Background:Adult-onset diabetes comprises subgroups differing in pathophysiology, clinical presentation, and risk of comorbidities. We investigated early phenotypic differences between individuals who later developed diabetes, stratified by subgroup at diabetes diagnosis. Methods:We conducted a pooled analysis of nine prospective European cohorts with 3309 individuals developing incident diabetes and 13,963 age- and sex-matched controls without diabetes. Cases were assigned to previously defined cluster-based subgroups: severe autoimmune (SAID), insulin-deficient (SIDD), or insulin-resistant diabetes (SIRD), and moderate obesity- (MOD) or age-related diabetes (MARD). Clinical and metabolic characteristics were retroactively assessed for three time periods (>12, 6-12, 1-6 years) before diagnosis. Findings:Despite similarly high body mass index (BMI) in MOD and SIRD at diagnosis, MOD differed from controls already >12 years before diagnosis (31% higher than controls), while BMI increased progressively in SIRD (from 14% to 25% higher than controls). Compared to controls in period 1-6 years, age-, sex-, and BMI-adjusted insulin-glucose ratio was higher in SIRD, MOD and MARD at fasting (88%, 45% and 14%, respectively) and 120 min (110%, 70%, 26%) during an oral glucose tolerance test (p < 0.0001 for all), and the first-phase insulin-glucose ratio was higher in SIRD (23% [6; 43] p = 0.0072) but lower in SIDD (-30% [-37; -22], p < 0.0001) and MARD (-29% [-34; -24], p < 0.0001). The autoimmune subgroup SAID also exhibited features of metabolic syndrome. Despite differences in HOMA2-B and HbA1c at diagnosis, insulin and glucose levels did not differ significantly between the SIDD and MARD subgroups 1-6 years earlier suggesting a rapid deterioration in glycemic control in SIDD around diagnosis. Interpretation:Subgroups of diabetes display different trajectories of insulin resistance, insulin deficiency, and features of the metabolic syndrome before diagnosis. Funding:ERC, local governments, private foundations, University of Helsinki, and Research councils of Finland and Sweden.

Genetic predisposition and alcohol consumption are risk factors for increased blood pressure (BP), but their interactions influencing BP remain understudied. We conducted population-specific and cross-population meta-analyses of genome-wide gene-alcohol (GxAlc) interactions affecting BP in >1.1M individuals from multiple populations. We identified 46 GxAlc interaction loci for BP, including 21 from one-degree-of-freedom interaction tests (PGxAlc<5×10-8; or <0.05/Meff, Meff independent BP associations at P<10-5), and 25 from two-degree-of-freedom tests of main and interaction effects (PGxAlc<0.05/M2df, M2df independent 2df-associations at P2df<5×10-8), including 7 novel and 39 known BP loci. The 12q24 locus highlights the genetic effect of BRAP-rs11066001 on BP, being ~6 times larger in current drinkers than in non-drinkers. Gene prioritization with 46 GxAlc loci identified 15 genes with ≥3 lines of evidence (location, literature, druggability, functional/regulatory annotation, or pathway analyses). Several loci showed sex- and population-specific effects and revealed biological pathways of alcohol's influence on BP, suggesting mechanisms underlying alcohol-induced hypertension.

