Association between atherogenic index and risk of falls: a decade-long prospective cohort study

To investigate the association between the Atherogenic Index (AI) and several other untraditional lipid parameters with the risk of falls. The findings may offer novel insights for the prevention and management of falls among middle-aged and older adults.

This cohort study analyzed data from the China Health and Retirement Longitudinal Study (CHARLS). AI and other untraditional lipid parameters were calculated from blood samples collected in 2011 and 2015, and fall incidents were self-reported during the follow-up surveys until 2020. Untraditional lipid parameters were analyzed in quartiles. Logistic regression was used to assess their associations with risk of falls with stepwise adjustments. Restricted cubic spline (RCS) and receiver operating characteristic (ROC) analyses evaluated dose-response relationships and predictive performance. Subgroup and interaction analyses were conducted to identify potential effect modifiers.

Among the 6,241 participants, higher levels of AI were significantly associated with an increased risk of falls. After adjusting for potential confounders, participants in the highest quartile (Q4) exhibited a significantly higher odds ratio (OR) for risk of falls (OR = 1.29, 95% CI: 1.11-1.50) compared to those in the lowest quartile (Q1). Among all nontraditional lipid parameters assessed, AI demonstrated the highest predictive accuracy for falls. Subgroup analysis further indicated that the association between AI and risk of falls was more pronounced in individuals younger than 65 years.

The findings underscore the potential of incorporating AI measurements alongside traditional assessment methods to offer a more comprehensive evaluation framework for reducing risk of falls.