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Physiology and Biochemistry

Emerging therapeutic options in the management of diabetes: recent trends, challenges and future directions

International Journal of Obesity volume 47, pages 1179–1199 (2023)Cite this article

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Abstract

Diabetes is a serious health issue that causes a progressive dysregulation of carbohydrate metabolism due to insufficient insulin hormone, leading to consistently high blood glucose levels. According to the epidemiological data, the prevalence of diabetes has been increasing globally, affecting millions of individuals. It is a long-term condition that increases the risk of various diseases caused by damage to small and large blood vessels. There are two main subtypes of diabetes: type 1 and type 2, with type 2 being the most prevalent. Genetic and molecular studies have identified several genetic variants and metabolic pathways that contribute to the development and progression of diabetes. Current treatments include gene therapy, stem cell therapy, statin therapy, and other drugs. Moreover, recent advancements in therapeutics have also focused on developing novel drugs targeting these pathways, including incretin mimetics, SGLT2 inhibitors, and GLP-1 receptor agonists, which have shown promising results in improving glycemic control and reducing the risk of complications. However, these treatments are often expensive, inaccessible to patients in underdeveloped countries, and can have severe side effects. Peptides, such as glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide-1 (GLP-1), are being explored as a potential therapy for diabetes. These peptides are postprandial glucose-dependent pancreatic beta-cell insulin secretagogues and have received much attention as a possible treatment option. Despite these advances, diabetes remains a major health challenge, and further research is needed to develop effective treatments and prevent its complications. This review covers various aspects of diabetes, including epidemiology, genetic and molecular basis, and recent advancements in therapeutics including herbal and synthetic peptides.

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Fig. 1
Fig. 2: The underlying molecular mechanism of novel peptides that alleviate IR via the IRS-1/PI3K/Akt and AMPK signaling pathways.

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The datasets utilized and/or analyzed during this study are included in the manuscript.

References

  1. American Diabetes A. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2009;32:S62–7.

  2. Fareed M, Chauhan W, Fatma R, Din I, Afzal M, Ahmed Z. Next-generation sequencing technologies in diabetes research. Diabetes Epidemiol Manag. 2022;7:100097.

    Google Scholar 

  3. Lampasona V, Petrone A, Tiberti C, Capizzi M, Spoletini M, di Pietro S, et al. Zinc transporter 8 antibodies complement GAD and IA-2 antibodies in the identification and characterization of adult-onset autoimmune diabetes: Non Insulin Requiring Autoimmune Diabetes (NIRAD) 4. Diabetes Care. 2010;33:104–8.

    CAS  PubMed  Google Scholar 

  4. Galicia-Garcia U, Benito-Vicente A, Jebari S, Larrea-Sebal A, Siddiqi H. et al. Pathophysiology of type 2 diabetes mellitus. Int J Mol Sci. 2020;21:6275.

    PubMed  PubMed Central  Google Scholar 

  5. Plows JF, Stanley JL, Baker PN, Reynolds CM, Vickers MH. The pathophysiology of gestational diabetes mellitus. Int J Mol Sci. 2018;19:3342.

    PubMed  PubMed Central  Google Scholar 

  6. Skoczek D, Dulak J, Kachamakova-Trojanowska N. Maturity onset diabetes of the young-new approaches for disease modelling. Int J Mol Sci. 2021;14:7553.

    Google Scholar 

  7. Cole JB, Florez JC. Genetics of diabetes mellitus and diabetes complications. Nat Rev Nephrol. 2020;16:377–90.

    PubMed  PubMed Central  Google Scholar 

  8. Ikle JM, Gloyn AL. 100 YEARS OF INSULIN: a brief history of diabetes genetics: insights for pancreatic beta-cell development and function. J Endocrinol. 2021;250:R23–R35.

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Redondo M, Yu L, Hawa M, Mackenzie T, Pyke D, Eisenbarth G, et al. Heterogeneity of type I diabetes: analysis of monozygotic twins in Great Britain and the United States. Diabetologia. 2001;44:354–62.

    CAS  PubMed  Google Scholar 

  10. Mychaleckyj JC, Noble JA, Moonsamy PV, Carlson JA, Varney MD, Post J, et al. HLA genotyping in the international Type 1 Diabetes Genetics Consortium. Clinical Trials. 2010;7:S75–S87.

    PubMed  Google Scholar 

  11. Ounissi-Benkalha H, Polychronakos C. The molecular genetics of type 1 diabetes: new genes and emerging mechanisms. Trends Mol Med. 2008;14:268–75.

    CAS  PubMed  Google Scholar 

  12. Noble JA, Valdes AM. Genetics of the HLA region in the prediction of type 1 diabetes. Curr Diab Rep. 2011;11:533–42.

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Sanchez-Mazas A, Meyer D. The relevance of HLA sequencing in population genetics studies. J Immunol Res. 2014;2014.

  14. Buhler S, Sanchez-Mazas A. HLA DNA sequence variation among human populations: molecular signatures of demographic and selective events. PloS One. 2011;6:e14643.

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Eisenbarth GSJD. Banting Lecture 2009: An unfinished journey: molecular pathogenesis to prevention of type 1A diabetes. Diabetes. 2010;59:759.

  16. Wyatt RC, Lanzoni G, Russell MA, Gerling I, Richardson SJ. What the HLA-I!-Classical and non-classical HLA Class I and their potential roles in type 1 diabetes. Curr Diab Rep. 2019;19:159.

    PubMed  PubMed Central  Google Scholar 

  17. Redondo MJ, Steck AK. Pugliese AJPd. Genetics of type 1. Diabetes. 2018;19:346–53.

    Google Scholar 

  18. Noble JA, Erlich HA. Genetics of type 1 diabetes. Cold Spring Harb Perspect Med. 2012;2:a007732.

    PubMed  PubMed Central  Google Scholar 

  19. Laine AP, Valta M, Toppari J, et al. Non-HLA Gene Polymorphisms in the Pathogenesis of Type 1 Diabetes: Phase and Endotype Specific Effects. Front Immunol. 2022;13:909020.

    PubMed  PubMed Central  Google Scholar 

  20. Pociot F, Akolkar B, Concannon P, et al. Genetics of type 1 diabetes: what’s next?. Diabetes. 2010;59:1561–71.

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Pérez de Nanclares G, Bilbao JR, Calvo B, Vitoria JC, Vázquez F, Castaño L. 5′‐Insulin gene VNTR polymorphism is specific for type 1 diabetes: no association with celiac or Addison’s disease. Ann N Y Acad Sci. 2003;1005:319–23.

    PubMed  Google Scholar 

  22. Pugliese A, Zeller M, Fernandez A, Zalcberg LJ, Bartlett RJ, Ricordi C, et al. The insulin gene is transcribed in the human thymus and transcription levels correlate with allelic variation at the INS VNTR-IDDM2 susceptibility locus for type 1 diabetes. Nat Genet. 1997;15:293–7.

    CAS  PubMed  Google Scholar 

  23. Bennett S, Lucassen A, Gough S, Powell E, Undlien D, Pritchard L, et al. Susceptibility to human type 1 diabetes at IDDM2 is determined by tandem repeat variation at the insulin gene minisatellite locus. Nat Genet. 1995;9:284–92.

    CAS  PubMed  Google Scholar 

  24. Wang J, Liu L, Ma J, Sun F, Zhao Z, Gu M. Common variants on cytotoxic T lymphocyte antigen-4 polymorphisms contributes to type 1 diabetes susceptibility: evidence based on 58 studies. PloS One. 2014;9:e85982.

    PubMed  PubMed Central  Google Scholar 

  25. Kavvoura FK, Ioannidis JP. CTLA-4 gene polymorphisms and susceptibility to type 1 diabetes mellitus: a HuGE review and meta-analysis. Am J Epidemiol. 2005;162:3–16.

    PubMed  Google Scholar 

  26. Bottini N, Musumeci L, Alonso A, Rahmouni S, Nika K, Rostamkhani M, et al. A functional variant of lymphoid tyrosine phosphatase is associated with type I diabetes. Nat Genet. 2004;36:337–8.

