Association of the Trajectories of Metabolic Component and Outcomes in Patients with Chronic Kidney Disease: The National Health Insurance Service-National Health Screening Cohort (NHIS-HEALS) Study

Association of the Trajectories of Metabolic Component and Outcomes in Patients with Chronic Kidney Disease: The National Health Insurance Service-National Health Screening Cohort (NHIS-HEALS) Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association of the Trajectories of Metabolic Component and Outcomes in Patients with Chronic Kidney Disease: The National Health Insurance Service-National Health Screening Cohort (NHIS-HEALS) Study Hyuk Huh, Je Hun Song, Yunmi Kim, Gwang Sil Kim, Hoseok Koo, Kyung Don Yoo, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-959764/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: Chronic kidney disease (CKD) could cause and exacerbate metabolic disturbances, including hypertension and dyslipidemia. Conversely, metabolic disturbances affect renal outcome and mortality in CKD patients. However, studies on the relationship between the pattern of metabolic disturbance and prognosis in CKD during the observation period are lacking. Methods: Through trajectory analysis, we found that subjects with CKD were divided into two groups in a pattern of metabolic disturbances over time. Subjects were divided into low (A) and high (B) groups using K-means clustering based blood pressure, total cholesterol (TC), triglyceride (TG), and low-density lipoprotein (LDL) cholesterol measurement at two time-points. The optimal number of clustering was selected using the Calinski-Harabasz index. The outcome of our study was a decline in renal function and mortality. Results: This study is a large-scale retrospective study of 51,313 subjects with CKD from The National Health Insurance Service-National Health Screening Cohort. The mean age of the subjects was 65.7±9.7 years, and 50.4% were male. During the study period, the mean systolic blood pressure (SBP) was 127.8±15.8 mmHg and diastolic blood pressure (DBP) was maintained at 83.6±8.4 mmHg. Mean serum LDL cholesterol and TG levels were 196.4±40.9 and 147.1±89.8 mg/dL, respectively After clustering, the low group (A group) had the mean SBP of 118.9±10.9mmHg and a TG of 118.8±46.1 mg/dl. However, in the high group (B group), it was found that the mean SBP was maintained at 138.9 ± 13.2 mmHg, and the TG was maintained at was 266.1 ± 116.7 mg/dL. In logistic regression analysis, the high group of SBP was associated with the decline of renal function and increased mortality (odds ratios [OR] 1.13 95%confidence intervals (CI) 1.066-1.212). and the high group of TG was independently associated with a decrease in renal function (OR 1.15, 95% CI 1.069-1.240). Conclusion: The results of this study showed the association of the pattern of metabolic disturbances with the prognosis of CKD over time. Additionally, it could be useful to control intensively SBP for renal outcome and mortality in CKD, and management of high TG could be necessary to improve renal outcome. Urology & Nephrology Chronic kidney disease metabolic component trajectory analysis Figures Figure 1 Figure 2 Introduction Chronic kidney disease (CKD) increases the risk of chronic metabolic illnesses, including hypertension (HTN), diabetes mellitus (DM), and hyperlipidemia [ 1 , 2 ]. CKD also affects renal function deterioration reciprocally [ 3 – 5 ]. According to USRDS annual data report, HTN is the second most common cause of end-stage kidney disease in the US, followed by DM. Similar results of epidemiology has been reported in Korea. [ 6 ]. Prolonged high blood pressure causes a decrease in renal function [ 7 ]. Consequently, the deterioration in kidney function and renal fibrosis lead to increased blood pressure due to the accumulation of salt [ 8 ]. Although it is crucial to control blood pressure to reduce the mortality and incidence of cardiovascular disease in patients with HTN and CKD, the optimal blood pressure level is still controversial [ 9 ]. Obesity is also associated with an increased risk of developing CKD [ 10 , 11 ]. Obesity causes glomerular hyperfiltration [ 12 ], and unlike the general population, a reversal of the obesity-mortality association is observed in the CKD population [ 13 ]. CKD is associated with dyslipidemia from the early stages of microalbuminuria [ 14 ]. As proteinuria increases, total cholesterol (TC), very-low-density lipoprotein, low-density lipoprotein (LDL) cholesterol, and triglycerides (TG) also increase [ 15 ]. However, the ideal cholesterol values, including TG for CKD patients, have not been determined, and debates regarding optimal blood pressure and body mass index (BMI) has been continued. One of the reasons why controversy persists is that the outcomes were inconsistent because of the reverse epidemiology in CKD patient. During the observation period, we tried to find whether these metabolic factors were individually trajected and to determine the effect of these trajectories on the outcome. To evaluate this, we used the National Health Insurance Service-National Health Screening Cohort (NHIS-HEALS) performed over the last four years in South Korea. Materials And Methods The National Health insurance service and National Health screening in Korea South Koreans are obliged to purchase health insurance. Thus, 97% of the population, excluding beneficiaries of the basic national livelihood and national meritorious persons, were covered for universal healthcare service. The National Health Insurance Database of Korea is a vast, informative resource that includes all Koreans who have health insurance. This database contains data on insured medical services, health screenings, and sociodemographic variables. In addition, information on hospital claims with the International Classification of Disease, 10th edition (ICD-10) coding and death was available. Through National Health Screening, Korean adults over the age of 40 were provided free general health screening once every 1-2 years [16, 17]. This study was approved by the Institutional Review Board of the Korea National Institute for Bioethics Policy (No. P01–201603–21-005), and written informed consent was waived, as this is a retrospective analysis of de-identified administrative data [16,17]. Study population We screened adults aged 40 to 79 who underwent health screening from 2010 to 2013. A total of 866,310 screening examinations over four years were verified. Among 866,310 cases of screening, 51,313 patients with chronic kidney disease (CKD) who had undergone 2 and more screening were included final analysis from 2010 to 2013 (Total cases of screening test were 63,537 cases). The CKD is defined as a MDRD estimated GFR of less than 60 mL/min/1.73 m 2 Definition of chronic diseases and study outcomes The CKD was defined as a MDRD estimated GFR of less than 60 mL/min/1.73 m 2 body surface area. DM was defined as fasting plasma glucose levels ≥ 126 mg/dL at measurement or case with diagnostic code (the International Classification of Diseases, 10th Revision [ICD-10] codes E11–14) or prescription of antidiabetic medication. HTN was defined by blood pressure ≥ 140/90 mm Hg or case with diagnostic code (ICD-10 codes I10–I15) or prescription of antihypertensive medication. Dyslipidemia was defined by TC level ≥ 240 mg/dL or case with diagnostic code (ICD-10 code E78) or prescription of antihyperlipidemic medication. Cardiovascular disease was identified when the participant gave an affirmative answer to questionnaire. Cancer was defined by the presence of the specific diagnostic criteria for cancer and diagnosed and certified by a physician. The outcome of our study was all-cause mortality and a decrease in eGFR compared with the first screening between 2010 and 2013. Statistical analysis We divided and clustered participants into group-based trajectory modeling for comparing changeable aspect of metabolic component. For group comparisons, we used the t-test for continuous variables and the χ2 test for proportions. Multivariate logistic regression analysis was applied for relative risk evaluation of all-cause mortality and renal outcomes. Cox regression analysis was used to calculate the hazard ratios (HRs) and 95% CIs for cluster variables. Principal component analysis of multivariate Gaussian distribution was used in exploratory data analysis. The dimension consists of the eigenvectors of the covariance matrix scaled by the square root of the eigenvalue. All variables trajectory modeling was as follows: To categorize the trend of SBP, DBP, TC, TG, LDL, BMI, serum creatinine over time, we applied group-based trajectory modeling using the kml package in R statistics. K-means is a hill-climbing algorithm belonging to the expectation-maximization class. First, each observation was assigned to one cluster. Then, the optimal clustering was completed by the alternation of two phases. (kml and kml3d: R Packages to Cluster Longitudinal Data. 2015, volume 65, issue 4. journal of statistical software) During the expectation phase, the centers between two different clusters were computed. And during the maximization phase, each observation was matched to the nearest cluster. For the initialization method, K-means used random partition. For complexity, K-means analysis used Euclidean distance to assign optimal distance. For the partitioning clustering plot, we used the factoextra package in R statistical software by using the Calinski-Harabasz index. In the resulting plot. All observations are represented by points using principal components. Results Baseline characteristics according to the year of screening from 2010 to 2013 The participants’ average age was 65.7 ± 9.7 years, and 50.4% were male. The history of HTN, DM, and hyperlipidemia was 57.8%, 24.2%, and 9%, respectively. Further, among cardiovascular diseases, 4.1% reported cerebral infarction, and 10.9% had cardiac disease. The mean SBP of the participants were 127.8 ± 15.8 mmHg. Additionally, BMI and waist circumferences were 24.4 ± 3.0 kg/m 2 and 83.6 ± 8.4 cm, respectively. For kidney function, the serum creatinine was 2.4 ± 3.4 mg/dL, and Modification of Diet in Renal Disease (MDRD) eGFR was 46.0 ± 17.6 ml/min/1.73m 2 . From 2010 to 2013, the examinee’s age increased from 63.1±9.8 to 68.6±8.9 years, and the proportion of women gradually increased from 49.6% to 54.0%. The prevalence of HTN (from 51% to 30.9%), DM (from 82.7% to 65.7%), and hyperlipidemia (from 94.3% to 84.4%) showed a gradual decreasing pattern. As metabolic factors, the mean level of LDL and high-density lipoprotein (HDL) cholesterol decreased, and GFR increased. (Table 1). Trajectory analysis of variables with a metabolic component within four years In trajectory analysis, a mean maintenance level of SBP was 138.9±13.2 mmHg in cluster B and 118.9±10.9 mmHg in cluster A ( P <0.001). DBP was maintained at a mean of 84±8.0 mmHg in cluster A, 71.3±7.3 mmHg in cluster B ( P <0.001). In lipid profile, TC (167.5±26.6 mg/dL in cluster A, 223.4±33.0 mg/dL in cluster B, P <0.001), TG (118.8±46.1 mg/dL in cluster A, 266.1±116.7 mg/dL in cluster B, P <0.001), and LDL cholesterol (89.9±24.3 mg/dL in cluster A, 139.7±31.2 mg/dL in cluster B, P <0.001) were lower in cluster A than cluster B. BMI was clustered into five groups from A to E in the order of the number of distributions of the participants. Most participants maintained at 22 – 25.9 (BMI cluster A: 24.0±0.8 kg/m²; B: 25.9±0.9 kg/m²; C:22.1±0.9 kg/m²; D: 28.2±1.1 kg/m²; E: 19.6±1.3 kg/m²; F: 31.9±2.2 kg/m², P <0.001, respectively) and it was 19.6±1.3 kg/m² at a value close to the criterion of underweight in BMI cluster E group. (Fig 1). Difference comparison for cluster groups according to all-cause mortality A total of 307 deaths occurred in clusters A and B from 2010 to 2013. Among the deceased, 233 cases (75.9%) were screened once during the study period. Ten cases (3.3%) had a screening interval of 1 year, 58 cases (18.9%) of 2 years, and six cases (2.0%) of 3 years. In survivors compared to the deceased, the proportion of participants who received screening only once during the study period was low and there were more cases where the screening interval was once a year. The difference in cluster group distribution between the deceased and survivor was compared; among the cluster variables, SBP, DBP, TC, and TG were not significantly different. Proportions of BMI cluster E was more in the deceased (event-free group: Cluster E 9.4%; event group cluster E 20.5%, respectively) (Table 2). Difference comparison for cluster groups according to the decline of eGFR There was no difference in the number of deaths in the GFR decrement group compared to the non-GFR decrement group. The interval between most participants’ health screens was two years (59.2% in the non-GFR decrement group, 62.8% in the GFR decrement group). In the group with decreased renal function, eGFR change was 4.7 ± 6.9 ml/min/1.73m 2 . Among the metabolic components, the proportions of SBP cluster B were 46.9% and 48.5%, respectively, which was higher in the GFR decrement group. The proportions of DBP cluster B were higher in the GFR decrement group (47.7% and 49.3%, respectively). In the GFR decrement group, TG cluster B were 20.9%, which was higher than the non-GFR decrement group. As for other factors, there was no significant difference between the two groups (Table 3). Regression analysis for events using clustering variables In logistic regression analysis, SBP cluster B and BMI cluster E were associated with all-cause mortality (SBP cluster B: OR 1.823, 95% CI 1.066-1.212, P = 0.027; BMI cluster E: OR 2.194, 95% CI 1.071-1.4.387, P = 0.027). Factors related to GFR decline included SBP cluster B (HR 1.130, 95% CI 1.066-1.212, P < 0.001), DBP cluster B (HR 1.105, 95% CI 1.036-1.178, P = 0.002), TG cluster A (OR 1.151, 95% CI 1.069-1.240, P < 0.001) showed a significant correlation (Table 4). In multivariable Cox regression analysis, it was not observed to increase in the hazard ratios in cluster B group according to the metabolic component except for BMI (Fig 2). However, we found a lower risk of mortality (HR 0.35, 95% CI 0.15-0.83) in the cluster D (BMI 28.2±1.1 kg/m²) compared to the cluster A (24.0±0.8 kg/m²) (Table 4). Discussion Our study is an observational study using data from the large-scale National Health Insurance Service-National Health Screening Cohort. This study showed the importance of controlling chronic diseases, including HTN and dyslipidemia, by performing trajectory cluster analysis on data obtained through continuous measurement of metabolic factors in the CKD patient group. Blood pressure control in the CKD patient group is important for preventing CKD progression and mortality. However, controversy remains about the optimal blood pressure that can prevent renal function decline and improve mortality in CKD patients. Furthermore, most randomized controlled trials (RCTs) about blood pressure control have limitations that exclude or include only a small number of patients with CKD. In terms of mortality, results of the International Verapamil-Trandolapril Study suggest a possible J-curve relationship between blood pressure and mortality through previous observational studies [ 18 ]. The Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial in patients with Type 2 DM showed no improvement in all-cause mortality with the intensive control of SBP (< 120 mmHg) [ 19 ]. In a study including 77,765 CKD patients, the effect of intensive treatment compared to standard treatment (SBP<120 vs 120–139 mmHg) was not proven [ 20 ]. However, diabetic nephropathy patients with an SBP of 120 mmHg had the most protective cardiovascular event in the Irbesartan Diabetic Nephropathy Trial [ 21 ]. In terms of renal outcomes, studies on the effect of blood pressure control on renal function also show mixed results. Two representative studies for blood pressure control in CKD (the MDRD study and African-American Study of Kidney Disease and Hypertension (AASK) study) did not demonstrate an improvement in CKD progression through intensive control [ 22 , 23 ]. A meta-analysis and Renin-2 trial also reported that intensive control did not provide additional benefit for renal outcomes for CKD progression [ 24 , 25 ]. However, the advantage of intensive treatment was highlighted through the SPRINT trial. [ 26 ]. The SPRINT Research Group reported that major cardiovascular events and all-cause death decreased in the intensive control group through subgroup analysis of 2,646 CKD patients [ 27 ]. In 2019, pooled analyses from 4 RCTs, including AASK, ACCORD, MDRD and the SPRINT, have been published demonstrating mortality benefits of SBP < 130 mmHg in CKD patients [ 28 ]. In the guideline for the management of blood pressure in CKD announced by KDIGO in 2021, it is suggested to control SBP < 120 mmHg if tolerable (2B) [ 29 ]. The mean SBP of the cohort participants included in our study was 127.8 ± 15.8 mmHg, which was well controlled compared to the standard concept. The cluster A group, controlled to mean 118.9±10.9 mmHg, showed a lower risk of mortality and better renal outcome through trajectory analysis. This is consistent with the recently accepted trend in which the benefits of intensive control are highlighted. Hyperlipidemia is a major risk factor for cardiovascular disease, and TG and HDL cholesterol are evaluated for metabolic syndrome. LDL cholesterol is the main therapeutic target for secondary prevention of atherosclerotic cardiovascular disease. CKD patients were classified as a high-risk group in the 2019 guideline of dyslipidemia in the European Society of Cardiology [ 30 ]. Dyslipidemia is associated with a decrease in renal function [ 31 , 32 ]. In CKD patients, an increase in TG and LDL is commonly observed, and multiple lipid-lowering agents are often required [ 33 ]. Although it is emphasized to identify the lipid profile in CKD patients, the main therapeutic target is LDL cholesterol. In the SHARP trial, using a lipid-lowering agent could not reduce the risk of ESRD, nor did it reduce major atherosclerotic events in dialysis-dependent patients. [ 34 ]. In our study, the TG cluster A group (maintained the mean 118 ± 46.1 mg/dL) had a better renal outcome than the TG cluster B group with hypertriglyceridemia. This finding is consistent with the results of other cohort studies showing the need for control of hypertriglyceridemia in CKD patients [ 35 ]. Reverse epidemiology of the obesity paradox has been observed in chronic disease patients, including CKD patients [ 36 , 37 ]. As observed in the general population, it is also known to increase the mortality rate of patients with low BMI. Low weight in CKD patients, including those dependent on dialysis, is closely related to protein-energy wasting syndrome [ 38 , 39 ], a major cause of increased mortality [ 40 ]. Although most of the participants in our study maintained a BMI of 22-24 kg/m 2 , the risk of death in the cluster E group increased. This result suggests that it is important to improve the nutritional status of patients with low body weight. In cox regression analysis, our results showed that the mortality risk of cluster D was lower than those of cluster A. These findings are thought to reflect reverse epidemiology of the obesity paradox. Korean citizens are registered for mandatory to health insurance and could have health screen every 1-2years. Through database containing many subjects, many studies have been conducted. Among the representative studies on metabolic syndrome, there is a study that showed the relationship between changes in metabolic syndrome status and prognosis [ 41 ]. However, the effect of changes in individual factors of metabolic syndrome was not confirmed. We used Korean large-scale cohort database. Trajectory analysis was performed for each item of metabolic syndrome, and the effect of trajectory factors on the outcome of CKD was confirmed. Our study has some limitations. As a retrospective observational study, there is a possibility that the selection and lead-time bias of participants in the cohort may be involved. Additional confounding factors, such as differences in drugs taken by participants and the causative disease of CKD, may remain in relation to the mortality and renal outcome. Conclusion In conclusion, prolonged hypertension, glucose, hyperlipidemia have negative impacts on GFR confirmed by trajectory analysis. Cluster B has lower GFR than cluster A; this is caused by patients with chronic diseases in cluster B having not been controlled over four years as values of BMI, SBP and DBP. To not affect the function of the kidney, chronic diseases including HTN, DM, and hyperlipidemia need to be effectively controlled. Abbreviations ACCORD Action to Control Cardiovascular Risk in Diabetes CI Confidence intervals CKD Chronic kidney disease DM Diabetes mellitus DBP Diastolic Blood Pressure ESKD End-stage kidney disease GFR Glomerular filtration rate HR Hazard ratios HTN Hypertension INVEST International Verapamil-Trandolapril Study KORDS Korean Renal Data System LDL Low-density lipoprotein MDRD Modification of Diet in Renal Disease NHIS National Health Screening PCA Partitioning component of analysis RCT Randomized controlled trials SBP Systolic and diastolic blood pressure TC Total cholesterol VLDL Very-low-density lipoprotein AASK African-American Study of Kidney Disease and Hypertension study BMI Body mass index ESKD End stage kidney disease HDL High density lipoprotein KDIGO The Kidney Disease: Improving Global Outcomes SHARP Study of Heart and Renal Protection SPRINT Systolic Blood Pressure Intervention Trial USRDS United States Renal Data System Declarations Ethics approval and consent to participate This study complied with the Declaration of Helsinki and was approved by the Institutional Review Board of the Korea National Institute for Bioethics Policy (No. P01–201603–21-005), and written informed consent was waived, as this is a retrospective analysis of de-identified administrative data. Consent for publication N/A Availability of data and materials The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests None Funding None Authors’ contributions Research idea and study design: KDY, HK; data acquisition: HH, JHS, GSK, KDY, YK; data analysis/interpretation: HH, JHS, HK, KDY, Each author contributed important intellectual content during manuscript drafting. All authors read and approved the final manuscript. Acknowledgements The data have not been published elsewhere except a poster presentation at the ERA-EDTA Congress (June 13-16, 2019 - Budapest, Hungary), and ASN kidney week (November 5-9, Washington). References Ferro CJ, Mark PB, Kanbay M, Sarafidis P, Heine GH, Rossignol P, Massy ZA, Mallamaci F, Valdivielso JM, Malyszko J et al : Lipid management in patients with chronic kidney disease . Nat Rev Nephrol 2018, 14 (12):727–749. Tsimihodimos V, Mitrogianni Z, Elisaf M: Dyslipidemia associated with chronic kidney disease . Open Cardiovasc Med J 2011, 5 :41–48. Tsai WC, Wu HY, Peng YS, Ko MJ, Wu MS, Hung KY, Wu KD, Chu TS, Chien KL: Risk Factors for Development and Progression of Chronic Kidney Disease: A Systematic Review and Exploratory Meta-Analysis . Medicine (Baltimore) 2016, 95 (11):e3013. Taal MW, Brenner BM: Predicting initiation and progression of chronic kidney disease: Developing renal risk scores . Kidney Int 2006, 70 (10):1694–1705. Tozawa M, Iseki K, Iseki C, Kinjo K, Ikemiya Y, Takishita S: Blood pressure predicts risk of developing end-stage renal disease in men and women . Hypertension 2003, 41 (6):1341–1345. Hong YA, Ban TH, Kang CY, Hwang SD, Choi SR, Lee H, Jung HY, Kim K, Kwon YE, Kim SH et al : Trends in epidemiologic characteristics of end-stage renal disease from 2019 Korean Renal Data System (KORDS) . Kidney Res Clin Pract 2021, 40 (1):52–61. Son HE, Ryu JY, Go S, Yi Y, Kim K, Oh YK, Oh KH, Chin HJ: Association of ambulatory blood pressure monitoring with renal outcome in patients with chronic kidney disease . Kidney Res Clin Pract 2020, 39 (1):70–80. Wright JA, Cavanaugh KL: Dietary sodium in chronic kidney disease: a comprehensive approach . Semin Dial 2010, 23 (4):415–421. Reynolds K, Gu D, Muntner P, Kusek JW, Chen J, Wu X, Duan X, Chen CS, Klag MJ, Whelton PK et al : A population-based, prospective study of blood pressure and risk for end-stage renal disease in China . J Am Soc Nephrol 2007, 18 (6):1928–1935. Kramer H, Luke A, Bidani A, Cao G, Cooper R, McGee D: Obesity and prevalent and incident CKD: the Hypertension Detection and Follow-Up Program . Am J Kidney Dis 2005, 46 (4):587–594. Foster MC, Hwang SJ, Larson MG, Lichtman JH, Parikh NI, Vasan RS, Levy D, Fox CS: Overweight, obesity, and the development of stage 3 CKD: the Framingham Heart Study . Am J Kidney Dis 2008, 52 (1):39–48. Henegar JR, Bigler SA, Henegar LK, Tyagi SC, Hall JE: Functional and structural changes in the kidney in the early stages of obesity . J Am Soc Nephrol 2001, 12 (6):1211–1217. Adams KF, Schatzkin A, Harris TB, Kipnis V, Mouw T, Ballard-Barbash R, Hollenbeck A, Leitzmann MF: Overweight, obesity, and mortality in a large prospective cohort of persons 50 to 71 years old . N Engl J Med 2006, 355 (8):763–778. Jones SL, Close CF, Mattock MB, Jarrett RJ, Keen H, Viberti GC: Plasma lipid and coagulation factor concentrations in insulin dependent diabetics with microalbuminuria . Bmj 1989, 298 (6672):487–490. Marre M, Bouhanick B, Berrut G: Microalbuminuria . Curr Opin Nephrol Hypertens 1994, 3 (5):558–563. Cheol Seong S, Kim YY, Khang YH, Heon Park J, Kang HJ, Lee H, Do CH, Song JS, Hyon Bang J, Ha S et al : Data Resource Profile: The National Health Information Database of the National Health Insurance Service in South Korea . Int J Epidemiol 2017, 46 (3):799–800. Seong SC, Kim YY, Park SK, Khang YH, Kim HC, Park JH, Kang HJ, Do CH, Song JS, Lee EJ et al : Cohort profile: the National Health Insurance Service-National Health Screening Cohort (NHIS-HEALS) in Korea . BMJ Open 2017, 7 (9):e016640. Messerli FH, Mancia G, Conti CR, Hewkin AC, Kupfer S, Champion A, Kolloch R, Benetos A, Pepine CJ: Dogma disputed: can aggressively lowering blood pressure in hypertensive patients with coronary artery disease be dangerous? Ann Intern Med 2006, 144 (12):884–893. Cushman WC, Evans GW, Byington RP, Goff DC, Jr., Grimm RH, Jr., Cutler JA, Simons-Morton DG, Basile JN, Corson MA, Probstfield JL et al : Effects of intensive blood-pressure control in type 2 diabetes mellitus . N Engl J Med 2010, 362 (17):1575–1585. Kovesdy CP, Lu JL, Molnar MZ, Ma JZ, Canada RB, Streja E, Kalantar-Zadeh K, Bleyer AJ: Observational modeling of strict vs conventional blood pressure control in patients with chronic kidney disease . JAMA Intern Med 2014, 174 (9):1442–1449. Berl T, Hunsicker LG, Lewis JB, Pfeffer MA, Porush JG, Rouleau JL, Drury PL, Esmatjes E, Hricik D, Pohl M et al : Impact of achieved blood pressure on cardiovascular outcomes in the Irbesartan Diabetic Nephropathy Trial . J Am Soc Nephrol 2005, 16 (7):2170–2179. Wright JT, Jr., Bakris G, Greene T, Agodoa LY, Appel LJ, Charleston J, Cheek D, Douglas-Baltimore JG, Gassman J, Glassock R et al : Effect of blood pressure lowering and antihypertensive drug class on progression of hypertensive kidney disease: results from the AASK trial . Jama 2002, 288 (19):2421–2431. Klahr S, Levey AS, Beck GJ, Caggiula AW, Hunsicker L, Kusek JW, Striker G: The effects of dietary protein restriction and blood-pressure control on the progression of chronic renal disease. Modification of Diet in Renal Disease Study Group . N Engl J Med 1994, 330 (13):877–884. Tsai WC, Wu HY, Peng YS, Yang JY, Chen HY, Chiu YL, Hsu SP, Ko MJ, Pai MF, Tu YK et al : Association of Intensive Blood Pressure Control and Kidney Disease Progression in Nondiabetic Patients With Chronic Kidney Disease: A Systematic Review and Meta-analysis . JAMA Intern Med 2017, 177 (6):792–799. Ruggenenti P, Perna A, Loriga G, Ganeva M, Ene-Iordache B, Turturro M, Lesti M, Perticucci E, Chakarski IN, Leonardis D et al : Blood-pressure control for renoprotection in patients with non-diabetic chronic renal disease (REIN-2): multicentre, randomised controlled trial . Lancet 2005, 365 (9463):939–946. Wright JT, Jr., Williamson JD, Whelton PK, Snyder JK, Sink KM, Rocco MV, Reboussin DM, Rahman M, Oparil S, Lewis CE et al : A Randomized Trial of Intensive versus Standard Blood-Pressure Control . N Engl J Med 2015, 373 (22):2103–2116. Cheung AK, Rahman M, Reboussin DM, Craven TE, Greene T, Kimmel PL, Cushman WC, Hawfield AT, Johnson KC, Lewis CE et al : Effects of Intensive BP Control in CKD . J Am Soc Nephrol 2017, 28 (9):2812–2823. Aggarwal R, Petrie B, Bala W, Chiu N: Mortality Outcomes With Intensive Blood Pressure Targets in Chronic Kidney Disease Patients . Hypertension 2019, 73 (6):1275–1282. Tomson CRV, Cheung AK, Mann JFE, Chang TI, Cushman WC, Furth SL, Hou FF, Knoll GA, Muntner P, Pecoits-Filho R et al : Management of Blood Pressure in Patients With Chronic Kidney Disease Not Receiving Dialysis: Synopsis of the 2021 KDIGO Clinical Practice Guideline . Ann Intern Med 2021. 