Impact of Frailty and Clonal Hematopoiesis on Cardiovascular Outcomes in Elderly Patients with Renal Artery Stenosis Undergoing Stenting

Impact of Frailty and Clonal Hematopoiesis on Cardiovascular Outcomes in Elderly Patients with Renal Artery Stenosis Undergoing Stenting | 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 Biological Sciences - Article Impact of Frailty and Clonal Hematopoiesis on Cardiovascular Outcomes in Elderly Patients with Renal Artery Stenosis Undergoing Stenting Peng Li, Yiyang Wang, Yang Wang, Hu Ai, Yongjun Li, Junhong Ren This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5117728/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Background: Frailty and clonal hematopoiesis of indeterminate potential (CHIP) have emerged as crucial predictors of adverse cardiovascular outcomes in older adults. However, their combined impact on major adverse cardiovascular events (MACE) in patients with severe atherosclerotic renal artery stenosis (ARAS) remains unclear. Methods: We conducted a prospective cohort study involving 175 patients aged 60 years and older with severe ARAS (luminal stenosis ≥ 70%) who underwent renal artery stenting at Beijing Hospital between January 2019 and December 2022. Frailty was assessed using the Fried phenotype, categorizing patients into robust, prefrail, and frail subgroups. CHIP status was determined through targeted gene sequencing of peripheral blood, stratifying patients into No CHIP (VAF < 2%), Small CHIP (VAF 2%-<10%), and Large CHIP (VAF ≥ 10%) subgroups. All patients were systematically followed up until June 30, 2024. The primary outcome was the incidence of MACE, which was a composite of renal function deterioration (RFD), initiation of renal replacement therapy, renal artery revascularization, nonfatal myocardial infarction, hospitalization for heart failure, nonfatal stroke, and cardiorenovascular death. We employed Cox proportional hazards models, Kaplan-Meier survival analysis, and heatmaps to explore the combined impact of frailty and CHIP on MACE risk. Results: The mean age of the patients was 68.3 years. Of the cohort, 64.6% had no CHIP, 26.8% had Small CHIP, and 8.6% had Large CHIP. Frail patients showed a higher prevalence of CHIP, particularly in the Small (34.7%) and Large (10.2%) CHIP categories. During a median follow-up of 32 months, 54 MACE occurred. Kaplan-Meier survival curve revealed that frailty was associated with a higher incidence of MACE (35.7% in frail vs. 29.5% in prefrail vs. 24.6% in robust, P = 0.045) and RFD (16.3% in frail vs. 11.5% in prefrail vs. 7.7% in robust, P = 0.034). Patients with Large CHIP experienced significantly higher rates of MACE (60.0% vs. 36.2% in Small CHIP vs. 24.8% in No CHIP, P = 0.004) and RFD (26.7% vs. 14.9% in prefrail vs. 8.0% in robust, P = 0.019). Findings for RFD appeared to be consistent with those for MACE. Frailty and CHIP status showed independent contribution to overall risk. The greatest spread for MACE and RFD risk was obtained in models that incorporated frail and Large CHIP. Conclusion: Frailty and CHIP, independently and jointly, contribute to a significantly higher risk of MACE and RFD in elderly patients with severe ARAS undergoing stenting. These findings highlight the necessity for integrated risk stratification and targeted management strategies in this high-risk population. Health sciences/Medical research/Outcomes research Health sciences/Diseases/Cardiovascular diseases/Vascular diseases/Peripheral vascular disease Frailty Clonal hematopoiesis Renal artery stenosis Cardiovascular events Elderly patients Aging Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Renal artery stenosis (RAS) is a prevalent and significant clinical challenge, particularly in the elderly [ 1 ]. RAS is closely linked with secondary hypertension, progressive renal dysfunction, and an elevated risk of major adverse cardiovascular events (MACE). The pathophysiology of RAS extends beyond simple luminal narrowing, encompassing a complex interplay of systemic and local factors that contribute to disease progression and increased MACE risk [ 2 ]. Among these, frailty and clonal hematopoiesis of indeterminate potential (CHIP) have emerged as independent predictors of poor cardiovascular outcomes [ 3 – 4 ]; however, their combined impact, particularly in the context of severe RAS, remains unclear. Frailty, a multifactorial geriatric syndrome characterized by reduced physiological reserves and increased vulnerability to external stressors, has been consistently associated with higher mortality and increased rates of cardiovascular events across various patient populations[ 5 – 6 ]. In patients with RAS, frailty may exacerbate the clinical course by diminishing the capacity to recover from ischemic insults and by contributing to a higher burden of comorbidities. This increased susceptibility underscores the need for a more nuanced understanding of frailty’s role in the pathogenesis of cardiovascular complications in the elderly [ 7 – 8 ]. Concurrently, CHIP, defined by the presence of somatic mutations in hematopoietic stem cells, has been increasingly recognized as a driver of chronic inflammation, endothelial dysfunction, and atherosclerosis [ 9 ]. Mutations in key genes such as DNMT3A and TET2 are frequently observed in CHIP and have been linked to a heightened risk of cardiovascular disease, independent of traditional risk factors [ 10 – 11 ]. The presence of CHIP may thus represent a critical, yet underappreciated, factor in the progression of RAS and its associated cardiovascular complications. Despite these insights, the combined impact of frailty and CHIP on MACE in elderly patients with ARAS has not been well studied. It is possible that the interaction between frailty and CHIP amplifies the risk of cardiovascular events. Frailty, with its physical vulnerability, and CHIP, with its role in inflammation, may work together to worsen outcomes [ 12 – 13 ]. Understanding this relationship is essential for better risk stratification and management of elderly patients with RAS. Therefore, we hypothesized that frailty and CHP together might have an combined impact on cardiovascular risk. We addressed this hypothesis in the prospective cohort study, which included a cohort of 175 elderly severe RAS patients who underwent endovascular therapy Patients and Methods Study Design This prospective cohort study was conducted at Beijing Hospital and registered with the China Clinical Trial Registration Center (ChiCTR2300077390). The study protocol adhered to the principles outlined in the Declaration of Helsinki, and has been approved by the Ethics Committee of Beijing Hospital (2023BJYYEC-342-01). Furthermore, the study was designed and reported in accordance with the STROCSS criteria[ 14 ]. Study Population We collected data from patients diagnosed with severe atherosclerotic RAS (ARAS), defined as luminal stenosis of ≥ 70%, who were treated at our hospital between January 2019 and December 2022. The final follow-up was conducted on June 30, 2024. Eligibility criteria included patients aged 60 years or older with severe RAS, confirmed by either contrast-enhanced ultrasound (CEUS) or computed tomography angiography (CTA)[ 15 ], who had undergone renal artery endovascular therapy (stent placement) and completed a minimum follow-up period of 18 months. Inclusion in the study also required complete data on frailty status and CHIP status. Exclusion criteria encompassed patients with a history of prior renal artery endovascular therapy or major vascular surgery. Moreover, patients with end-stage renal disease (estimated glomerular filtration rate (eGFR) of < 15 mL/min/1.73 m²), and those who had active malignancies were also excluded. Methods Baseline data were collected at the time of study enrollment and recorded in our Clintrial Database. The collected data encompassed demographic information, medical history, laboratory results, stenosis degree, and treatment details. In addition, renal microvascular perfusion (RMP) was also evaluated using CEUS, including area under the ascending curve (AUC1), area under the descending curve (AUC2), peak intensity (PI), time to peak intensity (TTP), and mean transit time (MTT)[ 16 – 17 ]. Frailty was evaluated using the Fried phenotype, which consisted of five criteria: unintentional weight loss, exhaustion, low grip strength, slow walking speed, and low activity (Supplementary file 1) . Patients with a score ≥ 3 were diagnosed with frailty, with a score of 1–2 were diagnosed as prefrail, and with a score of 0 were diagnosed with robust [ 18 ]. Concurrently, CHIP was assessed through targeted gene sequencing of peripheral blood samples, focusing on forty-six mutations commonly associated with CHIP (Supplementary file 2) . Based on the variant allele frequency (VAF), patients were categorized into No CHIP (VAF < 2%), Small CHIP (VAF 2%-<10%), and Large CHIP (VAF ≥ 10%) groups[ 19 ]. Follow-Up All patients were systematically followed up from the time of their initial procedure until the last follow-up date on June 30, 2024. Follow-up assessments were conducted at regular intervals, including 6, and 12 months post-procedure, and annually thereafter. The primary outcome was the incidence of MACE, which was a composite of renal function deterioration (RFD), initiation of renal replacement therapy, renal artery revascularization, nonfatal myocardial infarction, hospitalization for heart failure (HHF), nonfatal stroke, and cardiorenovascular death. RFD was defined as a reduction of the eGFR from baseline of 30% or more, with the reduction sustained for 60 days or longer and not attributable to other causes[ 20 ]. The flow chart was shown in Supplementary file 3 . Statistical Analysis Continuous variables will be presented as means with standard deviations (SD) or medians with interquartile ranges (IQR), depending on the normality of their distribution. Categorical variables will be reported as frequencies and percentages. Baseline characteristics across frailty and CHIP subgroups will be compared using one-way ANOVA for continuous variables and chi-square tests for categorical variables. Spearman’s correlation coefficients were used to discern relationships between frailty and CHIP status. For survival analysis, Kaplan-Meier curves will be generated to compare time-to-event outcomes for MACE and RFD across different frailty and CHIP subgroups. Hazard ratios (HR) for incident MACE were assessed in Cox proportional-hazard models that compared high frailty (prefrail, frail subgroups) or CHIP (small, large CHIP subgroups) burden with robust or No CHIP subgroup (the reference subgroup). Estimates of HR were obtained in model 1: adjusted for age; in model 2: additionally adjusted for diastolic blood pressure (DBP), eGFR, NT-proBNP, and the presence of diabetes; and in model 3: adjusted for the other factor (frailty or CHIP status). HR and 95% confidence intervals (CIs) will be reported for each frailty and CHIP category. Additionally, heatmaps will be utilized to visualize the combined impact of frailty and CHIP on MACE risk. To assess potential joint effects between frailty and CHIP status, we also conducted risk factor adjusted analysis that evaluate the MACE rates according to whether the patients had frail (no frail [robust + prefrail] vs. Frail subgroups) or CHIP (no CHIP vs. CHIP [Small + Large CHIP] subgroups). To evaluate the potential combined effects of frailty and CHIP status, we repeated the above analysis after dividing patients into three subgroups: subgroup 1: having frail or Large CHIP; subgroup 2: having frail and large CHIP; subgroup 3: not having frail, or Large CHIP. All statistical tests will be two-sided, with a P-value of < 0.05 considered statistically significant. Statistical analyses will be performed using SPSS software (version 26.0; IBM Corp., Armonk, NY). Results Distribution of Frailty and CHIP Status The Fig. 1 illustrated that in a population of severe RAS patients, the majority (64.6%) fall into the No CHIP category, with a progressively smaller proportion in Small CHIP (26.8%) and Large CHIP (8.6%). Among robust patients, most (70.8%) had No CHIP, while frail patients showed a higher prevalence of CHIP, particularly in the Small (34.7%) and Large (10.2%) CHIP categories. This distribution suggested a potential correlation between increasing frailty and a higher CHIP burden, indicating that frail patients may be more likely to exhibit significant levels of CHIP. Baseline Characteristics Across Varying Frailty The mean age of the patients was 68.3 years. In Table 1 , the results demonstrated that frail patients tended to be older (P < 0.001) and have longer durations of hypertension (P = 0.001). Additionally, frail patients exhibited higher levels of neutrophils (P = 0.002) and lower lymphocyte counts (P = 0.001), reflecting a more pro-inflammatory state. Frail patients also had significantly higher serum creatinine levels (P = 0.012) and BUN levels (P = 0.034), indicating poorer renal function compared to their robust counterparts. Moreover, increased frailty is associated with impaired RMP (P < 0.05). Table 1 Basic characteristics across patients with different levels of frailty Characteristics Total (n = 175) Robust group (n = 65) Pre-frail group (n = 61) Frail group (n = 49) P value Baseline data Age,yr 68.3 ± 7.2 66.2 ± 6.0 69.3 ± 7.1 71.8 ± 7.5 < 0.001 Male,n(%) 88(50.3%) 38 (58.5%) 31 (50.8%) 19 (38.8%) 0.112 BMI,kg/m 2 25.4 ± 2.6 25.8 ± 2.2 25.3 ± 2.5 24.8 ± 2.9 0.112 Current smoking,n(%) 54(30.9%) 17 (26.2%) 18 (29.5%) 19 (38.8%) 0.346 Previous history Diabetes,n(%) 70(40.0%) 22(33.8%) 26(42.6%) 22(44.9%) 0.409 Hypertension,n(%) 154(88.0%) 56 (86.2%) 54 (88.5%) 44 (89.8%) 0.754 Duration of hypertension,yr 10.8 ± 4.4 9.5 ± 3.6 11.3 ± 4.5 12.5 ± 4.8 0.001 Hyperlipidemia ,n(%) 90(51.4%) 28 (43.1%) 31 (50.8%) 31 (63.3%) 0.102 OMI,n(%) 21(12.0%) 5 (7.7%) 7 (11.5%) 9 (18.4%) 0.222 Atrial fibrillation,n(%) 25(14.3%) 6 (9.2%) 9 (14.8%) 10 (20.4%) 0.267 COPD,n(%) 20(11.4%) 6 (9.2%) 6 (9.8%) 8 (16.3%) 0.453 CKD,n(%) 41(23.4%) 9 (13.8%) 14 (23.0%) 18 (36.7%) 0.399 PAD,n(%) 24(13.7%) 6 (9.2%) 9 (14.8%) 9 (18.4%) 0.355 Stroke/TIA,n(%) 18(10.3%) 5 (7.7%) 7 (11.5%) 6 (12.2%) 0.671 Cancer,n(%) 14(8.0%) 3 (4.6%) 6 (9.8%) 5 (10.2%) 0.438 SLE,n(%) 10(5.7%) 2 (3.1%) 4 (6.6%) 4 (8.2%) 0.457 Blood pressure(mmHg) SBP 148.6 ± 20.1 145.2 ± 17.5 149.6 ± 18.7 152.8 ± 22.5 0.113 DBP 89.3 ± 18.4 85.5 ± 16.1 88.7 ± 17.3 92.4 ± 20.6 0.127 MAP 108.6 ± 22.4 107.5 ± 17.6 109.0 ± 19.4 112.3 ± 21.5 0.419 Lab. test WBC count(×10 9 /L) 6.5 ± 1.8 6.1 ± 1.6 6.6 ± 1.9 7.0 ± 2.4 0.051 Neutrophils(×10 9 /L) 3.8 ± 1.2 3.5 ± 1.1 3.7 ± 1.3 4.4 ± 1.8 0.002 Lymphocytes(×10 9 /L) 1.9 ± 0.6 2.1 ± 0.5 2.0 ± 0.4 1.7 ± 0.6 0.001 Platelet count(×10 9 /L) 252.2 ± 45.7 245.5 ± 43.1 250.4 ± 47.2 258.7 ± 51.8 0.334 Hemoglobin(g/dL) 13.4 ± 1.7 13.7 ± 1.4 13.2 ± 1.6 12.8 ± 2.5 0.034 FPG(mg/dL) 102.4 ± 23.7 98.2 ± 21.6 100.5 ± 23.3 106.3 ± 30.7 0.225 TC(mg/dL) 181.2 ± 41.2 178.1 ± 32.5 180.2 ± 35.7 183.4 ± 45.3 0.673 TG(mg/dL) 150.2 ± 35.3 145.6 ± 31.4 148.7 ± 33.5 150.5 ± 38.7 0.740 LDL-C(mg/dL) 112.5 ± 32.4 109.4 ± 30.1 111.1 ± 32.6 113.8 ± 35.2 0.773 HDL-C(mg/dL) 50.5 ± 16.1 53.2 ± 14.8 49.7 ± 15.9 49.3 ± 17.1 0.333 Total albumin(g/dL) 4.1 ± 1.4 4.2 ± 1.2 4.1 ± 1.3 3.9 ± 1.6 0.502 hs-CRP(mg/L) 5.1 ± 1.7 4.7 ± 1.4 5.0 ± 1.6 5.5 ± 2.1 0.045 NT-proBNP(pg/mL) 366.4 ± 65.3 356.1 ± 50.7 360.7 ± 54.2 376.8 ± 65.1 0.138 Serum creatinine (mg/dL) 1.2 ± 0.3 1.1 ± 0.3 1.2 ± 0.3 1.4 ± 0.5 0.012 BUN(mg/dL) 20.5 ± 7.3 18.8 ± 5.1 19.4 ± 6.2 21.8 ± 7.6 0.034 eGFR(ml/min/1.73m 2 ) 68.5 ± 17.5 70.2 ± 16.3 66.5 ± 18.7 63.7 ± 20.4 0.168 Echocardiography LVESD(mm) 46.7 ± 7.3 46.0 ± 6.8 46.6 ± 7.4 47.6 ± 8.1 0.519 LVEDD(mm) 55.1 ± 8.6 54.7 ± 7.9 55.0 ± 8.2 56.9 ± 9.3 0.345 LVMI(g/m 2 ) 125.4 ± 30.7 123.0 ± 28.1 124.5 ± 29.3 130.3 ± 32.4 0.407 LVEF(%) 60.1 ± 11.4 61.1 ± 10.7 60.2 ± 11.2 57.5 ± 12.6 0.238 Degree of RAS,% 83.5 ± 10.4 83.0 ± 9.5 83.4 ± 10.7 84.1 ± 15.3 0.864 RMP AUC1(dB×s) 1203.4 ± 386.2 1269.3 ± 380.4 1183.5 ± 341.2 1063.6 ± 447.9 0.021 AUC2(dB×s) 5212.1 ± 1404.2 5336.3 ± 2015.2 5193.4 ± 1838.4 4944.1 ± 1433.7 0.517 PI(dB) 137.4 ± 26.5 144.6 ± 36.8 135.9 ± 40.9 126.7 ± 45.3 0.069 TTP(s) 18.6 ± 5.7 17.4 ± 5.5 19.2 ± 6.3 21.5 ± 5.1 0.011 MTT(s) 48.8 ± 15.7 44.6 ± 13.2 52.3 ± 16.4 58.3 ± 19.2 0.001 Drug treatment Statins,n(%) 90(51.4%) 34 (52.3%) 35 (57.4%) 21 (42.9%) 0.374 SGLT2i,n(%) 38 (21.7%) 14 (21.5%) 16 (26.2%) 8 (16.3%) 0.286 GLP-1RA,n(%) 16 (9.1%) 8 (12.3%) 6 (9.8%) 2 (4.1%) 0.311 β-blocker,n(%) 32(18.3%) 13(20.0%) 11(18.0%) 7(14.3%) 0.726 RAAS inhibitor,n(%) 18(10.3%) 8(12.3%) 7(11.5%) 3(6.1%) 0.516 Antiplatelet therapy,n(%) 41(23.4%) 10 (15.4%) 16 (26.2%) 15 (30.6%) 0.130 Anticoagulant,n(%) 23(13.1%) 6 (9.2%) 8 (13.1%) 9 (18.4%) 0.362 BMI, body mass index; OMI, old myocardial infarction; COPD, chronic obstructive pulmonary disease; CKD, chronic kidney disease; PAD, peripheral artery disease; TIA, transient ischemic attack; SLE, systemic lupus erythematosus; SBP, systolic