Keywords:
web of science
journal impact factor
document types
authorship patterns
citation impacts

  • Abstract

    Hypoglycemia poses a critical challenge in managing diabetes. Existing literature, while extensive, lacks a holistic perspective. This study aims to bridge this gap by combining bibliometric analysis and a comprehensive review of Bayesian analysis-related hypoglycemic issues. This study employed data from the SCI-EXPANDED database for bibliometric analysis. The keywords "symptom" or "symptoms," "hypoglycemic" or "hypoglycemia," or "hypoglycaemia" or "hypoglycaemic," and "Diabetes" or "Diabetic" or "Diabetics" were used to locate 1,596 documents from 2000 to 2022. Document types, authorship patterns, and citation metrics were examined. Bayesian methodologies were systematically reviewed across various diabetes types and evaluated using specific assessment tools. Most of the articles published in "Endocrinology & Metabolism" contributed 37.2% of total articles, with a notable CPP2022 (Citations Per Publication (CPP)) of 35, and the main publication type were articles with an average of about six authors and over 32,000 citations in 2022. The United States (US) consistently leads in the number of published articles, followed by China, Japan, and India. Novo Nordisk led institutions with 36 publications and a substantial CPP2022 of 60.9. The comprehensive review emphasized that Bayesian statistical modeling is widely used for adult Type 1 and Type 2 diabetes but is limited in child Type 1 and absent in Gestational Diabetes (GAD) research. In contrast, Bayesian Networks (BNs) are mainly applied to adult Type 2 diabetes, with gaps in other types. Furthermore, Bayesian Neural Networks (BNNs) are prevalent in adult and child Type 1 studies but not applied to Type 2 or GAD. Since 2010, Total Publications (TP) have increased rapidly, indicating increased interest in researching hypoglycemia. Outlining potential research directions and emphasizing the transformative impact of Bayesian methodologies provides valuable insights for clinicians, researchers, and healthcare stakeholders.

  • References

    1. Bailey, C. J., Gross, J. L., Pieters, A., Bastien, A., & List, J. F. (2010). Effect of dapagliflozin in patients with type 2 diabetes who have inadequate glycaemic control with metformin: a randomised, double-blind, placebo-controlled trial. Lancet, 375(9733), 2223-2233. https://doi.org/ 10.1016/S0140-6736(10)60407-2
    2. Bolinder, J., Antuna, R., Geelhoed-Duijvestijn, P., Kröger, J., & Weitgasser, R. (2016). Novel glucose-sensing technology and hypoglycaemia in type 1 diabetes: a multicentre, non-masked, randomised controlled trial. Lancet, 388(10057), 2254-2263. https://doi.org/ 10.1016/S0140-6736(16)31535-5.
    3. Bonds, D. E., Miller, M. E., Bergenstal, R. M., Buse, J. B., Byington, R. P., Cutler, J. A., Hoogwerf, B. J. B. (2010). The association between symptomatic, severe hypoglycaemia and mortality in type 2 diabetes: retrospective epidemiological analysis of the ACCORD study. BMJ (Online), 340(7738), 137.https://doi.org/10.1136/bmj.b4909
    4. Cai, D. H., Liu, W., Ji, L. N., & Shi, D. W. (2021). Bayesian optimization assisted meal bolus decision based on Gaussian processes learning and risk-sensitive control. Control Engineering Practice, 114, 11. https://doi.org/10.1016/j.conengprac.2021.104881
    5. Ciechanowski, P. S., Katon, W. J., & Russo, J. E. J. A. o. i. m. (2000). Depression and diabetes: impact of depressive symptoms on adherence, function, and costs. Archives of internal medicine, 160(21), 3278-3285. https://doi.org/ 10.1001/archinte.160.21.3278
