Biomedical subjects

Yumeng Jia

Publications and source records attributed to Yumeng Jia.

3 recordsLinked to original sources

PubMed2026

Adolescent depression as a systemic multimorbidity catalyst: integrated genetic and metabolic pathway analysis.

BACKGROUND: Although adolescent depression has been linked to individual chronic conditions, its broader role in shaping multimorbidity risk remains understudied. METHODS: A total of 87,562 UK Biobank participants were included, of whom 18,851 had documented adolescent depression. Cox proportional hazards models were applied to evaluate associations between adolescent depression and 24 chronic diseases, followed by stratified analyses by sex and age. Two-sample Mendelian randomization (MR) was then conducted to infer causality for diseases showing significant associations. Genomic colocalization analyses were performed using relevant GWAS data to identify shared causal variants. Mediation analyses were performed to detect possible mediating factors, including the frailty index, KDM biological age acceleration, allostatic load and 30 circulating biomarkers. RESULTS: Adolescent depression was associated with elevated risk for 12 chronic diseases, with strongest associations for hypothyroidism (HR = 1.29 [1.18-1.42]), diabetes (HR = 1.25 [1.13-1.38]) and chronic obstructive pulmonary disease (COPD) (HR = 1.74 [1.50-2.01]). Risks were notably higher among females and younger adults. MR confirmed likely causal relationships for hypothyroidism (OR = 1.45 [1.03-2.05]), diabetes (OR = 1.01 [1.01-1.02]) and COPD (OR = 1.04 [1.02-1.06]). Genomic colocalization revealed a shared genetic signal at the CDSN/PSORS1C1 locus between adolescent depression and hypothyroidism. Mediation analyses revealed disease-specific pathways: creatinine for hypothyroidism, testosterone for diabetes, KDM biological ageing for COPD and frailty index across all three conditions. CONCLUSIONS: Adolescent depression confers systemic vulnerability through genetic and metabolic mechanisms, with amplified risks in females and individuals aged ≤55 years. These findings support early, integrated interventions to mitigate long-term multimorbidity.

Humans↗

PubMed2026

Genetic heterogeneity affects the risk of incident depression, comorbidity, and response to environment: A prospective trajectory study.

BACKGROUND: Depression exhibits significant heterogeneity in its genetic underpinnings. The role of genetic components in the development of depression and its comorbidities remains insufficiently explored. METHODS: First, depression risk loci from a large-scale genome-wide meta-analysis were annotated to Gene Ontology (GO) terms by functional enrichment. GO-based polygenic risk scores (GO-PRS) were then calculated for individuals in the UK Biobank. Principal component analysis (PCA) was applied for dimensionality reduction, followed by cluster analysis to identify genetic subtypes of depression. Multistate models were applied to assess the impact of genetic patterns on the trajectory from healthy status to incident depression, and depression to 26 subsequent diseases, as well as the associations between environmental factors and disease trajectories across genetic subtypes. RESULTS: Participants were categorized into three genetic subtypes: immune-dominant, neuro-dominant, and comprehensive-risk. Significant differences in risk of depression and subsequent diseases, and susceptibility to environmental factors were observed across subtypes. Comprehensive-risk subtype showed higher risks of depression compared to immune-dominant (HR: 1.10, 95% CI: 1.05-1.15) and neuro-dominant subtype (HR: 1.12, 95% CI: 1.08-1.16). Comprehensive-risk subtype exhibited higher risks of transition from depression to subsequent diseases, such as anemia compared to immune-dominant subtype, and diseases of the digestive system compared to neuro-dominant subtype. Environmental factors were more strongly associated with the transition from depression to subsequent diseases in immune-dominant and comprehensive-risk subtypes, including cardiovascular, respiratory, and metabolic diseases. CONCLUSIONS: Our findings highlight the genetic heterogeneity of depression and comorbidities, and shed light on how genetic components modulate responses to environmental factors.

Humans↗

PubMed2025

Polygenic enrichment analysis in multi-omics levels identifies cell/tissue specific associations with schizophrenia based on single-cell RNA sequencing data.

OBJECTIVE: Understanding the specific cellular origin and tissue heterogeneity in schizophrenia is critically important for exploring the disease etiology. This study aims to investigate these aspects by performing multiple analyses based on omics data. METHOD: We performed single-cell disease relevance score (scDRS) algorithm to link brain single-cell RNA sequencing (scRNA-seq) with schizophrenia risk across multi-omics scales at single-cell resolution. This approach identified cell types with overexpression of schizophrenia-related genes implicated by multi-omics panels (ATAC-seq, RNA-seq, TWAS, and GWAS). Schizophrenia-related genes from these multi-omics panels were extracted and combined with scRNA-seq data to calculate scDRS. Subsequently, the cell-type vs. disease association and tissue heterogeneity were assessed using scDRS for each omics panel. RESULTS: We identified two novel cell subpopulations in the brain that differentially express SCUBE3 (59 cells, 7.0 %) and FN1 (21 cells, 2.5 %). At the individual cell level, schizophrenia-associated cell subpopulations included microglial cell associated with ATAC-seq panel (Passociation = 0.002, Pheterogeneity = 0.009) and deep layer neuron suggestively associated with GWAS panel (Passociation = 0.033, Pheterogeneity = 0.017). At the brain tissue level, microglial cell was significantly associated with cortical plate in ATAC-seq panel (Passociation = 0.002, Pheterogeneity = 0.011). Gene level analysis identified several genes associated with schizophrenia across multi-omics panels. CONCLUSIONS: Our study outlines the signature of cell subpopulations, brain regions, and disease risk genes in schizophrenia at single-cell resolution across multi-omics scales. These findings provide a reference for future precision medicine approaches targeting specific cell types and brain regions in schizophrenia.

Schizophrenia↗