The emergence of proton nuclear magnetic resonance metabolomics in the cardiovascular arena as viewed from a clinical perspective

The ability to phenotype metabolic profiles in serum has increased substantially in recent years with the advent of metabolomics. Metabolomics is the study of the metabolome, defined as those molecules with an atomic mass less than 1.5 kDa. There are two main metabolomics methods: mass spectrometry (MS) and proton nuclear magnetic resonance (1H NMR) spectroscopy, each with its respective benefits and limitations. MS has greater sensitivity and so can detect many more metabolites. However, its cost (especially when heavy labelled internal standards are required for absolute quantitation) and quality control is sub-optimal for large cohorts. 1H NMR is less sensitive but sample preparation is generally faster and analysis times shorter, resulting in markedly lower analysis costs. 1H NMR is robust, reproducible and can provide absolute quantitation of many metabolites. Of particular relevance to cardio-metabolic disease is the ability of 1H NMR to provide detailed quantitative data on amino acids, fatty acids and other metabolites as well as lipoprotein subparticle concentrations and size. Early epidemiological studies suggest promise, however, this is an emerging field and more data is required before we can determine the clinical utility of these measures to improve disease prediction and treatment. This review describes the theoretical basis of 1H NMR; compares MS and 1H NMR and provides a tabular overview of recent 1H NMR-based research findings in the atherosclerosis field, describing the design and scope of studies conducted to date. 1H NMR metabolomics-CVD related research is emerging, however further large, robustly conducted prospective, genetic and intervention studies are needed to advance research on CVD risk prediction and to identify causal pathways amenable to intervention

Utility of mass spectrometry for the diagnosis of the unstable coronary plaque.

Mass spectrometry is a powerful technique that is used to identify unknown compounds, to quantify known materials, and to elucidate the structure and chemical properties of molecules. Recent advances in the accuracy and speed of the technology have allowed data acquisition for the global analysis of lipids from complex samples such as blood plasma or serum. Here, mass spectrometry as a tool is described, its limitations explained and its application to biomarker discovery in coronary artery disease is considered. In particular an application of mass spectrometry for the discovery of lipid biomarkers that may indicate plaque morphology that could lead to myocardial infarction is elucidated

Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine

Despite the tremendous success, pitfalls have been observed in every step of a clinical metabolomics workflow, which impedes the internal validity of the study. Furthermore, the demand for logistics, instrumentations, and computational resources for metabolic phenotyping studies has far exceeded our expectations. In this conceptual review, we will cover inclusive barriers of a metabolomics-based clinical study and suggest potential solutions in the hope of enhancing study robustness, usability, and transferability. The importance of quality assurance and quality control procedures is discussed, followed by a practical rule containing five phases, including two additional "pre-pre-" and "post-post-" analytical steps. Besides, we will elucidate the potential involvement of machine learning and demonstrate that the need for automated data mining algorithms to improve the quality of future research is undeniable. Consequently, we propose a comprehensive metabolomics framework, along with an appropriate checklist refined from current guidelines and our previously published assessment, in the attempt to accurately translate achievements in metabolomics into clinical and epidemiological research. Furthermore, the integration of multifaceted multi-omics approaches with metabolomics as the pillar member is in urgent need. When combining with other social or nutritional factors, we can gather complete omics profiles for a particular disease. Our discussion reflects the current obstacles and potential solutions toward the progressing trend of utilizing metabolomics in clinical research to create the next-generation healthcare system.11Ysciescopu

Multiomics tools for improved atherosclerotic cardiovascular disease management

Multiomics studies offer accurate preventive and therapeutic strategies for atherosclerotic cardiovascular disease (ASCVD) beyond traditional risk factors. By using artificial intelligence (AI) and machine learning (ML) approaches, it is possible to integrate multiple ‘omics and clinical data sets into tools that can be utilized for the development of personalized diagnostic and therapeutic approaches. However, currently multiple challenges in data quality, integration, and privacy still need to be addressed. In this opinion, we emphasize that joined efforts, exemplified by the AtheroNET COST Action, have a pivotal role in overcoming the challenges to advance multiomics approaches in ASCVD research, with the aim to foster more precise and effective patient care

Serum proteomics to detect early changes in type 1 diabetes and carotid atherosclerosis

