Type 2 diabetes genetic loci informed by multi-trait associations point to disease mechanisms and subtypes: A soft clustering analysis

Precision nutrition in diabetes: when population-based dietary advice gets personal

Diet plays a fundamental role in maintaining long-term health, with healthful diets being endorsed by current dietary guidelines for the prevention and management of type 2 diabetes. However, the response to dietary interventions varies widely, highlighting the need for refinement and personalisation beyond population-based 'one size fits all'. This article reviews the clinical evidence supporting precision nutrition as a fundamental approach for dietary advice in diabetes. Further, it proposes a framework for the eventual implementation of precision nutrition and discusses key challenges for the application of this approach in the prevention of diabetes. One implication of this approach is that precision nutrition would not exclude the parallel goal of population-based healthy dietary advice. Nevertheless, the shift in prioritising precision nutrition is needed to reflect the dynamic nature of responses to dietary interventions that vary among individuals and change over the life course.

Phenotypic and genetic classification of diabetes

The historical subclassification of diabetes into predominantly types 1 and 2 is well appreciated to inadequately capture the heterogeneity seen in patient presentations, disease course, response to therapy and disease complications. This review summarises proposed data-driven approaches to further refine diabetes subtypes using clinical phenotypes and/or genetic information. We highlight the benefits as well as the limitations of these subclassification schemas, including practical barriers to their implementation that would need to be overcome before incorporation into clinical practice.

Lipodystrophy for the Diabetologist-What to Look For

PURPOSE OF REVIEW

Genetic or acquired lipodystrophies are characterized by selective loss of body fat along with predisposition towards metabolic complications of insulin resistance, such as diabetes mellitus, hypertriglyceridemia, hepatic steatosis, polycystic ovarian syndrome, and acanthosis nigricans. In this review, we discuss the various subtypes and when to suspect and how to diagnose lipodystrophy.

RECENT FINDINGS

The four major subtypes are autosomal recessive, congenital generalized lipodystrophy (CGL); acquired generalized lipodystrophy (AGL), mostly an autoimmune disorder; autosomal dominant or recessive familial partial lipodystrophy (FPLD); and acquired partial lipodystrophy (APL), an autoimmune disorder. Diagnosis of lipodystrophy is mainly based upon physical examination findings of loss of body fat and can be supported by body composition analysis by skinfold measurements, dual-energy x-ray absorptiometry, and whole-body magnetic resonance imaging. Confirmatory genetic testing is helpful in the proband and at-risk family members with suspected genetic lipodystrophies. The treatment is directed towards the specific comorbidities and metabolic complications, and there is no treatment to reverse body fat loss. Metreleptin should be considered as the first-line therapy for metabolic complications in patients with generalized lipodystrophy and for prevention of comorbidities in children. Metformin and insulin therapy are the best options for treating hyperglycemia and fibrates and/or fish oil for hypertriglyceridemia. Lipodystrophy should be suspected in lean and muscular subjects presenting with diabetes mellitus, hypertriglyceridemia, non-alcoholic fatty liver disease, polycystic ovarian syndrome, or amenorrhea. Diabetologists should be aware of lipodystrophies and consider genetic varieties as an important subtype of monogenic diabetes.

On the Verge of Precision Medicine in Diabetes

The epidemic of type 2 diabetes (T2D) is a significant global public health challenge and a major cause of morbidity and mortality. Despite the recent proliferation of pharmacological agents for the treatment of T2D, current therapies simply treat the symptom, i.e. hyperglycemia, and do not directly address the underlying disease process or modify the disease course. This article summarizes how genomic discovery has contributed to unraveling the heterogeneity in T2D, reviews relevant discoveries in the pharmacogenetics of five commonly prescribed glucose-lowering agents, presents evidence supporting how pharmacogenetics can be leveraged to advance precision medicine, and calls attention to important research gaps to its implementation to guide treatment choices.

Circulating microRNAs as clinically useful biomarkers for Type 2 Diabetes Mellitus: miRNomics from bench to bedside

Type 2 diabetes (T2D), a chronic metabolic disease, has attained the status of a global epidemic with steadily increasing incidence worldwide. Improved diagnosis, stratification and prognosis of T2D patients and the development of more effective treatments are needed. In this era of personalized medicine, the discovery and evaluation of innovative circulating biomarkers can be an effective tool for better stratification, prognosis and therapeutic selection/management of T2D patients. MicroRNAs (miRNAs), a class of small non-coding RNAs that modulate gene expression, have been investigated as potential circulating biomarkers in T2D. Several studies have investigated the expression of circulating miRNAs in T2D patients from various biological fluids, including plasma and serum, and have demonstrated their potential as diagnostic and prognostic biomarkers, as well as biomarkers of response to therapy. In this review, we provide an overview of the current state of knowledge, focusing on circulating miRNAs that have been consistently expressed in at least two independent studies, in order to identify a set of consistent biomarker candidates in T2D. The expression levels of miRNAs, correlation with clinical parameters, functional roles of miRNAs and their potential as biomarkers are reported. A systematic literature search and assessment of studies led to the selection and review of 10 miRNAs (miR-126-3p, miR-223-3p, miR-21-5p, miR-15a-5p, miR-24-3p, miR-34a-5p, miR-146a-5p, miR-148a-3p, miR-30d-5p and miR-30c-5p). We also present technical challenges and our thoughts on the potential validation of circulating miRNAs and their application as biomarkers in the context of T2D.

Polygenic Risk Scores for Cardiovascular Disease: A Scientific Statement From the American Heart Association

Cardiovascular disease is the leading contributor to years lost due to disability or premature death among adults. Current efforts focus on risk prediction and risk factor mitigation' which have been recognized for the past half-century. However, despite advances, risk prediction remains imprecise with persistently high rates of incident cardiovascular disease. Genetic characterization has been proposed as an approach to enable earlier and potentially tailored prevention. Rare mendelian pathogenic variants predisposing to cardiometabolic conditions have long been known to contribute to disease risk in some families. However, twin and familial aggregation studies imply that diverse cardiovascular conditions are heritable in the general population. Significant technological and methodological advances since the Human Genome Project are facilitating population-based comprehensive genetic profiling at decreasing costs. Genome-wide association studies from such endeavors continue to elucidate causal mechanisms for cardiovascular diseases. Systematic cataloging for cardiovascular risk alleles also enabled the development of polygenic risk scores. Genetic profiling is becoming widespread in large-scale research, including in health care-associated biobanks, randomized controlled trials, and direct-to-consumer profiling in tens of millions of people. Thus, individuals and their physicians are increasingly presented with polygenic risk scores for cardiovascular conditions in clinical encounters. In this scientific statement, we review the contemporary science, clinical considerations, and future challenges for polygenic risk scores for cardiovascular diseases. We selected 5 cardiometabolic diseases (coronary artery disease, hypercholesterolemia, type 2 diabetes, atrial fibrillation, and venous thromboembolic disease) and response to drug therapy and offer provisional guidance to health care professionals, researchers, policymakers, and patients.

Challenges and Opportunities for Developing More Generalizable Polygenic Risk Scores

Polygenic risk scores (PRS) estimate an individual's genetic likelihood of complex traits and diseases by aggregating information across multiple genetic variants identified from genome-wide association studies. PRS can predict a broad spectrum of diseases and have therefore been widely used in research settings. Some work has investigated their potential applications as biomarkers in preventative medicine, but significant work is still needed to definitively establish and communicate absolute risk to patients for genetic and modifiable risk factors across demographic groups. However, the biggest limitation of PRS currently is that they show poor generalizability across diverse ancestries and cohorts. Major efforts are underway through methodological development and data generation initiatives to improve their generalizability. This review aims to comprehensively discuss current progress on the development of PRS, the factors that affect their generalizability, and promising areas for improving their accuracy, portability, and implementation.

An affordable approach to classifying type 2 diabetes based on fasting plasma glucose, TyG index and BMI: a retrospective cohort study of NHANES Data from 1988 to 2014

BACKGROUND

The β-cell function and insulin resistance required by existing methods of classifying type 2 diabetes are not routinely adopted in most medical institutions of developing countries and regions. This study aims to propose a novel, affordable classification approach and evaluate its predictive ability for several health and mortality outcomes, including cardiovascular health (CVH), retinopathy, chronic kidney disease (CKD), nonalcoholic fatty liver disease (NAFLD), advanced liver fibrosis, and mortality caused by all-cause, cardiovascular disease (CVD), cancer.

