Association between the C-Reactive Protein-Triglyceride-Glucose Index and Mortality in Patients with Metabolic Dysfunction-Associated Fatty Liver Disease

Association between the C-Reactive Protein-Triglyceride-Glucose Index and Mortality in Patients with Metabolic Dysfunction-Associated Fatty Liver Disease | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association between the C-Reactive Protein-Triglyceride-Glucose Index and Mortality in Patients with Metabolic Dysfunction-Associated Fatty Liver Disease Yifei He, Feng Xiao, Nan Zhu, Bin Yi, Jin Lu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7535688/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective The C-reactive protein (CRP)-triglyceride-glucose (TyG) index (CTI) is a clinical marker that simultaneously reflects inflammation and insulin resistance (IR) and is associated with adverse cardiovascular outcomes. However, its prognostic value in metabolic dysfunction-associated fatty liver disease (MAFLD) remains unclear. This study aimed to investigate the associations of CTI with all-cause and cardiovascular disease (CVD) mortality in patients with MAFLD. Methods Data were utilized from adults who participated in the National Health and Nutrition Examination Survey (NHANES) III (1988–1994), with their records linked to mortality data from the National Death Index (NDI). CTI was calculated as 0.412 × Ln(CRP [mg/L]) + Ln( (triglycerides [mg/dL] × fasting glucose [mg/dL]) / 2 ). To assess the association between CTI and mortality, we employed multivariable Cox proportional hazards models, restricted cubic splines (RCS) analysis, and Kaplan-Meier curves. Furthermore, stratified analyses were conducted to evaluate potential heterogeneity across subgroups. Results Among 3,102 MAFLD participants, RCS analyses revealed significant non-linear associations between CTI and mortality risks (both P < 0.05), with inflection points at CTI = 8.1. After comprehensive adjustment,participants in the highest CTI tertile exhibited significantly elevated risks of both all-cause mortality (HR = 1.58,95% CI 1.23–2.02) and CVD mortality (HR = 2.09, 95% CI 1.33–3.28) compared to those in the lowest tertile. Conclusions Elevated CTI exceeding the threshold of 8.1 was independently associated with significantly increased risks of all-cause and CVD mortality. These findings establish CTI as a novel prognostic biomarker for long-term mortality risk stratification in patients with MAFLD. C-reactive protein-triglyceride-glucose index(CTI) Metabolic dysfunction-associated fatty liver disease (MAFLD) All-cause mortality Cardiovascular diseases (CVD) mortality Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction MAFLD is a chronic progressive liver disorder driven by nutrient excess and IR in genetically susceptible individuals 1 . Its diagnosis requires radiologically or histologically confirmed hepatic steatosis plus at least one of the following metabolic abnormalities: overweight/obesity, type 2 diabetes mellitus (T2DM), or ≥ 2 features of other metabolic dysregulation 2 . Against the backdrop of the global obesity and T2DM pandemics, the prevalence of MAFLD continues to rise and is projected to affect approximately 2.4 billion people by 2030 3 . This condition substantially increases mortality risk. CVD constitutes a predominant cause of mortality in nonalcoholic fatty liver disease (NAFLD) populations 4 , 5 . Despite substantial phenotypic overlap between NAFLD and MAFLD cohorts, Huang et al. 6 demonstrated significantly elevated CVD risk in MAFLD patients compared to NAFLD counterparts (HR 2.01 vs.1.53). These findings indicate that MAFLD confers heightened susceptibility to adverse cardiovascular outcomes. Consequently, systematic risk stratification for MAFLD-related mortality carries paramount clinical significance. IR constitutes the central pathophysiological nexus unifying MAFLD, T2DM, and obesity 7 . Through promoting chronic inflammation, oxidative stress, and endothelial dysfunction, IR directly accelerates atherosclerosis and stroke 8 , 9 . Current IR assessment spans from the gold-standard hyperinsulinemic-euglycemic clamp 10 to clinical surrogates (HOMA-IR 11 , fasting insulin), though simplified indices based on routine biomarkers are preferred for large-scale epidemiological studies. The triglyceride-glucose index (TyG = Ln[fasting triglycerides (mg/dL) × fasting glucose (mg/dL)/2]) effectively captures metabolic status and IR severity 12 . Its derivative C-reactive protein - TyG index (CTI) further incorporates C-reactive protein 13 , enhancing CVD risk prediction through integrated inflammation metrics 14 . Given that chronic inflammation synergistically amplifies oxidative stress and endothelial injury-accelerating both CVD and steatohepatitis-CTI represents a promising prognostic biomarker. Accumulating evidence has demonstrated its predictive value for cancer mortality 15 , coronary heart disease 16 , and stroke incidence 17 , particularly in high-risk populations including individuals with varying glycemic status 17 or cardiovascular-kidney-metabolic syndrome(CKM) 16 . However, its prognostic utility for cardiovascular outcomes in the MAFLD population remains unexplored. To resolve this significant evidence void,our study utilized the NHANES III - a nationally representative cohort conducted by the Centers for Disease Control and Prevention (CDC) that collected comprehensive health, nutritional, and risk factor data through standardized interviews, mobile examination center assessments, and laboratory testing. Leveraging the National Death Index (NDI) linkage through the National Center for Health Statistics (NCHS) 18 providing extended follow-up until 2019, we established an analytic cohort of participants with ultrasonographically confirmed MAFLD criteria. Within this population, we comprehensively evaluated the association of the CTI with all-cause and CVD mortality to generate evidence for clinical risk stratification. Study Population and Data Source The NHANES III included 31,312 baseline participants, among these, 13,856 underwent hepatic ultrasonography, which detected hepatic steatosis in 3,683 individuals. Mortality data through December 31, 2019, were linked to NHANES III records. After excluding participants lacking essential baseline data, mortality status information, or who did not meet criteria for metabolic dysfunction, the final analytical cohort comprised 3,102 individuals (Fig. 1 ). All participants provided written informed consent under approval by the Institutional Review Board. MAFLD diagnosis is contingent upon ultrasonographic evidence of hepatic steatosis (defined by the primary outcome variable GUPHSPFR from the NHANES III database as present or absent) plus fulfillment of one of three criteria 19 : overweight/obesity (body mass index [BMI] ≥ 25 kg/m²), T2DM, or metabolic dysregulation. T2DM is defined by self-reported diagnosis, use of glucose-lowering agents, hemoglobin A1c (HbA1c) ≥ 6.5%, or fasting plasma glucose (FPG) ≥ 7.0 mmol/L. For individuals with BMI < 25 kg/m², metabolic dysregulation is characterized by the presence of at least two metabolic risk abnormalities derived from examination, questionnaire, and laboratory data. These abnormalities include: (1) waist circumference ≥ 102 cm (men) or ≥ 88 cm (women); (2) blood pressure ≥ 130/85 mmHg, self-reported hypertension, or antihypertensive medication use; (3) plasma triglycerides (TG ) ≥ 150 mg/dL (≥ 1.70 mmol/L), self-reported dyslipidemia, or lipid-lowering medication use; (4) plasma high-density lipoprotein cholesterol (HDL-C) < 40 mg/dL (< 1.0 mmol/L) in men or < 50 mg/dL (< 1.3 mmol/L) in women, self-reported dyslipidemia, or lipid-lowering medication use; (5) prediabetes (HbA1c 5.7–6.4% or fasting plasma glucose 5.6–6.9 mmol/L); or (6) plasma C-reactive protein (CRP) level > 2 mg/L 19 . The TyG index is calculated as the natural logarithm (Ln) of [TG(mg/dl) ×FPG (mg/dl)] divided by 2 20 . The CTI is computed using the formula: CTI = 0.412 × Ln(CRP [mg/L]) + Ln(TG [mg/dl] × FPG [mg/dl])/2 17 . Covariate data were collected across four primary domains: sociodemographic characteristics (age, sex, race/ethnicity, education level, marital status, physical activity, income-to-poverty ratio, and smoking status) obtained from the NHANES III Household Adult Data File; anthropometric measurements (BMI) derived from examination data; and laboratory parameters (FPG, insulin, HbA1c, alanine aminotransferase [ALT], aspartate aminotransferase [AST], TG, total cholesterol [TC], HDL-C, low density lipoprotein cholesterol (LDL-C), uric acid [UA], and platelet count [PLT]) extracted from laboratory files. The fourth domain is liver fibrosis indices(FIB-4, NFS). Fibrosis-4 (FIB-4) index 21 (FIB-4 = [age (years) × AST (U/L)] / [PLT (×10⁹/L) × ALT (U/L)]).and NAFLD Fibrosis Score 22 (NFS = -1.675 + 0.037 × age [years] + 0.094 × BMI [kg/m²] + 1.13 × impaired fasting glucose/diabetes [yes = 1, no = 0] + 0.99 × [ALT/AST ratio] − 0.013 ×PLT [×10⁹/L] − 0.66 × albumin [g/dl]) . All-cause mortality was defined as death from any cause occurring within the study cohort. CVD mortality was classified according to the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) codes I00-I09, I11, I13, I20-I51, and I60-I69, as documented in death certificates. Statistical Analysis Participants were categorized by CTI tertiles (Q1: ≤8.3; Q2: 8.3–9.1; Q3: >9.1), with CTI also analyzed continuously. For quantitative data that are normally distributed, we report the means along with the standard errors, and group comparisons are performed using analysis of variance (ANOVA). In cases where quantitative variables deviate from normality, the median and interquartile range are presented, and the Kruskal–Wallis test is employed to examine differences across groups. Categorical variables are summarized as frequencies and percentages, and associations are tested using the Chi-square test. Cox proportional hazards models assessed CTI-mortality associations using three adjustment levels: Crude; Model 1 (demographics: age, sex, race, education, marital status, income, smoking, physical activity); Model 2 (Model 1 + diabetes, hypertension, BMI, FIB-4,HDL). Restricted cubic splines evaluated dose-response relationships. Kaplan-Meier curves with log-rank tests visualized survival differences. ROC analysis compared CTI, TyG, and CRP for mortality prediction (DeLong method). Subgroup analyses assessed interactions by sex, age (≥ 60/<60 years), race, hypertension, diabetes, BMI (< 25/≥25 kg/m²), and fibrosis (FIB-4 ≥ 1.3/<1.3). Sensitivity analyses included: (1) Multiple imputation (MI, 5 datasets) for missing data; (2) Logistic regression; (3) Exclusion of deaths within 2 years post-baseline. Analyses used R V4.2.2 (survival, rms, pROC, mice packages) with two-sided P < 0.05 indicating significance. Baseline characteristics of participants As illustrated in Table 1 , this study enrolled 3,102 participants. Participants were categorized into tertiles based on CTI (Q1: lowest group, n = 1034; Q2: intermediate group, n = 1034; Q3: highest group, n = 1034). The cohort comprised 50.68% males with a median age of 47.0 years. Mexican Americans constituted the predominant ethnic group (73.0%).Notably, the highest CTI tertile (Q3) demonstrated significantly adverse metabolic profiles (elevated fasting glucose, dyslipidemia, and increased liver fibrosis risk scores), higher prevalence of diabetes and hypertension, and elevated all-cause and CVD mortality compared with Q1. Conversely, the lowest CTI tertile (Q1) consistently exhibited more favorable metabolic characteristics with reduced disease burden and mortality rates. Table 1 Baseline characteristics of participants with MAFLD Characteristics Total (n = 3102) CTI quartiles Q1 Q2 Q3 P-value (n = 1034) (n = 1034) (n = 1034) Male, n (%) 1598 (50.68%) 516 (49.9%) 572 (55.3%) 494 (47.8%) 0.002 Age(years) 47.9 ± 15.3 41.2 ± 14.8 48.9 ± 15.0 53.8 ± 13.4 < 0.001 Race, n (%) Mexican American NonHispanicWhite Non-Hipanic Black Other Race 2289 (73.0%) 711 (22.7%)) 134 (4.3%)) 2 (0.1%) 652(63.1%) 333 (32.2%)) 48 (4.6%)) 1(0.1%) 795 (76.9%) 193 (18.7%)) 45 (4.4%)) 1 (1%) 818 (79.1%) 175 (16.9%) 41 (4.0%)) 0 (4.42%) < 0.001 College degree, n (%) 929 (29.6%) 242 (23.5%) 300 (29.0%) 374 (36.2%) < 0.001 Low income, n (%) 1038 (36.5%) 370 (39.1%) 308 (32.6%) 346 (37.4%) 0.01 Sedentary lifestyle, n (%) 436 (13.9%) 166 (16.1% 137 (13.2%) 128 (12.4%) 0.039 Married, n (%) 2006 (64.0%) 588 (57.0%) 699 (67.6%)) 698 (67.6%) < 0.001 BMI(kg/m 2 ) 30.3 ± 6.6 28.1 ± 6.7 31.0 ± 6.2 32.0 ± 6.2 < 0.001 Glucose (mg/dL) 109.3 (94.0-142.7) 94.7 (87.1-104.4) 109.4 (96.2-129.6) 153.0 (115.4–236.0) < 0.001 Insulin (uU/mL) 29.06 ± 33.99 29.06 ± 33.99 23.56 ± 26.38 33.73 ± 37.56 < 0.001 HbA1c(%) 5.5 (5.1-6.0) 5.3 (5.0-5.6) 5.5 (5.1–5.8) 6.0 (5.4-8.0) < 0.001 TC(mg/dl) 212.2 ± 46.9 191.6 ± 39.8 212.5 ± 40.4 233.0 ± 50.4 < 0.001 TG(mg/ dl ) 156.0 (104.5–234.0) 92.0 (71.0-119.0) 169.0 (133.0-212.0) 257.0 (188.2-368.8) < 0.001 HDL(mg/ dl ) 216.8 ± 47.2 195.9 ± 40.6 217.6 ± 41.9 237.3 ± 49.4 < 0.001 LDL(mg/ dl ) 129.6 ± 38.6 122.1 ± 38.3 132.7 ± 37.0 135.9 ± 39.2 < 0.001 UA(mg/l) 5.8 ± 1.6 5.3 ± 1.5 6.0 ± 1.4 6.0 ± 1.7 < 0.001 ALT(U/L) 19.0(13.0–29.0) 17.0 (12.0–27.0) 19.0 (14.0–31.0) 19.0 (14.0–29.0) < 0.001 AST(U/L) 21.0(17.0–29.0) 20.0 (17.0–27.0) 22.0 (18.0–29.0) 21.0 (17.0–29.0) 0.001 PLT(10 9 /L) 277.6 ± 72.3 273.0 ± 69.0 277.9 ± 72.6 281.9 ± 74.8 0.072 Albumin(g/dl) 41.3 ± 3.7 41.7 ± 3.6 41.5 ± 3.6 40.8 ± 3.8 < 0.001 CTI 8.8 ± 0.9 7.8 ± 0.4 8.7 ± 0.2 9.8 ± 0.6 < 0.001 NFS -1.6 ± 1.7 -2.3 ± 1.6 -1.5 ± 1.7 -0.9 ± 1.6 < 0.001 FIB-4 0.9 (0.6–1.3) 0.7 (0.5–1.1) 0.9 (0.6–1.3) 1.0 (0.7–1.3) < 0.001 Diabetes, n (%) 1206 (38.5%) 106 (10.3%) 346 (33.5%) 733 (70.9%) < 0.001 Hypertension, n (%) 1082 (34.5%) 219 (21.2%) 375 (36.3%) 474 (45.8%) < 0.001 Mortality, n (%) All cause 1441 (46.0%) 322 (31.1%)) 472 (45.6%) 635 (61.4%) < 0.001 CVD 474 (15.1%) 90 (8.7%) 164 (15.9%) 219 (21.2%) < 0.001 Association of CTI index with all-cause and CVD mortality in MAFLD patients Table 2 details Cox regression analyses for mortality risks associated with CTI in MAFLD. In the crude model, each 1-unit increase in continuous CTI was associated with a significantly elevated risk of all-cause mortality (HR = 1.71; 95% CI 1.55–1.89; P < 0.001). This association remained robust after sequentially adjusting for confounding factors, with the increased risk remaining significant in all multivariable-adjusted models (Model 2: HR = 1.32; 95% CI1.18–1.47; P < 0.001). In stratified analyses, Q3 maintained a 58% higher mortality risk versus Q1 after full adjustment (HR 1.58, 95% CI 1.23–2.02; P < 0.001), whereas Q2 demonstrated a non-significant association (HR 1.11; P = 0.41). CVD mortality demonstrated a more pronounced association. Per 1-unit CTI increase, the crude model indicated an 88% higher CVD mortality risk (HR 1.88, 95% CI 1.64–2.16; P < 0.001), with a 52% residual increase in the fully adjusted Model 2 (HR 1.52, 95% CI 1.26–1.85; P < 0.001). Stratified analysis revealed non-significant risk elevation for Q2 versus Q1 (HR 1.38, 95% CI 0.88–2.14; P = 0.16). Conversely, Q3 exhibited substantially elevated CVD mortality risk across all adjustment levels, demonstrating a 2.09-fold higher risk than Q1 (HR 2.09, 95% CI 1.33–3.28; P < 0.001) in Model 2, establishing the highest CTI tertile as a robust independent predictor of CVD death. Table 2 HRs of CTI on all-cause mortality and CVD mortality in patients with MAFLD CTI Crude model Model 1 Model 2 HR (95% CI) P HR (95% CI) P HR (95% CI) P All cause mortality Continuous 1.71(1.55,1.89) < 0.001 1.41(1.28,1.55) < 0.001 1.32(1.18,1.47) < 0.001 Categories Q1 Ref Ref Ref Q2 1.89(1.50,2.39) < 0.001 1.28(1.02,1.61) 0.04 1.11(0.87,1.41) 0.41 Q3 3.23(2.57,4.06) < 0.001 1.93(1.54,2.40) < 0.001 1.58(1.23,2.02) < 0.001 CVD mortality Continuous 1.88(1.64,2.16) < 0.001 1.62(1.37,1.91) < 0.001 1.52(1.26, 1.85) < 0.001 Categories Q1 Ref Ref Ref Q2 1.8(1.50,2.39) < 0.001 1.68(1.10,2.55) 0.02 1.38(0.88,2.14) 0.16 Q3 3.2(2.57,4.06) < 0.001 2.74(1.83,4.10) < 0.001 2.09(1.33,3.28) < 0.001 Crude model: Unadjusted.Model 1: Adjusted for age, sex, race/ethnicity, marital status, education, smoking, physical activity, and income.Model 2: Additionally adjusted for history of diabetes, history of hypertension, BMI, and FIB-4 score,HDL-C. RCS and threshold effect analysis Restricted cubic splines revealed significant non-linearity in the CTI-all-cause mortality association (P for non-linearity < 0.05). Two-piecewise regression identified an inflection point at CTI = 8.1 (Fig. 2 A-C). Below this threshold (CTI < 8.1), each unit CTI increase showed non-significant mortality association (HR 0.78, 95%CI 0.55–1.12; P = 0.179), whereas above threshold (CTI ≥ 8.1) each unit increase conferred significantly elevated risk (HR 1.32, 95%CI 1.21–1.45; P < 0.001). The high-segment demonstrated 169% greater mortality risk versus low-segment (ΔHR 1.69, 95%CI 1.15–2.49; P = 0.008), with the threshold model significantly outperforming linear model by log-likelihood ratio test (P = 0.01)(Table S1 ). Similarly, CVD mortality exhibited non-linear association with CTI (P for non-linearity < 0.05) and shared identical inflection point (CTI = 8.1; Fig. 2 D-F). Sub-threshold CTI increases showed non-significant CVD risk (HR 0.57, 95%CI 0.31–1.06; P = 0.077), while supra-threshold increases demonstrated significant 49% elevated risk per unit (HR 1.49, 95%CI 1.28–1.73; P < 0.001). The high-segment manifested 260% greater CVD mortality versus low-segment (ΔHR 2.60, 95%CI 1.33–5.09; P = 0.005), with threshold model superiority confirmed (P = 0.009) (Table S1 ). Kaplan–Meier (K–M) survival curves Kaplan-Meier analysis demonstrated progressively increased cumulative incidence of all-cause mortality (Fig. 3 A) and CVD mortality (Fig. 3 B) from Q1 to Q3 tertiles, with statistically significant inter-group differences (log-rank P < 0.001). Subgroup and interaction analyses To evaluate potential heterogeneity in the association between CTI and mortality, we conducted subgroup analyses using Cox proportional hazards models across key demographic and clinical variables, including age, gender, race, obesity (BMI ≥ 25), diabetes, hypertension, and liver fibrosis status (as defined by FIB-4). Formal interaction tests were performed for each subgroup (Fig. 4 ). For all-cause mortality(Fig. 4 A), elevated CTI was consistently associated with increased risk across nearly all subgroups. Specifically, higher CTI was significantly associated with greater all-cause mortality in both age groups (≤ 60: HR 1.50, 95% CI 1.17–1.61; >60: HR 1.60, 95% CI 1.31–1.78), both genders (Male: HR 1.23, 95% CI 1.04–1.46; Female: HR 1.42, 95% CI 1.22–1.66), and all racial and comorbidity subgroups. The association was non-significant only among non-diabetic individuals (HR 1.20, 95% CI 0.98–1.46). However, no significant interaction effects were observed for any of the subgroups (all p-interaction > 0.05). For cardiovascular disease (CVD) mortality(Fig. 4 B), a significant positive association with CTI was observed in most subgroups, except among Non-Mexican Americans (HR 1.11, 95% CI 0.81–1.51). Notably, stronger associations were found in individuals aged > 60 (HR 1.77, 95% CI 1.34–2.34) and those with FIB-4 < 1.3 (HR 1.74, 95% CI 1.36–2.23). A significant interaction was detected for age (p-interaction = 0.01). No other subgroups show statistically significant interaction effect. AUC and ROC For all-cause mortality risk, CTI and TyG demonstrated comparable discriminatory power (AUC 0.655 vs. 0.647), whereas CRP showed lower discrimination (AUC 0.564). Similarly, for CVD mortality, CTI exhibited moderate predictive utility (AUC 0.622, 95% CI 0.595–0.648), marginally superior to TyG (AUC 0.615, 95% CI 0.588–0.641). CRP lacked discrimination capacity (AUC 0.553, 95% CI 0.527–0.579). Sensitivity analyses To evaluate the robustness of our results, we conducted a series of sensitivity analyses. First, to address potential limitations due to missing data, we applied multiple imputation techniques, which minimized bias by creating plausible estimates for incomplete observations (Table S2. S3). The primary associations between CTI levels and all-cause mortality remained consistent across these analyses. Second, we examined the robustness of our findings using alternative statistical models, such as logistic regression, to confirm that the observed effects were not specific to a particular analytical approach (Table S4). Third, we jointly assessed the associations of TyG and CRP with all-cause mortality and cardiovascular mortality to ensure the robustness of the results(Table S5).Finally, to assess potential bias from early mortality events, we excluded individuals who died within 2 years after baseline assessment, and the resulting effect estimates remained largely unchanged, maintaining the robustness of our principal conclusions (Table S6). Discussion Leveraging large-scale prospective cohort data from the NHANES III, this study is the first to demonstrate a nonlinear relationship between the CTI and both all-cause and CVD mortality in individuals with MAFLD.The results demonstrated a distinct threshold effect of CTI at 8.1 for both outcomes. When CTI exceeded 8.1, each unit increase in CTI was significantly associated with a higher risk of all-cause mortality (HR = 1.32, 95% CI 1.21–1.45, P < 0.001). Below this threshold, the association was non-significant and suggested a potentially lower risk (HR = 0.78, 95% CI 0.55–1.12, P = 0.1788). Similarly, for CVD mortality, a significant association with increased CVD mortality risk was evident only when CTI surpassed this threshold (HR = 1.49, 95% CI 1.28–1.73, P < 0.001). The CTI demonstrated superior predictive performance for mortality compared to its individual components (CRP or the TyG index alone), underscoring the clinical utility of integrating inflammatory and metabolic signals. As a composite index integrating TyG and CRP, CTI enables a synergistic assessment of mortality risk in patients with MAFLD through dual pathways involving insulin resistance and chronic inflammation. TyG as an insulin resistance marker,although previous studies have suggested an association between CTI and all-cause mortality in MAFLD patients 23 – 25 , its relationship with CVD mortality remains controversial. For instance, Chen et al. report null association between TyG and CVD mortality (HR 1.036, 95% CI :0.904–1.187, P>0.05) 23 , whereas Zhang et al. reported a significant positive correlation (HR 1.63, 95% CI 1.03–2.57, P < 0.05) 25 . In contrast, CRP as an inflammatory marker consistently demonstrates associations with increased CVD mortality risk in both the general population 26 , 27 and MAFLD patients 28 .Ni et al.'s meta-analysis (incorporating 22 studies) definitively showed that in the general population 26 , moderate and high CRP levels were associated with 43% (HR 1.43, 95% CI 1.22–1.68, P < 0.001) and 102% (HR 2.02, 95% CI 1.70–2.41, P < 0.001) increased risks of CVD mortality, respectively, compared to low CRP levels. In the MAFLD population, Wang et al.'s study also confirmed that elevated CRP was significantly associated with increased CVD mortality risk (HR 1.35, 95% CI 1.03–1.77, P < 0.001) 28 .The pivotal finding of the present study lies in the enhanced predictive power achieved by constructing CTI to integrate TyG and CRP: high CTI levels conferred a 109% (HR 2.09, 95% CI 1.33–3.28, P < 0.001) higher excess CVD mortality risk compared to low levels. ROC analysis indicated that CTI demonstrated a trend toward superior predictive value for both all-cause and CVD mortality compared to either CRP or the TyG index alone. This strongly supports the necessity of concurrently evaluating insulin resistance and chronic inflammatory status for accurate CVD prognosis assessment in MAFLD patients. The elevated all-cause and CVD mortality observed in MAFLD patients originates from insulin resistance and chronic low-grade inflammation 29 .IR directly triggers atherogenic dyslipidemia (characterized by hypertriglyceridemia, low HDL-C, and increased small dense LDL particles), hypertension, endothelial dysfunction, and a prothrombotic state 30 . These factors collectively and potently promote the initiation, progression, and plaque destabilization of atherosclerosis 31 . Simultaneously, chronic low-grade inflammation not only directly drives vascular endothelial injury, leukocyte infiltration, foam cell formation, and plaque rupture 32 but also forms a vicious cycle with IR (wherein inflammatory cytokines disrupt insulin signaling, and IR in turn exacerbates inflammation) 33 . This synergy between IR and inflammation further amplifies metabolic dysregulation and vascular damage, leading to myocardial metabolic derangements, fibrosis, and remodeling. Ultimately, these pathological processes significantly elevate the incidence of fatal CVD events (such as myocardial infarction, ischemic