SEARCH · PubMed Health

Results for “Polygenic Risk Score”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 163 records · Page 9Linked to original sources

PubMed2026

Integrative Analysis Uncover the Effects and Multi-Omics Features of Thigh Muscle Fat Infiltration.

The health impacts and underlying biological pathways of thigh muscle fat infiltration (TMFI) remain incompletely understood. In this study, we analyzed TMFI measured by magnetic resonance imaging in 55,120 UK Biobank participants and found that higher TMFI was significantly associated with all-cause mortality as well as with all major system-specific diseases examined (p values ranged from 2.50 × 10-88 to 9.97 × 10-04). TMFI also mediated the effects of lifestyle factors on health-related outcomes, with mediation proportions ranging from 6.7% to 71.7%. A genome-wide association study (GWAS) identified 79 lead single nucleotide polymorphisms (SNPs) linked to TMFI, and the polygenic risk score for TMFI was significantly associated with mortality and all incident diseases across examined organ systems in an independent subset of UK Biobank participants of European ancestry who were not included in the TMFI GWAS (n = 362,286, all p < 0.05). Gene-drug interactions identified multiple drugs that could potentially modulate TMFI. Analysis of single-cell transcriptomic data indicated that myogenic cells were strongly linked to TMFI (p = 7.08 × 10-08). Summary-data-based Mendelian randomization and Transcriptome-Wide Association Study analyses revealed numerous genes whose expression in specific tissues was associated with TMFI. Proteomic and metabolomic profiling uncovered a broad array of circulating biomarkers associated with TMFI, many of which mediated the effects of modifiable factors and genetic risk on TMFI. Overall, our results highlight the biological relevance of TMFI to human health and provide insights into the multi-omics mechanisms underlying TMFI, identifying potential targets for interventions.

Humans↗

PubMed2025

Sex as a modifier of genetic risk for type 1 diabetes.

Sex differences influence the pathogenesis of type 1 diabetes (T1D), yet most genetic studies have treated sex as a control covariate rather than a dynamic effect modifier. Sex influences immune cell behaviour, including CD4+ and CD8+ T cell activation, regulatory T cell stability, B cell autoantibody production, dendritic cell priming and monocyte/macrophage inflammation. Underlying mechanisms include hormone-responsive enhancers, X-escape gene dosage and sex-biassed chromatin states, intersecting with T1D-associated variants to produce sex-specific immune phenotypes. These insights help explain regional variation in sex ratios of T1D incidence, such as male predominance in high-risk populations and female excess in low-risk populations. Biological sex shapes T1D risk across multiple layers, including polygenic load; environmental exposures such as vitamin D deficiency and enteroviral infection; and sex-specific hormonal, chromosomal and epigenetic influences. An integrative G × E × S (genetic × environmental × sex-specific) liability-threshold framework is thus supported. Clinical and translational implications include developing sex-specific polygenic risk scores, biomarker panels and interventional strategies targeting pathways such as hormone signalling, vitamin D metabolism and the microbiome. Future multi-omic, longitudinal studies are warranted to test genotype-sex interactions, integrate sex as a core effect modifier and enable precision prevention and treatment of T1D in both males and females.

Humans↗

PubMed2026

Genomic Medicine Sweden: Advancing precision medicine at the national level.

