Independent Validation and Assay Standardization of Improved Metabolic Biomarker Signature to Differentiate Pancreatic Ductal Adenocarcinoma From Chronic Pancreatitis.

BACKGROUND & AIMS: Pancreatic ductal adenocarcinoma cancer (PDAC) is a highly lethal malignancy requiring efficient detection when the primary tumor is still resectable. We previously developed the MxPancreasScore comprising 9 analytes and serum carbohydrate antigen 19-9 (CA19-9), achieving an accuracy of 90.6%. The necessity for 5 different analytical platforms and multiple analytical runs, however, hindered clinical applicability. We therefore aimed to develop a simpler single-analytical run, single-platform diagnostic signature. METHODS: We evaluated 941 patients (PDAC, 356; chronic pancreatitis [CP], 304; nonpancreatic disease, 281) in 3 multicenter independent tests, and identification (ID) and validation cohort 1 (VD1) and 2 (VD2) were evaluated. Targeted quantitative plasma metabolite analysis was performed on a liquid chromatography-tandem mass spectrometry platform. A machine learning-aided algorithm identified an improved (i-Metabolic) and minimalistic metabolic (m-Metabolic) signatures, and compared them for performance. RESULTS: The i-Metabolic Signature, (12 analytes plus CA19-9) distinguished PDAC from CP with area under the curve (95% confidence interval) of 97.2% (97.1%-97.3%), 93.5% (93.4%-93.7%), and 92.2% (92.1%-92.3%) in the ID, VD1, and VD2 cohorts, respectively. In the VD2 cohort, the m-Metabolic signature (4 analytes plus CA19-9) discriminated PDAC from CP with a sensitivity of 77.3% and specificity of 89.6%, with an overall accuracy of 82.4%. For the subset of 45 patients with PDAC with resectable stages IA-IIB tumors, the sensitivity, specificity, and accuracy were 73.2%, 89.6%, and 82.7%, respectively; for those with detectable CA19-9 >2 U/mL, 81.6%, 88.7%, and 84.5%, respectively; and for those with CA19-9 <37 U/mL, 39.7%, 94.1%, and 76.3%, respectively. CONCLUSIONS: The single-platform, single-run, m-Metabolic signature of just 4 metabolites used in combination with serum CA19-9 levels is an innovative accurate diagnostic tool for PDAC at the time of clinical presentation, warranting further large-scale evaluation.

Metabolic Background, Not Photosynthetic Physiology, Determines Drought and Drought Recovery Responses in C3 and C2 Moricandias.

Distinct photosynthetic physiologies are found within the Moricandia genus, both C3-type and C2-type representatives being known. As C2-physiology is an adaptation to drier environments, a study of physiology, biochemistry and transcriptomics was conducted to investigate whether plants with C2-physiology are more tolerant of low water availability and recover better from drought. Our data on Moricandia moricandioides (Mmo, C3), M. arvensis (Mav, C2) and M. suffruticosa (Msu, C2) show that C3 and C2-type Moricandias are metabolically distinct under all conditions tested (well-watered, severe drought, early drought recovery). Photosynthetic activity was found to be largely dependent upon the stomatal opening. The C2-type M. arvensis was able to secure 25-50% of photosynthesis under severe drought as compared to the C3-type M. moricandioides. Nevertheless, the C2-physiology does not seem to play a central role in M. arvensis drought responses and drought recovery. Instead, our biochemical data indicated metabolic differences in carbon and redox-related metabolism under the examined conditions. The cell wall dynamics and glucosinolate metabolism regulations were found to be major discriminators between M. arvensis and M. moricandioides at the transcription level.

Identification of Survival-Related Metabolic Genes and a Novel Gene Signature Predicting the Overall Survival for Patients with Uveal Melanoma.

