A lineshape fitting model for 1H NMR spectra of human blood plasma

Biomarker Discovery in Atherosclerotic Diseases Using Quantitative Nuclear Magnetic Resonance Metabolomics

Despite great progress in the management of atherosclerosis (AS), its subsequent cardiovascular disease (CVD) remains the leading cause of morbidity and mortality. This is probably due to insufficient risk detection using routine lipid testing; thus, there is a need for more effective approaches relying on new biomarkers. Quantitative nuclear magnetic resonance (qNMR) metabolomics is able to phenotype holistic metabolic changes, with a unique advantage in regard to quantifying lipid-protein complexes. The rapidly increasing literature has indicated that qNMR-based lipoprotein particle number, particle size, lipid components, and some molecular metabolites can provide deeper insight into atherogenic diseases and could serve as novel promising determinants. Therefore, this article aims to offer an updated review of the qNMR biomarkers of AS and CVD found in epidemiological studies, with a special emphasis on lipoprotein-related parameters. As more researches are performed, we can envision more qNMR metabolite biomarkers being successfully translated into daily clinical practice to enhance the prevention, detection and intervention of atherosclerotic diseases.

1H NMR spectroscopy quantifies visibility of lipoproteins, subclasses, and lipids at varied temperatures and pressures

NMR-based quantification of human lipoprotein (sub)classes is a powerful high-throughput method for medical diagnostics. We evaluated select proton NMR signals of serum lipoproteins for elucidating the physicochemical features and the absolute NMR visibility of their lipids. We separated human lipoproteins of different subclasses by ultracentrifugation and analyzed them by 1H NMR spectroscopy at different temperatures (283-323 K) and pressures (0.1-200 MPa). In parallel, we determined the total lipid content by extraction with chloroform/methanol. The visibility of different lipids in the 1H NMR spectra strongly depends on temperature and pressure: it increases with increasing temperatures but decreases with increasing pressures. Even at 313 K, only part of the lipoprotein is detected quantitatively. In LDL and in HDL subclasses HDL2 and HDL3, only 39%, 62%, and 90% of the total cholesterol and only 73%, 70%, and 87% of the FAs are detected, respectively. The choline head groups show visibilities of 43%, 75%, and 87% for LDL, HDL2, and HDL3, respectively. The description of the NMR visibility of lipid signals requires a minimum model of three different compartments, A, B, and C. The thermodynamic analysis of compartment B leads to melting temperatures between 282 K and 308 K and to enthalpy differences that vary for the different lipoproteins as well as for the reporter groups selected. In summary, we describe differences in NMR visibility of lipoproteins and variations in biophysical responses of functional groups that are crucial for the accuracy of absolute NMR quantification.

A field-invariant method for quantitative analysis with benchtop NMR

Recently developed benchtop instruments have the potential of bringing the benefits of NMR spectroscopy to the wide variety of industrial applications. Unfortunately, their low spectral resolution poses significant challenges for traditional quantification approach. Here we present a novel model-based method designed to overcome these challenges. By defining our models in terms of quantum mechanical properties of the underlying spin system, we make our approach invariant to the spectrometer field strength and especially suitable for analyzing benchtop data. Our experimental results on prepared samples and natural fruit juices confirm the applicability of our method for quantitative analysis of medium-field ¹H NMR spectra. The developed method succeeds in accurately separating the spectra of glucose anomers and even monitoring their interconversion in non-deuterated water. Furthermore, the compositions of unbuffered natural fruit juices estimated using data from 43 MHz to 400 MHz spectrometers are in good agreement with each other and with the reference values from nutrition databases.

An experimental validation of a Bayesian model for quantification in NMR spectroscopy

The traditional peak integration method for quantitative analysis in nuclear magnetic resonance (NMR) spectroscopy is inherently limited by its ability to resolve overlapping peaks and is susceptible to noise. The alternative model-based approaches not only extend quantification capabilities to these challenging examples but also provide a means for automation of the entire process of NMR data analysis. In this paper, we present a general model for an NMR signal that, in a principled way, takes into account the effects of chemical shifts, relaxation, lineshape imperfections, phasing, and baseline distortions. We test the model using both simulations and experiments, concentrating on simple spectra with well-resolved peaks where we expect conventional analysis to be effective. Our results of quantifying mixture compositions compare favorably with the established methods. At high SNR (>40dB), all approaches usually achieve for these test systems an absolute accuracy of at least 0.01mol/mol for the concentrations of all species. Our model-based approach is successful even for SNR<20dB; it achieves 0.05-0.1mol/mol accuracy in cases where precise phasing is practically impossible due to high levels of noise in the data.

