Concise review: gamma-glutamyl transferase - evolution from an indiscriminate liver test to a biomarker of cardiometabolic risk

Abstract

This concise review article critically examines the recent medical literature regarding gamma glutamyl transferase (GGT) with a special emphasis on newly proposed indications for GGT use, including cardiovascular risk assessment. GGT is a ubiquitous glycosylated protein embedded in the outer surface of cell membranes, which catalyzes the transfer of glutamyl groups from various substrates and plays a key role in the antioxidant/pro-oxidant balance. In the past, the enzyme was considered a non-specific liver test. Current evidence supports the role of GGT in the assessment of portal hypertension in cystic fibrosis, porto-sinusoidal vascular disease, malignant mesothelioma, and incident type 2 diabetes and as a biomarker of cardiometabolic risk and cardiovascular disease. Several specific points including the use of GGT in hepatology as a sensitive but poorly specific test and the association of GGT with metabolic syndrome, nonalcoholic fatty liver disease and its fibrotic stages, cardiometabolic risk, chronic kidney disease, neurodegenerative disorders and dementia, idiopathic pulmonary arterial hypertension, and Corona Virus Disease 2019 (COVID-19) are addressed based on the most recent research in these fields. Putative mechanisms linking GGT with increased metabolic stress and the effects of various therapeutic interventions on GGT values are also discussed. We conclude that GGT has evolved from an indiscriminate liver test and an index of alcohol consumption to a biomarker of cardiometabolic health. The proper interpretation of GGT values (i.e., of hepatic vs. extrahepatic origin) is deeply affected by the clinical and epidemiological context. We propose that GGT may be utilized in public health campaigns, in the research arena, and in clinical practice to identify those individuals who can benefit most from the proactive preventive and therapeutic approaches, given that they are at high cardiometabolic risk.

An international Delphi consensus statement on metabolic dysfunction-associated fatty liver disease and risk of chronic kidney disease

“…Current evidence indicates that MAFLD and CKD are two risk factors for adverse cardiovascular outcomes and all-cause mortality ( 106 - 109 ). Increasing evidence recommends that patients with MAFLD should be treated early and aggressively for obesity and other coexisting cardiometabolic risk factors ( 110 , 111 ). Most available drugs that target cardiometabolic risk factors exert their actions either directly or indirectly on glucose and lipid metabolism.…”

Prevalence of hepatic steatosis and fibrosis in Turner syndrome: A prospective case–control study

“…However, multiple population‐based studies suggest that GGT is an independent risk factor for SLD, and that increased GGT levels may be associated with the severity of steatosis and fibrosis 14,15 . Elevated GGT has also been suggested to predict the development of portal hypertension in cardiometabolic disease in the general population 16–18 . It is hypothesized that GGT elevation is not just due to release from injured cells in the setting of chronic liver disease, but also accelerated synthesis of GGT and/or release of GGT into the bloodstream 16–18 .…”

“…14,15 Elevated GGT has also been suggested to predict the development of portal hypertension in cardiometabolic disease in the general population. [16][17][18] It is hypothesized that GGT elevation is not just due to release from injured cells in the setting of chronic liver disease, but also accelerated synthesis of GGT and/or release of GGT into the bloodstream. [16][17][18] GGT has low specificity, and its role in TS will need continued study, but our results suggest that it may assist in SLD risk assessment in TS.…”

Four hepatic steatosis indices in predicting quantitative computed tomography-based metabolic dysfunction-associated fatty liver disease

“…Several studies have reported a reciprocal comparison of the prediction efficacy of FLI, HSI, FSI, and ZJU index [16,18,44]. Obviously, the algorithms and equations differ from each other.…”