{"id":63005,"date":"2024-12-30T10:18:34","date_gmt":"2024-12-30T10:18:34","guid":{"rendered":"https:\/\/biomedpharmajournal.org\/?p=63005"},"modified":"2025-01-07T04:14:53","modified_gmt":"2025-01-07T04:14:53","slug":"evaluation-of-gamma-glutamine-transferase-%ce%b3-gt-as-a-marker-of-alcohol-abuse","status":"publish","type":"post","link":"https:\/\/biomedpharmajournal.org\/staging\/vol17no4\/evaluation-of-gamma-glutamine-transferase-%ce%b3-gt-as-a-marker-of-alcohol-abuse\/","title":{"rendered":"Evaluation of Gamma-Glutamine Transferase (\u03b3-GT) as a Marker of Alcohol Abuse."},"content":{"rendered":"\n

Introduction<\/strong><\/p>\n\n\n\n

Gamma-glutamyl transferase (GGT) is an enzyme that is compared with ALP levels to distinguish between skeletal disease and liver disease. Elevations of this enzyme occur in several disparate clinical situations, including all manners of liver disease fatty liver, viral hepatitis, bile duct obstruction, and most drug reactions involving the liver.1<\/sup>. The World Health Organization reports that 2 billion global alcoholics and 76.3 million with alcohol use disorders, causing significant morbidity and mortality worldwide 2<\/sup>. Alcoholic beverages have been a staple in human societies since the beginning of recorded history 3<\/sup>. Experimental studies support a causal relation between heavy alcohol use and increased GGT levels, but also in experimental settings response of GGT to alcohol varies depending on individual characteristics, such as sex, age, and previous drinking habits 4<\/sup>. Alcohol consumption is linked to increased mortality rates from cardiovascular disease, cirrhosis, suicide, accidental death, and certain cancers in the oropharynx, larynx, esophagus, liver, lungs, colon, and rectum 5<\/sup>. Chronic alcohol consumption may negatively impact the peripheral immune system, potentially leading to increased cytokine production and difficulty in adherence to antiretroviral treatment regimens 6<\/sup>. Alcohol consumption among young people poses serious health, welfare, and life hazards, attracting public, policy, and research attention 7<\/sup>. Ethanol may reduce coronary heart disease risk, but benefits are limited to middle-aged and older individuals in high-risk populations and may be confined to specific subgroups 8<\/sup>.<\/p>\n\n\n\n

Alcoholism and related medical disorders are increasing globally, but patients with hazardous drinking practices often go unnoticed. Objective laboratory tests are needed to respond sensitively to excessive alcohol intake, but none have provided enough diagnostic accuracy 9<\/sup>. Gamma-glutamyl transferase (\u03b3-GT), a microsomal enzyme, has been found to be useful in detecting alcohol abuse by measuring its activity in serum 10<\/sup>.<\/p>\n\n\n\n

Gamma-glutamyl transferase, a membrane-bound glycoprotein enzyme, is used to measure excessive alcohol intake, with reported sensitivities ranging from 15% to 85%. Studies show a positive correlation between alcohol consumption and GGT activities11<\/sup>.<\/p>\n\n\n\n

Alcohol abuse monitoring and rehabilitation programs rely on biomarkers such as chemical, hematological, and clinical markers. These markers, including methanol level, reticulocyte count, and CDT (carbohydrate-deficient transferrin), indicate alcohol intake and liver enzyme activity, as well as non-enzymatic clinical markers like uric acid and HDL. Common markers have modest sensitivity and specificity, limited specificity due to their impact on common medications and conditions like non-alcoholic liver disease, hepatic congestion, and biliary disease 12<\/sup>.<\/p>\n\n\n\n

Chronic disease epidemiologists and researchers outside alcohol epidemiology may misinterpret studies on alcohol consumption and health outcomes, as they may not understand the methodological subtleties involved in measuring alcohol consumption 13<\/sup>.<\/p>\n\n\n\n

The\nstudy aims to investigate the role of gamma-glutamyl transferase (GGT) activity\nin heavy and moderate alcoholic male drinkers and controls, categorized by age\ngroup.<\/p>\n\n\n\n

Materials and Methods<\/strong><\/p>\n\n\n\n

The study, conducted in\nKarnataka, India, was a prospective cross-sectional study conducted from\nNovember 2009 to April 2010 at Padmashree Diagnostic Centre, Vijayanagar,\nBangalore and Sneha Mano Vikasa Kendra De-Addiction Center, Tumkur. The study involved 100\nparticipants, divided into two groups based on alcohol consumption: group A, 50\nalcohol abuse cases admitted for detoxification at Sneha Mano Vikasa Kendra\nDe-addiction Center, Tumkur, and group B, 50 healthy male volunteers.<\/p>\n\n\n\n

