{"paper_id":"0a6ad1ff-d208-4710-8caa-e6ce7cfa2b24","body_text":"Association of neutrophil-to-lymphocyte ratio with all-cause and cardiovascular mortality among cardiovascular-kidney-metabolic syndrome: a national cross-sectional study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Association of neutrophil-to-lymphocyte ratio with all-cause and cardiovascular mortality among cardiovascular-kidney-metabolic syndrome: a national cross-sectional study Peng Wu, Zhenghui Huang, Juan Ma, Baozhen Zhu, Mohan Wang, Ali Ma, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6611872/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The neutrophil-to-lymphocyte ratio (NLR) as inflammatory biomarker across cardiovascular outcomes. However, its relationship with all-cause and cardiovascular mortality in cardiovascular-kidney-metabolic (CKM) syndrome remains poorly characterized. We analyzed 7,929 CKM patients from National Health and Nutrition Examination Survey (NHANES) (2011–2018), with mortality follow-up through 2019. Cox proportional hazards models and restricted cubic splines (RCS) assessed NLR-mortality relationships. Survival disparities were quantified through Kaplan-Meier estimates. Sensitivity and stratification analyses were used to demonstrate the stability of the relationship. The receiver operating characteristic curve (ROC) analysis was conducted to access the predictive ability of NLR for survival. Mediation analysis explored estimated glomerular filtration rate (eGFR)-mediated effects.The cohort comprised 7,929 participants with 473 documented deaths during follow-up, including 125 cardiovascular-specific events. Elevated NLR independently predicted higher all-cause mortality (HR=1.13, 95%CI:1.09–1.17) and cardiovascular mortality (HR=1.17,1.11–1.24). RCS revealed a U-shaped NLR-all-cause mortality relationship (inflection: NLR=1.26, P for nonlinear=0.016), contrasting with linear cardiovascular mortality association (P for nonlinear =0.378). The highest NLR tertile demonstrating markedly higher mortality risks [all-cause mortality: HR (95CI%)1.40 (1.09, 1.81); cardiovascular mortality: HR (95CI%) 2.17 (1.24, 3.81)]. Sensitivity analysis and subgroup analyses were conducted to prove the stability of the model. ROC analysis demonstrated that the NLR had area under the curve (AUC) values of 0.651 and 0.703 for predicting all-cause mortality and cardiovascular mortality, respectively, showing superior predictive value compared to individual neutrophil or lymphocyte counts alone. Mediation analysis identified that eGFR mediated 1.7% of the NLR-all-cause mortality association and 1.6% of the cardiovascular mortality relationship. Elevated NLR levels were independently associated with increased risks of both all-cause mortality and cardiovascular mortality in patients with CKM syndrome. Moreover, these findings underscore the potential clinical utility of NLR to refine the detection of mortality in CKM population. Health sciences/Endocrinology Health sciences/Endocrinology/Endocrine system and metabolic diseases Health sciences/Endocrinology/Endocrine system and metabolic diseases/Metabolic syndrome Cardiovascular-kidney-metabolic syndrome Neutrophil-to-lymphocyte ratio All-cause mortality Cardiovascular mortality NHANES Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction The convergence of cardiovascular, renal, and metabolic dysfunction has recently been redefined as cardiovascular-kidney-metabolic (CKM) syndrome through an American Heart Association (AHA) scientific statement, establishing new diagnostic criteria that recognize these interconnected disorders as a unified clinical entity 1 . Given the intricate interplay between cardiovascular, renal, and metabolic disorders and their collective contribution to global disease burden, these interconnected conditions present substantial challenges to public health systems worldwide 2, 3 . The emerging concept of CKM syndrome highlights this pathophysiological convergence, necessitating the identification of reliable biomarkers for early detection and therapeutic monitoring 4 . The neutrophil-to-lymphocyte ratio (NLR) has gained attention as a hematologic indicator that quantifies inflammatory processes underlying these comorbidities while offering potential clinical value for tracking disease progression and therapeutic responses 5, 6 . The neutrophil-to-lymphocyte ratio (NLR) has gained recognition as a dual-pathway inflammatory integrator, mechanistically reflecting neutrophil-mediated tissue damage coupled with lymphocyte-depleted immune surveillance 7, 8 . The NLR has emerged as a robust inflammatory prognostic indicator 9 . It has emerged as a promising biomarker for diverse pathological conditions ranging from oncological processes and inflammatory states to metabolic dysregulation, cardiovascular outcomes 10-12 . Its clinical utility has been extensively validated in large-scale cohort studies, demonstrating independent predictive value for hypertension progression 5 , coronary artery disease severity 13 , and cardiovascular mortality risk stratification 14 . In a nationwide, population-based prospective cohort study encompassing 32,454 adults with median 12.3-year follow-up, elevated NLR emerged as an independent predictor of all-cause mortality 15 . Chronic inflammation serves as a critical pathophysiological nexus in CKM progression, mediating cross-organ damage through macrophage activation, cytokine storm amplification, and endothelial glycocalyx degradation 16 . Inflammation serves as a pathogenic linchpin in chronic disease pathogenesis, exerting deleterious effects across integrated organ systems including cardiac function, glomerular filtration dynamics, and cardiometabolic homeostasis 4 . Despite these advances, NLR's mortality associations in CKM syndrome remain uncharacterized. This population-based retrospective cohort study utilized data from 9 continuous cycles (2001-2018) of the National Health and Nutrition Examination Survey (NHANES) to investigate the prognostic value of NLR in all-cause and cardiovascular mortality among adults diagnosed with CKM syndrome Methods Data and sample source This investigation utilized publicly available data from the National Health and Nutrition Examination Survey (NHANES 2011-2018), a stratified multistage probability survey conducted by the CDC's National Center for Health Statistics to monitor population health in non-institutionalized U.S. civilians 17 . The study protocol received institutional review board approval, with documented informed consent obtained from all participants prior to data collection 18 . The analytic sample derivation process followed rigorous epidemiological standards: From 39,156 potentially eligible individuals across four biennial survey cycles, we sequentially excluded those aged <20 years (n=16,539), missing sampling weights (WTSAF2YR, n=13,373), participants with incomplete mortality linkage data (n=22), missing neutrophil/lymphocyte ratio measurements (n=235), missing CKM data and other covariates(n=1,058) .The final cohort comprised 7,929 adults with complete baseline characteristics and outcome data 19 . Data acquisition involved three complementary modalities: 1) Personal interviews capturing demographic and medical history, 2) Standardized physical examinations including anthropometric and blood pressure measurements, and 3) Centralized laboratory analyses of hematological/biochemical parameters. Finally,7,929 participants were included in our study. (Figure 1). Assessment of the NLR Neutrophil and lymphocyte counts were determined by conducting a complete blood count on blood specimens using a Beckman Coulter automated blood analyzer in an MEC, expressed as × 10 3 cells/µL. NLR was calculated as the absolute neutrophil count divided by the absolute lymphocyte count 20 . Ascertainment of CKM syndrome stages CKM classification followed an adapted implementation of the AHA 2023 criteria, operationalized through a multidimensional staging system integrating metabolic, renal, and cardiovascular parameters 21 : Stage 1: Metabolic Dysregulation: Characterized by excess adiposity (BMI ≥23 kg/m² [Asian] / ≥25 kg/m² [non-Asian]; waist circumference ≥80/90 cm [Asian women/men] or ≥88/102 cm [non-Asian women/men]) and dysglycemic thresholds (HbA1c 5.7–6.4% or fasting glucose 100–125 mg/dL). Stage 2: Metabolic-Kidney Codominance: Requires coexistence of ≥2 metabolic drivers with concurrent target organ involvement, Hypertension (≥130/85 mmHg); Hypertriglyceridemia (≥150 mg/dL); Albuminuria (UACR ≥30 mg/g); Confirmed diabetes (HbA1c ≥6.5%) Stage 3: Subclinical Cardiovascular Risk: Defined by: Chronic kidney disease (KDIGO G3b-A3: eGFR 30–44 mL/min/1.73m² + UACR >300 mg/g); Elevated 10-year CVD risk (≥20% via AHA PREVENT equations) Stage 4: Symptomatic Cardiovascular Disease: Atherosclerotic cardiovascular disease; Heart failure; Cerebrovascular events. Ascertainment of covariates Demographic characteristics of participants were obtained from the NHANES database, including age, gender, ethnicity (Mexican American, non-Hispanic Black, non-Hispanic White, and other racial/ethnic groups), education level (less than high school, high school/equivalent, or college/above) and marital status (Married, Single/Separated). Lifestyle behaviors were operationalized as: Smoking status: never, former, or current smoker; Alcohol consumption: never, former, or current drinker. Comorbidities included self-reported physician-diagnosed diabetes mellitus, hypertension, coronary heart disease (CVD), and dyslipidemia. Clinically measured covariates encompassed: Anthropometrics: body mass index (BMI); Hematological indices: leukocyte count (WBC), neutrophil, lymphocyte, hemoglobin (Hb), platelet count (PLT), and neutrophil (NEUT). Metabolic profiles: glycated hemoglobin (HbA1c), fast blood glucose (FBG), hemoglobin A1c (HbA1c), alanine aminotransferase (ALT), creatinine, triglycerides (TC), total cholesterol (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C). Renal function: estimated glomerular filtration rate (eGFR) using CKD-EPI 2021 creatinine-cystatin C equation. Mortality status was ascertained through probabilistic linkage with the National Death Index through December 31, 2019. The primary endpoint was all-cause mortality, with secondary analysis of cardiovascular mortality. Statistical analysis Given the intricate sampling design of the NHANES survey, we incorporated the sample weights for the various study periods in our analytical methods to ensure accurate estimates of health-related statistics 22 . For continuous variables, mean ± standard deviation (SD) was used for statistical description if they met normal distribution; independent samples t-test was used forinter-group comparison. the median (25-75%) was used for description if the variables did not meet normal distribution; and the rank sum test was used for inter-group comparison. For counting data, the number of cases (%) was used to describe it, the chi-square test was used for comparison between groups, and Fisher’s exact probability was used when the chi-square test was not satisfied. Adjusting for confounding variables using multivariable Cox proportional hazards regression models were used to calculate hazard ratios (HRs) between NLR and all-cause /cardiovascular mortality. Specifically, multivariable Cox proportional hazards regression models performed across three different models: Model 1 included no covariate adjustments; Model 2 adjusted for age, sex, BMI, ethnicity status, education level; smoke status, alcohol consumption; hypertension, diabetes. Model 3 further included adjustments for CVD, TG, TC, HDL, LDL, HbA1c and eGFR. Our study used a test for multicollinearity among all variables included in the analysis. The variance inflation factor (VIF) for all variables was less than 5, suggesting the absence of significant multicollinearity (Additional file: Table S1). In order to evaluate the stability of the models, we performed several sensitivity analyses. Firstly, to mitigate the influence of extreme values, we excluded participants with NLR exceeding the mean ± 3SD（Additional file: Table S2） 23 . Secondly, the association between NLR and mortality continued to be explored in the propensity score matched cohort (Additional file: Tables S3, Table S4 and Figure S1) 24 . Kaplan–Meier estimates were used to calculate survival curves, which were compared using the log-rank test. Additionally, we applied restricted cubic splines to explore the nonlinear relationship between NLR and all-cause and cardiovascular mortality. We