title: A novel statistical approach shows evidence for multi-system physiological dysregulation during aging.
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.
authors: Groupe de recherche PRIMUS, Department of Family Medicine, University of Sherbrooke, CHUS-Fleurimont, 3001 12e Avenue North, Sherbrooke, QC J1H 5N4, Canada. Alan.Cohen@USherbrooke.ca
authors: Alan A
authors: AA
authors: Cohen
authors: Emmanuel
authors: E
authors: Milot
authors: Jian
authors: J
authors: Yong
authors: Christopher L
authors: CL
authors: Seplaki
authors: Tamàs
authors: T
authors: Fülöp
authors: Karen
authors: K
authors: Bandeen-Roche
authors: Linda P
authors: LP
authors: Fried
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000375:Aging; D015415:Biomarkers / Q000378:metabolism*; D005260:Female; D006304:Health Status; D006801:Humans; D015233:Models, Statistical; D015999:Multivariate Analysis; D012044:Regression Analysis
chemical_list: D015415:Biomarkers
references: Exp Gerontol. 2003 Jul;38(7):731-4
references: 12855278
references: Exp Gerontol. 2005 May;40(5):438-49
references: 15919596
references: CMAJ. 2005 Aug 30;173(5):489-95
references: 16129869
references: J Am Geriatr Soc. 2009 Sep;57(9):1525-31
references: 19682116
references: Math Biosci. 2007 Aug;208(2):538-51
references: 17300818
references: Curr Aging Sci. 2009 Dec;2(3):193-9
references: 20021413
references: Psychosom Med. 2007 Nov;69(8):769-76
references: 17942833
references: Demography. 2006 Feb;43(1):165-83
references: 16579213
references: Biol Rev Camb Philos Soc. 1990 Aug;65(3):375-98
references: 2205304
references: J Gerontol A Biol Sci Med Sci. 2000 Jan;55(1):M43-52
references: 10719772
references: Mech Ageing Dev. 2011 Mar;132(3):93-102
references: 21262255
references: Horm Behav. 2003 Jan;43(1):2-15
references: 12614627
references: J Am Geriatr Soc. 2006 Jun;54(6):991-1001
references: 16776798
references: Arch Intern Med. 2005 Oct 24;165(19):2222-7
references: 16246987
references: Mech Ageing Dev. 2011 Oct;132(10):496-502
references: 21864562
references: Proc Natl Acad Sci U S A. 2001 Apr 10;98(8):4770-5
references: 11287659
references: Trends Ecol Evol. 2012 Aug;27(8):428-35
references: 22613457
references: J Gerontol A Biol Sci Med Sci. 2001 Mar;56(3):M146-56
references: 11253156
references: Age Ageing. 2011 Jul;40(4):494-500
references: 21622673
references: J Clin Epidemiol. 2002 Jul;55(7):696-710
references: 12160918
references: Biol Res Nurs. 2009 Jan;10(3):248-56
references: 18829589
references: Adv Clin Chem. 2008;46:161-216
references: 19004190