Type 2 diabetes (T2D) is a highly heritable, polygenic disease with over 600 loci identified through genome-wide association studies (GWAS). However, despite possessing unique genetic variation shaped by demographic history and admixture, Latin American populations remain markedly underrepresented in global genomic research. To address this gap, we conducted genome- and exome-wide analyses of 19,431 T2D cases and 105,611 controls from the Mexico City Prospective Study (MCPS). We identified 86 independent GWAS associations, including 21 novel signals, 15 of which replicated in external cohorts. Risk alleles at novel loci were enriched in individuals with Indigenous American ancestry. Exome analyses revealed rare and ultra-rare missense variants with substantial risk effects at HNF1A and GCK , as well as a protein-damaging variant in SLC30A8 that reduced T2D risk by 45% in carriers. Integrative analyses indicate that T2D genetic architecture in Mexico is predominantly driven by common regulatory variation acting in the endocrine pancreas. Polygenic risk scores strongly stratified T2D risk and transferred to Indigenous Mexican populations. These findings demonstrate the power of large-scale genetic discovery in diverse populations to refine disease architecture and identify loci with potential therapeutic relevance.

Background:Most polygenic risk scores (PRS) are derived and validated using genetic data from European populations. However, European-based PRS perform poorly in individuals of non-European ancestry, and their transferability to the admixed Brazilian population remains unknown. Methods:PRS derived from the United Kingdom Biobank (UKB) were selected from the PGS Catalog, including 33 scores for body mass index (BMI), 36 for systolic blood pressure (SBP), and 33 for diastolic blood pressure (DBP). PRS were applied to 4,758 participants from two geographically distinct Brazilian cohorts (São Paulo and North Minas Gerais) and a UKB sample. Performance was evaluated across self-identified racial subgroups and in Brazilian individuals genetically similar to the UK sample, as determined by genomic clustering techniques (Uniform Manifold Approximation and Projection, UMAP; and Principal Component Analysis, PCA). Effect sizes were compared using multivariable mixed-effects models. Results:Most BMI PRS were validated in São Paulo (96.7%) and North Minas Gerais (90.9%), whereas blood pressure PRS showed lower validation rates (SBP: 66.7 and 38.9%; DBP: 69.7 and 54.5%, respectively). Validated PRS consistently exhibited lower effect sizes in Brazilian cohorts compared to the UKB (p < 0.001). PRS calibration for obesity and hypertension using cohort-specific quintiles and precision-recall F1 scores did not improve performance. BMI PRS effects were slightly higher in São Paulo than in North Minas Gerais (+0.39 kg/m2 per SD, p < 0.001), whereas SBP and DBP effects did not differ significantly between regions. BMI PRS effects were higher in Brazilian Whites than Non-Whites (+0.60 kg/m2 per SD, p < 0.001), but blood pressure PRS effects were similar in the racial subgroups. Across all traits, PRS effects were higher in the UKB than in Brazilian individuals clustering with the British by UMAP/PCA (p < 0.001), and no differences were observed between Brazilian UMAP/PCA subgroups. Conclusion:European-derived PRS are not directly transferable to the Brazilian population without prior empirical validation. Regional origin, self-identified race, and genetic similarity to the UKB do not reliably identify Brazilian subgroups with differential PRS performance. These findings highlight the urgent need for polygenic scores derived and trained on Brazilian genomic data.

BACKGROUND:Hypertrophic cardiomyopathy (HCM) is a genetically inherited cardiac disease with significant clinical impact. Although its epidemiology is well described in developed countries, the prevalence of HCM in Latin American populations remains poorly defined. OBJECTIVE:To estimate the prevalence of the HCM phenotype and describe its clinical and echocardiographic characteristics in a large adult Brazilian cohort. METHODS:The ELSA-Brasil study is a multicenter cohort including 15,105 active and retired public servants aged 35-74 years at baseline. Comprehensive clinical data collection was conducted at each investigation center. Baseline echocardiographic examinations were reviewed, and participants were classified as having Definitive HCM if established diagnostic criteria were met. The prevalence of HCM and its 95% CI were calculated. Individuals meeting borderline criteria were classified as Possible HCM. A p-value < 0.05 was considered statistically significant. RESULTS:Among 3,267 participants who underwent echocardiography at baseline (mean age 64 ± 9 years; 53% women), five individuals met diagnostic criteria for HCM, of whom 80% were women. The estimated prevalence of HCM was 0.15% (95% CI, 0.05%-0.36%). Three participants exhibited a septal hypertrophy phenotype, and all cases of Definitive HCM showed abnormal electrocardiographic findings. A total of 19 additional participants were classified as Possible HCM, with no statistically significant differences compared with the Definitive HCM group. CONCLUSION:The prevalence of HCM in this Brazilian cohort was approximately 1.5 per 1,000 individuals, consistent with rates reported in other populations. Most cases of HCM occurred in women and were frequently associated with metabolic conditions. These findings provide novel data on the epidemiology and clinical profile of HCM in Brazil and highlight the need for further research on genetic cardiomyopathies in Latin America.