Clearing the undergrowth: detection and quantification of low level impurities using 19F NMR

A new method for the analysis of low level impurities in sparsely fluorinated species allows measurement of clean high dynamic range ¹⁹F spectra, fully decoupled and free of interfering signals from ¹³C isotopomers.

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.

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¹H NMR metabolomics is being increasingly applied to large cohort studies.

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Studies have identified potentially novel lipoprotein and metabolite predictors for CVD.

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Potential exists for the use of metabolomics in cardiovascular clinical practice.

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Current findings are too preliminary to translate into clinical recommendations.

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Further large scale studies are now needed to advance the field in a robust manner.

A novel approach for baseline correction in 1H-MRS signals based on ensemble empirical mode decomposition

Proton magnetic resonance spectroscopy ((1)H-MRS) is a non-invasive diagnostic tool for measuring biochemical changes in the human body. Acquired (1)H-MRS signals may be corrupted due to a wideband baseline signal generated by macromolecules. Recently, several methods have been developed for the correction of such baseline signals, however most of them are not able to estimate baseline in complex overlapped signal. In this study, a novel automatic baseline correction method is proposed for (1)H-MRS spectra based on ensemble empirical mode decomposition (EEMD). This investigation was applied on both the simulated data and the in-vivo (1)H-MRS of human brain signals. Results justify the efficiency of the proposed method to remove the baseline from (1)H-MRS signals.

Line-shape analysis of J-resolved NMR spectra: application to metabolomics and quantification of intensity errors from signal processing and high signal congestion

NMR spectroscopy remains one of the primary analytical approaches in metabolomics. Although 1D (1)H NMR spectroscopy is versatile, highly reproducible and currently the most widely used technique in NMR metabolomics, analysis of complex biological samples typically yields highly congested spectra with severely overlapping signals making unambiguous metabolite identification and quantification almost impossible. Consequently there is a growing use of 2D NMR methods, in particular (1)H J-resolved (JRES) spectroscopy, which spreads the high signal density into a second dimension. One potentially powerful method to deconvolute these JRES spectra, facilitating metabolite quantification, is via line-shape fitting. However, the mathematical functions describing the JRES NMR line-shape, in particular after applying apodisation functions and JRES specific processing, including tilting and symmetrisation, remain uncharacterised. Furthermore, possible quantitation errors arising from processing JRES spectra have not been evaluated, nor have the potentially adverse quantitative effects of overlapping dispersive tails of closely spaced signals in the 2D spectrum. Here we address these issues and evaluate the suitability of the JRES experiment for accurate complex mixture analysis. Specifically, we have examined changes in NMR line-shape and signal intensity after application of different apodisation functions (SINE and SEM) and JRES specific processing (tilting and symmetrising), comparing simulated and experimental data. We also report a significant quantitation error of up to 33%, dependent upon apodisation, due to overlap of the dispersive tails of closely spaced resonances. Finally, we have validated the use of these mathematical line-shape functions for metabolite quantitation of 2D JRES spectra, by comparison to corresponding 1D NMR datasets, using both gravimetrically-prepared chemically defined mixtures as well as biological tissue extracts.

A new FIR filter technique for solvent suppression in MRS signals

A large water resonance is usually present in MRS signals. Time-domain quantitation methods require a good suppression of these components in order to obtain accurate parameter estimates. In this paper, we analyze one of the most successful methods for solvent suppression, the maximum-phase finite impulse response filter (MP-FIR), and identify its drawbacks. A new filtering method is proposed to overcome the limitation of MP-FIR.

MRS signal quantitation: a review of time- and frequency-domain methods

In this paper an overview of time-domain and frequency-domain quantitation methods is given. Advantages and drawbacks of these two families of quantitation methods are discussed. An overview of preprocessing methods, such as lineshape correction methods or unwanted component removal methods, is also given. The choice of the quantitation method depends on the data under investigation and the pursued objectives.

