Exploratory analysis using machine learning of predictive factors for falls in persons with type 2 diabetes: A Longitudinal Study

The risk of falls in elderly individuals with diabetes was reported to be 1.5 - 3 times higher than in those without diabetes. However, it is not clear what risk factors are strongly related to falls in those with diabetes. In this study, we aimed to investigate the status of falls and to identify important risk factors for falls in persons with type 2 diabetes (T2D) including the non-elderly. Participants were 316 persons with T2D who were admitted to the University of Tsukuba Hospital for treatment of diabetes. They were assessed for medical history, laboratory data and physical capabilities during the hospitalization and were given a questionnaire on falls one year after discharge. Two different statistical models, logistic regression and random forest classifier, were used to investigate important predictors of falls. The response rate to the survey was 72%; of the 226 respondents, there were 129 males and 97 females (median age 62 years). The fall rate during the first year after discharge was 19% and increased with age; fall rates were 17% for those <60 years, 20% for those aged 60 – 69 years and 24% for those ≥70 years. Logistic regression revealed that knee extension strength (β= -0.698, P = 0.002), fasting C-peptide (F-CPR) level (β= 0.492, P = 0.009) and dorsiflexion strength (β= -0.432, P = 0.047) were independent predictors of falls. The random forest classifier placed knee extension strength (covariate importance = 0.304), grip strength (0.234), F-CPR level (0.232) and dorsiflexion strength (0.230) in the top 4 important variables for falls. The rate of falls in persons with T2D was high even in middle age. Lower extremity muscle weakness as well as elevated F-CPR levels and reduced grip strength were shown to be important risk factors for falls in T2D.