NMR Metabolomics and Random Forests Models to Identify Potential Plasma Biomarkers of Blood Stasis Syndrome With Coronary Heart Disease Patients

Relationship between human serum metabolites and angina pectoris: a Mendelian randomization study

PURPOSE

We aimed to explore the causal relationship between human serum metabolites and angina pectoris.

METHODS

This study used two-sample Mendelian randomization (MR) analysis to assess the association between 486 serum metabolites and angina pectoris. The analytical methods employed to reduce study bias included inverse variance weighted, MR-Egger, and weighted median method. A comprehensive sensitivity analysis was performed using the leave-one-out method, while instrumental variable pleiotropy was tested with MR-Pleiotropy RESidual Sum and Outlier. Metabolic pathways of angina-associated metabolites were analysed on the MetaboAnalyst metabolomics analysis tool platform.

RESULTS

In this study, 42 serum metabolites were found to be strongly associated with angina pectoris. They mainly belonged to seven groups: amino acids, carbohydrates, cofactors and vitamins, lipids, nucleotides, unknown metabolites, and exogenous substances. Pipecolate posed the highest risk for the development of angina pectoris among the 42 serum metabolites. The main metabolic pathways associated with angina pectoris were glycine, serine, threonine metabolism, primary bile acid biosynthesis, and caffeine metabolism.

CONCLUSION

We identified 25 high-risk and 17 protective human serum metabolites associated with angina pectoris. Their associated major metabolic pathways were also determined. The serum metabolite pipecolate was significantly and positively correlated with the risk of angina pectoris. This finding may serve as a valuable reference for testing serum markers associated with angina pectoris.

The interactions and biological pathways among metabolomics products of patients with coronary heart disease

BACKGROUND

Through bioinformatics analysis, this study explores the interactions and biological pathways involving metabolomic products in patients diagnosed with coronary heart disease (CHD).

METHODS

A comprehensive search for relevant studies focusing on metabolomics analysis in CHD patients was conducted across databases including CNKI, Wanfang, VIP, CBM, PubMed, Cochrane Library, Nature, Web of Science, Springer, and Science Direct. Metabolites reported in the literature underwent statistical analysis and summarization, with the identification of differential metabolites. The pathways associated with these metabolites were examined using the Kyoto Encyclopedia of Genes and Genomes (KEGG). Molecular annotation of metabolites and their relationships with enzymes or transporters were elucidated through analysis with the Human Metabolome Database (HMDB). Visual representation of the properties related to these metabolites was achieved using Metabolomics Pathway Analysis (metPA).

RESULTS

A total of 13 literatures satisfying the criteria for enrollment were included. A total of 91 metabolites related to CHD were preliminarily screened, and 87 effective metabolites were obtained after the unrecognized metabolites were excluded. A total of 45 pathways were involved. Through the topology analysis (TPA) of pathways, their influence values were calculated, and 13 major metabolic pathways were selected. The pathways such as Phenylalanine, tyrosine, and tryptophan biosynthesis, Citrate cycle (TCA cycle), Glyoxylate and dicarboxylate metabolism, and Glycine, serine, and threonine metabolism primarily involved the regulation of processes and metabolites related to inflammation, oxidative stress, one-carbon metabolism, energy metabolism, lipid metabolism, immune regulation, and nitric oxide expression.

CONCLUSION

Multiple pathways, including Phenylalanine, tyrosine, and tryptophan biosynthesis, Citrate cycle (TCA cycle), Glyoxylate and dicarboxylate metabolism, and Glycine, serine, and threonine metabolism, were involved in the occurrence of CHD. The occurrence of CHD is primarily associated with the regulation of processes and metabolites related to inflammation, oxidative stress, one-carbon metabolism, energy metabolism, lipid metabolism, immune regulation, and nitric oxide expression.

Performance evaluate of different chemometrics formalisms used for prostate cancer diagnosis by NMR-based metabolomics

INTRODUCTION

In general, two characteristics are ever present in NMR-based metabolomics studies: (1) they are assays aiming to classify the samples in different groups, and (2) the number of samples is smaller than the feature (chemical shift) number. It is also common to observe imbalanced datasets due to the sampling method and/or inclusion criteria. These situations can cause overfitting. However, appropriate feature selection and classification methods can be useful to solve this issue.

OBJECTIVES

Investigate the performance of metabolomics models built from the association between feature selectors, the absence of feature selection, and classification algorithms, as well as use the best performance model as an NMR-based metabolomic method for prostate cancer diagnosis.

