BMC medicine · 2026
Quantifying lifetime risk for 1,401 infectious diseases across the diabetes spectrum using a Bayesian approach.
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Olsen Boomer B, Tristani-Firouzi Martin, Eilbeck Karen, Yandell Mark, Hernandez Edgar Javier
Аннотация
BACKGROUND: While diabetes-related complications have been widely investigated, the burden of infectious diseases across the diabetes spectrum remains relatively understudied.
METHODS: 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.
RESULTS: 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 most infections among patients with either T1D, T2D, or 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).
CONCLUSIONS: 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.
METHODS: 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.
RESULTS: 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 most infections among patients with either T1D, T2D, or 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).
CONCLUSIONS: 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.