Association between urinary trace metals and multiple health outcomes

Abstract

Objective

To explore the association between trace metals and multiple health outcomes.

Methods

The cross-sectional study was from eight cycles of National Health and Nutrition Examination Survey (NHANES) from 2005 to 2020. We evaluated the relationship between 11 types of urinary trace metals and multiple health outcomes.

Results

A total of 3051 participants were included in this study. The levels of barium, cesium, molybdenum, lead, antimony, thallium were significantly reduced in women, while cobalt was increased (p<0.01). Except for kidney stones and sleep disorders, there were significant difference in at least one type of trace metals among the participants with or without diseases. Cadmium was the most common trace metal had significant differences. Antimony could significantly increase the risk of thyroid problem and sleep disorders in women, while cobalt and manganese were just related to the risk of arthritis in man. There was a nonlinear (L-shaped) relationship between tin and heart disease, cadmium and stroke, cadmium and emphysema respectively.

Conclusions

Urinary trace metals, particularly cadmium, are modifiable risk factors for multisystem health outcomes, with effects modulated by biological sex and metal co-exposure patterns.

Keywords

trace metals multisystem health outcome gender cadmium

Introduction

The ubiquity of metal exposure in modern environments—through contaminated water, air pollution, industrial emissions, and consumer products—has intensified concerns about their systemic health impacts.¹ Among the health outcomes associated with trace metals, cardiovascular diseases (CVDs),² renal disorders,³ and endocrine-metabolic conditions^4,5 exhibit particularly strong epidemiological links. Emerging epidemiological evidence suggests that trace metals, including barium (Ba), cadmium (Cd), cobalt (Co), cesium (Cs), molybdenum (Mo), manganese (Mn), lead (Pb), antimony (Sb), tin (Sn), thallium (Tl), and tungsten (W), may contribute to the pathogenesis of chronic diseases such as cardiovascular disorders, renal dysfunction, metabolic syndrome,⁶ and neurodegenerative conditions.⁷ Urinary concentrations of these metals serve as biomarkers of recent exposure, reflecting dietary intake, environmental contamination, and occupational hazards. For metals such as cadmium, urinary levels reflect cumulative body burden, while for others they indicate recent exposure. However, the relationship between urinary metal profiles and disease susceptibility remains poorly characterized.

Trace metals, both essential and non-essential, play complex roles in human health. The adverse effects of trace elements on health mainly include three types: (1) One trace metal is associated with a variety of health outcomes. Yu et al. have reported the comprehensive health effects (the liver, kidney, lung and other potential health effects) underlying chronic Tl exposure at trace levels. There was a relationship between Tl concentration and liver function disorder. The Odds ratio (OR) was 1.70, and 95 % confidence intervals (CI) was 1.30∼2.22. While, urinary Tl was negatively associated with lung health outcome⁸ . (2) Multiple trace metals can cause the same disease. Feng’s group found that urinary multiple metals (including aluminium, titanium, cobalt, nickel, copper, zinc, selenium, rubidium, strontium, molybdenum, cadmium, antimony, barium, tungsten and lead) were associated with altered fasting plasma glucose, impaired fasting glucose or diabetes risk⁸. (3) Some trace elements are highly correlated with some diseases. For example, aluminum is closely related to some neurodegenerative diseases. Although the role of aluminium (Al) and Alzheimer’s disease (AD) has been heavily disputed, it was generally believed that Al has a significant effect on AD.^9,10 Many studies have confirmed that it could influence the amyloid β (Aβ) production and tau protein hyperphosphorylation following many ways, thus exerting neurotoxicity effects.^11,12 In short, the relationship between trace elements and diseases is very complicated.

