eGFR is a risk factor for Long-Term All-Cause Death : a case control study in Middle-Aged and Elderly Patients with Hypertension

eGFR is a risk factor for Long-Term All-Cause Death : a case control study in Middle-Aged and Elderly Patients with Hypertension | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article eGFR is a risk factor for Long-Term All-Cause Death : a case control study in Middle-Aged and Elderly Patients with Hypertension Meng Ning, Chong Zhang, Zhiyuan Li, Kun Hu, Tingting Guo, Lei Zhang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4268748/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Hypertension combined with CKD is on the rise in middle-aged and elderly people. However, the association of early subclinical decline in kidney function with long-term mortality in these populations remains unclear. In this study, we developed a novel method for evaluating kidney function in middle-aged and elderly patients with hypertension and predicting their long-term survival outcomes based on the thresholds of estimated glomerular filtration rate (eGFR). Methods: We constructed a retrospective cohort study with a sample of 350 patients and used time-dependent COX regression analysis to analyze the effect of eGFR threshold changes over time on survival outcomes. Patients were divided into three subgroups based on eGFR values and age (eGFRc=1, eGFRc=2 eGFRc=3). We analyzed the potential prognostic clinicopathological factors via univariate and multivariate Cox regression. A prediction model combined the prognostic clinicopathological factors with age-related eGFRc grouping was builded. Results: The lower eGFR groups had significantly lower rates of survival (hazard ratio [HR] eGFRc=2 = 2.407, 95% confidence interval [CI]: 1.663–3.484, P = 0.000; HR eGFRc=3 = 7.081, 95% CI: 4.925–10.179, P = 0.000). The prediction model combined urinary albumintocreatinine ratio (ACR), Diabetes mellitus (DM), stroke, systolic blood pressure (SBP), diastolic blood pressure (DBP), with age-related eGFRc grouping significantly predicted the long-term survival of patients with hypertension (AUC = 0.827, P = 0.0105). Conclusions: The model above can be utilized for determining the thresholds of estimated renal function and assessing long-term survival in middle-aged and elderly patients with hypertension. Hypertension Chronic kidney disease Forecast eGFR Long-term cause of death Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Background Approximately one-third of the world’s population suffers from high blood pressure, 1 which can go unnoticed and pose considerable risks in its early stages. Studies have shown that at present, 30–40% of patients with hypertension have other chronic diseases, such as those of the heart, brain, and kidney. 2 , 3 This statistic highlights the harmful nature of hypertension. CKD is a condition with high incidence and mortality and incurs high medical costs. 4 Many studies have confirmed that decreased kidney function will lead to increased mortality in the medium and long term; 5 , 6 especially in individuals with an estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m 2 , the morbidity and mortality risk of cardiovascular and cerebrovascular diseases will increase significantly. 7 Therefore, the early detection and treatment of kidney function decline is crucial. Current guidelines recommend the use of both urine testing and eGFR to screen and diagnose CKD. 8 eGFR values below the threshold of 60 mL/min/1.73 m 2 are generally believed to indicate CKD, as they reflect at least 50% loss of kidney function. 9 However, some studies have pointed out that eGFR in the elderly decreases physiologically with age, and simply judging the kidney function of middle-aged and elderly patients with a 60 mL/min/1.73 m 2 threshold will produce the wrong result, 9 thereby increasing the medical burden of the elderly. 10 Based on the above reasons, and drawing from the latest research articles on eGFR threshold, 9 – 11 we proposed a new method for assessing kidney function in middle-aged and elderly patients with hypertension to predict their long-term survival outcomes. 2. Method Research object This retrospective cohort study was based on data collected from the “14th Five-Year Plan” Key Research Program database and approved by the Ethics Committee of Tianjin Third Central Hospital (approval number IRB2023-021-01). The analysis cohort included patients admitted to Tianjin Third Central Hospital during 2015–2016 with a history of hypertension or three consecutive SBP readings ≥ 140 mmHg in the consulting room. Those with a history of kidney disease were excluded from the study. Participation in the study was voluntary, and all the patients were required to sign an informed consent form. Cohort design Patients were divided into two groups according to their age: middle-aged (group Y, 40–64 years old) and elderly (group A, ≥ 65 years old). Patients in group Y were further divided into three groups according to their eGFR at enrollment: Y1 group (eGFR ≥ 80 mL/min/1.73 m 2 ), Y2 group (80 mL/min/1.73 m 2 ≥ eGFR ≥ 60 mL/min/1.73 m 2 ), and Y3 group (60 mL/min/1.73 m 2 ≥ eGFR). Patients in group A were similarly divided further into three groups: group A1 (eGFR ≥ 60 mL/min/1.73 m 2 ), group A2 (60 mL/min/1.73 m 2 ≥ eGFR ≥ 45 mL/min/1.73 m 2 ), and group A3 (45 mL/min/1.73 m 2 ≥ eGFR) (Fig. 1 ). Detection index Height, weight, age, sex, medical history, medication history, smoking history, blood lipid, kidney function indicators, serum creatinine, urine routine, eGFR, and systolic and diastolic blood pressure were recorded on the day of enrollment. The trace albumin content of the patients’ urine was detected on the same day. Follow-up outcome All patients received a first telephone follow-up one year after enrollment and a second one in 2023. Patient all-cause death was the only endpoint event, and the time of death was recorded. The duration of follow-up was accurately recorded for all patients who received a follow-up. Statistical analysis STATA 18 was used to conduct statistical analysis for this study. We performed independent sample T-test analysis on the cohort according to patient mortality, as well as subgroup analysis on the patients according to their eGFR at the time of enrollment. Time-dependent COX regression analysis was used to analyze the influence of independent variable changes over time on survival outcomes and plot K–M survival curves. We performed a univariate analysis of all-cause deaths and then adjusted for confounders for related factors to form a multivariate analysis. We conducted univariate and multivariate Cox regression analyses using clinical indicators. Then, integrating the risk score, we established a multivariate regression using a nomogram to predict survival rates over a 7.6-year period. In addition, survival rate was predicted through calibration and time-dependent ROC analysis. 