Abstract
This study investigated the potential association between uranium exposure and mortality from cerebrovascular diseases, with a focus on the mediating effects of lipid indicators. Employing recommended sampling weights to account for National Health and Nutrition Examination Survey’ complex survey design, this analysis drew from data collected between 2005 and 2016. The study examined the impact of uranium on mortality from cerebrovascular diseases using various statistical approaches, including Cox regression to assess linear relationships within metal mixtures. It also evaluated the role of lipid-adjusted plutonium (LAP) as a mediator and verified the persistence of associations across different subgroups. The study encompassed 4312 participants and established a significant direct link between uranium levels and mortality from cerebrovascular diseases (hazard ratio (95%CI) = 20.4243 (20.1347–20.7181), P = .0266). It also identified LAP as a mediating factor in the relationship, accounting for a mediated proportion of 1.35%. The findings highlight a pivotal connection between uranium exposure and increased mortality due to cerebrovascular diseases, with LAP playing a significant intermediary role.
Keywords: cerebrovascular diseases mortality, LAP, mediation analysis, NHANES, uranium
1. Introduction
As industrialization progresses, activities like wastewater treatment and fuel combustion have notably escalated the prevalence of heavy metals in environments closely associated with humans, surpassing natural remediation capacities.[1] These metals gain entry into the human body through various routes such as air inhalation, water consumption, food intake, and skin contact.[2–4] Studies have revealed a direct link between cadmium exposure and an increase in all-cause mortality,[5] and similarly, high blood lead levels have been associated with a rise in all-cause mortality within the Korean community.[6] Moreover, the surge in the mining of rare metals like tungsten and uranium (U) has further increased their presence in both the environment and human proximity.[7,8]
In 2005, Kahn explored the lipid accumulation product (LAP) as a tool for assessing the risk of cardiovascular disease (CVD) and found it to be an effective measure of lipid accumulation and visceral fat compared to traditional body fat assessments.[9] Subsequent research has suggested that LAP could be a more accurate predictor of CVD, metabolic syndrome, and diabetes compared to other indicators.[10–15] This has led to debates over whether differences in sample sizes or racial demographics might influence LAP’s effectiveness, leaving its reliability in question.
In environmental and biomedical research, heavy metals such as lead and cadmium have been extensively studied due to their widespread industrial use and significant toxicity.[16–18] However, uranium, as both a heavy metal and radioactive element, has received relatively less attention in environmental and health studies. While the radioactive properties of uranium are often the focus, its characteristics as a heavy metal also pose potential health risks, especially in nonoccupational exposure environments. Unlike lead and cadmium, uranium’s biogeochemical behaviors, including its solubility, mobility, and bioavailability in natural waters, significantly influence its environmental distribution and human exposure pathways.
Uranium is unique in its ability to undergo complex reactions with various environmental media, such as forming complexes with organic matter or displaying different solubilities under acidic and alkaline conditions. These characteristics determine uranium’s behavior in water and soil, which in turn affects human intake through drinking water and the food chain. Additionally, uranium primarily accumulates in the kidneys in the human body, unlike lead and cadmium, which mainly accumulate in the bones and liver, potentially leading to different health outcomes.[19,20]
Studying the environmental and biological impacts of uranium, particularly its role as a chronic disease factor, is a crucial and cutting-edge topic. Although uranium is less toxic compared to other heavy metals, its potential health impacts, especially the long-term effects on the cardiovascular system, remain underexplored. Therefore, this study’s focus on the relationship between uranium exposure and cerebrovascular disease mortality, and the mediating role of the LAP, fills a gap in the existing literature, providing new perspectives and data support. This research not only enhances our understanding of uranium as an environmental pollutant’s impact on public health but could also offer scientific evidence for policymakers in developing relevant health and environmental policies.
Given these insights, it is hypothesized that excessive exposure to uranium may elevate mortality rates from cerebrovascular diseases via the mediation of the LAP index. Our study, utilizing data from National Health and Nutrition Examination Survey (NHANES), aims to investigate this potential correlation.
2. Method
2.1. Study population
The NHANES, managed by the National Center for Health Statistics (NCHS) and a component of the CDC, performs biennial evaluations to assess the health and nutritional status of American adults and children outside of institutional environments. This survey employs a sophisticated, stratified, multi-stage clustered sampling approach that targets the civilian population. It systematically incorporates selection criteria including age, gender, racial/ethnic identities, and economic status, publishing its findings biennially.
