Metabolic Risk Factors in NonCVD Individuals and Their Trajectory Toward Cardiovascular Incidence

Metabolic Risk Factors in NonCVD Individuals and Their Trajectory Toward Cardiovascular Incidence | 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 Risk Factors in NonCVD Individuals and Their Trajectory Toward Cardiovascular Incidence Maryam Mahdavi, Anoshirvan Kazemnejad, Abbas Asosheh, Davood Khalili This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6690336/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background In 2019, cardiovascular disease (CVD) was the primary cause of death worldwide, responsible for approximately 18.6 million fatalities, with its prevalence and incidence continuing to rise. In Iran, CVD accounts for 46.04% of all deaths, with demographic aging and sedentary lifestyles exacerbating the burden. This study evaluated the impact of metabolic risk factors and their trajectories on CVD development in an Iranian cohort. Methods In accordance with the Tehran Lipid and Glucose Study (TLGS), this longitudinal study included 1872 adults aged 40–79 years without prior CVD at baseline. The participants were selected through multistage random cluster sampling from 1999–2018. Data were collected on demographic, lifestyle, and metabolic factors, with laboratory analyses conducted via standardized protocols. Generalized estimating equations (GEEs) were used to assess age- and sex-adjusted trajectories of metabolic indicators. Results Over the 10-year follow-up period, 117 individuals (6.3%) were diagnosed with cardiovascular disease (CVD). Baseline CVD converters presented increased age, weight, blood pressure, fasting glucose, and lipid levels, and diabetes incidence. The key metabolic risk factor trajectories included the TyG index, FPG, and SBP, which significantly increased 6 years before diagnosis. Conclusions Longitudinal trajectories of metabolic risk factors, particularly SBP, FPG, and the TyG index, demonstrated strong predictive value for CVD development years before onset, with SBP emerging as the most potent predictor. These findings emphasize the importance of early detection and preventive strategies targeting metabolic risk factors. Lifestyle modifications can significantly mitigate CVD risk, underscoring the utility of longitudinal data in understanding risk factor heterogeneity and disease progression. Greater attention should be given to patients with unstable cardiometabolic risk factors. Trajectory Cardiovascular disease TLGS CVD risk factors Figures Figure 1 Background Non-communicable diseases (NCDs) represent a significant worldwide health challenge. The incidence of NCDs is increasing annually worldwide, explaining the majority of annual deaths. Importantly, nearly 25% of these deaths happen before individuals reach 60 years of age ( 1 ). One of the non-communicable diseases is cardiovascular disease (CVD), which affects a significant part of the population. From 1990 to 2019, the number of individuals affected by CVD nearly doubled, showing a significant increase. Over the same period, CVD-related deaths also rose steadily. Global rates of disability-adjusted life years (DALYs) and years of life lost have shown a marked increase. During this time, the number of individuals living with disabilities doubled ( 2 ). Cardiovascular conditions are the predominant cause of death nationwide ( 3 ). The age-adjusted incidence rate, age-adjusted death rate, and DALYs attributable to cardiovascular disease in Iran exceed the global averages ( 4 ). The Non-Communicable Diseases Risk Factors Surveillance Study (STEPS) utilized nationwide data collection from 2005, 2006, 2007, 2008, 2009, 2011, and 2016 to evaluate the 10-year and 30-year CVD risk scores, the most recent STEPS study conducted in 2016 reported that approximately 15% of individuals aged 30–74 and 25–59 years are at risk of experiencing CVD events within the next 10 and 30 years, respectively ( 5 ). The national report on deaths in 29 provinces of the country from 1385–1389 ( 6 ) revealed that various causes contributed to 1,172,278 deaths nationwide during this period, with cardiovascular diseases accounting for 46.04%, or 539,679 cases ( 6 ). Cardiovascular diseases have resulted in more deaths among men than women during the referenced years. In contrast, Iran currently has a predominantly young population, with only 6.6% aged 65 and older as of 2020. However, projections suggest that by 2050, this proportion will exceed 20%, indicating a significant demographic shift toward an aging population ( 7 ). The Iranian population is adopting a sedentary lifestyle, resulting in a projected rise in obesity rates and decreased physical activity in the coming years ( 8 ). The increase in cardiovascular disease incidence in the coming years is unavoidable due to demographic aging and lifestyle modifications. Cardiovascular disease is primarily driven by four major behavioral risk factors: tobacco use, excessive alcohol intake, lack of physical activity, and poor dietary habits. These behaviors contribute to metabolic and physiological changes, including hypertension, excess body weight, hyperglycemia, and dyslipidemia ( 4 , 9 ). High blood pressure (BP) is a significant and modifiable risk factor for CVD. Long-term trends of BP may indicate the cumulative risk and vascular damage caused by long-term exposure to elevated BP levels. Recent research has started to conceptualize BP patterns as trajectories that span an individual’s lifetime. These trajectories integrate both absolute BP levels and the rate of BP changes over time, providing a comprehensive metric that captures longitudinal BP patterns ( 10 ). Li et al. conducted a study examining BP trajectories and reported a positive correlation between BP trajectories and the risk of stroke or myocardial infarction (MI) in a non-hypertensive Chinese population. They emphasized the importance of BP management in pre-hypertensive adults to reduce the risk of stroke and MI later in life ( 11 ). Patients with CVD exhibit varying body mass index (BMI) levels when diagnosed. Assessing BMI at a single time point is insufficient to fully understand the association between body fat and cardiovascular risk. Obesity is a complex and evolving condition that must be evaluated across the life course. Integrating various metrics of obesity patterns, intensity, and temporal changes provides a more comprehensive understanding of its impact on cardiovascular health ( 12 ). Maintaining stable overweight or obesity throughout adulthood is linked to an increased risk of coronary heart disease (CHD). However, transitioning from a normal weight at baseline to overweight or obesity was not associated with increased CHD risk. This highlights the importance of preventing obesity early in life to reduce the risk of CHD ( 13 ). An unfavorable lipid profile is a well-established risk factor for the development and progression of CVD. Furthermore, age-related changes in lipid and lipoprotein concentrations have been observed, with lipid levels increasing until middle age and subsequently declining ( 14 ). Most studies have focused on cardio-metabolic risk factors assessed at a single time point or averaged over a period, which