Qin Hui

Heart failure (HF) affects 6.7 million people in the US and includes two major subtypes, HF with reduced ejection fraction (HFrEF) and HF with preserved ejection fraction (HFpEF), with distinct genetic architectures. We meta-analyze genome-wide association studies (GWAS) of 38,781 HFrEF cases, 38,163 HFpEF cases, and 526,135 controls across European, African, Hispanic, and Asian ancestries using the Million Veteran Program and Vanderbilt University DNA Databank (BioVU). We identify 46 genome-wide significant loci for HFrEF (9 novel) and 3 loci for HFpEF (1 novel). Four HFrEF loci are detected in African ancestry participants near CD36, SPI1, TRIM48 , and SPNS3 , with lead SNPs showing low risk-allele frequencies in European populations. In the all-cause HF meta-analysis (200,070 cases, 2,076,466 controls), we identify 136 loci (12 novel). Gene-based tests, tissue enrichment, transcriptome-wide association, and fine-mapping implicate vascular, metabolic, and TGF-β/Smad signaling pathways and nominate candidate causal genes, clarifying shared and subtype-specific risk across ancestries.

Background/Objectives: Human immunodeficiency virus (HIV) infection is characterized by chronic systemic inflammation. Antiretroviral therapy (ART) can reduce persistent inflammation, as measured by C-reactive protein (CRP). The CRP methylation risk score (MRS_CRP) acts as proxy for plasma CRP, but longitudinal changes in this score for people with HIV (PWH) before and after initiating ART have not been described. Methods: We evaluated a previously published MRS_CRP in the Emory Twin Study (N = 352), testing its correlation with plasma CRP and CRP-responsive biomarkers, as well as associations with cardiometabolic traits using generalized estimating equations (GEEs). We then applied MRS_CRP in the HIV AIDS Drug Resistance Surveillance Study (ADReSS) cohort (N = 440) to assess longitudinal changes before and after ART, using paired t-tests and linear mixed-effects models. Results: MRS_CRP showed moderate correlation with CRP (r = 0.38) and other inflammatory biomarkers (r = 0.25–0.34). MRS_CRP was associated with hypertension, type 2 diabetes, coronary artery disease, and obesity, and remained independently associated with obesity (odds ratio = 1.49) after adjusting for measured CRP. Z-score-standardized MRS_CRP decreased by 0.254 standard deviation units between baseline and follow-up among PWH receiving ART, confirmed by linear mixed-effects models. Conclusions: MRS_CRP showed associations with chronic inflammation and cardiometabolic diseases. The observed decline in MRS_CRP following ART initiation may reflect changes in inflammatory status among PWH. These findings highlight the potential of MRS_CRP as a marker of inflammation-related changes and comorbidity risk in PWH.

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Background: Proteomic profiling may improve the understanding of obesity and cardiovascular risk prediction. This study explores the use of protein-predicted scores for body mass index (PPS_BMI), body fat percentage (PPS_BFP), and waist–hip ratio (PPS_WHR) to estimate risk for major adverse cardiovascular events (MACEs). Methods: We used data from the UK Biobank with proteome profiling. PPS_BMI, PPS_BFP, and PPS_WHR were derived using the LASSO algorithm. The association between these protein scores and incident MACEs was evaluated using a competing risk model. Results: Strong to moderate correlations were observed between protein-predicted obesity phenotypes and their measured counterparts (R²: BMI = 0.78, BFP = 0.85, WHR = 0.63). Each standard deviation increment of PPS_BFP and PPS_WHR, but not PPS_BMI, was associated with greater risk of MACEs (hazard ratio [HR] 1.25, 95% CI 1.14–1.38, p < 0.0001; HR 1.15, 95% CI 1.06–1.24, p = 0.001, respectively). For predicting MACEs, compared with the PREVENT equation (C statistic 0.694), the models adjusted for only age, sex, current smoking, and protein scores showed comparable performance (C statistics 0.684–0.688). Conclusion: Protein-predicted scores of obesity showed strong independent associations and predictive performance for MACEs, suggesting they may capture additional biological risk beyond anthropometry. These scores may complement existing risk models by providing a biologically informed approach to assessing obesity-related cardiovascular risk and improving risk stratification.

