Cross-population validation of statistical distance as a measure of physiological dysregulation during aging

Abstract

Measuring physiological dysregulation during aging could be a key tool both to understand underlying aging mechanisms and to predict clinical outcomes in patients. However, most existing indices are either circular or hard to interpret biologically. Recently, we showed that statistical distance of 14 common blood biomarkers (a measure of how strange an individual's biomarker profile is) was associated with age and mortality in the WHAS II data set, validating its use as a measure of physiological dysregulation. Here, we extend the analyses to other data sets (WHAS I and InCHIANTI) to assess the stability of the measure across populations. We found that the statistical criteria used to determine the original 14 biomarkers produced diverging results across populations; in other words, had we started with a different data set, we would have chosen a different set of markers. Nonetheless, the same 14 markers (or the subset of 12 available for InCHIANTI) produced highly similar predictions of age and mortality. We include analyses of all combinatorial subsets of the markers and show that results do not depend much on biomarker choice or data set, but that more markers produce a stronger signal. We conclude that statistical distance as a measure of physiological dysregulation is stable across populations in Europe and North America.

Keywords: Aging; Biomarker; InCHIANTI; Mahalanobis distance; Physiological dysregulation; WHAS.

Figure 1

Means and 95% quantiles of D_M with and without transformation and standardization. a) and b): unstandardized; c) and d) standardized (mean=0, sd=1); a) and c): untransformed; b) and d): log-transformed. Blue indicates WHAS and red InCHIANTI; triangles the 2.5^th percentile value, circles the mean value, and pluses the 97.5^th percentile value. There is a separate set of points for D_M calculated from each possible biomarker combination in each data set.

Figure 2

Correlations of raw variables and their deviances with age. Variables are sorted from lowest to highest deviance correlation coefficient in WHAS II (third column). Colored boxes indicate significant correlations (blue=positive, red=negative) with darker shading indicating lower p-values. Note that of the 12 original variables retained for WHAS II (those shaded blue at the bottom), only one would have been retained for WHAS I and four for InCHIANTI. On the other hand, eight additional variables not retained for WHAS II would have been retained for WHAS I, and 18 for InCHIANTI.

Figure 3

Changes in predictive power of D_M in WHAS with increasing numbers of variables used in its calculation. Each circle represents an analysis based on one of the 16383 combinatorial subsets of the 14 variables in WHAS. 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. (a)–(c): average individual slope of D_M with age (units of increase in D_M per year). (d)–(f): hazard ratio of mortality per unit D_M, controlling for age. (a), (d): The full WHAS data set. (b), (e): WHAS I. (c), (f): WHAS II.

Figure 4

Changes in predictive power of D_M in InCHIANTI with increasing numbers of variables used in its calculation. Each circle represents an analysis based on one of the 4095 combinatorial subsets of the 12 variables in InCHIANTI. 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. (a) and (b): average individual slope of D_M with age (units of increase in D_M per year). (c) and (c): hazard ratio of mortality per unit D_M, controlling for age. (a) and (c): The full InCHIANTI data set. (b) and (d): The subset of women aged 70+ (for comparison with the original WHAS II data set).