stroke, and sudden cardiac death), establishing CVD as the primary cause of death in MAFLD patients 34 . Furthermore, the chronic inflammatory state itself increases the risk of other lethal conditions, including liver disease progression (e.g., cirrhosis, hepatocellular carcinoma), malignancies, and chronic kidney disease, collectively contributing to elevated all-cause mortality 35 . Thus, liver-derived systemic IR and chronic inflammation form the central pathogenic axis driving adverse outcomes in MAFLD.This study identified a critical threshold (CTI > 8.1) beyond which the risks of all-cause and CVD mortality in MAFLD patients exhibit a significant surge. The threshold effect of the CTI index signifies a critical transition from compensated to decompensated metabolic homeostasis. This phenomenon is probably driven by a vicious cycle between insulin resistance and chronic inflammation reaching a tipping point, which precipitously accelerates vascular injury. Exceeding this threshold is associated with a nonlinear and significant increase in the risk of CVD, thereby providing a crucial cutoff for risk stratification in patients with MAFLD. Identifying this inflection point is essential for implementing early and precise interventions to halt the progression of cardiovascular complications. In the current study, a statistically significant interaction was identified between age and CTI with respect to CVD mortality (P for interaction = 0.01). Notably, elevated CTI was associated with a slightly higher hazard ratio for cardiovascular death among participants aged ≤ 60 years (HR 1.78, 95% CI 1.34–2.18) compared to those older than 60 years (HR 1.73, 95% CI 1.34–2.34), with both estimates remaining statistically significant. This pattern of association is consistent with previous reports involving the TyG index in populations with established cardiovascular 36 , 37 or cerebrovascular 38 diseases, wherein a high TyG index demonstrated more pronounced effects on cardiovascular mortality in younger and middle-aged adults than in older adults. The underlying mechanisms for this age-dependent risk amplification may be attributed to a combination of greater genetic susceptibility and more severe cardiometabolic abnormalities in younger individuals, leading to prolonged and intensified exposure to cardiovascular risk factors 39 . Consequently, this results in an accelerated accumulation of risk and manifest cardiovascular mortality at a relatively younger age. However, evidence regarding the association of CTI with clinical outcomes remains limited, and further studies are warranted to validate its interaction with age. This study has several limitations. First, the single baseline measurement of the TyG and CRP levels cannot capture their dynamic trajectories; the clinical value of monitoring these changes for MAFLD management requires validation through longitudinal studies. Second, despite adjusting for multidimensional covariates, residual confounding from unmeasured factors (e.g., genetic susceptibility, evolving lifestyle) may influence results, and imprecise smoking quantification could introduce bias. Finally, findings derived from a US cohort necessitate validation across diverse ethnicities and MAFLD phenotypes in multicenter studies. This study is the first to identify a significant nonlinear threshold effect (critical value: 8.1) of CTI for predicting all-cause and CVD mortality in MAFLD patients, providing a quantitative tool for precise risk stratification. By integrating MAFLD’s core pathological features -insulin resistance and chronic inflammation - CTI effectively captures the pivotal transition point driving progression to end-organ damage. Abbreviations NAFLD :nonalcoholic fatty liver disease MAFLD:metabolic dysfunction-associated fatty liver disease TG: total cholesterol FPG: fast blood glucose TyG: triglyceride-glucose CRP: C-reactive protein CTI C:reactive protein-triglyceride-glucose index CVD:Cardiovascular disease CKD chronic kidney disease BMI: body mass index T2DM :type 2 diabetes mellitus HbA1c: Hemoglobin A1c IR :insulin resistance TC: total cholesterol LDL-C: low density lipoprotein cholesterol HDL-C: high-density lipoprotein cholesterol; UA,uric acid ; ALT:alanine aminotransferase; AST:aspartate aminotransferase; PLT:platelet count ; NFS: NAFLD Fibrosis Score; FIB-4: fibrosis-4 index; CKM:cardiovascular-kidney-metabolic syndrome; CDC:Centers for Disease Control and Prevention; NDI:National Death Index; NHANES III:National Health and Nutrition Examination Survey III (1988–1994) ; ROC:Receiver Operating Characteristic; AUC:Area Under the Curve. HR:Hazard ratios 95%CI:95% confidence interval RCS:Restricted cubic spline K–M :Kaplan–Meier Declarations Funding The authors acknowledge funding support from the National Natural Science Foundation of China (82471657, 82171599, and 82201792), as well as from the 2022 Anhui Provincial Social Science Innovation and Development Research Project (Grant No. 2022CX082). Conflicts of interest The authors declare no conflicts of interest. Ethics approval The study protocol was approved by the Medical Research Ethics Committee of the First Affiliated Hospital of USTC on November 18, 2024 (Ethical approval No. 2024 KY 535). Consent to participate All participants provided written informed consent prior to inclusion in the study. The study protocol was reviewed and approved by the institutional ethics committee. Written Consent for publication Written consent for publication of anonymized data was obtained from all participants. Availability of data and material Data regarding any of the subjects in the study can be shared after reasonable request to the corresponding author. No identifying data will be provided. Code availability Not applicable Authors' contributions Q. J., X.J., and S.B. contributed equally to this work and share first authorship. Q.J. was responsible for sample collection, manuscript drafting, and revisions. X.J. constructed the figures and tables. S.B. and L.Z. performed the statistical analyses and interpreted the data. S.Z., L.W., and B.X. jointly supervised the study, contributed to the study design and critical revision of the manuscript, and share corresponding authorship. All authors read and approved the final version of the manuscript. References Eslam M, Sanyal AJ, George J. International Consensus Panel. MAFLD: A Consensus-Driven Proposed Nomenclature for Metabolic Associated Fatty Liver Disease. Gastroenterology. 2020;158:1999–e20141. Cusi K, et al. American Association of Clinical Endocrinology Clinical Practice Guideline for the Diagnosis and Management of Nonalcoholic Fatty Liver Disease in Primary Care and Endocrinology Clinical Settings: Co-Sponsored by the American Association for the Study of Liver Diseases (AASLD). Endocr Pract Off J Am Coll Endocrinol Am Assoc Clin Endocrinol. 2022;28:528–62. Riazi K, et al. The prevalence and incidence of NAFLD worldwide: a systematic review and meta-analysis. Lancet Gastroenterol Hepatol. 2022;7:851–61. Targher G, Day CP, Bonora E. Risk of cardiovascular disease in patients with nonalcoholic fatty liver disease. N Engl J Med. 2010;363:1341–50. Duell PB, et al. Nonalcoholic Fatty Liver Disease and Cardiovascular Risk: A Scientific Statement From the American Heart Association. Arterioscler Thromb Vasc Biol. 2022;42:e168–85. Huang DQ, El-Serag HB, Loomba R. Global epidemiology of NAFLD-related HCC: trends, predictions, risk factors and prevention. Nat Rev Gastroenterol Hepatol. 2021;18:223–38. Maldonado-Rojas ADC, Zuarth-Vázquez JM, Uribe M. Barbero-Becerra, V. J. Insulin resistance and Metabolic dysfunction-associated steatotic liver disease (MASLD): Pathways of action of hypoglycemic agents. Ann Hepatol. 2024;29:101182. Hill MA, et al. Insulin resistance, cardiovascular stiffening and cardiovascular disease. Metabolism. 2021;119:154766. Kosmas CE, et al. Insulin resistance and cardiovascular disease. J Int Med Res. 2023;51:3000605231164548. DeFronzo RA, Tobin JD, Andres R. Glucose clamp technique: a method for quantifying insulin secretion and resistance. Am J Physiol. 1979;237:E214–223. Fasipe OJ, Ayoade OG, Enikuomehin AC. Severity Grade Assessment Classifications for Both Insulin Resistance Syndrome and Status of Pancreatic Beta Cell Function in Clinical Practice Using Homeostasis Model Assessment Method Indices. Can J Diabetes. 2020;44:663–9. Tahapary DL, et al. Challenges in the diagnosis of insulin resistance: Focusing on the role of HOMA-IR and Tryglyceride/glucose index. Diabetes Metab Syndr. 2022;16:102581. Ruan G-T, et al. A Novel Inflammation and Insulin Resistance Related Indicator to Predict the Survival of Patients With Cancer. Front Endocrinol. 2022;13:905266. Ma X, Ma X, Wang Y, Qiu G, Zhang C. Associations of cumulative exposure and dynamic trajectories of the C-reactive protein-triglyceride-glucose index with incident cardiovascular disease in middle-aged and older Chinese adults: a nationwide cohort study. Cardiovasc Diabetol. 2025;24:303. Zhao D-F. Value of C-Reactive Protein-Triglyceride Glucose Index in Predicting Cancer Mortality in the General Population: Results from National Health and Nutrition Examination Survey. Nutr Cancer. 2023;75:1934–44. Ou H, Wei M, Li X, Xia X. C-reactive protein-triglyceride glucose index in evaluating cardiovascular disease and all-cause mortality incidence among individuals across stages 0–3 of cardiovascular-kidney-metabolic syndrome: a nationwide prospective cohort study. Cardiovasc Diabetol. 2025;24:296. Huo G, et al. Association between C-reactive protein-triglyceride glucose index and stroke risk in different glycemic status: insights from the China Health and Retirement Longitudinal Study (CHARLS). Cardiovasc Diabetol. 2025;24:142. Parker JD, et al. National Center for Health Statistics Data Presentation Standards for Proportions. Vital Health Stat. 2017;2:1–22. Eslam M, et al. A new definition for metabolic dysfunction-associated fatty liver disease: An international expert consensus statement. J Hepatol. 2020;73:202–9. Simental-Mendía LE, Rodríguez-Morán M, Guerrero-Romero F. The product of fasting glucose and triglycerides as surrogate for identifying insulin resistance in apparently healthy subjects. Metab Syndr Relat Disord. 2008;6:299–304. Sterling RK, et al. Development of a simple noninvasive index to predict significant fibrosis in patients with HIV/HCV coinfection. Hepatol Baltim Md. 2006;43:1317–25. Angulo P, et al. The NAFLD fibrosis score: A noninvasive system that identifies liver fibrosis in patients with NAFLD. Hepatology. 2007;45:846–54. https://doi.org/10.1002/hep . Chen Q, et al. Association between triglyceride-glucose related indices and mortality among individuals with non-alcoholic fatty liver disease or metabolic dysfunction-associated steatotic liver disease. Cardiovasc Diabetol. 2024;23:232. Min Y, et al. Prognostic effect of triglyceride glucose-related parameters on all-cause and cardiovascular mortality in the United States adults with metabolic dysfunction-associated steatotic liver disease. Cardiovasc Diabetol. 2024;23:188. Zhang Y, et al. Association of triglyceride glucose-related parameters with all-cause mortality and cardiovascular disease in NAFLD patients: NHANES 1999–2018. Cardiovasc Diabetol. 2024;23:262. Ni P, et al. Dose-response association between C-reactive protein and risk of all-cause and cause-specific mortality: a systematic review and meta-analysis of cohort studies. Ann Epidemiol. 2020;51:20–e2711. Hartley A, et al. The relationship of baseline high-sensitivity C-reactive protein with incident cardiovascular events and all-cause mortality over 20 years. EBioMedicine. 2025;117:105786. Wang Y, et al. Prognostic impact of systemic inflammation indicators on all-cause and CVD mortality in adults with MASLD. BMC Gastroenterol. 2025;25:448. European Association for the Study of the Liver (EASL). European Association for the Study of Diabetes (EASD), & European Association for the Study of Obesity (EASO). EASL-EASD-EASO Clinical Practice Guidelines on the management of metabolic dysfunction-associated steatotic liver disease (MASLD). J Hepatol. 2024;81:492–542. Hill MA, et al. Insulin resistance, cardiovascular stiffening and cardiovascular disease. Metabolism. 2021;119:154766. Muzurović E, Mikhailidis DP, Mantzoros C. Non-alcoholic fatty liver disease, insulin resistance, metabolic syndrome and their association with vascular risk. Metabolism. 