Background: Non-alcoholic fatty liver disease (NAFLD) represents the most widespread chronic liver disorder globally, impacting roughly 30% of the general population. Numerous factors have been linked to NAFLD, including obesity, type 2 diabetes, diet, physical inactivity, age, sex, genetic factors, and metabolic syndrome. Previous research predominantly treated NAFLD as a categorical outcome, providing less granular data compared to the continuous fatty liver index (FLI). This investigation enrolled healthy young Taiwanese men and applied multivariate adaptive regression spline (MARS) modeling to develop a predictive equation. Our aims were twofold: 1. To assess the predictive accuracy of traditional multiple linear regression (MLR) versus MARS. 2. To construct a MARS-derived equation for estimating FLI in this demographic. Methods: Data originated from the Taiwan MJ Cohort, comprising 5496 men aged 20–50 years not using medications for metabolic syndrome. MARS was used to formulate the FLI estimation equation. Model performance was compared using symmetric mean absolute percentage error (SMAPE), relative absolute error (RAE), root relative squared error (RRSE), and root mean squared error (RMSE). Results: Evaluation indicated that MARS yielded lower estimation errors than MLR, demonstrating its superior performance. The derived equation is: FLI = 65.224 − 0.436 × B1 − 0.490 × B2 + 0.252 × B3 − 2.962 × B4 + 2.231 × B5 − 0.292 × B6 + 0.189 × B7 − 0.361 × B8 − 0.699 × B9 + 0.160 × B10 − 2.715 × B11 + 0.799 × B12 − 0.153 × B13 + 0.084 × B14 − 35.274 × B15 − 4.424 × B16. Conclusions: Using MLR as a benchmark, our analysis revealed that MARS delivered better predictive performance. The presented equation explains 62.7% of the variance in FLI (r² = 0.627). Based on standardized variable importance scores (nsubsets metric), CRP emerged as the most influential predictor, followed by WBC, UA, HDL-C, AST, age, ALT, FPG, SBP, and LDL in this cohort of healthy young Taiwanese men.

Background: Sarcopenia is a multidimensional musculoskeletal-aging phenotype defined by reduced muscle quantity together with weakness and impaired performance, but public genetic resources rarely capture sarcopenia as a single uniformly labeled phenotype. Methods: Official National Human Genome Research Institute-European Bioinformatics Institute (NHGRI-EBI) genome-wide association studies (GWAS) Catalog association files archived locally on 15 April 2026 were grouped into muscle-quantity and function-frailty domains. Exact shared rsIDs were identified, assigned to catalog representative/mapped genes, evaluated against exploratory skeletal-muscle transcriptomic context, benchmarked with trait-grouping sensitivity analyses, and ranked with a secondary multi-criteria evidence-integration framework. Results: The muscle-quantity domain contained 1580 unique single-nucleotide polymorphisms (SNPs) and the function-frailty domain contained 383 unique SNPs, with 14 exact rsIDs shared between domains. A trait-file permutation benchmark did not show that this overlap exceeded a catalog-level null expectation (empirical p = 0.514), so the shared set is interpreted as a descriptive, high-specificity candidate intersection rather than statistically enriched sharing. Sensitivity analyses showed that the 14-rsID set was retained after excluding falling/fall and chronic obstructive pulmonary disease (COPD)-related proxy traits, indicating that the primary shared set was driven mainly by grip/low-grip function traits. Exploratory transcriptomic context and evidence integration organized candidates for follow-up but did not validate causal or linkage disequilibrium (LD)-level sharing. Conclusions: This framework generates candidate hypotheses for sarcopenia-related musculoskeletal aging but does not establish causal, fine-mapped, or statistically enriched genetic sharing.

Dysmenorrhea (Dys) is a prevalent gynecological condition that significantly impairs women’s quality of life. This study investigates the key predict