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Martín-Masot, R.; Jiménez-Muñoz, M.; Herrador-López, M.; Navas-López, V.M.; Obis, E.; Jové, M.; Pamplona, R.; Nestares, T. Metabolomic Profiling in Children with Celiac Disease. Encyclopedia. Available online: https://encyclopedia.pub/entry/46576 (accessed on 12 August 2024).
Martín-Masot R, Jiménez-Muñoz M, Herrador-López M, Navas-López VM, Obis E, Jové M, et al. Metabolomic Profiling in Children with Celiac Disease. Encyclopedia. Available at: https://encyclopedia.pub/entry/46576. Accessed August 12, 2024.
Martín-Masot, Rafael, María Jiménez-Muñoz, Marta Herrador-López, Víctor Manuel Navas-López, Elia Obis, Mariona Jové, Reinald Pamplona, Teresa Nestares. "Metabolomic Profiling in Children with Celiac Disease" Encyclopedia, https://encyclopedia.pub/entry/46576 (accessed August 12, 2024).
Martín-Masot, R., Jiménez-Muñoz, M., Herrador-López, M., Navas-López, V.M., Obis, E., Jové, M., Pamplona, R., & Nestares, T. (2023, July 08). Metabolomic Profiling in Children with Celiac Disease. In Encyclopedia. https://encyclopedia.pub/entry/46576
Martín-Masot, Rafael, et al. "Metabolomic Profiling in Children with Celiac Disease." Encyclopedia. Web. 08 July, 2023.
Metabolomic Profiling in Children with Celiac Disease
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This entry is adapted from the peer-reviewed paper 10.3390/nu15132871

Celiac disease (CD) is included in the group of complex or multifactorial diseases, i.e., those caused by the interaction of genetic and environmental factors. Despite a growing understanding of the pathophysiological mechanisms of the disease, diagnosis is still often delayed and there are no effective biomarkers for early diagnosis. The only current treatment, a gluten-free diet (GFD), can alleviate symptoms and restore intestinal villi, but its cellular effects remain poorly understood. To gain a comprehensive understanding of CD’s progression, it is crucial to advance knowledge across various scientific disciplines and explore what transpires after disease onset. Metabolomics studies hold particular significance in unravelling the complexities of multifactorial and multisystemic disorders, where environmental factors play a significant role in disease manifestation and progression. 

celiac disease gluten-free diet metabolomics children immune intestinal

1. Introduction

Celiac disease (CD) is included in the group of complex or multifactorial diseases, i.e., those caused by the interaction of genetic and environmental factors [1]. It is a chronic disease whose severity and digestive and/or systemic symptoms show great variability among patients. However, the common characteristic to all of them is an exacerbated immune response to gluten and related proteins, so this systemic disorder is considered an immune-mediated disease. In fact, patients are characterized by the presence of high titers of specific antibodies and the vast majority are carriers of the DQ2 and/or DQ8 haplotypes of the major histocompatibility complex (Human Leukocyte Antigen, HLA class II), responsible for antigen presentation by the immune system [2][3][4]. In addition to gluten intake and the presence of risk alleles in HLA, the occurrence of intestinal and extra-intestinal symptoms in CD requires the activation of both types of immune response, innate and adaptive, and this overactivation of the immune system is observed at the intestinal level as well as at the peripheral and systemic levels [4].
The main gap in the knowledge of the pathogenesis of CD is an explanation of why 25–35% of the world’s healthy population has these haplotypes, but only about 1–3% will develop CD [5][6]. It is possible that other environmental and genetic factors influence an individual’s ability to induce and control the innate response and an individual’s susceptibility to gluten.
