Introduction

Cardiovascular diseases (CVDs) have emerged as a significant global health concern over the past few decades, largely because of accelerated economic development, shifts in lifestyle, and the aging population1. The triglyceride-glucose (TyG) index has emerged as a critical metabolic risk factor for cardiovascular incidents among patients suffering from cardiovascular or metabolic conditions2. This relatively novel biomarker reflects insulin resistance within lipid metabolism and is derived from the levels of triglycerides and glucose3. Current research indicates that an elevated TyG index is correlated with an increased risk of cardiovascular morbidity, including heart failure4. In-depth analyses revealed that a one-unit increment in the TyG index is correlated with a 2.06-fold higher risk of coronary heart disease (CHD), as indicated by multivariate logistic regression analysis (odds ratio, 2.060; 95% confidence interval, 1.16–3.650; P = 0.013)5. Prevailing epidemiological evidence has underscored the robust association between the TyG index and metabolic disorders, such as type 2 diabetes and hypertension6,7,8,9. Furthermore, an increasing body of research has linked higher TyG indices with cardiometabolic pathologies, including coronary heart disease and coronary atherosclerosis2,6. Recently, there has been a surge of studies aiming to probe the connection between the TyG index and a spectrum of additional conditions10,11.

Numerous epidemiological studies have demonstrated that long-term exposure to metal mixtures can lead to chronic health problems12. Recent studies have revealed strong associations between blood lead (Pb) and cadmium (Cd) levels and the TyG index in men13. Current studies of the TyG index have analysed only the relationships between the TyG index and lead, mercury, and cadmium, focusing only on the effects of a single metal without considering the effects of other metals on the TyG index or the complexity of mixed metal exposures, which cannot be addressed by traditional single-metal models. Moreover, there is a paucity of clinical and epidemiological evidence exploring the association between the TyG index and metal exposure. While studies have identified associations between blood lead and cadmium levels and the TyG index in males, research into sex-specific associations in this context remains limited13. However, given that the TyG index is derived from triglyceride and glucose levels, previous studies have emphasized sex-specific associations between glucose metabolism and plasma metals and their mixtures. However, given that the TyG index is derived from triglyceride and glucose levels, previous studies have emphasized sex-specific associations between glucose metabolism and the levels of plasma metals and their mixtures14. This implies that a more in-depth exploration may be necessary to discern potential sex disparities in the relationship between metal exposure and the TyG index.

Therefore, we included 3,746 elderly individuals from the community in Yinchuan and conducted a cross-sectional study. In this study, the metals were screened via least absolute shrinkage and selection operator (LASSO) regression. A multimetal generalized linear model (GLM) was constructed to explore the associations between the metals and the TyG index. Weighted quantile sum (WQS) regression was used to explore the weights of each metal. Bayesian kernel-machine regression (BKMR) was used to explore the joint effects of mixed metal exposure and intermetal interactions. Finally, we used a general additive model (GAM) to determine the interactions between the metals. This study aimed to investigate the sex-specific associations of 13 metals with the TyG index in urine and the interactions between metals in community-dwelling elderly people.

Results

Characteristics of the study population

The study included 3764 participants, 1757 males and 2007 females. The general demographic characteristics of the study population and the results of the blood biochemical parameters are shown in Table 1. The differences in the prevalence of DBP, FBG, TC, TG, HDL-c, LDL-c, dyslipidemia, and diabetes mellitus between males and females were also not statistically significant (P ≥ 0.05). In addition, men had higher levels of smoking, alcohol consumption, and exercise frequency than women did. In contrast, the baseline TyG index and prevalence of hypertension were significantly greater in women than in men (P < 0.05).

Table 1 Comparison of baseline characteristics of 1757 male and 2007 female in a cross-sectional study.
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Urinary metal concentrations

Table S1 lists the levels of the 13 metals in the urine of the current study population and the differences in metal levels between male and female participants. All metals were corrected for urinary creatinine. The results revealed that, with the exception of cadmium, urinary men had higher levels of zinc in urine than women did, whereas women had significantly higher levels of vanadium, iron, cobalt, copper, arsenic, selenium, molybdenum, tellurium, strontium, titanium, and lead than men did, P < 0.05. To explore the associations among the 13 metals, Spearman rank association analysis was performed to calculate the Spearman association coefficients (rs) of the 13 metals. The results are shown in Fig S1 rs was greater than 0 for all 12 metals, suggesting a possible positive correlation between the 13 metals, with rs values ranging from 0.16 to 0.63.

