In human population studies, proteogenomics integrates genomic and proteomic data to uncover how genetic variation shapes protein expression and function. A central focus of proteogenomics is the identification of protein quantitative trait loci (pQTL), which link genetic variants to protein abundance and offer critical insights into the molecular basis of disease. Recent years have seen pQTL studies scale rapidly, driven by advances in high-throughput proteomic platforms, the expansion of large biobank datasets and increasingly powerful cross-cohort meta-analyses, thereby greatly extending the depth, breadth and translational potential of proteogenomics. This Review highlights recent progress in proteogenomics with a focus on pQTL mapping and its downstream integration with complementary molecular data to provide insights into human disease. We also address current challenges and propose future directions to harness proteogenomics for the development of personalized therapies and improvement of health outcomes.

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.

Background Excess adiposity, most commonly indexed through body mass index (BMI), is strongly associated with the development of heart failure (HF). Weight loss therapies improve outcomes in patients with obesity and HF with preserved left ventricular ejection fraction (LVEF), but their effects in HF with reduced LVEF remain unclear. Objectives The aim of this work is to determine whether higher BMI is associated with adverse clinical outcomes in patients with HF and whether there is effect modification by LVEF subgroup. Methods Two-sample Mendelian randomization (MR) was used, with genome-wide significant loci associated with BMI as instrumental variables and outcome data from a genome-wide association study (GWAS) of time-to-event clinical outcomes in patients with HF. A total of 50,636 individuals of European ancestry with established HF from 22 cohorts were included in the genetic analysis: 12 HF trials, 1 prospective case-cohort study, 9 cohorts nested within non-HF cardiovascular trials, and 1 population-based cohort derived from the UK Biobank.The exposure was genetically predicted BMI and the outcome measures were all-cause mortality and a composite of cardiovascular mortality or HF hospitalization. Genetic associations for the outcomes were derived from our GWAS and MR was used to estimate the unbiased association of genetically predicted BMI with these clinical outcomes. Results The mean BMI was 29.2 ± 5.8 kg/m2. Over a median follow-up of 27.0 months, all-cause mortality occurred in 11,454 patients (23%), and 11,360 participants (22%) experienced the composite endpoint. Genetically predicted BMI was associated with an increased rate of both all-cause mortality (HR per SD [4.8 BMI units] 1.21; 95% CI: 1.13-1.29; P = 9 × 10-8) and the composite outcome (HR 1.29; 95% CI: 1.20-1.38; P = 8 × 10-13). Associations were consistent across LVEF ≤40% and >40%: for all-cause mortality, HR: 1.16 (95% CI: 0.99-1.37) and 1.20 (95% CI: 0.94-1.53); and for the composite outcome, HR: 1.30 (95% CI: 1.15-1.48) and 1.57 (95% CI: 1.29-1.91), respectively. Conclusions Among patients with HF, higher BMI was associated with increased all-cause mortality and cardiovascular death or HF hospitalization, supporting the potential role of weight-management strategies across the ejection fraction spectrum.

Smooth muscle cell-related transcription factors have been associated with coronary artery disease in large genome-wide association studies, increasing their translational value for therapeutic investigations. Our study shows for the first time that FOXC1 is a major transcriptional regulator in vascular injury and atherosclerotic lesions, causally implicated in the disease. Functionally, our data suggest that FOXC1 modulates smooth muscle cell activation vs quiescence by regulating key pathways involved in cell adhesion, cell cycle progression, and actin cytoskeleton organization. Further investigations should focus on elucidating the mechanistic basis of these regulatory effects and assessing whether this axis could be pharmacologically targeted to promote lesion stability.