blood pessure; DBP, diastolic blood pressure; MAP, mean arterial pressure; WBC, white blood cell; FPG, fasting plasma glucose; TC, total cholesterol; TG, triglycerides; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; hs-CRP, high-sensitivity C-reactive protein; NT-proBNP, N-terminal pro-B-type natriuretic peptide; BUN, blood urea nitrogen; eGFR, estimated glomerular filtration rate; LVESD, left ventricular end-systolic dimension; LVEDD, left ventricular end-diastolic dimension; LVMI, left ventricular mass index; LVEF, left ventricular ejection fraction; RAS, renal artery stenosis; CHIP, clonal hematopoiesis of indeterminate potential; RAAS, renin-angiotensin-aldosterone system Baseline Characteristics Across Varying CHIP In Table 2 , the results revealed that patients in the Large CHIP group tended to be older (P = 0.002) and more likely to be male (P = 0.024). Additionally, Large CHIP patients exhibited higher levels of neutrophils (P = 0.004) and lower lymphocyte counts (P = 0.001), indicating a pro-inflammatory state. These patients also had significantly worse renal function, as evidenced by higher serum creatinine levels (P = 0.002) and BUN levels (P = 0.003). Furthermore, the Large CHIP group was more likely to have a history of cardiovascular conditions such as myocardial infarction (P = 0.031) and peripheral artery disease (P = 0.033), suggesting a higher cardiovascular risk profile. Patients in the Large CHIP group showed significantly decreased renal microvascular perfusion compared to the No CHIP group. These findings underscored the association between higher CHIP burden and adverse clinical characteristics, particularly in terms of inflammation, renal dysfunction, cardiovascular comorbidities, and RMP. Table 2 Basic characteristics across patients with different levels of CHIP Characteristics Total (n = 175) No CHIP group (n = 113) Small CHIP group (n = 47) Large CHIP group (n = 15) P value Baseline data Age,yr 68.3 ± 7.2 66.5 ± 6.0 69.8 ± 7.5 72(64,87) 0.002 Male,n(%) 88(50.3%) 50 (44.2%) 26 (55.3%) 12 (80.0%) 0.024 BMI,kg/m 2 25.4 ± 2.6 26.3 ± 2.1 25.6 ± 2.4 24.8 ± 2.9 0.023 Current smoking,n(%) 54(30.9%) 32 (28.3%) 14 (29.8%) 8 (53.3%) 0.141 Previous history Diabetes,n(%) 70(40.0%) 40(35.4%) 21(44.7%) 9(60.0%) 0.140 Hypertension,n(%) 154(88.0%) 97 (85.8%) 43 (91.5%) 14 (95.0%) 0.486 Duration of hypertension,yr 10.8 ± 4.4 9.8 ± 3.6 11.5 ± 4.7 13(7,21) 0.003 Hyperlipidemia ,n(%) 90(51.4%) 51 (45.1%) 30 (63.8%) 9 (60.0%) 0.047 OMI,n(%) 21(12.0%) 9 (8.0%) 7 (14.9%) 5 (33.3%) 0.031 Atrial fibrillation,n(%) 25(14.3%) 12 (10.6%) 8 (17.0%) 5 (33.3%) 0.052 COPD,n(%) 20(11.4%) 10 (8.8%) 6 (12.8%) 4 (26.7%) 0.118 CKD,n(%) 41(23.4%) 20 (17.7%) 15 (31.9%) 6 (40.0%) 0.044 PAD,n(%) 24(13.7%) 11 (9.7%) 8 (17.0%) 5 (33.3%) 0.033 Stroke/TIA,n(%) 18(10.3%) 8 (7.1%) 6 (12.8%) 4 (26.7%) 0.051 Cancer,n(%) 14(8.0%) 5 (4.4%) 5 (10.6%) 4 (26.7%) 0.025 SLE,n(%) 10(5.7%) 3 (2.7%) 5 (10.6%) 2 (13.3%) 0.069 Blood pressure(mmHg) SBP 148.6 ± 20.1 145.1 ± 18.5 150.5 ± 20.3 152(133,178) 0.181 DBP 89.3 ± 18.4 86.7 ± 16.9 90.5 ± 19.2 95(82,116) 0.174 MAP 108.6 ± 22.4 104.0 ± 19.3 105.7 ± 20.6 110(78,139) 0.556 Lab. test WBC count(×10 9 /L) 6.5 ± 1.8 6.0 ± 1.5 6.8 ± 2.1 7.2 ± 2.7 0.006 Neutrophils(×10 9 /L) 3.8 ± 1.2 3.5 ± 1.1 4.0 ± 1.4 4.5 ± 1.8 0.004 Lymphocytes(×10 9 /L) 1.9 ± 0.6 2.2 ± 0.5 1.9 ± 0.6 1.8 ± 0.7 0.001 Platelet count(×10 9 /L) 252.2 ± 45.7 247.0 ± 43.2 254.5 ± 46.9 260.8 ± 50.7 0.399 Hemoglobin(g/dL) 13.4 ± 1.7 13.6 ± 1.5 13.1 ± 1.8 12.2 ± 3.0 0.009 FPG(mg/dL) 102.4 ± 23.7 98.5 ± 21.7 104.3 ± 24.2 112.1 ± 31.8 0.064 TC(mg/dL) 181.2 ± 41.2 178.3 ± 39.5 183.4 ± 42.7 188.7 ± 54.3 0.572 TG(mg/dL) 150.2 ± 35.3 145.4 ± 33.4 152.6 ± 36.1 160.3 ± 48.5 0.212 LDL-C(mg/dL) 112.5 ± 32.4 109.2 ± 30.8 115.8 ± 33.2 120.4 ± 45.7 0.301 HDL-C(mg/dL) 50.5 ± 16.1 53.3 ± 14.9 50.1 ± 16.4 47.8 ± 15.2 0.055 Total albumin(g/dL) 4.1 ± 1.4 4.2 ± 1.3 4.0 ± 1.4 3.8 ± 1.7 0.457 hs-CRP(mg/L) 5.1 ± 1.7 4.5 ± 1.7 5.1 ± 1.8 5.7 ± 2.2 0.017 NT-proBNP(pg/mL) 366.4 ± 65.3 354.8 ± 49.6 363.6 ± 53.5 380.1 ± 62.1 0.141 Serum creatinine (mg/dL) 1.2 ± 0.3 1.1 ± 0.2 1.3 ± 0.3 1.5(1.0,2.1) 0.002 BUN(mg/dL) 20.5 ± 7.3 18.9 ± 5.1 20.3 ± 6.1 24.1 ± 7.8 0.003 eGFR(ml/min/1.73m 2 ) 68.5 ± 17.5 71.5 ± 16.2 67.8 ± 18.5 63.0 ± 24.1 0.145 Echocardiography LVESD(mm) 46.7 ± 7.3 46.0 ± 6.9 46.7 ± 7.4 47.7 ± 8.2 0.633 LVEDD(mm) 55.1 ± 8.6 54.2 ± 7.9 56.0 ± 8.7 57.9 ± 9.3 0.167 LVMI(g/m 2 ) 125.4 ± 30.7 122.8 ± 29.1 126.3 ± 31.4 137.2 ± 43.1 0.035 LVEF(%) 60.1 ± 11.4 61.9 ± 10.6 59.8 ± 11.2 54(42,61) 0.231 Degree of RAS,% 83.5 ± 10.4 80.5 ± 9.8 84.7 ± 10.9 88(77,98) 0.011 RMP AUC1(dB×s) 1203.4 ± 386.2 1263.7 ± 413.7 1058.6 ± 347.5 767.2 ± 478.4 < 0.001 AUC2(dB×s) 5212.1 ± 1404.2 5493.4 ± 1638.7 5028.2 ± 1834.6 4732.8 ± 2221.4 0.132 PI(dB) 137.4 ± 26.5 145.7 ± 36.3 133.7 ± 42.7 106.4 ± 48.7 0.010 TTP(s) 18.6 ± 5.7 16.4 ± 4.8 22.1 ± 5.4 24.1 ± 8.5 < 0.001 MTT(s) 48.8 ± 15.7 40.7 ± 12.4 58.4 ± 14.7 64.8 ± 23.3 < 0.001 Drug treatment Statins,n(%) 90(51.4%) 52 (46.0%) 28 (59.6%) 10 (66.7%) 0.138 SGLT2i,n(%) 38 (21.7%) 21 (18.6%) 13 (27.7%) 4 (26.7%) 0.398 GLP-1RA,n(%) 16 (9.1%) 10 (8.8%) 5 (10.6%) 1(6.7%) 0.883 β-blocker,n(%) 32(18.3%) 23(20.4%) 8(17.0%) 2(13.3%) 0.753 RAAS inhibitor,n(%) 18(10.3%) 13(11.5%) 4(8.5%) 1(6.7%) 0.758 Antiplatelet therapy,n(%) 41(23.4%) 22 (19.5%) 12 (25.5%) 7 (46.7%) 0.057 Anticoagulant,n(%) 23(13.1%) 12 (10.6%) 7 (14.9%) 4 (26.7%) 0.206 BMI, body mass index; OMI, old myocardial infarction; COPD, chronic obstructive pulmonary disease; CKD, chronic kidney disease; PAD, peripheral artery disease; TIA, transient ischemic attack; SLE, systemic lupus erythematosus; SBP, systolic blood pessure; DBP, diastolic blood pressure; MAP, mean arterial pressure; WBC, white blood cell; FPG, fasting plasma glucose; TC, total cholesterol; TG, triglycerides; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; hs-CRP, high-sensitivity C-reactive protein; NT-proBNP, N-terminal pro-B-type natriuretic peptide; BUN, blood urea nitrogen; eGFR, estimated glomerular filtration rate; LVESD, left ventricular end-systolic dimension; LVEDD, left ventricular end-diastolic dimension; LVMI, left ventricular mass index; LVEF, left ventricular ejection fraction; RAS, renal artery stenosis; CHIP, clonal hematopoiesis of indeterminate potential; RAAS, renin-angiotensin-aldosterone system Primary Outcome During a median follow-up of 32 months (interquartile range, 22 to 49 months), 54 confirmed MACE occurred. The age-adjusted and covariable-adjusted risk for MACE across varying levels of frailty or CHIP are shown in Table 3 . In this cohort, the covariable-adjusted HR for MACE in a comparison of frail with robust subgroups was 1.38 (95% CI, 1.13 to 1.66), and 2.32 (95% CI, 1.37 to 3.95) in a comparison of Large CHIP with no CHIP subgroups. When the analysis was further adjusted for the other factor, the observed HR for MACE 1.35 (95%CI, 1.11 to 1.62) and 2.33 (95% CI, 1.35 to 4.02), respectively. Age-adjusted and competing risk-adjusted cumulative incidence curves for the probability of an incident MACE rose significantly with increasing levels of CHIP. However, risk was increased for frailty primarily among patients with frailty (Fig. 2 ). Table 3 Hazard ratios for MACEs across different levels of frailty or CHIP Variables HR (95%CI) for MACEs HR (95%CI) for RFD HR (95%CI) for HHF Frailty subgroup Robust group Pre-frail group Frail group Robust group Pre-frail group Frail group Robust group Pre-frail group Frail group Rate 16/65 (24.6%) 18/61 (29.5%) 20/49 (35.7%) 5/65 (7.7%) 7/61 (11.5%) 8/49 (16.3%) 3/65 (4.6%) 5/61 (8.2%) 7/49 (14.3%) Model 1 1.0 (reference) 1.19 (1.06–1.34) 1.44 (1.12–1.85) 1.0 (reference) 1.47 (1.13–1.91) 2.13 (1.36–3.35) 1.0 (reference) 1.75 (1.21–2.53) 3.02 (1.46–6.25) Model 2 1.0 (reference) 1.14 (0.97–1.33) 1.38 (1.13–1.66) 1.0 (reference) 1.43 (1.06–1.91) 2.04 (1.27–3.26) 1.0 (reference) 1.71 (1.15–2.55) 2.94 (1.38–6.26) Model 3§ 1.0 (reference) 1.11 (0.95–1.28) 1.35 (1.11–1.62) 1.0 (reference) 1.32 (0.98–1.75) 1.91 (1.13–3.28) 1.0 (reference) 1.62 (1.16–2.24) 2.87 (1.34–6.17) CHIP subgroup No CHIP group Small CHIP group Large CHIP group No CHIP group Small CHIP group Large CHIP group No CHIP group Small CHIP group Large CHIP group Rate 26/113 (24.8%) 17/47 (36.2%) 9/15 (60.0%) 9/113 (8.0%) 7/47 (14.9%) 4/15 (26.7%) 6/113 (5.3%) 5/47 (10.6%) 4/15 (26.7%) Model 1 1.0 (reference) 1.42 (1.18–1.71) 2.40 (1.43–4.01) 1.0 (reference) 1.83 (1.25–2.69) 3.37 (1.22–9.31) 1.0 (reference) 1.89 (1.26-) 4.35 (1.46–12.95) Model 2 1.0 (reference) 1.36 (1.14–1.62) 2.32 (1.37–3.95) 1.0 (reference) 1.74 (1.21–2.52) 3.21 (1.18–8.74) 1.0 (reference) 1.71 (1.20–2.84) 4.26 (1.37–13.23) Model 3 1.0 (reference) 1.33 (1.12–1.57) 2.33 (1.35–4.02) 1.0 (reference) 1.71 (1.18–2.49) 3.13 (1.07–9.18) 1.0 (reference) 1.63 (1.23–2.18) 4.13 (1.32–12.91) Model 1: age-adjusted; Model 2: covariable-adjusted; Model 3§: CHIP-adjusted; Model 3༆: CHIP-adjusted;MACE, major adverse cardiorenovascular event; CHIP, clonal hematopoiesis of indeterminate potential; HR, hazard ratio; CI, confidence interval Renal Function Deterioration And Hospitalization for Heart Failure Findings for RFD appeared to be consistent with those for MACE (Table 3 ). The covariable-adjusted HR for RFD in a comparison of frail with robust subgroups was 2.04 (95% CI, 1.27 to 3.26), and 3.21 (95% CI, 1.18 to 8.74) in a comparison of Large CHIP with no CHIP subgroups. When the analysis was further adjusted for the other factor, the observed HR for RFD 1.91 (95%CI, 1.13 to 3.28) and 3.13 (95% CI, 1.07 to 9.18), respectively. Age-adjusted and competing risk-adjusted cumulative incidence curves for the probability of an incident MACE rose significantly with increasing levels of CHIP. However, risk was increased for frailty primarily among patients with frailty (Fig. 2 ). Joint Effects The joint effect analysis of age-adjusted and competing risk-adjusted cumulative incidence curves according to whether the patients had frail (no frail vs. Frail subgroups) or CHIP (no CHIP vs. CHIP subgroups) are summarized in Table 4 . The risk of MACE was highest in patients who were both frail and had CHIP (HR = 2.32, 95%CI: 1.26–4.27), followed by those with CHIP but no frailty (HR = 1.51, 95%CI: 1.17–1.96). Frail patients without CHIP also had an elevated risk (HR = 1.24, 95%CI: 1.05–1.47) compared to robust patients without CHIP. Similar findings for the joint effects of frailty and CHIP on RFD and HHF. The joint effects of frailty and CHIP on prognosis are shown in Fig. 3 . Table 4 Joint effects of Covariable adjusted HRs (95%CI ) for patients with frail/CHIP Prognosis Rates HR covariable−adjusted 95%CI Major Cardiovascualr Events No frail + no CHIP 20/86(23.3%) 1.0 reference Frail + no CHIP 8/27(29.6%) 1.24 1.05–1.47 No frail + CHIP 14/40(35.0%) 1.51 1.17–1.96 Frail + CHIP 12/22(54.5%) 2.32 1.26–4.27 Renal Function Deterioration No frail + no CHIP 6/86(7.0%) 1.0 reference Frail + no CHIP 3/27(11.1%) 1.49 1.08–2.09 No frail + CHIP 7/40(17.5%) 2.47 1.23–4.96 Frail + CHIP 5/22(22.7%) 3.18 1.36–7.45 Hospitalization For Heart Failure No frail + no CHIP 4/86(4.7%) 1.0 reference Frail + no CHIP 2/27(7.4%) 1.51 1.11–2.06 No frail + CHIP 4/40(10.0%) 2.07 1.28–3.35 Frail + CHIP 5/22(22.7%) 4.35 1.41–13.42 No frail: robust + prefrail; CHIP: small CHIP + large CHIP Table 5 Combined effects of Covariable adjusted HRs (95%CI ) for patients with different number of frailty/Large CHIP Prognosis Number of frail or Large CHIP 0 1 2 Major Cardiovascualr Events n(%) 34/126 (26.9%) 16/44 (36.4%) 4/5 (80.0%) HR (95%CI ) 1.0 (reference) 1.35 (1.09–1.68) 2.84 (1.13–7.12) Renal Function Deterioration n(%) 12/126 (9.5%) 6/44 (13.6%) 2/5 (40.0%) HR (95%CI ) 1.0 (reference) 1.41 (1.12–1.79) 4.07 (1.25–13.26) Hospitalization For Heart Failure n(%) 8/126 (6.3%) 5/44 (11.4%) 2/5 (40.0%) HR (95%CI ) 1.0 (reference) 1.63 (1.18–2.26) 5.92 (2.26–15.52) Combined Effects Frailty and CHIP showed independent contribution to the MACE risk, and the greatest spread for risk was obtained in models that use both in combination. The covariable-adjusted HRs for MACE were 1.0 (reference group) for robust patients with no Large CHIP, were 1.35 (95% CI, 1.09–1.68) for patients with frail or Large CHIP, were 2.84 (95% CI, 1.13–7.12) for frail patents with Large CHIP. Similar combined effect were observed for the individual outcomes of RFD and HHF, with HRs of 4.07 (95% CI, 1.25–13.26) and 5.92 (95% CI, 2.26–15.52), respectively, for patients with frail and Large CHIP burden. Age-adjusted and competing risk-adjusted cumulative incidence curves for the probability of MACE, RFD and HHF according to the number of risk factors are shown in Fig. 4 . Discussion In this study, we explored the combined impact of frailty and CHIP on the risk of MACE in elderly patients with severe ARAS who underwent renal artery stenting. Our findings reveal that both frailty and CHIP independently contribute to a higher risk of MACE, and their combined presence significantly amplifies this risk. These results highlight the importance of considering both frailty and CHIP in cardiovascular risk stratification for elderly ARAS patients undergoing renal artery stenting. Frailty is well-known to increase the vulnerability of elderly individuals to adverse outcomes across various diseases, including cardiovascular conditions[ 21 ]. In our study, frail patients demonstrated significantly higher rates of MACE compared to robust individuals. This aligns with previous studies that have consistently shown frailty to be an independent predictor of worse outcomes in cardiovascular disease, including myocardial infarction, stroke, and heart failure[ 22 – 23 ]. The mechanisms underlying this relationship are thought to include a decline in physiological reserves, chronic inflammation, and impaired recovery capacity following acute events[ 24 ]. Similarly, CHIP has emerged as an important risk factor for cardiovascular events, even in individuals without traditional risk factors for atherosclerosis. Our study found that patients with large CHIP burdens had significantly higher rates of MACE, particularly when they were also frail. This finding reinforces the growing body of evidence linking CHIP with inflammation and cardiovascular disease progression. Mutations in genes associated with CHIP, such as TET2, DNMT3A, and ASXL1, have been shown to drive chronic inflammation through mechanisms involving the IL-1β and NLRP3 inflammasome pathways[ 25 – 26 ]. This pro-inflammatory state accelerates atherosclerosis, thereby increasing the risk of cardiovascular events[ 27 ]. Our results confirm the role of CHIP as an independent risk factor and suggest that it may interact with frailty to exacerbate poor outcomes. The synergistic effect observed between frailty and CHIP in our cohort underscores the need for a multidimensional approach to risk assessment in elderly patients with ARAS. The combination of physical frailty and underlying hematopoietic mutations likely creates a "double-hit" scenario, wherein both chronic inflammation and impaired physiological reserves converge to heighten the risk of MACE[ 28 – 29 ]. This is particularly concerning given that frail patients often have limited options for aggressive cardiovascular management due to their reduced ability to tolerate invasive procedures or medical therapies[ 30 ]. Therefore, identifying CHIP in frail patients could help refine their risk stratification and guide more personalized treatment strategies Our findings also emphasize the importance of longitudinal monitoring in this high-risk population. The median follow-up of 32 months provided a robust dataset to analyze the long-term outcomes of patients with varying levels of frailty and CHIP burden. The increased incidence of MACE in patients with large CHIP and frailty over time suggests that both factors have a sustained impact on cardiovascular risk, beyond the immediate peri-procedural period. This has important clinical implications, as it highlights the need for continued surveillance and proactive management of frailty and CHIP in elderly patients with ARAS[ 31 – 32 ]. Our study builds on prior research that has examined frailty and CHIP in the context of cardiovascular disease, but it is the first to investigate their combined impact in a cohort of patients with ARAS undergoing stenting. Previous studies have demonstrated the individual roles of frailty and CHIP in predicting adverse cardiovascular outcomes, but none have directly compared their synergistic effects[ 33 – 34 ]. A study by Marston et al. [ 35 ] studied 63,700 patients from five randomized trials that tested established therapies for CVD and found that CHIP was associated with a 30% increased risk of first MI, but not recurrent MI. The overall risk of CV events in CHIP + patients was modestly elevated, though not statistically significant. Additionally, no significant differences in treatment efficacy were observed between patients with or without CHIP for the therapies tested. These findings suggest that