Davies, Jenny L.

OBJECTIVE: Chronic hyperglycemia relates to the occurrence of diabetic polyneuropathy (DPN), but has not yet been shown to relate to its overall severity In addition, the degree and duration of hyperglycemia, which measure of chronic hyperglycemia is most predictive of defined levels of severity of DPN, and which other putative risk factors are involved remain unknown. RESEARCH DESIGN AND METHODS: In a longitudinal study of 264 diabetic individuals in Rochester, MN, risk factors and other diabetic complications assessed at regular intervals during an average of approximately 7 years were tested for their association with a composite score of severity of DPN at the last examination. RESULTS: In multivariate analysis, diabetic retinopathy severity level (at last examination), mean ln(24-h proteinuria x duration of diabetes), and mean GHb were the main covariates for severity of DPN (R2 = 0.33). Excluding markers of microvessel and macrovessel disease, the independent risk factors were mean In(GHb x duration of diabetes), GHb, and type of diabetes (R2 = 0.23). CONCLUSIONS: We found that diabetic microvessel disease, chronic hyperglycemia exposure, and type of diabetes are associated with severity of DPN, and we believe these factors are implicated in its cause. Each of the five markers of microvessel disease was a strong covariate for severity of DPN. Mean GHb predicts severity of DPN better than duration of diabetes, and the latter predicts severity of DPN better than mean fasting plasma glucose. Knowing the severity of microvessel disease, the degree of chronic hyperglycemia exposure, and the type of diabetes provides useful information to evaluate whether a coexisting polyneuropathy and its severity is probably due to diabetes.

OBJECTIVE—The degree to which chronic glycemic exposure (CGE) (fasting plasma glucose [FPG], HbA1c [A1C], duration of diabetes, age at onset of diabetes, or combinations of these) is associated with or predicts the severity of microvessel complications is unsettled. Specifically, we test whether combinations of components correlate and predict complications better than individual components. RESEARCH DESIGN AND METHODS—Correlations and predictions of CGE and complications were assessed in the Rochester Diabetic Neuropathy Study, a population-based, cross-sectional, and longitudinal epidemiologic survey of 504 patients with diabetes followed for up to 20 years. RESULTS—In multivariate analysis, A1C and duration of diabetes (and to a lesser degree age at onset of diabetes but not FPG) were the main significant CGE risk covariates for complications. A derived glycemic exposure index (GEi) correlated with and predicted complications better than did individual components. Composite or staged measures of polyneuropathy provided higher correlations and better predictions than did dichotomous measures of whether polyneuropathy was present or not. Generally, the mean GEi was significantly higher with increasing stages of severity of complications. CONCLUSIONS—A combination of A1C, duration of diabetes, and age at onset of diabetes (a mathematical index, GEi) correlates significantly with complications and predicts later complications better than single components of CGE. Serial measures of A1C improved the correlations and predictions. For polyneuropathy, continuous or staged measurements performed better than dichotomous judgments. Even with intensive assessment of CGE and complications over long times, only about one-third of the variability of the severity of complications is explained, emphasizing the role of other putative risk covariates.