Association of Triglyceride-Glucose Index and Its Derivatives With Incidence and Cause-Specific Mortality of Cardiovascular and Cerebrovascular Diseases Among Cancer Survivors

Association of Triglyceride-Glucose Index and Its Derivatives With Incidence and Cause-Specific Mortality of Cardiovascular and Cerebrovascular Diseases Among Cancer Survivors | 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 of Triglyceride-Glucose Index and Its Derivatives With Incidence and Cause-Specific Mortality of Cardiovascular and Cerebrovascular Diseases Among Cancer Survivors Shuhang Luo, Runhua Tang, Li Ma, Haoran Wang, Jianyong Liu, Huimin Hou, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7625609/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 Background The risk and specific mortality of cardiovascular and cerebrovascular diseases (CCVD) would greatly increase among cancer survivors, with prevalent cardiovascular risk scores for regular populations showing poor performance. We aim to evaluate four insulin resistance (IR) indices—the triglyceride-glucose (TyG) index and its derivatives (TyG-BMI, TyG-WC, TyG-WHtR)—for their association with CCVD outcomes in cancer survivors. Methods This prospective cohort study is based on data from the UK Biobank. Fasting glucose, triglycerides, and anthropometric measures were used to calculate IR indices. Primary outcomes were CCVD incidence (composite of cardiovascular disease [CVD], stroke, and heart failure) and CCVD-specific mortality. Cox regression models adjusted for demographics, lifestyle, and comorbidities assessed hazard ratios (HRs) by index quartiles (Q1–Q4). Sensitivity and subgroup analyses were used to evaluate robustness and effect modification. Results Among cancer survivors, higher IR indices correlated with higher CCVD incidence and specific mortality (p-trend < 0.001). For CCVD incidence, TyG-WC showed the strongest association (Q4 HR = 1.51 (1.33, 1.71), p < 0.001), while TyG-WHtR best predicted CCVD mortality (Q4 HR = 3.17, 1.72–5.85). CVD-specific mortality risk was highest with TyG-WHtR (5.89-fold increase per 3-unit increment, p < 0.001). Heart failure risk rose significantly in Q4 for obesity-adjusted indices (e.g., TyG-WC HR = 3.17 (1.72, 5.85), p < 0.001), but no associations emerged for stroke outcomes. Subgroup analyses revealed stronger CCVD risk prediction in female patients, nondiabetics, and alcohol consumers (interaction p < 0.05). Sensitivity analyses confirmed the robustness of the results after excluding early events. Conclusion Except for the TyG index, the IR indices showed a close association with CCVD incidence and specific mortality among cancer survivors. The relation of TyG-WHtR was stronger than that of TyG-BMI and TyG-WC in CCVD, CVD, and heart failure, and this association could further increase in specific subgroups. Trial registration The ethical approval for the UK Biobank research was granted by the North West Multicenter Research Ethical Committee. This current study was specifically approved by the UK Biobank under application number 332912. Cancer survivor cardiovascular and cerebrovascular diseases triglyceride-glucose index insulin resistance Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Research Insights What is currently known about this topic? Cancer survivors face elevated cardiovascular/cerebrovascular disease (CCVD) risks due to shared pathophysiology and treatment effects, while insulin resistance (IR) indices show disease-specific associations with both cancer and CCVD. Meanwhile, current risk assessment tools for CCVD remain inadequate for cancer survivors, underscoring the need for better metabolic monitoring strategies. What is the key research question? Do the TyG index and its derivatives (TyG-BMI, TyG-WC, TyG-WHtR) show differential associations with CCVD risk and mortality in cancer survivors, and which index demonstrates the strongest relationship with these outcomes? What is new? This study provides the first comprehensive comparison of four TyG-derived indices in cancer survivors, identifying TyG-WC as showing the strongest association with CCVD risk and TyG-WHtR as having the closest relationship with mortality outcomes. How might this study influence clinical practice? This study indicates that TyG-WC and TyG-WHtR, as straightforward metabolic markers, could improve the monitoring of cardiovascular disease (CVD) risk in cancer survivors. They provide significant connections to cardiovascular outcomes, especially in terms of assessing mortality risk. Incorporating these markers into routine follow-up care may help identify high-risk patients who require early intervention. Introduction Cancer poses a major global health challenge, accounting for nearly 10 million deaths each year worldwide[ 1 ]. In Europe alone, over 1.2 million fatalities were attributed to cancer in 2022[ 2 ]. Current projections indicate that the burden of cancer is expected to increase significantly, with annual mortality anticipated to reach 3.24 million cases by 2040 [ 2 ]. With a consistently growing population of cancer, the number of cancer survivors increases as well. It has been reported that there are currently more than 30 million cancer survivors, with a 3.5% annual increase from 2010 to 2020 [ 3 , 4 ]. Cardiovascular and cerebrovascular diseases (CCVD) require special attention in cancer survivors because the two diseases have complex causes and share common risk factors [ 5 – 8 ]. Recent studies show that both tumor biology and cancer treatments can greatly increase the risk of CCVD, which includes cardiovascular disease (CVD), heart failure, and stroke, as well as related mortality[ 9 – 12 ]. On the other hand, CCVD and related biological responses can conversely influence the progression and metastasis of malignancies via particular biological mechanisms, leading to a diminished life expectancy for this at-risk population [ 8 , 13 – 16 ]. These interdependent relationships between CCVD highlight the urgent necessity for early risk evaluation and preventive strategies designed for cancer survivors. Insulin resistance (IR) is a hallmark feature of diabetes and obesity, which has been reported to commonly exist among cancer survivors and has a close relation to CCVD [ 17 – 20 ]. As a widely used evaluation tool for IR, the triglyceride-glucose (TyG) index has been employed to predict cardiovascular disease (CVD) risk and mortality [ 21 – 23 ]. There are derivative indices of the TyG index, including TyG-BMI (body mass index-adjusted), TyG-WC (waist circumference-adjusted), and TyG-WHtR (waist-to-height ratio-adjusted). Together with the TyG index, these indices were also highly correlated with various malignancies [ 24 – 26 ]. NNotably, although the three indices mentioned above are all derived from the foundational TyG index, they exhibit differential predictive capacities for specific diseases. For instance, the TyG-WC and TyG-WhtR indices demonstrated better evaluation efficacy than the TyG index in CVD [ 27 ]. The TyG-BMI and TyG-WC indices demonstrated superior performance in predicting the risk of liver steatosi [ 28 ]. In conclusion, it is quite rational to use TyG and its derivatives as a bridge between cancers and CCVD. In this prospective cohort study, we aimed to explore the associations between TyG and its derivatives (TyG-BMI, TyG-WC, and TyG-WHtR) and CCVD risk and specific mortality among cancer survivors based on the extensive prospective cohort data from the UK Biobank. Furthermore, we aim to thoroughly investigate which index has a tighter relationship with CCVD risk and mortality, or which indices are more relevant to CCVD risk. In contrast, other indices have a stronger association with CCVD mortality. The analysis incorporated extensive adjustment for potential confounding variables and included comprehensive sensitivity analyses to assess the robustness of observed relationships. Materials and Methods This prospective, population-based study analyzed data from the UK Biobank [ 30 ], a large-scale cohort comprising over 500,000 participants aged 37–73 years recruited between 2006 and 2010 across England, Scotland, and Wales. At baseline, participants completed touchscreen questionnaires, underwent standardized physiological measurements, and provided biological samples. Longitudinal follow-up included linkage to national hospital admissions, cancer registries, and mortality records, with participants providing written informed consent for data access. The UK National Health Service’s National Research Ethics Service approved the UK Biobank study protocol. The present analysis was authorized by the UK Biobank Access Management Team under Application #332912, with additional ethical oversight provided by the Institutional Review Board and the National Institutes of Health. We excluded participants with a prevalent CCVD diagnosis at baseline (N = 34, 405). To evaluate the predictive performance of the TyG index, TyG-BMI, TyG-WC, and TyG-WHtR for CCVD incidence and mortality among cancer survivors, we further excluded individuals with missing data on BMI, waist circumstance, standing height, triglycerides, glucose, or HDL cholesterol (N = 37, 068). Additional exclusions applied to participants with incomplete covariate data, resulting in a final analytical cohort of 425, 767 participants for primary survival analysis and a cancer survivor cohort of 18, 526 (Fig. 1 ). Exposure Fasting plasma glucose, triglycerides (TG), and high-density lipoprotein cholesterol (HDL-C) levels were measured using a Beckman Coulter AU5800 automated analyzer following standardized protocols. All blood samples were processed immediately after collection, with plasma/serum components separated and stored at -80°C until analysis. Detailed analytical procedures and quality control measures are available through the UK Biobank resource (Reference ID: 1227). We evaluated four insulin resistance-related indices derived from measurements as follows according to established methodologies [ 29 – 31 ]: TyG index: Ln[fasting TG (mg/dL) × fasting glucose (mg/dL)/2] TyG-BMI: TyG index × body mass index (kg/m²) TyG-WC: TyG index × waist circumference (cm) TyG-WHtR = TyG index × [waist circumference (cm)/height (cm)] Assessment of cancer survivors Cancer status at baseline recruitment was determined using International Classification of Diseases, Tenth Revision (ICD-10) codes from linked national health records, including Hospital Episode Statistics and the National Cancer Registration and Analysis Service. The cancer case definition encompassed malignancies across multiple organ systems: digestive (anal [C21], colorectal [C18-C20], esophageal [C15], gastric [C16], liver [C22], pancreatic [C25], and small intestinal [C17]), genitourinary (bladder [C67], kidney [C64], ovarian [C56], and prostate [C61]), thoracic (lung [C33-C34] and mesothelioma [C45]), hematologic (leukemia [C91-C95], lymphoma [C81-C86,C88], and multiple myeloma [C90]), as well as other major sites (brain [C71], breast [C50], laryngeal [C32], melanoma [C43-C44], oral [C00-C14], soft tissue [C46-C49], thyroid [C73], and uterine [C54-C55]). Cancer survivors were identified by comparing the date of first cancer diagnosis (Field ID 41280) with the date of assessment center attendance, with the first recorded cancer event in any linked database used to determine both the timing of diagnosis and primary cancer site. This comprehensive approach ensured accurate classification of prevalent cancer cases at baseline while enabling precise identification of cancer survivors within the study population. Exposure Cardio-cerebrovascular diseases (CCVD) were defined as a composite endpoint comprising cardiovascular disease (CVD), stroke, and heart failure. Incident CCVD cases (including disease status and initial diagnosis date) were identified using ICD-10 codes: CVD (I20-I25), stroke (I60, I61, I63, I64), and heart failure (I50, I11.0, I13.0, and I13.2). CCVD-related mortality was determined through death records containing the underlying cause of death (Field ID 40001, ICD-10 coded) and date of death (Field ID 40000), following established criteria from previous studies. The time-to-event for CCVD incidence was calculated from enrollment to the first diagnosis of CCVD, while CCVD-related mortality was measured from enrollment to the date of death. For participants without events, censoring occurred at the last follow-up date or the date of loss to follow-up. These records were linked with national death registries, including NHS Digital for England and Wales (data complete as of May 30, 2024) and the National Records of Scotland (data complete as of December 31, 2023), ensuring comprehensive mortality ascertainment. Covariates This study examined a range of demographic characteristics at baseline, including age (analyzed as a continuous variable), genetic sex (male/female), ethnicity (White/non-White), and educational attainment (A levels/AS levels or equivalent, College/University degree, CSEs or equivalent, NVQ/HND/HNC or equivalent, O levels/GCSEs or equivalent, and Other professional qualifications). Socioeconomic status was assessed by the Townsend Deprivation Index (continuous, derived from postal code data on unemployment, car and home ownership, and household overcrowding, with higher scores indicating greater deprivation), which was subsequently divided into quartiles (Q1-Q4) for analysis. Health behaviours included smoking status (classified as never/ever smoked) and alcohol consumption (never/ever drank). Clinical factors comprised the family history of cancer (yes/no, determined through baseline records of cancer diagnoses in participants' parents and siblings), anticoagulant use (yes/no, identified from baseline medication records for pain relief, constipation, or heartburn), and histories of heart failure, hypertension, cardiovascular disease, and stroke (ascertained through ICD-10 codes in the UK Biobank's linked inpatient hospital records using established diagnostic criteria [ 32 ]). All variables were dichotomized as present or absent based on baseline self-reports, except where objective measures (such as ICD codes) were available. Statistical analysis The baseline characteristics were reported as means with standard deviations (SD) for continuous variables and percentages (%) for categorical variables. The Shapiro-Wilk test, P-P plots, and Q-Q plots were used to assess the normality of continuous variables. Differences between groups were analyzed using analysis of variance (ANOVA) for continuous variables and chi-square tests for categorical variables. Cox proportional hazards regression models were applied to examine the relationships between TyG, TyG-BMI, TyG-WC, and TyG-WHtR scores (with the ideal state as the reference group) and outcomes, including incidence and mortality of CCVD and its components (CVD, stroke, and heart failure separately). The proportional hazards assumption was checked using Schoenfeld residuals; transient covariates were incorporated into the model if the assumption was violated. Multicollinearity among the covariates was evaluated through the variance inflation factor (VIF), with values below 10 deemed acceptable. The final model, adjusted for age, sex, ethnicity, education, socioeconomic status, smoking status, alcohol consumption, family history of cancer, history of anticoagulant use, and history of hypertension, CVD, stroke, heart failure, and diabetes, calculated the hazard ratios and 95% confidence intervals (CIs) for each IR score about the outcome variables. Time-dependent ROC (Receiver Operating Characteristic) curves were generated using the C/D method, which dynamically adjusts for censoring. Sensitivity (true positive rate) and 1-specificity (false positive rate) were calculated across risk thresholds (0–100%) at each time point. AUC values with 95% CIs