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Trends in incidence of total or type 2 diabetes: systematic review

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Showing the turning point in diabetes incidence in 61 populations

Linked editorial

Trends in type 2 diabetes

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• Peer review
• Rakibul M Islam , postdoctoral research fellow 1 2 ,
• Elizabeth L M Barr , postdoctoral research fellow 1 ,
• Edward W Gregg , chair in diabetes and cardiovascular disease epidemiology 3 4 ,
• Meda E Pavkov , physician scientist 3 ,
• Jessica L Harding , research fellow 3 ,
• Maryam Tabesh , research study coordinator 1 2 ,
• Digsu N Koye , postdoctoral research fellow 1 2 ,
• Jonathan E Shaw , deputy director of Baker Heart and Diabetes Institute 1 2
• 1 Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia
• 2 School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
• 3 Centres for Diseases Control and Prevention, Division of Diabetes Translation, Atlanta, GA, USA
• 4 School of Public Health, Epidemiology and Biostatistics, Imperial College London, London, UK
• Correspondence to: D J Magliano dianna.magliano{at}baker.edu.au
• Accepted 16 July 2019

Objective To assess what proportions of studies reported increasing, stable, or declining trends in the incidence of diagnosed diabetes.

Design Systematic review of studies reporting trends of diabetes incidence in adults from 1980 to 2017 according to PRISMA guidelines.

Data sources Medline, Embase, CINAHL, and reference lists of relevant publications.

Eligibility criteria Studies of open population based cohorts, diabetes registries, and administrative and health insurance databases on secular trends in the incidence of total diabetes or type 2 diabetes in adults were included. Poisson regression was used to model data by age group and year.

Results Among the 22 833 screened abstracts, 47 studies were included, providing data on 121 separate sex specific or ethnicity specific populations; 42 (89%) of the included studies reported on diagnosed diabetes. In 1960-89, 36% (8/22) of the populations studied had increasing trends in incidence of diabetes, 55% (12/22) had stable trends, and 9% (2/22) had decreasing trends. In 1990-2005, diabetes incidence increased in 66% (33/50) of populations, was stable in 32% (16/50), and decreased in 2% (1/50). In 2006-14, increasing trends were reported in only 33% (11/33) of populations, whereas 30% (10/33) and 36% (12/33) had stable or declining incidence, respectively.

Conclusions The incidence of clinically diagnosed diabetes has continued to rise in only a minority of populations studied since 2006, with over a third of populations having a fall in incidence in this time period. Preventive strategies could have contributed to the fall in diabetes incidence in recent years. Data are limited in low and middle income countries, where trends in diabetes incidence could be different.

Systematic review registration Prospero CRD42018092287.

Introduction

Over the past few decades, the prevalence of diabetes in developed and developing countries has risen substantially, making diabetes a key health priority globally. 1 Examination of trends in total burden of diabetes is an essential part of the monitoring of this health priority area, but, to date, it has consisted primarily of studies looking at diabetes prevalence. 1 2 3 4 5 Prevalence estimates suggest that the diabetes burden is still rising in most countries, and this is often interpreted as evidence of increasing risk in the population. However, selective incidence studies 6 7 and some accompanying risk factor data 8 suggest otherwise. Prevalence can be a crude and misleading metric of the trajectory of an epidemic, because increasing prevalence of a disease might be due to either increasing incidence or to improved survival. Furthermore, prevalence cannot be reliably used to study the effects of changes in population risk factors, because their effects are detected earlier with incidence trends than with prevalence trends, and incidence is not affected by changes in survival.

Incidence measures the proportion of people who develop diabetes over a period of time among the population at risk. It is the appropriate measure of population risk, and a valuable way of assessing whether public health campaigns for diabetes prevention are succeeding. While prevalence can rise simply because mortality falls, incidence of diagnosed diabetes is affected only by the risk of the population and the amount of screening undertaken. Changes in prevalence might be an inadequate guide to the effects of prevention activities, and could lead to the inappropriate rejection of effective interventions. It is only by measuring both incidence and prevalence that a better understanding of the extent of diabetes can be achieved.

Among existing diabetes incidence data, a few studies suggest that diabetes incidence could be falling despite rising or stable prevalence, 6 7 9 but not all data are consistently showing the same trends. For example, studies from England and Wales (1994-98), 10 Portugal (1992-2015), 11 and Canada (1995-2007) 12 are reporting increases in diabetes incidence. To understand what is happening at a global level over time, a systematic approach to review all incidence trend data should be undertaken to study patterns and distributions of incidence trends by time, age, and sex. So far, no systematic reviews have reported on trends in the incidence of diabetes. Therefore, we conducted a systematic review of the literature reporting diabetes incidence trends.

Data sources and searches

We conducted a systematic review in accordance with PRISMA guidelines. 13 We searched Medline, Embase, and CINAHL from January 1980 to December 2017 without language restrictions. The full search strategy is available in supplementary table 1.

Study selection

Inclusion and exclusion criteria.

Eligible studies needed to report diabetes incidence in two or more time periods. Study populations derived from open, population based cohort studies (that is, with ongoing recruitment over time), diabetes registries, or administrative or health insurance databases based mainly or wholly in primary care (electronic medical records, health insurance databases, or health maintenance organisations). We also included serial, cross sectional, population based studies where incidence was defined as a person reporting the development of diabetes in the 12 months before the survey. Studies were required to report on the incidence of either total diabetes or type 2 diabetes. We excluded studies reporting incidence restricted to select groups (eg, people with heart failure) and studies reporting only on children or youth.

Each title and abstract was screened by at least two authors (DJM, JES, DNK, JLH, and MT) and discrepancies were resolved by discussion. We aimed to avoid overlap of populations between studies. Therefore, if national data and regional data were available from the same country over the same time period, we only included the national data. If multiple publications used the same data source, over the same time period, we chose the publication that covered the longest time period.

Outcome measure

Our outcome was diabetes incidence using various methods of diabetes ascertainment including: blood glucose, glycated haemoglobin (HbA1c), linkage to drug treatment or reimbursement registries, clinical diagnosis by physicians, administrative data (ICD codes (international classification of diseases)), or self report. Several studies developed algorithms based on several of these elements to define diabetes. We categorised the definition of diabetes into one of five groups: clinical diagnosis, diabetes treatment, algorithm derived, glycaemia defined (blood glucose or HbA1c, with or without treatment), and self report.

Data extraction and quality of studies

We extracted crude and standardised incidence by year (including counts and denominators) and the reported pattern of the trends (increasing, decreasing, or stable, (that is, no statistically significant change)) in each time period as well as study and population characteristics. Age specific data were also extracted if available. Data reported only in graphs were extracted by DigitizeIt software (European Organisation for Nuclear Research, Germany). We assessed study quality using a modified Newcastle-Ottawa scale for assessing the risk of bias of cohort studies 14 (supplementary material).

Statistical methods

Data were reported as incidence density (per person year) or yearly rates (percentage per year). From every study, we extracted data from every subpopulation reported, such that a study reporting incidence in men and women separately contributed two populations to this analysis. If studies reported two different trends over different time periods, we considered these as two populations. Further, if the study was over 10 years in duration, we treated these as two separate time periods. To avoid double counting, when the data were reported in the total population as well as by sex and ethnic groups, we only included data once and prioritised ethnicity specific data over sex specific data.

We extracted the age specific incidence data reported for every individual calendar year. These data were then categorised into four age bands (<40, 40-54, 55-69, and ≥70), and were plotted against calendar year. In studies where counts and denominators were reported by smaller age groups than we used, we recalculated incidence across our specified larger age groups. If we found multiple age groups within any of our broader age groups, but with insufficient information to combine the data into a new category, only data from one age group were used. To limit overcrowding on plots, if data were available for men, women, and the total population, only total population data were plotted. Data from populations with high diabetes incidence such as Mauritians 15 and First Nation populations from Canada 16 were plotted separately to allow the examination of most of the data more easily on a common scale (supplementary material). Furthermore, studies reporting data before 1991 or populations with fewer than three data points were not plotted. We also categorised studies into European and non-European populations on the basis of the predominant ethnicity of the population in which they were conducted. Studies conducted in Israel, Canada, and the United States were assigned to the European category.

We took two approaches to analyse trends of diabetes incidence over time. Firstly, we allocated the reported trend (increasing, decreasing, or stable (that is, no statistically significant change)) of each population to the mid-point of each study’s observational period, and then assigned this trend into one of five time periods (1960-79, 1980-89, 1990-99, 2000-05, and 2006-14). Where a test of significance of trends was not reported or when a time period was longer than 10 years, we performed Joinpoint trend analyses 17 18 to observe any significant trends in the data (assuming a constant standard deviation). Joinpoint Trend Analysis Software (version 4.5.0.1) uses permutation tests to identify points where linear trends change significantly in direction or in magnitude, and calculates an annual percentage change for each time period identified. In sensitivity analyses we also tested different cut points in the last two time periods.

The second approach was used to more accurately allocate trends to the prespecified time periods. Among the studies that reported raw counts of diabetes cases and denominators, we examined the association between calendar year and incidence, using Poisson models with the log person years as offset. The midpoints of age and calendar period were used as continuous covariates, and the effects of these were taken as linear functions. We analysed each study separately by prespecified time periods, and reported annual percentage change when the number of data points in the time period was at least four. For studies that did not provide raw data but did report a sufficient number of points, we analysed the relation between year and incidence using Joinpoint regression across the time periods specified above and reported annual percentage change. Analyses were conducted with Stata software version 14.0 (Stata Corporation, College Station, TX, USA), and Joinpoint (Joinpoint Desktop Software Version 4.5.0.1). 17 18

Patient and public involvement

No patients or members of the public were involved in setting the research question or the outcome measures for this study. No patients were asked to advise on interpretation or writing up of results. We intend to disseminate this research through press releases and at research meetings.

We found 22 833 unique abstracts from 1 January 1980 to the end of 2017. Among these, 80 described trends of diabetes incidence, of which 47 met all inclusion criteria. Articles describing trends were excluded for the following reasons: duplicated data (n=21), closed cohorts (n=5), populations included youth only (n=1), occupational cohorts (n=2), or no usable data presented (n=4; fig 1 ).

Flowchart of study selection

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Table 1 and supplementary material table 2 describe the characteristics of the included studies. Only 19% (9/47) of studies were from predominantly non-Europid populations and 4% (2/47) of studies were from low or middle income countries (China 25 and Mauritius 15 ). Administrative datasets, health insurance data, registry data, survey data, and cohort studies accounted for 38% (n=18), 21% (n=10), 19% (n=9), 11% (n=5), and 11% (n=5) of the 47 data sources, respectively. Among the 47 studies, diabetes was defined by a clinical diagnosis, diabetes treatment (via linkage to drug treatment registers), an algorithm, blood glucose, and self report in 28% (n=13), 9% (n=4), 47% (n=22), 11% (n=5), and 6% (n=3) of studies, respectively. Sample sizes of the populations were greater than 10 000 in every year in 85% (n=40) of the studies, and greater than 130 000 per year in 70% (n=33) of the studies. A total of 62% (n=29) of the 47 included studies exclusively reported on type 2 diabetes, and 38% (n=18) reported on total diabetes.

Characteristics of 47 included studies reporting on diabetes incidence trends, by country

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Summary of patterns of diabetes incidence trends based on analyses reported in publications in 1960-99

Trends of diabetes incidence

Among the 47 studies, 16 provided information on incidence by age group. Of these 16 studies, 14 were plotted in figure 2 , with those from high incidence countries plotted in supplementary figure 1. In these figures, incidence in most studies increased progressively until the mid-2000s in all age groups. Thereafter, most studies showed a stable or decreasing trend, apart from studies in Denmark 26 27 and Germany 31 and in a US health insurance population 9 where the incidence inflected upwards in the later years for some age groups.

Incidence of diabetes over time for populations aged under 40, 40-54, 55-69, and 70 or more, among studies reporting age specific data. Only populations with at least three points were plotted. NHIS=National Health Interview Survey

Using the first approach to analyse trends of diabetes incidence over time, we separated the data into populations based on sex and ethnicity, and allocated a time period to each population, generating 105 populations for analysis. Seventy four and 31 populations were predominantly Europid and non-Europid, respectively. Table 2 and table 3 show the reported trend for each population. Table 4 summarises the findings in table 2 and table 3 , and shows that the proportion of populations reporting increasing trends peaked in 1990-99 and fell progressively in the two later time periods. Between 1960 and 1989, 36% (8/22) of the populations studied had increasing trends in incidence of diabetes, 55% (12/22) had stable trends, and 9% (2/22) had decreasing trends. In 1990-2005, diabetes incidence increased in 66% (33/50) of populations, was stable in 32% (16/50), and decreased in 2% (1/50). In 2006-14, increasing trends were reported in 33% (11/33) of populations, whereas 30% (10/33) and 36% (12/33) had stable or declining incidence, respectively.

Summary of patterns of diabetes incidence trends based on analyses reported in publications in 2000-14

Summary of incidence trends over time of total or type 2 diabetes

Populations that reported a decrease in incidence after 2005 came from the US, 6 9 Israel, 34 Switzerland, 46 Hong Kong, 32 Sweden, 43 and Korea. 36 Populations reporting increasing incidence after 2005 included Portugal, 11 Denmark, 26 27 and Germany, 31 while populations from Canada, 19 Italy, 35 Scotland, 40 Norway, 39 US (non-Hispanic white), 56 and the United Kingdom 50 showed stable incidence. For two studies (16 populations), 16 29 we could not determine a direction of a trend (increasing, decreasing, or stable), because they showed three phases of change with the trend of the middle phase differing from the trend of the first and last phase. Across the total time period, we observed a higher proportion of populations reporting stable or decreasing trends in predominantly Europid than in non-Europid populations (52% v 41%).

Using the second approach to analyse trends of diabetes incidence over time, we modelled 21 studies (62 populations) that reported diabetes counts and denominators specifically within each time period ( table 5 ). The percentage of populations with a decreased or stable incidence was highest in 1980-89 (88%; 7/8), but this proportion was based on only eight populations in three studies. From 1990 onwards, the percentage with decreasing or stable incidence increased progressively, reaching 83% (19/23) of populations in 2006-14. Eight studies (21 populations) that were analysed by Joinpoint had no data on counts or denominators (supplementary table 3). When these data were considered with the data in table 5 , the percentage of populations in 2006-14 with decreasing or stable incidence fell to 70% (19/27), but this proportion was still the highest of all the time periods, whereas the percentage for 1990-99 remained the lowest at 31% (5/16).

Annual percentage change in diabetes incidence in men (M), women (W), or total population (T) among studies that provided counts and denominators, by time period

In a sensitivity analysis, we tested whether our selection of time periods was driving our results. When we defined the final time periods to be 2000-07 and 2008-14, our results were not altered, with 66% (21/32) of the populations in the last time period showing decreasing or stable trends. We also repeated the analysis in table 4 and excluded cohort studies and surveys, and found that the results were not materially altered, with 65% (20/31) of populations in the last time period (from 2006 onwards) showing decreasing or stable incidence of diabetes.

Quality of studies

The median score for study quality was 10 (interquartile range 8-11; supplementary table 4). We repeated the analyses reported in table 4 after excluding studies that had quality scores in the lowest quarter, and observed similar results to the main findings. For example, in 1960-89, 67% (10/15) of populations reported stable or decreasing incidence, while in the final time period, 67% (18/27) of populations reported stable or decreasing incidence of diagnosed diabetes.

Principal findings

In this systematic review of population based studies on diabetes incidence, we show evidence that the incidence of diagnosed diabetes increased in most populations from the 1960s to the early 2000s, after which a pattern emerged of levelling trends in 30% and declining trends in 36% of the reported populations. Although the lack of data for non-Europid populations leaves global trends in incidence unclear, these findings suggest that trends in the diabetes epidemic in some high income countries have turned in a more encouraging direction compared with previous decades. It is important to note that these results apply predominantly to type 2 diabetes, as even though many studies did not accurately define diabetes type, the incidence of type 2 diabetes in adults is an order of magnitude greater than that of type 1 diabetes.

The countries that showed stable or decreasing trends in the last time period were from Europe and east Asia, with no obvious clustering or commonalities. For the countries showing decreasing or stable diabetes trends, if the prevalence data were used to understand the diabetes epidemic in that country, a different message would be obtained. For example, national data from Korea showed that the prevalence of diabetes increased from 2000 to 2010. 59 Similarly in Sweden, the prevalence of pharmacologically treated diabetes increased moderately from 2006 to 2014. 43 In the US, the prevalence of diabetes reached a plateau when incidence began to decrease. However, we lacked incidence data from many areas of the world where the most steady and substantial increases in prevalence have been reported, including the Pacific Islands, Middle East, and south Asia. Large increases in incidence could still be occurring in these areas. The lack of incidence data for much of the world, combined with the common observation of discordance between incidence and prevalence rates where such data exist, both underscore the importance of using incidence data to understand the direction of the diabetes epidemic.

Incidence could be starting to fall for several reasons. Firstly, we might be starting to benefit from prevention activities of type 2 diabetes, including increased awareness, education, and risk factor modification. These activities have involved both targeted prevention among high risk individuals, similar to that conducted in the Diabetes Prevention study 60 and Diabetes Prevention Programme 61 62 in many countries, 63 and less intensive interventions with broader reach such as telephone counselling in the general community. 64 65 67 Secondly, health awareness and education programmes have also been implemented in schools and work places, and many changes to the physical environment, such as the introduction of bike tracks and exercise parks, have occurred. 68 Thirdly, favourable trends in selected risk factors of type 2 diabetes in some countries provide indirect evidence of positive changes to reduce diabetes incidence. Finally, in the US, there is some evidence in recent years of improved diets and related behaviours, which include reductions in intake of sugar sweetened beverages 69 and fat, 70 small declines in overall energy intake, and declines in some food purchases. 8 71

Similar reduction in consumptions of sugar sweetened beverages have occurred in Norway 72 and Australia 73 and fast food intake has decreased in Korea. 74 Some of these changes could be linked to a fall in diabetes incidence. Some places such as Scotland 75 have also had a plateauing of obesity prevalence, but this is not universal. In the US, despite earlier studies suggesting that the rate of increase in obesity might be slowing down, 76 77 more recent data show a small increase. 78 79 While some evidence supports the hypothesis that these prevention activities for type 2 diabetes and an improved environment could trigger sufficient behaviour change to have an effect on diabetes incidence, other data, such as the continuing rising obesity prevalence in the US, 79 casts some doubt over the explanations underpinning our findings on diabetes incidence trends.

Other factors might have also influenced reported diabetes incidence. Only 11% (n=5) of the studies reported here screened for undiagnosed diabetes, and therefore trends could have been influenced by secular changes in diagnostic behaviour. In 1997, the threshold for fasting plasma glucose for diagnosis of diabetes was reduced from 7.8 to 7.0 mmol/L, which could increase diagnosis of new cases of type 2 diabetes. In 2009-10, HbA1c was then introduced as an alternative way to diagnose diabetes. 80 Evidence from some studies suggests that the HbA1c diagnostic threshold detects fewer people with diabetes than do the thresholds for fasting plasma blood glucose, 80 81 potentially leading to a lowering of incidence estimates. However, across multiple studies, prevalence estimates based on fasting plasma glucose only versus HbA1c definitions are similar. 82 Furthermore, because HbA1c can be measured in the non-fasting state (unlike the fasting blood glucose or oral glucose tolerance test), the number of people who actually undergo diagnostic testing could be higher with HbA1c. Nichols and colleagues 56 reported that among seven million insured US adults, despite a shift towards HbA1c as the diagnostic test in 2010, the incidence of diabetes did not change from 2010 to 2011.