Background: Genetic testing is a Class I recommendation for patients with hypertrophic cardiomyopathy (HCM). Variant classification relies on evidence from publicly available case data, evolving classification rules, and gene-disease associations. Thus, as knowledge increases, genetic variant classifications change over time. We evaluated the occurrence and reasons for variant reclassification from a large multi-center international HCM registry (Sarcomeric Human Cardiomyopathy Registry; SHaRe), with the goal to minimize uncertainty for patients and clinicians. Methods: Participants receive clinical care at specialized HCM centres. Baseline classifications were derived from the clinical genetic test report (original or updated) or prior further adjudication by SHaRe geneticists. All variants were then computationally reannotated and reevaluated during 2024-2025. Variants underwent expedited curation if no new evidence was present. The remainder underwent full manual curation using accepted criteria and classified as pathogenic/likely pathogenic (P/LP), variant of uncertain significance (VUS) and benign/likely benign (B/LB). VUS were sub-classified to high, mid or low. Results: Of 12,187 HCM patients, 8,054 (66%) had genetic testing between 1990-2024, and 4,923 (61%) had a variant identified in one of 29 ClinGen-validated HCM genes (1606 unique variants). Expedited curation was performed for 704 (44%) variants and 902 (56%) underwent manual curation. There were 1279 (79%) variants that retained their classification: 148 B/LB, 663 VUS, and 468 P/LP. While 276 (17%) variants (n=557 patients) were reclassified, including 73 upgrades: 61 from VUS to P/LP (199 patients), and 12 from B/LB to VUS. There were 203 downgrades: 108 from P/LP to VUS (196 patients), and 95 from P/LP or VUS to B/LB. VUS were additionally subclassified: 90 VUS-High, 129 VUS-Mid, 115 VUS-Low. Sub-classification of VUS resulted in less uncertainty, with 369 (40.6%) variants reclassified as VUS-Low or B/LB, indicating a very strong probability of not being HCM associated. Conclusions: Clinically meaningful reclassification occurred in 10% of variants identified in HCM probands. Most VUS were unlikely to be causal, and sub-classification has potential to reduce their burden on clinicians and families. Contemporary approaches to classification can minimize uncertainty of genetic results and highlight the need for periodic reevaluation. ### Competing Interest Statement SHaRe is supported by unrestricted funding from Bristol Myers Squibb, Cytokinetics, Alexion, and Lexicon. The sponsors had no role in the study design, data collection, data analysis, data interpretation, manuscript preparation, or the decision to submit the manuscript for publication. Dr. Ho is a consultant for or receives research funding from Bristol Myers Squibb, Pfizer, Cytokinetics, Tenaya, Biomarin, viz.AI, and Lexicon. Dr. Lakdawala recieves personal fees from Bridge Bio, Alexion, Tenaya, Cytokinetics, Bayer, and Gemma and grants from Bristol Myers Squibb and Pfizer. Dr. Owens consults for Avidity, Alexion, Bristol Myers Squibb, Bayer, Cytokinetics, Bridgebio, Braveheart, Edgewise, Imbria, Kardigan, Lexeo, Stealth, and Tenaya. Dr. Helms receives grants from Tenaya Therapeutics and Preload Therapeutics and personal fees from Lexeo Therapeutics, Preload Therapeutics, and Cytokinetics. Dr. Saberi receives grants from Bristol Myers Squibb during the conduct of the study as well as personal fees from Bristol Myers Squibb and Cytokinetics and grants from Cytokinetics, Lexicon, Edgewise, and Novartis. Dr. Parikh receives scientific advisory fees from Lexeo Therapeutics, Solid Biosciences, Constantiam Biosciences, Borrealis, and BioMarin. Dr. Ashley reported other from Personalis (founder and publicly traded stock), DeepCell (founder), Svexa (founder), Saturnus Bio (founder), Swift Bio (founder), Candela (founder, advisor), Parameter Health (founder, advisor), Pacific Biosciences (advisor, publicly traded stocks, collaborative support in kind), AstraZeneca (nonexecutive director, publicly traded stock), Dexcom (nonexecutive director), Illumina (collaborative support in kind), Oxford Nanopore (collaborative support in kind). Dr. Gray has received advisory board and education honoraria from Bristol Myers Squibb. Dr. Olivotto is a consultant for Bristol Myers Squibb, Cytokinetics, Tenaya, Lexeo, Edgewise, and Rocket Pharma. Dr. Michels is a consultant or receives research funding from Bristol Myers Squibb, Cytokinetics, Bayer, Alnylam, Biomarin, and Sanofi. Dr. Ware has consulted for MyoKardia (now Bristol Myers Squibb), Foresite Labs, and Pfizer. Dr. Crotti has consulted for Bristol Myers Squibb. Dr. Bundgaard receives lecture fees from Amgen, MSD, Sanofi, Bristol Myers Squibb, and Pfizer. Dr. Rossano is a consultant for AskBio, Astellas, CRI Biotech, Bristol Myers Squibb, Bayer, and Merck. Dr. Abrams is a consultant for Dinaqor. Dr. Maurizi has received grants from Bristol Meier Squibb, Amicus, Foundation CVCL, AICARM APS Onlus, Bangarter-Rhyner Foundation and fees (honoraria or consulting) from Bristol Meier Squibb and Academic CM. Dr. Thompson receives compensation as editor for Merck Manuals. Dr. Day receives personal fees from Lexicon Pharmaceuticals and Cytokinetics and grants from Bristol Myers Squibb. Disclosures are unrelated to current manuscript. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Each participating site has received ethics approval in accordance with local policies as per the following: ethical approval requiring informed consent was obtained from Cincinnati Childrens Hospital USA; Childrens Hospital of Philadelphia USA; Michigan Medical USA; Yale Medical USA; Royal Brompton Hospital United Kingdom; Erasmus University Medical Center The Netherlands; Florence Centre for Cardiomyopathies Italy; Sydney Local Health District Royal Prince Alfred Hospital Australia; and InCor Heart Institute University of Sao Paulo Brazil ethics committees. Waiver of consent was granted by Stanford School of Medicine USA; Brigham and Womens Hospital USA; Boston Childrens Hospital USA; Pennsylvania University Medical Center USA and Sydney Local Health District Royal Prince Alfred Hospital Australia ethics committees. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors Australian Government, https://ror.org/0314h5y94 Bristol-Myers Squibb (United States), https://ror.org/00gtmwv55 Cytokinetics (United States), https://ror.org/03tx9ss94 Alexion Pharmaceuticals (United States), https://ror.org/031ywxc85 Lexicon Pharmaceuticals (United States), https://ror.org/00v64s089

