Exploring onset patterns and preventive strategies for early-stage locomotive syndrome through Bayesian network modeling

This study aimed to clarify factors contributing to early-stage locomotive syndrome stage 1 (LS1) and to identify onset patterns for prevention. We analyzed cross-sectional data from health check-ups conducted in the Iwaki area of Hirosaki City between 2015 and 2019, including 1,236 participants aged 20–85 years (non-LS: n = 884; LS1: n = 352). A Bayesian network of 43 variables—covering demographics, body composition, blood markers, lifestyle, and outcomes—was constructed to analyze relationships among variables. Edges directly connected to LS1 were evaluated for importance, and hierarchical clustering was applied to participant-specific edge importance. The following nine parent nodes in the estimated network influenced LS1: age, height, visceral fat area (VFA), albumin, interleukin-6, red blood cells, blood sugar, HbA1c, and aspartate aminotransferase. The VFA–LS1 edge showed the highest importance after age. Five onset patterns were identified: average (no direct edges), inflammation, aging, malnutrition, and visceral fat accumulation. A distinct pathway driven by VFA characterized one pattern. In summary, personalized prevention strategies were proposed for each onset pattern. Managing VFA may be key in preventing both metabolic and locomotive syndromes.