Characterization and molecular detection of atherothrombosis by magnetic resonance--potential tools for individual risk assessment and diagnostics

Quantitative serum nuclear magnetic resonance metabolomics in cardiovascular epidemiology and genetics

Metabolomics is becoming common in epidemiology due to recent developments in quantitative profiling technologies and appealing results from their applications for understanding health and disease. Our team has developed an automated high-throughput serum NMR metabolomics platform that provides quantitative molecular data on 14 lipoprotein subclasses, their lipid concentrations and composition, apolipoprotein A-I and B, multiple cholesterol and triglyceride measures, albumin, various fatty acids as well as on numerous low-molecular-weight metabolites, including amino acids, glycolysis related measures and ketone bodies. The molar concentrations of these measures are obtained from a single serum sample with costs comparable to standard lipid measurements. We have analyzed almost 250 000 samples from around 100 epidemiological cohorts and biobanks and the new international set-up of multiple platforms will allow an annual throughput of more than 250 000 samples. The molecular data have been used to study type 1 and type 2 diabetes etiology as well as to characterize the molecular reflections of the metabolic syndrome, long-term physical activity, diet and lipoprotein metabolism. The results have revealed new biomarkers for early atherosclerosis, type 2 diabetes, diabetic nephropathy, cardiovascular disease and all-cause mortality. We have also combined genomics and metabolomics in diverse studies. We envision that quantitative high-throughput NMR metabolomics will be incorporated as a routine in large biobanks; this would make perfect sense both from the biological research and cost point of view - the standard output of over 200 molecular measures would vastly extend the relevance of the sample collections and make many separate clinical chemistry assays redundant.

Nanoparticles as magnetic resonance imaging contrast agents for vascular and cardiac diseases

Advances in nanoparticle contrast agents for molecular imaging have made magnetic resonance imaging a promising modality for noninvasive visualization and assessment of vascular and cardiac disease processes. This review provides a description of the various nanoparticles exploited for imaging cardiovascular targets. Nanoparticle probes detecting inflammation, apoptosis, extracellular matrix, and angiogenesis may provide tools for assessing the risk of progressive vascular dysfunction and heart failure. The utility of nanoparticles as multimodal probes and/or theranostic agents has also been investigated. Although clinical application of these nanoparticles is largely unexplored, the potential for enhancing disease diagnosis and treatment is considerable.

Integration of metabolomics and transcriptomics data to aid biomarker discovery in type 2 diabetes

Type 2 diabetes (T2D), one of the most common diseases in the western world, is characterized by insulin resistance and impaired beta-cell function but currently it is difficult to determine the precise pathophysiology in individual T2D patients. Non-targeted metabolomics technologies have the potential for providing novel biomarkers of disease and drug efficacy, and are increasingly being incorporated into biomarker exploration studies. Contextualization of metabolomics results is enhanced by integration of study data from other platforms, such as transcriptomics, thus linking known metabolites and genes to relevant biochemical pathways. In the current study, urinary NMR-based metabolomic and liver, adipose, and muscle transcriptomic results from the db/db diabetic mouse model are described. To assist with cross-platform integration, integrative pathway analysis was used. Sixty-six metabolites were identified in urine that discriminate between the diabetic db/db and control db/+ mice. The combined analysis of metabolite and gene expression changes revealed 24 distinct pathways that were altered in the diabetic model. Several of these pathways are related to expected diabetes-related changes including changes in lipid metabolism, gluconeogenesis, mitochondrial dysfunction and oxidative stress, as well as protein and amino acid metabolism. Novel findings were also observed, particularly related to the metabolism of branched chain amino acids (BCAAs), nicotinamide metabolites, and pantothenic acid. In particular, the observed decrease in urinary BCAA catabolites provides direct corroboration of previous reports that have inferred that elevated BCAAs in diabetic patients are caused, in part, by reduced catabolism. In summary, the integration of metabolomics and transcriptomics data via integrative pathway mapping has facilitated the identification and contextualization of biomarkers that, presuming further analytical and biological validation, may be useful in future T2D clinical studies by identifying patient populations that share common disease pathophysiology and therefore may identify those patients that may respond better to a particular class of anti-diabetic drugs.

Anti-angiogenic perfluorocarbon nanoparticles for diagnosis and treatment of atherosclerosis

Complementary developments in nanotechnology, genomics, proteomics, molecular biology and imaging offer the potential for early, accurate diagnosis. Molecularly-targeted diagnostic imaging agents will allow noninvasive phenotypic characterization of pathologies and, therefore, tailored treatment close to the onset. For atherosclerosis, this includes anti-angiogenic therapy with specifically-targeted drug delivery systems to arrest the development of plaques before they impinge upon the lumen. Additionally, monitoring the application and effects of this targeted therapy in a serial fashion will be important. This review covers the specific application of alpha(nu)beta(3)-targeted anti-angiogenic perfluorocarbon nanoparticles in (1) the detection of molecular markers for atherosclerosis, (2) the immediate verification of drug delivery with image-based prediction of therapy outcomes, and (3) the serial, noninvasive observation of therapeutic efficacy.

