Association between Serum Metal Levels and Blood Pressure in Aluminium Smelting Workers: Mediating Role of Gamma-Glutamyltransferase

Association between Serum Metal Levels and Blood Pressure in Aluminium Smelting Workers: Mediating Role of Gamma-Glutamyltransferase | 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 Association between Serum Metal Levels and Blood Pressure in Aluminium Smelting Workers: Mediating Role of Gamma-Glutamyltransferase YOUXING LI, Yaqin Pang, Wenxue Li, Yufang Cen, Junhong Wei, Yinxia Lin, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8071936/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Feb, 2026 Read the published version in Biological Trace Element Research → Version 1 posted 10 You are reading this latest preprint version Abstract Occupational exposure to multiple metals in aluminium smelting environments may influence blood pressure through oxidative stress pathways, yet evidence on specific elemental effects and mediating mechanisms remains limited. In this cross-sectional study, 540 male aluminium smelter workers in China were investigated. Serum concentrations of 17 metals were quantified using inductively coupled plasma mass spectrometry (ICP-MS), and γ-glutamyltransferase (GGT) was measured as an oxidative stress biomarker. Least absolute shrinkage and selection operator (LASSO) regression was performed to identify key metals associated with systolic blood pressure (SBP) and diastolic blood pressure (DBP), followed by generalized linear models (GLM), Bayesian kernel machine regression (BKMR), and restricted cubic splines (RCS) to assess metal–blood pressure relationships. Mediation analysis was conducted using the PROCESS macro. Copper (Cu) was positively associated with SBP, while molybdenum (Mo) was inversely associated with both SBP and DBP. Cu was positively correlated with GGT, whereas Mo was negatively correlated, and GGT was positively associated with blood pressure. GGT mediated approximately 24–33% of these associations. These findings suggest that oxidative stress mediates the effects of Cu and Mo on blood pressure in aluminium smelter workers, highlighting the importance of early monitoring and intervention in occupational populations. Metal γ-glutamyltransferase Blood pressure Oxidative stress Mediator analysis Aluminium workers Figures Figure 1 Figure 2 Figure 3 1. Introduction Hypertension is one of the most prevalent chronic non-communicable diseases worldwide. According to the World Health Organization (WHO) Global Hypertension Report 2024, approximately 1.4 billion individuals are affected globally. Substantial evidence indicates that hypertension is a major contributor to heart disease, stroke, and premature mortality, placing a heavy burden on public health systems worldwide [9, 16, 22]. The WHO defines hypertension in adults as systolic blood pressure (SBP) ≥140 mmHg and/or diastolic blood pressure (DBP) ≥90 mmHg. However, even blood pressure levels below these thresholds, referred to as “prehypertension,” are associated with increased cardiovascular disease risk[5]. In recent years, rapid industrialisation has raised growing concern about the relationship between occupational metal exposure and hypertension[6]. Aluminium smelting, a key sector of the non-ferrous metal industry, releases substantial quantities of metallic elements during production. Workers engaged in aluminium smelting are chronically exposed to complex mixtures of metals at concentrations markedly higher than those encountered by the general population, which may elevate their risk of developing hypertension[4] Therefore, investigating the relationship between metal exposure and blood pressure in occupational populations is essential for improving cardiovascular disease prevention and control. A growing body of epidemiological evidence has demonstrated close associations between metal exposure and blood pressure regulation. For example, excessive sodium intake is strongly linked to an increased risk of hypertension[13]. An iron metabolism study reported a U-shaped relationship between serum Fe and both SBP and DBP [41] Population-based nutritional surveys have shown a significant positive correlation between serum selenium levels and hypertension, whereas no consistent associations were observed for zinc or copper [3]. In contrast, other studies have suggested that selenium may exert protective effects by reducing inflammation, oxidative stress, and vascular remodelling, thereby lowering blood pressure[38]. A prospective cohort study further reported that higher levels of copper, zinc, and molybdenum were positively associated with hypertension risk[51]. However, a multi-metal exposure study involving 13 elements (lead, antimony, arsenic, cadmium, mercury, thallium, chromium, cobalt, molybdenum, manganese, nickel, selenium, and tin) found that lead and arsenic were positively associated with hypertension, whereas molybdenum was negatively correlated with hypertension risk[31]. Occupational studies have shown that aluminium exposure is associated with a higher risk of hypertension among smelter workers [44, 48], whereas no such association was observed in the general population [45]. Taken together, these findings suggest that relationships between metals (e.g., copper , zinc, selenium, molybdenum, and aluminium) and blood pressure remain inconsistent and warrant further epidemiological confirmation. In addition, potential interactions among metals highlight the need to investigate the combined effects of mixed metal exposure on blood pressure. Gamma-glutamyl transferase (GGT) is an important enzymatic indicator of hepatic function and a sensitive biomarker of systemic oxidative stress [37]. Increasing evidence shows that GGT is closely associated with various cardiovascular diseases [21]. Epidemiological studies have demonstrated that in elderly Japanese men, higher serum GGT levels are positively correlated with hypertension and atherosclerosis severity, reflecting elevated oxidative stress and vascular endothelial injury [36]. Moreover, metal exposure markedly affects GGT expression and activity. In adults, serum copper levels have shown a significant positive correlation with GGT activity[29]. Similarly, a population-based survey reported that individuals with high combined exposure to toxic metals such as lead , cadmium, mercury, and arsenic had significantly higher GGT levels than those with low exposure [46]. Conversely, elevated urinary molybdenum levels were associated with lower GGT concentrations [19] . Collectively, these findings suggest that metal exposure may modulate GGT expression and activity by inducing systemic oxidative stress, which could subsequently contribute to increased blood pressure. However, research investigating the mediating role of GGT in the relationship between metal exposure and blood pressure remains limited. Building on this background, the present study applied multiple analytical approaches, including least absolute shrinkage and selection operator (LASSO) regression, generalized linear models (GLM), Bayesian kernel machine regression (BKMR), and restricted cubic splines (RCS), to identify key serum metals associated with blood pressure among aluminium smelter workers. Furthermore, the study explored the potential mediating role of oxidative stress in the relationship between metal exposure and blood pressure, aiming to provide scientific evidence for the early prevention and risk assessment of cardiovascular diseases in occupational populations. 2. Methods 2.1 Study population This cross-sectional study was conducted in June 2024 among workers from a medium-sized aluminium smelter in China. Participants were excluded if they met any of the following criteria: (1) presence of neurological disorders, malignant tumours, renal diseases (including renal failure or insufficiency), chronic obstructive pulmonary disease, thyroid disorders (hyperthyroidism or hypothyroidism), diabetes mellitus, hepatitis or cerebrovascular lesions [39]; (2) significant visual or hearing impairments or poor cooperation; or (3) current use of antihypertensive medication. Ultimately, 540 male workers were enrolled in the study. Ethical approval was obtained from the Ethics Committee of Youjiang Medical University for Nationalities (Approval No. 2023070601) and the Ethics Committee of Universiti Teknologi MARA (Approval No. REC/12/2023-PG/FB/29). Written informed consent was obtained from all participants prior to enrolment, and each underwent blood sampling and blood pressure measurement. 2.2 Blood Pressure Measurement and Definition SBP and DBP were measured for each participant using a calibrated mercury sphygmomanometer. All measurements were conducted in the morning after an overnight fast. Before measurement, participants were asked to rest quietly for at least five minutes in a seated position. Three consecutive readings were obtained at intervals of more than two minutes, and the average of the three readings was recorded as the final blood pressure value, expressed in millimetres of mercury (mmHg). 2.3 Determination of Metals Serum concentrations of 17 metals, including sodium (Na), iron (Fe), zinc (Zn), copper (Cu), selenium (Se), vanadium (V), chromium (Cr), cobalt (Co), molybdenum (Mo), aluminium (Al), titanium (Ti), arsenic (As), strontium (Sr), antimony (Sb), barium (Ba), mercury (Hg) and thallium (Tl), were determined using inductively coupled plasma mass spectrometry (ICP-MS, Agilent 7700 Series, USA). Metal concentrations were quantified using the external standard calibration method, and accuracy was verified through spiked recovery tests (90%–120%). Quality control samples were analysed concurrently to ensure analytical reliability. For measurements below the limit of detection (LOD), a value equal to LOD divided by the square root of two was assigned [11]. 2.4 Determination of GGT Whole blood samples were centrifuged at 1500 × g for 10 minutes to separate serum. The resulting serum was analysed using an automated biochemical analyser (Dierui Medical, Model CS-66B) to determine GGT levels. 2.5 Covariates Covariates included age, body mass index (BMI), ethnicity (Han Chinese or ethnic minority), marital status (living alone or cohabiting), educational attainment (junior secondary school or below, senior secondary school or above), and monthly income (5000 CNY). Lifestyle factors included smoking status (Yes: smoking at least one cigarette per day for six consecutive months or more; No) and alcohol consumption (Yes: consuming more than one standard drink per week for six consecutive months or more; No). 