Development of a predictive model for symptomatic neuropathy in diabetes

In evaluating therapeutic interventions aimed at preventing diabetic neuropathy, choosing a suitable measure of neural function is difficult, partly because the relation between most available objective measures and the development of symptomatic neuropathy (SN) is unclear. Using data from 67 diabetic patients, we developed a linear logistic regression model to assess the relationship between SN and a set of neural measures, including dark-adapted pupil size; pupillary latency; heart rate; a measure of respiratory sinus arrhythmia (R); the Valsalva ratio; and conduction velocities for the peroneal, median motor, and median sensory nerves. Models allowed for possible effects related to age, sex, duration and type of diabetes, glycosylated hemoglobin, and adiposity. Thirty-two of the patients reported SN (autonomic and/or sensorimotor). The best-fitting model for predicting the presence of any SN included only heart rate, duration of disease, and R. Exclusion of duration (P less than .01), or heart rate (P less than .05), or R (P less than .001) significantly impaired the fit of the model. To evaluate the temporally predictive power of the model, nine of the asymptomatic patients were reinterviewed 2 yr later by the same interviewer, who was unaware of the results of the modeling. Four of five to whom the model had assigned high probability of symptoms had indeed developed SN during the follow-up period, whereas none of the four assigned low probability had developed SN (P less than .05). Thus it seems that a measure of respiratory sinus arrhythmia provides an index of neural function strongly related to SN, and our follow-up data suggest that diminished R can be used to predict the development of SN in diabetes.