CHIP is linked to incident coronary events but does not predict enhanced benefit from standard CV treatments. Our findings extend these observations by demonstrating that CHIP not only increases cardiovascular risk but also interacts with frailty to further elevate the likelihood of MACE in elderly patients with ARAS. This suggests that the inflammatory pathways activated by CHIP may have an even greater impact in patients who are already physiologically compromised by frailty[ 36 ]. Similarly, the role of frailty in cardiovascular outcomes has been well-documented, and frailty significantly increases the risk of cardiovascular events, including myocardial infarction and stroke[ 37 – 38 ]. However, our study is unique in that it examines this relationship in the context of CHIP and ARAS, providing new insights into how frailty may compound the effects of underlying hematopoietic mutations. Given that CHIP drives inflammation through similar pathways, our study suggests that anti-inflammatory therapies may hold promise for reducing cardiovascular risk in patients with CHIP, particularly those who are also frail[ 39 ]. Svensson et al evaluate whether individuals with CHIP have greater cardiovascular event reduction in response to IL-1β neutralization in the CANTOS study[ 40 ]. A total of 338 participants were identified with CHIP, with TET2 variants being more common than DNMT3A in this population. While placebo-treated patients with CHIP showed a nonsignificant increase in MACE risk, those with TET2 variants had a reduced risk of MACE when treated with canakinumab. These findings suggest that patients with TET2-driven CHIP may benefit more from IL-1β neutralization. However, using canakinumab as a anti-inflammatory strategy to prevent CVD in TET2 mutation carriers faces significant challenges, primarily due to its high cost and the rejection by regulatory agencies of applications to expand its use for secondary prevention of atherosclerotic CVD[ 41 ]. Recently, Zuriaga et al conducted a study to evaluate whether colchicine, a widely available used anti-inflammatory drug, could prevent the accelerated atherosclerosis associated with TET2-mutant CH [ 42 ]. This study used a mouse model of TET2-mutant clonal haematopoiesis (CH) via bone marrow transplantation in atherosclerosis-prone Ldlr −/− mice, treated with colchicine or placebo. Colchicine prevented accelerated atherosclerosis and suppressed interleukin-1β overproduction in the TET2-mutant mice. In human cohorts from the Mass General Brigham Biobank and UK Biobank, colchicine prescription attenuated the association between TET2 mutations and myocardial infarction. These findings suggest colchicine may reduce cardiovascular risk in individuals with TET2-mutant CH. The broad use of colchicine may be limited by potential side effects, including a higher incidence of non-cardiovascular deaths in clinical trials, though without a clear mechanism. To improve its benefit/risk ratio, colchicine could be selectively used in individuals, such as TET2 mutation carriers, who are likely to gain the most benefit[ 43 ]. Further research is needed to explore its potential benefits in other cardiovascular conditions linked to TET2-mutant CHIP, such as heart failure. The findings of this study have several important clinical implications. First, frailty and CHIP should be considered as part of routine cardiovascular risk assessments in elderly patients with ARAS undergoing stenting. Screening for frailty and CHIP could help identify high-risk patients who may benefit from closer monitoring and more personalized treatment strategies[ 44 ]. Second, our results suggest that interventions targeting inflammation, such as anti-IL-1 therapies, may be beneficial for patients with CHIP. Given the growing body of evidence linking CHIP to cardiovascular disease through inflammatory pathways, clinical trials exploring the use of anti-inflammatory therapies in this population are warranted[ 45 ]. Additionally, efforts to address frailty, such as physical rehabilitation programs, may help improve outcomes in frail patients undergoing cardiovascular interventions[ 46 ]. Future research should focus on validating these findings in larger, prospective cohorts and exploring the underlying mechanisms of the frailty-CHIP interaction. Studies examining the role of anti-inflammatory and frailty-targeting interventions in patients with ARAS and CHIP are also needed to determine the best approaches for improving outcomes in this high-risk population. Study Limitations Despite the valuable insights provided by our study, several limitations should be acknowledged. (1) This is a retrospective cohort study, and although we made efforts to control for potential confounders through multivariable analysis, unmeasured variables may still have influenced the results. (2) The relatively small sample size, particularly in the Large CHIP group, may significantly limit the generalizability of our findings. Larger prospective studies are needed to confirm our observations and further elucidate the mechanisms underlying the interaction between frailty and CHIP in cardiovascular disease. (3) Another limitation is the reliance on targeted gene sequencing for CHIP detection. While this approach allowed us to identify mutations in commonly studied CHIP-associated genes, it may have missed less frequent mutations that could also contribute to cardiovascular risk. Whole-exome or whole-genome sequencing could provide a more comprehensive view of the mutational landscape in future studies. (4) While our study focused on MACE as the primary outcome, other important clinical endpoints, such as quality of life, were not assessed. Given the significant impact of frailty on overall well-being, future studies should include these outcomes to provide a more holistic understanding of the effects of frailty and CHIP on elderly patients with ARAS. Conclusion This study highlights the independent and synergistic effects of frailty and CHIP on cardiovascular outcomes in elderly patients with severe ARAS undergoing stenting. Both frailty and CHIP are significant risk factors for MACE, and their combination amplifies this risk, particularly in frail patients with large CHIP burdens. These findings underscore the importance of comprehensive risk assessments that integrate frailty and CHIP status when managing elderly patients with ARAS. Declarations Funding Our study is supported by National High Level Hospital Clinical Research Funding (BJ-2023-206, BJ-2018-198), Basic Research Project of the Central Academy of Medical Sciences of China (2019PT320012), Beijing Science and Technology Project (Z211100002921011) and the Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences. Conflicts of interest None. Ethical statement The study protocol was complied with the guidelines of the Declaration of Helsinki and relevant national regulations and approved by the Ethics Committee of Beijing Hospital (2023BJYYEC-342-01). 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Yalcinkaya M, Liu W, Thomas LA, Olszewska M, Xiao T, Abramowicz S, Papapetrou EP, Westerterp M, Wang N, Tabas I, Tall AR. BRCC3-Mediated NLRP3 Deubiquitylation Promotes Inflammasome Activation and Atherosclerosis in Tet2 Clonal Hematopoiesis. Circulation. 2023;148(22):1764–1777. doi: 10.1161/CIRCULATIONAHA.123.065344 . Liang YD, Zhang YN, Li YM, Chen YH, Xu JY, Liu M, Li J, Ma Z, Qiao LL, Wang Z, Yang JF, Wang H. Identification of Frailty and Its Risk Factors in Elderly Hospitalized Patients from Different Wards: A Cross-Sectional Study in China. Clin Interv Aging. 2019;14:2249–2259. doi: 10.2147/CIA.S225149 . Lin AE, Bapat AC, Xiao L, Niroula A, Ye J, Wong WJ, Agrawal M, Farady CJ, Boettcher A, Hergott CB, McConkey M, Flores-Bringas P, Shkolnik V, Bick AG, Milan D, Natarajan P, Libby P, Ellinor PT, Ebert BL. Clonal Hematopoiesis of Indeterminate Potential With Loss of Tet2 Enhances Risk for Atrial Fibrillation Through Nlrp3 Inflammasome Activation. Circulation. 2024;149(18):1419–1434. doi: 10.1161/CIRCULATIONAHA.123.065597 . Amancherla K, Wells JA 4th, Bick AG. Clonal hematopoiesis and vascular disease. Semin Immunopathol. 2022;44(3):303–308. doi: 10.1007/s00281-022-00913-z . Cao X, Li X, Zhang J, Sun X, Yang G, Zhao Y, Li S, Hoogendijk EO, Wang X, Zhu Y, Allore H, Gill TM, Liu Z. Associations Between Frailty and the Increased Risk of Adverse Outcomes Among 38,950 UK Biobank Participants With Prediabetes: Prospective Cohort Study. JMIR Public Health Surveill. 2023;9:e45502. doi: 10.2196/45502 . Ahmad H, Jahn N, Jaiswal S. Clonal Hematopoiesis and Its Impact on Human Health. Annu Rev Med. 2023;74:249–260. doi: 10.1146/annurev-med-042921-112347 . Evans MA, Walsh K. Clonal hematopoiesis, somatic mosaicism, and age-associated disease. Physiol Rev. 2023;103(1):649–716. doi: 10.1152/physrev.00004.2022 . Kusne Y, Xie Z, Patnaik MM. Clonal hematopoiesis: Molecular and clinical implications. Leuk Res. 2022;113:106787. doi: 10.1016/j.leukres.2022.106787 . Maciejewski JP. Clonal hematopoiesis. Semin Hematol. 2024;61(1):1–2. doi: 10.1053/j.seminhematol.2024.01.014 . Marston NA, Pirruccello JP, Melloni GEM, Kamanu F, Bonaca MP, Giugliano RP, Scirica BM, Wiviott SD, Bhatt DL, Steg PG, Raz I, Braunwald E, Libby P, Ellinor PT, Bick AG, Sabatine MS, Ruff CT. Clonal hematopoiesis, cardiovascular events and treatment benefit in 63,700 individuals from five TIMI randomized trials. Nat Med. 2024. doi: 10.1038/s41591-024-03188-z . Avagyan S, Zon LI. Clonal hematopoiesis and inflammation - the perpetual cycle. Trends Cell Biol. 2023;33(8):695–707. doi: 10.1016/j.tcb.2022.12.001 . Yu Q, Guo D, Peng J, Wu Q, Yao Y, Ding M, Wang J. Prevalence and adverse outcomes of frailty in older patients with acute myocardial infarction after percutaneous coronary interventions: A systematic review and meta-analysis. Clin Cardiol. 2023;46(1):5–12. doi: 10.1002/clc.23929 . Burton JK, Stewart J, Blair M, Oxley S, Wass A, Taylor-Rowan M, Quinn TJ. Prevalence and implications of frailty in acute stroke: systematic review & meta-analysis. Age Ageing. 2022;51(3):afac064. doi: 10.1093/ageing/afac064. . Hannan M, Chen J, Hsu J, Zhang X, Saunders MR, Brown J, McAdams-DeMarco M, Mohanty MJ, Vyas R, Hajjiri Z, Carmona-Powell E, Meza N, Porter AC, Ricardo AC, Lash JP; CRIC Study Investigators. Frailty and Cardiovascular Outcomes in Adults With CKD: Findings From the Chronic Renal Insufficiency Cohort (CRIC) Study. Am J Kidney Dis. 2024;83(2):208–215. doi: 10.1053/j.ajkd.2023.06.009 . Svensson EC, Madar A, Campbell CD, He Y, Sultan M, Healey ML, Xu H, D'Aco K, Fernandez A, Wache-Mainier C, Libby P, Ridker PM, Beste MT, Basson CT. TET2-Driven Clonal Hematopoiesis and Response to Canakinumab: An Exploratory Analysis of the CANTOS Randomized Clinical Trial. JAMA Cardiol. 2022;7(5):521–528. doi: 10.1001/jamacardio.2022.0386 . Boczar KE, Beanlands R, Wells G, Coyle D. Cost-Effectiveness of Canakinumab From a Canadian Perspective for Recurrent Cardiovascular Events. CJC Open. 2022;4(5):441–448. doi: 10.1016/j.cjco.2022.01.003 . Zuriaga MA, Yu Z, Matesanz N, Truong B, Ramos-Neble BL, Asensio-López MC, Uddin MM, Nakao T, Niroula A, Zorita V, Amorós-Pérez M, Moro R, Ebert BL, Honigberg MC, Pascual-Figal D, Natarajan P, Fuster JJ. Colchicine prevents accelerated atherosclerosis in TET2-mutant clonal haematopoiesis. Eur Heart J. 2024:ehae546. doi: 10.1093/eurheartj/ehae546 . Buckley LF, Libby P. Colchicine's Role in Cardiovascular Disease Management. Arterioscler Thromb Vasc Biol. 2024;44(5):1031–1041. doi: 10.1161/ATVBAHA.124.319851 . Fan J, Yu C, Guo Y, Bian Z, Sun Z, Yang L, Chen Y, Du H, Li Z, Lei Y, Sun D, Clarke R, Chen J, Chen Z, Lv J, Li L; China Kadoorie Biobank Collaborative Group. Frailty index and all-cause and cause-specific mortality in Chinese adults: a prospective cohort study. Lancet Public Health. 2020;5(12):e650-e660. doi: 10.1016/S2468-2667(20)30113-4 . Jensen JL, Easaw S, Anderson T, Varma Y, Zhang J, Jensen BC, Coombs CC. Clonal Hematopoiesis and the Heart: a Toxic Relationship. Curr Oncol Rep. 2023;25(5):455–463. doi: 10.1007/s11912-023-01398-1 . Kanagal-Shamanna R, Beck DB, Calvo KR. Clonal Hematopoiesis, Inflammation, and Hematologic Malignancy. Annu Rev Pathol. 2024;19:479–506. doi: 10.1146/annurev-pathmechdis-051222-122724 . Additional Declarations There is NO Competing Interest. Supplementary Files supplementaryfile13.pdf Supplementary Files: Supplementary file 1: Frailty criteria Supplementary file 2: Targeted gene sequencing of peripheral blood samples Supplementary file 3: Flow Chart Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5117728","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Biological Sciences - Article","associatedPublications":[],"authors":[{"id":360813129,"identity":"23f882d7-fef8-4d82-9ef4-5476e8ac9280","order_by":0,"name":"Peng Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuElEQVRIiWNgGAWjYBACPmYGNhDNw8/MfPABUVrYYFok29mSDYjTwgDRwmBwnsdMgDgt7LzHHnzcUSdjfJjBjIGhxiaaCIfxpRvOPMPGY3aYIe0Bw7G03AbCWnjMpHnbeEBajhswNhwmUsvfNgke42bGNgnitTC2GfAYMDOzEasF6JfetgQeicNszAYJxPiFn//ssQc/2+rs+fvPf3zwocaGsBZgJCKxEwgrR9cyCkbBKBgFowAbAADn9TCRv/FopAAAAABJRU5ErkJggg==","orcid":"","institution":"Beijing Hospital","correspondingAuthor":true,"prefix":"","firstName":"Peng","middleName":"","lastName":"Li","suffix":""},{"id":360813130,"identity":"6c33c584-0dec-4fb9-b2e4-dff9afacfbf0","order_by":1,"name":"Yiyang Wang","email":"","orcid":"","institution":"Beijing Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yiyang","middleName":"","lastName":"Wang","suffix":""},{"id":360813131,"identity":"5c80d5fe-ebc7-4441-b43b-3181ccc3176d","order_by":2,"name":"Yang Wang","email":"","orcid":"","institution":"National Center for Cardiovascular Diseases and Fuwai Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Wang","suffix":""},{"id":360813132,"identity":"2ccc1574-ed8f-4bfd-87ca-51dbcf975862","order_by":3,"name":"Hu Ai","email":"","orcid":"","institution":"National Center for Cardiovascular Diseases and Fuwai Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hu","middleName":"","lastName":"Ai","suffix":""},{"id":360813133,"identity":"35e705c9-c02f-4765-9d29-b0fb45e9dec4","order_by":4,"name":"Yongjun Li","email":"","orcid":"","institution":"National Center for Cardiovascular Diseases and Fuwai Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yongjun","middleName":"","lastName":"Li","suffix":""},{"id":360813134,"identity":"8edf2c4a-a86d-4cd6-8fe0-4ebf956b0b3e","order_by":5,"name":"Junhong Ren","email":"","orcid":"","institution":"National Center for Cardiovascular Diseases and Fuwai Hospital","correspondingAuthor":false,"prefix":"","firstName":"Junhong","middleName":"","lastName":"Ren","suffix":""}],"badges":[],"createdAt":"2024-09-19 14:26:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5117728/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5117728/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69356450,"identity":"43c32ee7-73b1-491a-ad71-4cb9348a9c1e","added_by":"auto","created_at":"2024-11-19 13:50:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":125034,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of frailty and CHIP status among severe RAS patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCHIP, clonal hematopoiesis of indeterminate potential; RAS, renal artery stenosis\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5117728/v1/4671154c8d25e14fb28e2b70.png"},{"id":69356449,"identity":"ac9b59a8-d1de-403c-86b1-df3d6a7883d2","added_by":"auto","created_at":"2024-11-19 13:50:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":265265,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAge-adjusted and competing risk-adjusted cumulative incidence of cardiovascular events.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 2A, risk of MACE in frailty subgroups; Figure 2B, risk of RFD in frailty subgroups; Figure 2C, risk of HHF in frailty subgroups; Figure 2D, risk of MACE in CHIP subgroups; Figure 2E, risk of RFD in CHIP subgroups; Figure 2F, risk of HHF in CHIP subgroups\u003c/p\u003e\n\u003cp\u003eMACE, major adverse cardiovascular event; CHIP, clonal hematopoiesis of indeterminate potential; RFD, renal function deterioration; HHF, hospitalization for heart failure\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5117728/v1/eb626af5cad0e375b687bfe8.png"},{"id":69355586,"identity":"6d9edf7a-72e2-4cae-9df6-7710aff8edb6","added_by":"auto","created_at":"2024-11-19 13:42:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":161264,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eJoint effect of frailty and CHIP on the cumulative incidence of cardiovascular events.