2.2. Ethical approval
Informed consent was obtained from all participants. Ethic approval received from NCHS Ethics Review Board in accordance with the Declaration of Helsinki (Protocol #2011-17 and Protocol #2005-06).
2.3. Inclusion and exclusion criteria
Initially, the sample comprised 6205 individuals from the NHANES data collected between 2005 and 2016. Due to incomplete data, 1893 participants were removed from the study. Ultimately, 4312 participants were included in the final analysis (Fig. 1).
Figure 1.
Study flowchart.
2.4. Exposure assessment
The analysis method involved mass spectrometry to examine various urinary metals, beginning with a primary dilution step. Samples are fed into a mass spectrometer via an inductively coupled plasma ionization source. The specimens are atomized into fine droplets using a nebulizer, which are then carried into the inductively coupled plasma by argon gas. The ions pass through a focusing zone and a dynamic reaction cell, continue through a quadrupole mass filter, and are detected in sequence, which allows for precise isotope identification of each element.
2.5. Outcome assessment
The causes of death were categorized using the 10th revision of the International Classification of Diseases (ICD-10). The primary endpoint was mortality from cerebrovascular diseases, defined as deaths related to cerebrovascular diseases (ICD-10 codes 070). Mortality data were sourced by linking NHANES records with the National Death Index. The follow-up period for each subject started on their survey participation date and concluded on either their death date or the end of the study period (December 31, 2019).
2.6. Covariates
Our analysis integrated various clinical covariates previously identified as relevant,[21,22] such as age at interview, gender, race and ethnicity, educational level, marital status, family poverty income ratio (PIR), alcohol intake, smoking habits, diabetes, and hypertension.
NHANES organizes race and ethnicity into several categories, including Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black, non-Hispanic Asian, and multi-racial. Educational levels are classified from less than 9th grade to college graduate or higher. Marital status is diversified from married to unspecified, reflecting different living arrangements. The PIR evaluates annual income relative to poverty thresholds adjusted by family size.
Alcohol consumption is assessed by inquiring whether participants had consumed 12 or more alcoholic drinks annually from 2005 to 2016, and since 2017, if they ever drank alcohol. Smoking status is ascertained by whether individuals smoked over 100 cigarettes in their lifetime. Both diabetes and hypertension are confirmed through self-reported medical diagnoses.
2.7. Statistical analysis
We conducted a thorough statistical analysis of baseline characteristics according to cadmium (Cd) quartiles. Continuous variables were analyzed using the Kruskal–Wallis rank sum test, while categorical data with low expected counts used Fisher exact test. Logarithmic transformations normalized heavy metal concentrations.
We utilized the Cox proportional hazards model to assess the relationship between low attenuation regions and CVD mortality. Two risk models were established: Model 1, an unadjusted model, and Model 2, which included all covariates. Mediation analysis using nonparametric bootstrapping (n = 1000) examined direct and indirect effects and the extent of mediation. Subgroup analyses adjusted all models by covariates and evaluated interactions.
All statistical procedures applied the appropriate NHANES sampling weights to accommodate the survey’s complex design, unless specified otherwise in the tables. All analyses, including QG-comp model, Bayesian Kernel Machine Regression, and mediation methods, were performed in R software, with the significance threshold set at P < .05.[23,24]
3. Result
3.1. Study population
The study analyzed 4312 individuals with an average follow-up of 10.1792 years (Table 1). The participants were categorized into 4 quartiles based on uranium (U) exposure, with significant differences noted across variables such as gender, race/ethnicity, educational levels, marital status, PIR, smoking habits, and the presence of hypertension and diabetes, as well as levels of various heavy metals.
Table 1.
General information.
