case study dm type 2

Open access
Published: 17 January 2024

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

2115 Accesses

1 Altmetric

Metrics details

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.

Download references

Acknowledgements

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

This research received no external funding.

Author information

Authors and affiliations.

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

You can also search for this author in PubMed Google Scholar

Contributions

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.

Corresponding author

Correspondence to Mohammad Amin Kashani .

Ethics declarations

Ethics approval and consent to participate.

This study was performed in line with the principles of the Declaration of Helsinki. Informed consent was obtained from all participants or their legal guardians. Approval was granted by the Research Ethics Committee of Islamic Azad University of Medical Sciences (Approval number: IR.AUI.MEDICINE. REC.1401.147).

Consent for publication

Not applicable.

Competing interests

The authors declared no conflicts of interest.

Additional information

Publisher’s note.

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

Rights and permissions

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

Reprints and permissions

About this article

Cite this article.

haramshahi, M., A-Elgadir, T.M.E., Daabo, H.M.A. et al. Nutrient patterns and risk of diabetes mellitus type 2: a case-control study. BMC Endocr Disord 24 , 10 (2024). https://doi.org/10.1186/s12902-024-01540-5

Download citation

Received : 04 November 2023

Accepted : 09 January 2024

Published : 17 January 2024

DOI : https://doi.org/10.1186/s12902-024-01540-5

Share this article

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

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

Provided by the Springer Nature SharedIt content-sharing initiative

Nutrient pattern

BMC Endocrine Disorders

ISSN: 1472-6823

General enquiries: [email protected]

Breakthrough Studies on Automated Insulin Delivery and CGM for Type 2 Diabetes Unveiled at ADA Scientific Sessions

Results Demonstrate Enhanced Diabetes Management and Quality of Life with Advanced Technology

New data focused on advanced technology innovations for managing type 2 diabetes (T2D) highlight the positive impact of automated insulin delivery systems (AID) and continuous glucose monitoring (CGM) in improving glycemic control and overall diabetes management. Three studies showing advancements for type 2 diabetes were presented at the American Diabetes Association’s ® (ADA) 84th Scientific Sessions in Orlando, FL.

Of the nearly 40 million Americans with diabetes, more than 90% have type 2 diabetes. As the prevalence continues to rise globally, effective management strategies are more critical than ever. The studies showcased at the ADA Scientific Sessions emphasize the transformative potential of integrating advanced technologies into diabetes care, particularly for under-resourced populations.

"These studies represent a significant advancement in diabetes management technologies, showing substantial improvements in glycemic control and quality of life for people with type 2 diabetes," said Robert Gabbay, MD, PhD, ADA chief scientific and medical officer. “By leveraging these innovations, we can empower patients with more effective and manageable treatment options, ultimately transforming the landscape of diabetes care.”

SECURE-T2D Pivotal Trial Demonstrates Significant Benefits of Omnipod® 5 Automated Insulin Delivery System in Adults with Type 2 Diabetes

Findings from the SECURE-T2D pivotal trial, the first large-scale, multicenter study evaluating the Omnipod® 5 AID System, a novel insulin pump, in a racially diverse group of adults with type 2 diabetes were presented as a late-breaking poster.

The Omnipod 5 AID System is a tubeless insulin pump that automatically adjusts insulin delivery based on CGM data. This system aims to improve glycemic control by responding to glucose levels in real-time, reducing the burden of manual insulin dosing for people with diabetes.

The multicenter pivotal clinical trial included 305 adults aged 18-75 years with type 2 diabetes who were using various insulin regimens (basal-bolus, premix, or basal-only) and had a baseline HbA1c of less than 12.0%. After a 14-day standard therapy phase to establish baseline glycemic control, participants transitioned to 13 weeks of using the Omnipod 5 AID System. The primary endpoint was the change in HbA1c from baseline to 13 weeks. The study population was also notably diverse, with 24% Black and 22% Hispanic/Latino participants.

Key findings from the trial showed that the use of the Omnipod® 5 AID System led to a significant reduction in HbA1c levels, from a baseline average of 8.2±1.3% to 7.4±0.9% at the end of the study (treatment effect: -0.8%, 95% CI: -1.0 to -0.7, p<0.001). The greatest improvements were observed in participants with the highest baseline HbA1c.

"The results from the SECURE-T2D trial underscore the potential of the Omnipod 5 AID System to transform diabetes management for adults with type 2 diabetes,” said Francisco J. Pasquel, MD, MPH, Associate Professor of Medicine and Global Health at Emory University, and lead author of the study. “The substantial improvements in glycemic control and quality of life, particularly among minority populations, are promising steps toward more equitable diabetes care."

Future research will focus on long-term outcomes and the potential of a new solution to address other aspects of diabetes management. The authors also note that studies may explore its effectiveness in different populations and its impact on quality of life for people with type 2 diabetes. Additionally, ongoing analyses will aim to refine and enhance the system's algorithms to maximize its benefits for users.

Improved Glycemic Outcomes with Continuous Glucose Monitoring (CGM) in Type 2 Diabetes Patients: Real-World Analysis Reveals Significant Benefits

Findings from an oral presentation, Glycemic Outcomes with CGM Use in Patients with Type 2 Diabetes—Real-World Analysis, showcase the significant impact of continuous glucose monitoring on patients with type 2 diabetes, revealing the use of CGM substantially improves glucose control in type 2 diabetes patients across all therapeutic treatments.

The study evaluated the impact of CGM on adults with type 2 diabetes using non-insulin therapies (NIT), basal insulin (BIT), and prandial insulin (PIT). This 12-month retrospective analysis used data from a large claims database of over 7.1 million type 2 diabetes patients and compared HbA1c levels before and after CGM use, focusing on the change closest to 12 months post-CGM acquisition.

Among the 6,030 adults with type 2 diabetes (NIT: 1,533; BIT: 1,375; PIT: 3,122), with a mean baseline HbA1c of 8.8% and a mean age of 59 years, significant HbA1c improvements (by 1% across all therapies) were observed across all therapy groups after 12 months. The study underscores CGM's potential to enhance glycemic control and reduce healthcare costs in both insulin and non-insulin-treated type 2 diabetes patients.

"These results suggest that CGM can play a crucial role in enhancing health outcomes for all diabetes patients, regardless of their treatment regimen," said Satish K. Garg, MD, University of Colorado Denver, and lead author of the study. "The real-world analysis underscores the potential of CGM to not only improve glycemic outcomes but also reduce healthcare resource utilization and overall healthcare costs."

Looking ahead, longer-term studies and randomized controlled trials are recommended to further validate these results and explore the broader implications of CGM use in diabetes care. Future research will focus on confirming the sustained benefits of CGM and understanding its impact on various patient subgroups to tailor diabetes management strategies effectively.

Using the same database, findings from a related late-breaking abstract reveal that CGM use in type 2 diabetes results in more than a 50% reduction in all-cause hospitalizations and acute diabetes-related hospitalizations. Dr. Garg presented the results of the late-breaking abstract, Impact of Continuous Glucose Monitoring Use on Hospitalizations in People with Type 2 Diabetes—Real-World Analysis , as an e-theatre poster on Sunday, June 23, 2024.

Continuous Glucose Monitoring (CGM) Improves Glycemic Control in Adults with Type 2 Diabetes Not Using Insulin

Findings from a new study demonstrate that CGM significantly enhances glycemic control in adults with type 2 diabetes who are not using insulin. These results, presented during the general poster session and simultaneously published in Diabetes Technology and Therapeutics , underscore the potential of CGM to improve diabetes management and support expanding CGM access for adults with type 2 diabetes not using insulin.

The real-world study analyzed data from over 3,800 adults using Dexcom G6 and G7 sensors. The participants, initially not meeting their glycemic targets, showed significant improvements after six months of CGM use, with further progress at one year.

Key findings include a 0.5% reduction in the glucose management indictor, a CGM approximation of A1C, and a 17% increase in Time in Range (TIR), which translates to an additional four hours per day spent within the target glucose range. The study also highlighted the advantages of the Dexcom High Alert feature, which notifies users when glucose levels exceed their selected targets. Participants who used this feature showed the greatest improvements in their glucose levels. The consistent CGM use over the year suggests sustained benefits and a positive impact on long-term diabetes care.

“We are encouraged by the significant long-term improvements in glycemic control observed in our study,” said Jennifer E. Layne, PhD, Dexcom. “These findings highlight the importance of CGM for managing non-insulin treated type 2 diabetes for clinicians and for patient self-management.”

Looking ahead, the authors plan to continue studying this cohort and other CGM users not taking insulin to explore ongoing patterns of glycemic improvement and real-world behavior change enabled by CGM. The team also intends to evaluate the impact of other Dexcom system features on glycemic control.

Research presentation details:

Dr. Pasquel will present the findings at the late-breaking poster session presentation sessions:

Glycemic Improvement with Use of the Omnipod 5 Automated Insulin Delivery System in Adults with Type 2 Diabetes—Results of the SECURE-T2D Pivotal Trial
Presented on Saturday, June 22, 2024 at 12:30 PM EDT
Presented on Sunday, June 24, 2024 at 1:50 PM EDT

Dr. Garg will present the findings at the following oral presentation session:

Glycemic Outcomes with CGM Use in Patients with Type 2 Diabetes—Real-World Analysis
Presented on Monday, June 24, 2024 at 8:00 AM EDT

Dr. Layne will present the findings at the general poster session:

Long-Term Improvement in CGM-Measured Glycemic Control in Adults with Type 2 Diabetes Not Treated with Insulin—Real-Word

About the ADA’s Scientific Sessions The ADA's 84th Scientific Sessions, the world's largest scientific meeting focused on diabetes research, prevention, and care, will be held in Orlando, FL on June 21-24. More than 11,000 leading physicians, scientists, and health care professionals from around the world are expected to convene both in person and virtually to unveil cutting-edge research, treatment recommendations, and advances toward a cure for diabetes. Attendees will receive exclusive access to thousands of original research presentations and take part in provocative and engaging exchanges with leading diabetes experts. Join the Scientific Sessions conversation on social media using #ADAScientificSessions.

About the American Diabetes Association The American Diabetes Association (ADA) is the nation’s leading voluntary health organization fighting to bend the curve on the diabetes epidemic and help people living with diabetes thrive. For 83 years, the ADA has driven discovery and research to treat, manage, and prevent diabetes while working relentlessly for a cure. Through advocacy, program development, and education we aim to improve the quality of life for the over 136 million Americans living with diabetes or prediabetes. Diabetes has brought us together. What we do next will make us Connected for Life®. To learn more or to get involved,

visit us at diabetes.org or call 1-800-DIABETES (1-800-342-2383). Join the fight with us on Facebook ( American Diabetes Association ), Spanish Facebook ( Asociación Americana de la Diabetes ), LinkedIn ( American Diabetes Association ), Twitter ( @AmDiabetesAssn ), and Instagram ( @AmDiabetesAssn ).

Contact Virginia Cramer for press-related questions.

Your support helps drive lifesaving research, advocacy, and programs to help improve the lives of people living with diabetes.

Donate Now and DOUBLE your impact.

Diabetes & Primary Care
Vol:23 | No:02

Interactive case study: Making a diagnosis of type 2 diabetes

12 Apr 2021

Share this article + Add to reading list – Remove from reading list ↓ Download pdf

Diabetes & Primary Care ’s series of interactive case studies is aimed at GPs, practice nurses and other professionals in primary and community care who would like to broaden their understanding of type 2 diabetes.

The three mini-case studies presented with this issue of the journal take you through what to consider in making an accurate diagnosis of type 2 diabetes.

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.

Working through the case studies will improve your knowledge and problem-solving skills in type 2 diabetes by encouraging you to make evidence-based decisions in the context of individual cases.

Crucially, you are invited to respond to the questions by typing in your answers. In this way, you are actively involved in the learning process, which is a much more effective way to learn.

By actively engaging with these case histories, I hope you will feel more confident and empowered to manage such presentations effectively in the future.

Colin is a 51-year-old construction worker. A recent blood test shows an HbA 1c of 67 mmol/mol. Is this result enough to make a diagnosis of diabetes?

Rao, a 42-year-old accountant of Asian origin, is currently asymptomatic but has a strong family history of type 2 diabetes. Tests have revealed a fasting plasma glucose level of 6.7 mmol/L and an HbA 1c of 52 mmol/mol. How would you interpret these results?

43-year-old Rachael has complained of fatigue. She has a BMI of 28.4 kg/m 2 and her mother has type 2 diabetes. Rachael’s HbA 1c is 46 mmol/mol. How would you interpret her HbA 1c measurement?

By working through these interactive cases, you will consider the following issues and more:

The criteria for the correct diagnosis of diabetes and non-diabetic hyperglycaemia.
Which tests to use in different circumstances to determine a diagnosis.
How to avoid making errors in classification of the type of diabetes being diagnosed.
The appropriate steps to take following diagnosis.

Semaglutide effective treatment for HFpEF in people with type 2 diabetes

Select trial: further analysis shows preventative effects of semaglutide on type 2 diabetes development, pcds national conference 2024: request for poster abstracts, pcds news: obesity survey results, diabetes distilled: keeping kidneys flowing – semaglutide improves renal outcomes, at a glance factsheet: intermittent fasting for the management of weight and diabetes, diabetes distilled: deep dive into diabetes and infection.

ADA 2024: Semaglutide improves HFpEF-related symptoms and physical function in people with type 2 diabetes.

27 Jun 2024

ADA 2024: Semaglutide reduces risk of developing type 2 diabetes and increases likelihood of reverting to normoglycaemia in those with prediabetes.

26 Jun 2024

Poster abstract submissions are invited for the 20th National Conference of the PCDS, which will be held on 6 and 7 November.

25 Jun 2024

Key insights from the PCDS survey on obesity management, conducted in April 2024.

CPD Learning
Journal of Diabetes Nursing
Diabetes Care for Children & Young People
The Diabetic Foot Journal
Diabetes Digest

Useful information

Terms and conditions
Privacy policy
Editorial policies and ethics

By clicking ‘Subscribe’, you are agreeing that DiabetesontheNet.com are able to email you periodic newsletters. You may unsubscribe from these at any time. Your info is safe with us and we will never sell or trade your details. For information please review our Privacy Policy .

Are you a healthcare professional? This website is for healthcare professionals only. To continue, please confirm that you are a healthcare professional below.

We use cookies responsibly to ensure that we give you the best experience on our website. If you continue without changing your browser settings, we’ll assume that you are happy to receive all cookies on this website. Read about how we use cookies .

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Publications
Account settings
My Bibliography
Collections
Citation manager

Save citation to file

Email citation, add to collections.

Create a new collection
Add to an existing collection

Add to My Bibliography

Your saved search, create a file for external citation management software, your rss feed.

Search in PubMed
Search in NLM Catalog
Add to Search

Association between adverse childhood experiences and type 2 diabetes mellitus in later life: A case-control study

Affiliations.

1 Department of Genetic Engineering and Biotechnology, University of Dhaka, Dhaka, Bangladesh.
2 Bangladesh Institute of Research and Rehabilitation of Diabetes, Endocrine and Metabolic Disorder (BIRDEM) General Hospital, Dhaka, Bangladesh.
3 Department of Public Health and Informatics, Bangabandhu Sheikh Mujib Medical University, Dhaka, Bangladesh.
PMID: 38917185
PMCID: PMC11198900
DOI: 10.1371/journal.pgph.0002715

Adverse childhood experiences (ACEs) are potentially traumatic events that occur before 18 years of age. Studies emphasize the importance of childhood adversity as a risk factor for developing non-communicable diseases, including type-2 diabetes mellitus (T2DM) in adulthood. This case-control study involved 137 patients with T2DM and 134 non-diabetic adults of both genders (mean age 46.9 and 45.7 years, respectively). In addition to collecting socio-demographic, behavioral, and anthropological data, a 10-item ACE scale was utilized to gather information regarding childhood adversities, while perceived stress was assessed using the perceived stress scale-4. Fasting and 2-hour post glucose load blood sugar levels, HbA1c, and fasting lipid profiles were measured. Both univariable and multivariable binary logistic regression analyses were performed to investigate whether ACE is a potential risk factor for T2DM, with a significance level of 0.05. Around two-thirds of T2DM patients reported having ACE scores of 4 or higher, with the mean ACE score significantly higher in the case group than in the control group (3.96 vs. 3.34; p<0.0001). The logistic regression analysis found that T2DM was linked to female gender, hypertension, dyslipidemia, family history of DM, higher perceived stress, and a higher ACE score of 4 and above. After adjusting for confounding factors, individuals with an ACE score of 4 or higher had a significantly greater risk of developing T2DM (OR: 2.24; 95% CI 1.238-4.061). The study revealed a significant association between higher ACE scores and an increased risk of developing T2DM. As a recommendation, further investigation into the epigenetic mechanisms underlying this relationship is warranted.

Copyright: © 2024 Barman et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

PubMed Disclaimer

Conflict of interest statement

The authors have declared that no competing interests exist.

Related information

Linkout - more resources, research materials.

NCI CPTC Antibody Characterization Program

Miscellaneous

NCI CPTAC Assay Portal
Citation Manager

NCBI Literature Resources

MeSH PMC Bookshelf Disclaimer

The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Unauthorized use of these marks is strictly prohibited.

DOI: 10.2337/DIASPECT.16.1.32
Corpus ID: 73083750

Case Study: A Patient With Uncontrolled Type 2 Diabetes and Complex Comorbidities Whose Diabetes Care Is Managed by an Advanced Practice Nurse

G. Spollett
Published 2003
Diabetes Spectrum

5 Citations

Management of ketosis-prone type 2 diabetes mellitus., integrating a pico clinical questioning to the ql4pomr framework for building evidence-based clinical case reports, nursing practice guideline for foot care for patients with diabetes in thailand, goal-driven structured argumentation for patient management in a multimorbidity setting, logic and argumentation: third international conference, clar 2020, hangzhou, china, april 6–9, 2020, proceedings, 18 references, using a primary nurse manager to implement dcct recommendations in a large pediatric program, diabetes in urban african americans. iii. management of type ii diabetes in a municipal hospital setting., primary care outcomes in patients treated by nurse practitioners or physicians: a randomized trial., caring for a child with diabetes: the effect of specialist nurse care on parents' needs and concerns., standards of medical care for patients with diabetes mellitus, management of patients with diabetes by nurses with support of subspecialists., a practical approach to type 2 diabetes., the diabetes control and complications trial (dcct): the trial coordinator perspective, oral antihyperglycemic therapy for type 2 diabetes: scientific review., caring for feet: patients and nurse practitioners working together., related papers.

Showing 1 through 3 of 0 Related Papers

Open access
Published: 24 June 2024

ApoA1/HDL-C ratio as a predictor for coronary artery disease in patients with type 2 diabetes: a matched case-control study

Farzaneh Ghaemi 1 ,
Soghra Rabizadeh 1 ,
Amirhossein Yadegar 1 ,
Fatemeh Mohammadi 1 ,
Hassan Asadigandomani 1 ,
Melika Arab Bafrani 1 ,
Sahar Karimpour Reyhan 1 ,
Alireza Esteghamati 1 &
Manouchehr Nakhjavani 1

BMC Cardiovascular Disorders volume 24 , Article number: 317 ( 2024 ) Cite this article

175 Accesses

Metrics details

Introduction

This study investigated the possible relationship between the Apo lipoprotein A1 /high-density lipoprotein cholesterol (ApoA1/HDL-C) ratio and coronary artery disease (CAD) in patients with type 2 diabetes (T2D).

This was a matched case-control study of 482 patients with T2D in two groups of CAD and ( n = 241) non-CAD ( n = 241). The patients were classified into four quartiles according to the ApoA1/HDL-C ratio, and multivariate logistic regression analysis was performed to assess the relationship between ApoA1/HDL-C and CAD. ROC analysis was also conducted.

This study showed that the ApoA1/HDL-C ratio has an independent association with CAD in individuals with T2D. The CAD group exhibited a significantly higher ApoA1/HDL-C ratio than those without CAD ( p -value = 0.004). Moreover, the risk of CAD increased significantly across the ApoA1/HDL-C ratio quartiles, with the highest odds in the fourth quartile. The second quartile showed an odds ratio (OR) of 2.03 ( p -value = 0.048) compared to the first. Moving to the third quartile, the OR increased to 2.23 ( p -value = 0.023). The highest OR was noted in the fourth, reaching 3.41 ( p -value = 0.001). Employing a cut-off value of 2.66 and an area under the curve (AUC) of 0.885, the ApoA1/HDL-C ratio predicts CAD among patients with T2D with a sensitivity of 75% and a specificity of 91% ( p -value < 0.001).

The current study revealed an independent association between ApoA1/HDL-C ratio and CAD in patients with T2D. This ratio can be a promising tool for predicting CAD during the follow-up of patients with T2D, aiding in identifying those at higher risk for CAD.

Peer Review reports

In recent decades, the prevalence of diabetes has risen, attributed to factors including global aging, economic expansion, rapid urbanization, sedentary lifestyle, and shifts in nutrition [ 1 ]. The International Diabetes Federation (IDF) has estimated a rise to 643 million patients with diabetes by 2030 and a further increase to 783 million by 2045 [ 2 ]. Diabetes caused 6.7 million deaths in 2021, constituting 12.2% of global deaths [ 3 ].