INTRODUCTION: Uveal melanoma (UM) is the most common primary intraocular malignancy among adults. Altered metabolism has been shown to contribute to the development of cancer closely, but the prognostic role of metabolism in UM remains to be explored. This study aimed to construct a metabolic-related signature for UM. METHOD: We collected the mRNA sequencing data and corresponding clinical information from The Cancer Genome Atlas and Gene Expression Omnibus databases. A univariate Cox regression analysis, the Lasso-penalized Cox regression analysis, and multivariate Cox regression analyses were used to construct a metabolic signature based on TCGA. The time-dependent ROC and Kaplan-Meier survival curves were calculated to validate the prognostic ability of the signature. The immune-related features and mutation profile were characterized by CIBERSORT and maftools between high- and low-risk groups. RESULT: A novel metabolic-related signature (risk score = -0.246*SLC25A38 - 0.50186*ABCA12 + 0.032*CA12 + 0.086*SYNJ2) was constructed to predict the prognosis of UM patients. In TCGA and GSE22138, the signature had high sensitivity and specificity in predicting the prognosis of UM patients (survival probability; p < 0.0001, p = 0.012) . Gene Ontology pathway enrichment analysis and GSEA were used to discriminate several significantly enriched metabolism-related pathways, including channel activity and passive transmembrane transporter activity, which may reveal the underlying mechanisms. The high-risk group had more immune cell infiltration and greater distribution of BAP1 mutations. CONCLUSION: Our study developed a robust metabolic-gene signature based on TCGA to predict the prognosis of UM patients. The signature indicates a dysregulated metabolic microenvironment and provides new metabolic biomarkers and therapeutic targets for UM patients.

[Immunohistochemical analysis of a hypoxia-associated signature in melanomas with positive and negative sentinel lymph nodes : Hypoxia-associated signature of primary cutaneous melanomas]. / Immunhistochemische Analyse einer Hypoxie-assoziierten Signatur in Melanomen mit positivem und negativem Schildwächterlymphknoten : Hypoxie-assoziierte Signatur primär kutaner Melanome.

Metabolic reprogramming mediated by hypoxia-inducible factors and its downstream targets plays a crucial role in many human malignancies. Excessive proliferation of tumor cells under hypoxic conditions leads to metabolic reprogramming and altered gene expression enabling tumors to adapt to their hypoxic environment. Here we analyzed the metabolic signatures of primary cutaneous melanomas with positive and negative sentinel node status in order to evaluate potential differences in their metabolic signature. We found a positive correlation of the expression of glucose transporter 1 (GLUT-1) with tumor thickness and ulceration in all melanomas with subgroup analyses as well as in the subgroup with a negative sentinel node. Furthermore, the expression of vascular endothelial growth factor (VEGF) was positively correlated with the presence of ulceration in melanomas with positive sentinel node.

Metabolomic Profile of Aggressive Meningiomas by Using High-Resolution Magic Angle Spinning Nuclear Magnetic Resonance.

Meningiomas are in most cases benign brain tumors. The WHO 2016 classification defines three grades of meningiomas. This classification had a prognosis value because grade III meningiomas have a worse prognosis value compared to grades I and II meningiomas. However, some benign or atypical meningiomas can have a clinical aggressive behavior. There are currently no reliable markers which allow distinguishing between the meningiomas with a good prognosis and those which may recur. High-resolution magic angle spinning (HRMAS) spectrometry is a noninvasive method able to determine the metabolite profile of a tissue sample. We retrospectively analyzed 62 meningioma samples by using HRMAS spectrometry (43 metabolites). We described a metabolic profile defined by a high concentration for acetate, threonine, N-acetyl-lysine, hydroxybutyrate, myoinositol, ascorbate, scylloinositol, and total choline and a low concentration for aspartate, glucose, isoleucine, valine, adenosine, arginine, and alanine. This metabolomic signature was associated with poor prognosis histological markers [Ki-67 ≥ 40%, high histological grade and negative progesterone receptor (PR) expression]. We also described a similar metabolomic spectrum between grade III and grade I meningiomas. Moreover, all grade I meningiomas with a low Ki-67 expression and a positive PR expression did not have the same metabolomic profile. Metabolomic analysis could be used to determine an aggressive meningioma in order to discuss a personalized treatment. Further studies are needed to confirm these results and to correlate this metabolic profile with survival data.

