Associations between exposure to multiple metals/metalloid and periodontitis risk in US adults: an NHANES study using multipollutant approaches

doi:10.21203/rs.3.rs-3654991/v1

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Associations between exposure to multiple metals/metalloid and periodontitis risk in US adults: an NHANES study using multipollutant approaches

https://doi.org/10.21203/rs.3.rs-3654991/v1

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Background:

Periodontitis is a common chronic inflammatory disease affected by multiple factors. Heavy metals exposure may be environmental risk factors for periodontitis. However, relationships between multiple metals/metalloid and periodontitis risk remain unclear.

Methods:

We analyzed associations between blood levels of cadmium, lead, manganese, mercury and selenium and periodontitis prevalence in 4,982 adults aged 30–80 years from National Health and Nutrition Examination Survey (NHANES) 2011–2018. Multivariable logistic regression assessed individual and combined metals/metalloid effects. Restricted cubic splines (RCS) and weighted quantile sum (WQS) regression evaluated potential non-linear and joint relationships. Mediation analysis was used to explore their potential mediated effects of neutrophils with periodontitis.

Results:

With increasing blood cadmium and lead, periodontitis risk increased significantly, while decreasing selenium was associated with elevated risk. Combined exposure to cadmium and lead conferred greater risk than individual metals. RCS confirmed monotonically increasing trends between cadmium, lead and periodontitis risk at higher levels. WQS found cadmium and lead positively correlated and selenium negatively correlated with periodontitis.

Conclusion:

Increased environmental exposure to cadmium and lead as well as insufficient selenium may be independent risk factors for periodontitis. This study provided important evidence regarding relationships between environmental pollutants, nutritional factors and periodontal health. Further research is warranted to validate our findings and elucidate underlying mechanisms.

Metals

Metalloid

Periodontitis

Cadmium

Lead

Selenium

Risk factors

Environmental exposure

Periodontitis is a highly prevalent chronic inflammatory disease affecting the tissues surrounding and supporting the teeth, which can lead to irreversible alveolar bone loss and tooth loss if left untreated^[1]. In the United States, over 47% of adults aged 30 years and older have different form of periodontitis^[2]. Periodontitis has been associated with multiple systemic diseases, including cardiovascular disease, diabetes mellitus, rheumatoid arthritis, chronic obstructive pulmonary disease, chronic kidney disease, cancer, and adverse pregnancy outcomes^[3–5]. Therefore, periodontitis is now recognized as an important public health issue with significant medical and financial burdens.

The pathogenesis of periodontitis involves complex interactions between pathogenic biofilms and the host immune-inflammatory response^[6]. However, the contributors to individual susceptibility remain incompletely understood. Emerging evidence from epidemiological studies suggests that environmental pollutants, especially heavy metals, may play a role in periodontitis development^{[7, 8]}. Heavy metals such as lead, cadmium, and mercury are prevalent environmental and occupational toxicants. Prolonged exposure to these metals leads to their accumulation in the body and induces oxidative stress and chronic inflammation^{[9, 10]}. Recent studies found positive associations between heavy metal levels in blood and urine and increased prevalence and severity of periodontitis^{[7, 11]}. Several mechanisms have been proposed regarding how metals/metalloid could impact periodontal health, including impairment of the immune-inflammatory response, interference with tissue remodeling, and inhibition of bone formation^[12]. Nevertheless, the relationships between multiple heavy metal exposure and periodontitis risk are not fully characterized.

In addition to the toxic heavy metals, some essential metalloid such as selenium may also play a role in periodontitis. Selenium is an essential micronutrient with antioxidant properties. Studies have shown that selenium deficiency is associated with increased susceptibility to oxidative stress-induced tissue damage and inflammation ^[13]. Blood selenium levels were found to be significantly lower in patients with periodontitis compared to healthy controls in some studies^{[14, 15]}. Therefore, selenium status could impact periodontal health and should also be taken into consideration when investigating the associations between metals/metalloid and periodontitis risk. In this study, we measured blood levels of selenium along with toxic heavy metals including lead, cadmium and mercury, to provide a comprehensive assessment of their associations with periodontitis.

In this study, we analyzed the associations between blood levels of lead, cadmium, mercury, manganese and selenium and periodontitis prevalence using data from the National Health and Nutrition Examination Survey (NHANES). This study aimed to elucidate the links between environmental heavy metal exposures and periodontitis susceptibility in US adults. The findings will contribute to a better understanding of the role of metals/metalloid in periodontitis pathogenesis.

