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Poverty and InequalitySexual and Reproductive HealthFamily, Maternal & Child HealthMethodology

A novel statistical approach shows evidence for multi-system physiological dysregulation during aging

TitleA novel statistical approach shows evidence for multi-system physiological dysregulation during aging
Publication TypeJournal Article
Year of Publication2013
AuthorsCohen, AA, Milot, E, Yong, J, Seplaki, CL, Fulop, T, Bandeen-Roche, K, Fried, LP
JournalMechanisms of Ageing and Development
Volume134
Pagination110-7
Date PublishedMar
ISBN Number1872-6216 (Electronic)0047-6374 (Linking)
Accession Number23376244
KeywordsAged, Aged, 80 and over, Aging, Biological Markers/ metabolism, Female, Health Status, Humans, Models, Statistical, Multivariate Analysis, Regression Analysis
Abstract

Previous studies have identified many biomarkers that are associated with aging and related outcomes, but the relevance of these markers for underlying processes and their relationship to hypothesized systemic dysregulation is not clear. We address this gap by presenting a novel method for measuring dysregulation via the joint distribution of multiple biomarkers and assessing associations of dysregulation with age and mortality. Using longitudinal data from the Women's Health and Aging Study, we selected a 14-marker subset from 63 blood measures: those that diverged from the baseline population mean with age. For the 14 markers and all combinatorial sub-subsets we calculated a multivariate distance called the Mahalanobis distance (MHBD) for all observations, indicating how "strange" each individual's biomarker profile was relative to the baseline population mean. In most models, MHBD correlated positively with age, MHBD increased within individuals over time, and higher MHBD predicted higher risk of subsequent mortality. Predictive power increased as more variables were incorporated into the calculation of MHBD. Biomarkers from multiple systems were implicated. These results support hypotheses of simultaneous dysregulation in multiple systems and confirm the need for longitudinal, multivariate approaches to understanding biomarkers in aging.