Metal Cocktail in the Bloodstream Linked to High Blood Sugar in Aluminum Workers

Inside aluminum smelters, workers are exposed to far more than the metal they produce. The molten electrolysis cells, the recycled potlining, the dust that settles on every surface, and the fumes that rise from reduction pots carry a complex cocktail of trace elements into the body. A new study from Shanxi Province, China, now suggests that this mixed exposure may be quietly reshaping the metabolic health of the people who do the work. Researchers at Shanxi Medical University, working with colleagues at Sinopharm Tongmei General Hospital and the Sixth Hospital of Shanxi Medical University, report that elevated plasma levels of aluminum, selenium, and copper are each associated with a markedly higher likelihood of abnormally raised fasting blood glucose among male aluminum plant workers. The findings, published in Environmental Geochemistry and Health, add an occupational and environmental dimension to one of the most pressing public health problems in China and worldwide: the relentless rise of dysglycemia and type 2 diabetes.

The research team surveyed 384 male workers from a large aluminum plant in Shanxi Province between July and August 2024. Each participant provided fasting blood samples, from which the investigators measured fasting plasma glucose alongside the plasma concentrations of eight metals. Abnormally elevated blood glucose was defined according to World Health Organization diagnostic criteria as a fasting plasma glucose of at least 6.1 millimoles per liter, a threshold that captures both frank diabetes and the high-risk zone immediately beneath it. Because occupational cohorts are rarely uniform, the researchers also collected information on potential confounders, including age, body mass index, smoking, alcohol consumption, and other lifestyle and workplace characteristics, so that the statistical models could separate the metal signal from the noise of everyday habits and working conditions.

What makes this study technically interesting is not simply that it measured metals, but how it interrogated the mixture. Environmental epidemiology has long struggled with the problem of co-exposure: workers inhaling aluminum-laden dust are simultaneously exposed to a shifting ensemble of other elements, and the traditional one-metal-at-a-time analysis can miss both the joint effect and the individual contributions within it. The Shanxi team therefore deployed a battery of complementary statistical tools. Logistic regression provided the conventional single-metal estimates. Restricted cubic splines mapped the shape of each dose-response relationship without forcing it into a straight line. Least absolute shrinkage and selection operator regression, a machine-learning technique that penalizes weak predictors to zero, identified which metals mattered most. Weighted quantile sum regression estimated the overall mixture effect and apportioned it into weights for each component. Finally, Bayesian kernel machine regression modeled the mixture nonparametrically and probed for interactions between metals.

The single-metal results were striking. After adjustment for confounders, workers in the highest quartile of plasma aluminum had nearly three times the odds of abnormal fasting glucose compared with those in the lowest quartile, with an adjusted odds ratio of 2.93 and a 95 percent confidence interval of 1.16 to 7.40. Copper told a similar story: the top quartile carried an adjusted odds ratio of 2.71 (95 percent CI 1.17 to 6.27) relative to the bottom. Selenium, an element more often discussed as a protective micronutrient, was also implicated, with the highest quartile showing an odds ratio of 2.30 (95 percent CI 1.05 to 5.05). Restricted cubic spline analyses reinforced these findings, indicating positive and approximately linear dose-response relationships across the observed concentration ranges for all three metals, meaning the risk climbed steadily rather than appearing only at some threshold.

When the methods turned to the mixture as a whole, the picture sharpened rather than blurred. LASSO regression independently selected aluminum, selenium, and copper as the key metals in the panel, discarding the others as redundant. Weighted quantile sum regression then revealed a positive overall association between the mixed metal burden and abnormal blood glucose, and within that mixture aluminum contributed the largest share, with a weight of 0.434, followed closely by selenium at 0.375. In other words, roughly four-fifths of the mixture’s statistical weight in the model was carried by just two elements, one an unavoidable occupational exposure in an aluminum plant and the other an essential trace element whose excess appears anything but benign. Bayesian kernel machine regression confirmed positive exposure-response trends for the individual metals while finding no strong evidence of interactions, suggesting the metals act largely in parallel rather than amplifying one another.