    CAS  PubMed  Google Scholar 

  27. Begovich AB, Carlton VE, Honigberg LA, Schrodi SJ, Chokkalingam AP, Alexander HC, et al. A missense single-nucleotide polymorphism in a gene encoding a protein tyrosine phosphatase (PTPN22) is associated with rheumatoid arthritis. Am J Hum Genet. 2004;75:330–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Lempainen J, Laine AP, Hammais A, et al. Non-HLA gene effects on the disease process of type 1 diabetes: From HLA susceptibility to overt disease. J Autoimmun. 2015;61:45–53.

    CAS  PubMed  Google Scholar 

  29. Steck AK, Dong F, Wong R, Fouts A, Liu E, Romanos J, et al. Improving prediction of type 1 diabetes by testing non‐HLA genetic variants in addition to HLA markers. Pediatr Diabetes. 2014;15:355–62.

    CAS  PubMed  Google Scholar 

  30. Lempainen J, Hermann R, Veijola R, Simell O, Knip M, Ilonen J. Effect of the PTPN22 and INS risk genotypes on the progression to clinical type 1 diabetes after the initiation of β-cell autoimmunity. Diabetes. 2012;61:963–966.

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Bugawan TL, Mirel DB, Valdes AM, Panelo A, Pozzilli P, Erlich HA. Association and interaction of the IL4R, IL4, and IL13 loci with type 1 diabetes among Filipinos. Am J Hum Genet. 2003;72:1505–14.

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Noble JA. Immunogenetics of type 1 diabetes: a comprehensive review. J Autoimmun. 2015;64:101–12.

    CAS  PubMed  Google Scholar 

  33. Shastry A, Sedimbi S, Rajalingam R, Nikitina‐Zake L, Rumba I, Wigzell H, et al. Combination of KIR 2DL2 and HLA‐C1 (Asn80) confers susceptibility to type 1 diabetes in Latvians. Int J of Immunogenet. 2008;35:439–46.

    CAS  Google Scholar 

  34. Sabater L, Ferrer-Francesch X, Sospedra M, Caro P, Juan M, Pujol-Borrell R. Insulin alleles and autoimmune regulator (AIRE) gene expression both influence insulin expression in the thymus. J Autoimmun. 2005;25:312–8.

    CAS  PubMed  Google Scholar 

  35. Visperas A, Vignali DA. Are regulatory T cells defective in type 1 diabetes and can we fix them? J Immunol. 2016;197:3762–70.

    CAS  PubMed  Google Scholar 

  36. Flanagan SE, Haapaniemi E, Russell MA, Caswell R, Allen HL, De Franco E, et al. Activating germline mutations in STAT3 cause early-onset multi-organ autoimmune disease. Nat Genet. 2014;46:812–4.

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Udler MS, Kim J, von Grotthuss M, Bonàs-Guarch S, Cole JB, Chiou J, et al. Type 2 diabetes genetic loci informed by multi-trait associations point to disease mechanisms and subtypes: a soft clustering analysis. PLoS Med. 2018;15:e1002654.

    PubMed  PubMed Central  Google Scholar 

  38. Mahajan A, Taliun D, Thurner M, Robertson NR, Torres JM, Rayner NW, et al. Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nat Genet. 2018;50:1505–13.

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Udler MS. Type 2 diabetes: multiple genes, multiple diseases. Curr Diab Rep. 2019;19:1–9.

    Google Scholar 

  40. Ge Y, Onengut-Gumuscu S, Quinlan AR, Mackey AJ, Wright JA, Buckner JH, et al. Targeted deep sequencing in multiple-affected sibships of European ancestry identifies rare deleterious variants in PTPN22 that confer risk for type 1 diabetes. Diabetes. 2016;65:794–802.

    CAS  PubMed  Google Scholar 

  41. Fajans SS, Bell GI. MODY: history, genetics, pathophysiology, and clinical decision making. Diabetes Care. 2011;34:1878–84.

    PubMed  PubMed Central  Google Scholar 

  42. Chen J, Sun M, Adeyemo A, Pirie F, Carstensen T, Pomilla C, et al. Genome-wide association study of type 2 diabetes in Africa. Diabetologia. 2019;62:1204–11.

    PubMed  PubMed Central  Google Scholar 

  43. Qi Q, Stilp AM, Sofer T, Moon J-Y, Hidalgo B, Szpiro AA, et al. Genetics of type 2 diabetes in US Hispanic/Latino individuals: results from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). Diabetes. 2017;66:1419–25.

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Spracklen CN, Horikoshi M, Kim YJ, Lin K, Bragg F, Moon S, et al. Identification of type 2 diabetes loci in 433,540 East Asian individuals. Nature. 2020;582:240–5.

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Mahajan A, Wessel J, Willems SM, Zhao W, Robertson NR, Chu AY, et al. Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes. Nat Genet. 2018;50:559–71.

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Mahajan A, Go MJ, Zhang W, Below JE, Gaulton KJ, Ferreira T, et al. Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility. Nat Genet. 2014;46:234–44.

    CAS  PubMed  Google Scholar 

  47. Mahajan A, Sim X, Zhang W, Below JE, Kitajima H, Speidel L, et al. 303-OR: ADA presidents’ select abstract: transethnic association study of type 2 diabetes in more than a million individuals. Diabetes. 2019;68.

  48. Vujkovic M, Keaton JM, Lynch JA, Miller DR, Zhou J, Tcheandjieu C, Huffman JE, et al. Discovery of 318 new risk loci for type 2 diabetes and related vascular outcomes among 1.4 million participants in a multi-ancestry meta-analysis. Nat Genet. 2020;52:680–91.

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Langenberg C, Lotta LA. Genomic insights into the causes of type 2 diabetes. Lancet. 2018;391:2463–74.

    CAS  PubMed  Google Scholar 

  50. Flannick J, Mercader JM, Fuchsberger C, Udler MS, Mahajan A, Wessel J, et al. Exome sequencing of 20,791 cases of type 2 diabetes and 24,440 controls. Nature. 2019;570:71–6.

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Dwivedi OP, Lehtovirta M, Hastoy B, Chandra V, Krentz NA, Kleiner S, et al. Loss of ZnT8 function protects against diabetes by enhanced insulin secretion. Nat Genet. 2019;51:1596–606.

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Fuchsberger C, Flannick J, Teslovich TM, Mahajan A, Agarwala V, Gaulton KJ, et al. The genetic architecture of type 2 diabetes. Nature. 2016;536:41–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Jambaljav B, Tanaka D, Nagashima K, Harashima SI, Harada N, Harada T, et al. Whole-exome sequencing in a Japanese family with highly aggregated diabetes identifies a candidate susceptibility mutation in ADAMTSL3. Diabetes Res Clin Pract. 2018;135:143–9.

    CAS  PubMed  Google Scholar 

  54. Johansson S, Irgens H, Chudasama KK, Molnes J, Aerts J, Roque FS, et al. Exome sequencing and genetic testing for MODY. PLoS One. 2012;7:e38050.

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Yki-Jarvinen H, Koivisto VA. Natural course of insulin resistance in type I diabetes. N Engl J Med. 1986;315:224–30.

    CAS  PubMed  Google Scholar 

  56. Cleland SJ, Fisher BM, Colhoun HM, Sattar N, Petrie JR. Insulin resistance in type 1 diabetes: what is ‘double diabetes’ and what are the risks? Diabetologia. 2013;56:1462–70.

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Morris AP, Voight BF, Teslovich TM, Ferreira T, Segre AV, Steinthorsdottir V, et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat Genet. 2012;44:981–90.

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Scott RA, Lagou V, Welch RP, Wheeler E, Montasser ME, Luan J, et al. Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways. Nat Genet. 2012;44:991–1005.

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Gaulton KJ, Ferreira T, Lee Y, Raimondo A, Magi R, Reschen ME, et al. Genetic fine mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci. Nat Genet. 2015;47:1415–25.

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Portha B, Chavey A, Movassat J. Early-life origins of type 2 diabetes: fetal programming of the beta-cell mass. Exp Diabetes Res. 2011;2011:105076.

    PubMed  PubMed Central  Google Scholar 

  61. Matveyenko AV, Butler PC. Relationship between beta-cell mass and diabetes onset. Diabetes Obes Metab. 2018;10:23–31.

    Google Scholar 

  62. Campbell-Thompson M, Fu A, Kaddis JS, Wasserfall C, Schatz DA, Pugliese A, et al. Insulitis and beta-cell mass in the natural history of type 1 diabetes. Diabetes. 2016;65:719–31.

