What is the clinical evidence from this study?

Study design: Cohort. Population: Risk of falls (n=6241). Intervention: Atherogenic Index (AI) vs. Lowest quartile (Q1) of Atherogenic Index. Primary outcome: Risk of falls (OR 1.29, 95% CI 1.11-1.50).

October 31, 2025BMC Public Health1 citationsOpen Access

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

JLJingwen LiuCalifornia State University, Fullerton WLWenfeng LuDonghua University YHYuan HeMinistry of Natural Resources

Key Result

Participants in the highest quartile of Atherogenic Index had a significantly increased risk of falls compared to those in the lowest quartile (OR 1.29).

Study Design

Type

Cohort (n=6,241)

Multicenter

Yes

Structured PICO

Does a higher Atherogenic Index increase the risk of falls in middle-aged and older adults?

Population

6,241 community-dwelling adults aged 45 and older from China were followed for up to a decade to assess the association between atherogenic index and self-reported falls.

Exposure

Higher levels of Atherogenic Index (AI) (highest quartile Q4)

Comparator

Lowest quartile (Q1) of Atherogenic Index

Outcome

Self-reported falls during the decade-long follow-up periodpatient reported

Higher Atherogenic Index is significantly associated with an increased risk of falls in middle-aged and older adults, particularly those under 65 years of age.

Main Result

Odds Ratio: 1.29 (95% CI 1.11–1.5)

Limitations

Relying on older adults to recall fall incidents over a two-year period introduces potential recall bias.
Using the average AI from 2011 to 2015 may not capture subtle changes in biochemical markers over time.
AI calculation is based on TC and HDL-C, which may predominantly reflect cardiovascular health issues rather than overall health status.

Abstract

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

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