High-throughput sequencing has transformed clinical diagnostics of rare diseases (RD), cancer and infectious diseases by enabling the identification of disease-causing genetic alterations and facilitating individualised treatment and care. In response to these advances, Genomic Medicine Sweden (GMS) was established in 2017 as a national collaborative effort to accelerate implementation of genomics-based precision medicine within Sweden's regionally organized, publicly funded healthcare system. GMS brings together the seven university healthcare regions and their associated medical faculties, in collaboration with healthcare regions across Sweden, Science for Life Laboratory, patient organizations, industry and governmental agencies. Activities are coordinated through national disease-specific expert groups, supported by cross-cutting functions in bioinformatics, health economics, ethics, education and patient engagement. At the operational level, seven Genomic Medicine Centres, embedded at university hospitals, develop and deliver harmonised genomic diagnostics nationwide. The National Genomics Platform provides secure infrastructure for large-scale data storage, analysis, and national and international data sharing. Following initial project-based funding, GMS now receives long-term governmental support. This review describes the national implementation of genomic-based precision diagnostics, discusses challenges and lessons learnt, and highlights key milestones across disease areas, including whole-genome sequencing in RD and paediatric cancer, comprehensive genomic profiling of haematological malignancies and solid tumours, pathogen genomics in microbiology, pharmacogenomic testing and emerging applications of polygenic risk scores in complex diseases. Collectively, these efforts have contributed to more than 500,000 genomic tests being performed within Swedish healthcare between 2017 and 2025. Finally, we outline future diagnostic needs and priority areas to ensure sustainable, scalable and equitable access to precision medicine.

Precision Medicine↗

PubMed2026

Incidence patterns and genetic validation of primary glaucoma subtypes among 1 million adults in China and the UK.

BACKGROUND/AIMS: Primary open-angle glaucoma (POAG) and primary angle-closure glaucoma (PACG) are distinct diseases, yet many glaucoma cases in population-based datasets lack subtype specification. We assessed incidence patterns of glaucoma subtypes in China and the UK and used genetic evidence to infer the likely subtype composition of cases recorded as unspecified glaucoma. METHODS: Incident primary glaucoma was identified from linked inpatient records in the prospective China Kadoorie Biobank (CKB; n=512 504) and UK Biobank (UKB; n=492 329) studies. Cohort-specific phenotyping algorithms defined POAG, PACG and unspecified glaucoma. Adjusted incidence rates were estimated by direct standardisation. To support subtype inference, polygenic risk scores (PRSs) were constructed using ancestry-specific genome-wide association studies, including a new East Asian PACG meta-analysis, and tested for association with glaucoma phenotypes using multivariable logistic regression. RESULTS: Over 12 years of follow-up, 1658 primary glaucoma cases were identified in CKB and 7643 in UKB. Most (>68%) cases lacked subtype specification. Incidence increased with age and was twofold higher among women for PACG in both cohorts and for unspecified glaucoma in CKB. In UKB, POAG incidence was fivefold higher among Black than White participants, with a similar but attenuated pattern for unspecified glaucoma. PRS analyses indicated that unspecified glaucoma closely aligned with PACG in CKB but was more heterogeneous in UKB. CONCLUSION: Healthcare-recorded incidence patterns for POAG and PACG were consistent with established demographic risk factors, whereas unspecified glaucoma showed differences in subtype composition between populations. Integrating epidemiological and genetic evidence improves interpretation of glaucoma phenotypes when detailed clinical information is unavailable.

Epidemiology↗

PubMed2025

Genetic relationships between systemic lupus erythematosus and a positive antinuclear antibody test in the absence of autoimmune disease.

OBJECTIVE: We defined the genetic factors associated with a positive ANA test (ANA+) in the absence of autoimmune disease and tested the association with SLE. METHODS: Using a case-control design, we performed a genome-wide association study (GWAS) in individuals of European ancestry without an autoimmune disease who had ANA tested as part of clinical care from DNA biobanks linked to de-identified electronic medical records: BioVU and Electronic Medical Records and Genomics. GWAS results were meta-analysed and single nucleotide polymorphism (SNP) heritability was calculated. A polygenic risk score (PRS) for ANA+ and for SLE was constructed and compared in patients with SLE, ANA+ and ANA negative (ANA-) individuals without autoimmune disease and general controls who never had ANA testing performed. RESULTS: A total of 7287 individuals of European ancestry were included in the meta-analyses (2169 ANA+ and 5118 ANA-); an SNP upstream of the TSBP1 in the HLA locus (rs1967688) was associated with ANA+ (p=4.84×10-8). SNP heritability for ANA+ was low (h2 SNP= 0.04), and the PRS for ANA+ was not significantly different in ANA+ and ANA- individuals. In contrast, the PRS for SLE was significantly higher in SLE compared with ANA+ individuals (p<2.2×10-16) but did not differ among ANA+, ANA- and general control groups (p=0.17). CONCLUSIONS: ANA+ occurring in the absence of autoimmune disease has a genetic association with the HLA region, but overall heritability is low. In addition, few SLE-associated SNPs were associated with ANA+, and the PRS for SLE was not associated with ANA+, indicating limited genetic overlap.