Overall and sex-specific associations of 13 metal levels with the TyG index in GLMs

The data presented in Table 2 illustrate the β values and their corresponding 95% confidence intervals (CIs) for the comparison between the highest quartile (Q4) and the lowest quartile (Q1) in Model 1. The findings revealed β values of − 0.128 (− 0.193, − 0.062) for Fe, 0.159 (0.094, 0.158) for As, 0.075 (0.010, 0.141) for Co, 0.307 (0.242, 0.371) for Zn, 0.125 (0.059, 0.190) for Cu, 0.172 (0.105, 0.239) for Se, 0.244 (0.179, 0.308) for Mo, 0.133 (0.068, 0.198) for Te, 0.103 (0.038, 0.168) for Tl, and 0.141 (0.076, 0.206) for Sr. The trend test results indicated statistically significant p values for Fe, As, Co, Zn, Cu, Se, Mo, Te, Tl, and Sr (p value < 0.05). Adjustments were made for several covariates in Model 2, yielding β values and 95% CIs for the highest versus the lowest quartile as follows: − 0.141 (− 0.204, − 0.078) for Fe, 0.142 (0.078, 0.205) for As, 0.288 (0.226, 0.351) for Zn, 0.106 (0.043, 0.169) for Cu, 0.135 (0.072, 0.198) for Se, 0.227 (0.164, 0.290) for Mo, 0.093 (0.030, 0.156) for Te, 0.075 (0.012, 0.138) for Tl, and 0.121 (0.058, 0.184) for Sr. The trend test results revealed significant p values for Fe, As, Zn, Cu, Se, Mo, Te, Tl, and Sr (p value < 0.05).

Table 2 All population in a generalized linear model based on beta coefficients (β) and 95% confidence intervals (95% CI) of the triglyceride-glucose index versus urinary metal levels.
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The associations between the selected metals and the TyG index were examined after stratification by sex, with male participants’ data presented in Table S2. In Model 1, the β values and 95% confidence intervals (CIs) for the top quartile (Q4) versus the bottom quartile (Q1) were as follows: − 0.114 (− 0.218, − 0.011) for Fe, 0.259 (0.156, 0.362) for As, 0.164 (0.063, 0.266) for Co, 0.436 (0.335, 0.538) for Zn, 0.244 (0.144, 0.344) for Cu, 0.246 (0.146, 0.346) for Se, 0.331 (0.232, 0.430) for Mo, 0.202 (0.101, 0.304) for Te, 0.211 (0.111, 0.311) for Tl, 0.138 (0.037, 0.238) for Pb, and 0.237 (0.136, 0.337) for Sr. The trend test results revealed significant differences for As, Zn, Co, Cu, Se, Mo, Te, Tl, Pb, and Sr (p value < 0.05). After adjusting for confounders in Model 2, the β values and 95% CIs for Q4 versus Q1 were − 0.107 (− 0.200, − 0.013) for Fe, 0.215 (0.121, 0.309) for As, 0.139 (0.047, 0.231) for Co, 0.335 (0.242, 0.428) for Zn, 0.198 (0.106, 0.289) for Cu, 0.197 (0.106, 0.288) for Se, 0.272 (0.182, 0.363) for Mo, 0.150 (0.059, 0.241) for Tl, and 0.152 (0.060, 0.244) for Sr. The trend test indicated significance for As, Zn, Co, Cu, Se, Mo, Te, Tl, and Sr (p value < 0.05).

The results for the female participants are shown in Table S3. In Model 1, compared with the lowest quartile (Q1), the highest quartile (Q4) had β values and 95% CIs of − 0.158 (− 0.242, − 0.073) for Fe, 0.237 (0.153, 0.321) for Zn, 0.156 (0.070, 0.241) for Mo, and − 0.092 (− 0.180, − 0.004) for Cd. The results of the trend test revealed that the p values of Fe, Zn, and Cd were significant (p value < 0.05). After adjusting for covariates (Model 2), the β values were − 0.154 (-0.236, -0.072) for Fe, 0.218 (0.136, 0.300) for Zn, and 0.163 (0.079, 0.246) for Mo. The results of the trend test revealed that the p values of Fe, As, Zn, and Mo were significant (p value < 0.05).

