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Challenges in Diagnosing Type 1 Diabetes in Different Populations
Marian Rewers
Diabetes Metab J. 2012;36(2):90-97.   Published online April 17, 2012
DOI: https://doi.org/10.4093/dmj.2012.36.2.90
  • 4,121 View
  • 48 Download
  • 33 Crossref
AbstractAbstract PDFPubReader   

Diabetes affects today an estimated 366 million people world-wide, including 20 million to 40 million of patients with type 1 diabetes (T1D). While T1D accounts for 5% to 20% of those with diabetes, it is associated with higher morbidity, mortality and health care cost than the more prevalent type 2 diabetes. Patients with T1D require exogenous insulin for survival and should be identified as soon as possible after diagnosis to avoid high morbidity due to a delay in insulin treatment. It is also important to present to the patient correct prognosis that differs by the type of diabetes. From the research point of view, correct classification should help to identify the etiologies and to develop specific prevention for T1D. This review summarizes evidence that may be helpful in diagnosing T1D in various ethnic groups. Challenges in interpretation of results commonly used to determine the type of diabetes are highlighted.

Citations

Citations to this article as recorded by  
  • Evaluation of Corneal Endothelium and Central Corneal Thickness in Children and Adolescents with Type 1 Diabetes
    Semra Tiryaki Demir, Ahmet Uçar, Gizem Kara Elitok, Sümeyra Keleş Yeşiltaş, Emine Betül Akbaş Özyürek, Saniye Üke Uzun
    Hamidiye Medical Journal.2023; 4(1): 50.     CrossRef
  • Analysis of Corneal and Lens Densitometry Changes in Patients With Type 1 Diabetes Mellitus
    Yusuf Cem Yilmaz, Serife Ciloglu Hayat, Sefik Can Ipek
    American Journal of Ophthalmology.2023; 254: 23.     CrossRef
  • Altered Prevalence of Pulp Diagnoses in Diabetes Mellitus Patients: A Retrospective Study
    Yandy Gonzalez Marrero, Yoshifumi Kobayashi, Mohammad Saqib Ihsan, Lisa A. Pilch, Liyaa Chen, Shuying Jiang, Yi Ye, Daniel H. Fine, Carla Y. Falcon, Paul A. Falcon, Craig S. Hirschberg, Emi Shimizu
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  • The relationship between GAD65 autoantibody and the risk of T1DM onset
    Elham Keshavarzi, Behnoud Baradaran Noveiry, Nima Rezaei
    Journal of Diabetes & Metabolic Disorders.2022; 21(2): 1935.     CrossRef
  • Ketosis-Prone Type 2 Diabetes (Flatbush Diabetes) in Remission: A Report of Two Cases
    Beisi Ji, SumathaChannapatna Suresh, Klynt Bally, Kamrun Naher, Mary A Banerji
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    Journal of Translational Medicine.2021;[Epub]     CrossRef
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    Diabetic Medicine.2020; 37(12): 2067.     CrossRef
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  • Differential association of body mass index on glycemic control in type 1 diabetes
    Eun Young Lee, Yong‐ho Lee, Sang‐Man Jin, Hae Kyung Yang, Chang Hee Jung, Cheol‐Young Park, Jae Hyoung Cho, Woo Je Lee, Byung‐Wan Lee, Jae Hyeon Kim
    Diabetes/Metabolism Research and Reviews.2017;[Epub]     CrossRef
  • Progress and challenges in macroencapsulation approaches for type 1 diabetes (T1D) treatment: Cells, biomaterials, and devices
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    Biotechnology and Bioengineering.2016; 113(7): 1381.     CrossRef
  • Covariation of the Incidence of Type 1 Diabetes with Country Characteristics Available in Public Databases
    Paula Andrea Diaz-Valencia, Pierre Bougnères, Alain-Jacques Valleron
    PLOS ONE.2015; 10(2): e0118298.     CrossRef
  • Glycated albumin and the risk of micro- and macrovascular complications in subjects with Type 1 Diabetes
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  • The Glycated Albumin to Glycated Hemoglobin Ratio Might Not Be Associated with Carotid Atherosclerosis in Patients with Type 1 Diabetes
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  • Diabetes mellitus associada à desnutrição proteica: realidade ou ficção?
    Ana Rita Caldas, André Couto Carvalho, Anabela Giestas, Marta Almeida Ferreira, Cláudia Amaral, Cláudia Freitas, Maria Helena Cardoso
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  • Diagnostic criteria for acute-onset type 1 diabetes mellitus (2012)
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  • Phenylmethimazole Suppresses dsRNA-Induced Cytotoxicity and Inflammatory Cytokines in Murine Pancreatic Beta Cells and Blocks Viral Acceleration of Type 1 Diabetes in NOD Mice
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  • Variation of C peptide decay rate in diabetic patients with positive glutamic acid decarboxylase antibody: better discrimination with initial fasting C peptide
    Xia Li, Gan Huang, Jian Lin, Lin Yang, Zhiguang Zhou
    BMC Endocrine Disorders.2013;[Epub]     CrossRef
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    Jaakko Tuomilehto
    Current Diabetes Reports.2013; 13(6): 795.     CrossRef
  • Clinical characteristics and insulin independence of Koreans with new‐onset type 2 diabetes presenting with diabetic ketoacidosis
    H. Seok, C. H. Jung, S. W. Kim, M. J. Lee, W. J. Lee, J. H. Kim, B‐W. Lee
    Diabetes/Metabolism Research and Reviews.2013; 29(6): 507.     CrossRef
  • Diabetes mellitus and inflammatory pulpal and periapical disease: a review
    S. M. F. Lima, D. C. Grisi, E. M. Kogawa, O. L. Franco, V. C. Peixoto, J. F. Gonçalves‐Júnior, M. P. Arruda, T. M. B. Rezende
    International Endodontic Journal.2013; 46(8): 700.     CrossRef
  • UM OLHAR SOBRE O DIABETES NA INFÂNCIA E NA JUVENTUDE: NEM TODOS SÃO TIPO 1
    Mauren Isfer ANGHEBEM-OLIVEIRA
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  • Proteome‐base biomarkers in diabetes mellitus: Progress on biofluids' protein profiling using mass spectrometry
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    PROTEOMICS – Clinical Applications.2012; 6(9-10): 447.     CrossRef
  • Diabetes mellitus: formas de presentación clínica y diagnóstico diferencial de la hiperglucemia en la infancia y adolescencia
    Ó. Rubio Cabezas, J. Argente
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The Importance of Global Studies of the Genetics of Type 2 Diabetes
Mark I. McCarthy
Diabetes Metab J. 2011;35(2):91-100.   Published online April 30, 2011
DOI: https://doi.org/10.4093/dmj.2011.35.2.91
  • 4,387 View
  • 29 Download
  • 11 Crossref
AbstractAbstract PDFPubReader   