Human genetics has become a cornerstone of drug target discovery, yet the value of Mendelian randomization (MR) for predicting clinical success remains uncertain. Here, we systematically evaluated MR across 11,482 target-indication pairs with documented Phase II clinical outcomes to assess its utility for drug development. We find that MR statistical significance alone does not enrich for Phase II success, in contrast to genome-wide association study (GWAS) support, which confers an increase in success probability. However, this apparent limitation reflects the heterogeneous nature of clinical failure and the fact that MR encodes information beyond P values. When MR-derived features, including instrument strength and explained variance, are integrated into machine learning models, predictive performance improves substantially. An MR-informed XGBoost classifier identifies target-indication pairs with a 55% overall approval rate, corresponding to a 6.4-fold enrichment over unstratified programs and a 2.8-fold improvement over GWAS-supported targets in Phase II. Notably, this enrichment is achieved without reliance on statistically significant MR results. Our findings demonstrate that MR is most informative when treated as a graded, context-dependent source of causal evidence rather than a binary hypothesis test, and that its integration with machine learning enables scalable, genetics-informed prioritization of drug targets across the clinical pipeline. ### Competing Interest Statement C.N.J.R., M.A., H.A.B., J.L.C., M.R.J.L., J.K.S, R.F. and M.F.S. are employees of biotx.ai GmbH. A.M. was an employee of Pfizer Research and Development at the time this work started and is now an employee of Novo Nordisk A/S. ### Funding Statement This work was partially supported by Germany`s Federal Agency for Breakthrough Innovation (SPRIN-D) through a validation project. The analyses were supported by NVIDIA Corporation through the NVIDIA Inception program and by Google LLC through the Google for Startups Cloud Program. ### 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: The study used (or will use) ONLY openly available human data that were originally located at: NHGRI-EBI GWAS Catalog (https://www.ebi.ac.uk/gwas/home) 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.

BACKGROUND:Plasma and cerebrospinal fluid (CSF) protein biomarkers in amyotrophic lateral sclerosis (ALS) may provide insight into disease mechanisms and yield clinically useful biomarkers. METHODS:Overall, 363 proteins in plasma and CSF from 198 patients with ALS and 125 matched controls were profiled using Olink assays. Associations with disease status, survival, and functional decline, as well as longitudinal biomarker stability across the disease course were assessed, together with network and enrichment analyses. ALS risk-associated biomarkers were externally validated in the UK Biobank (UKB). RESULTS:Overall, 125 proteins were significantly associated with at least one outcome (i.e., case status, risk, survival, or functional decline), and 21 were associated with three or more outcomes. NEFL was the most robust biomarker in plasma and CSF, alongside TNFRSF12A in plasma and CSF, EDA2R in plasma, and FABP4 in plasma and CSF. Most biomarkers remained stable longitudinally across the disease course. ALS risk-associated biomarkers were replicated in UKB, in which > 3000 plasma proteins were measured in 52,990 participants, including 298 with ALS. Network and enrichment analyses highlighted their roles in immune response and extracellular-matrix remodeling, and their enrichments in the brain and T-cell subsets. Construction of an ALS risk-prediction model achieved an ROC-AUC of 0.72 in the UKB validation cohort. CONCLUSIONS:These findings suggest candidate protein biomarkers for ALS risk stratification, early detection, and clinical therapeutic monitoring.

Postprandial variability in glucose and protein levels is one of the elements of insulin resistance (IR) and prediabetes, which is an area precursor to type 2 diabetes mellitus (DM). The objective of the study was a comprehensive proteomic analysis according to glucose tolerance in the general population who did not self-report DM or other diseases. We used Olink® Reveal, a novel, high-throughput platform by Olink Proteomics based on their Proximity Extension Assay (PEA), to identify levels of 1034 circulating proteins in small volumes (4 µL) of plasma samples. The study enrolled 508 participants (mean age 52 ± 10.5 years, 47.2% men) from the population-based study, Bialystok PLUS Polish Longitudinal University Study. The study population was categorized according to glucose metabolism in comparison to impaired fasting blood glucose (IFG), impaired glucose tolerance (IGT), and newly diagnosed DM. Analysis of variance (ANOVA) adjusted for age, weight, fat mass, lean mass, and body mass index (BMI), identified 19 proteins significantly associated with categories of glucose tolerance. Of the five markers with the greatest ability to distinguish newly diagnosed diabetes from non-diabetic participants, paralemmin 2 performed best (AUC = 0.81; 77% sensitivity, 75% specificity), whereas furin was the most accurate for detecting any abnormal glucose regulation (AUC = 0.69). A linear regression model adjusted for the same confounding factors showed statistically significant associations between HbA1c levels and 37 proteins. Our findings highlight multiple proteins with significantly different levels across categories of glucose tolerance, especially between the healthy controls and the group with newly diagnosed DM. The consistent patterns of protein level differences, independent of body composition, suggest potential involvement in the progression of glucose metabolism disturbances and provide unique insights into pathomechanisms. These findings identify PALM2, FURIN, PDZK1, ACAA1, and IL18R1 as potential biomarkers of early dysglycemia.