2.6 Statistical Analysis Categorical variables were expressed as counts and percentages, while continuous variables were presented as mean ± standard deviation. As serum metal and GGT concentrations exhibited skewed distributions, log10 transformations were applied prior to analysis. Spearman’s rank correlation analysis was used to assess correlations among the 17 metals. To minimise selection bias during variable screening and address potential collinearity and confounding among metals, the LASSO regression was employed to identify key metals associated with blood pressure, adjusting for covariates. GLM was then applied to examine individual associations between metals and blood pressure. The primary metals identified were further incorporated into a BKMR model to evaluate the overall effect of mixed metal exposure on blood pressure. This analysis included: (1) exposure–response function curves for individual metals (with other metals held at their median levels); (2) component effects of each metal (assessing blood pressure changes when the concentration of one metal increased from the 25th to the 75th percentile, with other metals fixed at the 25th, 50th, and 75th percentiles); and (3) combined and interactive effects among metals. RCS were used to model the dose–response relationships between individual metals and blood pressure. GLM was also used to examine associations between key metals and GGT, as well as between GGT and blood pressure. Mediation analysis was performed using the PROCESS macro in SPSS to assess the mediating effect of GGT in the relationship between metals and blood pressure, following the Baron and Kenny framework [30]. The procedure included: (1) testing the direct effect of metals on blood pressure, the effect of metals on GGT, and the effect of GGT on blood pressure; (2) conducting a bootstrap mediation analysis (1000 resamples) to estimate indirect, direct, and total effects with corresponding 95% confidence intervals (CIs) and P-values; and (3) calculating the mediation proportion as indirect effect ÷ total effect. Full mediation was considered present when the direct effect was non-significant ( P ≥ 0.05), and partial mediation was indicated when both direct and indirect effects were significant. All models were adjusted for age, BMI, ethnicity, marital status, educational attainment, income, smoking status, and alcohol consumption. Statistical analyses were conducted using SPSS version 22.0 (IBM Corp., USA) and R version 4.3.2. All statistical tests were two-tailed, and P < 0.05 was considered statistically significant. 3. Results 3.1 Characteristics of the participants As shown in Table 1 , among the 540 participants, the mean age was 39.1 years and the mean body mass index (BMI) was 24.8 kg/m². The majority of participants were cohabiting (86.3%) and belonged to ethnic minority groups (63.9%). Regarding educational attainment, 71.3% had completed secondary education or higher. In terms of household monthly income, 63.5% of participants earned between ¥3,000 and ¥5,000. More than half of the participants were current smokers (54.4%) and reported alcohol consumption (60.4%). Table 1 Baseline Characteristics of Study Population(n = 540) Characteristic Mean ± SD or n (%) Age (years) 39.09±6.69 BMI 24.82±3.54 Marital status Living without a spouse 74 (13.7) Living with a spouse 466 (86.3) Ethnicity Han 195 (36.1) National minority 345(63.9) Education level Junior high school and below 155 (28.7) High school and above 385 (71.3) Income ＜3000（CNY） 128 (23.7) 3000-5000（CNY） 343 (63.5) ＞5000（CNY） 69 (12.8) Smoking No 246 (45.6) Yes 394 (54.4) Drinking No 213 (39.4) Yes 326 (60.4) Abbreviations: BMI, body mass index is measured and defined as weight (kg)/height (m) 2 ; Continuous variables are expressed as mean ± SD; Categorical variables were presented as n (%); CNY, Chinese yuan. 3.2 Correlation Among Metal Concentrations As shown in Figure. S1 , significant correlations were observed among most serum metals, with correlation coefficients (r) ranging from −0.27 to 0.62 (all P < 0.05). Strong positive correlations were noted between Zn and Se (r = 0.53), Zn and Cr (r = 0.47), Se and Cr (r = 0.52), V and Ti (r = 0.62), and Cr and Ti (r = 0.55). Cu exhibited significant positive correlations with Se (r = 0.37) and Cr (r = 0.36). Mo showed a positive correlation with Hg (r = 0.18) and a negative correlation with Fe (r = −0.14). Sr demonstrated positive correlations with Ba (r = 0.53) and Sb (r = 0.41). In contrast, Tl displayed negative correlations with Ba (r = −0.27), Sb (r = −0.18), and Sr (r = −0.13). 3.3 LASSO Regression Screening for Primary Metals Associated with Blood Pressure To optimise model performance and identify metals significantly associated with blood pressure, LASSO regression was applied for variable selection. As shown in Figure. S2 , the left panel displays the coefficient paths, and the right panel presents the cross-validation curve used to determine the optimal λ value for predicting SBP(a–b) and DBP(c–d). With increasing λ, most variable coefficients gradually approached zero. Ultimately, Cu, Mo, and Sr were selected as the key variables in both SBP and DBP models. 3.4 Association between Major Metal Concentrations and Blood Pressure (GLM) Table 2 summarises the associations between Cu, Mo, and Sr concentrations and blood pressure based on the GLM analysis. In both the single-metal (Model 1) and multi-metal (Model 2) analyses, Cu showed a significant positive association with SBP (Model 2: β = 21.83, 95% CI: 5.74–38.19, P = 0.009). In contrast, Mo demonstrated significant negative associations with both SBP (Model 2: β = −8.97, 95% CI: −16.90 to −1.03, P = 0.027) and DBP (Model 2: β = −6.75, 95% CI: −12.45 to −1.06, P = 0.020). Although Sr was significantly associated with blood pressure in the single-metal analysis, this relationship was no longer significant after adjustment for other metals in the multi-metal model. Table 2 Associations between Cu, Mo, and Sr Concentrations and Blood Pressure Metals SBP DBP β (95% CI) P value β (95% CI) P value Model1 Cu 23.48 (7.66, 39.30) 0.004 8.54 (-2.81, 19.89) 0.140 Mo -8.02 (-16.01, -0.23) 0.049 -6.39 (-12.09, -0.69) 0.028 Sr 10.91 (0.53, 21.28) 0.039 5.28 (-2.13, 12.70) 0.163 Model 2 Cu 21.83 (5.47, 38.19) 0.009 7.89 (-3.85, 19.62) 0.188 Mo -8.97 (-16.90, -1.03) 0.027 -6.75 (-12.45, -1.06) 0.020 Sr 7.38 (-3.29, 18.01) 0.176 4.10 (-3.55, 11.74 0.294 Note: Model 1 represents the single-metal GLM analysis; Model 2 represents the multi-metal GLM analysis. Both models were adjusted for age, BMI, marital status, ethnicity, education level, income, smoking and drinking. All metal concentrations were log₁₀-transformed prior to analysis. 3.5 BKMR Analysis The BKMR model was applied to further assess the combined effects of Cu, Mo, and Sr on blood pressure, with all covariates adjusted. Figure. S3 displays the exposure–response functions for individual metals in relation to SBP and DBP. Cu and Sr showed positive linear associations with both SBP and DBP, whereas Mo exhibited negative linear associations with both outcomes. Figure. 1 illustrates the component effects of the three metals. Cu was positively associated with SBP, while Mo showed a negative association with SBP (Figure. 1a). Similarly, Mo was negatively associated with DBP, particularly when concentrations of other metals were fixed at low or median quantiles (Figure. 1b). Analysis of the overall mixture effects revealed no significant association between combined metal exposure and blood pressure (Figure. 2), and no evident interactions among metals (Figure. S4). Within the mixture, Cu and Mo were identified as the primary contributors to SBP (PIP = 0.390 and 0.221, respectively), whereas Mo was the major contributor to DBP (PIP = 0.365) (Table S1). Overall, the BKMR results were consistent with those from the GLM analysis, supporting the robustness of the observed associations. 3.6 RCS Analysis Figure. S5 illustrates the dose–response relationships between Cu and Mo exposure levels and blood pressure, modelled using the RCS method. The number of spline knots was automatically determined based on the minimum Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) to achieve optimal model fit. Figures. S5 (a, c, e, g) present single-metal models adjusted for all covariates, while Figures. S5 (b, d, f, h) depict multi-metal models further adjusted for Cu, Mo, and Sr after covariate adjustment. Results showed that Cu levels exhibited a significant linear positive association with SBP ( P for overall = 0.012 and 0.026) (Figures. S5-a,b), whereas associations with DBP were not statistically significant ( P for overall = 0.150 and 0.181) (Figures. S5-c,d). Conversely, elevated Mo levels demonstrated linear negative associations with both SBP ( P for overall = 0.080 and 0.064) and DBP ( P for overall = 0.051 and 0.047) (Figures. S5-e, f, g, h). Although some individual associations did not reach statistical significance, the overall negative trends for Mo approached or achieved significance. 3.7 Association between Major Metal Concentrations and GGT (GLM) Table 3 summarises the associations between Cu, Mo and Sr concentrations and GGT. In the single-metal model (Model 1), Cu exhibited a significant positive correlation with GGT (β = 0.71, 95% CI: 0.46 to 0.96, P < 0.001), Mo showed a significant negative correlation (β = −0.19, 95% CI: −0.32 to −0.07, P = 0.003), and Sr displayed a significant positive correlation (β = 0.26, 95% CI: 0.09 to 0.42, P = 0.002). In the multi-metal model (Model 2), the positive association between Cu and GGT remained significant (β = 0.69, 95% CI: 0.43 to 0.94, P < 0.001), as did the negative association between Mo and GGT (β = −0.22, 95% CI: −0.34 to −0.10, P < 0.001), whereas the association between Sr and GGT lost statistical significance. Furthermore, in the multivariate GLM including all 17 metals, only Cu and Mo maintained statistically significant associations with GGT, whereas Sr continued to show no significant relationship ( Table S2). Table 3 Correlation between Cu, Mo and Sr Concentrations and GGT Metals GGT β (95% CI) P value Model1 Cu 0.71 (0.46, 0.96) ＜ 0.001 Mo -0.19 (-0.32, -0.07) 0.003 Sr 0.26 (0.09, 0.42) 0.002 Model 2 Cu 0.69 (0.43, 0.94) ＜ 0.001 Mo -0.22 (-0.34, -0.10) ＜ 0.001 Sr 0.14 (-0.02, 0.31) 0.090 Note: Model 1 represents the single-metal GLM analysis; Model 2 represents the multi-metal GLM analysis. Both models were adjusted for age, BMI, marital status, ethnicity, education level, income, smoking and drinking. All metal and GGT concentrations were log₁₀-transformed prior to analysis. 3.8 Association between GGT and Blood Pressure (GLM) Table 4 presents the associations between GGT concentrations and blood pressure levels. After adjusting for all covariates, GGT concentrations showed significant positive associations with both SBP (β= 12.21, 95% CI: 6.93 to17.30, P < 0.001) and DBP (β = 10.88, 95% CI: 7.23 to14.54, P < 0.001). Table 4 Correlation between GGT Concentration and Blood Pressure Levels Variables SBP DBP β (95% CI) P value β (95% CI) P value GGT 12.12 (6.93, 17.30) ＜ 0.001 10.88 (7.23, 14.54) ＜ 0.001 Note: The models were adjusted for age, BMI, marital status, ethnicity, education level, income, smoking and drinking. GGT concentrations were log₁₀-transformed prior to analysis. 3.9 Mediational Analysis Based on the linear regression results, mediation analysis was performed to evaluate the potential mediating role of GGT in the associations of Cu and Mo with blood pressure outcomes (Figure. 3). Figure. 3(a-c) show the results of the single-metal models after adjusting for all covariates. In these models, Cu demonstrated a significant indirect effect on SBP (β= 7.721, 95% CI: 3.271 to12.891, P < 0.001) with a mediation proportion of 32.7%. Similarly, Mo exhibited a significant indirect effect on SBP via GGT (β= −2.236, 95% CI: −4.155 to −0.684, P = 0.002) with a mediation proportion of 27.56%, and an indirect effect on DBP with a mediation proportion of 31.56%. Figure. 