Metal Mixture-Related Inflammatory Burden and Stroke Risk: A NHANES-Based Mediation Analysis via Central Adiposity

Metal Mixture-Related Inflammatory Burden and Stroke Risk: A NHANES-Based Mediation Analysis via Central Adiposity | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Metal Mixture-Related Inflammatory Burden and Stroke Risk: A NHANES-Based Mediation Analysis via Central Adiposity Keyi Ren, Yang Liu, Jianguo Zhou, Bin Wang, Yu Zhang, Hongquan Yu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6950019/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 Growing evidence suggests that exposure to heavy metal mixtures may contribute to increased risk of stroke and premature mortality, potentially through inflammation-mediated mechanisms. The Metal Mixture Inflammatory Index (MMII) has recently been proposed as a composite metric to quantify the systemic inflammatory potential of metal co-exposure. However, the relationship between MMII and cerebrovascular outcomes, and the potential mediating role of central adiposity measured by the weight-adjusted waist index (WWI), remains unclear. Methods We analyzed data from 11,563 adults in the U.S. National Health and Nutrition Examination Survey (NHANES, 2005–2018). MMII was calculated by standardizing and averaging the concentrations of nine urinary heavy metals. WWI was computed as waist circumference divided by the square root of weight. Multivariable logistic and Cox regression models were used to assess the associations of MMII and WWI with stroke and all-cause mortality. Restricted cubic spline (RCS) models evaluated dose–response patterns, and mediation analysis was performed to assess the indirect effect of WWI on the MMII–stroke relationship. Results Higher MMII and WWI were independently associated with increased odds of stroke and risk of all-cause mortality after adjusting for demographic and clinical covariates. The associations were approximately linear in RCS models. Subgroup analyses confirmed robustness across various strata. Mediation analysis revealed that WWI explained 9.87% of the association between MMII and stroke (indirect effect: 3.09 × 10⁻³, P < 0.001). Conclusion This study provides evidence that MMII is positively associated with stroke and mortality risk, and that WWI partially mediates the relationship between MMII and stroke. These findings highlight the interplay between environmental exposure and central adiposity in shaping cerebrovascular risk and support the utility of MMII and WWI as informative risk indicators in population health research. Health sciences/Cardiology Health sciences/Risk factors Health sciences/Health care/Public health Metal Mixture Inflammatory Index weight-adjusted waist index stroke all-cause mortality NHANES mediation analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Stroke remains a leading cause of morbidity and mortality worldwide, contributing substantially to the global burden of cardiovascular disease 1 – 3 . In parallel, exposure to environmental toxicants—particularly heavy metals—has emerged as a significant and modifiable risk factor for cardiometabolic and cerebrovascular disorders 4 , 5 . Widespread industrialization and environmental contamination have led to ubiquitous human exposure to toxic metals such as arsenic, cadmium, and lead, which are known to induce oxidative stress, endothelial dysfunction, and systemic inflammation 6 , 7 . These mechanisms are increasingly recognized as key contributors to stroke pathogenesis and premature mortality 8 , yet the long-term population-level impacts of chronic low-level metal exposure remain poorly defined. Although individual metals have been linked to adverse cardiovascular outcomes in previous studies 9 – 12 , real-world exposure typically involves complex mixtures of multiple co-occurring toxicants. This has prompted the development of novel indices to capture the cumulative biological burden of metal mixtures. One such metric—the Metal Mixture Inflammatory Index (MMII)—has recently been proposed to quantify the systemic inflammatory potential of combined metal exposures 13 . However, epidemiologic evidence on the association between MMII and major health outcomes remains scarce. Furthermore, emerging data suggest that changes in body fat distribution may serve as a mediating pathway linking metal exposure to stroke risk 14 , 15 , yet few studies have evaluated this potential pathway using mediation analysis or body shape-specific metrics such as the weight-adjusted waist index (WWI), which may better capture central adiposity than conventional measures like BMI. To address these gaps, we leveraged nationally representative data from the U.S. National Health and Nutrition Examination Survey (NHANES) from 2005 to 2018 to investigate the associations of MMII with stroke and all-cause mortality. We further explored whether WWI mediates the relationship between MMII and stroke, and conducted stratified, nonlinear, and sensitivity analyses to assess robustness. By integrating mixture-based exposure metrics with anthropometric and clinical data, this study aims to provide new insights into the pathophysiological links between environmental metal exposure, adiposity distribution, and cerebrovascular outcomes. Materials and methods Study Population This study utilized data from the NHANES, covering the years 2005 to 2018. NHANES is an ongoing, nationally representative, cross-sectional program aimed at evaluating the health and nutritional profiles of non-institutionalized U.S. residents. It adopts a multistage, stratified, and probability-based sampling strategy. Detailed protocols and design information are accessible on the official NHANES website ( https://www.cdc.gov/nchs/nhanes/ ) 16 . A total of 70,190 individuals participated across the 2005–2018 cycles. We excluded those under 20 years of age or pregnant at the time of their physical examination (n = 31,152), resulting in 39,038 eligible adults. Additional exclusions included individuals lacking data necessary for MMII computation (n = 26,881), and those with incomplete stroke or WWI information (n = 594). The final study population consisted of 11,563 participants (Fig. 1 ). All participants provided informed consent, and the study protocol was reviewed and approved by the Ethics Review Board of the National Center for Health Statistics (NCHS). Exposure Assessment: MMII Urinary concentrations of selected heavy metals were used to construct the MMII. Specifically, nine metals measured in urine were included: arsenic, cadmium, lead, antimony, barium, cesium, molybdenum, thallium, and tungsten. Urinary metal levels were adjusted for urinary creatinine and log-transformed to normalize their distributions. For each metal, Z-scores were calculated to standardize values across participants. The MMII was then derived by calculating the arithmetic mean of the standardized Z-scores of the nine metals. This composite index reflects the overall burden of systemic exposure to metal mixtures. Higher MMII values represent a greater degree of metal-induced pro-inflammatory potential. This approach was informed by previous studies assessing co-exposure to multiple toxicants using additive or weighted combination indices 13 . Urinary metal measurements were conducted by the CDC’s Division of Laboratory Sciences using inductively coupled plasma mass spectrometry (ICP-MS) or dynamic reaction cell mass spectrometry (ICP-DRC-MS), with detailed quality assurance procedures available in the NHANES Laboratory Procedures Manual. Outcome Definition Stroke Stroke status was determined based on self-reported questionnaire data. Participants were classified as having a history of stroke if they answered “yes” to the Medical Conditions Questionnaire item MCQ160f, which asked, “Has a doctor or other health professional ever told you that you had a stroke?” This method has been widely used in previous epidemiological studies utilizing NHANES data. Participants with missing responses to this item were excluded from stroke-related analyses. Mortality Mortality status was ascertained by linking NHANES participants to the National Death Index (NDI) through December 31, 2019. The primary endpoint was all-cause mortality. Cardiovascular mortality was also examined as a secondary endpoint, defined using ICD-10 codes I00–I09, I11, I13, and I20–I51 as the underlying cause of death. Time-to-event data were calculated from the NHANES interview date to the date of death or censoring (December 31, 2019), whichever came first. Mortality linkage files and variable definitions followed the guidelines issued by the NCHS. Covariates A comprehensive set of covariates was included in the multivariable models to control for potential confounding factors. All covariates were selected a priori based on their established associations with metal exposure, stroke, adiposity, and mortality, as well as their availability and completeness in the NHANES dataset. Demographic variables included age (categorized as 20–40, 41–60, > 60 years), sex (male/female), race/ethnicity (Non-Hispanic White, Non-Hispanic Black, Mexican American, Other), educational level (below high school vs. high school or above), marital status (married/living with partner vs. not), and poverty income ratio (PIR), dichotomized as poor (< 1.3) vs. not poor (≥ 1.3). Clinical comorbidities were defined as follows: Hypertension: defined by a self-reported diagnosis, use of antihypertensive medications, or measured systolic blood pressure ≥ 140 mmHg or diastolic blood pressure ≥ 90 mmHg 17 . Diabetes: defined by self-reported history, HbA1c ≥ 6.5%, or fasting glucose ≥ 126 mg/dL 18 . Hyperlipidemia was classified as meeting any of the following criteria: serum triglyceride levels ≥ 150 mg/dL, total cholesterol ≥ 200 mg/dL, LDL cholesterol ≥ 130 mg/dL, HDL cholesterol < 40 mg/dL in men or < 50 mg/dL in women, or current use of lipid-lowering agents 19 . All covariates were included in adjusted models unless otherwise specified. The definitions and coding procedures followed the NHANES analytic guidelines and are detailed in Supplementary Table S1 . WWI Calculation The WWI was calculated as an anthropometric indicator of central adiposity, independent of overall body weight. WWI was computed using the following formula: \(\:\mathbf{W}\mathbf{W}\mathbf{I}=\:\frac{\mathbf{W}\mathbf{a}\mathbf{i}\mathbf{s}\mathbf{t}\:\mathbf{c}\mathbf{i}\mathbf{r}\mathbf{c}\mathbf{u}\mathbf{m}\mathbf{f}\mathbf{e}\mathbf{r}\mathbf{e}\mathbf{n}\mathbf{c}\mathbf{e}\:\left(\mathbf{c}\mathbf{m}\right)}{\sqrt{\mathbf{W}\mathbf{e}\mathbf{i}\mathbf{g}\mathbf{h}\mathbf{t}\:\left(\mathbf{k}\mathbf{g}\right)}}\) Waist circumference and weight were measured during the physical examination component of NHANES using standardized procedures. Compared with traditional indices such as BMI or waist-to-height ratio (WHtR), WWI has been shown to more accurately reflect fat distribution, particularly visceral adiposity. Participants were further classified into tertiles based on WWI distribution to facilitate categorical analyses. Higher WWI values indicate greater central fat accumulation relative to body mass. Statistical Analysis Baseline Characteristics Analysis Baseline demographic and clinical characteristics were analyzed according to stroke history. Continuous variables were summarized as weighted means with corresponding standard errors (SEs), and differences were assessed using survey-weighted Student’s t-tests. Categorical variables were expressed as weighted percentages and evaluated using survey-adjusted chi-square tests. All statistical procedures were adjusted for NHANES’s complex sampling framework, accounting for strata, primary sampling units (PSUs), and sampling weights. Logistic Regression for Stroke Multivariable logistic regression models were used to examine the associations of MMII and WWI with the prevalence of stroke. Both MMII and WWI were treated as continuous and categorical variables (divided into tertiles). To examine associations, three nested logistic regression models were employed: Model 1 : Crude estimates without adjustment. Model 2 : Adjusted for demographic and socioeconomic factors, including age, sex, education level, marital status, poverty-income ratio (PIR), and race/ethnicity. Model 3 : Further adjusted for clinical comorbidities such as hypertension, diabetes, and hyperlipidemia. Odds ratios (ORs) with 95% confidence intervals (CIs) were reported, and P for trend was computed across tertile groups of MMII and WWI. Cox Proportional Hazards Modeling for Mortality To examine associations with mortality outcomes, Cox proportional hazards models were applied to assess the relationships between MMII, WWI, and both all-cause and cardiovascular mortality. The adjustment strategy followed the same three-tiered model framework as previously described. Person-time was calculated from the date of NHANES interview until the date of death or December 31, 2019, whichever came first. HRs with 95% CIs were estimated. Restricted Cubic Spline Analysis To assess potential nonlinear associations between MMII or WWI and outcomes including stroke and mortality, restricted cubic spline models incorporating three knots (positioned at the 10th, 50th, and 90th percentiles) were applied. Departure from linearity was evaluated using likelihood ratio tests by comparing spline-based models against their linear counterparts. Adjusted ORs or HRs with 95% CIs were illustrated to depict dose–response trend. Mediation Analysis To evaluate whether WWI mediated the relationship between MMII and stroke, a causal mediation analysis was performed. The total effect of MMII on stroke was decomposed into direct and indirect effects via WWI using a counterfactual-based framework. The indirect effect (path A × path B), direct effect (path C’), and total effect (path C) were estimated using bootstrapping with 1,000 replications. The proportion mediated was calculated as the ratio of the indirect effect to the total effect. All mediation models were adjusted for the same covariates included in Model 3. Survey Weights and Software All analyses incorporated NHANES-provided sample weights to account for the complex multistage sampling design, oversampling, and nonresponse. Appropriate 14-year sampling weights were calculated following NCHS guidelines to combine multiple survey cycles. Statistical analyses were performed using R (version 4.2.0) with the survey, rms, mediation, and survival packages. A two-sided P value < 0.05 was considered statistically significant. Results Baseline characteristics by stroke status A total of 11,563 participants representing approximately 68.6 million U.S. adults were included in this study, of whom 424 had a history of stroke and 11,139 did not. The weighted prevalence of stroke was 2.5%. As shown in Table 1 , stroke participants were significantly older, with 64% aged over 60 years, compared to 22% in the non-stroke group (P < 0.001). The proportion of Non-Hispanic Black individuals was higher in the stroke group (18% vs. 11%), while the proportions of Mexican Americans and other races were lower (P < 0.001). Participants with stroke also had lower educational attainment, with 27% having less than a high school education, compared to 16% in the non-stroke group (P < 0.001). A higher proportion of individuals with stroke were living in poverty (32% vs. 21%, P < 0.001) and had comorbid conditions including hypertension (77% vs. 36%), diabetes (36% vs. 13%), and hyperlipidemia (84% vs. 67%) (all P < 0.001). Notably, the stroke group exhibited higher levels of MMII (mean: 0.20 vs. 0.09) and WWI (mean: 11.46 vs. 10.94), and a greater proportion of individuals in the highest tertile of MMII (51%) and WWI (58%) (all P < 0.001). Associations of MMII and WWI with stroke As presented in Table 2 , elevated MMII levels were significantly linked to a higher likelihood of stroke. In the crude model (Model 1), each unit increment in MMII corresponded to a 6.19-fold increase in stroke odds (OR = 6.19; 95% CI: 3.45–11.11; P < 0.001). After adjusting for sociodemographic variables (Model 2), the association remained statistically significant (OR = 2.45; 95% CI: 1.25–4.82; P = 0.010). When further controlling for clinical conditions including hypertension, diabetes, and hyperlipidemia (Model 3), the association persisted (OR = 2.33; 95% CI: 1.20–4.51; P = 0.013). Additionally, when MMII was divided into tertiles, individuals in the highest group (T3) exhibited a significantly elevated stroke risk compared with those in the lowest group (T1), with a fully adjusted OR of 1.67 (95% CI: 1.05–2.65; P = 0.032). A significant trend was also observed across tertiles (P for trend = 0.036). Continuous variables are presented as mean (standard error), with P values derived from survey-weighted Student’s t-tests. Categorical variables are shown as weighted counts and percentages, and group differences were assessed using survey-adjusted chi-square tests. Abbreviations: MMII, Metal Mixture Inflammatory Index; WWI, Weight-Adjusted Waist Index; PIR, Poverty-Income Ratio. Similarly, elevated WWI was associated with increased stroke risk. In Model 1, each unit increase in WWI was associated with 2.15-fold higher odds of stroke (OR: 2.15; 95% CI: 1.85–2.49; P < 0.001). After full adjustment (Model 3), the association remained significant (OR: 1.29; 95% CI: 1.06–1.58; P = 0.010). Compared with the lowest WWI tertile, the highest tertile had an OR of 1.56 (95% CI: 1.02–2.46; P = 0.036), with a consistent linear trend (P for trend = 0.037). These findings suggest that both MMII and WWI are independently associated with increased stroke risk in the U.S. adult population. Table 2. Multivariable-Adjusted Associations between MMII, WWI, and Stroke. Characteristics Model 1 [OR (95% CI)] p-value Model 2 [OR (95% CI)] p-value Model 3 [OR (95% CI)] p-value MMII - stroke Continuous 6.19(3.45,11.11) <0.001 2.45(1.25, 4.82) 0.010 2.33(1.20, 4.51) 0.013 Tertile T1 1 (ref.) 1 (ref.) 1 (ref.) T2 1.59(1.12,2.26) 0.010 1.04(0.71, 1.51) 0.842 1.01(0.70, 1.47) 0.949 T3 3.72(2.39,5.79) <0.001 1.77(1.09, 2.87) 0.021 1.67(1.05, 2.65) 0.032 P for trend <0.001 0.024 0.036 WWI - stroke Continuous 2.15(1.85,2.49) <0.001 1.45(1.19, 1.76) <0.001 1.29(1.06, 1.58) 0.010 Tertile T1 1 (ref.) 1 (ref.) 1 (ref.) T2 2.45(1.50,3.98) <0.001 1.55(0.92, 2.61) 0.100 1.38(0.81, 2.33) 0.230 T3 4.89(3.26,7.34) <0.001 1.95(1.23, 3.08) 0.005 1.56(1.02, 2.46) 0.036 P for trend <0.001 0.002 0.037 Model 1: Unadjusted (no covariates included). Model 2: Adjusted for sociodemographic variables: age, sex, education level, marital status, poverty-income ratio (PIR), and race/ethnicity. Model 3: Further adjusted for clinical factors: hypertension, diabetes, and hyperlipidemia. Abbreviations: MMII, Metal Mixture Inflammatory Index; WWI, Weight-Adjusted Waist Index; PIR, Poverty-Income Ratio; OR, Odds Ratio; CI, Confidence Interval. Restricted cubic spline analysis RCS analysis was conducted to evaluate the potential nonlinear associations of MMII and WWI with stroke and all-cause mortality. Both MMII and WWI demonstrated approximately linear positive associations with stroke risk ( Figure 2 ). For MMII, the hazard ratio increased steadily as MMII rose above the reference point (median), with no evidence of nonlinearity (P for nonlinearity = 0.522). A similar linear trend was observed for WWI, where the stroke risk increased gradually with rising WWI levels (P for nonlinearity = 0.995). These findings suggest that higher levels of MMII and WWI are linearly associated with an elevated risk of stroke in the general population. RCS analysis for all-cause mortality is presented in Figure S1 . A consistent positive and linear relationship was observed between MMII and mortality (P for overall < 0.001; P for nonlinearity = 0.082), indicating that the risk of death increases steadily with higher MMII values. Similarly, WWI also exhibited a monotonic increase in mortality risk across its range (P for overall < 0.001; P for nonlinearity = 0.106), suggesting a dose-dependent association. The absence of significant nonlinear effects in all models supports the appropriateness of treating MMII and WWI as continuous linear predictors in subsequent modeling. Together, these results reinforce the utility of MMII and WWI as informative and scalable markers for predicting stroke and mortality risk in the U.S. adult population. Multivariate analysis of MMII and WWI with mortality Multivariable Cox models indicated that elevated MMII levels were independently linked to a greater risk of all-cause mortality, with the association being more pronounced in participants without a prior history of stroke ( Table 3 ). In the fully adjusted model, each unit increase in MMII was associated with a 2.42-fold higher risk of all-cause mortality in the overall population (HR: 2.42; 95% CI: 1.71–3.43; P < 0.001), and the association remained significant in the non-stroke subgroup (HR: 2.51; 95% CI: 1.71–3.68; P < 0.001), but not in stroke survivors (HR: 1.60; 95% CI: 0.88–2.90; P = 0.090). When analyzed by tertiles, individuals in the highest MMII tertile (T3) had a significantly elevated risk of all-cause mortality compared to those in the lowest tertile (T1), with a clear dose–response relationship observed in the overall (P for trend 0.05). These findings suggest that MMII is a strong and independent predictor of all-cause mortality, particularly in individuals without a history of stroke, whereas its association with cardiovascular mortality remains inconclusive. Table 3. Associations between MMII and risks of all-cause and cardiovascular mortality, expressed as HRs (95% CIs). Characteristics All-cause mortality [HR (95% CI)] * p-value Cardiovascular mortality [HR (95% CI)] * p-value All participants Continuous 2.42(1.71,3.43) <0.001 0.87(0.41,1.86) 0.723 Tertile T1 1 (ref.) 1 (ref.) T2 1.25(1.03,1.50) <0.001 1.13(0.76,1.67) 0.550 T3 2.14(1.62,2.82) <0.001 1.05(0.63,1.77) 0.840 P for trend <0.001 0.664 stroke Continuous 1.60(0.88,2.90) 0.090 1.18(0.23,6.11) 0.840 Tertile T1 1 (ref.) 1 (ref.) T2 1.44(0.81,2.55) 0.210 2.10(0.75,5.89) 0.160 T3 1.60(0.88,2.90) 0.120 1.47(0.40,5.36) 0.560 P for trend 0.100 0.300 Non-stroke Continuous 2.51(1.71,3.68) <0.001 0.83(0.35,1.99) 0.670 Tertile T1 1 (ref.) 1 (ref.) T2 1.26(1.02,1.55) 0.030 1.06(0.69,1.65) 0.780 T3 2.23(1.64,3.04) <0.001 1.01(0.57,1.79) 0.960 P for trend <0.001 0.850 *Models were adjusted for age, sex, education, marital status, poverty-income ratio (PIR), race/ethnicity, hypertension, diabetes, and hyperlipidemia. Abbreviations: PIR, Poverty-Income Ratio; MMII, Metal Mixture Inflammatory Index; HR, Hazard Ratio; CI, Confidence Interval. Subgroup analysis The associations between MMII and stroke as well as all-cause mortality were