2019 ESC/EAS guidelines for the management of dyslipidaemias: Lipid modification to reduce cardiovascular risk . Atherosclerosis 2019, 290 :140–205. Mänttäri M, Tiula E, Alikoski T, Manninen V: Effects of hypertension and dyslipidemia on the decline in renal function . Hypertension 1995, 26 (4):670–675. Hunsicker LG, Adler S, Caggiula A, England BK, Greene T, Kusek JW, Rogers NL, Teschan PE: Predictors of the progression of renal disease in the Modification of Diet in Renal Disease Study . Kidney Int 1997, 51 (6):1908–1919. Harper CR, Jacobson TA: Managing dyslipidemia in chronic kidney disease . J Am Coll Cardiol 2008, 51 (25):2375–2384. Ahmed MH, Khalil AA: Ezetimibe as a potential treatment for dyslipidemia associated with chronic renal failure and renal transplant . Saudi J Kidney Dis Transpl 2010, 21 (6):1021–1029. Liang X, Ye M, Tao M, Zheng D, Cai R, Zhu Y, Jin J, He Q: The association between dyslipidemia and the incidence of chronic kidney disease in the general Zhejiang population: a retrospective study . BMC Nephrol 2020, 21 (1):252. Kovesdy CP, Anderson JE: Reverse epidemiology in patients with chronic kidney disease who are not yet on dialysis . Semin Dial 2007, 20 (6):566–569. Kalantar-Zadeh K, Block G, Humphreys MH, Kopple JD: Reverse epidemiology of cardiovascular risk factors in maintenance dialysis patients . Kidney Int 2003, 63 (3):793–808. Young P, Lombi F, Finn BC, Forrester M, Campolo-Girard V, Pomeranz V, Iriarte R, Bruetman JE, Trimarchi H: ["Malnutrition-inflammation complex syndrome" in chronic hemodialysis] . Medicina (B Aires) 2011, 71 (1):66–72. Kalantar-Zadeh K, Kopple JD, Block G, Humphreys MH: A malnutrition-inflammation score is correlated with morbidity and mortality in maintenance hemodialysis patients . Am J Kidney Dis 2001, 38 (6):1251–1263. Kovesdy CP, Kalantar-Zadeh K: Why is protein-energy wasting associated with mortality in chronic kidney disease? Semin Nephrol 2009, 29 (1):3–14. Park S, Lee S, Kim Y, Lee Y, Kang MW, Han K, Han SS, Lee H, Lee JP, Joo KW et al : Altered Risk for Cardiovascular Events With Changes in the Metabolic Syndrome Status: A Nationwide Population-Based Study of Approximately 10 Million Persons . Ann Intern Med 2019, 171 (12):875–884. Tables Table 1 Baseline characteristics according to the year of screening from 2010 to 2013 Total 2010 2011 2012 2013 P (N=63537) (N=18772) (N=17282) (N=13283) (N=14200) AGE (years) 65.7 ± 9.7 63.1 ± 9.8 65.4 ± 9.6 66.6 ± 9.3 68.6 ± 8.9 <0.001 Sex (%) <0.001 Male 32030 (50.4%) 10136 (54.0%) 8758 (50.7%) 6598 (49.7%) 6538 (46.0%) Female 31507 (49.6%) 8636 (46.0%) 8524 (49.3%) 6685 (50.3%) 7662 (54.0%) Smoking history <0.001 Non-smoker 42812 (67.9%) 12069 (65.3%) 11584 (67.4%) 9050 (68.3%) 10109 (71.3%) Ex-smoker 12800 (20.3%) 3847 (20.8%) 3577 (20.8%) 2700 (20.4%) 2676 (18.9%) Current smoker 7470 (11.8%) 2554 (13.8%) 2027 (11.8%) 1494 (11.3%) 1395 (9.8%) Alcohol history 1.7 ± 1.4 1.8 ± 1.5 1.7 ± 1.4 1.6 ± 1.4 1.6 ± 1.3 <0.001 Hypertension History <0.001 No 22184 (42.2%) 8209 (51.9%) 6640 (45.2%) 3792 (35.6%) 3543 (30.9%) Yes 30416 (57.8%) 7593 (48.1%) 8039 (54.8%) 6848 (64.4%) 7936 (69.1%) Diabetes mellitus History <0.001 No 36666 (75.8%) 13012 (82.7%) 11396 (78.8%) 6097 (69.1%) 6161 (65.7%) Yes 11735 (24.2%) 2724 (17.3%) 3067 (21.2%) 2726 (30.9%) 3218 (34.3%) Hyperlipidemia History <0.001 No 42425 (91.0%) 14835 (94.3%) 13385 (93.1%) 7038 (87.7%) 7167 (84.4%) Yes 4190 (9.0%) 891 (5.7%) 990 (6.9%) 986 (12.3%) 1323 (15.6%) Total cholesterol (mg/dL) 196.4 ± 40.9 199.8 ± 40.2 196.6 ± 40.3 194.7 ± 41.1 193.5 ± 42.1 <0.001 Triglycerides (mg/dL) 147.1 ± 89.8 150.3 ± 97.1 145.2 ± 86.6 146.2 ± 88.2 145.9 ± 85.0 <0.001 HDL cholesterol (mg/dL) 53.9 ± 36.3 59.1 ± 61.0 51.9 ± 14.7 52.2 ± 20.1 51.0 ± 16.6 <0.001 LDL cholesterol (mg/dL) 115.5 ± 37.2 118.3 ± 37.6 115.5 ± 36.3 113.9 ± 37.1 113.5 ± 37.5 <0.001 Cerebral infarction History <0.001 44054 (95.9%) 15264 (97.2%) 13839 (96.4%) 7356 (94.7%) 7595 (93.7%) 1876 (4.1%) 445 (2.8%) 510 (3.6%) 410 (5.3%) 511 (6.3%) Heart disease History <0.001 41557 (89.1%) 14497 (92.2%) 12994 (90.3%) 6954 (85.9%) 7112 (84.0%) 5103 (10.9%) 1220 (7.8%) 1395 (9.7%) 1137 (14.1%) 1351 (16.0%) BMI (kg/m 2 ) 24.4 ± 3.0 24.3 ± 3.0 24.4 ± 3.0 24.4 ± 3.1 24.4 ± 3.1 <0.001 Waist (cm) 83.6 ± 8.4 83.4 ± 8.2 83.6 ± 8.3 83.6 ± 8.7 83.8 ± 8.6 <0.001 SBP (mmHg) 127.8 ± 15.8 127.3 ± 15.4 128.2 ± 16.0 128.1 ± 16.0 127.8 ± 15.6 0.003 DBP (mmHg) 77.6 ± 10.0 78.0 ± 9.9 77.8 ± 10.0 77.6 ± 10.2 77.0 ± 10.0 <0.001 FBS (mg/dL) 106.1 ± 31.0 104.6 ± 29.4 105.6 ± 29.9 106.8 ± 31.4 108.1 ± 33.9 <0.001 Hemoglobin (g/dL) 13.4 ± 1.7 13.6 ± 1.7 13.4 ± 1.7 13.3 ± 1.7 13.2 ± 1.7 <0.001 Creatinine (mg/dL) 2.4 ± 3.4 3.3 ± 4.0 2.7 ± 3.7 1.8 ± 2.7 1.6 ± 2.2 <0.001 MDRD eGFR 46.0 ± 17.6 41.4 ± 21.1 44.2 ± 19.1 49.9 ± 13.0 50.5 ± 11.6 <0.001 HDL, high density lipoprotein; LDL, low density lipoprotein; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; FBS, fasting blood sugar; eGFR. estimated glomerular filtration rate Table 2 Difference comparison for cluster groups according to all-cause mortality Survivor Deceased P (N=63230) (N=307) Interval of screening 0.000 Once screened 39210 (62.0%) 233 (75.9%) 1 year 6056 (9.6%) 10 (3.3%) 2 years 14789 (23.4%) 58 (18.9%) 3 years 3175 (5.0%) 6 (2.0%) SBP clusters 0.161 A 12503 (52.1%) 32 (43.2%) B 11509 (47.9%) 42 (56.8%) DBP clusters 0.744 A 12300 (51.2%) 36 (48.6%) B 11712 (48.8%) 38 (51.4%) TC clusters 0.918 A 12963 (54.0%) 39 (52.7%) B 11053 (46.0%) 35 (47.3%) TG clusters 0.471 A 19111 (79.7%) 62 (83.8%) B 4858 (20.3%) 12 (16.2%) LDL clusters 0.954 A 12305 (53.8%) 40 (54.8%) B 10580 (46.2%) 33 (45.2%) BMI clusters 0.007 A 6750 (28.1%) 19 (26.0%) B 5725 (23.8%) 14 (19.2%) C 5378 (22.4%) 20 (27.4%) D 3161 (13.2%) 3 (4.1%) E 2247 (9.4%) 15 (20.5%) F 754 (3.1%) 2 (2.7%) SBP, systolic blood pressure; DBP, diastolic blood pressure; TC, total cholesterol; TG, triglyceride; LDL, low density lipoprotein; BMI, body mass index Table 3 Difference comparison for cluster groups according to the decline of eGFR ` No decrement (N=7803) Decrement (N=16291) P Death 0.540 Survivor 7782 (99.7%) 16238 (99.7%) Deceased 21 (0.3%) 53 (0.3%) Change of eGFR (ml/min/1.73m 2 ) -8.5 ± 11.8 4.7 ± 6.9 0.000 Interval of screening 0.000 1 year 2134 (27.3%) 3932 (24.1%) 2 years 4621 (59.2%) 10226 (62.8%) 3 years 1048 (13.4%) 2133 (13.1%) SBP clusters 0.023 A 4143 (53.1%) 8392 (51.5%) B 3658 (46.9%) 7893 (48.5%) DBP clusters 0.025 A 4077 (52.3%) 8259 (50.7%) B 3724 (47.7%) 8026 (49.3%) TC clusters 0.392 A 4180 (53.6%) 8822 (54.2%) B 3623 (46.4%) 7465 (45.8%) TG clusters 0.001 A 6300 (81.0%) 12873 (79.1%) B 1474 (19.0%) 3396 (20.9%) LDL clusters 0.650 A 3881 (54.0%) 8464 (53.7%) B 3306 (46.0%) 7307 (46.3%) BMI_clusters 0.901 A 2223 (28.5%) 4546 (27.9%) B 1866 (23.9%) 3873 (23.8%) C 1727 (22.1%) 3671 (22.5%) D 1028 (13.2%) 2136 (13.1%) E 720 (9.2%) 1542 (9.5%) F 239 (3.1%) 517 (3.2%) SBP, systolic blood pressure; DBP, diastolic blood pressure; TC, total cholesterol; TG, triglyceride; LDL, low density lipoprotein; BMI, body mass index; eGFR. estimated glomerular filtration rate Table 4 Logistic regression analysis for events using clustering variables Death beta OR p -value 95%CI SBP clusters B (Ref. cluster A) 0.601 1.823 0.027 1.066~1.212 DBP clusters B (Ref. cluster A) 0.283 1.327 0.294 0.780~2.253 TC clusters B (Ref. cluster A) 0.272 1.312 0.442 0.893~1.055 TG clusters A (Ref. cluster B) 0.202 1.223 0.553 0.652~2.511 LDL clusters A (Ref. cluster B) 0.194 1.214 0.581 0.611~2.417 BMI Ref. cluster A BMI clusters B -0.153 0.858 0.664 0.909~1.063 BMI clusters C 0.270 1.310 0.401 0.694~2.479 BMI clusters D -1.117 0.327 0.072 0.889~1.072 BMI clusters E 0.785 2.194 0.027 1.071~4.387 BMI clusters F -0.132 0.876 0.859 0.851~1.191 GFR decline beta OR p -value 95%CI SBP clusters B (Ref. cluster A) 0.128 1.130 <0.001 1.066~1.212 DBP clusters B (Ref. cluster A) 0.100 1.105 0.002 1.036~1.178 TC clusters B (Ref. cluster A) 0.029 1.029 0.492 0.947~1.119 TG clusters A (Ref. cluster B) 0.141 1.151 <0.001 1.069~1.240 LDL clusters A (Ref. cluster B) 0.045 1.046 0.279 0.963~1.136 BMI Ref. cluster A BMI clusters B -0.016 0.983 0.680 0.909~1.063 BMI clusters C 0.027 1.027 0.680 0.949~1.112 BMI clusters D -0.023 0.976 0.618 0.889~1.072 BMI clusters E 0.038 1.039 0.474 0.935~1.155 BMI clusters F 0.005 1.005 0.945 0.851~1.191 SBP, systolic blood pressure; DBP, diastolic blood pressure; TC, total cholesterol; TG, triglyceride; LDL, low density lipoprotein; BMI, body mass index; eGFR. estimated glomerular filtration rate 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-959764","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":58406715,"identity":"753cb17d-bb5a-4436-b091-a26a84170952","order_by":0,"name":"Hyuk Huh","email":"","orcid":"","institution":"Seoul National University Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hyuk","middleName":"","lastName":"Huh","suffix":""},{"id":58406716,"identity":"39c6457d-6940-44a6-8b52-a6a21fbafc7b","order_by":1,"name":"Je Hun Song","email":"","orcid":"","institution":"University of Ulsan College of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Je","middleName":"Hun","lastName":"Song","suffix":""},{"id":58406719,"identity":"cd180f3f-40eb-4d0a-812e-ec0f63b86922","order_by":2,"name":"Yunmi Kim","email":"","orcid":"","institution":"Inje University Busan Paik Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yunmi","middleName":"","lastName":"Kim","suffix":""},{"id":58406721,"identity":"f0734478-ccfe-4f48-bd33-8c9d3fafaae2","order_by":3,"name":"Gwang Sil Kim","email":"","orcid":"","institution":"Inje University College of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Gwang","middleName":"Sil","lastName":"Kim","suffix":""},{"id":58406723,"identity":"23fa3000-31f7-4abb-a6c9-8a3ad0b4a973","order_by":4,"name":"Hoseok Koo","email":"","orcid":"","institution":"Inje University College of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hoseok","middleName":"","lastName":"Koo","suffix":""},{"id":58406726,"identity":"ec501f98-cd61-467f-90f0-aacd2ecd8dd8","order_by":5,"name":"Kyung Don Yoo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYDACHgbDBwkVNnIg9oEHRGoxNvhwJs0YrCWBSC1mkjNbDic2gDhEaTE4c3ibNG8Dc/r8sMMPgbbYyek2ENJytq3YmncHW+7G22kGQC3JxmYHCGk5z2N4m/cMT+7G2QkgLQcStxGhxUCat00i3XB2+gcitZztMZKc2WaQIC+dQ6QtkmeOFQMDOcFwg3ROwYEEAyL8wncmeSMwKv/Ly89O3/zhQ4WdHEEtCjAFBmCGAQHlICDfgM4YBaNgFIyCUYAOAKw/S7zS/faOAAAAAElFTkSuQmCC","orcid":"","institution":"University of Ulsan College of Medicine","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Kyung","middleName":"Don","lastName":"Yoo","suffix":""},{"id":58406727,"identity":"5949552f-5a2c-4ea5-a983-29164c936cab","order_by":6,"name":"Jong Soo Lee","email":"","orcid":"","institution":"University of Ulsan College of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jong","middleName":"Soo","lastName":"Lee","suffix":""}],"badges":[],"createdAt":"2021-10-08 02:44:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-959764/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-959764/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":14875266,"identity":"fec9d946-2ff4-4df4-9ad7-0418bde7634a","added_by":"auto","created_at":"2021-10-25 15:51:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":7456925,"visible":true,"origin":"","legend":"Trajectory analysis for the component of metabolic syndrome using NHIS-HEALS data\n\nIn trajectory modeling, we divided subjects into two clusters according to variables of their metabolic components during study period. Cluster A is shown in red and cluster B is shown in light blue. (A) Systolic blood pressure (mean 118.9±10.9 mmHg in cluster A, 138.9±13.2 mmHg in cluster B, P\u003c0.001); (B) Diastolic blood pressure (84±8.0 mmHg in cluster A, 71.3±7.3 mmHg in cluster B, P\u003c0.001); (C) Total cholesterol (167.5±26.6 mg/dL in cluster A, 223.4±33.0 mg/dL in cluster B, P\u003c0.001); (D) Triglyceride (18.8±46.1 mg/dL in cluster A, 266.1±116.7 mg/dL in cluster B, P\u003c0.001); (E) Low density lipoprotein (89.9±24.3 mg/dL in cluster A, 139.7±31.2 mg/dL in cluster B, P\u003c0.001); (F) Body mass index (cluster A: 24.0±0.8 kg/m²; B: 25.9±0.9 kg/m²; C:22.1±0.9 kg/m²; D: 28.2±1.1 kg/m²; E: 19.6±1.3 kg/m²; F: 31.9±2.2 kg/m², P\u003c0.001)\n","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-959764/v1/b8cbe2bc0c3f5764e55b7577.png"},{"id":14875265,"identity":"e8b514da-bb02-4d97-9b8d-a56e862069cd","added_by":"auto","created_at":"2021-10-25 15:51:29","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":352625,"visible":true,"origin":"","legend":"Forest plot for Cox regression analysis according to the trajectory group\n\nHazard ratios of all variables for all-cause mortality \n","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-959764/v1/f31a19408804a1672c85c16b.jpg"},{"id":15957625,"identity":"603fadca-c5fd-4be2-8313-33720ef4080a","added_by":"auto","created_at":"2021-11-29 07:44:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1604061,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-959764/v1/7a6d0727-c634-431e-a8df-a8f15af51db9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of the Trajectories of Metabolic Component and Outcomes in Patients with Chronic Kidney Disease: The National Health Insurance Service-National Health Screening Cohort (NHIS-HEALS) Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChronic kidney disease (CKD) increases the risk of chronic metabolic illnesses, including hypertension (HTN), diabetes mellitus (DM), and hyperlipidemia [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. CKD also affects renal function deterioration reciprocally [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. According to USRDS annual data report, HTN is the second most common cause of end-stage kidney disease in the US, followed by DM. Similar results of epidemiology has been reported in Korea. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Prolonged high blood pressure causes a decrease in renal function [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Consequently, the deterioration in kidney function and renal fibrosis lead to increased blood pressure due to the accumulation of salt [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Although it is crucial to control blood pressure to reduce the mortality and incidence of cardiovascular disease in patients with HTN and CKD, the optimal blood pressure level is still controversial [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Obesity is also associated with an increased risk of developing CKD [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Obesity causes glomerular hyperfiltration [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], and unlike the general population, a reversal of the obesity-mortality association is observed in the CKD population [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. CKD is associated with dyslipidemia from the early stages of microalbuminuria [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. As proteinuria increases, total cholesterol (TC), very-low-density lipoprotein, low-density lipoprotein (LDL) cholesterol, and triglycerides (TG) also increase [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, the ideal cholesterol values, including TG for CKD patients, have not been determined, and debates regarding optimal blood pressure and body mass index (BMI) has been continued.\u003c/p\u003e \u003cp\u003eOne of the reasons why controversy persists is that the outcomes were inconsistent because of the reverse epidemiology in CKD patient. During the observation period, we tried to find whether these metabolic factors were individually trajected and to determine the effect of these trajectories on the outcome. To evaluate this, we used the National Health Insurance Service-National Health Screening Cohort (NHIS-HEALS) performed over the last four years in South Korea.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003ch2\u003eThe National Health insurance service and National Health screening in Korea\u003c/h2\u003e\n\u003cp\u003eSouth Koreans are obliged to purchase health insurance. Thus, 97% of the population, excluding beneficiaries of the basic national livelihood and national meritorious persons, were covered for universal healthcare service. The National Health Insurance Database of Korea is a vast, informative resource that includes all Koreans who have health insurance. This database contains data on insured medical services, health screenings, and sociodemographic variables. In addition, information on hospital claims with the International Classification of Disease, 10th edition (ICD-10) coding and death was available. Through National Health Screening, Korean adults over the age of 40 were provided free general health screening once every 1-2 years [16, 17]. This study was approved by the Institutional Review Board of the Korea National Institute for Bioethics Policy (No. P01\u0026ndash;201603\u0026ndash;21-005), and written informed consent was waived, as this is a retrospective analysis of de-identified administrative data [16,17].