Biology subjects

Quyyumi, A. A.

Publications and source records attributed to Quyyumi, A. A..

4 recordsLinked to original sources

bioRxiv2026

Immune determinants of leuprolide-associated cardiovascular risk in prostate cancer

Leuprolide, a widely used androgen-deprivation therapy (ADT) for prostate cancer (PC), has been associated with increased cardiovascular events (CVE), although the underlying mechanisms remain unclear. Through integrated spatial transcriptomic analyses of murine heart and immune-stromal profiling in patient blood, we identified immune programs linked to leuprolide-associated CVE risk. Spatial analyses revealed the distinct organization of lymphoid (T+B cells) aggregates and inflammatory macrophages in the plaque following leuprolide exposure that accompanied by ventricular hypertrophy, relative to normal vasculature. Parallel profiling of circulatory soluble and cellular components in patients demonstrated CD8+T cell enriched pattern in ~44% of patients, including memory and type 1 cytotoxic subsets that linked to treatment-induced CVE, such as ventricular dysfunctions, but no contribution of adipocytokines. These translational findings highlight the stratification of leuprolide-associated CVE risk in PC patients based on circulating T cell profiling and could guide the risk mitigation with alternative ADT agents.

immunology↗

bioRxiv2026

Identification and characterization of a human antibody profile inversely associated with adverse cardiovascular events

Objective: The immune response is linked to the progression of atherosclerotic cardiovascular disease (CVD). We sought to characterize a predictive cardiovascular antibody biomarker using two independent cohorts. Approach and Results: A human IgG profile serendipitously identified by ELISA was quantified in a well characterized cohort of 359 patients with a history of coronary artery disease (CAD) who experienced a total of 71 incident adverse CVD events (death, myocardial infarction, and stroke) over a median 4.1-year follow-up. Using Cox proportional hazard regression analysis, low biomarker levels were an independent predictor of adverse cardiovascular outcomes after adjustment for age, sex, diabetes mellitus, estimated glomerular filtration rate, presence of obstructive CAD, heart failure, total cholesterol, and high-density lipoprotein (HDL) cholesterol (adjusted hazard ratio of 1.90 [95% CI: 1.03 to 3.49; p=0.038] between lowest and highest tertiles). Validation was then performed in a larger secondary cohort using 4356 baseline samples from the Multi-Ethnic Study of Atherosclerosis (MESA), a prospective study of cardiovascular outcomes resulting in adjusted odds ratio of 50.5 per unit decrease in log-transformed biomarker [95% CI: 2.4 to 2773.1; p=0.030] over one year by logistic regression analysis. Conclusions: Low levels of human IgG antibodies targeting Bovidae IgG are independently associated with increased incidence of CVD events in patients with or without a history of CAD, indicating the potential clinical predictive power of this antibody profile as an inverse biomarker for CVD.

immunology↗

bioRxiv2025

Metabolic Trajectories During Surgical Stress in Patients Undergoing Cardiac Surgery

Stress hyperglycemia (SH) during acute illness is linked to adverse surgical outcomes, yet the accompanying metabolic perturbations are incompletely characterized. We profiled longitudinal metabolic changes in adults without diabetes undergoing cardiac surgery to identify pathways associated with perioperative SH (defined as point-of-care glucose [≥]140 mg/dL on [≥]3 readings or [≥]180 mg/dL once). Blood was collected at baseline before surgery (T0) and at 2 h (T1), 24-48 h (T2), and 72-96 h (T3) after surgical initiation. High-resolution metabolomics (LC-MS) was integrated with continuous glucose monitoring and inflammatory/cardiac biomarkers. At T0, several pathways were associated with subsequent SH, including bile acid metabolism, the carnitine shuttle, and fatty-acid oxidation, suggesting preoperative metabolic susceptibility. In longitudinal analyses, participants who developed SH showed coordinated postoperative changes with significant enrichment of pathways not evident at baseline, C21-steroid hormone biosynthesis, glycerophospholipid metabolism, and glycosphingolipid (ceramide) metabolism, consistent with lipid remodeling and inflammatory signaling during surgical stress. Individuals with SH also exhibited higher inflammatory biomarker levels (high-sensitivity C-reactive protein and soluble urokinase plasminogen activator receptor). A machine-learning model using early metabolomic features predicted SH with an area under the receiver operating characteristic curve of 0.86. These findings highlight distinct preoperative and perioperative metabolic trajectories associated with SH and implicate established dysglycemia-related pathways, as well as stress-induced pathways in perioperative metabolic dysregulation. Pathway enrichment analyses were exploratory and hypothesis-generating; validation in larger cohorts and assessment of implications for clinical outcomes are warranted.

biochemistry↗

bioRxiv2016

Constraints on eQTL fine mapping in the presence of multi-site local regulation of gene expression

Expression QTL (eQTL) detection has emerged as an important tool for unravelling of the relationship between genetic risk factors and disease or clinical phenotypes. Most studies use single marker linear regression to discover primary signals, followed by sequential conditional modeling to detect secondary genetic variants affecting gene expression. However, this approach assumes that functional variants are sparsely distributed and that close linkage between them has little impact on estimation of their precise location and magnitude of effects. In this study, we address the prevalence of secondary signals and bias in estimation of their effects by performing multi-site linear regression on two large human cohort peripheral blood gene expression datasets (each greater than 2,500 samples) with accompanying whole genome genotypes, namely the CAGE compendium of Illumina microarray studies, and the Framingham Heart Study Affymetrix data. Stepwise conditional modeling demonstrates that multiple eQTL signals are present for ~40% of over 3500 eGenes in both datasets, and the number of loci with additional signals reduces by approximately two-thirds with each conditioning step. However, the concordance of specific signals between the two studies is only ~30%, indicating that expression profiling platform is a large source of variance in effect estimation. Furthermore, a series of simulation studies imply that in the presence of multi-site regulation, up to 10% of the secondary signals could be artefacts of incomplete tagging, and at least 5% but up to one quarter of credible intervals may not even include the causal site, which is thus mis-localized. Joint multi-site effect estimation recalibrates effect size estimates by just a small amount on average. Presumably similar conclusions apply to most types of quantitative trait. Given the strong empirical evidence that gene expression is commonly regulated by more than one variant, we conclude that the fine-mapping of causal variants needs to be adjusted for multi-site influences, as conditional estimates can be highly biased by interference among linked sites.

genetics↗