Trajectories of cardiometabolic risk factors before diagnosis of three subtypes of type 2 diabetes: a post-hoc analysis of the longitudinal Whitehall II cohort study

Fasting and postprandial plasma metabolite responses to a 12-wk dietary intervention in tissue-specific insulin resistance: a secondary analysis of the PERSonalized glucose Optimization through Nutritional intervention (PERSON) randomized trial

BACKGROUND

We previously showed that dietary intervention effects on cardiometabolic health were driven by tissue-specific insulin resistance (IR) phenotype: individuals with predominant muscle IR (MIR) benefited more from a low-fat, high-protein, and high-fiber (LFHP) diet, whereas individuals with predominant liver insulin resistance (LIR) benefited more from a high-monounsaturated fatty acid (HMUFA) diet.

OBJECTIVES

To further characterize the effects of LFHP and HMUFA diets and their interaction with tissue-specific IR, we investigated dietary intervention effects on fasting and postprandial plasma metabolite profile.

METHODS

Adults with MIR or LIR (40-75 y, BMI 25-40 kg/m2) were randomly assigned to a 12-wk HMUFA or LFHP diet (n = 242). After the exclusion of statin use, 214 participants were included in this prespecified secondary analysis. Plasma samples were collected before (T = 0) and after (T = 30, 60, 120, and 240 min) a high-fat mixed meal for quantification of 247 metabolite measures using nuclear magnetic resonance spectroscopy.

RESULTS

A larger reduction in fasting VLDL-triacylglycerol (TAG) and VLDL particle size was observed in individuals with MIR following the LFHP diet and those with LIR following the HMUFA diet, although no longer statistically significant after false discovery rate (FDR) adjustment. No IR phenotype-by-diet interactions were found for postprandial plasma metabolites assessed as total area under the curve (tAUC). Irrespective of IR phenotype, the LFHP diet induced greater reductions in postprandial plasma tAUC of the larger VLDL particles and small HDL particles, and TAG content in most VLDL subclasses and the smaller LDL and HDL subclasses (for example, VLDL-TAG tAUC standardized mean change [95% CI] LFHP = -0.29 [-0.43, -0.16] compared with HMUFA = -0.04 [-0.16, 0.09]; FDR-adjusted P for diet × time = 0.041).

CONCLUSIONS

Diet effects on plasma metabolite profiles were more pronounced than phenotype-by-diet interactions. An LFHP diet may be more effective than an HMUFA diet for reducing cardiometabolic risk in individuals with tissue-specific IR, irrespective of IR phenotype. Am J Clin Nutr 20xx;x:xx. This trial was registered at the clinicaltrials.gov registration (https://clinicaltrials.gov/study/NCT03708419?term=NCT03708419&rank=1) as NCT03708419 and CCMO registration (https://www.toetsingonline.nl/to/ccmo_search.nsf/fABRpop?readform&unids=3969AABCD9BA27FEC12587F1001BCC65) as NL63768.068.17.

Longitudinal metabolite and protein trajectories prior to diabetes mellitus diagnosis in Danish blood donors: a nested case-control study

AIMS/HYPOTHESIS

Metabolic risk factors and plasma biomarkers for diabetes have previously been shown to change prior to a clinical diabetes diagnosis. However, these markers only cover a small subset of molecular biomarkers linked to the disease. In this study, we aimed to profile a more comprehensive set of molecular biomarkers and explore their temporal association with incident diabetes.

METHODS

We performed a targeted analysis of 54 proteins and 171 metabolites and lipoprotein particles measured in three sequential samples spanning up to 11 years of follow-up in 324 individuals with incident diabetes and 359 individuals without diabetes in the Danish Blood Donor Study (DBDS) matched for sex and birth year distribution. We used linear mixed-effects models to identify temporal changes before a diabetes diagnosis, either for any incident diabetes diagnosis or for type 1 and type 2 diabetes mellitus diagnoses specifically. We further performed linear and non-linear feature selection, adding 28 polygenic risk scores to the biomarker pool. We tested the time-to-event prediction gain of the biomarkers with the highest variable importance, compared with selected clinical covariates and plasma glucose.

RESULTS

We identified two proteins and 16 metabolites and lipoprotein particles whose levels changed temporally before diabetes diagnosis and for which the estimated marginal means were significant after FDR adjustment. Sixteen of these have not previously been described. Additionally, 75 biomarkers were consistently higher or lower in the years before a diabetes diagnosis. We identified a single temporal biomarker for type 1 diabetes, IL-17A/F, a cytokine that is associated with multiple other autoimmune diseases. Inclusion of 12 biomarkers improved the 10-year prediction of a diabetes diagnosis (i.e. the area under the receiver operating curve increased from 0.79 to 0.84), compared with clinical information and plasma glucose alone.

CONCLUSIONS/INTERPRETATION

Systemic molecular changes manifest in plasma several years before a diabetes diagnosis. A particular subset of biomarkers shows distinct, time-dependent patterns, offering potential as predictive markers for diabetes onset. Notably, these biomarkers show shared and distinct patterns between type 1 diabetes and type 2 diabetes. After independent replication, our findings may be used to develop new clinical prediction models.

Longitudinal HbA1c patterns before the first treatment of diabetes in routine clinical practice: A latent class trajectory analysis

AIMS

To examine the longitudinal heterogeneity of HbA1c preceding the initiation of diabetes treatment in clinical practice.

METHODS

In this population-based study, we used HbA1c from routine laboratory and healthcare databases. Latent class trajectory analysis was used to classify individuals according to their longitudinal HbA1c patterns before first glucose-lowering drug prescription irrespective of type of diabetes.

RESULTS

Among 21,556 individuals initiating diabetes treatment during 2017-2018, 20,733 (96 %) had HbA1c measured (median 4 measurements [IQR 2-7]) in the 5 years preceding treatment initiation. Four classes with distinct HbA1c trajectories were identified, with varying steepness of increase in HbA1c. The largest class (74 % of the individuals) had mean HbA1c above the 48 mmol/mol threshold 9 months before treatment initiation. Mean HbA1c was 52 mmol/mol (95 % CI 52-52) at treatment initiation. In the remaining three classes, mean HbA1c exceeded 48 mmol/mol almost 1.5 years before treatment initiation and reached 79 mmol/mol (95 % CI 78-80), 105 mmol/mol (95 % CI 104-106), and 137 mmol/mol (95 % CI 135-140) before treatment initiation.

CONCLUSION

We identified four distinct longitudinal HbA1c patterns before initiation of diabetes treatment in clinical practice. All had mean HbA1c levels exceeding the diagnostic threshold many months before treatment initiation, indicating therapeutic inertia.

MTNR1B genotype and effects of carbohydrate quantity and dietary glycaemic index on glycaemic response to an oral glucose load: the OmniCarb trial

AIMS/HYPOTHESIS

A type 2 diabetes-risk-increasing variant, MTNR1B (melatonin receptor 1B) rs10830963, regulates the circadian function and may influence the variability in metabolic responses to dietary carbohydrates. We investigated whether the effects of carbohydrate quantity and dietary glycaemic index (GI) on glycaemic response during OGTTs varied by the risk G allele of MTNR1B-rs10830963.

METHODS

This study included participants (n=150) of a randomised crossover-controlled feeding trial of four diets with high/low GI levels and high/low carbohydrate content for 5 weeks. The MTNR1B-rs10830963 (C/G) variant was genotyped. Glucose response during 2 h OGTT was measured at baseline and the end of each diet intervention.

RESULTS

Among the four study diets, carrying the risk G allele (CG/GG vs CC genotype) of MTNR1B-rs10830963 was associated with the largest AUC of glucose during 2 h OGTT after consuming a high-carbohydrate/high-GI diet (β 134.32 [SE 45.69] mmol/l × min; p=0.004). The risk G-allele carriers showed greater increment of glucose during 0-60 min (β 1.26 [0.47] mmol/l; p=0.008) or 0-90 min (β 1.10 [0.50] mmol/l; p=0.028) after the high-carbohydrate/high-GI diet intervention, but not after consuming the other three diets. At high carbohydrate content, reducing GI levels decreased 60 min post-OGTT glucose (mean -0.67 [95% CI: -1.18, -0.17] mmol/l) and the increment of glucose during 0-60 min (mean -1.00 [95% CI: -1.67, -0.33] mmol/l) and 0-90 min, particularly in the risk G-allele carriers (pinteraction <0.05 for all).

CONCLUSIONS/INTERPRETATION

Our study shows that carrying the risk G allele of MTNR1B-rs10830963 is associated with greater glycaemic responses after consuming a diet with high carbohydrates and high GI levels. Reducing GI in a high-carbohydrate diet may decrease post-OGTT glucose concentrations among the risk G-allele carriers.

Deteriorating beta cell function is the dominant determinant of progression from normal glucose tolerance to prediabetes/diabetes in young women following pregnancy

AIMS/HYPOTHESIS

Excess adiposity, insulin resistance and beta cell dysfunction each contribute to the development of prediabetes (impaired glucose tolerance and/or impaired fasting glucose)/diabetes but their comparative impact in relation to one another remains uncertain. We thus ranked their contributions to incident dysglycaemia over the first 5 years postpartum in women reflecting the full spectrum of gestational glucose tolerance (spanning normoglycaemia to gestational diabetes) and hence a range of future diabetic risk.