2021;119:154770. Rohm TV, Meier DT, Olefsky JM, Donath MY. Inflammation in obesity, diabetes, and related disorders. Immunity. 2022;55:31–55. Püschel GP, Klauder J, Henkel J, Macrophages. Low-Grade Inflammation, Insulin Resistance and Hyperinsulinemia: A Mutual Ambiguous Relationship in the Development of Metabolic Diseases. J Clin Med. 2022;11:4358. Simon TG, Roelstraete B, Hagström H, Sundström J, Ludvigsson JF. Non-alcoholic fatty liver disease and incident major adverse cardiovascular events: results from a nationwide histology cohort. Gut. 2022;71:1867–75. Lee DY, et al. Impact of systemic inflammation on the relationship between insulin resistance and all-cause and cancer-related mortality. Metabolism. 2018;81:52–62. Ma X, et al. Triglyceride glucose index for predicting cardiovascular outcomes after percutaneous coronary intervention in patients with type 2 diabetes mellitus and acute coronary syndrome. Cardiovasc Diabetol. 2020;19:31. Wang L, et al. Influence of age on the association between the triglyceride-glucose index and all-cause mortality in patients with cardiovascular diseases. Lipids Health Dis. 2022;21:135. Liu R, Li L, Wang L, Zhang S. Triglyceride-glucose index predicts death in patients with stroke younger than 65. Front Neurol. 2023;14:1198487. Chen J, Wu K, Lin Y, Huang M, Xie S. Association of triglyceride glucose index with all-cause and cardiovascular mortality in the general population. Cardiovasc Diabetol. 2023;22:320. Additional Declarations No competing interests reported. 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1","display":"","copyAsset":false,"role":"figure","size":98314,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of the study population\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7535688/v1/6e8151386abd443cced32928.png"},{"id":91828219,"identity":"f313238b-be85-48fc-9148-b6c8b608ded2","added_by":"auto","created_at":"2025-09-22 08:53:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":143360,"visible":true,"origin":"","legend":"\u003cp\u003eRCS curves for CTI associations with all-cause (A-C) and CVD (D-F) mortality in MAFLD.Crude model: Unadjusted.Model 1: Adjusted for age, sex, race/ethnicity, marital status, education, smoking, physical activity, and income.Model 2: Additionally adjusted for history of diabetes, history of hypertension, body mass index (BMI), and FIB-4 score,HDL-C.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7535688/v1/cee63cb8f002c131e4f9deca.png"},{"id":91828215,"identity":"afd570be-a204-488e-b9be-9d20bb0200d3","added_by":"auto","created_at":"2025-09-22 08:53:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":46568,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier curves of the survival pattern in patients with MAFLD. A:All-cause mortality.B: CVD mortality.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7535688/v1/550defaf604be5ba3633eca5.png"},{"id":91825784,"identity":"d11875c7-2d2d-4601-9a7e-5dccd543f976","added_by":"auto","created_at":"2025-09-22 08:37:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":29819,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots of CTI associations with all-cause (A) and CVD (B) mortality across subgroups in patients with MAFLD.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7535688/v1/c1b9e7610e50d866e2e0dcd7.png"},{"id":91827020,"identity":"c7d49254-8427-4323-bec4-3ba2d8c82e53","added_by":"auto","created_at":"2025-09-22 08:45:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":92044,"visible":true,"origin":"","legend":"\u003cp\u003eTime-dependent ROC curves for predicting mortality in MAFLD by CTI, CRP, and TyG. A: the predictive value for all-cause mortality; B: the predictive value for CVD mortality.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7535688/v1/cd795290435c4a48f659af2f.png"},{"id":94466607,"identity":"e5170016-9bda-4177-8f1c-7e7a29da2d7a","added_by":"auto","created_at":"2025-10-27 15:20:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1197922,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7535688/v1/0df19f20-e9a2-4677-9d12-4bfb2fe400b2.pdf"},{"id":91825780,"identity":"2288a10f-09db-4044-8faa-a9dfb620fb08","added_by":"auto","created_at":"2025-09-22 08:37:06","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":39337,"visible":true,"origin":"","legend":"","description":"","filename":"file.docx","url":"https://assets-eu.researchsquare.com/files/rs-7535688/v1/b881b654ad6804298b676161.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between the C-Reactive Protein-Triglyceride-Glucose Index and Mortality in Patients with Metabolic Dysfunction-Associated Fatty Liver Disease","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMAFLD is a chronic progressive liver disorder driven by nutrient excess and IR in genetically susceptible individuals\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Its diagnosis requires radiologically or histologically confirmed hepatic steatosis plus at least one of the following metabolic abnormalities: overweight/obesity, type 2 diabetes mellitus (T2DM), or \u0026ge;\u0026thinsp;2 features of other metabolic dysregulation\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Against the backdrop of the global obesity and T2DM pandemics, the prevalence of MAFLD continues to rise and is projected to affect approximately 2.4\u0026nbsp;billion people by 2030\u003csup\u003e3\u003c/sup\u003e. This condition substantially increases mortality risk. CVD constitutes a predominant cause of mortality in nonalcoholic fatty liver disease (NAFLD) populations\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Despite substantial phenotypic overlap between NAFLD and MAFLD cohorts, Huang et al.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e demonstrated significantly elevated CVD risk in MAFLD patients compared to NAFLD counterparts (HR 2.01 vs.1.53). These findings indicate that MAFLD confers heightened susceptibility to adverse cardiovascular outcomes. Consequently, systematic risk stratification for MAFLD-related mortality carries paramount clinical significance.\u003c/p\u003e\u003cp\u003eIR constitutes the central pathophysiological nexus unifying MAFLD, T2DM, and obesity\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Through promoting chronic inflammation, oxidative stress, and endothelial dysfunction, IR directly accelerates atherosclerosis and stroke\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Current IR assessment spans from the gold-standard hyperinsulinemic-euglycemic clamp \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003eto clinical surrogates (HOMA-IR\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, fasting insulin), though simplified indices based on routine biomarkers are preferred for large-scale epidemiological studies. The triglyceride-glucose index (TyG\u0026thinsp;=\u0026thinsp;Ln[fasting triglycerides (mg/dL) \u0026times; fasting glucose (mg/dL)/2]) effectively captures metabolic status and IR severity\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Its derivative C-reactive protein - TyG index (CTI) further incorporates C-reactive protein\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, enhancing CVD risk prediction through integrated inflammation metrics\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Given that chronic inflammation synergistically amplifies oxidative stress and endothelial injury-accelerating both CVD and steatohepatitis-CTI represents a promising prognostic biomarker. Accumulating evidence has demonstrated its predictive value for cancer mortality\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, coronary heart disease\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, and stroke incidence\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, particularly in high-risk populations including individuals with varying glycemic status\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e or cardiovascular-kidney-metabolic syndrome(CKM) \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. However, its prognostic utility for cardiovascular outcomes in the MAFLD population remains unexplored.\u003c/p\u003e\u003cp\u003eTo resolve this significant evidence void,our study utilized the NHANES III - a nationally representative cohort conducted by the Centers for Disease Control and Prevention (CDC) that collected comprehensive health, nutritional, and risk factor data through standardized interviews, mobile examination center assessments, and laboratory testing. Leveraging the National Death Index (NDI) linkage through the National Center for Health Statistics (NCHS)\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e providing extended follow-up until 2019, we established an analytic cohort of participants with ultrasonographically confirmed MAFLD criteria. Within this population, we comprehensively evaluated the association of the CTI with all-cause and CVD mortality to generate evidence for clinical risk stratification.\u003c/p\u003e"},{"header":"Study Population and Data Source","content":"\u003cp\u003eThe NHANES III included 31,312 baseline participants, among these, 13,856 underwent hepatic ultrasonography, which detected hepatic steatosis in 3,683 individuals. Mortality data through December 31, 2019, were linked to NHANES III records. After excluding participants lacking essential baseline data, mortality status information, or who did not meet criteria for metabolic dysfunction, the final analytical cohort comprised 3,102 individuals (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). All participants provided written informed consent under approval by the Institutional Review Board.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eMAFLD diagnosis is contingent upon ultrasonographic evidence of hepatic steatosis (defined by the primary outcome variable GUPHSPFR from the NHANES III database as present or absent) plus fulfillment of one of three criteria\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e: overweight/obesity (body mass index [BMI]\u0026thinsp;\u0026ge;\u0026thinsp;25 kg/m\u0026sup2;), T2DM, or metabolic dysregulation. T2DM is defined by self-reported diagnosis, use of glucose-lowering agents, hemoglobin A1c (HbA1c)\u0026thinsp;\u0026ge;\u0026thinsp;6.5%, or fasting plasma glucose (FPG)\u0026thinsp;\u0026ge;\u0026thinsp;7.0 mmol/L. For individuals with BMI\u0026thinsp;\u0026lt;\u0026thinsp;25 kg/m\u0026sup2;, metabolic dysregulation is characterized by the presence of at least two metabolic risk abnormalities derived from examination, questionnaire, and laboratory data. These abnormalities include: (1) waist circumference\u0026thinsp;\u0026ge;\u0026thinsp;102 cm (men) or \u0026ge;\u0026thinsp;88 cm (women); (2) blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;130/85 mmHg, self-reported hypertension, or antihypertensive medication use; (3) plasma triglycerides (TG )\u0026thinsp;\u0026ge;\u0026thinsp;150 mg/dL (\u0026ge;\u0026thinsp;1.70 mmol/L), self-reported dyslipidemia, or lipid-lowering medication use; (4) plasma high-density lipoprotein cholesterol (HDL-C)\u0026thinsp;\u0026lt;\u0026thinsp;40 mg/dL (\u0026lt;\u0026thinsp;1.0 mmol/L) in men or \u0026lt;\u0026thinsp;50 mg/dL (\u0026lt;\u0026thinsp;1.3 mmol/L) in women, self-reported dyslipidemia, or lipid-lowering medication use; (5) prediabetes (HbA1c 5.7\u0026ndash;6.4% or fasting plasma glucose 5.6\u0026ndash;6.9 mmol/L); or (6) plasma C-reactive protein (CRP) level\u0026thinsp;\u0026gt;\u0026thinsp;2 mg/L\u003csup\u003e19\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe TyG index is calculated as the natural logarithm (Ln) of [TG(mg/dl) \u0026times;FPG (mg/dl)] divided by 2\u003csup\u003e20\u003c/sup\u003e. The CTI is computed using the formula: CTI\u0026thinsp;=\u0026thinsp;0.412 \u0026times; Ln(CRP [mg/L])\u0026thinsp;+\u0026thinsp;Ln(TG [mg/dl] \u0026times; FPG [mg/dl])/2\u003csup\u003e17\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eCovariate data were collected across four primary domains: sociodemographic characteristics (age, sex, race/ethnicity, education level, marital status, physical activity, income-to-poverty ratio, and smoking status) obtained from the NHANES III Household Adult Data File; anthropometric measurements (BMI) derived from examination data; and laboratory parameters (FPG, insulin, HbA1c, alanine aminotransferase [ALT], aspartate aminotransferase [AST], TG, total cholesterol [TC], HDL-C, low density lipoprotein cholesterol (LDL-C), uric acid [UA], and platelet count [PLT]) extracted from laboratory files. The fourth domain is liver fibrosis indices(FIB-4, NFS). Fibrosis-4 (FIB-4) index\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e (FIB-4 = [age (years) \u0026times; AST (U/L)] / [PLT (\u0026times;10⁹/L) \u0026times; ALT (U/L)]).and NAFLD Fibrosis Score\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e (NFS = -1.675\u0026thinsp;+\u0026thinsp;0.037 \u0026times; age [years]\u0026thinsp;+\u0026thinsp;0.094 \u0026times; BMI [kg/m\u0026sup2;]\u0026thinsp;+\u0026thinsp;1.13 \u0026times; impaired fasting glucose/diabetes [yes\u0026thinsp;=\u0026thinsp;1, no\u0026thinsp;=\u0026thinsp;0]\u0026thinsp;+\u0026thinsp;0.99 \u0026times; [ALT/AST ratio] \u0026minus;\u0026thinsp;0.013 \u0026times;PLT [\u0026times;10⁹/L] \u0026minus;\u0026thinsp;0.66 \u0026times; albumin [g/dl]) .