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 3A, joint effect on MACE; Figure 3B, joint effect on RFD; Figure 3C, joint effect on HHF\u003c/p\u003e\n\u003cp\u003eMACE, major adverse cardiovascular event; CHIP, clonal hematopoiesis of indeterminate potential; RFD, renal function deterioration; HHF, hospitalization for heart failure\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5117728/v1/21aa32d15e383d46c2d8d1ad.png"},{"id":69358156,"identity":"d219a6cc-c296-4d32-a8a2-48f6ff7813d8","added_by":"auto","created_at":"2024-11-19 13:58:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":112110,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCombined effect of frailty and Large CHIP on the cumulative incidence of cardiovascular events.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 4A, combined effect on MACE; Figure 4B, combined effect on RFD; Figure 4C, combined effect on HHF\u003c/p\u003e\n\u003cp\u003eMACE, major adverse cardiovascular event; CHIP, clonal hematopoiesis of indeterminate potential; RFD, renal function deterioration; HHF, hospitalization for heart failure\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5117728/v1/22146fad07a38044381dd3ab.png"},{"id":69358468,"identity":"adfff4f8-581a-4a37-8ce5-54aae538b098","added_by":"auto","created_at":"2024-11-19 14:06:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1858546,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5117728/v1/a2b83305-07a1-4115-84d7-c99821c49591.pdf"},{"id":69355588,"identity":"0a1d6d00-e39f-4bc4-b920-d82a029b1884","added_by":"auto","created_at":"2024-11-19 13:42:20","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":234672,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Files\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eSupplementary file 1: Frailty criteria\u003c/p\u003e\n\u003cp\u003eSupplementary file 2: Targeted gene sequencing of peripheral blood samples\u003c/p\u003e\n\u003cp\u003eSupplementary file 3: Flow Chart\u003c/p\u003e","description":"","filename":"supplementaryfile13.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5117728/v1/e4aebd3196e90c3b73e94130.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Impact of Frailty and Clonal Hematopoiesis on Cardiovascular Outcomes in Elderly Patients with Renal Artery Stenosis Undergoing Stenting","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRenal artery stenosis (RAS) is a prevalent and significant clinical challenge, particularly in the elderly [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. RAS is closely linked with secondary hypertension, progressive renal dysfunction, and an elevated risk of major adverse cardiovascular events (MACE). The pathophysiology of RAS extends beyond simple luminal narrowing, encompassing a complex interplay of systemic and local factors that contribute to disease progression and increased MACE risk [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Among these, frailty and clonal hematopoiesis of indeterminate potential (CHIP) have emerged as independent predictors of poor cardiovascular outcomes [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]; however, their combined impact, particularly in the context of severe RAS, remains unclear.\u003c/p\u003e \u003cp\u003eFrailty, a multifactorial geriatric syndrome characterized by reduced physiological reserves and increased vulnerability to external stressors, has been consistently associated with higher mortality and increased rates of cardiovascular events across various patient populations[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In patients with RAS, frailty may exacerbate the clinical course by diminishing the capacity to recover from ischemic insults and by contributing to a higher burden of comorbidities. This increased susceptibility underscores the need for a more nuanced understanding of frailty\u0026rsquo;s role in the pathogenesis of cardiovascular complications in the elderly [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Concurrently, CHIP, defined by the presence of somatic mutations in hematopoietic stem cells, has been increasingly recognized as a driver of chronic inflammation, endothelial dysfunction, and atherosclerosis [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Mutations in key genes such as DNMT3A and TET2 are frequently observed in CHIP and have been linked to a heightened risk of cardiovascular disease, independent of traditional risk factors [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The presence of CHIP may thus represent a critical, yet underappreciated, factor in the progression of RAS and its associated cardiovascular complications.\u003c/p\u003e \u003cp\u003eDespite these insights, the combined impact of frailty and CHIP on MACE in elderly patients with ARAS has not been well studied. It is possible that the interaction between frailty and CHIP amplifies the risk of cardiovascular events. Frailty, with its physical vulnerability, and CHIP, with its role in inflammation, may work together to worsen outcomes [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Understanding this relationship is essential for better risk stratification and management of elderly patients with RAS.\u003c/p\u003e \u003cp\u003eTherefore, we hypothesized that frailty and CHP together might have an combined impact on cardiovascular risk. We addressed this hypothesis in the prospective cohort study, which included a cohort of 175 elderly severe RAS patients who underwent endovascular therapy\u003c/p\u003e"},{"header":"Patients and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eThis prospective cohort study was conducted at Beijing Hospital and registered with the China Clinical Trial Registration Center (ChiCTR2300077390). The study protocol adhered to the principles outlined in the Declaration of Helsinki, and has been approved by the Ethics Committee of Beijing Hospital (2023BJYYEC-342-01). Furthermore, the study was designed and reported in accordance with the STROCSS criteria[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Population\u003c/h3\u003e\n\u003cp\u003eWe collected data from patients diagnosed with severe atherosclerotic RAS (ARAS), defined as luminal stenosis of \u0026ge;\u0026thinsp;70%, who were treated at our hospital between January 2019 and December 2022. The final follow-up was conducted on June 30, 2024. Eligibility criteria included patients aged 60 years or older with severe RAS, confirmed by either contrast-enhanced ultrasound (CEUS) or computed tomography angiography (CTA)[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], who had undergone renal artery endovascular therapy (stent placement) and completed a minimum follow-up period of 18 months. Inclusion in the study also required complete data on frailty status and CHIP status. Exclusion criteria encompassed patients with a history of prior renal artery endovascular therapy or major vascular surgery. Moreover, patients with end-stage renal disease (estimated glomerular filtration rate (eGFR) of \u0026lt;\u0026thinsp;15 mL/min/1.73 m\u0026sup2;), and those who had active malignancies were also excluded.\u003c/p\u003e\n\u003ch3\u003eMethods\u003c/h3\u003e\n\u003cp\u003eBaseline data were collected at the time of study enrollment and recorded in our Clintrial Database. The collected data encompassed demographic information, medical history, laboratory results, stenosis degree, and treatment details. In addition, renal microvascular perfusion (RMP) was also evaluated using CEUS, including area under the ascending curve (AUC1), area under the descending curve (AUC2), peak intensity (PI), time to peak intensity (TTP), and mean transit time (MTT)[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFrailty was evaluated using the Fried phenotype, which consisted of five criteria: unintentional weight loss, exhaustion, low grip strength, slow walking speed, and low activity \u003cb\u003e(Supplementary file 1)\u003c/b\u003e. Patients with a score\u0026thinsp;\u0026ge;\u0026thinsp;3 were diagnosed with frailty, with a score of 1\u0026ndash;2 were diagnosed as prefrail, and with a score of 0 were diagnosed with robust [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Concurrently, CHIP was assessed through targeted gene sequencing of peripheral blood samples, focusing on forty-six mutations commonly associated with CHIP \u003cb\u003e(Supplementary file 2)\u003c/b\u003e. Based on the variant allele frequency (VAF), patients were categorized into No CHIP (VAF\u0026thinsp;\u0026lt;\u0026thinsp;2%), Small CHIP (VAF 2%-\u0026lt;10%), and Large CHIP (VAF\u0026thinsp;\u0026ge;\u0026thinsp;10%) groups[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eFollow-Up\u003c/h3\u003e\n\u003cp\u003eAll patients were systematically followed up from the time of their initial procedure until the last follow-up date on June 30, 2024. Follow-up assessments were conducted at regular intervals, including 6, and 12 months post-procedure, and annually thereafter. The primary outcome was the incidence of MACE, which was a composite of renal function deterioration (RFD), initiation of renal replacement therapy, renal artery revascularization, nonfatal myocardial infarction, hospitalization for heart failure (HHF), nonfatal stroke, and cardiorenovascular death. RFD was defined as a reduction of the eGFR from baseline of 30% or more, with the reduction sustained for 60 days or longer and not attributable to other causes[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The flow chart was shown in \u003cb\u003eSupplementary file 3\u003c/b\u003e.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eContinuous variables will be presented as means with standard deviations (SD) or medians with interquartile ranges (IQR), depending on the normality of their distribution. Categorical variables will be reported as frequencies and percentages. Baseline characteristics across frailty and CHIP subgroups will be compared using one-way ANOVA for continuous variables and chi-square tests for categorical variables.\u003c/p\u003e \u003cp\u003eSpearman\u0026rsquo;s correlation coefficients were used to discern relationships between frailty and CHIP status. For survival analysis, Kaplan-Meier curves will be generated to compare time-to-event outcomes for MACE and RFD across different frailty and CHIP subgroups. Hazard ratios (HR) for incident MACE were assessed in Cox proportional-hazard models that compared high frailty (prefrail, frail subgroups) or CHIP (small, large CHIP subgroups) burden with robust or No CHIP subgroup (the reference subgroup). Estimates of HR were obtained in model 1: adjusted for age; in model 2: additionally adjusted for diastolic blood pressure (DBP), eGFR, NT-proBNP, and the presence of diabetes; and in model 3: adjusted for the other factor (frailty or CHIP status). HR and 95% confidence intervals (CIs) will be reported for each frailty and CHIP category. Additionally, heatmaps will be utilized to visualize the combined impact of frailty and CHIP on MACE risk.\u003c/p\u003e \u003cp\u003eTo assess potential joint effects between frailty and CHIP status, we also conducted risk factor adjusted analysis that evaluate the MACE rates according to whether the patients had frail (no frail [robust\u0026thinsp;+\u0026thinsp;prefrail] vs. Frail subgroups) or CHIP (no CHIP vs. CHIP [Small\u0026thinsp;+\u0026thinsp;Large CHIP] subgroups).\u003c/p\u003e \u003cp\u003eTo evaluate the potential combined effects of frailty and CHIP status, we repeated the above analysis after dividing patients into three subgroups: subgroup 1: having frail or Large CHIP; subgroup 2: having frail and large CHIP; subgroup 3: not having frail, or Large CHIP.\u003c/p\u003e \u003cp\u003eAll statistical tests will be two-sided, with a P-value of \u0026lt;\u0026thinsp;0.05 considered statistically significant. Statistical analyses will be performed using SPSS software (version 26.0; IBM Corp., Armonk, NY).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eDistribution of Frailty and CHIP Status\u003c/h2\u003e \u003cp\u003eThe Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrated that in a population of severe RAS patients, the majority (64.6%) fall into the No CHIP category, with a progressively smaller proportion in Small CHIP (26.8%) and Large CHIP (8.6%). Among robust patients, most (70.8%) had No CHIP, while frail patients showed a higher prevalence of CHIP, particularly in the Small (34.7%) and Large (10.2%) CHIP categories. This distribution suggested a potential correlation between increasing frailty and a higher CHIP burden, indicating that frail patients may be more likely to exhibit significant levels of CHIP.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBaseline Characteristics Across Varying Frailty\u003c/h3\u003e\n\u003cp\u003eThe mean age of the patients was 68.3 years. In Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the results demonstrated that frail patients tended to be older (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and have longer durations of hypertension (P\u0026thinsp;=\u0026thinsp;0.001). Additionally, frail patients exhibited higher levels of neutrophils (P\u0026thinsp;=\u0026thinsp;0.002) and lower lymphocyte counts (P\u0026thinsp;=\u0026thinsp;0.001), reflecting a more pro-inflammatory state. Frail patients also had significantly higher serum creatinine levels (P\u0026thinsp;=\u0026thinsp;0.012) and BUN levels (P\u0026thinsp;=\u0026thinsp;0.034), indicating poorer renal function compared to their robust counterparts. Moreover, increased frailty is associated with impaired RMP (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBasic characteristics across patients with different levels of frailty\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;175)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRobust group (n\u0026thinsp;=\u0026thinsp;65)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePre-frail group (n\u0026thinsp;=\u0026thinsp;61)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFrail group (n\u0026thinsp;=\u0026thinsp;49)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge,yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.2\u0026thinsp;\u0026plusmn;\u0026thinsp;6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71.8\u0026thinsp;\u0026plusmn;\u0026thinsp;7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88(50.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (58.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31 (50.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19 (38.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI,kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoking,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54(30.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (26.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (29.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19 (38.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.346\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70(40.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22(33.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26(42.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22(44.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.409\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e154(88.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56 (86.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54 (88.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44 (89.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.754\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of hypertension,yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipidemia ,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90(51.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (43.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31 (50.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31 (63.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOMI,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21(12.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (7.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (11.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (18.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtrial fibrillation,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25(14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (9.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (14.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 (20.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.267\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20(11.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (9.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (9.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 (16.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.453\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKD,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41(23.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (13.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (23.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18 (36.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.399\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePAD,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24(13.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (9.