This project aims to unravel the complex biological interactions underlying geriatric multimorbidity, with a focus on cancer, metabolic disorders, cardiovascular diseases, and neurodegenerative conditions. By employing an integrated multi-omics approach, we will systematically investigate the interplay between genomic susceptibility, metabolic dysregulation, microbiome alterations, radiomic phenotypes, and plasma proteomic profiles to identify shared pathways driving disease co-occurrence in aging populations. The study will leverage cutting-edge genomic sequencing to pinpoint high-impact variants in aging-related genes such as TP53 and APOE, while metabolomic profiling will reveal systemic perturbations in insulin signaling, lipid metabolism, and inflammatory markers. Plasma proteomics will be employed to characterize senescence-associated secretory phenotypes and neuroinflammatory markers linked to brain pathologies. Special emphasis will be placed on gut microbiome-derived metabolites including short-chain fatty acids and trimethylamine N-oxide, which may serve as molecular bridges connecting different disease states. Advanced radiomic analysis will extract quantitative features from CT, MRI (including structural and functional neuroimaging), and DEXA scans to characterize subclinical disease manifestations and predict progression trajectories, with particular attention to white matter hyperintensities and other neuroimaging markers of brain pathology. These multidimensional datasets will be integrated through machine learning algorithms to develop predictive models for risk stratification and personalized intervention strategies. The ultimate goal is to transform our understanding of geriatric multimorbidity by identifying novel biomarkers and therapeutic targets that could enable more precise management of complex aging-related conditions.