Overall and sex-specific relationships of 13 metals with the TyG index via multimetal generalized linear models

The results of polymetallic generalized linear modelling revealed that in the overall population, the β values and 95% CIs were − 0.030 (− 0.042, − 0.019) for Fe, 0.035 (0.005, 0.065) for As, 0.037 (0.024, 0.050) for Zn, − 0.056 (− 0.101, − 0.011) for Se, and − 0.056 (− 0.101, − 0.011) for Mo. The values were 0.073 (0.040, 0.106) for the other metals, − 0.053 (− 0.079, − 0.029) for Cd, and 0.017 (0.005, 0.029) for Te. The results revealed that As, Zn, Mo, and Te were positively correlated with TyG index levels, whereas Fe and Cd were negatively correlated with the TyG index. The results of the trend test revealed that the p values of Fe, As, Zn, Se, Mo, Cd, and Te were significant (p value < 0.05) (Table 3).

Table 3 Beta coefficients(β) and 95% confidence intervals (95% CI) according to levels of Triglyceride-glucose index with urine metals in multi-metal model.
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In the male population, the β values and 95% CIs were as follows: Fe − 0.030 (− 0.047, − 0.014), Zn 0.042 (0.022, 0.062), Mo 0.053 (0.005, 0.100), and Cd − 0.040 (− 0.077, − 0.004). The results indicated that Fe and Cd were negatively correlated with the TyG index, whereas As, Zn, and Mo were positively correlated with the TyG index. The results of the trend test revealed that the p values of Fe, As, Zn, Se, and Mo were significant (p value < 0.05) (Table 3).

In the female population, the β values and 95% CIs were as follows: Fe − 0.024 (− 0.039, − 0.009), Se − 0.064 (− 0.124, − 0.004), Mo 0.093 (0.049, 0.136), and Cd − 0.066 (− 0.103, − 0.029). The results indicated that Fe, Cd, and Se were negatively correlated with the TyG index, whereas Zn and Mo were positively correlated with the TyG index. The results of the trend test revealed that the p values of Fe, Se, Zn, Cd, and Mo were significant (p value < 0.05). We included the seven metals with statistical significance from the multimetal generalized linear model in the subsequent statistical analysis of the overall population (Table 3).

Screening for independent metals associated with the TyG index

Since multiple covariances may exist between multiple metals, to mitigate this effect, we used the LASSO regression model to screen the 12 metals, as shown in Fig S2. When λ = 0.03667362, only seven metals, Zn, As, Mo, Fe, Cd, Se, and Te, are included in the polymetallic GLM (Fig. S2).

Overall and sex-specific associations of the 7 metal levels with the TyG index in the WQS model

To explore the weights of the seven metals in the positive and negative correlations between mixed metal exposure and TyG index levels, we developed a WQS regression model. The results revealed statistically significant negative and positive correlations between mixed exposures to the seven metals and the TyG index in the overall population after adjustment for covariates. The highest positive correlation weights were 0.579 for Zn, 0.366 for Mo, 0.040 for As, 0.008 for Se, 0.007 for Te, and < 0.001 for both Fe and Cd. Iron had the highest negative correlation weight of 0.929, followed by cadmium with 0.071, while the rest of the metals had weights of < 0.001(Fig. 1, Table S4). The positive and negative correlations between mixed metal exposure and the TyG index were also statistically significant in the male population. Among the positive correlation weights, Te had the highest weight value of 0.365, Zn had 0.346, Mo had 0.190, As had 0.044, Cd had 0.004, and Fe was < 0.001. Among the negative correlation weights, Fe had the highest weight value of 0.998, cadmium weighted 0.001, and the other five metals had weights of less than 0.001. The positive and negative correlations between mixed metal exposure and the TyG index were also statistically significant in the female population. Among the positive correlation weights, Mo had the highest weight value of 0.496, Zn had a value of 0.455, Te had a value of 0.019, Se had a value of 0.008, and the remaining three metals had positive correlation weights of less than 0.001. In the negative correlation weights, Fe had the highest weight value of 0.775, As was 0.218, Cd was 0.216, Se was 0.001, Te was 0.007, and both As and Mo had weights of < 0.001(Fig. 2, Table S4).

Fig. 1
Fig. 1The alternative text for this image may have been generated using AI.
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The weights of each metal in negative WQS model regression index for levels of TyG index. This model was adjusted for age, BMI, smoking state, alcohol consumption, eating habit, exercise frequency, hypertension, diabetes, and dyslipidemia.