The detection of early markers is the key issue in predicting the outcome of inflammatory diseases such as type 1 diabetes and atherosclerosis. Whilst biochemical testing approaches have improved prediction of inflammatory diseases, validated biomarkers with better diagnostic specificities are still needed. Currently, majority of the disease-related proteomics studies have focused on their endpoints. The work presented in this thesis includes the first comprehensive proteomics analyses on serum samples collected from two unique Finnish longitudinal cohorts, namely The Diabetes Prediction and Prevention Project (DIPP) and The Cardiovascular Risk in Young Finns Study (YFS), to identify early markers associated with type 1 diabetes and carotid atherosclerosis. Using mass spectrometry (MS)-based quantitative serum proteomics, profiling was carried out to the study temporal variation in pre-diabetic samples and early markers of plaque formation with the T1D and YFS cohorts, respectively. The analyses revealed consistent differences in the abundance of a number of proteins in subjects having an ongoing asymptomatic changes, several of which are functionally relevant to the disease process. Taken together, the discovered markers are candidates for further validation studies in an independent cohorts and may be used to characterize an increased risk, progression and early onset of these diseases.Tyypin 1 diabeteksen ja ateroskleroosin kehittymiseen liittyvät varhaiset muutokset seerumiproteomissa Yksi keskeinen haaste tulehduksellisten sairauksien, kuten tyypin 1 diabeteksen ja ateroskleroosin, ennustamisessa on varhaisten tautimarkkerien löytäminen. Vaikka erilaiset biokemialliset testit ovat jo parantaneet tulehdusperäisten sairauksien ennustamista, uusia tarkempia biomarkkereita tarvitaan edelleen. Tästä huolimatta monissa näiden alojen proteomiikkatöissä on nykyisin keskitytty sairastumishetken tutkimiseen. Tämän väitöskirjatyön aikana olemme tehneet laajamittaiset proteomiikka-analyysit seeruminäytteille, jotka on kerätty osana kahta ainutlaatuista suomalaista seurantatutkimusta: DIPP-tutkimusta (tyypin 1 diabeteksen ennustaminen ja ennaltaehkäisy) ja YFS-tutkimusta (sydän- ja verisuonitautien riski nuorilla suomalaisilla). Näissä tutkimuksissa seerumiproteomiikkaa hyödynnettiin ensimmistä kertaa varhaisten tyypin 1 diabetes- ja ateroskleroosimarkkerien etsimiseen. Tutkimme tyypin 1 diabeteksen kehittymiseen ja ateroskleroottisten plakkien muodostumiseen liittyviä muutoksia seerumin proteomiprofiileissa massaspektrometriaan perustuvan kvantitatiivisen proteomiikan avulla. Nämä analyysit paljastivat johdonmukaisia eroja lukuisissa proteiineissa myöhemmin sairastuneiden oireettomien henkilöiden ja terveinä pysyneiden kontrollien välillä. Monet näistä proteiineista saattavat myös liittyä olennaisesti tautien kehittymiseen. Tutkimuksissamme löydetyt markkerit tarjoavat lähtökohdan tuleville validointitutkimuksille, ja niitä voitaisiin tulevaisuudessa käyttää yksilön kohonneen sairastumisriskin, taudin etenemisen sekä taudin varhaisen puhkeamisen kartoittamiseen

Potential Impact and Study Considerations of Metabolomics in Cardiovascular Health and Disease: A Scientific Statement From the American Heart Association.

Through the measure of thousands of small-molecule metabolites in diverse biological systems, metabolomics now offers the potential for new insights into the factors that contribute to complex human diseases such as cardiovascular disease. Targeted metabolomics methods have already identified new molecular markers and metabolomic signatures of cardiovascular disease risk (including branched-chain amino acids, select unsaturated lipid species, and trimethylamine-N-oxide), thus in effect linking diverse exposures such as those from dietary intake and the microbiota with cardiometabolic traits. As technologies for metabolomics continue to evolve, the depth and breadth of small-molecule metabolite profiling in complex systems continue to advance rapidly, along with prospects for ongoing discovery. Current challenges facing the field of metabolomics include scaling throughput and technical capacity for metabolomics approaches, bioinformatic and chemoinformatic tools for handling large-scale metabolomics data, methods for elucidating the biochemical structure and function of novel metabolites, and strategies for determining the true clinical relevance of metabolites observed in association with cardiovascular disease outcomes. Progress made in addressing these challenges will allow metabolomics the potential to substantially affect diagnostics and therapeutics in cardiovascular medicine

Linking quantitative radiology to molecular mechanism for improved vascular disease therapy selection and follow-up