\u003c/p\u003e\u003cp\u003eAll-cause mortality was defined as death from any cause occurring within the study cohort. CVD mortality was classified according to the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) codes I00-I09, I11, I13, I20-I51, and I60-I69, as documented in death certificates.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eParticipants were categorized by CTI tertiles (Q1: \u0026le;8.3; Q2: 8.3\u0026ndash;9.1; Q3: \u0026gt;9.1), with CTI also analyzed continuously. For quantitative data that are normally distributed, we report the means along with the standard errors, and group comparisons are performed using analysis of variance (ANOVA). In cases where quantitative variables deviate from normality, the median and interquartile range are presented, and the Kruskal\u0026ndash;Wallis test is employed to examine differences across groups. Categorical variables are summarized as frequencies and percentages, and associations are tested using the Chi-square test. Cox proportional hazards models assessed CTI-mortality associations using three adjustment levels: Crude; Model 1 (demographics: age, sex, race, education, marital status, income, smoking, physical activity); Model 2 (Model 1\u0026thinsp;+\u0026thinsp;diabetes, hypertension, BMI, FIB-4,HDL). Restricted cubic splines evaluated dose-response relationships. Kaplan-Meier curves with log-rank tests visualized survival differences. ROC analysis compared CTI, TyG, and CRP for mortality prediction (DeLong method). Subgroup analyses assessed interactions by sex, age (\u0026ge;\u0026thinsp;60/\u0026lt;60 years), race, hypertension, diabetes, BMI (\u0026lt;\u0026thinsp;25/\u0026ge;25 kg/m\u0026sup2;), and fibrosis (FIB-4\u0026thinsp;\u0026ge;\u0026thinsp;1.3/\u0026lt;1.3). Sensitivity analyses included: (1) Multiple imputation (MI, 5 datasets) for missing data; (2) Logistic regression; (3) Exclusion of deaths within 2 years post-baseline. Analyses used R V4.2.2 (survival, rms, pROC, mice packages) with two-sided P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicating significance.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eBaseline characteristics of participants\u003c/h3\u003e\n\u003cp\u003eAs illustrated in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, this study enrolled 3,102 participants. Participants were categorized into tertiles based on CTI (Q1: lowest group, n\u0026thinsp;=\u0026thinsp;1034; Q2: intermediate group, n\u0026thinsp;=\u0026thinsp;1034; Q3: highest group, n\u0026thinsp;=\u0026thinsp;1034). The cohort comprised 50.68% males with a median age of 47.0 years. Mexican Americans constituted the predominant ethnic group (73.0%).Notably, the highest CTI tertile (Q3) demonstrated significantly adverse metabolic profiles (elevated fasting glucose, dyslipidemia, and increased liver fibrosis risk scores), higher prevalence of diabetes and hypertension, and elevated all-cause and CVD mortality compared with Q1. Conversely, the lowest CTI tertile (Q1) consistently exhibited more favorable metabolic characteristics with reduced disease burden and mortality rates.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline characteristics of participants with MAFLD\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;3102)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e\u003cp\u003eCTI quartiles\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1034)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1034)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1034)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1598 (50.68%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e516 (49.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e572 (55.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e494 (47.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge(years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e47.9\u0026thinsp;\u0026plusmn;\u0026thinsp;15.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e41.2\u0026thinsp;\u0026plusmn;\u0026thinsp;14.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e48.9\u0026thinsp;\u0026plusmn;\u0026thinsp;15.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e53.8\u0026thinsp;\u0026plusmn;\u0026thinsp;13.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRace, n (%)\u003c/p\u003e\u003cp\u003eMexican American\u003c/p\u003e\u003cp\u003eNonHispanicWhite\u003c/p\u003e\u003cp\u003eNon-Hipanic Black\u003c/p\u003e\u003cp\u003eOther Race\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2289 (73.0%)\u003c/p\u003e\u003cp\u003e711 (22.7%))\u003c/p\u003e\u003cp\u003e134 (4.3%))\u003c/p\u003e\u003cp\u003e2 (0.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e652(63.1%)\u003c/p\u003e\u003cp\u003e333 (32.2%))\u003c/p\u003e\u003cp\u003e48 (4.6%))\u003c/p\u003e\u003cp\u003e1(0.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e795 (76.9%)\u003c/p\u003e\u003cp\u003e193 (18.7%))\u003c/p\u003e\u003cp\u003e45 (4.4%))\u003c/p\u003e\u003cp\u003e1 (1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e818 (79.1%)\u003c/p\u003e\u003cp\u003e175 (16.9%)\u003c/p\u003e\u003cp\u003e41 (4.0%))\u003c/p\u003e\u003cp\u003e0 (4.42%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCollege degree, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e929 (29.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e242 (23.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e300 (29.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e374 (36.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow income, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1038 (36.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e370 (39.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e308 (32.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e346 (37.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSedentary lifestyle, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e436 (13.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e166 (16.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e137 (13.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e128 (12.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2006 (64.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e588 (57.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e699 (67.6%))\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e698 (67.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI(kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e30.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.0\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e32.0\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlucose (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e109.3 (94.0-142.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e94.7 (87.1-104.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e109.4 (96.2-129.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e153.0 (115.4\u0026ndash;236.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInsulin (uU/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e29.06\u0026thinsp;\u0026plusmn;\u0026thinsp;33.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29.06\u0026thinsp;\u0026plusmn;\u0026thinsp;33.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23.56\u0026thinsp;\u0026plusmn;\u0026thinsp;26.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e33.73\u0026thinsp;\u0026plusmn;\u0026thinsp;37.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHbA1c(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.5 (5.1-6.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.3 (5.0-5.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.5 (5.1\u0026ndash;5.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.0 (5.4-8.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTC(mg/dl)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e212.2\u0026thinsp;\u0026plusmn;\u0026thinsp;46.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e191.6\u0026thinsp;\u0026plusmn;\u0026thinsp;39.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e212.5\u0026thinsp;\u0026plusmn;\u0026thinsp;40.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e233.0\u0026thinsp;\u0026plusmn;\u0026thinsp;50.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTG(mg/ dl )\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e156.0 (104.5\u0026ndash;234.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e92.0 (71.0-119.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e169.0 (133.0-212.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e257.0 (188.2-368.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL(mg/ dl )\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e216.8\u0026thinsp;\u0026plusmn;\u0026thinsp;47.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e195.9\u0026thinsp;\u0026plusmn;\u0026thinsp;40.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e217.6\u0026thinsp;\u0026plusmn;\u0026thinsp;41.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e237.3\u0026thinsp;\u0026plusmn;\u0026thinsp;49.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL(mg/ dl )\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e129.6\u0026thinsp;\u0026plusmn;\u0026thinsp;38.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e122.1\u0026thinsp;\u0026plusmn;\u0026thinsp;38.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e132.7\u0026thinsp;\u0026plusmn;\u0026thinsp;37.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e135.9\u0026thinsp;\u0026plusmn;\u0026thinsp;39.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUA(mg/l)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALT(U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e19.0(13.0\u0026ndash;29.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17.0 (12.0\u0026ndash;27.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19.0 (14.0\u0026ndash;31.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e19.0 (14.0\u0026ndash;29.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAST(U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21.0(17.0\u0026ndash;29.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20.0 (17.0\u0026ndash;27.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22.0 (18.0\u0026ndash;29.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e21.0 (17.0\u0026ndash;29.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLT(10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e277.6\u0026thinsp;\u0026plusmn;\u0026thinsp;72.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e273.0\u0026thinsp;\u0026plusmn;\u0026thinsp;69.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e277.9\u0026thinsp;\u0026plusmn;\u0026thinsp;72.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e281.9\u0026thinsp;\u0026plusmn;\u0026thinsp;74.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.072\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlbumin(g/dl)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e41.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e41.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e41.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e40.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCTI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNFS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFIB-4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.9 (0.6\u0026ndash;1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.7 (0.5\u0026ndash;1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.9 (0.6\u0026ndash;1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.0 (0.7\u0026ndash;1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1206 (38.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e106 (10.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e346 (33.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e733 (70.