The relationship between the CUN-BAE body fatness index and incident diabetes: a longitudinal retrospective study

Author:

Peng Qing,Feng Zihao,Cai Zhuojian,Liu Dixing,Zhong Jiana,Zhao Hejia,Zhang Xiuwei,Chen Weikun

Abstract

Abstract Background The Clínica Universidad de Navarra-Body Adiposity Estimator (CUN-BAE) index has been recommended as an ideal indicator of body fat and exhibited significant correlation with cardiometabolic risk factors. However, whether the CUN-BAE index correlates with incident diabetes in Asian populations is unknown. Therefore, this longitudinal study was designed to evaluate the association between baseline CUN-BAE index and type 2 diabetes mellitus (T2DM). Methods This retrospective longitudinal study involved 15,464 participants of 18–79 years of age in the NAGALA (NAfld in the Gifu Area Longitudinal Analysis) study over the period of 2004–2015. Cox proportional hazards regression was performed to test the relationship between the baseline CUN-BAE index and diabetes incidence. Further stratification analysis was conducted to ensure that the results were robust. The diagnostic utility of the CUN-BAE index was tested by the receiver operating characteristic (ROC) curve. Results Over the course of an average follow-up of 5.4 years, 373 (2.41%) participants developed diabetes. A higher diabetes incidence was associated with higher CUN-BAE quartiles (P for trend< 0.001). Each 1 unit increase in CUN-BAE index was associated with a 1.08-fold and 1.14-fold increased risk of diabetes after adjustment for confounders in males and females, respectively (both P < 0.001). Stratification analysis demonstrated a consistent positive correlation between baseline CUN-BAE and diabetes incidence. Moreover, based on ROC analysis, CUN-BAE exhibited a better capacity for diabetes prediction than both body mass index (BMI) and waist circumference (WC) in both sexes. Conclusions The baseline CUN-BAE level was independently related to the incidence of diabetes. Increased adiposity determined by CUN-BAE could be used as a strong nonlaboratory predictor of incident diabetes in clinical practice.

Publisher

Springer Science and Business Media LLC

Subject

Biochemistry (medical),Clinical Biochemistry,Endocrinology,Endocrinology, Diabetes and Metabolism

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