Group A consisted of\nindividuals aged 40-80 with over 5 years of alcohol consumption, classified as\nheavy or moderate drinkers, and Group B consisted of non-alcoholics.<\/p>\n\n\n\n

Serum GGT was measured\nfor both heavy and moderate drinkers. Heavy drinkers had a history of continuous\nalcohol consumption or binge drinking, with a mean consumption of over 280\ngrams per week or 4-6 standard drinks on one occasion. Moderate drinkers had a\nmean consumption of <280 grams per week. The study excluded patients with\nhormonal treatment, liver disease, or jaundice due to potential effects on\nserum GGT levels.<\/p>\n\n\n\n

Patients’ venous blood\nsamples were collected, centrifuged, stored at -80C, and analyzed using a\nBS-300 automated chemistry analyzer (Mindray) to detect GGT concentration and\nthe values were documented for statistical analysis.<\/p>\n\n\n\n

As this study involves\nhuman subjects, the clearance has been obtained from the ethical committee of\nPadmashree Institute of Medical Laboratory Technology, Nagarbhavi, Bangalore \u2013\n560 072 as per ethical guidelines research from biomedical research on human\nsubject, 2000; ICMR, New Delhi and Informed consent was obtained from all the\nparticipants.<\/p>\n\n\n\n

Results<\/strong><\/p>\n\n\n\n

The study conducted a\nprospective cross-sectional analysis of age, alcohol intake, and GGT levels in\nalcoholic and non-alcoholic healthy subjects, comparing and correlating these\nfindings.<\/p>\n\n\n\n

After selecting all participants between the ages of 40 and 80, they are divided into four class intervals: 40\u201349, 50\u201359, 60\u201369, and 70\u201380. The age distribution Figure 1 indicates that the mean age in the alcoholic case group was 51.86\u00b112.38, while the mean age in the control group of healthy, non-alcoholic participants was 52.30\u00b112.52, with a p-value of 0.860. Thus, implying that the age groups in the study were distributed equally.<\/p>\n\n\n\n\n\n
\"\"<\/td>\n\n

Figure 1: Age distribution of the participants.<\/strong><\/p>\n<\/p>\n

Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Depending on their level of alcohol consumption, the Group A alcoholic case subjects were further separated into two subgroups: moderate and strong alcohol abusers. Figure 2 presents the distribution of alcoholics in group A: 36% were heavy alcohol users and 64% were moderate users.<\/p>\n\n\n\n\n\n
\"\"<\/td>\n\n

Figure 2: Alcohol abuse of group A participants.<\/strong><\/p>\n<\/p>\n

Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

As shown in Figure 3 subjects who consumed alcohol had blood GGT levels that were more than three times higher than those of non-drinking subjects (mean 78.06\u00b111.01 U\/L), demonstrating a substantial significance with p<0.001 and student t=4.761.<\/p>\n\n\n\n\n\n
\"\"<\/td>\n\n

Figure 3: Levels of GGT (U\/L) in A & B groups.<\/strong><\/p>\n<\/p>\n

Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Compared to Group B, the association between age and GGT is skewed in Group A as a result of alcohol addiction. In group A, there is a negative connection (r = -0.076, p = 0.600) between age and GGT on alcohol use as shown in Table 1.<\/p>\n\n\n\n

Table 1: Pearson correlation of age and GGT in two groups<\/strong><\/p>\n\n\n\n\n\n\n\n
\n

Age vs GGT (U\/L)<\/strong><\/p>\n<\/td>\n

\n

Group A<\/strong><\/p>\n<\/td>\n

\n

Group B<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

r value<\/p>\n<\/td>\n

\n

-0.076<\/p>\n<\/td>\n

\n

0.390<\/p>\n<\/td>\n<\/tr>\n

\n

p value<\/p>\n<\/td>\n

\n

0.600<\/p>\n<\/td>\n

\n

0.006**<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n

GGT is increased when the Patients had severe alcohol abuse with t=3.639; P=0.001** as shown in Table 2.<\/p>\n\n\n\n

Table 2: Alcohol abuse and GGT (U\/L)<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n
\n

