Blood mercury and depressive symptoms: a longitudinal study combining metabolomics

Blood mercury and depressive symptoms: a longitudinal study combining metabolomics | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Blood mercury and depressive symptoms: a longitudinal study combining metabolomics Xinyuan Li, Yang Ma, Lingyan Qiao, Mingyu Feng, Shengjun Sun, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4385885/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Mercury exposure may increase the risk of depression. This study aimed to examine the association between blood mercury and depressive symptoms in Chinese young adults. We collected 477 fasting venous blood samples and questionnaire data from the Chinese undergraduate cohort study in 2019 and 2021. Patient Health Questionnaire-9 was used to estimate depressive symptoms. Blood mercury levels and metabolomic levels were measured using inductively coupled plasma mass spectrometry (ICP-MS) and liquid chromatography-mass spectrometry. Using linear mixed-effects models and ANOVAs based on baseline blood samples (N = 468) to examine metabolomic changes associated with mercury exposure. Our study found for every 2-fold increase in blood mercury levels, the depression scale score increased by 0.50 [95% confidence interval (CI): 0.14, 0.86]. And in males, a 2-fold increase in blood mercury levels was associated with a 0.87 (95% CI: 0.12, 1.61) increase in depression scale scores, while it wasn’t observed in females. Individuals consuming fish ≥ once a month shows similar negative correlation. Metabolomic analysis identified 10 differential metabolites enriched in 4 metabolic pathways. Blood mercury may deteriorate depressive symptoms. Males and those who consume more fish are susceptible subjects. Mercury exposure may affect depressive symptoms through neurotransmitter, energy and inflammation-related pathways. Blood mercury Depressive symptoms Metabolomics Chinese young adults Cohort study Figures Figure 1 Figure 2 Figure 3 1. Introduction Depression is one of the most prevalent mental disorders and is ranked 13th of the disease burden in the world and 11th in China in 2019 [ 1 , 2 ]. It is estimated that 5.00% of adults suffered from depression in 2019, with the highest incidence among young adults [ 3 , 4 ]. In China, the lifetime prevalence of depressive disorder among adults is 6.80% [ 5 ]. Multidimensional factors interact to make young adults vulnerable to depression [ 6 ]. Heavy metal has been identified as one of the environmental risk factors for depression [ 7 ]. As a ubiquitous neurotoxin, mercury has drawn great concern [ 8 ]. China is the greatest emitter of anthropogenic mercury [ 9 ]. China’s anthropogenic mercury emissions to the atmosphere alone were estimated at 444 tons in 2017 [ 10 ]. Human are exposed to mercury through air pollution, seafood consumption, dental amalgams, and occupational settings [ 11 ]. Mercury concentrations in whole blood reveal recent (1 to 2 months) exposure to methylmercury and inorganic mercury [ 12 ]. A cross-sectional study of 15,140 Americans (age 31–63) indicated that greater levels of mercury were inversely associated with depressive symptoms [adjusted prevalence ratios = 0.62, 95% confidence interval (CI) = 0.50, 0.78] [ 13 ]. Another cross-sectional study of 11,754 Korean participants aged 43 to 49 years showed a positive correlation between higher blood mercury levels and the risk of depression symptoms in females (multivariate odds ratio = 2.05; 95% CI = 1.20, 3.48) [ 14 ]. However, a cross-sectional study of 6,911 adults aged 32 to 57 years from 2005–2008 NHANES in the United States has demonstrated that high blood mercury levels(>23.8mg/L)were not related to increased odds of depressive symptoms [ 15 ]. Current studies on mercury and depressive symptoms have inconsistent results. In addition, the available evidence is mainly conducted in developed countries such as South Korea and the United States. There is a shortage of studies examining the association between blood mercury levels and depressive symptoms among Chinese young adults. Meanwhile, the potential metabolomic pathways associated with mercury exposure remain unclear. The objectives of this study were: (1) to assess the degree of depressive symptoms and blood mercury levels (2) to determine the association of blood mercury with depressive symptoms and to estimate potential health risks related to gender and diet (3) to estimate changes in plasma metabolites associated with mercury exposure. 2. Materials and Methods 2.1. Participants and study design The data was from a follow-up study of college students at Binzhou Medical University in Yantai, Shandong Province [ 16 ]. The first wave of data was collected between August 23 and September 23, in 2019. Baseline information including questionnaires and blood samples [ 17 ]. The questionnaire contained the following: Demographic data (age, sex, BMI). Lifestyle information (smoking, drinking). Frequency of food consumption (fish consumption). Mental health information (depressive symptoms). The follow-up information collection was conducted when they were set to leave school for internships. The follow-up was conducted between 25–28 May 2021 for nursing majors and between 19–21 December 2021 for clinical medicine majors. The collection of follow-up data comprised a self-reported questionnaire and blood samples. Inclusion criteria: high school and residence locations in Shandong Province; age of 18 or older; admission to Binzhou Medical University was in 2019. Exclusion Criteria: have chronic medical conditions; surgery within the past month; have hearing or speech impairments. Finally, we included 477 subjects with total results of blood mercury and questionnaire data from both 2019 and 2021 in the analyses. The whole blood samples were centrifuged into serum and plasma, cryopreserved at -80°C for mercury assay and metabolomics evaluation. Figure 1 shows the distribution map of the study subjects. This study was reviewed and approved by the Ethics Committee of Binzhou Medical University, and all study subjects signed the informed consent form. 2.2. Collection and Measurement of Mercury The methodological details of laboratory analysis have been described in previous studies [ 18 ]. Venous blood samples were collected by an experienced nurse using a royal blue trace metal vacuum tube containing EDTA and kept at -80°C until analysis. All blood samples were examined by inductively coupled plasma mass spectrometry (ICP-MS) at the Peking University Health Science Center, which the China Standards Council accredits. The blood mercury concentration was reported in nanograms per milliliter (ng/mL). 2.3. Measurement of Depressive Symptoms The 9-item Patient Health Questionnaire (PHQ-9) was used to assess participants’ depressive symptoms. Each of the nine items in the questionnaire is rated on a scale from 0 (not at all) to 3 (almost every day), for a total score of 0–27 [ 19 ]. Based on the diagnostic criteria for depression in the diagnostic and statistical manual of mental disorders-Ⅳ, the PHQ-9 is widely used in screening for depression and is considered reliable and effective [ 20 ]. 2.4. Liquid chromatography and Mass spectrometry We employed a liquid-liquid extraction method to extract metabolites and lipids from plasma samples [ 21 , 22 ]. Subsequently, the resulting dried samples were frozen at − 80°C until subsequent LC-MS analysis. The Ultimate 3000 UHPLC system with Q-Exactive HF MS (Thermo Fisher Scientific, Waltham, MA, USA) was used for metabolomics and lipidomics analysis. Specifically, an Xbridge amide column (100×2.1 mm i.d., 3.5 µm; Waters, USA) was utilized at 30°C for the metabolomics analysis, while a reversed-phase BEH C18 column (2.1 mm×100 mm, 2.5 µm, Waters, USA) was employed at 40°C for the lipidomic analysis. Further details are provided in the Supplementary Methods section. 2.5. Covariates Age, sex, body mass index (BMI), smoking, passive smoking, drinking, household income, fish consumption frequency, and physical activity frequency were collected by the questionnaire. Annual household income exceeding 100,000RMB (≈ US $ 15,000) is defined as high household income, and the rest is defined as low household income. [ 23 ]. Fish consumption was separated into two categories (fish consumption less than once a month versus fish consumption at least once a month) [ 15 ]. The following lifestyle behaviors were dichotomously defined: smoking, at least one cigarette per day for 6 months; passive smoking, breathing in other people's smoke at least one cigarette per day for 6 months; drinking, at least once per month for the past 3 months; physical activity, physical activity at least once a week [ 24 ]. 