Modeling the consistency of hypoglycemic symptoms: high variability in diabetes.

BACKGROUND: The aim of the present study was to examine symptoms of hypoglycemia, to develop a method to quantify individual differences in the consistency of symptom reporting, and to investigate which factors affect these differences.
METHODS: Participants recorded their symptoms with every episode of hypoglycemia over a 9-12-month period. A novel logistic-type latent variable model was developed to quantify the consistency of each individual's symptom complex and was used to analyze data from 59 subjects (median age, 57.5 years [range, 22-74 years], 65% male, 77% type 1 diabetes) who had experienced 19 or more hypoglycemic episodes. The association between the calculated consistency parameter and age, sex, type and duration of diabetes, and C-peptide and serum angiotensin converting enzyme concentration was examined using a generalized linear model. Analyses were performed under a Bayesian framework, using Markov chain Monte-Carlo methodology.
RESULTS: Individuals exhibited substantial differences in between-episode consistency of their symptom reports, with only a small number of individuals exhibiting high levels of consistency. Men were more consistent than women. No other factors affected consistency in patients with normal hypoglycemia awareness.
CONCLUSIONS: By using a novel stochastic model as a quantitative tool to compare the consistency of hypoglycemic symptom reporting, much greater intra-individual variability in symptom reporting was identified than has been recognized previously. This is relevant when instructing patients on identification of hypoglycemic symptoms and in interpreting symptomatic responses during experimentally induced hypoglycemia.