Plasma metabolic signatures for intracranial aneurysm and its rupture identified by pseudotargeted metabolomics

Metabolic pathway and genetically causal links of 1,400 circulating metabolites on the risk of intracranial aneurysms and aneurysmal subarachnoid hemorrhage

BACKGROUND

The rupture of intracranial aneurysms (IAs) leads to aneurysmal subarachnoid hemorrhage (aSAH), which is associated with significant disability and mortality rates. This study aims to identify metabolic markers causally linked to the occurrence of IAs and aSAH through Mendelian randomization (MR), thereby offering novel predictive and therapeutic targets.

METHODS

We conducted a genome-wide association study (GWAS) on IAs and aSAH, analyzing 1,400 metabolomic indices from the Canadian Longitudinal Study on Aging (CLSA) cohort (n = 8,299). Subsequently, we employed two-sample Mendelian randomization to ascertain potential causal relationships between each metabolite and the conditions IAs and aSAH by various MR methodologies, including MR Egger, Weighted median, Inverse variance weighted (IVW), MR-PRESSO, Simple mode, and Weighted mode. The heterogeneity of instrumental variables was assessed using Cochran's Q statistics, and metabolic pathway analyses were performed via the Metaconflict 5.0 platform.

RESULTS

Our analysis found that 87 metabolites/metabolic ratios were associated with IAs, and 85 metabolites/metabolic ratios were associated with aSAH. After false discovery rate (FDR) correction and sensitivity analyses, nine metabolites/metabolic ratios were significantly causally associated with aSAH. Conversely, while 87 metabolites and their ratios initially showed potential causal links with IA, none demonstrated significant causal associations post-FDR correction. The study also pinpointed eight significant metabolic pathways implicated in both IAs and aSAH.

CONCLUSION

This study found that nine circulating metabolites and their ratios with significant causal associations to aSAH, while no metabolites and their ratios were causally linked to IAs. These results suggest possible mechanisms and predictive molecular targets for IAs and aSAH.

Integrative metabolomics dictate distinctive signature profiles in patients with Tetralogy of Fallot

BACKGROUND

Tetralogy of Fallot (TOF) is the most common cyanotic congenital heart disease (CCHD) with multifactorial etiology. We aimed to investigate the metabolic profiles of CCHD and their independent contributions to TOF.

METHODS

A cohort comprising 42 individuals with TOF and atrial septal defect (ASD) was enrolled. Targeted ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) was employed to systematically analyze metabolite levels and identify TOF-associated metabolic profiles.

RESULTS

Of 370 identified metabolites in tissue and 284 in plasma, over one-third of metabolites showed an association with microbiome. Differential metabolic pathways including amino acids biosynthesis, ABC (ATP-binding cassette) transporters, carbon metabolism, and fatty acid biosynthesis, shed light on TOF biological phenotypes. Additionally, ROC curves identified potential biomarkers, such as erythronic acid with an AUC of 0.868 in plasma, and 3-β-hydroxy-bisnor-5-cholenic acid, isocitric acid, glutaric acid, ortho-Hydroxyphenylacetic acid, picolinic acid with AUC close to 1 in tissue, whereas the discriminative performance of those substances significantly improved when combined with clinical phenotypes.

CONCLUSIONS

Distinct metabolic profiles exhibited robust discriminatory capabilities, effectively distinguishing TOF from ASD patients. These metabolites may serve as biomarkers or key molecular players in the intricate metabolic pathways involved in CCHD development.

IMPACT

Distinct metabolic profiles exhibited robust discriminatory capabilities, effectively distinguishing Tetralogy of Fallot from atrial septal defect patients. Similar profiling but inconsistent differential pathways between plasma and tissue. More than one-third metabolites in plasma and tissue are associated with the microbiome. The discovery of biomarkers is instrumental in facilitating early detection and diagnosis of Tetralogy of Fallot. Disturbed metabolism offers insights into interpretation of pathogenesis of Tetralogy of Fallot.

