Diabetes poses a growing global public health burden, with impaired fasting glucose (IFG) representing a key prediabetic stage. Body fat percentage (BF%) is a commonly used indicator of adiposity and has been shown to be a better predictor of metabolic risk than BMI. However, evidence regarding the association between BF% and the risk of IFG in Chinese populations remains limited. This study aims to explore the relationship between BF% and the risk of incident IFG in Chinese adults. The study analyzed data from 184,308 Chinese adults with normal fasting glucose, derived from a large retrospective cohort database. The exposure was BF%, estimated using a previously validated prediction formula incorporating BMI, age, and gender. The primary outcome was new-onset IFG, defined as a fasting plasma glucose level between 5.6 and 6.9 mmol/L. The relationship between BF% and IFG risk was examined using multivariable Cox proportional hazards regression models. Restricted cubic splines (RCS) were used to explore nonlinearity, and subgroup analyses were conducted to assess consistency. During a median follow-up of 3.0 years, 11.28% of participants developed IFG. Higher BF% levels were independently associated with increased IFG risk: per 1-unit increase, the HR was 1.03 (95% CI: 1.02-1.03) in the fully adjusted model. A dose-response relationship was observed across BF% quartiles. RCS analysis revealed a nonlinear association with an inflection point at BF% = 30.205; below this point, the HR per 1-unit increase was 1.013 (95% CI: 1.009-1.017), and above it, the HR was 1.046 (95% CI: 1.038-1.054). Elevated BF% was consistently and positively associated with an increased risk of IFG across all predefined subgroups, with all corresponding HRs being greater than 1, indicating a robust and consistent association. Elevated BF% is an independent risk factor for IFG in Chinese adults, exhibiting a dose-response and nonlinear relationship. BF%, which can be easily estimated from routine parameters, may serve as a practical biomarker for early identification of high-risk individuals, informing targeted diabetes prevention strategies.
Yang et al. (Sat,) studied this question.