may fail to fully capture the dynamic changes in these risk factors over time. However, longitudinal changes in risk factors are closely linked to CVD and mortality ( 15 ). Risk factor trajectories provide valuable clinical insights beyond single time-point measurements. Methods Participants The Tehran Lipid and Glucose Study (TLGS) is a large, ongoing population-based study in Tehran's district 13 that tracks risk factors for chronic diseases and cardiovascular health. Comprehensive details about the study protocol have been provided in previous publications ( 16 ). Participants were chosen through multistage random cluster sampling, with 73% participation per phase, and tracked over five follow-up phases. This study included 5,479 adults aged 40–79 years from the third examination cycle. After applying exclusion criteria, a subset was selected for the final analysis. The study ultimately included 1872 adults without CVD at the third examination cycle (used as the baseline) and followed them until March 2018. Further details on participant selection and exclusion criteria are available in another of our publications ( 17 ). This study builds on previous findings ( 17 ) that conventional CVD risk scores may fail to identify individuals at high risk of severe events. By analyzing temporal trajectories of metabolic factors in a statin-naïve cohort, the study aims to identify early, significant changes in these indicators preceding CVD onset—changes that traditional risk scores might overlook—thereby enhancing risk stratification and informing earlier interventions. Measurements Participants underwent standardized assessments for anthropometrics, blood pressure, and blood tests after fasting. Comprehensive details about the measurements have been provided in previous publications. Cardiovascular risk was assessed using the 2013 ACC/AHA guidelines and categorized into four risk groups. The official ASCVD Risk Estimator from the American Heart Association website was used for calculations. Definitions of outcomes Participants were followed annually for medical events, which were confirmed by an expert panel. CHD, MI, unstable angina, and CVD were defined based on clinical criteria, ECG findings, biomarkers, and cause of death. Cardiovascular mortality included deaths from MI, heart failure, cardiac arrest, or cerebrovascular events. Further details of CVD outcomes are available in previous publications ( 18 ). Analysis Method Continuous variables following a normal distribution are expressed as means with standard deviations (SDs). Non-normally distributed continuous variables are presented as medians with interquartile ranges (IQRs) spanning the 25th to 75th percentiles, and categorical baseline characteristics are described as frequencies (%). Independent t-tests were employed for normally distributed continuous variables, the Mann-Whitney test was applied to skewed continuous variables, and the chi-square test was utilized for categorical variable. These statistical tests were applied to evaluate differences in baseline characteristics between study participants with and without CVD. The date of CVD diagnosis for participants who developed CVD and the final visit for those who did not were designated as time 0. Participant data were then retrospectively examined at 3-year intervals from baseline to assess the progression of risk factors. The Generalized Estimating Equations (GEE) approach, utilizing an autoregressive working correlation structure and a linear model with an identity link function, was employed to analyze metabolic indicators throughout the follow-up period. The retrospective examination assessed the trajectories of metabolic indicators and their differences between participants with and without CVD. In the model, each metabolic indicator was treated as the outcome, with follow-up time, CVD conversion status, and their interaction serving as covariates. Graphical representations were used to demonstrate the actual changes in these indicators over time. All metabolic indicator trajectories were adjusted for age and sex. All analyses were conducted via the SPSS, with a two-tailed p-value < 0.05 deemed statistically significant. Results A total of 887 men and 985 women were followed up. Over a 10-year follow-up period, among 1,872 participants without CVD in the TLGS, 117 (6.3%) developed CVD. Table 1 presents a comparison of baseline characteristics between participants who developed CVD and those who did not. At baseline, participants who developed CVD were older, had higher body weight, elevated systolic and diastolic blood pressure, increased fasting plasma glucose (FPG) and 2-hour plasma glucose (2hPG) levels, higher total cholesterol, low-density lipoprotein (LDL), and non-high-density lipoprotein (non-HDL) levels, and greater triglyceride-glucose (TyG) index values compared to those who did not develop CVD. Participants who developed cardiovascular disease (CVD) had significantly higher mean waist circumference (98.21 cm vs. 93.95 cm) and mean age (58.21 years vs. 50.33 years) compared to those who did not develop CVD. The mean serum creatinine level and frequency of men were borderline significant. No significant differences were observed in mean BMI, AI, HDL, or TG levels between participants who developed CVD and those who did not. Table 1: Baseline characteristics of participants Variables Non-CVD (n=1755) CVD (n=117) P-value Gender (Male) 822(46.8) 65(55.6) 0.067 Age (year) 50.33±8.21 58.21±9.52 <0.001 BMI (kg/m2 ) 28.30±4.39 28.98±4.21 0.101 Wc (cm) 93.95±10.89 98.21±9.56 <0.001 SBP (mmHg) 117.09±16.38 127.17±18.91 <0.001 DBP (mmHg) 76.28±8.93 78.67±8.43 0.005 Serum creatinine (μmol/L) 93.29±14.41 95.92±15.26 0.056 FPG (mmol/l) 5.26±1.39 6.12±2.49 <0.001 2-h PG (mmol/l) 6.12±2.44 7.07±3.05 0.003 TG (mmol/l) † 1.52(1.12,2.07) 1.58(1.18,1.98) 0.640 TC (mmol/l) 4.95±0.75 5.11±0.76 0.023 HDL-C (mmol/l) 1.08±0.25 1.08±0.26 0.951 LDL-C (mmol/l) 3.11±0.66 3.26±0.68 0.019 Non-HDL-C (mmol/L) 3.87±0.74 4.03±0.77 0.023 AI 3.80±1.20 3.96±1.26 0.155 TyG index 8.74±0.49 8.88±0.53 0.004 BMI, body mass index; Wc, Waist circumference; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG, fasting plasma glucose; 2-hPG, 2-hour post-challenge plasma glucose; TG, triglyceride; TC, Total Cholesterol; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; AI, Atherogenic index; TyG index, Triglyceride-glucose index. Data are given as the mean±SD or median (IQ 25–75) for continuous variables unless otherwise indicated († ), and data are given as the n(%) for categorical variables Figure 1 illustrates the trajectories of metabolic risk factors among participants. The trajectory of the TyG index varied significantly between those who developed CVD and those who did not. Remarkably, the slope of the trajectory line exhibited a significant change three years prior to the diagnosis of CVD (Pinteraction = .032). The trajectories of FPG paralleled those of the TyG index, showing a marked increase in the six years preceding the diagnosis of CVD (Pinteraction = .031). FPG showed a linear increase in both non-CVD and CVD participants, with a more noticeable difference between groups starting six years prior to CVD diagnosis. Systolic blood pressure (SBP) showed an upward trend in the CVD group, with differences from the non-CVD group evident nine years before the endpoint. Trajectories of the CVD risk score demonstrated a significant rise in the six years leading up to CVD diagnosis (P interaction <0.001). Figure 1 shows that the risk score of CVD increases progressively before it occurs, but the results of our previous article on the same population ( 17 ) showed that the AHA risk score classification, despite its good performance in identifying high-risk individuals, cannot necessarily distinguish individuals with severe cardiovascular outcomes very well. This highlights the need to address each risk factor individually, which optimizes CVD risk factors. The AHA risk score is designed for individuals not currently undergoing statin therapy, effectively representing a relatively healthy population. However, this score has limited predictive power in identifying individuals at high risk of severe cardiovascular outcomes. To address this limitation, we analyze the longitudinal trajectory of the AHA risk score alongside other metabolic markers prior to the onset of CVD. Our aim is to identify which indicators exhibit significant differences between individuals who eventually develop CVD and those who remain disease-free. Discussion The study followed 1872 participants for 10 years and revealed significant differences in the trajectories of metabolic risk factors between CVD converters and non-CVD converters many years before the onset of CVD. When a non-CVD individual converted to CVD, the TyG index, SBP, and plasma glucose showed significant changes in metabolic risk factors over time. In this study, the greatest differences were observed in FPG, SBP, and TyG index over the six years preceding the onset of CVD. Kohi et al. demonstrated that although overall risk trajectories remained consistent over time, individual risk factors exhibited distinct patterns of variation within each trajectory ( 19 ). This study highlights the significance of addressing individual risk factors independently to develop preventive strategies that not only target overall CVD risk but also optimize the management of each contributing factor. Consequently, modifications in unfavorable metabolic factors through lifestyle changes can enhance the long-term outlook for CVD. A substantial body of epidemiological research has consistently demonstrated that adopting a healthy lifestyle is associated with a reduced risk of cardiovascular disease ( 20 ). In the present study, the trajectory of the TyG index as an insulin resistance marker differed between individuals who developed CVD and those who did not. According to prior research, compelling evidence indicates that insulin resistance plays a significant role in the prediction of cardiovascular events. A prospective urban‒rural epidemiological study assessed the impact of adjustable risk factors on CVD risk in 21 nations, revealing a correlation between various modifiable lifestyle habits and a reduced risk of cardiovascular mortality ( 21 ). Furthermore, the study revealed that poor dietary choices and tobacco use contribute more to CVD cases than insufficient physical activity does at this risk ( 22 ). Regardless of metabolic status, lifestyle factors significantly impact major adverse cardiovascular events. The interplay between lifestyle and metabolic health clearly shows a gradient of risk, emphasizing the need for lifestyle changes regardless of current metabolic conditions ( 23 ). Multiple prospective, population-based studies have identified a link between insulin resistance and the occurrence of CVD outcomes ( 24 ). A meta-analysis found that a 1 standard deviation increase in HOMA-IR was linked to a 46% higher risk of coronary heart disease in individuals without diabetes ( 25 ). Research by Li Mian et al. provided a comprehensive analysis underscoring the contribution of metabolic components to CVD risk ( 22 ). Insulin resistance, a key underlying mechanism of type 2 diabetes mellitus (T2DM), is recognized as a significant predictor of atherosclerotic cardiovascular disease (CVD) ( 26 ). The 2019 ADA-EASD consensus report strongly recommends managing hyperglycemia in T2DM patients at high risk for CVD ( 27 ). Long-term blood pressure trajectories offer added predictive value beyond single-time measurements. Several studies have shown that time-averaged blood pressure is more strongly associated with cardiovascular disease prognosis than isolated blood pressure readings ( 28 ). Despite a decline in the prevalence of CVD in individuals with diabetes, CVD continues to be the primary cause of mortality in those with T2DM and remains a significant consequence of the condition. Conventional cardiovascular risk factors inadequately account for the heightened risk of CVD and CVD mortality in individuals with diabetes. The evidence indicates that hereditary risk factors, insulin resistance, and hypoglycemia may explain the inexplicable cardiovascular risk in type 2 diabetes mellitus patients. Moreover, extensive lifestyle alterations can increase cardiovascular risk factors ( 29 ). In this research, the trajectory of the CVD risk score was examined. We found that LDL levels did not change significantly before the disease. These findings align with the study by Kohi et al., which reported that despite a decline in LDL-C levels over time, a substantial residual risk of CVD persists. This underscores the importance of evaluating risk factor levels at each clinical visit to more effectively mitigate residual CVD risk ( 30 ). The strength of the current study lies in its longitudinal design, which allows us to examine the heterogeneity in risk factors and evaluate their effects on CVD events. A key limitation of this study is that the data were collected from a metropolitan area in Iran, which may limit the generalizability of the findings to the broader national population or to populations in other regions. Conclusions It is essential to assess the risk factor levels at each visit to reduce the risk of CVD. Longitudinal trajectories of metabolic risk factors, particularly SBP, FPG, and the TyG index, demonstrated an increasing trend for CVD development years before onset. These findings emphasize the importance of early detection and preventive strategies targeting metabolic risk factors. Abbreviations The following abbreviations are used in this manuscript: Cardiovascular disease: CVD Tehran Lipid and Glucose Study: TLGS Generalized Estimating Equations: GEE Non-communicable diseases: NCDs American College of Cardiology/American Heart Association: ACC/AHA STEPwise approach to NCD risk factor surveillance: STEPs Disability-Adjusted Life Years: DALYs Coronary heart disease: CHD Myocardial Infarction: MI ElectroCardioGraphy: ECG Type 2 Diabetes Mellitus: T2DM Serum Creatinine: Cr Body Mass Index: BMI Waist circumference : Wc High blood pressure: BP Systolic Blood Pressure: SBP Diastolic Blood Pressure: DBP fasting plasma glucose: FPG 2 Hours Post 75 g Glucose: 2hPG Triglyceride: TG Total Cholesterol: TC High-Density Lipoprotein-Cholesterol: HDL-C Low-Density Lipoprotein-Cholesterol: LDL-C Atherogenic Index: AI Triglyceride-Glucose Index: TyG index Declarations The