    6. Contador, S., Velasco, J. M., Garnica, O., & Hidalgo, J. I. (2021). Glucose forecasting using genetic programming and latent glucose variability features. Applied Soft Computing, 110, 12. https://doi.org/10.1016/j.asoc.2021.107609
    7. Cryer, P. E., Axelrod, L., Grossman, A. B., Heller, S. R., Montori, V. M., Seaquist, E. R., . . . Metabolism. (2009). Evaluation and management of adult hypoglycemic disorders: an Endocrine Society Clinical Practice Guideline. The Journal of Clinical Endocrinology & Metabolism, 94(3), 709-728. https://doi.org/ 10.1210/jc.2008-1410
    8. Cryer, P. E., Davis, S. N., & Shamoon, H. J. D. c. (2003). Hypoglycemia in diabetes. Diabetes care, 26(6), 1902-1912. https://doi.org/10.2337/diacare.26.6.1902
    9. Cryer, P. J. D. (2002). Hypoglycaemia: the limiting factor in the glycaemic management of type I and type II diabetes. Diabetologia, 45, 937-948. https://doi.org/10.1007/s00125-002-0822-9
    10. Emami, A., El Youssef, J., Rabasa-Lhoret, R., Pineau, J., Castle, J. R., & Haidar, A. (2017). Modeling Glucagon Action in Patients With Type 1 Diabetes. IEEE Journal of Biomedical and Health Informatics, 21(4), 1163-1171. https://doi.org/10.1109/jbhi.2016.2593630
    11. Facchinetti, A., Sparacino, G., & Cobelli, C. J. I. T. o. B. E. (2011). Online denoising method to handle intraindividual variability of signal-to-noise ratio in continuous glucose monitoring. IEEE Transactions on Biomedical Engineering, 58(9), 2664-2671. https://doi.org/ 10.1109/TBME.2011.2161083
    12. Fathi, A. E., Kearney, R. E., Palisaitis, E., Boulet, B., & Haidar, A. (2021). A Model-Based Insulin Dose Optimization Algorithm for People with Type 1 Diabetes on Multiple Daily Injections Therapy. IEEE Transactions on Biomedical Engineering, 68(4), 1208-1219. https://doi.org/10.1109/TBME.2020.3023555
    13. Ferrannini, E., Ramos, S. J., Salsali, A., & Tang, W. (2010). Dapagliflozin monotherapy in type 2 diabetic patients with inadequate glycemic control by diet and exercise: a randomized, double-blind, placebo-controlled, phase 3 trial. Diabetes care, 33(10), 2217-2224. https://doi.org/10.2337/dc10-0612
    14. Heine, R. J., Van Gaal, L. F., Johns, D., Mihm, M. J., Widel, M. H., Brodows, R. G., & medicine, G. S. G. J. A. o. i. (2005). Exenatide versus insulin glargine in patients with suboptimally controlled type 2 diabetes: a randomized trial. Annals of internal medicine, 143(8), 559-569.
    15. Ho, Y.-S., Fahad Halim, A., Islam, M. T. J. F. i. B., & Biotechnology. (2022). The trend of bacterial nanocellulose research published in the science citation index expanded from 2005 to 2020: a bibliometric analysis. Frontiers in Bioengineering and Biotechnology, 9, 795341. https://doi.org/10.3389/fbioe.2021.795341
    16. Ho, Y.-S., Sharmin, A. A., Islam, M. T., & Halim, A. F. (2022). Future direction of wound dressing research: Evidence From the bibliometric analysis. Journal of Industrial Textiles, 52, 15280837221130518. https://doi.org/10.1177/15280837221130518
    17. Hussain, A., Bhowmik, B., do Vale Moreira, N. C. J. D. r., & practice, c. (2020). COVID-19 and diabetes: Knowledge in progress. 162, 108142.
    18. Islam, M. T., Farhan, M. S., Faiza, F., Halim, A. F., & Sharmin, A. A. (2022). Pigment coloration research published in the science citation index expanded from 1990 to 2020: a systematic review and bibliometric analysis. Colorants, 1(1), 38-57. https://doi.org/10.3390/colorants1010005
    19. Jeyam, A., Gibb, F. W., McKnight, J. A., Kennon, B., O'Reilly, J. E., Caparrotta, T. M., & Scottish Diabet Res Network, S. E. (2021). Marked improvements in glycaemic outcomes following insulin pump therapy initiation in people with type 1 diabetes: a nationwide observational study in Scotland. Diabetologia, 64(6), 1320-1331. https://doi.org/10.1007/s00125-021-05413-7
