Magdalena Sevilla-González

Importance: Higher coffee intake has been associated with lower risk of type 2 diabetes (T2D), but the underlying biological pathways remain incompletely understood. Objective: To examine associations of coffee intake with insulin sensitivity, adiposity, and T2D risk, and assess whether coffee intake modifies associations between pathway-specific genetic susceptibility and incident T2D. Design, Setting, and Participants: Cross-sectional analyses among 806 participants without T2D in the VITamin D and OmegA-3 TriaL (VITAL) clinical sub-cohort, who underwent repeated dietary assessment, clinical phenotyping, and dual-energy X-ray absorptiometry imaging at baseline and year-2. Prospective analyses among 333,053 UK Biobank participants without T2D at baseline who had dietary and genetic data and were followed for a median of 13.3 years. Exposures: Coffee intake assessed by food frequency questionnaires. In UK Biobank, 12 pathway-specific polygenic scores (pPS) representing distinct T2D pathophysiological mechanisms were evaluated. Main Outcomes and Measures: The primary outcomes, in VITAL, were HbA1c, oral glucose tolerance test-derived measures of glucose response and insulin sensitivity, β-cell function, and overall, truncal, and visceral adiposity; in UK Biobank, was incident T2D. Results: In VITAL, higher coffee intake was associated with higher insulin sensitivity (standardized β per cup/day, 0.046; P = .004) and lower visceral adipose tissue mass (β, −0.047; P = .006), after adjusting for demographic, lifestyle, and clinical factors, including body mass index. In UK Biobank, higher coffee intake was associated with lower T2D incidence (hazard ratio per cup/day, 0.96; 95% CI, 0.95-0.97), lower triglyceride-to-HDL cholesterol ratio (β,−0.01; P = 2.51 × 10^-19), and lower visceral adipose tissue mass (β, −0.01; P = 4.28 × 10^-9). Associations of 3 pPS related to insulin resistance and fat distribution with incident T2D were attenuated among participants consuming higher amount of coffee than among non-consumers (P for interaction < .0043). Conclusions and Relevance: Higher coffee intake was associated with greater insulin sensitivity, lower visceral adiposity, and lower risk of T2D. Together with the attenuation of associations between pathway-specific genetic susceptibility and T2D risk among higher coffee consumers, these findings suggest that insulin resistance and visceral adiposity-related pathways may contribute to the association between coffee intake and T2D risk. Key Points: Question: Is coffee intake associated with specific insulin sensitivity and adiposity markers, and type 2 diabetes risk, and does it modify associations between pathway-specific genetic susceptibility and type 2 diabetes? Findings: In analyses repeated dietary, clinical, and imaging phenotyping in 806 VITAL participants and prospective data from 333,053 UK Biobank participants, higher coffee intake was associated with greater insulin sensitivity, lower visceral adiposity, and lower type 2 diabetes risk. Higher coffee intake also attenuated associations of three pathway-specific polygenic scores related to insulin resistance and fat distribution with type 2 diabetes risk. Meaning: These findings suggest that pathways related to insulin sensitivity and visceral adiposity may contribute to the associations between coffee intake and lower type 2 diabetes risk.

Large-scale multiancestry genome-wide association studies have identified hundreds of loci associated with type 2 diabetes (T2D) and glycemic traits, yet imputed genotyping arrays limit the detection of low-frequency and rare variants. Whole-genome sequencing (WGS) offers a more complete view of genetic variation, especially across diverse populations. We analyzed high-coverage (38×) WGS data from 21,913 T2D cases, 61,036 controls, and up to 50,011 individuals with no diabetes with fasting glucose, fasting insulin, and HbA1c from the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine Program. We performed single-variant association testing, conditional analysis, fine-mapping, and Bayesian colocalization to identify genetic signals and assess regulatory relevance in diabetes-related tissues. We identified 76 distinct association signals across 34 loci, including novel variants at DUSP9 for T2D, and ROBO1, NDN, and MYT1 for HbA1c. Fine-mapping narrowed credible sets and improved causal variant resolution. Colocalization highlighted 80 expression signals in diabetes-related tissues, linking genetic associations to functional regulatory mechanisms. Our findings demonstrate the utility of WGS to uncover novel variants in diverse populations, enhance locus resolution, and link regulatory variation to disease-relevant tissues. This work refines the genetic architecture of T2D and glycemic traits and supports precision medicine efforts targeting diverse populations. Article Highlights: We aimed to improve understanding of the genetic architecture of type 2 diabetes and glycemic traits by leveraging whole-genome sequencing in diverse populations. Our goal was to identify novel variants, refine known loci, and link genetic signals to regulatory mechanisms through colocalization with expression quantitative trait loci. We discovered novel variants, significantly improved fine-mapping resolution, and identified 80 regulatory colocalization signals in diabetes-relevant tissues. These findings support precision medicine approaches by connecting genetic variation to functional biology in type 2 diabetes.