METHODS

In this study, 302 women with normal glucose tolerance (NGT) on OGTT at 3 months postpartum underwent repeat OGTT at 1 year, 3 years and 5 years, enabling serial assessment of glucose tolerance, insulin sensitivity/resistance (Matsuda index, HOMA-IR) and beta cell function (insulin secretion-sensitivity index-2 [ISSI-2], insulinogenic index [IGI]/HOMA-IR). Determinants of prediabetes/diabetes were ranked by change in concordance index (CCI) of Cox proportional hazard regression models.

RESULTS

Over 5 years of follow-up, 89 women progressed from NGT to prediabetes/diabetes (progressors). At 3 months postpartum, though all women were normoglycaemic, future progressors had higher fasting glucose (p=0.03) and 2 h glucose (p<0.0001) than non-progressors, coupled with higher BMI (p=0.001), greater insulin resistance (both Matsuda index and HOMA-IR, p≤0.02) and poorer beta cell function (both ISSI-2 and IGI/HOMA-IR, p≤0.006). Unlike their peers, progressors exhibited deteriorating beta cell function from 1 year to 5 years (both p<0.0001). On regression analyses, the dominant determinants of progression to prediabetes/diabetes were time-varying ISSI-2 (change in CCI 25.2%) and IGI/HOMA-IR (13.0%), in contrast to time-varying Matsuda index (2.9%) and HOMA-IR (0.5%). Neither time-varying BMI nor waist were significant predictors after adjustment for beta cell function and insulin sensitivity/resistance.

CONCLUSION/INTERPRETATION

Declining beta cell function is the dominant determinant of incident prediabetes/diabetes in young women following pregnancy.

Precision subclassification of type 2 diabetes: a systematic review

BACKGROUND

Heterogeneity in type 2 diabetes presentation and progression suggests that precision medicine interventions could improve clinical outcomes. We undertook a systematic review to determine whether strategies to subclassify type 2 diabetes were associated with high quality evidence, reproducible results and improved outcomes for patients.

METHODS

We searched PubMed and Embase for publications that used 'simple subclassification' approaches using simple categorisation of clinical characteristics, or 'complex subclassification' approaches which used machine learning or 'omics approaches in people with established type 2 diabetes. We excluded other diabetes subtypes and those predicting incident type 2 diabetes. We assessed quality, reproducibility and clinical relevance of extracted full-text articles and qualitatively synthesised a summary of subclassification approaches.

RESULTS

Here we show data from 51 studies that demonstrate many simple stratification approaches, but none have been replicated and many are not associated with meaningful clinical outcomes. Complex stratification was reviewed in 62 studies and produced reproducible subtypes of type 2 diabetes that are associated with outcomes. Both approaches require a higher grade of evidence but support the premise that type 2 diabetes can be subclassified into clinically meaningful subtypes.

CONCLUSION

Critical next steps toward clinical implementation are to test whether subtypes exist in more diverse ancestries and whether tailoring interventions to subtypes will improve outcomes.

Serum branch-chained amino acids are increased in type 2 diabetes and associated with atherosclerotic cardiovascular disease

BACKGROUND AND AIM

Circulating biomarkers of metabolic and cardiovascular diseases can help in the early detection and prevention of those diseases. Using proton nuclear magnetic resonance (1H-NMR), we aimed to study the plasma levels of low-molecular-weight metabolites (LMWMs) in a cohort of 307 patients with metabolic diseases to assess their relationships with type-2 diabetes (T2D) and incident atherosclerotic cardiovascular disease (ASCVD).

METHODS

We conducted a cross-sectional and prospective study. We included 307 patients attending the Lipid Unit of our University Hospital for the treatment of the following metabolic disturbances and associated disorders: T2D (73.9%), obesity (58.7%), and hypertension (55.1%). 1H-NMR was used to study the plasma levels of 13 LMWMs. LMWM serum concentrations were evaluated in patients with and without T2D. and the correlations with several parameters and their associations with T2D were analyzed. The association between LMWM levels at baseline and the development of ASCVD in patients with T2D after 10 years of follow-up was also evaluated.

RESULTS

Among the LMWMs measured, the branched-chain amino acids (BCAAs) valine, leucine and isoleucine showed a positive association with several clinical and lipid-related biochemical parameters and inflammatory markers (p < 0.05). Likewise, these three BCAAS were associated with diabetes even after adjusting for covariates (p < 0.05). During the follow-up period of 10 years, 29 of the 185 patients with diabetes at baseline (15.68%) developed ASCVD. After adjusting for clinical covariates, baseline levels of valine and alanine were associated with the development of ASCVD (p < 0.05).

CONCLUSION

Overall, our results indicated that plasma levels of LMWMs measured by 1H-NMR could be potential biomarkers associated with T2D. Moreover, alanine and valine can help in the early detection of the cardiovascular risk associated with this metabolic disease.

Uncovering heterogeneous cardiometabolic risk profiles in US adults: the role of social and behavioral determinants of health

INTRODUCTION

Social and behavioral determinants of health (SBDH) have been linked to diabetes risk, but their role in explaining variations in cardiometabolic risk across race/ethnicity in US adults is unclear. This study aimed to classify adults into distinct cardiometabolic risk subgroups using SBDH and clinically measured metabolic risk factors, while comparing their associations with undiagnosed diabetes and pre-diabetes by race/ethnicity.

RESEARCH DESIGN AND METHODS

We analyzed data from 38,476 US adults without prior diabetes diagnosis from the National Health and Nutrition Examination Survey (NHANES) 1999-2018. The k-prototypes clustering algorithm was used to identify subgroups based on 16 SBDH and 13 metabolic risk factors. Each participant was classified as having no diabetes, pre-diabetes or undiagnosed diabetes using contemporaneous laboratory data. Logistic regression was used to assess associations between subgroups and diabetes status, focusing on differences by race/ethnicity.

RESULTS

Three subgroups were identified: cluster 1, primarily middle-aged adults with high rates of smoking, alcohol use, short sleep duration, and low diet quality; cluster 2, mostly young non-white adults with low income, low health insurance coverage, and limited healthcare access; and cluster 3, mostly older males who were the least physically active, but with high insurance coverage and healthcare access. Compared with cluster 2, adjusted ORs (95% CI) for undiagnosed diabetes were 14.9 (10.9, 20.2) in cluster 3 and 3.7 (2.8, 4.8) in cluster 1. Clusters 1 and 3 (vs cluster 2) had high odds of pre-diabetes, with ORs of 1.8 (1.6, 1.9) and 2.1 (1.8, 2.4), respectively. Race/ethnicity was found to modify the relationship between identified subgroups and pre-diabetes risk.

CONCLUSIONS

Self-reported SBDH combined with metabolic factors can be used to classify adults into subgroups with distinct cardiometabolic risk profiles. This approach may help identify individuals who would benefit from screening for diabetes and pre-diabetes and potentially suggest effective prevention strategies.

Clinical Characterization of Data-Driven Diabetes Clusters of Pediatric Type 2 Diabetes

Background

Pediatric Type 2 diabetes (T2D) is highly heterogeneous. Previous reports on adult-onset diabetes demonstrated the existence of diabetes clusters. Therefore, we set out to identify unique diabetes subgroups with distinct characteristics among youth with T2D using commonly available demographic, clinical, and biochemical data.

Methods

We performed data-driven cluster analysis (K-prototypes clustering) to characterize diabetes subtypes in pediatrics using a dataset with 722 children and adolescents with autoantibody-negative T2D. The six variables included in our analysis were sex, race/ethnicity, age, BMI Z-score and hemoglobin A1c at the time of diagnosis, and non-HDL cholesterol within first year of diagnosis.

Results

We identified five distinct clusters of pediatric T2D, with different features, treatment regimens and risk of diabetes complications: Cluster 1 was characterized by higher A1c; Cluster 2, by higher non-HDL; Cluster 3, by lower age at diagnosis and lower A1c; Cluster 4, by lower BMI and higher A1c; and Cluster 5, by lower A1c and higher age. Youth in Cluster 1 had the highest rate of diabetic ketoacidosis (DKA) (p = 0.0001) and were most prescribed metformin (p = 0.06). Those in Cluster 2 were most prone to polycystic ovarian syndrome (p = 0.001). Younger individuals with lowest family history of diabetes were least frequently diagnosed with diabetic ketoacidosis (p = 0.001) and microalbuminuria (p = 0.06). Low-BMI individuals with higher A1c had the lowest prevalence of acanthosis nigricans (p = 0.0003) and hypertension (p = 0.03).

Conclusions

Utilizing clinical measures gathered at the time of diabetes diagnosis can be used to identify subgroups of pediatric T2D with prognostic value. Consequently, this advancement contributes to the progression and wider implementation of precision medicine in diabetes management.

Systematic review of precision subclassification of type 2 diabetes

Heterogeneity in type 2 diabetes presentation, progression and treatment has the potential for precision medicine interventions that can enhance care and outcomes for affected individuals. We undertook a systematic review to ascertain whether strategies to subclassify type 2 diabetes are associated with improved clinical outcomes, show reproducibility and have high quality evidence. We reviewed publications that deployed 'simple subclassification' using clinical features, biomarkers, imaging or other routinely available parameters or 'complex subclassification' approaches that used machine learning and/or genomic data. We found that simple stratification approaches, for example, stratification based on age, body mass index or lipid profiles, had been widely used, but no strategy had been replicated and many lacked association with meaningful outcomes. Complex stratification using clustering of simple clinical data with and without genetic data did show reproducible subtypes of diabetes that had been associated with outcomes such as cardiovascular disease and/or mortality. Both approaches require a higher grade of evidence but support the premise that type 2 diabetes can be subclassified into meaningful groups. More studies are needed to test these subclassifications in more diverse ancestries and prove that they are amenable to interventions.