\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eStudy population\u003c/h2\u003e\n\u003cp\u003eWe screened adults aged 40 to 79 who underwent health screening from 2010 to 2013. A total of 866,310 screening examinations over four years were verified. Among 866,310 cases of screening, 51,313 patients with chronic kidney disease (CKD) who had undergone 2 and more screening were included final analysis from 2010 to 2013 (Total cases of screening test were 63,537 cases). The CKD is defined as a MDRD estimated GFR of less than 60 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e \u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eDefinition of chronic diseases and study outcomes\u003c/h2\u003e\n\u003cp\u003eThe CKD was defined as a MDRD estimated GFR of less than 60 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e body surface area. DM was defined as fasting plasma glucose levels \u0026ge; 126 mg/dL at measurement or case with diagnostic code (the International Classification of Diseases, 10th Revision [ICD-10] codes E11\u0026ndash;14) or prescription of antidiabetic medication. HTN was defined by blood pressure \u0026ge; 140/90 mm Hg or case with diagnostic code (ICD-10 codes I10\u0026ndash;I15) or prescription of antihypertensive medication. Dyslipidemia was defined by TC level \u0026ge; 240 mg/dL or case with diagnostic code (ICD-10 code E78) or prescription of antihyperlipidemic medication. Cardiovascular disease was identified when the participant gave an affirmative answer to questionnaire. Cancer was defined by the presence of the specific diagnostic criteria for cancer and diagnosed and certified by a physician. The outcome of our study was all-cause mortality and a decrease in eGFR compared with the first screening between 2010 and 2013.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eStatistical analysis\u003c/h2\u003e\n\u003cp\u003eWe divided and clustered participants into group-based trajectory modeling for comparing changeable aspect of metabolic component. For group comparisons, we used the t-test for continuous variables and the \u0026chi;2 test for proportions. Multivariate logistic regression analysis was applied for relative risk evaluation of all-cause mortality and renal outcomes. Cox regression analysis was used to calculate the hazard ratios (HRs) and 95% CIs for cluster variables. Principal component analysis of multivariate Gaussian distribution was used in exploratory data analysis. The dimension consists of the eigenvectors of the covariance matrix scaled by the square root of the eigenvalue. All variables trajectory modeling was as follows: To categorize the trend of SBP, DBP, TC, TG, LDL, BMI, serum creatinine over time, we applied group-based trajectory modeling using the kml package in R statistics. K-means is a hill-climbing algorithm belonging to the expectation-maximization class. First, each observation was assigned to one cluster. Then, the optimal clustering was completed by the alternation of two phases. (kml and kml3d: R Packages to Cluster Longitudinal Data. 2015, volume 65, issue 4. journal of statistical software) During the expectation phase, the centers between two different clusters were computed. And during the maximization phase, each observation was matched to the nearest cluster. For the initialization method, K-means used random partition. For complexity, K-means analysis used Euclidean distance to assign optimal distance. For the partitioning clustering plot, we used the factoextra package in R statistical software by using the Calinski-Harabasz index. In the resulting plot. All observations are represented by points using principal components.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eBaseline characteristics according to the year of screening from 2010 to 2013\u003c/h2\u003e\n\u003cp\u003eThe participants\u0026rsquo; average age was 65.7 \u0026plusmn; 9.7 years, and 50.4% were male. The history of HTN, DM, and hyperlipidemia was 57.8%, 24.2%, and 9%, respectively. Further, among cardiovascular diseases, 4.1% reported cerebral infarction, and 10.9% had cardiac disease. The mean SBP of the participants were 127.8 \u0026plusmn; 15.8 mmHg. Additionally, BMI and waist circumferences were\u0026nbsp;24.4 \u0026plusmn; 3.0 kg/m\u003csup\u003e2\u003c/sup\u003e and 83.6 \u0026plusmn; 8.4 cm, respectively. For kidney function, the serum creatinine was 2.4 \u0026plusmn; 3.4 mg/dL, and Modification of Diet in Renal Disease (MDRD) eGFR was 46.0 \u0026plusmn; 17.6 ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e. From 2010 to 2013, the examinee\u0026rsquo;s age increased from 63.1\u0026plusmn;9.8 to 68.6\u0026plusmn;8.9 years, and the proportion of women gradually increased from 49.6% to 54.0%. The prevalence of HTN (from 51% to 30.9%), DM (from 82.7% to 65.7%), and hyperlipidemia (from 94.3% to 84.4%) showed a gradual decreasing pattern. As metabolic factors, the mean level of LDL and high-density lipoprotein (HDL) cholesterol decreased, and GFR increased. (Table 1).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eTrajectory analysis of variables with a metabolic component within four years\u003c/h2\u003e\n\u003cp\u003eIn trajectory analysis,\u0026nbsp;a mean maintenance level of SBP was 138.9\u0026plusmn;13.2 mmHg in cluster B and 118.9\u0026plusmn;10.9 mmHg in cluster A (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). DBP was maintained at a mean of 84\u0026plusmn;8.0 mmHg in cluster A, 71.3\u0026plusmn;7.3 mmHg in cluster B (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). In lipid profile, TC (167.5\u0026plusmn;26.6 mg/dL in cluster A, 223.4\u0026plusmn;33.0 mg/dL in cluster B, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001), TG (118.8\u0026plusmn;46.1 mg/dL in cluster A, 266.1\u0026plusmn;116.7 mg/dL in cluster B, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001), and LDL cholesterol (89.9\u0026plusmn;24.3 mg/dL in cluster A, 139.7\u0026plusmn;31.2 mg/dL in cluster B, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001) were lower in cluster A than cluster B. BMI was clustered into five groups from A to E in the order of the number of distributions of the participants. Most participants maintained at 22 \u0026ndash; 25.9 (BMI cluster A: 24.0\u0026plusmn;0.8 kg/m\u0026sup2;; B: 25.9\u0026plusmn;0.9 kg/m\u0026sup2;; C:22.1\u0026plusmn;0.9 kg/m\u0026sup2;; D: 28.2\u0026plusmn;1.1 kg/m\u0026sup2;; E: 19.6\u0026plusmn;1.3 kg/m\u0026sup2;; F: 31.9\u0026plusmn;2.2 kg/m\u0026sup2;, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001, respectively) and it was 19.6\u0026plusmn;1.3 kg/m\u0026sup2; at a value close to the criterion of underweight in BMI cluster E group. (Fig 1).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eDifference comparison for cluster groups according to all-cause mortality\u003c/h2\u003e\n\u003cp\u003eA total of 307 deaths occurred in clusters A and B from 2010 to 2013. Among the deceased, 233 cases (75.9%) were screened once during the study period. Ten cases (3.3%) had a screening interval of 1 year, 58 cases (18.9%) of 2 years, and six cases (2.0%) of 3 years. In survivors compared to the deceased, the proportion of participants who received screening only once during the study period was low and there were more cases where the screening interval was once a year. The difference in cluster group distribution between the deceased and survivor was compared; among the cluster variables, SBP, DBP, TC, and TG were not significantly different. Proportions of BMI cluster E was more in the deceased (event-free group: Cluster E 9.4%; event group cluster E 20.5%, respectively) (Table 2).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eDifference comparison for cluster groups according to the decline of eGFR\u003c/h2\u003e\n\u003cp\u003eThere was no difference in the number of deaths in the GFR decrement group compared to the non-GFR decrement group. The interval between most participants\u0026rsquo; health screens was two years (59.2% in the non-GFR decrement group, 62.8% in the GFR decrement group). In the group with decreased renal function, eGFR change was 4.7 \u0026plusmn; 6.9 ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e. Among the metabolic components, the proportions of SBP cluster B were 46.9% and 48.5%, respectively, which was higher in the GFR decrement group. The proportions of DBP cluster B were higher in the GFR decrement group (47.7% and 49.3%, respectively). In the GFR decrement group, TG cluster B were 20.9%, which was higher than the non-GFR decrement group. As for other factors, there was no significant difference between the two groups (Table 3).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eRegression analysis for events using clustering variables\u003c/h2\u003e\n\u003cp\u003eIn logistic regression analysis, SBP cluster B and BMI cluster E were associated with all-cause mortality (SBP cluster B: OR 1.823, 95% CI 1.066-1.212, \u003cem\u003eP\u003c/em\u003e = 0.027; BMI cluster E: OR 2.194, 95% CI 1.071-1.4.387, \u003cem\u003eP\u003c/em\u003e = 0.027). Factors related to GFR decline included SBP cluster B (HR 1.130, 95% CI 1.066-1.212, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), DBP cluster B (HR 1.105, 95% CI 1.036-1.178, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.002), TG cluster A (OR 1.151, 95% CI 1.069-1.240, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) showed a significant correlation (Table 4). In multivariable Cox regression analysis, it was not observed to increase in the hazard ratios in cluster B group according to the metabolic component except for BMI (Fig 2). However, we found a lower risk of mortality (HR 0.35, 95% CI 0.15-0.83) in the cluster D (BMI 28.2\u0026plusmn;1.1 kg/m\u0026sup2;) compared to the cluster A (24.0\u0026plusmn;0.8 kg/m\u0026sup2;) (Table 4).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study is an observational study using data from the large-scale National Health Insurance Service-National Health Screening Cohort. This study showed the importance of controlling chronic diseases, including HTN and dyslipidemia, by performing trajectory cluster analysis on data obtained through continuous measurement of metabolic factors in the CKD patient group.\u003c/p\u003e \u003cp\u003eBlood pressure control in the CKD patient group is important for preventing CKD progression and mortality. However, controversy remains about the optimal blood pressure that can prevent renal function decline and improve mortality in CKD patients. Furthermore, most randomized controlled trials (RCTs) about blood pressure control have limitations that exclude or include only a small number of patients with CKD. In terms of mortality, results of the International Verapamil-Trandolapril Study suggest a possible J-curve relationship between blood pressure and mortality through previous observational studies [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial in patients with Type 2 DM showed no improvement in all-cause mortality with the intensive control of SBP (\u0026lt; 120 mmHg) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In a study including 77,765 CKD patients, the effect of intensive treatment compared to standard treatment (SBP\u0026lt;120 vs 120\u0026ndash;139 mmHg) was not proven [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, diabetic nephropathy patients with an SBP of 120 mmHg had the most protective cardiovascular event in the Irbesartan Diabetic Nephropathy Trial [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In terms of renal outcomes, studies on the effect of blood pressure control on renal function also show mixed results. Two representative studies for blood pressure control in CKD (the MDRD study and African-American Study of Kidney Disease and Hypertension (AASK) study) did not demonstrate an improvement in CKD progression through intensive control [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. A meta-analysis and Renin-2 trial also reported that intensive control did not provide additional benefit for renal outcomes for CKD progression [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. However, the advantage of intensive treatment was highlighted through the SPRINT trial. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The SPRINT Research Group reported that major cardiovascular events and all-cause death decreased in the intensive control group through subgroup analysis of 2,646 CKD patients [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In 2019, pooled analyses from 4 RCTs, including AASK, ACCORD, MDRD and the SPRINT, have been published demonstrating mortality benefits of SBP \u0026lt; 130 mmHg in CKD patients [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In the guideline for the management of blood pressure in CKD announced by KDIGO in 2021, it is suggested to control SBP \u0026lt; 120 mmHg if tolerable (2B) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The mean SBP of the cohort participants included in our study was 127.8 \u0026plusmn; 15.8 mmHg, which was well controlled compared to the standard concept. The cluster A group, controlled to mean 118.9\u0026plusmn;10.9 mmHg, showed a lower risk of mortality and better renal outcome through trajectory analysis. This is consistent with the recently accepted trend in which the benefits of intensive control are highlighted.\u003c/p\u003e \u003cp\u003eHyperlipidemia is a major risk factor for cardiovascular disease, and TG and HDL cholesterol are evaluated for metabolic syndrome. LDL cholesterol is the main therapeutic target for secondary prevention of atherosclerotic cardiovascular disease. CKD patients were classified as a high-risk group in the 2019 guideline of dyslipidemia in the European Society of Cardiology [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Dyslipidemia is associated with a decrease in renal function [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In CKD patients, an increase in TG and LDL is commonly observed, and multiple lipid-lowering agents are often required [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Although it is emphasized to identify the lipid profile in CKD patients, the main therapeutic target is LDL cholesterol. In the SHARP trial, using a lipid-lowering agent could not reduce the risk of ESRD, nor did it reduce major atherosclerotic events in dialysis-dependent patients. [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In our study, the TG cluster A group (maintained the mean 118 \u0026plusmn; 46.1 mg/dL) had a better renal outcome than the TG cluster B group with hypertriglyceridemia. This finding is consistent with the results of other cohort studies showing the need for control of hypertriglyceridemia in CKD patients [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Reverse epidemiology of the obesity paradox has been observed in chronic disease patients, including CKD patients [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. As observed in the general population, it is also known to increase the mortality rate of patients with low BMI. Low weight in CKD patients, including those dependent on dialysis, is closely related to protein-energy wasting syndrome [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], a major cause of increased mortality [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Although most of the participants in our study maintained a BMI of 22-24 kg/m\u003csup\u003e2\u003c/sup\u003e, the risk of death in the cluster E group increased. This result suggests that it is important to improve the nutritional status of patients with low body weight. In cox regression analysis, our results showed that the mortality risk of cluster D was lower than those of cluster A. These findings are thought to reflect reverse epidemiology of the obesity paradox.