further examined across predefined subgroups to evaluate its consistency and potential effect modifiers. The association between higher MMII and stroke risk remained generally consistent across subgroups ( Figure 3A ), with no statistically significant interactions observed (all P for interaction > 0.05). Notably, the strength of association appeared more pronounced among middle-aged individuals (41–60 years, OR: 5.11; 95% CI: 1.61–10.17), males (OR: 3.03; 95% CI: 1.36–6.77), Non-Hispanic Black individuals (OR: 4.21; 95% CI: 0.90–19.29), and those classified as living in poverty (OR: 6.01; 95% CI: 2.37–15.23). Although these differences did not reach statistical significance for interaction, they may indicate potential population-specific vulnerability. Similarly, as shown in the Figure S2A , MMII was associated with elevated risk of all-cause mortality across nearly all subgroups, particularly among younger and middle-aged adults, those without hypertension, and individuals without diabetes. The interactions were not significant, but the consistent trends reinforce the robustness of MMII as a predictor of stroke and mortality risk. Subgroup analyses for WWI are presented in Figure 3B (stroke outcome) and Figure S2B (all-cause mortality). The association between higher WWI and stroke risk also remained stable across most strata, with slightly stronger associations observed among individuals over 60 years (OR: 1.39; 95% CI: 1.10–1.76), males (OR: 1.63; 95% CI: 1.18–2.27), and those with hyperlipidemia (OR: 1.42; 95% CI: 1.14–1.76), though none of the interactions reached statistical significance. Regarding all-cause mortality, the increased risk associated with higher WWI appeared more pronounced among non-poor individuals (HR: 2.65; 95% CI: 1.61–4.35), those with hyperlipidemia (HR: 2.70; 95% CI: 1.67–4.36), and those without diabetes (HR: 2.96; 95% CI: 1.80–4.84). These findings collectively suggest that while the WWI–outcome associations do not appear to be statistically modified by subgroup factors, variations in effect sizes may warrant further investigation in more targeted populations. Mediation analysis To evaluate the potential mediating role of WWI in the relationship between MMII and stroke, we first assessed the linear association between MMII and WWI. MMII was positively associated with WWI in multivariable linear regression analysis (β = 0.26; 95% CI: 0.19–0.33; P < 0.001), independent of demographic and clinical covariates ( Table 4 ). Subsequently, a formal mediation analysis was conducted, treating WWI as the intermediary variable between MMII and stroke. MMII exhibited a significant association with stroke risk via both direct and indirect pathways involving WWI ( Figure 4 ). The overall effect of MMII on stroke was estimated at 3.22 × 10⁻² (P = 0.004), with the direct component (Path C′) remaining significant after adjusting for WWI (2.91 × 10⁻², P = 0.012). The indirect pathway through WWI (Path A × B) accounted for 3.09 × 10⁻³ (P < 0.001), representing 9.87% of the total estimated effect. These results indicate that WWI partially mediates the MMII–stroke relationship, suggesting that MMII-associated changes in central fat distribution may play a mechanistic role in cerebrovascular risk linked to metal exposure. Table 4. Multivariate linear regression of MMII and WWI. β 95%CI P-value MMII - WWI 0.26 (0.19, 0.33) <0.001 Adjusted for age, sex, education level, marital status, PIR, race, hypertension, diabetes, and hyperlipidemia. Discussion In this large, population-based study of U.S. adults, we observed that elevated MMII levels—an index reflecting the cumulative pro-inflammatory burden of urinary metal mixtures—were independently associated with heightened risks of stroke and all-cause mortality. Notably, the associations remained robust after adjusting for multiple sociodemographic and clinical covariates. In addition, WWI, a surrogate measure of central adiposity, was independently associated with both outcomes. Mediation analysis further indicated that WWI served as a partial mediator in the association between MMII and stroke, suggesting that body fat distribution may serve as a biological pathway through which metal exposure influences cerebrovascular health. These findings are consistent with a growing body of evidence linking environmental metal exposure to vascular injury and mortality. Previous studies have reported that chronic exposure to toxic metals such as arsenic, cadmium, and lead can induce oxidative stress, endothelial dysfunction, and low-grade systemic inflammation, all of which are implicated in the pathogenesis of stroke and atherosclerosis 5,7,20 . For example, blood cadmium and urinary arsenic have been associated with increased carotid intima-media thickness and ischemic stroke risk in large population-based cohort 10,21,22 . A recent study by Wang et al. introduced the MMII as a composite index to quantify the pro-inflammatory burden of multi-metal exposure, showing strong associations with C-reactive protein and elevated all-cause mortality in U.S. adults 13 . Our results build upon that work by extending the clinical relevance of MMII to cerebrovascular endpoints and identifying potential downstream anthropometric mediators. The observed mediating role of WWI provides new insight into the metabolic consequences of metal exposure. Central adiposity, particularly visceral fat accumulation, is not only a hallmark of metabolic dysregulation but also an active source of inflammatory cytokines such as IL-6, TNF-α, and resistin, which may amplify metal-induced vascular injury 23,24 . WWI, calculated as waist circumference divided by the square root of body weight, has emerged as a more robust indicator of central fat burden than BMI, especially in older adults. Recent studies have linked elevated WWI with increased risk of cardiovascular events, stroke, and premature death 25,26 . Our mediation analysis demonstrates that a modest proportion (~10%) of the effect of MMII on stroke risk is attributable to increases in WWI, underscoring the interrelationship between environmental toxicants and metabolic risk factors. Beyond its etiologic insights, this study has important public health implications. First, it highlights the need to consider environmental exposures such as metal mixtures as part of routine cardiovascular risk assessment, particularly in vulnerable populations. Second, the integration of novel composite indicators like MMII and WWI into epidemiologic surveillance systems could improve early identification of at-risk individuals and inform targeted prevention strategies. Third, our findings support the potential value of environmental interventions—such as source reduction of metal pollution or dietary modulation of metal bioavailability—in mitigating systemic inflammation and downstream cardiometabolic outcomes. Several limitations merit consideration. Due to the cross-sectional nature of the NHANES data, causality cannot be inferred. Longitudinal or interventional studies are needed to verify temporal relationships and explore reversibility. Second, MMII in this study was constructed using a simplified Z-score averaging approach, whereas more advanced statistical methods like reduced rank regression or Bayesian kernel machine regression could better account for interactions among metals. Third, self-reported stroke status is subject to recall bias, although previous validations suggest acceptable sensitivity and specificity. Lastly, urinary metal levels reflect recent exposure and may not capture chronic cumulative burden, particularly for metals with long biological half-lives. Despite these limitations, our study contributes to the growing recognition of environmental determinants of cerebrovascular disease and provides new evidence that the relationship between metal exposure and stroke may be partially mediated through adverse fat distribution. These findings warrant further investigation in prospective studies with detailed exposure assessments, mechanistic biomarkers of inflammation and vascular damage, and refined models of environmental mixture analysis. Conclusion In conclusion, elevated MMII levels were independently linked to a greater likelihood of stroke and all-cause mortality among U.S. adults. WWI was also independently associated with these outcomes and partially mediated the relationship between MMII and stroke. These findings highlight the importance of considering environmental metal exposure and central adiposity in the assessment and prevention of cerebrovascular and mortality risk. Further prospective studies are needed to validate these associations and explore underlying mechanisms. Declarations Data availability statement The datasets utilized in this study are publicly accessible via the NHANES database at https://www.cdc.gov/nchs/nhanes/. Ethics statement This study was based on secondary analysis of publicly available, anonymized data from the NHANES project. The original NHANES protocol was approved by the Ethics Review Board of the National Center for Health Statistics, and all participants had provided written informed consent prior to data collection. Given the de-identified and open-access nature of the dataset, no additional ethical approval was required for this analysis. Author Contributions KR : Conceptualization, Methodology, Formal analysis, Visualization, Writing – original draft, Writing – review & editing; YL : Methodology, Software, Visualization, Writing – original draft; JZ : Data curation, Writing – review & editing. BW : Data curation, Writing – review & editing; YZ : Software, Visualization, Writing – review & editing; HY : Supervision, Project administration, Funding acquisition, Writing – review & editing. Funding This work was supported by the Science and Technology Development Project of Jilin Province (20240601024RC). Acknowledgments We thank the participants and staff of NHANES and the National Center for Health Statistics for providing access to the data used in this study. Conflict of interest The authors declare that they have no competing interests. Generative AI statement The author(s) declare that no Generative AI was used in the creation of this manuscript. References Mensah GA, Fuster V, Murray CJL, Roth GA. 