Cardiovascular disease (CVD), including coronary artery disease (CAD), myocardial infarction (MI), stroke, and congestive heart failure, is the primary cause of mortality and morbidity among individuals with diabetes [ 4 , 5 , 6 ]. Diabetes is an independent risk factor for CVD [ 7 ]. CVD risk factors such as dyslipidemia, hypertension, obesity, and autonomic dysfunction are also more prevalent in patients with diabetes [ 8 , 9 ]. Timely diagnosis of CVD in patients with diabetes faces challenges due to altered or obscured typical CVD symptoms [ 10 ]. Additionally, diabetes often coexists with other conditions that can interfere with diagnostic tests [ 11 ]. Moreover, while indicated in certain situations, diagnostic interventions, including cardiac exercise test, cardiac radionuclide scan, computed tomography (CT) angiography, and cardiac catheterization, are costly and come with several associated complications [ 12 ]. Consequently, for early diagnosis and screening of high-risk patients, the tendency to use biochemical markers has increased [ 13 , 14 ].

Earlier studies have consistently shown that increased low-density lipoprotein cholesterol (LDL-C) levels and decreased high-density lipoprotein cholesterol (HDL-C) levels have significant risks for CVD, especially in individuals with T2D [ 15 , 16 , 17 ]. HDL-C plays a critical role in preventing and treating atherosclerotic disease. This is attributed to its anti-inflammatory properties and positive impact on reverse cholesterol transport (RCT), a mechanism responsible for eliminating surplus cholesterol from peripheral tissues and transporting it to the liver [ 18 ].

ApoA1, as one of the main components of HDL-C, plays a vital role in the formation of most plasma-esterified cholesterol and helps to promote cholesterol efflux from tissues to the liver by being a cofactor for lecithin cholesterol acyl transferase (LCAT) [ 19 , 20 ]. ApoA1 modulates LDL-C function and its clearance by the liver. ApoA1 reduces the adverse effects of LDL-C on intravascular atherosclerotic plaque formation and endothelial cell damage. Furthermore, ApoA1 provides protection against functional impairment in the islet beta cells [ 16 , 21 , 22 ]. HDL and ApoA1 are cardioprotective [ 23 ]. Studies have demonstrated that elevated ApoA1 and HDL-C concentrations are correlated with a reduction in the severity of coronary artery stenosis [ 24 ]. In addition, a lower Apo lipoprotein B/ Apo lipoprotein A1 (ApoB/ApoA1) ratio has been linked to a decreased risk of CVD [ 25 ]. Moreover, current literature suggests an association between the ApoA1/HDL-C ratio and diabetes [ 26 ].

Prior research has investigated the association between biochemical markers and CVD among patients with T2D. Evidence indicates that levels of circulating growth differentiation factor 15 (GDF-15), the ApoB/ApoA1 ratio, high-sensitivity cardiac troponin I (hs-cTnI), N-terminal prohormone of brain natriuretic peptide (NT-proBNP), and high-sensitivity C-reactive protein (hs-CRP) can be used as predictors of CVD in diabetes [ 13 , 27 ]. In addition, elevated lipoprotein(a) levels in individuals with diabetes are linked to higher ASCVD risk, and adding lipoprotein(a) to traditional risk factors improves ASCVD risk prediction [ 28 ]. However, it remains unclear whether the ApoA1/HDL-C ratio is linked to CVD in individuals with diabetes. This study aimed to investigate the correlation between the ApoA1/HDL-C ratio and CVD in patients with T2D and to assess its potential for predicting CVD occurrence.

Study population

This cross-sectional, matched case-control study was conducted among patients with T2D who attended the diabetes clinic at a tertiary hospital affiliated with Tehran University of Medical Sciences from March 2020 to March 2021. All patients with a diagnosis of T2D according to American Diabetes Association (ADA) criteria [ 29 ] and older than 18 years were included in the study. Patients with a history of chronic liver disease, End Stage Renal Disease (ESRD), pregnancy, and cancer were excluded. A total of 482 patients with T2D in two groups of CAD and ( n = 241) non-CAD ( n = 241) were enrolled in this study. CAD was defined as the presence of one of the following conditions: history of myocardial infarction (MI), acute coronary syndrome (ACS) leading to hospitalization, Percutaneous Coronary Intervention (PCI), and Coronary Artery Bypass Grafting (CABG) (Fig. 1 ). The research ethics committee of the Tehran University of Medical Sciences approved the study (IR.TUMS.IKHC.REC.1400.193). The research was conducted in accordance with the Helsinki Declaration. All participants provided informed consent.

Diagram of patient selection

FBS: fast blood sugar; 2-hpp: 2-hour postprandial blood glucose; HbA1c: hemoglobin A1c; T2D: type 2 diabetes; CAD: coronary artery disease

Data collection

A structured questionnaire including age, gender, diabetes duration, history of any disease, history of tobacco and alcohol use, smoking, drug history, history of hospitalization and surgery, and history of coronary artery disease was completed for each patient. Microvascular complications, including retinopathy, neuropathy, and diabetic kidney disease (DKD), were evaluated according to a comprehensive physical exam, laboratory findings, or medical history. Well-trained examiners performed anthropometric measurements, including weight, height, waist circumference (WC), and hip circumference (HC). A calibrated balance beam scale was used to determine the weight (rounded to the nearest 0.1 kg). Using a portable stadiometer, standing height was measured (rounded to the nearest 0.1 cm). This study measured WC at the middle point between the lower borders of the rib cage and the iliac crest (rounded to the nearest 0.1 cm). HC was measured at the widest circumference of the buttock (rounded to the nearest 0.1 cm). To calculate body mass index (BMI), weight was divided by height squared (m 2 ). By dividing WC (cm) by HC (cm), the waist-to-hip ratio (WHR) was calculated. Well-trained nurses measured blood pressure (systolic and diastolic) after 10 min of resting in the seated position with a calibrated mercury sphygmomanometer. The definition of hypertension was as follows: systolic blood pressure (SBP) ≥ 140 mm Hg or diastolic blood pressure (DBP) ≥ 90 mm Hg or treatment with antihypertensives, according to ADA guidelines [ 30 ]. Blood samples were taken from the patients. The glucose oxidase method was used to determine fasting blood sugar (FBS) and 2-hour postprandial blood glucose (2hpp) (Parsazmun, Karaj, Iran) (Auto Analyzer, BT-3000(plus), Biotechnica, Italy). To measure hemoglobin A1c (HbA1c), high-performance liquid chromatography (HPLC) was employed (DS5, DREW, England). Triglycerides (TG), total cholesterol (TC), LDL-C, HDL-C, and serum creatinine were quantified by enzymatic methods (Parsazmun, Karaj, Iran) (Auto Analyzer, BT-3000(plus), Biotechnica, Italy). Turbidometry was utilized to assess the level of ApoA1. ApoA1/HDL-C ratio was also calculated. The Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation was used to calculate the estimated glomerular filtration rate (eGFR).

Statistical analysis

Statistical analysis was conducted using Python version 3.12 with NumPy version 1.26, Pandas version 2.1.4, Matplotlib version 3.8.1, and Scikit-learn version 1.4.0 libraries [ 31 , 32 , 33 , 34 ], and SPSS version 22.0 for Windows (IBM Corporation, New York, USA). Mean and standard deviation (SD) were used to describe continuous variables, while categorical variables were expressed as frequency and percentage (%). The study population was tested for normality using Kolmogorov-Smirnov and Shapiro-Wilk tests, P-P plots, and histograms. As quantitative variables were normally distributed, Student’s t-test analysis was performed to compare the means between different groups. A Mann-Whitney U test was also conducted to assess differences in nonparametric variables such as ‘duration of diabetes.’ The chi-square test and Cochrane’s Mantel-Haenszel statistics were employed to compare categorical variables. A multivariate logistic regression analysis was performed to evaluate the association between ApoA1/HDL-C and other indicators with CAD. The results were expressed as odds ratios (ORs) and a 95% confidence interval (CI). A receiver operating characteristic (ROC) curve was used to estimate the predictive value of ApoA1/HDL-C for the CAD group, and the cut-off for ApoA1/HDL-C was calculated using the maximum Youden index. In this study, a p -value < 0.05 was considered statistically significant.

Baseline characteristic

In this study, 482 patients with T2D were included, including 241 with CAD and 241 without CAD. The mean age in the CAD group was 61.6 ± 8.1 years, and in the group without CAD was 61.3 ± 8.1years. Approximately 59% of subjects in each group were male. Duration of diabetes, smoking, hypertension, insulin use, and ApoA1/HDL-C ratio was significantly higher in the CAD group than in the group without CAD ( p -values were < 0.001, < 0.001, 0.006, 0.001, and 0.004, respectively). Using oral agents for diabetes, TC, HDL-C, and LDL-C were significantly lower in the CAD group ( p -values were 0.001, < 0.001, 0.002, and < 0.001, respectively). Regarding other variables, there was no significant difference between the two groups. Table 1 provides a summary of the baseline characteristics of the study population.

Association between ApoA1/HDL-C ratio and CAD in patients with T2D

This analysis divided patients into four quartiles to assess the correlation of ApoA1/HDL-C with CAD in patients with T2D. The first quartile included the patients with ApoA1/HDL-C between 0.5 and 2.4. The ApoA1/HDL-C ratio between 2.4 and 3, 3-3.51, and 3.51–5.89 comprised the second to fourth quartile, respectively. As shown in Fig. 2 , the first quartile consists of 48 (20%) patients with T2D and CAD, and the fourth quartile includes the highest number of patients (28%). The number of patients in the group without CAD decreased as we moved towards the fourth quartile of the ApoA1/HDL-C ratio. In multivariable logistic regression, after adjusting for confounding factors, including duration of diabetes, HbA1c, smoking, BMI, hypertension, antidiabetic agents, and anti-lipid drugs, the ApoA1/HDL-C ratio was significantly associated with CAD in individuals with T2D.

Prevalence of patients in four groups according to ApoA1/HDL-C ratio quartiles

CAD: coronary artery disease; ApoA1: apo lipoprotein A1; HDL-C: high-density lipoprotein cholesterol.

The first quartile was considered as reference. The second quartile’s OR was 2.03 (95% CI: 1.01–4.10; p -value = 0.048). The third quartile had a higher OR, at about 2.23 (95% CI: 1.11–4.46; p -value = 0.023), and the fourth quartile had the highest OR, at about 3.41 (95% CI: 1.68–6.91; p value = 0.001) (Table 2 ).

ROC curve analysis

Fig. 3 shows the predictive value of the ApoA1/HDL-C ratio for CAD in patients with T2D based on ROC analysis. Using the maximum Youden Index, the cut-off was set at 2.66 with 75% sensitivity and 91% specificity (AUC = 0.885, 95% CI = 0.827–0.939, p -value < 0.001) (Table 3 ).

ROC curve, sensitivity, and specificity of ApoA1/HDL-C ratio in diagnosis of CAD in patients with T2D; adjusted for duration of diabetes, HbA1c, smoking, body mass index, hypertension, antidiabetic agents, and anti-lipid drugs

ROC: Receiver operating characteristic; ApoA1: apo lipoprotein A1; HDL-C: high-density lipoprotein cholesterol; CAD: coronary artery disease; T2D: type 2 diabetes.

This study investigated the association between ApoA1/HDL-C ratio and CAD in patients with T2D, aiming to clarify whether this ratio has predictive value for CAD in diabetes. The ApoA1/HDL-C ratio was significantly higher in the CAD group than in those without CAD. The current analysis showed that the ApoA1/HDL-C ratio has an independent association with CAD in patients with T2D. Furthermore, the odds ratio for CAD increased across the ApoA1/HDL-C ratio quartiles, with the fourth quartile having the highest OR. With a cut-off of 2.66 and AUC of 0.885, the ApoA1/HDL-C ratio can predict CAD in patients with T2D with a sensitivity of 75% and a specificity of 91%.

Prompt diagnosis of CVD in patients with T2D is vital, as CVD ranks first in terms of cause of death in these patients [ 5 ]. Recent studies have investigated the role of biomarkers in predicting CVD in patients with T2D. Mei et al. surveyed patients with T2D with or without CAD and showed that serum GDF-15 levels and ApoB/ApoA1 ratio were higher in the CAD group. Furthermore, CAD was positively correlated with serum GDF-15 or ApoB/ApoA1 ratio. They suggested that these markers may help predict the occurrence of CAD in patients with T2D [ 13 ]. In another study, Haller et al. found that hs-cTnI, NT-proBNP, and hs-CRP were independently associated with cardiovascular events and can improve cardiovascular risk prediction [ 27 ]. To the best of our knowledge, this study is the first to investigate the association between the ApoA1/HDL-C ratio and CAD in patients with T2D. A cross-sectional survey by Jian et al. investigated the relationship between the ApoA1/HDL-C ratio and diabetes. It showed that an increase in the ApoA1/HDL-C ratio was associated with the incidence of diabetes and was a primary risk factor for diabetes in both genders [ 26 ]. In addition, a case-control study by Nakhjavani et al. showed that a higher ApoA1/ HDL-C ratio was correlated with microalbuminuria in females with T2D [ 35 ]. However, the association of this ratio with CAD in patients with T2D has yet to be reported. The independent association between ApoA1/HDL-C ratio and CAD that this study showed not only helps predict CAD but can also help elucidate the possible underlying pathways of CAD in patients with T2D, which further studies can investigate.

Several studies have shown that HDL-C and its major component, ApoA1, are cardioprotective [ 23 ]. A study in the general population found that ApoA1 levels were lower among patients with CAD, and low ApoA1 levels were independently associated with CAD presence and severity [ 24 ]. Another study compared ApoA1 and other lipid parameters in three groups of patients, including those with CAD but without T2D, patients with CAD and T2D, and a control group. Researchers found significantly lower ApoA1 levels in the CAD-positive group than in the control group [ 36 ]. In addition, studies showed that the patients with atrial fibrillation had significantly lower ApoA1 levels than the control group [ 37 , 38 ]. A case-control study compared the ApoA1 levels in patients with diabetes and non-diabetic healthy individuals. They demonstrated that the mean ApoA1 levels were lower in patients with diabetes compared to the healthy participants [ 39 ]. In a meta-analysis of patients with or without diabetes treated with statins, an increase in HDL-C levels after treatment had no significant cardiovascular benefit, whereas an increase in ApoA1 resulted in a substantial reduction in cardiovascular events [ 40 ]. In the present study, the ApoA1/HDL-C ratio was significantly higher in the CAD group, and the odds ratio for CAD was greater in the higher quartiles of the ApoA1/HDL-C ratio compared to the first quartile. Although HDL-C levels are critical, recent studies indicate its function holds even greater importance. It has been demonstrated that the simple measurement of HDL-C concentration is not always associated with cardiovascular risk [ 41 ]. HDL-C can be influenced by biomarkers such as 15-lipoxygenase (15-LPO), myeloperoxidase (MPO), symmetric dimethylarginine (SDMA), and other markers. These influences alter HDL-C, decreasing the availability of endothelial nitric oxide, causing problems in endothelial repair, triggering pro-inflammatory activation, and leading to macrophage efflux, ultimately affecting its function [ 18 ]. Huang et al. illustrated that HDL-C and ApoA1, obtained from human atheroma, were dysfunctional and oxidized by MPO [ 42 ]. Moreover, in vitro studies revealed that oxidized ApoA1 and HDL-C particles reduce their ability for optimal cholesterol acceptance and make these particles poor in lipid content. As a result, they could behave as pro-inflammatory molecules and initiate the process of augmented atherogenesis and increased risk of CVD [ 42 , 43 , 44 , 45 ]. Studies illustrated that the function and structure of HDL-C change in patients with diabetes [ 46 ]. Additionally, evidence suggests that ApoA1 is a more reliable and stable marker than HDL-C [ 47 ]. Therefore, the ApoA1/HDL-C ratio may better represent HDL-C structure and function and better predict CVD in patients with diabetes.

The ApoA1/HDL-C ratio’s predictive accuracy for CAD in patients with T2D was relatively high in this study (AUC = 0.885), and it holds promise for clinical and epidemiological research. The lower cost and easy accessibility make ApoA1 and HDL-C measurements advantageous over other diagnostic tests, especially invasive procedures. Moreover, given the crucial need for early CAD diagnosis in patients with T2D, ApoA1/HDL-C ratio testing adds significant value.

This study found that the duration of diabetes, smoking, and hypertension were significantly higher, while HDL-C levels were significantly lower in the CAD group. Given that the abovementioned factors are known risk factors for CAD, it is not surprising that they are more prevalent in the CAD group compared to the group without CAD [ 48 , 49 ]. On the contrary, TC and LDL-C levels were significantly lower in the CAD group. These differences may be due to more severe lipid-lowering therapies in patients with CAD. In addition, patients with CAD may be more adherent to lifestyle and nutritional modifications.

Strengths and limitations

This study was the first to examine the relationship between ApoA1/HDL-C and CAD in patients with T2D and its role as a predictive factor for CAD. In addition, this was an age- and sex-matched case-control study that minimized potential confounding factors related to age and gender, allowing for a more accurate assessment of the association. Nonetheless, there were some limitations to this study. Firstly, the number of patients was limited. Secondly, the findings of this analysis may not be fully generalizable to the entire diabetic population, as this study was conducted at a single center and included a limited number of participants. Thirdly, due to its case-control nature, this study cannot conclude a causal link between the ApoA1/HDL-C ratio and CAD. Moreover, there is a possibility of the presence of selection bias. Future research could explore additional factors, such as GDF-15, that may influence the ApoA1/HDL-C ratio and its association with CAD in patients with T2D. In addition, further studies could examine the predictive value of the ApoA1/HDL-C ratio for CAD over time through longitudinal and more extensive studies. In future research, the inclusion of a healthy control group could enhance the comprehensiveness of the findings.

This study showed that the ApoA1/HDL-C ratio has an independent association with CAD in patients with T2D. Incorporating the ApoA1/HDL-C ratio into current risk assessment models may improve their predictive accuracy. Furthermore, ApoA1/HDL-C ratio could assist in the early identification of patients with T2D who are at high risk for CAD, leading to more targeted patient management strategies. The ApoA1/HDL-C ratio holds potential as a valuable tool for clinicians in the risk stratification and personalized patient care.

Data availability

The data that support the findings of this study are available on request from the corresponding author.

Liu J, Ren Z-H, Qiang H, Wu J, Shen M, Zhang L, et al. Trends in the incidence of diabetes mellitus: results from the global burden of Disease Study 2017 and implications for diabetes mellitus prevention. BMC Public Health. 2020;20(1):1415.

Article PubMed PubMed Central Google Scholar

Magliano DJ, Boyko EJ, committee IDFDAtes. IDF Diabetes Atlas. Idf diabetes atlas. Brussels: International Diabetes Federation © International Diabetes Federation, 2021.; 2021.

Kim D, Alshuwaykh O, Sandhu KK, Dennis BB, Cholankeril G, Ahmed A. Trends in all-cause and cause-specific mortality among individuals with diabetes before and during the COVID-19 pandemic in the U.S. Diabetes Care. 2022;45(6):e107–9.

Article CAS PubMed Google Scholar

Zheng Y, Ley SH, Hu FB. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat Rev Endocrinol. 2018;14(2):88–98.

Article PubMed Google Scholar

Morrish N, Wang S-L, Stevens L, Fuller J, Keen H, Group WMS. Mortality and causes of death in the WHO multinational study of Vascular Disease in Diabetes. Diabetologia. 2001;44:S14–21.

Yadegar A, Mohammadi F, Rabizadeh S, Qahremani R, Esteghamati A, Nakhjavani M. Prevalence of different patterns of dyslipidemia in patients with type 2 diabetes in an Iranian population. Translational Med Commun. 2022;7(1):23.

Article Google Scholar

Sarwar N, Gao P, Seshasai SR, Gobin R, Kaptoge S, Di Angelantonio E, et al. Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies. Lancet. 2010;375(9733):2215–22.

Leon BM, Maddox TM. Diabetes and cardiovascular disease: Epidemiology, biological mechanisms, treatment recommendations and future research. World J Diabetes. 2015;6(13):1246–58.

Yadegar A, Mohammadi F, Rabizadeh S, Meysamie A, Nabipoorashrafi SA, Seyedi SA, et al. Decreasing trend of blood lipid profile in type 2 diabetes: not a promising change in HDL-C, a serial cross-sectional study. PLoS ONE. 2023;18(10):e0293410.

Article CAS PubMed PubMed Central Google Scholar

Grundy SM, Benjamin IJ, Burke GL, Chait A, Eckel RH, Howard BV, et al. Diabetes Cardiovasc Disease Circulation. 1999;100(10):1134–46.

CAS Google Scholar

Ofstad AP. Myocardial dysfunction and cardiovascular disease in type 2 diabetes. Scand J Clin Lab Investig. 2016;76(4):271–81.

Article CAS Google Scholar

Manda YR, Baradhi KM. Cardiac Catheterization Risks and Complications: StatPearls Publishing, Treasure Island (FL); 2022 2022.

Mei Y, Zhao Z, Lyu Y, Li Y. Circulating growth differentiation factor 15 levels and apolipoprotein B to apolipoprotein A1 ratio in coronary artery disease patients with type 2 diabetes mellitus. Lipids Health Dis. 2022;21(1):59.

Wu HK, Zhang Y, Cao CM, Hu X, Fang M, Yao Y, et al. Glucose-sensitive Myokine/Cardiokine MG53 regulates systemic insulin response and metabolic homeostasis. Circulation. 2019;139(7):901–14.

Du Y, Chen J, Chen MH, Yang SH, Li S, Guo YL, et al. Relationship of lipid and lipoprotein ratios with coronary severity in patients with new on-set coronary artery disease complicated with type 2 diabetics. J Geriatr Cardiol. 2016;13(8):685–92.

