Elevated Estimated Pulse Wave Velocity and the Risk of Type 2 Diabetes in Non-Obese Young Adults: A Longitudinal Cohort Study

Elevated Estimated Pulse Wave Velocity and the Risk of Type 2 Diabetes in Non-Obese Young Adults: A Longitudinal Cohort Study | 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 Elevated Estimated Pulse Wave Velocity and the Risk of Type 2 Diabetes in Non-Obese Young Adults: A Longitudinal Cohort Study Chunxia Zhang, Li Chen, Ri Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5359838/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Jun, 2025 Read the published version in BMC Endocrine Disorders → Version 1 posted 10 You are reading this latest preprint version Abstract Background Arterial stiffness, measured by estimated pulse wave velocity (ePWV), is associated with a higher risk of cardiovascular diseases and type 2 diabetes mellitus (T2DM) in older and obese individuals. However, the role of arterial stiffness as an early predictor of T2DM in non-obese, young adults remains underexplored. Identifying alternative predictors like arterial stiffness is crucial for detecting diabetes onset in non-obese and younger populations who may not exhibit traditional risk factors such as high Body Mass Index (BMI).This study investigates the association between elevated ePWV and the risk of T2DM in non-obese young adults, who are often overlooked in diabetes prevention efforts. Methods The study included 9,543 non-obese participants (BMI < 25 kg/m², age < 50 years) from the NAGALA cohort in the Gifu area. ePWV was calculated, and participants were followed for the development of diabetes. Cox proportional hazard models assessed the association between ePWV and T2DM risk, adjusting for age, sex, BMI, alcohol consumption, exercise, smoking status, and metabolic biomarkers. Subgroup analyses examined the association's consistency across sex, age, smoking, and alcohol consumption. Results During a median follow-up period of 6.3 years, 110 participants (1.2%) developed T2DM.In the unadjusted model, elevated ePWV was significantly linked to a higher risk of T2DM (HR 1.85, 95% CI 1.53–2.23, p < 0.001).The association remained significant after adjusting for confounders (HR 1.36, 95% CI 1.05–1.75, p = 0.018).Subgroup analysis showed no significant interactions across sex, age, alcohol consumption, or smoking status. Conclusions Elevated ePWV independently correlates with a higher risk of T2DM in non-obese young adults. ePWV may serve as a valuable tool for early diabetes risk identification in this population. Pulse wave velocity Type 2 diabetes Non-obese young adults Arterial stiffness Longitudinal study. Figures Figure 1 Figure 2 Figure 3 Lay Summary This study investigates the relationship between elevated arterial stiffness, measured by estimated pulse wave velocity (ePWV), and the risk of developing type 2 diabetes mellitus (T2DM) in non-obese young adults. While arterial stiffness has been linked to higher T2DM risk in older or obese individuals, this study uniquely focuses on non-obese young adults, a group not typically associated with high diabetes risk. By analyzing data from over 9,500 participants, the research found that even in individuals with a normal body mass index (BMI), higher ePWV is significantly associated with an increased risk of T2DM. This suggests that measuring ePWV could help detect early diabetes risk in people who may not exhibit traditional risk factors, such as high BMI. The findings highlight the importance of vascular health in diabetes prevention and propose ePWV as a potential tool for early detection in clinical practice. Background Type 2 diabetes mellitus (T2DM) is a growing global health issue, increasingly affecting younger and non-obese populations. Traditionally, the primary risk factors for T2DM have included obesity, age, and family history[1, 2]. However, recent studies have suggested that individuals without obesity are not immune to the risk of developing T2DM, highlighting the need to investigate alternative early indicators[3–6]. Arterial stiffness has been shown to play a critical role in the development of cardiovascular diseases[7], and recent evidence suggests that it may also be associated with the pathogenesis of T2DM[8]. Estimated pulse wave velocity (ePWV) offers a practical and cost-effective alternative to traditional arterial stiffness measurements like pulse wave velocity (PWV) and ambulatory arterial stiffness index (AASI), which require specialized equipment and are less feasible in routine clinical settings[9]. Calculated from age and mean blood pressure (MBP), ePWV reliably reflects arterial stiffness and has predictive value for cardiovascular risk, making it an accessible tool for broader clinical use[7]. Understanding the relationship between ePWV and T2DM in a younger, non-obese population is critical, as early detection could offer new pathways for preventive interventions. Previous research has shown that elevated arterial stiffness can indicate early vascular damage[10]. This vascular dysfunction can lead to endothelial damage, affecting insulin sensitivity and contributing to the onset of diabetes[11]. Furthermore, arterial stiffness has been associated with impaired glucose metabolism, even in populations that are not typically considered high-risk for diabetes, such as young and non-obese individuals[12]. Therefore, exploring the role of ePWV as an independent predictor of T2DM in non-obese young adults is particularly important, as it may help identify high-risk individuals before the disease manifests. In light of the increasing prevalence of T2DM in non-obese populations, there is a pressing need to shift the focus of diabetes prevention strategies towards early detection and novel risk factors. Traditional risk factors, such as obesity and age, may no longer be sufficient for effective risk stratification, especially as evolving lifestyles and dietary habits contribute to shifts in metabolic risk profiles[2]. Emerging markers, like ePWV, provide valuable insights into cardiovascular and metabolic health, particularly in younger, non-obese adults. This study explores the association between elevated ePWV and the incidence of T2DM in non-obese young adults, aiming to determine whether arterial stiffness could serve as a reliable early predictor of diabetes in this underserved population. Methods Data Source This study utilized data from the NAGALA cohort, a well-established longitudinal dataset from Murakami Memorial Hospital in Japan[13, 14]. The NAGALA database provides extensive health and metabolic data from adult participants, making it suitable for analyzing the development of metabolic disorders such as T2DM. The data are accessible via the DRYAD database (https://datadryad.org/stash/dataset/doi: 10.5061/dryad.8q0p192 ), for secondary analysis, respecting the rights of the original researchers. Study Population The initial population for this study comprised 20,944 participants from the NAGALA cohort. A total of 5,480 participants were excluded due to incomplete data, such as missing measurements of High-Density Lipoprotein Cholesterol (HDL-C), or the presence of conditions like liver disease or excessive alcohol consumption. The remaining 15,464 participants were further filtered to focus on non-obese young adults. Participants aged 50 years or older (n = 4,085) and those with a BMI over 25 kg/m² (n = 1,825) were excluded. After these exclusions, the final study population consisted of 9,543 non-obese participants aged between 20 and 49 years. Data Collection and Measurements Data were collected during routine health check-ups, encompassing demographics (age, sex), lifestyle factors (smoking status, alcohol consumption, exercise habits), and clinical measurements. Key clinical parameters included BMI, systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting plasma glucose (FPG), triglycerides (TG), total cholesterol (TC), and HDL-C. Liver function markers such as alanine aminotransferase (ALT), aspartate aminotransferase (AST), and gamma-glutamyl transferase (GGT) were also assessed. Blood pressure was assessed with a mercury sphygmomanometer, and BMI was determined by dividing weight in kilograms by height in meters squared. All laboratory tests were performed after an overnight fast. Non-obesity was defined as a BMI of less than 25 kg/m², in accordance with World Health Organization guidelines [15]. Young adults were defined as those aged 20 to 49 years [16]. ePWV[7] was calculated using the following formula: \(\:ePWV=9.587-0.402\times\:age+4.560\times\:{10}^{-3}\times\:{age}^{2}-2.621\times\:{10}^{-5}\times\:{age}^{2}\times\:MBP+3.176\times\:{10}^{-3}\times\:age\times\:MBP-1.832\times\:{10}^{-2}\times\:MBP\) , where mean blood pressure (MBP) was calculated follows: \(\:MBP=DBP+0.4\times\:(SBP-DBP)\) .The primary outcome was the incidence of T2DM, which was recorded as a binary variable (0 = no diabetes, 1 = diabetes) during follow-up assessments. Statistical Analysis Data were processed and analyzed using Free Statistics software version 1.9, which incorporates the R statistical software version 4.3.2 ( https://www.R-project.org , R Foundation). Continuous variables with normal distributions were expressed as mean ± standard deviation (SD), while those with skewed distributions were represented as medians with interquartile ranges (IQR). Comparisons between the three ePWV groups were conducted using the t-test for normally distributed variables and the Wilcoxon rank-sum test for skewed data. Categorical variables were expressed as numbers (percentages) and analyzed using the chi-square test. To assess differences across the three ePWV groups, the Kruskal-Wallis test or one-way ANOVA was applied, depending on the distribution of the variables. The relationship between ePWV and T2DM risk was analyzed using Cox proportional hazard models. Subgroup analyses were conducted to identify potential effect modifiers based on age (20–34 vs. 35–49 years), sex (male vs. female), alcohol consumption (none vs. light/moderate/heavy), and smoking status (never vs. past/current smoker).Analyses were deemed statistically significant with a p-value below 0.05. Results 3.1. Baseline Characteristics of Selected Participants. A total of 9,543 participants were included in this study. Table 1 presents the baseline characteristics grouped by tertile of ePWV. The mean age of participants was 39.4 ± 5.7 years, and 51.3% were male. During the follow-up, 110 participants (1.2%) developed T2DM. Participants in the highest ePWV tertile (T3) were older and exhibited higher values for BMI, SBP, DBP, TG, and FPG. Additionally, T3 included a larger proportion of smokers and alcohol consumers compared to the lower tertiles (T1 and T2). Table 1 Baseline characteristics of selected participants. Variables Total (n = 9543) ePWV T1 (n = 3180) T2 (n = 3180) T3 (n = 3183) p value Sex, n (%) < 0.001 Male 4892 (51.3) 2331 (73.3) 1477 (46.4) 1084 (34.1) Female 4651 (48.7) 849 (26.7) 1703 (53.6) 2099 (65.9) Age(years), Mean ± SD 39.4 ± 5.7 36.7 ± 5.3 38.9 ± 5.3 42.5 ± 4.8 < 0.001 SBP(mmHg), Mean ± SD 110.6 ± 13.2 98.2 ± 6.9 110.2 ± 6.8 123.5 ± 10.5 < 0.001 DBP(mmHg), Mean ± SD 68.8 ± 9.5 59.6 ± 4.9 68.4 ± 4.3 78.3 ± 7.3 < 0.001 Habit of exercise, n (%) 0.611 No 8038 (84.2) 2694 (84.7) 2676 (84.2) 2668 (83.8) Yes 1505 (15.8) 486 (15.3) 504 (15.8) 515 (16.2) Alcohol consumption, n (%) < 0.001 None 7547 (79.1) 2801 (88.1) 2550 (80.2) 2196 (69) Light/Moderate/Heavy 1996 (20.9) 379 (11.9) 630 (19.8) 987 (31) Smoking status, n (%) < 0.001 Never 6014 (63.0) 2296 (72.2) 1956 (61.5) 1762 (55.4) Past/Current 3529 (37.0) 884 (27.8) 1224 (38.5) 1421 (44.6) BMI(kg/m 2 ), Mean ± SD 21.0 ± 2.1 20.0 ± 2.0 21.1 ± 2.1 21.9 ± 2.0 < 0.001 HDL-C(mmol/L), Mean ± SD 1.5 ± 0.4 1.6 ± 0.4 1.5 ± 0.4 1.5 ± 0.4 < 0.001 TC(mmol/L), Mean ± SD 4.9 ± 0.8 4.8 ± 0.8 4.9 ± 0.8 5.2 ± 0.8 < 0.001 TG(mmol/L), Mean ± SD 0.8 ± 0.6 0.6 ± 0.3 0.8 ± 0.5 1.0 ± 0.7 < 0.001 HbA1c (mmol/mol), Mean ± SD 32.4 ± 3.3 32.2 ± 3.2 32.4 ± 3.3 32.6 ± 3.4 < 0.001 FPG(mmol/L), Mean ± SD 5.1 ± 0.4 4.9 ± 0.4 5.1 ± 0.4 5.2 ± 0.4 < 0.001 ePWV(m/s), Mean ± SD 6.6 ± 0.8 5.8 ± 0.3 6.5 ± 0.2 7.5 ± 0.6 < 0.001 Follow-up(years), Mean ± SD 6.3 ± 3.8 6.1 ± 3.8 6.4 ± 3.8 6.5 ± 3.9 < 0.001 T2DM, n(%) < 0.001 No 9433 (98.8) 3162 (99.4) 3154 (99.2) 3117 (97.9) Yes 110 ( 1.2) 18 (0.6) 26 (0.8) 66 (2.1) ALT(IU/L), Median (IQR) 15.0 (12.0, 21.0) 13.0 (11.0, 17.0) 15.0 (12.0, 21.0) 17.0 (13.0, 24.0) < 0.001 AST(IU/L), Median (IQR) 16.0 (13.0, 20.0) 15.0 (13.0, 18.2) 16.0 (13.0, 20.0) 17.0 (14.0, 21.0) < 0.001 GGT(IU/L), Median (IQR) 14.0 (11.0, 19.0) 12.0 (10.0, 15.0) 14.0 (11.0, 19.0) 17.0 (12.0, 25.0) < 0.001 Note: Data presented are mean ± SD, median (Q1–Q4), or n (%); T1, T2, T3 are tertile of ePWV. Abbreviations: SBP, systolic blood pressure; DBP, diastolic blood pressure; BMI, body mass index; HDL-C, high-density lipoprotein cholesterol; TC, total cholesterol; TG, Triglycerides; HbA1c, hemoglobin A1c; FPG, fasting plasma glucose; ; ePWV, estimated pulse wave velocity; T2DM, Type 2 Diabetes; ALT, alanine aminotransferase; AST, aspartate aminotransferase; GGT, gamma-glutamyl transferase. 3.2. Cox Proportional Hazard Model The Cox proportional hazard models revealed a significant association between elevated ePWV and increased risk of T2DM. In the unadjusted model, higher ePWV was linked to a hazard ratio (HR) of 1.85 (95% CI: 1.53–2.23, p < 0.001). After adjusting for age, sex, and BMI in Model 1, the association remained significant but was attenuated (HR 1.33, 95% CI: 1.03–1.71, p = 0.026). With additional adjustments for alcohol consumption, exercise, and smoking status in Model 2, the HR increased slightly to 1.42 (95% CI: 1.1–1.83, p = 0.007). In the fully adjusted Model 3, which included liver enzymes (ALT, AST, GGT) and lipid profiles (HDL-C, TC, TG), the association remained significant, with an HR of 1.36 (95% CI: 1.05–1.75, p = 0.018). These results confirm that elevated ePWV independently predicts a higher risk of T2DM, even after controlling for multiple confounding factors (Table 2 ). Table 2 Associations between ePWV and T2DM in the multiple COX regression model. Variable Non-adjusted Model Model 1 Model 2 Model 3 HR(95%) CI P -value HR(95%) CI P -value HR(95%) CI P -value HR(95%) CI P -value ePWV 1.85 (1.53 ~ 2.23) < 0.001 1.33 (1.03 ~ 1.71) 0.026 1.42 (1.1 ~ 1.83) 0.007 1.36 (1.05 ~ 1.75) 0.018 Notes: Data presented are HRs and 95% CIs. Model I: adjusted for age, sex, BMI. Model II: adjusted for model1 + Alcohol consumption, Habit of exercise, Smoking status. Model III: adjusted for model2 + ALT, AST, GGT, HDL-C, TC, TG. Abbreviations: ePWV,estimated pulse wave velocity; BMI, body mass index; T2DM, type 2 diabetes mellitus; ALT, alanine aminotransferase; AST, aspartate aminotransferase; GGT, gamma-glutamyl transferase; HDL-C, high-density lipoprotein cholesterol; TC, total cholesterol; TG, triglyceride. 3.3. Threshold Effect Analysis of ePWV on Incident T2DM. A Cox proportional hazards regression model with cubic spline functions was employed to investigate the relationship between ePWV and T2DM incidence(Fig. 2 ). After adjusting for potential confounders, including age, sex, BMI, alcohol consumption, exercise habits, smoking status, liver enzymes (ALT, AST, GGT), and lipid profile (HDL-C, TC, TG), the analysis revealed a positive linear relationship between ePWV and the risk of T2DM.. 3.4. Subgroup Analysis A subgroup analysis was conducted to examine the relationship between ePWV and T2DM risk across categories such as sex, age, alcohol consumption, and smoking status. The adjusted HR for ePWV and T2DM risk was consistently significant at 1.36 (95% CI: 1.05–1.75), demonstrating a stable link between elevated ePWV and higher T2DM risk across all subgroups. As illustrated in Fig. 3 , the positive association between ePWV and the risk of developing diabetes persisted across all subgroups, with no significant variation in effect observed between different demographic and lifestyle categories. Discussion This retrospective cohort study based of Japanese population found a positive linear relationship between elevated ePWV and the T2DM incidence in non-obese young adults. This association remained significant even after adjusting for multiple covariates. Diabetes is a chronic metabolic disease that can cause multi-organ dysfunction and failure, significantly impacting health; thus, early screening and prevention are essential[2, 17, 18]. Arterial stiffness, a risk factor for hypertension, cardiovascular disease, chronic kidney disease, and cognitive decline, has also been identified as a potential risk factor for diabetes[12, 19]. Our findings align with previous studies that have demonstrated a connection between arterial stiffness and the risk of T2DM[10, 11, 20, 21]. For example, Muhammad et al. reported an association between increased arterial stiffness measured by PWV and the incidence of T2DM in a broader population[22]. A study conducted by Mengyi Zheng et al. in 2020 in China evaluated the relationship between diabetes and brachial-ankle PWV (baPWV), identifying arterial stiffness as an independent risk factor for diabetes[12]. Similarly, a Swedish study involving 3,734 individuals found that the relative risk of developing diabetes in the highest carotid-femoral PWV (cfPWV) tertile was 3.24 times higher than that in the lowest tertile[8]. The Candesartan Anti-hypertensive Survival Assessment in Japan (CASE-J) trial showed that for every standard deviation increase in pulse pressure, the risk of developing diabetes increased by 44%, identifying peripheral pulse pressure as an independent predictor of new-onset diabetes in high-risk Japanese hypertensive patients[22]. Our study demonstrates that ePWV serves as an independent predictor of T2DM in non-obese young adults. Compared to other arterial stiffness indicators, ePWV can be derived from routine clinical data such as age and blood pressure, making it a more accessible and cost-effective tool for assessing cardiovascular health in large epidemiological studies[23]. This method enhances the feasibility of identifying individuals at risk of T2DM, particularly in non-obese populations, across various healthcare settings. The potential mechanisms by which arterial stiffness led to type 2 