Genome-wide association studies (GWAS) have identified >1,200 signals associated with type 2 diabetes (T2D), yet identifying functional variants remains challenging because the majority of them lie in noncoding regions of the genome and are in areas of high linkage disequilibrium (LD). While chromatin accessibility QTL (caQTL) and expression QTL (eQTL) analyses are useful for nominating regulatory mechanisms underlying GWAS signals, limitations still exist in pinpointing functional variants within regions of high LD. A complementary approach that has been less frequently applied is to focus on the allele-specific effect on chromatin accessibility at heterozygous single-nucleotide polymorphisms (SNPs), hereafter referred to as "allelic imbalance". We analyzed the allelic imbalance of reads generated from an assay for transposase-accessible chromatin with sequencing (ATAC-seq) across genotyped samples from 490 donors in T2D-relevant tissues: skeletal muscle, liver, pancreatic islets, adipose tissue, and relevant cell types. We identified 119,949 allelically imbalanced SNPs (FDR<0.05) across the genome. The allelic imbalance was often most prominent in one tissue and showed an enrichment overlapping with tissue-specific transcription factor (TF) binding footprints. Focusing on the 8,581 SNPs in previously published 99% credible sets from 338 T2D GWAS signals, we identified 256 imbalanced SNPs across 123 (36.4% of) signals, each showing allelic imbalance in at least one tissue or cell type. Of these, 71 signals contained only a single imbalanced SNP, representing excellent candidate causative variants. As a proof-of-concept, we showed that 23 of the 256 imbalanced SNPs were supported by allelic assays from previous studies. Further, we experimentally validated two imbalanced SNPs as likely functional variants: rs34584161 among a seven-SNP T2D credible set at the RNF6 signal in islets and rs849134 among a 13-SNP credible set at the JAZF1 signal in liver. This study demonstrates the power of integrating ATAC-seq allelic imbalance (ASAI) with GWAS statistical fine-mapping to identify candidate functional regulatory variants from among tightly linked GWAS variants in disease-relevant tissues. While applied here in T2D, this approach represents a widely applicable high-throughput framework for refining the genetic architecture of complex traits.

Patatin-like phosphatase domain-containing 3 (PNPLA3) gene and dietary fat are important factors for metabolic dysfunction-associated steatotic liver disease (MASLD). We studied the impact of dietary fat quality modification on liver adiposity in men homozygotes for PNPLA3 (GG, carriers of the risk allele and CC, non-carriers). Ninety-eight men (age: 67.8 ± 4.2 years, body mass index: 27.2 ± 2.5 kg/m2), homozygous for PNPLA3 rs738409 variant (I148M), randomly assigned for two diet intervention arms, participated in a 12-week diet intervention. Recommended diet (RD) arm ate fat according to the National and Nordic nutrition recommendations, average diet (AD) arm ate according to the average fat intake in Finland. Liver imaging by ultrasound with 2D-shear wave elastography (2D-SWE) and magnetic resonance imaging (MRI) in combination with magnetic resonance spectroscopy (MRS) were performed. MRI-based liver fat proportion decreased in the RD arm (CC: from 3.8 ± 3.2 to 3.2 ± 3.3

[This corrects the article DOI: 10.1016/j.lanepe.2026.101715.].

Background Obesity-related cardiometabolic disease is linked to impaired adipose tissue function, but the underlying molecular programs are difficult to assign to specific adipose-resident cell types, to mechanistically connect to the activation state of macrophages, and to distinguish from alterations that may normalize with weight loss. Methods We integrated a layered design combining untargeted proteomics and lipidomics to define obesity-associated, cell-type-resolved molecular phenotypes across adipocytes and adipose microvascular endothelial cells, explore whether an inflammatory milieu reproduces adipose-resident cell dysfunction, and identify features that show evidence of recovery after weight loss. Next, we validated in adipose tissue transcriptomes the elements that exhibit the strongest association with dyslipidaemia, hypertriglyceridemia, and/or hyperglycaemia to identify gene signatures of cardiometabolic relevance. Results Adipocytes from subjects with obesity show suppression of mitochondrial energy metabolism together with impaired lipid plasticity, as reflected by triglyceride remodelling. By mimicking an inflammatory milieu with macrophage-conditioned media, we reproduced many of these changes in adipocyte cultures. Endothelial cells exhibited yet another, opposite trajectory in obesity, with reduced cell-cycle signalling and increased mitochondrial activation, which were recapitulated in vitro when these cells were exposed, respectively, to the secretions of inflamed macrophages and adipocytes. Bulk adipose tissue proteomes and lipidomes showed evidence of metabolic improvement after weight loss, including restoration of mitochondrial and substrate-handling pathways alongside reciprocal triglyceride remodelling. Together with inflammation-responsive adipocyte mitochondrial and lipid-handling dysfunction, our cell-type-informed framework probes macrophage and adipocyte-to-endothelial activation in obesity, and delineates cross-context cellular programs associated with weight loss. Notably, when integrated with transcriptomic resources, these layers of information prioritized determinants linked to impaired metabolism, and were used to generate models that can assess cardiometabolic vulnerability in subjects with obesity (AUROC values between 0.88 and 0.99). Conclusions Our study reveals adipocyte and endothelial cell-specific elements acting as molecular signatures of adipose tissue inflammation with cardiometabolic implications.

