The impact of peanut allergy is large and accidental ingestion of peanut can lead to severe reactions. Currently used diagnostic tests, such as skin prick tests (SPT) and determination of specific immunoglobulins (IgE) have, however, limited sensitivity and specificity. Therefore, new tools have to be developed to improve the accuracy of the diagnostic work-up of food-allergic patients. Comprehensive metabolite analysis may provide biomarkers for diagnosing food allergy as metabolite levels reflect actual physiological conditions. We investigated whether metabolites can be found that discriminate between peanut-allergic patients and non-peanut-allergic subjects. Such metabolites may be used for future diagnostic purposes.
Plasma and saliva samples were obtained from 23 participants (12 peanut allergic and 11 peanut tolerant) prior to and after a peanut challenge and measured with (1)H nuclear magnetic resonance (NMR) spectroscopy with subsequent multivariate data analysis.
Clear differences were observed between NMR spectra of peanut-allergic and peanut-tolerant subjects in plasma as well as saliva. Allergic patients already showed aberrant metabolite levels prior to peanut ingestion, thus before the onset of allergic reactions.
This pilot study shows that aberrant metabolite levels as determined by NMR in combination with multivariate statistics may serve as novel biomarkers for food allergy.
"The gold-standard skin prick test has a low specificity of only 20-50% in detecting peanut allergy; therefore this diagnosis needs to be confirmed by performing a peanut challenge to distinguish peanut-allergic from peanut-tolerant patients. A metabolomics approach applied in the peanut challenge test has been shown to outperform the gold-standard skin prick test, as allergic patients already showed deviant metabolite levels prior to peanut ingestion, thus before the onset of allergic reactions (Peeters et al., 2011). Furthermore, the postprandial metabolic profile before and after a (standardized) meal challenge was also used to identify pathways associated with colon motility in order to find potential targets for the treatment of children with constipation (Rodriguez et al., 2013). "
[Show abstract][Hide abstract] ABSTRACT: Challenge tests are used to assess the resilience of human beings to perturbations by analyzing responses to detect functional abnormalities. Well known examples are allergy tests and glucose tolerance tests. Increasingly, metabolomics analysis of blood or serum samples is used to analyze the biological response of the individual to these challenges. The information content of such metabolomics challenge test data involves both the disturbance and restoration of homeostasis on a metabolic level and is thus inherently different from the analysis of steady state data. It opens doors to study the variation of resilience between individuals beyond the classical biomarkers; preferably in terms of underlying biological processes. We review challenge tests in which metabolomics was used to analyze the biological response. Specifically, we describe strategies to perform statistical analyses on the responses and we will show some examples of these strategies applied to a postprandial challenge that was used to study a diet with anti-inflammatory properties. Finally we discuss open issues and give recommendation for further research.
". The methodological limitations of this research need to be mentioned. Indeed, although the component-resolved diagnosis approach showed a detailed picture of the molecules implicated in the mechanism of peanut sensitization, the lack of complementary diagnostic tests (e.g., skin prick test, oral provocation test, and metabolite evaluation) is a bias of this study . "
[Show abstract][Hide abstract] ABSTRACT: Peanuts are one of the most relevant foods implicated in IgE-mediated adverse reactions in pediatric population. This study aimed to evaluate the pattern of sensitization towards five peanut allergenic components (rAra h 1, 2, 3, 8 and 9) in a population of Italian children and adolescents with specific IgE (sIgE) to peanut. rAra h 9 was the main allergen implicated in peanut sensitization (58%), followed by rAra h 8 (35%), rAra h 2 (27%), rAra h 3 (23%) and rAra h 1 (12.5%). rAra h 1, 2, and 3 were the main allergenic components in young children: 8/13 (62%) between 2 and 5 years, 8/23 (35%) between 6 and 11 years, and 3/12 (25%) between 1 and 16 years. No differences were found among the levels of sIgE towards rAra h 1, 2, 3, and 9 in the three groups; in contrast, the levels of sIgE against rAra h 8 showed an increasing trend according to age. In conclusion rAra h 1, 2, and 3 were the prevalent sensitizing allergens during the first years of life in Italian patients with sIgE to peanuts ("genuine" allergy); in contrast rAra h 9 and 8 were mainly involved in school-age children and adolescents with pollen allergy ("secondary" sensitization).
"Until now only few studies examined the influence of skin related diseases on the metabolite changes in blood e.g. for peanut allergy
 or systemic lupus erythematosus
. We here present a metabolic approach to identify changes in serum that occur during an autoimmune skin blistering disease. "
[Show abstract][Hide abstract] ABSTRACT: Epidermolysis bullosa acquisita (EBA) is a rare skin blistering disease with a prevalence of 0.2/ million people. EBA is characterized by autoantibodies against type VII collagen. Type VII collagen builds anchoring fibrils that are essential for the dermal-epidermal junction. The pathogenic relevance of antibodies against type VII collagen subdomains has been demonstrated both in vitro and in vivo. Despite the multitude of clinical and immunological data, no information on metabolic changes exists.
We used an animal model of EBA to obtain insights into metabolomic changes during EBA. Sera from mice with immunization-induced EBA and control mice were obtained and metabolites were isolated by filtration. Proton nuclear magnetic resonance (NMR) spectra were recorded and analyzed by principal component analysis (PCA), partial least squares discrimination analysis (PLS-DA) and random forest.
The metabolic pattern of immunized mice and control mice could be clearly distinguished with PCA and PLS-DA. Metabolites that contribute to the discrimination could be identified via random forest. The observed changes in the metabolic pattern of EBA sera, i.e. increased levels of amino acid, point toward an increased energy demand in EBA.
Knowledge about metabolic changes due to EBA could help in future to assess the disease status during treatment. Confirming the metabolic changes in patients needs probably large cohorts.
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