    20. LaLonde, A., & Qu, Y. (2020). Estimation of group means using Bayesian generalized linear mixed models. Pharmaceutical Statistics, 19(4), 482-491. https://doi.org/10.1002/pst.2006
    21. Mamun, M. A. A., Haji, A., Mahmud, M. H., Repon, M. R., & Islam, M. T. J. C. (2023). Bibliometric Evidence on the Trend and Future Direction of the Research on Textile Coloration with Natural Sources.Coatings, 13(2), 413. https://doi.org/10.3390/coatings13020413
    22. McCoy, R. G., Van Houten, H. K., Ziegenfuss, J. Y., Shah, N. D., Wermers, R. A., & Smith, S. A. J. D. c. (2012). Increased mortality of patients with diabetes reporting severe hypoglycemia. Diabetes care, 35(9), 1897-1901. https://doi.org/10.2337/dc11-2054
    23. Mueller, L., Berhanu, P., Bouchard, J., Alas, V., Elder, K., Thai, N., & Miller-Wilson, L. A. (2020). Application of Machine Learning Models to Evaluate Hypoglycemia Risk in Type 2 Diabetes. Diabetes Therapy, 11(3), 681-699. https://doi.org/10.1007/s13300-020-00759-4
    24. Nauck, M. A., Heimesaat, M. M., Behle, K., Holst, J. J., Nauck, M. S., Ritzel, R., . . . Metabolism. (2002). Effects of glucagon-like peptide 1 on counterregulatory hormone responses, cognitive functions, and insulin secretion during hyperinsulinemic, stepped hypoglycemic clamp experiments in healthy volunteers. The Journal of Clinical Endocrinology & Metabolism, 87(3), 1239-1246. https://doi.org/10.1210/jcem.87.3.8355
    25. Ngo, C. Q., Chai, R. F., Jones, T. W., & Nguyen, H. T. (2021). The Effect of Hypoglycemia on Spectral Moments in EEG Epochs of Different Durations in Type 1 Diabetes Patients. IEEE Journal of Biomedical and Health Informatics, 25(8), 2857-2865. https://doi.org/10.1109/jbhi.2021.3054876
    26. Ngo, C. Q., Chai, R. F., Nguyen, T. V., Jones, T. W., Nguyen, H. T., & Ieee. (2019, Jul 23-27). Nocturnal Hypoglycemia Detection using Optimal Bayesian Algorithm in an EEG Spectral Moments Based System. Paper presented at the 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, GERMANY.
    27. Ngo, C. Q., Chai, R., Nguyen, T. V., Jones, T. W., & Nguyen, H. T. (2020). Electroencephalogram Spectral Moments for the Detection of Nocturnal Hypoglycemia. IEEE Journal of Biomedical and Health Informatics, 24(5), 1237-1245. https://doi.org/10.1109/JBHI.2019.2931782
    28. Ngo, C. Q., Truong, B. C. Q., Jones, T. W., Nguyen, H. T., & Ieee. (2018, Jul 18-21). Occipital EEG Activity for the Detection of Nocturnal Hypoglycemia. Paper presented at the 40th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC), Honolulu, HI.
    29. Ngo, P., Tejedor, M., Tayefi, M., Chomutare, T., & Godtliebsen, F. (2020). Risk-averse food recommendation using bayesian feedforward neural networks for patients with type 1 diabetes doing physical activities. Applied Sciences (Switzerland), 10(22), 1-13. https://doi.org/10.3390/app10228037
    30. Nguyen, H. T. (2008). Intelligent technologies for real-time biomedical engineering applications. International Journal of Automation and Control, 2(2-3), 274-285. https://doi.org/10.1504/IJAAC.2008.022181
    31. Nguyen, H. T., Ghevondian, N., Nguyen, S. T., & Jones, T. W. (2007). Optimal Bayesian neural-network detection of hypoglycemia in children with type 1 diabetes using a non-invasive and continuous monitor (HypoMon). Diabetes, 56, A115-A115.
    32. Nguyen, H. T., Jones, T. W., & Ieee. (2010). Detection of Nocturnal Hypoglycemic Episodes using EEG Signals. Paper presented at the 32nd Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBC 10), Buenos Aires, ARGENTINA.
