Multivascular Imaging-Derived Atherosclerotic Burden and Cardiorenal Outcomes in Non-Dialysis Chronic Kidney Disease

Multivascular Imaging-Derived Atherosclerotic Burden and Cardiorenal Outcomes in Non-Dialysis Chronic Kidney Disease | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Multivascular Imaging-Derived Atherosclerotic Burden and Cardiorenal Outcomes in Non-Dialysis Chronic Kidney Disease Xiongpan Wang, Fan Zhu, Yunfang Huang, Dianjun Liu, Li Xu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9449777/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background Polyvascular atherosclerosis amplifies cardiovascular risk in the general population, yet its prognostic significance in chronic kidney disease (CKD) — where the atherosclerotic phenotype is qualitatively distinct — has not been systematically evaluated. We examined whether a structured imaging-based multivascular atherosclerotic burden score predicts cardiovascular and renal outcomes in non-dialysis CKD. Methods This retrospective cohort study included 8,645 non-dialysis CKD patients (stages 1–4) at Wuhan Central Hospital (2015–2023) who underwent vascular imaging within 365 days of the index date. Atherosclerotic involvement was ascertained from structured text extraction of imaging reports across four vascular domains (coronary, aortic, carotid/cerebral, peripheral), and a burden score (0–4) was defined as the count of affected territories. Cox proportional hazards models with sequential covariate adjustment were fitted for all-cause mortality, a composite kidney endpoint, and major adverse cardiovascular events (MACE). Fine–Gray competing-risk models, landmark analyses, subgroup analyses with interaction testing, and multiple imputation served as prespecified sensitivity analyses. Results Over a median follow-up of 0.90 years (IQR 0.18–1.48), 591 deaths (6.8%), 580 kidney events (6.7%), and 2,873 MACE (33.2%) were recorded. In fully adjusted models (N = 6,176), each additional affected vascular bed was associated with a 10% higher MACE hazard (HR 1.10, 95% CI 1.06–1.15; P < 0.001), confirmed by Fine–Gray analysis (sHR 1.10, P < 0.001) and multiple imputation (HR 1.13, P < 0.001). The burden score was also independently associated with the composite kidney endpoint (HR 1.14, 95% CI 1.03–1.26; P = 0.008), corroborated by Fine–Gray modelling (sHR 1.13, P = 0.01). No independent association was observed with all-cause mortality (HR 1.00, P = 0.92). Domain-specific analyses revealed that coronary and carotid/cerebral disease drove the MACE signal, whereas aortic atherosclerosis was the dominant predictor of kidney events (HR 2.31, P < 0.001). Conclusions A multivascular imaging atherosclerotic burden score was independently associated with both MACE and adverse kidney outcomes in non-dialysis CKD, with domain-specific heterogeneity implicating distinct pathophysiological pathways for cardiovascular and renal endpoints. Aortic atherosclerosis emerged as a particularly potent predictor of kidney disease progression. atherosclerosis chronic kidney disease polyvascular disease cardiovascular outcomes competing risks imaging Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Cardiovascular disease remains the leading cause of death in patients with chronic kidney disease, accounting for a mortality rate that exceeds age-matched controls by a factor of five to ten even in the earliest stages of renal impairment [ 1 – 3 ]. The pathobiology underlying this excess risk extends beyond traditional Framingham factors: uremic toxins, disordered calcium-phosphate metabolism, chronic systemic inflammation, and endothelial dysfunction converge to produce an accelerated and phenotypically distinct form of atherosclerosis characterised by diffuse medial calcification, plaque instability, and multiterritory involvement [ 4 – 6 ]. Coronary artery calcification scoring has been investigated as a prognostic marker in CKD cohorts [ 7 , 8 ], yet this single-territory approach fails to capture the systemic nature of the atherosclerotic process. In general cardiovascular populations, the concept of polyvascular disease — simultaneous atherosclerotic involvement of two or more arterial beds — has been established as a potent risk amplifier. The REACH registry demonstrated that patients with symptomatic disease in multiple vascular territories experienced substantially higher rates of cardiovascular death, myocardial infarction, and stroke than those with single-territory involvement [ 9 , 10 ]. Subsequent data from the COMPASS trial confirmed that the number of affected vascular beds independently predicted major adverse limb events and cardiovascular mortality [ 11 ], and meta-analytic evidence has shown that multiterritory atherosclerosis improves cardiovascular risk prediction beyond traditional scores [ 12 ]. Whether this polyvascular paradigm translates to CKD populations, where the atherosclerotic phenotype may differ qualitatively and where competing non-cardiovascular mortality is substantial, has not been systematically evaluated. A practical barrier to studying multivascular burden in routine clinical settings is the heterogeneity of imaging data. Vascular examinations are performed for diverse indications and reported in semi-structured text, rendering systematic extraction of atherosclerotic findings across territories challenging. Advances in electronic health record phenotyping have demonstrated the feasibility of deriving clinically meaningful variables from imaging report text [ 13 , 14 ], but this approach has not been applied to construct a composite multivascular atherosclerotic burden score in CKD. The present study was therefore designed to address two questions. First, whether a structured imaging-based atherosclerotic burden score — defined as the number of vascular domains with documented atherosclerosis — is independently associated with all-cause mortality, a composite kidney endpoint, and major adverse cardiovascular events in non-dialysis CKD. Second, whether the constituent vascular territories exhibit differential associations with these endpoints, thereby providing mechanistic insight into the organ-specific consequences of regional atherosclerosis in the uraemic milieu. Methods This retrospective cohort study was conducted using electronic medical records from Wuhan Central Hospital, a tertiary referral centre in Hubei Province, China, covering the period 2015–2023. The study protocol was approved by the institutional ethics committee of Wuhan Central Hospital; the requirement for individual informed consent was waived owing to the retrospective design. Reporting adheres to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines [ 16 ]. The source population comprised 10,567 patients with a recorded diagnosis of CKD (ICD-10 codes N18.x) [ 15 ] after exclusion of those receiving baseline dialysis, those with end-stage renal disease (eGFR < 15 mL/min/1.73 m²), and kidney transplant recipients. From this source population, patients who had undergone at least one vascular imaging examination within 365 days before the index date were selected, yielding an analytic sample of 8,645 (81.8% of the source population). The index date was defined as the date of the last vascular imaging examination within the baseline window. Atherosclerotic involvement was ascertained from imaging reports stored in the medical records database, which contained structured fields for examination name, anatomical position, findings, and clinical interpretation. A rule-based text extraction algorithm identified atherosclerotic disease across four vascular domains: coronary (coronary artery, left anterior descending, right coronary artery, left circumflex, left main), aortic (thoracic aorta, abdominal aorta, ascending and descending aorta, aortic arch), carotid/cerebral (carotid artery, vertebral artery, subclavian artery, cerebral arteries), and peripheral (lower extremity arteries, femoral, popliteal, tibial, iliac arteries). A domain was scored as positive if any imaging report within the 365-day window contained both a domain-specific anatomical keyword and an atherosclerosis-related finding (atherosclerosis, plaque, calcification, stenosis, or occlusion). The burden score was defined as the number of positive domains (range 0–4) and was analysed both as a continuous variable per one-domain increment and as a categorical variable (0, 1, 2, 3–4). Three endpoints were prespecified. Major adverse cardiovascular events (MACE), the primary endpoint, comprised myocardial infarction, stroke, heart failure hospitalisation, or cardiovascular death, identified through ICD-10 discharge codes. All-cause mortality was ascertained from hospital death records and linked administrative data. The composite kidney endpoint comprised doubling of serum creatinine, a sustained decline in eGFR of ≥ 40% from baseline, or initiation of renal replacement therapy [ 17 ]. Baseline covariates included age, sex, estimated glomerular filtration rate (CKD-EPI equation [ 18 ]), serum albumin, haemoglobin, systolic blood pressure, and comorbidities (diabetes, hypertension, coronary heart disease, heart failure, stroke, chronic obstructive pulmonary disease). Medication exposure to renin–angiotensin–aldosterone system inhibitors, statins, and diuretics was recorded from prescription data. Baseline characteristics were summarised by burden score category and compared using one-way analysis of variance for continuous variables and the chi-square test for categorical variables. Cox proportional hazards regression was performed at four levels of covariate adjustment: unadjusted (Model 1); adjusted for age and sex (Model 2); additionally adjusted for diabetes and hypertension (Model 3); and further adjusted for eGFR, albumin, haemoglobin, systolic blood pressure, coronary heart disease, heart failure, stroke, chronic obstructive pulmonary disease, renin–angiotensin–aldosterone system inhibitor use, statin use, and diuretic use (Model 4; complete cases, N = 6,176). The proportional hazards assumption was evaluated by scaled Schoenfeld residuals [ 19 ]; where violated, eGFR-stratified models were fitted as a secondary approach. Fine–Gray subdistribution hazard models accounted for death as a competing event for the kidney and MACE endpoints [ 20 , 21 ]. Prespecified subgroup analyses for MACE were conducted across seven strata (age, sex, diabetes, hypertension, eGFR category, coronary heart disease, statin use), with effect modification assessed by likelihood ratio interaction tests. Domain-specific analyses examined the independent association of each vascular territory with each endpoint in separate fully adjusted models. Sensitivity analyses comprised landmark analyses excluding events within 30, 90, and 365 days to address reverse causation; multiple imputation using predictive mean matching (5 datasets, mice package [ 22 ]) to evaluate the impact of missing covariate data [ 23 ]; and assessment of incremental discrimination by comparing Harrell's C-statistics [ 25 ] of models with and without the burden score [ 24 ]. Missing continuous covariates in the primary analysis were handled by median imputation with a missing-indicator variable. All analyses were conducted in R version 4.2 (survival, rms, cmprsk, and mice packages). Two-sided P < 0.05 was considered statistically significant. Results The analytic cohort comprised 8,645 patients with CKD stages 1–4 (mean age 69.8 ± 12.7 years; 57.6% male; Fig. 1 ). Median follow-up was 0.90 years (interquartile range 0.18–1.48), contributing 7,889 person-years. During follow-up, 591 deaths (6.8%), 580 kidney events (6.7%), and 2,873 MACE (33.2%) were recorded. Atherosclerotic involvement was most prevalent in the aortic domain (81.2%), followed by coronary (68.1%), peripheral (44.5%), and carotid/cerebral (40.2%; Supplementary Table S1 ). Nearly half the cohort (45.9%) had disease in three or four territories; only 6.8% had no documented atherosclerosis. Compared with non-imaged CKD patients, the imaged cohort was older (69.8 vs 58.0 years), had lower eGFR (69.8 vs 80.9 mL/min/1.73 m²), and carried a higher comorbidity burden (Supplementary Table S2). Patients with higher burden scores were older, had lower serum albumin and haemoglobin concentrations, and carried a higher prevalence of heart failure, stroke, and chronic obstructive pulmonary disease (Table 1 ; Fig. 2 ). Missing data rates ranged from 3.0% for albumin to 27.9% for systolic blood pressure. Table 1 Baseline characteristics of the analytic cohort stratified by atherosclerotic burden group Variable Overall Burden 0 Burden 1 Burden 2 Burden 3–4 P N 8645 586 1452 2643 3964 Age, years 69.8 ± 12.7 54.2 ± 14.4 63.9 ± 12.2 69.9 ± 11.4 74.2 ± 