Alcohol abuse<\/strong><\/p>\n<\/td>\n

\n

Number of patients<\/strong><\/p>\n<\/td>\n

\n

GGT (U\/L)<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

Min-Max<\/strong><\/p>\n<\/td>\n

\n

Mean \u00b1 SD<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

Moderate<\/p>\n<\/td>\n

\n

32<\/p>\n<\/td>\n

\n

8.0-105<\/p>\n<\/td>\n

\n

51.16\u00b129.82<\/p>\n<\/td>\n<\/tr>\n

\n

Severe<\/p>\n<\/td>\n

\n

18<\/p>\n<\/td>\n

\n

14.0-376.0<\/p>\n<\/td>\n

\n

125.89\u00b1109.96<\/p>\n<\/td>\n<\/tr>\n

\n

Total<\/p>\n<\/td>\n

\n

50<\/p>\n<\/td>\n

\n

8.00-376.0<\/p>\n<\/td>\n

\n

78.06\u00b177.91<\/p>\n<\/td>\n<\/tr>\n

\n

Inference<\/p>\n<\/td>\n

\n

GGT is increased when the Patients had severe alcohol abuse with t=3.639; P=0.001**<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n

The mean serum GGT levels in heavy drinkers are 125.89\u00b1109.96 U\/L, higher than the means of moderate drinkers (51.16\u00b129.82) and abstainers (25.12\u00b110.61 U\/L), with a strong statistical significance at p<0.001 and F=27.318 as shown in Table 3 and figure 4.<\/p>\n\n\n\n

Table 3: Alcohol abuse and GGT (U\/L)<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n
\n

Alcohol abuse<\/strong><\/p>\n<\/td>\n

\n

Number of patients<\/strong><\/p>\n<\/td>\n

\n

GGT (U\/L)<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

Min-Max<\/strong><\/p>\n<\/td>\n

\n

Mean \u00b1 SD<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

Non-alcoholic<\/p>\n<\/td>\n

\n

50<\/p>\n<\/td>\n

\n

9.0-52.0<\/p>\n<\/td>\n

\n

25.12\u00b110.61<\/p>\n<\/td>\n<\/tr>\n

\n

Moderate<\/p>\n<\/td>\n

\n

32<\/p>\n<\/td>\n

\n

8.0-105<\/p>\n<\/td>\n

\n

51.16\u00b129.82<\/p>\n<\/td>\n<\/tr>\n

\n

Severe<\/p>\n<\/td>\n

\n

18<\/p>\n<\/td>\n

\n

14.0-376.0<\/p>\n<\/td>\n

\n

125.89\u00b1109.96<\/p>\n<\/td>\n<\/tr>\n

\n

Inference<\/p>\n<\/td>\n

\n

GGT is increased when the Patients had severe alcohol abuse with F=27.318; P<0.001**<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u00a0<\/p>\n\n\n\n
\"\"<\/td>\n\n

Figure 4:<\/strong> Alcohol abuse and GGT (U\/L)<\/strong><\/p>\n

Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Discussion<\/strong><\/p>\n\n\n\n

The findings of this study highlight \u03b3-GT (gamma-glutamyl transferase) as an important biomarker for assessing alcohol consumption in men. Serum GGT levels were found to be significantly elevated in participants classified as heavy drinkers compared to moderate drinkers and non-drinkers. The findings exhibited that participants engaged in heavy alcohol consumption had serum GGT levels significantly exceeding those of moderate drinkers and abstainers, which aligns with findings from numerous research indicating that elevated GGT levels correlate strongly with alcohol intake 14,15<\/sup>.<\/p>\n\n\n\n

In our study, heavy drinkers exhibited a mean serum GGT concentration of 125.89 U\/L, which far surpassed the levels observed in moderate drinkers (51.16 U\/L) and non-drinkers (25.12 U\/L). Such results align with the observation that chronic alcohol consumption leads to marked changes in liver enzyme activity, particularly GGT, which is produced mainly in the liver and can indicate liver stress or damage 16,17<\/sup>.<\/p>\n\n\n\n

Interestingly, we noted a negative correlation between age and GGT levels among those who consume alcohol. This could reflect a unique metabolic adaptation or age-related changes that affect how the body processes alcohol. Recent studies have pointed to variations in metabolic responses to alcohol among older adults, suggesting that age may influence GGT activity and its interpretation as a marker of abuse 18.<\/sup><\/p>\n\n\n\n

Furthermore, \u03b3-GT’s relevance goes beyond just indicating alcohol consumption; it also serves as a potential marker for liver health, helping to identify risks associated with alcohol-related diseases such as cirrhosis and liver cancer. These findings indicate that \u03b3-GT could play a crucial role in early identification of individuals at risk for alcohol-related liver diseases, including cirrhosis. Our findings supported by many studies indicated that the elevated plasma GGT enzymatic activity is a significant predictor of the metabolic syndrome and is associated with oxidative stress, and elevated GGT levels are occasionally shown in fatty liver, and individuals with elevated plasma GGT are probably manifesting a sign of a liver disorder 19,20<\/sup>. a higher serum GGT levels were associated with a higher risk of mortality and several studies have demonstrated that GGT positively correlated with the development of various cancers including breast, lung, endometrium, gastrointestinal tract, and liver 21,22,23<\/sup>.<\/p>\n\n\n\n

