Pentraxin 3 Is an Inflammation-Related Biomarker That Distinguishes Early-Stage from Mid-Advanced Cardiovascular-Kidney-Metabolic Syndrome

Pentraxin 3 Is an Inflammation-Related Biomarker That Distinguishes Early-Stage from Mid-Advanced Cardiovascular-Kidney-Metabolic Syndrome | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Pentraxin 3 Is an Inflammation-Related Biomarker That Distinguishes Early-Stage from Mid-Advanced Cardiovascular-Kidney-Metabolic Syndrome Zhen Xu, Shuo Yang, Yuan Tan, Qian Zhang, He Wang, Jingjin Tao, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9249191/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Background: The cardiovascular-kidney-metabolic (CKM) syndrome represents a continuum linking metabolic dysfunction, chronic kidney disease, and cardiovascular disease, in which chronic inflammation plays a central role. However, conventional inflammatory biomarkers may not fully capture local inflammatory processes involved in CKM stage transitions. Pentraxin 3 (PTX3), a long pentraxin produced locally at sites of inflammation, may provide complementary information, yet its association with CKM staging has not been systematically evaluated. Methods: In this cross-sectional study, circulating PTX3 levels were measured in 240 adults, including healthy controls (stage 0, S0; n = 60) and individuals with stage 2 (S2; n = 60), stage 3 (S3; n = 60), and stage 4 (S4; n = 60) CKM, classified according to the CKM staging framework. Associations between PTX3 and inflammatory, metabolic, cardiac, and renal biomarkers were assessed using Spearman correlation analysis. Multivariable logistic regression models were constructed within the CKM population (S2-S4) to distinguish early-stage CKM (S2) from mid-advanced-stage CKM (S3 + S4). Model discrimination and calibration were evaluated using receiver operating characteristic (ROC) analysis and the Hosmer-Lemeshow goodness-of-fit test. Results: PTX3 levels were significantly elevated in early-stage CKM (S2) compared with healthy controls ( p < 0.001) and showed further differentiation between S2 and S3 ( p < 0.01). PTX3 showed moderate correlations with biomarkers reflecting inflammatory activation, metabolic dysregulation, myocardial injury, and renal dysfunction, including high-sensitivity C-reactive protein (hs-CRP; ρ = 0.361, p < 0.001), glycated hemoglobin (HbA1c; ρ = 0.434, p < 0.001), triglycerides (TG; ρ = 0.296, p < 0.001), and high-sensitivity cardiac troponin T (hs-cTnT; ρ = 0.411, p < 0.001), and was inversely correlated with estimated glomerular filtration rate (eGFR; ρ = -0.419, p < 0.001). In multivariable logistic regression models adjusting for demographic factors, metabolic indices, and cardiorenal biomarkers, PTX3 remained independently associated with classification into mid-advanced-stage CKM (S3 + S4 vs S2; odds ratio [OR] per unit increase = 1.05, 95% confidence interval [CI]: 1.03–1.08; p < 0.001), whereas hs-CRP and procalcitonin (PCT) showed no independent associations. Incorporation of PTX3 significantly improved model discrimination (area under the curve [AUC], 0.892 vs 0.804 for the baseline model; ΔAUC = 0.088, p = 0.001 by DeLong test), without evidence of compromised calibration. Conclusions: Circulating PTX3 is cross-sectionally associated with CKM stage classification and demonstrates incremental discriminative value beyond conventional inflammatory markers in distinguishing early-stage from mid-advanced-stage CKM. These findings suggest that PTX3 may reflect inflammatory processes not fully captured by systemic markers, supporting its potential role in CKM risk stratification. Pentraxin 3 Cardiovascular-Kidney-Metabolic syndrome Inflammation CKM staging Cardiorenal biomarkers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction The escalating global prevalence of obesity, type 2 diabetes mellitus (T2DM), chronic kidney disease (CKD), and cardiovascular disease (CVD) has highlighted the intricate and bidirectional interactions among metabolic, renal, and cardiovascular disorders. In recognition of their substantial pathophysiological overlap and mutual disease amplification, the American Heart Association (AHA) recently introduced cardiovascular-kidney-metabolic (CKM) syndrome as a unified systemic disorder that integrates metabolic risk factors, CKD, and cardiovascular dysfunction into a single clinical continuum [ 1 ] . CKM syndrome is characterized by progressive multi-organ involvement and is associated with a markedly increased risk of adverse cardiovascular outcomes and premature mortality [ 1 ] . To facilitate early identification and stage-specific intervention, the AHA further proposed a staging construct for CKM syndrome that reflects disease evolution across the life course [ 1 ] . Specifically, Stage 0 represents optimal cardiometabolic health in otherwise healthy individuals; Stage 1 is characterized by excess or dysfunctional adiposity; Stage 2 encompasses the presence of metabolic risk factors; Stage 3 denotes subclinical cardiovascular disease or moderate- to high-risk chronic kidney disease; and Stage 4 comprises overt clinical cardiovascular disease with or without kidney failure. This framework underscores the progressive accumulation of metabolic abnormalities, renal impairment, and cardiovascular involvement, providing a conceptual basis for integrated risk stratification, prevention, and multidisciplinary management of CKM syndrome. Chronic inflammation is considered a key pathophysiological feature of CKM syndrome, functioning both as a critical mediator of disease initiation and as a molecular nexus linking cardiac, renal, and metabolic dysfunction. Persistent low-grade inflammation promotes insulin resistance, oxidative stress, endothelial dysfunction, lipotoxicity, and dysregulated activation of the renin-angiotensin-aldosterone system (RAAS) [ 2 ] . These processes converge on multiple inflammatory signaling pathways—including NF-κB, Wnt, PI3K-AKT, and JAK-STAT—thereby establishing self-amplifying positive feedback loops across metabolic tissues, the kidney, and the cardiovascular system, ultimately accelerating multi-organ injury and disease progression [ 2 ] . Despite the recognized central role of inflammation in CKM syndrome, currently available clinical inflammatory biomarkers remain suboptimal for capturing the complex, multi-organ inflammatory burden that characterizes this condition. Widely used systemic inflammatory markers, such as C-reactive protein (CRP), primarily reflect hepatically derived acute-phase responses driven by interleukin-6 (IL-6) signaling. Although CRP has been extensively validated for cardiovascular risk assessment and provides valuable information on systemic inflammatory status, it may not adequately reflect localized or tissue-specific inflammatory processes within the cardiovascular and renal systems [ 3 ] . Hematologic inflammation-related indices, including the neutrophil-to-lymphocyte ratio (NLR), have also attracted attention as inexpensive and readily available markers associated with cardiometabolic and renal risk [ 4 ] . Elevated NLR has been linked to adverse cardiovascular outcomes and CKD progression, reflecting systemic immune activation and imbalance [ 5 – 7 ] . In addition, procalcitonin (PCT), a biomarker widely used for detecting bacterial infection, has been reported to be elevated in cardiovascular and renal diseases, suggesting a potential association with systemic inflammatory stress [ 8 , 9 ] . However, the specificity of these circulating markers in non-infectious cardiometabolic conditions remains limited. Even mild infections may obscure underlying inflammatory signals, and impaired renal clearance—common in cardiovascular disease—may lead to pseudo-elevations of circulating biomarkers, complicating their interpretation in cardiorenal disorders. These limitations highlight the need for integrated biomarkers capable of capturing multi-organ inflammatory signaling within the CKM continuum. Pentraxin 3 (PTX3), a prototypical long pentraxin, has emerged as a promising candidate in this context. Unlike CRP, PTX3 is produced locally at sites of inflammation by multiple cell types directly implicated in CKM pathophysiology, including endothelial cells, monocytes/macrophages, adipocytes, and renal parenchymal cells [ 10 ] . Through its involvement in innate immune activation, complement regulation, endothelial dysfunction, and tissue remodeling, PTX3 may reflect inflammatory pathways more directly related to organ injury [ 11 – 13 ] . Although accumulating evidence links elevated PTX3 levels to metabolic disturbances, CKD progression, and adverse cardiovascular outcomes, its role within the integrated CKM framework—and its association with disease severity across CKM stages—remains incompletely defined. Therefore, the present study aimed to evaluate the cross-sectional association between circulating PTX3 levels and CKM stage classification, with particular emphasis on its discriminatory value across CKM stages and its relationship with metabolic, renal, and cardiovascular injury markers, as well as its discriminative performance in comparison with commonly used inflammatory markers such as hs-CRP and PCT. 2. Material and Methods 2.1 Participants The design and overall workflow of this cross-sectional study are summarized in Figure 1. Participants were consecutively recruited between August 2024 and January 2025 from hospitalized patients in the Departments of Endocrinology, Nephrology, and Cardiology at Peking University Third Hospital, as well as from apparently healthy individuals undergoing routine health examinations at the hospital’s Health Examination Center. Hospitalized patients were screened through the hospital electronic medical record system, and those meeting the diagnostic framework for CKM syndrome based on documented clinical diagnoses in the medical records were consecutively included in the study, whereas serum samples from patients who did not meet the CKM diagnostic criteria were not collected. These patients were admitted primarily for evaluation or management of metabolic, renal, or cardiovascular conditions. Following completion of routine clinical testing, residual serum samples were collected from all participants and stored at -80°C until further laboratory analyses. Inclusion criteria were as follows: (1) age ≥ 18 years; (2) fulfillment of the diagnostic framework for CKM syndrome proposed by the AHA, defined by the presence of one or more of the following conditions: obesity, T2DM, CKD, and CVD, including peripheral artery disease, coronary artery disease, atrial fibrillation, heart failure, and stroke; (3) for apparently healthy individuals, no prior history of obesity, T2DM, CKD, or CVD based on medical records and routine health examination results. To ensure diagnostic consistency, clinical conditions were defined according to established clinical guidelines. Obesity was defined based on body mass index (BMI) according to the criteria for the Chinese population. T2DM was defined according to the diagnostic criteria of the American Diabetes Association. Hypertension was defined according to established hypertension guidelines or documented clinical diagnosis recorded in the medical records. CKD was defined according to the KDIGO guidelines. CVD included clinically documented conditions such as coronary artery disease, peripheral artery disease, atrial fibrillation, heart failure, and stroke. Metabolic syndrome (MetS) was defined according to the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) criteria. Exclusion criteria included: (1) presence of acute inflammatory conditions; (2) acute infections, including severe cases such as bacterial or fungal bloodstream infections, sepsis, or septic shock; (3) malignant tumors or acquired immunodeficiency disorders; (4) current use of immunosuppressive medications; (5) incomplete clinical or laboratory data. According to the CKM staging construct proposed by the AHA [1] , participants were categorized into four groups. Stage 0 (S0) included apparently healthy individuals without metabolic risk factors, CKD, or CVD. Stage 2 (S2) included individuals with established metabolic risk factors such as obesity, T2DM, or MetS without overt CVD. Stage 3 (S3) included individuals with evidence of subclinical CVD or CKD. Evidence of subclinical CVD documented in the medical records, including carotid plaque detected by carotid ultrasound, was used to support Stage 3 classification when available. Stage 4 (S4) included individuals with clinically manifest CVD. An a priori sample size estimation was performed using G*Power software based on pilot data. Stage 1 (S1) individuals were not included in the final inpatient-based analyses because this stage generally represents individuals with early metabolic risk factors who are predominantly managed in outpatient settings and are therefore infrequently encountered among hospitalized patients. However, pilot data including S1 participants were available, and the effect size (Cohen’s d = 0.661) was derived from a preliminary comparison between S0 and S1. This S0-S1 comparison was used as a conservative reference for sample size estimation under the minimal-difference principle. To account for prespecified multiple comparisons, a Bonferroni-adjusted two-sided significance level of α = 0.0125 and a statistical power of 80% were applied. This yielded an estimated sample size of approximately 53 participants per group. Allowing for an anticipated ~10% proportion of unusable samples (e.g., inadequate specimen quality or missing key variables), the target sample size was set at 60 participants per group. Accordingly, a total of 240 eligible participants were consecutively included, with 60 individuals in each group. The flowchart illustrates the screening and inclusion process of the study population between August 2024 and January 2025. Hospitalized patients from the Departments of Endocrinology, Nephrology, and Cardiology were screened through the hospital electronic medical record system, and those meeting CKM diagnostic criteria were included. A total of 240 eligible participants were enrolled and stratified into four groups (n = 60 each) representing CKM Stages 0, 2, 3, and 4. Statistical analyses included group comparisons (ANOVA and Kruskal-Wallis), correlation analysis (Spearman), and multivariable modeling with 5-fold cross-validation. Abbreviations: AHA, American Heart Association; CKM, cardiovascular-kidney-metabolic syndrome; ANOVA, analysis of variance; CV, cross-validation; ROC, receiver operating characteristic. Figure 1. Study population screening, grouping, and analytical workflow 2.2 Laboratory measurements After an overnight fast of at least 12 hours, venous blood samples were collected in the early morning by trained ward nurses. All laboratory analyses were performed at the Clinical Laboratory Center of Peking University Third Hospital in accordance with standardized operating procedures. Routine biochemical measurements All routine biochemical parameters were measured using a Beckman Coulter AU5800 automated chemistry analyzer (Beckman Coulter, Brea, CA, USA). Fasting plasma glucose (FPG) was determined using the hexokinase method. High-sensitivity C-reactive protein (hs-CRP) was measured using a particle-enhanced immunoturbidimetric assay (DiaSys Diagnostic Systems GmbH, Holzheim, Germany). Lipid parameters included total cholesterol (T-CHO) and triglycerides (TG), both measured using the enzymatic end-point method. Low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C) were measured using a homogeneous enzymatic colorimetric assay (Sekisui Medical Co., Ltd, Japan). Lipoprotein(a) [Lp(a)] was determined by immunoturbidimetry (Leadman Biochemistry Co., Ltd., China). The triglyceride-glucose (TyG) index was calculated as ln [TG (mg/dL) × FPG (mg/dL) / 2]. Alanine aminotransferase (ALT), aspartate aminotransferase (AST), and creatine kinase (CK) were measured using enzymatic rate methods. Creatine kinase-MB (CK-MB) was determined using an immunoinhibition method (Kanto Chemical Co., Inc., Japan). Renal function parameters included urea, measured using the urease-glutamate dehydrogenase method; uric acid (UA), measured using the uricase-peroxidase method (Shino-Test Corporation, Tokyo, Japan); and serum creatinine (Cr), measured using the picric acid (Jaffe) method (Biosino Bio-Technology and Science Incorporation, Beijing, China). The estimated glomerular filtration rate (eGFR) was calculated using the CKD-EPI equation. HbA1c measurement Glycated hemoglobin (HbA1c) was measured by high-performance liquid chromatography (HPLC) using a Tosoh HLC-723G11 automated hemoglobin analyzer (Tosoh Corporation, Tokyo, Japan). The assay was standardized according to the National Glycohemoglobin Standardization Program (NGSP). PCT, hs-cTnT, and NT-proBNP measurements Serum procalcitonin (PCT), high-sensitivity cardiac troponin T (hs-cTnT), and N-terminal pro-B-type natriuretic peptide (NT-proBNP) were measured using electrochemiluminescence immunoassay (ECLIA) on a cobas e411 analyzer (Roche Diagnostics, Mannheim, Germany). PTX3 measurement Serum PTX3 levels were measured using a commercially available enzyme-linked immunosorbent assay (ELISA) kit (Wuhan Elabscience Biotechnology Co., Ltd., Wuhan, China; Catalog number: E-EL-H6081). According to the manufacturer’s validation data, the intra-assay coefficients of variation ranged from 4.19% to 5.36%, and the inter-assay coefficients of variation ranged from 7.05% to 8.82%, indicating good assay reproducibility. All procedures were performed strictly according to the manufacturer’s instructions. Optical densities were read at the specified wavelength, and concentrations were calculated from the standard calibration curve. Quality control samples were included in each batch to ensure assay accuracy and precision. Urinary biomarker measurements Urinary protein (uPRO), urinary creatinine (uCr), and urinary microalbumin (mALB) were measured using a Hitachi 3500 specific protein analyzer (Hitachi High-Tech Diagnostics, Tokyo, Japan). Urinary protein was quantified using the pyrogallol red-molybdate colorimetric method (DiaSys Diagnostic Systems GmbH, Holzheim, Germany). Urinary creatinine was determined using the picric acid (Jaffe) method (Biosino Bio-Technology and Science Incorporation, Beijing, China). Urinary microalbumin was measured by immunoturbidimetry (DiaSys Diagnostic Systems GmbH, Holzheim, Germany). 2.3 Statistical analysis All statistical analyses were performed using R software (version 4.5.0) and SPSS (version 25.0). Graphical visualizations were generated using GraphPad Prism (version 10.6). All tests were two-sided, and a p value < 0.05 was considered statistically significant. Baseline demographic and clinical characteristics were summarized according to variable type. Variables following a normal distribution were expressed as mean ± standard deviation (SD), whereas non-normally distributed variables were presented as median (interquartile range, IQR). Categorical variables were summarized as frequencies. The proportion of missing data was less than 3% across variables. Given the low proportion of missingness, analyses were conducted using complete-case analysis without additional imputation. Values below the limit of detection (LOD) were imputed as LOD/2. Categorical variables across four groups were compared using the chi-square test. For continuous variables, normality was assessed using the Shapiro-Wilk test, and homogeneity of variances was evaluated using Levene’s test. When data were normally distributed ( p > 0.05) and variances were homogeneous, one-way analysis of variance (ANOVA) was performed. If the assumption of homogeneity of variances was violated (Levene’s test, p < 0.05), Welch’s ANOVA was used to calculate p values. When any group failed to meet the normality assumption, the Kruskal-Wallis H test was applied. When the overall test indicated a statistically significant difference, pairwise comparisons were conducted. For normally distributed data with homogeneous variances, Tukey’s honestly significant difference (HSD) test was used; when variances were unequal, the Games-Howell test was applied. For non-normally distributed data, Dunn’s post hoc test was performed, with Bonferroni correction applied to adjust for multiple comparisons. Correlations between continuous variables were evaluated using Spearman’s rank correlation analysis. Multivariable linear regression analysis was performed to identify factors independently associated with PTX3. Multivariable logistic regression models were constructed to assess independent associations between covariates and study outcomes. Multicollinearity among covariates was assessed using variance inflation factors (VIFs), with VIF values < 5 considered indicative of acceptable collinearity. Given the sample size, the number of covariates included in multivariable models was determined with consideration of the events-per-variable (EPV) principle to reduce the risk of model overfitting. Results were reported as adjusted odds ratios (ORs) with 95% confidence intervals (CIs). To reduce the risk of overfitting and to assess model robustness and generalizability, stratified 5-fold cross-validation was employed, and the mean area under the receiver operating characteristic (ROC) curve (AUC) across folds was calculated. Model calibration was assessed using the Hosmer-Lemeshow (H-L) goodness-of-fit test, whereas discriminative performance was evaluated using the AUC. Differences in AUCs between models were compared using the DeLong test. 2.4 Ethics approval and consent to participate This study was approved by the Medical Science Research Ethics Committee of Peking University Third Hospital (approval number: IRB00006761-M20250008). The requirement for informed consent was waived by the Ethics Committee because the study involved minimal risk to participants and used anonymized data, making the acquisition of informed consent impracticable. The waiver was determined not to adversely affect the rights or welfare of the participants. All serum samples analyzed in this study were residual serum specimens obtained after routine clinical testing, with no additional interventions or harm to the participants. All procedures were conducted in accordance with the principles of the Declaration of Helsinki. 3. Results 3.1 Baseline characteristics Baseline demographic and clinical characteristics of the study population are presented in Table 1. Age differed significantly among groups ( p < 0.001), with higher mean age observed in more advanced CKM stages. The mean age increased from 45.8 ± 6.9 years in the Healthy group to 67.3 ± 9.5 years in S4. Sex distribution was comparable across groups ( p = 0.203). Compared with healthy controls, participants in S2-S4 exhibited higher BMI and waist circumference (WC) (overall p < 0.001). Blood pressure indices, including systolic blood pressure (SBP) and diastolic blood pressure (DBP), also differed significantly among groups (both p < 0.001). Markers of glucose metabolism, including FPG and HbA1c, showed significant intergroup differences (both p < 0.001). Median FPG was 5.0 (4.7-5.4) mmol/L in the Healthy group compared with 8.7 (6.5-10.5) mmol/L in S3. Lipid parameters, including T-CHO, TG, and LDL-C, differed significantly across groups (all p < 0.001), whereas HDL-C was lower in CKM stages compared with the Healthy group ( p < 0.001). In addition, the TyG index differed significantly across the four groups ( p < 0.001), with higher levels observed in all CKM stages than in the Healthy group. Regarding hepatic and myocardial enzymes, ALT and CK-MB levels varied significantly across groups ( p = 0.003 and p < 0.001, respectively), whereas no significant differences were observed in AST and CK. Renal function markers, including serum urea, UA, and Cr, differed significantly among the four groups ( p = 0.006 for Cr; others p < 0.001), while eGFR was significantly lower in more advanced CKM stages ( p < 0.001). The median eGFR was 101.5 (95.0-107.0) mL/min/1.73 m 2 in the Healthy group and 78.0 (43.0-88.0) mL/min/1.73 m 2 in S4. The prevalence of MetS differed significantly among groups ( p < 0.001), being rare in healthy individuals but common in participants with CKM, particularly in S3. Likewise, the prevalence of CKD and CVD differed significantly among groups (both p < 0.001). CKD was more common in S3 and S4 than in the Healthy and S2 groups, whereas CVD was observed mainly in S3 and S4. Overall, baseline metabolic, cardiovascular, and renal characteristics differed significantly among the four groups. Table 1. Baseline characteristics of participants stratified by CKM stage Variable Healthy (n=60) S2 (n=60) S3 (n=60) S4 (n=60) p value Age (years) 45.8 ± 6.9 45.2 ± 9.4 52.8 ± 12.9 67.3 ± 9.5 < 0.001 Sex (M/F) 26/34 33/27 27/33 36/24 0.203 BMI (kg/m²) 22.88 ± 1.97 26.32 ± 3.39 26.02 ± 4.24 25.91 ± 2.27 < 0.001 WC (cm) 84.63 ± 5.83 94.74 ± 11.20 92.99 ± 11.98 91.92 ± 7.13 < 0.001 SBP (mmHg) 113.4 ± 7.7 131.8 ± 14.2 137.6 ± 16.6 135.2 ± 18.0 < 0.001 DBP (mmHg) 73.0 ± 8.1 81.8 ± 11.4 83.2 ± 11.2 80.1 ± 10.9 < 0.001 FPG (mmol/L) 5.0 (4.7-5.4) 7.8 (5.3-10.6) 8.7 (6.5-10.5) 8.2 (7.0-10.2) < 0.001 HbA1c (%) 5.4 (5.3-5.7) 7.4 (5.8-11.1) 8.3 (7.1-10.6) 7.7 (6.9-9.2) < 0.001 T-CHO (mmol/L) 3.91 (3.38-4.43) 5.04 (4.23-5.65) 4.47 (3.75-5.33) 4.37 (3.46-5.40) < 0.001 TG (mmol/L) 0.95 (0.78-1.14) 1.79 (1.28-2.83) 1.67 (1.22-2.41) 1.47 (1.01-2.00) < 0.001 TyG index 8.24 (8.02-8.46) 9.25 (8.87-9.79) 9.35 (8.81-9.76) 9.15 (8.88-9.60) < 0.001 LDL-C (mmol/L) 2.30 (1.84-2.57) 3.32 (2.54-3.87) 2.72 (2.14-3.26) 2.74 (1.88-3.27) < 0.001 HDL-C (mmol/L) 1.42 (1.29-1.60) 0.97 (0.85-1.22) 1.02 (0.87-1.19) 1.04 (0.90-1.32) < 0.001 ALT (U/L) 19.5 (15.0-26.0) 29.0 (15.5-46.8) 26.0 (17.0-42.5) 26.5 (14.3-41.8) 0.003 AST (U/L) 21.5 (18.3-25.0) 23.0 (18.3-31.8) 23.0 (18.0-36.0) 24.0 (18.3-30.8) 0.337 CK (U/L) 73.0 (53.3-94.3) 73.5 (51.0-114.0) 68.0 (52.0-98.0) 75.0 (50.5-97.8) 0.934 CK-MB (U/L) 7.5 (6.0-9.0) 8.0 (6.0-10.8) 9.0 (6.0-11.0) 10.5 (8.0-15.0) < 0.001 Urea (mmol/L) 4.4 (3.7-5.5) 4.7 (3.9-6.5) 5.5 (4.8-7.8) 6.9 (5.7-11.0) < 0.001 UA µmol/L 317.5 (274.0-342.5) 356.0 (287.3-435.0) 351.5 (298.5-419.8) 362.5 (296.8-435.0) < 0.001 Cr (µmol/L) 68.0 (62.3-80.8) 74.0 (64.0-85.0) 80.5 (63.0-98.0) 82.0 (67.0-115.3) 0.006 eGFR (mL/min/1.73m²) 101.5 (95.0-107.0) 101.0 (93.0-112.8) 85.0 (66.3-96.5) 78.0 (43.0-88.0) < 0.001 MetS, n (%) 1 (1.7%) 38 (63.3%) 48 (80.0%) 38 (63.3%) < 0.001 CKD, n (%) 0 (0%) 16 (26.7%) 37 (61.7%) 48 (80.0%) < 0.001 CVD, n (%) 0 (0%) 0 (0%) 46 (76.7%) 60 (100.0%) < 0.001 Abbreviations: BMI, body mass index; WC, waist circumference; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG, fasting plasma glucose; HbA1c, glycated hemoglobin; T-CHO, total cholesterol; TG, triglycerides; TyG index, triglyceride-glucose index; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; ALT, alanine aminotransferase; AST, aspartate aminotransferase; CK, creatine kinase; CK-MB, creatine kinase-MB; UA, uric acid; Cr, creatinine; eGFR, estimated glomerular filtration rate; MetS, metabolic syndrome; CKD, chronic kidney disease; CVD, cardiovascular disease. Note: Normally distributed data are presented as mean ± SD, and non-normally distributed data are presented as median (IQR). p values represent overall comparisons across the four groups. 