10.6 < 0.001 Male sex 4976 (57.6%) 325 (55.5%) 804 (55.4%) 1542 (58.3%) 2305 (58.1%) 0.163 eGFR, mL/min/1.73m² 69.8 ± 26.5 84.8 ± 28.1 75.4 ± 26.6 69.3 ± 26.1 65.9 ± 25.2 < 0.001 Albumin, g/L 38.2 ± 6.5 40.3 ± 6.7 39.5 ± 6.4 38.2 ± 6.6 37.4 ± 6.3 < 0.001 Hemoglobin, g/L 119.1 ± 24.1 125.9 ± 24.6 123.3 ± 24 119.4 ± 24.5 116.7 ± 23.3 < 0.001 SBP, mmHg 127.9 ± 20 126.5 ± 18.3 126.7 ± 19.2 127.2 ± 20 128.9 ± 20.4 0.001 DBP, mmHg 74.1 ± 12.4 78.2 ± 12.3 75.8 ± 12.3 74.2 ± 12.5 73 ± 12.3 < 0.001 Fasting glucose, mmol/L 7.2 ± 3.3 6.5 ± 2.8 6.9 ± 3.2 7 ± 3.1 7.5 ± 3.5 < 0.001 LDL-C, mmol/L 2.4 ± 1.0 2.6 ± 1.0 2.5 ± 1.0 2.4 ± 1.0 2.2 ± 0.9 < 0.001 Triglycerides, mmol/L 1.6 ± 1.4 1.9 ± 1.8 1.8 ± 1.8 1.6 ± 1.3 1.5 ± 1.3 < 0.001 Potassium, mmol/L 4.2 ± 0.5 4.1 ± 0.5 4.2 ± 0.5 4.2 ± 0.5 4.2 ± 0.5 0.899 Phosphorus, mmol/L 1.1 ± 0.3 1.1 ± 0.3 1.1 ± 0.3 1.1 ± 0.3 1.1 ± 0.3 0.002 Diabetes 4525 (52.3%) 227 (38.7%) 632 (43.5%) 1231 (46.6%) 2435 (61.4%) < 0.001 Hypertension 7084 (81.9%) 362 (61.8%) 1033 (71.1%) 2135 (80.8%) 3554 (89.7%) < 0.001 Coronary heart disease 5535 (64%) 193 (32.9%) 705 (48.6%) 1632 (61.7%) 3005 (75.8%) < 0.001 Heart failure 3746 (43.3%) 114 (19.5%) 398 (27.4%) 1096 (41.5%) 2138 (53.9%) < 0.001 Stroke 4376 (50.6%) 109 (18.6%) 484 (33.3%) 1107 (41.9%) 2676 (67.5%) < 0.001 COPD 3912 (45.3%) 133 (22.7%) 497 (34.2%) 1201 (45.4%) 2081 (52.5%) < 0.001 Hyperlipidemia 4634 (53.6%) 282 (48.1%) 748 (51.5%) 1276 (48.3%) 2328 (58.7%) < 0.001 RAAS inhibitor 4467 (51.7%) 209 (35.7%) 571 (39.3%) 1223 (46.3%) 2464 (62.2%) < 0.001 Statin 6220 (71.9%) 251 (42.8%) 844 (58.1%) 1718 (65%) 3407 (85.9%) < 0.001 Diuretic 5125 (59.3%) 233 (39.8%) 657 (45.2%) 1561 (59.1%) 2674 (67.5%) < 0.001 Data are presented as mean ± SD for continuous variables and n (%) for categorical variables. P values were derived from one-way ANOVA (continuous) or chi-square test (categorical) across burden groups. Abbreviations: eGFR, estimated glomerular filtration rate (CKD-EPI equation); SBP, systolic blood pressure; DBP, diastolic blood pressure; LDL-C, low-density lipoprotein cholesterol; COPD, chronic obstructive pulmonary disease; RAAS, renin-angiotensin-aldosterone system. Burden score = number of vascular domains with documented atherosclerosis (range 0-4). To evaluate the independent association between atherosclerotic burden and MACE, Cox proportional hazards models with sequential covariate adjustment were fitted (Table 2 ). The unadjusted hazard ratio per one-domain increment was 1.47 (95% CI 1.43–1.53; P < 0.001). Sequential adjustment progressively attenuated this estimate: age- and sex-adjusted HR 1.30 (95% CI 1.26–1.35; P < 0.001), core-adjusted HR 1.28 (95% CI 1.23–1.32; P < 0.001), and fully adjusted HR 1.10 (95% CI 1.06–1.15; P < 0.001; N = 6,176). Scaled Schoenfeld residuals indicated marginal violation of the proportional hazards assumption for the burden score in the fully adjusted model (P = 0.037); eGFR stratification did not resolve this violation (P = 0.008 after stratification), indicating that the hazard ratio varied over time. In categorical analysis, both the intermediate (burden 2 vs 0: HR 1.46, 95% CI 1.10–1.95; P = 0.010) and highest burden groups (3–4 vs 0: HR 1.61, 95% CI 1.20–2.14; P = 0.001) reached significance. Fine–Gray subdistribution hazard modelling, which accounted for death as a competing event, confirmed the association (sHR 1.10, 95% CI 1.06–1.15; P < 0.001), as did multiple imputation (HR 1.13, 95% CI 1.09–1.17; P < 0.001; Table 3 ). Table 2 Association between atherosclerotic burden score and clinical outcomes across sequential Cox regression models Outcome Events/N Model 1 HR (95% CI) P Model 2 HR (95% CI) P Model 3 HR (95% CI) P Model 4 HR (95% CI) P PH P (Model 4) MACE 2873/8645 (Model 4: 2414/6176) 1.47 (1.43, 1.53) < 0.001 1.30 (1.26, 1.35) < 0.001 1.28 (1.23, 1.32) < 0.001 1.10 (1.06, 1.15) < 0.001 0.037 Composite kidney endpoint 580/8645 (Model 4: 446/6176) 1.31 (1.22, 1.41) < 0.001 1.22 (1.13, 1.32) < 0.001 1.21 (1.12, 1.31) < 0.001 1.14 (1.03, 1.26) 0.008 0.103 All-cause mortality 591/8645 (Model 4: 562/6176) 1.22 (1.13, 1.30) < 0.001 0.97 (0.89, 1.05) 0.4 1.03 (0.95, 1.12) 0.423 1.00 (0.91, 1.09) 0.917 0.504 Hazard ratios are per one-domain increment in atherosclerotic burden score. Model 1: unadjusted. Model 2: adjusted for age and sex. Model 3: Model 2 + diabetes and hypertension. Model 4: Model 3 + eGFR, albumin, hemoglobin, SBP, coronary heart disease, heart failure, stroke, COPD, RAAS inhibitor, statin, and diuretic use (complete cases, N = 6,176). PH P: P value for the proportional hazard assumption based on scaled Schoenfeld residuals. MACE: major adverse cardiovascular events (myocardial infarction, stroke, heart failure hospitalization, or cardiovascular death). Composite kidney endpoint: doubling of serum creatinine, sustained eGFR decline > = 40%, or initiation of renal replacement therapy. Table 3 Sensitivity analyses: competing risks and multiple imputation Outcome Fine-Gray competing risk HR (95% CI) P Landmark 30d HR (95% CI) P Landmark 90d HR (95% CI) P Landmark 365d HR (95% CI) P Multiple imputation (m = 5) HR (95% CI) P MACE 1.10 (1.06, 1.15) < 0.001 1.08 (1.02, 1.15) 0.01 1.05 (0.98, 1.12) 0.19 1.13 (0.97, 1.32) 0.13 1.13 (1.09, 1.17) < 0.001 Composite kidney endpoint 1.13 (1.03, 1.24) 0.01 1.14 (1.03, 1.26) 0.01 1.12 (1.01, 1.24) 0.04 1.11 (0.96, 1.28) 0.15 1.17 (1.07, 1.27) < 0.001 All-cause mortality — — 1.03 (0.91, 1.17) 0.64 0.99 (0.85, 1.16) 0.95 1.01 (0.77, 1.32) 0.95 0.99 (0.91, 1.08) 0.86 Fine-Gray competing-risk models used death as the competing event for MACE and kidney endpoints, not applicable for all-cause mortality. Landmark analyses excluded events occurring within the specified time window (30, 90, or 365 days) after the index date to address reverse causation. Multiple imputation: 5 datasets generated using predictive mean matching (mice package); estimates pooled using Rubin's rules. Hazard ratios (or sub distribution hazard ratios for Fine-Gray) are per one-domain increment in burden score, fully adjusted (Model 4 covariates). The burden score was not independently associated with all-cause mortality in any adjusted model (fully adjusted HR 1.00, 95% CI 0.91–1.09; P = 0.92; Table 2 ), with the proportional hazards assumption satisfied (Schoenfeld P = 0.50). Multiple imputation corroborated this null finding (HR 0.99, P = 0.86; Table 3 ). For the composite kidney endpoint, the burden score was independently associated with adverse outcomes after full adjustment (HR 1.14, 95% CI 1.03–1.26; P = 0.008), with the proportional hazards assumption satisfied (Schoenfeld P = 0.10). Fine–Gray analysis confirmed this association (sHR 1.13, 95% CI 1.03–1.24; P = 0.01), and multiple imputation yielded a stronger estimate (HR 1.17, 95% CI 1.07–1.27; P < 0.001). To assess the temporal stability of the MACE association, landmark analyses were performed excluding events within 30, 90, and 365 days (Table 3 ). The association persisted at the 30-day landmark (HR 1.08, 95% CI 1.02–1.15; P = 0.007) but was attenuated at 90 days (HR 1.05, P = 0.19) and at one year (HR 1.13, P = 0.13), indicating that the signal was driven predominantly by early events. Notably, the kidney endpoint association persisted at the 30-day (HR 1.14, P = 0.01) and 90-day landmarks (HR 1.12, P = 0.04), suggesting a more temporally stable effect on renal outcomes. Individual domain analyses revealed marked heterogeneity in the vascular territories driving each endpoint (Fig. 4 ). Coronary atherosclerosis (HR 1.32, 95% CI 1.19–1.47; P < 0.001) and carotid/cerebral disease (HR 1.30, 95% CI 1.19–1.41; P < 0.001) were the primary drivers of the MACE association, whereas aortic and peripheral disease showed no significant association. For the composite kidney endpoint, aortic atherosclerosis emerged as the dominant predictor (HR 2.31, 95% CI 1.62–3.31; P < 0.001), with coronary disease also reaching significance (HR 1.28, 95% CI 1.01–1.62; P = 0.04). For all-cause mortality, no individual domain reached statistical significance, though peripheral disease showed a borderline inverse association (HR 0.84, 95% CI 0.71–1.00; P = 0.05). Subgroup analyses for MACE demonstrated significant effect modification by age (interaction P = 0.009), hypertension (P = 0.008), coronary heart disease (P < 0.001), and statin use (P = 0.007; Fig. 3 ). The burden score was more strongly associated with MACE in patients younger than 65 years (HR 1.15 vs 1.09), those without pre-existing coronary heart disease (HR 1.21 vs 1.06), those without hypertension (HR 1.19 vs 1.09), and those not receiving statins (HR 1.14 vs 1.10). No significant interaction was observed for sex (P = 0.21), diabetes (P = 0.72), or eGFR category (P = 0.05). The addition of the burden score to a model containing all clinical covariates produced modest improvement in discrimination: the C-statistic increased from 0.6979 to 0.6993 for MACE (delta 0.0014, likelihood ratio P < 0.001), from 0.7231 to 0.7270 for the kidney endpoint (delta 0.0039, P = 0.008), and was unchanged for mortality (delta 0.0000, P = 0.92). Discussion The principal finding of this study is that a structured multivascular imaging atherosclerotic burden score was independently associated with both MACE and adverse kidney outcomes in non-dialysis CKD, with each additional affected vascular bed conferring a 10% higher MACE hazard and a 14% higher kidney event hazard after comprehensive covariate adjustment. These associations were robust to competing-risk modelling and multiple imputation. Domain-specific analyses uncovered a striking dissociation: coronary and carotid/cerebral atherosclerosis drove the MACE signal, whereas aortic disease was the dominant predictor of adverse kidney outcomes. These observations extend the polyvascular disease paradigm, established in general cardiovascular populations through the REACH [ 9 , 10 ] and COMPASS [ 11 ] registries, to the CKD setting. The magnitude of confounding attenuation — from an unadjusted HR of 1.47 to a fully adjusted HR of 1.10 for MACE — warrants emphasis. This 72% reduction on the log-hazard scale indicates that a substantial proportion of the crude association between multivascular burden and MACE was explained by shared risk factors, principally age, comorbidity burden, and medication use. Such attenuation is consistent with the observation by Matsushita et al. that traditional risk factors account for a substantial proportion of the excess cardiovascular risk attributable to reduced kidney function [ 26 , 36 ]. Nonetheless, the residual 10% per-domain hazard increment remained highly significant (P < 0.001) and was corroborated by Fine-Gray competing-risk modelling (SHR 1.10, P < 0.001). The delta C-statistic for MACE was 0.0014 — modest but statistically significant — while the kidney endpoint showed a more meaningful increment of 0.0039 (P = 0.008), suggesting that the burden score may have greater discriminative value for renal than for cardiovascular outcomes [ 24 , 34 ]. The violation of the proportional hazards assumption for the MACE endpoint (Schoenfeld P = 0.0002) and the progressive attenuation across landmark analyses provide complementary evidence that the burden score captures acute or peri-assessment cardiovascular risk rather than a stable long-term hazard. Given that the median time to MACE was short and that a substantial proportion of events occurred during or shortly after the index hospitalisation, the reported hazard ratio should be interpreted as a time-averaged estimate over a non-constant effect. Future investigations employing time-varying coefficient models or restricted mean survival time analyses may more accurately characterise this temporal heterogeneity. The domain-specific findings merit particular attention. The strong and independent association between aortic atherosclerosis and the composite kidney endpoint (HR 2.31, P < 0.001) is biologically plausible: atherosclerotic disease of the abdominal aorta can compromise renal perfusion through atheroembolism, renal artery ostial stenosis, or reduced aortic compliance with consequent transmission of pulsatile haemodynamic stress to the glomerular microvasculature [ 27 , 28 ]. This finding aligns with prior work demonstrating that aortic calcification predicts CKD progression independently of traditional risk factors [ 29 , 30 ]. By contrast, coronary (HR 1.32, P < 0.001) and carotid/cerebral disease (HR 1.30, P < 0.001) drove the MACE association, consistent with their direct pathophysiological relevance to myocardial infarction and stroke. Notably, the composite burden score showed no independent association with all-cause mortality after adjustment (fully adjusted HR 0.995, P = 0.92), despite a significant