complications of diabetes research article

Advertisement

Global trends in diabetes complications: a review of current evidence

• Published: 31 August 2018
• Volume 62 , pages 3–16, ( 2019 )

Cite this article

• Jessica L. Harding 1 ,
• Meda E. Pavkov 1 ,
• Dianna J. Magliano 2 , 3 ,
• Jonathan E. Shaw 2 &
• Edward W. Gregg 1  

65k Accesses

849 Citations

123 Altmetric

13 Mentions

Explore all metrics

In recent decades, large increases in diabetes prevalence have been demonstrated in virtually all regions of the world. The increase in the number of people with diabetes or with a longer duration of diabetes is likely to alter the disease profile in many populations around the globe, particularly due to a higher incidence of diabetes-specific complications, such as kidney failure and peripheral arterial disease. The epidemiology of other conditions frequently associated with diabetes, including infections and cardiovascular disease, may also change, with direct effects on quality of life, demands on health services and economic costs. The current understanding of the international burden of and variation in diabetes-related complications is poor. The available data suggest that rates of myocardial infarction, stroke and amputation are decreasing among people with diabetes, in parallel with declining mortality. However, these data predominantly come from studies in only a few high-income countries. Trends in other complications of diabetes, such as end-stage renal disease, retinopathy and cancer, are less well explored. In this review, we synthesise data from population-based studies on trends in diabetes complications, with the objectives of: (1) characterising recent and long-term trends in diabetes-related complications; (2) describing regional variation in the excess risk of complications, where possible; and (3) identifying and prioritising gaps for future surveillance and study.

Similar content being viewed by others

Interpreting global trends in type 2 diabetes complications and mortality

A systematic review of trends in all-cause mortality among people with diabetes

Epidemiology of diabetes and complications among adults in the republic of ireland 1998-2015: a systematic review and meta-analysis.

Avoid common mistakes on your manuscript.

In recent decades, large increases in diabetes prevalence have been demonstrated in virtually all regions of the world, with 415 million people worldwide now living with diabetes [ 1 ]. This is most concerning because an increase in diabetes prevalence will increase the number of chronic and acute diseases in the general population, with profound effects on quality of life, demand on health services and economic costs. Macrovascular complications of diabetes, including coronary heart disease, stroke and peripheral vascular disease, and microvascular complications, such as end-stage renal disease (ESRD), retinopathy and neuropathy, along with lower-extremity amputations (LEA), are responsible for much of the burden associated with diabetes. There is also growing recognition of a diversifying set of causally-linked conditions, including cancers, ageing-related outcomes (e.g. dementia), infections and liver disease. Since current data suggests that rates of all-cause and cardiovascular disease (CVD) mortality are decreasing in individuals with diabetes [ 2 ], trends in other complications of diabetes may become proportionately more prominent in the future.

Despite widespread international assessment of the growth of diabetes prevalence, quantification of the international burden and variation in the incidence of diabetes-related complications is notably lacking. This stems largely from the fact that data systems and population-based studies assessing diabetes complications are concentrated in Europe, North America and other high-income countries, with little to no availability in low- and middle-income countries, where the absolute increase in diabetes prevalence is largest. The lack of both uniform diagnosis of diabetes and of standardised measurement of diabetes-related complications has caused additional barriers in comparing trends worldwide. In this review, we synthesise data from adult population-based studies on trends in diabetes complications based on original articles, review articles and meta-analyses, with the objectives of: (1) characterising recent and long-term trends in diabetes-related complications; (2) describing regional variation in the excess risk of complications, where possible; and (3) identifying and prioritising gaps for future surveillance and study.

To this end, we conducted an extensive review of the literature in order to identify the majority of relevant publications. However, we did not adopt the formalities of a systematic literature review. Relevant publications were identified through a PubMed and Medline search using the following medical subject heading (MesH) terms: Diabetes Mellitus AND Diabetes Complications OR Mortality; End-Stage Renal Disease; Hyperglycaemia; Amputations; Cardiovascular Disease; Retinopathy; Nephropathy; Infections; Cancer; Dementia AND Epidemiology AND Trend. We also hand-searched reference lists of identified publications to determine additional eligible articles. The search was limited to papers in the English language. Throughout this Review, unless otherwise stated, data are reported among populations of people with diabetes, not general populations. All studies included were population-based, not clinic-based. Where more than one study per country (per outcome) existed, we chose the study reporting the most recent trends.

Macrovascular complications

CVD is a major cause of death and disability among people with diabetes. As the number of people with diabetes is predicted to increase, it is expected that the number of people with CVD will also increase [ 2 ]. However, data from several studies suggest that risk of CVD in people with diabetes has been declining since the 1990s (Table 1 ). Despite these improvements, people with diabetes continue to have a two- to fourfold higher risk of hospitalisation for major CVD events and CVD-associated clinical procedures compared with those without diabetes [ 2 ].

CVD mortality

Among the general population, mortality rates owing to CVD have declined in most high-income countries [ 12 ]. However, worldwide, CVD remains a leading cause of death in both people with and without diabetes [ 2 , 13 ], and individuals with diabetes still have a two- to fourfold increased rate of CVD mortality compared with those without [ 14 ]. Data from several studies suggest a decline in CVD-associated mortality among people with diabetes.

In the USA, a 53% relative decline in CVD mortality was observed between 1988 and 1994, and 2010 and 2015, as well as a reduction in the excess risk between populations with and without diabetes [ 15 ]. In Australia, a 50% decline in CVD-mortality rates was observed between 2000 and 2011 [ 16 ] and, in Iceland, a 46% decline was observed between 1993 and 2004 [ 17 ]. In Canada, in-hospital mortality for acute myocardial infarction (AMI) and stroke fell by 44.1% and 17.1%, respectively, between 1992 and 1999, but individuals with diabetes were still 1.6 times more likely to die from these events than those without diabetes [ 3 ]. Similar declines for CVD mortality in individuals with type 1 diabetes have also been shown in Australia [ 16 ] and Switzerland [ 18 ].

Microvascular complications

LEAs are a major complication for adults with diabetes because of their physical, economical and psychosocial burden. Since several aetiological pathways are associated with conditions leading to LEAs, LEAs are also an important indicator of the success of preventive care, such as that targeting glycaemic control, CVD risk factor management, and screening and treatment of people at high risk of foot complications. Population-based studies indicate that, in general, there have been reductions in the rates of LEAs between 1982 and 2011 (by ~3% to 85%) across diverse populations [ 9 , 19 , 20 , 21 , 22 , 23 ] (Fig. 1 and Table 2 ). Only two studies have specifically examined trends among people with type 1 diabetes; significant declines were observed in Spain [ 24 ] and non-significant declines were seen in Australia [ 21 ].

Trends in LEAs among people with diabetes, by country, between 1988 and 2011. Data in the figure were derived from population-based studies of countries or major regions of countries in which rates of LEAs were examined using the same methods within populations over time. Differences in absolute rates between countries may be affected by variation in age and differences in criteria for diagnosis of both LEA and diabetes. Data are intended to be interpreted as trends over time and should not be used for comparison of absolute rates between countries at any one time point. a Unadjusted rate; b rate per 100,000 person-years. This figure is available as part of a downloadable slideset

Among the 13 countries and major regions of countries with available data, the decline in total LEA incidence appears to be driven by declines in major LEAs (Fig. 2 a, Table 2 ). Smaller relative declines have been reported for minor LEAs, with some countries even reporting increases (Fig. 2 b, Table 2 ). This suggests that there may be a relative increase in the number of minor LEAs being performed in the clinical setting to prevent major LEAs. There also remain important disparities in rates of LEA between subgroups within populations. For example, in the USA, decreases in LEA rates are mainly attributable to greater reductions in LEAs in the elderly, with reductions in rates in young and middle-age people being modest [ 22 ]. In addition, the number of LEAs remain higher in non-whites and the male population in the USA [ 25 ], and large geographical differences exist [ 26 ].

Trends in ( a ) major and ( b ) minor LEAs among people with diabetes, by country, between 1982 and 2010. Data in the figure were derived from population-based studies of countries or major regions of countries in which rates of LEAs were examined using the same methods within populations over time. Differences in absolute rates between countries may be affected by variation in age and differences in criteria for diagnosis of both LEA and diabetes. Data are intended to be interpreted as trends over time and should not be used for comparison of absolute rates between countries at any one time point. a Unadjusted rate. This figure is available as part of a downloadable slideset

Worldwide, it is estimated that 80% of ESRD cases are caused by diabetes or hypertension [ 28 ]. Between 2002 and 2015, steep increases (approximately 40–700%) in the incidence of diabetes-associated ESRD were reported for Russia, the Philippines, Malaysia, the Republic of Korea, the Jalisco region of Mexico and Singapore, as well as Australia, Taiwan, Bosnia and Herzegovina and Scotland. In the USA, the increase was 11% for the same period [ 28 ] (Fig. 3 ). By contrast, diabetes-associated ESRD incidence declined over the same period in Austria (by 26%), Belgium (16%), Finland (11%), Denmark (2%), and Sweden (1%). All of these rates are reported for overall country-specific populations, not for diabetes populations, and increases likely reflect the increasing prevalence of both type 1 and type 2 diabetes in these populations [ 28 ].

Trends in the incidence rate (per million people in the general population/year) of diabetes-related ESRD, by country, between 2002 and 2015. The graph was generated based on data from the United States Renal Data System ( USRDS) annual data report 2017 [ 28 ]. This figure is available as part of a downloadable slideset

Among adults with type 2 diabetes, the incidence of ESRD declined by approximately 6% per year between 2000 and 2012 in a nationwide study of Chinese participants [ 29 ]. In the USA, incidence of ESRD in those with diabetes declined by 28% between 1990 and 2010, with a statistically significant decrease across all age groups after the year 2000 [ 19 ]. This decline was smaller than for other reported complications of diabetes, such as AMI, stroke, LEAs and death from hypoglycaemia, possibly owing to more inclusive criteria for initiating renal replacement therapy in the earlier years and large reductions in cardiovascular complications, both improving morbidity and mortality rates among people with diabetes.

Trends in the incidence of treated ESRD (i.e. dialysis initiation) among people with diabetes are also known to differ by race/ethnicity. In the USA, the incidence rate of treated ESRD declined between 2000 and 2013, by 28%, 22%, 14%, and 13% in American Indian/Alaska Native, Hispanic, non-Hispanic white and non-Hispanic black people with diabetes, respectively. Within the same timeframe, ESRD incidence remained relatively stable in Asian individuals with diabetes [ 30 ].

According to the United States Renal Data System (USRDS) reports, of all new cases of diabetes-associated ESRD, an estimated 91% were attributable to type 2 diabetes. Epidemiological data on trends in the incidence of treated ESRD in type 1 diabetes are less clear, partly because type 1 diabetes is less frequent than type 2 diabetes and also because of uncertainties related to the diagnosis of type 1 diabetes; young people with diabetes or those treated with insulin are often misclassified as having type 1 diabetes. Nonetheless, a review of ESRD in eight countries or regions of Europe, and in non-indigenous Canadians and Australians, found that incidence of type 1 diabetes-related ESRD declined between 1998 and 2002 [ 31 ]. Unlike type 2 diabetes, there are no studies among national cohorts with type 1 diabetes populations as the denominator; however, several cohort studies indicate that for a given duration of type 1 diabetes, people diagnosed in more recent decades have a lower incidence of ESRD than those diagnosed in the 1960s and 1970s [ 32 ]. Declines in type 1 diabetes-related ESRD may be attributed to the widespread use of renin–angiotensin system inhibitors and statin therapy at younger ages in this population, and recent improvements in insulin delivery technologies. On the other hand, in Taiwan, the incidence of type 1 diabetes-related ESRD increased substantially between 1999 and 2010 (from 0.13 to 3.52 per 1000 people; p  < 0.001) [ 33 ].

Retinopathy

Retinopathy affects approximately one third of adults with diabetes and represents the leading cause of blindness in these individuals [ 34 ]. Despite how common diabetic retinopathy is, there are few population-based data on incidence trends. Of the few studies that do report objectively measured annual incidence of retinopathy over time, findings are mixed (Table 3 ).

Generally, population-based studies conducted from the 1990s onwards report a 50–67% lower incidence of diabetic retinopathy compared with earlier studies [ 34 ]. A meta-analysis of 28 studies and 27,120 participants with type 1 and type 2 diabetes showed that the pooled incidence of proliferative diabetic retinopathy was lower in 1986–2008 (2.6%) compared with 1975–1985 (19.5%) [ 35 ]. Likewise, in the Pittsburgh Epidemiology of Diabetes Complications Study, incidence of proliferative diabetic retinopathy reduced from 38% in 1965–1969 to 26.5% in 1975–1980 [ 36 ]. These trends are likely to be owing to earlier identification and treatment of both diabetes and diabetic retinopathy and reductions in smoking rates. Moreover, lessons learned from the UK Prospective Diabetes Study (UKPDS) and DCCT trial, leading to better glycaemic and blood pressure control in diabetes, may have also contributed to the reduced incidence of diabetic retinopathy over recent years.

Information on trends in the prevalence or incidence of neuropathy are virtually non-existent due to the lack of data from repeated population surveys. Surveillance data from the US Diabetes Surveillance System (USDSS) show that the rate of hospitalisations for neuropathy (both first admission and any readmissions) increased by 42.1% (from 29.7 to 42.2 per 1000 people with diabetes) between 2000 and 2014; although these data are likely influenced by changes in coding of neuropathy and increased awareness of neuropathy among individuals with diabetes [ 37 ]. Historical data from the Pittsburgh Epidemiology of Diabetes Complications Study indicate a decline in the incidence of distal symmetrical polyneuropathy in participants with a 25-year duration of type 1 diabetes who were diagnosed between 1970 and 1974 compared with those diagnosed between 1965 and 1969 [ 36 ].

Acute complications

Acute complications of diabetes, such as diabetic ketoacidosis (DKA), the hyperglycaemic hyperosmolar state (HHS), lactic acidosis and hypoglycaemia are largely preventable, yet they still account for high morbidity and mortality among people with diabetes and contribute significantly to the high costs of diabetes care [ 43 ]. In the USA, the SEARCH for Diabetes in Youth study reported that 29% of individuals aged <20 years with type 1 diabetes, and 10% with type 2 diabetes presented with DKA at diagnosis [ 44 ]. The incidence of DKA in children and adolescents with type 1 diabetes also remains high, with approximately 1–12 episodes per 100 patient-years [ 43 ]. Comparable population-based data for adults are not currently available.

Overall, data suggest that DKA-related mortality and hospitalisation rates for acute complications are decreasing among people with diabetes (Table 4 ). However, in the USA, since 2010, significant increases in hospitalisations for hyperglycaemia and death from hyperglycaemic crisis have been reported by the USDSS, although continued declines in hospitalisations for hypoglycaemia were observed [ 37 ].

Decreasing temporal trends in hospitalisations and deaths from acute diabetes complications suggest improvements in in-hospital management of DKA and HHS and outpatient care, and better patient education in disease management. Reasons for increases in acute complications, as observed in the USA, are, at this stage, unclear.

Non-cardiovascular mortality

Diabetes is associated with a diverse set of specific, non-cardiovascular causes of death. An international meta-analysis of 97 prospective studies representing 820,900 individuals with diabetes and 123,205 deaths throughout North America and Europe found that diabetes was associated with an increased risk for mortality from several cancers (17–116% increased risk, depending on the cancer site), renal disease, infections, liver disease, digestive system disorders, falls, pneumonia, mental health issues, intentional self-harm, external causes, nervous system disorders, chronic obstructive pulmonary disease (COPD) and related conditions, and other non-cancer, non-vascular causes [ 48 ].

Observations of trends in non-cardiovascular mortality are restricted to a few studies. In the USA, the rate of cancer-related deaths declined by 16% every 10 years between 1988–1994 and 2010–2015, while the rate of non-vascular, non-cancer-related deaths declined by a smaller magnitude (8% every 10 years) [ 15 ]. In Australia, age-standardised mortality rates (ASMRs) for all-cause, CVD and diabetes decreased significantly between 2000 and 2011, while cancer-related ASMRs remained unchanged in people with type 1 and type 2 diabetes [ 16 ]. Data from the same national registry in Australia demonstrated that cancer is now the second leading cause of death among people with diabetes, increasing from 25% of all deaths to 35% between 1997 and 2010 [ 49 ]. Similar findings have been reported in the USA [ 50 ] and Taiwan [ 51 ]. This is important in light of the increasing prevalence of diabetes that is coinciding with an ageing population, the latter being an inherent risk factor for both diabetes and cancer.

All-cause mortality

Mortality rates due to diabetes are often estimated from vital statistics systems (based on death certificate data), the efficacy of which may be affected by diabetes prevalence, coding practices and country-level awareness of diabetes. Therefore, to adequately monitor mortality rates among populations with diabetes, rates should ideally be estimated among defined cohorts with diagnosed diabetes. However, data on all-cause and cause-specific mortality among people with diabetes are difficult to compare and come from a relatively small number of high-income countries within North America, Europe, Australia and Asia. Population-based data on all-cause mortality from several of these countries are shown in Fig. 4 and Table 5 . These data are intended to be interpreted as trends over time, rather than as a comparison of absolute rates between countries, as methodologies differ between the studies. Nonetheless, a consistent reduction in mortality among people with diabetes (either type 2 diabetes or all [type 1 and type 2] diabetes) has been observed since the late 1980s, ranging from a 4% relative decline in mortality among Taiwanese women with diabetes (27% in Taiwanese men) between 2000 and 2009 [ 51 ], to a 37% decline in Canadians between 1996 and 2009 [ 52 ].

Trends in all-cause mortality among people with diabetes, by country, between 1988 and 2015. Data in the figure were derived from population-based studies of countries or major regions of countries in which all-cause mortality rates were examined using the same methods within populations over time. Differences in absolute rates between countries may be affected by variation in age, differences in diabetes diagnosis, country-level awareness of diabetes and collection of vital statistics. Data are intended to be interpreted as trends over time and should not be used for comparison of absolute rates between countries at any one time point. a Rate per 100,000 person-years. This figure is available as part of a downloadable slideset

Studies that compare populations with and without diabetes show that the relative difference between the two populations is decreasing over time, but excess risk remains among people with diabetes, even at more recent time points [ 53 ]. For example, in Ontario, Canada, the mortality rate ratio decreased from 1.90 (95% CI 1.86, 1.94) in 1996 to 1.51 (95% CI 1.48, 1.54) in 2009 [ 52 ], and similar declines have been noted in the UK [ 52 ], USA [ 15 ] and Australia [ 49 ].

For type 1 diabetes, there is a 3–18-fold excess risk for death compared with individuals without diabetes [ 54 ]. However, continued improvements in mortality rates have been noted by a few studies. For example, in the USA, between 1950 and 2009, marked declines in the number of deaths attributed to type 1 diabetes were observed across all age groups (by 45–90%) [ 54 ]. An analysis by the Centers for Disease Control and Prevention also showed a 61% decrease in diabetes-related mortality prior to age 20 years between 1968–1969 and 2008–2009 [ 55 ]. Outside of the USA, Japan and Finland reported declines in mortality rates of 69% and 8%, respectively, when comparing mortality among those diagnosed with childhood-onset type 1 diabetes in 1965–1969 with those diagnosed in 1975–1979 [ 56 ]. The smaller declines in Finland are most likely explained by the lower absolute mortality in this country as compared with Japan [ 56 ]. In Norway, mortality rates among individuals diagnosed with type 1 diabetes between 1973 and 1982, before 15 years of age, was reduced by 81% (from 286 to 53 per 100,000 person-years) compared with those diagnosed in 1999–2012 [ 57 ]. In Australia, mortality rates among individuals with type 1 diabetes who were diagnosed before 45 years of age declined by 33% between 2000 and 2011 [ 16 ].

Emerging complications of diabetes

The increase in diabetes incidence since the 1980s, combined with declining mortality among people with diabetes, has increased the total years of life spent with diabetes. Longer life expectancy among those with diabetes has also driven the emergence of newly recognised complications, including cancer, infections and physical and cognitive disability. Observations of trends in ‘emerging’ diabetes complications are restricted to a few select studies.

Individuals with diabetes have an increased risk for tuberculosis, severe gram-positive infections, hospital-acquired postoperative infections, urinary tract infections (UTIs) and tropical diseases compared with people without diabetes [ 63 ]. Whether the rate of infections among populations with diabetes has changed over time is not clear. In the USA, data from the National Vital Statistics System show that the per cent of deaths with infections listed anywhere on the death certificate decreased from 3.1% in 1999 to 2.7% in 2010 in people with diabetes and from 4.5% to 4.1% in people without, with respiratory tract infections accounting for the highest percentage of deaths in both groups [ 63 ]. An analysis of data from the National Nursing Home Surveys between 1999 and 2004 showed that the age-standardised proportion of nursing home residents with infections among people with diabetes increased from 6.1% to 10.3% between 1999 and 2004, while in people without diabetes this increased from 6.0% to 8.5% [ 63 ]. In Spain, a 61.3% increase in hospitalisation rates for sepsis was observed between 2008 and 2012 [ 64 ], though changes in ICD-9-clinical modification (ICD-9-CM; www.cdc.gov/nchs/icd/icd9cm.htm ) codes make it difficult to assess the change in sepsis over time.

A growing body of research suggests that people with diabetes are at increased risk for major depressive disorder [ 65 ], anxiety [ 66 ], eating disorders (particularly in female adolescents with type 1 diabetes) [ 67 ], serious mental illness (e.g. schizophrenia) [ 68 ], dementia [ 69 ], and several domains of disability, including mobility loss, reduced instrumental activities of daily living (IADL) or basic activities of daily living, and work disability [ 70 ]. Again, whether risk has changed over time remains unknown as for many of these complications, prospective data with adequate follow-up is not available. For depression, two studies have explored trends over time. In Spain, the prevalence of depression among hospitalised individuals with type 2 diabetes increased significantly from 3.5% to 5.8% between 2001 and 2011, with increases being much higher in women [ 71 ]. In Finland, the use of antidepressants was more common in people with diabetes compared with those without and use of these drugs increased more rapidly between 1997 and 2007 in people with diabetes, particularly younger individuals with type 2 diabetes [ 72 ]. For physical disability, data from the USA show that the prevalence of both impaired mobility and IADLs have not changed in recent decades, while work disability declined from 23.8% in 1997 to 17.9% in 2006; however, this then increased to 19.7% in 2011 [ 70 ]. In relative terms, similar trends in rates of disability were reported among the non-diabetic population, but, in absolute terms, rates over time were smaller (from 9.8% in 1997 to 5.8% in 2010).

This review of international trends in diabetes-related complications reveals several key conclusions (see Text box); first, rates of LEAs, acute complications, CVD and all-cause and CVD-related mortality among populations of people with diabetes are declining. Data on trends in ESRD, diabetic retinopathy and neuropathy, non-CVD-related causes of death and ‘emerging’ complications in these populations are scarce, however, and, as such, conclusions are limited. Second, in spite of notable declines in several diabetes complications, people with diabetes remain at significantly higher risk for these complications compared with people without diabetes. Third, declines in all-cause and CVD-related mortality are leading to proportional increases in other forms of morbidity, including renal disease, infections, cancers, and physical and cognitive disability, with important implications for the clinical and public health burden of diabetes. Last, there is a genuine lack of comparable data on trends in rates of diabetes complications, specifically from low- and middle-income countries. Therefore, conclusions drawn from this work are limited to about a dozen high-income countries in North America, Europe and East Asia and, as such, this leaves the status of global trends in diabetes complications unclear.

The explanation for the decline in rates of diabetes complications among selected countries around the world is likely multifactorial, involving trends in the underlying risk factors of the population and changes in preventive care and medical treatment. Reductions in macrovascular complications in high-income countries are likely influenced by improved pharmacotherapy, CVD treatment procedures and better prevention strategies [ 73 ]. For example, large reductions in smoking rates occurred in the 1970s and 1980s, followed by gradual reductions thereafter [ 74 , 75 ]. Blood pressure control also improved in the 1980s and 1990s, driven by new evidence for treatment efficacy from clinical trials and better awareness of blood pressure as a key risk factor for CVD [ 74 , 75 ]. In addition, lipid levels have declined over time, likely due to increased use of lipid-lowering medications as well as reductions in trans-fat intake [ 73 , 76 ]. These improvements in risk factor management in high-income countries have likely had additional benefits in terms of microvascular complications, which have been further buoyed by improvements in glycaemic control since 2000 [ 73 , 76 , 77 ]. In the USA, the improvements in risk factors are also likely driven by improvements in the organisation of care and initiatives to improve quality of diabetes care. Whether improvements in risk factors, treatment options and medical care also occurs in the majority of other countries in the world is unclear due to the lack of continuous monitoring systems.

Trends in rates of diabetes complications are also influenced by background trends in mortality. For example, the large reductions in CVD-related mortality in populations with diabetes that have been observed in the USA, Australia and several other countries in Northern Europe have increased survival rates, resulting in proportional increases in other causes of death, including those due to cancer, renal disease and infections.

The interpretation of trends in rates of diabetes complications also depends on which denominator population (diabetes or whole population) is used. This review has focused primarily on the average risk for the average person with diagnosed diabetes, independent of changes in prevalence of diabetes in the underlying population. When rates are calculated as the frequency of diabetes-related complications in the general population, many countries reveal flat or even increasing trends because the increases in diabetes prevalence offset reductions in risk of complications within the diabetic population [ 19 ]. For example, while the average adult with diabetes in the USA has a lower risk of CVD than in previous decades, the average adult in the general population has an increased risk of diabetes-related CVD than in previous decades because of the large increase in diabetes prevalence. The fact that trends differ depending on the choice of general population denominator is a reminder that the burden of the wide spectrum of complications in those with diabetes will ultimately be influenced by efforts to prevent diabetes.

In this review, we have highlighted the scarcity of data outside North America, Europe and high-income Asia-Pacific countries, leaving the global status of diabetes complications rates unclear, especially in low and middle-income countries. This gap in data stems largely from the lack of population-based systems quantifying healthcare utilisation because surveys and cohort studies are generally inadequate for the assessment of diabetic complications. The comparison of trends in complications has also been hampered by varied reporting methods, definitions of complications and methods to identify people with diabetes. Future monitoring of global trends in diabetes complications could be enhanced by implementing standardised reporting methods and establishing practical registries that suit the dual needs of population monitoring and providing feedback and decision support for clinical systems.

Abbreviations

Acute myocardial infarction

Age-standardised mortality rates

Cardiovascular disease

Diabetic ketoacidosis

End-stage renal disease

Hyperglycaemic hyperosmolar state

Instrumental activities of daily living

Lower-extremity amputation

United States Diabetes Surveillance System

Ogurtsova K, da Rocha Fernandes JD, Huang Y et al (2017) IDF Diabetes Atlas: global estimates for the prevalence of diabetes for 2015 and 2040. Diabetes Res Clin Pract 128:40–50

Article   CAS   PubMed   Google Scholar  

International Diabetes Federation (2016) Diabetes and cardiovascular disease. Brussels, Belgium: International Diabetes Federation 2016. Available from www.idf.org/our-activities/care-prevention/cardiovascular-disease/cvd-report . Accessed 24 July 2018

Booth GL, Kapral MK, Fung K, Tu JV (2006) Recent trends in cardiovascular complications among men and women with and without diabetes. Diabetes Care 29:32–37

Article   PubMed   Google Scholar  

Burrows NR, Li Y, Gregg EW, Geiss LS (2018) Declining rates of hospitalization for selected cardiovascular disease conditions among adults aged ≥35 years with diagnosed diabetes, U.S., 1998–2014. Diabetes Care 41:293–302

Jung CH, Chung JO, Han K et al (2017) Improved trends in cardiovascular complications among subjects with type 2 diabetes in Korea: a nationwide study (2006–2013). Cardiovasc Diabetol 16:1

Article   PubMed   PubMed Central   Google Scholar  

Lopez-de-Andres A, Jimenez-Garcia R, Hernandez-Barrera V et al (2014) National trends over one decade in hospitalization for acute myocardial infarction among Spanish adults with type 2 diabetes: cumulative incidence, outcomes and use of percutaneous coronary intervention. PLoS One 9:e85697

Article   CAS   PubMed   PubMed Central   Google Scholar  

Lopez-de-Andres A, Jimenez-Trujillo I, Jimenez-Garcia R et al (2015) National trends in incidence and outcomes of abdominal aortic aneurysm among elderly type 2 diabetic and non-diabetic patients in Spain (2003-2012). Cardiovasc Diabetol 14:48

Munoz-Rivas N, Mendez-Bailon M, Hernandez-Barrera V et al (2015) Time trends in ischemic stroke among type 2 diabetic and non-diabetic patients: analysis of the Spanish National Hospital Discharge Data (2003-2012). PLoS One 10:e0145535

Ringborg A, Lindgren P, Martinell M, Yin DD, Schon S, Stalhammar J (2008) Prevalence and incidence of type 2 diabetes and its complications 1996–2003—estimates from a Swedish population-based study. Diabet Med 25:1178–1186

Vamos EP, Millett C, Parsons C, Aylin P, Majeed A, Bottle A (2012) Nationwide study on trends in hospital admissions for major cardiovascular events and procedures among people with and without diabetes in England, 2004–2009. Diabetes Care 35:265–272

Yashkin AP, Picone G, Sloan F (2015) Causes of the change in the rates of mortality and severe complications of diabetes mellitus: 1992-2012. Med Care 53:268–275

Levi F, Lucchini F, Negri E, La Vecchia C (2002) Trends in mortality from cardiovascular and cerebrovascular diseases in Europe and other areas of the world. Heart 88:119–124

Abi Khalil C, Roussel R, Mohammedi K, Danchin N, Marre M (2012) Cause-specific mortality in diabetes: recent changes in trend mortality. Eur J Prev Cardiol 19:374–381

Taylor KS, Heneghan CJ, Farmer AJ et al (2013) All-cause and cardiovascular mortality in middle-aged people with type 2 diabetes compared with people without diabetes in a large U.K. primary care database. Diabetes Care 36:2366–2371

Gregg EW, Cheng YJ, Srinivasan M et al (2018) Trends in cause-specific mortality among adults with and without diagnosed diabetes in the USA: an epidemiological analysis of linked national survey and vital statistics data. Lancet 391:2430–2440

Harding JL, Shaw JE, Peeters A, Davidson S, Magliano DJ (2016) Age-specific trends from 2000–2011 in all-cause and cause-specific mortality in type 1 and type 2 diabetes: a cohort study of more than one million people. Diabetes Care 39:1018–1026

Olafsdottir E, Aspelund T, Sigurdsson G et al (2013) Similar decline in mortality rate of older persons with and without type 2 diabetes between 1993 and 2004 the Icelandic population-based Reykjavik and AGES-Reykjavik cohort studies. BMC Public Health 13:36

Allemann S, Saner C, Zwahlen M, Christ ER, Diem P, Stettler C (2009) Long-term cardiovascular and non-cardiovascular mortality in women and men with type 1 and type 2 diabetes mellitus: a 30-year follow-up in Switzerland. Swiss Med Wkly 139:576–583

PubMed   Google Scholar  

Gregg EW, Li Y, Wang J et al (2014) Changes in diabetes-related complications in the United States, 1990–2010. N Engl J Med 370:1514–1523

Kennon B, Leese GP, Cochrane L et al (2012) Reduced incidence of lower-extremity amputations in people with diabetes in Scotland: a nationwide study. Diabetes Care 35:2588–2590

Kurowski JR, Nedkoff L, Schoen DE, Knuiman M, Norman PE, Briffa TG (2015) Temporal trends in initial and recurrent lower-extremity amputations in people with and without diabetes in Western Australia from 2000 to 2010. Diabetes Res Clin Pract 108:280–287

Moxey PW, Gogalniceanu P, Hinchliffe RJ et al (2011) Lower-extremity amputations — a review of global variability in incidence. Diabet Med 28:1144–1153

