case study of type 2 diabetes

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Nutrient patterns and risk of diabetes mellitus type 2: a case-control study

Morteza haramshahi 1 ,
Thoraya Mohamed Elhassan A-Elgadir 2 ,
Hamid Mahmood Abdullah Daabo 3 ,
Yahya Altinkaynak 4 ,
Ahmed Hjazi 5 ,
Archana Saxena 6 ,
Mazin A.A. Najm 7 ,
Abbas F. Almulla 8 ,
Ali Alsaalamy 9 &
Mohammad Amin Kashani 10

BMC Endocrine Disorders volume 24 , Article number: 10 ( 2024 ) Cite this article

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Backgrounds

Although the significance of diet in preventing or managing diabetes complications is highlighted in current literature, there is insufficient evidence regarding the correlation between nutrient patterns and these complications. The objective of this case-control study is to investigate this relationship by analyzing the dietary intake of nutrients in participants with and without type 2 diabetes (T2D).

A case-control study was conducted at the Tabriz Center of Metabolism and Endocrinology to investigate the relationship between nutrient patterns and type 2 diabetes (T2D). The study enrolled 225 newly diagnosed cases of T2D and 225 controls. The dietary intake of nutrients was assessed using a validated semi-quantitative food frequency questionnaire (FFQ). Principal component analysis using Varimax rotation was used to obtain nutrient patterns. Logistic regression analysis was performed to estimate the risk of T2D.

The participants’ mean (SD) age and BMI were 39.8 (8.8) years and 27.8 (3.6) kg/m2, respectively. The results identified three major nutrient patterns. The first nutrient pattern was characterized by high consumption of sucrose, animal protein, vitamin E, vitamin B1, vitamin B12, calcium, phosphorus, zinc, and potassium. The second nutrient pattern included fiber, plant protein, vitamin D, Riboflavin, Vitamin B5, copper, and Magnesium. The third nutrient pattern was characterized by fiber, plant protein, vitamin A, riboflavin, vitamin C, calcium, and potassium. Individuals in the highest tertile of nutrient pattern 3 (NP3) had a lower risk of T2D compared to those in the lowest tertile after adjusting for confounders. The odds ratio was 0.52 with a 95% confidence interval of 0.30–0.89 and a P_trend of 0.039.

This study found that conforming to a nutrient pattern consisting of plant protein, vitamin C, vitamin A, vitamin B2, potassium, and calcium is linked to a lower likelihood of developing T2D.The initial results suggest that following a nutrient pattern that includes these nutrients may reduce the risk of T2D. However, further research is required to confirm the relationship between nutrient patterns and T2D.

Peer Review reports

Type 2 diabetes is a significant concern for public health in developed nations. It leads to high rates of illness and death and places a significant financial burden on healthcare systems [ 1 , 2 ]. In the past few decades, there has been a sharp increase in the occurrence of diabetes, and is expected to continue increasing, with an estimated 693 million people living with the disease by 2045 [ 1 ]. Complications associated with type 2 diabetes can also contribute to premature death. A concerning aspect of the disease is that a significant proportion of cases (40%) go undetected [ 3 ], and there is also an increasing prevalence of prediabetes, which raises the risk of developing type 2 diabetes and other chronic diseases [ 1 ].

The connection between diet and type 2 diabetes has been extensively studied, including the examination of dietary patterns and individual foods or nutrient patterns [ 4 , 5 , 6 , 7 ]. Various sources have suggested that chronic diseases may be influenced by a combination of nutrients [ 8 ]. In the field of nutritional epidemiology, the examination of dietary patterns has emerged as a viable approach to investigate the correlation between diet and disease. This method involves using statistical techniques to combine multiple foods or nutrients into dietary or nutrient patterns, which are believed to provide a more detailed understanding of the connection between diet and disease. It has been suggested that the impact of individual nutrients or foods on chronic disease may be too subtle to detect, but their collective effect within a pattern may be more indicative [ 9 ].

There have been some recent studies examining the effect of nutrient patterns on chronic disease such as, non-alcoholic fatty liver, breast and gastric cancer, Polycystic Ovary Syndrome (PCOs) and metabolic syndrome [ 10 , 11 , 12 , 13 , 14 ]. For example, it was found that a nutrient pattern consisting mainly of protein, carbohydrates, and various sugars was linked to a higher risk of Metabolic Syndrome (MetS) in both men and women, whereas a pattern characterized by copper, selenium, and several vitamins was linked to greater odds of MetS [ 14 ]. A prospective study conducted among participants of the Tehran Lipid and Glucose Study indicates that a nutrient pattern rich in vitamin A, vitamin C, vitamin B6, potassium, and fructose is associated with a reduced risk of insulin-related disorders [ 15 ]. Although there have been limited investigations on the connection between nutrient patterns and the likelihood of developing diabetes, the present study seeks to explore this relationship by analyzing the adherence to different nutrient patterns and its effect on the risk of type 2 diabetes.

Study population

This study utilized a case-control design and involved participants between the ages of 18 and 60 who had been diagnosed with type 2 diabetes within the previous six months based on specific glucose level criteria (FBS levels of ≥ 126 mg/dl and 2 h-PG levels of ≥ 200 mg/dl [ 17 ]). Healthy individuals within the same age range were also included, with specific glucose level criteria (FBS levels of < 100 mg/dl and 2 h-PG levels of < 200 mg/dl [ 17 ]). The study excluded individuals with certain chronic diseases, Type 1 Diabetes, gestational diabetes, those following specific dietary patterns or taking certain medications, pregnant and breastfeeding women, those with a family history of diabetes or hypertension, and those who did not complete the food frequency questionnaire (more than 35 items) or whose reported energy intake was outside of a specific range (range of 800–4200 kcal [ 18 ]).

This study enrolled 450 adult participants, with 225 individuals in the case group and 225 in the control group. The case group was selected using a simple sampling method from patients diagnosed with diabetes at the Tabriz Center of Metabolism and Endocrinology as a referral center affiliated to tabriz University of Medical Sciences from January 2021 to March 2022, as well as through a two-stage cluster sampling method among patients referred to private endocrinologists to enhance the sample’s external validity. Participants in the control group were also selected through a two-stage cluster sampling method from individuals who had undergone blood glucose checkups at the Tabriz Center of Metabolism and Endocrinology, a referral center affiliated with Tabriz University of Medical Sciences, within the past six months. All participants provided informed consent at the beginning of the study. The study was financially supported by Tabriz University of Medical Sciences and is related to project NO. 1400/63,145.

Dietary assessment

To collect dietary intake information, personal interviews and a semi-quantitative food frequency questionnaire (FFQ) consisting of 168 food items were used [ 16 ]. The FFQ asked about the frequency of consumption for each item over the course of one year, with the year before diagnosis for the case group and the year before the interview for the control group. Participants were also asked about the frequency of consumption (per day, week, month, or year) for each type of food. to ensure consistency in measurements, a nutritionist provided instructions on converting the size of reported food items from household measures to grams using four scales. The quantity of food consumed by each individual was calculated based on their intake in grams and reported on a daily basis. The nutrient composition of all foods was derived by using modified nutritionist IV software.

Nutrient pattern assessment

We conducted factor analyses using a comprehensive set of 34 nutrients, encompassing various macronutrients, micronutrients, and other dietary components. These included sucrose, lactose, fructose, fiber, animal protein, plant protein, saturated fatty acids, monounsaturated fatty acids, polyunsaturated fatty acids, cholesterol, as well as an array of vitamins and minerals such as A, D, E, K, C, thiamine (B1), riboflavin (B2), niacin (B3), pantothenic acid (B5), pyridoxine (B6), folate (B9), B12, calcium, phosphorus, iron, zinc, copper, magnesium, manganese, chromium, selenium, sodium, potassium, and caffeine. The dietary intake of these 34 nutrients per 1,000 Kcal of energy intake was computed and utilized as input variables. Subsequently, nutrient patterns (NPs) were derived through principal component analysis (PCA) with varimax rotation, based on the correlation matrix. Factor scores for each participant were then calculated by aggregating the frequency of consumption and multiplying it by the factor loadings across all 34 nutrients. To assess the statistical correlation between variables and evaluate the adequacy of the sample size, we employed the Bartlett test of sphericity ( P < 0.001) and the Kaiser-Mayer-Olkin test (0.71), respectively.

Assessment of other variables

To obtain the participants’ anthropometric measurements, weight and height were measured using a seca scale, and the participants’ BMI was determined by dividing their weight in kilograms by the square of their height in meters. Waist circumference was measured using a metal anthropometric tape, and the participants’ hip circumference was measured using a metal anthropometric tape while standing [ 17 ]. Daily physical activity was measured using a physical activity questionnaire [ 18 ], and personal questioning was employed to gather information on population and socioeconomic characteristics, including marital status, academic degree, and smoking.

Statistical analysis

Statistical analysis was performed using the Statistical Package Software for Social Science, version 21. The normality of the data was assessed using Kolmogorov-Smirnov’s test and histogram chart. The characteristics and dietary intakes of the case and control groups were presented as mean ± SD or median and frequency (percentages). Independent sample t-tests and chi-square tests were used to compare continuous and categorical variables, respectively, between the case and control groups.

The participants’ mean (SD) age and BMI were 39.8 (8.8) years and 27.8 (3.6) kg/m2, respectively. The mean (SD) BMI in the case group was 30.5 ± 4.1, and in the control group, it was 25.2 ± 3.2 kg/m2. The mean (SD) physical activity in the case group was 1121 ± 611 MET/min/week, and in the control group, it was 1598 ± 940 MET/min/week. There were significant differences in BMI and physical activity between the two groups. The mean (SD) waist circumference in the case group was 109.32 ± 10.28 cm, and in the control group, it was 87.25 ± 9.35 cm. The mean (SD) hip circumference in the case group was 107.25 ± 8.61 cm, and in the control group, it was 91.44 ± 6.17 cm. The study identified three primary nutrient patterns (NPs) with eigenvalues greater than 2. Table 1 displays the factor loadings for nutrient patterns, which accounted for 56.11% of the total nutrient variation. The high intake of sucrose, animal protein, phosphorus, zinc, potassium, calcium, vitamin E, vitamin B1 and vitamin B12 were the distinguishing features of the first pattern. The second nutrient pattern was positively associated with copper, magnesium, fiber, vitamin D, B2, B5 and plant protein but had a negative correlation with lactose and saturated fatty acids. On the other hand, the high intake of fiber, vitamin A, B2, vitamin C, plant protein and potassium were the distinguishing features of the third pattern.

The following are the characteristics of T2D patients compared to the control group, as shown in Table 2 : Higher BMI, More likely to be smokers, Lower physical activity levels, higher FBS, HbA1C, Insulin ( p < 0.05). Other variables did not differ significantly between the two groups ( p > 0.05). Additionally, T2D patients had a greater intake of energy and vitamin B3 but consumed less plant protein, vitamin A, vitamin E, vitamin B2, and zinc ( p < 0.05).

Table 3 summarizes the partial correlation coefficient between NPs and food sources, with NP1 showing a strong positive correlation with low-fat dairy, NP2 with refined grains, and NP3 with fruits and vegetables.

Table 4 demonstrates the relationships between NPs and T2D. After adjusting for age and sex, there was no significant link between each nutrient pattern (NP) and T2D. However, when adjusting for other factors such as BMI, physical activity, smoking, and energy intake, individuals in the highest tertile of NP1 and NP2 did not show a significant association with T2D compared to those in the lowest tertile. On the other hand, those in the highest tertile of NP3 had a lower probability of developing T2D than those in the lowest tertile (OR: 0.52, 95%CI: 0.30–0.89, P_trend = 0.039).

In this study, three major NPs were identified. After adjusting for potential confounders, we observed a significant inverse association between the Third NP and the odds of T2D. The high intake of fiber, vitamin A, B2, vitamin C, plant protein and potassium were the distinguishing features of the third pattern.

Dietary patterns, such as healthy, Mediterranean, traditional, and Western dietary patterns, have recently received significant attention in studying the connection between diet and health. When looking at the relationship between nutrients and disease incidence, it is more challenging to evaluate when considering individual foods and the metabolism of all nutrients together [ 19 ]. It is therefore more effective to take a broader view and consider diet as a whole. Dietary and nutrient patterns can have a greater impact on health than specific nutrients or nutritional groups. There is supporting evidence that links high calorie or high glycemic index foods with an increased risk of T2D. The quality of one’s diet is also associated with the risk, progression, and side effects of T2D [ 20 ]. Establishing a desirable food pattern has become a priority in public health efforts to prevent T2D. By studying dietary and nutrient patterns, we can gain a comprehensive understanding of an individual’s overall diet beyond just the consumption of specific nutrients and food groups. Moreover, it is easier for people to understand health recommendations when presented as dietary patterns rather than focusing solely on individual nutrients [ 19 ].

A previous cross-sectional study investigated the relationship between NPs and fasting glucose and glycated hemoglobin levels among apparently healthy black South Africans. The study stratified 2,010 participants by gender and urban/rural status and identified three nutrient patterns per stratum. In rural women, a nutrient pattern driven by starch, dietary fiber, and B vitamins was significantly associated with lower fasting glucose and glycated hemoglobin levels. A nutrient pattern that included vitamin B1, zinc, and plant protein was linked to notable decreases in glycated hemoglobin and fasting glucose levels in rural men. These findings suggest that nutrient patterns that are plant-based are linked to lower levels of fasting glucose and glycated hemoglobin [ 21 ].

Iwasaki et al. found that specific nutrient patterns were associated with lower risks of MetS. One nutrient pattern high in potassium, fiber, and vitamins, while another pattern high in vitamin B2, saturated fatty acids and calcium [ 22 ]. A recent study found that a nutrient pattern characterized by high intake of calcium, potassium, fats, cholesterol, vitamins B2, B12, A, D, K and C was positively linked to MetS [ 23 ]. Salehi-Sahlabadi et al. found that adhering to a nutrient pattern rich in potassium, vitamin A, fructose, vitamin C and vitamin B6 was negatively associated with the likelihood of NAFLD [ 11 ]. A nutrient pattern high in potassium, vitamin A, vitamin B6, vitamin C and fructose was associated with a reduced risk of hyperinsulinemia, IR, and dyslipidemia among participants in Tehran, according to a prospective study [ 11 , 24 , 25 ].

Due to several variations among studies exploring NPs linked to chronic diseases, including differences in the number of nutrients, populations, study designs and outcomes there has been a considerable diversity in the identified NPs, with only a few NPs being replicated across studies. Our study is the first of its kind to explore the correlation between nutrient patterns and T2D in this context.