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (14.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (18.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.355\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke/TIA,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18(10.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (7.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (11.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (12.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.671\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14(8.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (4.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (9.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (10.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.438\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSLE,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10(5.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (3.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (6.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (8.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.457\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood pressure(mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e148.6\u0026thinsp;\u0026plusmn;\u0026thinsp;20.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e145.2\u0026thinsp;\u0026plusmn;\u0026thinsp;17.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e149.6\u0026thinsp;\u0026plusmn;\u0026thinsp;18.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e152.8\u0026thinsp;\u0026plusmn;\u0026thinsp;22.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89.3\u0026thinsp;\u0026plusmn;\u0026thinsp;18.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.5\u0026thinsp;\u0026plusmn;\u0026thinsp;16.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88.7\u0026thinsp;\u0026plusmn;\u0026thinsp;17.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e92.4\u0026thinsp;\u0026plusmn;\u0026thinsp;20.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e108.6\u0026thinsp;\u0026plusmn;\u0026thinsp;22.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107.5\u0026thinsp;\u0026plusmn;\u0026thinsp;17.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109.0\u0026thinsp;\u0026plusmn;\u0026thinsp;19.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e112.3\u0026thinsp;\u0026plusmn;\u0026thinsp;21.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLab. test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC count(\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophils(\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocytes(\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet count(\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e252.2\u0026thinsp;\u0026plusmn;\u0026thinsp;45.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e245.5\u0026thinsp;\u0026plusmn;\u0026thinsp;43.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e250.4\u0026thinsp;\u0026plusmn;\u0026thinsp;47.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e258.7\u0026thinsp;\u0026plusmn;\u0026thinsp;51.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.334\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin(g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPG(mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102.4\u0026thinsp;\u0026plusmn;\u0026thinsp;23.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98.2\u0026thinsp;\u0026plusmn;\u0026thinsp;21.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100.5\u0026thinsp;\u0026plusmn;\u0026thinsp;23.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e106.3\u0026thinsp;\u0026plusmn;\u0026thinsp;30.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.225\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC(mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e181.2\u0026thinsp;\u0026plusmn;\u0026thinsp;41.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e178.1\u0026thinsp;\u0026plusmn;\u0026thinsp;32.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e180.2\u0026thinsp;\u0026plusmn;\u0026thinsp;35.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e183.4\u0026thinsp;\u0026plusmn;\u0026thinsp;45.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.673\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG(mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e150.2\u0026thinsp;\u0026plusmn;\u0026thinsp;35.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e145.6\u0026thinsp;\u0026plusmn;\u0026thinsp;31.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e148.7\u0026thinsp;\u0026plusmn;\u0026thinsp;33.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e150.5\u0026thinsp;\u0026plusmn;\u0026thinsp;38.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.740\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C(mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e112.5\u0026thinsp;\u0026plusmn;\u0026thinsp;32.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109.4\u0026thinsp;\u0026plusmn;\u0026thinsp;30.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e111.1\u0026thinsp;\u0026plusmn;\u0026thinsp;32.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e113.8\u0026thinsp;\u0026plusmn;\u0026thinsp;35.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.773\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C(mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.5\u0026thinsp;\u0026plusmn;\u0026thinsp;16.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.2\u0026thinsp;\u0026plusmn;\u0026thinsp;14.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.7\u0026thinsp;\u0026plusmn;\u0026thinsp;15.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49.3\u0026thinsp;\u0026plusmn;\u0026thinsp;17.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal albumin(g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.502\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehs-CRP(mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNT-proBNP(pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e366.4\u0026thinsp;\u0026plusmn;\u0026thinsp;65.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e356.1\u0026thinsp;\u0026plusmn;\u0026thinsp;50.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e360.7\u0026thinsp;\u0026plusmn;\u0026thinsp;54.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e376.8\u0026thinsp;\u0026plusmn;\u0026thinsp;65.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum creatinine (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN(mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.5\u0026thinsp;\u0026plusmn;\u0026thinsp;7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.8\u0026thinsp;\u0026plusmn;\u0026thinsp;7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR(ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.5\u0026thinsp;\u0026plusmn;\u0026thinsp;17.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.2\u0026thinsp;\u0026plusmn;\u0026thinsp;16.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.5\u0026thinsp;\u0026plusmn;\u0026thinsp;18.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63.7\u0026thinsp;\u0026plusmn;\u0026thinsp;20.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEchocardiography\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVESD(mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.7\u0026thinsp;\u0026plusmn;\u0026thinsp;7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.0\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.6\u0026thinsp;\u0026plusmn;\u0026thinsp;8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.519\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEDD(mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.1\u0026thinsp;\u0026plusmn;\u0026thinsp;8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.7\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.0\u0026thinsp;\u0026plusmn;\u0026thinsp;8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.345\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVMI(g/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125.4\u0026thinsp;\u0026plusmn;\u0026thinsp;30.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e123.0\u0026thinsp;\u0026plusmn;\u0026thinsp;28.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e124.5\u0026thinsp;\u0026plusmn;\u0026thinsp;29.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e130.3\u0026thinsp;\u0026plusmn;\u0026thinsp;32.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.407\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEF(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.1\u0026thinsp;\u0026plusmn;\u0026thinsp;11.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60.2\u0026thinsp;\u0026plusmn;\u0026thinsp;11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57.5\u0026thinsp;\u0026plusmn;\u0026thinsp;12.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.238\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDegree of RAS,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.0\u0026thinsp;\u0026plusmn;\u0026thinsp;9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83.4\u0026thinsp;\u0026plusmn;\u0026thinsp;10.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84.1\u0026thinsp;\u0026plusmn;\u0026thinsp;15.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.864\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAUC1(dB\u0026times;s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1203.4\u0026thinsp;\u0026plusmn;\u0026thinsp;386.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1269.3\u0026thinsp;\u0026plusmn;\u0026thinsp;380.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1183.5\u0026thinsp;\u0026plusmn;\u0026thinsp;341.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1063.6\u0026thinsp;\u0026plusmn;\u0026thinsp;447.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAUC2(dB\u0026times;s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5212.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1404.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5336.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2015.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5193.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1838.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4944.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1433.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.517\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePI(dB)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e137.4\u0026thinsp;\u0026plusmn;\u0026thinsp;26.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e144.6\u0026thinsp;\u0026plusmn;\u0026thinsp;36.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e135.9\u0026thinsp;\u0026plusmn;\u0026thinsp;40.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e126.7\u0026thinsp;\u0026plusmn;\u0026thinsp;45.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTTP(s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.2\u0026thinsp;\u0026plusmn;\u0026thinsp;6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMTT(s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.8\u0026thinsp;\u0026plusmn;\u0026thinsp;15.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.6\u0026thinsp;\u0026plusmn;\u0026thinsp;13.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.3\u0026thinsp;\u0026plusmn;\u0026thinsp;16.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58.3\u0026thinsp;\u0026plusmn;\u0026thinsp;19.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrug treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatins,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90(51.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (52.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35 (57.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21 (42.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.374\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSGLT2i,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (21.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (21.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (26.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 (16.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.286\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGLP-1RA,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (9.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (12.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (9.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (4.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ-blocker,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32(18.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13(20.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11(18.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7(14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.726\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRAAS inhibitor,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18(10.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(12.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7(11.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3(6.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.516\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntiplatelet therapy,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41(23.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (15.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (26.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15 (30.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnticoagulant,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23(13.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (9.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (13.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (18.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.362\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eBMI, body mass index; OMI, old myocardial infarction; COPD, chronic obstructive pulmonary disease; CKD, chronic kidney disease; PAD, peripheral artery disease; TIA, transient ischemic attack; SLE, systemic lupus erythematosus; SBP, systolic blood pessure; DBP, diastolic blood pressure; MAP, mean arterial pressure; WBC, white blood cell; FPG, fasting plasma glucose; TC, total cholesterol; TG, triglycerides; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; hs-CRP, high-sensitivity C-reactive protein; NT-proBNP, N-terminal pro-B-type natriuretic peptide; BUN, blood urea nitrogen; eGFR, estimated glomerular filtration rate; LVESD, left ventricular end-systolic dimension; LVEDD, left ventricular end-diastolic dimension; LVMI, left ventricular mass index; LVEF, left ventricular ejection fraction; RAS, renal artery stenosis; CHIP, clonal hematopoiesis of indeterminate potential; RAAS, renin-angiotensin-aldosterone system\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eBaseline Characteristics Across Varying CHIP\u003c/h2\u003e \u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the results revealed that patients in the Large CHIP group tended to be older (P\u0026thinsp;=\u0026thinsp;0.002) and more likely to be male (P\u0026thinsp;=\u0026thinsp;0.024). Additionally, Large CHIP patients exhibited higher levels of neutrophils (P\u0026thinsp;=\u0026thinsp;0.004) and lower lymphocyte counts (P\u0026thinsp;=\u0026thinsp;0.001), indicating a pro-inflammatory state. These patients also had significantly worse renal function, as evidenced by higher serum creatinine levels (P\u0026thinsp;=\u0026thinsp;0.002) and BUN levels (P\u0026thinsp;=\u0026thinsp;0.003). Furthermore, the Large CHIP group was more likely to have a history of cardiovascular conditions such as myocardial infarction (P\u0026thinsp;=\u0026thinsp;0.031) and peripheral artery disease (P\u0026thinsp;=\u0026thinsp;0.033), suggesting a higher cardiovascular risk profile. Patients in the Large CHIP group showed significantly decreased renal microvascular perfusion compared to the No CHIP group. These findings underscored the association between higher CHIP burden and adverse clinical characteristics, particularly in terms of inflammation, renal dysfunction, cardiovascular comorbidities, and RMP.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBasic characteristics across patients with different levels of CHIP\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;175)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo CHIP group (n\u0026thinsp;=\u0026thinsp;113)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSmall CHIP group (n\u0026thinsp;=\u0026thinsp;47)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLarge CHIP group (n\u0026thinsp;=\u0026thinsp;15)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge,yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69.8\u0026thinsp;\u0026plusmn;\u0026thinsp;7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72(64,87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88(50.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 (44.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (55.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12 (80.