Joint association of cadmium exposure and inflammatory indicators with mortality in US cancer survivors

Joint association of cadmium exposure and inflammatory indicators with mortality in US 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 Joint association of cadmium exposure and inflammatory indicators with mortality in US cancer survivors Ya Wang, Cancan Luo, Qingqing Luo, Lili Zhou, Tiantian Yu, Li Yu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7191853/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Cadmium (Cd) exposure and high inflammation status are independent risk factors for mortality in cancer survivors, yet their combined impact on mortality remains unexplored. The objective of this study was to investigate joint effect of cadmium exposure and complete blood cell count (CBC)-derived inflammation markers on mortality in cancer survivors. Methods This study utilized information collected from the National Health and Nutrition Examination Survey (NHANES) spanning the years 2007 to 2018, with a participant pool of 2,450 individuals who had survived cancer. Blood cadmium levels and inflammation markers derived from CBC were systematically evaluated. We used weighted multivariable Cox regression models to evaluate the joint effect of blood cadmium levels and inflammation markers on all-cause mortality. Survival probabilities under different exposure conditions were compared using Kaplan-Meier curves. To investigate the independent effects of blood cadmium levels and inflammatory indicators on mortality, restricted cubic spline curve (RCS) modeling was used. Additionally, subgroup analyses were further conducted by age, sex, hypertension status, diabetes, smoking history, alcohol consumption, and other factors to explore potential interactions across different populations. Results Over a median follow-up period of 77 months, a total of 608 deaths were documented. The results showed that the combined exposure to elevated cadmium levels and high inflammatory markers was significantly associated with increased mortality risk. Notably, the highest mortality risk was observed in participants with both high cadmium levels and elevated MLR (hazard ratio [HR] = 3.12; 95% confidence interval [CI],2.07–4.72). Kaplan-Meier curves further demonstrated the poorest survival outcomes in subgroups with concurrent high cadmium exposure and elevated inflammatory indices. RCS analysis revealed significant linear associations between several inflammatory markers (including PIV, MLR, NLR) and all-cause mortality, whereas PLR, SII, SIRI, and blood cadmium levels exhibited nonlinear relationships with all-cause mortality. These findings were consistently supported by subgroup analyses. Conclusion This study represents the first systematic investigation evaluating the combined impact of cadmium exposure and inflammatory markers on mortality risk among cancer survivors. Our findings suggest that the joint effect of cadmium exposure and systemic inflammation significantly impacts survival outcomes in this population, providing novel perspectives for personalized interventions in this population. Cadmium exposure CBC-derived inflammation markers Cancer survivors Mortality NHANES Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The size of the community of cancer survivors has grown in recent years. According to future projections, this number will surge by 2040, when it is expected that the number of cancer survivors worldwide could reach 26 million[ 1 ].This trend reflects advances in medical technology and improved cancer treatments, empowering an increasing number of patients to combat cancer, achieve victory, and prolong their survival. However, the life expectancy of cancer survivors is still limited compared to that of individuals without cancer. Therefore, identifying and managing modifiable risk factors, such as physiological characteristics, living environment, and implementing personalized treatments, are extremely critical to improving mortality in cancer patients. Growing evidence has established a robust linkage between environmental heavy metal exposure and multiple detrimental health effects, prompting extensive research investigations. Cadmium(Cd), classified as a Group 1 carcinogen by the International Agency for Research on Cancer (IARC)[ 2 ], is widely distributed in the environment. Primary exposure sources include dietary intake, tobacco consumption, and industrial activities[ 3 – 4 ].The cytotoxic effects of cadmium are largely attributed to its binding to mitochondria, which interferes with mitochondrial function[ 5 ]. This disruption leads to the excessive generation of free radicals, which can cause DNA damage[ 6 ]. In addition to destabilizing the genome, cadmium also disrupts cellular homeostasis and induces apoptosis by increasing the production of reactive oxygen species (ROS) and inducing a chronic inflammatory response[ 7 ], which in turn exacerbates metabolic dysfunctions and the extent of tissue damage[ 8 – 9 ], affecting the prognosis of cancer patients. Cadmium toxicity plays a key role in inflammation, with cadmium having immunoregulatory effects on immune cells[ 10 ]. These mechanisms are critically involved in tumor initiation, progression, and prognosis[ 11 – 12 ]. In the tumor microenvironment, inflammation and tissue damage trigger cell renewal and proliferation, creating a favorable space and conditions for the growth of malignant clone cells, thus promoting tumor progression[ 13 – 14 ]. Furthermore, inflammatory cells secrete proinflammatory cytokines, which activate epigenetic regulation in cancer cells through signaling pathways, leading to an increase in tumor cell proliferation[ 15 ].The inflammatory status of systemic circulation can be comprehensively evaluated through multiple hematological parameters derived from CBC, particularly the systemic immune-inflammation index (SII), platelet-immune inflammation value (PIV), and various cellular ratios (MLR, PLR, NLR), complemented by the systemic inflammation response index (SIRI). These biomarkers provide a multidimensional and multilayered approach to evaluating systemic immune-inflammatory status[ 16 ]. Although inflammatory markers have emerged as valuable prognostic tools in oncology, the comprehensive effects of cadmium exposure combined with multiple inflammatory markers on cancer patients remain insufficiently understood. A combined evaluation of cadmium exposure and inflammatory markers provides new insights into understanding the mortality of cancer survivors. Through this integrated assessment, we can more accurately identify environmental factors that impact the health of survivors, providing theoretical support for future personalized interventions. This study aims to utilize data from U.S. cancer survivors between 2007 and 2018 to explore the role of combined cadmium exposure and inflammatory markers in assessing cancer survivor mortality, providing new insights into understanding mortality rates among cancer survivors. Through this integrated assessment, we can more accurately identify environmental factors that impact survivor health, offering theoretical support for future personalized intervention strategies. Methods The NHANES program combined personal interviews, standardized physical examinations, and laboratory tests to comprehensively evaluate multiple health domains, including chronic diseases, nutritional status, dietary habits, and environmental exposures. The study population was selected through a multistage, stratified random sampling design to ensure national representativeness across racial/ethnic groups, age strata, genders, and socioeconomic status[ 17 ]. As a comprehensive health survey, all study protocols were approved by the NCHS Institutional Review Board, with written informed consent obtained from all participants. Our study utilized publicly available data from the NHANES website for a total of 59,482 participants surveyed between 2007 and 2018. By asking participants if they had cancer and screening individuals older than 20 years, we initially identified 3,368 cancer survivors for the study. Subsequently, excluding individuals with missing data on survival outcomes, blood cadmium levels, or inflammatory biomarkers, the final analytical cohort comprised 2,450 eligible cancer survivors. Figure 1 presents the complete participant selection flowchart. Mortality outcomes Follow-up spanned from baseline until mortality or study closure (December 31, 2019). All-cause mortality was the primary outcome, and ICD-10 criteria were applied for cause-specific mortality classification. Blood cadmium levels Blood cadmium concentrations were measured by inductively coupled plasma mass spectrometry (ICP-MS). For values below the limit of detection (LOD), we imputed cadmium concentrations as LOD/√2, ensuring proper handling of undetectable levels and minimizing potential biases. Throughout the measurement process, strict adherence to the laboratory's established quality control protocols was maintained to ensure the accuracy and reliability of the results. inflammation biomarkers The inflammatory biomarkers evaluated in this study were derived from CBC-related indices, calculated as follows: PLR = Platelet count / Lymphocyte count[ 18 ], SII = Platelet count × Neutrophil count / Lymphocyte count[ 19 ], PIV = SII × Monocyte count[ 20 ], MLR = Monocyte count / Lymphocyte count[ 21 ], NLR = Neutrophil count / Lymphocyte count[ 22 ], SIRI = Neutrophil count × Monocyte count / Lymphocyte count[ 23 ]. Covariates The covariates included in this study encompassed demographic, socioeconomic, and anthropometric factors. Key variables included gender, age, race/ethnicity, and educational attainment; Educational attainment; Marital status was categorized as married, unmarried, or divorced/separated/widowed; Socioeconomic status was assessed using the Poverty Income Ratio (PIR), with a cutoff of 3.5 (PIR < 3.5 vs. PIR ≥ 3.5) based on prior research[ 24 ];categorized according to WHO criteria: <25 kg/m², 25–30 kg/m², and ≥ 30 kg/m². Health-related behaviors and conditions were assessed as follows: Sleep disturbances were evaluated using the question "Difficulty sleeping or excessive sleep" from the questionnaire, where a response of "Not at all" was considered no sleep disturbance, and responses of "Nearly every day", "More than half the days" or "Several days" were considered to indicate sleep disturbance; Diabetes mellitus was diagnosed if participants fulfilled any of the following conditions: (a) physician-diagnosed diabetes per self-report; (b) Fasting blood glucose ≥ 7.0 mmol/L; (c) HbA1c ≥ 6.5%; (d) current use of oral antidiabetic drugs or insulin; Hypertension history was defined by meeting any of the following conditions: (a) self-reported physician-confirmed diagnosis of hypertension; (b) Currently using antihypertensive medication; (c) sustained blood pressure ≥ 140/90 mmHg[ 25 ]; Alcohol consumption was classified into three mutually exclusive categories based on self-reported drinking patterns: "Never" (< 12 lifetime drinks), "Former" (≥ 12 drinks in a lifetime but abstinent for ≥ 1 year ), and "Current" (past-year consumption with ≥ 12 lifetime drinks); Smoking status was categorized into three groups: "Never" (< 100 lifetime cigarettes), "Former" (≥ 100 cigarettes with cessation), and "Current" (≥ 100 cigarettes with ongoing tobacco use on some days) [ 26 ]. Statistical analysis We weighted the data according to the NHANES analytical guidelines using its recommended sample weight calculation.The weights corresponding to the smallest subset of variables WTMEC2YR were selected. Missing data were handled using multiple imputation. For categorical data representation, weighted frequency distributions (%) were employed, with weighted chi-square tests evaluating group differences. Continuous measures were described using weighted quartiles and analyzed using weighted Kruskal-Wallis tests. Study participants were stratified into high- and low-cadmium groups based on median blood cadmium concentrations. CBC-derived inflammatory markers were similarly dichotomized using median cutoffs. To explore the joint effect of cadmium levels and inflammatory markers on mortality, participants were divided into four groups: low cadmium levels and inflammatory markers ≤ median, low cadmium levels and inflammatory markers ≥ median, high cadmium levels and inflammatory markers ≤ median, and high cadmium levels and inflammatory markers ≥ median. Survey-weighted Cox regression models were used to evaluate the associations of blood cadmium and inflammatory markers with all-cause mortality, with adjustment for potential confounders. Three regression models were established: Model 1 Unadjusted; Model 2 demographic factors (age, gender, race) and socioeconomic status (PIR, education, marital status); Additionally adjusted for lifestyle factors (BMI, sleep duration, alcohol consumption, smoking) and comorbidities (diabetes, hypertension). Survival analyses were performed using Kaplan-Meier methods with log-rank testing to compare survival in different combined groups. Additionally, restricted cubic spline (RCS)tested non-linear relationships between cadmium, inflammatory markers, and mortality. Subgroup analyses stratified by age, gender, diabetes, and hypertension were performed. All statistical analyses were conducted using R statistical software (version 4.3.2). Weighted analysis was performed using the survey package, multiple imputation was conducted with the mice package. For survival analyses, we employed the survival package to conduct Cox proportional hazards regression, Kaplan-Meier estimation, and stratified subgroup analyses. Graphical representations were generated using the survminer and ggplot2 packages. Throughout all analyses, Statistical significance was assessed using a two-tailed p < 0.05 criterion. Results Baseline characteristics of cancer survivors This study analyzed a nationally representative sample of 2,450 cancer patient samples, which, after weighting, are representative of a population of 21,020,006 cancer survivors in the United States. Over a median follow-up of 77 months, A totally of 608 deaths were recorded. An analysis of the participant characteristics revealed that 82.02% of the patients were over 50 years old (n = 2,093). In terms of race, 85.95% of the patients were White (n = 1,645). The gender distribution showed that 56.86% of the patients were female (n = 1,289). Further comparative analysis identified significant differences between cancer survivors and deceased patients across several factors. Specifically, there were statistically significant differences in age distribution, gender, race, education attainment, marital status, PIR, sleep disturbances, smoking, alcohol consumption history, hypertension, and diabetes (all p < 0.01). Moreover, levels of inflammatory markers derived