Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Feb 12:69:102486.
doi: 10.1016/j.eclinm.2024.102486. eCollection 2024 Mar.

The association of long-term trajectories of BMI, its variability, and metabolic syndrome: a 30-year prospective cohort study

Affiliations

The association of long-term trajectories of BMI, its variability, and metabolic syndrome: a 30-year prospective cohort study

Tongshuai Guo et al. EClinicalMedicine. .

Abstract

Background: Limited data exists on how early-life weight changes relate to metabolic syndrome (MetS) risk in midlife. This study examines the association between long-term trajectories of body mass index (BMI), its variability, and MetS risk in Chinese individuals.

Methods: In the Hanzhong Adolescent Hypertension study (March 10, 1987-June 3, 2017), 1824 participants with at least five BMI measurements from 1987 to 2017 were included. Using group-based trajectory modeling, different BMI trajectories were identified. BMI variability was assessed through standard deviation (SD), variability independent of the mean (VIM), and average real variability (ARV). Logistic regression analyzed the relationship between BMI trajectory, BMI variability, and MetS occurrence in midlife (URL: https://www.clinicaltrials.gov; Unique identifier: NCT02734472).

Findings: BMI trajectories were categorized as low-increasing (34.4%), moderate-increasing (51.8%), and high-increasing (13.8%). Compared to the low-increasing group, the odds ratios (ORs) [95% CIs] for MetS were significantly higher in moderate (4.27 [2.63-6.91]) and high-increasing groups (13.11 [6.30-27.31]) in fully adjusted models. Additionally, higher BMI variabilities were associated with increased MetS odds (ORs for SDBMI, VIMBMI, and ARVBMI: 2.30 [2.02-2.62], 1.22 [1.19-1.26], and 4.29 [3.38-5.45]). Furthermore, BMI trajectories from childhood to adolescence were predictive of midlife MetS, with ORs in moderate (1.49 [1.00-2.23]) and high-increasing groups (2.45 [1.22-4.91]). Lastly, elevated BMI variability in this period was also linked to higher MetS odds (ORs for SDBMI, VIMBMI, and ARVBMI: 1.24 [1.08-1.42], 1.00 [1.00-1.01], and 1.21 [1.05-1.38]).

Interpretation: Our study suggests that both early-life BMI trajectories and BMI variability could be predictive of incident MetS in midlife.

Funding: This work was supported by the National Natural Science Foundation of China No. 82070437 (J.-J.M.), the Clinical Research Award of the First Affiliated Hospital of Xi'an Jiaotong University of China (No. XJTU1AF-CRF-2022-002, XJTU1AF2021CRF-021, and XJTU1AF-CRF-2023-004), the Key R&D Projects in Shaanxi Province (Grant No. 2023-ZDLSF-50), the Chinese Academy of Medical Sciences & Peking Union Medical College (2017-CXGC03-2), and the International Joint Research Centre for Cardiovascular Precision Medicine of Shaanxi Province (2020GHJD-14).

Keywords: Body mass index; Body mass index trajectory; Body mass index variability; Central obesity; Metabolic syndrome.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1
Fig. 1
Long-term body mass index (BMI) trajectories from childhood to middle age.
Fig. 2
Fig. 2
Comparison of each MetS component among BMI trajectory groups. A. Waist circumference (WC); B. Fasting blood glucose (FBG); C. triglyceride (TG); D. high-density lipoprotein cholesterol (HDL-C); E. systolic blood pressure (SBP); F. diastolic blood pressure (DBP).

Similar articles

Cited by

References

    1. O'Neill S., O'Driscoll L. Metabolic syndrome: a closer look at the growing epidemic and its associated pathologies. Obes Rev. 2015;16(1):1–12. - PubMed
    1. Lu Y., Li X.M., Liu Y.T., et al. Trends in prevalence and treatment of metabolic syndrome and individual components by race/ethnicity, 1999-2020. Circulation. 2022;146:3.
    1. Li X.L., Li X.L., Lin H.L., et al. Metabolic syndrome and stroke: a meta-analysis of prospective cohort studies. J Clin Neurosci. 2017;40:34–38. - PubMed
    1. Kurl S., Laaksonen D.E., Jae S.Y., et al. Metabolic syndrome and the risk of sudden cardiac death in middle-aged men. Int J Cardiol. 2016;203:792–797. - PubMed
    1. Mente A., Yusuf S., Islam S., et al. Metabolic syndrome and risk of acute myocardial infarction A case-control study of 26,903 Subjects from 52 countries. J Am Coll Cardiol. 2010;55(21):2390–2398. - PubMed

Associated data