In our study, there was no association between NPs 1 and 2 and T2D. This lack of correlation may be attributed to the absence of harmful nutrients or food categories linked to diabetes in these NPs. NP3 in this study, unlike other NPs, is positively associated with beneficial food groups such as nuts, fruits, plant oil and vegetables, and negatively associated with unhealthy food groups like red-processed meat, snacks, high-fat dairy and refined grains. A recent systematic review and meta-analysis found that individuals who consumed higher amounts of fruits and vegetables had a lower risk of developing type 2 diabetes [ 26 ]. Moreover, the consumption of vegetables was found to have an inverse relationship with ALT, TC and LDL levels among adults, while fruit consumption was associated with a positive reduction in visceral fat [ 27 , 28 ]. Another study suggested that an increased intake of vegetables and fruits could potentially lower the risk of MetS [ 29 ]. According to a study, greater nut consumption was significantly linked to a reduced prevalence of T2D [ 30 ]. Consuming fruits and vegetables is a crucial component of a healthful dietary pattern that can lower the risk of type 2 diabetes [ 31 ]. On the other hand, Consuming a Western dietary pattern, which primarily consists of fast foods, high-fat dairy, refined grains, soft drinks and processed meat has been found to be correlated with an increased risk of type 2 diabetes [ 31 ].

Several mechanisms have been identified that explain the positive associations between the components of NP 3 and T2D or its risk factors. Vitamin intake has been shown to play a role in the development of T2D through various pathways. Consuming vitamin C has been found to have beneficial effects in reducing the risk of type 2 diabetes mellitus. These effects can be attributed to the following actions of vitamin C: vasodilator, cytoprotective, platelet anti-aggregator and anti-mutagenic. To achieve this, the body increases the production of several substances including prostaglandin E1, PGI2, endothelial nitric oxide, and lipoxin A4. Additionally, the body restores the Arachidonic Acid content to normal levels [ 32 ]. Vitamin A has a multifaceted role in cell regulation beyond its antioxidant function. It contributes to gene regulation, epithelial cell integrity, and resistance to infection. Research suggests that vitamin A also enhances antioxidant enzyme function in the body. Research has indicated a link between vitamin A deficiency and type 2 diabetes mellitus (T2DM), which suggests that vitamin A may have a role in the biology of T2DM [ 33 ]. Moreover, a meta-analysis has found that replacing animal protein with plant protein can lead to minor improvements in glycemic control for individuals with diabetes [ 34 ]. According to a recent meta-analysis, increasing the consumption of fruits, especially berries, yellow vegetables, cruciferous vegetables, green leafy vegetables is associated with a lower risk of developing type 2 diabetes. These results support the recommendation to incorporate more fruits and vegetables into the diet as a way to prevent various chronic diseases, including type 2 diabetes [ 35 ]. A study showed that maintaining adequate potassium intake could regulate insulin secretion and carbohydrate metabolism, leading to the prevention of obesity and metabolic syndrome (MetS) [ 36 ].

A number of research studies conducted in the Western societies have shown that Western dietary pattern including higher intake of red meat, processed meat, and refined grains is significantly associated with increased risk of T2D [ 37 , 38 ]. For example, in the 12-years cohort prospective study, van Dam et al. investigated dietary pattern of 42,504 American white men at the age range of 40–75 years old using the FFQ. After controlling the confounders, the risk of T2D increased 60% in people adherent to the western-like dietary pattern [ 38 ]. The rapid process of change in lifestyle, diets, and physical activity that have been occurred as a result of extended urbanization, improved economic status, change of work pattern toward jobs, and change in the processes of producing and distributing nutrients during the recent years in developing countries have led people to more consumption of fast food and processed foods [ 20 ].

Significant research has been conducted on the impact of nutrient type and sequence on glucose tolerance. Multiple studies have shown that manipulating the sequence of food intake can enhance glycemic control in individuals with type 2 diabetes in real-life situations. The glucose-lowering effect of preload-based nutritional strategies has been found to be more pronounced in type 2 diabetes patients compared to healthy individuals. Moreover, consuming carbohydrates last, as part of meal patterns, has been proven to improve glucose tolerance and reduce the risk of weight gain [ 39 ]. Recent findings on meal sequence further emphasize the potential of this dietary approach in preventing and managing type 2 diabetes [ 40 ].

Several studies have shown that food from a short supply chain has a significant impact on metabolic syndrome. The length of the food supply chain is important in determining the risk of metabolic syndrome in a population [ 41 ]. Research indicates that people who consume food from short supply chains have a lower prevalence of metabolic syndrome compared to those who consume food from long supply chains. Specifically, food from short supply chains is associated with lower levels of triglycerides and glucose, which leads to a reduced occurrence of metabolic syndrome [ 42 ]. Adhering to the Mediterranean diet with a short supply chain is also found to significantly reduce the prevalence of metabolic syndrome. Therefore, these studies provide evidence that food from short supply chains positively affects metabolic parameters and the occurrence of metabolic syndrome [ 41 ].

The study we conducted presented several advantages. It was the first case-control research to investigate the correlation between nutrient patterns and the likelihood of developing type 2 diabetes (T2D). While numerous studies have explored the relationship between dietary patterns and diabetes, there is a scarcity of research specifically focusing on nutrient patterns in individuals with type 2 diabetes. Furthermore, the collection of dietary intake data was carried out through face-to-face interviews conducted by trained dieticians to minimize measurement errors. However, this study also had some limitations. Case-control studies are susceptible to selection and recall biases. Additionally, the use of factor analysis to identify patterns, and the potential influence of research decisions on the number of factors and nutrient factor loadings in each pattern, should be considered. Lastly, despite the use of a validated semi-quantitative FFQ (food frequency questionnaire), there remains a possibility of measurement error due to dietary recall. The study’s findings and limitations contribute to the ongoing discourse on the role of nutrient patterns in the development of T2D and the importance of considering these factors in future research and preventive strategies.

Conclusions

The results of this study indicate that conforming to a nutrient pattern consisting of plant protein, vitamin C, vitamin A, vitamin B2, potassium, and calcium is linked to a lower likelihood of developing T2D. Our investigation did not reveal any significant correlation between other nutrient patterns and T2D risk. However, additional research is necessary to authenticate these initial findings and establish the correlation between nutrient patterns and T2D.

Data availability

Upon reasonable request, the corresponding author can provide the datasets that were produced and analyzed during the current study.

Ogurtsova K, Guariguata L, Barengo NC, Ruiz PL-D, Sacre JW, Karuranga S, et al. IDF Diabetes Atlas: global estimates of undiagnosed diabetes in adults for 2021. Diabetes Res Clin Pract. 2022;183:109118.

Article PubMed Google Scholar

Teo ZL, Tham Y-C, Yu M, Chee ML, Rim TH, Cheung N, et al. Global prevalence of diabetic retinopathy and projection of burden through 2045: systematic review and meta-analysis. Ophthalmology. 2021;128(11):1580–91.

Sugiyama T, Yanagisawa-Sugita A, Tanaka H, Ihana-Sugiyama N, Imai K, Ohsugi M, et al. Different incidences of diabetic retinopathy requiring treatment since diagnosis according to the course of diabetes diagnosis: a retrospective cohort study. Sci Rep. 2023;13(1):10527.

Article CAS PubMed PubMed Central Google Scholar

Hodge AM, English DR, O’Dea K, Giles GG. Dietary patterns and diabetes incidence in the Melbourne Collaborative Cohort Study. Am J Epidemiol. 2007;165(6):603–10.

Jannasch F, Kröger J, Schulze MB. Dietary patterns and type 2 diabetes: a systematic literature review and meta-analysis of prospective studies. J Nutr. 2017;147(6):1174–82.

Article CAS PubMed Google Scholar

Sami W, Ansari T, Butt NS, Ab Hamid MR. Effect of diet on type 2 diabetes mellitus: a review. Int J Health Sci. 2017;11(2):65.

Google Scholar

Miller V, Micha R, Choi E, Karageorgou D, Webb P, Mozaffarian D. Evaluation of the quality of evidence of the association of foods and nutrients with cardiovascular disease and diabetes: a systematic review. JAMA Netw open. 2022;5(2):e2146705–e.

Article PubMed PubMed Central Google Scholar

Salehi-Sahlabadi A, Teymoori F, Jabbari M, Momeni A, Mokari-Yamchi A, Sohouli M, et al. Dietary polyphenols and the odds of non-alcoholic fatty liver disease: a case-control study. Clin Nutr ESPEN. 2021;41:429–35.

Salehi-Abargouei A, Esmaillzadeh A, Azadbakht L, Keshteli AH, Feizi A, Feinle-Bisset C, et al. Nutrient patterns and their relation to general and abdominal obesity in Iranian adults: findings from the SEPAHAN study. Eur J Nutr. 2016;55:505–18.

Panjeshahin A, Salehi-Abargouei A, Ghadiri-Anari A, Rasouli A, Hosseinzadeh M. The Association between nutrient patterns and polycystic ovary syndrome: a case-control study. J Nutr Food Secur. 2022.

Salehi-Sahlabadi A, Teymoori F, Ahmadirad H, Mokhtari E, Azadi M, Seraj SS, et al. Nutrient patterns and non-alcoholic fatty liver disease in Iranian Adul: a case-control study. Front Nutr. 2022;9:977403.

Fereidani SS, Eini-Zinab H, Heidari Z, Jalali S, Sedaghat F, Rashidkhani B. Nutrient patterns and risk of breast cancer among Iranian women: a case-control study. Asian Pac J cancer Prevention: APJCP. 2018;19(9):2619.

CAS Google Scholar

Narmcheshm S, Sasanfar B, Hadji M, Zendehdel K, Toorang F, Azadbakht L. Patterns of nutrient intake in relation to gastric cancer: a case control study. Nutr Cancer. 2022;74(3):830–9.

Khayyatzadeh SS, Moohebati M, Mazidi M, Avan A, Tayefi M, Parizadeh SMR, et al. Nutrient patterns and their relationship to metabolic syndrome in Iranian adults. Eur J Clin Invest. 2016;46(10):840–52.

Teymoori F, Mokhtari E, Salehi P, Hosseini-Esfahani F, Mirmiran P, Azizi F. A nutrient pattern characterized by vitamin A, C, B6, potassium, and fructose is associated with reduced risk of insulin-related disorders: a prospective study among participants of Tehran lipid and glucose study. Diabetol Metab Syndr. 2021;13(1):12.

Esmaillzadeh A, Azadbakht L. Major dietary patterns in relation to general obesity and central adiposity among Iranian women. J Nutr. 2008;138(2):358–63.

Wang J, Thornton JC, Bari S, Williamson B, Gallagher D, Heymsfield SB, et al. Comparisons of waist circumferences measured at 4 sites. Am J Clin Nutr. 2003;77(2):379–84.

Committee IR. Guidelines for data processing and analysis of the International Physical Activity Questionnaire (IPAQ)-short and long forms. http://www.ipaq.ki.se/scoring.pdf ; 2005.

Mandalazi E, Drake I, Wirfält E, Orho-Melander M, Sonestedt E. A high diet quality based on dietary recommendations is not associated with lower incidence of type 2 diabetes in the Malmö Diet and Cancer Cohort. Int J Mol Sci. 2016;17(6):901.

Beigrezaei S, Ghiasvand R, Feizi A, Iraj B. Relationship between dietary patterns and incidence of type 2 diabetes. Int J Prev Med. 2019;10:122.

Chikowore T, Pisa PT, Van Zyl T, Feskens EJ, Wentzel-Viljoen E, Conradie KR. Nutrient patterns associated with fasting glucose and glycated haemoglobin levels in a black South African population. Nutrients. 2017;9(1):9.

Iwasaki Y, Arisawa K, Katsuura-Kamano S, Uemura H, Tsukamoto M, Kadomatsu Y, et al. Associations of nutrient patterns with the prevalence of metabolic syndrome: results from the baseline data of the Japan multi-institutional collaborative cohort study. Nutrients. 2019;11(5):990.

Sadeghi O, Sadeghi A, Mozaffari-Khosravi H, Shokri A. The association between nutrient patterns and metabolic syndrome among Iranian adults: cross-sectional analysis of Shahedieh cohort study. Public Health Nutr. 2021;24(11):3379–88.

Mottaghian M, Salehi P, Teymoori F, Mirmiran P, Hosseini-Esfahani F, Azizi F. Nutrient patterns and cardiometabolic risk factors among Iranian adults: Tehran lipid and glucose study. BMC Public Health. 2020;20:1–12.

Article Google Scholar

Halvorsen RE, Elvestad M, Molin M, Aune D. Fruit and vegetable consumption and the risk of type 2 diabetes: a systematic review and dose–response meta-analysis of prospective studies. BMJ Nutr Prev Health. 2021;4(2):519.

Mollahosseini M, Daneshzad E, Rahimi MH, Yekaninejad MS, Maghbooli Z, Mirzaei K. The association between fruit and vegetable intake and liver enzymes (aspartate and alanine transaminases) in Tehran, Iran. Ethiop J Health Sci. 2017;27(4):401–10.

Plaz Torres MC, Bodini G, Furnari M, Marabotto E, Zentilin P, Giannini EG. Nuts and non-alcoholic fatty liver disease: are nuts safe for patients with fatty liver disease? Nutrients. 2020;12(11):3363.

Lee M, Lim M, Kim J. Fruit and vegetable consumption and the metabolic syndrome: a systematic review and dose–response meta-analysis. Br J Nutr. 2019;122(7):723–33.

Muley A, Fernandez R, Ellwood L, Muley P, Shah M. Effect of tree nuts on glycemic outcomes in adults with type 2 diabetes mellitus: a systematic review. JBI Evid Synthesis. 2021;19(5):966–1002.

Beigrezaei S, Ghiasvand R, Feizi A, Iraj B. Relationship between dietary patterns and incidence of type 2 diabetes. Int J Prev Med. 2019;10.

Das UN. Vitamin C for type 2 diabetes mellitus and hypertension. Arch Med Res. 2019;50(2):11–4.

Iqbal S, Naseem I. Role of vitamin A in type 2 diabetes mellitus biology: effects of intervention therapy in a deficient state. Nutrition. 2015;31(7–8):901–7.

Viguiliouk E, Stewart SE, Jayalath VH, Ng AP, Mirrahimi A, De Souza RJ, et al. Effect of replacing animal protein with plant protein on glycemic control in diabetes: a systematic review and meta-analysis of randomized controlled trials. Nutrients. 2015;7(12):9804–24.

Wang PY, Fang JC, Gao ZH, Zhang C, Xie SY. Higher intake of fruits, vegetables or their fiber reduces the risk of type 2 diabetes: a meta-analysis. J Diabetes Invest. 2016;7(1):56–69.

Cai X, Li X, Fan W, Yu W, Wang S, Li Z, et al. Potassium and obesity/metabolic syndrome: a systematic review and meta-analysis of the epidemiological evidence. Nutrients. 2016;8(4):183.

van Dam RM, Rimm EB, Willett WC, Stampfer MJ, Hu FB. Dietary patterns and risk for type 2 diabetes mellitus in US men. Ann Intern Med. 2002;136(3):201–9.

Fung TT, Schulze M, Manson JE, Willett WC, Hu FB. Dietary patterns, meat intake, and the risk of type 2 diabetes in women. Arch Intern Med. 2004;164(20):2235–40.

Wheeler ML, Dunbar SA, Jaacks LM, Karmally W, Mayer-Davis EJ, Wylie-Rosett J, et al. Macronutrients, Food groups, and eating patterns in the management of diabetes: a systematic review of the literature, 2010. Diabetes Care. 2012;35(2):434–45.