Another potential explanation for declining or stable diabetes incidence after the mid-2000s is a reduction in the pool of undiagnosed diabetes 83 through the intensification of diagnostic and screening activities 83 84 and changing diagnostic criteria during the previous decade. 80 Data from Read and colleagues provide some evidence to support this notion. 41

Among the included studies, two studies specifically examined clinical screening patterns in parallel with incidence trends. These studies reported that the proportion of the population screened for diabetes increased over time, and the incidence of diabetes remained stable 56 or fell. 34 While the Karpati study 34 combined data for glucose testing with HbA1c testing, the study by Nichols and colleagues 56 separated the two, and showed that both glucose testing and HbA1c testing increased over time. A third study, in Korea, 36 also noted that the incidence of diabetes decreased in the setting of an increase in the uptake of the national health screening programme. Despite the introduction of HbA1c for diagnosis of diabetes by the World Health Organization, this practice has not been adopted everywhere. For example, neither Scotland nor Hong Kong have introduced the use of HbA1c for screening or diagnosis of diabetes, and studies in these areas showed a levelling of diabetes incidence trends and decreasing trends, respectively.

Our findings appear to contrast with data showing increasing global prevalence of diabetes. 1 3 However, increasing prevalence could be influenced by improved survival of people with diabetes, because this increases the length of time that each individual remains within the diabetes population. As is shown in several studies in this review, 23 41 mortality from diabetes and incidence of diabetes might both be falling but as long as mortality is lower than incidence, prevalence will rise. Therefore, we argue that prevalence alone is an insufficient measure to track the epidemic of diabetes and other non-communicable diseases.

Strengths and weaknesses of this study

A key strength of this work was the systematic approach and robust methodology to describe trends in diagnosed diabetes incidence. We also presented the reported trends allocated to approximate time periods, as well as conducting our own regression within exact time periods. The following limitations should also be considered. Firstly, we did not formally search the grey literature, because a preliminary grey literature search revealed only low quality studies, with inadequate methodological detail to provide confidence in any observed incidence trends, and thus review could be subject to publication bias. Secondly, we were not able to source age or sex specific data on all populations. Thirdly, it was not possible to adjust for different methods of diabetes diagnosis or ascertain trends by different definitions of diabetes. Fourthly, most data sources reported only on clinically diagnosed diabetes and so were subject to influence from diagnostic behaviour and coding practices. Fifthly, study type changed over time, with large administrative datasets becoming more common and cohort studies becoming less common over time. Nevertheless, the size and absence of volunteer bias in administrative datasets likely make them less biased. Finally, data were limited in low and middle income countries.

Conclusions and unanswered questions

This systematic review shows that in most countries for which data are available, the incidence of diagnosed diabetes was rising from the 1990s to the mid-2000s, but has been stable or falling since. Preventive strategies and public health education and awareness campaigns could have contributed to this recent trend. Data are limited in low and middle income countries where trends in diabetes incidence might be different. Improvement of the collection, availability, and analysis of incidence data will be important to effectively monitor the epidemic and guide prevention efforts into the future.

What is already known on this topic

Monitoring of the diabetes epidemic has mainly focused on reporting diabetes prevalence, which continues to rise; however, increasing prevalence is partly driven by improved medical treatment and declining mortality

Studies on diabetes incidence are scarce, but among those that exist, some report a fall or stabilisation of diabetes incidence;

Whether the proportion of studies reporting falling incidence has changed over time is not known

What this study adds

This systematic review of published data reporting diabetes incidence trends over time shows that in most countries with available data, incidence of diabetes (mainly diagnosed diabetes) increased from the 1990s to the mid-2000s, and has been stable or falling since

Preventive strategies and public health education and awareness campaigns could have contributed to this flattening of rates, suggesting that worldwide efforts to curb the diabetes epidemic over the past decade might have been effective

Published data were very limited in low and middle income countries, where trends in diabetes incidence might be different

Acknowledgments

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

Contributors: MT, DNK, JLH, and RMI are postdoctoral fellows who screened abstracts for selection into the systematic review. JES and DJM also screened abstracts. ELMB applied the quality criteria to the selected articles. RMI extracted data, applied quality criteria to selected articles, and contributed to preparing the manuscript. DJM conceived the project, screened abstracts, extracted the data, analysed the data, and wrote the manuscript. JES, MEP, and EWG conceived the project, edited the manuscript, and provided intellectual input throughout the process. The funder of the study (CDC) was part of the study group and contributed to data collection, data analysis, data interpretation, and writing of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication. DJM is guarantor. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Funding: Funded by the CDC. The researchers were independent from the funders.

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf and declare: support from the CDC for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.

Ethical approval: Not required because this work was a systematic review.

Data sharing: Data are available from the corresponding author ([email protected]).

The lead author affirms that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.

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 and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ .

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• Published: 23 July 2015

Type 2 diabetes mellitus

• Ralph A. DeFronzo 1 ,
• Ele Ferrannini 2 ,
• Leif Groop 3 ,
• Robert R. Henry 4 ,
• William H. Herman 5 ,
• Jens Juul Holst 6 ,
• Frank B. Hu 7 ,
• C. Ronald Kahn 8 ,
• Itamar Raz 9 ,
• Gerald I. Shulman 10 ,
• Donald C. Simonson 11 ,
• Marcia A. Testa 12 &
• Ram Weiss 13  

Nature Reviews Disease Primers volume  1 , Article number:  15019 ( 2015 ) Cite this article

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• Diabetes complications
• Type 2 diabetes

Type 2 diabetes mellitus (T2DM) is an expanding global health problem, closely linked to the epidemic of obesity. Individuals with T2DM are at high risk for both microvascular complications (including retinopathy, nephropathy and neuropathy) and macrovascular complications (such as cardiovascular comorbidities), owing to hyperglycaemia and individual components of the insulin resistance (metabolic) syndrome. Environmental factors (for example, obesity, an unhealthy diet and physical inactivity) and genetic factors contribute to the multiple pathophysiological disturbances that are responsible for impaired glucose homeostasis in T2DM. Insulin resistance and impaired insulin secretion remain the core defects in T2DM, but at least six other pathophysiological abnormalities contribute to the dysregulation of glucose metabolism. The multiple pathogenetic disturbances present in T2DM dictate that multiple antidiabetic agents, used in combination, will be required to maintain normoglycaemia. The treatment must not only be effective and safe but also improve the quality of life. Several novel medications are in development, but the greatest need is for agents that enhance insulin sensitivity, halt the progressive pancreatic β-cell failure that is characteristic of T2DM and prevent or reverse the microvascular complications. For an illustrated summary of this Primer, visit: http://go.nature.com/V2eGfN

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Acknowledgements

The authors acknowledge grants from: the South Texas Veterans Healthcare System to R.A.D.; the National Institutes of Health (grants R01DK24092 to R.A.D.; DK58845 and P30 DK46200 to F.B.H.; R01 DK-040936, R01 DK-049230, R24 DK-085836, UL1 RR-045935, R01 DK-082659 and R24 DK085610 to G.I.S.; P30 DK036836 to C.R.K. Novo Nordisk Foundation for Basic Metabolic Research and the University of Copenhagen to G.I.S. and C.R.K.; DVA-Merit Review grant and VA San Diego Healthcare System to R.H.; National Institute for Diabetes and Digestive and Kidney Disease (grant P30DK092926) to W.H.; the Swedish Research Council (grants 2010–3490 and 2008–6589) and European Council (grants GA269045) to L.G.; Italian Ministry of University & Research (MIUR 2010329EKE) to E.F.; the Patient-Centered Outcomes Research Institute (PCORI) Program Award (CE1304-6756) to D.C.S. and M.A.T.; NovoNordisk Foundation to the NNF Center for Basic Metabolic Research to J.H. W.H. acknowledges the Michigan Center for Diabetes Translational Research and I.R. thanks R. Sprung for editorial assistance.

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Ralph A. DeFronzo

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Ele Ferrannini

Department of Clinical Science Malmoe, Diabetes & Endocrinology, Lund University Diabetes Centre, Lund, Sweden

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Division of Internal Medicine, Diabetes Unit, Hadassah Hebrew University Hospital, Jerusalem, Israel

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Contributions

Introduction (R.R.H.); Epidemiology (F.B.H.); Mechanisms/pathophysiology (L.C.G., C.R.K., E.F., G.I.S. and R.A.D.); Diagnosis, screening and prevention (W.H.H.); Management (R.A.D.); Quality of life (D.C.S. and M.A.T.); Outlook (I.R., J.J.H. and R.W.); overview of Primer (R.A.D.).

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Correspondence to Ralph A. DeFronzo .

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The authors declare the following potential COI: (1) R.A.D.: Research Grant Support - AstraZeneca, Bristol Myers Squibb, Janssen; Speaker's Bureau - AstraZeneca, Novo Nordisk, Advisory Board/Consultant - AstraZeneca, Janssen, Novo Nordisk, Boehringer Ingelheim, Lexicon, Intarcia; (2) E.F.: Research Grant Support - Boehringer Ingelheim, Eli Lilly; Consultant/Speaker Bureau-Boehringer Ingelheim, Eli Lilly, Sanofi, Novo Nordisk, Janssen, AstraZeneca, Takeda, Medtronic, Intarcia; (3) C.R.K. serves as a consultant for Medimmune, Merck, Five Prime Therapeutics, CohBar, Antriabio, and Catabasis; (4) L.G. has no conflict of interest; (5) R.H. has received grant support from Hitachi, Janssen, Eli Lilly, Sanofi-Aventis and Viacyte and is a consultant/advisory board member for Alere, Amgen, AstraZeneca, Boehringer Ingelheim, Bristol Myers Squibb, Clin Met, Eisai, Elcelyx, Gilead, Intarcia, Isis, Janssen, Merck, Novo Nordisk, Sanofi-Aventis, and Vivus; (6) W.H.H. has no conflict of interest; (7) J.J.H. has received grant support from Novartis and Merck and is a consultant/advisory board member for Glaxo, Smith, Kline, Novo Nordisk, and Zealand Pharmaceuticals; (8) M.A.T. has no conflict of interest; (9) R.W. serves as a consultant for Medtronics and Kamada and is on the speaker's bureau for Medtronics and Novo Nordisk; (10) F.H. has received research support from California Walnut Commission and Metegenics; (11) G.I.S. serves on scientific advisory boards for Merck and Novartis and he has received research grant support from Gilead Pharmaceuticals; (12) D.C.S. has no conflict of interest; (13) I.R. – Advisory Board: Novo Nordisk, Astra Zeneca/BMS, MSD, Eli Lilly, Sanofi, Medscape Cardiology; Consultant: Astra Zeneca/BMS, Insuline; Speaker's Bureau: Eli Lilly, Novo Nordisk, Astra Zeneca/BMS, J&J, Sanofi, MSD, Novartis, Teva; Shareholder: Insuline, Labstyle.

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DeFronzo, R., Ferrannini, E., Groop, L. et al. Type 2 diabetes mellitus. Nat Rev Dis Primers 1 , 15019 (2015). https://doi.org/10.1038/nrdp.2015.19

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Diabetes Mellitus Review

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Diabetes mellitus is a group of physiological dysfunctions characterized by hyperglycemia resulting directly from insulin resistance, inadequate insulin secretion, or excessive glucagon secretion. Type 1 diabetes (T1D) is an autoimmune disorder leading to the destruction of pancreatic beta-cells. Type 2 diabetes (T2D), which is much more common, is primarily a problem of progressively impaired glucose regulation due to a combination of dysfunctional pancreatic beta cells and insulin resistance. The purpose of this article is to review the basic science of type 2 diabetes and its complications, and to discuss the most recent treatment guidelines.

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Introduction

Definitions of health disparities, health equity, and sdoh, sdoh nomenclatures and contextual factors, review of sdoh and diabetes, linkages across health care and community sectors to address sdoh, article information, social determinants of health and diabetes: a scientific review.

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Felicia Hill-Briggs , Nancy E. Adler , Seth A. Berkowitz , Marshall H. Chin , Tiffany L. Gary-Webb , Ana Navas-Acien , Pamela L. Thornton , Debra Haire-Joshu; Social Determinants of Health and Diabetes: A Scientific Review. Diabetes Care 1 January 2021; 44 (1): 258–279. https://doi.org/10.2337/dci20-0053

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Decades of research have demonstrated that diabetes affects racial and ethnic minority and low-income adult populations in the U.S. disproportionately, with relatively intractable patterns seen in these populations’ higher risk of diabetes and rates of diabetes complications and mortality ( 1 ). With a health care shift toward greater emphasis on population health outcomes and value-based care, social determinants of health (SDOH) have risen to the forefront as essential intervention targets to achieve health equity ( 2 – 4 ). Most recently, the COVID-19 pandemic has highlighted unequal vulnerabilities borne by racial and ethnic minority groups and by disadvantaged communities. In the wake of concurrent pandemic and racial injustice events in the U.S., the American College of Physicians, American Academy of Pediatrics, Society of General Internal Medicine, National Academy of Medicine, and other professional organizations have published statements on SDOH ( 5 – 8 ), and calls to action focus on amelioration of these determinants at individual, organizational, and policy levels ( 9 – 11 ).

In diabetes, understanding and mitigating the impact of SDOH are priorities due to disease prevalence, economic costs, and disproportionate population burden ( 12 – 14 ). In 2013, the American Diabetes Association (ADA) published a scientific statement on socioecological determinants of prediabetes and type 2 diabetes ( 15 ). Toward the goal ofunderstanding and advancing opportunities for health improvement among the population with diabetes through addressing SDOH, ADA convened the current SDOH and diabetes writing committee, prepandemic, to review the literature on 1 ) associations of SDOH with diabetes risk and outcomes and 2 ) impact of interventions targeting amelioration of SDOH on diabetes outcomes. This article begins with an overview of key definitions and SDOH frameworks. The literature review focuses primarily on U.S.-based studies of adults with diabetes and on five SDOH: socioeconomic status (education, income, occupation); neighborhood and physical environment (housing, built environment, toxic environmental exposures); food environment (food insecurity, food access); health care (access, affordability, quality); and social context (social cohesion, social capital, social support). This review concludes with recommendations for linkages across health care and community sectors from national advisory committees, recommendations for diabetes research, and recommendations for research to inform practice.

Table 1 displays definitions of key terms. Differences in diabetes risk and outcomes can result from multiple contributors, including biological, clinical, and nonclinical factors ( 1 ). A substantial body of scientific literature demonstrates the adverse impact of a particular type of difference, health disparities ( 16 ) in diabetes ( 1 , 17 , 18 ). A preponderance of health disparities research in the U.S. has examined disparities by race and ethnicity ( 3 , 19 ). Internationally, the term health equity has traditionally been used to encompass the range of population inequalities resulting from demographic and economic characteristics, and this term is used increasingly in the U.S. ( 20 – 24 ). Addressing healthy equity necessitates an understanding of social and environmental factors that combined account for 50% to 60% of health outcomes ( 22 , 25 ). These social and environmental factors collectively are known as SDOH ( 21 , 26 , 27 ).

Definitions

The writing committee reviewed the following commonly referenced SDOH frameworks for classifications and terminology: the World Health Organization (WHO) Commission on Social Determinants of Health ( 28 ), Healthy People 2020 ( 29 , 30 ), the County Health Rankings Model ( 31 , 32 ), and Kaiser Family Foundation Social Determinants of Health factors ( 33 ).

No single consensus set of factors define SDOH. Nomenclatures used by each framework, for shared SDOH factors, are depicted in Fig. 1 . Common among the frameworks is placement of economic and socioeconomic determinants as foremost. Food SDOH factors (e.g., insecurity and access) are classified as economic stability ( 29 ), neighborhood and built environment ( 29 ), material circumstances ( 28 ), or as an independent category ( 33 ). Housing is classified as material circumstances ( 28 ), economic stability ( 29 ), or neighborhood and built environment ( 33 ). Environmental exposures (i.e., toxins, air pollution, and water quality) are classified as neighborhood and physical environment ( 31 ) or built environment ( 29 ). Social environment is represented as community and societal context ( 29 , 33 ), social cohesion and social capital ( 28 ), or social factors ( 31 ). Health care is a SDOH in the WHO, Healthy People 2020, and County Health Rankings models, with access factors as primary, with or without quality of care factors.

Nomenclatures for shared determinants among four social determinants of health frameworks, the World Health Organization Commission on the Social Determinants of Health, the U.S. Department of Health and Human Services Healthy People 2020, the County Health Rankings Model, and the Kaiser Family Foundation Social Determinants of Health framework.

Each framework posits complex interactions among SDOH factors. WHO additionally maps causal priority among the determinants. For example, the upstream sociopolitical context and resulting socioeconomic position are root cause, structural determinants of health inequities that work through intermediary sets of determinants to cause health inequities. WHO positions the health system as an SDOH that plays a role in mediating impact of intermediary determinants on health outcomes ( 28 ).

SDOH inclusion in this review was determined by representation within one or more existing SDOH frameworks and presence of a sufficient body of literature to demonstrate influence of the determinant on diabetes. The reviewed SDOH are shown in Table 2 . Studies of primarily adult populations are described, and the terms type 2 diabetes mellitus (T2DM), type 1 diabetes mellitus (T1DM), and “diabetes” (indicating either a mixed T2DM and T1DM sample or diabetes case ascertainment methods that do not enable specifying clinical diagnostic type) are used in accordance with the terminology used in the respective studies.

SDOH and component factors included in the diabetes review

Socioeconomic Status and Diabetes

Socioeconomic status (SES) is a multidimensional construct that includes educational, economic, and occupational status ( 34 – 36 ). SES is a consistently strong predictor of disease onset and progression at all levels of SES for many diseases, including diabetes ( 37 ). SES is linked to virtually all of the established SDOH. It is associated with the extent to which individuals and communities can access material resources including health care, housing, transportation, and nutritious food and social resources such as political power, social engagement, and control.

The three components of SES are intercorrelated ( 38 ), but each aspect has unique implications for health. Each component can be assessed at the individual or population level ( 39 ). For example, economic status is often measured by determining a person’s own income. However, it is also assessed by the income of the household in which the person resides and by the income level of the community (e.g., mean household income of the census track in which a person resides) as a proxy for the individual’s household income. Census-level household income also operates as a contextual variable, reflecting the composition and available resources in a defined area.

Educational status can be quantified either in years of schooling or highest degree earned. It may be assessed at the level of the individual (e.g., the person’s own educational attainment), the household (the highest grade completed by anyone in the household), or the community (e.g., percent of high school or college graduates in a census track). Quantity of education does not capture differences in quality of education that may be relevant to SES measurement ( 38 ). Literacy has emerged as a measure of educational quality and as potentially more reflective of SES than years of schooling among African Americans and low-income Whites ( 40 , 41 ). Health literacy, which is directly associated with literacy and is context specific ( 42 – 44 ), and literacy are included as SDOH in Healthy People 2020 ( 29 ).

Occupation is itself multidimensional. It has been measured as employment status (e.g., employed vs. unemployed), stability (e.g., job insecurity), job type (e.g., manual vs. nonmanual, prestige of the occupation), and working conditions (e.g., shift work, number of hours worked, job demands, and control) ( 39 , 45 ). For members of large organizations, occupational hierarchies of job titles capture work conditions as well as qualifications and pay (e.g., civil service grades).