conducted subgroup analyses to investigate the association between NLR and all-cause mortality and cardiovascular mortality, using stratifying factors such as age (≤65 years, > 65 years), gender(male/female), BMI (≤24, >24), ethnicity (Mexican American, non-Hispanic Black, non-Hispanic White, and other racial/ethnic) ,hypertension (yes/no), diabetes (yes/no), smoke status (never/former/current) and alcohol consumption (never/former/current). The ROC curve was employed to evaluate the accuracy of NLR in predicting outcomes. A mediation analysis was carried out to access the indirect impact of NLR on mortality mediated through eGFR. All analyses were performed using R software (http://www.r-project.org) and Empower Stats (http://www.empowerstats.com), with a significance level set at P < 0.05. Results Participants’ characteristics The study included 7,929 participants stratified into NLR tertiles T1 (N=2646), T2 (N=2643), and T3(N=2640). The cohort had a mean age of 49.53±17.49 years, and 49.36% were male. Among 473 recorded deaths, 125 (26.4%) were attributed to cardiac causes. Significant differences across tertiles were observed for age, ethnicity, marital status, BMI, alcohol consumption, smoking status, comorbidities (hypertension, diabetes mellitus, CVD, hyperlipidemia), and laboratory parameters (WBC, neutrophil, lymphocyte counts, Hb, PLT, FBG, HbA1c, ALT, TC, LDL, HDL, creatinine, and eGFR) (all P <0.05). Participants in T3 group were older, had higher prevalence of White ethnicity, hypertension, diabetes, CVD, hyperlipidemia. T3 group exhibited higher BMI, WBC, neutrophil, FBG, HbA1c, creatinine and lower lymphocyte, ALT, TC, HDL, LDL and eGFR. (all P <0.05). Notably, all-cause mortality and cardiovascular mortality rates increased progressively across NLR tertiles (T1: 0.72% vs. T3: 2.99%; and T1: 3.74% vs. T3: 10.11%, respectively, both P <0.05). Non-significant differences were observed for gender, education level, HB, PLT, and TG (all P ＞0.05). (Table 1）. Association between NLR and all-cause mortality with CKM syndrome NLR as a continuous variable, each unit increase in NLR was associated with a 25% higher risk of all-cause mortality in the non-adjusted model (HR=1.25, 95% CI: 1.22–1.28; P <0.0001) (Table 2). This association remained robust after full adjustment (age, sex, BMI, ethnicity status, education level, smoke status, alcohol consumption, hypertension, diabetes, CVD, TG, TC, HDL, LDL, HBA1C, eGFR) in the model 3 HR=1.13, 95% CI: 1.09–1.17; P <0.0001) (Table 2). RCS analysis revealed a U-shape association between NLR and all-cause mortality ( P for nonlinear = 0.016) (Fig. 2A). Segmented Cox regression analysis pinpointed an inflection point at 1.23 (Table 3). Survival curve analysis showed a significant decrease in the survival rate in the highest NLR group(T3) compared to the lower NLR group ( P < 0.0001) (Fig. 3A). Cox regression analysis demonstrated a substantial increase in all-cause mortality in the highest NLR group(T3), from model 1(HR 2.71, 95% CI 2.15–3.42, P < 0.0001) to Model 2 (HR 1.51, 95% CI 1.17–1.94, P =0.0013) and model 3 (HR 1.40, 95% CI 1.09–1.81, P =0.0007), compared with lowest NLR group(T1) (Table 2). Sensitivity analysis was conducted to prove the stability of the model. (Additional file: Table S2, Tables S3, Table S4 and Figure S1). Subgroup analyses evaluated effect modification across clinically relevant strata (age, sex, BMI, ethnicity status, alcohol consumption, smoke status, history of hypertension and diabetes). We observed consistent positive NLR and all-cause mortality associations across all subgroups without evidence of effect modification ( P for interaction > 0.05) (Figure 4A). Association between NLR and cardiovascular mortality with CKM syndrome In model 1, the risk for cardiovascular mortality increased with higher NLR (HR 1.26, 95% CI 1.21-1.32, P < 0.0001) (Table 2). After comprehensive adjustment, each one-unit increase in the NLR value was associated with a 17% increase in the risk of cardiovascular mortality (Model 3, HR 1.17, 95% CI 1.11–1.24, P < 0.0001) (Table 2). RCS analysis revealed a positive linear association between NLR and cardiovascular mortality ( P for nonlinear = 0.378) (Fig. 2B). Survival curve analysis showed a significant decrease in the survival rate in the highest NLR group(T3) compared to the lower NLR group ( P < 0.0001) (Fig. 3B). Cox regression analysis demonstrated a substantial increase in cardiovascular mortality in the highest NLR group(T3), from model 1(HR 4.17, 95% CI 2.53–6.89, P < 0.0001) to Model 2 (HR2.27, 95% CI 1.3–3.90, P = 0.0028) and model 3 (HR 2.17, 95% CI 1.24–3.81, P = 0.0065), compared with lowest NLR group(T1) (Table 2). Sensitivity analysis was conducted to prove the stability of the model. (Additional file: Table S2, Tables S3, Table S4 and Figure S1). Subgroup analyses evaluated effect modification across clinically relevant strata (age, sex, BMI, ethnicity status, alcohol consumption, smoke status, history of hypertension and diabetes). We observed consistent positive NLR and all-cause mortality associations across all subgroups without evidence of effect modification ( P for interaction > 0.05) (Figure 4B). The predictive ability of NLR for all‑cause and cardiovascular mortality in patients with CKM syndrome Receiver operating characteristic (ROC) curve analysis demonstrated that the NLR exhibited an area under the curve (AUC) of 0.651 (95% CI: 0.622–0.679) for predicting all-cause mortality, significantly outperforming neutrophil count (AUC = 0.584) or lymphocyte count (AUC = 0.631) alone. The optimal NLR cut-off value for all-cause mortality prediction was 2.38, with a sensitivity of 52.8% and specificity of 72.2% (Figure 5A). Moreover, the NLR showed predictive performance for cardiovascular mortality (AUC = 0.703; 95% CI: 0.648-0.757) compared to neutrophil count (AUC = 0.638) or lymphocyte count (AUC = 0.671). The corresponding NLR cut-off value was 2.70, yielding a sensitivity of 52.8% and specificity of 80.1% (Figure 5B). Mediation analysis of NLR for all‑cause and cardiovascular mortality Mediation analysis explored the mediating effect of eGFR on the relationship between NLR and both all-cause and cardiovascular mortality. Specifically, NLR was negatively correlated with eGFR (β=-0.1602, P ＜0.0001), while eGFR was negatively correlated with survival for all-cause mortality (β=-0.2600, P ＜0.0001),and cardiovascular mortality (β=-0.1601, P ＜0.0001). Ultimately, 1.7% (95% CI 0.3% -3.4%) and 1.6% (95% CI 0.3%–3.7%) of the observational association of NLR with risk of all-cause and cardiovascular mortality was mediated through eGFR (Figure. 6A and B). Discussion Based on data from a cross-sectional study involving 7929 adults, our research has uncovered an association between the prognosis of CKM syndrome and the NLR. Specifically, our study provides novel evidence that elevated NLR is independently associated with increased risks of both all-cause and cardiovascular mortality in patients with CKM syndrome These results were consistent across sensitivity and stratified analyses. Notably, we observed a U-shaped relationship between NLR and all-cause mortality, with an inflection point at NLR=1.26, contrasting the linear association observed for cardiovascular mortality. In addition, NLR was superior to lymphocyte and neutrophils alone in predicting all-cause and cardiogenic death. Mediation analysis showed that eGFR played a mediating role in the relationship between NLR and mortality. These findings align with emerging evidence that systemic inflammation, as reflected by NLR, plays a pivotal role in the pathophysiology of multiorgan dysfunction characteristic of CKM syndrome 4 . The NLR derived from two routinely measured hematological indices, serves as a cost-effective biomarker reflecting systemic immunoinflammatory balance. The NLR operationalizes the bidirectional interplay between innate immunity (quantified via neutrophil enumeration) and adaptive immunity (gauged through lymphocyte quantification), with this integrative metric demonstrating greater clinical utility in risk stratification than compartmentalized analysis of isolated parameters 25 .Mounting evidence positions NLR at the nexus of inflammatory pathophysiology, particularly in hypertension where chronic low-grade inflammation perpetuates vascular dysfunction through endothelial activation and oxidative stress cascades 26 . Clinically validated across the cardiovascular spectrum, NLR demonstrates robust risk stratification capacity for acute coronary events , chronic heart failure exacerbations, and atherosclerotic progression 5 . The NLR predictive capacity transcends cardiovascular medicine, with dysregulated immune responses quantified by NLR correlating with fatal outcomes in both general population and distinct clinicopathological states: infectious crises, respiratory failure syndromes (COVID-19 ARDS), and malignancy-associated cachexia 27 . However, the prognostic significance of NLR in patients with CKM syndrome remains undetermined. CKM syndrome characterized by the pathophysiological convergence of cardiovascular dysfunction, chronic kidney disease, and metabolic dysregulation – has escalated into a pressing global health crisis, demanding urgent multidisciplinary intervention strategies 28 . Driven by aging demographics and obesogenic environments, this multisystem disorder progresses through synergistic inflammatory pathways that concurrently impair vascular integrity, renal filtration capacity, and myocardial remodeling 29 . The syndrome's pathogenesis arises from interconnected metabolic dysregulation, characterized by insulin resistance, sustained low-grade inflammation, and oxidative stress amplification. Inflammatory mediators impair high-density lipoprotein (HDL) functionality, reducing its anti-inflammatory and reverse cholesterol transport capacities, thereby accelerating atherosclerosis 30, 31 . Concurrently, inflammation induces endothelial dysfunction in hypertension by suppressing nitric oxide bioavailability and promoting oxidative stress, which elevates vascular resistance and perpetuates cardiac remodeling 32 . Hyperglycemia, a key metabolic component of CKM syndrome, is exacerbated by pro-inflammatory cytokine that disrupt insulin signaling and promote insulin resistance, further fueling dyslipidemia and renal injury 33 . The prognostic significance of NLR in CKM syndrome stems from its dual reflection of innate immune activation (via neutrophils) and adaptive immune suppression (via lymphocytes). Neutrophils, as first responders to tissue injury, release pro-inflammatory cytokines (e.g., IL-6, TNF-α), reactive oxygen species (ROS), and matrix metalloproteinases (MMPs), which exacerbate endothelial dysfunction and promote atherosclerotic plaque instability 33, 34 . Conversely, lymphopenia, often observed in chronic inflammatory states, reflects impaired regulatory T-cell activity, weakening anti-inflammatory responses and accelerating vascular and renal fibrosis 35 . This imbalance is particularly detrimental in CKM syndrome, where metabolic stressors such as insulin resistance and dyslipidemia amplify neutrophilic inflammation while depleting lymphocyte reserves 36 .Elevated NLR correlates with heightened neutrophil extracellular trap (NET) formation, a key driver of inflammation in atherosclerosis and myocardial infarction 37 . NETs promote plaque rupture and microvascular occlusion, directly contributing to cardiovascular mortality. Simultaneously, lymphopenia reduces IL-10 production, a cytokine critical for mitigating post-infarction remodeling 38 . These mechanisms align with our linear NLR-cardiovascular mortality relationship, underscoring inflammation’s central role in acute cardiac events. The U-shaped NLR-all-cause mortality relationship suggests that both immunosuppression (NLR <1.26) and hyperinflammation (NLR >1.26) drive mortality through distinct pathways. Low NLR may indicate lymphopenia-induced vulnerability to infections or malignancy, as seen in cancer survivors with impaired lymphocyte recovery 39 . High NLR, conversely, reflects uncontrolled inflammation accelerating multiorgan failure. This biphasic risk mirrors observations in heart failure populations, where extreme NLR values predict poor outcomes regardless of etiology 40 . Our findings harmonize with extensive evidence linking NLR to adverse cardiovascular outcomes. For instance, in hypertensive cohorts, Zhang et al. demonstrated NLR’s linear association with cardiovascular mortality (HR=2.33 for NLR>3.5) 5 , while a