Fig. 2
Fig. 2The alternative text for this image may have been generated using AI.
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Positive effect of mixed metal exposure with TyG index in the WQS model. There was no statistical significance in either overall, male, or female participants (p > 0.05). This model was adjusted for age, BMI, smoking state, alcohol consumption, eating habit, exercise frequency, hypertension, diabetes, and dyslipidemia.

Correlations between the TyG index and Fe, As, Zn, Se, Mo, Cd, and Te were assessed by RCS for the entire population as well as after stratification by sex, after adjustment for covariates

The relationships between the seven metals and the TyG index were obtained through RCS analysis on the basis of the results from LASSO regression and generalized linear modelling of polymetallic data. As shown in Fig. 3, in the whole population, As and the TyG index were linearly correlated, whereas Zn, Mo, Fe, and Cd were nonlinearly correlated with the TyG index. In the male population, the RCS results revealed that Te, Se, and As were linearly correlated with the TyG index, whereas Zn and Mo were nonlinearly correlated, as depicted in Fig S3. In the female population, as shown in Fig S4, Mo and the TyG index were linearly correlated, whereas Zn and Fe were nonlinearly correlated with the TyG index.

Fig. 3
Fig. 3The alternative text for this image may have been generated using AI.
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Correlations between TyG and iron, arsenic, zinc, selenium, molybdenum, cadmium, and tellurium in the whole population were assessed by RCS after adjustment for covariates.

Overall associations of the 7 metal levels with the TyG index in the BKMR model

We also constructed a BKMR model of the effects of mixed multimetal exposures because the WQS regression model could only obtain the weights of each metal in a single direction in mixed exposures. First, we calculated the posterior inclusion probabilities (PIPs) for each metal, which were used to evaluate the importance of each metal to the outcome of mixed metal exposure. When the PIPs were close to 1, the importance of the metal was demonstrated to be high, and vice versa. The results revealed that in the overall population, the PIPs were 0.912 for Fe, 0.912 for As, 0.912 for Zn, 0.106 for Se, 0.142 for Mo, 0.078 for Cd, and 0.098 for Te (Table S5).

Figure 4a depicts the change in the effect of increasing and decreasing the TyG index for specific metals from P25 to P75 when the concentrations of other metals were fixed at P25, P50, and P75. The P50 for iron was less than 0 when the concentrations of other metals were fixed at P25, P50, and P75, which indicated that elevated iron concentrations decreased the TyG index. Additionally, the results of the present study revealed that as the concentration of zinc increased, its positive effect on the TyG index increased. When As was at P25–P50, however, the positive effect of As on the TyG index increased, but the negative effect of As on the TyG index increased when the As concentration was at P75.

Fig. 4
Fig. 4The alternative text for this image may have been generated using AI.
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BKMR models of the incidence of TyG index for each metal and mixed exposures. (A) The effect of a single metal exposure at P75, compared to the effect of that metal exposure at P25, where all other exposures were fixed at P75. (B) Univariate exposure-response function (95% CI) for each metal, with the other fixed at its median (P50). (C) Overall effect of mixed exposures of metals fixed at different percentiles compared to when they are at the median (P50). (D) Bivariate cross-sectional effects of exposure-response functions for a single metal, where the second metal was fixed at P25, P50, and P75.

Figure 4b shows that the effects of Fe, Cd, and Zn on the TyG index slowly decreased. Moreover, the effects of Se, As, and Mo all increased but then decreased.

Figure 4c shows the overall correlation between the metal mixtures and the potential continuous results for the TyG index. When all the metals were at or above the 45th percentile, the TyG index increased compared with the 45th percentile, indicating a positive correlation between the metal mixture and the TyG index.

Finally, bivariate exposure‒response curves for the essential metals were generated (Fig. 4d). Among the seven metals, only Zn interacted with Cd, and no interaction was found with the other metals.

Overall associations of the 7 metal levels with the TyG index in the GAM model

The BKMR model suggested a potential interaction between Zn and Cd, which was further explored via the GAM. Our final analysis revealed that within certain Zn concentration ranges in the human body, increasing Cd levels could lead to a higher TyG index (Fig S5), but this interaction was not statistically significant (Table S6). Our BKMR results align with emerging evidence on essential metal mixtures.