study adhered to the ethical principles of the Helsinki Declaration and received approval from relevant ethics committees in Iran. All participants provided written informed consent. Clinical trial number: not applicable Ethics approval and consent to participate: The data for this study were sourced from the Tehran Lipid and Glucose Study (TLGS), conducted by the Endocrine Research Center at Shahid Beheshti University of Medical Sciences. The authors gratefully acknowledge the efforts of the TLGS team and the participation of all study subjects. Ethical approval for this project was granted by the Ethics Committee of Tarbiat Modares University (IR.MODARES.REC.1403.100). Consent for publication: Not applicable. Availability of data and materials: The data used in this study can be obtained from the corresponding author upon a justified request. Competing interest: The authors declare no financial or nonfinancial conflicts of interest that could have affected the integrity of this research. Funding: None Author's contributions: "MM" contributed to data collection, literature review, data analysis, data interpretation, and manuscript preparation. "DK" was responsible for study design, manuscript revision, and final approval of the manuscript. "AA " and "AK" were involved in study design, manuscript preparation, manuscript revision, and final approval. All authors reviewed and approved the final version of the manuscript. Acknowledgments : The data for this research were obtained from the Tehran Lipid and Glucose Study (TLGS), conducted by the Endocrine Research Center at Shahid Beheshti University of Medical Sciences. The authors sincerely thank all individuals involved in the design and data collection of the TLGS, as well as the study participants for their valuable contributions. This project was approved by the Ethics Committee of Tarbiat Modares University (IR.MODARES.REC.1403.100). References Taheri Soodejani M. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6690336","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":477951083,"identity":"a6f7fab5-1e6c-4c91-9203-3850adff6b50","order_by":0,"name":"Maryam Mahdavi","email":"","orcid":"","institution":"Tarbiat Modares University","correspondingAuthor":false,"prefix":"","firstName":"Maryam","middleName":"","lastName":"Mahdavi","suffix":""},{"id":477951084,"identity":"1022ea15-fd8a-4a39-b3e0-9f3297361c1b","order_by":1,"name":"Anoshirvan Kazemnejad","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYBACCRiDH5nDkECMFskGkrUYHEDWgg9Ith9/+OgGg12e8bXDB2/83GNjz8B++AHDwz24tUjzJCQb5zAkF5vdTku27HmWltjAk2bAkPAMtxY5hoRj0jkMzInbbueYSfAcOAz0RQ7QLwfwaOF/2P47h6E+cfPsHDPJPwf+2zPwv8GvRVoimY05h+Fw4gbpHDNpngMHGBskCNgiOeMZM9BhxxNnAP1iLXMgObFN4pnBAXxaJM6nP/ycw1Cd2D87+eDNNwfs7Pn5kx8+/IFHCxgw/kPisAExIQ2jYBSMglEwCggAAN46TksG6DLfAAAAAElFTkSuQmCC","orcid":"","institution":"Tarbiat Modares University","correspondingAuthor":true,"prefix":"","firstName":"Anoshirvan","middleName":"","lastName":"Kazemnejad","suffix":""},{"id":477951085,"identity":"c6946194-201d-4e78-b5d8-04a6e2ff3cf2","order_by":2,"name":"Abbas Asosheh","email":"","orcid":"","institution":"Tarbiat Modares University","correspondingAuthor":false,"prefix":"","firstName":"Abbas","middleName":"","lastName":"Asosheh","suffix":""},{"id":477951086,"identity":"cc777a35-857f-4340-a29c-490c6851b7b2","order_by":3,"name":"Davood Khalili","email":"","orcid":"","institution":"Shahid Beheshti University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Davood","middleName":"","lastName":"Khalili","suffix":""}],"badges":[],"createdAt":"2025-05-18 07:38:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6690336/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6690336/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85824700,"identity":"7c46b472-be25-4c8e-bf45-bcd2b5f1d39a","added_by":"auto","created_at":"2025-07-02 07:05:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":222857,"visible":true,"origin":"","legend":"\u003cp\u003eLongitudinal patterns of clinical and metabolic factors. (A) LDL (low-density lipoprotein-cholesterol), (B) Cr (serum creatinine), (C) FPG (fasting plasma glucose), (D) TG (triglyceride), (E) TyG index (triglyceride-glucose index), (F) 2hPG (2 hours post 75 g glucose), (G) HDL (high-density lipoprotein-cholesterol), (H) TC (total cholesterol), (I) non-HDL, (J) AI (atherogenic index), (K) SBP (systolic blood pressure), (L) DBP (diastolic blood pressure), (M) Wc (waist circumference), (N) BMI (body mass index), (O) Risk score of ASCVD in the CVD population with (solid line) and without (dashed line) CVD incidence adjusted by sex and age.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6690336/v1/45fee4a9f9c8af1dac3452b5.png"},{"id":101494600,"identity":"34cfd65b-203f-40f0-a9e1-e0343103509b","added_by":"auto","created_at":"2026-01-30 11:56:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":736059,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6690336/v1/1f973642-43f8-48bd-ab3e-29aab15a60e6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Metabolic Risk Factors in NonCVD Individuals and Their Trajectory Toward Cardiovascular Incidence","fulltext":[{"header":"Background","content":"\u003cp\u003eNon-communicable diseases (NCDs) represent a significant worldwide health challenge. The incidence of NCDs is increasing annually worldwide, explaining the majority of annual deaths. Importantly, nearly 25% of these deaths happen before individuals reach 60 years of age (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). One of the non-communicable diseases is cardiovascular disease (CVD), which affects a significant part of the population. From 1990 to 2019, the number of individuals affected by CVD nearly doubled, showing a significant increase. Over the same period, CVD-related deaths also rose steadily. Global rates of disability-adjusted life years (DALYs) and years of life lost have shown a marked increase. During this time, the number of individuals living with disabilities doubled (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCardiovascular conditions are the predominant cause of death nationwide (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The age-adjusted incidence rate, age-adjusted death rate, and DALYs attributable to cardiovascular disease in Iran exceed the global averages (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). The Non-Communicable Diseases Risk Factors Surveillance Study (STEPS) utilized nationwide data collection from 2005, 2006, 2007, 2008, 2009, 2011, and 2016 to evaluate the 10-year and 30-year CVD risk scores, the most recent STEPS study conducted in 2016 reported that approximately 15% of individuals aged 30\u0026ndash;74 and 25\u0026ndash;59 years are at risk of experiencing CVD events within the next 10 and 30 years, respectively (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe national report on deaths in 29 provinces of the country from 1385\u0026ndash;1389 (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) revealed that various causes contributed to 1,172,278 deaths nationwide during this period, with cardiovascular diseases accounting for 46.04%, or 539,679 cases (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Cardiovascular diseases have resulted in more deaths among men than women during the referenced years. In contrast, Iran currently has a predominantly young population, with only 6.6% aged 65 and older as of 2020. However, projections suggest that by 2050, this proportion will exceed 20%, indicating a significant demographic shift toward an aging population (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). The Iranian population is adopting a sedentary lifestyle, resulting in a projected rise in obesity rates and decreased physical activity in the coming years (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The increase in cardiovascular disease incidence in the coming years is unavoidable due to demographic aging and lifestyle modifications.