High-throughput serum NMR metabonomics for cost-effective holistic studies on systemic metabolism

A high-throughput proton (1H) nuclear magnetic resonance (NMR) metabonomics approach is introduced to characterise systemic metabolic phenotypes. The methodology combines two molecular windows that contain the majority of the metabolic information available by 1H NMR from native serum, e.g. serum lipids, lipoprotein subclasses as well as various low-molecular-weight metabolites. The experimentation is robotics-controlled and fully automated with a capacity of about 150-180 samples in 24 h. To the best of our knowledge, the presented set-up is unique in the sense of experimental high-throughput, cost-effectiveness, and automated multi-metabolic data analyses. As an example, we demonstrate that the NMR data as such reveal associations between systemic metabolic phenotypes and the metabolic syndrome (n = 4407). The high-throughput of up to 50,000 serum samples per year is also paving the way for this technology in large-scale clinical and epidemiological studies. In contradiction to single 'biomarkers', the application of this holistic NMR approach and the integrated computational methods provides a data-driven systems biology approach to biomedical research.

Non-invasive assessment of vulnerable plaque

BACKGROUND

Sudden cardiac death or unheralded acute coronary syndromes are common initial manifestations of coronary atherosclerosis and most such events occur at sites of non-flow limiting coronary atherosclerosis.

OBJECTIVE

Non-invasive detection of high-risk plaques might provide a means to improve risk stratification in primary and secondary prevention settings.

METHODS

This review is focused on the potential of multidetector computed tomography coronary angiography (MDCT-CA) to provide the opportunity to identify different aspects of plaque vulnerability throughout the coronary tree in an accurate, fast, safe and non-invasive manner.

CONCLUSION

Coronary artery calcium scoring, on top of established risk stratification, could potentially be a cost-effective strategy for primary prevention. MDCT-CA allows a non-invasive evaluation of several features commonly seen in vulnerable plaques and has demonstrated an independent prognostic value on a patient basis. The value of the technique itself might result, potentially, in a better estimation of the relative risk of an invidual plaque to rupture.

Metabolic phenotypes, vascular complications, and premature deaths in a population of 4,197 patients with type 1 diabetes

OBJECTIVE

Poor glycemic control, elevated triglycerides, and albuminuria are associated with vascular complications in diabetes. However, few studies have investigated combined associations between metabolic markers, diabetic kidney disease, retinopathy, hypertension, obesity, and mortality. Here, the goal was to reveal previously undetected association patterns between clinical diagnoses and biochemistry in the FinnDiane dataset.

RESEARCH DESIGN AND METHODS

At baseline, clinical records, serum, and 24-h urine samples of 2,173 men and 2,024 women with type 1 diabetes were collected. The data were analyzed by the self-organizing map, which is an unsupervised pattern recognition algorithm that produces a two-dimensional layout of the patients based on their multivariate biochemical profiles. At follow-up, the results were compared against all-cause mortality during 6.5 years (295 deaths).

RESULTS

The highest mortality was associated with advanced kidney disease. Other risk factors included 1) a profile of insulin resistance, abdominal obesity, high cholesterol, triglycerides, and low HDL(2) cholesterol, and 2) high adiponectin and high LDL cholesterol for older patients. The highest population-adjusted risk of death was 10.1-fold (95% CI 7.3-13.1) for men and 10.7-fold (7.9-13.7) for women. Nonsignificant risk was observed for a profile with good glycemic control and high HDL(2) cholesterol and for a low cholesterol profile with a short diabetes duration.

CONCLUSIONS

The self-organizing map analysis enabled detailed risk estimates, described the associations between known risk factors and complications, and uncovered statistical patterns difficult to detect by classical methods. The results also suggest that diabetes per se, without an adverse metabolic phenotype, does not contribute to increased mortality.

Critical evaluation of 1H NMR metabonomics of serum as a methodology for disease risk assessment and diagnostics

This review focuses on (i) the current status of 1H NMR spectroscopy to quantify lipoprotein subclasses directly from serum or plasma, and (ii) the applications of 1H NMR metabonomics of serum in biomedicine. Related to both themes, experimental and data analysis methodologies are discussed together with the biochemical rationales. Particular emphasis is placed on the concepts of risk assessment and diagnostics in relation to the potential clinical role of 1H NMR metabonomics; recent applications in the area of coronary heart disease and diabetes are addressed in more detail.

