To Type 2 Diabetes Risk Research Articles

Outdoor light at night, air pollution and risk of incident type 2 diabetes

BackgroundAir pollution and outdoor light at night (LAN) have been reported to be related to type 2 diabetes (T2D). However, their interaction with risk of T2D remains uncertain. Therefore, our study aimed to explore the relationship between outdoor LAN, air pollution and incident T2D. MethodsOur study included a cohort of 24,147 subjects recruited from 2015 to 2018 in Ningbo, China. Land use regression models were used to evaluate particulate matter with a diameter ≤2.5 μm (PM2.5), ≤10 μm (PM10) and nitrogen dioxide (NO2). Satellite images data with a spatial resolution of 500m was used to estimate outdoor LAN levels. T2D new cases were identified by medical records based on health information system. Cox proportional hazards models were used to estimate Hazard ratios (HRs) and 95% confidence intervals (CIs). Moreover, we investigated the multiplicative and additive interactions between air pollution and outdoor LAN. ResultsDuring 108,908 person-years of follow-up period, 1016 T2D incident cases were identified. The HRs (95% CIs) were 1.22 (1.15, 1.30) for outdoor LAN, 1.20 (1.00, 1.45) for PM2.5, 1.23 (1.11, 1.35) for PM10 and 1.19 (1.04, 1.37) for NO2 in every interquartile range increase, respectively. Furthermore, significant interactions were observed between outdoor LAN and NO2. ConclusionsOur findings indicated that air pollution and outdoor LAN were positively associated with T2D. Moreover, we observed an interaction between outdoor LAN and NO2 suggesting that stronger associations for outdoor LAN and T2D in areas with higher levels of NO2, and for NO2 and T2D in areas with higher levels of outdoor LAN.

Hypoglycemia in Oral Glucose Tolerance Test during Pregnancy and Risk for Type 2 Diabetes-A Five-Year Cohort Study.

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. Included were parturients with no prior diabetesand available oGTT values during pregnancy. Reactive hypoglycemia was defined as glucose levels lower than 60 mg/dL in at least one of 3 post-glucose load values in oGTT. The cohort was divided into 3 groups: normal glucose status, reactive hypoglycemia, and GDM. Maternal characteristics, laboratory data, and progression to T2D over 5 years were compared. Univariate and survival analyses assessed the adjusted hazard ratio for T2D, stratified by obesity Results: Among 14,122 parturients, 16.8% had reactive hypoglycemia, 71% had normal glucose status, and 12.2% had GDM. Adjusted for age, obesity, and hypertension, Parturients with reactive hypoglycemia had similar T2D risk compared to normal glucose status and a lower risk compared to GDM patients, regardless of obesity status. Conclusions: Reactive hypoglycemia during oGTT does not increase the risk of progressing to T2D.

MicroRNAs Associated with Metformin Treatment in the Diabetes Prevention Program.

The Diabetes Prevention Program (DPP) randomized controlled trial demonstrated that metformin treatment reduced progression to type 2 diabetes (T2D) by 31% compared to placebo in adults with prediabetes. Circulating micro-ribonucleic acids (miRs) are promising biomarkers of T2D risk, but little is known about their associations with metformin regimens for T2D risk reduction. We compared the change in 24 circulating miRs from baseline to 2 years in a subset from DPP metformin intervention (n = 50) and placebo (n = 50) groups using Wilcoxon signed rank tests. Spearman correlations were used to evaluate associations between miR change and baseline clinical characteristics. Multiple linear regression was used to adjust for covariates. The sample was 73% female, 17% Black, 13% Hispanic, and 50 ± 11 years. Participants were obese, normotensive, prediabetic, and dyslipidemic. Change in 12 miR levels from baseline to 2 years was significantly different in the metformin group compared with placebo after adjusting for multiple comparisons: six (let-7c-5p, miR-151a-3p, miR-17-5p, miR-20b-5p, miR-29b-3p, and miR-93-5p) were significantly upregulated and six (miR-130b-3p, miR-22-3p, miR-222-3p, miR-320a-3p, miR-320c, miR-92a-3p) were significantly downregulated in the metformin group. These miRs help to explain how metformin is linked to T2D risk reduction, which may lead to novel biomarkers, therapeutics, and precision health strategies.

Association of dietary nutrient intake with type 2 diabetes: A Mendelian randomization study.