were estimated via trapezoidal integration and bootstrap resampling (1000 replicates). Pairwise AUC comparisons were performed using DeLong's test. We conducted extensive subgroup analyses to evaluate the robustness of our findings and examine potential effect modifiers. Participants were stratified by key demographic and clinical characteristics, including age (< 60 vs. ≥60 years), genetic sex (male vs. female), BMI (< 25 kg/m² vs. ≥25 kg/m²), smoking status (yes vs. no), alcohol consumption (yes vs. No), hypertension (yes vs. no), diabetes (yes vs. no), and hypertension (yes vs. no). Statistical differences between subgroups were assessed using interaction terms. To rigorously evaluate our results, we performed comprehensive sensitivity analyses addressing potential confounding from medication use and moderate adherence. These analyses systematically excluded participants taking antidiabetic, antihyperlipidemic, or antihypertensive medications, as well as those with moderate adherence or medication persistence. We further excluded participants whose CCVD diagnosis or death occurred within one or two years after recruitment. Associations between TyG-BMI, TyG-WC (including their components BMI and waist circumference) and cancer outcomes were assessed using Cox regression. All statistical analyses were performed using R version 4.3.2 (R Foundation for Statistical Computing), with two-sided tests and significance threshold of p < 0.05. Results Overall survival, Event-free survival, and Disease-specific survival among regular participants and cancer survivors During a median follow-up of 14.9 years, 3827 deaths were recorded among cancer survivors. Compared to regular participants, the level of TyG, TyG-BMI, TyG-WC, and TyG-WHtR indices in cancer survivors were slightly higher (8.771 ± 0.555 vs 8.728 ± 0.567, p < 0.001, for TyG; 241.911 ± 49.810 vs 241.179 ± 50.591, p < 0.001, for TyG-BMI; 797.364 ± 147.218 vs 793.332 ± 149.694, p < 0.001, for TyG-WC; 4.764 ± 0.837 vs 4.707 ± 0.847, p < 0.001, for TyG-WHtR). We initially investigated the overall survival (OS), event-free survival (EFS), and disease-specific survival (DSS) between regular participants without cancer history and cancer survivors. The results demonstrated significantly worse OS in cancer survivors (p < 0.05, Fig. 2 A), which was expected given the generally poor prognosis of many malignancies. More importantly, cancer survivors also exhibited significantly higher incidence of CCVD events and CCVD-specific mortality compared to cancer-free participants (p < 0.05, Fig. 2 B-C). These findings suggest that cancer survivors may face elevated risks of developing CCVD and higher CCVD-related mortality than the general population. Participants characteristics Table 1 presents the baseline characteristics of the 18,526 cancer survivors. The cohort had a mean age of 60.3 years (SD = 6.8), with a female predominance (60.1%). Ethnically, the majority of participants were White (92.3%). Regarding educational attainment, 30.0% of participants held a college or university degree, whereas 21.5% reported no formal qualifications. The mean Townsend Index was − 1.47 (SD 2.98), reflecting overall low levels of deprivation. Among the participants, only 8.7% of them were currently smokers, while most of them (91.3%) had a history of alcohol consumption. Table 1 Baseline characteristics Characteristic Overall N = 18, 526 Q1 N = 4, 636 Q2 N = 4, 638 Q3 N = 4, 632 Q4 N = 4, 620 P -value 1 Age, n (%) < 0.001 < 60 years 6772 (36.6%) 2127 (45.9%) 1667 (35.9%) 1481 (32.0%) 1497 (32.4%) ≥ 60 years 11754 (63.4%) 2509 (54.1%) 2971 (64.1%) 3151 (68.0%) 3123 (67.6%) Sex, n (%) < 0.001 Male 7392 (39.9%) 1472 (31.8%) 1700 (36.7%) 1938 (41.8%) 2282 (49.4%) Female 11134 (60.1%) 3164 (68.2%) 2938 (63.3%) 2694 (58.2%) 2338 (50.6%) Ethnicity, n (%) < 0.001 White 17105 (92.3%) 4226 (91.2%) 4260 (91.8%) 4304 (92.9%) 4315 (93.4%) Non-White 1421 (7.7%) 410 (8.8%) 378 (8.2%) 328 (7.1%) 305 (6.6%) Education level, n (%) < 0.001 College or University degree 5550 (30.0%) 1634 (35.2%) 1394 (30.1%) 1339 (28.9%) 1183 (25.6%) A levels/AS levels or equivalent 1893 (10.2%) 542 (11.7%) 477 (10.3%) 417 (9.0%) 457 (9.9%) CSEs or equivalent 707 (3.8%) 190 (4.1%) 164 (3.5%) 169 (3.6%) 184 (4.0%) NVQ or HND or HNC or equivalent 1232 (6.7%) 231 (5.0%) 297 (6.4%) 337 (7.3%) 367 (7.9%) O levels/GCSEs or equivalent 4044 (21.8%) 998 (21.5%) 1067 (23.0%) 985 (21.3%) 994 (21.5%) Other professional qualifications eg: nursing, teaching 1118 (6.0%) 268 (5.8%) 288 (6.2%) 263 (5.7%) 299 (6.5%) None of the above 3982 (21.5%) 773 (16.7%) 951 (20.5%) 1122 (24.2%) 1136 (24.6%) Townsend deprivation index, n (%) < 0.001 Q1: -6.26~-3.70 4692 (25.3%) 1187 (25.6%) 1163 (25.1%) 1194 (25.8%) 1148 (24.8%) Q2: -3.70~-2.27 4642 (25.1%) 1173 (25.3%) 1195 (25.8%) 1202 (25.9%) 1108 (24.0%) Q3: -2.27 ~ 0.21 4662 (25.2%) 1211 (26.1%) 1188 (25.6%) 1110 (24.0%) 1153 (25.0%) Q4: 0.21 ~ 10.28 4494 (24.3%) 1065 (23.0%) 1092 (23.5%) 1126 (24.3%) 1211 (26.2%) Current smoking status, n (%) < 0.001 No 16923 (91.3%) 4302 (92.8%) 4259 (91.8%) 4240 (91.5%) 4122 (89.2%) Yes 1603 (8.7%) 334 (7.2%) 379 (8.2%) 392 (8.5%) 498 (10.8) Alcohol use, n (%) < 0.001 No 1572 (8.5%) 344 (7.4%) 367 (7.9%) 414 (8.9%) 447 (9.7%) Yes 16954 (91.5%) 4292 (92.6%) 4271 (92.2%) 4218 (91.1%) 4173 (90.3%) Diabetes history, n (%) < 0.001 No 16482 (89.0%) 4436 (95.7%) 4294 (92.6%) 4169 (90.0%) 3583 (77.6%) Yes 2044 (11.0%) 200 (4.3%) 344 (7.4%) 463 (10.0%) 1037 (22.4%) Hypertension history, n (%) < 0.001 No 7328 (39.6%) 2405 (51.9%) 1928 (41.6%) 1624 (35.1%) 1371 (29.7%) Yes 11198 (60.4%) 2231 (48.1%) 2710 (58.4%) 3008 (64.9%) 3249 (70.3%) 1 Kruskal-Wallis rank sum test; Pearson's Chi-squared test Associations between IR indexes and overall survival of cancer survivors The restricted cubic spline (RCS) analysis indicated a progressive increase in mortality risk associated with higher insulin resistance (IR) index levels when compared to the lowest 5% reference group, a trend consistently observed across all four indices (Supplementarty Fig. 1 A-D). Following this, Kaplan-Meier survival analysis was performed after categorizing participants into quartiles based on their IR index levels (Supplementarty Fig. 2 A-D). This analysis revealed significantly poorer overall survival rates in the higher-scoring groups (Q3 and Q4) in comparison to the lower-scoring groups (Q1 and Q2) (p < 0.001), a pattern that was confirmed for all four IR indices. We employed multivariable Cox proportional hazards regression with four progressively adjusted models to examine the relationship between IR indices and overall survival. Model 1 was unadjusted; Model 2 adjusted for age, genetic sex, race, and Townsend deprivation index; Model 3 additionally included education level, smoking status, and alcohol consumption; and Model 4 further incorporated history of diabetes and hypertension. Regarding CCVD risk, after full adjustment in Model 4, we found significantly elevated risk in the highest quartile (Q4) for TyG (HR = 1.10, 95% CI: 1.01–1.21; p = 0.038, Fig. 3 A), TyG-WC (HR = 1.20, 1.08–1.34; p < 0.001, Fig. 3 A), and TyG-WHtR (HR = 1.21, 1.10–1.34; p < 0.001, Fig. 3 A) compared to the reference group. Interestingly, for TyG-BMI, the Q2 group showed a modest but significant reduction in mortality risk (HR = 0.89, 0.81–0.98; p = 0.015) relative to the reference group. Detailed information was presented in Supplementary Table 1–4. Associations between IR indexes and CCVD Incidence and mortality Our analyses revealed significant associations between insulin resistance (IR) indices and the risk of cardio-cerebrovascular disease (CCVD) in cancer survivors. Using restricted cubic spline models, we identified a consistent dose-response relationship, where progressively higher CCVD incidence and mortality risks were observed at increasing levels of all four IR indices compared to the lowest 5% reference group (p for trend < 0.001, Fig. 4 A-D). Kaplan-Meier survival analyses, stratified by quartiles of IR indices, confirmed these findings, showing significantly worse CCVD outcomes in the higher quartile groups (Q3/Q4) compared to the lower quartiles (Q1/Q2) for all indices (log-rank p < 0.001, Fig. 5 A-D). In fully adjusted Cox models (Model 4), the TyG index demonstrated a significantly elevated risk of CCVD in both the third quartile (Q3) (HR = 1.12, 95% CI: 1.01–1.25; p = 0.038, Fig. 3 B) and the fourth quartile (Q4) (HR = 1.14, 95% CI: 1.02–1.27; p = 0.019, Fig. 3 B). In a similar vein, the TyG-WC indicated substantial risk increases in Q3 (HR = 1.17, 95% CI: 1.03–1.32; p = 0.015) and Q4 (HR = 1.51, 95% CI: 1.33–1.71; p < 0.001). The TyG-BMI, however, showed statistical significance only in Q4 (HR = 1.35, 95% CI: 1.21–1.51; p < 0.001). Analysis of standardized unit increases revealed notable risk gradients: with each 3-unit increase in TyG and TyG-WHtR, there was an associated 1.54-fold (95% CI: 1.11–2.15; p = 0.011) and a 2.04-fold (95% CI: 1.77–2.35; p < 0.001) increase in CCVD risk, respectively. Furthermore, every 100-unit increase in TyG-BMI and TyG-WC corresponded to a risk elevation of 1.41-fold (95% CI: 1.30–1.52; p < 0.001) and 1.15-fold (95% CI: 1.12–1.17; p < 0.001), respectively. The ROC curve illustrated that TyG-WHtR index had the highest AUC than other indices (0.711 (0.703, 0.726) at 10-year follow-up, Fig. 6 A). For CCVD-specific mortality, RCS illustrated CCVD specific mortality were related to increasing levels of all four IR indices (p for trend < 0.001, Fig. 4 E-H). Kaplan-Meier survival confirmed these findings, showing significantly higher CCVD specific mortality in the higher quartile groups (Q3/Q4) compared to the lower quartiles (Q1/Q2) for all indices (log-rank p < 0.001, Fig. 5 E-H ). TyG-BMI showed notable associations in Q2 (HR = 2.19, 95% CI: 1.26–3.80; p = 0.006, Fig. 3 C), Q3 (HR = 1.88, 95% CI: 1.08–3.29; p = 0.026, Fig. 3 C), and Q4 (HR = 2.59, 95% CI: 1.49–4.50; p < 0.001, Fig. 3 C). Meanwhile, TyG-WHtR demonstrated a similar but more pronounced quartile-dependent gradient (Q2: HR = 2.39, 95% CI: 1.29–4.46; Q3: HR = 2.07, 95% CI: 1.11–3.87; Q4: HR = 3.17, 95% CI: 1.72–5.85; all p < 0.05). Standardized unit analyses revealed particularly strong mortality risk associations for TyG-WHtR (4.35-fold increase per 3 units, 95% CI: 2.50–7.56; p < 0.001) and TyG-BMI (1.93-fold increase per 100 units, 95% CI: 1.45–2.58; p < 0.001). The ROC curve illustrated that TyG-WHtR index also had the highest AUC than other indices (0.779 (0.755, 0.834) at 10-year follow-up, Fig. 6 B).Detailed results were presented in Supplementary Table 5–12. Associations between IR indexes and CVD Incidence and mortality We further examined the associations between IR indices and the incidence of individual CCVD components (CVD, stroke, and heart failure) using four progressively adjusted Cox regression models. The RCS of CVD indicated that IR indices were positively associated with CVD incidence and mortality (Supplementary Fig. 3A-H). For CVD incidence, the TyG index showed significantly elevated risks in both Q3 (HR = 1.16, 95% CI: 1.04–1.29; p = 0.010) and Q4 (HR = 1.18, 95% CI: 1.05–1.31; p = 0.004) quartiles compared to the reference group. Similar patterns were observed for TyG-WC (Q3: HR = 1.19, 1.04–1.35, p = 0.009; Q4: HR = 1.53, 1.35–1.74, p < 0.001), while TyG-BMI and TyG-WHtR demonstrated significant associations only in Q4 (HR = 1.36, 1.22–1.53, p < 0.001 and HR = 1.48, 1.31–1.66, p < 0.001, respectively). Per-unit analysis revealed that each 3-unit increase in TyG and TyG-WHtR was associated with 1.31-fold (1.06–1.60; p = 0.010) and 2.09-fold (1.81–2.42; p < 0.001) higher CVD risk, respectively, while each 100-unit increase in TyG-BMI and TyG-WC increased risk by 1.43-fold (1.32–1.54; p < 0.001) and 1.15-fold (1.12–1.19; p < 0.001). The ROC analysis illustrated that TyG-WHtR index had a slightly higher AUC than other indices (0.712 (0.704, 0.727), Fig. 6 C). Regarding CVD-specific mortality, the TyG-BMI and TyG-WHtR indices demonstrated superior discriminative ability compared to the TyG and TyG-WC indices. While TyG showed no significant associations across quartiles, TyG-WC exhibited elevated mortality only in Q4 (HR = 2.12, 1.06–4.25; p = 0.035). More pronounced effects were observed for TyG-BMI (Q3: HR = 2.06, 1.04–4.06, p = 0.038; Q4: HR = 3.27, 1.68–6.38, p < 0.001) and TyG-WHtR (Q3: HR = 2.83, 1.25–6.42, p = 0.013; Q4: HR = 4.39, 1.96–9.81, p < 0.001). Per-unit analyses demonstrated significant mortality risk gradients for all indices: each 3-unit increase in TyG and TyG-WHtR corresponded to 2.74-fold (1.15–6.55; p = 0.023) and 5.89-fold (3.15-11.0; p < 0.001) higher risk, respectively, while each 100-unit increase in TyG-BMI and TyG-WC increased risk by 2.19-fold (1.58–3.03; p < 0.001) and 1.37-fold (1.21–1.55; p < 0.001). The TyG-WHtR index still showed a better prediction efficacy in ROC plot (0.806 (0.786, 0.868), Fig. 6 D). These results were presented in Supplementary Table 13–20. Associations between IR indexes and heart failure Incidence and mortality Our analysis showed significant associations between IR indices and the risk of heart failure in cancer survivors. RCS models indicated a progressive increase in heart failure incidence with higher levels of all four IR indices (Supplementary Fig. 4). Quartile-based analyses showed that three obesity-modified indices exhibited significant associations in their highest quartiles: TyG-BMI Q4 (HR = 1.29, 95% CI: 1.06–1.57, p = 0.010), TyG-WC Q4 (HR = 1.46, 1.17–1.83, p < 0.001), and TyG-WHtR Q4 (HR = 1.34, 1.09–1.64, p = 0.005), while no significant associations were observed in lower quartiles (Q2-Q3) or for the TyG index. Continuous analyses further revealed strong per-unit risk gradients for TyG-WHtR (HR = 2.37 per 3-unit increase, 1.85–3.04, p < 0.001), TyG-BMI (HR = 1.58 per 100-unit increase, 1.39–1.80, p < 0.001), and TyG-WC (HR = 1.19 per 100-unit increase, 1.13–1.25, p < 0.001). The ROC curves illustrated that TyG-WHtR, TyG-BMI, and TyG-WC had almost equal AUC, with TyG-WHtR was slightly higher in some time-point (Fig. 6 E). In contrast to the incidence findings, heart failure-specific mortality analyses revealed non-linear associations on RCS plots, and the limited event numbers (N = 10) restricted our analysis to unadjusted models. These findings suggest that adiposity-adjusted insulin resistance indices, especially in their highest ranges, may be key predictors of heart failure risk for cancer survivors. Nevertheless, further research is needed to explore their association with heart failure mortality in larger cohorts. The results mentioned above were listed in Supplementary Table 21–28. Associations between IR indexes and stroke Incidence and mortality Analysis of stroke outcomes revealed discordant patterns between initial RCS trends and formal statistical testing. While restricted cubic splines suggested a positive association between all four IR indices and stroke incidence (Supplementary Fig. 5), formal quartile-based comparisons in fully adjusted models showed no statistically significant differences in stroke risk across TyG quartiles (Q1-Q4, all p > 0.05). This null finding was consistently observed for all other IR indices (TyG-WC, TyG-BMI, and TyG-WHtR). Similarly, continuous analyses of per-unit increases in each index did not demonstrate significant associations with stroke incidence (all p > 0.05). Similar to heart failure, TyG-WHtR, TyG-BMI, and TyG-WC had almost equal AUC, with TyG-WHtR was slightly higher in some time-point (Fig. 6 F). For stroke-specific mortality, the limited number of events (N = 34) constrained our analytical approach, permitting only Model 2 adjustment (for age, sex, race, and Townsend deprivation index). In these analyses, none of the IR indices showed significant: (1) quartile-dependent risk gradients (all between-quartile