Research Paper Volume 13, Issue 20 pp 23471—23516

Predicting physiological aging rates from a range of quantitative traits using machine learning


Figure 2. Physiological aging rates are associated with mortality. (A) Age-matched mortality analysis: 265 deceased participants were randomly paired with age- Matched ± 0.5 years) living participants in the baseline SardiNIA study. We calculated the difference in the mean PAR measurements of the two groups, ΔPAR = PARdeceased – PARliving and the corresponding p-value from a one-sided, one-sample t-test for ΔPAR > 0.The age-matched grouping was performed 10000 times and ΔPAR and p-values were calculated for each of the 10000 comparisons. 77.4% of the age-matched comparisons produced significantly greater than zero ΔPAR values (p < 0.05) and the mean ΔPAR across all comparisons was 0.016. Nearly all comparisons (>99%) had ΔPAR > 0, which indicated that PARdeceased > PARliving on average. (B) Randomized, age-matched control comparisons produced a 5.2% frequency of significantly greater than zero (p < 0.05) ΔPAR values and the mean ΔPAR was 0.00. Consistent with random assignment, 50.8% of the ΔPAR values were greater than zero. (C) Lifespans for individuals were negatively correlated with PARs (r = −0.491).