Diabetic neuropathy is a common complication of diabetes. While multiple pathways are implicated in the pathophysiology of diabetic neuropathy, there are no specific treatments and no means to predict diabetic neuropathy onset or progression. Here, we identify gene expression signatures related to diabetic neuropathy and develop computational classification models of diabetic neuropathy progression. Microarray experiments were performed on 50 samples of human sural nerves collected during a 52-week clinical trial. A series of bioinformatics analyses identified differentially expressed genes and their networks and biological pathways potentially responsible for the progression of diabetic neuropathy. We identified 532 differentially expressed genes between patient samples with progressing or non-progressing diabetic neuropathy, and found these were functionally enriched in pathways involving inflammatory responses and lipid metabolism. A literature-derived co-citation network of the differentially expressed genes revealed gene subnetworks centred on apolipoprotein E, jun, leptin, serpin peptidase inhibitor E type 1 and peroxisome proliferator-activated receptor gamma. The differentially expressed genes were used to classify a test set of patients with regard to diabetic neuropathy progression. Ridge regression models containing 14 differentially expressed genes correctly classified the progression status of 92% of patients (P < 0.001). To our knowledge, this is the first study to identify transcriptional changes associated with diabetic neuropathy progression in human sural nerve biopsies and describe their potential utility in classifying diabetic neuropathy. Our results identifying the unique gene signature of patients with progressive diabetic neuropathy will facilitate the development of new mechanism-based diagnostics and therapies.