meta-analysis of 25,000+ coronary artery disease patients confirmed NLR’s independent predictive value for major adverse cardiac events (pooled HR=1.45 per unit increase) 41 . Similarly, in heart failure, NLR>4.0 predicted 50% higher 1-year mortality risk 42 . The novelty of our study lies in extending these observations to CKM syndrome, where NLR integrates multisystemic inflammation, offering superior prognostic granularity compared to single biomarkers like CRP or creatinine 43 . In CKM syndrome, NLR elevation is closely tied to adipose tissue inflammation and renal hypoxia. Neutrophil infiltration into visceral fat exacerbates insulin resistance via IL-1β secretion, while renal tubular injury from neutrophil-derived myeloperoxidase (MPO) worsens albuminuria and glomerulosclerosis 44 . Lymphocyte depletion further impairs renal repair mechanisms, as CD4+ T cells are essential for resolving acute kidney injury 45 . Our mediation analysis, showing eGFR’s partial mediation, supports this bidirectional NLR-kidney interaction, consistent with findings in diabetic nephropathy cohorts 46 .Therefore, the NLR could act as a vital biomarker for forecasting mortality in individuals with CKM syndrome. The NLR could be an important target for CKM syndrome treatment in the future. This study has several limitations that warrant consideration. First, the observational cross-sectional design precludes causal inference between the NLR and outcomes in CKM syndrome. Second, despite comprehensive adjustments for covariates, residual confounding from unmeasured variables may persist. Third, the geographically and demographically homogeneous cohort limits the generalizability of our findings to populations with greater ethnic diversity or socioeconomic disparities. Additionally, the exclusive focus on NLR, without comparative analyses of other biomarkers such as C-reactive protein, interleukin-6, or novel omics-derived markers, may overlook indicators with superior predictive capacity for CKM-related mortality. Despite these limitations, our findings underscore NLR as a novel, cost-effective biomarker for risk stratification in CKM syndrome, particularly in resource-limited settings where routine hematologic testing is widely accessible. Conclusion Elevated NLR independently predicts increased all-cause and cardiovascular mortality in CKM syndrome. eGFR mediation partially links NLR-driven inflammation to multiorgan failure, positioning NLR as a pivotal biomarker for risk stratification and anti-inflammatory targeting in CKM management. Declarations Data availability The datasets for this study can be found in the NHANES (https://www.cdc.gov/nchs/nhanes/index.html). Ethics approval and consent to participate The protocol was approved by the Institutional Review Board of National Center for Health Statistics and no new data was added. Author contributions Peng Wu and Zhenghui Huang : Conceptualization; data curation; formal analysis; investigation; methodology; software; supervision; validation; visualization; writing—original draft; writing—review & editing. Juan Ma , Baozhen Zhu and Mohan Wang : Writing—original draft. Ali Maand Xin Wang : Writing —original draft. Ruixin Hai : Data curation; writing—original draft. Shaobin Jia,Xueping Ma and Ning Yan : Investigation; supervision; validation; writing—original draft; writing—review & editing. Peng Wu and Zhenghui Huang contributed equally as co-first authors. . Shaobin Jia,Xueping Ma and Ning Yan contributed equally as corresponding co-authors. All authors have read and agreed to the published version of the manuscript. Funding The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was supported by Open competition mechanism to select the best candidates for key research projects of Ningxia Medical University (No. XJKF230205); the Central Government Guiding Local Science and Technology Development Special Project (No. 2024FRD05139); the National Natural Science Foundation of China (No. 82260086) and the National Natural Science Foundation of China (No. 8206020191); Ningxia Natural Science Foundation project (No.2023AAC02069). Acknowledgments We would like to gratefully acknowledge all of the investigators and patients participating in this work. Conflict of interest The authors declare that they have no competing interests. References Ndumele CE, Rangaswami J, Chow SL, et al. Cardiovascular-Kidney-Metabolic Health: A Presidential Advisory From the American Heart Association. Circulation . 148 (20):1606-35. (2023). Rao Kondapally Seshasai S, Kaptoge S, Thompson A, et al. Diabetes mellitus, fasting glucose, and risk of cause-specific death. 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Mediators of inflammation . 2021 :6889733. (2021). Arbel Y, Finkelstein A, Halkin A, et al. Neutrophil/lymphocyte ratio is related to the severity of coronary artery disease and clinical outcome in patients undergoing angiography. Atherosclerosis . 225 (2):456-60. (2012). Ridker PM. High-sensitivity C-reactive protein and cardiovascular risk: rationale for screening and primary prevention. The American journal of cardiology . 92 (4b):17k-22k. (2003). Antonelou M, Evans RDR, Henderson SR, et al. Neutrophils are key mediators in crescentic glomerulonephritis and targets for new therapeutic approaches. Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association . 37 (2):230-8. (2022). Kinsey GR, Sharma R, Okusa MD. Regulatory T cells in AKI. Journal of the American Society of Nephrology : JASN . 24 (11):1720-6. (2013). Li L, Shen Q, Rao S. Association of Neutrophil-to-Lymphocyte Ratio and Platelet-to-Lymphocyte Ratio with Diabetic Kidney Disease in Chinese Patients with Type 2 Diabetes: A Cross-Sectional Study. Therapeutics and clinical risk management . 18 :1157-66. (2022). Tables Table 1. Baseline characteristics stratified by NLR tertiles(N=7929). Variable Tertile 1 (N=2646) Tertile2 (N=2643) Tertile 3 (N=2640) P -value Age, years 47.23 ± 16.90 48.51 ± 16.97 52.86 ± 18.08 <0.001 Female, n (%) 1342 (50.72) 1356 (51.31) 1317 (49.89) 0.584 Ethnicity, n (%) <0.001 White 771 (29.14) 1041 (39.39) 1282 (48.56) Black 787 (29.74) 464 (17.56) 399 (15.11) Mexican American 350 (13.23) 416 (15.74) 342 (12.95) Other 738 (27.89) 722 (27.32) 617 (23.37) Marital status, n (%) 1537 (58.09%) 1639 (62.01%) 1555 (58.90%) 0.009 Education level, n (%) 0.167 Some college or above 2122 (80.20%) 2047 (77.45%) 2080 (78.79%) High School 307 (11.60%) 357 (13.51%) 340 (12.88%) Middle School 217 (8.20%) 239 (9.04%) 220 (8.33%) BMI(Kg/m 2 ) 28.75 ± 6.71 29.21 ± 6.88 30.15 ± 7.70 <0.001 Alcohol consumption, n (%) <0.001 Never 405 (15.31%) 380 (14.38%) 378 (14.32%) Current 1947 (73.58%) 1913 (72.38%) 1847 (69.96%) Former 294 (11.11%) 350 (13.24%) 415 (15.72%) Smoke status, n (%) <0.001 Never 1611 (60.88%) 1529 (57.85%) 1363 (51.63%) Current 456 (17.23%) 511 (19.33%) 556 (21.06%) Former 579 (21.88%) 603 (22.81%) 721 (27.31%) Hypertension, n (%) 1010 (38.17) 1073 (40.60) 1340 (50.76) <0.001 Diabetes Mellitus, n (%) 447 (16.98) 540 (20.52) 719 (27.72) <0.001 CVD, n (%) 200 (7.56) 242 (9.16) 412 (15.61) <0.001 Hyperlipidemia, n (%) 1775 (67.08) 1880 (71.13) 1941 (73.52) <0.001 WBC, ×10⁹/L 6.13 ± 3.07 6.63 ± 1.70 7.74 ± 2.24 <0.001 Neutrophil, ×10⁹/L 2.86 ± 0.92 3.83 ± 1.04 5.23 ± 1.75 <0.001 Lymphocyte, ×10⁹/L 2.51 ± 2.56 2.03 ± 0.55 1.67 ± 0.51 <0.001 Hb(g/L) 14.10 (13.10-15.10) 14.10 (13.20-15.20) 14.10 (13.10-15.20) 0.310 PLT×10⁹/L 232.34±58.75 236.02 ±60.22 236.99 ±65.34 0.059 FBG, mmol/L 5.95 ± 1.80 6.10 ± 1.93 6.29 ± 2.18 <0.001 HbA1c, % 5.75 ± 1.07 5.79 ± 1.12 5.87 ± 1.20 <0.001 ALT, U/L 24.75 ± 16.18 25.24 ± 18.69 23.73 ± 17.50 0.006 TG, mmol/L 1.02 (0.69-1.56) 1.11 (0.77-1.65) 1.13 (0.78-1.64) 0.166 TC, mmol/L 4.89 (4.21-5.61) 4.91 (4.24-5.64) 4.76 (4.06-5.48) <0.001 HDL, mmol/L 1.37 (1.11-1.63) 1.32 (1.09-1.60) 1.32 (1.09-1.60) 0.017 LDL, mmol/L 2.87 (2.30-3.52) 2.90 (2.30-3.54) 2.74 (2.15-3.36) <0.001 Creatinine, μmol/L 74.26 (62.76-87.52) 72.49 (61.00-85.75) 74.26 (62.76-90.17) <0.001 eGFR, mL/min/1.73m² 97.82 ± 21.44 96.59 ± 22.38 91.00 ± 25.83 <0.001 All-cause mortality, n (%) 19 (0.72) 27 (1.02) 79 (2.99) <0.001 Cardiovascular mortality, n (%) 99 (3.74) 107 (4.05) 267 (10.11) <0.001 Data presented as mean ± standard deviation (SD) or median (interquartile range) for continuous variables, and n (%) for categorical variables. NLR tertile: (T1: NLR <1.57; T2: 1.57≤NLR <2.27; T3: NLR≥2.27). Abbreviations: NLR: Neutrophil-to-lymphocyte ratio, BMI: body mass index; CVD: cardiovascular disease; WBC: white blood cell count; Hb: hemoglobin; PLT: platelet count; FBG: fasting blood glucose; HbA1c: hemoglobin A1c; ALT: Alanine Aminotransferase; TG: triglycerides; TC: total cholesterol; HDL: high-density lipoprotein; LDL: low-density lipoprotein; eGFR: estimated glomerular filtration rate Table 2. Association of NLR and NLR Tertiles with all-cause and cardiovascular mortality (N=7929). Exposure Model 1 Model 2 Model 3 HR (95%CI) P -value HR (95%CI) P -value HR (95%CI) P -value All-cause mortality NLR 1.25 (1.22, 1.28) <0.0001 1.12 (1.09, 1.16) <0.0001 1.13 (1.09, 1.17) <0.0001 NLR tertile T1 Reference Reference Reference T2 1.06 (0.81, 1.40) 0.6587 0.93 (0.70, 1.24) 0.6359 0.89 (0.67, 1.19) 0.4362 T3 2.71 (2.15, 3.42) <0.0001 1.51 (1.17, 1.94) 0.0013 1.40 (1.09, 1.81) 0.0093 P for trend <0.0001 <0.0001 0.0007 Cardiovascular mortality NLR 1.26 (1.21, 1.32) <0.0001 1.15 (1.09, 1.21) <0.0001 1.17 (1.11, 1.24) <0.0001 NLR tertile T1 Reference Reference Reference T2 1.40 (0.78, 2.52) 0.2615 1.18 (0.65, 2.18) 0.5844 1.13 (0.61, 2.12) 0.6926 T3 4.17 (2.53, 6.89) <0.0001 2.27 (1.33, 3.90) 0.0028 2.17 (1.24, 3.81) 0.0065 P for trend <0.0001 0.0004 0.0009 NLR tertile: (T1: NLR <1.57; T2: 1.57≤NLR <2.27; T3: NLR≥2.27). HR: hazard ratios; CI: confidence Model 1: Non-adjusted. Model 2：adjust for age, sex, BMI, ethnicity status, education level, smoke status, alcohol consumption, hypertension, diabetes. Model 3：adjust for age, sex, BMI, ethnicity status, education level, smoke status, alcohol consumption, hypertension, diabetes, CVD, TG, TC, HDL, LDL, HBA1C, eGFR. Table 3 Threshold effect analysis of NLR on all-cause mortality Outcome All-cause mortality HR (95%CI) P -value Model I One line effect 1.21 (1.14, 1.29) <0.0001 Model II Inflection point (K) 1.23 HRR < K 0.45 (0.21, 0.93) 0.0324 HRR> K 1.23 (1.16, 1.31) <0.0001 P for Log-likelihood ration* 0.016 HR hazard ratio, CI confidence interval, NLR: Neutrophil-to-lymphocyte ratio. Model I, linear analysis; Model II, nonlinear analysis. *Model II differs significantly from Model I by the logarithm likelihood ratio test (LRT) by p < 0.05. Adjusted for age, sex, BMI, ethnicity status, education level, smoke status, alcohol consumption, hypertension, diabetes, CVD, TG, TC, HDL, LDL, HBA1C, eGFR. Additional Declarations No competing interests reported. Supplementary Files SUPPLEMENTMaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-6611872\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":473638396,\"identity\":\"ea12d59b-cc76-4b3c-b076-87f435297054\",\"order_by\":0,\"name\":\"Peng 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Syndrome.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage1.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6611872/v1/0ab4b34e9ecff118715fe160.jpeg\"},{\"id\":85386945,\"identity\":\"2b26aa41-515c-4d32-83e2-6ff3e42dffc1\",\"added_by\":\"auto\",\"created_at\":\"2025-06-25 09:55:21\",\"extension\":\"jpeg\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":254972,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eThe association of NLR with all-cause (A) and cardiovascular mortality (B) among CKM syndrome visualized by restricted cubic spline.\\u003c/p\\u003e\\n\\u003cp\\u003eHazard ratios were adjusted for age, sex, BMI, ethnicity status, education level, smoke status, alcohol consumption, hypertension, diabetes, CVD, TG, TC, HDL, LDL, HBA1C, eGFR.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage2.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6611872/v1/ae5ff8cdbc953e6a7166fac8.jpeg\"},{\"id\":85386948,\"identity\":\"107a078d-ebbf-438c-862f-5c83af5f96bd\",\"added_by\":\"auto\",\"created_at\":\"2025-06-25 09:55:21\",\"extension\":\"jpeg\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":203866,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eKaplan–Meier survival curve for all-cause (A) and cardiovascular mortality (B) among CKM syndrome patients according to NLR tertiles.\\u003c/p\\u003e\\n\\u003cp\\u003eNLR tertile: (T1: NLR \\u0026lt;1.57; T2: 1.57≤NLR \\u0026lt;2.27; T3: 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eGFR.