What new technologies will lead the field of DN forward?
Translation of the knowledge of the molecular consequences of diabetes to effective therapies requires better measures of disease progression, faithful models of the molecular and cellular pathology, and application of cutting-edge technologies. Validated biomarkers and surrogate end points will allow rapid screening of clinical interventions prior to larger clinical trials, and can assess risk factors and treatment adequacy ...more »
Translation of the knowledge of the molecular consequences of diabetes to effective therapies requires better measures of disease progression, faithful models of the molecular and cellular pathology, and application of cutting-edge technologies. Validated biomarkers and surrogate end points will allow rapid screening of clinical interventions prior to larger clinical trials, and can assess risk factors and treatment adequacy for patients. Surrogate end points, if adequately validated as predictors, could enable shorter randomized clinical trials and require smaller sample sizes, factors that would accelerate acquisition of clinical information. The challenge is finding biomarkers that reliably characterize risk or the disease state among numerous biomarker candidates. Animal models exist or can be developed for specific aspects of DN, but cannot completely replicate the human clinical disease. Ready access to human samples and noninvasive imaging would allow testing hypotheses within the complexity of real people with diabetes. Without question, future advances in DN will come from emerging technologies and those not yet imagined. Will the artificial pancreas or other technologies that control the blood sugar prevent the systemic complications of diabetes? Currently, the field is poised to benefit from new imaging methods, systems biology approaches, and bioinformatics tools. Can early diabetes-induced changes in tissues and organs be detected by noninvasive imaging? Will computational models that incorporate several biomarkers and imaging results create a composite analysis that is a better measure of disease progression than the individual components? What are the indicators that predict an irreversible step in the progression of DN? How can the large amount of data generated by genomic, epigenomic, and high-throughput screening experiments be synthesized into new, testable hypotheses? DN arises at the molecular, cell, and tissue level, so novel high-throughput assays are needed to encompass these interactions. Is this an area where model organisms like C. elegans, drosophila and zebrafish could be exploited?
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