3. Result The baseline characteristics of the study participants are shown in Table 1. A total of 402 eligible participants with a mean age of 69.05 years were enrolled at the start of the study. Of this number, 52 participants were lost between the first and second follow-up. Of the 350 participants who were fully enrolled in the study and followed up 7.6 years later, 173 were alive (49.43%) while 177 had died (50.57%). We compared the baseline characteristics of the patients with these two outcomes at the time of enrollment and found the following results: The age of the death group was significantly higher than that of the survival group (64.8 vs. 73.3, P < 0.001), and the proportion of patients with coronary heart disease, stroke, and diabetes in the death group was higher than that in the survival group ( P = 0.041, P < 0.001, P = 0.004, respectively). These outcomes indicate that a history of cardiovascular and cerebrovascular disease and diabetes has a substantial impact on the long-term prognosis of patients. Owing to the non-normal distribution of the overall data, we calculated the eGFR of the two groups using the interquartile distance (92.0 vs . 65.0), and the result was significantly higher in the survival group than in the death group ( P < 0.001). We used the same method to calculate the SBP and DBP of the two groups and found that the survival group was significantly lower than the death group (152 vs . 159, P < 0.001; 91 vs . 95, P = 0.003). We divided the patients into three subgroups based on their eGFR values and age (Fig. 1). The eGFRc=1 subgroup was the population without kidney disease, the eGFRc=3 subgroup comprised those with kidney damage, and the eGFRc=2 subgroup consistent of the subclinical population with decreased eGFR. Among the three subgroups, eGFRc=1 had the highest survival rate (85.5%), followed by eGFRc=2 (12.1%), and lastly, eGFRc=3 (2.3%). These findings indicate that kidney function is associated with long-term all-cause death in patients with hypertension. Table 1: Baseline characteristics of study participants Characteristics Survivors n = 173 Death n = 177 P value Demographic Age (SD*) 64.8 (9.3) 73.3 (10.6) ＜0.001 Male, n (%) 77 (44.5%) 76 (42.9%) 0.770 Medical history, n (%) CHD* 74 (42.8%) 95 (53.7%) 0.041 antihypertensive drugs 157 (90.8%) 161 (91.0%) 0.951 stroke 13 (7.5%) 43 (24.3%) ＜0.001 DM* 46 (26.6%) 73 (41.2%) 0.004 Laboratory tests (IQR*) eGFR, mL/min/1.73 m 2 92.0 (77.0, 102.0) 65.0 (43.0, 85.0) ＜0.001 TC*, mg/dL 4.5 (4.0, 5.4) 4.5 (3.9 5.3) 0.45 TG*, mg/dL 1.3 (1.0, 1.9) 1.2 (0.9, 1.8) 0.060 LDL-C, mg/dL 2.8 (2.3, 3.3) 2.7 (2.2, 3.3) 0.18 HDL-C, mg/dL 1.1 (0.9, 1.3) 1.1 (1.0, 1.3) 0.87 Vital signs SBP*, mmHg 152.0 (140.0, 164.0) 159.0 (146.0, 172.0) ＜0.001 DBP*, mmHg 91.0 (83.0, 99.0) 95.0 (87.0, 106.0) 0.003 eGFR, mL/min/1.73 m 2 ＜0.001 1 148 (85.5%) 86 (48.6%) 2 21 (12.1%) 42 (23.7%) 3 4 (2.3%) 49 (27.7%) Abbreviations: *SD, standard deviation; CHD, coronary atherosclerotic heart disease; DM, diabetes mellitus; IQR, interquartile range; TC, total cholesterol; TG, triglyceride; SBP, systolic blood pressure; DBP, diastolic blood pressure. Association of eGFR with survival outcomes Based on the latest literature, 9–11 patients were divided into three subgroups according to age and eGFR value: eGFRc=1, eGFRc=2, and eGFRc=3 (Fig. 1). K–M analyses showed that the low eGFR groups had seriously inferior rates of survival over time. In addition, the survival curve of the eGFRc=3 population decreased the fastest, followed by the eGFRc=2 population, and lastly by the eGFRc=1 population, which had the lowest mortality. A log-rank test with p = 0.000 indicates a close association between the mentioned groups and long-term mortality in patients with hypertension in the long-term follow-up (Fig. 2). The COX proportional risk model was established with eGFRc=1 as reference (Fig. 3). The result showed that lower eGFR groups had significantly lower rates of survival (hazard ratio [HR] eGFRc=2 = 2.407, 95% confidence interval [CI]: 1.663–3.484, P = 0.000; HR eGFRc=3 = 7.081, 95% CI: 4.925–10.179, P = 0.000). From the results, we can see that the combination of age and eGFR grouping can effectively correlate eGFR and outcome. Interestingly, the average risk of death of patients in the eGFRc=3 group is 7.081 times higher than that in the eGFRc=1 group. Relationship between covariates and outcomes We analyze the potential prognostic clinicopathological factors by univariate and multivariate Cox regression and calculate the independent prognostic parameters (age, ACR, CHD, anti-htn drugs, DM, stroke, SBP, DBP) and risk score using univariate Cox regression (Table 2). The results show the following: Age (HR = 1.067, 95% CI: 1.051–1.083), ACR (HR = 1.006, 95% CI: 1.005–1.009), CHD (HR = 1.380, 95% CI: 1.027–1.856), anti-HTN drugs (HR = 0.964, 95% CI: 0.577–1.612), DM (HR = 1.640, 95% CI: 1.215–2.213), stroke (HR = 2.376, 95% CI (1.682–3.357)], SBP (HR = 1.026, 95% CI (1.006–1.028), and DBP (HR = 1.087, 95% CI (1.022–1.035)]. We then adjusted the confounding factors according to the above results. Finally, DM, stroke, age, SBP, DBP, ACR, and eGFRc were included in the prediction model (Table 2) (Figure 4). Since the eGFRc grouping was related to age, age could not be included in the model. The results showed that after adjusting for confounding factors, ACR (HR = 1.006, 95% CI: 1.004–1.008), DM (HR = 1.476, 95% CI: 1.076–2.026), stroke (HR = 1.949, 95% CI: 1.362–2.789), SBP (HR = 1.013, 95% CI: 1.006–1.021), and DBP (HR = 1.011, 95% CI: 1.000–1.022) were still associated with all-cause death outcomes ( P < 0.05). eGFRc=2 and eGFRc=3 still had strong predictive value for outcome, especially eGFRc=2, which exhibited a higher HR after adjusting for confounding factors. This result indicates a very close relationship between subclinical eGFR decline and survival. Table 2: Results of analysis of factors associated with all-cause mortality Time-dependent ROC analysis The results of time-dependent ROC analysis for model 1 revealed that the discriminatory ability of the all-cause mortality was robust. DM, stroke, age, SBP, DBP, and Acr were integrated into nomogram model 1, whose AUC = 0.798; the other model added age-adapted eGFR criteria based on the relevant factors of model 1. The AUC of this new model is 0.827, and its predictive value is significantly higher than that of model 1, P = 0.0105 (Table 3). Table 3: Predictive ability of the age-adapted eGFR criteria for death. Model AUC P value *Model 1 0.798 Ref. Model 1+ age-adapted eGFR criteria 0.827 0.0105 Abbreviations: AUC, area under curve. *Model 1: DM, Stroke, Age, SBP, DBP, ACR. 4. Discussion In this cohort study of 350 patients with hypertension, survival outcomes were measured 7.6 years after enrollment. Results indicate that the lower the eGFR, the higher the mortality. Interestingly, we found that subclinical eGFR decline (i.e., eGFR higher than the current CKD definition but lower than expected for age) was also associated with a higher risk of late death, and that subclinical eGFR decline was more strongly associated with the risk of late death after adjusting for confounding factors. Time-dependent ROC analysis indicated that common clinical laboratory measures plus age-adapted eGFR criteria had very good predictive results. Hypertension is closely related to the pathogenesis of CKD, and it is both a complication and a driving factor of kidney disease progression. 