\u003c/p\u003e \u003cp\u003eCardiovascular disease is primarily driven by four major behavioral risk factors: tobacco use, excessive alcohol intake, lack of physical activity, and poor dietary habits. These behaviors contribute to metabolic and physiological changes, including hypertension, excess body weight, hyperglycemia, and dyslipidemia (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). High blood pressure (BP) is a significant and modifiable risk factor for CVD. Long-term trends of BP may indicate the cumulative risk and vascular damage caused by long-term exposure to elevated BP levels. Recent research has started to conceptualize BP patterns as trajectories that span an individual\u0026rsquo;s lifetime. These trajectories integrate both absolute BP levels and the rate of BP changes over time, providing a comprehensive metric that captures longitudinal BP patterns (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Li et al. conducted a study examining BP trajectories and reported a positive correlation between BP trajectories and the risk of stroke or myocardial infarction (MI) in a non-hypertensive Chinese population. They emphasized the importance of BP management in pre-hypertensive adults to reduce the risk of stroke and MI later in life (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Patients with CVD exhibit varying body mass index (BMI) levels when diagnosed. Assessing BMI at a single time point is insufficient to fully understand the association between body fat and cardiovascular risk. Obesity is a complex and evolving condition that must be evaluated across the life course. Integrating various metrics of obesity patterns, intensity, and temporal changes provides a more comprehensive understanding of its impact on cardiovascular health (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Maintaining stable overweight or obesity throughout adulthood is linked to an increased risk of coronary heart disease (CHD). However, transitioning from a normal weight at baseline to overweight or obesity was not associated with increased CHD risk. This highlights the importance of preventing obesity early in life to reduce the risk of CHD (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). An unfavorable lipid profile is a well-established risk factor for the development and progression of CVD. Furthermore, age-related changes in lipid and lipoprotein concentrations have been observed, with lipid levels increasing until middle age and subsequently declining (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Most studies have focused on cardio-metabolic risk factors assessed at a single time point or averaged over a period, which may fail to fully capture the dynamic changes in these risk factors over time. However, longitudinal changes in risk factors are closely linked to CVD and mortality (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Risk factor trajectories provide valuable clinical insights beyond single time-point measurements.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eThe Tehran Lipid and Glucose Study (TLGS) is a large, ongoing population-based study in Tehran's district 13 that tracks risk factors for chronic diseases and cardiovascular health. Comprehensive details about the study protocol have been provided in previous publications (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Participants were chosen through multistage random cluster sampling, with 73% participation per phase, and tracked over five follow-up phases. This study included 5,479 adults aged 40\u0026ndash;79 years from the third examination cycle. After applying exclusion criteria, a subset was selected for the final analysis. The study ultimately included 1872 adults without CVD at the third examination cycle (used as the baseline) and followed them until March 2018. Further details on participant selection and exclusion criteria are available in another of our publications (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). This study builds on previous findings (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) that conventional CVD risk scores may fail to identify individuals at high risk of severe events. By analyzing temporal trajectories of metabolic factors in a statin-na\u0026iuml;ve cohort, the study aims to identify early, significant changes in these indicators preceding CVD onset\u0026mdash;changes that traditional risk scores might overlook\u0026mdash;thereby enhancing risk stratification and informing earlier interventions.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasurements\u003c/h3\u003e\n\u003cp\u003eParticipants underwent standardized assessments for anthropometrics, blood pressure, and blood tests after fasting. Comprehensive details about the measurements have been provided in previous publications. Cardiovascular risk was assessed using the 2013 ACC/AHA guidelines and categorized into four risk groups. The official ASCVD Risk Estimator from the American Heart Association website was used for calculations.\u003c/p\u003e \u003cp\u003e \u003cem\u003eDefinitions\u003c/em\u003e of outcomes\u003c/p\u003e \u003cp\u003eParticipants were followed annually for medical events, which were confirmed by an expert panel. CHD, MI, unstable angina, and CVD were defined based on clinical criteria, ECG findings, biomarkers, and cause of death. Cardiovascular mortality included deaths from MI, heart failure, cardiac arrest, or cerebrovascular events. Further details of CVD outcomes are available in previous publications (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eAnalysis Method\u003c/h3\u003e\n\u003cp\u003eContinuous variables following a normal distribution are expressed as means with standard deviations (SDs). Non-normally distributed continuous variables are presented as medians with interquartile ranges (IQRs) spanning the 25th to 75th percentiles, and categorical baseline characteristics are described as frequencies (%). Independent t-tests were employed for normally distributed continuous variables, the Mann-Whitney test was applied to skewed continuous variables, and the chi-square test was utilized for categorical variable. These statistical tests were applied to evaluate differences in baseline characteristics between study participants with and without CVD.