Observational research suggests that the evidence linking dietary nutrient intake (encompassing minerals, vitamins, amino acids, and unsaturated fatty acids) to type 2 diabetes (T2D) is both inconsistent and limited. This study aims to explore the potential causal relationship between dietary nutrients and T2D. Causal estimation utilized Mendelian randomization techniques. Single nucleotide polymorphisms linked to dietary nutrients were identified from existing genome-wide association studies and used as instrumental variables. Genome-wide association studies data pertinent to T2D were sourced from the DIMANTE consortium and the FinnGen database. Techniques including inverse variance weighting (IVW), weighted mode, weighted median, and Mendelian randomization-Egger were employed for causal inference, complemented by sensitivity analysis. Genetically predicted higher phenylalanine (IVW: odds ratio = 1.10 95% confidence interval 1.04-1.17, P = 1.5 × 10-3, q_pval = 3.4 × 10-2) and dihomo-gamma-linolenic acid (IVW: odds ratio = 1.001 95% confidence interval 1.0006-1.003, P = 3.7 × 10-3, q_pval = 4.1 × 10-2) levels were directly associated with T2D risk. Conversely, no causal relationships between other nutrients and T2D were established. We hypothesize that phenylalanine and dihomo-gamma-linolenic acid contribute to the pathogenesis of T2D. Clinically, the use of foods with high phenylalanine content may pose potential risks for patients with a heightened risk of T2D. Our study provides evidence supporting a causal link between dietary nutrient intake and the development of T2D.

International Diabetes Federation Position Statement on the 1-hour post-load plasma glucose for the diagnosis of intermediate hyperglycaemia and type 2 diabetes

Many individuals with intermediate hyperglycaemia (IH), including impaired fasting glycaemia (IFG) and impaired glucose tolerance (IGT), as presently defined, will progress to type 2 diabetes (T2D). There is confirmatory evidence that T2D can be prevented by lifestyle modification and/or medications, in people with IGT diagnosed by 2-h plasma glucose (PG) during a 75-gram oral glucose tolerance test (OGTT). Over the last 40 years, a wealth of epidemiological data has confirmed the superior value of 1-h plasma glucose (PG) over fasting PG (FPG), glycated haemoglobin (HbA1c) and 2-h PG in populations of different ethnicity, sex and age in predicting diabetes and associated complications including death. Given the relentlessly rising prevalence of diabetes, a more sensitive, practical method is needed to detect people with IH and T2D for early prevention or treatment in the often lengthy trajectory to T2D and its complications. The International Diabetes Federation (IDF) Position Statement reviews findings that the 1-h post-load PG ≥ 155 mg/dL (8.6 mmol/L) in people with normal glucose tolerance (NGT) during an OGTT is highly predictive for detecting progression to T2D, micro- and macrovascular complications, obstructive sleep apnoea, cystic fibrosis-related diabetes mellitus, metabolic dysfunction-associated steatotic liver disease, and mortality in individuals with risk factors. The 1-h PG of 209 mg/dL (11.6 mmol/L) is also diagnostic of T2D. Importantly, the 1-h PG cut points for diagnosing IH and T2D can be detected earlier than the recommended 2-h PG thresholds. Taken together, the 1-h PG provides an opportunity to avoid misclassification of glycaemic status if FPG or HbA1c alone are used. The 1-h PG also allows early detection of high-risk people for intervention to prevent progression to T2D which will benefit the sizeable and growing population of individuals at increased risk of T2D. Using a 1-h OGTT, subsequent to screening with a non-laboratory diabetes risk tool, and intervening early will favourably impact the global diabetes epidemic. Health services should consider developing a policy for screening for IH based on local human and technical resources. People with a 1-h PG ≥ 155 mg/dL (8.6 mmol/L) are considered to have IH and should be prescribed lifestyle intervention and referred to a diabetes prevention program. People with a 1-h PG ≥ 209 mg/dL (11.6 mmol/L) are considered to have T2D and should have a repeat test to confirm the diagnosis of T2D and then referred for further evaluation and treatment. The substantive data presented in the Position Statement provides strong evidence for redefining current diagnostic criteria for IH and T2D by adding the 1-h PG.

Plasma metabolite profiles associated with the World Cancer Research Fund/American Institute for Cancer Research lifestyle score and future risk of cardiovascular disease and type 2 diabetes