| Population | Quartile 1 | Quartile 2 | Quartile 3 | Quartile 4 | P |
|---|---|---|---|---|---|
| 1078 | 1078 | 1078 | 1078 | ||
| Gender | .0053 | ||||
| Male | 501 (46.47%) | 536 (49.72%) | 533 (49.44%) | 583 (54.08%) | |
| Female | 577 (53.53%) | 542 (50.28%) | 545 (50.56%) | 495 (45.92%) | |
| Age | 48.49 ± 17.00 | 49.28 ± 17.77 | 49.01 ± 18.11 | 48.19 ± 17.56 | .4664 |
| Race and ethnicity | .0000 | ||||
| Mexican American | 111 (10.30%) | 160 (14.84%) | 177 (16.42%) | 240 (22.26%) | |
| Other Hispanic | 132 (12.24%) | 102 (9.46%) | 113 (10.48%) | 80 (7.42%) | |
| Non-Hispanic White | 542 (50.28%) | 487 (45.18%) | 509 (47.22%) | 467 (43.32%) | |
| Non-Hispanic Black | 178 (16.51%) | 240 (22.26%) | 214 (19.85%) | 212 (19.67%) | |
| Other Race – including multi-racial | 115 (10.67%) | 89 (8.26%) | 65 (6.03%) | 79 (7.33%) | |
| Educational background | .0000 | ||||
| <9th grade | 81 (7.51%) | 109 (10.11%) | 111 (10.30%) | 111 (10.30%) | |
| 9–11th grade (includes 12th grade with no diploma) | 135 (12.52%) | 135 (12.52%) | 164 (15.21%) | 184 (17.07%) | |
| High school graduate/GED or equivalent | 222 (20.59%) | 243 (22.54%) | 249 (23.10%) | 265 (24.58%) | |
| Some college or AA degree | 306 (28.39%) | 317 (29.41%) | 352 (32.65%) | 300 (27.83%) | |
| College graduate or above | 334 (30.98%) | 274 (25.42%) | 202 (18.74%) | 218 (20.22%) | |
| Marital status | .0027 | ||||
| Married | 624 (57.88%) | 567 (52.60%) | 540 (50.09%) | 563 (52.23%) | |
| Widowed | 60 (5.57%) | 85 (7.88%) | 92 (8.53%) | 69 (6.40%) | |
| Divorced | 100 (9.28%) | 107 (9.93%) | 132 (12.24%) | 118 (10.95%) | |
| Separated | 23 (2.13%) | 31 (2.88%) | 42 (3.90%) | 47 (4.36%) | |
| Never married | 186 (17.25%) | 205 (19.02%) | 178 (16.51%) | 180 (16.70%) | |
| Living with partner | 85 (7.88%) | 83 (7.70%) | 94 (8.72%) | 101 (9.37%) | |
| PIR | 2.76 ± 1.65 | 2.65 ± 1.57 | 2.52 ± 1.61 | 2.38 ± 1.57 | .0000 |
| Drinking | .0770 | ||||
| No | 304 (28.20%) | 327 (30.33%) | 273 (25.32%) | 297 (27.55%) | |
| Yes | 774 (71.80%) | 751 (69.67%) | 805 (74.68%) | 781 (72.45%) | |
| Smoking | .0003 | ||||
| No | 624 (57.88%) | 623 (57.79%) | 568 (52.69%) | 541 (50.19%) | |
| Yes | 454 (42.12%) | 455 (42.21%) | 510 (47.31%) | 537 (49.81%) | |
| Hypertension | .0274 | ||||
| No | 743 (68.92%) | 712 (66.05%) | 696 (64.56%) | 679 (62.99%) | |
| Yes | 335 (31.08%) | 366 (33.95%) | 382 (35.44%) | 399 (37.01%) | |
| Diabetes | .0000 | ||||
| No | 1001 (92.86%) | 937 (86.92%) | 946 (87.76%) | 930 (86.27%) | |
| Yes | 77 (7.14%) | 141 (13.08%) | 132 (12.24%) | 148 (13.73%) | |
| Heavy metal | |||||
| Ba | 0.38 ± 0.12 | 0.43 ± 0.12 | 0.47 ± 0.12 | 0.48 ± 0.12 | .0000 |
| Cd | 0.34 ± 0.17 | 0.42 ± 0.16 | 0.47 ± 0.16 | 0.49 ± 0.16 | .0000 |
| Co | 0.31 ± 0.11 | 0.37 ± 0.10 | 0.38 ± 0.10 | 0.40 ± 0.09 | .0000 |
| Cs | 0.60 ± 0.11 | 0.65 ± 0.10 | 0.68 ± 0.09 | 0.69 ± 0.09 | .0000 |
| Mo | 0.54 ± 0.12 | 0.61 ± 0.11 | 0.64 ± 0.11 | 0.66 ± 0.11 | .0000 |
| Pb | 0.33 ± 0.11 | 0.39 ± 0.10 | 0.42 ± 0.10 | 0.44 ± 0.11 | .0000 |
| Sb | 0.15 ± 0.12 | 0.22 ± 0.14 | 0.27 ± 0.15 | 0.31 ± 0.16 | .0000 |
| Tl | 0.43 ± 0.14 | 0.49 ± 0.13 | 0.52 ± 0.12 | 0.53 ± 0.12 | .0000 |
| W | 0.16 ± 0.14 | 0.25 ± 0.14 | 0.30 ± 0.14 | 0.36 ± 0.16 | .0000 |
3.2. Association between uranium and cerebrovascular diseases mortality assessed by Cox proportional hazards model
Cox regression analysis revealed a strong positive correlation between mixed heavy metal exposure and mortality due to cerebrovascular diseases. Initial results indicated a marked increase in mortality from these conditions associated with higher uranium levels (hazard ratio [HR] (95%CI) = 20.4243 (20.1347–20.7181), P = .0266). This relationship remained statistically significant after adjusting for all variables considered in the study (HR (95%CI) = 26.4768 (26.0891–26.8702), P = .0184) (Table 2).