2.6. Statistical analysis Blood mercury levels were analyzed as a continuous measure and log 2 -transformed to meet normality. We performed longitudinal data analysis using the linear mixed-effects model with adjustment for all covariates to examine the association between blood mercury levels and depressive symptoms scores. We used restricted maximum likelihood estimation (REML) to estimate regression coefficients and standard deviations. The linear mixed-effects model adjusted potential confounders based on the association between depressive symptom score and blood mercury levels. We used the same model to conduct stratification analyses by sex and fish consumption. We also conducted several sensitivity analyses. First, separate analyses were performed in non-smoking or non-drinking populations to assess the robustness of our results. Second, the two-metal model of mercury and selenium was constructed to test the antagonistic effect [ 25 ]. All analyses were conducted in R (version 4.2.1) software. Metabolites and lipids were identified based on MS1 and MS2 spectra using MSDIAL software. To identify metabolites, the MassBank database was searched. Also, to identify lipids, a Lipid Blast-based silica spectral database (version: LipidDBs-VS23-FiehnO) was used. MS1 and MS/MS search tolerances were set to 0.01 Da and 0.05 Da, respectively, with an identification score cutoff of 70%. Other parameters used in MS-DIAL are set to default values. To investigate the effect of blood mercury exposure on metabolites, we conducted a metabolomic analysis on samples stratified by quartile blood mercury levels. Firstly, single-factor analysis of variance and post-hoc Duncan analysis were utilized to compare statistical metabolic changes associated with mercury exposure, with significance determined at a corrected p-value less than 0.05. Secondly, a linear mixed-effect model was applied to evaluate the associations between identified metabolites and other confounders, with mercury exposure level as the primary exposure indicator and controlling for factors such as age, BMI, gender, fish consumption, and socioeconomic status. P-values less than 0.05 were considered significant. To identify the main metabolic pathways involved in the differential metabolites, we subsequently performed pathway enrichment analysis. 3. Results Table 1 shows the demographic characteristics, lifestyle characteristics, depressive symptoms, and blood mercury levels in 2019 and in 2021. There were in total 477 participants in this cohort (32.08% male). The mean age of the cohort at baseline was 18.44 ± 0.58 years (Table 1 ). There were 37.74% participants consumed fish less than once a month. The subjects of high household income accounted for 21.80%. The mean (SD) PHQ-9 score was 2.81 (± 3.65) at baseline, and slightly decreased to 2.79 (± 4.10) in 2021. The mean (SD) blood mercury level was 0.87 (± 0.48) ng/mL at baseline, and decreased to 0.83 (± 0.37) ng/mL in 2021. PHQ-9 scores at different blood mercury levels are shown in Supplementary Material Table S1 . Table 1 Demographic characteristics of participants (N = 477) Participants characteristics Year 2019 2021 Age (years), mean (SD) 18.44(0.58) 20.24(0.64) Sex, n (%) Male 153(32.08%) 153(32.08%) Female 324(67.92%) 324(67.92%) BMI (kg/m 2 ), mean (SD) 22.18(4.71) 22.10(4.71) Household income, n (%) Low 373(78.20%) 37(77.78%)1 High 104(21.80%) 106(22.22%) Smoking exposure, n (%) Yes 5(1.05%) 7(1.47%) No 472(98.95%) 470(98.53%) Passive smoking exposure, n (%) Yes 65(13.63%) 16(3.35%) No 412(86.37%) 461(96.65%) Drinking exposure, n (%) Yes 19(3.98%) 10(2.10%) No 458(96.02%) 467(97.90%) Physical activity, n (%) Yes 348(72.96%) 257(53.88%) No 129(27.04%) 220(46.12%) Fish consumption, n (%) <Once a month 180(37.74%) 174(36.48%) ≥Once a month 297(62.26%) 303(63.52%) PHQ-9 scores, mean (SD) 2.81(3.65) 2.79(4.10) Blood mercury levels (ng/mL), mean (SD) 0.87(0.48) 0.83(0.37) Note: SD: Standard deviation. Table 2 shows the blood mercury levels and PHQ-9 scores in different groups. Males (n = 153) had higher depressive symptoms and higher blood mercury levels than females (n = 324) in 2019 and 2021. During follow-up, the mean of blood mercury levels increased in the groups of smoking, drinking, and fish consumption at least once a month. However, blood mercury concentrations had no change in passive smoking exposure and high household income group. We also found that the depressive symptoms score increased in low household income, non-exercising, and fish consumption at least once a month groups in the follow-up. Table 2 The description of PHQ-9 scores and blood mercury levels by different population characteristics in 2019 and 2021 2019 2021 Variables PHQ-9 Hg PHQ-9 Hg Mean (SD) Mean (SD) Median (range) Mean (SD) Mean (SD) Median (range) Sex Male 3.08(4.11) 0.95(0.51) 0.86(0.2,3.65) 3.05(5.18) 0.86(0.42) 0.8(0.22,2.56) Female 2.67(3.42) 0.84(0.46) 0.76(0.09,3.34) 2.67(3.48) 0.81(0.34) 0.76(0.11,2.37) Household income Low 2.82(3.7) 0.83(0.47) 0.75(0.09,3.65) 2.75(3.87) 0.85(0.39) 0.78(0.11,2.56) High 2.74(3.51) 1(0.49) 0.9(0.2,2.79) 2.95(4.84) 0.75(0.27) 0.7(0.3,1.56) Smoking Yes 2.6(0.89) 0.77(0.5) 0.51(0.3,1.54) 1.86(3.48) 0.84(0.26) 0.88(0.53,1.13) No 2.81(3.67) 0.87(0.48) 0.78(0.09,3.65) 2.81(4.11) 0.83(0.37) 0.76(0.11,2.56) Passive smoking Yes 2.47(4.56) 0.79(0.35) 0.66(0.4,1.54) 1.4(2.01) 0.68(0.31) 0.59(0.29,1.29) No 2.82(3.62) 0.87(0.48) 0.78(0.09,3.65) 2.82(4.13) 0.83(0.37) 0.77(0.11,2.56) Drinking Yes 4.05(4.56) 0.78(0.4) 0.67(0.27,2.21) 3.12(3.61) 0.93(0.59) 0.74(0.38,2.37) No 2.61(3.46) 0.89(0.49) 0.79(0.09,3.65) 2.78(4.12) 0.82(0.36) 0.76(0.11,2.56) Physical activity Yes 2.61(3.64) 0.89(0.5) 0.78(0.09,3.65) 2.3(3.67) 0.85(0.37) 0.78(0.11,2.49) No 3.33(3.65) 0.82(0.39) 0.76(0.17,2.17) 3.37(4.49) 0.8(0.36) 0.73(0.24,2.56) Fish consumption <Once a month 3.31(3.79) 0.77(0.38) 0.7(0.17,3.34) 3.16(4.25) 0.79(0.32) 0.74(0.11,2.1) ≥Once a month 2.5(3.54) 0.93(0.52) 0.84(0.09,3.65) 2.58(4) 0.85(0.39) 0.79(0.22,2.56) Figure 2 shows the association between young adults’ blood mercury levels and PHQ-9 score and results stratified by sex and fish consumption frequency. Detailed results of the association can be found in Table S2. The results showed that blood mercury levels were positively associated with depressive symptoms scores in the fully adjusted model. For 2-fold increase in blood mercury levels, depressive symptoms score increased by 0.50 (95% CI: 0.14, 0.86; P < 0.01). We found that blood mercury levels were positively associated with deterioration of depressive symptoms in males. For each 2-fold increase in blood mercury levels, the depressive symptoms score increased by 0.87 (95% CI: 0.12, 1.61; P = 0.02) in males, while such association was not observed in females. We also found that blood mercury levels were positively associated with depressive symptoms score in the groups of fish consumption at least once a month. For each 2-fold increase in blood mercury levels, the depressive symptoms score increased by 0.56 (95% CI: 0.13, 0.99; P = 0.02). The results of the sensitivity analyses are presented in Table S3. The population of no smoking (n = 472) or no drinking (n = 458) were analyzed. For every 2-fold increase in blood mercury levels, depressive symptom score increased by 0.51 (95% CI: 0.15, 0.88; P value < 0.01) in the non-smoking model and 0.50 (95% CI: 0.14, 0.87; P value < 0.01) in the non-drinking model. The two-metal model of mercury and selenium was also constructed, and the results were still robust. Depressive symptom score increased by 0.47 (0.10, 0.85; P value 0.01) for each 2-fold of blood mercury in two-metal model. Figure 3 shows the results of the KEGG metabolic pathway enrichment analysis. Based on analysis of variance and linear mixed model analysis, 213 differential metabolites were screened out with a significance threshold of P < 0.05. The identified metabolites were then put into the HMDB database and KEGG database for cross-matching, and finally, 44 differential metabolites were identified. Further metabolic pathways analysis showed that the metabolic dysregulation associated with mercury exposure was concentrated in amino acid metabolism. There were 10 dysregulated metabolites enriched in four differential metabolic pathways, specifically arginine biosynthesis, arginine and proline metabolism, aminyl-tRNA biosynthesis, and phenylalanine, tyrosine, and tryptophan biosynthesis. 4. Discussion As far as we know, this is the first longitudinal cohort study to explore the association between blood mercury levels and depressive symptoms in Chinese young adults. We found that the increased blood mercury levels among Chinese undergraduates were positively associated with depressive symptoms scores. This association was sex-specific and affected males more than females. In addition, we found a more significant association between blood mercury levels and depressive symptom in those who ate fish at least once a month. In the metabolomic analysis, high blood mercury level was associated with 44 altered metabolites, mainly enriched in amino acid metabolism pathways. This study revealed a positive association between blood mercury levels and depressive symptoms. In line with our findings, a cross-sectional study of 11,754 Korean adults indicated a positive association between greater blood mercury concentration and the incidence of depression in Korean females [ 14 ]. According to a case-control study with 3,517 participants from Japan, those in the Minamata region who were severely exposed to methylmercury were more likely to experience psychiatric symptoms [ 26 ]. However, the cross-sectional 2005–2008 NHANES study of 6,911 US adults age ≥ 20 years revealed that total blood mercury levels were not associated with depression after adjusting for sociodemographic variables (income-poverty ratio, education, marital status). However, in the elderly, an inverse effect of total blood mercury on depression was found [ 15 ]. The authors attributed this finding to residual factors such as socioeconomic factors and seafood consumption. Another cross-sectional study of 15,140 US adults age ≥ 18 years from the 2005–2010 NHANES found that higher concentration of mercury was negatively associated with depression (adjusted Prevalence Ratios (PR) = 0.62, 95% CI = 0.50, 0.78) [ 13 ]. Differences in study design could partly explain heterogeneity in the above-mentioned results and our results. Additionally, although depression was assessed in both studies using the PHQ-9 scale, the 2005–2008 NHANES study employed a cut-off point for the diagnosis of depression was 4/5, and the 2005–2010 NHANES was 9/10. In our study, the PHQ-9 score was used as a quantitative variable. Different types of variables and diagnostic criteria for the outcome may account for the discrepancies in study results. Inconsistency may also be attributable to research populations, which may differ according to the individual's physical condition, lifestyle, dietary habits, education, and mercury exposure levels. Toxicological studies have revealed that mercury metabolism and toxicity can be affected by sex, diet, or co-exposure to other pollutants [ 27 ]. Nevertheless, knowledge regarding the role of potential modifying factors in the association between mercury exposure and depressive symptoms is currently limited. Dietary intake and environmental characteristics are considered to increase blood mercury concentrations in human beings. In subgroup analysis, our results indicated sex-specific associations between blood mercury levels and depressive symptoms. Males seem to be more sensitive to mercury exposure in terms of depressive symptoms. The finding of a cross-sectional study of 408 individuals in Zhoushan City, China, supported our results that prenatal methylmercury exposure from fish intake caused a neurodevelopmental risk for males but not females [ 28 ]. We can consider several potential causes for this difference: metabolic levels and the protective effects of estrogen. In the study on mercury-induced immunotoxicity effects on mice, males had more tissue mercury retention than females, particularly in the kidneys [ 29 ]. Additionally, estrogen may perform a protective function in the association between blood mercury and depressive symptoms. Estrogen has antioxidant properties that can provide an additional defense against oxidative stress by working as scavengers or activating estrogen receptors to stimulate the synthesis of protective molecules [ 30 ]. This may indicate a protective role of estrogen in psychiatric disorders. We also found sex-specific blood mercury level differences in our study. The mean mercury levels were significantly higher in males than in females during the follow-up. In a 2010–2011 cross-sectional study including 4,000 Korean participants aged 0 to 83, the mean concentration of total blood mercury was higher in men (3.11 µg/L) than women (2.77 µg/L) [ 31 ]. Males have higher blood mercury levels than females, which may induce more severe mental health problems. Given that the mechanisms of distribution and susceptibility in mercury remain obscure, further study is essential to clarify it. Our stratified analysis revealed that the effect of blood mercury levels on depressive symptoms was more significant among those who consumed fish at least once a month. The Japan Public Health Center-based Prospective Study (JPHC) demonstrated that moderate fish consumption (average 111 grams of fish per day )could reduce depression risk. [ 32 ]. However, consuming contaminated fish may increase neurotoxicity, as fish may be the primary source of human mercury exposure [ 33 ]. Mercury concentrations were higher in Korean from coastal regions than in Korean from inland areas due to their more intake of seafood [ 34 ]. A study in Hong Kong, China, analyzed 151 plasma samples and found a positive association between fish consumption rates and plasma methylmercury concentrations [ 35 ]. It is clear from all of the above studies that a high intake of fish could affect blood mercury levels. This supports our finding that people who eat fish at least once a month are more likely to be exposed to mercury, which affects depressive symptoms. High blood mercury exposure altered the levels of amino acids enriched in various metabolic pathways. The levels of phenylalanine, pyroglutamic acid and arginine were down-regulated, while the levels of L-leucine and taurine were up-regulated. Disruptions in amino acid metabolism have a strong correlation with the development of depression [ 36 ]. Phenylalanine serve as a precursor for dopamine, a monoamine transmitter in the brain, the impairment of which could lead to depression [ 37 ]. Pyroglutamic acid is a cyclic lactam of glutamic acid and acts as a glutamic acid reservoir [ 38 ]. The downregulated pyroglutamic acid levels may indicate that participants with high blood mercury levels could have a reduction in the excitatory neurotransmitter glutamate. [ 39 ]. In addition, the non-essential amino acid taurine is a major intracellular free beta-amino acid. It is a protectant against oxidative stress damage [ 40 ]. Upregulation of taurine levels indicated that participants with higher mercury exposure could possibly experience oxidative stress damage, a major manifestation of depression. High blood mercury exposure could possibly influence neurotransmitter levels and oxidative stress responses resulting in depression symptoms by disrupting serum amino acid homeostasis. We found that serum levels of energy-related metabolites, oxaloacetate, were downregulated after exposure to higher levels of mercury. It is known that oxaloacetate can condense with acetyl coenzyme to produce citrate, which is one of the key reactions of the tricarboxylic acid cycle. This suggests that high mercury exposure leads to downregulation of key metabolites that initiate the Krebs cycle. Similarly, previous studies found that there were altered energy metabolites in women with postpartum depression [ 41 ]. Mercury exposure could possibly lead to depressive symptoms through oxaloacetate-related energy metabolic pathways. We also found that high mercury exposure resulted in abnormal lipid metabolism in participants. In the present study, the serum levels of arachidonic acid and leukotrienes were upregulated. Arachidonic acid is a precursor for the production of inflammatory mediators such as prostaglandins, thrombin, and leukotrienes [ 42 ]. Inflammatory response pathway could possibly lead to depressive symptoms caused by mercury exposure. The primary merit of the present research is that it presents the first report on the association between blood mercury and depressive symptoms among Chinese young adults. At the same time, the association between mercury exposure and serum metabolomics was preliminarily discussed. Potential biomarkers and metabolic pathways for mercury exposure to affect depressive symptoms were identified. Moreover, given the cohort study design, we could infer causality between blood mercury concentration and depressive symptoms. However, this study had some limitations. A recall bias on the food frequency questionnaire might have occurred owing to the subjects’ seasonal or daily dietary habits. In addition, depression symptoms were evaluated by a self-assessment questionnaire. Although the study has some inevitable limitations, our findings still give fresh insights into the neurotoxicity of mercury exposure among young adults. 5. Conclusion Blood mercury levels were positively associated with depressive severity in this population-based cohort study. Males and more fish consumption groups were susceptible to mercury in terms of depression symptoms. Mercury exposure may affect depressive symptoms through neurotransmitter, energy, inflammation and oxidative stress related metabolimic pathways. Stricter mercury pollution legislations are warranted to protect human health. Declarations Author Contributions M.F., X.L., H.L., Y.G., P.L., planned the study and drafted the manuscript. H.L., Y.M., P.L., contributed to data analysis and interpretation. S.S., S.W., L.Y., T.Y., L.Y., J.W., P.X., S.L., S.F., P.L., participated in manuscript revision. All authors have given final approval for the manuscript to be published and have agreed to be responsible for all aspects of the manuscript. Institutional Review Board Statement The study was approved by the Binzhou Medical University ethics committee (NO.2019075). All experiments were performed in accordance with relevant specified guidelines and regulations. Informed consent was obtained from all participants. Data availability statement The datasets analysed during the current study are available from the corresponding author on reasonable request. Consent to participate declaration Written informed consent was obtained from all participants. Acknowledgements The authors are grateful to all students of Binzhou Medical College who participated in this study. Funding declaration No funding. Declaration of interest The authors declare no competing interest. 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Environment international, 2011. 37 (5): p. 907-913. Llop, S., et al., Prenatal exposure to mercury and infant neurodevelopment in a multicenter cohort in Spain: study of potential modifiers. American journal of epidemiology, 2012. 175 (5): p. 451-465. Tchounwou, P.B., et al., Environmental exposure to mercury and its toxicopathologic implications for public health. Environmental Toxicology: An International Journal, 2003. 