Principal component directed partial least squares analysis for combining nuclear magnetic resonance and mass spectrometry data in metabolomics: Application to the detection of breast cancer

Acrylamide is probably carcinogenic to humans (International Agency for Research on Cancer, group 2A) with major occurrence in heated, mainly carbohydrate-rich foods. For roasted coffee, a European Union benchmark level of 400 µg/kg acrylamide is of importance. Regularly, the acrylamide contents are controlled using liquid chromatography combined with tandem mass spectrometry (LC–MS/MS). This reference method is reliable and precise but laborious because of the necessary sample clean-up procedure and instrument requirements. This research investigates the possibility of predicting the acrylamide content from proton nuclear magnetic resonance (NMR) spectra that are already recorded for other purposes of coffee control. In the NMR spectrum acrylamide is not directly quantifiable, so that the aim was to establish a correlation between the reference value and the corresponding NMR spectrum by means of a partial least squares (PLS) regression. Therefore, 40 commercially available coffee samples with already available LC–MS/MS data and NMR spectra were used as calibration data. To test the accuracy and robustness of the model and its limitations, 50 coffee samples with extreme roasting degrees and blends were additionally prepared as the test set. The PLS model shows an applicability for the varieties Coffea arabica and C. canephora, which were medium to very dark roasted using drum or infrared roasters. The root mean square error of prediction (RMSEP) is 79 µg/kg acrylamide (n = 32). The current PLS model is judged as suitable to predict the acrylamide values of commercially available coffee samples.

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Differentiation Between Sepsis Survivors and Sepsis Non-survivors Through Blood Serum Metabolomics: A Proton Nuclear Magnetic Resonance Spectroscopy (NMR) Study.

10.21203/rs.3.rs-412947/v1 ◽

2021 ◽

Author(s):

Naveen kumar M S ◽

Virendra Kumar ◽

Jagannathan N R ◽

Sanjeev Sinha ◽

Sujeet Mewar ◽

...

Keyword(s):

Nuclear Magnetic Resonance ◽

Discriminant Analysis ◽

Magnetic Resonance ◽

Least Squares ◽

Partial Least Squares ◽

Proton Nuclear Magnetic Resonance ◽

Healthy Controls ◽

Potential Biomarker ◽

Sepsis Survivors ◽

Nuclear Magnetic

Abstract Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. Septic shock is a subset of sepsis with underlying circulatory cellular and metabolic abnormalities associated with higher mortality rates. However, a detailed understanding of sepsis is still limited. The present study reports the differences in the metabolic profile of serum samples of patients with sepsis compared to healthy controls using Nuclear Magnetic Resonance (NMR) spectroscopy. The study also compares the NMR metabolomics on day zero of admission among sepsis survivors (those who survived till day seven) and sepsis non-survivors (those who succumbed on day zero). Furthermore, the different metabolites in serum were analysed by univariate and multivariate analysis, ROC analysis, principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) methods. Metabolites with VIP score (>1·0) were considered as potential biomarker/s to discriminate sepsis survivors from non- survivors at day zero. Data showed that phenylalanine was significantly higher in sepsis patients compared to healthy controls, whereas isoleucine, valine and histidine were significantly lower in sepsis patients compared to healthy controls. Also, non-survivors had higher serum levels of creatine, phosphocreatine, choline, betaine, tyrosine, histidine and phenylalanine concentrations than survivors. These findings suggest that the metabolic alterations at day zero may predict the survival of patients with sepsis. The significant differences seen in metabolites concentration of amino acids, phospholipids and creatine may be used as early prognostic markers to discriminate non-survivors from survivors of sepsis patients at day zero. Our findings indicate that the metabolite alterations are associated with the progression of the disease.

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