Association between heavy metals’exposure and depression: Findings of the NHANES from 2003 to 2020

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This study assessed multi-metal exposure's collective risk and identified key contributors. Methods Analyzing National Health and Nutrition Examination Survey data, adults with complete data on nine urinary metals (antimony, barium, cadmium, cobalt, cesium, molybdenum, lead, thallium and tungsten), three blood metals (cadmium, mercury and lead), depression status, and key covariates were assessed via four methods (multivariate logistic regression, restricted cubic spline (RCS) regression, weighted quantile sum (WQS) regression and Bayesian kernel machine regression (BKMR) to evaluate metal-depression associations. Results Among 8,814 participants (731 with depression), those with depression showed higher urine and blood cadmium levels, but lower blood mercury and urine thallium levels compared to controls. Adjusted analyses linked elevated urine antimony (OR = 1.34, p = 0.029) and tungsten (OR = 1.42, p = 0.008) to increased depression risk, while higher urine thallium (OR = 0.52, p < 0.001) and blood mercury (OR = 0.7, p = 0.005) reduced risk. RCS analysis revealed nonlinear relationships between depression and urine cadmium ( p = 0.004), cobalt ( p = 0.005), lead ( p = 0.024), antimony ( p < 0.001), tungsten( p < 0.001), as well as blood cadmium ( p < 0.001) and mercury ( p < 0.001). However, the WQS index was postive associated with depression (OR:1.17, 95% CI: 1.02–1.35, p = 0.026) but a negative correlation (OR:0.81, 95% CI: 0.7–0.94, p = 0.006). BKMR analysis confirmed multi-metal co-exposure elevates depression risk, and urinary barium showed the highest BKMR-derived posterior inclusion probability (PIP = 0.448). Conclusion Heavy metal mixture exposure elevates depressive disorder risk, with tungsten and antimony as key risk drivers, mercury and thallium showing protective effects, and barium emerging as a potential contributor. Further studies needed to validate these metal-specific impacts and uncover additional depression-linked metals. Heavy metals Depression Joint exposure Barium NHANES Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Depressive disorders, characterized by intense emotional fluctuations and a persistent dysphoric mood, represent a major category of mood disorders with high prevalence, disability rates, recurrence rates and suicide risk. As such, it imposes substantial psychological and economic burdens on both individuals and society [1, 2]. Globally, the prevalence of depression reached 4.4% in 2020, reflecting a 60% increase over the past three decades [3]. Furthermore, according to Greenberg et al. adult depression accounted for an economic burden of US $ 326.2 billion in 2020, with projections indicating that it will become the leading contributor to the global disease burden by 2030 [4,5]. However, despite ongoing therapeutic advancements, achieving complete remission currently remains a significant challenge. In particular, pharmacological treatments based on the "monoaminergic neurotransmitter imbalance" hypothesis often result in considerable side effects, thereby contributing to suboptimal treatment outcomes [6–8]. Consequently, preventing disease onset and progression remains the most practical and critical strategy. Previous studies indicated that depression results from interactions between environmental and genetic factors, with heritability accounting for less than 40% of the observed variance [9]. Indeed, the pathogenesis of depressive disorders involves multifaceted mechanisms that extend beyond the well-established links to lifestyle factors, cerebral structural abnormalities, immune-metabolic dysregulation and genetic predisposition [10, 11], with increasing evidence now underscoring the crucial role of environmental metals (EMs) in disease development. Global industrialization has exacerbated contamination of air, soil and water, leading to widespread human exposure to multiple heavy metals, including cadmium, mercury and arsenic [12–14]. Upon systemic absorption through the circulatory, respiratory and digestive systems, these metals accumulate in various organs and tissues. Subsequently, at critical concentrations, they disrupt essential element homeostasis (e.g., zinc, iron, copper) in a dose-dependent manner, impair hypothalamic-pituitary-adrenal (HPA) axis function, alter glucose/lipid metabolism and compromise mitochondrial integrity. Altogether, these disruptions trigger oxidative stress and inflammatory responses, resulting in irreversible damage to biomacromolecules, such as DNA and RNA, and contributing to disease pathogenesis. The nervous system is particularly susceptible to such metal-induced pathophysiological damage. For instance, in studies involving rodents, chronic cadmium exposure was shown to increase hippocampal levels of thiobarbituric acid reactive substances (TBARS), nitric oxide (NO) as well as catalase (CAT) activity while suppressing superoxide dismutase (SOD) activity, ultimately leading to CA3 neuronal degeneration and depressive-like behaviors [15, 16]. Similarly, thallium has been found to interact selectively with oligonucleotide repair genes, such as OGG1, to induce exogenous DNA damage comparable to that caused by ultraviolet/ionizing radiation and alkylating agents, which can manifest as neuropsychiatric dysfunction [17]. Additionally, barium can disrupt potassium ion channel dynamics and alter neuronal action potentials, thereby enhancing neurotoxicity and exacerbating depressive symptoms, especially in elderly females [18, 19]. However, not all metal accumulations are positively associated with depressive disorders. For example, Sun et al. reported an inverse relationship between nickel levels and the risk of major depression [20]. Furthermore, in an analysis of 2017–2018 National Health and Nutrition Examination Survey (NHANES) data involving seven metals (lead, mercury, cadmium, manganese, selenium, chromium, cobalt), Fang et al. found a positive association between cadmium and depression but an inverse association with mercury, while the remaining metals showed no significant correlations [21]. While current research predominantly focuses on assessing depression risks associated with exposure to individual metals, comprehensive evaluations of the effects of environmental metal mixtures remain limited. Indeed, in real-world scenarios, environmental metals commonly coexist as complex mixtures, with individual heavy metals exhibiting distinct toxicological mechanisms, including differences in metabolic kinetics, interactions between ultimate toxicants and target molecules, alterations in cellular signaling pathways as well as disruptions in biological repair processes [18]. Moreover, these metals interact in multifaceted ways, producing additive, synergistic, antagonistic or even independent neurotoxic effects. Additionally, multi-metal exposures may also generate complex, nonlinear associations with health outcomes and related biomarkers, with conventional multivariable parametric regression models often struggling to capture such relationships due to limitations in addressing multicollinearity, model misspecification and insufficient capacity to estimate the combined effects of multiple exposures [22–25]. This study addresses the critical need for large-scale epidemiological evidence on environmental metal mixtures and depression, analyzing NHANES data (2003–2020) through advanced modeling: Restricted Cubic Spline (RCS), Weighted Quantile Sum (WQS) regression and Bayesian Kernel Machine Regression (BKMR). These approaches overcome the limitations of traditional linear models and account for multicollinearity, enabling a more comprehensive evaluation of both individual and combined effects of heavy metals. The findings aim to clarify the critical role of mixed metal exposures in the pathogenesis of depression and identify modifiable environmental risk factors that could inform targeted prevention strategies. Materials and methods Study population The NHANES study protocols were approved by the Ethics Review Board of the National Center for Health Statistics. Detailed information about the database is available at https://www.cdc.gov/nchs/nhanes/default.aspx . This study’s analytical sample included data from eight consecutive NHANES cycles (2003–2020). Due to the coronavirus disease 2019 (COVID-19) pandemic, data collected between 2019 and March 2020 were combined with the 2017–2018 cycle, following NHANES guidelines [26]. The inclusion criteria were as follows: (1) participants aged 20 years or older; (2) participants who took part in the blood and urine sub-study of heavy metals; and (3) participant depression status assessed using NHANES questionnaire data. Furthermore, the following exclusion criteria were applied: (1) Missing or undetectable data on blood trace elements (cadmium, lead, mercury) or urine trace elements (barium, cadmium, cobalt, cesium, molybdenum, lead, antimony, thallium, tungsten); (2) incomplete Patient Health Questionnaire-9 (PHQ-9) responses, and (3) missing data on key covariates, including body mass index (BMI), race/ethnicity, education level, marital status, income-to-poverty ratio, physical activity, smoking status, alcohol consumption, cardiovascular disease, chronic kidney disease, hypertension and diabetes. After applying the above criteria, the final analytical sample consisted of 8,814 participants. The study design flow is depicted in Fig. 1 . Assessment of depression The PHQ-9 is a validated and reliable screening tool for assessing the severity of depression symptoms experienced over the past two weeks, and it has demonstrated a sensitivity and specificity of 88% for detecting depressive disorders [27]. The PHQ-9 consists of nine items covering the following symptoms: fatigue, appetite disturbances, psychomotor retardation or agitation, concentration difficulties, sleep disturbances, anhedonia, depressed mood, feelings of worthlessness and suicidal thoughts, with each item rated on a four-point Likert scale (0 = “not at all”, 1 = “several days”, 2 = “more than half the days”, and 3 = “Nearly every day”), resulting in a total score ranging from 0 to 27. For the purposes of this research, depression was operationally defined as a score of 10 or higher, in alignment with previous studies [28]. Assessment of heavy metals All blood and urine specimens were collected, processed, stored and eventually shipped to the Environmental Health Sciences Laboratory of the National Center for Environmental Health, Centers for Disease Control and Prevention. Specifically, blood levels of cadmium, lead and mercury as well as urinary levels of barium, cadmium, cobalt, cesium, molybdenum, lead, antimony, thallium and tungsten were measured using inductively coupled plasma mass spectrometry. All NHANES procedures adhere to quality assurance and quality control (QA/QC) protocols which comply with the 1988 Clinical Laboratory Improvement Act mandates [29]. Assessment of covariates Covariates were selected based on previous literature and theoretical rationale that suggested their potential associations with both depression prevalence and heavy metal exposure levels [30]. In particular, this study examined a range of demographic, socio-economic, lifestyle and health-related variables. The demographic factors included sex (male and female), age, BMI and ethnicity (Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black or other racial groups), while the socio-economic variables encompassed educational level (less than high school, high school diploma or equivalent and college degree or higher), marital status (married vs. unmarried/other) and poverty-to-income ratio (PIR), categorized as high (> 3), medium (1–3) or low (< 1) [31]. Lifestyle variables included physical activity level (vigorous, moderate or other forms of exercise), smoking status (current smokers – individuals who have smoked over 100 cigarettes in their lifetime, former smokers – those who have smoked more than 100 cigarettes but no longer smoke or non-smokers – those who have smoked less than 100 cigarettes in their lifetime) [30] and alcohol consumption, with the latter classified into heavy drinkers (≥ 2 drinks per day for men and ≥ 1 drink per day for women), low-to-moderate drinkers (< 2 drinks per day for men and < 1 drink per day for women) or non-drinkers (less than 12 drinks per year) [31]. The health-related covariates included a history of cardiovascular disease (CVD), defined when at least one of the following conditions was present: coronary artery disease, congestive heart failure, angina pectoris, myocardial infarction or stroke. In addition, chronic kidney disease (CKD) was identified based on an estimated glomerular filtration rate (eGFR) of < 60 mL/min/1.73 m², calculated using the Chronic Kidney Disease Epidemiology Collaboration equation, or an albumin-to-creatinine ratio of at least 30 mg/g, while hypertension was defined as a mean systolic blood pressure of ≥ 130 mmHg, a mean diastolic blood pressure of ≥ 80 mmHg or the current use of antihypertensive medications [32]. Finally, diabetes was determined by self-reported use of hypoglycemic medications or a diagnosis confirmed by a healthcare professional, with fasting blood glucose levels ≥ 126 mg/dL or hemoglobin A1c values ≥ 6.5% also used to identify diabetic cases [31]. Statistical analysis All statistical analyses were performed using RStudio version 4.1.2 and EmpowerStats software. Descriptive statistics were used to analyze the baseline characteristics of participants in the depression and non-depression groups. For this purpose, continuous variables were first expressed as mean ± standard deviation (SD), while categorical ones were presented as percentages. The baseline characteristics were then compared using logistic regression models and chi-square tests for continuous and categorical variables, respectively. The concentrations of heavy metals in blood and urine were also categorized into four quartiles (Q1, Q2, Q3 and Q4), with Q1 serving as the reference group in subsequent logistic regression analyses. Furthermore, the association between individual heavy metals and depression risk was evaluated using multivariable logistic regression, with the results reported as odds ratios (OR) and their corresponding 95% confidence intervals (CI) (OR, 95% CI). To assess the combined and individual effects of heavy metal mixtures on the prevalence of depression, WQS regression was conducted by calculating a weighted linear index and assigning appropriate weights to each component metal. In addition, RCS regression was performed to investigate nonlinear associations between heavy metal concentrations and depression risk. Finally, Pearson's correlation coefficients were calculated to assess inter-metal correlations among the 12 heavy metals, while BKMR modeling was employed to examine the combined effects of multiple heavy metals, identify potential interactions and determine the relative contribution of each metal within the exposure mixture. A p -value of < 0.05 was considered statistically significant throughout all analyses. Results Baseline characteristics This study included 8,814 participants (45.7% male; 54.3% female) with baseline characteristics in Table 1 . Mean age and BMI were 48.2 years and 29.3 kg/m² respectively. The cohort comprised 43.6% Non-Hispanic White and 9.1% Other Hispanic. Most held college degrees (54.4%), were married (53.1%), reported vigorous activity (37.3%), non-smoking (56%), or heavy drinking (63.6%), with 42.3% having medium income (PIR 1–3). Prevalence of chronic conditions included hypertension (40%), diabetes (16.6%), cardiovascular disease (9.1%), and chronic kidney disease (93.9%). Urinary metal concentrations (µg/L): barium 1.99, cadmium 0.37, cobalt 0.53, cesium 5.22, molybdenum 53.12, lead 0.68, antimony 0.09, thallium 0.19, tungsten 0.12; blood metal concentrations: cadmium 0.50 (µg/L), lead 1.50 (µg/dL), mercury 1.55 (µg/L). Compared with the non-depressed group, those in the depression group showed significant differences in several variables, including sex, BMI, race/ethnicity, education level, marital status, PIR, physical activity, presence of cardiovascular disease, hypertension, diabetes, urinary levels of cadmium and thallium as well as blood concentrations of cadmium and mercury. Table 1 Baseline characteristics of the 8,814 participants with and without depression. Variables Total (n = 8,814) Non-Depression (n = 8,083) Depression (n = 731) P -value Age, (years) 48.2 ± 17.6 48.2 ± 17.7 47.5 ± 16.4 0.289 Sex, n (%) < 0.001 Male 4032(45.7%) 3796(46.9%) 236(32.3%) Female 4782(54.3%) 4287(53.1%) 495(67.7%) BMI (kg/m 2 ) 29.3 ± 7.0 29.1 ± 6.9 30.8 ± 8.3 < 0.001 Race, n (%) 0.0017 Mexican American 1343(15.2%) 1229(15.2%) 114(15.6%) Other Hispanic 838(9.1%) 738(9.1%) 100(13.7%) Non Hispanic White 3831(43.6%) 3524(43.6%) 307(42%) Non Hispanic Black 1906(21.7%) 1756(21.7%) 150(20.5%) Other Race 896(10.3%) 836(10.3%) 60(8.2%) Education level, n (%) < 0.001 College or above 4797(54.4%) 4488(55.5%) 309(42.3%) High school or equiva len 2061(23.4%) 1889(23.4%) 172(23.5%) Less than high school 1956(22.2%) 1706(21.1%) 250(34.2%) Marital status, n (%) < 0.001 Married 4679(53.1%) 4403(54.5%) 276(37.8%) Unmarried 3512(39.8%) 3122(38.6%) 390(53.4%) Others 623(7.1%) 558(6.9%) 65(8.9%) PIR, n (%) 3) 3372(38.3%) 3241(40.1%) 131(17.9%) Medium(1 ~ 3) 3732(42.3%) 3405(42.1%) 327(44.7%) Low(<1) 1710(19.4%) 1437(17.8%) 273(37.3%) Physical activity, n (%) < 0.001 Vigorous 3291(37.3%) 3070(38%) 221(30.2%) Moderate 2698(30.6%) 2501(30.9%) 197(26.9%) Other 2825(32.1%) 2512(31.1%) 313(42.8%) Smoking status, n (%) < 0.001 Current smoker 1803(20.5%) 1517(18.8%) 286(39.1%) Ex-smoker 2077(23.6%) 1934(23.9%) 143(19.6%) None 4934(56%) 4632(57.3%) 302(41.3%) Alcohol consumption, n (%) 0.182 Heavy drinker 5603(63.6%) 5134(63.5%) 469(64.2%) Low-to-moderate drinker 1470(16.7%) 1336(16.5%) 134(18.3%) Non drinker 1741(19.8%) 1613(20%) 128(17.5%) CVD, n (%) < 0.001 Yes 802(9.1%) 672(8.3%) 130(17.8%) No 8012(90.9%) 7411(91.7%) 601(82.2%) CKD, n (%) 0.782 Yes 8280(93.9%) 7595(94%) 685(93.7%) No 534(6.1%) 488(6%) 46(6.3%) Hypertension, n (%) < 0.001 Yes 3524(40%) 3180(39.3%) 344(47.1%) No 5290(60%) 4903(60.7%) 387(52.9%) Diabetes, n (%) < 0.001 Yes 1460(16.6%) 1281(15.8%) 179(24.5%) No 7354(83.4%) 6802(84.2%) 552(75.5%) Urine Barium (ug/L) 1.99 ± 3.55 2.00 ± 3.65 1.93 ± 2.28 0.599 Urine Cadmium (ug/L) 0.37 ± 0.45 0.36 ± 0.43 0.49 ± 0.59 < 0.001 Urine Cobalt (ug/L) 0.53 ± 1.06 0.52 ± 1.07 0.59 ± 1.05 0.073 Urine Cesium (ug/L) 5.22 ± 3.82 5.23 ± 3.83 5.13 ± 3.63 0.49 Urine Molybdenum (ug/L) 53.12 ± 49.17 52.93 ± 49.22 55.16 ± 48.65 0.241 Urine Lead (ug/L) 0.68 ± 1.26 0.68 ± 1.29 0.67 ± 0.83 0.771 Urine Antimony (ug/L) 0.09 ± 0.17 0.09 ± 0.18 0.09 ± 0.13 0.653 Urine Thallium (ug/L) 0.19 ± 0.16 0.19 ± 0.17 0.18 ± 0.13 0.007 Urine Tungsten (ug/L) 0.12 ± 0.44 0.13 ± 0.45 0.15 ± 0.30 0.192 Blood Cadmium (ug/L) 0.50 ± 0.56 0.48 ± 0.53 0.73 ± 0.80 < 0.001 Blood Lead (ug/dL) 1.50 ± 1.52 1.51 ± 1.54 1.45 ± 1.33 0.315 Blood mercury, total (ug/L) 1.55 ± 2.55 1.59 ± 2.62 1.04 ± 1.57 < 0.001 Mean ± SE for continuous variables: P -value was calculated by the weighted t-test. % (SE) for categorical variables: P -value was calculated by the weighted chi-square test. BMI, body mass index. PIR, family poverty income ratio. CVD, cardiovascular diseases. CKD, chronic kidney disease. Association between heavy metals and depression In the fully adjusted model, urinary tungsten levels exhibited a significant association with depression in both the Q2 and Q4 quartiles compared with the Q1 group. Specifically, higher tungsten concentrations were associated with an increased risk of depression (Q2: OR = 1.42, 95% CI: 1.10–1.83, p = 0.008; Q4: OR = 1.20, 95% CI: 1.04–1.88, p = 0.026). Similarly, urinary thallium levels showed a significant association in the Q3 and Q4 groups relative to Q1, indicating that lower concentrations of urinary thallium were linked to a reduced risk of depression (Q3: OR = 0.63, 95% CI: 0.46–0.86, p = 0.003; Q4: OR = 0.52, 95% CI: 0.36–0.73, p < 0.001). Additionally, urinary antimony levels were significantly associated with depression in the Q3 group compared with Q1 (Q3: OR = 1.34, 95% CI: 1.03–1.75, p = 0.029). In this case, even though associations in the Q2 (Q2: OR = 1.07, 95% CI: 0.83–1.39, p = 0.595) and Q4 (Q4: OR = 1.25, 95% CI: 0.93–1.68, p = 0.134) groups, where hazard ratios (HR) exceeded one, did not reach statistical significance, the upward trend suggested a potential positive relationship between antimony exposure and depression risk. Finally, blood levels of mercury exhibited a significant association with depression in the Q3 and Q4 groups compared with Q1, hence suggesting that lower mercury concentrations in the blood were linked to a decreased risk of depression (Q3: OR = 0.80, 95% CI: 0.64–1.00, p = 0.047; Q4: OR = 0.70, 95% CI: 0.54–0.90, p = 0.005). In contrast, no statistically significant associations were observed for urinary levels of molybdenum, lead, cobalt, cesium, cadmium and barium or for blood levels of lead and cadmium (Fig. 2 ). Nonlinear relationship between heavy metal exposure and depression risk Evidence of significant nonlinearity was observed for several heavy metals, including urinary cadmium ( p = 0.004), cobalt ( p = 0.005), lead ( p = 0.024), antimony ( p < 0.001) and tungsten ( p < 0.001) as well as blood cadmium ( p < 0.001) and mercury ( p < 0.01). Conversely, no significant nonlinear relationships were identified for urinary barium, cesium, molybdenum and thallium, or for blood lead levels, with their corresponding p -values exceeding 0.05. Altogether, the findings suggest that the relationship between these specific metal exposures and depression risk did not follow a linear pattern within the studied range (Fig. 3 ). Heavy metal co-exposure and depression in WQS model The WQS index demonstrated a positive correlation between heavy metal co-exposure and depression risk. In the crude model, the OR was 1.76 (95% CI: 1.54–2.02, p < 0.001), and it decreased to 1.17 (95% CI: 1.02–1.35, p = 0.026) after adjusting for covariates in Model I (Table. S1). Among the 12 heavy metals analyzed, the urinary levels of tungsten contributed the most to the risk of depression, with a relative weight of 29.3% (Fig. 4 A). Similarly, the analysis yielded a WQS index of 0.69 (95% CI: 0.62–0.76, p < 0.001) in the crude model, and it increased to 0.81 (95% CI: 0.70–0.94, p = 0.006) in Model I, indicating a statistically significant inverse relationship between heavy metal co-exposures and the prevalence of depressive symptoms (Table S1 ). Furthermore, within this model, blood mercury was identified as the most influential component, contributing 44% to the overall effect (Fig. 4 B). The correlation among 12 heavy metals was assessed by Pearson’s correlation coefficients (Fig. S1 ). There was a medium correlation between urine thallium and urine cesium (r = 0.66), blood and urine lead (r = 0.66). Other heavy metals were all weak or no correlations. BKMR model to assess the association between heavy metal coexposure and depression risk The results indicated an increased risk of depression among individuals exposed to heavy metal mixtures above the 55th percentile compared with those at median exposure levels (Fig. 5 ). Among the analyzed heavy metals, urinary barium exhibited the highest posterior inclusion probabili probabilities ( PIPs ) of 0.448, suggesting that it could be the most influential contributor to depression risk. The PIPs values, determined by the BKMR model, are presented in Table S2 . Furthermore, as illustrated in Fig. 6 , under standardized conditions (where concentrations of all other heavy metals were fixed at their median levels), significant nonlinear dose-response relationships were observed between depression risk and urinary concentrations of barium, cadmium, cobalt, molybdenum, thallium, tungsten as well as blood levels of mercury. These associations displayed a generally positive correlation across the range of measured concentrations. Interestingly, distinct exposure-response patterns were also identified for lead and antimony. Specifically, urinary lead, urinary antimony and blood lead concentrations exhibited inverted U-shaped relationships with depression risk, indicating that the risk peaked at moderate exposure levels before declining at higher concentrations. Discussion This study represents that employs multiple advanced statistical methods on a large-scale, nationally representative dataset to assess the combined effects of heavy metal mixtures in blood and urine on the risk of depression. The findings indicated that participants diagnosed with depressive disorders exhibited significantly higher concentrations of cadmium in both urine and blood, along with lower levels of urinary thallium and blood mercury compared with non-depressed individuals. Multivariate logistic regression analysis further demonstrated that exposure to urinary tungsten and antimony was independently associated with an increased risk of depression, while lower concentrations of urinary thallium and blood mercury were significantly correlated with a reduced risk. Furthermore, WQS and RCS regression analyses revealed that urinary tungsten and antimony had significant positive nonlinear associations with depression, with notable negative nonlinear correlations between urinary thallium, blood mercury and depression risk also identified. Importantly, although urinary barium and cobalt emerged as the most significant contributors to the overall effect in the BKMR model, these associations did not reach statistical significance in the other analytical models. In recent years, an increasing body of evidence has highlighted the significant impact of heavy metals on mental health [16, 20, 21, 33]. These metals, which can enter the human body through multiple routes, including dietary intake, personal care products and environmental contamination [11–13], are believed to contribute to neurodegenerative processes via mechanisms such as oxidative stress, inflammatory cascades as well as disruption to the brain-gut axis and neuronal integrity. Existing research has further suggested that residing in areas with elevated soil concentrations of heavy metals was associated with a higher risk of developing mental health disorders [9, 12]. Parallel to these findings, there has been a marked rise in the prevalence of neuropsychiatric conditions such as autism spectrum disorder, attention deficit/hyperactivity disorder (ADHD), learning difficulties and aggressive behavior [26, 34]. Tungsten, known for its stable chemical properties, is widely used across both defense and industrial sectors [35, 36]. However, it has recently been