Abstract
IntroductionTo explore the temporal relationship between blood lipids and insulin resistance in perimenopausal women.Research design and methodsThe longitudinal cohort consisted of 1386 women (mean age 46.4 years at baseline) in the Study of Women’s Health Across the Nation. Exploratory factor analysis was used to identify appropriate latent factors of lipids (total cholesterol (TC); triglyceride (TG); high-density lipoprotein cholesterol (HDL-C); low-density lipoprotein cholesterol (LDL-C); lipoprotein A-I (LpA-I); apolipoprotein A-I (ApoA-I); apolipoprotein B (ApoB)). Cross-lagged path analysis was used to explore the temporal sequence of blood lipids and homeostasis model assessment of insulin resistance (HOMA-IR).ResultsThree latent lipid factors were defined as: the TG factor, the cholesterol transport factor (CT), including TC, LDL-C, and ApoB; the reverse cholesterol transport factor (RCT), including HDL-C, LpA-I, and ApoA-I. The cumulative variance contribution rate of the three factors was 86.3%. The synchronous correlations between baseline TG, RCT, CT, and baseline HOMA-IR were 0.284, −0.174, and 0.112 (p<0.05 for all). After adjusting for age, race, smoking, drinking, body mass index, and follow-up years, the path coefficients of TG→HOMA-IR (0.073, p=0.004), and HOMA-IR→TG (0.057, p=0.006) suggested a bidirectional relationship between TG and HOMA-IR. The path coefficients of RCT→HOMA-IR (−0.091, P < 0.001) and HOMA-IR→RCT (−0.058, p=0.002) were also significant, but the path coefficients of CT→HOMA-IR (0.031, p=0.206) and HOMA-IR→CT (−0.028, p=0.113) were not. The sensitivity analyses showed consistent results.ConclusionsThese findings provide evidence that TG and the reverse cholesterol transport-related lipids are related with insulin resistance bidirectionally, while there is no temporal relationship between the cholesterol transport factor and insulin resistance.
Funder
Cheeloo Young Scholars Program of Shandong University
Beihang University & Capital Medical University Advanced Innovation Center for Big Data-Based Precision Medicine Plan
National Natural Science Foundation of China
Shandong University multidisciplinary research and innovation team of young scholars
Subject
Endocrinology, Diabetes and Metabolism
Cited by
2 articles.
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