Association between BMI trajectories and metabolic syndrome in postmenopausal women - a cross-sectional study from NHANES 1999-2018

Q: What population and data source were used to study BMI trajectories and MetS risk?

The study used postmenopausal women aged ≥60 years from NHANES 1999–2018.

Q: How were BMI trajectories identified in the analysis?

BMI trajectories over time were derived using latent class growth mixture modeling.

Q: Which BMI trajectory groups showed higher MetS risk compared with the stable trajectory group?

Participants in the increasing BMI trajectory group had higher MetS risk, and the increasing–decreasing trajectory group had the highest risk relative to the stable group.

Sep 30, 2026

Upload by Rainbow Cat

Obesity is a well-established driver of metabolic syndrome (MetS), and both are highly prevalent among postmenopausal women, yet the link between long-term body mass index (BMI) change patterns and MetS in this group remains insufficiently characterized. This cross-sectional analysis uses NHANES 1999–2018 data to identify BMI trajectories via latent class growth mixture modeling, then tests their relationship with MetS risk using multivariable logistic regression and reproductive-characteristic subgroup analyses.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages12

Reading Time30 min

AccessFree to download

Background

Methods

Results

Conclusions

Background: Metabolic syndrome and menopause

Liu et al. BMC Public Health (2026) 26:121 BMC Public Health [https://doi.org/10.1186/s12889-025-25480-4](https://doi.org/10.1186/s12889-025-25480-4) RESEARCH Open Access Association between BMI trajectories and metabolic syndrome in postmenopausal women: a cross-sectional study from NHANES 1999–2018 Yingying Liu 1, Zhixiang Liang 1, Xingying Li 1 and Juan Xiong 1* Abstract Background Obesity is an established risk factor for metabolic syndrome (MetS), with both conditions being particularly prevalent among postmenopausal women. However, research examining the relationship between body mass index (BMI) changes across the lifespan and MetS in this specific population remains limited. This study aims to explore the association between BMI trajectories and MetS risk in postmenopausal women. Methods This cross-sectional study included postmenopausal women aged ≥ 60 years from the National Health and Nutrition Examination Survey 1999–2018. MetS was defined by 2009 Joint Interim Statement criteria. BMI trajectories over time were identified using latent class growth mixture modeling. Multivariable logistic regression analyses were performed to evaluate the association between distinct BMI trajectories and MetS risk. Subgroup analyses based on reproductive characteristics were conducted to investigate potential effect modification in the relationship between BMI trajectories and MetS risk. Results Among 4061 eligible participants, the overall prevalence of MetS was 71. 57%(n = 3070) . Three distinct BMI trajectories were identified: stable trajectory (n = 3047, 76 . 34%), increasing trajectory (n = 540, 12. 30%), and increasing– decreasing trajectory (n = 474, 11. 36%) . Compared with the stable trajectory group, participants in the increasing trajectory group had a higher risk of MetS (OR = 5 . 09, 95% CI: 3 .19–8. 13), while those in the increasing–decreasing trajectory group had the highest risk of MetS (OR = 5 . 89, 95% CI: 3 .73–9. 31) . Subgroup analyses revealed significant effect modification of the association between BMI trajectories and MetS risk by early menopause (P = 0 . 059), early age at first live birth (P < 0. 05), and high parity (P < 0. 05) . Conclusions The increasing and increasing–decreasing BMI trajectory groups are associated with a higher risk of MetS in postmenopausal women. This association is significantly modified by reproductive characteristics. Maintaining a long-term stable BMI may benefit metabolic health. Keywords Body mass index, Metabolic syndrome, Postmenopause, Trajectory analysis, NHANES *Correspondence: Juan Xiong [jxiong@szu.edu.cn](jxiong@szu.edu.cn) 1Medical School, Shenzhen University, 1066 Xueyuan Avenue, Nanshan District, Shenzhen , Guangdong Province 518055, China © The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creati](http://creati)[vecommons.org/l](vecommons.org/l)icenses/by-nc-nd/4.0/. Liu et al. BMC Public Health (2026) 26:121 Background Metabolic Syndrome (MetS) is characterized by a cluster of metabolic disorde

FAQs

What population and data source were used to study BMI trajectories and MetS risk?

The study used postmenopausal women aged ≥60 years from NHANES 1999–2018.

How were BMI trajectories identified in the analysis?

BMI trajectories over time were derived using latent class growth mixture modeling.

Which BMI trajectory groups showed higher MetS risk compared with the stable trajectory group?

Participants in the increasing BMI trajectory group had higher MetS risk, and the increasing–decreasing trajectory group had the highest risk relative to the stable group.

Sep 30, 2026

Upload by Rainbow Cat