Associations of SES With Diabetes Incidence, Prevalence, and Outcomes

Income, education, and occupation show a graded association with diabetes prevalence and complications across all levels of SES, up to the very top. Those lower on the SES ladder are more likely to develop T2DM, experience more complications, and die sooner than those higher up on the SES ladder ( 46 , 47 ). The higher a person’s income, the greater their educational attainment, and the higher their occupational grade, the less likely they are to develop T2DM or to experience its complications. The gradient is steeper at the bottom, however, and research has focused primarily on those with the lowest levels of income and education.

Prevalence of diabetes increases on a gradient from highest to lowest income ( 48 , 49 ). In data from the National Health Interview Survey (NHIS) covering 2011–2014, Beckles and Chou ( 50 ) found increasing diabetes prevalence at lower levels of income as reflected in the levels of ratio of income to poverty level. Compared with those with high income, the relative percentage difference in prevalence of diabetes for those classified as middle income, near poor, and poor, was 40.0%, 74.1%, and 100.4%, respectively. The difference in diabetes prevalence by income was greater during this time period than it had been in a prior period (1999–2002), pointing to widening disparities in diabetes prevalence associated with income.

At the neighborhood level, differences in diabetes prevalence by census track are attributable to SES ( 51 , 52 ). For example, in a recent study by Kolak et al. ( 52 ), rate of T2DM was found to be significantly higher and concentrated in census tracts characterized by factors including lower incomes, lower high school graduation rates, more single-parent households, and crowded housing. Living in neighborhood census tracts with lower educational attainment, lower annual income, and larger percentage of households receiving Supplemental Nutrition Assistance Program benefits has been associated with higher risk of progression to T2DM among adults with prediabetes ( 53 ).

Gaskin et al. ( 49 ) examined the interaction of individual poverty with neighborhood poverty and found that, compared with nonpoor adults living in nonpoor neighborhoods, poor adults living in nonpoor neighborhoods have increased odds of having diabetes, and poor adults living in poor neighborhoods have twofold higher odds of having diabetes. In addition, a race-poverty-place gradient was observed. Compared with nonpoor Whites in nonpoor neighborhoods, odds of diabetes were highest for poor Whites in poor neighborhoods (odds ratio [OR] 2.51, 95% CI = 1.31–4.81), followed by poor Blacks in poor neighborhoods and nonpoor Blacks in poor neighborhoods (OR 2.45, 95% CI 1.50–4.01, and OR 2.49, 95% CI 1.48–4.19), and finally poor Whites in nonpoor neighborhoods (OR 1.73, 95% CI 1.16–2.57) ( 49 ).

Adults with T2DM who have a family income below the federal poverty level have a twofold higher risk of diabetes-related mortality compared with their counterparts in the highest family income levels ( 54 ). This pattern of diabetes-related mortality has been observed specifically in adults with T1DM as well ( 55 ). A meta-analysis by Bijlsma-Rutte et al. ( 56 ) observed an inverse association between income and HbA 1c levels in people with T2DM, with a pooled mean difference in HbA 1c of 0.20% (95% CI −0.05 to 0.46) between people with low and high income. Low income is associated with a higher risk of experiencing diabetic ketoacidosis among youth and adults with T1DM ( 57 ) and with higher HbA 1c levels, particularly among racial/ethnic minority youth with lower SES ( 58 , 59 ).

Age-adjusted incidence of diagnosed diabetes in adults is associated also with educational level in a stepwise pattern. Diabetes incidence is highest (10.4 per 1,000 persons) for adults with less than a high school education, 7.8 per 1,000 persons for those with a terminal high school education, and 5.3 per 1,000 persons for those with more than a high school education ( 60 ). Diabetes prevalence in the adult U.S. population is similarly inversely associated with educational level in a stepwise pattern. In the U.S., the age-adjusted prevalence of diagnosed diabetes is 12.6% for those with less than a high school education, 9.5% for those with a high school education, and 7.2% for those with more than a high school education ( 61 ). Having a college education or more is associated with the lowest odds of diabetes ( 62 ). Mirroring findings on income, temporal trends in diabetes prevalence at different levels of education show increasing disparities in prevalence associated with educational attainment ( 50 ).

The risk of diabetes-related mortality demonstrates a gradient from lowest to highest education level. Compared with adults with a college degree or higher, having less than high school education is associated with a twofold higher mortality from diabetes (relative hazard 2.05, 95% CI 1.78–2.35) ( 54 ). In adults with T1DM, not having a college degree is associated with a threefold higher mortality from diabetes compared with counterparts with a college degree ( 63 ). Lower educational level is associated with higher HbA 1c , with a meta-analysis ( 56 ) reporting a pooled mean difference in HbA 1c of 0.26% (95% CI, 0.09–0.43) between people with low and high educational levels. Regarding literacy/health literacy as a SDOH, Marciano et al. ( 64 ) conducted a meta-analysis of 61 studies of 18,905 adults with T1DM or T2DM to determine associations of health literacy with several diabetes outcomes and found that higher levels of health literacy were significantly associated with lower HbA 1c levels and better diabetes knowledge, but not with more frequent self-management activities.

Occupation.

Systematic reviews and meta-analyses have examined several aspects of occupation in relation to diabetes risk, although most of this research has been conducted outside of the U.S. Ferrie et al. ( 65 ) conducted a meta-analysis of associations of job insecurity with incident diabetes and found an association of high job insecurity with higher risk of incident diabetes (OR 1.19, 95% CI 1.09–1.30). A meta-analysis by Varanka-Ruuska et al. ( 66 ) found that unemployment was associated with increased odds of both prediabetes (OR 1.58, 95% CI 1.07–2.35) and T2DM (OR 1.72, 95% CI 1.14–2.58). Exposure to shift work is associated with higher risk of diabetes than working normal daytime schedules ( 67 ). A meta-analysis by Kivimäki et al. ( 68 ) reported an association of long work hours (≥55 h per week) as compared with standard work hours (35–40 h per week) with higher incident diabetes in adults with low SES but not in adults with high SES. A U.S. population-based survey on diabetes and occupation found highest prevalence of diabetes among transportation workers and lowest prevalence of diabetes among physicians ( 69 , 70 ).

SES Interventions and Diabetes Outcomes

To date, there is no body of literature describing impact of change in income, change to higher educational status, or different employment/occupational status on diabetes outcomes, although income and wage changes, and job changes and loss, do occur naturalistically. Similarly, no diabetes outcomes have been reported from interventions directly targeting living wages, early childhood education, educational quality, or educational access for poor children and families. Studies have examined diabetes self-management interventions in the setting of low literacy/health literacy, particularly among racial/ethnic minority adults with T2DM and have demonstrated effectiveness of low-literacy adaptions ( 71 ) and health literacy and numeracy tools in improving diabetes knowledge and self-care ( 72 – 74 ). A meta‐analysis of nine intervention trials with 1,874 adults with T2DM found that literacy-sensitive interventions were associated with a small but statistically significant decrease in HbA 1c (–0.18%; 95% CI –0.36 to –0.004) in comparison with usual clinical care ( 75 ) in patients regardless of health literacy status. Literacy-adapted education and tools may need to be combined with more comprehensive evidence-based behavioral self-management intervention approaches to achieve substantive clinical improvements in racial/ethnic minority populations with T2DM and low literacy/health literacy ( 76 , 77 ). In conclusion, despite the long-standing evidence for SES as a key determinant both of diabetes risk and outcomes, systematic investigation of impact on diabetes of change in SES remains a gap in the literature.

Neighborhood and Physical Environment and Diabetes

The neighborhood environment in which one lives has been of major interest as a setting in which to understand contextual and multilevel influences on health ( 78 ). Diez Roux and Mair ( 78 ) have described the role of historical and contemporary residential segregation by race, ethnicity, and SES as the socioeconomic and political context that produced the patterns of unequal resource distribution resulting in neighborhood environments that maintain health inequities. Tung et al. ( 79 ) also discuss the multiple intricacies associated with how race, place, and poverty converge in a dynamic way across various spatial contexts and circumstances to influence health and propose that understanding the intersection of these contextual influences is needed to prevent diabetes inequities. Neighborhood and physical environment factors of housing, built environment, and environmental exposures are reviewed.

Stable housing is a key indicator of economic stability ( 80 ) and a core SDOH ( 80 ). Housing instability refers to a spectrum of situations that can range from living in one’s car, staying with relatives or friends, having trouble paying rent, suffering evictions or frequent moves, paying more than 50% of income in rent, and living in crowded conditions (historically defined as having more than one person per room) to homelessness—the most extreme form of unstable housing ( 81 – 85 ). Homelessness is defined as “lacking a regular nighttime residence or having a primary nighttime residence that is a temporary shelter or other place not designed for sleeping” ( 86 ). As of 2020, the U.S. government reported 567,715 or 17 of 10,000 people in the country are homeless; African Americans accounted for 40% of people experiencing homelessness, while those identifying as Hispanic or Latino comprised 22% of the homeless population ( 87 ). A common theme in conceptual models linking housing instability to poor health is that the instability inherent to the situation makes it difficult to attend to preventive services and self-care ( 83 , 88 – 90 ), leading to worse control of chronic conditions, higher use of acute-care services like emergency departments, and higher likelihood of complications ( 91 – 93 ).

Associations of Housing Instability With Diabetes Incidence, Prevalence, and Outcomes.

The prevalence of diabetes among those with housing instability in the U.S., and whether it differs from that among those without housing instability, is not known. A key limitation for the field is that there is no single, accepted definition of housing instability or a commonly used assessment instrument. Further, because housing instability is more likely to occur among individuals with lower SES—which is independently associated with higher diabetes prevalence—it is unclear whether housing instability is causally related to developing diabetes. One systematic review did not find higher diabetes prevalence than in the general population among persons experiencing homelessness, estimating approximately 8% prevalence in adults who do and do not experience homelessness ( 94 ). A recent study using nationally representative data from individuals seen in community health centers found that approximately 37% of individuals with diabetes reported housing instability. This study also found that individuals with diabetes and housing instability were more likely to self-report having an emergency department visit or hospitalization for their diabetes (adjusted OR 5.17, 95% CI 2.08–12.87) ( 82 ). A cross-sectional study in a single health care system found that housing instability among individuals with diabetes was associated with higher outpatient utilization (incident rate ratio 1.31, 95% CI 1.14–1.51) ( 95 ). Though not specific to diabetes, additional work has linked housing instability to poor health outcomes and reduced health care access ( 91 , 96 – 100 ). A longitudinal study in the Department of Veterans Affairs (VA) health care system found that experiencing homelessness was associated with higher adjusted odds of having an HbA 1c >8.0% and >9.0%. Vijayaraghavan et al. ( 84 ) identified unstable housing as a key barrier to diabetes care among low-income individuals. There was an observed linear decrease in diabetes self-efficacy as housing instability increased (β-coefficient −0.94, 95% CI −1.88 to −0.01, P < 0.01), which was partially mediated by food insecurity. Qualitative work has found that unstable housing makes it more difficult to engage in self-care, follow self-management routines, afford diabetes medications and supplies, and eat healthy foods ( 91 , 92 ). Choice of medication is important, and considerations should include medication cost and the ability to store medication and diabetes care supplies safely. Brooks et al. provide a narrative review of considerations for diabetes treatment among individuals experiencing homelessness ( 101 ).

Housing Instability Interventions and Diabetes Outcomes.

Given its expense, housing is one of the most difficult health-related social needs to intervene upon. Housing intervention studies reporting diabetes outcomes are few; however, there is some high-quality evidence for housing interventions. The Moving To Opportunity for Fair Housing Demonstration Project (MTO), a randomized social experiment conducted through the Department of Housing and Urban Development, in partnership with behavioral scientists and other federal agencies, was designed to determine what impact moving from a high-poverty to a low-poverty census tract would have on multiple outcomes ( 102 , 103 ). In 1994–1998, MTO randomized 4,498 women with children living in public housing within high-poverty census tracts in five cities to one of three study arms. The 1,788 women in the experimental arm received Section 8 vouchers usable only in low-poverty areas (census tracts with <10% of the population below the poverty line) along with counseling and assistance in finding a private rental unit. The 1,312 women in the Section 8 arm received traditional unrestricted vouchers and the usual briefing the local Section 8 program provided. The 1,398 women in the control arm received no vouchers but continued to receive MTO project-based assistance. Those who received vouchers could choose whether to use the vouchers or not. Findings from the follow-up survey in 2008 through 2010 found a 21.6% relative reduction in prevalence of an elevated HbA 1c (>6.5%) in the group that moved to low-poverty census tracts compared with the control group, with an absolute difference of 4.31 percentage points (95% CI −7.82 to −0.80). The low-poverty group also had relative reductions of 13.0% in prevalence of BMI ≥35 and relative reduction of 19.1% in BMI ≥40 kg/m 2 , with absolute differences of 4.61 percentage points (95% CI −8.54 to −0.69) and 3.38 percentage points (95% CI −6.39 to −0.36), respectively ( 102 ). The usual vouchers and control arms did not differ. Other MTO outcomes among the group randomized to low-poverty census tracts included higher housing quality, education, employment, and earnings as well as multiple additional improvements to child and adult health ( 103 ). A 10–15 year follow-up study found substantial and sustained reductions in diabetes prevalence, rates of extreme obesity, and improvement in mental health outcomes among the adults who received vouchers to move to low-poverty neighborhoods and reduction in extreme obesity among the adults who received Section 8 vouchers ( 104 ). While not specific to diabetes, a meta-analysis of randomized trials that provided low-barrier housing support for individuals experiencing homelessness found significant reductions in health care utilization ( 105 ).

Housing interventions may facilitate access to diabetes care. The Collaborative Initiative to End Chronic Homelessness provided adults who were chronically homeless with permanent housing and supportive primary health care and mental health services ( 106 ). Placed persons were more likely to receive evaluation and management services (relative risk [RR] 1.03, 95% CI 1.01–1.04) than unplaced persons ( 107 ). Placed persons were more likely to receive HbA 1c tests (RR 1.10, 95% CI 1.02–1.19) and lipid tests (RR 1.09, 95% CI 1.02–1.17), while for those without baseline diabetes placement was associated with lower risk of new diabetes diagnoses (RR 0.87, 95% CI 0.76–0.99). Keene et al. ( 91 ) suggest the relationship of stable housing to diabetes management is due to its role as a foundation for prioritizing care and allowing for the routinization of diabetes management, critical to disease control. This suggests the benefits of supportive and stable housing may be extended to diabetes care and prevention. A naturalistic qualitative study of the impact of transitioning to rental-assisted housing among low-income, housing-insecure adults with T2DM reported that rental assistance afforded individuals more environmental and financial control over life circumstances, thereby enabling diabetes routines and allocation of financial resources to diabetes care ( 108 ).

Built Environment

The built environment, as defined by the U.S. Centers for Disease Control and Prevention (CDC), includes the physical parts of where people live and work, such as infrastructure, buildings, streets, and open spaces ( 109 ). Here, built environment factors of walkability and greenspace are reviewed.

Associations of Built Environment with Diabetes Incidence, Prevalence, and Outcomes.

A robust literature has demonstrated associations of the built environment with obesity-related outcomes ( 110 – 113 ). However, a smaller body of research has examined associations of the built environment with diabetes specifically. Smalls et al. ( 114 ) reported significant associations of both walking environment (β = −0.040) and neighborhood activities (β = −0.104) with exercise in a southeastern U.S. population with diabetes. A recent U.S. review and meta-analysis by Chandrabose et al. ( 113 ) examined longitudinal studies of the built environment and cardiometabolic health. Results showed strong evidence for impact of walkability on T2DM outcomes, with four of seven studies (57%) showing significant findings in the aggregated analyses using objective and perceived measures of walkability. Although the methods to determine mediation by physical activity in most studies were ineffective to make conclusions, one study tested the indirect effect of walkability on 10-year change in HbA 1c and found a partial mediation effect for self-reported physical activity using structural equation modeling. For other measures of built environment, such as neighborhood recreational facilities or destinations/routes, there was insufficient data to examine the relationship with T2DM outcomes. A larger body of research on built environment and diabetes has been conducted in countries outside of the U.S ( 110 – 112 ). In these studies, neighborhood physical activity (PA) environments, specifically better walkability of neighborhoods and access to greenspace, have been consistently associated with lower risk of T2DM and better outcomes ( 115 , 116 ). Numerous studies have been conducted on walkability measured by macroscale aspects of the neighborhood, including higher population density, land use mix, and aesthetics, to microscale aspects, including sidewalks, street connectivity, and street safety. A review by Bilal et al. ( 115 ) on walkability and diabetes incidence and prevalence found that more walkable neighborhoods are associated with a lower incidence and prevalence of T2DM. Similarly, Twohig-Bennett and Jones ( 117 ) conducted a systematic review and meta-analysis examining the relationship to diabetes outcomes of “high” and “low” exposure to greenspace in neighborhoods (defined as open, undeveloped land with natural vegetation and/or spaces such as parks and tree-lined areas). The meta-analysis, representing 462,220 participants, showed an association of high exposure with reductions in the incidence of T2DM (OR 0.71, 95% CI 0.61–0.85) ( 117 ). After decades of research, many built environment factors related to PA and obesity risk have been identified for consideration in urban planning ( 118 ).

Neighborhood-Level Interventions on the Built Environment and Diabetes Outcomes.

Because it is often not feasible or ethical to randomize neighborhoods to receive certain structural interventions, natural experiment designs are used in which the researcher does not control or withhold intervention allocation to particular areas; rather, natural or predetermined variation in allocation occurs, often as a result of policy intervention ( 119 ). Several review articles of natural experiments summarize the benefits of policy and built environment changes on obesity-related outcomes ( 112 ) and diet and PA outcomes ( 120 , 121 ). The strongest diet-related studies were those that evaluated regulations to the food environment, and the strongest PA-related studies were those that improved infrastructure for active transport. Although this literature does not directly address diabetes outcomes, improvements in obesity and diet and PA behaviors are relevant to populations with diabetes and warrant rigorous evaluation ( 122 ).

Toxic Environmental Exposures

Toxic environmental exposures can be naturally occurring (e.g., arsenic in private wells, radon) or introduced into the environment through human activity (e.g., pollution, synthetic pesticides) ( 123 ). Marginalized communities in the U.S. are disproportionately exposed to environmental agents that have evidence of an association with diabetes, including air pollution, environmental toxicants, and ambient noise ( 124 – 129 ), and subgroups that generate the least pollution have highest exposures ( 130 ).

Factors contributing to inequities in toxic environmental exposures include residential segregation and inequity in goods and services, due in part to systemic racism in environmental regulation and opportunities ( 128 , 130 – 133 ). Explanatory factors are closer proximity of underserved neighborhoods to nearby pollution sources, poor enforcement of regulations, and inadequate response to community complaints ( 134 – 138 ). In rural and suburban communities, including Native American Indian communities, unregulated private wells are a source of water contaminants including arsenic and other metals/metalloids, pesticides, and hazardous chemicals, affecting millions of people ( 139 – 141 ). Both food packaging and fast-food consumption, which can be high in low-income neighborhoods, can expose people to chemicals known to be endocrine disrupters ( 142 – 145 ). Examples include chemicals released from plastic packaging during microwave heating ( 142 ), higher urinary phthalate levels associated with fast food ( 145 ), and higher urinary bisphenol A levels from canned foods ( 146 ). Certain personal care and cosmetic products, which are a source of phthalates and metals (e.g., skin-lightening products, which are high in mercury), are disproportionately marketed to marginalized population subgroups ( 147 ).