Type 2 diabetes (T2D) is a heterogeneous disease shaped by genetic pathways related to insulin resistance and beta cell dysfunction, but how this heterogeneity is reflected molecularly remains unclear. We integrated partitioned polygenic scores (pPS) with proteomic and metabolomic profiling to define molecular signatures of T2D and their clinical relevance. We analyzed UK Biobank participants with genomic, proteomic, and metabolomic data. In a disease-free training subset, we used LASSO regression to identify multi-omic signatures associated with each pPS by jointly modeling proteins and metabolites. In an independent testing set, we constructed multi-omic scores and examined their associations with clinical traits and diabetes-related outcomes. Mediation analyses were used to investigate putative causal pathways. Key findings were evaluated in the Multi-Ethnic Study of Atherosclerosis (MESA). We identified distinct multi-omic signatures that capture the molecular architecture of T2D genetic risk across physiological subtypes. Compared with genetic scores alone, multi-omic pPS showed larger effect sizes and better disease discrimination. These scores recapitulated subtype-specific physiology and were associated with T2D risk. The Beta-Cell 2 multi-omic score showed marked stratification for insulin use, which was replicated in MESA, where it also predicted future insulin use. Mediation analyses implicated lipoprotein remodeling and fatty acid metabolism in the Lipodystrophy 1 cluster, accounting for up to 45% of the total effect of pPS on T2D risk. Integrating process-specific genetic risk with circulating multi-omic profiles reveals biologically distinct endotypes of T2D and supports a framework for improved patient stratification and risk assessment.

Measures from affinity-proteomics platforms often correlate poorly, challenging interpretation of protein associations with genetic variants and phenotypes. Here, we examine 2157 proteins measured on both SomaScan 7k and Olink Explore 3072 across 1930 participants with genetic similarity to European, African, East Asian, and Admixed American ancestry references. Inter-platform correlation coefficients for these 2157 proteins follow a bimodal distribution (median r = 0.30). We evaluate protein measure associations with genetic variants, and find approximately 25-30% of the signals on each platform are likely driven by protein-altering variants. We highlight 80 proteins that correlate differently across ancestry groups likely in part due to differing protein-altering variant frequencies by ancestry. Furthermore, adjustment for protein-altering variants with opposite directions of effect by platform improves inter-platform protein measure correlation and results in more concordant genetic and phenotypic associations. Hence, protein-altering variants need to be accounted for across ancestries to facilitate platform-concordant and accurate protein measurement.

Aims/hypothesis Type 2 diabetes has been associated with increased risk of colorectal cancer (CRC), but the specific diabetogenic pathways contributing to this risk remain unclear. Methods We analysed individual-level data from 129,420 participants of European ancestry in the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO) and the Colon Cancer Family Registry (CCFR), comprising 58,531 patients with CRC and 70,889 control participants. We applied eight validated partitioned polygenic scores (PPSs) representing distinct diabetogenic processes: two related to relative insulin secretion insufficiency and six to insulin resistance with varying degrees of preserved insulin secretion. Adjusted ORs and 95% CIs for CRC and early-onset CRC were estimated using conditional logistic regression. Results PPSs reflecting insulin resistance-linked hyperinsulinaemia, particularly those related to lipodystrophy, body fat and obesity, were associated with higher odds of CRC (pp value for interaction with BMI <0.001). Conclusions/interpretation Diabetogenic processes characterised by insulin resistance-linked hyperinsulinaemia were associated with increased odds of CRC, including early-onset disease. These findings offer new insights into diabetes–CRC pathogenesis, and may inform targeted prevention strategies. Graphical Abstract

Background Type 2 diabetes (T2D) is a heterogeneous disease shaped by both genetic, environmental, cultural, and socioeconomic factors, with well-documented disparities in incidence across populations. The molecular pathways underlying these disparities, however, remain poorly understood. Plasma metabolites and proteins integrate both genetic and environmental influences on type 2 diabetes (T2D) risk, providing insight into disease mechanisms. We aimed to quantify the variance in these molecular profiles explained by environmental and genetic ancestry domains and to apply causal inference approaches to identify environmentally and genetic ancestry influenced pathways contributing to T2D risk. Methods We analyzed plasma proteomic and metabolomic profiles from 3,360 MESA participants (51.6% female), and in 1,333 participants from the Women’s Health Initiative. To characterize the sources of variance in plasma proteomic and metabolomic profiles, we performed variance decomposition partitioning into four domains: biological (age, sex, BMI), genetic ancestry (principal components), lifestyle (smoking, alcohol intake, diet), and social determinants (self-reported race and ethnicity, income, education). To assess causal pathways towards T2D risk, we applied two-sample Mendelian Randomization to disentangle environmental and genetic contributors to T2D risk. Results The largest share of variance in proteomic and metabolomic profiles was explained by biological and lifestyle factors, while race and ethnicity and genetic ancestry accounted for smaller but non-redundant contributions. Genetic ancestry was primarily associated with lipid and apolipoprotein variation, whereas race and ethnicity and socioeconomic factors were associated with immune and inflammatory signatures. Environmentally influenced metabolites (e.g., diacylglycerols, phosphatidylethanolamines, lysophosphatidylcholines) and vascular–inflammatory proteins were consistently linked to higher T2D risk, while genetic ancestry influenced triglycerides and IGFBP3 reflected inherited risk pathways. Mediation analyses showed that selected lipids and proteins (e.g., IGFBP2, HGF, SSC4D) explained 10–25% of racial/ethnic disparities in T2D. Mendelian randomization identified causal roles for seven lipid species and IGFBP3 in T2D risk. Conclusions Our results reveal both genetic and non-genetic sources of variation in proteomic and metabolomic profiles, uncovering environmental and genetic pathways contributing to T2D risk. These findings advance precision medicine by identifying modifiable molecular mediators of disparities and potential causal targets for prevention.