from CBC and blood cadmium levels differed between the two groups (p < 0.05), with deceased patients exhibiting significantly higher levels of both inflammatory markers and blood cadmium compared to survivors. Detailed data can be found in Table 1 . Table 1 Baseline characteristics of the of cancer survivors, weighted. Variables Overall (n = 21,020,006) Live (n = 17,277,887) Death (n = 3,742,119) P value Age(years), n (%) < 0.001 ≥ 20 & <50 3,779,526 (17.98) 3,692,435 (21.37) 87,091 (2.33) ≥ 50 17,240,480 (82.02) 13,585,452 (78.63) 3,655,028 (97.67) Sex, n (%) < 0.001 Male 9,068,027 (43.14) 6,986,521 (40.44) 2,081,506 (55.62) Female 11,951,979 (56.86) 10,291,366 (59.56) 10,291,366 (59.56) Race, n (%) < 0.001 Mexican American 577,372 (2.75) 531,120 (3.07) 46,252 (1.24) Other Hispanic 530,920 (2.53) 494,150 (2.86) 36,770 (0.98) Non-Hispanic White 18,067,448 (85.95) 14,721,257 (85.20) 3,346,192 (89.42) Non-Hispanic Black 1,089,469 (5.18) 853,829 (4.94) 235,640 (6.30) Other Race 754,797 (3.59) 677,532 (3.92) 77,264 (2.06) Education level, n (%) < 0.001 Less than high school 7,313,225 (34.79) 5,500,983 (31.84) 1,812,242 (48.43) High school or equivalent 6,332,333 (30.13) 5,301,755 (30.69) 1,030,578 (27.54) College or above 7,374,448 (35.08) 6,475,150 (37.48) 899,298 (24.03) PIR, n (%) < 0.001 < 3.5 10,680,539 (50.81) 8,109,948 (46.94) 2,570,591 (68.69) ≥ 3.5 10,339,467 (49.19) 9,167,940 (53.06) 1,171,528 (31.31) BMI (kg/m2) 0.485 < 25 4,520,286 (21.50) 3,645,586 (21.10) 874,701 (23.37) ≥ 25 & <30 5,979,469 (28.45) 4,863,481 (28.15) 1,115,988 (29.82) ≥ 30 10,520,251 (50.05) 8,768,820 (50.75) 1,751,430 (46.80) Marital status, n (%) < 0.001 Married/Living with partner 13,167,763 (62.64) 11,239,822 (65.05) 1,927,942 (51.52) Widowed / Divorced / Separated 7,852,243 (37.36) 6,038,066 (34.95) 1,814,177 (48.48) Never married 2,165 (12.31) 508 (17.30) 621 (12.22) Sleepdisorder, n (%) 0.029 No 12,934,484 (61.53) 10,449,241 (60.48) 2,485,243 (66.41) Yes 8,085,523 (38.47) 6,828,647 (39.52) 1,256,876 (33.59) Smoking status, n (%) < 0.001 Never 9,986,901 (47.51) 8,585,488 (49.69) 1,401,413 (37.45) Former 7,792,672 (37.07) 6,012,221 (34.80) 1,780,451 (47.58) Now 3,240,433 (15.42) 2,680,178 (15.51) 560,255 (14.97) Alcohol status, n(%) 0.009 None 4,390,876 (20.89) 2,988,895 (17.30) 1,401,981 (37.46) Moderate 2,392,465 (11.38) 1,960,507 (11.35) 431,958 (11.54) Excessive 14,236,665 (67.73) 12,328,486 (71.35) 1,908,180 (50.99) Hypertension ,n (%) < 0.001 No 9,030,497 (42.96) 8,102,130 (46.89) 928,368 (24.81) Yes 11,989,509 (57.04) 9,175,758 (53.11) 2,813,751 (75.19) Diabetes, n (%) < 0.001 Yes 16,302,093 (77.56) 13,739,230 (79.52) 2,562,863 (68.49) No 4,717,914 (22.44) 3,538,657 (20.48) 1,179,256 (31.51) PIV, IQR 276.25 (177.60–431.20) 270.16 (176.18–410.98) 328.57 (198.09–522.63) < 0.001 NLR, IQR 2.19 (1.63–3.00) 2.13 (1.57–2.94) 2.53 (1.88–3.63) < 0.001 MLR, IQR 0.30 (0.23–0.38) 0.29 (0.22–0.36) 0.36 (0.27–0.47) < 0.001 PLR, IQR 125.60 (95.45–160.71) 124.21 (95.29–158.57) 131.25 (96.67–173.33) 0.029 SII, IQR 501.28 (353.69–723.09) 490.00 (352.08–704.00) 556.50 (382.94–834.75) < 0.001 SIRI, IQR 1.20 (0.82–1.80) 1.15 (0.79–1.69) 1.51 (1.04–2.27) < 0.001 Cd, IQR 3.20 (2.05–5.16) 3.02 (1.96–5.07) 3.83 (2.67–5.96) < 0.001 WBC,IQR)(×10 9 /L) 6.90 (5.60–8.40) 6.80 (5.60–8.30) 7.10 (5.70–8.40) 0.260 Lymphocyte ,IQR)(×10 9 /L) 1.90 (1.40–2.40) 1.90 (1.50–2.40) 1.60 (1.30–2.10) < 0.001 Monocyte,IQR)(×10 9 /L) 0.60 (0.40–0.70) 0.60 (0.40–0.70) 0.60 (0.50–0.70) < 0.001 Neutrophil,IQR)(×10 9 /L) 4.10 (3.20–5.30) 4.00 (3.20–5.20) 4.40 (3.40–5.50) 0.002 PLT,IQR(×10 9 /L) 230.00 (191.00–271.00) 232.00 (195.00–273.00) 217.00 (172.00–265.00) < 0.001 PIR, poverty income ratio; BMI: body mass index; PIV: platelet-immune inflammation value; NLR: neutrophil-to-lymphocyte ratio; MLR: monocyte-to-lymphocyte ratio; PLR: platelet-to-lymphocyte ratio; SII: systemic immune-inflammation index; SIRI: systemic inflammation response index; WBC: white blood cell count; HGB: Hemoglobin PLT: platelets; IQR, Interquartile range. P-value of < 0.05 was considered statistically significant. The combined association of blood cadmium and inflammatory markers with mortality Table 2 demonstrates the synergistic impact of systemic inflammation (measured via CBC-derived markers) and blood cadmium concentrations on mortality risk among cancer survivors. The analysis reveals a pronounced elevation in mortality risk for individuals exhibiting concurrent elevations in both inflammatory markers and blood cadmium levels. To further elucidate the association between inflammatory markers, blood cadmium levels, and mortality risk in cancer patients, we conducted a weighted multivariable-adjusted Cox regression analysis. Using a reference group comprising participants with both low blood cadmium levels (< median) and submedian inflammatory markers, our fully adjusted model identified the highest mortality risk in the subgroup with dual exposure to high cadmium and elevated inflammation(P for trend < 0.001). Notably, patients with concomitant high cadmium levels and an elevated MLR demonstrated the most substantial risk increase, with an adjusted hazard ratio (HR) of 3.12 (95% CI: 2.07–4.72). In the analysis of CVD-specific mortality, a significant upward trend in the mortality rate was observed in the highest exposure group compared to the lowest exposure group (p < 0.05, trend test P value < 0.05). However, for cancer-specific mortality, a significant trend was only observed in individuals with both high blood cadmium exposure and a high MLR index, and this trend remained significant after multivariate adjustment (trend test P value < 0.05). Detailed data can be found in Tables S1 and S2. Table 2 Baseline characteristics of the of cancer survivors. Subgroups Case Model I Model II Model III HR (95% CI) P value HR (95% CI) P value HR (95% CI) P value Cd and PIV Group 1 581 (23.71) 1.00 (ref) 1.00 (ref) 1.00 (ref) Group 2 542 (22.12) 1.47(1.09–1.98) 0.198 1.42 (1.02–1.99) 0.038 1.23(0.90–1.70) 0.199 Group 3 631 (25.76) 1.65(1.24–2.21) < 0.001 1.63(1.20–2.21) 0.002 1.55(1.14–2.11) 0.006 Group 4 696(28.41) 2.38(1.82–3.13) < 0.001 2.02(1.52–2.69) < 0.001 1.86(1.40–2.47) < 0.001 P for trend < 0.001 < 0.001 < 0.001 Cd and PLR Group 1 561(22.90) 1.00 (ref) 1.00 (ref) 1.00 (ref) Group 2 562(22.94) 1.03 (0.72–1.46) 0.879 0.97 (0.70–1.36) 0.863 0.92(0.65–1.32) 0.661 Group 3 690(28.16) 1.35 (1.00-1.81) 0.048 1.26 (0.94–1.68) 0.121 1.22 (0.89–1.67) 0.219 Group 4 637(26.00) 2.11 (1.54–2.89) < 0.001 1.81(1.30–2.51) < 0.001 1.76 (1.22–2.54) 0.003 P for trend < 0.001 < 0.001 < 0.001 Cd and MLR Group 1 566 (23.10) 1.00 (ref) 1.00 (ref) 1.00 (ref) Group 2 557 (22.73) 2.74(1.80–4.20) < 0.001 2.53(1.65–3.90) < 0.001 2.23(1.45–3.43) < 0.001 Group 3 653 (26.65) 1.78(1.22–2.59) 0.003 1.72 (1.19–2.50) 0.004 1.73 (1.18–2.54) 0.005 Group 4 674 (27.51) 4.50 (3.02–6.72) < 0.001 3.39 (2.54–5.10) < 0.001 3.12 (2.07–4.72) < 0.001 P for trend < 0.001 < 0.001 < 0.001 Cd and NLR Group 1 545 (22.24) 1.00 (ref) 1.00 (ref) 1.00 (ref) Group 2 578 (23.59) 1.71(1.13–2.60) 0.012 1.44(0.97–2.15) 0.07 1.28(0.84–1.94) 0.25 Group 3 643 (26.24) 1.58(1.06–2.37) 0.025 1.40 (0.95–2.08) 0.091 1.35 (0.90–2.04) 0.15 Group 4 684 (27.92) 3.07 (2.13–4.40) < 0.001 2.44(1.70–3.51) < 0.001 2.20 (1.48–3.27) < 0.001 P for trend < 0.001 < 0.001 < 0.001 Cd and SII Group 1 555 (22.6)5 1.00 (ref) 1.00 (ref) 1.00 (ref) Group 2 568 (23.19) 1.17(0.85–1.61) 0.333 1.11(0.79–1.55) 0.563 0.96(0.69–1.34) 0.802 Group 3 650 (26.53) 1.48(1.08–2.03) 0.015 1.40 (1.00-1.96) 0.053 1.31 (0.94–1.83) 0.119 Group 4 677 (27.63) 2.14 (1.60–2.85) < 0.001 1.83(1.34–2.50) < 0.001 1.70 (1.23–2.31) < 0.001 P for trend < 0.001 < 0.001 < 0.001 Cd and SIRI Group 1 570 (23.27) 1.00 (ref) 1.00 (ref) 1.00 (ref) Group 2 553 (22.57) 2.15(1.47–3.14) < 0.001 1.90(1.28–2.80) 0.001 1.68(1.13–2.51) 0.010 Group 3 617 (25.18) 1.61(1.20–2.17) 0.002 1.58(1.18–2.11) 0.002 1.56 (1.13–2.17) 0.007 Group 4 710 (28.98) 3.40(2.45–4.72) < 0.001 2.66(1.90–3.73) < 0.001 2.45 (1.70–3.52) < 0.001 P for trend < 0.001 < 0.001 < 0.001 Model I: non-adjusted; Model II: adjusted for age, gender, race, PIR, education level, marital status; Model III: further adjusted for BMI, sleep disorder, smoking status, alcohol status, Hypertension, Diabetes. Group 1 refers to Low blood cadmium levels & CBC-derived inflammatory biomarkers ≤ median; Group 2 refers to Low blood cadmium levels & CBC-derived inflammatory biomarkers ≥ median; Group 3 refers to High blood cadmium levels & CBC-derived inflammatory biomarkers ≤ median; Group 4 refers to High blood cadmium levels & CBC-derived inflammatory biomarkers ≥ median. PIV: platelet-immune inflammation value; NLR: neutrophil-to-lymphocyte ratio; MLR: monocyte-to-lymphocyte ratio; PLR: platelet-to-lymphocyte ratio; SII: systemic immune-inflammation index; SIRI: systemic inflammation response index; Cd: blood cadmium. The weighted Kaplan-Meier analysis showed significant survival differences among groups (P < 0.0001; Fig. 2 ), with Group 4 (high cadmium and high inflammation) demonstrating markedly worse survival than Group 1 (low cadmium and low inflammation). Similar results were obtained for the rates of CVD mortality and cancer-specific mortality (P < 0.05, Figures S1 , S2). Group 1 refers to Low blood cadmium levels & CBC-derived inflammatory biomarkers ≤ median; Group 2 refers to Low blood cadmium levels & CBC-derived inflammatory biomarkers ≥ median; Group 3 refers to High blood cadmium levels & CBC-derived inflammatory biomarkers ≤ median; Group 4 refers to High blood cadmium levels & CBC-derived inflammatory biomarkers ≥ median. PIV: platelet-immune inflammation value; NLR: neutrophil-to-lymphocyte ratio; MLR: monocyte-to-lymphocyte ratio; PLR: platelet-to-lymphocyte ratio; SII: systemic immune-inflammation index; SIRI: systemic inflammation response index; Cd: blood cadmium. Independent effects between blood cadmium or inflammatory markers and mortality To examine the potential exposure-response relationships between blood cadmium concentrations, CBC-derived inflammatory biomarkers, and all-cause mortality, we performed RCS regression analyses. As shown in Fig. 3 and Figure S3, After adjusting for potential confounders, the RCS analysis revealed that blood cadmium levels were nonlinearly associated with all-cause mortality (P for overall < 0.001; nonlinearity test p = 0.001). The HR shows a gentle trend at low Cd concentrations but escalates rapidly after crossing a critical threshold. Among the inflammation indicators, PIV, MLR and NLR demonstrated a substantial linear positive correlation with all-cause mortality (P for overall < 0.001; nonlinearity test p = 0.001), whereas PLR, SII and SIRI showed a nonlinear correlation (P for overall < 0.001; nonlinearity test p = 0.001).We performed a Cox regression analysis, adjusting for weighted multivariable factors, to assess the independent effects of seven indicators on all-cause mortality in cancer survivors. The median blood cadmium level and CBC-derived inflammatory markers were used as cutoffs. The results showed that the risk of all-cause mortality was significantly higher in the high-level group compared to the low-level group (Table S3). The spline analysis was adjusted based on factors such as age, gender, education level, poverty level, marital status, BMI, smoking status, drinking status, sleep disorder status, hypertension, and diabetes.PIV: platelet-immune inflammation value; NLR: neutrophil-to-lymphocyte ratio; MLR: monocyte-to-lymphocyte ratio; PLR: platelet-to-lymphocyte ratio; SII: systemic immune-inflammation index; SIRI: systemic inflammation response index; Cd: blood cadmium. Subgroup analysis The robustness of the regression results evaluating the association between blood cadmium levels combined with inflammatory indicators and mortality among cancer survivor subgroups was confirmed in Fig. 4 and Table (S1-S6). The analysis demonstrated a consistent positive relationship between elevated blood cadmium levels, inflammatory markers, and increased all-cause mortality across most demographic and clinical subgroups. Importantly, statistical analyses revealed no significant effect modification by gender, age, race, education attainment, PIR, marital status, smoking history, alcohol consumption history, hypertension, and diabetes (all P-interaction > 0.05). This supports the conclusion that the detrimental effects of cadmium exposure on mortality risk persist independently of these demographic and socioeconomic factors. Discussion This investigation provides the first systematic evaluation of the impact of toxic metal exposure, particularly blood cadmium levels, in conjunction with various hematologic inflammatory indices (including PIV, SII, PLR, MLR, NLR and SIRI) on survival outcomes in a nationally representative cohort of U.S. cancer survivors. Our results demonstrate the prognostic significance of heavy metal burden and systemic inflammation, indicating that their co-exposure is associated with significantly increased all-cause mortality risk. In multivariable-adjusted models, adjusted for potential confounders including demographic characteristics, lifestyle factors, and clinical comorbidities, the combination of high blood cadmium levels and elevated inflammatory markers was significantly associated with increased all-cause mortality. Survival curves analysis further confirmed significant survival differences between subgroups with different combinations of blood cadmium and inflammatory markers. Notably, subgroup analyses demonstrated that this joint effect was consistent across various demographic and clinical subgroups, suggesting that these associations are robust and widely applicable. The results of our study are consistent with previous literature. Prior research has established that cadmium exposure contributes to both elevated all-cause mortality in cancer population and disease progression in major chronic conditions, particularly cardiovascular and metabolic disorders[ 27 – 28 ]. While cadmium's association with mortality is established, the underlying pathophysiological mechanisms remain to be fully characterized. From a molecular mechanism perspective, studies have found that cadmium can induce mutations in key genes involved in stress response, transcriptional regulation, and translational control[ 29 ]. Moreover, cadmium may interact with the HSP90-β, potentially playing a role in maintaining malignant cell protein homeostasis, simultaneously suppressing apoptosis and replicative senescence while promoting tumor angiogenesis and activating invasive potential[ 30 ]. These mechanisms contribute to the increased mortality risk identified among the cancer survivor population in our study. Notably, cadmium exposure and inflammatory responses may interact through complex multifactorial mechanisms. Cd can impair immune system function by activating pro-inflammatory cytokines, enhancing