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Identifying risk factors for sarcopenia using machine learning : insights from multimodal data
Felicita Urzi, Domen Šoberl, Ornella Caputo, Marco Vincenzo Narici, 2025, original scientific article

Abstract: Purpose This study aims to identify key risk factors for sarcopenia using machine learning models, leveraging anthropomet- ric, biochemical, functional, nutritional, and genetic data. By developing predictive models, the research seeks to improve early detection, enhance diagnostic accuracy, and facilitate personalized interventions for individuals at risk of sarcopenia. Methods We analysed multimodal data from 484 older adults. Two scenarios: Set-a (including SARC–CalF, excluding SARC-F) and Set-b (including SARC-F, excluding SARC–CalF) were applied in a three-stage modeling process with progressively reduced features and optimized predictive performance using machine learning models. Key predictors were ranked using SHAP values, and model performance was evaluated using AUC, accuracy, sensitivity, and specificity. Internal validation and DeLong’s test were applied to assess robustness and statistical differences. Results The most predictive risk factors included functional measures (chair stand, gait speed), nutritional indicators (pro- tein, folate, copper, vitamin B7), clinical conditions (diabetes, comorbidities, low-density lipoprotein (LDL)), and anthro- pometric markers (body mass index (BMI), calf circumference). Genetic features also contributed to risk stratification. The best-performing model Set-b (with screening test SARC-F) achieved an AUC of 0.951 and an accuracy of 93.62%. While SARC–CalF showed higher individual feature importance, the model achieved an AUC of 0.945 and accuracy of 92.2%. Conclusions This study highlights that traditional sarcopenia screening can be enhanced by capturing complex interplay of functional, nutritional, clinical, and genetic factors, offering clinicians a more accurate and tailored tool for early detec- tion and risk stratification. Future research should focus on validating these models in larger, independent, and longitudinal cohorts to assess their predictive utility across diverse populations and over time.
Keywords: genetics, nutrition, risk factors, sarcopenia
Published in RUP: 23.07.2025; Views: 1284; Downloads: 20
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Greater loss in muscle mass and function but smaller metabolic alterations in older compared to younger men following two weeks of bed rest and recovery
Rado Pišot, Uroš Marušič, Gianni Biolo, Sara Mazzucco, Stefano Lazzer, Bruno Grassi, Carlo Reggiani, Luana Toniolo, Pietro Enrico Di Prampero, Angelina Passaro, Marco Vincenzo Narici, Shahid Mohammed, Joern Rittweger, Mladen Gasparini, Mojca Gabrijelčič Blenkuš, Boštjan Šimunič, 2016, original scientific article

Keywords: ageing, prolonged physical inactivity, functional decline, reconditioning
Published in RUP: 08.08.2016; Views: 6679; Downloads: 204
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Single muscle fibre contractile properties differ between body-builders, power athletes and control subjects
J. P. Meijer, R. T. Jaspers, Joern Rittweger, Olivier Seynnes, S. Kamandulis, M. Brazaitis, A. Skurvydas, Rado Pišot, Boštjan Šimunič, Marco Vincenzo Narici, Hans Degens, 2015, original scientific article

Keywords: skeletal muscles, muscle fibers, contractile properties, body-builders, power athletes
Published in RUP: 08.08.2016; Views: 7206; Downloads: 212
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Costamere remodeling with muscle loading and unloading in healthy young men
Ruowei Li, Marco Vincenzo Narici, Robert M. Erskine, Olivier Seynnes, Joern Rittweger, Rado Pišot, Boštjan Šimunič, M. Flück, 2013, original scientific article

Keywords: costamere, atrophy, focal adhesion kinase, hypertrophy, physical inactivity, bed rest, skeletal muscle, myofibril, vinculin
Published in RUP: 15.10.2013; Views: 6328; Downloads: 163
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