A Metabonomics Study on Celiac Disease by CART

Fathi, Fariba; Ektefa, Fatemeh; Sohrabzadeh, Kaveh; Oskouie, Afsaneh Arefi; Tafazzoli, Mohsen; Rostami, Kamran; Zali, Mohammad Reza; Rostami‐Nejad, Mohammad

doi:10.12691/ijcd-2-2-3

Cited by 12 publications

(17 citation statements)

References 13 publications

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“…Rezaei-Tavirani et al (2012) Serum, NMR Lipids, lactate, and choline were changed in CD patients. Fathi et al (2013a) Serum, NMR Lactate, valin and lipid levels in CD patient are lower than healthy controls Aprea et al (2014) Exhaled breath, PTR-TOF-MS Exhaled breath of patients with CD on GFD was similar to the exhaled breath of healthy people. autoimmune diseases.…”

Section: Referencementioning

confidence: 93%

“…Early diagnosis and compliance with a gluten-free diet are important for potential coeliac disease patients. Several studies have used NMR and MS methods to identify biomarkers in coeliac disease (Bertini et al, 2009;Bernini et al, 2011;Rezaei-Tavirani et al, 2012;Sellitto et al, 2012;Fathi et al, 2013a;Aprea et al, 2014). Those results showed the following findings: 1) overt CD, potential CD and healthy controls could be distinguished by metabolic profiling; 2) a strict gluten-free diet (GFD) is benefit to disease control; and 3) alterations of gut microbiota were associated with onset of coeliac disease.…”

Section: Coeliac Diseasementioning

confidence: 99%

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Application of metabolomics in autoimmune diseases: Insight into biomarkers and pathology

Kang

Zhu

Lü

et al. 2015

Journal of Neuroimmunology

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Section: Referencementioning

confidence: 93%

Section: Coeliac Diseasementioning

confidence: 99%

Application of metabolomics in autoimmune diseases: Insight into biomarkers and pathology

Kang

Zhu

Lü

et al. 2015

Journal of Neuroimmunology

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“…Classification and Regression using the method tree (CART) was performed to identify predictors of CD based on metabolic profile. This analysis revealed that the most important predictor of CD was lactat metabolite (with a lower level than 0.1026), so it was revealed a reduction in blood serum level of milk compared to healthy controls [12].…”

Section: Discussionmentioning

confidence: 93%

Biostatistics Methods Used in Celiac Disease Population Studies

Iancu¹,

Tilincă²,

Cadariu³

2016

IJCD

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Introduction. Research on celiac disease is based on a descriptive and inferential variety of techniques that allow summarizing data and extrapolating the target population. The aim of this study was to identify descriptive and inferential statistical methods used in International Journal of Celiac Disease. Material and method. The sample of this study was defined based on all original articles published in the journal mentioned above in 2013-2014 period. We evaluated the descriptive statistics, the number and type of inferential statistical methods used. Results. Of the eight articles analyzed, one study was a randomzat clinical trial, the remaining studies were crosssectional studies. The articles have used both quantitative and qualitative data. The indicator of central tendency on data was mean (n = 5, 62.5 %) or the median with interquartile range (n = 1, 12.5 %) and the dispersion of data was estimatedonly by the standard deviation in 5 studies (62.5 %). Quantitative data were used in raw form, except for one study using Z scores. Qualitative data were summarized using absolute and relative frequencies (n = 7, 87.5 %). The statistical software was mentioned in all studies. All articles except one used the inferential statistical methods or statistical tests. Most articles (87.5 %) used common tests as Student's t-test (37.5), analysis of variance/covariance (25 %), Kruskal -Wallis test (12.5 %), χ2 test (50 %), and Fisher's exact test (12.5 %). A specified significance level was noted in 62.5 % of the articles and the significance level was estimated in 6 studies (75 %). Conclusion. Results showed that a proper understanding of standard statistical methods leads to a proper interpretation of clinical research hypothesis. If necessary, the advanced statistical analysis should be performed by a multidisciplinary collaboration with specialists in biostatistics or epidemiology.

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“…Some of these metabolites are also important in other brain diseases. The changes in metabolites' levels may indicate the disease state (8). In another way, specific kinds of metabolites may be recognized as potential biomarkers for disease determinations (9).…”

Section: Original Articlementioning

confidence: 99%

Metabolite- protein interaction (MPI) network analysis of obsessive- compulsive disorder (OCD) from reported metabolites

Azodi¹,

Mortazavi-Tabatabaei²,

Mansouri³

et al. 2016

Arvand Journal of Health and Medical Sciences

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Obsessive-Compulsive Disorder (OCD) is a lifetime mental condition with the prevalence of 1-3% in general population. Molecular evaluation of OCD is prominent to understand the complex basis of underlying mechanisms. Here, by the application of MPI construction of significant changed metabolites in OCD patients, it is tried to obtain a preliminarily insight of related molecular interactions. These interactions are fundamental for cell processes and drug design. For this purpose, a number of ten OCD related metabolites were obtained from literature, by the use of PubMed and Google Scholar Resources. Then, the ten candidate metabolites were examined by the application of STITCH 4.0 Online Resource, for investigating the ten significant linked proteins with our query metabolites and the related BPs. Top ten scored interacting proteins with the query metabolites were retrieved and the action types were identified. Some of the protein partners were previously detected as potential polymorphisms in OCD etiology. The Regulation of hormone secretion and regulation of phosphorylation are the most significant (corrected p value< 0.05) related biological process of the ten investigated metabolites in the whole network. The result suggests that, insulin may be influenced significantly in OCD profile as it is significantly regulated by the six of our metabolites in the whole network. In fact, either metabolites or proteins changes may influence each other levels and promotes OCD risk. Thus, further systematic analysis is suggested for confirmation and introduction of new biomarkers.

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A Metabonomics Study on Celiac Disease by CART

Cited by 12 publications

References 13 publications

Application of metabolomics in autoimmune diseases: Insight into biomarkers and pathology

Application of metabolomics in autoimmune diseases: Insight into biomarkers and pathology

Biostatistics Methods Used in Celiac Disease Population Studies

Metabolite- protein interaction (MPI) network analysis of obsessive- compulsive disorder (OCD) from reported metabolites

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