Addition of Inflammatory Biomarkers Did Not Improve Diabetes Prediction in the Community: The Framingham Heart Study

Abstract

Background Prior studies have reported conflicting findings with regard to the association of biomarkers in the prediction of incident type 2 diabetes. We evaluated 12 biomarkers as possible diabetes predictors in the F ramingham H eart S tudy. Methods and Results Biomarkers representing inflammation ( C ‐reactive protein, interleukin‐6, monocyte chemoattractant protein‐1, tumor necrosis factor receptor 2, osteoprotegerin, and fibrinogen), endothelial dysfunction (intercellular adhesion molecule‐1), vascular damage ( CD 40‐ligand, P ‐selectin, and lipoprotein‐associated phospholipase A2 mass and activity), and oxidative stress (urinary isoprostanes) were measured in participants without diabetes attending the Offspring seventh (n=2499) or multiethnic Omni second (n=189) examination (1998–2001). Biomarkers were log e transformed and standardized. Multivariable logistic regression tested each biomarker in association with incident diabetes at a follow‐up examination (the Offspring eighth and Omni third examination; mean 6.6 years later), with adjustment for age, sex, cohort, body mass index, fasting glucose, systolic blood pressure, high‐density lipoprotein cholesterol, triglycerides, and smoking. C statistics were evaluated with and without inflammatory markers. In 2638 participants (56% women, mean age 59 years), 162 (6.1%) developed type 2 diabetes. All biomarkers, excluding osteoprotegerin, were associated with the outcome with adjustment for age, sex, and cohort; however, none remained significant after multivariable adjustment (all P >0.05). The c statistic from the model including only clinical covariates (0.89) did not statistically significantly improve after addition of biomarkers (all P >0.10). Conclusions Biomarkers representing different inflammatory pathways are associated with incident diabetes but do not remain statistically significant after adjustment for established clinical covariates. Inflammatory biomarkers might not be an effective resource to predict type 2 diabetes in community‐based samples.