unadjusted association (HR 1.22, P < 0.001) that was entirely attenuated by age and sex adjustment alone (HR 0.97, P = 0.40). This pattern indicates that the crude mortality–burden link was driven by age confounding rather than by atherosclerosis per se. The null finding is further explained by the heterogeneous causes of death in CKD, where non-cardiovascular mortality — infection, malignancy, metabolic derangement — constitutes a substantial proportion of total deaths, diluting any signal from a vascular-specific exposure [ 35 , 37 ]. Similar dissociations between polyvascular burden and all-cause mortality have been reported in the REACH registry [ 9 , 10 ]. Peripheral atherosclerosis showed a borderline inverse association with mortality (HR 0.84, P = 0.05) but was not significantly associated with MACE (HR 0.94, P = 0.14) or kidney outcomes (HR 1.16, P = 0.16). The borderline mortality finding was unexpected. Potential explanations include confounding by indication: patients with documented peripheral vascular disease may receive more aggressive cardiovascular risk factor management, including intensified statin therapy and antiplatelet regimens, thereby attenuating their event rates [ 31 , 32 , 33 ]. Alternatively, peripheral arterial disease in this cohort may serve as a marker of more comprehensive vascular imaging evaluation, with the apparent protective effect reflecting ascertainment bias rather than a true biological phenomenon. Given the borderline significance and the absence of a consistent pattern across endpoints, this finding should be interpreted with caution and requires replication. The subgroup analyses revealed a coherent pattern of effect modification. The burden score was more informative in patients without established coronary heart disease (interaction P < 0.001), without hypertension (P = 0.008), younger than 65 years (P = 0.009), and not receiving statins (P = 0.007). This pattern is consistent with the concept of risk reclassification at the margin: the burden score adds prognostic information primarily in patients whose cardiovascular risk is not already clinically apparent or pharmacologically managed. In patients with known coronary disease or those already receiving aggressive risk factor modification, the incremental value of quantifying polyvascular extent is diminished [ 33 , 34 ]. Several limitations should be acknowledged. The retrospective single-centre design limits generalisability. Of the 10,567 non-dialysis CKD patients identified, 8,645 (81.8%) had undergone at least one vascular imaging examination within 365 days of the index date and constituted the analytic cohort. These imaged patients were older and had higher comorbidity burden than the 1,922 non-imaged patients (Supplementary Table S2), introducing potential selection bias; the results should therefore be interpreted as applying to CKD patients who undergo vascular imaging in routine clinical care, not to the broader CKD population. The atherosclerotic burden score was derived from rule-based text extraction without formal validation against manual chart review; misclassification is possible, though likely non-differential with respect to outcomes, biasing estimates toward the null. The median follow-up of 0.90 years, while improved by anchoring to the imaging date, remains relatively short and limits conclusions about long-term prognostic value; the progressive attenuation of MACE associations across landmark analyses (30-day HR 1.08, 90-day HR 1.05, 365-day HR 1.13 [all non-significant beyond 90 days]) underscores this temporal limitation. The fully adjusted model was restricted to 71.4% of the cohort due to missing covariate data; although multiple imputation produced consistent or stronger results, residual selection bias cannot be excluded. The MACE event rate (33.2%) was high, partly reflecting the broad composite definition that included heart failure hospitalisation; this should be considered when comparing effect sizes with studies using narrower MACE definitions. Finally, the proportional hazards assumption was violated for the MACE endpoint, and the modest improvement in C-statistic (0.0014 for MACE, 0.0039 for kidney) suggests that the burden score provides limited incremental discrimination beyond established clinical variables, though the kidney endpoint showed more promising discriminative gains. Conclusions A structured multivascular imaging atherosclerotic burden score derived from routine clinical imaging was independently associated with both MACE and adverse kidney outcomes in non-dialysis CKD. The MACE association was driven by coronary and carotid/cerebral disease and concentrated in the early post-assessment period, while aortic atherosclerosis emerged as a distinct and potent predictor of kidney endpoints (HR 2.31). The modest incremental discrimination for MACE (delta C = 0.0014) and the temporal attenuation of associations beyond 90 days suggest that the burden score, in its current form, has limited utility for long-term cardiovascular risk stratification. However, the more meaningful discriminative gain for kidney outcomes (delta C = 0.0039) and the strong domain-specific signals highlight the potential value of vascular imaging in identifying CKD patients at elevated renal risk. These findings underscore the domain-specific heterogeneity of atherosclerotic risk in CKD and support the hypothesis that different vascular territories contribute to distinct organ-specific outcomes through divergent pathophysiological mechanisms. Declarations Ethics approval and consent to participate This study was approved by the Ethics Committee of Wuhan Central Hospital (Approval No. WHZXKYL2022-083-03). The requirement for informed consent was waived given the retrospective design. This study was conducted in accordance with the Declaration of Helsinki. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding [This study was supported by Wuhan Central Hospital Institutional Fund (22YJ45), the Chen-Xiaoping Foundation for the Development of Science and Technology of Hubei Province (CXPJJH122001-2239), and the 2022 Wuhan Traditional Chinese Medicine Research Project (WZ24B03). The funders had no role in the study design, data collection, data analysis, data interpretation, manuscript preparation, or the decision to submit the article for publication.] Author Contribution Xiongpan Wang: Conceptualization, study design, data curation, data cleaning, formal analysis, and manuscript drafting.Dianjun Liu: Study supervision, project administration, conceptual guidance, and critical revision of the manuscript.Yunfang Huang: Data verification, data correction, statistical methodology, formal analysis, and manuscript review.Fan Zhu: Conceptualization, study design, statistical methodology, formal analysis, and manuscript review.Li Xu: Study supervision, methodological guidance, project administration, and critical revision of the manuscript.All authors read and approved the final manuscript. Xiongpan Wang, Dianjun Liu and Yunfang Huang contributed equally to this work and share first authorship. Fan Zhu and Li Xu jointly supervised this study and serve as co-corresponding authors. Acknowledgements Not applicable. Availability of data and materials The datasets analysed during the current study are not publicly available due to patient privacy regulations but are available from the corresponding author on reasonable request, subject to institutional ethics approval. References Go AS, Chertow GM, Fan D, et al. Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. N Engl J Med. 2004;351(13):1296–305. Tonelli M, Muntner P, Lloyd A, et al. Risk of coronary events in people with chronic kidney disease compared with those with diabetes: a population-level cohort study. Lancet. 2012;380(9844):807–14. Sarnak MJ, Amann K, Bangalore S, et al. Chronic kidney disease and coronary artery disease: JACC state-of-the-art review. J Am Coll Cardiol. 2019;74(14):1823–38. Valdivielso JM, Rodríguez-Puyol D, Pascual J, et al. Atherosclerosis in chronic kidney disease: more, less, or just different? Arterioscler Thromb Vasc Biol. 2019;39(10):1938–66. Jankowski J, Floege J, Fliser D, et al. Cardiovascular disease in chronic kidney disease: pathophysiological insights and therapeutic options. Circulation. 2021;143(7):518–32. London GM, Guérin AP, Marchais SJ, et al. Arterial media calcification in end-stage renal disease: impact on all-cause and cardiovascular mortality. Nephrol Dial Transpl. 2003;18(9):1731–40. Budoff MJ, Rader DJ, Reilly MP, et al. Relationship of estimated GFR and coronary artery calcification in the CRIC (Chronic Renal Insufficiency Cohort) study. Am J Kidney Dis. 2011;58(4):519–26. Chen J, Budoff MJ, Reilly MP, et al. Coronary artery calcification and risk of cardiovascular disease and death among patients with chronic kidney disease. JAMA Cardiol. 2017;2(6):635–43. Bhatt DL, Steg PG, Ohman EM, et al. International prevalence, recognition, and treatment of cardiovascular risk factors in outpatients with atherothrombosis. JAMA. 2006;295(2):180–9. Steg PG, Bhatt DL, Wilson PW, et al. One-year cardiovascular event rates in outpatients with atherothrombosis. JAMA. 2007;297(11):1197–206. Anand SS, Bosch J, Eikelboom JW, et al. Rivaroxaban with or without aspirin in patients with stable peripheral or carotid artery disease: an international, randomised, double-blind, placebo-controlled trial. Lancet. 2018;391(10117):219–29. Fowkes FG, Murray GD, Butcher I, et al. Ankle brachial index combined with Framingham Risk Score to predict cardiovascular events and mortality: a meta-analysis. JAMA. 2008;300(2):197–208. Liao KP, Cai T, Savova GK, et al. Development of phenotype algorithms using electronic medical records and incorporating natural language processing. BMJ. 2015;350:h1885. Kirby JC, Speltz P, Rasmussen LV, et al. PheKB: a catalog and workflow for creating electronic phenotype algorithms for transportability. J Am Med Inf Assoc. 2016;23(6):1046–52. Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group. KDIGO 2012 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. Kidney Int Suppl. 2013;3(1):1–150. von Elm E, Altman DG, Egger M, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement. Lancet. 2007;370(9596):1453–7. Levey AS, Inker LA, Matsushita K, et al. GFR decline as an end point for clinical trials in CKD: a scientific workshop sponsored by the National Kidney Foundation and the US Food and Drug Administration. Am J Kidney Dis. 2014;64(6):821–35. Levey AS, Stevens LA, Schmid CH, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150(9):604–12. Grambsch PM, Therneau TM. Proportional hazards tests and diagnostics based on weighted residuals. Biometrika. 1994;81(3):515–26. Fine JP, Gray RJ. A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc. 1999;94(446):496–509. Austin PC, Lee DS, Fine JP. Introduction to the analysis of survival data in the presence of competing risks. Circulation. 2016;133(6):601–9. van Buuren S, Groothuis-Oudshoorn K. mice: Multivariate Imputation by Chained Equations in R. J Stat Softw. 2011;45(3):1–67. White IR, Royston P, Wood AM. Multiple imputation using chained equations: issues and guidance for practice. Stat Med. 2011;30(4):377–99. Pencina MJ, D'Agostino RB, Sr, Agostino RB Jr, Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med. 2008;27(2):157–72. Harrell FE Jr, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med. 1996;15(4):361–87. Matsushita K, van der Velde M, Astor BC, et al. Association of estimated glomerular filtration rate and albuminuria with all-cause and cardiovascular mortality in general population cohorts: a collaborative meta-analysis. Lancet. 2010;375(9731):2073–81. Safian RD, Textor SC. Renal-artery stenosis. N Engl J Med. 2001;344(6):431–42. Townsend RR, Anderson AH, Chirinos JA, et al. Association of pulse wave velocity with chronic kidney disease progression and mortality: findings from the CRIC study (Chronic Renal Insufficiency Cohort). Hypertension. 2018;71(6):1101–7. Blacher J, Guérin AP, Pannier B, et al. Arterial calcifications, arterial stiffness, and cardiovascular risk in end-stage renal disease. Hypertension. 2001;38(4):938–42. Sigrist MK, Taal MW, Bungay P, et al. Progressive vascular calcification over 2 years is associated with arterial stiffening and increased mortality in patients with stages 4 and 5 chronic kidney disease. Clin J Am Soc Nephrol. 2007;2(6):1241–8. O'Hare AM, Glidden DV, Fox CS, Hsu CY. High prevalence of peripheral arterial disease in persons with renal insufficiency: results from the National Health and Nutrition Examination Survey 1999–2000. Circulation. 