The human SLC39A8 (hSLC39A8) gene encodes a plasma membrane protein SLC39A8 (ZIP8) that mediates the specific uptake of the metals Cd2+, Mn2+, Zn2+, Fe2+, Co2+, and Se4+. Pathogenic variants within hSLC39A8 are associated with congenital disorder of glycosylation type 2 (CDG type II) or Leigh-like syndrome. However, numerous mutations of uncertain significance are also linked to different conditions or benign traits. Our study characterized 21 hSLC39A8 variants and measured their impact on protein localization and intracellular levels of Cd2+, Zn2+, and Mn2+. We identified four variants that disrupt protein expression, five variants with high retention in the endoplasmic reticulum, and 12 variants with localization to the plasma membrane. From the 12 variants with plasma membrane localization, we identified three with complete loss of detectable ion uptake by the cell and five with differential uptake between metal ions. Further in silico analysis on protein stability identified variants that may affect the stability of homodimer interfaces. This study elucidates the variety of effects of hSLC39A8 variants on ZIP8 and on diseases involving disrupted metal ion homeostasis.

BACKGROUND:Pharmacologic blood pressure (BP) lowering is typically a lifelong treatment, and both clinicians and patients may have concerns about the long-term use of antihypertensive agents and the risk for cancer. However, evidence from randomized controlled trials (RCTs) regarding the effect of long-term pharmacologic BP lowering on the risk for new-onset cancer is limited, with most knowledge derived from observational studies. OBJECTIVES:The aim of this study was to assess whether long-term BP lowering affects the risk for new-onset cancer, cause-specific cancer death, and selected site-specific cancers. METHODS:Individual-level data from 42 RCTs were pooled using a one-stage individual participant data meta-analysis. The primary outcome was incident cancer of all types, and secondary outcomes were cause-specific cancer death and selected site-specific cancers. Prespecified subgroup analyses were conducted to assess the heterogeneity of the BP-lowering effect by baseline variables and over follow-up time. Cox proportional hazards regression, stratified by trial, was used for the statistical analysis. For site-specific cancers, analyses were complemented with Mendelian randomization, using naturally randomized genetic variants associated with BP lowering to mimic the design of a long-term RCT. RESULTS:Data from 314,016 randomly allocated participants without known cancer at baseline were analyzed. Over a median follow-up of 4 years (Q1-Q3: 3-5 years), 17,954 participants (5.7%) developed cancer, and 4,878 (1.5%) died of cancer. In the individual participant data meta-analysis, no associations were found between reductions in systolic or diastolic BP and cancer risk (HR per 5 mm Hg reduction in systolic BP: 1.03 [95% CI: 0.99-1.06]; HR per 3 mm Hg reduction in diastolic BP: 1.03 [95% CI: 0.98-1.07]). No changes in relative risk for incident cancer were observed over follow-up time, nor was there evidence of heterogeneity in treatment effects across baseline subgroups. No effect on cause-specific cancer death was found. For site-specific cancers, no evidence of an effect was observed, except a possible link with lung cancer risk (HR for systolic BP reduction: 1.17; 99.5% CI: 1.02-1.32). Mendelian randomization studies showed no association between systolic or diastolic BP reduction and site-specific cancers, including overall lung cancer and its subtypes. CONCLUSIONS:Randomized data analysis provided no evidence to indicate that pharmacologic BP lowering has a substantial impact, either increasing or decreasing, on the risk for incident cancer, cause-specific cancer death, or selected site-specific cancers.

