Association of eGFR index category with All-Cause Mortality and Cardiovascular Events in middle and older individuals: a prospective cohort study

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The aim of our study was to examine the relationship between the risk of cardiovascular disease, all cause mortality and the evolution of renal function over time. Methods This study utilized longitudinal data from the Mashhad stroke and heart atherosclerotic disorder (MASHAD) cohort, a prospective study conducted from 01/01/2010 to 30/12/2020 involving 6382 participants aged 35–65 years in Mashhad, Iran. eGFR was calculated using the Cockcroft-Gault equation and categorized per KDIGO guidelines. Cardiovascular events (CVE) and all-cause mortality were assessed at multiple follow-up points. Sociodemographic, anthropometric, clinical, and mental health data were collected, while CVE diagnoses were confirmed through detailed clinical evaluations. Cox proportional hazards models were used to analyze the association between eGFR and outcomes, adjusting for confounding variables, with statistical significance set at p < 0.05. Results From the 6382 eligible participants (median age 52 years, 60.3% male), lower eGFR was associated with increased risk of cardiovascular events (CVE) and all cause mortality over a median follow-up of 115 months. Participants in the < 60 ml/min/1.73m² eGFR group had the highest CVE risk (adjusted HR: 2.51, 95% CI: 1.84 − 3.42, P < 0.001) and mortality risk (adjusted HR: 2.24, 95% CI: 1.52–3.30, P < 0.001), compared to the reference group (90–120 ml/min/1.73m²). The 60–89 ml/min/1.73m² group also exhibited elevated risks for CVE (HR: 2.07, P < 0.001) and mortality (HR: 1.73, P 120 ml/min/1.73m² group showed no significant associations. Conclusion These findings suggest that glomerular filtration rate is independently associated with increased cardiovascular risk and all cause mortality in middle aged and older healthy individuals. Health sciences/Cardiology Health sciences/Diseases Health sciences/Medical research Health sciences/Nephrology Health sciences/Risk factors glomerular filtration rate All Cause Mortality cardiovascular disease chronic kidney disease Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Chronic kidney disease (CKD) represents a critical global health challenge affecting approximately 9–13% of the adult population and contributing significantly to worldwide morbidity and mortality( 1 ). The estimated glomerular filtration rate serves as a fundamental marker of kidney function, widely employed in clinical practice for renal health assessment, CKD diagnosis and prediction of adverse outcomes such as all-cause mortality and cardiovascular events (CVE)( 2 ).Multiple studies across diverse settings have demonstrated that lower eGFR is strongly associated with cardiovascular disease (CVD) and all-cause mortality( 3 , 4 ). Individuals with decreased eGFR are more likely to die from cardiovascular causes than from kidney failure itself( 5 ). The decline in GFR characteristically marks the progression of kidney disease toward kidney failure, with an eGFR below 60 mL/min/1.73 m² consistently associated with increased risk of all-cause and cardiovascular mortality in various populations( 6 ). The relationship between eGFR and cardiovascular health is complex, influenced by various comorbidities such as diabetes, hypertension, and obesity, which are prevalent among individuals with CKD( 7 ). These conditions amplify the systemic effects of reduced kidney function, potentially leading to a higher burden of cardiovascular events( 8 ). Reduced eGFR reflects diminished kidney function, which has been closely linked to systemic processes including inflammation, oxidative stress, and vascular dysfunction all significant contributors to increased cardiovascular risk( 9 ). Prior studies have demonstrated that even slight differences in eGFR classifications can have a significant impact on cardiovascular and mortality risks especially in patients with proteinuria or high albumin to creatinine ratios( 10 ). However, these studies often focus on specialized subgroups rather than general populations, leaving knowledge gaps in broader applications. While prior investigations have established the prognostic value of eGFR, they frequently fail to consider the nuanced effects across specific categories, such as mildly decreased function or hyper filtration( 11 ). Emerging evidence suggests that age and sex may modulate the impact of eGFR on health outcomes, with older adults showing a less pronounced association between mild eGFR reductions and cardiovascular events compared to younger cohorts( 12 ). Additionally, sociodemographic factors, including race and socioeconomic status, influence eGFR-associated risks( 13 ). These factors, combined with the underlying heterogeneity of study populations, highlight the importance of conducting large scale, prospective cohort studies to capture these dynamics comprehensively. The objective of this study was to evaluate the association between eGFR and all-cause mortality (ACM) and the incidence of cardiovascular disease in individuals aged 35 to 65 years in Northeast Iran's population. This research aims to address existing knowledge gaps by examining these relationships in a general population cohort, considering various eGFR categories and their specific impacts on health outcomes. Materials and methods Study Design and Population This study utilized longitudinal follow up data from the MASHAD cohort study, conducted from 01/01/2010 to 30/12/2020. Using a stratified cluster random sampling method, 9704 healthy individuals aged 35–65 years were initially recruited from three regions in Mashhad, northeastern Iran( 14 , 15 ). Participants were free of cardiovascular disease and other chronic diseases at baseline. After excluding individuals with a history of heart disease at study initiation, those lost to follow up, and those with missing data, the final analysis included 6382 subjects (708 with CVD and 5674 without overt CVD). The Human Research Ethics Committee of Mashhad University of Medical Sciences (MUMS) approved the study protocol, and all participants provided written informed consent. Follow up and Outcome Assessment The incidence of CVD was assessed at multiple follow up time points (2011, 2014, 2016, and 2020). Participants were monitored for a minimum of 10 years, with contact maintained at 3 yearly intervals to minimize loss to follow up. At each follow up, participants completed questionnaires to identify changes in their health status and lifestyle. While certain data were collected more frequently for specific sub projects, comprehensive follow-up analyses were performed every three years. Morbidity, mortality, myocardial infarction (MI), and stroke rates were regularly collected from reference community sources. Baseline data including demographic, lifestyle, and clinical history were collected using the structured questionnaire developed for the MASHAD cohort, as previously described by Ghayour Mobarhan et al ( 14 ). Cardiovascular Disease Assessment Cardiovascular events were diagnosed through a comprehensive evaluation process. This included: Detailed medical history collection, Physical examination by a specialist cardiologist, Electrocardiogram analysis using the Minnesota Code for evidence of P, QRS, T, and Q wave alterations Additional examinations when CVD was suspected, including: Echocardiography, Stress echocardiography, Radioisotope studies, Angiography, Computed Tomography (CT) angiography, Exercise Tolerance Test (ETT). The definitive diagnosis was established through consensus agreement by a panel of experts. The Framingham cardiovascular examination questionnaire was completed for all participants. GFR Measurement and Categorization Glomerular Filtration Rate was estimated using the Cockcroft Gault equation: eGFR (mL/min) = (140 − age) × body weight/plasma creatinine × 72 (× 0.85 if female)​ Following the 2012 Kidney Disease Improving Global Outcomes (KDIGO) guidelines, baseline eGFR was categorized into four groups: Normal kidney function (eGFR ≥ 90 mL/min/1.73 m²) Mildly decreased kidney function (eGFR = 60 to 89 mL/min/1.73 m²) Moderately to severely decreased kidney function (eGFR < 60 mL/min/1.73 m²) Glomerular hyperfiltration rate (GHF) (eGFR ≥ 120 to ≤ 200 mL/min/1.73 m²) Clinical and Laboratory Measurements Anthropometric Assessments Height, weight, body mass index, waist circumference (WC), hip circumference (HC), waist to hip ratio (WHR), and mid upper arm circumference (MAC) were measured according to standardized protocols. Height, waist circumference, hip circumference, and mid upper arm circumference were measured to the nearest millimeter using a tape measure. Weight was measured to the nearest 0.1 kg using electronic scales. BMI was calculated as weight (kg) divided by height squared (m²). Laboratory Evaluation Blood samples were collected after a 14 hour overnight fast. The following parameters were measured using enzymatic methods on an automated analyzer: Triglycerides (TG), Low density lipoprotein cholesterol (LDL-C), High density lipoprotein cholesterol (HDL-C), Total cholesterol (TC), Fasting blood glucose (FBG) Blood Pressure Assessment Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured using calibrated mercury sphygmomanometers. Hypertension was diagnosed in individuals with systolic blood pressure ≥ 140 mmHg and/or diastolic blood pressure ≥ 90 mmHg, or in those on anti hypertension medication. Additional Variables Mental Health Assessment The data used in the present analysis were derived from the Mashhad Stroke and Heart Atherosclerotic Disorder (MASHAD) cohort study( 14 ), a large prospective population-based study designed to evaluate cardiovascular risk factors in middle-aged and older adults. Within the framework of the MASHAD study, psychological assessments were performed using the Beck Anxiety Inventory (BAI) and Beck Depression Inventory-II (BDI-II) to evaluate anxiety and depressive symptoms. These assessments were administered as part of the standardized baseline data collection protocol of the parent cohort. Other Variables Demographic and socioeconomic characteristics, including age, sex, marital status, education, lifestyle data (smoking status), drug history (lipid lowering and anti-hypertensive medications), and family history of CVD were collected through healthcare