Safety-related issues account for approximately 25% of failures in new drug discovery programs. On top of that, many are discovered during post-marketing surveillance, significantly limiting drug utility and application. To proactively address these concerns, we developed a genetics-led strategy leveraging Mendelian Randomization (MR) across large-scale genetic datasets from the Million Veteran Program, FinnGen, and UK Biobank. By mapping genetic variants associated with gene expression and protein abundance to 1,449 harmonized human phenotypes, we systematically identified potential adverse drug reactions (ADR). Our extensive MR analysis, encompassing 16,915 protein-coding genes, demonstrated the capacity to predict hundreds of known ADR for approved medications, with approximately 40% corroborated by FDA Adverse Event Reporting System (FAERS) data. Additionally, we found significant enrichment of identified gene-mechanism pairs in clinical trials terminated early due to safety concerns, highlighting the clinical utility of genetics-informed safety prediction. Notably, immune-related pathways were prominently associated with ADR, indicating particular sensitivity within immune modulation targets. Our comprehensive atlas, integrating genetic evidence with pharmacological mechanisms, provides a robust predictive framework for anticipating drug safety, potentially enhancing decision-making in drug development and pharmacovigilance. An interactive web interface allowing filtering by gene, phenotype, drug phase, and mechanism of action is available at https://shiny.parse-health.org/safety/.

