Hyperlipidemia has become an increasingly common concern in Europe and the US, as well as in developing countries. This condition includes various genetic and acquired disorders that are associated with high lipid levels, including cholesterol and triglycerides. From 2015 to 2018, approximately 12% of adults aged ≥20 years reported total cholesterol levels higher than 240 mg/dL, and nearly 17% reported high-density lipoprotein (HDL) cholesterol levels <40 mg/dL in the US (1). In the US, 28 million adults reported total cholesterol levels higher than 240 mg/dL (1). Hyperlipidemia increases the risk of cardiovascular disease and is a leading cause of stroke and death (2).

Lead (Pb) is a neurotoxic heavy metal that exists widely in the environment. The use of Pb has increased globally in the past decades, and the mining, smelting, and refining industries have led to substantial increases in environmental levels. Much of the Pb exposure mixtures come from a variety of industry products around the life space, including paint, ceramic, and piping materials (3). Pb is generally absorbed by the human body through ingestion and inhalation and accumulates in bone (4). It is estimated that adults could absorb 3–10% of water-soluble Pb at oral dose, whereas for children, the estimates may increase to 40–50% (4). Pregnancy was associated with higher absorption of Pb (5). Furthermore, mothers can transfer Pb to the fetus via the blood and to infants via breastfeeding (5). Pb toxicity damages the human central nervous system, and a previous report has shown that, in children, the amount of Pb adsorption is linked to lower intelligence and impaired motor function (4).

The association between Pb exposure and hyperlipidemia is inconsistent, and epidemiological studies on blood Pb levels and hyperlipidemia are rare. Evidence from rat experiments showed that exposure to Pb can cause oxidative stress, which induces alterations in lipid metabolism and gives rise to lipid disorders (6). This result indicates that exposure to Pb is associated with hyperlipidemia. Similarly, a longitudinal study in the US of 426 aging males showed that blood Pb levels were positively correlated with total cholesterol and HDL concentrations. Moreover, a national cross-sectional study in the US among adults found that exposure to Pb for long periods may significantly increase the risk of low-density lipoprotein, C-reactive protein, systolic blood pressure, and diastolic blood pressure (7). Moreover, in pregnant women, Pb exposure was significantly associated with increased non-HDL cholesterol and gamma-glutamyl transferase in the US (7). However, the artisans in Abeokuta, Nigeria were more likely to have high low-density lipoprotein levels than the control group, but there was no significant association with HDL cholesterol or triglycerides (8).

The purpose of this study was to examine the association between blood Pb levels and hyperlipidemia for 10 pooled cycles from the US NHANES to find potential evidence that could shed light on the contradictory association between lipid biomarkers and Pb exposure found in previous studies, and the limitation on hyperlipidemia associated with long-term Pb exposure.

There were 43,196 participants included in the final analysis across the quartiles of blood Pb levels, as shown in Table 1. Participants in the Q4 group tended to be older, female, high school education or above, non-Hispanic White, mild alcohol users, never smokers, overweight, with total MET >1,200 min/week, ratio of family income to poverty <1.5, no diabetes, and hypertension. There was a significant difference among the blood Pb quartiles in terms of age, sex, education, races, alcohol intake, smoking status, BMI, MET, ratio of family income to poverty, diabetes, hypertension and hyperlipidemia (P < 0.001).

High blood Pb levels were positively associated with hyperlipidemia in the null model, and the OR for hyperlipidemia across the quartiles of blood Pb levels was 1.00, 1.44 (95%CI: 1.34–1.54), 1.79 (95%CI: 1.65–1.95), and 1.85 (95%CI: 1.69–2.03) for Q1, Q2, Q3, and Q4, respectively. After adjusting for sex and age, the association was not significantly changed, and the OR of the blood Pb level quartiles was 1.00, 1.12 (95%CI: 1.04–1.20), 1.18 (95%CI: 1.08–1.29), and 1.08 (95%CI: 0.97–1.19) for Q1, Q2, Q3, and Q4, respectively. In the full mode, after further adjusting for race, education level, alcohol intake, smoking status, ratio of family income to poverty, diabetes, BMI, hypertension, and MET, the OR for hyperlipidemia across the blood Pb level quartiles was 1.00, 1.16 (95%CI: 1.04–1.29), 1.39 (95%CI: 1.21–1.59), and 1.33 (95%CI: 1.15–1.54) for Q1, Q2, Q3, and Q4, respectively (Table 2).

