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Distinct tissue-specific mechanisms mediate insulin action in fasting and postprandial states. Previous genetic studies have largely focused on insulin resistance in the fasting state, where hepatic insulin action dominates. Here we studied genetic variants influencing insulin levels measured 2 h after a glucose challenge in >55,000 participants from three ancestry groups. We identified ten new loci (P

Patients with established type 2 diabetes display both β-cell dysfunction and insulin resistance. To define fundamental processes leading to the diabetic state, we examined the relationship between type 2 diabetes risk variants at 37 established susceptibility loci, and indices of proinsulin processing, insulin secretion, and insulin sensitivity. We included data from up to 58,614 nondiabetic subjects with basal measures and 17,327 with dynamic measures. We used additive genetic models with adjustment for sex, age, and BMI, followed by fixed-effects, inverse-variance meta-analyses. Cluster analyses grouped risk loci into five major categories based on their relationship to these continuous glycemic phenotypes. The first cluster (PPARG, KLF14, IRS1, GCKR) was characterized by primary effects on insulin sensitivity. The second cluster (MTNR1B, GCK) featured risk alleles associated with reduced insulin secretion and fasting hyperglycemia. ARAP1 constituted a third cluster characterized by defects in insulin processing. A fourth cluster (TCF7L2, SLC30A8, HHEX/IDE, CDKAL1, CDKN2A/2B) was defined by loci influencing insulin processing and secretion without a detectable change in fasting glucose levels. The final group contained 20 risk loci with no clear-cut associations to continuous glycemic traits. By assembling extensive data on continuous glycemic traits, we have exposed the diverse mechanisms whereby type 2 diabetes risk variants impact disease predisposition.

Gene-lifestyle interactions have been suggested to contribute to the development of type 2 diabetes. Glucose levels 2 h after a standard 75-g glucose challenge are used to diagnose diabetes and are associated with both genetic and lifestyle factors. However, whether these factors interact to determine 2-h glucose levels is unknown. We meta-analyzed single nucleotide polymorphism (SNP) × BMI and SNP × physical activity (PA) interaction regression models for five SNPs previously associated with 2-h glucose levels from up to 22 studies comprising 54,884 individuals without diabetes. PA levels were dichotomized, with individuals below the first quintile classified as inactive (20%) and the remainder as active (80%). BMI was considered a continuous trait. Inactive individuals had higher 2-h glucose levels than active individuals (β = 0.22 mmol/L [95% CI 0.13-0.31], P = 1.63 × 10(-6)). All SNPs were associated with 2-h glucose (β = 0.06-0.12 mmol/allele, P ≤ 1.53 × 10(-7)), but no significant interactions were found with PA (P > 0.18) or BMI (P ≥ 0.04). In this large study of gene-lifestyle interaction, we observed no interactions between genetic and lifestyle factors, both of which were associated with 2-h glucose. It is perhaps unlikely that top loci from genome-wide association studies will exhibit strong subgroup-specific effects, and may not, therefore, make the best candidates for the study of interactions.

Background: Retrospective research on real-world data provides the ability to gain evidence on specific topics especially when running across different sites in research networks. Those research networks have become increasingly relevant in recent years; not least due to the special situation caused by the COVID-19 pandemic. An important requirement for those networks is the data harmonization by ensuring the semantic interoperability. Aims: In this paper we demonstrate (1) how to facilitate digital infrastructures to run a retrospective study in a research network spread across university and non-university hospital sites; and (2) to answer a medical question on COVID-19 related change in diagnostic counts for diabetes-related eye diseases. Materials and methods: The study is retrospective and non-interventional and runs on medical case data documented in routine care at the participating sites. The technical infrastructure consists of the OMOP CDM and other OHDSI tools that is provided in a transferable format. An ETL process to transfer and harmonize the data to the OMOP CDM has been utilized. Cohort definitions for each year in observation have been created centrally and applied locally against medical case data of all participating sites and analyzed with descriptive statistics. Results: The analyses showed an expectable drop of the total number of diagnoses and the diagnoses for diabetes in general; whereas the number of diagnoses for diabetes-related eye diseases surprisingly decreased stronger compared to non-eye diseases. Differences in relative changes of diagnoses counts between sites show an urgent need to process multi-centric studies rather than single-site studies to reduce bias in the data. Conclusions: This study has demonstrated the ability to utilize an existing portable and standardized infrastructure and ETL process from a university hospital setting and transfer it to non-university sites. From a medical perspective further activity is needed to evaluate data quality of the utilized real-world data documented in routine care and to investigate its eligibility of this data for research. [ABSTRACT FROM AUTHOR]

The question as to how to screen diabetes risk in a multi-ethnic population is not easy to answer. There are a number of diagnostic procedures and risk score tools which may help identify people with increased risk. Some of the risk factors for diabetes have a clear ethnic component, thus the risk stratification is different in Caucasian, Asian and Latin American populations. However, we can expect that the pathophysiology for diabetes development consisting of insulin resistance and progressive beta-cell failure is very similar in its pathomechanistic background between ethnic groups, although the speed and progressive destruction may have ethnic and varying genetic components. In this environment, we have to find clinically applicable approaches to identify those with increased diabetes risk which have to be easy to understand, transparent and replicable for diabetes risk detection. The International Diabetes Federation recently started the PREDICT-2 study to develop a global diabetes risk score. In this article, we discuss some of the strategies to identify diabetes risk and give some ideas about ethnic variation. Copyright © 2013 FEND. Published by John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

AimTo find out whether diabetes prevention via a lifestyle intervention programme is feasible in a primary healthcare setting in Poland.MethodsThe intervention (Diabetes in Europe: Prevention using Lifestyle, physical Activity and Nutritional intervention; DE-PLAN project) was completed by 175 middle-aged, slightly obese participants in nine primary healthcare centres in Krakow, Poland. The inclusion criterion was diabetes risk (Finnish Diabetes Risk score (FRS) >14). The nurse-delivered intervention consisted of 10 group sessions on lifestyle changes, six motivational telephone sessions, two motivational letters and the opportunity to participate in once or twice weekly physical activity sessions.ResultsAt 12 months there were mean reductions in weight, 85.7 to 83.7 kg; body mass index, 31.8 to 31.1 kg/m2 (p<0.05); waist circumference, 98.8 to 95.5 cm (p<0.05); total cholesterol 5.6 to 5.3 mmol/L (p<0.05), systolic and diastolic blood pressure 133 to 130 and 83 to 81 mmHg respectively (p<0.05); FRS 18–16 (p<0.05) and 25% of participants lost ≥5% of initial body weight. No changes were observed in fasting or 2-h post-oral glucose tolerance test plasma glucose, triglycerides, or high-density lipoproteins. Significant improvements were found in lifestyle risk factors like diet and physical activity.ConclusionsPrevention of type 2 diabetes through lifestyle intervention is feasible in primary healthcare settings in Poland. [ABSTRACT FROM PUBLISHER]