INTRODUCTION:Non-GFR determinants of filtration markers reduce the accuracy of estimated GFR (eGFR) and genetic factors may explain part of this reduced performance. Here, we identify genetic variants associated with serum levels of creatinine or cystatin C (but not GFR), compute their effect, and investigate candidate genes. METHODS:We used data from the UK Biobank (approximately 470,000 people) and the CKD-EPI creatinine (eGFRcr) and cystatin C (eGFRcys) equations. Using genome-wide association studies (GWAS), we defined strict criteria to classify loci as creatinine-specific or cystatin C-specific, refined with co-localization studies, and identified associated pathways. We computed biomarker and eGFR polygenic scores as the cumulative effect of biomarker-specific loci (genetic bias) and tested their associations to surrogate markers of identified pathways. Findings were validated by independent studies with measured GFR and mapped candidate genes using expression quantitative trait loci (eQTL) resources. RESULTS:We identified 52 creatinine-specific and 48 cystatin C-specific loci. Using eQTLs to identify candidate causal genes, enriched pathways were energy and muscle metabolism for creatinine, and inflammatory response, cancer, cysteine endopeptidase activity, wound healing, and immune modulatory functions for cystatin C. Polygenic scores showed that individual-level genetic bias in eGFRcr and eGFRcys had mean values of 3.8 (standard deviation 0.94, range -1.5 to 7.9) mL/min per 1.73m2 and 0.6 (standard deviation 0.93, range -3.5 to 4.7) mL/min per 1.73m2 , respectively. Genetic bias was different per self-reported race for creatinine and correlated with surrogate markers of selected pathways (muscle mass, for creatinine; body mass index and C-reactive protein, for cystatin C). Lastly, we showed that the difference between polygenic scores derived from GWAS with and without adjustments for the biomarker-specific variants was associated with GFR bias in two independent cohorts (1304 and 939 individuals). CONCLUSIONS:Our study identified loci related to serum creatinine or cystatin C but not to GFR and quantified the populational and individual-level effect of genetic determinants on filtration markers and eGFRs.

Cardiovascular diseases (CVD) are a leading cause of global mortality, with dyslipidemia playing a central role in their pathogenesis. The influence of alcohol consumption on lipid profiles, particularly high-density lipoprotein cholesterol (HDL-c), in relation to obesity status remains insufficiently explored. We evaluated the association between alcohol consumption and HDL-c levels in individuals with and without obesity in a Brazilian population. This cross-sectional analysis used data from the Baependi Heart Study, comprising 2345 participants aged 18-100 years. Alcohol intake was categorized according to weekly ethanol consumption, and HDL-c levels were measured through standard biochemical methods. No significant differences were observed across alcohol consumption groups for total cholesterol, LDL-c, triacylglycerols, and fasting glucose; however, the HDL-c/LDL-c ratio was significantly higher among male moderate consumers. A significant interaction was found between obesity and moderate alcohol consumption (β = 0.90, p = 0.015), indicating that the relationship between alcohol intake and low HDL-c varies according to obesity status. Moderate alcohol consumers exhibited significantly higher HDL-c levels compared to abstainers, an association observed exclusively among nonobese participants. In this group, moderate alcohol intake was linked to a 65% reduction in the odds of low HDL-c in women and a 66% reduction in men. No significant association was observed among individuals with obesity. Moderate alcohol consumption was associated with higher HDL-c levels and lower odds of low HDL-c, particularly among individuals without obesity. These findings contribute to the understanding of the complex interplay between alcohol intake, lipid metabolism, and adiposity.

Understanding the genetic regulation of circulating protein levels can provide new insights into disease mechanisms. Here, we present the largest proteogenomic study to date (n = 78,664 participants across 38 studies), identifying >24,000 protein quantitative trait loci (QTLs) associated with 1,116 proteins, acting near to (n = 5,040) or distant (n = 19,698) from the cognate gene. Using machine learning-guided effector gene assignment, we provide genetic evidence for pathways, cell types, and tissues that modulate circulating protein levels, highlighting N-linked glycosylation as an important regulatory pathway. We demonstrate that genetic instruments of protein production/function (“cis”) versus modulation (“trans”) reveal distinct phenotypic insights. We identify proteins as candidates for drug targets and engagement (e.g., plasma furin and cardiovascular diseases) by comparing cis-based genetic evidence with protein-disease associations. Systematic triangulation of trans-protein QTLs (pQTLs) with genetic and protein associations across many diseases highlights potential drug repurposing opportunities, e.g., tyrosine kinase 2 (TYK2) inhibitors for rheumatoid arthritis. Our multi-cohort meta-analyses generate proteogenomic insights into disease mechanisms and new treatment opportunities.