METHODS

Based on 4060 participants with diabetes (aged ≥ 30 at the time of diagnosis) selected from the National Health and Nutrition Examination Survey III & 1999-2014, we proposed a novel, but simple classification approach based on the threshold of fasting plasma glucose (FPG), triglyceride-glucose (TyG) index and body mass index (BMI). We used logistic regression model to assess its predictability for diabetes complications, and Cox regression model to estimate the mortality risks.

RESULTS

By utilizing this approach, we characterized the subjects into four subgroups: subgroup A (obesity-related), which accounts for 37% of the total, subgroup B (age-related), 38%, subgroup C (insulin resistance), 20%, and subgroup D (severe insulin deficiency), 5%. Subjects in subgroup D had a higher risk of retinopathy, in subgroup B had a lower risk of poor cardiovascular health, nonalcoholic fatty liver disease, and advanced liver fibrosis, in subgroup C had a higher risk of all-cause mortality.

CONCLUSIONS

This study proposes an affordable and practical method for classifying patients with type 2 diabetes into different subgroups, with a view to yield a high predictability of patient outcomes and to assist clinicians in providing better treatment.

Genetics of Type 2 Diabetes: Past, Present, and Future

Diabetes has reached epidemic proportions worldwide. Currently, approximately 537 million adults (20–79 years) have diabetes, and the total number of people with diabetes is continuously increasing. Diabetes includes several subtypes. About 80% of all cases of diabetes are type 2 diabetes (T2D). T2D is a polygenic disease with an inheritance ranging from 30 to 70%. Genetic and environment/lifestyle factors, especially obesity and sedentary lifestyle, increase the risk of T2D. In this review, we discuss how studies on the genetics of diabetes started, how they expanded when genome-wide association studies and exome and whole-genome sequencing became available, and the current challenges in genetic studies of diabetes. T2D is heterogeneous with respect to clinical presentation, disease course, and response to treatment, and has several subgroups which differ in pathophysiology and risk of micro- and macrovascular complications. Currently, genetic studies of T2D focus on these subgroups to find the best diagnoses and treatments for these patients according to the principles of precision medicine.

Multi-Trait Genetic Analysis Reveals Clinically Interpretable Hypertension Subtypes

Background:

Hypertension comprises a heterogeneous range of phenotypes. We asked whether underlying genetic structure could explain a part of this heterogeneity.

Methods:

Our study sample comprised N=198 148 FinnGen participants (56% women, mean age 58 years) and N=21 168 well-phenotyped FINRISK participants (53% women, mean age 50 years). First, we identified genetic hypertension components with an unsupervised Bayesian non-negative matrix factorization algorithm using public genome-wide association data for 144 genetic hypertension variants and 16 clinical traits. For these components, we computed their (1) cross-sectional associations with clinical traits in FINRISK using linear regression and (2) longitudinal associations with incident adverse outcomes in FinnGen using Cox regression.

Results:

We observed 4 genetic hypertension components corresponding to recognizable clinical phenotypes: obesity (high body mass index), dyslipidemia (low high-density lipoprotein cholesterol and high triglycerides), hypolipidemia (low low-density lipoprotein cholesterol and low total cholesterol), and short stature. In FINRISK, all hypertension components had robust associations with their respective clinical characteristics. In FinnGen, the Obesity component was associated with increased diabetes risk (hazard ratio per 1 SD increase 1.08 [Bonferroni corrected CI, 1.05–1.10]) and the Hypolipidemia component with increased autoimmune disease risk (hazard ratio per 1 SD increase 1.05 [Bonferroni corrected CI, 1.03–1.07]). In addition, all hypertension components were related to both hypertension and cardiovascular disease.

Conclusions:

Our unsupervised analysis demonstrates that the genetic basis of hypertension can be understood as a mixture of 4 broad, clinically interpretable components capturing disease heterogeneity. These components could be used to stratify individuals into specific genetic subtypes and, therefore, to benefit personalized health care and pharmaceutical research.

TCF7L2 plays a complex role in human adipose progenitor biology, which might contribute to genetic susceptibility to type 2 diabetes

INTRODUCTION

Non-coding genetic variation at TCF7L2 is the strongest genetic determinant of type 2 diabetes (T2D) risk in humans. TCF7L2 encodes a transcription factor mediating the nuclear effects of WNT signaling in adipose tissue (AT). In vivo studies in transgenic mice have highlighted important roles for TCF7L2 in adipose tissue biology and systemic metabolism.

OBJECTIVE

To map the expression of TCF7L2 in human AT, examine its role in human adipose cell biology in vitro, and investigate the effects of the fine-mapped T2D-risk allele at rs7903146 on AT morphology and TCF7L2 expression.

METHODS

Ex vivo gene expression studies of TCF7L2 in whole and fractionated human AT. In vitro TCF7L2 gain- and/or loss-of-function studies in primary and immortalized human adipose progenitor cells (APCs) and mature adipocytes (mADs). AT phenotyping of rs7903146 T2D-risk variant carriers and matched controls.

RESULTS

Adipose progenitors (APs) exhibited the highest TCF7L2 mRNA abundance compared to mature adipocytes and adipose-derived endothelial cells. Obesity was associated with reduced TCF7L2 transcript levels in whole subcutaneous abdominal AT but paradoxically increased expression in APs. In functional studies, TCF7L2 knockdown (KD) in abdominal APs led to dose-dependent activation of WNT/β-catenin signaling, impaired proliferation and dose-dependent effects on adipogenesis. Whilst partial KD enhanced adipocyte differentiation, near-total KD impaired lipid accumulation and adipogenic gene expression. Over-expression of TCF7L2 accelerated adipogenesis. In contrast, TCF7L2-KD in gluteal APs dose-dependently enhanced lipid accumulation. Transcriptome-wide profiling revealed that TCF7L2 might modulate multiple aspects of AP biology including extracellular matrix secretion, immune signaling and apoptosis. The T2D-risk allele at rs7903146 was associated with reduced AP TCF7L2 expression and enhanced AT insulin sensitivity.

CONCLUSIONS

TCF7L2 plays a complex role in AP biology and has both dose- and depot-dependent effects on adipogenesis. In addition to regulating pancreatic insulin secretion, genetic variation at TCF7L2 might also influence T2D risk by modulating AP function.

Highly perturbed genes and hub genes associated with type 2 diabetes in different tissues of adult humans: a bioinformatics analytic workflow

Type 2 diabetes (T2D) has a complex etiology which is not yet fully elucidated. The identification of gene perturbations and hub genes of T2D may deepen our understanding of its genetic basis. We aimed to identify highly perturbed genes and hub genes associated with T2D via an extensive bioinformatics analytic workflow consisting of five steps: systematic review of Gene Expression Omnibus and associated literature; identification and classification of differentially expressed genes (DEGs); identification of highly perturbed genes via meta-analysis; identification of hub genes via network analysis; and downstream analysis of highly perturbed genes and hub genes. Three meta-analytic strategies, random effects model, vote-counting approach, and p value combining approach, were applied. Hub genes were defined as those nodes having above-average betweenness, closeness, and degree in the network. Downstream analyses included gene ontologies, Kyoto Encyclopedia of Genes and Genomes pathways, metabolomics, COVID-19-related gene sets, and Genotype-Tissue Expression profiles. Analysis of 27 eligible microarrays identified 6284 DEGs (4592 downregulated and 1692 upregulated) in four tissue types. Tissue-specific gene expression was significantly greater than tissue non-specific (shared) gene expression. Analyses revealed 79 highly perturbed genes and 28 hub genes. Downstream analyses identified enrichments of shared genes with certain other diabetes phenotypes; insulin synthesis and action-related pathways and metabolomics; mechanistic associations with apoptosis and immunity-related pathways; COVID-19-related gene sets; and cell types demonstrating over- and under-expression of marker genes of T2D. Our approach provided valuable insights on T2D pathogenesis and pathophysiological manifestations. Broader utility of this pipeline beyond T2D is envisaged.