CAS PubMed PubMed Central Google Scholar

Raj R, Bhatti JS, Badada SK, Ramteke PW. Genetic basis of dyslipidemia in disease precipitation of coronary artery disease (CAD) associated type 2 diabetes mellitus (T2DM). Diabetes Metab Res Rev. 2015;31(7):663–71.

Mohammadi F, Yadegar A, Rabizadeh S, Ayati A, Seyedi SA, Nabipoorashrafi SA, et al. Correlates of normal and decreased HDL cholesterol levels in type 2 diabetes: a cohort-based cross-sectional study. Lipids Health Dis. 2024;23(1):18.

Marques LR, Diniz TA, Antunes BM, Rossi FE, Caperuto EC, Lira FS, et al. Reverse Cholesterol Transport: Molecular mechanisms and the non-medical Approach to enhance HDL cholesterol. Front Physiol. 2018;9:526.

Phillips JC, Wriggers W, Li Z, Jonas A, Schulten K. Predicting the structure of apolipoprotein A-I in reconstituted high-density lipoprotein disks. Biophys J. 1997;73(5):2337–46.

Qahremani R, Rabizadeh S, Mirmiranpoor H, Yadegar A, Mohammadi F, Sahebi L, et al. Lipid profile, ox-LDL, and LCAT activity in patients with endometrial carcinoma and type 2 diabetes: the effect of concurrent disease based on a case–control study. Health Sci Rep. 2023;6(9):e1537.

Khatana C, Saini NK, Chakrabarti S, Saini V, Sharma A, Saini RV, et al. Mechanistic insights into the Oxidized Low-Density Lipoprotein-Induced atherosclerosis. Oxid Med Cell Longev. 2020;2020:5245308.

Ouimet M, Barrett TJ, Fisher EA. HDL and Reverse Cholesterol Transport. Circ Res. 2019;124(10):1505–18.

Zamanian-Daryoush M, Lindner D, Tallant TC, Wang Z, Buffa J, Klipfell E, et al. The cardioprotective protein apolipoprotein A1 promotes potent anti-tumorigenic effects. J Biol Chem. 2013;288(29):21237–52.

Saez Y, Vacas M, Santos M, Saez de Lafuente J, Sagastagoitia J, Molinero E et al. Relation of high-density lipoprotein cholesterol and apoprotein A1 levels with presence and severity of coronary obstruction. International Scholarly Research Notices. 2012;2012.

Walldius G, Jungner I. The apoB/apoA-I ratio: a strong, new risk factor for cardiovascular disease and a target for lipid-lowering therapy–a review of the evidence. J Intern Med. 2006;259(5):493–519.

Jian Z-H, Lung C-C, Ko P-C, Sun Y-H, Huang J-Y, Ho C-C, et al. The association between the apolipoprotein A1/ high density lipoprotein -cholesterol and diabetes in Taiwan — a cross-sectional study. BMC Endocr Disorders. 2013;13(1):42.

Haller PM, Goßling A, Magnussen C, Brenner H, Schöttker B, Iacoviello L, et al. Biomarker-based prediction of fatal and non-fatal cardiovascular outcomes in individuals with diabetes mellitus. Eur J Prev Cardiol. 2023;30(12):1218–26.

Saeed A, Sun W, Agarwala A, Virani SS, Nambi V, Coresh J, et al. Lipoprotein(a) levels and risk of cardiovascular disease events in individuals with diabetes mellitus or prediabetes: the atherosclerosis risk in communities study. Atherosclerosis. 2019;282:52–6.

Association AD. 2. Classification and diagnosis of diabetes: standards of medical care in diabetes—2020. Diabetes Care. 2020;43(Supplement1):S14–31.

Passarella P, Kiseleva TA, Valeeva FV, Gosmanov AR. Hypertension management in diabetes: 2018 update. Diabetes Spectr. 2018;31(3):218–24.

Hunter JD, Matplotlib. A 2D graphics environment. Comput Sci Eng. 2007;9(03):90–5.

Harris CR, Millman KJ, Van Der Walt SJ, Gommers R, Virtanen P, Cournapeau D, et al. Array programming with NumPy. Nature. 2020;585(7825):357–62.

Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: machine learning in Python. J Mach Learn Res. 2011;12:2825–30.

Google Scholar

McKinney W, editor. Data structures for statistical computing in python. Proceedings of the 9th Python in Science Conference; 2010: Austin, TX.

Nakhjavani M, Nargesi A, Mostafavi E, Mirmiranpour H, Esteghamati A. Gender-related differences in HDL structure with the progression of microalbuminuria in patients with type 2 diabetes. J Diabetes Metab Disord Control. 2015;2:110–5.

Sheriff DS, Sachu P, Elshaari F. HDL, apo B/apo A1 ratio, Diabetes Mellitus and Cardiovascular Disease. Type 2 Diabetes: IntechOpen; 2013.

Zhong X, Yu J, Zhao D, Teng J, Jiao H. Association between serum apolipoprotein A1 and atrial fibrillation in the Chinese population: a case–control study. BMC Cardiovasc Disord. 2023;23(1):269.

Çınar T, Tanık VO, Gürkan K. Comparison of apolipoprotein-A1 levels between paroxysmal atrial fibrillation patients and healthy subjects. J Cardiovasc Thorac Res. 2020;12(2):140–4.

Patel VI, Patel KP, Makadia MG, Shah AD, Chaudhari KS, Nilayangode HN. Levels of apolipoprotein A1, B100 and lipoprotein (a) in Controlled and Uncontrolled Diabetic patients and in non-diabetic healthy people. J Clin Diagn Res. 2017;11(2):Bc01–5.

Boekholdt SM, Arsenault BJ, Hovingh GK, Mora S, Pedersen TR, LaRosa JC, et al. Levels and changes of HDL cholesterol and apolipoprotein AI in relation to risk of cardiovascular events among statin-treated patients: a meta-analysis. Circulation. 2013;128(14):1504–12.

Movva R, Rader DJ. Laboratory Assessment of HDL heterogeneity and function. Clin Chem. 2008;54(5):788–800.

Huang Y, DiDonato JA, Levison BS, Schmitt D, Li L, Wu Y, et al. An abundant dysfunctional apolipoprotein A1 in human atheroma. Nat Med. 2014;20(2):193–203.

Peng DQ, Brubaker G, Wu Z, Zheng L, Willard B, Kinter M, et al. Apolipoprotein A-I tryptophan substitution leads to resistance to myeloperoxidase-mediated loss of function. Arterioscler Thromb Vasc Biol. 2008;28(11):2063–70.

Wu Z, Wagner MA, Zheng L, Parks JS, Shy JM 3rd, Smith JD, et al. The refined structure of nascent HDL reveals a key functional domain for particle maturation and dysfunction. Nat Struct Mol Biol. 2007;14(9):861–8.

Zheng L, Nukuna B, Brennan ML, Sun M, Goormastic M, Settle M, et al. Apolipoprotein A-I is a selective target for myeloperoxidase-catalyzed oxidation and functional impairment in subjects with cardiovascular disease. J Clin Invest. 2004;114(4):529–41.

Kontush A, Chapman MJ. Why is HDL functionally deficient in type 2 diabetes? Curr Diab Rep. 2008;8(1):51–9.

Voight BF, Peloso GM, Orho-Melander M, Frikke-Schmidt R, Barbalic M, Jensen MK, et al. Plasma HDL cholesterol and risk of myocardial infarction: a mendelian randomisation study. Lancet. 2012;380(9841):572–80.

Dal Canto E, Ceriello A, Rydén L, Ferrini M, Hansen TB, Schnell O, et al. Diabetes as a cardiovascular risk factor: an overview of global trends of macro and micro vascular complications. Eur J Prev Cardiol. 2019;26(2suppl):25–32.

Venuraju SM, Lahiri A, Jeevarethinam A, Cohen M, Darko D, Nair D, et al. Duration of type 2 diabetes mellitus and systolic blood pressure as determinants of severity of coronary stenosis and adverse events in an asymptomatic diabetic population: PROCEED study. Cardiovasc Diabetol. 2019;18(1):51.

Download references

Acknowledgements

The authors wish to thank the patients for their participation and kind cooperation.

The authors did not receive support from any organization for the submitted work.

Author information

Authors and affiliations.

Endocrinology and Metabolism Research Center (EMRC), Vali-Asr Hospital, Tehran University of Medical Sciences, Tehran, Iran

Farzaneh Ghaemi, Soghra Rabizadeh, Amirhossein Yadegar, Fatemeh Mohammadi, Hassan Asadigandomani, Melika Arab Bafrani, Sahar Karimpour Reyhan, Alireza Esteghamati & Manouchehr Nakhjavani

You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization: MN, AE, SR; Methodology: FG, SR, AY, FM; Formal analysis: SR, FM, AY; Investigation: FG, HA, MA; Writing-original draft preparation: HA, MA, FM; Writing-review and editing: AY, SR, SK, AE; Supervision: MN, AE. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Manouchehr Nakhjavani .

Ethics declarations

Ethics approval and patient consent.

This study was conducted according to the declaration of Helsinki and was approved by the research ethics committee of the Tehran University of Medical Sciences (Approval number: IR.TUMS.IKHC.REC.1400.193). Informed consent was obtained from all subjects to publish their information without mentioning their names.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note.

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

Rights and permissions

Reprints and permissions

About this article

Cite this article.

Ghaemi, F., Rabizadeh, S., Yadegar, A. et al. ApoA1/HDL-C ratio as a predictor for coronary artery disease in patients with type 2 diabetes: a matched case-control study. BMC Cardiovasc Disord 24 , 317 (2024). https://doi.org/10.1186/s12872-024-03986-w

Download citation

Received : 01 February 2024

Accepted : 18 June 2024

Published : 24 June 2024

DOI : https://doi.org/10.1186/s12872-024-03986-w

Share this article

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

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

Provided by the Springer Nature SharedIt content-sharing initiative

ApoA1/HDL-C ratio
Apolipoprotein A-I
High-density lipoprotein
Coronary artery disease

BMC Cardiovascular Disorders

ISSN: 1471-2261

Submission enquiries: [email protected]
General enquiries: [email protected]

Study links gut microbiome changes to increased risk of type 2 diabetes

Brigham, Broad, and Harvard Chan School researchers found that specific species and strains of bacteria were linked to changes in the functioning of the gut microbiome and a person’s risk of type 2 diabetes

For immediate release: June 25, 2024

Boston, MA—The largest and most ethnically and geographically comprehensive investigation to date of the gut microbiome of people with type 2 diabetes (T2D), prediabetes, and healthy glucose status has found that specific viruses and genetic variants within bacteria correspond with changes in gut microbiome function and T2D risk. Results of the study—which represents a collaboration across Brigham and Women’s Hospital (a founding member of the Mass General Brigham healthcare system), the Broad Institute of MIT and Harvard, and Harvard T.H. Chan School of Public Health—are published in Nature Medicine .

“The microbiome is highly variable across different geographic locations and racial and ethnic groups. If you only study a small, homogeneous population, you will probably miss something,” said co-corresponding author Daniel (Dong) Wang , of the Channing Division of Network Medicine at Brigham and Women’s Hospital, Broad, and Harvard Chan School. “Our study is by far the largest and most diverse study of its kind.”

“The gut microbiome’s relationship to complex, chronic, heterogeneous diseases like T2D is quite subtle,” said co-corresponding author Curtis Huttenhower, of Harvard Chan School and Broad. “Much like studies of large human populations have been crucial for understanding human genetic variation, large and diverse populations are necessary—and increasingly feasible—for detailed microbiome variation studies as well.”

T2D affects approximately 537 million people worldwide. In T2D, the body gradually loses its ability to regulate blood sugar effectively. Research over the last decade has linked changes in the gut microbiome—the collection of bacteria, fungi, and viruses that inhabit our intestines—to the development of T2D. However, prior studies of the gut microbiome and its role in T2D have been too small and varied in study design to draw significant conclusions.

This paper analyzed data from the newly established Microbiome and Cardiometabolic Disease Consortium (MicroCardio). The investigation included newly generated data and those originally captured during several other experiments, encompassing a total of 8,117 gut microbiome metagenomes from ethnically and geographically diverse participants. People included in the study had T2D, prediabetes, or no changes in their blood sugar levels and hailed from the U.S., Israel, Sweden, Finland, Denmark, Germany, France, and China. Co-first authors on the paper are Zhendong Mei, of the Channing Division of Network Medicine at Brigham and Women’s Hospital and Broad, as well as Fenglei Wang , of Harvard Chan School and Broad.

“With this large study, we asked two questions. One is, ‘What are the roles of species and strains that make up the gut microbiome in type 2 diabetes?’ The other question is, ‘What are these microbes doing?’” Wang said. “When we analyzed this data, we found a relatively consistent set of microbial species linked to type 2 diabetes across our study populations. Many of those species have never been reported before.”

To understand the role of these microbes in the gut, the team analyzed species’ functional abilities. Different strains of a microbial species can have varied functions, like the ability to make a specific amino acid. The team found that certain strains had functions that may be linked to varied T2D disease risk.

One major functional difference they saw was that a strain of Prevotella copri —a common microbe in the gut that has the capacity to produce large amounts of branched-chain amino acids (BCAAs)—was more commonly seen in diabetes patients’ gut microbiomes. Previous studies have shown that people with chronically high blood levels of BCAAs have a higher risk of obesity and T2D.

The researchers also found evidence suggesting that bacteriophages—viruses that infect bacteria—could be driving some of the changes they detected within certain strains of gut bacteria.

“Our findings related to bacteriophages were very surprising,” Wang said. “This could mean that the virus infects the bacteria and changes its function in a way that increases or decreases type 2 diabetes risk, but more work is needed to understand this connection.”

In another analysis, the team studied a small subset of samples from patients newly diagnosed with T2D to assess microbiomes that are less likely to have been impacted by medication use or long-term high glucose status. Their results were similar to their larger findings, according to Wang.

“We believe that changes in the gut microbiome cause type 2 diabetes,” said Wang. “The changes to the microbiome may happen first, and diabetes develops later, not the other way around—although future prospective or interventional studies are needed to prove this relation firmly.”

“If these microbial features are causal, we can find a way to change the microbiome and reduce type 2 diabetes risk,” he added. “The microbiome is amenable to intervention—meaning you can change your microbiome, for example, with dietary changes, probiotics, or fecal transplants.”

One major limitation of the study is that, for the most part, it looked at patients’ microbiomes at one point in time. It didn’t look at changes to the gut microbiome or disease status over time. Future studies that build on this work include studying this link over an extended period and examining the strain-specific functions to understand better how they lead to T2D.

“A benefit and a challenge of the human microbiome is that it is highly personalized,” said Huttenhower. “The fact that we each have highly distinct microbial communities and microbial genetics means that very large population studies are needed to find consistent patterns. But once we do, individual microbiomes have the potential to be reshaped to help reduce disease risk.”

Other Harvard Chan authors include Fenglei Wang , Amrisha Bhosle , Andrew Ghazi, Yancong Zhang , Yuxi Liu, Eric Rimm , Walter Willett , Frank Hu , Qibin Qi , Meir Stampfer , and Iris Shai .

The study was funded by National Institute of Diabetes and Digestive and Kidney Diseases (R00 DK119412) and Boston Nutrition Obesity Research Center Pilot & Feasibility Program (P30 DK046200; R24 DK110499), National Institute of Nursing Research (R01 NR01999), National Institute on Aging (R01 AG077489; RF1 AG083764), and National Cancer Institute (R35 CA253185). Fenglei Wang is supported by the American Heart Association Postdoctoral Fellowship (Grant 897161).

“Strain-Specific gut microbial signatures in Type 2 Diabetes Revealed by a Cross-Cohort Analysis of 8,117 Metagenomes,” Zhendong Mei, Fenglei Wang, Amrisha Bhosle, Danyue Dong, Raaj Mehta, Andrew Ghazi, Yancong Zhang, Yuxi Liu, Ehud Rinott, Siyuan Ma, Eric B. Rimm, Martha Daviglus, Walter C. Willett, Rob Knight, Frank B. Hu, Qibin Qi, Andrew T. Chan, Robert D. Burk, Meir J. Stampfer, Iris Shai, Robert C. Kaplan, Curtis Huttenhower, Dong D. Wang, Nature Medicine, June 25, 2024, doi: 10.1038/s41591-024-03067-7

Visit the Harvard Chan School website for the latest news , press releases , and multimedia offerings .

Image: iStock/Elif Bayraktar

For more information:

Maya Brownstein [email protected]

Serena Bronda [email protected]

Karen Zusi-Tran [email protected]

Harvard T.H. Chan School of Public Health brings together dedicated experts from many disciplines to educate new generations of global health leaders and produce powerful ideas that improve the lives and health of people everywhere. As a community of leading scientists, educators, and students, we work together to take innovative ideas from the laboratory to people’s lives—not only making scientific breakthroughs, but also working to change individual behaviors, public policies, and health care practices. Each year, more than 400 faculty members at Harvard Chan School teach 1,000-plus full-time students from around the world and train thousands more through online and executive education courses. Founded in 1913 as the Harvard-MIT School of Health Officers, the School is recognized as America’s oldest professional training program in public health.

Brigham and Women’s Hospital is a founding member of Mass General Brigham and a teaching affiliate of Harvard Medical School. With nearly 1,000 inpatient beds, approximately 50,000 inpatient stays, and over 2.6 million outpatient encounters annually, clinicians across the Brigham provide compassionate, high-quality care in virtually every medical and surgical specialty to patients locally, regionally, nationally and around the world. An international leader in basic, clinical, and translational research, Brigham and Women’s Hospital has nearly 5,000 scientists, including physician-investigators, renowned biomedical researchers and faculty supported by nearly $750 million in funding. The Brigham’s medical preeminence and service to the community dates to 1832, with the opening of the Boston Lying In, one of the nation’s first maternity hospitals designed to care for women unable to afford in-home medical care. Its merger with the Free Hospital for Women resulted in the Boston Hospital for Women in 1966. In 1980, the Boston Hospital for Women, the Peter Bent Brigham Hospital and the Robert Breck Brigham Hospital officially merged to become Brigham and Women’s Hospital. With nearly 21,000 employees across the Brigham family – including the Brigham and Women’s Physicians Organization and Brigham and Women’s Faulkner Hospital – that rich history is the foundation for our commitment to providing superb care for some of the most complex cases, pursuing breakthroughs in biomedical research, training the next generation of health care providers, and serving the local and global community.

Broad Institute of MIT and Harvard was launched in 2004 to empower this generation of creative scientists to transform medicine. The Broad Institute seeks to describe the molecular components of life and their connections; discover the molecular basis of major human diseases; develop effective new approaches to diagnostics and therapeutics; and disseminate discoveries, tools, methods and data openly to the entire scientific community. Founded by MIT, Harvard, Harvard-affiliated hospitals, and the visionary Los Angeles philanthropists Eli and Edythe L. Broad, the Broad Institute includes faculty, professional staff and students from throughout the MIT and Harvard biomedical research communities and beyond, with collaborations spanning over a hundred private and public institutions in more than 40 countries worldwide.

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

View all journals
Explore content
About the journal
Publish with us
Sign up for alerts
Published: 25 June 2024

Strain-specific gut microbial signatures in type 2 diabetes identified in a cross-cohort analysis of 8,117 metagenomes

Zhendong Mei ORCID: orcid.org/0000-0001-6235-8647 1 , 2 na1 ,
Fenglei Wang ORCID: orcid.org/0000-0002-3850-2482 2 , 3 na1 ,
Amrisha Bhosle 2 , 4 ,
Danyue Dong ORCID: orcid.org/0009-0003-3256-2876 1 , 2 ,
Raaj Mehta 2 , 5 , 6 , 7 ,
Andrew Ghazi 2 , 4 ,
Yancong Zhang ORCID: orcid.org/0000-0002-2768-2975 2 , 4 ,
Yuxi Liu ORCID: orcid.org/0000-0003-2484-151X 1 , 2 , 8 ,
Ehud Rinott 9 ,
Siyuan Ma 10 ,
Eric B. Rimm ORCID: orcid.org/0000-0002-1402-7250 1 , 3 , 8 ,
Martha Daviglus 11 ,
Walter C. Willett 1 , 3 , 8 ,
Rob Knight ORCID: orcid.org/0000-0002-0975-9019 12 , 13 , 14 ,
Frank B. Hu 1 , 3 , 8 ,
Qibin Qi ORCID: orcid.org/0000-0002-2687-1758 3 , 15 ,
Andrew T. Chan ORCID: orcid.org/0000-0001-7284-6767 2 , 5 , 6 ,
Robert D. Burk ORCID: orcid.org/0000-0002-8376-8458 15 , 16 , 17 , 18 ,
Meir J. Stampfer 1 , 3 , 8 ,
Iris Shai 3 , 19 ,
Robert C. Kaplan 15 , 20 ,
Curtis Huttenhower ORCID: orcid.org/0000-0002-1110-0096 2 , 4 , 21 , 22 &
Dong D. Wang ORCID: orcid.org/0000-0002-0897-3048 1 , 2 , 3

Nature Medicine ( 2024 ) Cite this article

1741 Accesses

187 Altmetric

Metrics details

Clinical microbiology
Type 2 diabetes

The association of gut microbial features with type 2 diabetes (T2D) has been inconsistent due in part to the complexity of this disease and variation in study design. Even in cases in which individual microbial species have been associated with T2D, mechanisms have been unable to be attributed to these associations based on specific microbial strains. We conducted a comprehensive study of the T2D microbiome, analyzing 8,117 shotgun metagenomes from 10 cohorts of individuals with T2D, prediabetes, and normoglycemic status in the United States, Europe, Israel and China. Dysbiosis in 19 phylogenetically diverse species was associated with T2D (false discovery rate < 0.10), for example, enriched Clostridium bolteae and depleted Butyrivibrio crossotus . These microorganisms also contributed to community-level functional changes potentially underlying T2D pathogenesis, for example, perturbations in glucose metabolism. Our study identifies within-species phylogenetic diversity for strains of 27 species that explain inter-individual differences in T2D risk, such as Eubacterium rectale . In some cases, these were explained by strain-specific gene carriage, including loci involved in various mechanisms of horizontal gene transfer and novel biological processes underlying metabolic risk, for example, quorum sensing. In summary, our study provides robust cross-cohort microbial signatures in a strain-resolved manner and offers new mechanistic insights into T2D.