3.2 Changes in disease-related biomarkers across CKM stages As shown in Table 2, significant differences in multiple disease-related biomarkers were observed across the four groups (all p < 0.001). Inflammatory markers, including PTX3, hs-CRP, and PCT, differed significantly among CKM stages. Notably, PTX3 levels were higher across CKM stages, rising from a median of 32.51 (19.48-41.58) pg/mL in the Healthy group to 99.40 (68.50-113.42) pg/mL in S4. Similarly, hs-CRP and PCT levels were higher in disease groups compared with healthy controls and differed significantly across the four groups ( p < 0.001 for both). Cardiovascular-related biomarkers also demonstrated significant intergroup differences. Lp(a) levels were significantly elevated in S2-S4 groups compared with healthy participants ( p < 0.001). Markers of myocardial injury and cardiac function, including hs-cTnT and NT-proBNP, increased significantly across CKM stages. Median hs-cTnT levels increased from 5.5 (4.0-7.0) pg/mL in the Healthy group to 14.5 (9.0-32.0) pg/mL in S4, while NT-proBNP levels rose markedly from 29.5 (19.0-42.8) pg/mL to 129.0 (61.5-637.0) pg/mL (both p < 0.001). Renal injury-related biomarkers also varied significantly among groups. uPRO and mALB levels were higher in later CKM stages ( p < 0.001). In contrast, uCr levels declined significantly across groups, decreasing from 12,857.5 (11,030.5-13,937.8) μmol/L in the Healthy group to 5,972.5 (4,460.3-9,664.5) μmol/L in S4 ( p < 0.001). Collectively, these findings indicate significant alterations in inflammatory, cardiovascular, and renal-related biomarkers across CKM stages. Table 2. Levels of inflammatory, cardiovascular, and renal-related biomarkers across CKM Biomarkers Healthy (n=60) S2 (n=60) S3 (n=60) S4 (n=60) p value PTX3 (pg/mL) 32.51 (19.48-41.58) 52.02 (36.12-66.53) 70.72 (49.52-93.14) 99.40 (68.50-113.42) < 0.001 hs-CRP (mg/dL) 0.43 (0.21-0.66) 1.52 (0.57-4.51) 1.40 (0.87-3.71) 1.50 (0.83-4.41) < 0.001 PCT (pg/mL) 17.5 (10.0-25.0) 19.0 (10.0-35.8) 32.0 (10.0-52.5) 37.0 (10.0-71.5) < 0.001 Lp(a) (mg/L) 67.50 (44.75-106.00) 163.00 (69.25-433.75) 129.50 (74.75-283.75) 125.00 (69.50-333.75) < 0.001 hs-cTnT (pg/mL) 5.5 (4.0-7.0) 5.0 (1.5-9.8) 10.0 (6.0-14.0) 14.5 (9.0-32.0) < 0.001 NT-proBNP (pg/mL) 29.5 (19.0-42.8) 23.0 (5.0-69.5) 42.0 (26.3-81.8) 129.0 (61.5-637.0) < 0.001 uPRO (mg/L) 69.00 (57.50-81.75) 116.00 (56.25-438.25) 124.00 (69.25-541.50) 169.00 (74.25-875.00) < 0.001 uCr (µmol/L) 12857.5 (11030.5-13937.8) 8839.0 (5292.0-13775.0) 8120.5 (4904.3-11990.0) 5972.5 (4460.3-9664.5) < 0.001 mALB (mg/L) 5.4 (2.7-7.0) 19.9 (6.4-236.8) 17.9 (6.6-173.6) 39.1 (11.1-557.7) < 0.001 Abbreviations: PTX3, pentraxin 3; hs-CRP, high-sensitivity C-reactive protein; PCT, procalcitonin; Lp(a), lipoprotein(a); hs-cTnT, high-sensitivity cardiac troponin T; NT-proBNP, N-terminal pro-B-type natriuretic peptide; uPRO, urinary protein; uCr, urinary creatinine; mALB, microalbumin. Note: Non-normally distributed data are presented as median (IQR). p values represent overall comparisons across the four groups. 3.3 Distribution of key inflammatory, metabolic, and cardiorenal biomarkers across CKM stages To visualize the distributions of key inflammatory, metabolic, and cardiorenal biomarkers across CKM stages, box-and-whisker plots were generated. Biomarkers with markedly skewed distributions were log 10 -transformed prior to plotting to improve data symmetry and visualization. 3.3.1 Differences in inflammatory biomarkers across CKM stages The distributions of inflammatory biomarkers across CKM stages are shown in Figure 2. Serum PTX3 levels differed across CKM stages (Figure 2A). Compared with healthy individuals, PTX3 concentrations were significantly higher in S2 and were also higher in S3 and S4. Pairwise comparisons demonstrated significant differences between Healthy and S2, Healthy and S3, as well as Healthy and S4 (all p < 0.001). PTX3 levels also differed significantly between S2 and S3 ( p < 0.01). In contrast, no statistically significant difference was observed between S3 and S4, despite a numerically higher median value in S4. For log 10 -transformed hs-CRP (Figure 2B), levels were significantly elevated in all CKM stages (S2-S4) compared with healthy controls (all p < 0.0001). However, pairwise comparisons among the disease stages themselves showed no significant differences, indicating that hs-CRP levels were comparable across S2-S4. Similarly, PCT levels (log 10 -transformed) differed across CKM stages (Figure 2C). Compared with healthy individuals, PCT concentrations were significantly higher in S3 and S4, whereas the difference between Healthy and S2 did not reach statistical significance. Pairwise analysis showed a significant difference between S2 and S3 ( p < 0.05), while no significant difference was detected between S3 and S4. Collectively, these findings indicate differences in systemic inflammatory markers across CKM stages, with PTX3 showing broader interstage distinctions, whereas hs-CRP and PCT exhibited elevations in CKM stages relative to healthy controls but limited differentiation among later stages. Box-and-whisker plots showing the distributions of PTX3 (A), log 10 -transformed hs-CRP (B), and log 10 -transformed PCT (C) across CKM stages (Healthy, S2, S3, and S4; n = 60 per group). The central line represents the median, boxes indicate the interquartile range (IQR), and whiskers represent the minimum and maximum values. Statistical significance is indicated as p < 0.05 (*), p < 0.01 (**), p < 0.001 (***), and p < 0.0001 (****); ns indicates not significant. All p values are two-sided. Figure 2. Distribution of inflammatory biomarkers across CKM stages 3.3.2 Distribution of glycemic and lipid biomarkers across CKM stages As shown in Figure 3, markers of glucose metabolism differed significantly across CKM stages. Both FPG (Figure 3A) and HbA1c (Figure 3B) were significantly elevated in all disease groups compared with healthy controls ( p < 0.0001). Pairwise comparisons revealed that FPG and HbA1c were significantly higher in S2 compared with the Healthy group ( p < 0.0001), whereas no significant differences were observed among S2, S3, and S4. TG (Figure 3C) levels were also significantly higher in all disease groups compared with healthy controls ( p < 0.0001). Specifically, TG concentrations were markedly elevated in S2 ( p < 0.0001), while no significant differences were observed among S2, S3, and S4. Lp(a), visualized after log 10 -transformation due to its skewed distribution, showed higher levels in S2-S4 compared with healthy controls ( p < 0.01), whereas no significant differences were observed among disease stages (Figure 3D), indicating limited variation across CKM stages. Additional lipid parameters are presented in Supplementary Figure 1. T-CHO and LDL-C exhibited significant overall group differences, with elevated levels primarily observed in S2 compared with healthy controls. In contrast, HDL-C differed significantly across groups ( p < 0.0001), with lower levels in all disease groups relative to the Healthy group, but no significant differences among S2, S3, and S4. In summary, abnormalities in glucose metabolism and lipid profiles were observed across CKM stages, with significant differences between healthy individuals and CKM groups but limited differentiation among disease stages. Box-and-whisker plots showing the distribution of FPG (A), HbA1c (B), TG (C), and log 10 -transformed Lp(a) (D) across CKM stages (Healthy, S2, S3, and S4; n = 60 per group). The central line represents the median, boxes indicate the interquartile range (IQR), and whiskers represent the minimum and maximum values. Statistical significance is indicated as p < 0.05 (*), p < 0.01 (**), p < 0.001 (***), and p < 0.0001 (****); ns indicates not significant. All p values are two-sided. Figure 3. Distribution of glycemic and selected lipid biomarkers across CKM stages 3.3.3 Distribution of cardiovascular and renal injury biomarkers across CKM stages Pairwise comparisons revealed differences across CKM stages for cardiovascular and renal injury biomarkers (Figure 4 and Supplementary Figures 2-3). As shown in Figure 4, cardiovascular injury biomarkers showed clear differences across CKM stages. Levels of hs-cTnT (Figure 4A, log 10 -transformed) were significantly higher in the S3 group than in S2 ( p < 0.001), and were also higher in S4 compared with S3 ( p < 0.05). In contrast, no significant difference was observed between the Healthy and S2 groups. For NT-proBNP (Figure 4B, log 10 -transformed), pairwise comparisons showed higher levels in the S4 group than in the Healthy, S2, and S3 groups (all p < 0.0001). In addition, NT-proBNP levels in S3 were higher than those in the Healthy group ( p < 0.05), whereas other pairwise differences among Healthy, S2, and S3 were not statistically significant. Routine myocardial enzyme biomarkers showed limited pairwise differences (Supplementary Figure 2). AST and CK did not differ significantly between any pair of groups. For CK-MB, higher levels were observed in S4 compared with the Healthy group ( p < 0.0001) and compared with S2 ( p < 0.01), whereas CK-MB levels in S2 and S3 did not differ significantly from those in Healthy controls. Renal function, as assessed by eGFR and urinary mALB, differed across CKM stages (Figure 4C, D). eGFR did not differ significantly between the Healthy and S2 groups, but was significantly lower in S3 and S4 compared with the Healthy group (both p < 0.0001). In pairwise comparisons, eGFR was significantly lower in S3 than in S2 ( p < 0.0001), whereas the difference between S3 and S4 was not statistically significant. Urinary mALB (log 10 -transformed), was significantly higher in S2 compared with the Healthy group ( p < 0.0001). No statistically significant differences were observed among S2, S3, and S4. Additional renal biomarkers differed among CKM groups, as shown in Supplementary Figure 3. Serum urea levels were significantly higher in S3 and S4 compared with Healthy controls (both p < 0.0001), and were also higher relative to S2. UA levels were significantly higher in all CKM groups (S2, S3, and S4) compared with Healthy participants (all p < 0.01), whereas no significant differences were observed among CKM stages. Serum Cr levels were significantly increased in S3 ( p < 0.05) and S4 ( p < 0.01) compared with the Healthy group; however, no significant differences were detected between S2 and S4 or between S3 and S4. uPRO, shown after logarithmic transformation, was significantly higher in all CKM groups compared with Healthy controls (all p < 0.0001), while no significant differences were observed among CKM stages. Conversely, uCr levels were significantly lower in all CKM groups compared with the Healthy group, with no significant differences detected among disease stages. Overall, cardiovascular and renal biomarkers exhibited cross-sectional differences across CKM stages, with myocardial injury markers differing more prominently between later stages, while renal injury markers showed differences across multiple CKM groups. Box-and-whisker plots showing the distribution of log 10 -transformed hs-cTnT (A), log 10 -transformed NT-proBNP (B), eGFR (C), and log 10 -transformed mALB (D) across CKM stages (Healthy, S2, S3, and S4; n = 60 per group). The central line represents the median, boxes indicate the interquartile range (IQR), and whiskers represent the minimum and maximum values. Statistical significance is indicated as p < 0.05 (*), p < 0.01 (**), p < 0.001 (***), and p < 0.0001 (****); ns indicates not significant. All p values are two-sided. Figure 4. Distribution of key cardiovascular and renal injury biomarkers across CKM stages 3.4 Correlation between PTX3 and clinical biomarkers Spearman correlation analysis was performed to assess pairwise associations among inflammatory, metabolic, and cardiorenal biomarkers using data pooled from all study participants across the four CKM groups, with the results visually presented as a correlation matrix heatmap (Figure 5). Detailed correlation coefficients and their corresponding p values are presented in Table 3, Supplementary Table 1, and Supplementary Table 2. 3.4.1 Correlation matrix of inflammatory, metabolic, and cardiorenal biomarkers As shown in Figure 5, inflammatory, metabolic, and cardiorenal biomarkers exhibited identifiable correlation pattern. Glycemic markers showed strong internal consistency, with a particularly strong correlation between FPG and HbA1c (ρ = 0.847). Renal injury markers also showed positive correlations with one another, exemplified by the association between uPRO and mALB (ρ = 0.873), with correlation coefficients provided in Supplementary Table 1. In contrast, inverse correlations were observed between eGFR and several markers of renal injury and cardiovascular stress. Within this correlation matrix, PTX3 showed predominantly positive associations with most inflammatory, metabolic, and injury-related biomarkers, while exhibiting inverse correlations with indices of renal filtration. Quantitative associations between PTX3 and individual biomarkers are summarized in Table 3. PTX3 showed statistically significant positive correlations with hs-CRP (ρ = 0.361, p < 0.001) and PCT (ρ = 0.233, p < 0.001). Similar positive correlations were observed with metabolic indices, including FPG (ρ = 0.458, p < 0.001) and HbA1c (ρ = 0.434, p < 0.001), as well as TG (ρ = 0.296, p < 0.001). In contrast, PTX3 was not significantly correlated with Lp(a) (ρ = 0.052, p = 0.426). For cardiorenal injury markers, PTX3 showed positive correlations with hs-cTnT (ρ = 0.411, p < 0.001) and NT-proBNP (ρ = 0.297, p < 0.001), and was inversely correlated with eGFR (ρ = -0.419, p < 0.001). Among renal biomarkers, PTX3 correlated positively with uPRO (ρ = 0.216, p < 0.001) and mALB (ρ = 0.337, p < 0.001), while demonstrating a negative association with uCr (ρ = -0.296, p < 0.001). Aside from its inverse correlations with eGFR and uCr, PTX3 showed positive correlations with most other evaluated biomarkers, reflecting co-variation across inflammatory, metabolic, and cardiorenal measures in this cross-sectional dataset. To further identify factors independently associated with circulating PTX3 levels, a multivariable linear regression analysis was performed including Age, Sex, BMI, SBP, FPG, hs-cTnT, and eGFR as covariates. Age, Sex, FPG, hs-cTnT, and eGFR were independently associated with PTX3 levels, whereas BMI and SBP were not significantly associated with PTX3 (Supplementary Table 3). Overall, these results describe the observed cross-sectional association patterns among inflammatory, metabolic, and cardiorenal biomarkers. A heatmap of the Spearman correlation coefficients (ρ) matrix is shown. Red indicates positive correlations and blue indicates negative correlations, with color intensity reflecting the magnitude of the correlation. In the upper triangle, pie charts are used to visualize correlations, where the proportion of the filled area represents correlation strength and color indicates correlation direction. In the lower triangle, shaded tiles are displayed, with forward slashes (/) indicating positive correlations and backslashes (\) indicating negative correlations. Figure 5. Correlation matrix heatmap of inflammatory, metabolic, and cardiorenal biomarkers Table 3. Correlation of PTX3 with inflammatory, metabolic, and cardiorenal biomarkers Biomarkers ρ (vs. PTX3) p value hs-CRP 0.361 < 0.001 PCT 0.233 < 0.001 FPG 0.458 < 0.001 HbA1c 0.434 < 0.001 TG 0.296 < 0.001 Lp(a) 0.052 0.426 CK-MB 0.168 0.009 hs-cTnT 0.411 < 0.001 NT-proBNP 0.297 < 0.001 eGFR -0.419 < 0.001 uPRO 0.216 < 0.001 uCr -0.296 < 0.001 mALB 0.337 < 0.001 Data are Spearman correlation coefficients (ρ) describing the associations between PTX3 and individual inflammatory, metabolic, and cardiorenal biomarkers, with corresponding two-sided p values. 3.4.2 Correlations between PTX3 and selected glycemic, lipid, and cardiorenal biomarkers As shown in Figure 6, based on the correlation results summarized in Table 3, scatter plots were generated to visualize the associations between PTX3 and selected key glycemic, lipid, and cardiorenal biomarkers in the overall study population. PTX3 exhibited a moderate positive correlation with HbA1c (ρ = 0.434, p < 0.001; Figure 6A) and a modest positive correlation with TG (ρ = 0.296, p < 0.001; Figure 6B), reflecting statistically significant associations between these variables. In addition, PTX3 showed a moderate positive association with hs-cTnT (ρ = 0.411, p < 0.001; Figure 6C). Conversely, PTX3 was moderately and inversely correlated with eGFR (ρ = -0.419, p < 0.001; Figure 6D). Although several correlations were modest to moderate in magnitude, these associations reached statistical significance in this cross-sectional dataset. Together, these visualized associations describe the observed relationships between PTX3 and selected biomarkers and provided context for subsequent multivariable analyses. Scatter plots showing correlations between PTX3 and HbA1c (A), TG (B), hs-cTnT (C), and eGFR (D). Correlations were assessed using Spearman’s rank correlation analysis. Solid lines indicate fitted regression trends, and shaded areas represent 95% confidence intervals. Each dot represents an individual participant. Spearman’s correlation coefficients are denoted by ρ. All p values are two-sided. Figure 6. Correlations of PTX3 with HbA1c, TG, hs-cTnT, and eGFR 3.5 Multivariable logistic regression analysis of PTX3 and CKM stages 3.5.1 Primary association analysis (S2 vs S3+S4) To further evaluate factors associated with CKM stage classification, multivariable logistic regression analyses were conducted within the CKM population (stages S2-S4), with the outcome defined as early-stage CKM (S2) versus mid-advanced-stage CKM (S3+S4). From a clinical and pathophysiological perspective, although S3 represents subclinical CVD or moderate-to-high risk CKD and S4 reflects overt clinical cardiovascular or renal events, both stages involve structural or functional organ abnormalities beyond isolated metabolic risk factors. This overlap provided the rationale for grouping S3 and S4 as mid-advanced-stage CKM stages in the primary analysis. Covariates for the multivariable logistic regression models were pre-specified according to clinical relevance and the CKM staging conceptual framework. Model 1 incorporated core demographic and anthropometric variables (Age, Sex, WC, and SBP), reflecting baseline cardiovascular risk factors. Model 2 additionally included key metabolic and cardiorenal indicators (HbA1c, TG, hs-cTnT, and eGFR), representing established components of CKM pathophysiology. In order to evaluate the incremental contribution of inflammatory biomarkers, hs-CRP, PCT, and PTX3 were introduced separately into Model 2 (Models 3-5), thereby avoiding simultaneous inclusion of multiple correlated inflammatory markers and allowing independent assessment of their associations with CKM stage classification. As shown in Table 4, age was consistently associated with higher odds of classification into mid-advanced-stage CKM (S3+S4) across all models (all p 0.05 across models). Sex showed no significant association in Models 1-4; however, in Model 5, which included PTX3, sex became significantly associated with CKM stage classification (OR = 0.29, 95% CI: 0.09-0.81, p = 0.02), indicating a potential difference after full adjustment. Among cardiorenal-related variables, hs-cTnT was significantly associated with classification into mid-advanced-stage CKM (S3+S4) in Model 2 (OR = 1.08, 95% CI: 1.01-1.15, p = 0.02) and Model 3, with the association attenuating to borderline significance after further adjustment in Model 4 (OR = 1.06, 95% CI: 1.00-1.14, p = 0.06). eGFR showed an inverse association with S3+S4 classification in Models 2 and 3 (OR = 0.98, 95% CI: 0.96-1.00, p = 0.03), whereas this association was no longer statistically significant after additional adjustment for PCT or PTX3. Notably, differential patterns were observed among inflammatory biomarkers. Neither hs-CRP (Model 3; OR = 1.02, p = 0.74) nor PCT (Model 4; OR = 1.02, p = 0.17) showed statistically significant associations with CKM stage classification after adjustment. In contrast, PTX3 remained significantly associated with classification into mid-advanced-stage CKM (S3+S4) in Model 5 (OR = 1.05, 95% CI: 1.03-1.08, p < 0.001), after controlling for demographic variables, metabolic indices, and cardiorenal markers. To improve interpretability of the effect estimate, PTX3 was additionally standardized, and effect estimates were expressed per 1-SD increase. When modeled per SD increase, PTX3 remained significantly associated with CKM stage classification (OR = 5.30, 95% CI: 2.77-11.56, p < 0.001), consistent with the primary per-unit analysis. Furthermore, multicollinearity was assessed in Model 5 using variance inflation factors (VIFs). All VIF values were below 2 (range: 1.05-1.65), suggesting no evidence of substantial collinearity among included covariates (Age: 1.386; Sex: 1.112; WC: 1.092; SBP: 1.052; HbA1c: 1.079; TG: 1.071; hs-cTnT: 1.391; eGFR: 1.650; PTX3: 1.227). Taken together, these results suggest that, within the CKM population, PTX3 shows a more consistent cross-sectional association with CKM stage classification compared with conventional inflammatory markers, and may have value in distinguishing early-stage CKM (S2) from mid-advanced-stage CKM (S3+S4). Table 4. Multivariable logistic regression analysis for CKM stage classification (S2 vs S3+S4) Variable Model 1 OR (95% CI) p value Model 2 OR (95% CI) p value Model 3 OR (95% CI) p value Model 4 OR (95% CI) p value Model 5 OR (95% CI) p value Age 1.10 (1.07-1.14) < 0.001 1.08 (1.04-1.12) < 0.001 1.08 (1.04-1.12) < 0.001 1.09 (1.05-1.13) < 0.001 1.08 (1.04-1.13) < 0.001 Sex 0.95 (0.45-1.99) 0.88 0.64 (0.27-1.50) 0.31 0.66 (0.27-1.60) 0.36 0.68 (0.28-1.60) 0.38 0.29 (0.09-0.81) 0.02 WC 1.00 (0.96-1.03) 0.87 0.99 (0.95-1.03) 0.62 0.99 (0.95-1.03) 0.55 0.99 (0.95-1.03) 0.59 1.00 (0.96-1.05) 0.95 SBP 1.02 (1.00-1.04) 0.12 1.02 (0.99-1.05) 0.14 1.02 (0.99-1.05) 0.17 1.02 (0.99-1.04) 0.25 1.03 (1.00-1.06) 0.08 HbA1c - - 1.11 (0.94-1.31) 0.22 1.11 (0.94-1.31) 0.22 1.09 (0.92-1.30) 0.31 1.11 (0.92-1.34) 0.29 TG - - 1.08 (0.80-1.51) 0.63 1.08 (0.80-1.52) 0.62 1.05 (0.79-1.48) 0.74 1.00 (0.71-1.42) 0.98 hs-cTnT - - 1.08 (1.01-1.15) 0.02 1.08 (1.01-1.15) 0.02 1.06 (1.00-1.14) 0.06 1.10 (1.03-1.18) 0.01 eGFR - - 0.98 (0.96-1.00) 0.03 0.98 (0.96-1.00) 0.03 0.98 (0.96-1.00) 0.07 0.98 (0.96-1.00) 0.09 hs-CRP - - - - 1.02 (0.91-1.14) 0.74 - - - - PCT - - - - - - 1.02 (0.99-1.04) 0.17 - - PTX3 (unit) - - - - - - - - 1.05 (1.03-1.08) < 0.001 PTX3 (SD) 5.30 (2.77-11.56) < 0.001 Outcome was defined as S2 vs S3+S4, and analyses were restricted to participants with CKM stages S2-S4. Odds ratios (ORs) are presented with 95% confidence intervals (CIs) and two-sided p values. Model specifications were as follows: Model 1: age, sex, WC, and SBP; Model 2: Model 1 plus HbA1c, TG, hs-cTnT, and eGFR; Model 3: Model 2 plus hs-CRP; Model 4: Model 2 plus PCT; Model 5: Model 2 plus PTX3. For PTX3 (unit), ORs represent the change in odds associated with a 1-unit increase in circulating PTX3 levels (pg/mL). For PTX3 (SD), ORs represent the change in odds associated with a 1-standard deviation increase in PTX3 within the CKM (S2-S4) study population. All other continuous variables were modeled per 1-unit increase in their original measurement scales. 