professional and nurse interviews. Statistical Analysis Data were coded and entered into Excel version 7.2.0.1 and analyzed using Stata version 14. Descriptive statistics were computed for all study variables. Categorical data were compared using Pearson χ² tests. To evaluate the association between GFR and first occurrence of CVE and to minimize confounding bias, Cox proportional hazards models were developed. Testing the proportional hazards assumption, which is fundamental to Cox regression was conducted. Also Schoenfeld residuals testing and other diagnostic procedures were conducted for evaluate whether the Cox models are appropriate for these data or not. Analyses were performed using IBM SPSS Statistics and Stata version 14 software. Adjusted odds ratios (AORs) with 95% confidence intervals (CIs) were calculated. Statistical significance was set at p < 0.05 for all analyses. RESULTS Of the 9704 patients initially enrolled in the study, 6382 participants met the eligibility criteria and were included in the final analysis. The study population had a median follow up duration of 115 months and a median age of 52 years. The gender distribution showed that 3847 (60.3%) were men and 2535 (39.7%) were women. Analysis of baseline characteristics revealed significant age-related trends across eGFR categories (P < 0.001). Older participants (≥ 60 years) were predominantly represented in the < 60 ml/min/1.73m² category (18.0%), while younger participants ( 120 ml/min/1.73m² group (40.0%). Gender distribution varied significantly across categories, with women showing higher representation in the 60–89 and 90–120 ml/min/1.73m² groups, while men were more prevalent in the > 120 ml/min/1.73m² category. Body mass index (BMI) demonstrated strong associations with eGFR categories (P 35 found in the > 120 ml/min/1.73m² group, suggesting a correlation between obesity and increased glomerular filtration. Conversely, participants with BMI < 25 showed higher prevalence in the < 60 ml/min/1.73m² category (14.9%). (Table 1 ). Table 1 Baseline demographics by index eGFR category Variables 120ml P-overall* P-trend Age ,years < 40 37(2.6) 298(20.8) 527(36.7) 574(40.0) < 0.001 < 0.001 40–49 126(5.7) 569(25.9) 890(40.5) 610(27.8) 50–59 208(10.1) 821(40.0) 710(34.6) 312(15.2) ≥ 60 126(18.0) 335(47.9) 184(26.3) 55(7.9) Sex Female 345(9.0) 1299(33.8) 1354(35.2) 849(22.1) < 0.001 < 0.001 Male 152(6.0) 724(28.6) 957(37.8) 702(27.7) Marital status Single 68(14.4) 178(37.8) 150(31.8) 75(15.9) < 0.001 < 0.001 Married 429(7.3) 1845(31.2) 2161(36.6) 1476(25.0) Employment status Housewife 197(7.6) 846(32.8) 921(35.7) 615(23.8) 0.007 0.007 Worker 44(6.3) 196(28.0) 268(38.3) 192(27.4) self-employed 190(8.9) 704(32.9) 742(34.7) 505(23.6) Employee 66(6.9) 277(28.8) 380(39.5) 239(24.8) Education level Illiterate 90(13.3) 287(42.3) 199(29.4) 102(15.0) < 0.001 < 0.001 Elementary 216(9.1) 791(33.4) 830(35.1) 529(22.4) High school 121(5.1) 673(28.1) 912(38.1) 687(28.7) College graduate 70(7.4) 272(28.8) 370(39.2) 233(24.7) BMI < 25 260(14.9) 814(46.5) 542(31.0) 133(7.6) < 0.001 35 14(2.9) 58(12.1) 123(25.6) 285(59.4) Cardiovascular events No 430(7.4) 1792(30.7) 2171(37.2) 1444(24.7) < 0.001 < 0.001 Yes 67(12.3) 231(42.4) 140(25.7) 107(19.6) Diabetes No 406(7.4) 1741(31.7) 2011(36.7) 1329(24.2) 0.021 0.021 Yes 91(10.2) 282(31.5) 300(33.5) 222(24.8) Hypertension No 430(8.0) 1724(32.1) 1953(36.4) 1260(23.5) 0.003 0.003 Yes 67(6.6) 299(29.5) 358(35.3) 291(28.7) Hypercholesterolemia No 293(7.6) 1218(31.4) 1410(36.4) 957(24.7) 0.66 0.66 Yes 204(8.1) 805(32.1) 901(36.0) 594(23.7) Smoking Never smoker 316(7.2) 1380(31.7) 1630(37.4) 1033(23.7) 0.026 0.026 Former smoker 63(10.0) 202(31.9) 214(33.8) 154(24.3) Active smoker 118(8.5) 441(31.7) 467(33.6) 364(26.2) Family history of heart disease No 337(8.1) 1377(32.9) 1502(35.9) 967(23.1) 0.003 0.003 Yes 160(7.3) 646(29.4) 809(36.8) 584(26.6) Depression Minimal 255(6.6) 1241(31.9) 1459(37.5) 935(24.0) < 0.001 < 0.001 Moderate 191(9.3) 646(31.4) 707(34.4) 512(24.9) High 51(11.7) 136(31.2) 145(33.3) 104(23.9) Anxiety Minimal 214(7.1) 973(32.2) 1122(37.2) 710(23.5) 0.05 0.05 Mild 128(7.3) 565(32.0) 645(36.6) 425(24.1) Moderate 101(10.4) 293(30.2) 309(31.9) 266(27.5) Severe 54(8.6) 192(30.4) 235(37.2) 150(23.8) Unless otherwise indicated, data are expressed as No. (%).* Chi-squared test During the follow up period, 296 deaths and 545 cardiovascular events were recorded. Cardiovascular events were most prevalent in the < 60 ml/min/1.73m² group (12.3%), highlighting the relationship between declining renal function and cardiovascular risk. Cox proportional hazards analysis revealed that participants in the < 60 ml/min/1.73m² group demonstrated the highest risk of cardiovascular events (adjusted HR: 2.51, 95% CI: 1.84–3.42, P < 0.001), followed by the 60–89 ml/min/1.73m² group (adjusted HR: 2.07, 95% CI: 1.66–2.57, P 120 ml/min/1.73m² category showed no significant risk increase (HR: 1.06, P = 0.60) compared to the reference group (90–120 ml/min/1.73m²).(Fig. 3 ) Similar patterns emerged for all-cause mortality. The < 60 ml/min/1.73m² group exhibited the highest mortality risk (adjusted HR: 2.24, 95% CI: 1.52–3.30, P < 0.001), while the 60–89 ml/min/1.73m² group also showed significantly elevated risk (adjusted HR: 1.73, P 120 ml/min/1.73m² group showed no significant difference from the reference group (adjusted HR: 1.10, P = 0.59). (Fig. 4 ) Analysis of comorbidities revealed that while hypercholesterolemia and hypertension showed weaker associations, they were more common in lower eGFR groups. Active smokers showed higher prevalence in intermediate eGFR categories (60–89 and 90–120 ml/min/1.73m²). Mental health assessments demonstrated significant trends across eGFR categories, with participants having high depression scores showing greater prevalence in the < 60 ml/min/1.73m² group (11.7%). Similar patterns were observed for anxiety levels, with moderate anxiety being more prevalent in lower eGFR categories. (Table 2 ) (Fig. 1 ) (Fig. 2 ). Table 2 Risk of Cardiovascular events and All-cause mortality outcomes by eGFR index category. †Cox proportional hazard model for time-to-event, adjusted for age, sex, employment status, education level, BMI, hypercholesterolemia, hypertension, diabetes, family history of heart disease, smoking, depression and anxiety plus interaction terms for the multivariate model. Outcome Index eGFR Category,ml/min/1.73m 2 Patients with event,n(%) Crude model HR(95%Cl) P-value Adjusted model HR(95%Cl) P-overall P-trend CVD events < 60 67(12.3) 2.37(1.77–3.17) < 0.001 2.51(1.84–3.42) < 0.001 < 0.001 60–89 231(42.4) 1.95(1.58–2.40) < 0.001 2.07(1.66–2.57) < 0.001 120 107(19.6) 1.15(0.89–1.48) 0.27 1.06(0.81–1.38) 0.60 0.60 All-cause mortality < 60 49(16.6) 3.41(2.37–4.90) < 0.001 2.24(1.52–3.30) < 0.001 < 0.001 60–89 124(41.9) 2.05(1.53–2.74) < 0.001 1.73(1.28–2.35) < 0.001 120 50(16.9) 1.03(0.72–1.48) 0.84 1.10(0.75–1.62) 0.59 0.59 All analyses were adjusted for potential confounders including age, sex, employment status, education level, BMI, hypercholesterolemia, hypertension, diabetes, family history of heart disease, smoking status, depression, and anxiety. The 90–120 ml/min/1.73m² category served as the reference group for all comparisons. Discussion Our prospective cohort analysis of the Mashhad stroke and heart atherosclerotic disorder study revealed that participants with declining eGFR faced significantly elevated risks of all-cause mortality and cardiovascular events, even after adjusting for confounding factors such as age, BMI, and comorbidities. This finding emphasizes the fundamental role of renal impairment in driving adverse cardiovascular and mortality outcomes. Specifically, patients with eGFR < 60 mL/min/1.73m² demonstrated markedly higher risks for CVE (HR: 2.51, 95% CI: 1.84–3.42) and mortality (HR: 2.24, 95% CI: 1.52–3.30) compared to those with normal kidney function (eGFR 90–120 mL/min/1.73m²). The 60–89 ml/min/1.73m² group also exhibited elevated risk with an adjusted HR of 2.07 (95% CI: 1.66–2.57, P < 0.001). These findings align with and expand upon previous research in this field( 16 , 17 ). Yidan Guo et al. demonstrated that reduced eGFR is strongly associated with cardiovascular disease and all-cause mortality across diverse settings( 18 ). Our results particularly complement the work of Matsushita et al. who established that eGFR below 60 mL/min/1.73m² consistently correlates with increased risk of all-cause and cardiovascular mortality in various populations( 19 ). The relationship we observed between eGFR decline and adverse outcomes persisted even after controlling for common comorbidities such as diabetes and hypertension, which Coresh et al identified as significant amplifiers of reduced kidney function's systemic effects( 20 ). In our large population-based cohort of Iranian community dwellers, we found that strong and mild declines in eGFR were respectively associated with a 2.24 and 1.73 times increase in all-cause mortality, as well as a 2.51 and 2.07 times increase in the incidence of CVD events within 10 years. It is important to note that we used the Cockcroft-Gault formula to estimate eGFR. The Cockcroft-Gault equation is a widely used formula for estimating creatinine clearance (CrCl) and assessing renal function, helping clinicians estimate glomerular filtration rate (GFR) based on serum creatinine levels, age, weight, and gender of the patient( 21 ). Because our data were derived using the Cockcroft-Gault equation, the conclusions of our analysis are likely more accurate and provide greater confidence in the evidence. Meanwhile, our sensitivity analysis also indicated that any decline in eGFR (mild or rapid) is associated with a significantly increased risk of all-cause mortality and CVD, regardless of CKD incidence. Thus, in the context of current knowledge, our data from this population-based prospective cohort study support the relevance of eGFR decline over time as a predictor of adverse outcomes and extend the applicability of this finding to a different ethnic population. Additionally, we assessed the association between glomerular hyperfiltration and clinical outcomes; however, we did not discover any noteworthy rise in the risk of cardiovascular disease (CVD) or all-cause mortality in these people. Despite a lot of recent attention being paid to the clinical significance of a sequential increase in eGFR, the findings have