\u003c/p\u003e \u003cp\u003eThe date of CVD diagnosis for participants who developed CVD and the final visit for those who did not were designated as time 0. Participant data were then retrospectively examined at 3-year intervals from baseline to assess the progression of risk factors. The Generalized Estimating Equations (GEE) approach, utilizing an autoregressive working correlation structure and a linear model with an identity link function, was employed to analyze metabolic indicators throughout the follow-up period. The retrospective examination assessed the trajectories of metabolic indicators and their differences between participants with and without CVD. In the model, each metabolic indicator was treated as the outcome, with follow-up time, CVD conversion status, and their interaction serving as covariates. Graphical representations were used to demonstrate the actual changes in these indicators over time. All metabolic indicator trajectories were adjusted for age and sex. All analyses were conducted via the SPSS, with a two-tailed p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 deemed statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 887 men and 985 women were followed up. Over a 10-year follow-up period, among 1,872 participants without CVD in the TLGS, 117 (6.3%) developed CVD. Table\u0026nbsp;1 presents a comparison of baseline characteristics between participants who developed CVD and those who did not. At baseline, participants who developed CVD were older, had higher body weight, elevated systolic and diastolic blood pressure, increased fasting plasma glucose (FPG) and 2-hour plasma glucose (2hPG) levels, higher total cholesterol, low-density lipoprotein (LDL), and non-high-density lipoprotein (non-HDL) levels, and greater triglyceride-glucose (TyG) index values compared to those who did not develop CVD. Participants who developed cardiovascular disease (CVD) had significantly higher mean waist circumference (98.21 cm vs. 93.95 cm) and mean age (58.21 years vs. 50.33 years) compared to those who did not develop CVD. The mean serum creatinine level and frequency of men were borderline significant. No significant differences were observed in mean BMI, AI, HDL, or TG levels between participants who developed CVD and those who did not.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"492\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 492px;\"\u003e\n \u003cp\u003eTable 1: Baseline characteristics of participants\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eNon-CVD\u003c/p\u003e\n \u003cp\u003e(n=1755)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eCVD\u003c/p\u003e\n \u003cp\u003e(n=117)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eGender (Male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e822(46.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e65(55.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eAge (year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e50.33\u0026plusmn;8.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e58.21\u0026plusmn;9.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eBMI (kg/m2 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e28.30\u0026plusmn;4.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e28.98\u0026plusmn;4.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eWc (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e93.95\u0026plusmn;10.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e98.21\u0026plusmn;9.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eSBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e117.09\u0026plusmn;16.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e127.17\u0026plusmn;18.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eDBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e76.28\u0026plusmn;8.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e78.67\u0026plusmn;8.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003eSerum creatinine (\u0026mu;mol/L)\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e93.29\u0026plusmn;14.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e95.92\u0026plusmn;15.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eFPG (mmol/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e5.26\u0026plusmn;1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e6.12\u0026plusmn;2.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e2-h PG (mmol/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e6.12\u0026plusmn;2.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e7.07\u0026plusmn;3.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eTG (mmol/l) \u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e1.52(1.12,2.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.58(1.18,1.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.640\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eTC (mmol/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e4.95\u0026plusmn;0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e5.11\u0026plusmn;0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eHDL-C (mmol/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e1.08\u0026plusmn;0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.08\u0026plusmn;0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.951\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eLDL-C (mmol/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e3.11\u0026plusmn;0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e3.26\u0026plusmn;0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eNon-HDL-C (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e3.87\u0026plusmn;0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e4.03\u0026plusmn;0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e3.80\u0026plusmn;1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e3.96\u0026plusmn;1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eTyG index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e8.74\u0026plusmn;0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e8.88\u0026plusmn;0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 492px;\"\u003e\n \u003cp\u003eBMI, body mass index; Wc, Waist circumference; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG, fasting plasma glucose; 2-hPG,\u0026nbsp;2-hour post-challenge plasma glucose; TG, triglyceride; TC, Total Cholesterol; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; AI, Atherogenic index; TyG index, Triglyceride-glucose index.