\u003c/p\u003e \u003cp\u003eKorean citizens are registered for mandatory to health insurance and could have health screen every 1-2years. Through database containing many subjects, many studies have been conducted. Among the representative studies on metabolic syndrome, there is a study that showed the relationship between changes in metabolic syndrome status and prognosis [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. However, the effect of changes in individual factors of metabolic syndrome was not confirmed. We used Korean large-scale cohort database. Trajectory analysis was performed for each item of metabolic syndrome, and the effect of trajectory factors on the outcome of CKD was confirmed. Our study has some limitations. As a retrospective observational study, there is a possibility that the selection and lead-time bias of participants in the cohort may be involved. Additional confounding factors, such as differences in drugs taken by participants and the causative disease of CKD, may remain in relation to the mortality and renal outcome.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, prolonged hypertension, glucose, hyperlipidemia have negative impacts on GFR confirmed by trajectory analysis. Cluster B has lower GFR than cluster A; this is caused by patients with chronic diseases in cluster B having not been controlled over four years as values of BMI, SBP and DBP. To not affect the function of the kidney, chronic diseases including HTN, DM, and hyperlipidemia need to be effectively controlled.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eACCORD Action to Control Cardiovascular Risk in Diabetes\u003c/p\u003e\n\u003cp\u003eCI Confidence intervals\u003c/p\u003e\n\u003cp\u003eCKD Chronic kidney disease\u003c/p\u003e\n\u003cp\u003eDM Diabetes mellitus\u003c/p\u003e\n\u003cp\u003eDBP Diastolic Blood Pressure\u003c/p\u003e\n\u003cp\u003eESKD End-stage kidney disease\u003c/p\u003e\n\u003cp\u003eGFR Glomerular filtration rate\u003c/p\u003e\n\u003cp\u003eHR Hazard ratios\u003c/p\u003e\n\u003cp\u003eHTN Hypertension\u003c/p\u003e\n\u003cp\u003eINVEST International Verapamil-Trandolapril Study\u003c/p\u003e\n\u003cp\u003eKORDS Korean Renal Data System\u003c/p\u003e\n\u003cp\u003eLDL Low-density lipoprotein\u003c/p\u003e\n\u003cp\u003eMDRD Modification of Diet in Renal Disease\u003c/p\u003e\n\u003cp\u003eNHIS National Health Screening\u003c/p\u003e\n\u003cp\u003ePCA Partitioning component of analysis\u003c/p\u003e\n\u003cp\u003eRCT Randomized controlled trials\u003c/p\u003e\n\u003cp\u003eSBP Systolic and diastolic blood pressure\u003c/p\u003e\n\u003cp\u003eTC Total cholesterol\u003c/p\u003e\n\u003cp\u003eVLDL Very-low-density lipoprotein\u003c/p\u003e\n\u003cp\u003eAASK African-American Study of Kidney Disease and Hypertension study\u003c/p\u003e\n\u003cp\u003eBMI Body mass index\u003c/p\u003e\n\u003cp\u003eESKD End stage kidney disease\u003c/p\u003e\n\u003cp\u003eHDL High density lipoprotein\u003c/p\u003e\n\u003cp\u003eKDIGO The Kidney Disease: Improving Global Outcomes\u003c/p\u003e\n\u003cp\u003eSHARP Study of Heart and Renal Protection\u003c/p\u003e\n\u003cp\u003eSPRINT Systolic Blood Pressure Intervention Trial\u003c/p\u003e\n\u003cp\u003eUSRDS United States Renal Data System\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eThis study complied with the Declaration of Helsinki and was approved by the Institutional Review Board of the Korea National Institute for Bioethics Policy (No. P01\u0026ndash;201603\u0026ndash;21-005), and written informed consent was waived, as this is a retrospective analysis of de-identified administrative data.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eN/A\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026rsquo; contributions\u003c/h2\u003e\n\u003cp\u003eResearch idea and study design: KDY, HK; data acquisition: HH, JHS, GSK, KDY, YK; data analysis/interpretation: HH, JHS, HK, KDY, Each author contributed important intellectual content during manuscript drafting. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThe data have not been published elsewhere except a poster presentation at the ERA-EDTA Congress (June 13-16, 2019 - Budapest, Hungary), and ASN kidney week (November 5-9, Washington). \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFerro CJ, Mark PB, Kanbay M, Sarafidis P, Heine GH, Rossignol P, Massy ZA, Mallamaci F, Valdivielso JM, Malyszko J \u003cem\u003eet al\u003c/em\u003e: \u003cb\u003eLipid management in patients with chronic kidney disease\u003c/b\u003e. \u003cem\u003eNat Rev Nephrol\u003c/em\u003e 2018, \u003cb\u003e14\u003c/b\u003e(12):727\u0026ndash;749.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsimihodimos V, Mitrogianni Z, Elisaf M: \u003cb\u003eDyslipidemia associated with chronic kidney disease\u003c/b\u003e. \u003cem\u003eOpen Cardiovasc Med J\u003c/em\u003e 2011, \u003cb\u003e5\u003c/b\u003e:41\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsai WC, Wu HY, Peng YS, Ko MJ, Wu MS, Hung KY, Wu KD, Chu TS, Chien KL: \u003cb\u003eRisk Factors for Development and Progression of Chronic Kidney Disease: A Systematic Review and Exploratory Meta-Analysis\u003c/b\u003e. \u003cem\u003eMedicine (Baltimore)\u003c/em\u003e 2016, \u003cb\u003e95\u003c/b\u003e(11):e3013.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaal MW, Brenner BM: \u003cb\u003ePredicting initiation and progression of chronic kidney disease: Developing renal risk scores\u003c/b\u003e. \u003cem\u003eKidney Int\u003c/em\u003e 2006, \u003cb\u003e70\u003c/b\u003e(10):1694\u0026ndash;1705.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTozawa M, Iseki K, Iseki C, Kinjo K, Ikemiya Y, Takishita S: \u003cb\u003eBlood pressure predicts risk of developing end-stage renal disease in men and women\u003c/b\u003e. \u003cem\u003eHypertension\u003c/em\u003e 2003, \u003cb\u003e41\u003c/b\u003e(6):1341\u0026ndash;1345.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHong YA, Ban TH, Kang CY, Hwang SD, Choi SR, Lee H, Jung HY, Kim K, Kwon YE, Kim SH \u003cem\u003eet al\u003c/em\u003e: \u003cb\u003eTrends in epidemiologic characteristics of end-stage renal disease from 2019 Korean Renal Data System (KORDS)\u003c/b\u003e. \u003cem\u003eKidney Res Clin Pract\u003c/em\u003e 2021, \u003cb\u003e40\u003c/b\u003e(1):52\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSon HE, Ryu JY, Go S, Yi Y, Kim K, Oh YK, Oh KH, Chin HJ: \u003cb\u003eAssociation of ambulatory blood pressure monitoring with renal outcome in patients with chronic kidney disease\u003c/b\u003e. \u003cem\u003eKidney Res Clin Pract\u003c/em\u003e 2020, \u003cb\u003e39\u003c/b\u003e(1):70\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWright JA, Cavanaugh KL: \u003cb\u003eDietary sodium in chronic kidney disease: a comprehensive approach\u003c/b\u003e. \u003cem\u003eSemin Dial\u003c/em\u003e 2010, \u003cb\u003e23\u003c/b\u003e(4):415\u0026ndash;421.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReynolds K, Gu D, Muntner P, Kusek JW, Chen J, Wu X, Duan X, Chen CS, Klag MJ, Whelton PK \u003cem\u003eet al\u003c/em\u003e: \u003cb\u003eA population-based, prospective study of blood pressure and risk for end-stage renal disease in China\u003c/b\u003e. \u003cem\u003eJ Am Soc Nephrol\u003c/em\u003e 2007, \u003cb\u003e18\u003c/b\u003e(6):1928\u0026ndash;1935.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKramer H, Luke A, Bidani A, Cao G, Cooper R, McGee D: \u003cb\u003eObesity and prevalent and incident CKD: the Hypertension Detection and Follow-Up Program\u003c/b\u003e. \u003cem\u003eAm J Kidney Dis\u003c/em\u003e 2005, \u003cb\u003e46\u003c/b\u003e(4):587\u0026ndash;594.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFoster MC, Hwang SJ, Larson MG, Lichtman JH, Parikh NI, Vasan RS, Levy D, Fox CS: \u003cb\u003eOverweight, obesity, and the development of stage 3 CKD: the Framingham Heart Study\u003c/b\u003e. \u003cem\u003eAm J Kidney Dis\u003c/em\u003e 2008, \u003cb\u003e52\u003c/b\u003e(1):39\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHenegar JR, Bigler SA, Henegar LK, Tyagi SC, Hall JE: \u003cb\u003eFunctional and structural changes in the kidney in the early stages of obesity\u003c/b\u003e. \u003cem\u003eJ Am Soc Nephrol\u003c/em\u003e 2001, \u003cb\u003e12\u003c/b\u003e(6):1211\u0026ndash;1217.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdams KF, Schatzkin A, Harris TB, Kipnis V, Mouw T, Ballard-Barbash R, Hollenbeck A, Leitzmann MF: \u003cb\u003eOverweight, obesity, and mortality in a large prospective cohort of persons 50 to 71 years old\u003c/b\u003e. \u003cem\u003eN Engl J Med\u003c/em\u003e 2006, \u003cb\u003e355\u003c/b\u003e(8):763\u0026ndash;778.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJones SL, Close CF, Mattock MB, Jarrett RJ, Keen H, Viberti GC: \u003cb\u003ePlasma lipid and coagulation factor concentrations in insulin dependent diabetics with microalbuminuria\u003c/b\u003e. \u003cem\u003eBmj\u003c/em\u003e 1989, \u003cb\u003e298\u003c/b\u003e(6672):487\u0026ndash;490.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarre M, Bouhanick B, Berrut G: \u003cb\u003eMicroalbuminuria\u003c/b\u003e. \u003cem\u003eCurr Opin Nephrol Hypertens\u003c/em\u003e 1994, \u003cb\u003e3\u003c/b\u003e(5):558\u0026ndash;563.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheol Seong S, Kim YY, Khang YH, Heon Park J, Kang HJ, Lee H, Do CH, Song JS, Hyon Bang J, Ha S \u003cem\u003eet al\u003c/em\u003e: \u003cb\u003eData Resource Profile: The National Health Information Database of the National Health Insurance Service in South Korea\u003c/b\u003e. \u003cem\u003eInt J Epidemiol\u003c/em\u003e 2017, \u003cb\u003e46\u003c/b\u003e(3):799\u0026ndash;800.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeong SC, Kim YY, Park SK, Khang YH, Kim HC, Park JH, Kang HJ, Do CH, Song JS, Lee EJ \u003cem\u003eet al\u003c/em\u003e: \u003cb\u003eCohort profile: the National Health Insurance Service-National Health Screening Cohort (NHIS-HEALS) in Korea\u003c/b\u003e. \u003cem\u003eBMJ Open\u003c/em\u003e 2017, \u003cb\u003e7\u003c/b\u003e(9):e016640.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMesserli FH, Mancia G, Conti CR, Hewkin AC, Kupfer S, Champion A, Kolloch R, Benetos A, Pepine CJ: \u003cb\u003eDogma disputed: can aggressively lowering blood pressure in hypertensive patients with coronary artery disease be dangerous?\u003c/b\u003e \u003cem\u003eAnn Intern Med\u003c/em\u003e 2006, \u003cb\u003e144\u003c/b\u003e(12):884\u0026ndash;893.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCushman WC, Evans GW, Byington RP, Goff DC, Jr., Grimm RH, Jr., Cutler JA, Simons-Morton DG, Basile JN, Corson MA, Probstfield JL \u003cem\u003eet al\u003c/em\u003e: \u003cb\u003eEffects of intensive blood-pressure control in type 2 diabetes mellitus\u003c/b\u003e. \u003cem\u003eN Engl J Med\u003c/em\u003e 2010, \u003cb\u003e362\u003c/b\u003e(17):1575\u0026ndash;1585.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKovesdy CP, Lu JL, Molnar MZ, Ma JZ, Canada RB, Streja E, Kalantar-Zadeh K, Bleyer AJ: \u003cb\u003eObservational modeling of strict vs conventional blood pressure control in patients with chronic kidney disease\u003c/b\u003e. \u003cem\u003eJAMA Intern Med\u003c/em\u003e 2014, \u003cb\u003e174\u003c/b\u003e(9):1442\u0026ndash;1449.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerl T, Hunsicker LG, Lewis JB, Pfeffer MA, Porush JG, Rouleau JL, Drury PL, Esmatjes E, Hricik D, Pohl M \u003cem\u003eet al\u003c/em\u003e: \u003cb\u003eImpact of achieved blood pressure on cardiovascular outcomes in the Irbesartan Diabetic Nephropathy Trial\u003c/b\u003e. \u003cem\u003eJ Am Soc Nephrol\u003c/em\u003e 2005, \u003cb\u003e16\u003c/b\u003e(7):2170\u0026ndash;2179.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWright JT, Jr., Bakris G, Greene T, Agodoa LY, Appel LJ, Charleston J, Cheek D, Douglas-Baltimore JG, Gassman J, Glassock R \u003cem\u003eet al\u003c/em\u003e: \u003cb\u003eEffect of blood pressure lowering and antihypertensive drug class on progression of hypertensive kidney disease: results from the AASK trial\u003c/b\u003e. \u003cem\u003eJama\u003c/em\u003e 2002, \u003cb\u003e288\u003c/b\u003e(19):2421\u0026ndash;2431.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKlahr S, Levey AS, Beck GJ, Caggiula AW, Hunsicker L, Kusek JW, Striker G: \u003cb\u003eThe effects of dietary protein restriction and blood-pressure control on the progression of chronic renal disease. Modification of Diet in Renal Disease Study Group\u003c/b\u003e. \u003cem\u003eN Engl J Med\u003c/em\u003e 1994, \u003cb\u003e330\u003c/b\u003e(13):877\u0026ndash;884.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsai WC, Wu HY, Peng YS, Yang JY, Chen HY, Chiu YL, Hsu SP, Ko MJ, Pai MF, Tu YK \u003cem\u003eet al\u003c/em\u003e: \u003cb\u003eAssociation of Intensive Blood Pressure Control and Kidney Disease Progression in Nondiabetic Patients With Chronic Kidney Disease: A Systematic Review and Meta-analysis\u003c/b\u003e. \u003cem\u003eJAMA Intern Med\u003c/em\u003e 2017, \u003cb\u003e177\u003c/b\u003e(6):792\u0026ndash;799.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuggenenti P, Perna A, Loriga G, Ganeva M, Ene-Iordache B, Turturro M, Lesti M, Perticucci E, Chakarski IN, Leonardis D \u003cem\u003eet al\u003c/em\u003e: \u003cb\u003eBlood-pressure control for renoprotection in patients with non-diabetic chronic renal disease (REIN-2): multicentre, randomised controlled trial\u003c/b\u003e. \u003cem\u003eLancet\u003c/em\u003e 2005, \u003cb\u003e365\u003c/b\u003e(9463):939\u0026ndash;946.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWright JT, Jr., Williamson JD, Whelton PK, Snyder JK, Sink KM, Rocco MV, Reboussin DM, Rahman M, Oparil S, Lewis CE \u003cem\u003eet al\u003c/em\u003e: \u003cb\u003eA Randomized Trial of Intensive versus Standard Blood-Pressure Control\u003c/b\u003e. \u003cem\u003eN Engl J Med\u003c/em\u003e 2015, \u003cb\u003e373\u003c/b\u003e(22):2103\u0026ndash;2116.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheung AK, Rahman M, Reboussin DM, Craven TE, Greene T, Kimmel PL, Cushman WC, Hawfield AT, Johnson KC, Lewis CE \u003cem\u003eet al\u003c/em\u003e: \u003cb\u003eEffects of Intensive BP Control in CKD\u003c/b\u003e. \u003cem\u003eJ Am Soc Nephrol\u003c/em\u003e 2017, \u003cb\u003e28\u003c/b\u003e(9):2812\u0026ndash;2823.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAggarwal R, Petrie B, Bala W, Chiu N: \u003cb\u003eMortality Outcomes With Intensive Blood Pressure Targets in Chronic Kidney Disease Patients\u003c/b\u003e. \u003cem\u003eHypertension\u003c/em\u003e 2019, \u003cb\u003e73\u003c/b\u003e(6):1275\u0026ndash;1282.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTomson CRV, Cheung AK, Mann JFE, Chang TI, Cushman WC, Furth SL, Hou FF, Knoll GA, Muntner P, Pecoits-Filho R \u003cem\u003eet al\u003c/em\u003e: \u003cb\u003eManagement of Blood Pressure in Patients With Chronic Kidney Disease Not Receiving Dialysis: Synopsis of the 2021 KDIGO Clinical Practice Guideline\u003c/b\u003e. \u003cem\u003eAnn Intern Med\u003c/em\u003e 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cb\u003e2019 ESC/EAS guidelines for the management of dyslipidaemias: Lipid modification to reduce cardiovascular risk\u003c/b\u003e. \u003cem\u003eAtherosclerosis\u003c/em\u003e 2019, \u003cb\u003e290\u003c/b\u003e:140\u0026ndash;205.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM\u0026auml;ntt\u0026auml;ri M, Tiula E, Alikoski T, Manninen V: \u003cb\u003eEffects of hypertension and dyslipidemia on the decline in renal function\u003c/b\u003e. \u003cem\u003eHypertension\u003c/em\u003e 1995, \u003cb\u003e26\u003c/b\u003e(4):670\u0026ndash;675.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHunsicker LG, Adler S, Caggiula A, England BK, Greene T, Kusek JW, Rogers NL, Teschan PE: \u003cb\u003ePredictors of the progression of renal disease in the Modification of Diet in Renal Disease Study\u003c/b\u003e. \u003cem\u003eKidney Int\u003c/em\u003e 1997, \u003cb\u003e51\u003c/b\u003e(6):1908\u0026ndash;1919.