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Environment and Health: Heavy Metal Toxicity. FP Essent. Oct 2024;545:13-18. Jaishankar M, Tseten T, Anbalagan N, Mathew BB, Beeregowda KN. Toxicity, mechanism and health effects of some heavy metals. Interdiscip Toxicol. Jun 2014;7(2):60-72. doi:10.2478/intox-2014-0009 Bellinger DC. Lead Contamination in Flint--An Abject Failure to Protect Public Health. N Engl J Med. Mar 24 2016;374(12):1101-3. doi:10.1056/NEJMp1601013 Tellez-Plaza M, Navas-Acien A, Menke A, Crainiceanu CM, Pastor-Barriuso R, Guallar E. Cadmium exposure and all-cause and cardiovascular mortality in the U.S. general population. Environ Health Perspect. Jul 2012;120(7):1017-22. doi:10.1289/ehp.1104352 Chen Y, Parvez F, Gamble M, et al. Arsenic exposure at low-to-moderate levels and skin lesions, arsenic metabolism, neurological functions, and biomarkers for respiratory and cardiovascular diseases: review of recent findings from the Health Effects of Arsenic Longitudinal Study (HEALS) in Bangladesh. Toxicol Appl Pharmacol. Sep 1 2009;239(2):184-92. doi:10.1016/j.taap.2009.01.010 Navas-Acien A, Guallar E, Silbergeld EK, Rothenberg SJ. Lead exposure and cardiovascular disease--a systematic review. Environ Health Perspect. Mar 2007;115(3):472-82. doi:10.1289/ehp.9785 Lamas GA, Bhatnagar A, Jones MR, et al. Contaminant Metals as Cardiovascular Risk Factors: A Scientific Statement From the American Heart Association. J Am Heart Assoc. Jul 4 2023;12(13):e029852. doi:10.1161/jaha.123.029852 Wang Y, Wang Y, Li R, et al. Low-grade systemic inflammation links heavy metal exposures to mortality: A multi-metal inflammatory index approach. Sci Total Environ. Oct 15 2024;947:174537. doi:10.1016/j.scitotenv.2024.174537 He J, Zhang W, Zhao F, et al. Investigation of the relationship between lead exposure in heavy metals mixtures and the prevalence of stroke: a cross-sectional study. BMC Public Health. Dec 18 2024;24(1):3474. doi:10.1186/s12889-024-21000-y Winter Y, Rohrmann S, Linseisen J, et al. Contribution of obesity and abdominal fat mass to risk of stroke and transient ischemic attacks. Stroke. Dec 2008;39(12):3145-51. doi:10.1161/strokeaha.108.523001 Johnson CL, Dohrmann SM, Burt VL, Mohadjer LK. National health and nutrition examination survey: sample design, 2011-2014. Vital Health Stat 2. Mar 2014;(162):1-33. Whelton PK, Carey RM, Aronow WS, et al. 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults: Executive Summary: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Hypertension. Jun 2018;71(6):1269-1324. doi:10.1161/hyp.0000000000000066 Harreiter J, Roden M. [Diabetes mellitus: definition, classification, diagnosis, screening and prevention (Update 2023)]. Wien Klin Wochenschr. Jan 2023;135(Suppl 1):7-17. Diabetes mellitus – Definition, Klassifikation, Diagnose, Screening und Prävention (Update 2023). doi:10.1007/s00508-022-02122-y Grundy SM, Stone NJ, Bailey AL, et al. 2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the Management of Blood Cholesterol: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation. Jun 18 2019;139(25):e1082-e1143. doi:10.1161/cir.0000000000000625 Ali MU, Liu G, Yousaf B, Ullah H, Abbas Q, Munir MAM. A systematic review on global pollution status of particulate matter-associated potential toxic elements and health perspectives in urban environment. Environ Geochem Health. Jun 2019;41(3):1131-1162. doi:10.1007/s10653-018-0203-z Vandenberghe N, Vallet AE, Petitjean T, et al. Absence of airway secretion accumulation predicts tolerance of noninvasive ventilation in subjects with amyotrophic lateral sclerosis. Respir Care. Sep 2013;58(9):1424-32. doi:10.4187/respcare.02103 Lin JS, O'Connor E, Rossom RC, Perdue LA, Eckstrom E. Screening for cognitive impairment in older adults: A systematic review for the U.S. Preventive Services Task Force. Ann Intern Med. Nov 5 2013;159(9):601-12. doi:10.7326/0003-4819-159-9-201311050-00730 Ouchi N, Parker JL, Lugus JJ, Walsh K. Adipokines in inflammation and metabolic disease. Nat Rev Immunol. Feb 2011;11(2):85-97. doi:10.1038/nri2921 Cesaro A, De Michele G, Fimiani F, et al. Visceral adipose tissue and residual cardiovascular risk: a pathological link and new therapeutic options. Front Cardiovasc Med. 2023;10:1187735. doi:10.3389/fcvm.2023.1187735 Zheng Y, Nie Z, Zhang Y, Sun T. The weight-adjusted-waist index predicts all-cause and cardiovascular mortality in hypertension. Front Cardiovasc Med. 2025;12:1501551. doi:10.3389/fcvm.2025.1501551 Guo S, Chen D, Zhang Y, Cao K, Xia Y, Yang D. Association of weight-adjusted waist index with all-cause and cardiovascular mortality in individuals with osteoarthritis. BMC Musculoskelet Disord. Apr 21 2025;26(1):390. doi:10.1186/s12891-025-08638-4 Table 1 Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 28 Apr, 2026 Reviewers agreed at journal 19 Apr, 2026 Reviewers invited by journal 28 Jul, 2025 Editor assigned by journal 28 Jul, 2025 Editor invited by journal 24 Jun, 2025 Submission checks completed at journal 24 Jun, 2025 First submitted to journal 22 Jun, 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. 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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-6950019","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":492431287,"identity":"7d7232f8-384c-451a-8c0e-5d693c3869cf","order_by":0,"name":"Keyi Ren","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIie3PIQvCQBjG8RsHZzlZfQXRr3ByMP04OwamCSZZGGqQW1Cxum+xaFQGS+eyccNqsS0oKBbbdlHwfvDChecfDiHD+E34fdAjrV1ZuEGon3CbKswKlWknSMR7n3TKFW7eD6Ntyu5yZCXHSRaIJUF2tHZrk67KPRFLwOyYjy/i0EWgzkltAuDztC2BsNPSuQhFEINJQ9K/8fQpgbIUOVMhsUYClHuWBOhI6iC9hPp8sMmB2ZR44KqMNv+lpThUs/lC9q+nexWEPTva1icfFvm+afP846G5MwzD+E8vf1RGF/nqIyEAAAAASUVORK5CYII=","orcid":"","institution":"First Hospital of Jilin University","correspondingAuthor":true,"prefix":"","firstName":"Keyi","middleName":"","lastName":"Ren","suffix":""},{"id":492431288,"identity":"38211822-a59a-4a64-b590-ce5894e4c712","order_by":1,"name":"Yang Liu","email":"","orcid":"","institution":"Runda Medical-Hefei Runda Wantong Medical Technology Co., Ltd. Hefei","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Liu","suffix":""},{"id":492431289,"identity":"d6b2c3d1-5d95-4adf-ba32-1beb0d8cb14b","order_by":2,"name":"Jianguo Zhou","email":"","orcid":"","institution":"First Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Jianguo","middleName":"","lastName":"Zhou","suffix":""},{"id":492431290,"identity":"cf334bd7-a61a-4b16-839d-c326369e707e","order_by":3,"name":"Bin Wang","email":"","orcid":"","institution":"First Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Bin","middleName":"","lastName":"Wang","suffix":""},{"id":492431291,"identity":"c40838d5-099e-444e-8ce3-efe90feec293","order_by":4,"name":"Yu Zhang","email":"","orcid":"","institution":"First Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Zhang","suffix":""},{"id":492431292,"identity":"f051ea48-8d32-48bd-8c78-37913c22cd65","order_by":5,"name":"Hongquan Yu","email":"","orcid":"","institution":"First Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Hongquan","middleName":"","lastName":"Yu","suffix":""}],"badges":[],"createdAt":"2025-06-22 14:23:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6950019/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6950019/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87995490,"identity":"34180a8f-5b12-4420-97df-df5e574247f7","added_by":"auto","created_at":"2025-07-31 09:22:21","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":143275,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart illustrating the selection process of eligible participants from the NHANES dataset.\u003c/p\u003e\n\u003cp\u003eAbbreviations: MMII, Metal Mixture Inflammatory Index; WWI, Weight-Adjusted Waist Index.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6950019/v1/8360b2c9955118e34396e562.jpeg"},{"id":87995261,"identity":"240fa34f-4b48-4674-adc4-512f8ccf6ba2","added_by":"auto","created_at":"2025-07-31 09:14:21","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":365830,"visible":true,"origin":"","legend":"\u003cp\u003eNonlinear associations of MMII and WWI with stroke risk. Panel A illustrates the relationship between MMII and stroke; Panel B, WWI and stroke.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6950019/v1/fa346c216a6b471c0368d75d.jpeg"},{"id":87995262,"identity":"e882f044-85bb-4509-b347-ec67af3541b6","added_by":"auto","created_at":"2025-07-31 09:14:21","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":822083,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analysis of the associations between MMII, WWI, and stroke. Panel A presents results for MMII and stroke, and Panel B for WWI and stroke.\u003c/p\u003e\n\u003cp\u003eOdds ratios were estimated per unit increase in MMII and per standard deviation increase in WWI. All models were adjusted for age, sex, education, marital status, PIR, race/ethnicity, hypertension, diabetes, and hyperlipidemia.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6950019/v1/9879fa77374110ce4744767f.jpeg"},{"id":87995491,"identity":"b91bce33-f6ed-45c9-b7c5-06cf8532efa9","added_by":"auto","created_at":"2025-07-31 09:22:21","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":247476,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual framework illustrating the mediation analysis. Path C represents the total effect, while path C′ reflects the direct effect. The indirect effect is defined as the product of paths A and B (A × B). The proportion mediated is calculated as: indirect effect / (direct effect + indirect effect) × 100%.\u003c/p\u003e\n\u003cp\u003eAbbreviations: MMII, Metal Mixture Inflammatory Index; WWI, Weight-Adjusted Waist Index.\u003c/p\u003e\n\u003cp\u003eRegression models were adjusted for age, sex, educational attainment, marital status, poverty-income ratio (PIR), race/ethnicity, hypertension, diabetes, and hyperlipidemia.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6950019/v1/98788b9a471411c4fc3d5850.jpeg"},{"id":87996974,"identity":"49873450-f9e8-4db6-b412-65618928debe","added_by":"auto","created_at":"2025-07-31 09:38:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2565691,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6950019/v1/ea3711fc-275c-4d04-915f-320cf86031c7.pdf"},{"id":87995259,"identity":"17877d3b-1806-4c30-b16f-364334aa91a4","added_by":"auto","created_at":"2025-07-31 09:14:21","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":20960,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6950019/v1/d8385fdc68c061fffd667096.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Metal Mixture-Related Inflammatory Burden and Stroke Risk: A NHANES-Based Mediation Analysis via Central Adiposity","fulltext":[{"header":"Introduction","content":"\u003cp\u003eStroke remains a leading cause of morbidity and mortality worldwide, contributing substantially to the global burden of cardiovascular disease\u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. In parallel, exposure to environmental toxicants\u0026mdash;particularly heavy metals\u0026mdash;has emerged as a significant and modifiable risk factor for cardiometabolic and cerebrovascular disorders\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Widespread industrialization and environmental contamination have led to ubiquitous human exposure to toxic metals such as arsenic, cadmium, and lead, which are known to induce oxidative stress, endothelial dysfunction, and systemic inflammation\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. These mechanisms are increasingly recognized as key contributors to stroke pathogenesis and premature mortality\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, yet the long-term population-level impacts of chronic low-level metal exposure remain poorly defined.\u003c/p\u003e\u003cp\u003eAlthough individual metals have been linked to adverse cardiovascular outcomes in previous studies\u003csup\u003e\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, real-world exposure typically involves complex mixtures of multiple co-occurring toxicants. This has prompted the development of novel indices to capture the cumulative biological burden of metal mixtures. One such metric\u0026mdash;the Metal Mixture Inflammatory Index (MMII)\u0026mdash;has recently been proposed to quantify the systemic inflammatory potential of combined metal exposures\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. However, epidemiologic evidence on the association between MMII and major health outcomes remains scarce. Furthermore, emerging data suggest that changes in body fat distribution may serve as a mediating pathway linking metal exposure to stroke risk\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, yet few studies have evaluated this potential pathway using mediation analysis or body shape-specific metrics such as the weight-adjusted waist index (WWI), which may better capture central adiposity than conventional measures like BMI.