diabetes as follows. First, arterial pulse pressure elevation due to AS may lead to endothelial dysfunction[24]. Endothelial dysfunction and impaired endothelium-dependent vasodilation may exacerbate insulin resistance by impairing glucose delivery to key target tissues, such as the pancreas, liver, and muscles, which precedes the development of diabetes[25]. Second, Arterial stiffness may affect the microvascular health of the pancreas, potentially leading to endocrine dysfunction. Microvascular dysfunction and skeletal muscle remodeling may contribute to insulin resistance[26]. The microvascular changes associated with increased arterial stiffness may impair insulin-mediated muscle perfusion and alter glucose metabolism[27]. Mendelian randomization studies have shown that genetically determined reduced insulin secretion is linked to arterial stiffness [28, 29]. Finally, chronic low-grade inflammation and increased oxidative stress may be common risk factors for both diabetes and arterial stiffness [30]. One of the strengths of our study is the use of ePWV, a non-invasive marker of arterial stiffness. Unlike traditional PWV measurements, which require specialized equipment, ePWV can be derived from routine clinical data such as age and blood pressure, making it a more accessible and cost-effective tool for assessing cardiovascular health in large-scale epidemiological studies[23]. This approach improves the feasibility of identifying individuals at risk of T2DM, particularly in non-obese populations, making it especially useful in large-scale studies across various healthcare settings. This study benefited from the comprehensive follow-up data provided by the NAGALA cohort, which allowed us to conduct long-term assessments of outcomes. We adjusted for a wide range of confounding factors, including lifestyle behaviors such as smoking and alcohol consumption, thereby enhancing the reliability of our findings. Unlike studies that focus primarily on traditional risk factors such as BMI and insulin resistance, our research identified ePWV, an indicator of arterial stiffness, as an independent predictor of diabetes risk. This finding not only broadens the perspective of diabetes risk assessment but also highlights the critical role of vascular health in early prevention strategies. Furthermore, the consistency of our findings across different subgroups reinforces the stability and validity of ePWV as a predictor of T2DM. However, several limitations should be noted. Although ePWV is a feasible surrogate marker for arterial stiffness is feasible, it may not fully capture the nuances of direct PWV measurements obtained through more specialized techniques like cfPWV, which is considered the gold standard[10]. Our study's focus on a specific cohort of non-obese young adults in Japan, which may limit the generalizability of the findings to other populations with different risk profiles[1]. Furthermore, despite controlling for many potential confounders, residual confounding from unmeasured variables, such as dietary factors or genetic predisposition, cannot be completely ruled out. Future research should focus on using direct arterial stiffness measurement methods to validate the current findings and expand the study sample to include more diverse ethnic groups. By integrating genetic and dietary data, we can achieve a more comprehensive understanding of the interaction mechanisms between vascular health and diabetes risk. In addition to ePWV, studies should explore other biomarkers, such as inflammatory markers, to further elucidate the mechanisms linking arterial stiffness and type 2 diabetes. Conclusions In conclusion, elevated ePWV was positively and linearly associated with an increased risk of T2DM in a cohort of non-obese young adults in Japan. This relationship remained significant after adjusting for age, sex, BMI, smoking status, alcohol consumption, liver enzymes, and lipid profiles. Abbreviations BMI, Body Mass Index ePWV, Estimated Pulse Wave Velocity baPWV, brachial-ankle PWV cfPWV, carotid-femoral PWV AASI, Ambulatory Arterial Stiffness Index FPG, Fasting Plasma Glucose GGT, Gamma-Glutamyl Transferase HDL-C, High-Density Lipoprotein Cholesterol HR, Hazard Ratio SBP, Systolic Blood Pressure DBP, Diastolic Blood Pressure MBP, Mean Blood Pressure T2DM, Type 2 Diabetes Mellitus ALT, Alanine Aminotransferase AST, Aspartate Aminotransferase TC, Total Cholesterol TG, Triglycerides TyG, Triglyceride-Glucose SD, Standard Deviation IQR, Interquartile Ranges Declarations Ethics approval and consent to participate The study was conducted in accordance with the principles of the Declaration of Helsinki. Ethical approval was obtained from the Ethics Committee of Ningbo Medical Center LiHuiLi Hospital, China, under the approval number KY2024ML118 . All participants provided written informed consent prior to their inclusion in the study, ensuring voluntary participation and data confidentiality. For secondary data analysis, additional consent was not required, as the data were anonymized prior to use. Consent for publication Not applicable Availability of data and materials The data used in this study were obtained from the NAGALA (NAfld in Gifu Area, Longitudinal Analysis) cohort, which is publicly available through the DRYAD database. Access to the dataset can be granted through the following link: https://doi.org/10.5061/dryad.8q0p192 Competing interests The authors declare that they have no competing interests. Funding This study was supported by the Doctoral Development Fundation of LiHuiLi Hospital (2023BSKY-LR), the Medical and Health Science and Technology Project of Zhejiang Province (Grant Number: 2024KY1483 Author’s Contributions ZCX was responsible for the study design, data analysis, and drafting the manuscript. LC assisted with data interpretation, statistical analysis, and manuscript revisions. RL, as the corresponding author, supervised the entire research process, provided critical revisions to the manuscript, and ensured the integrity and accuracy of the data. All authors read and approved the final version of the manuscript before submission. Acknowledgements We would like to express our gratitude to all the participants of this study for their valuable contributions. 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Muris DMJ, Houben AJHM, Schram MT, Stehouwer CDA. Microvascular dysfunction is associated with a higher incidence of type 2 diabetes mellitus: a systematic review and meta-analysis. Arterioscler Thromb Vasc Biol. 2012;32:3082–94. Xu M, Huang Y, Xie L, Peng K, Ding L, Lin L, et al. Diabetes and risk of arterial stiffness: a mendelian randomization analysis. Nestle Nutr Works Se. 2016;65:1731–40. Cohen JB, Mitchell GF, Gill D, Burgess S, Rahman M, Hanff TC, et al. Arterial Stiffness and Diabetes Risk in Framingham Heart Study and UK Biobank. Circ Res. 2022;131:545–54. Wang X, Bao W, Liu J, Ouyang Y-Y, Wang D, Rong S, et al. Inflammatory markers and risk of type 2 diabetes: a systematic review and meta-analysis. Diabetes Care. 2013;36:166–75. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 06 Jun, 2025 Read the published version in BMC Endocrine Disorders → Version 1 posted Editorial decision: Revision requested 29 Jan, 2025 Reviews received at journal 19 Dec, 2024 Reviewers agreed at journal 19 Dec, 2024 Reviews received at journal 16 Dec, 2024 Reviewers agreed at journal 16 Dec, 2024 Reviewers invited by journal 09 Dec, 2024 Editor invited by journal 05 Nov, 2024 Editor assigned by journal 05 Nov, 2024 Submission checks completed at journal 31 Oct, 2024 First submitted to journal 30 Oct, 2024 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. 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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-5359838","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":376282976,"identity":"ac7f63b7-6c2c-4ea3-82eb-9ba4e8e40b4d","order_by":0,"name":"Chunxia Zhang","email":"","orcid":"","institution":"Ningbo Medical Center LiHuiLi Hospital (The Affiliated LiHuiLi Hospital of Ningbo University)","correspondingAuthor":false,"prefix":"","firstName":"Chunxia","middleName":"","lastName":"Zhang","suffix":""},{"id":376282977,"identity":"6ec75ce2-8ad3-4661-b185-b1c0315003be","order_by":1,"name":"Li Chen","email":"","orcid":"","institution":"Ningbo Medical Center LiHuiLi Hospital (The Affiliated LiHuiLi Hospital of Ningbo University)","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Chen","suffix":""},{"id":376282978,"identity":"5ba34bff-25fc-4b3d-986a-4bd6e8e64945","order_by":2,"name":"Ri Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwUlEQVRIiWNgGAWjYBACPgnmxgMJDAw8/Aw8ID4zYS1sEowNYC2SDSRpATEMDhCtRbqx4cDDtnsyxrd7j0kwVFgnNrCfPYBfi8zBhgOJbcU8ZnfOpUkwnElPbODJSyDgsESQlgQesxs5ZhKMbYcTGyR4DIjTYjwDpOUfKVoMJEBaGojVknAugUfiRl6yRcKxdOM2nhz8Wvglkg8+/FGWYM8/I/fgjQ811rL97Gfwa0EFCSB7SVA/CkbBKBgFowAHAAAl1EHVYvDiDwAAAABJRU5ErkJggg==","orcid":"","institution":"Ningbo Medical Center LiHuiLi Hospital (The Affiliated LiHuiLi Hospital of Ningbo University)","correspondingAuthor":true,"prefix":"","firstName":"Ri","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-10-30 08:53:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5359838/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5359838/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12902-025-01967-4","type":"published","date":"2025-06-06T15:57:34+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":69950490,"identity":"98690e7e-29db-46a8-9641-d3348d828a5f","added_by":"auto","created_at":"2024-11-27 02:12:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":198076,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of participant selection in the NAGALA Study.