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

Access options

Access Nature and 54 other Nature Portfolio journals

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

24,99 € / 30 days

cancel any time

Subscribe to this journal

Receive 12 print issues and online access

195,33 € per year

only 16,28 € per issue

Buy this article

Purchase on Springer Link
Instant access to full article PDF

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

Revealing links between gut microbiome and its fungal community in Type 2 Diabetes Mellitus among Emirati subjects: A pilot study

Functional alterations and predictive capacity of gut microbiome in type 2 diabetes

Microbiome connections with host metabolism and habitual diet from 1,098 deeply phenotyped individuals

Data availability.

The individual-level raw shotgun sequencing data and metadata have been deposited in the European Nucleotide Archive with accession codes PRJEB37249 , PRJEB38742 , PRJEB41311 and PRJEB46098 for the Fromentin_2022 dataset (MetaCardis); the Sequence Read Archive (SRA) under accession code ERP002469 for the Karlsson_2013 dataset; the NCBI SRA under accession numbers SRA045646 and SRA050230 for the Qin_2012 dataset (Shenzhen cohort); the China NGDC Genome Sequence Archive: HRA000020 or EGA: EGAS00001004480 for the Wu_2020 dataset; and the China Nucleotide Sequence Archive (CNSA) with the dataset identifier CNP0000175 for the Zhong_2019 dataset (Suzhou cohort). The shotgun metagenomic sequencing data from the Nurses’ Health Study II (NHSII) and Health Professionals Follow-up Study (HPFS) are publicly available at the BIOM-Mass Data Portal ( https://biom-mass.org/ ; project names: HPFS and MBS). Due to the gaining of informed consent from the participants, all of the individual-level phenotype data from NHSII and HPFS are available via a request for external collaboration and upon approval of a letter of intent and a research proposal. Details on how to request external collaboration with NHSII and HPFS can be found at https://nurseshealthstudy.org/researchers (contact principal investigator: A. H. Eliassen, email: [email protected]) and https://sites.sph.harvard.edu/hpfs/for-collaborators/ (contact principal investigator L. Mucci, email: [email protected]). The individual-level metadata in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) are archived at the National Institutes of Health repositories dbGap (study accession: phs000810.v2.p2 ) and BIOLINCC (accession number: HLB01141423a). Shotgun metagenomic sequencing data from the HCHS/SOL samples described in this study are deposited in QIITA (study ID: 11666). HCHS/SOL has established a process for the scientific community to apply for access to participant data and materials, with such requests reviewed by the project’s Steering Committee. These policies are described at https://sites.cscc.unc.edu/hchs/ (contact HCHS/SOL at [email protected]). The DIRECT-PLUS Study recruited participants in Israel and was designed as a clinical trial. That study used only baseline, pre-randomization data from the DIRECT-PLUS Study for an observational analysis. Due to gaining of informed consent from the participants, the individual-level de-identified metadata and metagenomic sequencing data in the DIRECT-PLUS Study will be available for general research purposes through a request to I. Shai (email: [email protected]) and D. D. Wang (email: [email protected]) after publication. All of the source data for creating figures and extended data figures are available as supplementary information. Source data are provided with this paper.

Code availability

This study mainly relies on open-source bioinformatic tools described in detail in Methods . The analysis-specific programs are publicly available through https://github.com/DW-Group/T2D_Microbiome_Meta-analysis .

IDF Diabetes Atlas https://diabetesatlas.org/atlas/tenth-edition/ (2021).

American Diabetes Association Professional Practice Committee 2. Classification and diagnosis of diabetes: standards of medical care in diabetes – 2022. Diabetes Care 45 (Suppl. 1), S17–S38 (2022).

Article Google Scholar

Canfora, E. E., Meex, R. C. R., Venema, K. & Blaak, E. E. Gut microbial metabolites in obesity, NAFLD and T2DM. Nat. Rev. Endocrinol. 15 , 261–273 (2019).

Article CAS PubMed Google Scholar

Forslund, K. et al. Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota. Nature 528 , 262–266 (2015).

Article CAS PubMed PubMed Central Google Scholar

Karlsson, F. H. et al. Gut metagenome in European women with normal, impaired and diabetic glucose control. Nature 498 , 99–103 (2013).

Qin, J. et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 490 , 55–60 (2012).

Reitmeier, S. et al. Arrhythmic gut microbiome signatures predict risk of type 2 diabetes. Cell Host Microbe 28 , 258–272 (2020).

Sankaranarayanan, K. et al. Gut microbiome diversity among Cheyenne and Arapaho individuals from Western Oklahoma. Curr. Biol. 25 , 3161–3169 (2015).

Thingholm, L. B. et al. Obese individuals with and without type 2 diabetes show different gut microbial functional capacity and composition. Cell Host Microbe 26 , 252–264 (2019).

Wu, H. et al. The gut microbiota in prediabetes and diabetes: a population-based cross-sectional study. Cell Metab. 32 , 379–390 (2020).

Zhong, H. et al. Distinct gut metagenomics and metaproteomics signatures in prediabetics and treatment-naïve type 2 diabetics. EBioMedicine 47 , 373–383 (2019).

Article PubMed PubMed Central Google Scholar

Sonnenburg, J. L. & Bäckhed, F. Diet–microbiota interactions as moderators of human metabolism. Nature 535 , 56–64 (2016).

Pedersen, H. K. et al. Human gut microbes impact host serum metabolome and insulin sensitivity. Nature 535 , 376–381 (2016).

Dobrindt, U., Chowdary, M. G., Krumbholz, G. & Hacker, J. Genome dynamics and its impact on evolution of Escherichia coli . Med. Microbiol. Immunol. 199 , 145–154 (2010).

Van Rossum, T., Ferretti, P., Maistrenko, O. M. & Bork, P. Diversity within species: interpreting strains in microbiomes. Nat. Rev. Microbiol. 18 , 491–506 (2020).

Fromentin, S. et al. Microbiome and metabolome features of the cardiometabolic disease spectrum. Nat. Med. 28 , 303–314 (2022).

Yaskolka Meir, A. et al. Effect of green-Mediterranean diet on intrahepatic fat: the DIRECT PLUS randomised controlled trial. Gut 70 , 2085–2095 (2021).

Article PubMed Google Scholar

Pirzada, A. et al. Evolving science on cardiovascular disease among Hispanic/Latino adults. J. Am. Coll. Cardiol. 81 , 505–1520 (2023).

Mehta, R. S. et al. Stability of the human faecal microbiome in a cohort of adult men. Nat. Microbiol. 3 , 347–355 (2018).

Bao, Y. Origin, methods, and evolution of the Three Nurses' Health Studies. Am. J. Public Health. 105 , 1573–1581 (2016).

Beghini, F. et al. Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3. Elife 10 , e65088 (2021).

Ma, S. et al. Population structure discovery in meta-analyzed microbial communities and inflammatory bowel disease using MMUPHin. Genome Biol. 23 , 208 (2022).

Mallick, H. et al. Multivariable association discovery in population-scale meta-omics studies. PLoS Comput. Biol. 17 , e1009442 (2021).

Thomas, A. M. et al. Metagenomic analysis of colorectal cancer datasets identifies cross-cohort microbial diagnostic signatures and a link with choline degradation. Nat. Med. 25 , 667–678 (2019).

Ruuskanen, M. O. et al. Gut microbiome composition is predictive of incident type 2 diabetes in a population cohort of 5,572 Finnish adults. Diabetes Care 45 , 811–818 (2022).

Atarashi, K. et al. Ectopic colonization of oral bacteria in the intestine drives T(H)1 cell induction and inflammation. Science 358 , 359–365 (2017).

Clooney, A. G. et al. Ranking microbiome variance in inflammatory bowel disease: a large longitudinal intercontinental study. Gut 70 , 499–510 (2021).

Cohen-Poradosu, R., McLoughlin, R. M., Lee, J. C. & Kasper, D. L. Bacteroides fragilis -stimulated interleukin-10 contains expanding disease. J. Infect. Dis. 204 , 363–371 (2011).

Garcia-Lopez, M. et al. Analysis of 1,000 type-strain genomes improves taxonomic classification of Bacteroidetes. Front. Microbiol. 10 , 2083 (2019).

Petersen, C. et al. T cell-mediated regulation of the microbiota protects against obesity. Science 365 , eaat9351 (2019).

Fung, T. C. et al. Intestinal serotonin and fluoxetine exposure modulate bacterial colonization in the gut. Nat. Microbiol. 4 , 2064–2073 (2019).

Riester, M. et al. Risk prediction for late-stage ovarian cancer by meta-analysis of 1,525 patient samples. J. Natl Cancer Inst. 106 , dju048 (2014).

Wu, H. et al. Metformin alters the gut microbiome of individuals with treatment-naïve type 2 diabetes, contributing to the therapeutic effects of the drug. Nat. Med. 23 , 850–858 (2017).

Forslund, S. K. et al. Combinatorial, additive and dose-dependent drug–microbiome associations. Nature 600 , 500–505 (2021).

Caspi, R. et al. The MetaCyc database of metabolic pathways and enzymes: a 2019 update. Nucleic Acids Res. 48 , D445–D453 (2020).

Anastasi, A., Knight, C. G. & Barrett, A. J. Characterization of the bacterial metalloendopeptidase pitrilysin by use of a continuous fluorescence assay. Biochem. J. 290 , 601–607 (1993).

Roden, M. & Shulman, G. I. The integrative biology of type 2 diabetes. Nature 576 , 51–60 (2019).

Vatanen, T. et al. Variation in microbiome LPS immunogenicity contributes to autoimmunity in humans. Cell 165 , 842–853 (2016).

Wang, D. D. et al. The gut microbiome modulates the protective association between a Mediterranean diet and cardiometabolic disease risk. Nat. Med. 27 , 333–343 (2021).

Wang, D. D. et al. The gut microbiome modifies the association between a Mediterranean diet and diabetes in USA Hispanic/ Latino population. J. Clin. Endocrinol. Metab. 107 , e924–e934 (2022).

Tett, A. et al. The Prevotella copri complex comprises four distinct clades underrepresented in westernized populations. Cell Host Microbe 26 , 666–679 (2019).

Truong, D. T., Tett, A., Pasolli, E., Huttenhower, C. & Segata, N. Microbial strain-level population structure and genetic diversity from metagenomes. Genome Res. 27 , 626–638 (2017).

Vangay, P. et al. US immigration westernizes the human gut microbiome. Cell 175 , 962–972 (2018).

Wang, T. J. et al. Metabolite profiles and the risk of developing diabetes. Nat. Med. 17 , 448–453 (2011).

Karcher, N. et al. Analysis of 1,321 Eubacterium rectale genomes from metagenomes uncovers complex phylogeographic population structure and subspecies functional adaptations. Genome Biol. 21 , 138 (2020).

Beghini, F. et al. Large-scale comparative metagenomics of Blastocystis , a common member of the human gut microbiome. ISME J. 11 , 2848–2863 (2017).

Hildebrand, F. et al. Dispersal strategies shape persistence and evolution of human gut bacteria. Cell Host Microbe 29 , 1167–1176 (2021).

Kaper, J. B., Nataro, J. P. & Mobley, H. L. Pathogenic Escherichia coli . Nat. Rev. Microbiol. 2 , 123–140 (2004).

Borodovich, T., Shkoporov, A. N., Ross, R. P. & Hill, C. Phage-mediated horizontal gene transfer and its implications for the human gut microbiome. Gastroenterol. Rep. 10 , goac012 (2022).

Bobay, L. M., Traverse, C. C. & Ochman, H. Impermanence of bacterial clones. Proc. Natl Acad. Sci. USA 112 , 8893–8900 (2015).

Navarro-Garcia, F. & Elias, W. P. Autotransporters and virulence of enteroaggregative E. coli . Gut Microbes 2 , 13–24 (2011).

Cani, P. D. et al. Metabolic endotoxemia initiates obesity and insulin resistance. Diabetes 56 , 1761–1772 (2007).

Vazquez-Lopez, J. & Navarro-Garcia, F. In silico analyses of core proteins and putative effector and immunity proteins for T6SS in enterohemorrhagic E. coli . Front. Cell. Infect. Microbiol. 10 , 195 (2020).

Ahmed, S. A. et al. Genomic comparison of Escherichia coli O104:H4 isolates from 2009 and 2011 reveals plasmid, and prophage heterogeneity, including Shiga toxin encoding phage stx2. PLoS One 7 , e48228 (2012).

Sun, H. et al. Regulation of flagellar motility and biosynthesis in enterohemorrhagic Escherichia coli O157:H7. Gut Microbes 14 , 2110822 (2022).

Chaban, B., Hughes, H. V. & Beeby, M. The flagellum in bacterial pathogens: for motility and a whole lot more. Semin. Cell Dev. Biol. 46 , 91–103 (2015).

Lux, R. & Shi, W. Chemotaxis-guided movements in bacteria. Crit. Rev. Oral Biol. Med. 15 , 207–220 (2004).

Ng, W. L. & Bassler, B. L. Bacterial quorum-sensing network architectures. Annu. Rev. Genet. 43 , 197–222 (2009).

Everard, A. et al. Cross-talk between Akkermansia muciniphila and intestinal epithelium controls diet-induced obesity. Proc. Natl Acad. Sci. USA 110 , 9066–9071 (2013).

Hillmann, B. et al. Evaluating the information content of shallow shotgun metagenomics. mSystems 3 , e00069-18 (2018).

IHMS Consortium. IHMS_SOP 03 V1: Standard Operating Procedure for Fecal Samples self-collection, laboratory analysis handled within 4 to 24 hours (4 hours < x ≤ 24 hours) , (International Human Microbiome Standards, 2015).

Salonen, A. et al. Comparative analysis of fecal DNA extraction methods with phylogenetic microarray: effective recovery of bacterial and archaeal DNA using mechanical cell lysis. J. Microbiol. Methods 81 , 127–134 (2010).

Courtois, S. et al. Recombinant environmental libraries provide access to microbial diversity for drug discovery from natural products. Appl. Environ. Microbiol. 69 , 49–55 (2003).

Deschasaux, M. et al. Depicting the composition of gut microbiota in a population with varied ethnic origins but shared geography. Nat. Med. 24 , 1526–1531 (2018).

Fang, C. et al. Assessment of the cPAS-based BGISEQ-500 platform for metagenomic sequencing. Gigascience 7 , 1–8 (2018).

Matthews, D. R. et al. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 28 , 412–419 (1985).

Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9 , 357–359 (2012).

Suzek, B. E., Huang, H., McGarvey, P., Mazumder, R. & Wu, C. H. UniRef: comprehensive and non-redundant UniProt reference clusters. Bioinformatics 23 , 1282–1288 (2007).

Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12 , 59–60 (2015).

Caspi, R. et al. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res. 44 , D471–D480 (2016).

Ye, Y. & Doak, T. G. A parsimony approach to biological pathway reconstruction/inference for genomes and metagenomes. PLoS Comput. Biol. 5 , e1000465 (2009).

Mirdita, M. et al. Uniclust databases of clustered and deeply annotated protein sequences and alignments. Nucleic Acids Res. 45 , D170–D176 (2017).

Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8 , 118–127 (2007).

Pasolli, E., Truong, D. T., Malik, F., Waldron, L. & Segata, N. Machine learning meta-analysis of large metagenomic datasets: tools and biological insights. PLoS Comput. Biol. 12 , e1004977 (2016).

Carpenter, B. et al. Stan: a probabilistic programming language. J. Stat. Softw. 76 , 1 (2017).

Paradis, E., Claude, J. & Strimmer, K. APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20 , 289–290 (2004).

Vehtari, A., Gelman, A. & Gabry, J. Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat. Comput. 27 , 1413–1432 (2017).

Zhou, X., Kao, M. C. & Wong, W. H. Transitive functional annotation by shortest-path analysis of gene expression data. Proc. Natl Acad. Sci. USA 99 , 12783–12788 (2002).

Download references

Acknowledgements

The authors thank K. Dennis for coordinating the collection and transfer of the data, and F. Bäckhed, K. Kristiansen, J. Li, H. Zhong and J. Qin for sharing their data and helping with the data transfer. The authors are indebted to the participants in the Health Professionals Follow-up Study (HPFS) and Nurses’ Health Study II (NHSII) for their continuing outstanding level of cooperation, and to the staff of the HPFS and NHSII for their valuable contributions. The authors also thank the staff and participants of the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) for their important contributions; the DIRECT-PLUS Study participants for their valuable contributions; and A. Yaskolka-Meir, G. Tsaban, A. Kaplan, H. Zelica, I. Youngster, K. Tuohy and O. Koren for their contribution to the DIRECT-PLUS Study. This work is funded by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK; R00 DK119412 and Boston Nutrition Obesity Research Center Pilot and Feasibility Program grant supported by P30 DK046200 to D.D.W.; R24 DK110499 to C.H.), National Institute of Nursing Research (R01 NR01999 to D.D.W.), National Institute on Aging (R01 AG077489 and RF1 AG083764 to D.D.W.) and National Cancer Institute (NCI; R35 CA253185 to A.T.C.). A.T.C. is an American Cancer Society Research Professor. F.W. is supported by the American Heart Association Postdoctoral Fellowship (Grant number: 897161 to F.W.). The HPFS is supported by research grants U01 CA167552 (to W.C.W.) and R01 HL035464 (to E.B.R.) from the National Institutes of Health (NIH). The Men’s Lifestyle Validation Study in HPFS was supported by U01 CA152904 (to M.J.S. and E.B.R.) from NCI. The fecal sample collection and metagenomic data sequencing in HPFS were supported by the STARR Cancer Consortium Award (I7-A714 to C.H.). NHSII was supported by U01 CA176726 from NIH and P01 CA055075 (to W.C.W.) from NCI. The fecal sample collection and metagenomic data sequencing in NHSII were supported by the R01 CA202704 (to A.T.C. and C.H.) from NCI. The HCHS/SOL is a collaborative study supported by contracts from the National Heart, Lung and Blood Institute (NHLBI) to the University of North Carolina (HHSN268201300001I/N01-HC-65233), University of Miami (HHSN268201300004I/N01-HC-65234), Albert Einstein College of Medicine (HHSN268201300002I/N01-HC-65235), University of Illinois at Chicago (HHSN268201300003I/N01-HC-65236 Northwestern University) and San Diego State University (HHSN268201300005I/N01-HC-65237). The following institutes, centers and/or offices have contributed to the HCHS/SOL through a transfer of funds to the NHLBI: National Institute on Minority Health and Health Disparities (NIMHD), National Institute on Deafness and Other Communication Disorders, National Institute of Dental and Craniofacial Research, NIDDK, National Institute of Neurological Disorders and Stroke, and NIH Institution-Office of Dietary Supplements. Additional funding for the ‘Gut Origins of Latino Diabetes’ ancillary study to HCHS/SOL was provided by R01 MD011389 (to R.C.K., R.D.B. and R.K.) from the NIMHD and the Life Course Methodology Core at Albert Einstein College of Medicine and the New York Regional Center for Diabetes Translation Research (P30 DK111022-8786 and P30 DK111022) through funds from NIDDK. Additional funding for this work was provided by R01 HL060712 (to F.B.H. and Q.Q.) from NHLBI. The DIRECT-PLUS Study was funded by grants from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) Collaborative Research Center SFB1052 ‘Obesity Mechanisms’ (SFB-1052/B11 to I.S.); Israel Ministry of Health grant 87472511 (to I.S.); Israel Ministry of Science and Technology grant 3-13604 (to I.S.); California Walnuts Commission (to I.S.) and the CABALA_DIET&HEALTH Project, which received funding from the European Union’s Horizon 2020 Programme. The funding source had no role in the design and conduct of the study; collection, management, analysis and interpretation of the data; preparation, review, or approval of the manuscript; and the decision to submit the manuscript for publication. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. The computations in this paper were run in part on the FASRC Cannon cluster supported by the FAS Division of Science Research Computing Group at Harvard University.

Author information

These authors contributed equally: Zhendong Mei, Fenglei Wang.