Abstract Background Subcutaneous adipose tissue (SAT), the key human fat depot for cardiometabolic health, exhibits high cellular heterogeneity. However, the contributions of contexts and cardiometabolic diseases (CMDs) to this heterogeneity are poorly understood, especially in admixed populations. Despite the substantially increased risk of obesity and obesity-related CMDs in Mexicans, cell-type-level mechanisms behind their elevated CMD risk have remained elusive. Methods To investigate how cell-type and subcell-type level profiles of SAT are impacted by sex, admixed American ancestry, CMD traits, and cell-type level cis regulation in Mexicans, we generated a Mexican SAT single nucleus RNA sequencing cohort (n = 49). We performed cell-type level differential expression testing, weighted gene co-expression analysis, and cis-expression quantitative trait locus (eQTL) mappings. We then integrated genome-wide association study (GWAS) and Mexican population level data to assess partitioned polygenic risk for lipid outcomes and colocalization between SAT cell-type level cis-eQTL variants and lipid GWAS variants. Results First, we discovered and validated a sex-associated adipocyte subtype, overlapping an adipocyte co-expression network with 132 adipocyte function centered genes, including key triglyceride biosynthesis genes, GPAM, DGAT2, ACSL1, and LPL, that are differentially expressed (DE) by sex. The cis regional variants of these network genes DE by sex confer a significant sex-specific polygenic risk to serum triglycerides, a clinically important atherogenic lipid trait. We also found significant enrichment of progesterone receptor binding at these variant sites, contributing to the sex-specific findings. Second, we identified 34 colocalized genes for three lipid traits, of which 25 (74%) genes have not been identified in previous European colocalization studies using SAT bulk tissue. Among the discovered 25 lipid GWAS genes, 12 are regulated by Mexican enriched and seven by European enriched cis-eQTL variants, thus mechanistically elucidating the genetic dyslipidemia susceptibility at the cell-type level in both Europeans and Mexicans. The identified 12 lipid GWAS genes regulated by a Mexican enriched variant include an important adipogenesis gene, CYP26B1, and a regulator of adipocyte browning and beiging, GPR180. Conclusions We identify sex- and ancestry-stratified genes and variants contributing to the risk of adverse cardiometabolic outcomes and improve understanding of the complex cell-type level biological mechanisms underlying CMDs in Mexicans.

Fibro-adipogenic progenitors (FAPs) in skeletal muscle have been implicated in type 2 diabetes (T2D) risk, yet their heterogeneity and context-dependent regulation remain poorly understood. Here, we establish induced pluripotent stem cell (iPSC)-derived FAPs as a faithful model of primary FAPs by leveraging a unique resource: iPSC lines and skeletal muscle biopsies obtained from the same 30 individuals. Donor-matched comparisons reveal that iPSC-FAPs recapitulate the transcriptome, epigenome, and subtype composition of muscle tissue FAPs. Using single-nucleus multiomics, we show that high-insulin exposure drives iPSC-FAPs toward an adipogenic fate - and that this adipogenic subtype is enriched for T2D GWAS signals, an enrichment undetectable under baseline conditions. We map the T2D-associated rs3814707 non-coding signal to LTBP3, a gene that influences FAP adipogenic differentiation. These findings reveal how disease-relevant regulatory mechanisms can be masked in unstimulated cells and establish iPSC-FAPs as a powerful platform for dissecting the state-dependent biology of complex metabolic disease.