Integrated omics analyses of retrograde signaling mutant delineate interrelated stress-response strata.

To maintain homeostasis in the face of intrinsic and extrinsic insults, cells have evolved elaborate quality control networks to resolve damage at multiple levels. Interorganellar communication is a key requirement for this maintenance, however the underlying mechanisms of this communication have remained an enigma. Here we integrate the outcome of transcriptomic, proteomic, and metabolomics analyses of genotypes including ceh1, a mutant with constitutively elevated levels of both the stress-specific plastidial retrograde signaling metabolite methyl-erythritol cyclodiphosphate (MEcPP) and the defense hormone salicylic acid (SA), as well as the high MEcPP but SA deficient genotype ceh1/eds16, along with corresponding controls. Integration of multi-omic analyses enabled us to delineate the function of MEcPP from SA, and expose the compartmentalized role of this retrograde signaling metabolite in induction of distinct but interdependent signaling cascades instrumental in adaptive responses. Specifically, here we identify strata of MEcPP-sensitive stress-response cascades, among which we focus on selected pathways including organelle-specific regulation of jasmonate biosynthesis; simultaneous induction of synthesis and breakdown of SA; and MEcPP-mediated alteration of cellular redox status in particular glutathione redox balance. Collectively, these integrated multi-omic analyses provided a vehicle to gain an in-depth knowledge of genome-metabolism interactions, and to further probe the extent of these interactions and delineate their functional contributions. Through this approach we were able to pinpoint stress-mediated transcriptional and metabolic signatures and identify the downstream processes modulated by the independent or overlapping functions of MEcPP and SA in adaptive responses.

Impact of Dietary Fiber Consumption on Insulin Resistance and the Prevention of Type 2 Diabetes.

Large prospective cohort studies consistently show associations of a high dietary fiber intake (>25 g/d in women and >38 g/d in men) with a 20-30% reduced risk of developing type 2 diabetes (T2D), after correction for confounders. It is less well recognized that these effects appear to be mainly driven by high intakes of whole grains and insoluble cereal fibers, which typically are nonviscous and do not relevantly influence postprandial glucose responses [i.e., glycemic index (GI)] or are strongly fermented by the gut microbiota in the colon. In contrast, a dietary focus on soluble, viscous, gel-forming, more readily fermentable fiber intakes derived from fruit and certain vegetables yields mixed results and generally does not appear to reduce T2D risk. Although disentangling types of fiber-rich foods and separating these from possible effects related to the GI is an obvious challenge, the common conclusion that key metabolic effects of high-fiber intake are explained by mechanisms that should mainly apply to the soluble, viscous type can be challenged. More recently, studies in humans and animal models focused on gaining mechanistic insights into why especially high-cereal-fiber (HCF) diets appear to improve insulin resistance (IR) and diabetes risk. Although effects of HCF diets on weight loss are only moderate and comparable to other types of dietary fibers, possible novel mechanisms have emerged, which include the prevention of the absorption of dietary protein and modulation of the amino acid metabolic signature. Here we provide an update of our previous review from 2008, with a focus on mechanistic insights of how HCF diets may improve IR and the risk of developing T2D.

Chronic Diseases and Lifestyle Biomarkers Identification by Metabolomics.

