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Guo T, Zheng S, Chen T, Chu C, Ren J, Sun Y, Wang Y, He M, Yan Y, Jia H, Liao Y, Cao Y, Du M, Wang D, Yuan Z, Wang D, Mu J. The association of long-term trajectories of BMI, its variability, and metabolic syndrome: a 30-year prospective cohort study. EClinicalMedicine 2024; 69:102486. [PMID: 38370536 PMCID: PMC10874716 DOI: 10.1016/j.eclinm.2024.102486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 01/21/2024] [Accepted: 01/30/2024] [Indexed: 02/20/2024] Open
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).
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Affiliation(s)
- Tongshuai Guo
- Department of Cardiology, First Affiliated Hospital of Medical School, Xi'an Jiaotong University, Xi'an, 710061, China
| | - Sirui Zheng
- Biostatistics Unit, Department of Clinical Sciences, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA, UK
| | - Tao Chen
- Centre for Health Economics, University of York, Heslington, York, YO10 5DD, UK
| | - Chao Chu
- Department of Cardiology, First Affiliated Hospital of Medical School, Xi'an Jiaotong University, Xi'an, 710061, China
| | - Jie Ren
- Department of Cardiology, First Affiliated Hospital of Medical School, Xi'an Jiaotong University, Xi'an, 710061, China
| | - Yue Sun
- Department of Cardiology, First Affiliated Hospital of Medical School, Xi'an Jiaotong University, Xi'an, 710061, China
| | - Yang Wang
- Department of Cardiology, First Affiliated Hospital of Medical School, Xi'an Jiaotong University, Xi'an, 710061, China
| | - Mingjun He
- Department of Cardiology, First Affiliated Hospital of Medical School, Xi'an Jiaotong University, Xi'an, 710061, China
| | - Yu Yan
- Department of Cardiology, First Affiliated Hospital of Medical School, Xi'an Jiaotong University, Xi'an, 710061, China
| | - Hao Jia
- Department of Cardiology, First Affiliated Hospital of Medical School, Xi'an Jiaotong University, Xi'an, 710061, China
| | - Yueyuan Liao
- Department of Cardiology, First Affiliated Hospital of Medical School, Xi'an Jiaotong University, Xi'an, 710061, China
| | - Yumeng Cao
- Department of Cardiology, First Affiliated Hospital of Medical School, Xi'an Jiaotong University, Xi'an, 710061, China
| | - Mingfei Du
- Department of Cardiology, First Affiliated Hospital of Medical School, Xi'an Jiaotong University, Xi'an, 710061, China
| | - Dan Wang
- Department of Cardiology, First Affiliated Hospital of Medical School, Xi'an Jiaotong University, Xi'an, 710061, China
| | - Zuyi Yuan
- Department of Cardiology, First Affiliated Hospital of Medical School, Xi'an Jiaotong University, Xi'an, 710061, China
| | - Duolao Wang
- Biostatistics Unit, Department of Clinical Sciences, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA, UK
| | - Jianjun Mu
- Department of Cardiology, First Affiliated Hospital of Medical School, Xi'an Jiaotong University, Xi'an, 710061, China
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