The Early detection of frailty syndrome using a model that employs a combination of omic data

Frailty syndrome (FS) is an age-related condition characterised by a loss of physiological reserves across multiple organs and systems, resulting in high vulnerability to even mild stressors [1]. This state of physiological deterioration and generalized loss of homeostasis has been shown to increase the risk of premature mortality, falls, fractures, hospitalization and institutionalization among the elderly [2]. An early and accurate diagnosis of FS is therefore critical for improving patient quality of life and guiding clinical decision-making.

FS is a complex phenotype influenced by multiple factors, with approximately 40% of its development attributable to genetic determinants. Genome-wide association studies have identified significant variants in genes involved in inflammation, neurotransmission, and aging pathways [3]. Concurrently, more evidence has emerged indicating a correlation between gut microbiota dysbiosis and the progression of FS [4].

In this study, a cohort comprising 936 genomic samples (whole-genome DNA microarrays) and 199 microbiome profiles obtained thro­ugh 16S rRNA sequencing was analysed. The cohort included both frail and healthy individuals aged ≥ 65 years. Supplementary clinical data provided additional context on participant health status. Predictive models were generated for each type of data: genomic, microbiome and clinical. Subsequently, an ensemble learning approach was implemented for the purpose of integrating all three model predictions, with a view to enhancing predictive accuracy.

The findings suggest that the combined ensemble model demonstrates superior performance in comparison to single-source predictors. The conclusions of the present study demonstrate the potential of omic data fusion and advanced machine learning techniques for FS diagnosis.

References:

1. Kim DH, Rockwood K. Frailty in Older Adults. N Engl J Med. 2024;391(6):538-548.

2. Khan KT, Hemati K, Donovan AL. Geriatric Physiology and the Frailty Syndrome. Anesthesiology clinics. 2019;37:453-474.

3. Weiss CO. Frailty and chronic diseases in older adults. Clinics in geriatric medicine. 2011;27:39-52.

4. Tongeren SP, Slaets JPJ, Harmsen HJM, Welling GW. Fecal microbiota composition and frailty. Applied and environmental microbiology. 2005;71:6438-6442.