Metabolic Profiling and Metabolites Fingerprints in Human Hypertension: Discovery and Potential

From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies

In the dynamic landscape of targeted therapeutics, drug discovery has pivoted towards understanding underlying disease mechanisms, placing a strong emphasis on molecular perturbations and target identification. This paradigm shift, crucial for drug discovery, is underpinned by big data, a transformative force in the current era. Omics data, characterized by its heterogeneity and enormity, has ushered biological and biomedical research into the big data domain. Acknowledging the significance of integrating diverse omics data strata, known as multi-omics studies, researchers delve into the intricate interrelationships among various omics layers. This review navigates the expansive omics landscape, showcasing tailored assays for each molecular layer through genomes to metabolomes. The sheer volume of data generated necessitates sophisticated informatics techniques, with machine-learning (ML) algorithms emerging as robust tools. These datasets not only refine disease classification but also enhance diagnostics and foster the development of targeted therapeutic strategies. Through the integration of high-throughput data, the review focuses on targeting and modeling multiple disease-regulated networks, validating interactions with multiple targets, and enhancing therapeutic potential using network pharmacology approaches. Ultimately, this exploration aims to illuminate the transformative impact of multi-omics in the big data era, shaping the future of biological research.

Association between human blood metabolome and the risk of pre-eclampsia

Pre-eclampsia is a complex multi-system pregnancy disorder with limited treatment options. Therefore, we aimed to screen for metabolites that have causal associations with preeclampsia and to predict target-mediated side effects based on Mendelian randomization (MR) analysis. A two-sample MR analysis was firstly conducted to systematically assess causal associations of blood metabolites with pre-eclampsia, by using metabolites related large-scale genome-wide association studies (GWASs) involving 147,827 European participants, as well as GWASs summary data about pre-eclampsia from the FinnGen consortium R8 release data that included 182,035 Finnish adult female subjects (5922 cases and 176,113 controls). Subsequently, a phenome-wide MR (Phe-MR) analysis was applied to assess the potential on-target side effects associated with hypothetical interventions that reduced the burden of pre-eclampsia by targeting identified metabolites. Four metabolites were identified as potential causal mediators for pre-eclampsia by using the inverse-variance weighted method, including cholesterol in large HDL (L-HDL-C) [odds ratio (OR): 0.88; 95% confidence interval (95% CI): 0.83-0.93; P = 2.14 × 10-5), cholesteryl esters in large HDL (L-HDL-CE) (OR: 0.88; 95% CI: 0.83-0.94; P = 5.93 × 10-5), free cholesterol in very large HDL (XL-HDL-FC) (OR: 0.88; 95% CI: 0.82-0.94; P = 1.10 × 10-4) and free cholesterol in large HDL (L-HDL-FC) (OR: 0.89; 95% CI: 0.84-0.95; P = 1.45 × 10-4). Phe-MR analysis showed that targeting L-HDL-CE had beneficial effects on the risk of 24 diseases from seven disease chapters. Based on this systematic MR analysis, L-HDL-C, L-HDL-CE, XL-HDL-FC, and L-HDL-FC were inversely associated with the risk of pre-eclampsia. Interestingly, L-HDL-CE may be a promising drug target for preventing pre-eclampsia with no predicted detrimental side effects. The study consists of a two-stage design that conducts MR at both stages. First, we assessed the causality for the associations between 194 blood metabolites and the risk of pre-eclampsia. Second, we investigated a broad spectrum of side effects associated with the targeting identified metabolites in 693 non-preeclampsia diseases. Our results suggested that Cholesteryl esters in large HDL may serve as a promising drug target for the prevention or treatment of pre-eclampsia with no predicted detrimental side effects.

Blood metabolites mediate the impact of lifestyle factors on the risk of urolithiasis: a multivariate, mediation Mendelian randomization study

