Metabolic Syndrome Complexity among Older Adults in Southwest China: An In-Depth Study Using Heterogeneous Linear Mixed Models

Metabolic Syndrome Complexity among Older Adults in Southwest China: An In-Depth Study Using Heterogeneous Linear Mixed Models | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Metabolic Syndrome Complexity among Older Adults in Southwest China: An In-Depth Study Using Heterogeneous Linear Mixed Models Xuzheng Shan, Ruihong Song, Zhi Huang, Shiyun Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7330697/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Objective: To identify distinct longitudinal trajectories of metabolic syndrome (MetS) and their determinants among older adults in Southwest China using a Heterogenous Linear Mixed Model (HLMM), addressing the heterogeneous nature of MetS progression. Methods: This longitudinal study analyzed health records from a tertiary hospital in Chengdu (2018-2023). MetS was defined per Chinese guidelines (2017). HLMM was applied to identify latent trajectory classes based on AIC/BIC criteria. Cox regression determined predictors of trajectory class membership. Results: 868 participants (3,233 observations) were included in the study. Three distinct MetS trajectories were identified: stable (β=0.002, P=0.042), progressive (β=0.043, P<0.001). regressive (β=-0.179, P<0.001). Baseline MetS prevalence was 22.9%, significantly higher in males (35.2% vs. 20.1%, P<0.001). Cox regression revealed that progression was primarily driven by high triglycerides (HR=3.19, 95%CI: 2.41-4.21), central obesity (HR=2.52, 95%CI: 1.92-3.31), and hyperglycemia (HR=2.48, 95%CI: 1.91-3.23). Regression was strongly associated with reductions in central obesity (HR=3.78, 95%CI: 1.69-8.46) and high triglycerides (HR=2.92, 95%CI: 1.34-6.37). Conclusion: MetS exhibits heterogeneous longitudinal patterns in older adults. Central obesity and dyslipidemia are critical determinants, with central obesity playing a dual role—significantly driving progression but offering even greater potential for driving regression when targeted. These findings underscore the need for trajectory-stratified management strategies focusing on visceral adiposity and lipid control to mitigate MetS burden. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Metabolic syndrome (MetS) is a significant risk factor for cardiovascular disease (CVD) and type 2 diabetes mellitus (T2DM) [ 1 , 2 ]. In China, national data reveal that 10.4% of adults aged ≥ 18 years have diabetes, 35.7% exhibit prediabetes, and 42.0% are overweight or obese [ 3 ]. Over recent decades, metabolic-related chronic diseases (e.g., diabetes, obesity, hyperlipidemia) have maintained persistently high prevalence rates with upward trajectories. Despite its clinical significance, MetS pathophysiology remains incompletely understood[ 4 ]. Factor analyses have identified structural connections among MetS components and potential common etiologies [ 5 – 7 ], yet these approaches oversimplify MetS as a homogeneous entity. Current studies predominantly rely on cross-sectional designs, limiting insights into longitudinal dynamics and intrinsic metabolic trends. Reported component prevalences vary substantially—e.g., hypertension (39.1%), abdominal obesity (37.9%), hypertriglyceridemia (30.2%), dyslipidemia (30.1%), and hyperglycemia (21.1%)—with abdominal obesity exhibiting the strongest association with MetS (OR: 353.13; 95% CI: 136.16–915.81) [ 8 ] [ 9 ]. Obesity (OR: 16.34) and systemic inflammation (hs-CRP > 11 mg/L) further elevate MetS risk [ 10 ], underscoring its multifactorial complexity. The literature review findings suggest that between two and four factors can explain the relationships between the components of MetS, indicating that there is no single pathophysiological pathway for its development. However, factor analysis has limitations. Extensive research efforts have been dedicated to understanding MetS and its associated risk factors. However, significant gaps in knowledge persist, particularly regarding the heterogeneous nature of MetS. Traditional analyses often oversimplify MetS as a uniform condition, failing to capture its complex mechanisms. Segmenting elderly individuals into these cardiometabolic categories has the potential to enhance the monitoring and management of cardiometabolic risk factors. This strategy may help reduce the severe outcomes of metabolic syndrome in this susceptible population. Longitudinal data is important to identify the difference of improving. Critically, traditional methodologies fail to capture heterogeneity in MetS manifestation, particularly among high-risk populations like older adults. This gap impedes personalized risk stratification and intervention. The heterogeneous linear mixed model (HLMM) addresses this limitation by identifying distinct subpopulations with unique risk-factor patterns [ 11 , 12 ], enabling deeper mechanistic insights and tailored interventions. Given the elevated MetS burden in aging populations and the scarcity of longitudinal studies in Southwest China, we apply HLMM to: (1) Characterize metabolic complexity in older adults, (2) Derive clinically meaningful MetS subtypes, and (3) Inform personalized health strategies for this vulnerable demographic. Methods Study design and participants This longitudinal study utilized electronic health records from a tertiary hospital in Chengdu, Southwest China. Participants included adults aged ≥60 years with ≥3 documented health examinations between January 2015 and December 2022. Exclusion criteria comprised incomplete metabolic parameter records, severe comorbidities (e.g., cancer, end-stage renal disease), and follow-up duration <1 year. All participants provided written informed consent, and the consent forms are securely archived by the research team. Definition of metabolic syndrome MetS was defined according to the Chinese Guidelines for the Prevention and Control of Type 2 Diabetes (2017 Edition)[13] , requiring ≥3 of the following: (1) Elevated triglycerides: ≥1.7 mmol/L or lipid-lowering medication use; (2) Reduced HDL-C: <1.04 mmol/L; (3) Hypertension: Systolic BP ≥130 mmHg, Diastolic BP ≥85 mmHg, or antihypertensive medication use; (4) Hyperglycemia: Fasting glucose ≥6.1 mmol/L or glucose-lowering medication use; (5) Central obesity: Waist circumference ≥90 cm (men) or ≥85 cm (women). Statistical analysis HLMM were employed to identify latent metabolic subgroups through a three-stage analytical approach. First, model fitting was performed for configurations specifying 1–7 latent classes. Optimal class selection was then determined by comparing the Akaike (AIC) and Bayesian (BIC) information criteria, where lower values indicated superior model fit. Finally, clinical validity was ensured by excluding classes comprising less than 5% of the cohort to maintain subgroup stability and interpretability. Subsequently, Cox proportional hazards regression was applied to identify determinants of class membership, with adjustments for age, sex, and baseline metabolic syndrome components. All analyses were conducted in R version 4.3.2, utilizing the lcmm package for HLMM implementation and the survival package for Cox regression, with statistical significance defined as two-tailed p < 0.05. Results 1. Baseline Characteristics The longitudinal study included 868 participants contributing 3,233 observations (Fig. 1 ). The cohort comprised 68.1% females with a mean baseline age of 67.1 ± 8.6 years. Metabolic syndrome (MetS) prevalence was 22.9%, with component-specific prevalences as follows: central obesity (32.3%), hypertension (72.4%), hyperglycemia (29.4%), hypertriglyceridemia (33.9%), and low HDL-C (4.1%). Gender-stratified analysis revealed significantly higher MetS prevalence in males (35.2% vs 20.1%, χ²=16.10, P < 0.001) (Table 1 ). Table 1 Baseline Characteristics and Metabolic Component Prevalence by Gender gender N Age ( \(\:\stackrel{-}{x}\pm\:sd\) ) Central Obesity(%) High BP(%) High Glucose (%) High TG(%) Low HDL(%) MetS(%) male 706 69.6 ± 7.4 45.1 78.4 33.3 38.9 9.88 35.2 female 162 66.6 ± 8.7 29.3 71.0 28.5 32.7 2.83 20.1 Total 868 67.1 ± 8.6 32.3 72.4 29.4 33.9 4.1 22.9 \(\:{\chi\:}^{2}\) (t) 4.15 14.23 3.28 1.28 1.97 14.72 16.10 P < 0.001 < 0.001 0.070 0.259 0.160 < 0.001 < 0.001 Note: Age (t = 4.15, P < 0.001); Central obesity (χ²=14.23, P < 0.001); MetS prevalence (χ²=16.10, P < 0.001) The prevalence rate of Mets from 2018 to 2023 shew fluctuations in the rate over the follow-up years, with a decline from 2018 to 2019, a sharp increase in 2020, a subsequent decrease from 2020 to 2022, and a marked rise again in 2023 (Fig. 1 ). 