Humans↗

PubMed2026

Genetic Basis of Pancreatic Steatosis: A Systematic Review of Comparison between African and Non-African Populations.

This systematic review compared genetic evidence of pancreatic steatosis across African and non-African populations to illuminate ancestry-specific mechanisms and precision-prevention opportunities. Following PRISMA guidelines for reporting, a search was conducted across PubMed, Scopus, Web of Science, and NHGRI-EBI GWAS Catalog for studies spanning 2011 to 31st March 2026. Eligible studies included genome-wide association studies (GWAS), polygenic risk score (PRS), and Mendelian randomization (MR) analyses that reported genetic associations with pancreatic fat phenotypes and had explicit ancestry stratification or comparison. Narrative thematic synthesis was performed due to methodological heterogeneity. Six core genetic studies (N > 120,000 participants) were included. The only multi-ethnic GWAS found a strong protective variant of African ancestry, rs73449607 (near PDX1/PLUTO), which reduced pancreatic fat (β = -0.67, P = 4.50 × 10⁻⁸) and explained 14.3% of the variance in African Americans (versus 5.3% overall). UK Biobank race-stratified PRS analyses confirmed the lowest pancreatic fat fraction in Black participants, with the strongest HbA1c PRS-fat association in this group (ρ = 0.23, P < 0.0001). European-dominant GWAS highlighted risk loci, including FUT2 rs601338 (higher fat and chronic pancreatitis risk, OR 1.26). MR studies demonstrated causal links between genetically predicted intra-pancreatic fat deposition (IPFD) and pancreatic ductal adenocarcinoma (PDAC) (OR 2.46 per SD) but not diabetes. African-ancestry genomes confer substantial protection against pancreatic steatosis, whereas non-African genomes are enriched for risk alleles that amplify the non-alcoholic fatty pancreas disease (NAFPD)-to-PDAC cascade. These ancestry-differentiated mechanisms position NAFPD as a precision medicine target.

Africa↗

PubMed2026

The Clinical Application of Refined Risk Estimates Study in BRCA1 and BRCA2 Pathogenic Variant Carriers: A Randomized Controlled Trial.

UNLABELLED: Individuals with germline BRCA1 or BRCA2 pathogenic variants (PV) may struggle with risk management decision-making. Advancements in technology could provide more specific risk information to patients, but the impact of this information is unknown. The Clinical Application of Refined Risk Estimates Study is a two-arm randomized controlled trial in women with a BRCA1/BRCA2 PV. The primary objective was to determine whether genotype-informed personalized cancer risk estimates (GRE) compared with standard lifetime cancer risk estimates (SRE) decreased decisional conflict related to cancer risk management decision-making. Women were recruited following the disclosure of their PV results. Participants completed a baseline survey and were randomized 1:1 to receive a GRE or SRE. After receiving their results, participants completed a follow-up survey. Likert and continuous data measures were analyzed using linear regression. There were no differences in decisional conflict between study arms at follow-up. However, individuals in the SRE arm showed an increased need for personal structure compared with those in the GRE arm (P = 0.02). Compared with baseline, individuals within the SRE arm showed decreased decisional conflict (P = 0.003) and increased perceived stress (P = 0.02) at follow-up. A more personalized cancer risk estimate did not decrease decisional conflict in women with BRCA1/BRCA2 PVs. Future studies will determine whether a GRE affects actual decision-making behaviors. PREVENTION RELEVANCE: Women with a germline PV in BRCA1 or BRCA2 have significantly elevated risks of developing breast and ovarian cancers. This randomized controlled trial evaluates the impact of polygenic risk scores on decisional conflict related to breast and ovarian cancer prevention and risk management in those with BRCA1/BRCA2 PVs.

