Composite Measure of Physiological Dysregulation as a Predictor of Mortality: The Long Life Family Study

Frontiers in Public Health

article

Konstantin G. Arbeev¹  Bharat Thyagarajan²  Joseph M. Zmuda³  Anatoliy I. Yashin¹  Olivia Bagley¹  Svetlana V. Ukraintseva¹  Hongzhe Duan¹  Alexander M. Kulminski¹  Eric Stallard¹  Deqing Wu¹  Kaare Christensen⁴  Mary F. Feitosa⁵ 

[1] Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, United States;Department of Laboratory Medicine and Pathology, University of Minnesota, United States;Department of Epidemiology, University of Pittsburgh, United States;Danish Aging Research Center, Department of Public Health, University of Southern Denmark;Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, United States

关键词: physiological dysregulation;    statistical distance;    mortality;    prediction;    Long Life Family Study;    deficits index;    aging;   

DOI  :  10.3389/fpubh.2020.00056

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

Biological aging results in changes in an organism that accumulate over age in a complex fashion across different regulatory systems, and their cumulative effect manifests in increased physiological dysregulation (PD) and declining robustness and resilience that increase risks of health disorders and death. Several composite measures involving multiple biomarkers that capture complex effects of aging have been proposed. We applied one such approach, the Mahalanobis distance (D M ), to baseline measurements of various biomarkers (inflammation, hematological, diabetes-associated, lipids, endocrine, renal) in 3,279 participants from the Long Life Family Study (LLFS) with complete biomarker data. We used D M to estimate the level of PD by summarizing information about multiple deviations of biomarkers from specified “norms” in the reference population (here, LLFS participants younger than 60 years at baseline). An increase in D M was associated with significantly higher mortality risk (hazard ratio per standard deviation of D M : 1.42; 95% confidence interval: [1.3, 1.54]), even after adjustment for a composite measure summarizing 85 health-related deficits (disabilities, diseases, less severe symptoms), age, and other covariates. Such composite measures significantly improved mortality predictions especially in the subsample of participants from families enriched for exceptional longevity (the areas under the receiver operating characteristic curves are 0.88 vs. 0.85, in models with and without the composite measures, p = 2.9 × 10 −5 ). Sensitivity analyses confirmed that our conclusions are not sensitive to different aspects of computational procedures. Our findings provide the first evidence of association of PD with mortality and its predictive performance in a unique sample selected for exceptional familial longevity.

【 授权许可】

CC BY