Clusters of prediabetes and type 2 diabetes stratify all-cause mortality in a cohort of participants undergoing invasive coronary diagnostics

Abstract Background Heterogeneous metabolic clusters have been identified in diabetic and prediabetic states. It is not known whether such pathophysiologic clusters impact survival in at-risk persons being evaluated for coronary heart disease. Methods The LURIC Study recruited patients referred for coronary angiography at a median age of 63 (IQR 56–70) with a follow-up of 16.1 (IQR 9.6, 17.7) years. Clustering of 1269 subjects without diabetes was performed with oGTT-derived glucose and insulin; fasting triglyceride, high-density lipoprotein, BMI, waist and hip circumference. Patients with T2D (n = 794) were clustered using age, BMI, glycemia, homeostasis model assessment, and islet autoantibodies. Associations of clusters with mortality were analysed using Cox regression. Results Individuals without diabetes were classified into six subphenotypes, with 884 assigned to subjects at low-risk (cluster 1,2,4) and 385 at high-risk (cluster 3,5,6) for diabetes. We found significantly increased mortality in clusters 3 (hazard ratio (HR)1.42), 5 (HR 1.43), and 6 (HR 1.46) after adjusting for age, BMI, HbA1c and sex. In the T2D group, 508 were assigned to mild age-related diabetes (MARD), 183 to severe insulin-resistant diabetes (SIRD), 84 to mild obesity-related diabetes (MOD), 19 to severe insulin-deficient diabetes (SIDD). Compared to the low-risk non-diabetes group, crude mortality was not different in MOD. Increased mortality was found for MARD (HR 2.2), SIRD (HR 2.2), and SIDD (HR 2.5). Conclusions Metabolic clustering successfully stratifies survival even among persons undergoing invasive coronary diagnostics. Novel clustering approaches based on glucose metabolism can identify persons who require special attention as they are at risk of increased mortality

Comprehensive validation of fasting-based and oral glucose tolerance test-based indices of insulin secretion against gold standard measures

INTRODUCTION: With pre-diabetes and diabetes increasingly recognized as heterogeneous conditions, assessment of beta-cell function is gaining clinical importance to identify disease subphenotypes. Our study aims to comprehensively validate all types of surrogate indices based on oral glucose tolerance test (OGTT) and fasting measurements in comparison with gold standard methods. RESEARCH DESIGN AND METHODS: The hyperglycemic clamp extended with glucagon-like peptide 1 (GLP-1) infusion and intravenous glucose tolerance test (IVGTT), as well as OGTT, was performed in two well-phenotyped cohorts. The gold standard-derived indices were compared with surrogate insulin secretion markers, derived from fasting state and OGTT, using both Pearson's and Spearman's correlation coefficients. The insulin-based and C-peptide-based indices were analyzed separately in different groups of glucose tolerance and the entire cohorts. RESULTS: The highest correlation coefficients were found for area under curve (AUC) (I0-30)/AUC (G0-30), I30/G30, first-phase Stumvoll and Kadowaki model. These indices have high correlation coefficients with measures obtained from both insulin and C-peptide levels from IVGTT and hyperglycemic clamp. AUC (I0-120)/AUC (G0-120), BIGTT-AIR0-60-120, I30/G30, first-phase Stumvoll and AUC (I0-30)/AUC (G0-30) demonstrated the strongest association with incretin-stimulated insulin response. CONCLUSIONS: We have identified glucose-stimulated and GLP-1-stimulated insulin secretion indices, derived from OGTT and fasting state, that have the strongest correlation with gold standard measures and could be potentially used in future researches and clinical practice

Postprandial dynamics of proglucagon cleavage products and their relation to metabolic health.