Cerebrospinal Fluid Metabolomics and Proteomics Integration in Neurological Syndromes

The integration of multi-omics data has increasingly been recognized as an effective approach to addressing complex problems and advancing precision medicine. Metabolomics and proteomics are closely related, and their integration provides complementary insights and enables cross-validation of experimental results. The integration of proteomics and metabolomics in cerebrospinal fluid is expected to reconstruct the complex biological networks underlying nervous system diseases, enhance understanding of molecular mechanisms, and aid in disease classification and prognosis prediction. However, integrating multi-omics data still faces numerous challenges, limiting the application of combined proteomic and metabolomic analyses in neurological diseases. Based on the advantages of integrated proteomics and metabolomics, this chapter introduces, for the first time, common strategies for the integrated analyses of omics data. Furthermore, we review advances in cerebrospinal fluid proteomics and metabolomics for neurological syndromes, highlighting current challenges and future directions.

Untargeted metabolomics to detect and identify plasma metabolic signatures associated with intracranial aneurysm and its rupture

The biological basis for metabolic differences between unruptured and ruptured intracranial aneurysm (UIA and RIA) populations and their potential role in triggering IA rupture remain unclear. The aim of this study was to analyze the plasma metabolic profiles of patients with UIA and RIA using an untargeted metabolomic approach and to develop a model for early rupture classification. Plasma samples were analyzed using an ultra-high-performance liquid chromatography high-resolution tandem mass spectrometry-based platform. Least absolute shrinkage and selection operator regression and random forest machine learning methods were employed for metabolite feature selection and predictive model construction. Among 49 differential plasma metabolites identified, 31 were increased and 18 were decreased in the plasma of RIA patients. Five key metabolites-canrenone, piperine, 1-methyladenosine, betaine, and trigonelline-were identified as having strong potential to discriminate between UIA and RIA patients. This combination of metabolites demonstrated high diagnostic accuracy, with an area under the curve exceeding 0.95 in both the training and validation datasets. Our finding highlights the significance of plasma metabolites as potential biomarkers for early detection of IA rupture risk, offering new insights for clinical practice and future research on IA management.

Identification of chronic non-atrophic gastritis and intestinal metaplasia stages in the Correa's cascade through machine learning analyses of SERS spectral signature of non-invasively-collected human gastric fluid samples

The progression of gastric cancer involves a complex multi-stage process, with gastroscopy and biopsy being the standard procedures for diagnosing gastric diseases. This study introduces an innovative non-invasive approach to differentiate gastric disease stage using gastric fluid samples through machine-learning-assisted surface-enhanced Raman spectroscopy (SERS). This method effectively identifies different stages of gastric lesions. The XGBoost algorithm demonstrates the highest accuracy of 96.88% and 91.67%, respectively, in distinguishing chronic non-atrophic gastritis from intestinal metaplasia and different subtypes of gastritis (mild, moderate, and severe). Through blinded testing validation, the model can achieve more than 80% accuracy. These findings offer new possibilities for rapid, cost-effective, and minimally invasive diagnosis of gastric diseases.

Integrated metagenomic and metabolomic analysis reveals distinctive stage-specific gut-microbiome-derived metabolites in intracranial aneurysms

OBJECTIVE

Our study aimed to explore the influence of gut microbiota and their metabolites on intracranial aneurysms (IA) progression and pinpoint-related metabolic biomarkers derived from the gut microbiome.

DESIGN

We recruited 358 patients with unruptured IA (UIA) and 161 with ruptured IA (RIA) from two distinct geographical regions for conducting an integrated analysis of plasma metabolomics and faecal metagenomics. Machine learning algorithms were employed to develop a classifier model, subsequently validated in an independent cohort. Mouse models of IA were established to verify the potential role of the specific metabolite identified.