Narres M, Kvitkina T, Claessen H et al (2017) Incidence of lower-extremity amputations in the diabetic compared with the non-diabetic population: a systematic review. PLoS One 12:e0182081

Carinci F, Massi Benedetti M, Klazinga NS, Uccioli L (2016) Lower extremity amputation rates in people with diabetes as an indicator of health systems performance. A critical appraisal of the data collection 2000–2011 by the Organization for Economic Cooperation and Development (OECD). Acta Diabetol 53:825–832

Li Y, Burrows NR, Gregg EW, Albright A, Geiss LS (2012) Declining rates of hospitalization for nontraumatic lower-extremity amputation in the diabetic population aged 40 years or older: U.S., 1988-2008. Diabetes Care 35:273–277

Margolis DJ, Hoffstad O, Nafash J et al (2011) Location, location, location: geographic clustering of lower-extremity amputation among Medicare beneficiaries with diabetes. Diabetes Care 34:2363–2367

Lopez-de-Andres A, Martinez-Huedo MA, Carrasco-Garrido P, Hernandez-Barrera V, Gil-de-Miguel A, Jimenez-Garcia R (2011) Trends in lower-extremity amputations in people with and without diabetes in Spain, 2001–2008. Diabetes Care 34:1570–1576

United States Renal Data System. 2017 USRDS annual data report: epidemiology of kidney disease in the United States. National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, 2017. Available from www.usrds.org/adr.aspx . Accessed 26 July 2018

Luk AOY, Hui EMT, Sin MC et al (2017) Declining trends of cardiovascular-renal complications and mortality in type 2 diabetes: The Hong Kong Diabetes Database. Diabetes Care 40:928–935

Bullock A, Burrows NR, Narva AS et al (2017) Vital signs: decrease in incidence of diabetes-related end-stage renal disease among American Indians/Alaska Natives — United States, 1996–2013. MMWR Morb Mortal Wkly Rep 66:26–32

Group EIS, Stewart JH, McCredie MR, Williams SM (2006) Divergent trends in the incidence of end-stage renal disease due to type 1 and type 2 diabetes in Europe, Canada and Australia during 1998–2002. Diabet Med 23:1364–1369

Article   Google Scholar  

Helve J, Sund R, Arffman M et al (2018) Incidence of end-stage renal disease in patients with type 1 diabetes. Diabetes Care 41:434–439

Lin WH, Li CY, Wang WM, Yang DC, Kuo TH, Wang MC (2014) Incidence of end stage renal disease among type 1 diabetes: a nationwide cohort study in Taiwan. Medicine 93:e274

Liew G, Wong VW, Ho IV (2017) Mini Review: changes in the incidence of and progression to proliferative and sight-threatening diabetic retinopathy over the last 30 years. Ophthalmic Epidemiol 24:73–80

Wong TY, Mwamburi M, Klein R et al (2009) Rates of progression in diabetic retinopathy during different time periods: a systematic review and meta-analysis. Diabetes Care 32:2307–2313

Pambianco G, Costacou T, Ellis D, Becker DJ, Klein R, Orchard TJ (2006) The 30-year natural history of type 1 diabetes complications: the Pittsburgh Epidemiology of Diabetes Complications Study experience. Diabetes 55:1463–1469

Centers for Disease Control and Prevention. Diabetes Atlas, United States Diabetes Surveillance System 2018. Available from: https://gis.cdc.gov/grasp/diabetes/DiabetesAtlas.html . Accessed 24 July 2018

Mathur R, Bhaskaran K, Edwards E et al (2017) Population trends in the 10-year incidence and prevalence of diabetic retinopathy in the UK: a cohort study in the Clinical Practice Research Datalink 2004–2014. BMJ Open 7:e014444

Klein R, Lee KE, Gangnon RE, Klein BE (2010) The 25-year incidence of visual impairment in type 1 diabetes mellitus: the Wisconsin Epidemiologic Study of Diabetic Retinopathy. Ophthalmology 117:63–70

Song SJ, Han K, Choi KS et al (2018) Trends in diabetic retinopathy and related medical practices among type 2 diabetes patients: results from the National Insurance Service Survey 2006–2013. J Diabetes Investig 9:173–178

Tracey ML, McHugh SM, Fitzgerald AP, Buckley CM, Canavan RJ, Kearney PM (2016) Trends in blindness due to diabetic retinopathy among adults aged 18–69 years over a decade in Ireland. Diabetes Res Clin Pract 121:1–8

Hall HN, Chinn DJ, Sinclair A, Styles CJ (2013) Epidemiology of blindness attributable to diabetes in Scotland: change over 20 years in a defined population. Diabet Med 30:1349–1354

Rewers A (2017) Acute metabolic complications in diabetes. In: Diabetes in America, 3rd edn. National Institutes of Health, NIH Pub No. 17-1468; pp 17-1–17-19

Klingensmith GJ, Tamborlane WV, Wood J et al (2013) Diabetic ketoacidosis at diabetes onset: still an all too common threat in youth. J Pediatr 162:330–4.e1

Booth GL, Hux JE, Fang J, Chan BT (2005) Time trends and geographic disparities in acute complications of diabetes in Ontario, Canada. Diabetes Care 28:1045–1050

Lombardo F, Maggini M, Gruden G, Bruno G (2013) Temporal trend in hospitalizations for acute diabetic complications: a nationwide study, Italy, 2001–2010. PLoS One 8:e63675

Liu CC, Chen KR, Chen HF, Huang HL, Ko MC, Li CY (2010) Trends in hospitalization for diabetic ketoacidosis in diabetic patients in Taiwan: analysis of national claims data, 1997–2005. J Formos Med Assoc 109:725–734

Rao Kondapally Seshasai S, Kaptoge S et al (2011) Diabetes mellitus, fasting glucose, and risk of cause-specific death. N Engl J Med 364:829–841

Harding JL, Shaw JE, Peeters A, Guiver T, Davidson S, Magliano DJ (2014) Mortality trends among people with type 1 and type 2 diabetes in Australia: 1997–2010. Diabetes Care 37:2579–2586

Thomas RJ, Palumbo PJ, Melton LJ III et al (2003) Trends in the mortality burden associated with diabetes mellitus: a population-based study in Rochester, Minn, 1970-1994. Arch Intern Med 163:445–451

Li HY, Jiang YD, Chang CH, Chung CH, Lin BJ, Chuang LM (2012) Mortality trends in patients with diabetes in Taiwan: a nationwide survey in 2000–2009. J Formos Med Assoc 111:645–650

Lind M, Garcia-Rodriguez LA, Booth GL et al (2013) Mortality trends in patients with and without diabetes in Ontario, Canada and the UK from 1996 to 2009: a population-based study. Diabetologia 56:2601–2608

Rosenquist KJ, Fox CS (2017) Mortality trends in type 2 diabetes. In: Diabetes in America, 3rd edn. National Institutes of Health, NIH Pub No. 17-1468; pp 36-1–36-14

Secrest AM, Washington RE, Orchard TJ 2017 Mortality in type 1 diabetes. In: Diabetes in America, 3rd edn. National Institutes of Health, NIH Pub No. 17-1468; pp 35-1–35-16

Centers for Disease Control and Prevention (2012) Diabetes death rates among youths aged ≤19 years — United States, 1968–2009. MMWR Morb Mortal Wkly Rep 61:869–872

Google Scholar  

Asao K, Sarti C, Forsen T et al (2003) Long-term mortality in nationwide cohorts of childhood-onset type 1 diabetes in Japan and Finland. Diabetes Care 26:2037–2042

Gagnum V, Stene LC, Sandvik L et al (2015) All-cause mortality in a nationwide cohort of childhood-onset diabetes in Norway 1973–2013. Diabetologia 58:1779–1786

Forssas E, Arffman M, Koskinen S, Reunanen A, Keskimaki I (2010) Socioeconomic differences in mortality among diabetic people in Finland. Scand J Public Health 38:691–698

Zghebi SS, Steinke DT, Carr MJ, Rutter MK, Emsley RA, Ashcroft DM (2017) Examining trends in type 2 diabetes incidence, prevalence and mortality in the UK between 2004 and 2014. Diabetes Obes Metab 19:1537–1545

Green A, Sortso C, Jensen PB, Emneus M (2015) Incidence, morbidity, mortality, and prevalence of diabetes in Denmark, 2000–2011: results from the Diabetes Impact Study 2013. Clin Epidemiol 7:421–430

Karpati T, Cohen-Stavi CJ, Leibowitz M, Hoshen M, Feldman BS, Balicer RD (2014) Towards a subsiding diabetes epidemic: trends from a large population-based study in Israel. Popul Health Metrics 12:32

Read SH, Kerssens JJ, McAllister DA et al (2016) Trends in type 2 diabetes incidence and mortality in Scotland between 2004 and 2013. Diabetologia 59:2106–2113

Egede LE, Hull BJ, Williams JS (2017) Infections associated with diabetes. In: Diabetes in America, 3rd edn. National Institutes of Health, NIH Pub No. 17-1468; pp 30-1-30-25

de Miguel-Yanes JM, Mendez-Bailon M, Jimenez-Garcia R, Hernandez-Barrera V, Perez-Farinos N, Lopez-de-Andres A (2015) Trends in sepsis incidence and outcomes among people with or without type 2 diabetes mellitus in Spain (2008–2012). Diabetes Res Clin Pract 110:266–275

Ali S, Stone MA, Peters JL, Davies MJ, Khunti K (2006) The prevalence of co-morbid depression in adults with type 2 diabetes: a systematic review and meta-analysis. Diabet Med 23:1165–1173

Fisher L, Skaff MM, Mullan JT, Arean P, Glasgow R, Masharani U (2008) A longitudinal study of affective and anxiety disorders, depressive affect and diabetes distress in adults with type 2 diabetes. Diabet Med 25:1096–1101

Ward A, Troop N, Cachia M, Watkins P, Treasure J (1995) Doubly disabled: diabetes in combination with an eating disorder. Postgrad Med J 71:546–550

Vancampfort D, Correll CU, Galling B et al (2016) Diabetes mellitus in people with schizophrenia, bipolar disorder and major depressive disorder: a systematic review and large scale meta-analysis. World Psychiatry 15:166–174

Li J, Shao YH, Gong YP, Lu YH, Liu Y, Li CL (2014) Diabetes mellitus and dementia - a systematic review and meta-analysis. Eur Rev Med Pharmacol Sci 18:1778–1789

CAS   PubMed   Google Scholar  

Gregg EW, Menke A (2017) Diabetes and disability. In: Diabetes in America, 3rd edn. National Institutes of Health, NIH Pub No. 17-1468; pp 34-1–34-15

Lopez-de-Andres A, Jimenez-Trujillo MI, Hernandez-Barrera V et al (2015) Trends in the prevalence of depression in hospitalized patients with type 2 diabetes in Spain: analysis of hospital discharge data from 2001 to 2011. PLoS One 10:e0117346

Manderbacka K, Sund R, Koski S, Keskimaki I, Elovainio M (2011) Diabetes and depression? Secular trends in the use of antidepressants among persons with diabetes in Finland in 1997–2007. Pharmacoepidemiol Drug Saf 20:338–343

Ford ES (2011) Trends in the control of risk factors for cardiovascular disease among adults with diagnosed diabetes: findings from the National Health and Nutrition Examination Survey 1999–2008*. J Diabetes 3:337–347

Imperatore G, Cadwell BL, Geiss L et al (2004) Thirty-year trends in cardiovascular risk factor levels among US adults with diabetes: National Health and Nutrition Examination Surveys, 1971–2000. Am J Epidemiol 160:531–539

GBD 2016 Risk Factors Collaborators (2017) Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet 390:1345–1422

Heart Protection Study Collaborative Group (2002) MRC/BHF Heart Protection Study of cholesterol lowering with simvastatin in 20536 high-risk individuals: a randomised placebo-controlled trial. Lancet 360:7–22

Ali MK, Bullard KM, Saaddine JB, Cowie CC, Imperatore G, Gregg EW (2013) Achievement of goals in U.S. diabetes care, 1999–2010. N Engl J Med 368:1613–1624

Download references

Acknowledgements

The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.

The interpretation and reporting of the ESRD data supplied by the United States Renal Data System (USRDS) are the responsibility of the authors and in no way should be seen as an official policy or interpretation of the US government.

Author information

Authors and affiliations.

Division of Diabetes Translation, Centers for Disease Control and Prevention (CDC), 4770 Buford Hwy NE, MS-F75, Atlanta, GA, 30341, USA

Jessica L. Harding, Meda E. Pavkov & Edward W. Gregg

Department of Clinical Diabetes and Epidemiology, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia

Dianna J. Magliano & Jonathan E. Shaw

School of Population Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia

Dianna J. Magliano

You can also search for this author in PubMed   Google Scholar

Contributions

JLH contributed to the literature search and data analyses and interpretation and wrote the manuscript. MEP contributed to the literature search and data analyses and interpretation and reviewed the manuscript. DJM and JES contributed to interpretation of data and reviewed the manuscript. EWG contributed to interpretation of data and writing of the manuscript. All authors approved the version to be published.

Corresponding author

Correspondence to Jessica L. Harding .

Ethics declarations

The authors declare that there is no duality of interest associated with this manuscript.

Electronic supplementary material

(PPTX 621 kb)

Rights and permissions

Reprints and permissions

About this article

Harding, J.L., Pavkov, M.E., Magliano, D.J. et al. Global trends in diabetes complications: a review of current evidence. Diabetologia 62 , 3–16 (2019). https://doi.org/10.1007/s00125-018-4711-2

Download citation

Received : 23 April 2018

Accepted : 04 July 2018

Published : 31 August 2018

Issue Date : January 2019

DOI : https://doi.org/10.1007/s00125-018-4711-2

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Complications (all)
• Epidemiology
• Find a journal
• Publish with us
• Track your research

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Review Article
• Published: 12 May 2020

Genetics of diabetes mellitus and diabetes complications

• Joanne B. Cole   ORCID: orcid.org/0000-0001-9520-2788 1 , 2 , 3 &
• Jose C. Florez 1 , 2 , 4  

Nature Reviews Nephrology volume  16 ,  pages 377–390 ( 2020 ) Cite this article

18k Accesses

662 Citations

86 Altmetric

Metrics details

• Diabetes complications

Diabetes is one of the fastest growing diseases worldwide, projected to affect 693 million adults by 2045. Devastating macrovascular complications (cardiovascular disease) and microvascular complications (such as diabetic kidney disease, diabetic retinopathy and neuropathy) lead to increased mortality, blindness, kidney failure and an overall decreased quality of life in individuals with diabetes. Clinical risk factors and glycaemic control alone cannot predict the development of vascular complications; numerous genetic studies have demonstrated a clear genetic component to both diabetes and its complications. Early research aimed at identifying genetic determinants of diabetes complications relied on familial linkage analysis suited to strong-effect loci, candidate gene studies prone to false positives, and underpowered genome-wide association studies limited by sample size. The explosion of new genomic datasets, both in terms of biobanks and aggregation of worldwide cohorts, has more than doubled the number of genetic discoveries for both diabetes and diabetes complications. We focus herein on genetic discoveries for diabetes and diabetes complications, empowered primarily through genome-wide association studies, and emphasize the gaps in research for taking genomic discovery to the next level.

A moderate genetic component and significant genetic overlap exists for diabetes and microvascular and macrovascular diabetes complications.

Large biobanks and aggregation of diabetes cohorts have more than doubled the number of genetic associations with diabetes and diabetes complications discovered in genome-wide association studies.

Sequencing studies remain limited by sample size, although work in type 2 diabetes mellitus highlights their use in gene variant characterization.

Future genetic discovery of diabetes and its complications will rely on large sample sizes, interrogation of sequencing datasets, diverse populations and improved phenotyping and sub-phenotyping.

This is a preview of subscription content, access via your institution

Access options

Access Nature and 54 other Nature Portfolio journals

Get Nature+, our best-value online-access subscription

24,99 € / 30 days

cancel any time

Subscribe to this journal

Receive 12 print issues and online access

195,33 € per year

only 16,28 € per issue

Buy this article

• Purchase on Springer Link
• Instant access to full article PDF

Prices may be subject to local taxes which are calculated during checkout

Similar content being viewed by others

Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation

Genome-wide association analyses highlight etiological differences underlying newly defined subtypes of diabetes

Discovery of 318 new risk loci for type 2 diabetes and related vascular outcomes among 1.4 million participants in a multi-ancestry meta-analysis

Cho, N. H. et al. IDF diabetes atlas: global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes Res. Clin. Pract. 138 , 271–281 (2018).

Article   CAS   PubMed   Google Scholar  

Morrish, N. J. et al. Mortality and causes of death in the WHO multinational study of vascular disease in diabetes. Diabetologia 44 (Suppl. 2), 14–21 (2001).

Article   Google Scholar  

da Rocha Fernandes, J. et al. IDF diabetes atlas estimates of 2014 global health expenditures on diabetes. Diabetes Res. Clin. Pract. 117 , 48–54 (2016).

Article   PubMed   Google Scholar  

American Diabetes Association. Economic costs of diabetes in the US in 2017. Diabetes Care , 41 , 917–928 (2018).

Article   PubMed Central   Google Scholar  

Liyanage, T. et al. Worldwide access to treatment for end-stage kidney disease: a systematic review. Lancet 385 , 1975–1982 (2015).

Brownlee, M. Biochemistry and molecular cell biology of diabetic complications. Nature 414 , 813–820 (2001).

American Diabetes Association. Classification and diagnosis of diabetes: standards of medical care in diabetes. Diabetes Care 41 , S13–S27 (2018).

Udler, M. S. et al. Type 2 diabetes genetic loci informed by multi-trait associations point to disease mechanisms and subtypes: a soft clustering analysis. PLoS Med. 15 , e1002654 (2018).

Article   PubMed   PubMed Central   CAS   Google Scholar  

Ahlqvist, E. et al. Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. Lancet Diabetes Endocrinol. 6 , 361–369 (2018).

Anders, H. J., Huber, T. B., Isermann, B. & Schiffer, M. CKD in diabetes: diabetic kidney disease versus nondiabetic kidney disease. Nat. Rev. Nephrol. 14 , 361–377 (2018).

Deckert, T. & Poulsen, J. Diabetic nephropathy: fault or destiny? Diabetologia 21 , 178–183 (1981).

CAS   PubMed   Google Scholar  

Quinn, M., Angelico, M. C., Warram, J. H. & Krolewski, A. S. Familial factors determine the development of diabetic nephropathy in patients with IDDM. Diabetologia 39 , 940–945 (1996).

Seaquist, E. R., Goetz, F. C., Rich, S. & Barbosa, J. Familial clustering of diabetic kidney disease. N. Engl. J. Med. 320 , 1161–1165 (1989).

Earle, K., Walker, J., Hill, C. & Viberti, G. Familial clustering of cardiovascular disease in patients with insulin-dependent diabetes and nephropathy. N. Engl. J. Med. 326 , 673–677 (1992).

Tuomilehto, J. et al. Incidence of cardiovascular disease in type 1 (insulin-dependent) diabetic subjects with and without diabetic nephropathy in Finland. Diabetologia 41 , 784–790 (1998).

Redondo, M. J., Jeffrey, J., Fain, P. R., Eisenbarth, G. S. & Orban, T. Concordance for islet autoimmunity among monozygotic twins. N. Engl. J. Med. 359 , 2849–2850 (2008).

Fajans, S. S. & Bell, G. I. MODY: history, genetics, pathophysiology, and clinical decision making. Diabetes Care 34 , 1878–1884 (2011).

Article   PubMed   PubMed Central   Google Scholar  

Mahajan, A. et al. Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nat. Genet. 50 , 1505–1513 (2018).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Udler, M. S. Type 2 diabetes: multiple genes, multiple diseases. Curr. Diab. Rep. 19 , 55 (2019).

Robertson, C. C. & Rich, S. S. Genetics of type 1 diabetes. Curr. Opin. Genet. Dev. 50 , 7–16 (2018).

Noble, J. A. & Erlich, H. A. Genetics of type 1 diabetes. Cold Spring Harb. Perspect. Med. 2 , a007732 (2012).

Barrett, J. C. et al. Genome-wide association study and meta-analysis find that over 40 loci affect risk of type 1 diabetes. Nat. Genet. 41 , 703 (2009).

Cooper, N. J. et al. Type 1 diabetes genome-wide association analysis with imputation identifies five new risk regions. bioRxiv https://doi.org/10.1101/120022 (2017).

Onengut-Gumuscu, S. et al. Fine mapping of type 1 diabetes susceptibility loci and evidence for colocalization of causal variants with lymphoid gene enhancers. Nat. Genet. 47 , 381 (2015).

Ge, Y. et al. Targeted deep sequencing in multiple-affected sibships of European ancestry identifies rare deleterious variants in PTPN22 that confer risk for type 1 diabetes. Diabetes 65 , 794–802 (2016).

Ingelsson, E. & McCarthy Mark, I. Human genetics of obesity and type 2 diabetes mellitus. Circ. Genom. Precis. Med. 11 , e002090 (2018).

Prasad, R. B. & Groop, L. Genetics of type 2 diabetes — pitfalls and possibilities. Genes 6 , 87–123 (2015).

Florez, J. C. The Genetics of Type 2 Diabetes and Related Traits (Springer, 2016).

Chen, J. et al. Genome-wide association study of type 2 diabetes in Africa. Diabetologia 62 , 1204–1211 (2019).

Ng, M. C. Y. et al. Meta-analysis of genome-wide association studies in African Americans provides insights into the genetic architecture of type 2 diabetes. PLoS Genet. 10 , e1004517 (2014).

Imamura, M. et al. Genome-wide association studies in the Japanese population identify seven novel loci for type 2 diabetes. Nat. Commun. 7 , 10531 (2016).

Qi, Q. et al. Genetics of type 2 diabetes in U.S. Hispanic/Latino individuals: results from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). Diabetes 66 , 1419–1425 (2017).

Cho, Y. S. et al. Meta-analysis of genome-wide association studies identifies eight new loci for type 2 diabetes in East Asians. Nat. Genet. 44 , 67 (2012).

Article   CAS   Google Scholar  

Kooner, J. S. et al. Genome-wide association study in individuals of South Asian ancestry identifies six new type 2 diabetes susceptibility loci. Nat. Genet. 43 , 984 (2011).

Mahajan, A. et al. Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility. Nat. Genet. 46 , 234–244 (2014).

Williams, A. L. et al. Sequence variants in SLC16A11 are a common risk factor for type 2 diabetes in Mexico. Nature 506 , 97–101 (2014).

Estrada, K. et al. Association of a low-frequency variant in HNF1A with type 2 diabetes in a Latino population. JAMA 311 , 2305–2314 (2014).

Article   PubMed   CAS   Google Scholar  

Spracklen, C. N. et al. Identification of type 2 diabetes loci in 433,540 East Asian individuals. bioRxiv https://doi.org/10.1101/685172 (2019).

Mahajan, A. et al. Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes. Nat. Genet. 50 , 559–571 (2018).

Mahajan, A. et al. 303-OR: ADA Presidents’ select abstract: transethnic association study of type 2 diabetes in more than a million individuals. Diabetes 68 (Suppl. 1), 303-OR (2019).

Vujkovic, M. et al. Discovery of 318 novel loci for type-2 diabetes and related micro- and macrovascular outcomes among 1.4 million participants in a multi-ethnic meta-analysis. medRxiv https://doi.org/10.1101/19012690 (2019).

Andersen, M. K. et al. Genetics of type 2 diabetes: the power of isolated populations. Curr. Diab. Rep. 16 , 65 (2016).

Langenberg, C. & Lotta, L. A. Genomic insights into the causes of type 2 diabetes. Lancet 391 , 2463–2474 (2018).

Flannick, J. et al. Exome sequencing of 20,791 cases of type 2 diabetes and 24,440 controls. Nature 570 , 71–76 (2019).

Dwivedi, O. P. et al. Loss of ZnT8 function protects against diabetes by enhanced insulin secretion. Nat. Genet. 51 , 1596–1606 (2019).

Zuk, O. et al. Searching for missing heritability: designing rare variant association studies. Proc. Natl Acad. Sci. USA 111 , E455–464 (2014).

Deshpande, A. D., Harris-Hayes, M. & Schootman, M. Epidemiology of diabetes and diabetes-related complications. Phys. Ther. 88 , 1254–1264 (2008).

Martínez-Castelao, A., Navarro-González, J., Górriz, J. & de Alvaro, F. The concept and the epidemiology of diabetic nephropathy have changed in recent years. J. Clin. Med. 4 , 1207–1216 (2015).

Tuttle, K. R. et al. Diabetic kidney disease: a report from an ADA consensus conference. Am. J. Kidney Dis. 64 , 510–533 (2014).

Pettitt, D. J., Saad, M. F., Bennett, P. H., Nelson, R. G. & Knowler, W. C. Familial predisposition to renal disease in two generations of Pima Indians with type 2 (non-insulin-dependent) diabetes mellitus. Diabetologia 33 , 438–443 (1990).

Harjutsalo, V., Katoh, S., Sarti, C., Tajima, N. & Tuomilehto, J. Population-based assessment of familial clustering of diabetic nephropathy in type 1 diabetes. Diabetes 53 , 2449–2454 (2004).

Borch-Johnsen, K. et al. Is diabetic nephropathy an inherited complication? Kidney Int. 41 , 719–722 (1992).

Sandholm, N. et al. The genetic landscape of renal complications in type 1 diabetes. J. Am. Soc. Nephrol. 28 , 557–574 (2016).

Sandholm, N. et al. Genome-wide association study of urinary albumin excretion rate in patients with type 1 diabetes. Diabetologia 57 , 1143–1153 (2014).

van Zuydam, N. R. et al. A genome-wide association study of diabetic kidney disease in subjects with type 2 diabetes. Diabetes 67 , 1414–1427 (2018).

Imperatore, G. et al. Pima Diabetes Genes Group. Sib-pair linkage analysis for susceptibility genes for microvascular complications among Pima Indians with type 2 diabetes. Diabetes 47 , 821–830 (1998).

Iyengar, S. K. et al. Genome-wide scans for diabetic nephropathy and albuminuria in multiethnic populations. Diabetes 56 , 1577–1585 (2007).

Schelling, J. R. et al. Genome-wide scan for estimated glomerular filtration rate in multi-ethnic diabetic populations. Diabetes 57 , 235–243 (2008).

Vardarli, I. et al. Gene for susceptibility to diabetic nephropathy in type 2 diabetes maps to 18q22.3-23. Kidney Int. 62 , 2176–2183 (2002).

Janssen, B. et al. Carnosine as a protective factor in diabetic nephropathy: association with a leucine repeat of the carnosinase gene CNDP1. Diabetes 54 , 2320–2327 (2005).

Mooyaart, A. L. et al. Genetic associations in diabetic nephropathy: a meta-analysis. Diabetologia 54 , 544–553 (2011).

Tong, Z. et al. Promoter polymorphism of the erythropoietin gene in severe diabetic eye and kidney complications. Proc. Natl Acad. Sci. USA 105 , 6998–7003 (2008).

Reutens, A. T. Epidemiology of diabetic kidney disease. Med. Clin. 97 , 1–18 (2013).

Google Scholar  

Pezzolesi, M. G. et al. Genome-wide association scan for diabetic nephropathy susceptibility genes in type 1 diabetes. Diabetes 58 , 1403 (2009).

Martini, S. et al. From single nucleotide polymorphism to transcriptional mechanism: a model for FRMD3 in diabetic nephropathy. Diabetes 62 , 2605–2612 (2013).

Sandholm, N. et al. New susceptibility loci associated with kidney disease in type 1 diabetes. PLoS Genet. 8 , e1002921 (2012).

Sandholm, N. et al. Chromosome 2q31. 1 associates with ESRD in women with type 1 diabetes. J. Am. Soc. Nephrol. 24 , 1537–1543 (2013).

Salem, R. M. et al. Genome-wide association study of diabetic kidney disease highlights biology involved in glomerular basement membrane collagen. J. Am. Soc. Nephrol. 30 , 2000–2016 (2019).

Longo, I. et al. COL4A3/COL4A4 mutations: from familial hematuria to autosomal-dominant or recessive Alport syndrome. Kidney Int. 61 , 1947–1956 (2002).

Altshuler, D. et al. The common PPARγ Pro12Ala polymorphism is associated with decreased risk of type 2 diabetes. Nat. Genet. 26 , 76–80 (2000).

Taira, M. et al. A variant within the FTO confers susceptibility to diabetic nephropathy in Japanese patients with type 2 diabetes. PLoS One 13 , e0208654 (2018).

Sandholm, N. & Groop, P. H. Genetic basis of diabetic kidney disease and other diabetic complications. Curr. Opin. Genet. Dev. 50 , 17–24 (2018).

Ahlqvist, E., Van Zuydam, N. R., Groop, L. C. & McCarthy, M. I. The genetics of diabetic complications. Nat. Rev. Nephrol. 11 , 277 (2015).

Sandholm, N. et al. Confirmation of GLRA3 as a susceptibility locus for albuminuria in Finnish patients with type 1 diabetes. Sci. Rep. 8 , 12408 (2018).

Iyengar, S. K. et al. Genome-wide association and trans-ethnic meta-analysis for advanced diabetic kidney disease: family investigation of nephropathy and diabetes (FIND). PLoS Genet. 11 , e1005352 (2015).

Guan, M. et al. Genome-wide association study identifies novel loci for type 2 diabetes-attributed end-stage kidney disease in African Americans. Hum. Genomics 13 , 21 (2019).

Köttgen, A. et al. Multiple loci associated with indices of renal function and chronic kidney disease. Nat. Genet. 41 , 712–217 (2009).

Köttgen, A. et al. New loci associated with kidney function and chronic kidney disease. Nat. Genet. 42 , 376–384 (2010).

Böger, C. A. et al. CUBN is a gene locus for albuminuria. J. Am. Soc. Nephrol. 22 , 555–570 (2011).

Pattaro, C. et al. Genome-wide association and functional follow-up reveals new loci for kidney function. PLoS Genet. 8 , e1002584 (2012).

Gorski, M. et al. Genome-wide association study of kidney function decline in individuals of European descent. Kidney Int. 87 , 1017–1029 (2015).

Teumer, A. et al. Genome-wide association studies identify genetic loci associated with albuminuria in diabetes. Diabetes 65 , 803–817 (2016).

Pattaro, C. et al. Genetic associations at 53 loci highlight cell types and biological pathways relevant for kidney function. Nat. Commun. 7 , 10023 (2016).

Li, M. et al. SOS2 and ACP1 loci identified through large-scale exome chip analysis regulate kidney development and function. J. Am. Soc. Nephrol. 28 , 981–994 (2017).

Gorski, M. et al. 1000 Genomes-based meta-analysis identifies 10 novel loci for kidney function. Sci. Rep. 7 , 45040 (2017).

Haas, M. E. et al. Genetic association of albuminuria with cardiometabolic disease and blood pressure. Am. J. Hum. Genet. 103 , 461–473 (2018).

Ahluwalia, T. S. et al. A novel rare CUBN variant and three additional genes identified in Europeans with and without diabetes: results from an exome-wide association study of albuminuria. Diabetologia 62 , 292–305 (2019).

Teumer, A. et al. Genome-wide association meta-analyses and fine-mapping elucidate pathways influencing albuminuria. Nat. Commun. 10 , 4130 (2019).

Wuttke, M. et al. A catalog of genetic loci associated with kidney function from analyses of a million individuals. Nat. Genet. 51 , 957–972 (2019).

Hellwege, J. N. et al. Mapping eGFR loci to the renal transcriptome and phenome in the VA million veteran program. Nat. Commun. 10 , 1–11 (2019).

Chu, J. & Ali, Y. Diabetic retinopathy: a review. Drug. Dev. Res. 69 , 1–14 (2008).