Genome wide association analyses have revealed large numbers of common variants influencing predisposition to type 2 diabetes and related phenotypes. These studies have predominantly featured European populations, but are now being extended to samples from a wider range of ethnic groups. The transethnic analysis of association data is already providing insights into the genetic, molecular and biological causes of diabetes, and the relevance of such studies will increase as human discovery genetics increasingly moves towards sequencing-based approaches and a focus on low frequency and rare variants.

Citations

Citations to this article as recorded by  
  • Validation and genetic heritability estimation of known type 2 diabetes related variants in the Korean population
    Hye-Mi Jang, Mi Yeong Hwang, Bong-Jo Kim, Young Jin Kim
    Genomics & Informatics.2021; 19(4): e37.     CrossRef
  • Distinct subtypes of polycystic ovary syndrome with novel genetic associations: An unsupervised, phenotypic clustering analysis
    Matthew Dapas, Frederick T. J. Lin, Girish N. Nadkarni, Ryan Sisk, Richard S. Legro, Margrit Urbanek, M. Geoffrey Hayes, Andrea Dunaif, Jenny E. Myers
    PLOS Medicine.2020; 17(6): e1003132.     CrossRef
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    Current Diabetes Reports.2014;[Epub]     CrossRef
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    Ian Harrow, Wendy Filsell, Peter Woollard, Ian Dix, Michael Braxenthaler, Richard Gedye, David Hoole, Richard Kidd, Jabe Wilson, Dietrich Rebholz-Schuhmann
    Drug Discovery Today.2013; 18(9-10): 428.     CrossRef
  • Genome-Wide Association Study for Type 2 Diabetes in Indians Identifies a New Susceptibility Locus at 2q21
    Rubina Tabassum, Ganesh Chauhan, Om Prakash Dwivedi, Anubha Mahajan, Alok Jaiswal, Ismeet Kaur, Khushdeep Bandesh, Tejbir Singh, Benan John Mathai, Yogesh Pandey, Manickam Chidambaram, Amitabh Sharma, Sreenivas Chavali, Shantanu Sengupta, Lakshmi Ramakris
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    Wassim Y. Almawi, Rita Nemr, Sose H. Keleshian, Akram Echtay, Fabiola Lisa Saldanha, Fatima A. AlDoseri, Eddie Racoubian
    Diabetes Research and Clinical Practice.2013; 102(2): 117.     CrossRef
  • Single nucleotide polymorphisms in JAZF1 and BCL11A gene are nominally associated with type 2 diabetes in African-American families from the GENNID study
    Kurt A Langberg, Lijun Ma, Neeraj K Sharma, Craig L Hanis, Steven C Elbein, Sandra J Hasstedt, Swapan K Das
    Journal of Human Genetics.2012; 57(1): 57.     CrossRef
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    J. Kriebel, H. Grallert, T. Illig
    Der Diabetologe.2012; 8(1): 26.     CrossRef
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    Jennifer Kriebel, Thomas Illig, Harald Grallert
    BIOspektrum.2012; 18(5): 508.     CrossRef
  • T2DM: Why Epigenetics?
    Delphine Fradin, Pierre Bougnères
    Journal of Nutrition and Metabolism.2011; 2011: 1.     CrossRef
The Epidemiology of Diabetes in Korea: From the Economics to Genetics
Nam H. Cho
Korean Diabetes J. 2010;34(1):10-15.   Published online February 28, 2010
DOI: https://doi.org/10.4093/kdj.2010.34.1.10
  • 2,889 View
  • 52 Download
  • 17 Crossref
AbstractAbstract PDFPubReader   

To determine the factors responsible for the dramatic increase in the prevalence of diabetes in Korea. A computerized literature survey was conducted to evaluate the risk factors for Type 2 diabetes mellitus (T2DM) in Korea, including genome-wide association studies. National Statistics gross national income data was integrated with the reported prevalence of diabetes to evaluate the relationship between diabetes and the economic growth. The strength of the association was evaluated using measures of effect size, such as odds ratio and relative risks. The putative risk factors identified in Korean studies are very similar to the risk factors identified from the other countries, including genetic background. Genome-wide association studies reported relative risks of 1.5 or less, indicating that no single gene is associated with the risk of T2DM. The scientific evidence suggests that the dramatic increase in the incidence and prevalence of T2DM in Korea is related to the economic development of Korea, which has a direct influence on health policy, as well as an individual's health behaviors. We expect to observe the current diabetes incidence rates until the key risk factors are present for long enough in our society, at which point we would expect to start observing a more gradual increase in both the incidence and prevalence of T2DM in Korea.

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