Diagnosis of diabetic autonomic neuropathy: A multivariate approach

Aim/hypothesis: In the diagnosis of diabetic autonomic neuropathy (DAN) various autonomic tests are used. We took a novel statistical approach to find a combination of autonomic tests that best separates normal controls from patients with DAN. Methods: Twenty-four patients with Type-1 diabetes mellitus considered as having mild to moderate DAN as well as 10 normal, nondiabetic control subjects were analysed, searching for a test or a combination of tests that would optimally discriminate Type-1 diabetes mellitus from controls. Variations of heart rate during deep breathing (AR6) and during a Valsalva manoeuvre (VR), the number of reactive sweat glands on the foot (testing sympathetic sudomotor function), and the response of human pancreatic polypeptide to hypoglycaemia [ln(ΔhPP + 1)] were evaluated. Results: Respective values for respective sensitivity and specificity values were: ΔR6, 96 and 70%; VR, 96 and 60%; sweat gland function, 71 and 90%; and ln(ΔhPP + 1), 71 and 90%. In a multivariate analysis approach a single discriminant function separating patients with Type-1 diabetes mellitus from nondiabetic controls was generated {Logit P = 288.5 - [14.7 ΔR6] - [26.6 ln(ΔhPP + 1)]}. This function allowed complete separation of patients with Type-1 diabetes mellitus from normal controls. Conclusion/interpretation: We conclude that the combined determination of ΔR6 and of ln (ΔhPP + 1) optimally separates subjects with parasympathetic impairment from normal subjects. In addition, this combination of tests may serve as a sensitive method for the assessment of DAN.