BACKGROUND: Cardio-kidney-metabolic (CKM) disease represents a significant public health challenge. While proteomics-based risk scores (ProtRS) enhance cardiovascular risk prediction, their utility in improving risk prediction for a composite CKM outcome beyond traditional risk factors remains unknown. METHODS: We analyzed 23 815 UK Biobank participants without baseline CKM disease, defined by International Classification of Diseases -Tenth Revision codes as cardiovascular disease (coronary artery disease, heart failure, stroke, peripheral arterial disease, atrial fibrillation/flutter), kidney disease (chronic kidney disease or end-stage renal disease), or metabolic disease (type 2 diabetes or obesity). The sample was randomly divided into ProtRS training (70%, n=16 671) and validation (30%, n=7144) cohorts. A least absolute shrinkage and selection operator-based Cox regression model of 2913 Olink-based proteins was utilized to develop the ProtRS in the training cohort. We then assessed the association of the ProtRS with incident CKM disease risk in the validation cohort with competing-risk regression after adjusting for traditional risk factors and evaluated its ability to discriminate incident CKM disease risk with C-indices. RESULTS: The study sample had a mean age of 56.1 years; 44% were men, and 94% were White. Over a median follow-up of 13.5 years, 3235 and 1407 incident CKM disease events occurred in the training and validation cohorts, respectively. A ProtRS based on the weighted sum of the 238 least absolute shrinkage and selection operator-selected proteins was significantly associated with incident CKM disease risk (subdistribution hazard ratio per 1-SD, 1.87 [95% CI, 1.73–2.03]; P <0.001) in the validation cohort after adjustment for traditional risk factors. The addition of the ProtRS to a traditional risk factor model significantly improved incident CKM disease risk discrimination beyond the traditional risk factor model (C-index, 0.73 [0.72–0.74] versus 0.71 [0.69–0.72]; ΔC-index, 0.03 [0.02–0.04]). CONCLUSIONS: A ProtRS was independently associated with incident CKM disease risk and improved risk prediction beyond traditional risk factors in a population free of CKM disease at baseline.

Background and Objectives: Type 2 diabetes affects an increasing number of people worldwide. Although genome-wide association studies (GWAS) of type 2 diabetes have identified hundreds of loci, their interactions with other risk factors aren’t well understood. We investigated genetic interactions with three sex hormones (total testosterone, bioavailable testosterone, and sex hormone binding globulin (SHBG)) to identify additional type 2 diabetes-related loci that were undetected in traditional GWAS. Methods: The study population consisted of white European UK Biobank participants. Individuals with type 1 diabetes were excluded. We examined sex-stratified interactions of polygenic risk score (PRS) for type 2 diabetes with sex hormone levels. We analyzed sex-stratified, genome-wide SNP × sex hormone interactions, adjusting for age and the top ten principal ancestry components. Results: We found significant (P < 0.05) interactions for each of the sex hormones with PRS in both men and women, with the most significant being between SHBG and PRS in women (OR 0.88; 95% CI: 0.85-0.90; P = 1.09E-18). We identified 3 SNP × sex hormone interactions in men and 14 in women that achieved genome-wide significance (GWS; P < 5 × 10^-8). Applying a 2-degree of freedom test, we identified GWS loci (10 in men and 23 in women) that were not GWS when testing marginal genetic effects alone. Conclusion: Including interaction terms in GWAS may identify additional risk loci and improve the understanding of genetic architecture for type 2 diabetes. Different genetic interactions with sex hormones in men and women emphasize the importance of sex-stratified analysis in sex differential diseases.