p > 0.05), or (2) per-unit risk associations (all p > 0.05). These null findings were visually confirmed by RCS plots, which showed no evidence of linear or nonlinear associations between IR indices and stroke mortality. The results mentioned above were listed in Supplementary Table 29–36. Subgroup analysis To evaluate potential effect modifications, we conducted detailed subgroup analyses to examine the interactions between covariates and IR indices across various outcomes. For the incidence of CCVD, we found significant interactions between all IR indices and both alcohol consumption and smoking status, with a p-value for interaction of less than 0.05. Diabetes history showed significant interactions with TyG-BMI, TyG-WC, and TyG-WHtR, while genetic sex interacted significantly with TyG and TyG-WC (p-values for interaction 0.05, Supplementary Table 41–44). Similar to CCVD incidence, CVD risk showed significant interactions between all four IR indices and alcohol consumption, smoking status, and diabetes history (Supplementary Table 45–48). Besides, age and sex are two additional interaction factors for TyG index. However, as with CCVD mortality, no significant covariate interactions emerged for CVD-specific mortality (Supplementary Table 49–52). Heart failure risk demonstrated more complex interaction patterns: genetic sex interacted with TyG and TyG-WC, hypertension with TyG-BMI and TyG-WHtR, while TyG-BMI additionally showed interactions with smoking status and ethnicity, and TyG with age and smoking status. Notably, TyG-WC and TyG-WHtR showed the fewest significant interactions among all indices (Supplementary Table 53–56). For stroke outcomes, except for TyG (age was the only interaction factor), no significant interactions were found between any covariates and IR indices across all analyses (Supplementary Table 47–60). These findings suggest that the predictive performance of IR indices varies substantially across different cardiovascular outcomes and is particularly influenced by lifestyle factors (smoking and alcohol use) for composite and CVD outcomes, while showing more limited interactions for mortality endpoints. The differential interaction patterns among indices highlight the importance of considering specific clinical and demographic characteristics when applying these metrics in risk prediction. Sensitivity Analysis In sensitivity analyses excluding individuals with follow-up durations of less than 1 year and less than 2 years, we reassessed the associations of insulin resistance (IR) indices (TyG, TyG-BMI, TyG-WC, and TyG-WHtR) with incident CCVD, CVD, heart failure, stroke, and cause-specific mortality. TyG-WHtR and TyG-BMI maintained significant discriminative capacity for CCVD-specific mortality (p < 0.05, Supplementary Table 61–64). Except for TyG, the highest quartile of the other indices remained associated with significantly elevated CCVD risk compared to Q1, whereas TyG failed to stratify either CCVD risk or mortality (p > 0.05, Supplementary Table 61–64). For CVD risk, TyG-WC demonstrated the strongest association, with progressively increasing HRs in Q3 and Q4 (p 1, p < 0.05 for Q2–Q4 vs. Q1, Supplementary Table 65–68), characterised by an initial decline between Q2 and Q3, followed by an increase from Q3 to Q4. TyG again showed no discriminative value for CVD outcomes. Regarding heart failure, all indices except TyG exhibited statistically significant risk associations in Q4 (p 0.05, Supplementary Table 69–70). No significant association was found between all IR indices and stroke incidence (p > 0.05, Supplementary Table 61–72). Discussion It is crucial and urgent to develop suitable prediction tools for CCVD risk and prognosis in this vulnerable population. However, currently, the prevalent cardiovascular risk scores for regular populations showed poor performance among cancer survivors [ 33 ]. While newly developed evaluation tools for specific sub-components of CCVD were either based on multiple predictors or depended on a combination of multiple indices [ 34 , 35 ], this increased the complexity of clinical work. On the other hand, the correlations among IR, malignancies, and CCVD can be complex and interrelated [ 36 – 39 ]. IR is quite common among cancer survivors, which may be due to the tumour itself or treatments related to the tumour [ 17 , 40 ]. Therefore, starting from IR finding tools that can both assess whether cancer survivors are currently in an IR status and predict the risk and mortality of CCVD in this specific group, thereby enabling early intervention to improve the long-term prognosis of cancer survivors, can be feasible and highly valuable. In this prospective cohort study, four representative IR indices (TyG, TyG-BMI, TyG-WC, and TyG-WHtR) were introduced to find out their correlation with CCVD among cancer survivors and determine the association strength of CCVD risk and mortality, as well as the incidence and specific mortality of its sub-items (CVD, stroke, and heart failure). The IR indices were slightly higher in cancer survivors than in regular participants. The survival curve illustrated that the overall survival of cancer survivors is poorer than that of regular participants. At the same time, both event incidence and CCVD-related mortality were much higher in cancer survivors than in regular participants. These results, which followed previous studies, highlighted the need for extra attention to the risk of CCVD occurrence among cancer survivors to improve their long-term prognosis, and IR indices could potentially be ideal prediction tools [ 5 , 17 , 41 , 42 ]. The RCS results illustrated that all IR indices are positively related to CCVD risk and CCVD-related mortality among cancer survivors. Therefore, participants were subsequently divided into four groups according to quartiles, and the Kaplan-Meier survival curves illustrate that higher IR indices were significantly related to poor event-free survival and disease-specific survival. To assess the association strength of the four IR indices, we performed COX regression analysis with multiple adjusted models and subgroup analysis. Interesting results began to appear from this moment on. However, there is a noticeable increase in the risk of CCVD among all four IR indices, as well as in CVD-specific deaths, except for the TyG index. The TyG and TyG-WC indices demonstrated a stronger ability to distinguish CCVD risk compared to the other two indices, as both the third and fourth grade groups experienced higher CCVD incidence, with HR increasing in a step-like pattern. In contrast, TyG-BMI and TyG-WHtR demonstrated stronger association strength than the TyG and TyG-WC indices, as all three grade groups showed higher CCVD mortality rates than the reference group. The subgroup analysis illustrated that the association between TyG and TyG-WC indices and CCVD risk were much stronger in female patients, and the correlation between CCVD risk and all the IR indices was stronger in non-smoking patients with diabetes and alcohol consumption history. We noticed that the proportion of female patients is higher in the population being studied. Besides, it has been reported that waist circumference could be an indicator of metabolic disorder and IR [ 43 , 44 ]. Therefore, we attribute the stronger association of TyG-WC with CCVD risk to the gender distribution imbalance in the study population. Smoking is highly related to IR and TyG index, not to mention diabetes and the related treatment would influence the TyG level [ 45 , 46 ]. In contrast, the association between alcohol and IR remain controversial; in some studies, alcohol consumption was reported to be negatively related to IR [ 47 , 48 ]. Therefore, the results of the subgroup analysis are rational. On the other hand, WHtR has been reported to be highly associated with CCVD-related death [ 49 , 50 ]. Thus, it is rational that TyG-WHtR showed a strong predictive value for CCVD-related death. The ROC curves not only support this result but also indicate that the prediction efficacy of TyG-WHtR was slightly higher than the other three IR indices. Regarding the sub-items of CCVD, the RCS revealed that IR indices are also positively associated with the risk of CVD, stroke, and heart failure. However, as for disease-related mortality, only CVD-related mortality was associated with higher IR indices, while their association with heart failure and stroke-related death was not statistically significant. We attributed this result to the fact that the number of death events of heart failure and stroke was quite lower than that of CVD (12 and 42 vs 181) [ 51 ]. Similar findings were detected in the associations between IR indices and CVD outcomes. TyG and TyG-WC had better discriminability for CVD risk, while TyG-BMI and TyG-WHtR showed better discriminability for CVD-related mortality. Like the results of ROC in CCVD, TyG-WHtR also had the highest AUC value. Regarding the analysis of heart failure and stroke, due to the limited number of events, the analysis is limited to assessing the risk of heart failure and stroke [ 52 ]. Except for the TyG index, TyG-BMI, TyG-WC, and TyG-WHtR showed similar discriminability for heart failure risk, where the fourth grade of participants had a significantly higher risk of heart failure than the reference group. The results of ROC support those of Cox regression analysis, showing that the AUC of the TyG-BMI, TyG-WC, and TyG-WHtR indices at five-year and ten-year event-free survival were almost similar but higher than the TyG index. For the stroke event, unfortunately, the four IR indices were not associated with stroke risk after adjusting for covariates. The sensitivity analysis confirmed the results above. In conclusion, although all the IR indices showed a dose-response relationship with CCVD and CVD outcomes among cancer survivors, TyG-WHtR could be a more reliable tool for prediction, especially for CCVD and CVD-related death. Nonetheless, our study has several limitations. First, IR indices were calculated at baseline and treated as static; changes in behavior or clinical metrics over time were not captured. Future studies incorporating repeated assessments help elucidate the effect of longitudinal health improvements or deterioration. Second, the number of deaths related to heart failure and stroke was quite limited; therefore, the actual relation of IR indices to disease-specific survival still needs validation. Additionally, although ROC analysis verified the superior predictive value of TyG-WHtR for CCVD and CVD risk and mortality, the phenomenon of Cox regression, where two of the four indices are suitable for predicting CCVD and CVD risk, while the other two are suitable for predicting CCVD and CVD-specific mortality, deserves further study. Notably, as large-scale datasets become increasingly popular, safeguarding data privacy remains a critical concern. Recent studies have raised a classification system for data sensitivity and recommended ethical guidelines tailored to each level. This approach promotes responsible data sharing while ensuring adherence to ethical standards [ 53 ]. Finally, the study was only based on participants' data acquired from the UK Biobank. Therefore, other large-scale cohorts are still needed for further validation. Conclusion Through comprehensive analysis, we found that IR indices are highly associated with the risk and specific mortality of cardiovascular and cerebrovascular diseases among cancer survivors. TyG-WHtR had the highest association strength, especially for non-smoking women, patients without a history of diabetes, and those with a history of alcohol consumption. Abbreviations CCVD cardiovascular and cerebrovascular diseases CVD cardiovascular disease IR insulin resistance TyG index triglyceride-glucose index BMI body mass index WC waist circumference TG triglycerides HDL-C high-density lipoprotein cholesterol SD standard deviations CIs confidence intervals ANOVA analysis of variance VIF variance inflation factor OS overall survival EFS event-free survival DSS disease-specific survival RCS restricted cubic spline ROC Receiver Operating Characteristic Declarations Competing interests The authors declare no competing interests. Ethics approval and consent to participate The ethical approval for the UK Biobank research was granted by the North West Multicenter Research Ethical Committee. This current study was specifically approved by the UK Biobank under application number 332912. Consent for publication Written informed consent was obtained from all the participants during the baseline recruitment to the UK Biobank. The authors consent to the publication of the submitted manuscript. Funding This work was supported by the Beijing Natural Science Foundation (7232138), Gansu Provincial Joint Research Fund Project (24JRRA9330). Author Contribution Jianye Wang had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: Shuhang Luo, Runhua Tang, Haoran Wang, Li Ma. Acquisition, analysis, or interpretation of data: Jianye Wang. Drafting of the manuscript: Shuhang Luo, Runhua Tang, Haoran Wang, Li Ma. Critical review of the manuscript for important intellectual content: Jianyong Liu, Huimin Hou, Li Ma. Statistical analysis: Jianye Wang. Administrative, technical, or material support: Ming Liu, Huimin Hou. Supervision: Ming Liu, Huimin Hou, Jianyong Liu. Funding Acquisition: Ming Liu, Li Ma. Acknowledgements This study has been conducted using the UK Biobank resource (Application Number 332912). We are grateful to all staff from UK Biobank for their contributions to the study. Data Availability UK Biobank data are available through application to the database. Restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of UK Biobank References Siegel R, Kratzer T, Giaquinto A, Sung H, Jemal A. Cancer statistics, 2025. Ca. 2025;75:10–45. Shariat SF. A pan-European total cancer prevalence canvas: a benchmark for advancing strategic interventions. Lancet Oncol. 2024;25(3):266–7. De Angelis R, Demuru E, Baili P, et al. Complete cancer prevalence in Europe in 2020 by disease duration and country (EUROCARE-6): a population-based study. Lancet Oncol. 2024;25(3):293–307. Miller KD, Nogueira L, Mariotto AB, et al. Cancer treatment and survivorship statistics, 2019. Cancer J Clin. 2019;69(5):363–85. 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The use of restricted cubic splines to approximate complex hazard functions in the analysis of time-to-event data: a simulation study. J Stat Comput Simul. 2015;85:777–93. Peduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR. A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol. 1996;49(12):1373–9. Zhang Y, Fan S, Hui H, et al. Privacy Protection for Open Sharing of Psychiatric and Behavioral Research Data: Ethical Considerations and Recommendations. Alpha psychiatry. 2025;26(1):38759. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigure1.jpg Supplementary Figure 1 Restricted cubic spline (RCS) plot illustrated the associations between overall survival and IR indices. A) the association between overall survival and TyG index. B) the association between overall survival and TyG-BMI index. C) the association between overall survival and TyG-WC index. D) the association between overall survival and TyG-WHtR index. SupplementaryFigure2.tif Supplementary Figure 2 Overall Survival Stratified by Quartiles of IR Indices Kaplan-Meier curves display overall survival for cancer survivors grouped by quartiles (Q1-Q4) of four TyG-derived indices: (A) TyG index, (B) TyG-BMI, (C) TyG-WC, and (D) TyG-WHtR. Q1 represents the lowest 25% of values (best metabolic profile), while Q4 represents the highest 25% (worst metabolic profile). All indices showed statistically significant differences in survival across quartiles (log-rank p < 0.001), with a graded decrease in overall survival probability from Q1 to Q4. SupplementaryFigure3.tif Supplementary Figure 3 Restricted cubic spline (RCS) plot illustrated the associations between CVD incidence and specific mortality and IR indices. TyG index were positively associated with CVD incidence and specific mortality (panel A, E). TyG-BMI index was positively associated with CVD incidence and specific mortality (panel B, F). TyG-WC index was positively associated with CVD incidence and specific mortality (panel C, G). TyG-WHtR index were positively associated with CVD incidence and specific mortality (panel D, H). SupplementaryFigure4.tif Supplementary Figure 4Restricted cubic spline (RCS) plot illustrated the associations between heart failure incidence and specific mortality and IR indices. TyG index were positively associated with heart failure incidence while no significant correlation was found between index and heart failure specific mortality (panel A, E). TyG-BMI index was positively associated with heart failure incidence while no significant correlation was found between index and heart failure specific mortality (panel B, F). TyG-WC index was positively associated with heart failure incidence while no significant correlation was found between index and heart failure specific mortality (panel C, G). TyG-WHtR index was positively associated with heart failure incidence while no significant correlation was found between index and heart failure specific mortality (panel D, H). SupplementaryFigure5.tif Supplementary Figure 5Restricted cubic spline (RCS) plot illustrated the associations between stroke incidence and specific mortality and IR indices. TyG index was positively associated with stroke incidence while no significant correlation was found between index and stroke mortality (panel A, E). TyG-BMI index was positively associated with stroke incidence while no significant correlation was found between index and stroke specific mortality (panel B, F). TyG-WC index was positively associated with stroke incidence while no significant correlation was found between index and stroke specific mortality (panel C, G). TyG-WHtR index was positively associated with stroke incidence while no significant correlation was found between index and stroke specific mortality (panel D, H). 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19:23:22","extension":"xml","order_by":28,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":154226,"visible":true,"origin":"","legend":"","description":"","filename":"39c785cbc4b444c991aa996d50ae3a821structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7625609/v1/77334b92b3ef89933b22dd3b.xml"},{"id":94139047,"identity":"eb174938-d406-4cb1-b4be-bbb6af9d79aa","added_by":"auto","created_at":"2025-10-22 19:31:21","extension":"html","order_by":29,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":168748,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7625609/v1/a2690ed1b4f88e4a87d7228b.html"},{"id":94137550,"identity":"b7d04c1d-03c9-4935-ad03-7be2cfb53b91","added_by":"auto","created_at":"2025-10-22 19:23:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":303694,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of the study\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7625609/v1/6aaae82775c9010c184cdcd3.png"},{"id":94137569,"identity":"8ad60fd0-4702-4af2-b16a-8555fa8194c7","added_by":"auto","created_at":"2025-10-22 19:23:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":167323,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival curves for overall survival, event-free survival and disease-specific survival. Overall survival was compared among regular participants and cancer survivors (panel A). Cancer survivors also had a poorer CVVD event-free survival (panel B). Cumulative incidence curves for disease-specific mortality (panels C) showed that higher cumulative CVVD-related deaths were found in cancer survivors.\u003c/p\u003e","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7625609/v1/b367674c4e5f8742968b7c1f.png"},{"id":94137543,"identity":"6132fc2a-d768-4d5b-935c-d8872ad7c7d6","added_by":"auto","created_at":"2025-10-22 19:23:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":253923,"visible":true,"origin":"","legend":"\u003cp\u003eForest map for overall survival, CCVD incidence and specific mortality among cancer survivors. Forest map illustrates associations between IR indices (TyG, TyG-BMI, TyG-WC, TyG-WHtR) and overall survival (panel A), CCVD incidence (panel B) and CCVD specific mortality (panel C).\u003c/p\u003e","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7625609/v1/8b976f08b4beb2bbfbe7569c.png"},{"id":94139049,"identity":"487cc4a5-5c31-4606-893c-7d82deaf9215","added_by":"auto","created_at":"2025-10-22 19:31:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":242446,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline (RCS) plot illustrated the associations between CCVD incidence and specific mortality and IR indices. TyG index were positively associated with CCVD incidence and specific mortality (panel A, E). TyG-BMI index were positively associated with CCVD incidence and specific mortality (panel B, F). TyG-WC index was positively associated with CCVD incidence and specific mortality (panel C, G). TyG-WHtR index were positively associated with CCVD incidence and specific mortality (panel D, H).\u003c/p\u003e","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7625609/v1/f32404ea76a2c7f545f85369.png"},{"id":94137547,"identity":"828078c8-7af7-4444-b566-de198be73822","added_by":"auto","created_at":"2025-10-22 19:23:19","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":310958,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival curves illustrated the stepped trends among CCVD incidence and specific mortality and IR indices. The cumulative number of CCVD events increased gradually from Q1 group to Q4 group of TyG, TyG-BMI, TyG-WC, and TyG-WHtR indices (panel A-D). The cumulative number of CCVD-related death increased gradually from Q1 group to Q4 group of TyG, TyG-BMI, TyG-WC, and TyG-WHtR indices (panel E-H).\u003c/p\u003e","description":"","filename":"OnlineFigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7625609/v1/756f3f7a46c6105daef0bbef.png"},{"id":94137568,"identity":"c4052606-178a-45ca-9b70-c9fd7583e129","added_by":"auto","created_at":"2025-10-22 19:23:20","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":808441,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves across IR indices for CCVD, CVD, heart failure and stroke. ROC curves of IR indices prediction efficacy of CCVD incidence and specific mortality at 3-year, 5-year, and 10-year time point (panel A-B). ROC curves of IR indices prediction efficacy of CVD incidence and specific mortality at 3-year, 5-year, and 10-year time point (panel C-D). ROC curves of IR indices prediction efficacy of heart failure incidence at 3-year, 5-year, and 10-year time point (panel E). ROC curves of IR indices prediction efficacy of heart failure incidence at 3-year, 5-year, and 10-year time point (panel F).\u003c/p\u003e","description":"","filename":"OnlineFigure6.png","url":"https://assets-eu.researchsquare.com/files/rs-7625609/v1/d89bfc63c9441e5e8f1b4f0d.png"},{"id":97671838,"identity":"76541a67-e2cb-4693-bb68-08a4d8b738d3","added_by":"auto","created_at":"2025-12-08 09:33:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4186037,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7625609/v1/42d000ad-9687-44cc-9f07-3a50dcfbd2d8.pdf"},{"id":94137555,"identity":"575d6f63-ac5c-4b93-b42f-a48e8ed88311","added_by":"auto","created_at":"2025-10-22 19:23:20","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":682892,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 1 \u003c/strong\u003eRestricted cubic spline (RCS) plot illustrated the associations between overall survival and IR indices. A) the association between overall survival and TyG index. B) the association between overall survival and TyG-BMI index. C) the association between overall survival and TyG-WC index. D) the association between overall survival and TyG-WHtR index.\u003c/p\u003e","description":"","filename":"SupplementaryFigure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7625609/v1/5431e8ada1d99f316879b34a.jpg"},{"id":94137556,"identity":"7f382371-24d6-4b54-b87a-c1715612ddca","added_by":"auto","created_at":"2025-10-22 19:23:20","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1854016,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 2 Overall Survival Stratified by Quartiles of IR Indices \u003c/strong\u003eKaplan-Meier curves display overall survival for cancer survivors grouped by quartiles (Q1-Q4) of four TyG-derived indices: (A) TyG index, (B) TyG-BMI, (C) TyG-WC, and (D) TyG-WHtR. Q1 represents the lowest 25% of values (best metabolic profile), while Q4 represents the highest 25% (worst metabolic profile). All indices showed statistically significant differences in survival across quartiles (log-rank p \u0026lt; 0.001), with a graded decrease in overall survival probability from Q1 to Q4.\u003c/p\u003e","description":"","filename":"SupplementaryFigure2.tif","url":"https://assets-eu.researchsquare.com/files/rs-7625609/v1/719f50c8689eccb3abd149eb.tif"},{"id":94137572,"identity":"e32e2767-eab0-41d0-93a6-3bef1b7c5146","added_by":"auto","created_at":"2025-10-22 19:23:20","extension":"tif","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":1076388,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 3 \u003c/strong\u003eRestricted cubic spline (RCS) plot illustrated the associations between CVD incidence and specific mortality and IR indices. TyG index were positively associated with CVD incidence and specific mortality (panel A, E). TyG-BMI index was positively associated with CVD incidence and specific mortality (panel B, F). TyG-WC index was positively associated with CVD incidence and specific mortality (panel C, G). TyG-WHtR index were positively associated with CVD incidence and specific mortality (panel D, H).\u003c/p\u003e","description":"","filename":"SupplementaryFigure3.tif","url":"https://assets-eu.researchsquare.com/files/rs-7625609/v1/7ed758cb64de5b1041bbee1a.tif"},{"id":94137531,"identity":"2598b6dc-f3fb-4e27-8e5a-1b634e3c8af8","added_by":"auto","created_at":"2025-10-22 19:23:19","extension":"tif","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":1074444,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 4\u003c/strong\u003eRestricted cubic spline (RCS) plot illustrated the associations between heart failure incidence and specific mortality and IR indices. TyG index were positively associated with heart failure incidence while no significant correlation was found between index and heart failure specific mortality (panel A, E). TyG-BMI index was positively associated with heart failure incidence while no significant correlation was found between index and heart failure specific mortality (panel B, F). TyG-WC index was positively associated with heart failure incidence while no significant correlation was found between index and heart failure specific mortality (panel C, G). TyG-WHtR index was positively associated with heart failure incidence while no significant correlation was found between index and heart failure specific mortality (panel D, H).\u003c/p\u003e","description":"","filename":"SupplementaryFigure4.tif","url":"https://assets-eu.researchsquare.com/files/rs-7625609/v1/01a4b60aa8aa3b1e56a35562.tif"},{"id":94137565,"identity":"99a81ebf-13dc-4aed-b695-44c01e21a042","added_by":"auto","created_at":"2025-10-22 19:23:20","extension":"tif","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":1082244,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 5\u003c/strong\u003eRestricted cubic spline (RCS) plot illustrated the associations between stroke incidence and specific mortality and IR indices. TyG index was positively associated with stroke incidence while no significant correlation was found between index and stroke mortality (panel A, E). TyG-BMI index was positively associated with stroke incidence while no significant correlation was found between index and stroke specific mortality (panel B, F). TyG-WC index was positively associated with stroke incidence while no significant correlation was found between index and stroke specific mortality (panel C, G). TyG-WHtR index was positively associated with stroke incidence while no significant correlation was found between index and stroke specific mortality (panel D, H).\u003c/p\u003e","description":"","filename":"SupplementaryFigure5.tif","url":"https://assets-eu.researchsquare.com/files/rs-7625609/v1/67c8b82a95bcd03a9eec4081.tif"},{"id":94139028,"identity":"490ddb7c-8f1e-42a0-9c4a-4b5c0423e7b4","added_by":"auto","created_at":"2025-10-22 19:31:19","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":589843,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable.docx","url":"https://assets-eu.researchsquare.com/files/rs-7625609/v1/d77e9861fc10b2455ed2a91b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of Triglyceride-Glucose Index and Its Derivatives With Incidence and Cause-Specific Mortality of Cardiovascular and Cerebrovascular Diseases Among Cancer Survivors","fulltext":[{"header":"Research Insights","content":"\u003cp\u003e\u003cstrong\u003eWhat is currently known about this topic?\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCancer survivors face elevated cardiovascular/cerebrovascular disease (CCVD) risks due to shared pathophysiology and treatment effects, while insulin resistance (IR) indices show disease-specific associations with both cancer and CCVD. Meanwhile, current risk assessment tools for CCVD remain inadequate for cancer survivors, underscoring the need for better metabolic monitoring strategies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhat is the key research question?\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDo the TyG index and its derivatives (TyG-BMI, TyG-WC, TyG-WHtR) show differential associations with CCVD risk and mortality in cancer survivors, and which index demonstrates the strongest relationship with these outcomes?\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhat is new?\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study provides the first comprehensive comparison of four TyG-derived indices in cancer survivors, identifying TyG-WC as showing the strongest association with CCVD risk and TyG-WHtR as having the closest relationship with mortality outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHow might this study influence clinical practice?\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study indicates that TyG-WC and TyG-WHtR, as straightforward metabolic markers, could improve the monitoring of cardiovascular disease (CVD) risk in cancer survivors. They provide significant connections to cardiovascular outcomes, especially in terms of assessing mortality risk. Incorporating these markers into routine follow-up care may help identify high-risk patients who require early intervention.