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI,kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoking,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54(30.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (28.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (29.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 (53.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70(40.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40(35.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21(44.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9(60.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e154(88.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97 (85.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43 (91.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14 (95.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.486\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of hypertension,yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13(7,21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipidemia ,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90(51.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51 (45.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 (63.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (60.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOMI,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21(12.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (8.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (14.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtrial fibrillation,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25(14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (10.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (17.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20(11.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (8.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (12.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (26.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKD,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41(23.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (17.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (31.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (40.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePAD,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24(13.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (9.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (17.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke/TIA,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18(10.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (7.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (12.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (26.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14(8.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (4.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (10.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (26.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSLE,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10(5.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (2.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (10.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (13.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood pressure(mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e148.6\u0026thinsp;\u0026plusmn;\u0026thinsp;20.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e145.1\u0026thinsp;\u0026plusmn;\u0026thinsp;18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e150.5\u0026thinsp;\u0026plusmn;\u0026thinsp;20.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e152(133,178)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89.3\u0026thinsp;\u0026plusmn;\u0026thinsp;18.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86.7\u0026thinsp;\u0026plusmn;\u0026thinsp;16.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90.5\u0026thinsp;\u0026plusmn;\u0026thinsp;19.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95(82,116)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e108.6\u0026thinsp;\u0026plusmn;\u0026thinsp;22.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104.0\u0026thinsp;\u0026plusmn;\u0026thinsp;19.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e105.7\u0026thinsp;\u0026plusmn;\u0026thinsp;20.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e110(78,139)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.556\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLab. test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC count(\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophils(\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocytes(\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet count(\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e252.2\u0026thinsp;\u0026plusmn;\u0026thinsp;45.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e247.0\u0026thinsp;\u0026plusmn;\u0026thinsp;43.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e254.5\u0026thinsp;\u0026plusmn;\u0026thinsp;46.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e260.8\u0026thinsp;\u0026plusmn;\u0026thinsp;50.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.399\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin(g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPG(mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102.4\u0026thinsp;\u0026plusmn;\u0026thinsp;23.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98.5\u0026thinsp;\u0026plusmn;\u0026thinsp;21.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e104.3\u0026thinsp;\u0026plusmn;\u0026thinsp;24.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e112.1\u0026thinsp;\u0026plusmn;\u0026thinsp;31.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC(mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e181.2\u0026thinsp;\u0026plusmn;\u0026thinsp;41.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e178.3\u0026thinsp;\u0026plusmn;\u0026thinsp;39.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e183.4\u0026thinsp;\u0026plusmn;\u0026thinsp;42.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e188.7\u0026thinsp;\u0026plusmn;\u0026thinsp;54.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.572\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG(mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e150.2\u0026thinsp;\u0026plusmn;\u0026thinsp;35.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e145.4\u0026thinsp;\u0026plusmn;\u0026thinsp;33.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e152.6\u0026thinsp;\u0026plusmn;\u0026thinsp;36.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e160.3\u0026thinsp;\u0026plusmn;\u0026thinsp;48.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C(mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e112.5\u0026thinsp;\u0026plusmn;\u0026thinsp;32.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109.2\u0026thinsp;\u0026plusmn;\u0026thinsp;30.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e115.8\u0026thinsp;\u0026plusmn;\u0026thinsp;33.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e120.4\u0026thinsp;\u0026plusmn;\u0026thinsp;45.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.301\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C(mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.5\u0026thinsp;\u0026plusmn;\u0026thinsp;16.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.3\u0026thinsp;\u0026plusmn;\u0026thinsp;14.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.1\u0026thinsp;\u0026plusmn;\u0026thinsp;16.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.8\u0026thinsp;\u0026plusmn;\u0026thinsp;15.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal albumin(g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.457\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehs-CRP(mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNT-proBNP(pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e366.4\u0026thinsp;\u0026plusmn;\u0026thinsp;65.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e354.8\u0026thinsp;\u0026plusmn;\u0026thinsp;49.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e363.6\u0026thinsp;\u0026plusmn;\u0026thinsp;53.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e380.1\u0026thinsp;\u0026plusmn;\u0026thinsp;62.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum creatinine (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.5(1.0,2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN(mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.5\u0026thinsp;\u0026plusmn;\u0026thinsp;7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.1\u0026thinsp;\u0026plusmn;\u0026thinsp;7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR(ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.5\u0026thinsp;\u0026plusmn;\u0026thinsp;17.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71.5\u0026thinsp;\u0026plusmn;\u0026thinsp;16.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67.8\u0026thinsp;\u0026plusmn;\u0026thinsp;18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63.0\u0026thinsp;\u0026plusmn;\u0026thinsp;24.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEchocardiography\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVESD(mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.7\u0026thinsp;\u0026plusmn;\u0026thinsp;7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.0\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.7\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.7\u0026thinsp;\u0026plusmn;\u0026thinsp;8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.633\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEDD(mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.1\u0026thinsp;\u0026plusmn;\u0026thinsp;8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.2\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.0\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVMI(g/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125.4\u0026thinsp;\u0026plusmn;\u0026thinsp;30.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e122.8\u0026thinsp;\u0026plusmn;\u0026thinsp;29.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e126.3\u0026thinsp;\u0026plusmn;\u0026thinsp;31.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e137.2\u0026thinsp;\u0026plusmn;\u0026thinsp;43.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEF(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.1\u0026thinsp;\u0026plusmn;\u0026thinsp;11.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.9\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.8\u0026thinsp;\u0026plusmn;\u0026thinsp;11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54(42,61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDegree of RAS,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.5\u0026thinsp;\u0026plusmn;\u0026thinsp;9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84.7\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88(77,98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAUC1(dB\u0026times;s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1203.4\u0026thinsp;\u0026plusmn;\u0026thinsp;386.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1263.7\u0026thinsp;\u0026plusmn;\u0026thinsp;413.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1058.6\u0026thinsp;\u0026plusmn;\u0026thinsp;347.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e767.2\u0026thinsp;\u0026plusmn;\u0026thinsp;478.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAUC2(dB\u0026times;s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5212.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1404.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5493.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1638.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5028.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1834.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4732.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2221.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePI(dB)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e137.4\u0026thinsp;\u0026plusmn;\u0026thinsp;26.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e145.7\u0026thinsp;\u0026plusmn;\u0026thinsp;36.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e133.7\u0026thinsp;\u0026plusmn;\u0026thinsp;42.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e106.4\u0026thinsp;\u0026plusmn;\u0026thinsp;48.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTTP(s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.1\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMTT(s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.8\u0026thinsp;\u0026plusmn;\u0026thinsp;15.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.7\u0026thinsp;\u0026plusmn;\u0026thinsp;12.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.4\u0026thinsp;\u0026plusmn;\u0026thinsp;14.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64.8\u0026thinsp;\u0026plusmn;\u0026thinsp;23.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrug treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatins,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90(51.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52 (46.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (59.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSGLT2i,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (21.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (18.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (27.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (26.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.398\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGLP-1RA,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (9.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (8.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (10.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ-blocker,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32(18.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23(20.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8(17.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2(13.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.753\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRAAS inhibitor,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18(10.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13(11.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4(8.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.758\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntiplatelet therapy,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41(23.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (19.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (25.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (46.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnticoagulant,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23(13.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (10.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (14.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (26.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eBMI, body mass index; OMI, old myocardial infarction; COPD, chronic obstructive pulmonary disease; CKD, chronic kidney disease; PAD, peripheral artery disease; TIA, transient ischemic attack; SLE, systemic lupus erythematosus; SBP, systolic blood pessure; DBP, diastolic blood pressure; MAP, mean arterial pressure; WBC, white blood cell; FPG, fasting plasma glucose; TC, total cholesterol; TG, triglycerides; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; hs-CRP, high-sensitivity C-reactive protein; NT-proBNP, N-terminal pro-B-type natriuretic peptide; BUN, blood urea nitrogen; eGFR, estimated glomerular filtration rate; LVESD, left ventricular end-systolic dimension; LVEDD, left ventricular end-diastolic dimension; LVMI, left ventricular mass index; LVEF, left ventricular ejection fraction; RAS, renal artery stenosis; CHIP, clonal hematopoiesis of indeterminate potential; RAAS, renin-angiotensin-aldosterone system\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePrimary Outcome\u003c/h2\u003e \u003cp\u003eDuring a median follow-up of 32 months (interquartile range, 22 to 49 months), 54 confirmed MACE occurred. The age-adjusted and covariable-adjusted risk for MACE across varying levels of frailty or CHIP are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. In this cohort, the covariable-adjusted HR for MACE in a comparison of frail with robust subgroups was 1.38 (95% CI, 1.13 to 1.66), and 2.32 (95% CI, 1.37 to 3.95) in a comparison of Large CHIP with no CHIP subgroups. When the analysis was further adjusted for the other factor, the observed HR for MACE 1.35 (95%CI, 1.11 to 1.62) and 2.33 (95% CI, 1.35 to 4.02), respectively. Age-adjusted and competing risk-adjusted cumulative incidence curves for the probability of an incident MACE rose significantly with increasing levels of CHIP. However, risk was increased for frailty primarily among patients with frailty (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHazard ratios for MACEs across different levels of frailty or CHIP\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eHR (95%CI) for MACEs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eHR (95%CI) for RFD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eHR (95%CI) for HHF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrailty subgroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRobust group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePre-frail group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFrail group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRobust group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePre-frail group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFrail group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRobust group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePre-frail group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eFrail group\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16/65\u003c/p\u003e \u003cp\u003e(24.