Yau, J. W. Y. et al. Global prevalence and major risk factors of diabetic retinopathy. Diabetes Care 35 , 556–564 (2012).

Sivaprasad, S., Gupta, B., Crosby-Nwaobi, R. & Evans, J. Prevalence of diabetic retinopathy in various ethnic groups: a worldwide perspective. Surv. Ophthalmol. 57 , 347–370 (2012).

United States Department of Health and Human Services. National diabetes fact sheet: national estimates and general information on diabetes and prediabetes in the United States, 2011 (CDC, 2011).

Bunce, C. & Wormald, R. Causes of blind certifications in England and Wales: April 1999–March 2000. Eye 22 , 905–911 (2008).

Klein, R., Klein, B. E. & Moss, S. E. Visual impairment in diabetes. Ophthalmology 91 , 1–9 (1984).

Leslie, R. & Pyke, D. Diabetic retinopathy in identical twins. Diabetes 31 , 19–21 (1982).

The Diabetes Control and Complications Trial Research Group. Clustering of long-term complications in families with diabetes in the diabetes control and complications trial. Diabetes 46 , 1829–1839 (1997).

Looker, H. C. et al. Genome-wide linkage analyses to identify loci for diabetic retinopathy. Diabetes 56 , 1160–1166 (2007).

Arar, N. H. et al. Heritability of the severity of diabetic retinopathy: the FIND-eye study. Invest. Ophthalmol. Vis. Sci. 49 , 3839–3845 (2008).

Hietala, K., Forsblom, C., Summanen, P. & Groop, P. H. Heritability of proliferative diabetic retinopathy. Diabetes 57 , 2176–2180 (2008).

Hallman, D. M. et al. Familial aggregation of severity of diabetic retinopathy in Mexican Americans from Starr County, Texas. Diabetes Care 28 , 1163–1168 (2005).

Hampton, B. M., Schwartz, S. G., Brantley, M. A. Jr. & Flynn, H. W. Jr. Update on genetics and diabetic retinopathy. Clin. Ophthalmol. 9 , 2175–2193 (2015).

CAS   PubMed   PubMed Central   Google Scholar  

Cho, H. & Sobrin, L. Genetics of diabetic retinopathy. Curr. Diab. Rep. 14 , 515 (2014).

Burdon, K. P. et al. Genome-wide association study for sight-threatening diabetic retinopathy reveals association with genetic variation near the GRB2 gene. Diabetologia 58 , 2288–2297 (2015).

Meng, W. et al. A genome-wide association study suggests new evidence for an association of the NADPH Oxidase 4 (NOX4) gene with severe diabetic retinopathy in type 2 diabetes. Acta Ophthalmol. 96 , e811–e819 (2018).

Li, J., Wang, J. J. & Zhang, S. X. NADPH oxidase4-derived H2O2 promotes aberrant retinal neovascularization via activation of VEGF receptor 2 pathway in oxygen-induced retinopathy. Invest. Ophthalmol. Vis. Sci. 55 , 2955–2955 (2014).

Wang, H., Yang, Z., Jiang, Y. & Hartnett, M. E. Endothelial NADPH oxidase 4 mediates vascular endothelial growth factor receptor 2-induced intravitreal neovascularization in a rat model of retinopathy of prematurity. Mol. Vis. 20 , 231–241 (2014).

Pollack, S. et al. Multiethnic genome-wide association study of diabetic retinopathy using liability threshold modeling of duration of diabetes and glycemic control. Diabetes 68 , 441–456 (2019).

Shtir, C. et al. Exome-based case–control association study using extreme phenotype design reveals novel candidates with protective effect in diabetic retinopathy. Hum. Genet. 135 , 193–200 (2016).

Ung, C. et al. Whole exome sequencing identification of novel candidate genes in patients with proliferative diabetic retinopathy. Vis. Res. 139 , 168–176 (2017).

Johannsen, L. et al. Evaluation of patients with symptoms suggestive of chronic polyneuropathy. J. Clin. Neuromuscul. Dis. 3 , 47–52 (2001).

Pop-Busui, R. et al. Diabetic neuropathy: a position statement by the American Diabetes Association. Diabetes Care 40 , 136–154 (2017).

Singh, N., Armstrong, D. G. & Lipsky, B. A. Preventing foot ulcers in patients with diabetes. JAMA 293 , 217–228 (2005).

Most, R. S. & Sinnock, P. The epidemiology of lower extremity amputations in diabetic individuals. Diabetes Care 6 , 87–91 (1983).

Young, M. J., Boulton, A. J. M., Macleod, A. F., Williams, D. R. R. & Sonksen, P. H. A multicentre study of the prevalence of diabetic peripheral neuropathy in the United Kingdom hospital clinic population. Diabetologia 36 , 150–154 (1993).

Maser, R. E. et al. Epidemiological correlates of diabetic neuropathy: report from pittsburgh epidemiology of diabetes complications study. Diabetes 38 , 1456–1461 (1989).

Tesfaye, S. et al. Prevalence of diabetic peripheral neuropathy and its relation to glycaemic control and potential risk factors: the EURODIAB IDDM complications study. Diabetologia 39 , 1377–1384 (1996).

Tesfaye, S. et al. Vascular risk factors and diabetic neuropathy. N. Engl. J. Med. 352 , 341–350 (2005).

Monti, M. C. et al. Familial risk factors for microvascular complications and differential male-female risk in a large cohort of American families with type 1 diabetes. J. Clin. Endocrinol. Metab. 92 , 4650–4655 (2007).

Meng, W. et al. A genome-wide association study provides evidence of sex-specific involvement of Chr1p35.1 (ZSCAN20-TLR12P) and Chr8p23.1 (HMGB1P46) with diabetic neuropathic pain. EBioMedicine 2 , 1386–1393 (2015).

Meng, W. et al. A genome-wide association study suggests an association of Chr8p21.3 (GFRA2) with diabetic neuropathic pain. Eur. J. Pain. 19 , 392–399 (2015).

Meng, W. et al. A genome-wide association study suggests that MAPK14 is associated with diabetic foot ulcers. Br. J. Dermatol. 177 , 1664–1670 (2017).

Politi, C. et al. Recent advances in exploring the genetic susceptibility to diabetic neuropathy. Diabetes Res. Clin. Pract. 120 , 198–208 (2016).

Witzel, I. I. et al. Identifying common genetic risk factors of diabetic neuropathies. Front. Endocrinol. 6 , 88 (2015).

Tang, Y. et al. A genetic locus on chromosome 2q24 predicting peripheral neuropathy risk in type 2 diabetes: results from the ACCORD and BARI 2D studies. Diabetes 68 , 1649–1662 (2019).

Blesneac, I. et al. Rare NaV1.7 variants associated with painful diabetic peripheral neuropathy. Pain 159 , 469–480 (2018).

Stamler, J., Vaccaro, O., Neaton, J. D. & Wentworth, D. Diabetes, other risk factors, and 12-yr cardiovascular mortality for men screened in the multiple risk factor intervention trial. Diabetes Care 16 , 434–444 (1993).

Haffner, S. M., Lehto, S., Rönnemaa, T., Pyörälä, K. & Laakso, M. Mortality from coronary heart disease in subjects with type 2 diabetes and in nondiabetic subjects with and without prior myocardial infarction. N. Engl. J. Med. 339 , 229–234 (1998).

Soedamah-Muthu, S. S. et al. High risk of cardiovascular disease in patients with type 1 diabetes in the UK. Diabetes Care 29 , 798–804 (2006).

Turnbull, F. M. et al. Intensive glucose control and macrovascular outcomes in type 2 diabetes. Diabetologia 52 , 2288–2298 (2009).

Parry, H. M. et al. Both high and low HbA1c predict incident heart failure in type 2 diabetes mellitus. Circ. Heart Fail. 8 , 236–242 (2015).

Fall, T. et al. The role of adiposity in cardiometabolic traits: a Mendelian randomization analysis. PLoS Med. 10 , e1001474 (2013).

De Ferranti, S. D. et al. Type 1 diabetes mellitus and cardiovascular disease: a scientific statement from the American Heart Association and American Diabetes Association. Circulation 130 , 1110–1130 (2014).

Klein, B. E. K. et al. Cardiovascular disease, mortality, and retinal microvascular characteristics in type 1 diabetes: Wisconsin epidemiologic study of diabetic retinopathy. Arch. Intern. Med. 164 , 1917–1924 (2004).

Zdravkovic, S. et al. Heritability of death from coronary heart disease: a 36-year follow-up of 20 966 Swedish twins. J. Intern. Med. 252 , 247–254 (2002).

Gusev, A. et al. Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases. Am. J. Hum. Genet. 95 , 535–552 (2014).

Simonson, M. A., Wills, A. G., Keller, M. C. & McQueen, M. B. Recent methods for polygenic analysis of genome-wide data implicate an important effect of common variants on cardiovascular disease risk. BMC Med. Genet. 12 , 146 (2011).

Wagenknecht Lynne, E. et al. Familial aggregation of coronary artery calcium in families with type 2 diabetes. Circulation 103 , 1353–1353 (2001).

Lange, L. A. et al. Heritability and expression of C-reactive protein in type 2 diabetes in the diabetes heart study. Ann. Hum. Genet. 70 , 717–725 (2006).

Lange, L. A. et al. Heritability of carotid artery intima-medial thickness in type 2 diabetes. Stroke 33 , 1876–1881 (2002).

Erdmann, J., Kessler, T., Munoz Venegas, L. & Schunkert, H. A decade of genome-wide association studies for coronary artery disease: the challenges ahead. Cardiovasc. Res. 114 , 1241–1257 (2018).

Qi, L. et al. Genetic susceptibility to coronary heart disease in type 2 diabetes. J. Am. Coll. Cardiol. 58 , 2675–2682 (2011).

Parry, H. M. et al. Genetic variants predicting left ventricular hypertrophy in a diabetic population: a Go-DARTS study including meta-analysis. Cardiovasc. Diabetol. 12 , 109 (2013).

Cox, A. J. et al. Genetic risk score associations with cardiovascular disease and mortality in the Diabetes Heart Study. Diabetes Care 37 , 1157–1164 (2014).

Doria, A. et al. Interaction between poor glycemic control and 9p21 locus on risk of coronary artery disease in type 2 diabetes. JAMA 300 , 2389–2397 (2008).

Zhang, L. W. et al. Interaction of type 2 diabetes mellitus with chromosome 9p21 rs10757274 polymorphism on the risk of myocardial infarction: a case–control study in Chinese population. BMC Cardiovasc. Disord. 14 , 170 (2014).

Qi, L. et al. Association between a genetic variant related to glutamic acid metabolism and coronary heart disease in individuals with type 2 diabetes. JAMA 310 , 821–828 (2013).

Prudente, S. et al. Genetic variant at the GLUL locus predicts all-cause mortality in patients with type 2 diabetes. Diabetes 64 , 2658–2663 (2015).

The Look AHEAD Research Group. Prospective association of GLUL rs10911021 with cardiovascular morbidity and mortality among individuals with type 2 diabetes: the look AHEAD study. Diabetes 65 , 297–302 (2016).

Shah, H. S. et al. Genetic predictors of cardiovascular mortality during intensive glycemic control in type 2 diabetes: findings from the ACCORD clinical trial. Diabetes Care 39 , 1915–1924 (2016).

Shah, H. S. et al. Modulation of GLP-1 levels by a genetic variant that regulates the cardiovascular effects of intensive glycemic control in ACCORD. Diabetes Care 41 , 348–355 (2018).

Marso, S. P. et al. Semaglutide and cardiovascular outcomes in patients with type 2 diabetes. N. Engl. J. Med. 375 , 1834–1844 (2016).

Marso, S. P. et al. Liraglutide and cardiovascular outcomes in type 2 diabetes. N. Engl. J. Med. 375 , 311–322 (2016).

Nathan, D. M. et al. Intensive diabetes treatment and cardiovascular disease in patients with type 1 diabetes. N. Engl. J. Med. 353 , 2643–2653 (2005).

UK Prospective Diabetes Study (UKPDS) Group. Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). Lancet 352 , 837–853 (1998).

The ADVANCE Collaborative Group. Intensive blood glucose control and vascular outcomes in patients with type 2 diabetes. N. Engl. J. Med. 358 , 2560–2572 (2008).

Gerstein, H. C. et al. Effects of intensive glucose lowering in type 2 diabetes. N. Engl. J. Med. 358 , 2545–2559 (2008).

Investigators, O. T. Basal insulin and cardiovascular and other outcomes in dysglycemia. N. Engl. J. Med. 367 , 319–328 (2012).

Gerstein, H. C. et al. Basal insulin and cardiovascular and other outcomes in dysglycemia. N. Engl. J. Med. 367 , 319–328 (2012).

Ray, K. K. et al. Effect of intensive control of glucose on cardiovascular outcomes and death in patients with diabetes mellitus: a meta-analysis of randomised controlled trials. Lancet 373 , 1765–1772 (2009).

Tkac, I. Effect of intensive glycemic control on cardiovascular outcomes and all-cause mortality in type 2 diabetes: overview and metaanalysis of five trials. Diabetes Res. Clin. Pract. 86 (Suppl. 1), S57–S62 (2009).

Boussageon, R. et al. Effect of intensive glucose lowering treatment on all cause mortality, cardiovascular death, and microvascular events in type 2 diabetes: meta-analysis of randomised controlled trials. BMJ 343 , d4169 (2011).

Holman, R. R., Paul, S. K., Bethel, M. A., Matthews, D. R. & Neil, H. A. W. 10-year follow-up of intensive glucose control in type 2 diabetes. N. Engl. J. Med. 359 , 1577–1589 (2008).

Gerstein, H. C. et al. Effects of intensive glycaemic control on ischaemic heart disease: analysis of data from the randomised, controlled ACCORD trial. Lancet 384 , 1936–1941 (2014).

Hayward, R. A., Reaven, P. D. & Emanuele, N. V. Follow-up of glycemic control and cardiovascular outcomes in type 2 diabetes. N. Engl. J. Med. 373 , 978 (2015).

PubMed   Google Scholar  

Zinman, B., Lachin, J. M. & Inzucchi, S. E. Empagliflozin, cardiovascular outcomes, and mortality in type 2 diabetes. N. Engl. J. Med. 374 , 1094 (2016).

Merino, J. et al. Genetically driven hyperglycemia increases risk of coronary artery disease separately from type 2 diabetes. Diabetes Care 40 , 687–693 (2017).

Rani, P. K. et al. Albuminuria and diabetic retinopathy in type 2 diabetes mellitus Sankara nethralaya diabetic retinopathy epidemiology and molecular genetic study (SN-DREAMS, report 12). Diabetol. Metab. Syndr. 3 , 9 (2011).

Groop, P. H. et al. The presence and severity of chronic kidney disease predicts all-cause mortality in type 1 diabetes. Diabetes 58 , 1651–1658 (2009).

Chavers, B. M., Mauer, S. M., Ramsay, R. C. & Steffes, M. W. Relationship between retinal and glomerular lesions in IDDM patients. Diabetes 43 , 441–446 (1994).

Klein, R. et al. The relationship of diabetic retinopathy to preclinical diabetic glomerulopathy lesions in type 1 diabetic patients. Diabetes 54 , 527–533 (2005).

Dyck, P. J. et al. The prevalence by staged severity of various types of diabetic neuropathy, retinopathy, and nephropathy in a population – based cohort. Neurology 43 , 817–817 (1993).

Hill, M. N. et al. Risk of foot complications in long-term diabetic patients with and without ESRD: a preliminary study. ANNA J. 23 , 381–389 (1996).

Eggers, P. W., Gohdes, D. & Pugh, J. Nontraumatic lower extremity amputations in the Medicare end-stage renal disease population. Kidney Int. 56 , 1524–1533 (1999).

Drury, P. L. et al. Estimated glomerular filtration rate and albuminuria are independent predictors of cardiovascular events and death in type 2 diabetes mellitus: the Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) study. Diabetologia 54 , 32–43 (2011).

Nichols, G. A., Déruaz-Luyet, A., Hauske, S. J. & Brodovicz, K. G. The association between estimated glomerular filtration rate, albuminuria, and risk of cardiovascular hospitalizations and all-cause mortality among patients with type 2 diabetes. J. Diabetes Complications 32 , 291–297 (2018).

Fuller, J. H., Stevens, L. K. & Wang, S. L., and the WHO Multinational Study Group. Risk factors for cardiovascular mortality and morbidity: the WHO multinational study of vascular disease in diabetes. Diabetologia 44 , S54 (2001).

Cheung, N. et al. Is diabetic retinopathy an independent risk factor for ischemic stroke? Stroke 38 , 398–401 (2007).

Cheung, N. et al. Diabetic retinopathy and the risk of coronary heart disease: the Atherosclerosis risk in communities study. Diabetes Care 30 , 1742–1746 (2007).

Mukhopadhyay, N., Noble, J. A., Govil, M., Marazita, M. L. & Greenberg, D. A. Identifying genetic risk loci for diabetic complications and showing evidence for heterogeneity of type 1 diabetes based on complications risk. PLoS One 13 , e0192696 (2018).

Scott, R. A. et al. An expanded genome-wide association study of type 2 diabetes in Europeans. Diabetes 66 , 2888–2902 (2017).

McCarthy, S. et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48 , 1279 (2016).

Nathan, D. M. et al. The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N. Engl. J. Med. 329 , 977–986 (1993).

UK Prospective Diabetes Study Group. Tight blood pressure control and risk of macrovascular and microvascular complications in type 2 diabetes: UKPDS 38. BMJ 317 , 703–713 (1998).

Lewis, E. J., Hunsicker, L. G., Bain, R. P. & Rohde, R. D. The effect of angiotensin-converting-enzyme inhibition on diabetic nephropathy. N. Engl. J. Med. 329 , 1456–1462 (1993).

Brenner, B. M. et al. Effects of losartan on renal and cardiovascular outcomes in patients with type 2 diabetes and nephropathy. N. Engl. J. Med. 345 , 861–869 (2001).

Zelniker, T. A. et al. Comparison of the effects of glucagon-like peptide receptor agonists and sodium-glucose cotransporter 2 inhibitors for prevention of major adverse cardiovascular and renal outcomes in type 2 diabetes mellitus. Circulation 139 , 2022–2031 (2019).

Mahaffey Kenneth, W. et al. Canagliflozin and cardiovascular and renal outcomes in type 2 diabetes mellitus and chronic kidney disease in primary and secondary cardiovascular prevention groups. Circulation 140 , 739–750 (2019).

Perkovic, V. et al. Canagliflozin and renal outcomes in type 2 diabetes and nephropathy. N. Engl. J. Med. 380 , 2295–2306 (2019).

Santer, R. et al. Molecular analysis of the SGLT2 gene in patients with renal glucosuria. J. Am. Soc. Nephrol. 14 , 2873–2882 (2003).

Download references

Acknowledgements

J.B.C. is supported by an American Diabetes Postdoctoral Fellowship (1-19-PDF-028). J.C.F. is supported by NIDDK K24 DK110550 and R01 DK105154.

Author information

Authors and affiliations.

Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA

Joanne B. Cole & Jose C. Florez

Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA

Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children’s Hospital, Boston, MA, USA

Joanne B. Cole

Department of Medicine, Harvard Medical School, Boston, MA, USA

Jose C. Florez

You can also search for this author in PubMed   Google Scholar

Contributions

J.B.C. researched data for the article, made a substantial contribution to discussion of the content and wrote and reviewed/edited the manuscript before submission. J.C.F. reviewed/edited the manuscript before submission.

Corresponding author

Correspondence to Jose C. Florez .

Ethics declarations

Competing interests.

J.C.F. has received consulting honoraria from Janssen Pharmaceuticals and Goldfinch Bio, and a speaker honorarium from Novo Nordisk. J.B.C. declares no competing interests.

Additional information

Peer review information.

Nature Reviews Nephrology thanks Peter Rossing, Marcus Pezzolesi and Carol Forsblom for their contribution to the peer review of this work.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Cite this article.

Cole, J.B., Florez, J.C. Genetics of diabetes mellitus and diabetes complications. Nat Rev Nephrol 16 , 377–390 (2020). https://doi.org/10.1038/s41581-020-0278-5

Download citation

Accepted : 29 March 2020

Published : 12 May 2020

Issue Date : July 2020

DOI : https://doi.org/10.1038/s41581-020-0278-5

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

Progress and application of adipose-derived stem cells in the treatment of diabetes and its complications.

Stem Cell Research & Therapy (2024)

Advances in secondary prevention mechanisms of macrovascular complications in type 2 diabetes mellitus patients: a comprehensive review

• Huifang Guan
• Jiaxing Tian
• Xiaolin Tong

European Journal of Medical Research (2024)

Insulin resistance-related features are associated with cognitive decline: a cross-sectional study in adult patients with type 1 diabetes

• Wenjing Zou

Diabetology & Metabolic Syndrome (2024)

Knowledge, attitudes and practices among medical workers toward outpatient diabetes information platform

• Jianying Duan

BMC Health Services Research (2024)

Nanozyme as a rising star for metabolic disease management

Journal of Nanobiotechnology (2024)

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

• Previous Article
• Next Article

Introduction

Research design and methods, conclusions, article information, the diabetes control and complications trial/epidemiology of diabetes interventions and complications study at 30 years: summary and future directions.

*A complete list of participants in the DCCT/EDIC Research Group can be found in N Engl J Med 2011;365:2366–2376.

• Split-Screen
• Article contents
• Figures & tables
• Supplementary Data
• Peer Review
• Open the PDF for in another window
• Cite Icon Cite
• Get Permissions

Rose A. Gubitosi-Klug , for the DCCT/EDIC Research Group; The Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications Study at 30 Years: Summary and Future Directions. Diabetes Care 1 January 2014; 37 (1): 44–49. https://doi.org/10.2337/dc13-2148

Download citation file:

• Ris (Zotero)
• Reference Manager

The Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) study continues to address knowledge gaps in our understanding of type 1 diabetes and the effects of intensive therapy on its long-term complications.

During the DCCT (1982–1993), a controlled clinical trial of 1,441 subjects with type 1 diabetes, and the EDIC (1994–present), an observational study of the DCCT cohort, core data collection has included medical history questionnaires, surveillance health exams, and frequent laboratory and other evaluations for microvascular and macrovascular disease. Numerous collaborations have expanded the outcome data with more detailed investigations of cardiovascular disease, cognitive function, neuropathy, genetics, and potential biological pathways involved in the development of complications.

The longitudinal follow-up of the DCCT/EDIC cohort provides the opportunity to continue monitoring the durability of intensive treatment as well as to address lingering questions in type 1 diabetes research. Future planned analyses will address the onset and progression of microvascular triopathy, evidence-based screening for retinopathy and nephropathy, effects of glycemic variability and nonglycemic risk factors on outcomes, long-term impact of intensive therapy on cognitive decline, and health economics. Three new proposed investigations include an examination of residual C-peptide secretion and its impact, prevalence of hearing impairment, and evaluation of gastrointestinal dysfunction.

With the comprehensive data collection and the remarkable participant retention over 30 years, the DCCT/EDIC continues as an irreplaceable resource for understanding type 1 diabetes and its long-term complications.

The closing article in the series celebrating the 30th anniversary of Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) study reflects on how past investigations have shaped the study’s future directions. The preceding articles on retinopathy, nephropathy, neuropathy, and cardiovascular disease have provided detailed updates on the state of the complications of diabetes in the cohort over the past 30 years. Collectively, the message is clear: chronic glycemia is the major modifiable factor driving the development and progression of the complications of type 1 diabetes ( 1 – 6 ). With intensive management of their diabetes, patients can achieve lower glycemia and reduce the risk of developing complications, including severe disease ( 7 ). In turn, patients free of complications report a good and sustained diabetes-related quality of life ( 8 ). The substantial effects of intensive therapy on primary prevention during DCCT, compounded by metabolic memory established during EDIC, make it imperative to intervene with intensive therapy as early after diagnosis as possible to effectively slow the course of complications.

As we move forward, the durability of our intervention will continue to be monitored: Will the effects of intensive therapy—metabolic memory—decrease over time? EDIC will continue to monitor the trajectory of the development and progression of microvascular and cardiovascular disease. DCCT/EDIC core studies have been the foundation for many collaborations, each initiative advancing our understanding of the multisystem effects of diabetes and guiding future clinical and basic science investigations ( Fig. 1 ). In this spirit, several new ancillary studies are planned to investigate clinical topics across diverse fields, such as residual β-cell function, hearing dysfunction, and gastric disturbances.

The DCCT/EDIC core investigations have been the center of a diverse array of supplemental studies and collaborations over the past 30 years. The black circle denotes the major outcomes (i.e., retinopathy, nephropathy, neuropathy, and cardiovascular disease) studied during the DCCT (1983–1993) and throughout EDIC (1994–present). Extending from these core investigations, a multitude of supplemental studies have been performed, starting from 1990–2005, the late DCCT/early EDIC period; continuing with 2006–2013, the first EDIC extension period; and expanding to 2013–forward, the current extension of EDIC. Family genetics study, first-degree relative genetic studies ( 12 ); CVD-CAC, coronary calcium CT scan ( 13 ); CVD-NMR lipid, lipid studies ( 14 ); URO-EDIC 1, study of urological dysfunction ( 15 ); CVD-IMT, carotid ultrasounds ( 16 – 18 ); AGEs, advanced glycation end products ( 19 ); cognitive function, neurocognition testing ( 20 – 22 ); CAN, cardiac autonomic neuropathy testing ( 23 ); cardiac MRI, enhanced cardiac MRI ( 11 ); dermal AGEs, dermal advanced glycation end product study ( 24 , 25 ); retention, participant retention study ( 26 ); glycated albumin measurements ( 27 , 28 ); haptoglobin levels ( 29 ); cardiovascular biomarkers; epigenetics; mobility, joint mobility; and URO-EDIC 2, repeat study of urological dysfunction.

Participant Retention

As reviewed previously, the DCCT interventional study was conducted between 1982–1993, and since 1994 EDIC has continued as an observational follow-up of the consenting DCCT participants, comprising 95% of the surviving members of the original DCCT cohort ( 9 , 10 ). Retention during EDIC has been consistent (93–96%) whether considering original recruitment into primary prevention or secondary intervention groups or random assignment to intensive or conventional treatment. DCCT/EDIC ancillary studies are offered to all subjects, and compliance has routinely been >90% for studies involving minor procedures and/or questionnaires. Even with procedure-intensive ancillary studies, such as the cardiac magnetic resonance imaging (MRI), participation rates were 81% or better ( 11 ).

Current Study Design and Core Outcomes

For each DCCT/EDIC participant, the current core protocol is performed on an annual basis and includes the major data collection form that reviews the participants’ health history by system and updates intercurrent events; denotes critical diabetes-related health information including all treatments and hypoglycemia severity, awareness, and frequency during the prior 3 months; and records the results of the standardized annual physical examination focusing on diabetes complications—yearly HbA 1c and electrocardiogram, fasting lipid profiles every other year, urine albumin-to-creatinine ratio every other year, and ophthalmologist exam and fundus photographs once every 4 years. Core data collection of HbA 1c , fasting lipids, and renal collections has exceeded 89%, 90%, and 91%, respectively, on an annual basis.

Additional Study Outcomes

In addition to the core data collection, the breadth of DCCT/EDIC investigations has been increased through supplemental studies proposed by the DCCT/EDIC Study Group and collaborations with experts in various fields of diabetes research ( Fig. 1 ). These studies are typically performed at the time of an annual visit. Data from the supplemental studies and collaborations have been incorporated into the longitudinal dataset and will be used in the planned analyses. This includes data from the following studies: first-degree relative genetic studies ( 12 ), coronary calcium computed tomography scan ( 13 ), lipid studies ( 14 ), urological dysfunction (URO-EDIC 1) ( 15 ), carotid ultrasounds ( 16 – 18 ), advanced glycation end products (AGEs) ( 19 ), neurocognition testing ( 20 – 22 ), peripheral nerve conduction testing ( 5 , 20 – 22 ), cardiac autonomic neuropathy testing ( 23 ), enhanced cardiac MRI ( 11 ), dermal AGE study ( 24 , 25 ), participant retention study ( 26 ), glycated albumin measurements ( 27 , 28 ), and haptoglobin levels ( 29 ). More recent supplemental studies with ongoing analyses include cardiovascular biomarkers, epigenetics, joint mobility, and repeat of urological dysfunction (URO-EDIC 2).

Metabolic Memory

As presented in the earlier accompanying manuscripts, the data through EDIC year 18 suggest that the beneficial risk reductions afforded by intensive relative to conventional therapy, while persistent and significant, are decreasing over time ( 30 – 33 ). Declining risk reductions are evident across microvascular complications: risk reduction for further progression of retinopathy falling from 70% to 53% to 46% at EDIC year 4, year 10, and year 18, respectively; risk reduction for confirmed clinical neuropathy decreased from 64% at DCCT closeout to 30% at EDIC year 14; and risk reduction for albuminuria with intensive therapy dropping from a high of nearly 60% at EDIC year 8 to nearly 40% at EDIC year 18, which is identical to the risk reduction seen at DCCT closeout. The fall in relative benefit of intensive therapy is almost certainly due to the adoption of intensive therapy by the original conventional therapy group and the waning of metabolic memory over time. The relative contribution of DCCT HbA 1c , which is also declining in the case of retinopathy, to these risk reductions over time has important clinical as well as biological implications. The underlying mechanistic link(s) between hyperglycemia and the development of complications in various organ systems remains a critical unanswered question. The potential mechanisms for the lasting effects of metabolic memory may involve genetic factors, epigenetic changes, and/or glycation of proteins, all of which have been and continue to be explored in DCCT/EDIC. The DCCT/EDIC family genetic study has identified several loci that regulate risk of developing retinopathy ( 34 ), nephropathy ( 35 , 36 ), and erectile dysfunction ( 37 ). Initial epigenetic investigations were presented during the 2013 American Diabetes Association Scientific Sessions and suggest intriguing links between glycemia-associated histone acetylation and activation of recognized pathogenic inflammatory cascades. In previous DCCT/EDIC publications, AGEs have been shown to correlate with the presence of microvascular complications at DCCT closeout and to predict the development and progression of complications in EDIC, all independent of the HbA 1c ( 19 , 38 ). The potential genetic factors predisposing to accelerated AGE formation are currently under investigation.

Core Initiatives

The 30-year longitudinal data collected and the remarkable retention of our research participants provide an invaluable diabetes resource, and the DCCT/EDIC and its collaborators hope to address several novel questions in diabetes care in the next few years.

Defining the relative time course of the development of retinopathy, nephropathy, and neuropathy (“triopathy”) to answer such clinical questions as: “Can the rate of progression of retinopathy predict individuals that will progress to more advanced renal disease?” and “What is the frequency of triopathy and what are the dominant risk factors?”

Establishing the evidence-based frequency of screening of retinopathy and nephropathy

Refining our prior 7-point self-monitoring of blood glucose data to investigate the role of glycemic variability on outcomes as well as perform a current cross-sectional examination of glycemic variability using continuous glucose monitoring

Exploring the contributions of nonglycemic risk factors to risk reduction for macrovascular as well as microvascular outcomes

Monitoring for effects of glycemic control on neurocognition: Will the rate of age-related cognitive decline in our cohort be different when compared with the general population?

Continuing to monitor the bottom line—that intensive diabetes management prevents costly complications and is economically sage

Ancillary Initiatives

Building on these core initiatives, additional ancillary studies are planned. First, prior DCCT data demonstrated that intensive therapy preserved insulin secretion, measured as C-peptide ( 39 ). Those with preserved C-peptide secretion had lower HbA 1c with less frequent hypoglycemia and reduced development of retinopathy and nephropathy. Now, in EDIC year 20, the following questions are posed.

Using modern, ultrasensitive assays, is there residual β-cell function after an average diabetes duration of 30 years?

