Type 2 diabetes constitutes an imposing epidemiological, economic, and scientific global challenge. The chronic complications of type 2 diabetes are a major cause of mortality and disability worldwide [1,2]. Clinical research is the main way to gain knowledge about long-term diabetic complications and reduce the burden of diabetes. This allows for designing effective programs for screening and follow-up and fine-targeted therapeutic interventions. However, new research methodologies are needed to obtain more accurate and useful insights into the biological and clinical processes involved in diabetic complication development.
During the last few years, new approaches for clinical research have incorporated digital tools to analyze the complex physiopathological background of type 2 diabetes. In this Special Issue, entitled “Clinical Research on Type 2 Diabetes and Its Complications” and published in the Journal of Clinical Medicine (https://www.mdpi.com/journal/jcm/special_issues/Type_2_Diabetes_Complications), some valuable digital methodologies were used in different studies focusing on the type 2 diabetes syndrome. Novel machine learning techniques for predicting long-term complications are one of these approaches, as the studies of Huang, Rashid, and Shin et al. depict [3,4,5]. The data presented by these authors suggest that machine learning may be more accurate in predicting diabetic microvascular complications than traditional methods. Additionally, digital tools such as artificial intelligence and machine learning can be implemented through an automated and rapid process.
Among the frequent causes of frustration for people with diabetes and the health care providers involved in their management is the delayed detection of diabetic complications. The outlook of clinical research appears promising in the near future owing to the development and implementation of advanced methods for the detection of early alterations in the micro- and macrovascular complications associated with diabetes. Two papers in this Special Issue cover the use of specific biomarkers tracing the progress of diabetic cardiovascular complications [6,7]. In another contribution, Lee et al. revisit the long-term glycemic variability and its relationship with end-stage kidney disease [8].
Besides the genetic approach, the application of digital techniques, including machine learning and artificial intelligence, and novel biomarkers could be crucial for individualized type 2 diabetes management, which is the backbone of precision medicine.
Two review papers address the complications that are non-traditionally linked to type 2 diabetes, although currently under exhaustive research: bone health and non-alcoholic fatty liver disease [9,10]. The multifaceted nature of type 2 diabetes is clearly visualized owing to the holistic angle used by these approaches.
The efficacy and safety of new type 2 diabetes pharmacological treatment are covered by three original papers [11,12,13]. The Yu-Chuan Kang et al. study includes a large population sample and an extended follow-up to evaluate the association between dipeptidyl peptidase-4 inhibitors and diabetic retinopathy [13]. This could be the first signal for a new safety risk of a pharmacological class of drugs used by millions worldwide.
The COVID-19 pandemic was first reported in China in December 2019 and continues to be a devastating condition for global health and economy. The COVID-19 disease has immediate implications for common chronic metabolic disorders such as type 2 diabetes. Both direct infection and the associated distress due to preventive measures in the general population have worsened the control of type 2 diabetes. Some factors indicate that COVID-19 or other coronavirus-caused diseases can be seasonal or persistent in the future. Type 2 diabetes has a strong negative effect on the prognosis of patients with COVID-19. Three papers in this Special Issue review the implications of this disease in relation to diabetes [14,15,16].
Finally, the aim of researchers in this field should be to make all these remarkable advances accessible to those populations experiencing more difficulties due to sociodemographic factors such as cultural deprivation, sex discrimination, or limited income [17,18,19].
Acknowledgments
The authors acknowledge the continuous editorial assistance of Nicole Quinn, Always English S.L.
Author Contributions
Conceptualization, writing—original draft preparation, writing—review and editing were equally done by F.G.-P. and C.A. All authors have read and agreed to the published version of the manuscript.
Conflicts of Interest
The authors declare no conflict of interest.
Funding Statement
This research received no external funding.