Dwibedi C, Mellergård E, Gyllensten AC, Nilsson K, Axelsson AS, Bäckman M, et al. Effect of self-managed lifestyle treatment on glycemic control in patients with type 2 diabetes. Npj Digit Med. 2022;5(1):60.

Santulli G, Pascale V, Finelli R, Visco V, Giannotti R, Massari A, et al. We are what we eat: impact of food from short supply chain on metabolic syndrome. J Clin Med. 2019;8(12):2061.

De Rosa M, Giannotti R, Pascale A, Finelli R, Ilario M, Ciccarelli M, et al. P6280 food of short supply chain impacts metabolism and cardiovascular risk. A survey in Southern Italy. Eur Heart J. 2018;39(suppl1):ehy566. P6280.

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Acknowledgements

The researchers express their gratitude towards all the individuals who volunteered to take part in the study.

This research received no external funding.

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Faculty of medicine, Tabriz University of medical sciences, Tabriz, Iran

Morteza haramshahi

Department of clinical biochemistry, College of medicine, King Khalid University, Abha, Saudi Arabia

Thoraya Mohamed Elhassan A-Elgadir

Fharmacy Department, Duhok polytechnic, University Duhok, Kurdistan, Iraq

Hamid Mahmood Abdullah Daabo

Department of Medical Services and Techniques, Ardahan University, Ardahan, Turkey

Yahya Altinkaynak

Department of Medical Laboratory Sciences, College of Applied Medical Sciences, Prince Sattam bin Abdulaziz University, Jeddah, Saudi Arabia

Ahmed Hjazi

Department of Management, Uttaranchal Institute of Management, Uttaranchal University, Dehradun, Uttarakhand, India

Archana Saxena

Pharmaceutical Chemistry Department, College of Pharmacy, Al-Ayen University, Thi-Qar, Iraq

Mazin A.A. Najm

College of technical engineering, The Islamic University, Najaf, Iraq

Abbas F. Almulla

College of technical engineering, Imam Ja’afar Al-Sadiq University, Al‐Muthanna, 66002, Iraq

Ali Alsaalamy

Department of Medicinal Chemistry, Faculty of Pharmacy, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran

Mohammad Amin Kashani

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The study’s protocol was designed by M.K., M.H., and T.E., while H.A., Y.A., and A.H. carried out the research. A.S. analyzed the data and prepared the initial draft of the manuscript. M.N., A.FA., and A.A. interpreted the data and provided critical feedback on the manuscript. All authors reviewed and approved the final version of the manuscript.

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Correlation of Platelet Indices in Patients With Type 2 Diabetes Mellitus and Associated Microvascular Complications: A Hospital-Based, Prospective, Case-Control Study

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1 Department of General Medicine, Sri Guru Ram Das University of Health Sciences, Amritsar, IND.
2 Department of General Surgery, Institute of Medical Sciences, Banaras Hindu University, Varanasi, IND.
PMID: 38601371
PMCID: PMC11005942
DOI: 10.7759/cureus.55959

Background Diabetic patients exhibit increased platelet activity. Insulin inhibits the activation of platelets. Therefore, a relative or absolute deficiency of insulin would increase platelet reactivity. The younger (larger) platelets are also more metabolically and enzymatically active. If detected early, microvascular complications could alert us regarding the possible macrovascular complications. Thus, the aims and objectives of the present study were to determine platelet indices in patients with type 2 diabetes mellitus with controls (non-diabetics) and to find an association of platelet indices with microvascular complications. Material & methods In this prospective case-control study conducted from 2021 to 2022 (2 years), a total number of 200 subjects were taken and were divided into two groups of 100 each, cases (I) and controls (II). The cases included patients of diabetes mellitus (DM) of a duration of more than 5 years, which were further divided into two groups of 50 each, IA and IB. Group IA consisted of patients with diabetes mellitus of a duration of more than five years with at least one microvascular complication and group IB was diabetics of more than five years duration without any microvascular complications, which includes diabetic retinopathy, diabetic nephropathy, and diabetic neuropathy. An automated cell counter (Thermo Fisher Scientific, Waltham, MA, US) provided hemoglobin values along with the platelet count and platelet indices, i.e. mean platelet volume (MPV), platelet large cell ratio (P-LCR), and platelet distribution width (PDW). Results The present study consisted of 200 subjects divided into 2 groups of 100 each, cases (I) and controls (II). The average MPV (9.4-12.3 femtolitre) in diabetics was 12.089±1.450 fL as compared to the controls where it was 9.464±1.424 fL with a statistically significant p-value of 0.001. PDW among the cases was 16.868±2.352 fL while in controls, it was 12.753±10.559 fL (p=0.001). The mean P-LCR was 34.975±8.056% among the cases, in comparison to the mean P-LCR among the controls, which was 26.031±7.004 (p=0.001). In this study, the MPV, PDW, and P-LCR were significantly raised in individuals having diabetes with microvascular complications when compared with patients without complications. The mean MPV in diabetics with complications was 12.5960±0.95660 fL and in those without complications was 11.5820±1.67609 fL (with a p-value of P = 2×10 -3 )which is statistically significant. Similar results were obtained in cases of PDW and P-LCR. The mean PDW in diabetics with complications was 17.1140±2.58228 fL and without complications was 15.6220±2.10532 fL ((with a p-value of P = 2×10 -3 )). The mean P-LCR in diabetics with microvascular complications was 35.408±3.5490% and without complications was 33.542±4.8694% (with a p-value of P = 3.1×10 -3 ). Conclusion Based on the findings of the present study, there is a statistical correlation between type 2 diabetes and variations in platelet indices, resulting in the associated microvascular complications. Higher MPV, PDW, and P-LCR values suggest that these parameters are more reliable predictors of early vascular complications in individuals with type 2 diabetes mellitus and can be utilized as an easy-to-use, low-cost method. They are a readily available, economical, practical, noninvasive, and simple-to-understand approach for assessing platelet dysfunction, which in turn helps anticipate the existence of microvascular complications.

Keywords: diabetic microvascular complications; endocrinology and diabetes; mean platelet volume(mpv); platelet indices; type 2 diabetes mellites.

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Published: 11 January 2005

A case study of type 2 diabetes self-management

Hsin-i Wu 1

BioMedical Engineering OnLine volume 4 , Article number: 4 ( 2005 ) Cite this article

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It has been established that careful diabetes self-management is essential in avoiding chronic complications that compromise health. Disciplined diet control and regular exercise are the keys for the type 2 diabetes self-management. An ability to maintain one's blood glucose at a relatively flat level, not fluctuating wildly with meals and hypoglycemic medical intervention, would be the goal for self-management. Hemoglobin A1c (HbA1c or simply A1c) is a measure of a long-term blood plasma glucose average, a reliable index to reflect one's diabetic condition. A simple regimen that could reduce the elevated A1c levels without altering much of type 2 diabetic patients' daily routine denotes a successful self-management strategy.

A relatively simple model that relates the food impact on blood glucose excursions for type 2 diabetes was studied. Meal is treated as a bolus injection of glucose. Medical intervention of hypoglycaemic drug or injection, if any, is lumped with secreted insulin as a damping factor. Lunch was used for test meals. The recovery period of a blood glucose excursion returning to the pre-prandial level, the maximal reach, and the area under the excursion curve were used to characterize one's ability to regulate glucose metabolism. A case study is presented here to illustrate the possibility of devising an individual-based self-management regimen.

Results of the lunch study for a type 2 diabetic subject indicate that the recovery time of the post-prandial blood glucose level can be adjusted to 4 hours, which is comparable to the typical time interval for non-diabetics: 3 to 4 hours. A moderate lifestyle adjustment of light supper coupled with morning swimming of 20 laps in a 25 m pool for 40 minutes enabled the subject to reduce his A1c level from 6.7 to 6.0 in six months and to maintain this level for the subsequent six months.

Conclusions

The preliminary result of this case study is encouraging. An individual life-style adjustment can be structured from the extracted characteristics of the post-prandial blood glucose excursions. Additional studies are certainly required to draw general applicable guidelines for lifestyle adjustments of type 2 diabetic patients.

It is well established that diabetes can lead to acute and chronic complications, compromising the health and quality of life. Results from various studies [ 1 ] have demonstrated that improved control of blood glucose in type 2 diabetes reduces related complications. Type 2 diabetes results from the metabolic problem that is related to certain tissue resistance to insulin action and to the inability of the pancreas to appropriately regulate the quantity of insulin for glucose metabolism. These metabolic abnormalities lead to the many complications of diabetes. Type 2 diabetes historically occurs predominantly in adults aged 40 and over. A recent trend, however, indicates that children and adolescents of minority ethnic groups, especially in African Americans and American Indians, are increasingly susceptible to type 2 diabetes [ 2 ]. With the prevalence of type 2 diabetes and its associated risk for serious complications, issues related to proactive self-management become an urgent concern.

Dietary management is frequently referred as the cornerstone, or the initial step, in treating of type 2 diabetes mellitus. Foods containing carbohydrates play an important role in the diet. The glycemic Index (GI) ranks foods according to their post-prandial glycemic responses. The GI was introduced more than twenty years ago and has been widely adopted in diabetes management in Australia, New Zealand, Canada, the United Kingdoms, and France [ 3 ]. The World Health Organization states that it is important to consider the GI in constructing a healthful diet because low GI foods help control blood sugar levels by producing minimal fluctuations in blood glucose [ 4 ]. For diabetic patients, choosing low GI foods is particularly important because consumption of high GI foods often results in far more exaggerated glycemic responses, creating a need for drug or insulin therapy [ 3 , 5 ].

Most published GI lists are for single food items only. A GI is a numerical measure of how a carbohydrate would increase one's blood glucose level over a period of two (for normal) or three hours (for diabetic patients) after eating [ 6 , 7 ]. The area of elevated blood glucose level from the baseline (the pre-prandial measure) is expressed as a percent of the area for the same amount of a reference carbohydrate such as a pure glucose or a white bread (usually 50 g) [ 8 , 9 ]. To plan a complete meal using the weighted mean [ 6 ] for various food items is not only tedious, but also impractical.

Diet exchange lists are usually recommended for diabetic patients to use in formulating a sensible meal plan. However, an exchange list is not always convenient to use. Moreover, there is a lack of ethnic diet exchange lists. For a member of an ethnic minority to follow a diet exchange list, he or she must prepare his or her own meal away from the rest of the family. Nutall and Chasuk [ 10 ] have stressed that dietary recommendations for type 2 diabetes should be flexible and highly individualized, yet most of the prepared meal programs and exchange-list diets for diabetes have not had individualization in mind nor are they designed for ethnic minorities.

When diet alone cannot effectively control the type 2 diabetic conditions, medical interventions, such as insulin injections or dispensing hypoglycaemic pills, are usually the next step of managing type 2 diabetes mellitus. Medical interventions notoriously exacerbate the fluctuation of blood glucose excursions. Even with the smallest dosage of hypoglycaemic drug (5 mg glucotrol or glyburide) once in the morning, the subject of this study still experienced frequent acute hypoglycaemias. Besides, his A1c levels hovered around 6.5 levels for many years following his physician's advice of taking 5 mg glucotrol per day. It became obvious that a properly designed drug dispensing regimen was needed to avoid hypoglycaemic bouts and effectively reduce A1c levels.

Fasting blood glucose measurements are not consistent indicators, fluctuating widely from a low of 70 mg/dL to a high of 200 mg/dL (with most frequent range lay between 90 to 150 mg/dL) that were experienced by this type 2 diabetic subject prior to the model-based lifestyle adjustment. Initially, the subject tried to adjust lifestyle based on fasting glucose measurements, but it was not successful. His A1c measurements crept from 6.3 to 6.7 in a year. As glucose binds irreversibly to haemoglobin molecules within red blood cells, the amount of glucose that is bound to haemoglobin is directly tied to the concentration of glucose in the blood. The average life span of erythrocytes is about 120 days [ 11 ], measuring the amount of glucose bound to haemoglobin – by the A1c measurement – can provide an estimate of average blood sugar level during the 3 to 4 months period. It is obvious that A1c is a more reliable indicator than fasting glucose measurements for an effective blood glucose control self-management.

It has been established that exercise can effectively alleviate diabetic conditions. Although no rigorous investigation has been performed here, nor is the focus of this current study, a forty-minute exercise of swimming, or weight lifting, or jogging, or any combination of these, prior to a meal or 3 to 4 hours after a meal, can significantly depress the volunteer's post-prandial blood glucose levels. However, it is impractical to substitute hypoglycemic pills with a multiple daily exercise schedule. A sensible lifestyle adjustment is required to manage the diabetic conditions without altering much of daily routines.

Post-prandial blood glucose excursions (time series) for type 2 diabetes vary widely depending on the variety and the amount of food consumed. It also depends on long and short term physical conditions (exercise routines and stress levels such as insomnia) to a lesser scale. The recovery periods of blood glucose excursions returning to the pre-prandial level (or baseline) for diabetics are generally longer than those for non-diabetics. Although a simple glucose-insulin interaction compartmental model exists [ 12 ], not all the model parameters are readily interpretable. In addition, no case study is given to illustrate its potential applications. Compartmental models can provide first-order approximations that may be sufficient for specific goals. Simple models may not duplicate real phenomena but may reveal enough clues for which alternative approaches or experimental designs may come to light.

A biophysically-based model of impulse-force-generated heavily damped oscillatory system is used here to capture the post-prandial blood glucose characteristics of type 2 diabetes. The model follows the general approach of glucose-insulin interaction model (bolus injection of glucose) with a few modifications, for which parameters can readily be interpreted and a case study is presented for exploring its potential applications. Rather than using single food items for their published GI values, or its cumbersome weighted mean of multiple ingredients in a meal, normally consumed lunch for the subject was used for the test meal. Based on the preliminary results obtained from the model, a moderate lifestyle adjustment was devised for the subject: swimming 20 laps for 40 minutes in a 25 m pool in the morning and dispensing 1/4 of 5 mg glyburide 1/2 to 1 hour before lunch and dinner – that enables him to reduce 10% of his A1c level in six months and maintain the desirable lower level for the subsequent six months.

The subject is a mid-sixty healthy male of 180 lbs with 5'10" frame, leading a productive professional life. He has been diagnosed with type 2 diabetes for more than 30 years. Initially, he was on diet regimen for nearly twenty years and then was instructed by his physician to dispense 5 mg glucotrol once every morning. He experienced frequent acute hypoglycemia that led him to discuss a possible self-managed regimen with his family physician.

Lunch was chosen as the test meal for having sufficient time to take post-prandial measurements. The test meals were 15 sets of lunches that consisted either (1) 10 to 12 oz of steamed rice, stir-fried vegetables with 4 oz canned tuna (or steamed cod), or (2) 10 to 12 oz spaghetti with 6 medium sized meat balls (from Sam's family package). Five sets of data each were collected from: (i) without taking hypoglycemic pills before test meals; (ii) 1/4 size of 5 mg glyburide pills were dispensed pre-prandially right before the meal and (iii) 1/4 size of 5 mg glyburide pills were dispensed pre-prandially an hour before the test meals. One pre- and 8 to 12 post-prandial blood glucose measurements were taken at 30-minute intervals starting at the beginning of a meal (meal is usually consumed in 15 minutes): (i) for 6 hours, (ii) for 5 hours, and (iii) for 4 hours. In addition, for case (iii) two reference measurements were taken with one right before dispensing the pill and one an hour after completion of the 8 post prandial measurements, i.e ., at hour 5, for a total of 11 readings.