    CAS  PubMed  Google Scholar 

  63. Ferrannini E, Gastaldelli A, Miyazaki Y, Matsuda M, Mari A, DeFronzo RA. beta-Cell function in subjects spanning the range from normal glucose tolerance to overt diabetes: a new analysis. J Clin Endocrinol Metab. 2005;90:493–500.

    CAS  PubMed  Google Scholar 

  64. Skyler JS, Bakris GL, Bonifacio E, Darsow T, Eckel RH, Groop L, et al. Differentiation of diabetes by pathophysiology, natural history, and prognosis. Diabetes. 2017;66:241–55.

    CAS  PubMed  Google Scholar 

  65. Christensen AA, Gannon M. The beta cell in type 2 diabetes. Curr Diab Rep. 2019;19:81.

    PubMed  Google Scholar 

  66. Halban PA, Polonsky KS, Bowden DW, Hawkins MA, Ling C, Mather KJ, et al. beta-cell failure in type 2 diabetes: postulated mechanisms and prospects for prevention and treatment. Diabetes Care. 2014;37:1751–8.

    PubMed  PubMed Central  Google Scholar 

  67. Yamamoto WR, Bone RN, Sohn P, Syed F, Reissaus CA, Mosley AL, et al. Endoplasmic reticulum stress alters ryanodine receptor function in the murine pancreatic beta cell. J Biol Chem. 2019;294:168–81.

    CAS  PubMed  Google Scholar 

  68. Esser N, Legrand-Poels S, Piette J, Scheen AJ, Paquot N. Inflammation as a link between obesity, metabolic syndrome and type 2 diabetes. Diabetes Res Clin Pract. 2014;105:141–50.

    CAS  PubMed  Google Scholar 

  69. Pradhan AD, Manson JE, Rifai N, Buring JE, Ridker PM. C-reactive protein, interleukin 6, and risk of developing type 2 diabetes mellitus. JAMA. 2001;286:327–34.

    CAS  PubMed  Google Scholar 

  70. Bunney PE, Zink AN, Holm AA, Billington CJ, Kotz CM. Orexin activation counteracts decreases in nonexercise activity thermogenesis (NEAT) caused by high-fat diet. Physiol Behav. 2017;176:139–48.

    CAS  PubMed  PubMed Central  Google Scholar 

  71. Venkatasamy VV, Pericherla S, Manthuruthil S, Mishra S, Hanno R. Effect of physical activity on insulin resistance, inflammation and oxidative stress in diabetes mellitus. J Clin Diagn Res. 2013;7:1764–6.

    PubMed  PubMed Central  Google Scholar 

  72. Sircana A, Framarin L, Leone N, Berrutti M, Castellino F, Parente R, et al. Altered gut microbiota in type 2 diabetes: just a coincidence? Curr Diab Rep. 2018;18:98.

    PubMed  Google Scholar 

  73. Wang R, Tang R, Li B, Ma X, Schnabl B, Tilg H. Gut microbiome, liver immunology, and liver diseases. Cell Mol Immunol. 2021;18:4–17.

    PubMed  Google Scholar 

  74. Crittenden S, Goepp M, Pollock J, Robb CT, Smyth DJ, Zhou Y, et al. Prostaglandin E(2) promotes intestinal inflammation via inhibiting microbiota-dependent regulatory T cells. Sci Adv. 2021;7.

  75. Han Q, Wang J, Li W, Chen ZJ, Du Y. Androgen-induced gut dysbiosis disrupts glucolipid metabolism and endocrinal functions in polycystic ovary syndrome. Microbiome. 2021;9:101.

    CAS  PubMed  PubMed Central  Google Scholar 

  76. Zhou Z, Sun B, Yu D, Zhu C. Gut microbiota: an important player in type 2 diabetes mellitus. Front Cell Infect Microbiol. 2022;12:834485.

    CAS  PubMed  PubMed Central  Google Scholar 

  77. Takagi T, Naito Y, Kashiwagi S, Uchiyama K, Mizushima K, Kamada K, et al. Changes in the gut microbiota are associated with hypertension, hyperlipidemia, and type 2 diabetes mellitus in Japanese subjects. Nutrients. 2020;12.

  78. Wang TY, Zhang XQ, Chen AL, Zhang J, Lv BH, Ma MH, et al. A comparative study of microbial community and functions of type 2 diabetes mellitus patients with obesity and healthy people. Appl Microbiol Biotechnol. 2020;104:7143–53.

    CAS  PubMed  Google Scholar 

  79. Yang K, Niu J, Zuo T, Sun Y, Xu Z, Tang W, et al. Alterations in the gut virome in obesity and type 2 diabetes mellitus. Gastroenterology. 2021;161:1257–69.e13.

    CAS  PubMed  Google Scholar 

  80. Al Bataineh MT, Dash NR, Bel Lassen P, Banimfreg BH, Nada AM, Belda E, et al. Revealing links between gut microbiome and its fungal community in Type 2 Diabetes Mellitus among Emirati subjects: a pilot study. Sci Rep. 2020;10:9624.

    CAS  PubMed  PubMed Central  Google Scholar 

  81. Federation I IDF Diabetes Atlas Eighth edition 2019. International Diabetes Federation. IDF Diabetes Atlas, 9th edn. Brussels. Belgium: International Diabetes Federation 2019.

  82. Forouhi NG, Wareham N. Epidemiology of diabetes. Medicine. 2019;47:22–7.

    Google Scholar 

  83. Fitzmaurice C, Abate D, Abbasi N. Global Burden of Disease Cancer Collaboration Global, regional, and national cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life-years for 29 cancer groups, 1990 to 2017: a systemic analysis for the Global Burden of Disease Study. Jama Oncol. 2020;6:444.

    Google Scholar 

  84. Saeedi P, Petersohn I, Salpea P, et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes Res Clin Pract. 2019;157:107843.

    PubMed  Google Scholar 

  85. Herman WH, Ye W, Griffin SJ, Simmons RK, Davies MJ, Khunti K, et al. Early detection and treatment of type 2 diabetes reduce cardiovascular morbidity and mortality: a simulation of the results of the Anglo-Danish-Dutch Study of Intensive Treatment in People With Screen-Detected Diabetes in Primary Care (ADDITION-Europe). Diabetes Care. 2015;38:1449–55.

    PubMed  PubMed Central  Google Scholar 

  86. Chong S, Ding D, Byun R, Comino E, Bauman A, Jalaludin B. Lifestyle changes after a diagnosis of type 2 diabetes. diabetes spectr. Diabetes Spectr. 2017;30:43–50.

    PubMed  PubMed Central  Google Scholar 

  87. Cotter AP, Durant N, Agne AA, Cherrington A. Internet interventions to support lifestyle modification for diabetes management: a systematic review of the evidence. J Diabetes Complications. 2014;28:243–51.

    PubMed  Google Scholar 

  88. Contreras I, Vehi J. Artificial intelligence for diabetes management and decision support: literature review. J Med Internet Res. 2018;20:e10775.

    PubMed  PubMed Central  Google Scholar 

  89. Ellahham S. Artificial intelligence: the future for diabetes care. Am J Med. 2020;133:895–900.

    CAS  PubMed  Google Scholar 

  90. Buch V, Varughese G, Maruthappu M. Artificial intelligence in diabetes care. Diabetic Med. 2018;35:495–7.

    CAS  PubMed  Google Scholar 

  91. Food U Drug Administration (FDA). FDA permits marketing of artificial intelligence-based device to detect certain diabetes-related eye problems. 2018.

  92. Association AD. 11. Microvascular complications and foot care: standards of medical care in diabetes—2020. Diabetes Care. 2020;43:S135–S51.

    Google Scholar 

  93. Ellahham S, Ellahham N. Use of artificial intelligence for improving patient flow and healthcare delivery. J Comp Sci Syst Biol. 2019;12:2.

    Google Scholar 

  94. Bellemo V, Lim G, Rim TH, Tan GS, Cheung CY, Sadda S, et al. Artificial intelligence screening for diabetic retinopathy: the real-world emerging application. Curr Diab Rep. 2019;19:1–12.