What factors influence residual β-cell function?

What is the physiologic significance of low levels of C-peptide?

Will there remain a beneficial reduction in rates of complications associated with preserved insulin secretion?

Based on pilot data obtained with 4-h mixed-meal tolerance tests on 58 DCCT/EDIC subjects, mixed-meal tolerance tests are planned for the entire EDIC cohort. Three different ultrasensitive C-peptide assays will be used to provide comparative analyses of detection limits. The C-peptide results, in combination with the extensive, historical database, will extend our understanding of the relationship of long-term C-peptide production with HbA 1c levels over time, insulin dose requirements, hypoglycemia rates, and complications. Of importance, the study will also seek to identify the factors that mediate β-cell preservation and provide hope for future treatment options for type 1 diabetes.

A second initiative stems from the observation that hearing impairment is more common in type 2 diabetes than in the nondiabetic population ( 40 , 41 ). Early detection of hearing loss allows for earlier intervention, which preserves quality of life. The current proposal includes standardized hearing tests at all EDIC centers with oversight by a centralized audiology unit. The DCCT/EDIC participants’ nondiabetic partners/spouses will be used as control subjects. The prevalence of hearing impairment in type 1 diabetes, its potential correlation with other microvascular complications, and the relationship with glycemia and other risk factors will be investigated.

Third, abnormal gastric emptying is a manifestation of autonomic neuropathy that challenges many patients’ daily routine and their glycemic control. This initiative aims to advance our understanding of the prevalence of gastroparesis, symptomatic and asymptomatic as well as delayed and rapid types. In addition to symptom-related questionnaires, participants will complete the validated stable isotope 13 C-Spirulina platensis gastric emptying breath test ( 42 ). Like the C-peptide study, a pilot investigation with 80 participants across seven EDIC centers is currently in progress.

The DCCT/EDIC set the standard of care for clinical management of type 1 diabetes, which has forever changed the course of its complications. Intensive diabetes management remains the primary approach to prevent or slow the progression of complications. Now entering the fourth decade of study, the DCCT/EDIC core objectives continue to emphasize the rigorous assessment of microvascular and cardiovascular disease and traditional and novel risk factors. The comprehensive, longitudinal data collection and continued successful collaboration between the DCCT/EDIC investigators and research partners should provide answers to questions that will guide the clinical care of type 1 diabetes. The improvement in the long-term prospects for people with type 1 diabetes brought about through DCCT/EDIC should continue with these efforts.

Clinical trial reg. nos. NCT00360815 and NCT00360893, clinicaltrials.gov .

See accompanying articles, pp. 5 , 8 , 9 , 17 , 24 , 31 , and 39 .

Acknowledgments. These investigations were the result of the dedication and collaboration between many individuals. The author thanks the DCCT/EDIC participants, the DCCT/EDIC’s partners in discovery, who have rewritten the course of diabetes for future generations; the National Institute of Diabetes and Digestive and Kidney Diseases, a long-time sponsor, whose support has maintained the DCCT/EDIC’s abilities to find answers to their questions; the DCCT/EDIC’s pharmaceutical donors, whose assistance with diabetes supplies has been a gracious support to the participants throughout the study; the DCCT/EDIC study coordinators, who are the core of this study and remain always mindful of the delicate balance between scientific inquiry and participant burden; and the investigators and collaborators, who have carefully guided the course of past and future investigations.

Funding. The DCCT/EDIC has been supported by U01 Cooperative Agreement grants (1982–1993, 2011–2016) and contracts (1982–2011) with the Division of Diabetes Endocrinology and Metabolic Diseases of the National Institute of Diabetes and Digestive and Kidney Diseases (current grant numbers U01-DK-094176 and U01-DK-094157) and through support of the National Eye Institute, the National Institute of Neurological Disorders and Stroke, the Genetic Clinical Research Centers Program (1993–2007), and Clinical and Translational Science Center Program (2006–present), Bethesda, MD.

Industry contributors have had no role in the DCCT/EDIC but have provided free or discounted supplies or equipment to support participants’ adherence to the study: Abbott Diabetes Care (Alameda, CA); Animas (West Chester, PA); Bayer Diabetes Care (Tarrytown, NY); Becton, Dickinson and Company (Franklin Lakes, NJ); CanAm (Atlanta, GA); Eli Lilly (Indianapolis, IN); LifeScan (Milpitas, CA); Medtronic Diabetes (Minneapolis, MI); Nova Diabetes Care (Bedford, MA); Omron (Shelton, CT); OmniPod Insulin Management System (Bedford, MA); Roche Diabetes Care (Indianapolis, IN); and Sanofi (Bridgewater, NJ).

Duality of Interest. No potential conflicts of interest relevant to this article were reported.

Author Contributions. R.A.G.-K. researched and wrote the manuscript. R.A.G.-K. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis .

Email alerts

• Neuropathy and Related Findings in the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications Study
• Kidney Disease and Related Findings in the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications Study
• Diabetic Retinopathy and Other Ocular Findings in the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications Study
• The Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications Study at 30 Years: Overview
• The Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications Study at 30 Years: The “Gift” That Keeps on Giving!
• The Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications Study: 30th Anniversary Presentations
• Update on Cardiovascular Outcomes at 30 Years of the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications Study
• In This Issue of Diabetes Care
• Online ISSN 1935-5548
• Print ISSN 0149-5992
• Diabetes Care
• Clinical Diabetes
• Diabetes Spectrum
• Standards of Medical Care in Diabetes
• Scientific Sessions Abstracts
• BMJ Open Diabetes Research & Care
• ShopDiabetes.org
• ADA Professional Books

Clinical Compendia

• Clinical Compendia Home
• Latest News
• DiabetesPro SmartBrief
• Special Collections
• DiabetesPro®
• Diabetes Food Hub™
• Insulin Affordability
• Know Diabetes By Heart™
• About the ADA
• Journal Policies
• For Reviewers
• Advertising in ADA Journals
• Reprints and Permission for Reuse
• Copyright Notice/Public Access Policy
• ADA Professional Membership
• ADA Member Directory
• Diabetes.org
• X (Twitter)
• Cookie Policy
• Accessibility
• Terms & Conditions
• Get Adobe Acrobat Reader
• © Copyright American Diabetes Association

This Feature Is Available To Subscribers Only

Sign In or Create an Account

Diabetes and Its Complications: Therapies Available, Anticipated and Aspired

Affiliations.

• 1 Ipca Laboratories, Mumbai - 400063, India.
• 2 Bhupal Nobles' Institute of Pharmaceutical Sciences, Udaipur, India.
• 3 Department of Biochemistry, Pacific Institute of Medical Sciences, Udaipur, India.
• 4 Ravinder Nath Tagore Medical College and Maharana Bhupal Govt. Hospital, Udaipur, India.
• 5 School of Pharmaceutical Sciences, Lovely Professional University, Phagwara, Punjab- 144411, India.
• PMID: 33143627
• DOI: 10.2174/1573399816666201103144231

Worldwide, diabetes ranks among the ten leading causes of mortality. Prevalence of diabetes is growing rapidly in low and middle income countries. It is a progressive disease leading to serious co-morbidities, which results in increased cost of treatment and over-all health system of the country. Pathophysiological alterations in Type 2 Diabetes (T2D) progressed from a simple disturbance in the functioning of the pancreas to triumvirate to ominous octet to egregious eleven to dirty dozen model. Due to complex interplay of multiple hormones in T2D, there may be multifaceted approach in its management. The 'long-term secondary complications' in uncontrolled diabetes may affect almost every organ of the body, and finally may lead to multi-organ dysfunction. Available therapies are inconsistent in maintaining long term glycemic control and their long term use may be associated with adverse effects. There is need for newer drugs, not only for glycemic control but also for prevention or mitigation of secondary microvascular and macrovascular complications. Increased knowledge of the pathophysiology of diabetes has contributed to the development of novel treatments. Several new agents like Glucagon Like Peptide - 1 (GLP-1) agonists, Dipeptidyl Peptidase IV (DPP-4) inhibitors, amylin analogues, Sodium-Glucose transport -2 (SGLT- 2) inhibitors and dual Peroxisome Proliferator-Activated Receptor (PPAR) agonists are available or will be available soon, thus extending the range of therapy for T2D, thereby preventing its long term complications. The article discusses the pathophysiology of diabetes along with its comorbidities, with a focus on existing and novel upcoming antidiabetic drugs which are under investigation. It also dives deep to deliberate upon the novel therapies that are in various stages of development. Adding new options with new mechanisms of action to the treatment armamentarium of diabetes may eventually help improve outcomes and reduce its economic burden.

Keywords: Cardioprotection; Diabetic complications; GLP-1; Management of T2D; Pathophysiology; Type 2 Diabetes.

Copyright© Bentham Science Publishers; For any queries, please email at [email protected].

• Diabetes Mellitus, Type 2* / complications
• Diabetes Mellitus, Type 2* / drug therapy
• Diabetes Mellitus, Type 2* / epidemiology
• Dipeptidyl-Peptidase IV Inhibitors* / therapeutic use
• Glucagon-Like Peptide 1
• Hypoglycemic Agents / therapeutic use
• Dipeptidyl-Peptidase IV Inhibitors
• Hypoglycemic Agents

Log in using your username and password

• Search More Search for this keyword Advanced search
• Latest content
• BMJ Journals More You are viewing from: Google Indexer

You are here

• Volume 8, Issue 1
• Chronic complications in patients with newly diagnosed type 2 diabetes: prevalence and related metabolic and clinical features: the Verona Newly Diagnosed Type 2 Diabetes Study (VNDS) 9
• Article Text
• Article info
• Citation Tools
• Rapid Responses
• Article metrics

• http://orcid.org/0000-0003-1074-5164 Enzo Bonora 1 ,
• Maddalena Trombetta 1 ,
• Marco Dauriz 1 ,
• Daniela Travia 2 ,
• Vittorio Cacciatori 2 ,
• Corinna Brangani 2 ,
• Carlo Negri 2 ,
• Fabrizia Perrone 2 ,
• Isabella Pichiri 2 ,
• Vincenzo Stoico 2 ,
• http://orcid.org/0000-0003-1085-554X Giacomo Zoppini 1 ,
• Elisabetta Rinaldi 1 ,
• Giuliana Da Prato 1 ,
• Maria Linda Boselli 1 ,
• Lorenza Santi 1 ,
• Federica Moschetta 1 ,
• Monica Zardini 1 ,
• Riccardo C Bonadonna 3
• 1 Department of Medicine, Division of Endocrinology, Diabetes and Metabolism , University of Verona , Verona , Italy
• 2 Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Azienda Ospedaliera Universitaria Integrata Verona , Verona , Italy
• 3 Department of Medicine and Surgery , University of Parma , Parma , Italy
• Correspondence to Professor Enzo Bonora; enzo.bonora{at}univr.it

Introduction We explored the presence of chronic complications in subjects with newly diagnosed type 2 diabetes referred to the Verona Diabetes Clinic. Metabolic (insulin secretion and sensitivity) and clinical features associated with complications were also investigated.

Research design and methods The comprehensive assessment of microvascular and macrovascular complications included detailed medical history, resting ECG, ultrasonography of carotid and lower limb arteries, quantitative neurological evaluation, cardiovascular autonomic tests, ophthalmoscopy, kidney function tests. Insulin sensitivity and beta-cell function were assessed by state-of-the-art techniques (insulin clamp and mathematical modeling of glucose/C-peptide curves during oral glucose tolerance test).

Results We examined 806 patients (median age years, two-thirds males), of whom prior clinical cardiovascular disease (CVD) was revealed in 11.2% and preclinical CVD in 7.7%. Somatic neuropathy was found in 21.2% and cardiovascular autonomic neuropathy in 18.6%. Retinopathy was observed in 4.9% (background 4.2%, proliferative 0.7%). Chronic kidney disease (estimated glomerular filtration rate <60 mL/min/1.73 m 2 ) was found in 8.8% and excessive albuminuria in 13.2% (microalbuminuria 11.9%, macroalbuminuria 1.3%).

Isolated microvascular disease occurred in 30.8%, isolated macrovascular disease in 9.3%, a combination of both in 9.1%, any complication in 49.2% and no complications in 50.8%.

Gender, age, body mass index, smoking, hemoglobin A1c and/or hypertension were independently associated with one or more complications. Insulin resistance and beta-cell dysfunction were associated with macrovascular but not microvascular disease.

Conclusions Despite a generally earlier diagnosis for an increased awareness of the disease, as many as ~50% of patients with newly diagnosed type 2 diabetes had clinical or preclinical manifestations of microvascular and/or macrovascular disease. Insulin resistance might play an independent role in macrovascular disease.

Trial registration number NCT01526720 .

• diabetes mellitus
• insulin resistance
• insulin secretion
• diabetes complications

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/ .

https://doi.org/10.1136/bmjdrc-2020-001549

Statistics from Altmetric.com

Request permissions.

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

Significance of this study

What is already known about this subject.

Type 2 diabetes mellitus (T2DM) can remain undiagnosed for years before being detected.

Unrecognized and untreated hyperglycemia could result in organ damage and contribute to classic macrovascular and microvascular complications.

What are the new findings?

When carefully searched, chronic complications of diabetes are revealed in as many as 50% of patients with newly diagnosed T2DM.

Microvascular, mainly neurological, are more prevalent than macrovascular complications.

State-of-the-art assessed insulin resistance and beta-cell dysfunction are independently associated with macrovascular complications.

How might these results change the focus of research or clinical practice?

More effective strategies to anticipate T2DM diagnosis seem to be warranted.

A more detailed and comprehensive search for organ/system damage should be implemented as soon as the diagnosis of T2DM is established.

Introduction

The stages of type 2 diabetes mellitus (T2DM) include a period in which the disease is undiagnosed. In the 90s, the time elapsing before diagnosis was estimated to be up to 10 years. 1 Hyperglycemia can generate functional and structural damages which might yield macrovascular and/or microvascular complications during this more or less long period of undetected disease. In fact, a number of studies reported that a sizable proportion of subjects with newly diagnosed T2DM already have chronic complications. 2–16 Nowadays, at least in Western countries, T2DM is probably diagnosed at an earlier stage 17 and this might have reduced the prevalence of complications at time of diagnosis. However, an updated information on the prevalence of chronic complications when T2DM is first diagnosed is rather scant and generally incomplete. Most studies, in fact, did not focus on all potential complications and none of them carefully evaluated subclinical vascular disease. Yet, very few studies have explored the association of complications with main pathogenic defects of T2DM, that is, insulin resistance and beta-cell dysfunction.

The aim of the present study was to assess the prevalence and associated clinical and metabolic features of all traditional chronic complications of T2DM in a large cohort of newly diagnosed patients referred to the Diabetes Clinic of Verona in the last years.

Subjects and methods

Study population.

The Verona Newly Diagnosed Type 2 Diabetes Study is an ongoing study on genetics, pathophysiology and clinics of patients with newly diagnosed T2DM. 18–23 As of January 1, 2002, all patients with T2DM referred to the Diabetes Clinic embedded into the Division of Endocrinology, Diabetes and Metabolic Diseases of the University and Hospital Trust of Verona and whose disease was diagnosed in the past 6 months were offered to participate in this study. Recruitment was ended on December 31, 2015 and a follow-up was then planned and is ongoing. All participants signed an informed consent form. The clinical evidence on which the diagnosis of T2DM had been made was reviewed at the recruitment and the diagnosis was confirmed according to standard criteria. The large majority of patients were drug-naïve (~95%) or, if already treated with antidiabetic drugs (~5%), underwent a treatment washout of at least 1 week before metabolic tests were performed. Exclusion criteria were age >75 years, non-Italian ancestry, current insulin treatment, presence of anti- glutamic acid decarboxylase antibodies and history of malignancies or any condition severely impairing liver and/or kidney function. In this paper, we report data collected from 806 patients. Not all of them accepted to undergo the proposed complete assessment but most tests were carried out in >85% of patients. Cardiovascular autonomic tests were performed in 68% of the cohort.

Clinical data

Weight and height were measured and body mass index (BMI) calculated by dividing weight in kilograms by the square of height in meters. Waist circumference (to the nearest 0.5 cm) was measured with a plastic tape meter at the level of the umbilicus. Blood pressure was measured with a standard mercury manometer on the right arm when sitting. Hypertension was diagnosed when systolic blood pressure was ≥140 mm Hg and/or diastolic blood pressure was ≥90 mm Hg and/or antihypertensive drugs were used. A confirmed history of myocardial infarction, angina, coronary revascularization, stroke, transitory ischemic attack, carotid revascularization, non-traumatic amputation, gangrene and/or lower limb revascularization was considered a valid proxy for prior clinical cardiovascular disease (CVD). A resting 12-lead ECG was performed (CardioDirect 12 unit; Metasoft 3.9 software) and interpreted according to Minnesota coding system. 24 In particular, ECG abnormalities were categorized as ‘definite’, ‘probable’ or ‘possible coronary heart disease’ and only ‘definite’ ECG abnormalities were used for diagnosing myocardial ischemia. Ultrasonography scanning of common and internal carotid arteries was performed as previously described (Esaote Wall Track System, Esaote S.p.A., Genova, Italy) and a cut-off of 40% was used to define a significant arterial stenosis. 22 Ultrasonography scanning of lower limb arteries was performed and any detected stenosis or moderate-to-severe reduction of blood flow at proximal and/or distal level was considered as a marker of peripheral artery disease. Presence of diabetic retinopathy (DR) was detected by indirect ophthalmoscopy after pupillary dilation by a single expert ophthalmologist. DR was categorized into background and proliferative. Distal symmetric polyneuropathy (DSPN) was looked for by assessing ankle reflex, touch sensation by Semmes-Weinstein monofilament and vibration perception threshold by biothesiometer. A dichotomous approach (yes/no) was used to categorize it. Cardiovascular autonomic neuropathy (CAN) was searched and diagnosed as previously described. 23

Laboratory testing and metabolic studies

Venous blood was drawn in the morning after an overnight fast in all patients. Plasma glucose and serum creatinine and lipids were assayed by standard laboratory procedures. Hypercholesterolemia was arbitrarily defined when statins were used and/or low-density lipoprotein (LDL) cholesterol was above the current recommended target of <70 mg/dL (<1.8 mmol/L). Hemoglobin A1c (HbA1c) was measured with a high performance liquid chromatography method, standardized according to IFCC. In case of discrepancy between the three tests (fasting plasma glucose, 2-hour plasma glucose, HbA1c), the one documenting diabetes (value above the diagnostic cut-off) was used for diagnosis according to standard criteria. 25 Glomerular filtration rate (GFR) was estimated from the four-variable Modification of Diet in Renal Disease study equation. 26 Chronic kidney disease (CKD) was established when estimated glomerular filtration rate was <60 mL/min/1.73 m 2 . Urinary albumin excretion rate was measured from a 24-hour urine sample by an immunonephelometric method on at least two occasions. Microalbuminuria and macroalbuminuria were defined as urinary excretion of 30–300 and >300 mg/day, respectively. Subjects underwent a euglycemic hyperinsulinemic clamp and a 75 g oral glucose tolerance test (OGTT) with frequent and prolonged sampling (up to 4–5 hours) for assessment of beta-cell function which was reconstructed by mathematical modeling, as previously described. 20 21

Statistical analysis

Statistical analyses were carried out with standard techniques (χ 2 test, multiple logistic regression analysis). Skewed variables were logarithmically transformed to improve normality before analyses were performed. Data are presented as median and IQR or as percentage of total.

Table 1 reports main clinical features of subjects under study. Two-thirds of them were males. Median age was 60 years. Most patients were overweight or had obesity. Average fasting and 2-hour OGTT plasma glucose levels were mildly elevated and the same holds true for HbA1c. A number of subjects were diagnosed by OGTT or HbA1c rather than fasting plasma glucose. More than half of the subjects were treated with antihypertensive medications and one-fifth with statins. Blood pressure was generally well controlled but LDL cholesterol was above current target in most subjects.

• View inline

Main clinical features of subjects under study

Data on prior clinical CVD were available in all subjects. Ultrasonography of carotid artery or lower limb arteries in 89% and 88%, respectively. Neurological assessment was available in all subjects and cardiovascular autonomic tests in 68%. Fundus oculi was examined by ophthalmoscopy in 88% of subjects. Subjects undergoing insulin clamp and OGTT were 96% and 93%, respectively.

Table 2 summarizes the prevalence of various chronic complications. A prior clinical cardiovascular event was revealed in >10% of subjects. Another ~8% had preclinical manifestations of CVD (ischemic ECG and/or plaques into carotid or lower limbs arteries). CKD was found in ~9% and albuminuria in ~13% (mostly microalbuminuria). DSPN was observed in ~21% and CAN in ~19%. Retinopathy was observed in ~5% of subjects (proliferative in less than one out of five).

Prevalence of chronic complications of diabetes

In subjects who had a complete staging of organ/system damage (heart, arteries, kidney, eye, nerves) (n=614, 76%), microvascular disease occurred in 30.8%, macrovascular disease in 9.3%, both in 9.1% and either in 49.2%. As a consequence, 50.8% had no detectable complications. The inclusion of CAN into this analysis reduced the number of subjects (n=438, 54%) but did not substantially change these proportions (eg, at least one complication was found in 50.2%).

We performed multivariate logistic regression analyses in which single chronic complications were the dependent variables and gender, age, smoking, BMI, HbA1c, hypertension and hypercholesterolemia were the independent variables ( table 3 ). In these analyses, CVD was associated with male gender, age, smoking and hypertension, CKD was associated with female gender and age, microalbuminuria-macroalbuminuria was associated with male gender, smoking and HbA1c, DR was not significantly associated with any variable in the model, DSPN was associated with BMI and smoking, CAN was associated with BMI, smoking and hypertension. The replacement in the analyses of hypercholesterolemia with LDL cholesterol and hypertension with systolic blood pressure did not change the results. When carotid stenosis or lower limb atherosclerosis were treated as dependent variables, the former was significantly associated with age and smoking, and the latter with male gender, age, hypertension and hypercholesterolemia (data not shown). The inclusion of triglycerides and HDL cholesterol in the models yielded similar results with triglycerides being positively associated with CKD (OR 2.53, 95% CI 1.29 to 4.98, p=0.007) and HDL cholesterol being negatively associated with albuminuria (OR 0.25, 95% CI 0.09 to 0.65; p=0.005). The replacement of BMI with waist circumference showed that the latter, as for BMI, was a predictor of CAN (OR 1.02, 95% CI 1.00 to 1.04, p=0.036; per each cm of waist) but not of other complications.

Independent predictors of single chronic complications in multivariate analyses

We have also run multivariate analyses where microvascular (pooled) or macrovascular complications were the dependent variables and insulin sensitivity and beta-cell function parameters (derivative or, alternatively, proportional control) were included in the models as independent variables. In these analyses, both insulin sensitivity and beta-cell function (derivative control) were negatively associated with macrovascular disease ( table 4 ). No association of microvascular disease with these metabolic variables was found. This finding was confirmed when CAN was excluded or when single microvascular complications were treated as dependent variables (data not shown). When BMI was replaced by waist circumference and triglycerides and HDL cholesterol were included in the model data were substantially confirmed, although the association of insulin sensitivity with macrovascular disease lost its statistical significance. In this analysis, waist circumference, as for BMI, was a negative predictor of macrovascular disease (OR 0.98, 95% CI 0.96 to 1.00, p=0.043; per each cm of waist) and the latter was not significantly associated with triglycerides or HDL cholesterol.

Independent predictors of macrovascular and pooled microvascular complications in multivariate analyses including insulin sensitivity and beta-cell function

We observed that approximately 50% of subjects in this cohort with newly diagnosed T2DM had target organ/system damage if the latter was searched in-depth with several techniques, including ultrasonography scanning of carotid and lower limb arteries, comprehensive neurological evaluation and ophthalmoscopy. Focusing on microvascular complications (eye, kidney, nerves), test abnormalities compatible with neuropathy were more common than those documenting retinopathy or nephropathy. This observation is noteworthy as neuropathy is often a neglected microvascular complication of diabetes because the eye and the kidney generally receive more attention than nerves. Overall, as many as 40% of subjects had microvascular disease, with a proportion twofold higher than macrovascular disease. Noteworthy, as many as 10% had both microvascular and macrovascular damage and as many as ~50% had either.

We have observed that gender, age, BMI, smoking, HbA1c and hypertension were variably associated with specific microvascular complications and gender, age, smoking and hypertension were associated with macrovascular complications. Interestingly, male gender was associated with CVD whereas female gender was associated with CKD. A classic risk factor such as hypercholesterolemia was not associated with macrovascular complications and hypertension was not associated with CKD. The cross-sectional setting of the study is the likely explanation. Yet, almost all subjects were on statins or had cholesterol levels above the cut-off of 70 mg/dL and the majority of hypertensive subjects were on treatment.

Insulin sensitivity and insulin secretion were negatively associated with macrovascular complications. In previous longitudinal studies, we found that insulin resistance was a predictor of CVD in T2DM and in the general population. 27 28 In a study conducted several years ago in the UK, Roy Chowdhury et al 29 observed an association between impaired insulin secretion and retinopathy but no association of this complication with insulin sensitivity. However, they used different and surrogate methods (eg, Homeostasis Model Assessment) to assess insulin secretion and sensitivity. Martinell et al 11 observed an inverse association between insulin secretion and retinopathy and no association with insulin resistance. However, they have used surrogate methods to assess these metabolic functions. We were unable to observed any association of insulin secretion with microvascular disease.

In this study, we explored virtually all classic sites of chronic complications (heart, arteries, eye, kidney, nerves) and this is at variance with most previous studies, some of which are also quite dated. In these studies, conducted in the last 40 years in large cohorts of patients with newly diagnosed T2DM recruited in Western countries, the prevalence of complications was quite variable, most likely for substantial differences in the methods of their detection. 2–16 Prevalence of retinopathy ranged from 1% 4 to 21%. 2 Prevalence of DSPN ranged from 3% 13 to 42%. 16 Prevalence of microalbuminuria/macroalbuminuria ranged from 7% 3 to 20%. 10 CKD was observed in 3% 2 13 up to 21%. 9 In these studies, prior cardiovascular events were often presented separately: myocardial infarction ranged from 5% 13 to 11%, 12 stroke ranged from 2% 10 to 5%, 8 peripheral vascular disease ranged from 2% 4 14 to 40%. 8 In none of these studies, carotid or lower limb ultrasonography were used to detect plaques and only one of them explored CAN, finding a prevalence of 4%. 16 Therefore, we feel that our study is more comprehensive than those previous studies.

Multivariate analyses were run only in few of the above referenced studies. Looker et al 5 found associations of retinopathy with male gender, HbA1c, BMI and blood pressure. An association of retinopathy with male gender, HbA1c and blood pressure was observed also by Kostev and Rathmann. 6 Kostev et al 8 have reported that DSPN was associated with male gender and age. Interestingly, we found an association between smoking and neuropathy (both DSPN and CAN). This finding is consistent with recent data from others 30 31 and could be attributed to the damage exerted by smoking on vasa nervorum as well as its direct detrimental effects on nerve structure and function. The latter includes an increased oxidative stress, with reactive oxygen species and Advanced Glycosilation End-Products as mediators, leading to mitochondrial dysfunction, inflammation, DNA damage and apoptosis. 32 33 The lack of significant associations of HbA1c with some of the complications (eg, neuropathy or retinopathy) is reasonably due to the cross-sectional design of the study.

As far as we know, this is the only study exploring in the same cohort all major complications of diabetes and relating them to classic risk factors and to major pathogenic determinants of T2DM (ie, insulin resistance and beta-cell dysfunction). We feel that our data are important as they point out to what extent the diabetes milieu can deteriorate health status of subjects before diagnosis even in the presence of mild-to-moderate hyperglycemia and how often chronic complications might be detected at time of diabetes diagnosis if they are carefully and comprehensively searched. This happens despite the increased awareness for diabetes occurred in the last 20–30 years. Yet, a role of insulin resistance in macrovascular disease emerged independently of classic risk factors, thus consolidating previous findings in subjects with and without diabetes. 27 29

Strengths of the study are: large number of subjects; no selection of patients (only those older than 75 years were not examined); exclusion of patients with Latent Autoimmune Diabetes of Adults; lack of any interference by antihyperglycemic drugs; assessment of all major organs/systems suffering from chronic hyperglycemia, including autonomic nervous system; investigation of carotid and lower limb arteries and not solely of prior CVD clinical events; measurement of insulin sensitivity and insulin secretion with state-of-the-art techniques.

Limits of the study are: inclusion of Caucasian subjects only; lack of investigation of CHD with dynamic techniques (eg, stress ECG or stress echocardiography).

In conclusion, despite a generally earlier diagnosis of T2DM occurring in the last two decades as compared with previous decades for an increased awareness of the disease, as many as ~50% of newly diagnosed patients have clinical or preclinical manifestations of microvascular and/or macrovascular disease. Our findings might promote an additional effort to further anticipate T2DM diagnosis by tracing undetected cases. Yet, our study might translate into a stronger commitment for staging organ/system damage in T2DM as soon as the diagnosis is established.

Acknowledgments

Dr Stefano Casati who performed ophthalmoscopy in all subjects is gratefully acknowledged. The superb assistance of the nurses of the Division of Endocrinology, Diabetes and Metabolism of the Hospital Trust of Verona in performing metabolic studies is greatly acknowledged.

• UKPDS Group
• Stevens RJ ,
• Manley SE , et al
• Hillier TA ,
• Looker HC ,
• Nyangoma SO ,
• Cromie D , et al
• Winkley K ,
• Thomas SM ,
• Sivaprasad S , et al
• Jockwig A ,
• Hallwachs A , et al
• Piniés JA ,
• González-Carril F ,
• Arteagoitia JM , et al
• Sandbæk A ,
• Griffin SJ ,
• Sharp SJ , et al
• Martinell M ,
• Dorkhan M ,
• Stålhammar J , et al
• Simmons RK ,
• Lauritzen T , et al
• Christensen DH ,
• Nicolaisen SK ,
• Berencsi K , et al
• Gedebjerg A ,
• Almdal TP ,
• Ahlqvist E ,
• Käräjämäki A , et al
• Zaharia OP ,
• Strassburger K ,
• Strom A , et al
• Curletto G ,
• Cipullo D , et al
• Bonetti S ,
• Trombetta M ,
• Malerba G , et al
• Boselli ML , et al
• Boselli M , et al
• Bonetti S , et al
• Zoppini G ,
• Cacciatori V ,
• Raimondo D , et al
• Epstein FH ,
• Ostrander LD ,
• JOHNSON BC , et al
• International Diabetes Federation
• Lewis JB , et al
• Formentini G ,
• Calcaterra F , et al
• Willeit J , et al
• Roy Chowdhury S ,
• Thomas RL ,
• Dunseath GJ , et al
• Knudsen ST ,
• Gylfadottir SS , et al
• Szwarcbard N ,
• Villani M ,
• Earnest A , et al
• Nicholl I , et al
• Krishnan J , et al

EB and MT contributed equally.

Contributors EB, MT and RCB designed the protocol and planned statistical analyses. All authors collected data and contributed to their interpretation and discussion. FM and MZ performed laboratory work. MLB modeled data of insulin secretion. LS made data entry and statistical analyses. EB wrote the manuscript, MT and RCB edited it and all authors reviewed it.

Funding The study was supported by grants from the Italian Ministry of the Education, University and Research (PRIN 2009WYP4AS to EB; PRIN 2015373Z39_002 to EB; PRIN 2015373Z39_004 to RCB; PRIN 2010098WFZ2 to RCB), the University of Verona (scientific achievement-based grants to EB, MT, RCB), the University of Parma (scientific achievement-based grants to RCB), the Foundation of the European Association for the Study of Diabetes (EFSD/Novartis grant to RCB), the Foundation of the Italian Diabetes Society (research grant to MT).

Competing interests None declared.

