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Updated: Apr 19, 2026

High Throughput Sequential ELISA for Validation of Biomarkers of Acute Graft-Versus-Host Disease
Published on: October 31, 2012
Circulating gamma glutamyltransferase and prediction of cardiovascular disease
Setor K Kunutsor1, Stephan J L Bakker2, Jenny E Kootstra-Ros3
1Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom; School of Clinical Sciences, University of Bristol, Bristol, United Kingdom.
Background:
The value of measuring levels of gamma glutamyltransferase (GGT) for the prediction of first cardiovascular events is uncertain. We aimed to determine whether adding information on GGT values to conventional cardiovascular risk factors is associated with improvement in prediction of CVD risk.
Methods:
Circulating GGT levels were measured at baseline in the PREVEND prospective cohort study. We included 6969 participants without a prevalent history of CVD. Hazard ratios (95% confidence intervals [CI]) and measures of risk discrimination for CVD outcomes (e.g., C-index) and reclassification (i.e., net reclassification improvement) of participants across predicted 10-year risk categories of low (<5%), intermediate (5%to <7.5%), and high (≥7.5%) risk were assessed.
Results:
During a median follow-up of 10.5 years, 735 incident CVD events were recorded. Loge GGT was linearly associated with CVD risk. In analyses adjusted for conventional plus several potential cardiovascular risk factors, the hazard ratio (95% CI) for CVD per 1 standard deviation increase in loge GGT was 1.24 (1.12-1.37; P < 0.001), which was attenuated somewhat after further adjustment for C-reactive protein 1.18 (1.06-1.30; P = 0.002). Addition of information on GGT to a CVD risk prediction model containing conventional risk factors was associated with a C-index change of 0.0003 (-0.0015 to 0.0022; P = 0.73) and a net reclassification improvement of 1.19% (-0.11-2.49%; P = 0.07) for the categories of predicted 10-year CVD risk.
Conclusions:
In the general population, adding GGT to conventional CVD risk factors is unlikely to improve prediction of first-ever cardiovascular events.
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