Modeling Chronic Glycemic Exposure Variables as Correlates and Predictors of Microvascular Complications of Diabetes
Year: 2006
Type: article
Study design: Observational Study
Abstract: 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 (GE(i)) 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 GE(i) 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, GE(i)) 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.
Source: Diabetes Care
DOI: 10.2337/dc06-0525
Institutions: Mayo Clinic, University of Wisconsin–Madison
Language: English
Subfield: Endocrinology, Diabetes and Metabolism
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
Funder: U.S. Public Health Service
Award: G3624835761
OpenAlex ID: W2161984474