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage4.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6611872/v1/6757fd3ead308ef107139612.jpeg\"},{\"id\":85386954,\"identity\":\"9d35baaa-a8bf-4db8-b351-b220d7a2ca88\",\"added_by\":\"auto\",\"created_at\":\"2025-06-25 09:55:21\",\"extension\":\"jpeg\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":279679,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eROC curves of the NLR for predicting all-cause mortality(A) and cardiovascular mortality (B).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage5.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6611872/v1/ac56d6f2a4d08209160e5bcf.jpeg\"},{\"id\":85387597,\"identity\":\"6dceb655-4de3-4509-bdb1-62fa4d8c9eca\",\"added_by\":\"auto\",\"created_at\":\"2025-06-25 10:03:21\",\"extension\":\"jpeg\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":79280,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eThe mediating effect of eGFR on the relationship between NLR and mortality (A, all-cause mortality; B, cardiovascular mortality).\\u003c/p\\u003e\\n\\u003cp\\u003eAdjusted for age, sex, BMI, ethnicity status, education level, smoke status, alcohol consumption, hypertension, diabetes, CVD, TG, TC, HDL, LDL, HBA1C.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage6.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6611872/v1/7cf5ce7ab53182d34cce4584.jpeg\"},{\"id\":100854013,\"identity\":\"045e0e97-f091-436b-9538-45661d7b27d1\",\"added_by\":\"auto\",\"created_at\":\"2026-01-22 06:42:09\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":3130951,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6611872/v1/d77c5b27-b765-48f5-9eff-987c88944c95.pdf\"},{\"id\":85387593,\"identity\":\"0edd0a4a-74b8-40ad-a068-69f01c4e256a\",\"added_by\":\"auto\",\"created_at\":\"2025-06-25 10:03:21\",\"extension\":\"docx\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":107316,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SUPPLEMENTMaterial.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6611872/v1/2aecf2cf009d9c9c8b40edc4.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Association of neutrophil-to-lymphocyte ratio with all-cause and cardiovascular mortality among cardiovascular-kidney-metabolic syndrome: a national cross-sectional study\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eThe convergence of cardiovascular, renal, and metabolic dysfunction has recently been redefined as cardiovascular-kidney-metabolic (CKM) syndrome through an American Heart Association (AHA) scientific statement, establishing new diagnostic criteria that recognize these interconnected disorders as a unified clinical entity\\u003csup\\u003e1\\u003c/sup\\u003e. Given the intricate interplay between cardiovascular, renal, and metabolic disorders and their collective contribution to global disease burden, these interconnected conditions present substantial challenges to public health systems worldwide\\u003csup\\u003e2, 3\\u003c/sup\\u003e. The emerging concept of CKM syndrome highlights this pathophysiological convergence, necessitating the identification of reliable biomarkers for early detection and therapeutic monitoring\\u003csup\\u003e4\\u003c/sup\\u003e. The neutrophil-to-lymphocyte ratio (NLR) has gained attention as a hematologic indicator that quantifies inflammatory processes underlying these comorbidities while offering potential clinical value for tracking disease progression and therapeutic responses\\u003csup\\u003e5, 6\\u003c/sup\\u003e.\\u003c/p\\u003e\\n\\u003cp\\u003eThe neutrophil-to-lymphocyte ratio (NLR) has gained recognition as a dual-pathway inflammatory integrator, mechanistically reflecting neutrophil-mediated tissue damage coupled with lymphocyte-depleted immune surveillance\\u003csup\\u003e7, 8\\u003c/sup\\u003e. The NLR has emerged as a robust inflammatory prognostic indicator\\u003csup\\u003e9\\u003c/sup\\u003e. It has emerged as a promising biomarker for diverse pathological conditions ranging from oncological processes and inflammatory states to metabolic dysregulation, cardiovascular outcomes\\u003csup\\u003e10-12\\u003c/sup\\u003e. Its clinical utility has been extensively validated in large-scale cohort studies, demonstrating independent predictive value for hypertension progression\\u003csup\\u003e5\\u003c/sup\\u003e, coronary artery disease severity\\u003csup\\u003e13\\u003c/sup\\u003e, and cardiovascular mortality risk stratification \\u003csup\\u003e14\\u003c/sup\\u003e. \\u0026nbsp; In a nationwide, population-based prospective cohort study encompassing 32,454 adults with median 12.3-year follow-up, elevated NLR emerged as an independent predictor of all-cause mortality\\u003csup\\u003e15\\u003c/sup\\u003e. Chronic inflammation serves as a critical pathophysiological nexus in CKM progression, mediating cross-organ damage through macrophage activation, cytokine storm amplification, and endothelial glycocalyx degradation\\u003csup\\u003e16\\u003c/sup\\u003e. Inflammation serves as a pathogenic linchpin in chronic disease pathogenesis, exerting deleterious effects across integrated organ systems including cardiac function, glomerular filtration dynamics, and cardiometabolic homeostasis\\u003csup\\u003e4\\u003c/sup\\u003e.\\u003c/p\\u003e\\n\\u003cp\\u003eDespite these advances, NLR\\u0026apos;s mortality associations in CKM syndrome remain uncharacterized. This population-based retrospective cohort study utilized data from 9 continuous cycles (2001-2018) of the National Health and Nutrition Examination Survey (NHANES) to investigate the prognostic value of NLR in all-cause and cardiovascular mortality among adults diagnosed with CKM syndrome\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eData and sample source\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis investigation utilized publicly available data from the National Health and Nutrition Examination Survey (NHANES 2011-2018), a stratified multistage probability survey conducted by the CDC's National Center for Health Statistics to monitor population health in non-institutionalized U.S. civilians\\u003csup\\u003e17\\u003c/sup\\u003e. The study protocol received institutional review board approval, with documented informed consent obtained from all participants prior to data collection\\u003csup\\u003e18\\u003c/sup\\u003e.\\u003c/p\\u003e\\n\\u003cp\\u003eThe analytic sample derivation process followed rigorous epidemiological standards: From 39,156 potentially eligible individuals across four biennial survey cycles, we sequentially excluded those aged \\u0026lt;20 years (n=16,539), missing sampling weights (WTSAF2YR, n=13,373), participants with incomplete mortality linkage data (n=22), missing neutrophil/lymphocyte ratio measurements (n=235), missing CKM data and other covariates(n=1,058) .The final cohort comprised 7,929 adults with complete baseline characteristics and outcome data\\u003csup\\u003e19\\u003c/sup\\u003e.\\u003c/p\\u003e\\n\\u003cp\\u003eData acquisition involved three complementary modalities: 1) Personal interviews capturing demographic and medical history, 2) Standardized physical examinations including anthropometric and blood pressure measurements, and 3) Centralized laboratory analyses of hematological/biochemical parameters. Finally,7,929 participants were included in our study. (Figure 1).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAssessment of the NLR\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNeutrophil and lymphocyte counts were determined by conducting a complete blood count on blood specimens using a Beckman Coulter automated blood analyzer in an MEC, expressed as × 10\\u003csup\\u003e3\\u0026nbsp;\\u003c/sup\\u003ecells/µL. NLR was calculated as the absolute neutrophil count divided by the absolute lymphocyte count\\u003csup\\u003e20\\u003c/sup\\u003e.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAscertainment of CKM syndrome stages\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eCKM classification followed an adapted implementation of the AHA 2023 criteria, operationalized through a multidimensional staging system integrating metabolic, renal, and cardiovascular parameters\\u003csup\\u003e21\\u003c/sup\\u003e:\\u003c/p\\u003e\\n\\u003cp\\u003eStage 1: Metabolic Dysregulation: Characterized by excess adiposity (BMI ≥23 kg/m² [Asian] / ≥25 kg/m² [non-Asian]; waist circumference ≥80/90 cm [Asian women/men] or ≥88/102 cm [non-Asian women/men]) and dysglycemic thresholds (HbA1c 5.7–6.4% or fasting glucose 100–125 mg/dL).\\u003c/p\\u003e\\n\\u003cp\\u003eStage 2: Metabolic-Kidney Codominance: Requires coexistence of ≥2 metabolic drivers with concurrent target organ involvement, Hypertension (≥130/85 mmHg); Hypertriglyceridemia (≥150 mg/dL); Albuminuria (UACR ≥30 mg/g); Confirmed diabetes (HbA1c ≥6.5%)\\u003c/p\\u003e\\n\\u003cp\\u003eStage 3: Subclinical Cardiovascular Risk: Defined by: Chronic kidney disease (KDIGO G3b-A3: eGFR 30–44 mL/min/1.73m² + UACR \\u0026gt;300 mg/g); Elevated 10-year CVD risk (≥20% via AHA PREVENT equations)\\u003c/p\\u003e\\n\\u003cp\\u003eStage 4: Symptomatic Cardiovascular Disease: Atherosclerotic cardiovascular disease; Heart failure; Cerebrovascular events.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAscertainment of covariates\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eDemographic characteristics of participants were obtained from the NHANES database, including age, gender, ethnicity (Mexican American, non-Hispanic Black, non-Hispanic White, and other racial/ethnic groups), education level (less than high school, high school/equivalent, or college/above) and marital status (Married, Single/Separated). Lifestyle behaviors were operationalized as: Smoking status: never, former, or current smoker; Alcohol consumption: never, former, or current drinker. Comorbidities included self-reported physician-diagnosed diabetes mellitus, hypertension, coronary heart disease (CVD), and dyslipidemia. Clinically measured covariates encompassed: Anthropometrics: body mass index (BMI); Hematological indices: leukocyte count (WBC), neutrophil, lymphocyte, hemoglobin (Hb), platelet count (PLT), and neutrophil (NEUT). Metabolic profiles: glycated hemoglobin (HbA1c), fast blood glucose (FBG), hemoglobin A1c (HbA1c), alanine aminotransferase (ALT), creatinine, triglycerides (TC), total cholesterol (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C). Renal function: estimated glomerular filtration rate (eGFR) using CKD-EPI 2021 creatinine-cystatin C equation. Mortality status was ascertained through probabilistic linkage with the National Death Index through December 31, 2019. The primary endpoint was all-cause mortality, with secondary analysis of cardiovascular mortality.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eStatistical analysis\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eGiven the intricate sampling design of the NHANES survey, we incorporated the sample weights for the various study periods in our analytical methods to ensure accurate estimates of health-related statistics\\u003csup\\u003e22\\u003c/sup\\u003e. For continuous variables, mean ± standard deviation (SD) was used for statistical description if they met normal distribution; independent samples t-test was used forinter-group comparison. the median (25-75%) was used for description if the variables did not meet normal distribution; and the rank sum test was used for inter-group comparison. For counting data, the number of cases (%) was used to describe it, the chi-square test was used for comparison between groups, and Fisher’s exact probability was used when the chi-square test was not satisfied.