18 (3): p. 149-175. Thomas, D.J., et al., Sexual differences in the distribution and retention of organic and inorganic mercury in methyl mercury-treated rats. Environmental research, 1986. 41 (1): p. 219-234. Brann, D.W., et al., Neurotrophic and neuroprotective actions of estrogen: basic mechanisms and clinical implications. Steroids, 2007. 72 (5): p. 381-405. Eom, S.-Y., et al., Lead, mercury, and cadmium exposure in the Korean general population. Journal of Korean Medical Science, 2018. 33 (2). Matsuoka, Y., et al., Dietary fish, n-3 polyunsaturated fatty acid consumption, and depression risk in Japan: a population-based prospective cohort study. Translational psychiatry, 2017. 7 (9): p. e1242-e1242. Fernandes, A.C., et al., Benefits and risks of fish consumption for the human health. Revista de Nutrição, 2012. 25 : p. 283-295. You, C.-H., et al., The relationship between the fish consumption and blood total/methyl-mercury concentration of costal area in Korea. Neurotoxicology, 2012. 33 (4): p. 676-682. Liang, P., et al., Plasma mercury levels in Hong Kong residents: in relation to fish consumption. Sci Total Environ, 2013. 463-464 : p. 1225-9. Pu, J., et al., An integrated meta-analysis of peripheral blood metabolites and biological functions in major depressive disorder. Molecular psychiatry, 2021. 26 (8): p. 4265-4276. Wang, Y., et al., Chinese herbal medicine for the treatment of depression: applications, efficacies and mechanisms. Current pharmaceutical design, 2017. 23 (34): p. 5180-5190. Mahan, V.L., Neurointegrity and europhysiology: astrocyte, glutamate, and carbon monoxide interactions. Medical gas research, 2019. 9 (1): p. 24. Kumar, P., et al., Dietary glutamic acid, obesity, and depressive symptoms in patients with schizophrenia. Frontiers in Psychiatry, 2021. 11 : p. 620097. Shi, C., et al., Metabolic profiling of liver tissues in mice after instillation of fine particulate matter. Science of the Total Environment, 2019. 696 : p. 133974. Papadopoulou, Z., et al., Unraveling the serum metabolomic profile of post-partum depression. Frontiers in neuroscience, 2019. 13 : p. 833. Hanna, V.S. and E.A.A. Hafez, Synopsis of arachidonic acid metabolism: A review. Journal of advanced research, 2018. 11 : p. 23-32. Additional Declarations No competing interests reported. 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University","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Ma","suffix":""},{"id":304354677,"identity":"d80e696c-7c56-4b3a-a459-6310f596e37f","order_by":2,"name":"Lingyan Qiao","email":"","orcid":"","institution":"Binzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lingyan","middleName":"","lastName":"Qiao","suffix":""},{"id":304354678,"identity":"c35c70bd-f45f-4b54-ada3-ceaf513da743","order_by":3,"name":"Mingyu Feng","email":"","orcid":"","institution":"Binzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Mingyu","middleName":"","lastName":"Feng","suffix":""},{"id":304354679,"identity":"217e1f80-003d-417d-99ed-be10d59e923b","order_by":4,"name":"Shengjun Sun","email":"","orcid":"","institution":"Yantaishan 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University","correspondingAuthor":false,"prefix":"","firstName":"Lailai","middleName":"","lastName":"Yan","suffix":""},{"id":304354684,"identity":"f0e7b31e-c99d-4971-b3e1-233d0eb284aa","order_by":8,"name":"Tingting Ye","email":"","orcid":"","institution":"Monash University","correspondingAuthor":false,"prefix":"","firstName":"Tingting","middleName":"","lastName":"Ye","suffix":""},{"id":304354686,"identity":"84b1a05c-dbe5-4476-b191-5fcfb7ef6bc0","order_by":9,"name":"Jianyu Wang","email":"","orcid":"","institution":"Binzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jianyu","middleName":"","lastName":"Wang","suffix":""},{"id":304354688,"identity":"4aa70074-011a-4f74-b1a3-ac527d003ba2","order_by":10,"name":"Ping Xu","email":"","orcid":"","institution":"Binzhou Medical 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University","correspondingAuthor":false,"prefix":"","firstName":"Yuming","middleName":"","lastName":"Guo","suffix":""},{"id":304354694,"identity":"fe672bb0-b0ad-4606-a04c-12c267312ecb","order_by":14,"name":"Peng Lu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYNCCAgY5MM3YACQOEKXFgMGYdC2JDURrMWfvPfyCweBw+tr23oMff+5gkOO7kYBfi2XPuTQLoJbcbWfOJUvznmEwliSkxeBGjpkBWMuNHANpxjaGxA0Etdx/A9aSbnYjx/jnzzaGesJabvAYPwBqSQBqMZPgbWNIMCDslxwzoLJ0w21nzphZ87ZJGM488wC/FnP2M8YfPlRYy5sd7zG++bPNRp7vOCGHMTCwSSCpkcCvHKqF+QNhZaNgFIyCUTCiAQCSPEb4lDRctgAAAABJRU5ErkJggg==","orcid":"","institution":"Monash University","correspondingAuthor":true,"prefix":"","firstName":"Peng","middleName":"","lastName":"Lu","suffix":""}],"badges":[],"createdAt":"2024-05-08 02:09:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4385885/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4385885/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57451577,"identity":"20a3fd91-becb-4aff-b679-04afa19d1f8a","added_by":"auto","created_at":"2024-05-30 20:52:14","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":88262,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution map of study subjects\u003c/p\u003e\n\u003cp\u003eNote: The picture is the map of Shandong Province; The yellow dots represent participants' home addresses.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4385885/v1/e86fe68883fc6335c8d9a6e6.jpeg"},{"id":57451576,"identity":"7782e611-9bea-480e-a1e5-0823215a8342","added_by":"auto","created_at":"2024-05-30 20:52:13","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":40665,"visible":true,"origin":"","legend":"\u003cp\u003eThe result of linear mixed effects model of the association between PHQ-9 scores and blood mercury levels and stratified analysis\u003c/p\u003e\n\u003cp\u003eNote: The total model included subject as random effect andadjusted for sex, age, BMI, lifestyle factors (smoking status, passive smoking, physical activity), and fish consumption.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4385885/v1/7776ad6a73243d7a23680df2.jpeg"},{"id":57451579,"identity":"f9dd6599-ede6-45bc-82d4-b4e043940032","added_by":"auto","created_at":"2024-05-30 20:52:14","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":123329,"visible":true,"origin":"","legend":"\u003cp\u003eEnrichment results for metabolic pathway\u003c/p\u003e\n\u003cp\u003eNote: The vertical coordinate is the metabolic pathway name and the horizontal coordinate is -log(p-value), the color from green to red indicates that the p-value decreases in order, the larger the point, the more the number of metabolites enriched to that pathway.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4385885/v1/098fac5527b2d49024a95c3d.jpeg"},{"id":64036747,"identity":"32c52a04-e235-411d-908d-4c2d05d2a79f","added_by":"auto","created_at":"2024-09-05 11:09:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":890927,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4385885/v1/065e9a31-0a49-42c0-9378-5a6d40c13e04.pdf"},{"id":57451578,"identity":"c15c5c25-ca5d-4bd5-becb-fbe95ed0a93b","added_by":"auto","created_at":"2024-05-30 20:52:14","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":22916,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-4385885/v1/1cc41972d0bb0e07885680f3.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Blood mercury and depressive symptoms: a longitudinal study combining metabolomics","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eDepression is one of the most prevalent mental disorders and is ranked 13th of the disease burden in the world and 11th in China in 2019 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. It is estimated that 5.00% of adults suffered from depression in 2019, with the highest incidence among young adults [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In China, the lifetime prevalence of depressive disorder among adults is 6.80% [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Multidimensional factors interact to make young adults vulnerable to depression [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHeavy metal has been identified as one of the environmental risk factors for depression [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. As a ubiquitous neurotoxin, mercury has drawn great concern [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. China is the greatest emitter of anthropogenic mercury [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. China\u0026rsquo;s anthropogenic mercury emissions to the atmosphere alone were estimated at 444 tons in 2017 [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Human are exposed to mercury through air pollution, seafood consumption, dental amalgams, and occupational settings [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Mercury concentrations in whole blood reveal recent (1 to 2 months) exposure to methylmercury and inorganic mercury [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA cross-sectional study of 15,140 Americans (age 31\u0026ndash;63) indicated that greater levels of mercury were inversely associated with depressive symptoms [adjusted prevalence ratios\u0026thinsp;=\u0026thinsp;0.62, 95% confidence interval (CI)\u0026thinsp;=\u0026thinsp;0.50, 0.78] [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Another cross-sectional study of 11,754 Korean participants aged 43 to 49 years showed a positive correlation between higher blood mercury levels and the risk of depression symptoms in females (multivariate odds ratio\u0026thinsp;=\u0026thinsp;2.05; 95% CI\u0026thinsp;=\u0026thinsp;1.20, 3.48) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, a cross-sectional study of 6,911 adults aged 32 to 57 years from 2005\u0026ndash;2008 NHANES in the United States has demonstrated that high blood mercury levels(\u0026gt;23.8mg/L)were not related to increased odds of depressive symptoms [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Current studies on mercury and depressive symptoms have inconsistent results. In addition, the available evidence is mainly conducted in developed countries such as South Korea and the United States. There is a shortage of studies examining the association between blood mercury levels and depressive symptoms among Chinese young adults. Meanwhile, the potential metabolomic pathways associated with mercury exposure remain unclear.