The ratio of dihomo-γ-linolenic acid to deoxycholic acid species is a potential biomarker for the metabolic abnormalities in obesity

Nonparametric empirical Bayes biomarker imputation and estimation

Biomarkers are often measured in bulk to diagnose patients, monitor patient conditions, and research novel drug pathways. The measurement of these biomarkers often suffers from detection limits that result in missing and untrustworthy measurements. Frequently, missing biomarkers are imputed so that down-stream analysis can be conducted with modern statistical methods that cannot normally handle data subject to informative censoring. This work develops an empirical Bayesg $$ g $$ -modeling method for imputing and denoising biomarker measurements. We establish superior estimation properties compared to popular methods in simulations and with real data, providing the useful biomarker measurement estimations for down-stream analysis.

Understanding the gastrointestinal tract in obesity: From gut motility patterns to enzyme secretion

BACKGROUND AND PURPOSE

The pathophysiology of obesity has been the product of extensive research, revealing multiple interconnected mechanisms contributing to body weight regulation. The regulation of energy balance involves an intricate network, including the gut-neuroendocrine interplay. As a consequence, research on the gut-brain-microbiota axis in obesity has grown extensively. The physiology of the gastrointestinal tract, far from being underexplored, has significant implications for the development of specific complications in people living with obesity across the fields of gastroenterology, nutrition, and pharmacology. Clinical research indicates higher fasting bile acids serum levels, and blunted postprandial increases in bilious secretions in people living with obesity. Findings are less straightforward for the impact of obesity on gastric emptying with various studies reporting accelerated, normal, or delayed gastric emptying rates. Conversely, the effect of obesity on gastrointestinal pH, gastrointestinal transit, and gastric and pancreatic enzyme secretion is largely unknown. In this review, we explore the current evidence on the gastrointestinal physiology of obesity.

Identification of Differential Metabolites Between Type 2 Diabetes and Postchronic Pancreatitis Diabetes (Type 3c) Based on an Untargeted Metabolomics Approach

OBJECTIVE

A nontargeted metabolomics approach was established to characterize serum metabolic profile in type 3c diabetes mellitus (T3cDM) secondary to chronic pancreatitis and compare with T2DM.

METHODS

Forty patients were recruited for metabolite analysis based on liquid chromatography-mass spectrometry. Cluster heatmap and KEGG metabolic pathway enrichment analysis were used to analyze the specific and differential metabolites. The receiver operating characteristics (ROCs) were generated and correlation analysis with clinical data was conducted.

RESULTS

Metabolites including sphingosine, lipids, carnitine, bile acid, and hippuric acid were found to be different between T2DM and T3cDM, mainly enriched in bile acid biosynthesis, fatty acid biosynthesis, and sphingolipid metabolic pathways. The ROCs were generated with an area under the curve of 0.907 (95% confidence interval, 0.726-1) for the model with 15 metabolites.

CONCLUSION

T3cDM is characterized by increased sphingosine, carnitine, bile acid, and most lipids, providing novel biomarkers for clinical diagnosis and a future direction in research on pathophysiological mechanisms.

Detection of Bile Acids in Complex Matrices Using a Transcription Factor-Based Biosensor

Bile acids play an important role in digestion and human health, are found throughout the gastrointestinal tract, and are excreted in feces. Therefore, bile acids are promising biomarkers for monitoring health and detecting fecal contamination in water sources. Here, we engineered a bile acid sensor by expressing the transcription factor BreR, a TetR-like repressor from Vibrio cholorae, in Escherichia coli. The sensor was further optimized by screening a promoter library. To further characterize the BreR sensor and increase its utility, we moved expression to a cell-free expression (CFE) system, resulting in an approximately 3 orders of magnitude increase in deoxycholic acid sensitivity. We next optimized this sensor to detect bile acids in fecal water, wastewater, and serum and transferred the CFE sensor to a paper-based assay to enhance fieldability.

Association of Serum Bile Acid and Unsaturated Fatty Acid Profiles with the Risk of Diabetic Retinopathy in Type 2 Diabetic Patients

Aim

We aimed to identify the ability of serum bile acids (BAs) and unsaturated fatty acids (UFAs) profiles to predict the development of diabetic retinopathy (DR) in type 2 diabetes mellitus (T2DM) patients.