Given the growing global attention on alcohol consumption’s health impacts, there is a pressing need for markers that can reliably detect harmful drinking patterns for early intervention and improved patient outcomes. The serum GGT levels may be useful for risk assessment of all-cause and disease-specific mortality in general population23<\/sup>. While \u03b3-GT is a valuable tool in this context, it should not be viewed as isolation. Integrating GGT with additional biomarkers, such as carbohydrate-deficient transferring (CDT) and mean corpuscular volume (MCV), can provide a more robust and accurate assessment of alcohol consumption and its effects on health 24.<\/sup><\/p>\n\n\n\n

Limitations<\/strong><\/p>\n\n\n\n

Our prevalence rates are likely to\nbe lower than those of the general population because these findings were only\nobtained from subjects who attended the Sneha Mano Vikasa Kendra De-Addiction\nCenter in Tumkur, Karnataka, India, for alcohol detoxification during a limited\nperiod of time, from November 2009 to April 2010 (6 months).<\/p>\n\n\n\n

Our research\ndoes not contain a uniform distribution of ages. One of our limitations was\nthat the age intervals between 40 and 49 years were 62% in alcoholic\nparticipants, whereas the age intervals between 50 and 59, 60 and 69, and 70\nand 80 years were 12%, 12%, and 14%, respectively.<\/p>\n\n\n\n

Future Perspective of your Study<\/strong><\/p>\n\n\n\n

Future\nstudies should be conducted to evaluate the Gamma glutamyl transferase enzyme\nas a risk factor for cardiovascular diseases.<\/p>\n\n\n\n

Conclusion<\/strong><\/p>\n\n\n\n

This\nstudy emphasizes \u03b3-GT as a reliable marker for monitoring alcohol use. The\nsignificant differences in GGT levels among drinkers compared to non-drinkers\nunderscore its effectiveness in clinical assessments. The relationship between\nage, alcohol consumption, and GGT levels serves as an important reminder of the\nneed for further investigation into how these factors intertwine.<\/p>\n\n\n\n

Acknowledgment<\/strong><\/p>\n\n\n\n

The author would like to thank Rajiv Gandhi\nUniversity of Health Sciences, Karnataka, Bangalore for supporting this M.Sc.\nresearch work.<\/p>\n\n\n\n

Funding Sources<\/strong><\/p>\n\n\n\n

The author(s) received no\nfinancial support for the research, authorship, and\/or publication of this\narticle<\/p>\n\n\n\n

Conflict of Interest<\/strong><\/p>\n\n\n\n

The author(s) do not have any conflict of interest.<\/p>\n\n\n\n

Data Availability Statement<\/strong><\/p>\n\n\n\n

This statement does not apply to this article.<\/p>\n\n\n\n

Ethics Statement<\/strong><\/p>\n\n\n\n

As this study involves human subjects, the clearance has been obtained from the ethical committee of Padmashree Institute of Medical Laboratory Technology, Nagarbhavi, Bangalore \u2013 560 072 as per ethical guidelines research from biomedical research on human subject, 2000; ICMR, New Delhi and Informed consent was obtained from all the participants.<\/p>\n\n\n\n

Informed Consent Statement<\/strong><\/p>\n\n\n\n

This study did not involve human participants, and therefore, informed consent was not required.<\/p>\n\n\n\n

Clinical Trial Registration<\/strong><\/p>\n\n\n\n

This research does not involve any clinical trials<\/p>\n\n\n\n

Author Contributions<\/strong> <\/p>\n\n\n\n

Mohamed Magzoub: Data Collection, Original Draft , Analysis, Writing.<\/p>\n\n\n\n

Elrayh Ali: Conceptualization, Methodology, Writing.<\/p>\n\n\n\n

Ayman Alfeel: Conceptualization, Methodology, Writing.<\/p>\n\n\n\n

Israa Yousif: Conceptualization, Methodology, Writing.<\/p>\n\n\n\n

Kiran Gopinath: Writing \u2013 Review.<\/p>\n\n\n\n

Sofiyat Zayyad: Writing \u2013 Review.<\/p>\n\n\n\n

Osman Elsadig: Writing \u2013 Review.<\/p>\n\n\n\n

Mosab Omer: Supervision, and Review & Editing.<\/p>\n\n\n\n

Kshama K. Hiremath: Supervision, and Review & Editing.<\/p>\n\n\n\n

Qubaa Ahmed Elzubair: Writing \u2013 Review.<\/p>\n\n\n\n

Marwan Ismail : Supervision and  Review & Editing.<\/p>\n\n\n\n

References<\/strong><\/p>\n\n\n\n

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