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A new algorithm for the diagnosis of celiac disease

Cellular & Molecular Immunology volume 8, pages 146–149 (2011)Cite this article

Abstract

Celiac disease (CD) affects at least 1% of the Western population but remains largely unrecognized. In our laboratory, we utilize a novel algorithm to diagnose pediatric CD that offers both high sensitivity and high specificity for diagnosis in an outpatient setting. The aim of the present study was to challenge this algorithm and to test its performance in children and adults suspected of having CD. Using a three-assay algorithm, screening with the most sensitive tissue transglutaminase (tTG) complexed with deamidated gliadin peptide neoepitope immunoglobulin A (IgA)+IgG assay and confirming with the two specific tTG IgA and tTG IgA+IgG assays, we examined the serological results from 112 children aged 0–17 years old and 60 adults in comparison to their respective biopsy results. The algorithm performance was calculated by statistical analysis. The use of the new algorithm enabled us to diagnose CD with 98% sensitivity, 93% specificity and 95% accuracy in the pediatric group and 94% sensitivity, 92% specificity and 93% accuracy in the total population studied. The false-negative cases in the adult group were attributed to previous adherence to a gluten-free diet, and the single false-negative result in a young child became a true positive after 6 months. We have also monitored three celiac patients before and after diagnosis and found that the algorithm may be suitable for disease monitoring. The newly proposed three-assay algorithm for celiac detection is very reliable in both children and adults. Due to the high performance of this assay, the further need for confirmatory intestinal biopsies will be reassessed.

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Author notes

  1. Orit Rozenberg, Aaron Lerner and Mira Barak: These authors contributed equally to this work

Authors and Affiliations

  1. Central Laboratory of Haifa and Western Galilee, Clalit Health Services and Pediatric Gastroenterology, Haifa, Israel

    Orit Rozenberg, Maya Grinberg, Dina Reginashvili, Clara Henig & Mira Barak

  2. Nutrition Unit, Carmel Medical Center, Haifa, Israel

    Aaron Lerner & Avi Pacht

  3. Technion – Israel Institute of Technology, Haifa, Israel

    Aaron Lerner

Authors
  1. Orit Rozenberg
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  2. Aaron Lerner
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  3. Avi Pacht
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  4. Maya Grinberg
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  5. Dina Reginashvili
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  6. Clara Henig
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  7. Mira Barak
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Corresponding author

Correspondence to Mira Barak.

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Rozenberg, O., Lerner, A., Pacht, A. et al. A new algorithm for the diagnosis of celiac disease. Cell Mol Immunol 8, 146–149 (2011). https://doi.org/10.1038/cmi.2010.63

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