recognized as an emerging environmental pollutant [37]. In this context, research has shown that exposure to sodium tungstate could induce mild neurobehavioral changes in rats [38]. Additionally, hard alloys containing 79–95% tungsten carbide and approximately 10% cobalt have been linked to declines in memory and sensory perception among exposed workers [39]. In this study, a positive nonlinear relationship was observed between urinary tungsten levels and depression risk, with the association being particularly pronounced in the case of metal co-exposure. Tungsten was also significantly correlated with several other heavy metals detected in urine samples. Notably, a combined exposure to tungsten, cobalt and nickel has been shown to produce neurotoxic effects through their synergistic interactions [39, 40]. In experiments involving mouse macrophages, tungsten carbide-cobalt compounds exhibited greater cytotoxicity than either cobalt metal or tungsten carbide alone [39]. Moreover, tungsten can interfere with molybdenum absorption, thereby inhibiting the synthesis of sulfite oxidase, an essential enzyme in redox homeostasis. Such disruption was shown to lead to oxidative imbalance and bioenergetic dysfunction within rat brains, resulting in seizures and neurological damage [41]. Overall, the toxicity profile of tungsten is complex, often intensifying the adverse effects of co-exposure with other metals [36, 37]. However, despite these concerns, tungsten levels are not routinely monitored in environmental air or water sources, thus highlighting the urgent need for regulatory policies aimed at minimizing exposure risk. As a metalloid, antimony has been classified as a priority pollutant due to its potential carcinogenicity [42]. Consequently, previous studies have mostly focused on its toxicity to cardiovascular and respiratory systems, with limited attention given to its neurological and psychiatric effects [43, 44]. However, animal studies have demonstrated that antimony exposure in zebrafish embryos could induce oxidative stress and inflammation, characterized by elevated levels of interleukins (IL-1β, IL-6), tumor necrosis factor αand pro-oxidative substances such as glutathione peroxidase and malondialdehyde. These changes could subsequently suppress acetylcholinesterase activity and cause astrocytic dysfunction, ultimately impairing nervous system development [45]. Furthermore, antimony has been found to trigger ferroptosis via chaperone-mediated autophagy, with ferritinophagy potentially contributing to its neurotoxic mechanisms [46, 47]. In this study, a positive nonlinear association was identified between antimony exposure and depression risk, although this association was borderline significant. In addition, antimony levels were significantly correlated with other heavy metals, including tungsten, molybdenum, cesium, thallium, lead and cadmium, in urine. Consistent with the current findings, a previous report also indicated a significant nonlinear relationship between antimony and a higher risk of depressive symptoms [42]. In contrast, Shiue’s study did not find a significant association between antimony exposure and depression, with the dose-response relationship also not investigated [48]. These discrepancies could be attributed to the nonlinear nature of the correlation or to the lack of adjustment for key covariates in Shiue’s analysis. Thallium, a widely distributed trace metal used in high-tech, medical and chemical industries, is highly toxic due to its bio-accumulative properties and potent neurotoxicity. Thus, exposure can lead to acute poisoning (accidental, intentional or criminal) or chronic toxicity through occupational exposure or contaminated food sources [49]. The neurological effects of thallium poisoning often develop insidiously, manifesting initially as sensorimotor dysfunction, along with early psychiatric symptoms, such as anxiety and depression as well as cognitive impairments, including memory and attention deficits [50–51]. In this study, a significant reduction in urinary thallium levels was observed among individuals with depression compared with healthy controls. These findings are supported by previous studies reporting a negative relationship between urinary thallium concentration and depression risk [42]. Furthermore, Liu et al. demonstrated that thallium exposure could induce irreversible neurotoxicity, primarily via neuronal synaptic damage, transcriptional dysregulation and mitochondrial oxidative stress, which eventually lead to neuronal cell death [52]. Impaired renal function can reduce the excretion of urinary thallium, resulting in excessive thallium exposure if it is not effectively eliminated [53]. This study also identified a significant correlation between thallium and various heavy metal ions, including cesium, molybdenum, cadmium, barium, cobalt, lead, and tungsten. However, the precise mechanisms underlying their interactions remain unclear. It is hypothesized that these heavy metals may engage in complex interactions concerning environmental exposure, in vivo distribution, facilitation of enzymatic processes, reduction of oxidative stress and inflammatory responses, as well as the maintenance of normal physiological functions in neurons [54]. Mercury, one of the most toxic heavy metals as ranked by the U.S. Agency for Toxic Substances and Disease Registry, exists in elemental, organic and inorganic forms, each exhibiting distinct pharmacokinetic behaviors and clinical implications due to their interconversion in vivo [55–57]. Extensive evidence links mercury exposure to depression which may occur through mechanisms such as oxidative stress, neuroinflammation, dysregulation of neurotrophic factors and altered neurotransmitter systems [58, 59]. In this study, the findings revealed a nonlinear association between blood mercury levels and depression risk, with mercury not only emerging as a significant contributor within heavy metal mixtures but also exhibiting inverse correlations with other urinary and blood heavy metals. Interestingly, mercury also demonstrated a dual-effect relationship with depression, whereby both low and high levels could confer neurotoxic effects. However, while Fu et al. reported a negative association between blood mercury levels and depression risk in adults [43]. Nguyen et al. found no significant differences in blood mercury concentrations between depressed individuals and non-depressed controls [15]. Furthermore, it is worth noting that AnGongNiuHuang Pill (AGNH), a traditional Chinese medicine containing realgar (arsenic disulfide, As2S2) and cinnabar (mercuric sulfide, HgS), has demonstrated therapeutic efficacy in managing neurological disorders such as epileptic seizures, cerebrovascular events and related sleep disturbances, including insomnia and fragmented dreaming [60]. These discrepancies likely reflect a nonlinear dose-response relationship between mercury exposure and depression, with such association further modulated by unaccounted variables such as selenium co-exposure, dietary habits, socioeconomic status, exposure duration and individual susceptibility or comorbidities [61, 62]. Blood levels of cadmium, a non-essential and toxic heavy metal, has been shown to exhibit a significant positive correlation with the risk of depressive symptoms [63, 64]. In this context, previous studies have suggested that cadmium can penetrate the blood-brain barrier, thereby inducing glial oxidative damage through mechanisms such as lipid peroxidation, disruption of antioxidant defenses, interference with zinc-calcium homeostasis, metallothionein induction and activation of apoptotic pathways. These neurotoxic effects are subsequently manifested as attention deficits, memory impairments, learning disabilities, headaches, vertigo and other neurological symptoms [42, 62, 65, 66]. In the current study, elevated blood and urinary levels of cadmium were observed among individuals with depression compared with the general population. However, paradoxically, multivariate regression models adjusted for all covariates showed no significant association between cadmium concentrations and depression risk. This finding aligns with that of Sun et al. who reported an inverse correlation between higher serum cadmium levels and the incidence of major depressive disorder, with no significant differences observed in the case of urinary cadmium levels [20]. Similarly, other studies found no association between blood cadmium levels and depression, particularly in elderly populations [67, 68]. These inconsistencies may stem from the complexity of cadmium metabolism in humans. Indeed, factors such as interactions with essential metals (e.g., zinc, iron and selenium can competitively inhibit cadmium absorption), smoking-related cadmium exposure, the protective role of moderate physical activity and altered cadmium burden in metabolic disorders (e.g., diabetes and hypertension) likely contribute to the intricate and multifaceted relationship between cadmium exposure and mental health outcomes [14, 20, 69–71]. This highlights the need for more comprehensive exposure assessments and mechanistic studies to elucidate cadmium’s role in depressive symptomatology. Barium, an alkaline earth metal, exhibits toxicity that depends largely on its chemical form (salt type) and absorption route [72]. However, its neurotoxicity remains poorly characterized, with existing studies also reporting conflicting results [20, 34, 48, 72, 73]. For instance, Sun et al. found that serum barium levels were positively correlated with the severity of depressive symptoms in a dose-dependent manner [20]. In contrast, Rokoff et al. observed no clinically meaningful association between serum barium levels and depressive symptoms during the perinatal period [74]. Moreover, Zhang et al. reported that children with autism spectrum disorder exhibited higher barium levels compared with healthy controls [35]. Toxicologically, highly soluble forms, such as BaCl 2 , are known to induce diarrhea, liver and kidney dysfunction, anxiety, neurological impairments, dyspnea and even cerebral edema [74]. The BKMR modeling applied in this study identified barium as the most influential contributor to depression risk within metal mixtures, even though its concentration differences across depression and non-depression groups were not statistically significant. The central role of barium may stem from its dual neurotoxic mechanisms: electrophysiological interference via potassium channel blockade and molecular reprogramming through oxidative and epigenetic pathways. These pathways position barium as a latent contributor to depression pathogenesis [18, 75–77]. However, this hypothesis remains largely speculative, underscoring the need for rigorous mechanistic studies to clarify barium's role in neuropsychiatric disorders. The primary strength of this study lies in the application of multiple advanced statistical models, thus enabling a comprehensive assessment of the associations between metal mixtures, individual metal exposures and major depressive disorder. This multifaceted analytical approach enhances the robustness and reliability of the findings while addressing the limitations inherent in traditional epidemiological methods that typically assess single-metal exposures in isolation. Furthermore, this study successfully identified key metals that contribute most significantly to the observed associations between metal mixtures and depression, an area that has remained largely underexplored in previous research. However, several limitations should be acknowledged. Firstly, the cross-sectional design of this study precludes the ability to infer causal relationships or evaluate temporal dynamics between metal exposure and depression risk. Secondly, the omission of neuroactive metals, such as iron, copper, zinc and manganese, limits the interpretability of the results, as these elements may interact synergistically or antagonistically with toxic metals to influence neuropsychiatric outcomes. Additionally, the inability to incorporate NHANES's complex sampling design may affect the generalizability of the findings to broader populations. Finally, despite adjusting for a wide range of covariates, the potential for residual confounding from unmeasured factors (e.g., occupational exposure history, dietary habits and medication use) cannot be entirely excluded. Conclusions In summary, this study revealed significant differences in heavy metal exposure profiles between individuals with depressive disorders and healthy controls, thus highlighting a positive association between cumulative exposure to metal mixtures and the risk of depression among U.S. adults. Through mixed-exposure analyses, tungsten and antimony were identified as the primary contributors to elevated depression risk, while mercury and thallium exhibited inverse associations. Furthermore, barium also emerged as a potential key contributor to these interactions. These findings emphasize the need for stricter industrial regulations and enhanced personal hygiene practices to reduce toxic metal exposure. Future studies should adopt prospective cohort designs, incorporate multiple biomarkers and define clear clinical endpoints to better elucidate the biological mechanisms underlying the relationship between cumulative metal exposure and depression. Ultimately, the results underscore the critical importance of comprehensive environmental metal exposure management in mitigating depression as an emerging public health burden. Abbreviations NHANES National Health and Nutrition Examination Survey RCS Restricted cubic spline WQS Weighted quantile sum regression BKMR Bayesian kernel machine regression EMs Environmental metals PHQ-9 Patient Health Questionnaire-9 BMI Body mass index PIR Poverty-to-income ratio CVD Cardiovascular disease CKD Chronic kidney disease IL-1β Interleukin-β IL-6 Interleukin-6 TNF-ɑ Tumor Necrosis Factor-ɑ Declarations The NHANES agreement has been reviewed and approved by the NCHS Research Ethics Committee. All participants provided written informed consent before participating.. (https://wwwn.cdc.gov/nchs/nhanes/). Consent for publication Supplementary Information Data is provided within the manuscript or supplementary information files. Acknowledgements The authors are grateful to all colleagues of Chengdu University of Traditional Chinese Medicine Affiliated Fifth People's hospital who participated in this study. Author contributions Yuanxin Guo and Zhongrui Ma conceived the presented idea. Yuanxin Guo and Yixu Chen performed the data analysis, figure preparation, and manuscript writing. Houfeng Zhou, Yuting Fan and Tao Feng were involved in the processing of the data and figures. Zhongrui Ma provided fnal approval of the version submitted for publication. All authors contributed to the article and approved the submitted version. Funding Not applicable. Data availability The survey data is available to researchers and data users globally via the website www.cdc.gov/nchs/nhanes/. Not applicable. Competing interests The authors declare no competing interests. References 1. Beurel E, Toups M, Nemeroff CB: The Bidirectional Relationship of Depression and Inflammation: Double Trouble. Neuron 2020, 107(2):234-256. 