Predicting Diabetic Nephropathy Using a Multifactorial Genetic Model

AIMS: The tendency to develop diabetic nephropathy is, in part, genetically determined, however this genetic risk is largely undefined. In this proof-of-concept study, we tested the hypothesis that combined analysis of multiple genetic variants can improve prediction. METHODS: Based on previous reports, we selected 27 SNPs in 15 genes from metabolic pathways involved in the pathogenesis of diabetic nephropathy and genotyped them in 1274 Ashkenazi or Sephardic Jewish patients with Type 1 or Type 2 diabetes of >10 years duration. A logistic regression model was built using a backward selection algorithm and SNPs nominally associated with nephropathy in our population. The model was validated by using random "training" (75%) and "test" (25%) subgroups of the original population and by applying the model to an independent dataset of 848 Ashkenazi patients. RESULTS: The logistic model based on 5 SNPs in 5 genes (HSPG2, NOS3, ADIPOR2, AGER, and CCL5) and 5 conventional variables (age, sex, ethnicity, diabetes type and duration), and allowing for all possible two-way interactions, predicted nephropathy in our initial population (C-statistic = 0.672) better than a model based on conventional variables only (C = 0.569). In the independent replication dataset, although the C-statistic of the genetic model decreased (0.576), it remained highly associated with diabetic nephropathy (χ(2) = 17.79, p<0.0001). In the replication dataset, the model based on conventional variables only was not associated with nephropathy (χ(2) = 3.2673, p = 0.07). CONCLUSION: In this proof-of-concept study, we developed and validated a genetic model in the Ashkenazi/Sephardic population predicting nephropathy more effectively than a similarly constructed non-genetic model. Further testing is required to determine if this modeling approach, using an optimally selected panel of genetic markers, can provide clinically useful prediction and if generic models can be developed for use across multiple ethnic groups or if population-specific models are required

SUDOSCAN: A Simple, Rapid, and Objective Method with Potential for Screening for Diabetic Peripheral Neuropathy.

Clinical methods of detecting diabetic peripheral neuropathy (DPN) are not objective and reproducible. We therefore evaluated if SUDOSCAN, a new method developed to provide a quick, non-invasive and quantitative assessment of sudomotor function can reliably screen for DPN. 70 subjects (45 with type 1 diabetes and 25 healthy volunteers [HV]) underwent detailed assessments including clinical, neurophysiological and 5 standard cardiovascular reflex tests (CARTs). Using the American Academy of Neurology criteria subjects were classified into DPN and No-DPN groups. Based on CARTs subjects were also divided into CAN, subclinical-CAN and no-CAN. Sudomotor function was assessed with measurement of hand and foot Electrochemical Skin Conductance (ESC) and calculation of the CAN risk score. Foot ESC (μS) was significantly lower in subjects with DPN [n = 24; 53.5(25.1)] compared to the No-DPN [77.0(7.9)] and HV [77.1(14.3)] groups (ANCOVA p<0.001). Sensitivity and specificity of foot ESC for classifying DPN were 87.5% and 76.2%, respectively. The area under the ROC curve (AUC) was 0.85. Subjects with CAN had significantly lower foot [55.0(28.2)] and hand [53.5(19.6)] ESC compared to No-CAN [foot ESC, 72.1(12.2); hand ESC 64.9(14.4)] and HV groups (ANCOVA p<0.001 and 0.001, respectively). ROC analysis of CAN risk score to correctly classify CAN revealed a sensitivity of 65.0% and specificity of 80.0%. AUC was 0.75. Both foot and hand ESC demonstrated strong correlation with individual parameters and composite scores of nerve conduction and CAN. SUDOSCAN, a non-invasive and quick test, could be used as an objective screening test for DPN in busy diabetic clinics, insuring adherence to current recommendation of annual assessments for all diabetic patients that remains unfulfilled