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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
  • 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 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
    Journal of Endodontics.2022; 48(2): 208.     CrossRef
  • 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
    Cureus.2022;[Epub]     CrossRef
  • Analysis of immune cell components and immune-related gene expression profiles in peripheral blood of patients with type 1 diabetes mellitus
    Jian Lin, Yuanhua Lu, Bizhou Wang, Ping Jiao, Jie Ma
    Journal of Translational Medicine.2021;[Epub]     CrossRef
  • Identification of a subgroup of black South Africans with type 1 diabetes who are older at diagnosis but have lower levels of glutamic acid decarboxylase and islet antigen 2 autoantibodies
    C. J. Padoa, P. Rheeder, F. J. Pirie, A. A. Motala, J. C. van Dyk, N. J. Crowther
    Diabetic Medicine.2020; 37(12): 2067.     CrossRef
  • Evaluation of retinal neurovascular structures by optical coherence tomography and optical coherence tomography angiography in children and adolescents with type 1 diabetes mellitus without clinical sign of diabetic retinopathy
    Semra Tiryaki Demir, Ahmet Ucar, Gizem Kara Elitok, Mehmet Egemen Karatas, Murat Karapapak, Oguz Kaan Kutucu, Saniye Uke Uzun, Dilek Guven
    Graefe's Archive for Clinical and Experimental Ophthalmology.2020; 258(11): 2363.     CrossRef
  • A Genome-Wide Analysis of Long Noncoding RNAs in Circulating Leukocytes and Their Differential Expression in Type 1 Diabetes Patients
    Yihan Liu, Xiaoming Du, Jia Cui, Changlong Li, Meng Guo, Jianyi Lv, Xin Liu, Jingtao Dou, Xiaoyan Du, Hongjuan Fang, Zhenwen Chen, Bernd Stratmann
    Journal of Diabetes Research.2020; 2020: 1.     CrossRef
  • Uncommon Presentations of Diabetes: Zebras in the Herd
    Karen L. Shidler, Lisa R. Letourneau, Lucia M. Novak
    Clinical Diabetes.2020; 38(1): 78.     CrossRef
  • Changes in Retinal Microcirculation Precede the Clinical Onset of Diabetic Retinopathy in Children With Type 1 Diabetes Mellitus
    Merve Inanc, Kemal Tekin, Hasan Kiziltoprak, Servan Ozalkak, Sibel Doguizi, Zehra Aycan
    American Journal of Ophthalmology.2019; 207: 37.     CrossRef
  • Autoimmune signatures for prediction and diagnosis of autoimmune diabetes in Kuwait
    Mohamed Jahromi, Fahd Al-Mulla, Ebaa Al-Ozairi
    Autoimmunity Reviews.2019; 18(6): 642.     CrossRef
  • Human Leukocyte Antigen (HLA) and Islet Autoantibodies Are Tools to Characterize Type 1 Diabetes in Arab Countries: Emphasis on Kuwait
    Mohamed Jahromi, Ebaa Al-Ozairi
    Disease Markers.2019; 2019: 1.     CrossRef
  • Complete loss of insulin secretion capacity in type 1A diabetes patients during long‐term follow up
    Sae Uno, Akihisa Imagawa, Junji Kozawa, Kenji Fukui, Hiromi Iwahashi, Iichiro Shimomura
    Journal of Diabetes Investigation.2018; 9(4): 806.     CrossRef
  • Incidence of type 1 diabetes has doubled in Kuwaiti children 0-14 years over the last 20 years
    Azza A. Shaltout, Deborah Wake, Thangavel A. Thanaraj, Dina M. Omar, Dalia Al-AbdulRazzaq, Arshad Channanath, Hessa AlKandari, Majedah Abdulrasoul, Sophie Miller, Nicholas Conway, Jaakko Tuomilehto, Lena Davidsson
    Pediatric Diabetes.2017; 18(8): 761.     CrossRef
  • Objective Evaluation of Corneal and Lens Clarity in Children With Type 1 Diabetes Mellitus
    Kemal Tekin, Merve Inanc, Erdal Kurnaz, Elvan Bayramoglu, Emre Aydemir, Mustafa Koc, Zehra Aycan
    American Journal of Ophthalmology.2017; 179: 190.     CrossRef
  • 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
    Shang Song, Shuvo Roy
    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
    Hye-jin Yoon, Yong-ho Lee, So Ra Kim, Tyler Hyungtaek Rim, Eun Young Lee, Eun Seok Kang, Bong-Soo Cha, Hyun Chul Lee, Byung-Wan Lee
    Cardiovascular Diabetology.2015;[Epub]     CrossRef
  • Lipid peroxidation and antioxidant protection in girls with type 1 diabetes mellitus during reproductive system development
    Lubov I. Kolesnikova, Marina A. Darenskaya, Natalia V. Semenova, Lyudmila A. Grebenkina, Larisa V. Suturina, Marya I. Dolgikh, Svetlana V. Gnusina
    Medicina.2015; 51(2): 107.     CrossRef