reactive oxygen species production, disrupting cellular signaling pathways, and inducing apoptosis[ 31 – 33 ]. Preclinical data indicate that cadmium exposure dose-dependently promotes splenocyte apoptosis in murine models, suggesting that it may compromise the body's anti-cancer defense by damaging immune cells, particularly CD8 + T cells, which have a significant anti-tumor role[ 34 ]. Moreover, cadmium-induced dysregulation of CD4+/CD8 + T cell ratios in lymphoid organs compromises immune surveillance, potentially facilitating tumor growth and metastasis[ 35 ]. Blood cell-derived inflammatory markers, as part of routine blood tests, can comprehensively reflect an individual's immune and inflammatory status. These markers are based on changes in the proportions and numbers of various blood cell types, including lymphocytes, monocytes, neutrophils, and platelets. Neutrophils, as the frontline defenders of the innate immune system, can clear pathogens through the formation of neutrophil extracellular traps[ 36 ]. Lymphocytes participate in adaptive immune regulation by secreting cytokines and exhibiting cytotoxic activity[ 37 ]. As precursors of tumor-associated macrophages,monocytes contribute significantly to the formation of an immunosuppressive tumor microenvironment[ 38 ]. In recent years, composite inflammatory indices such as SII and NLR have demonstrated potential value in prognostic assessment for cancer survivors. However, current research on the synergistic effects of metal exposure, inflammatory status, and their relationship with mortality risk remains limited. Our investigation represents the first systematic evaluate the combined effect of blood cadmium levels and blood cell-derived inflammatory markers on mortality risk in cancer survivors. Comprehensive assessment of multiple risk factor combinations enables more robust evaluation of survival outcomes in cancer survivors. Our findings offer novel insights into the interplay of heavy metal toxicity, systemic inflammation, and clinical outcomes in cancer survivors. We found a synergistic effect between blood cadmium levels and hematologic inflammation indices in jointly predicting the mortality risk of cancer survivors. This finding suggests that assessing patients' levels of heavy metal exposure and inflammatory status may hold clinical value in cancer rehabilitation management, particularly in the development of personalized survival intervention strategies. Subsequent research should further focus on mechanistic exploration and develop potential intervention pathways to improve the long-term survival prognosis of cancer survivors. Strengths and limitations This research demonstrates several notable strengths. First, it employed a large, nationally U.S. sample and utilized weighted analyses to enhance the generalizability of its findings. Second, the 77-month follow-up period, combined with adjustments for demographic, lifestyle, and clinical covariates, strengthens the reliability of the results. Finally, whereas prior studies have focused on metal exposure and mortality, this study is the first to systematically analyze the combined impact of metal exposure and inflammatory markers on mortality in cancer survivors. This finding underscores the importance of considering both metal exposure and inflammatory responses in the development of interventions and support strategies for cancer survival. However, limitations should be noted. First, self-reported cancer history in NHANES may introduce recall bias. Second, the dataset lacks critical clinical parameters, including tumor stage and therapeutic interventions. Finally, the limited sample size for certain cancer subtypes constrains the ability to conduct more in-depth and detailed analyses for these specific subtypes. Conclusion Our study findings indicate that the combination of blood cadmium levels and inflammatory markers is significantly associated with the mortality risk of cancer survivors. The interaction between different levels of blood cadmium and inflammatory status jointly affects the mortality risk in cancer survivors. Based on these results, this study emphasizes the importance of avoiding cadmium exposure and regularly monitoring inflammatory markers for improving the survival outcomes of cancer survivors. As an environmental risk factor, cadmium exposure may increase the mortality risk in cancer survivors, while monitoring inflammatory markers helps detect abnormal inflammatory responses, which is critical for the health management of cancer survivors. Actively avoiding cadmium exposure and reducing inflammation levels could enhance both survival rates and quality of life in cancer survivor populations. Abbreviations Cd Cadmium CBC Blood cell count PLR Platelet-to-lymphocyte ratio SII Systemic immune-inflammation index PIV Platelet-immune inflammation value MLR Monocyte-to-lymphocyte ratio NLR Neutrophil-to-lymphocyte ratio SIRI Systemic inflammation response index NHANES National Health and Nutrition Examination Survey KM Kaplan-Meier RCS Restricted cubic splines Declarations Acknowledgements We sincerely express our gratitude for the data provided by the NHANES database. Author contributions WY: Original draft, Data curation, Formal analysis, Investigation, Methodology, Visualization, review & editing. LCC: Conceptualization, Data curation, Formal analysis, review & editing. LQQ: Investigation, Methodology, Project administration, review & editing. ZLL: Data curation, Validation, review & editing. TTY: Conceptualization, Methodology, Software, Validation, Visualization, review & editing. LY: Funding acquisition, Project administration, Resources, Supervision, review & editing. Funding This study received support by the Major Discipline Academic and Technical Leaders Training Program of Jiangxi Province (20225BCJ22001). Data availability The dataset analyzed in this study can be requested by the corresponding author for access under the condition of meeting reasonable requirements. Ethics approval and consent to participate The protocols of NHANES were approved by the institutional review board of the National Center for Health Statistics, CDC (https://www.cdc.gov/nchs/nhanes/irba98.htm). All participants signed the written informed consent form on the basis of their informed consent. Consent for publication Not applicable. Competing interests The authors declare no competing interests References Miller KD, Nogueira L, Devasia T, Mariotto AB, Yabroff KR, Jemal A, Kramer J, Siegel RL. Cancer treatment and survivorship statistics, 2022. CA Cancer J Clin. 2022;72(5):409–36. Peana M, Pelucelli A, Chasapis CT, Perlepes SP, Bekiari V, Medici S, Zoroddu MA. Biological Effects of Human Exposure to Environmental Cadmium. Biomolecules 2022, 13(1). Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229–63. Xue W, Zhang C, Huang Y, Wang C, Zhang X, Liu Z. Rice organs concentrate cadmium by chelation of amino acids containing dicarboxyl groups and enhance risks to human and environmental health in Cd-contaminated areas. J Hazard Mater. 2022;426:128130. Alshehri S, Ahmad SF, Albekairi NA, Alqarni SS, Al-Harbi NO, Al-Ayadhi LY, Attia SM, Alfardan AS, Bakheet SA, Nadeem A. Thioredoxin 1 and Thioredoxin Reductase 1 Redox System Is Dysregulated in Neutrophils of Subjects with Autism: In Vitro Effects of Environmental Toxicant, Methylmercury. Toxics 2023, 11(9). Gasmi A, Peana M, Arshad M, Butnariu M, Menzel A, Bjørklund G. Krebs cycle: activators, inhibitors and their roles in the modulation of carcinogenesis. Arch Toxicol. 2021;95(4):1161–78. Cao X, Fu M, Bi R, Zheng X, Fu B, Tian S, Liu C, Li Q, Liu J. Cadmium induced BEAS-2B cells apoptosis and mitochondria damage via MAPK signaling pathway. Chemosphere. 2021;263:128346. Wang LY, Fan RF, Yang DB, Zhang D, Wang L. Puerarin reverses cadmium-induced lysosomal dysfunction in primary rat proximal tubular cells via inhibiting Nrf2 pathway. Biochem Pharmacol. 2019;162:132–41. Qiao Z, Sun X, Fu M, Zhou S, Han Y, Zhao X, Gong K, Peng C, Zhang W, Liu F, et al. Co-exposure of decabromodiphenyl ethane and cadmium increases toxicity to earthworms: Enrichment, oxidative stress, damage and molecular binding mechanisms. J Hazard Mater. 2024;473:134684. Ijaz MU, Shahzadi S, Hamza A, Azmat R, Anwar H, Afsar T, Shafique H, Bhat MA, Naglah AM, Al-Omar MA, et al. Alleviative effects of pinostrobin against cadmium-induced renal toxicity in rats by reducing oxidative stress, apoptosis, inflammation, and mitochondrial dysfunction. Front Nutr. 2023;10:1175008. Grivennikov SI, Greten FR, Karin M. Immunity, inflammation, and cancer. Cell. 2010;140(6):883–99. Diakos CI, Charles KA, McMillan DC, Clarke SJ. Cancer-related inflammation and treatment effectiveness. Lancet Oncol. 2014;15(11):e493–503. Greten FR, Grivennikov SI. Inflammation and Cancer: Triggers, Mechanisms, and Consequences. Immunity. 2019;51(1):27–41. Kuraishy A, Karin M, Grivennikov SI. Tumor promotion via injury- and death-induced inflammation. Immunity. 2011;35(4):467–77. Grivennikov SI. Inflammation and colorectal cancer: colitis-associated neoplasia. Semin Immunopathol. 2013;35(2):229–44. Fu C, Chen J, Wang Y, Yang Y, Li X, Liu K. Association between complete blood cell count-derived inflammatory biomarkers and gallstones prevalence in American adults under 60 years of age. Front Immunol. 2024;15:1497068. Zipf G, Chiappa M, Porter KS, Ostchega Y, Lewis BG, Dostal J. National health and nutrition examination survey: plan and operations, 1999–2010. Vital Health Stat 1 2013(56):1–37. Gong P, Liu Y, Gong Y, Chen G, Zhang X, Wang S, Zhou F, Duan R, Chen W, Huang T, et al. The association of neutrophil to lymphocyte ratio, platelet to lymphocyte ratio, and lymphocyte to monocyte ratio with post-thrombolysis early neurological outcomes in patients with acute ischemic stroke. J Neuroinflammation. 2021;18(1):51. Templeton AJ, McNamara MG, Šeruga B, Vera-Badillo FE, Aneja P, Ocaña A, Leibowitz-Amit R, Sonpavde G, Knox JJ, Tran B, et al. Prognostic role of neutrophil-to-lymphocyte ratio in solid tumors: a systematic review and meta-analysis. J Natl Cancer Inst. 2014;106(6):dju124. Wang L, Li X, Liu M, Zhou H, Shao J. Association between monocyte-to-lymphocyte ratio and prostate cancer in the U.S. population: a population-based study. Front Cell Dev Biol. 2024;12:1372731. Zhao Y, Shao W, Zhu Q, Zhang R, Sun T, Wang B, Hu X. Association between systemic immune-inflammation index and metabolic syndrome and its components: results from the National Health and Nutrition Examination Survey 2011–2016. J Transl Med. 2023;21(1):691. Jin C, Li X, Luo Y, Zhang C, Zuo D. Associations between pan-immune-inflammation value and abdominal aortic calcification: a cross-sectional study. Front Immunol. 2024;15:1370516. Qi Q, Zhuang L, Shen Y, Geng Y, Yu S, Chen H, Liu L, Meng Z, Wang P, Chen Z. A novel systemic inflammation response index (SIRI) for predicting the survival of patients with pancreatic cancer after chemotherapy. Cancer. 2016;122(14):2158–67. Li G, Zhang D, Li M, Yuan F, Wang Y, Fu Y. Association between triglyceride-glucose index and hypertension in adults with cancer from NHANES 2005–2018: a cross-sectional study. BMC Cancer. 2025;25(1):993. Xie W, Liu H, Lin Q, Lian L, Liang B. Association of non-high-density lipoprotein to high-density lipoprotein ratio (NHHR) with prognosis in cancer survivors: a population-based study in the United States. Front Nutr. 2024;11:1430835. Liu H, Wang L, Chen C, Dong Z, Yu S. Association between Dietary Niacin Intake and Migraine among American Adults: National Health and Nutrition Examination Survey. Nutrients 2022, 14(15). Yan Y, Jin L, Li J, Chen G. Association of cadmium and lead exposure with mortality in cancer survivors: A prospective cohort study. Ecotoxicol Environ Saf. 2025;292:117960. Liu J, Chen K, Tang M, Mu Q, Zhang S, Li J, Liao J, Jiang X, Wang C. Oxidative stress and inflammation mediate the adverse effects of cadmium exposure on all-cause and cause-specific mortality in patients with diabetes and prediabetes. Cardiovasc Diabetol. 2025;24(1):145. Rani A, Kumar A, Lal A, Pant M. Cellular mechanisms of cadmium-induced toxicity: a review. Int J Environ Health Res. 2014;24(4):378–99. Den RB, Lu B. Heat shock protein 90 inhibition: rationale and clinical potential. Ther Adv Med Oncol. 2012;4(4):211–8. Cormet-Boyaka E, Jolivette K, Bonnegarde-Bernard A, Rennolds J, Hassan F, Mehta P, Tridandapani S, Webster-Marketon J, Boyaka PN. An NF-κB-independent and Erk1/2-dependent mechanism controls CXCL8/IL-8 responses of airway epithelial cells to cadmium. Toxicol Sci. 2012;125(2):418–29. Son YO, Wang L, Poyil P, Budhraja A, Hitron JA, Zhang Z, Lee JC, Shi X. Cadmium induces carcinogenesis in BEAS-2B cells through ROS-dependent activation of PI3K/AKT/GSK-3β/β-catenin signaling. Toxicol Appl Pharmacol. 2012;264(2):153–60. Qu F, Zheng W. Cadmium Exposure: Mechanisms and Pathways of Toxicity and Implications for Human Health. Toxics 2024, 12(6). Pathak N, Khandelwal S. Oxidative stress and apoptotic changes in murine splenocytes exposed to cadmium. Toxicology. 2006;220(1):26–36. Raskov H, Orhan A, Christensen JP, Gögenur I. Cytotoxic CD8(+) T cells in cancer and cancer immunotherapy. Br J Cancer. 2021;124(2):359–67. Brinkmann V, Reichard U, Goosmann C, Fauler B, Uhlemann Y, Weiss DS, Weinrauch Y, Zychlinsky A. Neutrophil extracellular traps kill bacteria. Science. 2004;303(5663):1532–5. Franklin RA, Liao W, Sarkar A, Kim MV, Bivona MR, Liu K, Pamer EG, Li MO. The cellular and molecular origin of tumor-associated macrophages. Science. 2014;344(6186):921–5. Cupp MA, Cariolou M, Tzoulaki I, Aune D, Evangelou E, Berlanga-Taylor AJ. Neutrophil to lymphocyte ratio and cancer prognosis: an umbrella review of systematic reviews and meta-analyses of observational studies. BMC Med. 2020;18(1):360. Additional Declarations No competing interests reported. Supplementary Files supplementaryfileTableS1S9andFigureS1S3.