2. Malhi GS, Mann JJ: Depression. Lancet 2018, 392(10161):2299-2312. 3. 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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-7038470","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":495576632,"identity":"e17bc499-4d71-443c-8d71-e60adbcc4c3e","order_by":0,"name":"Yuanxin Guo","email":"","orcid":"","institution":"Chengdu University of Traditional Chinese Medicine Affiliated Fifth People's hospital","correspondingAuthor":false,"prefix":"","firstName":"Yuanxin","middleName":"","lastName":"Guo","suffix":""},{"id":495576633,"identity":"f1e73ae7-7fde-428a-b7a8-2d8ab5e234a6","order_by":1,"name":"Yixu Chen","email":"","orcid":"","institution":"Chengdu University of Traditional Chinese Medicine Affiliated Fifth People's hospital","correspondingAuthor":false,"prefix":"","firstName":"Yixu","middleName":"","lastName":"Chen","suffix":""},{"id":495576634,"identity":"167c2c32-187d-4e0c-8d9c-9a9015e80001","order_by":2,"name":"Houfeng Zhou","email":"","orcid":"","institution":"Chengdu University of Traditional Chinese Medicine Affiliated Fifth People's hospital","correspondingAuthor":false,"prefix":"","firstName":"Houfeng","middleName":"","lastName":"Zhou","suffix":""},{"id":495576635,"identity":"4003569b-bd8c-407d-b71f-e4266545bb3d","order_by":3,"name":"Yuting Fan","email":"","orcid":"","institution":"Chengdu University of Traditional Chinese Medicine Affiliated Fifth People's hospital","correspondingAuthor":false,"prefix":"","firstName":"Yuting","middleName":"","lastName":"Fan","suffix":""},{"id":495576636,"identity":"4c3c59ce-3297-4931-be50-5001e252f92c","order_by":4,"name":"Tao Feng","email":"","orcid":"","institution":"Chengdu University of Traditional Chinese Medicine Affiliated Fifth People's hospital","correspondingAuthor":false,"prefix":"","firstName":"Tao","middleName":"","lastName":"Feng","suffix":""},{"id":495576637,"identity":"e1d04b4e-727c-4612-a178-6ebd78ff1595","order_by":5,"name":"Zhongrui Ma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYPACCWY2ZuYDBz78IF6LDTsfe1viwZk9xGtJ45fjOWN8mIONCLW67ccfPi74dViaTSLnw2EGHgZ5frED+LWYnckxNp7Zd9iYTSJ3w+ECCwbDmbMTCGg5kMMmzdtzOBmsZQYPQ4LBbUJazj9/BtJS3yaR8+AwDxsxWm4kmEnz/EhjZuM5w0CsljfGxrwNNsxs7G0GwECWIMIv59MfPub5I8Es38z8+MOHHzby/NIEtIABYxucKUGEcjD4Q6zCUTAKRsEoGJEAAEKkROQtN74+AAAAAElFTkSuQmCC","orcid":"","institution":"Chengdu University of Traditional Chinese Medicine Affiliated Fifth People's hospital","correspondingAuthor":true,"prefix":"","firstName":"Zhongrui","middleName":"","lastName":"Ma","suffix":""}],"badges":[],"createdAt":"2025-07-03 12:53:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7038470/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7038470/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40001-026-03850-x","type":"published","date":"2026-01-14T16:30:19+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":88349663,"identity":"ce5a1459-0bf9-432a-904a-aa8925d968b7","added_by":"auto","created_at":"2025-08-05 14:08:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":193690,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of participant selection process for this study. Abbreviations: NHANES, National Health and Nutrition Examination Survey; PHQ-9, Patient Health Questionnaire-9; BMI, body mass index; PIR, family poverty-to-income ratio; CVD, cardiovascular diseases; CKD, chronic kidney disease.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7038470/v1/b1b4a1fe03c5584d2ebc5c9e.png"},{"id":88348492,"identity":"c7d7601c-16a3-445d-bac3-afedec06af29","added_by":"auto","created_at":"2025-08-05 14:00:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":382177,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between heavy metal concentrations and risk of depression, adjusted for sex, age, BMI, race/ethnicity, education level, marital status, PIR, physical activity, smoking history, alcohol consumption, CVD, CKD, hypertension and diabetes.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7038470/v1/d3ce67eefde42d3eb7ea2935.png"},{"id":88348468,"identity":"40dcf44b-124c-4d80-917a-396ebc150b35","added_by":"auto","created_at":"2025-08-05 14:00:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":103916,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline plots illustrating the nonlinear relationships between heavy metal exposure and depression risk (urinary levels of A: barium. B: cadmium. C: cobalt. D: cesium. E: molybdenum. F: lead. G: antimony. H: thallium. I: tungsten. blood levels of J: cadmium. K: lead. L: mercury.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7038470/v1/7b7fa85457d0fae02fb12490.png"},{"id":88348470,"identity":"ad71db51-8f4e-472b-916f-124536d7f3e7","added_by":"auto","created_at":"2025-08-05 14:00:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":123594,"visible":true,"origin":"","legend":"\u003cp\u003eWeights of individual heavy metals for the prevalence of depression as determined in the WQS model in the (A) positive direction. (B) negative direction. This model was adjusted for all covariates. WQS, weighted quantile sum.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7038470/v1/acba3b1768bf3c7d6511925d.png"},{"id":88348487,"identity":"e6f3296d-9880-48e1-9bb8-f9fc462d35ff","added_by":"auto","created_at":"2025-08-05 14:00:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":73599,"visible":true,"origin":"","legend":"\u003cp\u003eOverall effect estimates (95% CI) of heavy metal mixtures on depression risk.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7038470/v1/755a820e9808fd7e96bc88d4.png"},{"id":88348489,"identity":"5e84143d-7dbf-4513-9952-963ff9bb3362","added_by":"auto","created_at":"2025-08-05 14:00:09","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":149915,"visible":true,"origin":"","legend":"\u003cp\u003eUnivariate exposure–response functions for each heavy metal, with all other metals fixed at the 50th percentile.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7038470/v1/0f39d8ff2dccdd3e9343f72b.png"},{"id":100615922,"identity":"49aeff6a-27a1-45c6-8108-0b0502a3e652","added_by":"auto","created_at":"2026-01-19 17:38:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1974476,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7038470/v1/0c9db991-ed07-44c1-a64e-3e92fa2eeb4d.pdf"},{"id":88348466,"identity":"abf22b85-9fda-439a-ba86-3169c96c95d3","added_by":"auto","created_at":"2025-08-05 14:00:07","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1410129,"visible":true,"origin":"","legend":"","description":"","filename":"Researchdata.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7038470/v1/443bc8e805e244272af61deb.xlsx"},{"id":88348469,"identity":"b44cc82b-4baa-4eea-b570-aa76e2476230","added_by":"auto","created_at":"2025-08-05 14:00:07","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":484466,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-7038470/v1/16af98127908e085090ce159.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between heavy metals’exposure and depression: Findings of the NHANES from 2003 to 2020","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDepressive disorders, characterized by intense emotional fluctuations and a persistent dysphoric mood, represent a major category of mood disorders with high prevalence, disability rates, recurrence rates and suicide risk. As such, it imposes substantial psychological and economic burdens on both individuals and society [1, 2]. Globally, the prevalence of depression reached 4.4% in 2020, reflecting a 60% increase over the past three decades [3]. Furthermore, according to Greenberg et al. adult depression accounted for an economic burden of US\u003cspan\u003e$\u003c/span\u003e326.2\u0026nbsp;billion in 2020, with projections indicating that it will become the leading contributor to the global disease burden by 2030 [4,5]. However, despite ongoing therapeutic advancements, achieving complete remission currently remains a significant challenge. In particular, pharmacological treatments based on the \"monoaminergic neurotransmitter imbalance\" hypothesis often result in considerable side effects, thereby contributing to suboptimal treatment outcomes [6\u0026ndash;8]. Consequently, preventing disease onset and progression remains the most practical and critical strategy.\u003c/p\u003e\u003cp\u003ePrevious studies indicated that depression results from interactions between environmental and genetic factors, with heritability accounting for less than 40% of the observed variance [9]. Indeed, the pathogenesis of depressive disorders involves multifaceted mechanisms that extend beyond the well-established links to lifestyle factors, cerebral structural abnormalities, immune-metabolic dysregulation and genetic predisposition [10, 11], with increasing evidence now underscoring the crucial role of environmental metals (EMs) in disease development. Global industrialization has exacerbated contamination of air, soil and water, leading to widespread human exposure to multiple heavy metals, including cadmium, mercury and arsenic [12\u0026ndash;14]. Upon systemic absorption through the circulatory, respiratory and digestive systems, these metals accumulate in various organs and tissues. Subsequently, at critical concentrations, they disrupt essential element homeostasis (e.g., zinc, iron, copper) in a dose-dependent manner, impair hypothalamic-pituitary-adrenal (HPA) axis function, alter glucose/lipid metabolism and compromise mitochondrial integrity. Altogether, these disruptions trigger oxidative stress and inflammatory responses, resulting in irreversible damage to biomacromolecules, such as DNA and RNA, and contributing to disease pathogenesis. The nervous system is particularly susceptible to such metal-induced pathophysiological damage. For instance, in studies involving rodents, chronic cadmium exposure was shown to increase hippocampal levels of thiobarbituric acid reactive substances (TBARS), nitric oxide (NO) as well as catalase (CAT) activity while suppressing superoxide dismutase (SOD) activity, ultimately leading to CA3 neuronal degeneration and depressive-like behaviors [15, 16]. Similarly, thallium has been found to interact selectively with oligonucleotide repair genes, such as OGG1, to induce exogenous DNA damage comparable to that caused by ultraviolet/ionizing radiation and alkylating agents, which can manifest as neuropsychiatric dysfunction [17]. Additionally, barium can disrupt potassium ion channel dynamics and alter neuronal action potentials, thereby enhancing neurotoxicity and exacerbating depressive symptoms, especially in elderly females [18, 19]. However, not all metal accumulations are positively associated with depressive disorders. For example, Sun et al. reported an inverse relationship between nickel levels and the risk of major depression [20]. Furthermore, in an analysis of 2017\u0026ndash;2018 National Health and Nutrition Examination Survey (NHANES) data involving seven metals (lead, mercury, cadmium, manganese, selenium, chromium, cobalt), Fang et al. found a positive association between cadmium and depression but an inverse association with mercury, while the remaining metals showed no significant correlations [21].