BackgroundA healthy lifestyle (HL) has been inversely related to type 2 diabetes (T2D) and cardiovascular disease (CVD). However, few studies have identified a metabolite profile associated with HL. The present study aims to identify a metabolite profile of a HL score and assess its association with the incidence of T2D and CVD in individuals at high cardiovascular risk.MethodsIn a subset of 1833 participants (age 55-80y) of the PREDIMED study, we estimated adherence to a HL using a composite score based on the 2018 Word Cancer Research Fund/American Institute for Cancer Research recommendations. Plasma metabolites were analyzed using LC-MS/MS methods at baseline (discovery sample) and 1-year of follow-up (validation sample). Cross-sectional associations between 385 known metabolites and the HL score were assessed using elastic net regression. A 10-cross-validation procedure was used, and correlation coefficients or AUC were assessed between the identified metabolite profiles and the self-reported HL score. We estimated the associations between the identified metabolite profiles and T2D and CVD using multivariable Cox regression models.ResultsThe metabolite profiles that identified HL as a dichotomous or continuous variable included 24 and 58 metabolites, respectively. These are amino acids or derivatives, lipids, and energy intermediates or xenobiotic compounds. After adjustment for potential confounders, baseline metabolite profiles were associated with a lower risk of T2D (hazard ratio [HR] and 95% confidence interval (CI): 0.54, 0.38–0.77 for dichotomous HL, and 0.22, 0.11–0.43 for continuous HL). Similar results were observed with CVD (HR, 95% CI: 0.59, 0.42–0.83 for dichotomous HF and HR, 95%CI: 0.58, 0.31–1.07 for continuous HL). The reduction in the risk of T2D and CVD was maintained or attenuated, respectively, for the 1-year metabolomic profile.ConclusionsIn an elderly population at high risk of CVD, a set of metabolites was selected as potential metabolites associated with the HL pattern predicting the risk of T2D and, to a lesser extent, CVD. These results support previous findings that some of these metabolites are inversely associated with the risk of T2D and CVD.Trial registrationThe PREDIMED trial was registered at ISRCTN (http://www.isrctn.com/, ISRCTN35739639).

285-OR: Surveying Specific and Shared Responses of Human Islets to T2D-Associated Stressors

Endoplasmic reticulum (ER) and inflammatory stress in islets is linked to Type 2 Diabetes (T2D). We hypothesize that a subset of non-coding T2D-associated genetic variants (T2D SNPs) modulate these pathophysiologic responses in human islets. To test this, we compared chromatin accessibility and gene expression in human islets before and after exposure to ER stress (thapsigargin) or pro-inflammatory cytokines (IL-1β+IFN-γ) to identify specific and shared stress-responsive cis-regulatory elements (CREs) and their target genes. Approximately 13% of CREs (n=14,966) and 30% of genes (n=5,131) are modulated by ≥1 stressor (ER stress-specific: ~41% CREs & ~33% genes, inflammation-specific: ~52% CREs & ~42% genes, shared: ~7% CREs & ~25% genes), implying that changes in accessibility and expression in islets are stress-specific. For example, ER stress-specific genes (ex: DDIT3 & ATF4) are involved in unfolded protein response, and ER stress-specific CREs enrich for 157 transcription factor (TF) binding sites (ex: CHOP & ATF4). Many genes nearest to stress-responsive CREs are also stress-responsive genes (ER stress-specific: n=596, inflammation-specific: n=827, shared: n=91) suggesting a context-specific modulation of gene expression by CREs. Of these CREs, 92 (ER stress: n=53, inflammation: n=39) harbor 114 T2D SNPs, of which 19 T2D SNPs become accessible only upon ER stress. For example, rs6444081 (A>G) overlaps an ER stress-specific CRE and resides downstream of ETV5 (ER stress-responsive gene). The T2D risk allele (G) is predicted to be bound by ER stress-specific TF ETV1. In a separate cohort, ETV5 expression is higher in T2D vs non-T2D beta cells, suggesting that ETV5 is a putative target gene of rs6444081 and that its activity is genetically modulated in islets. Our results nominate T2D SNPs that we propose modulate stress-specific islet responses. Further study of these CREs and their target genes should help to functionally compartmentalize T2D risk variants. Disclosure E.K.Sokolowski: None. R.M.Bhuiyan: None. R.Kursawe: None. M.L.Stitzel: None. D.Ucar: None. Funding National Institutes of Health (R01DK118011-01A1); U.S. Department of Defense (W81XWH-18-0401)

Potato consumption, polygenic scores, and incident type 2 diabetes: An observational study

Whether the consumption of different processed potatoes is detrimental to type 2 diabetes (T2D) is highly debated. This study aimed to assess the relations between potato consumption and the risk of T2D and whether the relationship was modified by the genetic predisposition to T2D. We included 174,665 participants from the UK Biobank at baseline. Potato consumption was evaluated using the 24-hour dietary questionnaire. The genetic risk score (GRS) was calculated based on 424 variants associated with T2D. After adjustment for demographic, lifestyle, and dietary factors, the consumption of total potatoes was significantly and positively associated with T2D risk [hazard ratio (HR) comparing two or more servings/day with non-consumers was 1.28 (95% CI: 1.13–1.45)]. HRs (95% CIs) of T2D for each 1-SD increment in boiled/baked potatoes, mashed potatoes, and fried potatoes were 1.02 (0.99–1.05), 1.05 (1.02–1.08), and 1.05 (1.02–1.09), respectively. There were no significant interactions between the consumption of total or different processed potatoes and overall GRS on T2D risk. Theoretically, replacing one serving/day of total potatoes with the same amount of non-starchy vegetables was related to a 12% (95% CI: 0.84–0.91) lower T2D risk. These results showed the positive associations of the consumption of total potatoes, mashed potatoes or fried potatoes and genetic risk with higher incident T2D. An unhealthy potato-based diet is associated with higher diabetes risk regardless of genetic risk.