\u003c/p\u003e\u003cp\u003eTo address these gaps, we leveraged nationally representative data from the U.S. National Health and Nutrition Examination Survey (NHANES) from 2005 to 2018 to investigate the associations of MMII with stroke and all-cause mortality. We further explored whether WWI mediates the relationship between MMII and stroke, and conducted stratified, nonlinear, and sensitivity analyses to assess robustness. By integrating mixture-based exposure metrics with anthropometric and clinical data, this study aims to provide new insights into the pathophysiological links between environmental metal exposure, adiposity distribution, and cerebrovascular outcomes.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Population\u003c/h2\u003e\u003cp\u003eThis study utilized data from the NHANES, covering the years 2005 to 2018. NHANES is an ongoing, nationally representative, cross-sectional program aimed at evaluating the health and nutritional profiles of non-institutionalized U.S. residents. It adopts a multistage, stratified, and probability-based sampling strategy. Detailed protocols and design information are accessible on the official NHANES website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e16\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eA total of 70,190 individuals participated across the 2005\u0026ndash;2018 cycles. We excluded those under 20 years of age or pregnant at the time of their physical examination (n\u0026thinsp;=\u0026thinsp;31,152), resulting in 39,038 eligible adults. Additional exclusions included individuals lacking data necessary for MMII computation (n\u0026thinsp;=\u0026thinsp;26,881), and those with incomplete stroke or WWI information (n\u0026thinsp;=\u0026thinsp;594). The final study population consisted of 11,563 participants (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). All participants provided informed consent, and the study protocol was reviewed and approved by the Ethics Review Board of the National Center for Health Statistics (NCHS).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eExposure Assessment: MMII\u003c/h3\u003e\n\u003cp\u003eUrinary concentrations of selected heavy metals were used to construct the MMII. Specifically, nine metals measured in urine were included: arsenic, cadmium, lead, antimony, barium, cesium, molybdenum, thallium, and tungsten. Urinary metal levels were adjusted for urinary creatinine and log-transformed to normalize their distributions. For each metal, Z-scores were calculated to standardize values across participants. The MMII was then derived by calculating the arithmetic mean of the standardized Z-scores of the nine metals. This composite index reflects the overall burden of systemic exposure to metal mixtures. Higher MMII values represent a greater degree of metal-induced pro-inflammatory potential. This approach was informed by previous studies assessing co-exposure to multiple toxicants using additive or weighted combination indices\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eUrinary metal measurements were conducted by the CDC\u0026rsquo;s Division of Laboratory Sciences using inductively coupled plasma mass spectrometry (ICP-MS) or dynamic reaction cell mass spectrometry (ICP-DRC-MS), with detailed quality assurance procedures available in the NHANES Laboratory Procedures Manual.\u003c/p\u003e\n\u003ch3\u003eOutcome Definition\u003c/h3\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStroke\u003c/h2\u003e\u003cp\u003eStroke status was determined based on self-reported questionnaire data. Participants were classified as having a history of stroke if they answered \u0026ldquo;yes\u0026rdquo; to the Medical Conditions Questionnaire item MCQ160f, which asked, \u0026ldquo;Has a doctor or other health professional ever told you that you had a stroke?\u0026rdquo; This method has been widely used in previous epidemiological studies utilizing NHANES data. Participants with missing responses to this item were excluded from stroke-related analyses.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMortality\u003c/h3\u003e\n\u003cp\u003eMortality status was ascertained by linking NHANES participants to the National Death Index (NDI) through December 31, 2019. The primary endpoint was all-cause mortality. Cardiovascular mortality was also examined as a secondary endpoint, defined using ICD-10 codes I00\u0026ndash;I09, I11, I13, and I20\u0026ndash;I51 as the underlying cause of death. Time-to-event data were calculated from the NHANES interview date to the date of death or censoring (December 31, 2019), whichever came first. Mortality linkage files and variable definitions followed the guidelines issued by the NCHS.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eCovariates\u003c/h2\u003e\u003cp\u003eA comprehensive set of covariates was included in the multivariable models to control for potential confounding factors. All covariates were selected a priori based on their established associations with metal exposure, stroke, adiposity, and mortality, as well as their availability and completeness in the NHANES dataset.\u003c/p\u003e\u003cp\u003eDemographic variables included age (categorized as 20\u0026ndash;40, 41\u0026ndash;60, \u0026gt;\u0026thinsp;60 years), sex (male/female), race/ethnicity (Non-Hispanic White, Non-Hispanic Black, Mexican American, Other), educational level (below high school vs. high school or above), marital status (married/living with partner vs. not), and poverty income ratio (PIR), dichotomized as poor (\u0026lt;\u0026thinsp;1.3) vs. not poor (\u0026ge;\u0026thinsp;1.3).\u003c/p\u003e\u003cp\u003eClinical comorbidities were defined as follows:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eHypertension: defined by a self-reported diagnosis, use of antihypertensive medications, or measured systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg or diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eDiabetes: defined by self-reported history, HbA1c\u0026thinsp;\u0026ge;\u0026thinsp;6.5%, or fasting glucose\u0026thinsp;\u0026ge;\u0026thinsp;126 mg/dL\u003csup\u003e18\u003c/sup\u003e.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eHyperlipidemia was classified as meeting any of the following criteria: serum triglyceride levels\u0026thinsp;\u0026ge;\u0026thinsp;150 mg/dL, total cholesterol\u0026thinsp;\u0026ge;\u0026thinsp;200 mg/dL, LDL cholesterol\u0026thinsp;\u0026ge;\u0026thinsp;130 mg/dL, HDL cholesterol\u0026thinsp;\u0026lt;\u0026thinsp;40 mg/dL in men or \u0026lt;\u0026thinsp;50 mg/dL in women, or current use of lipid-lowering agents\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eAll covariates were included in adjusted models unless otherwise specified. The definitions and coding procedures followed the NHANES analytic guidelines and are detailed in \u003cb\u003eSupplementary Table S1\u003c/b\u003e.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eWWI Calculation\u003c/h3\u003e\n\u003cp\u003eThe WWI was calculated as an anthropometric indicator of central adiposity, independent of overall body weight. WWI was computed using the following formula:\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\mathbf{W}\\mathbf{W}\\mathbf{I}=\\:\\frac{\\mathbf{W}\\mathbf{a}\\mathbf{i}\\mathbf{s}\\mathbf{t}\\:\\mathbf{c}\\mathbf{i}\\mathbf{r}\\mathbf{c}\\mathbf{u}\\mathbf{m}\\mathbf{f}\\mathbf{e}\\mathbf{r}\\mathbf{e}\\mathbf{n}\\mathbf{c}\\mathbf{e}\\:\\left(\\mathbf{c}\\mathbf{m}\\right)}{\\sqrt{\\mathbf{W}\\mathbf{e}\\mathbf{i}\\mathbf{g}\\mathbf{h}\\mathbf{t}\\:\\left(\\mathbf{k}\\mathbf{g}\\right)}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003eWaist circumference and weight were measured during the physical examination component of NHANES using standardized procedures. Compared with traditional indices such as BMI or waist-to-height ratio (WHtR), WWI has been shown to more accurately reflect fat distribution, particularly visceral adiposity. Participants were further classified into tertiles based on WWI distribution to facilitate categorical analyses. Higher WWI values indicate greater central fat accumulation relative to body mass.\u003c/p\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003eBaseline Characteristics Analysis\u003c/h2\u003e\u003cp\u003eBaseline demographic and clinical characteristics were analyzed according to stroke history. Continuous variables were summarized as weighted means with corresponding standard errors (SEs), and differences were assessed using survey-weighted Student\u0026rsquo;s t-tests. Categorical variables were expressed as weighted percentages and evaluated using survey-adjusted chi-square tests. All statistical procedures were adjusted for NHANES\u0026rsquo;s complex sampling framework, accounting for strata, primary sampling units (PSUs), and sampling weights.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eLogistic Regression for Stroke\u003c/h2\u003e\u003cp\u003eMultivariable logistic regression models were used to examine the associations of MMII and WWI with the prevalence of stroke. Both MMII and WWI were treated as continuous and categorical variables (divided into tertiles). To examine associations, three nested logistic regression models were employed:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eModel 1\u003c/b\u003e: Crude estimates without adjustment.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eModel 2\u003c/b\u003e: Adjusted for demographic and socioeconomic factors, including age, sex, education level, marital status, poverty-income ratio (PIR), and race/ethnicity.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eModel 3\u003c/b\u003e: Further adjusted for clinical comorbidities such as hypertension, diabetes, and hyperlipidemia.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eOdds ratios (ORs) with 95% confidence intervals (CIs) were reported, and P for trend was computed across tertile groups of MMII and WWI.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eCox Proportional Hazards Modeling for Mortality\u003c/h2\u003e\u003cp\u003eTo examine associations with mortality outcomes, Cox proportional hazards models were applied to assess the relationships between MMII, WWI, and both all-cause and cardiovascular mortality. The adjustment strategy followed the same three-tiered model framework as previously described. Person-time was calculated from the date of NHANES interview until the date of death or December 31, 2019, whichever came first. HRs with 95% CIs were estimated.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eRestricted Cubic Spline Analysis\u003c/h2\u003e\u003cp\u003eTo assess potential nonlinear associations between MMII or WWI and outcomes including stroke and mortality, restricted cubic spline models incorporating three knots (positioned at the 10th, 50th, and 90th percentiles) were applied. Departure from linearity was evaluated using likelihood ratio tests by comparing spline-based models against their linear counterparts. Adjusted ORs or HRs with 95% CIs were illustrated to depict dose\u0026ndash;response trend.