Abstract
Objectives
Given their established role in hepatic function and insulin resistance for adults, early screening of type 2 diabetes mellitus (T2DM) in the pediatric population may potentially be improved by the assessment of elevated liver enzymes.
Methods
Our study enrolled 151 nondiabetic children aged 10–14 years. Patients were assessed for demographics and five risk factors for T2DM. The levels of γ-glutamyl transpeptidase (GGT), alanine aminotransferase (ALT), and alkaline phosphatase (ALP) levels were determined in serum samples. The effects of demographics and risk factors on abnormal liver enzyme levels were assessed with univariate chi-square analyses and also with multivariate logistic regression analyses, which were controlled for gender.
Results
Frequencies for abnormal liver enzyme values were as follows: 13 (9%) for GGT, 5 (3%) for ALT, and 20 (13%) for ALP. Across analyses, two results were consistently statistically significant. Females were more likely to have abnormal ALP levels, and patients with BMI percentile ≥95% and with acanthosis nigricans were more likely to have abnormal GGT levels.
Conclusions
Our study suggests GGT as potential marker for T2DM discovery in children. Subsequent long-term longitudinal studies would help to more clearly delineate GGT’s association with T2DM. Additionally, future studies that elucidate the molecular contribution of GGT elevation to T2DM pathogenesis are needed.
Acknowledgments
This study was conducted as part of the University of North Texas Health Science Center and Cook Children’s Pediatric Research Program (PRP).
Research funding: None declared.
Author contributions: Victor Lin assisted with interpretation of data and drafting the manuscript. Tyler Hamby assisted with interpretation of data, statistical analysis of data, and drafting the manuscript. Siddharth Das assisted with drafting the manuscript. Amritpaul Chatrath assisted with drafting the manuscript. Riyaz Basha assisted with interpretation of data, and drafting the manuscript. Kimberly G. Fulda assisted with acquisition of data and drafting the manuscript. Nusrath Habiba assisted with acquisition of data, interpretation of data, and drafting the manuscript. All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Competing interests: The funding organization(s) (none declared) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.
Ethical approval: All study procedures were approved by the UNTHSC Institutional Review Board. Data were collected prospectively as part of a larger study [5]. A consent form was signed prior to enrollment, and a questionnaire was completed regarding demographic information.
Employment or leadership: None declared.
Honorarium: None declared.
References
1. Ward, ZJ, Long, MW, Resch, SC, Giles, CM, Cradock, AL, Gortmaker, SL. Simulation of growth trajectories of childhood obesity into adulthood. N Engl J Med 2017;377:2145–53. https://doi.org/10.1056/nejmoa1703860.Search in Google Scholar PubMed PubMed Central
2. Younossi, ZM, Koenig, AB, Abdelatif, D, Fazel, Y, Henry, L, Wymer, M. Global epidemiology of nonalcoholic fatty liver disease--meta-analytic assessment of prevalence, incidence, and outcomes. Hepatology 2016;64:73–84. https://doi.org/10.1002/hep.28431.Search in Google Scholar PubMed
3. Sherif, ZA, Saeed, A, Ghavimi, S, Nouraie, SM, Laiyemo, AO, Brim, H, et al.. Global epidemiology of nonalcoholic fatty liver disease and perspectives on US minority populations. Dig Dis Sci 2016;61:1214–25. https://doi.org/10.1007/s10620-016-4143-0.Search in Google Scholar PubMed PubMed Central
4. Cho, NH, Jang, HC, Choi, SH, Kim, HR, Lee, HK, Chan, JC, et al.. Abnormal liver function test predicts type 2 diabetes: a community-based prospective study. Diabetes Care 2007;30:2566–8.https://doi.org/10.2337/dc07-0106.Search in Google Scholar PubMed
5. Habiba, NM, Fulda, KG, Basha, R, Shah, D, Fernando, S, Nguyen, B, et al.. Correlation of lipid profile and risk of developing type 2 diabetes mellitus in 10–14 year old children. Cell Physiol Biochem 2016;39:1695–704. https://doi.org/10.1159/000447870.Search in Google Scholar PubMed
6. American Diabetes Association. Classification and diagnosis of diabetes: standards of medical care in diabetes-2020. Diabetes Care 2020;43:S14–31. https://doi.org/10.2337/dc20-s002.Search in Google Scholar PubMed
7. Grossman, DC, Bibbins-Domingo, K, Curry, SJ, Barry, MJ, Davidson, KW, Doubeni, CA, et al.. Screening for obesity in children and adolescents: US Preventive Services Task Force recommendation statement. J Am Med Assoc 2017;317:2417–26.10.1001/jama.2017.6803Search in Google Scholar PubMed
8. Vos, MB, Abrams, SH, Barlow, SE, Caprio, S, Daniels, SR, Kohli, R, et al.. NASPGHAN clinical practice guideline for the diagnosis and treatment of nonalcoholic fatty liver disease in children: recommendations from the Expert Committee on NAFLD (ECON) and the North American Society of Pediatric Gastroenterology, Hepatology and Nutrition (NASPGHAN). J Pediatr Gastroenterol Nutr 2017;64:319–34. https://doi.org/10.1097/mpg.0000000000001482.Search in Google Scholar
9. Mora, S, Pitukcheewanont, P, Kaufman, FR, Nelson, JC, Gilsanz, V. Biochemical markers of bone turnover and the volume and the density of bone in children at different stages of sexual development. J Bone Miner Res 1999;14:1664–71. https://doi.org/10.1359/jbmr.1999.14.10.1664.Search in Google Scholar PubMed
10. Fernandez-Gaxiola, AC, Valdes-Ramos, R, Fulda, KG, Lopez, ALG, Martinez-Carrillo, BE, Franks, SF, et al.. Liver biomarkers and lipid profiles in Mexican and Mexican-American 10- to 14-year-old adolescents at risk for type 2 diabetes. J Diabetes Res 2017;2017:4262536. https://doi.org/10.1155/2017/4262536.Search in Google Scholar PubMed PubMed Central
11. Nakanishi, N, Nishina, K, Li, W, Sato, M, Suzuki, K, Tatara, K. Serum gamma-glutamyltransferase and development of impaired fasting glucose or type 2 diabetes in middle-aged Japanese men. J Intern Med 2003;254:287–95. https://doi.org/10.1046/j.1365-2796.2003.01198.x.Search in Google Scholar PubMed
12. Kunutsor, SK, Abbasi, A, Adler, AI. Gamma-glutamyl transferase and risk of type II diabetes: an updated systematic review and dose-response meta-analysis. Ann Epidemiol 2014;24:809–16. https://doi.org/10.1016/j.annepidem.2014.09.001.Search in Google Scholar PubMed
13. Koenig, G, Seneff, S. Gamma-glutamyltransferase: a predictive biomarker of cellular antioxidant inadequacy and disease risk. Dis Markers 2015;2015:818570. https://doi.org/10.1155/2015/818570.Search in Google Scholar PubMed PubMed Central
14. Lee, SY, Sung, E, Chang, Y. Elevated serum gamma-glutamyltransferase is a strong marker of insulin resistance in obese children. Int J Endocrinol 2013;2013:578693. https://doi.org/10.1155/2013/578693.Search in Google Scholar PubMed PubMed Central
15. Wang, H, Li, L, Zhang, S. Non-linear relationship between gamma-glutamyl transferase and type 2 diabetes mellitus risk: secondary analysis of a prospective cohort study. J Int Med Res 2020;48. https://doi.org/10.1177/0300060520937911.Search in Google Scholar PubMed PubMed Central
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