Inverse Correlation of Superoxide Dismutase and Catalase with Type 2 Diabetes among Rural Thais

Oxidative stress contributes to defective antioxidant defenses, which may lead to type 2 diabetes (T2D). This study aimed to elucidate the T2D risks and antioxidant defenses by investigating the superoxide dismutase (SOD), catalase (CAT), vitamin A, and vitamin E status. We observed 102 participants aged 35–66 years from Sung Neon, Nakhon Ratchasima, Thailand. The blood samples were collected to measure the SOD, CAT, vitamin A, and vitamin E concentrations. The SOD and CAT activities were inversely associated with T2D risk. When compared with participants in the highest quartile of SOD and CAT, those in the lowest quartile for T2D risk obtained multivariable-adjusted odds ratios of 4.77 (SOD: 95% confident interval CI, 1.01–22.40; p = 0.047) and 4.22 (CAT: 95% CI, 1.07–16.60; p = 0.039). The possible influencing factors (e.g., physical activity, total cholesterol, and triglyceride) might mediate the association of SOD and CAT with T2D risk. Meanwhile, the relationship between vitamin A and vitamin E concentrations and T2D risk was insignificant. In conclusion, lower concentrations of antioxidant enzyme activity (SOD and CAT) may be an additional risk factor for T2D.

Gestational Diabetes Mellitus, Postpartum Lipidomic Signatures, and Subsequent Risk of Type 2 Diabetes: A Lipidome-Wide Association Study.

To identify a postpartum lipidomic signature associated with gestational diabetes mellitus (GDM) and investigate the role of the identified lipids in the progression to type 2 diabetes (T2D). This prospective cohort study enrolled 1,409 women at 24-72 h after delivery of a singleton baby and followed them prospectively at the Boston Medical Center. The lipidome was profiled by liquid chromatography-tandem mass spectrometry. Diagnoses of GDM and incident T2D were extracted from medical records and verified using plasma glucose levels. Mean (SD) age of study women at baseline was 28.5 (6.6) years. A total of 219 (16.4%) women developed incident diabetes over a median follow-up of 11.8 (interquartile range 8.2-14.8) years. We identified 33 postpartum lipid species associated with GDM, including 16 inverse associations (primarily cholesterol esters and phosphatidylcholine plasmalogens), and 17 positive associations (primarily diacyglycerols and triacyglycerols). Of these, four were associated with risk of incident T2D and mediated ∼12% of the progression from GDM to T2D. The identified lipid species modestly improved the predictive performance for incident T2D above classical risk factors when the entire follow-up period was considered. GDM was associated with a wide range of lipid metabolic alterations at early postpartum, among which some lipid species were also associated with incident T2D and mediated the progression from GDM to T2D. The improvements attained by including lipid species in the prediction of T2D provides new insights regarding the early detection and prevention of progression to T2D.

Association of physical activity and air pollution exposure with the risk of type 2 diabetes: a large population-based prospective cohort study

BackgroundThe interplay between physical activity (PA) and air pollution in relation to type 2 diabetes (T2D) remains largely unknown. Based on a large population-based cohort study, this study aimed to examine whether the benefits of PA with respect to the risk of T2D are moderated by exposure to air pollution.MethodsUK Biobank participants (n = 359,153) without diabetes at baseline were included. Information on PA was obtained using the International Physical Activity Questionnaire short form. Exposure to air pollution, including PM2.5, PMcoarse (PM2.5−10), PM10, and NO2, was estimated from land use regression models. Cox regression models were used to estimate the hazard ratios (HRs) and 95% confidence intervals (95% CIs).ResultsDuring a median of 8.9 years of follow-up, 13,706 T2D events were recorded. Compared with a low PA level, the HRs for the risk of T2D among individuals with moderate and high PA were 0.82 (95% CI, 0.79–0.86) and 0.73 (95% CI, 0.70–0.77), respectively. Compared with low levels of air pollution, the HRs for risk of T2D for high levels of air pollution (PM2.5, PMcoarse, PM10, and NO2) were 1.19 (1.14–1.24), 1.06 (1.02–1.11), 1.13 (1.08–1.18), and 1.19 (1.14–1.24), respectively. There was no effect modification of the associations between PA and T2D by air pollution (all P-interactions > 0.05). The inverse associations between PA and T2D in each air pollution stratum were generally consistent (all P for trend < 0.05).ConclusionA higher PA and lower air pollution level were independently associated with a lower risk of T2D. The beneficial effects of PA on T2D generally remained stable among participants exposed to different levels of air pollution. Further studies are needed to replicate our findings in moderately and severely polluted areas.