\u003c/p\u003e\u003cp\u003eWhile current research predominantly focuses on assessing depression risks associated with exposure to individual metals, comprehensive evaluations of the effects of environmental metal mixtures remain limited. Indeed, in real-world scenarios, environmental metals commonly coexist as complex mixtures, with individual heavy metals exhibiting distinct toxicological mechanisms, including differences in metabolic kinetics, interactions between ultimate toxicants and target molecules, alterations in cellular signaling pathways as well as disruptions in biological repair processes [18]. Moreover, these metals interact in multifaceted ways, producing additive, synergistic, antagonistic or even independent neurotoxic effects. Additionally, multi-metal exposures may also generate complex, nonlinear associations with health outcomes and related biomarkers, with conventional multivariable parametric regression models often struggling to capture such relationships due to limitations in addressing multicollinearity, model misspecification and insufficient capacity to estimate the combined effects of multiple exposures [22\u0026ndash;25].\u003c/p\u003e\u003cp\u003eThis study addresses the critical need for large-scale epidemiological evidence on environmental metal mixtures and depression, analyzing NHANES data (2003\u0026ndash;2020) through advanced modeling: Restricted Cubic Spline (RCS), Weighted Quantile Sum (WQS) regression and Bayesian Kernel Machine Regression (BKMR). These approaches overcome the limitations of traditional linear models and account for multicollinearity, enabling a more comprehensive evaluation of both individual and combined effects of heavy metals. The findings aim to clarify the critical role of mixed metal exposures in the pathogenesis of depression and identify modifiable environmental risk factors that could inform targeted prevention strategies.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cb\u003eStudy population\u003c/b\u003e\u003c/p\u003e\u003cp\u003e The NHANES study protocols were approved by the Ethics Review Board of the National Center for Health Statistics. Detailed information about the database is available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/default.aspx\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes/default.aspx\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eThis study\u0026rsquo;s analytical sample included data from eight consecutive NHANES cycles (2003\u0026ndash;2020). Due to the coronavirus disease 2019 (COVID-19) pandemic, data collected between 2019 and March 2020 were combined with the 2017\u0026ndash;2018 cycle, following NHANES guidelines [26]. The inclusion criteria were as follows: (1) participants aged 20 years or older; (2) participants who took part in the blood and urine sub-study of heavy metals; and (3) participant depression status assessed using NHANES questionnaire data.\u003c/p\u003e\u003cp\u003eFurthermore, the following exclusion criteria were applied: (1) Missing or undetectable data on blood trace elements (cadmium, lead, mercury) or urine trace elements (barium, cadmium, cobalt, cesium, molybdenum, lead, antimony, thallium, tungsten); (2) incomplete Patient Health Questionnaire-9 (PHQ-9) responses, and (3) missing data on key covariates, including body mass index (BMI), race/ethnicity, education level, marital status, income-to-poverty ratio, physical activity, smoking status, alcohol consumption, cardiovascular disease, chronic kidney disease, hypertension and diabetes. After applying the above criteria, the final analytical sample consisted of 8,814 participants. The study design flow is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eAssessment of depression\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe PHQ-9 is a validated and reliable screening tool for assessing the severity of depression symptoms experienced over the past two weeks, and it has demonstrated a sensitivity and specificity of 88% for detecting depressive disorders [27]. The PHQ-9 consists of nine items covering the following symptoms: fatigue, appetite disturbances, psychomotor retardation or agitation, concentration difficulties, sleep disturbances, anhedonia, depressed mood, feelings of worthlessness and suicidal thoughts, with each item rated on a four-point Likert scale (0 = \u0026ldquo;not at all\u0026rdquo;, 1 = \u0026ldquo;several days\u0026rdquo;, 2 = \u0026ldquo;more than half the days\u0026rdquo;, and 3 = \u0026ldquo;Nearly every day\u0026rdquo;), resulting in a total score ranging from 0 to 27. For the purposes of this research, depression was operationally defined as a score of 10 or higher, in alignment with previous studies [28].\u003c/p\u003e\u003cp\u003e\u003cb\u003eAssessment of heavy metals\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAll blood and urine specimens were collected, processed, stored and eventually shipped to the Environmental Health Sciences Laboratory of the National Center for Environmental Health, Centers for Disease Control and Prevention. Specifically, blood levels of cadmium, lead and mercury as well as urinary levels of barium, cadmium, cobalt, cesium, molybdenum, lead, antimony, thallium and tungsten were measured using inductively coupled plasma mass spectrometry. All NHANES procedures adhere to quality assurance and quality control (QA/QC) protocols which comply with the 1988 Clinical Laboratory Improvement Act mandates [29].\u003c/p\u003e\u003cp\u003e\u003cb\u003eAssessment of covariates\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCovariates were selected based on previous literature and theoretical rationale that suggested their potential associations with both depression prevalence and heavy metal exposure levels [30]. In particular, this study examined a range of demographic, socio-economic, lifestyle and health-related variables. The demographic factors included sex (male and female), age, BMI and ethnicity (Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black or other racial groups), while the socio-economic variables encompassed educational level (less than high school, high school diploma or equivalent and college degree or higher), marital status (married vs. unmarried/other) and poverty-to-income ratio (PIR), categorized as high (\u0026gt;\u0026thinsp;3), medium (1\u0026ndash;3) or low (\u0026lt;\u0026thinsp;1) [31]. Lifestyle variables included physical activity level (vigorous, moderate or other forms of exercise), smoking status (current smokers \u0026ndash; individuals who have smoked over 100 cigarettes in their lifetime, former smokers \u0026ndash; those who have smoked more than 100 cigarettes but no longer smoke or non-smokers \u0026ndash; those who have smoked less than 100 cigarettes in their lifetime) [30] and alcohol consumption, with the latter classified into heavy drinkers (\u0026ge;\u0026thinsp;2 drinks per day for men and \u0026ge;\u0026thinsp;1 drink per day for women), low-to-moderate drinkers (\u0026lt;\u0026thinsp;2 drinks per day for men and \u0026lt;\u0026thinsp;1 drink per day for women) or non-drinkers (less than 12 drinks per year) [31]. The health-related covariates included a history of cardiovascular disease (CVD), defined when at least one of the following conditions was present: coronary artery disease, congestive heart failure, angina pectoris, myocardial infarction or stroke. In addition, chronic kidney disease (CKD) was identified based on an estimated glomerular filtration rate (eGFR) of \u0026lt;\u0026thinsp;60 mL/min/1.73 m\u0026sup2;, calculated using the Chronic Kidney Disease Epidemiology Collaboration equation, or an albumin-to-creatinine ratio of at least 30 mg/g, while hypertension was defined as a mean systolic blood pressure of \u0026ge;\u0026thinsp;130 mmHg, a mean diastolic blood pressure of \u0026ge;\u0026thinsp;80 mmHg or the current use of antihypertensive medications [32]. Finally, diabetes was determined by self-reported use of hypoglycemic medications or a diagnosis confirmed by a healthcare professional, with fasting blood glucose levels\u0026thinsp;\u0026ge;\u0026thinsp;126 mg/dL or hemoglobin A1c values\u0026thinsp;\u0026ge;\u0026thinsp;6.5% also used to identify diabetic cases [31].\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were performed using RStudio version 4.1.2 and EmpowerStats software. Descriptive statistics were used to analyze the baseline characteristics of participants in the depression and non-depression groups. For this purpose, continuous variables were first expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), while categorical ones were presented as percentages. The baseline characteristics were then compared using logistic regression models and chi-square tests for continuous and categorical variables, respectively. The concentrations of heavy metals in blood and urine were also categorized into four quartiles (Q1, Q2, Q3 and Q4), with Q1 serving as the reference group in subsequent logistic regression analyses. Furthermore, the association between individual heavy metals and depression risk was evaluated using multivariable logistic regression, with the results reported as odds ratios (OR) and their corresponding 95% confidence intervals (CI) (OR, 95% CI). To assess the combined and individual effects of heavy metal mixtures on the prevalence of depression, WQS regression was conducted by calculating a weighted linear index and assigning appropriate weights to each component metal. In addition, RCS regression was performed to investigate nonlinear associations between heavy metal concentrations and depression risk. Finally, Pearson's correlation coefficients were calculated to assess inter-metal correlations among the 12 heavy metals, while BKMR modeling was employed to examine the combined effects of multiple heavy metals, identify potential interactions and determine the relative contribution of each metal within the exposure mixture. A \u003cem\u003ep\u003c/em\u003e-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant throughout all analyses.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eBaseline characteristics\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study included 8,814 participants (45.7% male; 54.3% female) with baseline characteristics in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Mean age and BMI were 48.2 years and 29.3 kg/m\u0026sup2; respectively. The cohort comprised 43.6% Non-Hispanic White and 9.1% Other Hispanic. Most held college degrees (54.4%), were married (53.1%), reported vigorous activity (37.3%), non-smoking (56%), or heavy drinking (63.6%), with 42.3% having medium income (PIR 1\u0026ndash;3). Prevalence of chronic conditions included hypertension (40%), diabetes (16.6%), cardiovascular disease (9.1%), and chronic kidney disease (93.9%). Urinary metal concentrations (\u0026micro;g/L): barium 1.99, cadmium 0.37, cobalt 0.53, cesium 5.22, molybdenum 53.12, lead 0.68, antimony 0.09, thallium 0.19, tungsten 0.12; blood metal concentrations: cadmium 0.50 (\u0026micro;g/L), lead 1.50 (\u0026micro;g/dL), mercury 1.55 (\u0026micro;g/L). Compared with the non-depressed group, those in the depression group showed significant differences in several variables, including sex, BMI, race/ethnicity, education level, marital status, PIR, physical activity, presence of cardiovascular disease, hypertension, diabetes, urinary levels of cadmium and thallium as well as blood concentrations of cadmium and mercury.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline characteristics of the 8,814 participants with and without depression.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;8,814)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-Depression\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;8,083)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDepression\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;731)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48.2\u0026thinsp;\u0026plusmn;\u0026thinsp;17.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48.2\u0026thinsp;\u0026plusmn;\u0026thinsp;17.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47.5\u0026thinsp;\u0026plusmn;\u0026thinsp;16.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.289\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4032(45.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3796(46.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e236(32.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4782(54.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4287(53.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e495(67.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30.8\u0026thinsp;\u0026plusmn;\u0026thinsp;8.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRace, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMexican American\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1343(15.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1229(15.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e114(15.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Hispanic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e838(9.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e738(9.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100(13.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon Hispanic White\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3831(43.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3524(43.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e307(42%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon Hispanic Black\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1906(21.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1756(21.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e150(20.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Race\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e896(10.