Yue Xi, Susanne Breitner-Busch, Qiuling Dong, Kathrin Wolf, Marco Dallavalle, Nikolaos Nikolaou, Josef Cyrys, Harald Grallert, Birgit Linkohr, Wolfgang Rathmann, CHRISTIAN HERDER, Lars Schwettmann, Barbara Thorand, REINER JUMPERTZ VON SCHWARTZENBERG, Annette Peters
To examine the association between environmental exposures and specific type 2 diabetes (T2D) subphenotypes. We categorized T2D participants from the KORA F4 (2006–2008) and FF4 (2013–2014) study waves into three phenotypes using k-means clustering: Cluster A (insulin deficiency); Cluster B (age-related diabetes); and Cluster C (higher insulin resistance). The annual averages of fine particulate matter (PM 2.5 ) and PM 2.5 absorbance (PM 2.5 abs), annual air temperature mean (T m ) and standard deviations (T sd ), and greenness (NDVI), were assessed at participants’ residences. Covariate-adjusted mixed multinomial logistic regression models were fitted to examine the effects of environmental exposures on diabetes subphenotypes. We also calculated joint odds ratios (ORs) to estimate the additive effects of exposure mixtures. The longitudinal analysis showed that interquartile range (IQR) increases in PM 2.5 (OR = 1.29, 95 % confidence interval [CI]: 1.01, 1.64) and PM 2.5 abs (OR = 1.30, 95 % CI: 1.01, 1.67) were associated with higher odds of being in T2D Cluster C, compared to normoglycemic individuals. Furthermore, we found that IQR increases in PM 2.5 and T sd , alongside with decreases in NDVI and T m increased the odds of being in Cluster B (joint OR = 1.41, 95 % CI: 1.03, 1.93) and Cluster C (joint OR = 1.55, 95 % CI: 1.02, 2.36), while the combination of PM 2.5 abs with other exposures increased the odds of Cluster C (joint OR = 1.54, 95 % CI: 1.01, 2.33). Our study contributes to an enhanced understanding of the associations between environmental exposures and diabetes, indicating increased risks for age-related and insulin-resistant diabetes.