Human metabolic phenotypes link directly to specific dietary preferences in healthy individuals

Individual human health is determined by a complex interplay between genes, environment, diet, lifestyle, and symbiotic gut microbial activity. Here, we demonstrate a new "nutrimetabonomic" approach in which spectroscopically generated metabolic phenotypes are correlated with behavioral/psychological dietary preference, namely, "chocolate desiring" or "chocolate indifferent". Urinary and plasma metabolic phenotypes are characterized by differential metabolic biomarkers, measured using 1H NMR spectroscopy, including the postprandial lipoprotein profile and gut microbial co-metabolism. These data suggest that specific dietary preferences can influence basal metabolic state and gut microbiome activity that in turn may have long-term health consequences to the host. Nutrimetabonomics appears as a promising approach for the classification of dietary responses in populations and personalized nutritional management.

High resolution proton magnetic resonance spectroscopy of human cervical mucus

High resolution 1H NMR spectroscopy is a powerful method for qualitative and quantitative analysis of the highly viscous human cervical mucus (CM). Up to 23 compounds could be identified in this study and can be observed in the 1H NMR spectra of native mucus without the need of any complicated preparative chemistry. Storage conditions could be excluded as a possible reason for variations observed between different samples. pH values decreased after freezing and storing at 253 K. For NMR studies, lyophilization proved to be most useful, allowing the determination of the water content, replacement of H2O by D2O, and most importantly, absolute quantification of low molecular mass compounds. In a small collective of women the concentrations of some of the small constituents of the CM are strongly correlated; an example is the mutual positive correlation of taurine, citrate and creatinine. In conclusion, high resolution 1H NMR spectroscopy is a valid method to investigate mucus composition and to determine absolute concentrations of low molecular mass compounds in CM.

MR spectroscopy quantitation: a review of frequency domain methods

There has been a vast increase in applications of magnetic resonance spectroscopy (MRS) in biomedical research during the last few years. This is not surprising since MRS provides both in vivo and in vitro a non-invasive tool for various biochemical and biomedical studies. There are also expectations that clinical MRS will have an important role as a diagnostic tool. An essential prerequisite for the future success of MRS for applicability in biomedical sciences will be accurate and biochemically relevant data analysis (at as high a level of automation as possible). This review briefly describes principles of the methodology available for advanced quantitative data analysis in the frequency domain. Various biomedical applications are discussed in order to illustrate the practical aspects of the analyses and to show the applicability and power of biochemical prior knowledge-based lineshape fitting analysis.

MR spectroscopy quantitation: a review of time-domain methods

In this article an overview of time-domain quantitation methods is given. Advantages of processing the data in the measurement domain are discussed. The basic underlying principles of the methods are outlined and from them the situations under which these algorithms perform well are derived. Also an overview of methods to preprocess the data is given. In that respect, signal-to-noise and/or resolution enhancement, the removal of unwanted components and corrections for model imperfections are discussed.

Application of directly coupled HPLC NMR to separation and characterization of lipoproteins from human serum

Disorders in lipoprotein metabolism are critical in the etiology of several disease states such as coronary heart disease and atherosclerosis. Thus, there is considerable interest in the development of novel methods for the analysis of lipoprotein complexes. We report here a simple chromatographic method for the separation of high-density lipoprotein, low-density lipoprotein, and very low-density lipoprotein from intact serum or plasma. The separation was achieved using a hydroxyapatite column and elution with pH 7.4 phosphate buffer with 100-microL injections of whole plasma. Coelution of HDL with plasma proteins such as albumin occurred, and this clearly limits quantitation of that species by HPLC peak integration. We also show, for the first time, the application of directly coupled HPLC 1H NMR spectroscopy to confirm the identification of the three major lipoproteins. The full chromatographic run time was 90 min with stopped-flow 600-MHz NMR spectra of each lipoprotein being collected using 128 scans, in 7 min. The 1H NMR chemical shifts of lipid signals were identical to conventional NMR spectra of freshly prepared lipoprotein standards, confirming that the lipoproteins were not degraded by the HPLC separation and that their gross supramolecular organization was intact.