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarper CR, Jacobson TA: \u003cb\u003eManaging dyslipidemia in chronic kidney disease\u003c/b\u003e. \u003cem\u003eJ Am Coll Cardiol\u003c/em\u003e 2008, \u003cb\u003e51\u003c/b\u003e(25):2375\u0026ndash;2384.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmed MH, Khalil AA: \u003cb\u003eEzetimibe as a potential treatment for dyslipidemia associated with chronic renal failure and renal transplant\u003c/b\u003e. \u003cem\u003eSaudi J Kidney Dis Transpl\u003c/em\u003e 2010, \u003cb\u003e21\u003c/b\u003e(6):1021\u0026ndash;1029.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiang X, Ye M, Tao M, Zheng D, Cai R, Zhu Y, Jin J, He Q: \u003cb\u003eThe association between dyslipidemia and the incidence of chronic kidney disease in the general Zhejiang population: a retrospective study\u003c/b\u003e. \u003cem\u003eBMC Nephrol\u003c/em\u003e 2020, \u003cb\u003e21\u003c/b\u003e(1):252.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKovesdy CP, Anderson JE: \u003cb\u003eReverse epidemiology in patients with chronic kidney disease who are not yet on dialysis\u003c/b\u003e. \u003cem\u003eSemin Dial\u003c/em\u003e 2007, \u003cb\u003e20\u003c/b\u003e(6):566\u0026ndash;569.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKalantar-Zadeh K, Block G, Humphreys MH, Kopple JD: \u003cb\u003eReverse epidemiology of cardiovascular risk factors in maintenance dialysis patients\u003c/b\u003e. \u003cem\u003eKidney Int\u003c/em\u003e 2003, \u003cb\u003e63\u003c/b\u003e(3):793\u0026ndash;808.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoung P, Lombi F, Finn BC, Forrester M, Campolo-Girard V, Pomeranz V, Iriarte R, Bruetman JE, Trimarchi H: \u003cb\u003e[\"Malnutrition-inflammation complex syndrome\" in chronic hemodialysis]\u003c/b\u003e. \u003cem\u003eMedicina (B Aires)\u003c/em\u003e 2011, \u003cb\u003e71\u003c/b\u003e(1):66\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKalantar-Zadeh K, Kopple JD, Block G, Humphreys MH: \u003cb\u003eA malnutrition-inflammation score is correlated with morbidity and mortality in maintenance hemodialysis patients\u003c/b\u003e. \u003cem\u003eAm J Kidney Dis\u003c/em\u003e 2001, \u003cb\u003e38\u003c/b\u003e(6):1251\u0026ndash;1263.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKovesdy CP, Kalantar-Zadeh K: \u003cb\u003eWhy is protein-energy wasting associated with mortality in chronic kidney disease?\u003c/b\u003e \u003cem\u003eSemin Nephrol\u003c/em\u003e 2009, \u003cb\u003e29\u003c/b\u003e(1):3\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark S, Lee S, Kim Y, Lee Y, Kang MW, Han K, Han SS, Lee H, Lee JP, Joo KW \u003cem\u003eet al\u003c/em\u003e: \u003cb\u003eAltered Risk for Cardiovascular Events With Changes in the Metabolic Syndrome Status: A Nationwide Population-Based Study of Approximately 10 Million Persons\u003c/b\u003e. \u003cem\u003eAnn Intern Med\u003c/em\u003e 2019, \u003cb\u003e171\u003c/b\u003e(12):875\u0026ndash;884.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp style=\"text-align: center;\"\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003e\u003cstrong\u003e\u0026nbsp;Baseline characteristics according to the year of screening from 2010 to 2013\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.4897%;\" width=\"20.427807486631018%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 13.2732%;\" width=\"13.262032085561497%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.4897%;\" width=\"20.427807486631018%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 13.2732%;\" width=\"13.262032085561497%\"\u003e\n \u003cp\u003e(N=63537)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e(N=18772)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e(N=17282)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e(N=13283)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e(N=14200)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.4897%;\" width=\"20.427807486631018%\"\u003e\n \u003cp\u003eAGE (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2732%;\" width=\"13.262032085561497%\"\u003e\n \u003cp\u003e65.7 \u0026plusmn; 9.7\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e63.1 \u0026plusmn; 9.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e65.4 \u0026plusmn; 9.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e66.6 \u0026plusmn; 9.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e68.6 \u0026plusmn; 8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.4897%;\" width=\"20.427807486631018%\"\u003e\n \u003cp\u003eSex (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2732%;\" width=\"13.262032085561497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.4897%;\" width=\"20.427807486631018%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2732%;\" width=\"13.262032085561497%\"\u003e\n \u003cp\u003e32030 (50.4%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e10136 (54.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e8758 (50.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e6598 (49.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e6538 (46.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.4897%;\" width=\"20.427807486631018%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2732%;\" width=\"13.262032085561497%\"\u003e\n \u003cp\u003e31507 (49.6%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e8636 (46.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e8524 (49.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e6685 (50.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e7662 (54.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.4897%;\" width=\"20.427807486631018%\"\u003e\n \u003cp\u003eSmoking history\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2732%;\" width=\"13.262032085561497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.4897%;\" width=\"20.427807486631018%\"\u003e\n \u003cp\u003eNon-smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2732%;\" width=\"13.262032085561497%\"\u003e\n \u003cp\u003e42812 (67.9%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e12069 (65.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e11584 (67.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e9050 (68.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e10109 (71.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.4897%;\" width=\"20.427807486631018%\"\u003e\n \u003cp\u003eEx-smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2732%;\" width=\"13.262032085561497%\"\u003e\n \u003cp\u003e12800 (20.3%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e3847 (20.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e3577 (20.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e2700 (20.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e2676 (18.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.4897%;\" width=\"20.427807486631018%\"\u003e\n \u003cp\u003eCurrent smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2732%;\" width=\"13.262032085561497%\"\u003e\n \u003cp\u003e7470 (11.8%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e2554 (13.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e2027 (11.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e1494 (11.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e1395 (9.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.4897%;\" width=\"20.427807486631018%\"\u003e\n \u003cp\u003eAlcohol history\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2732%;\" width=\"13.262032085561497%\"\u003e\n \u003cp\u003e1.7 \u0026plusmn; 1.4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e\u0026nbsp;1.8 \u0026plusmn; 1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e\u0026nbsp;1.7 \u0026plusmn; 1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e\u0026nbsp;1.6 \u0026plusmn; 1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e\u0026nbsp;1.6 \u0026plusmn; 1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.4897%;\" width=\"20.427807486631018%\"\u003e\n \u003cp\u003eHypertension History\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2732%;\" width=\"13.262032085561497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.4897%;\" width=\"20.427807486631018%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2732%;\" width=\"13.262032085561497%\"\u003e\n \u003cp\u003e22184 (42.2%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e8209 (51.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e6640 (45.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e3792 (35.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e3543 (30.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.4897%;\" width=\"20.427807486631018%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2732%;\" width=\"13.262032085561497%\"\u003e\n \u003cp\u003e30416 (57.8%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e7593 (48.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e8039 (54.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e6848 (64.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e7936 (69.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.4897%;\" width=\"20.427807486631018%\"\u003e\n \u003cp\u003eDiabetes mellitus History\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2732%;\" width=\"13.262032085561497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.4897%;\" width=\"20.427807486631018%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2732%;\" width=\"13.262032085561497%\"\u003e\n \u003cp\u003e36666 (75.8%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e13012 (82.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e11396 (78.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e6097 (69.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e6161 (65.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.4897%;\" width=\"20.427807486631018%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2732%;\" width=\"13.262032085561497%\"\u003e\n \u003cp\u003e11735 (24.2%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e2724 (17.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e3067 (21.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e2726 (30.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e3218 (34.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.4897%;\" width=\"20.427807486631018%\"\u003e\n \u003cp\u003eHyperlipidemia History\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2732%;\" width=\"13.262032085561497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.4897%;\" width=\"20.427807486631018%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2732%;\" width=\"13.262032085561497%\"\u003e\n \u003cp\u003e42425 (91.0%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e14835 (94.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e13385 (93.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e7038 (87.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e7167 (84.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.4897%;\" width=\"20.427807486631018%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2732%;\" width=\"13.262032085561497%\"\u003e\n \u003cp\u003e4190 (9.0%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e891 (5.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e990 (6.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e986 (12.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e1323 (15.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.4897%;\" width=\"20.427807486631018%\"\u003e\n \u003cp\u003eTotal cholesterol (mg/dL)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2732%;\" width=\"13.262032085561497%\"\u003e\n \u003cp\u003e196.4 \u0026plusmn; 40.9\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e199.8 \u0026plusmn; 40.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e196.6 \u0026plusmn; 40.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e194.7 \u0026plusmn; 41.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e193.5 \u0026plusmn; 42.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.4897%;\" width=\"20.427807486631018%\"\u003e\n \u003cp\u003eTriglycerides (mg/dL)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2732%;\" width=\"13.262032085561497%\"\u003e\n \u003cp\u003e147.1 \u0026plusmn; 89.8\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e150.3 \u0026plusmn; 97.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e145.2 \u0026plusmn; 86.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e146.2 \u0026plusmn; 88.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e145.9 \u0026plusmn; 85.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.4897%;\" width=\"20.427807486631018%\"\u003e\n \u003cp\u003eHDL cholesterol (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2732%;\" width=\"13.262032085561497%\"\u003e\n \u003cp\u003e53.9 \u0026plusmn; 36.3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e59.1 \u0026plusmn; 61.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e51.9 \u0026plusmn; 14.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e52.2 \u0026plusmn; 20.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e51.0 \u0026plusmn; 16.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.4897%;\" width=\"20.427807486631018%\"\u003e\n \u003cp\u003eLDL cholesterol (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2732%;\" width=\"13.262032085561497%\"\u003e\n \u003cp\u003e115.5 \u0026plusmn; 37.2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e118.3 \u0026plusmn; 37.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e115.5 \u0026plusmn; 36.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e113.9 \u0026plusmn; 37.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e113.5 \u0026plusmn; 37.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.4897%;\" width=\"20.427807486631018%\"\u003e\n \u003cp\u003eCerebral infarction History\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2732%;\" width=\"13.262032085561497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.4897%;\" width=\"20.427807486631018%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 13.2732%;\" width=\"13.262032085561497%\"\u003e\n \u003cp\u003e44054 (95.9%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e15264 (97.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e13839 (96.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e7356 (94.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e7595 (93.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.4897%;\" width=\"20.427807486631018%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 13.2732%;\" width=\"13.262032085561497%\"\u003e\n \u003cp\u003e1876 (4.1%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e445 (2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e510 (3.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e410 (5.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e511 (6.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.4897%;\" width=\"20.427807486631018%\"\u003e\n \u003cp\u003eHeart disease History\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2732%;\" width=\"13.262032085561497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.4897%;\" width=\"20.427807486631018%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 13.2732%;\" width=\"13.262032085561497%\"\u003e\n \u003cp\u003e41557 (89.1%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e14497 (92.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e12994 (90.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e6954 (85.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e7112 (84.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.4897%;\" width=\"20.427807486631018%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 13.2732%;\" width=\"13.262032085561497%\"\u003e\n \u003cp\u003e5103 (10.9%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e1220 (7.