\u003c/p\u003e\n\u003cp\u003eLegend: This flowchart illustrates the selection process of participants for the study from the NAGALA cohort, originally consisting of 20,944 individuals enrolled from 1994 to 2016. A total of 5,480 participants were excluded due to missing data, known liver disease, excessive alcohol consumption (over 60 g/day for men and 40 g/day for women), medication usage, existing T2DM at baseline, or fasting plasma glucose over 6.1 mmol/L at baseline. The remaining 15,464 participants were further filtered, excluding 5,921 individuals due to missing HDL-C data, age ≥50 years, or BMI ≥25 kg/m². This resulted in a final study cohort of 9,543 non-obese participants aged 20-49 years.\u003c/p\u003e","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-5359838/v1/53478a4cb50046da97cd95b4.png"},{"id":69950489,"identity":"c07cba98-62b6-4aa2-8357-f90bf9b8ea7d","added_by":"auto","created_at":"2024-11-27 02:12:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":77089,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between ePWV and risk of T2DM incidence.\u003c/p\u003e\n\u003cp\u003eLegend: This figure illustrates the relationship between estimated pulse wave velocity (ePWV) and the hazard ratio (HR) for the risk of T2DM incidence. The red line represents the HR curve, while the shaded area indicates the 95% confidence interval. The histogram at the bottom shows the distribution of ePWV in the study population. No significant non-linearity was observed (P = 0.971).\u003c/p\u003e","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-5359838/v1/df04b288cb9ecb59d49653ec.png"},{"id":69950491,"identity":"98660bc0-9e72-49ab-b261-6775892d01c2","added_by":"auto","created_at":"2024-11-27 02:12:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":570368,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of hazard ratios (HRs) for the association between ePWV and T2DM risk across subgroups.\u003c/p\u003e\n\u003cp\u003eLegend: This forest plot presents the hazard ratios (HRs) for the association between estimated pulse wave velocity (ePWV) and the risk of T2DM in various subgroups. The overall adjusted HR was 1.36 (95% CI: 1.05–1.75). Subgroup analyses were performed by sex, age, alcohol consumption, and smoking status, showing no significant interaction effects. The HRs for each subgroup are displayed with 95% confidence intervals, with p-values for interaction indicating no statistically significant differences across subgroups.\u003c/p\u003e","description":"","filename":"fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-5359838/v1/85c83d1a1ae12fe1c9b55085.png"},{"id":84242573,"identity":"8e736c00-0752-40fb-b404-171ba523ef67","added_by":"auto","created_at":"2025-06-09 16:09:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1378380,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5359838/v1/913d9b7a-c328-4a6c-a9c2-581926ed887b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Elevated Estimated Pulse Wave Velocity and the Risk of Type 2 Diabetes in Non-Obese Young Adults: A Longitudinal Cohort Study","fulltext":[{"header":"Lay Summary","content":"\u003cp\u003eThis study investigates the relationship between elevated arterial stiffness, measured by estimated pulse wave velocity (ePWV), and the risk of developing type 2 diabetes mellitus (T2DM) in non-obese young adults. While arterial stiffness has been linked to higher T2DM risk in older or obese individuals, this study uniquely focuses on non-obese young adults, a group not typically associated with high diabetes risk. By analyzing data from over 9,500 participants, the research found that even in individuals with a normal body mass index (BMI), higher ePWV is significantly associated with an increased risk of T2DM. This suggests that measuring ePWV could help detect early diabetes risk in people who may not exhibit traditional risk factors, such as high BMI. The findings highlight the importance of vascular health in diabetes prevention and propose ePWV as a potential tool for early detection in clinical practice.\u003c/p\u003e"},{"header":"Background","content":"\u003cp\u003eType 2 diabetes mellitus (T2DM) is a growing global health issue, increasingly affecting younger and non-obese populations. Traditionally, the primary risk factors for T2DM have included obesity, age, and family history[1, 2]. However, recent studies have suggested that individuals without obesity are not immune to the risk of developing T2DM, highlighting the need to investigate alternative early indicators[3\u0026ndash;6]. Arterial stiffness has been shown to play a critical role in the development of cardiovascular diseases[7], and recent evidence suggests that it may also be associated with the pathogenesis of T2DM[8]. Estimated pulse wave velocity (ePWV) offers a practical and cost-effective alternative to traditional arterial stiffness measurements like pulse wave velocity (PWV) and ambulatory arterial stiffness index (AASI), which require specialized equipment and are less feasible in routine clinical settings[9]. Calculated from age and mean blood pressure (MBP), ePWV reliably reflects arterial stiffness and has predictive value for cardiovascular risk, making it an accessible tool for broader clinical use[7]. Understanding the relationship between ePWV and T2DM in a younger, non-obese population is critical, as early detection could offer new pathways for preventive interventions.\u003c/p\u003e \u003cp\u003ePrevious research has shown that elevated arterial stiffness can indicate early vascular damage[10]. This vascular dysfunction can lead to endothelial damage, affecting insulin sensitivity and contributing to the onset of diabetes[11]. Furthermore, arterial stiffness has been associated with impaired glucose metabolism, even in populations that are not typically considered high-risk for diabetes, such as young and non-obese individuals[12]. Therefore, exploring the role of ePWV as an independent predictor of T2DM in non-obese young adults is particularly important, as it may help identify high-risk individuals before the disease manifests.\u003c/p\u003e \u003cp\u003eIn light of the increasing prevalence of T2DM in non-obese populations, there is a pressing need to shift the focus of diabetes prevention strategies towards early detection and novel risk factors. Traditional risk factors, such as obesity and age, may no longer be sufficient for effective risk stratification, especially as evolving lifestyles and dietary habits contribute to shifts in metabolic risk profiles[2]. Emerging markers, like ePWV, provide valuable insights into cardiovascular and metabolic health, particularly in younger, non-obese adults. This study explores the association between elevated ePWV and the incidence of T2DM in non-obese young adults, aiming to determine whether arterial stiffness could serve as a reliable early predictor of diabetes in this underserved population.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Source\u003c/h2\u003e \u003cp\u003eThis study utilized data from the NAGALA cohort, a well-established longitudinal dataset from Murakami Memorial Hospital in Japan[13, 14]. The NAGALA database provides extensive health and metabolic data from adult participants, making it suitable for analyzing the development of metabolic disorders such as T2DM. The data are accessible via the DRYAD database (https://datadryad.org/stash/dataset/doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5061/dryad.8q0p192\u003c/span\u003e\u003cspan address=\"10.5061/dryad.8q0p192\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), for secondary analysis, respecting the rights of the original researchers.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Population\u003c/h3\u003e\n\u003cp\u003eThe initial population for this study comprised 20,944 participants from the NAGALA cohort. A total of 5,480 participants were excluded due to incomplete data, such as missing measurements of High-Density Lipoprotein Cholesterol (HDL-C), or the presence of conditions like liver disease or excessive alcohol consumption. The remaining 15,464 participants were further filtered to focus on non-obese young adults. Participants aged 50 years or older (n\u0026thinsp;=\u0026thinsp;4,085) and those with a BMI over 25 kg/m\u0026sup2; (n\u0026thinsp;=\u0026thinsp;1,825) were excluded. After these exclusions, the final study population consisted of 9,543 non-obese participants aged between 20 and 49 years.