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 (pdim1<0·0001, pdim2=0·00097) across the tree. Individuals in the region with the lowest insulin sensitivity had the highest hepatic lipid content (n=205, pdim1<0·0001, pdim2=0·037), pro-inflammatory biomarkers (IL-6: n=348, pdim1<0·0001, pdim2=0·013; IL-18: n=350, pdim1<0·0001, pdim2=0·38), and elevated cardiovascular risk (nevents=143, pdim1=0·14, pdim2<0·00081), whereas individuals positioned in the branch with the lowest insulin secretion were more prone to require insulin therapy (nevents=85, pdim1=0·032, pdim2=0·12) and had the highest risk of diabetic sensorimotor polyneuropathy (nevents=184, pdim1=0·012, pdim2=0·044) and cardiac autonomic neuropathy (nevents=118, pdim1=0·0094, pdim2=0·06). In the LURIC cohort, all-cause mortality was highest in the tree branch showing insulin resistance (nevents=488, pdim1=0·12, pdim2=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.

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.

Brain insulin action on peripheral insulin sensitivity in women depends on menstrual cycle phase

Insulin action in the human brain modulates eating behaviour, whole-body metabolism and body fat distribution1,2. In particular, brain insulin action increases whole-body insulin sensitivity, but these studies were mainly performed in lean men3,4. Here we investigate metabolic and hypothalamic effects of brain insulin action in women with a focus on the impact of menstrual cycle ( ClinicalTrials.gov registration: NCT03929419 ).Eleven women underwent four hyperinsulinemic-euglycemic clamps, two in the follicular phase and two in the luteal phase. Brain insulin action was introduced using nasal insulin spray5-7 and compared to placebo spray in a fourfold crossover design with change in glucose infusion rate as the primary endpoint. Here we show that during the follicular phase, more glucose has to be infused after administration of nasal insulin than after administration of placebo. This remains significant after adjustment for blood glucose and insulin. During the luteal phase, no significant influence of brain insulin action on glucose infusion rate is detected after adjustment for blood glucose and insulin (secondary endpoint). In 15 other women, hypothalamic insulin sensitivity was assessed in a within-subject design by functional magnetic resonance imaging with intranasal insulin administration8. Hypothalamus responsivity is influenced by insulin in the follicular phase but not the luteal phase.Our study therefore highlights that brain insulin action improves peripheral insulin sensitivity also in women but only during the follicular phase. Thus, brain insulin resistance could contribute to whole-body insulin resistance in the luteal phase of the menstrual cycle.

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

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.

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.

Interscapular fat is associated with impaired glucose tolerance and insulin resistance independent of visceral fat mass

OBJECTIVE

Dysregulated body fat distribution is a major determinant of various diseases. In particular, increased visceral fat mass and ectopic lipids in the liver are linked to metabolic disorders such as insulin resistance and type 2 diabetes. Furthermore, interscapular fat is considered to be a metabolically active fat compartment.

METHODS

This study measured interscapular fat mass and investigated its relationship with glucose metabolism in 822 individuals with a wide range of BMI values and different glucose tolerance statuses. Magnetic resonance imaging was used to quantify body fat depots, and an oral glucose tolerance test was performed to determine glucose metabolism.

RESULTS

Elevated interscapular fat mass was positively associated with age, BMI, and total body, visceral, and subcutaneous adipose tissue mass. High interscapular fat mass associated with elevated fasting glucose levels, glucose levels at 2 hours during the oral glucose tolerance test, glycated hemoglobin, and insulin resistance, independent of sex, age, and total body and visceral fat mass.

CONCLUSIONS

In conclusion, interscapular fat might be a highly specific fat compartment with a potential impact on glucose metabolism and the pathogenesis of diabetes mellitus.

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) (I_0-30)/AUC (G_0-30), 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 (I_0-120)/AUC (G_0-120), BIGTT-AIR_0-60-120, I₃₀/G₃₀, first-phase Stumvoll and AUC (I_0-30)/AUC (G_0-30) 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.