Choice of spectroscopic lineshape model affects metabolite peak areas and area ratios

The use of Lorentzian model lineshapes leads to systematic errors in the quantification of in vivo (1)H NMR spectra. Experimental lineshapes are better modeled by the Voigt (mixed Lorentzian-Gaussian) function, leading to more accurate fits (reduced chi(2)). In this work, results from a group of 41 subjects are presented. It is shown that not only are the estimated metabolite peak areas affected by the choice of lineshape model, but so too are the metabolite ratios. For example, the NAA/choline ratio was 1.92 +/- 0.06 (mean +/- standard error) using the Lorentzian lineshape model and 1.85 +/- 0.05 using the Voigt lineshape model. The corresponding figures for NAA/creatine were 2.32 +/- 0.06 and 2. 10 +/- 0.05 respectively, which are significantly different for the two lineshape models. An explanation of this previously unreported effect is given. This finding clearly has serious implications for the methodology and reporting of spectroscopic studies.

Frequency-selective quantification of biomedical magnetic resonance spectroscopy data

In this paper the possibility of obtaining accurate estimates of parameters of selected peaks in the presence of unknown or uninteresting spectral features in biomedical magnetic resonance spectroscopy (MRS) signals is investigated. This problem is denoted by frequency-selective parameter estimation. A new time-domain technique based on maximum-phase finite impulse response (FIR) filters is presented. The proposed method is compared to a number of existing approaches: the application of a weighting function in the time domain, frequency domain fitting using a polynomial baseline, and the time-domain HSVD filter method. The ease of use and low computational complexity of the FIR filter method make it an attractive approach for frequency-selective parameter estimation. The methods are validated using simulations of relevant (13)C and (31)P MRS examples.

A fast variant of (1)H spectroscopic U-FLARE imaging using adjusted chemical shift phase encoding

So far, fast spectroscopic imaging (SI) using the U-FLARE sequence has provided metabolic maps indirectly via Fourier transformation (FT) along the chemical shift (CS) dimension and subsequent peak integration. However, a large number of CS encoding steps N(omega) is needed to cover the spectral bandwidth and to achieve sufficient spectral resolution for peak integration even if the number of resonance lines is small compared to N(omega) and even if only metabolic images are of interest and not the spectra in each voxel. Other reconstruction algorithms require extensive prior knowledge, starting values, and/or model functions. An adjusted CS phase encoding scheme (APE) can be used to overcome these drawbacks. It incorporates prior knowledge only about the resonance frequencies present in the sample. Thus, N(omega) can be reduced by a factor of 4 for many (1)H in vivo studies while no spectra have to be reconstructed, and no additional user interaction, prior knowledge, starting values, or model function are required. Phantom measurements and in vivo experiments on rat brain have been performed at 4.7 T to test the feasibility of the method for proton SI.

Incorporation of metabolite prior knowledge for data analysis: biochemical implications of dynamic 31P NMR ex vivo pig liver studies

A semi-automated, metabolite prior-knowledge-based, lineshape fitting analysis has been developed to assess the dynamic biochemical changes found in ex vivo 31P NMR pig liver preservation studies. Due to the inherent experimental limitations of the ex vivo study and the complexity of the composite phosphorus resonances, metabolite information obtained in vitro was incorporated into the ex vivo analysis. This approach has allowed complete metabolite analysis (phosphomonoesters, inorganic phosphate, phosphodiesters and nucleotide triphosphates) in over 2000 spectra in a fraction of the time compared with more conventional analysis methods. The developed analysis will enable complete and rapid assessment of the biochemical changes in ongoing cold preservation studies of the pig liver which will result in thousands of ex vivo 31P NMR spectra. It is also envisaged that comparative studies on human donor livers will be carried out, in which this type of analysis would be the method of choice. Moreover, this kind of analysis approach could be advantageous in many complex in vivo NMR spectroscopy applications.

Quantification of plasma lipoprotein fractions by wavelet transform time-domain data processing of the proton nuclear magnetic resonance methylene spectral region

Quantitative analysis of lipoprotein major fractions, LDL, VLDL and HDL, is of great interest for medical purposes, for instance in liver or heart diseases, diet management or cancer. The presently available biochemical methods require time consuming ultracentrifugation. A potentially automated method is proposed, using time domain quantification by Wavelet Transform (WT-NMR) method. The aim of the present study was to evaluate, on a preliminary series of nine human plasmas, the potential interest of WT-NMR in the quantification of both NMR-visible lipids and total lipoprotein fractions. The correlation coefficients between low and intermediate density (LDL+IDL), very low density (VLDL) and high density (HDL) lipoprotein visible lipid quantifications, obtained on nine human plasmas with WT-NMR and standard biochemical methods, were 0.79, 0.84 and 0.92, respectively. For the total lipoprotein assay, i.e. including an estimation of non NMR-visible protein and free cholesterol, the correlation between WT-NMR and the biochemistry were 0.87 for LDL+IDL, 0.81 for VLDL and 0.88 for HDL.