Urolithiasis is closely linked to lifestyle factors. However, the causal relationship and underlying mechanisms remain unclear. This study aims to investigate the relationship between lifestyle factors and the onset of urolithiasis and explore potential blood metabolite mediators and their role in mediating this relationship. In this study, we selected single nucleotide polymorphisms (SNPs) as instrumental variables if they exhibited significant associations with our exposures in genome-wide association studies (GWAS) (p < 5.0 × 10-8). Summary data for urolithiasis came from the FinnGen database, including 8597 cases and 333,128 controls. We employed multiple MR analysis methods to assess causal links between genetically predicted lifestyle factors and urolithiasis, as well as the mediating role of blood metabolites. A series of sensitivity and pleiotropy analyses were also conducted. Our results show that cigarettes smoked per day (odds ratio [OR] = 1.159, 95% confidence interval [CI] = 1.004-1.338, p = 0.044) and alcohol intake frequency (OR = 1.286, 95% CI = 1.056-1.565, p = 0.012) were positively associated with increased risk of urolithiasis, while tea intake (OR = 0.473, 95% CI = 0.299-0.784, p = 0.001) was positively associated with reduced risk of urolithiasis. Mediation analysis identifies blood metabolites capable of mediating the causal relationship between cigarettes smoked per day, tea intake and urolithiasis. We have come to the conclusion that blood metabolites serve as potential causal mediators of urolithiasis, underscoring the importance of early lifestyle interventions and metabolite monitoring in the prevention of urolithiasis.

Metabolomic profiling reveals key metabolites associated with hypertension progression

Introduction

Pre-hypertension is a prevalent condition among the adult population worldwide. It is characterized by asymptomatic elevations in blood pressure beyond normal levels but not yet reaching the threshold for hypertension. If left uncontrolled, pre-hypertension can progress to hypertension, thereby increasing the risk of serious complications such as heart disease, stroke, kidney damage, and others.

Objective

The precise mechanisms driving the progression of hypertension remain unknown. Thus, identifying the metabolic changes associated with this condition can provide valuable insights into potential markers or pathways implicated in the development of hypertension.

Methods

In this study, we utilized untargeted metabolomics profiling, which examines over 1,000 metabolites to identify novel metabolites contributing to the progression from pre-hypertension to hypertension. Data were collected from 323 participants through Qatar Biobank.

Results

By comparing metabolic profiles between pre-hypertensive, hypertensive and normotensive individuals, six metabolites including stearidonate, hexadecadienoate, N6-carbamoylthreonyladenosine, 9 and 13-S-hydroxyoctadecadienoic acid (HODE), 2,3-dihydroxy-5-methylthio- 4-pentenoate (DMTPA), and linolenate were found to be associated with increased risk of hypertension, in both discovery and validation cohorts. Moreover, these metabolites showed a significant diagnostic performance with area under curve >0.7.

Conclusion

These findings suggest possible biomarkers that can predict the risk of progression from pre-hypertension to hypertension. This will aid in early detection, diagnosis, and management of this disease as well as its associated complications.

Association between human blood metabolome and the risk of delirium: a Mendelian Randomization study

Background

Delirium significantly contributes to both mortality and morbidity among hospitalized older adults. Furthermore, delirium leads to escalated healthcare expenditures, extended hospital stays, and enduring cognitive deterioration, all of which are acknowledged detrimental outcomes. Nonetheless, the current strategies for predicting and managing delirium remain constrained. Our aim was to employ Mendelian randomization (MR) to investigate the potential causal relationship between metabolites and delirium, as well as to identify potential therapeutic targets.

Methods

We identified 129 distinct blood metabolites from three genome-wide association studies (GWASs) conducted on the metabolome, involving a total of 147,827 participants of European descent. Genetic information pertaining to delirium was sourced from the ninth iteration of the Finngen Biobank, encompassing 359,699 individuals of Finnish ancestry. We conducted MR analyses to evaluate the connections between blood metabolites and delirium. Additionally, we extended our analysis to encompass the entire phenome using MR, aiming to uncover potential on-target consequences resulting from metabolite interventions.

Results

In our investigation, we discovered three metabolites serving as causal mediators in the context of delirium: clinical low density lipoprotein cholesterol (LDL-C) (odds ratio [OR]: 1.47, 95% confidence interval [CI]: 1.25-1.73, p = 3.92 x 10-6), sphingomyelin (OR: 1.47, 95% CI: 1.25-1.74, p = 5.97 x 10-6), and X-11593-O-methylascorbate (OR: 0.21, 95% CI: 0.10-0.43, p = 1.86 x 10-5). Furthermore, utilizing phenome-wide MR analysis, we discerned that clinical LDL-C, sphingomyelin, and O-methylascorbate not only mediate delirium susceptibility but also impact the risk of diverse ailments.