Resumo Fundamento A cardiomiopatia hipertrófica (CMH) é uma doença cardíaca de origem genética, associada a impacto clínico significativo. Embora sua epidemiologia esteja bem descrita em países desenvolvidos, a prevalência da CMH em populações latino-americanas permanece pouco definida. Objetivo Estimar a prevalência do fenótipo ecocardiográfico de CMH e descrever suas características clínicas e ecocardiográficas em uma grande coorte de adultos brasileiros. Métodos O estudo ELSA-Brasil é uma coorte multicêntrica que inclui 15.105 servidores públicos ativos e aposentados, com idades entre 35 e 74 anos na linha de base com extensa coleta de dados clínicos em cada centro de investigação. Os exames ecocardiográficos realizados na linha de base foram revisados, e os participantes foram classificados como portadores de CMH Definitiva quando os critérios diagnósticos estabelecidos foram atendidos. A prevalência de CMH e seus respectivos intervalos de confiança de 95% (IC 95%) foram calculados. Indivíduos que preencheram critérios limítrofes foram classificados como CMH Possível. Valores p < 0,05 foram considerados estatisticamente significativos. Resultados Entre os 3.267 participantes submetidos à ecocardiografia na linha de base (idade média de 64 ± 9 anos; 53% mulheres), cinco indivíduos preencheram os critérios diagnósticos para CMH, dos quais 80% eram mulheres. A prevalência estimada de CMH foi de 0,15% (IC 95%, 0,05%-0,36%). Três participantes apresentaram fenótipo de hipertrofia septal, e todos os casos classificados como CMH Definitiva exibiram alterações eletrocardiográficas. Outros 19 participantes foram classificados como CMH Possível, sem diferenças estatisticamente significativas em comparação com o grupo de CMH Definitiva. Conclusão A prevalência de CMH nesta coorte brasileira foi de aproximadamente 1,5 por 1.000 indivíduos, valor consistente com o observado em outras populações. A maioria dos casos de CMH ocorreu em mulheres e frequentemente com condiçoes metabólicas associadas. Esses achados fornecem dados inéditos sobre a epidemiologia e o perfil clínico da CMH no Brasil e destacam a necessidade de pesquisas adicionais sobre cardiomiopatias genéticas na América Latina.

Abstract Heart failure (HF) is a leading cause of morbidity and mortality. We conducted multi-ancestry genome-wide association studies of 345,687 HF cases (4,468,166 individuals), and 47,192 and 46,934 cases of HF with preserved (HFpEF) and reduced ejection fraction (HFrEF), respectively, integrating plasma proteomics and multi-tissue transcriptomics to identify druggable targets. Across HF, HFrEF, and HFpEF, we identified 383 loci (166 novel) and 568 genes (375 novel). Eleven novel genes are targets of approved or investigational cardiovascular therapies, supporting indication expansion of aldosterone synthase inhibitors ( CYP11B2 ) and type-II activin receptor antagonists ( ACVR2A ) to HF. Six cardiomyopathy genes were novel for HF and associated with cardiac structure and function. We identified nearly 100 genes involved in food intake and energy expenditure; metabolism of fatty acids, glucose, and branched-chain amino acids; and mitochondrial proteome, sustaining myocardial energy production. Our findings highlight the primordial role of metabolic pathways and adipokines as therapeutic targets for HF management.

Missense variants (MVs) influence clinical phenotypes, but our understanding of their phenotypic consequences remains constrained. Existing computational approaches to interpret MVs predominantly assess their pathogenicity, without considering phenotypic heterogeneity. We present a machine-learning-based method, PheMART, to predict the clinical phenotypic consequences of MVs. PheMART integrates comprehensive variant and phenotype characterizations by leveraging a robust combination of multiple resources involving protein language models, protein-protein interactions, protein domains, medical knowledge graphs and electronic health records. Exploiting contrastive learning, PheMART establishes connections between MVs and 4,179 phenotypes by jointly projecting them into a cohesive low-dimensional metric space where proximity signifies relevance. Besides substantially outperforming existing models, PheMART aids in diagnosing individuals with rare diseases by effectively pinpointing clinical diagnoses and causative MVs. As a resource to the community, we provide a database of phenotypic predictions for 5.1 million putative pathogenic amino acid alterations.

Background Heart failure (HF) has multiple etiologies, but genetic testing remains mainly recommended for selected subgroups with suspected inherited cardiomyopathy. Objectives This study identified and classified genetic variants in patients within a HF cohort with reduced ejection fraction or mildly reduced ejection fraction from diverse etiologies in an admixed population. Methods The authors analyzed whole-genome sequencing data from 1,013 unrelated individuals in the GENIUS-HF (Genetic and ElectroNic medIcal records to predict oUtcomeS in Heart Failure patients) cohort. Rare variants in 123 ClinGen-curated cardiovascular genes were classified according to American College of Medical Genetics and Genomics (ACMG) criteria. Local ancestry inference evaluated associations with variant distribution. Results Overall, 6.5% of individuals carried at least one pathogenic/likely pathogenic variant considered causal (no significant difference between HF cohort with reduced ejection fraction and HF with mildly reduced ejection fraction, P = 0.500), 77.7% had at least 1 variant of uncertain significance, and 15.8% had only benign/likely benign variants observed. TTN variants accounted for nearly half of the causal findings, including 13 novel variants, with additional causal variants in MYBPC3, FLNC, BAG3, and DSP. Idiopathic dilated cardiomyopathy showed the highest yield (9.9%), but pathogenic variants were also detected in ischemic (4.6%), Chagasic HF (4.9%), and hypertensive (3.8%). Local ancestry analyses showed that variant of uncertain significance were significantly more frequent in regions with African and Native American ancestry. Conclusions These findings replicate a significant diagnostic yield of molecular genetic testing in individuals with dilated cardiomyopathy but extend the relevance of genetic testing beyond traditionally selected HF subgroups. They also underscore the importance of including non-European and admixed populations in cardiovascular genomics to reduce interpretation bias and improve equity in precision medicine.