Even though CD is actually one of the most frequent genetic diseases, affecting 1–3% of the world’s population, and to a greater extent women and children [7], it is clearly underestimated and underdiagnosed. Despite advances in knowledge and the development of serological tests, CD continues to be difficult and costly to diagnose, largely due to the systemic nature of CD, the lack of specificity of its clinical manifestations and the existence of silent or latent forms [8][9]. The problem is that untreated celiacs or those whose diagnosis has been delayed, despite having no symptoms, may have an exacerbated and chronic activation of the immune system, which is uncontrolled for a longer time, leading to a worse prognosis, an accentuation of symptoms and the appearance of other autoimmune diseases such as type 1 diabetes or gluten-dependent hepatitis, which are frequent in CD patients [10][11]. Also, they may be affected by a variety of adverse consequences, some serious such as the development of malignant tumors [12]. Therefore, at the present time, the main challenge, like for other genetic-based diseases (such as some types of cancer and diabetes, among others), is to study in genetically predisposed individuals, on the one hand, which factors are involved in the development or not of CD and, on the other, to find biomarkers for its early diagnosis and follow-up. The idea is to avoid the side effects when the disease has already made its debut and even prevent its appearance, something relevant since it is currently incurable.
The only current treatment for CD is a lifelong strict gluten-free diet (GFD) that achieves a remission of symptoms within a few days or weeks and a restoration of intestinal villi and immune homeostasis within a few months. Researchers have found that, after 18 months of a strict GFD follow-up, the celiac has most of the parameters equivalent to those of a healthy child [13][14][15]. This is an important finding, but, even so, the usual delay in diagnosis (because of its difficulty), in combination with the time it takes to stabilize the disease, is too long, and in this period they may develop complications that will result in sequelae that range from minor (e.g., permanent short stature, dental enamel failure and psychiatric problems) to serious, such as tumors, in the long term. The fact is that the GFD seems to play an important role in the pathogenesis of tumor development, since some studies have described a greater development of tumors the later the diagnosis is made and in patients who have not followed the GFD [16]. Thus, untreated CD is associated above all with T-cell lymphoma (EATL) [12] and small bowel adenocarcinoma [17], although a higher incidence of non-Hodgkin’s lymphoma and colorectal cancer has also been described [18][19].
As CD is a systemic and complex disease, it is necessary to look at it from different perspectives. Personalized or precision medicine refers to the application of biotechnology, genetic profiling, “omics” sciences and the incorporation of clinical and environmental factors to evaluate individual risks and design strategies for the prevention, diagnosis, treatment or follow-up of the disease at the right time and in the right patient, with the minimum toxicity and maximum possible efficacy. One of the sciences that has been booming in recent years is metabolomics, which deals with the study of chemical processes in which small molecules, called metabolites, which can be both endogenous and xenobiotic, are measured. These molecules give information about a metabolic process that has taken place in the organism, and metabolomics can be considered, from this perspective, as an approach to cellular metabolism that other “omics” sciences cannot provide [20][21]. In this sense, metabolomics is presented as a fast and non-invasive tool that could represent a step forward in the knowledge of many diseases through the study of the metabolic profile by obtaining what are known as “metabolomic fingerprints or signatures” resulting from the interaction of the genome, epigenome, transcriptome, proteome and the environment.
Metabolomics studies are especially relevant in those multifactorial and multisystemic pathological situations where the environmental factor plays a relevant role in the appearance and development of the disease. Technologies such as metabolomics could define the alterations that occur in the genetically predisposed individual, as well as after certain changes, such as the GFD, helping to better understand these complex interactions, and thus may be useful for the diagnosis and monitoring of CD [22].

2. Plasma Metabolomic Profile

2.1.  CD’s Inherent Footprint and Role of the GFD

Several molecules have been proposed as potential CD biomarkers. In this regard, Auricchio et al., 2019 [23] found that the serum phospholipid profile is different in children who develop CD compared to healthy children with similar genetic profiles (specific celiac human HLA DQ2 or DQ8), even before the introduction of gluten to the diet at 4 months of age. They followed a cohort of children from families with a CD case from birth to 8 years of age, with sampling at 4 and 12 months of age (and at CD diagnosis in cases >24 months of age), finding in lipidomic analysis based on LC coupled with MS and multiple reaction monitoring (MRM) that the lipid profile is fairly constant in each individual, in both groups, suggesting that it could be constitutive. In the age-grouped analysis, they found that children who developed CD had increased lyso- and phosphatidylcholine (PC) serum levels (PC 40:4 showed the greatest difference between the two groups), as well as alkylacylphosphatidylcholine (PC-O). Specifically, two alkylacylphosphatidylcholipids (PC O-42:0 and PC O-38:3), together with breastfeeding and one phosphatidylcholine (PC 34:1), were defined as predictors of CD development. They found that phosphatidylethanolamines (PE) PE 34:1 and PE 36:1 were decreased in celiac patients.
