Objective: This study aims to investigate the relationship between baseline, cumulative exposure, and changes in the atherogenic index of plasma (AIP) and its related indices—namely AIP-body mass index (AIP-BMI), AIP-waist circumference (AIP-WC), and AIP-Waist-to-height-ratio (AIP-WHtR)—in relation to the risk of diabetes among middle-aged and elderly populations in China. Design and method: Data were collected from the China Health and Retirement Longitudinal Study conducted in 2012 and 2015. K-Means clustering analysis was employed to determine the change in the AIP and its related indices. Logistic regression and restricted cubic spline regression were used to explore the associations between baseline, cumulative exposure, and changes in the AIP and its related indices with diabetes risk. Additionally, Receiver Operating Characteristic curves and DeLong's test were utilized to compare the predictive performance of different indices. Weighted quantile sum regression was applied to calculate the weights of optimal predictive indicators, thereby providing a comprehensive understanding of the contribution of each component. Results: A total of 3,924 Chinese participants aged 45 years and older were included in the study, comprising 1,766 males (45.01%), with a mean baseline age of 59.72 ± 8.74 years. During the follow-up period, 356 subjects (9.07%) developed new-onset diabetes. Logistic regression analysis revealed that compared to Q1, Q4 of each indicator were associated with an increased risk of diabetes onset (AIP2012: OR=1.34; AIP-WHtR2012: OR=2.13; AIP-WC2012: OR=2.05; AIP-BMI2012: OR=2.15). The cumulative effects of various indicators at the Q4 were also linked to an increased risk of new-onset diabetes (cumulative AIP: OR=2.52; cumulative AIP-WHtR: OR=2.70; cumulative AIP-WC: OR=2.64; cumulative AIP-BMI: OR=2.66). Changes in the indicators of class 3 were associated with an elevated risk of new-onset diabetes (AIP: OR=2.68; AIP-WHtR: OR=3.34; AIP-WC: OR=2.89; AIP-BMI: OR=3.33). AIP-WHtR emerged as the optimal predictor for diabetes risk, with WHtR identified as the primary driving factor in the association between AIP-WHtR and diabetes incidence. Conclusions: AIP and its related indices are closely associated with the risk of diabetes, particularly AIP-WHtR. Long-term monitoring of the dynamic changes in these indicators should be a crucial consideration in diabetes prevention strategies.
Zheng et al. (Fri,) studied this question.