Evaluations of different imputation methods using labeled approaches.

<p>Pearson's correlation between log-transformed p-values of student’s t-tests on FFA dataset (upper left) and BA dataset (upper right) along with different numbers of missing variables based on four imputation methods: HM (red circle), QRILC (green triangle), GSimp (blue square), and kNN-TN (purple cross). PLS-Procrustes sum of squared errors on FFA dataset (lower left) and BA dataset (lower right) along with different numbers of missing variables based on four imputation methods: HM (red circle), QRILC (green triangle), GSimp (blue square), and kNN-TN (purple cross).</p

GSimp: A Gibbs sampler based left-censored missing value imputation approach for metabolomics studies

<div><p>Left-censored missing values commonly exist in targeted metabolomics datasets and can be considered as missing not at random (MNAR). Improper data processing procedures for missing values will cause adverse impacts on subsequent statistical analyses. However, few imputation methods have been developed and applied to the situation of MNAR in the field of metabolomics. Thus, a practical left-censored missing value imputation method is urgently needed. We developed an iterative Gibbs sampler based left-censored missing value imputation approach (GSimp). We compared GSimp with other three imputation methods on two real-world targeted metabolomics datasets and one simulation dataset using our imputation evaluation pipeline. The results show that GSimp outperforms other imputation methods in terms of imputation accuracy, observation distribution, univariate and multivariate analyses, and statistical sensitivity. Additionally, a parallel version of GSimp was developed for dealing with large scale metabolomics datasets. The R code for GSimp, evaluation pipeline, tutorial, real-world and simulated targeted metabolomics datasets are available at: <a href="https://github.com/WandeRum/GSimp" target="_blank">https://github.com/WandeRum/GSimp</a>.</p></div

Sequentially parameters updating in GSimp.

<p>The first 500 iterations out of a total of 2000 (100×20) iterations using GSimp where <i>ŷ</i>, <i>ỹ</i> and <i>σ</i> represent fitted value, sample value and standard deviation correspondingly.</p

Comparisons of imputed values and original values on one variable.

<p>Scatter plots of imputed values (X-axis) and original values (Y-axis) on one example missing variable while non-missing elements represented as blue dots and missing elements as red dots based on four imputation methods: HM (upper left), QRILC (upper right), kNN-TN (lower left), and GSimp (lower right). Rug plots show the distributions of imputed values and original values.</p

Evaluations of different imputation methods using TPR for various <i>p</i>-cutoffs on simulation dataset.

<p><i>TPR</i> along with different numbers of missing variables based on three imputation methods: QRILC (green triangle), GSimp (blue square), and kNN-TN (purple cross) among different p-cutoff = 0.05 (left panel), and 0.01 (right panel).</p

Evaluations of different imputation methods using labeled approaches.

<p>Pearson's correlation between log-transformed p-values of student’s t-tests on FFA dataset (upper left) and BA dataset (upper right) along with different numbers of missing variables based on four imputation methods: HM (red circle), QRILC (green triangle), GSimp (blue square), and kNN-TN (purple cross). PLS-Procrustes sum of squared errors on FFA dataset (lower left) and BA dataset (lower right) along with different numbers of missing variables based on four imputation methods: HM (red circle), QRILC (green triangle), GSimp (blue square), and kNN-TN (purple cross).</p

Tryptophan Predicts the Risk for Future Type 2 Diabetes

<div><p>Recently, 5 amino acids were identified and verified as important metabolites highly associated with type 2 diabetes (T2D) development. This report aims to assess the association of tryptophan with the development of T2D and to evaluate its performance with existing amino acid markers. A total of 213 participants selected from a ten-year longitudinal Shanghai Diabetes Study (SHDS) were examined in two ways: 1) 51 subjects who developed diabetes and 162 individuals who remained metabolically healthy in 10 years; 2) the same 51 future diabetes and 23 strictly matched ones selected from the 162 healthy individuals. Baseline fasting serum tryptophan concentrations were quantitatively measured using ultra-performance liquid chromatography triple quadruple mass spectrometry. First, serum tryptophan level was found significantly higher in future T2D and was positively and independently associated with diabetes onset risk. Patients with higher tryptophan level tended to present higher degree of insulin resistance and secretion, triglyceride and blood pressure. Second, the prediction potential of tryptophan is non-inferior to the 5 existing amino acids. The predictive performance of the combined score improved after taking tryptophan into account. Our findings unveiled the potential of tryptophan as a new marker associated with diabetes risk in Chinese populations. The addition of tryptophan provided complementary value to the existing amino acid predictors.</p></div

ROCs of 5AAs-combined score and 6-AAs combined score in discriminating a) T2D and matched NGT (n = 74); and b) T2D and NGT (n = 213).

<p>ROCs of 5AAs-combined score and 6-AAs combined score in discriminating a) T2D and matched NGT (n = 74); and b) T2D and NGT (n = 213).</p

Metabolic Transformation of DMBA-Induced Carcinogenesis and Inhibitory Effect of Salvianolic Acid B and Breviscapine Treatment

Oral cancer typically develops from hyperplasia through dysplasia to carcinoma with a multistep process of carcinogenesis involving genetic alterations resulting in aberrant cellular appearance, deregulated cell growth, and carcinoma. The metabolic transformation during the process of oral carcinogenesis and its implications for cancer therapy have not been extensively investigated. Here, we report a metabonomic study on a classical model of 7,12-dimethylbenz(a)anthracene (DMBA)-induced oral carcinogenesis in hamsters to delineate characteristic metabolic transformation during the carcinogenesis using gas chromatography time-of-flight mass spectrometry (GC–TOF MS). Salvianolic acid B (Sal-B), isolated from <i>Salvia miltiorrhiza</i> Bge, and Breviscapine, a flavonoid isolated from Herba Erigerontis, were used to treat the hamsters exposed to DMBA to investigate the molecular mechanism of the inhibitory effect of the two agents on oral carcinogenesis. The dynamic changes of serum metabolic profiles indicated that both Sal-B and Breviscapine were able to attenuate DMBA-induced metabolic perturbation, which is consistent with the histopathological findings that Sal-B and Breviscapine significantly decreased the squamous cell carcinoma (SCC) incidence in the two treatment groups. Significant alterations of key metabolic pathways, including elevated glutaminolysis and glycolysis, and decreased cholesterol and myo-inositol metabolism, were observed in the DMBA-induced model group, which were attenuated or normalized by Sal-B or Breviscapine treatment. Elevated inflammation and tumor angiogenesis at gene and metabolite expression levels were also observed in DMBA-induced oral dysplasia and SCC but were attenuated or normalized by Sal-B and Breviscapine along with significantly decreased incidences of SCC formation

Additional file 16: Table S10. of Bile acid is a significant host factor shaping the gut microbiome of diet-induced obese mice

Feed ingredients. (XLSX 10 kb