METHODS

We evaluated the performance of NMR-based metabolomics models for prostate cancer diagnosis using seven feature selectors and five classification formalisms. We also obtained metabolomics models without feature selection. In this study, thirty-eight volunteers with a positive diagnosis of prostate cancer and twenty-three healthy volunteers were enrolled.

RESULTS

Thirty-eight models obtained were evaluated using AUROC, accuracy, sensitivity, specificity, and kappa's index values. The best result was obtained when Genetic Algorithm was used with Linear Discriminant Analysis with 0.92 sensitivity, 0.83 specificity, and 0.88 accuracy.

CONCLUSION

The results show that the pick of a proper feature selection method and classification model, and a resampling method can avoid overfitting in a small metabolomic dataset. Furthermore, this approach would decrease the number of biopsies and optimize patient follow-up. 1H NMR-based metabolomics promises to be a non-invasive tool in prostate cancer diagnosis.

Differences in the serum metabolic profile to identify potential biomarkers for cyanotic versus acyanotic heart disease

BACKGROUND

Growth retardation, malnutrition, and failure to thrive are some of the consequences associated with congenital heart diseases. Several metabolic factors such as hypoxia, anoxia, and several genetic factors are believed to alter the energetics of the heart. Timely diagnosis and patient management is one of the major challenges faced by the clinicians in understanding the disease and provide better treatment options. Metabolic profiling has shown to be potential diagnostic tool to understand the disease.

OBJECTIVE

The present experiment was designed as a single center observational pilot study to classify and create diagnostic metabolic signatures associated with the energetics of congenital heart disease in cyanotic and acyanotic groups.

METHODS

Metabolic sera profiles were obtained from 35 patients with cyanotic congenital heart disease (TOF) and 23 patients with acyanotic congenital heart disease (ASD and VSD) using high resolution 1D ¹H NMR spectra. Univariate and multivariate statistical analysis were performed to classify particular metabolic disorders associated with cyanotic and acyanotic heart disease.

RESULTS

The results show dysregulations in several metabolites in cyanotic CHD patients versus acyanotic CHD patients. The discriminatory metabolites were further analyzed with area under receiver operating characteristic (AUROC) curve and identified four metabolic entities (i.e. mannose, hydroxyacetone, myoinositol, and creatinine) which could differentiate cyanotic CHDs from acyanotic CHDs with higher specificity.

CONCLUSION

An untargeted metabolic approach proved to be helpful for the detection and distinction of disease-causing metabolites in cyanotic patients from acyanotic ones and can be useful for designing better and personalized treatment protocol.

Modern concepts and biomarkers of blood stasis in cardio- and cerebrovascular diseases from the perspectives of Eastern and Western medicine: a scoping review protocol

OBJECTIVE

The objective of this review is to provide a modern definition and identify potential biomarkers of blood stasis in cardio- and cerebrovascular diseases by mapping, comparing, and combining Eastern and Western concepts.

INTRODUCTION

Blood stasis is a pathological concept found in both Eastern and Western medical literature. In traditional East Asian medicine, blood stasis is a differential syndrome characterized by stagnant blood flow in various parts of the body. Similarly, in Western medicine, various diseases, especially cardio- and cerebrovascular diseases, are known to be accompanied by blood stasis. Numerous scientific studies on blood stasis have been conducted over the last decade, and there is a need to synthesize those results.

INCLUSION CRITERIA

We will use the keywords "blood stasis," "blood stagnation," "blood stagnant," and "blood congestion" in 3 electronic databases: PubMed, Cochrane CENTRAL, and Google Scholar. In addition, we will use the keywords "어혈" and "혈어" in 4 Korean electronic databases (ie, NDSL, OASIS, KISS, and DBpia). Peer-reviewed articles published from 2010 to the present that focus on blood stasis in cardio- and cerebrovascular diseases in human subjects according to the International Classification of Diseases 11 th revision categories BA00-BE2Z, 8B00-8B2Z, 8E64, and 8E65 will be included. Reviews, opinion articles, in vivo, in vitro, and in silico preclinical studies will be excluded.

METHODS

We will follow the frameworks by Arksey and O'Malley and Levac et al. as well as JBI guidelines and the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews. Two reviewers will independently search and screen titles and abstracts followed by full-text screening of eligible studies. If there are discrepancies between the 2 reviewers, a third reviewer will be consulted to make the final decision. We will use descriptive narrative, tabular, and graphical displays, and content analysis to present the results.