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 24 Oct, 2025 Reviewers agreed at journal 17 Oct, 2025 Reviewers invited by journal 31 Jul, 2025 Editor invited by journal 29 Jul, 2025 Editor assigned by journal 28 Jul, 2025 Submission checks completed at journal 28 Jul, 2025 First submitted to journal 22 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7191853","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":494697609,"identity":"b428b62a-c02a-4c2d-a9ec-be81aa87297c","order_by":0,"name":"Ya Wang","email":"","orcid":"","institution":"Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Ya","middleName":"","lastName":"Wang","suffix":""},{"id":494697611,"identity":"9f25531a-14db-4e32-a2c5-039c80389cae","order_by":1,"name":"Cancan Luo","email":"","orcid":"","institution":"Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Cancan","middleName":"","lastName":"Luo","suffix":""},{"id":494697612,"identity":"cdd5fc42-98c2-45da-a494-5e7b0a581363","order_by":2,"name":"Qingqing Luo","email":"","orcid":"","institution":"Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Qingqing","middleName":"","lastName":"Luo","suffix":""},{"id":494697613,"identity":"b746fdae-3380-44cb-8d55-6d2331f75d2d","order_by":3,"name":"Lili Zhou","email":"","orcid":"","institution":"Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Lili","middleName":"","lastName":"Zhou","suffix":""},{"id":494697614,"identity":"bad00109-4a2f-4e0f-8586-7fb4b3db9bc2","order_by":4,"name":"Tiantian Yu","email":"","orcid":"","institution":"Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Tiantian","middleName":"","lastName":"Yu","suffix":""},{"id":494697615,"identity":"ec63678f-4f96-46ff-a159-5c999a320af0","order_by":5,"name":"Li Yu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYFCCBIYPDAY2PPzMzAcgAgcIa2GcwVCRJifZzpZAipYzh40NzvMYEKfFnD35YDNvG3Niw2Gebw9+tjHI8d1IYPxcgEeLZc+zRKAWtsTGZt7thr1tDMaSNxKYpWfg0WJwI8f8MW8bT2IzM+82acY2hsQNNxLYmHnwasn/CLRFIrGNmecZSEs9EVpyGJt5zhgY8zDzsIG0JBgQ1HLmmWHjnIoEOQlmNjPJnnMShjPPPGyWxqvlePLDhjcG/3nszx9+JvGjzEae73jywc/4tIAAE5ICCSBmbCCgAajkB0Elo2AUjIJRMKIBAOYMTFuKUmbDAAAAAElFTkSuQmCC","orcid":"","institution":"Nanchang University","correspondingAuthor":true,"prefix":"","firstName":"Li","middleName":"","lastName":"Yu","suffix":""}],"badges":[],"createdAt":"2025-07-23 04:08:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7191853/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7191853/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88489153,"identity":"5472f470-d749-4127-889e-89536b37d512","added_by":"auto","created_at":"2025-08-07 04:03:08","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":310719,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow chart of inclusion and exclusion criteria of individuals\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7191853/v1/aed2d8d23fbad4bfb6b2197f.jpeg"},{"id":88489160,"identity":"bb101e76-836a-4fb0-9ec7-05cfac34783e","added_by":"auto","created_at":"2025-08-07 04:03:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":745130,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWeighted K-M curve of all-cause mortality in cancer survivors based on blood cadmium levels combined with inflammatory indicators\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGroup 1 refers to Low blood cadmium levels \u0026amp; CBC-derived inflammatory biomarkers ≤ median; Group 2 refers to Low blood cadmium levels \u0026amp; CBC-derived inflammatory biomarkers ≥ median; Group 3 refers to High blood cadmium levels \u0026amp; CBC-derived inflammatory biomarkers \u0026nbsp;≤ median; Group 4 refers to High blood cadmium levels \u0026amp; CBC-derived inflammatory biomarkers ≥ median. PIV: platelet-immune inflammation value; NLR: neutrophil-to-lymphocyte ratio; MLR: monocyte-to-lymphocyte ratio; PLR: platelet-to-lymphocyte ratio; SII: systemic immune-inflammation index; SIRI: systemic inflammation response index; Cd: blood cadmium.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7191853/v1/fea220fd270916d7b8868af0.png"},{"id":88490307,"identity":"c09050ea-18a3-4bed-9b6f-cf92940b67ee","added_by":"auto","created_at":"2025-08-07 04:11:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":389943,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe independent influence of inflammatory markers and blood cadmium levels on the all-cause mortality of cancer survivors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe spline analysis was adjusted based on factors such as age, gender, education level, poverty level, marital status, BMI, smoking status, drinking status, sleep disorder status, hypertension, and diabetes.PIV: platelet-immune inflammation value; NLR: neutrophil-to-lymphocyte ratio; MLR: monocyte-to-lymphocyte ratio; PLR: platelet-to-lymphocyte ratio; SII: systemic immune-inflammation index; SIRI: systemic inflammation response index; Cd: blood cadmium.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7191853/v1/2f14b0bd64b0142c569da8e6.png"},{"id":88489159,"identity":"848432b0-f6e9-411f-96b7-9ce73e4769a7","added_by":"auto","created_at":"2025-08-07 04:03:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1030125,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSubgroup Analysis of Joint Associations of Blood Cadmium and Inflammatory Markers with Mortality in Cancer Survivors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePIV: platelet-immune inflammation value; NLR: neutrophil-to-lymphocyte ratio; MLR: monocyte-to-lymphocyte ratio; PLR: platelet-to-lymphocyte ratio; SII: systemic immune-inflammation index; SIRI: systemic inflammation response index; Cd: blood cadmium.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7191853/v1/918865fcd6107602d1bcac8c.png"},{"id":88491191,"identity":"714b6d91-d92b-4d43-99c5-e5a6221b0656","added_by":"auto","created_at":"2025-08-07 04:19:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3591467,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7191853/v1/84b58add-691e-4c91-9c8c-1f122a477c17.pdf"},{"id":88489155,"identity":"8620ee70-858f-480d-8adc-4e85d972b005","added_by":"auto","created_at":"2025-08-07 04:03:08","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1356177,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryfileTableS1S9andFigureS1S3.docx","url":"https://assets-eu.researchsquare.com/files/rs-7191853/v1/c4e1cb7b8074828e15092685.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Joint association of cadmium exposure and inflammatory indicators with mortality in US cancer survivors","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe size of the community of cancer survivors has grown in recent years. According to future projections, this number will surge by 2040, when it is expected that the number of cancer survivors worldwide could reach 26 million[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].This trend reflects advances in medical technology and improved cancer treatments, empowering an increasing number of patients to combat cancer, achieve victory, and prolong their survival. However, the life expectancy of cancer survivors is still limited compared to that of individuals without cancer. Therefore, identifying and managing modifiable risk factors, such as physiological characteristics, living environment, and implementing personalized treatments, are extremely critical to improving mortality in cancer patients.\u003c/p\u003e\u003cp\u003eGrowing evidence has established a robust linkage between environmental heavy metal exposure and multiple detrimental health effects, prompting extensive research investigations. Cadmium(Cd), classified as a Group 1 carcinogen by the International Agency for Research on Cancer (IARC)[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], is widely distributed in the environment. Primary exposure sources include dietary intake, tobacco consumption, and industrial activities[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].The cytotoxic effects of cadmium are largely attributed to its binding to mitochondria, which interferes with mitochondrial function[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This disruption leads to the excessive generation of free radicals, which can cause DNA damage[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In addition to destabilizing the genome, cadmium also disrupts cellular homeostasis and induces apoptosis by increasing the production of reactive oxygen species (ROS) and inducing a chronic inflammatory response[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], which in turn exacerbates metabolic dysfunctions and the extent of tissue damage[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], affecting the prognosis of cancer patients.\u003c/p\u003e\u003cp\u003eCadmium toxicity plays a key role in inflammation, with cadmium having immunoregulatory effects on immune cells[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. These mechanisms are critically involved in tumor initiation, progression, and prognosis[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In the tumor microenvironment, inflammation and tissue damage trigger cell renewal and proliferation, creating a favorable space and conditions for the growth of malignant clone cells, thus promoting tumor progression[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Furthermore, inflammatory cells secrete proinflammatory cytokines, which activate epigenetic regulation in cancer cells through signaling pathways, leading to an increase in tumor cell proliferation[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].The inflammatory status of systemic circulation can be comprehensively evaluated through multiple hematological parameters derived from CBC, particularly the systemic immune-inflammation index (SII), platelet-immune inflammation value (PIV), and various cellular ratios (MLR, PLR, NLR), complemented by the systemic inflammation response index (SIRI). These biomarkers provide a multidimensional and multilayered approach to evaluating systemic immune-inflammatory status[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Although inflammatory markers have emerged as valuable prognostic tools in oncology, the comprehensive effects of cadmium exposure combined with multiple inflammatory markers on cancer patients remain insufficiently understood. A combined evaluation of cadmium exposure and inflammatory markers provides new insights into understanding the mortality of cancer survivors. Through this integrated assessment, we can more accurately identify environmental factors that impact the health of survivors, providing theoretical support for future personalized interventions.\u003c/p\u003e\u003cp\u003eThis study aims to utilize data from U.S. cancer survivors between 2007 and 2018 to explore the role of combined cadmium exposure and inflammatory markers in assessing cancer survivor mortality, providing new insights into understanding mortality rates among cancer survivors. Through this integrated assessment, we can more accurately identify environmental factors that impact survivor health, offering theoretical support for future personalized intervention strategies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThe NHANES program combined personal interviews, standardized physical examinations, and laboratory tests to comprehensively evaluate multiple health domains, including chronic diseases, nutritional status, dietary habits, and environmental exposures. The study population was selected through a multistage, stratified random sampling design to ensure national representativeness across racial/ethnic groups, age strata, genders, and socioeconomic status[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. As a comprehensive health survey, all study protocols were approved by the NCHS Institutional Review Board, with written informed consent obtained from all participants. Our study utilized publicly available data from the NHANES website for a total of 59,482 participants surveyed between 2007 and 2018. By asking participants if they had cancer and screening individuals older than 20 years, we initially identified 3,368 cancer survivors for the study. Subsequently, excluding individuals with missing data on survival outcomes, blood cadmium levels, or inflammatory biomarkers, the final analytical cohort comprised 2,450 eligible cancer survivors. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the complete participant selection flowchart.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMortality outcomes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFollow-up spanned from baseline until mortality or study closure (December 31, 2019). All-cause mortality was the primary outcome, and ICD-10 criteria were applied for cause-specific mortality classification.\u003c/p\u003e\u003cp\u003e\u003cb\u003eBlood cadmium levels\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBlood cadmium concentrations were measured by inductively coupled plasma mass spectrometry (ICP-MS). For values below the limit of detection (LOD), we imputed cadmium concentrations as LOD/\u0026radic;2, ensuring proper handling of undetectable levels and minimizing potential biases. Throughout the measurement process, strict adherence to the laboratory's established quality control protocols was maintained to ensure the accuracy and reliability of the results.\u003c/p\u003e\u003cp\u003e\u003cb\u003einflammation biomarkers\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe inflammatory biomarkers evaluated in this study were derived from CBC-related indices, calculated as follows:\u003c/p\u003e\u003cp\u003ePLR\u0026thinsp;=\u0026thinsp;Platelet count / Lymphocyte count[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e],\u003c/p\u003e\u003cp\u003eSII\u0026thinsp;=\u0026thinsp;Platelet count \u0026times; Neutrophil count / Lymphocyte count[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e],\u003c/p\u003e\u003cp\u003ePIV\u0026thinsp;=\u0026thinsp;SII \u0026times; Monocyte count[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e],\u003c/p\u003e\u003cp\u003eMLR\u0026thinsp;=\u0026thinsp;Monocyte count / Lymphocyte count[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e],\u003c/p\u003e\u003cp\u003eNLR\u0026thinsp;=\u0026thinsp;Neutrophil count / Lymphocyte count[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e],\u003c/p\u003e\u003cp\u003eSIRI\u0026thinsp;=\u0026thinsp;Neutrophil count \u0026times; Monocyte count / Lymphocyte count[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eCovariates\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe covariates included in this study encompassed demographic, socioeconomic, and anthropometric factors. Key variables included gender, age, race/ethnicity, and educational attainment; Educational attainment; Marital status was categorized as married, unmarried, or divorced/separated/widowed; Socioeconomic status was assessed using the Poverty Income Ratio (PIR), with a cutoff of 3.5 (PIR\u0026thinsp;\u0026lt;\u0026thinsp;3.5 vs. PIR\u0026thinsp;\u0026ge;\u0026thinsp;3.5) based on prior research[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e];categorized according to WHO criteria: \u0026lt;25 kg/m\u0026sup2;, 25\u0026ndash;30 kg/m\u0026sup2;, and \u0026ge;\u0026thinsp;30 kg/m\u0026sup2;. Health-related behaviors and conditions were assessed as follows: Sleep disturbances were evaluated using the question \"Difficulty sleeping or excessive sleep\" from the questionnaire, where a response of \"Not at all\" was considered no sleep disturbance, and responses of \"Nearly every day\", \"More than half the days\" or \"Several days\" were considered to indicate sleep disturbance; Diabetes mellitus was diagnosed if participants fulfilled any of the following conditions: (a) physician-diagnosed diabetes per self-report; (b) Fasting blood glucose\u0026thinsp;\u0026ge;\u0026thinsp;7.0 mmol/L; (c) HbA1c\u0026thinsp;\u0026ge;\u0026thinsp;6.5%; (d) current