Introduction: Varenicline is an alpha(4)beta(2) nicotinic acetylcholine receptor partial agonist with the highest therapeutic efficacy of any pharmacological smoking cessation aid and a 12-month cessation rate of 26%. Genetic variation may be associated with varenicline response, but to date, no genome-wide association studies of varenicline response have been published. Methods: In this study, we investigated the genetic contribution to varenicline effectiveness using two electronic health record-derived phenotypes. We defined short-term varenicline effectiveness (SVE) and long-term varenicline effectiveness (LVE) by assessing smoking status at 3 and 12 months, respectively, after initiating varenicline treatment. In Stage 1, comprising five European cohort studies, we tested genome-wide associations with SVE (1405 cases, 2074 controls) and LVE (1576 cases, 2555 controls), defining sentinel variants (the most strongly associated variant within 1 Mb) with p-value < 5 x 10(-6) to follow up in Stage 2. In Stage 2, we tested association between sentinel variants and comparable smoking cessation endpoints in varenicline randomized controlled trials. We subsequently meta-analyzed Stages 1 and 2. Results: No variants reached genome-wide significance in the meta-analysis. In Stage 1, 10 sentinel variants were associated with SVE and five with LVE at a suggestive significance threshold (p-value < 5 x 10(-6)); none of these sentinels were previously implicated in varenicline-aided smoking cessation or in genetic studies of smoking behavior. Conclusions: We provide initial insights into the biological underpinnings of varenicline-aided smoking cessation, through implicating genes involved in various processes, including gene expression, cilium assembly, and early-stage development.

Pharmacologic blood pressure (BP) lowering is typically a lifelong treatment, and both clinicians and patients may have concerns about the long-term use of antihypertensive agents and the risk for cancer. However, evidence from randomized controlled trials (RCTs) regarding the effect of long-term pharmacologic BP lowering on the risk for new-onset cancer is limited, with most knowledge derived from observational studies. The aim of this study was to assess whether long-term BP lowering affects the risk for new-onset cancer, cause-specific cancer death, and selected site-specific cancers. Individual-level data from 42 RCTs were pooled using a one-stage individual participant data meta-analysis. The primary outcome was incident cancer of all types, and secondary outcomes were cause-specific cancer death and selected site-specific cancers. Prespecified subgroup analyses were conducted to assess the heterogeneity of the BP-lowering effect by baseline variables and over follow-up time. Cox proportional hazards regression, stratified by trial, was used for the statistical analysis. For site-specific cancers, analyses were complemented with Mendelian randomization, using naturally randomized genetic variants associated with BP lowering to mimic the design of a long-term RCT. Data from 314,016 randomly allocated participants without known cancer at baseline were analyzed. Over a median follow-up of 4 years (Q1-Q3: 3-5 years), 17,954 participants (5.7%) developed cancer, and 4,878 (1.5%) died of cancer. In the individual participant data meta-analysis, no associations were found between reductions in systolic or diastolic BP and cancer risk (HR per 5 mm Hg reduction in systolic BP: 1.03 [95% CI: 0.99-1.06]; HR per 3 mm Hg reduction in diastolic BP: 1.03 [95% CI: 0.98-1.07]). No changes in relative risk for incident cancer were observed over follow-up time, nor was there evidence of heterogeneity in treatment effects across baseline subgroups. No effect on cause-specific cancer death was found. For site-specific cancers, no evidence of an effect was observed, except a possible link with lung cancer risk (HR for systolic BP reduction: 1.17; 99.5% CI: 1.02-1.32). Mendelian randomization studies showed no association between systolic or diastolic BP reduction and site-specific cancers, including overall lung cancer and its subtypes. Randomized data analysis provided no evidence to indicate that pharmacologic BP lowering has a substantial impact, either increasing or decreasing, on the risk for incident cancer, cause-specific cancer death, or selected site-specific cancers.