Discussion

In the present study, we explored sex differences between combined exposure to multiple metals and the TyG index in the Yinchuan community-dwelling elderly cohort. There may be a positive association between human urinary levels of As, Zn, Mo and Te and the TyG index, whereas Fe, Se and Cd may be negatively correlated with the TyG index. There may be some sex-specific associations between polymetal exposure and the TyG index. Among the seven metals, only Zn interacted with Cd, and no interaction was found with the other metals. The results of the WQS regression model show that in the male population, Te has the highest positive correlation weight, and Fe has the highest negative correlation weight. Similarly, in the female population, Mo has the highest positive correlation weight, whereas Fe has the highest negative correlation weight. The BKMR results revealed an increasing trend in the combined effect of multiple metal exposures with increasing metal quartiles and revealed that of the seven metals, only Zn interacted with Cd, Wang et al. similarly applied BKMR to six essential metals (V, Se, Mg, Co, Ca, Mo), finding the mixture negatively associated with fasting plasma glucose in a dose-response pattern. Notably, magnesium demonstrated the strongest contribution (PIP = 1.000), consistent with our observed negative association between Fe/Se/Cd and TyG index(Wang et al., 2023). This convergence suggests that: (1) essential metal mixtures may exert coordinated effects on glucose metabolism biomarkers; (2) magnesium and iron potentially play dominant regulatory roles across different metabolic indices. Finally, through the GAM, we found that the interaction between Zn and Cd was not statistically significant.

We found a positive correlation between As and the TyG index in our study, and this correlation was detected only in the whole population. In the exposure‒outcome association in the BKMR results, a negative correlation between As and the TyG index was found when the As concentration was at P75. There is evidence from other animal experiments that exposure to other metals, such as arsenic (As), induces cardiac dysfunction in rodent models15. Low arsenite concentrations are proatherogenic, and macrophage lipid homeostasis is more sensitive to arsenite-induced perturbations than SMCs are16. The results of several epidemiological studies are consistent with data from animal models, suggesting that arsenic exposure induces cardiovascular diseases17,18,19,20,21,22. Because the TyG index can predict the occurrence of cardiovascular diseases, our results can also indirectly validate that elevated TyG index values increase the risk of developing cardiovascular diseases. We will also conduct more studies in the future to confirm the relationship between As and the TyG index.

Zn was found to increase the TyG index when zinc was elevated in urine in the present cross-sectional study and in the overall population as well as in the male and female populations. Studies have shown that high dietary zinc intake and high zinc‒iron ratios are associated with a high incidence of cardiovascular disease in women23. It has also been shown that dietary zinc intake should be increased moderately, but not excessively, to benefit cardiovascular disease24,25. The urinary zinc concentrations in the elderly individuals in our study may have been higher than the average values reported in other studies, leading to a positive correlation between zinc and the TyG index, suggesting that zinc is a risk factor for the TyG index.

In the present cross-sectional study, Mo reported that elevated zinc in urine led to elevated TyG indices in the total population as well as in both male and female populations. Studies have linked exposure to heavy metal mixtures to an increased risk of cardiovascular disease, with mixed metal exposures involving molybdenum26. Urinary tungsten was positively associated with the incidence of cardiovascular disease, but our prospective analysis in the Strong Heart Study suggested that the association may be influenced by molybdenum exposure27. The TyG index predicts the risk of cardiovascular disease. Epidemiologic evidence of metals and heart issues indirectly supports the positive correlation of the TyG index with molybdenum. Further experiments will verify these findings.

Te has been found only in the whole population, and elevated Te in urine increases the TyG index. Tellurium exposure may increase the risk of increased blood pressure28. The results of our study show that an increase in the amount of Te in the urine increases the triglyceride‒glucose index and thus increases the risk of cardiovascular disease, hypertension and other diseases.

Iron is the most abundant essential metal in the human body and is involved in various vital cellular processes. However, excess or deficient iron can be harmful to the body, leading to the development of diseases29. The results of our study, both in the whole population and in the male and female populations, indicated that elevated urinary iron levels decreased triglyceride‒glucose‒glucose indices. As small cohort study involving 287 patients with both type 2 diabetes (T2D) and coronary artery disease revealed that serum transferrin saturation (serum iron level/total iron-binding capacity (TIBC) × 100%) was negatively linearly correlated with all-cause mortality30. Findings from a Mendelian randomization study suggest that high levels of iron in humans may be associated with a reduced risk of CHD31. A large number of studies published to date with no high level of evidence support the hypothesis that iron may be associated with CVD, but the role of iron in CVD continues to be the subject of considerable debate owing to the marked variation in results32. Therefore, it is reasonable to assume that more experimental and prospective epidemiologic studies are needed to confirm the negative correlation between urinary iron levels and the triglyceride‒glucose index at this stage.