In real life, the common exposure of various trace metals is very common in most population. Despite these findings, critical gaps persist in understanding how co-exposure to multiple metals synergistically or antagonistically. Here, in order to explore the association between trace metals and multiple health outcomes, we investigated the National Health and Nutrition Examination Survey (NHANES, 2005∼2020).^13,14 We evaluated the relationship between 11 types of urinary trace metals and multiple health outcomes, excavated the key trace metals which mainly contribute to different diseases, and initially assess the sex-based differences. This study offers new insights into potential health risks associated with race metals, and hope to contribute to improving public health outcomes.

Methods

Data source and study population

The cross-sectional study was from eight cycles of NHANES (2005∼2020) and conducted in accordance with the Helsinki Declaration of 1975 as revised in 2024, and its reporting follows the STROBE guidelines.¹⁵ The participant’s selection is illustrated in Figure 1. Of 76496 initial subjects, 39597 were excluded due to missing demographics data and body mass index (BMI). 16493 were excluded due to missing the data of urine trace metals.17355 were excluded due to missing the information of multiple health outcomes. Finally, 3051 participants were included in this study (1511 males and 1540 females). No personally identifiable information is disclosed in this manuscript.

Figure 1.

Flowchart of participants selection from the NHANES in present study.

Definition of multiple health outcomes

Just as the previous study,¹⁶ multiple health outcomes were self-reported via validated questionnaires, mainly including medical conditions (MCQ), diabetes (DIQ), kidney conditions (KIQ), sleep disorders (SLQ) and blood pressure & cholesterol (BPQ). Participants were considered as have the condition when they answered “yes” to the question “Has a doctor or other health professional ever told you that you have?”.

In this study, the health outcomes were selected a priori based on prior evidence of associations with trace metals and their representation in the NHANES questionnaire data, covering cardiovascular, respiratory, metabolic, musculoskeletal, and other systems.16 conditions and 26 subtypes were included in the study, including heart disease (heart attack, angina pectoris, coronary heart disease, congestive heart failure), stroke, emphysema, asthma, chronic bronchitis, liver condition, failing kidney, kidney stones, cancer (breast, cervix, ovary, uterus), overweight, arthritis (osteoarthritis, rheumatoid arthritis), gout, thyroid problem, diabetes, high blood pressure, sleep disorder.

Measurements of urinary trace metals

The urinary trace metals were obtained from laboratory data (UM), including 11 types of urinary trace metals: barium (ug/L), cadmium (ug/L), cobalt (ug/L), cesium (ug/L), molybdenum (ug/L), manganese (ug/L), lead (ug/L), antimony (ug/L), tin (ug/L), thallium (ug/L) and tungsten (ug/L). This method directly measures multiple metals in urine specimens using mass spectrometry after a simple dilution sample preparation step. These 11 metals were selected because they were consistently measured in urine across all NHANES cycles from 2005 to 2020; other essential elements (such as copper, selenium, and zinc) were not available in the urinary metal dataset.

Other covariates

Similar to some other studies,^17–19 age, gender, race education level, race and poverty ratio were obtained from demographics data. BMI and weight were collected from examination data.

Statistical analysis

DecisionLinnc1.0 software²⁰ was employed for data analysis, which is a platform that integrates multiple programming language environments. Logistic regression analysis model was employed to across two distinct models to examine the relationship between urinary trace metals and multiple health outcomes. Next, restricted cubic splines (RCS) was utilized to explore potential non-linear relationships between urinary trace metals and the risk of multiple health outcomes. Weighted Quantile Sum (WQS) regression was used to explore the overall effect of trace metals. P < 0.05 was considered statistically significant.

Results

Baseline participant characteristics

A total of 3051 participants with complete data from NHANES (2005∼2020) were included in this study. Table 1 presented the baseline characteristics of the participants according to gender. Compared to the man, the women had higher BMI (29.37±7.77 vs 28.28±6.33 kg/m², p<0.001). At the same time, there were significant different levels of several urinary trace metals. The levels of barium, cesium, molybdenum, lead, antimony, thallium were significantly reduced in women, while cobalt was increased (p<0.01).

Table 1.

Baseline participant characteristics according to gender.