Inherited basis of visceral, abdominal subcutaneous and gluteofemoral fat depots

For any given level of overall adiposity, individuals vary considerably in fat distribution. The inherited basis of fat distribution in the general population is not fully understood. Here, we study up to 38,965 UK Biobank participants with MRI-derived visceral (VAT), abdominal subcutaneous (ASAT), and gluteofemoral (GFAT) adipose tissue volumes. Because these fat depot volumes are highly correlated with BMI, we additionally study six local adiposity traits: VAT adjusted for BMI and height (VATadj), ASATadj, GFATadj, VAT/ASAT, VAT/GFAT, and ASAT/GFAT. We identify 250 independent common variants (39 newly-identified) associated with at least one trait, with many associations more pronounced in female participants. Rare variant association studies extend prior evidence for PDE3B as an important modulator of fat distribution. Local adiposity traits (1) highlight depot-specific genetic architecture and (2) enable construction of depot-specific polygenic scores that have divergent associations with type 2 diabetes and coronary artery disease. These results - using MRI-derived, BMI-independent measures of local adiposity - confirm fat distribution as a highly heritable trait with important implications for cardiometabolic health outcomes.

TSABL: Trait Specific Annotation Based Locus predictor

BACKGROUND

The majority of Genome Wide Associate Study (GWAS) loci fall in the non-coding genome, making causal variants difficult to identify and study. We hypothesized that the regulatory features underlying causal variants are biologically specific, identifiable from data, and that the regulatory architecture that influences one trait is distinct compared to biologically unrelated traits.

RESULTS

To better characterize and identify these variants, we used publicly available GWAS loci and genomic annotations to build 17 Trait Specific Annotation Based Locus (TSABL) predictors to identify differences between GWAS loci associated with different phenotypic trait groups. We used a penalized binomial logistic regression model to select trait relevant annotations and tested all models on a holdout set of loci not used for training in any trait. We were able to successfully build models for autoimmune, electrocardiogram, lipid, platelet, red blood cell, and white blood cell trait groups. We used these models both to prioritize variants in existing loci and to identify new genomic regions of interest.

CONCLUSIONS

We found that TSABL models identified biologically relevant regulatory features, and anticipate their future use to enhance the design and interpretation of genetic studies.

Shared components of heritability across genetically correlated traits

Most disease-associated genetic variants are pleiotropic, affecting multiple genetically correlated traits. Their pleiotropic associations can be mechanistically informative: if many variants have similar patterns of association, they may act via similar pleiotropic mechanisms, forming a shared component of heritability. We developed pleiotropic decomposition regression (PDR) to identify shared components and their underlying genetic variants. We validated PDR on simulated data and identified limitations of existing methods in recovering the true components. We applied PDR to three clusters of five to six traits genetically correlated with coronary artery disease (CAD), asthma, and type II diabetes (T2D), producing biologically interpretable components. For CAD, PDR identified components related to BMI, hypertension, and cholesterol, and it clarified the relationship among these highly correlated risk factors. We assigned variants to components, calculated their posterior-mean effect sizes, and performed out-of-sample validation. Our posterior-mean effect sizes pool statistical power across traits and substantially boost the correlation (r2) between true and estimated effect sizes (compared with the original summary statistics) by 94% and 70% for asthma and T2D out of sample, respectively, and by a predicted 300% for CAD.

Genetic analysis of right heart structure and function in 40,000 people

Congenital heart diseases often involve maldevelopment of the evolutionarily recent right heart chamber. To gain insight into right heart structure and function, we fine-tuned deep learning models to recognize the right atrium, right ventricle and pulmonary artery, measuring right heart structures in 40,000 individuals from the UK Biobank with magnetic resonance imaging. Genome-wide association studies identified 130 distinct loci associated with at least one right heart measurement, of which 72 were not associated with left heart structures. Loci were found near genes previously linked with congenital heart disease, including NKX2-5, TBX5/TBX3, WNT9B and GATA4. A genome-wide polygenic predictor of right ventricular ejection fraction was associated with incident dilated cardiomyopathy (hazard ratio, 1.33 per standard deviation; P = 7.1 × 10-13) and remained significant after accounting for a left ventricular polygenic score. Harnessing deep learning to perform large-scale cardiac phenotyping, our results yield insights into the genetic determinants of right heart structure and function.

Genome-Wide Association Study Identifies Genetic Loci Associated With Fat Cell Number and Overlap With Genetic Risk Loci for Type 2 Diabetes

Interindividual differences in generation of new fat cells determine body fat and type 2 diabetes risk. In the GENetics of Adipocyte Lipolysis (GENiAL) cohort, which consists of participants who have undergone abdominal adipose biopsy, we performed a genome-wide association study (GWAS) of fat cell number (n = 896). Candidate genes from the genetic study were knocked down by siRNA in human adipose-derived stem cells. We report 318 single nucleotide polymorphisms (SNPs) and 17 genetic loci displaying suggestive (P < 1 × 10-5) association with fat cell number. Two loci pass threshold for GWAS significance, on chromosomes 2 (lead SNP rs149660479-G) and 7 (rs147389390-deletion). We filtered for fat cell number-associated SNPs (P < 1.00 × 10-5) using evidence of genotype-specific expression. Where this was observed we selected genes for follow-up investigation and hereby identified SPATS2L and KCTD18 as regulators of cell proliferation consistent with the genetic data. Furthermore, 30 reported type 2 diabetes-associated SNPs displayed nominal and consistent associations with fat cell number. In functional follow-up of candidate genes, RPL8, HSD17B12, and PEPD were identified as displaying effects on cell proliferation consistent with genetic association and gene expression findings. In conclusion, findings presented herein identify SPATS2L, KCTD18, RPL8, HSD17B12, and PEPD of potential importance in controlling fat cell numbers (plasticity), the size of body fat, and diabetes risk.

Risk assessment for hip and knee osteoarthritis using polygenic risk scores

Objective

Polygenic risk scores (PRS) allow risk stratification using common single‐nucleotide polymorphisms (SNPs), and clinical applications are currently explored for several diseases. This study was undertaken to assess the risk of hip and knee osteoarthritis (OA) using PRS.

Methods

We analyzed 12,732 individuals from a population‐based cohort from the Rotterdam Study (n = 11,496), a clinical cohort (Cohort Hip and Cohort Knee [CHECK] study; n = 908), and a high‐risk cohort of overweight women (Prevention of Knee OA in Overweight Females [PROOF] study; n = 328), for the association of the PRS with prevalence/incidence of radiographic OA, of clinical OA, and of total hip replacement (THR) or total knee replacement (TKR). The hip PRS and knee PRS contained 44 and 24 independent SNPs, respectively, and were derived from a recent genome‐wide association study meta‐analysis. Standardized PRS (with Z transformation) were used in all analyses.

Results

We found a stronger association of the PRS for clinically defined OA compared to radiographic OA phenotypes, and we observed the highest PRS risk stratification for TKR/THR. The odds ratio (OR) per SD was 1.3 for incident THR (95% confidence interval [95% CI] 1.1–1.5) and 1.6 (95% CI 1.3–1.9) for incident TKR in the Rotterdam Study. The knee PRS was associated with incident clinical knee OA in the CHECK study (OR 1.3 [95% CI 1.1–1.5]), but not for the PROOF study (OR 1.2 [95% CI 0.8–1.7]). The OR for OA increased gradually across the PRS distribution, up to 2.1 (95% CI 1.4–3.2) for individuals with the 10% highest PRS compared to the middle 50% of the PRS distribution.

Conclusion

Our findings validated the association of PRS across OA definitions. Since OA is becoming frequent and primary prevention is not commonly applicable, PRS‐based risk assessment could play a role in OA prevention. However, the utility of PRS is dependent on the setting. Further studies are needed to test the integration of genetic risk assessment in diverse health care settings.