\u003c/p\u003e \u003cp\u003eThe objectives of this study were: (1) to assess the degree of depressive symptoms and blood mercury levels (2) to determine the association of blood mercury with depressive symptoms and to estimate potential health risks related to gender and diet (3) to estimate changes in plasma metabolites associated with mercury exposure.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Participants and study design\u003c/h2\u003e \u003cp\u003eThe data was from a follow-up study of college students at Binzhou Medical University in Yantai, Shandong Province [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The first wave of data was collected between August 23 and September 23, in 2019. Baseline information including questionnaires and blood samples [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The questionnaire contained the following:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eDemographic data (age, sex, BMI).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eLifestyle information (smoking, drinking).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFrequency of food consumption (fish consumption).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eMental health information (depressive symptoms).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe follow-up information collection was conducted when they were set to leave school for internships. The follow-up was conducted between 25\u0026ndash;28 May 2021 for nursing majors and between 19\u0026ndash;21 December 2021 for clinical medicine majors. The collection of follow-up data comprised a self-reported questionnaire and blood samples. Inclusion criteria: high school and residence locations in Shandong Province; age of 18 or older; admission to Binzhou Medical University was in 2019. Exclusion Criteria: have chronic medical conditions; surgery within the past month; have hearing or speech impairments. Finally, we included 477 subjects with total results of blood mercury and questionnaire data from both 2019 and 2021 in the analyses. The whole blood samples were centrifuged into serum and plasma, cryopreserved at -80\u0026deg;C for mercury assay and metabolomics evaluation. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the distribution map of the study subjects. This study was reviewed and approved by the Ethics Committee of Binzhou Medical University, and all study subjects signed the informed consent form.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Collection and Measurement of Mercury\u003c/h2\u003e \u003cp\u003eThe methodological details of laboratory analysis have been described in previous studies [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Venous blood samples were collected by an experienced nurse using a royal blue trace metal vacuum tube containing EDTA and kept at -80\u0026deg;C until analysis. All blood samples were examined by inductively coupled plasma mass spectrometry (ICP-MS) at the Peking University Health Science Center, which the China Standards Council accredits. The blood mercury concentration was reported in nanograms per milliliter (ng/mL).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Measurement of Depressive Symptoms\u003c/h2\u003e \u003cp\u003eThe 9-item Patient Health Questionnaire (PHQ-9) was used to assess participants\u0026rsquo; depressive symptoms. Each of the nine items in the questionnaire is rated on a scale from 0 (not at all) to 3 (almost every day), for a total score of 0\u0026ndash;27 [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Based on the diagnostic criteria for depression in the diagnostic and statistical manual of mental disorders-Ⅳ, the PHQ-9 is widely used in screening for depression and is considered reliable and effective [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Liquid chromatography and Mass spectrometry\u003c/h2\u003e \u003cp\u003eWe employed a liquid-liquid extraction method to extract metabolites and lipids from plasma samples [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Subsequently, the resulting dried samples were frozen at \u0026minus;\u0026thinsp;80\u0026deg;C until subsequent LC-MS analysis. The Ultimate 3000 UHPLC system with Q-Exactive HF MS (Thermo Fisher Scientific, Waltham, MA, USA) was used for metabolomics and lipidomics analysis. Specifically, an Xbridge amide column (100\u0026times;2.1 mm i.d., 3.5 \u0026micro;m; Waters, USA) was utilized at 30\u0026deg;C for the metabolomics analysis, while a reversed-phase BEH C18 column (2.1 mm\u0026times;100 mm, 2.5 \u0026micro;m, Waters, USA) was employed at 40\u0026deg;C for the lipidomic analysis. Further details are provided in the Supplementary Methods section.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Covariates\u003c/h2\u003e \u003cp\u003e Age, sex, body mass index (BMI), smoking, passive smoking, drinking, household income, fish consumption frequency, and physical activity frequency were collected by the questionnaire. Annual household income exceeding 100,000RMB (\u0026asymp;\u0026thinsp;US\u003cspan\u003e$\u003c/span\u003e15,000) is defined as high household income, and the rest is defined as low household income. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Fish consumption was separated into two categories (fish consumption less than once a month versus fish consumption at least once a month) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The following lifestyle behaviors were dichotomously defined: smoking, at least one cigarette per day for 6 months; passive smoking, breathing in other people's smoke at least one cigarette per day for 6 months; drinking, at least once per month for the past 3 months; physical activity, physical activity at least once a week [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Statistical analysis\u003c/h2\u003e \u003cp\u003eBlood mercury levels were analyzed as a continuous measure and log\u003csub\u003e2\u003c/sub\u003e-transformed to meet normality. We performed longitudinal data analysis using the linear mixed-effects model with adjustment for all covariates to examine the association between blood mercury levels and depressive symptoms scores. We used restricted maximum likelihood estimation (REML) to estimate regression coefficients and standard deviations. The linear mixed-effects model adjusted potential confounders based on the association between depressive symptom score and blood mercury levels. We used the same model to conduct stratification analyses by sex and fish consumption. We also conducted several sensitivity analyses. First, separate analyses were performed in non-smoking or non-drinking populations to assess the robustness of our results. Second, the two-metal model of mercury and selenium was constructed to test the antagonistic effect [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. All analyses were conducted in R (version 4.2.1) software.\u003c/p\u003e \u003cp\u003eMetabolites and lipids were identified based on MS1 and MS2 spectra using MSDIAL software. To identify metabolites, the MassBank database was searched. Also, to identify lipids, a Lipid Blast-based silica spectral database (version: LipidDBs-VS23-FiehnO) was used. MS1 and MS/MS search tolerances were set to 0.01 Da and 0.05 Da, respectively, with an identification score cutoff of 70%. Other parameters used in MS-DIAL are set to default values.\u003c/p\u003e \u003cp\u003eTo investigate the effect of blood mercury exposure on metabolites, we conducted a metabolomic analysis on samples stratified by quartile blood mercury levels. Firstly, single-factor analysis of variance and post-hoc Duncan analysis were utilized to compare statistical metabolic changes associated with mercury exposure, with significance determined at a corrected p-value less than 0.05. Secondly, a linear mixed-effect model was applied to evaluate the associations between identified metabolites and other confounders, with mercury exposure level as the primary exposure indicator and controlling for factors such as age, BMI, gender, fish consumption, and socioeconomic status. P-values less than 0.05 were considered significant. To identify the main metabolic pathways involved in the differential metabolites, we subsequently performed pathway enrichment analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the demographic characteristics, lifestyle characteristics, depressive symptoms, and blood mercury levels in 2019 and in 2021. There were in total 477 participants in this cohort (32.08% male). The mean age of the cohort at baseline was 18.