generated debate( 18 ). The strength of our findings is particularly noteworthy given our use of the Cockcroft-Gault formula for eGFR estimation. This widely-used equation incorporates serum creatinine levels, age, weight, and gender, providing a comprehensive assessment of renal function. Our sensitivity analyses further confirmed that both mild and rapid eGFR decline significantly increased the risk of all-cause mortality and CVD, independent of chronic kidney disease (CKD) status at baseline. The mechanisms underlying the relationship between declining kidney function and adverse outcomes are complex and multifaceted. As highlighted by Julia Carracedo reduced eGFR may exacerbate cardiovascular risk factors through multiple pathways, including endothelial dysfunction, oxidative stress, and vascular damage( 22 ). Additionally, Kexin Ma noted that the activation of the renin-angiotensin system plays a crucial role in this relationship( 23 ). The gradual decrease in kidney function can also lead to reduced appetite, loss of lean body mass, and diminished physical function, indirectly contributing to higher mortality risk. Notably, our analysis of glomerular hyper filtration (GHF) revealed no significant increase in the risk of all-cause mortality and CVD among individuals with eGFR > 120 ml/min/1.73m². This finding contributes to the ongoing debate about the clinical relevance of sequential increases in eGFR. While some studies have reported associations between increasing eGFR and mortality, our population-based cohort, recruited from community-dwelling individuals undergoing routine health examinations, showed no such relationship( 24 , 25 ). Our study has several limitations that warrant consideration. First, as eGFR represents an indirect parameter of kidney function, we cannot definitively confirm that observed eGFR changes accurately reflect true changes in kidney function, as direct GFR measurements were not conducted in the MASHAD cohort study. Second, the absence of baseline albuminuria measurements limits our ability to fully characterize kidney function. Finally, despite the rigorous measurement protocols employed in the MASHAD study and our comprehensive coverage of important covariates, we cannot completely exclude the potential influence of unmeasured confounders, such as inflammatory or oxidative stress biomarkers. Conclusions Our findings provide robust evidence that declining eGFR over time is independently associated with higher risk for all-cause mortality and CVD, regardless of initial eGFR and other baseline risk factors. This relationship holds true for both percentage and absolute changes in annual eGFR, and remains significant in individuals with and without CKD at baseline. These results underscore the importance of monitoring kidney function as a critical predictor of cardiovascular and mortality outcomes in middle-aged and older adults. Declarations Ethics approval and consent to participate All participants provided written informed consent before their inclusion in the study. The consent process ensured that each individual was fully informed about the objectives, procedures, potential risks, and benefits of the study. Participants were made aware that their involvement was entirely voluntary and that they could withdraw at any point without any consequence. The consent forms were approved by the Ethics Committee of Mashhad University of Medical Sciences (approval code: IR.MUMS.FHMPM.REC.1403.029) and were obtained in accordance with the ethical standards outlined in the Declaration of Helsinki. Consent for publication Not Applicable. Competing interests The authors declare that they have no known competing for financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data Sharing Statement The data supporting the findings of this study are available upon reasonable request from the corresponding author. Due to privacy and ethical considerations, access to the dataset may be subject to institutional and regulatory approvals. Aggregated results and summary statistics are available in the published article and supplementary materials. Clinical trial number not applicable. Funding This study was supported by Mashhad University of Medical Sciences under grant number 4022054.The funding body had no role in the study design, data collection, analysis, or manuscript preparation. Author Contribution AM is the principal investigator and research leader. MD and EM and MG designed the Study and drafted the manuscript and analyzed the data.HS contributed to the conception, design, data interpretation, and revising the manuscript. All authors read and approved the final manuscript. Acknowledgements Not Applicable. Data Availability The data supporting the findings of this study are available upon reasonable request from the corresponding author. Due to privacy and ethical considerations, access to the dataset may be subject to institutional and regulatory approvals. Aggregated results and summary statistics are available in the published article and supplementary materials. References Kovesdy, C. P. Epidemiology of chronic kidney disease: an update 2022. 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Serum Creatinine versus Corrected Cockcroft-Gault Equation According to Poggio Reference Values in Patients with Arterial Hypertension. Int. J. Appl. Basic. Med. Res. 12 (1), 9–13 (2022). Carracedo, J. et al. Mechanisms of Cardiovascular Disorders in Patients With Chronic Kidney Disease: A Process Related to Accelerated Senescence. Front. Cell. Dev. Biol. 8 , 185 (2020). Ma, K., Gao, W., Xu, H., Liang, W. & Ma, G. Role and Mechanism of the Renin-Angiotensin-Aldosterone System in the Onset and Development of Cardiorenal Syndrome. J. Renin Angiotensin Aldosterone Syst. 2022 , 3239057 (2022). Dupuis, M. E., Nadeau-Fredette, A. C., Madore, F., Agharazii, M. & Goupil, R. Association of Glomerular Hyperfiltration and Cardiovascular Risk in Middle-Aged Healthy Individuals. JAMA Netw. Open. 3 (4), e202377 (2020). Reboldi, G. et al. Glomerular hyperfiltration is a predictor of adverse cardiovascular outcomes. Kidney Int. 93 (1), 195–203 (2018). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 24 Apr, 2026 Reviewers invited by journal 23 Apr, 2026 Editor assigned by journal 22 Apr, 2026 Editor invited by journal 06 Mar, 2026 Submission checks completed at journal 17 Feb, 2026 First submitted to journal 17 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8872629","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":633627359,"identity":"6a40b549-de66-4938-8a67-e4de38bab47a","order_by":0,"name":"Amin Moradi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYBACNggpASIZHwAJHj5CWviRtDAbgLSwEdIi2YBuF0EtBtcOH/vwo8yCQbf/8LPKrzl2MmwMzA8f3cCn5XZa8syecxIMZjfSzG7LbksGOozN2DgHr5YcYwbeNpAWHrbbktuYgVp42KTxabEHamH8C9Jy/gxbseS2esJaQLYwg205kMPG+HHbYWK0pCUzy5yT4AH6xViacdtxHjZmgn5JPsz4pqxOzuz84Ycff26rtudnb374GJ8WGOABEcwQkgjlcMD4gxTVo2AUjIJRMGIAAE7eQJvBM4P/AAAAAElFTkSuQmCC","orcid":"","institution":"Mashhad University of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Amin","middleName":"","lastName":"Moradi","suffix":""},{"id":633627361,"identity":"02ff27e2-a3c8-4a2d-8a69-e35b1b75377a","order_by":1,"name":"Mohsen Dehghani","email":"","orcid":"","institution":"Mashhad University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Mohsen","middleName":"","lastName":"Dehghani","suffix":""},{"id":633627363,"identity":"8c5d1e02-5127-4a11-868e-511eb83206fb","order_by":2,"name":"Mehdi Norouzi","email":"","orcid":"","institution":"Mashhad University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Mehdi","middleName":"","lastName":"Norouzi","suffix":""},{"id":633627370,"identity":"88e0d703-2140-46d4-a03b-ee852d4a1f53","order_by":3,"name":"Habibollah Esmaily","email":"","orcid":"","institution":"Mashhad University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Habibollah","middleName":"","lastName":"Esmaily","suffix":""},{"id":633627371,"identity":"2886a480-4adc-4761-8817-ac9215391807","order_by":4,"name":"Majid Ghayour-Mobarhan","email":"","orcid":"","institution":"Majid Ghayour-Mobarhan, Professor of Nutritional Sciences, Mashhad University of Medical Sciences, Mashhad University of Medical Sciences (MUMS), Mashhad University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Majid","middleName":"","lastName":"Ghayour-Mobarhan","suffix":""},{"id":633627373,"identity":"10d0a8a9-a4d5-4e40-94c1-566e64654739","order_by":5,"name":"Ehsan Mosafarkhani","email":"","orcid":"","institution":"Mashhad University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ehsan","middleName":"","lastName":"Mosafarkhani","suffix":""}],"badges":[],"createdAt":"2026-02-13 13:53:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8872629/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8872629/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108736529,"identity":"3b7f3d92-7642-4c5b-ae9e-bdb1d8d09c81","added_by":"auto","created_at":"2026-05-07 20:16:30","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":115105,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of Study cohort\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8872629/v1/975c24e18fdd0226693d5ca6.jpg"},{"id":108806560,"identity":"b6b110c1-a424-45e0-aea0-07982cb754f4","added_by":"auto","created_at":"2026-05-08 15:28:55","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":53794,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure1\u003c/strong\u003e: Cumulative Incidence of Adverse Cardiovascular Events in Individuals With different eGFR index category Compared With Normal Glomerular Filtration rate(90-120 ml)\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8872629/v1/d31df0b127d7defa3091be2b.jpg"},{"id":108736530,"identity":"0227a2f5-da46-437d-912e-da4093f80c08","added_by":"auto","created_at":"2026-05-07 20:16:30","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":45903,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure2\u003c/strong\u003e: Cumulative Incidence of All-Cause mortality in Individuals With different eGFR index category Compared With Normal Glomerular Filtration rate(90-120 ml)\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8872629/v1/9ded78062bf3f790b30cc078.jpg"},{"id":108736532,"identity":"6de51803-9f18-4c46-830e-4c8e274e370b","added_by":"auto","created_at":"2026-05-07 20:16:30","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":41321,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure3\u003c/strong\u003e: Association between eGFR and risk of CVE (Cardiovascular events) in population study. The black line represents the hazard ratio and the shaded area, the 95%confidence interval. (1=eGFR\u0026lt;60,2=eGFR60-89,3=eGFR90-120,4=eGFR\u0026gt;120)\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8872629/v1/2796e9bbc97d185848afdbcc.jpg"},{"id":108736533,"identity":"b3d1f37c-8866-4f16-b155-34974c80ff6c","added_by":"auto","created_at":"2026-05-07 20:16:30","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":50760,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure4\u003c/strong\u003e: Association between eGFR and risk of ACM (All-cause-mortality) in population study. The black line represents the hazard ratio and the shaded area, the 95%confidence interval. (1=eGFR\u0026lt;60,2=eGFR60-89,3=eGFR90-120,4=eGFR\u0026gt;120)\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8872629/v1/bc81cef72e69871845025aa8.jpg"},{"id":108809913,"identity":"e54175d9-b858-4e74-bfeb-fb7a3a6bfa92","added_by":"auto","created_at":"2026-05-08 15:56:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":730254,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8872629/v1/c7a9ed68-ae48-4825-90a5-d058ccfafbd5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of eGFR index category with All-Cause Mortality and Cardiovascular Events in middle and older individuals: a prospective cohort study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChronic kidney disease (CKD) represents a critical global health challenge affecting approximately 9\u0026ndash;13% of the adult population and contributing significantly to worldwide morbidity and mortality(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The estimated glomerular filtration rate serves as a fundamental marker of kidney function, widely employed in clinical practice for renal health assessment, CKD diagnosis and prediction of adverse outcomes such as all-cause mortality and cardiovascular events (CVE)(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).Multiple studies across diverse settings have demonstrated that lower eGFR is strongly associated with cardiovascular disease (CVD) and all-cause mortality(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Individuals with decreased eGFR are more likely to die from cardiovascular causes than from kidney failure itself(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). The decline in GFR characteristically marks the progression of kidney disease toward kidney failure, with an eGFR below 60 mL/min/1.73 m\u0026sup2; consistently associated with increased risk of all-cause and cardiovascular mortality in various populations(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe relationship between eGFR and cardiovascular health is complex, influenced by various comorbidities such as diabetes, hypertension, and obesity, which are prevalent among individuals with CKD(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). These conditions amplify the systemic effects of reduced kidney function, potentially leading to a higher burden of cardiovascular events(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Reduced eGFR reflects diminished kidney function, which has been closely linked to systemic processes including inflammation, oxidative stress, and vascular dysfunction all significant contributors to increased cardiovascular risk(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrior studies have demonstrated that even slight differences in eGFR classifications can have a significant impact on cardiovascular and mortality risks especially in patients with proteinuria or high albumin to creatinine ratios(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). However, these studies often focus on specialized subgroups rather than general populations, leaving knowledge gaps in broader applications. While prior investigations have established the prognostic value of eGFR, they frequently fail to consider the nuanced effects across specific categories, such as mildly decreased function or hyper filtration(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEmerging evidence suggests that age and sex may modulate the impact of eGFR on health outcomes, with older adults showing a less pronounced association between mild eGFR reductions and cardiovascular events compared to younger cohorts(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Additionally, sociodemographic factors, including race and socioeconomic status, influence eGFR-associated risks(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). These factors, combined with the underlying heterogeneity of study populations, highlight the importance of conducting large scale, prospective cohort studies to capture these dynamics comprehensively. The objective of this study was to evaluate the association between eGFR and all-cause mortality (ACM) and the incidence of cardiovascular disease in individuals aged 35 to 65 years in Northeast Iran's population. This research aims to address existing knowledge gaps by examining these relationships in a general population cohort, considering various eGFR categories and their specific impacts on health outcomes.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Population\u003c/h2\u003e \u003cp\u003eThis study utilized longitudinal follow up data from the MASHAD cohort study, conducted from 01/01/2010 to 30/12/2020. Using a stratified cluster random sampling method, 9704 healthy individuals aged 35\u0026ndash;65 years were initially recruited from three regions in Mashhad, northeastern Iran(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Participants were free of cardiovascular disease and other chronic diseases at baseline. After excluding individuals with a history of heart disease at study initiation, those lost to follow up, and those with missing data, the final analysis included 6382 subjects (708 with CVD and 5674 without overt CVD). The Human Research Ethics Committee of Mashhad University of Medical Sciences (MUMS) approved the study protocol, and all participants provided written informed consent.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFollow up and Outcome Assessment\u003c/h3\u003e\n\u003cp\u003eThe incidence of CVD was assessed at multiple follow up time points (2011, 2014, 2016, and 2020). Participants were monitored for a minimum of 10 years, with contact maintained at 3 yearly intervals to minimize loss to follow up. At each follow up, participants completed questionnaires to identify changes in their health status and lifestyle. While certain data were collected more frequently for specific sub projects, comprehensive follow-up analyses were performed every three years. Morbidity, mortality, myocardial infarction (MI), and stroke rates were regularly collected from reference community sources. Baseline data including demographic, lifestyle, and clinical history were collected using the structured questionnaire developed for the MASHAD cohort, as previously described by Ghayour Mobarhan et al (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eCardiovascular Disease Assessment\u003c/h3\u003e\n\u003cp\u003eCardiovascular events were diagnosed through a comprehensive evaluation process. This included:\u003c/p\u003e \u003cp\u003eDetailed medical history collection, Physical examination by a specialist cardiologist, Electrocardiogram analysis using the Minnesota Code for evidence of P, QRS, T, and Q wave alterations Additional examinations when CVD was suspected, including: Echocardiography, Stress echocardiography, Radioisotope studies, Angiography, Computed Tomography (CT) angiography, Exercise Tolerance Test (ETT).\u003c/p\u003e \u003cp\u003eThe definitive diagnosis was established through consensus agreement by a panel of experts. The Framingham cardiovascular examination questionnaire was completed for all participants.\u003c/p\u003e\n\u003ch3\u003eGFR Measurement and Categorization\u003c/h3\u003e\n\u003cp\u003eGlomerular Filtration Rate was estimated using the Cockcroft Gault equation:\u003c/p\u003e \u003cp\u003eeGFR (mL/min) = (140\u0026thinsp;\u0026minus;\u0026thinsp;age) \u0026times; body weight/plasma creatinine \u0026times; 72 (\u0026times; 0.85 if female)​\u003c/p\u003e \u003cp\u003e Following the 2012 Kidney Disease Improving Global Outcomes (KDIGO) guidelines, baseline eGFR was categorized into four groups:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eNormal kidney function (eGFR\u0026thinsp;\u0026ge;\u0026thinsp;90 mL/min/1.73 m\u0026sup2;)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eMildly decreased kidney function (eGFR\u0026thinsp;=\u0026thinsp;60 to 89 mL/min/1.73 m\u0026sup2;)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eModerately to severely decreased kidney function (eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 mL/min/1.73 m\u0026sup2;)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eGlomerular hyperfiltration rate (GHF) (eGFR\u0026thinsp;\u0026ge;\u0026thinsp;120 to \u0026le;\u0026thinsp;200 mL/min/1.73 m\u0026sup2;)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e\n\u003ch3\u003eClinical and Laboratory Measurements\u003c/h3\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAnthropometric Assessments\u003c/h2\u003e \u003cp\u003eHeight, weight, body mass index, waist circumference (WC), hip circumference (HC), waist to hip ratio (WHR), and mid upper arm circumference (MAC) were measured according to standardized protocols. Height, waist circumference, hip circumference, and mid upper arm circumference were measured to the nearest millimeter using a tape measure. Weight was measured to the nearest 0.1 kg using electronic scales. BMI was calculated as weight (kg) divided by height squared (m\u0026sup2;).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLaboratory Evaluation\u003c/h3\u003e\n\u003cp\u003eBlood samples were collected after a 14 hour overnight fast. The following parameters were measured using enzymatic methods on an automated analyzer: Triglycerides (TG), Low density lipoprotein cholesterol (LDL-C), High density lipoprotein cholesterol (HDL-C), Total cholesterol (TC), Fasting blood glucose (FBG)\u003c/p\u003e\n\u003ch3\u003eBlood Pressure Assessment\u003c/h3\u003e\n\u003cp\u003eSystolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured using calibrated mercury sphygmomanometers. Hypertension was diagnosed in individuals with systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg and/or diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg, or in those on anti hypertension medication.