A Polygenic Score for Type 2 Diabetes Improves Risk Stratification Beyond Current Clinical Screening Factors in an Ancestrally Diverse Sample

A substantial proportion of the adult United States population with type 2 diabetes (T2D) are undiagnosed, calling into question the comprehensiveness of current screening practices, which primarily rely on age, family history, and body mass index (BMI). We hypothesized that a polygenic score (PGS) may serve as a complementary tool to identify high-risk individuals. The T2D polygenic score maintained predictive utility after adjusting for family history and combining genetics with family history led to even more improved disease risk prediction. We observed that the PGS was meaningfully related to age of onset with implications for screening practices: there was a linear and statistically significant relationship between the PGS and T2D onset (-1.3 years per standard deviation of the PGS). Evaluation of U.S. Preventive Task Force and a simplified version of American Diabetes Association screening guidelines showed that addition of a screening criterion for those above the 90th percentile of the PGS provided a small increase the sensitivity of the screening algorithm. Among T2D-negative individuals, the T2D PGS was associated with prediabetes, where each standard deviation increase of the PGS was associated with a 23% increase in the odds of prediabetes diagnosis. Additionally, each standard deviation increase in the PGS corresponded to a 43% increase in the odds of incident T2D at one-year follow-up. Using complications and forms of clinical intervention (i.e., lifestyle modification, metformin treatment, or insulin treatment) as proxies for advanced illness we also found statistically significant associations between the T2D PGS and insulin treatment and diabetic neuropathy. Importantly, we were able to replicate many findings in a Hispanic/Latino cohort from our database, highlighting the value of the T2D PGS as a clinical tool for individuals with ancestry other than European. In this group, the T2D PGS provided additional disease risk information beyond that offered by traditional screening methodologies. The T2D PGS also had predictive value for the age of onset and for prediabetes among T2D-negative Hispanic/Latino participants. These findings strengthen the notion that a T2D PGS could play a role in the clinical setting across multiple ancestries, potentially improving T2D screening practices, risk stratification, and disease management.

Integrating polygenic risk scores in the prediction of type 2 diabetes risk and subtypes in British Pakistanis and Bangladeshis: A population-based cohort study

BACKGROUND

Type 2 diabetes (T2D) is highly prevalent in British South Asians, yet they are underrepresented in research. Genes & Health (G&H) is a large, population study of British Pakistanis and Bangladeshis (BPB) comprising genomic and routine health data. We assessed the extent to which genetic risk for T2D is shared between BPB and European populations (EUR). We then investigated whether the integration of a polygenic risk score (PRS) for T2D with an existing risk tool (QDiabetes) could improve prediction of incident disease and the characterisation of disease subtypes.

METHODS AND FINDINGS

In this observational cohort study, we assessed whether common genetic loci associated with T2D in EUR individuals were replicated in 22,490 BPB individuals in G&H. We replicated fewer loci in G&H (n = 76/338, 22%) than would be expected given power if all EUR-ascertained loci were transferable (n = 101, 30%; p = 0.001). Of the 27 transferable loci that were powered to interrogate this, only 9 showed evidence of shared causal variants. We constructed a T2D PRS and combined it with a clinical risk instrument (QDiabetes) in a novel, integrated risk tool (IRT) to assess risk of incident diabetes. To assess model performance, we compared categorical net reclassification index (NRI) versus QDiabetes alone. In 13,648 patients free from T2D followed up for 10 years, NRI was 3.2% for IRT versus QDiabetes (95% confidence interval (CI): 2.0% to 4.4%). IRT performed best in reclassification of individuals aged less than 40 years deemed low risk by QDiabetes alone (NRI 5.6%, 95% CI 3.6% to 7.6%), who tended to be free from comorbidities and slim. After adjustment for QDiabetes score, PRS was independently associated with progression to T2D after gestational diabetes (hazard ratio (HR) per SD of PRS 1.23, 95% CI 1.05 to 1.42, p = 0.028). Using cluster analysis of clinical features at diabetes diagnosis, we replicated previously reported disease subgroups, including Mild Age-Related, Mild Obesity-related, and Insulin-Resistant Diabetes, and showed that PRS distribution differs between subgroups (p = 0.002). Integrating PRS in this cluster analysis revealed a Probable Severe Insulin Deficient Diabetes (pSIDD) subgroup, despite the absence of clinical measures of insulin secretion or resistance. We also observed differences in rates of progression to micro- and macrovascular complications between subgroups after adjustment for confounders. Study limitations include the absence of an external replication cohort and the potential biases arising from missing or incorrect routine health data.

CONCLUSIONS

Our analysis of the transferability of T2D loci between EUR and BPB indicates the need for larger, multiancestry studies to better characterise the genetic contribution to disease and its varied aetiology. We show that a T2D PRS optimised for this high-risk BPB population has potential clinical application in BPB, improving the identification of T2D risk (especially in the young) on top of an established clinical risk algorithm and aiding identification of subgroups at diagnosis, which may help future efforts to stratify care and treatment of the disease.

Heterogeneity in phenotype, disease progression and drug response in type 2 diabetes

Type 2 diabetes (T2D) is a complex chronic disease characterized by considerable phenotypic heterogeneity. In this study, we applied a reverse graph embedding method to routinely collected data from 23,137 Scottish patients with newly diagnosed diabetes to visualize this heterogeneity and used partitioned diabetes polygenic risk scores to gain insight into the underlying biological processes. Overlaying risk of progression to outcomes of insulin requirement, chronic kidney disease, referable diabetic retinopathy and major adverse cardiovascular events, we show how these risks differ by patient phenotype. For example, patients at risk of retinopathy are phenotypically different from those at risk of cardiovascular events. We replicated our findings in the UK Biobank and the ADOPT clinical trial, also showing that the pattern of diabetes drug monotherapy response differs for different drugs. Overall, our analysis highlights how, in a European population, underlying phenotypic variation drives T2D onset and affects subsequent diabetes outcomes and drug response, demonstrating the need to incorporate these factors into personalized treatment approaches for the management of T2D.

Genetics of chronic obstructive pulmonary disease: understanding the pathobiology and heterogeneity of a complex disorder

Chronic obstructive pulmonary disease (COPD) is a deadly and highly morbid disease. Susceptibility to and heterogeneity of COPD are incompletely explained by environmental factors such as cigarette smoking. Family-based and population-based studies have shown that a substantial proportion of COPD risk is related to genetic variation. Genetic association studies have identified hundreds of genetic variants that affect risk for COPD, decreased lung function, and other COPD-related traits. These genetic variants are associated with other pulmonary and non-pulmonary traits, demonstrate a genetic basis for at least part of COPD heterogeneity, have a substantial effect on COPD risk in aggregate, implicate early-life events in COPD pathogenesis, and often involve genes not previously suspected to have a role in COPD. Additional progress will require larger genetic studies with more ancestral diversity, improved profiling of rare variants, and better statistical methods. Through integration of genetic data with other omics data and comprehensive COPD phenotypes, as well as functional description of causal mechanisms for genetic risk variants, COPD genetics will continue to inform novel approaches to understanding the pathobiology of COPD and developing new strategies for management and treatment.

Extending precision medicine tools to populations at high risk of type 2 diabetes

In this Perspective, Shivani Misra and Jose C Florez discuss the application of precision medicine tools in under-represented populations.

Polygenic scores, diet quality, and type 2 diabetes risk: An observational study among 35,759 adults from 3 US cohorts

BACKGROUND

Both genetic and lifestyle factors contribute to the risk of type 2 diabetes, but the extent to which there is a synergistic effect of the 2 factors is unclear. The aim of this study was to examine the joint associations of genetic risk and diet quality with incident type 2 diabetes.

METHODS AND FINDINGS

We analyzed data from 35,759 men and women in the United States participating in the Nurses' Health Study (NHS) I (1986 to 2016) and II (1991 to 2017) and the Health Professionals Follow-up Study (HPFS; 1986 to 2016) with available genetic data and who did not have diabetes, cardiovascular disease, or cancer at baseline. Genetic risk was characterized using both a global polygenic score capturing overall genetic risk and pathway-specific polygenic scores denoting distinct pathophysiological mechanisms. Diet quality was assessed using the Alternate Healthy Eating Index (AHEI). Cox models were used to calculate hazard ratios (HRs) for type 2 diabetes after adjusting for potential confounders. With over 902,386 person-years of follow-up, 4,433 participants were diagnosed with type 2 diabetes. The relative risk of type 2 diabetes was 1.29 (95% confidence interval [CI] 1.25, 1.32; P < 0.001) per standard deviation (SD) increase in global polygenic score and 1.13 (1.09, 1.17; P < 0.001) per 10-unit decrease in AHEI. Irrespective of genetic risk, low diet quality, as compared to high diet quality, was associated with approximately 30% increased risk of type 2 diabetes (Pinteraction = 0.69). The joint association of low diet quality and increased genetic risk was similar to the sum of the risk associated with each factor alone (Pinteraction = 0.30). Limitations of this study include the self-report of diet information and possible bias resulting from inclusion of highly educated participants with available genetic data.