2. Trajectories of Metabolic Syndrome HLMM Fitting Results The three-class model was chosen due to clinical interpretability and model parsimony. Each study participant was allocated to a single class with the greatest probability of membership. The HLMM identified three distinct trajectories: Stable (Class 1): Minimal annual change (β = 0.002, SE = 0.004, P = 0.042) Progressive (Class 2): Significant worsening (β = 0.043, SE = 0.004, P < 0.001) Regressive (Class 3): Clinical improvement (β=-0.179, SE = 0.019, P < 0.001) Model fit indices demonstrated good discrimination (AIC = 1486.50, BIC = 1570.37) (Table 2 , Fig. 2 ). Table 2 HLMM Trajectory Parameter Estimates Parameters Coefficient SE \(\:\text{W}\text{a}\text{l}\text{d}\:{\chi\:}^{2}\) P Trajectories Class Class 1 Intercept 0.195 0.010 2.031 0.042 Stable Class 2 Intercept 0.580 0.036 15.956 < 0.001 Progressive Class 3 Intercept 0.762 0.063 12.058 < 0.001 Regressive Class 1 Slope 0.002 0.004 0.580 0.562 Stable Class 2 Slope 0.043 0.004 5.105 < 0.001 Progressive Class 3 Slope -0.179 0.019 -9.473 < 0.001 Regressive Figure 3 illustrates longitudinal patterns of metabolic syndrome (MetS) components (central obesity, hypertriglyceridemia, hyperglycemia, low HDL-C, and hypertension) stratified by trajectory class (2018–2023). The stable class maintained consistent component prevalence. The progressive class exhibited statistically significant annual increases in high TG and high BP, peaking in 2019 and 2022. Conversely, the regressive class demonstrated substantial reductions, paralleled by declining MetS incidence. 3. Prognostic Determinants Cox regression analyses revealed distinct risk profiles across trajectory classes (reference: stable group). In the progressive trajectory, High TG (HR = 3.19, 95%CI:2.41–4.21), central obesity (HR = 2.52, 95%CI:1.92–3.31), and High Glu (HR = 2.48, 95%CI:1.91–3.23) emerged as significant independent predictors of metabolic deterioration. Conversely, the regressive trajectory showed central obesity (HR = 3.78, 95%CI: 1.69–8.46, P = 0.001) and High TG (HR = 2.92, 95%CI:1.34–6.37, P = 0.007) as primary drivers of metabolic improvement, with central obesity demonstrating 49.6% greater effect magnitude in regression versus progression. Hypertension demonstrated borderline significance for regression (HR = 7.34, 95%CI:0.97–55.65, P = 0.054) (Table 3 , Figs. 4 – 5 ). Table 3 Multivariable Cox Regression of Metabolic Trajectories Trajectory Class β z-score P HR HR 95%CI down up progressive Age 0.013 1.344 0.179 1.014 0.994 1.033 Gender(female) -0.195 -1.287 0.198 0.823 0.612 1.107 High_tg 1.159 8.157 < 0.001 3.186 2.412 4.209 High_glu 0.909 6.749 < 0.001 2.482 1.906 3.231 High_bp 0.830 3.618 < 0.001 2.293 1.463 3.594 Low_hdl 0.063 0.294 0.769 1.065 0.701 1.619 obesity 0.926 6.667 < 0.001 2.524 1.923 3.314 regressive Age 0.019 0.704 0.481 1.019 0.966 1.076 Gender(female) -0.614 -1.643 0.100 0.541 0.260 1.126 High_tg 1.072 2.697 0.007 2.923 1.341 6.371 High_glu 0.054 0.148 0.883 1.055 0.518 2.147 High_bp 1.993 1.929 0.054 7.341 0.968 55.646 Low_hdl 0.543 1.130 0.258 1.722 0.671 4.418 obesity 1.329 3.229 0.001 3.776 1.686 8.456 Note: Reference groups: Male gender; Stable trajectory as baseline. Abbreviations: BP = blood pressure; HDL = high-density lipoprotein; HR = hazard ratio; CI = confidence interval. Discussion This longitudinal study identified three distinct metabolic syndrome (MetS) trajectories—stable, progressive, and regressive—in a cohort of old adults, providing novel insights into the dynamic nature of MetS and its determinants. The findings highlight the heterogeneous progression patterns of MetS and underscore the differential roles of metabolic components in driving clinical deterioration or improvement. Atieh A et al. found that its severity generally remained stable throughout adulthood over ten years of follow-up, although most adults exhibited an unhealthy metabolic score[ 14 ], which differed from our findings. This discrepancy may be attributed to differences in the study cohort, which included individuals aged 20–60 years without diabetes. The progressive metabolic trajectory—characterized by worsening health—showed strong associations with elevated triglycerides (HR = 3.19), central obesity (HR = 2.52), and hyperglycemia (HR = 2.48). These findings align with established literature underscoring the synergistic roles of dyslipidemia, adiposity, and insulin resistance in MetS progression[ 1 ]. The most prevalent component of metabolic syndrome was hypertension followed by abdominal obesity[ 15 ]. Notably, central obesity emerged as a pivotal factor across all trajectories but exhibited a 49.8% greater effect magnitude in the regressive group compared to the progressive trajectory. This suggests that interventions targeting visceral adiposity may yield disproportionate benefits in MetS reversal, likely due to the reversible nature of obesity-related metabolic dysfunction. Consequently, public health strategies should emphasize visceral and ectopic fat reduction alongside weight management to address the global obesity epidemic [ 16 ]. Conversely, the regressive trajectory was primarily driven by improvements in central obesity (HR = 3.78) and triglycerides (HR = 2.92), with hypertension showing borderline significance (HR = 7.34, p = 0.054). The stronger association of abdominal fat reduction with regression highlights its potential as a critical lever for metabolic improvement. While hypertension control remains a cornerstone of MetS management [ 17 , 18 ], its marginal role in regression warrants further investigation. Importantly, early-life cardiometabolic monitoring may be pivotal, as BMI trajectories predict midlife MetS risk [ 19 ]. Interventions targeting waist circumference changes and hypertriglyceridemia—key predictors of diabetes risk [ 20 ]—could optimize preventive strategies. A notable gender disparity in MetS prevalence was observed (35.2% males vs. 20.1% females), contrasting with global trends of higher female susceptibility [ 6 , 21 , 22 ]. This discrepancy may reflect regional lifestyle factors or biological mechanisms such as estrogen’s protective effects in this aging cohort [ 23 ]. However, gender did not independently predict trajectory class, indicating that metabolic severity—rather than sex—drives progression patterns. Temporal fluctuations in MetS prevalence (2018–2023), including a sharp 2020 surge, likely reflect COVID-19 pandemic disruptions that exacerbated sedentary behaviors and dietary imbalances in China [ 24 – 26 ]. The subsequent decline aligns with healthcare system recovery and public health interventions. This underscores the preventive role of physical activity