Biomarkers for type 1 diabetes

Abstract

Type 1 diabetes (T1D) is an autoimmune disorder characterized by the immune destruction of the insulin producing beta cells of the pancreatic islets. Autoimmunity towards pancreatic antigens results from complex interactions between multiple genes, environmental factors and the immune system. The autoimmune process may occur many years before the onset of clinical diabetes and this long asymptomatic period provides excellent opportunities for the prediction and prevention of the disease. Research in past four decades has identified a number of risk factors including susceptibility genes, gene and protein expression changes, cellular changes as well as environmental triggers, which may serve as excellent biomarkers for risk assessment. Furthermore, demographic and clinical parameters such as age and family history of diabetes and other autoimmune diseases are also important for risk assessment. Despite the identification of multiple useful biomarkers, the existing tests for T1D prediction are still imperfect and earlier biomarkers are also urgently needed. Because of the insufficient predictive power of individual risk factors, future biomarkers with better predictive power will most likely take advantage of the combinatorial power of multiple biomarkers of different nature and the integration of various biomarkers and demographic/clinical information will be the key to success.

Keywords: Type-1 diabetes; bioinformatics; biomarkers; genomics; prediction; proteomics.

Figure 1

Literature survey on systemic levels of cytokines, chemokines and other serum proteins. A: type of studies performed, B: Number of subjects involved in the study including controls and patients, C: Number of markers analyzed in each study, D: Mean expression differences observed between the controls and T1D patients.

Figure 2

Heatmap showing the mean expression ratios between T1D and age-matched controls for serum levels of cytokines, chemokines and MMPs. Each plate consists of 40 patients and 40 controls. PI: Plate 1, P2: Plate 2, P3: Plate 3, Comb: combined data from all three plates.

Figure 3

Representative charts showing distribution of mean fluorescent intensities (MFI) for selected proteins relative to their standard curves. Standards were serially diluted 1:3 to create 9 point standard curve(s). Each concentration was assayed in duplicates (blue diamonds). MFI's of the samples (dashes) were then plotted along these standards to display distribution of MFI's across the standard curve(s). Most samples have MFI's below the lower end of the linear range of their respective standard curve.