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eAdjusting for confounding variables using multivariable Cox proportional hazards regression models were used to calculate hazard ratios (HRs) between NLR and all-cause /cardiovascular mortality. Specifically, multivariable Cox proportional hazards regression models performed across three different models: Model 1 included no covariate adjustments; Model 2 adjusted for age, sex, BMI, ethnicity status, education level; smoke status, alcohol consumption; hypertension, diabetes. Model 3 further included adjustments for CVD, TG, TC, HDL, LDL, HbA1c and eGFR. Our study used a test for multicollinearity among all variables included in the analysis. The variance inflation factor (VIF) for all variables was less than 5, suggesting the absence of significant multicollinearity (Additional file: Table S1). In order to evaluate the stability of the models, we performed several sensitivity analyses. Firstly, to mitigate the influence of extreme values, we excluded participants with NLR exceeding the mean ± 3SD（Additional file: Table S2）\\u003csup\\u003e23\\u003c/sup\\u003e. Secondly, the association between NLR and mortality continued to be explored in the propensity score matched cohort (Additional file: Tables S3, Table S4 and Figure S1)\\u003csup\\u003e24\\u003c/sup\\u003e. Kaplan–Meier estimates were used to calculate survival curves, which were compared using the log-rank test. Additionally, we applied restricted cubic splines to explore the nonlinear relationship between NLR and all-cause and cardiovascular mortality. We conducted subgroup analyses to investigate the association between NLR and all-cause mortality and cardiovascular mortality, using stratifying factors such as age (≤65 years, \\u0026gt; 65 years), gender(male/female), BMI (≤24, \\u0026gt;24), ethnicity (Mexican American, non-Hispanic Black, non-Hispanic White, and other racial/ethnic) ,hypertension (yes/no), diabetes (yes/no), smoke status (never/former/current) and alcohol consumption (never/former/current). The ROC curve was employed to evaluate the accuracy of NLR in predicting outcomes. A mediation analysis was carried out to access the indirect impact of NLR on mortality mediated through eGFR.\\u003c/p\\u003e\\n\\u003cp\\u003eAll analyses were performed using R software (http://www.r-project.org) and Empower Stats (http://www.empowerstats.com), with a significance level set at \\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.05.\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eParticipants\\u0026rsquo; characteristics\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe study included 7,929 participants stratified into NLR tertiles T1 (N=2646), T2 (N=2643), and T3(N=2640). The cohort had a mean age of 49.53\\u0026plusmn;17.49 years, and 49.36% were male. Among 473 recorded deaths, 125 (26.4%) were attributed to cardiac causes.\\u003c/p\\u003e\\n\\u003cp\\u003eSignificant differences across tertiles were observed for age, ethnicity, marital status, BMI, alcohol consumption, smoking status, comorbidities (hypertension, diabetes mellitus, CVD, hyperlipidemia), and laboratory parameters (WBC, neutrophil, lymphocyte counts, Hb, PLT, FBG, HbA1c, ALT, TC, LDL, HDL, creatinine, and eGFR) (all \\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt;0.05). Participants in T3 group were older, had higher prevalence of White ethnicity, hypertension, diabetes, CVD, hyperlipidemia. T3 group exhibited higher BMI, WBC, neutrophil, FBG, HbA1c, creatinine and lower lymphocyte, ALT, TC, HDL, LDL and eGFR. (all \\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt;0.05).\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eNotably, all-cause mortality and cardiovascular mortality rates increased progressively across NLR tertiles (T1: 0.72% vs. T3: 2.99%; and T1: 3.74% vs. T3: 10.11%, respectively, both\\u003cem\\u003e\\u0026nbsp;P\\u003c/em\\u003e \\u0026lt;0.05). Non-significant differences were observed for gender, education level, HB, PLT, and TG (all \\u003cem\\u003eP\\u003c/em\\u003e＞0.05). (Table 1）.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAssociation between NLR and all-cause mortality with CKM syndrome\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNLR as a continuous variable, each unit increase in NLR was associated with a 25% higher risk of all-cause mortality in the non-adjusted model (HR=1.25, 95% CI: 1.22\\u0026ndash;1.28; \\u003cem\\u003eP\\u003c/em\\u003e\\u0026lt;0.0001) (Table 2). This association remained robust after full adjustment (age, sex, BMI, ethnicity status, education level, smoke status, alcohol consumption, hypertension, diabetes, CVD, TG, TC, HDL, LDL, HBA1C, eGFR) in the model 3 HR=1.13, 95% CI: 1.09\\u0026ndash;1.17; \\u003cem\\u003eP\\u003c/em\\u003e\\u0026lt;0.0001) (Table 2). RCS analysis revealed a U-shape association between NLR and all-cause mortality (\\u003cem\\u003eP\\u003c/em\\u003e for nonlinear = 0.016) (Fig. 2A). Segmented Cox regression analysis pinpointed an inflection point at 1.23 (Table 3).\\u003c/p\\u003e\\n\\u003cp\\u003eSurvival curve analysis showed a significant decrease in the survival rate in the highest NLR group(T3) compared to the lower NLR group (\\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.0001) (Fig. 3A). Cox regression analysis demonstrated a substantial increase in all-cause mortality in the highest NLR group(T3), from model 1(HR 2.71, 95% CI 2.15\\u0026ndash;3.42, \\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.0001) to Model 2 (HR 1.51, 95% CI 1.17\\u0026ndash;1.94, \\u003cem\\u003eP\\u003c/em\\u003e =0.0013) and model 3 (HR 1.40, 95% CI 1.09\\u0026ndash;1.81, \\u003cem\\u003eP\\u003c/em\\u003e =0.0007), compared with lowest NLR group(T1) (Table 2). Sensitivity analysis was conducted to prove the stability of the model. (Additional file: Table S2, Tables S3, Table S4 and Figure S1).\\u003c/p\\u003e\\n\\u003cp\\u003eSubgroup analyses evaluated effect modification across clinically relevant strata (age, sex, BMI, ethnicity status, alcohol consumption, smoke status, history of hypertension and diabetes). We observed consistent positive NLR and all-cause mortality associations across all subgroups without evidence of effect modification (\\u003cem\\u003eP\\u003c/em\\u003e for interaction \\u0026gt; 0.05) (Figure 4A).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAssociation between NLR and cardiovascular mortality with CKM syndrome\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eIn model 1, the risk for cardiovascular mortality increased with higher NLR (HR 1.26, 95% CI 1.21-1.32, \\u003cem\\u003eP\\u0026nbsp;\\u003c/em\\u003e\\u0026lt; 0.0001) (Table 2). After comprehensive adjustment, each one-unit increase in the NLR value was associated with a 17% increase in the risk of cardiovascular mortality (Model 3, HR 1.17, 95% CI 1.11\\u0026ndash;1.24, \\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.0001) (Table 2). RCS analysis revealed a positive linear association between NLR and cardiovascular mortality (\\u003cem\\u003eP\\u003c/em\\u003e for nonlinear = 0.378) (Fig. 2B).\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eSurvival curve analysis showed a significant decrease in the survival rate in the highest NLR group(T3) compared to the lower NLR group (\\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.0001) (Fig. 3B). Cox regression analysis demonstrated a substantial increase in cardiovascular mortality in the highest NLR group(T3), from model 1(HR 4.17, 95% CI 2.53\\u0026ndash;6.89, \\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.0001) to Model 2 (HR2.27, 95% CI 1.3\\u0026ndash;3.90, \\u003cem\\u003eP\\u003c/em\\u003e= 0.0028) and model 3 (HR 2.17, 95% CI 1.24\\u0026ndash;3.81, \\u003cem\\u003eP\\u003c/em\\u003e = 0.0065), compared with lowest NLR group(T1) (Table 2). Sensitivity analysis was conducted to prove the stability of the model. (Additional file: Table S2, Tables S3, Table S4 and Figure S1).\\u003c/p\\u003e\\n\\u003cp\\u003eSubgroup analyses evaluated effect modification across clinically relevant strata (age, sex, BMI, ethnicity status, alcohol consumption, smoke status, history of hypertension and diabetes). We observed consistent positive NLR and all-cause mortality associations across all subgroups without evidence of effect modification (\\u003cem\\u003eP\\u003c/em\\u003e for interaction \\u0026gt; 0.05) (Figure 4B).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eThe predictive ability of NLR for all‑cause and cardiovascular mortality in\\u0026nbsp;patients with CKM syndrome\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eReceiver operating characteristic (ROC) curve analysis demonstrated that the NLR exhibited an area under the curve (AUC) of 0.651 (95% CI: 0.622\\u0026ndash;0.679) for predicting all-cause mortality, significantly outperforming neutrophil count (AUC = 0.584) or lymphocyte count (AUC = 0.631) alone. The optimal NLR cut-off value for all-cause mortality prediction was 2.38, with a sensitivity of 52.8% and specificity of 72.2% (Figure 5A).\\u003c/p\\u003e\\n\\u003cp\\u003eMoreover, the NLR showed predictive performance for cardiovascular mortality (AUC = 0.703; 95% CI: 0.648-0.757) compared to neutrophil count (AUC = 0.638) or lymphocyte count (AUC = 0.671). The corresponding NLR cut-off value was 2.70, yielding a sensitivity of 52.8% and specificity of 80.1% (Figure 5B).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMediation analysis of NLR for all‑cause and cardiovascular mortality\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eMediation analysis explored the mediating effect of eGFR on the relationship between NLR and both all-cause and cardiovascular mortality. Specifically, NLR was negatively correlated with eGFR (\\u0026beta;=-0.1602, \\u003cem\\u003eP\\u003c/em\\u003e＜0.0001), while eGFR was negatively correlated with survival for all-cause mortality (\\u0026beta;=-0.2600, \\u003cem\\u003eP\\u003c/em\\u003e＜0.0001),and cardiovascular mortality (\\u0026beta;=-0.1601, \\u003cem\\u003eP\\u003c/em\\u003e＜0.0001). Ultimately, 1.7% (95% CI 0.3% -3.4%) and 1.6% (95% CI 0.3%\\u0026ndash;3.7%) of the observational association of NLR with risk of all-cause and cardiovascular mortality was mediated through eGFR (Figure. 6A and B).\\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eBased on data from a cross-sectional study involving 7929 adults, our research has uncovered an association between the prognosis of CKM syndrome and the NLR. Specifically, our study provides novel evidence that elevated NLR is independently associated with increased risks of both all-cause and cardiovascular mortality in patients with CKM syndrome These results were consistent across sensitivity and stratified analyses. Notably, we observed a U-shaped relationship between NLR and all-cause mortality, with an inflection point at NLR=1.26, contrasting the linear association observed for cardiovascular mortality. In addition, NLR was superior to lymphocyte and neutrophils alone in predicting all-cause and cardiogenic death. Mediation analysis showed that eGFR played a mediating role in the relationship between NLR and mortality. These findings align with emerging evidence that systemic inflammation, as reflected by NLR, plays a pivotal role in the pathophysiology of multiorgan dysfunction characteristic of CKM syndrome\\u003csup\\u003e4\\u003c/sup\\u003e.\\u003c/p\\u003e\\n\\u003cp\\u003eThe NLR derived from two routinely measured hematological indices, serves as a cost-effective biomarker reflecting systemic immunoinflammatory balance. The NLR operationalizes the bidirectional interplay between innate immunity (quantified via neutrophil enumeration) and adaptive immunity (gauged through lymphocyte quantification), with this integrative metric demonstrating greater clinical utility in risk stratification than compartmentalized analysis of isolated parameters\\u003csup\\u003e25\\u003c/sup\\u003e.Mounting evidence positions NLR at the nexus of inflammatory pathophysiology, particularly in hypertension where chronic low-grade inflammation perpetuates vascular dysfunction through endothelial activation and oxidative stress cascades \\u003csup\\u003e26\\u003c/sup\\u003e. \\u0026nbsp;Clinically validated across the cardiovascular spectrum, NLR demonstrates robust risk stratification capacity for acute coronary events , chronic heart failure exacerbations, and atherosclerotic progression \\u003csup\\u003e5\\u003c/sup\\u003e. The NLR predictive capacity transcends cardiovascular medicine, with dysregulated immune responses quantified by NLR correlating with fatal outcomes in both general population and distinct clinicopathological states: infectious crises, respiratory failure syndromes (COVID-19 ARDS), and malignancy-associated cachexia\\u003csup\\u003e27\\u003c/sup\\u003e. However, the prognostic significance of NLR in patients with CKM syndrome remains undetermined.