Limitations

(1) Limited representation of the complete blood metabolome, (2) reliance on the PheCode system based on hospital diagnoses may underrepresent conditions with infrequent hospital admissions, and (3) limited to European ancestry.

Conclusion

The genetic prediction of heightened O-methylascorbate levels seems to correspond to a diminished risk of delirium, in contrast to the association of elevated clinical LDL-C and sphingomyelin levels with an amplified risk. A comprehensive analysis of side-effect profiles has been undertaken to facilitate the prioritization of drug targets. Notably, O-methylascorbate emerges as a potentially auspicious target for mitigating and treating delirium, offering the advantage of lacking predicted adverse side effects.

Comprehensive analysis of metabolic changes in spontaneously hypertensive rats

OBJECTIVES

Hypertension is a chronic disease with multiple causative factors that involve metabolic disturbances and can cause various complications. However, the metabolic characteristics of hypertension at different stages are still unclear. This study aimed to explore the metabolic changes induced by hypertension at different ages.

METHODS

Spontaneously hypertensive rats (SHR) and Wistar Kyoto (WKY) rats were divided into four groups according to age: 5-week-old SHR (n = 6), 5-week-old WKY rats (n = 6), 32-week-old SHR (n = 6), and 32-week-old WKY rats (n = 6). Metabolites were analyzed in primary tissues (serum, heart, lung, kidney, brain, and brown adipose) using a non-targeted metabolomics approach.

RESULTS

Thirty-five metabolites and nine related metabolic pathways were identified in 5-week-old SHR, mainly related to the metabolism of amino acids. Fifty-one metabolites and seven related metabolic pathways were identified in the 32-week-old SHR, involving glycolysis, lipid, and amino acid metabolisms.

CONCLUSION

This experiment elucidates the metabolic profile of SHR at different ages and provides a basis for predicting and diagnosing hypertension. It also provides a reference for the pathogenesis of hypertension.

The Crosstalk between Gut Microbiota and Nervous System: A Bidirectional Interaction between Microorganisms and Metabolome

Several studies have shown that the gut microbiota influences behavior and, in turn, changes in the immune system associated with symptoms of depression or anxiety disorder may be mirrored by corresponding changes in the gut microbiota. Although the composition/function of the intestinal microbiota appears to affect the central nervous system (CNS) activities through multiple mechanisms, accurate epidemiological evidence that clearly explains the connection between the CNS pathology and the intestinal dysbiosis is not yet available. The enteric nervous system (ENS) is a separate branch of the autonomic nervous system (ANS) and the largest part of the peripheral nervous system (PNS). It is composed of a vast and complex network of neurons which communicate via several neuromodulators and neurotransmitters, like those found in the CNS. Interestingly, despite its tight connections to both the PNS and ANS, the ENS is also capable of some independent activities. This concept, together with the suggested role played by intestinal microorganisms and the metabolome in the onset and progression of CNS neurological (neurodegenerative, autoimmune) and psychopathological (depression, anxiety disorders, autism) diseases, explains the large number of investigations exploring the functional role and the physiopathological implications of the gut microbiota/brain axis.

Lipidome, central carbon metabolites, and sleep rhythm in coronary heart disease with nontraditional risks: An exploratory pilot study

Aims

Altered lipid, energy metabolism and sleep disorders had been linked with coronary heart disease (CHD), however, the metabolic signatures and sleep rhythm in non-obstructive coronary atherosclerosis-CHD remain unclear. This pilot study aims to investigate the lipidome and central carbon metabolites profiles and associated sleep characteristics among CHD patients without traditional risk factors.

Methods

From January to July 2021, 15 CHD patients and 15 healthy controls were randomly selected from the cardiology unit of Zhongshan Hospital, Shanghai. A total of 464 lipids and 45 central carbon metabolites (CCM) were quantified in blood plasma. Metabolic signatures were selected through orthogonal projections to latent structures discriminant analysis (OPLS-DA) and principal component analysis (PCA) was conducted to link the profiles of identified metabolites with CHD risk, sleep patterns, cardiometabolic traits and cardiac electrophysiologic parameters.