3.5.2 Sensitivity analyses by individual CKM stages To further assess the robustness of the pooled-stage analysis and to explore potential differences between S3 and S4, additional stage-specific sensitivity analyses were performed by separately comparing S2 vs S3 and S2 vs S4 (Table 5). As shown in Table 5, PTX3 remained significantly associated with both S3 and S4 relative to S2 after multivariable adjustment. In the S2 vs S3 comparison, the base clinical model (Model 2) showed moderate discrimination (AUC = 0.694). After the addition of PTX3 (Model 5), the AUC increased to 0.757 (ΔAUC = 0.063), although the difference did not reach statistical significance according to the DeLong test ( p = 0.118). In Model 5, PTX3 was significantly associated with S3 (OR: 1.05, 95% CI: 1.03-1.07; p < 0.001). Similarly, in the S2 vs S4 comparison, Model 2 already demonstrated high discriminative performance (AUC = 0.919). Addition of PTX3 further increased the AUC to 0.945 (ΔAUC = 0.026), although the improvement was not statistically significant (DeLong p = 0.099). PTX3 nevertheless remained independently associated with S4 in Model 5 (OR: 1.10, 95% CI: 1.05-1.19; p = 0.002). Overall, the direction of associations was consistent across both comparisons. These findings indicate that PTX3 was independently associated with both S3 and S4 relative to S2 in the stage-specific sensitivity analyses, consistent with the results observed in the pooled-stage analysis. Table 5. Stage-specific sensitivity analyses for CKM stage classification (S2 vs S3 and S2 vs S4) Comparison Model PTX3 OR (95% CI) p value AUC (95% CI) ΔAUC p (DeLong) S2 vs S3 Model 2 - - 0.694 (0.600-0.789) - - Model 5 1.05 (1.03-1.07) < 0.001 0.757 (0.669-0.844) 0.063 0.118 S2 vs S4 Model 2 - - 0.919 (0.868-0.969) - - Model 5 1.10 (1.05-1.19) 0.002 0.945 (0.904-0.986) 0.026 0.099 Stage-specific multivariable logistic regression analyses were performed by separately comparing S2 vs S3 and S2 vs S4 within the CKM (S2-S4) study population. Odds ratios (ORs) are presented with 95% confidence intervals (CIs) and two-sided p values. Discrimination performance is reported as area under the receiver operating characteristic curve (AUC) with 95% CIs. ΔAUC represents the difference between Model 5 and Model 2. AUCs were compared using DeLong’s test. 3.6 Comprehensive evaluation of multivariable model performance 3.6.1 Discriminative performance of multivariable models based on ROC curve analysis As summarized in Table 6 and visualized in Figure 7, the baseline model incorporating demographic and anthropometric variables (Model 1) showed discrimination for classifying early-stage CKM (S2) versus mid-advanced-stage CKM (S3+S4), with an AUC of 0.804 (95% CI: 0.741-0.868). After adding metabolic and cardiorenal-related variables (Model 2), the AUC increased to 0.833 (95% CI: 0.771-0.894; ΔAUC = 0.029). However, this increment was not statistically significant when compared with Model 1 using the DeLong test ( p = 0.213), consistent with the overlap of ROC curves between Model 1 and 2 (Figure 7). Further inclusion of conventional inflammatory biomarkers yielded limited changes in discrimination. Specifically, adding hs-CRP (Model 3) resulted in an AUC of 0.829 (95% CI: 0.767-0.891; ΔAUC = 0.025; DeLong p = 0.281 vs Model 1), while adding PCT (Model 4) produced an AUC of 0.839 (95% CI: 0.778-0.900; ΔAUC = 0.035; DeLong p = 0.148 vs Model 1). These findings indicate that, relative to the baseline model, hs-CRP and PCT did not significantly improve discrimination in this cross-sectional stage-classification setting. Incorporating PTX3 (Model 5) yielded an AUC of 0.892 (95% CI: 0.844-0.940). The improvement over Model 1 (ΔAUC = 0.088) reached statistical significance by the DeLong test ( p = 0.001). Direct pairwise comparisons further showed that Model 5 had higher discrimination than Model 3 (DeLong p = 0.005) and Model 4 (DeLong p = 0.018). In Figure 7, the ROC curve for Model 5 was generally positioned above those of Models 1-4 across much of the false-positive range. Collectively, these results indicate that inclusion of PTX3 was associated with improved model discrimination beyond demographic, metabolic, and cardiorenal markers in distinguishing early-stage CKM (S2) from mid-advanced-stage CKM (S3+S4), whereas hs-CRP and PCT showed limited incremental discrimination in the evaluated models. Table 6. Discriminative performance of multivariable models Model Variables included AUC (95% CI) ΔAUC p (DeLong) Model 1 Age, Sex, WC, SBP 0.804 (0.741-0.868) - - Model 2 Model 1 + HbA1c, TG, hs-cTnT, eGFR 0.833 (0.771-0.894) 0.029 0.213 Model 3 Model 2 + hs-CRP 0.829 (0.767-0.891) 0.025 0.281 Model 4 Model 2 + PCT 0.839 (0.778-0.900) 0.035 0.148 Model 5 Model 2 + PTX3 0.892 (0.844-0.940) 0.088 0.001 a, b AUCs are presented with 95% confidence intervals (CIs). ΔAUC indicates the absolute difference in AUC compared with Model 1. p values were calculated using two-sided DeLong test for correlated ROC curves, comparing each model against Model 1. a: Model 5 compared with Model 3 using DeLong’s test ( p = 0.005); b: Model 5 compared with Model 4 using DeLong’s test ( p = 0.018). ROC curves are shown for Models 1-5. The diagonal dashed line indicates no-discrimination performance (AUC = 0.500). Model specifications and corresponding AUCs (95% CIs) are reported in Table 6. Figure 7. ROC curves of multivariable models for CKM stage classification (S2 vs S3+S4) 3.6.2 Calibration performance of multivariable models assessed by the H-L test Calibration results assessed by the H-L test are summarized in Table 7. Overall, Models 1, 3, and 5 showed no statistically significant evidence of lack of fit, with HL p values above 0.05 (Model 1: χ 2 = 14.050, p = 0.080; Model 3: χ 2 = 12.908, p = 0.115; Model 5: χ 2 = 14.968, p = 0.060), indicating consistency between observed and expected event frequencies across risk strata. Model 2 showed borderline calibration (χ 2 = 15.538, p = 0.050), suggesting that the addition of metabolic and cardiorenal variables was associated with small differences between predicted and observed classification probabilities, although the result was at the conventional significance threshold. In contrast, Model 4 showed statistically significant lack of fit (χ 2 = 156.260, p < 0.001), indicating deviation between predicted and observed probabilities in this dataset. This finding suggests that the model specification incorporating PCT (Model 4) may produce predicted probabilities that differ from observed proportions, despite its discrimination being similar to other models. Overall, the H-L results show that most candidate models did not demonstrate statistically significant lack of fit, whereas the PCT-augmented model (Model 4) showed evidence of miscalibration in this sample. Table 7. Calibration performance of multivariable models Model Hosmer-Lemeshow χ 2 df p value Model 1 14.050 8 0.080 Model 2 15.538 8 0.050 Model 3 12.908 8 0.115 Model 4 156.260 8 0.05 indicates no evidence of lack of fit, suggesting acceptable model calibration, whereas p < 0.05 indicates a statistically significant deviation between observed and expected outcomes, suggestive of potential miscalibration. 4. Discussion In this cross-sectional study, we systematically characterized the distributional patterns, inter-marker correlations, and multivariable classification performance of inflammatory, metabolic, and cardiorenal biomarkers across CKM stages, with a particular focus on PTX3. The main findings can be summarized as follows. First, circulating PTX3 levels differed across CKM stages and were already elevated in individuals with early-stage CKM compared with healthy controls. Second, PTX3 was associated with multiple biomarker domains, including markers of systemic inflammation, glycemic dysregulation, myocardial injury, and renal dysfunction. Third, in multivariable models comparing early-stage CKM (S2) with mid-advanced-stage CKM (S3+S4), PTX3 remained significantly associated with stage classification after adjustment for demographic characteristics, metabolic indices, and cardiorenal biomarkers, whereas hs-CRP and PCT did not retain statistically significant associations under full adjustment. Finally, incorporation of PTX3 into the clinical model was associated with improved discrimination in the pooled-stage analysis while maintaining acceptable model calibration. Taken together, these findings suggest that PTX3 may serve as a complementary biomarker associated with inflammatory and cardiorenal features within the CKM framework. CKM syndrome is conceptualized as a disease continuum encompassing metabolic risk factors, CKD, and CVD, in which chronic low-grade inflammation is thought to link metabolic dysregulation, renal impairment, and cardiovascular dysfunction across multiple target organs [14-17] . Conventional inflammatory markers such as hs-CRP are predominantly synthesized by hepatocytes and mainly reflect IL-6–driven systemic acute-phase responses, which may not fully differentiate the specific sources of inflammatory signals contributing to CKM. Consistent with this view, population-based analyses suggest that hs-CRP levels are strongly influenced by obesity-related inflammation rather than independent cardiometabolic dysfunction [18] . Emerging multi-organ frameworks of the cardiovascular-renal-hepato-metabolic syndrome further highlight the liver—particularly metabolic dysfunction-associated steatotic liver disease (MASLD)—as an inflammatory contributor, suggesting that reliance on liver-derived hs-CRP alone may not fully capture inflammatory signals arising from other tissues [19] . In the present dataset, hs-CRP did not differ significantly among CKM stages (S2, S3, and S4), suggesting limited discriminatory capacity within the CKM spectrum. Furthermore, when hs-CRP was incorporated into a multivariable model that already included glycemic, lipid, and cardiorenal indicators, no statistically significant improvement in discrimination was observed. These findings suggest that hs-CRP may reflect a background inflammatory burden common to CKM rather than differences across CKM stages. PCT, originally developed as a biomarker of bacterial infection and sepsis, has also been examined in metabolic disorders, heart failure, and CKD. Available evidence indicates that PCT may show low-level elevations in obesity, diabetes, heart failure, or CKD even in the absence of overt infection, reflecting non-specific inflammatory influences [20-25] . In the present dataset, although PCT differed across CKM stages, its addition to the clinical model was associated with only a small change in AUC, and the PCT-containing model demonstrated evidence of miscalibration. These findings suggest that PCT may not provide additional discriminatory information for CKM stage classification beyond established clinical and metabolic markers. In contrast, PTX3 is biologically distinct from liver-derived acute-phase reactants. PTX3 is produced at sites of inflammation by multiple cell types—including endothelial and vascular smooth muscle cells, monocytes/macrophages, adipocytes, and renal parenchymal cells—and may reflect inflammatory processes occurring within affected tissues [26-29] . Accumulating evidence across metabolic, renal, and cardiovascular populations indicates that PTX3 is associated with inflammatory activation at the interface of metabolic stress, vascular injury, and renal dysfunction. In metabolic disorders, PTX3 has been reported to correlate with glycemic dysregulation and obesity-related inflammatory burden [30] . In CKD, elevated PTX3 levels have been associated with mortality and cardiovascular events [31] . In cardiovascular epidemiology, PTX3 has been associated with subclinical atherosclerosis and coronary artery calcification beyond traditional risk factors [32] . Rather than representing isolated findings, these observations suggest that PTX3 is associated with inflammatory features across multiple disease domains. Within the CKM classification framework, PTX3 levels exhibited stage-related variation, with differences primarily observed between early-stage and more advanced CKM. Notably, PTX3 levels did not differ significantly between S3 (subclinical CVD) and S4 (clinical CVD). This pattern suggests that PTX3 may reflect inflammatory activation associated with early cardiorenal injury, which likely emerges at the stage of subclinical organ involvement and may subsequently remain at relatively stable levels as the disease progresses to overt cardiovascular manifestations. In contrast, conventional inflammatory and metabolic markers—including hs-CRP, FPG, HbA1c, and TG—showed significant differences between healthy individuals and CKM groups but demonstrated limited ability to differentiate among S2, S3, and S4. Taken together, these findings suggest that PTX3 may be more closely associated with CKM stage classification than several commonly used inflammatory or metabolic markers. Correlation analyses further supported this pattern. PTX3 showed correlations with biomarkers reflecting metabolic status as well as myocardial and renal injury. In contrast, correlations among traditional metabolic markers were largely confined within metabolic domains and showed weaker associations with cardiorenal injury indicators. At the multivariable level, PTX3 remained significantly associated with CKM stage classification distinguishing early-stage CKM (S2) from mid-advanced-stage CKM (S3+S4) after adjustment for demographic characteristics, metabolic indices, and cardiorenal biomarkers, whereas hs-CRP and PCT did not retain statistically significant associations under full adjustment. This finding suggests that PTX3 may capture inflammatory signals that are not fully reflected by conventional systemic inflammatory markers or by traditional metabolic and cardiorenal indicators. Given that PTX3 is produced locally at sites of tissue injury by vascular, immune, and parenchymal cells, circulating PTX3 levels may partly reflect inflammatory activity occurring across multiple organs involved in CKM pathophysiology. In discrimination analyses, incorporation of PTX3 into the clinical model was associated with a statistically significant improvement in AUC in the pooled-stage analysis, whereas the addition of hs-CRP or PCT was not associated with meaningful changes in model performance. This pattern suggests that PTX3 may provide complementary information beyond conventional metabolic and cardiorenal markers within the CKM spectrum. Calibration analyses further indicated that the PTX3-containing model demonstrated acceptable agreement between predicted and observed stage classifications in this dataset. In stage-specific sensitivity analyses separating S3 and S4, the addition of PTX3 to Model 2 yielded numerically higher AUC values for both the S2 versus S3 and S2 versus S4 comparisons; however, the corresponding DeLong tests were not statistically significant. This may partly reflect reduced statistical power after subgroup stratification together with the already strong discriminative performance of Model 2, particularly for the S2 versus S4 comparison where the baseline AUC was relatively high. Accordingly, these analyses should be interpreted as supportive evidence indicating a consistent direction of association rather than definitive evidence of incremental discrimination at each individual CKM stage. It should be noted that, owing to the cross-sectional design of the present study, the observed patterns represent associations across CKM stages rather than evidence of temporal changes at the individual level. Nevertheless, the results suggest that PTX3 may serve as a biomarker reflecting inflammatory and cardiorenal characteristics within the CKM spectrum. In populations with prevalent metabolic abnormalities, PTX3 may provide complementary information for CKM stage classification beyond conventional systemic inflammatory markers. Several limitations should be considered when interpreting the present findings. First, owing to the cross-sectional design, the observed associations reflect contemporaneous differences across CKM stages and do not permit causal inference or evaluation of temporal changes in CKM stage at the individual level. Prospective longitudinal studies are required to determine whether PTX3 is associated with future stage transitions or adverse cardiovascular outcomes. In addition, PTX3 was measured at a single time point, precluding assessment of intra-individual variability or temporal dynamics; such measurement variability may attenuate observed associations. Second, although the sample size was reasonable for a single-center CKM staging study, the overall cohort remains modest, and CKM S1 was not included. Therefore, the applicability of the findings is primarily limited to individuals with established metabolic or cardiorenal abnormalities (S2-S4). In addition, CKM stages encompass heterogeneous clinical conditions, including different combinations of metabolic abnormalities, cardiovascular diseases, and renal impairment. Consequently, the composition of patients within each stage may vary, and differences in inflammatory marker levels could partly reflect underlying disease heterogeneity rather than stage classification alone. Renal impairment was generally mild, with preserved or only mildly reduced eGFR and predominantly microalbuminuria, and BMI levels were relatively low in this study population, reflecting a comparatively mild overall disease profile that may limit generalizability. Notably, previous studies have suggested that sex-related differences may influence inflammatory responses and cardiometabolic risk. Although the distribution of sex did not differ significantly across CKM stages in the present cohort, the potential impact of sex on PTX3-associated inflammatory pathways warrants further investigation in future studies. Furthermore, despite adjustment for multiple demographic, metabolic, myocardial injury, and renal function markers, residual confounding from unmeasured inflammatory mediators, lifestyle factors, or medication use cannot be fully excluded. Detailed information on medication use (e.g., renin-angiotensin system inhibitors or statins) was not systematically collected in the present study. As patients with more advanced CKM stages may receive pharmacological treatments for underlying cardiometabolic conditions, the potential influence of medications on circulating PTX3 levels cannot be excluded. Validation in larger, multicenter, and ethnically diverse cohorts is warranted. 5. Conclusion This study evaluated the association between circulating PTX3 and AHA-defined CKM stages. PTX3 levels were significantly elevated in early-stage CKM (S2) compared with healthy controls and remained significantly associated with classification between early-stage CKM (S2) and mid-advanced-stage CKM (S3+S4) after multivariable adjustment. Compared with hs-CRP and PCT, inclusion of PTX3 was associated with higher discriminative performance in stage classification, without evidence of impaired calibration. These findings indicate that PTX3 is associated with inflammatory and cardiorenal features within the CKM framework. Prospective studies are needed to determine whether PTX3 is associated with subsequent CKM stage changes or adverse cardiorenal outcomes. Integration with imaging-based markers and multi-omics approaches may further clarify the biological context of PTX3-related inflammation and its potential relevance for CKM characterization in diverse populations. Abbreviations T2DM type 2 diabetes mellitus CKD chronic kidney disease CVD cardiovascular disease CKM cardiovascular-kidney-metabolic AHA American Heart Association RAAS renin-angiotensin-aldosterone system CRP C-reactive protein IL-6 interleukin-6 NLR neutrophil-to-lymphocyte ratio PCT procalcitonin PTX3 pentraxin 3 FPG fasting plasma glucose hs-CRP high-sensitivity C-reactive protein T-CHO total cholesterol TG triglycerides LDL-C low-density lipoprotein cholesterol HDL-C high-density lipoprotein cholesterol Lp(a) lipoprotein(a) ALT alanine aminotransferase AST aspartate aminotransferase CK creatine kinase CK-MB creatine kinase-MB UA uric acid Cr creatinine eGFR estimated glomerular filtration rate HbA1c glycated hemoglobin HPLC high-performance liquid chromatography hs-cTnT high-sensitivity cardiac troponin T NT-proBNP N-terminal pro-B-type natriuretic peptide ELISA enzyme-linked immunosorbent assay uPRO urinary protein uCr urinary creatinine mALB microalbumin SD standard deviation IQR interquartile range LOD limit of detection ANOVA analysis of variance VIFs variance inflation factors EPV events-per-variable ORs odds ratios CIs confidence intervals ROC receiver operating characteristic AUC area under the curve WC waist circumference SBP systolic blood pressure DBP diastolic blood pressure MASLD metabolic dysfunction-associated steatotic liver disease Declarations 1. Ethics approval and consent to participate This study was approved by the Medical Science Research Ethics Committee of Peking University Third Hospital (IRB00006761-M20250008). The requirement for informed consent was waived due to the minimal risk nature of the study and the use of anonymized residual serum samples obtained after routine clinical testing. All procedures were conducted in accordance with the Declaration of Helsinki. 2. Consent for publication All authors have read and approved the publication of this manuscript. 3. Availability of data and material The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. 4. Competing interests The authors declare no competing interests. 5. Clinical trial number Not applicable. 6. Funding Beijing Research Ward Excellence Program, BRWEP. Grant Number: BRWEP2024W014090210 7. Author Contributions Zhen Xu was responsible for study conception, sample collection, data analysis, and drafting of the manuscript. Yuan Tan, Qian Zhang, He Wang, and Jingjin Tao contributed to sample organization and management. Qi Liu, Zhongxin Li, and Chong Wang contributed to the critical revision and refinement of the initial draft. Shuo Yang and Liyan Cui were responsible for the overall review and editing of the manuscript. All authors have read and approved the final version of the manuscript. 8. Acknowledgements We sincerely thank every member of our team for their valuable contributions to this manuscript. From sample collection and data acquisition to manuscript framework and detailed refinement, the collaborative efforts and dedication of all members were essential to the completion of this study. 9. Authors' information Zhen Xu: Graduate student in medicine; Peking University Third Hospital; [email protected] Shuo Yang: Master of medical science; Peking University Third Hospital; [email protected] Yuan Tan: PhD student in medicine; Peking University Third Hospital; [email protected] Qian Zhang: Doctoral student in clinical medicine; Peking University Third Hospital; [email protected] He Wang: PhD student in medicine; Peking University Third Hospital; [email protected] Jingjin Tao: Graduate student in medicine; Peking University Third Hospital; [email protected] Qi Liu: PhD student in medicine; Peking University Third Hospital; [email protected] Zhongxin Li: Doctoral student in clinical medicine; Peking University Third Hospital; [email protected] Chong Wang: Graduate student in medicine; Peking University Third Hospital; [email protected] Liyan Cui: PhD in Medicine; Peking University Third Hospital; [email protected] References Ndumele CE, Rangaswami J, Chow SL, Neeland IJ, Tuttle KR, Khan SS, Coresh J, Mathew RO, Baker-Smith CM, Carnethon MR, Despres JP, Ho JE, Joseph JJ, Kernan WN, Khera A, Kosiborod MN, Lekavich CL, Lewis EF, Lo KB, Ozkan B, Palaniappan LP, Patel SS, Pencina MJ, Powell-Wiley TM, Sperling LS, Virani SS, Wright JT, Rajgopal Singh R, Elkind MSV (2023) American Heart Association. Cardiovascular-Kidney-Metabolic Health: A Presidential Advisory From the American Heart Association. Circulation. ;148(20):1606–1635. doi: 10.1161/CIR.0000000000001184. Epub 2023 Oct 9. Erratum in: Circulation. 2024;149(13):e1023. 10.1161/CIR.0000000000001241 . PMID: 37807924 Xu Z, Yang S, Tan Y, Zhang Q, Wang H, Tao J, Liu Q, Wang Q, Feng W, Li Z, Wang C, Cui L (2025) Inflammation in cardiovascular-kidney-metabolic syndrome: key roles and underlying mechanisms-a comprehensive review. Mol Cell Biochem 480(12):6039–6075. 10.1007/s11010-025-05379-9 Epub 2025 Sep 10. PMID: 40928582; PMCID: PMC12594699 Roy A, Zeller J, Nero TL, Klepetko J, Eisenhardt SU, Parker MW, McFadyen JD, Peter K (2025) C-reactive protein: the nexus between inflammation and protein misfolding diseases. Front Immunol 16:1612703. 10.3389/fimmu.2025.1612703 PMID: 40534869; PMCID: PMC12173917 Carollo C, Sorce A, Cirafici E, Ciuppa ME, Mulè G, Caimi G (2025) Silent Inflammation, Loud Consequences: Decoding NLR Across Renal, Cardiovascular and Metabolic Disorders. Int J Mol Sci 26(17):8256. 10.3390/ijms26178256 PMID: 40943179; PMCID: PMC12428752 Chen HL, Wu C, Cao L, Wang R, Zhang TY, He Z (2024) The association between the neutrophil-to-lymphocyte ratio and type 2 diabetes mellitus: a cross-sectional study. BMC Endocr Disord 24(1):107. 10.1186/s12902-024-01637-x PMID: 38982402; PMCID: PMC11232124 Yoshitomi R, Nakayama M, Sakoh T, Fukui A, Katafuchi E, Seki M, Tsuda S, Nakano T, Tsuruya K, Kitazono T (2019) High neutrophil/lymphocyte ratio is associated with poor renal outcomes in Japanese patients with chronic kidney disease. Ren Fail 41(1):238–243 PMID: 30942116; PMCID: PMC6450582 Luo J, Thomassen JQ, Nordestgaard BG, Tybjærg-Hansen A, Frikke-Schmidt R (2023) Neutrophil counts and cardiovascular disease. Eur Heart J 44(47):4953–4964. 10.1093/eurheartj/ehad649 PMID: 37950632; PMCID: PMC10719495 Yu L, Sun J, Liu X (2023) Serum C reactive protein and procalcitonin are valuable predictors of coronary heart disease and poor prognosis in the elderly. Am J Transl Res 15(6):4188–4195 PMID: 37434846; PMCID: PMC10331649 Bontekoe J, Bansal V, Lee J, Syed M, Hoppensteadt D, Maia P, Walborn A, Liles J, Vasaiwala S, Fareed J Procalcitonin as a Marker of Comorbid Atrial Fibrillation in Chronic Kidney Disease and History of Sepsis. Clin Appl Thromb Hemost 2020 Jan-Dec ;26:1076029620932228. doi: 10.1177/1076029620932228. PMID: 32539447; PMCID: PMC7427007. Massimino AM, Colella FE, Bottazzi B, Inforzato A (2023) Structural insights into the biological functions of the long pentraxin PTX3. Front Immunol 14:1274634. 10.3389/fimmu.2023.1274634 PMID: 37885881; PMCID: PMC10598717 Daigo K, Inforzato A, Barajon I, Garlanda C, Bottazzi B, Meri S, Mantovani A (2016) Pentraxins in the activation and regulation of innate immunity. Immunol Rev 274(1):202–217. 10.1111/imr.12476 PMID: 27782337; PMCID: PMC5412954 Inforzato A, Doni A, Barajon I, Leone R, Garlanda C, Bottazzi B, Mantovani A (2013) PTX3 as a paradigm for the interaction of pentraxins with the complement system. Semin Immunol 25(1):79–85 Epub 2013 Jun 6. PMID: 23747040 Zlibut A, Bocsan IC, Agoston-Coldea L (2019) Pentraxin-3 and endothelial dysfunction. Adv Clin Chem. ;91:163–179. 10.1016/bs.acc.2019.03.005 . Epub 2019 May 4. PMID: 31331488 Kawai T, Autieri MV, Scalia R (2021) Adipose tissue inflammation and metabolic dysfunction in obesity. Am J Physiol Cell Physiol 320(3):C375–C391. 10.1152/ajpcell.00379.2020 Epub 2020 Dec 23. PMID: 33356944; PMCID: PMC8294624 Grant CH, Bell S (2025) Obesity and Chronic Kidney Disease: The Dual Epidemic in Cardiovascular Health. Eur Cardiol 20:e28. 10.15420/ecr.2025.33 PMID: 41210917; PMCID: PMC12590400 Rohm TV, Meier DT, Olefsky JM, Donath MY (2022) Inflammation in obesity, diabetes, and related disorders. Immunity 55(1):31–55. 10.1016/j.immuni.2021.12.013 PMID: 35021057; PMCID: PMC8773457 Manabe I (2011) Chronic inflammation links cardiovascular, metabolic and renal diseases. Circ J 75(12):2739–2748. 10.1253/circj.cj-11-1184 Epub 2011 Nov 8. PMID: 22067929 Kim BM, Ryu SY, Han MA, Choi SW (2025) Loss of significant association between high-sensitivity C-reactive protein (hs-CRP) and metabolic syndrome after adjustment for waist circumference found in 2022 Korea National Health and Nutrition Examination Survey data. J Physiol Anthropol 44(1):16. 10.1186/s40101-025-00396-5 PMID: 40474309; PMCID: PMC12139091 Nzobokela J, Muchaili L, Mwambungu A, Masenga SK, Kirabo A (2025) Pathophysiology and emerging biomarkers of cardiovascular-renal-hepato-metabolic syndrome. Front Cardiovasc Med 12:1661563. 10.3389/fcvm.2025.1661563 PMID: 41235339; PMCID: PMC12605086 Abbasi A, Corpeleijn E, Postmus D, Gansevoort RT, de Jong PE, Gans RO, Struck J, Hillege HL, Stolk RP, Navis G, Bakker SJ (2010) Plasma procalcitonin is associated with obesity, insulin resistance, and the metabolic syndrome. J Clin Endocrinol Metab 95(9):E26–31. 10.1210/jc.2010-0305 Epub 2010 Jun 9. PMID: 20534760 Canbay A, Celebi OO, Celebi S, Aydogdu S, Diker E (2015) Procalcitonin: a marker of heart failure. Acta Cardiol. ;70(4):473-8. 10.1080/ac.70.4.3096896 . PMID: 26455251 Villanueva MP, Mollar A, Palau P, Carratalá A, Núñez E, Santas E, Bodí V, Chorro FJ, Miñana G, Blasco ML, Sanchis J, Núñez J (2015) Procalcitonin and long-term prognosis after an admission for acute heart failure. Eur J Intern Med 26(1):42–48 Epub 2015 Jan 12. PMID: 25592074 Cvetinovic N, Isakovic AM, Lainscak M, Dungen HD, Nikolic NM, Loncar G (2017) Procalcitonin in heart failure: hic et nunc. Biomark Med. ;11(10):893–903. 10.2217/bmm-2017-0160 . Epub 2017 Oct 4. PMID: 28976777 Aïssou L, Sorbets E, Lallmahomed E, Goudot FX, Pop N, Es-Sebbani S, Benouda L, Nuel G, Meune C (2018) Prognostic and diagnostic value of elevated serum concentration of procalcitonin in patients with suspected heart failure. A review and meta-analysis. Biomarkers. ;23(5):407–413. doi: 10.1080/1354750X.2018.1443511. Epub 2018 Mar 12. PMID: 29465002 Wu SC, Liang CX, Zhang YL, Hu WP (2020) Elevated serum procalcitonin level in patients with chronic kidney disease without infection: A case-control study. J Clin Lab Anal 34(2):e23065. 10.1002/jcla.23065 Epub 2019 Oct 16. PMID: 31617251; PMCID: PMC7031592 Xu Z, Gao Z, Fu H, Zeng Y, Jin Y, Xu B, Zhang Y, Pan Z, Chen X, Zhang X, Wang X, Yan H, Yang X, Yang B, He Q, Luo P (2023) PTX3 from vascular endothelial cells contributes to trastuzumab-induced cardiac complications. Cardiovasc Res 119(5):1250–1264. 