\\u003c/p\\u003e\\n\\u003cp\\u003eCKM syndrome characterized by the pathophysiological convergence of cardiovascular dysfunction, chronic kidney disease, and metabolic dysregulation \\u0026ndash; has escalated into a pressing global health crisis, demanding urgent multidisciplinary intervention strategies \\u003csup\\u003e28\\u003c/sup\\u003e. Driven by aging demographics and obesogenic environments, this multisystem disorder progresses through synergistic inflammatory pathways that concurrently impair vascular integrity, renal filtration capacity, and myocardial remodeling\\u003csup\\u003e29\\u003c/sup\\u003e. The syndrome\\u0026apos;s pathogenesis arises from interconnected metabolic dysregulation, characterized by insulin resistance, sustained low-grade inflammation, and oxidative stress amplification. Inflammatory mediators impair high-density lipoprotein (HDL) functionality, reducing its anti-inflammatory and reverse cholesterol transport capacities, thereby accelerating atherosclerosis\\u003csup\\u003e30, 31\\u003c/sup\\u003e. Concurrently, inflammation induces endothelial dysfunction in hypertension by suppressing nitric oxide bioavailability and promoting oxidative stress, which elevates vascular resistance and perpetuates cardiac remodeling \\u003csup\\u003e32\\u003c/sup\\u003e. Hyperglycemia, a key metabolic component of CKM syndrome, is exacerbated by pro-inflammatory cytokine that disrupt insulin signaling and promote insulin resistance, further fueling dyslipidemia and renal injury \\u003csup\\u003e33\\u003c/sup\\u003e.\\u003c/p\\u003e\\n\\u003cp\\u003eThe prognostic significance of NLR in CKM syndrome stems from its dual reflection of innate immune activation (via neutrophils) and adaptive immune suppression (via lymphocytes). Neutrophils, as first responders to tissue injury, release pro-inflammatory cytokines (e.g., IL-6, TNF-\\u0026alpha;), reactive oxygen species (ROS), and matrix metalloproteinases (MMPs), which exacerbate endothelial dysfunction and promote atherosclerotic plaque instability \\u003csup\\u003e33, 34\\u003c/sup\\u003e. Conversely, lymphopenia, often observed in chronic inflammatory states, reflects impaired regulatory T-cell activity, weakening anti-inflammatory responses and accelerating vascular and renal fibrosis\\u003csup\\u003e35\\u003c/sup\\u003e. This imbalance is particularly detrimental in CKM syndrome, where metabolic stressors such as insulin resistance and dyslipidemia amplify neutrophilic inflammation while depleting lymphocyte reserves\\u003csup\\u003e36\\u003c/sup\\u003e.Elevated NLR correlates with heightened neutrophil extracellular trap (NET) formation, a key driver of inflammation in atherosclerosis and myocardial infarction \\u003csup\\u003e37\\u003c/sup\\u003e. NETs promote plaque rupture and microvascular occlusion, directly contributing to cardiovascular mortality. Simultaneously, lymphopenia reduces IL-10 production, a cytokine critical for mitigating post-infarction remodeling \\u003csup\\u003e38\\u003c/sup\\u003e. These mechanisms align with our linear NLR-cardiovascular mortality relationship, underscoring inflammation\\u0026rsquo;s central role in acute cardiac events. The U-shaped NLR-all-cause mortality relationship suggests that both immunosuppression (NLR \\u0026lt;1.26) and hyperinflammation (NLR \\u0026gt;1.26) drive mortality through distinct pathways. Low NLR may indicate lymphopenia-induced vulnerability to infections or malignancy, as seen in cancer survivors with impaired lymphocyte recovery \\u003csup\\u003e39\\u003c/sup\\u003e. High NLR, conversely, reflects uncontrolled inflammation accelerating multiorgan failure. This biphasic risk mirrors observations in heart failure populations, where extreme NLR values predict poor outcomes regardless of etiology \\u003csup\\u003e40\\u003c/sup\\u003e. Our findings harmonize with extensive evidence linking NLR to adverse cardiovascular outcomes. For instance, in hypertensive cohorts, Zhang et al. demonstrated NLR\\u0026rsquo;s linear association with cardiovascular mortality (HR=2.33 for NLR\\u0026gt;3.5) \\u003csup\\u003e5\\u003c/sup\\u003e, while a meta-analysis of 25,000+ coronary artery disease patients confirmed NLR\\u0026rsquo;s independent predictive value for major adverse cardiac events (pooled HR=1.45 per unit increase) \\u003csup\\u003e41\\u003c/sup\\u003e. Similarly, in heart failure, NLR\\u0026gt;4.0 predicted 50% higher 1-year mortality risk\\u003csup\\u003e42\\u003c/sup\\u003e. The novelty of our study lies in extending these observations to CKM syndrome, where NLR integrates multisystemic inflammation, offering superior prognostic granularity compared to single biomarkers like CRP or creatinine \\u003csup\\u003e43\\u003c/sup\\u003e.\\u003c/p\\u003e\\n\\u003cp\\u003eIn CKM syndrome, NLR elevation is closely tied to adipose tissue inflammation and renal hypoxia. Neutrophil infiltration into visceral fat exacerbates insulin resistance via IL-1\\u0026beta; secretion, while renal tubular injury from neutrophil-derived myeloperoxidase (MPO) worsens albuminuria and glomerulosclerosis \\u003csup\\u003e44\\u003c/sup\\u003e. Lymphocyte depletion further impairs renal repair mechanisms, as CD4+ T cells are essential for resolving acute kidney injury\\u003csup\\u003e45\\u003c/sup\\u003e. Our mediation analysis, showing eGFR\\u0026rsquo;s partial mediation, supports this bidirectional NLR-kidney interaction, consistent with findings in diabetic nephropathy cohorts\\u003csup\\u003e46\\u003c/sup\\u003e.Therefore, the NLR could act as a vital biomarker for forecasting mortality in individuals with CKM syndrome. The NLR could be an important target for CKM syndrome treatment in the future.\\u003c/p\\u003e\\n\\u003cp\\u003eThis study has several limitations that warrant consideration. First, the observational cross-sectional design precludes causal inference between the NLR and outcomes in CKM syndrome. \\u0026nbsp;Second, despite comprehensive adjustments for covariates, residual confounding from unmeasured variables may persist. \\u0026nbsp;Third, the geographically and demographically homogeneous cohort limits the generalizability of our findings to populations with greater ethnic diversity or socioeconomic disparities. \\u0026nbsp;Additionally, the exclusive focus on NLR, without comparative analyses of other biomarkers such as C-reactive protein, interleukin-6, or novel omics-derived markers, may overlook indicators with superior predictive capacity for CKM-related mortality.\\u003c/p\\u003e\\n\\u003cp\\u003eDespite these limitations, our findings underscore NLR as a novel, cost-effective biomarker for risk stratification in CKM syndrome, particularly in resource-limited settings where routine hematologic testing is widely accessible.\\u003c/p\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eElevated NLR independently predicts increased all-cause and cardiovascular mortality in CKM syndrome. eGFR mediation partially links NLR-driven inflammation to multiorgan failure, positioning NLR as a pivotal biomarker for risk stratification and anti-inflammatory targeting in CKM management.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eData availability\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe datasets for this study can be found in the NHANES (https://www.cdc.gov/nchs/nhanes/index.html).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe protocol was approved by the Institutional Review Board of National Center for Health Statistics and no new data was added.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthor contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003ePeng Wu and Zhenghui Huang\\u003c/strong\\u003e: Conceptualization; data curation; formal analysis; investigation; methodology; software; supervision; validation; visualization; writing\\u0026mdash;original draft; writing\\u0026mdash;review \\u0026amp; editing.\\u003cstrong\\u003e\\u0026nbsp;Juan Ma\\u003c/strong\\u003e,\\u003cstrong\\u003eBaozhen Zhu and Mohan Wang\\u003c/strong\\u003e: Writing\\u0026mdash;original draft. \\u003cstrong\\u003eAli Maand Xin Wang\\u003c/strong\\u003e: Writing \\u0026mdash;original draft. \\u003cstrong\\u003eRuixin Hai\\u003c/strong\\u003e: Data curation; writing\\u0026mdash;original draft.\\u003cstrong\\u003e\\u0026nbsp;Shaobin Jia,Xueping Ma and Ning Yan\\u003c/strong\\u003e: Investigation; supervision; validation; writing\\u0026mdash;original draft; writing\\u0026mdash;review \\u0026amp; editing.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003ePeng Wu and Zhenghui Huang\\u003c/strong\\u003e contributed equally as co-first authors. .\\u003cstrong\\u003e\\u0026nbsp;Shaobin Jia,Xueping Ma and Ning Yan\\u003c/strong\\u003e contributed equally as corresponding co-authors. All authors have read and agreed to the published version of the manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was supported by Open competition mechanism to select the best candidates for key research projects of Ningxia Medical University (No. XJKF230205); the Central Government Guiding Local Science and Technology Development Special Project (No. 2024FRD05139); the National Natural Science Foundation of China (No. 82260086) and the National Natural Science Foundation of China (No. 8206020191); Ningxia Natural Science Foundation project (No.2023AAC02069).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgments\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe would like to gratefully acknowledge all of the investigators and patients participating in this work.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConflict of interest\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare that they have no competing interests.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eNdumele CE, Rangaswami J, Chow SL, et al. 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(2022).\\u003c/li\\u003e\\n\\u003cli\\u003eKinsey GR, Sharma R, Okusa MD. Regulatory T cells in AKI.\\u003cem\\u003e Journal of the American Society of Nephrology : JASN\\u003c/em\\u003e.\\u003cstrong\\u003e24\\u003c/strong\\u003e(11):1720-6. (2013).\\u003c/li\\u003e\\n\\u003cli\\u003eLi L, Shen Q, Rao S. Association of Neutrophil-to-Lymphocyte Ratio and Platelet-to-Lymphocyte Ratio with Diabetic Kidney Disease in Chinese Patients with Type 2 Diabetes: A Cross-Sectional Study.\\u003cem\\u003e Therapeutics and clinical risk management\\u003c/em\\u003e.\\u003cstrong\\u003e18\\u003c/strong\\u003e:1157-66. (2022).\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"},{\"header\":\"Tables\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eTable 1. Baseline characteristics stratified by NLR tertiles(N=7929).\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"539\\\"\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 161px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eVariable\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eTertile 1\\u003cbr\\u003e\\u0026nbsp;(N=2646)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eTertile2\\u003cbr\\u003e\\u0026nbsp;(N=2643)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eTertile 3\\u003cbr\\u003e\\u0026nbsp;(N=2640)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eP\\u003c/em\\u003e\\u003c/strong\\u003e\\u003cstrong\\u003e-value\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 161px;\\\"\\u003e\\n \\u003cp\\u003eAge, years\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e47.23 \\u0026plusmn; 16.90\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e48.51 \\u0026plusmn; 16.97\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e52.86 \\u0026plusmn; 18.08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 161px;\\\"\\u003e\\n \\u003cp\\u003eFemale, n (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e1342 (50.72)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e1356 (51.31)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e1317 (49.89)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e0.584\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 161px;\\\"\\u003e\\n \\u003cp\\u003eEthnicity, n (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 161px;\\\"\\u003e\\n \\u003cp\\u003eWhite\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e771 (29.14)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e1041 (39.39)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e1282 (48.56)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 66px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 161px;\\\"\\u003e\\n \\u003cp\\u003eBlack\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e787 (29.74)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e464 (17.56)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e399 (15.11)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 66px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 161px;\\\"\\u003e\\n \\u003cp\\u003eMexican American\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e350 (13.23)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e416 (15.74)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e342 (12.95)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 66px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 161px;\\\"\\u003e\\n \\u003cp\\u003eOther\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e738 (27.89)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e722 (27.32)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e617 (23.37)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 66px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 161px;\\\"\\u003e\\n \\u003cp\\u003eMarital status, n (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e1537 (58.09%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e1639 (62.01%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e1555 (58.90%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e0.009\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 161px;\\\"\\u003e\\n \\u003cp\\u003eEducation level, n (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e0.167\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 161px;\\\"\\u003e\\n \\u003cp\\u003eSome college or above\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e2122 (80.20%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e2047 (77.45%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e2080 (78.79%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 161px;\\\"\\u003e\\n \\u003cp\\u003eHigh School\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e307 (11.60%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e357 (13.51%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e340 (12.88%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 161px;\\\"\\u003e\\n \\u003cp\\u003eMiddle School\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e217 (8.20%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e239 (9.04%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e220 (8.33%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 161px;\\\"\\u003e\\n \\u003cp\\u003eBMI(Kg/m\\u003csup\\u003e2\\u003c/sup\\u003e)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e28.75 \\u0026plusmn; 6.71\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e29.21 \\u0026plusmn; 6.88\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e30.15 \\u0026plusmn; 7.70\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 161px;\\\"\\u003e\\n \\u003cp\\u003eAlcohol consumption, n (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 161px;\\\"\\u003e\\n \\u003cp\\u003eNever\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e405 (15.31%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e380 (14.38%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e378 (14.32%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 161px;\\\"\\u003e\\n \\u003cp\\u003eCurrent\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e1947 (73.58%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e1913 (72.38%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e1847 (69.96%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 161px;\\\"\\u003e\\n \\u003cp\\u003eFormer\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e294 (11.11%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e350 (13.24%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e415 (15.72%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 161px;\\\"\\u003e\\n \\u003cp\\u003eSmoke status, n (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 161px;\\\"\\u003e\\n \\u003cp\\u003eNever\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e1611 (60.88%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e1529 (57.85%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e1363 (51.63%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 161px;\\\"\\u003e\\n \\u003cp\\u003eCurrent\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e456 (17.23%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e511 (19.33%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e556 (21.06%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 161px;\\\"\\u003e\\n \\u003cp\\u003eFormer\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e579 (21.88%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e603 (22.81%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e721 (27.31%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 161px;\\\"\\u003e\\n \\u003cp\\u003eHypertension, n (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e1010 (38.17)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e1073 (40.60)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e1340 (50.76)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 161px;\\\"\\u003e\\n \\u003cp\\u003eDiabetes Mellitus, n (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e447 (16.98)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e540 (20.52)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e719 (27.72)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 161px;\\\"\\u003e\\n \\u003cp\\u003eCVD, n (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e200 (7.56)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e242 (9.16)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e412 (15.61)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 161px;\\\"\\u003e\\n \\u003cp\\u003eHyperlipidemia, n (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e1775 (67.08)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e1880 (71.13)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e1941 (73.52)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 161px;\\\"\\u003e\\n \\u003cp\\u003eWBC, \\u0026times;10⁹/L\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e6.13 \\u0026plusmn; 3.07\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e6.63 \\u0026plusmn; 1.70\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e7.74 \\u0026plusmn; 2.24\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 161px;\\\"\\u003e\\n \\u003cp\\u003eNeutrophil, \\u0026times;10⁹/L\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e2.86 \\u0026plusmn; 0.92\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e3.83 \\u0026plusmn; 1.04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e5.23 \\u0026plusmn; 1.75\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 161px;\\\"\\u003e\\n \\u003cp\\u003eLymphocyte, \\u0026times;10⁹/L\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e2.51 \\u0026plusmn; 2.56\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e2.03 \\u0026plusmn; 0.55\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e1.67 \\u0026plusmn; 0.51\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 161px;\\\"\\u003e\\n \\u003cp\\u003eHb(g/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e14.10 (13.10-15.10)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e14.10 (13.20-15.20)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e14.10 (13.10-15.20)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e0.310\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 161px;\\\"\\u003e\\n \\u003cp\\u003ePLT\\u0026times;10⁹/L\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e232.34\\u0026plusmn;58.75\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e236.02 \\u0026plusmn;60.22\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e236.99 \\u0026plusmn;65.34\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e0.059\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 161px;\\\"\\u003e\\n \\u003cp\\u003eFBG, mmol/L\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e5.95 \\u0026plusmn; 1.80\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e6.10 \\u0026plusmn; 1.93\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e6.29 \\u0026plusmn; 2.18\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 161px;\\\"\\u003e\\n \\u003cp\\u003eHbA1c, %\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e5.75 \\u0026plusmn; 1.07\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e5.79 \\u0026plusmn; 1.12\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e5.87 \\u0026plusmn; 1.20\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 161px;\\\"\\u003e\\n \\u003cp\\u003eALT, U/L\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e24.75 \\u0026plusmn; 16.18\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e25.24 \\u0026plusmn; 18.69\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e23.73 \\u0026plusmn; 17.50\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e0.006\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 161px;\\\"\\u003e\\n \\u003cp\\u003eTG, mmol/L\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e1.02 (0.69-1.56)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e1.11 (0.77-1.65)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e1.13 (0.78-1.64)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e0.166\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 161px;\\\"\\u003e\\n \\u003cp\\u003eTC, mmol/L\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e4.89 (4.21-5.61)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e4.91 (4.24-5.64)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e4.76 (4.06-5.48)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 161px;\\\"\\u003e\\n \\u003cp\\u003eHDL, mmol/L\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e1.37 (1.11-1.63)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e1.32 (1.09-1.60)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e1.32 (1.09-1.60)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e0.017\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 161px;\\\"\\u003e\\n \\u003cp\\u003eLDL, mmol/L\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e2.87 (2.30-3.52)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e2.90 (2.30-3.54)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e2.74 (2.15-3.36)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 132px;\\\"\\u003e\\n \\u003cp\\u003eCreatinine, \\u0026mu;mol/L\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"0\\\" style=\\\"width: 123px;\\\"\\u003e\\n \\u003cp\\u003e74.26 (62.76-87.52)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e72.49 (61.00-85.75)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e74.26 (62.76-90.17)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 161px;\\\"\\u003e\\n \\u003cp\\u003eeGFR, mL/min/1.73m\\u0026sup2;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e97.82 \\u0026plusmn; 21.44\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e96.59 \\u0026plusmn; 22.38\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e91.00 \\u0026plusmn; 25.83\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 161px;\\\"\\u003e\\n \\u003cp\\u003eAll-cause mortality, n (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e19 (0.72)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e27 (1.02)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e79 (2.99)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 161px;\\\"\\u003e\\n \\u003cp\\u003eCardiovascular mortality, n (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e99 (3.74)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e107 (4.05)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e267 (10.11)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003eData presented as mean \\u0026plusmn; standard deviation (SD) or median (interquartile range) for continuous variables, and n (%) for categorical variables.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eNLR tertile: (T1: NLR \\u0026lt;1.57; T2: 1.57\\u0026le;NLR \\u0026lt;2.27; T3: NLR\\u0026ge;2.27).\\u003c/p\\u003e\\n\\u003cp\\u003eAbbreviations: NLR: Neutrophil-to-lymphocyte ratio, BMI: body mass index; CVD: cardiovascular disease; WBC: white blood cell count; Hb: hemoglobin; PLT: platelet count; FBG: fasting blood glucose; HbA1c: hemoglobin A1c; ALT: Alanine Aminotransferase; TG: triglycerides; TC: total cholesterol; HDL: high-density lipoprotein; LDL: low-density lipoprotein; eGFR: estimated glomerular filtration rate\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 2. Association of NLR and NLR Tertiles with all-cause and cardiovascular mortality (N=7929).