RESULTS

Distinct shifts in taxonomic and functional profiles of gut microbiota and their related metabolites were observed in different IA stages. Notably, tryptophan metabolites, particularly indoxyl sulfate (IS), were significantly higher in plasma of RIA. Meanwhile, upregulated tryptophanase expression and indole-producing microbiota were observed in gut microbiome of RIA. A model harnessing gut-microbiome-derived tryptophan metabolites demonstrated remarkable efficacy in distinguishing RIA from UIA patients in the validation cohort (AUC=0.97). Gut microbiota depletion by antibiotics decreased plasma IS concentration, reduced IA formation and rupture in mice, and downregulated matrix metalloproteinase-9 expression in aneurysmal walls with elastin degradation reduction. Supplement of IS reversed the effect of gut microbiota depletion.

CONCLUSION

Our investigation highlights the potential of gut-microbiome-derived tryptophan metabolites as biomarkers for distinguishing RIA from UIA patients. The findings suggest a novel pathogenic role for gut-microbiome-derived IS in elastin degradation in the IA wall leading to the rupture of IA.

Mendelian randomization demonstrates a causal link between peripheral circulating acylcarnitines and intracranial aneurysms

Intracranial aneurysm (IA) is the most prevalent type of cerebral vascular disease causing life-threatening subarachnoid hemorrhages (SAH). A long-term vascular structure remodeling is considered as the main pathophysiological feature of IAs. However, the causal factors triggering the pathophysiological process are not clear. Recently, the abnormalities of peripheral circulating proteins and metabolites have been found in IAs patients and associated with the ruptures. We comprehensively investigated the potential causal relationship between blood metabolites and proteins and IAs using the mendelian randomization (MR) analysis. We applied two-sample MR to explore the potential causal association between peripheral circulating metabolites (191 blood metabolites) and proteins (1398 proteins) and IAs using data from the FinnGen study and the GWAS datasets published by Bakker et al. We identified palmitoylcarnitine, stearoylcarnitine and 2-tetradecenoylcarnitine as causal contributors of IAs and ruptures. Further two-step mediation MR analysis suggested that hypertension as one of the contributors of IAs and ruptures mediated the causal relationship between palmitoylcarnitine, stearoylcarnitine and 2-tetradecenoylcarnitine and IAs. Together, our study demonstrates that blood metabolic palmitoylcarnitine, stearoylcarnitine and 2-tetradecenoylcarnitine are causally linked to the formation and rupture of IAs. Hypertension partially mediates the causal effects.

Study the local metabolic changes of aneurysms through microcatheter sampling

Intracranial aneurysm is the primary cause of nontraumatic subarachnoid hemorrhage. To assess aneurysm metabolism, we present a method of intra-operatively collecting blood samples from the aneurysm neck, as well as the proximal and distal responsible vessels, using microcatheters. Through these paired comparisons, we can eliminate the interpatient variation usually observed in plasma samples taken from the peripheral vein. We utilized 39 plasma samples from 13 intracranial patients to characterize the metabolite profiles using untargeted liquid chromatography-mass spectrometry. Our findings revealed that L-tyrosine is upregulated at relatively high levels at the aneurysm neck than the proximal and distal aneurysm, whereas phenylpyruvic acid, L-cystine, and L-ornithine are downregulated. Based on this, there was also a significant decrease in arginine within small aneurysm of the internal carotid artery. The 6-month follow-up indicated that patients who experienced good recovery had lower levels of biliverdin, bilirubin, and metabolites of coenzyme Q within the aneurysm. In conclusion, our investigation provides a comprehensive overview of plasma metabolites in patients with intracranial aneurysms, shedding light on potential pathogenetic mechanisms in unruptured intracranial aneurysms. Moreover, the study proposes innovative ideas for establishing postoperative follow-up timelines for flow diverter devices.

