Leveraging precision medicine analytics to optimize inflammation reduction and enhance physical function in older adults
Abstract
Background: Chronic inflammation in older adults is a key contributor to functional decline and mortality. Although anti-inflammatory medications have shown limited success in improving physical function, emerging targeted approaches offer new promise. We applied machine learning to identify clusters of older adults with shared patterns of inflammatory and cardiometabolic dysregulation and evaluated their responses to specific interventions.
Methods: We conducted a secondary analysis of the Enabling Reduction of low-grade Inflammation in Seniors (ENRGISE) multicenter, double-blind, placebo-controlled, factorial trial. This trial assessed the effects of losartan, omega-3, combination therapy, or placebo on interleukin-6 (IL-6) levels and 400-m walking speed. Key variables were selected using least absolute shrinkage and selection operator (LASSO), followed by linear regression to identify those significantly affecting the outcome slope. The optimal intervention was defined as the one that maximized the estimated slope improvement.
Results: We included 287 participants (47.4% female; mean age 77.6 ± 5.4 years) with a baseline IL-6 of 4.81 pg/mL. If all participants had received the recommended interventions, the estimated mean IL-6 slope would be -0.70 pg/mL/year (95% CI, -3.71, 1.41), compared to -0.51 pg/mL/year (-1.47, 0.35) among those randomized. The estimated improvement in walking speed was +0.0017 m/s/year (-0.0336, 0.0407) for the recommended interventions versus…
How strong is the evidence?
Evidence Hub reads this as a randomized trial, done in people. Its evidence level is 2 of 5 (High · randomized trial). Randomized trials of a reasonable size are the strongest single-study design. Reported size: 287 participants or records.
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