Study populations

Our study is a national cross-sectional survey and database by NHANES(The National Health and Nutrition Examination Survey) was downloaded on the website of the America Centers for Disease Control and Prevention (CDC) (https://www.cdc.gov/nchs/nhanes/index.htm). All participants in study provided consent of enrollment. The National Center for Health Statistics (NCHS) Research Ethics Review Board approved the study protocol. The study included 39,156 participants from NHANES 2011–2018, age from 30–80 years old, of which 13,520 with no blood metal/metalloid levels and 20,650 with unevaluable periodontitis were excluded, resulting in a final inclusion of 4,982 with 2,511 diagnosed periodontitis.

Periodontitis assessment

In NHANES, there are professionals who perform oral health examination, including tooth counts, basic screening exam (assess the presence of at least one tooth with dental decay, a dental restoration, and a dental sealant), dental fluorosis assessment, periodontal pockets, recession, and loss of attachment and so on. Periodontitis was defined on the basis of clinical loss of attachment (LOA) and probing depth (PD)^[16], and periodontitis was categorized into a dichotomous variable, with and without periodontitis.

Blood composition assessment

Five blood metals/metalloid ion concentrations, including cadmium, lead, manganese, total mercury, and selenium, were extracted from the NHANES database, and whole blood specimens extracted from the participants was mixed and diluted and then introduced into the mass spectrometer through inductively coupled plasma (ICP) ionization source and details on the website of https://wwwn.cdc.gov/Nchs/Nhanes/2017-2018/PBCD_J.htm#LBDBMNLC. Blood cadmium, manganese, mercury, selenium, and blood lead were measured in ug/l, and blood lead was measured in ug/dl. Continuous and categorical variables were used for the analyses, and for categorical variables, quartiles were calculated for each of the five metals/metalloid concentrations and grouped into four equal groups, which were denoted as Q1, Q2, Q3, and Q4. Cadmium was grouped in 0.20ug/l, 0.32 ug/l, and 0.60 ug/l, and lead was grouped in 0.75 ug/dl, 1.15 ug/dl and 1.15 ug/dl. 1.15 ug/dl and 1.76 ug/dl, mercury was grouped at 0.47 ug/l, 0.90 ug/l and 1.76 ug/l, manganese was grouped at 7.34 ug/l, 9.24 ug/l and 11.67 ug/l, and selenium was grouped at 179.19 ug/l, 194.07 ug/l and 209.90 ug/l. Neutrophils in blood was divided evenly into four equal groups in 3.0*10⁹/L, 3.9*10⁹/L, 5.0*10⁹/L.

Assessment of the association between metals/metalloid and periodontitis

For assessing the relationship between periodontitis and blood metals/metalloid levels, multivariate logistic regression analysis was applied to perform separate calculations to assess the correlation between the five metals/metalloid singly or jointly and the risk of developing periodontitis. In the analysis of periodontitis and the five metals/metalloid alone using covariate-adjusted logistic regression models, the adjusted models were three, with model 1 combining age (< 65 and ≥ 65), sex (Male and Female), race (Mexican American, Non-Hispanic Black, Non-Hispanic White, Other Hispanic and Other race/multiracial), and education level (Under high school, High school, Above high school, Some college or AA degree and College graduate or above) were included as covariates in the analysis, model 2 added BMI (< 18.5, 18.5–25, 25–30, ≥ 30), smoking status (Never and Ever), drinking status (Never and Ever) to model 1, and model 3 added diabetes, hypertension, and hyperlipidemia to model 2. Finally the odds ratios (OR) and 95% confidence intervals (95% CI) were obtained. In the subsequent logistic regression analysis of mixed metals/metalloid and periodontitis, the covariates of five metals/metalloid and all covariates in model 3 above were included, and ORs and 95% CIs were obtained. Restricted cubic spline (RCS) plots were applied to analyze the nonlinear relationship and dose-response relationships between metals/metalloid and periodontitis, and the weighted quantile sum regression (WQS) model was applied to evaluate the potential joint effect. At last, mediation analysis was used to explore their potential mediated effects of neutrophils with periodontitis.