10.1093/cvr/cvad012 PMID: 36651911; PMCID: PMC10202646 Suzuki S, Takeishi Y, Niizeki T, Koyama Y, Kitahara T, Sasaki T, Sagara M, Kubota I (2008) Pentraxin 3, a new marker for vascular inflammation, predicts adverse clinical outcomes in patients with heart failure. Am Heart J 155(1):75–81 Epub 2007 Sep 27. PMID: 18082493 Lin TY, Guo H, Chen X (2021) Unraveling mechanisms of pentraxin 3 secretion in adipocytes during inflammation. J Mol Endocrinol. ;67(3):55–69. 10.1530/JME-21-0026 . PMID: 34213423 Nauta AJ, de Haij S, Bottazzi B, Mantovani A, Borrias MC, Aten J, Rastaldi MP, Daha MR, van Kooten C, Roos A (2005) Human renal epithelial cells produce the long pentraxin PTX3. Kidney Int. ;67(2):543 – 53. 10.1111/j.1523-1755.2005.67111.x . PMID: 15673302 Ahrițculesei RV, Boldeanu L, Caragea DC, Vladu IM, Clenciu D, Mitrea A, Ungureanu AM, Văduva CC, Dijmărescu AL, Popescu AIS, Assani MZ, Boldeanu MV, Vere CC (2025) Association Between Pentraxins and Obesity in Prediabetes and Newly Diagnosed Type 2 Diabetes Mellitus Patients. Int J Mol Sci 26(8):3661. 10.3390/ijms26083661 PMID: 40332236; PMCID: PMC12027536 Li L, Liu H, Zhang Q, Jin H, Tao H, Chen H, Zhou Z (2024) Pentraxin-3 and Outcomes in CKD: A Systematic Review and Meta-analysis. Kidney Med 6(4):100800. 10.1016/j.xkme.2024.100800 PMID: 38645733; PMCID: PMC11026967 Jenny NS, Blumenthal RS, Kronmal RA, Rotter JI, Siscovick DS, Psaty BM (2014) Associations of pentraxin 3 with cardiovascular disease: the Multi-Ethnic Study of Atherosclerosis. J Thromb Haemost 12(6):999–1005. 10.1111/jth.12557 PMID: 24628740; PMCID: PMC4055511 Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterials.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 14 May, 2026 Reviews received at journal 11 May, 2026 Reviewers agreed at journal 11 May, 2026 Reviewers agreed at journal 10 May, 2026 Reviews received at journal 22 Apr, 2026 Reviewers agreed at journal 06 Apr, 2026 Reviewers invited by journal 04 Apr, 2026 Editor assigned by journal 02 Apr, 2026 Submission checks completed at journal 02 Apr, 2026 First submitted to journal 27 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9249191","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":621728776,"identity":"44439e70-22cb-4bd9-98be-3f9a8837e5bc","order_by":0,"name":"Zhen Xu","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhen","middleName":"","lastName":"Xu","suffix":""},{"id":621728778,"identity":"6f2e040e-8fc5-40f1-a6b0-d554c1ef9fb8","order_by":1,"name":"Shuo Yang","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shuo","middleName":"","lastName":"Yang","suffix":""},{"id":621728779,"identity":"82288a20-d1d4-448c-a95c-47ab15b376f7","order_by":2,"name":"Yuan Tan","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Tan","suffix":""},{"id":621728780,"identity":"ea1f4ef4-05b6-4ecd-b335-3fa80d03bab6","order_by":3,"name":"Qian Zhang","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qian","middleName":"","lastName":"Zhang","suffix":""},{"id":621728781,"identity":"342a52a0-974b-4d92-9e44-aa8ffa707289","order_by":4,"name":"He Wang","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"He","middleName":"","lastName":"Wang","suffix":""},{"id":621728782,"identity":"7aae4209-f05d-4cf1-852c-47239e4756b0","order_by":5,"name":"Jingjin Tao","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jingjin","middleName":"","lastName":"Tao","suffix":""},{"id":621728783,"identity":"9f25a866-5b2f-4f05-87e1-17d16d4f45dc","order_by":6,"name":"Qi Liu","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qi","middleName":"","lastName":"Liu","suffix":""},{"id":621728784,"identity":"05a23db9-2562-4c08-aa6f-75c8fcd960b6","order_by":7,"name":"Zhongxin Li","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhongxin","middleName":"","lastName":"Li","suffix":""},{"id":621728785,"identity":"1f27ed3d-e722-428b-8989-f73b61aaabf4","order_by":8,"name":"Chong Wang","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chong","middleName":"","lastName":"Wang","suffix":""},{"id":621728786,"identity":"d2a6621d-e2a5-4970-88e6-a2dee207c78b","order_by":9,"name":"Liyan Cui","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsElEQVRIiWNgGAWjYDACdsYGIGnDw8/fQKwWZrCWNBnJGQeI1gImD9sYNCQQqYO/mblN4ueO8zwGDAcYP3zMIUKLxGHGNsneM7d5zJkbmCVnbiNCiwEzY5sEb9ttHsuGA2zMvMRqkfzbdo7H4EACCVqkedsOkKAF6Jdma9m2ZB7JGQebifMLf3v7w5tv2+zs+fmbD374SIwWIGCRgNDgOCUOMH8gWukoGAWjYBSMTAAAQQkxW4Ih3skAAAAASUVORK5CYII=","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":true,"prefix":"","firstName":"Liyan","middleName":"","lastName":"Cui","suffix":""}],"badges":[],"createdAt":"2026-03-28 02:53:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9249191/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9249191/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106784037,"identity":"11e651ca-7377-4e0c-9862-c37d44f1094e","added_by":"auto","created_at":"2026-04-13 12:12:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1230578,"visible":true,"origin":"","legend":"\u003cp\u003eThe flowchart illustrates the screening and inclusion process of the study population between August 2024 and January 2025. Hospitalized patients from the Departments of Endocrinology, Nephrology, and Cardiology were screened through the hospital electronic medical record system, and those meeting CKM diagnostic criteria were included. A total of 240 eligible participants were enrolled and stratified into four groups (n = 60 each) representing CKM Stages 0, 2, 3, and 4. Statistical analyses included group comparisons (ANOVA and Kruskal-Wallis), correlation analysis (Spearman), and multivariable modeling with 5-fold cross-validation. Abbreviations: AHA, American Heart Association; CKM, cardiovascular-kidney-metabolic syndrome; ANOVA, analysis of variance; CV, cross-validation; ROC, receiver operating characteristic.\u003c/p\u003e\n\u003cp\u003eFigure 1. Study population screening, grouping, and analytical workflow\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9249191/v1/12ace12ed3f6ae7eecea838b.png"},{"id":106784112,"identity":"074c7f97-c5b6-4f00-b3a7-b7249588eba5","added_by":"auto","created_at":"2026-04-13 12:13:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1668228,"visible":true,"origin":"","legend":"\u003cp\u003eBox-and-whisker plots showing the distributions of PTX3 (A), log\u003csub\u003e10\u003c/sub\u003e-transformed hs-CRP (B), and log\u003csub\u003e10\u003c/sub\u003e-transformed PCT (C) across CKM stages (Healthy, S2, S3, and S4; n = 60 per group). The central line represents the median, boxes indicate the interquartile range (IQR), and whiskers represent the minimum and maximum values. Statistical significance is indicated as \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 (*), \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01 (**), \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001 (***), and \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001 (****); ns indicates not significant. All \u003cem\u003ep\u003c/em\u003e values are two-sided.\u003c/p\u003e\n\u003cp\u003eFigure 2. Distribution of inflammatory biomarkers across CKM stages\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9249191/v1/0f9022c49edd3e072769e4c5.png"},{"id":106784099,"identity":"6b2c8535-6d0a-40f5-97b6-1268a51a8b3e","added_by":"auto","created_at":"2026-04-13 12:12:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2144863,"visible":true,"origin":"","legend":"\u003cp\u003eBox-and-whisker plots showing the distribution of FPG (A), HbA1c (B), TG (C), and log\u003csub\u003e10\u003c/sub\u003e-transformed Lp(a) (D) across CKM stages (Healthy, S2, S3, and S4; n = 60 per group). The central line represents the median, boxes indicate the interquartile range (IQR), and whiskers represent the minimum and maximum values. Statistical significance is indicated as \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 (*), \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01 (**), \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001 (***), and \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001 (****); ns indicates not significant. All \u003cem\u003ep\u003c/em\u003e values are two-sided.\u003c/p\u003e\n\u003cp\u003eFigure 3. Distribution of glycemic and selected lipid biomarkers across CKM stages\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9249191/v1/4e1a62c7537f89c9f23ad31a.png"},{"id":106784034,"identity":"9c41fe02-e136-4ee1-aca4-c331884243e8","added_by":"auto","created_at":"2026-04-13 12:12:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1695302,"visible":true,"origin":"","legend":"\u003cp\u003eBox-and-whisker plots showing the distribution of log\u003csub\u003e10\u003c/sub\u003e-transformed hs-cTnT (A), log\u003csub\u003e10\u003c/sub\u003e-transformed NT-proBNP (B), eGFR (C), and log\u003csub\u003e10\u003c/sub\u003e-transformed mALB (D) across CKM stages (Healthy, S2, S3, and S4; n = 60 per group). The central line represents the median, boxes indicate the interquartile range (IQR), and whiskers represent the minimum and maximum values. Statistical significance is indicated as \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 (*), \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01 (**), \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001 (***), and \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001 (****); ns indicates not significant. All \u003cem\u003ep\u003c/em\u003e values are two-sided.\u003c/p\u003e\n\u003cp\u003eFigure 4. Distribution of key cardiovascular and renal injury biomarkers across CKM stages\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-9249191/v1/0dde6fc58a18ec89fb72ffe1.png"},{"id":106784036,"identity":"a8eb25e8-1ac2-4eac-97cc-0cee78f28e46","added_by":"auto","created_at":"2026-04-13 12:12:46","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":16198177,"visible":true,"origin":"","legend":"\u003cp\u003eA heatmap of the Spearman correlation coefficients (ρ) matrix is shown. Red indicates positive correlations and blue indicates negative correlations, with color intensity reflecting the magnitude of the correlation. In the upper triangle, pie charts are used to visualize correlations, where the proportion of the filled area represents correlation strength and color indicates correlation direction. In the lower triangle, shaded tiles are displayed, with forward slashes (/) indicating positive correlations and backslashes (\\) indicating negative correlations.\u003c/p\u003e\n\u003cp\u003eFigure 5. Correlation matrix heatmap of inflammatory, metabolic, and cardiorenal biomarkers\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-9249191/v1/5c941af97df433a40554b365.png"},{"id":106784035,"identity":"f9375b19-fba5-4d1b-82cc-8a2b18b0c609","added_by":"auto","created_at":"2026-04-13 12:12:46","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2762287,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plots showing correlations between PTX3 and HbA1c (A), TG (B), hs-cTnT (C), and eGFR (D). Correlations were assessed using Spearman’s rank correlation analysis. Solid lines indicate fitted regression trends, and shaded areas represent 95% confidence intervals. Each dot represents an individual participant. Spearman’s correlation coefficients are denoted by ρ. All \u003cem\u003ep\u003c/em\u003e values are two-sided.\u003c/p\u003e\n\u003cp\u003eFigure 6. Correlations of PTX3 with HbA1c, TG, hs-cTnT, and eGFR\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-9249191/v1/e6fd72121a83254cada7bee3.png"},{"id":106784033,"identity":"44e29a16-bdae-4ffa-8af6-47536a0100d6","added_by":"auto","created_at":"2026-04-13 12:12:46","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":607526,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves are shown for Models 1-5. The diagonal dashed line indicates no-discrimination performance (AUC = 0.500). Model specifications and corresponding AUCs (95% CIs) are reported in Table 6.\u003c/p\u003e\n\u003cp\u003eFigure 7. ROC curves of multivariable models for CKM stage classification (S2 vs S3+S4)\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-9249191/v1/86d8442898358aeecb0405f1.png"},{"id":106784343,"identity":"98b16171-9e01-42b7-9486-f3c7004b939b","added_by":"auto","created_at":"2026-04-13 12:13:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":20653578,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9249191/v1/d1dbf61d-4587-4354-ba7b-123a6a8bdf7e.pdf"},{"id":106784103,"identity":"da37a48b-db5c-40b0-94f3-aa42651c18c8","added_by":"auto","created_at":"2026-04-13 12:13:01","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":251636,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-9249191/v1/070b000495c988bcb558026b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Pentraxin 3 Is an Inflammation-Related Biomarker That Distinguishes Early-Stage from Mid-Advanced Cardiovascular-Kidney-Metabolic Syndrome","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe escalating global prevalence of obesity, type 2 diabetes mellitus (T2DM), chronic kidney disease (CKD), and cardiovascular disease (CVD) has highlighted the intricate and bidirectional interactions among metabolic, renal, and cardiovascular disorders. In recognition of their substantial pathophysiological overlap and mutual disease amplification, the American Heart Association (AHA) recently introduced cardiovascular-kidney-metabolic (CKM) syndrome as a unified systemic disorder that integrates metabolic risk factors, CKD, and cardiovascular dysfunction into a single clinical continuum \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. CKM syndrome is characterized by progressive multi-organ involvement and is associated with a markedly increased risk of adverse cardiovascular outcomes and premature mortality \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo facilitate early identification and stage-specific intervention, the AHA further proposed a staging construct for CKM syndrome that reflects disease evolution across the life course \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Specifically, Stage 0 represents optimal cardiometabolic health in otherwise healthy individuals; Stage 1 is characterized by excess or dysfunctional adiposity; Stage 2 encompasses the presence of metabolic risk factors; Stage 3 denotes subclinical cardiovascular disease or moderate- to high-risk chronic kidney disease; and Stage 4 comprises overt clinical cardiovascular disease with or without kidney failure. This framework underscores the progressive accumulation of metabolic abnormalities, renal impairment, and cardiovascular involvement, providing a conceptual basis for integrated risk stratification, prevention, and multidisciplinary management of CKM syndrome.\u003c/p\u003e \u003cp\u003eChronic inflammation is considered a key pathophysiological feature of CKM syndrome, functioning both as a critical mediator of disease initiation and as a molecular nexus linking cardiac, renal, and metabolic dysfunction. Persistent low-grade inflammation promotes insulin resistance, oxidative stress, endothelial dysfunction, lipotoxicity, and dysregulated activation of the renin-angiotensin-aldosterone system (RAAS) \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. These processes converge on multiple inflammatory signaling pathways\u0026mdash;including NF-κB, Wnt, PI3K-AKT, and JAK-STAT\u0026mdash;thereby establishing self-amplifying positive feedback loops across metabolic tissues, the kidney, and the cardiovascular system, ultimately accelerating multi-organ injury and disease progression \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDespite the recognized central role of inflammation in CKM syndrome, currently available clinical inflammatory biomarkers remain suboptimal for capturing the complex, multi-organ inflammatory burden that characterizes this condition. Widely used systemic inflammatory markers, such as C-reactive protein (CRP), primarily reflect hepatically derived acute-phase responses driven by interleukin-6 (IL-6) signaling. Although CRP has been extensively validated for cardiovascular risk assessment and provides valuable information on systemic inflammatory status, it may not adequately reflect localized or tissue-specific inflammatory processes within the cardiovascular and renal systems \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHematologic inflammation-related indices, including the neutrophil-to-lymphocyte ratio (NLR), have also attracted attention as inexpensive and readily available markers associated with cardiometabolic and renal risk \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Elevated NLR has been linked to adverse cardiovascular outcomes and CKD progression, reflecting systemic immune activation and imbalance \u003csup\u003e[\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. In addition, procalcitonin (PCT), a biomarker widely used for detecting bacterial infection, has been reported to be elevated in cardiovascular and renal diseases, suggesting a potential association with systemic inflammatory stress \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. However, the specificity of these circulating markers in non-infectious cardiometabolic conditions remains limited. Even mild infections may obscure underlying inflammatory signals, and impaired renal clearance\u0026mdash;common in cardiovascular disease\u0026mdash;may lead to pseudo-elevations of circulating biomarkers, complicating their interpretation in cardiorenal disorders.\u003c/p\u003e \u003cp\u003eThese limitations highlight the need for integrated biomarkers capable of capturing multi-organ inflammatory signaling within the CKM continuum. Pentraxin 3 (PTX3), a prototypical long pentraxin, has emerged as a promising candidate in this context. Unlike CRP, PTX3 is produced locally at sites of inflammation by multiple cell types directly implicated in CKM pathophysiology, including endothelial cells, monocytes/macrophages, adipocytes, and renal parenchymal cells \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Through its involvement in innate immune activation, complement regulation, endothelial dysfunction, and tissue remodeling, PTX3 may reflect inflammatory pathways more directly related to organ injury \u003csup\u003e[\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Although accumulating evidence links elevated PTX3 levels to metabolic disturbances, CKD progression, and adverse cardiovascular outcomes, its role within the integrated CKM framework\u0026mdash;and its association with disease severity across CKM stages\u0026mdash;remains incompletely defined.\u003c/p\u003e \u003cp\u003eTherefore, the present study aimed to evaluate the cross-sectional association between circulating PTX3 levels and CKM stage classification, with particular emphasis on its discriminatory value across CKM stages and its relationship with metabolic, renal, and cardiovascular injury markers, as well as its discriminative performance in comparison with commonly used inflammatory markers such as hs-CRP and PCT.\u003c/p\u003e"},{"header":"2. Material and Methods","content":"\u003cp\u003e2.1 Participants\u003c/p\u003e\n\u003cp\u003eThe design and overall workflow of this cross-sectional study are summarized in Figure 1. Participants were consecutively recruited between August 2024 and January 2025 from hospitalized patients in the Departments of Endocrinology, Nephrology, and Cardiology at Peking University Third Hospital, as well as from apparently healthy individuals undergoing routine health examinations at the hospital\u0026rsquo;s Health Examination Center. Hospitalized patients were screened through the hospital electronic medical record system, and those meeting the diagnostic framework for CKM syndrome based on documented clinical diagnoses in the medical records were consecutively included in the study, whereas serum samples from patients who did not meet the CKM diagnostic criteria were not collected. These patients were admitted primarily for evaluation or management of metabolic, renal, or cardiovascular conditions. Following completion of routine clinical testing, residual serum samples were collected from all participants and stored at -80\u0026deg;C until further laboratory analyses.\u003c/p\u003e\n\u003cp\u003eInclusion criteria were as follows: (1) age \u0026ge; 18 years; (2) fulfillment of the diagnostic framework for CKM syndrome proposed by the AHA, defined by the presence of one or more of the following conditions: obesity, T2DM, CKD, and CVD, including peripheral artery disease, coronary artery disease, atrial fibrillation, heart failure, and stroke; (3) for apparently healthy individuals, no prior history of obesity, T2DM, CKD, or CVD based on medical records and routine health examination results.\u003c/p\u003e\n\u003cp\u003eTo ensure diagnostic consistency, clinical conditions were defined according to established clinical guidelines. Obesity was defined based on body mass index (BMI) according to the criteria for the Chinese population. T2DM was defined according to the diagnostic criteria of the American Diabetes Association. Hypertension was defined according to established hypertension guidelines or documented clinical diagnosis recorded in the medical records. CKD was defined according to the KDIGO guidelines. CVD included clinically documented conditions such as coronary artery disease, peripheral artery disease, atrial fibrillation, heart failure, and stroke. Metabolic syndrome (MetS) was defined according to the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) criteria.\u003c/p\u003e\n\u003cp\u003eExclusion criteria included: (1) presence of acute inflammatory conditions; (2) acute infections, including severe cases such as bacterial or fungal bloodstream infections, sepsis, or septic shock; (3) malignant tumors or acquired immunodeficiency disorders; (4) current use of immunosuppressive medications; (5) incomplete clinical or laboratory data.\u003c/p\u003e\n\u003cp\u003eAccording to the CKM staging construct proposed by the AHA \u003csup\u003e[1]\u003c/sup\u003e, participants were categorized into four groups. Stage 0 (S0) included apparently healthy individuals without metabolic risk factors, CKD, or CVD. Stage 2 (S2) included individuals with established metabolic risk factors such as obesity, T2DM, or MetS without overt CVD. Stage 3 (S3) included individuals with evidence of subclinical CVD or CKD. Evidence of subclinical CVD documented in the medical records, including carotid plaque detected by carotid ultrasound, was used to support Stage 3 classification when available. Stage 4 (S4) included individuals with clinically manifest CVD.\u003c/p\u003e\n\u003cp\u003eAn a priori sample size estimation was performed using G*Power software based on pilot data. Stage 1 (S1) individuals were not included in the final inpatient-based analyses because this stage generally represents individuals with early metabolic risk factors who are predominantly managed in outpatient settings and are therefore infrequently encountered among hospitalized patients. However, pilot data including S1 participants were available, and the effect size (Cohen\u0026rsquo;s d = 0.661) was derived from a preliminary comparison between S0 and S1. This S0-S1 comparison was used as a conservative reference for sample size estimation under the minimal-difference principle. To account for prespecified multiple comparisons, a Bonferroni-adjusted two-sided significance level of \u0026alpha; = 0.0125 and a statistical power of 80% were applied. This yielded an estimated sample size of approximately 53 participants per group. Allowing for an anticipated ~10% proportion of unusable samples (e.g., inadequate specimen quality or missing key variables), the target sample size was set at 60 participants per group. Accordingly, a total of 240 eligible participants were consecutively included, with 60 individuals in each group.\u003c/p\u003e\n\u003cp\u003eThe flowchart illustrates the screening and inclusion process of the study population between August 2024 and January 2025. Hospitalized patients from the Departments of Endocrinology, Nephrology, and Cardiology were screened through the hospital electronic medical record system, and those meeting CKM diagnostic criteria were included. A total of 240 eligible participants were enrolled and stratified into four groups (n = 60 each) representing CKM Stages 0, 2, 3, and 4. Statistical analyses included group comparisons (ANOVA and Kruskal-Wallis), correlation analysis (Spearman), and multivariable modeling with 5-fold cross-validation. Abbreviations: AHA, American Heart Association; CKM, cardiovascular-kidney-metabolic syndrome; ANOVA, analysis of variance; CV, cross-validation; ROC, receiver operating characteristic.\u003c/p\u003e\n\u003cp\u003eFigure 1.\u0026nbsp;Study population screening, grouping, and analytical workflow\u003c/p\u003e\n\u003cp\u003e2.2 Laboratory measurements\u003c/p\u003e\n\u003cp\u003eAfter an overnight fast of at least 12 hours, venous blood samples were collected in the early morning by trained ward nurses. All laboratory analyses were performed at the Clinical Laboratory Center of Peking University Third Hospital in accordance with standardized operating procedures.\u003c/p\u003e\n\u003cp\u003eRoutine biochemical measurements\u003c/p\u003e\n\u003cp\u003eAll routine biochemical parameters were measured using a Beckman Coulter AU5800 automated chemistry analyzer (Beckman Coulter, Brea, CA, USA).\u003c/p\u003e\n\u003cp\u003eFasting plasma glucose (FPG) was determined using the hexokinase method.\u0026nbsp;High-sensitivity C-reactive protein (hs-CRP) was measured using a particle-enhanced immunoturbidimetric assay (DiaSys Diagnostic Systems GmbH, Holzheim, Germany).\u003c/p\u003e\n\u003cp\u003eLipid parameters included total cholesterol (T-CHO) and triglycerides (TG), both measured using the enzymatic end-point method. Low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C) were measured using a homogeneous enzymatic colorimetric assay (Sekisui Medical Co., Ltd, Japan). Lipoprotein(a) [Lp(a)] was determined by immunoturbidimetry (Leadman Biochemistry Co., Ltd., China).\u0026nbsp;The triglyceride-glucose (TyG) index was calculated as ln [TG (mg/dL) \u0026times; FPG (mg/dL) / 2].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlanine aminotransferase (ALT), aspartate aminotransferase (AST), and creatine kinase (CK) were measured using enzymatic rate methods. Creatine kinase-MB (CK-MB) was determined using an immunoinhibition method (Kanto Chemical Co., Inc., Japan).\u003c/p\u003e\n\u003cp\u003eRenal function parameters included urea, measured using the urease-glutamate dehydrogenase method; uric acid (UA), measured using the uricase-peroxidase method (Shino-Test Corporation, Tokyo, Japan); and serum creatinine (Cr), measured using the picric acid (Jaffe) method (Biosino Bio-Technology and Science Incorporation,\u0026nbsp;Beijing, China). The estimated glomerular filtration rate (eGFR) was calculated using the CKD-EPI equation.