Modeling Chronic Glycemic Exposure Variables as Correlates and Predictors of Microvascular Complications of Diabetes

  1. Peter J. Dyck, MD1,
  2. Jenny L. Davies, BA1,
  3. Vicki M. Clark1,
  4. William J. Litchy, MD1,
  5. P. James B. Dyck, MD1,
  6. Christopher J. Klein, MD1,
  7. Robert A. Rizza, MD2,
  8. John M. Pach, MD3,
  9. Ronald Klein, MD4,
  10. Timothy S. Larson, MD5,
  11. L. Joseph Melton III, MD6 and
  12. Peter C. O’Brien, PHD7
  1. 1Department of Neurology, Mayo Clinic College of Medicine, Rochester, Minnesota
  2. 2Division of Endocrinology, Mayo Clinic College of Medicine, Rochester, Minnesota
  3. 3Department of Ophthalmology, Mayo Clinic College of Medicine, Rochester, Minnesota
  4. 4Department of Ophthalmology, University of Wisconsin Madison, Madison, Wisconsin
  5. 5Division of Nephrology and Hypertension, Mayo Clinic College of Medicine, Rochester, Minnesota
  6. 6Division of Epidemiology, Mayo Clinic College of Medicine, Rochester, Minnesota
  7. 7Division of Biostatistics, Mayo Clinic College of Medicine, Rochester, Minnesota
  1. Address correspondence and reprint requests to Peter J. Dyck, MD, Mayo Clinic College of Medicine, Department of Neurology, 200 First St. SW, Rochester, MN 55905. E-mail: dyck.peter{at}mayo.edu

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 (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.

Footnotes

  • P.C.O.B. receives royalties from WR Medical Electronics.

    A table elsewhere in this issue shows conventional and Système International (SI) units and conversion factors for many substances.

    The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked “advertisement” in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

    • Accepted June 22, 2006.
    • Received March 8, 2006.
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  1. doi: 10.2337/dc06-0525 Diabetes Care October 2006 vol. 29 no. 10 2282-2288
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