Variable names	Overall	Gender		p-value	FDR-adjusted q-values
Variable names	Overall	Male	Female	p-value	FDR-adjusted q-values
n	3051	1511	1540
Age (year)	47.56±17.39	47.59±17.44	47.52±17.35	0.920	0.920
Race (%)				0.364	0.442
Mexican American	337 (11.05)	159 (10.52)	178 (11.56)
Other Hispanic	280 (9.18)	126 (8.34)	154 (10.00)
Non-Hispanic White	1246 (40.84)	620 (41.03)	626 (40.65)
Non-Hispanic Black	700 (22.94)	361 (23.89)	339 (22.01)
Other Race	488 (15.99)	245 (16.21)	243 (15.78)
Poverty income ratio	2.504±1.67	2.542±1.687	2.466±1.67	0.213	0.278
<5.0	2485 (81.45)	1216 (80.48)	1269 (82.40)
>=5.0	566 (18.55)	295 (19.52)	271 (17.60)
Education level (%)				0.026	0.049
Less than 9th grade	255 (8.36)	144 (9.53)	111 (7.21)
9-11th grade	371 (12.16)	193 (12.77)	178 (11.56)
High school graduate	645 (21.14)	331 (21.91)	314 (20.39)
Some college	960 (31.47)	444 (29.38)	516 (33.51)
College graduate or above	820 (26.88)	399 (26.41)	421 (27.34)
BMI (kg/m2)	28.83±7.11	28.28±6.33	29.37±7.77	<0.001	0.017
Normal weight (%)	986 (32.32)	483 (31.97)	503 (32.66)
Overweight (%)	970 (31.79)	553 (36.60)	417 (27.08)
Obesity (%)	1095 (35.89)	475 (31.44)	620 (40.26)
Smoking status				<0.001	0.009
No	2453 (80.39)	1179 (77.96)	1274 (82.78)
Yes	598 (19.61)	333 (22.04)	265 (17.22)
Urinary Barium (ug/L)	1 (0.5-2.05)	1.09 (0.53-2.11)	0.91 (0.47-1.97)	0.02	0.043
Urinary Cadmium (ug/L)	0.2 (0.09-0.41)	0.19 (0.1-0.39)	0.2 (0.09-0.42)	0.17	0.247
Urinary Cobalt (ug/L)	0.34 (0.2-0.57)	0.33 (0.19-0.52)	0.36 (0.2-0.65)	<0.01	0.0567
Urinary Cesium (ug/L)	4.12 (2.45-6.41)	4.48 (2.67-6.63)	3.78 (2.27-6.24)	<0.01	0.043
Urinary Molybdenum (ug/L)	37.3 (18.82-65.2)	41.8 (22-70.95)	32.1 (16.75-59.85)	<0.01	0.034
Urinary Manganese (ug/L)	0.09 (0.09-0.16)	0.09 (0.09-0.16)	0.09 (0.09-0.17)	0.04	0.062
Urinary Lead (ug/L)	0.37 (0.19-0.65)	0.41 (0.23-0.73)	0.32 (0.17-0.56)	<0.01	0.028
Urinary Antimony (ug/L)	0.04 (0.03-0.08)	0.05 (0.03-0.09)	0.04 (0.03-0.07)	<0.01	0.024
Urinary Tin (ug/L)	0.46 (0.22-1.05)	0.46 (0.22-0.98)	0.47 (0.23-1.11)	0.66	0.701
Urinary Thallium (ug/L)	0.16 (0.09-0.25)	0.16 (0.1-0.26)	0.15 (0.08-0.24)	0.03	0.051
Urinary Tungsten (ug/L)	0.06 (0.03-0.12)	0.07 (0.03-0.14)	0.05 (0.02-0.11)	0.59	0.669

The occurrence of multiple health outcomes

Figure 2 and Supplementary Table 1 compared the occurrence of multiple health outcomes. The disease with the highest incidence rate was high blood pressure, with an incidence rate of 34.51%, followed by overweight (33.82%), arthritis (24.55%), asthma (15.34%) and diabetes (12.23%). Among some diseases, there were significant differences between gender. For example, the incidence of thyroid problem in female was significantly higher than that in male (14.68% vs 4.24%, p<0.05).