\u003c/p\u003e\n\u003ch3\u003eData Collection and Measurements\u003c/h3\u003e\n\u003cp\u003eData were collected during routine health check-ups, encompassing demographics (age, sex), lifestyle factors (smoking status, alcohol consumption, exercise habits), and clinical measurements. Key clinical parameters included BMI, systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting plasma glucose (FPG), triglycerides (TG), total cholesterol (TC), and HDL-C. Liver function markers such as alanine aminotransferase (ALT), aspartate aminotransferase (AST), and gamma-glutamyl transferase (GGT) were also assessed. Blood pressure was assessed with a mercury sphygmomanometer, and BMI was determined by dividing weight in kilograms by height in meters squared. All laboratory tests were performed after an overnight fast.\u003c/p\u003e \u003cp\u003e Non-obesity was defined as a BMI of less than 25 kg/m\u0026sup2;, in accordance with World Health Organization guidelines [15]. Young adults were defined as those aged 20 to 49 years [16]. ePWV[7] was calculated using the following formula:\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:ePWV=9.587-0.402\\times\\:age+4.560\\times\\:{10}^{-3}\\times\\:{age}^{2}-2.621\\times\\:{10}^{-5}\\times\\:{age}^{2}\\times\\:MBP+3.176\\times\\:{10}^{-3}\\times\\:age\\times\\:MBP-1.832\\times\\:{10}^{-2}\\times\\:MBP\\)\u003c/span\u003e\u003c/span\u003e, where mean blood pressure (MBP) was calculated follows:\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:MBP=DBP+0.4\\times\\:(SBP-DBP)\\)\u003c/span\u003e\u003c/span\u003e.The primary outcome was the incidence of T2DM, which was recorded as a binary variable (0\u0026thinsp;=\u0026thinsp;no diabetes, 1\u0026thinsp;=\u0026thinsp;diabetes) during follow-up assessments.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eData were processed and analyzed using Free Statistics software version 1.9, which incorporates the R statistical software version 4.3.2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.R-project.org\u003c/span\u003e\u003cspan address=\"https://www.R-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, R Foundation). Continuous variables with normal distributions were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), while those with skewed distributions were represented as medians with interquartile ranges (IQR). Comparisons between the three ePWV groups were conducted using the t-test for normally distributed variables and the Wilcoxon rank-sum test for skewed data. Categorical variables were expressed as numbers (percentages) and analyzed using the chi-square test.\u003c/p\u003e \u003cp\u003eTo assess differences across the three ePWV groups, the Kruskal-Wallis test or one-way ANOVA was applied, depending on the distribution of the variables. The relationship between ePWV and T2DM risk was analyzed using Cox proportional hazard models. Subgroup analyses were conducted to identify potential effect modifiers based on age (20\u0026ndash;34 vs. 35\u0026ndash;49 years), sex (male vs. female), alcohol consumption (none vs. light/moderate/heavy), and smoking status (never vs. past/current smoker).Analyses were deemed statistically significant with a p-value below 0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e3.1. Baseline Characteristics of Selected Participants.\u003c/p\u003e \u003cp\u003eA total of 9,543 participants were included in this study. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the baseline characteristics grouped by tertile of ePWV. The mean age of participants was 39.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7 years, and 51.3% were male. During the follow-up, 110 participants (1.2%) developed T2DM. Participants in the highest ePWV tertile (T3) were older and exhibited higher values for BMI, SBP, DBP, TG, and FPG. Additionally, T3 included a larger proportion of smokers and alcohol consumers compared to the lower tertiles (T1 and T2).\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 of selected participants.\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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;9543)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eePWV\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT1 (n\u0026thinsp;=\u0026thinsp;3180)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT2 (n\u0026thinsp;=\u0026thinsp;3180)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eT3 (n\u0026thinsp;=\u0026thinsp;3183)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, n (%)\u003c/p\u003e \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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\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\u003e4892 (51.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2331 (73.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1477 (46.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1084 (34.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\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\u003e4651 (48.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e849 (26.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1703 (53.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2099 (65.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(years), Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP(mmHg), Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e110.6\u0026thinsp;\u0026plusmn;\u0026thinsp;13.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e98.2\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e110.2\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e123.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP(mmHg), Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e68.8\u0026thinsp;\u0026plusmn;\u0026thinsp;9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHabit of exercise, n (%)\u003c/p\u003e \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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.611\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8038 (84.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2694 (84.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2676 (84.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2668 (83.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1505 (15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e486 (15.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e504 (15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e515 (16.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol consumption, n (%)\u003c/p\u003e \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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7547 (79.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2801 (88.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2550 (80.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2196 (69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLight/Moderate/Heavy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1996 (20.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e379 (11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e630 (19.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e987 (31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status, n (%)\u003c/p\u003e \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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6014 (63.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2296 (72.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1956 (61.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1762 (55.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePast/Current\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3529 (37.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e884 (27.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1224 (38.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1421 (44.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI(kg/m\u003csup\u003e2\u003c/sup\u003e), Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C(mmol/L), Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC(mmol/L), Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG(mmol/L), Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c (mmol/mol), Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPG(mmol/L), Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eePWV(m/s), Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFollow-up(years), Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2DM, n(%)\u003c/p\u003e \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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9433 (98.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3162 (99.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3154 (99.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3117 (97.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e110 ( 1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT(IU/L), Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.0 (12.0, 21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.0 (11.0, 17.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.0 (12.0, 21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.0 (13.0, 24.