SCOPING REVIEW REGISTRATION

Open Science Framework https://osf.io/gv4ym.

Discovery of Drug-Responsive Phenomic Alteration-Related Driver Genes in the Treatment of Coronary Heart Disease

Background

The Xueyu Zheng (XYZ) phenome is central to coronary heart disease (CHD), but efforts to detect genetic associations in the XYZ phenome have been disappointing.

Methods

The phenomic alteration-related genes (PARGs) for the XYZ phenome were screened using |ρ| > 0.4 and p < 0.05 after treatment with Danhong injection at day 14 and day 30. Then, the driver genes for the Protein-Protein Interaction (PPI) networks of the PARGs established using STRING 11.0 were detected using a personalized network control algorithm (PNC). Finally, the molecular correlations of the driver genes with the XYZ phenome were analyzed with the Gene Ontology (GO) biological processes and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways from a holistic viewpoint.

Results

A total of 525 and 309 PARGs in the XYZ phenome at day 14 and day 30 were identified. These genes were separately enriched in 48 and 35 pathways. Furthermore, five driver genes were detected. These genes were mainly correlated with endoplasmic reticulum stress-mediated apoptosis and autophagy regulation, which could suppress atherosclerosis progression.

Conclusion

Our study detected the drug-responsive PARGs of the XYZ phenome in CHD and provides an exemplary strategy to investigate the genetic associations among this common phenome and its component symptoms in patients with CHD.

Trial Registration

ClinicalTrials.gov, NCT01681316; registered on September 7, 2012.

Machine learning based on metabolomics reveals potential targets and biomarkers for primary Sjogren’s syndrome

Background: Using machine learning based on metabolomics, this study aimed to construct an effective primary Sjogren’s syndrome (pSS) diagnostics model and reveal the potential targets and biomarkers of pSS.

Methods: From a total of 39 patients with pSS and 38 healthy controls (HCs), serum specimens were collected. The samples were analyzed by ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry. Three machine learning algorithms, including the least absolute shrinkage and selection operator (LASSO), random forest (RF), and extreme gradient boosting (XGBoost), were used to build the pSS diagnosis models. Afterward, four machine learning methods were used to reduce the dimensionality of the metabolomics data. Finally, metabolites with significant differences were screened and pathway analysis was conducted.

Results: The area under the curve (AUC), sensitivity, and specificity of LASSO, RF and XGBoost test set all reached 1.00. Orthogonal partial least squares discriminant analysis was used to classify the metabolomics data. By combining the results of the univariate false discovery rate and the importance of the variable in projection, we identified 21 significantly different metabolites. Using these 21 metabolites for diagnostic modeling, the AUC, sensitivity, and specificity of LASSO, RF, and XGBoost all reached 1.00. Metabolic pathway analysis revealed that these 21 metabolites are highly correlated with amino acid and lipid metabolisms. On the basis of 21 metabolites, we screened the important variables in the models. Further, five common variables were obtained by intersecting the important variables of three models. Based on these five common variables, the AUC, sensitivity, and specificity of LASSO, RF, and XGBoost all reached 1.00.2-Hydroxypalmitic acid, L-carnitine and cyclic AMP were found to be potential targets and specific biomarkers for pSS.

Conclusion: The combination of machine learning and metabolomics can accurately distinguish between patients with pSS and HCs. 2-Hydroxypalmitic acid, L-carnitine and cyclic AMP were potential targets and biomarkers for pSS.

UPLC-G2Si-HDMS Untargeted Metabolomics for Identification of Yunnan Baiyao's Metabolic Target in Promoting Blood Circulation and Removing Blood Stasis

Yunnan Baiyao is a famous Chinese patent medicine in Yunnan Province. However, its mechanism for promoting blood circulation and removing blood stasis is not fully explained. Our study used metabonomics technology to reveal the regulatory effect of Yunnan Baiyao on small molecular metabolites in promoting blood circulation and removing blood stasis, and exploring the related urine biomarkers. The coagulation function, blood rheology, and pathological results demonstrated that after Yunnan Baiyao treatment, the pathological indexes in rats with epinephrine hydrochloride-induced blood stasis syndrome improved and returned to normal levels. This is the basis for the effectiveness of Yunnan Baiyao. UPLC-G2Si-HDMS was used in combination with multivariate statistical analysis to conduct metabonomic analysis of urine samples. Finally, using mass spectrometry technology, 28 urine biomarkers were identified, clarifying the relevant metabolic pathways that play a vital role in the Yunnan Baiyao treatment. These were used as the target for Yunnan Baiyao to promote blood circulation and remove blood stasis. This study showed that metabolomics strategies provide opportunities and conditions for a deep and systematic understanding of the mechanism of action of prescriptions.