3(d-f) display the results from the multi-metal model, which additionally adjusted for Cu, Mo, and Sr after controlling for all covariates. In this model, Cu’s indirect effect on SBP through GGT remained significant (β= 6.831, 95% CI: 2.717 to 12.116, P < 0.001), corresponding to a mediation proportion of 30.96%. Meanwhile, the mediation proportions of Mo via GGT for SBP and DBP were 24.28% and 33.40%, respectively. 4. Discussion This study identified Cu, Mo, and Sr as key metals significantly associated with blood pressure among Al smelter workers. A linear positive association was observed between Cu and SBP, whereas Mo showed a linear negative association with both SBP and DBP; The association between Sr and blood pressure was unstable. No significant association was detected between overall mixed metal exposure and blood pressure. In addition, Cu was positively correlated with GGT, Mo was negatively correlated with GGT, and GGT was associated with both SBP and DBP. Mediation analysis further demonstrated that GGT mediated the associations of Cu and Mo with blood pressure. These findings suggest that oxidative stress may serve as a critical biological pathway linking metal exposure to blood pressure regulation. Cu is an essential trace element in the human body that functions as a catalytic cofactor for various enzymes and participates in mitochondrial energy metabolism, antioxidant defence, and the synthesis of bioactive substances. Cu homeostasis is crucial for maintaining physiological balance and overall health [7]. The association between Cu and blood pressure has attracted considerable attention. An analysis of the NHANES database reported that higher dietary Cu intake was associated with an increased risk of hypertension [26]. Among children and adolescents, elevated serum Cu levels were significantly correlated with higher blood pressure [24], and a case–control study in adults also observed significantly higher serum Cu levels in the hypertensive group compared with controls [50]. However, other studies found no association between serum Cu and hypertension or blood pressure levels, possibly due to the confounding effects of antihypertensive medication use [3, 43]. Overall, existing evidence generally supports that excessive Cu exposure may increase hypertension risk. The findings of the present study are consistent with this view and, as the participants taking antihypertensive drugs were excluded, the results provide more reliable evidence. The mechanism by which Cu affects blood pressure appears closely related to oxidative stress [10]. Under Cu deficiency, reduced activity of copper–superoxide dismutase (Cu–SOD) and ceruloplasmin in the antioxidant defence system leads to excessive production of reactive oxygen species (ROS) [2, 32]. Conversely, Cu overload can catalyse Fenton-like reactions, generating hydroxyl radicals (-OH) and superoxide anions (O₂⁻) [12, 18]. These ROS damage vascular endothelial cells, reduce nitric oxide (NO) synthesis, and cause vasoconstriction. In addition, ROS rapidly react with NO to form peroxynitrite (ONOO⁻), further impairing endothelial cells, triggering inflammation and fibrosis, and ultimately leading to vascular stiffness and elevated blood pressure [25]. Thus, both Cu deficiency and excess can contribute to hypertension, consistent with a reported U-shaped association between Cu intake and new-onset hypertension [14]. In this study, Cu was positively associated only with SBP, possibly because serum Cu levels in our population were within the non-deficient range. In addition, GGT levels were significantly and positively correlated with Cu. GGT is a sensitive biomarker of systemic oxidative stress that becomes upregulated under oxidative stress conditions. By degrading extracellular glutathione (GSH), GGT provides substrates for intracellular GSH resynthesis, thereby strengthening antioxidant defence [42]. These findings further support the role of oxidative stress in mediating the effect of Cu on blood pressure. It is noteworthy that this study did not observe a significant association between Cu and DBP. Under physiological conditions, blood pressure levels are primarily determined by vascular compliance and peripheral vascular resistance. When compliance in large and medium-sized arteries decreases, SBP tends to rise, while diastolic pressure remains normal or slightly reduced [20]. In contrast, when peripheral arterioles undergo sustained constriction and luminal narrowing, the resulting increase in peripheral resistance may elevate diastolic pressure [34]. Therefore, the present findings suggest that the vasotoxic effects of Cu may predominantly affect large and medium-sized arteries rather than peripheral resistance vessels. Nevertheless, this hypothesis requires further experimental and longitudinal validation. Mo is an essential trace element that serves as a cofactor for flavin enzymes such as xanthine oxidase and xanthine dehydrogenase (XO/XDH), playing a key role in purine metabolism [1]. The relationship between Mo and blood pressure remains controversial. Epidemiological evidence has shown a dose–response relationship between elevated plasma Mo levels and a lower risk of hypertension [23]. In middle-aged and elderly populations with metabolic syndrome, plasma Mo levels were negatively correlated with both SBP and DBP [17]. Similarly, a cross-sectional study reported that higher urinary Mo levels were associated with a significantly lower prevalence of hypertension [31]. These findings are consistent with the inverse associations between Mo and SBP/DBP observed in the present study. Mo may exert protective effects through antioxidant pathways. A population-based and cellular study demonstrated a significant negative correlation between urinary Mo levels and systemic oxidative stress, while in vitro experiments using HK-2 cells revealed that Mo upregulated manganese superoxide dismutase (Mn-SOD) expression, thereby reducing oxidative stress [19]. Another mechanistic study found that Mo salts enhanced the activity of superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GPx), and reduced GSH, leading to decreased oxidative stress, improved vascular smooth muscle function, and increased NO synthesis, ultimately resulting in lower blood pressure [27]. In the present study, the observed negative correlation between Mo and GGT further supports the hypothesis that Mo may confer vasoprotective effects by mitigating oxidative stress. However, inconsistent findings have also been reported. An occupational exposure cohort study observed a positive association between elevated Mo levels and hypertension risk [35], and community-based studies in elderly populations similarly linked higher urinary Mo levels with an increased risk of hypertension [8]. The proposed mechanism involves secondary Cu deficiency induced by excessive Mo exposure. Excess Mo antagonises Cu, impairing the activity of Cu-dependent antioxidant enzymes such as Cu–SOD, thereby triggering oxidative stress and vascular dysfunction, ultimately contributing to elevated blood pressure [28]. Therefore, the effect of Mo on blood pressure may be dose-dependent and exhibit biphasic characteristics. In the present study, Mo showed only a negative linear association with blood pressure, possibly because the Mo exposure levels among Al smelter workers were moderate and did not reach toxic thresholds. Under these exposure conditions, Mo likely exerts primarily antioxidant and vasoprotective effects. In addition, although Sr was identified as a key metal associated with blood pressure in this study, its correlation proved unstable. In the univariate model, Sr showed a positive association with SBP; however, this relationship disappeared after adjusting for Cu and Mo. No significant association was observed between Sr and DBP. These results suggest that Sr may have only a modest effect on blood pressure, a conclusion further supported by the BKMR analysis. The relationship between Sr and blood pressure has been inconsistent across previous studies. A community-based cohort study in older adults reported that higher serum Sr concentrations were associated with a lower risk of hypertension, suggesting a potential protective effect of Sr [8], possibly through mechanisms involving the inhibition of oxidative stress and inflammatory cytokine activity [33]. Conversely, two other studies conducted among elderly populations found that elevated plasma Sr levels were associated with an increased risk of hypertension [47, 49]. Overall, research on the Sr–blood pressure association remains limited, and further validation through population-based and experimental studies is needed. Moreover, this study did not detect a significant association between overall metal mixture exposure and blood pressure. This may be due to antagonistic or offsetting interactions among metals. For instance, Cu and Sr were positively associated with blood pressure, whereas Mo showed an inverse association, possibly masking individual metal effects. Beyond these metals, other elements such as Zn, Se, and Al have also been implicated in blood pressure regulation. Previous studies have reported associations between Zn, Se, and Al and blood pressure levels [15, 40, 44]; however, no such associations were observed in the present study. These discrepancies may be attributable to differences in study populations, sample sizes, and exposure levels, and the underlying reasons require further investigation. This study provides novel epidemiological evidence linking occupational metal exposure to blood pressure dysregulation; however, several limitations should be acknowledged. First, its cross-sectional design precludes causal inference, and future longitudinal, animal, and cellular studies are required to verify these findings. Second, although multiple confounding factors were controlled for, residual confounding from unmeasured variables—such as genetic background, blood lipids, glucose levels, and dietary habits—cannot be ruled out. Follow-up studies should include these variables to improve model robustness. Third, GGT was used as the sole oxidative stress biomarker, without simultaneous assessment of other oxidative or inflammatory indicators (e.g., glutathione peroxidase, malondialdehyde, superoxide dismutase). Future research should incorporate multi-marker approaches to better characterise oxidative stress mechanisms. Finally, metal exposure levels were measured at a single time point, which may not accurately reflect long-term or cumulative exposure. Inter-individual variability in metal metabolism could also contribute to measurement error. Repeated or longitudinal measurements may improve exposure assessment precision, though such efforts require greater logistical and financial resources. 