Methods

We first used univariate and multivariate analysis to compare 15 serum BA and 11 UFA levels in healthy control (HC) group (n = 82), T2DM patients with DR (n = 58) and T2DM patients without DR (n = 60). Forty T2DM patients were considered for validation. Then, the receiver operating characteristic curve (ROC) and decision curve analysis were used to assess the diagnostic value and clinical benefit of serum biomarkers alone, clinical variables alone or in combination, and the area under the curve (AUC), integrated discrimination improvement (IDI), and net reclassification improvement (NRI) were used to further assess whether the addition of biomarkers significantly improved the predictive ability of the model.

Results

Orthogonal partial least squares-discriminant analysis (OPLS-DA) of serum BAs and UFAs separated the three cohorts including HC, T2DM patients with or without DR. The difference in serum BA and UFA profiles of T2DM patients with or without DR was mainly manifested in the three metabolites of taurolithocholic acid (TLCA), tauroursodeoxycholic acid (TUDCA) and arachidonic acid (AA). Together, they had an AUC of 0.785 (0.918 for validation cohort) for predicting DR in T2DM patients. After adjusting for numerous confounding factors, TLCA, TUDCA, and AA were independent predictors that differentiated T2DM with or without DR. The results of AUC, IDI, and NRI demonstrated that adding these three biomarkers to a model with clinical variables statistically increased their predictive value and were replicated in our independent validation cohort.

Conclusion

These findings highlight the association of three metabolites, TLCA, TUDCA and AA, with DR and may indicate their potential value in the pathogenesis of DR.

Bile acid metabolism and signaling, the microbiota, and metabolic disease

The diversity, composition, and function of the bacterial community inhabiting the human gastrointestinal tract contributes to host health through its role in producing energy or signaling molecules that regulate metabolic and immunologic functions. Bile acids are potent metabolic and immune signaling molecules synthesized from cholesterol in the liver and then transported to the intestine where they can undergo metabolism by gut bacteria. The combination of host- and microbiota-derived enzymatic activities contribute to the composition of the bile acid pool and thus there can be great diversity in bile acid composition that depends in part on the differences in the gut bacteria species. Bile acids can profoundly impact host metabolic and immunological functions by activating different bile acid receptors to regulate signaling pathways that control a broad range of complex symbiotic metabolic networks, including glucose, lipid, steroid and xenobiotic metabolism, and modulation of energy homeostasis. Disruption of bile acid signaling due to perturbation of the gut microbiota or dysregulation of the gut microbiota-host interaction is associated with the pathogenesis and progression of metabolic disorders. The metabolic and immunological roles of bile acids in human health have led to novel therapeutic approaches to manipulate the bile acid pool size, composition, and function by targeting one or multiple components of the microbiota-bile acid-bile acid receptor axis.

MiR-203 is an anti-obese miRNA by targeting ASBT

Obesity is characterized by excessive fat deposition within the body. Bile acids (BA) are important regulators for controlling the absorption of lipid. Here we show that miR-203 exerts weight-loss and lipid-lowering effects by increasing total BA excretion in obese rodents. miR-203 overexpression transgenic mice are resistant to high-fat diet (HFD)-induced obesity and dyslipidemia. Moreover, the knockdown of miR-203 deteriorates metabolic disorders. ASBT plays important role in regulating BA homeostasis and is a direct target of miR-203. In human intestinal epithelial cells, overexpression of miR-203 decreases the cellular uptake of BA by inhibiting ASBT. Furthermore, TCF7L2 is downregulated in obese mice and acts as a transcription factor of miR-203. The ASBT mRNA level was positively correlated with the body mass index (BMI) of population, while the miR-203 level was negatively associated with BMI. Taken together, these data suggest miR-203 could be a new therapeutic BA regulator for obesity and dyslipidemia.

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miR-203 is downregulated in obese rodents and overweight/obese population

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ASBT is a direct target of miR-203 in obesity

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TCF7L2 acts as an upstream activator of miR-203 in obesity

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miR-203 ameliorates obesity and dyslipidemia by increasing TBAs and lipids excretion

Changes in bile acid subtypes and long-term successful weight-loss in response to weight-loss diets: The POUNDS lost trial

BACKGROUND AND AIMS

Primary bile acids (BAs) are synthesized in the liver and secondary BAs result from intestinal microbial activity. Different subtypes of BAs may be involved in regulating adiposity and energy homeostasis. We examined how changes in circulating BA subtypes induced by weight-loss diets were associated with improvements in adiposity, regional fat deposition and energy metabolism among overweight and obese adults.