Chronic diseases, also known as noncommunicable diseases (NCDs), are complex disorders that last for long periods of time and progress slowly. They currently account for the major cause of death worldwide with an alarming increase in rate both in developed and developing countries. In this chapter, the principal metabolomic-based investigations on chronic diseases (cardiovascular diseases, diabetes, and respiratory chronic diseases) and their major risk factors (particularly overweight/obesity) are described by focusing both on metabolites and metabolic pathways. Additional information on the contribution of metabolomics strategies in the ambit of the biomarker discovery for NCDs is also provided by exploring the major prospective studies of the last years (i.e., Framingham Heart Study, EPIC, MONICA, KORA, FINRIK, ECLIPSE). The metabolic signature of diseases, which arises from the metabolomic-based investigation, is therefore depicted in the chapter by pointing out the potential of metabolomics to explain the pathophysiological mechanisms underlying a disease, as well as to propose new therapeutic targets for alternative treatments.

Metabolic signatures of Besnoitia besnoiti-infected endothelial host cells and blockage of key metabolic pathways indicate high glycolytic and glutaminolytic needs of the parasite.

Besnoitia besnoiti is an obligate intracellular and emerging coccidian parasite of cattle with a significant economic impact on cattle industry. During acute infection, fast-proliferating tachyzoites are continuously formed mainly in endothelial host cells of infected animals. Given that offspring formation is a highly energy and cell building block demanding process, the parasite needs to exploit host cellular metabolism to meet its metabolic demands. Here, we analyzed the metabolic signatures of B. besnoiti-infected endothelial host cells and aimed to influence parasite proliferation by inhibitors of specific metabolic pathways. The following inhibitors were tested: fluoro 2-deoxy-D-glucose and 2-deoxy-D-glucose (FDG, DG; inhibitors of glycolysis), 6-diazo-5-oxo-L-norleucin (DON; inhibitor of glutaminolysis), dichloroacetate (DCA; inhibitor of pyruvate dehydrogenase kinase which favorites channeling of glucose carbons into the TCA cycle) and adenosine-monophosphate (AMP; inhibitor of ribose 5-P synthesis). Overall, B. besnoiti infections of bovine endothelial cells induced a significant and infection rate-dependent increase of glucose, lactate, glutamine, glutamate, pyruvate, alanine, and serine conversion rates which together indicate a parasite-triggered up-regulation of glycolysis and glutaminolysis. Thus, addition of DON, FDG, and DG into the cultivation medium of B. besnoiti infected endothelial cells led to a dose-dependent inhibition of parasite replication (4 µM DON, 99.5 % inhibition; 2 mM FDG, 99.1 % inhibition; 2 mM DG, 93 % inhibition; and 8 mM DCA, 71.9 % inhibition). In contrast, AMP had no significant effects on total tachyzoite production up to a concentration of 20 mM. Together, these data may open new strategies for the development of therapeutics for B. besnoiti infections.

Molecular characterization and clinical relevance of metabolic signature subtypes in gastric cancer.

Metabolic reprogramming dictates tumor molecular attributes and therapeutic potentials. However, the comprehensive metabolic characteristics in gastric cancer (GC) remain obscure. Here, metabolic signature-based clustering analysis identifies three subtypes with distinct molecular and clinical features: MSC1 showed better prognosis and upregulation of the tricarboxylic acid (TCA) cycle and lipid metabolism, combined with frequent TP53 and RHOA mutation; MSC2 had moderate prognosis and elevated nucleotide and amino acid metabolism, enriched by intestinal histology and mismatch repair deficient (dMMR); and MSC3 exhibited poor prognosis and enhanced glycan and energy metabolism, accompanied by diffuse histology and frequent CDH1 mutation. The Shandong Provincial Hospital (SDPH) in-house dataset with matched transcriptomic, metabolomic, and spatial-metabolomic analysis also validated these findings. Further, we constructed the metabolic subtype-related prognosis gene (MSPG) scoring model to quantify the activity of individual tumors and found a positive correlation with cuproptosis signaling. In conclusion, comprehensive recognition of the metabolite signature can enhance the understanding of diversity and heterogeneity in GC.

GALNT2 expression is associated with glucose control and serum metabolites in patients with type 2 diabetes.