Associations of Environmental Risk Factors With Diabetes Incidence, Prevalence, and Outcomes.

In 2011, the National Toxicology Program at the National Institute of Environmental Health Sciences convened an international workshop to evaluate the experimental and epidemiologic evidence on the relationship of environmental chemicals with obesity, diabetes, and metabolic syndrome ( 148 – 150 ). Evidence was deemed strongest for arsenic, with relative risks of diabetes found to range from 1.11 to 10.05 in different studies (median 2.69) at high arsenic exposure levels. More recent systematic reviews and meta-analyses present the growing literature examining multiple groups of chemicals ( 148 , 151 ) or specific groups of chemicals ( 152 – 154 ). Overall, the evidence supports an increased risk of diabetes in populations exposed to environmental chemicals including arsenic, persistent organic pollutants, phthalates, and possibly bisphenol.

In 11 prospective studies of air pollution exposure and incident diabetes in adults, the pooled hazard ratio (HR) (95% CI) per 10 µg/m 3 increment particulate matter of <2.5 µm aerodynamic diameter was 1.10 (1.04–1.17) ( 155 ). Other reviews have reached conclusions consistent with this increased diabetes risk finding ( 156 – 158 ). The epidemiologic evidence is also supported by animal experiments showing that air pollution exposure can increase susceptibility to insulin resistance and T2DM ( 159 – 161 ). These findings highlight that populations more exposed to air pollution are also disproportionately at risk for developing diabetes.

There is epidemiologic and experimental evidence that environmental exposures increase susceptibility to cardiovascular disease (CVD) in people with diabetes. The evidence is extensive for air pollution exposures ( 162 , 163 ). For example, in Medicare patients, a daily increase of 10 µg/m 3 in particulate matter <10 µm of aerodynamic diameter was associated with 2.01% increase in CVD hospitalizations for those with diabetes compared with 0.94% increase among those without diabetes ( 162 ). Short-term increases in air pollution exposure are also related to higher risk of stroke mortality in patients with diabetes compared with those without ( 164 ). In an experimental model, mice with diabetes exposed to diesel exhaust particles showed increased cardiovascular susceptibility compared with mice without diabetes ( 165 ). In natural experiments in human populations, air pollution exposure also resulted in increased vascular reactivity ( 166 ) and inflammation in patients with diabetes compared with those without ( 167 ). In addition to air pollution, some evidence is also available for metals. In the Strong Heart Study of American Indian adults followed since 1989–1991, the risk of CVD associated with higher exposure to arsenic and cadmium was higher among participants with diabetes compared with those without diabetes ( 168 , 169 ). In a clinical trial in patients with a previous myocardial infarction (Trial to Assess Chelation Therapy [TACT]), the beneficial effects of repeated chelation with disodium edetate on cardiovascular outcomes were greater in patients with diabetes ( 170 ).

Environmental Exposures Interventions and Diabetes.

Few studies have evaluated the effect of population-based or clinical interventions related to environmental exposures and diabetes prevention or control. The increased risk of diabetes in populations exposed to environmental chemicals and the increased susceptibility for diabetes complications in individuals with diabetes exposed to air pollution potentially provides an opportunity for prevention and treatment that can be particularly relevant for the most vulnerable populations. For example, a comparison of preterm births among four studies in different countries, before and after the implementation of smoke-free legislation, has shown reductions in diabetes risk (pooled risk change −18.4%, 95% CI −18.8 to −2), although the long-term benefits have not yet been evaluated ( 171 ).

Because individuals generally have limited control over environmental agents, the most effective interventions will be at the population level, through policy and regulation, with a particular focus on protecting marginalized and underserved populations. There is evidence that declines in air pollution levels and metal exposures have contributed to improvements in CVD development ( 172 , 173 ); benefits for diabetes development are pending. Research is also needed to test intermediate strategies at the clinical level, such as exposure screening (e.g., asking about living near highways or using private wells for drinking water) and recommendations to test air or water, reduce known sources of exposure (e.g., minimize packaged foods, avoid heating food in plastic containers, and minimize use of certain cosmetic products), and make home interventions (e.g., install filters for air or water contaminants) ( 174 – 176 ).

Food Environment and Diabetes

The food environment can be defined as the physical presence of food that affects a person’s diet; a person’s proximity to food store locations; the distribution of food stores, food service, and any physical entity by which food may be obtained; or a connected system that allows access to food ( 177 ). It is the “collective physical, economic, policy and sociocultural surroundings, opportunities and conditions that influence people’s food and beverage choices and nutritional status” ( 178 ). It is also referred to as the community food environment (e.g., number, type, location, and accessibility of food outlets such as food stores, markets, or both) and the consumer-level environment (e.g., healthful, affordable foods in stores, markets, or both), which interact to affect food choices and diet quality ( 179 , 180 ). Key dimensions of the food environment include accessibility, availability, affordability, and quality ( 181 – 184 ). These factors, which define the quality of the food environment, are of particular importance in marginalized communities, which may have poor access to supermarkets and healthy foods but abundant access to fast-food outlets and energy-dense foods and are often disproportionately impacted by physical hazards (e.g., vacant houses, traffic, and crime) ( 78 ). At their root, differences in the food environment can be caused by government policies and incentives, and the legacy of such policies as redlining and segregation.

Associations of Food Environment With Diabetes Incidence, Prevalence, and Outcomes

Food access and availability..

Cross-sectional studies have shown associations between food access, availability, geographic characteristics, and T2DM prevalence. Ahern et al. ( 185 ) examined 3,128 counties across the U.S. for food access (assessed as percent of households with no car living more than one mile from a grocery store) and food availability (assessed as number of fast-food restaurants, full-service restaurants, grocery stores, convenience stores, and per capita sales in dollars from local farms made directly to consumers). Higher access to food was associated with lower T2DM rates in metro and nonmetro counties, and higher availability of full-service restaurants and grocery stores and lower availability of fast-food and convenience stores were associated with lower diabetes rates ( 185 ). Haynes-Maslow and Leone ( 186 ) similarly found availability of full-service restaurants to be associated with lower prevalence of diabetes in adults and availability of fast-food restaurants generally to be associated with higher diabetes prevalence. However, the study reported variability in associations among numerous food environment characteristics based on county composition (low poverty/low minority, low poverty/medium minority, high poverty/low minority), highlighting complexities in understanding patterns among variables of county socioeconomic status, demographics, food availability, and diabetes prevalence ( 186 ).

Several observational, longitudinal studies report neighborhood resources in general, and access and availability of the food environment in particular, as associated with diabetes prevalence and incidence ( 187 ). A systematic review by den Braver et al. ( 188 ) found availability of fast-food outlets and convenience stores to be associated with higher T2DM risk/prevalence and perceived healthfulness of the food environment to be associated with lower diabetes risk/prevalence, but no association was found between density of grocery stores and T2DM risk/prevalence. Heterogeneity across the studies prevented the conduct of meta-analyses. Gabreab et al. ( 189 ) examined neighborhood, social, and physical environments and T2DM in 3,700 African Americans through the Jackson Heart Study and found higher density of unfavorable food stores was associated with a 34% higher T2DM incidence after adjusting for individual-level risk factors. In a longitudinal employee cohort, Herrick et al. ( 190 ) found that living in a zip code with higher supermarket density was associated with a reduction in T2DM risk, while zip codes with a higher percentage of poverty and zip codes with higher walkability scores were both associated with higher diabetes risk. Christine et al. ( 191 ) reported long-term exposure to residential environments that offer resources to support healthy diets and PA was associated with a lower incidence of T2DM, although results varied by measurement method.

Studies have also examined both food and PA environments in combination and diabetes risk. Meyer et al. ( 192 ) combined measures of neighborhood food and PA environments and weight-related outcomes ( N = 14,379) of the Coronary Artery Risk Development in Young Adults (CARDIA) study, examining population density–specific (less than vs. greater than 1,750 people per square kilometer) clusters of neighborhood indicators: road connectivity, parks and PA facilities, and food stores/restaurants. In lower–population density areas, higher food and PA resource diversity relative to other clusters was significantly associated with higher diet quality ( 192 ). In higher–population density areas, a cluster with relatively more natural food/specialty stores, fewer convenience stores, and more PA resources was associated with higher diet quality. Neighborhood clusters were inconsistently associated with BMI or insulin resistance and not associated with fast-food consumption, or walking, biking, or running ( 192 ). Tabaei et al. ( 193 ) examined associations of residential socioeconomic, food, and built environments with glycemic control in adults with diabetes ascertained from the New York City A1C Registry from 2007 to 2013. Individuals who lived continuously in the most advantaged residential areas, including greater ratio of healthy food outlets to unhealthy food outlets and residential walkability, achieved increased glycemic control and took less time to achieve glycemic control compared with the individuals who lived continuously in the least advantaged residential areas ( 193 ).

Food Affordability.

Kern et al. ( 194 ) note that it is reasonable to expect that large differences in price between healthy and unhealthy foods would lead to differences in purchasing patterns and resulting diets and that those differences would be more prominent for individuals of lower SES. In a longitudinal study, they examined food affordability and neighborhood price of healthier food relative to unhealthy food and its association with T2DM and insulin resistance. Higher prices of healthy foods relative to unhealthy foods were found to be associated with lower odds of having a high-quality diet; however, there was no association with diabetes incidence or prevalence ( 194 ). More studies are needed in this area.

Food Insecurity.

Food insecurity is defined as not having adequate quantity and quality of food at all times for all household members to have an active, healthy life ( 195 , 196 ). Approximately 20% of diabetes patients report household food insecurity ( 197 ), and food insecurity is a risk factor for poor diabetes management ( 196 ). Researchers have investigated several pathways through which food insecurity may worsen T2DM outcomes ( 198 – 200 ). First, in the nutritional pathway , food insecurity is associated with lower diet quality ( 201 ), which is in turn associated with higher HbA 1c . Food insecurity incentivizes more affordable, energy-dense foods that can directly raise serum glucose (e.g., refined carbohydrates, processed snacks and sweets, sugar-sweetened beverages, etc.) and may lead to greater insulin resistance ( 202 , 203 ). Conversely, low or inconsistent food availability can increase risk of hypoglycemia. Second, via a compensatory pathway , behavioral strategies necessary to cope with the immediate problem of food insecurity can inadvertently undermine T2DM management. For example, financial resources that might otherwise have been used for medications or diabetes care supplies are diverted to meet dietary needs ( 197 , 204 – 206 ). Third, through the psychological pathway , the state of food insecurity, in which meeting basic needs is outside an individual’s control, undermines self-efficacy and increases depressive symptoms and diabetes distress ( 207 – 210 ). Several studies have reported a relationship between food insecurity and adverse diabetes outcomes ( 211 , 212 ), and a review by Barnard et al. ( 213 ) has suggested that food insecurity among patients with and at high risk for T2DM may be particularly toxic because, in addition to issues of accessing sufficient calories overall, the dietary quality of the foods eaten is even more important than for the general population. Several cross-sectional studies report a relationship between food insecurity and T2DM diabetes outcomes ( 214 – 216 ), including poor metabolic control ( 217 , 218 ), experience of severe hypoglycemia in low-income and low-education samples ( 218 ), lower diabetes self-management behavioral adherence and worse glycemic control ( 219 ), and increased outpatient visits but not increased emergency department/inpatient visits ( 95 , 212 ).

Food Environment Interventions and Diabetes

Three studies reported food bank and pantry interventions with food insecure clients with T2DM ( 196 , 220 , 221 ). Seligman et al. ( 196 ) conducted a pilot program in Texas, California, and Ohio with a pre/post design, encompassing provision of diabetes-appropriate food, blood glucose monitoring, self-management support, and primary care referrals. The study resulted in improvements in HbA 1c , fruit and vegetable consumption, self-efficacy, and medication adherence. In a randomized controlled trial of the intervention, Seligman et al. ( 220 ) found improvements in nutritional consumption, food security, and distress but no clinical changes. Palar et al. ( 221 ) found reduction in BMI but not HbA 1c and better nutritional and psychosocial outcomes.

Studies have examined effect of supermarket gain or loss on T2DM outcomes. A study conducted within the setting of the Kaiser Permanente Northern California Diabetes Registry linked clinical measures to metrics from a geographic information system based on participants’ residential addresses ( 115 , 222 , 223 ). Results over 4 years of tracking supermarket change in low-income neighborhoods showed that relative to no change in supermarket presence, supermarket loss was associated with worse HbA 1c trajectories, especially among those with highest HbA 1c . Supermarket gain in neighborhoods was associated with marginally better HbA 1c outcomes, but only for those with near-normal HbA 1c baseline values ( 223 ). In a natural experiment design, the Pittsburgh Hill/Homewood Study on Eating, Shopping, and Health (PHRESH) tested the effects of adding a supermarket, along with other neighborhood investments, on cardiometabolic risk factors among a randomly selected cohort of residents from two low-income, urban, and predominately African American matched neighborhoods ( 222 , 224 ). Results for the intervention neighborhood (receiving the supermarket) showed improved perceived access to healthy food ( 225 ), and the prevalence of diabetes increased less in the neighborhood with the supermarket than in the comparison neighborhood. Since the initiation of the supermarket, many other investments including greenspace, housing, and commercial spaces have been implemented in the intervention neighborhood ( 226 ). Results of these neighborhood investments on measured BMI, blood pressure, HbA 1c , and HDL cholesterol will be forthcoming. In sum, food environment factors of food unavailability, inaccessibility, and insecurity each demonstrate associations with worse diabetes risk and outcomes, and interventions including diabetes-targeted food and self-management care at food banks and pantries and increasing grocery store presence in low-income neighborhoods are few, but collectively they demonstrate the potential to impact diabetes risk, clinical outcomes, and psychosocial outcomes.

Health Care and Diabetes

Health care as a SDOH includes access, affordability, and quality of care factors. In the U.S., these factors are highly correlated with race/ethnicity, SES, and place/geographic region ( 19 ).

Associations of Health Care With Diabetes Incidence, Prevalence, and Outcomes

In population-based studies, having health insurance is the strongest predictor of whether adults with diabetes have access to diabetes screenings and care ( 227 ). Uninsured adults in the U.S. population have a higher likelihood of having undiagnosed diabetes than adults with insurance ( 228 ). Compared with insured adults with diabetes, the uninsured have 60% fewer office visits with a physician, are prescribed 52% fewer medications, and have 168% more emergency department visits ( 229 ). Liese et al. ( 230 ) found that, among adolescents and young adults with T1DM or T2DM, compared with having private insurance, having state or federal health insurance was associated with higher HbA 1c values by 0.68%, and having no insurance was associated with higher HbA 1c by 1.34%. Having insurance has also been found to attenuate associations of financial barriers with higher HbA 1c ( 231 ).

Geographic access to adult and pediatric endocrinologists varies substantially by state and county in the U.S ( 232 ), with disparities in access in many of the geographic regions with highest diabetes prevalence and socioeconomic disadvantage ( 232 , 233 ). Similarly, factors that increase odds of having a diabetes self-management education program in a geographic area include a higher percentage of the population with at least a high school education, a higher percentage of insured individuals, and a lower rate of unemployment ( 234 ). DeVoe et al. ( 235 ) found that among adults with diabetes, having both insurance and a usual source of care, rather than one or the other, conferred the greatest odds of receiving at least minimum diabetes health care. Being uninsured and without a usual source of care was associated with three to five times lower odds of adults receiving an HbA 1c screen, blood pressure check, or access to urgent care when needed ( 235 ). Among adolescents and young adults with diabetes who had state or federal health insurance, not having any usual source of provider (primary care or diabetes specialist) was associated with higher HbA 1c than having a usual source of provider, and HbA 1c was similar whether in primary care or specialist care ( 230 ).

Affordability.

On average, health care costs of people with diabetes are 2.3 times those of people without diabetes ( 229 ). Approximately 14% to 20% of adults with diabetes report reducing or delaying medications due to cost ( 236 – 238 ). Among adults with diabetes who are prescribed insulin, rates may be >25% ( 236 , 239 ). Cost-related or cost-reducing nonadherence (CRN) is associated with income, insured status, and type of insurance. Adults with diabetes with an annual household income of <$50,000 are more likely to engage in CRN than their counterparts with income ≥$50,000, and uninsured adults with diabetes are more likely to engage in CRN than those with insurance ( 236 ). Within a diabetes clinic population of adults with T1DM or T2DM prescribed insulin, odds of CRN were three times higher for those with Medicaid or no insurance compared with those with Medicare ( 239 ). Piette et al. ( 240 ) found differences based on health system model. Compared with VA patients with diabetes, risk of CRN was found to be almost three times higher for privately insured patients and four to eight times higher for patients with Medicare, Medicaid, or no health insurance ( 240 ). Higher financial stress, financial insecurity, and financial barriers are associated with likelihood of CRN ( 231 , 238 ). People with CRN experience poorer diabetes management, higher HbA 1c , and decreased functional status ( 231 , 240 ). Deaths have been reported from insulin CRN among youth and adults with T1DM ( 241 ).

Having insurance is the strongest single predictor of whether adults with diabetes are likely to meet individual quality measures of diabetes care ( 242 ). Sociodemographic disparities in care quality are well documented in national reports and recommendations ( 2 ) and appear to remain consistent over time ( 243 ). In a U.S. population-based study of achievement of a composite diabetes treatment goal from 2005 to 2016, data from 2013 to 2016 showed that non-Hispanic Blacks had lower odds of achieving a composite diabetes quality measure than non-Hispanic Whites (adjusted OR 0.57, 95% CI 0.39–0.83), and women had lower odds than men (adjusted OR 0.60, 95% CI 0.45–0.80), with no improvement in diabetes treatment gaps from prior time periods (2005–2008 and 2009–2012), especially for minorities, women, and younger adults ( 227 ). Within insured settings, disparities have been reported among Blacks as compared with Whites—in measures including dilated eye exam taken; LDL test taken; LDL, blood pressure, or HbA 1c control; and statin therapy ( 244 – 246 ). A study of 21 VA facilities found Blacks with diabetes were more likely than Whites with diabetes to receive care at lower-performing facilities overall, which explained some racial differences in diabetes quality measures ( 246 ).

Health Care Interventions and Diabetes

Community health workers..

Several systematic reviews have concluded that community health worker (CHW) interventions using trained lay workforces are effective for multiple outcomes in underserved African American and Hispanic adults with T2DM and comorbid conditions ( 247 – 250 ). CHWs have been integrated into care delivery ( 251 , 252 ) with reimbursement in some states ( 253 ). Roles of CHWs include patient navigation, appointment scheduling, visit attendance, patient education, home-based monitoring, assessment of social needs and connection with social services, social support, and advocacy ( 252 , 254 ). Reported outcomes include better diabetes knowledge and self-care behaviors, increased quality of life, reduced emergency visits and hospitalizations, reduced costs, and modest improvements in glycemic control ( 247 – 250 , 255 ), using home-based or integrated health team delivery models ( 252 , 256 ). A majority of the CHW interventions designed for adult populations with diabetes have been diabetes-focused in content and goals and have utilized structured curricula ( 254 ); however, one series of studies reported use of a standardized, all-condition CHW intervention and found modest gain in diabetes outcomes along with additional health benefits ( 257 , 258 ).

Organizational Interventions.