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18/61\u003c/p\u003e \u003cp\u003e(29.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20/49\u003c/p\u003e \u003cp\u003e(35.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5/65\u003c/p\u003e \u003cp\u003e(7.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7/61\u003c/p\u003e \u003cp\u003e(11.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8/49\u003c/p\u003e \u003cp\u003e(16.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3/65\u003c/p\u003e \u003cp\u003e(4.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5/61\u003c/p\u003e \u003cp\u003e(8.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7/49\u003c/p\u003e \u003cp\u003e(14.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003cp\u003e(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003cp\u003e(1.06\u0026ndash;1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003cp\u003e(1.12\u0026ndash;1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003cp\u003e(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003cp\u003e(1.13\u0026ndash;1.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.13\u003c/p\u003e \u003cp\u003e(1.36\u0026ndash;3.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003cp\u003e(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003cp\u003e(1.21\u0026ndash;2.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.02\u003c/p\u003e \u003cp\u003e(1.46\u0026ndash;6.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003cp\u003e(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003cp\u003e(0.97\u0026ndash;1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003cp\u003e(1.13\u0026ndash;1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003cp\u003e(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003cp\u003e(1.06\u0026ndash;1.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.04\u003c/p\u003e \u003cp\u003e(1.27\u0026ndash;3.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003cp\u003e(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003cp\u003e(1.15\u0026ndash;2.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.94\u003c/p\u003e \u003cp\u003e(1.38\u0026ndash;6.26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u0026sect;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003cp\u003e(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003cp\u003e(0.95\u0026ndash;1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003cp\u003e(1.11\u0026ndash;1.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003cp\u003e(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003cp\u003e(0.98\u0026ndash;1.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.91\u003c/p\u003e \u003cp\u003e(1.13\u0026ndash;3.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003cp\u003e(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003cp\u003e(1.16\u0026ndash;2.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.87\u003c/p\u003e \u003cp\u003e(1.34\u0026ndash;6.17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHIP subgroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eCHIP group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSmall CHIP group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLarge CHIP group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eCHIP group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSmall CHIP group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLarge CHIP group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eCHIP group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSmall CHIP group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eLarge CHIP group\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26/113\u003c/p\u003e \u003cp\u003e(24.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17/47\u003c/p\u003e \u003cp\u003e(36.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9/15\u003c/p\u003e \u003cp\u003e(60.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9/113\u003c/p\u003e \u003cp\u003e(8.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7/47\u003c/p\u003e \u003cp\u003e(14.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4/15\u003c/p\u003e \u003cp\u003e(26.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6/113\u003c/p\u003e \u003cp\u003e(5.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5/47\u003c/p\u003e \u003cp\u003e(10.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4/15\u003c/p\u003e \u003cp\u003e(26.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003cp\u003e(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003cp\u003e(1.18\u0026ndash;1.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.40\u003c/p\u003e \u003cp\u003e(1.43\u0026ndash;4.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003cp\u003e(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.83\u003c/p\u003e \u003cp\u003e(1.25\u0026ndash;2.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.37\u003c/p\u003e \u003cp\u003e(1.22\u0026ndash;9.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003cp\u003e(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.89\u003c/p\u003e \u003cp\u003e(1.26-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.35\u003c/p\u003e \u003cp\u003e(1.46\u0026ndash;12.95)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003cp\u003e(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003cp\u003e(1.14\u0026ndash;1.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.32\u003c/p\u003e \u003cp\u003e(1.37\u0026ndash;3.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003cp\u003e(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003cp\u003e(1.21\u0026ndash;2.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.21\u003c/p\u003e \u003cp\u003e(1.18\u0026ndash;8.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003cp\u003e(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003cp\u003e(1.20\u0026ndash;2.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.26\u003c/p\u003e \u003cp\u003e(1.37\u0026ndash;13.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003cp\u003e(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003cp\u003e(1.12\u0026ndash;1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.33\u003c/p\u003e \u003cp\u003e(1.35\u0026ndash;4.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003cp\u003e(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003cp\u003e(1.18\u0026ndash;2.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.13\u003c/p\u003e \u003cp\u003e(1.07\u0026ndash;9.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003cp\u003e(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.63\u003c/p\u003e \u003cp\u003e(1.23\u0026ndash;2.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.13\u003c/p\u003e \u003cp\u003e(1.32\u0026ndash;12.91)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eModel 1: age-adjusted; Model 2: covariable-adjusted; Model 3\u0026sect;: CHIP-adjusted; Model 3༆: CHIP-adjusted;MACE, major adverse cardiorenovascular event; CHIP, clonal hematopoiesis of indeterminate potential; HR, hazard ratio; CI, confidence interval\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eRenal Function Deterioration And Hospitalization for Heart Failure\u003c/h2\u003e \u003cp\u003eFindings for RFD appeared to be consistent with those for MACE (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The covariable-adjusted HR for RFD in a comparison of frail with robust subgroups was 2.04 (95% CI, 1.27 to 3.26), and 3.21 (95% CI, 1.18 to 8.74) in a comparison of Large CHIP with no CHIP subgroups. When the analysis was further adjusted for the other factor, the observed HR for RFD 1.91 (95%CI, 1.13 to 3.28) and 3.13 (95% CI, 1.07 to 9.18), respectively. Age-adjusted and competing risk-adjusted cumulative incidence curves for the probability of an incident MACE rose significantly with increasing levels of CHIP. However, risk was increased for frailty primarily among patients with frailty (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eJoint Effects\u003c/h2\u003e \u003cp\u003eThe joint effect analysis of age-adjusted and competing risk-adjusted cumulative incidence curves according to whether the patients had frail (no frail vs. Frail subgroups) or CHIP (no CHIP vs. CHIP subgroups) are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The risk of MACE was highest in patients who were both frail and had CHIP (HR\u0026thinsp;=\u0026thinsp;2.32, 95%CI: 1.26\u0026ndash;4.27), followed by those with CHIP but no frailty (HR\u0026thinsp;=\u0026thinsp;1.51, 95%CI: 1.17\u0026ndash;1.96). Frail patients without CHIP also had an elevated risk (HR\u0026thinsp;=\u0026thinsp;1.24, 95%CI: 1.05\u0026ndash;1.47) compared to robust patients without CHIP. Similar findings for the joint effects of frailty and CHIP on RFD and HHF. The joint effects of frailty and CHIP on prognosis are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eJoint effects of Covariable adjusted HRs (95%CI ) for patients with frail/CHIP\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrognosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR\u003csub\u003ecovariable\u0026minus;adjusted\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMajor Cardiovascualr Events\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo frail\u0026thinsp;+\u0026thinsp;no CHIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20/86(23.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrail\u0026thinsp;+\u0026thinsp;no CHIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8/27(29.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05\u0026ndash;1.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo frail\u0026thinsp;+\u0026thinsp;CHIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14/40(35.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.17\u0026ndash;1.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrail\u0026thinsp;+\u0026thinsp;CHIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12/22(54.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.26\u0026ndash;4.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRenal Function Deterioration\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo frail\u0026thinsp;+\u0026thinsp;no CHIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6/86(7.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrail\u0026thinsp;+\u0026thinsp;no CHIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3/27(11.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08\u0026ndash;2.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo frail\u0026thinsp;+\u0026thinsp;CHIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7/40(17.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.23\u0026ndash;4.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrail\u0026thinsp;+\u0026thinsp;CHIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5/22(22.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.36\u0026ndash;7.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHospitalization For Heart Failure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo frail\u0026thinsp;+\u0026thinsp;no CHIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4/86(4.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrail\u0026thinsp;+\u0026thinsp;no CHIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2/27(7.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.11\u0026ndash;2.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo frail\u0026thinsp;+\u0026thinsp;CHIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4/40(10.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.28\u0026ndash;3.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrail\u0026thinsp;+\u0026thinsp;CHIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5/22(22.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.41\u0026ndash;13.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNo frail: robust\u0026thinsp;+\u0026thinsp;prefrail; CHIP: small CHIP\u0026thinsp;+\u0026thinsp;large CHIP\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCombined effects of Covariable adjusted HRs (95%CI ) for patients with different number of frailty/Large CHIP\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePrognosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eNumber of frail or Large CHIP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMajor Cardiovascualr Events\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34/126\u003c/p\u003e \u003cp\u003e(26.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16/44\u003c/p\u003e \u003cp\u003e(36.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4/5\u003c/p\u003e \u003cp\u003e(80.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR (95%CI )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003cp\u003e(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003cp\u003e(1.09\u0026ndash;1.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.84\u003c/p\u003e \u003cp\u003e(1.13\u0026ndash;7.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRenal Function Deterioration\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12/126\u003c/p\u003e \u003cp\u003e(9.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6/44\u003c/p\u003e \u003cp\u003e(13.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2/5\u003c/p\u003e \u003cp\u003e(40.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR (95%CI )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003cp\u003e(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003cp\u003e(1.12\u0026ndash;1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.07\u003c/p\u003e \u003cp\u003e(1.25\u0026ndash;13.26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHospitalization For Heart Failure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8/126\u003c/p\u003e \u003cp\u003e(6.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5/44\u003c/p\u003e \u003cp\u003e(11.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2/5\u003c/p\u003e \u003cp\u003e(40.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR (95%CI )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003cp\u003e(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.63\u003c/p\u003e \u003cp\u003e(1.18\u0026ndash;2.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.92\u003c/p\u003e \u003cp\u003e(2.26\u0026ndash;15.52)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCombined Effects\u003c/h2\u003e \u003cp\u003eFrailty and CHIP showed independent contribution to the MACE risk, and the greatest spread for risk was obtained in models that use both in combination. The covariable-adjusted HRs for MACE were 1.0 (reference group) for robust patients with no Large CHIP, were 1.35 (95% CI, 1.09\u0026ndash;1.68) for patients with frail or Large CHIP, were 2.84 (95% CI, 1.13\u0026ndash;7.12) for frail patents with Large CHIP. Similar combined effect were observed for the individual outcomes of RFD and HHF, with HRs of 4.07 (95% CI, 1.25\u0026ndash;13.26) and 5.92 (95% CI, 2.26\u0026ndash;15.52), respectively, for patients with frail and Large CHIP burden. Age-adjusted and competing risk-adjusted cumulative incidence curves for the probability of MACE, RFD and HHF according to the number of risk factors are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we explored the combined impact of frailty and CHIP on the risk of MACE in elderly patients with severe ARAS who underwent renal artery stenting. Our findings reveal that both frailty and CHIP independently contribute to a higher risk of MACE, and their combined presence significantly amplifies this risk. These results highlight the importance of considering both frailty and CHIP in cardiovascular risk stratification for elderly ARAS patients undergoing renal artery stenting.