The working group of Sen et al., 2019 [24] also applied lipidomics in the study of a cohort of Finnish children in the context of the Type 1 Diabetes Prediction and Prevention study. Based on MS and comparing plasma samples from children who developed CD with plasma samples from healthy controls, matched for HLA risk, sex and age, they found that CD progressors (children who developed CD) had increased triacylgycerols (TGs) of low carbon number, double-bond count plasma levels and decreased phosphatidylcholines and cholesterol esters levels at 3 months of age compared to controls. These differences were not apparent at birth (cord blood) and exacerbated with age. It is proposed that the increase in TGs of low carbon number and double-bond count is due to de novo lipogenesis compensating for lipid malabsorption, which would occur at a very young age; this increase in TGs has been linked in adults to increased liver fat in non-alcoholic fatty liver disease [25]. In addition, they found decreased total essential TG levels in the plasma of the CD progressors after gluten intake, reversing this trend, but not significantly, after the onset of GFD, and there was an inverse relationship with the tissue transglutaminase IgA titer (tTGA). There was also an increase in cholesterol levels after the start of GFD in the CD progressors. On the other hand, the endogenous TGs plasma levels were decreased in CD progressors independently of gluten intake. PCs were elevated in both CD progressors and controls after the start of gluten intake. A difference in sphingomyelin plasma levels was observed in CD progressors at a later age, after the introduction of GFD.
These findings suggest that a dysregulation in lipid metabolism may be associated with the development of CD, and that it occurs in the first months of life, even before the introduction of gluten to the diet. This could help predict the development of CD in infants at genetic risk, even years before the appearance of specific antibodies or clinical symptoms/signs.
However, a previous study (2016) based on the PreventCD project suggested that the metabolic profile at 4 months (before the introduction of gluten to the diet) did not predict the development of CD, but that metabolic pathways are affected later in life [26]. In this study, which studied serum samples from infants at genetic risk for CD who developed CD compared to those who did not develop the disease at 8 years of age, a trend of decreased phospholipids levels was found in children who subsequently developed CD, although not significantly, with a greater decrease in the subsample of children exclusively breastfed until 4 months of age. They conclude that metabolomic studies should focus on children who have already had gluten introduced to their diet. However, this study focused the analysis on phospholipids and acylcarnitines, and TGs and cholesterol esters were not measured.
Following the lipidomics approach, in a pilot study conducted by ourselves [27], plasma lipid profile was affected in celiac patients, despite GFD treatment. Using an LC-MS/MS platform, there plasma from 17 celiac children under a GFD treatment and 17 healthy controls (siblings) was analyzed. Among the significant molecules, it was found that 64% were increased and 36% decreased in CD patients. Two carboxylic acids and derivatives were increased in CD; other molecules whose levels were affected in patients were four fatty acyls (thromboxanes and leykotrienes involved in inflammatory pathways), five glycerolipids, eleven glycerophospholipids, one organoxigen compound and two sphingolipids, lipid species belonging to steroid metabolism and other molecules involved in bilirubin metabolism. In celiac patients elevated levels of molecules involved in cell signaling pathways (ceramides, diacylglycerides and lysophospholipids) were found. Diacylglycerides play a central role in the control of neuronal communication, phagocytosis and the control of immune responses, and as a second messenger they play an important role in the regulation of mTOR, recently described as a key factor in maintaining a sustained inflammatory response in CD [28].
Aside from the lipid profile, one-carbon metabolism alterations were also found by this group [29] under a targeted plasma metabolomics study. They observed a down-regulation of the trans-sulphuration pathway in CD patients, despite GFD, with decreased cysteine and cystathionine, which, together with normal glutathione and vitamin B6, suggests a specific defect at the level of the enzymes involved in antioxidant defense, oxygen sensing, mitochondrial function, inflammation and second-messenger signaling. This finding, moreover, could be explained by a S-adenosyl-L-homocysteine (SAH) hydrolase mutation that causes typical symptoms of the disease, such as growth retardation, dental anomalies or hypertransaminasemia. In contrast, other pathways involved in one-carbon metabolism appeared to be preserved (choline metabolism, the methionine cycle and the folate cycle), suggesting that adherence to a strict GFD could reverse certain metabolic changes in celiac patients, making them resemble the profile of healthy subjects. This group notes that these metabolomic changes are, however, minor, as only approximately 4% of the total plasma metabolome analyzed was affected [27].