Males and females exhibit differences in proteome profiles associated with disease risk. However, sex-dimorphic protein quantitative trait loci (SD-pQTL) and their effects on sex differences in health disorders have not been thoroughly investigated. We conducted a sex-stratified, genome-wide association study on 2,922 proteins using data from 30,272 individuals of Caucasian ancestry from the UK Biobank and compared the estimated effects on protein levels of these variants in the men and women to identify SD-pQTLs. The identified SD-pQTLs were replicated using data from two Japanese cohorts (comprising 2,886 and 1,394 individuals, respectively), as well as from 1,990 Finnish, 630 South Asian, and 662 Black ancestry individuals. Sex-dimorphic pleiotropy and the causal relationship between protein levels and health disorders were assessed using the identified SD-pQTLs. We identified 113 SD-pQTLs associated with 65 proteins. Of the 113 SD-pQTLs, 52 were significant in both sexes, five were not significant in either sex, and 42 and 14 were significant only in males and females, respectively. Variant rs2270416 was significantly associated with the CDH15 protein in both sexes but showed opposite effect direction in men and women. Of the 113 SD-pQTLs identified, a total of 41 were replicated in a meta-analysis encompassing Japanese, South Asian, and Black ancestry individuals. SD-pQTLs for proteins APOE (rs157581) and SNAP25 (rs4420638) exhibited sex-dimorphic associations with dementia, indicating sex dimorphic pleiotropy in both proteins and health disorders. From sex-stratified Mendelian randomization using the SD-pQTLs, proteins NCAM1 and PZP showed significant causal relationship with dementia in males and females, respectively. The present study provides evidence of sex-dimorphic genetic architecture in protein-level regulation, elucidating the proteo-genetic architecture for sex differences in human variation.

BACKGROUND:Protein quantitative trait loci (pQTLs) remain underexplored in asthma but might provide valuable insights into the underlying molecular mechanisms. This study aimed to investigate associations between genetic variation and inflammation-related plasma proteins and to assess differences in the levels of genetically determined proteins in subjects with signatures of type-2 inflammation and/or asthma. METHODS:A pQTL mapping of 92 inflammation-related plasma proteins was conducted in young adults from the Swedish BAMSE cohort (n = 1538). Replication of sentinel pQTLs was attempted, and the overlap and colocalization of pQTLs with expression quantitative trait loci (eQTLs) were investigated using publicly available data. Proteins with significant pQTLs were tested for association with type-2 signatures defined as high levels of fractional exhaled nitric oxide, blood eosinophils, and/or sensitization to airborne allergens in subjects with or without asthma in BAMSE. RESULTS:Forty-five sentinel pQTLs (33 cis, 12 trans) for 39 inflammation-related proteins were identified (p ≤ 7.14 × 10-11), and a high proportion of these were validated in independent populations. A high likelihood for colocalization of cis-pQTLs and cis-eQTLs was observed for 19 proteins in different tissues. Six of the 39 circulating proteins with significant pQTLs were associated with type-2 signatures and/or asthma, and matrix metalloproteinase-10 (MMP-10) showed the most significant associations. CONCLUSIONS:These findings underscore the existence of a genetic component influencing the plasma levels of proteins involved in inflammatory processes, including MMP-10, which is suggested to have a role in high type-2 inflammation in asthma subjects.

Aortic stiffness is a key determinant of cardiovascular disease (CVD) risk. Advances in deep learning on cardiac magnetic resonance (CMR) imaging enable detailed phenotyping of cardiac traits, providing novel insights into CVD genetics. We developed a novel CMR-based, pressure-independent measure of aortic stiffness (β0) and identified genetic determinants using genome-wide association studies (GWAS) of UK Biobank data. A pre-trained neural network segmented CMR images of 45 789 European-ancestry UK Biobank participants to quantify ascending aorta diameter. Combined with contemporaneous blood pressure readings, this was used to calculate β0, assuming an exponential relationship between pressure and aortic diameter. GWAS excluded individuals with prior CVDs, identifying independent lead variants through conditional analysis. Genomic loci were defined by a 500 kb flanking region, and putative effector genes were prioritized using variant-to-gene mapping, polygenic priority scores, gene-based association tests, and proximity. Pathway enrichment analysis identified biological processes associated with β0. GWAS identified 17 independent lead variants (Fig. A). We identified putative effector genes at 16 of 17 genomic loci (Fig. B, one locus was not resolved). Among the putative effectors were ELN and LTBP4, genes implicated in elastin fiber formation pathway (P = 0.019); ELN, HAS2 and LTBP4 associated with extracellular matrix assembly pathway (P = 0.0075), and ULK4, ARHGAP24, ELN, HAS2, SVIL, ARHGAP22, CDH13, SMG6, and LTBP4 linked to regulation of cellular component organization (P = 0.0026). Our findings link aortic stiffness to genetic pathways regulating extracellular matrix and cellular organization, providing a foundation for future mechanistic studies and potential therapeutic targets.

