This study investigated the association between metabolomic profiles, genetic risk scores (GRS), and brain volumes in 8,635 UK Biobank participants. Machine learning revealed that individuals in the top 20% of metabolomic state had 2.4–35.7% larger brain volumes compared to the bottom 20%, while GRS showed a similar but weaker effect (1.5–32.8%). Metabolomic state explained 2.2–19.4% of brain volume variance, whereas GRS accounted for 0.8–8.7%. While metabolomic state added minimal predictive value beyond age and sex, GRS provided moderate additional predictive power (0.8–8.8%). No significant interaction between metabolomic state and GRS was found, though some associations varied by sex or age. Key metabolomic predictors included lipids and fatty acids. The findings highlight metabolomic state and GRS as important, independent predictors of brain phenotypes.

This study aimed to identify novel biomarkers for early glaucoma detection and treatment using network-based multiomics analysis. Researchers constructed disease-biomarker and disease-target-drug networks, applying greedy search algorithms to pinpoint key biomarkers and drug targets. Genome-wide association studies and UK Biobank data validated 10 hub biomarkers, including BMP1 and MMP9, with diagnostic and therapeutic potential for glaucoma. Network analysis revealed critical pathways connecting glaucoma with related diseases. The findings suggest these hub biomarkers could significantly improve early diagnosis and treatment strategies for glaucoma.

Alzheimer’s disease (AD) is a common neurodegenerative disorder with poorly understood genetic comorbidities. This study identified ten genetic comorbidities and 16 pleiotropic genes linked to AD, providing insights into the molecular basis of AD. Additionally, 50 potential diagnostic biomarkers were discovered, offering new directions for AD diagnosis.