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST(IU/L), Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.0 (13.0, 20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.0 (13.0, 18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.0 (13.0, 20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.0 (14.0, 21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGGT(IU/L), Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.0 (11.0, 19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.0 (10.0, 15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.0 (11.0, 19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.0 (12.0, 25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\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=\"6\"\u003eNote: Data presented are mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, median (Q1\u0026ndash;Q4), or n (%); T1, T2, T3 are tertile of ePWV. Abbreviations: SBP, systolic blood pressure; DBP, diastolic blood pressure; BMI, body mass index; HDL-C, high-density lipoprotein cholesterol; TC, total cholesterol; TG, Triglycerides; HbA1c, hemoglobin A1c; FPG, fasting plasma glucose; ; ePWV, estimated pulse wave velocity; T2DM, Type 2 Diabetes; ALT, alanine aminotransferase; AST, aspartate aminotransferase; GGT, gamma-glutamyl transferase.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e3.2. Cox Proportional Hazard Model\u003c/p\u003e \u003cp\u003eThe Cox proportional hazard models revealed a significant association between elevated ePWV and increased risk of T2DM. In the unadjusted model, higher ePWV was linked to a hazard ratio (HR) of 1.85 (95% CI: 1.53\u0026ndash;2.23, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eAfter adjusting for age, sex, and BMI in Model 1, the association remained significant but was attenuated (HR 1.33, 95% CI: 1.03\u0026ndash;1.71, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026). With additional adjustments for alcohol consumption, exercise, and smoking status in Model 2, the HR increased slightly to 1.42 (95% CI: 1.1\u0026ndash;1.83, p\u0026thinsp;=\u0026thinsp;0.007). In the fully adjusted Model 3, which included liver enzymes (ALT, AST, GGT) and lipid profiles (HDL-C, TC, TG), the association remained significant, with an HR of 1.36 (95% CI: 1.05\u0026ndash;1.75, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018). These results confirm that elevated ePWV independently predicts a higher risk of T2DM, even after controlling for multiple confounding factors (Table\u0026nbsp;\u003cspan refid=\"Tab2\" 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\u003eAssociations between ePWV and T2DM in the multiple COX regression model.\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=\"left\" 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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNon-adjusted Model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR(95%) CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR(95%) CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR(95%) CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHR(95%) CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eePWV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.85 (1.53\u0026thinsp;~\u0026thinsp;2.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.33 (1.03\u0026thinsp;~\u0026thinsp;1.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.42 (1.1\u0026thinsp;~\u0026thinsp;1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.36 (1.05\u0026thinsp;~\u0026thinsp;1.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNotes: Data presented are HRs and 95% CIs. Model I: adjusted for age, sex, BMI. Model II: adjusted for model1\u0026thinsp;+\u0026thinsp;Alcohol consumption, Habit of exercise, Smoking status. Model III: adjusted for model2\u0026thinsp;+\u0026thinsp;ALT, AST, GGT, HDL-C, TC, TG. Abbreviations: ePWV,estimated pulse wave velocity; BMI, body mass index; T2DM, type 2 diabetes mellitus; ALT, alanine aminotransferase; AST, aspartate aminotransferase; GGT, gamma-glutamyl transferase; HDL-C, high-density lipoprotein cholesterol; TC, total cholesterol; TG, triglyceride.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e3.3. Threshold Effect Analysis of ePWV on Incident T2DM.\u003c/p\u003e \u003cp\u003eA Cox proportional hazards regression model with cubic spline functions was employed to investigate the relationship between ePWV and T2DM incidence(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). After adjusting for potential confounders, including age, sex, BMI, alcohol consumption, exercise habits, smoking status, liver enzymes (ALT, AST, GGT), and lipid profile (HDL-C, TC, TG), the analysis revealed a positive linear relationship between ePWV and the risk of T2DM..\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e3.4. Subgroup Analysis\u003c/p\u003e \u003cp\u003eA subgroup analysis was conducted to examine the relationship between ePWV and T2DM risk across categories such as sex, age, alcohol consumption, and smoking status. The adjusted HR for ePWV and T2DM risk was consistently significant at 1.36 (95% CI: 1.05\u0026ndash;1.75), demonstrating a stable link between elevated ePWV and higher T2DM risk across all subgroups. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the positive association between ePWV and the risk of developing diabetes persisted across all subgroups, with no significant variation in effect observed between different demographic and lifestyle categories.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis retrospective cohort study based of Japanese population found a positive linear relationship between elevated ePWV and the T2DM incidence in non-obese young adults. This association remained significant even after adjusting for multiple covariates.\u003c/p\u003e \u003cp\u003eDiabetes is a chronic metabolic disease that can cause multi-organ dysfunction and failure, significantly impacting health; thus, early screening and prevention are essential[2, 17, 18]. Arterial stiffness, a risk factor for hypertension, cardiovascular disease, chronic kidney disease, and cognitive decline, has also been identified as a potential risk factor for diabetes[12, 19]. Our findings align with previous studies that have demonstrated a connection between arterial stiffness and the risk of T2DM[10, 11, 20, 21]. For example, Muhammad et al. reported an association between increased arterial stiffness measured by PWV and the incidence of T2DM in a broader population[22]. A study conducted by Mengyi Zheng et al. in 2020 in China evaluated the relationship between diabetes and brachial-ankle PWV (baPWV), identifying arterial stiffness as an independent risk factor for diabetes[12]. Similarly, a Swedish study involving 3,734 individuals found that the relative risk of developing diabetes in the highest carotid-femoral PWV (cfPWV) tertile was 3.24 times higher than that in the lowest tertile[8]. The Candesartan Anti-hypertensive Survival Assessment in Japan (CASE-J) trial showed that for every standard deviation increase in pulse pressure, the risk of developing diabetes increased by 44%, identifying peripheral pulse pressure as an independent predictor of new-onset diabetes in high-risk Japanese hypertensive patients[22]. Our study demonstrates that ePWV serves as an independent predictor of T2DM in non-obese young adults. Compared to other arterial stiffness indicators, ePWV can be derived from routine clinical data such as age and blood pressure, making it a more accessible and cost-effective tool for assessing cardiovascular health in large epidemiological studies[23]. This method enhances the feasibility of identifying individuals at risk of T2DM, particularly in non-obese populations, across various healthcare settings.\u003c/p\u003e \u003cp\u003eThe potential mechanisms by which arterial stiffness led to type 2 diabetes as follows. First, arterial pulse pressure elevation due to AS may lead to endothelial dysfunction[24]. Endothelial dysfunction and impaired endothelium-dependent vasodilation may exacerbate insulin resistance by impairing glucose delivery to key target tissues, such as the pancreas, liver, and muscles, which precedes the development of diabetes[25]. Second, Arterial stiffness may affect the microvascular health of the pancreas, potentially leading to endocrine dysfunction. Microvascular dysfunction and skeletal muscle remodeling may contribute to insulin resistance[26]. The microvascular changes associated with increased arterial stiffness may impair insulin-mediated muscle perfusion and alter glucose metabolism[27]. Mendelian randomization studies have shown that genetically determined reduced insulin secretion is linked to arterial stiffness [28, 29]. Finally, chronic low-grade inflammation and increased oxidative stress may be common risk factors for both diabetes and arterial stiffness [30].\u003c/p\u003e \u003cp\u003eOne of the strengths of our study is the use of ePWV, a non-invasive marker of arterial stiffness. Unlike traditional PWV measurements, which require specialized equipment, ePWV can be derived from routine clinical data such as age and blood pressure, making it a more accessible and cost-effective tool for assessing cardiovascular health in large-scale epidemiological studies[23]. This approach improves the feasibility of identifying individuals at risk of T2DM, particularly in non-obese populations, making it especially useful in large-scale studies across various healthcare settings.