The impact of ethnicity and intra-pancreatic fat on the postprandial metabolome response to whey protein in overweight Asian Chinese and European Caucasian women with prediabetes.

The "Thin on the Outside Fat on the Inside" TOFI_Asia study found Asian Chinese to be more susceptible to Type 2 Diabetes (T2D) compared to European Caucasians matched for gender and body mass index (BMI). This was influenced by degree of visceral adipose deposition and ectopic fat accumulation in key organs, including liver and pancreas, leading to altered fasting plasma glucose, insulin resistance, and differences in plasma lipid and metabolite profiles. It remains unclear how intra-pancreatic fat deposition (IPFD) impacts TOFI phenotype-related T2D risk factors associated with Asian Chinese. Cow's milk whey protein isolate (WPI) is an insulin secretagogue which can suppress hyperglycemia in prediabetes. In this dietary intervention, we used untargeted metabolomics to characterize the postprandial WPI response in 24 overweight women with prediabetes. Participants were classified by ethnicity (Asian Chinese, n=12; European Caucasian, n=12) and IPFD (low IPFD < 4.66%, n=10; high IPFD ≥ 4.66%, n=10). Using a cross-over design participants were randomized to consume three WPI beverages on separate occasions; 0g (water control), 12.5g (low protein, LP) and 50g (high protein, HP), consumed when fasted. An exclusion pipeline for isolating metabolites with temporal (T0-240mins) WPI responses was implemented, and a support vector machine-recursive feature elimination (SVM-RFE) algorithm was used to model relevant metabolites by ethnicity and IPFD classes. Metabolic network analysis identified glycine as a central hub in both ethnicity and IPFD WPI response networks. A depletion of glycine relative to WPI concentration was detected in Chinese and high IPFD participants independent of BMI. Urea cycle metabolites were highly represented among the ethnicity WPI metabolome model, implicating a dysregulation in ammonia and nitrogen metabolism among Chinese participants. Uric acid and purine synthesis pathways were enriched within the high IPFD cohort's WPI metabolome response, implicating adipogenesis and insulin resistance pathways. In conclusion, the discrimination of ethnicity from WPI metabolome profiles was a stronger prediction model than IPFD in overweight women with prediabetes. Each models' discriminatory metabolites enriched different metabolic pathways that help to further characterize prediabetes in Asian Chinese women and women with increased IPFD, independently.

Genetic regulation of RNA splicing in human pancreatic islets

BackgroundNon-coding genetic variants that influence gene transcription in pancreatic islets play a major role in the susceptibility to type 2 diabetes (T2D), and likely also contribute to type 1 diabetes (T1D) risk. For many loci, however, the mechanisms through which non-coding variants influence diabetes susceptibility are unknown.ResultsWe examine splicing QTLs (sQTLs) in pancreatic islets from 399 human donors and observe that common genetic variation has a widespread influence on the splicing of genes with established roles in islet biology and diabetes. In parallel, we profile expression QTLs (eQTLs) and use transcriptome-wide association as well as genetic co-localization studies to assign islet sQTLs or eQTLs to T2D and T1D susceptibility signals, many of which lack candidate effector genes. This analysis reveals biologically plausible mechanisms, including the association of T2D with an sQTL that creates a nonsense isoform in ERO1B, a regulator of ER-stress and proinsulin biosynthesis. The expanded list of T2D risk effector genes reveals overrepresented pathways, including regulators of G-protein-mediated cAMP production. The analysis of sQTLs also reveals candidate effector genes for T1D susceptibility such as DCLRE1B, a senescence regulator, and lncRNA MEG3.ConclusionsThese data expose widespread effects of common genetic variants on RNA splicing in pancreatic islets. The results support a role for splicing variation in diabetes susceptibility, and offer a new set of genetic targets with potential therapeutic benefit.

Dietary Protein Sources, Mediating Biomarkers, and Incidence of Type 2 Diabetes: Findings From the Women's Health Initiative and the UK Biobank.

Whether and how dietary protein intake is linked to type 2 diabetes (T2D) remains unclear. The aim of this study was to investigate the associations of protein intake with development of T2D and the potential mediating roles of T2D biomarkers. We included 108,681 postmenopausal women without T2D at baseline from the Women's Health Initiative (WHI) (primary cohort) and 34,616 adults without T2D from the U.K. Biobank (UKB) (replication cohort). Cox proportional hazard models were used for estimation of protein-T2D associations. Mediation analysis was performed to assess the mediating roles of biomarkers in case-control studies nested in the WHI. In the WHI, 15,842 incident T2D cases were identified during a median follow-up of 15.8 years. Intake of animal protein was associated with increased T2D risk (hazard ratio in comparing the highest to the lowest quintile = 1.31 [95% CI 1.24-1.37]) and plant protein with decreased risk (0.82 [0.78-0.86]). Intakes of red meat, processed meat, poultry, and eggs were associated with increased T2D risk and whole grains with decreased risk. Findings from the UKB were similar. These findings were materially attenuated after additional adjustment for BMI. Substituting 5% energy from plant protein for animal protein was associated with 21% decreased T2D risk (0.79 [0.74-0.84]), which was mediated by levels of hs-CRP, interleukin-6, leptin, and SHBG. Findings from these two large prospective cohorts support the notion that substituting plant protein for animal protein may decrease T2D risk mainly by reducing obesity-related inflammation.