Summary: Background: Visceral adipose tissue (VAT) may causally contribute to cardiovascular disease (CVD); however, evidence in Latin America is limited. Here, we evaluated the association between estimated VAT (eVAT) and incident CVD among individuals without diabetes and estimated its population-attributable fraction (PAF) using data from the Cohorts Consortium of Latin America and the Caribbean (CC-LAC). Methods: We pooled data from 15 prospective cohorts across 7 countries (n = 23,097; 62% women [n = 14,383], median age 51 years). Baseline eVAT (in grams [g]) was estimated using the Metabolic Score for Visceral Fat (METS-VF) index, incorporating age, sex, an insulin resistance index, and waist-to-height ratio. The primary outcome was incident CVD events, including fatal and non-fatal outcomes. Cause-specific Cox proportional hazards models estimated adjusted hazard ratios (aHR). PAF estimates used scenario-based eVAT quartile reductions. Findings: Over a median follow-up of 4 years (113,622 person-years), 436 participants (1.9%) experienced an incident CVD event (174 fatal; 262 non-fatal). Uniformly age- and sex-standardized incidence rates were 3.8 (95% CI: 3.5–4.2) per 1000 person-years. Each 100 g increase in eVAT was associated with a 4% higher CVD hazard (aHR 1.04, 1.03–1.06). Compared with Q1 (<735 g), those in Q3 (1069–1441 g) and Q4 (≥1441 g) had a higher CVD hazard (Q3 aHR: 1.78 [1.28–2.49]; Q4 aHR: 2.03 [1.48–2.79]; p-for-trend <0.001). Associations were stronger for non-fatal events. In PAF estimates, shifting individuals from Q4 to lower quartiles could prevent 8.8% (2.8%–14.7%) of CVD events over 10 years. Interpretation: Higher eVAT was associated with increased CVD risk in Latin America, supporting the view that modest VAT reductions could decrease regional CVD burden. Funding: CC-LAC was funded by the Wellcome Trust.

Background:Heart failure (HF) is a major and growing public health problem, and prior studies support a meaningful genetic contribution to HF susceptibility. Clinically, HF is commonly categorized into the major clinical sub-types of HF with reduced ejection fraction (HFrEF) and HF with preserved ejection fraction (HFpEF), which differ in pathophysiology and clinical profiles. However, previous genome-wide association studies have focused on autosomal variation and have routinely excluded the X chromosome, leaving X-linked genetic contributions to HF and its subtypes under-characterized. Methods:We performed X-chromosome wide association study (XWAS) utilizing directly genotyped data from 590,568 Million Veteran Program participants, including 90,694 HF cases across European, African, Hispanic, and Asian Americans. Sex- and ancestry-stratified logistic regression was used with XWAS quality control measures, adjusting for age and population structure, followed by fixed-effects multi-ancestry meta-analysis. Functional annotation, gene-based testing, fine-mapping, and colocalization were performed. We replicated genetic associations with all-cause HF in the UK Biobank. Results:In the multi-ancestry meta-analysis, we identified five X-chromosome-wide significant loci for all-cause HF, five for HFrEF, and one locus for HFpEF in males. No loci reached significance in female-specific analyses. In sex-combined analyses, we identified six loci for all-cause HF and four for HFrEF. The strongest and most emphasized signals mapped to genes were BRWD3, FHL1 , and CHRDL1 . Ancestry-specific analyses revealed additional loci, including NDP and WDR44 in African ancestry and PHF8 in Hispanic ancestry. One locus, BRWD3 , was replicated in UK Biobank HF cohort. Integrated post-GWAS analyses (fine-mapping, colocalization and pleiotropy trait association studies) reinforced the biological plausibility of the X-linked signals. Conclusions:This multi-ancestry, sex-stratified XWAS identifies X-linked genetic contributions to HF and its subtypes and highlights the role of X-chromosome in heart failure pathogenesis.

