Inflamm-aging does not simply reflect increases in pro-inflammatory markers - PubMed (original) (raw)

Randomized Controlled Trial

Inflamm-aging does not simply reflect increases in pro-inflammatory markers

Vincent Morrisette-Thomas et al. Mech Ageing Dev. 2014 Jul.

Abstract

Many biodemographic studies use biomarkers of inflammation to understand or predict chronic disease and aging. Inflamm-aging, i.e. chronic low-grade inflammation during aging, is commonly characterized by pro-inflammatory biomarkers. However, most studies use just one marker at a time, sometimes leading to conflicting results due to complex interactions among the markers. A multidimensional approach allows a more robust interpretation of the various relationships between the markers. We applied principal component analysis (PCA) to 19 inflammatory biomarkers from the InCHIANTI study. We identified a clear, stable structure among the markers, with the first axis explaining inflammatory activation (both pro- and anti-inflammatory markers loaded strongly and positively) and the second axis innate immune response. The first but not the second axis was strongly correlated with age (r=0.56, p<0.0001, r=0.08 p=0.053), and both were strongly predictive of mortality (hazard ratios per PCA unit (95% CI): 1.33 (1.16-1.53) and 0.87 (0.76-0.98) respectively) and multiple chronic diseases, but in opposite directions. Both axes were more predictive than any individual markers for baseline chronic diseases and mortality. These results show that PCA can uncover a novel biological structure in the relationships among inflammatory markers, and that key axes of this structure play important roles in chronic disease.

Keywords: Aging; Biomarker; Chronic disease; Inflammation; Multivariate.

Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

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Figures

Fig. 1

Fig. 1

Boxplot of variance explained for each of the 19 axes using 5000 random samples from the bootstrap. The first two axes (PCA1 and PCA2) are presented separately to allow appropriate _y_-axis scaling.

Fig. 2

Fig. 2

Boxplot of correlations between the original scores and those created by the 5000 random samples from the bootstrap for each of the 19 axes.

Fig. 3

Fig. 3

Strength and stability of axis loadings for PCA1 (left) and PCA2 (right) across non-random, often mutually exclusive population subsamples (the entire population, women, men, those from Greve in Chianti, those from Bagno a Ripoli, those aged less than 65, those aged more than 65). Here, each color represents one of the 19 markers, ordered from bottom to top by their importance in the full population analysis, represented by the height of the color. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 4

Fig. 4

Biplot of loadings for the first two PCA axes. A loading far from zero on a given axis indicates that the variable in question plays an important role in determining the axis. Green names are anti-inflammatory markers, red names are pro-inflammatory, blue names are part of the innate immune system and black names are the rest. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 5

Fig. 5

Correlations between age and the standardized scores for PCA1 (left), PCA2 (right). Blue lines indicated cubic spline fits.

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