AIMS: Aim of this study was to investigate in type 2 diabetes whether expression level of GALNT2, a positive modulator of insulin sensitivity, is associated with a metabolic signature. METHODS: Five different metabolite families, including acylcarnitines, aminoacids, biogenic amines, phospholipids and sphingolipids were investigated in fasting serum of 70 patients with type 2 diabetes, by targeted metabolomics. GALNT2 expression levels were measured in peripheral white blood cells by RT-PCR. The association between GALNT2 expression and serum metabolites was assessed using false discovery rate followed by stepwise selection and, finally, multivariate model including several clinical parameters as confounders. The association between GALNT2 expression and the same clinical parameters was also investigated. RESULTS: GALNT2 expression was independently correlated with HbA1c levels (P value = 0.0052), a finding that is the likely consequence of the role of GALNT2 on insulin sensitivity. GALNT2 expression was also independently associated with serum levels of the aminoacid glycine (P value = 0.014) and two biogenic amines phenylethylamine (P value = 0.0065) and taurine (P value = 0.0011). The association of GALNT2 expression with HbA1c was not mediated by these three metabolites. CONCLUSIONS: Our data indicate that in type 2 diabetes the expression of GALNT2 is associated with several serum metabolites. This association needs to be further investigated to understand in depth its role in mediating the effect of GALNT2 on insulin sensitivity, glucose control and other clinical features in people with diabetes.

Development of a (Poly)phenol Metabolic Signature for Assessing (Poly)phenol-Rich Dietary Patterns.

The objective assessment of habitual (poly)phenol-rich diets in nutritional epidemiology studies remains challenging. This study developed and evaluated the metabolic signature of a (poly)phenol-rich dietary score (PPS) using a targeted metabolomics method comprising 105 representative (poly)phenol metabolites, analyzed in 24 h of urine samples collected from healthy volunteers. The metabolites that were significantly associated with PPS after adjusting for energy intake were selected to establish a metabolic signature using a combination of linear regression followed by ridge regression to estimate penalized weights for each metabolite. A metabolic signature comprising 51 metabolites was significantly associated with adherence to PPS in 24 h urine samples, as well as with (poly)phenol intake estimated from food frequency questionnaires and diaries. Internal and external data sets were used for validation, and plasma, spot urine, and 24 h urine samples were compared. The metabolic signature proposed here has the potential to accurately reflect adherence to (poly)phenol-rich diets, and may be used as an objective tool for the assessment of (poly)phenol intake.

Metabotropic Glutamate Receptors Type 3 and 5 Feature the "NeuroTransmitter-type" of Glioblastoma: A Bioinformatic Approach.

BACKGROUND: Glioblastoma (GBM) represents an aggressive and common tumor of the central nervous system. The prognosis of GBM is poor, and despite a refined genetic and molecular characterization, pharmacological treatment is largely suboptimal. OBJECTIVE: Contribute to defining a therapeutic line in GBM targeting the mGlu3 receptor in line with the principles of precision medicine. METHODS: Here, we performed a computational analysis focused on the expression of type 3 and 5 metabotropic glutamate receptor subtypes (mGlu3 and mGlu5, respectively) in high- and low-grade gliomas. RESULTS: The analysis allowed the identification of a particular high-grade glioma type, characterized by a high expression level of both receptor subtypes and by other markers of excitatory and inhibitory neurotransmission. This so-called neurotransmitter-GBM (NT-GBM) also shows a distinct immunological, metabolic, and vascularization gene signature. CONCLUSION: Our findings might lay the groundwork for a targeted therapy to be specifically applied to this putative novel type of GBM.

A Metabolic Plasticity-Based Signature for Molecular Classification and Prognosis of Lower-Grade Glioma.