METHODS

The study included 551 subjects who participated in a 2-year weight-loss diet intervention trial. Circulating 14 BA subtypes (primary and secondary unconjugated BAs and their taurine-/glycine-conjugates) were measured at baseline and 6 months. Associations of changes in BAs with changes in weight, waist circumference, resting energy expenditure (REE), body fat composition and fat distribution were evaluated.

RESULTS

Greater decreases in primary BAs (cholate and chenodeoxycholate) and secondary BAs (deoxycholate and lithocholate) and their conjugates (except for glycolithocholate) were associated with more decreases in weight and waist circumference at 6 months (P_{-after-false-discovery-rate-correction} [P_FDR ] < .05). We found that changes in glycocholate and glycoursodeoxycholate were consistently associated with reductions of general and central adiposity, REE, whole-body fat and visceral adipose tissue (P_FDR  < .05). Further, the initial (6-month) changes in BA subtypes were differently predictive of successful weight loss over 2 years.

CONCLUSIONS

The decreases in primary and secondary BA subtypes after eating low-calorie weight-loss diets were significantly associated with improving adiposity, fat accumulation and energy metabolism, suggesting that specific BA subtypes would be predictive of long-term successful weight loss and individuals' response to the treatment of weight-loss diets.

Improved fatty acid profile reduces body fat and arterial stiffness in obese adolescents upon combinatorial intervention with exercise and dietary restriction

Objective

In order to examine the effect of 4-week combinatorial intervention with exercise training and dietary restriction on serum fatty acids, and to explore the correlation of intervention-induced improvement of serum fatty acid profile with the reduction of body fat and arterial stiffness.

Methods

Thirty-three obese adolescents were randomized into the intervention (n = 19) and control (n = 14) groups. The participants from the intervention group were subjected to 4-week combinatorial intervention with exercise training and dietary restriction while the participants from the control group maintained regular activities and diet habits. Anthropometry, serum fatty acids and arterial stiffness were measured before and after 4-week intervention.

Results

The participants upon combinatorial intervention revealed the improved body compositions and serum fatty acid profile, and reduced arterial stiffness when compared with their basal levels and the control participants (p < 0.05). Moreover, the decrease in myristic acid, stearic acid, arachidic acid, behenic acid, palmitoleic acid, and dihomo-γ-linolenic acid, was associated with the reduction in body fat. A positive correlation between arachidonic acid and left brachial ankle pulse velocity was observed, and the increase in docosahexaenoic acid was associated with the reduction of left brachial ankle pulse wave velocity and the enhancement of right ankle brachial index.

Conclusion

The 4-week combinatorial intervention is a useful strategy to improve serum fatty acid profile along with the reduction of body fat and arterial stiffness in obese adolescents.

Hyocholic acid species improve glucose homeostasis through a distinct TGR5 and FXR signaling mechanism

Hyocholic acid (HCA) and its derivatives are found in trace amounts in human blood but constitute approximately 76% of the bile acid (BA) pool in pigs, a species known for its exceptional resistance to type 2 diabetes. Here, we show that BA depletion in pigs suppressed secretion of glucagon-like peptide-1 (GLP-1) and increased blood glucose levels. HCA administration in diabetic mouse models improved serum fasting GLP-1 secretion and glucose homeostasis to a greater extent than tauroursodeoxycholic acid. HCA upregulated GLP-1 production and secretion in enteroendocrine cells via simultaneously activating G-protein-coupled BA receptor, TGR5, and inhibiting farnesoid X receptor (FXR), a unique mechanism that is not found in other BA species. We verified the findings in TGR5 knockout, intestinal FXR activation, and GLP-1 receptor inhibition mouse models. Finally, we confirmed in a clinical cohort, that lower serum concentrations of HCA species were associated with diabetes and closely related to glycemic markers.