Remodeling of HDL by phospholipid transfer protein: demonstration of particle fusion by 1H NMR spectroscopy

There is evidence that phospholipid transfer protein (PLTP) can increase reverse cholesterol transport by inducing favorable subclass distribution in the high density lipoprotein (HDL) fraction. This includes generation of initial cholesterol acceptor particles, pre beta-HDL, and of enlarged particles that are rapidly cleared from the circulation. However, partly because of methodological difficulties, the mechanisms behind the PLTP-mediated interconversion of HDL particles are not fully understood. In this communication, we describe the use of a novel methodology, based on 1H NMR spectroscopy, to study the PLTP-induced size changes in the HDL particles. In accordance with native gradient gel electrophoresis, the 1H NMR data revealed a gradual production of enlarged HDL particles in the HDL3+ PLTP mixtures. In addition, according to a physical model for lipoprotein particles, relating the frequency shifts observable with NMR to the size of the lipoprotein particles, the NMR data demonstrated that PLTP-mediated HDL remodeling involves fusion of the HDL particles.

Assessment of cholesteryl ester transfer protein function in lipoprotein mixtures by 1H NMR spectroscopy

Studies of cholesteryl ester transfer protein (CETP) function in lipoprotein mixtures pose many difficulties by conventional biochemical methods. For instance, studies on the effects of CETP on the composition of apolipoprotein B containing lipoproteins (very low and low density lipoproteins) in lipoprotein mixtures are tedious due to repeated ultracentrifugational isolations and have thus rarely been performed. Here we present a new 1H NMR spectroscopy technique to assess the CETP function in lipoprotein mixtures. This technique does not require repeated physical isolations of the lipoprotein particles but uses mathematical separation of the fractions on the basis of biochemical prior knowledge based lineshape fitting analysis of specific lipid resonances in the 1H NMR spectra. The lipoproteins are separated according to their size related chemical shift which allows for distinct quantification between very low and low density lipoproteins, the two major apolipoprotein B containing fractions. The methodological basis of the technique is discussed here together with a demonstration that this kind of approach allows dynamic follow up of the lipid transfer reactions in complex lipoprotein and CETP mixtures. The results revealed a consistent behaviour which corroborated the recent findings suggesting that the neutral lipid mass transfer among lipoproteins is not an equimolar heteroexchange.

A computational strategy for the deconvolution of NMR spectra with multiplet structures and constraints: analysis of overlapping 13C-2H multiplets of 13C enriched metabolites from cell suspensions incubated in deuterated media

A computational strategy for the deconvolution of complex spectra involving scalar multiplet patterns is presented. This approach fits spectra that can be composed of single resonances as well as scalar coupling multiplets for which resonance frequencies, intensities, and lineshape parameters can be optimized. For multiplets, the coupling constant also is optimized. Any external information about the optimizable parameters can be taken into account as external constraints. A lineshape described by absorptive and dispersive Lorentzian and Gaussian contributions and the baseline with up to 40 Fourier and polynomial terms can likewise be optimized. The effectiveness of the procedure is assessed on the basis of computer simulated deconvolutions of a composite of 1J(13C-2H) multiplets arising from a mixture of all possible 13C-2H isotopomers of deuterated L-[3-13C]lactate generated from cell preparations incubated with D-[1-13C]glucose in D2O, which was analyzed previously with a manual deconvolution procedure (R. Willem, M. Biesemans, F. Kayser, W. J. Malaisse, Magn, Reson. Med. 31, 259-267 (1994)). The use of constraints is shown to lead to an improvement in the results. The fitting strategies and the importance of the baseline as an origin of bias are discussed.