\u003c/p\u003e\n \u003cp\u003eData are given as the mean\u0026plusmn;SD or median (IQ 25\u0026ndash;75) for continuous variables unless otherwise indicated (\u0026dagger; ), and data are given as the n(%) for categorical variables\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the trajectories of metabolic risk factors among participants. The trajectory of the TyG index varied significantly between those who developed CVD and those who did not. Remarkably, the slope of the trajectory line exhibited a significant change three years prior to the diagnosis of CVD (Pinteraction\u0026thinsp;=\u0026thinsp;.032). The trajectories of FPG paralleled those of the TyG index, showing a marked increase in the six years preceding the diagnosis of CVD (Pinteraction\u0026thinsp;=\u0026thinsp;.031). FPG showed a linear increase in both non-CVD and CVD participants, with a more noticeable difference between groups starting six years prior to CVD diagnosis. Systolic blood pressure (SBP) showed an upward trend in the CVD group, with differences from the non-CVD group evident nine years before the endpoint. Trajectories of the CVD risk score demonstrated a significant rise in the six years leading up to CVD diagnosis (P\u003csub\u003einteraction\u003c/sub\u003e \u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows that the risk score of CVD increases progressively before it occurs, but the results of our previous article on the same population (\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e) showed that the AHA risk score classification, despite its good performance in identifying high-risk individuals, cannot necessarily distinguish individuals with severe cardiovascular outcomes very well. This highlights the need to address each risk factor individually, which optimizes CVD risk factors. The AHA risk score is designed for individuals not currently undergoing statin therapy, effectively representing a relatively healthy population. However, this score has limited predictive power in identifying individuals at high risk of severe cardiovascular outcomes. To address this limitation, we analyze the longitudinal trajectory of the AHA risk score alongside other metabolic markers prior to the onset of CVD. Our aim is to identify which indicators exhibit significant differences between individuals who eventually develop CVD and those who remain disease-free.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e The study followed 1872 participants for 10 years and revealed significant differences in the trajectories of metabolic risk factors between CVD converters and non-CVD converters many years before the onset of CVD. When a non-CVD individual converted to CVD, the TyG index, SBP, and plasma glucose showed significant changes in metabolic risk factors over time. In this study, the greatest differences were observed in FPG, SBP, and TyG index over the six years preceding the onset of CVD.\u003c/p\u003e \u003cp\u003eKohi et al. demonstrated that although overall risk trajectories remained consistent over time, individual risk factors exhibited distinct patterns of variation within each trajectory (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). This study highlights the significance of addressing individual risk factors independently to develop preventive strategies that not only target overall CVD risk but also optimize the management of each contributing factor. Consequently, modifications in unfavorable metabolic factors through lifestyle changes can enhance the long-term outlook for CVD. A substantial body of epidemiological research has consistently demonstrated that adopting a healthy lifestyle is associated with a reduced risk of cardiovascular disease (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the present study, the trajectory of the TyG index as an insulin resistance marker differed between individuals who developed CVD and those who did not. According to prior research, compelling evidence indicates that insulin resistance plays a significant role in the prediction of cardiovascular events. A prospective urban‒rural epidemiological study assessed the impact of adjustable risk factors on CVD risk in 21 nations, revealing a correlation between various modifiable lifestyle habits and a reduced risk of cardiovascular mortality (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Furthermore, the study revealed that poor dietary choices and tobacco use contribute more to CVD cases than insufficient physical activity does at this risk (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRegardless of metabolic status, lifestyle factors significantly impact major adverse cardiovascular events. The interplay between lifestyle and metabolic health clearly shows a gradient of risk, emphasizing the need for lifestyle changes regardless of current metabolic conditions (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Multiple prospective, population-based studies have identified a link between insulin resistance and the occurrence of CVD outcomes (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). A meta-analysis found that a 1 standard deviation increase in HOMA-IR was linked to a 46% higher risk of coronary heart disease in individuals without diabetes (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eResearch by Li Mian et al. provided a comprehensive analysis underscoring the contribution of metabolic components to CVD risk (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Insulin resistance, a key underlying mechanism of type 2 diabetes mellitus (T2DM), is recognized as a significant predictor of atherosclerotic cardiovascular disease (CVD) (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). The 2019 ADA-EASD consensus report strongly recommends managing hyperglycemia in T2DM patients at high risk for CVD (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Long-term blood pressure trajectories offer added predictive value beyond single-time measurements. Several studies have shown that time-averaged blood pressure is more strongly associated with cardiovascular disease prognosis than isolated blood pressure readings (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite a decline in the prevalence of CVD in individuals with diabetes, CVD continues to be the primary cause of mortality in those with T2DM and remains a significant consequence of the condition. Conventional cardiovascular risk factors inadequately account for the heightened risk of CVD and CVD mortality in individuals with diabetes. The evidence indicates that hereditary risk factors, insulin resistance, and hypoglycemia may explain the inexplicable cardiovascular risk in type 2 diabetes mellitus patients. Moreover, extensive lifestyle alterations can increase cardiovascular risk factors (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this research, the trajectory of the CVD risk score was examined. We found that LDL levels did not change significantly before the disease. These findings align with the study by Kohi et al., which reported that despite a decline in LDL-C levels over time, a substantial residual risk of CVD persists. This underscores the importance of evaluating risk factor levels at each clinical visit to more effectively mitigate residual CVD risk (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe strength of the current study lies in its longitudinal design, which allows us to examine the heterogeneity in risk factors and evaluate their effects on CVD events. A key limitation of this study is that the data were collected from a metropolitan area in Iran, which may limit the generalizability of the findings to the broader national population or to populations in other regions.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIt is essential to assess the risk factor levels at each visit to reduce the risk of CVD. Longitudinal trajectories of metabolic risk factors, particularly SBP, FPG, and the TyG index, demonstrated an increasing trend for CVD development years before onset. These findings emphasize the importance of early detection and preventive strategies targeting metabolic risk factors.