A pseudo-targeted metabolomics for discovery of potential biomarkers of cardiac hypertrophy in rats

Cardiac hypertrophy (CH) is one of the stages in the occurrence and development of severe cardiovascular diseases, and exploring its biomarkers is beneficial for delaying the progression of severe cardiovascular diseases. In this research, we established a comprehensive and highly efficient pseudotargeted metabolomics method, which demonstrated a superior capacity to identify differential metabolites when compared to traditionaluntargeted metabolomics. The intra/inter-day precision and reproducibility results proved the method is reliable and precise. The established method was then applied to seek the potential differentiated metabolic biomarkers of cardiac hypertrophy (CH) rats, and oxylipins, phosphorylcholine (PC), lysophosphatidylcholine (LysoPC), lysophosphatidylethanolamine (LysoPE), Krebs cycle intermediates, carnitines, amino acids, and bile acids were disclosed to be the possible differentiate components. Their metabolic pathway analysis revealed that the potential metabolic alterations in CH rats were mainly associated with phenylalanine, tyrosine and tryptophan biosynthesis, phenylalanine metabolism, arachidonic acid metabolism, citrate cycle, glyoxylate and dicarboxylate metabolism, and tyrosine metabolism. In sum, this research provided a comprehensiveand reliable LC-MS/MS MRM platform for pseudo-targeted metabolomics investigation of disease condition, and some interesting potential biomarkers were disclosed for CH, which merit further exploration in the future.

Machine learning-based clinical decision support using laboratory data

Artificial intelligence (AI) and machine learning (ML) are becoming vital in laboratory medicine and the broader context of healthcare. In this review article, we summarized the development of ML models and how they contribute to clinical laboratory workflow and improve patient outcomes. The process of ML model development involves data collection, data cleansing, feature engineering, model development, and optimization. These models, once finalized, are subjected to thorough performance assessments and validations. Recently, due to the complexity inherent in model development, automated ML tools were also introduced to streamline the process, enabling non-experts to create models. Clinical Decision Support Systems (CDSS) use ML techniques on large datasets to aid healthcare professionals in test result interpretation. They are revolutionizing laboratory medicine, enabling labs to work more efficiently with less human supervision across pre-analytical, analytical, and post-analytical phases. Despite contributions of the ML tools at all analytical phases, their integration presents challenges like potential model uncertainties, black-box algorithms, and deskilling of professionals. Additionally, acquiring diverse datasets is hard, and models' complexity can limit clinical use. In conclusion, ML-based CDSS in healthcare can greatly enhance clinical decision-making. However, successful adoption demands collaboration among professionals and stakeholders, utilizing hybrid intelligence, external validation, and performance assessments.

Liquid chromatography coupled to mass spectrometry metabolomic analysis of cerebrospinal fluid revealed the metabolic characteristics of moyamoya disease

Objective

Metabolomics has found extensive applications in the field of neurological diseases, significantly contributing to their diagnosis and treatment. However, there has been limited research applying metabolomics to moyamoya disease (MMD). This study aims to investigate and identify differential metabolites associated with MMD.

Methods

We employed a liquid chromatography coupled with mass spectrometry (LC-MS) approach, complemented by univariate and multivariate analyses, to discern metabolic biomarkers in cerebrospinal fluid samples. We then compared these biomarkers between MMD patients and healthy controls (Ctl).

Results

Sixteen patients diagnosed with MMD via cerebral angiography and eight healthy controls were enrolled in this study. Comparative analyses, including univariate and multivariate analyses, correlation studies, heatmaps, Volcano Plots, and KEGG pathway enrichment, were performed between MMD patients and controls. As a result, we identified 129 significant differential metabolites in the cerebrospinal fluid between MMD patients and controls. These metabolic biomarkers are associated with various pathways, with notable involvement in purine and pyrimidine metabolism.

Conclusion

Utilizing an LC-MS-based metabolomics approach holds promise for enhancing the clinical diagnosis of MMD. The identified biomarkers offer potential avenues for the development of novel diagnostic methods for MMD and offer fresh insights into the pathogenesis of the disease.