Statistical analysis

All statistical analyses were performed using SPSS 26.0 and R 4.0.5. Demographic characteristics between periodontitis and non-periodontitis groups were compared by Chi-square tests for categorical variables and t-tests for continuous variables. To assess the associations between individual blood metals/metalloid levels and periodontitis, multivariable logistic regression models were utilized to obtain odds ratios (ORs) and 95% confidence intervals (CIs). Metal/metalloid concentrations were analyzed as both continuous and categorical (quartiles) predictors. Three models with progressive degrees of adjustment were performed - Model 1: adjusted for demographics; Model 2: further adjusted for lifestyle factors; Model 3: additionally adjusted for comorbidities. Dose-response relationships were evaluated by restricted cubic spline (RCS) models with 4 knots, adjusting for all covariates in Model 3. Non-linearity was assessed by likelihood ratio tests. To estimate the combined effects and interactions of multiple metals/metalloid, weighted quantile sum (WQS) regression was used. WQS considers the joint exposure distribution and does not require a priori knowledge of interactions or relationships. The WQS index was calculated by weighting each individual metals/metalloid based on their associations with periodontitis. The magnitude and directionality of influence for each metal and metalloid were determined and ranked. Mediation analysis was conducted to examine neutrophil counts as a potential mediator in the pathway between metal exposures and periodontitis, quantifying direct and indirect effects. Two-tailed P < 0.05 was considered statistically significant. Logistic regression, RCS, WQS, and mediation analysis were appropriately utilized to fully explore the relationships between multiple metal/metalloid exposures and periodontitis risk, adjusting for confounders and considering potential non-linearity, interactions, and mechanistic pathways.

Participants characteristics of periodontitis

The distribution of participants can be seen in Fig. 1. A total of 4982 participants were finally included in the study, divided into two groups, with 2511 participants in the periodontitis group and 2471 in the non-periodontitis group. The baseline characteristics of the two groups can be seen in Table 1. 27.2% of patients older than 65 had periodontitis, while only 14.3% did not have periodontitis, and a greater number of participants younger than 65 years old did not have periodontitis with 85.7%. The periodontitis group was 58.2% male, which was higher than the non-periodontitis group. The race of 32.6% of the participants in the periodontitis group was non-Hispanic White compared to 47.0% in the non-periodontitis group, and the periodontitis group was 13.9% Mexican American, 27.6% non-Hispanic Black, 10.2% other Hispanic, and 10.2% other race /multiracial at 15.7%. Overall, age, gender, race, educational status, smoking status, presence of diabetes, hypertension, and hyperlipidemia were statistically different between the two groups, with participants with a history of previous smoking and alcohol consumption having more periodontitis (51.5% vs. 48.5%, 84.4% vs. 15.6%), and participants with diabetes having more periodontitis (75.1% vs 24.9%).