In a more recent study based on a targeted plasma metabolomics analysis, Girdhar et al., 2023 [30] found increased levels of 2-methyl-3-ketovalric acid, taurodeoxycholic acid (TDCA), glucono-D-lactone and isoburyryl-L-carnitine, as well as significantly low oleic acid levels (anti-inflammatory metabolite) in CD progressors (compared to healthy children matched for age, HLA genotype, breastfeeding duration and gluten exposure duration). Other metabolic pathways were also affected in the CD progressors, such as the pentose phosphate pathway, unsaturated fatty acid biosynthesis and glycolipid and linoleic acid metabolism. Notably, TDCA levels were increased to twice the normal values. TDCA, a metabolite derived from the gut microbiota, may play a role in small intestinal inflammation and CD pathogenesis, as its administration to C57BL/6J mice by supplementing their diet caused a distortion in crypt structure and total or partial villous atrophy; increased CD4+ T cells, natural killer cells and Qa-1 and NKG2D expression on T cells (two immunomodulatory proteins); and decreased regulatory T cells in intraepithelial lymphocytes. Therefore, TDCA could be used as an early diagnosis biomarker, and more importantly, targeted therapies to eliminate TDCA-producing bacteria (Clostridium XIVa and Clostridium XI) early in life could be used as a strategy to decrease the CD development risk. On the other hand, they found that the cytokine plasma profile and other metabolites were altered in CD progressors, even before diagnosis (other recent work (Auricchio et al., 2023 [31]) has also focused on the serum cytokine profile and proinflammatory genes expression in infants at CD risk), and differences were also found in the gut microbiota composition (studied in stool) (other authors have also studied microbiota and metabolome alterations in infants at risk of CD, in stool [32][33]). In the CD progressors, before diagnosis, they found significantly increased levels of three proinflammatory cytokines (IFNA2, IL-1a and IL-17E/(IL25)) and a chemokine (MIP-1b/CCl4).
Another interesting aspect to be addressed is alternative biomarkers that allow the disease to be monitored and can also be used in the evaluation of celiac patients’ relatives. Plasma citrulline was assessed by an LC auto sampler (and in dried blood spots) by Lomash et al., 2021 [34], as a potential biomarker useful in the diagnosis and monitoring of the disease, as well as in the evaluation of celiac patients’ first-degree relatives (FDRs) (predictive value in the distinction of seronegative CD and in the progression of potential to overt CD). This non-essential amino acid is specifically produced by proximal small intestine enterocyte villi, so it has been proposed as a possible marker of residual intestinal function in pathologies such as necrotizing enterocolitis in newborns, enterophaties, small intestine transplantation or small bowel resections [35]. This work found statistically significant differences in the median plasma citrulline levels in celiac children (20.1 μM (IQR, 13.35–29.15)) compared to controls (serology-negative FDRs) (37.33 μM (IQR, 29.8–42.6)). They also found an inverse correlation between plasma citrulline levels and anti-tTG IgA levels throughout the establishment of GFD and, in addition, in the different Marsh grades at diagnosis; so, citrulline could be used as a surrogate biomarker for serology in disease monitoring and in predicting the histopathological damage degree (it was effective in distinguishing grades 3b and above but not in distinguishing 3a or less in celiac patients and healthy asymptomatic FDRs). In addition, in patients with inconclusive serology and biopsy results, the median plasma citrulline reflected mucosal damage (12.26 μM) like in potential celiacs (median plasma citrulline levels: 23.25 μM).

2.2. Genetic Influence (HLA)

In the above-mentioned work [34], plasma citrulline levels were also correlated with HLA genotype. Significantly low plasma citrulline levels were observed in subjects with the HLA DQ 2.5 genotype with subtypes DQA1*0501 and DQB1*0201. The HLA-DQ genotype has already been reported to influence early intestinal microbial colonization, thus influencing the metabolome [36].
Kirchberg et al. (2016) found that the HLA genotype did not have any influence on the serum metabolic profile in infants who were at risk for celiac disease before introducing gluten to their diet [26].