Skeletal muscle aging is characterized by the deterioration of muscle function, which can lead to negative quality-of-life outcomes including frailty and sarcopenia. While understanding the mechanisms of this process is increasingly important as the global population ages, previous molecular studies of skeletal muscle aging have been limited by statistical power and cell type resolution. In this study, we analyzed single-nucleus gene expression and chromatin accessibility data from 287 human skeletal muscle samples from individuals aged 20-79 years to explore sex- and cell type- specific aging effects. Across 467,126 nuclei from 13 cell types, we identify 384 age-associated genes and 4,061 age-associated chromatin regions. These age-associated molecular features are enriched for functional pathways, including metabolic processes, cell-to-cell communication, and senescence Kyoto Encyclopedia of Genes and Genomes KEGG terms. Age-associated closing chromatin was more common across fiber types and sexes than opening chromatin, and was enriched in active enhancer regions while depleted for active transcription start sites. We observe enrichment for specific transcription factor motifs in closing chromatin, including those of glucocorticoid and androgen receptors, both of which play a key role in the maintenance of healthy skeletal muscle. Together, these findings identify an age-associated regulatory shift, largely invisible in matched transcriptomic data, characterized by closing chromatin which reduces accessibility to hormone receptor binding sites and enhancer regions in the muscle fiber epigenome.

AIMS/HYPOTHESIS:Comprehensive assessment of pancreatic islet β-cell function (PIF) is crucial for diabetes management. We proposed a multidimensional, relative quantification system for PIF measurement. METHODS:Our novel approach evaluates PIF using 3 dimensions: stationary baseline (PIF-S), load peak (PIF-L), and accelerated slope (PIF-A). The system was evaluated in 814 JuRong City, Jiangsu Province cohort volunteers (195 metabolically healthy, 619 abnormal), 12 Botnia clamp study participants, 3394 type 2 diabetes patients, and 6345 Metabolic Syndrome in Men study (METSIM) cohort study participants. Restricted cubic spline modeling determined ideal values based on human physiological parameters. Each subject's actual values were compared with predicted ideals and converted into percentile indices. RESULTS:The Botnia clamp experiment confirmed the distinct meaning of 3 PIF indices. Cluster analysis in metabolically abnormal individuals identified 3 clusters. Cluster 1, with the highest PIF-A, had the best metabolic profiles and lowest cardiovascular and renal disease risks. Cluster 3, with the highest PIF-S and PIF-L but lowest PIF-A, had the poorest metabolic profiles and highest disease risks. Type 2 diabetes patients with high PIF-S and PIF-L were more prone to complications. Similar patterns were observed in the METSIM cohort, with cluster 1 showing the lowest diabetes risk; hazard ratios for clusters 2 and 3 were 2.499 [95% confidence interval (CI) 1.932-3.233, P = 3.11E-12] and 3.185 (95% CI 2.353-4.311, P = 6.35E-12), respectively. The novel 3-dimensional PIF indices surpass previous indicators in predicting diabetes. Combined with existing diabetes risk scores, novel PIFs also significantly improved their predictive efficiency. CONCLUSION:This novel system offers an effective method for PIF assessment, enhancing diabetes prediction and management by deepening the understanding of diabetes complexity and aiding in precise therapy.