5. Conclusions This study identified that higher serum Cu levels were associated with elevated SBP, whereas higher Mo levels were associated with reduced blood pressure among occupational workers. The oxidative stress biomarker GGT mediated approximately 24–33% of these associations, suggesting that oxidative stress may serve as an important biological pathway linking metal exposure and blood pressure regulation. These findings provide novel epidemiological evidence on the role of metal exposure in occupational hypertension. Declarations Credit authorship contribution statement Youxing Li: Writing – original draft, Writing – review & editing. Yaqin Pang: Writing – original draft, Writing – review & editing. Wenxue Li: Writing – original draft, Writing – review & editing. Yufang Cen: Investigation, Data curation. Junhong Wei: Investigation, Data curation. Yinxia Lin:Methodology, Formal analysis. Jingyi Lu: Investigation, Data curation. Ahmad Razali Bin Ishak: Project administration. Mohd Shukri Bin Mohd Aris: Supervision, Project administration. Guangzi Qi: Supervision, Project administration. Funding Statement This study was supported by the Natural Science Foundation of Guangxi Province (GUIKEAB24010060), and the Guangxi Natural Science Fund aids a project financially (2025GXNSFHA069036). Declaration of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data availability Data supporting the findings of this study are openly available in Mendeley Data at: https://doi: 10.17632/5834bm3c82.1. Acknowledgments We would like to express our gratitude to all the staff and participants involved in this study. Special thanks to Dr. Mohd Shukri Bin Mohd Aris and Dr. Qi Guangzi for his guidance and suggestions on project design. References Adamus JP, Ruszczyńska A, Wyczałkowska-Tomasik A (2024) Molybdenum's Role as an Essential Element in Enzymes Catabolizing Redox Reactions: A Review. Biomolecules 14(7) https://doi.org/10.3390/biom14070869 Altobelli GG, Van Noorden S, Balato A, Cimini V (2020) Copper/Zinc Superoxide Dismutase in Human Skin: Current Knowledge. 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Supplementary Files supplementarymaterials.docx Cite Share Download PDF Status: Published Journal Publication published 11 Feb, 2026 Read the published version in Biological Trace Element Research → Version 1 posted Editorial decision: Revision requested 10 Jan, 2026 Reviews received at journal 10 Jan, 2026 Reviewers agreed at journal 02 Jan, 2026 Reviews received at journal 07 Dec, 2025 Reviewers agreed at journal 07 Dec, 2025 Reviewers agreed at journal 12 Nov, 2025 Reviewers invited by journal 12 Nov, 2025 Editor assigned by journal 12 Nov, 2025 Submission checks completed at journal 11 Nov, 2025 First submitted to journal 09 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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10:08:33","extension":"xml","order_by":34,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":163450,"visible":true,"origin":"","legend":"","description":"","filename":"bc1f9738e71c4af0a9348388ecb42b331structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8071936/v1/4b4cc67faa5fb948a27ddff8.xml"},{"id":96640451,"identity":"0f1a9e74-a97e-4667-b816-47271410b6ad","added_by":"auto","created_at":"2025-11-24 14:25:19","extension":"html","order_by":35,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":180034,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8071936/v1/8540795854704a6065809511.html"},{"id":96640416,"identity":"ab422797-7360-4e24-babe-f86b1aecf8cf","added_by":"auto","created_at":"2025-11-24 14:25:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":896596,"visible":true,"origin":"","legend":"\u003cp\u003eComponent effects of Cu, Mo and Sr on blood pressure. Estimated effects of Cu, Mo, and Sr on (a) SBP and (b) DBP with 95% confidence intervals, when all other metals were fixed at the 25th (P25), 50th (P50), and 75th (P75) percentiles. SBP: Systolic Blood Pressure；DBP:Diastolic Blood Pressure.\u003c/p\u003e","description":"","filename":"figure.1.png","url":"https://assets-eu.researchsquare.com/files/rs-8071936/v1/d1e2314e2ddfdffbf2212d61.png"},{"id":96640418,"identity":"391db056-6825-4388-9117-ea6e242331f0","added_by":"auto","created_at":"2025-11-24 14:25:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":694293,"visible":true,"origin":"","legend":"\u003cp\u003eEstimated overall combined effects of metal mixture exposure on (a) SBP and (b) DBP. The median exposure level of the combined metal mixture was used as the reference point, illustrating the overall effect trends for SBP and DBP across different exposure quantiles. SBP: Systolic Blood Pressure；DBP:Diastolic Blood Pressure.\u003c/p\u003e","description":"","filename":"figure.2.png","url":"https://assets-eu.researchsquare.com/files/rs-8071936/v1/ce34d78fa489cf7234f0ab41.png"},{"id":96640420,"identity":"47469921-7fc8-4507-ab80-dee57e4ba0bb","added_by":"auto","created_at":"2025-11-24 14:25:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2981052,"visible":true,"origin":"","legend":"\u003cp\u003eGGT-mediated associations between Cu, Mo, and blood pressure. \u003cstrong\u003e(a–c)\u003c/strong\u003e Single-metal models adjusted for all covariates;\u003cstrong\u003e (d–f) \u003c/strong\u003emulti-metal models further adjusted for Cu, Mo, and Sr concentrations after controlling for all covariates. All metal and GGT concentrations were log₁₀-transformed prior to analysis.\u003c/p\u003e","description":"","filename":"figure.3.png","url":"https://assets-eu.researchsquare.com/files/rs-8071936/v1/3bcf0498d909926d536436e5.png"},{"id":102786156,"identity":"8f95e425-11e9-4758-8212-07d352c59316","added_by":"auto","created_at":"2026-02-16 16:12:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5290018,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8071936/v1/135bb01d-c4d2-46e0-b4cd-58f6833d41bc.pdf"},{"id":96640425,"identity":"579e62b2-f475-4c23-82ce-fadedd345e9b","added_by":"auto","created_at":"2025-11-24 14:25:18","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":7739053,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-8071936/v1/048f68410ab52d96f1893ad2.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between Serum Metal Levels and Blood Pressure in Aluminium Smelting Workers: Mediating Role of Gamma-Glutamyltransferase","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eHypertension is one of the most prevalent chronic non-communicable diseases worldwide. According to the World Health Organization (WHO) Global Hypertension Report 2024, approximately 1.4 billion individuals are affected globally. Substantial evidence indicates that hypertension is a major contributor to heart disease, stroke, and premature mortality, placing a heavy burden on public health systems worldwide [9, 16, 22]. The WHO defines hypertension in adults as systolic blood pressure (SBP) \u0026ge;140 mmHg and/or diastolic blood pressure (DBP) \u0026ge;90 mmHg. However, even blood pressure levels below these thresholds, referred to as \u0026ldquo;prehypertension,\u0026rdquo; are associated with increased cardiovascular disease risk[5]. In recent years, rapid industrialisation has raised growing concern about the relationship between occupational metal exposure and hypertension[6]. Aluminium smelting, a key sector of the non-ferrous metal industry, releases substantial quantities of metallic elements during production. Workers engaged in aluminium smelting are chronically exposed to complex mixtures of metals at concentrations markedly higher than those encountered by the general population, which may elevate their risk of developing hypertension[4] Therefore, investigating the relationship between metal exposure and blood pressure in occupational populations is essential for improving cardiovascular disease prevention and control.\u003cbr\u003e\u0026nbsp; \u0026nbsp; A growing body of epidemiological evidence has demonstrated close associations between metal exposure and blood pressure regulation. For example, excessive sodium intake is strongly linked to an increased risk of hypertension[13]. An iron metabolism study reported a U-shaped relationship between serum Fe and both SBP and DBP [41] Population-based nutritional surveys have shown a significant positive correlation between serum selenium levels and hypertension, whereas no consistent associations were observed for zinc or copper [3]. In contrast, other studies have suggested that selenium may exert protective effects by reducing inflammation, oxidative stress, and vascular remodelling, thereby lowering blood pressure[38]. A prospective cohort study further reported that higher levels of copper, zinc, and molybdenum were positively associated with hypertension risk[51]. However, a multi-metal exposure study involving 13 elements (lead, antimony, arsenic, cadmium, mercury, thallium, chromium, cobalt, molybdenum, manganese, nickel, selenium, and tin) found that lead and arsenic were positively associated with hypertension, whereas molybdenum was negatively correlated with hypertension risk[31]. Occupational studies have shown that aluminium exposure is associated with a higher risk of hypertension among smelter workers [44, 48], whereas no such association was observed in the general population [45]. Taken together, these findings suggest that relationships between metals (e.g., copper , zinc, selenium, molybdenum, and aluminium) and blood pressure remain inconsistent and warrant further epidemiological confirmation. In addition, potential interactions among metals highlight the need to investigate the combined effects of mixed metal exposure on blood pressure.\u003c/p\u003e\n\u003cp\u003eGamma-glutamyl transferase (GGT) is an important enzymatic indicator of hepatic function and a sensitive biomarker of systemic oxidative stress\u0026nbsp;[37]. Increasing evidence shows that GGT is closely associated with various cardiovascular diseases\u0026nbsp;[21]. Epidemiological studies have demonstrated that in elderly Japanese men, higher serum GGT levels are positively correlated with hypertension and atherosclerosis severity, reflecting elevated oxidative stress and vascular endothelial injury\u0026nbsp;[36]. Moreover, metal exposure markedly affects GGT expression and activity. In adults, serum copper levels have shown a significant positive correlation with GGT activity[29]. Similarly, a population-based survey reported that individuals with high combined exposure to toxic metals such as lead , cadmium, mercury, and arsenic had significantly higher GGT levels than those with low exposure\u0026nbsp;[46]. Conversely, elevated urinary molybdenum levels were associated with lower GGT concentrations\u0026nbsp;[19]\u003cstrong\u003e.\u003c/strong\u003e Collectively, these findings suggest that metal exposure may modulate GGT expression and activity by inducing systemic oxidative stress, which could subsequently contribute to increased blood pressure. However, research investigating the mediating role of GGT in the relationship between metal exposure and blood pressure remains limited.\u003c/p\u003e\n\u003cp\u003eBuilding on this background, the present study applied multiple analytical approaches, including least absolute shrinkage and selection operator (LASSO) regression, generalized linear models (GLM), Bayesian kernel machine regression (BKMR), and restricted cubic splines (RCS), to identify key serum metals associated with blood pressure among aluminium smelter workers. Furthermore, the study explored the potential mediating role of oxidative stress in the relationship between metal exposure and blood pressure, aiming to provide scientific evidence for the early prevention and risk assessment of cardiovascular diseases in occupational populations.