\u003c/p\u003e \u003cp\u003eThis study benefited from the comprehensive follow-up data provided by the NAGALA cohort, which allowed us to conduct long-term assessments of outcomes. We adjusted for a wide range of confounding factors, including lifestyle behaviors such as smoking and alcohol consumption, thereby enhancing the reliability of our findings. Unlike studies that focus primarily on traditional risk factors such as BMI and insulin resistance, our research identified ePWV, an indicator of arterial stiffness, as an independent predictor of diabetes risk. This finding not only broadens the perspective of diabetes risk assessment but also highlights the critical role of vascular health in early prevention strategies. Furthermore, the consistency of our findings across different subgroups reinforces the stability and validity of ePWV as a predictor of T2DM.\u003c/p\u003e \u003cp\u003eHowever, several limitations should be noted. Although ePWV is a feasible surrogate marker for arterial stiffness is feasible, it may not fully capture the nuances of direct PWV measurements obtained through more specialized techniques like cfPWV, which is considered the gold standard[10]. Our study's focus on a specific cohort of non-obese young adults in Japan, which may limit the generalizability of the findings to other populations with different risk profiles[1]. Furthermore, despite controlling for many potential confounders, residual confounding from unmeasured variables, such as dietary factors or genetic predisposition, cannot be completely ruled out.\u003c/p\u003e \u003cp\u003eFuture research should focus on using direct arterial stiffness measurement methods to validate the current findings and expand the study sample to include more diverse ethnic groups. By integrating genetic and dietary data, we can achieve a more comprehensive understanding of the interaction mechanisms between vascular health and diabetes risk. In addition to ePWV, studies should explore other biomarkers, such as inflammatory markers, to further elucidate the mechanisms linking arterial stiffness and type 2 diabetes.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, elevated ePWV was positively and linearly associated with an increased risk of T2DM in a cohort of non-obese young adults in Japan. This relationship remained significant after adjusting for age, sex, BMI, smoking status, alcohol consumption, liver enzymes, and lipid profiles.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBMI, Body Mass Index\u003c/p\u003e\n\u003cp\u003eePWV, Estimated Pulse Wave Velocity\u003c/p\u003e\n\u003cp\u003ebaPWV, brachial-ankle PWV\u003c/p\u003e\n\u003cp\u003ecfPWV, carotid-femoral PWV\u003c/p\u003e\n\u003cp\u003eAASI, Ambulatory Arterial Stiffness Index\u003c/p\u003e\n\u003cp\u003eFPG, Fasting Plasma Glucose\u003c/p\u003e\n\u003cp\u003eGGT, Gamma-Glutamyl Transferase\u003c/p\u003e\n\u003cp\u003eHDL-C, High-Density Lipoprotein Cholesterol\u003c/p\u003e\n\u003cp\u003eHR, Hazard Ratio\u003c/p\u003e\n\u003cp\u003eSBP, Systolic Blood Pressure\u003c/p\u003e\n\u003cp\u003eDBP, Diastolic Blood Pressure\u003c/p\u003e\n\u003cp\u003eMBP, Mean Blood Pressure\u003c/p\u003e\n\u003cp\u003eT2DM, Type 2 Diabetes Mellitus\u003c/p\u003e\n\u003cp\u003eALT, Alanine Aminotransferase\u003c/p\u003e\n\u003cp\u003eAST, Aspartate Aminotransferase\u003c/p\u003e\n\u003cp\u003eTC, Total Cholesterol\u003c/p\u003e\n\u003cp\u003eTG, Triglycerides\u003c/p\u003e\n\u003cp\u003eTyG, Triglyceride-Glucose\u003c/p\u003e\n\u003cp\u003eSD, Standard Deviation \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIQR, Interquartile Ranges\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch4\u003eEthics approval and consent to participate\u003c/h4\u003e\n\u003cp\u003eThe study was conducted in accordance with the principles of the Declaration of Helsinki. Ethical approval was obtained from the Ethics Committee of Ningbo Medical Center LiHuiLi Hospital, China, under the approval number \u003cstrong\u003eKY2024ML118\u003c/strong\u003e. All participants provided written informed consent prior to their inclusion in the study, ensuring voluntary participation and data confidentiality. For secondary data analysis, additional consent was not required, as the data were anonymized prior to use.\u0026nbsp;\u003c/p\u003e\n\u003ch4\u003eConsent for publication\u003c/h4\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch4\u003eAvailability of data and materials\u003c/h4\u003e\n\u003cp\u003eThe data used in this study were obtained from the NAGALA (NAfld in Gifu Area, Longitudinal Analysis) cohort, which is publicly available through the DRYAD database. Access to the dataset can be granted through the following link: https://doi.org/10.5061/dryad.8q0p192\u0026nbsp;\u003c/p\u003e\n\u003ch4\u003eCompeting interests\u003c/h4\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch4\u003eFunding\u003c/h4\u003e\n\u003cp\u003eThis study was supported by the Doctoral Development Fundation of LiHuiLi Hospital (2023BSKY-LR), the Medical and Health Science and Technology Project of Zhejiang Province (Grant Number: 2024KY1483\u003c/p\u003e\n\u003ch4\u003eAuthor\u0026rsquo;s Contributions\u003c/h4\u003e\n\u003cp\u003eZCX was responsible for the study design, data analysis, and drafting the manuscript. LC assisted with data interpretation, statistical analysis, and manuscript revisions. RL, as the corresponding author, supervised the entire research process, provided critical revisions to the manuscript, and ensured the integrity and accuracy of the data. All authors read and approved the final version of the manuscript before submission.\u003c/p\u003e\n\u003ch4\u003eAcknowledgements\u003c/h4\u003e\n\u003cp\u003eWe would like to express our gratitude to all the participants of this study for their valuable contributions. Additionally, we extend our thanks to Takuro Okamura from the Department of Endocrinology and Metabolism at Kyoto Prefectural University of Medicine for providing the original dataset that made this research possible.\u003c/p\u003e\n\u003ch4\u003eClinical trial number\u003c/h4\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZheng Y, Ley SH, Hu FB. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat Rev, Endocrinol. 2018;14:88\u0026ndash;98.\u003c/li\u003e\n\u003cli\u003eAmerican Diabetes Association Professional Practice Committee. 2. Diagnosis and classification of diabetes: standards of care in diabetes-2024. Diabetes Care. 2024;47 Suppl 1:S20\u0026ndash;42.\u003c/li\u003e\n\u003cli\u003eHan J, Dai W, Chen L, Huang Z, Li C, Wang K. Elevated triglyceride-glucose index associated with increased risk of diabetes in non-obese young adults: a longitudinal retrospective cohort study from multiple asian countries. Front Endocrinol. 2024;15:1427207.\u003c/li\u003e\n\u003cli\u003eNarisada A, Shibata E, Hasegawa T, Masamura N, Taneda C, Suzuki K. Sex differences in the association between fatty liver and type 2 diabetes incidence in non-obese japanese: a retrospective cohort study. J Diabetes Investig. 2021;12:1480\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eCai X-T, Zhu Q, Liu S-S, Wang M-R, Wu T, Hong J, et al. Associations between the metabolic score for insulin resistance index and the risk of type 2 diabetes mellitus among non-obese adults: insights from a population-based cohort study. Int J Gen Med. 2021;14:7729\u0026ndash;40.\u003c/li\u003e\n\u003cli\u003eCai X-T, Ji L-W, Liu S-S, Wang M-R, Heizhati M, Li N-F. Derivation and validation of a prediction model for predicting the 5-year incidence of type 2 diabetes in non-obese adults: a population-based cohort study. Diabetes Metab Syndr Obes: Targets Ther. 2021;14:2087\u0026ndash;101.\u003c/li\u003e\n\u003cli\u003eReference Values for Arterial Stiffness\u0026rsquo; Collaboration. Determinants of pulse wave velocity in healthy people and in the presence of cardiovascular risk factors: \u0026ldquo;establishing normal and reference values.\u0026rdquo; Eur Heart J. 2010;31:2338\u0026ndash;50.\u003c/li\u003e\n\u003cli\u003eMuhammad IF, Born\u0026eacute; Y, \u0026Ouml;stling G, Kennb\u0026auml;ck C, Gotts\u0026auml;ter M, Persson M, et al. Arterial stiffness and incidence of diabetes: a population-based cohort study. Diabetes Care. 2017;40:1739\u0026ndash;45.\u003c/li\u003e\n\u003cli\u003eWang X, Keith JC, Struthers AD, Feuerstein GZ. Assessment of arterial stiffness, a translational medicine biomarker system for evaluation of vascular risk. Cardiovasc Ther. 2008;26:214\u0026ndash;23.\u003c/li\u003e\n\u003cli\u003eLaurent S, Cockcroft J, Van Bortel L, Boutouyrie P, Giannattasio C, Hayoz D, et al. Expert consensus document on arterial stiffness: methodological issues and clinical applications. Eur Heart J. 2006;27:2588\u0026ndash;605.\u003c/li\u003e\n\u003cli\u003eSchram MT, Henry RMA, van Dijk RAJM, Kostense PJ, Dekker JM, Nijpels G, et al. Increased central artery stiffness in impaired glucose metabolism and type 2 diabetes: the hoorn study. Hypertens (Dallas Tex,: 1979). 