Table 1

Basic characteristics of patients by periodontitis, NHANES 2011–2018.
Characteristics	Total (N = 4982)	Periodontitis (N = 2511)	Non-periodontitis (N = 2471)	P
Age, n (%)				< 0.001
<65	3945(79.2)	1828(72.8)	2117(85.7)
≥65	1037(20.8)	683(27.2)	354(14.3)
Sex, n (%)				< 0.001
Male	2454(49.3)	1461(58.2)	993(40.2)
Female	2528(50.7)	1050(41.8)	1478(59.8)
Race/ethnicity, n (%)				< 0.001
Mexican American	590(11.8)	349(13.9)	241(9.8)
Non-Hispanic Black	1133(22.7)	693(27.6)	440(17.8)
Non-Hispanic White	1979(39.7)	818(32.6)	1161(47.0)
Other Hispanic	480(9.6)	257(10.2)	223(9.0)
Other race/multiracial	800(16.1)	394(15.7)	406(16.4)
Education, n (%)				< 0.001
Under high school	455(9.1)	338(13.5)	117(4.7)
High school	605(12.1)	381(15.2)	224(9.1)
High school graduate or GRE	1074(21.6)	653(26.0)	421(17.0)
Some college or AA degree	1412(28.3)	678(27.0)	734(29.7)
College graduate or higher	1434(28.8)	460(18.3)	974(39.4)
BMI, n (%)				0.221
< 18.5	54(1.1)	26(1.1)	28(1.1)
18.5–25	1340(27.3)	644(26.1)	696(28.5)
25–30	1657(33.7)	835(33.8)	822(33.6)
≥ 30	1864(37.9)	965(39.1)	899(36.8)
Alcohol drinking status, n (%)				0.187
Never	675(14.7)	362(15.6)	313(13.7)
Ever	3930(85.3)	1958(84.4)	1972(86.3)
Smoke status, n (%)				< 0.001
Never	2831(56.8)	1215(48.5)	1616(65.4)
Ever	2149(43.2)	1294(51.5)	855(34.6)
Diabetes, n (%)				< 0.001
Yes	961(19.4)	624(24.9)	337(13.7)
No	3995(80.6)	1880(75.1)	2115(86.3)
Hypertension, n (%)				< 0.001
Yes	2243(45.0)	1315(52.4)	928(37.6)
No	2739(55.0)	1196(47.6)	1543(62.4)
Hyperlipidemia, n (%)				0.015
Yes	3667(73.6)	1886(75.1)	1781(72.1)
No	1315(26.4)	625(24.9)	690(27.9)
Cadmium		0.609 ± 0.691	0.447 ± 0.492	< 0.001
Q1	1250(25.1)	519(20.7)	731(29.6)	< 0.001
Q2	1245(25.0)	569(22.7)	676(27.4)
Q3	1255(25.2)	629(25.0)	626(25.3)
Q4	1232(24.7)	794(31.6)	438(17.7)
Lead		1.796 ± 2.096	1.250 ± 1.386	< 0.001
Q1	1265(25.4)	431(17.2)	834(33.8)	< 0.001
Q2	1228(24.6)	559(22.3)	669(27.1)
Q3	1244(25.0)	687(27.4)	557(22.5)
Q4	1245(25.0)	834(33.2)	411(16.6)
Mercury		1.729 ± 2.863	1.888 ± 2.894	0.052
Q1	1282(25.7)	685(27.3)	597(24.2)	0.001
Q2	1221(24.5)	644(25.6)	577(23.4)
Q3	1235(24.8)	604(24.1)	631(25.5)
Q4	1244(25.0)	578(23.0)	666(27.0)
Manganese		9.769 ± 4.064	10.176 ± 3.929	< 0.001
Q1	1246(25.0)	681(27.1)	565(22.9)	< 0.001
Q2	1253(25.2)	650(25.9)	603(24.4)
Q3	1238(24.8)	601(23.9)	637(25.8)
Q4	1245(25.0)	579(23.1)	666(27.0)
Selenium		195.299 ± 27.87	197.166 ± 29.167	0.021
Q1	1244(25.0)	688(27.4)	556(22.5)	0.001
Q2	1247(25.0)	598(23.8)	649(26.3)
Q3	1246(25.0)	613(24.4)	633(25.6)
Q4	1245(25.0)	612(24.4)	633(25.6)
Categorical variables were presented as n (%). Continuous variables were presented as mean ± SE. NHANES: National Health and Nutrition Examination Survey, BMI: body mass index.

In the bottom half of the Table 1 describes the characteristics of the five blood metals/metalloid concentrations of cadmium, lead, mercury, manganese, and selenium as continuous and categorical variables between the two groups. As continuous variables, cadmium, lead, manganese, and selenium were all significantly different from each other, except for mercury. Both cadmium and lead in the periodontitis group were lower than in the non-periodontitis group, while selenium showed the opposite results. When these five metals/metalloid analyzed as categorical variables, there were significant differences for all five metals/metalloid, and for cadmium and lead, the number of people in the Q1-Q4 groups with progressively higher concentrations was progressively higher (Cadmium: Q1vsQ4 = 20.7%vs31.6%; Lead: Q1vsQ4 = 17.2%vs33.2%, All P < 0.001), while for mercury, manganese, and selenium the results were reversed (Mercury: Q1vsQ4 = 27.3%vs23.0%; Manganese: Q1vsQ4 = 27.1%vs23.1%; Selenium: Q1vsQ4 = 27.4%vs24.4%, All P < 0.001).

Associations between the concentration of metals/metalloid in blood and periodontitis risk.