3. Urine Metabolomic Profile

Other studies have also compared the urine metabolomic profile of celiac children with healthy controls, some of them focusing on changes in the volatile organic compounds (VOCs) profile. An example of this is Di Cagno et al., 2011 [37], who, using gas chromatography mass spectrometry/solid-phase microextraction (GC-MS/SPME) analysis, demonstrated that VOCs and free-amino-acid levels are altered in the urine (and stool) of celiac children with more than 2 years of GFD, relating these imbalances to qualitative and quantitative differences in the microbiota of celiac patients compared to healthy people. They found that the CD group had higher dimethyl trisulfide and dimethyl disulfide urine levels. In addition, with some exceptions, they also had higher urine hydrocarbon levels. No differences in urine aldehyde levels were found between the two groups. These findings were confirmed by NMR, which also found that the CD group had higher lysine, arginine, creatine and methylamine mean levels, while carnosine, glucose, glutamine and 3-methyl-2-oxobutanoic acid were the highest in healthy children. This study emphasizes that a GFD does not completely restore the microbiota or, consequently, the metabolome of children with CD, and that there are possible metabolic markers of CD; furthermore, it suggests that the addition of prebiotics and probiotics to the GFD could restore the microbiota–microbiome balance in celiac children.

In relation to this aspect, Drabinska et al., 2019 [38], studied the effect of GFD supplementation with a prebiotic (oligofructose-enriched inulin) on VOC urine concentration in celiac children and adolescents, using GC-MS/SPME analysis. This work is based on the idea that changes in the VOC profile in biological fluids that occur in various gastrointestinal diseases (studied by the recent so-called “volatolomics”) are due in part to alterations in microbiota metabolism, especially its fermentative activity, and not so much to variations in its composition. However, GFD supplementation with this prebiotic had no impact on most VOC urine profiles of celiac patients; only a significant change was observed in benzaldehyde concentrations, which decreased by 36% after 12 weeks of intervention, which may be related to a decrease in Lactobacillus counts in the prebiotic-supplemented group, as Lactobacillus produces an aminotransferase that converts phenylalanine to benzaldehyde.
Another study, also led by Drabinska [39], aimed to optimize the GC-MS/SPME method for the detection of changes in VOC urine profiles in celiac children compared to healthy children. Based on Variable Importance in the Projection (VIP) scores, several CD biomarkers could be suggested: 1,3-di-tert-butylbenzene (only found in the urine of celiac children) and other VOCs present in higher concentrations in the urine of healthy children (2,3-butanedione, 2-heptanone, dimethyl disulfide and octanal, and, with lower VIP scores, 2-butanone, hexanal and 4-heptanone). Again, these differences could be explained by alterations in the celiac patients’ gut microbiota, as many VOCs are fermentation products of the microbiota. On the other hand, the VOC levels in biological fluids (blood, urine, sweat…) could be increased by the altered intestinal permeability present in many gastrointestinal tract diseases.

References

  1. Martín-Masot, R.; Diaz-Castro, J.; Moreno-Fernandez, J.; Navas-López, V.M.; Nestares, T. The Role of Early Programming and Early Nutrition on the Development and Progression of Celiac Disease: A Review. Nutrients 2020, 12, 3427.
  2. Ludvigsson, J.F.; Bai, J.C.; Biagi, F.; Card, T.R.; Ciacci, C.; Ciclitira, P.J.; Green, P.H.R.; Hadjivassiliou, M.; Holdoway, A.; Van Heel, D.A.; et al. Diagnosis and Management of Adult Coeliac Disease: Guidelines from the British Society of Gastroenterology. Gut 2014, 63, 1210–1228.
  3. Szajewska, H.; Shamir, R.; Mearin, L.; Ribes-Koninckx, C.; Catassi, C.; Domellof, M.; Fewtrell, M.S.; Husby, S.; Papadopoulou, A.; Vandenplas, Y.; et al. Gluten Introduction and the Risk of Coeliac Disease: A Position Paper by the European Society for Pediatric Gastroenterology, Hepatology, and Nutrition. J. Pediatr. Gastroenterol. Nutr. 2016, 62, 507–513.
