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Identification of individuals at risk of developing disease comorbidities represents an important task in tackling the growing personal and societal burdens associated with chronic diseases. We employed machine learning techniques to investigate to what extent data from longitudinal, nationwide Danish health registers can be used to predict individuals at high risk of developing type 2 diabetes (T2D) comorbidities. Leveraging logistic regression-, random forest- and gradient boosting models and register data spanning hospitalizations, drug prescriptions and contacts with primary care contractors from >200,000 individuals newly diagnosed with T2D, we predicted five-year risk of heart failure (HF), myocardial infarction (MI), stroke (ST), cardiovascular disease (CVD) and chronic kidney disease (CKD). For HF, MI, CVD, and CKD, register-based models outperformed a reference model leveraging canonical individual characteristics by achieving area under the receiver operating characteristic curve improvements of 0.06, 0.03, 0.04, and 0.07, respectively. The top 1,000 patients predicted to be at highest risk exhibited observed incidence ratios exceeding 4.99, 3.52, 1.97 and 4.71 respectively. In summary, prediction of T2D comorbidities utilizing Danish registers led to consistent albeit modest performance improvements over reference models, suggesting that register data could be leveraged to systematically identify individuals at risk of developing disease comorbidities.

C-reactive protein, neopterin and lactoferrin are biomarkers of atherosclerotic disease. We aimed to assess changes in these biomarkers after conservative and surgical weight loss interventions in individuals with morbid obesity, to evaluate associations between biomarker changes and changes in body mass index and HbA1c, and to study associations between changes in the biomarkers. C-reactive protein, neopterin and lactoferrin were measured before and after conservative weight loss intervention and bariatric surgery. Data were analysed with mixed models. 137 individuals (mean age 43 years) were included. Body mass index decreased from 42.1 kg/m 2 to 38.9 kg/m 2 after the conservative intervention, and further to 30.5 kg/m 2 after bariatric surgery. All biomarkers decreased after the conservative weight loss intervention. C-reactive protein and lactoferrin continued to decrease following bariatric surgery whereas neopterin remained stable. After adjustments for change in body mass index and HbA1c, all biomarkers decreased significantly after the conservative weight loss intervention, whereas none changed after bariatric surgery. There were no consistent correlations between changes in C-reactive protein, neopterin and lactoferrin. In conclusion, biomarkers of atherosclerosis decreased after weight loss interventions but had different trajectories. Neopterin, a marker related to atherosclerotic plaque stability, decreased after conservative weight loss but not following bariatric surgery.