The purpose of the first set of measurements was to establish the baseline for this diabetic subject: the recovery period of post-prandial blood glucose excursion without medication. The second and the third sets of the trials were designed to quantitatively measure the hypoglycemic drug effects and the most optimal time frame to administer the pills. Raw data were averaged and the corresponding standard deviations were also calculated for 5 replicates at given times. The averaged data were then used for modeling analysis.

Model formulation

The post-prandial blood glucose excursion can be considered as a hormone regulated resilient system. The food intake is treated as a bolus injection of glucose, and thus the impulse force f ( t ); effects of exercises and hypoglycemic medication are lumped as the damping factor, β . The differential equation of such an oscillatory system, that is used to describe post-prandial blood glucose excursions, can be found in many physics texts:

where x represents blood glucose level over the baseline at time t , ω 0 is the system natural frequency [ 12 ]. The pre-prandial blood glucose levels are generally fluctuating with relatively insignificant magnitudes thus can be approximated as a flat level. If the impulse force f ( t ) takes the form of the Dirac delta function, F δ ( t -0) with F being a food intake dependent parameter, the solution of Eq. (1) is

is the frequency of the system. Equation (2) is a three parameter model: F , ω and β . Implications of these three parameters not only could reveal distinctive characteristics between diabetic and non-diabetic individuals but also provide guidelines to adjust one's lifestyle.

Parametric estimation

For a given blood glucose excursion, data was taken every 30 minute interval from the time a meal was initially consumed, from which the excursion peak ( MR ), x max , and the corresponding time τ to reach MR can both be estimated. Setting dx / dt = 0 in Eq. (2), the time τ can be expressed as:

Substituting Eq. (3) into Eq.(2), we have

The area under an excursion curve, AUC , can also be obtained:

where T = 2 π / ω is the period of oscillation. The reason for setting the upper integral limit to T /2 is because the damping factor β effectively depresses the glucose excursion levels x near zero for t > T /2, i.e ., it ripples about pre-prandial level. The time T /2 is therefore defined as the recovery period ( RP ). For type 2 diabetic patients who are not in a properly structured regimen, the recovery periods are often longer than 5 hours, by which time the next meal arrives and induces another blood glucose upswing.

Equations (3) – (5) can be used to estimate the three parameters, F , ω and β , from the measurable quantities of τ , x max , and AUC . The procedure is briefly described below:

Assign T as twice the roughly estimated recovery period in hours, which can be obtained from the raw data and thus ω = 2 π / T .

Fine tune these three parameters by using MATLAB function fminsearch to minimize [ AUC data - AUC ( F , β , ω )] 2 , where AUC data is calculated from the averaged data points by the trapezoidal rule and AUC ( F , β , ω ) is calculated from Eq. (5).

These three parameters can further be fine-tuned by fminsearch (sum of squared errors between the averaged data points and the model predicted values).

Two MATLAB user defined functions: GlucoseModel (for No pill and Pill at meal) and GlucoseModel1 (for Pill one hour prior) to estimate these model parameters and calculating the relevant diabetic characteristic measures: τ , x max , AUC are listed in the Additional files 1 and 2 , respectively.

Table 1 lists the fine-tuned values of model parameters: F , ω , β , and those characteristic parameters: RP , τ , x max , and AUC , the latter three are calculated from Eqs. (3) to (5). Also included in Table 1 are the fitting statistics R 2 values that indicate how well model curves fit the data.

The parametric value of F is the result of food impact, or the rate of glucose being absorbed into the blood stream. The interpretation of F is rather difficult as the liver acts as a storage compartment for glucose [ 12 ]. Liver regulates blood plasma glucose levels; if it is too high, the excess will be stored in the liver, and the reverse process will take place if the plasma glucose is too low. Although all three model parameters: F , ω , and β are more or less influenced by the liver function, the impact on F deems more pronounced as it has a direct impact on the glucose levels in the blood stream. As the function of the liver is not included in the current model, the estimated F values can only be loosely inferred as a function of insulin level, F increases as hypoglycemic drug depresses the blood glucose levels that in turn increases the absorption rate of glucose into the blood stream as in the case of 1/4 pill taken right before the meal. When the drug is taken an hour before the meal, the liver may have sufficient time to regulate blood glucose levels that additional glucose absorption becomes less intensive.

No pill trial

Parametric values for no-pill trial reveal that glucose absorption rate is generally slower (low F value) in comparison with the other two cases. The exceedingly long RP of nearly 7 hours is undesirable: as it implies that the next meal time arrives before the blood glucose level could return to the baseline, i.e ., an elevated blood glucose level would be sustained for a prolonged period of time. The high RP and AUC are unmistakably the characteristics for type 2 diabetes. Figure 1 compares the model and the data with the corresponding standard deviation bars. Model curves are extended for an additional hour beyond the last data point (and in all the figures herewith) to denote the trend of blood glucose excursion.

Post-prandial glucose excursion: no pill trial

1/4 of 5 mg glyburide taken right before the meal

The blood glucose characteristics are significantly improved with a 1/4 size of 5 mg glyburide taken right before lunch. Increased ω and β values translate to significantly lower RP and AUC with virtually unchanged x max . Although the mean RP is less than 5 hours, it is still a bit too long in comparison with the non-diabetics [ 12 ] (~ 4 hours). A higher F value than the one for no-pill trial may partly due to the liver intervention. Figure 2 compares the model and the data. From the figure one can tell that hypoglycemic drug has an effective delayed effect of about two hours as the rising portion of the model is almost identical to the one for no-pill trial with both x max are about 60, which may be the result of liver function that with initial stimulation of hypoglycemic drug, liver may also release glucose. As the hypoglycemic drug effect persists, the liver ceases to interfere.

Post-prandial glucose excursion: 1/4 pill right before the meal

1/4 of 5 mg glyburide taken an hour before the meal

From the personal experience of the participating subject, the hypoglycemia usually occurs 3 to 4 hours after taking the pill. The trial described in the previous section also reveals that no significant hypoglycemic drug effect is detected in the initial two hours. In order to learn the drug impact on an empty stomach, an additional glucose measurement was made prior to taking the hypoglycemic pill at -1 hour. Another measurement was also taken an hour after the blood glucose excursion returned to the baseline ( i.e ., at hour 5). This is meant to check if the blood glucose would remain near the baseline level. The drop of blood glucose levels between -1 and 0 hours are roughly 10 mg/dL, which can be contributed to the mild liver intervention. No net hypoglycemic drug effect is taking place before the meal as evidenced from the initial rise of the blood excursion curve as shown in Fig. 3 (in comparison with Fig. 2 ), where only data between hour 0 and hour 4 were used to generate the model curve. Indeed, all parametric values are improved significantly: both PR and x max are decreased by 20% and their combination that reflected in AUC dropped nearly 35% in comparison to those for pill taken at meal trial as shown in Table 1 . The food impact parameter F decreased a little from the one for pill at meal trial, which may indicate an hour after dispensing the pill, a quasi-equilibrium state has been reached among the liver function, hypoglycemic drug effects, and the bolus injection of glucose. The system frequency ω increased for more than 25%, which gives a shorter RP that compares favorably with non-diabetics. The drop of damping factor β may be the result of low F , as both τ and x max are already significantly reduced that further strengthening of β becomes unnecessary. The hour 5 measurements confirm that although the model curve shows a decreasing trend, upon returning to the base level the blood glucose excursions practically stabilizes. In addition, the volunteer patient did not experience any hypoglycemia even two to three hours after the final post-prandial measurement.

Post-prandial glucose excursion: 1/4 pill an hour before the meal

This simple impulse-forced model provides a means to shape a self-management regimen for the type 2 diabetic subject: a moderate meal coupled with minimal amount of medical intervention has effectively modulated the blood glucose excursion by reducing its recovery periods and fluctuation amplitudes. Based on the model, the type 2 diabetic subject was able to adjust a lifestyle that include (a) 40 minute swimming in a 25 m pool in the morning, (b) a fruit of mid-size apple or its equivalent and a cup of coffee with cream for breakfast without taking hypoglycaemic pill, (c) moderate lunch with 1/4 size of 5 mg glyburide taken 1/2 to 1 hour before the meal, (d) moderate early dinner, 4 hours prior to bed time, with 1/4 size of 5 mg glyburide taken 1/2 to 1 hour before the meal, (e) snack a mid-size banana, or a small bag (3.5 oz) of peanuts, or 6 crackers when needed in between meals. With this regimen, he was able to reduce his A1c level from 6.7 to 6.0 in 6 months and maintained at this level for the subsequent 6 months. Moreover, he has not had any hypoglycaemic bouts ever since he particitipated in this study more than two years ago.

Elevated blood glucose excursions during the night would boost the A1c levels. To keep a low average fluctuation of blood glucose excursion amplitudes, the evening meal is crucial. In order to avoid hypoglycaemia during the sleep, an early dinner is advised. The subject has been able to keep post-prandial blood glucose levels within 200 mg/dL with the mean fasting reading of 90 ± 20 mg/dL. Occasionally he consumes a can of beer or sugar free deserts. Although no rigorous study has been performed, a forty-minute exercise of swimming, or weight lifting, or jogging, or any combination of these is roughly equivalent to the effect of 1/4 size of 5 mg glyburide. Nonetheless, it is impractical to exercise more than once a day, thus the subject takes 2.5 mg of hypoglycemic pill a day instead. His physician originally prescribed him to take one 5 mg hypoglycemic pill daily. That was more that 10 years ago. The regimen did not work very well as he experienced hypoglycaemic bouts often. This model-based regimen not only reduced A1c level but entirely eliminated hypoglycaemic symptoms. In addition, one fasting blood glucose measurement in the morning is sufficient for him to maintain a healthy daily routine of exercise, consuming meals/snacks and leading a productive life with mental and physical activities.

Lifestyle adjustments are the best regimens for many chronicle ailments such as diabetes, hypertension, high cholesterol levels, etc . Although this model-based self-management regimen for the type 2 diabetic subject is only a case study, it certainly provides a general guideline for an applicable life-style adjustment. Currently not all the model parameters are entirely clear, additional data are required to draw a meaningful general conclusion. A pilot project of testing this regimen on six type 2 diabetic patients in a regional nursing home is proposed for the next phase of study.

Although derived characteristic parameters: RP and AUC (to a lesser degree, τ and x max ), carry clear meaning that can be used to characterize type 2 diabetic subjects from non-diabetics, the implications of model parameters, F , ω and β are not as translucent. With additional data, one may be able to draw plausible conclusions about (a) how F is influenced by food intakes, drug (delaying) effects, and liver (regulatory) functions; and (b) how ω and β behave, whether they are independent of F and of each other, or all three somewhat mutually dependent. Better understanding of these parameters would definitely enhance the self-management for type 2 diabetes.

This model-based lifestyle adjustment has another advantage: it can be used to manage each individual needs. Nutall and Chasuk [ 10 ] have stressed that dietary recommendation for type 2 diabetes should be flexible and highly individualized; most of prepared meal programs and exchange-list diets for diabetes have not had individualization in mind nor are they designed for ethnic minorities. Once we have a comprehensive understanding of these parameters, it is possible to tailor individual lifestyle adjustment accordingly.

For those individuals who are interested in self-managing the type 2 diabetes, the general advice is: avoiding big meals, may snack moderately between meals, eat an early dinner – about 4 hours before bedtime, and exercise regularly. If one is interested in "normal" meal effects on one's post-prandial blood glucose excursion, taking a pre-prandial blood glucose measurement prior to a typical lunch and 8 to 10 post-prandial measurements at half-hour intervals for 5 or more replicates and follow the procedure described here to obtain these characteristic parameters RP , τ , x max , and AUC . Applying a small dosage of medical intervention prior to a meal can keep the blood glucose at a relatively flat level and depress the overnight blood glucose excursion; however, this practice needs the approval from one's family physician and is not recommended here.

Ratner RE: Type 2 diabetes mellitus: the grand overview. Diabet Med 1998, 15: S4–7.

Article Google Scholar

Jiwa F: Diabetes in the 1990s – an overview. Stat Bull Metrop Co 1997, 78(1):2–8.

Google Scholar

Brand-Miller J: The Glucose Revolution . Marlowe & Company; 1999.

Linder L: What's your number, sweetie? The Washington Post, May 1, HE08 . 2001.

Franz M: In defence of the American Diabetes Association's recommendations on the glycemic index. Nutrition Today 1999, 34(2):78–81.

Article MathSciNet Google Scholar

Wolever TMS, Jenkins D, Jenkins AL, Josse RG: The glycemic index: methodology and clinical implication. American Journal of Clinical Nutrition 1991, 54: 846–854.

Brand-Miller J: Diets with a low glycemic index: From theory to practice. Nutrition Today 1999, 34(2):64–72.

Gannon MC, Nuttall FQ: Factors Affecting Interpretation of Postprandial Glucose and Insulin Areas. Diabetes Care 1987, 10(6):759–763.

Truswell AS: Glycemic index of foods. Eur J Clin Nutr 1992, 46(S2):S91-S101.

Nuttall FQ, Chasuk RM: Nutrition and the management of type 2 diabetes. Journal of Family Practice 1998, 47: S45–53.

Fournier RL: Basic Transport Phenomena in Biomedical Engineering . Taylor & Francis; 1998.

Fisher RJ: Compartmental analysis. In Introduction to Biomedical Engineering . Edited by: Enderle J, Blanchard S, Bronzino J. London: Academic Press; 2000:369–410.

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The author wishes to express his appreciation to Ms. Katherine Jakubik for her editing efforts, to Professor Jame B. Bassingthwaighte and two other anonymous reviewers for their critical comments to an earlier version of this manuscript.