use of oral antidiabetic drugs or insulin; Hypertension history was defined by meeting any of the following conditions: (a) self-reported physician-confirmed diagnosis of hypertension; (b) Currently using antihypertensive medication; (c) sustained blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140/90 mmHg[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]; Alcohol consumption was classified into three mutually exclusive categories based on self-reported drinking patterns: \"Never\" (\u0026lt;\u0026thinsp;12 lifetime drinks), \"Former\" (\u0026ge;\u0026thinsp;12 drinks in a lifetime but abstinent for \u0026ge;\u0026thinsp;1 year ), and \"Current\" (past-year consumption with \u0026ge;\u0026thinsp;12 lifetime drinks); Smoking status was categorized into three groups: \"Never\" (\u0026lt;\u0026thinsp;100 lifetime cigarettes), \"Former\" (\u0026ge;\u0026thinsp;100 cigarettes with cessation), and \"Current\" (\u0026ge;\u0026thinsp;100 cigarettes with ongoing tobacco use on some days) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003e We weighted the data according to the NHANES analytical guidelines using its recommended sample weight calculation.The weights corresponding to the smallest subset of variables WTMEC2YR were selected. Missing data were handled using multiple imputation. For categorical data representation, weighted frequency distributions (%) were employed, with weighted chi-square tests evaluating group differences. Continuous measures were described using weighted quartiles and analyzed using weighted Kruskal-Wallis tests. Study participants were stratified into high- and low-cadmium groups based on median blood cadmium concentrations. CBC-derived inflammatory markers were similarly dichotomized using median cutoffs. To explore the joint effect of cadmium levels and inflammatory markers on mortality, participants were divided into four groups: low cadmium levels and inflammatory markers\u0026thinsp;\u0026le;\u0026thinsp;median, low cadmium levels and inflammatory markers\u0026thinsp;\u0026ge;\u0026thinsp;median, high cadmium levels and inflammatory markers\u0026thinsp;\u0026le;\u0026thinsp;median, and high cadmium levels and inflammatory markers\u0026thinsp;\u0026ge;\u0026thinsp;median. Survey-weighted Cox regression models were used to evaluate the associations of blood cadmium and inflammatory markers with all-cause mortality, with adjustment for potential confounders. Three regression models were established: Model 1 Unadjusted; Model 2 demographic factors (age, gender, race) and socioeconomic status (PIR, education, marital status); Additionally adjusted for lifestyle factors (BMI, sleep duration, alcohol consumption, smoking) and comorbidities (diabetes, hypertension). Survival analyses were performed using Kaplan-Meier methods with log-rank testing to compare survival in different combined groups. Additionally, restricted cubic spline (RCS)tested non-linear relationships between cadmium, inflammatory markers, and mortality. Subgroup analyses stratified by age, gender, diabetes, and hypertension were performed.\u003c/p\u003e\u003cp\u003eAll statistical analyses were conducted using R statistical software (version 4.3.2). Weighted analysis was performed using the survey package, multiple imputation was conducted with the mice package. For survival analyses, we employed the survival package to conduct Cox proportional hazards regression, Kaplan-Meier estimation, and stratified subgroup analyses. Graphical representations were generated using the survminer and ggplot2 packages. Throughout all analyses, Statistical significance was assessed using a two-tailed p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 criterion.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eBaseline characteristics of cancer survivors\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study analyzed a nationally representative sample of 2,450 cancer patient samples, which, after weighting, are representative of a population of 21,020,006 cancer survivors in the United States. Over a median follow-up of 77 months, A totally of 608 deaths were recorded. An analysis of the participant characteristics revealed that 82.02% of the patients were over 50 years old (n\u0026thinsp;=\u0026thinsp;2,093). In terms of race, 85.95% of the patients were White (n\u0026thinsp;=\u0026thinsp;1,645). The gender distribution showed that 56.86% of the patients were female (n\u0026thinsp;=\u0026thinsp;1,289). Further comparative analysis identified significant differences between cancer survivors and deceased patients across several factors. Specifically, there were statistically significant differences in age distribution, gender, race, education attainment, marital status, PIR, sleep disturbances, smoking, alcohol consumption history, hypertension, and diabetes (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Moreover, levels of inflammatory markers derived from CBC and blood cadmium levels differed between the two groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with deceased patients exhibiting significantly higher levels of both inflammatory markers and blood cadmium compared to survivors. Detailed data can be found in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline characteristics of the of cancer survivors, weighted.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;21,020,006)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLive (n\u0026thinsp;=\u0026thinsp;17,277,887)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDeath (n\u0026thinsp;=\u0026thinsp;3,742,119)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\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(years), 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=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;20 \u0026amp; \u0026lt;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3,779,526 (17.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3,692,435 (21.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e87,091 (2.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e17,240,480 (82.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13,585,452 (78.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3,655,028 (97.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\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=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9,068,027 (43.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6,986,521 (40.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2,081,506 (55.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11,951,979 (56.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10,291,366 (59.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10,291,366 (59.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRace, 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=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMexican American\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e577,372 (2.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e531,120 (3.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e46,252 (1.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Hispanic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e530,920 (2.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e494,150 (2.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e36,770 (0.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic White\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18,067,448 (85.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14,721,257 (85.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3,346,192 (89.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic Black\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,089,469 (5.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e853,829 (4.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e235,640 (6.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Race\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e754,797 (3.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e677,532 (3.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e77,264 (2.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\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=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLess than high school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7,313,225 (34.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5,500,983 (31.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,812,242 (48.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school or equivalent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6,332,333 (30.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5,301,755 (30.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,030,578 (27.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCollege or above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7,374,448 (35.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6,475,150 (37.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e899,298 (24.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePIR, 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=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10,680,539 (50.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8,109,948 (46.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2,570,591 (68.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10,339,467 (49.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9,167,940 (53.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,171,528 (31.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBMI (kg/m2)\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=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.485\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4,520,286 (21.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3,645,586 (21.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e874,701 (23.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;25 \u0026amp; \u0026lt;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5,979,469 (28.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4,863,481 (28.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,115,988 (29.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10,520,251 (50.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8,768,820 (50.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,751,430 (46.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMarital 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=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried/Living with partner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13,167,763 (62.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11,239,822 (65.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,927,942 (51.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWidowed / Divorced / Separated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7,852,243 (37.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6,038,066 (34.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,814,177 (48.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever married\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,165 (12.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e508 (17.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e621 (12.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSleepdisorder, 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=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.029\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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12,934,484 (61.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10,449,241 (60.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2,485,243 (66.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8,085,523 (38.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6,828,647 (39.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,256,876 (33.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSmoking 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=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9,986,901 (47.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8,585,488 (49.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,401,413 (37.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFormer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7,792,672 (37.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6,012,221 (34.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,780,451 (47.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3,240,433 (15.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2,680,178 (15.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e560,255 (14.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAlcohol 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=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4,390,876 (20.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2,988,895 (17.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,401,981 (37.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,392,465 (11.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,960,507 (11.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e431,958 (11.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExcessive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14,236,665 (67.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12,328,486 (71.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,908,180 (50.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHypertension ,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=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9,030,497 (42.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8,102,130 (46.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e928,368 (24.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11,989,509 (57.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9,175,758 (53.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2,813,751 (75.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiabetes, 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=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16,302,093 (77.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13,739,230 (79.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2,562,863 (68.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4,717,914 (22.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3,538,657 (20.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,179,256 (31.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePIV, IQR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e276.25 (177.60\u0026ndash;431.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e270.16 (176.18\u0026ndash;410.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e328.57 (198.09\u0026ndash;522.