Literature DB >> 17130187

Accuracy and predictive value of classification schemes for ketosis-prone diabetes.

Ashok Balasubramanyam1, Gilberto Garza, Lucille Rodriguez, Christiane S Hampe, Lakshmi Gaur, Ake Lernmark, Mario R Maldonado.   

Abstract

OBJECTIVE: Ketosis-prone diabetes (KPD) is an emerging, heterogeneous syndrome. A sound classification scheme for KPD is essential to guide clinical practice and pathophysiologic studies. Four schemes have been used and are based on immunologic criteria, immunologic criteria and insulin requirement, BMI, and immunologic criteria and beta-cell function (Abeta classification). The aim of the present study is to compare the four schemes for accuracy and predictive value in determining whether KPD patients have absent or preserved beta-cell function, which is a strong determinant of long-term insulin dependence and clinical phenotype. RESEARCH DESIGN AND METHODS: Consecutive patients (n = 294) presenting with diabetic ketoacidosis and followed for 12-60 months were classified according to all four schemes. They were evaluated longitudinally for beta-cell autoimmunity, clinical and biochemical features, beta-cell function, and insulin dependence. beta-Cell function was defined by peak plasma C-peptide response to glucagon >or=1.5 ng/ml. The accuracy of each scheme to predict absent or preserved beta-cell function after 12 months of follow-up was tested using multiple statistical analyses.
RESULTS: The "Abeta" classification scheme was the most accurate overall, with a sensitivity and specificity of 99.4 and 95.9%, respectively, positive and negative likelihood ratios of 24.55 and 0.01, respectively, and an area under the receiver operator characteristic curve of 0.972.
CONCLUSIONS: The Abeta scheme has the highest accuracy and predictive value in classifying KPD patients with regard to clinical outcomes and pathophysiologic subtypes.

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Mesh:

Year:  2006        PMID: 17130187     DOI: 10.2337/dc06-0749

Source DB:  PubMed          Journal:  Diabetes Care        ISSN: 0149-5992            Impact factor:   19.112


  43 in total

1.  Presence or absence of a known diabetic ketoacidosis precipitant defines distinct syndromes of "A-β+" ketosis-prone diabetes based on long-term β-cell function, human leukocyte antigen class II alleles, and sex predilection.

Authors:  Ramaswami Nalini; Kerem Ozer; Mario Maldonado; Sanjeet G Patel; Christiane S Hampe; Anu Guthikonda; Jesus Villanueva; E O'Brian Smith; Lakshmi K Gaur; Ashok Balasubramanyam
Journal:  Metabolism       Date:  2010-02-19       Impact factor: 8.694

Review 2.  Approach to the patient with atypical diabetes.

Authors:  Devin W Steenkamp; Sara M Alexanian; Elliot Sternthal
Journal:  CMAJ       Date:  2014-01-06       Impact factor: 8.262

3.  Personalized medicine for diabetes.

Authors:  David C Klonoff
Journal:  J Diabetes Sci Technol       Date:  2008-05

4.  The villain with a thousand faces.

Authors:  Ashok Balasubramanyam
Journal:  J Diabetes Complications       Date:  2014-02-18       Impact factor: 2.852

5.  Elevated unmethylated and methylated insulin DNA are unique markers of A+β+ ketosis prone diabetes.

Authors:  Surya N Mulukutla; Sarah A Tersey; Christiane S Hampe; Raghavendra G Mirmira; Ashok Balasubramanyam
Journal:  J Diabetes Complications       Date:  2017-10-31       Impact factor: 2.852

Review 6.  Diabetes at the crossroads: relevance of disease classification to pathophysiology and treatment.

Authors:  R David Leslie; Jerry Palmer; Nanette C Schloot; Ake Lernmark
Journal:  Diabetologia       Date:  2016-01       Impact factor: 10.122

Review 7.  Syndromes of ketosis-prone diabetes mellitus.

Authors:  Ashok Balasubramanyam; Ramaswami Nalini; Christiane S Hampe; Mario Maldonado
Journal:  Endocr Rev       Date:  2008-02-21       Impact factor: 19.871

Review 8.  Precision medicine in diabetes: an opportunity for clinical translation.

Authors:  Jordi Merino; Jose C Florez
Journal:  Ann N Y Acad Sci       Date:  2018-01       Impact factor: 5.691

9.  A-beta-subtype of ketosis-prone diabetes is not predominantly a monogenic diabetic syndrome.

Authors:  Wade C Haaland; Diane I Scaduto; Mario R Maldonado; Dena L Mansouri; Ramaswami Nalini; Dinakar Iyer; Sanjeet Patel; Anu Guthikonda; Christiane S Hampe; Ashok Balasubramanyam; Michael L Metzker
Journal:  Diabetes Care       Date:  2009-02-19       Impact factor: 17.152

10.  Predicting adult-onset autoimmune diabetes: clarity from complexity.

Authors:  R David Leslie
Journal:  Diabetes       Date:  2010-02       Impact factor: 9.461

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