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Additional File 1: MATLAB user defined function: GlucoseModel (for No pill and Pill at meal) to estimate model parameters: F , β , ω and to calculate the relevant diabetic characteristic measures: τ , x max , AUC . (DOC 30 KB)

12938_2004_76_MOESM2_ESM.doc

Additional File 2: MATLAB user defined function: GlucoseModel1 (for Pill one-hour prior) to estimate model parameters: F , β , ω and to calculate the relevant diabetic characteristic measures: τ , x max , AUC . (DOC 28 KB)

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Wu, Hi. A case study of type 2 diabetes self-management. BioMed Eng OnLine 4 , 4 (2005). https://doi.org/10.1186/1475-925X-4-4

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Healthcare Utilization and Its Correlates in Comorbid Type 2 Diabetes Mellitus and Generalized Anxiety Disorder

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Published: 19 April 2024

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Chun-Jen Huang ORCID: orcid.org/0000-0002-6556-2448 1 , 2 , 3 ,
Ching-Hua Lin 3 , 4 , 5 ,
Tai-Ling Liu 1 , 6 ,
Pai-Cheng Lin 7 ,
Chin-Chen Chu 8 ,
Jhi‑Joung Wang 8 , 9 ,
Chun-Wang Wei 10 na1 &
Shih-Feng Weng 10 , 11 , 12 na1

This study investigated the healthcare utilization and medical expenditure of type 2 diabetes mellitus (T2DM) patients with generalized anxiety disorder (GAD) and identified the associated factors. The healthcare utilization and expenditure of T2DM patients with (case group) and without (control group) GAD between 2002 and 2013 were examined using the population-based Taiwan National Health Insurance Research Database. Healthcare utilization included outpatient visits and hospitalization; health expenditure included outpatient, inpatient, and total medical expenditure. Moreover, nonpsychiatric healthcare utilization and medical expenditure were distinguished from total healthcare utilization and medical expenditure. The average healthcare utilization, including outpatient visits and hospitalization, was significantly higher for the case group than for the control group (total and nonpsychiatric). The results regarding differences in average outpatient expenditure (total and nonpsychiatric), inpatient expenditure (total and nonpsychiatric), and total expenditure (total and nonpsychiatric) between the case and control groups are inconsistent. Sex, age, income, comorbidities/complications, and the diabetes mellitus complication severity index were significantly associated with outpatient visits, medical expenditure, and hospitalization in the case group (total and nonpsychiatric). Greater knowledge of factors affecting healthcare utilization and expenditure in comorbid individuals may help healthcare providers intervene to improve patient management and possibly reduce the healthcare burden in the future.

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Availability of Data and Materials

This study is based in part on data from the Health and Welfare Data Science Center, Ministry of Health and Welfare (HWDC, MOHW) (H106094 and H109076). Access to the dataset is restricted to research purposes only, and eligibility for data access is limited to citizens of the Republic of China who meet the requirements for conducting research projects. Applicants are required to comply with the Computer-Processed Personal Data Protection Law and the relevant regulations set by the Health and Welfare Data Science Center, Ministry of Health and Welfare. Therefore, we are unable to deposit the datasets in publicly available repositories or include them in the manuscript or additional supporting.

Lin WH, Hsu CH, Chen HF, Liu CC, Li CY. Mortality of patients with type 2 diabetes in Taiwan: a 10-year nationwide follow-up study. Diabetes Res Clin Pract. 2015;107:178–86.

Article PubMed Google Scholar

Holman N, Forouhi N, Goyder E, Wild S. The Association of Public Health Observatories (APHO) diabetes prevalence model: estimates of total diabetes prevalence for England, 2010–2030. Diabet Med. 2011;28:575–82.

Article CAS PubMed Google Scholar

Akena D, Kadama P, Ashaba S, Akello C, Kwesiga B, Rejani L, et al. The association between depression, quality of life, and the health care expenditure of patients with diabetes mellitus in Uganda. J Affect Disord. 2015;174:7–12.

Murray CJ, Vos T, Lozano R, Naghavi M, Flaxman AD, Michaud C, et al. Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet. 2012;380:2197–223.

Molosankwe I, Patel A, Gagliardino JJ, Knapp M, McDaid D. Economic aspects of the association between diabetes and depression: a systematic review. J Affect Disord. 2012;142:S42–55.

Harris MI. Diabetes in America: epidemiology and scope of the problem. Diabetes Care. 1998;21(Suppl 3):C11–4.

Association AD. Economic consequences of diabetes mellitus in the U.S. in 1997. Diabetes Care. 1998;21:296–309.

Article Google Scholar

Barrera TL, Mott JM, Hundt NE, Mignogna J, Yu HJ, Stanley MA, et al. Diagnostic specificity and mental health service utilization among veterans with newly diagnosed anxiety disorders. Gen Hosp Psychiatry. 2014;36:192–8.

Maier W, Gänsicke M, Freyberger H, Linz M, Heun R, Lecrubier Y. Generalized anxiety disorder (ICD-10) in primary care from a cross-cultural perspective: a valid diagnostic entity? Acta Psychiatr Scand. 2000;101:29–36.

Kujanpaa T, Ylisaukko-Oja T, Jokelainen J, Linna M, Timonen M. Comparative cost analysis of generalized anxiety disorder and major depressive disorder patients in secondary care from a national hospital registry in Finland. Nord J Psychiatry. 2014;68:306–10.

Hoffman DL, Dukes EM, Wittchen HU. Human and economic burden of generalized anxiety disorder. Depress Anxiety. 2008;25:72–90.

Chien IC, Wu EL, Lin CH, Chou YJ, Chou P. Prevalence of diabetes in patients with major depressive disorder: a population-based study. Compr Psychiatry. 2012;53:569–75.

Murray CJ, Lopez AD. Alternative projections of mortality and disability by cause 1990–2020: global burden of disease study. Lancet. 1997;349:1498–504.

Clarke PM, Glasziou P, Patel A, Chalmers J, Woodward M, Harrap SB, et al. Event rates, hospital utilization, and costs associated with major complications of diabetes: a multicountry comparative analysis. PLoS Med. 2010;7:1000236.

Article PubMed PubMed Central Google Scholar

Gulliford MC, Latinovic R, Charlton J. Diabetes diagnosis, resource utilization, and health outcomes. Am J Manag Care. 2008;14:32–8.

PubMed Google Scholar

Nichols GA, Brown JB. The impact of cardiovascular disease on medical care costs in subjects with and without type 2 diabetes. Diabetes Care. 2002;25:482–6.

Williams R, Van Gaal L, Lucioni C. Assessing the impact of complications on the costs of Type II diabetes. Diabetologia. 2002;45:S13–7.

Huang CJ, Lin CH, Lee MH, Chang KP, Chiu HC. Prevalence and incidence of diagnosed depression disorders in patients with diabetes: a national population-based cohort study. Gen Hosp Psychiatry. 2012;34:242–8.

Huang CJ, Chiu HC, Lee MH, Wang SY. Prevalence and incidence of anxiety disorders in diabetic patients: a national population-based cohort study. Gen Hosp Psychiatry. 2011;33:8–15.

Hsu JH, Chien IC, Lin CH, Chou YJ, Chou P. Incidence of Diabetes in Patients With Schizophrenia: a Population-based Study. Can J Psychiatry. 2011;56:19–26.

Anderson RJ, Freedland KE, Clouse RE, Lustman PJ. The prevalence of comorbid depression in adults with diabetes: a meta-analysis. Diabetes Care. 2001;24:1069–78.

Vamos EP, Mucsi I, Keszei A, Kopp MS, Novak M. Comorbid depression is associated with increased healthcare utilization and lost productivity in persons with diabetes: a large nationally representative Hungarian population survey. Psychosom Med. 2009;71:501–7.

Nichols L, Barton PL, Glazner J, McCollum M. Diabetes, minor depression and health care utilization and expenditures: a retrospective database study. Cost Eff Resour Alloc. 2007;5:4.

Simon GE, Katon WJ, Lin EH, Ludman E, VonKorff M, Ciechanowski P, et al. Diabetes complications and depression as predictors of health service costs. Gen Hosp Psychiatry. 2005;27:344–51.

Finkelstein EA, Bray JW, Chen H, Larson MJ, Miller K, Tompkins C, et al. Prevalence and costs of major depression among elderly claimants with diabetes. Diabetes Care. 2003;26:415–20.

Egede LE, Zheng D, Simpson K. Comorbid depression is associated with increased health care use and expenditures in individuals with diabetes. Diabetes Care. 2002;25:464–70.

Ciechanowski PS, Katon WJ, Russo JE. Depression and diabetes: impact of depressive symptoms on adherence, function, and costs. Arch Intern Med. 2000;160:3278–85.

Huang CJ, Chiu HC, Hsieh HM, Yen JY, Lee MH, Chang KP, et al. Health care utilization and expenditures of persons with diabetes comorbid with anxiety disorder: a national population-based cohort study. Gen Hosp Psychiatry. 2015;37:299–304.

Huang C-J, Wang S-Y, Lee M-H, Chiu H-C. Prevalence and incidence of mental illness in diabetes: a national population-based cohort study. Diabetes Res Clin Pract. 2011;93:106–14.

Young BA, Lin E, Von Korff M, Simon G, Ciechanowski P, Ludman EJ, et al. Diabetes complications severity index and risk of mortality, hospitalization, and healthcare utilization. Am J Manag Care. 2008;14:15–23.

PubMed PubMed Central Google Scholar

Chiu HC, Shi HY, Mau LW, Wang GJ. The effects of a prospective case payment system on hospital charges for total hip arthroplasty in Taiwan. J Arthroplasty. 2007;22:65–71.

Sullivan G, Han X, Moore S, Kotrla K. Disparities in hospitalization for diabetes among persons with and without co-occurring mental disorders. Psychiatr Serv. 2006;57:1126–31.

Garrison MM, Katon WJ, Richardson LP. The impact of psychiatric comorbidities on readmissions for diabetes in youth. Diabetes Care. 2005;28:2150–4.

Hutter N, Schnurr A, Baumeister H. Healthcare costs in patients with diabetes mellitus and comorbid mental disorders—a systematic review. Diabetologia. 2010;53:2470–9.

Black G, Roberts RM, Li-Leng T. Depression in rural adolescents: relationships with gender and availability of mental health services. Rural Remote Health. 2012;12:2092.

Wang W, Fu C, Zhuo H, Luo J, Xu B. Factors affecting costs and utilization of type 2 diabetes healthcare: a cross-sectional survey among 15 hospitals in urban China. BMC Health Serv Res. 2010;10:244.

Chaikledkaew U, Pongchareonsuk P, Chaiyakunapruk N, Ongphiphadhanakul B. Factors affecting health-care costs and hospitalizations among diabetic patients in Thai public hospitals. Value Health. 2008;11:S69–74.

Rost K, Zhang M, Fortney J, Smith J, Smith GR Jr. Rural-urban differences in depression treatment and suicidality. Med Care. 1998;36:1098–107.

Su C-H, Chiu H-C, Hsieh H-M, Yen J-Y, Lee M-H, Li C-Y, et al. Healthcare Utilization and Expenditures for Persons with Diabetes Comorbid with Mental Illnesses. Psychiatr Q. 2016;87:545–57.

Blazer DG, Kessler RC, McGonagle KA, Swartz MS. The prevalence and distribution of major depression in a national community sample: the National Comorbidity Survey. Am J Psychiatry. 1994;151(7):979–86.

Jackson-Triche ME, Sullivan JG, Wells KB, Rogers W, Camp P, Mazel R. Depression and health-related quality of life in ethnic minorities seeking care in general medical settings. J Affect Disord. 2000;58:89–97.

Amos AF, McCarty DJ, Zimmet P. The rising global burden of diabetes and its complications: estimates and projections to the year 2010. Diabet Med. 1997;14(Suppl 5):S1-85.

Gu D, Reynolds K, Duan X, Xin X, Chen J, Wu X, et al. Prevalence of diabetes and impaired fasting glucose in the Chinese adult population: International Collaborative Study of Cardiovascular Disease in Asia (InterASIA). Diabetologia. 2003;46:1190–8.

Lin CC, Lai MS, Syu CY, Chang SC, Tseng FY. Accuracy of diabetes diagnosis in health insurance claims data in Taiwan. J Formos Med Assoc. 2005;104:157–63.

Liptzin B, Regier DA, Goldberg ID. Utilization of health and mental health services in a large insured population. Am J Psychiatry. 1980;137:553–8.

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Acknowledgements

This study is based in part on data from the Health and Welfare Data Science Center, Ministry of Health and Welfare (HWDC, MOHW) (H106094 and H109076). The authors thank the Center for Medical Informatics and Statistics of Kaohsiung Medical University for providing statistical and administrative support. The interpretation and conclusions contained herein do not represent those of the aforementioned agencies. This manuscript was edited by Wallace Academic Editing.

This study was supported by a grant from Kaohsiung Medical University Hospital (KMUH108-8T06 and KMUH111-1R65). The funding sources had no involvement in the study design, data collection, analysis, interpretation of data, writing of the report, or the decision to submit the paper for publication.

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Shih-Feng Weng and Chun-Wang Wei are contributed equally.

Authors and Affiliations

Department of Psychiatry, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan

Chun-Jen Huang & Tai-Ling Liu

Department of Psychiatry, School of Post-Baccalaureate Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan

Chun-Jen Huang

Department of Psychiatry, School of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan

Chun-Jen Huang & Ching-Hua Lin

Kaohsiung Municipal Kai-Syuan Psychiatric Hospital, Kaohsiung, Taiwan

Ching-Hua Lin

Department of Post-Baccalaureate Medicine, College of Medicine, National Sun Yat-Sen University, Kaohsiung, Taiwan

Department of Psychiatry, School of Medicine and Graduate Institute of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan

Tai-Ling Liu

Department of Psychiatry, Kaohsiung Municipal Ta-Tung Hospital, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan

Pai-Cheng Lin

Department of Anesthesiology, Chi-Mei Medical Center, Tainan, Taiwan

Chin-Chen Chu & Jhi‑Joung Wang

Department of Medical Research, Chi-Mei Medical Center, Tainan, Taiwan

Jhi‑Joung Wang

Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung, Taiwan

Chun-Wang Wei & Shih-Feng Weng

Center for Medical Informatics and Statistics, Office of R&D, Kaohsiung Medical University, Kaohsiung, Taiwan

Shih-Feng Weng

Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan

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Authors Chun-Jen Huang, Jhi‑Joung Wang and Shih-Feng Weng designed the study and wrote the protocol. Authors Tai-Ling Liu and Chin-Chen Chu managed the literature searches and analyses. Authors Shih-Feng Weng and Chun-Wang Wei undertook the statistical analysis, and authors Pai-Cheng Lin and Ching-Hua Lin wrote the part of discussion draft of the manuscript. All authors contributed to and have approved the final manuscript.

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Correspondence to Chun-Wang Wei or Shih-Feng Weng .

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This study was conducted according to the Declaration of Helsinki and was approved by the Institutional Review Board of Kaohsiung Medical University Hospital.

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Huang, CJ., Lin, CH., Liu, TL. et al. Healthcare Utilization and Its Correlates in Comorbid Type 2 Diabetes Mellitus and Generalized Anxiety Disorder. Psychiatr Q (2024). https://doi.org/10.1007/s11126-024-10072-z

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DOI : https://doi.org/10.1007/s11126-024-10072-z

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Clinical pearls, suggested readings, case study: hemachromatosis in type 2 diabetes.

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Peter Capell; Case Study: Hemachromatosis in Type 2 Diabetes. Clin Diabetes 1 April 2004; 22 (2): 101–102. https://doi.org/10.2337/diaclin.22.2.101

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G.O. is a 50-year-old white man referred for help in managing his diabetes. Two years before his visit, diabetes was diagnosed during a routine exam. He was started on oral hypoglycemic agents. He initially responded to this treatment, but over the ensuing 2 years, his medication doses were slowly raised until he was on 15 mg glyburide and 2,000 mg metformin. At the time of referral, his fasting blood glucose levels were in the range of 150 mg/dl and his hemoglobin A 1c (A1C) was 8%. He requested a consultation when he was advised to start on insulin therapy.