12 The complex relationship between high blood pressure and kidney disease has profound implications for global health. 13 Recent studies 14 have shown no association between simple diastolic hypertension and complex kidney outcomes, complex cardiovascular events, or all-cause mortality. Therefore, patients with simple diastolic hypertension whose SBP is ≥140 mmHg were included in this study, while those with simple diastolic hypertension were not considered. Studies have confirmed that left ventricular diastolic dysfunction also occurs in patients with early CKD, but patients with both CKD and hypertension have a higher prevalence of diastolic and systolic dysfunction than do patients with normal blood pressure. 15 Therefore, patients with early CKD must be aware of hypertension risk. In this study, clinical eGFR decline showed that its association with death outcomes remained strong even after adjusting for confounding factors. This finding suggests that focusing on eGFR decline in the non-elderly population is better than relying on the standard 60 mL/min/1.73 m 2 threshold. High blood pressure can cause hypertrophy of blood vessels, eventually leading to vascular stiffness and fibrosis. Moreover, exposure to high blood pressure at the cellular level leads to changes in biological function and signaling pathways that may persist even after these risk factors are normalized. 15 Existing evidence shows that the prevalence rate of metabolic diseases, nephropathy, and cardiovascular and cerebrovascular diseases is between 30% and 40%, 2 which is consistent with the results of the present study. The overall mortality rate of patients with multiple diseases is also higher than that of other patients. However, because the exact cause of death was not registered at the final follow-up, the reasons for the higher mortality of patients were not supported by data. Heart failure and sudden cardiac death are generally considered to be the primary causes of death in patients with CKD. 16 In addition, it is crucial to consider the significant number of Chinese people infected with the covid-19 virus in 2022, as this has also contributed to an increase in mortality. 5. Conclusion In middle-aged and elderly people, an eGFR lower than age expectations is associated with increased long-term all-cause mortality. The prediction model that uses age-adapted eGFR criteria can accurately predict the long-term survival of these populations. Abbreviations ACR, urinary albumintocreatinine ratio; CHD, history of coronary heart disease; DM, diabetes mellitus SBP, systolic blood pressure; DBP , diastolic blood pressure. Declarations Ethics approval and consent to participate This retrospective cohort study was based on data collected from the “14th Five-Year Plan” Key Research Program database and approved by the Ethics Committee of Tianjin Third Central Hospital (approval number IRB2023-021-01) Consent for publication All authors agree to publish this manuscript Availability of data and materials The datasets generated and/or analyzed during the current study are not publicly available due to the involvement of patients' personal privacy in the original data. However, they are available from the corresponding author upon reasonable request. Competing interests There is no conflict of interest in this study Funding Not applicable Authors' contributions Meng Ning, Chong Zhang and Zhiyuan Li contributed equally to this work and should be considered co-first authors. Ning and Li conceived and designed the project; Zhan collected the clinical samples; all authors wrote and approved the manuscript. Acknowledgements Thank the nurses of Tianjin Third Central Hospital for taking care of the patients. References Wu G, Jose PA, Zeng C. Noncoding RNAs in the regulatory network of hypertension[J]. Hypertension. 2018;72(5):1047–59. 10.1161/HYPERTENSIONAHA.118.11126 . Guo Y, Cui L, Ye P, et al. Change of kidney function is associated with all-cause mortality and cardiovascular diseases: results from the Kailuan study[J]. J Am Heart Assoc. 2018;7(21):e010596. 10.1161/JAHA.118.010596 . Shrestha PL, Shrestha PA, Vivo RP. Epidemiology of comorbidities in patients with hypertension[J]. Curr Opin Cardiol. 2016;31(4):376–80. 10.1097/hco.0000000000000298 . van der Velde M, Matsushita K, Coresh J, et al. Lower estimated glomerular filtration rate and higher albuminuria are associated with all-cause and cardiovascular mortality. A collaborative meta-analysis of high-risk population cohorts[J]. Kidney Int. 2011;79(12):1341–52. 10.1038/ki.2010.536 . Martinez-Quinones P, McCarthy CG, Watts SW, et al. Hypertension-induced morphological and physiological changes in cells of the arterial wall[J]. Am J Hypertens. 2018;31(10):1067–78. 10.1093/ajh/hpy083 . Wang J, Wang R, Qin H, et al. Impaired kidney function portended a bleak prognosis for surgically treated hypertensive intracerebral hemorrhage patients[J]. Ann Indian Acad Neurol. 2023;26(4):520–9. 10.4103/aian.aian_195_23 . Braden GL, Chapman A, Ellison DH, et al. Advancing nephrology: division leaders advise ASN[J]. Clin J Am Soc Nephrol. 2021;16(2):319–27. 10.2215/cjn.01550220 . Balevski I, Burja S, Petreski T, et al. The role of kidney function on patient survival after percutaneous coronary intervention for acute coronary syndrome in left main coronary artery disease[J]. Clin Nephrol. 2021;96(1):1–5. 10.5414/cnp96s01 . Levey AS, de Jong PE, Coresh J et al. The definition, classification, and prognosis of chronic kidney disease: a KDIGO Controversies Conference report[J]. Kidney Int. 2011;80(1);17–28. 10.1038/ki.2010.483 . Hussain J, Imsirovic H, Canney M, et al. Impaired renal function and major cardiovascular events in young adults[J]. J Am Coll Cardiol. 2023;82(13):1316–27. 10.1016/j.jacc.2023.07.012 . Denic A, Glassock RJ. Rule, structural and functional changes with the aging kidney[J]. Adv Chronic Kidney Dis. 2016;23(1):19–28. 10.1053/j.ackd.2015.08.004 . Thomas B, Matsushita K, Abate KH, et al. Global cardiovascular and renal outcomes of reduced GFR[J]. J Am Soc Nephrol. 2017;28(7):2167–79. 10.1681/asn.2016050562 . Bhattacharya A, Rana K, Vanpariya N, et al. Cardiovascular involvement in patients with chronic kidney disease[J]. J Assoc Physicians India. 