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.

Fig. 1

Changes in predictive power of MHBD with increasing numbers of variables from the positive suite used in its calculation. Each circle represents an analysis based on one of the 16,383 combinations of the 14 variables in the positive suite. Color indicates p-value: black: p ≥0.1; blue: 0.05≤p<0.1; cyan: 0.01≤p<0.05; yellow- green: 0.001≤p<0.01; orange: 0.0001≤p<0.001; red: p<0.0001. The line represents a linear regression of number of variables on relevant effect size. Effect size trend shows the results of a Pearson correlation analysis of variable number with relevant effect size, and P-value trend shows the results of a Pearson correlation analysis of variable number with log₁₀(p-value). (a) Correlation of MHBD with age; (b) mean intra-individual slope of MHBD with age; (c) relative risk based on Cox proportional hazards of mortality before next visit. In (b) – (c), effect sizes are standardized as indicated in text.

Fig. 2

Changes in predictive power of MHBD with increasing numbers of variables from the negative suite used in its calculation. Each circle represents an analysis based on one of the 31 combinations of the 5 variables in the negative suite. Color indicates p-value: black: p ≥0.1; blue: 0.05≤p<0.1; cyan: 0.01≤p<0.05; yellow- green: 0.001≤p<0.01; orange: 0.0001≤p<0.001; red: p<0.0001. The line represents a linear regression of number of variables on relevant effect size. Effect size trend shows the results of a Pearson correlation analysis of variable number with relevant effect size, and P-value trend shows the results of a Pearson correlation analysis of variable number with log₁₀(p-value). (a) Correlation of MHBD with age; (b) mean intra-individual slope of MHBD with age; (c) relative risk based on Cox proportional hazards of mortality before next visit. In (b) – (c), effect sizes are standardized as indicated in text.