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Visceral adiposity (metabolic dysregulation) plays a role in chronic inflammation and contributes to many chronic diseases. However, it is unclear whether visceral adiposity index (VAI) can be used to predict arthritis-diabetes risk. In our study, we investigated this connection and explored the potential of VAI as a noninvasive and clinically feasible candidate tool. We used data from CHARLS, China Health and Retirement Longitudinal Survey of Chinese adults 45 years old or above. VAI (per increase in interquartile range and quartiles) and incident arthritis-diabetes comorbidity were analyzed using multivariate logistic regression analyses adjusted for sociodemographic, lifestyle and clinical confounders. Predictive power was assessed using receiver-operating characteristic curves. Dose-response and effect changes among subgroups (e.g., gender, smoking) were also investigated. VAI has a positive correlation with arthritis-diabetes comorbidity. After full adjustment (Model 3), each increase in interquartile range of VAI increases the risk by 10% (odds ratio = 1.10, 95% confidence interval = 1.04-1.17). Individuals in the highest VAI quartile (Q4) have a significantly higher risk (odds ratio = 4.30, 95% confidence interval = 2.18-9.30) than those in the lowest quartile (Q1), with a significant dose-response trend (P for trend 0.001). The fully adjusted model had moderate discriminative ability (area under the curve = 0.703, consistent with the full result). Subgroup analyses showed consistent positive associations between VAI and arthritis-diabetes comorbidity in female participants and nonsmokers. A nonlinear dose-response curve showed that the comorbidity risk increases with increasing VAI. Increased VAI independently contributes to a higher risk of arthritis-diabetes comorbidity in middle-aged/elderly Chinese adults, with significant nonlinearity, subgroup heterogeneity, and robustness after confounder adjustment. Targeted visceral adiposity reduction in high-risk subgroups may be a potential strategy for reducing comorbidity burden, which needs to be verified by subsequent interventional studies, and it may provide a basis for chronic disease precision prevention in this population.