1H NMR metabonomics approach to the disease continuum of diabetic complications and premature death

Subtle metabolic changes precede and accompany chronic vascular complications, which are the primary causes of premature death in diabetes. To obtain a multimetabolite characterization of these high-risk individuals, we measured proton nuclear magnetic resonance (1H NMR) data from the serum of 613 patients with type I diabetes and a diverse spread of complications. We developed a new metabonomics framework to visualize and interpret the data and to link the metabolic profiles to the underlying diagnostic and biochemical variables. Our results indicate complex interactions between diabetic kidney disease, insulin resistance and the metabolic syndrome. We illustrate how a single 1H NMR protocol is able to identify the polydiagnostic metabolite manifold of type I diabetes and how its alterations translate to clinical phenotypes, clustering of micro- and macrovascular complications, and mortality during several years of follow-up. This work demonstrates the diffuse nature of complex vascular diseases and the limitations of single diagnostic biomarkers. However, it also promises cost-effective solutions through high-throughput analytics and advanced computational methods, as applied here in a case that is representative of the real clinical situation.

1H NMR metabonomics of plasma lipoprotein subclasses: elucidation of metabolic clustering by self-organising maps

(1)H NMR spectra of plasma are known to provide specific information on lipoprotein subclasses in the form of complex overlapping resonances. A combination of (1)H NMR and self-organising map (SOM) analysis was applied to investigate if automated characterisation of subclass-related metabolic interactions can be achieved. To reliably assess the intrinsic capability of (1)H NMR for resolving lipoprotein subclass profiles, sum spectra representing the pure lipoprotein subclass part of actual plasma were simulated with the aid of experimentally derived model signals for 11 distinct lipoprotein subclasses. Two biochemically characteristic categories of spectra, representing normolipidaemic and metabolic syndrome status, were generated with corresponding lipoprotein subclass profiles. A set of spectra representing a metabolic pathway between the two categories was also generated. The SOM analysis, based solely on the aliphatic resonances of these simulated spectra, clearly revealed the lipoprotein subclass profiles and their changes. Comparable SOM analysis in a group of 69 experimental (1)H NMR spectra of serum samples, which according to biochemical analyses represented a wide range of lipoprotein lipid concentrations, corroborated the findings based on the simulated data. Interestingly, the choline-N(CH(3))(3) region seems to provide more resolved clustering of lipoprotein subclasses in the SOM analyses than the methyl-CH(3) region commonly used for subclass quantification. The results illustrate the inherent suitability of (1)H NMR metabonomics for automated studies of lipoprotein subclass-related metabolism and demonstrate the power of SOM analysis in an extensive and representative case of (1)H NMR metabonomics.

A novel Bayesian approach to quantify clinical variables and to determine their spectroscopic counterparts in 1H NMR metabonomic data

BACKGROUND

A key challenge in metabonomics is to uncover quantitative associations between multidimensional spectroscopic data and biochemical measures used for disease risk assessment and diagnostics. Here we focus on clinically relevant estimation of lipoprotein lipids by 1H NMR spectroscopy of serum.

RESULTS

A Bayesian methodology, with a biochemical motivation, is presented for a real 1H NMR metabonomics data set of 75 serum samples. Lipoprotein lipid concentrations were independently obtained for these samples via ultracentrifugation and specific biochemical assays. The Bayesian models were constructed by Markov chain Monte Carlo (MCMC) and they showed remarkably good quantitative performance, the predictive R-values being 0.985 for the very low density lipoprotein triglycerides (VLDL-TG), 0.787 for the intermediate, 0.943 for the low, and 0.933 for the high density lipoprotein cholesterol (IDL-C, LDL-C and HDL-C, respectively). The modelling produced a kernel-based reformulation of the data, the parameters of which coincided with the well-known biochemical characteristics of the 1H NMR spectra; particularly for VLDL-TG and HDL-C the Bayesian methodology was able to clearly identify the most characteristic resonances within the heavily overlapping information in the spectra. For IDL-C and LDL-C the resulting model kernels were more complex than those for VLDL-TG and HDL-C, probably reflecting the severe overlap of the IDL and LDL resonances in the 1H NMR spectra.

CONCLUSION

The systematic use of Bayesian MCMC analysis is computationally demanding. Nevertheless, the combination of high-quality quantification and the biochemical rationale of the resulting models is expected to be useful in the field of metabonomics.

Endothelial cells and macrophages, partners in atherosclerotic plaque progression

Heart disease and stroke, the main cardiovascular diseases (CVD), have become global epidemics in our days. High levels of cholesterol and other abnormal lipids are among the main risk factors of atherosclerosis, the number one killer in the world. However, recent advances in CVD treatment together with improvements in surgical techniques have increased the quality of life and reduced premature death rates and disabilities. Nevertheless, they still add a heavy burden to the rising global costs of health care. The medical priorities highlight not only the need for early recognition of the warning signs of a heart attack, but also the need for early biomarkers for prevention. Two active partners in the development and progression of atherosclerotic plaques are the macrophages and endothelial cells that influence each other and modify the microenvironment composition of the plaque leading to either rapid progression or regression of individual lesions in patients. In this review we address two specific aspects related to atherosclerosis: i) the way in which folic acid and folic acid conjugates may be helpful to identify activated macrophages and ii) the high potential of proteomic analysis to evidence and identify the multiple changes induced in activated vascular cells.