TY  - JOUR
AB  - Numerous predictive models for the risk of type 2 diabetes mellitus (T2DM) exist, but a minority of them has implemented nutrition data so far, even though the significant effect of nutrition on the pathogenesis, prevention and management of T2DM has been established. Thus, in the present study, we aimed to build a predictive model for the risk of T2DM that incorporates nutrition data and calculates its predictive performance. We analysed cross-sectional data from 1591 individuals from the population-based Cooperative Health Research in the Region of Augsburg (KORA) FF4 study (2013-14) and used a bootstrap enhanced elastic net penalised multivariate regression method in order to build our predictive model and select among 193 food intake variables. After selecting the significant predictor variables, we built a logistic regression model with these variables as predictors and T2DM status as the outcome. The values of area under the receiver operating characteristic (ROC) curve, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy of our predictive model were calculated. Eleven out of the 193 food intake variables were selected for inclusion in our model, which yielded a value of area under the ROC curve of 0⋅79 and a maximum PPV, NPV and accuracy of 0⋅37, 0⋅98 and 0⋅91, respectively. The present results suggest that nutrition data should be implemented in predictive models to predict the risk of T2DM, since they improve their performance and they are easy to assess.
AU  - Katsimpris, A.*
AU  - Brahim, A.*
AU  - Rathmann, W.*
AU  - Peters, A.
AU  - Strauch, K.*
AU  - Flaquer, A.*
C1  - 62419
C2  - 50809
TI  - Prediction of type 2 diabetes mellitus based on nutrition data.
JO  - J. Nutr. Sci.
VL  - 10
PY  - 2021
SN  - 2048-6790
ER  - 

TY  - JOUR
AB  - Type 2 diabetes mellitus (T2DM) is a global public health epidemic. Diet and lifestyle changes have been demonstrated as effective measures in managing T2DM and preventing or delaying the progression from prediabetes to diabetes, yet the relationship between diet, prediabetes and diabetes is still not entirely clear. The present study aimed to further elucidate the relationship between diet, diabetes and especially prediabetes. A total of 1542 participants of the cross-sectional, population-based Cooperative Health Research in the Region of Augsburg (KORA) FF4 study (2013/2014) were included in this analysis. Dietary intake was derived using a method combining information from a FFQ and repeated 24-h food lists. Glucose tolerance status was assessed via oral glucose tolerance tests in all participants without a previous physician-confirmed diagnosis of T2DM, and was classified according to the 2003 American Diabetes Association criteria. Crude and fully adjusted multinomial logistic regression models were fitted to examine associations between diet and prediabetes, undetected diabetes mellitus (UDM) and prevalent T2DM. After adjusting for major covariates, fruit was significantly inversely and total meat, processed meat, sugar-sweetened beverages and moderate alcohol significantly associated with UDM and/or prevalent diabetes. Sex-specific analyses showed that in men, coffee was significantly inversely (OR 0·80; 95 % CI 0·67, 0·96) and heavy alcohol significantly (OR 1·84; 95 % CI 1·14, 2·95) associated with prediabetes. Our findings on diet and T2DM are consistent with current literature, while our results regarding coffee, heavy alcohol consumption and prediabetes highlight new possible targets for primary prevention of the derangement of glucose homeostasis.
AU  - Breuninger, T.
AU  - Riedl, A.
AU  - Wawro, N.
AU  - Rathmann, W.*
AU  - Strauch, K.
AU  - Quante, A.S.
AU  - Peters, A.
AU  - Thorand, B.
AU  - Meisinger, C.
AU  - Linseisen, J.
C1  - 55089
C2  - 46049
TI  - Differential associations between diet and prediabetes or diabetes in the KORA FF4 study.
JO  - J. Nutr. Sci.
VL  - 7
PY  - 2018
SN  - 2048-6790
ER  -