His medical history was significant for heavy alcohol intake and hepatitis B with full recovery. Family history was negative for diabetes and hemochromatosis. His review of systems was positive for joint discomfort in his hands and erectile dysfunction.

Physical exam revealed normal vital signs and no retinopathy or other signs of diabetic complications. His hand joints showed mild swelling and tenderness over the proximal interphalangeal joints, and his skin was slightly, diffusely hyperpigmented.

Lab data included a random glucose of 253 mg/dl, A1C of 7.9%, normal creatinine and electrolytes, aspartate aminotransferase (GOT) of 66 units/l (normal < 44), alanine aminotransferase (ALT) of 133 units/l (normal < 31 units/l), normal bilirubin and alkaline phosphatase levels, normal testosterone level, and negative hepatitis antigen screen. His iron level was 306 μg/dl (normal < 155) with an iron-binding capacity of 315 μg/dl (normal < 400) and percent saturation of 97% (normal < 50%). Serum ferritin was 2,920 μg/l (normal < 160). The polymerase chain restriction assay demonstrated homozygosity for the C282Y chromosome. Referral to the hepatology clinic resulted in a liver biopsy, which identified increased iron stores and early periportal fibrosis.

Following confirmation of a diagnosis of hemochromatosis, he was started on phlebotomy therapy. Family screening was encouraged and resulted in the finding of asymptomatic diabetes associated with hemochromatosis in his brother. His medication doses have not changed, nor have his fasting glucose level or A1C results after 4 months of phlebotomies.

What is the prevalence of hemochromatosis in the general and diabetic population?

What is the effect of treatment on diabetic control in patients with hemochromatosis?

Should all people with diabetes over age 30 be screened for hemochromatosis?

Hereditary hemochromatosis is an autosomal recessive genetic disorder caused by a mutation in the HFE gene located on the short arm of chromosome 6. This mutation results in increased intestinal absorption of iron and eventually to iron overload. About 10% of the white population in the United States is heterozygote, with the frequency for homozygosity at 0.2–0.5%. Heterozygote individuals are gene carriers but are not medically affected.

Onset of symptoms is seldom apparent before age 40 because it takes years to build up enough iron to cause tissue damage. Liver function abnormalities are the most frequent finding leading to a diagnosis. Other important organ systems usually involved include the pancreas (diabetes), skin (hyper-pigmentation), joints (arthralgias and arthritis), heart (arrhythmias), and gonads (hypogonadism).

Approximately 50% of patients diagnosed with hemochromatosis will have either type 1 or type 2 diabetes. The likelihood of finding hemochromatosis in the adult population of diabetic patients is reportedly between 1–2%. Diabetes is not uncommonly the only apparent manifestation of hemochromatosis in unrecognized cases.

Early recognition of the presence of hemochromatosis is extremely important. Prompt therapy can prevent cirrhosis of the liver, development of a hepatoma, joint and gonadal damage, and the development of diabetes. In addition, as in this case, it can lead to early recognition of the disease in family members. Unrecognized, advanced hemochromatosis carries a high risk for premature death.

Development of diabetes in hemochromatosis is likely multifactorial. Selective β-cell damage, due to uptake of iron, leads to impaired insulin synthesis and release. α-Cell function is not impaired. In addition, liver fibrosis leads to insulin resistance and contributes to some patients requiring large amounts of insulin to obtain optimal blood glucose control. A family history of diabetes is observed in 25% of patients with hemochromatosis who develop diabetes. In contrast, only 4% of those with hemochromatosis who fail to develop diabetes have a positive family history. Therefore, it is likely that all three factors—β-cell damage, insulin resistance, and underlying genetic tendencies—play a causal role in patients with hemochromatosis developing diabetes.

Phlebotomy therapy has a variable impact on diabetes control. In a large study exploring the effect of therapy on diabetes control, 40% of 72 patients on insulin or oral agents showed improved glucose control following phlebotomy therapy. This same study reported that 6% of patients were able to stop insulin therapy during phlebotomy therapy, but 12% of the study group required increased medication to achieve good glycemic control. The majority of diabetic patients will experience no change or a progressive worsening in their diabetes management despite phlebotomy treatment.

The issue of screening all diabetic patients for hemochromatosis is currently debated. Screening by transferrin saturation using a level of > 50% is reasonably inexpensive. The dilemma is that some reports indicate no increased risk of hemochromatosis in an adult diabetic population. Furthermore, an elevated transferrin level is nonspecific, and a positive result will lead to many unnecessary evaluations being performed. Certainly type 2 diabetes and abnormal liver tests (as in this case), arthritis, or a family history of iron overload disease (as seen with this patient’s brother) should trigger an order for a transferrin level.

Hemochromatosis is present in 1–2% of all diabetic patients, and diabetes is often the first clinical manifestation of the disease.

Early recognition and treatment is imperative to prevent fatal liver or heart abnormalities and can prevent the onset of diabetes or improve diabetes control.

Screening all diabetic patients for hemochromatosis may not be cost-effective. However, screening patients with a family history of iron overload disease, abnormal liver enzymes, or arthritis seems prudent.

Peter Capell, MD, is a clinical professor of medicine in the Division of Endocrinology and Metabolism at the University of Washington School of Medicine in Seattle, Wash.

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A large body of research on Type 2 diabetes has helped to develop guidance, informing how patients are diagnosed, treated, and manage their lifestyle. In contrast, Type 1 diabetes, often mistakenly associated only with childhood, has received less attention.

In this Q&A, adapted from the April 17 episode of Public Health On Call , Stephanie Desmon speaks to Johns Hopkins epidemiologists Elizabeth Selvin , PhD '04, MPH, and Michael Fang , PhD, professor and assistant professor, respectively, in the Department of Epidemiology, about recent findings that challenge common beliefs about type 1 diabetes. Their conversation touches on the misconception that it’s solely a childhood condition, the rise of adult-onset cases linked to obesity, and the necessity for tailored approaches to diagnosis and care. They also discuss insulin prices and why further research is needed on medications like Ozempic in treating Type 1 diabetes.

I want to hear about some of your research that challenges what we have long understood about Type 1 diabetes, which is no longer called childhood diabetes.

MF: Type 1 diabetes was called juvenile diabetes for the longest time, and it was thought to be a disease that had a childhood onset. When diabetes occurred in adulthood it would be type 2 diabetes. But it turns out that approximately half of the cases of Type 1 diabetes may occur during adulthood right past the age of 20 or past the age of 30.

The limitations of these initial studies are that they've been in small clinics or one health system. So, it's unclear whether it's just that particular clinic or whether it applies to the general population more broadly.

We were fortunate because the CDC has collected new data that explores Type 1 diabetes in the U.S. Some of the questions they included in their national data were, “Do you have diabetes? If you do, do you have Type 1 or Type 2? And, at what age were you diagnosed?”

With these pieces of information, we were able to characterize how the age of diagnosis of Type 1 diabetes differs in the entire U.S. population.

Are Type 1 and Type 2 diabetes different diseases?

ES: They are very different diseases and have a very different burden. My whole career I have been a Type 2 diabetes epidemiologist, and I’ve been very excited to expand work with Type 1 diabetes.

There are about 1.5 million adults with Type 1 diabetes in the U.S., compared to 21 million adults with Type 2 diabetes. In terms of the total cases of diabetes, only 5 to 10 percent have Type 1 diabetes. Even in our largest epidemiologic cohorts, only a small percentage of people have Type 1 diabetes. So, we just don't have the same national data, the same epidemiologic evidence for Type 1 diabetes that we have for Type 2. The focus of our research has been trying to understand and characterize the general epidemiology and the population burden of Type 1 diabetes.

What is it about Type 1 that makes it so hard to diagnose?

MF: The presentation of symptoms varies by age of diagnosis. When it occurs in children, it tends to have a very acute presentation and the diagnosis is easier to make. When it happens in adulthood, the symptoms are often milder and it’s often misconstrued as Type 2 diabetes.

Some studies have suggested that when Type 1 diabetes occurs in adulthood, about 40% of those cases are misdiagnosed initially as Type 2 cases. Understanding how often people get diagnosed later in life is important to correctly diagnose and treat patients.

Can you talk about the different treatments?

MF: Patients with Type 1 diabetes are going to require insulin. Type 2 diabetes patients can require insulin, but that often occurs later in the disease, as oral medications become less and less effective.

ES: Because of the epidemic of overweight and obese in the general population, we’re seeing a lot of people with Type 1 diabetes who are overweight and have obesity. This can contribute to issues around misdiagnosis because people with Type 1 diabetes will have signs and will present similarly to Type 2 diabetes. They'll have insulin resistance potentially as a result of weight gain metabolic syndrome. Some people call it double diabetes—I don't like that term—but it’s this idea that if you have Type 1 diabetes, you can also have characteristics of Type 2 diabetes as well.

I understand that Type 1 used to be considered a thin person's disease, but that’s not the case anymore. MF: In a separate paper, we also explored the issue of overweight and obesity in persons with Type 1 diabetes. We found that approximately 62% of adults with Type 1 diabetes were either overweight or obese, which is comparable to the general U.S. population.

But an important disclaimer is that weight management in this population [with Type 1 diabetes] is very different. They can't just decide to go on a diet, start jogging, or engage in rigorous exercise. It can be a very, very dangerous thing to do.

Everybody's talking about Ozempic and Mounjaro—the GLP-1 drugs—for diabetes or people who are overweight to lose weight and to solve their diabetes. Where does that fit in with this population?

ES: These medications are used to treat Type 2 diabetes in the setting of obesity. Ozempic and Mounjaro are incretin hormones. They mediate satiation, reduce appetite, slow gastric emptying, and lower energy intake. They're really powerful drugs that may be helpful in Type 1 diabetes, but they're not approved for the management of obesity and Type 1 diabetes. At the moment, there aren't data to help guide their use in people with Type 1 diabetes, but I suspect they're going to be increasingly used in people with Type 1 diabetes.

MF: The other piece of managing weight—and it's thought to be foundational for Type 1 or Type 2—is dieting and exercising. However, there isn’t good guidance on how to do this in persons with Type 1 diabetes, whereas there are large and rigorous trials in Type 2 patients. We’re really just starting to figure out how to safely and effectively manage weight with lifestyle changes for Type 1 diabetics, and I think that's an important area of research that should continue moving forward.

ES: Weight management in Type 1 diabetes is complicated by insulin use and the risk of hypoglycemia, or your glucose going too low, which can be an acute complication of exercise. In people with Type 2 diabetes, we have a strong evidence base for what works. We know modest weight loss can help prevent the progression and development of Type 2 diabetes, as well as weight gain. In Type 1, we just don't have that evidence base.

Is there a concern about misdiagnosis and mistreatment? Is it possible to think a patient has Type 2 but they actually have Type 1?

MF: I think so. Insulin is the overriding concern. In the obesity paper, we looked at the percentage of people who said their doctors recommended engaging in more exercise and dieting. We found that people with Type 1 diabetes were less likely to receive the same guidance from their doctor. I think providers may be hesitant to say, “Look, just go engage in an active lifestyle.”

This is why it's important to have those studies and have that guidance so that patients and providers can be comfortable in improving lifestyle management.

Where is this research going next?

ES: What's clear from these studies is that the burden of overweight and obesity is substantial in people with Type 1 diabetes and it's not adequately managed. Going forward, I think we're going to need clinical trials, clear clinical guidelines, and patient education that addresses how best to tackle obesity in the setting of Type 1 diabetes.

It must be confusing for people with Type 1 diabetes who are hearing about people losing all this weight on these drugs, but they go to their doctor who says, “Yeah, but that's not for you.”

ES: I hope it's being handled more sensitively. These drugs are being used by all sorts of people for whom they are not indicated, and I'm sure that people with Type 1 diabetes are accessing these drugs. I think the question is, are there real safety issues? We need thoughtful discussion about this and some real evidence to make sure that we're doing more good than harm.

MF: Dr. Selvin’s group has published a paper, estimating that about 15% of people with Type 1 diabetes are on a GLP-1. But we don't have great data on what potentially can happen to individuals.

The other big part of diabetes that we hear a lot about is insulin and its price. Can you talk about your research on this topic?

MF: There was a survey that asked, “Has there been a point during the year when you were not using insulin because you couldn’t afford it?” About 20% of adults under the age of 65 said that at some point during the year, they couldn't afford their insulin and that they did engage in what sometimes is called “cost-saving rationing” [of insulin].

Medicare is now covering cheaper insulin for those over 65, but there are a lot of people for whom affordability is an issue. Can you talk more about that?

MF: The fight is not over. Just because there are national and state policies, and now manufacturers have been implementing price caps, doesn't necessarily mean that the people who need insulin the most are now able to afford it.

A recent study in the Annals of Internal Medicine looked at states that adopted or implemented out-of-pocket cost caps for insulin versus those that didn't and how that affected insulin use over time. They found that people were paying less for insulin, but the use of insulin didn't change over time. The $35 cap is an improvement, but we need to do more.

ES: There are still a lot of formulations of insulin that are very expensive. $35 a month is not cheap for someone who is on insulin for the rest of their lives.

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This scenario covers the clinical implications, risk factors and diagnosis of non-diabetic hyperglycaemia (prediabetes), as well as the actions primary care professionals should take following diagnosis.

The format uses typical clinical scenarios as tools for learning. Information is provided in short sections, with most ending in a question to answer before moving on to the next section.

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Shona , a 38-year-old lady of South Asian ethnicity, contacts the surgery to discuss the results of her annual blood test, which she undergoes because of a previous history of gestational diabetes. Her HbA 1c has risen to 44 mmol/mol (6.2%).

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v.16(4); 2024 Apr
PMC11006619

Microbial and metabolomic profiles of type 1 diabetes with depression: A case–control study

1 Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Sun Yat‐sen University, Guangdong Diabetes Prevention and Control Research Center, Guangdong Provincial Key Laboratory of Diabetology, Guangzhou China

2 Department of Endocrinology, The Sixth Affiliated Hospital of Sun Yat‐sen University, Guangzhou China

3 Department of Endocrinology, Institute of Endocrine and Metabolic Diseases, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, Clinical Research Hospital of the Chinese Academy of Sciences (Hefei), University of Science and Technology of China, Hefei China

Xueying Zheng

Jianping weng, daizhi yang, chaofan wang.

Depression is the most common psychological disorder in patients with type 1 diabetes (T1D). However, the characteristics of microbiota and metabolites in these patients remain unclear. This study aimed to investigate microbial and metabolomic profiles and identify novel biomarkers for T1D with depression.

A case–control study was conducted in a total of 37 T1D patients with depression (TD+), 35 T1D patients without depression (TD−), and 29 healthy controls (HCs). 16S rRNA gene sequencing and liquid chromatography–mass spectrometry (LC–MS) metabolomics analysis were conducted to investigate the characteristics of microbiota and metabolites. The association between altered microbiota and metabolites was explored by Spearman's rank correlation and visualized by a heatmap. The microbial signatures to discriminate TD+ from TD− were identified by a random forest (RF) classifying model.