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e836(10.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60(8.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation level, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCollege or above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4797(54.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4488(55.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e309(42.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school or equiva len\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2061(23.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1889(23.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e172(23.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLess than high school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1956(22.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1706(21.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e250(34.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital status, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4679(53.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4403(54.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e276(37.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnmarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3512(39.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3122(38.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e390(53.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e623(7.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e558(6.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e65(8.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePIR, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh(\u0026gt;3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3372(38.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3241(40.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e131(17.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedium(1\u0026thinsp;~\u0026thinsp;3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3732(42.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3405(42.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e327(44.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow(\u0026lt;1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1710(19.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1437(17.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e273(37.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhysical activity, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVigorous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3291(37.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3070(38%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e221(30.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2698(30.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2501(30.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e197(26.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2825(32.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2512(31.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e313(42.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking status, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1803(20.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1517(18.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e286(39.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEx-smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2077(23.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1934(23.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e143(19.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4934(56%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4632(57.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e302(41.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlcohol consumption, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.182\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeavy drinker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5603(63.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5134(63.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e469(64.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow-to-moderate drinker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1470(16.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1336(16.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e134(18.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon drinker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1741(19.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1613(20%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e128(17.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCVD, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e802(9.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e672(8.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e130(17.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8012(90.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7411(91.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e601(82.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCKD, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.782\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8280(93.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7595(94%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e685(93.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e534(6.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e488(6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46(6.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3524(40%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3180(39.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e344(47.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5290(60%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4903(60.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e387(52.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1460(16.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1281(15.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e179(24.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7354(83.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6802(84.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e552(75.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrine Barium (ug/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.99\u0026thinsp;\u0026plusmn;\u0026thinsp;3.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.00\u0026thinsp;\u0026plusmn;\u0026thinsp;3.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.93\u0026thinsp;\u0026plusmn;\u0026thinsp;2.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.599\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrine Cadmium (ug/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrine Cobalt (ug/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.53\u0026thinsp;\u0026plusmn;\u0026thinsp;1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.52\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.59\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.073\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrine Cesium (ug/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.22\u0026thinsp;\u0026plusmn;\u0026thinsp;3.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.23\u0026thinsp;\u0026plusmn;\u0026thinsp;3.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.13\u0026thinsp;\u0026plusmn;\u0026thinsp;3.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrine Molybdenum (ug/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53.12\u0026thinsp;\u0026plusmn;\u0026thinsp;49.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52.93\u0026thinsp;\u0026plusmn;\u0026thinsp;49.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e55.16\u0026thinsp;\u0026plusmn;\u0026thinsp;48.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.241\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrine Lead (ug/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.68\u0026thinsp;\u0026plusmn;\u0026thinsp;1.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.68\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.771\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrine Antimony (ug/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.653\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrine Thallium (ug/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrine Tungsten (ug/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.192\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood Cadmium (ug/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood Lead (ug/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.50\u0026thinsp;\u0026plusmn;\u0026thinsp;1.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.51\u0026thinsp;\u0026plusmn;\u0026thinsp;1.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.45\u0026thinsp;\u0026plusmn;\u0026thinsp;1.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.315\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood mercury, total (ug/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.55\u0026thinsp;\u0026plusmn;\u0026thinsp;2.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.59\u0026thinsp;\u0026plusmn;\u0026thinsp;2.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.04\u0026thinsp;\u0026plusmn;\u0026thinsp;1.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE for continuous variables: \u003cem\u003eP\u003c/em\u003e-value was calculated by the weighted t-test. % (SE) for categorical variables: \u003cem\u003eP\u003c/em\u003e-value was calculated by the weighted chi-square test. BMI, body mass index. PIR, family poverty income ratio. CVD, cardiovascular diseases. CKD, chronic kidney disease.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eAssociation between heavy metals and depression\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn the fully adjusted model, urinary tungsten levels exhibited a significant association with depression in both the Q2 and Q4 quartiles compared with the Q1 group. Specifically, higher tungsten concentrations were associated with an increased risk of depression (Q2: OR\u0026thinsp;=\u0026thinsp;1.42, 95% CI: 1.10\u0026ndash;1.83, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008; Q4: OR\u0026thinsp;=\u0026thinsp;1.20, 95% CI: 1.04\u0026ndash;1.88, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026). Similarly, urinary thallium levels showed a significant association in the Q3 and Q4 groups relative to Q1, indicating that lower concentrations of urinary thallium were linked to a reduced risk of depression (Q3: OR\u0026thinsp;=\u0026thinsp;0.63, 95% CI: 0.46\u0026ndash;0.86, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003; Q4: OR\u0026thinsp;=\u0026thinsp;0.52, 95% CI: 0.36\u0026ndash;0.73, \u003cem\u003ep \u0026lt;\u003c/em\u003e 0.001). Additionally, urinary antimony levels were significantly associated with depression in the Q3 group compared with Q1 (Q3: OR\u0026thinsp;=\u0026thinsp;1.34, 95% CI: 1.03\u0026ndash;1.75, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029). In this case, even though associations in the Q2 (Q2: OR\u0026thinsp;=\u0026thinsp;1.07, 95% CI: 0.83\u0026ndash;1.39, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.595) and Q4 (Q4: OR\u0026thinsp;=\u0026thinsp;1.25, 95% CI: 0.93\u0026ndash;1.68, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.134) groups, where hazard ratios (HR) exceeded one, did not reach statistical significance, the upward trend suggested a potential positive relationship between antimony exposure and depression risk. Finally, blood levels of mercury exhibited a significant association with depression in the Q3 and Q4 groups compared with Q1, hence suggesting that lower mercury concentrations in the blood were linked to a decreased risk of depression (Q3: OR\u0026thinsp;=\u0026thinsp;0.80, 95% CI: 0.64\u0026ndash;1.00, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.047; Q4: OR\u0026thinsp;=\u0026thinsp;0.70, 95% CI: 0.54\u0026ndash;0.90, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005). In contrast, no statistically significant associations were observed for urinary levels of molybdenum, lead, cobalt, cesium, cadmium and barium or for blood levels of lead and cadmium (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eNonlinear relationship between heavy metal exposure and depression risk\u003c/b\u003e\u003c/p\u003e\u003cp\u003eEvidence of significant nonlinearity was observed for several heavy metals, including urinary cadmium (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004), cobalt (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005), lead (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024), antimony (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and tungsten (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) as well as blood cadmium (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and mercury (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Conversely, no significant nonlinear relationships were identified for urinary barium, cesium, molybdenum and thallium, or for blood lead levels, with their corresponding \u003cem\u003ep\u003c/em\u003e-values exceeding 0.05. Altogether, the findings suggest that the relationship between these specific metal exposures and depression risk did not follow a linear pattern within the studied range (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eHeavy metal co-exposure and depression in WQS model\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe WQS index demonstrated a positive correlation between heavy metal co-exposure and depression risk. In the crude model, the OR was 1.76 (95% CI: 1.54\u0026ndash;2.02, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and it decreased to 1.17 (95% CI: 1.02\u0026ndash;1.35, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026) after adjusting for covariates in Model I (Table. S1). Among the 12 heavy metals analyzed, the urinary levels of tungsten contributed the most to the risk of depression, with a relative weight of 29.3% (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003eSimilarly, the analysis yielded a WQS index of 0.69 (95% CI: 0.62\u0026ndash;0.76, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in the crude model, and it increased to 0.81 (95% CI: 0.70\u0026ndash;0.94, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006) in Model I, indicating a statistically significant inverse relationship between heavy metal co-exposures and the prevalence of depressive symptoms (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Furthermore, within this model, blood mercury was identified as the most influential component, contributing 44% to the overall effect (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003eThe correlation among 12 heavy metals was assessed by Pearson\u0026rsquo;s correlation coefficients (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). There was a medium correlation between urine thallium and urine cesium (r\u0026thinsp;=\u0026thinsp;0.66), blood and urine lead (r\u0026thinsp;=\u0026thinsp;0.66). Other heavy metals were all weak or no correlations.