2022;70(4):11–2. Al Saleh S, Dobre M, DeLozier S, et al. Isolated diastolic hypertension and kidney and cardiovascular outcomes in CKD: the chronic renal insufficiency cohort (CRIC) study[J]. Kidney Med. 2023;5(12):100728. 10.1016/j.xkme.2023.100728 . Coresh J, Turin TC, Matsushita K, et al. Decline in estimated glomerular filtration rate and subsequent risk of end-stage renal disease and mortality[J]. JAMA. 2014;311(24):2518–31. 10.1001/jama.2014.6634 . Thompson S, James M, Wiebe N, et al. Cause of death in patients with reduced kidney function[J]. J Am Soc Nephrol. 2015;26(10):2504–11. 10.1681/asn.2014070714 . Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4268748","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":293381371,"identity":"bd9e8b1c-c1b5-4efe-8752-6a80a295f57f","order_by":0,"name":"Meng Ning","email":"","orcid":"","institution":"The Third Central Hospital of Tianjin","correspondingAuthor":false,"prefix":"","firstName":"Meng","middleName":"","lastName":"Ning","suffix":""},{"id":293381372,"identity":"5a6e175e-df48-45b7-8260-fce5de89faf3","order_by":1,"name":"Chong Zhang","email":"","orcid":"","institution":"The Third Central Clinical College of Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chong","middleName":"","lastName":"Zhang","suffix":""},{"id":293381373,"identity":"68d55e2a-a228-43c0-8091-c5506ccae2ae","order_by":2,"name":"Zhiyuan Li","email":"","orcid":"","institution":"The Third Central Hospital of Tianjin","correspondingAuthor":false,"prefix":"","firstName":"Zhiyuan","middleName":"","lastName":"Li","suffix":""},{"id":293381374,"identity":"b452389c-e6ed-448a-889f-706dcb6169ef","order_by":3,"name":"Kun Hu","email":"","orcid":"","institution":"The Third Central Clinical College of Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Kun","middleName":"","lastName":"Hu","suffix":""},{"id":293381375,"identity":"d381ae74-eb50-477c-b538-7f14cedde575","order_by":4,"name":"Tingting Guo","email":"","orcid":"","institution":"The Third Central Clinical College of Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Tingting","middleName":"","lastName":"Guo","suffix":""},{"id":293381376,"identity":"4b70d115-b73e-4159-ac4c-c7008e039cb2","order_by":5,"name":"Lei Zhang","email":"","orcid":"","institution":"The Third Central Hospital of Tianjin","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Zhang","suffix":""},{"id":293381377,"identity":"1d7c83cd-8f10-4142-a5db-2ec9a82e7ffe","order_by":6,"name":"Yunfang Chen","email":"","orcid":"","institution":"The Third Central Hospital of Tianjin","correspondingAuthor":false,"prefix":"","firstName":"Yunfang","middleName":"","lastName":"Chen","suffix":""},{"id":293381378,"identity":"49b4e992-2030-4f73-84be-6c9c48bbe56f","order_by":7,"name":"Xin Lu","email":"","orcid":"","institution":"The Third Central Hospital of Tianjin","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Lu","suffix":""},{"id":293381380,"identity":"029a6945-c56d-4b6b-9a11-9fa1da0a373c","order_by":8,"name":"Yingwu Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYBACPmYgkQBi8TcwMHxgkCCshQ2uReIAA+MMorQgmAkMzDzEOIyNncdM4uGO2sQNB44/k7b5Y5HH38D88NENvA4Dakk8czxxw+GGZOPcNoliiQNsxsY5BLW0HcvdcODAwce5DRKJDQd42KSJ1JLYcNjij0TifCK11AC1JDM+ZmCTAHqKoBa2YovEtgP1M28cYzbsbZNI3HiYgF/4+Q9vvPmzrc6Y73z7M4kff+oS5x1vfvgYnxYgYAFG32EkPjN+5WAlHxgY6ggrGwWjYBSMgpELANaaS5aLdFisAAAAAElFTkSuQmCC","orcid":"","institution":"The Third Central Hospital of Tianjin","correspondingAuthor":true,"prefix":"","firstName":"Yingwu","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-04-15 09:38:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4268748/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4268748/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55321942,"identity":"5829cf5b-6375-499c-b04d-6438ddf96557","added_by":"auto","created_at":"2024-04-25 16:25:41","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":104144,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePatient grouping diagram and study flow chart\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4268748/v1/726873e6efa13fe86d811ffe.jpeg"},{"id":55321944,"identity":"6f18abed-4f3f-4bc1-8f18-2ea1f8222380","added_by":"auto","created_at":"2024-04-25 16:25:42","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":70920,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e–M survival curves of different eGFR groups at enrollment. The survival curve of the eGFRc=3 population decreased the fastest, followed by the eGFRc=2 population, and lastly, by the eGFRc=1 population, which had the lowest mortality. Log-rank test \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e = 0.000.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4268748/v1/36b6c7f84b1c967bf70ec393.jpeg"},{"id":55321940,"identity":"20f12e60-9cfd-4485-bafd-fdb13d1f93a9","added_by":"auto","created_at":"2024-04-25 16:25:40","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":68815,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCOX proportional risk model with eGFRc=1 as reference\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4268748/v1/f92484439e986f61dade993a.jpeg"},{"id":55323065,"identity":"a1c18d9f-86f4-4c0f-ae6e-2fb10d3dd576","added_by":"auto","created_at":"2024-04-25 16:33:41","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":30575,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCrude model results of analysis of factors associated with all-cause mortality\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4268748/v1/92bd967a9d945f78649bf703.jpeg"},{"id":74981792,"identity":"f2765de1-20f7-4c7c-aba6-5bdc1cf7df13","added_by":"auto","created_at":"2025-01-29 05:01:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1202114,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4268748/v1/b1c75229-2116-4df4-b123-eb48c3f12661.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"eGFR is a risk factor for Long-Term All-Cause Death : a case control study in Middle-Aged and Elderly Patients with Hypertension","fulltext":[{"header":"1. Background","content":"\u003cp\u003eApproximately one-third of the world\u0026rsquo;s population suffers from high blood pressure,\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e which can go unnoticed and pose considerable risks in its early stages. Studies have shown that at present, 30\u0026ndash;40% of patients with hypertension have other chronic diseases, such as those of the heart, brain, and kidney.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e This statistic highlights the harmful nature of hypertension.\u003c/p\u003e \u003cp\u003eCKD is a condition with high incidence and mortality and incurs high medical costs.