Background: Aortic stiffness is associated with increased risk of cardiovascular events. Deep learning on large cardiac magnetic resonance (CMR) imaging samples has enabled detailed characterization of cardiac shape and function for use in genome-wide association studies (GWAS), leading to improved understanding of genetic aetiology of cardiovascular diseases (CVDs). Aim: We present a novel CMR-based pressure-independent measure of aortic stiffness, identify genetic factors and biological determinants using GWAS analysis of CMR imaging data from the UK Biobank. Methods: A pre-trained neural network was used to segment CMR images of participants in the UK Biobank to quantify ascending aorta diameter, which together with contemporaneous systolic and diastolic blood pressure measurements were used to calculate a pressure-independent measure of aortic stiffness ( β 0 ). The measurement assumes an exponential relationship between pressure and aortic diameter. We performed a GWAS on β 0 in 45,789 participants of European ancestry, excluding individuals with a prior history of CVDs. We performed stepwise conditional joint analysis to identify conditionally independent lead variants, and then defined a locus based on a 500kb flanking region centered on the variant. For each locus, we evaluated and combined scores from four complimentary approaches: variant-to-gene, polygenic priority score, gene-based association test and nearest gene. Candidate gene with the highest aggregate score was identified as the putative effector gene. We performed a pathway enrichment analysis for putative effector gene to identify potential biological pathways involving β 0 . Results: We identified 17 independent lead variants from the GWAS (Figure A). We were able to identify putative effector genes at 16 of 17 genomic loci (Figure B, one locus was not resolved). Among the putative effectors were ELN and LTBP4, genes implicated in elastin fiber formation pathway (p=1.9E-2); ELN, HAS2 and LTBP4 associated with extracellular matrix assembly pathway (p=7.5E-3), and ULK4, ARHGAP24, ELN, HAS2, SVIL, ARHGAP22, CDH13, SMG6 , and LTBP4 linked to regulation of cellular component organization (p=2.6E-2). Conclusion: A GWAS of CMR-derived aortic stiffness identified 17 independent loci, suggesting its links with genetic influences on extracellular matrix and cellular component organization. These findings provide insights into the determinants of aortic stiffness that may inform future mechanistic studies.

The human solute carrier (SLC) superfamily of ~460 membrane transporters remains the largest understudied protein family despite its therapeutic potential. To advance SLC research, we developed a comprehensive knowledgebase that integrates systematic multi-omics data sets with selected curated information from public sources. We annotated SLC substrates through literature curation, compiled SLC disease associations using data mining techniques, and determined the subcellular localization of SLCs by combining annotations from public databases with an immunofluorescence imaging approach. This SLC-centric knowledge is made accessible to the scientific community via a web portal featuring interactive dashboards and visualization tools. Utilizing this systematically collected and curated resource, we computationally derived an integrated functional landscape for the entire human SLC superfamily. We identified clusters with distinct properties and established functional distances between transporters. Based on all available data sets and their integration, we assigned biochemical/biological functions to each SLC, making this study one of the largest systematic annotations of human gene function and a potential blueprint for future research endeavors.