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAdditional Variables\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003eMental Health Assessment\u003c/h2\u003e \u003cp\u003eThe data used in the present analysis were derived from the Mashhad Stroke and Heart Atherosclerotic Disorder (MASHAD) cohort study(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), a large prospective population-based study designed to evaluate cardiovascular risk factors in middle-aged and older adults. Within the framework of the MASHAD study, psychological assessments were performed using the Beck Anxiety Inventory (BAI) and Beck Depression Inventory-II (BDI-II) to evaluate anxiety and depressive symptoms. These assessments were administered as part of the standardized baseline data collection protocol of the parent cohort.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eOther Variables\u003c/h2\u003e \u003cp\u003eDemographic and socioeconomic characteristics, including age, sex, marital status, education, lifestyle data (smoking status), drug history (lipid lowering and anti-hypertensive medications), and family history of CVD were collected through healthcare professional and nurse interviews.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eData were coded and entered into Excel version 7.2.0.1 and analyzed using Stata version 14. Descriptive statistics were computed for all study variables. Categorical data were compared using Pearson χ\u0026sup2; tests. To evaluate the association between GFR and first occurrence of CVE and to minimize confounding bias, Cox proportional hazards models were developed. Testing the proportional hazards assumption, which is fundamental to Cox regression was conducted. Also Schoenfeld residuals testing and other diagnostic procedures were conducted for evaluate whether the Cox models are appropriate for these data or not. Analyses were performed using IBM SPSS Statistics and Stata version 14 software. Adjusted odds ratios (AORs) with 95% confidence intervals (CIs) were calculated. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all analyses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eOf the 9704 patients initially enrolled in the study, 6382 participants met the eligibility criteria and were included in the final analysis. The study population had a median follow up duration of 115 months and a median age of 52 years. The gender distribution showed that 3847 (60.3%) were men and 2535 (39.7%) were women.\u003c/p\u003e \u003cp\u003eAnalysis of baseline characteristics revealed significant age-related trends across eGFR categories (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Older participants (\u0026ge;\u0026thinsp;60 years) were predominantly represented in the \u0026lt;\u0026thinsp;60 ml/min/1.73m\u0026sup2; category (18.0%), while younger participants (\u0026lt;\u0026thinsp;40 years) were more concentrated in the \u0026gt;\u0026thinsp;120 ml/min/1.73m\u0026sup2; group (40.0%). Gender distribution varied significantly across categories, with women showing higher representation in the 60\u0026ndash;89 and 90\u0026ndash;120 ml/min/1.73m\u0026sup2; groups, while men were more prevalent in the \u0026gt;\u0026thinsp;120 ml/min/1.73m\u0026sup2; category. Body mass index (BMI) demonstrated strong associations with eGFR categories (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with 59.4% of participants with BMI\u0026thinsp;\u0026gt;\u0026thinsp;35 found in the \u0026gt;\u0026thinsp;120 ml/min/1.73m\u0026sup2; group, suggesting a correlation between obesity and increased glomerular filtration. Conversely, participants with BMI\u0026thinsp;\u0026lt;\u0026thinsp;25 showed higher prevalence in the \u0026lt;\u0026thinsp;60 ml/min/1.73m\u0026sup2; category (14.9%). (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline demographics by index eGFR category\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;60ml\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60-89ml\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90-120ml\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;120ml\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-overall*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-trend\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eAge ,years\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37(2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e298(20.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e527(36.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e574(40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\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\u003e40\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e126(5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e569(25.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e890(40.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e610(27.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e208(10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e821(40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e710(34.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e312(15.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e126(18.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e335(47.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e184(26.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55(7.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e345(9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1299(33.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1354(35.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e849(22.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e152(6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e724(28.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e957(37.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e702(27.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68(14.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e178(37.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e150(31.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75(15.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\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\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e429(7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1845(31.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2161(36.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1476(25.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEmployment status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousewife\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e197(7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e846(32.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e921(35.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e615(23.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44(6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e196(28.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e268(38.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e192(27.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eself-employed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e190(8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e704(32.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e742(34.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e505(23.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66(6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e277(28.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e380(39.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e239(24.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIlliterate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90(13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e287(42.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e199(29.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e102(15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\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\u003eElementary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e216(9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e791(33.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e830(35.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e529(22.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121(5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e673(28.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e912(38.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e687(28.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege graduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70(7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e272(28.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e370(39.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e233(24.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e260(14.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e814(46.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e542(31.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e133(7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\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\u003e25\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e164(6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e849(31.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1088(40.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e565(21.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59(4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e302(20.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e558(37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e568(38.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14(2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58(12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e123(25.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e285(59.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCardiovascular events\u003c/b\u003e\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e430(7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1792(30.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2171(37.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1444(24.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67(12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e231(42.