Evolution of the diagnostic value of "the sugar of the blood": hitting the sweet spot to identify alterations in glucose dynamics

In this paper, we provide an overview of the evolution of the definition of hyperglycemia during the past century and the alterations in glucose dynamics that cause fasting and postprandial hyperglycemia. We discuss how extensive mechanistic, physiological research into the factors and pathways that regulate the appearance of glucose in the circulation and its uptake and metabolism by tissues and organs has contributed knowledge that has advanced our understanding of different types of hyperglycemia, namely prediabetes and diabetes and their subtypes (impaired fasting plasma glucose, impaired glucose tolerance, combined impaired fasting plasma glucose, impaired glucose tolerance, type 1 diabetes, type 2 diabetes, gestational diabetes mellitus), their relationships with medical complications, and how to prevent and treat hyperglycemia.

Trajectories of Cognition and Daily Functioning Before and After Incident Diabetes

OBJECTIVE

The temporal pattern of cognitive and functional change before and after incident diabetes remains unknown.

RESEARCH DESIGN AND METHODS

Data from wave 2 to wave 9 (2004-2018) of the English Longitudinal Study of Ageing were used. Global cognition (assessed by orientation, memory, and executive function) and daily functioning (calculated as the sum of impaired basic and instrumental activities of daily living) were measured in each wave. Incident diabetes was defined as glycated hemoglobin A1c ≥6.5% (47.5 mmol/mol), self-reported doctor diagnosis of diabetes, or glucose-lowering medication use during follow-up.

RESULTS

Among the 6,342 participants (mean age 65.0 years, 57.8% women) included, 576 participants (9.1%) with incident diabetes were identified during a median follow-up of 13.3 years. The annual rates of change in global cognition (β = -0.035 SD/year; 95% CI -0.054 to -0.015), orientation (-0.031 SD/year; -0.060 to -0.002), memory (-0.016 SD/year; -0.029 to -0.003), and executive function (-0.027 SD/year; -0.042 to -0.013) were accelerated after diabetes diagnosis compared with before the event. The postdiabetes annual changes in daily functioning (0.093 points/year; 95% CI 0.056-0.131) were also accelerated compared with the prediabetes diagnosis. However, the rate of cognitive and functional decline before the diabetes diagnosis in participants with future incident diabetes was similar to the rate in participants without diabetes. Also, no significant acute change was observed during its onset.

CONCLUSIONS

Incident diabetes is associated with accelerated cognitive and functional decline after, but not before, the event. We suggest careful monitoring for cognitive and physical dysfunction after a diabetes diagnosis.

Patterns of change in obesity indices and other cardiometabolic risk factors before the diagnosis of type 2 diabetes: two decades follow-up of the Tehran lipid and glucose study

BACKGROUND

Identifying patterns of variation in obesity indices and other cardiometabolic risk factors before the diagnosis of type 2 diabetes could provide insight into the critical period when drastic changes occurred and facilitate targeted interventions for the prevention of diabetes. Therefore, this study sought to explore patterns of change in obesity indices and other cardiometabolic risk factors before diabetes diagnosis.

METHODS

We investigated 6305 participants (43.7% men) aged 20-65 from the Tehran Lipid and Glucose Study (TLGS) who were free of diabetes at baseline. First, we jointly estimated developmental multi-trajectories of obesity indices using multivariate latent class growth mixed model, and then patterns of cardiometabolic risk factors within the identified multi-trajectories were assessed using mixed-effects models.

RESULTS

Three patterns of change in obesity indices were identified. Most participants belonged to the "progressing" group (83.4%; n = 742), with a slight but steadily rising in obesity indices until diagnosis in both men and women. All multi-trajectory groups showed similar exponential increases in fasting and 2-h plasma glucose concentrations 6 years before diagnosis and linear increases in blood pressure and total and LDL cholesterol throughout follow-up. Patterns of triglyceride and HDL cholesterol accompanied each group's patterns of change in obesity indices.

CONCLUSION

Three patterns of the joint progression of obesity indices before diabetes diagnosis were accompanied by similar blood glucose patterns and other cardiometabolic risk factors. These findings suggest the impact of the increasing trend of obesity indices and other metabolic factors on the incidence of diabetes and emphasize the importance of assessing the metabolic risk factors at each visit.

Predictive value of circulating NMR metabolic biomarkers for type 2 diabetes risk in the UK Biobank study

BACKGROUND

Effective targeted prevention of type 2 diabetes (T2D) depends on accurate prediction of disease risk. We assessed the role of metabolomic profiling in improving T2D risk prediction beyond conventional risk factors.

METHODS

Nuclear magnetic resonance (NMR) metabolomic profiling was undertaken on baseline plasma samples in 65,684 UK Biobank participants without diabetes and not taking lipid-lowering medication. Among a subset of 50,519 participants with data available on all relevant co-variates (sociodemographic characteristics, parental history of diabetes, lifestyle-including dietary-factors, anthropometric measures and fasting time), Cox regression yielded adjusted hazard ratios for the associations of 143 individual metabolic biomarkers (including lipids, lipoproteins, fatty acids, amino acids, ketone bodies and other low molecular weight metabolic biomarkers) and 11 metabolic biomarker principal components (PCs) (accounting for 90% of the total variance in individual biomarkers) with incident T2D. These 11 PCs were added to established models for T2D risk prediction among the full study population, and measures of risk discrimination (c-statistic) and reclassification (continuous net reclassification improvement [NRI], integrated discrimination index [IDI]) were assessed.

RESULTS

During median 11.9 (IQR 11.1-12.6) years' follow-up, after accounting for multiple testing, 90 metabolic biomarkers showed independent associations with T2D risk among 50,519 participants (1211 incident T2D cases) and 76 showed associations after additional adjustment for HbA1c (false discovery rate controlled p < 0.01). Overall, 8 metabolic biomarker PCs were independently associated with T2D. Among the full study population of 65,684 participants, of whom 1719 developed T2D, addition of PCs to an established risk prediction model, including age, sex, parental history of diabetes, body mass index and HbA1c, improved T2D risk prediction as assessed by the c-statistic (increased from 0.802 [95% CI 0.791-0.812] to 0.830 [0.822-0.841]), continuous NRI (0.44 [0.38-0.49]) and relative (15.0% [10.5-20.4%]) and absolute (1.5 [1.0-1.9]) IDI. More modest improvements were observed when metabolic biomarker PCs were added to a more comprehensive established T2D risk prediction model additionally including waist circumference, blood pressure and plasma lipid concentrations (c-statistic, 0.829 [0.819-0.838] to 0.837 [0.831-0.848]; continuous NRI, 0.22 [0.17-0.28]; relative IDI, 6.3% [4.1-9.8%]; absolute IDI, 0.7 [0.4-1.1]).

CONCLUSIONS

When added to conventional risk factors, circulating NMR-based metabolic biomarkers modestly enhanced T2D risk prediction.

Trajectories of metabolic risk factors during the development of type 2 diabetes in Chinese adults

AIMS

China has the largest number of adults with diabetes. Although multiple metabolic risk factors (MRFs) are implicated in the development of diabetes, it remains unclear how they progress during the development of diabetes among Chinese. We examined trajectories of multiple MRFs among Chinese and identified the critical period when drastic changes occurred during the development of diabetes.

METHODS

This prospective cohort study included participants since 2006-2007 in the Kailuan study. People attended biennial examinations until 2017 with additions of new participants at each examination cycle. The time when a participant first completed the examination was designated as the baseline. A total of 122,659 participants without prevalent diabetes at baseline and with complete follow-up data were included. MRFs were collected via biennial physical examinations and laboratory measures. Incident diabetes cases were identified via biennial fasting glucose tests and self-reported physician-diagnosis.

RESULTS

During up to 12 years of follow-up, 14,922 incident diabetes cases were identified. Compared with participants who did not develop diabetes, those who developed diabetes had more adverse levels of most MRFs at baseline and during follow-up. Abrupt increases in multiple MRFs (including fasting glucose, surrogate insulin resistance indicators, lipids, systolic blood pressure, pulse pressure, heart rate, alanine aminotransferase, and C-reactive protein) were observed 3 years before the diagnosis of diabetes.

CONCLUSIONS

We identified 3 years before diabetes diagnosis as a critical period when multiple MRFs displayed drastic changes. This would have implications for early monitoring and timely prevention for individuals who experience sudden adverse progression of multiple MRFs.

Cardiorenal Risk Profiles Among Data-Driven Type 2 Diabetes Sub-Phenotypes: A Post-Hoc Analysis of the China Health and Nutrition Survey

BACKGROUND AND AIM

Evidence about recently proposed data-driven clusters of type 2 diabetes (T2D) is mainly about its prognostic effects and Western populations. We tested the applicability of this clustering approach among the Chinese population. We further investigated the cardiorenal risk profiles among different T2D sub-phenotypes cross-sectionally and before diabetes diagnosis.