against MetS components, while prolonged sitting and irregular sleep patterns elevate risks for central obesity, dyslipidemia, and hypertension [ 27 ]. These findings advocate for personalized MetS management strategies: (1) Progressive trajectory: Intensive monitoring and early intervention targeting triglycerides, glycemic control, and central obesity. (2) Regressive trajectory: Reinforcement of weight loss and lipid-lowering therapies to sustain metabolic improvements. (3) Hypertension management: Further research is needed to clarify its role in MetS regression. Limitations and Strengths This study has several limitations. First, the single-center design may limit generalizability, though the large sample size and longitudinal follow-up strengthen internal validity. Second, unmeasured confounders, such as dietary habits and physical activity, were not adjusted for. Third, the use of hospital records may introduce selection bias toward individuals with greater health awareness. Nevertheless, the application of HLMM provides methodological rigor in capturing dynamic MetS trajectories, addressing a gap in traditional cross-sectional analyses. Conclusion Metabolic syndrome exhibits heterogeneous longitudinal patterns influenced differentially by central obesity, dyslipidemia, and hyperglycemia. Central obesity serves as both a key driver of progression and a potent target for regression, highlighting its dual role in MetS pathophysiology. These insights support stratified management approaches to mitigate the growing burden of metabolic disorders. Declarations Acknowledgments We thank C Zhang, L Zhu, J Gong, Y Sun, J Huang for assistance in data collections. Funding This work was supported by the Jinniu District Medical Research Project of Chengdu [Grant number JNKY2024-01]. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Authors’ contributors S Li supervised the study and helped revise drafts of the manuscript. X Shan conceived, designed the study and collected the data, finalized the analysis, wrote the drafts of the manuscript. R Song designed the study, wrote the drafts of the manuscript and interpreted the findings. Z Huang collected the data and analyzed the data. All authors read and approved the final manuscript. Ethics approval The study was approved by the ethics committee of Affiliated Hospital of Chengdu University. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. References Grundy, S.M., et al., Diagnosis and management of the metabolic syndrome - An American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation, 2005. 112 (17): p. 2735-2752. 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Kuete, Prevalence of Metabolic Syndrome and Its Components in Bamboutos Division's Adults, West Region of Cameroon. Biomed Res Int, 2019. 2019 : p. 9676984. Kwobah, E., et al., Prevalence and correlates of metabolic syndrome and its components in adults with psychotic disorders in Eldoret, Kenya. PLoS One, 2021. 16 (1): p. e0245086. Beckett, A., et al., The Prevalence of Metabolic Syndrome and Its Components in Firefighters: A Systematic Review and Meta-Analysis. Int J Environ Res Public Health, 2023. 20 (19). Ge, X., Y. Peng, and D. Tu, A threshold linear mixed model for identification of treatment-sensitive subsets in a clinical trial based on longitudinal outcomes and a continuous covariate. Stat Methods Med Res, 2020. 29 (10): p. 2919-2931. Bates, D., et al., Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software, 2015. 67 (1): p. 1 - 48. Society, C.D., Guidelines for the Prevention and Control of Type 2 Diabetes in China (2017 Edition). Chinese Journal of Practical Internal Medicine, 2018. 38 : p. 52. Amouzegar, A., et al., Trajectory patterns of metabolic syndrome severity score and risk of type 2 diabetes. Journal of Translational Medicine, 2023. 21 (1). Basu, S., et al., Burden, determinants and treatment status of metabolic syndrome among older adults in India: a nationally representative, community-based cross-sectional survey. BMJ public health, 2023. 1 (1): p. e000389-e000389. Neeland, I.J., et al., Visceral and ectopic fat, atherosclerosis, and cardiometabolic disease: a position statement. Lancet Diabetes & Endocrinology, 2019. 7 (9): p. 715-725. Mancia, G., et al., 2007 ESH-ESC practice guidelines for the management of arterial hypertension - ESH-ESC task force on the management of arterial hypertension. Journal of Hypertension, 2007. 25 (9): p. 1751-1762. Derer, W., The new european Guidelines for the management of arterial Hypertension. Diabetes Stoffwechsel Und Herz, 2013. 22 (5): p. 319-320. Guo, T., et al., The association of long-term trajectories of BMI, its variability, and metabolic syndrome: a 30-year prospective cohort study. Eclinicalmedicine, 2024. 69 . Florez, H., et al., Metabolic syndrome components and their response to lifestyle and metformin interventions are associated with differences in diabetes risk in persons with impaired glucose tolerance. Diabetes Obes Metab, 2014. 16 (4): p. 326-33. Saklayen, M.G., The Global Epidemic of the Metabolic Syndrome. Current Hypertension Reports, 2018. 20 (2). Strauss, M., et al., A systematic review of prevalence of metabolic syndrome in occupational groups - Does occupation matter in the global epidemic of metabolic syndrome? Progress in cardiovascular diseases, 2022. 75 : p. 69-77. Chang, E., M. Varghese, and K. Singer, Gender and Sex Differences in Adipose Tissue. Current Diabetes Reports, 2018. 18 (9). Helvaci, N., et al., Prevalence of Obesity and Its Impact on Outcome in Patients With COVID-19: A Systematic Review and Meta-Analysis. Frontiers in Endocrinology, 2021. 12 . Yang, Y., et al., Obesity or increased body mass index and the risk of severe outcomes in patients with COVID-19 A protocol for systematic review and meta-analysis. Medicine, 2022. 101 (1). Zhang, X., et al., A systematic review and meta-analysis of obesity and COVID-19 outcomes. Scientific Reports, 2021. 11 (1). Xiao, J., et al., Physical Activity and Sedentary Behavior Associated with Components of Metabolic Syndrome among People in Rural China. PLoS One, 2016. 11 (1): p. e0147062. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 21 Nov, 2025 Reviews received at journal 11 Oct, 2025 Reviews received at journal 11 Oct, 2025 Reviewers agreed at journal 06 Oct, 2025 Reviewers agreed at journal 06 Oct, 2025 Reviewers invited by journal 06 Oct, 2025 Editor invited by journal 09 Sep, 2025 Editor assigned by journal 13 Aug, 2025 Submission checks completed at journal 13 Aug, 2025 First submitted to journal 08 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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16:03:23","extension":"png","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":34752,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7330697/v1/c3246b270eccde7062d9f920.png"},{"id":93795208,"identity":"eab37eec-98e9-48fa-af35-a053532226ea","added_by":"auto","created_at":"2025-10-17 15:47:23","extension":"xml","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":76886,"visible":true,"origin":"","legend":"","description":"","filename":"1176572eb15643a6b93ba9f7a253d9e91structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7330697/v1/bc220099320a8d46df367c59.xml"},{"id":93797076,"identity":"94c49483-32de-4821-a313-cab87cc4ad56","added_by":"auto","created_at":"2025-10-17 15:55:23","extension":"html","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":85749,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7330697/v1/137f9fb39f0204ea35943006.html"},{"id":93797066,"identity":"8a33ea6a-73bc-4339-853b-5ae46fc99d1b","added_by":"auto","created_at":"2025-10-17 15:55:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":113688,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMetabolic Syndrome Prevalence Rate During 2018-2023 Follow-up Period\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-7330697/v1/f1cf66a8a267c793a93359c5.png"},{"id":93795184,"identity":"17347983-d03b-4fc7-a617-aae4bfda3616","added_by":"auto","created_at":"2025-10-17 15:47:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":113166,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClass-Specific Trajectories of Metabolic Syndrome During Follow-Up Years\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"22.png","url":"https://assets-eu.researchsquare.com/files/rs-7330697/v1/5ffee8f443e78ff44469abac.png"},{"id":93795191,"identity":"c59cf8e1-3ed5-46f0-807a-ea85f7a89f29","added_by":"auto","created_at":"2025-10-17 15:47:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":364394,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal trends in metabolic syndrome component prevalence (2018–2023) stratified by trajectory class.