Published online Sep 15, 2024. doi: 10.4239/wjd.v15.i9.1829
Revised: May 29, 2024
Accepted: June 28, 2024
Published online: September 15, 2024
Processing time: 253 Days and 5.1 Hours
Monogenic diabetes is caused by one or even more genetic variations, which may be uncommon yet have a significant influence and cause diabetes at an early age. Monogenic diabetes affects 1% to 5% of children, and early detection and gene
Core Tip: Monogenic diabetes, a rare yet impactful condition in childhood, results from genetic variations, causing early-onset diabetes. Affecting 1%-5% of children, early detection and tailored genetic treatments can enhance long-term health. Culprits include genetic variations in beta-cell regulatory genes and severe insulin resistance. Identifying specific types allows transitioning to sulfonylureas while maintaining glucose control. Tools to differentiate diabetes types underscore genetic screening's importance for prognosis and treatment guidance. This review delves into genetic insights into childhood monogenic diabetes, offering diagnosis and management guidance for affected youth's better health.
- Citation: Sanyal D. Exploring the genetic basis of childhood monogenic diabetes. World J Diabetes 2024; 15(9): 1829-1832
- URL: https://www.wjgnet.com/1948-9358/full/v15/i9/1829.htm
- DOI: https://dx.doi.org/10.4239/wjd.v15.i9.1829
In the ever-evolving landscape of pediatric healthcare, the intricate interplay between genetics and clinical outcomes becomes a central focus of research and treatment strategies. This review article by Sun and Lin[1] delves into the complex world of childhood monogenic diabetes, seamlessly connecting genetic mutations, clinical manifestations, and innovative management approaches. This comprehensive review serves as a beacon, illuminating the path toward improved understanding and personalized care for young patients grappling with this condition.
Diabetes mellitus (DM) is a metabolic disorder associated with increased blood glucose levels and its associated symptoms which significantly impact an individual’s health, life expectancy, and public health. The lifetime risk of DM is one in three for individuals born in the United States[2]. This multifaceted condition includes autoimmune-mediated type 1 diabetes, injury-induced diabetes, genetically influenced diabetes, and the prevalent type 2 diabetes[3,4]. This article focuses on monogenic diabetes, a less common but genetically distinct form, accounting for about 1% to 5% of cases in pediatric and young populations[5-7].
Monogenic diabetes arises from mutations in a single gene, representing a distinct subset that lends itself to more targeted therapies. There are several forms of monogenic diabetes including maternally inherited diabetes and deafness, gestational diabetes due to the glucokinase (GCK) gene and maturity-onset diabetes of the young (MODY)[8-10]. Despite its lower prevalence (1%-5% in pediatric and young populations), accurate diagnosis remains a challenge, with approximately 80% of cases undetected[7].
Sun and Lin[1] explore the variation in genes linked to childhood diabetes, particularly focusing on ATP-sensitive potassium channels. These genes, like KCNJ11 and ABCC8, play a crucial role in forming KATP channels. The article discusses how these gene variations impact insulin release and the importance of catching them early, opening up possibilities for moving away from insulin reliance to oral medications.
Monogenic diabetes offers a unique window into diabetes pathophysiology, with genetic variations serving as key players. While comprising a small percentage of overall diabetes cases, the well-defined genetic basis allows for precise prognostic and treatment procedures. However, due to its clinical and genetic heterogeneity, diagnosing monogenic diabetes, especially in pediatric patients, remains challenging[11].
The review unfolds the hidden mechanism within the INS and GCK genes, revealing their role in causing permanent neonatal diabetes. By focusing on disruptions caused by INS mutations and the contribution of GCK mutations to familial hyperglycemia, Sun and Lin stress the need for early diagnosis, especially in couples with close family ties. This decoding sets the stage for more effective treatments and understanding familial risks.
The narrative extends to rare genetic origins and variations in childhood diabetes. Sun and Lin[1] shed light on clinical landscapes, underlining the importance of accurate diagnoses in conditions like Wolfram syndrome. This review emphasizes the diversity within childhood diabetes, urging doctors to be vigilant in how they diagnose.
