A New Data Processing Method for  Metabonomic and Its Application in a Study of  Diabetes

Abstract

Abstract:

Multivariate statistical methods are frequently used in nuclear magnetic resonance (NMR)-based metabonomic researches to analyze NMR spectra of biofluids. Based on the fact that the NMR spectrum of a given sample are a sum of the NMR signals from all constituting ingredients, we developed a nonnegative matrix factorization (NMF) method, capable of finding parts-based and linear representations of non-negative data, for analyzing the data acquired in NMR-based metabonomic studies. Detail comparisons were made between the NMF method and the commonly use principal component analysis (PCA) method by employing the two methods to discriminate the urine and serum spectra of type-2 diabetic patients from those of the healthy controls. It was shown that, compared to the PCA method, the NMF method is a more effective and accurate method for processing NMR spectra acquired in the metabonomic studies, partially due to its unique features such as the non-negative constraints and part-based representation. The disadvantages of the PCA method were also analyzed and discussed.

Key words: NMR, metabonomics, type 2 diabetes, non-negative matrix factorization, principle component analysis

CLC Number:

O591

DONG Ji-yang1, XU Le1, CAO Hong-ting1, DAI Xiao-xia2, LI Xue-jun3, YANG Shu-yu3, CHEN Zhong1*. A New Data Processing Method for Metabonomic and Its Application in a Study of Diabetes[J]. Chinese Journal of Magnetic Resonance, 2007, 24(4): 381-393.

References

[1] Nicholson J K, Lindon J C, Holmes E. Metabonomics: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data[J]. Xenobiotica, 1999, 29: 1 181-1 189.

[2] Lindon J, Holmes E, Nicholoson J K. Pattern recognition methods and applications in biomedical magnetic resonance[J]. Prog Nucl Mag Res Spe, 2001, 39: 1- 40.

[3] Lindon J C, Holmes E, Nicholoson J K, et al. Metabonomics technologies and their applications in physiological monitoring, drug safety assessment and disease diagnosis [J]. Biomarkers, 2004, 9: 1-31.

[4] Clayton T A, Lindon J C, Cloarec O, et al. Pharmaco-metabonomic phenotyping and personalized drug treatment[J]. Nature, 2006, 440: 1 073-1 077.

[5] Hector C K. Metabonomic modeling of drug toxicity[J]. Pharmacology & Therapeutics, 2006, 109: 92-106.

[6] Griffin J L, Nicholls A W. Metabolomics as a functional genomic tool for understanding lipid dysfunction in diabetes, obesity and related disorders[J]. Pharmacogenomics, 2006, 7(7): 1 095-1 107.

[7] Nicholson J K, Oflynn M P, Sadler P J. Proton-nuclear-magnetic-resonance studies of serum, plasma and urine from fasting normal and diabetic subjects[J]. Biochem J, 1984, 217: 365-375.

[8] Wang C, Kong H W, Guan Y F, et al. Plasma phospholipid metabolic profiling and biomarkers of type 2 diabetes mellitus based on high-performance liquid chromatography/electrospray mass spectrometry and multivariate statistical analysis[J]. Anal Chem, 2005, 77(13): 4 108-4 116.

[9] Yuan K L, Kong H W, Guan Y F, et al. A GC-based metabonomics investigation of type 2 diabetes by organic acids metabolic profile[J]. J Chromatogr B, 2007, 850(1-2): 236-240.

[10] kinen V P, Soininen P, Forsblom C, et al. Diagnosing diabetic nephropathy by ¹H NMR metabonomics of serum J]. Mag Res Mat Phy, 2006, 19: 281-296.

[11] Jin E S, Burgess S C, Merritt M, et al. Differing mechanisms of hepatic glucose overproduction in triiodothyroninetreated rats vs. Zucker diabetic fatty rats by NMR analysis of plasma glucose[J]. Am J Physiol Endocrinol Metab, 2005, 288: E654-E662.

[12] Xu L, Dong J Y, Dai X X, et al. Non-negative matrix factorization for diabetes II metabolic profiling analysis[C]. ICBBE2007, 2007, 1(2): 651-653.

[13] Wen J B, Xiao X, Dai X X, et al. Data normalization for diabetes II metabonomics analysis[C]. ICBBE2007, 2007, 2(2): 694-697.