In microbiota, 15 genera enriched in TD− and 2 genera enriched in TD+, and in metabolites, 14 differential metabolites (11 upregulated and 3 downregulated) in TD+ versus TD− were identified. Additionally, 5 genera (including Phascolarctobacterium , Butyricimonas , and Alistipes from altered microbiota) demonstrated good diagnostic power (area under the curve [AUC] = 0.73; 95% CI, 0.58–0.87). In the correlation analysis, Butyricimonas was negatively correlated with glutaric acid ( r = −0.28, p = 0.015) and malondialdehyde ( r = −0.30, p = 0.012). Both Phascolarctobacterium ( r = 0.27, p = 0.022) and Alistipes ( r = 0.31, p = 0.009) were positively correlated with allopregnanolone.

Conclusions

T1D patients with depression were characterized by unique profiles of gut microbiota and serum metabolites. Phascolarctobacterium , Butyricimonas , and Alistipes could predict the risk of T1D with depression. These findings provide further evidence that the microbiota–gut–brain axis is involved in T1D with depression.

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Type 1 diabetes (T1D) patients with depression were characterized by unique profiles of gut microbiota and serum metabolites.
Phascolarctobacterium , Butyricimonas , and Alistipes could predict the risk of T1D with depression.

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1. INTRODUCTION

Type 1 diabetes (T1D), an autoimmune disease characterized by the destruction of insulin‐producing β cells in the pancreas, poses a significant health challenge worldwide. 1 Individuals with T1D have an increased risk of psychological disorders including depression, anxiety, eating disorder, and substance abuse. 2 These mental disorders in diabetes are associated with poor compliance with treatment and inadequate glycemic control, which increases the risk for severe diabetic complications and represents a major medical and economic burden worldwide. 3 , 4 , 5

Depression is the most common psychological disorder in patients with T1D. 6 A previous meta‐analysis including 14 studies showed that the pooled prevalence of depressive symptoms was 30% in children and adolescents with T1D, 7 and another study reported that the prevalence of depression was 24% in adults with T1D. 6 However, the pathogenesis of depression in T1D is still unclear. Biological risk factors affecting the central nervous system are likely to be involved. 8 , 9

It has been reported that significant differences have been observed in the composition of gut microbiota and their associated metabolites in patients with T1D. 10 Patients with T1D exhibit compositional alterations in the gut microbiota, including a decreased ratio of Firmicutes to Bacteroides , a decreased abundance of short‐chain fatty acid‐producing bacteria, and an increased abundance of Bacteroides and Bifidobacterium . 11 However, the alteration of Bifidobacterium and Streptococcus lacks consistency. 12 , 13 , 14 It was reported that gut microbiota might influence the psychological state via the microbiota–gut–brain axis. 15 , 16 , 17 Similarly, studies on the effects of probiotic or prebiotic intake indicated an important role for the microbiota in regulating depression, anxiety, and other emotional responses. 18 , 19 , 20 In addition, metabolomic tools contributed to a better understanding of the role of gut microbiota in the development of mental disorders. 21 , 22 While the common biological mechanisms of type 2 diabetes and depression (including insulin resistance, hippocampal atrophy, and endothelial dysfunction) are becoming increasingly understood, 23 , 24 the corresponding evidence for T1D is still limited 25 and the correlations between microbial and metabolomic changes in T1D are currently rarely discussed. 26 , 27

In this study, we aimed to investigate microbial and metabolomic profiles and identify novel biomarkers for T1D with depression.

2. MATERIALS AND METHODS

2.1. study populations.

This study was designed as a noninterventional cross‐sectional case–control study. All subjects were enrolled at the Third Affiliated Hospital of Sun Yat‐sen University, Guangzhou, China. T1D patients were participants of the T1D China Registry Study ( www.chictr.org.cn , ChiCTR2000034642). 28 Inclusion criteria were participants diagnosed with T1D based on previous studies 28 , 29 and were at least 18 years of age. Exclusion criteria were (1) acute or chronic gastrointestinal disease; (2) history of intestinal surgery; (3) dietary modulation that was known to affect the microbiota; (4) use of antibiotics in the previous 3 months; (5) use of probiotics, prebiotics, or synbiotics in the previous month; or (6) combined neurological illness confirmed by a detailed clinical assessment. The recruitment of participants is depicted in Figure 1 .

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The flow chart of study participant selection. SDS, self‐rating depression scale; T1D, type 1 diabetes.

We consecutively recruited 72 T1D patients, including 37 T1D patients with depression (TD+) and 35 T1D patients without depression (TD−), and 29 healthy controls (HCs) matched for age and gender were recruited through public advertisements. The protocol for this study conformed to the ethical guidelines of the Declaration of Helsinki and was approved by the ethics committee of the Third Affiliated Hospital of Sun Yat‐sen University (approval no. [2014]2‐1051). All participants provided informed written consent.

2.2. Psychological assessment

The self‐rating depression scale (SDS) was used to assess the depression of all participants. 30 The scale included 20 items, with each item scoring from 1 to 4. It was calculated as the sum scores of all the items and then multiplied by a weight of 1.25. A higher score suggests a more sever status of depression, and a score ≥53 suggests depression in the Chinese population. 31 The assessment training for researchers was conducted to ensure consistency. After assessment, patients with an SDS score ≥53 were further diagnosed by psychologists based on the International Classification of Diseases, Tenth Revision (ICD‐10) criteria.

2.3. Clinical data and sample collection

Anthropometric and clinical data were collected from all participants, and T1D patients were instructed to follow healthy dietary patterns for diabetes. For microbiota and metabolomics analysis, fresh feces and serum samples were collected from the study population and kept in sterile tubes. All the samples were stored at −80°C as soon as possible till processing.

2.4. Fecal DNA extraction and 16S rRNA sequencing

The fecal DNA was extracted by the MagPure Stool DNA KF Kit B (Magen, China) according to the manufacturer's instructions. 16S rRNA gene amplification was performed by directional primers (515F: 5′‐ACTCCTACGGGAGGCAGCAG‐3′; 806R: 5′‐GGACTACHVGGGTWTCTAAT‐3′) targeting the V4 hypervariable regions. The polymerase chain reaction (PCR) was performed in triplicate, each reaction comprising a volume of 50 μL. The PCR products were purified using AmpureXP beads and eluted in the elution buffer. The validated libraries were used for sequencing on the Illumina MiSeq platform (BGI, Shenzhen, China) and generating 2 × 250 bp paired‐end reads.

2.5. Sequencing data processing

Fastq file analysis was performed using the Quantitative Insights Into Microbial Ecology (QIIME2). 32 Reads of 250 bp were clipped for each consecutive three‐locus position with an average quality score of 20. Paired‐end reads were added to tags by the Fast Length Adjustment of Short Reads (FLASH, 1.2.11). 33 Tags were classified as operational tax units (OTUs) by UPARSE 7.0.1090 with a cutoff of 97%, and UCHIME 4.2.40 was used to verify and remove chimeric sequences for further analysis. The remaining high‐quality sequences were grouped into operationally amplified sequence variants (ASVs) by the DADA2 algorithm with 100% similarity. 34 Using the Ribosomal Database Project Classifier (RDP 11.5), representative ASVs were annotated with taxonomic information based on the Mothur algorithm.

2.6. Metabolite extraction and data processing

After being thawed slowly at 4°C, 300 μL of precooled methanol and acetonitrile (2:1, v:v) were added for extracting 100‐μL samples, and quality control was performed by Internal Standards Mix 1 (IS1) and Internal Standards Mix 2 (IS2). After a 1‐min vortex and incubation at −20°C for 2 h, the samples were centrifuged at 4000 rpm for 20 min, and then the supernatant was transferred to vacuum freeze‐drying. Metabolites were resuspended in 150 μL of 50% methanol, centrifuged at 4000 rpm for 30 min, and the supernatants were transferred to liquid chromatography–mass spectrometry (LC–MS) analysis. 35 , 36 Raw data were imported into Compound Discoverer 3.1 (Thermo Fisher Scientific, Waltham, Massachusetts) and MetaX for further processing.

2.7. Bioinformatics and statistical analysis

For microbiome analysis, Venn plots of ASVs were performed by the “Venn diagram” package in R. Nonmetric multidimensional scaling (NMDS), principal coordinate analysis (PCoA) were used to analyze the β‐diversity. The Wilcoxon rank sum test was used to compare α‐diversity and bacterial communities among the three groups, and α‐diversity, including Shannon, Simpson, Chao1, and abundance‐based coverage estimator (Ace), was estimated by Mothur 1.31.2. Furthermore, linear discriminant analysis effect size (LefSe) was used to identify altered microbiota with the linear discriminant analysis (LDA) score >2.0. 37 To further determine microbial biomarkers that can differentiate between TD− and TD+, a random forest (RF) model was constructed using the area under the curve (AUC)‐RF algorithm. 38 The input variable for the classification model was only included if it was present in more than 20% of samples and its relative abundance was greater than 0.05%. The RF model was established by receiver operating characteristic (ROC) curve analysis. 39

For metabolomics analysis, the unsupervised principal component analysis (PCA) and supervised orthogonal partial least squares discriminant analysis (OPLS‐DA) were conducted to determine the distributions and identify the metabolic difference in the two groups. The screening conditions for the differential metabolites were as follows: (1) fold change (FC) ≥1.20 or ≤0.83, (2) p < 0.05, and (3) variable importance in projection (VIP) value ≥1.0. The metabolites were determined with a combination of the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. 40

Data were expressed as the mean ± SD for continuous variables, and frequency and percentage (%) were used for categorical variables. Comparisons between different groups were analyzed by t test or nonparametric test. To analyze the correlation network in microbiota, Spearman's correlation coefficients among genera were calculated. Correlations with an absolute coefficient value ≥0.6 and p value <0.05 were transformed into associations between two genera. Network graphs were constructed by Cytoscape 3.9.1. Spearman's rank correlation was also conducted to show the correlation among microbiota, metabolites, and clinical parameters. Pairwise comparisons of environmental factors were shown by the Mantel test, with a color gradient indicating Spearman's correlation coefficients. A p value <0.05 was considered to be statistically significant. All statistical analyses were performed using SPSS version 23.0 software (IBM Corporation, New York), GraphPad Prism 8.4.3 software (GraphPad Software Inc., San Diego, California), and the statistical program R (version 4.0.2; The R Foundation, Vienna, Austria).

3.1. Clinical characteristics of study population

The clinical characteristics of the study population are presented in Table 1 . There were no significant differences in sex, age, duration, body mass index (BMI), waist‐to‐hip ratio (WHR), glycosylated hemoglobin (HbA1c), fasting C‐peptide (FCP), total cholesterol (TC), triglyceride (TG), high‐density lipoprotein cholesterol (HDL‐C) and low‐density lipoprotein cholesterol (LDL‐C) between patients in TD− and TD+. However, patients in TD+ had a higher insulin dose (0.77 ± 0.23 u/kg vs. 0.64 ± 0.20 u/kg for TD+ and TD− group, respectively; p = 0.018) and fasting plasma glucose (FPG) (9.19 ± 3.86 mmol/L vs. 7.29 ± 2.39 mmol/L for TD+ and TD− group, respectively; p = 0.015) than those in TD−.

Demographic and clinical characteristics of participants.

Note : Data are mean (SD) or n (%). p values were compared by the two groups (TD−; TD+).

Abbreviations: BMI, body mass index; FCP, fasting C‐peptide; FPG, fasting plasma glucose; HC, healthy control; HbA1c, glycosylated hemoglobin; HDL‐C, high‐density lipoprotein cholesterol; LDL‐C, low‐density lipoprotein cholesterol; T1D, type 1 diabetes; TC, total cholesterol; TD−, T1D patients without depression; TD+, T1D patients with depression; TG, triglyceride; WHR, waist‐to‐hip ratio.

3.2. Bacterial diversity among HC , TD −, and TD +

A total of 7 525 573 high‐quality reads (2 151 588 reads in HC, 2611588 reads in TD−, and 2 762 397 reads in TD+) with an average of 74 511 reads per sample were obtained for microbiota analysis. Deeper sequencing identified the majority of the bacterial phylotypes (1552 ASVs) present in T1D patients. Based on the Venn diagram, we observed a slightly lower number of ASVs in TD+ (Figure 2A ). In β‐diversity analyses, compared to NMDS analysis and PCoA based on weighted UniFrac distances, PCoA conducted based on unweighted UniFrac distances could more effectively separate the three groups into distinct clusters (Pr [> F ] < 0.001) (Figure 2B–D ). Regarding bacterial α‐diversity analyses, we observed significant differences in the Shannon, Simpson, Chao1, and Ace indices among the HC, TD−, and TD+ groups ( p < 0.05), where TD+ exhibited decreased diversity compared to TD− and HC ( p < 0.05) (Figure 2E–H ).

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Alpha and Beta diversity among HC, TD− and TD+. (A) The Venn diagram among HC, TD− and TD+; (B) NMDS analysis of β‐diversity based on the Bray–Curtis dissimilarity; (C, D) PCoA analyses based on weighted (left) and unweighted (right) UniFrac distances in HC, TD− and TD+; (E–H) Box plots of differences in α‐diversity of the gut microbiota of Shannon (E), Simpson (F), Chao1 (G) and Ace (H) indices. *: p < 0.05; **: p < 0.01. Ace, abundance‐based coverage estimator; HC, healthy controls; NMDS, non‐metric multidimensional scaling; PCoA, principal co‐ordinates analysis; TD−, T1D patients without depression; TD+, T1D patients with depression.

3.3. Altered fecal microbiota composition among HC , TD −, and TD +

The overall microbial compositions in the HC, TD−, and TD+ groups were examined at different taxonomic levels. The top 10 most abundant microbiota at the phylum, family, and genus levels among the three groups are shown in Figure 3A–C . We revealed significant differences in the relative abundance of Lentisphaerae (phylum), Porphyromonadaceae (family), Enterobacteriaceae (family), Acidaminococcaceae (family), Parabacteroides (genus), Phascolarctobacterium (genus), and Escherichia (genus) among the HC, TD−, and TD+ groups ( <p < 0.05). LEfSe identified many bacterial phylotypes that could potentially distinguish TD+ from TD− (Figure 3D ). At the phylum level, Lentisphaerae was observed to differ significantly between the two groups. At the family level, we found Enterococcaceae were enriched in TD+, while Eubacteriaceae, Victivallaceae, Acidaminococcaceae, Rikenellaceae, and Desulfovibrionaceae were significantly decreased. At the genus level, Phascolarctobacterium , Ruminococcus2 , Bilophila , Butyricicoccus , Clostridium XlVb, Alistipes , Intestinimonas , Oscillibacter , Odoribacter , Sporobacter , Butyricimonas , Victivallis , Desulfovibrio , Eubacterium , and Faecalicoccus were significantly enriched in TD−, while Eisenbergiella and Enterococcus were markedly reduced. Together, these results revealed differences in microbial diversity and structure between TD− and TD+.

