Leveraging precision medicine analytics to optimize inflammation reduction and enhance physical function in older...

Area of Science:

• Gerontology
• Immunology
• Computational Biology

Background:

• Chronic inflammation significantly contributes to functional decline and mortality in older adults.
• Current anti-inflammatory medications have shown limited efficacy in improving physical function.
• Targeted interventions offer a promising avenue for addressing inflammation-related health issues in aging populations.

Purpose of the Study:

• To apply machine learning to identify distinct patient clusters based on inflammatory and cardiometabolic profiles.
• To evaluate the effectiveness of tailored interventions for these identified clusters in older adults.
• To determine if personalized approaches can improve physical function and reduce mortality.

Main Methods:

• Secondary analysis of the Enabling Reduction of low-grade Inflammation in Seniors (ENRGISE) trial.
• Utilized LASSO regression for key variable selection and linear regression to analyze outcome slopes.
• Defined optimal intervention by maximizing estimated slope improvement for interleukin-6 (IL-6) and physical function metrics.

Main Results:

• Included 287 participants (mean age 77.6 years) with elevated baseline IL-6 levels.
• Recommended interventions showed a trend toward improved IL-6 slope (-0.70 pg/mL/year) versus trial randomization (-0.51 pg/mL/year).
• Estimated improvements in walking speed and grip strength were comparable between recommended and trial groups, with non-significant results.

Conclusions:

• Findings suggest potential benefits of tailored interventions for individuals' unique inflammatory profiles.
• Personalized medicine analytics may identify more effective and individualized treatments for older adults.
• Further powered studies are needed to confirm the efficacy of precision medicine approaches in this population.