Background The population with diabetes in China is increasing year by year. Current research has found that either air pollution or temperature has an impact on the occurrence and development of diabetes, but the interaction between the two is unclear yet.

Objective To investigate the effects and the lag effects of air pollutants and temperature on the risk of hospital admission for type 2 diabetes in Hefei, Anhui Province from 2016 to 2019, as well as to verify potential interaction between air pollutants and temperature.

Methods This study collected hospital admission data for patients with type 2 diabetes from a tertiary hospital in Hefei, Anhui Province, and the corresponding monitoring data on air pollutants and meteorological factors from 2016 to 2019. Firstly, a distributed lag non-linear model (DLNM) was used to explore the effects of air pollutants and temperature on the risk of hospital admission for type 2 diabetes. Subsequently, a bivariate response surface model was used to explore potential interaction between temperature and various pollutants on frequency of hospital admission due to diabetes. Temperature was further divided into lower, medium, and higher levels by percentiles during the study period, and the potential interaction between air pollutants and temperature strata were verified .

Results After controlling long-term trend, seasonal trend, holiday effect, and day of the week effect, the results of single pollutant models showed that for every 10 μg·m⁻³ increase in fine particulate matter (PM_2.5), inhalable particulate matter (PM₁₀), and nitrogen dioxide (NO₂), the relative risk (RR) values for hospital admission due to type 2 diabetes were 1.032 (95%CI: 1.021, 1.043), 1.018 (95%CI: 1.008, 1.026), and 1.037 (95%CI: 1.016, 1.058), respectively; for every 1 mg·m⁻³ increase in carbon monoxide (CO), the RR value for hospital admission due to type 2 diabetes was 1.319 (95%CI: 1.163, 1.495); the increases in sulfur dioxide (SO₂), ozone (O₃), and daily average temperature showed no statistically significant impact on hospital admission due to type 2 diabetes. The results of bivariate response surface models suggested that daily average temperature and various pollutant levels spontaneously affected the risk of hospital admission for type 2 diabetes, but the stratified analysis did not find significant differences in the effect of PM_2.5 on the risk of hospital admission due to type 2 diabetes across different temperature strata.

Conclusion Increases in the concentrations of PM_2.5, PM₁₀, NO₂, and CO elevate the risk of hospital admission for type 2 diabetes. This study could not confirm the interactions between daily average temperature and various pollutants.