2004;109(3):320–3. Criqui MH, Aboyans V. Epidemiology of peripheral artery disease. Circ Res. 2015;116(9):1509–26. Baigent C, Landray MJ, Reith C, et al. The effects of lowering LDL cholesterol with simvastatin plus ezetimibe in patients with chronic kidney disease (Study of Heart and Renal Protection): a randomised placebo-controlled trial. Lancet. 2011;377(9784):2181–92. Cook NR. Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation. 2007;115(7):928–35. Stenvinkel P, Carrero JJ, Axelsson J, et al. Emerging biomarkers for evaluating cardiovascular risk in the chronic kidney disease patient: how do new pieces fit into the uremic puzzle? Clin J Am Soc Nephrol. 2008;3(2):505–21. Matsushita K, Coresh J, Sang Y, et al. Estimated glomerular filtration rate and albuminuria for prediction of cardiovascular outcomes: a collaborative meta-analysis of individual participant data. Lancet Diabetes Endocrinol. 2015;3(7):514–25. Thompson S, James M, Wiebe N, et al. Cause of death in patients with reduced kidney function. J Am Soc Nephrol. 2015;26(10):2504–11. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTablesS12.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 04 May, 2026 Reviewers invited by journal 29 Apr, 2026 Editor invited by journal 21 Apr, 2026 Editor assigned by journal 20 Apr, 2026 Submission checks completed at journal 20 Apr, 2026 First submitted to journal 17 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9449777","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":633888080,"identity":"68ec84fd-3363-4cdd-b1a0-5dac58a33980","order_by":0,"name":"Xiongpan Wang","email":"","orcid":"","institution":"Central Hospital of Wuhan","correspondingAuthor":false,"prefix":"","firstName":"Xiongpan","middleName":"","lastName":"Wang","suffix":""},{"id":633888082,"identity":"a089e3ae-d1df-49c0-8cb9-3d225897a49a","order_by":1,"name":"Fan Zhu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIie3PsYrCMBjA8S8E2huirrnh7l4h4iQovkqDjhV8hEghLuqsk6/g5JzygZNvEGfnyi0iHUx70w3augnmP4QEvh/hA/D5XjKqDICBIEyMydw7CCsJ+SNNtpfpqiC0BoGCfPO4g6z8tgK0ZtMpTvKj1DwW2LvufpoUSHaO7xN+SBWu2ElqdprgeGnbmgL9XO/uE8GlQsZR6nC4xfHcEkcC2qgkwhGIBHbndlCTRI58jATCxcpKUu7CDHY020fpQtmhpiR5uEtrluAvy/Fr4y7ZJbd9d0mz8wPyP6LLU9WdL8qfGfb5fL536QbmLFuMb12yQwAAAABJRU5ErkJggg==","orcid":"","institution":"Central Hospital of Wuhan","correspondingAuthor":true,"prefix":"","firstName":"Fan","middleName":"","lastName":"Zhu","suffix":""},{"id":633888083,"identity":"886e7b8c-061e-4f61-ab84-73e55e7f665a","order_by":2,"name":"Yunfang Huang","email":"","orcid":"","institution":"Central Hospital of Wuhan","correspondingAuthor":false,"prefix":"","firstName":"Yunfang","middleName":"","lastName":"Huang","suffix":""},{"id":633888086,"identity":"332800f6-127d-4fad-8906-2503df146586","order_by":3,"name":"Dianjun Liu","email":"","orcid":"","institution":"Central Hospital of Wuhan","correspondingAuthor":false,"prefix":"","firstName":"Dianjun","middleName":"","lastName":"Liu","suffix":""},{"id":633888089,"identity":"eee723e4-75a2-445a-9bb2-353a5a63b536","order_by":4,"name":"Li Xu","email":"","orcid":"","institution":"Central Hospital of Wuhan","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2026-04-17 13:38:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9449777/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9449777/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108947227,"identity":"8818f998-4578-4916-af63-db1af6f77351","added_by":"auto","created_at":"2026-05-11 06:27:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":43230,"visible":true,"origin":"","legend":"\u003cp\u003eStudy flow diagram. The source population comprised 135,072 patients from the electronic medical records of Wuhan Central Hospital (2015–2023). After applying inclusion and exclusion criteria, 10,567 patients with non-dialysis CKD were identified. Of these, 8,645 (81.8%) who underwent at least one vascular imaging examination within 365 days of the index date constituted the analytic cohort. Patients were classified into four atherosclerotic burden groups based on the number of affected vascular domains: 0 (n = 586), 1 (n = 1,452), 2 (n = 2,643), and 3–4 (n = 3,964).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9449777/v1/fd03f8c828a1bfa7434ee212.png"},{"id":108947255,"identity":"e21848c5-16cf-44f4-aa88-840dfc93873d","added_by":"auto","created_at":"2026-05-11 06:27:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":118212,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier event-free survival curves stratified by atherosclerotic burden group (0, 1, 2, 3–4), truncated at 2 years. (A) All-cause mortality. (B) Composite kidney endpoint (doubling of serum creatinine, sustained eGFR decline ≥40%, or initiation of renal replacement therapy). (C) Major adverse cardiovascular events (MACE). Numbers at risk are displayed below each panel. Colour coding: blue (burden 0), teal (burden 1), red (burden 2), purple (burden 3–4). Log-rank P values are shown.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9449777/v1/ae986ea181d89a1f4694baf6.png"},{"id":108947249,"identity":"4cc39c15-73dd-40e9-b541-cb1d3e03bfb9","added_by":"auto","created_at":"2026-05-11 06:27:26","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":75813,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of subgroup analyses for the association between atherosclerotic burden score (per one-domain increment) and MACE in the fully adjusted Cox model (Model 4, N = 6,176). Subgroups were defined by age (\u0026lt;65 vs ≥65 years), sex, diabetes, hypertension, eGFR category (\u0026lt;60 vs ≥60 mL/min/1.73 m²), coronary heart disease, and statin use. Hazard ratios with 95% confidence intervals are plotted on a logarithmic scale. P values for interaction (likelihood ratio test) are displayed for each subgroup.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9449777/v1/e7a43edf394b6b91a214094c.png"},{"id":108947250,"identity":"7ff5a153-7a88-452d-8261-7eff68437844","added_by":"auto","created_at":"2026-05-11 06:27:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":31010,"visible":true,"origin":"","legend":"\u003cp\u003eDomain-specific associations between individual vascular territories (coronary, aortic, carotid/cerebral, peripheral) and clinical outcomes in separate fully adjusted Cox models. (A) All-cause mortality. (B) Composite kidney endpoint. (C) MACE. Each domain was entered as a binary variable (positive vs negative) in a model adjusted for age, sex, diabetes, hypertension, eGFR, albumin, haemoglobin, systolic blood pressure, comorbidities, and medications. Hazard ratios with 95% confidence intervals are plotted on a logarithmic scale; the dashed vertical line indicates HR = 1.00 (null).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9449777/v1/b357cfa0b3a0b817c8d25a90.png"},{"id":108947439,"identity":"9968e3c8-73de-4261-b85e-62b6de281e42","added_by":"auto","created_at":"2026-05-11 06:29:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":596085,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9449777/v1/4ff3575f-6273-4ed0-b125-d6130719fe05.pdf"},{"id":108947251,"identity":"e3c904c2-9920-4336-95f5-893e9e76fd2d","added_by":"auto","created_at":"2026-05-11 06:27:27","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17320,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTablesS12.docx","url":"https://assets-eu.researchsquare.com/files/rs-9449777/v1/af97b66d4ab116585cbb540a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multivascular Imaging-Derived Atherosclerotic Burden and Cardiorenal Outcomes in Non-Dialysis Chronic Kidney Disease","fulltext":[{"header":"Background","content":"\u003cp\u003eCardiovascular disease remains the leading cause of death in patients with chronic kidney disease, accounting for a mortality rate that exceeds age-matched controls by a factor of five to ten even in the earliest stages of renal impairment [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The pathobiology underlying this excess risk extends beyond traditional Framingham factors: uremic toxins, disordered calcium-phosphate metabolism, chronic systemic inflammation, and endothelial dysfunction converge to produce an accelerated and phenotypically distinct form of atherosclerosis characterised by diffuse medial calcification, plaque instability, and multiterritory involvement [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Coronary artery calcification scoring has been investigated as a prognostic marker in CKD cohorts [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], yet this single-territory approach fails to capture the systemic nature of the atherosclerotic process.\u003c/p\u003e \u003cp\u003eIn general cardiovascular populations, the concept of polyvascular disease \u0026mdash; simultaneous atherosclerotic involvement of two or more arterial beds \u0026mdash; has been established as a potent risk amplifier. The REACH registry demonstrated that patients with symptomatic disease in multiple vascular territories experienced substantially higher rates of cardiovascular death, myocardial infarction, and stroke than those with single-territory involvement [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Subsequent data from the COMPASS trial confirmed that the number of affected vascular beds independently predicted major adverse limb events and cardiovascular mortality [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and meta-analytic evidence has shown that multiterritory atherosclerosis improves cardiovascular risk prediction beyond traditional scores [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Whether this polyvascular paradigm translates to CKD populations, where the atherosclerotic phenotype may differ qualitatively and where competing non-cardiovascular mortality is substantial, has not been systematically evaluated.\u003c/p\u003e \u003cp\u003eA practical barrier to studying multivascular burden in routine clinical settings is the heterogeneity of imaging data. Vascular examinations are performed for diverse indications and reported in semi-structured text, rendering systematic extraction of atherosclerotic findings across territories challenging. Advances in electronic health record phenotyping have demonstrated the feasibility of deriving clinically meaningful variables from imaging report text [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], but this approach has not been applied to construct a composite multivascular atherosclerotic burden score in CKD.\u003c/p\u003e \u003cp\u003eThe present study was therefore designed to address two questions. First, whether a structured imaging-based atherosclerotic burden score \u0026mdash; defined as the number of vascular domains with documented atherosclerosis \u0026mdash; is independently associated with all-cause mortality, a composite kidney endpoint, and major adverse cardiovascular events in non-dialysis CKD. Second, whether the constituent vascular territories exhibit differential associations with these endpoints, thereby providing mechanistic insight into the organ-specific consequences of regional atherosclerosis in the uraemic milieu.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis retrospective cohort study was conducted using electronic medical records from Wuhan Central Hospital, a tertiary referral centre in Hubei Province, China, covering the period 2015\u0026ndash;2023. The study protocol was approved by the institutional ethics committee of Wuhan Central Hospital; the requirement for individual informed consent was waived owing to the retrospective design. Reporting adheres to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe source population comprised 10,567 patients with a recorded diagnosis of CKD (ICD-10 codes N18.x) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] after exclusion of those receiving baseline dialysis, those with end-stage renal disease (eGFR\u0026thinsp;\u0026lt;\u0026thinsp;15 mL/min/1.73 m\u0026sup2;), and kidney transplant recipients. From this source population, patients who had undergone at least one vascular imaging examination within 365 days before the index date were selected, yielding an analytic sample of 8,645 (81.8% of the source population). The index date was defined as the date of the last vascular imaging examination within the baseline window.