METHODS

With the use of data from the China Health and Nutrition Survey (1989-2009), 6,728 participants with available fasting blood samples and completed questionnaires in the 2009 survey were included. Glycemic statuses (normoglycemia, prediabetes, and new-onset T2D) were defined according to the 2020 American Diabetes Association criteria. Data-driven cluster analysis was conducted among new-onset T2D based on five variables: age at onset, body mass index (BMI), hemoglobin A1c, homeostasis model estimates of β-cell function, and insulin resistance. Linear regression models were used to cross-sectionally examine the differences of cardiorenal risk factors (body fat distribution, blood pressure, lipid profiles, and kidney function) between glycemic statuses. Mixed-effects models were used to explore a maximum of 20-year trajectories of cardiovascular risk factors (body fat distribution and blood pressure) before diabetes diagnosis.

RESULTS

Among 557 (8.3%) new-onset T2D, four sub-phenotypes were found, with 57 (10.2%) assigned to the severe insulin-resistant diabetes (SIRD), 72 (12.9%) to the severe insulin-deficient diabetes (SIDD), 167 (30.0%) to the mild obesity-related diabetes (MOD), and 261 (46.9%) to the mild age-related diabetes (MARD). People clustered within different T2D sub-phenotypes had different cardiorenal risk profiles. Three T2D sub-phenotypes (SIRD, SIDD, and MOD) had worse cardiorenal abnormalities, while the risk burden in the MARD sub-phenotype was similar to that in prediabetes. Compared with people with other T2D sub-phenotypes, people in the MOD sub-phenotype had a faster increment in BMI, waist, upper arm circumference, and triceps skinfold up to 10 years before diagnosis. Blood pressure was less distinct in different T2D sub-phenotypes; however, SIDD and MOD clusters had higher blood pressure levels before diabetes diagnosis.

CONCLUSIONS

Data-driven T2D sub-phenotyping is applicable in the Chinese population. Certain sub-phenotypes such as MARD only have a minor cardiorenal risk burden, and distinct cardiovascular risk development occurs long before diabetes diagnosis. Our findings can help improve early prevention and targeted treatment for diabetes.

The Association of Gene Variants in the Vitamin D Metabolic Pathway and Its Interaction with Vitamin D on Gestational Diabetes Mellitus: A Prospective Cohort Study

The present prospective study included 2156 women and investigated the effect of gene variants in the vitamin D (VitD) metabolic and glucose pathways and their interaction with VitD levels during pregnancy on gestational diabetes mellitus (GDM). Plasma 25(OH)D concentrations were measured at the first and second trimesters. GDM subtype 1 was defined as those with isolated elevated fasting plasma glucose; GDM subtype 2 were those with isolated elevated postprandial glucose at 1 h and/or 2 h; and GDM subtype 3 were those with both elevated fasting plasma glucose and postprandial glucose. Six Gc isoforms were categorized based on two GC gene variants rs4588 and rs7041, including 1s/1s, 1s/2, 1s/1f, 2/2, 1f/2 and 1f/1f. VDR-rs10783219 and MTNR1B-rs10830962 were associated with increased risks of GDM and GDM subtype 2; interactions between each other as well as with CDKAL1-rs7754840 were observed (P_interaction < 0.05). Compared with the 1f/1f isoform, the risk of GDM subtype 2 among women with 1f/2, 2/2, 1s/1f, 1s/2 and 1s/1s isoforms and with prepregnancy body mass index ≥24 kg/m² increased by 5.11, 10.01, 10, 14.23, 19.45 times, respectively. Gene variants in VitD pathway interacts with VitD deficiency at the first trimester on the risk of GDM and GDM subtype 2.

Anthropometric Indices as Long-Term Predictors of Diabetes in Impaired Fasting Glucose Metabolism: Findings in the PURE Study in Poland

This study aimed to assess the predictive value of anthropometric measurements in impaired fasting glucose progression to type 2 diabetes (T2DM) after 9 years of follow-up in the Prospective Urban and Rural Epidemiology (PURE) study run in Poland. The study group consisted of 283 patients aged 54.3 ± 8.9 years who had impaired fasting glucose at baseline and completed a 9-year-long follow-up. We analyzed body weight, height, waist (WC) and hip (HC) circumferences, waist-to-hip ratio (WHR), waist-to-height ratio (WHtR), body mass index (BMI), and body adiposity index (BAI). Most individuals were overweight or obese according to BMI. Obesity occurred more often in men than women. The analysis highlighted the following three anthropometric parameters WHtR, BMI, and WC, each having equally good predictive power concerning the development of full-fledged T2DM in people with impaired fasting glucose. In conclusion, we confirmed the distinct harmfulness of obesity and pointed out the potential of easy-measured anthropometric parameters to self-control the risk of passing the impaired fasting glucose into T2DM.

Metabolic risk factors among prediabetic individuals and the trajectory toward the diabetes incidence

BACKGROUND

This study investigates the trajectory of the risk factors of prediabetes progression to overt diabetes.

METHODS

The study retrospectively investigated 1610 prediabetic individuals. The trajectory of metabolic indicators was investigated using the generalized estimated equation method with autoregressive working correlation structure through a linear model with the identity link function.

RESULTS

During 15 years of follow-up, the trajectories of metabolic risk factors changed from 3 years before diabetes occurrence for fasting plasma glucose (FPG) and 2-hour plasma glucose (2hPG), 6 years for waist circumference (WC), 9 years for high-density lipoprotein cholesterol (HDL-C), and earlier for body mass index, triglyceride (TG), and TG:HDL ratio. It was shown that the differences in the trajectory of WC and HDL were stable after adjustment for other metabolic risk factors. The trajectories of FPG and 2hPG remained stable after considering multiple insulin resistance markers.

CONCLUSIONS

Deterioration of metabolic risk factor status can be a predictor of diabetes many years before its occurrence, but the abrupt change in plasma glucose is evident 3 years before diabetes mellitus onset. It seems that the HDL-C and WC trajectories are two independent predictors for diabetes incidence. It was also found that when the rising trend in plasma glucose starts, preventive strategies to lessen insulin resistance might not be efficient.

Type 2 Diabetes Mellitus and Cancer: Epidemiology, Physiopathology and Prevention

Individuals with type 2 diabetes mellitus are at greater risk of developing cancer and of dying from it. Both diseases are age-related, contributing to the impact of population aging on the long-term sustainability of health care systems in European Union countries. The purpose of this narrative review was to describe, from epidemiological, pathophysiological and preventive perspectives, the links between type 2 diabetes mellitus and the most prevalent cancers in these patients. Multiple metabolic abnormalities that may occur in type 2 diabetes mellitus, particularly obesity, could explain the increased cancer risk. In addition, the effectiveness of drugs commonly used to treat type 2 diabetes mellitus (e.g., metformin and thiazolidinediones) has been broadly evaluated in cancer prevention. Thus, a better understanding of the links between type 2 diabetes mellitus and cancer will help to identify the contributing factors and the pathophysiological pathways and to design personalized preventive strategies. The final goal is to facilitate healthy aging and the prevention of cancer and other diseases related with type 2 diabetes mellitus, which are among the main sources of disability and death in the European Union and worldwide.

Diabetic Polyneuropathy Early in Type 2 Diabetes Is Associated With Higher Incidence Rate of Cardiovascular Disease: Results From Two Danish Cohort Studies

OBJECTIVE

Symptoms indicative of diabetic polyneuropathy (DPN) early in type 2 diabetes may act as a marker for cardiovascular disease (CVD) and death.

RESEARCH DESIGN AND METHODS

We linked data from two Danish type 2 diabetes cohorts, the Anglo-Danish-Dutch Study of Intensive Treatment in People With Screen-Detected Diabetes in Primary Care (ADDITION-Denmark) and the Danish Centre for Strategic Research in Type 2 Diabetes (DD2), to national health care registers. The Michigan Neuropathy Screening Instrument questionnaire (MNSIq) was completed at diabetes diagnosis in ADDITION-Denmark and at a median of 4.6 years after diagnosis of diabetes in DD2. An MNSIq score ≥4 was considered as indicative of DPN. Using Poisson regressions, we computed incidence rate ratios (IRRs) of CVD and all-cause mortality comparing MNSIq scores ≥4 with scores <4. Analyses were adjusted for a range of established CVD risk factors.

RESULTS

In total, 1,445 (ADDITION-Denmark) and 5,028 (DD2) individuals were included in the study. Compared with MNSIq scores <4, MNSIq scores ≥4 were associated with higher incidence rate of CVD, with IRRs of 1.79 (95% CI 1.38-2.31) in ADDITION-Denmark, 1.57 (CI 1.27-1.94) in the DD2, and a combined IRR of 1.65 (CI 1.41-1.95) in a fixed-effect meta-analysis. MNSIq scores ≥4 did not associate with mortality; combined mortality rate ratio was 1.11 (CI 0.83-1.48).

CONCLUSIONS

The MNSIq may be a tool to identify a subgroup within individuals with newly diagnosed type 2 diabetes with a high incidence rate of subsequent CVD. MNSIq scores ≥4, indicating DPN, were associated with a markedly higher incidence rate of CVD, beyond that conferred by established CVD risk factors.