Humans↗

PubMed2025

Exome-wide association study of bleeding events in patients receiving direct oral anticoagulants.

BackgroundDirect oral anticoagulants (DOACs) are first-line medications for stroke prevention in non-valvular atrial fibrillation (AF). However, variability in drug response poses risks of hemorrhagic or thromboembolic events.ObjectivesAlthough genetic influences on DOACs safety are increasingly recognized, robust evidence directly linking specific polymorphisms to bleeding risk remains limited.DesignMulti-center observational case-control study including exome-wide association analysis of 196 non-valvular AF patients treated with rivaroxaban or apixaban, comprising 97 with bleeding complications and 99 without.MethodsDOAC plasma concentrations, urinary 6-β-hydroxycortisol and cortisol levels were measured for CYP3A4 phenotyping. Sequencing was performed on the DNBSEQ G-400 platform. Single-nucleotide variant (SNV) associations with bleeding risk were assessed using logistic regression with additive, dominant, and recessive genetic models. Polygenic risk scores (PRSs) were calculated to evaluate cumulative genetic effects.ResultsNo SNVs reached Bonferroni-corrected significance under any model. PRSs showed weak predictive ability for bleeding with apixaban. For rivaroxaban, regression indicated that ln Css min/D + 1 index increased with PRS, age, and 6-β-hydroxycortisol/cortisol ratio, but decreased with higher 6-β-hydroxycortisol and coronary heart disease presence. No statistically significant differences were found for the PharmGKB Level 3 variants rs1045642 (rivaroxaban) and rs2231142 (apixaban). Trends toward statistical significance were observed for the rs2472304-G variant in rivaroxaban users, rs6977165-C in apixaban users, and for the CYP3A4*1/*36 diplotype.ConclusionResidual equilibrium concentration of DOACs, including dose-adjusted, did not independently predict bleeding risk in non-valvular AF patients. Variants rs2472304 and rs6977165 may warrant further investigation as potential contributors to bleeding risk.

Humans↗

PubMed2025

Genomewide association study of a homogeneous multiple sclerosis cohort: Tumefactive demyelination.

BACKGROUND: Tumefactive demyelination (TD) is a rare variant of multiple sclerosis (MS) characterized by tumor-like lesions that often require aggressive management. Genome-wide association studies (GWAS) identified variants associated with MS; similar analyses in TD are lacking. OBJECTIVE: A GWAS was performed to identify variants associated with TD. METHODS: The case-control study included 142 TD cases and 293 controls. TD patients were required to have a demyelinating event and magnetic resonance imaging (MRI) showing one or more lesions. Controls were patients without a neurologic or systemic inflammatory disease or cancer. Logistic regression was used to compare cases versus controls for each variant; age, sex, and principal components were included as covariates. A p-value threshold of 5 × 10-8 was GWAS significant and 5 × 10-6 nominally significant. A polygenic risk score (PRS) was compared across TD and controls. RESULTS: Variants on chromosome 14 (rs117797734, p = 2.06 × 10-11, odds ratio (OR) = 13.14) and chromosome 6 (most significant rs6936540, p = 5.5 × 10-7, OR = 2.61) near DCBLD1 were significant. Seven non-MHC and two MHC variants associated with MS were associated with TD. The PRS was significantly higher in TD versus controls. CONCLUSION: We identified novel regions associated with TD, demonstrating the importance of performing GWAS in homogeneous subtypes of MS. Further validation and functional experiments are necessary.

Humans↗

PubMed2025

Interplay among lipoprotein(a), hepatic and vascular damage in individuals with metabolic dysfunction.