\u003c/p\u003e\n\u003cp\u003eHbA1c measurement\u003c/p\u003e\n\u003cp\u003eGlycated hemoglobin (HbA1c) was measured by high-performance liquid chromatography (HPLC) using a Tosoh HLC-723G11 automated hemoglobin analyzer (Tosoh Corporation, Tokyo, Japan). The assay was standardized according to the National Glycohemoglobin Standardization Program (NGSP).\u003c/p\u003e\n\u003cp\u003ePCT, hs-cTnT, and NT-proBNP\u0026nbsp;measurements\u003c/p\u003e\n\u003cp\u003eSerum procalcitonin (PCT), high-sensitivity cardiac troponin T (hs-cTnT), and N-terminal pro-B-type natriuretic peptide (NT-proBNP) were measured using electrochemiluminescence immunoassay (ECLIA) on a cobas e411 analyzer (Roche Diagnostics, Mannheim, Germany).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePTX3 measurement\u003c/p\u003e\n\u003cp\u003eSerum PTX3 levels were measured using a commercially available enzyme-linked immunosorbent assay (ELISA) kit (Wuhan Elabscience Biotechnology Co., Ltd., Wuhan, China; Catalog number: E-EL-H6081). According to the manufacturer\u0026rsquo;s validation data, the intra-assay coefficients of variation ranged from 4.19% to 5.36%, and the inter-assay coefficients of variation ranged from 7.05% to 8.82%, indicating good assay reproducibility. All procedures were performed strictly according to the manufacturer\u0026rsquo;s instructions. Optical densities were read at the specified wavelength, and concentrations were calculated from the standard calibration curve. Quality control samples were included in each batch to ensure assay accuracy and precision.\u003c/p\u003e\n\u003cp\u003eUrinary biomarker measurements\u003c/p\u003e\n\u003cp\u003eUrinary protein (uPRO), urinary creatinine (uCr), and urinary microalbumin (mALB) were measured using a Hitachi 3500 specific protein analyzer (Hitachi High-Tech Diagnostics, Tokyo, Japan). Urinary protein was quantified using the pyrogallol red-molybdate colorimetric method (DiaSys Diagnostic Systems GmbH, Holzheim, Germany). Urinary creatinine was determined using the picric acid (Jaffe) method (Biosino Bio-Technology and Science Incorporation, Beijing, China). Urinary microalbumin was measured by immunoturbidimetry (DiaSys Diagnostic Systems GmbH, Holzheim, Germany).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2.3 Statistical analysis\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were performed using R software (version 4.5.0) and SPSS (version 25.0). Graphical visualizations were generated using GraphPad Prism (version 10.6). All tests were two-sided, and a \u003cem\u003ep\u003c/em\u003e value \u0026lt; 0.05 was considered statistically significant. Baseline demographic and clinical characteristics were summarized according to variable type. Variables following a normal distribution were expressed as mean \u0026plusmn; standard deviation (SD), whereas non-normally distributed variables were presented as median (interquartile range, IQR). Categorical variables were summarized as frequencies.\u0026nbsp;The proportion of missing data was less than 3% across variables. Given the low proportion of missingness, analyses were conducted using complete-case analysis without additional imputation. Values below the limit of detection (LOD) were imputed as LOD/2.\u003c/p\u003e\n\u003cp\u003eCategorical variables across four groups were compared using the chi-square test. For continuous variables, normality was assessed using the Shapiro-Wilk test, and homogeneity of variances was evaluated using Levene\u0026rsquo;s test. When data were normally distributed (\u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05) and variances were homogeneous, one-way analysis of variance (ANOVA) was performed. If the assumption of homogeneity of variances was violated (Levene\u0026rsquo;s test, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05), Welch\u0026rsquo;s ANOVA was used to calculate \u003cem\u003ep\u003c/em\u003e values. When any group failed to meet the normality assumption, the Kruskal-Wallis H test was applied.\u0026nbsp;When the overall test indicated a statistically significant difference, pairwise comparisons were conducted. For normally distributed data with homogeneous variances, Tukey\u0026rsquo;s honestly significant difference (HSD) test was used; when variances were unequal, the Games-Howell test was applied. For non-normally distributed data, Dunn\u0026rsquo;s post hoc test was performed, with Bonferroni correction applied to adjust for multiple comparisons.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCorrelations between continuous variables were evaluated using Spearman\u0026rsquo;s rank correlation analysis. Multivariable linear regression analysis was performed to identify factors independently associated with PTX3. Multivariable logistic regression models were constructed to assess independent associations between covariates and study outcomes.\u0026nbsp;Multicollinearity among covariates was assessed using variance inflation factors (VIFs), with VIF values \u0026lt; 5 considered indicative of acceptable collinearity.\u0026nbsp;Given the sample size, the number of covariates included in multivariable models was determined with consideration of the events-per-variable (EPV) principle to reduce the risk of model overfitting. Results were reported as adjusted odds ratios (ORs) with 95% confidence intervals (CIs). To reduce the risk of overfitting and to assess model robustness and generalizability, stratified 5-fold cross-validation was employed, and the mean area under the receiver operating characteristic (ROC) curve (AUC) across folds was calculated. Model calibration was assessed using the Hosmer-Lemeshow (H-L) goodness-of-fit test, whereas discriminative performance was evaluated using the AUC. Differences in AUCs between models were compared using the DeLong test.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2.4 Ethics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Medical Science Research Ethics Committee of Peking University Third Hospital (approval number: IRB00006761-M20250008). The requirement for informed consent was waived by the Ethics Committee because the study involved minimal risk to participants and used anonymized data, making the acquisition of informed consent impracticable. The waiver was determined not to adversely affect the rights or welfare of the participants. All serum samples analyzed in this study were residual serum specimens obtained after routine clinical testing, with no additional interventions or harm to the participants. All procedures were conducted in accordance with the principles of the Declaration of Helsinki.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e3.1 Baseline characteristics\u003c/p\u003e\n\u003cp\u003eBaseline demographic and clinical characteristics of the study population are presented in Table 1.\u003c/p\u003e\n\u003cp\u003eAge differed significantly among groups (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), with higher mean age observed in more advanced CKM stages. The mean age increased from 45.8 \u0026plusmn; 6.9 years in the Healthy group to 67.3 \u0026plusmn; 9.5 years in S4. Sex distribution was comparable across groups (\u003cem\u003ep\u003c/em\u003e = 0.203). Compared with healthy controls, participants in S2-S4 exhibited higher BMI and waist circumference (WC) (overall \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). Blood pressure indices, including systolic blood pressure (SBP) and diastolic blood pressure (DBP), also differed significantly among groups (both \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMarkers of glucose metabolism, including FPG and HbA1c, showed significant intergroup differences (both \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001).\u0026nbsp;Median FPG was 5.0 (4.7-5.4) mmol/L in the Healthy group compared with 8.7 (6.5-10.5) mmol/L in S3. Lipid parameters, including T-CHO, TG, and LDL-C, differed significantly across groups (all \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), whereas HDL-C was lower in CKM stages compared with the Healthy group (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001).\u0026nbsp;In addition, the TyG index differed significantly across the four groups (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), with higher levels observed in all CKM stages than in the Healthy group.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRegarding hepatic and myocardial enzymes, ALT and CK-MB levels varied significantly across groups (\u003cem\u003ep\u003c/em\u003e = 0.003 and \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, respectively), whereas no significant differences were observed in AST and CK.\u003c/p\u003e\n\u003cp\u003eRenal function markers, including serum urea, UA, and Cr, differed significantly among the four groups (\u003cem\u003ep\u003c/em\u003e = 0.006 for Cr; others \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), while eGFR was significantly lower in more advanced CKM stages (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). The median eGFR was 101.5 (95.0-107.0) mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e in the Healthy group and 78.0 (43.0-88.0) mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e in S4.\u003c/p\u003e\n\u003cp\u003eThe prevalence of MetS differed significantly among groups (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), being rare in healthy individuals but common in participants with CKM, particularly in S3. Likewise, the prevalence of CKD and CVD differed significantly among groups (both \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). CKD was more common in S3 and S4 than in the Healthy and S2 groups, whereas CVD was observed mainly in S3 and S4.\u003c/p\u003e\n\u003cp\u003eOverall, baseline metabolic, cardiovascular, and renal characteristics differed significantly among the four groups.\u003c/p\u003e\n\u003cp\u003eTable 1. Baseline characteristics of participants stratified by CKM stage\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"621\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHealthy (n=60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS2 (n=60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS3 (n=60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS4 (n=60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e45.8 \u0026plusmn; 6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e45.2 \u0026plusmn; 9.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52.8 \u0026plusmn; 12.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e67.3 \u0026plusmn; 9.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSex (M/F)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26/34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33/27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e27/33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36/24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.203\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22.88 \u0026plusmn; 1.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26.32 \u0026plusmn; 3.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26.02 \u0026plusmn; 4.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25.91 \u0026plusmn; 2.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWC (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e84.63 \u0026plusmn; 5.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e94.74 \u0026plusmn; 11.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e92.99 \u0026plusmn; 11.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e91.92 \u0026plusmn; 7.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e113.4 \u0026plusmn; 7.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e131.8 \u0026plusmn; 14.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e137.6 \u0026plusmn; 16.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e135.2 \u0026plusmn; 18.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e73.0 \u0026plusmn; 8.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e81.8 \u0026plusmn; 11.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e83.2 \u0026plusmn; 11.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e80.1 \u0026plusmn; 10.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFPG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.0 (4.7-5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.8 (5.3-10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.7 (6.5-10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.2 (7.0-10.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHbA1c (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.4 (5.3-5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.4 (5.8-11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.3 (7.1-10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.7 (6.9-9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eT-CHO (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.91 (3.38-4.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.04 (4.23-5.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.47 (3.75-5.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.37 (3.46-5.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.95 (0.78-1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.79 (1.28-2.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.67 (1.22-2.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.47 (1.01-2.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTyG index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.24 (8.02-8.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.25 (8.87-9.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.35 (8.81-9.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.15 (8.88-9.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLDL-C (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.30 (1.84-2.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.32 (2.54-3.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.72 (2.14-3.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.74 (1.88-3.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHDL-C (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.42 (1.29-1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.97 (0.85-1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.02 (0.87-1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.04 (0.90-1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eALT (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19.5 (15.0-26.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e29.0 (15.5-46.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26.0 (17.0-42.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26.5 (14.3-41.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAST (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21.5 (18.3-25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23.0 (18.3-31.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23.0 (18.0-36.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24.0 (18.3-30.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.337\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCK (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e73.0 (53.3-94.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e73.5 (51.0-114.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e68.0 (52.0-98.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e75.0 (50.5-97.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.934\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCK-MB (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.5 (6.0-9.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.0 (6.0-10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.0 (6.0-11.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.5 (8.0-15.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eUrea (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.4 (3.7-5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.7 (3.9-6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.5 (4.8-7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.9 (5.7-11.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eUA \u0026micro;mol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e317.5 (274.0-342.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e356.0 (287.3-435.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e351.5 (298.5-419.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e362.5 (296.8-435.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCr (\u0026micro;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e68.0 (62.3-80.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e74.0 (64.0-85.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e80.5 (63.0-98.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e82.0 (67.0-115.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eeGFR (mL/min/1.73m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e101.5 (95.0-107.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e101.0 (93.0-112.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e85.0 (66.3-96.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e78.0 (43.0-88.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMetS, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 (1.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e38 (63.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e48 (80.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e38 (63.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCKD, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16 (26.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e37 (61.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e48 (80.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCVD, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e46 (76.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e60 (100.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\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\u003c/div\u003e\n\u003cp\u003eAbbreviations: BMI, body mass index; WC, waist circumference; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG, fasting plasma glucose; HbA1c, glycated hemoglobin; T-CHO, total cholesterol; TG, triglycerides; TyG index, triglyceride-glucose index; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; ALT, alanine aminotransferase; AST, aspartate aminotransferase; CK, creatine kinase; CK-MB, creatine kinase-MB; UA, uric acid; Cr, creatinine; eGFR, estimated glomerular filtration rate; MetS, metabolic syndrome; CKD, chronic kidney disease; CVD, cardiovascular disease. Note: Normally distributed data are presented as mean \u0026plusmn; SD, and non-normally distributed data are presented as median (IQR). \u003cem\u003ep\u003c/em\u003e values represent overall comparisons across the four groups.\u003c/p\u003e\n\u003cp\u003e3.2 Changes in disease-related biomarkers across CKM stages\u003c/p\u003e\n\u003cp\u003eAs shown in Table 2, significant differences in multiple disease-related biomarkers were observed across the four groups (all \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003eInflammatory markers, including PTX3, hs-CRP, and PCT, differed significantly among CKM stages. Notably, PTX3 levels were higher across CKM stages, rising from a median of 32.51 (19.48-41.58) pg/mL in the Healthy group to 99.40 (68.50-113.42) pg/mL in S4. Similarly, hs-CRP and PCT levels were higher in disease groups compared with healthy controls and differed significantly across the four groups (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001 for both).\u003c/p\u003e\n\u003cp\u003eCardiovascular-related biomarkers also demonstrated significant intergroup differences. Lp(a) levels were significantly elevated in S2-S4 groups compared with healthy participants (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). Markers of myocardial injury and cardiac function, including hs-cTnT and NT-proBNP, increased significantly across CKM stages. Median hs-cTnT levels increased from 5.5 (4.0-7.0) pg/mL in the Healthy group to 14.5 (9.0-32.0) pg/mL in S4, while NT-proBNP levels rose markedly from 29.5 (19.0-42.8) pg/mL to 129.0 (61.5-637.0) pg/mL (both \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003eRenal injury-related biomarkers also varied significantly among groups. uPRO and mALB levels were higher in later CKM stages (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). In contrast, uCr levels declined significantly across groups, decreasing from 12,857.5 (11,030.5-13,937.8) \u0026mu;mol/L in the Healthy group to 5,972.5 (4,460.3-9,664.5) \u0026mu;mol/L in S4 (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003eCollectively, these findings indicate significant alterations in inflammatory, cardiovascular, and renal-related biomarkers across CKM stages.\u003c/p\u003e\n\u003cp\u003eTable 2. Levels of inflammatory, cardiovascular, and renal-related biomarkers across CKM\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"621\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBiomarkers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHealthy (n=60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS2 (n=60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS3 (n=60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS4 (n=60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePTX3 (pg/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32.51 (19.48-41.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52.02 (36.12-66.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e70.72 (49.52-93.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e99.40 (68.50-113.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ehs-CRP (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.43 (0.21-0.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.52 (0.57-4.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.40 (0.87-3.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.50 (0.83-4.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePCT (pg/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17.5 (10.0-25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19.0 (10.0-35.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32.0 (10.0-52.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e37.0 (10.0-71.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLp(a) (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e67.50 (44.75-106.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e163.00 (69.25-433.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e129.50 (74.75-283.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e125.00 (69.50-333.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ehs-cTnT (pg/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.5 (4.0-7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.0 (1.5-9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.0 (6.0-14.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14.5 (9.0-32.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNT-proBNP\u0026nbsp;(pg/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e29.5 (19.0-42.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23.0 (5.0-69.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e42.0 (26.3-81.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e129.0 (61.5-637.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003euPRO (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e69.00 (57.50-81.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e116.00 (56.25-438.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e124.00 (69.25-541.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e169.00 (74.25-875.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003euCr (\u0026micro;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12857.5 (11030.5-13937.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8839.0 (5292.0-13775.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8120.5 (4904.3-11990.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5972.5 (4460.3-9664.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003emALB (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.4 (2.7-7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19.9 (6.4-236.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17.9 (6.6-173.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e39.1 (11.1-557.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\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\u003c/div\u003e\n\u003cp\u003eAbbreviations: PTX3, pentraxin 3; hs-CRP, high-sensitivity C-reactive protein; PCT, procalcitonin; Lp(a), lipoprotein(a); hs-cTnT, high-sensitivity cardiac troponin T; NT-proBNP, N-terminal pro-B-type natriuretic peptide; uPRO, urinary protein; uCr, urinary creatinine; mALB, microalbumin. Note: Non-normally distributed data are presented as median (IQR). \u003cem\u003ep\u003c/em\u003e values represent overall comparisons across the four groups.\u003c/p\u003e\n\u003cp\u003e3.3 Distribution of key inflammatory, metabolic, and cardiorenal biomarkers across CKM stages\u003c/p\u003e\n\u003cp\u003eTo visualize the distributions of key inflammatory, metabolic, and cardiorenal biomarkers across CKM stages, box-and-whisker plots were generated. Biomarkers with markedly skewed distributions were log\u003csub\u003e10\u003c/sub\u003e-transformed prior to plotting to improve data symmetry and visualization.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3.3.1\u0026nbsp;Differences in inflammatory biomarkers across CKM stages\u003c/p\u003e\n\u003cp\u003eThe distributions of inflammatory biomarkers across CKM stages are shown in Figure 2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSerum PTX3 levels differed across CKM stages (Figure 2A). Compared with healthy individuals, PTX3 concentrations were significantly higher in S2 and were also higher in S3 and S4. Pairwise comparisons demonstrated significant differences between Healthy and S2, Healthy and S3, as well as Healthy and S4 (all \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). PTX3 levels also differed significantly between S2 and S3 (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01). In contrast, no statistically significant difference was observed between S3 and S4, despite a numerically higher median value in S4.\u003c/p\u003e\n\u003cp\u003eFor log\u003csub\u003e10\u003c/sub\u003e-transformed hs-CRP (Figure 2B), levels were significantly elevated in all CKM stages (S2-S4) compared with healthy controls (all \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001). However, pairwise comparisons among the disease stages themselves showed no significant differences, indicating that hs-CRP levels were comparable across S2-S4.\u003c/p\u003e\n\u003cp\u003eSimilarly, PCT levels (log\u003csub\u003e10\u003c/sub\u003e-transformed) differed across CKM stages (Figure 2C). Compared with healthy individuals, PCT concentrations were significantly higher in S3 and S4, whereas the difference between Healthy and S2 did not reach statistical significance. Pairwise analysis showed a significant difference between S2 and S3 (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05), while no significant difference was detected between S3 and S4.