A Computational Method for Learning Disease Trajectories From Partially Observable EHR Data

Diseases can show different courses of progression even when patients share the same risk factors. Recent studies have revealed that the use of trajectories, the order in which diseases manifest throughout life, can be predictive of the course of progression. In this study, we propose a novel computational method for learning disease trajectories from EHR data. The proposed method consists of three parts: first, we propose an algorithm for extracting trajectories from EHR data; second, three criteria for filtering trajectories; and third, a likelihood function for assessing the risk of developing a set of outcomes given a trajectory set. We applied our methods to extract a set of disease trajectories from Mayo Clinic EHR data and evaluated it internally based on log-likelihood, which can be interpreted as the trajectories' ability to explain the observed (partial) disease progressions. We then externally evaluated the trajectories on EHR data from an independent health system, M Health Fairview. The proposed algorithm extracted a comprehensive set of disease trajectories that can explain the observed outcomes substantially better than competing methods and the proposed filtering criteria selected a small subset of disease trajectories that are highly interpretable and suffered only a minimal (relative 5%) loss of the ability to explain disease progression in both the internal and external validation.

Phenotyping the Prediabetic Population-A Closer Look at Intermediate Glucose Status and Cardiovascular Disease

Even though the new thresholds for defining prediabetes have been around for more than ten years, there is still controversy surrounding the precise characterization of this intermediate glucose metabolism status. The risk of developing diabetes and macro and microvascular disease linked to prediabetes is well known. Still, the prediabetic population is far from being homogenous, and phenotyping it into less heterogeneous groups might prove useful for long-term risk assessment, follow-up, and primary prevention. Unfortunately, the current definition of prediabetes is quite rigid and disregards the underlying pathophysiologic mechanisms and their potential metabolic progression towards overt disease. In addition, prediabetes is commonly associated with a cluster of risk factors that worsen the prognosis. These risk factors all revolve around a common denominator: inflammation. This review focuses on identifying the population that needs to be screened for prediabetes and the already declared prediabetic patients who are at a higher risk of cardiovascular disease and require closer monitoring.

Resistance to the Insulin and Elevated Level of Androgen: A Major Cause of Polycystic Ovary Syndrome

PCOS has a wide range of negative impacts on women's health and is one of the most frequent reproductive systemic endocrine disorders. PCOS has complex characteristics and symptom heterogeneity due to the several pathways that are involved in the infection and the absence of a comm14on cause. A recent study has shown that the main etiology and endocrine aspects of PCOS are the increased level of androgen, which is also known as "hyperandrogenemia (HA)" and secondly the "insulin resistance (IR)". The major underlying cause of the polycystic ovary is these two IR and HA, by initiating the disease and its severity or duration. As a consequence, study on Pathogenesis is crucial to understand the effect of "HA" and "IR" on the pathophysiology of numerous symptoms linked to PCOS. A deep understanding of the pattern of the growth in PCOS for HA and IR can help ameliorate the condition, along with adjustments in nutrition and life, as well as the discovery of new medicinal products. However, further research is required to clarify the mutual role of IR and HA on PCOS development.

Sex Differences in Rates of Change and Burden of Metabolic Risk Factors Among Adults Who Did and Did Not Go On to Develop Diabetes: Two Decades of Follow-up From the Tehran Lipid and Glucose Study

OBJECTIVE

We investigated the cumulative burden and linear rates of change of major metabolic risk factors (MRFs) among Iranian adults in whom type 2 diabetes did and did not develop.

RESEARCH DESIGN AND METHODS

We included 7,163 participants (3,069 men) aged 20-70 years at baseline with at least three examinations during 1999-2018. Individual growth curve modeling was used for data analysis. Statistical interactions for sex by diabetes status were adjusted for age, family history of diabetes, smoking status, and physical activity level.

RESULTS

Study sample included 743 (316 men) new case subjects with diabetes. In both men and women, compared with individuals in whom diabetes did not develop, individuals in whom diabetes developed had a higher burden of all MRFs and a greater rate of change in BMI, fasting plasma glucose (FPG), systolic blood pressure (SBP), and diastolic blood pressure; however, the differences in burden and rate of change between those who did and did not develop diabetes were greater in women than in men. During the transition to diabetes, women experienced more adverse change in BMI, FPG, triglyceride, and HDL cholesterol (HDL-C) (diabetes-sex interaction P values <0.05) and faster rates of change in BMI, FPG, HDL-C, and total cholesterol (interaction P values <0.01) and SBP (interaction P = 0.055) than men.

CONCLUSIONS

The greater exposure of women to and burden of MRFs before onset of diabetes may have implications for implementing sex-specific strategies in order to prevent or delay diabetes complications.

Pitfalls of HbA1c in the Diagnosis of Diabetes

Many health care providers screen high-risk individuals exclusively with an HbA1c despite its insensitivity for detecting dysglycemia. The 2 cases presented describe the inherent caveats of interpreting HbA1c without performing an oral glucose tolerance test (OGTT). The first case reflects the risk of overdiagnosing type 2 diabetes (T2D) in an older African American male in whom HbA1c levels, although variable, were primarily in the mid-prediabetes range (5.7-6.4% [39-46 mmol/mol]) for many years although the initial OGTT demonstrated borderline impaired fasting glucose with a fasting plasma glucose of 102 mg/dL [5.7 mmol/L]) without evidence for impaired glucose tolerance (2-hour glucose ≥140-199 mg/dl ([7.8-11.1 mmol/L]). Because subsequent HbA1c levels were diagnostic of T2D (6.5%-6.6% [48-49 mmol/mol]), a second OGTT performed was normal. The second case illustrates the risk of underdiagnosing T2D in a male with HIV having normal HbA1c levels over many years who underwent an OGTT when mild prediabetes (HbA1c = 5.7% [39 mmol/mol]) developed that was diagnostic of T2D. To avoid inadvertent mistreatment, it is therefore essential to perform an OGTT, despite its limitations, in high-risk individuals, particularly when glucose or fructosamine and HbA1c values are discordant. Innate differences in the relationship between fructosamine or fasting glucose to HbA1c are demonstrated by the glycation gap or hemoglobin glycation index.

Predicting Skeletal Muscle and Whole-Body Insulin Sensitivity Using NMR-Metabolomic Profiling

PURPOSE

Abnormal lipoprotein and amino acid profiles are associated with insulin resistance and may help to identify this condition. The aim of this study was to create models estimating skeletal muscle and whole-body insulin sensitivity using fasting metabolite profiles and common clinical and laboratory measures.

MATERIAL AND METHODS

The cross-sectional study population included 259 subjects with normal or impaired fasting glucose or type 2 diabetes in whom skeletal muscle and whole-body insulin sensitivity (M-value) were measured during euglycemic hyperinsulinemic clamp. Muscle glucose uptake (GU) was measured directly using [18F]FDG-PET. Serum metabolites were measured using nuclear magnetic resonance (NMR) spectroscopy. We used linear regression to build the models for the muscle GU (Muscle-insulin sensitivity index [ISI]) and M-value (whole-body [WB]-ISI). The models were created and tested using randomly selected training (n = 173) and test groups (n = 86). The models were compared to common fasting indices of insulin sensitivity, homeostatic model assessment-insulin resistance (HOMA-IR) and the revised quantitative insulin sensitivity check index (QUICKI).

RESULTS

WB-ISI had higher correlation with actual M-value than HOMA-IR or revised QUICKI (ρ = 0.83 vs -0.67 and 0.66; P < 0.05 for both comparisons), whereas the correlation of Muscle-ISI with the actual skeletal muscle GU was not significantly stronger than HOMA-IR's or revised QUICKI's (ρ = 0.67 vs -0.58 and 0.59; both nonsignificant) in the test dataset.

CONCLUSION

Muscle-ISI and WB-ISI based on NMR-metabolomics and common laboratory measurements from fasting serum samples and basic anthropometrics are promising rapid and inexpensive tools for determining insulin sensitivity in at-risk individuals.

Heritability of Curve Patterns in Oral Glucose Tolerance Test

Type 2 diabetes, which is caused by both genetic and environmental factors, may be diagnosed using the oral glucose tolerance test (OGTT). Recent studies demonstrated specific patterns in glucose curves during OGTT associated with cardiometabolic risk profiles. As the relative contribution of genetic and environmental influences on glucose curve patterns is unknown, we aimed to investigate the heritability of these patterns. We studied twins from the Danish GEMINAKAR cohort aged 18-67 years and free from diabetes at baseline during 1997-2000; glucose concentrations were measured three times during a 2-h OGTT. Heterogeneity of the glucose response during OGTT was examined with latent class mixed-effects models, evaluating goodness of fit by Bayes information criterion. The genetic influence on curve patterns was estimated using quantitative genetic modeling based on linear structural equations. Overall, 1455 twins (41% monozygotic) had valid glucose concentrations measured from the OGTT, and four latent classes with different glucose response patterns were identified. Statistical modeling demonstrated genetic influence for belonging to a specific class or not, with heritability estimated to be between 45% and 67%. During ∼12 years of follow-up, the four classes were each associated with different incidence of type 2 diabetes. Hence, glucose response curve patterns associated with type 2 diabetes risk appear to be moderately to highly heritable.

Review: Diabetes, Obesity, and Cancer-Pathophysiology and Clinical Implications

Obesity and diabetes have both been associated with an increased risk of cancer. In the face of increasing obesity and diabetes rates worldwide, this is a worrying trend for cancer rates. Factors such as hyperinsulinemia, chronic inflammation, antihyperglycemic medications, and shared risk factors have all been identified as potential mechanisms underlying the relationship. The most common obesity- and diabetes-related cancers are endometrial, colorectal, and postmenopausal breast cancers. In this review, we summarize the existing evidence that describes the complex relationship between obesity, diabetes, and cancer, focusing on epidemiological and pathophysiological evidence, and also reviewing the role of antihyperglycemic agents, novel research approaches such as Mendelian Randomization, and the methodological limitations of existing research. In addition, we also describe the bidirectional relationship between diabetes and cancer with a review of the evidence summarizing the risk of diabetes following cancer treatment. We conclude this review by providing clinical implications that are relevant for caring for patients with obesity, diabetes, and cancer and provide recommendations for improving both clinical care and research for patients with these conditions.