Results

Using OPLS-DA, we identified 40 metabolites (variable influence on projection >1) that were altered in CHD patients, with 38 lipids, including 25 triacylglycerols (TAGs), 8 diacylglycerols (DAGs), being elevated and two CCM metabolites (i.e., succinic acid and glycolic acid) being reduced. Using PCA, four principal components (PCs) were identified and associated with increased risk of CHD. Specifically, one standard unit increasement in the PC that was characterized by high levels of DAG (18:1) and low succinic acid and the PC that was characterized by high levels of two sphingomyelins [SM (26:0) and SM (24:0)] was associated with 21% [odds ratio (OR) = 1.21, 95% CI: 1.02,1.43] and 14% (OR = 1.14,1.02,1.29) increased risk of CHD, respectively. Further regression analyses confirmed that the identified metabolites and the four PCs were positively associated with TG and ALT. Interestingly, glycolic acid was negatively associated with sleep quality and PSQI. Participants with night sleep mode tended to have a high level of the identified lipids, especially FFA (20:4).

Conclusion

In the present pilot study, our findings provide clues on alterations of lipid and energy metabolism in CHD patients without traditional risk factors, with multiple triacylglycerols and diacylglycerols metabolites seemingly elevated and certain nonlipids metabolites (e.g., succinic acid and glycolic acid) decreased in cases. Considering the limit sample size, further studies are warranted to confirm our results.

Association between human blood metabolome and the risk of breast cancer

BACKGROUND

Breast cancer is the most common cancer among women with limited treatment options. To identify promising drug targets for breast cancer, we conducted a systematical Mendelian randomization (MR) study to screen blood metabolome for potential causal mediators of breast cancer and further predict target-mediated side effects.

METHODS

We selected 112 unique blood metabolites from 3 large-scale European ancestry-based genome-wide association studies (GWASs) with a total of 147,827 participants. Breast cancer data were obtained from a GWAS in the Breast Cancer Association Consortium (BCAC), involving 122,977 cases and 105,974 controls of European ancestry. We conducted MR analyses to systematically assess the associations of blood metabolites with breast cancer, and a phenome-wide MR analysis was further applied to ascertain the potential on-target side effects of metabolite interventions.

RESULTS

Two blood metabolites were identified as the potential causal mediators for breast cancer, including high-density lipoprotein cholesterol (HDL-C) (odds ratio [OR], 1.09; 95% confidence interval [CI], 1.06-1.12; P = 9.67 × 10^-10) and acetate (OR, 1.24; 95% CI, 1.13-1.37; P = 1.35 × 10^-5). In the phenome-wide MR analysis, lowering HDL-C might have deleterious effects on the risk of the circulatory system and foreign body injury, while lowering acetate had deleterious effects on mental disorders disease.

CONCLUSIONS

The present systematic MR analysis revealed that HDL-C and acetate may be the causal mediators in the risk of developing breast cancer. Side-effect profiles were characterized to help inform drug target prioritization for breast cancer prevention. HDL-C and acetate might be promising drug targets for preventing breast cancer, but they should be applied under weighting advantages and disadvantages.

Association Between Human Blood Metabolome and the Risk of Alzheimer's Disease

OBJECTIVE

Alzheimer's disease (AD) is the most common degenerative neurological disorder with limited therapeutic options. Therefore, it is particularly important to explore the potential biomarkers implicated in the occurrence and progression of AD prior to clinical testing.

METHODS

We selected 119 unique blood metabolites from 3 metabolome genome-wide association studies (GWASs) with 147,827 European participants. Summary data about AD were obtained from a GWAS meta-analysis with 63,926 European individuals from the International Genomics of Alzheimer's Project. MR analyses were performed to assess the associations of blood metabolites with AD, and a phenome-wide MR analysis was further applied to ascertain the potential on-target side effects of metabolite interventions.

RESULTS

Four metabolites were identified as causal mediators for AD, including epiandrosterone sulfate (odds ratio [OR] per SD increase: 0.60; 95% confidence interval [CI]: 0.51-0.71; P=6.14×10^-9 ), 5alpha-androstan-3beta-17beta-diol disulfate (OR per SD increase: 0.69; 95% CI: 0.57-0.84; P=1.98×10^-4 ), sphingomyelin (OR per SD increase: 2.53; 95% CI: 1.78-3.59; P=2.10×10^-7 ), and glutamine (OR per SD increase: 0.83; 95% CI: 0.77-0.89; P=2.09×10^-6 ). Phenome-wide MR analysis showed that epiandrosterone sulfate, 5alpha-androstan-3beta-17beta-diol disulfate and sphingomyelin mediated the risk of multiple diseases, and glutamine had beneficial effects on the risk of 4 diseases.