\u003c/p\u003e","description":"","filename":"33.png","url":"https://assets-eu.researchsquare.com/files/rs-7330697/v1/08cda5f7692725d7fab8918b.png"},{"id":93795186,"identity":"2afa314f-9896-4d08-bb50-598f8a09aa9a","added_by":"auto","created_at":"2025-10-17 15:47:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":181955,"visible":true,"origin":"","legend":"\u003cp\u003eForest Plot of Risk Factors Associated with Progressive Trajectory\u003c/p\u003e","description":"","filename":"44.png","url":"https://assets-eu.researchsquare.com/files/rs-7330697/v1/bd1fe141ab597034093d443b.png"},{"id":93795187,"identity":"84e179ee-f489-4662-bf03-3b380d097518","added_by":"auto","created_at":"2025-10-17 15:47:22","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":178124,"visible":true,"origin":"","legend":"\u003cp\u003eForest Plot of Risk Factors Associated with Regressive Trajectory\u003c/p\u003e","description":"","filename":"55.png","url":"https://assets-eu.researchsquare.com/files/rs-7330697/v1/afaf8641f5f8c3b25fe80e5b.png"},{"id":93962251,"identity":"8a9a4547-4607-4de2-82db-6815f7ea92cd","added_by":"auto","created_at":"2025-10-20 17:22:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1625189,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7330697/v1/ea44f029-7c34-4b88-b65e-786ec680e149.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Metabolic Syndrome Complexity among Older Adults in Southwest China: An In-Depth Study Using Heterogeneous Linear Mixed Models","fulltext":[{"header":"Background","content":"\u003cp\u003eMetabolic syndrome (MetS) is a significant risk factor for cardiovascular disease (CVD) and type 2 diabetes mellitus (T2DM) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In China, national data reveal that 10.4% of adults aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years have diabetes, 35.7% exhibit prediabetes, and 42.0% are overweight or obese [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Over recent decades, metabolic-related chronic diseases (e.g., diabetes, obesity, hyperlipidemia) have maintained persistently high prevalence rates with upward trajectories.\u003c/p\u003e\u003cp\u003eDespite its clinical significance, MetS pathophysiology remains incompletely understood[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Factor analyses have identified structural connections among MetS components and potential common etiologies [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], yet these approaches oversimplify MetS as a homogeneous entity. Current studies predominantly rely on cross-sectional designs, limiting insights into longitudinal dynamics and intrinsic metabolic trends. Reported component prevalences vary substantially\u0026mdash;e.g., hypertension (39.1%), abdominal obesity (37.9%), hypertriglyceridemia (30.2%), dyslipidemia (30.1%), and hyperglycemia (21.1%)\u0026mdash;with abdominal obesity exhibiting the strongest association with MetS (OR: 353.13; 95% CI: 136.16\u0026ndash;915.81) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Obesity (OR: 16.34) and systemic inflammation (hs-CRP\u0026thinsp;\u0026gt;\u0026thinsp;11 mg/L) further elevate MetS risk [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], underscoring its multifactorial complexity.\u003c/p\u003e\u003cp\u003eThe literature review findings suggest that between two and four factors can explain the relationships between the components of MetS, indicating that there is no single pathophysiological pathway for its development. However, factor analysis has limitations. Extensive research efforts have been dedicated to understanding MetS and its associated risk factors. However, significant gaps in knowledge persist, particularly regarding the heterogeneous nature of MetS. Traditional analyses often oversimplify MetS as a uniform condition, failing to capture its complex mechanisms. Segmenting elderly individuals into these cardiometabolic categories has the potential to enhance the monitoring and management of cardiometabolic risk factors. This strategy may help reduce the severe outcomes of metabolic syndrome in this susceptible population. Longitudinal data is important to identify the difference of improving.\u003c/p\u003e\u003cp\u003eCritically, traditional methodologies fail to capture heterogeneity in MetS manifestation, particularly among high-risk populations like older adults. This gap impedes personalized risk stratification and intervention. The heterogeneous linear mixed model (HLMM) addresses this limitation by identifying distinct subpopulations with unique risk-factor patterns [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], enabling deeper mechanistic insights and tailored interventions. Given the elevated MetS burden in aging populations and the scarcity of longitudinal studies in Southwest China, we apply HLMM to: (1) Characterize metabolic complexity in older adults, (2) Derive clinically meaningful MetS subtypes, and (3) Inform personalized health strategies for this vulnerable demographic.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eStudy design and participants\u003c/h2\u003e\n\u003cp\u003eThis longitudinal study utilized electronic health records from a tertiary hospital in Chengdu, Southwest China. Participants included adults aged \u0026ge;60 years with \u0026ge;3 documented health examinations between January 2015 and December 2022. Exclusion criteria comprised incomplete metabolic parameter records, severe comorbidities (e.g., cancer, end-stage renal disease), and follow-up duration \u0026lt;1 year. All participants provided written informed consent, and the consent forms are securely archived by the research team.\u003c/p\u003e\n\u003ch2\u003eDefinition of metabolic syndrome\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eMetS was defined according to the Chinese Guidelines for the Prevention and Control of Type 2 Diabetes (2017 Edition)[13] , requiring \u0026ge;3 of the following:\u003c/p\u003e\n\u003cp\u003e(1) Elevated triglycerides: \u0026ge;1.7 mmol/L or lipid-lowering medication use;\u003c/p\u003e\n\u003cp\u003e(2) Reduced HDL-C: \u0026lt;1.04 mmol/L;\u003c/p\u003e\n\u003cp\u003e(3) Hypertension: Systolic BP \u0026ge;130 mmHg, Diastolic BP \u0026ge;85 mmHg, or antihypertensive medication use;\u003c/p\u003e\n\u003cp\u003e(4) Hyperglycemia: Fasting glucose \u0026ge;6.1 mmol/L or glucose-lowering medication use;\u003c/p\u003e\n\u003cp\u003e(5) Central obesity: Waist circumference \u0026ge;90 cm (men) or \u0026ge;85 cm (women).