The Natural Course of Impaired Fasting Glucose

Impaired glucose regulation, including diabetes and prediabetes, poses a huge global problem not only in health but also in the epidemiological and economic areas. These disorders are often detected too late or remain unrecognized. The article aims to provide a review of the prevalence, etiology, and natural history of impaired fasting glucose (IFG). We focus on the progression of isolated IFG to full-fledged type 2 diabetes and the factors conducive to the development of diabetes. The knowledge about it could help design an optimal management program for the prevention of diabetes in patients with IFG; a program that would be patient-tailored and based on the underlying pathophysiology.

The interaction between prepregnancy BMI and gestational vitamin D deficiency on the risk of gestational diabetes mellitus subtypes with elevated fasting blood glucose

BACKGROUND & AIMS

To investigate the association of VitD with GDM, and examine the potential modifying effect of prepregnancy BMI in Chinese pregnant women.

METHODS

3318 pregnant women underwent oral glucose tolerance test (OGTT) were selected from Zhoushan Pregnant Women Cohort. Plasma VitD levels were measured in the first (T1) and/or second trimester (T2). Multiple linear and logistic regression models were used for evaluating the association of VitD with GDM.

RESULTS

Prepregnancy BMI was positively associated with all three time-point glucose of OGTT. 25(OH)D level in T1 (β = -0.003) and T2 (β = -0.004), and its change from T1 to T2 (β = -0.004) were significantly and inversely associated with fasting blood glucose (FBG) of OGTT, but not 1-h and 2-h postload blood glucose of OGTT, respectively. The negative associations of VitD and FBG were stronger among overweight/obese women. VitD deficiency (25(OH)D < 20 ng/ml) in T2 was associated with an increased risk of GDM with increased FBG, GDM subtype 1 (OR: 2.10) and subtype 3 (OR: 2.19). Moreover, prepregnancy BMI modified this effect on GDM subtype 1 (BMI < 24: OR = 1.42; BMI ≥ 24: OR = 9.61, P for interaction = 0.002). Lower VitD increment from T1 to T2 was associated with a higher risk for GDM among overweight/obese women. Additionally, GDM prevalence fluctuated with the season, i.e. lower in summer/fall and higher in winter/spring.

CONCLUSIONS

Maternal VitD deficiency was associated with a higher risk of GDM subtype with increased FBG, and the risk is much greater among overweight/obesity women. The lower the VitD increment during pregnancy, the greater the risk of GDM, especially in overweight/obesity women. Furthermore, seasonal variation of GDM may be exhibited as a critical confounder in the association of VitD and GDM.

Clusters of Glycemic Response to Oral Glucose Tolerance Tests Explain Multivariate Metabolic and Anthropometric Outcomes of Bariatric Surgery in Obese Patients

Glycemic responses to bariatric surgery are highly heterogeneous among patients and defining response types remains challenging. Recently developed data-driven clustering methods have uncovered subtle pathophysiologically informative patterns among patients without diabetes. This study aimed to explain responses among patients with and without diabetes to bariatric surgery with clusters of glucose concentration during oral glucose tolerance tests (OGTTs). We assessed 30 parameters at baseline and at four subsequent follow-up visits over one year on 154 participants in the Bialystok Bariatric Surgery Study. We applied latent trajectory classification to OGTTs and multinomial regression and generalized linear mixed models to explain differential responses among clusters. OGTT trajectories created four clusters representing increasing dysglycemias that were discordant from standard diabetes diagnosis criteria. The baseline OGTT cluster increased the predictive power of regression models by over 31% and aided in correctly predicting more than 83% of diabetes remissions. Principal component analysis showed that the glucose homeostasis response primarily occurred as improved insulin sensitivity concomitant with improved the OGTT cluster. In sum, OGTT clustering explained multiple, correlated responses to metabolic surgery. The OGTT is an intuitive and easy-to-implement index of improvement that stratifies patients into response types, a vital first step in personalizing diabetic care in obese subjects.

Individuals With Prediabetes Display Different Age-Related Pathophysiological Characteristics

CONTEXT

Impaired fasting glucose (IFG) and impaired glucose tolerance (IGT) are highly pathophysiologic heterogeneous prediabetes conditions that can occur in all age groups, from youth to elderly people.

OBJECTIVE

We evaluated whether distinct age-related phenotypes exist among individuals with IFG or IGT.

RESEARCH DESIGN

479 young (aged 18 to 35 years), 699 adult (45 to 55 years) and 240 older (≥65 years) subjects underwent an oral glucose tolerance test (OGTT). From the OGTT results, the participants were grouped as follows: young age and normal glucose tolerance (NGT), adult age and NGT, older age and NGT, IFG young subjects, IFG adult subjects, IFG older subjects, IGT young (Y-IGT) subjects, IGT adult (A-IGT) subjects, and IGT older (O-IGT) subjects.

MAIN OUTCOME MEASURES

Insulin sensitivity and secretion, insulin clearance, and β-cell function.

RESULTS

Peripheral insulin sensitivity assessed using the Matsuda index, basal and glucose-stimulated insulin secretion, and β-cell function estimated using the disposition index were decreased in IFG adult subjects and IFG older subjects compared with IFG young subjects. A-IGT and Y-IGT subjects exhibited a progressively greater degree of hepatic insulin resistance assessed using the liver insulin resistance index, and reduced insulin clearance compared with O-IGT subjects. In contrast, the Matsuda index did not differ among Y-IGT, A-IGT, and O-IGT subjects. Basal and glucose-stimulated insulin secretion and β-cell function were lower in A-IGT and O-IGT subjects compared with Y-IGT individuals.

CONCLUSIONS

Subjects with IFG or IGT exhibited different age-related pathophysiologic characteristics. A more precise phenotyping of subjects with IGT or IFG could help to better design individualized preventive approaches to counteract diabetes progression.

Heart Rate, Autonomic Function, and Future Changes in Glucose Metabolism in Individuals Without Diabetes: The Whitehall II Cohort Study

OBJECTIVE

Autonomic nervous system dysfunction is associated with impaired glucose metabolism, but the temporality of this association remains unclear in individuals without diabetes. We investigated the association of autonomic function with 5-year changes in glucose metabolism in individuals without diabetes.

RESEARCH DESIGN AND METHODS

Analyses were based on 9,000 person-examinations for 3,631 participants without diabetes in the Whitehall II cohort. Measures of autonomic function included 5-min resting heart rate and six heart rate variability (HRV) indices. Associations between baseline autonomic function measures and 5-year changes in fasting and 2-h plasma glucose, serum insulin concentrations, insulin sensitivity (insulin sensitivity index [ISI0-120] and HOMA of insulin sensitivity), and β-cell function (HOMA of β-cell function) were estimated in models adjusting for age, sex, ethnicity, metabolic factors, and medication.

RESULTS

A 10-bpm higher resting heart rate was associated with 5-year changes in fasting and 2-h insulin and ISI0-120 of 3.3% change (95% CI 1.8; 4.8), P < 0.001; 3.3% change (1.3; 5.3), P = 0.001; and -1.4% change (-2.4; -0.3), P = 0.009, respectively. In models adjusted for age, sex, and ethnicity, higher baseline values of several HRV indices were associated with a 5-year decrease in fasting and 2-h insulin and ISI0-120. However, significance was lost by full adjustment. A majority of HRV indices exhibited a trend toward higher values being associated with lower insulin levels and higher insulin sensitivity.

CONCLUSIONS

Higher resting heart rate in individuals without diabetes is associated with future unfavorable changes in insulin levels and insulin sensitivity. Associations may be mediated via autonomic function; however, results are inconclusive. Resting heart rate may be a risk marker for future pathophysiological changes in glucose metabolism.

Thromboxane-Dependent Platelet Activation in Obese Subjects with Prediabetes or Early Type 2 Diabetes: Effects of Liraglutide- or Lifestyle Changes-Induced Weight Loss

Thromboxane (TX)-dependent platelet activation and lipid peroxidation, as reflected in vivo by the urinary excretion of 11-dehydro-TXB₂ and 8-iso-prostaglandin (PG)F_2α, play a key role in atherothrombosis in obesity and type 2 diabetes mellitus (T2DM) since the earlier stages. Thirty-five metformin-treated obese subjects with prediabetes or newly-diagnosed T2DM were randomized to the glucagon-like peptide receptor agonist (GLP-RA) liraglutide (1.8 mg/day) or lifestyle counseling until achieving a comparable weight loss (−7% of initial body weight), to assess whether changes in subcutaneous (SAT) and visceral (VAT) adipose tissue distribution (MRI), insulin sensitivity (Matsuda Index) and beta-cell performance (multiple sampling OGTT beta-index), with either intervention, might affect TX-dependent platelet activation, lipid peroxidation and inflammation. At baseline, Ln-8-iso-PGF_2α (Beta = 0.31, p = 0.0088), glycosylated hemoglobin (HbA1c) (Beta = 2.64, p = 0.0011) Ln-TNF-α (Beta = 0.58, p = 0.0075) and SAT (Beta = 0.14, p = 0.044) were significant independent predictors of 11-dehydro-TXB₂. After achievement of the weight loss target, a comparable reduction in U-11-dehydro-TXB₂ (between-group p = 0.679) and 8-iso-PGF-_2α (p = 0.985) was observed in both arms in parallel with a comparable improvement in glycemic control, insulin sensitivity, SAT, high-sensitivity C-reactive protein (hs-CRP). In obese patients with initial impairment of glucose metabolism, the extent of platelet activation is related to systemic inflammation, isoprostane formation and degree of glycemic control and abdominal SAT. Successful weight loss, achieved with either lifestyle changes or an incretin-based therapy, is associated with a significant reduction in lipid peroxidation and platelet activation.