Patient consent for publication Obtained.

Ethics approval The protocol was approved by the local Ethics Committee of the Azienda Ospedaliera Universitaria Integrata di Verona (No. 955).

Provenance and peer review Not commissioned; externally peer reviewed.

Data availability statement All data relevant to the study are included in the article. Data are filed at the Endocrinology, Diabetes and Metabolism Division of the Department of Medicine, University of Verona.

Read the full text or download the PDF:

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here .

Loading metrics

Open Access

Peer-reviewed

Research Article

Knowledge of chronic complications of diabetes among persons living with type 2 diabetes mellitus in northern Ghana

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Validation, Visualization, Writing – original draft

* E-mail: [email protected]

Affiliations Department of Surgery, Tamale West Hospital, Tamale, Ghana, Department of Nursing, Faculty of Allied Health Sciences, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Supervision, Validation, Visualization, Writing – review & editing

Affiliation Department of Nursing, Faculty of Allied Health Sciences, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana

Roles Conceptualization, Methodology, Supervision, Validation, Writing – review & editing

Affiliation Department of Public Health, School of Allied Health Sciences, University for Development Studies, Tamale, Ghana

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Supervision, Validation, Visualization, Writing – review & editing

Affiliations Department of Nursing, School of Nursing and Midwifery, University of Health and Allied Sciences, Ho, Ghana, College of Nursing, Yonsei University, Seoul, Republic of South Korea

• Richard Adongo Afaya, 
• Victoria Bam, 
• Thomas Bavo Azongo, 
• Agani Afaya

• Published: October 28, 2020
• https://doi.org/10.1371/journal.pone.0241424
• Reader Comments

Introduction

Diabetes mellitus is a complex disease that affects many organ systems, leading to concerns about deteriorating population health status and ever-increasing healthcare expenditure. Many people with diabetes do not achieve optimal glycaemic control and other metabolic indices, leading to a heightened risk of developing complications. Adequate knowledge of diabetes complications is a prerequisite for risk-factor reduction and prevention of the consequences of the disease. Therefore, this study aimed to evaluate the knowledge of chronic complications of diabetes among persons living with type 2 diabetes mellitus in northern Ghana.

A descriptive cross-sectional study was conducted among 320 patients with type 2 diabetes mellitus in northern Ghana. The consecutive sampling technique was employed to recruit participants from September to November 2018. Data analysis was performed using IBM statistical package for social science version 23. Descriptive statistics such as frequencies and percentages were used. Both bivariate and multivariate logistic regression analysis were employed to determine associations between knowledge of diabetes complications and demographic/clinical characteristics of participants, at 95% confidence interval with statistical significance at P <0.05.

The majority of participants (54.1%) had inadequate knowledge and 45.9% had adequate knowledge of diabetes complications. The factors associated with inadequate level of knowledge were female gender [AOR = 0.29 (95%CI: 0.14–0.56), p<0.001], older age [AOR = 0.45 (95%CI:0.20–0.99), p = 0.049], primary education [AOR = 0.13 (95%CI: 0.03–0.51), p = 0.004], no formal education [AOR = 0.16 (95%CI: 0.05–0.50), p = 0.002], rural dwellers [AOR = 0.50 (95%CI: 0.27–0.95), p = 0.033] and unknown family history diabetes [AOR = 0.38 (95%CI: 0.17–0.82), p = 0.014].

More than half of the studied population had inadequate knowledge of diabetes complications. Female gender, rural dwellers, and low education level were factors positively associated with inadequate knowledge of diabetes complications. A multisectoral approach is needed, where the government of Ghana together with other sectors of the economy such as the health, education and local government sectors work collaboratively in the development of locally tailored diabetes education programmes to promote healthy self-care behaviours relevant for the prevention of diabetes and its complications.

Citation: Afaya RA, Bam V, Azongo TB, Afaya A (2020) Knowledge of chronic complications of diabetes among persons living with type 2 diabetes mellitus in northern Ghana. PLoS ONE 15(10): e0241424. https://doi.org/10.1371/journal.pone.0241424

Editor: Khin Thet Wai, Ministry of Health and Sports, MYANMAR

Received: July 27, 2020; Accepted: October 14, 2020; Published: October 28, 2020

Copyright: © 2020 Afaya et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are available in the paper and its Supporting Information file.

Funding: No funding was received for this study.

Competing interests: The authors have declared that no competing interests exist.

Abbreviations: DM, Diabetes mellitus; T2DM, Type 2 diabetes mellitus; BMI, Body mass index; WC, Waist circumference; WHO, World health organization; SD, Standard deviation; SHS, Senior high school; JHS, Junior high school; CHPS, Community health planning and services

Diabetes mellitus (DM) is a complex disease that affects many organ systems, leading to concerns about deteriorating population health status and ever-increasing healthcare expenditure [ 1 , 2 ]. It is recognised as one of the leading causes of death and disability worldwide [ 3 ]. In 2016, DM was the seventh leading cause of mortality globally with a projected 1.6 million deaths directly caused by diabetes [ 4 ]. As of 2019, 463 million people had diabetes worldwide. This figure is estimated to increase to 700.2 million by 2045 [ 5 ]. Around 80% of the global surge is expected to happen in Africa [ 6 ]. Diabetes prevalence studies in Ghana have recorded a marked increase. The earliest study in the 1960s reported 0.2% prevalence in a population of Ghanaian men in Ho [ 7 ]. The crude prevalence of diabetes among the general population was 6.3% in the late 1990s in Accra and the age-adjusted prevalence and impaired glucose tolerance were 6.1% and 10.7% respectively [ 8 ]. Type 2 diabetes mellitus (T2DM) accounts for 90% of all cases [ 5 ].

Uncontrolled diabetes can lead to life-threatening complications including both micro and macrovascular complications such as cardiovascular diseases, retinopathy, end-stage renal disease, and neuropathy [ 9 ]. The prevalence of diabetes and its complications is rising exponentially in China [ 10 ], Ireland [ 11 ] and northern Africa [ 12 ]. In Ghana, the prevalence of hypertention, retinopathy and neuropathy were 96.2%, 58.6%, and 60.5% respectively [ 13 ]. Diabetes-related health outcomes, treatment choices, care needs, and associated expenditures are exacerbated by the presence of these complications existing in addition to T2DM [ 14 ]. Policymakers, healthcare professionals, and persons with diabetes are increasingly focused on improving diabetes management, including achieving glycaemic control, lipid and blood levels, and blood pressure targets, which have been shown to decrease some diabetes complications [ 2 ]. Although many of these efforts have focused on improving diabetes management performance of healthcare providers [ 2 ], diabetes-related knowledge of patients is also critically important to achieve diabetes care targets and minimise complications.

Adequate knowledge is a major component of diabetes management. Increasing patients’ knowledge regarding DM and its complications has significant benefits concerning adherence to treatment and reducing complications [ 15 , 16 ]. Several studies have been done on knowledge and management of diabetes across the globe [ 17 – 22 ]. Although some studies have been conducted on diabetes-related knowledge in Ghana, a few have focused on assessing knowledge on complications in particular [ 23 , 24 ]. A recent study conducted among persons living with T2DM at a district hospital in Ghana reported low level of knowledge on diabetes complications where only 13.1% had adequate knowledge [ 20 ]. Despite these studies, most Ghanaians are still less knowledgeable about DM and its associated complications [ 25 ].

People with inadequate knowledge and low health literacy on diabetes complications will likely have suboptimal glycaemic control and increased risk of getting complications as they often have challenges understanding and following medical instructions [ 26 ]. Knowledge of diabetes complications is relevant for early recognition of the warning signs and symptoms necessary for the planning and implementation of appropriate preventive interventions to avert or delay complications [ 27 ]. There is limited data on knowledge of chronic complications of DM within the Ghanaian context [ 20 ]. Such data is important to policymakers and healthcare professionals for the development and implementation of appropriate clinical and public health strategies aimed at controlling and preventing the consequences of the disease. Therefore, this study aimed to evaluate the knowledge of chronic complications of diabetes among persons living with type 2 diabetic mellitus in northern Ghana.

Methodology

Study design.

A descriptive cross-sectional design was used to determine the knowledge of diabetes complications among persons living with T2DM in northern Ghana.

Study setting

This study was conducted at three government hospitals namely; Tamale Teaching Hospital, Tamale West, and Central Hospitals in the northern region of Ghana. The Tamale teaching hospital serves as a referral center for all the five regions of the north and some parts of the Brono and Ahafo regions. The Tamale west, and central hospitals are secondary level facilities and serve as referral centers for some districts in the region. These hospitals are located in the Tamale metropolis, the administrative capital. Healthcare in the metropolis is provided by public/private hospitals, health centers, clinics and traditional medicine clinics/traditional healers. The metropolis is predominantly urban, however, some residents in adjoining rural communities seek medical services in Tamale. Available health facilities in rural communities include clinics, health centers, and community health planning and services (CHPS) providing primary health services [ 28 ]. Due to financial challenges, and lack of equitable health services, most rural residents including non-affluent urban dwellers often resort to traditional healers or herbalist for their healthcare needs including treatment for diabetes [ 29 ].

Study population

The study population comprised all patients with T2DM registered with the outpatient diabetes clinics at the three health facilities. The population of patients with T2DM in these hospitals was estimated at 1800 for three months from clinical data. Patients who sought routine medical services at the outpatient endocrinology clinics of the selected hospitals were identified during the study period. Participants were included in the study if they had; a diagnosis of T2DM for at least one year, were aged 18 years and older, and attended the clinics for at least one year. Patients with Type 1 DM and patients who had mental instability were excluded.

Sampling and sample size

The consecutive sampling technique was employed to recruit participants who reported to the endocrinology clinics for routine medical services. The sample frame for the number of persons with T2DM who visit the clinics of the selected hospitals was estimated at 1800 for three months. The sample size for the study was calculated using the formula for sample size determination by Yamane [ 30 ].

Where n is required sample size.

N is the total population size which is 1800.

e is acceptable sampling error (0.05) at 95% Confidence Interval

Hence, the sample size for the study = 327 participants. A 5% non response rate was calculated, increasing the sample size to 344.

A structured instrument was used to obtain information from all the study participants. The questionnaire was developed from the review of related litereature in the subject area [ 20 , 31 , 32 ]. It was divided into two sections. The first section involved questions that elicited information on socio-demographic variables of participants including; age, gender, educational level, occupation, duration of DM, type of treatment, residence, duration and regularity of clinic visit, presence of complication and family history of diabetes. Information on complications was obtained by self-report and subsequent review of participants medical records for confirmatory diagnosis. The second section included questions that assessed participants knowledge on complications of diabetes and the kind of complications they knew. Each question had one correct answer from three answer choices. The answer choices for each question were “Yes”, “No” and “Don’t Know”. The “Don’t know” option was included to minimise guesswork. The participants’ responses were analysed as either correct or incorrect. One point was awarded for each right response, and zero for each incorrect or “Don’t know response”. The mean knowledge score was calculated and knowledge was categorised as either inadequate or adequate. Participants who had a score of (<Mean– 1 SD) were coded as having inadequate knowledge and scores that corresponded to (>Mean + 1 SD) were considered as adequate knowledge [ 33 ].

Reliability and validity

The questionnaire was pre-tested among 25 patients with T2DM to ascertain its content and clarity. Reliability coefficients ranging from 0.00 to 1.00, with higher coefficients indicating higher levels of reliability was used to determine the validity and the reliability of the questionnaire. The reliability coefficients for all the questions were 0.901.

Data collection

The data collection team comprised of three nurses who were trained as research assistants and supervised by the principal investigator. Participants were approached during the endocrinology clinic days of various hospitals to collect data. The objectives and procedures of the study were explained to participants both in English and in the local dialect. Written informed consent was obtained from each participant. The questionnaires were self-administered to participants who could read and write in English. Participants who could not read nor write in English were assisted by the research assistants in terms of translating the questions into their respective native languages. The research assistants received training on how to translate the questions to ensure uniformity of interviews. The data collection spanned from September to November 2018.

Data analysis

Out of the 344 participants invited, 14 declined participation and 10 participants’ medical records were not available at the time of data collection. Therefore, a total of 320 patients participated in the study, resulting in a 93.0% response rate. The data entry and analysis were performed using IBM statistical package for social science (SPSS) version 23. Descriptive statistics such as frequencies and percentages were used. Bivariate logistic regression was used to model the factors associated with participants knowledge on diabetes complications. Sex, age, education, monthly income, residence, duration of diabetes, type of treatment, family history of diabetes, duration of clinic visit, smoking status, were variables statistically significant in the bivariate logistic regression model and were considered for the multivariable logistic model to account for confounding effect. Both the bivariate and multivariate logistic regression model were used to test the association between categorical variables to predict factors associated with knowledge of diabetes complications. A P -value of less than 0.05 was considered statistically significant.

Ethical consideration

Ethical approval was obtained from the ethics review committee of the Kwame Nkrumah University of Science and Technology (CHRPE/AP/576/18). Permission was sought from the management of the various hospitals. Confidentiality and anonymity of participants were ensured as no personal identifiers were used in the study. Participation was voluntary and they were free to participate or not and if they declined, it would not affect their regular care.

Socio-demographic characteristics of respondents

As indicated in Table 1 , the mean (SD) age of participants was 57.5 (11.67) years. The majority of participants, 68.4% were women, 60.9% had no formal education and 43.4% were self-employed. More than three-fourth of the participants (79.7%) had family support. About two-thirds (70.3%) earned an income of less than GHS 500 each month. Nearly 74% lived in an urban setting and 37.8% had diabetes for a period of 1 to 3 years. The majority of participants (74.4%) were under oral hypoglycaemic treatment. A significant proportion of participants (40%) had medically confirmed diabetes complication. More than half (55.6%) of participants visited the clinic every 2 months and 43.8% visited the clinic monthly.

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

https://doi.org/10.1371/journal.pone.0241424.t001

Knowledge of participants on complications of diabetes mellitus

The overall level of knowledge revealed that less than half, 45.9% of participants had adequate knowledge of diabetes complications, and the remaining 54.1% had inadequate knowledge.

As indicated in Table 2 , the knowledge of diabetes complications revealed that only 57.8% of participants knew that diabetes could cause damage to the kidney. Most of them, 78.8% knew that one could develop neuropathy as a result of diabetes. Less than half, 45.0% and 49.4% had knowledge that diabetes could cause retinopathy (blurred vision) and hypertension respectively. Around 58.8% and 74.4% of the participants knew that heart diseases and diabetic foot ulcers are complications of diabetes. Only 26.3% of the participants were aware of hypo-sexual dysfunction as a complication of diabetes.

https://doi.org/10.1371/journal.pone.0241424.t002

Factors influencing knowledge of complications of diabetes

In Table 3 , the multivariable logistic regression revealed that women were 71% less likely to have adequate knowledge of diabetes complication compared with men [AOR = 0.29 (95%CI: 0.14–0.56), p<0.001]. Participants who were aged between 60 and 69 years were 55% less likely to have adequate knowledge compared to those who were less than 50 years [AOR = 0.45 (95%CI:0.20–0.99), p = 0.049]. Participants with primary education and those without formal education were 87% and 84% less likely to have adequate knowledge of diabetes complications compared with their counterparts who had tertiary education [AOR = 0.13 (95%CI: 0.03–0.51), p = 0.004]; [AOR = 0.16 (95%CI: 0.05–0.50), p = 0.002] respectively. Moreover, participants who lived in rural settings were 50% less likely to have adequate knowledge of diabetes complications compared to those who lived in urban areas [AOR = 0.50 (95%CI: 0.27–0.95), p = 0.033]. Similarly, participants who were unaware of their family history of diabetes were 62% less likely to have adequate knowledge of diabetes complications compared to those with a family history of diabetes [AOR = 0.38 (95%CI: 0.17–0.82), p = 0.014]. Moreover, participants who attended the diabetes clinic for more than 6 years were 14 times more likely to have adequate knowledge of diabetes complications [AOR = 14.43 (95%CI: 3.26–60.26), p<0.001].

https://doi.org/10.1371/journal.pone.0241424.t003

This study aimed to assess the knowledge of complications of diabetes among persons living with T2DM in northern Ghana. Adequate knowledge of DM and its complications is necessary for diabetes self-management. It is a prerequisite for reduction of unhealthy behaviours and subsequent prevention and/or reduction of the development of complications associated with the disease. In this study, it was observed that a substantial proportion of the study participants (40%) reported having one or more chronic complications of DM. This implies that, these patients probably coud not control their diabetes leading to complications. Healthcare providers need to take pragmatic plans and shift priorities to focus on patients with complication(s) for the prevention of disability and death. This finding also brings to the fore the need for the establishment of national diabetes programmes to provide prevention and control strategies, considering the huge economic burden associated with treating DM and its complications [ 34 ].

Generally, the majority of participants (54.1%) in this study had inadequate knowledge of diabetes complications. This may be explained by the low educational and socioeconomic status of the current study population. It is worth noting that almost two-thirds of the participants had no formal education. This is a matter of concern, as there are no available culturally and linguistically appropriate diabetes education resources in the Ghanaian context [ 35 ]. Consequently, these patients may not be able to communicate effectively with health professionals due to low literacy. This could pose a challenge to effective counseling on diabetes and its associated complications. The present study finding is consistent with previous studies conducted in Ghana [ 20 ], Ethiopia [ 36 ], and India [ 37 ], whereby most participants had inadequate knowledge of diabetes complications. Conflicting findings were reported in Nigeria where 90.5% of T2DM patients had adequate knowledge of diabetes complications [ 27 ]. The variation could be due to the difference in socioeconomic and cultural characteristics which have been established to have an influence on patients knowledge [ 36 ].

Concerning participants knowledge on specific complications, the most common complications known by participants were; neuropathy followed by diabetic foot ulcer, heart diseases, kidney disease, hypertension, retinopathy and hypo-sexual dysfunction. The disparity on knowledge of different complications could imply that participants might have experienced them. A previous study done in Ghana by Obirikorang et al. [ 20 ] found out that, participants knew diabetic foot (51.5%), hypertension (35.4%), neuropathy (29.2%), hypoactive sexual arousal (25.4%), arousal disorder (21.5%), retinopathy (17.7%), heart disease (9.2%), and nephropathy (5.4%) as the most common DM complications. Another study by Konduru, Ranjan, Karthik and Shaik [ 38 ] revealed that eye complications (69%) was commonly known by diabetic patients followed by cardiac complications (51%), and central nervous system complications (28%). The current study also differ from a study conducted in Malaysia where the majority of participants (61.25%) mentioned that diabetic foot ulcer is the most common complications of diabetes. However, heart disease (27.75%), kidney disease (38.25%), eye disease (32.50%), and stroke (20%) were also reported by the participants as the common complications of diabetes [ 39 ]. This disparity between our study and prior studies may be attributed to the variation in the diabetes education provided to participants. Moreover, variations in culture, race, and ethnicity among the populations may influence the pattern of knowledge on diabetes complications [ 36 ].

Another interesting finding of this study is the positive association between educational status and the level of participants’ knowledge of diabetes complications. Participants with tertiary education had significantly higher knowledge than their counterparts with low level of education. This is in line with findings reported in a study done among T2DM patients visiting the diabetes clinic at Sampa Government Hospital in Ghana where there was a relationship between the level of education and the degree of knowledge on diabetic complications [ 20 ]. This is not surprising as participants who completed tertiary education may have attended workshops, conferences, seminars and health talks on health-related issues. Moreover, they may browse the internet for more information on DM to enhance their knowledge [ 36 ].

It is worth noting that, there was a positive association between gender and knowledge of diabetes complications in this study. Women were less likely to have adequate knowledge compared with men. Similar findings were observed in previous studies conducted in Ethiopia [ 36 ], Pakistan [ 40 ], India [ 37 ] and Ghana [ 20 ] where men were found to have more knowledge on diabetes complications than women. This finding could be attributed to cultural influence which allows males to spend most of their time outside the home, attend different meetings and conferences which might have provided them with the opportunity to obtain more information on diabetes than their female counterparts who are always at home [ 36 ]. We recommend the implementation of targeted educational programmes to enhance the knowledge of women.

A previous study conducted among patients with T2DM in Pakistan indicated that urban settlers were more knowledgeable on diabetes complications than their colleagues residing in the rural areas [ 41 ]. This finding is congruent with the present study finding where urban dwellers compared with rural settlers had adequate knowledge of diabetes complications. A similar finding was observed by Hoque, Islam, Khan, Aziz, and Ahasan [ 31 ]. Participants from rural areas were probably less educated or may have had less regular diabetes clinic visits due to financial challenges, as there is lack of equitable access to facilities particularly in rural communities in Ghana. Efforts at promoting education and health literacy on diabetes and its associated complications among rural inhabitants need to be intensified by government and healthcare providers. Community-wide education programmes is recommended to raise awareness of modifiable risk factors of diabetes and its complications which will have benefits beyond diabetes [ 5 ].

Limitations of the study

The sample population might not reflect the overall T2DM patients in Ghana. The patients who seek care in traditional medicine clinics or private hospitals or those who did not seek care regularly in these hospitals might not be included in this study. Thus, the findings cannot be generalized. The cross-sectional design does not establish causality though it demonstrates association between variables. Notwithstanding, the current study provides vital information on the level of knowedge of long-term complications among participants that can form the basis for patient education.

More than half of the studied population had inadequate knowledge of diabetes complications. The most common complications of DM known by participants were; neuropathy followed by diabetic foot ulcers, heart diseases, kidney disease, hypertension, retinopathy (blurred vision), and hypo-sexual dysfunction. Female gender, rural dwellers, and low educational level were the factors positively associated with inadequate knowledge of diabetes complications. A multisectoral approach is needed, where the government of Ghana together with other sectors such as the health, education and local government sectors work collaboratively in the development of locally tailored diabetes education programmes to promote healthy self-care behaviours relevant for the prevention of diabetes and its complications. Healthcare providers need to intensify education on diabetes, treatment, and complications utilising linguistically and culturally appropriate educational resources to enhance patients' knowledge.

Supporting information

S1 dataset..

https://doi.org/10.1371/journal.pone.0241424.s001

Acknowledgments

We thank the research assistants, management of the hospitals and the patients.

• View Article
• PubMed/NCBI
• Google Scholar
• 4. Global Health Estimates. Disease burden by cause, age, sex, by country and by region, 2000–2016. Geneva: World Health Organization; 2016.
• 5. International Diabetes Federation. IDF Diabetes Atlas, 9th edn. Brussels: International Diabetes Federation, 2019. http://www.diabetesatlas.org/resources/2019-atlas.html
• 28. Ministry of Health. Pharmacy Council of Ghana. Available from http://www.moh.gov.gh/pharmacy/council-ghana/ . Accessed 6/3/2019, 2015.
• 30. Yamane T. Statistics, An Introductory Analysis. Harper and Row, 2nd edition. New York, NY, USA. 1967.
• 34. International Diabetes Federation. IDF Diabetes Atlas. 6 th edition. 2015.

REVIEW article

The progress of clinical research on the detection of 1,5-anhydroglucitol in diabetes and its complications.

• 1 Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
• 2 School of Clinical Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China

1,5-Anhydroglucitol (1,5-AG) is sensitive to short-term glucose fluctuations and postprandial hyperglycemia, which has great potential in the clinical application of diabetes as a nontraditional blood glucose monitoring indicator. A large number of studies have found that 1,5-AG can be used to screen for diabetes, manage diabetes, and predict the perils of diabetes complications (diabetic nephropathy, diabetic cardiovascular disease, diabetic retinopathy, diabetic pregnancy complications, diabetic peripheral neuropathy, etc.). Additionally, 1,5-AG and β cells are also associated with each other. As a noninvasive blood glucose monitoring indicator, salivary 1,5-AG has much more benefit for clinical application; however, it cannot be ignored that its detection methods are not perfect. Thus, a considerable stack of research is still needed to establish an accurate and simple enzyme assay for the detection of salivary 1,5-AG. More clinical studies will also be required in the future to confirm the normal reference range of 1,5-AG and its role in diabetes complications to further enhance the blood glucose monitoring system for diabetes.

1 Introduction

In recent years, diabetes prevalence in China has been soaring up in a remarkable way. Poor glycemic control and huge fluctuations in blood glucose over time can lead to complications and influence the prognosis of the heart, kidney, and brain together with other important target organs. Traditional indicators for diagnosing diabetes and monitoring blood glucose fluctuations include fasting plasma glucose (FPG), oral glucose tolerance test (OGTT), glycosylated hemoglobin (HbA1c), and glycated albumin (GA) ( 1 ). Nevertheless, FPG is unable to screen patients with isolated postprandial hyperglycemia; the OGTT2h blood glucose detection is complicated to operate, let alone patient compliance; HbA1c is highly susceptible to red blood cell lifespan ( 2 ); and HbA1c and GA cannot reflect short-term blood glucose fluctuations. Above all, traditional blood glucose monitoring indicators still have limitations in screening for diabetes and reflecting blood glucose fluctuations. 1,5-Anhydroglucitol (1,5-AG), a nontraditional blood glucose monitoring indicator, is prominently correlated with HbA1c and GA ( 3 ). Guidelines for the prevention and treatment of type 2 diabetes mellitus in China (2020 edition) also claimed that 1,5-AG is an auxiliary test indicator for blood glucose monitoring, diabetes screening, and guidance adjustment of therapeutic regimen; it can clearly reflect data from the previous 1 to 2 weeks and postprandial blood glucose fluctuations clearly ( 1 ).

Recently, there has been extensive research on the role of 1,5-AG in diabetes and its complications. At the same time, salivary 1,5-AG has also been extensively explored in clinical practice owing to its characteristic of being woundless and its simplicity ( Table 1 ). This article reviews the progress of clinical research on the detection of 1,5-AG in diabetes and its complications.

Table 1 Advantages and limitations of different screening tests.

2 Overview of 1,5-AG

2.1 characteristics and metabolism of 1,5-ag.

1,5-AG, a naturally occurring, chemically inert monosaccharide with a structure similar to glucose that is mainly derived from food, is absorbed in the intestine and widely distributed in various tissues and organs in free form, with little metabolic degradation in the body ( Figure 1 ). Almost all 1,5-AG excreted in urine is reabsorbed in the renal tubules by specific sodium-glucose cotransporter 4 or specific sodium-glucose cotransporter 5 (SGLT4 or SGLT5), and this process ( 4 ) is competitively inhibited by glucose since 1,5-AG and glucose share the transporter protein ( Figure 2 ). Previous research had suggested that SGLT4 is the renal transporter for 1,5-AG, but more recently it has been demonstrated that 1,5-AG is transported by SGLT5, while mannose is transformed by SGTL4 ( 5 , 6 ). Nevertheless, there could be some optional bias because these studies are focused on patients with neutropenia. Although the accuracy of this finding is still uncertain, it also provides a new direction for future research. We look forward to more multicenter, large-sample randomized controlled trials in the future to reveal the metabolic mechanisms of 1,5-AG in vivo . When the blood glucose exceeds the threshold of the renal glucose (8.9–10.0 mmol/L), 1,5-AG will excrete in large quantities in the urine while reabsorption decreases, resulting in a conspicuous decrease in serum 1,5-AG ( 7 ). L Ying et al. ( 8 ) found that the metabolic rate of 1,5-AG in hepatocytes and skeletal muscle cells was less than 3%, indicating that 1,5-AG was stable in the body. Meanwhile, 1,5-AG can be freely transported both in and out of the cell in accordance with the concentration gradient to achieve dynamic equilibrium, which exactly shows that 1,5-AG can serve as a biomarker of glucose fluctuations.

Figure 1 Structure of 1,5-AG and glucose.

Figure 2 Circulatory pathway of 1,5-AG in the body.

1,5-AG is sensitive to the reaction, with a half-life of about 1 to 2 weeks, and when blood glucose is controlled, serum 1,5-AG will accordingly increase at a rate of 0.3 mg/L per day, reaching equilibrium after 5 weeks ( 9 ). Therefore, 1,5-AG not only reflects blood glucose fluctuations but also records the duration of hyperglycemia. The mass of evidence ( 10 ) also indicates that 1,5-AG, a good indicator to monitor short-term blood glucose, is qualified to reflect short-term glucose fluctuations, postprandial hyperglycemia, and even daily glucose excursions ( Figure 3 ). JB McGill et al. ( 11 ) found that there was a strong correlation between changes in 1,5-AG within 5 days and changes in HbA1c over the subsequent 3 months. Therefore, 1,5-AG could be used as an intermediate marker between 3-month assessments of HbA1c to determine whether glycemic control is good or not for longitudinal monitoring. Because 1,5-AG is dynamic, monitoring glycemic control with 1,5-AG should not rely on a single-point measurement. We believe that in clinical applications, serum 1,5-AG should be tested once at the patient’s initial treatment to assess the patient’s glycemic control and then adopt an appropriate hypoglycemic program. During the patient’s secondary evaluation (such as 1 week later), serum 1,5-AG should be tested again to observe its fluctuation and to assess whether the hypoglycemic program is effective or not based on the degree of relief of the patient’s clinical symptoms and whether the blood glucose level has improved. After a comprehensive evaluation, the clinician will decide whether to change the hypoglycemic program and the timing of the next serum 1,5-AG test so as to realize the individualized approach. Therefore, the timing of serum 1,5-AG testing depends on the duration of the hypoglycemic program adopted by the clinician and the overall judgment of the patient’s condition. As shown in a clinical trial in the United States ( 12 ), 1,5-AG levels can sensitively and rapidly reflect glycemic changes after adjustments to personalized treatment strategies, including changes in drug type or dosage, as well as the initiation of insulin therapy or combinations of different insulin regimens. This confirms our proposal to tailor the treatment to the individual by reflecting glucose fluctuations dynamically for longitudinal monitoring.

Figure 3 Principle of detection of 1,5-AG.

2.2 Detection methods for 1,5-AG

1,5-AG can be detected in a variety of samples such as serum, saliva, and cerebrospinal fluid, in which detection methods have undergone several improvements, now mainly divided into mass spectrometry and enzyme assay two major categories ( Table 2 ). Gas chromatography/mass spectrometry ( 13 ) and ultra-performance liquid chromatography tandem mass spectrometry ( 14 ) are often used to detect 1,5-AG, and such methods have good sensitivity and high accuracy, yet the process is more cumbersome, making it rarely used in the clinical detection of serum 1,5-AG but often used in the detection of saliva and other samples with a smaller content of 1,5-AG. Halama et al. ( 15 ) found a significant positive correlation between the results of serum 1,5-AG using enzyme assay and mass spectrometry, indicating that enzyme assay is also very accurate for serum 1,5-AG. GlycoMark™ (GlycoMark, Inc., USA) and Determiner-L (Kyowa Medex, Japan) are the most commonly used enzymatic assay kits in the clinical detection ( 16 ). Both can automate and quantify the detection of serum 1,5-AG with high specificity, simple operation, and wide use. The reference ranges of serum 1,5-AG and associated inter-individual biological variation parameters measured by the GlycoMark™ kit and the Determiner-L kit were essentially the same, with comparable results. However, there were minor differences, possibly due to calibration differences between the two kits. Using the GlycoMark™ kit to measure serum 1,5-AG has a good correlation with the rate of blood glucose excursions within a day. This provides a better reflection of short-term blood glucose fluctuations, helps monitor glycemic control, and is simple for clinical application ( 11 ). The GlycoMark™ kit for serum 1,5-AG is only affected by glucose, while other monosaccharides are less affected. The freeze–thaw sample has little effect on the GlycoMark™ kit results, which is better than the Determiner-L kit. W Nowatzke et al. ( 17 ) concluded that the GlycoMark™ kit is more stable and less susceptible to interference; so, it was more commonly used in the United States to monitor medium-term glycemic control. However, the Determiner-L kit has been used for a long time ( 16 ), its detection is more stable, and people recognize it more; so, it is more widely used. Meanwhile, the Determiner-L kit has the unique advantage of a detection limit of 1.0 μg/mL for 1,5-AG ( 18 ). Therefore, the exact enzymatic assay kit to be used depends largely on the intent of the person sending the test.