Table 2.
Association between uranium and cerebrovascular diseases mortality assessed by Cox proportional hazards model.
| Model I (unadjusted) | Model II (adjusted) | |||
|---|---|---|---|---|
| HR (95%CI) | P | HR (95%CI) | P | |
| Ba | 0.0240 (0.0236–0.0244) | .0830 | 0.0813 (0.0799–0.0827) | .1964 |
| Cd | 4.3487 (4.2859–4.4124) | .3369 | 0.1994 (0.1958–0.2030) | .4262 |
| Co | 0.0744 (0.0723–0.0766) | .3402 | 0.0753 (0.0733–0.0774) | .2841 |
| Cs | 5.0092 (4.8582–5.1650) | .6136 | 14.2465 (13.7777–14.7313) | .5123 |
| Mo | 48.3150 (47.1103–49.5506) | .2059 | 52.9133 (51.5953–54.2650) | .1838 |
| Pb | 94.6918 (92.6215–96.8084) | .0124 | 10.8329 (10.5574–11.1155) | .2570 |
| Sb | 0.0446 (0.0439–0.0453) | .1270 | 0.2636 (0.2592–0.2680) | .5353 |
| Tl | 0.1959 (0.1913–0.2005) | .5632 | 1.9818 (1.9359–2.0289) | .7976 |
| W | 0.7086 (0.6980–0.7194) | .8164 | 0.7136 (0.7025–0.7250) | .8157 |
| U | 20.4243 (20.1347–20.7181) | .0266 | 26.4768 (26.0891–26.8702) | .0184 |
Note: Model II was adjusted for age, gender, race, educational level, smoking habits, alcohol intake, diabetes, hypertension, etc.
3.3. Mediated effect of LAP on the association between uranium and cerebrovascular diseases mortality
Mediation analysis showed that the LAP partially mediated the association between uranium exposure and mortality from cerebrovascular diseases, with a mediated proportion of 1.35% (Table 3).
Table 3.
Mediated effect of LAP on the association between uranium and cerebrovascular diseases mortality.
| Independent variable | Intermediary variable | Predictor variable | Direct effects β (95%CI) | Indirect effects β (95%CI) | Total effects β (95%CI) | Mediated proportion | P-value | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Estimate | CI lower | CI upper | Estimate | CI lower | CI upper | Estimate | CI lower | CI upper | |||||
| U | LAP | Cerebrovascular Diseases Mortality | 0.4345 | 0.1333 | 0.7357 | 0.0060 | 0.0037 | 0.0082 | 0.4406 | 0.1487 | 0.7324 | 0.0135 | .0000 |
3.4. Association between uranium and cerebrovascular diseases mortality in different subgroups
The association between uranium exposure and increased mortality from cerebrovascular diseases persisted across nearly all examined subgroups, with no significant interactions detected (Table 4).
Table 4.