Logistic regression analyses using different models adjusted for multiple covariates were used to obtain OR, 95CI%, and forest plots for individual as well as combined blood metals/metalloid concentrations and periodontitis can be seen in Table 2 and Supplementary Fig. 1–3. Model 1 (Table 2, Supplementary Fig. 1) showed that higher cadmium concentration were associated with a greater risk of periodontitis as compared to Q1 after adjusting for sex, age, race, and educational status (Q2: [OR:1.24, 95%CI: 1.04–1.47, P = 0.015]; Q3 [1.45, 95%CI: 1.22–1.74, P < 0.001]; Q4 [2.63, 95%CI: 2.20–3.16, P < 0.001]), and higher lead concentration were associated with a greater risk of periodontitis (Q2: [OR:1.34, 95%CI. 1.13–1.60, P = 0.001]; Q3 [1.78, 95%CI: 1.49–2.12, P < 0.001]; Q4 [2.61, 95%CI: 2.17–3.13, P < 0.001]) and higher mercury concentration were associated with a lower risk of periodontitis (Q3 [0.84, 95%CI: 0.71-1.00, P = 0.049]; Q4 [0.77, 95%CI: 0.64–0.92, P = 0.005]), and higher selenium concentration were associated with a lower risk of periodontitis (Q2: [OR:0.74, 95%CI: 0.63–0.88, P = 0.001]; Q3 [0.81, 95%CI: 0.68–0.96, P = 0.017]; Q4 [ 0.79, 95%CI: 0.66–0.94, P = 0.007]). Model2 (Table 2, Supplementary Fig. 2) additionally adjusted for BMI, smoking status, and drinking status based on Model1 and Model3 (Table 2, Supplementary Fig. 3) additionally adjusted for diabetes, hypertension, and hyperlipidemia based on Model2 yielded results roughly similar to those of Fig. 2, with the higher concentrations of cadmium and lead being associated with a higher risk of developing periodontitis risk, and higher concentrations of mercury and selenium were associated with a lower risk of periodontitis.

Table 2

Associations of periodontitis and single/mixed metal/metalloid in blood (cadmium, lead, mercury, manganese and selenium).
Category		Single metal/metalloid model									Mixed metal/metalloid model
		Model1			Model2			Model3
		OR	95%CI	P	OR	95%CI	P	OR	95%CI	P	OR	95%CI	P
Cadmium	Q1	Reference			Reference			Reference			Reference
	Q2	1.239	1.042,1.474	0.015	1.182	1.132	0.064	1.186	0.993,1.417	0.060	1.132	0.945,1.356	0.177
	Q3	1.453	1.217,1.735	< 0.001	1.356	1.231	0.001	1.367	1.135,1.646	0.001	1.231	1.017,1.490	0.033
	Q4	2.635	2.197,3.161	< 0.001	2.267	1.965	< 0.001	2.309	1.881,2.835	< 0.001	1.965	1.588,2.432	< 0.001
Lead	Q1	Reference			Reference			Reference			Reference
	Q2	1.342	1.128,1.596	0.001	1.294	1.251	0.004	1.285	1.075,1.536	0.006	1.251	1.044,1.499	0.015
	Q3	1.777	1.490,2.118	< 0.001	1.706	1.654	< 0.001	1.713	1.428,2.054	< 0.001	1.654	1.373,1.992	< 0.001
	Q4	2.606	2.171,3.129	< 0.001	2.454	2.253	< 0.001	2.458	2.029,2.979	< 0.001	2.253	1.847,2.749	< 0.001
Mercury	Q1	Reference			Reference			Reference			Reference
	Q2	0.930	0.784,1.102	0.400	0.931	0.947	0.422	0.927	0.776,1.099	0.372	0.947	0.792,1.131	0.546
	Q3	0.841	0.708,0.999	0.049	0.840	0.842	0.052	0.837	0.702,0.999	0.049	0.842	0.703,1.008	0.062
	Q4	0.771	0.643,0.924	0.005	0.774	0.732	0.007	0.768	0.637,0.925	0.005	0.732	0.605,0.887	0.001
Manganese	Q1	Reference			Reference			Reference			Reference
	Q2	1.013	0.853,1.202	0.885	1.017	1.066	0.854	1.033	0.867,1.231	0.718	1.066	0.891,1.276	0.482
	Q3	0.945	0.793,1.125	0.524	0.942	0.939	0.514	0.951	0.795,1.137	0.578	0.939	0.782,1.126	0.495
	Q4	0.929	0.774,1.115	0.428	0.916	0.886	0.354	0.935	0.775,1.127	0.480	0.886	0.731,1.073	0.215
Selenium	Q1	Reference			Reference			Reference			Reference
	Q2	0.743	0.626,0.882	0.001	0.740	0.747	0.001	0.740	0.621,0.882	0.001	0.747	0.624,0.893	0.001
	Q3	0.812	0.683,0.964	0.017	0.816	0.854	0.023	0.810	0.679,0.966	0.019	0.854	0.713,1.024	0.088
	Q4	0.788	0.663,0.936	0.007	0.780	0.796	0.005	0.761	0.638,0.909	0.003	0.796	0.663,0.955	0.014
Model1 was adjusted for sex, age, race/ethnicity, education; Model2 was adjusted by model1 and body mass index, smoking status, drinking alcohol status; Model3 was adjusted by model2 and diabetes, hypertension and hyperlipidemia. The multivariate logistic model of mixed metal/metalloid was adjusted for sex, age, race/ethnicity, education, body mass index, smoking status, drinking alcohol status, diabetes, hypertension and hyperlipidemia.