Table 2 Different methods to detect 1,5-AG.

Whereas the use of saliva samples for diabetes testing could facilitate diabetes screening in public places, there is no well-established enzymatic assay kit for salivary 1,5-AG. Saliva was found to be a stable substrate for biochemical assays by the GlycoMark™ assay kit for the detection of 1,5-AG in serum and saliva samples, respectively. However, the GlycoMark™ kit measured salivary 1,5-AG under the influence of galactose, resulting in a readout that does not correlate with salivary 1,5-AG values as measured by mass spectrometry ( 15 ). CH Jian et al. ( 19 ) discovered no significant correlation between the enzyme assay for salivary 1,5-AG and serum 1,5-AG. Therefore, the reliability of the enzyme assay for the determination of salivary 1,5-AG and its detection methods still need a lot of experimental research and improvement before they can be applied in clinical practice. In the future, we may try to improve the assay by adding the step of removing galactose before sample testing, which will increase the specificity of the Glycomark™ kit for the determination of salivary 1,5-AG and will be favorable for clinical application. The detection of urine samples is also worth exploring, as a significant correlation between glucose and 1,5-AG concentrations in urine has been reported ( 20 ). N Namba et al. ( 21 ) compared the serum and urinary 1,5-AG levels in 15 patients with insulin-dependent diabetes mellitus as well as in control subjects. They found that urinary glucose concentrations correlated linearly with the ratio of serum and urinary 1,5-AG concentrations. J Ren et al. ( 22 ) developed and validated a rapid ultra-performance liquid chromatography–tandem mass spectrometry method for the detection of urinary 1,5-AG, which is simple, efficient, sensitive, and robust. In summary, the detection of 1,5-AG in saliva and urine specimens expands the diversity of samples, and the diversity of the delivered indicators provides more possibilities for our clinical choices.

Over the years, researchers have continuously explored and updated 1,5-AG detection methods that are more suitable for the clinical diagnosis and management of diabetes. Z Zhou et al. ( 23 ) adopted a quantitative study for the colorimetric detection of serum 1,5-AG based on graphene quantum dots and enzyme-catalyzed reactions. Their findings indicated a linear correlation between the absorbance and the concentration of serum 1,5-AG in the range of 20.0–100.0 μg/mL, with a detection limit of approximately 0.144 μg/mL. This method is highly accurate, easy to perform, and inexpensive. The paper-based sensor directly measures serum 1,5-AG in just one step within 10 min, which reduces the effect of excessive glucose in serum samples on the test results with high accuracy and a 1,5-AG detection limit of approximately 3.2 μg/mL ( 24 ). The nanozyme-mediated cascade reaction system for the electrochemical detection of serum 1,5-AG has high specificity, sensitivity, stability, and reproducibility. It is also low-cost and easy to construct. Through quantitative studies, G Li et al. ( 25 ) found that the peak current of the electrochemical biosensor has a good linear relationship within the serum 1,5-AG concentration range of 0.1–2.0 mg/mL, with a detection limit of approximately 38.2 μg/mL. A novel light-addressable potentiometric sensor can detect serum 1,5-AG with high sensitivity, good recovery, and stability, making it suitable for routine detection. Quantitative studies showed that the potential shift of the light-addressable potentiometric sensor has a linear relationship at a serum 1,5-AG concentration of 10 μg/mL, with a detection limit of approximately 10 μg/mL ( 26 ).

2.3 Normal reference range of 1,5-AG and its influencing factors

Influenced by age, gender, race, regional environment, diet, and medication, to name just a few, there exist some differences in the normal reference range of serum 1,5-AG. M Welter et al. ( 27 ) studied 2,303 healthy subjects of different genders and ages, finding that there was a difference in their 1,5-AG reference range, which was consistent with the results of E Selvin ( 28 ). Chen et al. ( 29 ) conducted an OGTT test on 646 healthy subjects in Jiangsu Province, which showed that the reference values of 1,5-AG differed by gender, 15.8–52.6 μg/mL in male patients and 14.3–48.0 μg/mL in female patients. The study concluded that 1,5-AG was influenced by factors such as gender, age, and uric acid, which was also significantly lower in blacks than in whites, and its ability to predict complications was also different ( 30 ). Serum 1,5-AG concentrations are also affected by types and quantities of dietary carbohydrates ( 31 ), excessive intake of dairy products ( 32 ), differences in renal glucose threshold ( 4 ), and polygala Chinese herbs ( 33 ). For the treatment of diabetes, sodium-glucose cotransporter 2 inhibitors, such as dapagliflozin, inhibits renal glucose reabsorption, which can also indirectly inhibit the reabsorption of 1,5-AG by SGLT4 or SGLT5, leading to a decrease in 1,5-AG ( 34 ). S Li et al. ( 35 ) found that the manufacturer’s reference range of the 1,5-AG kit (>14 μg/mL) was not applicable, and the study results suggested that the reference values of 1,5-AG for male patients in Guangdong Province were 34.61–37.37 μg/mL, compared with 22.38–25.07 μg/mL for female patients.

Compared with blood, saliva is easier to collect and store, and the detection of salivary 1,5-AG is noninvasive, making this indicator work to clinical application. DO Mook-Kanamori et al. ( 36 ) found that salivary 1,5-AG was highly correlated with serum 1,5-AG and with blood glucose, HbA1c was negative, through a case–control study of type 2 diabetes mellitus (T2DM), indicating that salivary 1,5-AG can be used to screen for diabetes. CH Jian et al. ( 37 ) suggested collecting saliva samples by chewing cotton swabs 40–50 times in 1 min and storing at normal temperature or 4°C for a short period of time. The normal reference range of salivary 1,5-AG measured by liquid chromatography mass spectrometry was 0.09–1.63 mg/L ( 38 ).

3 Progress of clinical research on 1,5-AG for diabetes

3.1 role of 1,5-ag in screening for diabetes.

Most diabetes cases are very insidious at the outset, making the rate of missed diagnoses high, and some of the commonly used blood glucose detection indicators have advantages and disadvantages, while 1,5-AG has a unique advantage in reflecting short-term glucose fluctuations and postprandial hyperglycemia, which results in 1,5-AG detection being gradually applied in clinical practice. As early as the 1980s, some scholars ( 39 ) have proposed that the decrease in 1,5-AG level was closely related to diabetes, which could be used as a biomarker of hyperglycemia for screening diabetes. Y Wang et al. ( 40 ) conducted an OGTT test on 1,170 subjects, measuring indicators of 1,5-AG, HbA1c, FPG, and 2-h postprandial plasma glucose, respectively. The results showed that serum 1,5-AG level was significantly negatively associated with FPG, 2-h postprandial plasma glucose, and HbA1c and that the optimal cutoff value of 1,5-AG for the diagnosis of diabetes was 11.18 μg/mL, with a sensitivity of 92.6% higher than HbA1c (82.3%) and an area under the curve of 0.920 higher than HbA1c (0.887). T Yamanouchi ( 41 ) divided 1,620 subjects into non-diabetic group, impaired glucose tolerance group, diabetes group, and other diseases without impaired glucose tolerance group, with indicators such as 1,5-AG and HbA1c, respectively, and found that the overlap of 1,5-AG values in the four groups was less than those of other indicators, whose reduction was highly specific (93.1%) and sensitive (84.2%) for the diagnosis of diabetes, with an optimal cutoff value of 14 μg/mL.

However, using only 1,5-AG as a biomarker in screening for diabetes has no obvious advantages compared with traditional blood glucose indicators such as HbA1c, GA, and FPG. Combining 1,5-AG with HbA1c, FPG, and GA to screen for diabetes with high sensitivity and specificity is more conducive to clinical application. J Qian et al. ( 42 ) conducted a study of 2,184 people in Jiangsu Province which showed that the optimal threshold for 1,5-AG screening for diabetes was ≤23.0 μg/mL, and the sensitivity of HbA1c combined with 1,5-AG was 85% higher than HbA1c (70%). H Su et al. ( 43 ) discovered that the sensitivity of 1,5-AG combined with FPG screening for diabetes was 84.92% and the specificity was 91.45% higher than the GA combined with FPG (77.71% and 90.88%) or using the above-mentioned indicators alone. Therefore, 1,5-AG combined with HbA1c, FPG, and GA screening for diabetes can reduce the proportion of people who need OGTT. Moreover, salivary 1,5-AG can also screen for diabetes. C Jian et al. ( 44 ) studied 363 people at risk of diabetes and 278 healthy subjects in Shanghai showing that the optimal cutoff value of salivary 1,5-AG was 0.44 μg/mL, and the sensitivity of salivary 1,5-AG combined with HbA1c or FPG was 80.13% and 73.51%, respectively, significantly higher than the above-mentioned indicators alone, which improved the screening rate of diabetes and reduced the proportion of the population requiring OGTT by 51.41%. However, Loomis et al. ( 45 ) found that a gene in the human body (SLC5A10) affects the 1,5-AG levels. As a result, 1,5-AG cannot be used as a hyperglycemic biomarker in the case of genetic variations. In the future, more studies are needed to determine whether 1,5-AG can be an effective biomarker for hyperglycemia.

Serum 1,5-AG is applicable to screen for type 1 diabetes mellitus (T1DM), as an auxiliary diagnostic indicator for T1DM ( 46 ), and to identify T2DM and fulminant type 1 diabetes mellitus (FT1DM). FT1DM has an acute onset and rapid progression. If it is not diagnosed and treated in time, it will lead to various complications and even death; so, early identification and diagnosis of FT1DM is extremely important. A Pal ( 47 ) and M Koga ( 48 ) found that the average value of serum 1,5-AG in FT1DM was 3.09 µg/mL, lower than T2DM (5.43 µg/mL), while HbA1c had no significant difference; so, 1,5-AG was more suitable to identify FT1DM and T2DM. L Ying et al. ( 49 ) studied 226 subjects with HbA1c <8.7%, showing that the 1,5-AG/GA index contributed to the early identification of FT1DM and the new-onset type 1A diabetes, with an optimal cutoff value of 0.3, which when combined with HbA1c resulted in an improvement of the identification rate of 61.11%. However, the aforementioned studies are not enough. Thus, clinical studies are urgently needed to verify this conclusion.

3.2 Role of 1,5-AG in diabetes management

Blood glucose monitoring is especially important for diabetes management. Dynamic monitoring of blood glucose fluctuations can reflect the patient’s glycemic control, identify the risk of diabetes as early as possible, effectively prevent diabetes recurrence, prevent the occurrence of hypoglycemic events, and also serve as a basis for adjusting clinical medication. The familiar continuous glucose monitoring (CGM), which measures the glucose concentration in interstitial fluid using a skin sensor, can provide detailed information about glucose variability. The range of target glucose levels (3.9–10.0 mmol/L) is close to the renal glucose threshold, which reflects glucose fluctuations in a sustained manner, making it an effective monitoring indicator ( 50 ). However, CGM readings may be influenced by periods of hyperglycemic variability as well as paracetamol or ascorbic acid intake, and factors such as skin pigmentation and room temperature may also contribute to differences in readings ( 51 ). Studies have demonstrated that CGM can continuously record blood glucose levels, which is beneficial in assessing blood glucose fluctuations in T1DM. Unfortunately, CGM is both expensive and inconvenient. The patients’ daily lives may be disturbed and uncomfortable as a result of having to keep the needle immobilized for several days ( 52 ). In summary, CGM is not commonly used in clinical practice. Undoubtedly, 1,5-AG presented in this article can be sensitive to reflect short-term blood glucose fluctuations, not affected by mild or moderate renal insufficiency, which is a reliable indicator of glycemic control in T2DM with normal renal function and mild to moderate renal insufficiency ( 53 ), and can also be used as an early predictive indicator for T1DM progress ( 54 ). A large number of studies ( 18 , 55 ) have found that changes in 1,5-AG levels are significantly correlated with changes in many CGM variation indicators, indicating that both 1,5-AG and CGM are sensitive to blood glucose fluctuations. However, there is a fundamental difference between 1,5-AG, which uses venous blood, and CGM, which measures glucose concentration in interstitial fluid. There is no doubt that testing venous blood is more conducive to visualizing blood glucose fluctuations. Since CGM requires the wearing of a skin sensor, this is very noticeable and may cause anxiety in some patients. Meanwhile, for patients with insulin pumps, monitoring glucose fluctuations with CGM requires wearing two machines at the same time, which is very inconvenient, whereas testing serum 1,5-AG does not have this disadvantage. The differences between the CGM reading and the blood glucose concentration are approximately 0.55–1.11 mmol/L, with a 10- to 15-min lag time. Serum 1,5-AG, on the other hand, does not experience a delay and therefore responds more rapidly, allowing for better management of diabetic patients ( 56 ). It is because serum 1,5-AG is more responsive that its application is more meaningful for severe patients, and the detection of serum 1,5-AG can avoid the occurrence of diabetic critical illnesses such as severe hypoglycemia ( 45 ). Meanwhile, 1,5-AG can be detected using existing enzymatic kits, making clinical application easier. J Peabody et al. ( 57 ) found that the use of 1,5-AG in clinical practice could improve the quality of preliminary healthcare, better identify patients with poor glycemic control, and reduce the cost of the healthcare system.

BA Kappel et al. ( 58 ) confirmed that serum 1,5-AG is the most reliable predictive indicator of poor glycemic control through a comprehensive metabolomics study. J Lin ( 59 ) found that 1,5-AG could accurately detect the nuances in blood glucose, rapidly increasing after glycemic control in diabetic patients, and better assess the risk of diabetes. H Sone et al. ( 60 ) studied 22 hospitalized T2DM patients who had been educated on diabetes management and then followed up for 3 months after leaving the hospital to measure their 1,5-AG, HbA1c, BMI, and other indicators. The results found that patients with low 1,5-AG had a higher BMI and a higher risk of disease recurrence, while the reflection of HbA1c was not sensitive; so 1,5-AG is more conducive to identify patients with poor glycemic control, and monitoring 1,5-AG levels can effectively prevent diabetes recurrence. Salivary 1,5-AG also plays an important role in predicting the risk of diabetes. Kedarnath et al. ( 61 ) discovered that the sensitivity and specificity of 1,5-AG <0.054 µg/mL for predicting blood glucose >180 mg/dl were 86.4% and 87.2%, respectively.

The monitoring of 1,5-AG is also effective in preventing the occurrence of hypoglycemic events, and the lower the 1,5-AG level the greater the risk of developing severe hypoglycemia. MK Kim et al. ( 62 ) recruited 18 patients with T2DM treated with insulin, and the results showed a significant negative correlation between 1,5-AG and hypoglycemia score ( r = -0.510, P = 0.031), which remained after adjusting a series of indicators ( r = -0.468, P = 0.068). AK Lee et al. ( 63 ) examined a series of biomarkers and assessed the association of risk factors with severe hypoglycemia using the Cox proportional risk regression model in 1,206 diabetic patients at risk of atherosclerosis in the community, which showed a linear correlation between 1,5-AG levels and severe hypoglycemia. Thus, 1,5-AG can be used as a reliable indicator for predicting hypoglycemic events in diabetic patients.

It has also been shown that 1,5-AG can be used as a basis for adjusting the clinical medication of diabetic patients. A clinical trial in the United States ( 12 ) found that 1,5-AG levels can sensitively and rapidly reflect glycemic changes after adjustments to personalized treatment strategies, including changes in drug type or dosage as well as the initiation of insulin therapy or combinations of different insulin regimens. KM Dungan et al. ( 64 ) discovered that the combination of 1,5-AG and HbA1c may be a reliable indicator for initiating insulin therapy in T2DM patients with poor control of oral hypoglycemic agents in controlled experiments, but the optimal threshold is still unclear and needs to be further explored.

3.3 Relationship between 1,5-AG and pancreatic β cells

The main pathogenesis of T2DM involves β cell dysfunction and insulin resistance. In order to better manage diabetes, we should use appropriate methods to detect the β cell function status and the secretion of insulin. In a study of 302 newly diagnosed T2DM patients, X Ma et al. ( 65 ) found that 1,5-AG was associated with basal insulin sensitivity and secretion as well as the early insulin secretion of the newly diagnosed T2DM in China. A reduction in the level of 1,5-AG means a decrease in insulin secretion capacity and also reflects a decrease in insulin production index. C Jiménez-Sánchez et al. ( 66 ) believed that serum 1,5-AG concentration was closely correlated with the content of β cells, while other glycemic control indicators cannot monitor the loss of β cells; so, for people at a high risk of diabetes, attention should be paid to monitoring serum 1,5-AG to further identify the loss of β cells and monitor the progress of the patient’s condition. In addition to reflecting the content of β cells, 1,5-AG can also reflect its functional status. By comparing lean β-Phb2-/- mouse models and obese db/db mouse models, L Li et al. ( 67 ) found that 1,5-AG, a blood glucose biomarker reflecting the degree of β cell function, was closely associated with the decline of functional β cells before the onset of diabetes. Y Shen et al. ( 68 ) investigated the relationship between acute C peptide response to arginine and serum 1,5-AG in 623 T2DM patients, showing a linear relationship between the two, while acute C peptide response was an indicator of responsive β cell function, further illustrating that 1,5-AG was closely related to β cell function. H Su et al. ( 69 ) measured the levels of 1,5-AG × HbA1c/100 (AHI) in 3,562 people to evaluate islet function and insulin sensitivity in T2DM patients with different AHI levels. The results showed that the normal population had an AHI level of 1.0 (0.7–1.3), which was significantly higher than the T2DM group of 0.8 (0.5–1.2). Hence, AHI can reflect the changes and functions of pancreatic β cells in blood glucose disorders. The lower the AHI, the more severe the blood glucose disorder and the worse the pancreatic β cell function.

Salivary 1,5-AG is a new noninvasive indicator that reflects early insulin secretion function. L Ying et al. ( 70 ), through a study of 284 T2DM patients, found that salivary 1,5-AG was closely correlated with the C-peptide production index at 0–30 min and the ratio of the area under the C-peptide curve to the area under the glucose curve. However, A Morita ( 71 ) deemed that 1,5-AG had no significant correlation with insulin secretion function. Whether there is a link between the two remains to be verified.

4 Progress of clinical research on 1,5-AG for diabetes complications

4.1 role of 1,5-ag in diabetic nephropathy.

L Bernard et al. ( 72 ) studied 3,799 people at risk of atherosclerosis in the community and found 1,5-AG to be an early sign of chronic kidney disease, which was inversely correlated with glucose and fructose. H Peng ( 73 ) suggested that 1,5-AG decreased with impaired renal function and that a low 1,5-AG level predicted a higher risk of developing diabetic nephropathy and was also significantly correlated with the progression of diabetic nephropathy, which was in accordance with the follow-up results of B Yu ( 74 ). E Selvin et al. ( 75 ) followed 10,000 people at risk of atherosclerosis in the community for 20 years and found that the risk of developing chronic kidney disease was increased threefold in diabetic patients with 1,5-AG <6 μg/mL, even after adjustment of HbA1c or FPG. N Taya et al. ( 13 ) studied 31 T2DM and 30 healthy subjects, compared with the biomarkers before and after treatment using gas chromatography–mass spectrometry. According to the study, the decrease in 1,5-AG and the increase in monosaccharide levels implied poor glycemic control and a significant increase in amino acid levels, which aggravated the kidney burden and increased the risk of diabetic nephropathy. Lower levels of 1,5-AG are associated with the risk of developing end-stage renal disease. When a reduction in serum 1,5-AG level is detected, attention should be paid to screening for kidney damage and timely intervention to avoid irreversible outcomes ( 76 ). A study ( 77 ) of time in range with dynamic blood glucose monitoring showed a significant positive correlation of 1,5-AG with time in range ( r = 0.591). In conclusion, 1,5-AG not only helps to predict the risk of developing diabetic nephropathy but also serves as an evaluation indicator of glycemic control.

4.2 Role of 1,5-AG in diabetic cardiovascular disease

Postprandial hyperglycemia and blood glucose fluctuations contribute to the development of cardiovascular disease, while 1,5-AG is a reliable indicator of monitoring postprandial hyperglycemia and reflecting short-term blood glucose fluctuations, which can be used to reduce the occurrence of cardiovascular disease in diabetic patients by monitoring the 1,5-AG levels ( 78 , 79 ). A prospective observational study of 1,5-AG and cardiovascular diseases found that low levels of 1,5-AG (<6.0 µg/mL) were closely related to cardiovascular disease ( 80 ), and the lower levels of 1,5-AG indicated the higher mortality of cardiovascular events ( 81 ). K Torimoto ( 82 ) found that a low 1,5-AG level was associated with vascular endothelial dysfunction, which was a potential marker of vascular endothelial dysfunction. Wada et al. ( 83 ) studied 161 patients with cardiovascular disease receiving percutaneous coronary intervention and measured the calcification angle by intravascular ultrasound before intervention to reflect the degree of coronary artery calcification. The results showed that the low-1,5-AG group (<14.0 μg/mL) had a significantly higher calcification angle (144°) than the high-1,5-AG group (≥14.0 μg/mL, 107°). YH Zou et al. ( 84 ) divided 160 patients with unstable angina pectoris and HbA1c <7.0% into calcified and non-calcified groups. Then, the serum 1,5-AG and alkaline phosphatase levels were monitored, respectively. The results showed that the 1,5-AG levels were significantly decreased and the alkaline phosphatase levels were significantly increased in the calcified group, which also confirmed the correlation between 1,5-AG and coronary artery calcification, and the lower 1,5-AG predicted a higher risk of coronary artery calcification. Serum 1,5-AG also predicts whether coronary plaque ruptures in diabetic patients with acute coronary syndrome ( 85 ). On the contrary, B Warren ( 86 ) and MR Rooney ( 87 ) concluded that 1,5-AG <10 µg/mL was negatively associated with cardiovascular disease, but there was almost no correlation between the two when the value of 1,5-AG was high; so, 1,5-AG has a poor predictive effect on cardiovascular disease, shows distinct limitations, and cannot provide prognostic information on cardiovascular events in diabetic patients.

4.3 Role of 1,5-AG in diabetic retinopathy

E Selvin et al. ( 88 ) has followed-up patients over 5 years and found that 1, 5-AG was inversely related to microvascular events and mortality, which means that a lower 1,5-AG level significantly increased the incidence of microvascular events, increased patient mortality, and was closely associated with retinopathy. WJ Kim et al. ( 89 ) followed 267 T2DM patients for 5 years and discovered that the risk of developing diabetic retinopathy in the low-1,5-AG (<5.1 ug/mL) group was significantly higher than that in the high-1,5-AG (≥8.64 ug/mL) group. A study has also shown that low levels of 1,5-AG were positively correlated with the incidence of diabetic retinopathy, and the prevalence of 1,5-AG <6 µg/mL was 11 times higher than that of 1,5-AG ≥10 µg/mL ( 75 ). N Mukai et al. ( 90 ) studied 2,681 subjects to locate the optimal threshold for detection of diabetic retinopathy through measurements of 1,5-AG and GA and ophthalmic examinations. According to the study’s results, the optimal thresholds for each indicator were as follows: FPG—6.3 mmol/l and 1,5-AG—12.1 μg/mL. The incidence of diabetic retinopathy was significantly increased when 1,5-AG <12.1 μg/mL, which indicated that monitoring 1,5-AG was essential to prevent microangiopathy and could be widely used in clinical applications.

4.4 Role of 1,5-AG in diabetic pregnancy complications

1,5-AG, a hyperglycemia biomarker in pregnant women, serves to be a bad predictor of gestational diabetes. To better confirm this view, scholars have conducted numerous clinical studies ( 91 , 92 ). TA Pramodkumar et al. ( 93 ) measured serum 1,5-AG by recruiting 145 pregnant women without gestational diabetes and 75 pregnant women with gestational diabetes, and the study found that the mean value of 1,5-AG in pregnant women with gestational diabetes was 0.001 ± 16.2 μg/mL, which was significantly lower than that in pregnant women without gestational diabetes (1.5 ± 11.8 μg/mL). 1,5-AG remained significantly correlated with gestational diabetes, even after adjusting the potential confounders. Moreover, 1,5-AG has a unique role in predicting diabetic pregnancy complications. LA Wright et al. ( 94 ) compared the relationship between 1,5-AG, HbA1c and diabetic pregnancy complications in 17 gestational diabetes cases, 48 T1DM cases, and 37 T2DM cases. The study found that the level of 1,5-AG was significantly negatively correlated with diabetic pregnancy complications, especially in large-for-gestational-age (LGA) and neonatal hypoglycemia cases. There is a strong positive correlation between 1,5-AG and preeclampsia at 1 week of gestation, which was also supported by E Yefet ( 95 ). CL Meek et al. ( 96 ) monitored the relationship between the biochemical indicators of T1DM in 157 pregnant women and pregnancy complications and found that 1,5-AG, the most prevalent indicator of LGAs, was negatively correlated with LGA throughout the entire pregnancy, especially in the late trimester of pregnancy, with lower 1,5-AG levels meaning a greater risk of LGA ( 97 ). 1,5-AG also predicts growth restriction in full-term fetuses and is closely linked to neonatal mortality ( 98 ).

4.5 Role of 1,5-AG in diabetic peripheral neuropathy

M Yamawaki et al. ( 99 ) examined brain MRI, serum 1,5-AG, and cognitive function in 688 subjects. According to the study’s results, lower 1,5-AG levels were found to be positively correlated with severe periventricular hyperintensities and deep white matter hyperintensities, as well as a significant risk factor for cognitive decline and depression. For every 1,5-AG reduction of 5 μg/mL, the risk of dementia increases by 16%; so, the decrease of 1,5-AG is a risk factor for cognitive decline and dementia, and the monitoring of serum 1,5-AG can be an important method of preventing cognitive decline ( 100 ). Q Lou et al. ( 101 ) randomly divided 75 patients with T2DM after cerebral infarction into two groups for a 6-month randomized controlled experiment. The control group received conventional treatment, while the intervention group strengthened the monitoring of blood glucose fluctuations on the basis of conventional treatment and flexibly adjusted medication. The results showed a marked improvement of 1,5-AG in the intervention group, and the National Institutes of Health Stroke Scale score was also reduced significantly; so, monitoring 1,5-AG could reduce the damage to the patient’s neurological function and improve the quality of life. However, in recent years, studies have shown that poor glycemic control and a longer course of diabetes were significantly associated with cognitive impairment, which can be reflected through HbA1c, GA, and fructosamine, while 1,5-AG is not significantly related to the occurrence of dementia ( 102 ). CW Hicks et al. ( 103 ) also concluded that 1,5-AG was not significantly related to diabetic peripheral neuropathy. Therefore, more studies are needed to confirm whether 1,5-AG is associated with diabetic peripheral neuropathy.

Although many clinical studies have demonstrated the value and practicality of 1,5-AG in clinical applications, there are still some problems to think about and study. First, while mass spectrometry detects serum 1,5-AG with high sensitivity and accuracy, the process shows difficulty, inconvenience, and high cost. Enzyme detection is simple and easy to perform, but the results will be affected by the manufacturer and the quality of the kit. Salivary 1,5-AG has been verified to be correlated with serum 1,5-AG, which has attracted much attention due to its noninvasiveness and good application potential. However, mass spectrometry for the detection of salivary 1,5-AG is cumbersome and expensive, and due to the lack of mature enzyme assay kits, the present enzyme assay for salivary 1,5-AG has poor accuracy. Therefore, a large number of studies are still needed in the future to identify the best detection method for 1,5-AG in order to promote its clinical application.

Second, the normal reference range for 1,5-AG is not constant, which is influenced by age, gender, race, regional environment, diet, medication, and so on. Establishing a normal reference range is an essential step in the application of 1,5-AG in clinical practice, but the reference ranges given by many manufacturers are not applicable and vary from region to region. With the multiplication of clinical studies, the normal reference range of 1,5-AG has been proposed in most regions, and more large-scale clinical studies will be needed in the future to further determine the normal reference range in each region.

Third, 1,5-AG is sensitive to short-term blood glucose fluctuations and postprandial hyperglycemia, helping to assist the screening for diabetes and avoiding the missed diagnosis of postprandial hyperglycemia patients. However, 1,5-AG cannot reflect the specific time of poor glycemic control. More research can be done in the future to determine if it can reflect specific periods more conducive to accurate medication administration and glycemic control.

Fourth, 1,5-AG is widely used but is less accurate in patients with liver disease. Increased inositol levels in uremia patients can also interfere with the measurement of 1,5-AG. M Koga et al. ( 104 ) showed that serum 1,5-AG levels were lower in patients with chronic liver disease irrespective of their blood glucose levels, which was associated with impaired liver function. The liver produces a modest quantity of 1,5-AG, which is lowered when the liver function is impaired. The liver is an important site of glucagon metabolism and an important organ in the regulation of plasma glucose levels; so, patients with chronic hepatitis, cirrhosis, and other chronic liver diseases often have abnormal glucose metabolism and large fluctuations in blood glucose ( 105 ). The reabsorption of 1,5-AG in renal tubules is competitively inhibited by glucose, and when glucose metabolism is abnormal, it will also lead to fluctuations in the measured 1,5-AG value; so, the measurement of serum 1,5-AG in diabetic patients with chronic liver disease does not accurately reflect their glycemic control. Some studies ( 106 ) have also shown that 1,5-AG decreases significantly and gradually with the progression of liver fibrosis; so, even if a low serum 1,5-AG level is measured, it does not indicate poor glycemic control. At this time, the use of 1,5-AG to reflect the glycemic control of patients with liver disease is less accurate; so, many studies have concluded that 1,5-AG is not suitable as a glycemic monitoring indicator for patients with liver disease. Therefore, the applicability of 1,5-AG is limited and needs further clinical verification.

Fifth, 1,5-AG is a reliable indicator of poor glycemic control and has a predictive effect on the occurrence of adverse events such as hypoglycemia. However, whether 1,5-AG has a better predictive effect on adverse events than HbA1c, GA, and FPG and other traditional blood glucose indicators remains to be explored. In the future, more prospective studies are required to further clarify whether 1,5-AG has an advantage in predicting adverse events.

Sixth, FT1DM has an acute onset and rapid progression, which can easily pose to various complications and even death; so, early recognition and diagnosis of FT1DM are particularly important. Currently, it is suggested that 1,5-AG can distinguish between FT1DM and T2DM, but such clinical studies are few, not convincing, and may be a small-probability event. A large number of clinical trials will still be needed in the future to confirm this conclusion.

Seventh, 1,5-AG is correlated with pancreatic β cells, but the deeper mechanism remains unclear, and more experimental studies are needed to explore the mechanism of both. Eighth, there are few studies on the role of 1,5-AG in diabetic peripheral neuropathy, and existing studies have different conclusions. Therefore, it is impossible to determine whether 1,5-AG can effectively predict the occurrence of diabetic peripheral neuropathy, and more studies are needed to clarify this. Overall, there are many studies on 1,5-AG with complications, but there is still a lack of prospective cohort studies with large samples, which is not forceful enough. More experimental studies and longer follow-up observations are required to further confirm the clinical value of 1,5-AG for predicting diabetes complications. At the same time, it remains to be demonstrated whether 1,5-AG predicts the risk of diabetes complications better than traditional glycemic indicators such as HbA1c, GA, and FPG.

In summary, 1,5-AG is more sensitive to reflecting short-term glucose fluctuations and postprandial hyperglycemia than traditional glucose monitoring indicators such as HbA1c, FPG, GA, and so on. The combination of 1,5-AG and traditional glucose monitoring indicators improves the accuracy of diabetes screening, which is of great help to improve the glucose monitoring system. 1,5-AG shows great potential in the screening and management of diabetes, diabetes complications, and so on, which is conducive to clinical application. As a noninvasive detection indicator, salivary 1,5-AG is more convenient, but it still requires further research and improvement methods to be widely used. More clinical studies are needed to demonstrate the normal reference range of 1,5-AG and its role in diabetes complications, thus making it better to predict the risk of diabetes complications.