Dietary Interventions for the Prevention of Type 2 Diabetes in High-Risk Groups: Current State of Evidence and Future Research Needs

A series of large-scale randomised controlled trials have demonstrated the effectiveness of lifestyle change in preventing type 2 diabetes in people with impaired glucose tolerance. Participants in these trials consumed a low-fat diet, lost a moderate amount of weight and/or increased their physical activity. Weight loss appears to be the primary driver of type 2 diabetes risk reduction, with individual dietary components playing a minor role. The effect of weight loss via other dietary approaches, such as low-carbohydrate diets, a Mediterranean dietary pattern, intermittent fasting or very-low-energy diets, on the incidence of type 2 diabetes has not been tested. These diets-as described here-could be equally, if not more effective in preventing type 2 diabetes than the tested low-fat diet, and if so, would increase choice for patients. There is also a need to understand the effect of foods and diets on beta-cell function, as the available evidence suggests moderate weight loss, as achieved in the diabetes prevention trials, improves insulin sensitivity but not beta-cell function. Finally, prediabetes is an umbrella term for different prediabetic states, each with distinct underlying pathophysiology. The limited data available question whether moderate weight loss is effective at preventing type 2 diabetes in each of the prediabetes subtypes.

Prevention and Treatment of Type 2 Diabetes: A Pathophysiological-Based Approach

Prediabetes affects approximately 40% of American adults. Randomized trials report that a proportion of individuals with prediabetes develop diabetes despite caloric restriction, physical activity, and/or when treated with metformin, the first-line medication for patients with type 2 diabetes mellitus (T2DM). Currently, there are no valid predictors of the effectiveness of these measures in determining who will and who will not progress to the T2DM state. Few studies have examined the clinical and phenotypic predictors of better and worse glycemic response to lifestyle interventions and metformin in prediabetes and diabetes. Further studies incorporating 'omic' approaches to discover novel markers of phenotypes and treatment effectiveness may pave the way to personalizing the treatment of prediabetes and diabetes.

Elevations of metabolic risk factors 20 years or more before diagnosis of type 2 diabetes: Experience from the AMORIS study

AIMS

To describe trajectories for metabolic risk factors for type 2 diabetes (T2D) up to 25 years prior to diagnosis and to estimate the absolute 20-year risk for T2D based on a simple set of commonly measured key risk factors.

METHODS

From the Swedish AMORIS cohort we included 296 428 individuals with data on fasting glucose obtained in health examinations during 1985-1996 (baseline period). All participants were followed until 2012 for development of incident T2D. The 20-year T2D risk based on age, sex, body mass index (BMI), fasting glucose and triglycerides was estimated. Trajectories for biomedical risk factors of T2D starting from >20 years before diagnosis and including fasting glucose, triglycerides and BMI were evaluated according to yearly means for cases and controls.

RESULTS

We identified 28 244 new T2D cases during the study period, with an average 20-year risk of 8.1%. This risk was substantially increased in overweight and obese participants and those with elevated fasting glucose and triglyceride levels, in both men and women. T2D cases had higher mean BMI and fasting glucose and triglyceride levels compared with controls >20 years before diagnosis and the difference in fasting glucose levels increased over time.

CONCLUSIONS

Development of T2D is associated with subtle elevations in glucose and lipid levels >20 years before diagnosis. This suggests that diabetogenic processes tied to chronic insulin resistance operate for decades prior to the development of T2D. A simple risk classification can help in early identification of individuals who are at increased risk.

Type 2 Diabetes: When Does It Start?

Objective

We aimed to clarify the onset of diabetes.

Design

Data from 27,392 nondiabetic health examinees were retrospectively analyzed for a mean of 5.3 years. Trajectories of fasting plasma glucose (FPG), body mass index (BMI), and the single point insulin sensitivity (Si) estimator (SPISE), an index of Si, 10 years before diagnosis of prediabetes (PDM; n = 4781) or diabetes (n = 1061) were separately assessed by a mixed effects model. Diabetes and PDM were diagnosed by the American Diabetes Association definition on the basis of FPG and glycosylated hemoglobin A1c values.

Results

In individuals who developed diabetes, mean FPG and BMI were significantly higher (P < 0.01 each) and SPISE lower than those who did not at -10 years: FPG 101.5 mg/dL vs 94.5 mg/dL, BMI 24.0 kg/m² vs 22.7 kg/m², and SPISE 7.32 vs 8.34, P < 0.01 each. These measurements, in subjects who developed prediabetes, were slightly but definitely different from those who did not, already at -10 years: FPG 91.8 mg/dL vs 89.6 mg/dL, BMI 22.6 kg/m² vs 22.1 kg/m², and SPISE 8.44 vs 8.82, P < 0.01 each. In both cases, the differences were progressively greater toward year 0, the time of diabetes, or PDM diagnosis.

Conclusions

FPG was significantly elevated in those who developed diabetes at least 10 years before diagnosis of diabetes, and this was also the case in those who developed PDM. Glucose dysregulation precedes diagnosis of diabetes at least for 20 years.

Modeling Risk-Factor Trajectories When Measurement Tools Change Sequentially During Follow-up in Cohort Studies: Application to Dietary Habits in Prodromal Dementia

Modeling risk-factor trajectories is critical to understanding the natural history of diseases, yet the measurement tools used to assess risk factors often evolve during follow-up in cohorts, and such change prevents longitudinal analyses using standard models. We addressed this issue with a latent process model. Trajectories of average intakes of 5 food families (fish, meat, fruits, vegetables, and carbohydrate-rich foods) were described in prodromal dementia during the 10 years prior to diagnosis of cases and compared with those of controls, using a case-control sample nested within the Three-City Study, Bordeaux, France (1999-2012). Food intakes were measured by 2 or 3 different subquestionnaires across 5 repeated food frequency questionnaires. The sample comprised 205 incident cases and 410 controls matched for age, sex, education, and number of repeated food frequency questionnaires. Intakes of fish, fruits, and vegetables decreased at the approach of diagnosis among cases, suggesting reverse causation. This study demonstrated that the latent process model approach constitutes a powerful framework for modeling risk-factor trajectories, even when measurement tools change sequentially over time. Coupled with a case-control approach to contrast trajectories in prodromal disease versus healthy status, it can help us to understand the dynamic, causal relationships between risk factors and diseases.

Circulating triacylglycerols but not pancreatic fat associate with insulin secretion in healthy humans

BACKGROUND

Loss of adequate insulin secretion for the prevailing insulin resistance is critical for the development of type 2 diabetes and has been suggested to result from circulating lipids (triacylglycerols [TG] or free fatty acids) and/or adipocytokines or from ectopic lipid storage in the pancreas. This study aimed to address whether circulating lipids, adipocytokines or pancreatic fat primarily associates with lower insulin secretion.

SUBJECTS/METHODS

Nondiabetic persons (n=73), recruited from the general population, underwent clinical examinations, fasting blood drawing to measure TG and adipocytokines and oral glucose tolerance testing (OGTT) to assess basal and dynamic insulin secretion and sensitivity indices. Magnetic resonance imaging and ¹H-magnetic resonance spectroscopy were used to measure body fat distribution and ectopic fat content in liver and pancreas.

RESULTS

In age-, sex- and BMI-adjusted analyses, total and high-molecular-weight adiponectin were the strongest negative predictors of fasting beta-cell function (BCF; β=-0.403, p=0.0003 and β=-0.237, p=0.01, respectively) and adaptation index (AI; β=-0.210, p=0.006 and β=-0.133, p=0.02, respectively). Circulating TG, but not pancreatic fat content, related positively to BCF (β=0.375, p<0.0001) and AI (β=0.192, p=0.003). Similar results were obtained for the disposition index (DI).

CONCLUSIONS

The association of serum lipids and adiponectin with beta-cell function may represent a compensatory response to adapt for lower insulin sensitivity in nondiabetic humans.

Clinical phenotyping of newly diagnosed type 2 diabetes in Yemen

OBJECTIVE

To identify clinical phenotypes of type 2 diabetes (T2D) among adults presenting with a first diagnosis of diabetes.

RESEARCH DESIGN AND METHODS

A total of 500 consecutive patients were subject to clinical assessment and laboratory investigations. We used data-driven cluster analysis to identify phenotypes of T2D based on clinical variables and Homeostasis Model Assessment (HOMA2) of insulin sensitivity and beta-cell function estimated from paired fasting blood glucose and specific insulin levels.