Figure 2.

The incidence of multiple health outcomes.

For the 16 diseases of interest, individuals with no disease account for 5.57% (n=170), those with one disease make up 11.1% (n=339), with two diseases 27.8% (n=848), with three diseases 16.7% (n=510), and with four diseases 5.57% (n=170). In addition, network graph depicted the comorbidity relationships between diseases. The top three node size in the patient network diagram were emphysema, chronic bronchitis and high blood pressure. The most significant link between nodes was emphysema-chronic bronchitis (Supplementary Figure 1).

Associations between urinary trace metals and multiple health outcomes

Firstly, we compared the levels of urine trace metals according to multiple health outcomes in Figure 3 and Supplementary Table 2. Except for kidney stones and sleep disorders, there were significant difference in at least one type of trace metals among the participants with or without diseases. Cadmium was the most common trace metal had significant differences, and it increased in the cases with heart disease, cancer, stroke, emphysema, chronic bronchitis, liver condition, arthritis, gout, thyroid problem, diabetes and high blood pressure. In addition, the levels of thallium, tin and lead were also related to these diseases, and they had significant differences among 7 kinds, 6 kinds and 5 kinds of diseases respectively.

Figure 3.

Comparison of urinary trace metal levels in different health outcomes.

Next, we have established two models before and after adjusting for confounding factors respectively (Table 2 and Figure 4), two models established a statistically significant association between urinary trace metals and multiple health outcomes. After additionally adjusting for general data confounding factors such as age, gender, race, education, poverty income ratio, BMI and smoking status (Model 2), at least one trace metal is associated with the risks of heart disease, cancer, stroke, asthma, emphysema, chronic bronchitis, arthritis, high blood pressure and overweight. Most trace metals are associated with an increased risk of disease. For instance, cadmium exposure significantly increases the risk of stroke, the odds ratios (OR) were 1.57 and 95% confidence intervals (CIs) were 1.06∼2.25 (p=0.0164). Manganese was closely related to arthritis with OR=1.97 and 95% CIs=1.29∼3.13 (p=0.0026).

Table 2.

Multivariable-adjust ORs and 95%CI of urinary trace metals and health outcomes.