BACKGROUND: The relationship between plasma lipoprotein(a) [Lp(a)] levels and metabolic dysfunction-associated steatotic liver disease (MASLD) remains unclear. The aim of this study was to examine the combined effects of Lp(a) levels on liver and vascular damage. METHODS: The study was conducted using the Liver-Bible cohort of individuals with metabolic dysfunction (n = 859, 808 with genomic information) and the Milan Biobank (n = 6963). Genome-wide association studies (GWAS) and polygenic risk scores (PRS) were used to evaluate the inherited factors influencing plasma Lp(a) levels. RESULTS: In the Liver-Bible cohort, genetic variation in the LPA gene was the strongest determinant of Lp(a), followed by liver stiffness measurement (LSM). Additionally, circulating Lp(a) levels, but not genetic predisposition, were inversely related to LSM, suggesting that MASLD severity may affect Lp(a) secretion. Among participants with more severe insulin resistance (n = 250), Lp(a) levels (odds ratio 6.7, 95% CI 1.0-53.0, p = 0.046) and LSM (odds ratio 13.7, 95% CI 1.4-172.2, p = 0.023) were associated with greater prevalence of carotid atherosclerotic plaques, regardless of traditional cardiovascular risk factors. In the Milan Biobank, genetically predicted higher Lp(a) levels tended to increase the risk of liver-related outcomes, whereas genetically predicted MASLD was associated with lower circulating Lp(a) levels. CONCLUSIONS: The results of this study suggest that liver damage is more likely the cause of reduced plasma Lp(a) levels rather than a consequence. Assessing plasma Lp(a) levels and the extent of liver damage could improve the prediction of vascular damage.

Humans↗

PubMed2026

Large-scale multi-omics enhance risk prediction for type 2 diabetes.

BACKGROUND: Polygenic risk scores (PRS), metabolomics, and proteomics have each shown promise in improving type 2 diabetes risk prediction, but their combined utility beyond established clinical models remains unclear. We aimed to evaluate whether integrating multi-omics biomarkers enhances 10-year type 2 diabetes risk prediction beyond single-omics extensions and the clinical Cambridge Diabetes Risk Score (CDRS), which includes HbA1c measurements. METHODS: We analysed data from 42,840 UK Biobank participants without diagnosed diabetes at baseline. The study population was split into a derivation set (Phase 1 metabolomics release, N = 23,108) to fit models and an independent validation set (Phase 2 release, N = 19,732) to evaluate performance. Data for a PRS for type 2 diabetes, 11 metabolites, and 15 proteins were added to the CDRS to develop multi-omics prediction models. Model performance was evaluated using Harrell's C-index and the net reclassification index (NRI). RESULTS: During 10 years of follow-up, 1090 participants developed incident type 2 diabetes. Among individual omics layers, proteomics contributed the greatest improvement in predictive performance, increasing the C-index from 0.862 (clinical CDRS) to 0.884 (ΔC-index; + 0.022; P < 0.001), with a continuous NRI of 42.0%. The full multi-omics model further significantly increased the C-index compared to a model combining the clinical CDRS with proteomics data (C-index, 0.891; ΔC-index; + 0.007; P < 0.001). CONCLUSION: Integrating proteomics, metabolomics, and a diabetes-PRS into a clinical model substantially improves type 2 diabetes risk prediction beyond single-omics extensions. Several of the selected proteins and metabolites are on cardiovascular disease pathways, highlighting the link between diabetes and cardiovascular risk. However, the C-index difference between the proteomics extended and full multi-omics extended models is small, and the clinical models extended with proteomics data would be easier to translate into routine care because it needs only the measurement of 15 proteins. External validation and cost-effectiveness analyses are needed to support clinical adoption.

Humans↗

PubMed2025

Genomic structural equation modeling elucidates the shared genetic architecture of allergic disorders.