BACKGROUND: Glioma is one of the major health problems worldwide. Biomarkers for predicting the prognosis of Glioma are still needed. METHODS: The transcriptome data and clinic information on Glioma were obtained from the CGGA, TCGA, GDC, and GEO databases. The immune infiltration status in the clusters was compared. The genes with differential expression were identified, and a prognostic model was developed. Several assays were used to detect RPH3A's role in Glioma cells, including CCK-8, colony formation, wound healing, and transwell migration assay. RESULTS: Lower Grade Glioma (LGG) was divided into two clusters. The immune infiltration difference was observed between the two clusters. We screened for genes that differed between the two groups. WGCNA was used to construct a co-expressed network using the DEGs, and four co-expressed modules were identified, which are blue, green, grey, and yellow modules. High-risk patients have a lower overall survival rate than low-risk patients. In addition, the risk score is associated with histological subtypes. Finally, the role of RPH3A was detected. The overexpression of RPH3A in LGG cells can significantly inhibit cell proliferation and migration and regulate EMT-regulated proteins. CONCLUSION: Our study developed a metabolic-related model for the prognosis of Glioma cells. RPH3A is a potential therapeutic target for Glioma.

Sex differences in the human metabolome.

BACKGROUND: The sexual dimorphism represents one of the triggers of the metabolic disparities between the organisms, advising about wild implications in research or diagnostics contexts. Despite the mounting recognition of the importance of sex consideration in the biomedical fields, the identification of male- and female-specific metabolic signatures has not been achieved. MAIN BODY: This review pointed the focus on the metabolic differences related to the sex, evidenced by metabolomics studies performed on healthy populations, with the leading aim of understanding how the sex influences the baseline metabolome. The main shared signatures and the apparent dissimilarities between males and females were extracted and highlighted from the metabolome of the most commonly analyzed biological fluids, such as serum, plasma, and urine. Furthermore, the influence of age and the significant interactions between sex and age have been taken into account. CONCLUSIONS: The recognition of sex patterns in human metabolomics has been defined in diverse biofluids. The detection of sex- and age-related differences in the metabolome of healthy individuals are helpful for translational applications from the bench to the bedside to set targeted diagnostic and prevention approaches in the context of personalized medicine.

A metabolism-relevant signature as a predictor for prognosis and therapeutic response in pancreatic cancer.

Although several altered metabolic genes have been identified to be involved in the tumorigenesis and advance of pancreatic cancer (PC), their prognostic values remained unclear. The purpose of this study was to explore new targets and establish a metabolic signature to predict prognosis and chemotherapy response for optimal individualized treatment. The expression data of PC patients from two independent cohorts and metabolism-related genes from KEGG were utilized and analyzed for the establishment of the signature via lasso regression. Then, the differentially expressed candidate genes were further confirmed via online data mining platform and qRT-PCR of clinical specimens. Then, the analyses of gene set enrichment, mutation, and chemotherapeutic response were performed via R package. As results showed, 109 differentially expressed metabolic genes were screened out in PC. Then a metabolism-related five-gene signature comprising B3GNT3, BCAT1, KYNU, LDHA, and TYMS was constructed and showed excellent ability for predicting survival. A novel nomogram coordinating the metabolic signature and other independent prognostic parameters was developed and showed better predictive power in predicting survival. In addition, this metabolic signature was significantly involved in the activation of multiple oncological pathways and regulation of the tumor immune microenvironment. The patients with high risk scores had higher tumor mutation burdens and were prone to be more sensitive to chemotherapy. In summary, our work identified a new metabolic signature and established a superior prognostic nomogram which may supply more indications to explore novel strategies for diagnosis and treatment.

Self-Renewal of Macrophages: Tumor-Released Factors and Signaling Pathways.