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eMediation Analysis\u003c/h2\u003e\u003cp\u003eTo evaluate whether WWI mediated the relationship between MMII and stroke, a causal mediation analysis was performed. The total effect of MMII on stroke was decomposed into direct and indirect effects via WWI using a counterfactual-based framework. The indirect effect (path A \u0026times; path B), direct effect (path C\u0026rsquo;), and total effect (path C) were estimated using bootstrapping with 1,000 replications. The proportion mediated was calculated as the ratio of the indirect effect to the total effect. All mediation models were adjusted for the same covariates included in Model 3.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eSurvey Weights and Software\u003c/h2\u003e\u003cp\u003eAll analyses incorporated NHANES-provided sample weights to account for the complex multistage sampling design, oversampling, and nonresponse. Appropriate 14-year sampling weights were calculated following NCHS guidelines to combine multiple survey cycles. Statistical analyses were performed using R (version 4.2.0) with the survey, rms, mediation, and survival packages. A two-sided P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline characteristics by stroke status\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 11,563 participants representing approximately 68.6 million U.S. adults were included in this study, of whom 424 had a history of stroke and 11,139 did not. The weighted prevalence of stroke was 2.5%. As shown in\u003cstrong\u003e\u0026nbsp;Table 1\u003c/strong\u003e, stroke participants were significantly older, with 64% aged over 60 years, compared to 22% in the non-stroke group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The proportion of Non-Hispanic Black individuals was higher in the stroke group (18% vs. 11%), while the proportions of Mexican Americans and other races were lower (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Participants with stroke also had lower educational attainment, with 27% having less than a high school education, compared to 16% in the non-stroke group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A higher proportion of individuals with stroke were living in poverty (32% vs. 21%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and had comorbid conditions including hypertension (77% vs. 36%), diabetes (36% vs. 13%), and hyperlipidemia (84% vs. 67%) (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Notably, the stroke group exhibited higher levels of MMII (mean: 0.20 vs. 0.09) and WWI (mean: 11.46 vs. 10.94), and a greater proportion of individuals in the highest tertile of MMII (51%) and WWI (58%) (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociations of MMII and WWI with stroke\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs presented in \u003cstrong\u003eTable 2\u003c/strong\u003e, elevated MMII levels were significantly linked to a higher likelihood of stroke. In the crude model (Model 1), each unit increment in MMII corresponded to a 6.19-fold increase in stroke odds (OR = 6.19; 95% CI: 3.45\u0026ndash;11.11; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). After adjusting for sociodemographic variables (Model 2), the association remained statistically significant (OR = 2.45; 95% CI: 1.25\u0026ndash;4.82; P\u0026thinsp;=\u0026thinsp;0.010). When further controlling for clinical conditions including hypertension, diabetes, and hyperlipidemia (Model 3), the association persisted (OR = 2.33; 95% CI: 1.20\u0026ndash;4.51; P\u0026thinsp;=\u0026thinsp;0.013).\u003c/p\u003e\n\u003cp\u003eAdditionally, when MMII was divided into tertiles, individuals in the highest group (T3) exhibited a significantly elevated stroke risk compared with those in the lowest group (T1), with a fully adjusted OR of 1.67 (95% CI: 1.05\u0026ndash;2.65; P\u0026thinsp;=\u0026thinsp;0.032). A significant trend was also observed across tertiles (P for trend = 0.036).\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eContinuous variables are presented as mean (standard error), with P values derived from survey-weighted Student\u0026rsquo;s t-tests.\u003c/p\u003e\n\u003cp\u003eCategorical variables are shown as weighted counts and percentages, and group differences were assessed using survey-adjusted chi-square tests.\u003c/p\u003e\n\u003cp\u003eAbbreviations: MMII, Metal Mixture Inflammatory Index; WWI, Weight-Adjusted Waist Index; PIR, Poverty-Income Ratio.\u003c/p\u003e\n\u003cp\u003eSimilarly, elevated WWI was associated with increased stroke risk. In Model 1, each unit increase in WWI was associated with 2.15-fold higher odds of stroke (OR: 2.15; 95% CI: 1.85\u0026ndash;2.49; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). After full adjustment (Model 3), the association remained significant (OR: 1.29; 95% CI: 1.06\u0026ndash;1.58; P\u0026thinsp;=\u0026thinsp;0.010). Compared with the lowest WWI tertile, the highest tertile had an OR of 1.56 (95% CI: 1.02\u0026ndash;2.46; P\u0026thinsp;=\u0026thinsp;0.036), with a consistent linear trend (P for trend\u0026thinsp;=\u0026thinsp;0.037). These findings suggest that both MMII and WWI are independently associated with increased stroke risk in the U.S. adult population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eMultivariable-Adjusted Associations between MMII, WWI, and Stroke.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1\u003cbr\u003e\u0026nbsp;[OR (95% CI)]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003cbr\u003e\u0026nbsp;[OR (95% CI)]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 3\u003cbr\u003e\u0026nbsp;[OR (95% CI)]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMMII - stroke\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e\n \u003cp\u003eContinuous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e6.19(3.45,11.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e2.45(1.25, 4.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2.33(1.20, 4.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e\n \u003cp\u003eTertile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; T1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e1 (ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e1 (ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1 (ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; T2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e1.59(1.12,2.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e1.04(0.71, 1.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1.01(0.70, 1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.949\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;T3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e3.72(2.39,5.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e1.77(1.09, 2.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1.67(1.05, 2.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; P for trend\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWWI - stroke\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e\n \u003cp\u003eContinuous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e2.15(1.85,2.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e1.45(1.19, 1.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1.29(1.06, 1.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e\n \u003cp\u003eTertile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;T1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e1 (ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e1 (ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1 (ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;T2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e2.45(1.50,3.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e1.55(0.92, 2.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1.38(0.81, 2.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.230\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;T3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e4.89(3.26,7.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e1.95(1.23, 3.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1.56(1.02, 2.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; P for trend\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eModel 1: Unadjusted (no covariates included).\u003c/p\u003e\n\u003cp\u003eModel 2: Adjusted for sociodemographic variables: age, sex, education level, marital status, poverty-income ratio (PIR), and race/ethnicity.\u003c/p\u003e\n\u003cp\u003eModel 3: Further adjusted for clinical factors: hypertension, diabetes, and hyperlipidemia.\u003c/p\u003e\n\u003cp\u003eAbbreviations: MMII, Metal Mixture Inflammatory Index; WWI, Weight-Adjusted Waist Index; PIR, Poverty-Income Ratio; OR, Odds Ratio; CI, Confidence Interval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRestricted cubic spline analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRCS analysis was conducted to evaluate the potential nonlinear associations of MMII and WWI with stroke and all-cause mortality. Both MMII and WWI demonstrated approximately linear positive associations with stroke risk (\u003cstrong\u003eFigure\u0026nbsp;2\u003c/strong\u003e). For MMII, the hazard ratio increased steadily as MMII rose above the reference point (median), with no evidence of nonlinearity (P for nonlinearity\u0026thinsp;=\u0026thinsp;0.522). A similar linear trend was observed for WWI, where the stroke risk increased gradually with rising WWI levels (P for nonlinearity\u0026thinsp;=\u0026thinsp;0.995). These findings suggest that higher levels of MMII and WWI are linearly associated with an elevated risk of stroke in the general population.\u003c/p\u003e\n\u003cp\u003eRCS analysis for all-cause mortality is presented in \u003cstrong\u003eFigure\u0026nbsp;S1\u003c/strong\u003e. A consistent positive and linear relationship was observed between MMII and mortality (P for overall\u0026thinsp;\u0026lt;\u0026thinsp;0.001; P for nonlinearity\u0026thinsp;=\u0026thinsp;0.082), indicating that the risk of death increases steadily with higher MMII values. Similarly, WWI also exhibited a monotonic increase in mortality risk across its range (P for overall\u0026thinsp;\u0026lt;\u0026thinsp;0.001; P for nonlinearity\u0026thinsp;=\u0026thinsp;0.106), suggesting a dose-dependent association. The absence of significant nonlinear effects in all models supports the appropriateness of treating MMII and WWI as continuous linear predictors in subsequent modeling. Together, these results reinforce the utility of MMII and WWI as informative and scalable markers for predicting stroke and mortality risk in the U.S. adult population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultivariate analysis of MMII and WWI with mortality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultivariable Cox models indicated that elevated MMII levels were independently linked to a greater risk of all-cause mortality, with the association being more pronounced in participants without a prior history of stroke (\u003cstrong\u003eTable 3\u003c/strong\u003e). In the fully adjusted model, each unit increase in MMII was associated with a 2.42-fold higher risk of all-cause mortality in the overall population (HR: 2.42; 95% CI: 1.71\u0026ndash;3.43; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the association remained significant in the non-stroke subgroup (HR: 2.51; 95% CI: 1.71\u0026ndash;3.68; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but not in stroke survivors (HR: 1.60; 95% CI: 0.88\u0026ndash;2.90; P\u0026thinsp;=\u0026thinsp;0.090). When analyzed by tertiles, individuals in the highest MMII tertile (T3) had a significantly elevated risk of all-cause mortality compared to those in the lowest tertile (T1), with a clear dose\u0026ndash;response relationship observed in the overall (P for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and non-stroke populations. In contrast, MMII was not significantly associated with cardiovascular mortality in any subgroup (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). These findings suggest that MMII is a strong and independent predictor of all-cause mortality, particularly in individuals without a history of stroke, whereas its association with cardiovascular mortality remains inconclusive.