BACKGROUND: The intricate shared genetic architecture underlying allergic disorders-including allergic asthma, atopic dermatitis, contact dermatitis, allergic rhinitis, allergic conjunctivitis, allergic urticaria, anaphylaxis, and eosinophilic esophagitis-remains incompletely characterized. METHODS: Our study employed genomic structural equation modeling (Genomic SEM) to define the common factor representing the shared genetic architecture of allergic disorders. Coupled with diverse post-GWAS analytical methods, we aimed to discover susceptible loci and investigate genetic associations with external traits. Furthermore, we explored enriched genetic pathways, cellular layers, and genomic elements, and investigated putative plasma protein biomarkers. Polygenic risk score (PRS) analyses, leveraging our integrated GWAS data, were conducted to assess chromosomal-level risk associations for allergic disorders. RESULTS: A well-fitted genomic SEM integrated GWAS data, revealing the shared genetic architecture of allergic disorders. We identified a total of 2038 genome-wide significant SNP loci (p < 5e-8), including 31 previously unreported loci. Fine-mapping of variants and gene sets pinpointed 2 causal variants and 31 candidate susceptible genes. Genetic correlation analyses further illuminated the shared genetic architecture underlying multiple traits, notably psychiatric disorders. Preliminary findings identified four putative causal plasma protein biomarkers. CONCLUSION: Notably, this study presents the first comprehensive genetic characterization of allergic disorders through a GWAS analysis of an unmeasured composite phenotype, providing novel insights into shared etiological pathways across these conditions.

Humans↗

PubMed2025

Association of Genetic Predisposition to Inflammation With Cancer-Related Cognitive Impairment and Fatigue in Women Who Received Chemotherapy for Nonmetastatic Breast Cancer.

PURPOSE: Cancer-related cognitive impairment (CRCI) and cancer-related fatigue (CRF) are reported by approximately 75% of patients receiving chemotherapy for breast cancer and both have been linked to inflammation. We sought to test whether an inflammation polygenic risk score (iPRS) might be associated with CRCI and/or CRF. METHODS: Using data from the UK Biobank, we developed an iPRS for the INFLA-Score, a composite measure of C-reactive protein, white cell count, platelet count, and neutrophil-lymphocyte ratio. The iPRS was evaluated for association with CRCI and CRF among women with nonmetastatic breast cancer enrolled in two completed multisite clinical trials: URCC08106 and URCC10055. CRCI and CRF were measured before and after standard-of-care chemotherapy using the FACT-Cog and Multidimensional Fatigue Symptom Inventory-Short Form (MFSI-SF), respectively. Linear regression evaluated the change in FACT-Cog and MFSI scores; logistic regression evaluated binary outcomes of any versus no worsening of scores. Analyses were adjusted for patient and treatment factors. We also performed exploratory genome-wide analyses. RESULTS: The cohort included 802 women who received chemotherapy (anthracycline-based = 51.8%; previous surgery = 85.0%) at a median age of 55 years (range = 22-81). The iPRS was associated with a significant decrease in MFSI-SF score (β = -3.29 [95% CI, -6.25 to -0.34]; P = .029) and lower odds of decreased MFSI-SF score (odds ratio, 0.66 [95% CI, 0.47 to 0.93]; P = .016) following chemotherapy. This negative relationship was partially explained by a positive correlation of the iPRS with prechemotherapy MFSI-SF score (β = 4.33 [95% CI, 0.23 to 8.43]; P = .038). The iPRS was not associated with a change in FACT-Cog score (β = -1.12; P = .627), but single nucleotide polymorphism rs9365961 was (β = -10.05; P = 1.46 × 10-8). CONCLUSION: If validated, the iPRS could identify patients in need of supportive care interventions to reduce CRF.

Humans↗

PubMed2026

Partitioned blood pressure polygenic risk reveals differential genetic effects of tissue-specific enhancers and their interactions on cardiovascular disease.