Estimated Renal Metabolomics at Reperfusion Predicts One-Year Kidney Graft Function

Renal transplantation is the gold-standard procedure for end-stage renal disease patients, improving quality of life and life expectancy. Despite continuous advancement in the management of post-transplant complications, progress is still needed to increase the graft lifespan. Early identification of patients at risk of rapid graft failure is critical to optimize their management and slow the progression of the disease. In 42 kidney grafts undergoing protocol biopsies at reperfusion, we estimated the renal metabolome from RNAseq data. The estimated metabolites' abundance was further used to predict the renal function within the first year of transplantation through a random forest machine learning algorithm. Using repeated K-fold cross-validation we first built and then tuned our model on a training dataset. The optimal model accurately predicted the one-year eGFR, with an out-of-bag root mean square root error (RMSE) that was 11.8 ± 7.2 mL/min/1.73 m2. The performance was similar in the test dataset, with a RMSE of 12.2 ± 3.2 mL/min/1.73 m2. This model outperformed classic statistical models. Reperfusion renal metabolome may be used to predict renal function one year after allograft kidney recipients.

Global metabolome profiling of exhaled breath condensates in male smokers with asthma COPD overlap and prediction of the disease

Asthma-chronic obstructive pulmonary disease (COPD) overlap, termed as ACO, is a complex heterogeneous disease characterised by persistent airflow limitation, which manifests features of both asthma and COPD. These patients have a worse prognosis, in terms of more frequent and severe exacerbations, more frequent symptoms, worse quality of life, increased comorbidities and a faster lung function decline. In absence of clear diagnostic or therapeutic guidelines, ACO presents as a challenge to clinicians. The present study aims to investigate whether ACO patients have a distinct exhaled breath condensate (EBC) metabolic profile in comparison to asthma and COPD. A total of 132 age and BMI matched male smokers were recruited in the exploratory phase which consisted of (i) controls = 33 (ii) asthma = 34 (iii) COPD = 30 and (iv) ACO = 35. Using nuclear magnetic resonance (NMR) metabolomics, 8 metabolites (fatty acid, propionate, isopropanol, lactate, acetone, valine, methanol and formate) were identified to be significantly dysregulated in ACO subjects when compared to both, asthma and COPD. The expression of these dysregulated metabolites were further validated in a fresh patient cohort consisting of (i) asthma = 32 (ii) COPD = 32 and (iii) ACO = 40, which exhibited a similar expression pattern. Multivariate receiver operating characteristic (ROC) curves generated using these metabolites provided a robust ACO classification model. The findings were also integrated with previously identified serum metabolites and inflammatory markers to develop a robust predictive model for differentiation of ACO. Our findings suggest that NMR metabolomics of EBC holds potential as a platform to identify robust, non-invasive biomarkers for differentiating ACO from asthma and COPD.

Correlation of aspirin resistance with serological indicators in patients with coronary heart disease in the plateau

OBJECTIVE

To explore the incidence rate of aspirin resistance (AR) in patients with coronary heart disease (CHD) in the plateau, and analyze its correlation with clinical influencing factors and serological indicators.

METHODS

In this retrospective study, 90 patients with CHD who had lived in the plateau for a long time (>10 years) and received treatment were selected as the subjects. Patients were divided into the AR group (11-dehydrothromboxane B2 (11-DH-TXB₂) >1500 pg/mg) and aspirin sensitivity group (AS group, 11-DH-TXB₂ ≤1500 pg/mg) according to the content of 11-DH-TXB₂ in the urine. The differences in gender, body weight, blood pressure and heart rate between the two groups were compared, and the correlation of these indexes with the incidence rate of AR was analyzed. Moreover, serum indicators were detected. Multiple variable binary logistic regression was used to detect the independent risk factors for AR.

RESULTS

The incidence rate of AR in the enrolled patients with CHD was 27.78% (25/90). The body mass index (BMI) in the AR group was significantly higher than that in the AS group (P<0.05). Patients in the AR group had significantly higher C-reactive protein (CRP) and total bilirubin levels and lower mean corpuscular volume and mean corpuscular hemoglobin compared with the AS group (all P<0.05). Binary logistic regression showed that BMI and CRP were independent factors for AR.