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e1395 (9.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e1137 (14.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e1351 (16.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.4897%;\" width=\"20.427807486631018%\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2732%;\" width=\"13.262032085561497%\"\u003e\n \u003cp\u003e24.4 \u0026plusmn; 3.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e24.3 \u0026plusmn; 3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e24.4 \u0026plusmn; 3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e24.4 \u0026plusmn; 3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e24.4 \u0026plusmn; 3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.4897%;\" width=\"20.427807486631018%\"\u003e\n \u003cp\u003eWaist (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2732%;\" width=\"13.262032085561497%\"\u003e\n \u003cp\u003e83.6 \u0026plusmn; 8.4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e83.4 \u0026plusmn; 8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e83.6 \u0026plusmn; 8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e83.6 \u0026plusmn; 8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e83.8 \u0026plusmn; 8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.4897%;\" width=\"20.427807486631018%\"\u003e\n \u003cp\u003eSBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2732%;\" width=\"13.262032085561497%\"\u003e\n \u003cp\u003e127.8 \u0026plusmn; 15.8\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e127.3 \u0026plusmn; 15.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e128.2 \u0026plusmn; 16.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e128.1 \u0026plusmn; 16.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e127.8 \u0026plusmn; 15.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e0.003\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.4897%;\" width=\"20.427807486631018%\"\u003e\n \u003cp\u003eDBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2732%;\" width=\"13.262032085561497%\"\u003e\n \u003cp\u003e77.6 \u0026plusmn; 10.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e78.0 \u0026plusmn; 9.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e77.8 \u0026plusmn; 10.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e77.6 \u0026plusmn; 10.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e77.0 \u0026plusmn; 10.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.4897%;\" width=\"20.427807486631018%\"\u003e\n \u003cp\u003eFBS (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2732%;\" width=\"13.262032085561497%\"\u003e\n \u003cp\u003e106.1 \u0026plusmn; 31.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e104.6 \u0026plusmn; 29.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e105.6 \u0026plusmn; 29.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e106.8 \u0026plusmn; 31.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e108.1 \u0026plusmn; 33.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.4897%;\" width=\"20.427807486631018%\"\u003e\n \u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2732%;\" width=\"13.262032085561497%\"\u003e\n \u003cp\u003e13.4 \u0026plusmn; 1.7\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e13.6 \u0026plusmn; 1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e13.4 \u0026plusmn; 1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e13.3 \u0026plusmn; 1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e13.2 \u0026plusmn; 1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.4897%;\" width=\"20.427807486631018%\"\u003e\n \u003cp\u003eCreatinine (mg/dL)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2732%;\" width=\"13.262032085561497%\"\u003e\n \u003cp\u003e2.4 \u0026plusmn; 3.4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e\u0026nbsp;3.3 \u0026plusmn; 4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e\u0026nbsp;2.7 \u0026plusmn; 3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e\u0026nbsp;1.8 \u0026plusmn; 2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e\u0026nbsp;1.6 \u0026plusmn; 2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.4897%;\" width=\"20.427807486631018%\"\u003e\n \u003cp\u003eMDRD eGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2732%;\" width=\"13.262032085561497%\"\u003e\n \u003cp\u003e46.0 \u0026plusmn; 17.6\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e41.4 \u0026plusmn; 21.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e44.2 \u0026plusmn; 19.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e49.9 \u0026plusmn; 13.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e50.5 \u0026plusmn; 11.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.262032085561497%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 99.6134%;\"\u003eHDL, high density lipoprotein; LDL, low density lipoprotein; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; FBS, fasting blood sugar; eGFR. estimated glomerular filtration rate\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003e\u003cstrong\u003e\u0026nbsp;Difference comparison for cluster groups according to all-cause mortality\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.573426573426573%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\n \u003cp\u003eSurvivor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\n \u003cp\u003eDeceased\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.573426573426573%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\n \u003cp\u003e(N=63230)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\n \u003cp\u003e(N=307)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.573426573426573%\"\u003e\n \u003cp\u003eInterval of screening\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.573426573426573%\"\u003e\n \u003cp\u003e\u0026nbsp;Once screened\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\n \u003cp\u003e39210 (62.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\n \u003cp\u003e233 (75.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.573426573426573%\"\u003e\n \u003cp\u003e1 year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\n \u003cp\u003e6056 (9.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\n \u003cp\u003e10 (3.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.573426573426573%\"\u003e\n \u003cp\u003e2 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\n \u003cp\u003e14789 (23.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\n \u003cp\u003e58 (18.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.573426573426573%\"\u003e\n \u003cp\u003e3 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\n \u003cp\u003e3175 (5.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\n \u003cp\u003e6 (2.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.573426573426573%\"\u003e\n \u003cp\u003eSBP clusters\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\n \u003cp\u003e0.161\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.573426573426573%\"\u003e\n \u003cp\u003e\u0026nbsp; A\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\n \u003cp\u003e12503 (52.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\n \u003cp\u003e32 (43.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.573426573426573%\"\u003e\n \u003cp\u003e\u0026nbsp; B\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\n \u003cp\u003e11509 (47.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\n \u003cp\u003e42 (56.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.573426573426573%\"\u003e\n \u003cp\u003eDBP clusters\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\n \u003cp\u003e0.744\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.573426573426573%\"\u003e\n \u003cp\u003e\u0026nbsp; A\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\n \u003cp\u003e12300 (51.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\n \u003cp\u003e36 (48.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.573426573426573%\"\u003e\n \u003cp\u003e\u0026nbsp; B\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\n \u003cp\u003e11712 (48.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\n \u003cp\u003e38 (51.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.573426573426573%\"\u003e\n \u003cp\u003eTC clusters\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\n \u003cp\u003e0.918\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.573426573426573%\"\u003e\n \u003cp\u003e\u0026nbsp; A\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\n \u003cp\u003e12963 (54.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\n \u003cp\u003e39 (52.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.573426573426573%\"\u003e\n \u003cp\u003e\u0026nbsp; B\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\n \u003cp\u003e11053 (46.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\n \u003cp\u003e35 (47.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.573426573426573%\"\u003e\n \u003cp\u003eTG clusters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\n \u003cp\u003e0.471\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.573426573426573%\"\u003e\n \u003cp\u003e\u0026nbsp; A\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\n \u003cp\u003e19111 (79.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\n \u003cp\u003e62 (83.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.573426573426573%\"\u003e\n \u003cp\u003e\u0026nbsp; B\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\n \u003cp\u003e4858 (20.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\n \u003cp\u003e12 (16.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.573426573426573%\"\u003e\n \u003cp\u003eLDL clusters\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\n \u003cp\u003e0.954\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.573426573426573%\"\u003e\n \u003cp\u003e\u0026nbsp; A\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\n \u003cp\u003e12305 (53.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\n \u003cp\u003e40 (54.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.573426573426573%\"\u003e\n \u003cp\u003e\u0026nbsp; B\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\n \u003cp\u003e10580 (46.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\n \u003cp\u003e33 (45.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.573426573426573%\"\u003e\n \u003cp\u003eBMI clusters\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.573426573426573%\"\u003e\n \u003cp\u003e\u0026nbsp; A\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\n \u003cp\u003e6750 (28.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\n \u003cp\u003e19 (26.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.573426573426573%\"\u003e\n \u003cp\u003e\u0026nbsp; B\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\n \u003cp\u003e5725 (23.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\n \u003cp\u003e14 (19.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.573426573426573%\"\u003e\n \u003cp\u003e\u0026nbsp; C\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\n \u003cp\u003e5378 (22.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\n \u003cp\u003e20 (27.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.573426573426573%\"\u003e\n \u003cp\u003e\u0026nbsp; D\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\n \u003cp\u003e3161 (13.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\n \u003cp\u003e3 (4.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.573426573426573%\"\u003e\n \u003cp\u003e\u0026nbsp; E\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\n \u003cp\u003e2247 (9.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\n \u003cp\u003e15 (20.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.573426573426573%\"\u003e\n \u003cp\u003e\u0026nbsp; F\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\n \u003cp\u003e754 (3.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\n \u003cp\u003e2 (2.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.475524475524477%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 100%;\"\u003eSBP, systolic blood pressure; DBP, diastolic blood pressure; TC, total cholesterol; TG, triglyceride; LDL, low density lipoprotein; BMI, body mass index\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003e\u003cstrong\u003eDifference comparison for cluster groups according to the decline of eGFR\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.99822380106572%\"\u003e\n \u003cp\u003e`\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003eNo decrement\u003c/p\u003e\n \u003cp\u003e(N=7803)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003eDecrement\u003c/p\u003e\n \u003cp\u003e(N=16291)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.99822380106572%\"\u003e\n \u003cp\u003eDeath\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e0.540\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.99822380106572%\"\u003e\n \u003cp\u003e\u0026nbsp;Survivor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e7782 (99.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e16238 (99.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.99822380106572%\"\u003e\n \u003cp\u003e\u0026nbsp;Deceased\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e21 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e53 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.99822380106572%\"\u003e\n \u003cp\u003eChange of eGFR\u003c/p\u003e\n \u003cp\u003e(ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e-8.5 \u0026plusmn; 11.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e4.7 \u0026plusmn; 6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.99822380106572%\"\u003e\n \u003cp\u003eInterval of screening\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.99822380106572%\"\u003e\n \u003cp\u003e1 year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e2134 (27.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e3932 (24.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.99822380106572%\"\u003e\n \u003cp\u003e2 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e4621 (59.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e10226 (62.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.99822380106572%\"\u003e\n \u003cp\u003e3 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e1048 (13.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e2133 (13.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.99822380106572%\"\u003e\n \u003cp\u003eSBP clusters\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.99822380106572%\"\u003e\n \u003cp\u003e\u0026nbsp; A\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e4143 (53.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e8392 (51.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.99822380106572%\"\u003e\n \u003cp\u003e\u0026nbsp; B\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e3658 (46.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e7893 (48.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.99822380106572%\"\u003e\n \u003cp\u003eDBP clusters\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.99822380106572%\"\u003e\n \u003cp\u003e\u0026nbsp; A\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e4077 (52.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e8259 (50.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.99822380106572%\"\u003e\n \u003cp\u003e\u0026nbsp; B\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e3724 (47.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e8026 (49.