Fat Distribution Patterns and Future Type 2 Diabetes.

Fat accumulation in the liver, pancreas, skeletal muscle, and visceral bed relates to type 2 diabetes (T2D). However, the distribution of fat among these compartments is heterogenous and whether specific distribution patterns indicate high T2D risk is unclear. We therefore investigated fat distribution patterns and their link to future T2D. From 2,168 individuals without diabetes who underwent computed tomography in Japan, this case-cohort study included 658 randomly selected individuals and 146 incident cases of T2D over 6 years of follow-up. Using data-driven analysis (k-means) based on fat content in the liver, pancreas, muscle, and visceral bed, we identified four fat distribution clusters: hepatic steatosis, pancreatic steatosis, trunk myosteatosis, and steatopenia. In comparisons with the steatopenia cluster, the adjusted hazard ratios for incident T2D were 4.02 (95% CI 2.27-7.12) for the hepatic steatosis cluster, 3.38 (1.65-6.91) for the pancreatic steatosis cluster, and 1.95 (1.07-3.54) for the trunk myosteatosis cluster. The clusters were replicated in 319 German individuals without diabetes who underwent MRI and metabolic phenotyping. The distribution of the glucose area under the curve across the four clusters found in Germany was similar to the distribution of T2D risk across the four clusters in Japan. Insulin sensitivity and insulin secretion differed across the four clusters. Thus, we identified patterns of fat distribution with different T2D risks presumably due to differences in insulin sensitivity and insulin secretion.

Comparison of adiposity indices and their association with insulin resistance and β-cell dysfunction in primary prevention for diabetes in Mexican population

IntroductionImpaired adiposity function, Insulin resistance (IR), and β-cell dysfunction predispose to type 2 diabetes (T2D). Some indices, such as the Visceral Adiposity Index (VAI), Dysfunctional Adiposity Index (DAI), and Atherogenic Index of Plasma (AIP), have been developed as surrogates of dysfunctional adiposity. However, the association of these indices with IR and β-cell dysfunction in primary prevention for T2D has not been fully studied. Materials and MethodsThis was a cross-sectional study that included 401 asymptomatic subjects with at least two risk factors for T2D that underwent an oral glucose tolerance test. A HOMA-IR ≥2.5 and a Disposition Index ≤1.24 were used to define IR and β-cell dysfunction, respectively. ResultsMean age of the subjects was 50.4 ± 9.6 years, 71.6% were women, had a mean BMI of 30.3 ± 6.4 kg/m2 27.6% and 11.6% had newly diagnosed T2D. After multiple regression analysis the VAI, DAI, and AIP were associated with IR (1.218 [1.103–1.345], 1.355 [1.150–1.597], and 2.533 [1.733–3.703], respectively), and β-cell dysfunction (1.119 [1.029–1.217], 1.193 [1.034–1.376], and 1.960 [1.357–2.830], respectively). Likewise, the AIP was the only index associated for newly T2D diagnosis (1.974 [1.224–3.184]). ConclusionsThe VAI, DAI and AIP were independently associated with IR and β-cell dysfunction in patients at high risk for T2D.

118-OR: Fat Distribution Patterns Predict Type 2 Diabetes Risk and Provide Insights into Prediabetic Metabolism