CONCLUSION

AR occurs in patients with CHD who take aspirin in the plateau. Patients with high BMI or CRP level have an increased risk of AR. In addition, BMI and CRP are independent factors for AR, and bilirubin can be a predictive factor for AR.

Multisite evaluation of phenotypic plasticity for specialized metabolites, some involved in carrot quality and disease resistance

Renewed consumer demand motivates the nutritional and sensory quality improvement of fruits and vegetables. Specialized metabolites being largely involved in nutritional and sensory quality of carrot, a better knowledge of their phenotypic variability is required. A metabolomic approach was used to evaluate phenotypic plasticity level of carrot commercial varieties, over three years and a wide range of cropping environments spread over several geographical areas in France. Seven groups of metabolites have been quantified by HPLC or GC methods: sugars, carotenoids, terpenes, phenolic compounds, phenylpropanoids and polyacetylenes. A large variation in root metabolic profiles was observed, in relation with environment, variety and variety by environment interaction effects in decreasing order of importance. Our results show a clear diversity structuration based on metabolite content. Polyacetylenes, β-pinene and α-carotene were identified mostly as relatively stable varietal markers, exhibiting static stability. Nevertheless, environment effect was substantial for a large part of carrot metabolic profile and various levels of phenotypic plasticity were observed depending on metabolites and varieties. A strong difference of environmental sensitivity between varieties was observed for several compounds, particularly myristicin, 6MM and D-germacrene, known to be involved in responses to biotic and abiotic stress. This work provides useful information about plasticity in the perspective of carrot breeding and production. A balance between constitutive content and environmental sensitivity for key metabolites should be reached for quality improvement in carrot and other vegetables.

Plasma metabolites were associated with spatial working memory in major depressive disorder

Major depressive disorder (MDD) is a common disease with both affective and cognitive disorders. Alterations in metabolic systems of MDD patients have been reported, but the underlying mechanisms still remains unclear. We sought to identify abnormal metabolites in MDD by metabolomics and to explore the association between differential metabolites and neurocognitive dysfunction.Plasma samples from 53 MDD patients and 83 sex-, gender-, BMI-matched healthy controls (HCs) were collected. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) system was then used to detect metabolites in those samples. Two different algorithms were applied to identify differential metabolites in 2 groups. Of the 136 participants, 35 MDD patients and 48 HCs had completed spatial working memory test. Spearman rank correlation coefficient was applied to explore the relationship between differential metabolites and working memory in these 2 groups.The top 5 metabolites which were found in sparse partial least squares-discriminant analysis (sPLS-DA) model and random forest (RF) model were the same, and significant difference was found in 3 metabolites between MDD and HCs, namely, gamma-glutamyl leucine, leucine-enkephalin, and valeric acid. In addition, MDD patients had higher scores in spatial working memory (SWM) between errors and total errors than HCs. Valeric acid was positively correlated with working memory in MDD group.Gamma-glutamyl leucine, leucine-enkephalin, and valeric acid were preliminarily proven to be decreased in MDD patients. In addition, MDD patients performed worse in working memory than HCs. Dysfunction in working memory of MDD individuals was associated with valeric acid.

1H NMR-based metabolomics study of the dynamic effect of Xue-Fu-Zhu-Yu capsules on coronary heart disease rats induced by high-fat diet, coronary artery ligation

An ¹H NMR-based metabolomics approach was conducted to holisticly explore the effect of Xue Fu Zhu Yu (XFZY) capsule (a well-known Chinese herbal medicine) on high-fat diets combined with coronary artery ligation induced coronary heart disease (CHD) model rats. 1H NMR-based metabolomics approach combined with multivariate analysis was performed to explore potential biomarkers, a total of 20 metabolites were confirmed as contributors to the discrimination of model group and sham group. We investigated the dynamic metabolic characteristics of XFZY capsule on CHD rats, lactate, glutamine, pyruvate, citrate, choline and taurine were potential biomarkers of early effect. More potential biomarkers changed after 28 days of medication, XFZY capsules primarily influenced the taurine and hypotaurine metabolism, glycine, serine and threonine metabolism, glyoxylate and dicarboxylate metabolism, purine metabolism, glycolysis/gluconeogenesis, amino sugar and nucleotide sugar metabolism, primary bile acid biosynthesis.

Metabolomic profiling of patients with high gradient aortic stenosis undergoing transcatheter aortic valve replacement

Aim

Aim of our study was to evaluate metabolic changes in patients with aortic stenosis (AS) before and after transcatheter aortic valve replacement (TAVR) and to assess whether this procedure reverses metabolomic alterations.

