Zupo, Roberta, Moroni, Alessia, Castellana, Fabio, Gasparri, Clara, Catino, Feliciana, Lampignano, Luisa, Perna, Simone, Clodoveo, Maria Lisa, Sardone, Rodolfo 
ORCID: 0000-0003-1383-1850 and Rondanelli, Mariangela
(2023)
A Machine-Learning Approach to Target Clinical and Biological Features Associated with Sarcopenia: Findings from Northern and Southern Italian Aging Populations.
METABOLITES, 13 (4).
565-.
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A Machine-Learning Approach to Target Clinical and Biological Features Associated with Sarcopenia Findings from Northern and.pdf - Open Access published version Download (1MB) | Preview |
Abstract
Epidemiological and public health resonance of sarcopenia in late life requires further research to identify better clinical markers useful for seeking proper care strategies in preventive medicine settings. Using a machine-learning approach, a search for clinical and fluid markers most associated with sarcopenia was carried out across older populations from northern and southern Italy. A dataset of adults >65 years of age (<i>n</i> = 1971) made up of clinical records and fluid markers from either a clinical-based subset from northern Italy (Pavia) and a population-based subset from southern Italy (Apulia) was employed (<i>n</i> = 1312 and <i>n</i> = 659, respectively). Body composition data obtained by dual-energy X-ray absorptiometry (DXA) were used for the diagnosis of sarcopenia, given by the presence of either low muscle mass (i.e., an SMI < 7.0 kg/m<sup>2</sup> for males or <5.5 kg/m<sup>2</sup> for females) and of low muscle strength (i.e., an HGS < 27 kg for males or <16 kg for females) or low physical performance (i.e., an SPPB ≤ 8), according to the EWGSOP2 panel guidelines. A machine-learning feature-selection approach, the random forest (RF), was used to identify the most predictive features of sarcopenia in the whole dataset, considering every possible interaction among variables and taking into account nonlinear relationships that classical models could not evaluate. Then, a logistic regression was performed for comparative purposes. Leading variables of association to sarcopenia overlapped in the two population subsets and included SMI, HGS, FFM of legs and arms, and sex. Using parametric and nonparametric whole-sample analysis to investigate the clinical variables and biological markers most associated with sarcopenia, we found that albumin, CRP, folate, and age ranked high according to RF selection, while sex, folate, and vitamin D were the most relevant according to logistics. Albumin, CRP, vitamin D, and serum folate should not be neglected in screening for sarcopenia in the aging population. Better preventive medicine settings in geriatrics are urgently needed to lessen the impact of sarcopenia on the general health, quality of life, and medical care delivery of the aging population.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | sarcopenia, nutrition, body composition, machine learning, artificial intelligence, elderly, older adults, aging, Salus in Apulia, Italy |
| Divisions: | Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Life Courses and Medical Sciences |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 07 Jul 2023 14:52 |
| Last Modified: | 07 Jul 2023 14:52 |
| DOI: | 10.3390/metabo13040565 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3171536 |
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