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Study population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis cross-sectional study was conducted in June 2024 among workers from a medium-sized aluminium smelter in China. Participants were excluded if they met any of the following criteria: (1) presence of neurological disorders, malignant tumours, renal diseases (including renal failure or insufficiency), chronic obstructive pulmonary disease, thyroid disorders (hyperthyroidism or hypothyroidism), diabetes mellitus, hepatitis or cerebrovascular lesions [39]; (2) significant visual or hearing impairments or poor cooperation; or (3) current use of antihypertensive medication. Ultimately, 540 male workers were enrolled in the study. Ethical approval was obtained from the Ethics Committee of Youjiang Medical University for Nationalities (Approval No. 2023070601) and the Ethics Committee of Universiti Teknologi MARA (Approval No. REC/12/2023-PG/FB/29). Written informed consent was obtained from all participants prior to enrolment, and each underwent blood sampling and blood pressure measurement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Blood Pressure Measurement and Definition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSBP and DBP were measured for each participant using a calibrated mercury sphygmomanometer. All measurements were conducted in the morning after an overnight fast. Before measurement, participants were asked to rest quietly for at least five minutes in a seated position. Three consecutive readings were obtained at intervals of more than two minutes, and the average of the three readings was recorded as the final blood pressure value, expressed in millimetres of mercury (mmHg).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Determination of Metals\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSerum concentrations of 17 metals, including sodium (Na), iron (Fe), zinc (Zn), copper (Cu), selenium (Se), vanadium (V), chromium (Cr), cobalt (Co), molybdenum (Mo), aluminium (Al), titanium (Ti), arsenic (As), strontium (Sr), antimony (Sb), barium (Ba), mercury (Hg) and thallium (Tl), were determined using inductively coupled plasma mass spectrometry (ICP-MS, Agilent 7700 Series, USA). Metal concentrations were quantified using the external standard calibration method, and accuracy was verified through spiked recovery tests (90%\u0026ndash;120%). Quality control samples were analysed concurrently to ensure analytical reliability. For measurements below the limit of detection (LOD), a value equal to LOD divided by the square root of two was assigned [11].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Determination of GGT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhole blood samples were centrifuged at 1500 \u0026times; g for 10 minutes to separate serum. The resulting serum was analysed using an automated biochemical analyser (Dierui Medical, Model CS-66B) to determine GGT levels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Covariates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCovariates included age, body mass index (BMI), ethnicity (Han Chinese or ethnic minority), marital status (living alone or cohabiting), educational attainment (junior secondary school or below, senior secondary school or above), and monthly income (\u0026lt;3000 CNY, 3000\u0026ndash;5000 CNY, \u0026gt;5000 CNY). Lifestyle factors included smoking status (Yes: smoking at least one cigarette per day for six consecutive months or more; No) and alcohol consumption (Yes: consuming more than one standard drink per week for six consecutive months or more; No).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Statistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCategorical variables were expressed as counts and percentages, while continuous variables were presented as mean \u0026plusmn; standard deviation. As serum metal and GGT concentrations exhibited skewed distributions, log10 transformations were applied prior to analysis.\u003c/p\u003e\n\u003cp\u003eSpearman\u0026rsquo;s rank correlation analysis was used to assess correlations among the 17 metals. To minimise selection bias during variable screening and address potential collinearity and confounding among metals, the LASSO regression was employed to identify key metals associated with blood pressure, adjusting for covariates. GLM was then applied to examine individual associations between metals and blood pressure. The primary metals identified were further incorporated into a BKMR model to evaluate the overall effect of mixed metal exposure on blood pressure. This analysis included: (1) exposure\u0026ndash;response function curves for individual metals (with other metals held at their median levels); (2) component effects of each metal (assessing blood pressure changes when the concentration of one metal increased from the 25th to the 75th percentile, with other metals fixed at the 25th, 50th, and 75th percentiles); and (3) combined and interactive effects among metals. RCS were used to model the dose\u0026ndash;response relationships between individual metals and blood pressure.\u003c/p\u003e\n\u003cp\u003eGLM was also used to examine associations between key metals and GGT, as well as between GGT and blood pressure. Mediation analysis was performed using the PROCESS macro in SPSS to assess the mediating effect of GGT in the relationship between metals and blood pressure, following the Baron and Kenny framework\u0026nbsp;[30]. The procedure included: (1) testing the direct effect of metals on blood pressure, the effect of metals on GGT, and the effect of GGT on blood pressure; (2) conducting a bootstrap mediation analysis (1000 resamples) to estimate indirect, direct, and total effects with corresponding 95% confidence intervals (CIs) and P-values; and (3) calculating the mediation proportion as indirect effect \u0026divide; total effect. Full mediation was considered present when the direct effect was non-significant (\u003cem\u003eP\u003c/em\u003e \u0026ge; 0.05), and partial mediation was indicated when both direct and indirect effects were significant.\u003c/p\u003e\n\u003cp\u003eAll models were adjusted for age, BMI, ethnicity, marital status, educational attainment, income, smoking status, and alcohol consumption. Statistical analyses were conducted using SPSS version 22.0 (IBM Corp., USA) and R version 4.3.2. All statistical tests were two-tailed, and \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Characteristics of the participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in \u003cstrong\u003eTable 1\u003c/strong\u003e, among the 540 participants, the mean age was 39.1 years and the mean body mass index (BMI) was 24.8 kg/m\u0026sup2;. The majority of participants were cohabiting (86.3%) and belonged to ethnic minority groups (63.9%). Regarding educational attainment, 71.3% had completed secondary education or higher. In terms of household monthly income, 63.5% of participants earned between \u0026yen;3,000 and \u0026yen;5,000. More than half of the participants were current smokers (54.4%) and reported alcohol consumption (60.4%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Baseline Characteristics of Study Population(n = 540)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"441\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53.7415%;\"\u003eCharacteristic\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 46.2585%;\"\u003eMean \u0026plusmn; SD or n (%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53.7415%;\"\u003e\u003cstrong\u003eAge (years) \u0026nbsp;\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 46.2585%;\"\u003e39.09\u0026plusmn;6.69\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53.7415%;\"\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 46.2585%;\"\u003e24.82\u0026plusmn;3.54\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53.7415%;\"\u003e\u003cstrong\u003eMarital status\u0026nbsp;\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 46.2585%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53.7415%;\"\u003eLiving without a spouse\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 46.2585%;\"\u003e74 (13.7)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53.7415%;\"\u003eLiving with a spouse\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 46.2585%;\"\u003e466 (86.3)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53.7415%;\"\u003e\u003cstrong\u003eEthnicity\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 46.2585%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53.7415%;\"\u003eHan\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 46.2585%;\"\u003e195 (36.1)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53.7415%;\"\u003eNational minority\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 46.2585%;\"\u003e345(63.9)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53.7415%;\"\u003e\u003cstrong\u003eEducation level\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 46.2585%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53.7415%;\"\u003eJunior high school and below\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 46.2585%;\"\u003e155 (28.7)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53.7415%;\"\u003eHigh school and above\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 46.2585%;\"\u003e385 (71.3)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53.7415%;\"\u003e\u003cstrong\u003eIncome\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 46.2585%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53.7415%;\"\u003e＜3000（CNY）\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 46.2585%;\"\u003e128 (23.7)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53.7415%;\"\u003e3000-5000（CNY）\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 46.2585%;\"\u003e343 (63.5)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53.7415%;\"\u003e＞5000（CNY）\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 46.2585%;\"\u003e69 (12.8)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53.7415%;\"\u003e\u003cstrong\u003eSmoking\u0026nbsp;\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 46.2585%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53.7415%;\"\u003eNo\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 46.2585%;\"\u003e246 (45.6)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53.7415%;\"\u003eYes\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 46.2585%;\"\u003e394 (54.4)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53.7415%;\"\u003e\u003cstrong\u003eDrinking\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 46.2585%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53.7415%;\"\u003eNo\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 46.2585%;\"\u003e213 (39.4)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53.7415%;\"\u003eYes\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 46.2585%;\"\u003e326 (60.4)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: BMI, body mass index is measured and defined as weight (kg)/height (m)\u003csup\u003e2\u003c/sup\u003e; Continuous variables are expressed as mean \u0026plusmn; SD; Categorical variables were presented as n (%); CNY, Chinese yuan.