Human Physiology of Genetic Defects Causing Beta-cell Dysfunction

The last decade has revealed hundreds of genetic variants associated with type 2 diabetes, many especially with insulin secretion. However, the evidence for their single or combined effect on beta-cell function relies mostly on genetic association of the variants or genetic risk scores with simple traits, and few have been functionally fully characterized even in cell or animal models. Translating the measured traits into human physiology is not straightforward: none of the various indices for beta-cell function or insulin sensitivity recapitulates the dynamic interplay between glucose sensing, endogenous glucose production, insulin production and secretion, insulin clearance, insulin resistance-to name just a few factors. Because insulin sensitivity is a major determinant of physiological need of insulin, insulin secretion should be evaluated in parallel with insulin sensitivity. On the other hand, multiple physiological or pathogenic processes can either mask or unmask subtle defects in beta-cell function. Even in monogenic diabetes, a clearly pathogenic genetic variant can result in different phenotypic characteristics-or no phenotype at all. In this review, we evaluate the methods available for studying beta-cell function in humans, critically examine the evidence linking some identified variants to a specific beta-cell phenotype, and highlight areas requiring further study. (C) 2020 The Authors. Published by Elsevier Ltd.Peer reviewe

Clusters provide a better holistic view of type 2 diabetes than simple clinical features

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Munuaispotilaiden diabeteksen glukoositasapainon hoito

English summar

1-Hour Post-OGTT Glucose Improves the Early Prediction of Type 2 Diabetes by Clinical and Metabolic Markers

Context: Early prediction of dysglycemia is crucial to prevent progression to type 2 diabetes. The 1-hour postload plasma glucose (PG) is reported to be a better predictor of dysglycemia than fasting plasma glucose (FPG), 2-hour PG, or glycated hemoglobin (HbA1c). Objective: To evaluate the predictive performance of clinical markers, metabolites, HbA1c, and PG and serum insulin (INS) levels during a 75-g oral glucose tolerance test (OGTT). Design and Setting: We measured PG and INS levels at 0, 30, 60, and 120 minutes during an OGTT in 543 participants in the Botnia Prospective Study, 146 of whom progressed to type 2 diabetes within a 10-year follow-up period. Using combinations of variables, we evaluated 1527 predictive models for progression to type 2 diabetes. Results: The 1-hour PG outperformed every individual marker except 30-minute PG or mannose, whose predictive performances were lower but not significantly worse. HbA1c was inferior to 1-hour PG according to DeLong test P value but not false discovery rate. Combining the metabolic markers with PG measurements and HbA1c significantly improved the predictive models, and mannose was found to be a robust metabolic marker. Conclusions: The 1-hour PG, alone or in combination with metabolic markers, is a robust predictor for determining the future risk of type 2 diabetes, outperforms the 2-hour PG, and is cheaper to measure than metabolites. Metabolites add to the predictive value of PG and HbA1c measurements. Shortening the standard 75-g OGTT to 1 hour improves its predictive value and clinical usability.Peer reviewe

Birthweight, BMI in adulthood and latent autoimmune diabetes in adults : a Mendelian randomisation study

Aims/hypothesis Observational studies have found an increased risk of latent autoimmune diabetes in adults (LADA) associated with low birthweight and adult overweight/obese status. We aimed to investigate whether these associations are causal, using a two-sample Mendelian randomisation (MR) design. In addition, we compared results for LADA and type 2 diabetes. Methods We identified 43 SNPs acting through the fetal genome as instrumental variables (IVs) for own birthweight from a gcnomc-wide association study (GWAS) of the Early Growth Genetics Consortium (EGG) and the UK Biobank. We identified 820 SNPs as IVs for adult BMI from a GWAS of the UK Biobank and the Genetic Investigation of ANthropometric Traits consortium (GIANT). Summary statistics for the associations between IVs and LADA were extracted from the only GWAS involving 2634 cases and 5947 population controls. We used the inverse-variance weighted (IVW) estimator as our primary analysis, supplemented by a series of sensitivity analyses. Results Genetically determined own birthweight was inversely associated with LADA (OR per SD [similar to 500 g] decrease in birthweight 1.68 [95% CI 1.01, 2.82]). In contrast, genetically predicted BMI in adulthood was positively associated with LADA (OR per SD [similar to 4.8 kg/m(2)] increase in BMI 1.40 [95% CI 1.14, 1.71]). Robust results were obtained in a range of sensitivity analyses using other MR estimators or excluding some IVs. With respect to type 2 diabetes, the association with birthweight was not stronger than in LADA while the association with adult BMI was stronger than in LADA. Conclusions/ interpretation This study provides genetic support for a causal link between low birthweight, adult overweight/obese status and LADA.Peer reviewe