  • Diagnostic criteria for acute‐onset type 1 diabetes mellitus (2012): Report of the Committee of Japan Diabetes Society on the Research of Fulminant and Acute‐onset Type 1 Diabetes Mellitus
    Eiji Kawasaki, Taro Maruyama, Akihisa Imagawa, Takuya Awata, Hiroshi Ikegami, Yasuko Uchigata, Haruhiko Osawa, Yumiko Kawabata, Tetsuro Kobayashi, Akira Shimada, Ikki Shimizu, Kazuma Takahashi, Masao Nagata, Hideichi Makino, Toshiaki Hanafusa
    Journal of Diabetes Investigation.2014; 5(1): 115.     CrossRef
  • The Glycated Albumin to Glycated Hemoglobin Ratio Might Not Be Associated with Carotid Atherosclerosis in Patients with Type 1 Diabetes
    Wonjin Kim, Kwang Joon Kim, Byung-Wan Lee, Eun Seok Kang, Bong Soo Cha, Hyun Chul Lee
    Diabetes & Metabolism Journal.2014; 38(6): 456.     CrossRef
  • 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
    Revista Portuguesa de Endocrinologia, Diabetes e Metabolismo.2014; 9(1): 79.     CrossRef
  • Diagnostic criteria for acute-onset type 1 diabetes mellitus (2012)
    Eiji Kawasaki, Taro Maruyama, Akihisa Imagawa, Takuya Awata, Hiroshi Ikegami, Yasuko Uchigata, Haruhiko Osawa, Yumiko Kawabata, Tetsuro Kobayashi, Akira Shimada, Ikki Shimizu, Kazuma Takahashi, Masao Nagata, Hideichi Makino, Toshiaki Hanafusa
    Diabetology International.2013; 4(4): 221.     CrossRef
  • Phenylmethimazole Suppresses dsRNA-Induced Cytotoxicity and Inflammatory Cytokines in Murine Pancreatic Beta Cells and Blocks Viral Acceleration of Type 1 Diabetes in NOD Mice
    Kelly McCall, Martin Schmerr, Jean Thuma, Calvin James, Maria Courreges, Fabian Benencia, Ramiro Malgor, Frank Schwartz
    Molecules.2013; 18(4): 3841.     CrossRef
  • 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
  • The Emerging Global Epidemic of Type 1 Diabetes
    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
    Infarma - Ciências Farmacêuticas.2013; 25(4): 206.     CrossRef
  • Proteome‐base biomarkers in diabetes mellitus: Progress on biofluids' protein profiling using mass spectrometry
    Ana Isabel Padrão, Rita Ferreira, Rui Vitorino, Francisco Amado
    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
    Anales de Pediatría.2012; 77(5): 344.e1.     CrossRef
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
  • 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 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
  • Association of a genetic variant of the ZPR1 zinc finger gene with type 2 diabetes mellitus
    Biomedical Reports.2015; 3(1): 88.     CrossRef
  • Insights into the Genetic Susceptibility to Type 2 Diabetes from Genome-Wide Association Studies of Glycaemic Traits
    Letizia Marullo, Julia S. El-Sayed Moustafa, Inga Prokopenko
    Current Diabetes Reports.2014;[Epub]     CrossRef
  • Towards Virtual Knowledge Broker services for semantic integration of life science literature and data sources
    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
    Diabetes.2013; 62(3): 977.     CrossRef
  • A replication study of 19 GWAS-validated type 2 diabetes at-risk variants in the Lebanese population
    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
  • Typ-2-Diabetes-assoziierte Gene
    J. Kriebel, H. Grallert, T. Illig
    Der Diabetologe.2012; 8(1): 26.     CrossRef
  • Genomweite Assoziationsstudien (GWAS) — Möglichkeiten und Grenzen
    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
  • 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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    Advances in Nutrition & Food Science.2019;[Epub]     CrossRef
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    Jong Chul Won, Eun-Jung Rhee, Hyung Joon Yoo
    Diabetes & Metabolism Journal.2018; 42(6): 475.     CrossRef
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    Junghyun Noh
    Endocrinology and Metabolism.2016; 31(3): 349.     CrossRef
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    Annals of Occupational and Environmental Medicine.2015;[Epub]     CrossRef
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    Jongnam Hwang, Changwoo Shon
    BMJ Open.2014; 4(8): e005710.     CrossRef
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    Hye Kyoung Han
    Journal of the Korean Society of Food Science and Nutrition.2012; 41(12): 1716.     CrossRef
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