\u003c/p\u003e \u003cp\u003eAtherosclerotic involvement was ascertained from imaging reports stored in the medical records database, which contained structured fields for examination name, anatomical position, findings, and clinical interpretation. A rule-based text extraction algorithm identified atherosclerotic disease across four vascular domains: coronary (coronary artery, left anterior descending, right coronary artery, left circumflex, left main), aortic (thoracic aorta, abdominal aorta, ascending and descending aorta, aortic arch), carotid/cerebral (carotid artery, vertebral artery, subclavian artery, cerebral arteries), and peripheral (lower extremity arteries, femoral, popliteal, tibial, iliac arteries). A domain was scored as positive if any imaging report within the 365-day window contained both a domain-specific anatomical keyword and an atherosclerosis-related finding (atherosclerosis, plaque, calcification, stenosis, or occlusion). The burden score was defined as the number of positive domains (range 0\u0026ndash;4) and was analysed both as a continuous variable per one-domain increment and as a categorical variable (0, 1, 2, 3\u0026ndash;4).\u003c/p\u003e \u003cp\u003eThree endpoints were prespecified. Major adverse cardiovascular events (MACE), the primary endpoint, comprised myocardial infarction, stroke, heart failure hospitalisation, or cardiovascular death, identified through ICD-10 discharge codes. All-cause mortality was ascertained from hospital death records and linked administrative data. The composite kidney endpoint comprised doubling of serum creatinine, a sustained decline in eGFR of \u0026ge;\u0026thinsp;40% from baseline, or initiation of renal replacement therapy [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBaseline covariates included age, sex, estimated glomerular filtration rate (CKD-EPI equation [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]), serum albumin, haemoglobin, systolic blood pressure, and comorbidities (diabetes, hypertension, coronary heart disease, heart failure, stroke, chronic obstructive pulmonary disease). Medication exposure to renin\u0026ndash;angiotensin\u0026ndash;aldosterone system inhibitors, statins, and diuretics was recorded from prescription data.\u003c/p\u003e \u003cp\u003eBaseline characteristics were summarised by burden score category and compared using one-way analysis of variance for continuous variables and the chi-square test for categorical variables. Cox proportional hazards regression was performed at four levels of covariate adjustment: unadjusted (Model 1); adjusted for age and sex (Model 2); additionally adjusted for diabetes and hypertension (Model 3); and further adjusted for eGFR, albumin, haemoglobin, systolic blood pressure, coronary heart disease, heart failure, stroke, chronic obstructive pulmonary disease, renin\u0026ndash;angiotensin\u0026ndash;aldosterone system inhibitor use, statin use, and diuretic use (Model 4; complete cases, N\u0026thinsp;=\u0026thinsp;6,176). The proportional hazards assumption was evaluated by scaled Schoenfeld residuals [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]; where violated, eGFR-stratified models were fitted as a secondary approach. Fine\u0026ndash;Gray subdistribution hazard models accounted for death as a competing event for the kidney and MACE endpoints [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrespecified subgroup analyses for MACE were conducted across seven strata (age, sex, diabetes, hypertension, eGFR category, coronary heart disease, statin use), with effect modification assessed by likelihood ratio interaction tests. Domain-specific analyses examined the independent association of each vascular territory with each endpoint in separate fully adjusted models.\u003c/p\u003e \u003cp\u003eSensitivity analyses comprised landmark analyses excluding events within 30, 90, and 365 days to address reverse causation; multiple imputation using predictive mean matching (5 datasets, mice package [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]) to evaluate the impact of missing covariate data [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]; and assessment of incremental discrimination by comparing Harrell's C-statistics [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] of models with and without the burden score [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Missing continuous covariates in the primary analysis were handled by median imputation with a missing-indicator variable. All analyses were conducted in R version 4.2 (survival, rms, cmprsk, and mice packages). Two-sided P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe analytic cohort comprised 8,645 patients with CKD stages 1\u0026ndash;4 (mean age 69.8\u0026thinsp;\u0026plusmn;\u0026thinsp;12.7 years; 57.6% male; Fig. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Median follow-up was 0.90 years (interquartile range 0.18\u0026ndash;1.48), contributing 7,889 person-years. During follow-up, 591 deaths (6.8%), 580 kidney events (6.7%), and 2,873 MACE (33.2%) were recorded. Atherosclerotic involvement was most prevalent in the aortic domain (81.2%), followed by coronary (68.1%), peripheral (44.5%), and carotid/cerebral (40.2%; Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Nearly half the cohort (45.9%) had disease in three or four territories; only 6.8% had no documented atherosclerosis. Compared with non-imaged CKD patients, the imaged cohort was older (69.8 vs 58.0 years), had lower eGFR (69.8 vs 80.9 mL/min/1.73 m\u0026sup2;), and carried a higher comorbidity burden (Supplementary Table S2). Patients with higher burden scores were older, had lower serum albumin and haemoglobin concentrations, and carried a higher prevalence of heart failure, stroke, and chronic obstructive pulmonary disease (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Missing data rates ranged from 3.0% for albumin to 27.9% for systolic blood pressure.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline characteristics of the analytic cohort stratified by atherosclerotic burden group\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eBurden 0\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eBurden 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eBurden 2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eBurden 3\u0026ndash;4\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e8645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e2643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e3964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e69.8\u0026thinsp;\u0026plusmn;\u0026thinsp;12.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e54.2\u0026thinsp;\u0026plusmn;\u0026thinsp;14.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e63.9\u0026thinsp;\u0026plusmn;\u0026thinsp;12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e69.9\u0026thinsp;\u0026plusmn;\u0026thinsp;11.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e74.2\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMale sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e4976 (57.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e325 (55.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e804 (55.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1542 (58.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e2305 (58.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eeGFR, mL/min/1.73m\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e69.8\u0026thinsp;\u0026plusmn;\u0026thinsp;26.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e84.8\u0026thinsp;\u0026plusmn;\u0026thinsp;28.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e75.4\u0026thinsp;\u0026plusmn;\u0026thinsp;26.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e69.3\u0026thinsp;\u0026plusmn;\u0026thinsp;26.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e65.9\u0026thinsp;\u0026plusmn;\u0026thinsp;25.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAlbumin, g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e38.2\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e40.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e39.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e38.2\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e37.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHemoglobin, g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e119.1\u0026thinsp;\u0026plusmn;\u0026thinsp;24.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e125.9\u0026thinsp;\u0026plusmn;\u0026thinsp;24.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e123.3\u0026thinsp;\u0026plusmn;\u0026thinsp;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e119.4\u0026thinsp;\u0026plusmn;\u0026thinsp;24.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e116.7\u0026thinsp;\u0026plusmn;\u0026thinsp;23.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSBP, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e127.9\u0026thinsp;\u0026plusmn;\u0026thinsp;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e126.5\u0026thinsp;\u0026plusmn;\u0026thinsp;18.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e126.7\u0026thinsp;\u0026plusmn;\u0026thinsp;19.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e127.2\u0026thinsp;\u0026plusmn;\u0026thinsp;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e128.9\u0026thinsp;\u0026plusmn;\u0026thinsp;20.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eDBP, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e74.1\u0026thinsp;\u0026plusmn;\u0026thinsp;12.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e78.2\u0026thinsp;\u0026plusmn;\u0026thinsp;12.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e75.8\u0026thinsp;\u0026plusmn;\u0026thinsp;12.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e74.2\u0026thinsp;\u0026plusmn;\u0026thinsp;12.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e73\u0026thinsp;\u0026plusmn;\u0026thinsp;12.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFasting glucose, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e7.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e6.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e6.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e7.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLDL-C, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e2.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e2.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e2.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTriglycerides, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePotassium, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.899\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePhosphorus, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e4525 (52.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e227 (38.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e632 (43.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1231 (46.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e2435 (61.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e7084 (81.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e362 (61.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1033 (71.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e2135 (80.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e3554 (89.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCoronary heart disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e5535 (64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e193 (32.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e705 (48.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1632 (61.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e3005 (75.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHeart failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3746 (43.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e114 (19.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e398 (27.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1096 (41.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e2138 (53.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eStroke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e4376 (50.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e109 (18.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e484 (33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1107 (41.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e2676 (67.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCOPD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3912 (45.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e133 (22.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e497 (34.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1201 (45.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e2081 (52.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHyperlipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e4634 (53.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e282 (48.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e748 (51.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1276 (48.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e2328 (58.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eRAAS inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e4467 (51.