Outcomes	Model 1		Model 2
Outcomes	OR (95%CI)	p-value	OR (95%CI)	p-value
Overall (n=3051)
Heart disease
Barium	0.85 (0.75, 0.94)	0.0051	0.86 (0.76, 0.96)	0.0184
Cadmium	2.13 (1.64, 2.76)	<0.0001	1.32 (0.97, 1.78)	0.0675
Tin	1.03 (1.01, 1.05)	0.0027	1.03 (1.00, 1.05)	0.0256
Thallium	0.06 (0.01, 0.31)	0.0015	0.70 (0.13, 3.14)	0.6547
Cancer
Cadmium	1.39 (1.06, 1.78)	0.0127	0.82 (0.56, 1.16)	0.2892
Lead	1.12 (1.03, 1.22)	0.0063	1.10 (0.97, 1.20)	0.0651
Tin	1.04 (1.02, 1.06)	0.0001	1.04 (1.01, 1.06)	0.0007
Thallium	0.17 (0.04, 0.70)	0.0179	1.42 (0.34, 5.50)	0.6222
Stroke
Cadmium	2.16 (1.56, 2.97)	<0.0001	1.57 (1.06, 2.25)	0.0164
Lead	1.12 (0.98, 1.23)	0.0305	1.10 (0.90, 1.23)	0.2022
Thallium	0.01 (0.01, 0.09)	0.0007	0.03 (0.01, 0.61)	0.0337
Asthma
Lead	0.70 (0.53, 0.88)	0.0047	0.75 (0.57, 0.93)	0.0195
Emphysema
Cadmium	2.20 (1.52, 3.14)	<0.0001	1.82 (1.09, 2.85)	0.0119
Chronic bronchitis
Cadmium	2.20 (1.52, 3.14)	<0.0001	1.37 (0.98, 1.90)	0.0573
Liver condition
Cadmium	1.61 (1.16, 2.16)	0.0020	1.20 (0.78, 1.71)	0.3671
Failing kidneys
Cadmium	1.48 (0.97, 2.11)	0.0387	1.12 (0.64, 1.73)	0.6609
Tin	1.03 (1.00, 1.05)	0.0297	1.02 (0.99, 1.05)	0.0824
Thallium	0.04 (0.01, 0.62)	0.0311	0.31 (0.01, 4.02)	0.4196
Kidney stones
-	-	-	-	-
Arthritis
Cadmium	1.97 (1.61, 2.43)	<0.0001	1.04 (0.83, 1.31)	0.7135
Cobalt	1.17 (1.04, 1.35)	0.0223	1.20 (1.03, 1.41)	0.0171
Manganese	1.93 (1.34, 2.83)	0.0005	1.97 (1.29, 3.13)	0.0026
Lead	1.08 (1.00, 1.17)	0.0457	1.03 (0.91, 1.12)	0.6412
Tin	1.02 (1.00, 1.04)	0.0451	1.01 (0.99, 1.03)	0.4446
Thallium	0.09 (0.03, 0.23)	<0.0001	0.72 (0.26, 1.95)	0.5241
Gout
Cadmium	1.60 (1.15, 2.19)	0.0034	1.20 (0.77, 1.74)	0.3645
Thyroid problem
Cadmium	1.48 (1.14, 1.90)	0.0020	0.92 (0.67, 1.24)	0.6200
Thallium	0.18 (0.04, 0.71)	0.0183	1.54 (0.44, 5.01)	0.4825
Diabetes
Cadmium	1.46 (1.16, 1.84)	0.0012	0.92 (0.67, 1.24)	0.6200
Tin	1.02 (1.00, 1.04)	0.0442	1.01 (0.99, 1.03)	0.2857
High blood pressure
Cadmium	2.49 (2.01, 3.10)	<0.0001	1.26 (1.00, 1.59)	0.0475
Tin	1.03 (1.01, 1.06)	0.0009	1.02 (1.00, 1.04)	0.0546
Thallium	0.22 (0.10, 0.49)	0.0002	0.89 (0.36, 2.17)	0.8022
Overweight
Lead	0.80 (0.68, 0.91)	0.0020	0.79 (0.67, 0.91)	0.0036
Sleep disorders
-	-	-	-	-

Model 1 without adjustments.

Model 2 was additionally adjusted for age, gender, race, education level, poverty, BMI, smoking statue.

Note. For both the mode1 and model2 models, if any element has statistical significance, it will be displayed in Table 2. Significant associations (p<0.05) are bolded.

Figure 4.

Multivariable-adjust ORs and 95%CI of urinary trace metals and health outcomes.

Then, we analysed the sex-specific associations (Figure 4, Supplementary Table 3). For example, antimony could significantly increase the risk of thyroid problem and sleep disorders in women, but it does not increase the risk of men. While cobalt and manganese were related to the risk of arthritis only in man. However, they were not associated with the risk of arthritis in women.

Effects of urinary trace metals

RCS curves were adopted to display the association between the levels of urinary trace metals and the risk of multiple health outcomes. After adjusting for multiple variables, evidence for a nonlinear (L-shaped) relationship were observed in tin and heart disease, cadmium and stroke, cadmium and emphysema (Figure 5). However, there were no nonlinear relationship in other trace meals and other diseases.

Figure 5.

RCS curve with nonlinear relationship.