Authors and Affiliations

Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

Zhendong Mei, Danyue Dong, Yuxi Liu, Eric B. Rimm, Walter C. Willett, Frank B. Hu, Meir J. Stampfer & Dong D. Wang

Broad Institute of MIT and Harvard, Cambridge, MA, USA

Zhendong Mei, Fenglei Wang, Amrisha Bhosle, Danyue Dong, Raaj Mehta, Andrew Ghazi, Yancong Zhang, Yuxi Liu, Andrew T. Chan, Curtis Huttenhower & Dong D. Wang

Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA

Fenglei Wang, Eric B. Rimm, Walter C. Willett, Frank B. Hu, Qibin Qi, Meir J. Stampfer, Iris Shai & Dong D. Wang

Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA

Amrisha Bhosle, Andrew Ghazi, Yancong Zhang & Curtis Huttenhower

Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

Raaj Mehta & Andrew T. Chan

Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA

Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA

Yuxi Liu, Eric B. Rimm, Walter C. Willett, Frank B. Hu & Meir J. Stampfer

Department of Medicine, Hebrew University and Hadassah Medical Center, Jerusalem, Israel

Ehud Rinott

Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA

Institute for Minority Health Research, University of Illinois Chicago, Chicago, IL, USA

Martha Daviglus

Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA

Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA

Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA

Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA

Qibin Qi, Robert D. Burk & Robert C. Kaplan

Department of Pediatrics, Albert Einstein College of Medicine, Bronx, NY, USA

Robert D. Burk

Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, NY, USA

Department of Obstetrics, Gynecology and Women’s Health, Albert Einstein College of Medicine, Bronx, NY, USA

Faculty of Health Sciences, The Health and Nutrition Innovative International Research Center, Ben-Gurion University of the Negev, Be′er Sheva, Israel

Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA

Robert C. Kaplan

Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, USA

Curtis Huttenhower

Harvard Chan Microbiome in Public Health Center, Harvard T.H. Chan School of Public Health, Boston, MA, USA

You can also search for this author in PubMed Google Scholar

Contributions

Z.M., F.W., C.H. and D.D.W. conceptualized the study. Z.M. and F.W. performed the data analysis. Z.M., F.W., C.H. and D.D.W. drafted the paper. C.H. and D.D.W. supervised the study. E.B.R, M.D., W.C.W., R.K., F.B.H., Q.Q., A.T.C., R.D.B., M.J.S., E.R., I.S., R.C.K., C.H. and D.D.W. collected the sample and data, and obtained funding. Z.M., F.W., A.B., D.D., R.M., A.G., Y.Z., Y.L., E.R., S.M., E.B.R., M.D., W.C.W., R.K., F.B.H., Q.Q., A.T.C., R.D.B., M.J.S., I.S., R.C.K., C.H., and D.D.W. discussed the results, critically reviewed the text and approved the final paper.

Corresponding authors

Correspondence to Curtis Huttenhower or Dong D. Wang .

Ethics declarations

Competing interests.

C.H. is a member of the scientific advisory board for Zoe Nutrition, Empress Therapeutics, and Seres Therapeutics. All other authors have no competing interests.

Peer review

Peer review information.

Nature Medicine thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editor: Sonia Muliyil, in collaboration with the Nature Medicine team.

Additional information

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

Extended data

Extended data fig. 1 workflow..

We adjusted for the study effect by adopting a conservative meta-analysis approach in the downstream analyses. Our analyses examined the overall microbial community structure, specific microbial taxonomic and functional features, strain-specific biochemical pathways, and within-species phylogeny and gene families in a cross-cohort meta-analysis framework. This figure was created with BioRender.com .

Extended Data Fig. 2 Principal coordinate analysis of all samples using species-level Bray–Curtis dissimilarity colored by cohorts before and after correcting batch and study effects.

R 2 values are calculated from permutational multivariate analysis of variance (PERMANOVA, n = 999 permutations) and indicate the variance attributable to study and batch effects.

Source data

Extended data fig. 3 comparisons in associations between microbial species and type 2 diabetes across different statistical models..

Meta-analyzed associations of individual microbial species with type 2 diabetes (T2D) phenotype from the ordinal ( a ) and binary ( b ) models. The ordinal model modeled the disease status as an ordinal variable (T2D, prediabetes, or controls) and used data from all the participants. The binary model modeled the disease status as a binary variable (T2D or controls) and used data from T2D patients and normoglycemic controls. The blue-to-red and purple-to-orange gradients represent the magnitude and direction of the associations as quantified by meta-analyzed beta coefficients from linear mixed models adjusted for age, sex, and body mass index (BMI) and further adjusted for metformin use in MaAsLin2. All the results were corrected for multiple hypothesis testing by controlling the false discovery rate (FDR) using the Benjamini–Hochberg method with a target rate of 0.10. All models included each participant’s identifier as random effects and simultaneously adjusted for covariables. ( c ) Comparisons in associations between microbial species and T2D between multivariate MaAsLin2 models with and without further adjustment for BMI and metformin use from the ordinal model. ( d ) Comparisons in associations between microbial species and T2D between multivariate MaAsLin2 models with and without further adjustment for BMI and metformin use from the binary model. Dots in the scatter plots in (c) and (d) represent meta-analyzed beta coefficients from linear mixed models adjusted for covariables in MaAsLin2. All the statistical tests were two-sided. A total of 8,117 metagenomes from 1,851 T2D patients, 2,770 individuals with prediabetes, and 2,277 normoglycemic controls were included in the analyses in (a), (b), (c), and (d). Abbreviations: BMI, body mass index; Con, control; metf, metformin use; insul, insulin use; T2D, type 2 diabetes.

Extended Data Fig. 4 Metformin has a direct impact on the gut microbiome composition and confounds the associations between microbial species and type 2 diabetes.

( a ) Distance-based redundancy analysis (dbRDA) based on species-level Bray–Curtis dissimilarity colored by type 2 diabetes (T2D) and metformin use. The centers of the boxplot show medians with boxes indicating their inter-quartile ranges (IQRs) and upper and lower whiskers indicating 1.5 times the IQR from above the upper quartile and below the lower quartile, respectively. ( b ) Meta-analyzed and cohort-specific associations of microbial species with metformin use among T2D patients. We defined microbial signatures of metformin as those significantly associated with metformin use in T2D cases only but not associated with T2D after further adjusting for metformin use in all participants. We also identified 4 species associated with both metformin use and T2D. The centers of the error bars represent the β coefficients of the associations, and the error bars represent their standard errors (SEs). ( c ) Our modeling approach effectively accounted for the potential confounding effect of metformin use, as evidenced by the high correlation between the beta coefficients of species–T2D associations obtained in the primary analysis and those calculated in a sensitivity analysis excluding T2D patients treated with metformin. The beta coefficients in (b) and (c) represent the associations quantified by linear mixed models, adjusting for age, sex, body mass index (BMI), and metformin use where appropriate, in MaAsLin2. All the results were corrected for multiple hypothesis testing by controlling the false discovery rate (FDR) using the Benjamini–Hochberg method with a target rate of 0.10. All the analyses in (a), (b), and (c) were based on 5,114 metagenomes from 1,851 T2D patients and 2,277 normoglycemic controls. The statistical tests in (a) and (b) were two-sided. Abbreviations: Con, control; metf, metformin use; T2D, type 2 diabetes.

Extended Data Fig. 5 Sensitivity analyses demonstrate that identified microbial features of type 2 diabetes are unlikely to reflect the duration or comorbidities of this disease.

( a ) Comparisons in associations between microbial species and T2D in one analysis that includes all study participants and the other that excludes individuals with prevalent T2D in the Hispanic Community Health Study/Study of Latinos. ( b ) Comparisons in associations between microbial species and T2D in one analysis that includes all study participants and the other analysis that excludes insulin-treated T2D patients. The dots represent the associations quantified by linear mixed models, adjusting for age, sex, body mass index, and metformin use in MaAsLin2. Abbreviation: T2D, type 2 diabetes.

Extended Data Fig. 6 Associations of microbial features with circulating metabolic and inflammation biomarkers.

( a ) Meta-analyzed associations of individual MetaCyc pathways with circulating biomarkers of metabolic risk. ( b ) Meta-analyzed associations of individual microbial enzymes with circulating biomarkers of metabolic risk. Only pathways and enzymes listed in Fig. 3 were analyzed and presented in this figure. The blue-to-red gradients represent the magnitude and direction of the associations as quantified by meta-analyzed beta coefficients from linear mixed models adjusted for age, sex, body mass index, and metformin use in MaAsLin2. All the results were corrected for multiple hypothesis testing by controlling the false discovery rate (FDR) using the Benjamini–Hochberg method with a target rate of 0.10. Abbreviations: BMI, body mass index; HbA1c, hemoglobin A1c; HDL-C, high-density lipoprotein cholesterol; hs-CRP, high-sensitivity C-reactive protein; HOMA-B, homeostasis model assessment of β-cell function; HOMA-IR, homeostasis model assessment of insulin resistance; LDL-C, low-density lipoprotein cholesterol; TG, triglyceride.

Extended Data Fig. 7 Prevotella copri ’s differential carriage of branched-chain amino acid biosynthesis function is explained by its discrete subclade structure.

( a ) Distribution of different P. copri subclades across geographic regions and studies. We applied MetaPhlAn taxonomic profiling based on P. copri subclade-specific marker genes to detect the presence of a subclade in metagenomes. ( b ) Comparisons in adjusted relative abundance of branched-chain amino acid (BCAA) biosynthesis pathways and enzyme encoded by P. copri subclades dominated by clade A versus other clades. The adjusted relative abundance of pathways and enzymes is estimated by anpan (ANalysis of microbial Phylogenies And geNes)’s pathway random effects models ( Methods ) with simultaneous adjustment for the abundance of P. copri subclades. The centers of the boxplot show medians of adjusted relative abundance with boxes indicating their inter-quartile ranges (IQRs) and upper and lower whiskers indicating 1.5 times the IQR from above the upper quartile and below the lower quartile, respectively. P -values were generated from two-sided t-tests based on the adjusted relative abundance. ( c ) Clade A-dominant P. copri strains in type 2 diabetes (T2D) patients were more likely to retain pathways and enzymes of branched-chain amino acid biosynthesis compared to clade A-dominant nonT2D controls. The blue and red lines, fitted by linear regression in participants with T2D and control participants separately, represent the associations between the log-transformed relative abundance of P. copri subclade and the log-transformed relative abundance of a given pathway or enzyme encoded by P. copri . The numeric values in the top left corner are posterior differences and 98% posterior intervals of differences in log-transformed pathway abundance between case–control status, as determined by mixed effects models anpan ( Methods ). This model allows us to identify microbial functions encoded by a P. copri subclade that are differentially abundant between T2D cases versus controls while controlling for its subclade-level abundance. All the analyses in (a), (b), and (c) were based on 5,114 metagenomes from 1,851 T2D patients and 2,277 normoglycemic controls.

Extended Data Fig. 8 Phylogenetic trees of select species show divergent associations between subclades and type 2 diabetes within each species.

The annotation bars represent metformin use (metf), study, body mass index (BMI), sex, age, and type 2 diabetes (T2D) status, respectively. The boxplots in the bottom represent the posterior mean of the phylogenetic effect of each phylogenetic tree leaf (metagenome) estimated by the phylogenetic generalized linear mixed models (PGLMMs) in anpan (ANalysis of microbial Phylogenies And geNes, see Methods ) with whiskers representing the 95% credible intervals of the posterior means. By applying PGLMMs, we compared two generalized linear mixed models with and without incorporating within-species phylogeny as a random effect ( Methods ). Both models were adjusted for age, sex, body mass index, metformin use, and study membership as fixed effects. We generated within-species phylogenetic trees by randomly splitting the edges based on the Euclidean similarity matrix derived from clustered sets of protein sequences (UniRef90 gene families) after dimension reduction by principal components analysis.

Extended Data Fig. 9 Gene set enrichment analysis of gene ontology terms for biological process.

The line plots show the running enrichment score for the gene ontology (GO) term as the analysis ‘walks down’ the ranked list. The vertical black lines on the X-axis show where members of the GO term appear in the ranked list of UniRef90 gene families.

Supplementary information

Supplementary information.

Supplementary Text and Supplementary Figs. 1 and 2

Reporting Summary

Supplementary Tables 1–13

Supplementary Data

Source Data for Supplementary Figs. 1 and 2

Source Data Fig. 1

Statistical Source Data

Source Data Fig. 2

Source data fig. 3, source data fig. 4, source data fig. 5, source data extended data fig. 2, source data extended data fig.3, source data extended data fig. 4, source data extended data fig. 5, source data extended data fig. 6, source data extended data fig. 7, source data extended data fig. 8, source data extended data fig. 9, rights and permissions.

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Cite this article.

Mei, Z., Wang, F., Bhosle, A. et al. Strain-specific gut microbial signatures in type 2 diabetes identified in a cross-cohort analysis of 8,117 metagenomes. Nat Med (2024). https://doi.org/10.1038/s41591-024-03067-7

Download citation

Received : 01 December 2023

Accepted : 14 May 2024

Published : 25 June 2024

DOI : https://doi.org/10.1038/s41591-024-03067-7

Share this article

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

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

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

Explore articles by subject
Guide to authors
Editorial policies

Sign up for the Nature Briefing: Microbiology newsletter — what matters in microbiology research, free to your inbox weekly.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Publications
Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

Advanced Search
Journal List
BMC Endocr Disord
PMC10792791

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

Morteza haramshahi.

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

Thoraya Mohamed Elhassan A-Elgadir

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

Hamid Mahmood Abdullah Daabo

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

Yahya Altinkaynak

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

Ahmed Hjazi

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

Archana Saxena

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

Mazin A.A. Najm

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

Abbas F. Almulla

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

Ali Alsaalamy

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

Mohammad Amin Kashani

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

Associated Data

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

Backgrounds

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 – 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 – 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

Dietary assessment

Nutrient pattern assessment

Assessment of other variables

Statistical analysis

Factor loading matrix and explained variances for major nutrient patterns identified by factor analysis in 225 cases and 225 controls * †

Nutrient patterns
Nutrients	Pattern 1	Pattern 2	Pattern 3
Animal protein
Plant protein
Saturated fatty acids	0.36
Mono unsaturated fatty acids	-0.30
Poly unsaturated fatty acids
Cholesterol
Sucrose
Lactose
Fructose
Fiber
Vitamin B1			-0.32
Vitamin B2
Vitamin B3
Vitamin B5
Vitamin B6
Folate
Vitamin B12
Vitamin C
Vitamin A
Vitamin D
Vitamin E
Vitamin K
Iron
Zinc			0.35
Copper			0.31
Magnesium
Manganese
Calcium
Phosphor
Sodium
Potassium
	24.30	20.95	10.86
	24.30	45.25	56.11

* Principal Component Analysis performed on 34 nutrients calculated as intake per 1000 Kcal

Nutrients with loadings > 0.40 and less than − 0.40 (in bold) are being characteristic for the four patterns; loadings less than 0.3 (in absolute value) are suppressed

† Kaiser’s Measure of Sampling Adequacy, KMO = 0.71, Bartlett’s test of sphericity = < 0.001

Partial correlation coefficient of nutrient patterns with food sources *

	Nutrient pattern 1	Nutrient pattern 2	Nutrient pattern 3
Red and processed meat(g/d)	-0.025	-0.089	-0.115
White meats(g/d)	-0.048	-0.029	0.091
Plant oil (serving/d)	0.017	-0.018	0.115
Nuts(g/d)	-0.024	-0.161	0.247
Low fat dairy(g/d)	0.712	-0.165	0.034
High fat dairy(g/d)	0.244	-0.156	-0.134
Whole grain(g/d)	0.158	-0.031	-0.087
Refined grain(g/d)	-0.242	0.431	-0.372
Legume(g/d)	0.007	0.156	-0.005
Fruits(g/d)	0.051	-0.082	0.847
Vegetables(g/d)	0.089	0.038	0.673
Egg (serving/d)	0.091	0.003	0.038
Fruit juice (serving/d)	0.053	-0.079	0.366
Snacks (serving/d)	-0.135	-0.281	-0.075
Artificial beverages (serving/d)	-0.079	-0.141	0.074

* Adjusted for age, sex, and energy intake

a P < 0.001, b P < 0.05

Dietary intakes and Characteristics among cases and controls*

Demographic variables	Cases ( = 225)	Controls( = 225)	-value
Age(year)	39.6 ± 8.7	40.1 ± 8.9	0.206
Male, n (%)	119 (52.8)	112 (49.7)	0.347
BMI(Kg/m )	30.5 ± 4.1	25.2 ± 3.2	< 0.001
Smoking, n (%)	17 (7.5)	14 (6.2)	0.089
Physical activity (MET/min/week)	1121 ± 611	1598 ± 940	< 0.001
SES, n (%)			0.274
Low	63 (28)	71 (31.5)
Middle	106 (47.1)	101 (44.8)
High	56 (24.9)	53 (23.5)
FBS (mg/dl)	135.26 ± 14.52	91.26 ± 10.38	< 0.001
HbA1C	8.6 ± 0.4	6.5 ± 0.1	< 0.001
Insulin (mU/L)	26.23 ± 4.57	12.71 ± 0.98	0.02

Energy intake (Kcal/d)	2371 ± 624	2213 ± 619	0.008
Sucrose(g/1000Kcal)	12.8 ± 6.4	14.2 ± 8.6	0.547
Lactose(g/1000Kcal)	6.5 ± 4.2	7.1 ± 4.5	0.332
Fructose(g/1000Kcal)	7.8 ± 3.3	7.9 ± 3.6	0.997
Fiber(g/1000Kcal)	16.2 ± 8.3	15.8 ± 6.5	0.218
Animal protein(g/1000Kcal)	22.4 ± 8.6	21.9 ± 8.5	0.072
Plant protein(g/1000Kcal)	13.8 ± 3.7	15.4 ± 3.9	0.024
Saturated fatty acids(g/1000Kcal)	11.2 ± 3.4	11.5 ± 3.2	0.205
Mono unsaturated fatty acids(g/1000Kcal)	12.0 ± 3.3	11.9 ± 2.9	0.599
Poly unsaturated fatty acids(g/1000Kcal)	7.3 ± 2.8	7.1 ± 2.4	0.217
cholesterol(mg/1000Kcal)	97.4 ± 41.4	101.7 ± 56.0	0.057
Vitamin A(mg/1000Kcal)	186 ± 102	208 ± 110	0.009
Vitamin D(µg/1000Kcal)	0.74 ± 0.55	0.95 ± 0.74	0.811
Vitamin E(mg/1000Kcal)	4.93 ± 1.50	5.12 ± 1.74	< 0.001
Vitamin K(mg/1000Kcal)	78.5 ± 52.3	83.4 ± 57.2	0.214
Vitamin B1(mg/1000Kcal)	0.85 ± 0.18	0.81 ± 0.16	0.007
Vitamin B2(mg/1000Kcal)	0.79 ± 0.20	0.86 ± 0.21	0.040
Vitamin B3(mg /1000Kcal)	9.8 ± 2.3	9.6 ± 2.0	0.854
Vitamin B5(mg/1000Kcal)	2.32 ± 0.42	2.35 ± 0.44	0.147
Vitamin B6(mg/1000Kcal)	0.83 ± 0.16	0.81 ± 0.15	0.712
Folate(mg/1000Kcal)	236 ± 45	233 ± 38	0.625
Vitamin B12(mg/1000Kcal)	1.73 ± 0.86	1.79 ± 0.79	0.447
Vitamin C(mg/1000Kcal)	58.4 ± 31.1	60.2 ± 31.5	0.547
Calcium(mg/1000Kcal)	520 ± 163	525 ± 152	0.258
Phosphor(mg/1000Kcal)	621 ± 127	622 ± 119	0.741
Iron(mg/1000Kcal)	11.5 ± 5.4	11.4 ± 5.7	0.847
Zinc(mg/1000Kcal)	4.52 ± 0.80	5.44 ± 0.81	0.049
Copper(mg/1000Kcal)	0.65 ± 0.13	0.69 ± 0.12	0.811
Magnesium(mg/1000Kcal)	157 ± 29	158 ± 30	0.784
Manganese(mg/1000Kcal)	3.2 ± 1.1	3.2 ± 1.0	0.974
Chromium(mg/1000Kcal)	0.04 (0.03–0.06)	0.04 (0.02–0.06)	0.414
Selenium(mg/1000Kcal)	48.5 ± 12.2	47.6 ± 11.7	0.532
Sodium(mg/1000Kcal)	1984 ± 1960	2022 ± 1385	0.736
Potassium(mg/1000Kcal)	1512 ± 312	1610 ± 381	0.033
Caffeine(mg/1000Kcal)	54.4 ± 46.0	58.1 ± 44.8	0.355

*independent sample t-test and chi square

a p < 0.05

Odds ratios (ORs) and 95% confidence intervals (CIs) for T2D based on tertiles of nutrient patterns

	Tertiles of nutrient patterns			for trend
	T1	T2	T3	for trend

Median score	-0.94	-0.13	0.93
T2D /control	78 / 72	78 / 72	69 / 81
Model 1	1.00 (Ref)	1.00 (0.71–1.50)	0.79 (0.51–1.20)	0.412
Model 2	1.00 (Ref)	0.93 (0.55–1.57)	0.81 (0.49–1.41)	0.218

Median score	-0.91	-0.08	0.84
T2D /control	73 / 76	73 / 75	79 / 74
Model 1	1.00 (Ref)	1.00 (0.59–1.38)	1.31 (0.89–1.92)	0.287
Model 2	1.00 (Ref)	0.91 (0.48–1.41)	1.09 (0.54–1.67)	0.850

Median score	-0.96	-0.13	0.99
T2D /control	75 / 75	84 / 77	66 / 73
Model 1	1.00 (Ref)	1.12 (0.81–1.61)	0.94 (0.61–1.34)	0.387
Model 2	1.00 (Ref)	0.87 (0.50–1.49)	0.52 (0.30–0.89)	0.039

* Model 1: Adjusted for age and sex

† Model 2: Additionally adjusted for model 1 and BMI, physical activity, smoking, dietary intake of energy

Conclusions

Acknowledgements

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

Author contributions

This research received no external funding.

Data availability

Declarations.

Not applicable.

The authors declared no conflicts of interest.

Publisher’s Note

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

Ohio State nav bar

The Ohio State University

BuckeyeLink
Find People
Search Ohio State

CC, History, and PE

Chief Concern

“I feel so tired lately and can’t seem to shake this sinus infection.”