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58 years (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). There were 37.74% participants consumed fish less than once a month. The subjects of high household income accounted for 21.80%. The mean (SD) PHQ-9 score was 2.81 (\u0026plusmn;\u0026thinsp;3.65) at baseline, and slightly decreased to 2.79 (\u0026plusmn;\u0026thinsp;4.10) in 2021. The mean (SD) blood mercury level was 0.87 (\u0026plusmn;\u0026thinsp;0.48) ng/mL at baseline, and decreased to 0.83 (\u0026plusmn;\u0026thinsp;0.37) ng/mL in 2021. PHQ-9 scores at different blood mercury levels are shown in Supplementary Material Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic characteristics of participants (N\u0026thinsp;=\u0026thinsp;477)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticipants characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge (years), mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.44(0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.24(0.64)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSex, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e153(32.08%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e153(32.08%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e324(67.92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e324(67.92%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e), mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.18(4.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.10(4.71)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHousehold income, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e373(78.20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37(77.78%)1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104(21.80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106(22.22%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSmoking exposure, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(1.05%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7(1.47%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e472(98.95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e470(98.53%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePassive smoking exposure, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65(13.63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16(3.35%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e412(86.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e461(96.65%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDrinking exposure, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19(3.98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10(2.10%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e458(96.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e467(97.90%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePhysical activity, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e348(72.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e257(53.88%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e129(27.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e220(46.12%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFish consumption, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026lt;Once a month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e180(37.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e174(36.48%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026ge;Once a month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e297(62.26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e303(63.52%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePHQ-9 scores, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.81(3.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.79(4.10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBlood mercury levels (ng/mL), mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.87(0.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.83(0.37)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: SD: Standard deviation.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the blood mercury levels and PHQ-9 scores in different groups. Males (n\u0026thinsp;=\u0026thinsp;153) had higher depressive symptoms and higher blood mercury levels than females (n\u0026thinsp;=\u0026thinsp;324) in 2019 and 2021. During follow-up, the mean of blood mercury levels increased in the groups of smoking, drinking, and fish consumption at least once a month. However, blood mercury concentrations had no change in passive smoking exposure and high household income group. We also found that the depressive symptoms score increased in low household income, non-exercising, and fish consumption at least once a month groups in the follow-up.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe description of PHQ-9 scores and blood mercury levels by different population characteristics in 2019 and 2021\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c11\" namest=\"c8\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePHQ-9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePHQ-9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eHg\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedian (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eMedian (range)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.08(4.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.95(0.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.86(0.2,3.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e3.05(5.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.86(0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.8(0.22,2.56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.67(3.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.84(0.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.76(0.09,3.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e2.67(3.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.81(0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.76(0.11,2.37)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHousehold income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.82(3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.83(0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.75(0.09,3.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e2.75(3.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.85(0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.78(0.11,2.56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.74(3.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9(0.2,2.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e2.95(4.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.75(0.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.7(0.3,1.56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.6(0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.77(0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.51(0.3,1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.86(3.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.84(0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.88(0.53,1.13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.81(3.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.87(0.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.78(0.09,3.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e2.81(4.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.83(0.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.76(0.11,2.56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePassive smoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.47(4.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.79(0.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.66(0.4,1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.4(2.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.68(0.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.59(0.29,1.29)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.82(3.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.87(0.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.78(0.09,3.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e2.82(4.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.83(0.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.77(0.11,2.56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDrinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.05(4.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.78(0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.67(0.27,2.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e3.12(3.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.93(0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.74(0.38,2.37)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.61(3.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.89(0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.79(0.09,3.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e2.78(4.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.82(0.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.76(0.11,2.56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePhysical activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.61(3.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.89(0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.78(0.09,3.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e2.3(3.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.85(0.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.78(0.11,2.49)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.33(3.