Se, nother important trace element, plays a crucial role in many physiological processes, such as antioxidation, anticancer effects, and maintenance of metabolic homeostasis33. However, our observation of a negative association between urinary selenium and the TyG index (β = − 0.066, 95% CI − 0.103, − 0.029) presents a striking contrast to recent evidence on glucose metabolism. In a parallel study of Chinese elderly, Wang et al. reported that elevated urinary selenium was positively associated with FPG (β = 0.024, 95% CI 0.014–0.034)34.Prospective studies measuring both TyG and FPG concurrently are warranted to resolve these biomarker-specific associations, particularly in populations with documented selenium status. It also contributes to maintaining the normal function of endogenous antioxidative systems, promoting thyroid hormone metabolism, and supporting immune and anti-inflammatory processes. Therefore, the associations between the selenium status and health effects have been widely recognized33. Increased urinary selenium levels reduced the TyG glucose index in our study, but this trend was observed only in the overall population and in the female population and not in the male population. The literature suggests that an optimized selenium status may benefit cardiovascular health and prevent atherosclerotic cardiovascular disease35. High physiological selenium levels in humans are associated with a reduced risk of cardiovascular disease morbidity and mortality36. The risk of all-cause and cardiovascular disease mortality in patients with chronic kidney disease is lower at higher serum selenium concentrations37. The negative association between selenium intake and CVD risk was also confirmed in an epidemiological study from China38. According to other research evidence, it is also possible that Se is negatively correlated with the TyG index. However, more molecular biology studies and prospective epidemiologic studies are needed to confirm our results.

For the first time, in this cross-sectional study, we found that urinary cadmium levels affect the TyG index and found a negative correlation between cadmium levels and the TyG index. Most animal models and epidemiological studies have shown that cadmium is a risk factor for cardiovascular disease, diabetes and other diseases21,22,26,39,40,41. In complete contrast to the results of our study, it is possible that the negative correlation effect in our study is because we chose a population of older adults over the age of 60 years, as well as the fact that cadmium elemental levels within our chosen group of older adults were lower than the average of other studies. We will use additional animal models and epidemiological studies to verify these results.

This study reveals sex-specific urinary metal-TyG index associations in Northwest China’s elderly, suggesting targeted interventions: urinary monitoring of Mo and Te in women and Zn/Fe in men could identify high-risk individuals for cardiovascular prevention. Regional exposure mitigation should prioritize Cd and As given Ningxia’s industrial profile, where BKMR models indicate cumulative metal mixtures amplify metabolic risks. Implementation should first target elderly populations in industrial regions with metal exposure profiles mirroring this cohort, though validation across broader Asian groups remains essential before wider policy adoption.

Our findings demonstrate that specific metal exposures (As, Zn, Mo, Te positively; Fe, Se, Cd negatively) significantly modulate the TyG index, with notable sex-specific patterns. Critically, the clinical relevance of TyG dysregulation is powerfully substantiated by a recent nationwide study of 9,248 Chinese adults aged ≥ 45 years. Zheng et al. established a dose-dependent relationship: each quartile increase in TyG and its obesity-composite indices (TyG-BMI, TyG-WC, TyG-WHtR) corresponded to progressively higher hypertension risk (Q4 vs. Q1 ORs: 1.80–5.09). Notably, TyG-WC demonstrated superior hypertension predictive capacity (AUC = 0.642)42. This evidence directly positions TyG elevation as a mechanistic bridge between environmental metal exposure and clinical cardiovascular endpoints.

Our research is the first to evaluate the connection between 13 urinary metals and the triglyceride glycemic index. It is also the first study to authenticate this link through various statistical models. There are several advantages in this investigation. Initially, the study sample was derived from community members in Yinchuan city. Furthermore, we examined the correlation between gender and the TyG index. We subsequently analysed the sex-specific link between metals and the TyG index, incorporating actual metal exposure data. Additionally, multiple statistical models were developed and cross-verified in the study, ensuring the reliability and scientific soundness of the results. Nonetheless, there are areas for enhancement in our research. First, the study design is cross-sectional, limiting the ability to establish causal relationships compared with prospective studies. Second, certain demographic data relied on participants’ recollection via questionnaires, hence potentially introducing recall bias. Moreover, some confounding variables pertaining to metals and the TyG index were not accounted for. Finally, the study solely utilized urine samples for assessing metal levels in a rather uniform sample population.

