Association of Thallium with Diabetes Risk among Patients with Hearing Loss: Result from NHANES 2013 to 2018

Author:

Li Jing1,Lai Zhi-Gang1,Huang Xiao-Hua1ORCID

Affiliation:

1. Otolaryngology department, Longyan First Affiliated Hospital of Fujian Medical University, Longyan 364000, Fujian, China.

Abstract

To evaluate the correlation between thallium and diabetes risk among participants with hearing loss. This retrospective cohort study extracted related data such as demographic characteristics, lifestyle factors, and laboratory findings from the National Health and Nutrition Examination Survey (NHANES) database (2013–2018). Logistic regression analysis and interaction analysis were adopted to analyze the correlation between thallium and diabetes risk among patients with hearing loss. Then, the restricted cubic spline was employed to assess the nonlinear relationship between thallium and diabetes risk. The receiver operating characteristic curve and decision curve analysis were used to assess the predictive values of 3 multivariate models with or without thallium for diabetes risk. The Delong test was adopted to assess the significant change of the area under the curves (AUCs) upon thallium addition. A total of 425 participants with hearing loss were enrolled in the study: without diabetes group (n = 316) and diabetes group (n = 109). Patients with hearing loss in the diabetes group had significantly lower thallium (P < .05). The thallium was an independent predictor for diabetes risk after adjusting various covariates (P < .05). The restricted cubic spline (RCS) result showed that there was a linear correlation between thallium and diabetes risk (P nonlinear > .05). Finally, the receiver operating characteristic and decision curve analysis results revealed that adding thallium to the models slightly increased the performance in predicting diabetes risk but without significance in AUC change. Thallium was an independent predictor of diabetes risk among patients with hearing loss. The addition of thallium might help improve the predictive ability of models for risk reclassification. However, the conclusions should be verified in our cohort in the future due to the limitations inherent in the NHANES database.

Publisher

Ovid Technologies (Wolters Kluwer Health)

Reference45 articles.

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