Transitions in metabolic syndrome clustering patterns before and after menopause: a latent transition analysis in Korean women

Abstract

Objective: To identify metabolic syndrome (MetS) clustering patterns, examine transitions between classes from perimenopausal to postmenopausal stages, and identify factors associated with these transitions. Methods: This secondary analysis used data from 1,104 women in the Korean Genome Epidemiology Study, a population-based longitudinal cohort. Latent class analysis was conducted for perimenopausal and postmenopausal stages, and latent transition analysis class changes. Age at menopause, white blood cell count, and homeostasis model assessment of insulin resistance (HOMA-IR) were examined as predictors. Results: Four classes were identified in the perimenopausal stage: metabolic-diabetic, metabolic-hypertensive, low high-density lipoprotein cholesterol (HDL-c), and non-MetS. Five classes were identified in the postmenopausal stage: treated-hypertensive, metabolic-hypertensive, high-risk, low HDL-c, and non-MetS. Approximately 40% of women changed their classes during the menopause transition. The metabolic-diabetic group showed a markedly higher likelihood of progression to the high-risk group (88.3%). In the metabolic-hypertensive group at the perimenopausal stage, higher HOMA-IR was associated with increased transitions to low the HDL-c (odds ratio [OR]: 51.37, 95% CI: 15.27-172.78) and high-risk (OR: 78.10, 95% CI: 19.16-318.30) classes. Later menopause reduced the likelihood of remaining in (OR: 0.10, 95% CI: 0.02-0.50) or progressing to high-risk (OR: 0.05, 95% CI: 0.01-0.43) in the metabolic-hypertensive group but increased the probability of transitioning to treated-hypertensive in women in the non-MetS group (OR: 1.48, 95% CI: 1.13-1.94). Conclusions: Metabolic risk profiles shift substantially during menopause. Targeted interventions, especially for high-risk groups before menopause, and the inclusion of simple insulin resistance markers such as HOMA-IR in screening may improve prevention and management.

Affiliated Institutions

• Yonsei University KR
• Seojeong University KR

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