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.99822380106572%\"\u003e\n \u003cp\u003eTC clusters\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e0.392\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.99822380106572%\"\u003e\n \u003cp\u003e\u0026nbsp; A\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e4180 (53.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e8822 (54.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.99822380106572%\"\u003e\n \u003cp\u003e\u0026nbsp; B\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e3623 (46.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e7465 (45.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.99822380106572%\"\u003e\n \u003cp\u003eTG clusters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.99822380106572%\"\u003e\n \u003cp\u003e\u0026nbsp; A\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e6300 (81.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e12873 (79.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.99822380106572%\"\u003e\n \u003cp\u003e\u0026nbsp; B\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e1474 (19.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e3396 (20.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.99822380106572%\"\u003e\n \u003cp\u003eLDL clusters\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e0.650\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.99822380106572%\"\u003e\n \u003cp\u003e\u0026nbsp; A\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e3881 (54.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e8464 (53.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.99822380106572%\"\u003e\n \u003cp\u003e\u0026nbsp; B\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e3306 (46.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e7307 (46.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.99822380106572%\"\u003e\n \u003cp\u003eBMI_clusters\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e0.901\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.99822380106572%\"\u003e\n \u003cp\u003e\u0026nbsp; A\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e2223 (28.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e4546 (27.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.99822380106572%\"\u003e\n \u003cp\u003e\u0026nbsp; B\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e1866 (23.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e3873 (23.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.99822380106572%\"\u003e\n \u003cp\u003e\u0026nbsp; C\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e1727 (22.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e3671 (22.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.99822380106572%\"\u003e\n \u003cp\u003e\u0026nbsp; D\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e1028 (13.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e2136 (13.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.99822380106572%\"\u003e\n \u003cp\u003e\u0026nbsp; E\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e720 (9.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e1542 (9.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.99822380106572%\"\u003e\n \u003cp\u003e\u0026nbsp; F\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e239 (3.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\n \u003cp\u003e517 (3.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.33392539964476%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 99.6086%;\"\u003eSBP, systolic blood pressure; DBP, diastolic blood pressure; TC, total cholesterol; TG, triglyceride; LDL, low density lipoprotein; BMI, body mass index; eGFR. estimated glomerular filtration rate\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003e\u003cstrong\u003e\u0026nbsp;Logistic regression analysis for events using clustering variables\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.05227655986509%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDeath\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003ebeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.9106239460371%\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.05227655986509%\"\u003e\n \u003cp\u003eSBP clusters B (Ref. cluster A)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e0.601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e1.823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.9106239460371%\"\u003e\n \u003cp\u003e1.066~1.212\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.05227655986509%\"\u003e\n \u003cp\u003eDBP clusters B (Ref. cluster A)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e0.283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e1.327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e0.294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.9106239460371%\"\u003e\n \u003cp\u003e0.780~2.253\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.05227655986509%\"\u003e\n \u003cp\u003eTC clusters B (Ref. cluster A)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e0.272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e1.312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e0.442\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.9106239460371%\"\u003e\n \u003cp\u003e0.893~1.055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.05227655986509%\"\u003e\n \u003cp\u003eTG clusters A (Ref. cluster B)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e0.202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e1.223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e0.553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.9106239460371%\"\u003e\n \u003cp\u003e0.652~2.511\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.05227655986509%\"\u003e\n \u003cp\u003eLDL clusters A (Ref. cluster B)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e1.214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e0.581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.9106239460371%\"\u003e\n \u003cp\u003e0.611~2.417\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.05227655986509%\"\u003e\n \u003cp\u003eBMI Ref. cluster A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.9106239460371%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.05227655986509%\"\u003e\n \u003cp\u003eBMI clusters B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e-0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e0.858\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e0.664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.9106239460371%\"\u003e\n \u003cp\u003e0.909~1.063\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.05227655986509%\"\u003e\n \u003cp\u003eBMI clusters C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e0.270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e1.310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e0.401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.9106239460371%\"\u003e\n \u003cp\u003e0.694~2.479\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.05227655986509%\"\u003e\n \u003cp\u003eBMI clusters D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e-1.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e0.327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.9106239460371%\"\u003e\n \u003cp\u003e0.889~1.072\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.05227655986509%\"\u003e\n \u003cp\u003eBMI clusters E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e0.785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e2.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.9106239460371%\"\u003e\n \u003cp\u003e1.071~4.387\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.05227655986509%\"\u003e\n \u003cp\u003eBMI clusters F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e-0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e0.876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e0.859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.9106239460371%\"\u003e\n \u003cp\u003e0.851~1.191\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.05227655986509%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.9106239460371%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.05227655986509%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGFR decline\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003ebeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.9106239460371%\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.05227655986509%\"\u003e\n \u003cp\u003eSBP clusters B (Ref. cluster A)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e1.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.9106239460371%\"\u003e\n \u003cp\u003e1.066~1.212\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.05227655986509%\"\u003e\n \u003cp\u003eDBP clusters B (Ref. cluster A)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e1.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.9106239460371%\"\u003e\n \u003cp\u003e1.036~1.178\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.05227655986509%\"\u003e\n \u003cp\u003eTC clusters B (Ref. cluster A)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e1.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e0.492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.9106239460371%\"\u003e\n \u003cp\u003e0.947~1.119\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.05227655986509%\"\u003e\n \u003cp\u003eTG clusters A (Ref. cluster B)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e1.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.9106239460371%\"\u003e\n \u003cp\u003e1.069~1.240\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.05227655986509%\"\u003e\n \u003cp\u003eLDL clusters A (Ref. cluster B)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e1.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e0.279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.9106239460371%\"\u003e\n \u003cp\u003e0.963~1.136\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.05227655986509%\"\u003e\n \u003cp\u003eBMI Ref. cluster A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.9106239460371%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.05227655986509%\"\u003e\n \u003cp\u003eBMI clusters B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e-0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e0.983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e0.680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.9106239460371%\"\u003e\n \u003cp\u003e0.909~1.063\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.05227655986509%\"\u003e\n \u003cp\u003eBMI clusters C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e1.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e0.680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.9106239460371%\"\u003e\n \u003cp\u003e0.949~1.112\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.05227655986509%\"\u003e\n \u003cp\u003eBMI clusters D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e-0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e0.976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e0.618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.9106239460371%\"\u003e\n \u003cp\u003e0.889~1.072\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.05227655986509%\"\u003e\n \u003cp\u003eBMI clusters E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e1.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e0.474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.9106239460371%\"\u003e\n \u003cp\u003e0.935~1.155\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.05227655986509%\"\u003e\n \u003cp\u003eBMI clusters F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e1.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.345699831365936%\"\u003e\n \u003cp\u003e0.945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.9106239460371%\"\u003e\n \u003cp\u003e0.851~1.191\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 99.6769%;\"\u003eSBP, systolic blood pressure; DBP, diastolic blood pressure; TC, total cholesterol; TG, triglyceride; LDL, low density lipoprotein; BMI, body mass index; eGFR. estimated glomerular filtration rate\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\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":"Chronic kidney disease, metabolic component, trajectory analysis","lastPublishedDoi":"10.21203/rs.3.rs-959764/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-959764/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eChronic kidney disease (CKD) could cause and exacerbate metabolic disturbances, including hypertension and dyslipidemia. Conversely, metabolic disturbances affect renal outcome and mortality in CKD patients. However, studies on the relationship between the pattern of metabolic disturbance and prognosis in CKD during the observation period are lacking.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Through trajectory analysis, we found that subjects with CKD were divided into two groups in a pattern of metabolic disturbances over time. Subjects were divided into low (A) and high (B) groups using K-means clustering based blood pressure, total cholesterol (TC), triglyceride (TG), and low-density lipoprotein (LDL) cholesterol measurement at two time-points. The optimal number of clustering was selected using the Calinski-Harabasz index. The outcome of our study was a decline in renal function and mortality.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e This study is a large-scale retrospective study of 51,313 subjects with CKD from The National Health Insurance Service-National Health Screening Cohort. The mean age of the subjects was 65.7±9.7 years, and 50.4% were male. During the study period, the mean systolic blood pressure (SBP) was 127.8±15.8 mmHg and diastolic blood pressure (DBP) was maintained at 83.6±8.4 mmHg. Mean serum LDL cholesterol and TG levels were 196.4±40.9 and 147.1±89.8 mg/dL, respectively After clustering, the low group (A group) had the mean SBP of 118.9±10.9mmHg and a TG of 118.8±46.1 mg/dl. However, in the high group (B group), it was found that the mean SBP was maintained at 138.9 ± 13.2 mmHg, and the TG was maintained at was 266.1 ± 116.7 mg/dL. In logistic regression analysis, the high group of SBP was associated with the decline of renal function and increased mortality (odds ratios [OR] 1.13 95%confidence intervals (CI) 1.066-1.212). and the high group of TG was independently associated with a decrease in renal function (OR 1.15, 95% CI 1.069-1.240). \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThe results of this study showed the association of the pattern of metabolic disturbances with the prognosis of CKD over time. Additionally, it could be useful to control intensively SBP for renal outcome and mortality in CKD, and management of high TG could be necessary to improve renal outcome.\u0026nbsp;\u003c/p\u003e","manuscriptTitle":"Association of the Trajectories of Metabolic Component and Outcomes in Patients with Chronic Kidney Disease: The National Health Insurance Service-National Health Screening Cohort (NHIS-HEALS) Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-10-25 15:51:27","doi":"10.21203/rs.3.rs-959764/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":"5dd9dd3d-150a-42cb-980b-86b4c98d8433","owner":[],"postedDate":"October 25th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":8079322,"name":"Urology \u0026 Nephrology"}],"tags":[],"updatedAt":"2021-11-29T07:44:15+00:00","versionOfRecord":[],"versionCreatedAt":"2021-10-25 15:51:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-959764","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-959764","identity":"rs-959764","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}