One-Pot Extraction and Quantification Method for Bile Acids in the Rat Liver by Capillary Liquid Chromatography Tandem Mass Spectrometry

We developed a highly sensitive method for quantifying 21 bile acids (BAs) in the rat liver by capillary liquid chromatography tandem mass spectrometry (cLC/MS/MS) with one-pot extraction. High recovery rates were obtained for the one-pot methods with either methanol (MeOH) extraction or MeOH/acetonitrile (ACN) (1:1, v/v) mixture extraction; the results obtained for the MeOH/ACN mixture solution were better than the results obtained for MeOH. Thus, we determined that the one-pot method with MeOH/ACN was the most suitable method for the efficient extraction of BAs in the liver. Targeted BAs were well separated by cLC with gradient elution using ammonium acetate (NH₄OAc)-MeOH mobile phases. Method validation proved that the intra-day and inter-day accuracies and precisions were primarily less than ±20 and 20% relative standard deviation, respectively. Also, the limit of detection (LOD) and the limit of quantitation (LOQ) were 0.9-10 and 2.3-27 ng/g liver, which proves the high sensitivity of the method. Finally, we quantitated 21 BA concentrations in the liver samples of normal and nonalcoholic steatohepatitis (NASH) rats, both of which were derived from stroke-prone spontaneously hypertensive five (SHRSP5) /Dmcr rat. The hepatic BA profiles were found to be substantially different between the normal and NASH groups; the two groups were clearly separated along the first component axis in the score plots of the principal component analysis. In particular, 10 BAs (β-muricholic acid (MCA), glyco (G-) cholic acid (CA), G-chenodeoxycholic acid (CDCA), tauro (T-) CA, T-CDCA, T-ursodeoxycholic acid (UDCA), T-lithocholic acid (LCA), T-hiodeoxycholic acid (HDCA), T-α-MCA, and T-β-MCA) were significantly different between the two groups using Welch's t-test with the false discovery rate correction method, demonstrating BA disruption in the NASH model rat. In conclusion, this method was able to quantify 21 BAs in the rat liver and will evaluate the hepatic BA pathophysiology of rat disease models.

Ganoderic acid A from Ganoderma lucidum ameliorates lipid metabolism and alters gut microbiota composition in hyperlipidemic mice fed a high-fat diet

Ganoderic acid A (GA) is one of the most abundant triterpenoids in Ganoderma lucidum, and has been proved to possess a wide range of beneficial health effects. The aim of the current study is to investigate the amelioration effects and mechanism of GA on improving hyperlipidemia in mice fed a high-fat diet (HFD). The results showed that GA intervention significantly inhibited the abnormal growth of body weight and epididymal white adipose tissue (eWAT), prevented the hypertrophy of epididymal adipocytes, and ameliorated the biochemical parameters of serum and liver related to lipid metabolism in HFD-fed mice. Histological analysis also showed that the excessive accumulation of lipid droplets in the liver induced by HFD-feeding was greatly alleviated by GA intervention. In addition, GA intervention also increased the level of short chain fatty acids (SCFAs) in the intestine and promoted the excretion of bile acids (BAs) through feces. High-throughput sequencing of bacterial full-length 16S rDNA revealed that daily supplementation with GA made significant structural changes in the gut microbial population of mice fed with HFD, in particular modulating the relative abundance of some function related microbial phylotypes. The relationships between lipid metabolic parameters and gut microbial phylotypes were also revealed by correlation analysis based on a heatmap and network. The result showed that 46 key gut microbial phylotypes (OTUs) were markedly correlated with at least one lipid metabolic parameter. Moreover, UPLC-QTOF/MS-based liver metabolomics showed that 111 biomarkers (47 up-regulated metabolites and 64 down-regulated metabolites) were significantly changed after high-dose GA intervention (75 mg kg^-1 day^-1), compared with the HFD-fed hyperlipidemic mice. Metabolic pathway enrichment analysis of the differential hepatic metabolites demonstrated that GA intervention had significant regulatory effects on primary bile acid biosynthesis, fatty acid biosynthesis, amino sugar and nucleotide sugar metabolism, inositol phosphate metabolism, and so on. In addition, GA intervention regulated the mRNA levels of hepatic genes involved in fatty acid metabolism and bile acid homeostasis. These findings present new evidence supporting that GA from G. lucidum has the potential to alleviate lipid metabolic disorders and ameliorate the imbalance of gut microflora in a positive way.

