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CCM as a biomarker

Evaluation of CCM as a surrogate endpoint for the identification and prediction of diabetic neuropathy (NIH-DP3 multinational study).Principal Investigators:

Prof. Rayaz Malik (Professor of Medicine & Senior Consultant, WCM-Q & HMC) 
Professor Bruce Perkins (Toronto General Research Institute, Canada)
Professor Nathan Efron (Queensland University of Technology in Brisbane, Australia) 

Diabetic neuropathy is a significant clinical problem that currently has no effective therapy, and in advanced cases, it is a major cause of morbidity and mortality worldwide. The accurate detection, characterization and quantification of this condition are important to define at risk patients, anticipate deterioration, monitor progression and assess new therapies. There is thus a fundamental need to establish an objective marker that can accurately predict onset and assess progression or regression of neuropathy. We have robust data to support the thesis that the measurement of corneal nerve morphology parameters using in-vivo corneal confocal microscopy (CCM) represents an accurate ocular biomarker of diabetic neuropathy in patients with type 1 and type 2 diabetes. Importantly, this procedure could be harmonized with the annual eye examination for retinopathy, a part of current clinical practice. Results of our pooled, multinational longitudinal datasets will unequivocally determine the role of CCM as a method to determine future risk of neuropathy allowing clinicians to risk stratify their patients. It will also pave the way for evaluating putative interventions in clinical trials. Ultimately, CCM has the potential to serve as a broadly applicable means for disease identification and prognosis in clinical practice and may prove to be a valid biomarker for use in the clinical trials designed to identify disease-modifying interventions. We will study 516 (363 without DPN and 151 with DPN) subjects with T1DM and 524 subjects with T2DM (241 without DPN and 283 with DPN) at baseline. Further analyses will be performed in identical fashion to the baseline, except that case status will be determined according to longitudinal incident DPN. We assume a 70% follow-up rate (30% attrition) and as such, we anticipate including T1D Derivation (n=128), T1D Validation (n=128) and a T2D Derivation (n=84) and T2D Validation (n=84) sets.