Search PubMed⌕ Search

Biomedical subjects

Åsa K Hedman

Publications and source records attributed to Åsa K Hedman.

4 recordsLinked to original sources
PubMed2026

ReverseGWAS identifies combined phenotypes associated with a genotype in GWA studies.

MOTIVATION: Traditional genome-wide association studies (GWAS) aim to uncover the genetic variants associated with a single phenotype of interest (typically a disease), and to elucidate its genotypic architecture. However, many of today's GWAS simultaneously measure multiple related phenotypes, leading to the possibility of pursuing the reverse aim of elucidating the "phenotypic architecture" of a single genetic variant. In other words, we may ask what combination of measured phenotypes is associated with a given genotypic variant. ReverseGWAS is an algorithmic platform for answering such questions in the context of large-scale multi-phenotype GWAS. RESULTS: We demonstrate the effectiveness of ReverseGWAS on simulated data, showing its ability to identify logical combinations of phenotypes with a reasonable amount of noise. We then apply it to a selection of combined phenotypes from the UK Biobank, obtaining 719 candidate associations using autoimmune diseases and 205 using common ICD10 codes. We find that the majority of these associations (546/719 and 111/205, respectively) successfully replicate in an independent cohort, FinnGen. AVAILABILITY AND IMPLEMENTATION: The source code of ReverseGWAS is freely available to non-commercial users as an installable R package at https://github.com/Leonardini/rgwas.

Phenotype↗
PubMed2026

Protein Biomarkers in Risk and Prognosis of Amyotrophic Lateral Sclerosis.

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.

Humans↗
PubMed2025

Blood Pressure Lowering and Risk of Cancer: Individual Participant-Level Data Meta-Analysis and Mendelian Randomization Studies.

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.

epidemiology↗
PubMed2025

Genome-wide association study meta-analysis provides insights into the etiology of heart failure and its subtypes.

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.

Humans↗