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eCancer poses a major global health challenge, accounting for nearly 10\u0026nbsp;million deaths each year worldwide[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In Europe alone, over 1.2\u0026nbsp;million fatalities were attributed to cancer in 2022[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Current projections indicate that the burden of cancer is expected to increase significantly, with annual mortality anticipated to reach 3.24\u0026nbsp;million cases by 2040 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. With a consistently growing population of cancer, the number of cancer survivors increases as well. It has been reported that there are currently more than 30\u0026nbsp;million cancer survivors, with a 3.5% annual increase from 2010 to 2020 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Cardiovascular and cerebrovascular diseases (CCVD) require special attention in cancer survivors because the two diseases have complex causes and share common risk factors [\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Recent studies show that both tumor biology and cancer treatments can greatly increase the risk of CCVD, which includes cardiovascular disease (CVD), heart failure, and stroke, as well as related mortality[\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. On the other hand, CCVD and related biological responses can conversely influence the progression and metastasis of malignancies via particular biological mechanisms, leading to a diminished life expectancy for this at-risk population [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These interdependent relationships between CCVD highlight the urgent necessity for early risk evaluation and preventive strategies designed for cancer survivors.\u003c/p\u003e\u003cp\u003eInsulin resistance (IR) is a hallmark feature of diabetes and obesity, which has been reported to commonly exist among cancer survivors and has a close relation to CCVD [\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. As a widely used evaluation tool for IR, the triglyceride-glucose (TyG) index has been employed to predict cardiovascular disease (CVD) risk and mortality [\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. There are derivative indices of the TyG index, including TyG-BMI (body mass index-adjusted), TyG-WC (waist circumference-adjusted), and TyG-WHtR (waist-to-height ratio-adjusted). Together with the TyG index, these indices were also highly correlated with various malignancies [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. NNotably, although the three indices mentioned above are all derived from the foundational TyG index, they exhibit differential predictive capacities for specific diseases. For instance, the TyG-WC and TyG-WhtR indices demonstrated better evaluation efficacy than the TyG index in CVD [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The TyG-BMI and TyG-WC indices demonstrated superior performance in predicting the risk of liver steatosi [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In conclusion, it is quite rational to use TyG and its derivatives as a bridge between cancers and CCVD.\u003c/p\u003e\u003cp\u003eIn this prospective cohort study, we aimed to explore the associations between TyG and its derivatives (TyG-BMI, TyG-WC, and TyG-WHtR) and CCVD risk and specific mortality among cancer survivors based on the extensive prospective cohort data from the UK Biobank. Furthermore, we aim to thoroughly investigate which index has a tighter relationship with CCVD risk and mortality, or which indices are more relevant to CCVD risk. In contrast, other indices have a stronger association with CCVD mortality. The analysis incorporated extensive adjustment for potential confounding variables and included comprehensive sensitivity analyses to assess the robustness of observed relationships.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eThis prospective, population-based study analyzed data from the UK Biobank [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], a large-scale cohort comprising over 500,000 participants aged 37\u0026ndash;73 years recruited between 2006 and 2010 across England, Scotland, and Wales. At baseline, participants completed touchscreen questionnaires, underwent standardized physiological measurements, and provided biological samples. Longitudinal follow-up included linkage to national hospital admissions, cancer registries, and mortality records, with participants providing written informed consent for data access. The UK National Health Service\u0026rsquo;s National Research Ethics Service approved the UK Biobank study protocol. The present analysis was authorized by the UK Biobank Access Management Team under Application #332912, with additional ethical oversight provided by the Institutional Review Board and the National Institutes of Health.\u003c/p\u003e\u003cp\u003eWe excluded participants with a prevalent CCVD diagnosis at baseline (N\u0026thinsp;=\u0026thinsp;34, 405). To evaluate the predictive performance of the TyG index, TyG-BMI, TyG-WC, and TyG-WHtR for CCVD incidence and mortality among cancer survivors, we further excluded individuals with missing data on BMI, waist circumstance, standing height, triglycerides, glucose, or HDL cholesterol (N\u0026thinsp;=\u0026thinsp;37, 068). Additional exclusions applied to participants with incomplete covariate data, resulting in a final analytical cohort of 425, 767 participants for primary survival analysis and a cancer survivor cohort of 18, 526 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eExposure\u003c/h2\u003e\u003cp\u003eFasting plasma glucose, triglycerides (TG), and high-density lipoprotein cholesterol (HDL-C) levels were measured using a Beckman Coulter AU5800 automated analyzer following standardized protocols. All blood samples were processed immediately after collection, with plasma/serum components separated and stored at -80\u0026deg;C until analysis. Detailed analytical procedures and quality control measures are available through the UK Biobank resource (Reference ID: 1227).\u003c/p\u003e\u003cp\u003eWe evaluated four insulin resistance-related indices derived from measurements as follows according to established methodologies [\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTyG index: Ln[fasting TG (mg/dL) \u0026times; fasting glucose (mg/dL)/2]\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTyG-BMI: TyG index \u0026times; body mass index (kg/m\u0026sup2;)\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTyG-WC: TyG index \u0026times; waist circumference (cm)\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTyG-WHtR\u0026thinsp;=\u0026thinsp;TyG index \u0026times; [waist circumference (cm)/height (cm)]\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAssessment of cancer survivors\u003c/h3\u003e\n\u003cp\u003eCancer status at baseline recruitment was determined using International Classification of Diseases, Tenth Revision (ICD-10) codes from linked national health records, including Hospital Episode Statistics and the National Cancer Registration and Analysis Service. The cancer case definition encompassed malignancies across multiple organ systems: digestive (anal [C21], colorectal [C18-C20], esophageal [C15], gastric [C16], liver [C22], pancreatic [C25], and small intestinal [C17]), genitourinary (bladder [C67], kidney [C64], ovarian [C56], and prostate [C61]), thoracic (lung [C33-C34] and mesothelioma [C45]), hematologic (leukemia [C91-C95], lymphoma [C81-C86,C88], and multiple myeloma [C90]), as well as other major sites (brain [C71], breast [C50], laryngeal [C32], melanoma [C43-C44], oral [C00-C14], soft tissue [C46-C49], thyroid [C73], and uterine [C54-C55]). Cancer survivors were identified by comparing the date of first cancer diagnosis (Field ID 41280) with the date of assessment center attendance, with the first recorded cancer event in any linked database used to determine both the timing of diagnosis and primary cancer site. This comprehensive approach ensured accurate classification of prevalent cancer cases at baseline while enabling precise identification of cancer survivors within the study population.\u003c/p\u003e\n\u003ch3\u003eExposure\u003c/h3\u003e\n\u003cp\u003eCardio-cerebrovascular diseases (CCVD) were defined as a composite endpoint comprising cardiovascular disease (CVD), stroke, and heart failure. Incident CCVD cases (including disease status and initial diagnosis date) were identified using ICD-10 codes: CVD (I20-I25), stroke (I60, I61, I63, I64), and heart failure (I50, I11.0, I13.0, and I13.2). CCVD-related mortality was determined through death records containing the underlying cause of death (Field ID 40001, ICD-10 coded) and date of death (Field ID 40000), following established criteria from previous studies. The time-to-event for CCVD incidence was calculated from enrollment to the first diagnosis of CCVD, while CCVD-related mortality was measured from enrollment to the date of death. For participants without events, censoring occurred at the last follow-up date or the date of loss to follow-up. These records were linked with national death registries, including NHS Digital for England and Wales (data complete as of May 30, 2024) and the National Records of Scotland (data complete as of December 31, 2023), ensuring comprehensive mortality ascertainment.\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eThis study examined a range of demographic characteristics at baseline, including age (analyzed as a continuous variable), genetic sex (male/female), ethnicity (White/non-White), and educational attainment (A levels/AS levels or equivalent, College/University degree, CSEs or equivalent, NVQ/HND/HNC or equivalent, O levels/GCSEs or equivalent, and Other professional qualifications). Socioeconomic status was assessed by the Townsend Deprivation Index (continuous, derived from postal code data on unemployment, car and home ownership, and household overcrowding, with higher scores indicating greater deprivation), which was subsequently divided into quartiles (Q1-Q4) for analysis. Health behaviours included smoking status (classified as never/ever smoked) and alcohol consumption (never/ever drank). Clinical factors comprised the family history of cancer (yes/no, determined through baseline records of cancer diagnoses in participants' parents and siblings), anticoagulant use (yes/no, identified from baseline medication records for pain relief, constipation, or heartburn), and histories of heart failure, hypertension, cardiovascular disease, and stroke (ascertained through ICD-10 codes in the UK Biobank's linked inpatient hospital records using established diagnostic criteria [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]). All variables were dichotomized as present or absent based on baseline self-reports, except where objective measures (such as ICD codes) were available.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eThe baseline characteristics were reported as means with standard deviations (SD) for continuous variables and percentages (%) for categorical variables. The Shapiro-Wilk test, P-P plots, and Q-Q plots were used to assess the normality of continuous variables. Differences between groups were analyzed using analysis of variance (ANOVA) for continuous variables and chi-square tests for categorical variables. Cox proportional hazards regression models were applied to examine the relationships between TyG, TyG-BMI, TyG-WC, and TyG-WHtR scores (with the ideal state as the reference group) and outcomes, including incidence and mortality of CCVD and its components (CVD, stroke, and heart failure separately). The proportional hazards assumption was checked using Schoenfeld residuals; transient covariates were incorporated into the model if the assumption was violated. Multicollinearity among the covariates was evaluated through the variance inflation factor (VIF), with values below 10 deemed acceptable. The final model, adjusted for age, sex, ethnicity, education, socioeconomic status, smoking status, alcohol consumption, family history of cancer, history of anticoagulant use, and history of hypertension, CVD, stroke, heart failure, and diabetes, calculated the hazard ratios and 95% confidence intervals (CIs) for each IR score about the outcome variables. Time-dependent ROC (Receiver Operating Characteristic) curves were generated using the C/D method, which dynamically adjusts for censoring. Sensitivity (true positive rate) and 1-specificity (false positive rate) were calculated across risk thresholds (0\u0026ndash;100%) at each time point. AUC values with 95% CIs were estimated via trapezoidal integration and bootstrap resampling (1000 replicates). Pairwise AUC comparisons were performed using DeLong's test.\u003c/p\u003e\u003cp\u003eWe conducted extensive subgroup analyses to evaluate the robustness of our findings and examine potential effect modifiers. Participants were stratified by key demographic and clinical characteristics, including age (\u0026lt;\u0026thinsp;60 vs. \u0026ge;60 years), genetic sex (male vs. female), BMI (\u0026lt;\u0026thinsp;25 kg/m\u0026sup2; vs. \u0026ge;25 kg/m\u0026sup2;), smoking status (yes vs. no), alcohol consumption (yes vs. No), hypertension (yes vs. no), diabetes (yes vs. no), and hypertension (yes vs. no). Statistical differences between subgroups were assessed using interaction terms.\u003c/p\u003e\u003cp\u003eTo rigorously evaluate our results, we performed comprehensive sensitivity analyses addressing potential confounding from medication use and moderate adherence. These analyses systematically excluded participants taking antidiabetic, antihyperlipidemic, or antihypertensive medications, as well as those with moderate adherence or medication persistence. We further excluded participants whose CCVD diagnosis or death occurred within one or two years after recruitment. Associations between TyG-BMI, TyG-WC (including their components BMI and waist circumference) and cancer outcomes were assessed using Cox regression. All statistical analyses were performed using R version 4.3.2 (R Foundation for Statistical Computing), with two-sided tests and significance threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eOverall survival, Event-free survival, and Disease-specific survival among regular participants and cancer survivors\u003c/h2\u003e\u003cp\u003eDuring a median follow-up of 14.9 years, 3827 deaths were recorded among cancer survivors. Compared to regular participants, the level of TyG, TyG-BMI, TyG-WC, and TyG-WHtR indices in cancer survivors were slightly higher (8.771\u0026thinsp;\u0026plusmn;\u0026thinsp;0.555 vs 8.728\u0026thinsp;\u0026plusmn;\u0026thinsp;0.567, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, for TyG; 241.911\u0026thinsp;\u0026plusmn;\u0026thinsp;49.810 vs 241.179\u0026thinsp;\u0026plusmn;\u0026thinsp;50.591, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, for TyG-BMI; 797.364\u0026thinsp;\u0026plusmn;\u0026thinsp;147.218 vs 793.332\u0026thinsp;\u0026plusmn;\u0026thinsp;149.694, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, for TyG-WC; 4.764\u0026thinsp;\u0026plusmn;\u0026thinsp;0.837 vs 4.707\u0026thinsp;\u0026plusmn;\u0026thinsp;0.847, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, for TyG-WHtR). We initially investigated the overall survival (OS), event-free survival (EFS), and disease-specific survival (DSS) between regular participants without cancer history and cancer survivors. The results demonstrated significantly worse OS in cancer survivors (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), which was expected given the generally poor prognosis of many malignancies. More importantly, cancer survivors also exhibited significantly higher incidence of CCVD events and CCVD-specific mortality compared to cancer-free participants (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB-C). These findings suggest that cancer survivors may face elevated risks of developing CCVD and higher CCVD-related mortality than the general population.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eParticipants characteristics\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the baseline characteristics of the 18,526 cancer survivors. The cohort had a mean age of 60.3 years (SD\u0026thinsp;=\u0026thinsp;6.8), with a female predominance (60.1%). Ethnically, the majority of participants were White (92.3%). Regarding educational attainment, 30.0% of participants held a college or university degree, whereas 21.5% reported no formal qualifications. The mean Townsend Index was \u0026minus;\u0026thinsp;1.47 (SD 2.98), reflecting overall low levels of deprivation. Among the participants, only 8.7% of them were currently smokers, while most of them (91.3%) had a history of alcohol consumption.