Association between atherogenic index of plasma, body mass index, and sarcopenia: a cross-sectional and longitudinal analysis study based on older adults in China

Abstract

Objective

To investigate the correlation between the atherogenic index of plasma (AIP), body mass index (BMI), and sarcopenia in the older adults in China, and to analyze the predictive ability of AIP and BMI for sarcopenia.

Methods

This study utilized data from the 2011–2015 CHARLS database (China Health and Retirement Longitudinal Study, CHARLS), focusing on participants aged 60 years and older. The cross-sectional analysis included 7,744 samples, with 2,398 in the sarcopenia group and 5,346 in the non-sarcopenia group. In the retrospective cohort study, 1,441 participants without sarcopenia at baseline were selected and followed for the development of sarcopenia. Multivariable logistic regression was employed to analyze the association between AIP, BMI, and sarcopenia risk. A restricted cubic spline regression model was used to evaluate the dose-response association, and ROC curve analysis was performed to assess the predictive ability of individual and combined indicators (AIP and BMI). Additionally, subgroup analysis was conducted to explore the association between AIP, BMI, and sarcopenia risk across different demographic groups.

Results

The cross-sectional analysis demonstrated that sarcopenia was significantly associated with various factors, including age, marital status, education level, residence, smoking, BMI, uric acid (UA), glycosylated hemoglobin (HbA1c), total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), AIP, as well as hypertension, diabetes, dyslipidemia, and heart disease (p < 0.05). Logistic regression analysis, adjusted for potential confounders, revealed that the low AIP group was significantly associated with an increased risk of sarcopenia (OR = 1.22, 95% CI 1.03–1.44, p = 0.02), while no significant difference was observed in the high AIP group (OR = 0.83, 95% CI 0.69–1.01, p = 0.07). In the retrospective cohort study, the low AIP group showed a positive association with sarcopenia risk (OR = 1.79, 95% CI 1.18–2.72, p = 0.01), and a similar trend was observed in the high AIP group (OR = 1.69, 95% CI 1.03–2.77, p = 0.04). BMI was inversely associated with sarcopenia incidence, consistent with the cross-sectional findings. Both AIP and BMI showed a nonlinear dose-response relationship with sarcopenia risk, with AIP approximating a U-shaped curve and BMI approximating an L-shaped curve. Subgroup analysis indicated that, in the 65–69 age group, low AIP levels were significantly associated with an increased risk of sarcopenia. In participants aged 70 and above, as well as in females, both low and high AIP levels were significantly associated with higher incidence risk. ROC curve analysis showed that the combined use of AIP and BMI for predicting sarcopenia had an Area Under the Curve (AUC) of 0.8913, which was moderately better than the use of AIP (0.6499) or BMI (0.8888) alone.

Conclusion

The changes in AIP and BMI are associated with the risk of sarcopenia, and both provide some predictive value for sarcopenia. Taken together, the combined prediction using AIP and BMI appears to be somewhat more effective than using either indicator alone in assessing the risk of sarcopenia.