Abstract: Objective: To analyze the association between mixed metal exposure and sleep quality within the Guangxi natural population cohort, providing scientific evidence for developing prevention and control strategies for sleep disorders. Methods: A cross-sectional study design was implemented using baseline data from the "Prospective Cohort of Chronic Diseases in Guangxi Ethnic Minority Natural Population", enrolling 5, 486 participants. Sleep quality was evaluated with the Pittsburgh Sleep Quality Index (PSQI), while basic information was collected via standardized questionnaires and physical examinations. Concentrations of 22 metal elements in firstmorning urine samples were quantified via inductively coupled plasma mass spectrometry. The least absolute shrinkage and selection operator (LASSO) regression was employed for metal feature selection, followed by logistic regression to assess associations between the selected metals and sleep disorder risk. Furthermore, weighted quantile sum (WQS) regression, quantile g-computation (qgcomp), and Bayesian kernel machine regression (BKMR) models were integrated to systematically evaluate the joint effects of mixed metal exposure and identify the primary contributing metals. Results: The detection rate of sleep disturbance among the study participants was 29.4% (1, 613/5, 486). LASSO regression selected 10 metals associated with sleep disorder risk: titanium, manganese, zinc, strontium, molybdenum, cadmium, tin, antimony, barium, and thallium. Logistic regression results showed that concentrations of manganese (OR=1.1, 95% CI: 1.0-1.2), zinc (OR=1.2, 95% CI: 1.0-1.6), and barium (OR=1.1, 95% CI: 1.0-1.2) were significantly associated with an increased risk of sleep disorders, while antimony (OR=0.8, 95% CI: 0.7-0.9) concentration was associated with a decreased risk. Joint effect analysis revealed that the positive WQS index for mixed exposure to the 10 metals was significantly associated with an increased risk of sleep disorders (OR=1.15, 95% CI: 1.01-1.31). The primary contributing metals identified by the WQS regression, qgcomp, and BKMR models were consistent with the logistic regression findings. Stratified and sensitivity analyses further confirmed that the associations between manganese, zinc, barium, antimony, and sleep disorder risk were consistent with the logistic regression findings. Conclusion: Urinary levels of manganese, zinc, barium, and antimony are significantly associated with the risk of sleep disturbance in the general population of Guangxi. Furthermore, combined exposure to these metals may be associated with an elevated risk of sleep disturbance.