\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\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall \u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;18, 526\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQ1 \u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;4, 636\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQ2 \u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;4, 638\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eQ3 \u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;4, 632\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eQ4 \u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;4, 620\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\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\u003e\u0026lt;\u0026thinsp;60 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6772 (36.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2127 (45.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1667 (35.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1481 (32.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1497 (32.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;60 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11754 (63.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2509 (54.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2971 (64.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3151 (68.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3123 (67.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7392 (39.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1472 (31.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1700 (36.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1938 (41.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2282 (49.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11134 (60.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3164 (68.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2938 (63.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2694 (58.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2338 (50.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEthnicity, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\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\u003eWhite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17105 (92.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4226 (91.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4260 (91.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4304 (92.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4315 (93.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-White\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1421 (7.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e410 (8.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e378 (8.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e328 (7.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e305 (6.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation level, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\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 or University degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5550 (30.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1634 (35.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1394 (30.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1339 (28.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1183 (25.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA levels/AS levels or equivalent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1893 (10.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e542 (11.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e477 (10.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e417 (9.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e457 (9.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCSEs or equivalent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e707 (3.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e190 (4.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e164 (3.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e169 (3.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e184 (4.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNVQ or HND or HNC or equivalent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1232 (6.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e231 (5.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e297 (6.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e337 (7.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e367 (7.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eO levels/GCSEs or equivalent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4044 (21.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e998 (21.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1067 (23.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e985 (21.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e994 (21.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther professional qualifications eg: nursing, teaching\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1118 (6.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e268 (5.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e288 (6.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e263 (5.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e299 (6.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNone of the above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3982 (21.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e773 (16.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e951 (20.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1122 (24.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1136 (24.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTownsend deprivation index, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\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\u003eQ1: -6.26~-3.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4692 (25.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1187 (25.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1163 (25.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1194 (25.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1148 (24.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2: -3.70~-2.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4642 (25.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1173 (25.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1195 (25.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1202 (25.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1108 (24.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3: -2.27\u0026thinsp;~\u0026thinsp;0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4662 (25.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1211 (26.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1188 (25.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1110 (24.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1153 (25.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4: 0.21\u0026thinsp;~\u0026thinsp;10.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4494 (24.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1065 (23.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1092 (23.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1126 (24.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1211 (26.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCurrent smoking status, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\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\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16923 (91.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4302 (92.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4259 (91.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4240 (91.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4122 (89.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1603 (8.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e334 (7.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e379 (8.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e392 (8.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e498 (10.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAlcohol use, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\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\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1572 (8.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e344 (7.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e367 (7.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e414 (8.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e447 (9.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16954 (91.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4292 (92.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4271 (92.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4218 (91.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4173 (90.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiabetes history, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\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\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16482 (89.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4436 (95.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4294 (92.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4169 (90.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3583 (77.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2044 (11.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e200\u003c/p\u003e\u003cp\u003e(4.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e344\u003c/p\u003e\u003cp\u003e(7.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e463\u003c/p\u003e\u003cp\u003e(10.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1037\u003c/p\u003e\u003cp\u003e(22.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHypertension history, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\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\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7328 (39.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2405 (51.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1928 (41.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1624 (35.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1371 (29.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11198 (60.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2231 (48.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2710 (58.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3008 (64.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3249 (70.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eKruskal-Wallis rank sum test; Pearson's Chi-squared test\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eAssociations between IR indexes and overall survival of cancer survivors\u003c/h2\u003e\u003cp\u003eThe restricted cubic spline (RCS) analysis indicated a progressive increase in mortality risk associated with higher insulin resistance (IR) index levels when compared to the lowest 5% reference group, a trend consistently observed across all four indices (Supplementarty Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-D). Following this, Kaplan-Meier survival analysis was performed after categorizing participants into quartiles based on their IR index levels (Supplementarty Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-D). This analysis revealed significantly poorer overall survival rates in the higher-scoring groups (Q3 and Q4) in comparison to the lower-scoring groups (Q1 and Q2) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), a pattern that was confirmed for all four IR indices.\u003c/p\u003e\u003cp\u003eWe employed multivariable Cox proportional hazards regression with four progressively adjusted models to examine the relationship between IR indices and overall survival. Model 1 was unadjusted; Model 2 adjusted for age, genetic sex, race, and Townsend deprivation index; Model 3 additionally included education level, smoking status, and alcohol consumption; and Model 4 further incorporated history of diabetes and hypertension. Regarding CCVD risk, after full adjustment in Model 4, we found significantly elevated risk in the highest quartile (Q4) for TyG (HR\u0026thinsp;=\u0026thinsp;1.10, 95% CI: 1.01\u0026ndash;1.21; p\u0026thinsp;=\u0026thinsp;0.038, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), TyG-WC (HR\u0026thinsp;=\u0026thinsp;1.20, 1.08\u0026ndash;1.34; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), and TyG-WHtR (HR\u0026thinsp;=\u0026thinsp;1.21, 1.10\u0026ndash;1.34; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA) compared to the reference group. Interestingly, for TyG-BMI, the Q2 group showed a modest but significant reduction in mortality risk (HR\u0026thinsp;=\u0026thinsp;0.89, 0.81\u0026ndash;0.98; p\u0026thinsp;=\u0026thinsp;0.015) relative to the reference group. Detailed information was presented in Supplementary Table\u0026nbsp;1\u0026ndash;4.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eAssociations between IR indexes and CCVD Incidence and mortality\u003c/h2\u003e\u003cp\u003eOur analyses revealed significant associations between insulin resistance (IR) indices and the risk of cardio-cerebrovascular disease (CCVD) in cancer survivors. Using restricted cubic spline models, we identified a consistent dose-response relationship, where progressively higher CCVD incidence and mortality risks were observed at increasing levels of all four IR indices compared to the lowest 5% reference group (p for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-D). Kaplan-Meier survival analyses, stratified by quartiles of IR indices, confirmed these findings, showing significantly worse CCVD outcomes in the higher quartile groups (Q3/Q4) compared to the lower quartiles (Q1/Q2) for all indices (log-rank p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-D).