Quantification of metabolites from single-voxel in vivo 1H NMR data of normal human brain by means of time-domain data analysis

We present here a combination of time-domain signal analysis procedures for quantification of human brain in vivo 1H NMR spectroscopy (MRS) data. The method is based on a separate removal of a residual water resonance followed by a frequency-selective time-domain line-shape fitting analysis of metabolite signals. Calculation of absolute metabolite concentrations was based on the internal water concentration as a reference. The estimated average metabolite concentrations acquired from six regions of normal human brain with a single-voxel spin-echo technique for the N-acetylaspartate, creatine, and choline-containing compounds were 11.4 +/- 1.0, 6.5 +/- 0.5, and 1.7 +/- 0.2 mumol kg-1 wet weight, respectively. The time-domain analyses of in vivo 1H MRS data from different brain regions with their specific characteristics demonstrate a case in which the use of frequency-domain methods pose serious difficulties.

Quantification of biomedical NMR data using artificial neural network analysis: lipoprotein lipid profiles from 1H NMR data of human plasma

Artificial neural network (ANN) analysis is a new technique in NMR spectroscopy. It is very often considered only as an efficient "black-box' tool for data classification, but we emphasize here that ANN analysis is also powerful for data quantification. The possibility of finding out the biochemical rationale controlling the ANN outputs is presented and discussed. Furthermore, the characteristics of ANN analysis, as applied to plasma lipoprotein lipid quantification, are compared to those of sophisticated lineshape fitting (LF) analysis. The performance of LF in this particular application is shown to be less satisfactory when compared to neural networks. The lipoprotein lipid quantification represents a regular clinical need and serves as a good example of an NMR spectroscopic case of extreme signal overlap. The ANN analysis enables quantification of lipids in very low, intermediate, low and high density lipoprotein (VLDL, IDL, LDL and HDL, respectively) fractions directly from a 1H NMR spectrum of a plasma sample in < 1 h. The ANN extension presented is believed to increase the value of the 1H NMR based lipoprotein quantification to the point that it could be the method of choice in some advanced research settings. Furthermore, the excellent quantification performance of the ANN analysis, demonstrated in this study, serves as an indication of the broad potential of neural networks in biomedical NMR.

In vivo 31P MRS: absolute concentrations, signal-to-noise and prior knowledge

Absolute metabolite concentrations have been estimated for nucleoside triphosphate and P(i) from in vivo 31P MR measurements using ISIS localization in a rat tumour model, and the results have been compared to those obtained from acid extracts of the tumours. The aim of the experiment was to assess the performance of four different spectral analysis techniques used for absolute quantitation. The spectral analysis techniques used were two frequency domain methods (peak area integration and Lorentzian fitting--FITSPEC) and two time domain methods (VARPRO and HLSVD). The spectra were acquired in blocks so that the degradation in performance of the four spectral analysis methods with decreasing signal-to-noise ratio (SNR) could be compared and referenced. This and the inclusion of a sophisticated method incorporating prior knowledge yields a more realistic and comprehensive protocol than previously published comparisons. The results suggest that VARPRO is the method of choice for quantitative analysis of tumour 31P MR spectra, giving the most reliable results at low SNR.

Time and frequency domain analysis of NMR data compared: an application to 1D 1H spectra of lipoproteins

A comparison between a time domain analysis algorithm (VARPRO) and a frequency domain analysis algorithm (FITPLAC) for parameter estimation of magnetic resonance spectroscopy (MRS) data series is presented. VARPRO analyses the measured MRS signal (free induction decay; FID); FITPLAC analyses the discrete Fourier transform of the FID, the frequency domain magnetic resonance spectrum. A rapid time domain method, used to subtract the dominating water resonance from a 1H MRS FID, without affecting the metabolites of interest, is outlined and applied. Also a new "pseudofrequency selective" approach to time domain fitting is introduced. The possibilities of combining the most favorable features of time and frequency domain processing into one single MRS signal processing method are assessed. The 1H MRS signals of ultracentrifuged very low (VLDL), intermediate (IDL), and high (HDL) density lipoprotein fractions from human blood plasma were used for the comparisons. The results from both algorithms were in good agreement.

Nuclear magnetic resonance spectroscopy of plasma to distinguish between malignant and benign diseases causing jaundice and cholestasis

The sera of 51 patients with malignant (n = 25) and benign (n = 26) hepatopancreatobiliary disorders were analysed by 1H magnetic resonance spectroscopy (NMR) in order to distinguish between malignant and benign diseases causing jaundice and/or cholestasis. Macromolecular linewidths were determined both manually and automatically with a computed analysis, and both methylene (CH2) and methyl (CH3) resonances were evaluated. The mean linewidth of the CH3 peak was significantly narrower in the patients with malignant disease than in the patients with benign disease both in the manual and computed analyses, but no significant differences in the CH2 peak were detected. Diagnostic sensitivity and specificity of the CH3 peak determined in the computed analysis were 92% and 27% respectively. In the light of the current study, it seems obvious that because overlap between benign and malignant groups was too great, 1H NMR spectroscopy of plasma is not of practical value in distinguishing between benign and malignant causes of jaundice and/or cholestasis.