\u003c/p\u003e\n\u003ch2\u003eStatistical analysis\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eHLMM were employed to identify latent metabolic subgroups through a three-stage analytical approach. First, model fitting was performed for configurations specifying 1\u0026ndash;7 latent classes. Optimal class selection was then determined by comparing the Akaike (AIC) and Bayesian (BIC) information criteria, where lower values indicated superior model fit. Finally, clinical validity was ensured by excluding classes comprising less than 5% of the cohort to maintain subgroup stability and interpretability. Subsequently, Cox proportional hazards regression was applied to identify determinants of class membership, with adjustments for age, sex, and baseline metabolic syndrome components. All analyses were conducted in R version 4.3.2, utilizing the lcmm package for HLMM implementation and the survival package for Cox regression, with statistical significance defined as two-tailed p \u0026lt; 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003e1. Baseline Characteristics\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe longitudinal study included 868 participants contributing 3,233 observations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The cohort comprised 68.1% females with a mean baseline age of 67.1\u0026thinsp;\u0026plusmn;\u0026thinsp;8.6 years. Metabolic syndrome (MetS) prevalence was 22.9%, with component-specific prevalences as follows: central obesity (32.3%), hypertension (72.4%), hyperglycemia (29.4%), hypertriglyceridemia (33.9%), and low HDL-C (4.1%). Gender-stratified analysis revealed significantly higher MetS prevalence in males (35.2% vs 20.1%, χ\u0026sup2;=16.10, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline Characteristics and Metabolic Component Prevalence by Gender\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003egender\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAge (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{x}\\pm\\:sd\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCentral Obesity(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHigh BP(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHigh Glucose (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eHigh TG(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eLow HDL(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eMetS(%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e706\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e45.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e78.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e33.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e38.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e9.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e35.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003efemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e162\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66.6\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e29.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e71.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e28.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e32.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e20.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e868\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e67.1\u0026thinsp;\u0026plusmn;\u0026thinsp;8.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e72.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e29.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e33.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e22.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}^{2}\\)\u003c/span\u003e\u003c/span\u003e(t)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e14.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e16.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.070\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.259\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote: Age (t\u0026thinsp;=\u0026thinsp;4.15, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001); Central obesity (χ\u0026sup2;=14.23, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001); MetS prevalence (χ\u0026sup2;=16.10, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe prevalence rate of Mets from 2018 to 2023 shew fluctuations in the rate over the follow-up years, with a decline from 2018 to 2019, a sharp increase in 2020, a subsequent decrease from 2020 to 2022, and a marked rise again in 2023 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e ).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e2. Trajectories of Metabolic Syndrome HLMM Fitting Results\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe three-class model was chosen due to clinical interpretability and model parsimony. Each study participant was allocated to a single class with the greatest probability of membership. The HLMM identified three distinct trajectories:\u003c/p\u003e\u003cp\u003eStable (Class 1): Minimal annual change (β\u0026thinsp;=\u0026thinsp;0.002, SE\u0026thinsp;=\u0026thinsp;0.004, P\u0026thinsp;=\u0026thinsp;0.042)\u003c/p\u003e\u003cp\u003eProgressive (Class 2): Significant worsening (β\u0026thinsp;=\u0026thinsp;0.043, SE\u0026thinsp;=\u0026thinsp;0.004, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e\u003cp\u003eRegressive (Class 3): Clinical improvement (β=-0.179, SE\u0026thinsp;=\u0026thinsp;0.019, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e\u003cp\u003eModel fit indices demonstrated good discrimination (AIC\u0026thinsp;=\u0026thinsp;1486.50, BIC\u0026thinsp;=\u0026thinsp;1570.37) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eHLMM Trajectory Parameter Estimates\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{W}\\text{a}\\text{l}\\text{d}\\:{\\chi\\:}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTrajectories Class\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass 1 Intercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStable\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass 2 Intercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.580\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.956\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eProgressive\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass 3 Intercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.762\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12.058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRegressive\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass 1 Slope\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.580\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.562\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStable\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass 2 Slope\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eProgressive\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass 3 Slope\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.179\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-9.473\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRegressive\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates longitudinal patterns of metabolic syndrome (MetS) components (central obesity, hypertriglyceridemia, hyperglycemia, low HDL-C, and hypertension) stratified by trajectory class (2018\u0026ndash;2023). The stable class maintained consistent component prevalence. The progressive class exhibited statistically significant annual increases in high TG and high BP, peaking in 2019 and 2022. Conversely, the regressive class demonstrated substantial reductions, paralleled by declining MetS incidence.