CONCLUSIONS

These data provide evidence for the independent associations of genetic risk and diet quality with incident type 2 diabetes and suggest that a healthy diet is associated with lower diabetes risk across all levels of genetic risk.

Lp(a) and the Risk for Cardiovascular Disease: Focus on the Lp(a) Paradox in Diabetes Mellitus

Lipoprotein(a) (Lp(a)) is one of the strongest causal risk factors of atherosclerotic disease. It is rich in cholesteryl ester and composed of apolipoprotein B and apo(a). Plasma Lp(a) levels are determined by apo(a) transcriptional activity driven by a direct repeat (DR) response element in the apo(a) promoter under the control of (HNF)4α Farnesoid-X receptor (FXR) ligands play a key role in the downregulation of APOA expression. In vitro studies on the catabolism of Lp(a) have revealed that Lp(a) binds to several specific lipoprotein receptors; however, their in vivo role remains elusive. There are more than 1000 publications on the role of diabetes mellitus (DM) in Lp(a) metabolism; however, the data is often inconsistent and confusing. In patients suffering from Type-I diabetes mellitus (T1DM), provided they are metabolically well-controlled, Lp(a) plasma concentrations are directly comparable to healthy individuals. In contrast, there exists a paradox in T2DM patients, as many of these patients have reduced Lp(a) levels; however, they are still at an increased cardiovascular risk. The Lp(a) lowering mechanism observed in T2DM patients is most probably caused by mutations in the mature-onset diabetes of the young (MODY) gene and possibly other polymorphisms in key transcription factors of the apolipoprotein (a) gene (APOA).

Lp(a) and the Risk for Cardiovascular Disease: Focus on the Lp(a) Paradox in Diabetes Mellitus

Lipoprotein(a) (Lp(a)) is one of the strongest causal risk factors of atherosclerotic disease. It is rich in cholesteryl ester and composed of apolipoprotein B and apo(a). Plasma Lp(a) levels are determined by apo(a) transcriptional activity driven by a direct repeat (DR) response element in the apo(a) promoter under the control of (HNF)4α Farnesoid-X receptor (FXR) ligands play a key role in the downregulation of APOA expression. In vitro studies on the catabolism of Lp(a) have revealed that Lp(a) binds to several specific lipoprotein receptors; however, their in vivo role remains elusive. There are more than 1000 publications on the role of diabetes mellitus (DM) in Lp(a) metabolism; however, the data is often inconsistent and confusing. In patients suffering from Type-I diabetes mellitus (T1DM), provided they are metabolically well-controlled, Lp(a) plasma concentrations are directly comparable to healthy individuals. In contrast, there exists a paradox in T2DM patients, as many of these patients have reduced Lp(a) levels; however, they are still at an increased cardiovascular risk. The Lp(a) lowering mechanism observed in T2DM patients is most probably caused by mutations in the mature-onset diabetes of the young (MODY) gene and possibly other polymorphisms in key transcription factors of the apolipoprotein (a) gene (APOA).

Lp(a) and the Risk for Cardiovascular Disease: Focus on the Lp(a) Paradox in Diabetes Mellitus

Lipoprotein(a) (Lp(a)) is one of the strongest causal risk factors of atherosclerotic disease. It is rich in cholesteryl ester and composed of apolipoprotein B and apo(a). Plasma Lp(a) levels are determined by apo(a) transcriptional activity driven by a direct repeat (DR) response element in the apo(a) promoter under the control of (HNF)4α Farnesoid-X receptor (FXR) ligands play a key role in the downregulation of APOA expression. In vitro studies on the catabolism of Lp(a) have revealed that Lp(a) binds to several specific lipoprotein receptors; however, their in vivo role remains elusive. There are more than 1000 publications on the role of diabetes mellitus (DM) in Lp(a) metabolism; however, the data is often inconsistent and confusing. In patients suffering from Type-I diabetes mellitus (T1DM), provided they are metabolically well-controlled, Lp(a) plasma concentrations are directly comparable to healthy individuals. In contrast, there exists a paradox in T2DM patients, as many of these patients have reduced Lp(a) levels; however, they are still at an increased cardiovascular risk. The Lp(a) lowering mechanism observed in T2DM patients is most probably caused by mutations in the mature-onset diabetes of the young (MODY) gene and possibly other polymorphisms in key transcription factors of the apolipoprotein (a) gene (APOA).

Evidence From Men for Ovary-independent Effects of Genetic Risk Factors for Polycystic Ovary Syndrome

CONTEXT

Polycystic ovary syndrome (PCOS) is characterized by ovulatory dysfunction and hyperandrogenism and can be associated with cardiometabolic dysfunction, but it remains unclear which of these features are inciting causes and which are secondary consequences.

OBJECTIVE

To determine whether ovarian function is necessary for genetic risk factors for PCOS to produce nonreproductive phenotypes.

DESIGN, SETTING, AND PARTICIPANTS

Cohort of 176 360 men in the UK Biobank and replication cohort of 37 348 men in the Estonian Biobank.

MAIN OUTCOME MEASURES

We calculated individual PCOS polygenic risk scores (PRS), tested for association of these PRS with PCOS-related phenotypes using linear and logistic regression and performed mediation analysis.

RESULTS

For every 1 SD increase in the PCOS PRS, men had increased odds of obesity (odds ratio [OR]: 1.09; 95% CI, 1.08-1.10; P = 1 × 10-49), type 2 diabetes mellitus (T2DM) (OR: 1.08; 95% CI, 1.05-1.10; P = 3 × 10-12), coronary artery disease (CAD) (OR: 1.03; 95% CI, 1.01-1.04; P = 0.0029), and marked androgenic alopecia (OR: 1.03; 95% CI, 1.02-1.05; P = 3 × 10-5). Body mass index (BMI), hemoglobin A1c, triglycerides, and free androgen index increased as the PRS increased, whereas high-density lipoprotein cholesterol and SHBG decreased (all P < .0001). The association between the PRS and CAD appeared to be completely mediated by BMI, whereas the associations with T2DM and marked androgenic alopecia appeared to be partially mediated by BMI.

CONCLUSIONS

Genetic risk factors for PCOS have phenotypic consequences in men, indicating that they can act independently of ovarian function. Thus, PCOS in women may not always be a primary disorder of the ovaries.

Integration of genetic colocalizations with physiological and pharmacological perturbations identifies cardiometabolic disease genes

BACKGROUND

Identification of causal genes for polygenic human diseases has been extremely challenging, and our understanding of how physiological and pharmacological stimuli modulate genetic risk at disease-associated loci is limited. Specifically, insulin resistance (IR), a common feature of cardiometabolic disease, including type 2 diabetes, obesity, and dyslipidemia, lacks well-powered genome-wide association studies (GWAS), and therefore, few associated loci and causal genes have been identified.

METHODS

Here, we perform and integrate linkage disequilibrium (LD)-adjusted colocalization analyses across nine cardiometabolic traits (fasting insulin, fasting glucose, insulin sensitivity, insulin sensitivity index, type 2 diabetes, triglycerides, high-density lipoprotein, body mass index, and waist-hip ratio) combined with expression and splicing quantitative trait loci (eQTLs and sQTLs) from five metabolically relevant human tissues (subcutaneous and visceral adipose, skeletal muscle, liver, and pancreas). To elucidate the upstream regulators and functional mechanisms for these genes, we integrate their transcriptional responses to 21 relevant physiological and pharmacological perturbations in human adipocytes, hepatocytes, and skeletal muscle cells and map their protein-protein interactions.

RESULTS

We identify 470 colocalized loci and prioritize 207 loci with a single colocalized gene. Patterns of shared colocalizations across traits and tissues highlight different potential roles for colocalized genes in cardiometabolic disease and distinguish several genes involved in pancreatic β-cell function from others with a more direct role in skeletal muscle, liver, and adipose tissues. At the loci with a single colocalized gene, 42 of these genes were regulated by insulin and 35 by glucose in perturbation experiments, including 17 regulated by both. Other metabolic perturbations regulated the expression of 30 more genes not regulated by glucose or insulin, pointing to other potential upstream regulators of candidate causal genes.

