ArticleJournal of cachexia, sarcopenia and muscle2025
Development and Validation of Quantile Regression Forests for Prediction of Reference Quantiles in Handgrip and Chair-Stand Test.
Article in Journal of cachexia, sarcopenia and muscle, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Nonlinear association between age and verbal fluency and moderation by adiposity: cross-sectional evidence from the Look Up 8+ study.Frontiers in aging neuroscience · 2026Article
- Machine learning-based prediction of IVF/ICSI outcomes in male factor infertility highlighting couple-level BMI.Frontiers in endocrinology · 2026Article
- Nutritional Challenges in Older Cancer Patients: A Narrative Review of Assessment Tools and Management Strategies.Nutrients · 2025Review
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Authors and funding
4 authors.
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Abstract
backgroundMuscle strength is one of the key components in the diagnosis of sarcopenia. The aim of this study was to train a machine learning model to predict reference values and percentiles for handgrip strength and chair-stand test (CST), in a large cohort of community dwellers recruited in the Longevity check-up (Lookup) 8+ project.
methodsThe longevity checkup project is an ongoing initiative conducted in unconventional settings in Italy from 1 June 2015. Eligible participants were 18+ years and provided written informed consent. After a 70/20/10 split in training, validation and test set, a quantile regression forest (QRF) was trained. Performance metrics were R-squared (R
resultsBetween 1 June 2015 and 23 November 2024, a total of 21 171 individuals were enrolled, of which 19 995 were included in our analyses. In the overall population, 11 019 (55.1%) were females. Median age was 56 years (IQR 47.0-67.0). Five variables were included: age, sex, height, weight and BMI. After the train/validation/test split, 13 996 subjects were included in the train set, 4199 in validation set and 1800 in the test set. For handgrip strength, the R
conclusionsWe developed and validated a QRF model to predict subject-specific quantiles for handgrip and CST. These models hold promise for integration into clinical practice, facilitating cost-effective and time-efficient early identification of individuals at elevated risk of sarcopenia. The predictive outputs of these models may serve as surrogate biomarkers of the aging process, capturing functional decline.
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