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e3. Prognostic Determinants\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCox regression analyses revealed distinct risk profiles across trajectory classes (reference: stable group). In the progressive trajectory, High TG (HR\u0026thinsp;=\u0026thinsp;3.19, 95%CI:2.41\u0026ndash;4.21), central obesity (HR\u0026thinsp;=\u0026thinsp;2.52, 95%CI:1.92\u0026ndash;3.31), and High Glu (HR\u0026thinsp;=\u0026thinsp;2.48, 95%CI:1.91\u0026ndash;3.23) emerged as significant independent predictors of metabolic deterioration. Conversely, the regressive trajectory showed central obesity (HR\u0026thinsp;=\u0026thinsp;3.78, 95%CI: 1.69\u0026ndash;8.46, P\u0026thinsp;=\u0026thinsp;0.001) and High TG (HR\u0026thinsp;=\u0026thinsp;2.92, 95%CI:1.34\u0026ndash;6.37, P\u0026thinsp;=\u0026thinsp;0.007) as primary drivers of metabolic improvement, with central obesity demonstrating 49.6% greater effect magnitude in regression versus progression. Hypertension demonstrated borderline significance for regression (HR\u0026thinsp;=\u0026thinsp;7.34, 95%CI:0.97\u0026ndash;55.65, P\u0026thinsp;=\u0026thinsp;0.054) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariable Cox Regression of Metabolic Trajectories\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrajectory Class\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ez-score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eHR 95%CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003edown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eup\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eprogressive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.344\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.179\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.994\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.033\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGender(female)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.823\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.612\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.107\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh_tg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.159\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.186\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.412\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.209\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh_glu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.909\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.749\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.482\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.906\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.231\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh_bp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.830\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.618\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.293\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.463\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.594\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow_hdl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.294\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.769\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.701\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.619\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eobesity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.926\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.667\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.524\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.923\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.314\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eregressive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.704\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.481\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.966\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.076\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGender(female)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.614\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.643\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.541\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.260\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.126\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh_tg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.697\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.923\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.341\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6.371\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh_glu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.883\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.518\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.147\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh_bp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.993\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.929\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7.341\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.968\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e55.646\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow_hdl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.543\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.258\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.722\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.671\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.418\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eobesity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.329\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.229\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.776\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.686\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8.456\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote: Reference groups: Male gender; Stable trajectory as baseline. Abbreviations: BP\u0026thinsp;=\u0026thinsp;blood pressure; HDL\u0026thinsp;=\u0026thinsp;high-density lipoprotein; HR\u0026thinsp;=\u0026thinsp;hazard ratio; CI\u0026thinsp;=\u0026thinsp;confidence interval.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis longitudinal study identified three distinct metabolic syndrome (MetS) trajectories\u0026mdash;stable, progressive, and regressive\u0026mdash;in a cohort of old adults, providing novel insights into the dynamic nature of MetS and its determinants. The findings highlight the heterogeneous progression patterns of MetS and underscore the differential roles of metabolic components in driving clinical deterioration or improvement. Atieh A et al. found that its severity generally remained stable throughout adulthood over ten years of follow-up, although most adults exhibited an unhealthy metabolic score[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], which differed from our findings. This discrepancy may be attributed to differences in the study cohort, which included individuals aged 20\u0026ndash;60 years without diabetes.