Objective: Fat accumulation in liver, pancreas, skeletal muscle, and visceral bed relates to type 2 diabetes (T2D) . However, distribution of fat in these compartments is heterogenous and it is unclear if specific distribution patterns indicate high T2D risk. We therefore investigated fat distribution patterns and their link to future T2D. Methods: From 2168 individuals without diabetes undergoing computed tomography in Japan, this case-cohort study included 658 randomly selected individuals and 146 incident cases of T2D with a 6-year follow-up. Data-driven analysis (k-means) was applied to develop clusters based on fat content in liver, pancreas, muscle, and visceral bed. Hazard ratios (HRs) for association of clusters and incident T2D were estimated using weighted Cox regression. In 3 individuals without diabetes with magnetic resonance imaging and metabolic phenotyping in Germany, cluster validation with additional assessment of glycemic traits from OGTTs was conducted. Results: We identified four clusters of fat distribution: cluster 1 (Hepatic steatosis cluster) , cluster 2 (Pancreatic steatosis cluster) , cluster 3 (Myosteatosis dominant cluster) , cluster 4 (Steatopenic cluster) . Compared with Steatopenic cluster, the adjusted-HRs (95%CIs) for incident T2D were 4.02 (2.27-7.12) in Hepatic steatosis cluster, 3.38 (1.65-6.91) in Pancreatic steatosis cluster, and 1.95 (1.07-3.54) in Myosteatosis dominant cluster. The clusters were replicated in the German cohort with similar distribution of AUC-glucose to the diabetes hazard in the Japanese cohort. Insulin sensitivity and insulin secretion were different across clusters with the lowest insulin sensitivity and highest insulin secretion in Hepatic steatosis cluster. Conclusions: Extending evidence about fat accumulation in single compartments, we identified specific patterns of fat distribution with different T2D risk presumably due to differences in insulin sensitivity and insulin secretion. Disclosure H.Yamazaki: None. N.Stefan: Advisory Panel; Gilead Sciences, Inc., GlaxoSmithKline plc., Sanofi, Consultant; AstraZeneca, Intercept Pharmaceuticals, Inc., Novo Nordisk, Pfizer Inc., Speaker's Bureau; AstraZeneca, Boehringer Ingelheim International GmbH, Lilly Diabetes, Merck Sharp &amp; Dohme Corp. A.Fritsche: Advisory Panel; Boehringer Ingelheim International GmbH, Novo Nordisk, Sanofi-Aventis Deutschland GmbH. A.L.Birkenfeld: None. R.Wagner: Advisory Panel; Akcea Therapeutics, Daiichi Sankyo, Sanofi-Aventis Deutschland GmbH, Speaker's Bureau; Lilly, Novo Nordisk, Sanofi-Aventis Deutschland GmbH. M.Heni: Advisory Panel; Boehringer Ingelheim International GmbH, Research Support; Boehringer Ingelheim International GmbH, Sanofi, Speaker's Bureau; Amryt Pharma Plc, Boehringer Ingelheim International GmbH, Novo Nordisk. S.Tauchi: None. J.Machann: None. T.Haueise: None. Y.Yamamoto: None. M.Dohke: None. N.Hanawa: None. Y.Kodama: Other Relationship; Eisai Co., Ltd. A.Katanuma: None. Funding the German Center for Diabetes Research (DZD, 01GI0925) , the state of Baden-Württemberg (32-5400/58/2, Forum Gesundheitsstandort Baden-Württemberg) , and JSPS KAKENHI Grant Number 19K16978

856-P: Cardiovascular (CV) and Kidney Outcomes with Canagliflozin (CANA) According to Type 2 Diabetes (T2D) Treatment Targets at Baseline (BL) : Data from the CANVAS Program and CREDENCE

In T2D, treatment targets include maintaining HbA1c ≤7.0%, LDL-C &amp;lt;2 mmol/L (&amp;lt;77 mg/dL) , and BP &amp;lt;130/80 mmHg. It is also recommended to improve urinary albumin:creatinine ratio (UACR) , a marker of renal damage and CV risk. We examined effects of CANA vs. placebo on CV and kidney outcomes in patients with T2D and high CV risk and/or chronic kidney disease according to BL treatment targets and risk factors. Pooled data from the CANVAS Program (N=10,142) and the CREDENCE trial (N=4401) were analyzed according to treatment target achievement and by number of targets achieved at BL. Hazard ratios and 95% CIs were estimated using Cox regression models. Of 14,432 participants at BL, 3683 (26%) had achieved 0, 5851 (41%) had achieved 1, 3680 (25%) had achieved 2, and 1218 (8%) had achieved either 3 or 4 targets. At BL, 8415 (58%) participants had UACR &amp;gt;2 mg/mmol. CANA consistently reduced risk of MACE, HHF/CV death, and ESKD or doubling of serum creatinine vs. placebo, regardless of whether BL targets were met or if UACR was elevated (Figure) . The number of uncontrolled targets at BL did not impact the beneficial effect of CANA on CV and kidney outcomes (all P interaction &amp;gt;0.17) . In conclusion, CANA demonstrated consistent CV and renal benefits in patients with T2D, regardless of risk factor control. Disclosure V.C.Woo: Advisory Panel; AstraZeneca, Boehringer Ingelheim International GmbH, Lilly, Novo Nordisk, Speaker's Bureau; Janssen Pharmaceuticals, Inc. M.Tsoukas: Speaker's Bureau; AstraZeneca, Bausch Health, Canada, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Johnson &amp; Johnson, Novo Nordisk Canada Inc. S.Tobe: Other Relationship; Bayer AG, Speaker's Bureau; AstraZeneca, Otsuka Pharmaceutical Co., Ltd. A.Slee: Consultant; Alydia Health, Elixir Medical , IHP Therapeutics , iLumen Scientific, Intuitive Surgical, Janssen Global Services, LLC, Laborie, Providence Medical, Pulse Biosciences, SonoMotion. W.Rapattoni: Employee; Janssen Pharmaceuticals, Inc. F.Ang: Employee; Bristol-Myers Squibb Company, Janssen Pharmaceuticals, Inc. J.Seufert: Advisory Panel; Abbott, Sanofi-Aventis Deutschland GmbH, Research Support; Boehringer Ingelheim International GmbH, Speaker's Bureau; Abbott Diabetes, AstraZeneca, Bayer AG, Boehringer Ingelheim International GmbH, Lilly, Novo Nordisk, Sanofi-Aventis Deutschland GmbH. D.C.Wheeler: Advisory Panel; Bayer AG, Boehringer Ingelheim International GmbH, Gilead Sciences, Inc., GlaxoSmithKline plc., Janssen Global Services, LLC, Merck Sharp &amp; Dohme Corp., Mundipharma, Tricida, Inc., Vifor Pharma Management Ltd., Consultant; AstraZeneca, Speaker's Bureau; Amgen Inc., Astellas Pharma Inc. Funding Janssen Inc., Toronto, ON, Canada