Association between uranium and cerebrovascular diseases mortality in different subgroups.
| Variables | n (%) | HR (95%CI) | P | P for interaction |
|---|---|---|---|---|
| Gender | .048 | |||
| Male | 2153 (49.93) | 0.04 (0.00–2.35) | .121 | |
| Female | 2159 (50.07) | 14.95 (0.29–763.24) | .178 | |
| Race and ethnicity | .702 | |||
| Mexican American | 688 (15.96) | 0.73 (0.00–4715.21) | .944 | |
| Other Hispanic | 427 (9.90) | 4.09 (0.00–15473385.48) | .855 | |
| Non-Hispanic White | 2005 (46.50) | 1.95 (0.04–108.48) | .744 | |
| Non-Hispanic Black | 844 (19.57) | 0.01 (0.00–4.14) | .13 | |
| Other race – including multi-racial | 348 (8.07) | 3.38 (0.00–120632.34) | .82 | |
| Educational background | .213 | |||
| <9th grade | 412 (9.55) | 24.28 (0.26–2294.35) | .169 | |
| 9–11th grade (includes 12th grade with no diploma) | 618 (14.33) | 0.00 (0.01–0.90) | .047 | |
| High school graduate/GED or equivalent | 979 (22.70) | 0.80 (0.01–123.29) | .932 | |
| Some college or AA degree | 1275 (29.57) | 0.06 (0.00–73.98) | .437 | |
| College graduate or above | 1028 (23.84) | 0.61 (0.00–758.38) | .891 | |
| Marital status | .425 | |||
| Married | 2294 (53.20) | 2.88 (0.08–110.83) | .569 | |
| Widowed | 306 (7.10) | 0.05 (0.00–31.82) | .361 | |
| Divorced | 457 (10.60) | 0.00 (0.01–0.82) | .047 | |
| Separated | 143 (3.32) | |||
| Never married | 749 (17.37) | 0.42 (0.00–39919.64) | .883 | |
| Living with partner | 363 (8.42) | 20.94 (0.00–28749095.07) | .673 | |
| Drinking | .5 | |||
| No | 1201 (27.85) | 1.78 (0.02–162.09) | .802 | |
| Yes | 3111 (72.15) | 0.23 (0.01–10.28) | .448 | |
| Hypertension | .232 | |||
| No | 2830 (65.63) | 0.06 (0.00–8.44) | .27 | |
| Yes | 1482 (34.37) | 2.79 (0.07–105.30) | .579 | |
| Diabetes | .304 | |||
| No | 3814 (88.45) | 0.26 (0.01–7.27) | .427 | |
| Yes | 498 (11.55) | 7.74 (0.06–980.16) | .407 | |
| Smoking | .462 | |||
| No | 2356 (54.64) | 1.61 (0.03–94.07) | .818 | |
| Yes | 1956 (45.36) | 0.17 (0.00–13.27) | .423 |
4. Discussion
This investigation confirmed an association between uranium (U) exposure and increased mortality from cerebrovascular diseases, with the LAP serving as an intermediary.
As environmental contamination escalates, the detrimental health impacts of heavy metals gain prominence. While previous research primarily addressed the cardiovascular effects of lead (Pb) and cadmium (Cd), studies on uranium’s influence on cardiovascular health are scant.
Evidence suggests that heavy metals significantly affect cardiovascular wellness. Chowdhury et al discovered that exposure to arsenic, lead, cadmium, and copper increases cardiovascular risks, unlike mercury.[25] Further research indicates that heavy metals disrupt cardiovascular serum metabolites in healthy adults by activating the sphingolipid metabolism pathway.[26]
Toxic metals such as lead, cadmium, and arsenic disrupt the physiological functions of essential metals like calcium, iron, and zinc, affecting their absorption, transport, and metabolic roles.[27,28] Lead, by mimicking essential metals, can alter enzymatic functions by displacing metals like calcium, iron, and zinc at active sites. Cadmium and lead also compete with zinc for binding sites, affecting zinc utilization, particularly under zinc deficiency conditions.[29,30] Additionally, cadmium can interfere with iron absorption by competing for intestinal transporters, thereby increasing the risk of iron deficiency.[31,32] At low doses, selenium can mitigate arsenic toxicity by forming excretable arsenic–selenium complexes, although higher selenium levels might amplify the detrimental effects of arsenic.[33]
Calcium and magnesium may reduce the toxicity of metals such as lead and cadmium by competing for absorption sites in the intestines and attaching to enzyme active sites to prevent tissue damage.[34,35] Extensive research has linked lead exposure to elevated blood pressure,[36,37] and arsenic exposure has been correlated with hypertension in dose–response studies from systematic reviews.[38,39] Thus, these toxic metals are associated with an increased risk of hypertension, potentially leading to cardiovascular events like strokes and coronary heart disease.