  4. Lebwohl, B.; Sanders, D.S.; Green, P.H.R. Coeliac Disease. Lancet 2018, 391, 70–81.
  5. Caio, G.; Volta, U.; Sapone, A.; Leffler, D.A.; De Giorgio, R.; Catassi, C.; Fasano, A. Celiac Disease: A Comprehensive Current Review. BMC Med. 2019, 17, 142.
  6. Leonard, M.M.; Karathia, H.; Pujolassos, M.; Troisi, J.; Valitutti, F.; Subramanian, P.; Camhi, S.; Kenyon, V.; Colucci, A.; Serena, G.; et al. Multi-Omics Analysis Reveals the Influence of Genetic and Environmental Risk Factors on Developing Gut Microbiota in Infants at Risk of Celiac Disease. Microbiome 2020, 8, 130.
  7. King, J.A.; Jeong, J.; Underwood, F.E.; Quan, J.; Panaccione, N.; Windsor, J.W.; Coward, S.; Debruyn, J.; Ronksley, P.E.; Shaheen, A.A.; et al. Incidence of Celiac Disease Is Increasing over Time: A Systematic Review and Meta-Analysis. Am. J. Gastroenterol. 2020, 115, 507–525.
  8. Abrams, J.A.; Diamond, B.; Rotterdam, H.; Green, P.H.R. Seronegative Celiac Disease: Increased Prevalence with Lesser Degrees of Villous Atrophy. Dig. Dis. Sci. 2004, 49, 546–550.
  9. Charlesworth, R.P.G. Diagnosing Coeliac Disease: Out with the Old and in with the New? World J. Gastroenterol. 2020, 26, 1–10.
  10. Simpson, S.M.; Ciaccio, E.J.; Case, S.; Jaffe, N.; Mahadov, S.; Lebwohl, B.; Green, P.H. Celiac Disease in Patients with Type 1 Diabetes: Screening and Diagnostic Practices. Diabetes Educ. 2013, 39, 532–540.
  11. Volta, U.; Rostami, K.; Tovoli, F.; Caio, G.; Masi, C.; Ruggeri, E.; Cacciari, G.; Bon, I.; De Giorgio, R. Fulminant Type 1 Autoimmune Hepatitis in a Recently Diagnosed Celiac Disease Patient. Arch. Iran Med. 2013, 16, 683–685.
  12. Martín-Masot, R.; Herrador-López, M.; Navas-López, V.M.; Carmona, F.D.; Nestares, T.; Bossini-Castillo, L. Celiac Disease Is a Risk Factor for Mature T and NK Cell Lymphoma: A Mendelian Randomization Study. Int. J. Mol. Sci. 2023, 24, 7216.
  13. Diaz-Castro, J.; Muriel-Neyra, C.; Martin-Masot, R.; Moreno-Fernandez, J.; Maldonado, J.; Nestares, T. Oxidative Stress, DNA Stability and Evoked Inflammatory Signaling in Young Celiac Patients Consuming a Gluten-Free Diet. Eur. J. Nutr. 2019, 59, 1577–1584.
  14. Nestares, T.; Martín-Masot, R.; Flor-Alemany, M.; Bonavita, A.; Maldonado, J.; Aparicio, V.A. Influence of Ultra-Processed Foods Consumption on Redox Status and Inflammatory Signaling in Young Celiac Patients. Nutrients 2021, 13, 156.
  15. Nestares, T.; Martín-Masot, R.; Labella, A.; Aparicio, V.A.; Flor-Alemany, M.; López-Frías, M.; Maldonado, J. Is a Gluten-Free Diet Enough to Maintain Correct Micronutrients Status in Young Patients with Celiac Disease? Nutrients 2020, 12, 844.
  16. Silano, M.; Volta, U.; Mecchia, A.M.; Dessì, M.; Di Benedetto, R.; De Vincenzi, M. Delayed Diagnosis of Coeliac Disease Increases Cancer Risk. BMC Gastroenterol. 2007, 7, 8.
  17. Viljamaa, M.; Kaukinen, K.; Pukkala, E.; Hervonen, K.; Reunala, T.; Collin, P. Malignancies and Mortality in Patients with Coeliac Disease and Dermatitis Herpetiformis: 30-Year Population-Based Study. Dig. Liver Dis. 2006, 38, 374–380.