Hidradenitis suppurativa is a chronic inflammatory skin condition associated with an increased risk of all-cause mortality. Although it is known to be influenced by genetic and environmental factors, the genetic contributors remain poorly characterized. We performed a large-scale meta-analysis involving patients from the Million Veteran Program, UK Biobank, and FinnGen, including a total of 3941 cases and 1,435,603 controls. We identified 9 genome-wide significant loci, 5 of which have not been previously reported. Using a variant-to-gene analysis pipeline, we mapped these variants to 11 lead genes, supporting prior candidates while revealing previously unreported ones, to our knowledge, including SMPD4 and PSMA4, which we demonstrate are differentially expressed in hidradenitis suppurativa skin lesions. Using expression quantitative trait loci and protein quantitative trait loci, 2-sample Mendelian randomization identified additional genes likely involved in the pathogenesis of hidradenitis suppurativa, including MPO. We subsequently probed the protein interactome network of these candidates to reveal drug targets for prioritization in future studies. These results reaffirm the previously suspected role of KLF5 in hidradenitis suppurativa while linking the pathogenesis of the disease to pathways related to lipid and skin barrier homeostasis, proteasome function, and oxidative stress.

BackgroundChronic Chagas cardiomyopathy (CCC) remains a major cause of heart failure (HF)-related mortality in Latin America and is increasingly recognized as a global health concern. Prognostic models developed in non-Chagas populations often perform poorly in CCC, highlighting the need for etiology-specific risk stratification.Methodology/principal findingsWe applied high-throughput plasma proteomics to evaluate 2-year mortality risk in CCC compared with other HF etiologies. Baseline plasma from 1,212 adults with heart failure with reduced ejection fraction (HFrEF; LVEF <50%) was analyzed to quantify 734 circulating proteins. CCC was confirmed in 191 participants (16%) by dual Trypanosoma cruzi serology. Two-year mortality was higher in CCC than in the overall HF cohort (26% vs. 16%, p < 0.01). Feature-selection methods identified a nine-protein panel (P9: C1QA, CCL4, REN, EGLN1, COL9A1, GP1BA, ITM2A, CNPY2, NT-proBNP) that improved risk classification compared with NT-proBNP alone, increasing F1-macro by 20% (0.674 vs. 0.560) and integrated time-dependent discrimination for 2-year mortality (iAUC) by 6%. Performance gains varied by HF etiology. Improvements were greatest in hypertensive (+40%) and ischemic (+21%) HF, whereas in CCC the P9 panel underperformed NT-proBNP alone (-16%), suggesting distinct underlying disease biology. External validation in the UK Biobank confirmed generalizability: compared with NT-proBNP, P9 improved F1-macro by 18% and iAUC by 7.4%, reaching an F1-macro of 0.612 in the highest-risk tertile. Pathway enrichment identified 14 CCC-specific pathways, mainly related to fibrosis, integrin signaling, immune dysregulation, and impaired protein trafficking. Exploratory analyses also highlighted potential pathway-linked therapeutic targets consistent with distinct CCC mechanisms.Conclusions/significanceThe P9 proteomic panel improved mortality risk prediction beyond NT-proBNP and the MAGGIC clinical score across most HF etiologies and showed consistent performance in an independent population-based cohort. In contrast, in CCC P9 underperformed NT-proBNP alone, highlighting the distinct biological features of this disease. These findings underscore the limitations of universal biomarker models in CCC and support the need for etiology-specific risk stratification strategies.

IntroductionObesity and related metabolic disorders represent a major global health burden, yet their genetic determinants remain incompletely characterized, particularly in non-European populations. We aimed to identify body mass index (BMI)–associated loci in an admixed Brazilian population and to functionally characterize ancestry-enriched variants contributing to obesity risk.MethodsWe conducted a genome-wide association study (GWAS) of BMI in 1,079 admixed Brazilian individuals. Significant and suggestive loci were evaluated using integrative analysis, including epigenomic annotation and chromatin conformation data. Regulatory activity was assessed using allele-specific luciferase reporter assays and electrophoretic mobility shift assays. The functional role of the prioritized gene was examined using pharmacological inhibition in human preadipocytes and genetic deletion in mice.ResultsWe identified three BMI-associated loci reaching genome-wide significance (p ≤ 5 × 10-8) and 49 additional loci previously implicated in obesity-related traits at suggestive significance. Integrative analyses prioritized a non-coding variant at chromosome 20 (rs149309426), located within an active enhancer that physically interacts with the KCNB1 promoter during preadipocyte differentiation. The BMI risk allele (C) increased enhancer activity in luciferase assays and showed enhanced transcription factor binding. Pharmacological inhibition of KCNB1 impaired adipocyte differentiation and lipid accumulation in human preadipocytes. Consistently, Kcnb1 knockout mice exhibited reduced fat mass and increased lean mass. The rs149309426 risk allele was rare in European populations but enriched in individuals with African ancestry.DiscussionOur findings identify KCNB1 as a regulator of adipogenesis and body composition and highlight the importance of studying admixed populations to uncover ancestry-specific genetic mechanisms underlying obesity.