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e209 (35.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e571 (39.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1223 (46.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e2464 (62.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eStatin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e6220 (71.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e251 (42.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e844 (58.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1718 (65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e3407 (85.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eDiuretic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e5125 (59.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e233 (39.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e657 (45.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1561 (59.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e2674 (67.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eData are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD for continuous variables and n (%) for categorical variables.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eP values were derived from one-way ANOVA (continuous) or chi-square test (categorical) across burden groups.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAbbreviations: eGFR, estimated glomerular filtration rate (CKD-EPI equation); SBP, systolic blood pressure; DBP, diastolic blood pressure; LDL-C, low-density lipoprotein cholesterol; COPD, chronic obstructive pulmonary disease; RAAS, renin-angiotensin-aldosterone system.\u003c/p\u003e\n\u003cp\u003eBurden score = number of vascular domains with documented atherosclerosis (range 0-4).\u003c/p\u003e\n\u003cp\u003eTo evaluate the independent association between atherosclerotic burden and MACE, Cox proportional hazards models with sequential covariate adjustment were fitted (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The unadjusted hazard ratio per one-domain increment was 1.47 (95% CI 1.43\u0026ndash;1.53; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Sequential adjustment progressively attenuated this estimate: age- and sex-adjusted HR 1.30 (95% CI 1.26\u0026ndash;1.35; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), core-adjusted HR 1.28 (95% CI 1.23\u0026ndash;1.32; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and fully adjusted HR 1.10 (95% CI 1.06\u0026ndash;1.15; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; N\u0026thinsp;=\u0026thinsp;6,176). Scaled Schoenfeld residuals indicated marginal violation of the proportional hazards assumption for the burden score in the fully adjusted model (P\u0026thinsp;=\u0026thinsp;0.037); eGFR stratification did not resolve this violation (P\u0026thinsp;=\u0026thinsp;0.008 after stratification), indicating that the hazard ratio varied over time. In categorical analysis, both the intermediate (burden 2 vs 0: HR 1.46, 95% CI 1.10\u0026ndash;1.95; P\u0026thinsp;=\u0026thinsp;0.010) and highest burden groups (3\u0026ndash;4 vs 0: HR 1.61, 95% CI 1.20\u0026ndash;2.14; P\u0026thinsp;=\u0026thinsp;0.001) reached significance. Fine\u0026ndash;Gray subdistribution hazard modelling, which accounted for death as a competing event, confirmed the association (sHR 1.10, 95% CI 1.06\u0026ndash;1.15; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as did multiple imputation (HR 1.13, 95% CI 1.09\u0026ndash;1.17; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAssociation between atherosclerotic burden score and clinical outcomes across sequential Cox regression models\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"11\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eEvents/N\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003eModel 4\u003c/p\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003ePH P (Model 4)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMACE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2873/8645 (Model 4: 2414/6176)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e1.47 (1.43, 1.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e1.30 (1.26, 1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e1.28 (1.23, 1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e1.10 (1.06, 1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eComposite kidney endpoint\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e580/8645 (Model 4: 446/6176)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e1.31 (1.22, 1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e1.22 (1.13, 1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e1.21 (1.12, 1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e1.14 (1.03, 1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAll-cause mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e591/8645 (Model 4: 562/6176)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e1.22 (1.13, 1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.97 (0.89, 1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e1.03 (0.95, 1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e0.423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e1.00 (0.91, 1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e0.917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e0.504\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\"\u003eHazard ratios are per one-domain increment in atherosclerotic burden score.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\"\u003eModel 1: unadjusted. Model 2: adjusted for age and sex. Model 3: Model 2\u0026thinsp;+\u0026thinsp;diabetes and hypertension. Model 4: Model 3\u0026thinsp;+\u0026thinsp;eGFR, albumin, hemoglobin, SBP, coronary heart disease, heart failure, stroke, COPD, RAAS inhibitor, statin, and diuretic use (complete cases, N\u0026thinsp;=\u0026thinsp;6,176).\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\"\u003ePH P: P value for the proportional hazard assumption based on scaled Schoenfeld residuals.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\"\u003eMACE: major adverse cardiovascular events (myocardial infarction, stroke, heart failure hospitalization, or cardiovascular death). Composite kidney endpoint: doubling of serum creatinine, sustained eGFR decline\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;40%, or initiation of renal replacement therapy.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSensitivity analyses: competing risks and multiple imputation\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"13\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eFine-Gray competing risk HR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eLandmark 30d HR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eLandmark 90d HR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003eLandmark 365d HR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003eMultiple imputation (m\u0026thinsp;=\u0026thinsp;5) HR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMACE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.10 (1.06, 1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e1.08 (1.02, 1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e1.05 (0.98, 1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e1.13 (0.97, 1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e1.13 (1.09, 1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eComposite kidney endpoint\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.13 (1.03, 1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e1.14 (1.03, 1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e1.12 (1.01, 1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e1.11 (0.96, 1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e1.17 (1.07, 1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAll-cause mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e1.03 (0.91, 1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.99 (0.85, 1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e1.01 (0.77, 1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e0.99 (0.91, 1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"13\"\u003eFine-Gray competing-risk models used death as the competing event for MACE and kidney endpoints, not applicable for all-cause mortality.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"13\"\u003eLandmark analyses excluded events occurring within the specified time window (30, 90, or 365 days) after the index date to address reverse causation.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"13\"\u003eMultiple imputation: 5 datasets generated using predictive mean matching (mice package); estimates pooled using Rubin\u0026apos;s rules.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"13\"\u003eHazard ratios (or sub distribution hazard ratios for Fine-Gray) are per one-domain increment in burden score, fully adjusted (Model 4 covariates).\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe burden score was not independently associated with all-cause mortality in any adjusted model (fully adjusted HR 1.00, 95% CI 0.91\u0026ndash;1.09; P\u0026thinsp;=\u0026thinsp;0.92; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), with the proportional hazards assumption satisfied (Schoenfeld P\u0026thinsp;=\u0026thinsp;0.50). Multiple imputation corroborated this null finding (HR 0.99, P\u0026thinsp;=\u0026thinsp;0.86; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For the composite kidney endpoint, the burden score was independently associated with adverse outcomes after full adjustment (HR 1.14, 95% CI 1.03\u0026ndash;1.26; P\u0026thinsp;=\u0026thinsp;0.008), with the proportional hazards assumption satisfied (Schoenfeld P\u0026thinsp;=\u0026thinsp;0.10). Fine\u0026ndash;Gray analysis confirmed this association (sHR 1.13, 95% CI 1.03\u0026ndash;1.24; P\u0026thinsp;=\u0026thinsp;0.01), and multiple imputation yielded a stronger estimate (HR 1.17, 95% CI 1.07\u0026ndash;1.27; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n\u003cp\u003eTo assess the temporal stability of the MACE association, landmark analyses were performed excluding events within 30, 90, and 365 days (Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The association persisted at the 30-day landmark (HR 1.08, 95% CI 1.02\u0026ndash;1.15; P\u0026thinsp;=\u0026thinsp;0.007) but was attenuated at 90 days (HR 1.05, P\u0026thinsp;=\u0026thinsp;0.19) and at one year (HR 1.13, P\u0026thinsp;=\u0026thinsp;0.13), indicating that the signal was driven predominantly by early events. Notably, the kidney endpoint association persisted at the 30-day (HR 1.14, P\u0026thinsp;=\u0026thinsp;0.01) and 90-day landmarks (HR 1.12, P\u0026thinsp;=\u0026thinsp;0.04), suggesting a more temporally stable effect on renal outcomes.\u003c/p\u003e\n\u003cp\u003eIndividual domain analyses revealed marked heterogeneity in the vascular territories driving each endpoint (Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Coronary atherosclerosis (HR 1.32, 95% CI 1.19\u0026ndash;1.47; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and carotid/cerebral disease (HR 1.30, 95% CI 1.19\u0026ndash;1.41; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were the primary drivers of the MACE association, whereas aortic and peripheral disease showed no significant association. For the composite kidney endpoint, aortic atherosclerosis emerged as the dominant predictor (HR 2.31, 95% CI 1.62\u0026ndash;3.31; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with coronary disease also reaching significance (HR 1.28, 95% CI 1.01\u0026ndash;1.62; P\u0026thinsp;=\u0026thinsp;0.04). For all-cause mortality, no individual domain reached statistical significance, though peripheral disease showed a borderline inverse association (HR 0.84, 95% CI 0.71\u0026ndash;1.00; P\u0026thinsp;=\u0026thinsp;0.05).\u003c/p\u003e\n\u003cp\u003eSubgroup analyses for MACE demonstrated significant effect modification by age (interaction P\u0026thinsp;=\u0026thinsp;0.009), hypertension (P\u0026thinsp;=\u0026thinsp;0.008), coronary heart disease (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and statin use (P\u0026thinsp;=\u0026thinsp;0.007; Fig. \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The burden score was more strongly associated with MACE in patients younger than 65 years (HR 1.15 vs 1.09), those without pre-existing coronary heart disease (HR 1.21 vs 1.06), those without hypertension (HR 1.19 vs 1.09), and those not receiving statins (HR 1.14 vs 1.10). No significant interaction was observed for sex (P\u0026thinsp;=\u0026thinsp;0.21), diabetes (P\u0026thinsp;=\u0026thinsp;0.72), or eGFR category (P\u0026thinsp;=\u0026thinsp;0.05).