Recent progress in genetic testing, particularly through next-generation sequencing (NGS) technologies, has significantly enhanced the identification of specific genetic mutations linked to monogenic diabetes. NGS enables a more thorough genetic analysis, unveiling rare mutations that traditional methods might overlook. Machine learning algorithms are also emerging to assist in interpreting genetic data, streamlining the diagnostic process[12-14]. Therapeutically, the article talks about personalized treatment plans, especially using sulfonylureas for specific gene mutations like HNF1A-MODY3, showing how genetic insights can guide more effective treatment choices. The review provides valuable insights, but its relevance might vary due to regional genetic differences, and some recent advancements or lesser-known mutations may not be fully addressed, affecting how it can be applied and accessed equitably.
Tailoring treatment for monogenic diabetes is evolving, guided by a deeper understanding of the genetic basis. Gene-based therapies, including CRISPR-Cas9 gene editing, show promise in correcting genetic mutations responsible for monogenic diabetes subtypes. Initiatives including international collaborations and data-sharing could further enhance treatment strategies and outcomes[15-17].
The collaborative work of Sun and Lin[1] navigates a genetic exploration in pediatric diabetes. Their comprehensive review not only enhances our understanding of the intricate genetic tapestry but also paves the way for a future where personalized care becomes the cornerstone of pediatric diabetes management.
1. | Sun HY, Lin XY. Genetic perspectives on childhood monogenic diabetes: Diagnosis, management, and future directions. World J Diabetes. 2023;14:1738-1753. [PubMed] [DOI] [Cited in This Article: ] [Reference Citation Analysis (1)] |
2. | Yoshiji S, Horikawa Y, Kubota S, Enya M, Iwasaki Y, Keidai Y, Aizawa-Abe M, Iwasaki K, Honjo S, Hosomichi K, Yabe D, Hamasaki A. First Japanese Family With PDX1-MODY (MODY4): A Novel PDX1 Frameshift Mutation, Clinical Characteristics, and Implications. J Endocr Soc. 2022;6:bvab159. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 10] [Cited by in F6Publishing: 9] [Article Influence: 4.5] [Reference Citation Analysis (0)] |
3. | Aydogan HY, Gul N, Demirci DK, Mutlu U, Gulfidan G, Arga KY, Ozder A, Camli AA, Tutuncu Y, Ozturk O, Cacina C, Darendeliler F, Poyrazoglu S, Satman I. Precision Diagnosis of Maturity-Onset Diabetes of the Young with Next-Generation Sequencing: Findings from the MODY-IST Study in Adult Patients. OMICS. 2022;26:218-235. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis (0)] |
4. | Murphy R, Colclough K, Pollin TI, Ikle JM, Svalastoga P, Maloney KA, Saint-Martin C, Molnes J; ADA/EASD Precision Medicine Diabetes Initiative, Misra S, Aukrust I, de Franco A, Flanagan SE, Njølstad PR, Billings LK, Owen KR, Gloyn AL. A Systematic Review of the use of Precision Diagnostics in Monogenic Diabetes. medRxiv. 2023;. [PubMed] [DOI] [Cited in This Article: ] [Reference Citation Analysis (0)] |
5. | Rodrigues KF, Yong WTL, Bhuiyan MSA, Siddiquee S, Shah MD, Venmathi Maran BA. Current Understanding on the Genetic Basis of Key Metabolic Disorders: A Review. Biology (Basel). 2022;11. [PubMed] [DOI] [Cited in This Article: ] [Cited by in F6Publishing: 4] [Reference Citation Analysis (0)] |
6. | Thomas CC, Philipson LH. Update on diabetes classification. Med Clin North Am. 2015;99:1-16. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 90] [Cited by in F6Publishing: 101] [Article Influence: 11.2] [Reference Citation Analysis (0)] |