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.82(0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.76(0.17,2.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e3.37(4.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.8(0.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.73(0.24,2.56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFish consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;Once a month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.31(3.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.77(0.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.7(0.17,3.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e3.16(4.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.79(0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.74(0.11,2.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;Once a month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.5(3.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.93(0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.84(0.09,3.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e2.58(4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.85(0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.79(0.22,2.56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the association between young adults\u0026rsquo; blood mercury levels and PHQ-9 score and results stratified by sex and fish consumption frequency. Detailed results of the association can be found in Table S2. The results showed that blood mercury levels were positively associated with depressive symptoms scores in the fully adjusted model. For 2-fold increase in blood mercury levels, depressive symptoms score increased by 0.50 (95% CI: 0.14, 0.86; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). We found that blood mercury levels were positively associated with deterioration of depressive symptoms in males. For each 2-fold increase in blood mercury levels, the depressive symptoms score increased by 0.87 (95% CI: 0.12, 1.61; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02) in males, while such association was not observed in females. We also found that blood mercury levels were positively associated with depressive symptoms score in the groups of fish consumption at least once a month. For each 2-fold increase in blood mercury levels, the depressive symptoms score increased by 0.56 (95% CI: 0.13, 0.99; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe results of the sensitivity analyses are presented in Table S3. The population of no smoking (n\u0026thinsp;=\u0026thinsp;472) or no drinking (n\u0026thinsp;=\u0026thinsp;458) were analyzed. For every 2-fold increase in blood mercury levels, depressive symptom score increased by 0.51 (95% CI: 0.15, 0.88; \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.01) in the non-smoking model and 0.50 (95% CI: 0.14, 0.87; \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.01) in the non-drinking model. The two-metal model of mercury and selenium was also constructed, and the results were still robust. Depressive symptom score increased by 0.47 (0.10, 0.85; \u003cem\u003eP\u003c/em\u003e value 0.01) for each 2-fold of blood mercury in two-metal model.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the results of the KEGG metabolic pathway enrichment analysis. Based on analysis of variance and linear mixed model analysis, 213 differential metabolites were screened out with a significance threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The identified metabolites were then put into the HMDB database and KEGG database for cross-matching, and finally, 44 differential metabolites were identified. Further metabolic pathways analysis showed that the metabolic dysregulation associated with mercury exposure was concentrated in amino acid metabolism. There were 10 dysregulated metabolites enriched in four differential metabolic pathways, specifically arginine biosynthesis, arginine and proline metabolism, aminyl-tRNA biosynthesis, and phenylalanine, tyrosine, and tryptophan biosynthesis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eAs far as we know, this is the first longitudinal cohort study to explore the association between blood mercury levels and depressive symptoms in Chinese young adults. We found that the increased blood mercury levels among Chinese undergraduates were positively associated with depressive symptoms scores. This association was sex-specific and affected males more than females. In addition, we found a more significant association between blood mercury levels and depressive symptom in those who ate fish at least once a month. In the metabolomic analysis, high blood mercury level was associated with 44 altered metabolites, mainly enriched in amino acid metabolism pathways.\u003c/p\u003e \u003cp\u003eThis study revealed a positive association between blood mercury levels and depressive symptoms. In line with our findings, a cross-sectional study of 11,754 Korean adults indicated a positive association between greater blood mercury concentration and the incidence of depression in Korean females [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. According to a case-control study with 3,517 participants from Japan, those in the Minamata region who were severely exposed to methylmercury were more likely to experience psychiatric symptoms [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. However, the cross-sectional 2005–2008 NHANES study of 6,911 US adults age ≥ 20 years revealed that total blood mercury levels were not associated with depression after adjusting for sociodemographic variables (income-poverty ratio, education, marital status). However, in the elderly, an inverse effect of total blood mercury on depression was found [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The authors attributed this finding to residual factors such as socioeconomic factors and seafood consumption. Another cross-sectional study of 15,140 US adults age ≥ 18 years from the 2005–2010 NHANES found that higher concentration of mercury was negatively associated with depression (adjusted Prevalence Ratios (PR) = 0.62, 95% CI = 0.50, 0.78) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Differences in study design could partly explain heterogeneity in the above-mentioned results and our results. Additionally, although depression was assessed in both studies using the PHQ-9 scale, the 2005–2008 NHANES study employed a cut-off point for the diagnosis of depression was 4/5, and the 2005–2010 NHANES was 9/10. In our study, the PHQ-9 score was used as a quantitative variable. Different types of variables and diagnostic criteria for the outcome may account for the discrepancies in study results. Inconsistency may also be attributable to research populations, which may differ according to the individual's physical condition, lifestyle, dietary habits, education, and mercury exposure levels. Toxicological studies have revealed that mercury metabolism and toxicity can be affected by sex, diet, or co-exposure to other pollutants [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Nevertheless, knowledge regarding the role of potential modifying factors in the association between mercury exposure and depressive symptoms is currently limited. Dietary intake and environmental characteristics are considered to increase blood mercury concentrations in human beings.\u003c/p\u003e \u003cp\u003eIn subgroup analysis, our results indicated sex-specific associations between blood mercury levels and depressive symptoms. Males seem to be more sensitive to mercury exposure in terms of depressive symptoms. The finding of a cross-sectional study of 408 individuals in Zhoushan City, China, supported our results that prenatal methylmercury exposure from fish intake caused a neurodevelopmental risk for males but not females [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. We can consider several potential causes for this difference: metabolic levels and the protective effects of estrogen. In the study on mercury-induced immunotoxicity effects on mice, males had more tissue mercury retention than females, particularly in the kidneys [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Additionally, estrogen may perform a protective function in the association between blood mercury and depressive symptoms. Estrogen has antioxidant properties that can provide an additional defense against oxidative stress by working as scavengers or activating estrogen receptors to stimulate the synthesis of protective molecules [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. This may indicate a protective role of estrogen in psychiatric disorders. We also found sex-specific blood mercury level differences in our study. The mean mercury levels were significantly higher in males than in females during the follow-up. In a 2010–2011 cross-sectional study including 4,000 Korean participants aged 0 to 83, the mean concentration of total blood mercury was higher in men (3.11 µg/L) than women (2.77 µg/L) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Males have higher blood mercury levels than females, which may induce more severe mental health problems. Given that the mechanisms of distribution and susceptibility in mercury remain obscure, further study is essential to clarify it.