Author contributions

HX: Conceptualization, Writing – original draft, Writing – review & editing. JP: Writing – original draft, Writing – review & editing. QC: Supervision, Writing – review & editing.

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

Acknowledgments

First of all, I would like to extend my sincere gratitude to my supervisor, QC, for his instructive advice and useful suggestions on my thesis. I am deeply grateful of his help in the completion of this thesis. Furthermore, I am deeply indebted to my friends who have put considerable time and effort into their comments on the draft. I am indebted to these people for their direct and indirect help to me. Finally, I am indebted to my parents for their continuous support and encouragement.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fendo.2024.1383483/full#supplementary-material

1. Chinese Diabetes Society. Chinese guidelines for the prevention and control of type 2 diabetes (2020 edition). Chin J Pract Internal Med . (2021) 41:668–95. doi: 10.19538/j.nk2021080106

CrossRef Full Text | Google Scholar

2. Kiniwa N, Okumiya T, Tokuhiro S, Matsumura Y, Matsui H, Koga M. Hemolysis causes a decrease in HbA1c level but not in glycated albumin or 1,5-anhydroglucitol level. Scand J Clin Lab Invest . (2019) 79:377–80. doi: 10.1080/00365513.2019.1627577

PubMed Abstract | CrossRef Full Text | Google Scholar

3. Zhou M, Shen RJ, Wu WY, Yan HM, Guo W Pan BS, et al. Performance verification and clinical application evaluation of enzyme assay for serum 1,5-anhydrous sorbitol. Lab Med . (2019) 34:1015–20. doi: 10.3969/j.issn.1673-8640.2019.11.013

4. Jiang JJ, Zheng X, Ma MS, Cui XG, Jian S, Tang XY, et al. Short-term efficacy of empagliflozin in children with glycogen storage disease type Ib. Zhonghua Er Ke Za Zhi . (2023) 61:515–9. doi: 10.3760/cma.j.cn112140–20230131–00067

5. Diederich J, Mounkoro P, Tirado HA, Chevalier N, Van Schaftingen E, Veiga-da-Cunha M. SGLT5 is the renal transporter for 1,5-anhydroglucitol, a major player in two rare forms of neutropenia. Cell Mol Life Sci . (2023) 80:259. doi: 10.1007/s00018–023-04884–8

6. Dewulf JP, Chevalier N, Marie S, Veiga-da-Cunha M. DBS are suitable for 1,5-anhydroglucitol monitoring in GSD1b and G6PC3-deficient patients taking SGLT2 inhibitors to treat neutropenia. Mol Genet Metab . (2023) 140:107712. doi: 10.1016/j.ymgme.2023.107712

7. Yamanouchi T, Shinohara T, Ogata N, Tachibana Y, Akaoka I, Miyashita H. Common reabsorption system of 1,5-anhydro-D-glucitol, fructose, and mannose in rat renal tubule. Biochim Biophys Acta . (1996) 1291:89–95. doi: 10.1016/0304–4165(96)00050–5

8. Ying L, Ma X, Yin J, Wang Y, He X, Peng J, et al. The metabolism and transport of 1,5-anhydroglucitol in cells. Acta Diabetol . (2018) 55:279–86. doi: 10.1007/s00592–017-1093–8

9. Stickle D, Turk J. A kinetic mass balance model for 1,5-anhydroglucitol: applications to monitoring of glycemic control. Am J Physiol . (1997) 273:E821–30. doi: 10.1152/ajpendo.1997.273.4.E821

10. Stettler C, Stahl M, Allemann S, Diem P, Schmidlin K, Zwahlen M, et al. Association of 1,5-anhydroglucitol and 2-h postprandial blood glucose in type 2 diabetic patients. Diabetes Care . (2008) 31:1534–5. doi: 10.2337/dc08–0385

11. McGill JB, Cole TG, Nowatzke W, Houghton S, Ammirati EB, Gautille T, et al. Circulating 1,5-anhydroglucitol levels in adult patients with diabetes reflect longitudinal changes of glycemia: a U.S. trial of the GlycoMark assay. Diabetes Care . (2004) 27:1859–65. doi: 10.2337/diacare.27.8.1859

12. Beck R, Steffes M, Xing D, Ruedy K, Mauras N, Wilson DM, et al. The interrelationships of glycemic control measures: HbA1c, glycated albumin, fructosamine, 1,5-anhydroglucitrol, and continuous glucose monitoring. Pediatr Diabetes . (2011) 12:690–5. doi: 10.1111/j.1399–5448.2011.00764

13. Taya N, Katakami N, Omori K, Arakawa S, Hosoe S, Watanabe H, et al. Evaluation of change in metabolome caused by comprehensive diabetes treatment: A prospective observational study of diabetes inpatients with gas chromatography/mass spectrometry-based non-target metabolomic analysis. J Diabetes Investig . (2021) 12:2232–41. doi: 10.1111/jdi.13600

14. Li M, Yan D, Hao M, Huang X, Xu Y, Li W, et al. Quantification of Glucose, fructose and 1,5-Anhydroglucitol in plasma of diabetic patients by ultra performance liquid chromatography tandem mass spectrometry. J Chromatogr B Analyt Technol BioMed Life Sci . (2022) 1200:123277. doi: 10.1016/j.jchromb.2022.123277

15. Halama A, Kulinski M, Kader SA, Satheesh NJ, Abou-Samra AB, Suhre K, et al. Measurement of 1,5-anhydroglucitol in blood and saliva: from non-targeted metabolomics to biochemical assay. J Transl Med . (2016) 14:140. doi: 10.1186/s12967–016-0897–6

16. Selvin E, Rynders GP, Steffes MW. Comparison of two assays for serum 1,5-anhydroglucitol. Clin Chim Acta . (2011) 412:793–5. doi: 10.1016/j.cca.2011.01.007

17. Nowatzke W, Sarno MJ, Birch NC, Stickle DF, Eden T, Cole TG. Evaluation of an assay for serum 1,5-anhydroglucitol (GlycoMark) and determination of reference intervals on the Hitachi 917 analyzer. Clin Chim Acta . (2004) 350:201–9. doi: 10.1016/j.cccn.2004.08.013

18. Ohigashi M, Osugi K, Kusunoki Y, Washio K, Matsutani S, Tsunoda T, et al. Association of time in range with hemoglobin A1c, glycated albumin and 1,5-anhydro-d-glucitol. J Diabetes Investig . (2021) 12:940–9. doi: 10.1111/jdi.13437

19. Jian CH, Zhao AH, Ma XJ, Lu W, Zhu W, Wang YF, et al. Consistency of different assays for salivary 1,5-dehydroglucosol. Chin Med J . (2020) 100:3291–5. doi: 10.3760/cma.j.cn112137–20200312–00726

20. Akanuma Y, Morita M, Fukuzawa N, Yamanouchi T, Akanuma H. Urinary excretion of 1,5-anhydro-D-glucitol accompanying glucose excretion in diabetic patients. Diabetologia . (1988) 31:831–5. doi: 10.1007/BF00277486

21. Namba N, Watanabe F, Tokuda M, Mino M, Furuya E. A new method of quantitating serum and urinary levels of 1,5-anhydroglucitol in insulin-dependent diabetes mellitus. Diabetes Res Clin Pract . (1994) 24:55–61. doi: 10.1016/0168–8227(94)90086–8

22. Ren J, Ma Y, Ma M, Ding J, Jiang J, Zheng X, et al. Rapid ultra-performance liquid chromatography-tandem mass spectrometry method for the simultaneous determination of three characteristic urinary saccharide metabolites in patients with glycogen storage diseases (type Ib and II). J Chromatogr B Analyt Technol BioMed Life Sci . (2023) 1229:123900. doi: 10.1016/j.jchromb.2023.123900

23. Zhou Z, Zhao L, Wang Z, Xue W, Wang Y, Huang Y, et al. Colorimetric detection of 1,5-anhydroglucitol based on graphene quantum dots and enzyme-catalyzed reaction. Int J Biol Macromol . (2018) 112:1217–24. doi: 10.1016/j.ijbiomac.2018.02.093

24. Jang H, Oh J, Ki H, Kim MG. Paper-based 1,5-anhydroglucitol quantification using enzyme-based glucose elimination. Analyst . (2020) 145:5740–3. doi: 10.1039/D0AN00905A

25. Li G, Wu G, Huang J, Wang B, Li H, Chen W, et al. Nanozyme-mediated cascade reaction system for electrochemical detection of 1,5-anhydroglucitol. Bioelectrochemistry . (2022) 147:108204. doi: 10.1016/j.bioelechem.2022.108204

26. Liang J, Shi X, Feng H, Chen M, Li W, Lai J, et al. 1,5-anhydroglucitol biosensor based on light-addressable potentiometric sensor with RGO-CS-Fc/Au NPs nanohybrids. Bioelectrochemistry . (2021) 142:107938. doi: 10.1016/j.bioelechem.2021.107938

27. Welter M, Boritza KC, Anghebem-Oliveira MI, Henneberg R, Hauser AB, Rego FGM, et al. Reference intervals for serum 1,5-anhydroglucitol in children, adolescents, adults, and pregnant women. Clin Chim Acta . (2018) 486:54–8. doi: 10.1016/j.cca.2018.07.018

28. Selvin E, Warren B, He X, Sacks DB, Saenger AK. Establishment of community-based reference intervals for fructosamine, glycated albumin, and 1,5-anhydroglucitol. Clin Chem . (2018) 64:843–50. doi: 10.1373/clinchem.2017.285742

29. Chen C, Wang X, Tan Y, Yang J, Yuan Y, Chen J, et al. Reference intervals for serum 1,5-anhydroglucitol of a population with normal glucose tolerance in Jiangsu Province. J Diabetes . (2020) 12:447–54. doi: 10.1111/1753–0407.13016

30. Parrinello CM, Sharrett AR, Maruthur NM, Bergenstal RM, Grams ME, Coresh J, et al. Racial differences in and prognostic value of biomarkers of hyperglycemia. Diabetes Care . (2016) 39:589–95. doi: 10.2337/dc15–1360

31. Juraschek SP, Miller ER 3rd, Appel LJ, Christenson RH, Sacks FM, Selvin E. Effects of dietary carbohydrate on 1,5-anhydroglucitol in a population without diabetes: results from the OmniCarb trial. Diabetes Med . (2017) 34:1407–13. doi: 10.1111/dme.13391

32. Koga M, Murai J, Saito H, Mukai M, Kasayama S. Habitual intake of dairy products influences serum 1,5-anhydroglucitol levels independently of plasma glucose. Diabetes Res Clin Pract . (2010) 90:122–5. doi: 10.1016/j.diabres.2010.06.023

33. Kawasaki T, Yamanouchi T, Kashiwabara A, Inoue T, Yoshimura T, Fujimori S, et al. The influence of traditional Chinese herbal drugs on serum 1, 5-anhydroglucitol levels. Diabetes Res Clin Pract . (2000) 50:97–101. doi: 10.1016/s0168–8227(00)00167–4

34. Fortuna D, McCloskey LJ, Stickle DF. Model analysis of effect of canagliflozin (Invokana), a sodium-glucose cotransporter 2 inhibitor, to alter plasma 1,5-anhydroglucitol. Clin Chim Acta . (2016) 452:138–41. doi: 10.1016/j.cca.2015.11.010

35. Li S, Wen DM, Xie JY, Wang WJ. Performance validation of 1,5-Anhydroglucitol kit and establishment of reference intervals. Lab Med And Clinical . (2020) 17:1424–7. doi: 10.3969/j.issn.1672–9455.2020.10.032

36. Mook-Kanamori DO, Selim MM, Takiddin AH, Al-Homsi H, Al-Mahmoud KA, Al-Obaidli A, et al. 1,5-Anhydroglucitol in saliva is a noninvasive marker of short-term glycemic control. J Clin Endocrinol Metab . (2014) 99:E479–83. doi: 10.1210/jc.2013–3596

37. Jian CH, Zhao AH, Xu YT, Wang YS, Nie XM, Ying LW, et al. Establishment of a method for the detection of salivary 1,5-anhydroglucitol. Chin J Of Diabetes . (2020) 12:25–9. doi: 10.3760/cma.j.issn.1674–5809.2020.01.005

38. Jian CH, Zhao AH, Ma XJ, Lu W, Zhu W, Wang YF, et al. A new indicator for blood glucose monitoring: a study of the normal reference range of salivary 1,5-anhydroglucitol. Chin J Of Diabetes . (2020) 12:480–5. doi: 10.3760/cma.j.cn115791–20191030–00399

39. Yamanouchi T, Akanuma H, Nakamura T, Akaoka I, Akanuma Y. Reduction of plasma 1,5-anhydroglucitol (1-deoxyglucose) concentration in diabetic patients. Diabetologia . (1988) 31:41–5. doi: 10.1007/BF00279131

40. Wang Y, Yuan Y, Zhang Y, Lei C, Zhou Y, He J, et al. Serum 1,5-anhydroglucitol level as a screening tool for diabetes mellitus in a community-based population at high risk of diabetes. Acta Diabetol . (2017) 54:425–31. doi: 10.1007/s00592-016-0944-z

41. Yamanouchi T, Akanuma Y, Toyota T, Kuzuya T, Kawai T, Kawazu S, et al. Comparison of 1,5-anhydroglucitol, HbA1c, and fructosamine for detection of diabetes. Diabetes . (1991) 40:52–7. doi: 10.2337/diab.40.1.52

42. Qian J, Chen C, Wang X, Tan Y, Yang J, Yuan Y, et al. HbA1c combined with glycated albumin or 1,5-anhydroglucitol improves the efficiency of diabetes screening in a Chinese population. Diabetes Med . (2022) 39:e14685. doi: 10.1111/dme.14685

43. Su H, Ma XJ, Ying LW, He XX, Zhu W, Tang JL, et al. Comparison of the efficiency of 1,5-anhydroglucitol or glycated albumin combined with fasting blood glucose screening for diabetes. J Of Shanghai Jiaotong University(Medical Edition) . (2019) 39:1078–1082+1077. doi: 10.3969/j.issn.1674–8115.2019.09.022

44. Jian C, Zhao A, Ma X, Ge K, Lu W, Zhu W, et al. Diabetes Screening: detection and application of saliva 1,5-anhydroglucitol by liquid chromatography-mass spectrometry. J Clin Endocrinol Metab . (2020) 105:dgaa114. doi: 10.1210/clinem/dgaa114

45. Loomis SJ, Köttgen A, Li M, Tin A, Coresh J, Boerwinkle E, et al. Rare variants in SLC5A10 are associated with serum 1,5-anhydroglucitol (1,5-AG) in the Atherosclerosis Risk in Communities (ARIC) Study. Sci Rep . (2019) 9:5941. doi: 10.1038/s41598–019-42202–0

46. Martins BR, Gomes LC, Boritza KC, Anghebem-Oliveira MI, Souza EM, França SN, et al. Serum 1,5-anhydroglucitol concentration as a biomarker for type 1 diabetes in adults and children. Clin Lab . (2019) 65:9. doi: 10.7754/Clin.Lab.2019.190141

47. Pal A, Farmer AJ, Dudley C, Selwood MP, Barrow BA, Klyne R, et al. Evaluation of serum 1,5 anhydroglucitol levels as a clinical test to differentiate subtypes of diabetes. Diabetes Care . (2010) 33:252–7. doi: 10.2337/dc09–1246

48. Koga M, Murai J, Saito H, Mukai M, Kasayama S, Imagawa A, et al. Serum 1,5-anhydroglucitol levels in patients with fulminant type 1 diabetes are lower than those in patients with type 2 diabetes. Clin Biochem . (2010) 43:1265–7. doi: 10.1016/j.clinbiochem.2010.08.001

49. Ying L, Ma X, Shen Y, Lu J, Lu W, Zhu W, et al. Serum 1,5-anhydroglucitol to glycated albumin ratio can help early distinguish fulminant type 1 diabetes mellitus from newly onset type 1A diabetes mellitus. J Diabetes Res . (2020) 2020:1243630. doi: 10.1155/2020/1243630

50. Wadwa RP, Laffel LM, Shah VN, Garg SK. Accuracy of a factory-calibrated, real-time continuous glucose monitoring system during 10 days of use in youth and adults with diabetes. Diabetes Technol Ther . (2018) 20:395–402. doi: 10.1089/dia.2018.0150

51. Lucarelli F, Ricci F, Caprio F, Valgimigli F, Scuffi C, Moscone D, et al. GlucoMen Day continuous glucose monitoring system: a screening for enzymatic and electrochemical interferents. J Diabetes Sci Technol . (2012) 6:1172–81. doi: 10.1177/193229681200600522

52. Seok H, Huh JH, Kim HM, Lee BW, Kang ES, Lee HC, et al. 1,5-anhydroglucitol as a useful marker for assessing short-term glycemic excursions in type 1 diabetes. Diabetes Metab J . (2015) 39:164–70. doi: 10.4093/dmj.2015.39.2.164

53. Kim WJ, Park CY, Lee KB, Park SE, Rhee EJ, Lee WY, et al. Serum 1,5-anhydroglucitol concentrations are a reliable index of glycemic control in type 2 diabetes with mild or moderate renal dysfunction. Diabetes Care . (2012) 35:281–6. doi: 10.2337/dc11–1462

54. Buchwald P, Tamayo-Garcia A, Ramamoorthy S, Garcia-Contreras M, Mendez AJ, Ricordi C. Comprehensive metabolomics study to assess longitudinal biochemical changes and potential early biomarkers in nonobese diabetic mice that pogress to diabetes. J Proteome Res . (2017) 16:3873–90. doi: 10.1021/acs.jproteome.7b00512

55. Dungan KM, Buse JB, Largay J, Kelly MM, Button EA, Kato S, et al. 1,5-anhydroglucitol and postprandial hyperglycemia as measured by continuous glucose monitoring system in moderately controlled patients with diabetes. Diabetes Care . (2006) 29:1214–9. doi: 10.2337/dc06–1910

56. Monnier L, Colette C, Owens D. Calibration free continuous glucose monitoring (CGM) devices: Weighing up the benefits and limitations. Diabetes Metab . (2020) 46:79–82. doi: 10.1016/j.diabet.2019.101118

57. Peabody J, Paculdo D, Acelajado MC, Burgon T, Dahlen JR. Finding the clinical utility of 1,5-anhydroglucitol among primary care practitioners. J Clin Transl Endocrinol . (2020) 20:100224. doi: 10.1016/j.jcte.2020.100224

58. Kappel BA, Moellmann J, Thiele K, Rau M, Artati A, Adamski J, et al. Human and mouse non-targeted metabolomics identify 1,5-anhydroglucitol as SGLT2-dependent glycemic marker. Clin Transl Med . (2021) 11:e470. doi: 10.1002/ctm2.470

59. Lin J, Huang T, Wei H, Bao B, Gao T, Zheng X, et al. Does preoperative glycemic control restore immune defense against implant-related Infection in mice with diabetes? Clin Orthop Relat Res . (2022) 480:1008–17. doi: 10.1097/CORR.0000000000002041

60. Sone H, Okuda Y, Yamaoka T, Kawakami Y, Odawara M, Matsushima T, et al. Predicting long-term glycemic control of post-educational type II diabetic patients by evaluating serum 1,5-anhydroglucitol levels. Diabetes Res Clin Pract . (1996) 34:83–8. doi: 10.1016/S0168-8227(96)01340-X

61. Kedarnath PS, Subramanian SS, Bhaskar E, Kasi M, Pillai V, Subramanian S, et al. Salivary 1,5-anhydroglucitol and its correlation with postprandial hyperglycemia: development and validation of a novel assay. Int J Appl Basic Med Res . (2023) 13:23–8. doi: 10.4103/ijabmr.ijabmr_378_22

62. Kim MK, Jung HS, Kwak SH, Cho YM, Park KS, Kim SY. 1,5-Anhydroglucitol could reflect hypoglycemia risk in patients with type 2 diabetes receiving insulin therapy. Endocrinol Metab (Seoul) . (2016) 31:284–91. doi: 10.3803/EnM.2016.31.2.284

63. Lee AK, Lee CJ, Huang ES, Sharrett AR, Coresh J, Selvin E. Risk factors for severe hypoglycemia in black and white adults with diabetes: the atherosclerosis risk in communities (ARIC) study. Diabetes Care . (2017) 40:1661–7. doi: 10.2337/dc17–0819

64. Dungan KM, Buse JB, Herman WH, Arakaki RF, Jiang HH, Jacobson JG, et al. Potential for use of 1,5-anhydroglucitol when initiating insulin therapy in people with type 2 diabetes and suboptimal control with oral antidiabetic drugs. Diabetes Res Clin Pract . (2012) 96:e66–9. doi: 10.1016/j.diabres.2012.02.005

65. Ma X, Hao Y, Hu X, Luo Y, Deng Z, Zhou J, et al. 1,5-anhydroglucitol is associated with early-phase insulin secretion in chinese patients with newly diagnosed type 2 diabetes mellitus. Diabetes Technol Ther . (2015) 17:320–6. doi: 10.1089/dia.2014.0346

66. Jiménez-Sánchez C, Mezza T, Sinturel F, Li L, Di Giuseppe G, Quero G, et al. Circulating 1,5-anhydroglucitol as a biomarker of ß-cell mass independent of a diabetes phenotype in human subjects. J Clin Endocrinol Metab . (2022) 107:2833–43. doi: 10.1210/clinem/dgac444

67. Li L, Krznar P, Erban A, Agazzi A, Martin-Levilain J, Supale S, et al. Metabolomics identifies a biomarker revealing in vivo loss of functional β-cell mass before diabetes onset. Diabetes . (2019) 68:2272–86. doi: 10.2337/db19–0131

68. Shen Y, Si Y, Lu J, Ma X, Zhang L, Mo Y, et al. Association between 1,5-anhydroglucitol and acute C peptide response to arginine among patients with type 2 diabetes. J Diabetes Res . (2020) 21:4243053. doi: 10.1155/2020/4243053

69. Su H, Ma X, Shen Y, He X, Ying L, Zhu W, et al. 1,5-Anhydroglucitol × glycated hemoglobin A1c/100 as a potential biomarker for islet β-cell function among patients with type 2 diabetes. Acta Diabetol . (2020) 57:439–46. doi: 10.1007/s00592–019-01452–0

70. Ying L, Jian C, Ma X, Ge K, Zhu W, Wang Y, et al. Saliva 1,5-anhydroglucitol is associated with early-phase insulin secretion in Chinese patients with type 2 diabetes. BMJ Open Diabetes Res Care . (2021) 9:e002199. doi: 10.1136/bmjdrc-2021–002199

71. Morita A, Ouchi M, Satoh K, Terada M, Kon H, Wakashin H, et al. Development of an organ bath technique for isolated rat pancreas preparations to assess the effect of 1,5-AG on insulin secretion. Exp Anim . (2020) 69:127–34. doi: 10.1538/expanim.19–0059

72. Bernard L, Zhou L, Surapaneni A, Chen J, Rebholz CM, Coresh J, et al. Serum metabolites and kidney outcomes: the atherosclerosis risk in communities study. Kidney Med . (2022) 4:100522. doi: 10.1016/j.xkme.2022.100522

73. Peng H, Liu X, Ieong CA, Tou T, Tsai T, Zhu H, et al. A metabolomics study of metabolites associated with the glomerular filtration rate. BMC Nephrol . (2023) 24:105. doi: 10.1186/s12882–023-03147–9

74. Yu B, Zheng Y, Nettleton JA, Alexander D, Coresh J, Boerwinkle E. Serum metabolomic profiling and incident CKD among African Americans. Clin J Am Soc Nephrol . (2014) 9:1410–7. doi: 10.2215/CJN.11971113

75. Selvin E, Rawlings AM, Grams M, Klein R, Steffes M, Coresh J. Association of 1,5-anhydroglucitol with diabetes and microvascular conditions. Clin Chem . (2014) 60:1409–18. doi: 10.1373/clinchem.2014.229427

76. Rebholz CM, Grams ME, Chen Y, Gross AL, Sang Y, Coresh J, et al. Serum levels of 1,5-anhydroglucitol and risk of incident end-stage renal disease. Am J Epidemiol . (2017) 186:952–60. doi: 10.1093/aje/kwx167

77. Ju YJ, Zhang LQ, Wang GY, Kong YH. A study of the correlation between target glucose range and glycosylated hemoglobin, 1,5-anhydroglucitol and glycosylated serum albumin in patients with stage III diabetic nephropathy. J Of Hainan Med College . (2020) 26:199–203+208. doi: 10.13210/j.cnki.jhmu.20200210.003

78. Kuroda M, Shinke T, Sakaguchi K, Otake H, Takaya T, Hirota Y, et al. Effect of daily glucose fluctuation on coronary plaque vulnerability in patients pre-treated with lipid-lowering therapy: a prospective observational study. JACC Cardiovasc Interv . (2015) 8:800–11. doi: 10.1016/j.jcin.2014.11.025

79. Villena Chávez JE, Neira Sánchez ER, Poletti Ferrara LF. Dispersion of serum 1,5 anhydroglucitol values in patients with type 2 diabetes at goal of HbA1c. Diabetes Res Clin Pract . (2023) 199:110668. doi: 10.1016/j.diabres.2023.110668

80. Selvin E, Rawlings A, Lutsey P, Maruthur N, Pankow JS, Steffes M, et al. Association of 1,5-anhydroglucitol with cardiovascular disease and mortality. Diabetes . (2016) 65:201–8. doi: 10.2337/db15–0607

81. Ouchi S, Shimada K, Miyazaki T, Takahashi S, Sugita Y, Shimizu M, et al. Low 1,5-anhydroglucitol levels are associated with long-term cardiac mortality in acute coronary syndrome patients with hemoglobin A1c levels less than 7.0. Cardiovasc Diabetol . (2017) 16:151. doi: 10.1186/s12933–017-0636–1

82. Torimoto K, Okada Y, Mori H, Tanaka Y. Low levels of 1,5-anhydro-D-glucitol are associated with vascular endothelial dysfunction in type 2 diabetes. Cardiovasc Diabetol . (2014) 13:99. doi: 10.1186/1475–2840-13–99

83. Wada H, Dohi T, Miyauchi K, Takahashi N, Endo H, Kato Y, et al. Impact of serum 1,5-anhydro-D-glucitol level on the prediction of severe coronary artery calcification: an intravascular ultrasound study. Cardiovasc Diabetol . (2019) 18:69. doi: 10.1186/s12933–019-0878–1

84. Zou YH, Li DD, Dai B, He JX. Association of 1,5-anhydroglucitol with coronary artery calcification. J Of Pract Med . (2020) 36:902–5. doi: 10.3969/j.issn.1006–5725.2020.07.014

85. Su G, Gao MX, Shi GL, Dai XX, Yao WF, Zhang T, et al. Effect of 1,5-anhydroglucitol levels on culprit plaque rupture in diabetic patients with acute coronary syndrome. Cardiovasc Diabetol . (2020) 19:71. doi: 10.1186/s12933–020-01045–0

86. Warren B, Lee AK, Ballantyne CM, Hoogeveen RC, Pankow JS, Grams ME, et al. Associations of 1,5-anhydroglucitol and 2-hour glucose with major clinical outcomes in the atherosclerosis risk in communities (ARIC) study. J Appl Lab Med . (2020) 5:1296–306. doi: 10.1093/jalm/jfaa066

87. Rooney MR, Wang D, McEvoy JW, Juraschek SP, Chalmers J, Woodward M, et al. Glycemic excursions and subclinical cardiac damage in adults with type 2 diabetes: Results from the ADVANCE Trial. Diabetes Res Clin Pract . (2021) 182:109148. doi: 10.1016/j.diabres.2021.109148

88. Selvin E, Wang D, McEvoy JW, Juraschek SP, Lazo M, Hamet P, et al. Response of 1,5-anhydroglucitol level to intensive glucose- and blood-pressure lowering interventions, and its associations with clinical outcomes in the ADVANCE trial. Diabetes Obes Metab . (2019) 21:2017–23. doi: 10.1111/dom.13755

89. Kim WJ, Park CY, Park SE, Rhee EJ, Lee WY, Oh KW, et al. Serum 1,5-anhydroglucitol is associated with diabetic retinopathy in Type 2 diabetes. Diabetes Med . (2012) 29:1184–90. doi: 10.1111/j.1464-5491.2012.03613.x

90. Mukai N, Yasuda M, Ninomiya T, Hata J, Hirakawa Y, Ikeda F, et al. Thresholds of various glycemic measures for diagnosing diabetes based on prevalence of retinopathy in community-dwelling Japanese subjects: the Hisayama Study. Cardiovasc Diabetol . (2014) 13:45. doi: 10.1186/1475–2840-13–45

91. Scholtens DM, Muehlbauer MJ, Daya NR, Stevens RD, Dyer AR, Lowe LP, et al. Metabolomics reveals broad-scale metabolic perturbations in hyperglycemic mothers during pregnancy. Diabetes Care . (2014) 37:158–66. doi: 10.2337/dc13–0989

92. Boritza KC, dos Santos-Weiss IC, da Silva Couto Alves A, Réa RR, Pedrosa FO, de Souza EM, et al. 1,5 Anhydroglucitol serum concentration as a biomarker for screening gestational diabetes in early pregnancy. Clin Chem Lab Med . (2014) 52:e179–81. doi: 10.1515/cclm-2013–1042

93. Pramodkumar TA, Jayashri R, Gokulakrishnan K, Velmurugan K, Pradeepa R, Venkatesan U, et al. 1,5 Anhydroglucitol in gestational diabetes mellitus. J Diabetes Complications . (2019) 33:231–5. doi: 10.1016/j.jdiacomp.2018.11.010

94. Wright LA, Hirsch IB, Gooley TA, Brown Z. 1,5-anhydroglucitol and neonatal complications in pregnancy complicated by diabetes. Endocr Pract . (2015) 21:725–33. doi: 10.4158/EP14437.OR

95. Yefet E, Twafra S, Shwartz N, Hissin N, Hasanein J, Colodner R, et al. Inverse association between 1,5-anhydroglucitol and neonatal diabetic complications. Endocrine . (2019) 66:210–9. doi: 10.1007/s12020-019-02058-w

96. Meek CL, Tundidor D, Feig DS, Yamamoto JM, Scott EM, Ma DD, et al. Novel biochemical markers of glycemia to predict pregnancy outcomes in women with type 1 diabetes. Diabetes Care . (2021) 44:681–9. doi: 10.2337/dc20–2360

97. Nowak N, Skupien J, Cyganek K, Matejko B, Malecki MT. 1,5-Anhydroglucitol as a marker of maternal glycaemic control and predictor of neonatal birthweight in pregnancies complicated by type 1 diabetes mellitus. Diabetologia . (2013) 56:709–13. doi: 10.1007/s00125–013-2830–3

98. Sovio U, Goulding N, McBride N, Cook E, Gaccioli F, Charnock-Jones DS, et al. A maternal serum metabolite ratio predicts fetal growth restriction at term. Nat Med . (2020) 26:348–53. doi: 10.1038/s41591–020-0804–9

99. Yamawaki M, Wada-Isoe K, Yamamoto M, Nakashita S, Uemura Y, Takahashi Y, et al. Association of cerebral white matter lesions with cognitive function and mood in Japanese elderly people: a population-based study. Brain Behav . (2015) 5:e00315. doi: 10.1002/brb3.315

100. Rawlings AM, Sharrett AR, Mosley TH, Ballew SH, Deal JA, Selvin E. Glucose peaks and the risk of dementia and 20-year cognitive decline. Diabetes Care . (2017) 40:879–86. doi: 10.2337/dc16–2203

101. Lou Q, Yuan X, Hao S, Miller JD, Yan J, Zuo P, et al. Effects of glucose fluctuation targeted intervention on the prognosis of patients with type 2 diabetes following the first episode of cerebral infarction. J Diabetes Res . (2020) 28:2532171. doi: 10.1155/2020/2532171

102. Rawlings AM, Sharrett AR, Albert MS, Coresh J, Windham BG, Power MC, et al. The association of late-life diabetes status and hyperglycemia with incident mild cognitive impairment and dementia: The ARIC Study. Diabetes Care . (2019) 42:1248–54. doi: 10.2337/dc19–0120

103. Hicks CW, Wang D, Daya NR, Windham BG, Ballantyne CM, Matsushita K, et al. Associations of cardiac, kidney, and diabetes biomarkers with peripheral neuropathy among older adults in the atherosclerosis risk in communities (ARIC) Study. Clin Chem . (2020) 66:686–96. doi: 10.1093/clinchem/hvaa051

104. Koga M, Murai J, Saito H, Mukai M, Toya D, Tanaka N, et al. 1,5-Anhydroglucitol levels are low irrespective of plasma glucose levels in patients with chronic liver disease. Ann Clin Biochem . (2011) 48:121–5. doi: 10.1258/acb.2010.010053

105. Zeigerer A, Sekar R, Kleinert M, Nason S, Habegger KM, Müller TD. Glucagon's metabolic action in health and disease. Compr Physiol . (2021) 11:1759–83. doi: 10.1002/cphy.c200013

106. Hashiba M, Ono M, Hyogo H, Ikeda Y, Masuda K, Yoshioka R, et al. Glycemic variability is an independent predictive factor for development of hepatic fibrosis in nonalcoholic fatty liver disease. PloS One . (2013) 8:e76161. doi: 10.1371/journal.pone.0076161

Keywords: blood glucose monitoring, diabetes, screen for diabetes, diabetes management, diabetes complications, 1,5-Anhydroglucitol

Citation: Xu H, Pan J and Chen Q (2024) The progress of clinical research on the detection of 1,5-anhydroglucitol in diabetes and its complications. Front. Endocrinol. 15:1383483. doi: 10.3389/fendo.2024.1383483

Received: 07 February 2024; Accepted: 23 April 2024; Published: 13 May 2024.