\u003c/p\u003e\n\u003cp\u003eCollectively, these findings indicate differences in systemic inflammatory markers across CKM stages, with PTX3 showing broader interstage distinctions, whereas hs-CRP and PCT exhibited elevations in CKM stages relative to healthy controls but limited differentiation among later stages.\u003c/p\u003e\n\u003cp\u003eBox-and-whisker plots showing the distributions of PTX3 (A), log\u003csub\u003e10\u003c/sub\u003e-transformed hs-CRP (B), and log\u003csub\u003e10\u003c/sub\u003e-transformed PCT (C) across CKM stages (Healthy, S2, S3, and S4; n = 60 per group). The central line represents the median, boxes indicate the interquartile range (IQR), and whiskers represent the minimum and maximum values. Statistical significance is indicated as \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 (*), \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01 (**), \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001 (***), and \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001 (****); ns indicates not significant. All \u003cem\u003ep\u003c/em\u003e values are two-sided.\u003c/p\u003e\n\u003cp\u003eFigure 2. Distribution of inflammatory biomarkers across CKM stages\u003c/p\u003e\n\u003cp\u003e3.3.2 Distribution of glycemic and lipid biomarkers across CKM stages\u003c/p\u003e\n\u003cp\u003eAs shown in Figure 3, markers of glucose metabolism differed significantly across CKM stages. Both FPG (Figure 3A) and HbA1c (Figure 3B) were significantly elevated in all disease groups compared with healthy controls (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001). Pairwise comparisons revealed that FPG and HbA1c were significantly higher in S2 compared with the Healthy group (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001), whereas no significant differences were observed among S2, S3, and S4.\u003c/p\u003e\n\u003cp\u003eTG (Figure 3C) levels were also significantly higher in all disease groups compared with healthy controls (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001). Specifically, TG concentrations were markedly elevated in S2 (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001), while no significant differences were observed among S2, S3, and S4.\u003c/p\u003e\n\u003cp\u003eLp(a), visualized after log\u003csub\u003e10\u003c/sub\u003e-transformation due to its skewed distribution, showed higher levels in S2-S4 compared with healthy controls\u0026nbsp;(\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01), whereas no significant differences were observed among disease stages (Figure 3D), indicating limited variation across CKM stages.\u003c/p\u003e\n\u003cp\u003eAdditional lipid parameters are presented in Supplementary Figure 1. T-CHO and LDL-C exhibited significant overall group differences, with elevated levels primarily observed in S2 compared with healthy controls. In contrast, HDL-C differed significantly across groups (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001), with lower levels in all disease groups relative to the Healthy group, but no significant differences among S2, S3, and S4.\u003c/p\u003e\n\u003cp\u003eIn summary, abnormalities in glucose metabolism and lipid profiles were observed across CKM stages, with significant differences between healthy individuals and CKM groups but limited differentiation among disease stages.\u003c/p\u003e\n\u003cp\u003eBox-and-whisker plots showing the distribution of FPG (A), HbA1c (B), TG (C), and log\u003csub\u003e10\u003c/sub\u003e-transformed Lp(a) (D) across CKM stages (Healthy, S2, S3, and S4; n = 60 per group). The central line represents the median, boxes indicate the interquartile range (IQR), and whiskers represent the minimum and maximum values. Statistical significance is indicated as \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 (*), \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01 (**), \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001 (***), and \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001 (****); ns indicates not significant. All \u003cem\u003ep\u003c/em\u003e values are two-sided.\u003c/p\u003e\n\u003cp\u003eFigure 3. Distribution of glycemic and selected lipid biomarkers across CKM stages\u003c/p\u003e\n\u003cp\u003e3.3.3\u0026nbsp;Distribution of cardiovascular and renal injury biomarkers across CKM stages\u003c/p\u003e\n\u003cp\u003ePairwise comparisons revealed differences across CKM stages for cardiovascular and renal injury biomarkers (Figure 4 and Supplementary Figures 2-3).\u003c/p\u003e\n\u003cp\u003eAs shown in Figure 4, cardiovascular injury biomarkers showed clear differences across CKM stages. Levels of hs-cTnT (Figure 4A, log\u003csub\u003e10\u003c/sub\u003e-transformed) were significantly higher in the S3 group than in S2 (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), and were also higher in S4 compared with S3 (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). In contrast, no significant difference was observed between the Healthy and S2 groups. For NT-proBNP (Figure 4B, log\u003csub\u003e10\u003c/sub\u003e-transformed), pairwise comparisons showed higher levels in the S4 group than in the Healthy, S2, and S3 groups (all \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001). In addition, NT-proBNP levels in S3 were higher than those in the Healthy group (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05), whereas other pairwise differences among Healthy, S2, and S3 were not statistically significant.\u003c/p\u003e\n\u003cp\u003eRoutine myocardial enzyme biomarkers showed limited pairwise differences (Supplementary Figure 2). AST and CK did not differ significantly between any pair of groups. For CK-MB, higher levels were observed in S4 compared with the Healthy group (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001) and compared with S2 (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01), whereas CK-MB levels in S2 and S3 did not differ significantly from those in Healthy controls.\u003c/p\u003e\n\u003cp\u003eRenal function, as assessed by eGFR and urinary mALB, differed across CKM stages (Figure 4C, D). eGFR did not differ significantly between the Healthy and S2 groups, but was significantly lower in S3 and S4 compared with the Healthy group (both \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001). In pairwise comparisons, eGFR was significantly lower in S3 than in S2 (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001), whereas the difference between S3 and S4 was not statistically significant.\u0026nbsp;Urinary mALB (log\u003csub\u003e10\u003c/sub\u003e-transformed), was significantly higher in S2 compared with the Healthy group (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001). No statistically significant differences were observed among S2, S3, and S4.\u003c/p\u003e\n\u003cp\u003eAdditional renal biomarkers differed among CKM groups, as shown in Supplementary Figure 3. Serum urea levels were significantly higher in S3 and S4 compared with Healthy controls (both \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001), and were also higher relative to S2. UA levels were significantly higher in all CKM groups (S2, S3, and S4) compared with Healthy participants (all \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01), whereas no significant differences were observed among CKM stages. Serum Cr levels were significantly increased in S3 (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) and S4 (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01) compared with the Healthy group; however, no significant differences were detected between S2 and S4 or between S3 and S4. uPRO, shown after logarithmic transformation, was significantly higher in all CKM groups compared with Healthy controls (all \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001), while no significant differences were observed among CKM stages. Conversely, uCr levels were significantly lower in all CKM groups compared with the Healthy group, with no significant differences detected among disease stages.\u003c/p\u003e\n\u003cp\u003eOverall, cardiovascular and renal biomarkers exhibited cross-sectional differences across CKM stages, with myocardial injury markers differing more prominently between later stages, while renal injury markers showed differences across multiple CKM groups.\u003c/p\u003e\n\u003cp\u003eBox-and-whisker plots showing the distribution of log\u003csub\u003e10\u003c/sub\u003e-transformed hs-cTnT (A), log\u003csub\u003e10\u003c/sub\u003e-transformed NT-proBNP (B), eGFR (C), and log\u003csub\u003e10\u003c/sub\u003e-transformed mALB (D) across CKM stages (Healthy, S2, S3, and S4; n = 60 per group). The central line represents the median, boxes indicate the interquartile range (IQR), and whiskers represent the minimum and maximum values. Statistical significance is indicated as \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 (*), \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01 (**), \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001 (***), and \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001 (****); ns indicates not significant. All \u003cem\u003ep\u003c/em\u003e values are two-sided.\u003c/p\u003e\n\u003cp\u003eFigure 4. Distribution of key cardiovascular and renal injury biomarkers across CKM stages\u003c/p\u003e\n\u003cp\u003e3.4 Correlation between PTX3 and clinical biomarkers\u003c/p\u003e\n\u003cp\u003eSpearman correlation analysis was performed to assess pairwise associations among inflammatory, metabolic, and cardiorenal biomarkers using data pooled from all study participants across the four CKM groups, with the results visually presented as a correlation matrix heatmap (Figure 5). Detailed correlation coefficients and their corresponding \u003cem\u003ep\u003c/em\u003e values are presented in Table 3, Supplementary Table 1, and Supplementary Table 2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3.4.1 Correlation matrix of inflammatory, metabolic, and cardiorenal biomarkers\u003c/p\u003e\n\u003cp\u003eAs shown in Figure 5, inflammatory, metabolic, and cardiorenal biomarkers exhibited identifiable correlation pattern. Glycemic markers showed strong internal consistency, with a particularly strong correlation between FPG and HbA1c (\u0026rho; = 0.847). Renal injury markers also showed positive correlations with one another, exemplified by the association between uPRO and mALB (\u0026rho; = 0.873), with correlation coefficients provided in Supplementary Table 1. In contrast, inverse correlations were observed between eGFR and several markers of renal injury and cardiovascular stress.\u003c/p\u003e\n\u003cp\u003eWithin this correlation matrix, PTX3 showed predominantly positive associations with most inflammatory, metabolic, and injury-related biomarkers, while exhibiting inverse correlations with indices of renal filtration. Quantitative associations between PTX3 and individual biomarkers are summarized in Table 3. PTX3 showed statistically significant positive correlations with hs-CRP (\u0026rho; = 0.361, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) and PCT (\u0026rho; = 0.233, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). Similar positive correlations were observed with metabolic indices, including FPG (\u0026rho; = 0.458, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) and HbA1c (\u0026rho; = 0.434, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), as well as TG (\u0026rho; = 0.296, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). In contrast, PTX3 was not significantly correlated with Lp(a) (\u0026rho; = 0.052, \u003cem\u003ep\u003c/em\u003e = 0.426).\u003c/p\u003e\n\u003cp\u003eFor cardiorenal injury markers, PTX3 showed positive correlations with hs-cTnT (\u0026rho; = 0.411, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) and NT-proBNP (\u0026rho; = 0.297, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), and was inversely correlated with eGFR (\u0026rho; = -0.419, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). Among renal biomarkers, PTX3 correlated positively with uPRO (\u0026rho; = 0.216, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) and mALB (\u0026rho; = 0.337, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), while demonstrating a negative association with uCr (\u0026rho; = -0.296, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). Aside from its inverse correlations with eGFR and uCr, PTX3 showed positive correlations with most other evaluated biomarkers, reflecting co-variation across inflammatory, metabolic, and cardiorenal measures in this cross-sectional dataset.\u003c/p\u003e\n\u003cp\u003eTo further identify factors independently associated with circulating PTX3 levels, a multivariable linear regression analysis was performed including Age, Sex, BMI, SBP, FPG, hs-cTnT, and eGFR as covariates. Age, Sex, FPG, hs-cTnT, and eGFR were independently associated with PTX3 levels, whereas BMI and SBP were not significantly associated with PTX3 (Supplementary Table 3).\u003c/p\u003e\n\u003cp\u003eOverall, these results describe the observed cross-sectional association patterns among inflammatory, metabolic, and cardiorenal biomarkers.\u003c/p\u003e\n\u003cp\u003eA heatmap of the Spearman correlation coefficients (\u0026rho;) matrix is shown. Red indicates positive correlations and blue indicates negative correlations, with color intensity reflecting the magnitude of the correlation. In the upper triangle, pie charts are used to visualize correlations, where the proportion of the filled area represents correlation strength and color indicates correlation direction. In the lower triangle, shaded tiles are displayed, with forward slashes (/) indicating positive correlations and backslashes (\\) indicating negative correlations.\u003c/p\u003e\n\u003cp\u003eFigure 5. Correlation matrix heatmap of inflammatory, metabolic, and cardiorenal biomarkers\u003c/p\u003e\n\u003cp\u003eTable 3. Correlation of PTX3 with inflammatory, metabolic, and cardiorenal biomarkers\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBiomarkers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026rho;\u0026nbsp;(vs. PTX3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ehs-CRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFPG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHbA1c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLp(a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.426\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCK-MB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ehs-cTnT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNT-proBNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eeGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003euPRO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003euCr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003emALB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\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\u003c/div\u003e\n\u003cp\u003eData are Spearman correlation coefficients (\u0026rho;) describing the associations between PTX3 and individual inflammatory, metabolic, and cardiorenal biomarkers, with corresponding two-sided \u003cem\u003ep\u003c/em\u003e values.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3.4.2\u0026nbsp;Correlations between PTX3 and selected glycemic, lipid, and cardiorenal biomarkers\u003c/p\u003e\n\u003cp\u003eAs shown in Figure 6, based on the correlation results summarized in Table 3, scatter plots were generated to visualize the associations between PTX3 and selected key glycemic, lipid, and cardiorenal biomarkers in the overall study population. PTX3 exhibited a moderate positive correlation with HbA1c (\u0026rho; = 0.434, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001; Figure 6A) and a modest positive correlation with TG (\u0026rho; = 0.296, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001; Figure 6B), reflecting statistically significant associations between these variables. In addition, PTX3 showed a moderate positive association with hs-cTnT (\u0026rho; = 0.411, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001; Figure 6C). Conversely, PTX3 was moderately and inversely correlated with eGFR (\u0026rho; = -0.419, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001; Figure 6D).\u003c/p\u003e\n\u003cp\u003eAlthough several correlations were modest to moderate in magnitude, these associations reached statistical significance in this cross-sectional dataset. Together, these visualized associations describe the observed relationships between PTX3 and selected biomarkers and provided context for subsequent multivariable analyses.\u003c/p\u003e\n\u003cp\u003eScatter plots showing correlations between PTX3 and HbA1c (A), TG (B), hs-cTnT (C), and eGFR (D). Correlations were assessed using Spearman\u0026rsquo;s rank correlation analysis. Solid lines indicate fitted regression trends, and shaded areas represent 95% confidence intervals. Each dot represents an individual participant. Spearman\u0026rsquo;s correlation coefficients are denoted by \u0026rho;. All \u003cem\u003ep\u003c/em\u003e values are two-sided.\u003c/p\u003e\n\u003cp\u003eFigure 6. Correlations of PTX3 with HbA1c, TG, hs-cTnT, and eGFR\u003c/p\u003e\n\u003cp\u003e3.5 Multivariable logistic regression analysis of PTX3 and CKM stages\u003c/p\u003e\n\u003cp\u003e3.5.1 Primary association analysis (S2 vs S3+S4)\u003c/p\u003e\n\u003cp\u003eTo further evaluate factors associated with CKM stage classification, multivariable logistic regression analyses were conducted within the CKM population (stages S2-S4), with the outcome defined as early-stage CKM (S2) versus mid-advanced-stage CKM (S3+S4).\u003c/p\u003e\n\u003cp\u003eFrom a clinical and pathophysiological perspective, although S3 represents subclinical CVD or moderate-to-high risk CKD and S4 reflects overt clinical cardiovascular or renal events, both stages involve structural or functional organ abnormalities beyond isolated metabolic risk factors. This overlap provided the rationale for grouping S3 and S4 as mid-advanced-stage CKM stages in the primary analysis.\u003c/p\u003e\n\u003cp\u003eCovariates for the multivariable logistic regression models were pre-specified according to clinical relevance and the CKM staging conceptual framework. Model 1 incorporated core demographic and anthropometric variables (Age, Sex, WC, and SBP), reflecting baseline cardiovascular risk factors. Model 2 additionally included key metabolic and cardiorenal indicators (HbA1c, TG, hs-cTnT, and eGFR), representing established components of CKM pathophysiology. In order to evaluate the incremental contribution of inflammatory biomarkers, hs-CRP, PCT, and PTX3 were introduced separately into Model 2 (Models 3-5), thereby avoiding simultaneous inclusion of multiple correlated inflammatory markers and allowing independent assessment of their associations with CKM stage classification.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs shown in Table 4, age was consistently associated with higher odds of classification into mid-advanced-stage CKM (S3+S4) across all models (all \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). In contrast, WC and SBP were not significantly associated with CKM stage classification after adjustment (\u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05 across models). Sex showed no significant association in Models 1-4; however, in Model 5, which included PTX3, sex became significantly associated with CKM stage classification (OR = 0.29, 95% CI: 0.09-0.81, \u003cem\u003ep\u003c/em\u003e = 0.02), indicating a potential difference after full adjustment.\u003c/p\u003e\n\u003cp\u003eAmong cardiorenal-related variables, hs-cTnT was significantly associated with classification into mid-advanced-stage CKM (S3+S4) in Model 2 (OR = 1.08, 95% CI: 1.01-1.15, \u003cem\u003ep\u003c/em\u003e = 0.02) and Model 3, with the association attenuating to borderline significance after further adjustment in Model 4 (OR = 1.06, 95% CI: 1.00-1.14, \u003cem\u003ep\u003c/em\u003e = 0.06). eGFR showed an inverse association with S3+S4 classification in Models 2 and 3 (OR = 0.98, 95% CI: 0.96-1.00, \u003cem\u003ep\u003c/em\u003e = 0.03), whereas this association was no longer statistically significant after additional adjustment for PCT or PTX3.\u003c/p\u003e\n\u003cp\u003eNotably, differential patterns were observed among inflammatory biomarkers. Neither hs-CRP (Model 3; OR = 1.02, \u003cem\u003ep\u003c/em\u003e = 0.74) nor PCT (Model 4; OR = 1.02, \u003cem\u003ep\u003c/em\u003e = 0.17) showed statistically significant associations with CKM stage classification after adjustment. In contrast, PTX3 remained significantly associated with classification into mid-advanced-stage CKM (S3+S4) in Model 5 (OR = 1.05, 95% CI: 1.03-1.08, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), after controlling for demographic variables, metabolic indices, and cardiorenal markers.\u0026nbsp;To improve interpretability of the effect estimate, PTX3 was additionally standardized, and effect estimates were expressed per 1-SD increase. When modeled per SD increase, PTX3 remained significantly associated with CKM stage classification (OR = 5.30, 95% CI: 2.77-11.56, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), consistent with the primary per-unit analysis.\u003c/p\u003e\n\u003cp\u003eFurthermore, multicollinearity was assessed in Model 5 using variance inflation factors (VIFs). All VIF values were below 2 (range: 1.05-1.65), suggesting no evidence of substantial collinearity among included covariates (Age: 1.386; Sex: 1.112; WC: 1.092; SBP: 1.052; HbA1c: 1.079; TG: 1.071; hs-cTnT: 1.391; eGFR: 1.650; PTX3: 1.227).\u003c/p\u003e\n\u003cp\u003eTaken together, these results suggest that, within the CKM population, PTX3 shows a more consistent cross-sectional association with CKM stage classification compared with conventional inflammatory markers, and may have value in distinguishing early-stage CKM (S2) from mid-advanced-stage CKM (S3+S4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4. Multivariable logistic regression analysis for CKM stage classification (S2 vs S3+S4)\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"792\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eModel 1\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eOR\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eModel 2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eOR\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eModel 3\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eOR\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eModel 4\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eOR\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eModel 5\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eOR\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.10 (1.07-1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.08 (1.04-1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.08 (1.04-1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.09 (1.05-1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.08 (1.04-1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.95 (0.45-1.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.64 (0.27-1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.66 (0.27-1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.68 (0.28-1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.29 (0.09-0.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (0.96-1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.99 (0.95-1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.99 (0.95-1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.99 (0.95-1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (0.96-1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.02 (1.00-1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.02 (0.99-1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.02 (0.99-1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.02 (0.99-1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.03 (1.00-1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHbA1c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.11 (0.94-1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.11 (0.94-1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.09 (0.92-1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.11 (0.92-1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.08 (0.80-1.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.08 (0.80-1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.05 (0.79-1.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (0.71-1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ehs-cTnT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.08 (1.01-1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.08 (1.01-1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.06 (1.00-1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.10 (1.03-1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eeGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.98 (0.96-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.98 (0.96-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.98 (0.96-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.98 (0.96-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ehs-CRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.02 (0.91-1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.02 (0.99-1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePTX3 (unit)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.05 (1.03-1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePTX3 (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.30 (2.77-11.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\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\u003c/div\u003e\n\u003cp\u003eOutcome was defined as S2 vs S3+S4, and analyses were restricted to participants with CKM stages S2-S4. Odds ratios (ORs) are presented with 95% confidence intervals (CIs) and two-sided \u003cem\u003ep\u003c/em\u003e values. Model specifications were as follows: Model 1: age, sex, WC, and SBP; Model 2: Model 1 plus HbA1c, TG, hs-cTnT, and eGFR; Model 3: Model 2 plus hs-CRP; Model 4: Model 2 plus PCT; Model 5: Model 2 plus PTX3.\u0026nbsp;For PTX3 (unit), ORs represent the change in odds associated with a 1-unit increase in circulating PTX3 levels (pg/mL). For PTX3 (SD), ORs represent the change in odds associated with a 1-standard deviation increase in PTX3 within the CKM (S2-S4) study population. All other continuous variables were modeled per 1-unit increase in their original measurement scales.\u003c/p\u003e\n\u003cp\u003e3.5.2 Sensitivity analyses by individual CKM stages\u003c/p\u003e\n\u003cp\u003eTo further assess the robustness of the pooled-stage analysis and to explore potential differences between S3 and S4, additional stage-specific sensitivity analyses were performed by separately comparing S2 vs S3 and S2 vs S4 (Table 5).