Systematic reviews report improvements in quality of diabetes care among racial/ethnic minorities resulting from quality improvement employing health information technology (i.e., patient registries in the electronic health record, computerized decision support for providers, reminders, centralized outreach for diabetes patients overdue for specific services) ( 245 , 259 , 260 ). There is also evidence of effectiveness of self-management interventions delivered directly to underserved patients with diabetes when interventions are designed to overcome barriers. For example, the Centers for Medicare & Medicaid Services (CMS)-sponsored National Diabetes Prevention Program (DPP) Medicaid demonstration found CDC-recognized DPP lifestyle change programs were effective in achieving performance measures among Medicaid recipients in Maryland and Oregon, and additional strategies (i.e., transportation assistance and child care) facilitated the high retention reported over the 12 months of DPP visits ( 261 ). In a series of studies, a problem-based self-management training addressing multiple life barriers to care in low-income and minority populations was adapted for low literacy and prevalent diabetes-related functional limitations (e.g., low vision, physical disability, and mild cognitive impairment) that impede self-management education ( 73 , 262 ). The approach has proven effective in improving clinical outcomes (HbA 1c , blood pressure), self-care behaviors, and self-management knowledge and problem-solving skills in low-income, racial/ethnic minority, and rural populations ( 76 , 263 , 264 ).

Studies have examined the impact of the Affordable Care Act (ACA) on insurance coverage and health care access for patients with diabetes ( 265 ). Analyses of NHIS data from 2009 and 2016 found an increase nationwide of 770,000 more adults with diabetes aged 18 to 64 years with health insurance coverage in 2016, with a significant increase in coverage seen among Whites, Blacks, and Hispanics, people with family income <$35,000, and people across educational attainment strata (less than high school and more than high school) ( 266 ). Among people with diabetes in the lowest income strata, the proportion of income spent on health costs decreased significantly from 6.3% to 4.8% ( 266 ). Other studies found increased access to care, diabetes management, and health status among people with diabetes in Medicaid expansion states as compared with their counterparts in non–Medicaid expansion states ( 267 ); increased rates of diabetes detection and diagnosis among Medicaid patients with undiagnosed diabetes in states with Medicaid expansion ( 268 ); and reduction in cost-related medication nonadherence rates and uninsured rates among people with diabetes following ACA ( 269 ).

Social Context and Diabetes

Several multidimensional factors shape the social environment as a determinant of health ( 270 ), including social capital, social cohesion, and social support ( 28 , 29 ). Social capital is defined as the features of social structures that serve as resources for collective action (e.g., interpersonal trust, reciprocity norms, and mutual aid) ( 271 – 273 ). Bonding social capital refers to trusting and co-operative relations between members of a network who see themselves as being similar in terms of their shared social identity; by contrast, bridging social capital refers to aspects of respect and mutuality between people who do not share social identities (e.g., differing by race/ethnicity, social class, age) ( 274 – 276 ). Racism, discrimination, and inclusion versus exclusion are macro-level social capital factors that impact health ( 28 ).

Social cohesion refers to the extent of connectedness and solidarity among groups in a community ( 271 , 277 ) and has two dimensions: reduction of inequalities and patterns of social exclusion of population subgroups from full participation in society ( 278 ) and strengthening of social relationships and interactions ( 279 – 281 ). Social cohesion actions facilitate the goal of keeping the society united, not only through social relations, community ties, and intergroup harmony but also through reducing bias and discrimination toward economically disadvantaged groups within a society, such as women and ethnic minorities ( 28 ).

Social support describes experiences in individuals’ formal and informal personal relationships as well as their perceptions of those relationships. Categories include emotional support, tangible support, informational support, and companionship ( 282 – 285 ). Social support is theorized to operate by either buffering the effects of poor health or by directly impacting health ( 285 , 286 ).

Associations of Social Context With Diabetes Incidence, Prevalence, and Outcomes

A systematic review by Flôr et al. ( 287 ) concluded that social capital was positively associated with diabetes control among different populations, independent of the quality or quantity of social capital. However, the few studies available and variations among populations and measures limit the ability to draw firm conclusions related to dimensions of social capital and whether the association is the same at the individual or neighborhood level ( 272 , 288 – 290 ). Gebreab et al. ( 189 ), using data from the Jackson Heart Study, examined social cohesion, measured as trust in neighbors, shared values with neighbors, willingness to help neighbors, and extent to which neighbors get along. The study revealed higher neighborhood social cohesion was associated with a 22% lower incidence of T2DM ( 189 ). Studies demonstrating the relationship between social support and diabetes have associated increased social support with better glycemic control and improved quality of life ( 291 – 295 ), while lack of social support has been associated with increased mortality and diabetes-related complications ( 291 ).

A number of studies suggest social cohesion, social capital, and social support may influence—or be influenced by—racism and discrimination ( 296 ). Racism interacts with other social entities, creating a set of dynamic, interdependent components that reinforce each other, sustaining racial inequities and promoting both institutional- and individual-level discrimination across various sectors of society impacting diabetes incidence ( 296 , 297 ). For example, Whitaker et al. ( 298 ) documented associations of major and everyday discrimination experiences with incident diabetes among a diverse sample of 5,310 middle-aged to older adults from the Multi-Ethnic Study of Atherosclerosis. The Black Women’s Health Study found that, when compared with women in the lowest quartile of exposure, those in the highest quartile of exposure to everyday racism had a 31% increased risk of diabetes (HR 1.31, 95% CI 1.20–1.42), and women with the highest exposure to lifetime racism had a 16% increased risk (HR 1.16, 95% CI 1.05–1.27); both associations were mediated by BMI ( 298 , 299 ). Further work is needed to understand the multiple ways that the social environment influences inequities in diabetes outcomes.

Social Context Interventions and Diabetes Outcomes

To our knowledge, there is no empirical research on social capital or social cohesion interventions and impact on diabetes outcomes, but a body of literature has examined effects of social support. The systematic review by Strom and Egede ( 284 ) of 18 observational studies of adults with T2DM found that higher levels of social support were associated with outcomes including better glycemic control, knowledge, treatment adherence, quality of life, diagnosis awareness and acceptance, and stress reduction ( 284 ). Lack of social support has been linked with increased mortality and diabetes-related complications in T2DM ( 291 , 295 ). Strom and Egede’s review of 16 social support intervention studies demonstrated improved diabetes-related outcomes (clinical, psychosocial, and/or self-management behavior change) in adults with T2DM, and improvements in clinical outcomes (HbA 1c , blood pressure, lipids) appeared to be unrelated to the source or delivery (i.e., peer support, couples/spouse, or nurse manager).

With regard to preferences—in a study conducted before the coronavirus disease 2019 pandemic—Sarkar et al. ( 300 ) found that, compared with White adults with diabetes, Hispanics with diabetes preferred telephone-based and group support (including promotoras ), while African Americans demonstrated more variability in their preferences (i.e., telephone, group, internet). Reliance on support from family and community tended to be higher in minority populations, while Whites relied more on media and health care professionals ( 300 ).

International and U.S. national committees have convened to provide guidance on SDOH intervention approaches. These expert committee recommendations are not specific to any disease; rather, they are applicable to all conditions and populations of health inequity. Table 3 displays recommendations from the WHO Commission on Social Determinants of Health ( 27 ), the National Academies of Sciences, Engineering, and Medicine (NASEM) (formerly, Institute of Medicine) Committee on the Recommended Social and Behavioral Domains and Measures for Electronic Health Records ( 80 ), the NASEM Committee on Educating Health Professionals to Address the Social Determinants of Health ( 301 ), and the NASEM Committee on Integrating Social Needs Care into the Delivery of Health Care to Improve the Nation's Health ( 5 ).

SDOH intervention recommendations from international and national (U.S.) committees

AHRQ, Agency for Healthcare Research and Quality; NASEM, National Academies of Sciences, Engineering, and Medicine; NIH, National Institutes of Health; CMS, Centers for Medicare & Medicaid Services; PCORI, Patient-Centered Outcomes Research Institute.

SDOH measures.

The WHO recommendations are unique in their emphasis on root-cause, multisector interventions designed to remove the SDOH as a barrier to health equity. The NASEM recommendations are based in the health care sector and, collectively, focus on integration of SDOH into the health care mission, operations, and financial model. Accountable care organizations, value-based purchasing, and shared savings programs could be intentionally designed to support and incentivize health care systems to address patients’ health-related social needs as a strategy to improve health outcomes ( 5 ). The Accountable Health Communities is one current CMS demonstration project examining impact on health care costs of three models for health care response to SDOH through linkages with community services: awareness (screening for social needs within the health care setting and patient referral to services using an inventory of available local community services), assistance (screening, referral, plus navigation to enable access to and use of community services), and alignment (screening, referral, community service navigation, plus partner alignment using a “backbone” organization for capacity building, data sharing among community and health care partners, and scaling of services) ( 302 ). Many health care systems are utilizing electronic medical records and health information exchanges to capture SDOH data and commercially available SDOH algorithms to identify patients at social risk and trigger service referrals ( 303 ). NASEM provided assessment questions to capture SDOH domains and frequencies for assessment ( 304 ) with evidence of feasibility ( 305 ). In addition, Table 4 displays publicly available resources and tools to aid providers in addressing individual patients’ social needs.

Examples of resources on SDOH available for health care organizations and health care professionals

There is SDOH evidence supporting associations of SES, neighborhood and physical environment, food environment, health care, and social context with diabetes-related outcomes. Inequities in living and working conditions and the environments in which people reside have a direct impact on biological and behavioral outcomes associated with diabetes prevention and control ( 12 , 48 ). Life-course exposure based on the length of time one spends living in resource-deprived environments—defined by poverty, lack of quality education, or lack of health care—significantly impacts disparities in diabetes risk, diagnosis, and outcomes ( 12 , 48 , 306 ). Although the review reports SDOH intervention studies for aspects of housing, built and food environment, and health care, there appears to be relatively limited U.S.-based research examining impact on diabetes of interventions designed to target education, income, occupation, toxic environmental exposures, social cohesion, and social capital.

In the U.S., integrating social context into health care delivery has become a priority strategy ( 5 – 8 ). A clinical context alone, however, is too narrow to accommodate systemic SDOH influences. Structural and legal interventions are needed to address root causes driving SDOH ( 27 , 307 ). Similarly, additional emphasis is needed on a next generation of research that prioritizes interventions impacting the root causes of diabetes inequities, rather than compensatory interventions assisting the individual to adapt to inequities ( 18 , 308 ). For example, in the U.S., proficient literacy and resulting health literacy are disproportionately low in marginalized populations and communities ( 42 ), with historical sociopolitical root causes. U.S. antiliteracy laws for Blacks, which prohibited Blacks from being taught to read or write, persisted until the 1930s in some states ( 309 , 310 ), and laws prohibiting African Americans from attending public and private schools Whites attended continued until 1954 and 1976, respectively ( 311 ). Although adapting health materials for low-literacy suitability is an effective intervention to compensate for centuries of legal racial discrimination in educational access and quality, a next-generation intervention might target the education sector and implement delivery of high-quality early education to all within both the public and private school systems and with equitable educational funding for sociodemographic populations. Similarly, while partnerships to bring bags of healthy groceries to low-income families living in food deserts are important to compensate for food deserts, a next-generation approach might target historical redlining and zoning policies that are the root cause of absence of supermarkets and fresh food markets in minority and lower-income neighborhoods ( 312 – 314 ).

The review has limitations. First, the undertaking was designed to summarize literature on the range of SDOH identified as having impact on diabetes outcomes. As such, this article describes findings from systematic reviews and meta-analyses as well as more recent published studies on the named SDOH; it was not designed as a primary systematic review of all published research on the topic. Second are limitations of the research itself, including wide variability in measures and definitions used in studies within an SDOH area, making it more difficult to describe outcomes for an SDOH area in a consistent or uniform manner or to report quantitative outcomes derived from meta-analyses. Third, this review was U.S.-focused; conclusions from SDOH research in other countries, which in some instances may utilize more standardly defined SDOH variables (e.g., occupation) are not part of this initial review. Finally, the many complexities of SDOH and their potentially different pathways and impacts on populations are beyond the scope of this initial review and require attention to specificity in designs of future SDOH research in diabetes.

Recommendations for SDOH research in diabetes resulting from this SDOH review are described in Table 5 and include establishing consensus SDOH definitions and metrics, designing studies to examine specificities based on populations, prioritizing next-generation interventions, embedding SDOH context within dissemination and implementation science in diabetes, and training researchers in methodological techniques for future SDOH intervention studies. By addressing these critical elements, there is potential for progress to be realized in achieving greater health equity in diabetes and across health outcomes that are socially determined.

SDOH and diabetes research recommendations

See accompanying articles, pp. 1 , 8 , 11 , and 188 .

Acknowledgments. The authors express appreciation to Malaika I. Hill and Mindy Saraco of the American Diabetes Association; Elizabeth A. Vrany, Johns Hopkins University School of Medicine; and Shelly Johnson, Washington University in St. Louis, for providing technical assistance for this review.

Funding. F.H.-B. is supported in part by the Johns Hopkins Institute for Clinical and Translational Research (ICTR), which is funded in part by grant UL1TR003098 from the National Center for Advancing Translational Sciences (NCATS), a component of the National Institutes of Health (NIH) and NIH Roadmap for Medical Research. F.H.-B. is also supported in part by NIH National Heart, Lung, and Blood Institute (NHLBI) grant T32HL07180. D.H.-J. is supported in part by NIH National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) grant P30DK092950. M.H.C. is supported in part by NIH and NIDDK grant P30DK092949. T.L.G.-W. is supported in part by NHLBI grant R01HL131531. A.N.-A. is supported in part by National Institute of Environmental Health Sciences grants P42ES010349 and P30ES009089. S.A.B. is supported in part by NIDDK grant K23DK109200.

The findings and conclusion in this report are those of the authors and do not necessarily represent the official position of the Johns Hopkins ICTR, NCATS, NIH, NIDDK, or any other institution mentioned in the article.

Duality of Interest. M.H.C. reports being co-coordinator of the “Bridging the Gap: Reducing Disparities in Diabetes Care National Program Office,” supported by the Merck Foundation, a consultant to the Patient-Centered Outcomes Group, and a member of the Bristol-Myers Squibb Company Health Equity Advisory Board. S.A.B. received personal fees for service on an advisory board about prioritizing food insecurity research topics for the Aspen Institute. T.L.G.-W. received personal fees for service on an advisory board about prioritizing food insecurity research topics for the Aspen Institute. No other potential conflicts of interests relevant to this article were reported.

Author Contributions . F.H.-B. researched data and wrote the manuscript. N.E.A. contributed to writing and reviewing/editing the manuscript. S.A.B. researched data and contributed to writing and reviewing/editing the manuscript. M.H.C. contributed to writing and reviewing/editing the manuscript. T.L.G.-W. researched data and contributed to writing and reviewing/editing the manuscript. A.N.-A. researched data and contributed to writing the manuscript. P.L.T. researched data and contributed to writing and reviewing/editing the manuscript. D.H.-J. researched data and contributed to writing and reviewing/editing the manuscript.

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Emerging role of natriuretic peptides in diabetes care: a brief review of pertinent recent literature.

1. Introduction

2. pathophysiology of heart failure in diabetes, 3. natriuretic peptides and their clinical applications in heart failure, 4. natriuretic peptides for hf risk stratification and management in diabetes.

• The St Vincent’s Screening to Prevent Heart Failure (STOP-HF) study involved 1374 participants at HF risk (including those with diabetes), randomized into usual care or BNP screening groups. Those with BNP ≥ 50 pg/mL received echocardiography and collaborative care. The primary outcome showed a lower prevalence of LV dysfunction in the intervention group (5.3% vs. 8.7%) after a mean follow-up period of 4.2 years. Moreover, the incidence of cardiovascular hospitalizations was lower in the intervention group. BNP-based screening reduced the composite endpoint of incident asymptomatic LV dysfunction with or without newly diagnosed HF [ 28 ].
• The NT-proBNP Selected Prevention of Cardiac Events in a Population of Diabetic Patients without a History of Cardiac Disease (PONTIAC) study included 300 patients with T2DM and NT-proBNP > 125 pg/mL but without any history of cardiac disease. Participants were randomized into a control group (receiving usual care) and an intensified group, which received additional cardiac outpatient care for the up-titration of RAAS antagonists and beta-blockers and was followed up for 2 years. Notably, intensified therapy led to a significant reduction in cardiac hospitalizations/deaths compared to the control group (hazard ratio: 0.351; p = 0.044) [ 29 ].
• In the Examination of Cardiovascular Outcomes with Alogliptin versus Standard of Care (EXAMINE) trial, two NT-pro BNP measurements (6 months apart) in patients ( n = 5380) with T2DM effectively identified those at the highest risk of developing symptomatic HF. Patients with persistently high or increasing NT-pro BNP levels at 6 months had a significantly higher risk of CV death or HF compared to those with consistently low levels or initial high levels that declined [ 30 ].
• The Canagliflozin Cardiovascular Assessment Study (CANVAS) involved 4330 participants with T2DM and either CVD or other risk factors for cardiac events. Plasma NT-proBNP concentrations were measured at baseline, year 1, and year 6, and associations between NT-proBNP levels and various cardiovascular, renal, and mortality outcomes were investigated. NT-pro BNP levels ≥ 125 pg/mL predicted incident hospitalization for HF (hazard ratio 5.40; p < 0.001). Furthermore, it was suggested that elevated NT-pro BNP levels could predict a wide range of deleterious cardiovascular and renal outcomes in T2DM, HF death, and all-cause mortality [ 31 ].
• The Thousand and I study assessed the prognostic significance of elevated NT-pro BNP levels in patients ( n = 960) with type 1 DM with preserved LVEF and without known heart disease. During a median follow-up of 6.3 years, 121 participants experienced major cardiovascular events (MACE) and 51 died. Higher NT-pro BNP levels were linked to poorer outcomes, with adjusted hazard ratios for MACE at 1.56 and 4.29 per Loge increase for NT-proBNP [ 32 ].

5. Analytical and Clinical Considerations in Natriuretic Peptide Testing

6. future prospects and conclusions, author contributions, data availability statement, conflicts of interest, abbreviations.

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Tiwari, D.; Aw, T.C. Emerging Role of Natriuretic Peptides in Diabetes Care: A Brief Review of Pertinent Recent Literature. Diagnostics 2024 , 14 , 2251. https://doi.org/10.3390/diagnostics14192251

Tiwari D, Aw TC. Emerging Role of Natriuretic Peptides in Diabetes Care: A Brief Review of Pertinent Recent Literature. Diagnostics . 2024; 14(19):2251. https://doi.org/10.3390/diagnostics14192251

Tiwari, Dipti, and Tar Choon Aw. 2024. "Emerging Role of Natriuretic Peptides in Diabetes Care: A Brief Review of Pertinent Recent Literature" Diagnostics 14, no. 19: 2251. https://doi.org/10.3390/diagnostics14192251

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Impact of pesticides exposure and type 2 diabetes risk: a systematic review and meta-analysis

• Published: 09 October 2024

Cite this article

• Yang Chen 1 , 2 ,
• Yaqin Deng 1 , 2 ,
• Minjia Wu 1 , 2 ,
• Peixuan Ma 1 , 2 ,
• Wen Pan 1 , 2 ,
• Weiqi Chen 1 , 2 ,
• Lina Zhao 3 &
• Xiaowei Huang 1 , 2  

We conducted a systematic review and meta-analysis of observational studies that assessed the relationship between pesticides exposure and type 2 diabetes. We also examined the presence of heterogeneity and biases across the available studies.

