Although many diabetes complications have been extensively studied, less is known about the burden of infectious diseases. We developed a Bayesian approach to compare infection risk across 9,476 patients with type 1 diabetes (T1D), 74,270 with type 2 diabetes (T2D), and 32,095 with prediabetes. Patients with T1D, T2D, and prediabetes had multifold increased risk for all organ system- and pathogen-based composite infection outcomes. We also quantified risk for 1,401 individual infection outcomes, finding increased risk for 880 in T1D, 1,047 in T2D, and 991 in prediabetes. Patients had increased risk for well-established diabetes-associated infections (e.g., mucormycosis) and less commonly associated infections (e.g., West Nile Virus encephalitis). Finally, we found disparities in risk across sociodemographic subgroups (i.e., age, sex, ethnicity, ancestry, and insurance status). Our comprehensive findings advance previous research by quantifying risk for wide-ranging infection outcomes across diverse patients with T1D, T2D, and prediabetes through an innovative Bayesian approach.

The authors have declared no competing interest.

B.B.O. was supported in part by the Utah Stimulating Access to Research in Residency (StARR) program under NIH Award Number 1R38HL167282-01. M.Y. and M.T.F were supported in part under NIH Award Number U01HL128711.

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IRB of the University of Utah waived ethical approval for this work.

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The datasets generated and analyzed during the current study are available in the GitHub repository at https://boomerolsen.github.io. Data are also provided within the manuscript and supplementary information files.