Proton NMR analysis of plasma is a weak predictor of coronary artery disease

Abstract

Multivariate analysis of 1H-NMR spectra of blood sera was reported previously to predict angiographically defined advanced coronary artery disease (CAD) with >90% accuracy and specificity. The analysis depended mainly on the major lipid regions of the spectra, but many variables, including gender and drug treatment, affect lipid composition and are potential confounders. We have determined the predictive power of the same methodology for angiographically defined CAD using plasma samples from groups of male patients, classified by statin treatment, who had normal coronary arteries (NCAs) or CAD. Predictions for NCA and CAD groups were only 80.3% correct for patients not treated with statins and 61.3% for treated patients, compared with random correct predictions of 50%. A confidence limit of >99% was achieved for 36.2% of predictions for untreated groups and 6.2% for treated groups. Detection of CAD by 1H-NMR with >99% confidence was therefore very weak compared with angiography.

1H NMR metabonomics can differentiate the early atherogenic effect of dairy products in hyperlipidemic hamsters

“…Although it is a recent 15 introduction to the field of nutrition research, nutritional metabonomics has already delivered 16 interesting insights into the understanding of metabolic responses of humans or animals in 17 response to dietary interventions, as well as for the definition of metabolic phenotypes [10][11][12][13][14] . 18 Nevertheless, it has not yet been applied to the domain of diet-induced atherosclerosis as a 19 descriptive, nontargeted approach for evaluating the effects of dietary treatment on metabolic 20 phenotypes, which are also called metabotypes 15 . These metabotypes can be related to health 21 outcomes and provide predictive disease or risk assessment biomarkers 16 , 17 , although strong 22 clinical validations are required 17,18 .…”

“…18 Nevertheless, it has not yet been applied to the domain of diet-induced atherosclerosis as a 19 descriptive, nontargeted approach for evaluating the effects of dietary treatment on metabolic 20 phenotypes, which are also called metabotypes 15 . These metabotypes can be related to health 21 outcomes and provide predictive disease or risk assessment biomarkers 16 , 17 , although strong 22 clinical validations are required 17,18 . 23 Thus, we addressed whether different types of dairy products could affect the outcome of 24 atherogenesis, using both conventional blood chemistry and a metabolome-wide approach.…”

Metabolomics in Cardiovascular Disease: Towards Clinical Application

“…Unfortunately, the authors did not take into account the effects of medical treatments (particularly statins) and patient gender in their analysis and was later demonstrated that those two factors have a strong influence in the NMR pattern. As a result, the specificity and sensitivity first predicted of above 90% was reduced, once corrected by gender and statin use to approximately 60% [Kirschenlohr et al, 2006]. Also, age has also been described as a differential factor regarding lipoprotein subclasses measured by NMR spectroscopy [Freedman 2004].…”

“…Although this is one of the first areas were metabolomics were applied, in the case of cardiovascular diseases so far it has only been reported in animal studies (see above). Still, it has been show that drugs do influence the metabolic pattern in human studies of plasma [Kirschenlohr et al, 2006] and that altering metabolism could be a therapeutic target [Lee et al, 2005] thus paharmacogenomics application may not be far away in the area of cardiovascular disease.…”

Systems biology, metabolic modelling and metabolomics in drug discovery and development

“…Such diagnostic biomarkers can of course be surrogates [67], as well as being genuinely diagnostic. However, studies that seek novel biomarkers are not without their difficulties, and bias is an everpresent danger [68,69]. In addition, because we measure multiple metabolites, their statistical analysis involves multiple hypothesis testing and there is a profound danger of false discoveries, especially when sample sizes are low [70][71][72].…”