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eThe following abbreviations are used in this manuscript:\u003c/p\u003e\n\u003cp\u003eCardiovascular disease: CVD\u003c/p\u003e\n\u003cp\u003eTehran Lipid and Glucose Study: TLGS\u003c/p\u003e\n\u003cp\u003eGeneralized Estimating Equations: GEE\u003c/p\u003e\n\u003cp\u003eNon-communicable diseases: NCDs\u003c/p\u003e\n\u003cp\u003eAmerican College of Cardiology/American Heart Association: ACC/AHA\u003c/p\u003e\n\u003cp\u003eSTEPwise approach to NCD risk factor surveillance: STEPs\u003c/p\u003e\n\u003cp\u003eDisability-Adjusted Life Years: DALYs\u003c/p\u003e\n\u003cp\u003eCoronary heart disease: CHD\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMyocardial Infarction: MI\u003c/p\u003e\n\u003cp\u003eElectroCardioGraphy: ECG\u003c/p\u003e\n\u003cp\u003eType 2 Diabetes Mellitus: T2DM\u003c/p\u003e\n\u003cp\u003eSerum Creatinine: Cr\u003c/p\u003e\n\u003cp\u003eBody Mass Index: BMI\u003c/p\u003e\n\u003cp\u003eWaist circumference : Wc\u003c/p\u003e\n\u003cp\u003eHigh blood pressure: BP\u003c/p\u003e\n\u003cp\u003eSystolic Blood Pressure: SBP\u003c/p\u003e\n\u003cp\u003eDiastolic Blood Pressure: DBP\u003c/p\u003e\n\u003cp\u003efasting plasma glucose: FPG\u003c/p\u003e\n\u003cp\u003e2 Hours Post 75 g Glucose: 2hPG\u003c/p\u003e\n\u003cp\u003eTriglyceride: TG\u003c/p\u003e\n\u003cp\u003eTotal Cholesterol: TC\u003c/p\u003e\n\u003cp\u003eHigh-Density Lipoprotein-Cholesterol: HDL-C\u003c/p\u003e\n\u003cp\u003eLow-Density Lipoprotein-Cholesterol: LDL-C\u003c/p\u003e\n\u003cp\u003eAtherogenic Index: AI\u003c/p\u003e\n\u003cp\u003eTriglyceride-Glucose Index: TyG index\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe study adhered to the ethical principles of the Helsinki Declaration and received approval from relevant ethics committees in Iran. All participants provided written informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eThe data for this study were sourced from the Tehran Lipid and Glucose Study (TLGS), conducted by the Endocrine Research Center at Shahid Beheshti University of Medical Sciences. The authors gratefully acknowledge the efforts of the TLGS team and the participation of all study subjects. Ethical approval for this project was granted by the Ethics Committee of Tarbiat Modares University (IR.MODARES.REC.1403.100).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e The data used in this study can be obtained from the corresponding author upon a justified request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest:\u003c/strong\u003e The authors declare no financial or nonfinancial conflicts of interest that could have affected the integrity of this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e None\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026apos;s contributions:\u003c/strong\u003e \u003cem\u003e\u0026quot;MM\u0026quot;\u003c/em\u003e contributed to data collection, literature review, data analysis, data interpretation, and manuscript preparation. \u003cem\u003e\u0026quot;DK\u0026quot;\u003c/em\u003e was responsible for study design, manuscript revision, and final approval of the manuscript. \u003cem\u003e\u0026quot;AA\u003c/em\u003e\u0026quot; and \u003cem\u003e\u0026quot;AK\u0026quot;\u003c/em\u003e were involved in study design, manuscript preparation, manuscript revision, and final approval. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eThe data for this research were obtained from the Tehran Lipid and Glucose Study (TLGS), conducted by the Endocrine Research Center at Shahid Beheshti University of Medical Sciences. The authors sincerely thank all individuals involved in the design and data collection of the TLGS, as well as the study participants for their valuable contributions. This project was approved by the Ethics Committee of Tarbiat Modares University (IR.MODARES.REC.1403.100).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTaheri Soodejani M. Non-communicable diseases in the world over the past century: a secondary data analysis. Frontiers in Public Health. 2024;12:1436236.\u003c/li\u003e\n\u003cli\u003eRoth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, et al. 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Journal of Translational Medicine. 2021;19:1-13.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Trajectory, Cardiovascular disease, TLGS, CVD risk factors","lastPublishedDoi":"10.21203/rs.3.rs-6690336/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6690336/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eIn 2019, cardiovascular disease (CVD) was the primary cause of death worldwide, responsible for approximately 18.6\u0026nbsp;million fatalities, with its prevalence and incidence continuing to rise. In Iran, CVD accounts for 46.04% of all deaths, with demographic aging and sedentary lifestyles exacerbating the burden. This study evaluated the impact of metabolic risk factors and their trajectories on CVD development in an Iranian cohort.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn accordance with the Tehran Lipid and Glucose Study (TLGS), this longitudinal study included 1872 adults aged 40\u0026ndash;79 years without prior CVD at baseline. The participants were selected through multistage random cluster sampling from 1999\u0026ndash;2018. Data were collected on demographic, lifestyle, and metabolic factors, with laboratory analyses conducted via standardized protocols. Generalized estimating equations (GEEs) were used to assess age- and sex-adjusted trajectories of metabolic indicators.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOver the 10-year follow-up period, 117 individuals (6.3%) were diagnosed with cardiovascular disease (CVD). Baseline CVD converters presented increased age, weight, blood pressure, fasting glucose, and lipid levels, and diabetes incidence. The key metabolic risk factor trajectories included the TyG index, FPG, and SBP, which significantly increased 6 years before diagnosis.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eLongitudinal trajectories of metabolic risk factors, particularly SBP, FPG, and the TyG index, demonstrated strong predictive value for CVD development years before onset, with SBP emerging as the most potent predictor. These findings emphasize the importance of early detection and preventive strategies targeting metabolic risk factors. Lifestyle modifications can significantly mitigate CVD risk, underscoring the utility of longitudinal data in understanding risk factor heterogeneity and disease progression. Greater attention should be given to patients with unstable cardiometabolic risk factors.\u003c/p\u003e","manuscriptTitle":"Metabolic Risk Factors in NonCVD Individuals and Their Trajectory Toward Cardiovascular Incidence","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-02 06:48:59","doi":"10.21203/rs.3.rs-6690336/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"18f14100-5832-45c8-ba1e-0183e0c1d1ea","owner":[],"postedDate":"July 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-30T11:55:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-02 06:48:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6690336","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6690336","identity":"rs-6690336","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}