Non-invasive diagnosis of phenylketonuria by using artificial neural networking and nuclear magnetic resonance spectroscopy

Fatemeh Dorosti; Zahra Zanganeh; Roghayeh Mirzazadeh; Zahra Zamani; Mohammad Arjmand; Sedighe Sadeghi

Koomesh

Journal of Semnan University of Medical Sciences

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Non-invasive diagnosis of phenylketonuria by using artificial neural networking and nuclear magnetic resonance spectroscopy

Author(s):

Fatemeh Dorosti,

Zahra Zanganeh,

Roghayeh Mirzazadeh,

Zahra Zamani,

Mohammad Arjmand

^,*,

Sedighe Sadeghi

Koomesh:Vol. 17, issue 4; 910-917

Published online:Sep 28, 2016

Article type:Research Article

Received:Sep 01, 2015

Accepted:Dec 03, 2015

How to Cite:Fatemeh DorostiZahra ZanganehRoghayeh MirzazadehZahra ZamaniMohammad ArjmandSedighe Sadeghiet al.Non-invasive diagnosis of phenylketonuria by using artificial neural networking and nuclear magnetic resonance spectroscopy.koomesh.17(4):e151184.

Abstract

Introduction: Phenylketonuria (PKU) is a relatively common metabolic disease in world .The high incidence of the disease in our country is due to consanguineous marriages. The prevalence of this disease in Iran is reported to be about 1: 4000 to1: 8,000 births per year. Mental retardation, physical disabilities, neurological disorders are the clinical symptoms of the disease. Early diagnosis is very important to prevent the disabling consequences of the disease. The purpose of this study was to use a multi-layer neural network perceptron (MLP) to build a model for early detection and treatment of phenylketonuria patients.. Materials and Methods: Urine samples were obtained from healthy and PKU children. nuclear magnetic resonance spectroscopy was performed in NMR 400 MHz Bruker with the help of NOESY Protocol. Then peak resonance of each metabolite was identified, and modeling was done with multi-layer neural network perceptron. Results: The Model build in this study was able to classify the data in two groups of patient and healthy individuals successfully, with more than 90% sensitivity and 0.2% error rate with high predictive power Conclusion: Our results showed the high power capability of this technique to diagnose the Phenylketonuria with the help of NMR spectroscopy and artificial neural network