IMPORTANCE Valsartan has been shown to attenuate phenotypic progression among individuals with early-stage sarcomeric hypertrophic cardiomyopathy (HCM). Myocardial tissue characterization by cardiac magnetic resonance (CMR) imaging may enhance mechanistic insights, but whether valsartan influences these parameters remains uncertain. OBJECTIVE To evaluate the treatment effects of valsartan on myocardial structure, function, and tissue parameters in early-stage sarcomeric HCM. DESIGN, SETTING, AND PARTICIPANTS This prespecified CMR substudy of the VANISH (Valsartan for Attenuating Disease Evolution in Early Sarcomeric Hypertrophic Cardiomyopathy) randomized clinical trial evaluated treatment effects of valsartan vs placebo on myocardial structure, function, and tissue parameters and was conducted from April 2014 through July 2019 at 17 international sites. Individuals aged 8 to 45 years with early-stage HCM aged between 8 and 45 years and with no or minimal symptoms were eligible for inclusion. INTERVENTIONS Treatment with placebo or valsartan (80 mg per day for children weighing <35 kg, 160 mg per day for children weighing >= 35 kg, or 320 mg per day for adults aged 18 years or older). MAIN OUTCOMES AND MEASURES The primary outcome was mean change in CMR parameters between baseline and year 2, including indexed extracellular volume (iECV), indexed intracellular volume (iICV), and late gadolinium enhancement (LGE). Mean between-group differences in CMR parameters between baseline and year 2 were evaluated using multivariable mixed-effects linear regression models. RESULTS Overall, 137 of 178 VANISH participants (77.0%) underwent CMR imaging at baseline and year 2. Among these participants, mean (SD) age was 23 (10) years, and 51 participants (37.2%) were female. Baseline characteristics and CMR parameters were well balanced between treatment groups. Higher LGE, iECV, and iICV at baseline were associated with higher cardiac biomarker levels and more pronounced cardiac remodeling. Between baseline and year 2, valsartan appeared to increase left ventricular (LV) end-diastolic volume index (mean difference [MD], 3.3 mL/m(2); 95% CI, 0.4-6.2; P = .03), suggesting treatment benefit, but did not significantly impact LV mass index (MD, -2.9 g/m(2); 95% CI, -6.1 to 0.2; P = .07) or LV ejection fraction. Similarly, valsartan appeared to reduce decline in right ventricular volumes. Valsartan appeared to significantly reduce iICV progression (MD, -5.0 mL/m(2); 95% CI, -9.7 to -0.4; P = .03), but did not impact iECV (MD, 0.0 mL/m(2); 95% CI, -1.4 to 1.3; P = .95) or LGE progression (MD, 0.5%; 95% CI, -0.4 to 1.3; P = .30). CONCLUSIONS AND RELEVANCE These findings enhance mechanistic insights into the effect of valsartan in early-stage HCM, showing potential benefits on biventricular remodeling and myocardial intracellular volume. Further research to identify cellular mechanisms of valsartan on HCM progression is needed.

Heart failure (HF) is a complex, heterogeneous syndrome with rising prevalence and high morbidity and mortality. The pathophysiology and diverse etiologies of HF present significant challenges for developing effective therapies. Omics technologies-including genomics, proteomics, transcriptomics, metabolomics, and epigenomics-have reshaped our understanding of HF at the molecular level, uncovering new biomarkers and potential therapeutic targets. Omics also enable insights into individualized treatment responses, the risks of adverse drug effects, and patient stratification for clinical trials. This review explores how multi-omics can enhance heart failure drug discovery and development across all stages of the therapeutic pipeline: (1) target selection and lead identification, (2) preclinical studies, and (3) clinical trials. By integrating omics approaches throughout the drug development process, we can accelerate the discovery of more effective and personalized therapies for heart failure.

Interpreting proteomic associations with chronic kidney disease (CKD) is challenging due to the disease's clinical heterogeneity and complex overlap with systemic conditions. We present a framework that identifies causal circulating protein drivers of CKD and delineates their subtype-specific and systemic effects using electronic health record (EHR) data at biobank scale. Using proteome-wide Mendelian randomization, we instrumented cis-acting protein quantitative trait loci for 2,807 circulating proteins and tested them against detailed, EHR-based kidney function outcomes in 464,631 Million Veteran Program participants. Proteins were mapped to nine kidney disease subtypes defined by genome-wide association meta-analyses from the Million Veteran Program, UK Biobank, and FinnGen. Phenome-wide association studies across 1,020 traits distinguished renal versus extra-renal associations. This integrative strategy prioritizes 93 proteins with proteome-wide significance for kidney outcomes, demonstrates subtype-specific relevance, exposes systemic associations, and maps therapeutic targets to nominate candidates for drug development and repurposing.