  18. Mearin, M.L.; Catassi, C.; Brousse, N.; Brand, R.; Collin, P.; Fabiani, E.; Schweizer, J.J.; Abuzakouk, M.; Szajewska, H.; Hallert, C.; et al. European Multi-Centre Study on Coeliac Disease and Non-Hodgkin Lymphoma. Eur. J. Gastroenterol. Hepatol. 2006, 18, 187–194.
  19. Ilus, T.; Kaukinen, K.; Virta, L.J.; Pukkala, E.; Collin, P. Incidence of Malignancies in Diagnosed Celiac Patients: A Population-Based Estimate. Am. J. Gastroenterol. 2014, 109, 1471–1477.
  20. Kell, D.B.; Goodacre, R. Metabolomics and Systems Pharmacology: Why and How to Model the Human Metabolic Network for Drug Discovery. Drug Discov. Today 2014, 19, 171–182.
  21. Allen, J.; Davey, H.M.; Broadhurst, D.; Heald, J.K.; Rowland, J.J.; Oliver, S.G.; Kell, D.B. High-Throughput Classification of Yeast Mutants for Functional Genomics Using Metabolic Footprinting. Nat. Biotechnol. 2003, 21, 692–696.
  22. Sellitto, M.; Bai, G.; Serena, G.; Fricke, W.F.; Sturgeon, C.; Gajer, P.; White, J.R.; Koenig, S.S.K.; Sakamoto, J.; Boothe, D.; et al. Proof of Concept of Microbiome-Metabolome Analysis and Delayed Gluten Exposure on Celiac Disease Autoimmunity in Genetically at-Risk Infants. PLoS ONE 2012, 7, e33387.
  23. Auricchio, R.; Galatola, M.; Cielo, D.; Amoresano, A.; Caterino, M.; De Vita, E.; Illiano, A.; Troncone, R.; Greco, L.; Ruoppolo, M. A Phospholipid Profile at 4 Months Predicts the Onset of Celiac Disease in At-Risk Infants. Sci. Rep. 2019, 9, 14303.
  24. Sen, P.; Carlsson, C.; Virtanen, S.M.; Simell, S.; Hyöty, H.; Ilonen, J.; Toppari, J.; Veijola, R.; Hyötyläinen, T.; Knip, M.; et al. Persistent Alterations in Plasma Lipid Profiles Before Introduction of Gluten in the Diet Associated with Progression to Celiac Disease. Clin. Transl. Gastroenterol. 2019, 10, 44.
  25. Orešič, M.; Hyötyläinen, T.; Kotronen, A.; Gopalacharyulu, P.; Nygren, H.; Arola, J.; Castillo, S.; Mattila, I.; Hakkarainen, A.; Borra, R.J.H.; et al. Prediction of Non-Alcoholic Fatty-Liver Disease and Liver Fat Content by Serum Molecular Lipids. Diabetologia 2013, 56, 2266–2274.
  26. Kirchberg, F.F.; Werkstetter, K.J.; Uhl, O.; Auricchio, R.; Castillejo, G.; Korponay-Szabo, I.R.; Polanco, I.; Ribes-Koninckx, C.; Vriezinga, S.L.; Koletzko, B.; et al. Investigating the Early Metabolic Fingerprint of Celiac Disease—A Prospective Approach. J. Autoimmun. 2016, 72, 95–101.
  27. Martín-Masot, R.; Galo-Licona, J.D.; Mota-Martorell, N.; Sol, J.; Jové, M.; Maldonado, J.; Pamplona, R.; Nestares, T. Up-Regulation of Specific Bioactive Lipids in Celiac Disease. Nutrients 2021, 13, 2271.
  28. Sedda, S.; Dinallo, V.; Marafini, I.; Franzè, E.; Paoluzi, O.A.; Izzo, R.; Giuffrida, P.; Di Sabatino, A.; Corazza, G.R.; Monteleone, G. MTOR Sustains Inflammatory Response in Celiac Disease. Sci. Rep. 2020, 10, 10798.
  29. Martín-Masot, R.; Mota-Martorell, N.; Jové, M.; Maldonado, J.; Pamplona, R.; Nestares, T. Alterations in One-Carbon Metabolism in Celiac Disease. Nutrients 2020, 12, 3723.