\u003c/p\u003e \u003cp\u003eOur stratified analysis revealed that the effect of blood mercury levels on depressive symptoms was more significant among those who consumed fish at least once a month. The Japan Public Health Center-based Prospective Study (JPHC) demonstrated that moderate fish consumption (average 111 grams of fish per day )could reduce depression risk. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. However, consuming contaminated fish may increase neurotoxicity, as fish may be the primary source of human mercury exposure [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Mercury concentrations were higher in Korean from coastal regions than in Korean from inland areas due to their more intake of seafood [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. A study in Hong Kong, China, analyzed 151 plasma samples and found a positive association between fish consumption rates and plasma methylmercury concentrations [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. It is clear from all of the above studies that a high intake of fish could affect blood mercury levels. This supports our finding that people who eat fish at least once a month are more likely to be exposed to mercury, which affects depressive symptoms.\u003c/p\u003e \u003cp\u003eHigh blood mercury exposure altered the levels of amino acids enriched in various metabolic pathways. The levels of phenylalanine, pyroglutamic acid and arginine were down-regulated, while the levels of L-leucine and taurine were up-regulated. Disruptions in amino acid metabolism have a strong correlation with the development of depression [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Phenylalanine serve as a precursor for dopamine, a monoamine transmitter in the brain, the impairment of which could lead to depression [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Pyroglutamic acid is a cyclic lactam of glutamic acid and acts as a glutamic acid reservoir [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The downregulated pyroglutamic acid levels may indicate that participants with high blood mercury levels could have a reduction in the excitatory neurotransmitter glutamate. [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. In addition, the non-essential amino acid taurine is a major intracellular free beta-amino acid. It is a protectant against oxidative stress damage [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Upregulation of taurine levels indicated that participants with higher mercury exposure could possibly experience oxidative stress damage, a major manifestation of depression. High blood mercury exposure could possibly influence neurotransmitter levels and oxidative stress responses resulting in depression symptoms by disrupting serum amino acid homeostasis.\u003c/p\u003e \u003cp\u003eWe found that serum levels of energy-related metabolites, oxaloacetate, were downregulated after exposure to higher levels of mercury. It is known that oxaloacetate can condense with acetyl coenzyme to produce citrate, which is one of the key reactions of the tricarboxylic acid cycle. This suggests that high mercury exposure leads to downregulation of key metabolites that initiate the Krebs cycle. Similarly, previous studies found that there were altered energy metabolites in women with postpartum depression [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Mercury exposure could possibly lead to depressive symptoms through oxaloacetate-related energy metabolic pathways.\u003c/p\u003e \u003cp\u003eWe also found that high mercury exposure resulted in abnormal lipid metabolism in participants. In the present study, the serum levels of arachidonic acid and leukotrienes were upregulated. Arachidonic acid is a precursor for the production of inflammatory mediators such as prostaglandins, thrombin, and leukotrienes [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Inflammatory response pathway could possibly lead to depressive symptoms caused by mercury exposure.\u003c/p\u003e \u003cp\u003eThe primary merit of the present research is that it presents the first report on the association between blood mercury and depressive symptoms among Chinese young adults. At the same time, the association between mercury exposure and serum metabolomics was preliminarily discussed. Potential biomarkers and metabolic pathways for mercury exposure to affect depressive symptoms were identified. Moreover, given the cohort study design, we could infer causality between blood mercury concentration and depressive symptoms. However, this study had some limitations. A recall bias on the food frequency questionnaire might have occurred owing to the subjects’ seasonal or daily dietary habits. In addition, depression symptoms were evaluated by a self-assessment questionnaire. Although the study has some inevitable limitations, our findings still give fresh insights into the neurotoxicity of mercury exposure among young adults.\u003c/p\u003e "},{"header":"5. Conclusion","content":"\u003cp\u003eBlood mercury levels were positively associated with depressive severity in this population-based cohort study. Males and more fish consumption groups were susceptible to mercury in terms of depression symptoms. Mercury exposure may affect depressive symptoms through neurotransmitter, energy, inflammation and oxidative stress related metabolimic pathways. Stricter mercury pollution legislations are warranted to protect human health.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eM.F., X.L., H.L., Y.G., P.L., planned the study and drafted the manuscript. H.L., Y.M., P.L., contributed to data analysis and interpretation. S.S., S.W., L.Y., T.Y., L.Y., J.W., P.X., S.L., S.F., P.L., participated in manuscript revision. All authors have given final approval for the manuscript to be published and have agreed to be responsible for all aspects of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Binzhou Medical University ethics committee (NO.2019075).\u003c/p\u003e\n\u003cp\u003eAll experiments were performed in accordance with relevant specified guidelines and regulations.\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors are grateful to all students of Binzhou Medical College who participated in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Generative AI and AI-assisted technologies in the writing process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author did not use generative AI and AI-assisted technologies in the writing process.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCollaborators, G.M.D., \u003cem\u003eGlobal, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990\u0026ndash;2019: a systematic analysis for the Global Burden of Disease Study 2019.\u003c/em\u003e The Lancet Psychiatry, 2022.\u003c/li\u003e\n\u003cli\u003eIHME. \u003cem\u003eGBD Compare Data Visualization\u003c/em\u003e. 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Hafez, \u003cem\u003eSynopsis of arachidonic acid metabolism: A review.\u003c/em\u003e Journal of advanced research, 2018. \u003cstrong\u003e11\u003c/strong\u003e: p. 23-32.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Blood mercury, Depressive symptoms, Metabolomics, Chinese young adults, Cohort study","lastPublishedDoi":"10.21203/rs.3.rs-4385885/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4385885/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMercury exposure may increase the risk of depression. This study aimed to examine the association between blood mercury and depressive symptoms in Chinese young adults. We collected 477 fasting venous blood samples and questionnaire data from the Chinese undergraduate cohort study in 2019 and 2021. Patient Health Questionnaire-9 was used to estimate depressive symptoms. Blood mercury levels and metabolomic levels were measured using inductively coupled plasma mass spectrometry (ICP-MS) and liquid chromatography-mass spectrometry. Using linear mixed-effects models and ANOVAs based on baseline blood samples (N\u0026thinsp;=\u0026thinsp;468) to examine metabolomic changes associated with mercury exposure. Our study found for every 2-fold increase in blood mercury levels, the depression scale score increased by 0.50 [95% confidence interval (CI): 0.14, 0.86]. And in males, a 2-fold increase in blood mercury levels was associated with a 0.87 (95% CI: 0.12, 1.61) increase in depression scale scores, while it wasn\u0026rsquo;t observed in females. Individuals consuming fish\u0026thinsp;\u0026ge;\u0026thinsp;once a month shows similar negative correlation. Metabolomic analysis identified 10 differential metabolites enriched in 4 metabolic pathways. Blood mercury may deteriorate depressive symptoms. Males and those who consume more fish are susceptible subjects. Mercury exposure may affect depressive symptoms through neurotransmitter, energy and inflammation-related pathways.\u003c/p\u003e","manuscriptTitle":"Blood mercury and depressive symptoms: a longitudinal study combining metabolomics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-30 20:52:09","doi":"10.21203/rs.3.rs-4385885/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"07937cc4-0cf8-4f3c-af14-b0fe49093fe4","owner":[],"postedDate":"May 30th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-09-05T10:37:41+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-30 20:52:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4385885","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4385885","identity":"rs-4385885","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}