Biomarkers for Prediabetes, Type 2 Diabetes, and Associated Complications

Authors

  • Ali Mohamed Alsari Almheiri College of Medicine, University of Sharjah
  • Amna Sayah Alhammadi College of Medicine, University of Sharjah
  • Fatima Saeed AlShehhi College of Medicine, University of Sharjah
  • Asma Mohammad College of Medicine, University of Sharjah
  • Rodha Rashid Alshamsi College of Medicine, University of Sharjah
  • Khaled Yahya Alzaman College of Medicine, University of Sharjah
  • Saima Jabeen Department of Zoology, University of Agriculture
  • Burhan Ul Haq Department of Applied Psychology, GCUF

DOI:

https://doi.org/10.47672/ajhmn.1592

Abstract

Purpose: Diabetes mellitus is a chronic disorder caused by high blood glucose levels due to insulin resistance or insufficient insulin production in pancreatic β-cells. Due to its fastest-growing public health concerns worldwide, it is important to evaluate metabolic profile abnormalities before pre-diabetes or T2DM to anticipate and prevent disease progression. The purpose of the study was to examine the metabolite biomarkers by systematic review and meta-analysis to support early detection of pre-diabetes and T2DM.

Methodology: Studies published from the earliest online through May 31, 2023, were searched in the Cochrane Library, EMBASE, PubMed, and Scopus. Article titles, abstracts, and complete texts were reviewed after duplicate records were eliminated. Two writers (Long and Yang) created the following inclusion criteria for the publications before literature screening: The study was conducted on humans, did not involve gestational diabetes mellitus (GDM), type 1 diabetes mellitus (T1DM), or subjects under 18 years old, included a diabetic or prediabetes group, and followed international diagnostic guidelines (American Diabetes Association, 2013).

Findings: The study aimed to review the biomarkers that have been utilized for diabetes in previous research. The comparison of the biomarkers mentioned in the provided information revealed a complex interplay of factors influencing the risk and management of Type 2 Diabetes (T2D). These biomarkers encompass genetic, lifestyle, environmental, and insulin-related factors, each with varying degrees of accuracy and specificity in predicting T2D risk or guiding its management.

Recommendations: The research will help in spreading awareness among people regarding the identification of diabetes as understanding biomarker-based screening's economic impact can inform healthcare policies. Future studies should validate these biomarkers' diagnostic capacities across varied populations and circumstances. Assessment of these biomarkers' predictive usefulness should be done over time via longitudinal research. Understanding biomarker alterations and diabetes progression improves risk prediction.

Downloads

Download data is not yet available.

References

American Diabetes Association, (2013). Diagnosis and classification of diabetes mellitus. Diabetes Care 36(Suppl. 1):S67eS74.

American Diabetes Association. (2014). Standards of medical care in diabetes"”2014. Diabetes care, 37(Supplement_1), S14-S80.

Andersson, E.A., Allin, K.H., Sandholt, C.H., Borglykke, A., Lau, C.J., RibelMadsen, R., et al., (2013). Genetic risk score of 46 type 2 diabetes risk variants associated with changes in plasma glucose and estimates of pancreatic b-cell function over 5 years of follow-up. Diabetes 62:3610e3617.

Bansal, N. (2015). Prediabetes diagnosis and treatment: A review. World journal of diabetes, 6(2), 296.

Bellou, V., Belbasis, L., Tzoulaki, I., Evangelou, E., 2018. Risk factors for type 2 diabetes mellitus: an exposure-wide umbrella review of meta-analyses. Public Library of Science one 13:e0194127.

Centers for Disease Control and Prevention. (2014). National diabetes statistics report: estimates of diabetes and its burden in the United States, 2014. Atlanta, GA: US Department of Health and Human Services, 2014.

Chatterjee, S., Khunti, K., Davies, M.J., (2017). Type 2 diabetes. Lancet 389:2239e2251.

Chan, C. L., Pyle, L., Kelsey, M., Newnes, L., Zeitler, P. S., & Nadeau, K. J. (2016). Screening for type 2 diabetes and prediabetes in obese youth: evaluating alternate markers of glycemia-1, 5"anhydroglucitol, fructosamine, and glycated albumin. Pediatric diabetes, 17(3), 206-211.

Cho, N. H., Shaw, J. E., Karuranga, S., Huang, Y., da Rocha Fernandes, J. D., Ohlrogge, A. W., & Malanda, B. I. D. F. (2018). IDF Diabetes Atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes research and clinical practice, 138, 271-281.

Danese, E., Montagnana, M., Nouvenne, A., & Lippi, G. (2015). Advantages and pitfalls of fructosamine and glycated albumin in the diagnosis and treatment of diabetes. Journal of diabetes science and technology, 9(2), 169-176.

Dimas, A.S., Lagou, V., Barker, A., Knowles, J.W., Mägi, R., Hivert, M.F., et al., (2014). The impact of type 2 diabetes susceptibility variants on quantitative glycemic traits reveals mechanistic heterogeneity. Diabetes 63:2158e2171.