Logistic regression analyses with different model adjustments in mixed metals/metalloid still suggested much the same results as before (Table 2, Supplementary Fig. 4). Compared to Q1, higher cadmium concentrations were associated with a greater risk of periodontitis (Q3 [1.23, 95%CI: 1.02–1.49, P = 0.033]; Q4 [1.97, 95%CI: 1.59–2.43, P < 0.001]), and higher lead concentrations were associated with a greater risk of periodontitis (Q2: [OR:1.25, 95%CI: 1.04–1.50, P = 0.015]; Q3 [1.65, 95%CI: 1.37–1.99, P < 0.001]; Q4 [2.25, 95%CI: 1.85–2.75, P < 0.001]), and higher mercury concentrations were associated with a lower risk of periodontitis (Q4 [0.73, 95%CI: 0.60–0.89, P = 0.001]) and higher selenium concentrations were also associated with a lower risk of periodontitis (Q2: [OR:0.75, 95%CI: 0.62–0.89, P = 0.001]; Q4[0.80, 95%CI: 0.66–0.95, P = 0.014]).

The Restricted Cubic Splines of periodontitis and metals/metalloid.

The relationship between blood metals/metalloid and periodontitis was further analyzed by applying RCS plots as shown in Fig. 2 and correlation matrix of five metals/metalloid shown in Supplementary Fig. 5. It was seen that as the blood cadmium concentration increased, the risk of periodontitis gradually increased at concentrations greater than about 0.3ug/dl (P < 0.001), and lead demonstrated the same trend, but the OR peaked at about 3.0ug/dl, and after that, the OR no longer increase (P < 0.001). Mercury and selenium, on the other hand, showed the opposite trend, with smaller concentrations being associated with a greater risk of periodontitis at mercury concentrations less than about 1.0ug/dl and selenium less than about 200ug/dl (Mercury: P = 0.0045; Selenium: P = 0.0068). However, manganese concentration and the occurrence of periodontitis did not show positive results. In the linear correlation analysis between metals/metalloid concentrations and periodontitis, only selenium and periodontitis showed a linear correlation (Non-linearity: P = 0.0598).

The Weighted Quantile Sum regression

The WQS model was used to analyze the joint effect of mixed metals/metalloid and periodontitis and two models were obtained, positive model and negative model (Fig. 3, Supplementary Table 1). The WQS index in the positive model was seen as OR = 1.45, 95CI% 1.33–1.60) and the highest weighted metals/metalloid in the model was Cadmium (69.13%) followed by Lead (28.17%), Manganese (2.13%). The WQS index in the negative model was seen as OR = 0.84, 95CI% 0.76–0.93) and the highest weighted metals/metalloid in the model was selenium (46.15%) followed by mercury (36.11%), manganese (17.64%).

The mediation analysis

Supplementary Table 2 shows the relationship between neutrophil absolute values after using covariate-adjusted logistic regression analysis of neutrophils after homogenization into four equal groups, with higher neutrophils being associated with a greater risk of periodontitis (Q3 [1.43, 95%CI: 1.19–1.73, P < 0.001]; Q4 [1.72, 95%CI: 1.42–2.08, P < 0.001]). Applying the mediating role to assess the potential mediating role of neutrophils between metals/metalloid and periodontitis (Supplementary Table 3, Fig. 4), neutrophils mediated the presence of cadmium and mercury, with mediating ratios of 11.3% and 14.6% respectively.