A comparative study of 1H NMR lineshape fitting analyses and biochemical lipid analyses of the lipoprotein fractions VLDL, LDL and HDL, and total human blood plasma

The purpose of this work was two-fold. In the first instance, 1H NMR spectra of the ultracentrifuged lipoprotein fractions (VLDL, LDL and HDL) from six volunteers with different clinical conditions were measured. The methylene regions of the experimental spectra were modelled in the frequency domain using non-linear lineshape fitting analyses. In this way the resolvable Lorentzian component structures of the methylene regions of these lipoprotein fraction spectra could be determined. Second, the lipoprotein fraction analyses were used to construct simplified component structures, which interpreted the lipoprotein fraction spectra well, and were feasible to use in the total plasma spectra analyses. The considerable overlap problem of the resonances was properly handled in this way. The NMR-based relative amounts of the lipoproteins (relative integrated intensities of the lipoprotein model signals) obtained were compared to the biochemically resolved relative molar percentages of the lipoprotein fractions and also of the lipid contents between the lipoprotein complexes. It was noticed that nearly all correlations were extremely good. Thus, it is suggested that the developed methodology could be used as a fast method to predict the relative amounts of the lipoproteins and also possibly the relative lipid contents between the major lipoprotein categories directly from the proton NMR spectrum of a total blood plasma sample. Furthermore, if internal or external reference for the integrated intensities of the proton NMR resonances were used, it should also be possible to obtain the absolute amounts of these quantities.

High resolution NMR spectroscopy of physiological fluids: from metabolism to physiology

High resolution NMR spectroscopy of physiological fluids provides quantitative, qualitative and dynamic information on the metabolic status of the interstitial and plasma compartments under a variety of pathophysiological conditions. The simultaneous detection and quantitation by NMR spectroscopy of numerous compounds of the intermediary metabolism offers a new insight in the understanding of the milieu intérieur. NMR spectroscopy of physiological fluids offers a unique way to define and monitor the global metabolic homeostasis in humans. The development of this analytical approach is still limited by the scarcity of pluridisciplinary teams able to fully exploit the wealth of information present on the NMR spectrum of a fluid. While application in pharmacology and toxicology is already established, the main areas of current development are cancer, hereditary metabolic disorders, organ transplantation and neurological diseases.

Proton nuclear magnetic resonance lineshape studies on human blood plasma lipids from newborn infants, healthy adults, and adults with tumors

The usefulness of proton NMR spectroscopy of human blood plasma for cancer research has been extensively studied in recent years. Two main starting points have been offered by Fossel et al. (N. Engl. J. Med. 315, 1369 (1986)) and Mountford et al. (FEBS Lett. 203, 164 (1986)). In this work the experimental proton NMR spectra of blood plasma were analyzed with the aid of the multivariate lineshape fitting method. An appropriate model structure, in terms of the various lipoprotein (VLDL, LDL, and HDL) signals, for the methylene region was used. Neonates, healthy adults, and adults with nonmalignant and malignant tumors were studied. The linewidth of the methylene region was found to be linearly dependent on the relative concentrations of the lipoproteins. The correlation coefficient was -0.89 (P less than 0.001) for VLDL and 0.88 (P less than 0.001) for HDL. A correlation between VLDL concentration and age, 0.76 (P less than 0.001), was also established. VLDL was modeled using two components. The half-linewidth of the lower field component was slightly elevated for the adults with large metastases. This might be in association with the fucose-containing proteolipid complex detected earlier in cancer cells or in sera of cancer patients. Some signals of this complex may fall in the same region of the spectra. The spectra for the neonates were indicated to be totally different from the adults. This and other related questions were explained by means of the model parameters and the relative concentrations of the lipoproteins VLDL, LDL, and HDL. The presented technique can be used as a rapid research tool for figuring out the relative concentrations of the lipoproteins in blood plasma and explaining the reasons behind the changes in the spectra.