We conducted a comprehensive literature search of peer-reviewed studies published from 2011 to 2023, without language limitations. A random-effects model was employed to calculate the overall odds ratio (OR) and its corresponding 95% confidence interval (CI).

We included 19 studies (n = 12 case-control and n = 7 cross-sectional) for a total of 45,813 participants in our analysis. Our findings revealed a notable correlation between pesticide exposure and type 2 diabetes (non-specific definition) when not limiting pesticide types (OR: 1.19, 95% CI: 1.11–1.28). Subgroup analysis identified associations between pyrethroid (OR: 1.17, 95% CI: 1.05–1.30) and type 2 diabetes, as well as between organochlorine (OR: 1.26, 95% CI: 1.11–1.43) and type 2 diabetes. However, no statistically significant association was observed between herbicide exposure and the onset of type 2 diabetes (OR: 1.26, 95% CI: 0.91–1.75). In the elderly group, pesticide exposure significantly heightened the risk of type 2 diabetes (OR: 1.25, 95% CI: 1.14–1.38), with no statistically significant heterogeneity among studies (I 2  = 14.2%, p  = 0.323).

Conclusions

Pesticide (organochlorine and pyrethroid) exposure constitutes a risk factor for type 2 diabetes.

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This work was supported by the National Natural Science Foundation of China (No. 21966010) and Advance Innovation Teams and Xinghu Scholars Program of Guangxi Medical University.

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Yang Chen, Yaqin Deng, Minjia Wu, Peixuan Ma, Wen Pan, Weiqi Chen & Xiaowei Huang

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Yang Chen: Conceptualization, Data curation, Formal analysis, Methodology, Writing—original draft, Writing—review & editing. Yaqin Deng: Conceptualization, Data curation, Writing —original draft. Minjia Wu: Formal analysis, Methodology.Peixuan Ma: Formal analysis, Methodology.Wen Pan: Validation.Weiqi Chen: Validation.Lina Zhao: Writing—review & editing. Xiaowei Huang: Conceptualization, Funding acquisition, Methodology, Supervision, Writing—review & editing.

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Examining noncommunicable diseases using satellite imagery: a systematic literature review

• Elizabeth J. Folkmann 1 ,
• M. Courtney Hughes 2 ,
• Uzma Amzad Khan 3 &
• Mahdi Vaezi 4  

BMC Public Health volume  24 , Article number:  2774 ( 2024 ) Cite this article

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Introduction

Noncommunicable diseases (NCDs) are the leading cause of morbidity and mortality worldwide, accounting for 74% of deaths annually. Satellite imagery provides previously unattainable data about factors related to NCDs that overcome limitations of traditional, non-satellite-derived environmental data, such as subjectivity and requirements of a smaller geographic area of focus. This systematic literature review determined how satellite imagery has been used to address the top NCDs in the world, including cardiovascular diseases, cancers, chronic respiratory diseases, and diabetes.

A literature search was performed using PubMed (including MEDLINE), CINAHL, Web of Science, Science Direct, Green FILE, and Engineering Village for articles published through June 6, 2023. Quantitative, qualitative, and mixed-methods peer-reviewed studies about satellite imagery in the context of the top NCDs (cancer, cardiovascular disease, chronic respiratory disease, and diabetes) were included. Articles were assessed for quality using the criteria from the Oxford Centre for Evidence-Based Medicine.

A total of 43 studies were included, including 5 prospective comparative cohort trials, 22 retrospective cohort studies, and 16 cross-sectional studies. Country economies of the included studies were 72% high-income, 16% upper-middle-income, 9% lower-middle-income, and 0% low-income. One study was global. 93% of the studies found an association between the satellite data and NCD outcome(s). A variety of methods were used to extract satellite data, with the main methods being using publicly available algorithms (79.1%), preprocessing techniques (34.9%), external resource tools (30.2%) and publicly available models (13.9%). All four NCD types examined appeared in at least 20% of the studies.

Researchers have demonstrated they can successfully use satellite imagery data to investigate the world’s top NCDs. However, given the rapid increase in satellite technology and artificial intelligence, much of satellite imagery used to address NCDs remains largely untapped. In particular, with most existing studies focusing on high-income countries, future research should use satellite data, to overcome limitations of traditional data, from lower-income countries which have a greater burden of morbidity and mortality from NCDs. Furthermore, creating and refining effective methods to extract and process satellite data may facilitate satellite data’s use among scientists studying NCDs worldwide.

Peer Review reports

Noncommunicable diseases (NCDs) account for 74% of global deaths annually, with cardiovascular diseases, cancers, chronic respiratory diseases, and diabetes responsible for over 80% of premature NCD mortalities [ 1 ]. NCDs are not limited to older adults, with 17 million deaths before age 70, predominantly in low- and middle-income countries [ 1 ]. In the United States, direct health costs related to NCDs exceed $1 trillion annually [ 2 ]. Unhealthy behaviors like smoking, poor diet, and physical inactivity increase NCD susceptibility [ 3 ]. The third largest underlying risk factor of chronic disease (after high blood pressure and tobacco usage) is air pollution, an environmental risk factor most often increasing the risk for three of the top four NCDs - cardiovascular disease, cancers, and chronic respiratory diseases [ 4 ].

Achieving the World Health Organization Sustainable Development Goal Target 3.4 of reducing premature NCD mortality by one-third by 2030 [ 5 ] is challenging, with most countries making minimal progress [ 6 ]. Identifying geographic locations with populations most at risk for NCDs is one step toward directing prevention-related policies and programs to achieve this goal [ 7 ]. Satellite technologies offer tools to help identify at-risk geographic locations that overcome limitations of traditional, non-satellite-derived environmental data (e.g., surveys and ground monitoring stations) such as subjectivity and being limited to smaller geographic areas. Satellite data is open source, available on a global scale, and has four resolutions: temporal, spatial, radiometric and spectral [ 8 ]. With over 400 Earth observation satellites orbiting our planet [ 9 ], satellite imagery data, often coupled with artificial intelligence (AI), has shown great promise in advancing areas of research outside of healthcare, such as earth science [ 10 ] and economics [ 11 ], and within health sciences, particularly in infectious disease [ 12 , 13 , 14 ]. While satellite data helps mitigate the problem of traditional environmental data availability, it presents new challenges in understanding what satellite data to use and how to interpret the data. Fortunately, publicly available algorithms, tools, and tutorials exist to help scientists extract, process, and interpret satellite data. Satellite imagery has been less commonly used for analyzing and managing NCDs [ 15 ].

Traditional (non-satellite-derived) environmental measurements have been successfully used in research in the form of surveys (e.g., light at night (LAN), greenspace) and ground monitoring stations (e.g., air pollution), both of which have limitations [ 8 ]. Survey data can be subjective and non-uniform, while ground monitoring station data is limited to areas within a close proximity to a station, most often a developed urban area [ 8 ]. Satellite data has been found to overcome these limitations by its being open source, available on a global scale, and having four resolutions: temporal, spatial, radiometric and spectral [ 8 ].

Satellite data is derived from remote sensors located on satellites. The amount of energy reflected, absorbed, or transmitted by any item on Earth creates a “spectral fingerprint.” Remote sensors can detect a number (specific to the type of remote sensor and called its spectral resolution) of spectral bands, which allows items to be identified by their spectral fingerprint [ 16 , 17 ]. There are two types of sensors: passive sensors (e.g., radiometers and spectrometers operating in the visible, infrared, thermal infrared and microwave electromagnetic spectrum) that measure land and sea physical attributes (e.g., temperature, vegetation properties, aerosol properties, cloud properties) and active sensors (e.g., radar sensors, altimeters operating in the microwave band of the electromagnetic spectrum) that measure vertical profiles of land and sea attributes (e.g., forest structure, ice, aerosols). Satellites have specific orbits and sensor designs that dictate resolution [ 16 ]. How well a remote sensor can distinguish between small differences in energy is called its radiometric resolution, which is the amount of information in each pixel (e.g., 8 bit resolution that can store up to 256 values). Higher resolution means more detail, though this also requires more processing power. Spatial resolution is defined as the size of each pixel. For example, to see buildings you would need 10 m (m) spatial resolution, which represents a 10 m by 10 m square on the ground. Neighborhoods need 20 m spatial resolution, which represents a 20 m by 20 m square on the ground, while regional needs 1 km (km), which represents 1 km by 1 km square on the ground (national: 10 m, continent: 30 km and global: 110 km) [ 16 ]. Spectral resolution is defined by both the number of bands and how narrow the bands are. For example, 3–10 bands is referred to as multispectral, whereas hundreds or thousands of bans are hyperspectral. Temporal resolution is defined as the time it takes the satellite to complete one iteration of its orbit, which is dependent on its orbit, its swath, width and the specific sensor (e.g., Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Terra and Aqua satellite’s temporal resolution is 1–2 days) [ 16 ].

We aimed to provide the first systematic literature review focusing on how environmental factors data collected from satellite imagery has been used to examine risk, incidence, prevalence, or mortality related to an NCD, both methodologically and technically. This review can illuminate resources and methods for using emerging satellite imagery technologies to capture and analyze comprehensive data that can inform NCD prevention and control interventions and policies. By integrating satellite-derived data with ground-based monitoring systems, scientists and policymakers can better understand the risk and distribution of NCDs, allocate resources more effectively, and implement targeted strategies to lessen NCD burden.

This systematic review was registered at PROSPERO (CRD42023433472). We followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guidelines [ 18 ].

Data sources

We conducted a systematic review of literature related to satellite imagery and the top 4 NCDs (cardiovascular diseases, cancers, chronic respiratory diseases, and diabetes) in the world through June 6, 2023. We did not restrict our search to any start date. To gather relevant studies, we searched PubMed (including MEDLINE), CINAHL, Web of Science, Science Direct, Green FILE, and Engineering Village databases. See Additional file 1 for keyword search strings.

Study selection

First, we removed duplicate studies. Next, at least two study authors independently assessed the remaining abstracts based on predetermined inclusion criteria of needing to examine the top four NCDs in the world using satellite imagery. We considered all quantitative, qualitative, and mixed method study designs written in English. Then 2 study authors independently evaluated the full-text articles for inclusion, with discrepancies resolved through discussion. Studies that were not about one or more NCDs, not about satellite imagery, or review articles were excluded. Another way to describe our inclusion criteria is using the PECO (Population, Exposure, Comparator, Outcomes) framework recommended for exploring associations of environmental and other exposures with health outcomes [ 19 ]. Table  1 presents the inclusion and exclusion criteria.

Data extraction

At least two study authors independently extracted information for each study that met the inclusion criteria, including the study aim, disease, geographic level, year of data collection, methods, tools and resources, data extracted from images, measures, results, and findings. The authors discussed and resolved any discrepancies in the extracted data. We assessed the quality of the evidence for each study using the criteria from the Oxford Centre for Evidence-Based Medicine [ 20 ]. The quality of each study was independently graded by two study authors, with any discrepancies resolved through discussion. Following is a description of the quality ratings: 1 for properly powered randomized clinical trials, 2 for well-designed controlled trials without randomization and prospective comparative cohort trials, 3 for case-control studies and retrospective cohort studies, 4 for case series with or without intervention and cross-sectional studies, and 5 for case reports or opinions of respected authorities.

We conducted a qualitative synthesis of satellite data by determining if an association (statistically significant relationship) was found between the satellite data and each study’s dependent variable (e.g., NCD outcome) and explored the authors’ statements about the value of using satellite data. We also recorded statements that included wording about satellite data such as “overcame the problem,” “great tool,” and “enhanced.” We used a spreadsheet so that at least two authors could track these associations and statements and used codes to categorize aspects about the value of satellite imagery for examining NCD outcomes for each article. Our analysis also included all authors reviewing the frequency of the findings and the wording of the statements. Based on this analysis, the authors developed themes about the value of satellite imagery for examining NCDs.

We identified 1,495 articles from our database searches. After applying inclusion and exclusion criteria, 43 studies were selected for inclusion and 1,452 were excluded (Fig.  1 ). Table  2 includes details, quality assessment, and study authors’ statements about satellite value for all the reviewed studies and Table  3 presents the study characteristics, analysis, and data synthesis.

PRISMA flow diagram

The study publication dates spanned over 15 years, 2008–2023, with more than half published within the 5 years before our study search end date. Overall, 70% of the studies were from high-income countries, with over half of those from the United States. The remaining studies were from middle-income countries, and none were from low-income countries. The majority of studies (66%) used satellite data examined at the city, census tract or census block, or county level. About half of the study designs were retrospective cohort and about one-third were cross-sectional. The 12% of studies that fit in the most rigorous study design category for this review (had prospective study designs) all focused on cancer. There were no randomized control trials or case reports. Regarding disease outcomes examined, prevalence was an outcome in over half of the studies (53.5%), incidence in about 30% of the studies, mortality in just under 20%, and disease risk in just under 5% (Table  2 ).

30% of the studies used satellite images from MODIS [ 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 ], an instrument located on NASA’s Aura, Terra, and Aqua satellites, and 16% used unspecified instruments located on NASA’s Landsat satellites [ 24 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 ]. The other major satellite image source was the United States Space Force’s Department of Meteorological Satellite Program (DMSP) [ 46 , 47 , 48 , 49 , 50 , 51 , 52 ], with images used in 21% of the studies. MODIS and Landsat were primarily used to extract air pollution and greenspace data, while DMSP was primarily used to detect LAN. Greenspace was the most frequently extracted data, with nearly half of the studies examining this feature. The next most frequently extracted data was air pollution, appearing in 37.2% of the studies (Table  3 ).

All 4 major NCDs examined—cardiovascular disease, cancers, chronic respiratory disease, and diabetes—appeared in at least 20% of the studies, with chronic respiratory diseases and cancers each appearing in about 40%. Cardiovascular disease, chronic respiratory disease, and diabetes studies heavily used greenspace and air pollution data. All studies using LAN examined cancer outcomes [ 46 , 47 , 48 , 49 , 50 , 51 , 52 ], with 4 of the 7 specific to breast cancer [ 46 , 47 , 50 , 51 ]. DMSP data was from 1996 to 97 and was used as a baseline for determining LAN in 6 [ 47 , 48 , 49 , 50 , 51 , 52 ] of the 7 articles [ 46 , 47 , 48 , 49 , 50 , 51 , 52 ]. Approximately one-third of the cancer studies included air pollution data [ 23 , 25 , 29 , 35 , 53 , 54 ], while less than one-fifth included greenspace data [ 27 , 53 , 55 ] (Table  3 ). Air pollution was the primary data extracted for chronic respiratory disease, closely followed by greenspace. We found the reverse with the studies on cardiovascular disease; greenspace was the primary data extracted, closely followed by air pollution. Greenspace was the primary data extracted for diabetes studies (Fig.  2 ).

The number of studies of each type of satellite data for each noncommunicable disease. “LAN” is “light at night” and refers to ambient light exposure at night. “Extracted features” refers to features extracted using machine learning to inform a machine learning model. “Air pollution” refers to aerosol optic depth measures, particle matter in air with diameter less than 2.5 micrometers, particle matter in air with diameter less than 10 micrometers, “Flood” refers to changes in land and water surface due to rainfall. “Greenspace” refers to normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), photosynthetic vegetation (PV), and soil adjusted vegetation index (SAVI). “Temperature” refers to the land surface temperature (LST) and surface urban heat island (SUHI)

The majority of the articles (60%) used data from earlier than 5 years from the publication date and matched health data year(s) to satellite data year(s). Of the 10 articles [ 26 , 37 , 41 , 45 , 47 , 48 , 49 , 50 , 51 , 52 ] using data from earlier than 10 years from the publication date, 6 used data from DMSP [ 47 , 48 , 49 , 50 , 51 , 52 ], and 3 used data from LandSat [ 37 , 41 , 45 ]. All but 3 articles [ 39 , 48 , 49 ] in the review found associations between the satellite data and NCD outcomes of risk, incidence, prevalence, or mortality. For example, Fan and colleagues [ 38 ] found a positive association between neighborhood greenness and COPD prevalence using the NDVI based on satellite imagery. Two of the studies that found no association used LAN and cancer [ 48 , 49 ]; however, both found that LAN was a valid representation of circadian rhythm disruption. The third article found no association between greenspace in early life and insulin resistance in adolescence [ 39 ] (Table  2 ).

All data extracted in the studies in this review were related to a previously known disease risk factor. That is, no studies introduced a new disease risk factor that had not been established in prior research. Additionally, the majority of the studies included covariates such as sociodemographic factors like age and income level or health-related factors like body mass index (BMI) and smoking. Most studies focused on one type of satellite data, such as greenspace. However, 9% of the studies [ 22 , 24 , 28 , 37 , 38 , 40 , 44 , 53 , 56 ] examined multiple environmental factors extracted from satellites, such as greenspace and air pollution, in studying the incidence [ 28 , 44 ], prevalence [ 22 , 24 , 37 , 38 , 40 , 56 ], and mortality [ 28 , 53 ] of NCDs. Two articles used a type of AI, convolutional neural networks (CNN), to extract numerical features from satellite imaging. One article [ 57 ] used a CNN with t-SNE (t-distributed Stochastic Neighbor Embedding) to verify the capacity of the neural network to extract relevant features related to cancer prevalence. The other article [ 58 ] used a visual geometry group fast convolution neural network (VGG-CNN-F), a network previously described by Chatfield and colleagues [ 59 ] with elastic net regression to prevent overfitting of the model to training data and minimize mean cross-validation error in a study examining obesity prevalence.

All articles used satellite data extracted based on geolocation(s) specific to the population of interest’s location (versus using data from a convenience sample based on data availability) using one or two of a variety of methods (Table  3 ). Existing publicly available algorithms were the primary method for satellite data extraction, with such algorithms used for analyzing 85% of greenspace data (primarily the normalized difference vegetation index, or NDVI) and 50% of air pollution data (such as the multi-angle Implementation of Atmospheric Correction (MAIAC) and deriving aerosol optical depth (AOD) with geographically weighted regression (GWR), primarily for particulate matter 2.5, or PM2.5). Two articles used the Jenk’s Natural Break method algorithm to classify LAN data [ 46 , 47 ]. Image preprocessing methods performed on raw satellite image data to prepare it for further data processing were referenced in just under half of the studies. Examples of image preprocessing are LAN data transformed into radiance [ 49 , 50 , 52 ] or determining image inclusion according to criteria such as 10% or less cloud cover. Image preprocessing calibration, gauging the data with a standard scale, was used in two articles [ 50 , 60 ]. One article calibrated LAN with satellite sensor data to provide average daily radiance [ 50 ] and another calibrated satellite air pollution data with ground data using a land-use regression model [ 60 ]. One-quarter of the studies referenced external resource tools that extract data from satellite images, such as ArcGIS, MERRA-2, NASA’s Giovanni tool, and the Sentinel Application Platform [ 26 , 28 , 29 , 46 , 47 , 51 , 52 , 53 , 56 ]. Five articles used models, three of which were specific to chemistry: WRF-Chem [ 28 ], GEOS-chem [ 30 ], and an unspecified global chemical transport model [ 54 ]. Yuan and colleagues [ 54 ] also used the radiation transfer model and the differential absorption spectroscopy inversion technique. Qu and colleagues [ 31 ] used a spatial-statistical model with the NDVI to derive an estimate of residential greenness, and Qazi and colleagues [ 42 ] used a model for supervised classification. The “satellite data extraction method(s)” section in Table  3 shows the breakdown of satellite data extraction methods used in the studies.