CONCLUSIONS

Our use of transcriptional responses under metabolic perturbations to contextualize genetic associations from our custom colocalization approach provides a list of likely causal genes and their upstream regulators in the context of IR-associated cardiometabolic risk.

Effects of a 13-Week Personalized Lifestyle Intervention Based on the Diabetes Subtype for People with Newly Diagnosed Type 2 Diabetes

A type 2 diabetes mellitus (T2DM) subtyping method that determines the T2DM phenotype based on an extended oral glucose tolerance test is proposed. It assigns participants to one of seven subtypes according to their β-cell function and the presence of hepatic and/or muscle insulin resistance. The effectiveness of this subtyping approach and subsequent personalized lifestyle treatment in ameliorating T2DM was assessed in a primary care setting. Sixty participants, newly diagnosed with (pre)diabetes type 2 and not taking diabetes medication, completed the intervention. Retrospectively collected data of 60 people with T2DM from usual care were used as controls. Bodyweight (p < 0.01) and HbA1c (p < 0.01) were significantly reduced after 13 weeks in the intervention group, but not in the usual care group. The intervention group achieved 75.0% diabetes remission after 13 weeks (fasting glucose ≤ 6.9 mmol/L and HbA1c < 6.5% (48 mmol/mol)); for the usual care group, this was 22.0%. Lasting (two years) remission was especially achieved in subgroups with isolated hepatic insulin resistance. Our study shows that a personalized diagnosis and lifestyle intervention for T2DM in a primary care setting may be more effective in improving T2DM-related parameters than usual care, with long-term effects seen especially in subgroups with hepatic insulin resistance.

Circulating Fatty Acids and Genetic Predisposition to Type 2 Diabetes: Gene-Nutrient Interaction Analysis

OBJECTIVE

To assess the relationship of circulating fatty acids (FA) with risk of type 2 diabetes (T2D) and potential interactions with genetic risk.

RESEARCH DESIGN AND METHODS

A total of 95,854 participants with complete data on plasma FA from the UK Biobank were enrolled between 2006 and 2010 and were followed up to the end of 2020. Plasma concentrations of saturated fatty acids (SFA), monounsaturated fatty acids (MUFA), and polyunsaturated fatty acids (PUFA) were analyzed by a high-throughput nuclear magnetic resonance-based biomarker profiling platform. The genetic risk scores (GRS) were calculated on the basis of 424 variants associated with T2D. Pathway-specific GRS were calculated based on robust clusters of T2D loci.

RESULTS

There were 3,052 instances of T2D documented after an average follow-up of 11.6 years. Plasma concentrations of SFA and MUFA were positively associated with T2D risk, while plasma PUFA were inversely associated. After adjustment for major risk factors, hazard ratios (95% CI) of T2D for 1-SD increment were 1.03 (1.02-1.04) for SFA, 1.03 (1.02-1.05) for MUFA, 0.62 (0.56-0.68) for PUFA, 0.67 (0.61-0.73) for n-6 PUFA, 0.90 (0.85-0.95) for n-3 PUFA, and 1.01 (0.98-1.04) for n-6-to-n-3 ratio. Plasma MUFA had significant interactions with the overall GRS and GRS for proinsulin and liver/lipid clusters on T2D risk. The protective associations of n-3 PUFA with T2D risk were weaker among individuals with higher obesity GRS (P interaction = 0.040) and liver/lipid GRS (P interaction = 0.012). Additionally, increased plasma n-3 PUFA concentration was associated with more reductions in T2D risk among participants carrying more docosapentaenoic acid-associated alleles (P interaction = 0.007).

CONCLUSIONS

Plasma concentrations of SFA and MUFA were associated with a higher T2D risk, whereas plasma PUFA and n-6 and n-3 PUFA were related to a lower risk. Circulating MUFA and n-3 PUFA had significant interactions with genetic predisposition to T2D and FA-associated variants.

Genome-wide pleiotropy analysis identifies novel blood pressure variants and improves its polygenic risk scores

Systolic and diastolic blood pressure (S/DBP) are highly correlated modifiable risk factors for cardiovascular disease (CVD). We report here a bidirectional Mendelian Randomization (MR) and horizontal pleiotropy analysis of S/DBP summary statistics from the UK Biobank (UKB)-International Consortium for Blood Pressure (ICBP) (UKB-ICBP) BP genome-wide association study and construct a composite genetic risk score (GRS) by including pleiotropic variants. The composite GRS captures greater (1.11-3.26 fold) heritability for BP traits and increases (1.09- and 2.01-fold) Nagelkerke's R² for hypertension and CVD. We replicated 118 novel BP horizontal pleiotropic variants including 18 novel BP loci using summary statistics from the Million Veteran Program (MVP) study. An additional 219 novel BP signals and 40 novel loci were identified after a meta-analysis of the UKB-ICBP and MVP summary statistics but without further independent replication. Our study provides further insight into BP regulation and provides a novel way to construct a GRS by including pleiotropic variants for other complex diseases.

Type 2 Diabetes Partitioned Polygenic Scores Associate With Disease Outcomes in 454,193 Individuals Across 13 Cohorts

OBJECTIVE

Type 2 diabetes (T2D) has heterogeneous patient clinical characteristics and outcomes. In previous work, we investigated the genetic basis of this heterogeneity by clustering 94 T2D genetic loci using their associations with 47 diabetes-related traits and identified five clusters, termed β-cell, proinsulin, obesity, lipodystrophy, and liver/lipid. The relationship between these clusters and individual-level metabolic disease outcomes has not been assessed.

RESEARCH DESIGN AND METHODS

Here we constructed individual-level partitioned polygenic scores (pPS) for these five clusters in 12 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the UK Biobank (n = 454,193) and tested for cross-sectional association with T2D-related outcomes, including blood pressure, renal function, insulin use, age at T2D diagnosis, and coronary artery disease (CAD).

RESULTS

Despite all clusters containing T2D risk-increasing alleles, they had differential associations with metabolic outcomes. Increased obesity and lipodystrophy cluster pPS, which had opposite directions of association with measures of adiposity, were both significantly associated with increased blood pressure and hypertension. The lipodystrophy and liver/lipid cluster pPS were each associated with CAD, with increasing and decreasing effects, respectively. An increased liver/lipid cluster pPS was also significantly associated with reduced renal function. The liver/lipid cluster includes known loci linked to liver lipid metabolism (e.g., GCKR, PNPLA3, and TM6SF2), and these findings suggest that cardiovascular disease risk and renal function may be impacted by these loci through their shared disease pathway.

CONCLUSIONS

Our findings support that genetically driven pathways leading to T2D also predispose differentially to clinical outcomes.

Anti-obesity drug discovery: advances and challenges

Enormous progress has been made in the last half-century in the management of diseases closely integrated with excess body weight, such as hypertension, adult-onset diabetes and elevated cholesterol. However, the treatment of obesity itself has proven largely resistant to therapy, with anti-obesity medications (AOMs) often delivering insufficient efficacy and dubious safety. Here, we provide an overview of the history of AOM development, focusing on lessons learned and ongoing obstacles. Recent advances, including increased understanding of the molecular gut-brain communication, are inspiring the pursuit of next-generation AOMs that appear capable of safely achieving sizeable and sustained body weight loss.

Differences in complications, cardiovascular risk factor, and diabetes management among participants enrolled at veterans affairs (VA) and non-VA medical centers in the glycemia reduction approaches in diabetes: A comparative effectiveness study (GRADE)

AIMS

We evaluated differences in participants with type 2 diabetes (T2DM) enrolled in the GRADE study at VA vs non-VA sites, focusing on cardiovascular risk factors and rates of diabetes care target achievements.

METHODS

We compared baseline characteristics between participants at VA (n = 1216) and non-VA (n = 3831) sites, stratifying analyses by cardiovascular disease (CVD) history.