Objective: Therapeutic advancements in atherosclerotic cardiovascular disease have improved the prevention of ischemic stroke and myocardial infarction. However, diagnostic methods for atherosclerotic plaque phenotyping to aid individualized therapy are lacking. In this thesis, we aimed to elucidate plaque biology through the analysis of computed-tomography angiography (CTA) with sufficient sensitivity and specificity to capture the differentiated drivers of the disease. We then aimed to use such data to calibrate a systems biology model of atherosclerosis with adequate granularity to be clinically relevant. Such development may be possible with computational modeling, but given, the multifactorial biology of atherosclerosis, modeling must be based on complete biological networks that capture protein-protein interactions estimated to drive disease progression. Approach and Results: We employed machine intelligence using CTA paired with a molecular assay to determine cohort-level associations and individual patient predictions. Examples of predicted transcripts included ion transporters, cytokine receptors, and a number of microRNAs. Pathway analyses elucidated enrichment of several biological processes relevant to atherosclerosis and plaque pathophysiology. The ability of the models to predict plaque gene expression from CTAs was demonstrated using sequestered patients with transcriptomes of corresponding lesions. We further performed a case study exploring the relationship between biomechanical quantities and plaque morphology, indicating the ability to determine stress and strain from tissue characteristics. Further, we used a uniquely constituted plaque proteomic dataset to create a comprehensive systems biology disease model, which was finally used to simulate responses to different drug categories in individual patients. Individual patient response was simulated for intensive lipid-lowering, anti-inflammatory drugs, anti-diabetic, and combination therapy. Plaque tissue was collected from 18 patients with 6735 proteins at two locations per patient. 113 pathways were identified and included in the systems biology model of endothelial cells, vascular smooth muscle cells, macrophages, lymphocytes, and the integrated intima, altogether spanning 4411 proteins, demonstrating a range of 39-96% plaque instability. Simulations of drug responses varied in patients with initially unstable lesions from high (20%, on combination therapy) to marginal improvement, whereas patients with initially stable plaques showed generally less improvement, but importantly, variation across patients. Conclusion: The results of this thesis show that atherosclerotic plaque phenotyping by multi-scale image analysis of conventional CTA can elucidate the molecular signatures that reflect atherosclerosis. We further showed that calibrated system biology models may be used to simulate drug response in terms of atherosclerotic plaque instability at the individual level, providing a potential strategy for improved personalized management of patients with cardiovascular disease. These results hold promise for optimized and personalized therapy in the prevention of myocardial infarction and ischemic stroke, which warrants further investigations in larger cohorts

Biomarker research in thromboembolic stroke

Introduction Stroke is a leading cause of death and disability worldwide. Approximately one quarter of all strokes are secondary to carotid atherosclerosis. There is a clinical need to improve risk stratification of carotid atherosclerosis, to better target surgical or interventional therapy and prevent stroke. This study aimed to determine diagnostic biomarkers of high-risk carotid atherosclerosis, and ensure the validity of such markers in the presence of alternative phenotypes of atherosclerotic disease. Methods 150 patients were recruited according to the following criteria: Group 1: Symptomatic >50% carotid stenosis Group 2: Non-carotid stroke/TIA Group 3: Asymptomatic >50% carotid stenosis Group 4: Asymptomatic controls with <50% carotid stenosis Group 5: Abdominal aortic aneurysm Group 6: Intermittent claudication Disease groups were matched for age, gender, cardiovascular risk factors, haematological parameters, renal function and lipid status. Blood and urine was collected from all patients and analysed through global metabolic profiling (1H-NMR Spectroscopy, HILIC-Mass Spectrometry and Lipid Profiling-Mass Spectrometry). Acquired spectra were compared across groups using computational multivariate data analysis to determine markers of high-risk carotid atherosclerosis. Results Statistical models derived from urinary spectra proved stronger than serum datasets, in particular with HILIC-Mass Spectrometry (positive ionisation mode). Application of computational OPLS DA resulted in discrimination of symptomatic carotid atherosclerosis from asymptomatic disease, aneurysmal disease, and intermittent claudication. Differentiating metabolites span a vast array of compounds including lipid derivatives, amino acid derivatives and nucleotide derivatives. Conclusion This is the first study to identify urinary metabolic biomarkers of high-risk carotid atherosclerosis, differentiating symptomatic carotid atherosclerosis from asymptomatic disease, and aneurysmal and peripheral arterial disease. Targeted temporal studies are now required for clinical validation and to determine the variation of acute biomarkers with time.Open Acces

Lipidomics: A Tool for Studies of Atherosclerosis

Lipids, abundant constituents of both the vascular plaque and lipoproteins, play a pivotal role in atherosclerosis. Mass spectrometry-based analysis of lipids, called lipidomics, presents a number of opportunities not only for understanding the cellular processes in health and disease but also in enabling personalized medicine. Lipidomics in its most advanced form is able to quantify hundreds of different molecular lipid species with various structural and functional roles. Unraveling this complexity will improve our understanding of diseases such as atherosclerosis at a level of detail not attainable with classical analytical methods. Improved patient selection, biomarkers for gauging treatment efficacy and safety, and translational models will be facilitated by the lipidomic deliverables. Importantly, lipid-based biomarkers and targets should lead the way as we progress toward more specialized therapeutics

Chronic Kidney Disease Cohort Studies: A Guide to Metabolome Analyses

Kidney diseases still pose one of the biggest challenges for global health, and their heterogeneity and often high comorbidity load seriously hinders the unraveling of their underlying pathomechanisms and the delivery of optimal patient care. Metabolomics, the quantitative study of small organic compounds, called metabolites, in a biological specimen, is gaining more and more importance in nephrology research. Conducting a metabolomics study in human kidney disease cohorts, however, requires thorough knowledge about the key workflow steps: study planning, sample collection, metabolomics data acquisition and preprocessing, statistical/bioinformatics data analysis, and results interpretation within a biomedical context. This review provides a guide for future metabolomics studies in human kidney disease cohorts. We will offer an overview of important a priori considerations for metabolomics cohort studies, available analytical as well as statistical/bioinformatics data analysis techniques, and subsequent interpretation of metabolic findings. We will further point out potential research questions for metabolomics studies in the context of kidney diseases and summarize the main results and data availability of important studies already conducted in this field