The vast majority (90%) of studies found an association between their dependent variable and the satellite-derived data [ 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 40 , 42 , 43 , 44 , 45 , 46 , 47 , 50 , 51 , 52 , 53 , 55 , 56 , 57 , 58 , 60 , 61 , 62 , 63 , 64 , 65 ] (Table  2 ). For example, both Bauer and colleagues [ 46 ] and Kloog and colleagues [ 47 ] found breast cancer incidence was associated with high LAN exposure. Fan and colleagues [ 38 ] found a significant positive association between COPD prevalence and greenness (NDVI). Another 2% of studies [ 39 ] found value in satellite data but no association with their dependent variable [ 39 ]. In this article, Jimenez and colleagues [ 39 ] found that NDVI is the most widely used satellite-derived indicator of green space and can be used as a longitudinal exposure measure. Furthermore, 58% of the articles stated that satellite data overcomes the difficulties present in research when geographic areas do not have environmental data or the available ground data is sparse [ 21 , 22 , 23 , 24 , 25 , 26 , 28 , 30 , 32 , 33 , 34 , 35 , 36 , 38 , 39 , 42 , 44 , 51 , 53 , 55 , 57 , 58 , 63 , 64 , 65 ]. For example, Prud’homme [ 30 ] (2013) and Yitshak [ 64 ] (2015) specifically noted the limitation of ground data for air pollution, proximity to monitoring stations, and sparse spatial data, which can be overcome by using satellite data. The final two columns of Table  2 shows if the dependent variable and satellite-derived data were related and any claims stated by the study authors about the value of using satellite data. We found two themes regarding the value of using satellite imagery to examine risk, incidence, prevalence, and mortality related to NCDs. The first theme was that satellite data overcomes problems of sparse or missing spatial and temporal data. Traditional environmental data (non-satellite data) is limited by the range of each sensor and the completeness of data. Satellite data complements traditional data and extends the availability of environmental spatial and temporal data to a global scale. The following representative quote illustrates such sentiments:

In particular, we have clearly shown that, thanks to data availability and big data technologies, it is now possible to jointly study heterogenous data, such as health care and air pollution information extracted from satellites. This provides an unprecedented opportunity to improve our understanding of phenomena by extracting unseen temporal and spatial correlations [ 36 ]. Use of satellite images has become a great tool for epidemiology because with this technological advance we can determine the environment in which transmission occurs, the distribution of the disease and its evolution over time [ 44 ].

The second theme was the ability of open-source satellite data to enable studies to be extrapolated to other areas. Traditional data sources are specific to a distinct geographic area, whereas satellite data often allows for (spatial and temporal) data to be available globally. The following representative quote illustrates such sentiments:

The results of studies like ours could be extrapolated to other cities, given that the use of Sentinel 3 satellite images lies within the reach of the entire scientific community, and their use for determining land surface temperature (LST) and surface urban heat island (SUHI) is straight forward [ 53 ]. Our findings will have important public health implications for policy makers when they are planning the size, shape, density and accessibility of surrounding green spaces in living areas [ 38 ].

Satellite imagery has been used in a variety of ways to address NCDs. A key finding of this review is that nearly all studies found an association between at least one type of satellite data and one NCD. Most of the studies used existing publicly available algorithms to extract data from images. Furthermore, a couple of studies [ 57 , 58 ] attempted to harness the power of AI to see beyond predetermined features.

Over half of the studies analyzed satellite data based on geolocation(s) specific to the research population of interest’s location at the city, census tract, or census block level. One reason the authors may have chosen these geographic levels could be because sociodemographic and health-related data, types of data that have been integral to NCD studies [ 3 ], is often available at these levels [ 3 , 66 ]. Furthermore, by adding satellite data at these geographic levels, study authors may have been able to fill gaps in environmental factors when using ground data alone, such as in Allen and colleagues’ [ 35 ] examination of air pollution in the city of Ulaanbaatar, Mongolia. Just one article used satellite data globally; it replicated previous studies that either excluded areas without ground monitoring or were limited by “coarse spatial resolution” [ 23 ].

Over 80% of premature deaths caused by NCD occur in lower-income countries [ 1 ], yet just one-quarter of the studies in our review were from lower-income countries. Satellites orbiting the Earth capture data from across the globe, including areas without robust healthcare data collection, such as lower-income countries [ 8 ]. Using satellite data for these countries could better enable disease surveillance, tracking health trends and risk factors, and informed healthcare decision-making [ 15 , 67 ]. One potential strategy for using satellite data in lower-income countries is identifying trends between certain satellite-derived data and health outcomes in similar countries or regions that have health data available and extrapolating those trends to the lower-income country of focus. A similar method has been succeesfully used with satellite imagery data in the field of economics [ 68 ]. However, a limiting factor for using satellite imagery for health is a lack of proper tools, knowledge, and skills in collecting and analyzing satellite data among researchers, engineers, and government employees [ 69 ]. This is particularly problematic in lower-income countries with fewer educational resources to train people [ 69 , 70 ]. One potential solution to address the lack of training is to use the publicly available governmental and university-based tutorial programs and resources designed to make satellite data easier to use [ 69 , 70 , 71 , 72 ]. Additional file 2 presents resources to help researchers, scientists, and policymakers understand, find, and use satellite imagery data.

Most reviewed articles discussed how satellite data is an asset to NCD research by providing open-access environmental data that surmounts the constraints of ground-based data collection methods and availability. However, it is important that investigators consider tradeoffs between levels of spatial, temporal, and spectral resolution when choosing their satellite remote sensing data sources [ 15 ]. Researchers can use guidance from organizations such as NASA [ 73 , 74 ] and the European Space Agency [ 75 , 76 ] to help determine the optimal scale of data needed for their research. For example, MODIS (on Terra, Aura, Aqua, and Sentinel 1a satellites) is an instrument that produces moderate-resolution images, while Sentinel 2 and 3 instruments produce high-resolution images. The choice between using MODIS or Sentinel 2 or Sentinel 3 would depend on the investigator team’s resources (e.g., computing processing power and available investigator hours) and the data needs (e.g., general greenspace in a city versus specific greenspace by city block) for their research questions.

While satellite imagery has existed for over a half-century [ 77 ], not until recently did scientists have a method to process the vast amounts of data amassed by observing our planet from space [ 78 , 79 ]. AI can quickly and efficiently extract meaningful patterns, trends, and insights from satellite images. Most articles in this review (95%) examined previously known environmental factors (e.g., air pollution, LAN) using satellite image data. The paucity of studies employing AI to analyze satellite image data for NCD research–just two studies in this review–is notable given that other fields (e.g., waste management [ 80 , 81 ], agriculture [ 82 , 83 ], urban planning [ 82 , 84 ], and defense [ 82 , 85 ]) have more readily adopted AI methods to process satellite image data. Within health sciences, satellite imagery coupled with AI has been used to study patterns in infectious diseases [ 12 , 13 , 71 ]. With 241 in-orbit Earth observation satellites registered with the United Nations and that number growing [ 86 ], there is an opportunity to expand the use of AI and satellite data to monitor and analyze NCD risk for informing policy and programmatic decisions to improve noncommunicable disease outcomes.

Several studies used existing algorithms to measure satellite data. These algorithms allowed the researchers to avoid developing a new measure and better enabled comparisons between studies. We recommend that researchers continue testing these existing algorithms and develop and test new algorithms to measure satellite data to help facilitate future satellite imagery-based NCD research. Such algorithms could be constructed by researchers or generated using AI and validated through research studies. The existence of previously validated algorithms may help epidemiologists and other individuals focused on studying NCD conduct more robust satellite data-based studies and avoid the need to create and study the properties of a new measure.

Our study deviated from the original protocol in a few ways. First, due to the quantitative nature of the studies, we ended up using the assessment of quality from the Oxford Centre for Evidence-Based Medicine instead of the Mixed Methods Appraisal Tool. Second, we focused on the top four NCDs in the world because the World Health Organization highlights these four diseases as the deadliest diseases [ 1 ]. Third, we added more technical engineering and environmental databases to our literature search to ensure we captured as many articles as possible that fit our search criteria.

This review has some limitations. First, it was limited in scope to peer-reviewed literature; thus, it could be missing case reports and other grey literature contributions. Second, there is a risk of publication bias in a review of published studies. Third, due to the heterogeneity of the research methods across studies, we did not perform a meta-analysis to quantitatively examine how satellite imagery has been used to address the top NCDs in the world. This limits the depth of the analysis that could be achieved through more rigorous statistical exploration. Fourth, given that most studies are from developed regions, findings are skewed toward higher-income countries. Future studies should explore ways to include more diverse geographical inputs to research using satellite imagery in examining noncommunicable diseases.

Conclusions

Overall, this systematic review found satellite data to be an asset to NCD research. However, given the recent proliferation of satellites and the emerging capabilities of AI, using satellite imagery data to address the global health threat of NCDs has barely scratched the surface, particularly for locations most vulnerable to NCDs, such as low- and middle-income countries. Scientists and policymakers worldwide should take concerted and collaborative action to keep pace with the advancement of satellite imagery to produce better data-driven health outcomes.

Data availability

Data is provided within the manuscript or supplementary information files.

Abbreviations

Artificial intelligence

Aerosol Optical Depth

Body Mass Index

Convolutional Neural Networks

Department of Meteorological Satellite Program

Geographically weighted regression

Light at night

Multi-angle Implementation of Atmospheric Correction

Moderate Resolution Imaging Spectroradiometer

National Aeronautics and Space Administration

• Noncommunicable disease

Normalized Difference Vegetation Index

Particulate matter 2.5

Preferred Reporting Items for Systematic Reviews and Meta-analyses

t-distributed Stochastic Neighbor Embedding

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Folkmann, E.J., Hughes, M.C., Khan, U.A. et al. Examining noncommunicable diseases using satellite imagery: a systematic literature review. BMC Public Health 24 , 2774 (2024). https://doi.org/10.1186/s12889-024-20316-z

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Whole body vibration therapy and diabetes type 2: a systematic review and meta-analysis

Juan fabregat-fernández.

1 Department of Nursing, University of Extremadura, Plasencia, Spain

Vicente Rodríguez-Pérez

2 Department of Nursing and Physiotherapy, Universidad de Salamanca, Salamanca, Spain

Rocío Llamas-Ramos

3 Institute of Biomedical Research of Salamanca (IBSAL), Salamanca, Spain

Ana Felicitas López-Rodríguez

4 Department of Nursing and Physiotherapy, Faculty of Nursing and Physiotherapy, Universidad de León, León, Spain

María Cortés-Rodríguez

5 Department of Statistics, Universidad de Salamanca, Salamanca, Spain

Inés Llamas-Ramos

6 University Hospital of Salamanca, Salamanca, Spain

Adérito Ricardo Duarte Seixas, Escola Superior de Saúde Fernando Pessoa, Portugal

Associated Data

The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.

Vibration platforms have demonstrated systemic effects generated by the use of mechanical vibrations, which are similar to those of any physical activity. The effect that whole body vibration (WBV) generates on the organism could be recommended in Diabetes Mellitus 2 (DM 2) patients.

To systematically review and meta-analyze the available evidence on the effects of WBV on glycemic control in patients with DM 2

Material and methods

Exhaustive bibliographic searches were carried out until October 2023 in different biomedical portals and databases: Public Medline (PubMed), Scientific Electronic Library Online (SciELO), VHL Regional Portal, Cochrane Central and Latin American and Caribbean Literature in Health Sciences (LILACS). Randomized clinical trials based on the effects of Whole Body Vibration on glycosylated hemoglobin levels, with control group and participants were non-insulin dependent were the inclusion criteria. Two reviewers extracted data independently. A third reviewer was available for discrepancies.

Six articles with 223 participants met the criteria and were included in the systematic review; only four of them met the criteria to be part of the meta-analysis. This meta-analysis reveals a positive and significant effect size (μ ê=0.5731), indicating a substantial difference between the groups studied. Although there is some variability between studies (heterogeneity of 30.05%), the overall direction of the effects is consistent. These findings conclusively suggest the presence of a significant influence of the variable evaluated, underscoring the robustness and consistency of the relationship observed in the literature reviewed.

There are no conclusive results due to the lack of data for some variables, which prevents comparison; but WBV may be an effective therapy to improve glycemic control in DM 2 patients. More studies with more patients and longer follow-up are needed.

1. Introduction

Worldwide, 537 million people suffer from diabetes and this data will be increased up to 643 million by 2030 and 783 by 2045; in our country (Spain) the incidence of Diabetes Mellitus type 2 (DM 2) is estimated at 1 in 7 people, equivalent to 10.3% of the population (around 5.1 million adults). This situation generates a great impact on society, as well as concern among health professionals due to its high and ever-increasing prevalence, not to mention the high cost to the health system which could reach USD 966 billion dollars, which represents an increase of 316% over the last 15 years; raising awareness among these people is the key to combating this problem ( 1 ).

Several disciplines have made contributions to the current knowledge about DM 2; however, despite the passage of time, the fundamental pillars in the treatment of a diabetic person are based on diet and exercise, especially in DM 2. In addition, the physical disabilities and comorbidity present in most people with DM 2 are challenges to adherence to physical activity ( 2 ).

Due to the research during the last decades, the etiology, pathophysiological mechanisms, diagnosis and treatment of DM 2 are better understood. Traditionally, vibration exposure has been considered detrimental for causing harm in humans. However, several studies have shown beneficial effects of the application of whole-body vibration (WBV) with low frequencies, low amplitudes and short exposure times ( 3 , 4 ) as a novel treatment for DM 2 patients ( 5 ).

The application of WBV has been associated with favorable changes in hormone levels, strength, power, muscle mass, muscle electrical activity, jumping ability, balance, psycho-physical health and activation at the cortical level, among others ( 6 ). Nevertheless, the effects of WBV on DM 2 are less well known.

Various systematic reviews have evaluated the impact of WBV on glycemic control in patients with DM 2. The results obtained are contradictory. While some studies, conducted in 2016 ( 7 ) and 2019 ( 8 ), suggest a slight improvement in glucose and HbA1c levels, another systematic review published in 2018 ( 9 ) questions the strength of this evidence, pointing out a low methodological quality in the included studies.

Nowadays most WBV studies published are focused on the level of neuromuscular dysfunction ( 10 ) and the muscular system. Muscles are known to react to vibration by contracting and stretching automatically. Vibration produces a muscle contraction reflex, called the tonic vibratory reflex ( 11 – 13 ). This reflex has been related to decreased pain threshold, increased blood circulation ( 14 ) increased hormone secretion, activation of the Golgi tendon organ and inhibition of antagonist muscles ( 4 , 15 – 17 ).

The physiological modifications observed with WBV are analogous to those of any physical activity, having been described in addition to acute changes, chronic adaptations. Adaptation to physical exercise is what determines the positive changes that appear in our organism when performing any sport. The WBV effectiveness could be explained by the fact that, while with training work is performed on a certain number of tissues, with the use of mechanical vibrations the whole body is subjected to vibration, obtaining beneficial effects at a systemic level (16-118-19).

To summarize the current evidence, our objective was to perform a systematic review and meta-analysis of the effects of WBV intervention on blood glucose levels and glycosylated hemoglobin (HbA1c) levels in people with DM 2.

2. Materials and methods

2.1. systematic literature research.

An exhaustive literature search including randomized and controlled clinical trials obtained from the different biomedical portals and existing databases, from January 2019 to October 2023: Public Medline (PubMed), Scientific Electronic Library Online (SciELO), VHL Regional Portal, Cochrane Central Register of Controlled Trials (Cochrane CENTRAL) and Latin American and Caribbean Literature in Health Sciences (LILACS) has been conducted. Search terms included were Type 2 Diabetes Mellitus , physical exercise , whole body vibration and glycosylated hemoglobin or HbA1c .

The databases were searched using Boolean operators such as: “AND” and “OR”. Keywords have been combined with connectors in order to find valid articles for the aim of the present work. The “OR” connector has been used by joining the words that mean almost the same thing “exercise” and “Exercise on vibrating platforms”, and the “AND” connector has been used in order to give greater sensitivity and specificity to the search. No filters have been used and searches were made in “all fields” and “all index” ( Table 1 ).

Table 1

Search strategy.

PICO strategy has been followed as follows:

- POPULATIONS: diabetes type 2 patients

- INTERVENTIONS: whole body vibration therapy

- COMPARISONS: control groups

- OUTCOMES: fasting blood glucose, glycosylated hemoglobin

2.2. Selection criteria

Randomized controlled trials were considered eligible if they met inclusion criteria such as addressing the effects of WBV on glycosylated hemoglobin levels, the WBV intervention had to be at least 8 weeks, at least one control group did not perform WBV, and participants were non-insulin dependent. Exclusion criteria were studies in which individuals had a reported diabetic complication (neuropathy, retinopathy or diabetic peripheral nephropathy), animal studies and studies with insufficient description of WBV. Besides, a manual search was carried out based on the references of the selected articles as well as the references of other articles not included because they are a different type of study than the one selected in the present review.

2.3. Screening, selection and data extraction

Two independent reviewers in a first step carried out the bibliographic research and after that, examined the titles and abstracts of all studies identified through the search strategies. Studies that did not meet the inclusion criteria, based on titles or abstracts, were discarded. The two reviewers analyzed the full text of the remaining studies in a second review independently. If there is any disagreement a third reviewer was available to solve it.

Reviewers independently extracted data from the studies that met the inclusion criteria using a standardized data extraction form. Data extracted were: authors; year of publication; type of platform used; number of individuals forming the study sample; WBV parameters and intervention outcomes; mean and standard deviation of results.

2.4. Assessment of methodological quality and risk of bias

The physiotherapy evidence database (PEDro) scale was used to evaluate the methodological quality and the risk of bias of the randomized clinical trials included. This is a useful tool for assessing trial quality and the total score of this scale is 10 points. Punctuation more than 6 points is considered as a high-quality clinical trial. Sample selection, randomization, blinding (both participants and therapists), initial homogeneity and statistical analysis (intention to treat and comparisons) are included in this scale ( 20 ).

The quality of the included studies was scored by 2 investigators using the PEDro scale. The investigators rated the studies independently scoring from 0 to 10. If there is any disagreement a third reviewer was available to solve it.

Besides, the Van Heuvelen et al. ( 21 ) guidelines has been followed to report the quality of WBV studies. These guidelines encourage the authors to provide a detailed description of the WBV protocol applied (type of platform, frequency and amplitude of vibration, acceleration), the exposure parameters such as the duration and number of sessions as well as the total duration of the training program and the mode of application. In addition, the characteristics of the participants (demographic data, inclusion and exclusion criteria) should be presented. randomization and the existence of control groups should also be controlled, together with a presentation of the results and analysis of the data with statistical methods. finally, the existence of adverse effects and the existence or not of adherence, the interpretation of the results and the recommendations for clinical practice should be presented.

Besides, to test the quality of the meta-analysis the analysis implemented Random Effect Model using Jamovi software ( 22 ).