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNLR, IQR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.19 (1.63\u0026ndash;3.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.13 (1.57\u0026ndash;2.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.53 (1.88\u0026ndash;3.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMLR, IQR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.30 (0.23\u0026ndash;0.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.29 (0.22\u0026ndash;0.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.36 (0.27\u0026ndash;0.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePLR, IQR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e125.60 (95.45\u0026ndash;160.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e124.21 (95.29\u0026ndash;158.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e131.25 (96.67\u0026ndash;173.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.029\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSII, IQR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e501.28 (353.69\u0026ndash;723.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e490.00 (352.08\u0026ndash;704.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e556.50 (382.94\u0026ndash;834.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSIRI, IQR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.20 (0.82\u0026ndash;1.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.15 (0.79\u0026ndash;1.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.51 (1.04\u0026ndash;2.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCd, IQR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.20 (2.05\u0026ndash;5.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.02 (1.96\u0026ndash;5.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.83 (2.67\u0026ndash;5.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWBC,IQR)(\u0026times;10\u003c/b\u003e \u003csup\u003e\u003cb\u003e9\u003c/b\u003e\u003c/sup\u003e \u003cb\u003e/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.90 (5.60\u0026ndash;8.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.80 (5.60\u0026ndash;8.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.10 (5.70\u0026ndash;8.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.260\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLymphocyte ,IQR)(\u0026times;10\u003c/b\u003e \u003csup\u003e\u003cb\u003e9\u003c/b\u003e\u003c/sup\u003e \u003cb\u003e/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.90 (1.40\u0026ndash;2.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.90 (1.50\u0026ndash;2.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.60 (1.30\u0026ndash;2.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMonocyte,IQR)(\u0026times;10\u003c/b\u003e \u003csup\u003e\u003cb\u003e9\u003c/b\u003e\u003c/sup\u003e \u003cb\u003e/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.60 (0.40\u0026ndash;0.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.60 (0.40\u0026ndash;0.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.60 (0.50\u0026ndash;0.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNeutrophil,IQR)(\u0026times;10\u003c/b\u003e \u003csup\u003e\u003cb\u003e9\u003c/b\u003e\u003c/sup\u003e \u003cb\u003e/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.10 (3.20\u0026ndash;5.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.00 (3.20\u0026ndash;5.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.40 (3.40\u0026ndash;5.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePLT,IQR(\u0026times;10\u003c/b\u003e \u003csup\u003e\u003cb\u003e9\u003c/b\u003e\u003c/sup\u003e \u003cb\u003e/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e230.00 (191.00\u0026ndash;271.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e232.00 (195.00\u0026ndash;273.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e217.00 (172.00\u0026ndash;265.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ePIR, poverty income ratio; BMI: body mass index; PIV: platelet-immune inflammation value; NLR: neutrophil-to-lymphocyte ratio; MLR: monocyte-to-lymphocyte ratio; PLR: platelet-to-lymphocyte ratio; SII: systemic immune-inflammation index; SIRI: systemic inflammation response index; WBC: white blood cell count; HGB: Hemoglobin PLT: platelets; IQR, Interquartile range. P-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003cp\u003e\u003cb\u003eThe combined association of blood cadmium and inflammatory markers with mortality\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e demonstrates the synergistic impact of systemic inflammation (measured via CBC-derived markers) and blood cadmium concentrations on mortality risk among cancer survivors. The analysis reveals a pronounced elevation in mortality risk for individuals exhibiting concurrent elevations in both inflammatory markers and blood cadmium levels. To further elucidate the association between inflammatory markers, blood cadmium levels, and mortality risk in cancer patients, we conducted a weighted multivariable-adjusted Cox regression analysis. Using a reference group comprising participants with both low blood cadmium levels (\u0026lt;\u0026thinsp;median) and submedian inflammatory markers, our fully adjusted model identified the highest mortality risk in the subgroup with dual exposure to high cadmium and elevated inflammation(P for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Notably, patients with concomitant high cadmium levels and an elevated MLR demonstrated the most substantial risk increase, with an adjusted hazard ratio (HR) of 3.12 (95% CI: 2.07\u0026ndash;4.72). In the analysis of CVD-specific mortality, a significant upward trend in the mortality rate was observed in the highest exposure group compared to the lowest exposure group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, trend test P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, for cancer-specific mortality, a significant trend was only observed in individuals with both high blood cadmium exposure and a high MLR index, and this trend remained significant after multivariate adjustment (trend test P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Detailed data can be found in Tables S1 and S2.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline characteristics of the of cancer survivors.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSubgroups\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCase\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eModel I\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eModel II\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eModel III\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eHR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCd and PIV\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e581 (23.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00 (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00 (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.00 (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e542 (22.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.47(1.09\u0026ndash;1.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.42 (1.02\u0026ndash;1.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.038\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.23(0.90\u0026ndash;1.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.199\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e631 (25.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.65(1.24\u0026ndash;2.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.63(1.20\u0026ndash;2.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.55(1.14\u0026ndash;2.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e696(28.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.38(1.82\u0026ndash;3.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.02(1.52\u0026ndash;2.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.86(1.40\u0026ndash;2.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP for trend\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=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCd and PLR\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e561(22.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00 (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00 (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.00 (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e562(22.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.03 (0.72\u0026ndash;1.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.879\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.97 (0.70\u0026ndash;1.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.863\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.92(0.65\u0026ndash;1.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.661\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e690(28.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.35 (1.00-1.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.048\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.26 (0.94\u0026ndash;1.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.22 (0.89\u0026ndash;1.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.219\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e637(26.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.11 (1.54\u0026ndash;2.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.81(1.30\u0026ndash;2.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.76 (1.22\u0026ndash;2.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP for trend\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=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCd and MLR\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e566 (23.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00 (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00 (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.00 (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e557 (22.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.74(1.80\u0026ndash;4.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.53(1.65\u0026ndash;3.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.23(1.45\u0026ndash;3.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e653 (26.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.78(1.22\u0026ndash;2.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.72 (1.19\u0026ndash;2.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.73 (1.18\u0026ndash;2.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e674 (27.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.50 (3.02\u0026ndash;6.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.39 (2.54\u0026ndash;5.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.12 (2.07\u0026ndash;4.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP for trend\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=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCd and NLR\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e545 (22.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00 (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00 (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.00 (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e578 (23.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.71(1.13\u0026ndash;2.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.44(0.97\u0026ndash;2.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.28(0.84\u0026ndash;1.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e643 (26.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.58(1.06\u0026ndash;2.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.025\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.40 (0.95\u0026ndash;2.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.35 (0.90\u0026ndash;2.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e684 (27.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.07 (2.13\u0026ndash;4.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.44(1.70\u0026ndash;3.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.20 (1.48\u0026ndash;3.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP for trend\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=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCd and SII\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e555 (22.6)5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00 (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00 (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.00 (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e568 (23.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.17(0.85\u0026ndash;1.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.11(0.79\u0026ndash;1.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.563\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.96(0.69\u0026ndash;1.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.802\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e650 (26.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.48(1.08\u0026ndash;2.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.40 (1.00-1.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.31 (0.94\u0026ndash;1.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.119\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e677 (27.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.14 (1.60\u0026ndash;2.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.83(1.34\u0026ndash;2.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.70 (1.23\u0026ndash;2.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP for trend\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=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCd and SIRI\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e570 (23.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00 (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00 (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.00 (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e553 (22.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.15(1.47\u0026ndash;3.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.90(1.28\u0026ndash;2.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.68(1.13\u0026ndash;2.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e617 (25.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.61(1.20\u0026ndash;2.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.58(1.18\u0026ndash;2.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.56 (1.13\u0026ndash;2.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e710 (28.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.40(2.45\u0026ndash;4.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.66(1.90\u0026ndash;3.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.45 (1.70\u0026ndash;3.