RESULTS

The cluster analysis identified three statistically different clusters: cluster 1 (high insulin resistance and high beta-cell function group), which included patients with low insulin sensitivity and high beta-cell function; cluster 2 (low insulin resistance and low beta-cell function group), which included patients with high insulin sensitivity but very low beta-cell function; and cluster 3 (high insulin resistance and low beta-cell function group), which included patients with low insulin sensitivity and low beta-cell function. Insulin sensitivity, defined as median HOMA2-S, was progressively increasing from cluster 1 (35.4) to cluster 3 (40.9), to cluster 2 (76) (p<0.001). On the contrary, beta-cell function, defined as median HOMA2-β, was progressively declining from cluster 1 (78.3) to cluster 3 (30), to cluster 2 (22.3) (p<0.001). Clinical and biomarker variables associated with insulin resistance like obesity, abdominal adiposity, fatty liver, and high serum triglycerides were mainly seen in clusters 1 and 3. The highest median hemoglobin A1c value was noted in cluster 2 (88 mmol/mol) and the lowest in cluster 1.

CONCLUSION

Cluster analysis of newly diagnosed T2D in adults has identified three phenotypes based on clinical variables central to the development of diabetes and on specific clinical variables of each phenotype.

Prevalence of micro- and macrovascular diabetes complications at time of type 2 diabetes diagnosis and associated clinical characteristics: A cross-sectional baseline study of 6958 patients in the Danish DD2 cohort

AIMS

To examine the prevalence of micro- and macrovascular complications and their associated clinical characteristics at time of type 2 diabetes (T2D) diagnosis.

METHODS

We examined the prevalence of complications and associated clinical characteristics among 6958 newly diagnosed T2D patients enrolled in the prospective Danish Center for Strategic Research in T2D cohort during 2010-2016. We calculated age- and gender-adjusted prevalence ratios (aPRs) of complications using log-binomial and Poisson regression.

RESULTS

In total, 35% (n=2456) T2D patients had diabetic complications around diagnosis; 12% (n=828) had microvascular complications, 17% (n=1186) macrovascular complications, and 6% (n=442) had both. HbA1c levels of ≥7% were associated with microvascular complications [HbA1c 7%-8%; aPR: 1.35, 95% confidence interval (CI): 1.12-1.62] but not macrovascular complications [aPR: 0.91, 95% CI: 0.76-1.08]. High C-peptide≥800pmol/L was associated with macrovascular [aPR 1.34, 95% CI: 1.00-1.80] but not microvascular [aPR 0.97, 95% CI: 0.71-1.33] complications. Macrovascular complications were associated with male sex, age>50years, obesity, hypertriglyceridemia, low HDL cholesterol, smoking, elevated CRP levels, and anti-hypertensive therapy. Microvascular complications were associated with high blood pressure, hypertriglyceridemia, and absence of lipid-lowering therapy.

CONCLUSIONS

One-third of patients with T2D had diabetes complications around time of diagnosis. Our findings suggest different pathophysiological mechanisms behind micro- and macrovascular complications.

Genetic aetiology of glycaemic traits: approaches and insights

Glycaemic traits such as fasting and post-challenge glucose and insulin measures, as well as glycated haemoglobin (HbA1c), are used to diagnose and monitor diabetes. These traits are risk factors for cardiovascular disease even below the diabetic threshold, and their study can additionally yield insights into the pathophysiology of type 2 diabetes. To date, a diverse set of genetic approaches have led to the discovery of over 97 loci influencing glycaemic traits. In this review, we will focus on recent advances in the genetic aetiology of glycaemic traits, and the resulting biological insights. We will provide a brief overview of results ranging from common, to low- and rare-frequency variant-trait association studies, studies leveraging the diversity across populations, and studies harnessing the power of genetic and genomic approaches to gain insights into the biological underpinnings of these traits.

The cost-effectiveness of interventions targeting lifestyle change for the prevention of diabetes in a Swedish primary care and community based prevention program

BACKGROUND

Policymakers need to know the cost-effectiveness of interventions to prevent type 2 diabetes (T2D). The objective of this study was to estimate the cost-effectiveness of a T2D prevention initiative targeting weight reduction, increased physical activity and healthier diet in persons in pre-diabetic states by comparing a hypothetical intervention versus no intervention in a Swedish setting.

METHODS

A Markov model was used to study the cost-effectiveness of a T2D prevention program based on lifestyle change versus a control group where no prevention was applied. Analyses were done deterministically and probabilistically based on Monte Carlo simulation for six different scenarios defined by sex and age groups (30, 50, 70 years). Cost and quality adjusted life year (QALY) differences between no intervention and intervention and incremental cost-effectiveness ratios (ICERs) were estimated and visualized in cost-effectiveness planes (CE planes) and cost-effectiveness acceptability curves (CEA curves).

RESULTS

All ICERs were cost-effective and ranged from 3833 €/QALY gained (women, 30 years) to 9215 €/QALY gained (men, 70 years). The CEA curves showed that the probability of the intervention being cost-effective at the threshold value of 50,000 € per QALY gained was very high for all scenarios ranging from 85.0 to 91.1%.

DISCUSSION/CONCLUSION

The prevention or the delay of the onset of T2D is feasible and cost-effective. A small investment in healthy lifestyle with change in physical activity and diet together with weight loss are very likely to be cost-effective.

Two Physical Activity Measures: Paffenbarger Physical Activity Questionnaire Versus Aerobics Center Longitudinal Study as Predictors of Adult-Onset Type 2 Diabetes in a Follow-Up Study

PURPOSE

To compare 2 self-report methods of measuring weekly minutes of physical activity based on the Aerobics Center Longitudinal Study (ACLS) questionnaire and question 6 of the Paffenbarger Physical Activity Questionnaire (PPAQ) to determine the better predictor of adult-onset type 2 diabetes mellitus (T2DM).

DESIGN

An observational, prospective study.

SETTING

Survey data from the Adventist Health Study-2 (AHS-2) collected between 2002 and 2006 (baseline) and the Psychosocial Manifestations of Religion Sub-Study (PsyMRS), an AHS-2 subset collected 1 to 4 years later.

PATIENTS

Nine thousand eight hundred seventy-three male and female participants aged 23 to 106 years (mean, 63 years). Three hundred eighty participants reported adult-onset T2DM at follow-up.

MEASURES

Question 6 from the PPAQ and a question adopted from the ACLS were assessed at baseline. Incident diabetes was defined as participants who reported receiving treatment for adult-onset T2DM in the last 12 months in the PsyMRS and not at baseline.

ANALYSIS

Multivariate logistic regression analyses controlled for age, gender, ethnicity, education, body mass index (BMI), diet, and sedentary activity. Each exposure variable was compared to nonexercisers.

RESULTS

The PPAQ (odds ratio [OR]: 0.998; 95% confidence interval [CI]: 0.997-1.000) and the ACLS (OR: 0.999; 95% CI: 0.998-1.001) exhibited similar likelihood of predicting incident adult-onset T2DM in a healthy, mixed-gender population when controlling for several confounders.

CONCLUSIONS

The demonstrative nomenclature of the PPAQ may be more effectual in capturing physically active individuals than the ACLS notwithstanding generalizability and response bias limitations.

Trajectories of glycaemia, insulin sensitivity and insulin secretion in South Asian and white individuals before diagnosis of type 2 diabetes: a longitudinal analysis from the Whitehall II cohort study

AIMS/HYPOTHESIS

South Asian individuals have reduced insulin sensitivity and increased risk of type 2 diabetes compared with white individuals. Temporal changes in glycaemic traits during middle age suggest that impaired insulin secretion is a particular feature of diabetes development among South Asians. We therefore aimed to examine ethnic differences in early changes in glucose metabolism prior to incident type 2 diabetes.

METHODS

In a prospective British occupational cohort, subject to 5 yearly clinical examinations, we examined ethnic differences in trajectories of fasting plasma glucose (FPG), 2 h post-load plasma glucose (2hPG), fasting serum insulin (FSI), 2 h post-load serum insulin (2hSI), HOMA of insulin sensitivity (HOMA2-S) and secretion (HOMA2-B), and the Gutt insulin sensitivity index (ISI0,120) among 120 South Asian and 867 white participants who developed diabetes during follow-up (1991-2013). We fitted cubic mixed-effects models to longitudinal data with adjustment for a wide range of covariates.

RESULTS

Compared with white individuals, South Asians had a faster increase in FPG before diagnosis (slope difference 0.22 mmol/l per decade; 95% CI 0.02, 0.42; p = 0.03) and a higher FPG level at diagnosis (0.27 mmol/l; 95% CI 0.06, 0.48; p = 0.01). They also had higher FSI and 2hSI levels before and at diabetes diagnosis. South Asians had a faster decline and lower HOMA2-S (log e -transformed) at diagnosis compared with white individuals (0.33; 95% CI 0.21, 0.46; p < 0.001). HOMA2-B increased in both ethnic groups until 7 years before diagnosis and then declined; the initial increase was faster in white individuals. ISI0,120 declined steeply in both groups before diagnosis; levels were lower among South Asians before and at diagnosis. There were no ethnic differences in 2hPG trajectories.

CONCLUSIONS/INTERPRETATION

We observed different trajectories of plasma glucose, insulin sensitivity and secretion prior to diabetes diagnosis in South Asian and white individuals. This might be due to ethnic differences in the natural history of diabetes. South Asian individuals experienced a more rapid decrease in insulin sensitivity and faster increases in FPG compared with white individuals. These findings suggest more marked disturbance in beta cell compensation prior to diabetes diagnosis in South Asian individuals.