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1082 (34.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e219 (21.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e375 (36.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e474 (45.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMortality, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAll cause\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1441 (46.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e322 (31.1%))\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e472 (45.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e635 (61.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCVD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e474 (15.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e90 (8.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e164 (15.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e219 (21.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eAssociation of CTI index with all-cause and CVD mortality in MAFLD patients\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e details Cox regression analyses for mortality risks associated with CTI in MAFLD. In the crude model, each 1-unit increase in continuous CTI was associated with a significantly elevated risk of all-cause mortality (HR\u0026thinsp;=\u0026thinsp;1.71; 95% CI 1.55\u0026ndash;1.89; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This association remained robust after sequentially adjusting for confounding factors, with the increased risk remaining significant in all multivariable-adjusted models (Model 2: HR\u0026thinsp;=\u0026thinsp;1.32; 95% CI1.18\u0026ndash;1.47; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In stratified analyses, Q3 maintained a 58% higher mortality risk versus Q1 after full adjustment (HR 1.58, 95% CI 1.23\u0026ndash;2.02; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas Q2 demonstrated a non-significant association (HR 1.11; P\u0026thinsp;=\u0026thinsp;0.41). CVD mortality demonstrated a more pronounced association. Per 1-unit CTI increase, the crude model indicated an 88% higher CVD mortality risk (HR 1.88, 95% CI 1.64\u0026ndash;2.16; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with a 52% residual increase in the fully adjusted Model 2 (HR 1.52, 95% CI 1.26\u0026ndash;1.85; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Stratified analysis revealed non-significant risk elevation for Q2 versus Q1 (HR 1.38, 95% CI 0.88\u0026ndash;2.14; P\u0026thinsp;=\u0026thinsp;0.16). Conversely, Q3 exhibited substantially elevated CVD mortality risk across all adjustment levels, demonstrating a 2.09-fold higher risk than Q1 (HR 2.09, 95% CI 1.33\u0026ndash;3.28; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in Model 2, establishing the highest CTI tertile as a robust independent predictor of CVD death.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eHRs of CTI on all-cause mortality and CVD mortality in patients with MAFLD\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"12\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\" morerows=\"1\" nameend=\"c3\" namest=\"c1\" rowspan=\"2\"\u003e\u003cp\u003eCTI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003eCrude model\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u003cp\u003eHR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eHR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eAll cause mortality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eContinuous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1.71(1.55,1.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.41(1.28,1.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.32(1.18,1.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eCategories\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1.89(1.50,2.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.28(1.02,1.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.11(0.87,1.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e3.23(2.57,4.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.93(1.54,2.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.58(1.23,2.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eCVD mortality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eContinuous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003e1.88(1.64,2.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.62(1.37,1.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.52(1.26, 1.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eCategories\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003e1.8(1.50,2.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.68(1.10,2.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.38(0.88,2.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003e3.2(2.57,4.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.74(1.83,4.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e2.09(1.33,3.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eCrude model: Unadjusted.Model 1: Adjusted for age, sex, race/ethnicity, marital status, education, smoking, physical activity, and income.Model 2: Additionally adjusted for history of diabetes, history of hypertension, BMI, and FIB-4 score,HDL-C.\u003c/p\u003e\n\u003ch3\u003eRCS and threshold effect analysis\u003c/h3\u003e\n\u003cp\u003eRestricted cubic splines revealed significant non-linearity in the CTI-all-cause mortality association (P for non-linearity\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Two-piecewise regression identified an inflection point at CTI\u0026thinsp;=\u0026thinsp;8.1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-C). Below this threshold (CTI\u0026thinsp;\u0026lt;\u0026thinsp;8.1), each unit CTI increase showed non-significant mortality association (HR 0.78, 95%CI 0.55\u0026ndash;1.12; P\u0026thinsp;=\u0026thinsp;0.179), whereas above threshold (CTI\u0026thinsp;\u0026ge;\u0026thinsp;8.1) each unit increase conferred significantly elevated risk (HR 1.32, 95%CI 1.21\u0026ndash;1.45; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The high-segment demonstrated 169% greater mortality risk versus low-segment (ΔHR 1.69, 95%CI 1.15\u0026ndash;2.49; P\u0026thinsp;=\u0026thinsp;0.008), with the threshold model significantly outperforming linear model by log-likelihood ratio test (P\u0026thinsp;=\u0026thinsp;0.01)(Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSimilarly, CVD mortality exhibited non-linear association with CTI (P for non-linearity\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and shared identical inflection point (CTI\u0026thinsp;=\u0026thinsp;8.1; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD-F). Sub-threshold CTI increases showed non-significant CVD risk (HR 0.57, 95%CI 0.31\u0026ndash;1.06; P\u0026thinsp;=\u0026thinsp;0.077), while supra-threshold increases demonstrated significant 49% elevated risk per unit (HR 1.49, 95%CI 1.28\u0026ndash;1.73; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The high-segment manifested 260% greater CVD mortality versus low-segment (ΔHR 2.60, 95%CI 1.33\u0026ndash;5.09; P\u0026thinsp;=\u0026thinsp;0.005), with threshold model superiority confirmed (P\u0026thinsp;=\u0026thinsp;0.009) (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eKaplan–Meier (K–M) survival curves\u003c/h3\u003e\n\u003cp\u003eKaplan-Meier analysis demonstrated progressively increased cumulative incidence of all-cause mortality (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA) and CVD mortality (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB) from Q1 to Q3 tertiles, with statistically significant inter-group differences (log-rank P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e\u003cb\u003eSubgroup and interaction analyses\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eTo evaluate potential heterogeneity in the association between CTI and mortality, we conducted subgroup analyses using Cox proportional hazards models across key demographic and clinical variables, including age, gender, race, obesity (BMI\u0026thinsp;\u0026ge;\u0026thinsp;25), diabetes, hypertension, and liver fibrosis status (as defined by FIB-4). Formal interaction tests were performed for each subgroup (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFor all-cause mortality(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), elevated CTI was consistently associated with increased risk across nearly all subgroups. Specifically, higher CTI was significantly associated with greater all-cause mortality in both age groups (\u0026le;\u0026thinsp;60: HR 1.50, 95% CI 1.17\u0026ndash;1.61; \u0026gt;60: HR 1.60, 95% CI 1.31\u0026ndash;1.78), both genders (Male: HR 1.23, 95% CI 1.04\u0026ndash;1.46; Female: HR 1.42, 95% CI 1.22\u0026ndash;1.66), and all racial and comorbidity subgroups. The association was non-significant only among non-diabetic individuals (HR 1.20, 95% CI 0.98\u0026ndash;1.46). However, no significant interaction effects were observed for any of the subgroups (all p-interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003eFor cardiovascular disease (CVD) mortality(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB), a significant positive association with CTI was observed in most subgroups, except among Non-Mexican Americans (HR 1.11, 95% CI 0.81\u0026ndash;1.51). Notably, stronger associations were found in individuals aged\u0026thinsp;\u0026gt;\u0026thinsp;60 (HR 1.77, 95% CI 1.34\u0026ndash;2.34) and those with FIB-4\u0026thinsp;\u0026lt;\u0026thinsp;1.3 (HR 1.74, 95% CI 1.36\u0026ndash;2.23). A significant interaction was detected for age (p-interaction\u0026thinsp;=\u0026thinsp;0.01). No other subgroups show statistically significant interaction effect.\u003c/p\u003e\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eAUC and ROC\u003c/h2\u003e\u003cp\u003eFor all-cause mortality risk, CTI and TyG demonstrated comparable discriminatory power (AUC 0.655 vs. 0.647), whereas CRP showed lower discrimination (AUC 0.564). Similarly, for CVD mortality, CTI exhibited moderate predictive utility (AUC 0.622, 95% CI 0.595\u0026ndash;0.648), marginally superior to TyG (AUC 0.615, 95% CI 0.588\u0026ndash;0.641). CRP lacked discrimination capacity (AUC 0.553, 95% CI 0.527\u0026ndash;0.579).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eSensitivity analyses\u003c/h2\u003e\u003cp\u003eTo evaluate the robustness of our results, we conducted a series of sensitivity analyses. First, to address potential limitations due to missing data, we applied multiple imputation techniques, which minimized bias by creating plausible estimates for incomplete observations (Table S2. S3). The primary associations between CTI levels and all-cause mortality remained consistent across these analyses. Second, we examined the robustness of our findings using alternative statistical models, such as logistic regression, to confirm that the observed effects were not specific to a particular analytical approach (Table S4). Third, we jointly assessed the associations of TyG and CRP with all-cause mortality and cardiovascular mortality to ensure the robustness of the results(Table S5).Finally, to assess potential bias from early mortality events, we excluded individuals who died within 2 years after baseline assessment, and the resulting effect estimates remained largely unchanged, maintaining the robustness of our principal conclusions (Table S6).\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eLeveraging large-scale prospective cohort data from the NHANES III, this study is the first to demonstrate a nonlinear relationship between the CTI and both all-cause and CVD mortality in individuals with MAFLD.The results demonstrated a distinct threshold effect of CTI at 8.1 for both outcomes. When CTI exceeded 8.1, each unit increase in CTI was significantly associated with a higher risk of all-cause mortality (HR\u0026thinsp;=\u0026thinsp;1.32, 95% CI 1.21\u0026ndash;1.45, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Below this threshold, the association was non-significant and suggested a potentially lower risk (HR\u0026thinsp;=\u0026thinsp;0.78, 95% CI 0.55\u0026ndash;1.12, P\u0026thinsp;=\u0026thinsp;0.1788). Similarly, for CVD mortality, a significant association with increased CVD mortality risk was evident only when CTI surpassed this threshold (HR\u0026thinsp;=\u0026thinsp;1.49, 95% CI 1.28\u0026ndash;1.73, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The CTI demonstrated superior predictive performance for mortality compared to its individual components (CRP or the TyG index alone), underscoring the clinical utility of integrating inflammatory and metabolic signals.