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e Many studies have confirmed that decreased kidney function will lead to increased mortality in the medium and long term;\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e especially in individuals with an estimated glomerular filtration rate (eGFR)\u0026thinsp;\u0026lt;\u0026thinsp;60 mL/min/1.73 m\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, the morbidity and mortality risk of cardiovascular and cerebrovascular diseases will increase significantly.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e Therefore, the early detection and treatment of kidney function decline is crucial. Current guidelines recommend the use of both urine testing and eGFR to screen and diagnose CKD.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e eGFR values below the threshold of 60 mL/min/1.73 m\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e are generally believed to indicate CKD, as they reflect at least 50% loss of kidney function.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e However, some studies have pointed out that eGFR in the elderly decreases physiologically with age, and simply judging the kidney function of middle-aged and elderly patients with a 60 mL/min/1.73 m\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e threshold will produce the wrong result,\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e thereby increasing the medical burden of the elderly.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eBased on the above reasons, and drawing from the latest research articles on eGFR threshold,\u003csup\u003e\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e we proposed a new method for assessing kidney function in middle-aged and elderly patients with hypertension to predict their long-term survival outcomes.\u003c/p\u003e"},{"header":"2. Method","content":"\u003cp\u003e \u003cb\u003eResearch object\u003c/b\u003e \u003c/p\u003e \u003cp\u003e This retrospective cohort study was based on data collected from the \u0026ldquo;14th Five-Year Plan\u0026rdquo; Key Research Program database and approved by the Ethics Committee of Tianjin Third Central Hospital (approval number IRB2023-021-01). The analysis cohort included patients admitted to Tianjin Third Central Hospital during 2015\u0026ndash;2016 with a history of hypertension or three consecutive SBP readings\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg in the consulting room. Those with a history of kidney disease were excluded from the study. Participation in the study was voluntary, and all the patients were required to sign an informed consent form.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCohort design\u003c/b\u003e \u003c/p\u003e \u003cp\u003ePatients were divided into two groups according to their age: middle-aged (group Y, 40\u0026ndash;64 years old) and elderly (group A, \u0026ge; 65 years old). Patients in group Y were further divided into three groups according to their eGFR at enrollment: Y1 group (eGFR\u0026thinsp;\u0026ge;\u0026thinsp;80 mL/min/1.73 m\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e), Y2 group (80 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026ge;\u0026thinsp;eGFR\u0026thinsp;\u0026ge;\u0026thinsp;60 mL/min/1.73 m\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e), and Y3 group (60 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026ge;\u0026thinsp;eGFR). Patients in group A were similarly divided further into three groups: group A1 (eGFR\u0026thinsp;\u0026ge;\u0026thinsp;60 mL/min/1.73 m\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e), group A2 (60 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026ge;\u0026thinsp;eGFR\u0026thinsp;\u0026ge;\u0026thinsp;45 mL/min/1.73 m\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e), and group A3 (45 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026ge;\u0026thinsp;eGFR) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eDetection index\u003c/b\u003e \u003c/p\u003e \u003cp\u003eHeight, weight, age, sex, medical history, medication history, smoking history, blood lipid, kidney function indicators, serum creatinine, urine routine, eGFR, and systolic and diastolic blood pressure were recorded on the day of enrollment. The trace albumin content of the patients\u0026rsquo; urine was detected on the same day.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFollow-up outcome\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAll patients received a first telephone follow-up one year after enrollment and a second one in 2023. Patient all-cause death was the only endpoint event, and the time of death was recorded. The duration of follow-up was accurately recorded for all patients who received a follow-up.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStatistical analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSTATA 18 was used to conduct statistical analysis for this study. We performed independent sample T-test analysis on the cohort according to patient mortality, as well as subgroup analysis on the patients according to their eGFR at the time of enrollment. Time-dependent COX regression analysis was used to analyze the influence of independent variable changes over time on survival outcomes and plot K\u0026ndash;M survival curves. We performed a univariate analysis of all-cause deaths and then adjusted for confounders for related factors to form a multivariate analysis.\u003c/p\u003e \u003cp\u003eWe conducted univariate and multivariate Cox regression analyses using clinical indicators. Then, integrating the risk score, we established a multivariate regression using a nomogram to predict survival rates over a 7.6-year period. In addition, survival rate was predicted through calibration and time-dependent ROC analysis.\u003c/p\u003e"},{"header":"3. Result","content":"\u003cp\u003eThe baseline characteristics of the study participants are shown in Table 1. A total of 402 eligible participants with a mean age of 69.05 years were enrolled at the start of the study. Of this number, 52 participants were lost between the first and second follow-up. Of the 350 participants who were fully enrolled in the study and followed up 7.6 years later, 173 were alive (49.43%) while 177 had died (50.57%). We compared the baseline characteristics of the patients with these two outcomes at the time of enrollment and found the following results: The age of the death group was significantly higher than that of the survival group (64.8 vs. 73.3, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), and the proportion of patients with coronary heart disease, stroke, and diabetes in the death group was higher than that in the survival group (\u003cem\u003eP\u003c/em\u003e = 0.041, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, \u003cem\u003eP\u003c/em\u003e = 0.004, respectively). These outcomes indicate that a history of cardiovascular and cerebrovascular disease and diabetes has a substantial impact on the long-term prognosis of patients. Owing to the non-normal distribution of the overall data, we calculated the eGFR of the two groups using the interquartile distance (92.0 \u003cem\u003evs\u003c/em\u003e. 65.0), and the result was significantly higher in the survival group than in the death group (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). We used the same method to calculate the SBP and DBP of the two groups and found that the survival group was significantly lower than the death group (152 \u003cem\u003evs\u003c/em\u003e. 159, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001; 91 \u003cem\u003evs\u003c/em\u003e. 95,\u003cem\u003e\u0026nbsp;P\u003c/em\u003e = 0.003).