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Microbiotic characteristics among HC, TD− and TD+. (A–C) Top 10 abundant gut microbiota at the phyla (A), family (B) and genera (C) level among HC, TD− and TD+; (D) The bacterial phylotypes between TD− and TD+ based on LefSe analysis. *: p < 0.05; **: p < 0.01. HC, healthy controls; LefSe, linear discriminant analysis effect size; TD−, T1D patients without depression; TD+, T1D patients with depression.

3.4. Multivariate analyses and metabolites identification between TD − and TD +

The distribution of samples was determined by PCA. As shown in the score plot in positive or negative ion modes, TD− and TD+ were not separated (Figure 4A, B ). To optimize the separation of the two groups, OPLS‐DA was used to visualize their metabolic differences. The score plots of OPLS‐DA were shown in Figure 4C, D . Following the response test of score plots, no significant evidence of complete over‐fitting was observed, indicating the construction of a good OPLS‐DA model. These results showed that TD− and TD+ had different metabolic characteristics. Differential metabolites were further screened based on the VIP values obtained from the positive and negative OPLS‐DA models. A total of 54 metabolites (40 upregulated and 14 downregulated metabolites) in TD+ versus TD− were identified, and 14 differential metabolites (11 upregulated in TD+ [including 4‐methylene‐2‐oxoglutarate; 4‐tert‐octylphenol; di‐isodecyl phthalate; 12,13‐DHOME, 11‐aminoundecanoic acid; glutaric acid; bis (2‐ethylhexyl) phthalate; allamandin; diethyl pyrocarbonate; soyasapogenol A; and malondialdehyde] and 3 downregulated metabolites in TD+ [including polygodial, allopregnanolone, and uric acid]) were mapped to KEGG (Table 2 ).

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Metabolic characteristics between TD− and TD+. (A, B) The score plots of PCA models in positive‐ion mode (A) and negative‐ion mode (B) between TD− and TD+;<(C, D) The score plots of OPLS‐DA models in positive‐ion mode (C) and negative‐ion mode (D) between TD− and TD+.

Metabolite identification in serum from T1D patients with depression versus without depression.

Abbreviations: ESI, electron spray ionization; FC, fold change; KEGG, Kyoto Encyclopedia of Genes and Genomes; T1D, type 1 diabetes; TD+, T1D patients with depression; VIP, variable importance in projection.

3.5. Correlation analysis of altered microbiota, metabolites, and clinical characteristics between TD − and TD +

The co‐occurrence network between TD− and TD+ was totally different (Figure 5A,B ). The network was simplified in TD+ with a reduced number of nodes. More fragmented subnetworks appeared in TD+ due to the lower clustering coefficient of the network. Then we constructed a correlation heatmap to explore associations of altered microbiota and metabolites (Figure 5C ). Butyricimonas was positively correlated with polygodial but negatively correlated with three metabolites (glutaric acid, malondialdehyde, and allamandin); Phascolarctobacterium was positively correlated with allopregnanolone but negatively correlated with soyasapogenol A; Alistipes was positively correlated with allopregnanolone but negatively correlated with 12,13‐DHOME; Butyricicoccus was negatively correlated with 11‐aminoundecanoic acid and bis (2‐ethylhexyl) phthalate. We also found that Bilophila and Sporobacter were positively correlated with polygodial. Additionally, to identify the impact of environmental factors, we correlated altered microbiota ( Phascolarctobacterium , Ruminococcus2 , Bilophila , Butyricicoccus , Clostridium XlVb, Alistipes , Intestinimonas , Oscillibacter , Odoribacter , Sporobacter , Butyricimonas , Victivallis , Desulfovibrio , Eubacterium , Faecalicoccus , Eisenbergiella , and Enterococcus ) and metabolites (4‐methylene‐2‐oxoglutarate; 4‐tert‐octylphenol; di‐isodecyl phthalate; 12,13‐DHOME; 11‐aminoundecanoic acid; glutaric acid; bis (2‐ethylhexyl) phthalate; allamandin; diethyl pyrocarbonate; soyasapogenol A; malondialdehyde; polygodial; allopregnanolone; and uric acid) with clinical characteristics (including age, duration, insulin dose, BMI, WHR, HbA1c, FPG, FCP, TC, TG, HDL‐C, and LDL‐C). Based on the Mantel test, we found that FPG was the common correlate of both altered microbiota and metabolites (Figure 5D ).

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Correlation analysis of altered gut microbiota, serum metabolites and clinical characteristics between TD− and TD+. (A, B) The correlation network at the genera level of microbiota in TD− (A) and TD+ (B). The correlation coefficient was calculated with Spearman's rank correlation test (| r | ≥ 0.6); (C) The correlation heatmap of the altered fecal microbiota and serum metabolites between TD− and TD+; (D) The clinical characteristics correlated with altered fecal microbiota and serum metabolites between TD− and TD+ based on the Mantel test. BMI, body mass index; FCP, fasting C‐peptide; FPG, fasting plasma glucose; HbA 1c , glycosylated hemoglobin; HDLC, high‐density lipoprotein cholesterol; LDLC, low‐density lipoprotein cholesterol; TC, total cholesterol; TG, triglyceride; TD−, T1D patients without depression; TD+, T1D patients with depression; WHR, waist‐to‐hip ratio.

3.6. Microbial signature associated with TD +

To further identify microbial signatures that can differentiate TD+ from TD−, an RF classifying model was constructed using the AUC‐RF algorithm. The results demonstrated a minimal set of five genera that maximally distinguished TD− from TD+. It contained Phascolarctobacterium , Butyricimonas , Alistipes , Lachnospiracea incertae sedis , and Blautia . The probability value of this training model in TD+ increased significantly compared with that in TD− (Figure 6A ), and the AUC value was 0.73 (95% CI, 0.58–0.87) (Figure 6B ). Most of these (including Phascolarctobacterium , Butyricimonas , and Alistipes ) had an LDA value >2.0, which significantly reduced in TD+. We also explored the relationship among these microbiota, their related metabolites, and FPG (Figure 6C ). FPG was negatively correlated with Butyricimonas ( r = −0.40, p < 0.01) and its related metabolites (polygodial [ r = −0.25, p = 0.03], glutaric acid [ r = 0.55, p < 0.01], and malondialdehyde [ r = 0.56, p < 0.01]).

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Identification of microbial signature associated with TD+. (A) The probability value of RF model between TD− and TD+; (B) The ROC curve of the optimal model for classifying TD+ from TD−; (C) The correlation among key microbiota, their related metabolites and FPG. FPG, fasting plasma glucose; TD−, T1D patients without depression; TD+, T1D patients with depression; RF, random forest; receiver operating characteristic.

4. DISCUSSION

In the present study, we demonstrated the profile of the gut microbiota and serum metabolites in T1D with depression through high‐throughput sequencing technology, and constructed the relationship among microbiota, metabolites, and clinical characteristics.

Compared to those without mental disorders, patients with depression and diabetes had more serious complications and higher mortality. 41 Both depression and diabetes are connected with alterations in metabolic pathways and stress networks, leading to disorders of blood glucose and insulin resistance. 42 , 43 In this study, although the difference was not statistically significant, HbA1c was 0.59% higher in TD+ than TD−. Moreover, TD+ had higher FPG and daily insulin doses, which suggested that depression might play a significant role in adherence to diabetes care and result in poor glycemic control.

Growing evidence in recent years has established the role of gut microbiota in depression. Currently, there are few studies that identified the microbial signature in T1D with depression. Petrak et al. found that the composition of the gut microbiota was associated with the development of depression in T1D, and no difference in α‐diversity was observed between T1D with depression and T1D without depression, while T1D with depression had a higher abundance of Megasphaera . 25 These findings hint at a potential link between specific bacterial groups and depression in people with diabetes. In our study, the Shannon, Chao1, and Ace indices of TD− were significantly higher than those of TD+, suggesting that TD+ had a lower α‐diversity of gut microbiota. Consistent with our results, previous studies have found that α‐diversity is often lower in patients with depression than in controls. 44 , 45 Furthermore, the imbalance of microbiota was mainly reflected by changes at the genus level. We found 15 genera enriched in TD− and 2 genera enriched in TD+ by LefSe analysis, and most of them ( Phascolarctobacterium , 46 , 48 , 49 , 51 , 53 Bilophila , 50 Butyricicoccus , 47 Clostridium XlVb, 46 Alistipes , 46 , 50 , 52 , 53 , 54 Intestinimonas , 53 Oscillibacter , 46 , 49 , 52 Odoribacter , 54 Sporobacter , 51 Butyricimonas , 46 , 48 Desulfovibrio , 48 Eubacterium , 48 , 49 and Enterococcus 49 , 52 ) have been reported in human studies on depression. We generated an RF model for predicting T1D with depression and showed high predictive accuracy (AUC = 0.73). And the majority (3/5, including Phascolarctobacterium , Butyricimonas , and Alistipes ) of key bacteria identified by RF analyses were contained in the above 17 genera. In addition, metabolite changes between groups were distinguished based on the OPLS‐DA model. In the present study, a total of 14 differential metabolites mapped to KEGG were identified. Among them, allopregnanolone, 55 , 56 uric acid, 57 , 58 glutaric acid, 59 , 60 bis (2‐ethylhexyl) phthalate, 61 and malondialdehyde 62 , 63 have been reported in previous studies on depression. In these studies, allopregnanolone was found to be involved in steroid hormone metabolism, 55 uric acid in purine metabolism, 58 glutaric acid in lysine and fatty acid metabolism, 59 , 60 bis (2‐ethylhexyl) phthalate in glutamate metabolism, 61 and malondialdehyde in lipoprotein metabolism. 62

As stated above, we discovered a simplified network in TD+ in comparison with TD−. Bacteria coexist in complex interaction webs, and perturbations within these webs may contribute to disease. 64 Our finding suggested the loss of certain microbial interactions in TD+, leading to destruction of the homeostasis of the microbiota. Furthermore, the production of metabolites by microbes contributes to the host metabolic phenotype and influences the disease risk. Here, we explored the relationship between key bacteria and differential metabolites. Butyricimonas was negatively correlated with glutaric acid and malondialdehyde. Butyricimonas is a butyrate producer, which contributes to intestine epithelial integrity. 65 Decreased levels of Butyricimonas were found in T1D and depression. 11 , 46 Glutaric acid is produced during the metabolism of lysine. Fluctuations in blood glucose levels could affect this metabolic pathway. 66 Lu et al. found that the level of glutaric acid was higher in patients with fulminant T1D than in HCs. 67 Glutaric acid was also identified as a potential biomarker of chronic unpredictable mild stress (CUMS)‐induced depression. 59 Patients with postpartum depression had higher levels of glutaric acid than those without. 60 Malondialdehyde, the final product of polyunsaturated fatty acid peroxidation in the cells, is commonly known as a marker of oxidative stress. Elevated malondialdehyde levels have been observed in individuals with T1D, and malondialdehyde can further contribute to cellular damage and impair insulin sensitivity, exacerbating the progression of the disease. 68 , 69 In recent years, oxidative stress has received much attention with regard to psychiatric illnesses. Malondialdehyde was found to be elevated in patients with major depression and depressed patients with physical ailments, such as gastric adenocarcinoma, chronic heart failure, and stroke. 62 , 70 , 71

Furthermore, our results suggested that both Phascolarctobacterium and Alistipes were positively correlated with allopregnanolone. Phascolarctobacterium produces short‐chain fatty acids (including acetate and propionate) and is involved in energy metabolism and immune inflammation. 72 The levels of Phascolarctobacterium were higher in healthy subjects than T1D patients. 73 On the other hand, Humbel et al. found that Phascolarctobacterium was negatively correlated with depressive symptoms in major depression. 48 Alistipes , a relatively new genus of bacteria isolated from clinical samples, may promote mucous production and a healthy intestinal epithelial barrier in T1D. 74 However, Ma et al. found that Alistipes was upregulated in T1D rats injected with streptozotocin. 75 Evidence for the involvement of Alistipes in depression is also controversial. Caso et al. reported enrichment of the Alistipes in the depressed patients, but Zhang et al. found that Alistipes was relatively more abundant in the HCs compared to patients with major depressive disorder. 50 , 52 Allopregnanolone is a naturally occurring neurosteroid from the hormone progesterone and a positive allosteric modulator of γ‐aminobutyric acid (GABA). 76 It has been shown to have anti‐inflammatory and immunomodulatory effects, which could potentially help protect against cognitive deficit and dysbiosis in T1D. 77 And reduced levels of allopregnanolone in the peripheral blood or cerebrospinal fluid were found to be associated with depression. 78

In addition, our study discovered the relationship among key microbiota, their related metabolites, and FPG. These results suggested that Butyricimonas might affect FPG in T1D patients with depression through the glutaric acid and malondialdehyde metabolic pathways. In the future, we will explore their causal relationship in T1D with depression and investigate the biological mechanism through further study.

There are some limitations to our study. Firstly, this study included a relatively small sample size which was insufficient to completely reflect microbial and metabolic changes in T1D with depression. Secondly, all participants were recruited from the same site and were of Han ethnicity. Thus, site‐specific and ethnic biases in microbial phenotypes could not be ruled out. Thirdly, we used the 16S RNA sequencing method to assess the gut microbiota, which might not be as accurate for those species. Fourthly, it would be more scientifically rigorous to set up a separate control group for patients with depression but without T1D. The interdisciplinary collaboration between endocrinology and psychiatry is expected in the future.

In conclusion, our study indicated significant alterations in gut microbiota and serum metabolites in T1D with depression. T1D patients with depression were characterized by unique profiles of gut microbiota and serum metabolites. Phascolarctobacterium , Butyricimonas , and Alistipes could predict the risk of T1D with depression. These findings provide further evidence that the microbiota–gut–brain axis is involved in T1D with depression.

AUTHOR CONTRIBUTIONS

Conception and design of the study: X.Z., S.L., W.X., J.Y., J.W., D.Y., and C.W. Acquisition of data was performed by Z.L., X.Z., and S.L. Collection, analysis, and interpretation of data: Z.L., T.Y., S.L., and C.W. Preparation of figures and tables and writing of original draft: Z.L., D.Y., and C.W. All authors contributed to the article and approved the submitted version.

FUNDING INFORMATION

This study was supported by the National Key R&D Program of China (grant no. 2017YFC1309600), the Guangdong Basic and Applied Basic Research Foundation (grant no. 2019A1515010979), the National Natural Science Foundation of China (grant no. 82100822), the Anhui Provincial Natural Science Foundation (grant no. 2008085MH248 & 2008085MH278), and the Science and Technology Projects in Guangzhou (grant no. 2023A04J1087).

The authors declare that they have no conflict of interest.

ACKNOWLEDGEMENTS

We appreciate all participants from the T1D China Registry Study for their contribution in this study.

Liu Z, Yue T, Zheng X, et al. Microbial and metabolomic profiles of type 1 diabetes with depression: A case–control study . Journal of Diabetes . 2024; 16 ( 4 ):e13542. doi: 10.1111/1753-0407.13542 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

Contributor Information

Daizhi Yang, Email: nc.ude.usys.liam@3hzdgnay .

Chaofan Wang, Email: nc.ude.usys.liam@52fhcgnaw .

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