\u003c/p\u003e\u003cp\u003eAs a composite index integrating TyG and CRP, CTI enables a synergistic assessment of mortality risk in patients with MAFLD through dual pathways involving insulin resistance and chronic inflammation. TyG as an insulin resistance marker,although previous studies have suggested an association between CTI and all-cause mortality in MAFLD patients\u003csup\u003e\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, its relationship with CVD mortality remains controversial. For instance, Chen et al. report null association between TyG and CVD mortality (HR 1.036, 95% CI :0.904\u0026ndash;1.187, P\u0026gt;0.05)\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, whereas Zhang et al. reported a significant positive correlation (HR 1.63, 95% CI 1.03\u0026ndash;2.57, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003csup\u003e25\u003c/sup\u003e. In contrast, CRP as an inflammatory marker consistently demonstrates associations with increased CVD mortality risk in both the general population\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e and MAFLD patients\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.Ni et al.'s meta-analysis (incorporating 22 studies) definitively showed that in the general population\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, moderate and high CRP levels were associated with 43% (HR 1.43, 95% CI 1.22\u0026ndash;1.68, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 102% (HR 2.02, 95% CI 1.70\u0026ndash;2.41, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) increased risks of CVD mortality, respectively, compared to low CRP levels. In the MAFLD population, Wang et al.'s study also confirmed that elevated CRP was significantly associated with increased CVD mortality risk (HR 1.35, 95% CI 1.03\u0026ndash;1.77, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003csup\u003e28\u003c/sup\u003e.The pivotal finding of the present study lies in the enhanced predictive power achieved by constructing CTI to integrate TyG and CRP: high CTI levels conferred a 109% (HR 2.09, 95% CI 1.33\u0026ndash;3.28, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) higher excess CVD mortality risk compared to low levels. ROC analysis indicated that CTI demonstrated a trend toward superior predictive value for both all-cause and CVD mortality compared to either CRP or the TyG index alone. This strongly supports the necessity of concurrently evaluating insulin resistance and chronic inflammatory status for accurate CVD prognosis assessment in MAFLD patients.\u003c/p\u003e\u003cp\u003eThe elevated all-cause and CVD mortality observed in MAFLD patients originates from insulin resistance and chronic low-grade inflammation\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.IR directly triggers atherogenic dyslipidemia (characterized by hypertriglyceridemia, low HDL-C, and increased small dense LDL particles), hypertension, endothelial dysfunction, and a prothrombotic state\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. These factors collectively and potently promote the initiation, progression, and plaque destabilization of atherosclerosis\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Simultaneously, chronic low-grade inflammation not only directly drives vascular endothelial injury, leukocyte infiltration, foam cell formation, and plaque rupture \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003ebut also forms a vicious cycle with IR (wherein inflammatory cytokines disrupt insulin signaling, and IR in turn exacerbates inflammation)\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. This synergy between IR and inflammation further amplifies metabolic dysregulation and vascular damage, leading to myocardial metabolic derangements, fibrosis, and remodeling. Ultimately, these pathological processes significantly elevate the incidence of fatal CVD events (such as myocardial infarction, ischemic stroke, and sudden cardiac death), establishing CVD as the primary cause of death in MAFLD patients\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Furthermore, the chronic inflammatory state itself increases the risk of other lethal conditions, including liver disease progression (e.g., cirrhosis, hepatocellular carcinoma), malignancies, and chronic kidney disease, collectively contributing to elevated all-cause mortality\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Thus, liver-derived systemic IR and chronic inflammation form the central pathogenic axis driving adverse outcomes in MAFLD.This study identified a critical threshold (CTI\u0026thinsp;\u0026gt;\u0026thinsp;8.1) beyond which the risks of all-cause and CVD mortality in MAFLD patients exhibit a significant surge. The threshold effect of the CTI index signifies a critical transition from compensated to decompensated metabolic homeostasis. This phenomenon is probably driven by a vicious cycle between insulin resistance and chronic inflammation reaching a tipping point, which precipitously accelerates vascular injury. Exceeding this threshold is associated with a nonlinear and significant increase in the risk of CVD, thereby providing a crucial cutoff for risk stratification in patients with MAFLD. Identifying this inflection point is essential for implementing early and precise interventions to halt the progression of cardiovascular complications.\u003c/p\u003e\u003cp\u003eIn the current study, a statistically significant interaction was identified between age and CTI with respect to CVD mortality (P for interaction\u0026thinsp;=\u0026thinsp;0.01). Notably, elevated CTI was associated with a slightly higher hazard ratio for cardiovascular death among participants aged\u0026thinsp;\u0026le;\u0026thinsp;60 years (HR 1.78, 95% CI 1.34\u0026ndash;2.18) compared to those older than 60 years (HR 1.73, 95% CI 1.34\u0026ndash;2.34), with both estimates remaining statistically significant. This pattern of association is consistent with previous reports involving the TyG index in populations with established cardiovascular\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e or cerebrovascular\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e diseases, wherein a high TyG index demonstrated more pronounced effects on cardiovascular mortality in younger and middle-aged adults than in older adults. The underlying mechanisms for this age-dependent risk amplification may be attributed to a combination of greater genetic susceptibility and more severe cardiometabolic abnormalities in younger individuals, leading to prolonged and intensified exposure to cardiovascular risk factors\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Consequently, this results in an accelerated accumulation of risk and manifest cardiovascular mortality at a relatively younger age. However, evidence regarding the association of CTI with clinical outcomes remains limited, and further studies are warranted to validate its interaction with age.\u003c/p\u003e\u003cp\u003eThis study has several limitations. First, the single baseline measurement of the TyG and CRP levels cannot capture their dynamic trajectories; the clinical value of monitoring these changes for MAFLD management requires validation through longitudinal studies. Second, despite adjusting for multidimensional covariates, residual confounding from unmeasured factors (e.g., genetic susceptibility, evolving lifestyle) may influence results, and imprecise smoking quantification could introduce bias. Finally, findings derived from a US cohort necessitate validation across diverse ethnicities and MAFLD phenotypes in multicenter studies.\u003c/p\u003e\u003cp\u003eThis study is the first to identify a significant nonlinear threshold effect (critical value: 8.1) of CTI for predicting all-cause and CVD mortality in MAFLD patients, providing a quantitative tool for precise risk stratification. By integrating MAFLD\u0026rsquo;s core pathological features -insulin resistance and chronic inflammation - CTI effectively captures the pivotal transition point driving progression to end-organ damage.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eNAFLD :nonalcoholic fatty liver disease\u003c/p\u003e\n\u003cp\u003eMAFLD:metabolic dysfunction-associated fatty liver disease\u003c/p\u003e\n\u003cp\u003eTG: total cholesterol\u003c/p\u003e\n\u003cp\u003eFPG: fast blood glucose\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTyG: triglyceride-glucose\u003c/p\u003e\n\u003cp\u003eCRP: C-reactive protein \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCTI C:reactive protein-triglyceride-glucose index\u003c/p\u003e\n\u003cp\u003eCVD:Cardiovascular disease\u003c/p\u003e\n\u003cp\u003eCKD chronic kidney disease\u003c/p\u003e\n\u003cp\u003eBMI: body mass index\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eT2DM :type 2 diabetes mellitus\u003c/p\u003e\n\u003cp\u003eHbA1c: Hemoglobin A1c\u003c/p\u003e\n\u003cp\u003eIR :insulin resistance\u003c/p\u003e\n\u003cp\u003eTC: total cholesterol\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLDL-C: low density lipoprotein cholesterol\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHDL-C: high-density lipoprotein cholesterol;\u003c/p\u003e\n\u003cp\u003eUA,uric acid ;\u003c/p\u003e\n\u003cp\u003eALT:alanine aminotransferase;\u003c/p\u003e\n\u003cp\u003eAST:aspartate aminotransferase;\u003c/p\u003e\n\u003cp\u003ePLT:platelet count ;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNFS: NAFLD Fibrosis Score;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFIB-4: fibrosis-4 index;\u003c/p\u003e\n\u003cp\u003eCKM:cardiovascular-kidney-metabolic syndrome;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCDC:Centers for Disease Control and Prevention;\u003c/p\u003e\n\u003cp\u003eNDI:National Death Index;\u003c/p\u003e\n\u003cp\u003eNHANES III:National Health and Nutrition Examination Survey III (1988\u0026ndash;1994) ;\u003c/p\u003e\n\u003cp\u003eROC:Receiver Operating Characteristic;\u003c/p\u003e\n\u003cp\u003eAUC:Area Under the Curve.\u003c/p\u003e\n\u003cp\u003eHR:Hazard ratios\u003c/p\u003e\n\u003cp\u003e95%CI:95% confidence interval\u003c/p\u003e\n\u003cp\u003eRCS:Restricted cubic spline\u003c/p\u003e\n\u003cp\u003eK\u0026ndash;M :Kaplan\u0026ndash;Meier\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge funding support from the National Natural Science Foundation of China (82471657, 82171599, and 82201792), as well as from the 2022 Anhui Provincial Social Science Innovation and Development Research Project (Grant No. 2022CX082).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the Medical Research Ethics Committee of the First Affiliated Hospital of USTC on November 18, 2024 (Ethical approval No. 2024 KY 535).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants provided written informed consent prior to inclusion in the study. The study protocol was reviewed and approved by the institutional ethics committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWritten Consent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten consent for publication of anonymized data was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData regarding any of the subjects in the study can be shared after reasonable request to the corresponding author. No identifying data will be provided.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQ. J., X.J., and S.B. contributed equally to this work and share first authorship. Q.J. was responsible for sample collection, manuscript drafting, and revisions. X.J. constructed the figures and tables. S.B. and L.Z. performed the statistical analyses and interpreted the data. S.Z., L.W., and B.X. jointly supervised the study, contributed to the study design and critical revision of the manuscript, and share corresponding authorship. All authors read and approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eEslam M, Sanyal AJ, George J. International Consensus Panel. MAFLD: A Consensus-Driven Proposed Nomenclature for Metabolic Associated Fatty Liver Disease. Gastroenterology. 2020;158:1999\u0026ndash;e20141.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCusi K, et al. American Association of Clinical Endocrinology Clinical Practice Guideline for the Diagnosis and Management of Nonalcoholic Fatty Liver Disease in Primary Care and Endocrinology Clinical Settings: Co-Sponsored by the American Association for the Study of Liver Diseases (AASLD). Endocr Pract Off J Am Coll Endocrinol Am Assoc Clin Endocrinol. 