7. | American Diabetes Association. 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2020. Diabetes Care. 2020;43:S14-S31. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 1583] [Cited by in F6Publishing: 1949] [Article Influence: 487.3] [Reference Citation Analysis (0)] |
8. | Riddle MC, Philipson LH, Rich SS, Carlsson A, Franks PW, Greeley SAW, Nolan JJ, Pearson ER, Zeitler PS, Hattersley AT. Monogenic Diabetes: From Genetic Insights to Population-Based Precision in Care. Reflections From a Diabetes Care Editors' Expert Forum. Diabetes Care. 2020;43:3117-3128. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 44] [Cited by in F6Publishing: 51] [Article Influence: 12.8] [Reference Citation Analysis (0)] |
9. | Shepherd M, Shields B, Hammersley S, Hudson M, McDonald TJ, Colclough K, Oram RA, Knight B, Hyde C, Cox J, Mallam K, Moudiotis C, Smith R, Fraser B, Robertson S, Greene S, Ellard S, Pearson ER, Hattersley AT; UNITED Team. Systematic Population Screening, Using Biomarkers and Genetic Testing, Identifies 2.5% of the U.K. Pediatric Diabetes Population With Monogenic Diabetes. Diabetes Care. 2016;39:1879-1888. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 139] [Cited by in F6Publishing: 148] [Article Influence: 18.5] [Reference Citation Analysis (0)] |
10. | Hattersley AT, Patel KA. Precision diabetes: learning from monogenic diabetes. Diabetologia. 2017;60:769-777. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 221] [Cited by in F6Publishing: 180] [Article Influence: 25.7] [Reference Citation Analysis (0)] |
11. | Sanyoura M, Philipson LH, Naylor R. Monogenic Diabetes in Children and Adolescents: Recognition and Treatment Options. Curr Diab Rep. 2018;18:58. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 65] [Cited by in F6Publishing: 48] [Article Influence: 8.0] [Reference Citation Analysis (0)] |
12. | Nasykhova YA, Barbitoff YA, Serebryakova EA, Katserov DS, Glotov AS. Recent advances and perspectives in next generation sequencing application to the genetic research of type 2 diabetes. World J Diabetes. 2019;10:376-395. [PubMed] [DOI] [Cited in This Article: ] [Cited by in CrossRef: 15] [Cited by in F6Publishing: 9] [Article Influence: 1.8] [Reference Citation Analysis (0)] |
13. | Vora LK, Gholap AD, Jetha K, Thakur RRS, Solanki HK, Chavda VP. Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design. Pharmaceutics. 2023;15. [PubMed] [DOI] [Cited in This Article: ] [Cited by in F6Publishing: 61] [Reference Citation Analysis (0)] |
14. | Yang Y, Chen S, Liu Y, Huang Y, Cheong KL, Teng B, Liu W. Long-term treatment of polysaccharides-based hydrogel microparticles as oral insulin delivery in streptozotocin-induced type 2 diabetic mice. Biomed Pharmacother. 2021;133:110941. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 7] [Cited by in F6Publishing: 14] [Article Influence: 3.5] [Reference Citation Analysis (0)] |
15. | Pillon NJ, Loos RJF, Marshall SM, Zierath JR. Metabolic consequences of obesity and type 2 diabetes: Balancing genes and environment for personalized care. Cell. 2021;184:1530-1544. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 129] [Cited by in F6Publishing: 100] [Article Influence: 33.3] [Reference Citation Analysis (0)] |
16. | Sweeting A, Wong J, Murphy HR, Ross GP. A Clinical Update on Gestational Diabetes Mellitus. Endocr Rev. 2022;43:763-793. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 246] [Cited by in F6Publishing: 229] [Article Influence: 114.5] [Reference Citation Analysis (1)] |
17. | Kotagama OW, Jayasinghe CD, Abeysinghe T. Era of Genomic Medicine: A Narrative Review on CRISPR Technology as a Potential Therapeutic Tool for Human Diseases. Biomed Res Int. 2019;2019:1369682. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 33] [Cited by in F6Publishing: 22] [Article Influence: 4.4] [Reference Citation Analysis (0)] |