Targeting bile acid metabolism in obesity reduction: A systematic review and meta-analysis

A systematic review and meta-analysis was conducted of studies that address the association of bile acid (BA) with obesity and of studies on the effects of treatment in patients with obesity on BA metabolism, assessed from systemic BA, fibroblast growth factor 19 (FGF19), 7α-hydroxy-4-cholesten-3-one (C4) level, and faecal BA. We searched PubMed, Embase, and the Cochrane Library from inception to 1 August 2019 using the keywords obesity, obese, body mass index, and overweight with bile acid, FGF19, FXR, and TGR5. Two reviewers independently searched, selected, and assessed the quality of studies. Data were analysed using either fixed or random effect models with inverse variance weighting. Of 3771 articles, 33 papers were relevant for the association of BA with obesity of which 22 were included in the meta-analysis, and 50 papers were relevant for the effect of obesity interventions on BA of which 20 were included in the meta-analysis. Circulating fasting total BA was not associated with obesity. FGF19 was inversely and faecal BA excretion was positively associated with obesity. Roux-en-Y gastric bypass (RYGB) and sleeve gastrectomy (SG) modulated BA metabolism, ie, increased BA and FGF19. Our results indicate that BA metabolism is altered in obesity. Certain bariatric surgeries including RYGB and SG modulate BA, whether these underlie the beneficial effect of the treatment should be investigated.

Lipidomic Profiling Reveals Disruption of Lipid Metabolism in Valproic Acid-Induced Hepatotoxicity

Valproic acid (VPA) is one of the most widely prescribed antiepileptic drugs, as VPA-induced hepatotoxicity is one of the most severe adverse reaction that can lead to death. The objective of this study was to gain an understanding of dysregulated lipid metabolism in mechanism of hepatotoxicity. Nontargeted lipidomics analysis with liquid chromatography-quadrupole-time-of-flight mass spectrometry (LC-Q-TOF/MS) was performed to explore differential lipids from the patient serum and L02 cells. Lipidomics data interpretation was augmented by gene expression analyses for the key enzymes in lipid metabolism pathways. From patient serum lipidomics, pronouncedly changed lipid species between abnormal liver function (ALF) patients and normal liver function (NLF) patients were identified. Among these lipid species, LPCs, Cers, and SMs were markedly reduced in the ALF group and showed negative relationships with liver injury severity [alanine aminotransferase (ALT) levels], while significantly increased triacylglycerols (TAG) with higher summed carbon numbers demonstrated a positive relationship with ALT levels. Regarding lipidomics in hepatic L02 cells, TAG was markedly elevated after VPA exposure, especially in TAGs with more than 53 summed carbons. Besides, gene expression analysis revealed dysregulated lipid metabolism in VPA-treated L02 cells. Peroxime proliferators-activated receptor (PPARγ) pathway played an important role in VPA-induced lipid disruption through inducing long-chain fatty acid uptake and TAG synthesis, which was also regulated by Akt pathway. Our findings present that VPA-induced lipid metabolism disruption might lead to lipotoxicity in the liver. This approach is expected to be applicable for other drug-induced toxicity assessments.

Metabolomic Characterization of Human Model of Liver Rejection Identifies Aberrancies Linked to Cyclooxygenase (COX) and Nitric Oxide Synthase (NOS)