Reviewed by:

Copyright © 2024 Xu, Pan and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Qiu Chen, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

• Review article
• Open access
• Published: 10 May 2024

Metformin mitigates dementia risk among individuals with type 2 diabetes

• Nicholas Aderinto 1 ,
• Gbolahan Olatunji 2 ,
• Emmanuel Kokori 2 ,
• Praise Fawehinmi 3 ,
• Abdulrahmon Moradeyo 1 ,
• Stephen Igwe 2 ,
• Rebecca Ojabo 4 ,
• Badrudeen Olalekan Alabi 2 ,
• Emmanuel Chuka Okafor 4 ,
• Damilola Ologbe 5 ,
• Ayobami Olafimihan 6 &
• David B. Olawade 7  

Clinical Diabetes and Endocrinology volume  10 , Article number:  10 ( 2024 ) Cite this article

196 Accesses

Metrics details

This mini-narrative review explores the relationship between diabetes and dementia, focusing on the potential mitigating role of metformin in reducing cognitive decline among individuals with type 2 diabetes. The interplay of factors such as glycemic control, diabetic complications, and lifestyle influences characterises diabetes-related dementia. This review emphasises the significance of comprehensive diabetes management in addressing the heightened risk of dementia in this population. Methodologically, the review synthesises evidence from 23 studies retrieved through searches on PubMed, Embase, Google Scholar, and Scopus. Current evidence suggests a predominantly positive association between metformin use and a reduced risk of dementia in individuals with diabetes. However, the review shows the complex nature of these outcomes, revealing variations in results in some studies. These discrepancies show the importance of exploring dose–response relationships, long-term effects, and demographic diversity to unravel the complexities of metformin's impact on cognitive health. Limitations in the existing body of research, including methodological disparities and confounding variables, necessitate refined approaches in future studies. Large-scale prospective longitudinal studies and randomised controlled trials focusing specifically on cognitive effects are recommended. Propensity score matching and exploration of molecular mechanisms can enhance the validity of findings in clinical practice. From a clinical perspective, metformin can serve as a potential adjunctive therapy for individuals with diabetes at risk of cognitive decline.

Introduction

Diabetes-related dementia is a significant concern due to the increased risk of dementia in individuals with type 2 diabetes [ 1 ]. The relationship between diabetes and dementia is complex and multifaceted [ 1 ]. Studies have shown that both low and high HbA1C levels are associated with an increased risk of dementia in individuals with diabetes, indicating a non-linear relationship [ 1 , 2 ]. Additionally, uncontrolled diabetes has been linked to an elevated risk of Alzheimer's disease, highlighting the importance of glycemic control in mitigating dementia risk [ 3 ]. Furthermore, severe diabetic retinal disease has been identified as a potential risk factor for dementia in individuals with type 2 diabetes, emphasising the need for comprehensive management of diabetic complications to reduce the likelihood of developing dementia [ 4 ].

The impact of lifestyle factors on diabetes-related dementia has also been investigated, with studies suggesting that a combination of healthy lifestyle factors is associated with a reduced risk of dementia in patients with type 2 diabetes [ 5 ]. However, the aetiology of diabetes-related dementia remains unclear, and it has been proposed that dementia in diabetic patients should be regarded as an independent disease, distinct from Alzheimer's disease and vascular dementia, due to its unique pathophysiological characteristics related to diabetes [ 6 , 7 , 8 ].

The investigation into metformin as a potential mitigating agent for dementia risk among individuals with diabetes is grounded in the expanding body of evidence highlighting its plausible neuroprotective role [ 9 ]. Metformin's potential as a neuroprotective agent has been linked to its ability to lower mortality and age-related diseases independently of its impact on diabetes control [ 10 , 11 , 12 , 13 , 14 ]. Empirical evidence suggests that metformin might mitigate dementia risk by reducing oxidative stress, inflammation, and apoptosis and countering the deleterious effects of advanced glycosylation end products produced during hyperglycemia [ 10 , 11 ]. These collective findings show metformin's potential not only in diabetes management but also in addressing neurological disorders. This study aims to review the current evidence for metformin as a mitigating agent for dementia risk among individuals with diabetes.

Methodology

We searched PubMed, Embase, Google Scholar and Scopus to conduct this narrative review see Table  1 . We formulated a database search strategy based on keywords such as "diabetes," "diabetes mellitus," "diabetes mellitus, Type 2", "metformin," "biguanides," "metformin benefits," "anti-diabetic medications," "memory," "cognition," "cognitive-impairment," "amnestic mild cognitive impairment," "Alzheimer's disease," "Parkinson's disease," and "dementia." We also used other texts selected based on the existing literature and/or obtained from related bibliographies, combined using Boolean operators as follows: ((dementia) OR (cognitive-impairment) OR (cognitive function) OR (neurodegenerative diseases)) AND ((metformin) OR (anti-diabetic drugs)). Furthermore, we manually searched relevant articles cited within the retrieved studies to avoid omitting important research articles.

We only considered articles that a) presented results in English, b) had full text available, and c) specifically assessed dementia risk in patients with diabetes who were on metformin therapy. On the other hand, we excluded studies with a) missing data, b) articles that did not focus on metformin use in type 2 diabetes mellitus, c) studies performed on patients with significant neurological, psychiatric disease or cancer, and d) studies performed in vitro or animal models. We limited the study scope to randomised controlled trials, retrospective cohort studies, prospective observational studies, comparator studies, and case–control studies but excluded books, letters, editorials, conferences, and commentaries.

During the data extraction process, we evaluated the study characteristics such as the publication type, year, study design, study focus, sample size, and the number of positive and negative outcomes. It is important to note that we focused on the probable benefit of metformin in mitigating dementia risk among individuals with diabetes despite the controversial nature of the topic.

Current evidence in existing literature

Our review identified 23 studies, including sample sizes ranging from 305 to 446,105 participants see Table  2 . A majority of these studies, 17 out of the 23 [ 10 , 11 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 ], reported positive outcomes regarding the relationship between metformin use and dementia risk in individuals with diabetes. Metformin is the preferred first-line drug for the treatment of type 2 diabetes mellitus [ 9 ]. It can be safely administered with other antidiabetic drugs and has been demonstrated to reduce insulin resistance and improve glycaemic control [ 9 ]. However, a review of clinical trials paints a mixed picture of the connection between the use of metformin and the incidence of dementia among patients with diabetes.

The findings of observational studies examining the possible link between metformin and dementia risk have been inconclusive. Eleven (57.9%) of the 19 analysed publications had positive results, proving that metformin may help lower the risk of dementia [ 10 , 11 , 13 , 14 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 27 ]. Five articles (26.3%) had an elevated risk [ 25 , 26 , 28 , 29 , 30 ], whereas three (15.8%) provided a condition for decreased risk [ 15 , 16 , 17 ]. A retrospective cohort study by Chin-Hsiao Tseng indicated a lower risk when metformin was used with other medications, such as acarbose and pioglitazone [ 18 ]. At the end of a 6-month follow-up study, a significant difference in cognitive performance compared to baseline in frail women treated with extended-release metformin (p: 0.007) was observed [ 27 ]. Huang et al. highlighted the protective benefits of metformin when used at a low dose [ 16 ]. At the same time, Huang et al. reported higher doses of metformin with a higher intensity showed no protective role against dementia [ 16 ]. However, cohort studies by Yi-Chun Kuan showed mixed results. They raised questions because they linked long-term metformin use to a higher risk of dementia from all causes, including vascular disease and Alzheimer's disease [ 28 , 32 ]. Scherrer et al. showed that the effects of metformin vary in different subpopulations, indicating a lower risk in some individuals (> 50 years) [ 21 ].

Furthermore, the results from I-Shiang Tzeng raise questions about the possibility that metformin and DPP-4 inhibitor combination therapy alleviated the risk of dementia [ 26 ]. These varied results highlight the complex nature of the connection between dementia and metformin use and highlight the need for additional studies, especially examining dose–response interactions, long-term effects, and demographic diversity to offer a more thorough understanding. Among the notable findings is a study conducted by Chin-Hsiao Tseng in 2019, which indicated a reduction in the risk of dementia associated with metformin, particularly in the female population [ 18 ]. Furthermore, the use of a combination of three drugs (Metformin, acarbose, pioglitazone) was associated with the lowest risk of dementia, as highlighted in the same study [ 18 ]. Additionally, a study by Yonghwan Kim et al. demonstrated a dose–response relationship, revealing that Metformin use in an elderly population with diabetes mellitus contributed to a reduction in dementia risk [ 19 ]. However, a retrospective cohort study by Ariela R. Orkaby et al. in 2017 suggested that metformin was associated with a lower risk of subsequent dementia compared to sulfonylurea use in veterans aged 75 years and older [ 13 ]. Notably, a lower risk was also observed in a subset of younger veterans who maintained an HbA1C value of 7% and exhibited good renal function [ 13 ]. In the 2015 study by Kwang-pil Ko et al., a comprehensive evaluation of metformin's efficacy in modulating physical and mental profiles was undertaken, revealing favourable outcomes [ 22 ]. Specifically, within the age group of 65 to 74 years, metformin demonstrated a statistically significant association with a reduced risk of dementia across various racial categories. However, a distinctive pattern emerged among patients aged 75 years and older, as metformin exhibited no statistically significant association with dementia within this older demographic [ 23 ].

Theoretically, antidiabetic drugs designed to ameliorate insulin resistance within the brain hold promise in preventing Alzheimer's disease or dementia [ 18 , 31 ]. In a study involving 17,200 new users of metformin, a lower risk of dementia was reported in a subset of younger veterans exhibiting HbA1C values ≥ 7%, those with good renal function, and individuals of white ethnicity [ 13 ]. In a study conducted, T2DM compared with no medication, sulfonylureas alone reduced the HR from 1 to 0.85 (0.71–1.01), metformin alone to 0.76 (0.58–0.98), while with combined oral therapy, the HR was 0.65 (0.56–0.74) [ 20 ]. Adjustments included cerebrovascular diseases so that non-stroke-related dementias were found to be decreased in DM with sulfonylurea and metformin therapy. T2DM increases the risk of dementia more than 2-fold.

Elevated blood glucose levels pose a potential threat to cerebral function, contributing to an elevated risk of dementia in individuals with diabetes [ 19 , 31 ]. The link between diabetes and dementia is likely multifactorial, involving mechanisms such as inflammation, oxidative stress, atherosclerosis, amyloid-β deposition, brain insulin resistance accompanied by hyperinsulinemia, advanced glycation end-products (AGEs), and dysregulation of lipid metabolism [ 20 , 33 ]. Metformin, recognised as the primary first-line therapy for type 2 diabetes mellitus, operates by curbing hepatic gluconeogenesis and augmenting muscular glucose uptake by activating 5'-adenosine monophosphate-activated protein kinase (AMPK) [ 21 ]. Beyond its glucose-lowering effects, metformin has demonstrated additional benefits in individuals with type 2 diabetes, including reducing the risk of atherosclerotic events, protection against certain cancers, and an anti-ageing effect [ 20 ].

The potential neuroprotective effects of metformin are suggested to stem from its capacity to inhibit inflammatory responses and enhance cognitive function [ 16 ]. Apolipoprotein E (APOE), a crucial protein in lipid transport and brain injury repair, is implicated in Alzheimer's disease risk [ 21 ]. Specific APOE gene polymorphisms, particularly the ε4 allele, elevate the risk of AD, while the ε2 allele is associated with reduced risk [ 10 ]. The APOE ε4 allele is also linked to an increased risk of cerebral amyloid angiopathy and age-related cognitive decline. A recent study hinted at an association between metformin use and a faster decline in delayed memory among carriers of the APOE ε4 allele, prompting the need for further research to elucidate the potential influence of APOE ε4 genotype on the therapeutic effects of metformin [ 29 ].

Limitations and future directions

Existing studies on metformin’s involvement in reducing dementia risk in patients with diabetes have significant limitations that should be considered. First, many studies have methodological variances, such as differences in study design, sample size, and outcome measures. This variation makes obtaining standardised results difficult and direct comparisons between investigations difficult. Furthermore, the heterogeneity within the examined groups, which includes age and diabetes duration, complicates interpretation and restricts the generalizability of the findings. Most observational studies failed to address bias or did not address it clearly, making the evidence less efficient. Another significant issue is the possibility of confounding variables influencing the outcomes. Factors such as genetic predisposition, lifestyle decisions, and concurrent pharmaceutical use may all impact cognitive performance independent of metformin, making it difficult to assign observed effects to medication alone. Furthermore, contradictions in studies are exacerbated by differences in the definitions of dementia and cognitive decline between studies.

Future studies should target certain areas to address these constraints and to increase understanding. Large-scale, well-designed, prospective longitudinal studies with long follow-up periods can provide stronger data and aid in determining causation. In addition, randomised controlled trials (RCTs) focusing only on the cognitive effects of metformin would provide more control over confounding factors. Subgroup analyses within the diabetic population, considering variables such as age, sex, and diabetes management details, would help better understand the influence of metformin on various patient groups. Applying propensity score matching, or at the very least, a match for age, sex, and health status, will improve data validity by lowering baseline variability and, if possible, investigate the relationship between metformin usage, B-12 vitamin levels, and dementia. To inform clinical practice, it is critical to investigate dose–response relationships and optimal dosages for potential cognitive benefits.

Furthermore, a thorough examination of the molecular mechanisms underlying the influence of metformin on cognitive performance is required. This knowledge can guide focused therapies and identify individuals most benefit from metformin therapy. Future research should prioritise uniform study designs, investigate specific demographic subgroups, and explore molecular causes to improve the reliability and usefulness of the findings in clinical practice.

Implications for clinical practice

Clinically, the favourable results observed in multiple studies imply that metformin may be a feasible alternative for people with diabetes, particularly for those at risk of cognitive loss see Fig.  1 .

Metformin in dementia risk in type 2 diabetes

Healthcare practitioners should inform patients about the potential cognitive benefits in addition to glycemic control. However, care is advised owing to inconsistent findings and potential issues, such as the variation in the metformin outcome, increased risk of vitamin B-12 insufficiency, and identified risk with certain combinations, emphasising the importance of tailored treatment programs and regular cognitive monitoring. A multidisciplinary approach that combines endocrinologists, neurologists, and senior experts is required to address the complicated connection between diabetes control and cognitive health. Senior experts such as diabetologists are key in tailoring diabetes treatment plans to achieve optimal glycemic control [ 34 ]. In addition, it is essential also to involve psychologists and occupational therapists. These professionals play pivotal roles in the identification, comprehensive assessment, and rehabilitation processes associated with dementia [ 35 ]. They collaborate closely to develop tailored interventions that address cognitive deficits and consider the individual's emotional and functional aspects [ 36 ]. This collaborative effort ensures a more personalised approach to patient care.

At the public health level, awareness programs should be launched to educate diabetic patients about the potential cognitive consequences of metformin and the significance of making informed decisions. Comprehensive studies investigating dose–response connections, long-term consequences, and population-specific effects should receive research funding. Public health guidelines must be revised to reflect increasing evidence, giving healthcare practitioners clear advice on using metformin in diabetes management taking both glycaemic control and cognitive outcomes into account. Policymakers should consider these findings when developing diabetes management policies and public health initiatives to ensure that possible cognitive effects are integrated into broader healthcare programs.

Limitations and strengths of review

The review provides clear implications for clinical practice, suggesting that metformin may be a feasible adjunctive therapy for individuals with diabetes at risk of cognitive decline. The multidisciplinary approach recommended for navigating the complex relationship between diabetes control and cognitive health enhances the practicality of the review's recommendations. Also, the review identifies varied outcomes across studies, emphasising the complexity of the relationship between metformin use and dementia risk. This acknowledgement of diverse findings encourages a more cautious interpretation and highlights the need for further research. However, the included studies exhibit methodological disparities, including differences in study design, sample size, and outcome measures. This variation makes it challenging to obtain standardised results and directly compare findings between investigations.

The body of evidence exploring metformin's role in mitigating dementia risk among individuals with diabetes presents a complex yet promising landscape. The interplay between diabetes and dementia shows the importance of glycemic control and comprehensive management of diabetic complications in reducing the likelihood of cognitive decline. This mini-narrative review reveals a spectrum of outcomes regarding the potential connection between metformin use and dementia risk in patients with diabetes. While a majority of studies suggest a positive association between metformin use and a reduced risk of dementia, the complex nature of these findings prompts a cautious interpretation. Dose–response interactions, long-term effects, and demographic diversity emerge as critical factors requiring further investigation to understand metformin's impact on cognitive health. Noteworthy variations in outcomes across studies highlight the need for standardised methodologies and robust study designs in future research endeavours.

Availability of data and materials

Data sharing is not applicable to this article as no datasets were generated or analysed during the current study.

Abbreviations

Advanced Glycosylation End Products

Alzheimer's Disease

5'-Adenosine Monophosphate-Activated Protein Kinase

Apolipoprotein E

Vitamin B-12

Epsilon 2 (APOE gene polymorphism)

Epsilon 4 (APOE gene polymorphism)

Hemoglobin A1c

Hazard Ratio

Randomized Controlled Trials

Type 2 Diabetes Mellitus

Crane PK, Walker R, Hubbard RA, Li G, Nathan DM, Zheng H, et al. Glucose levels and risk of dementia. N Engl J Med. 2013;369:540–8. https://doi.org/10.1056/NEJMoa1215740 .

Article   CAS   PubMed   PubMed Central   Google Scholar  

Geijselaers SLC, Sep SJS, Stehouwer CDA, Biessels GJ. Glucose regulation, cognition, and brain MRI in type 2 diabetes: a systematic review. Lancet Diabetes Endocrinol. 2015;3:75–89. https://doi.org/10.1016/S2213-8587(14)70148-2 .

Article   CAS   PubMed   Google Scholar  

Xu WL, von Strauss E, Qiu CX, Winblad B, Fratiglioni L. Uncontrolled diabetes increases the risk of Alzheimer’s disease: a population-based cohort study. Diabetologia. 2009;52:1031–9. https://doi.org/10.1007/s00125-009-1323-x .

Exalto LG, Biessels GJ, Karter AJ, Huang ES, Quesenberry CP, Whitmer RA. Severe Diabetic retinal disease and dementia risk in type 2 diabetes. J Alzheimer’s Dis. 2014;42:S109–17. https://doi.org/10.3233/JAD-132570 .

Article   Google Scholar  

Wang B, Wang N, Sun Y, Tan X, Zhang J, Lu Y. Association of combined healthy lifestyle factors with incident dementia in patients with type 2 diabetes. Neurology. 2022;99:E2336–45. https://doi.org/10.1212/WNL.0000000000201231 .

Tsugawa A, Ogawa Y, Takenoshita N, Kaneko Y, Hatanaka H, Jaime E, et al. Decreased muscle strength and quality in diabetes-related dementia. Dement Geriatr Cogn Dis Extra. 2017;7:454–62. https://doi.org/10.1159/000485177 .

Article   PubMed   PubMed Central   Google Scholar  

Hanyu H, Hirose D, Fukasawa R, Hatanaka H, Namioka N, Sakurai H. Guidelines for the clinical diagnosis of diabetes mellitus-related dementia. J Am Geriatr Soc. 2015;63:1721–3. https://doi.org/10.1111/jgs.13581 .

Article   PubMed   Google Scholar  

Mayeda ER, Karter AJ, Huang ES, Moffet HH, Haan MN, Whitmer RA. Racial/ethnic differences in dementia risk among older type 2 diabetic patients: the diabetes and aging study. Diabetes Care. 2014;37:1009–15. https://doi.org/10.2337/dc13-0215 .

Campbell JM, Stephenson MD, de Courten B, Chapman I, Bellman SM, Aromataris E. Metformin use associated with reduced risk of dementia in patients with diabetes: a systematic review and meta-analysis. J Alzheimer’s Dis. 2018;65:1225–36. https://doi.org/10.3233/JAD-180263 .

Wium-Andersen IK, Osler M, Jørgensen MB, Rungby J, Wium-Andersen MK. Antidiabetic medication and risk of dementia in patients with type 2 diabetes: a nested case–control study. Eur J Endocrinol. 2019;181:499–507. https://doi.org/10.1530/EJE-19-0259 .

Hsu CC, Wahlqvist ML, Lee MS, Tsai HN. Incidence of dementia is increased in type 2 diabetes and reduced by the use of sulfonylureas and metformin. J Alzheimers Dis. 2011;24(3):485–93. https://doi.org/10.3233/JAD-2011-101524 .

Article   CAS   Google Scholar  

Campbell JM, Bellman SM, Stephenson MD, Lisy K. Metformin reduces all-cause mortality and diseases of ageing independent of its effect on diabetes control: a systematic review and meta-analysis. Ageing Res Rev. 2017;40:31–44. https://doi.org/10.1016/j.arr.2017.08.003 .

Orkaby AR, Cho K, Cormack J, Gagnon DR, Driver JA. Metformin vs sulfonylurea use and risk of dementia in US veterans aged ≥65 years with diabetes. Neurology. 2017;89:1877–85. https://doi.org/10.1212/WNL.0000000000004586 .

Samaras K, Makkar S, Crawford JD, Kochan NA, Wen W, Draper B, et al. Metformin use is associated with slowed cognitive decline and reduced incident dementia in older adults with type 2 diabetes: the sydney memory and ageing study. Diabetes Care. 2020;43:2691–701. https://doi.org/10.2337/DC20-0892 .

Ng TP, Feng L, Yap KB, Lee TS, Tan CH, Winblad B. Long-term metformin usage and cognitive function among older adults with diabetes. J Alzheimer’s Dis. 2014;41:61–8. https://doi.org/10.3233/JAD-131901 .

Huang K-H, Tsai Y-F, Lee CB, Gau S-Y, Tsai T-H, Chung N-J, et al. The correlation between metformin use and incident dementia in patients with new-onset diabetes mellitus: a population-based study. J Pers Med. 2023;13:738. https://doi.org/10.3390/jpm13050738 .

Luchsinger JA, Perez T, Chang H, Mehta P, Steffener J, Pradabhan G, et al. Metformin in amnestic mild cognitive impairment: results of a pilot randomized placebo controlled clinical trial. J Alzheimer’s Dis. 2016;51:501–14. https://doi.org/10.3233/JAD-150493 .

Tseng C-H. Dementia risk in type 2 diabetes patients: acarbose use and its joint effects with metformin and pioglitazone. Aging Dis. 2020;11:658.

Kim Y, Kim H-S, Lee J, Kim Y-S, You H-S, Bae Y-J, et al. Metformin use in elderly population with diabetes reduced the risk of dementia in a dose-dependent manner, based on the Korean NHIS-HEALS cohort. Diabetes Res Clin Pract. 2020;170:108496. https://doi.org/10.1016/j.diabres.2020.108496 .

Chin-Hsiao T. Metformin and the Risk of Dementia in Type 2 Diabetes Patients. Aging Dis. 2019;10(1):37–48. https://doi.org/10.14336/AD.2017.1202 .

Scherrer JF, Salas J, Floyd JS, Farr SA, Morley JE, Dublin S. Metformin and sulfonylurea use and risk of incident dementia. Mayo Clin Proc. 2019;94:1444–56. https://doi.org/10.1016/j.mayocp.2019.01.004 .

Ko K-P, Ma SH, Yang J-J, Hwang Y, Ahn C, Cho Y-M, et al. Metformin intervention in obese non-diabetic patients with breast cancer: phase II randomized, double-blind, placebo-controlled trial. Breast Cancer Res Treat. 2015;153:361–70. https://doi.org/10.1007/s10549-015-3519-8 .

Scherrer JF, Morley JE, Salas J, Floyd JS, Farr SA, Dublin S. Association between metformin initiation and incident dementia among African American and white veterans health administration patients. Ann Fam Med. 2019;17:352–62. https://doi.org/10.1370/afm.2415 .

Newby D, Linden AB, Fernandes M, Molero Y, Winchester L, Sproviero W, Ghose U, Li QS, Launer LJ, Duijn CMV, Nevado-Holgado AJ. Comparative effect of metformin versus sulfonylureas with dementia and Parkinson's disease risk in US patients over 50 with type 2 diabetes mellitus. BMJ Open Diabetes Res Care. 2022;10(5):e003036. https://doi.org/10.1136/bmjdrc-2022-003036 .

de Jager J, Kooy A, Lehert P, Wulffele MG, van der Kolk J, Bets D, et al. Long term treatment with metformin in patients with type 2 diabetes and risk of vitamin B-12 deficiency: randomised placebo controlled trial. BMJ. 2010;340:c2181–c2181. https://doi.org/10.1136/bmj.c2181 .

Tzeng I-S, Hsieh T-H. Collocation of metformin and dipeptidyl peptidase-4 inhibitor is associated with increased risk of diabetes-related vascular dementia: a single hospital study in Northern Taiwan. Expert Opin Investig Drugs. 2023;32:171–6. https://doi.org/10.1080/13543784.2023.2178417 .

Mone P, Martinelli G, Lucariello A, Leo AL, Marro A, De Gennaro S, Marzocco S, Moriello D, Frullone S, Cobellis L, Santulli G. Extended-release metformin improves cognitive impairment in frail older women with hypertension and diabetes: preliminary results from the LEOPARDESS Study. Cardiovasc Diabetol. 2023;22(1):94. https://doi.org/10.1186/s12933-023-01817-4 .

Kuan Y-C, Huang K-W, Lin C-L, Hu C-J, Kao C-H. Effects of metformin exposure on neurodegenerative diseases in elderly patients with type 2 diabetes mellitus. Prog Neuro-Psychopharmacol Biol Psychiatry. 2017;79:77–83. https://doi.org/10.1016/j.pnpbp.2017.06.002 .

Xue Y, Xie X. The association between metformin use and risk of developing severe dementia among ad patients with type 2 diabetes. Biomedicines. 2023;11:2935. https://doi.org/10.3390/biomedicines11112935 .

Luchsinger JA, Ma Y, Christophi CA, Florez H, Golden SH, Hazuda H, et al. Metformin, lifestyle intervention, and cognition in the diabetes prevention program outcomes study. Diabetes Care. 2017;40:958–65. https://doi.org/10.2337/dc16-2376 .

Zhou C, Peng B, Qin Z, Zhu W, Guo C. Metformin attenuates LPS-induced neuronal injury and cognitive impairments by blocking NF-κB pathway. BMC Neurosci. 2021;22(1):73. https://doi.org/10.1186/s12868-021-00678-5 .

Rabieipoor S, Zare M, Ettcheto M, Camins A, Javan M. Metformin restores cognitive dysfunction and histopathological deficits in an animal model of sporadic Alzheimer’s disease. Heliyon. 2023;9(7):e17873. https://doi.org/10.1016/j.heliyon.2023.e17873 .

Ji S, Wang L, Li L. Effect of metformin on short-term high-fat diet-induced weight gain and anxiety-like behavior and the gut microbiota. Front Endocrinol. 2019;10:704. https://doi.org/10.3389/fendo.2019.00704 .

Tong PC, Chan SC, Chan WB, Ho KK, Leung GT, Lo SH, Mak GY, Tse TS. Consensus statements from the diabetologists & endocrinologists alliance for the management of people with hypertension and type 2 diabetes mellitus. J Clin Med. 2023;12(10):3403. https://doi.org/10.3390/jcm12103403 .

Sridhar GR. On psychology and psychiatry in diabetes. Indian J Endocrinol Metab. 2020;24(5):387–95. https://doi.org/10.4103/ijem.IJEM_188_20 .

Aderinto N, Olatunji G, Abdulbasit M, Ashinze P, Faturoti O, Ajagbe A, Ukoaka B, Aboderin G. The impact of diabetes in cognitive impairment: a review of current evidence and prospects for future investigations. Medicine. 2023;102(43):e35557. https://doi.org/10.1097/MD.0000000000035557 .

Download references

Acknowledgements

No funding was received for this study.

Author information

Authors and affiliations.

Department of Medicine and Surgery, Ladoke Akintola University of Technology, Ogbomoso, Nigeria

Nicholas Aderinto & Abdulrahmon Moradeyo

Department of Medicine and Surgery, University of Ilorin, Ilorin, Nigeria

Gbolahan Olatunji, Emmanuel Kokori, Stephen Igwe & Badrudeen Olalekan Alabi

Southern Illinois University Edwardsville, Edwardsville, IL, USA

Praise Fawehinmi

Afe Babalola University, Ado-Ekiti, Ado, Nigeria

Rebecca Ojabo & Emmanuel Chuka Okafor

William Harvey Hospital, Ashford, UK

Damilola Ologbe

John H. Stroger Jr Hospital of Cook County, Chicago, IL, USA

Ayobami Olafimihan

Department of Allied and Public Health, School of Health, Sport and Bioscience, University of East London, London, UK

David B. Olawade

You can also search for this author in PubMed   Google Scholar

Contributions

NA conceptualised the study; All authors were involved in the literature review; GO & EK extracted the data from the reviewed studies; All authors wrote the final and first drafts. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Nicholas Aderinto .

Ethics declarations

Ethics approval and consent to participate.

Not applicable.

Consent for publication

Competing interests.

The authors declare that they have no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Aderinto, N., Olatunji, G., Kokori, E. et al. Metformin mitigates dementia risk among individuals with type 2 diabetes. Clin Diabetes Endocrinol 10 , 10 (2024). https://doi.org/10.1186/s40842-024-00168-7

Download citation

Received : 26 December 2023

Accepted : 18 January 2024

Published : 10 May 2024

DOI : https://doi.org/10.1186/s40842-024-00168-7

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Diabetes Mellitus
• Cognitive impairment

Clinical Diabetes and Endocrinology

ISSN: 2055-8260

• Submission enquiries: Access here and click Contact Us
• General enquiries: [email protected]