Dong X, Huai Z, Li C. (2020) Analysis of risk factors for insulin antibodies in T2DM patients. Adv Clin Med;10(6):920-5.

Echouffo-Tcheugui, J.B., Dieffenbach, S.D., Kengne, A.P., (2013). The added value of novel circulating and genetic biomarkers in type 2 diabetes prediction: a systematic review. Diabetes Research and Clinical Practice 101: 255e269.

Fuchsberger, C., Flannick, J., Teslovich, T.M., Mahajan, A., Agarwala, V., Gaulton, K.J., et al., (2016). Nature 536:41e47.

Go, M.J., Lee, Y., Park, S., Kwak, S.H., Kim, B.J., Lee, J., et al., (2016). Genetic risk assessment of GWAS-derived susceptibility loci for type 2 diabetes in a 10-year follow-up of a population-based cohort study. Journal of Human Genetics 61:1009e1012.

Guo, F., Moellering, D. R., & Garvey, W. T. (2014). Use of HbA1c for diagnoses of diabetes and prediabetes: comparison with diagnoses based on fasting and 2-hr glucose values and effects of gender, race, and age. Metabolic syndrome and related disorders, 12(5), 258-268.

Hu, X., & Chen, F. (2018). Exogenous insulin antibody syndrome (EIAS): a clinical syndrome associated with insulin antibodies induced by exogenous insulin in diabetic patients. Endocrine connections, 7(1), R47-R55.

Inaishi, J., Hirakawa, Y., Horikoshi, M., Akiyama, M., Higashioka, M., Yoshinari, M., et al., (2019). Association between genetic risk and development of type 2 diabetes in a general Japanese population: the Hisayama Study. Journal of Clinical Endocrinology & Metabolism 104:3213e3222.

Laakso, M., Kuusisto, J., Stancáková, A., Kuulasmaa, T., Pajukanta, P., Lusis, A.J., et al., (2017). The Metabolic Syndrome in Men study: a resource for studies of metabolic and cardiovascular diseases. The Journal of Lipid Research 58:481e493.

Langenberg, C., Lotta, L.A., (2018). Genomic insights into the causes of type 2 diabetes. Lancet 391:2463e2474.

Lee, J. E. (2015). Alternative biomarkers for assessing glycemic control in diabetes: fructosamine, glycated albumin, and 1, 5-anhydroglucitol. Annals of pediatric endocrinology & metabolism, 20(2), 74.

Li, G., Zhang, P., Wang, J., An, Y., Gong, Q., Gregg, E. W., ... & Bennett, P. H. (2014). Cardiovascular mortality, all-cause mortality, and diabetes incidence after lifestyle intervention for people with impaired glucose tolerance in the Da Qing Diabetes Prevention Study: a 23-year follow-up study. The Lancet Diabetes & endocrinology, 2(6), 474-480.

Li, J., Ma, H., Na, L., Jiang, S., Lv, L., Li, G., ... & Sun, C. (2015). Increased hemoglobin A1c threshold for prediabetes remarkably improved the agreement between A1c and oral glucose tolerance test criteria in the obese population. The Journal of Clinical Endocrinology & Metabolism, 100(5), 1997-2005.

Lyssenko, V., Laakso, M., (2013). Genetic screening for the risk of type 2 diabetes: worthless or valuable? Diabetes Care 36(Suppl. 2):S120e S126.

Mahajan, A., Taliun, D., Thurner, M., Robertson, N.R., Torres, J.M., Rayner, N.W., et al., (2018). Fine-mapping of an expanded set of type 2 diabetes loci to single-variant resolution using high-density imputation and is let specific epigenome maps. Nature Genetics 50:1505e1513.

Mahmoud, A. M., Ali, M. M., Miranda, E. R., Mey, J. T., Blackburn, B. K., Haus, J. M., & Phillips, S. A. (2017). Nox2 contributes to hyperinsulinemia-induced redox imbalance and impaired vascular function. Redox biology, 13, 288-300.

Mahmoud, A. M., Szczurek, M. R., Blackburn, B. K., Mey, J. T., Chen, Z., Robinson, A. T., ... & Haus, J. M. (2016). Hyperinsulinemia augments endothelin"1 protein expression and impairs vasodilation of human skeletal muscle arterioles. Physiological reports, 4(16), e12895.

Malkan, U. Y., Gunes, G., & Corakci, A. (2015). Rational diagnoses of diabetes: the comparison of 1, 5-anhydroglucitol with other glycemic markers. Springerplus, 4(1), 1-8.

Malmström, H., Walldius, G., Grill, V., Jungner, I., Gudbjörnsdottir, S., & Hammar, N. (2014). Fructosamine is a useful indicator of hyperglycemia and glucose control in clinical and epidemiological studies-cross-sectional and longitudinal experience from the AMORIS cohort. PloS one, 9(10), e111463.