WQS regression models was used to evaluate the impact of urinary trace metals on the risk of multiple health outcomes (Figure 6). For asthma, tungsten was the highest WQS weigh with the highest contributions 0.92. Cadmium has played the most important contributions in heart disease (weight=0.68) and cancer (weight=0.90). Tin (weight=0.53) and cadmium (weight=0.44) played a common contribution in the occurrence of arthritis.

Figure 6.

WOS weight of trace metals in different diseases.

Discussion

This comprehensive analysis of NHANES data (2005–2020) advances our understanding of the intricate relationships between urinary trace metals and major health outcomes. Our findings reveal three pivotal dimensions:¹: urinary trace metals are significant modifiable risk factors for multisystem health outcomes, especially cadmium.² profound sex-specific associations patterns, and³ evidence of nonlinear exposure-response dynamics and metal mixture effects. Below, we contextualize these discoveries within existing literature, address methodological considerations, and propose actionable public health strategies.

Cadmium emerged as the most consequential toxicant, its urinary levels represent long-term accumulation. In present study, cadmium significantly elevating risks of heart disease, cancer, stroke, diabetes, and hypertension. Our results demonstrated its multisystemic toxicity, and expanded the previous study on the correlation between urinary cadmium exposure and single disease. Previous major studies involved cardiovascular diseases,^21,22 various cancer,²³ chronic kidney disease,²⁴ arthritis,²⁵ stroke,²⁶ accelerated epigenetic age.²⁷ Our results are basically consistent with other studies, which excessive exposure to cadmium will significantly increase the risk of these diseases. Smoking is a major confounder in cadmium-disease associations. We have included smoking as a covariate in our models. The results show that cadmium remained significantly associated with several outcomes (e.g., stroke) even after adjusting for smoking, and the effect sizes were attenuated but still significant. Smoking is a key source of cadmium exposure and a risk factor for the diseases, and that our findings should be interpreted with this in mind. The nonlinear (L-shaped) cadmium-emphysema association suggests even low-level exposure may accelerate respiratory damage, consistent with in vitro evidence of Cd-induced alveolar cell apoptosis. Zhang’s group found that cadmium exposure could injury alveolar epithelial cell by inducing mitochondrial oxidative stress.²⁸ Notably, the prominence of cadmium in metal mixtures underscores its outsized role in disease etiology, such as heart disease and cancer. Mechanistically, cadmium’s pan-disease impact likely relates to its ability to induce oxidative stress, disrupt endocrine function, and promote chronic inflammation.²⁹ For cardiovascular disease, cadmium induces cardiotoxicity primarily through oxidative stress and inflammation, leading to endothelial dysfunction, platelet-leukocyte activation, and thromboinflammation. For respiratory diseases such as emphysema and chronic bronchitis, experimental studies have demonstrated that cadmium inhalation triggers pulmonary inflammation leading to increased median inter-wall distance indicative of emphysema development.

Notably, profound sex-specific disparities emerged in both disease susceptibility and metal toxicity patterns. Females exhibited 3.5-fold higher thyroid disorder prevalence than males, with antimony exposure elevating risk exclusively in women (OR=1.82). It was well known that antimony was an estrogenic metal. It can exert estrogen-disrupting effects by affecting estrogen levels and/or their receptor expression.³⁰ Dellovade et al. found that there was a complex interaction between estrogen and thyroid hormone in neuroendocrine regulation, which might be related to the occurrence and development of thyroid diseases.³¹ Stanley et al. thought that there were differences in the expression patterns of sex hormone receptors between male and female in the thyroid gland of newborn rats, which might provide a molecular basis for the gender differences in thyroid diseases.³² Conversely, manganese and cobalt showed male-specific associations with arthritis, possibly reflecting occupational exposures in male-dominated industries (e.g., welding, mining) combined with androgen-mediated metal uptake in joint tissues.³³ First, occupational exposures in male-dominated industries such as welding and mining may result in higher cumulative exposure to these metals. Second, experimental studies have demonstrated sex-dependent expression of metallothioneins (MT1 and MT2), which exhibit binding affinity for metals including cobalt and manganese. Third, androgens may mediate metal uptake in joint tissues, and cobalt-induced joint inflammation may be modulated by sex-specific immune responses. These findings underscore the necessity of sex-stratified risk assessments in environmental health. In recent years, the study focused on sex-specific differences between trace metals and health outcomes has been attracted much attention. For example, lead showed more important effects in renal function, especially in women.³⁴ But obviously, the study in this field is far from enough.