[14] Wen J B(温锦波). NMR based metabonomics´s data preprocess methods and its application on diabetes mellitus study(基于NMR的代谢组学的数据预处理方法及其在糖尿病研究的应用)[D]. Xiamen(厦门)： Xiamen University(厦门大学), 2007.

[15] Xiao X(肖娴), Yang S Y(杨叔禹), Dong J Y(董继扬), et al. Optimization of NMR pulse sequences for metabonomics(基于NMR代谢组学的脉冲序列优化) [J]. J Fuzhou Univ(福州学报), 2007, 35(S): 37-40.

[16] Zuppi C, Messana I, Forni F, et al. ¹H NMR spectra of normal urines reference ranges of the major metabolites[J]. Clinica Chimica Acta, 1997, 265: 85-97.

[17] Lee D D, Seung H S. Learning the parts of objects by non-negative matrix factorization[J]. Nature, 1999, 401: 788-791.

[18] Rao N N, Shepherd S J. Clustering gene expression data for periodic genes based on INMF[J]. Comp Sci, 2006, 4 115: 412-423.

[19] Lee D D, Seung H S. Algorithms for non-negative matrix factorization[J]. Adv Neural Info Proc Syst, 2001,13: 556-562.

[20] Garvey W T, Kwon S, Zheng D, et al. Effects of insulin resistance and type 2 diabetes on lipoprotein subclass particle size and concentration determined by nuclear magnetic resonance[J]. Diabetes, 2003, 52: 453-462.

[21] Soedamah-Muthu S S, Colhoun H M, Thomason M J, et al. The effect of atorvastatin on serum lipids, lipoproteins and NMR spectroscopy defined lipoprotein subclasses in type 2 diabetic patients with ischaemic heart disease[J]. Atherosclerosis, 2003, 167: 243-255.

[22] Ding S Y, Xenia T, Hansen B. Nuclear magnetic resonance-determined lipoprotein abnormalities in nonhuman primates with the metabolic syndrome and type 2 diabetes mellitus [J]. Metabolism, 2007, 56(6): 838-846.

[23] Messana I, Forni F, Ferrari F, et al. Proton nuclear magnetic resonance spectral profiles of urine in type II diabetic patients[J]. Clin Chem, 1998, 44(7): 1 529-1 534.

[24] Ciurtin C , Nicolescu A, et al. Metabolic profiling of urine by H-1-NMR spectroscopy A critical assessment of interpreting metabolite concentrations for normal and diabetes groups[J]. Revista de Chimie, 2007, 58 (1): 51-55.

[25] Williams R E, Lenz E M, Evans J A, et al. A combined ¹H NMR and HPLC-MS-based metabonomic study of urine from obese (fa/fa) Zucker and normal Wistar-derived rats[J]. J Pharmaceut Biomed, 2005, 38: 465-471.

[26] Williams R E, Lenz E M, Evans J A, et al. The comparative metabonomics of age-related changes in the urinary composition of male Wistar-derived and Zucker (fa/fa) obese rats[J]. Mol Biosys, 2006, 2(3-4): 193-202.

[27] Hodavance M S, Ralston S L, Pelczer I, et al. Beyond blood sugar: the potential of NMR-based metabonomics for type 2 human diabetes, and the horse as a possible model [J]. Anal Bioanal Chem, 2007, 387: 533-537.

[28] Lauridsen M, Hansen S H, Jaroszewski J W, et al. Human urine as test material in ¹H NMR-based metabonomics: recommendations for sample preparation and storage[J]. Anal Chem, 2007, 79: 1 181-1 186.

[29] Cloarec O, Dumas M E, Andrew C, et al. Statistical total correlation spectroscopy: an exploratory approach for latent biomarker identification from metabolic ¹H NMR data sets[J]. Anal Chem, 2005, 77: 1 282-1 289.

[30] Gipson G T, Tatsuoka K S, Brian C S, et al. Weighted least-squares deconvolution method for discovery of group differences between complex biofluid ¹H NMR spectra [J]. J Mag Res, 2006, 183: 269-277.

A New Data Processing Method for Metabonomic and Its Application in a Study of Diabetes

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