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn fully adjusted Cox models (Model 4), the TyG index demonstrated a significantly elevated risk of CCVD in both the third quartile (Q3) (HR\u0026thinsp;=\u0026thinsp;1.12, 95% CI: 1.01\u0026ndash;1.25; p\u0026thinsp;=\u0026thinsp;0.038, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB) and the fourth quartile (Q4) (HR\u0026thinsp;=\u0026thinsp;1.14, 95% CI: 1.02\u0026ndash;1.27; p\u0026thinsp;=\u0026thinsp;0.019, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). In a similar vein, the TyG-WC indicated substantial risk increases in Q3 (HR\u0026thinsp;=\u0026thinsp;1.17, 95% CI: 1.03\u0026ndash;1.32; p\u0026thinsp;=\u0026thinsp;0.015) and Q4 (HR\u0026thinsp;=\u0026thinsp;1.51, 95% CI: 1.33\u0026ndash;1.71; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The TyG-BMI, however, showed statistical significance only in Q4 (HR\u0026thinsp;=\u0026thinsp;1.35, 95% CI: 1.21\u0026ndash;1.51; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Analysis of standardized unit increases revealed notable risk gradients: with each 3-unit increase in TyG and TyG-WHtR, there was an associated 1.54-fold (95% CI: 1.11\u0026ndash;2.15; p\u0026thinsp;=\u0026thinsp;0.011) and a 2.04-fold (95% CI: 1.77\u0026ndash;2.35; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) increase in CCVD risk, respectively. Furthermore, every 100-unit increase in TyG-BMI and TyG-WC corresponded to a risk elevation of 1.41-fold (95% CI: 1.30\u0026ndash;1.52; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 1.15-fold (95% CI: 1.12\u0026ndash;1.17; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), respectively. The ROC curve illustrated that TyG-WHtR index had the highest AUC than other indices (0.711 (0.703, 0.726) at 10-year follow-up, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFor CCVD-specific mortality, RCS illustrated CCVD specific mortality were related to increasing levels of all four IR indices (p for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE-H). Kaplan-Meier survival confirmed these findings, showing significantly higher CCVD specific mortality in the higher quartile groups (Q3/Q4) compared to the lower quartiles (Q1/Q2) for all indices (log-rank p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE-H ). TyG-BMI showed notable associations in Q2 (HR\u0026thinsp;=\u0026thinsp;2.19, 95% CI: 1.26\u0026ndash;3.80; p\u0026thinsp;=\u0026thinsp;0.006, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), Q3 (HR\u0026thinsp;=\u0026thinsp;1.88, 95% CI: 1.08\u0026ndash;3.29; p\u0026thinsp;=\u0026thinsp;0.026, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), and Q4 (HR\u0026thinsp;=\u0026thinsp;2.59, 95% CI: 1.49\u0026ndash;4.50; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Meanwhile, TyG-WHtR demonstrated a similar but more pronounced quartile-dependent gradient (Q2: HR\u0026thinsp;=\u0026thinsp;2.39, 95% CI: 1.29\u0026ndash;4.46; Q3: HR\u0026thinsp;=\u0026thinsp;2.07, 95% CI: 1.11\u0026ndash;3.87; Q4: HR\u0026thinsp;=\u0026thinsp;3.17, 95% CI: 1.72\u0026ndash;5.85; all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Standardized unit analyses revealed particularly strong mortality risk associations for TyG-WHtR (4.35-fold increase per 3 units, 95% CI: 2.50\u0026ndash;7.56; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and TyG-BMI (1.93-fold increase per 100 units, 95% CI: 1.45\u0026ndash;2.58; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The ROC curve illustrated that TyG-WHtR index also had the highest AUC than other indices (0.779 (0.755, 0.834) at 10-year follow-up, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB).Detailed results were presented in Supplementary Table\u0026nbsp;5\u0026ndash;12.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eAssociations between IR indexes and CVD Incidence and mortality\u003c/h2\u003e\u003cp\u003eWe further examined the associations between IR indices and the incidence of individual CCVD components (CVD, stroke, and heart failure) using four progressively adjusted Cox regression models. The RCS of CVD indicated that IR indices were positively associated with CVD incidence and mortality (Supplementary Fig.\u0026nbsp;3A-H). For CVD incidence, the TyG index showed significantly elevated risks in both Q3 (HR\u0026thinsp;=\u0026thinsp;1.16, 95% CI: 1.04\u0026ndash;1.29; p\u0026thinsp;=\u0026thinsp;0.010) and Q4 (HR\u0026thinsp;=\u0026thinsp;1.18, 95% CI: 1.05\u0026ndash;1.31; p\u0026thinsp;=\u0026thinsp;0.004) quartiles compared to the reference group. Similar patterns were observed for TyG-WC (Q3: HR\u0026thinsp;=\u0026thinsp;1.19, 1.04\u0026ndash;1.35, p\u0026thinsp;=\u0026thinsp;0.009; Q4: HR\u0026thinsp;=\u0026thinsp;1.53, 1.35\u0026ndash;1.74, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while TyG-BMI and TyG-WHtR demonstrated significant associations only in Q4 (HR\u0026thinsp;=\u0026thinsp;1.36, 1.22\u0026ndash;1.53, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and HR\u0026thinsp;=\u0026thinsp;1.48, 1.31\u0026ndash;1.66, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, respectively). Per-unit analysis revealed that each 3-unit increase in TyG and TyG-WHtR was associated with 1.31-fold (1.06\u0026ndash;1.60; p\u0026thinsp;=\u0026thinsp;0.010) and 2.09-fold (1.81\u0026ndash;2.42; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) higher CVD risk, respectively, while each 100-unit increase in TyG-BMI and TyG-WC increased risk by 1.43-fold (1.32\u0026ndash;1.54; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 1.15-fold (1.12\u0026ndash;1.19; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The ROC analysis illustrated that TyG-WHtR index had a slightly higher AUC than other indices (0.712 (0.704, 0.727), Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003eRegarding CVD-specific mortality, the TyG-BMI and TyG-WHtR indices demonstrated superior discriminative ability compared to the TyG and TyG-WC indices. While TyG showed no significant associations across quartiles, TyG-WC exhibited elevated mortality only in Q4 (HR\u0026thinsp;=\u0026thinsp;2.12, 1.06\u0026ndash;4.25; p\u0026thinsp;=\u0026thinsp;0.035). More pronounced effects were observed for TyG-BMI (Q3: HR\u0026thinsp;=\u0026thinsp;2.06, 1.04\u0026ndash;4.06, p\u0026thinsp;=\u0026thinsp;0.038; Q4: HR\u0026thinsp;=\u0026thinsp;3.27, 1.68\u0026ndash;6.38, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and TyG-WHtR (Q3: HR\u0026thinsp;=\u0026thinsp;2.83, 1.25\u0026ndash;6.42, p\u0026thinsp;=\u0026thinsp;0.013; Q4: HR\u0026thinsp;=\u0026thinsp;4.39, 1.96\u0026ndash;9.81, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Per-unit analyses demonstrated significant mortality risk gradients for all indices: each 3-unit increase in TyG and TyG-WHtR corresponded to 2.74-fold (1.15\u0026ndash;6.55; p\u0026thinsp;=\u0026thinsp;0.023) and 5.89-fold (3.15-11.0; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) higher risk, respectively, while each 100-unit increase in TyG-BMI and TyG-WC increased risk by 2.19-fold (1.58\u0026ndash;3.03; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 1.37-fold (1.21\u0026ndash;1.55; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The TyG-WHtR index still showed a better prediction efficacy in ROC plot (0.806 (0.786, 0.868), Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). These results were presented in Supplementary Table\u0026nbsp;13\u0026ndash;20.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eAssociations between IR indexes and heart failure Incidence and mortality\u003c/h2\u003e\u003cp\u003eOur analysis showed significant associations between IR indices and the risk of heart failure in cancer survivors. RCS models indicated a progressive increase in heart failure incidence with higher levels of all four IR indices (Supplementary Fig.\u0026nbsp;4). Quartile-based analyses showed that three obesity-modified indices exhibited significant associations in their highest quartiles: TyG-BMI Q4 (HR\u0026thinsp;=\u0026thinsp;1.29, 95% CI: 1.06\u0026ndash;1.57, p\u0026thinsp;=\u0026thinsp;0.010), TyG-WC Q4 (HR\u0026thinsp;=\u0026thinsp;1.46, 1.17\u0026ndash;1.83, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and TyG-WHtR Q4 (HR\u0026thinsp;=\u0026thinsp;1.34, 1.09\u0026ndash;1.64, p\u0026thinsp;=\u0026thinsp;0.005), while no significant associations were observed in lower quartiles (Q2-Q3) or for the TyG index. Continuous analyses further revealed strong per-unit risk gradients for TyG-WHtR (HR\u0026thinsp;=\u0026thinsp;2.37 per 3-unit increase, 1.85\u0026ndash;3.04, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), TyG-BMI (HR\u0026thinsp;=\u0026thinsp;1.58 per 100-unit increase, 1.39\u0026ndash;1.80, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and TyG-WC (HR\u0026thinsp;=\u0026thinsp;1.19 per 100-unit increase, 1.13\u0026ndash;1.25, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The ROC curves illustrated that TyG-WHtR, TyG-BMI, and TyG-WC had almost equal AUC, with TyG-WHtR was slightly higher in some time-point (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). In contrast to the incidence findings, heart failure-specific mortality analyses revealed non-linear associations on RCS plots, and the limited event numbers (N\u0026thinsp;=\u0026thinsp;10) restricted our analysis to unadjusted models. These findings suggest that adiposity-adjusted insulin resistance indices, especially in their highest ranges, may be key predictors of heart failure risk for cancer survivors. Nevertheless, further research is needed to explore their association with heart failure mortality in larger cohorts. The results mentioned above were listed in Supplementary Table\u0026nbsp;21\u0026ndash;28.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eAssociations between IR indexes and stroke Incidence and mortality\u003c/h2\u003e\u003cp\u003eAnalysis of stroke outcomes revealed discordant patterns between initial RCS trends and formal statistical testing. While restricted cubic splines suggested a positive association between all four IR indices and stroke incidence (Supplementary Fig.\u0026nbsp;5), formal quartile-based comparisons in fully adjusted models showed no statistically significant differences in stroke risk across TyG quartiles (Q1-Q4, all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). This null finding was consistently observed for all other IR indices (TyG-WC, TyG-BMI, and TyG-WHtR). Similarly, continuous analyses of per-unit increases in each index did not demonstrate significant associations with stroke incidence (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Similar to heart failure, TyG-WHtR, TyG-BMI, and TyG-WC had almost equal AUC, with TyG-WHtR was slightly higher in some time-point (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF).\u003c/p\u003e\u003cp\u003eFor stroke-specific mortality, the limited number of events (N\u0026thinsp;=\u0026thinsp;34) constrained our analytical approach, permitting only Model 2 adjustment (for age, sex, race, and Townsend deprivation index). In these analyses, none of the IR indices showed significant: (1) quartile-dependent risk gradients (all between-quartile p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), or (2) per-unit risk associations (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). These null findings were visually confirmed by RCS plots, which showed no evidence of linear or nonlinear associations between IR indices and stroke mortality. The results mentioned above were listed in Supplementary Table\u0026nbsp;29\u0026ndash;36.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eSubgroup analysis\u003c/h2\u003e\u003cp\u003eTo evaluate potential effect modifications, we conducted detailed subgroup analyses to examine the interactions between covariates and IR indices across various outcomes. For the incidence of CCVD, we found significant interactions between all IR indices and both alcohol consumption and smoking status, with a p-value for interaction of less than 0.05. Diabetes history showed significant interactions with TyG-BMI, TyG-WC, and TyG-WHtR, while genetic sex interacted significantly with TyG and TyG-WC (p-values for interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Supplementary Table\u0026nbsp;37\u0026ndash;40). In contrast, no significant interactions were detected between any covariates and IR indices for CCVD-specific mortality (all p-values for interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Supplementary Table\u0026nbsp;41\u0026ndash;44). Similar to CCVD incidence, CVD risk showed significant interactions between all four IR indices and alcohol consumption, smoking status, and diabetes history (Supplementary Table\u0026nbsp;45\u0026ndash;48). Besides, age and sex are two additional interaction factors for TyG index. However, as with CCVD mortality, no significant covariate interactions emerged for CVD-specific mortality (Supplementary Table\u0026nbsp;49\u0026ndash;52).\u003c/p\u003e\u003cp\u003eHeart failure risk demonstrated more complex interaction patterns: genetic sex interacted with TyG and TyG-WC, hypertension with TyG-BMI and TyG-WHtR, while TyG-BMI additionally showed interactions with smoking status and ethnicity, and TyG with age and smoking status. Notably, TyG-WC and TyG-WHtR showed the fewest significant interactions among all indices (Supplementary Table\u0026nbsp;53\u0026ndash;56). For stroke outcomes, except for TyG (age was the only interaction factor), no significant interactions were found between any covariates and IR indices across all analyses (Supplementary Table\u0026nbsp;47\u0026ndash;60). These findings suggest that the predictive performance of IR indices varies substantially across different cardiovascular outcomes and is particularly influenced by lifestyle factors (smoking and alcohol use) for composite and CVD outcomes, while showing more limited interactions for mortality endpoints. The differential interaction patterns among indices highlight the importance of considering specific clinical and demographic characteristics when applying these metrics in risk prediction.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eSensitivity Analysis\u003c/h2\u003e\u003cp\u003eIn sensitivity analyses excluding individuals with follow-up durations of less than 1 year and less than 2 years, we reassessed the associations of insulin resistance (IR) indices (TyG, TyG-BMI, TyG-WC, and TyG-WHtR) with incident CCVD, CVD, heart failure, stroke, and cause-specific mortality. TyG-WHtR and TyG-BMI maintained significant discriminative capacity for CCVD-specific mortality (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Supplementary Table\u0026nbsp;61\u0026ndash;64). Except for TyG, the highest quartile of the other indices remained associated with significantly elevated CCVD risk compared to Q1, whereas TyG failed to stratify either CCVD risk or mortality (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Supplementary Table\u0026nbsp;61\u0026ndash;64). For CVD risk, TyG-WC demonstrated the strongest association, with progressively increasing HRs in Q3 and Q4 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 vs. Q1, Supplementary Table\u0026nbsp;65\u0026ndash;68). At the same time, TyG-BMI and TyG-WHtR showed significant but non-linear associations with CVD-specific mortality (HR\u0026thinsp;\u0026gt;\u0026thinsp;1, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for Q2\u0026ndash;Q4 vs. Q1, Supplementary Table\u0026nbsp;65\u0026ndash;68), characterised by an initial decline between Q2 and Q3, followed by an increase from Q3 to Q4. TyG again showed no discriminative value for CVD outcomes.\u003c/p\u003e\u003cp\u003eRegarding heart failure, all indices except TyG exhibited statistically significant risk associations in Q4 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 vs. Q1, Supplementary Table\u0026nbsp;69\u0026ndash;70), whereas none of the indices differentiated stroke risk across quartiles (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Supplementary Table\u0026nbsp;69\u0026ndash;70). No significant association was found between all IR indices and stroke incidence (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Supplementary Table\u0026nbsp;61\u0026ndash;72).\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIt is crucial and urgent to develop suitable prediction tools for CCVD risk and prognosis in this vulnerable population. However, currently, the prevalent cardiovascular risk scores for regular populations showed poor performance among cancer survivors [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. While newly developed evaluation tools for specific sub-components of CCVD were either based on multiple predictors or depended on a combination of multiple indices [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], this increased the complexity of clinical work. On the other hand, the correlations among IR, malignancies, and CCVD can be complex and interrelated [\u003cspan additionalcitationids=\"CR37 CR38\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. IR is quite common among cancer survivors, which may be due to the tumour itself or treatments related to the tumour [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Therefore, starting from IR finding tools that can both assess whether cancer survivors are currently in an IR status and predict the risk and mortality of CCVD in this specific group, thereby enabling early intervention to improve the long-term prognosis of cancer survivors, can be feasible and highly valuable.