This study primarily reflects metal-TyG index relationships in elderly Northwest Chinese populations. Caution is warranted when extending findings across diverse Asian groups due to substantial regional variations. Key factors include differential dietary patterns (e.g., shellfish consumption affecting molybdenum levels), heterogeneous environmental exposures (industrial vs. agricultural dominance), and genetic polymorphisms influencing metal metabolism. These elements may significantly modify observed associations. Future validation in multi-regional Asian cohorts with comparable age profiles is essential before broader application.

There may be a positive correlation between human urinary levels of As, Zn, Mo and Te and the TyG index, whereas Fe, Se and Cd may be negatively correlated with the TyG index. In our study, we found that there may be some sex-specific associations between polymetallic exposure and the TyG index. Therefore, to validate our research findings, we will conduct more prospective studies with large sample sizes in the future.

Methods

Study population

The study population was drawn from the baseline of the Yinchuan community-dwelling elderly cohort. We randomly selected older adults aged 60 years and older from five districts and counties in Yinchuan city, resulting in the inclusion of 5037 participants. We excluded participants who did not provide blood and urine samples, lacked general demographic information, did not complete urine metal testing, and had other medical conditions (including cerebrovascular disease, renal disease, heart disease, vascular disease, eye disease, and neurological disease), resulting in a total of 3764 study participants (Fig. 5). This study was performed in accordance with the principles of the Declaration of Helsinki. This study was performed in accordance with the principles of the Declaration of Helsinki. All study participants were informed and signed an informed consent form, and in addition, this study passed ethical review by the Ethics Committee of Ningxia Medical University (2021–N0098).

Fig. 5
Fig. 5The alternative text for this image may have been generated using AI.
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Flowchart of the study population.

Information collection

In this study, uniformly trained investigators conducted face‒to‒face surveys with participants via self-designed questionnaires to gather general information, including sex, age, smoking habits, alcohol consumption, exercise frequency, and dietary patterns. Additionally, a health examination was performed to record participants’ height and weight. A glucometer was used to measure fasting blood glucose (FBG), whereas a mercury sphygmomanometer was used to assess blood pressure, specifically systolic blood pressure (SBP) and diastolic blood pressure (DBP). Furthermore, blood and urine samples were collected from participants after an overnight fast for biochemical analysis, which included testing for blood creatinine, urine creatinine, total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-c), and low-density lipoprotein cholesterol (LDL-c) levels. All the researchers and investigators were affiliated with the Yinchuan Center for Disease Control and Prevention and were postgraduate students from Ningxia Medical University, with a strong medical background and extensive professional experience.

Covariates

For the current study, we gathered covariates from previous questionnaires and blood biochemistry analyses. These covariates included age (as a categorical variable), sex (male or female), smoking status (never, former, current), alcohol consumption (never, usually, sometimes, daily), dietary habits (balanced diet, vegetarian diet, meat-based diet), and frequency of exercise (daily, at least once a week, sometimes, never). Body mass index (BMI) was calculated via the following formula: weight (kg) divided by height (m) squared. The triglyceride‒glucose (TyG) index was determined via the following formula: Ln[triglyceride (mg/dL) × glucose (mg/dL)/2].

Hypertension was defined by at least one of the following criteria: systolic blood pressure (SBP) ≥ 140 mmHg, diastolic blood pressure (DBP) ≥ 90 mmHg, a prior diagnosis of hypertension, or current medication use for hypertension. Diabetes was classified as meeting at least one of the following criteria: fasting blood glucose (FBG) ≥ 7.00 mmol/L, random blood glucose ≥ 11.1 mmol/L, oral glucose tolerance test (OGTT) 2-hour blood glucose ≥ 11.1 mmol/L, glycated hemoglobin (HbA1c) ≥ 6.5%, or a diagnosis of diabetes with medication. Dyslipidemia was identified by at least one of the following conditions: total cholesterol (TC) ≥ 6.22 mmol/L, triglyceride (TG) ≥ 2.26 mmol/L, low-density lipoprotein (LDL) ≥ 4.14 mmol/L, high-density lipoprotein (HDL) < 1.04 mmol/L, or a diagnosis of dyslipidemia with medication. Hypercholesterolemia was defined as having at least one of the following: LDL-C ≥ 130 mg/dL (≥ 3.4 mmol/L), HDL-C ≥ 160 mg/dL (≥ 4.1 mmol/L), or a diagnosis of hypercholesterolemia with medication.