Introduction: While oral glucose ingestion typically leads to a decrease in circulating glucagon levels, a substantial number of persons display stable or rising glucagon concentrations when assessed by radioimmunoassay (RIA). However, these assays show cross-reactivity to other proglucagon cleavage products. Recently, more specific assays became available, therefore we systematically assessed glucagon and other proglucagon cleavage products and their relation to metabolic health. Research Design and Methods: We used samples from 52 oral glucose tolerance tests (OGTT) that were randomly selected from persons with different categories of glucose tolerance in an extensively phenotyped study cohort. Results: Glucagon concentrations quantified with RIA were non-suppressed at 2 hours of the OGTT in 36% of the samples. Non-suppressors showed lower fasting glucagon levels compared to suppressors (p=0.011). Similar to RIA measurements, ELISA-derived fasting glucagon was lower in non-suppressors (p<0.001). Glucagon 1-61 as well as glicentin and GLP-1 kinetics were significantly different between suppressors and non-suppressors (p=0.004, p=0.002, p=0.008 respectively) with higher concentrations of all three hormones in non-suppressors. Levels of insulin, C-peptide, and free fatty acids were comparable between groups. Non-suppressors were leaner and had lower plasma glucose concentrations (p=0.03 and p=0.047, respectively). Despite comparable liver fat content and insulin sensitivity (p≥0.3), they had lower 2-hour post-challenge glucose (p=0.01). Conclusions: Glucagon 1-61, glicentin and GLP-1 partially account for RIA-derived glucagon measurements due to cross-reactivity of the assay. However, this contribution is small, since the investigated proglucagon cleavage products contribute less than 10% to the variation in RIA measured glucagon. Altered glucagon levels and higher post-challenge incretins are associated with a healthier metabolic phenotype

Analysis of type 2 diabetes heterogeneity with a tree-like representation: insights from the prospective German Diabetes Study and the LURIC cohort

BACKGROUND Heterogeneity in type 2 diabetes can be represented by a tree-like graph structure by use of reversed graph-embedded dimensionality reduction. We aimed to examine whether this approach can be used to stratify key pathophysiological components and diabetes-related complications during longitudinal follow-up of individuals with recent-onset type 2 diabetes. METHODS For this cohort analysis, 927 participants aged 18-69 years from the German Diabetes Study (GDS) with recent-onset type 2 diabetes were mapped onto a previously developed two-dimensional tree based on nine simple clinical and laboratory variables, residualised for age and sex. Insulin sensitivity was assessed by a hyperinsulinaemic-euglycaemic clamp, insulin secretion was assessed by intravenous glucose tolerance test, hepatic lipid content was assessed by $^{1}$ H magnetic resonance spectroscopy, serum interleukin (IL)-6 and IL-18 were assessed by ELISA, and peripheral and autonomic neuropathy were assessed by functional and clinical measures. Participants were followed up for up to 16 years. We also investigated heart failure and all-cause mortality in 794 individuals with type 2 diabetes undergoing invasive coronary diagnostics from the Ludwigshafen Risk and Cardiovascular Health (LURIC) cohort. FINDINGS There were gradients of clamp-measured insulin sensitivity (both dimensions: p<0·0001) and insulin secretion (p$_{dim1}$<0·0001, p$_{dim2}$=0·00097) across the tree. Individuals in the region with the lowest insulin sensitivity had the highest hepatic lipid content (n=205, p$_{dim1}$<0·0001, p$_{dim2}$=0·037), pro-inflammatory biomarkers (IL-6: n=348, p$_{dim1}$<0·0001, p$_{dim2}$=0·013; IL-18: n=350, p$_{dim1}$<0·0001, p$_{dim2}$=0·38), and elevated cardiovascular risk (n$_{events}$=143, p$_{dim1}$=0·14, p$_{dim2}$<0·00081), whereas individuals positioned in the branch with the lowest insulin secretion were more prone to require insulin therapy (n$_{events}$=85, p$_{dim1}$=0·032, p$_{dim2}$=0·12) and had the highest risk of diabetic sensorimotor polyneuropathy (n$_{events}$=184, p$_{dim1}$=0·012, p$_{dim2}$=0·044) and cardiac autonomic neuropathy (n$_{events}$=118, p$_{dim1}$=0·0094, p$_{dim2}$=0·06). In the LURIC cohort, all-cause mortality was highest in the tree branch showing insulin resistance (n$_{events}$=488, p$_{dim1}$=0·12, p$_{dim2}$=0·0032). Significant gradients differentiated individuals having heart failure with preserved ejection fraction from those who had heart failure with reduced ejection fraction. INTERPRETATION These data define the pathophysiological underpinnings of the tree structure, which has the potential to stratify diabetes-related complications on the basis of routinely available variables and thereby expand the toolbox of precision diabetes diagnosis. FUNDING German Diabetes Center, German Federal Ministry of Health, Ministry of Culture and Science of the state of North Rhine-Westphalia, German Federal Ministry of Education and Research, German Diabetes Association, German Center for Diabetes Research, European Community, German Research Foundation, and Schmutzler Stiftung