\u003c/p\u003e\u003cp\u003eThe progressive metabolic trajectory\u0026mdash;characterized by worsening health\u0026mdash;showed strong associations with elevated triglycerides (HR\u0026thinsp;=\u0026thinsp;3.19), central obesity (HR\u0026thinsp;=\u0026thinsp;2.52), and hyperglycemia (HR\u0026thinsp;=\u0026thinsp;2.48). These findings align with established literature underscoring the synergistic roles of dyslipidemia, adiposity, and insulin resistance in MetS progression[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The most prevalent component of metabolic syndrome was hypertension followed by abdominal obesity[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Notably, central obesity emerged as a pivotal factor across all trajectories but exhibited a 49.8% greater effect magnitude in the regressive group compared to the progressive trajectory. This suggests that interventions targeting visceral adiposity may yield disproportionate benefits in MetS reversal, likely due to the reversible nature of obesity-related metabolic dysfunction. Consequently, public health strategies should emphasize visceral and ectopic fat reduction alongside weight management to address the global obesity epidemic [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eConversely, the regressive trajectory was primarily driven by improvements in central obesity (HR\u0026thinsp;=\u0026thinsp;3.78) and triglycerides (HR\u0026thinsp;=\u0026thinsp;2.92), with hypertension showing borderline significance (HR\u0026thinsp;=\u0026thinsp;7.34, p\u0026thinsp;=\u0026thinsp;0.054). The stronger association of abdominal fat reduction with regression highlights its potential as a critical lever for metabolic improvement. While hypertension control remains a cornerstone of MetS management [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], its marginal role in regression warrants further investigation. Importantly, early-life cardiometabolic monitoring may be pivotal, as BMI trajectories predict midlife MetS risk [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Interventions targeting waist circumference changes and hypertriglyceridemia\u0026mdash;key predictors of diabetes risk [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u0026mdash;could optimize preventive strategies.\u003c/p\u003e\u003cp\u003eA notable gender disparity in MetS prevalence was observed (35.2% males vs. 20.1% females), contrasting with global trends of higher female susceptibility [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This discrepancy may reflect regional lifestyle factors or biological mechanisms such as estrogen\u0026rsquo;s protective effects in this aging cohort [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, gender did not independently predict trajectory class, indicating that metabolic severity\u0026mdash;rather than sex\u0026mdash;drives progression patterns.\u003c/p\u003e\u003cp\u003eTemporal fluctuations in MetS prevalence (2018\u0026ndash;2023), including a sharp 2020 surge, likely reflect COVID-19 pandemic disruptions that exacerbated sedentary behaviors and dietary imbalances in China [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The subsequent decline aligns with healthcare system recovery and public health interventions. This underscores the preventive role of physical activity against MetS components, while prolonged sitting and irregular sleep patterns elevate risks for central obesity, dyslipidemia, and hypertension [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThese findings advocate for personalized MetS management strategies: (1) Progressive trajectory: Intensive monitoring and early intervention targeting triglycerides, glycemic control, and central obesity. (2) Regressive trajectory: Reinforcement of weight loss and lipid-lowering therapies to sustain metabolic improvements. (3) Hypertension management: Further research is needed to clarify its role in MetS regression.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eLimitations and Strengths\u003c/h2\u003e\u003cp\u003eThis study has several limitations. First, the single-center design may limit generalizability, though the large sample size and longitudinal follow-up strengthen internal validity. Second, unmeasured confounders, such as dietary habits and physical activity, were not adjusted for. Third, the use of hospital records may introduce selection bias toward individuals with greater health awareness. Nevertheless, the application of HLMM provides methodological rigor in capturing dynamic MetS trajectories, addressing a gap in traditional cross-sectional analyses.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eMetabolic syndrome exhibits heterogeneous longitudinal patterns influenced differentially by central obesity, dyslipidemia, and hyperglycemia. Central obesity serves as both a key driver of progression and a potent target for regression, highlighting its dual role in MetS pathophysiology. These insights support stratified management approaches to mitigate the growing burden of metabolic disorders.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgments\u003c/h2\u003e\n\u003cp\u003eWe thank C Zhang, L Zhu, J Gong, Y Sun, J Huang for assistance in data collections.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work was supported by the Jinniu District Medical Research Project of Chengdu [Grant number JNKY2024-01].\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026rsquo; contributors\u003c/h2\u003e\n\u003cp\u003eS Li supervised the study and helped revise drafts of the manuscript. X Shan conceived, designed the study and collected the data, finalized the analysis, wrote the drafts of the manuscript. R Song designed the study, wrote the drafts of the manuscript and interpreted the findings. Z Huang collected the data and analyzed the data. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eEthics approval\u003c/h2\u003e\n\u003cp\u003eThe study was approved by the ethics committee of Affiliated Hospital of Chengdu University.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGrundy, S.M., et al., \u003cem\u003eDiagnosis and management of the metabolic syndrome - An American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement.\u003c/em\u003e Circulation, 2005. \u003cstrong\u003e112\u003c/strong\u003e(17): p. 2735-2752.\u003c/li\u003e\n\u003cli\u003eScott, R., et al., \u003cem\u003eImpact of metabolic syndrome and its components on cardiovascular disease event rates in 4900 patients with type 2 diabetes assigned to placebo in the FIELD randomised trial.\u003c/em\u003e Cardiovasc Diabetol, 2011. \u003cstrong\u003e10\u003c/strong\u003e: p. 102.\u003c/li\u003e\n\u003cli\u003eShilian, H., et al., \u003cem\u003eAnalysis of epidemiological trends in chronic diseases of Chinese residents.\u003c/em\u003e Aging medicine (Milton (N.S.W)), 2020. \u003cstrong\u003e3\u003c/strong\u003e(4): p. 226-233.\u003c/li\u003e\n\u003cli\u003eEckel, R.H., et al., \u003cem\u003eThe metabolic syndrome.\u003c/em\u003e Lancet, 2010. \u003cstrong\u003e375\u003c/strong\u003e(9710): p. 181-183.\u003c/li\u003e\n\u003cli\u003ede Freitas, E.D., J.P. 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Kuete, \u003cem\u003ePrevalence of Metabolic Syndrome and Its Components in Bamboutos Division\u0026apos;s Adults, West Region of Cameroon.\u003c/em\u003e Biomed Res Int, 2019. \u003cstrong\u003e2019\u003c/strong\u003e: p. 9676984.\u003c/li\u003e\n\u003cli\u003eKwobah, E., et al., \u003cem\u003ePrevalence and correlates of metabolic syndrome and its components in adults with psychotic disorders in Eldoret, Kenya.\u003c/em\u003e PLoS One, 2021. \u003cstrong\u003e16\u003c/strong\u003e(1): p. e0245086.\u003c/li\u003e\n\u003cli\u003eBeckett, A., et al., \u003cem\u003eThe Prevalence of Metabolic Syndrome and Its Components in Firefighters: A Systematic Review and Meta-Analysis.\u003c/em\u003e Int J Environ Res Public Health, 2023. \u003cstrong\u003e20\u003c/strong\u003e(19).\u003c/li\u003e\n\u003cli\u003eGe, X., Y. Peng, and D. Tu, \u003cem\u003eA threshold linear mixed model for identification of treatment-sensitive subsets in a clinical trial based on longitudinal outcomes and a continuous covariate.\u003c/em\u003e Stat Methods Med Res, 2020. \u003cstrong\u003e29\u003c/strong\u003e(10): p. 2919-2931.