Measurement of metals in urine and quality control

To measure the concentration of metals in the participants’ urine, we utilized inductively coupled plasma‒mass spectrometry (ICP-MS). Prior to analysis, the urine samples were subjected to a concentrated nitric acid microwave digestion process. The urine was thawed at room temperature and centrifuged at 3000 r/min for 3 min at 4 °C, and 0.5 mL of the supernatant was combined with 2.5 mL of 67% (v/v) HNO3 (Kermel, Tianjin, China). This mixture was then placed in a microwave digestion system (CEM, MARS 6 CLASSIC) for 40 min. After digestion, the solution was subjected to an electric acid driving method (LabTech, 130 °C, 150 min) to reduce the volume to 0.5 mL. The final solution was adjusted to 2.5 mL with ultrapure water (18.2 MΩ) before being analysed via ICP‒MS to quantify the metal levels. To ensure quality control of the measurement process, for every 28 urine samples analysed, we included 3 random blank samples and a urine quality control standard (ClinChek® Control urine, level II). The metals assessed in this study included vanadium (V), iron (Fe), cobalt (Co), zinc (Zn), copper (Cu), arsenic (As), selenium (Se), molybdenum (Mo), cadmium (Cd), tellurium (Te), thallium (Tl), lead (Pb), and strontium (Sr).

Statistical analysis

We used descriptive statistics to describe the general demographic characteristics of the study participants. If continuous variables were normally distributed, they were described using the mean ± standard deviation (M ± SD). If variables were not normally distributed, they were described by the median (IQR). We used percentages (n%) to describe categorical variables. The t test was used for continuous variables, and the chi-square test was used for categorical variables to compare differences in general demographic characteristics between male and female participants.

For differences in urine metal content between male and female participants in the study population, we used the Wilcoxon rank sum test to compare differences in urine metal content between the two groups. Because urine may overconcentrate or overdilute urinary metals, we corrected urinary creatinine (micrograms per gram of creatinine, µg/g Cr) for urinary metal content to account for this effect. Because the distribution of urinary metal concentrations in the population is skewed, we log-transformed the metal concentrations. To explore the associations between metals, we performed a Spearman rank correlation test for the 13 metals to calculate Spearman correlation coefficients. To explore the relationships between the levels of 13 urinary metals and the triglyceride‒glucose index, we constructed a generalized linear model (GLM) and calculated the coefficients (β) and 95% confidence intervals (95% CI). We performed the GLM for quartiles of metal concentrations using the lowest quartile (Q1) as a reference to explore the changes in β values as the number of quartiles increased. In addition, each quartile was included as a continuous variable in the GLMs, and p-trends were calculated after replacing it with the median of that quartile to test for linear trends. Model 1 was unadjusted, and Model 2 was adjusted for the following factors: age, sex, body mass index, frequency of exercise, dietary habits, smoking status, alcohol consumption, and hypertension. Because of the potential for multicollinearity between metals, we screened these 12 metals and covariates by placing them in a LASSO regression model. To explore the associations between urinary metal levels and the TyG index, we included the metals screened via a LASSO regression model as well as covariates in a multimetal generalized linear model and calculated coefficients (β) and 95% confidence intervals (95% CI).

We included statistically significant and suggestive metals from a multimetal generalized linear WQS regression model to explore the weighting of a single metal in the association of mixed metals with the TyG index. In addition, we used RCS to explore the potential nonlinear relationship between urinary metal levels and the TyG index. The RCS was adjusted for age, body mass index, exercise frequency, dietary habits, smoking status, alcohol consumption, and hypertension. We built BKMR models to explore the influence of single components on the joint effects of pollutant mixtures and assessed the influence of single pollutants on the joint effects as well as the interactions between pollutants. The WQS regression model and the BKMR model were adjusted for age, body mass index, exercise frequency, dietary habits, smoking status, alcohol consumption, and hypertension. Finally, we used a GAM to determine the interactions between the metals.

In the full statistical analysis, we stratified by sex to compare whether there were differences in associations between the overall population and male and female participants.

All the statistical analyses for this study were performed in Stata MP18.0 and R4.3.2 software. All reported p values are two-sided, with p < 0.05 indicating statistical significance.