In the full model, the log-blood Pb concentrations were associated with a higher risk of hyperlipidemia (OR 1.41, 95%CI: 1.18–1.67). A significant linear association was found between the quartiles of blood Pb levels and hyperlipidemia (P for trend <0.001 and <0.05) (Table 2). No interaction of blood Pb levels and hyperlipidemia with the sex and BMI subgroups was identified (Table 3). Significant moderating effects were found in the subgroup analysis stratified by age, education, hypertension, and diabetes (P < 0.05), and it appeared stronger among participants aged below 60 years (OR 2.42, 95%CI: 2.03–2.89), among those with high school education or above (OR 2.57, 95%CI: 2.12–3.11), among those without diabetes (OR 2.59, 95%CI: 2.16–3.11) and hypertension (OR 2.35, 95%CI: 1.96–2.82) (Table 3).

An additional sensitively analysis was conducted in this study for which participants with arteriosclerotic cardiovascular disease were removed. In the full adjusted model, the OR for hyperlipidemia across the quartiles of blood Pb levels was 1.00, 1.17 (95%CI: 1.05–1.30), 1.42 (95%CI: 1.24–1.62), and 1.38 (95%CI: 1.19–1.60) for Q1, Q2, Q3, and Q4, respectively (P for trend <0.001) (Table 4). In the full analysis of including pregnant women, the OR for hyperlipidemia across the quartiles of blood Pb levels was 1.00, 1.13 (95%CI: 1.01–1.25), 1.38 (95%CI: 1.21–1.56), and 1.32 (95%CI: 1.16–1.52) for Q1, Q2, Q3, and Q4, respectively (P for trend <0.001) (Table 5).

The independent association between blood Pb levels and hyperlipidemia was determined in this study. Our results show an increasingly significant association between blood Pb levels and hyperlipidemia examined after full adjustment. Additionally, subgroup analyses showed significant moderating effects for age, hypertension, and diabetes.

A linear association between blood Pb levels and hyperlipidemia was highlighted in this study. A positive association between blood Pb levels and cholesterol has been observed in previous studies. For example, a national cross-sectional study in the US showed that persistent occupational exposure to Pb could significantly increase the cholesterol levels of workers (7). Another large national cross-sectional study conducted in the US for 7 years found that Pb exposure could exacerbate oxidative stress in pregnant women and elevate cholesterol concentrations (22). The Korean National Environmental Health Survey, including 2,591 Korean adults, reported that blood Pb, blood mercury (Hg), and urine Hg levels were significantly correlated with dyslipidemia (23). The association between Framingham risk scores (FRS) and blood Pb levels was supported in the Korean cross-sectional study involving 1,929 participants, which showed that blood Pb levels were correlated with high systolic blood pressure, FRS, and total cholesterol (24) and with low HDL levels. Therefore, our findings support the previous evidence that blood Pb levels could increase the risk of hyperlipidemia.

The underlying mechanism of blood Pb on hyperlipidemia could be related to oxidative stress (25, 26). The levels of 8-epi-PGF2alpha, a biomarker of oxidative stress, were positively associated with hypercholesterolemia (26). Pb can lead to oxidative stress by binding the sulfhydryl protein group and depleting glutathione, then releasing active oxygen species (26). Oxidative stress can lead to the onset of hyperlipidemia by inducing an inflammatory reaction (27). This reaction was involved in the pancreatic β-cells' apoptotic process associated with insulin generation, which modified glycemic homeostasis, leading to insulin resistance (28). This process can result in endothelial dysfunction due to a decrease in vasodilator generation and increase in vasoconstrictor production (29). Experimental studies have also found that chronic exposure to Pb could impair glucose metabolism and disrupt glucose homeostasis via pancreatic β-cell damage (6, 30).