Polygenic risk scores (PRS) compress genome-wide associations into a single predictor, but this aggregation obscures the distinct biological mechanisms through which genetic variation shapes complex traits. Here we introduce a framework that additively decomposes a trait's PRS, without loss of SNP heritability, into independent components defined by the tissue-specific and tissue-agnostic cis-regulatory elements (CREs) in which its variants act. Applied to blood pressure (BP) using ~0.5 million CREs across four BP-relevant tissues (adrenal gland, artery, heart, kidney), the framework reveals that regulatory effects are globally additive across tissues yet locally non-additive, and that the resulting partitioned scores carry pronounced, reproducible heterogeneity in their effects on BP and cardiovascular outcomes. We show this heterogeneity reflects gene-environment interactions, and trace one example to its mechanism: a kidney-CRE-partitioned score is protective against coronary artery disease and myocardial infarction through an interaction between ATP2B1 and antihypertensive medication. Explicitly modeling these interactions improves prediction and transferability, and tissue-focused partitioning increases power to resolve causal genes and reveals genes such as ADAMTS8 with antagonistic effects across BP components. Validated in an independent All of Us cohort, these findings recast the PRS from a blunt aggregate predictor into a mechanistic probe of context-dependent genetic architecture.

Journal Article↗

PubMed2026

Gene by environment interaction effects on the metabolic subtype of Polycystic Ovary Syndrome in Hispanic Community Health Study/Study of Latinos.

Polycystic Ovary Syndrome (PCOS) is a common polygenic endocrine disorder that is heterogenous in clinical presentation across genetic ancestry groups. PCOS is characterized by an array of symptoms such as hyperandrogenism, impaired mental health, and metabolic dysregulation. Studying the interaction of environmental factors (such as diet, physical activity, anxiety, and depression) with genetic variants on PCOS and its subtypes in populations with high cardiometabolic burden, e.g., Hispanic/Latinas, could aid in unraveling pathophysiological and genetic pathways through which PCOS functions. We sought to study gene by environment interactions with PCOS and its metabolic subtype (mPCOS) in a sample of US Hispanic/Latina female adults from the Hispanic Community Heath Study/Study of Latinos. In this large community-based study, we derived PCOS using self-reported condition and menstrual cycle information. We classified females with PCOS as having mPCOS if they had high metabolic impairment (fasting glucose, fasting insulin, or body mass index higher than the 75th percentile). There were 451 individuals with PCOS and 221 of them had mPCOS in our sample. We found that PCOS and mPCOS were significantly associated with hyperglycemia and high triglycerides in this population. While a polygenic risk score derived in European ancestry did not generalize to Hispanic/Latina females with PCOS, we identified the best proxy genetic variants in this population in known PCOS regions and investigated their interactions with four environment variables (diet, physical activity, anxiety and depression). Associations with known PCOS loci were generalized in our study at STAG3L4 and CACNA1G genomic regions. We observed GxE interactions between variants in/near three genes and physical activity on PCOS and mPCOS, including FGGY, FAT1, and PTHLH. Additionally, we noted interactions between diet and a variant in FANCC on PCOS, and diet and a variant near CMAS on both PCOS and mPCOS. We also detected GxE interactions between anxiety and depression and a variant in FGGY on PCOS, and depression and a variant near FBP1 on mPCOS. Our results point to potential protective effects of physical activity in females with PCOS and could inform future research on the mitigating effects of lifestyle management on PCOS genetic risk in Hispanic/Latino populations.

GxE↗

PubMed2026

Genetic and Lifestyle Factors Influence High 1-Hour Plasma Glucose, a Predictor of Type 2 Diabetes Mellitus.