The Random-Effect Model : The analysis was carried out using the standardized mean difference as the outcome measure. A random-effects model was fitted to the data. The amount of heterogeneity (i.e., tau²), was estimated using the restricted maximum-likelihood estimator ( 23 ). In addition to the estimate of tau², the Q-test for heterogeneity ( 24 ) and the I² statistic are reported. In case any amount of heterogeneity is detected (i.e., tau² > 0, regardless of the results of the Q-test), a prediction interval for the true outcomes is also provided. Studentized residuals and Cook’s distances are used to examine whether studies may be outliers and/or influential in the context of the model. Studies with a studentized residual larger than the 100 x (1 - 0.05/(2 X k))th percentile of a standard normal distribution are considered potential outliers (i.e., using a Bonferroni correction with two-sided alpha = 0.05 for k studies included in the meta-analysis). Studies with a Cook’s distance larger than the median plus six times the interquartile range of the Cook’s distances are considered to be influential. The rank correlation test and the regression test, using the standard error of the observed outcomes as predictor, are used to check for funnel plot asymmetry.

3.1. Study selection

After the initial search, 735 potential studies were found to evaluate its possible inclusion in the systematic review. 131 manuscripts were removed as duplicates. The first screening by title and abstract reduced the sample to 174. A carefully full- text read of these files was made by reviewers and finally 6 records were included in the systematic review and 4 in the meta-analysis due the lack of some values in the two studies removed, which avoid comparisons. Figure 1 shows the Prisma flowchart of the study selection.

Study selection.

3.2. Study characteristics

The sample analyzed in the articles reviewed ranged from 24 to 50 subjects, the mean of the sample being 37.1 patients. The selected articles show that 33.33% of the reviewed studies show the existence of a statistically significant decrease in glycosylated haemoglobin (HbA1c) after exposure to vibratory exercise ( 25 – 27 ). While for the remaining 66.66% ( 28 , 29 ) there is no statistically significant decrease in HbA1c. Fasting blood glucose (FBG), however, decreased in 66.66% of the studies analyzed ( 25 – 27 , 29 ), after the use of WBV, while for 33.33% ( 25 , 30 ) there was no significant decrease.

There are basically 2 types of vibration platforms: vertical platforms or oscillating platforms. Vertical platforms vibrate in a predominantly vertical direction, moving vertically under both feet at the same time. Oscillating platforms vibrate around a horizontal axis, resulting in a simultaneous and symmetrical movement of both sides of the body during exposure ( 31 , 32 ). The most commonly used platform in several studies ( 25 , 26 , 28 ), was the oscillating platform, obtaining positive results with respect to HbA1c and white blood cells (WBC). However, the use of the vertical platform ( 27 ), also concluded with positive effects on HbA1c and WBC results.

The duration of the intervention in the studies analyzed was between 8 ( 29 ) and 12 weeks ( 25 – 28 , 30 ), with a mean of 11.33 weeks. Regarding the rest time between exercises applied in the interventions, it is located in an average of 34 seconds for those that reflect rest time between exercises ( 25 – 27 ). The other authors do not reflect rest time between exercises ( 28 , 30 ). Most researchers propose an exercise frequency of 3 times per week, except Michels et al. ( 30 ) who carried out the intervention with 7 days of vibratory exercises. No study reports on possible adverse effects during the period of vibration work.

In Table 2 , the main results of these articles were showed.

Table 2

Study characteristic of the selected articles.

n, Sample size; WBV, whole body vibration; WBVG, whole body vibration group; CG, Control Group; DM 2, Diabetes mellitus type 2; FBG, fasting blood glucose; AEG, aerobic exercise group; Wk, week; Hz, Hertz; VE, vibration exercise; HbA1c, Glycosylated Hemoglobin; f, Frequency; AE, aerobic exercise; mm, millimeters; min, minutes.

Manimmanakorn ( 25 ) implemented a research with 36 patients with DM 2 divided into two groups. The WBV group performed for 3 times per week, 2 sets of 6 one-minute vibrating squats for 12 weeks and the control group performed their usual physical activity. These six positions were “(a) a deep squat position (knee angle 90o), (b) high squat position (knee angle 125o), (c) high squat position (with raised heels), (d) slight knee flexion 1 (holding hand straps with shoulder flexion), (e) slight knee flexion 2 (holding hand straps with shoulder abduction) and (f) slight knee flexion 3 (holding hand straps with elbow flexion)”. It was established a progression to 40 Hz and 4mm. They found no significant differences in HbA1c or FBG, concluding that WBV did not improve glycemic indices.

Alfonso-Rosa ( 26 ) published an article with 19 subjects with DM 2 exposed to a training based on 8 static and dynamic exercises with an elastic band (warm-up exercise: squat, up and down once with each foot, lunge, heel lift, squat, squat with weight changes, squats held with elastic bands, squat with elastic bands and side with elastic bands) on a vibrating platform at different frequencies (12, 14 and 16 Hz) while the control group continued with their usual activity. After 12 weeks it can be concluded that WBV produces an acute decrease in plasma levels, and the application of WBV at low frequencies is shown to be an effective and safe technique for the co-treatment and management of DM 2.

In this line, Del Pozo Cruz et al. ( 27 ) carried out a study with 50 subjects diagnosed with DM 2 were studied to test the feasibility, safety and efficacy of a whole body vibration intervention for 12 weeks in a primary health care setting performing 8 exercises in Physio Wave 700 oscillating platform (lunge, step up and down, squat, calf raises, left and right pivot, shoulder abduction with elastic bands, shoulder abduction with elastic bands while squatting, arm swinging with elastic bands) and the control group followed standard care. They establish a progression from 30 seconds for exercises and 30 seconds rest during the first month, while during the second and third months the exercise duration increased to 45-60 seconds and 30 seconds of rest and 2 more Hz each 4 weeks. They found a reduction in HbA1c and FGB compared to the control group concluding that the application of WBV in primary care is feasible, safe and effective in improving the glycemic profile.

Besides, Baum et al. ( 28 ) conducted another investigation with 40 non-insulin-dependent adult patients divided into 3 groups analyzed during 12 weeks of training with 3 training sessions per week. One group followed the WVB training with 30 Hz and 2mm from 1 to 9 week and 35 Hz the last weeks, the second, the strength group did the leg extension, seated leg flexion, leg press, seated calf raise, lat pulley, horizontal chest press, butterfly, and rowing exercises (1 set the first 6 weeks and 2 sets the last weeks) and the third group, flexibility group (control group) implemented 8 static exercises (the same progression was done increasing from 1 to 2 sets the last weeks). The main findings were that FBG remained unchanged after training and HbA1c tended to decrease below baseline in the vibration training group, while they increased in the other two intervention groups. Besides, the glucose tolerance improvement in WBV and strength groups. The authors concluded that these results suggest that vibration exercise may be an effective and low time-consuming tool to control glycemic control in patients with DM 2.

Behboudi et al. ( 29 ) selected a sample, 30 diabetic males, who followed a vibration exercise during 8–12-minute sessions of standing and semi-sitting positions at a frequency of 30 Hz and an amplitude of 2 mm, for 8 weeks three times a week. The aerobic exercise implemented 3 walking sessions a week and control group continued their routine activities. They detected no significant differences in HbA1c concentrations or fasting WBC between the conventional aerobic exercise groups and those subjected to vibration.

Finally, Michels et al. ( 30 ) investigated 22 adults with DM 2 who were taking oral antidiabetic agents were divided into 2 groups to submit one of them to 12-weeks intervention of WBV and control group received tips on how to change their lifestyle. After 12 weeks of intervention, they found a significant reduction in HbA1c in the WBV group, but no significant differences in FGB between groups.

3.3. Methodological quality and risk of bias

PEDro scale shows the quality of the randomized clinical trials. In this review, three articles showed high quality with 6, 7 and 9 points. Nevertheless, low methodological quality was shown in the other three articles with scores of 4, 5 and 5 points. In Table 3 , the PEDro Scale assessment of the six selected articles is presented. Given the variability of the data presented in each manuscript and the lack of data for comparison with the other articles in the review, two manuscripts were removed from the meta-analysis.

Table 3

PEDro score of the randomized clinical trials selected.

3.3.1. Quality of studies

Considering Van Heuvelen guidelines most of the articles have not adhered to these lines, so the results should be interpreted with caution ( Table 4 ).

Table 4

Van Heuvelen et al. guidelines.

3.3.2. Random-effect model

A total of k=4 studies were included in the analysis. The observed standardized mean differences ranged from 0.2582 to 1.3338, with most estimates being positive (100%). The estimated average standardized mean difference based on the random-effects model was \hat{\mu} = 0.5895 (95% CI: 0.1782 to 1.0008). Therefore, the average outcome differed significantly from zero (z= 2.8088, p = 0.0050). According to the Q-test, there was no significant amount of heterogeneity in the true outcomes (Q ( 3 ) = 4.2890, p = 0.2319, tau² = 0.0436, I² = 24.6411%). A 95% prediction interval for the true outcomes is given by 0.0092 to 1.1698. Hence, even though there may be some heterogeneity, the true outcomes of the studies are generally in the same direction as the estimated average outcome. An examination of the studentized residuals revealed that none of the studies had a value larger than ± 2.4977 and hence there was no indication of outliers in the context of this model. According to the Cook’s distances, none of the studies could be considered to be overly influential. Neither the rank correlation nor the regression test indicated any funnel plot asymmetry (p = 0.3333 and p = 0.1512, respectively). The funnel plot asymmetry was not assessed because there are less than 10 articles ( Tables 5 , 6 , Figure 2 ).

Table 5

Random-effects model.

Table 6

Heterogeneity statistics.

Forest plot.

4. Discussion

Whole body vibration is a simple, safe and effective non-pharmacological measure to reduce HbA1c and plasma glucose levels in patients with DM 2 whose physical condition prevents conventional aerobic exercise ( 33 ).

Regarding the pharmacological drugs for the treatment of DM 2, they do not inherently exhibit the ability to maintain sustained glycemic control over a prolonged period ( 34 ). In addition, adverse effects such as gastroenteritis, hypoglycemia and weight gain, among others, are undesirable manifestations associated with the use of orally administered pharmacological agents in patients with DM 2. These side effects lead to a decrease in adherence to pharmacological treatment by patients diagnosed with DM2 ( 35 , 36 ). For that reason, moderate-intensity aerobic physical exercise is recognized as an effective non-pharmacological approach to reducing blood glucose levels for people whose physical condition allows it to be performed. However, WBV could be a physical exercise option to reduce blood glucose and HbA1c levels for those who cannot perform aerobic exercise ( 26 – 29 ).

Regular physical exercise increases glucose uptake in activated muscle, which induces increased insulin sensitivity in diabetics. Regular WBV training has been shown to generate muscle adaptation similar to resistance exercise training ( 37 ). Thus, regular WBV training may help diabetic patients to control glucose metabolism ( 25 ).

WBV does not require a specific physical condition, nor prior supervision and may help in physical exercise adherence for patients with DM 2. Moreover, it has demonstrated its efficacy in animal ( 38 – 40 ) and human ( 16 , 41 , 42 ) studies. Some of the participants who underwent the WBV intervention showed a statistically significant reduction in HbA1c levels compared to those who did not receive the intervention.

Some studies ( 26 , 27 , 30 ) point out, it seems that 12 weeks of WBV intervention is sufficient for a statistically significant improvement in both WBC and HbA1c in people with DM 2 compared to those who did not undergo any intervention. This data is similar to those found in another meta-analysis ( 7 ) in which the sample was subjected to aerobic training for 12 weeks, achieving a reduction in HbA1c. On the other hand, authors stated that there is no significant differences in HbA1c concentrations or fasting WBC between the conventional aerobic exercise groups and those subjected to vibration ( 29 ).

However, there is no clear consensus on what the most beneficial parameters for these patients should be. Studies with longer duration and parameters of frequency, intensity and duration should be investigated in the future in order to adapt this therapy to patients with type 2 diabetes.

In this period, Baum et al. ( 28 ) and Manimmanakorn ( 25 ), using a step-up frequency of 30-40Hz, did not show a significant reduction in HbA1c which was only revealed after post-hoc dichotomization. However, Alfonso-Rosa ( 26 ) and Del Pozo-Cruz et al. ( 27 ), using a lower frequency, also of progressive increase (12-16Hz), did show a significant reduction in both HbA1c and WBC. These data coincide with another study ( 31 ), where an increase in the effects was demonstrated in relation to the progressive increase in frequency. The gradual increase in frequency may be the reason for the difference in the results. Therefore, it seems that a low frequency with progressive increase and the number of weeks of intervention play a determining role in obtaining a beneficial effect. On the other hand, Michels et al. ( 30 ), using a constant frequency of 28 Hz, showed a reduction in HbA1c and WBC. Behboudi et al. ( 29 ) using a constant high frequency of 30 Hz for a shorter number of weeks showed no significant differences in HbA1c or fasting glucose, but concentration was higher in the control group.

During exposure, the vibration of the vertical platform moves vertically under both feet at the same time, producing a simultaneous symmetrical movement of both sides of the body, whereas the oscillating platform generates an asymmetrical perturbation of the legs. Vertical platforms work at higher frequencies (between 30 and 50 Hz) than oscillating platforms (between 5 and 30 Hz). In addition, vertical platforms have shown greater chronic effects on strength training and oscillating platforms greater acute effects ( 31 ). However, the limited scientific literature on the use of these platforms in diabetes makes it difficult to determine which is the best choice. Regarding the type of platform used, different studies ( 26 , 28 ) have carried out an intervention using an oscillating type of platform, obtaining positive results with respect to HbA1c and WBC, as in the study by Lythgo et al. ( 43 ). However, in one of the studies ( 25 ), using the same type of platform, they did not obtain positive results for HbA1c, but did obtain positive results for WBC.

On the other hand, two of the studies ( 27 , 30 ) used a vertical type of vibrating platform and found positive effects on HbA1c and WBC results. These data agree with what was announced by another study ( 32 ) where they determined that vibration transmission was higher during vertical vibration compared to oscillating vibration. This could indicate that the type of platform alone is not determinant to assess its effectiveness, more conclusive studies are needed to evaluate the different vibratory devices in people with DM 2.

HbA1C is one of the most determinant parameters in the diagnosis and long-term control of DM. According to the American Diabetes Association (ADA) ( 44 ), an HbA1c > 6.5% is considered a diagnostic criterion for DM, while values < 7.0% HbA1c determine good blood glucose control in the last four months ( 45 , 46 ). Unfortunately, most of the studies using the HbA1chave performed short-term follow-ups, which prevents us from knowing the physiological effects in the long term,

Based on the information offered by the ADA ( 44 ), which considers that maintaining HbA1c levels below 7% acts as a protective factor, helping to manage glycemic imbalance and evaluate the risk of developing, Manimmanakorn ( 25 ) found a slight reduction in HbA1c, after 12 weeks of intervention in subjects with values above 8% using a post hoc dichotomization; in contrast, in a previous study, in which intervention exercises were applied for 8 weeks, no significant improvement in HbA1c was observed ( 47 ).

In relation to the control group, no significant changes in HbA1c levels were observed, according to the data provided by Manimmanakorn ( 25 ). On the other hand, Baum et al. compared the WBV group with a strength and flexibility group. After the vibration intervention, a reduction in HbA1c levels was found in both groups, although not reaching statistical significance ( 28 ). However, other authors, such as Alfonso-Rosa ( 16 ), Del Pozo-Cruz et al. ( 27 ) and Behboudi et al. ( 29 ) indicated that a conventional exercise program resulted in a decrease in HbA1c levels after the intervention period.

These data are in line with those provided by other studies ( 48 ), which show how a conventional training program reduces HbA1c levels.

The WBV guidelines reported by Van Heuvelen et al. ( 21 ) are essential to improve reproducibility and transparency in WBV studies, and their adherence allows for proper assessment of the efficacy and safety of WBV interventions. Follow them ensures that the necessary criteria for robust and replicable research are met. In this sense, all the authors of the selected articles ( 25 – 30 ) have reported the frequency of the intervention, as well as the amplitude, except for Michels et al. ( 30 ), the duration and frequency of the sessions and the total duration of the program, which was established at 12 weeks, except for Behboudi et al. ( 29 ) who applied 8 weeks. The application of the therapy, the results, the statistical analysis and the absence of adverse effects in all the selected articles were described. However, adherence has been variable, with 4 losses reported by Manimmanakorn et al. ( 25 ), 11 by Alonso-Rosa et al. ( 26 ) and del Pozo-Cruz et al. ( 27 ), and 2 by Michels et al. ( 30 ); only Baum et al. ( 28 ) and Behboudi et al. ( 29 ) did not report any losses. The recommendations for clinical practice and future lines of research recommend stratifying patients in the sampling according to the degree of severity of diabetes based on HbA1c or the duration of the disease ( 25 ) and analyzing cost effectiveness ( 27 ) as well as including more patients and longer follow-up ( 30 ) and optimizing frequency, amplitude, and duration of vibration exercises ( 28 ).

Finally, there is no consensus with the conclusions: WBV is applicable, safe and effective in reducing HbA1C and basal blood glucose level ( 26 – 28 , 30 ), while for others ( 25 ) there was no change in FBS or HbA1c, nor for Behboudi et al. ( 29 ) who proposed that insignificant results of the present study can be attributed to the small number of samples and improper time and intensity of exercise. However, whole body vibration can be considered as a better way to exercise in a shorter time for majority of diabetic patients who suffer from obesity and unwillingness to join active physical activities. Because of these results, the effect size of these interventions is small and the clinical implications, although they appear to be promising, should be interpreted with caution.

The review has several limitations. the review was not registered; however, all steps were taken to ensure the reliability, transparency and thoroughness of the process and data. Only full-text registries have been considered; the number of experimental clinical trials using WBV in these pathologies is small and some of the studies do not provide all the data on the variables used like disease duration or some outcomes which prevent the effect comparisons. One of the selected articles has not been submitted a per-review process, therefore, the results obtained in this manuscript should be treated with caution and most of them did not follow the quality guidelines and recommendations exposed by Van Heuvelen ( 21 ). In addition, the sample size has been very diverse and small; there is not a standard protocol or parameters to achieve the best effectiveness of this therapy although most of them agree with a frequency of three times a week for 12 weeks, and the follow-up of the studies has been only focused on the short-term effects of WBV. In addition, it should be noted that the treatments received by the patients of the selected studies were not composed only of WBV treatment, so that the sum of the effects of the prescribed pharmacological treatment and lifestyle may have contributed to the positive effects obtained.

Nevertheless, the clinical implications are promising, being a safe therapy with no adverse effects that can be used in this population as a complement to other therapies or as an alternative for patients who are unable to perform physical activity. In future lines of research larger samples should be recruited as well as similar measurements, platforms and long term follow up to be able to better determine the clinical effects of the use of WBV.

5. Conclusion

The habitual use of vibration platforms with a frequency of 14-16 Hz for 12 weeks is reflected in the literature. These results are encouraging and suggest that WBV may be an effective therapy to improve glycemic control in patients with DM 2. The addition of Whole Body Vibration therapy as a complement for exercise programs could be effective in sedentary people and could be a useful tool for clinicians. However, further studies with more patients and longer follow-up are needed to confirm these findings.

Acknowledgments

To our colleagues who have made this possible.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. University of Salamanca funded this manuscript. This publication is financed by the Ministry of Science, Innovation and Universities.

Data availability statement

Author contributions.

JF-F: Conceptualization, Methodology, Writing – original draft, Writing – review & editing. VR-P: Conceptualization, Funding acquisition, Methodology, Project administration, Writing – original draft, Writing – review & editing. RL-R: Conceptualization, Funding acquisition, Methodology, Project administration, Writing – original draft, Writing – review & editing. AL-R: Investigation, Methodology, Writing – review & editing. MC-R: Data curation, Formal analysis, Writing – review & editing. IL-R: Conceptualization, Investigation, Methodology, Writing – original draft, Writing – review & editing, Project administration.

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.

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