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Correlation Among Metal Concentrations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in \u003cstrong\u003eFigure. S1\u003c/strong\u003e, significant correlations were observed among most serum metals, with correlation coefficients (r) ranging from \u0026minus;0.27 to 0.62 (all \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). Strong positive correlations were noted between Zn and Se (r = 0.53), Zn and Cr (r = 0.47), Se and Cr (r = 0.52), V and Ti (r = 0.62), and Cr and Ti (r = 0.55). Cu exhibited significant positive correlations with Se (r = 0.37) and Cr (r = 0.36). Mo showed a positive correlation with Hg (r = 0.18) and a negative correlation with Fe (r = \u0026minus;0.14). Sr demonstrated positive correlations with Ba (r = 0.53) and Sb (r = 0.41). In contrast, Tl displayed negative correlations with Ba (r = \u0026minus;0.27), Sb (r = \u0026minus;0.18), and Sr (r = \u0026minus;0.13).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 LASSO Regression Screening for Primary Metals Associated with Blood Pressure\u003cbr\u003e\u0026nbsp;\u003c/strong\u003eTo optimise model performance and identify metals significantly associated with blood pressure, LASSO regression was applied for variable selection. As shown in \u003cstrong\u003eFigure. S2\u003c/strong\u003e, the left panel displays the coefficient paths, and the right panel presents the cross-validation curve used to determine the optimal \u0026lambda; value for predicting SBP(a\u0026ndash;b) and DBP(c\u0026ndash;d). With increasing \u0026lambda;, most variable coefficients gradually approached zero. Ultimately, Cu, Mo, and Sr were selected as the key variables in both SBP and DBP models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Association between Major Metal Concentrations and Blood Pressure (GLM)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 summarises the associations between Cu, Mo, and Sr concentrations and blood pressure based on the GLM analysis. In both the single-metal (Model 1) and multi-metal (Model 2) analyses, Cu showed a significant positive association with SBP (Model 2: \u0026beta; = 21.83, 95% CI: 5.74\u0026ndash;38.19, \u003cem\u003eP\u003c/em\u003e = 0.009). In contrast, Mo demonstrated significant negative associations with both SBP (Model 2: \u0026beta; = \u0026minus;8.97, 95% CI: \u0026minus;16.90 to \u0026minus;1.03, \u003cem\u003eP\u003c/em\u003e = 0.027) and DBP (Model 2: \u0026beta; = \u0026minus;6.75, 95% CI: \u0026minus;12.45 to \u0026minus;1.06, \u003cem\u003eP\u003c/em\u003e = 0.020). Although Sr was significantly associated with blood pressure in the single-metal analysis, this relationship was no longer significant after adjustment for other metals in the multi-metal model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003eAssociations between Cu, Mo, and Sr Concentrations and Blood Pressure\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"529\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 105px;\"\u003eMetals\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 230px;\"\u003eSBP\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 247px;\"\u003eDBP\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\u0026beta; (95% CI)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\u003cem\u003eP value\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\u0026beta; (95% CI)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\u003cem\u003eP value\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003eModel1\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003eCu\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\u003cstrong\u003e23.48 (7.66, 39.30)\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e8.54 (-2.81, 19.89)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e0.140\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003eMo\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\u003cstrong\u003e-8.02 (-16.01, -0.23)\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\u003cstrong\u003e0.049\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\u003cstrong\u003e-6.39 (-12.09, -0.69)\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\u003cstrong\u003e0.028\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003eSr\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\u003cstrong\u003e10.91 (0.53, 21.28)\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\u003cstrong\u003e0.039\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e5.28 (-2.13, 12.70)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e0.163\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003eModel 2\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003eCu\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\u003cstrong\u003e21.83 (5.47, 38.19)\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\u003cstrong\u003e0.009\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e7.89 (-3.85, 19.62)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e0.188\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003eMo\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\u003cstrong\u003e-8.97 (-16.90, -1.03)\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\u003cstrong\u003e0.027\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\u003cstrong\u003e-6.75 (-12.45, -1.06)\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\u003cstrong\u003e0.020\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003eSr\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e7.38 (-3.29, 18.01)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e0.176\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e4.10 (-3.55, 11.74\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e0.294\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote: Model 1 represents the single-metal GLM analysis; Model 2 represents the multi-metal GLM analysis. Both models were adjusted for age, BMI, marital status, ethnicity, education level, income, smoking and drinking. All metal concentrations were log₁₀-transformed prior to analysis.\u003cbr\u003e\u0026nbsp;\u003cbr\u003e\u003cstrong\u003e\u0026nbsp;3.5 BKMR Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe BKMR model was applied to further assess the combined effects of Cu, Mo, and Sr on blood pressure, with all covariates adjusted.\u0026nbsp;Figure. S3\u0026nbsp;displays the exposure\u0026ndash;response functions for individual metals in relation to SBP and DBP. Cu and Sr showed positive linear associations with both SBP and DBP, whereas Mo exhibited negative linear associations with both outcomes.\u0026nbsp;Figure. 1\u0026nbsp;illustrates the component effects of the three metals. Cu was positively associated with SBP, while Mo showed a negative association with SBP (Figure. 1a). Similarly, Mo was negatively associated with DBP, particularly when concentrations of other metals were fixed at low or median quantiles (Figure. 1b).\u003c/p\u003e\n\u003cp\u003eAnalysis of the overall mixture effects revealed no significant association between combined metal exposure and blood pressure (Figure. 2), and no evident interactions among metals (Figure. S4). Within the mixture, Cu and Mo were identified as the primary contributors to SBP (PIP = 0.390 and 0.221, respectively), whereas Mo was the major contributor to DBP (PIP = 0.365) (Table S1). Overall, the BKMR results were consistent with those from the GLM analysis, supporting the robustness of the observed associations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 RCS Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure. S5\u0026nbsp;illustrates the dose\u0026ndash;response relationships between Cu and Mo exposure levels and blood pressure, modelled using the RCS method. The number of spline knots was automatically determined based on the minimum Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) to achieve optimal model fit.\u0026nbsp;Figures. S5 (a, c, e, g)\u0026nbsp;present single-metal models adjusted for all covariates, while\u0026nbsp;Figures. S5 (b, d, f, h)\u0026nbsp;depict multi-metal models further adjusted for Cu, Mo, and Sr after covariate adjustment. Results showed that Cu levels exhibited a significant linear positive association with SBP (\u003cem\u003eP\u003c/em\u003e for overall = 0.012 and 0.026) (Figures. S5-a,b), whereas associations with DBP were not statistically significant (\u003cem\u003eP\u003c/em\u003e for overall = 0.150 and 0.181) (Figures. S5-c,d). Conversely, elevated Mo levels demonstrated linear negative associations with both SBP (\u003cem\u003eP\u003c/em\u003e for overall = 0.080 and 0.064) and DBP (\u003cem\u003eP\u003c/em\u003e for overall = 0.051 and 0.047) (Figures. S5-e, f, g, h). Although some individual associations did not reach statistical significance, the overall negative trends for Mo approached or achieved significance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.7 Association between Major Metal Concentrations and GGT (GLM)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 3 summarises the associations between Cu, Mo and Sr concentrations and GGT. In the single-metal model (Model 1), Cu exhibited a significant positive correlation with GGT (\u0026beta; = 0.71, 95% CI: 0.46 to 0.96, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), Mo showed a significant negative correlation (\u0026beta; = \u0026minus;0.19, 95% CI: \u0026minus;0.32 to \u0026minus;0.07, \u003cem\u003eP\u003c/em\u003e = 0.003), and Sr displayed a significant positive correlation (\u0026beta; = 0.26, 95% CI: 0.09 to 0.42, \u003cem\u003eP\u003c/em\u003e = 0.002). In the multi-metal model (Model 2), the positive association between Cu and GGT remained significant (\u0026beta; = 0.69, 95% CI: 0.43 to 0.94, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), as did the negative association between Mo and GGT (\u0026beta; = \u0026minus;0.22, 95% CI: \u0026minus;0.34 to \u0026minus;0.10, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), whereas the association between Sr and GGT lost statistical significance. Furthermore, in the multivariate GLM including all 17 metals, only Cu and Mo maintained statistically significant associations with GGT, whereas Sr continued to show no significant relationship ( Table S2).