Keywords

Phenylketonuria

Artificial neural networking

Magnetic Resonance Spectroscopy

فنیل‌کتونوری

شبکه‌های عصبی

طیف نمایی از طریق تشدید مغناطیسی

References

1.
Centerwall SA, Centerwall WR. The discovery of phenylketonuria: the story of a young couple, two retarded children, and a scientist. Pediatrics 2000; 105: 89-103.
2.
Farhud D, Shalileh M. Phenylketonuria and its dietary therapy in children. 2009.
3.
Blbl S. Novel approach for newborn errors in metabolism screening (NEMS) by NMR: Clinical NEMS-by-NMR Study in Turkey. Clin Biochem 2014; 9: 700-701.
4.
Mokhtari R, Bagga A. Consanguinity, genetic disorders and malformations in the Iranian population. Acta Biologica Szegediensis 2003; 47: 47-50.
5.
Rao AN, Kavitha J, Koch M, Kumar VS. Inborn errors of metabolism: Review and data from a tertiary care center. Indian J Clin Biochem 2009; 24: 215-222.
6.
Dougherty FE, Levy HL. Present newborn screening for phenylketonuria. Ment Retard Dev Disabil Res Rev 1999; 5: 144-149.
7.
Arjmand M, Kompany-Zareh M, Vasighi M, Parvizzadeh N, Zamani Z, Nazgooei F. Nuclear magnetic resonance-based screening of thalassemia and quantification of some hematological parameters using chemometric methods. Talanta 2010; 81: 1229-1236.
8.
Viant MR, Rosenblum ES, Tjeerdema RS. NMR-based metabolomics: a powerful approach for characterizing the effects of environmental stressors on organism health. Environ Sci Technol 2003; 37: 4982-4989.
9.
Zhang A, Sun H, Wu X, Wang X. Urine metabolomics. Clinica Chimica Acta 2012; 414: 65-69.
10.
Agatonovic-Kustrin S, Beresford R. Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. J Pharm Biomed Anal 2000; 22: 717-727.
11.
Brougham D, Ivanova G, Gottschalk M, Collins D, Eustace A, O'Connor R, Havel J. Artificial neural networks for classification in metabolomic studies of whole cells using 1 h nuclear magnetic resonance. J Biomed Biotechnol 2010; 2011: 158094.
12.
Long W. Medical diagnosis using a probabilistic causal network. Appl Artific Int Intern J 1989; 3: 367-383.
13.
Long WJ. Flexible reasoning about patient management using multiple models. Artific Int Med 1991; 3: 3-20.
14.
Arjmand M, Akbari Z, Taghizadeh N, Shahbazzadeh D, Zamani Z. NMR-based metabonomics survey in rats envenomed by Hemiscorpius lepturus venom. Toxicon 2015; 94: 16-22.
15.
Viant MR. Revealing the metabolome of animal tissues using 1H nuclear magnetic resonance spectroscopy. Methods Mol Biol 2007; 358: 229-246.
16.
Sadoughi F, Sheikhtaheri A. Applications of artificial intelligence in clinical decision making: Opportunities and challenges. Director General 2011; 8.
17.
Raghuveer TS, Garg U, Graf WD. Inborn errors of metabolism in infancy and early childhood: an update. Am Fam Phys 2006; 73: 1981-1990.
18.
Liebl B, Nennstiel-Ratzel U, Roscher A, von Kries R. Data required for the evaluation of newborn screening programmes. Eur J pediat 2003; 162: S57-S61.
19.
Liebl B, Nennstiel-Ratzel U, von Kries R, Fingerhut R, Olgemller B, Zapf A, Roscher AA. Expanded newborn screening in Bavaria: tracking to achieve requested repeat testing. Prev Med 2002; 34: 132-137.
20.
Hanley WB. Phenylketonuria (PKU)-A Success Story: INTECH Open Access Publisher; 2012.
21.
Guthrie R, Susi A. A simple phenylalanine method for detecting phenylketonuria in large populations of newborn infants. Pediatrics 1963; 32: 338-343.
22.
Koch J. Robert guthrie--the PKU story: a crusade against mental retardation: Hope Publishing House; 1997.
23.
Blumenfeld CM, Wallace MJ, Anderson R. Phenylketonuriathe guthrie screening testA method of quantitation, observations on reliability and suggestions for improvement. Calif Med 1966; 105: 429.
24.
Constantinou MA, Papakonstantinou E, Spraul M, Sevastiadou S, Costalos C, Koupparis MA, et al. 1 H NMR-based metabonomics for the diagnosis of inborn errors of metabolism in urine. Anal Chim Acta 2005; 542: 169-177.
25.
Ozand PT. Diagnosis of inborn errors of metabolism by tandem mass spectrometry. Annal Saudi Med 1998; 18: 234-238.
26.
Williams RA, Mamotte CD, Burnett JR. Phenylketonuria: an inborn error of phenylalanine metabolism. Clin Biochem Rev 2008; 29: 31.
27.
Gupta A. To err is genetics: diagnosis and management of inborn errors of metabolism (IEM). Anthropology Today: Trends, Scopes and Applications 2007; 415-423.
28.
Xia J, Broadhurst DI, Wilson M, Wishart DS. Translational biomarker discovery in clinical metabolomics: an introductory tutorial. Metabolomics 2013; 9: 280-299.
29.
Zhengzheng p, Haiwei G, Nari T, Huanwen C, Narasimhamurthy S, Bryan EH, R GC, Daniel R. Principal component analysis of urine metabolites detected by NMR and DESIMS in patients with inborn errors of metabolism. Anal Bioanal Chem 2007; 387: 539-549.

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