BACKGROUND Acute liver rejection (ALR), a significant complication of liver transplantation, burdens patients, healthcare payers, and the healthcare providers due to an increase in morbidity, cost, and resources. Despite clinical resolution, ALR is associated with an increased risk of graft loss. A unique protocol of delayed immunosuppression used in our institute provided a model to characterize metabolomic profiles in human ALR. MATERIAL AND METHODS Twenty liver allograft biopsies obtained 48 hours after liver transplantation in the absence of immunosuppression were studied. Hepatic metabolites were quantitated in these biopsies by liquid chromatography and mass spectroscopy (LC/MS). Metabolite profiles were compared among: 1) biopsies with reperfusion injury but no histological evidence of rejection (n=7), 2) biopsies with histological evidence of moderate or severe rejection (n=5), and 3) biopsies with histological evidence of mild rejection (n=8). RESULTS There were 133 metabolites consistently detected by LC/MS and these were prioritized using variable importance to projection (VIP) analysis, comparing moderate or severe rejection vs. no rejection or mild rejection using partial least squares discriminant statistical analysis (PLS-DA). Twenty metabolites were identified as progressively different. Further PLS-DA using these metabolites identified 3 metabolites (linoleic acid, γ-linolenic acid, and citrulline) which are associated with either cyclooxygenase or nitric oxide synthase functionality. CONCLUSIONS Hepatic metabolic aberrancies associated with cyclooxygenase and nitric oxide synthase function occur contemporaneous with ALR. Additional studies are required to better characterize the role of these metabolic pathways to enhance utility of the metabolomics approach in diagnosis and outcomes of ALR.

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

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: https://github.com/WandeRum/GSimp.

Missing values caused by the limit of detection/quantification (LOD/LOQ) were widely observed in mass spectrometry (MS)-based targeted metabolomics studies and could be recognized as missing not at random (MNAR). MNAR leads to biased parameter estimations and jeopardizes following statistical analyses in different aspects, such as distorting sample distribution, impairing statistical power, etc. Although a wide range of missing value imputation methods was developed for–omics studies, a limited number of methods was designed appropriately for the situation of MNAR currently. To alleviate problems caused by MNAR and to facilitate targeted metabolomics studies, we developed a Gibbs sampler based missing value imputation approach, called GSimp, which is public-accessible on GitHub. And we compared our method with existing approaches using an imputation evaluation pipeline on both of the real-world and simulated metabolomics datasets to demonstrate the superiority of our method from different perspectives.

Missing Value Imputation Approach for Mass Spectrometry-based Metabolomics Data

Missing values exist widely in mass-spectrometry (MS) based metabolomics data. Various methods have been applied for handling missing values, but the selection can significantly affect following data analyses. Typically, there are three types of missing values, missing not at random (MNAR), missing at random (MAR), and missing completely at random (MCAR). Our study comprehensively compared eight imputation methods (zero, half minimum (HM), mean, median, random forest (RF), singular value decomposition (SVD), k-nearest neighbors (kNN), and quantile regression imputation of left-censored data (QRILC)) for different types of missing values using four metabolomics datasets. Normalized root mean squared error (NRMSE) and NRMSE-based sum of ranks (SOR) were applied to evaluate imputation accuracy. Principal component analysis (PCA)/partial least squares (PLS)-Procrustes analysis were used to evaluate the overall sample distribution. Student's t-test followed by correlation analysis was conducted to evaluate the effects on univariate statistics. Our findings demonstrated that RF performed the best for MCAR/MAR and QRILC was the favored one for left-censored MNAR. Finally, we proposed a comprehensive strategy and developed a public-accessible web-tool for the application of missing value imputation in metabolomics ( https://metabolomics.cc.hawaii.edu/software/MetImp/ ).

Missing Value Imputation Approach for Mass Spectrometry-based Metabolomics Data

Missing values exist widely in mass-spectrometry (MS) based metabolomics data. Various methods have been applied for handling missing values, but the selection can significantly affect following data analyses. Typically, there are three types of missing values, missing not at random (MNAR), missing at random (MAR), and missing completely at random (MCAR). Our study comprehensively compared eight imputation methods (zero, half minimum (HM), mean, median, random forest (RF), singular value decomposition (SVD), k-nearest neighbors (kNN), and quantile regression imputation of left-censored data (QRILC)) for different types of missing values using four metabolomics datasets. Normalized root mean squared error (NRMSE) and NRMSE-based sum of ranks (SOR) were applied to evaluate imputation accuracy. Principal component analysis (PCA)/partial least squares (PLS)-Procrustes analysis were used to evaluate the overall sample distribution. Student's t-test followed by correlation analysis was conducted to evaluate the effects on univariate statistics. Our findings demonstrated that RF performed the best for MCAR/MAR and QRILC was the favored one for left-censored MNAR. Finally, we proposed a comprehensive strategy and developed a public-accessible web-tool for the application of missing value imputation in metabolomics ( https://metabolomics.cc.hawaii.edu/software/MetImp/ ).