\u003c/p\u003e\n\u003cp\u003eTable 3 \u0026nbsp;Correlation between Cu, Mo and Sr Concentrations and GGT\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"340\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 107px;\"\u003eMetals\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 233px;\"\u003eGGT\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 158px;\"\u003e\u0026beta; (95% CI)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\u003cem\u003eP value\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 107px;\"\u003eModel1\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 158px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 107px;\"\u003eCu\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 158px;\"\u003e\u003cstrong\u003e0.71 (0.46, 0.96)\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e＜\u003cstrong\u003e0.001\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 107px;\"\u003eMo\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 158px;\"\u003e\u003cstrong\u003e-0.19 (-0.32, -0.07)\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 107px;\"\u003eSr\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 158px;\"\u003e\u003cstrong\u003e0.26 (0.09, 0.42)\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 107px;\"\u003eModel 2\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 158px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 107px;\"\u003eCu\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 158px;\"\u003e\u003cstrong\u003e0.69 (0.43, 0.94)\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e＜\u003cstrong\u003e0.001\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 107px;\"\u003eMo\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 158px;\"\u003e\u003cstrong\u003e-0.22 (-0.34, -0.10)\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e＜\u003cstrong\u003e0.001\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 107px;\"\u003eSr\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 158px;\"\u003e0.14 (-0.02, 0.31)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e0.090\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Model 1 represents the single-metal GLM analysis; Model 2 represents the multi-metal GLM analysis. Both models were adjusted for age, BMI, marital status, ethnicity, education level, income, smoking and drinking. All metal and GGT concentrations were log₁₀-transformed prior to analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.8 Association between GGT and Blood Pressure (GLM)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 4 presents the associations between GGT concentrations and blood pressure levels. After adjusting for all covariates, GGT concentrations showed significant positive associations with both SBP (\u0026beta;= 12.21, 95% CI: 6.93 to17.30, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) and DBP (\u0026beta; = 10.88, 95% CI: 7.23 to14.54, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4 \u0026nbsp;Correlation between GGT Concentration and Blood Pressure Levels\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"529\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 80px;\"\u003eVariables\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 221px;\"\u003eSBP\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 228px;\"\u003eDBP\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\u0026beta; (95% CI)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cem\u003eP value\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\u0026beta; (95% CI)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\u003cem\u003eP value\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003eGGT\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\u003cstrong\u003e12.12 (6.93, 17.30)\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\u003cstrong\u003e10.88 (7.23, 14.54)\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: The models were adjusted for age, BMI, marital status, ethnicity, education level, income, smoking and drinking. GGT concentrations were log₁₀-transformed prior to analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.9 Mediational Analysis\u003c/strong\u003e\u003cbr\u003eBased on the linear regression results, mediation analysis was performed to evaluate the potential mediating role of GGT in the associations of Cu and Mo with blood pressure outcomes (Figure. 3). Figure. 3(a-c) show the results of the single-metal models after adjusting for all covariates. In these models, Cu demonstrated a significant indirect effect on SBP (\u0026beta;= 7.721, 95% CI: 3.271 to12.891, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) with a mediation proportion of 32.7%. Similarly, Mo exhibited a significant indirect effect on SBP via GGT (\u0026beta;= \u0026minus;2.236, 95% CI: \u0026minus;4.155 to \u0026minus;0.684, \u003cem\u003eP\u003c/em\u003e = 0.002) with a mediation proportion of 27.56%, and an indirect effect on DBP with a mediation proportion of 31.56%. Figure. 3(d-f) display the results from the multi-metal model, which additionally adjusted for Cu, Mo, and Sr after controlling for all covariates. In this model, Cu\u0026rsquo;s indirect effect on SBP through GGT remained significant (\u0026beta;= 6.831, 95% CI: 2.717 to 12.116, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), corresponding to a mediation proportion of 30.96%. Meanwhile, the mediation proportions of Mo via GGT for SBP and DBP were 24.28% and 33.40%, respectively.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study identified Cu, Mo, and Sr as key metals significantly associated with blood pressure among Al smelter workers. A linear positive association was observed between Cu and SBP, whereas Mo showed a linear negative association with both SBP and DBP; The association between Sr and blood pressure was unstable. No significant association was detected between overall mixed metal exposure and blood pressure. In addition, Cu was positively correlated with GGT, Mo was negatively correlated with GGT, and GGT was associated with both SBP and DBP. Mediation analysis further demonstrated that GGT mediated the associations of Cu and Mo with blood pressure. These findings suggest that oxidative stress may serve as a critical biological pathway linking metal exposure to blood pressure regulation.\u003c/p\u003e\n\u003cp\u003eCu is an essential trace element in the human body that functions as a catalytic cofactor for various enzymes and participates in mitochondrial energy metabolism, antioxidant defence, and the synthesis of bioactive substances. Cu homeostasis is crucial for maintaining physiological balance and overall health\u0026nbsp;[7]. The association between Cu and blood pressure has attracted considerable attention. An analysis of the NHANES database reported that higher dietary Cu intake was associated with an increased risk of hypertension\u0026nbsp;[26]. Among children and adolescents, elevated serum Cu levels were significantly correlated with higher blood pressure\u0026nbsp;[24], and a case\u0026ndash;control study in adults also observed significantly higher serum Cu levels in the hypertensive group compared with controls\u0026nbsp;[50]. However, other studies found no association between serum Cu and hypertension or blood pressure levels, possibly due to the confounding effects of antihypertensive medication use\u0026nbsp;[3, 43]. Overall, existing evidence generally supports that excessive Cu exposure may increase hypertension risk. The findings of the present study are consistent with this view and, as the participants taking antihypertensive drugs were excluded, the results provide more reliable evidence. The mechanism by which Cu affects blood pressure appears closely related to oxidative stress\u0026nbsp;[10]. Under Cu deficiency, reduced activity of copper\u0026ndash;superoxide dismutase (Cu\u0026ndash;SOD) and ceruloplasmin in the antioxidant defence system leads to excessive production of reactive oxygen species (ROS)\u0026nbsp;[2, 32]. Conversely, Cu overload can catalyse Fenton-like reactions, generating hydroxyl radicals (-OH) and superoxide anions (O₂⁻)\u0026nbsp;[12, 18]. These ROS damage vascular endothelial cells, reduce nitric oxide (NO) synthesis, and cause vasoconstriction. In addition, ROS rapidly react with NO to form peroxynitrite (ONOO⁻), further impairing endothelial cells, triggering inflammation and fibrosis, and ultimately leading to vascular stiffness and elevated blood pressure\u0026nbsp;[25]. Thus, both Cu deficiency and excess can contribute to hypertension, consistent with a reported U-shaped association between Cu intake and new-onset hypertension\u0026nbsp;[14]. In this study, Cu was positively associated only with SBP, possibly because serum Cu levels in our population were within the non-deficient range. In addition, GGT levels were significantly and positively correlated with Cu. GGT is a sensitive biomarker of systemic oxidative stress that becomes upregulated under oxidative stress conditions. By degrading extracellular glutathione (GSH), GGT provides substrates for intracellular GSH resynthesis, thereby strengthening antioxidant defence\u0026nbsp;[42]. These findings further support the role of oxidative stress in mediating the effect of Cu on blood pressure.\u003c/p\u003e\n\u003cp\u003eIt is noteworthy that this study did not observe a significant association between Cu and DBP. Under physiological conditions, blood pressure levels are primarily determined by vascular compliance and peripheral vascular resistance. When compliance in large and medium-sized arteries decreases, SBP tends to rise, while diastolic pressure remains normal or slightly reduced\u0026nbsp;[20]. In contrast, when peripheral arterioles undergo sustained constriction and luminal narrowing, the resulting increase in peripheral resistance may elevate diastolic pressure\u0026nbsp;[34]. Therefore, the present findings suggest that the vasotoxic effects of Cu may predominantly affect large and medium-sized arteries rather than peripheral resistance vessels. Nevertheless, this hypothesis requires further experimental and longitudinal validation.\u003c/p\u003e\n\u003cp\u003eMo is an essential trace element that serves as a cofactor for flavin enzymes such as xanthine oxidase and xanthine dehydrogenase (XO/XDH), playing a key role in purine metabolism\u0026nbsp;[1]. The relationship between Mo and blood pressure remains controversial. Epidemiological evidence has shown a dose\u0026ndash;response relationship between elevated plasma Mo levels and a lower risk of hypertension\u0026nbsp;[23]. In middle-aged and elderly populations with metabolic syndrome, plasma Mo levels were negatively correlated with both SBP and DBP\u0026nbsp;[17]. Similarly, a cross-sectional study reported that higher urinary Mo levels were associated with a significantly lower prevalence of hypertension\u0026nbsp;[31]. These findings are consistent with the inverse associations between Mo and SBP/DBP observed in the present study. Mo may exert protective effects through antioxidant pathways. A population-based and cellular study demonstrated a significant negative correlation between urinary Mo levels and systemic oxidative stress, while in vitro experiments using HK-2 cells revealed that Mo upregulated manganese superoxide dismutase (Mn-SOD) expression, thereby reducing oxidative stress\u0026nbsp;[19]. Another mechanistic study found that Mo salts enhanced the activity of superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GPx), and reduced GSH, leading to decreased oxidative stress, improved vascular smooth muscle function, and increased NO synthesis, ultimately resulting in lower blood pressure\u0026nbsp;[27]. In the present study, the observed negative correlation between Mo and GGT further supports the hypothesis that Mo may confer vasoprotective effects by mitigating oxidative stress. However, inconsistent findings have also been reported. An occupational exposure cohort study observed a positive association between elevated Mo levels and hypertension risk\u0026nbsp;[35], and community-based studies in elderly populations similarly linked higher urinary Mo levels with an increased risk of hypertension\u0026nbsp;[8]. The proposed mechanism involves secondary Cu deficiency induced by excessive Mo exposure. Excess Mo antagonises Cu, impairing the activity of Cu-dependent antioxidant enzymes such as Cu\u0026ndash;SOD, thereby triggering oxidative stress and vascular dysfunction, ultimately contributing to elevated blood pressure\u0026nbsp;[28]. Therefore, the effect of Mo on blood pressure may be dose-dependent and exhibit biphasic characteristics. In the present study, Mo showed only a negative linear association with blood pressure, possibly because the Mo exposure levels among Al smelter workers were moderate and did not reach toxic thresholds. Under these exposure conditions, Mo likely exerts primarily antioxidant and vasoprotective effects.