Development of a Simple Formula for Identifying Reduced Fat-Free Mass in Health Screening Participants

Background/Objectives: To develop and validate a simple prediction model for identifying individuals with low fat-free mass index (FFMI) using routinely available clinical variables. Methods: A total of 8736 individuals were included (mean age = 54.2 years). Among men, 1674 (40.1%) had a low FFMI (<18 kg/m2), while among women, 2160 (47.3%) had a low FFMI (<15 kg/m2). Candidate predictors were preprocessed to reduce redundancy and multicollinearity. Repeated LASSO logistic regression and XGBoost-based SHAP analysis were used to identify robust predictors, and sex-specific logistic regression models were developed using the final selected variables. Model performance was evaluated using 500 repeated 8:2 train-test splits, calibration analysis, risk stratification, and decision curve analysis (DCA). Results: BMI and estimated GFR (eGFR) were consistently selected as key predictors and were used to construct the final sex-specific models. The BMI-eGFR model showed excellent discrimination. Calibration plots demonstrated good agreement between predicted probabilities and observed event rates. Mean held-out sensitivities and specificities were 0.861/0.875 in men and 0.869/0.805 in women, respectively. DCA showed that the model provided a higher net benefit than treat-all or treat-none strategies over most of the evaluated threshold range of 0.05–0.50. Conclusions: Our model for predicting low FFMI may be helpful when direct muscle mass measurement is not readily available.