2004;43:176\u0026ndash;81.\u003c/li\u003e\n\u003cli\u003eZheng M, Zhang X, Chen S, Song Y, Zhao Q, Gao X, et al. Arterial stiffness preceding diabetes: a longitudinal study. Circ Res. 2020;127:1491\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eOkamura T, Hashimoto Y, Hamaguchi M, Obora A, Kojima T, Fukui M. Ectopic fat obesity presents the greatest risk for incident type 2 diabetes: a population-based longitudinal study. Int J Obes (2005). 2019;43:139\u0026ndash;48.\u003c/li\u003e\n\u003cli\u003eOkamura T, Hashimoto Y, Hamaguchi M, Ohobra A, Kojima T, Fukui M. Data from: ectopic fat obesity presents the greatest risk for incident type 2 diabetes: a population-based longitudinal study. 2019;:3273631 bytes.\u003c/li\u003e\n\u003cli\u003eObesity: preventing and managing the global epidemic. Report of a WHO consultation. World Health Organ Tech Rep Ser. 2000;894:i\u0026ndash;xii, 1\u0026ndash;253.\u003c/li\u003e\n\u003cli\u003eChambers AC, Dixon SW, White P, Williams AC, Thomas MG, Messenger DE. Demographic trends in the incidence of young-onset colorectal cancer: a population-based study. Br J Surg. 2020;107:595\u0026ndash;605.\u003c/li\u003e\n\u003cli\u003eDemir S, Nawroth PP, Herzig S, Ekim \u0026Uuml;st\u0026uuml;nel B. Emerging Targets in Type 2 Diabetes and Diabetic Complications. Adv Sci (Weinh Baden-Wurtt Ger). 2021;8:e2100275.\u003c/li\u003e\n\u003cli\u003eCloete L. Diabetes mellitus: an overview of the types, symptoms, complications and management. Nurs Stand (R Coll Nurs (G B): 1987). 2022;37:61\u0026ndash;6.\u003c/li\u003e\n\u003cli\u003eLamacchia O, Sorrentino MR. Diabetes mellitus, arterial stiffness and cardiovascular disease: clinical implications and the influence of SGLT2i. Curr Vasc Pharmacol. 2021;19:233\u0026ndash;40.\u003c/li\u003e\n\u003cli\u003eBao W, Chen C, Chen C, Zhang X, Miao H, Zhao X, et al. Association between estimated pulse wave velocity and risk of diabetes: A large sample size cohort study. Nutr metab cardiovasc dis: NMCD. 2023;33:1716\u0026ndash;24.\u003c/li\u003e\n\u003cli\u003eCai Y, Sha W, Deng H, Zhang T, Yang L, Wu Y, et al. Correlation between the triglyceride-glucose index and arterial stiffness in japanese individuals with normoglycaemia: a cross-sectional study. BMC Endocr Disord. 2024;24:30.\u003c/li\u003e\n\u003cli\u003eYasuno S, Ueshima K, Oba K, Fujimoto A, Hirata M, Ogihara T, et al. Is pulse pressure a predictor of new-onset diabetes in high-risk hypertensive patients?: a subanalysis of the candesartan antihypertensive survival evaluation in Japan (CASE-J) trial. Diabetes Care. 2010;33:1122\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eHeffernan KS, Stoner L, London AS, Augustine JA, Lefferts WK. Estimated pulse wave velocity as a measure of vascular aging. PLoS One. 2023;18:e0280896.\u003c/li\u003e\n\u003cli\u003ePetrie JR, Guzik TJ, Touyz RM. Diabetes, Hypertension, and Cardiovascular Disease: Clinical Insights and Vascular Mechanisms. Can J Cardiol. 2018;34:575\u0026ndash;84.\u003c/li\u003e\n\u003cli\u003eBalletshofer BM, Rittig K, Enderle MD, Volk A, Maerker E, Jacob S, et al. Endothelial dysfunction is detectable in young normotensive first-degree relatives of subjects with type 2 diabetes in association with insulin resistance. Circulation. 2000;101:1780\u0026ndash;4.\u003c/li\u003e\n\u003cli\u003eLevy BI, Schiffrin EL, Mourad J-J, Agostini D, Vicaut E, Safar ME, et al. Impaired tissue perfusion: a pathology common to hypertension, obesity, and diabetes mellitus. Circulation. 2008;118:968\u0026ndash;76.\u003c/li\u003e\n\u003cli\u003eMuris DMJ, Houben AJHM, Schram MT, Stehouwer CDA. Microvascular dysfunction is associated with a higher incidence of type 2 diabetes mellitus: a systematic review and meta-analysis. Arterioscler Thromb Vasc Biol. 2012;32:3082\u0026ndash;94.\u003c/li\u003e\n\u003cli\u003eXu M, Huang Y, Xie L, Peng K, Ding L, Lin L, et al. Diabetes and risk of arterial stiffness: a mendelian randomization analysis. Nestle Nutr Works Se. 2016;65:1731\u0026ndash;40.\u003c/li\u003e\n\u003cli\u003eCohen JB, Mitchell GF, Gill D, Burgess S, Rahman M, Hanff TC, et al. Arterial Stiffness and Diabetes Risk in Framingham Heart Study and UK Biobank. Circ Res. 2022;131:545\u0026ndash;54.\u003c/li\u003e\n\u003cli\u003eWang X, Bao W, Liu J, Ouyang Y-Y, Wang D, Rong S, et al. Inflammatory markers and risk of type 2 diabetes: a systematic review and meta-analysis. Diabetes Care. 2013;36:166\u0026ndash;75.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-endocrine-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bend","sideBox":"Learn more about [BMC Endocrine Disorders](http://bmcendocrdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bend/default.aspx","title":"BMC Endocrine Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Pulse wave velocity, Type 2 diabetes, Non-obese young adults, Arterial stiffness, Longitudinal study.","lastPublishedDoi":"10.21203/rs.3.rs-5359838/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5359838/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eArterial stiffness, measured by estimated pulse wave velocity (ePWV), is associated with a higher risk of cardiovascular diseases and type 2 diabetes mellitus (T2DM) in older and obese individuals. However, the role of arterial stiffness as an early predictor of T2DM in non-obese, young adults remains underexplored. Identifying alternative predictors like arterial stiffness is crucial for detecting diabetes onset in non-obese and younger populations who may not exhibit traditional risk factors such as high Body Mass Index (BMI).This study investigates the association between elevated ePWV and the risk of T2DM in non-obese young adults, who are often overlooked in diabetes prevention efforts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study included 9,543 non-obese participants (BMI \u0026lt; 25 kg/m², age \u0026lt; 50 years) from the NAGALA cohort in the Gifu area. ePWV was calculated, and participants were followed for the development of diabetes. Cox proportional hazard models assessed the association between ePWV and T2DM risk, adjusting for age, sex, BMI, alcohol consumption, exercise, smoking status, and metabolic biomarkers. Subgroup analyses examined the association's consistency across sex, age, smoking, and alcohol consumption.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring a median follow-up period of 6.3 years, 110 participants (1.2%) developed T2DM.In the unadjusted model, elevated ePWV was significantly linked to a higher risk of T2DM (HR 1.85, 95% CI 1.53–2.23, p \u0026lt; 0.001).The association remained significant after adjusting for confounders (HR 1.36, 95% CI 1.05–1.75, p = 0.018).Subgroup analysis showed no significant interactions across sex, age, alcohol consumption, or smoking status.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eElevated ePWV independently correlates with a higher risk of T2DM in non-obese young adults. ePWV may serve as a valuable tool for early diabetes risk identification in this population.\u003c/p\u003e","manuscriptTitle":"Elevated Estimated Pulse Wave Velocity and the Risk of Type 2 Diabetes in Non-Obese Young Adults: A Longitudinal Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-27 02:12:09","doi":"10.21203/rs.3.rs-5359838/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-01-29T15:32:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-19T16:18:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"174401492794824061042552202975771082456","date":"2024-12-19T11:21:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-16T23:16:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"327157590517876761659874628370416311767","date":"2024-12-16T22:33:35+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-12-09T10:44:19+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-11-05T08:22:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-05T08:18:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-01T02:00:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Endocrine Disorders","date":"2024-10-30T08:46:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-endocrine-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bend","sideBox":"Learn more about [BMC Endocrine Disorders](http://bmcendocrdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bend/default.aspx","title":"BMC Endocrine Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"87880189-a844-478d-8fa7-2d416bd2dde8","owner":[],"postedDate":"November 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-09T16:02:50+00:00","versionOfRecord":{"articleIdentity":"rs-5359838","link":"https://doi.org/10.1186/s12902-025-01967-4","journal":{"identity":"bmc-endocrine-disorders","isVorOnly":false,"title":"BMC Endocrine Disorders"},"publishedOn":"2025-06-06 15:57:34","publishedOnDateReadable":"June 6th, 2025"},"versionCreatedAt":"2024-11-27 02:12:09","video":"","vorDoi":"10.1186/s12902-025-01967-4","vorDoiUrl":"https://doi.org/10.1186/s12902-025-01967-4","workflowStages":[]},"version":"v1","identity":"rs-5359838","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5359838","identity":"rs-5359838","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}