\u003c/p\u003e\u003cp\u003e\u003cb\u003eBKMR model to assess the association between heavy metal coexposure and depression risk\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe results indicated an increased risk of depression among individuals exposed to heavy metal mixtures above the 55th percentile compared with those at median exposure levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Among the analyzed heavy metals, urinary barium exhibited the highest posterior inclusion probabili probabilities ( PIPs ) of 0.448, suggesting that it could be the most influential contributor to depression risk. The PIPs values, determined by the BKMR model, are presented in Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e. Furthermore, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, under standardized conditions (where concentrations of all other heavy metals were fixed at their median levels), significant nonlinear dose-response relationships were observed between depression risk and urinary concentrations of barium, cadmium, cobalt, molybdenum, thallium, tungsten as well as blood levels of mercury. These associations displayed a generally positive correlation across the range of measured concentrations. Interestingly, distinct exposure-response patterns were also identified for lead and antimony. Specifically, urinary lead, urinary antimony and blood lead concentrations exhibited inverted U-shaped relationships with depression risk, indicating that the risk peaked at moderate exposure levels before declining at higher concentrations.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study represents that employs multiple advanced statistical methods on a large-scale, nationally representative dataset to assess the combined effects of heavy metal mixtures in blood and urine on the risk of depression. The findings indicated that participants diagnosed with depressive disorders exhibited significantly higher concentrations of cadmium in both urine and blood, along with lower levels of urinary thallium and blood mercury compared with non-depressed individuals. Multivariate logistic regression analysis further demonstrated that exposure to urinary tungsten and antimony was independently associated with an increased risk of depression, while lower concentrations of urinary thallium and blood mercury were significantly correlated with a reduced risk. Furthermore, WQS and RCS regression analyses revealed that urinary tungsten and antimony had significant positive nonlinear associations with depression, with notable negative nonlinear correlations between urinary thallium, blood mercury and depression risk also identified. Importantly, although urinary barium and cobalt emerged as the most significant contributors to the overall effect in the BKMR model, these associations did not reach statistical significance in the other analytical models.\u003c/p\u003e\u003cp\u003eIn recent years, an increasing body of evidence has highlighted the significant impact of heavy metals on mental health [16, 20, 21, 33]. These metals, which can enter the human body through multiple routes, including dietary intake, personal care products and environmental contamination [11\u0026ndash;13], are believed to contribute to neurodegenerative processes via mechanisms such as oxidative stress, inflammatory cascades as well as disruption to the brain-gut axis and neuronal integrity. Existing research has further suggested that residing in areas with elevated soil concentrations of heavy metals was associated with a higher risk of developing mental health disorders [9, 12]. Parallel to these findings, there has been a marked rise in the prevalence of neuropsychiatric conditions such as autism spectrum disorder, attention deficit/hyperactivity disorder (ADHD), learning difficulties and aggressive behavior [26, 34].\u003c/p\u003e\u003cp\u003eTungsten, known for its stable chemical properties, is widely used across both defense and industrial sectors [35, 36]. However, it has recently been recognized as an emerging environmental pollutant [37]. In this context, research has shown that exposure to sodium tungstate could induce mild neurobehavioral changes in rats [38]. Additionally, hard alloys containing 79\u0026ndash;95% tungsten carbide and approximately 10% cobalt have been linked to declines in memory and sensory perception among exposed workers [39]. In this study, a positive nonlinear relationship was observed between urinary tungsten levels and depression risk, with the association being particularly pronounced in the case of metal co-exposure. Tungsten was also significantly correlated with several other heavy metals detected in urine samples. Notably, a combined exposure to tungsten, cobalt and nickel has been shown to produce neurotoxic effects through their synergistic interactions [39, 40]. In experiments involving mouse macrophages, tungsten carbide-cobalt compounds exhibited greater cytotoxicity than either cobalt metal or tungsten carbide alone [39]. Moreover, tungsten can interfere with molybdenum absorption, thereby inhibiting the synthesis of sulfite oxidase, an essential enzyme in redox homeostasis. Such disruption was shown to lead to oxidative imbalance and bioenergetic dysfunction within rat brains, resulting in seizures and neurological damage [41]. Overall, the toxicity profile of tungsten is complex, often intensifying the adverse effects of co-exposure with other metals [36, 37]. However, despite these concerns, tungsten levels are not routinely monitored in environmental air or water sources, thus highlighting the urgent need for regulatory policies aimed at minimizing exposure risk.\u003c/p\u003e\u003cp\u003eAs a metalloid, antimony has been classified as a priority pollutant due to its potential carcinogenicity [42]. Consequently, previous studies have mostly focused on its toxicity to cardiovascular and respiratory systems, with limited attention given to its neurological and psychiatric effects [43, 44]. However, animal studies have demonstrated that antimony exposure in zebrafish embryos could induce oxidative stress and inflammation, characterized by elevated levels of interleukins (IL-1β, IL-6), tumor necrosis factor αand pro-oxidative substances such as glutathione peroxidase and malondialdehyde. These changes could subsequently suppress acetylcholinesterase activity and cause astrocytic dysfunction, ultimately impairing nervous system development [45]. Furthermore, antimony has been found to trigger ferroptosis via chaperone-mediated autophagy, with ferritinophagy potentially contributing to its neurotoxic mechanisms [46, 47]. In this study, a positive nonlinear association was identified between antimony exposure and depression risk, although this association was borderline significant. In addition, antimony levels were significantly correlated with other heavy metals, including tungsten, molybdenum, cesium, thallium, lead and cadmium, in urine. Consistent with the current findings, a previous report also indicated a significant nonlinear relationship between antimony and a higher risk of depressive symptoms [42]. In contrast, Shiue\u0026rsquo;s study did not find a significant association between antimony exposure and depression, with the dose-response relationship also not investigated [48]. These discrepancies could be attributed to the nonlinear nature of the correlation or to the lack of adjustment for key covariates in Shiue\u0026rsquo;s analysis.\u003c/p\u003e\u003cp\u003eThallium, a widely distributed trace metal used in high-tech, medical and chemical industries, is highly toxic due to its bio-accumulative properties and potent neurotoxicity. Thus, exposure can lead to acute poisoning (accidental, intentional or criminal) or chronic toxicity through occupational exposure or contaminated food sources [49]. The neurological effects of thallium poisoning often develop insidiously, manifesting initially as sensorimotor dysfunction, along with early psychiatric symptoms, such as anxiety and depression as well as cognitive impairments, including memory and attention deficits [50\u0026ndash;51]. In this study, a significant reduction in urinary thallium levels was observed among individuals with depression compared with healthy controls. These findings are supported by previous studies reporting a negative relationship between urinary thallium concentration and depression risk [42]. Furthermore, Liu et al. demonstrated that thallium exposure could induce irreversible neurotoxicity, primarily via neuronal synaptic damage, transcriptional dysregulation and mitochondrial oxidative stress, which eventually lead to neuronal cell death [52]. Impaired renal function can reduce the excretion of urinary thallium, resulting in excessive thallium exposure if it is not effectively eliminated [53]. This study also identified a significant correlation between thallium and various heavy metal ions, including cesium, molybdenum, cadmium, barium, cobalt, lead, and tungsten. However, the precise mechanisms underlying their interactions remain unclear. It is hypothesized that these heavy metals may engage in complex interactions concerning environmental exposure, in vivo distribution, facilitation of enzymatic processes, reduction of oxidative stress and inflammatory responses, as well as the maintenance of normal physiological functions in neurons [54].\u003c/p\u003e\u003cp\u003eMercury, one of the most toxic heavy metals as ranked by the U.S. Agency for Toxic Substances and Disease Registry, exists in elemental, organic and inorganic forms, each exhibiting distinct pharmacokinetic behaviors and clinical implications due to their interconversion \u003cem\u003ein vivo\u003c/em\u003e [55\u0026ndash;57]. Extensive evidence links mercury exposure to depression which may occur through mechanisms such as oxidative stress, neuroinflammation, dysregulation of neurotrophic factors and altered neurotransmitter systems [58, 59]. In this study, the findings revealed a nonlinear association between blood mercury levels and depression risk, with mercury not only emerging as a significant contributor within heavy metal mixtures but also exhibiting inverse correlations with other urinary and blood heavy metals. Interestingly, mercury also demonstrated a dual-effect relationship with depression, whereby both low and high levels could confer neurotoxic effects. However, while Fu et al. reported a negative association between blood mercury levels and depression risk in adults [43]. Nguyen et al. found no significant differences in blood mercury concentrations between depressed individuals and non-depressed controls [15]. Furthermore, it is worth noting that AnGongNiuHuang Pill (AGNH), a traditional Chinese medicine containing realgar (arsenic disulfide, As2S2) and cinnabar (mercuric sulfide, HgS), has demonstrated therapeutic efficacy in managing neurological disorders such as epileptic seizures, cerebrovascular events and related sleep disturbances, including insomnia and fragmented dreaming [60]. These discrepancies likely reflect a nonlinear dose-response relationship between mercury exposure and depression, with such association further modulated by unaccounted variables such as selenium co-exposure, dietary habits, socioeconomic status, exposure duration and individual susceptibility or comorbidities [61, 62].