    Google Scholar 

  95. Dunbar CE, High KA, Joung JK, Kohn DB, Ozawa K, Sadelain M. Gene therapy comes of age. Science. 2018;359:eaan4672.

    PubMed  Google Scholar 

  96. Tsokos GC, Nepom GT. Gene therapy in the treatment of autoimmune diseases. J Clin Invest. 2000;106:181–3.

    CAS  PubMed  PubMed Central  Google Scholar 

  97. Chellappan DK, Yap WS, Na BAS, Gupta G, Dua K. Current therapies and targets for type 2 diabetes mellitus. Panminerva Med. 2018;60:117–31.

    PubMed  Google Scholar 

  98. Bakay M, Pandey R, Hakonarson H. Genes involved in type 1 diabetes: an update. Genes. 2013;4:499–521.

    PubMed  PubMed Central  Google Scholar 

  99. Chan L, Fujimiya M, Kojima H. In vivo gene therapy for diabetes mellitus. Trends Mol Med. 2003;9:430–5.

    CAS  PubMed  Google Scholar 

  100. Zalzman M, Gupta S, Giri RK, et al. Reversal of hyperglycemia in mice by using human expandable insulin-producing cells differentiated from fetal liver progenitor cells. Proc Natl Acad Sci USA. 2003;100:7253–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  101. Kwak SH, Park KS. Recent progress in genetic and epigenetic research on type 2 diabetes. Exp Mol Med. 2016;48:e220.

    CAS  PubMed  PubMed Central  Google Scholar 

  102. Abderrazak A, El Hadri K, Bosc E, Blondeau B, Slimane M-N, Büchele B, et al. Inhibition of the inflammasome NLRP3 by arglabin attenuates inflammation, protects pancreatic β-cells from apoptosis, and prevents type 2 diabetes mellitus development in ApoE2Ki mice on a chronic high-fat diet. J Pharmacol Exp Ther. 2016;357:487–94.

    CAS  PubMed  Google Scholar 

  103. O'Doherty RM, Jensen PB, Anderson P, et al. Activation of direct and indirect pathways of glycogen synthesis by hepatic overexpression of protein targeting to glycogen. J Clin Invest. 2000;105:479–88.

    CAS  PubMed  PubMed Central  Google Scholar 

  104. Newgard CB, Brady MJ, O'Doherty RM, Saltiel AR. Organizing glucose disposal: emerging roles of the glycogen targeting subunits of protein phosphatase-1. Diabetes. 2000;49:1967–77.

    CAS  PubMed  Google Scholar 

  105. Dong H, SLJTiE Woo. Hepatic insulin production for type 1 diabetes. Trends Endocrinol Metab. 2001;12:441–6.

    CAS  PubMed  Google Scholar 

  106. Auricchio A, Gao GP, Yu QC, et al. Constitutive and regulated expression of processed insulin following in vivo hepatic gene transfer. Gene Ther. 2002;9:963–71.

    CAS  PubMed  Google Scholar 

  107. Kojima H, Fujimiya M, Matsumura K, et al. NeuroD-betacellulin gene therapy induces islet neogenesis in the liver and reverses diabetes in mice. Nat Med. 2003;9:596–603.

    CAS  PubMed  Google Scholar 

  108. Pastors JG, Warshaw H, Daly A, Franz M, Kulkarni K. The evidence for the effectiveness of medical nutrition therapy in diabetes management. Diabetes Care. 2002;25:608–13.

    PubMed  Google Scholar 

  109. Tiwari P. Recent trends in therapeutic approaches for diabetes management: a comprehensive update. J Diabetes Res. 2015;2015.

  110. Association AD. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2014;37:S81–S90.

    Google Scholar 

  111. Evert AB, Boucher JL, Cypress M, Dunbar SA, Franz MJ, Mayer-Davis EJ, et al. Nutrition therapy recommendations for the management of adults with diabetes. Diabetes Care. 2014;37:S120–S43.

    PubMed  Google Scholar 

  112. Bantle JP, Wylie-Rosett J, Albright AL, Apovian CM, Clark NG, Franz MJ, et al. Nutrition recommendations and interventions for diabetes: a position statement of the American Diabetes Association. Diabetes Care. 2008;31:S61–S78.

    CAS  PubMed  Google Scholar 

  113. DiSanto RM, Subramanian V, Gu Z. Recent advances in nanotechnology for diabetes treatment. Wiley Interdiscip Rev Nanomed Nanobiotechnol. 2015;7:548–64.

    CAS  PubMed  PubMed Central  Google Scholar 

  114. Aloke C, Egwu CO, Aja PM, Obasi NA, Chukwu J, Akumadu BO, et al. Current advances in the management of diabetes mellitus. Biomedicines. 2022;10:2436.

    CAS  PubMed  PubMed Central  Google Scholar 

  115. Tamborlane W, Beck R, Bode B, Buckingham B, Chase H, Clemons R. Juvenile Diabetes Research Foundation Continuous Glucose Monitoring Study Group et al. Continuous glucose monitoring and intensive treatment of type 1 diabetes. N Engl J Med.2008;359:1464–76.

    PubMed  Google Scholar 

  116. Edelman SV, Argento NB, Pettus J, Hirsch IB. Clinical implications of real-time and intermittently scanned continuous glucose monitoring. Diabetes Care. 2018;41:2265–74.

    CAS  PubMed  Google Scholar 

  117. Veiseh O, Tang BC, Whitehead KA, Anderson DG, Langer R. Managing diabetes with nanomedicine: challenges and opportunities. Nat Rev Drug Discov. 2015;14:45–57.

    CAS  PubMed  Google Scholar 

  118. Scognamiglio V. Nanotechnology in glucose monitoring: advances and challenges in the last 10 years. Biosens Bioelectron. 2013;47:12–25.

    CAS  PubMed  Google Scholar 

  119. Grunberger G. The need for better insulin therapy. Diabetes Obes Metab. 2013;15:1–5.

    CAS  PubMed  Google Scholar 

  120. Lagopati N, Pavlatou E. Nanotechnology in diabetes management. Interv Obes Diabetes. 2021;5:419–24.

    Google Scholar 

  121. Lemmerman LR, Das D, Higuita-Castro N, Mirmira RG, Gallego-Perez D. Nanomedicine-based strategies for diabetes: diagnostics, monitoring, and treatment. Trends Endocrinol Metab. 2020;31:448–58.

    CAS  PubMed  PubMed Central  Google Scholar 

  122. McCall MD, Toso C, Baetge EE, Shapiro AJ. Are stem cells a cure for diabetes? Clin Sci. 2010;118:87–97.

    Google Scholar 

  123. Abdulazeez SS. Diabetes treatment: a rapid review of the current and future scope of stem cell research. Saudi Pharm J. 2015;23:333–40.

    Google Scholar 

  124. Bonner-Weir S, Baxter LA, Schuppin GT, Smith FE. A second pathway for regeneration of adult exocrine and endocrine pancreas: a possible recapitulation of embryonic development. Diabetes. 1993;42:1715–20.

    CAS  PubMed  Google Scholar 

  125. Bonner-Weir S, Taneja M, Weir GC, Tatarkiewicz K, Song K-H, Sharma A, et al. In vitro cultivation of human islets from expanded ductal tissue. Proc Natl Acad Sci. 2000;97:7999–8004.

    CAS  PubMed  PubMed Central  Google Scholar 

  126. Gao R, Ustinov J, Pulkkinen M-A, Lundin K, Korsgren O, Otonkoski T. Characterization of endocrine progenitor cells and critical factors for their differentiation in human adult pancreatic cell culture. Diabetes. 2003;52:2007–15.

    CAS  PubMed  Google Scholar 

  127. Ianus A, Holz GG, Theise ND, Hussain MA. In vivo derivation of glucose-competent pancreatic endocrine cells from bone marrow without evidence of cell fusion. J Clin Invest. 2003;111:843–50.

    CAS  PubMed  PubMed Central  Google Scholar 

  128. Hess D, Li L, Martin M, Sakano S, Hill D, Strutt B, et al. Bone marrow–derived stem cells initiate pancreatic regeneration. Nat Biotechnol. 2003;21:763–70.