In the subgroup analysis stratified by age, participants aged <60 years showed a stronger association between blood Pb levels and hyperlipidemia than those aged ≥60 years. Our study design was based on a cross-sectional study that cannot determine the influence of Pb exposure on individual children and adults. Therefore, the effect of blood Pb levels on chronic disease stratified by age could be underestimated. Furthermore, the onset of hyperlipidemia is more common in the aging population due to alterations of body function, but the hyperlipidemia group among adults <60 years old might indicate a worse environment or unhealthy diet habits and lifestyles (31, 32). Meanwhile, the hyperlipidemia status of young adults moderated the risk of subsequent coronary heart disease with aging (33). Hence, age stratification magnified the direct effect of blood Pb on hyperlipidemia. No other research has completely revealed the underlying association between Pb blood levels and potential mediating biological processes in the development of coronary vascular disease and time-varying effects, such as age.

In terms of education stratification, participants above high school had a stronger association between blood Pb levels and hyperlipidemia than other people. The causal evidence using mendelian randomization had demonstrated that high education attainment was associated with lower cardiovascular disease and ischemic stroke (34). Low education was always linked with unhealthier life choices (such as irregular diet, high frequency of smoking, and alcoholism), thereby increasing the risk of getting cardiovascular disease (35–37). Additionally, Pb exposure was associated with poverty, older housing, African and Hispanic dominated communities (38). In our subgroup model, people would experience the adverse effects of Pb exposure to hyperlipidemia at all education stages. However, participants with high education had a stronger risk for hyperlipidemia while exposure to high Pb environment. This result indicated that the hazard of Pb exposure on hyperlipidemia may be further enlarged in the group without long-term experience of Pb exposure.

In the subgroup analysis stratified by diabetes and hypertension, participants with no diabetes or hypertension had a stronger association between blood Pb levels and hyperlipidemia. “Survival-Selection” mechanism demonstrated that frail aging people might be premature death due to multiple disease and worse medical service, and the leaving aging people had some stronger characteristics, such as genetic and behavior (39). Additionally, participants with chronic disease may pay more attention on medical knowledge, thus decreasing injury of the hazardous substances to human body. Meanwhile, the cross-section involving Chinese and American participants across all age-stage population demonstrated that the obesity-diabetes association significantly decreased with aging, indicating that the modification of diabetes might be closer in adults than older people (40). In a previous study, the positive association between Pb and blood pressure, not hypertension, was determined (41). Therefore, the group with continuous blood pressure increase was a potentially sensitive subpopulation in this relationship chain.

The current study has several strengths. First, a 10-cycle national cross-sectional sample was used in the analysis, which improves the statistical power. Thus, the weighted results could be interpreted as a national representative sample for American adults. Second, sensitivity analysis decreases the risk of false positives. In this study, we removed the participants with arteriosclerotic cardiovascular disease, because this group might always have hypercholesterolemia. Third, the blood Pb levels were divided into quartiles, which increases between-group heterogeneity and increases interpretability.

This study has several limitations. First, a causal association between blood Pb levels and hyperlipidemia cannot be verified due to the limitation of the cross-sectional NHANES data. Second, the adverse effect of chronic Pb exposure on the body cannot be completely explained in our study. Some participants could have had contact with environmental Pb for a long time prior to the surveys; therefore, the research of life courses can be reliable for estimating the long-term causal effects. Third, we only investigated the independent relationship between Pb blood levels and hyperlipidemia. However, the relationship between heavy metal exposure and disease is interactive. The complex network of multiple heavy metals, such as lead, mercury, and cadmium, could be further examined.

The large-scale national US survey was analyzed in this study. Our findings highlight the significant association between high blood Pb levels and hyperlipidemia after adjusting for confounders. However, the results cannot establish a causal association, and further studies are needed.

Publicly available datasets were analyzed in this study. This data can be obtained at: https://www.cdc.gov/nchs/nhanes/index.htm.

The studies involving human participants were reviewed and approved by the studies involving human participants were reviewed and approved by the National Center for Health Statistics Institutional Ethics Review Board, and all participants agreed on the survey and provided their written informed consent to participate in this study.

YZ, WL, and XL contributed to the conception, analysis, interpretation of data, drafted the manuscript, and managed the final version for submission. WZ, RC, AT, SS, YX, and LZ contributed to analyzing and interpreting the data, commented on the report, and revised the manuscript. All authors have read and agreed on the published version of the manuscript. All authors contributed to the article and approved the submitted version.

This work was supported by the National Natural Science Foundation of China (No. 72174033) and the Science and Technology Research Project of Chongqing Education Commission (No. KJQN202100432).

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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