Health and disease phenotyping in old age using a cluster network analysis

• Main Authors: Valenzuela, Jesus Felix, Monterola, Christopher, Tong, Victor Joo Chuan, Ng, Tze Pin, Larbi, Anis
• Other Authors: School of Biological Sciences
• Format: Article
• Language: English
• Published: 2018
• Subjects: Disease; DRNTU::Science::Biological sciences; Network Analysis
• Online Access: https://hdl.handle.net/10356/87978; http://hdl.handle.net/10220/45584
• Institution: Nanyang Technological University

Human ageing is a complex trait that involves the synergistic action of numerous biological processes that interact to form a complex network. Here we performed a network analysis to examine the interrelationships between physiological and psychological functions, disease, disability, quality of life, lifestyle and behavioural risk factors for ageing in a cohort of 3,270 subjects aged ≥55 years. We considered associations between numerical and categorical descriptors using effect-size measures for each variable pair and identified clusters of variables from the resulting pairwise effect-size network and minimum spanning tree. We show, by way of a correspondence analysis between the two sets of clusters, that they correspond to coarse-grained and fine-grained structure of the network relationships. The clusters obtained from the minimum spanning tree mapped to various conceptual domains and corresponded to physiological and syndromic states. Hierarchical ordering of these clusters identified six common themes based on interactions with physiological systems and common underlying substrates of age-associated morbidity and disease chronicity, functional disability, and quality of life. These findings provide a starting point for indepth analyses of ageing that incorporate immunologic, metabolomic and proteomic biomarkers, and ultimately offer low-level-based typologies of healthy and unhealthy ageing.