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Glycaemic variability-based classification of impaired glucose tolerance vs. type 2 diabetes using continuous glucose monitoring data

Many glycaemic variability (GV) indices extracted from continuous glucose monitoring systems data have been proposed for the characterisation of various aspects of glucose concentration profile dynamics in both healthy and non-healthy individuals. However, the inter-index correlations have made it difficult to reach a consensus regarding the best applications or a subset of indices for clinical scenarios, such as distinguishing subjects according to diabetes progression stage. Recently, a logistic regression-based method was used to address the basic problem of differentiating between healthy subjects and those affected by impaired glucose tolerance (IGT) or type 2 diabetes (T2D) in a pool of 25 GV-based indices. Whereas healthy subjects were classified accurately, the distinction between patients with IGT and T2D remained critical. In the present work, by using a dataset of CGM time-series collected in 62 subjects, we developed a polynomial-kernel support vector machine-based approach and demonstrated the ability to distinguish between subjects affected by IGT and T2D based on a pool of 37 GV indices complemented by four basic parameters—age, sex, BMI, and waist circumference—with an accuracy of 87.1%.Peer reviewe

Management of Latent Autoimmune Diabetes in Adults : A Consensus Statement From an International Expert Panel

A substantial proportion of patients with adult-onset diabetes share features of both type 1 diabetes (T1D) and type 2 diabetes (T2D). These individuals, at diagnosis, clinically resemble T2D patients by not requiring insulin treatment, yet they have immunogenetic markers associated with T1D. Such a slowly evolving form of autoimmune diabetes, described as latent autoimmune diabetes of adults (LADA), accounts for 2-12% of all patients with adult-onset diabetes, though they show considerable variability according to their demographics and mode of ascertainment. While therapeutic strategies aim for metabolic control and preservation of residual insulin secretory capacity, endotype heterogeneity within LADA implies a personalized approach to treatment. Faced with a paucity of large-scale clinical trials in LADA, an expert panel reviewed data and delineated one therapeutic approach. Building on the 2020 American Diabetes Association (ADA)/European Association for the Study of Diabetes (EASD) consensus for T2D and heterogeneity within autoimmune diabetes, we propose "deviations" for LADA from those guidelines. Within LADA, C-peptide values, proxy for beta-cell function, drive therapeutic decisions. Three broad categories of random C-peptide levels were introduced by the panel:1) C-peptide levels 2) C-peptide values >= 0.3 and 0.7 nmol/L: suggests a modified ADA/EASD algorithm as for T2D but allowing for the potentially progressive nature of LADA by monitoring C-peptide to adjust treatment. The panel concluded by advising general screening for LADA in newly diagnosed non-insulin-requiring diabetes and, importantly, that large randomized clinical trials are warranted.Peer reviewe

Determinants of penetrance and variable expressivity in monogenic metabolic conditions across 77,184 exomes

Hundreds of thousands of genetic variants have been reported to cause severe monogenic diseases, but the probability that a variant carrier develops the disease (termed penetrance) is unknown for virtually all of them. Additionally, the clinical utility of common polygenetic variation remains uncertain. Using exome sequencing from 77,184 adult individuals (38,618 multi-ancestral individuals from a type 2 diabetes case-control study and 38,566 participants from the UK Biobank, for whom genotype array data were also available), we apply clinical standard-of-care gene variant curation for eight monogenic metabolic conditions. Rare variants causing monogenic diabetes and dyslipidemias display effect sizes significantly larger than the top 1% of the corresponding polygenic scores. Nevertheless, penetrance estimates for monogenic variant carriers average 60% or lower for most conditions. We assess epidemiologic and genetic factors contributing to risk prediction in monogenic variant carriers, demonstrating that inclusion of polygenic variation significantly improves biomarker estimation for two monogenic dyslipidemias. Penetrance of variants in monogenic disease and clinical utility of common polygenic variation has not been well explored on a large-scale. Here, the authors use exome sequencing data from 77,184 individuals to generate penetrance estimates and assess the utility of polygenic variation in risk prediction of monogenic variants.Peer reviewe