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140(25.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e107(19.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes\u003c/b\u003e\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e406(7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1741(31.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2011(36.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1329(24.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91(10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e282(31.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e300(33.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e222(24.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e430(8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1724(32.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1953(36.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1260(23.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67(6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e299(29.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e358(35.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e291(28.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypercholesterolemia\u003c/b\u003e\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e293(7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1218(31.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1410(36.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e957(24.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e204(8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e805(32.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e901(36.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e594(23.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e316(7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1380(31.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1630(37.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1033(23.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63(10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e202(31.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e214(33.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e154(24.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActive smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e118(8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e441(31.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e467(33.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e364(26.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFamily history of heart disease\u003c/b\u003e\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e337(8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1377(32.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1502(35.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e967(23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e160(7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e646(29.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e809(36.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e584(26.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDepression\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e255(6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1241(31.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1459(37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e935(24.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\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\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e191(9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e646(31.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e707(34.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e512(24.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51(11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e136(31.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e145(33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e104(23.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAnxiety\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e214(7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e973(32.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1122(37.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e710(23.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e128(7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e565(32.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e645(36.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e425(24.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101(10.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e293(30.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e309(31.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e266(27.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54(8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e192(30.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e235(37.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e150(23.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eUnless otherwise indicated, data are expressed as No. (%).* Chi-squared test\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDuring the follow up period, 296 deaths and 545 cardiovascular events were recorded. Cardiovascular events were most prevalent in the \u0026lt;\u0026thinsp;60 ml/min/1.73m\u0026sup2; group (12.3%), highlighting the relationship between declining renal function and cardiovascular risk. Cox proportional hazards analysis revealed that participants in the \u0026lt;\u0026thinsp;60 ml/min/1.73m\u0026sup2; group demonstrated the highest risk of cardiovascular events (adjusted HR: 2.51, 95% CI: 1.84\u0026ndash;3.42, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), followed by the 60\u0026ndash;89 ml/min/1.73m\u0026sup2; group (adjusted HR: 2.07, 95% CI: 1.66\u0026ndash;2.57, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The \u0026gt;\u0026thinsp;120 ml/min/1.73m\u0026sup2; category showed no significant risk increase (HR: 1.06, P\u0026thinsp;=\u0026thinsp;0.60) compared to the reference group (90\u0026ndash;120 ml/min/1.73m\u0026sup2;).(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSimilar patterns emerged for all-cause mortality. The \u0026lt;\u0026thinsp;60 ml/min/1.73m\u0026sup2; group exhibited the highest mortality risk (adjusted HR: 2.24, 95% CI: 1.52\u0026ndash;3.30, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while the 60\u0026ndash;89 ml/min/1.73m\u0026sup2; group also showed significantly elevated risk (adjusted HR: 1.73, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The \u0026gt;\u0026thinsp;120 ml/min/1.73m\u0026sup2; group showed no significant difference from the reference group (adjusted HR: 1.10, P\u0026thinsp;=\u0026thinsp;0.59). (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAnalysis of comorbidities revealed that while hypercholesterolemia and hypertension showed weaker associations, they were more common in lower eGFR groups. Active smokers showed higher prevalence in intermediate eGFR categories (60\u0026ndash;89 and 90\u0026ndash;120 ml/min/1.73m\u0026sup2;). Mental health assessments demonstrated significant trends across eGFR categories, with participants having high depression scores showing greater prevalence in the \u0026lt;\u0026thinsp;60 ml/min/1.73m\u0026sup2; group (11.7%). Similar patterns were observed for anxiety levels, with moderate anxiety being more prevalent in lower eGFR categories. (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e1\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" 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\u003eRisk of Cardiovascular events and All-cause mortality outcomes by eGFR index category. \u0026dagger;Cox proportional hazard model for time-to-event, adjusted for age, sex, employment status, education level, BMI, hypercholesterolemia, hypertension, diabetes, family history of heart disease, smoking, depression and anxiety plus interaction terms for the multivariate model.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003cp\u003eIndex eGFR Category,ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePatients with event,n(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCrude model\u003c/p\u003e \u003cp\u003eHR(95%Cl)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAdjusted model\u003c/p\u003e \u003cp\u003eHR(95%Cl)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-overall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-trend\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eCVD events\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67(12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.37(1.77\u0026ndash;3.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.51(1.84\u0026ndash;3.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\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\u003e60\u0026ndash;89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e231(42.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.95(1.58\u0026ndash;2.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.07(1.66\u0026ndash;2.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\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\u003e90\u0026ndash;120(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e140(25.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e107(19.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.15(0.89\u0026ndash;1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.06(0.81\u0026ndash;1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAll-cause mortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49(16.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.41(2.37\u0026ndash;4.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.24(1.52\u0026ndash;3.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\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\u003e60\u0026ndash;89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e124(41.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.05(1.53\u0026ndash;2.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.73(1.28\u0026ndash;2.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\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\u003e90\u0026ndash;120(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e73(24.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50(16.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03(0.72\u0026ndash;1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.10(0.75\u0026ndash;1.