The presence of metals/metalloid is essential for the economic development of a country, but heavy metal pollution occurs in the production and processing of toxic metals, which appears imperceptibly in different forms in the organism's surroundings^[17]. Cadmium can enter the human body through food, drinking water, and respiration. It accumulates in organs such as kidney, liver, and bones, with a long biological half-life of 10–30 years^{[7, 11]}. Lead exposure sources are wide-ranging, including contaminated soil, paints, and consumer products. Lead is absorbed into the bloodstream and soft tissues through ingestion or inhalation, with a half-life of around 30 days^{[11, 18, 19]}. Sources of mercury intake include food, dental amalgam fillings, and vaccines. Mercury accumulates in organs such as kidney, brain, and liver, with a half-life of about 60 days^{[18, 20]}. Selenium is ingested through food and primarily deposited in liver, kidney, and other tissues, with a half-life of around 100 days^[21], Fig. 5 showed the possible mechanism of periodontitis risk by metals/metalloid. Mixed metals/metalloid, such as cadmium, cobalt, and cesium, can increase the risk of osteoarthritis ^[22], arsenic, lead, cadmium, copper is associated with the risk of cardiovascular disease^[23], and even respiratory diseases including Covid-19^[24]。

In this study, we used blood specimens from the NHANES database, which can accurately reflect residual body burden of metals/metalloid compared to other specimen sources. The large sample size enhanced the statistical power and external validity. We conducted a comprehensive evaluation by examining five metals/metalloid simultaneously instead of single metals/metalloid analysis. Advanced statistical approaches were utilized to assess the dose-response relationships and combined effects of mixed metals/metalloid exposure through WQS regression and other mixture modeling methods. Potential confounding factors were adjusted extensively. These methodological strengths justified the reliability of our findings regarding the associations between mixed metals/metalloid exposures and periodontitis risk. Further experimental studies are warranted to validate the relationships and elucidate the underlying mechanisms.

Main findings and comparisons with previous studies

In this study, we found that higher blood lead, cadmium and manganese levels were associated with increased risk of periodontitis in a dose-response manner, which is consistent with previous epidemiological studies^{[7, 11]}. The positive dose-response relationships were further confirmed by RCS models, suggesting monotonically increasing trends between exposure levels and periodontitis risk. Our study expanded previous evidence by simultaneously evaluating mixed metals/metalloid in a large nationally representative population. The advanced statistical approaches enhanced the robustness of results. However, we observed that higher mercury and selenium concentrations were related to lower prevalence of periodontitis. A few studies found opposite associations between mercury exposure and periodontitis risk^{[8, 20]}. The reasons for the discrepant findings are unclear but may involve differences in exposure levels. Selenium is an essential trace element with antioxidant properties. Our results concur with some studies indicating that adequate selenium status may protect against periodontitis by combating oxidative stress and reactive oxygen species (ROC) production^{[25, 26]}. Previous study demonstrated that selenium can increase synthesis of basic fibroblastic growth factor and type I collagen from both gingival and periodontal ligament fibroblasts^[27]. The inverse relationship was also verified by RCS models in our study. Further research is warranted to validate the potential beneficial roles of mercury and selenium.

Possible biological mechanisms

Cadmium, lead and manganese are toxic heavy metals that could impact periodontal health through several potential mechanisms^{[7, 28, 29]}. By inducing oxidative stress, these metals cause lipid peroxidation, DNA damage and apoptosis in gingival epithelial cells and periodontal ligament fibroblasts^{[23, 29, 30]}. They also stimulate inflammatory cytokine secretion (e.g. Interleukin-1β, Interleukin − 6, tumor necrosis factor-α), Matrix Metalloproteinase (MMP) expression, and immune cell activation, leading to destruction of the extracellular matrix and supporting structures^[31–33]. Furthermore, cadmium and lead suppress osteoblastic differentiation and mineralization through inhibition of runt-related transcription factor 2(RUNX2) and bone morphogenetic protein-2(BMPs) signaling^{[28, 34–37]}. They may also downregulate OPG-RANKL ratio and promote osteoclast formation, resulting in alveolar bone loss^[38]. At high concentrations, these metals reduce fibroblast proliferation, alter cell cycle progression, and trigger cell necrosis and apoptosis in the periodontium^[31] .

