Identification of Pre-frailty Sub-Phenotypes in Elderly Using Metabolomics

Area of Science:

• Gerontology and Aging Research
• Metabolomics and Biomarker Discovery
• Personalized Medicine in Elderly Care

Background:

• Aging involves complex physiological transitions, with pre-frailty poorly understood but linked to systemic imbalances.
• Frailty and sarcopenia are key contributors to age-related disease morbidity and mortality.
• Robust, multidimensional biomarkers are crucial for personalized care in aging populations.

Purpose of the Study:

• To characterize the pre-frailty phenotype using untargeted metabolomics.
• To identify specific, stable biomarkers for pre-frailty.
• To explore gender-specific metabolic signatures associated with pre-frailty.

Main Methods:

• Utilized untargeted serum metabolomics on a sub-cohort (n=212) from the NU-AGE project (baseline T0 and follow-up T1).
• Analyzed pre-frail and non-frail elderly participants (65-79 years) from Italian and Polish centers.
• Employed univariate statistics, logistic regression, and ROC curve analyses for biomarker identification and model validation.

Main Results:

• Metabolomics successfully discriminated pre-frailty sub-phenotypes by gender and progression status.
• Gender-specific models with four metabolites achieved high predictive capacity (AUCs 0.93 for men, 0.94 for women).
• Identified early/predictive markers with good performance in gender-specific models (AUCs 0.82 for men, 0.92 for women).

Conclusions:

• Untargeted metabolomics can identify distinct pre-frailty phenotypes and potential biomarkers.
• Multivariate strategies offer early monitoring of pre-frailty progression and reversibility.
• Gender-specific metabolic profiles are important for understanding and managing pre-frailty.