    CAS  PubMed  Google Scholar 

  129. Shah RV, Goldfine AB. Statins and risk of new-onset diabetes mellitus. Circulation. 2012;126:e282–e4.

    PubMed  Google Scholar 

  130. Chen Y-H, Feng B, Chen Z-W. Statins for primary prevention of cardiovascular and cerebrovascular events in diabetic patients without established cardiovascular diseases: a meta-analysis. Exp Clin Endocrinol Diabetes. 2012;120:116–20.

    CAS  PubMed  Google Scholar 

  131. Buse J. Statin treatment in diabetes mellitus. Clin Diabetes. 2003;21:168–72.

    Google Scholar 

  132. Drummond RS, Lyall M, McKnight J. Statins should be routinely prescribed in all adults with diabetes. Pract Diabetes Int. 2010;27:404–6a.

    Google Scholar 

  133. Food U, Administration D FDA approves novel, dual-targeted treatment for type 2 diabetes. FDA News Release. 2022.

  134. Bertsch T. An introduction to tirzepatide. Clin Diabetes. 2022;40:371–2.

    PubMed  PubMed Central  Google Scholar 

  135. Bailey CJ, Flatt PR, Conlon JM. An update on peptide-based therapies for type 2 diabetes and obesity. Peptides. 2023;161:170939.

    CAS  PubMed  Google Scholar 

  136. Nauck MA, Mirna AEA, Quast DR. Meta-analysis of head-to-head clinical trials comparing incretin-based glucose-lowering medications and basal insulin: An update including recently developed glucagon-like peptide-1 (GLP-1) receptor agonists and the glucose-dependent insulinotropic polypeptide/GLP-1 receptor co-agonist tirzepatide. Diabetes Obes Metab. 2023;25:1361–71.

    CAS  PubMed  Google Scholar 

  137. Madsbad S, Holst JJ. Cardiovascular effects of incretins: focus on glucagon-like peptide-1 receptor agonists. Cardiovasc Res. 2023;119:886–904.

    CAS  PubMed  Google Scholar 

  138. Piemonti L, Keymeulen B, Gillard P, Linn T, Bosi E, Rose L, et al. Ladarixin, an inhibitor of the interleukin‐8 receptors CXCR1 and CXCR2, in new‐onset type 1 diabetes: a multicentre, randomized, double‐blind, placebo‐controlled trial. Diabetes Obes Metab. 2022;24:1840.

    CAS  PubMed  PubMed Central  Google Scholar 

  139. Kawai T, Sun B, Yoshino H, Feng D, Suzuki Y, Fukazawa M, et al. Structural basis for GLP-1 receptor activation by LY3502970, an orally active nonpeptide agonist. Proc Natl Acad Sci. 2020;117:29959–67.

    CAS  PubMed  PubMed Central  Google Scholar 

  140. Fesenko I, Azarkina R, Kirov I, Kniazev A, Filippova A, Grafskaia E, et al. Phytohormone treatment induces generation of cryptic peptides with antimicrobial activity in the Moss Physcomitrella patens. BMC Plant Biol. 2019;19:9.

    PubMed  PubMed Central  Google Scholar 

  141. Karami Z, Akbari-Adergani B. Bioactive food derived peptides: a review on correlation between structure of bioactive peptides and their functional properties. J Food Sci Technol. 2019;56:535–47.

    CAS  PubMed  PubMed Central  Google Scholar 

  142. Akbarian M, Khani A, Eghbalpour S, Uversky VN. Bioactive peptides: synthesis, sources, applications, and proposed mechanisms of action. Int J Mol Sci. 2022;23.

  143. Qiao Q, Chen L, Li X, Lu X, Xu Q. Roles of dietary bioactive peptides in redox balance and metabolic disorders. Oxid Med Cell Longev. 2021;2021:5582245.

    PubMed  PubMed Central  Google Scholar 

  144. Antony P, Vijayan R. Bioactive peptides as potential nutraceuticals for diabetes therapy: a comprehensive review. Int J Mol Sci. 2021;22.

  145. Muttenthaler M, King GF, Adams DJ, Alewood PF. Trends in peptide drug discovery. Nat Rev Drug Discov. 2021;20:309–25.

    CAS  PubMed  Google Scholar 

  146. Yan J, Zhao J, Yang R, Zhao W. Bioactive peptides with antidiabetic properties: a review. Int J Food Sci Technol. 2019;54:1909–19.

    CAS  Google Scholar 

  147. Singh AK. Dipeptidyl peptidase-4 inhibitors: novel mechanism of actions. Indian J Endocrinol Metab. 2014;18:753–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  148. Wang L, Wang N, Zhang W, Cheng X, Yan Z, Shao G, et al. Therapeutic peptides: current applications and future directions. Signal Transduct Target Ther. 2022;7:48.

    CAS  PubMed  PubMed Central  Google Scholar 

  149. Zaky AA, Simal-Gandara J, Eun JB, Shim JH, Abd El-Aty AM. Bioactivities, applications, safety, and health benefits of bioactive peptides from food and by-products: a review. Front Nutr. 2021;8:815640.

    PubMed  Google Scholar 

  150. Furman BL. The development of Byetta (exenatide) from the venom of the Gila monster as an anti-diabetic agent. Toxicon. 2012;59:464–71.

    CAS  PubMed  Google Scholar 

  151. Wajcberg E, Amarah A. Liraglutide in the management of type 2 diabetes. Drug Des Devel Ther. 2010;4:279–90.

    CAS  PubMed  PubMed Central  Google Scholar 

  152. Wang Z, Zhang X. Isolation and identification of anti-proliferative peptides from Spirulina platensis using three-step hydrolysis. J Sci Food Agric. 2017;97:918–22.

    CAS  PubMed  Google Scholar 

  153. Zambrowicz A, Eckert E, Pokora M, Bobak Ł, Dąbrowska A, Szołtysik M, et al. Antioxidant and antidiabetic activities of peptides isolated from a hydrolysate of an egg-yolk protein by-product prepared with a proteinase from Asian pumpkin (Cucurbita ficifolia). RSC Adv. 2015;5:10460–7.

    CAS  Google Scholar 

  154. Wang R, Zhao H, Pan X, Orfila C, Lu W, Ma Y. Preparation of bioactive peptides with antidiabetic, antihypertensive, and antioxidant activities and identification of alpha-glucosidase inhibitory peptides from soy protein. Food Sci Nutr. 2019;7:1848–56.

    CAS  PubMed  PubMed Central  Google Scholar 

  155. Karimi A, Azizi MH, Ahmadi Gavlighi H. Frationation of hydrolysate from corn germ protein by ultrafiltration: In vitro antidiabetic and antioxidant activity. Food Sci Nutr. 2020;8:2395–405.

    CAS  PubMed  PubMed Central  Google Scholar 

  156. Ktari N, Salem RBS-B, Bkhairia I, Slima SB, Nasri R, Salah RB, et al. Functional properties and biological activities of peptides from zebra blenny protein hydrolysates fractionated using ultrafiltration. Food Biosci. 2020;34:100539.

    Google Scholar 

  157. Dale HF, Jensen C, Hausken T, et al. Effect of a cod protein hydrolysate on postprandial glucose metabolism in healthy subjects: a double-blind cross-over trial [published correction appears in J Nutr Sci. 2019 Jan 18;8:e1]. J Nutr Sci. 2018;7:e33.

  158. Wang T, Zheng L, Zhao T, Zhang Q, Liu Z, Liu X, et al. Anti-diabetic effects of sea cucumber (Holothuria nobilis) hydrolysates in streptozotocin and high-fat-diet induced diabetic rats via activating the PI3K/Akt pathway. J Funct Foods. 2020;75.

  159. Godinho R, Mega C, Teixeira-de-Lemos E, Carvalho E, Teixeira F, Fernandes R, et al. The place of dipeptidyl peptidase-4 inhibitors in type 2 diabetes therapeutics: a “me too” or “the special one” antidiabetic class? J Diabetes Res. 2015;2015:806979.

    PubMed  PubMed Central  Google Scholar 

  160. Richter B, Bandeira-Echtler E, Bergerhoff K, Lerch C. Emerging role of dipeptidyl peptidase-4 inhibitors in the management of type 2 diabetes. Vasc Health Risk Manag. 2008;4:753–68.

    CAS  PubMed  PubMed Central  Google Scholar 

  161. Jin R, Teng X, Shang J, Wang D, Liu N. Identification of novel DPP-IV inhibitory peptides from Atlantic salmon (Salmo salar) skin. Food Res Int. 2020;133:109161.

