Subjective Walking Speed Predicted Metabolic Disease Or Syndrome In Patients With Specific Health Examination

PURPOSE: To investigate the possibility of using subjective walking speed in predicting the risk of hypertension (HT), diabetes mellitus (DM), dyslipidemia (DL), or metabolic syndrome (MetS) in participants undergoing specific health checkups held in Kyoto. METHODS: This study included 23,549 individuals (11,773 men and 11,776 women) aged 40–74 years whose baseline checkup data were available (HT: 17,412; DM: 21,942; DL: 12,862; MetS: 21,117). Participants were classified into fast or slow subjective walking speed groups. Age, sex, height, body mass index (BMI), and waist circumference were investigated. BMI ≥25 was defined as obese. Metabolic diseases (HT, DM, and DL) and MetS were identified using systolic and diastolic blood pressure, fasting blood glucose, glycosylated hemoglobin, low-density and high-density lipoprotein cholesterol, triglyceride levels, waist circumference, or medication. Each condition was analyzed using the Cox proportional hazards model after adjusting for age, sex, obesity, smoking, drinking, and exercise habits. RESULTS: The following were the mean follow-up periods for each condition: HT, 1490 ± 443 days; DM, 1636 ± 306 days; DL, 1347 ± 537 days; and MetS, 1577 ± 375 days. HT, DM, DL, and MetS occurred in 24.9%, 4.0%, 39.8%, and 11.4% of patients, respectively. In the Cox proportional hazards model, those with fast subjective walking speed were significantly associated with a lower risk of DM (hazard ratio (HR): 0.74, 95% confidence interval (CI) 0.65–0.85; p < 0.001) and MetS (HR: 0.88, 95% CI 0.81–0.95; p = 0.002). No significant associations were observed for HT (HR: 0.96, 95% CI 0.91–1.02; p = 0.217) and DL (HR: 1.01, 95% CI 0.95–1.06; p = 0.842). CONCLUSIONS: Fast subjective walking speed was significantly associated with a lower risk of DM and MetS, although there were no significant associations with HT and DL. This indicates that subjective walking speed may be useful in predicting health-related outcomes such as DM and MetS for those undergoing a specific medical checkup.