Heart failure (HF) is a major contributor to global morbidity and mortality. While distinct clinical subtypes, defined by etiology and left ventricular ejection fraction, are well recognized, their genetic determinants remain inadequately understood. In this study, we report a genome-wide association study of HF and its subtypes in a sample of 1.9 million individuals. A total of 153,174 individuals had HF, of whom 44,012 had a nonischemic etiology (ni-HF). A subset of patients with ni-HF were stratified based on left ventricular systolic function, where data were available, identifying 5,406 individuals with reduced ejection fraction and 3,841 with preserved ejection fraction. We identify 66 genetic loci associated with HF and its subtypes, 37 of which have not previously been reported. Using functionally informed gene prioritization methods, we predict effector genes for each identified locus, and map these to etiologic disease clusters through phenome-wide association analysis, network analysis and colocalization. Through heritability enrichment analysis, we highlight the role of extracardiac tissues in disease etiology. We then examine the differential associations of upstream risk factors with HF subtypes using Mendelian randomization. These findings extend our understanding of the mechanisms underlying HF etiology and may inform future approaches to prevention and treatment.

Accurate measurement of secreted proteins in serum and plasma is essential for understanding mechanisms and developing reliable biomarkers. Recent technological advancements, such as proximity extension assay (PEA), have enabled high-throughput multiplex protein analyses from small sample volumes in either serum or plasma. Despite the increasing use of PEA-based proteomics and the generation of extensive datasets, integrated data from these two mediums remains challenging due to inherent differences in sample processing. To address this issue, we developed and validated protein-specific transformation factors using linear modeling to normalize protein measurements between serum and plasma proteins quantified using Olink. Our analysis surveyed 1463 proteins across matched serum and plasma samples, identifying 686 transformation factors. The transformation factors were further validated using independent datasets generated from patients with different disease phenotypes and ages, and 551 of the models and transformation factors were reproducible. These transformation factors provide a valuable resource for normalizing PEA-based proteomic data across serum and plasma, ultimately enhancing the capacity for collaborative analyses and facilitating comprehensive insights across diverse disease phenotypes.

Glucose metabolism disturbances and especially type 2 diabetes (T2D) are key risk factors of cardiovascular disease. Most recent epidemiological analyses suggest that they may affect more than 40% of the adult population in European countries. Biomarkers for the development of atherosclerosis in patients with T2D or prediabetes are not yet fully elucidated. In this study we focused on identifying proteomic biomarkers in patients with untreated glucose metabolism disturbances from population without diagnosed cardiovascular disease. We ran a cross-sectional study on serum samples from 508 subjects (mean age 52±10.5) without previously diagnosed diabetes, chronic inflammatory, neoplastic or cardiovascular diseases, who were stratified based to: healthy controls, prediabetes and diabetes using ESC criteria. All participants underwent detailed anthropometric measurements, body mass composition assessment using Dual-energy X-ray absorptiometry (DXA). Participants with glucose metabolism disturbances were older, had higher BMI, fat mass and HbA1C concentration than ones with prediabetes or healthy controls. Statistical analysis with correction for factors independently associated with T2D (BMI, fat mass, lean mass and age) was performed. We used a novel method that analyses over 1,000 proteins, covering a wide range of biological pathways with enrichment for immune system pathways to effectively screen the proteome and disease perturbations. Over 92% of the proteins were detected in at least 50% of the samples with intra- and inter-plate coefficients of variation of 7.6% and 7.8%, respectively. Among proteins of interest - mevalonate kinase (MVK) – an enzyme associated with cholesterol synthesis, presented levels that independently from other variables, demarcated healthy controls , prediabetes and T2D progression. Basal glycemia correlated with protein associated with cardiovascular diseases, among others: IL10, CCL20, ACE2, FGF21. Glycemia after 120 minutes of oral glucose tolerance test was associated with lipoprotein lipase, hydroxysteroid 11-beta dehydrogenase 1. Proteomic tools allow discovery of new molecules associated with cardiovascular disease. Increased MVK concentration in serum marks patients with more profound metabolic disorders and may be an insight into pathogenesis of cardiovascular disease in diabetics.Protein abundance in relation to OGTTBasal glycemia correlations