    CAS  PubMed  Google Scholar 

  162. Gao J, Gong H, Mao X. Dipeptidyl peptidase-IV inhibitory activity and related molecular mechanism of bovine alpha-lactalbumin-derived peptides. Molecules. 2020;25.

  163. Gu H, Gao J, Shen Q, Gao D, Wang Q, Tangyu M, et al. Dipeptidyl peptidase-IV inhibitory activity of millet protein peptides and the related mechanisms revealed by molecular docking. LWT. 2021;138:110587.

    CAS  Google Scholar 

  164. Wang J, Wu T, Fang L, Liu C, Liu X, Li H, et al. Anti-diabetic effect by walnut (Juglans mandshurica Maxim.)-derived peptide LPLLR through inhibiting α-glucosidase and α-amylase, and alleviating insulin resistance of hepatic HepG2 cells. J Funct Foods. 2020;69.

  165. Depta J, Małkowska P, Wysokińska M, Todorska K, Sierawska O, Hrynkiewicz R, et al. Therapeutic role of antimicrobial peptides in diabetes mellitus. Biologics. 2022;2:92–106.

    Google Scholar 

  166. Moretta A, Scieuzo C, Petrone AM, Salvia R, Manniello MD, Franco A, et al. Antimicrobial peptides: a new hope in biomedical and pharmaceutical fields. Front Cell Infect Microbiol. 2021;11:668632.

    CAS  PubMed  PubMed Central  Google Scholar 

  167. Hirsch JG. Phagocytin: a bactericidal substance from polymorphonuclear leucocytes. J Exp Med. 1956;103:589–611.

    CAS  PubMed  PubMed Central  Google Scholar 

  168. Bahar AA, Ren D. Antimicrobial peptides. Pharmaceuticals (Basel). 2013;6:1543–75.

    PubMed  PubMed Central  Google Scholar 

  169. Miller A, Matera-Witkiewicz A, Mikolajczyk A, Wieczorek R, Rowinska-Zyrek M. Chemical “butterfly effect” explaining the coordination chemistry and antimicrobial properties of clavanin complexes. Inorg Chem. 2021;60:12730–4.

    CAS  PubMed  PubMed Central  Google Scholar 

  170. Zhang QY, Yan ZB, Meng YM, Hong XY, Shao G, Ma JJ, et al. Antimicrobial peptides: mechanism of action, activity and clinical potential. Mil Med Res. 2021;8:48.

    CAS  PubMed  PubMed Central  Google Scholar 

  171. van Harten RM, van Woudenbergh E, van Dijk A, Haagsman HP. Cathelicidins: immunomodulatory antimicrobials. Vaccines (Basel). 2018;6.

  172. Nagaoka I, Tamura H, Reich J. Therapeutic potential of cathelicidin peptide LL-37, an Antimicrobial agent, in a murine sepsis model. Int J Mol Sci. 2020;21.

  173. Contreras G, Shirdel I, Braun MS, Wink M. Defensins: transcriptional regulation and function beyond antimicrobial activity. Dev Comp Immunol. 2020;104:103556.

    CAS  PubMed  Google Scholar 

  174. Jenssen H, Hamill P, Hancock RE. Peptide antimicrobial agents. Clin Microbiol Rev. 2006;19:491–511.

    CAS  PubMed  PubMed Central  Google Scholar 

  175. Miani M, Le Naour J, Waeckel-Enee E, Verma SC, Straube M, Emond P, et al. Gut microbiota-stimulated innate lymphoid cells support beta-defensin 14expression in pancreatic endocrine cells, preventing autoimmune diabetes. Cell Metab. 2018;28:557–72.e6.

    CAS  PubMed  Google Scholar 

  176. Tsai YW, Dong JL, Jian YJ, Fu SH, Chien MW, Liu YW, et al. Gut Microbiota-modulated metabolomic profiling shapes the etiology and pathogenesis of autoimmune diseases. Microorganisms. 2021;9.

  177. Coughlan KA, Valentine RJ, Ruderman NB, Saha AK. AMPK activation: a therapeutic target for type 2 diabetes? Diabetes Metab Syndr Obes. 2014;7:241–53.

    PubMed  PubMed Central  Google Scholar 

  178. Zainab AJAA, Ashish N, Ragnath V. Salivary levels of antimicrobial peptides in chronic periodontitis patients with type 2 diabetes. J Int Acad Periodontol. 2019;21:36–44.

    PubMed  Google Scholar 

  179. Soltaninejad H, Zare-Zardini H, Ordooei M, Ghelmani Y, Ghadiri-Anari A, Mojahedi S, et al. Antimicrobial peptides from amphibian innate immune system as potent antidiabetic agents: a literature review and bioinformatics analysis. J Diabetes Res. 2021;2021:2894722.

    PubMed  PubMed Central  Google Scholar 

  180. Musale V, Moffett RC, Owolabi B, Conlon JM, Flatt PR, Abdel-Wahab YHA. Mechanisms of action of the antidiabetic peptide [S4K]CPF-AM1 in db/db mice. J Mol Endocrinol. 2021;66:115–28.

    CAS  PubMed  Google Scholar 

  181. Ramadhan AH, Nawas T, Zhang X, Pembe WM, Xia W, Xu Y. Purification and identification of a novel antidiabetic peptide from Chinese giant salamander (Andrias davidianus) protein hydrolysate against α-amylase and α-glucosidase. Int J Food Properties. 2017;20:S3360–S72.

    CAS  Google Scholar 

  182. Nishikawa T, Araki E. Investigation of a novel mechanism of diabetic complications: impacts of mitochondrial reactive oxygen species. Rinsho Byori. 2008;56:712–9.

    CAS  PubMed  Google Scholar 

  183. Rolo AP, Palmeira CM. Diabetes and mitochondrial function: role of hyperglycemia and oxidative stress. Toxicol Appl Pharmacol. 2006;212:167–78.

    CAS  PubMed  Google Scholar 

  184. Bonnefont-Rousselot D. Glucose and reactive oxygen species. Curr Opin Clin Nutr Metab Care. 2002;5:561–8.

    CAS  PubMed  Google Scholar 

  185. Erbguth F. Diabetes and the central nervous system. Nervenarzt. 2017;88:675–90.

    CAS  PubMed  Google Scholar 

  186. Zarse K, Ristow M. A mitochondrially encoded hormone ameliorates obesity and insulin resistance. Cell Metab. 2015;21:355–6.

    CAS  PubMed  Google Scholar 

  187. Onyango AN. Cellular stresses and stress responses in the pathogenesis of insulin resistance. Oxid Med Cell Longev. 2018;2018:4321714.

    PubMed  PubMed Central  Google Scholar 

  188. Martin-Montalvo A, Mercken EM, Mitchell SJ, Palacios HH, Mote PL, Scheibye-Knudsen M, et al. Metformin improves healthspan and lifespan in mice. Nat Commun. 2013;4:2192.

    PubMed  Google Scholar 

  189. Ikonen M, Liu B, Hashimoto Y, Ma L, Lee KW, Niikura T, et al. Interaction between the Alzheimer’s survival peptide humanin and insulin-like growth factor-binding protein 3 regulates cell survival and apoptosis. Proc Natl Acad Sci USA. 2003;100:13042–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  190. Kwon C, Sun JL, Jeong JH, Jung TW. Humanin attenuates palmitate-induced hepatic lipid accumulation and insulin resistance via AMPK-mediated suppression of the mTOR pathway. Biochem Biophys Res Commun. 2020;526:539–45.

    CAS  PubMed  Google Scholar 

  191. Kuliawat R, Klein L, Gong Z, Nicoletta-Gentile M, Nemkal A, Cui L, et al. Potent humanin analog increases glucose-stimulated insulin secretion through enhanced metabolism in the beta cell. FASEB J. 2013;27:4890–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  192. Cobb LJ, Lee C, Xiao J, Yen K, Wong RG, Nakamura HK, et al. Naturally occurring mitochondrial-derived peptides are age-dependent regulators of apoptosis, insulin sensitivity, and inflammatory markers. Aging (Albany NY). 2016;8:796–809.