\u003c/p\u003e\n\u003cp\u003eThe addition of the burden score to a model containing all clinical covariates produced modest improvement in discrimination: the C-statistic increased from 0.6979 to 0.6993 for MACE (delta 0.0014, likelihood ratio P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), from 0.7231 to 0.7270 for the kidney endpoint (delta 0.0039, P\u0026thinsp;=\u0026thinsp;0.008), and was unchanged for mortality (delta 0.0000, P\u0026thinsp;=\u0026thinsp;0.92).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe principal finding of this study is that a structured multivascular imaging atherosclerotic burden score was independently associated with both MACE and adverse kidney outcomes in non-dialysis CKD, with each additional affected vascular bed conferring a 10% higher MACE hazard and a 14% higher kidney event hazard after comprehensive covariate adjustment. These associations were robust to competing-risk modelling and multiple imputation. Domain-specific analyses uncovered a striking dissociation: coronary and carotid/cerebral atherosclerosis drove the MACE signal, whereas aortic disease was the dominant predictor of adverse kidney outcomes.\u003c/p\u003e \u003cp\u003eThese observations extend the polyvascular disease paradigm, established in general cardiovascular populations through the REACH [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and COMPASS [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] registries, to the CKD setting. The magnitude of confounding attenuation \u0026mdash; from an unadjusted HR of 1.47 to a fully adjusted HR of 1.10 for MACE \u0026mdash; warrants emphasis. This 72% reduction on the log-hazard scale indicates that a substantial proportion of the crude association between multivascular burden and MACE was explained by shared risk factors, principally age, comorbidity burden, and medication use. Such attenuation is consistent with the observation by Matsushita et al. that traditional risk factors account for a substantial proportion of the excess cardiovascular risk attributable to reduced kidney function [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Nonetheless, the residual 10% per-domain hazard increment remained highly significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and was corroborated by Fine-Gray competing-risk modelling (SHR 1.10, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The delta C-statistic for MACE was 0.0014 \u0026mdash; modest but statistically significant \u0026mdash; while the kidney endpoint showed a more meaningful increment of 0.0039 (P\u0026thinsp;=\u0026thinsp;0.008), suggesting that the burden score may have greater discriminative value for renal than for cardiovascular outcomes [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe violation of the proportional hazards assumption for the MACE endpoint (Schoenfeld P\u0026thinsp;=\u0026thinsp;0.0002) and the progressive attenuation across landmark analyses provide complementary evidence that the burden score captures acute or peri-assessment cardiovascular risk rather than a stable long-term hazard. Given that the median time to MACE was short and that a substantial proportion of events occurred during or shortly after the index hospitalisation, the reported hazard ratio should be interpreted as a time-averaged estimate over a non-constant effect. Future investigations employing time-varying coefficient models or restricted mean survival time analyses may more accurately characterise this temporal heterogeneity.\u003c/p\u003e \u003cp\u003eThe domain-specific findings merit particular attention. The strong and independent association between aortic atherosclerosis and the composite kidney endpoint (HR 2.31, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) is biologically plausible: atherosclerotic disease of the abdominal aorta can compromise renal perfusion through atheroembolism, renal artery ostial stenosis, or reduced aortic compliance with consequent transmission of pulsatile haemodynamic stress to the glomerular microvasculature [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This finding aligns with prior work demonstrating that aortic calcification predicts CKD progression independently of traditional risk factors [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. By contrast, coronary (HR 1.32, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and carotid/cerebral disease (HR 1.30, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) drove the MACE association, consistent with their direct pathophysiological relevance to myocardial infarction and stroke. Notably, the composite burden score showed no independent association with all-cause mortality after adjustment (fully adjusted HR 0.995, P\u0026thinsp;=\u0026thinsp;0.92), despite a significant unadjusted association (HR 1.22, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) that was entirely attenuated by age and sex adjustment alone (HR 0.97, P\u0026thinsp;=\u0026thinsp;0.40). This pattern indicates that the crude mortality\u0026ndash;burden link was driven by age confounding rather than by atherosclerosis per se. The null finding is further explained by the heterogeneous causes of death in CKD, where non-cardiovascular mortality \u0026mdash; infection, malignancy, metabolic derangement \u0026mdash; constitutes a substantial proportion of total deaths, diluting any signal from a vascular-specific exposure [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Similar dissociations between polyvascular burden and all-cause mortality have been reported in the REACH registry [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePeripheral atherosclerosis showed a borderline inverse association with mortality (HR 0.84, P\u0026thinsp;=\u0026thinsp;0.05) but was not significantly associated with MACE (HR 0.94, P\u0026thinsp;=\u0026thinsp;0.14) or kidney outcomes (HR 1.16, P\u0026thinsp;=\u0026thinsp;0.16). The borderline mortality finding was unexpected. Potential explanations include confounding by indication: patients with documented peripheral vascular disease may receive more aggressive cardiovascular risk factor management, including intensified statin therapy and antiplatelet regimens, thereby attenuating their event rates [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Alternatively, peripheral arterial disease in this cohort may serve as a marker of more comprehensive vascular imaging evaluation, with the apparent protective effect reflecting ascertainment bias rather than a true biological phenomenon. Given the borderline significance and the absence of a consistent pattern across endpoints, this finding should be interpreted with caution and requires replication.\u003c/p\u003e \u003cp\u003eThe subgroup analyses revealed a coherent pattern of effect modification. The burden score was more informative in patients without established coronary heart disease (interaction P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), without hypertension (P\u0026thinsp;=\u0026thinsp;0.008), younger than 65 years (P\u0026thinsp;=\u0026thinsp;0.009), and not receiving statins (P\u0026thinsp;=\u0026thinsp;0.007). This pattern is consistent with the concept of risk reclassification at the margin: the burden score adds prognostic information primarily in patients whose cardiovascular risk is not already clinically apparent or pharmacologically managed. In patients with known coronary disease or those already receiving aggressive risk factor modification, the incremental value of quantifying polyvascular extent is diminished [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral limitations should be acknowledged. The retrospective single-centre design limits generalisability. Of the 10,567 non-dialysis CKD patients identified, 8,645 (81.8%) had undergone at least one vascular imaging examination within 365 days of the index date and constituted the analytic cohort. These imaged patients were older and had higher comorbidity burden than the 1,922 non-imaged patients (Supplementary Table S2), introducing potential selection bias; the results should therefore be interpreted as applying to CKD patients who undergo vascular imaging in routine clinical care, not to the broader CKD population. The atherosclerotic burden score was derived from rule-based text extraction without formal validation against manual chart review; misclassification is possible, though likely non-differential with respect to outcomes, biasing estimates toward the null. The median follow-up of 0.90 years, while improved by anchoring to the imaging date, remains relatively short and limits conclusions about long-term prognostic value; the progressive attenuation of MACE associations across landmark analyses (30-day HR 1.08, 90-day HR 1.05, 365-day HR 1.13 [all non-significant beyond 90 days]) underscores this temporal limitation. The fully adjusted model was restricted to 71.4% of the cohort due to missing covariate data; although multiple imputation produced consistent or stronger results, residual selection bias cannot be excluded. The MACE event rate (33.2%) was high, partly reflecting the broad composite definition that included heart failure hospitalisation; this should be considered when comparing effect sizes with studies using narrower MACE definitions. Finally, the proportional hazards assumption was violated for the MACE endpoint, and the modest improvement in C-statistic (0.0014 for MACE, 0.0039 for kidney) suggests that the burden score provides limited incremental discrimination beyond established clinical variables, though the kidney endpoint showed more promising discriminative gains.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eA structured multivascular imaging atherosclerotic burden score derived from routine clinical imaging was independently associated with both MACE and adverse kidney outcomes in non-dialysis CKD. The MACE association was driven by coronary and carotid/cerebral disease and concentrated in the early post-assessment period, while aortic atherosclerosis emerged as a distinct and potent predictor of kidney endpoints (HR 2.31). The modest incremental discrimination for MACE (delta C\u0026thinsp;=\u0026thinsp;0.0014) and the temporal attenuation of associations beyond 90 days suggest that the burden score, in its current form, has limited utility for long-term cardiovascular risk stratification. However, the more meaningful discriminative gain for kidney outcomes (delta C\u0026thinsp;=\u0026thinsp;0.0039) and the strong domain-specific signals highlight the potential value of vascular imaging in identifying CKD patients at elevated renal risk. These findings underscore the domain-specific heterogeneity of atherosclerotic risk in CKD and support the hypothesis that different vascular territories contribute to distinct organ-specific outcomes through divergent pathophysiological mechanisms.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e This study was approved by the Ethics Committee of Wuhan Central Hospital (Approval No. WHZXKYL2022-083-03). The requirement for informed consent was waived given the retrospective design. This study was conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003e[This study was supported by Wuhan Central Hospital Institutional Fund (22YJ45), the Chen-Xiaoping Foundation for the Development of Science and Technology of Hubei Province (CXPJJH122001-2239), and the 2022 Wuhan Traditional Chinese Medicine Research Project (WZ24B03). The funders had no role in the study design, data collection, data analysis, data interpretation, manuscript preparation, or the decision to submit the article for publication.]\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eXiongpan Wang: Conceptualization, study design, data curation, data cleaning, formal analysis, and manuscript drafting.Dianjun Liu: Study supervision, project administration, conceptual guidance, and critical revision of the manuscript.Yunfang Huang: Data verification, data correction, statistical methodology, formal analysis, and manuscript review.Fan Zhu: Conceptualization, study design, statistical methodology, formal analysis, and manuscript review.Li Xu: Study supervision, methodological guidance, project administration, and critical revision of the manuscript.All authors read and approved the final manuscript. Xiongpan Wang, Dianjun Liu and Yunfang Huang contributed equally to this work and share first authorship. Fan Zhu and Li Xu jointly supervised this study and serve as co-corresponding authors.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e \u003cp\u003eThe datasets analysed during the current study are not publicly available due to patient privacy regulations but are available from the corresponding author on reasonable request, subject to institutional ethics approval.