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Large-Scale and Comprehensive Immune Profiling and Functional Analysis of Normal Human Aging

Fig 7

(A) Profile of mean-squared error of elastic-net regression for various values of lambda.

For a range of lambdas, an elastic-net regression is fit using 10-fold cross-validation. Vertical dotted lines indicate the value of lambda for which MSE is minimized, and the largest value of lambda within one standard error of that minimum. The numbers along the top border of the graph indicate the number of non-zero parameters that are included in the model for corresponding values of lambda. (B) Results of elastic-net regression combining data from multiple assays. Each bar represents the coefficients for a particular predictor of age. Predictors are selected by the algorithm, with 14 out of 312 analytes chosen. Analytes from multiple assays (clinical, serum Luminex, flow phenotyping, and the stimulated PBMC supernatant Luminex) were selected. Sex and CMV status were explicitly included in the model. Predictors were converted to Z-scores prior to model fitting so that all analytes could be interpreted on a common scale. Each unit represents one standard deviation for that particular analyte. Coefficients represent adjustment to a mean age of 69, based on the scaled value of the analyte. Thus, for example, the estimated age increases by approximately 1.6 years for every unit increase in absolute monocyte count (the analyte with the largest positive coefficient), holding all other values constant; and decreases by approximately 4.5 years for every unit increase in naïve CD8+ T cells (the analyte with the largest negative coefficient). (C) Comparison of predicted versus actual age for the elastic-net model. Each point represents one person. The blue diagonal line represents predicted age = actual age. Interestingly, the resulting model overestimates the age of younger participants, and underestimates the age of older participants.

Fig 7