Fecal Metabolites As Non-Invasive Biomarkers of Gut Diseases

Zhgun, E. S.; Ilyina, E. N.

doi:10.32607/actanaturae.10954

Cited by 11 publications

(10 citation statements)

References 81 publications

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“…Becaues of multiple factors including cost, insurance limitations, and the requirement for a second intravenous catheter to administer incompatible drugs, these potentially protective lipid formulations are not used for all infants in the NICU 9 . Therefore, there is a great need for identifying effective biomarkers (early in life) to predict which infants will develop PNAC in the NICU, prior to a clinical diagnosis of PNAC, to be able to target hepatoprotective strategies in those most at risk 10 …”

Section: Introductionmentioning

confidence: 99%

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Fecal sphingolipids predict parenteral nutrition–associated cholestasis in the neonatal intensive care unit

Moutinho

Powers

Hanson

et al. 2022

J Parenter Enteral Nutr

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Background: Parenteral nutrition-associated cholestasis (PNAC) in the neonatal intensive care unit (NICU) causes significant morbidity and associated healthcare costs. Laboratory detection of PNAC currently relies on elevated serum conjugated bilirubin levels in the aftermath of impaired bile flow. Here, we sought to identify fecal biomarkers, which when integrated with clinical data, would better predict risk for developing PNAC.Methods: Using untargeted metabolomics in 200 serial stool samples from 60 infants, we applied statistical and machine learning approaches to identify clinical features and metabolic biomarkers with the greatest associative potential for risk of developing PNAC. Stools were collected prospectively from infants receiving PN with soybean oil-based lipid emulsion at a level IV NICU.Results: Low birth weight, extreme prematurity, longer duration of PN, and greater number of antibiotic courses were all risk factors for PNAC (P < 0.05). We identified 78 stool biomarkers with early predictive potential (P < 0.05). From these 78 biomarkers, we further identified 12 sphingomyelin lipids with high association for the development of PNAC in precholestasis stool samples when combined with birth anthropometry. Conclusion:We demonstrate the potential for stool metabolomics to enhance early identification of PNAC risk. Earlier detection of high-risk infants would empower

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Section: Introductionmentioning

confidence: 99%

“…Stool and urine provide valuable information about physiological processes and their collection is noninvasive with minimal risk 10,11 . However, stool and urine remain an untapped resource for diagnostic applications in the NICU.…”

Section: Introductionmentioning

confidence: 99%

Fecal sphingolipids predict parenteral nutrition–associated cholestasis in the neonatal intensive care unit

Moutinho

Powers

Hanson

et al. 2022

J Parenter Enteral Nutr

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“…However, metabolites from intestinal microbiota are increasingly regarded as an important factor in human physiology [ 11 ]. In recent years, metabolomics has become popular in the research of finding useful new biomarkers [ 12 , 13 , 14 ]. Fecal metabolites are of great significance as potential biomarkers since they can reflect certain diseases of the large intestine, rectum, and colon, and can especially identify intestinal processes [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

“…There have been 6736 described fecal metabolites, including 5.9% of the characterized metabolites [ 16 ], indicating that fecal metabolites are potential biomarkers again. In a word, fecal metabonomics analysis provides a non-invasive method to study the relationship between IMF and metabolites to find biomarkers [ 14 ]. The gut microbial community plays an important role in animal performance and metabolism [ 17 , 18 , 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

Using Untargeted LC-MS Metabolomics to Identify the Association of Biomarkers in Cattle Feces with Marbling Standard Longissimus Lumborum