\u003c/p\u003e \u003cp\u003eFrailty is well-known to increase the vulnerability of elderly individuals to adverse outcomes across various diseases, including cardiovascular conditions[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In our study, frail patients demonstrated significantly higher rates of MACE compared to robust individuals. This aligns with previous studies that have consistently shown frailty to be an independent predictor of worse outcomes in cardiovascular disease, including myocardial infarction, stroke, and heart failure[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The mechanisms underlying this relationship are thought to include a decline in physiological reserves, chronic inflammation, and impaired recovery capacity following acute events[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Similarly, CHIP has emerged as an important risk factor for cardiovascular events, even in individuals without traditional risk factors for atherosclerosis. Our study found that patients with large CHIP burdens had significantly higher rates of MACE, particularly when they were also frail. This finding reinforces the growing body of evidence linking CHIP with inflammation and cardiovascular disease progression. Mutations in genes associated with CHIP, such as TET2, DNMT3A, and ASXL1, have been shown to drive chronic inflammation through mechanisms involving the IL-1β and NLRP3 inflammasome pathways[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. This pro-inflammatory state accelerates atherosclerosis, thereby increasing the risk of cardiovascular events[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Our results confirm the role of CHIP as an independent risk factor and suggest that it may interact with frailty to exacerbate poor outcomes.\u003c/p\u003e \u003cp\u003eThe synergistic effect observed between frailty and CHIP in our cohort underscores the need for a multidimensional approach to risk assessment in elderly patients with ARAS. The combination of physical frailty and underlying hematopoietic mutations likely creates a \"double-hit\" scenario, wherein both chronic inflammation and impaired physiological reserves converge to heighten the risk of MACE[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This is particularly concerning given that frail patients often have limited options for aggressive cardiovascular management due to their reduced ability to tolerate invasive procedures or medical therapies[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Therefore, identifying CHIP in frail patients could help refine their risk stratification and guide more personalized treatment strategies\u003c/p\u003e \u003cp\u003eOur findings also emphasize the importance of longitudinal monitoring in this high-risk population. The median follow-up of 32 months provided a robust dataset to analyze the long-term outcomes of patients with varying levels of frailty and CHIP burden. The increased incidence of MACE in patients with large CHIP and frailty over time suggests that both factors have a sustained impact on cardiovascular risk, beyond the immediate peri-procedural period. This has important clinical implications, as it highlights the need for continued surveillance and proactive management of frailty and CHIP in elderly patients with ARAS[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Our study builds on prior research that has examined frailty and CHIP in the context of cardiovascular disease, but it is the first to investigate their combined impact in a cohort of patients with ARAS undergoing stenting. Previous studies have demonstrated the individual roles of frailty and CHIP in predicting adverse cardiovascular outcomes, but none have directly compared their synergistic effects[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. A study by Marston et al. [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] studied 63,700 patients from five randomized trials that tested established therapies for CVD and found that CHIP was associated with a 30% increased risk of first MI, but not recurrent MI. The overall risk of CV events in CHIP\u003csup\u003e+\u003c/sup\u003e patients was modestly elevated, though not statistically significant. Additionally, no significant differences in treatment efficacy were observed between patients with or without CHIP for the therapies tested. These findings suggest that CHIP is linked to incident coronary events but does not predict enhanced benefit from standard CV treatments. Our findings extend these observations by demonstrating that CHIP not only increases cardiovascular risk but also interacts with frailty to further elevate the likelihood of MACE in elderly patients with ARAS. This suggests that the inflammatory pathways activated by CHIP may have an even greater impact in patients who are already physiologically compromised by frailty[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Similarly, the role of frailty in cardiovascular outcomes has been well-documented, and frailty significantly increases the risk of cardiovascular events, including myocardial infarction and stroke[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. However, our study is unique in that it examines this relationship in the context of CHIP and ARAS, providing new insights into how frailty may compound the effects of underlying hematopoietic mutations.\u003c/p\u003e \u003cp\u003eGiven that CHIP drives inflammation through similar pathways, our study suggests that anti-inflammatory therapies may hold promise for reducing cardiovascular risk in patients with CHIP, particularly those who are also frail[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Svensson et al evaluate whether individuals with CHIP have greater cardiovascular event reduction in response to IL-1β neutralization in the CANTOS study[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. A total of 338 participants were identified with CHIP, with TET2 variants being more common than DNMT3A in this population. While placebo-treated patients with CHIP showed a nonsignificant increase in MACE risk, those with TET2 variants had a reduced risk of MACE when treated with canakinumab. These findings suggest that patients with TET2-driven CHIP may benefit more from IL-1β neutralization. However, using canakinumab as a anti-inflammatory strategy to prevent CVD in TET2 mutation carriers faces significant challenges, primarily due to its high cost and the rejection by regulatory agencies of applications to expand its use for secondary prevention of atherosclerotic CVD[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Recently, Zuriaga et al conducted a study to evaluate whether colchicine, a widely available used anti-inflammatory drug, could prevent the accelerated atherosclerosis associated with TET2-mutant CH [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. This study used a mouse model of TET2-mutant clonal haematopoiesis (CH) via bone marrow transplantation in atherosclerosis-prone Ldlr\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e mice, treated with colchicine or placebo. Colchicine prevented accelerated atherosclerosis and suppressed interleukin-1β overproduction in the TET2-mutant mice. In human cohorts from the Mass General Brigham Biobank and UK Biobank, colchicine prescription attenuated the association between TET2 mutations and myocardial infarction. These findings suggest colchicine may reduce cardiovascular risk in individuals with TET2-mutant CH. The broad use of colchicine may be limited by potential side effects, including a higher incidence of non-cardiovascular deaths in clinical trials, though without a clear mechanism. To improve its benefit/risk ratio, colchicine could be selectively used in individuals, such as TET2 mutation carriers, who are likely to gain the most benefit[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Further research is needed to explore its potential benefits in other cardiovascular conditions linked to TET2-mutant CHIP, such as heart failure.\u003c/p\u003e \u003cp\u003eThe findings of this study have several important clinical implications. First, frailty and CHIP should be considered as part of routine cardiovascular risk assessments in elderly patients with ARAS undergoing stenting. Screening for frailty and CHIP could help identify high-risk patients who may benefit from closer monitoring and more personalized treatment strategies[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Second, our results suggest that interventions targeting inflammation, such as anti-IL-1 therapies, may be beneficial for patients with CHIP. Given the growing body of evidence linking CHIP to cardiovascular disease through inflammatory pathways, clinical trials exploring the use of anti-inflammatory therapies in this population are warranted[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Additionally, efforts to address frailty, such as physical rehabilitation programs, may help improve outcomes in frail patients undergoing cardiovascular interventions[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Future research should focus on validating these findings in larger, prospective cohorts and exploring the underlying mechanisms of the frailty-CHIP interaction. Studies examining the role of anti-inflammatory and frailty-targeting interventions in patients with ARAS and CHIP are also needed to determine the best approaches for improving outcomes in this high-risk population.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eStudy Limitations\u003c/h2\u003e \u003cp\u003eDespite the valuable insights provided by our study, several limitations should be acknowledged. (1) This is a retrospective cohort study, and although we made efforts to control for potential confounders through multivariable analysis, unmeasured variables may still have influenced the results. (2) The relatively small sample size, particularly in the Large CHIP group, may significantly limit the generalizability of our findings. Larger prospective studies are needed to confirm our observations and further elucidate the mechanisms underlying the interaction between frailty and CHIP in cardiovascular disease. (3) Another limitation is the reliance on targeted gene sequencing for CHIP detection. While this approach allowed us to identify mutations in commonly studied CHIP-associated genes, it may have missed less frequent mutations that could also contribute to cardiovascular risk. Whole-exome or whole-genome sequencing could provide a more comprehensive view of the mutational landscape in future studies. (4) While our study focused on MACE as the primary outcome, other important clinical endpoints, such as quality of life, were not assessed. Given the significant impact of frailty on overall well-being, future studies should include these outcomes to provide a more holistic understanding of the effects of frailty and CHIP on elderly patients with ARAS.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study highlights the independent and synergistic effects of frailty and CHIP on cardiovascular outcomes in elderly patients with severe ARAS undergoing stenting. Both frailty and CHIP are significant risk factors for MACE, and their combination amplifies this risk, particularly in frail patients with large CHIP burdens. These findings underscore the importance of comprehensive risk assessments that integrate frailty and CHIP status when managing elderly patients with ARAS.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study is supported by National High Level Hospital Clinical Research Funding (BJ-2023-206, BJ-2018-198), Basic Research Project of the Central Academy of Medical Sciences of China (2019PT320012), Beijing Science and Technology Project (Z211100002921011) and the Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was complied with the guidelines of the Declaration of Helsinki and relevant national regulations and approved by the Ethics Committee of Beijing Hospital (2023BJYYEC-342-01).\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRockwood K, Song X, MacKnight C, Bergman H, Hogan DB, McDowell I, Mitnitski A. 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Clonal Hematopoiesis, Inflammation, and Hematologic Malignancy. Annu Rev Pathol. 2024;19:479\u0026ndash;506. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1146/annurev-pathmechdis-051222-122724\u003c/span\u003e\u003cspan address=\"10.1146/annurev-pathmechdis-051222-122724\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Frailty, Clonal hematopoiesis, Renal artery stenosis, Cardiovascular events, Elderly patients; Aging","lastPublishedDoi":"10.21203/rs.3.rs-5117728/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5117728/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eFrailty and clonal hematopoiesis of indeterminate potential (CHIP) have emerged as crucial predictors of adverse cardiovascular outcomes in older adults. However, their combined impact on major adverse cardiovascular events (MACE) in patients with severe atherosclerotic renal artery stenosis (ARAS) remains unclear.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eWe conducted a prospective cohort study involving 175 patients aged 60 years and older with severe ARAS (luminal stenosis\u0026thinsp;\u0026ge;\u0026thinsp;70%) who underwent renal artery stenting at Beijing Hospital between January 2019 and December 2022. Frailty was assessed using the Fried phenotype, categorizing patients into robust, prefrail, and frail subgroups. CHIP status was determined through targeted gene sequencing of peripheral blood, stratifying patients into No CHIP (VAF\u0026thinsp;\u0026lt;\u0026thinsp;2%), Small CHIP (VAF 2%-\u0026lt;10%), and Large CHIP (VAF\u0026thinsp;\u0026ge;\u0026thinsp;10%) subgroups. All patients were systematically followed up until June 30, 2024. The primary outcome was the incidence of MACE, which was a composite of renal function deterioration (RFD), initiation of renal replacement therapy, renal artery revascularization, nonfatal myocardial infarction, hospitalization for heart failure, nonfatal stroke, and cardiorenovascular death. We employed Cox proportional hazards models, Kaplan-Meier survival analysis, and heatmaps to explore the combined impact of frailty and CHIP on MACE risk.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eThe mean age of the patients was 68.3 years. Of the cohort, 64.6% had no CHIP, 26.8% had Small CHIP, and 8.6% had Large CHIP. Frail patients showed a higher prevalence of CHIP, particularly in the Small (34.7%) and Large (10.2%) CHIP categories. During a median follow-up of 32 months, 54 MACE occurred. Kaplan-Meier survival curve revealed that frailty was associated with a higher incidence of MACE (35.7% in frail vs. 29.5% in prefrail vs. 24.6% in robust, P\u0026thinsp;=\u0026thinsp;0.045) and RFD (16.3% in frail vs. 11.5% in prefrail vs. 7.7% in robust, P\u0026thinsp;=\u0026thinsp;0.034). Patients with Large CHIP experienced significantly higher rates of MACE (60.0% vs. 36.2% in Small CHIP vs. 24.8% in No CHIP, P\u0026thinsp;=\u0026thinsp;0.004) and RFD (26.7% vs. 14.9% in prefrail vs. 8.0% in robust, P\u0026thinsp;=\u0026thinsp;0.019). Findings for RFD appeared to be consistent with those for MACE. Frailty and CHIP status showed independent contribution to overall risk. The greatest spread for MACE and RFD risk was obtained in models that incorporated frail and Large CHIP.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e \u003cp\u003eFrailty and CHIP, independently and jointly, contribute to a significantly higher risk of MACE and RFD in elderly patients with severe ARAS undergoing stenting. These findings highlight the necessity for integrated risk stratification and targeted management strategies in this high-risk population.\u003c/p\u003e","manuscriptTitle":"Impact of Frailty and Clonal Hematopoiesis on Cardiovascular Outcomes in Elderly Patients with Renal Artery Stenosis Undergoing Stenting","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-19 13:42:15","doi":"10.21203/rs.3.rs-5117728/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-medicine","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsmed","sideBox":"Learn more about [Communications Medicine](http://www.nature.com/commsmed)","snPcode":"43856","submissionUrl":"https://mts-commsmed.nature.com/cgi-bin/main.plex","title":"Communications Medicine","twitterHandle":"@commsmedicine","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"faf9d53d-70a3-4b3b-8b60-04287d3c8e6e","owner":[],"postedDate":"November 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":38387116,"name":"Health sciences/Medical research/Outcomes research"},{"id":38387117,"name":"Health sciences/Diseases/Cardiovascular diseases/Vascular diseases/Peripheral vascular disease"}],"tags":[],"updatedAt":"2024-11-19T13:42:16+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-19 13:42:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5117728","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5117728","identity":"rs-5117728","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}