\u003c/li\u003e\n\u003cli\u003eBates, D., et al., \u003cem\u003eFitting Linear Mixed-Effects Models Using lme4.\u003c/em\u003e Journal of Statistical Software, 2015. \u003cstrong\u003e67\u003c/strong\u003e(1): p. 1 - 48.\u003c/li\u003e\n\u003cli\u003eSociety, C.D., \u003cem\u003eGuidelines for the Prevention and Control of Type 2 Diabetes in China (2017 Edition).\u003c/em\u003e Chinese Journal of Practical Internal Medicine, 2018. \u003cstrong\u003e38\u003c/strong\u003e: p. 52.\u003c/li\u003e\n\u003cli\u003eAmouzegar, A., et al., \u003cem\u003eTrajectory patterns of metabolic syndrome severity score and risk of type 2 diabetes.\u003c/em\u003e Journal of Translational Medicine, 2023. \u003cstrong\u003e21\u003c/strong\u003e(1).\u003c/li\u003e\n\u003cli\u003eBasu, S., et al., \u003cem\u003eBurden, determinants and treatment status of metabolic syndrome among older adults in India: a nationally representative, community-based cross-sectional survey.\u003c/em\u003e BMJ public health, 2023. \u003cstrong\u003e1\u003c/strong\u003e(1): p. e000389-e000389.\u003c/li\u003e\n\u003cli\u003eNeeland, I.J., et al., \u003cem\u003eVisceral and ectopic fat, atherosclerosis, and cardiometabolic disease: a position statement.\u003c/em\u003e Lancet Diabetes \u0026amp; Endocrinology, 2019. \u003cstrong\u003e7\u003c/strong\u003e(9): p. 715-725.\u003c/li\u003e\n\u003cli\u003eMancia, G., et al., \u003cem\u003e2007 ESH-ESC practice guidelines for the management of arterial hypertension - ESH-ESC task force on the management of arterial hypertension.\u003c/em\u003e Journal of Hypertension, 2007. \u003cstrong\u003e25\u003c/strong\u003e(9): p. 1751-1762.\u003c/li\u003e\n\u003cli\u003eDerer, W., \u003cem\u003eThe new european Guidelines for the management of arterial Hypertension.\u003c/em\u003e Diabetes Stoffwechsel Und Herz, 2013. \u003cstrong\u003e22\u003c/strong\u003e(5): p. 319-320.\u003c/li\u003e\n\u003cli\u003eGuo, T., et al., \u003cem\u003eThe association of long-term trajectories of BMI, its variability, and metabolic syndrome: a 30-year prospective cohort study.\u003c/em\u003e Eclinicalmedicine, 2024. \u003cstrong\u003e69\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eFlorez, H., et al., \u003cem\u003eMetabolic syndrome components and their response to lifestyle and metformin interventions are associated with differences in diabetes risk in persons with impaired glucose tolerance.\u003c/em\u003e Diabetes Obes Metab, 2014. \u003cstrong\u003e16\u003c/strong\u003e(4): p. 326-33.\u003c/li\u003e\n\u003cli\u003eSaklayen, M.G., \u003cem\u003eThe Global Epidemic of the Metabolic Syndrome.\u003c/em\u003e Current Hypertension Reports, 2018. \u003cstrong\u003e20\u003c/strong\u003e(2).\u003c/li\u003e\n\u003cli\u003eStrauss, M., et al., \u003cem\u003eA systematic review of prevalence of metabolic syndrome in occupational groups - Does occupation matter in the global epidemic of metabolic syndrome?\u003c/em\u003e Progress in cardiovascular diseases, 2022. \u003cstrong\u003e75\u003c/strong\u003e: p. 69-77.\u003c/li\u003e\n\u003cli\u003eChang, E., M. Varghese, and K. Singer, \u003cem\u003eGender and Sex Differences in Adipose Tissue.\u003c/em\u003e Current Diabetes Reports, 2018. \u003cstrong\u003e18\u003c/strong\u003e(9).\u003c/li\u003e\n\u003cli\u003eHelvaci, N., et al., \u003cem\u003ePrevalence of Obesity and Its Impact on Outcome in Patients With COVID-19: A Systematic Review and Meta-Analysis.\u003c/em\u003e Frontiers in Endocrinology, 2021. \u003cstrong\u003e12\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eYang, Y., et al., \u003cem\u003eObesity or increased body mass index and the risk of severe outcomes in patients with COVID-19 A protocol for systematic review and meta-analysis.\u003c/em\u003e Medicine, 2022. \u003cstrong\u003e101\u003c/strong\u003e(1).\u003c/li\u003e\n\u003cli\u003eZhang, X., et al., \u003cem\u003eA systematic review and meta-analysis of obesity and COVID-19 outcomes.\u003c/em\u003e Scientific Reports, 2021. \u003cstrong\u003e11\u003c/strong\u003e(1).\u003c/li\u003e\n\u003cli\u003eXiao, J., et al., \u003cem\u003ePhysical Activity and Sedentary Behavior Associated with Components of Metabolic Syndrome among People in Rural China.\u003c/em\u003e PLoS One, 2016. \u003cstrong\u003e11\u003c/strong\u003e(1): p. e0147062.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7330697/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7330697/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e To identify distinct longitudinal trajectories of metabolic syndrome (MetS) and their determinants among older adults in Southwest China using a Heterogenous Linear Mixed Model (HLMM), addressing the heterogeneous nature of MetS progression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e This longitudinal study analyzed health records from a tertiary hospital in Chengdu (2018-2023). MetS was defined per Chinese guidelines (2017). HLMM was applied to identify latent trajectory classes based on AIC/BIC criteria. Cox regression determined predictors of trajectory class membership.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e 868 participants (3,233 observations) were included in the study. Three distinct MetS trajectories were identified: stable (β=0.002, P=0.042), progressive (β=0.043, P\u0026lt;0.001). regressive (β=-0.179, P\u0026lt;0.001). Baseline MetS prevalence was 22.9%, significantly higher in males (35.2% vs. 20.1%, P\u0026lt;0.001). Cox regression revealed that progression was primarily driven by high triglycerides (HR=3.19, 95%CI: 2.41-4.21), central obesity (HR=2.52, 95%CI: 1.92-3.31), and hyperglycemia (HR=2.48, 95%CI: 1.91-3.23). Regression was strongly associated with reductions in central obesity (HR=3.78, 95%CI: 1.69-8.46) and high triglycerides (HR=2.92, 95%CI: 1.34-6.37).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e MetS exhibits heterogeneous longitudinal patterns in older adults. Central obesity and dyslipidemia are critical determinants, with central obesity playing a dual role—significantly driving progression but offering even greater potential for driving regression when targeted. These findings underscore the need for trajectory-stratified management strategies focusing on visceral adiposity and lipid control to mitigate MetS burden.\u003c/p\u003e","manuscriptTitle":"Metabolic Syndrome Complexity among Older Adults in Southwest China: An In-Depth Study Using Heterogeneous Linear Mixed Models","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-17 15:47:18","doi":"10.21203/rs.3.rs-7330697/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-21T13:11:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-11T23:58:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-11T07:24:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"119317157530937695155595836530272137020","date":"2025-10-06T15:36:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"280671126640884142244001659212828403691","date":"2025-10-06T12:54:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-06T11:09:42+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-09T07:18:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-13T07:06:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-13T07:06:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-08-09T01:29:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1401fb74-c8d6-4b48-8bd6-1af90d98bfb6","owner":[],"postedDate":"October 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-03T01:53:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-17 15:47:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7330697","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7330697","identity":"rs-7330697","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}