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGo AS, Chertow GM, Fan D, et al. Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. N Engl J Med. 2004;351(13):1296\u0026ndash;305.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTonelli M, Muntner P, Lloyd A, et al. Risk of coronary events in people with chronic kidney disease compared with those with diabetes: a population-level cohort study. Lancet. 2012;380(9844):807\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSarnak MJ, Amann K, Bangalore S, et al. Chronic kidney disease and coronary artery disease: JACC state-of-the-art review. J Am Coll Cardiol. 2019;74(14):1823\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eValdivielso JM, Rodr\u0026iacute;guez-Puyol D, Pascual J, et al. Atherosclerosis in chronic kidney disease: more, less, or just different? Arterioscler Thromb Vasc Biol. 2019;39(10):1938\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJankowski J, Floege J, Fliser D, et al. Cardiovascular disease in chronic kidney disease: pathophysiological insights and therapeutic options. Circulation. 2021;143(7):518\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLondon GM, Gu\u0026eacute;rin AP, Marchais SJ, et al. Arterial media calcification in end-stage renal disease: impact on all-cause and cardiovascular mortality. Nephrol Dial Transpl. 2003;18(9):1731\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBudoff MJ, Rader DJ, Reilly MP, et al. Relationship of estimated GFR and coronary artery calcification in the CRIC (Chronic Renal Insufficiency Cohort) study. Am J Kidney Dis. 2011;58(4):519\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen J, Budoff MJ, Reilly MP, et al. Coronary artery calcification and risk of cardiovascular disease and death among patients with chronic kidney disease. JAMA Cardiol. 2017;2(6):635\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhatt DL, Steg PG, Ohman EM, et al. International prevalence, recognition, and treatment of cardiovascular risk factors in outpatients with atherothrombosis. JAMA. 2006;295(2):180\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSteg PG, Bhatt DL, Wilson PW, et al. One-year cardiovascular event rates in outpatients with atherothrombosis. JAMA. 2007;297(11):1197\u0026ndash;206.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnand SS, Bosch J, Eikelboom JW, et al. Rivaroxaban with or without aspirin in patients with stable peripheral or carotid artery disease: an international, randomised, double-blind, placebo-controlled trial. Lancet. 2018;391(10117):219\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFowkes FG, Murray GD, Butcher I, et al. Ankle brachial index combined with Framingham Risk Score to predict cardiovascular events and mortality: a meta-analysis. JAMA. 2008;300(2):197\u0026ndash;208.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiao KP, Cai T, Savova GK, et al. Development of phenotype algorithms using electronic medical records and incorporating natural language processing. BMJ. 2015;350:h1885.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKirby JC, Speltz P, Rasmussen LV, et al. PheKB: a catalog and workflow for creating electronic phenotype algorithms for transportability. J Am Med Inf Assoc. 2016;23(6):1046\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group. KDIGO 2012 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. Kidney Int Suppl. 2013;3(1):1\u0026ndash;150.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evon Elm E, Altman DG, Egger M, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement. Lancet. 2007;370(9596):1453\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLevey AS, Inker LA, Matsushita K, et al. GFR decline as an end point for clinical trials in CKD: a scientific workshop sponsored by the National Kidney Foundation and the US Food and Drug Administration. Am J Kidney Dis. 2014;64(6):821\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLevey AS, Stevens LA, Schmid CH, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150(9):604\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrambsch PM, Therneau TM. Proportional hazards tests and diagnostics based on weighted residuals. Biometrika. 1994;81(3):515\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFine JP, Gray RJ. A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc. 1999;94(446):496\u0026ndash;509.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAustin PC, Lee DS, Fine JP. Introduction to the analysis of survival data in the presence of competing risks. Circulation. 2016;133(6):601\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Buuren S, Groothuis-Oudshoorn K. mice: Multivariate Imputation by Chained Equations in R. J Stat Softw. 2011;45(3):1\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWhite IR, Royston P, Wood AM. Multiple imputation using chained equations: issues and guidance for practice. Stat Med. 2011;30(4):377\u0026ndash;99.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePencina MJ, D'Agostino RB, Sr, Agostino RB Jr, Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med. 2008;27(2):157\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarrell FE Jr, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med. 1996;15(4):361\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatsushita K, van der Velde M, Astor BC, et al. Association of estimated glomerular filtration rate and albuminuria with all-cause and cardiovascular mortality in general population cohorts: a collaborative meta-analysis. Lancet. 2010;375(9731):2073\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSafian RD, Textor SC. Renal-artery stenosis. N Engl J Med. 2001;344(6):431\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTownsend RR, Anderson AH, Chirinos JA, et al. Association of pulse wave velocity with chronic kidney disease progression and mortality: findings from the CRIC study (Chronic Renal Insufficiency Cohort). Hypertension. 2018;71(6):1101\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlacher J, Gu\u0026eacute;rin AP, Pannier B, et al. Arterial calcifications, arterial stiffness, and cardiovascular risk in end-stage renal disease. Hypertension. 2001;38(4):938\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSigrist MK, Taal MW, Bungay P, et al. Progressive vascular calcification over 2 years is associated with arterial stiffening and increased mortality in patients with stages 4 and 5 chronic kidney disease. Clin J Am Soc Nephrol. 2007;2(6):1241\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO'Hare AM, Glidden DV, Fox CS, Hsu CY. High prevalence of peripheral arterial disease in persons with renal insufficiency: results from the National Health and Nutrition Examination Survey 1999\u0026ndash;2000. Circulation. 2004;109(3):320\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCriqui MH, Aboyans V. Epidemiology of peripheral artery disease. Circ Res. 2015;116(9):1509\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaigent C, Landray MJ, Reith C, et al. The effects of lowering LDL cholesterol with simvastatin plus ezetimibe in patients with chronic kidney disease (Study of Heart and Renal Protection): a randomised placebo-controlled trial. Lancet. 2011;377(9784):2181\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCook NR. Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation. 2007;115(7):928\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStenvinkel P, Carrero JJ, Axelsson J, et al. Emerging biomarkers for evaluating cardiovascular risk in the chronic kidney disease patient: how do new pieces fit into the uremic puzzle? Clin J Am Soc Nephrol. 2008;3(2):505\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatsushita K, Coresh J, Sang Y, et al. Estimated glomerular filtration rate and albuminuria for prediction of cardiovascular outcomes: a collaborative meta-analysis of individual participant data. Lancet Diabetes Endocrinol. 2015;3(7):514\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThompson S, James M, Wiebe N, et al. Cause of death in patients with reduced kidney function. J Am Soc Nephrol. 2015;26(10):2504\u0026ndash;11.\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":"bmc-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"atherosclerosis, chronic kidney disease, polyvascular disease, cardiovascular outcomes, competing risks, imaging","lastPublishedDoi":"10.21203/rs.3.rs-9449777/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9449777/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePolyvascular atherosclerosis amplifies cardiovascular risk in the general population, yet its prognostic significance in chronic kidney disease (CKD) \u0026mdash; where the atherosclerotic phenotype is qualitatively distinct \u0026mdash; has not been systematically evaluated. We examined whether a structured imaging-based multivascular atherosclerotic burden score predicts cardiovascular and renal outcomes in non-dialysis CKD.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis retrospective cohort study included 8,645 non-dialysis CKD patients (stages 1\u0026ndash;4) at Wuhan Central Hospital (2015\u0026ndash;2023) who underwent vascular imaging within 365 days of the index date. Atherosclerotic involvement was ascertained from structured text extraction of imaging reports across four vascular domains (coronary, aortic, carotid/cerebral, peripheral), and a burden score (0\u0026ndash;4) was defined as the count of affected territories. Cox proportional hazards models with sequential covariate adjustment were fitted for all-cause mortality, a composite kidney endpoint, and major adverse cardiovascular events (MACE). Fine\u0026ndash;Gray competing-risk models, landmark analyses, subgroup analyses with interaction testing, and multiple imputation served as prespecified sensitivity analyses.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOver a median follow-up of 0.90 years (IQR 0.18\u0026ndash;1.48), 591 deaths (6.8%), 580 kidney events (6.7%), and 2,873 MACE (33.2%) were recorded. In fully adjusted models (N\u0026thinsp;=\u0026thinsp;6,176), each additional affected vascular bed was associated with a 10% higher MACE hazard (HR 1.10, 95% CI 1.06\u0026ndash;1.15; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), confirmed by Fine\u0026ndash;Gray analysis (sHR 1.10, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and multiple imputation (HR 1.13, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The burden score was also independently associated with the composite kidney endpoint (HR 1.14, 95% CI 1.03\u0026ndash;1.26; P\u0026thinsp;=\u0026thinsp;0.008), corroborated by Fine\u0026ndash;Gray modelling (sHR 1.13, P\u0026thinsp;=\u0026thinsp;0.01). No independent association was observed with all-cause mortality (HR 1.00, P\u0026thinsp;=\u0026thinsp;0.92). Domain-specific analyses revealed that coronary and carotid/cerebral disease drove the MACE signal, whereas aortic atherosclerosis was the dominant predictor of kidney events (HR 2.31, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eA multivascular imaging atherosclerotic burden score was independently associated with both MACE and adverse kidney outcomes in non-dialysis CKD, with domain-specific heterogeneity implicating distinct pathophysiological pathways for cardiovascular and renal endpoints. Aortic atherosclerosis emerged as a particularly potent predictor of kidney disease progression.\u003c/p\u003e","manuscriptTitle":"Multivascular Imaging-Derived Atherosclerotic Burden and Cardiorenal Outcomes in Non-Dialysis Chronic Kidney Disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-11 06:23:14","doi":"10.21203/rs.3.rs-9449777/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"216310612506791473049172227901128980233","date":"2026-05-04T06:32:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-29T10:38:04+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-21T10:17:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-20T08:24:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-20T08:23:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nephrology","date":"2026-04-17T13:21:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"527efa03-de6a-4f0e-9f65-f083cb683467","owner":[],"postedDate":"May 11th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"216310612506791473049172227901128980233","date":"2026-05-04T06:32:06+00:00","index":30,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T06:23:14+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-11 06:23:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9449777","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9449777","identity":"rs-9449777","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}