    CAS  PubMed  PubMed Central  Google Scholar 

  193. Lee C, Zeng J, Drew BG, Sallam T, Martin-Montalvo A, Wan J, et al. The mitochondrial-derived peptide MOTS-c promotes metabolic homeostasis and reduces obesity and insulin resistance. Cell Metab. 2015;21:443–54.

    CAS  PubMed  PubMed Central  Google Scholar 

  194. Lu H, Wei M, Zhai Y, Li Q, Ye Z, Wang L, et al. MOTS-c peptide regulates adipose homeostasis to prevent ovariectomy-induced metabolic dysfunction. J Mol Med (Berl). 2019;97:473–85.

    CAS  PubMed  Google Scholar 

  195. Zhai D, Ye Z, Jiang Y, Xu C, Ruan B, Yang Y, et al. MOTS-c peptide increases survival and decreases bacterial load in mice infected with MRSA. Mol Immunol. 2017;92:151–60.

    CAS  PubMed  Google Scholar 

  196. Bloomgarden ZT. Inflammation and insulin resistance. Diabetes Care. 2003;26:1619–23.

    CAS  PubMed  Google Scholar 

  197. Simsek S. Angiotensin I-converting enzyme, dipeptidyl peptidase-IV, and α-glucosidase inhibitory potential of hazelnut meal protein hydrolysates. J Food Measurement Charact. 2021;15:4490–6.

    Google Scholar 

  198. Yu Z, Yin Y, Zhao W, Liu J, Chen F. Anti-diabetic activity peptides from albumin against α-glucosidase and α-amylase. Food Chem. 2012;135:2078–85.

    CAS  PubMed  Google Scholar 

  199. Mudgil P, Kilari BP, Kamal H, Olalere OA, FitzGerald RJ, Gan C-Y, et al. Multifunctional bioactive peptides derived from quinoa protein hydrolysates: Inhibition of α-glucosidase, dipeptidyl peptidase-IV and angiotensin I converting enzymes. J Cereal Sci. 2020;96:103130.

    CAS  Google Scholar 

  200. Vilcacundo R, Martínez-Villaluenga C, Hernández-Ledesma B. Release of dipeptidyl peptidase IV, α-amylase and α-glucosidase inhibitory peptides from quinoa (Chenopodium quinoa Willd.) during in vitro simulated gastrointestinal digestion. J Funct Foods. 2017;35:531–9.

    CAS  Google Scholar 

  201. Fuentes LR, Richard C, Chen L. Sequential alcalase and flavourzyme treatment for preparation of α-amylase, α-glucosidase, and dipeptidyl peptidase (DPP)-IV inhibitory peptides from oat protein. J Funct Foods. 2021;87:104829.

    Google Scholar 

  202. Pathan S, Siddiqui RA. Nutritional composition and bioactive components in Quinoa (Chenopodium quinoa Willd.) greens: a review. Nutrients. 2022;14.

  203. Salami M, Sadeghian Motahar SF, Ariaeenejad S, Sheykh Abdollahzadeh Mamaghani A, Kavousi K, Moosavi-Movahedi AA, et al. The novel homologue of the human alpha-glucosidase inhibited by the non-germinated and germinated quinoa protein hydrolysates after in vitro gastrointestinal digestion. J Food Biochem. 2022;46:e14030.

    CAS  PubMed  Google Scholar 

  204. Luan F, Wang Z, Yang Y, Ji Y, Lv H, Han K, et al. Juglans mandshurica Maxim.: a review of its traditional usages, phytochemical constituents, and pharmacological properties. Front Pharmacol. 2020;11:569800.

    CAS  PubMed  Google Scholar 

  205. Ghafoor K, Özcan MM, AL-Juhaımı F, Babıker EE, Sarker ZI, Ahmed IAM, et al. Nutritional composition, extraction, and utilization of wheat germ oil: a review. Eur J Lipid Sci Technol. 2017;119:1600160.

    Google Scholar 

  206. Liu W, Li H, Wen Y, Liu Y, Wang J, Sun B. Molecular mechanism for the alpha-glucosidase inhibitory effect of wheat germ peptides. J Agric Food Chem. 2021;69:15231–9.

    CAS  PubMed  Google Scholar 

  207. Rasane P, Jha A, Sabikhi L, Kumar A, Unnikrishnan VS. Nutritional advantages of oats and opportunities for its processing as value added foods - a review. J Food Sci Technol. 2015;52:662–75.

    CAS  PubMed  Google Scholar 

  208. Grundy MM, Fardet A, Tosh SM, Rich GT, Wilde PJ. Processing of oat: the impact on oat’s cholesterol lowering effect. Food Funct. 2018;9:1328–43.

    CAS  PubMed  PubMed Central  Google Scholar 

  209. Zhang Y, Wu F, He Z, Fang X, Liu X. Optimization and molecular mechanism of novel alpha-glucosidase inhibitory peptides derived from camellia seed cake through enzymatic hydrolysis. Foods. 2023;12.

  210. Feng J, Ma YL, Sun P, Thakur K, Wang S, Zhang JG, et al. Purification and characterisation of α‐glucosidase inhibitory peptides from defatted camellia seed cake. Int J Food Sci Technol. 2021;56:138–47.

    CAS  Google Scholar 

  211. Brown R, Ware L, Tey SL. Effects of hazelnut consumption on cardiometabolic risk factors and acceptance: a systematic review. Int J Environ Res Public Health. 2022;19.

  212. Gul K, Yousuf B, Singh AK, Singh P, Wani AA. Rice bran: nutritional values and its emerging potential for development of functional food—a review. Bioact Carbohydr Diet Fibre. 2015;6:24–30.

    CAS  Google Scholar 

  213. Uraipong C, Zhao J. In vitro digestion of rice bran proteins produces peptides with potent inhibitory effects on alpha-glucosidase and angiotensin I converting enzyme. J Sci Food Agric. 2018;98:758–66.

    CAS  PubMed  Google Scholar 

  214. Tan SP, Kha TC, Parks SE, Roach PD. Bitter melon (Momordica charantia L.) bioactive composition and health benefits: a review. Food Rev Int. 2016;32:181–202.

    CAS  Google Scholar 

  215. Poovitha S, Parani M. In vitro and in vivo alpha-amylase and alpha-glucosidase inhibiting activities of the protein extracts from two varieties of bitter gourd (Momordica charantia L.). BMC Complement Altern Med. 2016;16:185.

    PubMed  PubMed Central  Google Scholar 

  216. Kumar P, Mahato DK, Kamle M, Borah R, Sharma B, Pandhi S, et al. Pharmacological properties, therapeutic potential, and legal status of Cannabis sativa L.: an overview. Phytother Res. 2021;35:6010–29.

    CAS  PubMed  Google Scholar 

  217. Ren Y, Liang K, Jin Y, Zhang M, Chen Y, Wu H, et al. Identification and characterization of two novel α-glucosidase inhibitory oligopeptides from hemp (Cannabis sativa L.) seed protein. J Funct Foods. 2016;26:439–50.

    CAS  Google Scholar 

  218. Telapolu S, Kalachavedu M, Punnoose AM, Bilikere DMD-1. a poly herbal formulation indicated in diabetes mellitus ameliorates glucose uptake and inhibits adipogenesis - an in vitro study. BMC Complement Altern Med. 2018;18:113.

    PubMed  PubMed Central  Google Scholar 

  219. Butala MA, Kukkupuni SK, Venkatasubramanian P, Vishnuprasad CN. An ayurvedic anti-diabetic formulation made from Curcuma longa L. and Emblica officinalis L. inhibits α-amylase, α-glucosidase, and starch digestion, in vitro. Starch Stärke. 2018;70:1700182.

    Google Scholar 

  220. Panda V, Deshmukh A, Singh S, Shah T, Hingorani L. An ayurvedic formulation of Emblica officinalis and Curcuma longa alleviates insulin resistance in diabetic rats: involvement of curcuminoids and polyphenolics. J Ayurveda Integr Med. 2021;12:506–13.

    CAS  PubMed  PubMed Central  Google Scholar 

  221. Mehrzadi S, Tavakolifar B, Huseini HF, Mosavat SH, Heydari M. The effects of Boswellia serrata gum resin on the blood glucose and lipid profile of diabetic patients: a double-blind randomized placebo-controlled clinical trial. J Evid Based Integr Med. 2018;23:2515690X18772728.