Plasma levels of CD36 and glutathione as biomarkers for ruptured intracranial aneurysm

Evidence has proved that intracranial aneurysm (IA) formation and rupture might be closely related to inflammatory response and oxidative stress. Our objective was to evaluate the potential of CD36 and glutathione (GSH) as biomarkers for IA. In this study, the enzyme-linked immunosorbent assay was used to measure the plasma levels of CD36 and GSH in 30 IA patients and 30 healthy controls. Then, correlation analysis, receiver operating characteristic (ROC) curve, and logistic regression analysis were performed. The results showed that the plasma level of CD36 in IA patients was significantly higher than that in the control group (P < 0.0001), and plasma GSH was significantly lower compared with that in the control group (P < 0.0001). ROC analysis showed that CD36 and GSH had high sensitivity (90.0 and 96.6%) and specificity (96.6 and 86.6%) for IA diagnosis. The combined sensitivity and specificity achieved were 100 and 100%, respectively. The plasma levels of CD36 and GSH did not show a significant correlation with age, the Glasgow Coma Scale, Hunter-Hess score, aneurysm size, aneurysm height, aneurysm neck, and aspect ratio. The AUC of the logistic regression model based on CD36 and GSH was 0.505. Our results suggested that the combination of plasma CD36 and GSH could serve as potential biomarkers for IA rupture.

Causal relationships between human blood metabolites and intracranial aneurysm and aneurysmal subarachnoid hemorrhage: a Mendelian randomization study

Objective

The aim of this study was to assess the causal relationships between blood metabolites and intracranial aneurysm, aneurysmal subarachnoid hemorrhage, and unruptured intracranial aneurysm.

Methods

Our exposure sample consisted of 7,824 individuals from a genome-wide association study of human blood metabolites. Our outcome sample consisted of 79,429 individuals (7,495 cases and 71,934 controls) from the International Stroke Genetics Consortium, which conducted a genome-wide association study of intracranial aneurysm, aneurysmal subarachnoid hemorrhage, and unruptured intracranial aneurysm. We identified blood metabolites with a potential causal effect on intracranial aneurysms and conducted sensitivity analyses to validate our findings.

Results

After rigorous screening and Mendelian randomization tests, we found four, two, and three serum metabolites causally associated with intracranial aneurysm, aneurysmal subarachnoid hemorrhage, and unruptured intracranial aneurysm, respectively (all P < 0.05). Sensitivity analyses confirmed the robustness of these associations.

Conclusions

Our Mendelian randomization analysis demonstrated causal relationships between human blood metabolites and intracranial aneurysm, aneurysmal subarachnoid hemorrhage, and unruptured intracranial aneurysm. Further research is required to explore the potential of targeting these metabolites in the management of intracranial aneurysm.

The markers and risk stratification model of intracranial aneurysm instability in a large Chinese cohort

Intracranial aneurysm is the leading cause of nontraumatic subarachnoid hemorrhage. Evaluating the unstable (rupture and growth) risk of aneurysms is helpful to guild decision-making for unruptured intracranial aneurysms (UIA). This study aimed to develop a model for risk stratification of UIA instability. The UIA patients from two prospective, longitudinal multicenter Chinese cohorts recruited from January 2017 to January 2022 were set as the derivation cohort and validation cohort. The primary endpoint was UIA instability, comprising aneurysm rupture, growth, or morphology change, during a 2-year follow-up. Intracranial aneurysm samples and corresponding serums from 20 patients were also collected. Metabolomics and cytokine profiling analysis were performed on the derivation cohort (758 single-UIA patients harboring 676 stable UIAs and 82 unstable UIAs). Oleic acid (OA), arachidonic acid (AA), interleukin 1β (IL-1β), and tumor necrosis factor-α (TNF-α) were significantly dysregulated between stable and unstable UIAs. OA and AA exhibited the same dysregulated trends in serums and aneurysm tissues. The feature selection process demonstrated size ratio, irregular shape, OA, AA, IL-1β, and TNF-α as features of UIA instability. A machine-learning stratification model (instability classifier) was constructed based on radiological features and biomarkers, with high accuracy to evaluate UIA instability risk (area under curve (AUC), 0.94). Within the validation cohort (492 single-UIA patients harboring 414 stable UIAs and 78 unstable UIAs), the instability classifier performed well to evaluate the risk of UIA instability (AUC, 0.89). Supplementation of OA and pharmacological inhibition of IL-1β and TNF-α could prevent intracranial aneurysms from rupturing in rat models. This study revealed the markers of UIA instability and provided a risk stratification model, which may guide treatment decision-making for UIAs.