Composite atherogenic indices reveal a superior lipid profile in a Chinese longevity population: a cross-sectional cohort study.

Authors

Jiaqi Zhang, Zhengkang Chen, Baihua Lu, Yaojin Huang, Yanzhi Yang, Zongkui Wang, Changqing Li, Rong Zhang

Published in

Scientific reports. Jun 23, 2026. Epub Jun 23, 2026.

Abstract

Our previous work revealed differences in conventional lipid parameters between individuals from the Bama longevity hotspot and general population controls. However, their comprehensive atherogenic risk profile, as assessed by novel composite indices, remains uncharacterized. This study aimed to perform an in-depth evaluation of key composite atherogenic indices in this unique longevity cohort. A total of 2,767 participants were enrolled, including 1,007 individuals from the Bama longevity area in Guangxi and 1,760 controls from Shimen County, Hunan. This analysis presents a novel evaluation of the Atherogenic Index of Plasma (AIP), Atherogenic Index (AI), Lipoprotein Combine Index (LCI), Remnant Cholesterol (RC), and Castelli's Risk indices (CRI-I, CRI-II). Statistical analyses included between-group comparisons (Student's t-test, Chi-square test) and Spearman correlation analysis. The Bama cohort demonstrated a significantly less atherogenic profile across most composite indices. Specifically, AIP, AI, LCI, RC, and CRI-I were markedly lower in Bama cohort than in Controls (all P < 0.001; Cohen's d ranging from - 0.23 to -1.04), whereas CRI-II did not differ significantly (P = 0.141). AIP, LCI and RC showed strong positive correlations with TG (r = 0.933, 0.882 and 0.700 respectively, all P < 0.001). In contrast, AI and CRI-I were strongly negatively correlated with HDL-C (r = -0.728 for both; all P < 0.001). The Bama longevity population possesses an associated capacity to regulate lipid metabolism, which may contribute to superior control of atherogenic risk, as captured by integrative indices like AIP, AI, LCI, and RC. These composite indices represent promising sensitive biomarkers that could improve clinical cardiovascular risk assessment in longevity populations, underscoring the potential translational value of identifying favorable metabolic profile shaped by genetic and local environmental factors.

PMID:
42336984
Bibliographic data and abstract were imported from PubMed on 24 Jun 2026.

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