    PubMed  PubMed Central  Google Scholar 

  222. Ahmed A, Zeng G, Azhar M, Lin H, Zhang M, Wang F, et al. Jiawei Shengmai San herbal formula ameliorates diabetic associate cognitive decline by modulating AKT and CREB in rats. Phytother Res. 2020;34:3249–61.

    CAS  PubMed  Google Scholar 

  223. Yella SST, Kumar RN, Ayyanna C, Varghese AM, Amaravathi P, Vangoori Y. The combined effect of Trigonella foenum seeds and Coriandrum sativum leaf extracts in alloxan-induced diabetes mellitus wistar albino rats. Bioinformation. 2019;15:716–22.

    PubMed  PubMed Central  Google Scholar 

  224. Mehrzadi S, Mirzaei R, Heydari M, Sasani M, Yaqoobvand B, Huseini HF. Efficacy and safety of a traditional herbal combination in patients with type II diabetes mellitus: a randomized controlled trial. J Diet. 2021;18:31–43.

    CAS  Google Scholar 

  225. Wasana KGP, Attanayake AP, Weerarathna TP, Jayatilaka K. Efficacy and safety of a herbal drug of Coccinia grandis (Linn.) Voigt in patients with type 2 diabetes mellitus: a double blind randomized placebo controlled clinical trial. Phytomedicine. 2021;81:153431.

    CAS  PubMed  Google Scholar 

  226. Wang H, Tan H, Zhan W, Song L, Zhang D, Chen X, et al. Molecular mechanism of Fufang Zhenzhu Tiaozhi capsule in the treatment of type 2 diabetes mellitus with nonalcoholic fatty liver disease based on network pharmacology and validation in minipigs. J Ethnopharmacol. 2021;274:114056.

    CAS  PubMed  Google Scholar 

  227. Perez Gutierrez RM, Martinez Jeronimo FF, Contreras Soto JG, Muniz Ramirez A, Estrella, Mendoza MF. Optimization of ultrasonic-assisted extraction of polyphenols from the polyherbal formulation of Cinnamomum verum, Origanum majorana, and Origanum vulgare and their anti-diabetic capacity in zebrafish (Danio rerio). Heliyon. 2022;8:e08682.

    CAS  PubMed  Google Scholar 

  228. Naik A, Adeyemi SB, Vyas B, Krishnamurthy R. Effect of co-administration of metformin and extracts of Costus pictus D.Don leaves on alloxan-induced diabetes in rats. J Tradit Complement Med. 2022;12:269–80.

    CAS  PubMed  Google Scholar 

  229. Kumar A, Negi AS, Chauhan A, Semwal R, Kumar R, Semwal RB, et al. Formulation and evaluation of SGLT2 inhibitory effect of a polyherbal mixture inspired from Ayurvedic system of medicine. J Tradit Complement Med. 2022;12:477–87.

    CAS  PubMed  PubMed Central  Google Scholar 

  230. Majd FS, Talebi SS, Ahmad Abadi AN, Poorolajal J, Dastan D. Efficacy of a standardized herbal product from Pistacia atlantica subsp. Kurdica in type 2 diabetic patients with hyperlipidemia: a triple-blind randomized clinical trial. Complement Ther Clin Pract. 2022;48:101613.

    PubMed  Google Scholar 

  231. Nguyen TT, Le QT, Hoang DT, et al. Massively parallel sequencing uncovered disease-associated variant spectra of glucose-6-phosphate dehydrogenase deficiency, phenylketonuria and galactosemia in Vietnamese pregnant women. Mol Genet Genomic Med. 2022;10:e1959.

    PubMed  PubMed Central  Google Scholar 

  232. Klak M, Gomółka M, Kowalska P, et al. Type 1 diabetes: genes associated with disease development. Cent Eur J Immunol. 2020;45:439–53.

    CAS  PubMed  Google Scholar 

  233. Ahlqvist E, Ahluwalia TS, Groop L. Genetics of type 2 diabetes. Clin Chem. 2011;57:241–54.

    CAS  PubMed  Google Scholar 

  234. Rafique I, Mir A, Saqib MAN, Naeem M, Marchand L, Polychronakos C. Causal variants in Maturity Onset Diabetes of the Young (MODY) - A systematic review. BMC Endocr Disord. 2021;21:223.

    PubMed  PubMed Central  Google Scholar 

  235. Huang SL, Jao CL, Ho KP, Hsu KC. Dipeptidyl-peptidase IV inhibitory activity of peptides derived from tuna cooking juice hydrolysates. Peptides. 2012;35:114–21.

    CAS  PubMed  Google Scholar 

  236. Nongonierma AB, FitzGerald RJ. Inhibition of dipeptidyl peptidase IV (DPP-IV) by proline containing casein-derived peptides. J Funct Foods. 2013;5:1909–17.

  237. Harnedy PA, Parthsarathy V, McLaughlin CM, et al. Atlantic salmon (Salmo salar) co-product-derived protein hydrolysates: A source of antidiabetic peptides. Food Res Int. 2018;106:598–606.

    CAS  PubMed  Google Scholar 

  238. Zhang Y, Chen R, Chen X, Zeng Z, Ma H, Chen S. Dipeptidyl peptidase iv-inhibitory peptides derived from silver carp (hypophthalmichthys molitrix val.) proteins. J Agric Food Chem. 2016;64:831–9.

    CAS  PubMed  Google Scholar 

  239. Valencia-Mejía E, Batista KA, Fernández JJA, Fernandes KF. Antihyperglycemic and hypoglycemic activity of naturally occurring peptides and protein hydrolysates from easy-to-cook and hard-to-cook beans (Phaseolus vulgaris L.). Food Res Int. 2019;121:238–46.

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Acknowledgements

WC and SS are thankful to Indian Council of Medical Research (ICMR), New Delhi and Council of Scientific and Industrial Research (CSIR), New Delhi for providing senior research fellowship, respectively.

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Authors and Affiliations

  1. Department of Epidemic Disease Research, Institute for Research and Medical Consultations (IRMC), Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam, 31441, Saudi Arabia

    Mohammad Azam Ansari

  2. Department of Hematology, Duke University, Durham, NC, 27710, USA

    Waseem Chauhan

  3. Department of Biochemistry, Faculty of Medicine, Aligarh Muslim University, Aligarh, Uttar Pradesh, India

    Shoaib Shoaib

  4. Wellness and Preventive Medicine Institute, King Abdulaziz City for Science and Technology (KACST), Riyadh, 11442, Saudi Arabia

    Sami A. Alyahya

  5. USF Health Byrd Alzheimer’s Center and Neuroscience Institute, Department of Molecular Medicine, Tampa, FL, USA

    Mubashshir Ali

  6. Rajiv Gandhi Center for Diabetes and Endocrinology, Faculty of Medicine, Aligarh Muslim University, Aligarh, Uttar Pradesh, India

    Hamid Ashraf

  7. Advanced Diagnostic and Therapeutic Institute, King Abdulaziz City for Science and Technology (KACST), Riyadh, 11442, Saudi Arabia

    Mohammad N. Alomary

  8. King Abdulaziz & his Companions Foundation for Giftedness & Creativity, Riyadh, Saudi Arabia

    Ebtesam A. Al-Suhaimi

Authors
  1. Mohammad Azam Ansari
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  2. Waseem Chauhan
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  3. Shoaib Shoaib
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  4. Sami A. Alyahya
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  5. Mubashshir Ali
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  6. Hamid Ashraf
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  7. Mohammad N. Alomary
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  8. Ebtesam A. Al-Suhaimi
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Contributions

Conceptualization: MAA, SS, and WC; writing—original draft: SS, WC, and MAA; writing—review and editing: SAA, MA, HA, MNA, and EAA; All authors were involved in drafting, reviewing and revising the final paper.

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Correspondence to Mohammad Azam Ansari, Mohammad N. Alomary or Ebtesam A. Al-Suhaimi.

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Ansari, M.A., Chauhan, W., Shoaib, S. et al. Emerging therapeutic options in the management of diabetes: recent trends, challenges and future directions. Int J Obes 47, 1179–1199 (2023). https://doi.org/10.1038/s41366-023-01369-3

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  • DOI: https://doi.org/10.1038/s41366-023-01369-3

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