A key methodological challenge for genome-wide association studies is how to leverage haplotype diversity and allelic heterogeneity to improve trait association power, especially in noncoding regions where it is difficult to predict variant impacts and define functional units for variant aggregation. Genealogy-based association methods have the potential to bridge this gap by testing combinations of common and rare haplotypes based purely on their ancestral relationships. In parallel work, we have developed an efficient local ancestry inference engine and a novel statistical method (LOCATER) for combining signals present on different branches of a locus-specific haplotype tree. Here, we develop a genome-wide LOCATER analysis pipeline and apply it to a genome sequencing study of 6795 Finnish individuals with 101 cardiometabolic traits and 18.9 million autosomal variants. We identify 351 significant trait associations at 47 distinct genomic loci and find that LOCATER boosts the single marker test (SMT) association signal at five loci by combining independent signals from distinct alleles. LOCATER successfully recovers known quantitative trait loci not found by SMT, including LIPG, recovers known allelic heterogeneity at the APOE/C1/C4/C2 gene cluster, and suggests one novel association. We find that confounders have a more pronounced effect on genealogy-based methods than SMT, and we propose a new randomization approach and a general method for genomic control to eliminate their effects. This study demonstrates that genealogy-based methods such as LOCATER excel when multiple causal variants are present and suggests that their application to larger and more diverse cohorts will be fruitful.

OBJECTIVE:To delineate organ-specific and systemic drivers of metabolic dysfunction-associated steatotic liver disease (MASLD), we applied integrative causal inference across clinical, imaging, and proteomic domains in individuals with and without type 2 diabetes (T2D). METHODS:Bayesian network analyses and complementary two-sample Mendelian randomization were used to quantify causal pathways linking adipose distribution, glycemia, and insulin dynamics with liver fat in the IMI-DIRECT prospective cohort study. Data included frequently sampled metabolic challenge tests, MRI-derived abdominal and hepatic fat content, serological biomarkers, and Olink plasma proteomics from 331 adults with new-onset T2D and 964 adults without diabetes, with harmonized protocols enabling replication. RESULTS:High basal insulin secretion rate (BasalISR), estimated via C-peptide deconvolution, emerged as the primary potential causal driver of liver fat accumulation in both cohorts. BasalISR, a clearance-independent measure of β-cell insulin output distinct from peripheral insulin levels, was independently linked to hepatic steatosis. Visceral adipose tissue exhibited bidirectional associations with liver fat, suggesting a self-reinforcing metabolic loop. Of 446 analyzed proteins, 34 mapped to these metabolic networks (27 in the non-diabetes network, 18 in the T2D network, and 11 shared). Key proteins directly associated with liver fat included GUSB, ALDH1A1, LPL, IGFBP1/2, CTSD, HMOX1, FGF21, AGRP, and ACE2. Sex-stratified analyses identified GUSB in females and LEP in males as the strongest protein predictors of liver fat. CONCLUSIONS:BasalISR may better capture early β-cell-driven disturbances contributing to MASLD. These findings outline a multifactorial, sex- and disease stage-specific proteo-metabolic architecture of hepatic steatosis and identify potential biomarkers or therapeutic targets.

A recent study has suggested eight clusters of genetic variants associated with type 2 diabetes. We aimed to characterise metabolite associations for these eight clusters. We constructed type 2 diabetes overall and cluster-partitioned polygenic risk scores (PRSs) in 10,015 Finnish men with 979 named plasma metabolites measured in Metabolon HD4 mass spectrometry platform. We evaluated metabolite–PRS associations using linear regression. We also performed a mediation analysis to examine whether metabolites statistically accounted for part of the association between genetic risk and incident type 2 diabetes that developed in a mean of 13.6 years’ follow-up. We identified 337 metabolites significantly associated with type 2 diabetes genetic risk, including 242 exclusive to cluster-partitioned PRSs. Of the significant metabolites, 26 exhibited significantly heterogeneous associations across clusters. We identified significant enrichment for 33 metabolic pathways among the cluster-associated metabolites. Notably, metabolites for the two pancreatic beta cell-related clusters exhibited enrichment in distinct pathways: the beta cell + proinsulin (PI) cluster in fructose, mannose and galactose metabolism; and the beta cell − PI cluster in branched-chain amino acid metabolism. Mediation analysis suggested that >50