In contrast, selenium is an essential trace element that may protect against periodontitis through its antioxidant properties. Selenium enhances activity of glutathione peroxidase and thioredoxin reductase, removing reactive oxygen species and preventing tissue damage^[13]. It also suppresses excessive immune responses by reducing leukocyte infiltration and pro-inflammatory cytokines^[39]. However, we did not find detrimental effects of mercury on periodontitis. Potential sequestration of mercury in marine foods may limit its bioavailability and toxicity^{[40, 41]}. More research is needed to clarify the mechanisms of both essential and toxic metals in periodontitis development.

Advantages of WQS model and mediation analysis

In this study, we applied WQS regression to comprehensively assess the joint effects of mixed metals/metalloid exposures on periodontitis risk. The WQS method considers the contribution of each individual metals/metalloid, overcoming the limitation of only examining single metals/metalloid in conventional analyses^[42]. We found that the WQS index of positive model was positively associated with periodontitis risk, indicating cadmium and lead as the top two contributors in the mixture model. Meanwhile, the WQS index of negative model was inversely related to periodontitis, suggesting a protective role of selenium. WQS analysis enabled an intuitive visualization of the direction and magnitude of influence for each metals/metalloid in periodontitis occurrence. Moreover, mediation analysis suggested that cadmium may lead to periodontal tissue destruction by stimulating neutrophilic inflammation. In contrast, mercury may exert protective effects on periodontium by suppressing neutrophil activity and subsequent inflammatory responses. In summary, the WQS model is a powerful tool to elucidate the complex combined effects of mixed metals/metalloid in relation to health outcomes.

Strengths, limitations and future directions

Major strengths of this study included using a large nationally representative sample of US adults, which enhanced the external validity and statistical power. The availability of data on mixed metals/metalloid and trace elements enabled a comprehensive evaluation of their joint effects on periodontitis risk. Various advanced statistical approaches were utilized to assess dose-response relationships and combined effects of mixed metals/metalloid exposure. Potential confounding factors were extensively adjusted for in the analyses.

However, there were also some limitations. The cross-sectional nature precluded causal inference, and the temporal sequence between metals/metalloid exposure and periodontitis development could not be established. Heavy metal concentrations in blood may not fully reflect long-term exposure levels. Data on metals/metalloid contents in periodontal tissue were unavailable. Further research directions include employing longitudinal cohort studies to determine temporal relationships, measuring metals/metalloid in periodontal tissue, and elucidating specific biological mechanisms using experimental approaches. These efforts will provide stronger evidence regarding the potential roles of environmental and nutritional factors in periodontitis etiology and prevention.

Ethical approval and consent to participate

The institutional review board of the National Center for Health Statistics, CDC approved the protocols of the National Health and Nutrition Examination Survey (NHANES). All participants in study provided consent of enrollment. The National Center for Health Statistics (NCHS) Research Ethics Review Board approved the study protocol (Protocol#2011-17 and #2018-01) on the website of https://www.cdc.gov/nchs/nhanes/irba98.htm. The authors would like to be immensely grateful to NHANES project for providing detailed data for analysis.

Declaration of Interest

The authors declare no conflict of interest.

Declaration of the financial disclosure

Non declared.

Data sharing statement

The database of the National Health and Nutrition Examination Survey (NHANES) was downloaded on the website of https://www.cdc.gov/nchs/nhanes/index.htm.

Authors’ contributions

Yuwei He, Yuguang Li and XiangLiang Liu performed the experiment, conducted and drafted the manuscript. Yuwei He, Yuguang Li and Yao Wang acquisite and analyzed the data. Wei Ji and Yixin Zhao contributed significantly to analysis and manuscript preparation. Kaiwen Zheng and Yining Liu contributed to statistical analysis. Jiuwei Cui and Xiangliang Liu helped perform the analysis with constructive discussions. All authors critically revised of the manuscript for important intellectual content.

Disclosure

Yuwei He and Yuguang Li are co-first authors for this study. All authors disclosed no relevant relationships.

Funding

This work was supported by Doctor of excellence program, The First Hospital of Jilin University (JDYY-DEP-2022024).

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Associations between exposure to multiple metals/metalloid and periodontitis risk in US adults: an NHANES study using multipollutant approaches

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Introduction

Methods

Study populations

Periodontitis assessment

Blood composition assessment

Assessment of the association between metals/metalloid and periodontitis

Statistical analysis

Results

Participants characteristics of periodontitis

The Weighted Quantile Sum regression

The mediation analysis

Discussion

Main findings and comparisons with previous studies

Possible biological mechanisms

Advantages of WQS model and mediation analysis

Strengths, limitations and future directions

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References

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