RESULTS

VA and non-VA participants had similar diabetes duration (4.0 years), HbA1c (7.5%), and BMI (34 kg/m²); however, VA participants had more individuals ≥ 65 years (37.3% vs 19.8%, p < 0.001), men (90.0% vs 55.2%, p < 0.001), hypertension (75.8% vs 63.6%, p < 0.001), hyperlipidemia (76.6% vs 64.6%, p < 0.001), current smokers (19.0% vs 12.1%, p < 0.001), nephropathy (20.4% vs 17.0%, p < 0.05), albuminuria (18.4% vs 15.1%, p < 0.05), and CVD (10.4% vs 5.2%, p < 0.001). In those without CVD, more VA participants were treated with lipid (70.8% vs 59.5%, p < 0.001) and blood pressure (74.9% vs 65.4%, p < 0.001) lowering medications, and had LDL-C < 70 mg/dl (32.9% vs 24.2%, p < 0.05). Among those with CVD, more VA participants had BP < 140/90 (80.2% vs 70.1%, p < 0.05) after adjusting for demographics.

CONCLUSION

GRADE participants at VA sites had more T2DM complications, greater CVD risk and were more likely to be treated with medications to reduce it, leading to more LDL-C at goal than non-VA participants, highlighting differences in diabetes populations and care.

Deep Resequencing of 9 Candidate Genes Identifies a Role for ARAP1 and IGF2BP2 in Modulating Insulin Secretion Adjusted for Insulin Resistance in Obese Southern Europeans

Type 2 diabetes is characterized by impairment in insulin secretion, with an established genetic contribution. We aimed to evaluate common and low-frequency (1–5%) variants in nine genes strongly associated with insulin secretion by targeted sequencing in subjects selected from the extremes of insulin release measured by the disposition index. Collapsing data by gene and/or function, the association between disposition index and nonsense variants were significant, also after adjustment for confounding factors (OR = 0.25, 95% CI = 0.11–0.59, p = 0.001). Evaluating variants individually, three novel variants in ARAP1, IGF2BP2 and GCK, out of eight reaching significance singularly, remained associated after adjustment. Constructing a genetic risk model combining the effects of the three variants, only carriers of the ARAP1 and IGF2BP2 variants were significantly associated with a reduced probability to be in the lower, worst, extreme of insulin secretion (OR = 0.223, 95% CI = 0.105–0.473, p < 0.001). Observing a high number of normal glucose tolerance between carriers, a regression posthoc analysis was performed. Carriers of genetic risk model variants had higher probability to be normoglycemic, also after adjustment (OR = 2.411, 95% CI = 1.136–5.116, p = 0.022). Thus, in our southern European cohort, nonsense variants in all nine candidate genes showed association with better insulin secretion adjusted for insulin resistance, and we established the role of ARAP1 and IGF2BP2 in modulating insulin secretion.

Four groups of type 2 diabetes contribute to the etiological and clinical heterogeneity in newly diagnosed individuals: An IMI DIRECT study

The presentation and underlying pathophysiology of type 2 diabetes (T2D) is complex and heterogeneous. Recent studies attempted to stratify T2D into distinct subgroups using data-driven approaches, but their clinical utility may be limited if categorical representations of complex phenotypes are suboptimal. We apply a soft-clustering (archetype) method to characterize newly diagnosed T2D based on 32 clinical variables. We assign quantitative clustering scores for individuals and investigate the associations with glycemic deterioration, genetic risk scores, circulating omics biomarkers, and phenotypic stability over 36 months. Four archetype profiles represent dysfunction patterns across combinations of T2D etiological processes and correlate with multiple circulating biomarkers. One archetype associated with obesity, insulin resistance, dyslipidemia, and impaired β cell glucose sensitivity corresponds with the fastest disease progression and highest demand for anti-diabetic treatment. We demonstrate that clinical heterogeneity in T2D can be mapped to heterogeneity in individual etiological processes, providing a potential route to personalized treatments.

Heterogeneity of Diabetes: β-Cells, Phenotypes, and Precision Medicine: Proceedings of an International Symposium of the Canadian Institutes of Health Research's Institute of Nutrition, Metabolism and Diabetes and the U.S. National Institutes of Health's National Institute of Diabetes and Digestive and Kidney Diseases

One hundred years have passed since the discovery of insulin-an achievement that transformed diabetes from a fatal illness into a manageable chronic condition. The decades since that momentous achievement have brought ever more rapid innovation and advancement in diabetes research and clinical care. To celebrate the important work of the past century and help to chart a course for its continuation into the next, the Canadian Institutes of Health Research's Institute of Nutrition, Metabolism and Diabetes and the U.S. National Institutes of Health's National Institute of Diabetes and Digestive and Kidney Diseases recently held a joint international symposium, bringing together a cohort of researchers with diverse interests and backgrounds from both countries and beyond to discuss their collective quest to better understand the heterogeneity of diabetes and thus gain insights to inform new directions in diabetes treatment and prevention. This article summarizes the proceedings of that symposium, which spanned cutting-edge research into various aspects of islet biology, the heterogeneity of diabetic phenotypes, and the current state of and future prospects for precision medicine in diabetes.

Noise-augmented directional clustering of genetic association data identifies distinct mechanisms underlying obesity

Clustering genetic variants based on their associations with different traits can provide insight into their underlying biological mechanisms. Existing clustering approaches typically group variants based on the similarity of their association estimates for various traits. We present a new procedure for clustering variants based on their proportional associations with different traits, which is more reflective of the underlying mechanisms to which they relate. The method is based on a mixture model approach for directional clustering and includes a noise cluster that provides robustness to outliers. The procedure performs well across a range of simulation scenarios. In an applied setting, clustering genetic variants associated with body mass index generates groups reflective of distinct biological pathways. Mendelian randomization analyses support that the clusters vary in their effect on coronary heart disease, including one cluster that represents elevated body mass index with a favourable metabolic profile and reduced coronary heart disease risk. Analysis of the biological pathways underlying this cluster identifies inflammation as potentially explaining differences in the effects of increased body mass index on coronary heart disease.

Mendelian randomization supports the causal role of fasting glucose on periodontitis

PURPOSE

The effect of hyperglycemia on periodontitis is mainly based on observational studies, and inconsistent results were found whether periodontal treatment favors glycemic control. The two-way relationship between periodontitis and hyperglycemia needs to be further elucidated. This study aims to evaluate the causal association of periodontitis with glycemic traits using bi-directional Mendelian randomization (MR) approach.

METHODS

Summary statistics were sourced from large-scale genome-wide association study conducted for fasting glucose (N = 133,010), HbA1c (N = 123,665), type 2 diabetes (T2D, N = 659,316), and periodontitis (N = 506,594) among European ancestry. The causal relationship was estimated using the inverse-variance weighted (IVW) model and further validated through extensive complementary and sensitivity analyses.

RESULTS

Overall, IVW showed that a genetically higher level of fasting glucose was significantly associated with periodontitis (OR = 1.119; 95% CI = 1.045-1.197; P_FDR= 0.007) after removing the outlying instruments. Such association was robust and consistent through other MR models. Limited evidence was found suggesting the association of HbA1C with periodontitis after excluding the outliers (IVW OR = 1.123; 95% CI = 1.026-1.229; P_FDR= 0.048). These linkages remained statistically significant in multivariate MR analyses, after adjusting for body mass index. The reverse direction MR analyses did not exhibit the causal association of genetic liability to periodontitis with any of the glycemic trait tested.

CONCLUSIONS

Our MR study reaffirms previous findings and extends evidence to substantiate the causal effect of hyperglycemia on periodontitis. Future studies with robust genetic instruments are needed to confirm the causal association of periodontitis with glycemic traits.

Validation of the classification for type 2 diabetes into five subgroups: a report from the ORIGIN trial

AIMS/HYPOTHESIS

Data analyses from Swedish individuals with newly diagnosed diabetes have suggested that diabetes could be classified into five subtypes that differ with respect to the progression of dysglycaemia and the incidence of diabetes consequences. We assessed this classification in a multiethnic cohort of participants with established and newly diagnosed diabetes, randomly allocated to insulin glargine vs standard care.

METHODS

In total, 7017 participants from the Outcome Reduction with Initial Glargine Intervention (ORIGIN) trial were assigned to the five predefined diabetes subtypes (namely, severe auto-immune diabetes, severe insulin-deficient diabetes, severe insulin-resistant diabetes, mild obesity-related diabetes, mild age-related diabetes) based on the age at diabetes diagnosis, BMI, HbA_1c, fasting C-peptide levels and the presence of glutamate decarboxylase antibodies at baseline. Differences between diabetes subtypes in cardiovascular and renal outcomes were investigated using Cox regression models for a median follow-up of 6.2 years. We also compared the effect of glargine vs standard care on hyperglycaemia, defined by having a mean post-randomisation HbA_1c ≥6.5%, between subtypes.