\u003c/p\u003e\u003cp\u003eIn this prospective cohort study, four representative IR indices (TyG, TyG-BMI, TyG-WC, and TyG-WHtR) were introduced to find out their correlation with CCVD among cancer survivors and determine the association strength of CCVD risk and mortality, as well as the incidence and specific mortality of its sub-items (CVD, stroke, and heart failure). The IR indices were slightly higher in cancer survivors than in regular participants. The survival curve illustrated that the overall survival of cancer survivors is poorer than that of regular participants. At the same time, both event incidence and CCVD-related mortality were much higher in cancer survivors than in regular participants. These results, which followed previous studies, highlighted the need for extra attention to the risk of CCVD occurrence among cancer survivors to improve their long-term prognosis, and IR indices could potentially be ideal prediction tools [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The RCS results illustrated that all IR indices are positively related to CCVD risk and CCVD-related mortality among cancer survivors. Therefore, participants were subsequently divided into four groups according to quartiles, and the Kaplan-Meier survival curves illustrate that higher IR indices were significantly related to poor event-free survival and disease-specific survival. To assess the association strength of the four IR indices, we performed COX regression analysis with multiple adjusted models and subgroup analysis. Interesting results began to appear from this moment on. However, there is a noticeable increase in the risk of CCVD among all four IR indices, as well as in CVD-specific deaths, except for the TyG index. The TyG and TyG-WC indices demonstrated a stronger ability to distinguish CCVD risk compared to the other two indices, as both the third and fourth grade groups experienced higher CCVD incidence, with HR increasing in a step-like pattern.\u003c/p\u003e\u003cp\u003eIn contrast, TyG-BMI and TyG-WHtR demonstrated stronger association strength than the TyG and TyG-WC indices, as all three grade groups showed higher CCVD mortality rates than the reference group. The subgroup analysis illustrated that the association between TyG and TyG-WC indices and CCVD risk were much stronger in female patients, and the correlation between CCVD risk and all the IR indices was stronger in non-smoking patients with diabetes and alcohol consumption history. We noticed that the proportion of female patients is higher in the population being studied. Besides, it has been reported that waist circumference could be an indicator of metabolic disorder and IR [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Therefore, we attribute the stronger association of TyG-WC with CCVD risk to the gender distribution imbalance in the study population. Smoking is highly related to IR and TyG index, not to mention diabetes and the related treatment would influence the TyG level [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. In contrast, the association between alcohol and IR remain controversial; in some studies, alcohol consumption was reported to be negatively related to IR [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Therefore, the results of the subgroup analysis are rational. On the other hand, WHtR has been reported to be highly associated with CCVD-related death [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Thus, it is rational that TyG-WHtR showed a strong predictive value for CCVD-related death. The ROC curves not only support this result but also indicate that the prediction efficacy of TyG-WHtR was slightly higher than the other three IR indices.\u003c/p\u003e\u003cp\u003eRegarding the sub-items of CCVD, the RCS revealed that IR indices are also positively associated with the risk of CVD, stroke, and heart failure. However, as for disease-related mortality, only CVD-related mortality was associated with higher IR indices, while their association with heart failure and stroke-related death was not statistically significant. We attributed this result to the fact that the number of death events of heart failure and stroke was quite lower than that of CVD (12 and 42 vs 181) [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Similar findings were detected in the associations between IR indices and CVD outcomes. TyG and TyG-WC had better discriminability for CVD risk, while TyG-BMI and TyG-WHtR showed better discriminability for CVD-related mortality. Like the results of ROC in CCVD, TyG-WHtR also had the highest AUC value.\u003c/p\u003e\u003cp\u003eRegarding the analysis of heart failure and stroke, due to the limited number of events, the analysis is limited to assessing the risk of heart failure and stroke [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Except for the TyG index, TyG-BMI, TyG-WC, and TyG-WHtR showed similar discriminability for heart failure risk, where the fourth grade of participants had a significantly higher risk of heart failure than the reference group. The results of ROC support those of Cox regression analysis, showing that the AUC of the TyG-BMI, TyG-WC, and TyG-WHtR indices at five-year and ten-year event-free survival were almost similar but higher than the TyG index. For the stroke event, unfortunately, the four IR indices were not associated with stroke risk after adjusting for covariates. The sensitivity analysis confirmed the results above. In conclusion, although all the IR indices showed a dose-response relationship with CCVD and CVD outcomes among cancer survivors, TyG-WHtR could be a more reliable tool for prediction, especially for CCVD and CVD-related death.\u003c/p\u003e\u003cp\u003eNonetheless, our study has several limitations. First, IR indices were calculated at baseline and treated as static; changes in behavior or clinical metrics over time were not captured. Future studies incorporating repeated assessments help elucidate the effect of longitudinal health improvements or deterioration. Second, the number of deaths related to heart failure and stroke was quite limited; therefore, the actual relation of IR indices to disease-specific survival still needs validation. Additionally, although ROC analysis verified the superior predictive value of TyG-WHtR for CCVD and CVD risk and mortality, the phenomenon of Cox regression, where two of the four indices are suitable for predicting CCVD and CVD risk, while the other two are suitable for predicting CCVD and CVD-specific mortality, deserves further study. Notably, as large-scale datasets become increasingly popular, safeguarding data privacy remains a critical concern. Recent studies have raised a classification system for data sensitivity and recommended ethical guidelines tailored to each level. This approach promotes responsible data sharing while ensuring adherence to ethical standards [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Finally, the study was only based on participants' data acquired from the UK Biobank. Therefore, other large-scale cohorts are still needed for further validation.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThrough comprehensive analysis, we found that IR indices are highly associated with the risk and specific mortality of cardiovascular and cerebrovascular diseases among cancer survivors. TyG-WHtR had the highest association strength, especially for non-smoking women, patients without a history of diabetes, and those with a history of alcohol consumption.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCCVD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ecardiovascular and cerebrovascular diseases\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCVD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ecardiovascular disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003einsulin resistance\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTyG index\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003etriglyceride-glucose index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ebody mass index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eWC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ewaist circumference\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003etriglycerides\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHDL-C\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ehigh-density lipoprotein cholesterol\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003estandard deviations\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCIs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003econfidence intervals\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eANOVA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eanalysis of variance\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eVIF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003evariance inflation factor\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eOS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eoverall survival\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eEFS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eevent-free survival\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDSS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003edisease-specific survival\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRCS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003erestricted cubic spline\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eReceiver Operating Characteristic\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ethical approval for the UK Biobank research was granted by the North West Multicenter Research Ethical Committee. This current study was specifically approved by the UK Biobank under application number 332912.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from all the participants during the baseline recruitment to the UK Biobank. The authors consent to the publication of the submitted manuscript.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work was supported by the Beijing Natural Science Foundation (7232138), Gansu Provincial Joint Research Fund Project (24JRRA9330).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eJianye Wang had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: Shuhang Luo, Runhua Tang, Haoran Wang, Li Ma. Acquisition, analysis, or interpretation of data: Jianye Wang. Drafting of the manuscript: Shuhang Luo, Runhua Tang, Haoran Wang, Li Ma. Critical review of the manuscript for important intellectual content: Jianyong Liu, Huimin Hou, Li Ma. Statistical analysis: Jianye Wang. Administrative, technical, or material support: Ming Liu, Huimin Hou. Supervision: Ming Liu, Huimin Hou, Jianyong Liu. Funding Acquisition: Ming Liu, Li Ma.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThis study has been conducted using the UK Biobank resource (Application Number 332912). We are grateful to all staff from UK Biobank for their contributions to the study.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eUK Biobank data are available through application to the database. Restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of UK Biobank\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel R, Kratzer T, Giaquinto A, Sung H, Jemal A. Cancer statistics, 2025. Ca. 2025;75:10\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShariat SF. A pan-European total cancer prevalence canvas: a benchmark for advancing strategic interventions. Lancet Oncol. 2024;25(3):266\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDe Angelis R, Demuru E, Baili P, et al. Complete cancer prevalence in Europe in 2020 by disease duration and country (EUROCARE-6): a population-based study. Lancet Oncol. 2024;25(3):293\u0026ndash;307.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMiller KD, Nogueira L, Mariotto AB, et al. Cancer treatment and survivorship statistics, 2019. 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Alpha psychiatry. 2025;26(1):38759.\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":"Cancer survivor, cardiovascular and cerebrovascular diseases, triglyceride-glucose index, insulin resistance","lastPublishedDoi":"10.21203/rs.3.rs-7625609/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7625609/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThe risk and specific mortality of cardiovascular and cerebrovascular diseases (CCVD) would greatly increase among cancer survivors, with prevalent cardiovascular risk scores for regular populations showing poor performance. We aim to evaluate four insulin resistance (IR) indices\u0026mdash;the triglyceride-glucose (TyG) index and its derivatives (TyG-BMI, TyG-WC, TyG-WHtR)\u0026mdash;for their association with CCVD outcomes in cancer survivors.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis prospective cohort study is based on data from the UK Biobank. Fasting glucose, triglycerides, and anthropometric measures were used to calculate IR indices. Primary outcomes were CCVD incidence (composite of cardiovascular disease [CVD], stroke, and heart failure) and CCVD-specific mortality. Cox regression models adjusted for demographics, lifestyle, and comorbidities assessed hazard ratios (HRs) by index quartiles (Q1\u0026ndash;Q4). Sensitivity and subgroup analyses were used to evaluate robustness and effect modification.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAmong cancer survivors, higher IR indices correlated with higher CCVD incidence and specific mortality (p-trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001). For CCVD incidence, TyG-WC showed the strongest association (Q4 HR\u0026thinsp;=\u0026thinsp;1.51 (1.33, 1.71), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while TyG-WHtR best predicted CCVD mortality (Q4 HR\u0026thinsp;=\u0026thinsp;3.17, 1.72\u0026ndash;5.85). CVD-specific mortality risk was highest with TyG-WHtR (5.89-fold increase per 3-unit increment, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Heart failure risk rose significantly in Q4 for obesity-adjusted indices (e.g., TyG-WC HR\u0026thinsp;=\u0026thinsp;3.17 (1.72, 5.85), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but no associations emerged for stroke outcomes. Subgroup analyses revealed stronger CCVD risk prediction in female patients, nondiabetics, and alcohol consumers (interaction p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Sensitivity analyses confirmed the robustness of the results after excluding early events.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eExcept for the TyG index, the IR indices showed a close association with CCVD incidence and specific mortality among cancer survivors. The relation of TyG-WHtR was stronger than that of TyG-BMI and TyG-WC in CCVD, CVD, and heart failure, and this association could further increase in specific subgroups.\u003c/p\u003e\u003ch2\u003eTrial registration\u003c/h2\u003e\u003cp\u003e The ethical approval for the UK Biobank research was granted by the North West Multicenter Research Ethical Committee. This current study was specifically approved by the UK Biobank under application number 332912.\u003c/p\u003e","manuscriptTitle":"Association of Triglyceride-Glucose Index and Its Derivatives With Incidence and Cause-Specific Mortality of Cardiovascular and Cerebrovascular Diseases Among Cancer Survivors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-22 19:23:11","doi":"10.21203/rs.3.rs-7625609/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":"33025b4f-b4d3-4251-be3a-590864d8334f","owner":[],"postedDate":"October 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-08T00:38:26+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-22 19:23:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7625609","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7625609","identity":"rs-7625609","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}