Gamma-glutamyltransferase and risk of cardiovascular disease mortality in people with and without diabetes: pooling of three British Health Surveys.
OBJECTIVE
The relation between gamma-glutamyltransferase (GGT) and mortality risk has been little explored in people with diabetes. We examined (a) the association of GGT with cardiovascular disease (CVD) and all-cause mortality in people with and without diabetes; and (b) the predictive validity observed when adding GGT to a CVD risk algorithm.


METHODS
Participants were 17,852 adults from three British cohorts representative of the general population in England (N=1) and Scotland (N=2). Follow-up was to December 2009 (Scottish cohorts) and February 2008 (English cohort). Cox models were used to compute the hazard ratio (HR) and 95% confidence interval (95% CI) for a standard deviation (SD) increase log(e)(GGT) in relation to mortality according to diabetes status. The value of adding GGT to a CVD risk engine was assessed through c-statistic and relative integrated discrimination improvement.


RESULTS
In an analytical sample of 17,852 participants, 583 (3.3%) had baseline diabetes. During 10.1 years of follow-up, there were 235 deaths from all causes (77 from CVD) in diabetics. Corresponding results for non-diabetics were 2859 and 719. The age- and sex-adjusted HR (95%CI) for a one SD increase in log(e)(GGT) in relation to CVD mortality risk in participants with diabetes was 1.43 (1.13-1.81) and in those without diabetes was 1.27 (1.18-1.37). Corresponding results for total mortality were 1.24 (1.08-1.44) and 1.30 (1.25-1.34). Thus, there was no evidence that diabetes status modified the strength of the GGT-mortality relationship (p value for interaction ≥0.16). Adding GGT to model with classical predictors only marginally enhanced the prediction of CVD in both people with and without diabetes.


CONCLUSIONS
Higher GGT levels are a risk factor for all-cause and cardiovascular disease death in people with and without diabetes. However, knowledge of GGT does not improve cardiovascular predictions beyond traditional risk factors.
South African Medical Research Council and University of Cape Town, South Africa. andre.kengne@mrc.ac.za
Andre Pascal
AP
Kengne
Sébastien
S
Czernichow
Emmanuel
E
Stamatakis
Mark
M
Hamer
G David
GD
Batty
D000328:Adult; D000368:Aged; D000465:Algorithms; D015415:Biomarkers / Q000097:blood; D002318:Cardiovascular Diseases / Q000097:blood / Q000453:epidemiology* / Q000401:mortality*; D015897:Comorbidity; D048909:Diabetes Complications / Q000097:blood / Q000453:epidemiology*; D003920:Diabetes Mellitus / Q000097:blood / Q000453:epidemiology*; D005260:Female; D005500:Follow-Up Studies; D006306:Health Surveys; D006801:Humans; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D012189:Retrospective Studies; D012307:Risk Factors; D006113:United Kingdom / Q000453:epidemiology; D005723:gamma-Glutamyltransferase / Q000097:blood*
D015415:Biomarkers; D005723:gamma-Glutamyltransferase