\u003c/p\u003e\n\u003cp\u003eAs shown in Table 5, PTX3 remained significantly associated with both S3 and S4 relative to S2 after multivariable adjustment. In the S2 vs S3 comparison, the base clinical model (Model 2) showed moderate discrimination (AUC = 0.694). After the addition of PTX3 (Model 5), the AUC increased to 0.757 (\u0026Delta;AUC = 0.063), although the difference did not reach statistical significance according to the DeLong test (\u003cem\u003ep\u003c/em\u003e = 0.118). In Model 5, PTX3 was significantly associated with S3 (OR: 1.05, 95% CI: 1.03-1.07; \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003eSimilarly, in the S2 vs S4 comparison, Model 2 already demonstrated high discriminative performance (AUC = 0.919). Addition of PTX3 further increased the AUC to 0.945 (\u0026Delta;AUC = 0.026), although the improvement was not statistically significant (DeLong \u003cem\u003ep\u003c/em\u003e = 0.099). PTX3 nevertheless remained independently associated with S4 in Model 5 (OR: 1.10, 95% CI: 1.05-1.19; \u003cem\u003ep\u003c/em\u003e = 0.002).\u003c/p\u003e\n\u003cp\u003eOverall, the direction of associations was consistent across both comparisons. These findings indicate that PTX3 was independently associated with both S3 and S4 relative to S2 in the stage-specific sensitivity analyses, consistent with the results observed in the pooled-stage analysis.\u003c/p\u003e\n\u003cp\u003eTable 5. Stage-specific sensitivity analyses for CKM stage classification (S2 vs S3 and S2 vs S4)\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eComparison\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePTX3\u003c/p\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003cp\u003e(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003cp\u003e(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026Delta;AUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e (DeLong)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eS2 vs S3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.694 (0.600-0.789)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eModel 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.05 (1.03-1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.757 (0.669-0.844)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eS2 vs S4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.919 (0.868-0.969)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eModel 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.10 (1.05-1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.945 (0.904-0.986)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.099\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\u003eStage-specific multivariable logistic regression analyses were performed by separately comparing S2 vs S3 and S2 vs S4 within the CKM\u0026nbsp;(S2-S4) study population. Odds ratios (ORs) are presented with 95% confidence intervals (CIs) and two-sided \u003cem\u003ep\u003c/em\u003e values. Discrimination performance is reported as area under the receiver operating characteristic curve (AUC) with 95% CIs. \u0026Delta;AUC represents the difference between Model 5 and Model 2. AUCs were compared using DeLong\u0026rsquo;s test.\u003c/p\u003e\n\u003cp\u003e3.6 Comprehensive evaluation of multivariable model performance\u003c/p\u003e\n\u003cp\u003e3.6.1 Discriminative performance of multivariable models based on ROC curve analysis\u003c/p\u003e\n\u003cp\u003eAs summarized in Table 6 and visualized in Figure 7, the baseline model incorporating demographic and anthropometric variables (Model 1) showed discrimination for classifying early-stage CKM (S2) versus mid-advanced-stage CKM (S3+S4), with an AUC of 0.804 (95% CI: 0.741-0.868).\u003c/p\u003e\n\u003cp\u003eAfter adding metabolic and cardiorenal-related variables (Model 2), the AUC increased to 0.833 (95% CI: 0.771-0.894; \u0026Delta;AUC = 0.029). However, this increment was not statistically significant when compared with Model 1 using the DeLong test (\u003cem\u003ep\u003c/em\u003e = 0.213), consistent with the overlap of ROC curves between Model 1 and 2 (Figure 7). Further inclusion of conventional inflammatory biomarkers yielded limited changes in discrimination. Specifically, adding hs-CRP (Model 3) resulted in an AUC of 0.829 (95% CI: 0.767-0.891; \u0026Delta;AUC = 0.025; DeLong \u003cem\u003ep\u003c/em\u003e = 0.281 vs Model 1), while adding PCT (Model 4) produced an AUC of 0.839 (95% CI: 0.778-0.900; \u0026Delta;AUC = 0.035; DeLong \u003cem\u003ep\u003c/em\u003e = 0.148 vs Model 1). These findings indicate that, relative to the baseline model, hs-CRP and PCT did not significantly improve discrimination in this cross-sectional stage-classification setting.\u003c/p\u003e\n\u003cp\u003eIncorporating PTX3 (Model 5) yielded an AUC of 0.892 (95% CI: 0.844-0.940). The improvement over Model 1 (\u0026Delta;AUC = 0.088) reached statistical significance by the DeLong test (\u003cem\u003ep\u003c/em\u003e = 0.001). Direct pairwise comparisons further showed that Model 5 had higher discrimination than Model 3 (DeLong \u003cem\u003ep\u003c/em\u003e = 0.005) and Model 4 (DeLong \u003cem\u003ep\u003c/em\u003e = 0.018). In Figure 7, the ROC curve for Model 5 was generally positioned above those of Models 1-4 across much of the false-positive range.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCollectively, these results indicate that inclusion of PTX3 was associated with improved model discrimination beyond demographic, metabolic, and cardiorenal markers in distinguishing early-stage CKM (S2) from mid-advanced-stage CKM (S3+S4), whereas hs-CRP and PCT showed limited incremental discrimination in the evaluated models.\u003c/p\u003e\n\u003cp\u003eTable 6. Discriminative performance of multivariable models\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eVariables included\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003cp\u003e(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026Delta;AUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e (DeLong)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAge, Sex,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eWC, SBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.804 (0.741-0.868)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eModel 1 + HbA1c, TG, hs-cTnT, eGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.833 (0.771-0.894)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.213\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eModel 2 + hs-CRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.829 (0.767-0.891)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.281\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eModel 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eModel 2 +\u003c/p\u003e\n \u003cp\u003ePCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.839 (0.778-0.900)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eModel 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eModel 2 + PTX3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.892 (0.844-0.940)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.001\u003csup\u003ea, b\u003c/sup\u003e\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\u003eAUCs are presented with 95% confidence intervals (CIs). \u0026Delta;AUC indicates the absolute difference in AUC compared with Model 1. \u003cem\u003ep\u003c/em\u003e values were calculated using two-sided DeLong test for correlated ROC curves, comparing each model against Model 1. a: Model 5 compared with Model 3 using DeLong\u0026rsquo;s test (\u003cem\u003ep\u003c/em\u003e = 0.005); b: Model 5 compared with Model 4 using DeLong\u0026rsquo;s test (\u003cem\u003ep\u003c/em\u003e = 0.018).\u003c/p\u003e\n\u003cp\u003eROC curves are shown for Models 1-5. The diagonal dashed line indicates no-discrimination performance (AUC = 0.500). Model specifications and corresponding AUCs (95% CIs) are reported in Table 6.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 7. ROC curves of multivariable models for CKM stage classification (S2 vs S3+S4)\u003c/p\u003e\n\u003cp\u003e3.6.2 Calibration performance of multivariable models assessed by the H-L test\u003c/p\u003e\n\u003cp\u003eCalibration results assessed by the H-L test are summarized in Table 7. Overall, Models 1, 3, and 5 showed no statistically significant evidence of lack of fit, with HL \u003cem\u003ep\u003c/em\u003e values above 0.05 (Model 1: \u0026chi;\u003csup\u003e2\u003c/sup\u003e = 14.050, \u003cem\u003ep\u003c/em\u003e = 0.080; Model 3: \u0026chi;\u003csup\u003e2\u003c/sup\u003e = 12.908, \u003cem\u003ep\u003c/em\u003e = 0.115; Model 5: \u0026chi;\u003csup\u003e2\u003c/sup\u003e = 14.968, \u003cem\u003ep\u003c/em\u003e = 0.060), indicating consistency between observed and expected event frequencies across risk strata.\u003c/p\u003e\n\u003cp\u003eModel 2 showed borderline calibration (\u0026chi;\u003csup\u003e2\u003c/sup\u003e = 15.538, \u003cem\u003ep\u003c/em\u003e = 0.050), suggesting that the addition of metabolic and cardiorenal variables was associated with small differences between predicted and observed classification probabilities, although the result was at the conventional significance threshold.\u003c/p\u003e\n\u003cp\u003eIn contrast, Model 4 showed statistically significant lack of fit (\u0026chi;\u003csup\u003e2\u003c/sup\u003e = 156.260, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), indicating deviation between predicted and observed probabilities in this dataset. This finding suggests that the model specification incorporating PCT (Model 4) may produce predicted probabilities that differ from observed proportions, despite its discrimination being similar to other models.\u003c/p\u003e\n\u003cp\u003eOverall, the H-L results show that most candidate models did not demonstrate statistically significant lack of fit, whereas the PCT-augmented model (Model 4) showed evidence of miscalibration in this sample.\u003c/p\u003e\n\u003cp\u003eTable 7. Calibration performance of multivariable models\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHosmer-Lemeshow \u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15.538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.908\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eModel 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e156.260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eModel 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14.968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.060\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\u003eCalibration was evaluated using the Hosmer-Lemeshow (H-L) goodness-of-fit test. \u0026chi;\u003csup\u003e2\u003c/sup\u003e and \u003cem\u003ep\u003c/em\u003e values are reported for each model (df = 8). \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05 indicates no evidence of lack of fit, suggesting acceptable model calibration, whereas \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 indicates a statistically significant deviation between observed and expected outcomes, suggestive of potential miscalibration.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this cross-sectional study, we systematically characterized the distributional patterns, inter-marker correlations, and multivariable classification performance of inflammatory, metabolic, and cardiorenal biomarkers across CKM stages, with a particular focus on PTX3. The main findings can be summarized as follows. First, circulating PTX3 levels differed across CKM stages and were already elevated in individuals with early-stage CKM compared with healthy controls. Second, PTX3 was associated with multiple biomarker domains, including markers of systemic inflammation, glycemic dysregulation, myocardial injury, and renal dysfunction. Third, in multivariable models comparing early-stage CKM (S2) with mid-advanced-stage CKM (S3+S4), PTX3 remained significantly associated with stage classification after adjustment for demographic characteristics, metabolic indices, and cardiorenal biomarkers, whereas hs-CRP and PCT did not retain statistically significant associations under full adjustment. Finally, incorporation of PTX3 into the clinical model was associated with improved discrimination in the pooled-stage analysis while maintaining acceptable model calibration. Taken together, these findings suggest that PTX3 may serve as a complementary biomarker associated with inflammatory and cardiorenal features within the CKM framework.\u003c/p\u003e\n\u003cp\u003eCKM syndrome is conceptualized as a disease continuum encompassing metabolic risk factors, CKD, and CVD, in which chronic low-grade inflammation is thought to link metabolic dysregulation, renal impairment, and cardiovascular dysfunction across multiple target organs \u003csup\u003e[14-17]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eConventional inflammatory markers such as hs-CRP are predominantly synthesized by hepatocytes and mainly reflect IL-6–driven systemic acute-phase responses, which may not fully differentiate the specific sources of inflammatory signals contributing to CKM. Consistent with this view, population-based analyses suggest that hs-CRP levels are strongly influenced by obesity-related inflammation rather than independent cardiometabolic dysfunction \u003csup\u003e[18]\u003c/sup\u003e. Emerging multi-organ frameworks of the cardiovascular-renal-hepato-metabolic syndrome further highlight the liver—particularly metabolic dysfunction-associated steatotic liver disease (MASLD)—as an inflammatory contributor, suggesting that reliance on liver-derived hs-CRP alone may not fully capture inflammatory signals arising from other tissues \u003csup\u003e[19]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn the present dataset, hs-CRP did not differ significantly among CKM stages (S2, S3, and S4), suggesting limited discriminatory capacity within the CKM spectrum.\u0026nbsp;Furthermore, when hs-CRP was incorporated into a multivariable model that already included glycemic, lipid, and cardiorenal indicators, no statistically significant improvement in discrimination was observed. These findings suggest that hs-CRP may reflect a background inflammatory burden common to CKM rather than differences across CKM stages.\u003c/p\u003e\n\u003cp\u003ePCT,\u0026nbsp;originally developed as a biomarker of bacterial infection and sepsis, has also been examined in metabolic disorders, heart failure, and CKD. Available evidence indicates that PCT may show low-level elevations in obesity, diabetes, heart failure, or CKD even in the absence of overt infection, reflecting non-specific inflammatory influences \u003csup\u003e[20-25]\u003c/sup\u003e. In the present dataset, although PCT differed across CKM stages, its addition to the clinical model was associated with only a small change in AUC, and the PCT-containing model demonstrated evidence of miscalibration. These findings suggest that PCT may not provide additional discriminatory information for CKM stage classification beyond established clinical and metabolic markers.\u003c/p\u003e\n\u003cp\u003eIn contrast, PTX3 is biologically distinct from liver-derived acute-phase reactants. PTX3 is produced at sites of inflammation by multiple cell types—including endothelial and vascular smooth muscle cells, monocytes/macrophages, adipocytes, and renal parenchymal cells—and may reflect inflammatory processes occurring within affected tissues \u003csup\u003e[26-29]\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAccumulating evidence across metabolic, renal, and cardiovascular populations indicates that PTX3 is associated with inflammatory activation at the interface of metabolic stress, vascular injury, and renal dysfunction. In metabolic disorders, PTX3 has been reported to correlate with glycemic dysregulation and obesity-related inflammatory burden \u003csup\u003e[30]\u003c/sup\u003e. In CKD, elevated PTX3 levels have been associated with mortality and cardiovascular events \u003csup\u003e[31]\u003c/sup\u003e. In cardiovascular epidemiology, PTX3 has been associated with subclinical atherosclerosis and coronary artery calcification beyond traditional risk factors \u003csup\u003e[32]\u003c/sup\u003e.\u0026nbsp;Rather than representing isolated findings, these observations suggest that PTX3 is associated with inflammatory features across multiple disease domains.\u003c/p\u003e\n\u003cp\u003eWithin the CKM classification framework, PTX3 levels exhibited stage-related variation, with differences primarily observed between early-stage and more advanced CKM. Notably, PTX3 levels did not differ significantly between S3 (subclinical CVD) and S4 (clinical CVD). This pattern suggests that PTX3 may reflect inflammatory activation associated with early cardiorenal injury, which likely emerges at the stage of subclinical organ involvement and may subsequently remain at relatively stable levels as the disease progresses to overt cardiovascular manifestations. In contrast, conventional inflammatory and metabolic markers—including hs-CRP, FPG, HbA1c, and TG—showed significant differences between healthy individuals and CKM groups but demonstrated limited ability to differentiate among S2, S3, and S4. Taken together, these findings suggest that PTX3 may be more closely associated with CKM stage classification than several commonly used inflammatory or metabolic markers.\u003c/p\u003e\n\u003cp\u003eCorrelation analyses further supported this pattern. PTX3 showed correlations with biomarkers reflecting metabolic status as well as myocardial and renal injury. In contrast, correlations among traditional metabolic markers were largely confined within metabolic domains and showed weaker associations with cardiorenal injury indicators.\u003c/p\u003e\n\u003cp\u003eAt the multivariable level, PTX3 remained significantly associated with CKM stage classification distinguishing early-stage CKM (S2) from mid-advanced-stage CKM (S3+S4) after adjustment for demographic characteristics, metabolic indices, and cardiorenal biomarkers, whereas hs-CRP and PCT did not retain statistically significant associations under full adjustment. This finding suggests that PTX3 may capture inflammatory signals that are not fully reflected by conventional systemic inflammatory markers or by traditional metabolic and cardiorenal indicators. Given that PTX3 is produced locally at sites of tissue injury by vascular, immune, and parenchymal cells, circulating PTX3 levels may partly reflect inflammatory activity occurring across multiple organs involved in CKM pathophysiology.\u003c/p\u003e\n\u003cp\u003eIn discrimination analyses, incorporation of PTX3 into the clinical model was associated with a statistically significant improvement in AUC in the pooled-stage analysis, whereas the addition of hs-CRP or PCT was not associated with meaningful changes in model performance. This pattern suggests that PTX3 may provide complementary information beyond conventional metabolic and cardiorenal markers within the CKM spectrum. Calibration analyses further indicated that the PTX3-containing model demonstrated acceptable agreement between predicted and observed stage classifications in this dataset.\u003c/p\u003e\n\u003cp\u003eIn stage-specific sensitivity analyses separating S3 and S4, the addition of PTX3 to Model 2 yielded numerically higher AUC values for both the S2 versus S3 and S2 versus S4 comparisons; however, the corresponding DeLong tests were not statistically significant. This may partly reflect reduced statistical power after subgroup stratification together with the already strong discriminative performance of Model 2, particularly for the S2 versus S4 comparison where the baseline AUC was relatively high. Accordingly, these analyses should be interpreted as supportive evidence indicating a consistent direction of association rather than definitive evidence of incremental discrimination at each individual CKM stage.\u003c/p\u003e\n\u003cp\u003eIt should be noted that, owing to the cross-sectional design of the present study, the observed patterns represent associations across CKM stages rather than evidence of temporal changes at the individual level. Nevertheless, the results suggest that PTX3 may serve as a biomarker reflecting inflammatory and cardiorenal characteristics within the CKM spectrum. In populations with prevalent metabolic abnormalities, PTX3 may provide complementary information for CKM stage classification beyond conventional systemic inflammatory markers.\u003c/p\u003e\n\u003cp\u003eSeveral limitations should be considered when interpreting the present findings. First, owing to the cross-sectional design, the observed associations reflect contemporaneous differences across CKM stages and do not permit causal inference or evaluation of temporal changes in CKM stage at the individual level. Prospective longitudinal studies are required to determine whether PTX3 is associated with future stage transitions or adverse cardiovascular outcomes. In addition, PTX3 was measured at a single time point, precluding assessment of intra-individual variability or temporal dynamics; such measurement variability may attenuate observed associations.\u003c/p\u003e\n\u003cp\u003eSecond, although the sample size was reasonable for a single-center CKM staging study, the overall cohort remains modest, and CKM S1 was not included. Therefore, the applicability of the findings is primarily limited to individuals with established metabolic or cardiorenal abnormalities (S2-S4). In addition, CKM stages encompass heterogeneous clinical conditions, including different combinations of metabolic abnormalities, cardiovascular diseases, and renal impairment. Consequently, the composition of patients within each stage may vary, and differences in inflammatory marker levels could partly reflect underlying disease heterogeneity rather than stage classification alone. Renal impairment was generally mild, with preserved or only mildly reduced eGFR and predominantly microalbuminuria, and BMI levels were relatively low in this study population, reflecting a comparatively mild overall disease profile that may limit generalizability. Notably, previous studies have suggested that sex-related differences may influence inflammatory responses and cardiometabolic risk. Although the distribution of sex did not differ significantly across CKM stages in the present cohort, the potential impact of sex on PTX3-associated inflammatory pathways warrants further investigation in future studies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, despite adjustment for multiple demographic, metabolic, myocardial injury, and renal function markers, residual confounding from unmeasured inflammatory mediators, lifestyle factors, or medication use cannot be fully excluded. Detailed information on medication use (e.g., renin-angiotensin system inhibitors or statins) was not systematically collected in the present study. As patients with more advanced CKM stages may receive pharmacological treatments for underlying cardiometabolic conditions, the potential influence of medications on circulating PTX3 levels cannot be excluded. Validation in larger, multicenter, and ethnically diverse cohorts is warranted.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study evaluated the association between circulating PTX3 and AHA-defined CKM stages. PTX3 levels were significantly elevated in early-stage CKM (S2) compared with healthy controls and remained significantly associated with classification between early-stage CKM (S2) and mid-advanced-stage CKM (S3+S4) after multivariable adjustment. Compared with hs-CRP and PCT, inclusion of PTX3 was associated with higher discriminative performance in stage classification, without evidence of impaired calibration. These findings indicate that PTX3 is associated with inflammatory and cardiorenal features within the CKM framework.\u003c/p\u003e\n\u003cp\u003eProspective studies are needed to determine whether PTX3 is associated with subsequent CKM stage changes or adverse cardiorenal outcomes. Integration with imaging-based markers and multi-omics approaches may further clarify the biological context of PTX3-related inflammation and its potential relevance for CKM characterization in diverse populations.