Macrophages are the most abundant immune cells of the tumor microenvironment (TME) and have multiple important functions in cancer. During tumor growth, both tissue-resident macrophages and newly recruited monocyte-derived macrophages can give rise to tumor-associated macrophages (TAMs), which have been associated with poor prognosis in most cancers. Compelling evidence indicate that the high degree of plasticity of macrophages and their ability to self-renew majorly impact tumor progression and resistance to therapy. In addition, the microenvironmental factors largely affect the metabolism of macrophages and may have a major influence on TAMs proliferation and subsets functions. Thus, understanding the signaling pathways regulating TAMs self-renewal capacity may help to identify promising targets for the development of novel anticancer agents. In this review, we focus on the environmental factors that promote the capacity of macrophages to self-renew and the molecular mechanisms that govern TAMs proliferation. We also highlight the impact of tumor-derived factors on macrophages metabolism and how distinct metabolic pathways affect macrophage self-renewal.

A Metabolic Signature to Monitor Endothelial Cell Differentiation, Activation, and Vascular Organization.

The formation of new blood vessels is an important step in the morphogenesis and organization of tissues and organs; hence, the success of regenerative medicine procedures is highly dependent on angiogenesis control. Despite the biotechnological advances, tissue engineering is still a challenge. Regarding vascular network formation, the regulators are well known, yet the identification of markers is pivotal in order to improve the monitoring of the differentiation and proliferation of endothelial cells, as well as the establishment of a vascular network supporting tissue viability for an efficacious implantation. The metabolic profile accompanies the physiological stages of cells involved in angiogenesis, being a fruitful hub of biomarkers, whose levels can be easily retrieved. Through NMR spectroscopy, we identified branched amino acids, acetate, and formate as central biomarkers of monocyte-to-endothelial-cell differentiation and endothelial cell proliferation. This study reinforces the successful differentiation process of monocytes into endothelial cells, allowing self-to-self transplantation of patient-derived vascular networks, which is an important step in tissue engineering, since monocytes are easily isolated and autologous transplantation reduces the immune rejection events.

Plasma metabolomics provides new insights into the relationship between metabolites and outcomes and left ventricular remodeling of coronary artery disease.

BACKGROUND: Coronary artery disease (CAD) is a metabolically perturbed pathological condition. However, the knowledge of metabolic signatures on outcomes of CAD and their potential causal effects and impacts on left ventricular remodeling remains limited. We aim to assess the contribution of plasma metabolites to the risk of death and major adverse cardiovascular events (MACE) as well as left ventricular remodeling. RESULTS: In a prospective study with 1606 Chinese patients with CAD, we have identified and validated several independent metabolic signatures through widely-targeted metabolomics. The predictive model respectively integrating four metabolic signatures (dulcitol, ß-pseudouridine, 3,3',5-Triiodo-L-thyronine, and kynurenine) for death (AUC of 83.7% vs. 76.6%, positive IDI of 0.096) and metabolic signatures (kynurenine, lysoPC 20:2, 5-methyluridine, and L-tryptophan) for MACE (AUC of 67.4% vs. 59.8%, IDI of 0.068) yielded better predictive value than trimethylamine N-oxide plus clinical model, which were successfully applied to predict patients with high risks of death (P = 0.0014) and MACE (P = 0.0008) in the multicenter validation cohort. Mendelian randomisation analysis showed that 11 genetically inferred metabolic signatures were significantly associated with risks of death or MACE, such as 4-acetamidobutyric acid, phenylacetyl-L-glutamine, tryptophan metabolites (kynurenine, kynurenic acid), and modified nucleosides (ß-pseudouridine, 2-(dimethylamino) guanosine). Mediation analyses show that the association of these metabolites with the outcomes could be partly explained by their roles in promoting left ventricular dysfunction. CONCLUSIONS: This study provided new insights into the relationship between plasma metabolites and clinical outcomes and its intermediate pathological process left ventricular dysfunction in CAD. The predictive model integrating metabolites can help to improve the risk stratification for death and MACE in CAD. The metabolic signatures appear to increase death or MACE risks partly by promoting adverse left ventricular dysfunction, supporting potential therapeutic targets of CAD for further investigation.