OBJECTIVE:We aimed to identify plasma metabolites significantly associated with existing type 2 diabetes (T2D), further characterize their associations with T2D-related laboratory measures and lifestyle-related risk factors, and evaluate their potential mediating and predictive roles for incident T2D. RESEARCH DESIGN AND METHODS:We performed metabolomic profiling in 10,163 Finnish men in the Metabolic Syndrome in Men (METSIM) study at baseline. Of these participants, 1,412 had prevalent T2D and 1,234 developed incident T2D during an average follow-up period of 13.6 years. We tested for associations of 979 named metabolites with prevalent T2D. For significant metabolites, we further evaluated their associations with 24 T2D-related laboratory measures of five groups and five T2D lifestyle-related risk factors. We performed an exploratory mediation analysis to examine the mediation effects of metabolites on incident T2D for the five risk factors. We evaluated metabolite associations with incident T2D and built exploratory metabolite predictive models for incident T2D. RESULTS:We identified 193 plasma metabolites significantly associated with prevalent T2D at baseline, including 71 previously unreported. Of these 193 metabolites, 88 were associated with incident T2D. In participants with prevalent T2D, the 193 metabolites showed associations with zero to 24 (mean 10.2) T2D-related laboratory measures. Eighty-one metabolites partially mediated the associations of the five risk factors with T2D onset under standard mediation assumptions. The significant metabolites showed improved predictive ability for future T2D. CONCLUSIONS:This study identifies T2D plasma metabolic biomarkers for further investigation in women and other populations. The findings enhance our understanding of T2D biology.

ABSTRACT Metabolic diseases such as type 2 diabetes (T2D) arise through complex interactions between physiological, molecular, and environmental processes. Clinical traits including age, sex, adiposity, and glycaemic status are strongly associated with disease risk and progression, yet most molecular studies examine these factors independently and assume relatively static molecular regulation. Consequently, how physiological state dynamically reshapes molecular organisation across omics layers remains poorly understood. Here, we integrated transcriptomic, proteomic, metabolomic, and genetic data from 3,027 individuals in the IMI DIRECT cohort to characterise the joint molecular effects of age, sex, body mass index (BMI), and glycated haemoglobin (HbA1c). We identified widespread associations between these traits and molecular phenotypes. However, interaction analyses revealed a more complex context-dependent regulation, showing that the molecular effect of one trait frequently depends on the state of another, with sex-specific effects of age being more prominent. We also investigated relationships between different types of molecular phenotypes and how these relationships are modulated by metabolic disease relevant traits, demonstrating that cross-omic molecular coordination is itself dynamically remodelled by physiological and metabolic state. Probabilistic causal inference identified a directionally structured network of age-associated molecules, revealing pathways through which age effects propagate across omics layers, showcased in the example of the mTOR signalling pathway. Integration of this directed network with genetic colocalisation analyses also identified a sub-network relevant for T2D. Collectively, our findings demonstrate that metabolic disease relevant traits not only independently influence molecular phenotype abundance but also jointly reshape the directional organisation of cross-omic molecular networks. These results support a model in which metabolic disease susceptibility emerges through dynamic rewiring of interconnected molecular systems and provide a framework for context-dependent biomarker discovery, disease stratification, and precision metabolic medicine.