\u003c/p\u003e\n\u003cp\u003eIn addition, although Sr was identified as a key metal associated with blood pressure in this study, its correlation proved unstable. In the univariate model, Sr showed a positive association with SBP; however, this relationship disappeared after adjusting for Cu and Mo. No significant association was observed between Sr and DBP. These results suggest that Sr may have only a modest effect on blood pressure, a conclusion further supported by the BKMR analysis. The relationship between Sr and blood pressure has been inconsistent across previous studies. A community-based cohort study in older adults reported that higher serum Sr concentrations were associated with a lower risk of hypertension, suggesting a potential protective effect of Sr [8], possibly through mechanisms involving the inhibition of oxidative stress and inflammatory cytokine activity [33]. Conversely, two other studies conducted among elderly populations found that elevated plasma Sr levels were associated with an increased risk of hypertension [47, 49]. Overall, research on the Sr\u0026ndash;blood pressure association remains limited, and further validation through population-based and experimental studies is needed.\u0026nbsp;\u003cbr\u003eMoreover, this study did not detect a significant association between overall metal mixture exposure and blood pressure. This may be due to antagonistic or offsetting interactions among metals. For instance, Cu and Sr were positively associated with blood pressure, whereas Mo showed an inverse association, possibly masking individual metal effects. Beyond these metals, other elements such as Zn, Se, and Al have also been implicated in blood pressure regulation. Previous studies have reported associations between Zn, Se, and Al and blood pressure levels [15, 40, 44]; however, no such associations were observed in the present study. These discrepancies may be attributable to differences in study populations, sample sizes, and exposure levels, and the underlying reasons require further investigation.\u003c/p\u003e\n\u003cp\u003eThis study provides novel epidemiological evidence linking occupational metal exposure to blood pressure dysregulation; however, several limitations should be acknowledged. First, its cross-sectional design precludes causal inference, and future longitudinal, animal, and cellular studies are required to verify these findings. Second, although multiple confounding factors were controlled for, residual confounding from unmeasured variables\u0026mdash;such as genetic background, blood lipids, glucose levels, and dietary habits\u0026mdash;cannot be ruled out. Follow-up studies should include these variables to improve model robustness. Third, GGT was used as the sole oxidative stress biomarker, without simultaneous assessment of other oxidative or inflammatory indicators (e.g., glutathione peroxidase, malondialdehyde, superoxide dismutase). Future research should incorporate multi-marker approaches to better characterise oxidative stress mechanisms. Finally, metal exposure levels were measured at a single time point, which may not accurately reflect long-term or cumulative exposure. Inter-individual variability in metal metabolism could also contribute to measurement error. Repeated or longitudinal measurements may improve exposure assessment precision, though such efforts require greater logistical and financial resources.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study identified that higher serum Cu levels were associated with elevated SBP, whereas higher Mo levels were associated with reduced blood pressure among occupational workers. The oxidative stress biomarker GGT mediated approximately 24\u0026ndash;33% of these associations, suggesting that oxidative stress may serve as an important biological pathway linking metal exposure and blood pressure regulation. These findings provide novel epidemiological evidence on the role of metal exposure in occupational hypertension.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCredit authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYouxing Li: Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eYaqin Pang: Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eWenxue Li: Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eYufang Cen: Investigation, Data curation.\u003c/p\u003e\n\u003cp\u003eJunhong Wei: Investigation, Data curation.\u003c/p\u003e\n\u003cp\u003eYinxia Lin:Methodology, Formal analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eJingyi Lu: Investigation, Data curation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAhmad Razali Bin Ishak: Project administration.\u003c/p\u003e\n\u003cp\u003eMohd Shukri Bin Mohd Aris: Supervision, Project administration.\u003c/p\u003e\n\u003cp\u003eGuangzi Qi: Supervision, Project administration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Natural Science Foundation of Guangxi Province (GUIKEAB24010060), and the Guangxi Natural Science Fund aids a project financially (2025GXNSFHA069036).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData supporting the findings of this study are openly available in Mendeley Data at: https://doi: 10.17632/5834bm3c82.1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our gratitude to all the staff and participants involved in this study. Special thanks to Dr. Mohd Shukri Bin Mohd Aris and Dr. Qi Guangzi for his guidance and suggestions on project design.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdamus JP, Ruszczyńska A, Wyczałkowska-Tomasik A (2024) Molybdenum\u0026apos;s Role as an Essential Element in Enzymes Catabolizing Redox Reactions: A Review. Biomolecules 14(7) https://doi.org/10.3390/biom14070869\u003c/li\u003e\n\u003cli\u003eAltobelli GG, Van Noorden S, Balato A, Cimini V (2020) Copper/Zinc Superoxide Dismutase in Human Skin: Current Knowledge. Front Med (Lausanne) Volume 7 - 2020\u003c/li\u003e\n\u003cli\u003eBastola MM, Locatis C, Maisiak R, Fontelo P (2020) Selenium, copper, zinc and hypertension: an analysis of the National Health and Nutrition Examination Survey (2011\u0026ndash;2016). 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Environ Int 153:106538. https://doi.org/https://doi.org/10.1016/j.envint.2021.106538\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"biological-trace-element-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bter","sideBox":"Learn more about [Biological Trace Element Research](https://www.springer.com/journal/12011)","snPcode":"12011","submissionUrl":"https://submission.nature.com/new-submission/12011/3","title":"Biological Trace Element Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Metal, γ-glutamyltransferase, Blood pressure, Oxidative stress, Mediator analysis, Aluminium workers","lastPublishedDoi":"10.21203/rs.3.rs-8071936/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8071936/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOccupational exposure to multiple metals in aluminium smelting environments may influence blood pressure through oxidative stress pathways, yet evidence on specific elemental effects and mediating mechanisms remains limited. In this cross-sectional study, 540 male aluminium smelter workers in China were investigated. Serum concentrations of 17 metals were quantified using inductively coupled plasma mass spectrometry (ICP-MS), and γ-glutamyltransferase (GGT) was measured as an oxidative stress biomarker. Least absolute shrinkage and selection operator (LASSO) regression was performed to identify key metals associated with systolic blood pressure (SBP) and diastolic blood pressure (DBP), followed by generalized linear models (GLM), Bayesian kernel machine regression (BKMR), and restricted cubic splines (RCS) to assess metal\u0026ndash;blood pressure relationships. Mediation analysis was conducted using the PROCESS macro. Copper (Cu) was positively associated with SBP, while molybdenum (Mo) was inversely associated with both SBP and DBP. Cu was positively correlated with GGT, whereas Mo was negatively correlated, and GGT was positively associated with blood pressure. GGT mediated approximately 24\u0026ndash;33% of these associations. These findings suggest that oxidative stress mediates the effects of Cu and Mo on blood pressure in aluminium smelter workers, highlighting the importance of early monitoring and intervention in occupational populations.\u003c/p\u003e","manuscriptTitle":"Association between Serum Metal Levels and Blood Pressure in Aluminium Smelting Workers: Mediating Role of Gamma-Glutamyltransferase","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-24 14:25:13","doi":"10.21203/rs.3.rs-8071936/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-10T17:19:24+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-10T16:44:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"25199699251877514594749506784450122908","date":"2026-01-02T16:00:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-07T14:32:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"128234075224431348614445800255225549884","date":"2025-12-07T13:51:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"230305773952292241915618268493335735122","date":"2025-11-12T15:11:25+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-12T14:41:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-12T14:05:39+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-12T02:33:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"Biological Trace Element Research","date":"2025-11-10T02:20:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"biological-trace-element-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bter","sideBox":"Learn more about [Biological Trace Element Research](https://www.springer.com/journal/12011)","snPcode":"12011","submissionUrl":"https://submission.nature.com/new-submission/12011/3","title":"Biological Trace Element Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"abf2be21-2940-4e7d-8e4e-cc4dcc0bdcc0","owner":[],"postedDate":"November 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-16T16:09:11+00:00","versionOfRecord":{"articleIdentity":"rs-8071936","link":"https://doi.org/10.1007/s12011-026-05025-5","journal":{"identity":"biological-trace-element-research","isVorOnly":false,"title":"Biological Trace Element Research"},"publishedOn":"2026-02-11 15:58:51","publishedOnDateReadable":"February 11th, 2026"},"versionCreatedAt":"2025-11-24 14:25:13","video":"","vorDoi":"10.1007/s12011-026-05025-5","vorDoiUrl":"https://doi.org/10.1007/s12011-026-05025-5","workflowStages":[]},"version":"v1","identity":"rs-8071936","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8071936","identity":"rs-8071936","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}