Diabetic neuropathy: a cross-sectional study of the relationships among tests of neurophysiology

Abstract

Objective: To determine the relationships among large, small, and autonomic fiber neurophysiological measures in a cross-sectional study of patients with diabetes.

Research design and methods: We assessed 130 individuals: 25 healthy subjects and 105 subjects with diabetes. Subjects were classified by the presence or absence of neuropathy by physical examination. All subjects underwent autonomic testing, nerve conduction studies, quantitative sensory testing, and nerve-axon reflex vasodilation in addition to quantifiable neurological examination and symptom scores. Correlation and cluster analysis were used to determine relationships between and among different neurophysiological testing parameters.

Results: Results of neurophysiological tests were abnormal in patients with clinical evidence of diabetic neuropathy compared with results in healthy control subjects and in those without neuropathy (P < 0.01, all tests). The correlations among individual tests varied widely, both within (r range <0.5->0.9, NS to <0.001) and between test groups (r range <0.2->0.5, NS to <0.01). A two-step hierarchical cluster analysis revealed that neurophysiological tests do not aggregate by typical "small," "large," or "autonomic" nerve fiber subtypes.

Conclusions: The modest correlation coefficients seen between the different testing modalities suggest that these techniques measure different neurophysiological parameters and are therefore not interchangeable. However, the data suggest that only a small number of neurophysiological tests are actually required to clinically differentiate individuals with neuropathy from those without. The natural clustering of both patients and healthy control subjects suggests that variations in the population will need to be considered in future studies of diabetic neuropathy.

Figures

Figure 1

A dendrogram highlighting the association between tests using a two-step hierarchical cluster analysis. Tests that cluster more closely together, such as the monofilament of the left and right legs, reveal more similar test results. Many tests follow expected clustering, such as heat-pain detection in the hand and foot, sural amplitude and velocity, and systolic and diastolic blood pressures. Other tests that were expected to be more similar, such as vibration detection and nerve conduction studies, were not.

Figure 2

This figure reports eight clusters (C1–C8) of individuals from the study. The numbers of healthy control and diabetic subjects with and without neuropathy are shown for each cluster. Clusters 1–4 are made up of healthy control subjects and subjects without neuropathy, whereas clusters 5–8 are made up of individuals with neuropathy. The tests that contributed to the formation of these clusters are listed in the lower portion of the table, with circle size demonstrating the relative weight of each test toward a particular cluster assignment: the larger the circle, the greater the weight. Black indicates a more normal (i.e., better) result, and white indicates a more abnormal (i.e., worse) result. For example, the large black circle on vibration detection at the toe indicates that a good result was the single most important factor in assigning individuals to cluster 1. Cluster 1 seems to be made up of individuals with entirely normal responses; clusters 2 and 4 are individuals with normal sensation and autonomic testing but some reduced vasomotor blood flow. Cluster 3 contains healthy individuals who have some decreased cold-pain detection. Cluster 5 indicates individuals with modest neuropathy across all neurophysiologic tests, while cluster 7 indicates those with autonomic neuropathy. Cluster 6 indicated significant neuropathy across all neurophysiologic tests with predominant “small nerve fiber” dysfunction, while cluster 8 indicates more “large nerve fiber” dysfunction.