Another important consideration is the complex interplay between essential and non-essential metals in co-exposure scenarios. Essential elements such as zinc, selenium, and copper may modulate the toxicity of non-essential metals like cadmium and lead through competitive binding, antioxidant mechanisms, or metallothionein induction.³⁵ For instance, zinc has been shown to protect against cadmium-induced oxidative stress and cellular damage by maintaining redox balance and preserving mitochondrial function.³⁶ While our WQS regression analysis captures the overall mixture effect, it does not specifically quantify pairwise interactions. Future studies employing advanced mixture methods such as Bayesian kernel machine regression (BKMR) or interaction analysis are needed to elucidate whether essential metals modify the adverse effects of toxic metals in a sex-specific manner. Such insights could inform targeted nutritional interventions for populations with high metal exposure burdens.

However, several methodological limitations warrant consideration. Most fundamental limitation of study is cross-sectional design, which significantly constrains ability to establish causality between urinary trace metals and multiple health outcomes.. Urinary biomarkers primarily reflect recent exposure, potentially underestimating cumulative burdens for metals like cadmium that accumulate in organs over decades. Another important limitation concerns co-exposure confounding. In real-world settings, individuals are often exposed to multiple metals simultaneously, and common co-exposure patterns are well documented in NHANES and other population-based studies. If such co-exposures are not adequately accounted for, observed associations between a given metal and a disease outcome may be partially or entirely attributable to another correlated metal that is the true etiologic agent. Although we included multiple metals in our analysis and employed WQS regression to assess mixture effects, our selection of metals was limited to those consistently measured in NHANES urine samples. We may have missed other potentially relevant metals (e.g., arsenic, nickel, chromium). Additionally, self-reported diagnoses introduce possible misclassification, particularly for heterogeneous conditions like “sleep disorders.” Survivorship bias may attenuate effects, as NHANES excludes institutionalized or terminally ill individuals with potentially high metal burdens. The power is limited for rare outcomes and that null findings should be interpreted cautiously.

Conclusion

In conclusion, this comprehensive analysis reveals complex relationships and establishes urinary trace metals as significant modifiable risk factors for multisystem health outcomes, with effects critically modulated by biological sex and co-exposure patterns.

Supplemental material

Supplemental material - Association between urinary trace metals and multiple health outcomes

Supplemental material for Association between urinary trace metals and multiple health outcomes by Song Wu, and Bin Yu in Science Progress.

Supplemental material

Supplemental material - Association between urinary trace metals and multiple health outcomes

Supplemental material for Association between urinary trace metals and multiple health outcomes by Song Wu, and Bin Yu in Science Progress.

Footnotes

Acknowledgements

We thank all of the project participants for their contributions.

ORCID iD

Bin Yu

Ethical considerations

The National Center for Health Statistics (NCHS) Ethics Review Board (ERB) ensures that research involving human participants protects the rights and welfare of study participants and conforms to U.S. federal regulations. The NCHS ERB, and the formal review bodies that preceded it, have approved each NHANES study protocol since the survey began running continuously in 1999.

Consent to participate

This study is derived from the mining of the NHANES database and is deemed exempt from ethical review and informed consent.

Author contributions

BY and SW conceived the study and carried out the assays. BY and SW wrote and reviewed the manuscript. All the authors agreed the publication.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by Project funding for the training of high-level health professionals in Changzhou (2022CZZY007).

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

Data are publicly available from the National Health and Nutrition Examination Survey website (). Identifiable individual data.

Supplemental material

Supplemental material for this article is available online.

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