Footnotes
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.Roth G.A., Abate D., Abate K.H., Abay S.M., Abbafati C., Abbasi N., Abbastabar H., Abd-Allah F., Abdela J., Abdelalim A., et al. Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: A systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392:1736–1788. doi: 10.1016/S0140-6736(18)32203-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.James S.L., Abate D., Abate K.H., Abay S.M., Abbafati C., Abbasi N., Abbastabar H., Abd-Allah F., Abdela J., Abdelalim A., et al. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: A systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392:1789–1858. doi: 10.1016/S0140-6736(18)32279-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Rashid M., Alkhodari M., Mukit A., Ahmed K.I.U., Mostafa R., Parveen S., Khandoker A.H. Machine Learning for Screening Microvascular Complications in Type 2 Diabetic Patients Using Demographic, Clinical, and Laboratory Profiles. J. Clin. Med. 2022;11:903. doi: 10.3390/jcm11040903. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Shin D.Y., Lee B., Yoo W.S., Park J.W., Hyun J.K. Prediction of Diabetic Sensorimotor Polyneuropathy Using Machine Learning Techniques. J. Clin. Med. 2021;10:4576. doi: 10.3390/jcm10194576. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Huang L.Y., Chen F.Y., Jhou M.J., Kuo C.H., Wu C.Z., Lu C.H., Chen Y.L., Pei D., Cheng Y.F., Lu C.J. Comparing Multiple Linear Regression and Machine Learning in Predicting Diabetic Urine Albumin–Creatinine Ratio in a 4-Year Follow-Up Study. J. Clin. Med. 2022;11:3661. doi: 10.3390/jcm11133661. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Filipov A., Fuchshuber H., Kraus J., Ebert A.D., Sandikci V., Alonso A. Measuring of Advanced Glycation End Products in Acute Stroke Care: Skin Autofluorescence as a Predictor of Ischemic Stroke Outcome in Patients with Diabetes Mellitus. J. Clin. Med. 2022;11:1625. doi: 10.3390/jcm11061625. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Luneva E.B., Vasileva A.A., Karelkina E.V., Boyarinova M.A., Mikhaylov E.N., Ryzhkov A.V., Babenko A.Y., Konradi A.O., Moiseeva O.M. Simple Predictors for Cardiac Fibrosis in Patients with Type 2 Diabetes Mellitus: The Role of Circulating Biomarkers and Pulse Wave Velocity. J. Clin. Med. 2022;11:2843. doi: 10.3390/jcm11102843. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Lee D.Y., Kim J., Park S., Park S.Y., Yu J.H., Seo J.A., Kim N.H., Yoo H.J., Kim S.G., Choi K.M., et al. Fasting Glucose Variability as a Risk Indicator for End-Stage Kidney Disease in Patients with Diabetes: A Nationwide Population-Based Study. J. Clin. Med. 2021;10:5948. doi: 10.3390/jcm10245948. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Martínez-Montoro J.I., García-Fontana B., García-Fontana C., Muñoz-Torres M. Evaluation of Quality and Bone Microstructure Alterations in Patients with Type 2 Diabetes: A Narrative Review. J. Clin. Med. 2022;11:2206. doi: 10.3390/jcm11082206. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Mantovani A., Dalbeni A., Beatrice G., Cappelli D., Gomez-Peralta F. Non-Alcoholic Fatty Liver Disease and Risk of Macro- and Microvascular Complications in Patients with Type 2 Diabetes. J. Clin. Med. 2022;11:968. doi: 10.3390/jcm11040968. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Bellido V., Abreu Padín C., Catarig A.M., Clark A., Barreto Pittol S., Delgado E. Once-Weekly Semaglutide Use in Patients with Type 2 Diabetes: Results from the SURE Spain Multicentre, Prospective, Observational Study. J. Clin. Med. 2022;11:4938. doi: 10.3390/jcm11174938. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Barboza J.J., Huamán M.R., Melgar B., Diaz-Arocutipa C., Valenzuela-Rodriguez G., Hernandez A.V. Efficacy of Liraglutide in Non-Diabetic Obese Adults: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. J. Clin. Med. 2022;11:2998. doi: 10.3390/jcm11112998. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Kang E.Y.C., Kang C., Wu W.C., Sun C.C., Chen K.J., Lai C.C., Chen T.H., Hwang Y.S. Association between Add-On Dipeptidyl Peptidase-4 Inhibitor Therapy and Diabetic Retinopathy Progression. J. Clin. Med. 2021;10:2871. doi: 10.3390/jcm10132871. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Bellido V., Pérez A. COVID-19 and Diabetes. J. Clin. Med. 2021;10:5341. doi: 10.3390/jcm10225341. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Gómez-Huelgas R., Gómez-Peralta F. Perceptions about the Management of Patients with DM2 and COVID-19 in the Hospital Care Setting. J. Clin. Med. 2022;11:4507. doi: 10.3390/jcm11154507. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Orozco-Beltrán D., Merino-Torres J.F., Pérez A., Cebrián-Cuenca A.M., Párraga-Martínez I., Ávila-Lachica L., Rojo-Martínez G., Pomares-Gómez F.J., Álvarez-Guisasola F., Sánchez-Molla M., et al. Diabetes Does Not Increase the Risk of Hospitalization Due to COVID-19 in Patients Aged 50 Years or Older in Primary Care—APHOSDIAB—COVID-19 Multicenter Study. J. Clin. Med. 2022;11:2092. doi: 10.3390/jcm11082092. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Ramírez-Morros A., Franch-Nadal J., Real J., Gratacòs M., Mauricio D. Sex Differences in Cardiovascular Prevention in Type 2: Diabetes in a Real-World Practice Database. J. Clin. Med. 2022;11:2196. doi: 10.3390/jcm11082196. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Desse T.A., Namara K.M., Yifter H., Manias E. Development of a Complex Intervention for Effective Management of Type 2 Diabetes in a Developing Country. J. Clin. Med. 2022;11:1149. doi: 10.3390/jcm11051149. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Dobbie L.J., Kassab M., Davison A.S., Grace P., Cuthbertson D.J., Hydes T.J. Low Screening Rates Despite a High Prevalence of Significant Liver Fibrosis in People with Diabetes from Primary and Secondary Care. J. Clin. Med. 2021;10:5755. doi: 10.3390/jcm10245755. [DOI] [PMC free article] [PubMed] [Google Scholar]