Development and Validation of Quantile Regression Forests for Prediction of Reference Quantiles in Handgrip and...

Muscle 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.

The 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 (R²), mean squared error (MSE), root mean squared error (RMSE) and mean Winkler interval score (MWIS) with 90% prediction coverage (PC). Metrics 95% confidence intervals (CI) were calculated using a bootstrap approach. Variable contribution was analysed using SHapley Additive exPlanations (SHAP) values. Probable sarcopenia (PS) was defined according to the European Working Group on Sarcopenia in Older People 2 (EWGSOP2) criteria.

Between 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 R² was 0.65 (95% CI 0.63-0.67) in the validation set and 0.64 (95% CI 0.62-0.67) in the test set. PCs were 91.5% and 91.2%, respectively. For CST test, the R² was 0.23 (95% CI 0.20-0.25) in the validation set and 0.24 (95% CI 0.20-0.28) in the test set. The PCs were 89.5% and 89.3%. Gender was the most influential variable for handgrip and age for CST. In the validation set, 23% of subjects in the first quartile for handgrip and 13% of subjects in the fourth quartile for CST test met criteria of PS.

We 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.