\u003c/p\u003e\n\u003cp\u003eWe divided the patients into three subgroups based on their eGFR values and age (Fig. 1). The eGFRc=1 subgroup was the population without kidney disease, the eGFRc=3 subgroup comprised those with kidney damage, and the eGFRc=2 subgroup consistent of the subclinical population with decreased eGFR. Among the three subgroups, eGFRc=1 had the highest survival rate (85.5%), followed by eGFRc=2 (12.1%), and lastly, eGFRc=3 (2.3%). These findings indicate that kidney function is associated with long-term all-cause death in patients with hypertension.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Baseline characteristics of study participants\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\" valign=\"top\"\u003e\n \u003cp\u003eSurvivors\u003c/p\u003e\n \u003cp\u003en = 173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.489795918367346%\" valign=\"top\"\u003e\n \u003cp\u003eDeath\u003c/p\u003e\n \u003cp\u003en = 177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\"\u003e\n \u003cp\u003eDemographic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.489795918367346%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\"\u003e\n \u003cp\u003eAge (SD*)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e64.8 (9.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.489795918367346%\" valign=\"top\"\u003e\n \u003cp\u003e73.3 (10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\"\u003e\n \u003cp\u003eMale,\u0026nbsp;n\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e77 (44.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.489795918367346%\" valign=\"top\"\u003e\n \u003cp\u003e76 (42.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e0.770\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\"\u003e\n \u003cp\u003eMedical history, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.489795918367346%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\"\u003e\n \u003cp\u003eCHD*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e74 (42.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.489795918367346%\" valign=\"top\"\u003e\n \u003cp\u003e95 (53.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\"\u003e\n \u003cp\u003eantihypertensive drugs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e157 (90.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.489795918367346%\" valign=\"top\"\u003e\n \u003cp\u003e161 (91.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e0.951\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\"\u003e\n \u003cp\u003estroke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e13 (7.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.489795918367346%\" valign=\"top\"\u003e\n \u003cp\u003e43 (24.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\"\u003e\n \u003cp\u003eDM*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e46 (26.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.489795918367346%\" valign=\"top\"\u003e\n \u003cp\u003e73 (41.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\"\u003e\n \u003cp\u003eLaboratory tests (IQR*)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.489795918367346%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\"\u003e\n \u003cp\u003eeGFR, mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e92.0 (77.0, 102.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.489795918367346%\" valign=\"top\"\u003e\n \u003cp\u003e65.0 (43.0, 85.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\"\u003e\n \u003cp\u003eTC*, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e4.5 (4.0, 5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.489795918367346%\" valign=\"top\"\u003e\n \u003cp\u003e4.5 (3.9 5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\"\u003e\n \u003cp\u003eTG*, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e1.3 (1.0, 1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.489795918367346%\" valign=\"top\"\u003e\n \u003cp\u003e1.2 (0.9, 1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\"\u003e\n \u003cp\u003eLDL-C, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e2.8 (2.3, 3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.489795918367346%\" valign=\"top\"\u003e\n \u003cp\u003e2.7 (2.2, 3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\"\u003e\n \u003cp\u003eHDL-C, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e1.1 (0.9, 1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.489795918367346%\" valign=\"top\"\u003e\n \u003cp\u003e1.1 (1.0, 1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\"\u003e\n \u003cp\u003eVital signs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.489795918367346%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\"\u003e\n \u003cp\u003eSBP*, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e152.0 (140.0, 164.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.489795918367346%\" valign=\"top\"\u003e\n \u003cp\u003e159.0 (146.0, 172.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\"\u003e\n \u003cp\u003eDBP*, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e91.0 (83.0, 99.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.489795918367346%\" valign=\"top\"\u003e\n \u003cp\u003e95.0 (87.0, 106.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\"\u003e\n \u003cp\u003eeGFR, mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.489795918367346%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e148 (85.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.489795918367346%\" valign=\"top\"\u003e\n \u003cp\u003e86 (48.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e21 (12.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.489795918367346%\" valign=\"top\"\u003e\n \u003cp\u003e42 (23.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e4 (2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.489795918367346%\" valign=\"top\"\u003e\n \u003cp\u003e49 (27.