RESULTS

The five diabetes subtypes were replicated in the ORIGIN trial and exhibited similar baseline characteristics in Europeans and Latin Americans, compared with the initially described clusters in the Swedish cohort. We confirmed differences in renal outcomes, with a higher incidence of events in the severe insulin-resistant diabetes subtype compared with the mild age-related diabetes subtype (i.e., chronic kidney disease stage 3A: HR 1.49 [95% CI 1.31, 1.71]; stage 3B: HR 2.25 [1.82, 2.78]; macroalbuminuria: HR 1.56 [1.22, 1.99]). No differences were observed in the incidence of retinopathy and cardiovascular diseases after adjusting for multiple hypothesis testing. Diabetes subtypes also differed in glycaemic response to glargine, with a particular benefit of receiving glargine (vs standard care) in the severe insulin-deficient diabetes subtype compared with the mild age-related diabetes subtype, with a decreased occurrence of hyperglycaemia by 13% (OR 1.36 [1.30, 1.41] on glargine; OR 1.49 [1.43, 1.57] on standard care; p for interaction subtype × intervention = 0.001).

CONCLUSIONS/INTERPRETATION

Cluster analysis enabled the characterisation of five subtypes of diabetes in a multiethnic cohort. Both the incidence of renal outcomes and the response to insulin varied between diabetes subtypes. These findings reinforce the clinical utility of applying precision medicine to predict comorbidities and treatment responses in individuals with diabetes.

TRIAL REGISTRATION

ORIGIN trial, ClinicalTrials.gov NCT00069784.

A pharmacogenetic pilot study of CYP2C9 common genetic variant and sulfonylureas therapeutic response in type 2 diabetes mellitus patients

Background

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disease that is associated with elevated blood glucose levels. Sulfonylureas (SFUs) are the most widely used among the oral antidiabetic drugs that are highly metabolized by cytochrome P450 family 2 subfamily C member 9 (CYP2C9). The CYP2C9 has been shown to be associated with a better glycemic response to SFUs and a lower treatment failure rate. The aim of the present study was to assess the influence of the CYP2C9 rs1067910 gene variant on the SFUs response in a group of Iranian patients for the first time.

Methods

Blood samples were taken from 30 patients with T2DM under sulfonylurea treatment. DNA extraction was performed using Salting out method, and then genotyping was performed by polymerase chain reaction (PCR) followed by Sanger sequencing.

Results

There was no significant difference in the fasting blood sugar (FBS) between T2DM patients with different genotypes before and after the treatment with SFUs (P = 0.073 and P = 0.893, respectively). Although HbA1c was significantly different among AA, CA and CC carriers before (P = 0.001) and after (P = 0.018) treatment, no significant change was observed after treatment in all three groups.

Conclusions

In the present study based on only 30 samples in pilot survey, it is shown that the therapeutic response to SFUs was not related to rs1057910 CYP2C9 variant.

Development and Validation of Decision Rules Models to Stratify Coronary Artery Disease, Diabetes, and Hypertension Risk in Preventive Care: Cohort Study of Returning UK Biobank Participants

Many predictive models exist that predict risk of common cardiometabolic conditions. However, a vast majority of these models do not include genetic risk scores and do not distinguish between clinical risk requiring medical or pharmacological interventions and pre-clinical risk, where lifestyle interventions could be first-choice therapy. In this study, we developed, validated, and compared the performance of three decision rule algorithms including biomarkers, physical measurements, and genetic risk scores for incident coronary artery disease (CAD), diabetes (T2D), and hypertension against commonly used clinical risk scores in 60,782 UK Biobank participants. The rules models were tested for an association with incident CAD, T2D, and hypertension, and hazard ratios (with 95% confidence interval) were calculated from survival models. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC), and Net Reclassification Index (NRI). The higher risk group in the decision rules model had a 40-, 40.9-, and 21.6-fold increased risk of CAD, T2D, and hypertension, respectively (p < 0.001 for all). Risk increased significantly between the three strata for all three conditions (p < 0.05). Based on genetic risk alone, we identified not only a high-risk group, but also a group at elevated risk for all health conditions. These decision rule models comprising blood biomarkers, physical measurements, and polygenic risk scores moderately improve commonly used clinical risk scores at identifying individuals likely to benefit from lifestyle intervention for three of the most common lifestyle-related chronic health conditions. Their utility as part of digital data or digital therapeutics platforms to support the implementation of lifestyle interventions in preventive and primary care should be further validated.

The importance of increasing population diversity in genetic studies of type 2 diabetes and related glycaemic traits

Type 2 diabetes has a global prevalence, with epidemiological data suggesting that some populations have a higher risk of developing this disease. However, to date, most genetic studies of type 2 diabetes and related glycaemic traits have been performed in individuals of European ancestry. The same is true for most other complex diseases, largely due to use of 'convenience samples'. Rapid genotyping of large population cohorts and case-control studies from existing collections was performed when the genome-wide association study (GWAS) 'revolution' began, back in 2005. Although global representation has increased in the intervening 15 years, further expansion and inclusion of diverse populations in genetic and genomic studies is still needed. In this review, I discuss the progress made in incorporating multi-ancestry participants in genetic analyses of type 2 diabetes and related glycaemic traits, and associated opportunities and challenges. I also discuss how increased representation of global diversity in genetic and genomic studies is required to fulfil the promise of precision medicine for all.

Heterogeneity of Diabetes: β-Cells, Phenotypes, and Precision Medicine: Proceedings of an International Symposium of the Canadian Institutes of Health Research's Institute of Nutrition, Metabolism and Diabetes and the U.S. National Institutes of Health's National Institute of Diabetes and Digestive and Kidney Diseases

One hundred years have passed since the discovery of insulin-an achievement that transformed diabetes from a fatal illness into a manageable chronic condition. The decades since that momentous achievement have brought ever more rapid innovation and advancement in diabetes research and clinical care. To celebrate the important work of the past century and help to chart a course for its continuation into the next, the Canadian Institutes of Health Research's Institute of Nutrition, Metabolism and Diabetes and the U.S. National Institutes of Health's National Institute of Diabetes and Digestive and Kidney Diseases recently held a joint international symposium, bringing together a cohort of researchers with diverse interests and backgrounds from both countries and beyond to discuss their collective quest to better understand the heterogeneity of diabetes and thus gain insights to inform new directions in diabetes treatment and prevention. This article summarizes the proceedings of that symposium, which spanned cutting-edge research into various aspects of islet biology, the heterogeneity of diabetic phenotypes, and the current state of and future prospects for precision medicine in diabetes.

Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits

Skeletal muscle accounts for the largest proportion of human body mass, on average, and is a key tissue in complex diseases and mobility. It is composed of several different cell and muscle fiber types. Here, we optimize single-nucleus ATAC-seq (snATAC-seq) to map skeletal muscle cell-specific chromatin accessibility landscapes in frozen human and rat samples, and single-nucleus RNA-seq (snRNA-seq) to map cell-specific transcriptomes in human. We additionally perform multi-omics profiling (gene expression and chromatin accessibility) on human and rat muscle samples. We capture type I and type II muscle fiber signatures, which are generally missed by existing single-cell RNA-seq methods. We perform cross-modality and cross-species integrative analyses on 33,862 nuclei and identify seven cell types ranging in abundance from 59.6% to 1.0% of all nuclei. We introduce a regression-based approach to infer cell types by comparing transcription start site-distal ATAC-seq peaks to reference enhancer maps and show consistency with RNA-based marker gene cell type assignments. We find heterogeneity in enrichment of genetic variants linked to complex phenotypes from the UK Biobank and diabetes genome-wide association studies in cell-specific ATAC-seq peaks, with the most striking enrichment patterns in muscle mesenchymal stem cells (∼3.5% of nuclei). Finally, we overlay these chromatin accessibility maps on GWAS data to nominate causal cell types, SNPs, transcription factor motifs, and target genes for type 2 diabetes signals. These chromatin accessibility profiles for human and rat skeletal muscle cell types are a useful resource for nominating causal GWAS SNPs and cell types.