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP for trend\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=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eModel I: non-adjusted; Model II: adjusted for age, gender, race, PIR, education level, marital status; Model III: further adjusted for BMI, sleep disorder, smoking status, alcohol status, Hypertension, Diabetes.\u003c/p\u003e\u003cp\u003eGroup 1 refers to Low blood cadmium levels \u0026amp; CBC-derived inflammatory biomarkers\u0026thinsp;\u0026le;\u0026thinsp;median; Group 2 refers to Low blood cadmium levels \u0026amp; CBC-derived inflammatory biomarkers\u0026thinsp;\u0026ge;\u0026thinsp;median; Group 3 refers to High blood cadmium levels \u0026amp; CBC-derived inflammatory biomarkers\u0026thinsp;\u0026le;\u0026thinsp;median; Group 4 refers to High blood cadmium levels \u0026amp; CBC-derived inflammatory biomarkers\u0026thinsp;\u0026ge;\u0026thinsp;median. PIV: platelet-immune inflammation value; NLR: neutrophil-to-lymphocyte ratio; MLR: monocyte-to-lymphocyte ratio; PLR: platelet-to-lymphocyte ratio; SII: systemic immune-inflammation index; SIRI: systemic inflammation response index; Cd: blood cadmium.\u003c/p\u003e\u003cp\u003eThe weighted Kaplan-Meier analysis showed significant survival differences among groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), with Group 4 (high cadmium and high inflammation) demonstrating markedly worse survival than Group 1 (low cadmium and low inflammation). Similar results were obtained for the rates of CVD mortality and cancer-specific mortality (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Figures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, S2).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eGroup 1 refers to Low blood cadmium levels \u0026amp; CBC-derived inflammatory biomarkers\u0026thinsp;\u0026le;\u0026thinsp;median; Group 2 refers to Low blood cadmium levels \u0026amp; CBC-derived inflammatory biomarkers\u0026thinsp;\u0026ge;\u0026thinsp;median; Group 3 refers to High blood cadmium levels \u0026amp; CBC-derived inflammatory biomarkers\u0026thinsp;\u0026le;\u0026thinsp;median; Group 4 refers to High blood cadmium levels \u0026amp; CBC-derived inflammatory biomarkers\u0026thinsp;\u0026ge;\u0026thinsp;median. PIV: platelet-immune inflammation value; NLR: neutrophil-to-lymphocyte ratio; MLR: monocyte-to-lymphocyte ratio; PLR: platelet-to-lymphocyte ratio; SII: systemic immune-inflammation index; SIRI: systemic inflammation response index; Cd: blood cadmium.\u003c/p\u003e\u003cp\u003e\u003cb\u003eIndependent effects between blood cadmium or inflammatory markers and mortality\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo examine the potential exposure-response relationships between blood cadmium concentrations, CBC-derived inflammatory biomarkers, and all-cause mortality, we performed RCS regression analyses. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Figure S3, After adjusting for potential confounders, the RCS analysis revealed that blood cadmium levels were nonlinearly associated with all-cause mortality (P for overall\u0026thinsp;\u0026lt;\u0026thinsp;0.001; nonlinearity test p\u0026thinsp;=\u0026thinsp;0.001). The HR shows a gentle trend at low Cd concentrations but escalates rapidly after crossing a critical threshold. Among the inflammation indicators, PIV, MLR and NLR demonstrated a substantial linear positive correlation with all-cause mortality (P for overall\u0026thinsp;\u0026lt;\u0026thinsp;0.001; nonlinearity test p\u0026thinsp;=\u0026thinsp;0.001), whereas PLR, SII and SIRI showed a nonlinear correlation (P for overall\u0026thinsp;\u0026lt;\u0026thinsp;0.001; nonlinearity test p\u0026thinsp;=\u0026thinsp;0.001).We performed a Cox regression analysis, adjusting for weighted multivariable factors, to assess the independent effects of seven indicators on all-cause mortality in cancer survivors. The median blood cadmium level and CBC-derived inflammatory markers were used as cutoffs. The results showed that the risk of all-cause mortality was significantly higher in the high-level group compared to the low-level group (Table S3).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe spline analysis was adjusted based on factors such as age, gender, education level, poverty level, marital status, BMI, smoking status, drinking status, sleep disorder status, hypertension, and diabetes.PIV: platelet-immune inflammation value; NLR: neutrophil-to-lymphocyte ratio; MLR: monocyte-to-lymphocyte ratio; PLR: platelet-to-lymphocyte ratio; SII: systemic immune-inflammation index; SIRI: systemic inflammation response index; Cd: blood cadmium.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSubgroup analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe robustness of the regression results evaluating the association between blood cadmium levels combined with inflammatory indicators and mortality among cancer survivor subgroups was confirmed in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Table (S1-S6). The analysis demonstrated a consistent positive relationship between elevated blood cadmium levels, inflammatory markers, and increased all-cause mortality across most demographic and clinical subgroups. Importantly, statistical analyses revealed no significant effect modification by gender, age, race, education attainment, PIR, marital status, smoking history, alcohol consumption history, hypertension, and diabetes (all P-interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05). This supports the conclusion that the detrimental effects of cadmium exposure on mortality risk persist independently of these demographic and socioeconomic factors.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis investigation provides the first systematic evaluation of the impact of toxic metal exposure, particularly blood cadmium levels, in conjunction with various hematologic inflammatory indices (including PIV, SII, PLR, MLR, NLR and SIRI) on survival outcomes in a nationally representative cohort of U.S. cancer survivors. Our results demonstrate the prognostic significance of heavy metal burden and systemic inflammation, indicating that their co-exposure is associated with significantly increased all-cause mortality risk. In multivariable-adjusted models, adjusted for potential confounders including demographic characteristics, lifestyle factors, and clinical comorbidities, the combination of high blood cadmium levels and elevated inflammatory markers was significantly associated with increased all-cause mortality. Survival curves analysis further confirmed significant survival differences between subgroups with different combinations of blood cadmium and inflammatory markers. Notably, subgroup analyses demonstrated that this joint effect was consistent across various demographic and clinical subgroups, suggesting that these associations are robust and widely applicable.\u003c/p\u003e\u003cp\u003eThe results of our study are consistent with previous literature. Prior research has established that cadmium exposure contributes to both elevated all-cause mortality in cancer population and disease progression in major chronic conditions, particularly cardiovascular and metabolic disorders[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. While cadmium's association with mortality is established, the underlying pathophysiological mechanisms remain to be fully characterized. From a molecular mechanism perspective, studies have found that cadmium can induce mutations in key genes involved in stress response, transcriptional regulation, and translational control[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Moreover, cadmium may interact with the HSP90-β, potentially playing a role in maintaining malignant cell protein homeostasis, simultaneously suppressing apoptosis and replicative senescence while promoting tumor angiogenesis and activating invasive potential[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. These mechanisms contribute to the increased mortality risk identified among the cancer survivor population in our study.\u003c/p\u003e\u003cp\u003eNotably, cadmium exposure and inflammatory responses may interact through complex multifactorial mechanisms. Cd can impair immune system function by activating pro-inflammatory cytokines, enhancing reactive oxygen species production, disrupting cellular signaling pathways, and inducing apoptosis[\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Preclinical data indicate that cadmium exposure dose-dependently promotes splenocyte apoptosis in murine models, suggesting that it may compromise the body's anti-cancer defense by damaging immune cells, particularly CD8\u0026thinsp;+\u0026thinsp;T cells, which have a significant anti-tumor role[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Moreover, cadmium-induced dysregulation of CD4+/CD8\u0026thinsp;+\u0026thinsp;T cell ratios in lymphoid organs compromises immune surveillance, potentially facilitating tumor growth and metastasis[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBlood cell-derived inflammatory markers, as part of routine blood tests, can comprehensively reflect an individual's immune and inflammatory status. These markers are based on changes in the proportions and numbers of various blood cell types, including lymphocytes, monocytes, neutrophils, and platelets. Neutrophils, as the frontline defenders of the innate immune system, can clear pathogens through the formation of neutrophil extracellular traps[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Lymphocytes participate in adaptive immune regulation by secreting cytokines and exhibiting cytotoxic activity[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. As precursors of tumor-associated macrophages,monocytes contribute significantly to the formation of an immunosuppressive tumor microenvironment[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In recent years, composite inflammatory indices such as SII and NLR have demonstrated potential value in prognostic assessment for cancer survivors. However, current research on the synergistic effects of metal exposure, inflammatory status, and their relationship with mortality risk remains limited. Our investigation represents the first systematic evaluate the combined effect of blood cadmium levels and blood cell-derived inflammatory markers on mortality risk in cancer survivors. Comprehensive assessment of multiple risk factor combinations enables more robust evaluation of survival outcomes in cancer survivors.\u003c/p\u003e\u003cp\u003eOur findings offer novel insights into the interplay of heavy metal toxicity, systemic inflammation, and clinical outcomes in cancer survivors. We found a synergistic effect between blood cadmium levels and hematologic inflammation indices in jointly predicting the mortality risk of cancer survivors. This finding suggests that assessing patients' levels of heavy metal exposure and inflammatory status may hold clinical value in cancer rehabilitation management, particularly in the development of personalized survival intervention strategies. Subsequent research should further focus on mechanistic exploration and develop potential intervention pathways to improve the long-term survival prognosis of cancer survivors.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStrengths and limitations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis research demonstrates several notable strengths. First, it employed a large, nationally U.S. sample and utilized weighted analyses to enhance the generalizability of its findings. Second, the 77-month follow-up period, combined with adjustments for demographic, lifestyle, and clinical covariates, strengthens the reliability of the results. Finally, whereas prior studies have focused on metal exposure and mortality, this study is the first to systematically analyze the combined impact of metal exposure and inflammatory markers on mortality in cancer survivors. This finding underscores the importance of considering both metal exposure and inflammatory responses in the development of interventions and support strategies for cancer survival.\u003c/p\u003e\u003cp\u003eHowever, limitations should be noted. First, self-reported cancer history in NHANES may introduce recall bias. Second, the dataset lacks critical clinical parameters, including tumor stage and therapeutic interventions. Finally, the limited sample size for certain cancer subtypes constrains the ability to conduct more in-depth and detailed analyses for these specific subtypes.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study findings indicate that the combination of blood cadmium levels and inflammatory markers is significantly associated with the mortality risk of cancer survivors. The interaction between different levels of blood cadmium and inflammatory status jointly affects the mortality risk in cancer survivors. Based on these results, this study emphasizes the importance of avoiding cadmium exposure and regularly monitoring inflammatory markers for improving the survival outcomes of cancer survivors. As an environmental risk factor, cadmium exposure may increase the mortality risk in cancer survivors, while monitoring inflammatory markers helps detect abnormal inflammatory responses, which is critical for the health management of cancer survivors. Actively avoiding cadmium exposure and reducing inflammation levels could enhance both survival rates and quality of life in cancer survivor populations.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCd\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCadmium\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCBC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBlood cell count\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePLR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePlatelet-to-lymphocyte ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSII\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSystemic immune-inflammation index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePIV\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePlatelet-immune inflammation value\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMLR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMonocyte-to-lymphocyte ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNLR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNeutrophil-to-lymphocyte ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSIRI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSystemic inflammation response index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNHANES\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNational Health and Nutrition Examination Survey\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eKM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eKaplan-Meier\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 splines\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely express our gratitude for the data provided by the NHANES database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWY: Original draft, Data curation, Formal analysis, Investigation, Methodology, Visualization, review \u0026amp; editing. LCC: Conceptualization, Data curation, Formal analysis, review \u0026amp; editing. LQQ: Investigation, Methodology, Project administration, review \u0026amp; editing. ZLL: Data curation, Validation, review \u0026amp; editing. TTY: Conceptualization, Methodology, Software, Validation, Visualization, review \u0026amp; editing. LY: Funding acquisition, Project administration, Resources, Supervision, \u0026nbsp;review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received support\u0026nbsp;by the Major Discipline Academic and Technical Leaders Training Program of Jiangxi Province (20225BCJ22001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset analyzed in this study can be requested by the corresponding author for access under the condition of meeting reasonable requirements.