History of Present Illness

Ms. Yazzie, a 63 year old Navajo female and long-time patient of your primary care office presents today with concerns related to general fatigue and ongoing sinusitis-like symptoms. She states that while her symptoms are “not so serious,” she has grown “sick and tired of being sick and tired.” Over the last four or five months Ms. Yazzie has grown fatigued and weak at times, as well as having symptoms of sinusitis and two back-to-back yeast infections. She recently bought a water bottle because she noticed she was thirsty “all the time.”

Two years ago, at a routine screening you diagnosed Ms. Yazzie as pre-diabetic. At that time, she appeared motivated to make lifestyle changes. She states that shortly after that appointment her sister fell ill and she moved back to the Navajo Nation to care for her. The last two years Ms. Yazzie has been dedicated to caring for her sister and sorting out her sister’s affairs since she passed. Ms. Yazzie states that she did not have time to take “good care of herself.” She says healthcare on the reservation was not easily accessible or of high quality and that exercise and a balanced diet were not priorities.

Past Medical History

Pre-diabetes: age 61
Hypertension: age 52
Hyperlipidemia: age 49
Obesity: age 38
Varicella: 8

Medications

Ms. Yazzie is not currently taking any medications but she previously had the following medications:

Bumex 0.5 mg po BID
Zocor 40 mg po daily

Family History

Both of Ms. Yazzie’s parents are dead. Her father died of a heart attack when he was 52 years old. Ms. Yazzie does not know much about his health because he, “didn’t like going to the doctor.” Her mother died fifteen years ago at the age of 71. Ms. Yazzie’s mother had type 2 diabetes mellitus and well-controlled hypertension.

Ms. Yazzie’s sister died at age 64, the specific cause is unknown. She had been diagnosed with obesity, hypertension, diabetes, and suspected coronary artery disease.

Ms. Yazzie’s other sister is alive at age 59. She was diagnosed with obesity at age 37 and hypertension at age 49.

Social History

Ms. Yazzie lives alone in a one bedroom apartment. Prior to moving to Arizona she worked as an artist’s assistant and docent at the art museum. She is currently volunteering again at the museum and looking for work.

She was married for 12 years but divorced nearly 30 years ago. Ms. Yazzie has one son who lives in town: she helps care for his three teenage children.

Ms. Yazzie denies drinking alcohol but says she smokes cigarettes. She says she’s been a smoker since she was 17 and smokes about a half pack a day (22 pack years).

Focused Physical Exam

VS: HR – 85, RR – 16, Temp – 94.3, BP – 136/86, O2 – 99% on room air
General: A&Ox4, appearance appropriate for age and race, central obesity (waist diameter 38”)
Skin: cool and dry. A 2” abrasion on right ankle that the patient says, “just won’t heal.”
HEENT: patient notes some floaters in her right eye “recently.”
GU: recent yeast infections, polyuria
GI: active bowel sounds in all quadrants
Respiratory: clear breath sounds in all fields
Cardiac: clear S1 and S2 noted. Absence of any murmur or rub.

You ask Ms. Yazzie to come back in for some diabetes-related diagnostic tests and a lipid profile over the following two weeks.

Fasting blood glucose: 145 mg/dL first test; 138 mg/dL second test
Glucose tolerance test: 212 mg/dL; 222 mg/dL second test
HbA1c: 6.5%
Triglycerides: 168 mg/dL
HDL: 46 mg/dL
LDL: 174 mg/dL
Total: 220 mg/dL

Photo from Healthline

PRESENTATION

Case study: a 30-year-old man with metformin-treated newly diagnosed diabetes and abdominal pain.

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

Ranjna Garg; Case Study: A 30-Year-Old Man With Metformin-Treated Newly Diagnosed Diabetes and Abdominal Pain. Clin Diabetes 1 April 2007; 25 (2): 77–78. https://doi.org/10.2337/diaclin.25.2.77

Download citation file:

Ris (Zotero)
Reference Manager

M.P. is a 30-year-old man who was diagnosed with type 2 diabetes 2 weeks before admission to the hospital. He has a strong family history of type 2 diabetes. He smokes heavily (> 20 cigarettes/day) and admits to some alcohol consumption. His primary care physician had started him on metformin,500 mg three times daily. There were no complications of diabetes at the time of presentation. Two days before his admission, he developed generalized abdominal discomfort, watery diarrhea, and bilious vomiting. He denied any history suggestive of food poisoning or recent surgery. He was apyrexial on admission. His blood pressure was 170/101 mmHg, pulse was 100 bpm, and temperature was 98.9°F. There were no signs suggestive of peritonitis, and his abdomen was soft without guarding. He had deep-seated tenderness in the epigastric region. Initial investigations showed a white blood count of 25.9× 109, hemoglobin of 15.8 g/dl, and C-reactive protein (CRP) of 200 mg. Renal functions were normal, and liver function tests were normal except for an increased lactic acid dehydrogenase (LDH) level of 848 IU/l.

Chest X-ray showing air under diaphragm (arrows).

His metformin was stopped because his gastrointestinal symptoms were attributed to metformin. After stopping metformin, he was able to eat normally and tolerate a regular diet. He received subcutaneous insulin therapy to control his glucose levels. He continued to have some abdominal discomfort,however, and on questioning reported right shoulder pain. Shoulder examination showed no signs of inflammation. There was no restriction of movement at the right shoulder. A chest X-ray appeared to demonstrate free air beneath the diaphragm.

Why is this patient having abdominal discomfort?

What investigation would confirm the diagnosis?

How should this condition be managed?

M.P. has pneumoperitoneum (PP). PP is the presence of air within the peritoneal cavity. Most commonly, it is caused by perforated viscous(perforated gastric ulcer, bowel perforation, tumour, or trauma). PP from perforation is associated with peritonitis in most cases. 1 In this setting, sign and symptoms of peritonism are present, and patients require prompt surgical intervention. On rare occasion, PP may occur without gastrointestinal perforation. Trauma, recent surgery, barotraumas, mechanical ventilation, diagnostic procedures (e.g., endoscopy and colposcopy) are other causes of spontaneous PP without peritonitis. The cause is often identifiable from the patient's history, but in this case it was not.

Nonsurgical PP is PP that is not associated with signs of peritonitis. Patients with nonsurgical PP present with vague abdominal symptoms but do not have signs suggestive of acute abdomen. Nonsurgical PP can occur in a number of situations (e.g., silent self-sealing perforation as in patients with diabetes, patients receiving steroids, elderly patients, or critically ill patients). Deliberate air introduction into the peritoneum could also account for nonsurgical PP, as in the case of peritoneal dialysis, injury to the female genital tract (e.g., from skiing accidents), postendoscopy leakage, or ruptured pneumatosis intestinal cysts. 2

The pain in the right shoulder noted in this case was likely referred pain. Initial abdominal discomfort and diarrhea was attributed to metformin-induced gastrointestinal side effects. When the symptoms persisted even after metformin was discontinued, search for another cause of abdominal pain revealed the correct diagnosis. This case illustrates the need to continue searching for other causes of gastrointestinal distress when symptoms do not resolve after stopping metformin. M.P.'s ultrasound scan of the abdomen showed the presence of fatty liver consistent with heavy alcohol abuse. It is also noteworthy that metformin is contraindicated in alcohol abuse and in patients at risk for dehydration. This patient, therefore, had multiple reasons to discontinue metformin. The chest X-ray showed the presence of air under the diaphragm ( Figure 1 ). An abdominal computed tomography (CT) scan showed the presence of free air within the peritoneal cavity ( Figure 2 ). There was no evidence of viscous perforation on further barium studies.

Figure 2. Abdominal CT scan showing presence of air within the peritoneal cavity. Arrow points to the falciparum ligament made prominent by the presence of air on both sides.

Abdominal CT scan showing presence of air within the peritoneal cavity. Arrow points to the falciparum ligament made prominent by the presence of air on both sides.

PP unaccompanied by peritonitis is usually asymptomatic. It can be diagnosed by erect chest X-ray showing the presence of air under the diaphragm. Abdominal CT scanning is the gold standard for confirming the diagnosis of PP. The CT scan is a sensitive tool and demarcates air within the peritoneal space. Once the diagnosis is confirmed, further investigations should be directed to uncover the cause and source of the air leak. Barium studies may show the perforation unless it is too small or has sealed spontaneously.

M.P. was managed conservatively. He was monitored closely. He tolerated normal meals. His diarrhea stopped. His CRP remained elevated for 2 weeks and then normalized at the time of discharge. Other markers of inflammation also improved in the same time period. Abdominal discomfort improved in 2 weeks. His blood glucose stabilized with insulin therapy. He remained fully mobile and independent and was discharged to home. He has not had any recurrences.

Nonsurgical PP has been described in the literature. Isolated cases from different pathophysiological origins have been reported. 3 - 5 Nonsurgical PP masquerading as metformin-induced gastrointestinal upset has not been reported previously. In addition to recognizing and diagnosing PP, it is important to be aware of rare nonsurgical causes of PP. Unnecessary surgery can be avoided in such cases.

Clinical Pearls

PP does not always require surgical intervention. In hemodynamically stable, minimally symptomatic patients, unusual causes of PP should be considered to avoid unnecessary surgery.

Patients with diabetes may have selflimiting small perforations that seal spontaneously. Absence of clinical signs of peritonism and the medical history of the patient can point to the nonsurgical nature of the condition.

Metformin can cause gastrointestinal upset, but other causes of such symptoms should be diligently searched for if patients remain symptomatic after stopping metformin.

Ranjna Garg, MRCP, MD, is specialist registrar at University Hospital in Birmingham, U.K.

Email alerts

Online ISSN 1945-4953
Print ISSN 0891-8929
Diabetes Care
Clinical Diabetes
Diabetes Spectrum
Standards of Medical Care in Diabetes
Scientific Sessions Abstracts
BMJ Open Diabetes Research & Care
ShopDiabetes.org
ADA Professional Books

Clinical Compendia

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

This Feature Is Available To Subscribers Only

Information

Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

Active Journals
Find a Journal
Proceedings Series
For Authors
For Reviewers
For Editors
For Librarians
For Publishers
For Societies
For Conference Organizers
Open Access Policy
Institutional Open Access Program
Special Issues Guidelines
Editorial Process
Research and Publication Ethics
Article Processing Charges
Testimonials
Preprints.org
SciProfiles
Encyclopedia

Article Menu

Subscribe SciFeed
Recommended Articles
Google Scholar
on Google Scholar
Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

Hypoglycemia in oral glucose tolerance test during pregnancy and risk for type 2 diabetes—a five-year cohort study.

Share and Cite

Haggiag, N.; Rotman, M.; Hallak, M.; Toledano, Y.; Gabbay-Benziv, R.; Maor-Sagie, E. Hypoglycemia in Oral Glucose Tolerance Test during Pregnancy and Risk for Type 2 Diabetes—A Five-Year Cohort Study. J. Clin. Med. 2024 , 13 , 3806. https://doi.org/10.3390/jcm13133806

Haggiag N, Rotman M, Hallak M, Toledano Y, Gabbay-Benziv R, Maor-Sagie E. Hypoglycemia in Oral Glucose Tolerance Test during Pregnancy and Risk for Type 2 Diabetes—A Five-Year Cohort Study. Journal of Clinical Medicine . 2024; 13(13):3806. https://doi.org/10.3390/jcm13133806

Haggiag, Noa, Moran Rotman, Mordechai Hallak, Yoel Toledano, Rinat Gabbay-Benziv, and Esther Maor-Sagie. 2024. "Hypoglycemia in Oral Glucose Tolerance Test during Pregnancy and Risk for Type 2 Diabetes—A Five-Year Cohort Study" Journal of Clinical Medicine 13, no. 13: 3806. https://doi.org/10.3390/jcm13133806

Article Metrics

Article access statistics, further information, mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals

Open access
Published: 24 June 2024

Assessment of subclinical LV myocardial dysfunction in T2DM patients with diabetic peripheral neuropathy: a cardiovascular magnetic resonance study

Xue-Ming Li 1 , 2 na1 ,
Ke Shi 1 na1 ,
Li Jiang 1 ,
Jing Wang 1 ,
Wei-Feng Yan 1 ,
Yue Gao 1 ,
Meng-Ting Shen 1 ,
Rui Shi 1 ,
Ge Zhang 1 , 2 ,
Xiao-Jing Liu 2 ,
Ying-Kun Guo 3 &
Zhi-Gang Yang 1

Cardiovascular Diabetology volume 23 , Article number: 217 ( 2024 ) Cite this article

258 Accesses

Metrics details

Diabetic peripheral neuropathy (DPN) is the most prevalent complication of diabetes, and has been demonstrated to be independently associated with cardiovascular events and mortality. This aim of this study was to investigate the subclinical left ventricular (LV) myocardial dysfunction in type 2 diabetes mellitus (T2DM) patients with and without DPN.

One hundred and thirty T2DM patients without DPN, 61 patients with DPN and 65 age and sex-matched controls who underwent cardiovascular magnetic resonance (CMR) imaging were included, all subjects had no symptoms of heart failure and LV ejection fraction ≥ 50%. LV myocardial non-infarct late gadolinium enhancement (LGE) was determined. LV global strains, including radial, circumferential and longitudinal peak strain (PS) and peak systolic and diastolic strain rates (PSSR and PDSR, respectively), were evaluated using CMR feature tracking and compared among the three groups. Multivariable linear regression analyses were performed to determine the independent factors of reduced LV global myocardial strains in T2DM patients.

The prevalence of non-infarct LGE was higher in patients with DPN than those without DPN (37.7% vs. 19.2%, p = 0.008). The LV radial and longitudinal PS (radial: 36.60 ± 7.24% vs. 33.57 ± 7.30% vs. 30.72 ± 8.68%; longitudinal: − 15.03 ± 2.52% vs. − 13.39 ± 2.48% vs. − 11.89 ± 3.02%), as well as longitudinal PDSR [0.89 (0.76, 1.05) 1/s vs. 0.80 (0.71, 0.93) 1/s vs. 0.77 (0.63, 0.87) 1/s] were decreased significantly from controls through T2DM patients without DPN to patients with DPN (all p < 0.001). LV radial and circumferential PDSR, as well as circumferential PS were reduced in both patient groups (all p < 0.05), but were not different between the two groups (all p > 0.05). Radial and longitudinal PSSR were decreased in patients with DPN (p = 0.006 and 0.003, respectively) but preserved in those without DPN (all p > 0.05). Multivariable linear regression analyses adjusting for confounders demonstrated that DPN was independently associated with LV radial and longitudinal PS (β = − 3.025 and 1.187, p = 0.014 and 0.003, respectively) and PDSR (β = 0.283 and − 0.086, p = 0.016 and 0.001, respectively), as well as radial PSSR (β = − 0.266, p = 0.007).

Conclusions

There was more severe subclinical LV dysfunction in T2DM patients complicated with DPN than those without DPN, suggesting further prospective study with more active intervention in this cohort of patients .

The substantial global increase in the incidence of diabetes has led to a parallel increase in the rates of diabetes-related deaths and complications [ 1 , 2 ]. Diabetic peripheral neuropathy (DPN) involving the outer nerves of the limbs is one of the most common complications of diabetes and affects over 50% of patients with type 2 diabetes mellitus (T2DM) [ 3 ]. In addition, it has been demonstrated to be independently associated with cardiovascular events and mortality in a number of studies [ 4 , 5 , 6 ]. Therefore, early detection of myocardial impairment in this cohort of patients is essential to prevent progression and subsequent increases in morbidity and mortality.

Current screening methods for diabetic cardiomyopathy mainly rely on left ventricular ejection fraction (LVEF) measurements that are based on global ventricular volume measurements, whether using echocardiography or CMR, which have inherent limitations, as they can only detect moderate to severe cardiac dysfunction. Interestingly, a large body of published data has shown that the echocardiography speckle tracking and cardiovascular magnetic resonance feature tracking (CMR-FT) techniques can detect and monitor the progression of subclinical myocardial dysfunction, which can further predict cardiovascular events [ 7 , 8 ]. Although echocardiography is currently the most convenient method for cardiac examination, it has a low spatial resolution and is highly dependent on the operator and angle, making it unsuitable for some patients with a poor echo window. CMR-FT, which is derived from a cine balanced steady-state free precession (bSSFP) sequence, has the advantages of a wide field of view, no anatomical plane restriction, and a semiautomatic and time-saving postprocessing procedure. In addition, late gadolinium enhancement (LGE) is a most advantage of MRI compared to the echocardiography, which is used to assess myocardial tissue characteristics [ 9 , 10 ].

Therefore, the aim of this study was to evaluate subclinical left ventricular (LV) myocardial dysfunction in type 2 diabetes mellitus (T2DM) patients with and without DPN using CMR-FT. The results might provide additional information on the link between the risk of cardiovascular disease and DPN.

Study population

Between January 2015 and June 2023, T2DM patients who had undergone CMR examinations were initially screened. T2DM was diagnosed according to the current American Diabetes Association guideline [ 11 ]. DPN was clinically diagnosed using the diagnostic criteria introduced by the American Diabetes Association in 2017 [ 12 ]. Chronic kidney disease (CKD) is clinically defined by the presence of persistent estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m 2 . The exclusion criteria included patients with coronary artery disease (confirmed by electrocardiogram, echocardiography, angiography, coronary computed tomographic angiography or CMR, or previous myocardial infarction or coronary revascularization), symptoms of heart failure, left ventricular ejection fraction < 50% on echocardiography or CMR imaging, other primary cardiomyopathies, moderate to severe valvular disease, atrial fibrillation, severe renal failure (eGFR < 30 mL/min/1.73 m 2 ), other causes of peripheral neuropathy (including chronic inflammatory demyelinating polyradiculoneuropathy, mononeuropathy, or conditions caused by vitamin B deficiency and thyroid dysfunction), incomplete clinical records and poor CMR image quality inadequate for analysis. Finally, 191 patients with T2DM were enrolled in this study, including 130 patients without DPN (75 males and 55 females, mean age 56.5 ± 9.7 years) and 61 patients with DPN (38 males and 23 females, mean age 55.3 ± 10.2). In addition, 65 age- and sex-matched healthy individuals (34 males and 31 females; mean age, 55.4 ± 9.9 years) were enrolled as the control group. The inclusion criteria for the control group were as follows: no diabetes or impaired glucose tolerance, hypertension, ischemic heart disease, cardiomyopathy, abnormal electrocardiogram, abnormalities detected with CMR (abnormal ventricular motion, valvular stenosis or regurgitation, decreased LVEF, etc.) or other cardiovascular disease-related symptoms.

This study (No. 2019-878) was approved by the Biomedical Research Ethics Committees of our hospital and complied with the Declaration of Helsinki. Written informed consent was waived due to the retrospective nature of the study.

CMR protocol

All subjects underwent CMR imaging on a 3 T whole-body scanner MAGNETOM Skyra or Trio Tim (Siemens Medical Solutions, Erlangen, Germany) in the supine position. The balanced steady-state free precession sequence (repetition time [TR] = 3.4 ms or 2.81 ms, echo time [TE] = 1.22 ms, flip angle = 50° or 40°, slice thickness = 8 mm, field of view [FOV] = 340 × 285 mm or 250 × 300 mm, matrix size = 256 × 166 or 208 × 139) with breath holding and ECG triggering was performed to acquire cine images, including a stack of contiguous short-axis slices covering the entire left ventricle from base to apex and one four- and two-chamber long-axis slice. Twenty-five frames were reconstructed per breath-hold acquisition. The LGE images in the entire LV short-axis stack and from the two-, three- and four-chamber views were acquired to exclude patients with infarct LGE and identify those with non-infarct LGE 10–15 min after contrast agent administration using the segmented-turbo-FLASH–phase-sensitive inversion recovery sequence (TR/TE, 300 ms/1.44 ms or 750 ms/1.18 ms, slice thickness, 8 mm, FOV, 275 × 400 mm or 400 × 270 mm, matrix size = 256 × 184 and flip angle, 40°).

Image analysis

All CMR data were uploaded to offline commercial software (Cvi42, v.5.11.2; Circle Cardiovascular Imaging, Inc., Calgary, Canada) and analyzed by two experienced radiologists with more than five years of experience in CMR interpretation, who were blinded to the clinical data.

The endo- and epicardial contours of left ventricle were semiautomatically delineated at the end-diastolic and end-systolic phases on the short-axis cine images in the Short-3D module. The papillary muscles and moderator bands were included in the ventricular cavity and excluded from the myocardial muscle. The global parameters of LV geometry and function, including end-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV), LVEF and LV mass, were computed automatically; LVEDV, LVESV, LVSV and LV mass were indexed to the body surface area (BSA) (LVEDVI, LVESVI, LVSVI and LVMI, respectively). The LV remodeling index was calculated as the ratio of LV mass to LVEDV.

The short-axis and long-axis four- and two-chamber cine images were loaded into the tissue tracking module for the LV myocardial strain analysis. The endo- and epicardial contours were semiautomatically delineated with papillary muscles and moderator bands excluded in all series at the end-diastolic phase. Subsequently, the LV global strain parameters (Fig. 1 ) were acquired automatically, including global radial, circumferential and longitudinal peak strain (PS) and peak systolic and diastolic strain rates (PSSR and PDSR, respectively). PS was defined as the relative thickening, shortening and lengthening of the myocardium from end diastole (reference phase). PSSR and PDSR are defined as the maximum strain rate during the contraction and relaxation phases, respectively.

