title: Statistical distance as a measure of physiological dysregulation is largely robust to variation in its biomarker composition.
abstract: Physiological dysregulation may underlie aging and many chronic diseases, but is challenging to quantify because of the complexity of the underlying systems. Recently, we described a measure of physiological dysregulation, DM, that uses statistical distance to assess the degree to which an individual's biomarker profile is normal versus aberrant. However, the sensitivity of DM to details of the calculation method has not yet been systematically assessed. In particular, the number and choice of biomarkers and the definition of the reference population (RP, the population used to define a "normal" profile) may be important. Here, we address this question by validating the method on 44 common clinical biomarkers from three longitudinal cohort studies and one cross-sectional survey. DMs calculated on different biomarker subsets show that while the signal of physiological dysregulation increases with the number of biomarkers included, the value of additional markers diminishes as more are added and inclusion of 10-15 is generally sufficient. As long as enough markers are included, individual markers have little effect on the final metric, and even DMs calculated from mutually exclusive groups of markers correlate with each other at r~0.4-0.5. We also used data subsets to generate thousands of combinations of study populations and RPs to address sensitivity to differences in age range, sex, race, data set, sample size, and their interactions. Results were largely consistent (but not identical) regardless of the choice of RP; however, the signal was generally clearer with a younger and healthier RP, and RPs too different from the study population performed poorly. Accordingly, biomarker and RP choice are not particularly important in most cases, but caution should be used across very different populations or for fine-scale analyses. Biologically, the lack of sensitivity to marker choice and better performance of younger, healthier RPs confirm an interpretation of DM physiological dysregulation and as an emergent property of a complex system.
authors: Groupe de recherche PRIMUS, Department of Family Medicine, University of Sherbrooke, 3001 12e Ave N, Sherbrooke, QC, J1H 5N4, Canada.
authors: Alan A
authors: AA
authors: Cohen
authors: Groupe de recherche PRIMUS, Department of Family Medicine, University of Sherbrooke, 3001 12e Ave N, Sherbrooke, QC, J1H 5N4, Canada.
authors: Qing
authors: Q
authors: Li
authors: Department of Chemistry, Biochemistry and Physics, Université du Québec à Trois-Rivières, 3351, boul. des Forges, C.P. 500, Trois-Rivières, QC, G9A 5H7, Canada.
authors: Emmanuel
authors: E
authors: Milot
authors: Economics Department, ESG, Université du Québec à Montréal, 315 rue Sainte-Catherine Est, Montréal, QC, H2X 3X2, Canada.
authors: Maxime
authors: M
authors: Leroux
authors: Groupe de recherche PRIMUS, Department of Family Medicine, University of Sherbrooke, 3001 12e Ave N, Sherbrooke, QC, J1H 5N4, Canada.
authors: Samuel
authors: S
authors: Faucher
authors: Groupe de recherche PRIMUS, Department of Family Medicine, University of Sherbrooke, 3001 12e Ave N, Sherbrooke, QC, J1H 5N4, Canada.
authors: Vincent
authors: V
authors: Morissette-Thomas
authors: Groupe de recherche PRIMUS, Department of Family Medicine, University of Sherbrooke, 3001 12e Ave N, Sherbrooke, QC, J1H 5N4, Canada.
authors: Véronique
authors: V
authors: Legault
authors: Mailman School of Public Health, Columbia University, 722 W. 168th Street, R1408, New York, NY, 10032, United States of America.
authors: Linda P
authors: LP
authors: Fried
authors: Translational Gerontology Branch, Longitudinal Studies Section, National Institute on Aging, National Institutes of Health, MedStar Harbor Hospital, 3001 S. Hanover Street, Baltimore, MD, 21225, United States of America.
authors: Luigi
authors: L
authors: Ferrucci
mesh_terms: D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D015415:Biomarkers; D002908:Chronic Disease; D019295:Computational Biology / Q000379:methods*; D003196:Computer Graphics; D003430:Cross-Sectional Studies; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D011446:Prospective Studies; D012984:Software; D055815:Young Adult
chemical_list: D015415:Biomarkers