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eT2DM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etype 2 diabetes mellitus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCKD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003echronic kidney disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCVD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecardiovascular disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCKM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecardiovascular-kidney-metabolic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAHA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAmerican Heart Association\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRAAS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003erenin-angiotensin-aldosterone system\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCRP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eC-reactive protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIL-6\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einterleukin-6\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNLR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eneutrophil-to-lymphocyte ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprocalcitonin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePTX3\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epentraxin 3\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFPG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003efasting plasma glucose\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ehs-CRP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehigh-sensitivity C-reactive protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eT-CHO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etotal cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etriglycerides\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLDL-C\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elow-density lipoprotein cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHDL-C\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehigh-density lipoprotein cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLp(a)\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elipoprotein(a)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eALT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ealanine aminotransferase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAST\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003easpartate aminotransferase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCK\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecreatine kinase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCK-MB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecreatine kinase-MB\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003euric acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCr\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecreatinine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eeGFR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eestimated glomerular filtration rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHbA1c\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eglycated hemoglobin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHPLC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehigh-performance liquid chromatography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ehs-cTnT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehigh-sensitivity cardiac troponin T\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNT-proBNP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eN-terminal pro-B-type natriuretic peptide\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eELISA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eenzyme-linked immunosorbent assay\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003euPRO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eurinary protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003euCr\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eurinary creatinine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003emALB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emicroalbumin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003estandard deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einterquartile range\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLOD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elimit of detection\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eANOVA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eanalysis of variance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVIFs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003evariance inflation factors\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEPV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eevents-per-variable\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eORs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eodds ratios\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCIs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econfidence intervals\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ereceiver operating characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003earea under the curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ewaist circumference\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esystolic blood pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ediastolic blood pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMASLD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emetabolic dysfunction-associated steatotic liver disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e1. Ethics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Medical Science Research Ethics Committee of Peking University Third Hospital (IRB00006761-M20250008). The requirement for informed consent was waived due to the minimal risk nature of the study and the use of anonymized residual serum samples obtained after routine clinical testing. All procedures were conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e2. Consent for publication\u003c/p\u003e\n\u003cp\u003eAll authors have read and approved the publication of this manuscript.\u003c/p\u003e\n\u003cp\u003e3. Availability of data and material\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e4. Competing interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e5. Clinical trial number\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e6. Funding\u003c/p\u003e\n\u003cp\u003eBeijing Research Ward Excellence Program, BRWEP.\u003c/p\u003e\n\u003cp\u003eGrant Number: BRWEP2024W014090210\u003c/p\u003e\n\u003cp\u003e7. Author Contributions\u003c/p\u003e\n\u003cp\u003eZhen Xu was responsible for study conception, sample collection, data analysis, and drafting of the manuscript. Yuan Tan, Qian Zhang, He Wang, and Jingjin Tao contributed to sample organization and management. Qi Liu, Zhongxin Li, and Chong Wang contributed to the critical revision and refinement of the initial draft. Shuo Yang and Liyan Cui were responsible for the overall review and editing of the manuscript. All authors have read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e8. Acknowledgements\u003c/p\u003e\n\u003cp\u003eWe sincerely thank every member of our team for their valuable contributions to this manuscript. From sample collection and data acquisition to manuscript framework and detailed refinement, the collaborative efforts and dedication of all members were essential to the completion of this study.\u003c/p\u003e\n\u003cp\u003e9. Authors' information\u003c/p\u003e\n\u003cp\u003eZhen Xu: Graduate student in medicine; Peking University Third Hospital; [email protected]\u003c/p\u003e\n\u003cp\u003eShuo Yang: Master of medical science; Peking University Third Hospital; [email protected]\u003c/p\u003e\n\u003cp\u003eYuan Tan: PhD student in medicine; Peking University Third Hospital; [email protected]\u003c/p\u003e\n\u003cp\u003eQian Zhang: Doctoral student in clinical medicine; Peking University Third Hospital; [email protected]\u003c/p\u003e\n\u003cp\u003eHe Wang: PhD student in medicine; Peking University Third Hospital; [email protected]\u003c/p\u003e\n\u003cp\u003eJingjin Tao: Graduate student in medicine; Peking University Third Hospital; [email protected]\u003c/p\u003e\n\u003cp\u003eQi Liu: PhD student in medicine; Peking University Third Hospital; [email protected]\u003c/p\u003e\n\u003cp\u003eZhongxin Li: Doctoral student in clinical medicine; Peking University Third Hospital; [email protected]\u003c/p\u003e\n\u003cp\u003eChong Wang: Graduate student in medicine; Peking University Third Hospital; [email protected]\u003c/p\u003e\n\u003cp\u003eLiyan Cui: PhD in Medicine; Peking University Third Hospital; [email protected]\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNdumele CE, Rangaswami J, Chow SL, Neeland IJ, Tuttle KR, Khan SS, Coresh J, Mathew RO, Baker-Smith CM, Carnethon MR, Despres JP, Ho JE, Joseph JJ, Kernan WN, Khera A, Kosiborod MN, Lekavich CL, Lewis EF, Lo KB, Ozkan B, Palaniappan LP, Patel SS, Pencina MJ, Powell-Wiley TM, Sperling LS, Virani SS, Wright JT, Rajgopal Singh R, Elkind MSV (2023) American Heart Association. Cardiovascular-Kidney-Metabolic Health: A Presidential Advisory From the American Heart Association. Circulation. ;148(20):1606\u0026ndash;1635. doi: 10.1161/CIR.0000000000001184. Epub 2023 Oct 9. Erratum in: Circulation. 2024;149(13):e1023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/CIR.0000000000001241\u003c/span\u003e\u003cspan address=\"10.1161/CIR.0000000000001241\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 37807924\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu Z, Yang S, Tan Y, Zhang Q, Wang H, Tao J, Liu Q, Wang Q, Feng W, Li Z, Wang C, Cui L (2025) Inflammation in cardiovascular-kidney-metabolic syndrome: key roles and underlying mechanisms-a comprehensive review. Mol Cell Biochem 480(12):6039\u0026ndash;6075. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11010-025-05379-9\u003c/span\u003e\u003cspan address=\"10.1007/s11010-025-05379-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003eEpub 2025 Sep 10. PMID: 40928582; PMCID: PMC12594699\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoy A, Zeller J, Nero TL, Klepetko J, Eisenhardt SU, Parker MW, McFadyen JD, Peter K (2025) C-reactive protein: the nexus between inflammation and protein misfolding diseases. Front Immunol 16:1612703. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2025.1612703\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2025.1612703\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003ePMID: 40534869; PMCID: PMC12173917\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarollo C, Sorce A, Cirafici E, Ciuppa ME, Mul\u0026egrave; G, Caimi G (2025) Silent Inflammation, Loud Consequences: Decoding NLR Across Renal, Cardiovascular and Metabolic Disorders. Int J Mol Sci 26(17):8256. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms26178256\u003c/span\u003e\u003cspan address=\"10.3390/ijms26178256\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003ePMID: 40943179; PMCID: PMC12428752\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen HL, Wu C, Cao L, Wang R, Zhang TY, He Z (2024) The association between the neutrophil-to-lymphocyte ratio and type 2 diabetes mellitus: a cross-sectional study. BMC Endocr Disord 24(1):107. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12902-024-01637-x\u003c/span\u003e\u003cspan address=\"10.1186/s12902-024-01637-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003ePMID: 38982402; PMCID: PMC11232124\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoshitomi R, Nakayama M, Sakoh T, Fukui A, Katafuchi E, Seki M, Tsuda S, Nakano T, Tsuruya K, Kitazono T (2019) High neutrophil/lymphocyte ratio is associated with poor renal outcomes in Japanese patients with chronic kidney disease. Ren Fail 41(1):238\u0026ndash;243 PMID: 30942116; PMCID: PMC6450582\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo J, Thomassen JQ, Nordestgaard BG, Tybj\u0026aelig;rg-Hansen A, Frikke-Schmidt R (2023) Neutrophil counts and cardiovascular disease. Eur Heart J 44(47):4953\u0026ndash;4964. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/eurheartj/ehad649\u003c/span\u003e\u003cspan address=\"10.1093/eurheartj/ehad649\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003ePMID: 37950632; PMCID: PMC10719495\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu L, Sun J, Liu X (2023) Serum C reactive protein and procalcitonin are valuable predictors of coronary heart disease and poor prognosis in the elderly. Am J Transl Res 15(6):4188\u0026ndash;4195 PMID: 37434846; PMCID: PMC10331649\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBontekoe J, Bansal V, Lee J, Syed M, Hoppensteadt D, Maia P, Walborn A, Liles J, Vasaiwala S, Fareed J Procalcitonin as a Marker of Comorbid Atrial Fibrillation in Chronic Kidney Disease and History of Sepsis. Clin Appl Thromb Hemost 2020 Jan-Dec ;26:1076029620932228. doi: 10.1177/1076029620932228. PMID: 32539447; PMCID: PMC7427007.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMassimino AM, Colella FE, Bottazzi B, Inforzato A (2023) Structural insights into the biological functions of the long pentraxin PTX3. Front Immunol 14:1274634. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2023.1274634\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2023.1274634\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003ePMID: 37885881; PMCID: PMC10598717\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDaigo K, Inforzato A, Barajon I, Garlanda C, Bottazzi B, Meri S, Mantovani A (2016) Pentraxins in the activation and regulation of innate immunity. Immunol Rev 274(1):202\u0026ndash;217. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/imr.12476\u003c/span\u003e\u003cspan address=\"10.1111/imr.12476\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003ePMID: 27782337; PMCID: PMC5412954\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInforzato A, Doni A, Barajon I, Leone R, Garlanda C, Bottazzi B, Mantovani A (2013) PTX3 as a paradigm for the interaction of pentraxins with the complement system. Semin Immunol 25(1):79\u0026ndash;85 Epub 2013 Jun 6. PMID: 23747040\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZlibut A, Bocsan IC, Agoston-Coldea L (2019) Pentraxin-3 and endothelial dysfunction. Adv Clin Chem. ;91:163\u0026ndash;179. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/bs.acc.2019.03.005\u003c/span\u003e\u003cspan address=\"10.1016/bs.acc.2019.03.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2019 May 4. PMID: 31331488\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKawai T, Autieri MV, Scalia R (2021) Adipose tissue inflammation and metabolic dysfunction in obesity. Am J Physiol Cell Physiol 320(3):C375\u0026ndash;C391. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1152/ajpcell.00379.2020\u003c/span\u003e\u003cspan address=\"10.1152/ajpcell.00379.2020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003eEpub 2020 Dec 23. PMID: 33356944; PMCID: PMC8294624\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrant CH, Bell S (2025) Obesity and Chronic Kidney Disease: The Dual Epidemic in Cardiovascular Health. Eur Cardiol 20:e28. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.15420/ecr.2025.33\u003c/span\u003e\u003cspan address=\"10.15420/ecr.2025.33\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003ePMID: 41210917; PMCID: PMC12590400\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRohm TV, Meier DT, Olefsky JM, Donath MY (2022) Inflammation in obesity, diabetes, and related disorders. Immunity 55(1):31\u0026ndash;55. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.immuni.2021.12.013\u003c/span\u003e\u003cspan address=\"10.1016/j.immuni.2021.12.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003ePMID: 35021057; PMCID: PMC8773457\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManabe I (2011) Chronic inflammation links cardiovascular, metabolic and renal diseases. Circ J 75(12):2739\u0026ndash;2748. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1253/circj.cj-11-1184\u003c/span\u003e\u003cspan address=\"10.1253/circj.cj-11-1184\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003eEpub 2011 Nov 8. PMID: 22067929\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim BM, Ryu SY, Han MA, Choi SW (2025) Loss of significant association between high-sensitivity C-reactive protein (hs-CRP) and metabolic syndrome after adjustment for waist circumference found in 2022 Korea National Health and Nutrition Examination Survey data. J Physiol Anthropol 44(1):16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s40101-025-00396-5\u003c/span\u003e\u003cspan address=\"10.1186/s40101-025-00396-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003ePMID: 40474309; PMCID: PMC12139091\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNzobokela J, Muchaili L, Mwambungu A, Masenga SK, Kirabo A (2025) Pathophysiology and emerging biomarkers of cardiovascular-renal-hepato-metabolic syndrome. Front Cardiovasc Med 12:1661563. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fcvm.2025.1661563\u003c/span\u003e\u003cspan address=\"10.3389/fcvm.2025.1661563\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003ePMID: 41235339; PMCID: PMC12605086\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbbasi A, Corpeleijn E, Postmus D, Gansevoort RT, de Jong PE, Gans RO, Struck J, Hillege HL, Stolk RP, Navis G, Bakker SJ (2010) Plasma procalcitonin is associated with obesity, insulin resistance, and the metabolic syndrome. J Clin Endocrinol Metab 95(9):E26\u0026ndash;31. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1210/jc.2010-0305\u003c/span\u003e\u003cspan address=\"10.1210/jc.2010-0305\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003eEpub 2010 Jun 9. PMID: 20534760\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCanbay A, Celebi OO, Celebi S, Aydogdu S, Diker E (2015) Procalcitonin: a marker of heart failure. Acta Cardiol. ;70(4):473-8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/ac.70.4.3096896\u003c/span\u003e\u003cspan address=\"10.1080/ac.70.4.3096896\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 26455251\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVillanueva MP, Mollar A, Palau P, Carratal\u0026aacute; A, N\u0026uacute;\u0026ntilde;ez E, Santas E, Bod\u0026iacute; V, Chorro FJ, Mi\u0026ntilde;ana G, Blasco ML, Sanchis J, N\u0026uacute;\u0026ntilde;ez J (2015) Procalcitonin and long-term prognosis after an admission for acute heart failure. Eur J Intern Med 26(1):42\u0026ndash;48 Epub 2015 Jan 12. PMID: 25592074\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCvetinovic N, Isakovic AM, Lainscak M, Dungen HD, Nikolic NM, Loncar G (2017) Procalcitonin in heart failure: hic et nunc. Biomark Med. ;11(10):893\u0026ndash;903. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2217/bmm-2017-0160\u003c/span\u003e\u003cspan address=\"10.2217/bmm-2017-0160\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2017 Oct 4. PMID: 28976777\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eA\u0026iuml;ssou L, Sorbets E, Lallmahomed E, Goudot FX, Pop N, Es-Sebbani S, Benouda L, Nuel G, Meune C (2018) Prognostic and diagnostic value of elevated serum concentration of procalcitonin in patients with suspected heart failure. A review and meta-analysis. Biomarkers. ;23(5):407\u0026ndash;413. doi: 10.1080/1354750X.2018.1443511. Epub 2018 Mar 12. PMID: 29465002\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu SC, Liang CX, Zhang YL, Hu WP (2020) Elevated serum procalcitonin level in patients with chronic kidney disease without infection: A case-control study. J Clin Lab Anal 34(2):e23065. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/jcla.23065\u003c/span\u003e\u003cspan address=\"10.1002/jcla.23065\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003eEpub 2019 Oct 16. PMID: 31617251; PMCID: PMC7031592\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu Z, Gao Z, Fu H, Zeng Y, Jin Y, Xu B, Zhang Y, Pan Z, Chen X, Zhang X, Wang X, Yan H, Yang X, Yang B, He Q, Luo P (2023) PTX3 from vascular endothelial cells contributes to trastuzumab-induced cardiac complications. Cardiovasc Res 119(5):1250\u0026ndash;1264. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/cvr/cvad012\u003c/span\u003e\u003cspan address=\"10.1093/cvr/cvad012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003ePMID: 36651911; PMCID: PMC10202646\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSuzuki S, Takeishi Y, Niizeki T, Koyama Y, Kitahara T, Sasaki T, Sagara M, Kubota I (2008) Pentraxin 3, a new marker for vascular inflammation, predicts adverse clinical outcomes in patients with heart failure. Am Heart J 155(1):75\u0026ndash;81 Epub 2007 Sep 27. PMID: 18082493\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin TY, Guo H, Chen X (2021) Unraveling mechanisms of pentraxin 3 secretion in adipocytes during inflammation. J Mol Endocrinol. ;67(3):55\u0026ndash;69. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1530/JME-21-0026\u003c/span\u003e\u003cspan address=\"10.1530/JME-21-0026\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 34213423\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNauta AJ, de Haij S, Bottazzi B, Mantovani A, Borrias MC, Aten J, Rastaldi MP, Daha MR, van Kooten C, Roos A (2005) Human renal epithelial cells produce the long pentraxin PTX3. Kidney Int. ;67(2):543\u0026thinsp;\u0026ndash;\u0026thinsp;53. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/j.1523-1755.2005.67111.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1523-1755.2005.67111.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 15673302\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhrițculesei RV, Boldeanu L, Caragea DC, Vladu IM, Clenciu D, Mitrea A, Ungureanu AM, Văduva CC, Dijmărescu AL, Popescu AIS, Assani MZ, Boldeanu MV, Vere CC (2025) Association Between Pentraxins and Obesity in Prediabetes and Newly Diagnosed Type 2 Diabetes Mellitus Patients. Int J Mol Sci 26(8):3661. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms26083661\u003c/span\u003e\u003cspan address=\"10.3390/ijms26083661\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003ePMID: 40332236; PMCID: PMC12027536\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi L, Liu H, Zhang Q, Jin H, Tao H, Chen H, Zhou Z (2024) Pentraxin-3 and Outcomes in CKD: A Systematic Review and Meta-analysis. Kidney Med 6(4):100800. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.xkme.2024.100800\u003c/span\u003e\u003cspan address=\"10.1016/j.xkme.2024.100800\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003ePMID: 38645733; PMCID: PMC11026967\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJenny NS, Blumenthal RS, Kronmal RA, Rotter JI, Siscovick DS, Psaty BM (2014) Associations of pentraxin 3 with cardiovascular disease: the Multi-Ethnic Study of Atherosclerosis. J Thromb Haemost 12(6):999\u0026ndash;1005. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/jth.12557\u003c/span\u003e\u003cspan address=\"10.1111/jth.12557\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003ePMID: 24628740; PMCID: PMC4055511\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"molecular-and-cellular-biochemistry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcbi","sideBox":"Learn more about [Molecular and Cellular Biochemistry](https://www.springer.com/journal/11010)","snPcode":"11010","submissionUrl":"https://submission.nature.com/new-submission/11010/3","title":"Molecular and Cellular Biochemistry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Pentraxin 3, Cardiovascular-Kidney-Metabolic syndrome, Inflammation, CKM staging, Cardiorenal biomarkers","lastPublishedDoi":"10.21203/rs.3.rs-9249191/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9249191/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eThe cardiovascular-kidney-metabolic (CKM) syndrome represents a continuum linking metabolic dysfunction, chronic kidney disease, and cardiovascular disease, in which chronic inflammation plays a central role. However, conventional inflammatory biomarkers may not fully capture local inflammatory processes involved in CKM stage transitions. Pentraxin 3 (PTX3), a long pentraxin produced locally at sites of inflammation, may provide complementary information, yet its association with CKM staging has not been systematically evaluated.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eIn this cross-sectional study, circulating PTX3 levels were measured in 240 adults, including healthy controls (stage 0, S0; n\u0026thinsp;=\u0026thinsp;60) and individuals with stage 2 (S2; n\u0026thinsp;=\u0026thinsp;60), stage 3 (S3; n\u0026thinsp;=\u0026thinsp;60), and stage 4 (S4; n\u0026thinsp;=\u0026thinsp;60) CKM, classified according to the CKM staging framework. Associations between PTX3 and inflammatory, metabolic, cardiac, and renal biomarkers were assessed using Spearman correlation analysis. Multivariable logistic regression models were constructed within the CKM population (S2-S4) to distinguish early-stage CKM (S2) from mid-advanced-stage CKM (S3\u0026thinsp;+\u0026thinsp;S4). Model discrimination and calibration were evaluated using receiver operating characteristic (ROC) analysis and the Hosmer-Lemeshow goodness-of-fit test.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003ePTX3 levels were significantly elevated in early-stage CKM (S2) compared with healthy controls (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and showed further differentiation between S2 and S3 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). PTX3 showed moderate correlations with biomarkers reflecting inflammatory activation, metabolic dysregulation, myocardial injury, and renal dysfunction, including high-sensitivity C-reactive protein (hs-CRP; ρ\u0026thinsp;=\u0026thinsp;0.361, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), glycated hemoglobin (HbA1c; ρ\u0026thinsp;=\u0026thinsp;0.434, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), triglycerides (TG; ρ\u0026thinsp;=\u0026thinsp;0.296, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and high-sensitivity cardiac troponin T (hs-cTnT; ρ\u0026thinsp;=\u0026thinsp;0.411, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and was inversely correlated with estimated glomerular filtration rate (eGFR; ρ = -0.419, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In multivariable logistic regression models adjusting for demographic factors, metabolic indices, and cardiorenal biomarkers, PTX3 remained independently associated with classification into mid-advanced-stage CKM (S3\u0026thinsp;+\u0026thinsp;S4 vs S2; odds ratio [OR] per unit increase\u0026thinsp;=\u0026thinsp;1.05, 95% confidence interval [CI]: 1.03\u0026ndash;1.08; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas hs-CRP and procalcitonin (PCT) showed no independent associations. Incorporation of PTX3 significantly improved model discrimination (area under the curve [AUC], 0.892 vs 0.804 for the baseline model; ΔAUC\u0026thinsp;=\u0026thinsp;0.088, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001 by DeLong test), without evidence of compromised calibration.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e \u003cp\u003eCirculating PTX3 is cross-sectionally associated with CKM stage classification and demonstrates incremental discriminative value beyond conventional inflammatory markers in distinguishing early-stage from mid-advanced-stage CKM. These findings suggest that PTX3 may reflect inflammatory processes not fully captured by systemic markers, supporting its potential role in CKM risk stratification.\u003c/p\u003e","manuscriptTitle":"Pentraxin 3 Is an Inflammation-Related Biomarker That Distinguishes Early-Stage from Mid-Advanced Cardiovascular-Kidney-Metabolic Syndrome","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-13 12:10:56","doi":"10.21203/rs.3.rs-9249191/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-14T22:56:56+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-11T07:31:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"10882425398863699049489683744336928650","date":"2026-05-11T05:33:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"276675619316829565194423535403312625928","date":"2026-05-10T18:31:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-23T01:04:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"313987546497770067669699926642118791224","date":"2026-04-06T17:07:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-04T21:59:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-02T17:53:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-02T17:53:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular and Cellular Biochemistry","date":"2026-03-28T02:34:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-and-cellular-biochemistry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcbi","sideBox":"Learn more about [Molecular and Cellular Biochemistry](https://www.springer.com/journal/11010)","snPcode":"11010","submissionUrl":"https://submission.nature.com/new-submission/11010/3","title":"Molecular and Cellular Biochemistry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"8b988fd5-8afd-49dd-88af-ac14fbef3b13","owner":[],"postedDate":"April 13th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-14T22:56:56+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-11T07:31:34+00:00","index":46,"fulltext":""},{"type":"reviewerAgreed","content":"10882425398863699049489683744336928650","date":"2026-05-11T05:33:36+00:00","index":44,"fulltext":""},{"type":"reviewerAgreed","content":"276675619316829565194423535403312625928","date":"2026-05-10T18:31:23+00:00","index":42,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-14T23:08:56+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-13 12:10:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9249191","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9249191","identity":"rs-9249191","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}