Analysis of type 2 diabetes heterogeneity with a tree-like representation:insights from the prospective German Diabetes Study and the LURIC cohort

Background: Heterogeneity in type 2 diabetes (T2D) can be represented by a tree-like structure using reversed graph-embedded dimensionality reduction. We examined whether this approach stratifies key pathophysiological components and diabetes-related complications during longitudinal follow-up of recent-onset T2D.Methods: Participants (n=927) of the German Diabetes Study (GDS) with recent-onset T2D were mapped onto a previously developed two-dimensional tree based on nine simple clinical and laboratory variables, residualized for age and sex. Insulin sensitivity was assessed by hyperinsulinaemic-euglycaemic clamp, insulin secretion by intravenous glucose tolerance test, hepatic lipid content (HLC) by 1H magnetic resonance spectroscopy, serum interleukin (IL)-6 and IL-18 by ELISA as well as peripheral and autonomic neuropathy by functional and clinical measures. Participants were followed-up for up to 16 years. In the Ludwigshafen Risk and Cardiovascular Health (LURIC) cohort in persons with diabetes undergoing invasive coronary diagnostics (n=794), we also investigated heart failure and all-cause mortality.Findings: There were gradients of clamp-measured insulin sensitivity (both dimensions: p&lt;0·001) and insulin secretion (both dimensions: p&lt;0·001) across the tree. Individuals in the region with the lowest insulin sensitivity had the highest HLC (pdim1&lt;0·001, pdim2=0·037), proinflammatory biomarkers (IL-6: pdim1&lt;0·001, pdim2=0·013; IL-18: pdim1&lt;0·001, pdim2=0·376) and elevated cardiovascular hazard (nevents=143, pdim1=0·144, pdim2&lt;0·001), whereas persons positioned in the branch with the lowest insulin secretion were more prone to require insulin therapy (nevents=85, pdim1=0·032, pdim2=0·116) and had the highest risk of peripheral (nevents=184, pdim1=0·012, pdim2=0·044) and autonomic neuropathy (nevents=118, pdim1=0·009, pdim2=0·060). In the LURIC cohort, mortality was highest in the tree branch exhibiting insulin resistance (nevents=448, pdim1=0·120, pdim2=0·003). Significant gradients differentiated persons having heart failure with preserved from those with reduced ejection fraction.Interpretation: These data define the pathophysiological underpinnings of the tree structure, with the potential to stratify diabetes-related complications based on routinely available variables and thereby extend the toolbox of precision diabetes diagnosis

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

Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine

Precision medicine is part of the logical evolution of contemporary evidence-based medicine that seeks to reduce errors and optimize outcomes when making medical decisions and health recommendations. Diabetes affects hundreds of millions of people worldwide, many of whom will develop life-threatening complications and die prematurely. Precision medicine can potentially address this enormous problem by accounting for heterogeneity in the etiology, clinical presentation and pathogenesis of common forms of diabetes and risks of complications. This second international consensus report on precision diabetes medicine summarizes the findings from a systematic evidence review across the key pillars of precision medicine (prevention, diagnosis, treatment, prognosis) in four recognized forms of diabetes (monogenic, gestational, type 1, type 2). These reviews address key questions about the translation of precision medicine research into practice. Although not complete, owing to the vast literature on this topic, they revealed opportunities for the immediate or near-term clinical implementation of precision diabetes medicine; furthermore, we expose important gaps in knowledge, focusing on the need to obtain new clinically relevant evidence. Gaps include the need for common standards for clinical readiness, including consideration of cost-effectiveness, health equity, predictive accuracy, liability and accessibility. Key milestones are outlined for the broad clinical implementation of precision diabetes medicine.</p