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAll analyses were adjusted for potential confounders including age, sex, employment status, education level, BMI, hypercholesterolemia, hypertension, diabetes, family history of heart disease, smoking status, depression, and anxiety. The 90\u0026ndash;120 ml/min/1.73m\u0026sup2; category served as the reference group for all comparisons.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur prospective cohort analysis of the Mashhad stroke and heart atherosclerotic disorder study revealed that participants with declining eGFR faced significantly elevated risks of all-cause mortality and cardiovascular events, even after adjusting for confounding factors such as age, BMI, and comorbidities. This finding emphasizes the fundamental role of renal impairment in driving adverse cardiovascular and mortality outcomes. Specifically, patients with eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 mL/min/1.73m\u0026sup2; demonstrated markedly higher risks for CVE (HR: 2.51, 95% CI: 1.84\u0026ndash;3.42) and mortality (HR: 2.24, 95% CI: 1.52\u0026ndash;3.30) compared to those with normal kidney function (eGFR 90\u0026ndash;120 mL/min/1.73m\u0026sup2;). The 60\u0026ndash;89 ml/min/1.73m\u0026sup2; group also exhibited elevated risk with an adjusted HR of 2.07 (95% CI: 1.66\u0026ndash;2.57, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eThese findings align with and expand upon previous research in this field(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Yidan Guo et al. demonstrated that reduced eGFR is strongly associated with cardiovascular disease and all-cause mortality across diverse settings(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Our results particularly complement the work of Matsushita et al. who established that eGFR below 60 mL/min/1.73m\u0026sup2; consistently correlates with increased risk of all-cause and cardiovascular mortality in various populations(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). The relationship we observed between eGFR decline and adverse outcomes persisted even after controlling for common comorbidities such as diabetes and hypertension, which Coresh et al identified as significant amplifiers of reduced kidney function's systemic effects(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn our large population-based cohort of Iranian community dwellers, we found that strong and mild declines in eGFR were respectively associated with a 2.24 and 1.73 times increase in all-cause mortality, as well as a 2.51 and 2.07 times increase in the incidence of CVD events within 10 years. It is important to note that we used the Cockcroft-Gault formula to estimate eGFR. The Cockcroft-Gault equation is a widely used formula for estimating creatinine clearance (CrCl) and assessing renal function, helping clinicians estimate glomerular filtration rate (GFR) based on serum creatinine levels, age, weight, and gender of the patient(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Because our data were derived using the Cockcroft-Gault equation, the conclusions of our analysis are likely more accurate and provide greater confidence in the evidence. Meanwhile, our sensitivity analysis also indicated that any decline in eGFR (mild or rapid) is associated with a significantly increased risk of all-cause mortality and CVD, regardless of CKD incidence. Thus, in the context of current knowledge, our data from this population-based prospective cohort study support the relevance of eGFR decline over time as a predictor of adverse outcomes and extend the applicability of this finding to a different ethnic population.\u003c/p\u003e \u003cp\u003eAdditionally, we assessed the association between glomerular hyperfiltration and clinical outcomes; however, we did not discover any noteworthy rise in the risk of cardiovascular disease (CVD) or all-cause mortality in these people. Despite a lot of recent attention being paid to the clinical significance of a sequential increase in eGFR, the findings have generated debate(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe strength of our findings is particularly noteworthy given our use of the Cockcroft-Gault formula for eGFR estimation. This widely-used equation incorporates serum creatinine levels, age, weight, and gender, providing a comprehensive assessment of renal function. Our sensitivity analyses further confirmed that both mild and rapid eGFR decline significantly increased the risk of all-cause mortality and CVD, independent of chronic kidney disease (CKD) status at baseline.\u003c/p\u003e \u003cp\u003eThe mechanisms underlying the relationship between declining kidney function and adverse outcomes are complex and multifaceted. As highlighted by Julia Carracedo reduced eGFR may exacerbate cardiovascular risk factors through multiple pathways, including endothelial dysfunction, oxidative stress, and vascular damage(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Additionally, Kexin Ma noted that the activation of the renin-angiotensin system plays a crucial role in this relationship(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). The gradual decrease in kidney function can also lead to reduced appetite, loss of lean body mass, and diminished physical function, indirectly contributing to higher mortality risk.\u003c/p\u003e \u003cp\u003eNotably, our analysis of glomerular hyper filtration (GHF) revealed no significant increase in the risk of all-cause mortality and CVD among individuals with eGFR\u0026thinsp;\u0026gt;\u0026thinsp;120 ml/min/1.73m\u0026sup2;. This finding contributes to the ongoing debate about the clinical relevance of sequential increases in eGFR. While some studies have reported associations between increasing eGFR and mortality, our population-based cohort, recruited from community-dwelling individuals undergoing routine health examinations, showed no such relationship(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur study has several limitations that warrant consideration. First, as eGFR represents an indirect parameter of kidney function, we cannot definitively confirm that observed eGFR changes accurately reflect true changes in kidney function, as direct GFR measurements were not conducted in the MASHAD cohort study. Second, the absence of baseline albuminuria measurements limits our ability to fully characterize kidney function. Finally, despite the rigorous measurement protocols employed in the MASHAD study and our comprehensive coverage of important covariates, we cannot completely exclude the potential influence of unmeasured confounders, such as inflammatory or oxidative stress biomarkers.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur findings provide robust evidence that declining eGFR over time is independently associated with higher risk for all-cause mortality and CVD, regardless of initial eGFR and other baseline risk factors. This relationship holds true for both percentage and absolute changes in annual eGFR, and remains significant in individuals with and without CKD at baseline. These results underscore the importance of monitoring kidney function as a critical predictor of cardiovascular and mortality outcomes in middle-aged and older adults.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e All participants provided written informed consent before their inclusion in the study. The consent process ensured that each individual was fully informed about the objectives, procedures, potential risks, and benefits of the study. Participants were made aware that their involvement was entirely voluntary and that they could withdraw at any point without any consequence. The consent forms were approved by the Ethics Committee of Mashhad University of Medical Sciences (approval code: IR.MUMS.FHMPM.REC.1403.029) and were obtained in accordance with the ethical standards outlined in the Declaration of Helsinki.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot Applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare that they have no known competing for financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eData Sharing Statement\u003c/h2\u003e \u003cp\u003eThe data supporting the findings of this study are available upon reasonable request from the corresponding author. Due to privacy and ethical considerations, access to the dataset may be subject to institutional and regulatory approvals. Aggregated results and summary statistics are available in the published article and supplementary materials.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eClinical trial number\u003c/h2\u003e \u003cp\u003enot applicable.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was supported by Mashhad University of Medical Sciences under grant number 4022054.The funding body had no role in the study design, data collection, analysis, or manuscript preparation.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAM is the principal investigator and research leader. MD and EM and MG designed the Study and drafted the manuscript and analyzed the data.HS contributed to the conception, design, data interpretation, and revising the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eNot Applicable.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data supporting the findings of this study are available upon reasonable request from the corresponding author. Due to privacy and ethical considerations, access to the dataset may be subject to institutional and regulatory approvals. Aggregated results and summary statistics are available in the published article and supplementary materials.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKovesdy, C. P. Epidemiology of chronic kidney disease: an update 2022. Kidney Int Suppl 2022;12(1):7\u0026ndash;11. (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeldal, K. et al. [Estimated glomerular filtration rate as a measurement of kidney function]. \u003cem\u003eTidsskr Nor. Laegeforen\u003c/em\u003e ;\u003cb\u003e141\u003c/b\u003e(1). (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbenavoli, C. et al. Role of Estimated Glomerular Filtration Rate in Clinical Research: The Never-Ending Matter. \u003cem\u003eRev. Cardiovasc. Med.