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003eAssociations between MMII and risks of all-cause and cardiovascular mortality, expressed as HRs (95% CIs).\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll-cause mortality\u003cbr\u003e\u0026nbsp;[HR (95% CI)] *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCardiovascular mortality\u003cbr\u003e\u0026nbsp;[HR (95% CI)] *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll participants\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp; Continuous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e2.42(1.71,3.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.87(0.41,1.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.723\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp; Tertile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; T1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e1 (ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e1 (ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; T2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e1.25(1.03,1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e1.13(0.76,1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.550\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; T3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e2.14(1.62,2.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e1.05(0.63,1.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.840\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp;P for trend\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.664\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003estroke\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp; Continuous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e1.60(0.88,2.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e1.18(0.23,6.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.840\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp; Tertile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; T1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e1 (ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e1 (ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; T2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e1.44(0.81,2.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.210\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e2.10(0.75,5.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.160\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; T3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e1.60(0.88,2.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.120\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e1.47(0.40,5.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.560\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp;P for trend\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.100\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.300\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-stroke\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp; Continuous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e2.51(1.71,3.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.83(0.35,1.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.670\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp; Tertile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; T1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e1 (ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e1 (ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; T2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e1.26(1.02,1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.030\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e1.06(0.69,1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.780\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; T3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e2.23(1.64,3.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e1.01(0.57,1.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.960\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp;P for trend\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.850\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Models were adjusted for age, sex, education, marital status, poverty-income ratio (PIR), race/ethnicity, hypertension, diabetes, and hyperlipidemia.\u003c/p\u003e\n\u003cp\u003eAbbreviations: PIR, Poverty-Income Ratio; MMII, Metal Mixture Inflammatory Index; HR, Hazard Ratio; CI, Confidence Interval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubgroup analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe associations between MMII and stroke as well as all-cause mortality were further examined across predefined subgroups to evaluate its consistency and potential effect modifiers. The association between higher MMII and stroke risk remained generally consistent across subgroups (\u003cstrong\u003eFigure 3A\u003c/strong\u003e), with no statistically significant interactions observed (all P for interaction \u0026gt; 0.05). Notably, the strength of association appeared more pronounced among middle-aged individuals (41\u0026ndash;60 years, OR: 5.11; 95% CI: 1.61\u0026ndash;10.17), males (OR: 3.03; 95% CI: 1.36\u0026ndash;6.77), Non-Hispanic Black individuals (OR: 4.21; 95% CI: 0.90\u0026ndash;19.29), and those classified as living in poverty (OR: 6.01; 95% CI: 2.37\u0026ndash;15.23). Although these differences did not reach statistical significance for interaction, they may indicate potential population-specific vulnerability. Similarly, as shown in the \u003cstrong\u003eFigure\u0026nbsp;S2A\u003c/strong\u003e, MMII was associated with elevated risk of all-cause mortality across nearly all subgroups, particularly among younger and middle-aged adults, those without hypertension, and individuals without diabetes. The interactions were not significant, but the consistent trends reinforce the robustness of MMII as a predictor of stroke and mortality risk.\u003c/p\u003e\n\u003cp\u003eSubgroup analyses for WWI are presented in \u003cstrong\u003eFigure\u0026nbsp;3B\u003c/strong\u003e (stroke outcome) and \u003cstrong\u003eFigure\u0026nbsp;S2B\u003c/strong\u003e (all-cause mortality). The association between higher WWI and stroke risk also remained stable across most strata, with slightly stronger associations observed among individuals over 60 years (OR: 1.39; 95% CI: 1.10\u0026ndash;1.76), males (OR: 1.63; 95% CI: 1.18\u0026ndash;2.27), and those with hyperlipidemia (OR: 1.42; 95% CI: 1.14\u0026ndash;1.76), though none of the interactions reached statistical significance. Regarding all-cause mortality, the increased risk associated with higher WWI appeared more pronounced among non-poor individuals (HR: 2.65; 95% CI: 1.61\u0026ndash;4.35), those with hyperlipidemia (HR: 2.70; 95% CI: 1.67\u0026ndash;4.36), and those without diabetes (HR: 2.96; 95% CI: 1.80\u0026ndash;4.84). These findings collectively suggest that while the WWI\u0026ndash;outcome associations do not appear to be statistically modified by subgroup factors, variations in effect sizes may warrant further investigation in more targeted populations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMediation analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate the potential mediating role of WWI in the relationship between MMII and stroke, we first assessed the linear association between MMII and WWI. MMII was positively associated with WWI in multivariable linear regression analysis (\u0026beta;\u0026thinsp;=\u0026thinsp;0.26; 95% CI: 0.19\u0026ndash;0.33; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), independent of demographic and clinical covariates (\u003cstrong\u003eTable 4\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eSubsequently, a formal mediation analysis was conducted, treating WWI as the intermediary variable between MMII and stroke. MMII exhibited a significant association with stroke risk via both direct and indirect pathways involving WWI (\u003cstrong\u003eFigure 4\u003c/strong\u003e). The overall effect of MMII on stroke was estimated at 3.22 \u0026times; 10⁻\u0026sup2; (P = 0.004), with the direct component (Path C\u0026prime;) remaining significant after adjusting for WWI (2.91 \u0026times; 10⁻\u0026sup2;, P = 0.012). The indirect pathway through WWI (Path A \u0026times; B) accounted for 3.09 \u0026times; 10⁻\u0026sup3; (P \u0026lt; 0.001), representing 9.87% of the total estimated effect. These results indicate that WWI partially mediates the MMII\u0026ndash;stroke relationship, suggesting that MMII-associated changes in central fat distribution may play a mechanistic role in cerebrovascular risk linked to metal exposure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e Multivariate linear regression of\u0026nbsp;MMII and\u0026nbsp;WWI.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003eMMII - WWI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e(0.19, 0.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAdjusted for age, sex, education level, marital status, PIR, race, hypertension, diabetes, and hyperlipidemia.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this large, population-based study of U.S. adults, we observed that elevated MMII levels\u0026mdash;an index reflecting the cumulative pro-inflammatory burden of urinary metal mixtures\u0026mdash;were independently associated with heightened risks of stroke and all-cause mortality. Notably, the associations remained robust after adjusting for multiple sociodemographic and clinical covariates. In addition, WWI, a surrogate measure of central adiposity, was independently associated with both outcomes. Mediation analysis further indicated that WWI served as a partial mediator in the association between MMII and stroke, suggesting that body fat distribution may serve as a biological pathway through which metal exposure influences cerebrovascular health.