\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"690\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 132px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eExposure\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 189px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eModel 1\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 180px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eModel 2\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 189px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eModel 3\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 132px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 189px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eHR (95%CI) \\u003cem\\u003eP\\u003c/em\\u003e-value\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 180px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eHR (95%CI)\\u003cem\\u003e\\u0026nbsp;P\\u003c/em\\u003e-value\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 189px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eHR (95%CI)\\u003cem\\u003e\\u0026nbsp;P\\u003c/em\\u003e-value\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"4\\\" style=\\\"width: 690px;\\\"\\u003e\\n \\u003cp\\u003eAll-cause mortality\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 132px;\\\"\\u003e\\n \\u003cp\\u003eNLR\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 189px;\\\"\\u003e\\n \\u003cp\\u003e1.25 (1.22, 1.28) \\u0026lt;0.0001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 180px;\\\"\\u003e\\n \\u003cp\\u003e1.12 (1.09, 1.16) \\u0026lt;0.0001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 189px;\\\"\\u003e\\n \\u003cp\\u003e1.13 (1.09, 1.17) \\u0026lt;0.0001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 132px;\\\"\\u003e\\n \\u003cp\\u003eNLR tertile\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 189px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 180px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 189px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 132px;\\\"\\u003e\\n \\u003cp\\u003eT1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 189px;\\\"\\u003e\\n \\u003cp\\u003eReference\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 180px;\\\"\\u003e\\n \\u003cp\\u003eReference\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 189px;\\\"\\u003e\\n \\u003cp\\u003eReference\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 132px;\\\"\\u003e\\n \\u003cp\\u003eT2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 189px;\\\"\\u003e\\n \\u003cp\\u003e1.06 (0.81, 1.40) 0.6587\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 180px;\\\"\\u003e\\n \\u003cp\\u003e0.93 (0.70, 1.24) 0.6359\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 189px;\\\"\\u003e\\n \\u003cp\\u003e0.89 (0.67, 1.19) 0.4362\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 132px;\\\"\\u003e\\n \\u003cp\\u003eT3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 189px;\\\"\\u003e\\n \\u003cp\\u003e2.71 (2.15, 3.42) \\u0026lt;0.0001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 180px;\\\"\\u003e\\n \\u003cp\\u003e1.51 (1.17, 1.94) 0.0013\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 189px;\\\"\\u003e\\n \\u003cp\\u003e1.40 (1.09, 1.81) 0.0093\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 132px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e for trend\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 189px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.0001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 180px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.0001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 189px;\\\"\\u003e\\n \\u003cp\\u003e0.0007\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"4\\\" style=\\\"width: 690px;\\\"\\u003e\\n \\u003cp\\u003eCardiovascular mortality\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 132px;\\\"\\u003e\\n \\u003cp\\u003eNLR\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 189px;\\\"\\u003e\\n \\u003cp\\u003e1.26 (1.21, 1.32) \\u0026lt;0.0001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 180px;\\\"\\u003e\\n \\u003cp\\u003e1.15 (1.09, 1.21) \\u0026lt;0.0001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 189px;\\\"\\u003e\\n \\u003cp\\u003e1.17 (1.11, 1.24) \\u0026lt;0.0001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 132px;\\\"\\u003e\\n \\u003cp\\u003eNLR tertile\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 189px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 180px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 189px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 132px;\\\"\\u003e\\n \\u003cp\\u003eT1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 189px;\\\"\\u003e\\n \\u003cp\\u003eReference\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 180px;\\\"\\u003e\\n \\u003cp\\u003eReference\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 189px;\\\"\\u003e\\n \\u003cp\\u003eReference\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 132px;\\\"\\u003e\\n \\u003cp\\u003eT2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 189px;\\\"\\u003e\\n \\u003cp\\u003e1.40 (0.78, 2.52) 0.2615\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 180px;\\\"\\u003e\\n \\u003cp\\u003e1.18 (0.65, 2.18) 0.5844\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 189px;\\\"\\u003e\\n \\u003cp\\u003e1.13 (0.61, 2.12) 0.6926\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 132px;\\\"\\u003e\\n \\u003cp\\u003eT3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 189px;\\\"\\u003e\\n \\u003cp\\u003e4.17 (2.53, 6.89) \\u0026lt;0.0001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 180px;\\\"\\u003e\\n \\u003cp\\u003e2.27 (1.33, 3.90) 0.0028\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 189px;\\\"\\u003e\\n \\u003cp\\u003e2.17 (1.24, 3.81) 0.0065\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 132px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eP\\u0026nbsp;\\u003c/em\\u003efor trend\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 189px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.0001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 180px;\\\"\\u003e\\n \\u003cp\\u003e0.0004\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 189px;\\\"\\u003e\\n \\u003cp\\u003e0.0009\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003eNLR tertile: (T1: NLR \\u0026lt;1.57; T2: 1.57\\u0026le;NLR \\u0026lt;2.27; T3: NLR\\u0026ge;2.27). HR: hazard ratios; CI: confidence\\u003c/p\\u003e\\n\\u003cp\\u003eModel 1: Non-adjusted.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eModel 2：adjust for age, sex, BMI, ethnicity status, education level, smoke status, alcohol consumption, hypertension, diabetes.\\u003c/p\\u003e\\n\\u003cp\\u003eModel 3：adjust for age, sex, BMI, ethnicity status, education level, smoke status, alcohol consumption, hypertension, diabetes, CVD, TG, TC, HDL, LDL, HBA1C, eGFR.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 3 Threshold effect analysis of NLR on all-cause mortality\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cdiv\\u003e\\n \\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"396\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 217px;\\\"\\u003e\\n \\u003cp\\u003eOutcome\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 180px;\\\"\\u003e\\n \\u003cp\\u003eAll-cause mortality\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 217px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 180px;\\\"\\u003e\\n \\u003cp\\u003eHR (95%CI) \\u003cem\\u003eP\\u003c/em\\u003e-value\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 217px;\\\"\\u003e\\n \\u003cp\\u003eModel I\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 180px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 217px;\\\"\\u003e\\n \\u003cp\\u003eOne line effect\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 180px;\\\"\\u003e\\n \\u003cp\\u003e1.21 (1.14, 1.29) \\u0026lt;0.0001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 217px;\\\"\\u003e\\n \\u003cp\\u003eModel II\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 180px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 217px;\\\"\\u003e\\n \\u003cp\\u003eInflection point (K)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 180px;\\\"\\u003e\\n \\u003cp\\u003e1.23\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 217px;\\\"\\u003e\\n \\u003cp\\u003eHRR \\u0026lt; K\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 180px;\\\"\\u003e\\n \\u003cp\\u003e0.45 (0.21, 0.93) 0.0324\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 217px;\\\"\\u003e\\n \\u003cp\\u003eHRR\\u0026gt; K\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 180px;\\\"\\u003e\\n \\u003cp\\u003e1.23 (1.16, 1.31) \\u0026lt;0.0001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 217px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eP\\u0026nbsp;\\u003c/em\\u003efor Log-likelihood ration*\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 180px;\\\"\\u003e\\n \\u003cp\\u003e0.016\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n\\u003c/div\\u003e\\n\\u003cp\\u003eHR hazard ratio, CI confidence interval,\\u0026nbsp;NLR: Neutrophil-to-lymphocyte ratio.\\u003c/p\\u003e\\n\\u003cp\\u003eModel I, linear analysis; Model II, nonlinear analysis.\\u0026nbsp;*Model II differs significantly from Model I by the logarithm likelihood ratio test (LRT) by p \\u0026lt; 0.05.\\u003c/p\\u003e\\n\\u003cp\\u003eAdjusted for age, sex, BMI, ethnicity status, education level, smoke status, alcohol consumption, hypertension, diabetes, CVD, TG, TC, HDL, LDL, HBA1C, eGFR.\\u003c/p\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Cardiovascular-kidney-metabolic syndrome, Neutrophil-to-lymphocyte ratio, All-cause mortality, Cardiovascular mortality, NHANES\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-6611872/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-6611872/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"The neutrophil-to-lymphocyte ratio (NLR) as inflammatory biomarker across cardiovascular outcomes. However, its relationship with all-cause and cardiovascular mortality in cardiovascular-kidney-metabolic (CKM) syndrome remains poorly characterized. We analyzed 7,929 CKM patients from National Health and Nutrition Examination Survey (NHANES) (2011–2018), with mortality follow-up through 2019. Cox proportional hazards models and restricted cubic splines (RCS) assessed NLR-mortality relationships. Survival disparities were quantified through Kaplan-Meier estimates. Sensitivity and stratification analyses were used to demonstrate the stability of the relationship. The receiver operating characteristic curve (ROC) analysis was conducted to access the predictive ability of NLR for survival. Mediation analysis explored estimated glomerular filtration rate (eGFR)-mediated effects.The cohort comprised 7,929 participants with 473 documented deaths during follow-up, including 125 cardiovascular-specific events. Elevated NLR independently predicted higher all-cause mortality (HR=1.13, 95%CI:1.09–1.17) and cardiovascular mortality (HR=1.17,1.11–1.24). RCS revealed a U-shaped NLR-all-cause mortality relationship (inflection: NLR=1.26, P for nonlinear=0.016), contrasting with linear cardiovascular mortality association (P for nonlinear =0.378). The highest NLR tertile demonstrating markedly higher mortality risks [all-cause mortality: HR (95CI%)1.40 (1.09, 1.81); cardiovascular mortality: HR (95CI%) 2.17 (1.24, 3.81)]. Sensitivity analysis and subgroup analyses were conducted to prove the stability of the model. ROC analysis demonstrated that the NLR had area under the curve (AUC) values of 0.651 and 0.703 for predicting all-cause mortality and cardiovascular mortality, respectively, showing superior predictive value compared to individual neutrophil or lymphocyte counts alone. Mediation analysis identified that eGFR mediated 1.7% of the NLR-all-cause mortality association and 1.6% of the cardiovascular mortality relationship. Elevated NLR levels were independently associated with increased risks of both all-cause mortality and cardiovascular mortality in patients with CKM syndrome. Moreover, these findings underscore the potential clinical utility of NLR to refine the detection of mortality in CKM population.\",\"manuscriptTitle\":\"Association of neutrophil-to-lymphocyte ratio with all-cause and cardiovascular mortality among cardiovascular-kidney-metabolic syndrome: a national cross-sectional study\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-06-25 09:55:16\",\"doi\":\"10.21203/rs.3.rs-6611872/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"69952cda-e82c-46c3-9cc1-fd498fb7b445\",\"owner\":[],\"postedDate\":\"June 25th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[{\"id\":50301096,\"name\":\"Health sciences/Endocrinology\"},{\"id\":50301097,\"name\":\"Health sciences/Endocrinology/Endocrine system and metabolic diseases\"},{\"id\":50301098,\"name\":\"Health sciences/Endocrinology/Endocrine system and metabolic diseases/Metabolic syndrome\"}],\"tags\":[],\"updatedAt\":\"2026-01-22T06:41:17+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-06-25 09:55:16\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-6611872\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-6611872\",\"identity\":\"rs-6611872\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}