\u003c/em\u003e \u003cb\u003e25\u003c/b\u003e (1), 1 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNichols, G. A. et al. Kidney disease progression and all-cause mortality across estimated glomerular filtration rate and albuminuria categories among patients with vs. without type 2 diabetes. \u003cem\u003eBMC Nephrol.\u003c/em\u003e \u003cb\u003e21\u003c/b\u003e (1), 167 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcCallum, W., Tighiouart, H., Ku, E., Salem, D. \u0026amp; Sarnak, M. J. Acute declines in estimated glomerular filtration rate on enalapril and mortality and cardiovascular outcomes in patients with heart failure with reduced ejection fraction. \u003cem\u003eKidney Int.\u003c/em\u003e \u003cb\u003e96\u003c/b\u003e (5), 1185\u0026ndash;1194 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMasrouri, S. et al. Kidney function decline is associated with mortality events: over a decade of follow-up from Tehran Lipid and Glucose Study. \u003cem\u003eJ. Nephrol.\u003c/em\u003e \u003cb\u003e37\u003c/b\u003e (1), 107\u0026ndash;118 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZoccali, C. et al. Cardiovascular complications in chronic kidney disease: a review from the European Renal and Cardiovascular Medicine Working Group of the European Renal Association. \u003cem\u003eCardiovasc. Res.\u003c/em\u003e \u003cb\u003e119\u003c/b\u003e (11), 2017\u0026ndash;2032 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaeed, D. et al. Navigating the Crossroads: Understanding the Link Between Chronic Kidney Disease and Cardiovascular Health. \u003cem\u003eCureus\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e (12), e51362 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVerma, S. et al. Implications of oxidative stress in chronic kidney disease: a review on current concepts and therapies. \u003cem\u003eKidney Res. Clin. Pract.\u003c/em\u003e \u003cb\u003e40\u003c/b\u003e (2), 183\u0026ndash;193 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFung, C. S., Wan, E. Y., Chan, A. K. \u0026amp; Lam, C. L. Association of estimated glomerular filtration rate and urine albumin-to-creatinine ratio with incidence of cardiovascular diseases and mortality in chinese patients with type 2 diabetes mellitus - a population-based retrospective cohort study. \u003cem\u003eBMC Nephrol.\u003c/em\u003e \u003cb\u003e18\u003c/b\u003e (1), 47 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiao, M. T. et al. The Association between Glomerular Hyperfiltration and Left Ventricular Structure and Function in Patients with Primary Aldosteronism. \u003cem\u003eInt. J. Med. Sci.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e (5), 369\u0026ndash;377 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFravel, M. A. et al. GFR Variability, Survival, and Cardiovascular Events in Older Adults. \u003cem\u003eKidney Med.\u003c/em\u003e \u003cb\u003e5\u003c/b\u003e (2), 100583 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatzer, R. E. \u0026amp; McClellan, W. M. Influence of race, ethnicity and socioeconomic status on kidney disease. \u003cem\u003eNat. Rev. Nephrol.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e (9), 533\u0026ndash;541 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGhayour-Mobarhan, M. et al. Mashhad stroke and heart atherosclerotic disorder (MASHAD) study: design, baseline characteristics and 10-year cardiovascular risk estimation. \u003cem\u003eInt. J. Public. Health\u003c/em\u003e. \u003cb\u003e60\u003c/b\u003e (5), 561\u0026ndash;572 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHedayatnia, M. et al. Dyslipidemia and cardiovascular disease risk among the MASHAD study population. \u003cem\u003eLipids Health Dis.\u003c/em\u003e \u003cb\u003e19\u003c/b\u003e (1), 42 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRamezankhani, A., Azizi, F. \u0026amp; Hadaegh, F. Association between estimated glomerular filtration rate slope and cardiovascular disease among individuals with and without diabetes: a prospective cohort study. \u003cem\u003eCardiovasc. Diabetol.\u003c/em\u003e \u003cb\u003e22\u003c/b\u003e (1), 270 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAtaklte, F. et al. Association of Mildly Reduced Kidney Function With Cardiovascular Disease: The Framingham Heart Study. \u003cem\u003eJ. Am. Heart Assoc.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e (16), e020301 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo, Y. et al. Change of Kidney Function Is Associated With All-Cause Mortality and Cardiovascular Diseases: Results From the Kailuan Study. \u003cem\u003eJ. Am. Heart Assoc.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e (21), e010596 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatsushita, K. et al. Association of estimated glomerular filtration rate and albuminuria with all-cause and cardiovascular mortality in general population cohorts: a collaborative meta-analysis. \u003cem\u003eLancet\u003c/em\u003e \u003cb\u003e375\u003c/b\u003e (9731), 2073\u0026ndash;2081 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCoresh, J. et al. Prevalence of chronic kidney disease in the United States. \u003cem\u003eJama\u003c/em\u003e \u003cb\u003e298\u003c/b\u003e (17), 2038\u0026ndash;2047 (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eŠečić, D. et al. Serum Creatinine versus Corrected Cockcroft-Gault Equation According to Poggio Reference Values in Patients with Arterial Hypertension. \u003cem\u003eInt. J. Appl. Basic. Med. Res.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e (1), 9\u0026ndash;13 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarracedo, J. et al. Mechanisms of Cardiovascular Disorders in Patients With Chronic Kidney Disease: A Process Related to Accelerated Senescence. \u003cem\u003eFront. Cell. Dev. Biol.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e, 185 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa, K., Gao, W., Xu, H., Liang, W. \u0026amp; Ma, G. Role and Mechanism of the Renin-Angiotensin-Aldosterone System in the Onset and Development of Cardiorenal Syndrome. \u003cem\u003eJ. Renin Angiotensin Aldosterone Syst.\u003c/em\u003e \u003cb\u003e2022\u003c/b\u003e, 3239057 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDupuis, M. E., Nadeau-Fredette, A. C., Madore, F., Agharazii, M. \u0026amp; Goupil, R. Association of Glomerular Hyperfiltration and Cardiovascular Risk in Middle-Aged Healthy Individuals. \u003cem\u003eJAMA Netw. Open.\u003c/em\u003e \u003cb\u003e3\u003c/b\u003e (4), e202377 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReboldi, G. et al. Glomerular hyperfiltration is a predictor of adverse cardiovascular outcomes. \u003cem\u003eKidney Int.\u003c/em\u003e \u003cb\u003e93\u003c/b\u003e (1), 195\u0026ndash;203 (2018).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"glomerular filtration rate, All Cause Mortality, cardiovascular disease, chronic kidney disease","lastPublishedDoi":"10.21203/rs.3.rs-8872629/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8872629/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe connection between changes in estimated glomerular filtration rate (eGFR) and clinical outcomes remains poorly understood. The aim of our study was to examine the relationship between the risk of cardiovascular disease, all cause mortality and the evolution of renal function over time.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized longitudinal data from the Mashhad stroke and heart atherosclerotic disorder (MASHAD) cohort, a prospective study conducted from 01/01/2010 to 30/12/2020 involving 6382 participants aged 35–65 years in Mashhad, Iran. eGFR was calculated using the Cockcroft-Gault equation and categorized per KDIGO guidelines. Cardiovascular events (CVE) and all-cause mortality were assessed at multiple follow-up points. Sociodemographic, anthropometric, clinical, and mental health data were collected, while CVE diagnoses were confirmed through detailed clinical evaluations. Cox proportional hazards models were used to analyze the association between eGFR and outcomes, adjusting for confounding variables, with statistical significance set at p \u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom the 6382 eligible participants (median age 52 years, 60.3% male), lower eGFR was associated with increased risk of cardiovascular events (CVE) and all cause mortality over a median follow-up of 115 months. Participants in the \u0026lt; 60 ml/min/1.73m² eGFR group had the highest CVE risk (adjusted HR: 2.51, 95% CI: 1.84 − 3.42, P \u0026lt; 0.001) and mortality risk (adjusted HR: 2.24, 95% CI: 1.52–3.30, P \u0026lt; 0.001), compared to the reference group (90–120 ml/min/1.73m²). The 60–89 ml/min/1.73m² group also exhibited elevated risks for CVE (HR: 2.07, P \u0026lt; 0.001) and mortality (HR: 1.73, P \u0026lt; 0.001), while the \u0026gt; 120 ml/min/1.73m² group showed no significant associations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThese findings suggest that glomerular filtration rate is independently associated with increased cardiovascular risk and all cause mortality in middle aged and older healthy individuals.\u003c/p\u003e","manuscriptTitle":"Association of eGFR index category with All-Cause Mortality and Cardiovascular Events in middle and older individuals: a prospective cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-07 20:16:26","doi":"10.21203/rs.3.rs-8872629/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"154870575059200222412372572960546157008","date":"2026-04-24T14:15:27+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-23T13:39:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-22T19:15:49+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-06T12:39:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-17T05:44:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-02-17T05:39:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"07bf5058-e287-472b-b362-cd1e148b576a","owner":[],"postedDate":"May 7th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":67438098,"name":"Health sciences/Cardiology"},{"id":67438099,"name":"Health sciences/Diseases"},{"id":67438100,"name":"Health sciences/Medical research"},{"id":67438101,"name":"Health sciences/Nephrology"},{"id":67438102,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-05-07T20:16:26+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-07 20:16:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8872629","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8872629","identity":"rs-8872629","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}