Potential Novel Serum Metabolic Markers Associated With Progression of Prediabetes to Overt Diabetes in a Chinese Population.

BackgroundIdentifying the metabolite profile of individuals with prediabetes who turned to type 2 diabetes (T2D) may give novel insights into early T2D interception. The purpose of this study was to identify metabolic markers that predict the development of T2D from prediabetes in a Chinese population.MethodsWe used an untargeted metabolomics approach to investigate the associations between serum metabolites and risk of prediabetes who turned to overt T2D (n=153, mean follow up 5 years) in a Chinese population (REACTION study). Results were compared with matched controls who had prediabetes at baseline [age: 56 ± 7 years old, body mass index (BMI): 24.2 ± 2.8 kg/m2] and at a 5-year follow-up [age: 61 ± 7 years old, BMI: 24.5 ± 3.1 kg/m2]. Confounding factors were adjusted and the associations between metabolites and diabetes risk were evaluated with multivariate logistic regression analysis. A 10-fold cross-validation random forest classification (RFC) model was used to select the optimal metabolites panels for predicting the development of diabetes, and to internally validate the discriminatory capability of the selected metabolites beyond conventional clinical risk factors.FindingsMetabolic alterations, including those associated with amino acid and lipid metabolism, were associated with an increased risk of prediabetes progressing to diabetes. The most important metabolites were inosine [odds ratio (OR) = 19.00; 95% confidence interval (CI): 4.23-85.37] and carvacrol (OR = 17.63; 95% CI: 4.98-62.34). Thirteen metabolites were found to improve T2D risk prediction beyond eight conventional T2D risk factors [area under the curve (AUC) was 0.98 for risk factors + metabolites vs 0.72 for risk factors, P < 0.05].InterpretationsUse of the metabolites identified in this study may help determine patients with prediabetes who are at highest risk of progressing to diabetes.

Dairy Product Intake Modifies MicroRNA Expression among Individuals with Hyperinsulinemia: A Post-Intervention Cross-Sectional Study

Introduction: MicroRNA (miRNA) profiles have been shown to change after intake of dairy products. Dysregulation of miRNA is associated with the changes in the level of glycemic parameters. The objectives are: (1) to investigate miRNA expression after consumption of dairy products and (2) to study the association between miRNAs and glycemic profile among individuals with hyperinsulinemia. Methods: In crossover design, 24 participants were randomized into 2 phases: high dairy (HD) (≥4 servings/day according to the Canadian food guide [2007]) and adequate dairy (AD) (≤2 servings/day) over 6 weeks. First, miRNAs were extracted from a pooled plasma sample of 10 subjects after HD and AD intervention which analyzed in duplicate by array hybridization (Affymetrix Gene Chip miRNA Array v. 4.0). Second, 6 miRNAs related to type 2 diabetes (T2D) were validated by quantitative reverse transcription polymerase chain reaction (qRT-PCR) from plasma of 24 participants. Results: Microarray analysis indicated that 297 miRNAs expressed differentially (FC ≥ ±1.2; p value <0.05) in a pooled plasma sample of 10 subjects. Among pooled miRNAs, the level of selected miRNAs, including miR-652-3p, miR-106b-5p, miR-93-5p, and miR-107 were downregulated; conversely, miR-223-3p and miR-122-5p were upregulated. After qRT-PCR validation, only the expression level of miR-106-5p tended to be increased after HD compared to AD (p = 0.06). After AD intervention, the level of fasting plasma glucose (FPG) and insulin and homeostatic model assessment of insulin resistance were negatively correlated with miR-122-5p. Similarly, negative correlation was found between miR-106-5p and FPG. Conclusion: The miRNAs profile was modified after HD compared to AD, and this may have role in modifying the risk of T2D (registration No. NCT02961179).