Dataset Information

Detection of celiac disease and lymphocytic enteropathy by parallel serology and histopathology in a population-based study.

ABSTRACT:

Background & aims

Although serologic analysis is used in diagnosis of celiac disease, histopathology is considered most reliable. We performed a prospective study to determine the clinical, pathologic, and serologic spectrum of celiac disease in a general population (Kalixanda study).

Methods

A random sample of an adult general population (n = 1000) was analyzed by upper endoscopy, duodenal biopsy, and serologic analysis of tissue transglutaminase (tTg) levels; endomysial antibody (EMA) levels were analyzed in samples that were tTg+. The cut off values for diagnosis of celiac disease were villous atrophy with 40 intraepithelial lymphocytes (IELs)/100 enterocytes (ECs).

Results

Samples from 33 subjects were tTg+, and 16 were EMA+. Histologic analysis identified 7 of 1000 subjects (0.7%) with celiac disease; all were tTg+, and 6 of 7 were EMA+. Another 26 subjects were tTg+ (7/26 EMA+). This was addressed by a second quantitative pathology study (nested case control design) using a threshold of 25 IELS/100 ECs. In this analysis, all 13 samples that were tTg+ and EMA+ had > or =25 IELs/100 ECs. In total, 16 subjects (1.6%) had serologic and histologic evidence of gluten-sensitive enteropathy. IELs were quantified in duodenal biopsy samples from seronegative individuals (n = 500); 19 (3.8%) had >25 IELs and lymphocytic duodenosis.

Conclusions

Measurement of > or =25 IELs/100 ECs correlated with serologic indicators of celiac disease; a higher IEL threshold could miss 50% of cases. Quantification of tTg is a sensitive test for celiac disease; diagnosis can be confirmed by observation of > or =25 IELs/100ECs in duodenal biopsy specimens. Lymphocytic enteropathy (celiac disease and lymphocytic duodenosis) is common in the population (5.4%).

SUBMITTER: Walker MM

PROVIDER: S-EPMC2902605 | biostudies-literature | 2010 Jul

REPOSITORIES: biostudies-literature

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Publications

Detection of celiac disease and lymphocytic enteropathy by parallel serology and histopathology in a population-based study.

Walker Marjorie M MM Murray Joseph A JA Ronkainen Jukka J Aro Pertti P Storskrubb Tom T D'Amato Mauro M Lahr Brian B Talley Nicholas J NJ Agreus Lars L

Gastroenterology 20100413 1

<h4>Background & aims</h4>Although serologic analysis is used in diagnosis of celiac disease, histopathology is considered most reliable. We performed a prospective study to determine the clinical, pathologic, and serologic spectrum of celiac disease in a general population (Kalixanda study).<h4>Methods</h4>A random sample of an adult general population (n = 1000) was analyzed by upper endoscopy, duodenal biopsy, and serologic analysis of tissue transglutaminase (tTg) levels; endomysial antibody ...[more]

PMID: 20398668

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Project description:ImportanceDuodenal biopsies from children with enteropathies associated with undernutrition, such as environmental enteropathy (EE) and celiac disease (CD), display significant histopathological overlap.ObjectiveTo develop a convolutional neural network (CNN) to enhance the detection of pathologic morphological features in diseased vs healthy duodenal tissue.Design, setting, and participantsIn this prospective diagnostic study, a CNN consisting of 4 convolutions, 1 fully connected layer, and 1 softmax layer was trained on duodenal biopsy images. Data were provided by 3 sites: Aga Khan University Hospital, Karachi, Pakistan; University Teaching Hospital, Lusaka, Zambia; and University of Virginia, Charlottesville. Duodenal biopsy slides from 102 children (10 with EE from Aga Khan University Hospital, 16 with EE from University Teaching Hospital, 34 with CD from University of Virginia, and 42 with no disease from University of Virginia) were converted into 3118 images. The CNN was designed and analyzed at the University of Virginia. The data were collected, prepared, and analyzed between November 2017 and February 2018.Main outcomes and measuresClassification accuracy of the CNN per image and per case and incorrect classification rate identified by aggregated 10-fold cross-validation confusion/error matrices of CNN models.ResultsOverall, 102 children participated in this study, with a median (interquartile range) age of 31.0 (20.3-75.5) months and a roughly equal sex distribution, with 53 boys (51.9%). The model demonstrated 93.4% case-detection accuracy and had a false-negative rate of 2.4%. Confusion metrics indicated most incorrect classifications were between patients with CD and healthy patients. Feature map activations were visualized and learned distinctive patterns, including microlevel features in duodenal tissues, such as alterations in secretory cell populations.Conclusions and relevanceA machine learning-based histopathological analysis model demonstrating 93.4% classification accuracy was developed for identifying and differentiating between duodenal biopsies from children with EE and CD. The combination of the CNN with a deconvolutional network enabled feature recognition and highlighted secretory cells' role in the model's ability to differentiate between these histologically similar diseases.

Project description:Background & aimsCeliac disease is detected using serology and endoscopy analyses. We used multiple statistical analyses of a geographically isolated population in the United States to determine whether a single serum screening can identify individuals with celiac disease.MethodsWe performed a retrospective study of 3555 pediatric patients (18 years old or younger) in the intermountain West region of the United States from January 1, 2008, through September 30, 2013. All patients had undergone serologic analyses for celiac disease, including measurement of antibodies to tissue transglutaminase (TTG) and/or deamidated gliadin peptide (DGP), and had duodenal biopsies collected within the following year. Modified Marsh criteria were used to identify patients with celiac disease. We developed models to identify patients with celiac disease using logistic regression and classification and regression tree (CART) analysis.ResultsSingle use of a test for serum level of IgA against TTG identified patients with celiac disease with 90% sensitivity, 90% specificity, a 61% positive predictive value (PPV), a 90% negative predictive value, and an area under the receiver operating characteristic curve value of 0.91; these values were higher than those obtained from assays for IgA against DGP or IgG against TTG plus DGP. Not including the test for DGP antibody caused only 0.18% of celiac disease cases to be missed. Level of TTG IgA 7-fold the upper limit of normal (ULN) identified patients with celiac disease with a 96% PPV and 100% specificity. Using CART analysis, we found a level of TTG IgA 3.2-fold the ULN and higher to most accurately identify patients with celiac disease (PPV, 89%). Multivariable CART analysis showed that a level of TTG IgA 2.5-fold the ULN and higher was sufficient to identify celiac disease in patients with type 1 diabetes (PPV, 88%). Serum level of IgA against TTG in patients with versus those without trisomy 21 did not affect diagnosis predictability in CART analysis.ConclusionsIn a population-based study, we found that serum level of IgA against TTG can identify patients with celiac disease with PPVs of about 90%. Predictive values increase greatly when levels are markedly above the ULN or when the assay is used in combination with other variables. Measurement of IgG against TTG or DGP does not increase the accuracy of detection of celiac disease based against TTG IgA levels. There is a low risk of false-positive results from serologic analysis in patients with type I diabetes or persistent increases in antibody against TTG on repeat testing.

Dataset Information

Detection of celiac disease and lymphocytic enteropathy by parallel serology and histopathology in a population-based study.

Background & aims

Methods

Results

Conclusions

Publications

Detection of celiac disease and lymphocytic enteropathy by parallel serology and histopathology in a population-based study.

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