INTERPRETATION

Genetically predicted increased epiandrosterone sulfate, 5alpha-androstan-3beta-17beta-diol disulfate and glutamine might be associated with a decreased risk of AD, while sphingomyelin was associated with an increased risk. Side-effect profiles were characterized to help inform drug target prioritization, and glutamine might be a promising target for the prevention and treatment of AD with no predicted detrimental side effects. This article is protected by copyright. All rights reserved.

NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches

NMR measurements combined with chemometrics allow achieving a great amount of information for the identification of potential biomarkers responsible for a precise metabolic pathway. These kinds of data are useful in different fields, ranging from food to biomedical fields, including health science. The investigation of the whole set of metabolites in a sample, representing its fingerprint in the considered condition, is known as metabolomics and may take advantage of different statistical tools. The new frontier is to adopt self-learning techniques to enhance clustering or classification actions that can improve the predictive power over large amounts of data. Although machine learning is already employed in metabolomics, deep learning and artificial neural networks approaches were only recently successfully applied. In this work, we give an overview of the statistical approaches underlying the wide range of opportunities that machine learning and neural networks allow to perform with accurate metabolites assignment and quantification.Various actual challenges are discussed, such as proper metabolomics, deep learning architectures and model accuracy.

Clinical Parameters and Metabolomic Biomarkers That Predict Inhospital Outcomes in Patients With ST-Segment Elevated Myocardial Infarctions

Background

Postoperative risk stratification is challenging in patients with ST-segment elevation myocardial infarction (STEMI) who undergo percutaneous coronary intervention. This study aimed to characterize the metabolic fingerprints of patients with STEMI with different inhospital outcomes in the early stage of morbidity and to integrate the clinical baseline characteristics to develop a prognostic prediction model.

Methods

Plasma samples were collected retrospectively from two propensity score-matched STEMI cohorts from May 6, 2020 to April 20, 2021. Cohort 1 consisted of 48 survivors and 48 non-survivors. Cohort 2 included 48 patients with unstable angina pectoris, 48 patients with STEMI, and 48 age- and sex-matched healthy controls. Metabolic profiling was generated based on ultra-performance liquid chromatography and a mass spectrometry platform. The comprehensive metabolomic data analysis was performed using MetaboAnalyst version 5.0. The hub metabolite biomarkers integrated into the model were tested using multivariate linear support vector machine (SVM) algorithms and a generalized estimating equation (GEE) model. Their predictive capabilities were evaluated using areas under the curve (AUCs) of receiver operating characteristic curves.

Results

Metabonomic analysis from the two cohorts showed that patients with STEMI with different outcomes had significantly different clusters. Seven differentially expressed metabolites were identified as potential candidates for predicting inhospital outcomes based on the two cohorts, and their joint discriminative capabilities were robust using SVM (AUC = 0.998, 95% CI 0.983–1) and the univariate GEE model (AUC = 0.981, 95% CI 0.969–0.994). After integrating another six clinical variants, the predictive performance of the updated model improved further (AUC = 0.99, 95% CI 0.981–0.998).

Conclusion

A survival prediction model integrating seven metabolites from non-targeted metabonomics and six clinical indicators may generate a powerful early survival prediction model for patients with STEMI. The validation of internal and external cohorts is required.

Metabolomic Fingerprinting for the Detection of Early-Stage Lung Cancer: From the Genome to the Metabolome

The five-year survival rate of lung cancer patients is very low, mainly because most newly diagnosed patients present with locally advanced or metastatic disease. Therefore, early diagnosis is key to the successful treatment and management of lung cancer. Unfortunately, early detection methods of lung cancer are not ideal. In this brief review, we described early detection methods such as chest X-rays followed by bronchoscopy, sputum analysis followed by cytological analysis, and low-dose computed tomography (LDCT). In addition, we discussed the potential of metabolomic fingerprinting, compared to that of other biomarkers, including molecular targets, as a low-cost, high-throughput blood-based test that is both feasible and affordable for early-stage lung cancer screening of at-risk populations. Accordingly, we proposed a paradigm shift to metabolomics as an alternative to molecular and proteomic-based markers in lung cancer screening, which will enable blood-based routine testing and be accessible to those patients at the highest risk for lung cancer.