\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate The protocols of NHANES were approved by the institutional review board\u0026nbsp;of the National Center for Health Statistics, CDC (https://www.cdc.gov/nchs/nhanes/irba98.htm). All participants signed the written informed consent form on the basis of their informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMiller KD, Nogueira L, Devasia T, Mariotto AB, Yabroff KR, Jemal A, Kramer J, Siegel RL. Cancer treatment and survivorship statistics, 2022. CA Cancer J Clin. 2022;72(5):409\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePeana M, Pelucelli A, Chasapis CT, Perlepes SP, Bekiari V, Medici S, Zoroddu MA. Biological Effects of Human Exposure to Environmental Cadmium. Biomolecules 2022, 13(1).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXue W, Zhang C, Huang Y, Wang C, Zhang X, Liu Z. Rice organs concentrate cadmium by chelation of amino acids containing dicarboxyl groups and enhance risks to human and environmental health in Cd-contaminated areas. J Hazard Mater. 2022;426:128130.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlshehri S, Ahmad SF, Albekairi NA, Alqarni SS, Al-Harbi NO, Al-Ayadhi LY, Attia SM, Alfardan AS, Bakheet SA, Nadeem A. Thioredoxin 1 and Thioredoxin Reductase 1 Redox System Is Dysregulated in Neutrophils of Subjects with Autism: In Vitro Effects of Environmental Toxicant, Methylmercury. \u003cem\u003eToxics\u003c/em\u003e 2023, 11(9).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGasmi A, Peana M, Arshad M, Butnariu M, Menzel A, Bj\u0026oslash;rklund G. Krebs cycle: activators, inhibitors and their roles in the modulation of carcinogenesis. Arch Toxicol. 2021;95(4):1161\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCao X, Fu M, Bi R, Zheng X, Fu B, Tian S, Liu C, Li Q, Liu J. Cadmium induced BEAS-2B cells apoptosis and mitochondria damage via MAPK signaling pathway. Chemosphere. 2021;263:128346.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang LY, Fan RF, Yang DB, Zhang D, Wang L. Puerarin reverses cadmium-induced lysosomal dysfunction in primary rat proximal tubular cells via inhibiting Nrf2 pathway. Biochem Pharmacol. 2019;162:132\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQiao Z, Sun X, Fu M, Zhou S, Han Y, Zhao X, Gong K, Peng C, Zhang W, Liu F, et al. Co-exposure of decabromodiphenyl ethane and cadmium increases toxicity to earthworms: Enrichment, oxidative stress, damage and molecular binding mechanisms. J Hazard Mater. 2024;473:134684.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIjaz MU, Shahzadi S, Hamza A, Azmat R, Anwar H, Afsar T, Shafique H, Bhat MA, Naglah AM, Al-Omar MA, et al. Alleviative effects of pinostrobin against cadmium-induced renal toxicity in rats by reducing oxidative stress, apoptosis, inflammation, and mitochondrial dysfunction. Front Nutr. 2023;10:1175008.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGrivennikov SI, Greten FR, Karin M. Immunity, inflammation, and cancer. Cell. 2010;140(6):883\u0026ndash;99.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDiakos CI, Charles KA, McMillan DC, Clarke SJ. Cancer-related inflammation and treatment effectiveness. Lancet Oncol. 2014;15(11):e493\u0026ndash;503.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGreten FR, Grivennikov SI. Inflammation and Cancer: Triggers, Mechanisms, and Consequences. Immunity. 2019;51(1):27\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKuraishy A, Karin M, Grivennikov SI. Tumor promotion via injury- and death-induced inflammation. Immunity. 2011;35(4):467\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGrivennikov SI. Inflammation and colorectal cancer: colitis-associated neoplasia. Semin Immunopathol. 2013;35(2):229\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFu C, Chen J, Wang Y, Yang Y, Li X, Liu K. Association between complete blood cell count-derived inflammatory biomarkers and gallstones prevalence in American adults under 60 years of age. Front Immunol. 2024;15:1497068.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZipf G, Chiappa M, Porter KS, Ostchega Y, Lewis BG, Dostal J. National health and nutrition examination survey: plan and operations, 1999\u0026ndash;2010. Vital Health Stat \u003cem\u003e1\u003c/em\u003e 2013(56):1\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGong P, Liu Y, Gong Y, Chen G, Zhang X, Wang S, Zhou F, Duan R, Chen W, Huang T, et al. The association of neutrophil to lymphocyte ratio, platelet to lymphocyte ratio, and lymphocyte to monocyte ratio with post-thrombolysis early neurological outcomes in patients with acute ischemic stroke. J Neuroinflammation. 2021;18(1):51.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTempleton AJ, McNamara MG, Šeruga B, Vera-Badillo FE, Aneja P, Oca\u0026ntilde;a A, Leibowitz-Amit R, Sonpavde G, Knox JJ, Tran B, et al. Prognostic role of neutrophil-to-lymphocyte ratio in solid tumors: a systematic review and meta-analysis. J Natl Cancer Inst. 2014;106(6):dju124.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang L, Li X, Liu M, Zhou H, Shao J. Association between monocyte-to-lymphocyte ratio and prostate cancer in the U.S. population: a population-based study. Front Cell Dev Biol. 2024;12:1372731.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhao Y, Shao W, Zhu Q, Zhang R, Sun T, Wang B, Hu X. Association between systemic immune-inflammation index and metabolic syndrome and its components: results from the National Health and Nutrition Examination Survey 2011\u0026ndash;2016. J Transl Med. 2023;21(1):691.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJin C, Li X, Luo Y, Zhang C, Zuo D. Associations between pan-immune-inflammation value and abdominal aortic calcification: a cross-sectional study. Front Immunol. 2024;15:1370516.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQi Q, Zhuang L, Shen Y, Geng Y, Yu S, Chen H, Liu L, Meng Z, Wang P, Chen Z. A novel systemic inflammation response index (SIRI) for predicting the survival of patients with pancreatic cancer after chemotherapy. Cancer. 2016;122(14):2158\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi G, Zhang D, Li M, Yuan F, Wang Y, Fu Y. Association between triglyceride-glucose index and hypertension in adults with cancer from NHANES 2005\u0026ndash;2018: a cross-sectional study. BMC Cancer. 2025;25(1):993.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXie W, Liu H, Lin Q, Lian L, Liang B. Association of non-high-density lipoprotein to high-density lipoprotein ratio (NHHR) with prognosis in cancer survivors: a population-based study in the United States. Front Nutr. 2024;11:1430835.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu H, Wang L, Chen C, Dong Z, Yu S. Association between Dietary Niacin Intake and Migraine among American Adults: National Health and Nutrition Examination Survey. Nutrients 2022, 14(15).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYan Y, Jin L, Li J, Chen G. Association of cadmium and lead exposure with mortality in cancer survivors: A prospective cohort study. Ecotoxicol Environ Saf. 2025;292:117960.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu J, Chen K, Tang M, Mu Q, Zhang S, Li J, Liao J, Jiang X, Wang C. Oxidative stress and inflammation mediate the adverse effects of cadmium exposure on all-cause and cause-specific mortality in patients with diabetes and prediabetes. Cardiovasc Diabetol. 2025;24(1):145.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRani A, Kumar A, Lal A, Pant M. Cellular mechanisms of cadmium-induced toxicity: a review. Int J Environ Health Res. 2014;24(4):378\u0026ndash;99.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDen RB, Lu B. Heat shock protein 90 inhibition: rationale and clinical potential. Ther Adv Med Oncol. 2012;4(4):211\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCormet-Boyaka E, Jolivette K, Bonnegarde-Bernard A, Rennolds J, Hassan F, Mehta P, Tridandapani S, Webster-Marketon J, Boyaka PN. An NF-κB-independent and Erk1/2-dependent mechanism controls CXCL8/IL-8 responses of airway epithelial cells to cadmium. Toxicol Sci. 2012;125(2):418\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSon YO, Wang L, Poyil P, Budhraja A, Hitron JA, Zhang Z, Lee JC, Shi X. Cadmium induces carcinogenesis in BEAS-2B cells through ROS-dependent activation of PI3K/AKT/GSK-3β/β-catenin signaling. Toxicol Appl Pharmacol. 2012;264(2):153\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQu F, Zheng W. Cadmium Exposure: Mechanisms and Pathways of Toxicity and Implications for Human Health. Toxics 2024, 12(6).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePathak N, Khandelwal S. Oxidative stress and apoptotic changes in murine splenocytes exposed to cadmium. Toxicology. 2006;220(1):26\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRaskov H, Orhan A, Christensen JP, G\u0026ouml;genur I. Cytotoxic CD8(+) T cells in cancer and cancer immunotherapy. Br J Cancer. 2021;124(2):359\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrinkmann V, Reichard U, Goosmann C, Fauler B, Uhlemann Y, Weiss DS, Weinrauch Y, Zychlinsky A. Neutrophil extracellular traps kill bacteria. Science. 2004;303(5663):1532\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFranklin RA, Liao W, Sarkar A, Kim MV, Bivona MR, Liu K, Pamer EG, Li MO. The cellular and molecular origin of tumor-associated macrophages. Science. 2014;344(6186):921\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCupp MA, Cariolou M, Tzoulaki I, Aune D, Evangelou E, Berlanga-Taylor AJ. Neutrophil to lymphocyte ratio and cancer prognosis: an umbrella review of systematic reviews and meta-analyses of observational studies. BMC Med. 2020;18(1):360.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cadmium exposure, CBC-derived inflammation markers, Cancer survivors, Mortality, NHANES","lastPublishedDoi":"10.21203/rs.3.rs-7191853/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7191853/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eCadmium (Cd) exposure and high inflammation status are independent risk factors for mortality in cancer survivors, yet their combined impact on mortality remains unexplored. The objective of this study was to investigate joint effect of cadmium exposure and complete blood cell count (CBC)-derived inflammation markers on mortality in cancer survivors.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis study utilized information collected from the National Health and Nutrition Examination Survey (NHANES) spanning the years 2007 to 2018, with a participant pool of 2,450 individuals who had survived cancer. Blood cadmium levels and inflammation markers derived from CBC were systematically evaluated. We used weighted multivariable Cox regression models to evaluate the joint effect of blood cadmium levels and inflammation markers on all-cause mortality. Survival probabilities under different exposure conditions were compared using Kaplan-Meier curves. To investigate the independent effects of blood cadmium levels and inflammatory indicators on mortality, restricted cubic spline curve (RCS) modeling was used. Additionally, subgroup analyses were further conducted by age, sex, hypertension status, diabetes, smoking history, alcohol consumption, and other factors to explore potential interactions across different populations.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eOver a median follow-up period of 77 months, a total of 608 deaths were documented. The results showed that the combined exposure to elevated cadmium levels and high inflammatory markers was significantly associated with increased mortality risk. Notably, the highest mortality risk was observed in participants with both high cadmium levels and elevated MLR (hazard ratio [HR]\u0026thinsp;=\u0026thinsp;3.12; 95% confidence interval [CI],2.07\u0026ndash;4.72). Kaplan-Meier curves further demonstrated the poorest survival outcomes in subgroups with concurrent high cadmium exposure and elevated inflammatory indices. RCS analysis revealed significant linear associations between several inflammatory markers (including PIV, MLR, NLR) and all-cause mortality, whereas PLR, SII, SIRI, and blood cadmium levels exhibited nonlinear relationships with all-cause mortality. These findings were consistently supported by subgroup analyses.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis study represents the first systematic investigation evaluating the combined impact of cadmium exposure and inflammatory markers on mortality risk among cancer survivors. Our findings suggest that the joint effect of cadmium exposure and systemic inflammation significantly impacts survival outcomes in this population, providing novel perspectives for personalized interventions in this population.\u003c/p\u003e","manuscriptTitle":"Joint association of cadmium exposure and inflammatory indicators with mortality in US cancer survivors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-07 04:03:03","doi":"10.21203/rs.3.rs-7191853/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-10-24T11:05:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"256908438013571820017688872641953351658","date":"2025-10-17T10:23:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-31T13:33:37+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-29T09:25:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-28T09:30:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-28T09:29:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2025-07-23T03:57:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"370cf8d2-8bb2-42a0-972b-4e9d3f7053fc","owner":[],"postedDate":"August 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-08-07T04:03:03+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-07 04:03:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7191853","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7191853","identity":"rs-7191853","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}