Representative CMR left ventricular pseudocolor images of long-axis two- and four-chamber images at the end-systole and CMR-derived global longitudinal peak strain (GLS) curves in a normal control ( A1 – A3 ), T2DM patient without DPN ( B1 – B3 ) and T2DM patient complicated with DPN ( C1 – C3 )

For late gadolinium enhancement (LGE) analysis, the LGE images were visually evaluated by two observers in combination and categorized into 3 patterns, that is none, infarct, or non-infarct patterns [ 13 ].

Statistical analysis

All continuous variables were assessed for normality with the Shapiro–Wilk test. Continuous variables are presented as the means ± standard deviations (SD) for normally distributed variables or the medians (25–75% interquartile ranges) for skewed variables. Comparisons between three groups were performed with one-way analysis of variance (ANOVA) with the post hoc Bonferroni correction for normally distributed variables and Kruskal–Wallis tests for variables with skewed distributions. The duration of T2DM and HbA1c levels were compared between the patient groups with Mann–Whitney U test. Categorical variables are presented as percentages and were compared using Chi-square tests. Variables with p < 0.1 in the univariable analysis, as well as age, sex and diabetes duration were included in the stepwise multivariable linear regression analyses to determine the predictors of LV systolic and diastolic function in patients with T2DM. The diabetic duration was divided into long (> 5 years) and short (≤ 5 years) term duration which were included in the univariable and multivariable analysis. Two-tailed p values < 0.05 were considered statistically significant, and statistical analyses were performed with SPSS version 23.0 (IBM, Armonk, New York, USA).

Baseline characteristics

The main clinical characteristics of the study cohort are shown in Table 1 . Although age was not significantly different between the patient groups, patients with DPN had a longer diabetes duration (p = 0.003) and higher incidences of dyslipidemia (63.9% vs. 42.3%, p = 0.008), retinopathy (29.5% vs. 4.6%, p < 0.001) and CKD (24.6% vs. 12.3%, p = 0.037) than those without DPN. As expected, both patient groups showed higher fasting blood glucose levels than the control group (all p < 0.001), and patients with DPN had significantly higher HbA1c levels than those without DPN (p = 0.001). In addition, BMI, mean SBP and DBP were significantly higher in patients with or without DPN than in controls (all p < 0.05), but they were not significantly different between the patient groups.

No significant differences in the use of medications were observed between patient groups except for statins and insulin, which were most frequently used in patients with DPN (p = 0.006 and < 0.001, respectively).

Characteristics of LV geometry and strain parameters

The CMR findings for the study cohort are shown in Table 2 . T2DM patients without and with DPN had a larger LV mass than the control group (p = 0.007 and 0.002, respectively), and the differences were present even after adjustment for BSA (p = 0.006 and 0.001, respectively). The LV remodeling index in the patient groups without and with DPN was significantly higher than that in the control group (p = 0.034 and = 0.017 respectively). Besides, the prevalence of non-infarct LGE was significantly higher in subjects with DPN compared with those without DPN (37.7% vs. 19.2%, p = 0.008). The LVEDV, LVEDVI, LVESV, LVESVI, LVSV, LVSVI and LVEF were not significantly different among the three groups (all p > 0.05).

Regarding the strain parameters (Fig. 2 ), the LV radial and longitudinal PS, as well as longitudinal PDSR were decreased progressively from controls through T2DM patients without DPN to patients with DPN (all p < 0.001). LV radial and circumferential PDSR, as well as circumferential PS were reduced in both patient groups (all p < 0.05), but were not significantly different between these groups (all p > 0.05). In addition, radial and longitudinal PSSR were decreased in patients with DPN (p = 0.006 and 0.003, respectively) but preserved in patients without DPN (all p > 0.05).

Comparion of left ventricular global strain parameters among controls, T2DM (DPN−) and T2DM (DPN+) groups. GRS global radial peak strain, GCS global circumferential peak strain, GLS global longitudinal peak strain, PSSR peak systolic strain rate, PDSR peak diastolic strain rate, R radial, C circumferential, L longitudinal, T2DM type 2 diabetes mellitus, DPN diabetic peripheral neuropathy

Determinants of subclinical LV dysfunction in T2DM patients

After univariable linear regression analyses (Tables 3 and 4 ), DPN was significantly associated with all three directions of LV global PS (all p < 0.05), radial and longitudinal PSSR and PDSR (all p < 0.1). Retinopathy was significantly associated with radial and longitudinal PS and PDSR, as well as radial and circumferential PSSR (all p < 0.05). Dyslipidemia was significantly associated with radial PS, PSSR and PDSR (all p < 0.05). In addition, LGE was significantly associated with all directions of LV global PS (all p ≤ 0.001), PSSR (p < 0.05) and PDSR (p < 0.1) except circumferential PSSR (p = 0.655). Results of other univariable analyses are shown in the tables.

Multivariable linear regression analyses adjusting for confounders demonstrated that DPN was independently associated with LV radial and longitudinal PS (β = − 3.030 and 1.187, p = 0.014 and 0.003, respectively) and PDSR (β = 0.281 and − 0.086, p = 0.016 and 0.001, respectively), as well as radial PSSR (β = − 0.266, p = 0.007). CKD was independently associated with LV longitudinal PS (β = 1.045, p = 0.042). Dyslipidemia was independently associated radial PS, PSSR and PDSR (β = 3.773, 0.212 and -0.450, all p < 0.05). Additionally, LGE was independently associated with radial, circumferential and longitudinal PS (β = − 4.070, 1.401 and 2.020, all p ≤ 0.002), radial and longitudinal PSSR (β = − 0.266 and 0.103, p = 0.012 and < 0.001, respectively), and longitudinal PDSR (β = − 0.076, P = 0.014).

Assessment of myocardial deformation by strain and strain rate is sensitive to detect subclinical myocardial systolic and diastolic dysfunction, in which PS and PSSR reflect myocardial systolic function, while PDSR is a sensitive marker of LV diastolic dysfunction. In T2DM patients without complicated DPN, we observed increases in LV myocardial mass and remodeling index and decreases in three directional PS and PDSR compared with the controls, but no significant differences in LVEDVI, LVESVI, LVSVI and LVEF among the groups. These findings indicate that LV myocardial systolic and diastolic function measured by CMR-FT were impaired at the early stage before the reduction of LVEF, which was consistent with previous studies [ 14 , 15 ]. In addition, the presence of LGE indicating myocardial fibrosis was also observed in these patients. Taken together, detection of early alternations in the LV myocardium enable early intervention and implementation of preventative strategies in T2DM patients.

In diabetes, hyperglycemia and lipotoxicity related to insulin resistance may lead to suppressed glucose oxidation, increased free fatty acid metabolism, inadequate calcium handling, mitochondrial dysfunction, increased oxidative stress, interstitial and perivascular fibrosis, and cardiomyocyte hypertrophy and stiffness, which may contribute to reduced ventricular compliance at the early stage [ 16 , 17 ]. A recent meta-analysis including a large number of patients (n = 5053) showed that diabetes was associated with a higher degree of myocardial fibrosis assessed by histological collagen volume fraction and extracellular volume fraction [ 18 ], and previous studies have detected diastolic dysfunction in the diabetic hearts without hypertrophy [ 19 , 20 ]. With the aggravation of aforementioned pathologies along with impairment in excitation–contraction coupling, microvascular abnormalities manifesting as microvascular endothelial inflammation, rarefaction and perivascular collagen, and end-product deposition, and increased LV wall thickness and mass may lead to systolic dysfunction [ 21 , 22 ]. Some studies have demonstrated an adverse effect of T2DM on subclinical LV systolic strains and myocardial microvascular impairment [ 23 , 24 ].

Further analysis in our patients revealed that the magnitude of LV radial and longitudinal PS as well as longitudinal PDSR were markedly lower in patients with DPN than in both controls and patients without DPN, and radial and longitudinal PSSR were reduced in patients with DPN but preserved in those without DPN. However, the LV geometry was not significantly different between the patient groups. In addition, we identified that DPN was independently associated with the magnitude of LV radial and longitudinal PS and PDSR, as well as radial PSSR after adjustment for confounding factors. Thus, we speculated that subclinical LV dysfunction was progressed in T2DM patients with DPN even without progressive alterations in LV geometry, which was consistent with previous speckle-tracking echocardiography study [ 25 ]. Subendocardial myocardial fibers predominantly affected by coronary microvascular dysfunction are impaired early and severer, then manifesting as independent association between DPN and LV longitudinal PS and PDSR. The results that DPN was not associated with circumferential PS and PDSR indicate subepicardial myocardial impairment was not decreased progressively in our patients with DPN. Because both subendocardial and subepicardial fibers contribute to LV radial function[ 26 ], decreased LV radial PS and PDSR may mainly be caused by impaired subendocardial fibers when circumferential function was not progressively decreased.

Several studies have shown that the main mechanisms involved in DPN are longstanding hyperglycemia, dyslipidemia and insulin resistance, which may cause common pathophysiological changes in multiple organs, such as mitochondrial dysfunction, oxidative stress, accumulation of advanced glycation end products, lipotoxicity, increased inflammatory cytokine synthesis and microvascular complications [ 3 , 27 , 28 ]. Myocardial dysfunction may be involved when these changes occur in the heart. An elevated HbA1c level is a known cardiovascular risk factor and associated with higher degrees of myocardial fibrosis [ 18 ], its reduction will lead to reduced risks of both macro- and microvascular disease [ 29 ]. Our results revealed higher HbA1c levels in patients with DPN, indicating poor glycemic control, higher metabolic disorder and myocardial fibrosis; however, it was not associated with myocardial dysfunction. A potential explanation for this discrepancy is that HbA1c levels may not be a good indicator of long-term glycemic control, as it only reflects glycemic control over the past 3–4 months. Besides, we found that patients with DPN had a higher incidence of dyslipidemia and it was independently associated with worsening LV radial PS, PSSR and PDSR, which may indicate that determining the pathophysiological mechanisms underlying the effect of dyslipidemia will provide mechanistic targets for developing new targeted therapies for DPN and related myocardial dysfunction.

A previous study revealed that microvascular alterations, similar to those observed in diabetic retinopathy and nephropathy, appear to be associated with pathological alterations of nerves [ 30 ], which may lead to reduced peripheral nerve nutrition and impaired nerve function. Chung et al. reported a more frequent prevalence of retinopathy in patients with T2DM presenting peripheral neuropathy [ 31 ], and it was consistent with our findings. Reduced flow in the left anterior descending artery was observed in patients with retinopathy [ 32 ]. The study by Sørensen et al. reported a decrease in myocardial perfusion reserve in patients with retinopathy that was associated with diastolic dysfunction [ 33 ]. According to our results, the retinopathy and CKD were significantly associated with LV myocardial dysfunction. Zhang et al. demonstrated that kidney dysfunction may aggravate the deterioration of LV strain in T2DM patients [ 34 ], and another study revealed that the LV global longitudinal strain is a superior predictor of all-cause and cardiovascular mortality when compared with ejection fraction in advanced CKD patients [ 35 ]. In addition, the study by Baltzis et al. showed that patients with DPN had a higher risk of myocardial ischemia than those without DPN using technetium-99 m sestamibi single-photon emission computed tomographic imaging [ 36 ], which was consistent with our finding that patients with DPN had higher proportion of non-infarct LGE indicating severer microvascular dsyfunction [ 37 ]. These observations suggest that the common mechanism of microvascular impairment in diabetic complications plays an important role in myocardial dysfunction in patients with DPN. It is highly stimulating developing future targeted medications improving microvascular function to improve prognosis in T2DM patients; for example, glucagon-like peptide-1(GLP-1) has been shown to have benefits for patients with microvascular complication [ 38 ].

LGE could be used as a surrogate for replacement fibrosis and very little is known regarding nonischemic LGE implications in T2DM patients [ 39 ]. In the present study, we found a high prevalence of non-infarct LGE in T2DM patients and even higher proportion in those with DPN, and it was independently associated with worsening LV systolic (three directional PS as well as radial ad longitudinal PSSR) and diastolic (longitudinal PDSR) dysfunction. A previous study showed that T2DM patients with non-ischemic LGE lesions had increased ECV [ 39 ], and a recent study revealed that coronary microvascular dysfunction was significantly associated with the development of myocardial fibrosis in patients with T2DM [ 40 ]. Considering higher prevalence of non-infarct LGE in patients with DPN, they may have severer myocardial structural and functional impairment in T2DM with DPN, which may explain the poor cardiac outcomes in these patients.

Limitations

There are several limitations in our study. Firstly, this was a retrospective, single-center, observational study involving a relatively limited sample size, which may introduce selection bias and limit the ability to establish causality. Therefore, a prospective, multicenter study is desirable to validate our results. Secondly, we only included diabetic patients without heart failure and preserved LVEF, the generalizability of our findings to patients with heart failure is worth to be further investigated. Thirdly, Although LGE is the technique of choice for diagnosis of replacement fibrosis, it cannot evaluate diffuse myocardial fibrosis. Native and postcontrast T1-mapping can assess the extent and distribution of diffuse myocardial fibrosis, however, it was not available in our series and will be implemented in our further studies. Furthermore, not all the patients underwent nerve conduction tests, and selection bias may exist because subclinical DPN with no signs or symptoms of neuropathy could not be diagnosed. However, our data reflect routine clinical practice in diagnosing DPN, and further studies are required to explore LV changes in patients with subclinical DPN. Finally, the inherent cross-sectional design of this study prevented us from drawing conclusions on causality. Long-term longitudinal studies are needed to investigate the ability of impaired LV strains and non-infarct LGE to predict cardiovascular outcomes in patients complicated with DPN.

There was more non-infarct LGE lesions and worsening subclinical LV dysfunction in T2DM patients complicated with DPN than those without DPN, which may suggest further prospective study with even more extensive therapeutic interventions in this cohort of patients to improve patient outcomes.

Availability of data and materials

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Type to diabetes mellitus

Diabetic peripheral neuropathy

Left ventricular

Cardiovascular magnetic resonance

CMR feature tracking

End-diastolic volume

Stroke volume

Cardiac output

Ejection fraction

LV mass index

Peak strain

Peak systolic strain rates

Peak diastolic strain rates

Late gadolinium enhancement

Saeedi P, Petersohn I, Salpea P, Malanda B, Karuranga S, Unwin N, et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: results from the International Diabetes Federation Diabetes Atlas, 9(th) edition. Diabetes Res Clin Pract. 2019;157: 107843.

Article PubMed Google Scholar

van Dieren S, Beulens JW, van der Schouw YT, Grobbee DE, Neal B. The global burden of diabetes and its complications: an emerging pandemic. Eur J Cardiovasc Prev Rehabil. 2010;17(Suppl 1):S3-8.

PubMed Google Scholar

Feldman EL, Callaghan BC, Pop-Busui R, Zochodne DW, Wright DE, Bennett DL, et al. Diabetic neuropathy. Nat Rev Dis Primers. 2019;5(1):41.

Forsblom CM, Sane T, Groop PH, Totterman KJ, Kallio M, Saloranta C, et al. Risk factors for mortality in Type II (non-insulin-dependent) diabetes: evidence of a role for neuropathy and a protective effect of HLA-DR4. Diabetologia. 1998;41(11):1253–62.

Article CAS PubMed Google Scholar

Margariti A. Peripheral neuropathy may be a potential risk of cardiovascular disease in diabetes mellitus. Heart. 2014;100(23):1823–4.

Brownrigg JR, Davey J, Holt PJ, Davis WA, Thompson MM, Ray KK, et al. The association of ulceration of the foot with cardiovascular and all-cause mortality in patients with diabetes: a meta-analysis. Diabetologia. 2012;55(11):2906–12.

Pedrizzetti G, Claus P, Kilner PJ, Nagel E. Principles of cardiovascular magnetic resonance feature tracking and echocardiographic speckle tracking for informed clinical use. J Cardiovasc Magn Reson. 2016;18(1):51.

Article PubMed PubMed Central Google Scholar

Kalam K, Otahal P, Marwick TH. Prognostic implications of global LV dysfunction: a systematic review and meta-analysis of global longitudinal strain and ejection fraction. Heart. 2014;100(21):1673–80.

Zulet P, Islas F, Ferrandez-Escarabajal M, Bustos A, Cabeza B, Gil-Abizanda S, et al. Diabetes mellitus is associated to high-risk late gadolinium enhancement and worse outcomes in patients with nonischemic dilated cardiomyopathy. Cardiovasc Diabetol. 2024;23(1):35.

Article CAS PubMed PubMed Central Google Scholar

Wang J, Li Y, Guo YK, Huang S, Shi R, Yan WF, et al. The adverse impact of coronary artery disease on left ventricle systolic and diastolic function in patients with type 2 diabetes mellitus: a 3.0T CMR study. Cardiovasc Diabetol. 2022;21(1):30.

Chamberlain JJ, Rhinehart AS, Shaefer CF Jr, Neuman A. Diagnosis and management of diabetes: synopsis of the 2016 American Diabetes Association Standards of Medical Care in Diabetes. Ann Intern Med. 2016;164(8):542–52.

Pop-Busui R, Boulton AJ, Feldman EL, Bril V, Freeman R, Malik RA, et al. Diabetic neuropathy: a position statement by the American Diabetes Association. Diabetes Care. 2017;40(1):136–54.

Schulz-Menger J, Bluemke DA, Bremerich J, Flamm SD, Fogel MA, Friedrich MG, et al. Standardized image interpretation and post-processing in cardiovascular magnetic resonance—2020 update: Society for Cardiovascular Magnetic Resonance (SCMR): Board of Trustees Task Force on Standardized Post-Processing. J Cardiovasc Magn Reson. 2020;22(1):19.

Yan WF, Xu HY, Jiang L, Zhang L, Guo YK, Li Y, et al. Early longitudinal changes in left ventricular function and morphology in diabetic pigs: evaluation by 3.0T magnetic resonance imaging. Cardiovasc Diabetol. 2023;22(1):6.

Zhou S, Zhang Z, Zhang Z, Gao Y, Li G, Lou M, et al. Evaluation of left ventricular systolic and diastolic function in subjects with prediabetes and diabetes using cardiovascular magnetic resonance-feature tracking. Acta Diabetol. 2022;59(4):491–9.

Miki T, Yuda S, Kouzu H, Miura T. Diabetic cardiomyopathy: pathophysiology and clinical features. Heart Fail Rev. 2013;18(2):149–66.

Grigorescu ED, Lacatusu CM, Floria M, Mihai BM, Cretu I, Sorodoc L. Left ventricular diastolic dysfunction in type 2 diabetes-progress and perspectives. Diagnostics. 2019;9(3):121.

Salvador DB, Gamba MR, Gonzalez-Jaramillo N, Gonzalez-Jaramillo V, Raguindin PFN, Minder B, et al. Diabetes and myocardial fibrosis: a systematic review and meta-analysis. JACC: Cardiovasc Imaging. 2022;15(5):796–808.

Schannwell CM, Schneppenheim M, Perings S, Plehn G, Strauer BE. Left ventricular diastolic dysfunction as an early manifestation of diabetic cardiomyopathy. Cardiology. 2002;98(1–2):33–9.

Ha JW, Lee HC, Kang ES, Ahn CM, Kim JM, Ahn JA, et al. Abnormal left ventricular longitudinal functional reserve in patients with diabetes mellitus: implication for detecting subclinical myocardial dysfunction using exercise tissue Doppler echocardiography. Heart. 2007;93(12):1571–6.

Cao Y, Zeng W, Cui Y, Kong X, Wang M, Yu J, et al. Increased myocardial extracellular volume assessed by cardiovascular magnetic resonance T1 mapping and its determinants in type 2 diabetes mellitus patients with normal myocardial systolic strain. Cardiovasc Diabetol. 2018;17(1):7.

Oka S, Kai T, Hoshino K, Watanabe K, Nakamura J, Abe M, et al. Effects of empagliflozin in different phases of diabetes mellitus-related cardiomyopathy: a prospective observational study. BMC Cardiovasc Disord. 2021;21(1):217.

Li XM, Jiang L, Guo YK, Ren Y, Han PL, Peng LQ, et al. The additive effects of type 2 diabetes mellitus on left ventricular deformation and myocardial perfusion in essential hypertension: a 3.0 T cardiac magnetic resonance study. Cardiovasc Diabetol. 2020;19(1):161.

Liu X, Yang ZG, Gao Y, Xie LJ, Jiang L, Hu BY, et al. Left ventricular subclinical myocardial dysfunction in uncomplicated type 2 diabetes mellitus is associated with impaired myocardial perfusion: a contrast-enhanced cardiovascular magnetic resonance study. Cardiovasc Diabetol. 2018;17(1):139.

Chen Y, Wang Y, Zhang Y, Li M, Zhang W, Zhou Y, et al. Association of peripheral neuropathy with subclinical left ventricular dysfunction in patients with type 2 diabetes. J Diabetes Complic. 2023;37(2): 108406.

Article CAS Google Scholar

Claus P, Omar AMS, Pedrizzetti G, Sengupta PP, Nagel E. Tissue tracking technology for assessing cardiac mechanics: principles, normal values, and clinical applications. JACC Cardiovasc Imaging. 2015;8(12):1444–60.

Rumora A, Kim B, Feldman EL. A role for fatty acids in peripheral neuropathy associated with type 2 diabetes and prediabetes. Antioxid Redox Signal. 2022;37(7–9):560–77.

Roustit M, Loader J, Deusenbery C, Baltzis D, Veves A. Endothelial dysfunction as a link between cardiovascular risk factors and peripheral neuropathy in diabetes. J Clin Endocrinol Metab. 2016;101(9):3401–8.