\u003c/p\u003e\u003cp\u003eBlood levels of cadmium, a non-essential and toxic heavy metal, has been shown to exhibit a significant positive correlation with the risk of depressive symptoms [63, 64]. In this context, previous studies have suggested that cadmium can penetrate the blood-brain barrier, thereby inducing glial oxidative damage through mechanisms such as lipid peroxidation, disruption of antioxidant defenses, interference with zinc-calcium homeostasis, metallothionein induction and activation of apoptotic pathways. These neurotoxic effects are subsequently manifested as attention deficits, memory impairments, learning disabilities, headaches, vertigo and other neurological symptoms [42, 62, 65, 66]. In the current study, elevated blood and urinary levels of cadmium were observed among individuals with depression compared with the general population. However, paradoxically, multivariate regression models adjusted for all covariates showed no significant association between cadmium concentrations and depression risk. This finding aligns with that of Sun et al. who reported an inverse correlation between higher serum cadmium levels and the incidence of major depressive disorder, with no significant differences observed in the case of urinary cadmium levels [20]. Similarly, other studies found no association between blood cadmium levels and depression, particularly in elderly populations [67, 68]. These inconsistencies may stem from the complexity of cadmium metabolism in humans. Indeed, factors such as interactions with essential metals (e.g., zinc, iron and selenium can competitively inhibit cadmium absorption), smoking-related cadmium exposure, the protective role of moderate physical activity and altered cadmium burden in metabolic disorders (e.g., diabetes and hypertension) likely contribute to the intricate and multifaceted relationship between cadmium exposure and mental health outcomes [14, 20, 69\u0026ndash;71]. This highlights the need for more comprehensive exposure assessments and mechanistic studies to elucidate cadmium\u0026rsquo;s role in depressive symptomatology.\u003c/p\u003e\u003cp\u003eBarium, an alkaline earth metal, exhibits toxicity that depends largely on its chemical form (salt type) and absorption route [72]. However, its neurotoxicity remains poorly characterized, with existing studies also reporting conflicting results [20, 34, 48, 72, 73]. For instance, Sun et al. found that serum barium levels were positively correlated with the severity of depressive symptoms in a dose-dependent manner [20]. In contrast, Rokoff et al. observed no clinically meaningful association between serum barium levels and depressive symptoms during the perinatal period [74]. Moreover, Zhang et al. reported that children with autism spectrum disorder exhibited higher barium levels compared with healthy controls [35]. Toxicologically, highly soluble forms, such as BaCl\u003csub\u003e2\u003c/sub\u003e, are known to induce diarrhea, liver and kidney dysfunction, anxiety, neurological impairments, dyspnea and even cerebral edema [74]. The BKMR modeling applied in this study identified barium as the most influential contributor to depression risk within metal mixtures, even though its concentration differences across depression and non-depression groups were not statistically significant. The central role of barium may stem from its dual neurotoxic mechanisms: electrophysiological interference via potassium channel blockade and molecular reprogramming through oxidative and epigenetic pathways. These pathways position barium as a latent contributor to depression pathogenesis [18, 75\u0026ndash;77]. However, this hypothesis remains largely speculative, underscoring the need for rigorous mechanistic studies to clarify barium's role in neuropsychiatric disorders.\u003c/p\u003e\u003cp\u003eThe primary strength of this study lies in the application of multiple advanced statistical models, thus enabling a comprehensive assessment of the associations between metal mixtures, individual metal exposures and major depressive disorder. This multifaceted analytical approach enhances the robustness and reliability of the findings while addressing the limitations inherent in traditional epidemiological methods that typically assess single-metal exposures in isolation. Furthermore, this study successfully identified key metals that contribute most significantly to the observed associations between metal mixtures and depression, an area that has remained largely underexplored in previous research. However, several limitations should be acknowledged. Firstly, the cross-sectional design of this study precludes the ability to infer causal relationships or evaluate temporal dynamics between metal exposure and depression risk. Secondly, the omission of neuroactive metals, such as iron, copper, zinc and manganese, limits the interpretability of the results, as these elements may interact synergistically or antagonistically with toxic metals to influence neuropsychiatric outcomes. Additionally, the inability to incorporate NHANES's complex sampling design may affect the generalizability of the findings to broader populations. Finally, despite adjusting for a wide range of covariates, the potential for residual confounding from unmeasured factors (e.g., occupational exposure history, dietary habits and medication use) cannot be entirely excluded.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, this study revealed significant differences in heavy metal exposure profiles between individuals with depressive disorders and healthy controls, thus highlighting a positive association between cumulative exposure to metal mixtures and the risk of depression among U.S. adults. Through mixed-exposure analyses, tungsten and antimony were identified as the primary contributors to elevated depression risk, while mercury and thallium exhibited inverse associations. Furthermore, barium also emerged as a potential key contributor to these interactions. These findings emphasize the need for stricter industrial regulations and enhanced personal hygiene practices to reduce toxic metal exposure. Future studies should adopt prospective cohort designs, incorporate multiple biomarkers and define clear clinical endpoints to better elucidate the biological mechanisms underlying the relationship between cumulative metal exposure and depression. Ultimately, the results underscore the critical importance of comprehensive environmental metal exposure management in mitigating depression as an emerging public health burden.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eNHANES National Health and Nutrition Examination Survey\u003c/p\u003e\u003cp\u003eRCS Restricted cubic spline\u003c/p\u003e\u003cp\u003eWQS Weighted quantile sum regression\u003c/p\u003e\u003cp\u003eBKMR Bayesian kernel machine regression\u003c/p\u003e\u003cp\u003eEMs Environmental metals\u003c/p\u003e\u003cp\u003ePHQ-9 Patient Health Questionnaire-9\u003c/p\u003e\u003cp\u003eBMI Body mass index\u003c/p\u003e\u003cp\u003ePIR Poverty-to-income ratio\u003c/p\u003e\u003cp\u003eCVD Cardiovascular disease\u003c/p\u003e\u003cp\u003eCKD Chronic kidney disease\u003c/p\u003e\u003cp\u003eIL-1β Interleukin-β\u003c/p\u003e\u003cp\u003eIL-6 Interleukin-6\u003c/p\u003e\u003cp\u003eTNF-ɑ Tumor Necrosis Factor-ɑ\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe NHANES agreement has been reviewed and approved by the NCHS Research Ethics Committee. All participants provided written informed consent before participating.. (https://wwwn.cdc.gov/nchs/nhanes/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is provided within the manuscript or supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful to all colleagues of Chengdu University of Traditional Chinese Medicine Affiliated Fifth People\u0026apos;s hospital who participated in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eYuanxin Guo and Zhongrui Ma conceived the presented idea. Yuanxin Guo and Yixu Chen performed the data analysis, figure preparation, and manuscript writing. Houfeng Zhou, Yuting Fan and Tao Feng were involved in the \u0026nbsp;processing of the data and figures. Zhongrui Ma provided fnal approval of the version submitted for publication. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe survey data is available to researchers and data users globally via the website www.cdc.gov/nchs/nhanes/.\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e1. 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J Clin Med 2024, 13(23).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Heavy metals, Depression, Joint exposure, Barium, NHANES","lastPublishedDoi":"10.21203/rs.3.rs-7038470/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7038470/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eHeavy metal exposure is established as depression-related, yet limited research examines combined metal impacts. This study assessed multi-metal exposure's collective risk and identified key contributors.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eAnalyzing National Health and Nutrition Examination Survey data, adults with complete data on nine urinary metals (antimony, barium, cadmium, cobalt, cesium, molybdenum, lead, thallium and tungsten), three blood metals (cadmium, mercury and lead), depression status, and key covariates were assessed via four methods (multivariate logistic regression, restricted cubic spline (RCS) regression, weighted quantile sum (WQS) regression and Bayesian kernel machine regression (BKMR) to evaluate metal-depression associations.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAmong 8,814 participants (731 with depression), those with depression showed higher urine and blood cadmium levels, but lower blood mercury and urine thallium levels compared to controls. Adjusted analyses linked elevated urine antimony (OR\u0026thinsp;=\u0026thinsp;1.34, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029) and tungsten (OR\u0026thinsp;=\u0026thinsp;1.42, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008) to increased depression risk, while higher urine thallium (OR\u0026thinsp;=\u0026thinsp;0.52, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) and blood mercury (OR\u0026thinsp;=\u0026thinsp;0.7, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005) reduced risk. RCS analysis revealed nonlinear relationships between depression and urine cadmium (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004), cobalt (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005), lead (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024), antimony (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), tungsten(\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as well as blood cadmium (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and mercury (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, the WQS index was postive associated with depression (OR:1.17, 95% CI: 1.02\u0026ndash;1.35, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026) but a negative correlation (OR:0.81, 95% CI: 0.7\u0026ndash;0.94, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006). BKMR analysis confirmed multi-metal co-exposure elevates depression risk, and urinary barium showed the highest BKMR-derived posterior inclusion probability (PIP\u0026thinsp;=\u0026thinsp;0.448).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eHeavy metal mixture exposure elevates depressive disorder risk, with tungsten and antimony as key risk drivers, mercury and thallium showing protective effects, and barium emerging as a potential contributor. Further studies needed to validate these metal-specific impacts and uncover additional depression-linked metals.\u003c/p\u003e","manuscriptTitle":"Association between heavy metals’exposure and depression: Findings of the NHANES from 2003 to 2020","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-05 14:00:03","doi":"10.21203/rs.3.rs-7038470/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-17T05:10:36+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-15T14:42:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-18T16:29:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-12T17:16:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"203546860976754643188860733896313220348","date":"2025-08-09T22:01:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"176720850562077222779542168225831010941","date":"2025-08-04T18:20:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-04T14:52:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"275935845103829561172260206758795061117","date":"2025-08-03T05:48:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"326389034975420348872780594099772096255","date":"2025-08-02T04:18:56+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-31T15:08:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-22T14:50:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Medical Research","date":"2025-07-19T11:24:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"eb5a1580-1906-43c1-b815-0222348608fc","owner":[],"postedDate":"August 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-19T17:05:10+00:00","versionOfRecord":{"articleIdentity":"rs-7038470","link":"https://doi.org/10.1186/s40001-026-03850-x","journal":{"identity":"european-journal-of-medical-research","isVorOnly":false,"title":"European Journal of Medical Research"},"publishedOn":"2026-01-14 16:30:19","publishedOnDateReadable":"January 14th, 2026"},"versionCreatedAt":"2025-08-05 14:00:03","video":"","vorDoi":"10.1186/s40001-026-03850-x","vorDoiUrl":"https://doi.org/10.1186/s40001-026-03850-x","workflowStages":[]},"version":"v1","identity":"rs-7038470","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7038470","identity":"rs-7038470","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}