Chen

et al. 2022

Animals

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Background: To improve the grade of beef marbling has great economic value in the cattle industry since marbling has the traits of high quality and comprehensive nutrition. And because of the marbling’s importance and complexity, it is indispensable to explore marbled beef at multiple levels. This experiment studied the relationship between fecal metabolites and marbling characters, and further screened biomarkers. Results: We performed fecal metabolomics analysis on 30 individuals selected from 100 crossbreed cattle (Luxi Yellow cattle ♀ × Japanese Wagyu cattle ♂), 15 with an extremely high-grade marbling beef and 15 with an extremely low-grade marbling beef. A total of 9959 and 8389 m/z features were detected in positive ionization and negative ionization mode by liquid chromatography-mass spectrometry (LC-MS). Unfortunately, the sample separation in the PCA is not obvious, and the predictive ability of the orthogonal partial least squares discrimination analysis (OPLS-DA) model is not good. However, we got six differential metabolites filtered by VIP > 1 and p < 0.05. After that, we used weighted correlation network analysis (WGCNA) and found out a module in each positive and negative mode most related to the trait of marbling beef, and then identified three metabolites in positive mode. By further annotation of the Kyoto encyclopedia of genes and genomes (KEGG), it was found that these metabolites involved a variety of metabolic ways, including sphingomyelin metabolism, linoleic acid metabolism, glycerophospholipid metabolism, and so on. Finally, receiver operating characteristic (ROC) analysis was used to evaluate the predictability of metabolites, and the result showed that SM(d18:0/16:1(9Z)) (AUC = 0.72), PC(15:0/18:2(9Z,12Z)) (AUC = 0.72), ADP (AUC = 0.71), PC(16:0/16:0) (AUC = 0.73), and 3-O-Sulfogalactosylceramide (d18:1/18:0) (AUC = 0.69) have an accuracy diagnosis. Conclusions: In conclusion, this study supports new opinions for the successive evaluation of marbling beef through metabolites. Furthermore, six non-invasive fecal metabolites that can evaluate beef marbling grade were found, including SM(d18:0/16:1(9Z)), PC(15:0/18:2(9Z,12Z)), ADP, PC(16:0/16:0), and 3-O-Sulfogalactosylceramide.

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“…Fecal mass consists to a great proportion of water and bacteria but also food components or metabolites (2). Fecal samples show great variability in their material content and characteristics, which makes it difficult to standardize the process from collection to processing and analysis including the analytical platform (3). The latter defines also the sensitivity of the analysis and the type of analytes available for analyses.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of different stool extraction methods for metabolomics measurements in human fecal samples

Erben

Poschet

Schrotz‐King

et al. 2020

Preprint

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Background: Measurements of metabolomics in human stool samples is of great interest for a broad range of applications in biomedical research including early detection of colorectal neoplasms. But due to the complexity of metabolites there is no consensus on how to process samples for stool metabolomics measurements to obtain a broad coverage of hydrophilic and hydrophobic substances. Methods: We used frozen stool samples (50mg) from healthy study participants. Stool samples were processed after thawing using 8 different processing protocols and different solvents. Metabolites were measured afterwards using the Mxp Quant 500 kit (Biocrates). The best performing protocol was subsequently applied to compare stool samples of participants with different dietary habits. Results: In this study, we were able to determine up to 340 metabolites of various chemical classes extracted from stool samples of healthy study participants with 8 different protocols. Polar metabolites such as amino acids could be measured with each method while other metabolite classes, particular lipid species, are more dependent on the solvent or combination of solvents used. Only a small number of triglycerides or acylcarnitines were detected in human feces. Extraction efficiency was higher for protocols using isopropanol or those using ethanol or methanol and MTBE including an evaporation and concentration than for other protocols. We detected significant fecal metabolite differences between vegetarians, semi-vegetarians and non-vegetarians. Conclusion: For the evaluation of metabolites in fecal samples we found protocols using solvents like isopropanol and those using ethanol or methanol and MTBE including an evaporation and concentration step to be superior over others tested in this study.

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Fecal Metabolites As Non-Invasive Biomarkers of Gut Diseases

Cited by 11 publications

References 81 publications

Fecal sphingolipids predict parenteral nutrition–associated cholestasis in the neonatal intensive care unit

Fecal sphingolipids predict parenteral nutrition–associated cholestasis in the neonatal intensive care unit

Using Untargeted LC-MS Metabolomics to Identify the Association of Biomarkers in Cattle Feces with Marbling Standard Longissimus Lumborum

Evaluation of different stool extraction methods for metabolomics measurements in human fecal samples

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