McCarthy, M.I., Mahajan, A., (2018). The value of genetic risk scores in precision medicine for diabetes. Expert Review of Precision Medicine and Drug Development 3:279e281.

Ogurtsova, K., da Rocha Fernandes, J. D., Huang, Y., Linnenkamp, U., Guariguata, L., Cho, N. H., ... & Makaroff, L. E. (2017). IDF Diabetes Atlas: Global estimates for the prevalence of diabetes for 2015 and 2040. Diabetes research and clinical practice, 128, 40-50.

Pfister, R., Sharp, S. J., Luben, R., Khaw, K. T., & Wareham, N. J. (2011). No evidence of an increased mortality risk associated with low levels of glycated hemoglobin in a non-diabetic UK population. Diabetologia, 54, 2025-2032.

Radin, M. S. (2014). Pitfalls in hemoglobin A1c measurement: when results may be misleading. Journal of General Internal Medicine, 29, 388-394.

Rodríguez-Segade, S., Rodríguez, J., & Camiña, F. (2017). Corrected Fructosamine improves both correlation with HbA1C and diagnostic performance. Clinical biochemistry, 50(3), 110-115.

Sanaki, Y., Nagata, R., Kizawa, D., Lopold, P., & Igaki, T. (2020). Hyperinsulinemia drives epithelial tumorigenesis by abrogating cell competition. Developmental cell, 53(4), 379-389.

Selvin, E., Francis, L. M., Ballantyne, C. M., Hoogeveen, R. C., Coresh, J., Brancati, F. L., & Steffes, M. W. (2011). Nontraditional markers of glycemia: associations with microvascular conditions. Diabetes care, 34(4), 960-967.

Stancáková, A., Kuulasmaa, T., Kuusisto, J., Mohlke, K.L., Collins, F.S., Boehnke, M., et al., (2017). Genetic risk scores in the prediction of plasma glucose, impaired insulin secretion, insulin resistance, and incident type 2 diabetes in the METSIM study. Diabetologia 60:1722e1730.

Sumner, A. E., Duong, M. T., Aldana, P. C., Ricks, M., Tulloch-Reid, M. K., Lozier, J. N., ... & Sacks, D. B. (2016). A1C combined with glycated albumin improves detection of prediabetes in Africans: the Africans in America study. Diabetes Care, 39(2), 271-277.

Sun, H., Saeedi, P., Karuranga, S., Pinkepank, M., Ogurtsova, K., Duncan, B. B., ... & Magliano, D. J. (2022). IDF Diabetes Atlas: Global, regional, and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes research and clinical practice, 183, 109119.

Tanaka, M. (2020). Improving obesity and blood pressure. Hypertension Research, 43(2), 79-89.

Tavares Ribeiro, R., Paula Macedo, M., & Filipe Raposo, J. (2016). HbA1c, fructosamine, and glycated albumin in the detection of dysglycaemic conditions. Current Diabetes Reviews, 12(1), 14-19.

Wang, J., Stancáková, A., Kuusisto, J., Laakso, M., (2010). Identification of undiagnosed type 2 diabetic individuals by the Finnish diabetes risk score and biochemical and genetic markers: a population-based study of 7232 Finnish men. Journal of Clinical Endocrinology & Metabolism 95:3858e3862.

Whiting, D. R., Guariguata, L., Weil, C., & Shaw, J. (2011). IDF diabetes atlas: global estimates of the prevalence of diabetes for 2011 and 2030. Diabetes research and clinical practice, 94(3), 311-321.

Vangipurapu, J., Stancáková, A., Jauhiainen, R., Kuusisto, J., Laakso, M., (2017). Short adult stature predicts impaired B-Cell function, insulin resistance, glycemia, and type 2 diabetes in Finnish men. Journal of Clinical Endocrinology & Metabolism 102:443e450.

Vaxillaire, M., Yengo, L., Lobbens, S., Rocheleau, G., Eury, E., Lantieri, O., (2014). Type 2 diabetes-related genetic risk scores associated with variations in fasting plasma glucose and development of impaired glucose homeostasis in the prospective DESIR study. Diabetologia 57:1601e1610.

Visscher, P.M., Wray, N.R., Zhang, Q., Sklar, P., McCarthy, M.I., Brown, M.A.,(2017). 10 years of GWAS discovery: biology, function, and translation. The American Journal of Human Genetics 101:5e22.

Downloads

Published

2023-09-27

How to Cite

Almheiri, A. ., Alhammadi, A. ., AlShehhi, F. ., Mohammad, A., Alshamsi, R. ., Alzaman, K. ., … Haq, B. (2023). Biomarkers for Prediabetes, Type 2 Diabetes, and Associated Complications. American Journal of Health, Medicine and Nursing Practice, 9(2), 1–21. https://doi.org/10.47672/ajhmn.1592

Issue

Section

Articles