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e*SD, standard deviation; CHD, coronary atherosclerotic heart disease; DM, diabetes mellitus; IQR, interquartile range; TC, total cholesterol; TG, triglyceride; SBP, systolic blood pressure; DBP, diastolic blood pressure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation of eGFR with survival outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the latest literature,\u003csup\u003e9\u0026ndash;11\u003c/sup\u003e patients were divided into three subgroups according to age and eGFR value: eGFRc=1, eGFRc=2, and eGFRc=3 (Fig. 1). K\u0026ndash;M analyses showed that the low eGFR groups had seriously inferior rates of survival over time. In addition, the survival curve of the eGFRc=3 population decreased the fastest, followed by the eGFRc=2 population, and lastly by the eGFRc=1 population, which had the lowest mortality. A log-rank test with p = 0.000 indicates a close association between the mentioned groups and long-term mortality in patients with hypertension in the long-term follow-up (Fig. 2). The COX proportional risk model was established with eGFRc=1 as reference (Fig. 3). The result showed that lower eGFR groups had significantly lower rates of survival (hazard ratio [HR] eGFRc=2 = 2.407, 95% confidence interval [CI]: 1.663\u0026ndash;3.484, P = 0.000; HR eGFRc=3 = 7.081, 95% CI: 4.925\u0026ndash;10.179, P = 0.000). From the results, we can see that the combination of age and eGFR grouping can effectively correlate eGFR and outcome. Interestingly, the average risk of death of patients in the eGFRc=3 group is 7.081 times higher than that in the eGFRc=1 group.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRelationship between covariates and outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe analyze the potential prognostic clinicopathological factors by univariate and multivariate Cox regression and calculate the independent prognostic parameters (age, ACR, CHD, anti-htn drugs, DM, stroke, SBP, DBP) and risk score using univariate Cox regression (Table 2). The results show the following: Age (HR = 1.067, 95% CI: 1.051\u0026ndash;1.083), ACR (HR = 1.006, 95% CI: 1.005\u0026ndash;1.009), CHD (HR = 1.380, 95% CI: 1.027\u0026ndash;1.856), anti-HTN drugs (HR = 0.964, 95% CI: 0.577\u0026ndash;1.612), DM (HR = 1.640, 95% CI: 1.215\u0026ndash;2.213), stroke (HR = 2.376, 95% CI (1.682\u0026ndash;3.357)], SBP (HR = 1.026, 95% CI (1.006\u0026ndash;1.028), and DBP (HR = 1.087, 95% CI (1.022\u0026ndash;1.035)]. We then adjusted the confounding factors according to the above results. Finally, DM, stroke, age, SBP, DBP, ACR, and eGFRc were included in the prediction model (Table 2) (Figure 4). Since the eGFRc grouping was related to age, age could not be included in the model. The results showed that after adjusting for confounding factors, ACR (HR = 1.006, 95% CI: 1.004\u0026ndash;1.008), DM (HR = 1.476, 95% CI: 1.076\u0026ndash;2.026), stroke (HR = 1.949, 95% CI: 1.362\u0026ndash;2.789), SBP (HR = 1.013, 95% CI: 1.006\u0026ndash;1.021), and DBP (HR = 1.011, 95% CI: 1.000\u0026ndash;1.022) were still associated with all-cause death outcomes (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). eGFRc=2 and eGFRc=3 still had strong predictive value for outcome, especially eGFRc=2, which exhibited a higher HR after adjusting for confounding factors. This result indicates a very close relationship between subclinical eGFR decline and survival.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Results of analysis of factors associated with all-cause mortality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTime-dependent ROC analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results of time-dependent ROC analysis for model 1 revealed that the discriminatory ability of the all-cause mortality was robust. DM, stroke, age, SBP, DBP, and Acr were integrated into nomogram model 1, whose AUC = 0.798; the other model added age-adapted eGFR criteria based on the relevant factors of model 1. The AUC of this new model is 0.827, and its predictive value is significantly higher than that of model 1, \u003cem\u003eP\u003c/em\u003e = 0.0105 (Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: Predictive ability of the age-adapted eGFR criteria for death.\u003c/strong\u003e\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"463\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAUC\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e*Model 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.798\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eModel 1+ age-adapted eGFR criteria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0105\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u0026nbsp;\u003c/strong\u003eAUC, area under curve.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e*Model 1: DM, Stroke, Age, SBP, DBP, ACR.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this cohort study of 350 patients with hypertension, survival outcomes were measured 7.6 years after enrollment. Results indicate that the lower the eGFR, the higher the mortality. Interestingly, we found that subclinical eGFR decline (i.e., eGFR higher than the current CKD definition but lower than expected for age) was also associated with a higher risk of late death, and that subclinical eGFR decline was more strongly associated with the risk of late death after adjusting for confounding factors. Time-dependent ROC analysis indicated that common clinical laboratory measures plus age-adapted eGFR criteria had very good predictive results.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; Hypertension is closely related to the pathogenesis of CKD, and it is both a complication and a driving factor of kidney disease progression.\u003csup\u003e12\u003c/sup\u003e The complex relationship between high blood pressure and kidney disease has profound implications for global health.\u003csup\u003e13\u003c/sup\u003e Recent studies\u003csup\u003e14\u003c/sup\u003e have shown no association between simple diastolic hypertension and complex kidney outcomes, complex cardiovascular events, or all-cause mortality. Therefore, patients with simple diastolic hypertension whose SBP is \u0026ge;140 mmHg were included in this study, while those with simple diastolic hypertension were not considered.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; Studies have confirmed that left ventricular diastolic dysfunction also occurs in patients with early CKD, but patients with both CKD and hypertension have a higher prevalence of diastolic and systolic dysfunction than do patients with normal blood pressure.\u003csup\u003e15\u003c/sup\u003e Therefore, patients with early CKD must be aware of hypertension risk. In this study, clinical eGFR decline showed that its association with death outcomes remained strong even after adjusting for confounding factors. This finding suggests that focusing on eGFR decline in the non-elderly population is better than relying on the standard 60 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e threshold.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; High blood pressure can cause hypertrophy of blood vessels, eventually leading to vascular stiffness and fibrosis. Moreover, exposure to high blood pressure at the cellular level leads to changes in biological function and signaling pathways that may persist even after these risk factors are normalized.