    33. Russell-Jones, D., Vaag, A., Schmitz, O., Sethi, B., Lalic, N., & Antic, S. (2009). Liraglutide vs insulin glargine and placebo in combination with metformin and sulfonylurea therapy in type 2 diabetes mellitus (LEAD-5 met+ SU): a randomised controlled trial. Diabetologia, 52, 2046-2055. https://doi.org/10.1007/s00125-009-1472-y
    34. Sharmin, A. A., & Munima, H. (2016). Susceptible Factors of Type-2 Diabetes in a Population of Bangladesh. Universal Journal of Public Health, 4(1), 40-42. https://doi.org/10.13189/ujph.2016.040106
    35. Sharmin, A. A., Zulkafli, H. S., & Ali, N. M. (2024). Establishing cut-off points for consistency in reporting hypoglycemia symptoms among diabetes patients. JP Journal of Biostatistics, 24(1), 31-46. https://doi.org/10.17654/0973514324004
    36. Shi, D., Dassau, E., & Doyle, F. J., 3rd. (2019). Multivariate learning framework for long-term adaptation in the artificial pancreas. Bioeng Transl Med, 4(1), 61-74. https://doi.org/10.1002/btm2.10119
    37. Si, Y. J., Palta, M., & Smith, M. (2020). Bayesian profiling multiple imputation for missing hemoglobin values in electronic health records. Annals of Applied Statistics, 14(4), 1903-1924. https://doi.org/10.1214/20-aoas1378
    38. Vinik, A. I., Maser, R. E., Mitchell, B. D., & Freeman, R. J. D. c. (2003). Diabetic autonomic neuropathy. Diabetes Care, 26(5), 1553-1579. https://doi.org/ 10.2337/diacare.26.5.1553
    39. Wang, W. J., Wang, S. P., Wang, X. J., Liu, D., Geng, Y. X., & Wu, T. (2021). A Glucose-Insulin Mixture Model and Application to Short-Term Hypoglycemia Prediction in the Night Time. IEEE Transactions on Biomedical Engineering, 68(3), 834-845. https://doi.org/10.1109/tbme.2020.3015199
    40. Wang, Y., Zhang, W. S., Hao, Y. T., Jiang, C. Q., Jin, Y. L., Cheng, K. K., & Xu, L. (2022). A Bayesian network model of new-onset diabetes in older Chinese: The Guangzhou biobank cohort study. Frontiers in Endocrinology, 13. https://doi.org/10.3389/fendo.2022.916851
    41. Wild, D., von Maltzahn, R., Brohan, E., Christensen, T., Clauson, P., Gonder-Frederick, L. J. P. e., & counseling. (2007). A critical review of the literature on fear of hypoglycemia in diabetes: implications for diabetes management and patient education. Patient education and counseling, 68(1), 10-15. https://doi.org/10.1016/j.pec.2007.05.003
    42. Yuan, K., Xie, M. D., Hou, H. J., Chen, J. J., Zhu, X. Y., Wang, H. M., & Liu, X. F. (2023). Association of glycemic gap with stroke recurrence in patients with ischemic stroke. Journal of Diabetes, 10. https://doi.org/10.1111/1753-0407.13432
    43. Zammitt, N. N., Streftaris, G., Gibson, G. J., Deary, I. J., & Frier, B. M. (2011). Modeling the consistency of hypoglycemic symptoms: High variability in diabetes. Diabetes Technology and Therapeutics, 13(5), 571-578. https://doi.org/10.1089/dia.2010.0207
    44. Zecchin, C., Facchinetti, A., Sparacino, G., Dalla Man, C., Manohar, C., Levine, J. A., & Cobelli, C. (2013). Physical activity measured by physical activity monitoring system correlates with glucose trends reconstructed from continuous glucose monitoring. Diabetes Technology and Therapeutics, 15(10), 836-844. https://doi.org/10.1089/dia.2013.0105
    45. Zhao, Y., Shen, W., & Fu, H. (2013). Joint modeling of clinical efficacy and safety with an application to diabetes studies. Journal of Biopharmaceutical Statistics, 23(5), 1155-1171. https://doi.org/10.1080/10543406.2013.813520
    46. Zulkafli, H. S., Streftaris, G., Gibson, G. J. J. M., & Statistics. (2020). Stochastic latent residual approach for consistency model assessment. Mathematics and Statistics, 8(5), 583-589. https://doi.org/10.13189/ms.2020.080513
    47. Zulkafli, H. S., Streftaris, G., Gibson, G. J., & Zammitt, N. N. (2016). Bayesian modelling of the consistency of symptoms reported during hypoglycaemia for individual patients. Malaysian Journal of Mathematical Sciences, 10(S), 27-39.
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Copyright (c) 2025 The Authors

How to cite

Sharmin, A. A., Zulkafli, H. S., Ali, N. M., Mamun, M. A. A., & Shafrin, R. (2024). Symptomatology of hypoglycemia in diabetes: A bibliometric analysis (2000-2022) of bayesian approaches. Multidisciplinary Reviews, 8(3), 2025081. https://doi.org/10.31893/multirev.2025081
Crossref
Scopus
Google Scholar
Europe PMC
  • Article viewed - 228
  • PDF downloaded - 153