Introduced by Professor Kahn in 2005, LAP is calculated from triglyceride and waist circumference values.[10] LAP has proven to be an effective predictor of insulin resistance, metabolic syndrome, and CVD risk, surpassing traditional body mass index, which, while still widely used, does not differentiate between fat and muscle and may not accurately represent body fat distribution or assess individuals with well-developed muscle mass.[40,41] Waist circumference alone does not accurately indicate visceral fat, yet visceral fat is closely linked to metabolic disorders.[42] In cases like familial hypercholesterolemia, normal body mass index and waist circumference can coexist with high LAP values.[43] LAP provides a more precise measure of central obesity, a risk factor for various chronic and acute diseases linked with hypertension, hyperglycemia, hyperlipidemia, and CVDs, all associated with overall mortality and cardiovascular deaths.[44]
In our study, significant gender differences were observed in the HRs for cerebrovascular disease mortality associated with uranium exposure, with females showing notably higher HRs compared to males. This underscores the importance of considering gender-specific biological, hormonal, and socio-behavioral factors in environmental health research. Females typically have higher body fat percentages, potentially influencing the bioaccumulation of lipophilic substances like uranium, and hormonal differences, especially involving estrogen, may alter the metabolism of heavy metals, increasing susceptibility to their toxic effects. Additionally, gender roles may lead to different levels of environmental exposure; for example, certain occupations predominantly occupied by women or domestic roles may involve greater contact with contaminated environments. Women also generally utilize healthcare services more frequently, which could lead to earlier detection and better reporting of diseases related to environmental exposures, affecting observed HRs in studies. Moreover, genetic predispositions may influence how each gender metabolizes toxins, with evidence suggesting genetic differences in detoxification pathways could make females more susceptible to the adverse effects of uranium. These observations highlight the need for targeted public health interventions and policies that consider gender-specific exposure and susceptibility to environmental toxins like uranium, and future research should continue to explore these gender differences more thoroughly to better understand the mechanisms behind them and to inform more effective public health strategies.
Overall, these results highlight that excessive uranium exposure may increase the mortality from cerebrovascular diseases, with LAP playing a mediating role, as evidenced in our study.
Our research has several strengths, including a novel examination of the relationship between uranium exposure and mortality from cerebrovascular diseases, utilizing a range of statistical methods and controlling for various confounders to enhance the reliability and precision of our results. Our data was sourced from a well-maintained, extensive population-based dataset.
However, our study has limitations. The NHANES database does not cover uncontrollable factors such as exposure to wastewater and cosmetics, which might influence our results. Additionally, our analysis did not consider the cumulative exposure to heavy metals, which could have impacted our findings.
5. Conclusion
Our research demonstrated a significant association between uranium (U) exposure and increased mortality from cerebrovascular diseases. Lipid markers were found to mediate this connection, underscoring the risks linked with exposure to heavy metals.
Author contributions
Conceptualization: Yuzhe Kong.
Data curation: Xiaoyi Zhu, Yuzhe Kong.
Formal analysis: Qu Zou, Xiaoyi Zhu, Yuzhe Kong.
Investigation: Xiaoyi Zhu, Yuzhe Kong.
Methodology: Yuzhe Kong.
Project administration: Yuzhe Kong.
Resources: Yuzhe Kong.
Software: Yuzhe Kong.
Supervision: Yuzhe Kong.
Validation: Yuzhe Kong.
Visualization: Xinling Tian, Yuzhe Kong.
Writing – original draft: Qu Zou, Qingsong Mao, Yuzhe Kong.
Writing – review & editing: Yuzhe Kong.
Abbreviations:
- CVD
- cardiovascular disease
- HR
- hazard ratio
- LAP
- lipid-adjusted plutonium
- NHANES
- National Health and Nutrition Examination Survey
- PIR
- poverty income ratio
The authors have no funding and conflicts of interest to disclose.
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
How to cite this article: Zou Q, Tian X, Mao Q, Zhu X, Kong Y. Lipid accumulation product mediating the association between uranium and cerebrovascular diseases mortality: Evidence from National Health and Nutrition Examination Survey. Medicine 2024;103:51(e40888).
Contributor Information
Qu Zou, Email: 1451861151@qq.com.
Xinling Tian, Email: 2985647934@qq.com.
Qingsong Mao, Email: 138840@hospital.cqmu.edu.cn.
Xiaoyi Zhu, Email: linhangirl2022@sina.com.
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