\u003csup\u003e15\u003c/sup\u003e Existing evidence shows that the prevalence rate of metabolic diseases, nephropathy, and cardiovascular and cerebrovascular diseases is between 30% and 40%,\u003csup\u003e2\u003c/sup\u003e which is consistent with the results of the present study. The overall mortality rate of patients with multiple diseases is also higher than that of other patients. However, because the exact cause of death was not registered at the final follow-up, the reasons for the higher mortality of patients were not supported by data. Heart failure and sudden cardiac death are generally considered to be the primary causes of death in patients with CKD.\u003csup\u003e16\u003c/sup\u003e In addition, it is crucial to consider the significant number of Chinese people infected with the covid-19 virus in 2022, as this has also contributed to an increase in mortality.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn middle-aged and elderly people, an eGFR lower than age expectations is associated with increased long-term all-cause mortality. The prediction model that uses age-adapted eGFR criteria can accurately predict the long-term survival of these populations.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eACR,\u0026nbsp;\u003c/strong\u003eurinary albumintocreatinine ratio;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCHD,\u0026nbsp;\u003c/strong\u003ehistory of coronary heart disease;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDM,\u0026nbsp;\u003c/strong\u003ediabetes mellitus\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSBP,\u0026nbsp;\u003c/strong\u003esystolic blood pressure;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDBP\u003c/strong\u003e, diastolic blood pressure.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis retrospective cohort study was based on data collected from the \u0026ldquo;14th Five-Year Plan\u0026rdquo; Key Research Program database and approved by the Ethics Committee of Tianjin Third Central Hospital (approval number IRB2023-021-01)\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAll authors agree to publish this manuscript\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available due to the involvement of patients\u0026apos; personal privacy in the original data. However, they are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThere is no conflict of interest in this study\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eMeng Ning, Chong Zhang and Zhiyuan Li contributed\u0026ensp;equally\u0026ensp;to\u0026ensp;this\u0026ensp;work\u0026ensp;and\u0026ensp;should\u0026ensp;be\u0026ensp;considered\u0026ensp;co-first\u0026ensp;authors. Ning and Li conceived and designed the project; Zhan collected the clinical samples; all authors wrote and approved the manuscript.\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eThank the nurses of Tianjin Third Central Hospital for taking care of the patients.\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWu G, Jose PA, Zeng C. Noncoding RNAs in the regulatory network of hypertension[J]. Hypertension. 2018;72(5):1047\u0026ndash;59. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/HYPERTENSIONAHA.118.11126\u003c/span\u003e\u003cspan address=\"10.1161/HYPERTENSIONAHA.118.11126\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo Y, Cui L, Ye P, et al. 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JAMA. 2014;311(24):2518\u0026ndash;31. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jama.2014.6634\u003c/span\u003e\u003cspan address=\"10.1001/jama.2014.6634\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThompson S, James M, Wiebe N, et al. Cause of death in patients with reduced kidney function[J]. J Am Soc Nephrol. 2015;26(10):2504\u0026ndash;11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1681/asn.2014070714\u003c/span\u003e\u003cspan address=\"10.1681/asn.2014070714\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Hypertension, Chronic kidney disease, Forecast, eGFR, Long-term cause of death","lastPublishedDoi":"10.21203/rs.3.rs-4268748/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4268748/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eHypertension combined with CKD is on the rise in middle-aged and elderly people. However, the association of early subclinical decline in kidney function with long-term mortality in these populations remains unclear. In this study, we developed a novel method for evaluating kidney function in middle-aged and elderly patients with hypertension and predicting their long-term survival outcomes based on the thresholds of estimated glomerular filtration rate (eGFR).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u0026nbsp;\u003c/strong\u003eWe constructed a retrospective cohort study with a sample of 350 patients and used time-dependent COX regression analysis to analyze the effect of eGFR threshold changes over time on survival outcomes. Patients were divided into three subgroups based on eGFR values and age (eGFRc=1, eGFRc=2 eGFRc=3). We analyzed the potential prognostic clinicopathological factors via univariate and multivariate Cox regression. A prediction model combined the prognostic clinicopathological factors with age-related eGFRc grouping was builded.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u0026nbsp;\u003c/strong\u003eThe lower eGFR groups had significantly lower rates of survival (hazard ratio [HR] eGFRc=2 = 2.407, 95% confidence interval [CI]: 1.663–3.484, P = 0.000; HR eGFRc=3 = 7.081, 95% CI: 4.925–10.179, P = 0.000). The prediction model combined urinary albumintocreatinine ratio (ACR), Diabetes mellitus (DM), stroke, systolic blood pressure (SBP), diastolic blood pressure (DBP), with age-related eGFRc grouping significantly predicted the long-term survival of patients with hypertension (AUC = 0.827, P = 0.0105).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003eThe model above can be utilized for determining the thresholds of estimated renal function and assessing long-term survival in middle-aged and elderly patients with hypertension.\u003c/p\u003e","manuscriptTitle":"eGFR is a risk factor for Long-Term All-Cause Death : a case control study in Middle-Aged and Elderly Patients with Hypertension","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-25 16:25:17","doi":"10.21203/rs.3.rs-4268748/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8a2799ba-f3a2-4ff0-bd1c-9ae77def2fb0","owner":[],"postedDate":"April 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-29T04:53:32+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-25 16:25:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4268748","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4268748","identity":"rs-4268748","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}