PubMed HealthSearch

Biomedical subjects

Jin J Zhou

Publications and source records attributed to Jin J Zhou.

2 recordsLinked to original sources

Novel approaches and applications in identifying DNA methylation markers of cardio-kidney-metabolic disease.

Cardio-kidney-metabolic (CKM) diseases represent a major public health challenge, accounting for a large proportion of global burden of morbidity and mortality. These conditions share risk factors, including genetic predisposition, environmental exposures, and lifestyle influences, which collectively drive disease development and progression. Epigenetic modifications, particularly DNA methylation (DNAm), serve as key mediators and biomarkers between these risk factors and disease phenotypes by regulating gene expression without altering the DNA sequence. Epigenome-wide association studies have identified DNAm markers associated with CKM diseases and related phenotypes, highlighting both shared pathways and disease-specific epigenetic signatures in inflammation, metabolic dysfunction, and aging-related processes. Longitudinal studies further demonstrate the dynamic nature of DNAm changes over time, offering insights into disease trajectories. Additionally, methylation risk scores integrating multiple epigenetic markers show promise in improving disease prediction and risk stratification beyond traditional clinical factors. To synthesize the current evidence, we conducted a targeted literature search in PubMed for English-language, peer-reviewed articles published between 2014 and the present. Future research leveraging large, well-phenotyped cohorts, advanced statistical methods, and innovative study designs will be critical for uncovering novel biomarkers, refining risk prediction models, and developing targeted epigenetic therapies to mitigate the global burden.

Humans

Heterogeneous effects of genetic variants and traits associated with fasting insulin on cardiometabolic outcomes.

Elevated fasting insulin levels (FI), indicative of altered insulin secretion and sensitivity, may precede type 2 diabetes (T2D) and cardiovascular disease onset. In this study, we group FI-associated genetic variants based on their genetic and phenotypic similarities and identify seven clusters with distinct mechanisms contributing to elevated FI levels. Clusters fall into two types: "non-diabetogenic hyperinsulinemia," where clusters are not associated with increased T2D risk, and "diabetogenic hyperinsulinemia," where T2D associations are driven by body fat distribution, liver function, circulating lipids, or inflammation. In over 1.1 million multi-ancestry individuals, we demonstrated that diabetogenic hyperinsulinemia cluster-specific polygenic scores exhibit varying risks for cardiovascular conditions, including coronary artery disease, myocardial infarction (MI), and stroke. Notably, the visceral adiposity cluster shows sex-specific effects for MI risk in males without T2D. This study underscores processes that decouple elevated FI levels from T2D and cardiovascular risk, offering new avenues for investigating process-specific pathways of disease.

Humans