2022;28:528\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRiazi K, et al. The prevalence and incidence of NAFLD worldwide: a systematic review and meta-analysis. Lancet Gastroenterol Hepatol. 2022;7:851\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTargher G, Day CP, Bonora E. Risk of cardiovascular disease in patients with nonalcoholic fatty liver disease. N Engl J Med. 2010;363:1341\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDuell PB, et al. Nonalcoholic Fatty Liver Disease and Cardiovascular Risk: A Scientific Statement From the American Heart Association. Arterioscler Thromb Vasc Biol. 2022;42:e168\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang DQ, El-Serag HB, Loomba R. Global epidemiology of NAFLD-related HCC: trends, predictions, risk factors and prevention. Nat Rev Gastroenterol Hepatol. 2021;18:223\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMaldonado-Rojas ADC, Zuarth-V\u0026aacute;zquez JM, Uribe M. Barbero-Becerra, V. J. Insulin resistance and Metabolic dysfunction-associated steatotic liver disease (MASLD): Pathways of action of hypoglycemic agents. Ann Hepatol. 2024;29:101182.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHill MA, et al. Insulin resistance, cardiovascular stiffening and cardiovascular disease. Metabolism. 2021;119:154766.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKosmas CE, et al. Insulin resistance and cardiovascular disease. J Int Med Res. 2023;51:3000605231164548.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDeFronzo RA, Tobin JD, Andres R. Glucose clamp technique: a method for quantifying insulin secretion and resistance. Am J Physiol. 1979;237:E214\u0026ndash;223.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFasipe OJ, Ayoade OG, Enikuomehin AC. Severity Grade Assessment Classifications for Both Insulin Resistance Syndrome and Status of Pancreatic Beta Cell Function in Clinical Practice Using Homeostasis Model Assessment Method Indices. Can J Diabetes. 2020;44:663\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTahapary DL, et al. Challenges in the diagnosis of insulin resistance: Focusing on the role of HOMA-IR and Tryglyceride/glucose index. Diabetes Metab Syndr. 2022;16:102581.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRuan G-T, et al. A Novel Inflammation and Insulin Resistance Related Indicator to Predict the Survival of Patients With Cancer. Front Endocrinol. 2022;13:905266.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMa X, Ma X, Wang Y, Qiu G, Zhang C. Associations of cumulative exposure and dynamic trajectories of the C-reactive protein-triglyceride-glucose index with incident cardiovascular disease in middle-aged and older Chinese adults: a nationwide cohort study. Cardiovasc Diabetol. 2025;24:303.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhao D-F. Value of C-Reactive Protein-Triglyceride Glucose Index in Predicting Cancer Mortality in the General Population: Results from National Health and Nutrition Examination Survey. Nutr Cancer. 2023;75:1934\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOu H, Wei M, Li X, Xia X. C-reactive protein-triglyceride glucose index in evaluating cardiovascular disease and all-cause mortality incidence among individuals across stages 0\u0026ndash;3 of cardiovascular-kidney-metabolic syndrome: a nationwide prospective cohort study. Cardiovasc Diabetol. 2025;24:296.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuo G, et al. Association between C-reactive protein-triglyceride glucose index and stroke risk in different glycemic status: insights from the China Health and Retirement Longitudinal Study (CHARLS). Cardiovasc Diabetol. 2025;24:142.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eParker JD, et al. National Center for Health Statistics Data Presentation Standards for Proportions. Vital Health Stat. 2017;2:1\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEslam M, et al. A new definition for metabolic dysfunction-associated fatty liver disease: An international expert consensus statement. J Hepatol. 2020;73:202\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSimental-Mend\u0026iacute;a LE, Rodr\u0026iacute;guez-Mor\u0026aacute;n M, Guerrero-Romero F. The product of fasting glucose and triglycerides as surrogate for identifying insulin resistance in apparently healthy subjects. Metab Syndr Relat Disord. 2008;6:299\u0026ndash;304.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSterling RK, et al. Development of a simple noninvasive index to predict significant fibrosis in patients with HIV/HCV coinfection. Hepatol Baltim Md. 2006;43:1317\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAngulo P, et al. The NAFLD fibrosis score: A noninvasive system that identifies liver fibrosis in patients with NAFLD. Hepatology. 2007;45:846\u0026ndash;54. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/hep\u003c/span\u003e\u003cspan address=\"10.1002/hep\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen Q, et al. Association between triglyceride-glucose related indices and mortality among individuals with non-alcoholic fatty liver disease or metabolic dysfunction-associated steatotic liver disease. Cardiovasc Diabetol. 2024;23:232.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMin Y, et al. Prognostic effect of triglyceride glucose-related parameters on all-cause and cardiovascular mortality in the United States adults with metabolic dysfunction-associated steatotic liver disease. Cardiovasc Diabetol. 2024;23:188.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang Y, et al. Association of triglyceride glucose-related parameters with all-cause mortality and cardiovascular disease in NAFLD patients: NHANES 1999\u0026ndash;2018. Cardiovasc Diabetol. 2024;23:262.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNi P, et al. Dose-response association between C-reactive protein and risk of all-cause and cause-specific mortality: a systematic review and meta-analysis of cohort studies. Ann Epidemiol. 2020;51:20\u0026ndash;e2711.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHartley A, et al. The relationship of baseline high-sensitivity C-reactive protein with incident cardiovascular events and all-cause mortality over 20 years. EBioMedicine. 2025;117:105786.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang Y, et al. Prognostic impact of systemic inflammation indicators on all-cause and CVD mortality in adults with MASLD. BMC Gastroenterol. 2025;25:448.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEuropean Association for the Study of the Liver (EASL). European Association for the Study of Diabetes (EASD), \u0026amp; European Association for the Study of Obesity (EASO). EASL-EASD-EASO Clinical Practice Guidelines on the management of metabolic dysfunction-associated steatotic liver disease (MASLD). J Hepatol. 2024;81:492\u0026ndash;542.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHill MA, et al. Insulin resistance, cardiovascular stiffening and cardiovascular disease. Metabolism. 2021;119:154766.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMuzurović E, Mikhailidis DP, Mantzoros C. Non-alcoholic fatty liver disease, insulin resistance, metabolic syndrome and their association with vascular risk. Metabolism. 2021;119:154770.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRohm TV, Meier DT, Olefsky JM, Donath MY. Inflammation in obesity, diabetes, and related disorders. Immunity. 2022;55:31\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eP\u0026uuml;schel GP, Klauder J, Henkel J, Macrophages. Low-Grade Inflammation, Insulin Resistance and Hyperinsulinemia: A Mutual Ambiguous Relationship in the Development of Metabolic Diseases. J Clin Med. 2022;11:4358.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSimon TG, Roelstraete B, Hagstr\u0026ouml;m H, Sundstr\u0026ouml;m J, Ludvigsson JF. Non-alcoholic fatty liver disease and incident major adverse cardiovascular events: results from a nationwide histology cohort. Gut. 2022;71:1867\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee DY, et al. Impact of systemic inflammation on the relationship between insulin resistance and all-cause and cancer-related mortality. Metabolism. 2018;81:52\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMa X, et al. Triglyceride glucose index for predicting cardiovascular outcomes after percutaneous coronary intervention in patients with type 2 diabetes mellitus and acute coronary syndrome. Cardiovasc Diabetol. 2020;19:31.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang L, et al. Influence of age on the association between the triglyceride-glucose index and all-cause mortality in patients with cardiovascular diseases. Lipids Health Dis. 2022;21:135.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu R, Li L, Wang L, Zhang S. Triglyceride-glucose index predicts death in patients with stroke younger than 65. Front Neurol. 2023;14:1198487.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen J, Wu K, Lin Y, Huang M, Xie S. Association of triglyceride glucose index with all-cause and cardiovascular mortality in the general population. Cardiovasc Diabetol. 2023;22:320.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"C-reactive protein-triglyceride-glucose index(CTI), Metabolic dysfunction-associated fatty liver disease (MAFLD), All-cause mortality, Cardiovascular diseases (CVD) mortality","lastPublishedDoi":"10.21203/rs.3.rs-7535688/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7535688/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eThe C-reactive protein (CRP)-triglyceride-glucose (TyG) index (CTI) is a clinical marker that simultaneously reflects inflammation and insulin resistance (IR) and is associated with adverse cardiovascular outcomes. However, its prognostic value in metabolic dysfunction-associated fatty liver disease (MAFLD) remains unclear. This study aimed to investigate the associations of CTI with all-cause and cardiovascular disease (CVD) mortality in patients with MAFLD.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eData were utilized from adults who participated in the National Health and Nutrition Examination Survey (NHANES) III (1988\u0026ndash;1994), with their records linked to mortality data from the National Death Index (NDI). CTI was calculated as 0.412 \u0026times; Ln(CRP [mg/L])\u0026thinsp;+\u0026thinsp;Ln( (triglycerides [mg/dL] \u0026times; fasting glucose [mg/dL]) / 2 ). To assess the association between CTI and mortality, we employed multivariable Cox proportional hazards models, restricted cubic splines (RCS) analysis, and Kaplan-Meier curves. Furthermore, stratified analyses were conducted to evaluate potential heterogeneity across subgroups.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAmong 3,102 MAFLD participants, RCS analyses revealed significant non-linear associations between CTI and mortality risks (both P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with inflection points at CTI\u0026thinsp;=\u0026thinsp;8.1. After comprehensive adjustment,participants in the highest CTI tertile exhibited significantly elevated risks of both all-cause mortality (HR\u0026thinsp;=\u0026thinsp;1.58,95% CI 1.23\u0026ndash;2.02) and CVD mortality (HR\u0026thinsp;=\u0026thinsp;2.09, 95% CI 1.33\u0026ndash;3.28) compared to those in the lowest tertile.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eElevated CTI exceeding the threshold of 8.1 was independently associated with significantly increased risks of all-cause and CVD mortality. These findings establish CTI as a novel prognostic biomarker for long-term mortality risk stratification in patients with MAFLD.\u003c/p\u003e","manuscriptTitle":"Association between the C-Reactive Protein-Triglyceride-Glucose Index and Mortality in Patients with Metabolic Dysfunction-Associated Fatty Liver Disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-22 08:37:01","doi":"10.21203/rs.3.rs-7535688/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d1ef7300-0b11-4c73-b2fa-c73f724b0b0e","owner":[],"postedDate":"September 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-27T13:25:07+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-22 08:37:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7535688","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7535688","identity":"rs-7535688","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}