The biology behind these associations is a subject of active research, and the study’s authors situate their findings within a substantial mechanistic literature. Oxidative stress is a central theme. Excess copper can catalyze the formation of reactive oxygen species, and laboratory work has shown that copper can mediate hydrogen peroxide production from the amylin peptide, a process proposed to contribute to the degeneration of insulin-producing islet cells in type 2 diabetes. Selenium presents a more paradoxical case. It is an essential component of antioxidant enzymes, yet several human studies, including randomized supplementation trials and dose-response meta-analyses, have linked higher selenium status to an increased risk of type 2 diabetes, and animal work has shown that high selenium can impair hepatic insulin sensitivity through dysregulated reactive oxygen species signaling. Aluminum, meanwhile, has been repeatedly associated in occupational cohorts with cognitive and metabolic disturbances, and earlier longitudinal work in northern China linked occupational aluminum exposure to changes in both blood pressure and blood glucose.

Context matters for interpreting the magnitude of the risk. Diabetes has become one of China’s defining health challenges, with epidemiological analyses in Nature Metabolism describing the scale of the epidemic and the Global Burden of Disease study projecting continued growth in prevalence through mid-century. Against that backdrop, identifying modifiable environmental contributors is a public health priority. Occupational cohorts are especially informative because exposure levels can be far above those seen in the general population, and because they can be reduced through engineering controls, personal protection, and workplace surveillance. The authors argue that their results justify targeted strengthening of occupational health protection in aluminum smelting, a recommendation that echoes earlier findings from the same research group on cognitive dysfunction among aluminum-exposed workers, where blood glucose itself appeared to mediate part of the effect of aluminum on cognition.

The study’s design imposes important caveats. It is cross-sectional, meaning metals and glucose were measured at the same time, so it cannot establish whether the metal exposures caused the metabolic disturbance or whether some shared factor, such as diet, kidney function, or disease-related changes in metal handling, drives the association. Reverse causation is a genuine possibility in trace element epidemiology, since impaired glucose metabolism can alter the transport and excretion of metals. The cohort was also limited to male workers at a single plant, which strengthens internal comparability but limits generalizability to women, to other industries, and to the wider population. Confidence intervals, while excluding the null, are wide for some estimates, reflecting the moderate sample size. The authors declare no competing financial interests, and the study was approved by the Medical Ethics Committee of Shanxi Medical University with written informed consent from all participants, but the data contain sensitive personal information and are available only from the corresponding author.

Even with those limitations, the methodological rigor of the mixture approach gives the findings unusual weight for a single cross-sectional study. Four independent techniques converged on the same trio of metals, and the dose-response curves were consistent with a monotonic relationship rather than a statistical fluke at one end of the distribution. For occupational physicians, the practical message is that glucose screening may deserve a place alongside the neurological and respiratory monitoring already routine in smelters, and that biomonitoring of aluminum, selenium, and copper could help identify workers whose cumulative metal burden places them at metabolic risk. For the broader field of environmental health, the study adds to a growing consensus that human exposure is inherently multi-element and that the health effects of any single metal can only be understood in the context of the mixture that accompanies it. As aluminum remains indispensable to transport, construction, and the renewable energy transition, the challenge is to keep the benefits of the metal while limiting its metabolic costs to the people who make it.

Subject of Research: Mixed metal exposure and abnormal fasting blood glucose among aluminum factory workers

Article Title: The association between mixed exposure to multiple metals and the risk of abnormally elevated blood glucose among aluminum factory workers

Article References: Zhang, Y., Xin, Y., Li, M., Xu, H., Guo, X., Wang, L., Zhang, H., Yin, J., Lu, X., Pan, B., & Song, J. (2026). The association between mixed exposure to multiple metals and the risk of abnormally elevated blood glucose among aluminum factory workers. Environmental Geochemistry and Health, 48(14), Article 564. https://doi.org/10.1007/s10653-026-03460-w

Image Credits: AI Generated

DOI: 10.1007/s10653-026-03460-w

Keywords: aluminum, selenium, copper, heavy metals, blood glucose, occupational health, aluminum smelting, metal mixture, diabetes risk, Bayesian kernel machine regression, weighted quantile sum regression, Shanxi