Abstract Skeletal muscle, a primary site of insulin-mediated glucose uptake, plays a central role in the pathogenesis of type 2 diabetes. It is therefore critical to understand the disease-associated alterations in skeletal muscle and identify the underlying drivers of this dysregulation. Here, we characterize type 2 diabetes associated transcriptional dysregulation using 301 skeletal muscle biopsies from living donors with and without diabetes. Using weighted gene co-expression network analysis, we identify 56 distinct gene modules, which we further characterize using single-nucleus RNA-seq-derived cell type signatures and pathway enrichment analysis. We identify numerous cell type-associated dysregulated pathways in skeletal muscle tissue from individuals with diabetes, including muscle fiber-associated mitochondrial function and mRNA splicing and processing; endothelial vascularization and phospholipase D signaling; and macrophage- and T-cell-associated inflammation. Through analysis of module hub genes and transcription factor regulatory network analysis, we further identify candidate driver genes of this dysregulation including ATP5L , ATF2 , SIRT1, and THRAP3 in muscle fibers; JAM2 and CLEC14A in endothelial cells; and F13A1 and IRF8 in immune cells. Finally, we integrate our co-expression networks with single-nucleus ATAC-seq data to identify proximal and distal genomic regulatory elements and identify context-specific enrichment for type 2 diabetes and related trait GWAS signals in muscle fiber and endothelial modules. Together, our results reveal dysregulation in pathways in muscle tissue from individuals with diabetes, identify candidate drivers, and connect the genomic drivers of this dysregulation across type 2 diabetes and related metabolic traits.

Obesity-related metabolic disease is linked to impaired adipose tissue function, but the underlying molecular programs are difficult to assign to specific adipose-resident cell types, to mechanistically connect to inflammation, and to distinguish from alterations that normalize with weight loss. We integrated here a layered design combining untargeted proteomics and lipidomics to define obesity-associated, cell-type-resolved molecular phenotypes across isolated adipocytes and adipose microvascular endothelial cells, explore whether an obesity-like inflammatory milieu reproduces adipose-resident cell dysfunction, and identify molecular features that show evidence of recovery after surgery-induced weight loss. As expected, adipocytes from people with obesity show suppression of mitochondrial energy metabolism together with impaired lipid plasticity, as reflected by triglyceride remodelling. By mimicking an obesity-like inflammatory milieu with macrophage-conditioned media, we reproduced most of these changes in adipocyte cultures. Endothelial cells exhibited yet another, opposite trajectory in obesity, with reduced cell-cycle signalling and increased mitochondrial activation, which were recapitulated in vitro when these cells were exposed, respectively, to the secretions of inflamed macrophages and adipocytes. Bulk adipose tissue proteomes and lipidomes showed evidence of metabolic improvement after weight loss, with broad restoration of mitochondrial and substrate-handling pathways and reciprocal triglyceride remodelling. Alongside the inflammation-responsive adipocyte mitochondrial and lipid-handling dysfunction, our cell-type-informed framework probes macrophage and adipocyte-to-endothelial activation in obesity and delineates cross-context cellular programs that recover with weight loss. Additionally, we identified the elements that exhibit the strongest association with dyslipidaemia, hypertriglyceridemia and hyperglycaemia in individuals with obesity, confirming molecular signatures relevant to metabolic obesity in two cross-sectional samples.

The identification of sex-differential gene regulatory elements is essential for understanding sex-differential patterns of health and disease. We leveraged bulk and single-nucleus RNA sequencing (RNA-seq) and single-nucleus ATAC-seq data from 281 skeletal muscle biopsies to characterize sex differences in gene expression and regulation at the cell-type and whole-tissue levels. We found highly concordant sex-biased expression of over 2,100 genes across the three muscle fiber types and bulk tissue. Gene pathways related to mitochondrial activity and energy metabolism were enriched for male-biased expression, whereas those related to signal transduction and cell differentiation were enriched for female-biased expression. We found widespread sex-biased chromatin accessibility enriched in proximal and distal gene regulatory states; in gene promoters, sex-biased chromatin accessibility was positively associated with sex-biased expression. Long noncoding RNAs (lncRNAs) and microRNAs (miRNAs) also showed extensive sex-biased expression in the fiber-type and bulk data, respectively. Together, these results highlight nuclear and cytoplasmic mechanisms for sex-differential gene regulation in skeletal muscle.