Methods

188 plasma metabolites of 30 patients with severe high-gradient aortic valve stenosis (pre-TAVR and 6 weeks post-TAVR) as well as 20 healthy controls (HC) were quantified by liquid chromatography tandem mass spectrometry. Significantly altered metabolites were then correlated to an extensive patient database of clinical parameters at the time of measurement.

Results

Out of the determined metabolites, 26.6% (n = 50) were significantly altered in patients with AS pre-TAVR compared to HC. In detail, 5/40 acylcarnitines as well as 10/42 amino acids and biogenic amines were mainly increased in AS, whereas 29/90 glycerophospholipids and 6/15 sphingomyelins were mainly reduced. In the post-TAVR group, 10.1% (n = 19) of metabolites showed significant differences when compared to pre-TAVR. Moreover, we found nine metabolites revealing reversible concentration levels. Correlation with clinically important parameters revealed strong correlations between sphingomyelins and cholesterol (r = 0.847), acylcarnitines and brain natriuretic peptide (r = 0.664) and showed correlation of acylcarnitine with an improvement of left ventricular (LV) ejection fraction (r = − 0.513) and phosphatidylcholines with an improvement of LV mass (r = − 0.637).

Conclusion

Metabolic profiling identified significant and reversible changes in circulating metabolites of patients with AS. The correlation of circulating metabolites with clinical parameters supports the use of these data to identify novel diagnostic as well as prognostic markers for disease screening, pathophysiological studies as well as patient surveillance.

Electronic supplementary material

The online version of this article (10.1007/s00392-020-01754-2) contains supplementary material, which is available to authorized users.

Non-Targeted Metabolomic Profiling of Coronary Heart Disease Patients With Taohong Siwu Decoction Treatment

Traditional Chinese medicine is one of the complementary and alternative therapies to improve the prognosis of coronary heart disease (CHD). Taohong Siwu Decoction (THSWD), a classical traditional Chinese medication that promotes blood circulation, is clinically beneficial in CHD. However, the underlying mechanism of THSWD is still unclear. To comprehensively understand the material foundation of the “blood”, it is significantly important to study the differential metabolites involved in the treatment of CHD with Chinese medicinal herb promoting blood circulation in TCM theory. Hence, this study investigated the metabolic profiles of the serum in CHD patients to determine the differential metabolites between the THSWD group and the placebo group. Eleven CHD patients were recruited and divided into two groups randomly and double-blindly. Serum samples were determined by performing non-targeted ultra-performance liquid chromatography with tandem mass spectrometry-based metabolomics. Pearson’s correlation analysis was used to assess the association between identified metabolites and clinical serum indexes of CHD. Based on the result, a total of 513 metabolites were found in the serum of CHD patients, of which 27, involved in 29 metabolic pathways, were significantly different between the two groups. Among the differential metabolites, THSWD upregulated succinylcarnitine in fatty acid metabolism and 5′-methylthioadenosine in cysteine and methionine metabolism compared with the placebo group. However, THSWD downregulated pelargonic acid, involved in FA metabolism; succinate, involved in the tricarboxylic acid cycle; gluconic acid, gluconolactone, and d-glucose, involved in pentose phosphate pathway; glycerophosphocholine, involved in glycerophospholipid metabolism; 8,9-dihydroxyeicosatrienoic acid (8,9-DiHETrE), l-lysine, N-acetyl-l-aspartic acid, N-alpha-acetyl-l-asparagine, hippurate, indoxyl sulfate, and 3-ureidopropionate involved in amino acid metabolism compared with the placebo group. Moreover, succinylcarnitine, pelargonic acid, succinate, d-glucose, gluconic acid, l-lysine, N-alpha-acetyl-l-asparagine, 5′-methylthioadenosine, indoxyl sulfate, 8,9-DiHETrE, and 3-ureidopropionate were associated with total cholesterol or low-density lipoprotein. Succinylcarnitine, pelargonic acid, gluconolactone, N-acetyl-l-aspartic acid, N-alpha-acetyl-l-asparagine, hippurate, and 5′-methylthioadenosine were associated with activated partial thromboplastin time. Our findings indicated that glycerophosphocholine, 8,9-DiHETrE, 5′-methylthioadenosine, hippurate, indoxyl sulfate, and 3-ureidopropionate might constitute the partial material foundation of the “blood” in CHD patients treated with THSWD.