  30. Girdhar, K.; Dogru, Y.D.; Huang, Q.; Yang, Y.; Tolstikov, V.; Raisingani, A.; Chrudinova, M.; Oh, J.; Kelley, K.; Ludvigsson, J.F.; et al. Dynamics of the Gut Microbiome, IgA Response, and Plasma Metabolome in the Development of Pediatric Celiac Disease. Microbiome 2023, 11, 9.
  31. Auricchio, R.; Galatola, M.; Cielo, D.; Rotondo, R.; Carbone, F.; Mandile, R.; Carpinelli, M.; Vitale, S.; Matarese, G.; Gianfrani, C.; et al. Antibody Profile, Gene Expression and Serum Cytokines in At-Risk Infants before the Onset of Celiac Disease. Int. J. Mol. Sci. 2023, 24, 6836.
  32. Fasano, A.; Leonard, M.M.; Kenyon, V.; Valitutti, F.; Pennacchio-Harrington, R.; Piemontese, P.; Francavilla, R.; Norsa, L.; Passaro, T.; Crocco, M.; et al. Cohort Profile: Celiac Disease Genomic, Environmental, Microbiome and Metabolome Study; a Prospective Longitudinal Birth Cohort Study of Children at-Risk for Celiac Disease. PLoS ONE 2023, 18, e2082739.
  33. Leonard, M.M.; Valitutti, F.; Karathia, H.; Pujolassos, M.; Kenyon, V.; Fanelli, B.; Troisi, J.; Subramanian, P.; Camhi, S.; Colucci, A.; et al. Microbiome Signatures of Progression toward Celiac Disease Onset in At-Risk Children in a Longitudinal Prospective Cohort Study. Proc. Natl. Acad. Sci. USA 2021, 118, e2020322118.
  34. Lomash, A.; Prasad, A.; Singh, R.; Kumar, S.; Gupta, R.; Dholakia, D.; Kumar, P.; Batra, V.V.; Puri, A.S.; Kapoor, S. Evaluation of the Utility of Amino Acid Citrulline as a Surrogate Metabolomic Biomarker for the Diagnosis of Celiac Disease. Nutr. Metab. Insights 2021, 14, 603.
  35. Crenn, P.; Messing, B.; Cynober, L. Citrulline as a Biomarker of Intestinal Failure Due to Enterocyte Mass Reduction. Clin. Nutr. 2008, 27, 328–339.
  36. Olivares, M.; Albrecht, S.; De Palma, G.; Ferrer, M.D.; Castillejo, G.; Schols, H.A.; Sanz, Y. Human Milk Composition Differs in Healthy Mothers and Mothers with Celiac Disease. Eur. J. Nutr. 2015, 54, 119–128.
  37. Di Cagno, R.; De Angelis, M.; De Pasquale, I.; Ndagijimana, M.; Vernocchi, P.; Ricciuti, P.; Gagliardi, F.; Laghi, L.; Crecchio, C.; Guerzoni, M.; et al. Duodenal and Faecal Microbiota of Celiac Children: Molecular, Phenotype and Metabolome Characterization. BMC Microbiol. 2011, 11, 219.
  38. Drabinska, N.; Jarocka-Cyrta, E.; Ratcliffe, N.M.; Krupa-Kozak, U. The Profile of Urinary Headspace Volatile Organic Compounds After 12-Week Intake of Oligofructose-Enriched Inulin by Children and Adolescents with Celiac Disease on a Gluten-Free Diet: Results of a Pilot, Randomized, Placebo-Controlled Clinical Trial. Molecules 2019, 24, 1341.
  39. Drabińska, N.; Azeem, H.A.; Krupa-Kozak, U. A Targeted Metabolomic Protocol for Quantitative Analysis of Volatile Organic Compounds in Urine of Children with Celiac Disease. RSC Adv. 2018, 8, 36534–36541.
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Subjects: Pediatrics
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Rafael Martín-Masot
,
María Jiménez-Muñoz
,
Marta Herrador-López
,
Víctor Manuel Navas-López
,
Elia Obis
,
Mariona Jové
,
Reinald Pamplona
,
Teresa Nestares
View Times: 453
Revisions: 5 times (View History)
Update Date: 10 Jul 2023
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