\u003c/p\u003e\n\u003cp\u003eThese findings are consistent with a growing body of evidence linking environmental metal exposure to vascular injury and mortality. Previous studies have reported that chronic exposure to toxic metals such as arsenic, cadmium, and lead can induce oxidative stress, endothelial dysfunction, and low-grade systemic inflammation, all of which are implicated in the pathogenesis of stroke and atherosclerosis\u003csup\u003e5,7,20\u003c/sup\u003e. For example, blood cadmium and urinary arsenic have been associated with increased carotid intima-media thickness and ischemic stroke risk in large population-based cohort\u003csup\u003e10,21,22\u003c/sup\u003e. A recent study by Wang et al. introduced the MMII as a composite index to quantify the pro-inflammatory burden of multi-metal exposure, showing strong associations with C-reactive protein and elevated all-cause mortality in U.S. adults\u003csup\u003e13\u003c/sup\u003e. Our results build upon that work by extending the clinical relevance of MMII to cerebrovascular endpoints and identifying potential downstream anthropometric mediators.\u003c/p\u003e\n\u003cp\u003eThe observed mediating role of WWI provides new insight into the metabolic consequences of metal exposure. Central adiposity, particularly visceral fat accumulation, is not only a hallmark of metabolic dysregulation but also an active source of inflammatory cytokines such as IL-6, TNF-\u0026alpha;, and resistin, which may amplify metal-induced vascular injury\u003csup\u003e23,24\u003c/sup\u003e. WWI, calculated as waist circumference divided by the square root of body weight, has emerged as a more robust indicator of central fat burden than BMI, especially in older adults. Recent studies have linked elevated WWI with increased risk of cardiovascular events, stroke, and premature death\u003csup\u003e25,26\u003c/sup\u003e. Our mediation analysis demonstrates that a modest proportion (~10%) of the effect of MMII on stroke risk is attributable to increases in WWI, underscoring the interrelationship between environmental toxicants and metabolic risk factors.\u003c/p\u003e\n\u003cp\u003eBeyond its etiologic insights, this study has important public health implications. First, it highlights the need to consider environmental exposures such as metal mixtures as part of routine cardiovascular risk assessment, particularly in vulnerable populations. Second, the integration of novel composite indicators like MMII and WWI into epidemiologic surveillance systems could improve early identification of at-risk individuals and inform targeted prevention strategies. Third, our findings support the potential value of environmental interventions\u0026mdash;such as source reduction of metal pollution or dietary modulation of metal bioavailability\u0026mdash;in mitigating systemic inflammation and downstream cardiometabolic outcomes.\u003c/p\u003e\n\u003cp\u003eSeveral limitations merit consideration. Due to the cross-sectional nature of the NHANES data, causality cannot be inferred. Longitudinal or interventional studies are needed to verify temporal relationships and explore reversibility. Second, MMII in this study was constructed using a simplified Z-score averaging approach, whereas more advanced statistical methods like reduced rank regression or Bayesian kernel machine regression could better account for interactions among metals. Third, self-reported stroke status is subject to recall bias, although previous validations suggest acceptable sensitivity and specificity. Lastly, urinary metal levels reflect recent exposure and may not capture chronic cumulative burden, particularly for metals with long biological half-lives. Despite these limitations, our study contributes to the growing recognition of environmental determinants of cerebrovascular disease and provides new evidence that the relationship between metal exposure and stroke may be partially mediated through adverse fat distribution. These findings warrant further investigation in prospective studies with detailed exposure assessments, mechanistic biomarkers of inflammation and vascular damage, and refined models of environmental mixture analysis.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, elevated MMII levels were independently linked to a greater likelihood of stroke and all-cause mortality among U.S. adults. WWI was also independently associated with these outcomes and partially mediated the relationship between MMII and stroke. These findings highlight the importance of considering environmental metal exposure and central adiposity in the assessment and prevention of cerebrovascular and mortality risk. Further prospective studies are needed to validate these associations and explore underlying mechanisms.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets utilized in this study are publicly accessible via the NHANES database at https://www.cdc.gov/nchs/nhanes/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was based on secondary analysis of publicly available, anonymized data from the NHANES project. The original NHANES protocol was approved by the Ethics Review Board of the National Center for Health Statistics, and all participants had provided written informed consent prior to data collection. Given the de-identified and open-access nature of the dataset, no additional ethical approval was required for this analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKR\u003c/strong\u003e: Conceptualization, Methodology, Formal analysis, Visualization, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing; \u003cstrong\u003eYL\u003c/strong\u003e: Methodology, Software, Visualization, Writing \u0026ndash; original draft; \u003cstrong\u003eJZ\u003c/strong\u003e: Data curation, Writing \u0026ndash; review \u0026amp; editing. \u003cstrong\u003eBW\u003c/strong\u003e: Data curation, Writing \u0026ndash; review \u0026amp; editing; \u003cstrong\u003eYZ\u003c/strong\u003e: Software, Visualization, Writing \u0026ndash; review \u0026amp; editing; \u003cstrong\u003eHY\u003c/strong\u003e: Supervision, Project administration, Funding acquisition, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Science and Technology Development Project of Jilin Province (20240601024RC).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the participants and staff of NHANES and the National Center for Health Statistics for providing access to the data used in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenerative AI statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) declare that no Generative AI was used in the creation of this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMensah GA, Fuster V, Murray CJL, Roth GA. 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Diabetes mellitus \u0026ndash; Definition, Klassifikation, Diagnose, Screening und Pr\u0026auml;vention (Update 2023). doi:10.1007/s00508-022-02122-y\u003c/li\u003e\n\u003cli\u003eGrundy SM, Stone NJ, Bailey AL, et al. 2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the Management of Blood Cholesterol: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation. Jun 18 2019;139(25):e1082-e1143. doi:10.1161/cir.0000000000000625\u003c/li\u003e\n\u003cli\u003eAli MU, Liu G, Yousaf B, Ullah H, Abbas Q, Munir MAM. A systematic review on global pollution status of particulate matter-associated potential toxic elements and health perspectives in urban environment. Environ Geochem Health. Jun 2019;41(3):1131-1162. doi:10.1007/s10653-018-0203-z\u003c/li\u003e\n\u003cli\u003eVandenberghe N, Vallet AE, Petitjean T, et al. 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The weight-adjusted-waist index predicts all-cause and cardiovascular mortality in hypertension. Front Cardiovasc Med. 2025;12:1501551. doi:10.3389/fcvm.2025.1501551\u003c/li\u003e\n\u003cli\u003eGuo S, Chen D, Zhang Y, Cao K, Xia Y, Yang D. Association of weight-adjusted waist index with all-cause and cardiovascular mortality in individuals with osteoarthritis. BMC Musculoskelet Disord. Apr 21 2025;26(1):390. doi:10.1186/s12891-025-08638-4\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Metal Mixture Inflammatory Index, weight-adjusted waist index, stroke, all-cause mortality, NHANES, mediation analysis","lastPublishedDoi":"10.21203/rs.3.rs-6950019/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6950019/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eGrowing evidence suggests that exposure to heavy metal mixtures may contribute to increased risk of stroke and premature mortality, potentially through inflammation-mediated mechanisms. The Metal Mixture Inflammatory Index (MMII) has recently been proposed as a composite metric to quantify the systemic inflammatory potential of metal co-exposure. However, the relationship between MMII and cerebrovascular outcomes, and the potential mediating role of central adiposity measured by the weight-adjusted waist index (WWI), remains unclear.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe analyzed data from 11,563 adults in the U.S. National Health and Nutrition Examination Survey (NHANES, 2005\u0026ndash;2018). MMII was calculated by standardizing and averaging the concentrations of nine urinary heavy metals. WWI was computed as waist circumference divided by the square root of weight. Multivariable logistic and Cox regression models were used to assess the associations of MMII and WWI with stroke and all-cause mortality. Restricted cubic spline (RCS) models evaluated dose\u0026ndash;response patterns, and mediation analysis was performed to assess the indirect effect of WWI on the MMII\u0026ndash;stroke relationship.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eHigher MMII and WWI were independently associated with increased odds of stroke and risk of all-cause mortality after adjusting for demographic and clinical covariates. The associations were approximately linear in RCS models. Subgroup analyses confirmed robustness across various strata. Mediation analysis revealed that WWI explained 9.87% of the association between MMII and stroke (indirect effect: 3.09 \u0026times; 10⁻\u0026sup3;, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis study provides evidence that MMII is positively associated with stroke and mortality risk, and that WWI partially mediates the relationship between MMII and stroke. These findings highlight the interplay between environmental exposure and central adiposity in shaping cerebrovascular risk and support the utility of MMII and WWI as informative risk indicators in population health research.\u003c/p\u003e","manuscriptTitle":"Metal Mixture-Related Inflammatory Burden and Stroke Risk: A NHANES-Based Mediation Analysis via Central Adiposity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-31 09:14:16","doi":"10.21203/rs.3.rs-6950019/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-28T15:33:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"59785966852240211519802436009881283012","date":"2026-04-19T11:47:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-28T10:01:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-28T09:55:55+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-24T13:40:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-24T06:25:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-06-22T14:12:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"704b6854-0a88-4b90-9b53-5c2cf0dfbd67","owner":[],"postedDate":"July 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":52283383,"name":"Health sciences/Cardiology"},{"id":52283384,"name":"Health sciences/Risk factors"},{"id":52283385,"name":"Health sciences/Health care/Public health"}],"tags":[],"updatedAt":"2025-07-31T09:14:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-31 09:14:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6950019","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6950019","identity":"rs-6950019","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}