Discriminatory value of alanine aminotransferase for diabetes prediction: The Insulin Resistance Atherosclerosis Study

Carlos Lorenzo, A. J. Hanley, M. J. Rewers, S. M. Haffner

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Aims: To examine the incremental usefulness of adding alanine aminotransferase to established risk factors for predicting future diabetes. Methods: The study population of the Insulin Resistance Atherosclerosis Study included 724 people aged 40-69 years. We excluded people who had excessive alcohol intake or were treated with lipid-lowering agents. Incident diabetes was assessed after a mean follow-up period of 5.2 years. Results: Alanine aminotransferase had a non-linear relationship with incident diabetes (Wald chi-squared test, P < 0.001; P for linearity = 0.005) independent of demographic variables, family history of diabetes, BMI and fasting glucose; therefore, we used Youden's J statistic to dichotomize alanine aminotransferase [threshold ≥ 0.43 μkat/L ( ≥ 26 IU/l)]. Dichotomized alanine aminotransferase increased the area under the receiver-operating characteristic curve (0.805 vs. 0.823; P = 0.007) of a model that included demographic variables, family history of diabetes, BMI and fasting glucose as independent variables. The net reclassification improvement was 9.6% (95% CI 1.8-17.4; P = 0.016), and the integrated discrimination improvement was 0.031 (95% CI 0.011-0.050; P = 0.002). Dichotomized alanine aminotransferase reclassified a net of 9.6% of individuals more appropriately. Conclusions: Alanine aminotransferase may be useful for classifying individuals who are at risk of future diabetes after accounting for the effect of other risk factors, including family history, adiposity and plasma glucose.

Original languageEnglish (US)
Pages (from-to)348-355
Number of pages8
JournalDiabetic Medicine
Volume33
Issue number3
DOIs
StatePublished - Mar 1 2016

ASJC Scopus subject areas

  • Internal Medicine
  • Endocrinology, Diabetes and Metabolism
  • Endocrinology

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