ProjectMultivariate analysis of biomarkers to better characterize their impact on the aging process
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. Principal component analysis and the Mahalanobis distance are relevant statistical tools for these analyzes and we assess their ability to describe aging.