BACKGRUOUND: High 1-hour plasma glucose (1-h PG) level has been proposed by the International Diabetes Federation to identify high-risk individuals and diagnose type 2 diabetes mellitus (T2DM). In a longitudinal cohort, we examined T2DM risk, β-cell function, and the effects of genetic and lifestyle factors on high 1-h PG. METHODS: We analyzed 7,464 participants without T2D at baseline from a community-based prospective cohort in Korea, who underwent biennial 2-h 75-g oral glucose tolerance tests over 14 years. Incident T2D risk were assessed across 1-h PG groups: < 155, 155-208, and ≥ 209 mg/dL. In 6,588 participants with at least two 1-h PG measurements, we analyzed 1-h PG trajectories by T2D polygenic risk score (PRS; low, 1st quintile; intermediate, 2nd-4th quintiles; high, 5th quintile) and lifestyle, assessed using Life's Essential 8. RESULTS: Compared to the <155 mg/dL group, hazard ratios for T2DM were 3.34 (95% confidence interval [CI], 2.99 to 3.74; P<0.001) for 155-208 mg/dL, and 6.81 (95% CI, 5.81 to 7.98; P<0.001) for ≥209 mg/dL. Both groups had lower baseline disposition index compared to the <155 mg/dL group (57.3% and 72.7%, respectively; both P<0.001). Higher T2DM PRS was associated with elevated baseline 1-h PG (low: 131 mg/dL, intermediate: 141 mg/dL, high: 151 mg/dL) and faster increase in 1-h PG (1.36 vs. 1.85 vs. 2.21 mg/dL/year; all P<0.001). Importantly, healthy lifestyle attenuated the rate of increase across all PRS groups. CONCLUSION: High 1-h PG predicts T2DM risk and is associated with β-cell dysfunction. The 1-h PG level is influenced by genetic risk and can be modified with a healthy lifestyle.

Humans↗

PubMed2025

Breast Cancer Risk Stratification in Black Women: Current Status and Potential Solutions to Improve Accuracy.

Breast cancer risk stratification models identify individuals at increased risk, allowing earlier screening than for those at average risk and potentially improving health outcomes. Due to the increasing rates of breast cancer in individuals aged <40 years, especially among Black females, the American College of Radiology now recommends all females initiate breast cancer risk assessment by age 25 years. Several breast cancer risk prediction models are readily available, including the Gail Model, Breast Cancer Surveillance Consortium Risk Calculator, BOADICEA, and Tyrer-Cuzick Model. However, because these models were primarily developed using data from White women of European ancestry, they may underestimate risk in Black women. Indeed, current evidence suggests that these models underpredict breast cancer risk among Black women, particularly those of African ancestry. Although cancer risk prediction models typically incorporate personal characteristics, family history of cancer, and hormonal and lifestyle factors, inherited breast cancer genes can also increase risk for breast cancer. Beyond monogenic inherited breast cancer genes that increase breast cancer risk, emerging data suggest that single nucleotide polymorphisms identified through genome-wide association studies (GWAS) may be used to generate polygenic risk scores, which may further refine breast cancer risk. However, GWAS data are also primarily gathered from European ancestry females, further reducing the ability to accurately stratify breast cancer risk in non-European ancestry populations. Current data highlight the importance of ensuring representation from all populations in developing cancer risk prediction models, conducting genomics research, and designing effective implementation strategies to enhance the use of these models in routine clinical care. Although new analytic methods and models are being developed to improve breast cancer risk stratification across populations, it remains critical to assess the utility and calibration of existing and new models to ensure applicability across non-European ancestry populations.

Humans↗

PubMed2026

MyGeneRisk Colon: A Web-Based Tool for Personalized Colorectal Cancer Risk Prediction Based on Genetics and Lifestyle.

Colorectal cancer (CRC) is a leading cause of cancer-related death, with incidence rising substantially among individuals under 50 years of age. Polygenic risk scores (PRS) hold promise for identifying high-risk individuals; when combined with lifestyle factors, they substantially improve prediction accuracy compared with models based on lifestyle factors alone. However, few clinical tools currently exist that facilitate this integrated, PRS-enhanced risk assessment. To bridge this gap, we developed MyGeneRisk Colo n, a publicly accessible web portal that delivers individualized CRC risk prediction by incorporating genetic, demographic, family history, and lifestyle factors. This paper details the development of the underlying risk prediction model, the portal's architecture and data security, our reporting framework, and engagement with a community advisory panel. Designed as a user-friendly platform, MyGeneRisk Colon aims to effectively communicate personalized CRC risk profiles and educate users and healthcare providers about prevention strategies.

Journal Article↗