Stratton IM, Adler AI, Neil HA, Matthews DR, Manley SE, Cull CA, et al. Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (UKPDS 35): prospective observational study. BMJ. 2000;321(7258):405–12.

Giannini C, Dyck PJ. Ultrastructural morphometric abnormalities of sural nerve endoneurial microvessels in diabetes mellitus. Ann Neurol. 1994;36(3):408–15.

Chung JO, Cho DH, Chung DJ, Chung MY. Association between diabetic polyneuropathy and cardiovascular complications in type 2 diabetic patients. Diabetes Metab J. 2011;35(4):390–6.

Akasaka T, Yoshida K, Hozumi T, Takagi T, Kaji S, Kawamoto T, et al. Retinopathy identifies marked restriction of coronary flow reserve in patients with diabetes mellitus. J Am Coll Cardiol. 1997;30(4):935–41.

Sorensen MH, Bojer AS, Broadbent DA, Plein S, Madsen PL, Gaede P. Cardiac perfusion, structure, and function in type 2 diabetes mellitus with and without diabetic complications. Eur Heart J Cardiovasc Imaging. 2020;21(8):887–95.

Zhang Y, Wang J, Ren Y, Yan WF, Jiang L, Li Y, et al. The additive effects of kidney dysfunction on left ventricular function and strain in type 2 diabetes mellitus patients verified by cardiac magnetic resonance imaging. Cardiovasc Diabetol. 2021;20(1):11.

Reboldi G, Krishnasamy R, Isbel NM, Hawley CM, Pascoe EM, Burrage M, et al. Left ventricular global longitudinal strain (GLS) Is a superior predictor of all-cause and cardiovascular mortality when compared to ejection fraction in advanced chronic kidney disease. PLoS ONE. 2015;10(5): e0127044.

Article Google Scholar

Baltzis D, Roustit M, Grammatikopoulou MG, Katsaboukas D, Athanasiou V, Iakovou I, et al. Diabetic peripheral neuropathy as a predictor of asymptomatic myocardial ischemia in type 2 diabetes mellitus: a cross-sectional study. Adv Ther. 2016;33(10):1840–7.

Li XM, Shi R, Shen MT, Yan WF, Jiang L, Min CY, et al. Subclinical left ventricular deformation and microvascular dysfunction in T2DM patients with and without peripheral neuropathy: assessed by 3.0 T cardiac magnetic resonance imaging. Cardiovasc Diabetol. 2023;22(1):256.

Ishibashi Y, Matsui T, Takeuchi M, Yamagishi S. Glucagon-like peptide-1 (GLP-1) inhibits advanced glycation end product (AGE)-induced up-regulation of VCAM-1 mRNA levels in endothelial cells by suppressing AGE receptor (RAGE) expression. Biocheml Biophys Res Commun. 2010;391(3):1405–8.

Bojer AS, Sorensen MH, Vejlstrup N, Goetze JP, Gaede P, Madsen PL. Distinct non-ischemic myocardial late gadolinium enhancement lesions in patients with type 2 diabetes. Cardiovasc Diabetol. 2020;19(1):84.

Zhao W, Li K, Tang L, Zhang J, Guo H, Zhou X, et al. Coronary microvascular dysfunction and diffuse myocardial fibrosis in patients with type 2 diabetes using quantitative perfusion MRI. J Magn Reson Imaging. 2024. https://doi.org/10.1002/jmri.29296 .

Download references

Acknowledgements

Not applicable.

This work was supported by grants from the National Natural Science Foundation of China (82371925, 81971586, 81771887, 821201080 and 82202115) and 1·3·5 project for disciplines of excellence, West China Hospital, Sichuan University (ZYGD23019).

Author information

Xue-Ming Li and Ke Shi have contributed equally to this work and should be considered as the equal first authors.

Authors and Affiliations

Department of Radiology, West China Hospital, Sichuan University, 37# Guo Xue Xiang, Chengdu, Sichuan, China

Xue-Ming Li, Ke Shi, Li Jiang, Jing Wang, Wei-Feng Yan, Yue Gao, Meng-Ting Shen, Rui Shi, Ge Zhang & Zhi-Gang Yang

Laboratory of Cardiovascular Diseases, Regenerative Medicine Research Center, West China Hospital, Sichuan University, 37# Guo Xue Xiang, Chengdu, Sichuan, China

Xue-Ming Li, Ge Zhang & Xiao-Jing Liu

Key Laboratory of Birth Defects and Related Diseases of Women and Children of Ministry of Education, Department of Radiology, West China Second University Hospital, Sichuan University, 20# South Renmin Road, Chengdu, Sichuan, China

Ying-Kun Guo

You can also search for this author in PubMed Google Scholar

Contributions

XML, KS and LJ contributed to the study design, data analysis and manuscript writing. XML, JW, WFY, YG and MTS were responsible for collecting, sorting and analyzed the data. RS, GZ and XJL participated in editing and review of the manuscript. YKG and ZGY participated in the study design and revised the manuscript. All authors read and approved the final version of submitted manuscript.

Corresponding author

Correspondence to Zhi-Gang Yang .

Ethics declarations

Ethics approval and consent to participate.

The Biomedical Research Ethics Committee of our hospital approved this study (No. 2019-878), which was performed in accordance with the Declaration of Helsinki. Written informed consent have been waived owing to the retrospective nature of this study.

Consent for publication

Competing interests.

The authors declare that there are no conflicts of interest.

Additional information

Publisher's note.

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

Rights and permissions

Reprints and permissions

About this article

Cite this article.

Li, XM., Shi, K., Jiang, L. et al. Assessment of subclinical LV myocardial dysfunction in T2DM patients with diabetic peripheral neuropathy: a cardiovascular magnetic resonance study. Cardiovasc Diabetol 23 , 217 (2024). https://doi.org/10.1186/s12933-024-02307-x

Download citation

Received : 08 March 2024

Accepted : 16 June 2024

Published : 24 June 2024

DOI : https://doi.org/10.1186/s12933-024-02307-x

Share this article

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

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

Provided by the Springer Nature SharedIt content-sharing initiative

Type 2 diabetes mellitus
Magnetic resonance imaging
Left ventricle

Cardiovascular Diabetology

ISSN: 1475-2840

Submission enquiries: [email protected]

Groundbreaking study shows why drinking from plastic bottles may increase your risk of type 2 diabetes

BPA is an industrial chemical that scientists have linked to hormone disruption and diabetes risk.
Plastic water bottles and food containers can leach BPA into what you eat and drink.
A new study found it can be risky at levels previously considered safe by government agencies.

Scientists have long suspected that industrial chemicals used in plastic water bottles can disrupt human hormones .

But, to date, evidence has been observational, meaning it shows an association between plastics exposure and certain diseases, but can't prove a causal effect.

Now, a groundbreaking new study shows direct evidence that bisphenol A — or, BPA, a chemical used to package food and drink — can reduce sensitivity to insulin, a hormone that helps regulate blood sugar.

An impaired ability to respond to insulin, known as insulin resistance , can mean chronically high blood sugar levels and a much higher risk of type 2 diabetes.

The researchers, who presented their findings at the 2024 Scientific Sessions of the American Diabetes Association , said this study shows the EPA may need to reconsider the safe limits for exposure to BPA in plastic bottles, food containers, and other containers.

Even so-called safe levels of BPA may cause health issues

Researchers from California Polytechnic State University studied 40 healthy adults who were randomly assigned to receive either a placebo or a small dose of BPA daily.

After four days, the participants who were given BPA were less responsive to insulin, while the placebo group did not experience any change.

Environmental contaminants can be a major threat to human health

The concern about BPA is part of a broader alarm being raised about our everyday exposure to substances that may be harmful to our health.

Other recent research has found microplastics , particles so tiny they can infiltrate human cells, may potentially wreak havoc with our health. They've been found everywhere, from human lungs to reproductive organs .

Understanding how the substances we encounter every day may affect our health long-term could help us make better decisions about how to reduce the risk of chronic illnesses like type 2 diabetes.

"Given that diabetes is a leading cause of death in the US, it is crucial to understand even the smallest factors that contribute to the disease," Hagobian said in the press release. " We were surprised to see that reducing BPA exposure, such as using stainless steel or glass bottles and BPA-free cans, may lower diabetes risk."

June 24, 2024 — An earlier version of this article misstated the difference between what the EPA considers safe BPA exposure and what the study found to be safe. It is 100 times higher, not 1000.

Watch: Every difference between US and UK Coca-Cola

Main content

IMAGES

Case Studies of Patients with Type 2 Diabetes Mellitus: Exercises in Problem Solving
DM Case Study
Case Study #54 DM Type 2.docx
Pathophysiology of type 2 diabetes mellitus
PPT
Tyep 2 DM Case Study

VIDEO

BIOCHEMISTRY
aflevering 10 Inleiding DM type 1 en Dm type 2
DM Type 2
Stem Cell Therapy Market in Asia-Pacific to 2018 Market Research Report
din bhar ki khabar
How To Automate Your Sales Process FULL GUIDE (Examples, Process, Results)

COMMENTS

Case Study: A Patient With Uncontrolled Type 2 Diabetes and Complex
The following case study illustrates the clinical role of advanced practice nurses in the management of a patient with type 2 diabetes. Case Presentation A.B. is a retired 69-year-old man with a 5-year history of type 2 diabetes.
Case 6-2020: A 34-Year-Old Woman with Hyperglycemia
The patient received a diagnosis of type 2 diabetes mellitus; lifestyle modification was recommended. Three months post partum, the glycated hemoglobin level was 5.9% and the BMI was 30.0. ... observational case control studies. PLoS One 2013; 8 (6): e65326. [PMC free article] [Google Scholar] 34. The HAPO Study Cooperative Research Group.
Case Study: A Woman With Type 2 Diabetes and Severe
In type 2 diabetes, characterized by insulin resistance and insulin deficiency, the pathophysiology of hypertriglyceridemia is an increased hepatic production of triglycerides as well as a decreased lipoprotein lipase activity leading to slower breakdown of VLDL cholesterol and chylomicrons.3 The American Diabetes Association (ADA) Clinical ...
Interactive diabetes case 17: A 47-year-old patient with ...
CASE. A 47-year-old woman was found to have hyperglycemia at a health fair when a random blood glucose level was 227 mg/dL (12.6 mmol/L). Several days later, a fasting blood glucose value was 147 mg/dL (8.2 mmol/L). She has no previous history of diabetes, is alarmed by the possibility of having this disorder, and seeks your advice.
Two Cases of Successful Type 2 Diabetes Control with Lifestyle
INTRODUCTION. Obesity and obesity-related diseases are serious public health issues worldwide, and the increased incidence of type 2 diabetes in children and adolescents is associated with the increased incidence of obesity.1,2 Excess weight gain is a risk factor for both type 2 diabetes and insulin resistance. Obesity refers to excessive fat accumulation and may affect the clinical course of ...
Case Study: A Patient With Type 2 Diabetes Working With an Advanced
B.L. is a 58-year-old white woman who has been referred to the pharmacist clinician for pharmacotherapy assessment and diabetes management. Her multiple medical conditions include type 2 diabetes diagnosed in 1995, hypertension, hyperlipidemia, asthma, coronary artery disease, persistent peripheral edema, and longstanding musculoskeletal pain secondary to a motor vehicle accident.
Clinical features of patients with type 2 diabetes with and without
The present case-control study was designed to primarily test whether the prevalence of cardiovascular disease, and secondarily of other underlying conditions, differ between patients with type 2 diabetes hospitalized for Covid-19 (cases), compared to patients with type 2 diabetes without signs or symptoms of SARS-CoV-2 infection (controls).
Nutrient patterns and risk of diabetes mellitus type 2: a case-control
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 []).Healthy individuals within the same age range were also included, with specific glucose level ...
Type 2 diabetes: a case study
This article examines the aetiology, pathophysiology, diagnosis and treatment of type 2 diabetes using a case study approach. The psychosocial implications for the patient are also discussed. The case study is based on a patient with diabetes who was admitted to hospital following a hypoglycaemic episode and cared for during a practice placement.
Case Studies of Patients with Type 2 Diabetes Mellitus ...
Diabetes mellitus currently affects 6.4% or 285 million adults worldwide, and that number is expected to increase to 7.7% or 439 million by 2030. 1 In the United States, the prevalence of diabetes in adults increased from 11.3% in 2010 to 12.3% in 2012. 2 The current type 2 diabetes mellitus (T2DM) epidemic is closely associated with a parallel obesity epidemic, with more than 85% of patients ...
Association between Diet Quality and Eating Behavior in Type 2 Diabetes
Type 2 diabetes mellitus (T2DM) has become a global epidemic. To effectively control T2DM, individuals must adhere to a high-quality diet that encompasses not only healthy dietary patterns but also promotes positive eating behaviors. We conducted a cross-sectional study on 314 patients with T2DM, and we evaluated the diet quality and also examined the associations between eating behavior, diet ...
Breakthrough Studies on Automated Insulin Delivery and CGM for Type 2
Results Demonstrate Enhanced Diabetes Management and Quality of Life with Advanced Technology. New data focused on advanced technology innovations for managing type 2 diabetes (T2D) highlight the positive impact of automated insulin delivery systems (AID) and continuous glucose monitoring (CGM) in improving glycemic control and overall diabetes management.
Nutrient patterns and risk of diabetes mellitus type 2: a case-control
Methods: 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 ...
Interactive case study: Making a diagnosis of type 2 diabetes
The new series of case studies from Diabetes & Primary Care is aimed at GPs, practice nurses and other professionals in primary and community care who would like to broaden their understanding of type 2 diabetes. This third case offering provides three mini-case studies that take you through the criteria for making an accurate diagnosis of diabetes and non-diabetic hyperglycaemia.
Intermittent fasting over two days can help people with Type 2 diabetes
The new study of the 5:2 diet took place in China, which has more people with Type 2 diabetes than any other country in the world. At least 141 million adults in China have diabetes and half the ...
Association between adverse childhood experiences and type 2 diabetes
Studies emphasize the importance of childhood adversity as a risk factor for developing non-communicable diseases, including type-2 diabetes mellitus (T2DM) in adulthood. This case-control study involved 137 patients with T2DM and 134 non-diabetic adults of both genders (mean age 46.9 and 45.7 years, respectively).
[PDF] Case Study: A Patient With Uncontrolled Type 2 Diabetes and
Nurse practitioners must recognize the clinical picture of ketosis-prone type 2 DM and use a multifaceted approach, encouraging dietary changes, increased physical activity, and medication adherence to build the self-management skills of the patient and ultimately decrease the long-term disease burden.
Changes to Gut Microbiome May Increase Type 2 Diabetes Risk
Researchers investigated the gut microbiome and type 2 diabetes risk in the largest and most diverse study of its kind to date. They found that specific species and strains of gut microbes were more common in people with type 2 diabetes. The findings, if confirmed, may mean that the gut microbiome can be altered to reduce type 2 diabetes risk.
ApoA1/HDL-C ratio as a predictor for coronary artery disease in
This study investigated the possible relationship between the Apo lipoprotein A1 /high-density lipoprotein cholesterol (ApoA1/HDL-C) ratio and coronary artery disease (CAD) in patients with type 2 diabetes (T2D). This was a matched case-control study of 482 patients with T2D in two groups of CAD and (n = 241) non-CAD (n = 241). The patients were classified into four quartiles according to the ...
Study links gut microbiome changes to increased risk of type 2 diabetes
"The changes to the microbiome may happen first, and diabetes develops later, not the other way around—although future prospective or interventional studies are needed to prove this relation firmly." "If these microbial features are causal, we can find a way to change the microbiome and reduce type 2 diabetes risk," he added.
Strain-specific gut microbial signatures in type 2 diabetes identified
The association of gut microbial features with type 2 diabetes (T2D) has been inconsistent due in part to the complexity of this disease and variation in study design. Even in cases in which ...
Nutrient patterns and risk of diabetes mellitus type 2: a case-control
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 ...
Diagnosis and Treatment
Treatment. Treatment of type 2 diabetes mellitus (T2DM) focuses on decreasing blood glucose, increasing insulin secretion, or countering insulin resistance. Treatment of symptoms, such as diabetic retinopathy, nephropathy, or neuropathy requires additional and involved patient education, medications, and therapies.
CC, History, and PE
Yazzie's mother had type 2 diabetes mellitus and well-controlled hypertension. Ms. Yazzie's sister died at age 64, the specific cause is unknown. She had been diagnosed with obesity, hypertension, diabetes, and suspected coronary artery disease. Ms. Yazzie's other sister is alive at age 59. She was diagnosed with obesity at age 37 and ...
PDF Presented by: Case study 2 The treatment of a newly diagnosed diabetic
Case 2 - Mr DvW, newly diagnosed with type 2 diabetes mellitus (T2DM) • 45-years-old, diagnosed three months ago by GP • Random glucose: 16.2 mmol/l, HbA 1c: 7.8% • Referred for assessment and opinion • 2Dyslipidaemia and hypertension, central obesity, BMI: 35 kg/m . Dr Lombard's clinical approach This patient requires intensive ...
Case Study: A 30-Year-Old Man With Metformin-Treated Newly Diagnosed
M.P. is a 30-year-old man who was diagnosed with type 2 diabetes 2 weeks before admission to the hospital. He has a strong family history of type 2 diabetes. He smokes heavily (> 20 cigarettes/day) and admits to some alcohol consumption. His primary care physician had started him on metformin,500 mg three times daily.
PDF Study links gut microbiome changes to increased risk of type 2 diabetes
diabetes (T2D), prediabetes, and healthy glucose status has found that specific viruses and genetic variants within bacteria correspond with changes in gut microbiome function and T2D risk.
JCM
Objective: To evaluate the risk of progression to type 2 diabetes (T2D) following reactive hypoglycemia in 100 g oral glucose tolerance test (oGTT). Methods: A retrospective analysis of parturients with up to 5-year follow-up postpartum. Data were extracted from the computerized laboratory system of Meuhedet, an Israeli HMO and cross-linked with the Israeli National Registry of Diabetes.
Assessment of subclinical LV myocardial dysfunction in T2DM patients
Background Diabetic peripheral neuropathy (DPN) is the most prevalent complication of diabetes, and has been demonstrated to be independently associated with cardiovascular events and mortality. This aim of this study was to investigate the subclinical left ventricular (LV) myocardial dysfunction in type 2 diabetes mellitus (T2DM) patients with and without DPN. Methods One hundred and thirty ...
Drinking From Plastic Bottles Can Increase Diabetes Risk: New Research
Groundbreaking study shows why drinking from plastic bottles may increase your risk of type 2 diabetes Gabby Landsverk 2024-06-24T20:00:36Z

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

Backgrounds

Study population

Dietary assessment

Nutrient pattern assessment

Assessment of other variables

Statistical analysis

Conclusions

Data availability

Acknowledgements

Author information

Contributions

Corresponding author

Ethics declarations

Consent for publication

Competing interests

Additional information

Rights and permissions

About this article

Share this article

BMC Endocrine Disorders

Breakthrough Studies on Automated Insulin Delivery and CGM for Type 2 Diabetes Unveiled at ADA Scientific Sessions

Contact Virginia Cramer for press-related questions.

Interactive case study: Making a diagnosis of type 2 diabetes

Semaglutide effective treatment for HFpEF in people with type 2 diabetes

Save citation to file

Add to My Bibliography

Association between adverse childhood experiences and type 2 diabetes mellitus in later life: A case-control study

Conflict of interest statement

Similar articles

Related information

Miscellaneous

Case Study: A Patient With Uncontrolled Type 2 Diabetes and Complex Comorbidities Whose Diabetes Care Is Managed by an Advanced Practice Nurse

5 Citations

ApoA1/HDL-C ratio as a predictor for coronary artery disease in patients with type 2 diabetes: a matched case-control study

Introduction

Study population

Data collection

Statistical analysis

Baseline characteristic

Association between ApoA1/HDL-C ratio and CAD in patients with T2D

ROC curve analysis

Strengths and limitations

Data availability

Acknowledgements

Author information

Contributions

Corresponding author

Ethics declarations

Consent for publication

Competing interests

Additional information

Rights and permissions

About this article

Share this article

BMC Cardiovascular Disorders

Study links gut microbiome changes to increased risk of type 2 diabetes

Strain-specific gut microbial signatures in type 2 diabetes identified in a cross-cohort analysis of 8,117 metagenomes

Access options

Similar content being viewed by others

Revealing links between gut microbiome and its fungal community in Type 2 Diabetes Mellitus among Emirati subjects: A pilot study

Functional alterations and predictive capacity of gut microbiome in type 2 diabetes

Microbiome connections with host metabolism and habitual diet from 1,098 deeply phenotyped individuals

Code availability

Acknowledgements

Author information

Authors and Affiliations

Contributions

Corresponding authors

Ethics declarations

Peer review

Additional information

Extended data

Extended Data Fig. 2 Principal coordinate analysis of all samples using species-level Bray–Curtis dissimilarity colored by cohorts before and after correcting batch and study effects.

Source data

Extended Data Fig. 4 Metformin has a direct impact on the gut microbiome composition and confounds the associations between microbial species and type 2 diabetes.

Extended Data Fig. 5 Sensitivity analyses demonstrate that identified microbial features of type 2 diabetes are unlikely to reflect the duration or comorbidities of this disease.

Extended Data Fig. 6 Associations of microbial features with circulating metabolic and inflammation biomarkers.

Extended Data Fig. 7 Prevotella copri ’s differential carriage of branched-chain amino acid biosynthesis function is explained by its discrete subclade structure.

Extended Data Fig. 8 Phylogenetic trees of select species show divergent associations between subclades and type 2 diabetes within each species.