Investigation of metabonomics technique by analyze of NMR data, which method is better? mean center or auto scale?

The factors such as disease can disrupt homeostasis, resulting in perturbations of endogenous biochemicals that are involved in key metabolic profiles. Metabonomics is useful technique to quantitative description of endogenous metabolites present in a biological sample such as urine, plasma and tissue. High resolution 1H nuclear magnetic resonance [NMR]-based metabonomics is a technique used to analyze and interpret multivariate metabolic data that correlate with changes of physiological conditions. Before any explanation for metabolite data, preprocessing the spectroscopic data is essential. In this paper, we show scaling effects in metabonomics investigation of patients diagnosed with Crohn's and Celiac disease. two techniques of scaling were applied as follows: mean centering and auto scaling. Results reveal that the mean centering is more useful to segregate patients from healthy subjects in the data set of Crohn's and Celiac disease

concentration of serum zinc in celiac patients compared to healthy subjects in Tehran

This study evaluated serum levels of zinc in patient with CD compare to healthy subjects. Celiac disease [CD] is characterized by small intestinal malabsorption of nutrients as a consequence of ingestion of wheat gluten. Zinc is an essential trace element that it has vital biological functions. Sera of 30 celiac cases and 30 healthy normal cohorts as control group were obtained. Atomic absorption spectrophotometer was employed for estimating serum zinc level. Zinc concentrations in patients diagnosed with CD were significantly lower than healthy subjects [75.97 +/- 12 compared with 92.83 +/- 18, P-value < 0.0001]. The result of this study shows that serum zinc concentration is decreased in celiac patients compare to healthy controls. Serum zinc may thus be a marker of CD in adults presenting with gastrointestinal symptoms

NMR based metabonomics study on celiac disease in the blood serum

The aim of this study is to look for the proper methods that would be a major step towards untreated CD diagnosis and seek the metabolic biomarkers causes of CD and compare them to control group. Celiac disease [CD] is a common autoimmune disorder that is not easily diagnosed using the clinical tests. Thirty cases and 30 controls were entered into this study. Metabolic profiling was obtained using proton nuclear magnetic resonance spectroscopy [HNMR] to seek metabolites that are helpful for the detection of CD. Classification of CD and healthy subject was done using random forest [RF]. The obtained classification model showed an 89% correct classification of CD and healthy subject for the external test set. The metabolites that caused changes in people with CD were identified using RF; these metabolites include lactate, valine and lipid. The findings of the present study reveal serum lactate, valin and lipid levels in CD patient are lower than healthy cohorts. This metabolite may provide diagnostic tools as well as insight into potential targets for disease therapy