Abstract
Age at menarche is strongly associated with cardiovascular disease risk in middle-aged and older adults, but little is known about the relationship between age at menarche and lipid levels and triglyceride-glucose (TyG) index in middle-aged women. This study aimed to examine the relationship between age at menarche and lipid levels and TyG index in middle-aged Chinese women based on nationally representative data from the China Health and Retirement Longitudinal Study (CHARLS). The study sample consisted of 5492 participants aged 45 years or older from CHARLS. The relationship between age at menarche and lipid levels as well as the TyG index was assessed using multiple linear regression and subgroup analyses. Nonlinear associations were examined using spline analysis and visualized. The median age at menarche in the study population was 16.00 years, with median values of 122.124 mg/dL for triglycerides (TG), 51.351 mg/dL for high-density lipoprotein cholesterol (HDL-C), 104.633 mg/dL for low-density lipoprotein cholesterol (LDL-C), and 8.680 for TyG. Multivariate linear regression results indicated that after adjusting for age, education, marital status, smoking, alcohol consumption, body mass index (BMI), and diabetes status, delaying menarche by 1 year was associated with an increase of 0.316 mg/dL in HDL-C (P = .005), a decrease of 2.900 mg/dL in TG (P = .002), and a decrease of 0.019 units in TyG (P < .001). Spline analysis showed a linear relationship between age at menarche and HDL-C (P = .094), LDL-C (P = .68), TG (P = .377), and TyG index (P = .127). Subgroup analysis revealed that diabetes and marital status moderated the relationship between age at menarche and lipid indices (P for interaction < .05). In middle-aged and elderly women, age at menarche was positively correlated with HDL-C and negatively correlated with TG and TyG index in a linear fashion. Clinical attention should be given to women's reproductive history and physiological development, such as age at menarche, to assist in the early identification of high-risk metabolic abnormalities.