Background: Although sex differences in coronary artery disease (CAD) risk have been observed, little is known about the role of sex hormones in CAD genetics. Accounting for sex hormone levels may help identify CAD‐risk loci and extend our knowledge of its genetic architecture. Methods and Results: A total of 365 662 individuals of European ancestry enrolled in the UK Biobank were considered. Genetic interaction of total testosterone, bioavailable testosterone, and SHBG (sex hormone‐binding globulin) were evaluated. Gene–environment interactions in millions of samples software was used to conduct sex‐stratified genome‐wide interaction analysis with prevalent CAD as the outcome. Participant age at enrollment and principal components 1 to 10 were adjusted as covariates. We identified 45 loci in men and 8 loci in women that reached genome‐wide significance ( P < 5 × 10 ⁻⁸ ) for CAD. Ten of the loci identified (5 loci in both men and women) were through joint effects and would not have been picked up using a traditional genome‐wide association study. Two of the joint effect loci in women were independently identified with significant single nucleotide polymorphism‐total testosterone interactions. Conclusions: This genome‐wide gene–sex hormone interaction study identified genomic‐risk loci that may contribute to the differential CAD risk between men and women, which otherwise would not have been discovered in a traditional genome‐wide association study solely including marginal genetic effects.

Background/Objectives: People with HIV (PWH) on antiretroviral therapy (ART) often gain weight, which increases their risk of type 2 diabetes and cardiovascular disease. The role of DNA methylation (DNAm) markers in obesity among PWH is understudied. This research explores the relationship between body mass index (BMI) and epigenetic patterns to better understand and manage obesity-related risks in PWH. Methods: We conducted an epigenome-wide association study (EWAS) on 892 African American male PWH from the Veterans Aging Cohort Study, examining BMI associations with DNAm using linear mixed models, adjusting for covariates, including soluble CD14. We compared our results with BMI-associated DNAm markers from non-HIV individuals and developed a methylation risk score (MRS) for BMI using machine learning and a cross-validation approach. Results: We identified four epigenome-wide significant CpG sites, including one in the RAP1B gene, indicating shared and unique BMI-related epigenetic markers between PWH and non-HIV individuals. The constructed BMI MRS explained approximately 19% of the BMI variance in PWH. Conclusions: DNAm markers and MRS are significantly linked to BMI in PWH, suggesting shared and distinct molecular mechanisms with non-HIV populations. These insights could lead to targeted interventions to reduce cardiometabolic disease risks in PWH under ART.

Heart failure (HF) is a significant health burden, with two major clinical subtypes: HF with reduced (HFrEF) and preserved ejection fraction (HFpEF). Blood pressure and lipid profile are established risk factors of HF. We performed univariable and multivariable Mendelian randomization (MR) analyses to assess potential causal effects of blood pressures and lipids on HF subtypes. Genetic instruments for blood pressures and lipids were derived from genome-wide association studies (GWASs) among the European participants of the UK Biobank. GWAS summaries of HFrEF and HFpEF were obtained from the meta-analysis of the European participants from the Million Veteran Program and the Vanderbilt University DNA Databank. Systolic blood pressure exhibited a supportive MR association primarily with HFpEF (odds ratio [OR], 1.14; 95% confidence interval [CI], 1.04–1.23), while diastolic blood pressure had an independent MR association with HFrEF (OR, 1.43; 95% CI, 1.13–1.77). MR associations also supported the observation that higher levels of low-density lipoprotein cholesterol increase the risk for both subtypes (HFrEF OR, 1.10 and 95% CI, 1.05–1.17; HFpEF OR, 1.05 and 95% CI, 1.02–1.09). These findings underscore differences in HF subtype-specific risk profiles and mechanisms, which may lead to different interventional strategies for different HF subtypes.

Obesity is a heritable disease, characterised by excess adiposity that is measured by body mass index (BMI). While over 1,000 genetic loci are associated with BMI, less is known about the genetic contribution to adiposity trajectories over adulthood. We derive adiposity-change phenotypes from 24.5 million primary-care health records in over 740,000 individuals in the UK Biobank, Million Veteran Program USA, and Estonian Biobank, to discover and validate the genetic architecture of adiposity trajectories. Using multiple BMI measurements over time increases power to identify genetic factors affecting baseline BMI by 14%. In the largest reported genome-wide study of adiposity-change in adulthood, we identify novel associations with BMI-change at six independent loci, including rs429358 (APOE missense variant). The SNP-based heritability of BMI-change (1.98%) is 9-fold lower than that of BMI. The modest genetic correlation between BMI-change and BMI (45.2%) indicates that genetic studies of longitudinal trajectories could uncover novel biology of quantitative traits in adulthood.