To better characterize the potential biological mechanisms underlying insulin resistance (IR) and dementia, we derive cross-population and population specific polygenic scores [PSs] for fasting insulin and IR-related partitioned PSs [pPSs]. We conduct a cross-sectional study of the associations of these genetic scores with neurological outcomes in >17k participants (36% men, mean age 55 yrs) from the Trans-Omics for Precision Medicine (TOPMed) program (50% Non-Hispanic White, 23% Black/African American, 21% Hispanic/Latino American, and 4% Asian American). We report significant negative associations (P < 0.002) of the cross-population (P = 1.3 × 10^-5) and European (P_EA = 3.0 × 10^-8) fasting insulin PSs with total cranial volume, and of a metabolic syndrome European PS with general cognitive function (B_EA = -0.13, P_EA = 0.0002) and lateral ventricular volume (B_EA = 0.09, P_EA = 0.002). We identify suggestive negative associations (P < 0.007) of metabolic syndrome and obesity pPSs with general cognitive function, and of lipodystrophy pPSs with total cranial volume. A higher genetic predisposition to IR is associated with lower brain size, and a genetic predisposition to specific IR-related type 2 diabetes subtypes, such as metabolic syndrome and mechanisms of IR mediated through obesity and lipodystrophy, is potentially involved in cognitive decline.

Context: The metabolic mechanisms underlying insulin resistance are not fully understood. Metabolomic profiling can reveal compartment-specific variations and identify individuals at risk for type 2 diabetes (T2D). Objective: To characterize insulin-induced metabolomic changes during a hyperinsulinemic–euglycemic clamp and evaluate a derived risk score's predictive value for T2D. Design, Setting, and Participants: Clamp studies were conducted in 80 adults (38 with T2D, 42 without) to measure plasma and muscle metabolites in fasting and hyperinsulinemic states. An insulin resistance metabolomic score was developed and tested in a prospective case–control study (367 cases, 910 controls) from the Women's Health Initiative (28.5-year follow-up). Main Outcome Measures: Metabolite changes during hyperinsulinemia and incident T2D. Results: Hyperinsulinemia altered 79.5% of plasma metabolites (notably fatty acids, lactate, and pyruvate) and 15.8% of muscle metabolites (eg, branched-chain and aromatic amino acids). T2D was associated with higher triglycerides and lower tricarboxylic acid intermediates during clamp. Adiposity amplified insulin-induced increases in plasma lipids. The risk score predicted incident T2D (hazard ratio, 1.20 per SD; 95% CI, 1.09-1.32; P = 7.4 × 10−4). Conclusion: Compartment-specific metabolic responses to insulin are shaped by adiposity and predict future T2D risk, supporting use of metabolomic signatures for early identification and prevention.

Type 2 diabetes (T2D) affects hundreds of millions of people worldwide, leading to significant morbidity, complications, and premature death. As the global burden rises, there is an urgent need for effective prevention strategies. Pre-diabetes—the early stage of glycemic dysfunction—offers a critical window for intervention, particularly as individuals with prediabetes, respond better to preventive strategies than those with normal glucose levels.1 However, current diagnostic criterial fail to capture the underlying pathophysiological heterogeneity or reliably predict progression to T2D2. A growing body of evidence suggests that “one-size-fits-all” prevention is insufficient. Understanding the heterogeneity of pre-diabetes is essential to identify those most likely to benefit from early, targeted interventions.3 Phenotypic subtyping -classifying individuals by distinct risk profiles- offers a promissing step toward more pecise and effective prevention strategies.

Elevated fasting insulin levels (FI), indicative of altered insulin secretion and sensitivity, may precede type 2 diabetes (T2D) and cardiovascular disease onset. In this study, we group FI-associated genetic variants based on their genetic and phenotypic similarities and identify seven clusters with distinct mechanisms contributing to elevated FI levels. Clusters fall into two types: “non-diabetogenic hyperinsulinemia,” where clusters are not associated with increased T2D risk, and “diabetogenic hyperinsulinemia,” where T2D associations are driven by body fat distribution, liver function, circulating lipids, or inflammation. In over 1.1 million multi-ancestry individuals, we demonstrated that diabetogenic hyperinsulinemia cluster-specific polygenic scores exhibit varying risks for cardiovascular conditions, including coronary artery disease, myocardial infarction (MI), and stroke. Notably, the visceral adiposity cluster shows sex-specific effects for MI risk in males without T2D. This study underscores processes that decouple elevated FI levels from T2D and cardiovascular risk, offering new avenues for investigating process-specific pathways of disease.