# Association between cumulative atherogenic index of plasma and dementia risk score in middle-aged and elderly adults: a longitudinal analysis of the CHARLS cohort

## TL;DR

Long-term high levels of a lipid-related marker are linked to increased dementia risk in Chinese adults over 60, especially in high-risk groups.

## Contribution

This study provides longitudinal evidence linking cumulative atherogenic index of plasma to dementia risk in Chinese adults.

## Key findings

- Higher cumulative atherogenic index of plasma is independently associated with increased dementia risk scores.
- The relationship between the lipid marker and dementia risk is linear and dose-dependent.
- The association is stronger in females, older adults, and other high-risk subgroups.

## Abstract

Dementia has become an important public health challenge as the aging population in China accelerates, highlighting the need to identify modifiable risk factors. The cumulative atherogenic index of plasma (CumAIP), a marker reflecting long-term dyslipidemia, may contribute to dementia via vascular and inflammatory pathways, but longitudinal evidence in Chinese adults around 60 years of age remains scarce.

Data were extracted from three waves of the China Health and Retirement Longitudinal Study (CHARLS): 2012 (Wave 1), 2015 (Wave 3), and 2018 (Wave 4). A total of 6473 participants with complete AIP measurements at 2012 and 2015 were included; CumAIP was computed as the time-weighted average of AIP values, normalized by the 2012–2015 observation duration. Dementia risk (primary outcome) was assessed via the Rotterdam Basic Dementia Risk Model (BDRM) using 2018 data, reflecting a 3-year follow-up with 2015 as the baseline. To validate the BDRM, we conducted a secondary analysis: Spearman rank correlation between 2018 BDRM scores and 2015 CHARLS cognitive function scores. Statistical analyses included: multivariable linear regression with three progressive adjustment tiers (sociodemographic, clinical, lifestyle factors); restricted cubic spline (RCS) curves (3 knots at 10th/50th/90th CumAIP percentiles) for linearity testing; quartile-based analyses (CumAIPQ1–Q4, Q1 as reference) for dose–response relationships; and subgroup (by gender, age, residence, and lifestyle/metabolic factors) and sensitivity analyses [multiple imputation, complete-case analysis, linear regression of CumAIP vs. 2015 cognitive function scores, linear regression of low-density lipoprotein cholesterol (LDL-C) vs. BDRM scores] to validate robustness.

First, the 2018 BDRM score was significantly negatively correlated with the 2015 cognitive function score (Spearman’s r = − 0.26, P < 0.001). Higher CumAIP was associated with elevated BDRM scores, remaining significant after full adjustment (β = 0.058, 95% CI 0.018–0.098, P = 0.004). RCS confirmed a linear relationship (nonlinear term P = 0.9671), and quartile analyses showed a dose–response trend (Q4 vs. Q1, β = 0.103, 95% CI 0.009–0.197, P = 0.031). Subgroup effects were more pronounced in females, those aged ≥ 60 years, rural residents, nondrinkers, smokers, and BMI ≥ 24 kg/m2. Sensitivity analyses validated robustness: (1) CumAIP correlated positively with 2015 cognitive function (β = 0.123, 95% CI 0.008–0.237, P = 0.037); (2) LDL-C correlated positively with BDRM scores (β = 0.019, 95% CI 0.009–0.028, P = 0.0001), with a smaller effect than CumAIP.

Cumulative elevated CumAIP is independently associated with higher BDRM-estimated dementia risk in Chinese adults (mean age 61.4 ± 8.5 years), with linear and dose-dependent relationships. The association is more prominent in high-risk subgroups: females, aged ≥ 60 years, rural residents, nondrinkers, smokers, and BMI ≥ 24 kg/m2. The BDRM’s validity supports its reliability. CumAIP’s stronger association with dementia risk than LDL-C underscores its value as a comprehensive lipid marker for risk stratification. Dynamic lipid monitoring to maintain low CumAIP may aid dementia prevention in these high-risk groups.