Application of the back-error propagation artificial neural network (BPANN) on genetic variants in the PPAR-γ and RXR-α gene and risk of metabolic syndrome in a Chinese Han population

Journal of Biomedical Research

Volumn 28, Issue 2, 2014, Pages 114-122

  Zhao, Xu ^a   Xir, Kang ^a   Shi, Hui ^a   Cheng, Jinluo ^a   Ma, Jianhua ^a   Gao, Yanqin ^a   Li, Qian ^a   Ye, Xinhua ^a   Lu, Ying ^a   Yu, Xiaofang ^a   Du, Juan ^a   Du, Wcncong ^a   Ye, Qing ^a   Zhou, Ling ^a  

^a NANJING MEDICAL UNIVERSITY   (China)

Author keywords

Adiponectin; Back error propagation artificial neural network (BPANN); Metabolic syndrome; Peroxisomeprolif erators activated receptoi (PPAR) gene; Retinoid X receptoi (RXR ) gene

Indexed keywords

ADIPONECTIN; PEROXISOME PROLIFERATOR ACTIVATED RECEPTOR GAMMA; RETINOID X RECEPTOR ALPHA;

ADULT; ARTICLE; ARTIFICIAL NEURAL NETWORK; BACK ERROR PROPAGATION ARTIFICIAL NEURAL NETWORK; BODY MASS; CONTROLLED STUDY; DRINKING BEHAVIOR; FAMILY HISTORY; FEMALE; GENETIC VARIABILITY; HAN CHINESE; HUMAN; HYPERLIPIDEMIA; HYPERTENSION; MAJOR CLINICAL STUDY; MALE; METABOLIC SYNDROME X; MULTIVARIATE LOGISTIC REGRESSION ANALYSIS; NON INSULIN DEPENDENT DIABETES MELLITUS; PHYSICAL ACTIVITY; POPULATION RISK; PRIORITY JOURNAL; RISK FACTOR; SINGLE NUCLEOTIDE POLYMORPHISM; SMOKING; UNIVARIATE ANALYSIS;

EID: 84921845685     PISSN: 16748301     EISSN: None     Source Type: Journal    
DOI: 10.7555/JBR.27.20120061     Document Type: Article

References (31)

1. Sygnowska E, Piwonska A, Waskiewicz A, Broda G. Socioeconomic factors and the risk of metabolic syndrome in the adult Polish population: the WOBASZ study. Kardiol Pol 2012: 70: 718-27.
2. Laaksonen DE, Lakka HM, Niskanen LK, Kaplan GA, Salonen JT, Lakka TA. Metabolic syndrome and development of diabetes mellitus: application and validation of recently suggested definitions of the metabolic syndrome in a prospective cohort study. Am J Epidemiol 2002; 156: 1070-7.
3. Onat A, Ceyhan K, Basar O, Erer B, Toprak S, Sansoy V. Metabolic syndrome: major impact on coronary risk in a population with low cholesterol levels--a prospective and cross-sectional evaluation. Atherosclerosis 2002: 165: 285-92.
4. Ford E, Giles W, Mokdad A. Mokdad, Increasing prevalence of the metabolic syndrome among u.s. Adults Diabetes Care 2004; 27: 2444-9.
5. Nestel P, Lyu R, Low LP, Sheu WH, Nitiyanant W, Saito I, et al. Metabolic syndrome: recent prevalence in East and Southeast Asian populations. Asia Pac J Clin Nutr 2007; 16: 362-7.
6. Athyros VG, Bouloukos VI, Pehlivanidis AN, Papageorgiou AA, Dionysopoulou SG, Symeonidis AN, et al. The prevalence of the metabolic syndrome in Greece: the Metabolic syndrome-Greece Multicentre Study. Diabetes Obes Metab 2005; 7: 397-405.
7. Okamoto Y, Kihara S, Funahashi T, Matsuzawa Y, Libby P. Adiponectin: a key adipocytokine in metabolic syndrome. Clin Sci (Lond) 2006; 110: 267-78.
8. Francke S, Manraj M, Lacquemant C, Lecoeur C, Lepretre F, Passa P, et al. A genome-wide scan for coronary heart disease suggests in Indo-Mauritians a susceptibility locus on chromosome 16p13 and replicates linkage with the metabolic syndrome on 3q27. Hum Mol Genet 2001; 10: 2751-65.
9. Lehmann JM, Moore LB, Smith-Oliver TA, Wilkison WO, Willson TM, Kliewer SA. An antidiabetic thiazolidinedione is a high affinity ligand for peroxisome proliferator-activated receptor gamma (PPAR gamma) J Biol Chem 1995; 270: 12953-6.
10. Iwaki M, Matsuda M, Maeda N, Funahashi T, Matsuzawa Y, Makishima M, et al. Induction of adiponectin, a fat-derived antidiabetic and antiatherogenic factor, by nuclear receptors. Diabetes 2003; 52: 1655-63.
11. Lu Y, Ye X, Cao Y, Li Q, Yu X, Cheng J, et al. Genetic variants in peroxisome proliferator-activated receptor-gamma and retinoid X receptor-alpha gene and type 2 diabetes risk, a case-control study of a Chinese Han population. Diabetes Technol Ther 2011; 13: 157-64.
12. Shi H, Yu X, Li Q, Ye X, Gao Y, Ma J, et al. Association between PPAR-γ and RXR-α gene polymorphism and metabolic syndrome risk: a case-control study of a Chinese Han population. Arch Med Res 2012; 43: 233-42.
13. Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation 2009; 120: 1640-5.
14. Lou XY, Chen GB, Yan L, Ma JZ, Zhu J, Elston RC, et al. A generalized combinatorial approach for detecting gene-by-gene and gene-by-environment interactions with application to nicotine dependence. Am J Hum Genet 2007; 80: 1125-37.
15. Mukherjee A, Deshpande JM. Application of artificial neural networks in structural design expert s- ystems. Comput Struct 1995; 54: 367-75.
16. Bragt MC, Popeijus HE. Peroxisome proliferator-activated receptors and the metabolic syndrome. Physiol Behav 2008; 94: 187-97.
17. Meirhaeghe A, Cottel D, Amouyel P, Dallongeville J. Dallongeville, Association between peroxisome proliferator-activated receptor gamma haplotypes and the metabolic syndrome in French men and women. Diabetes 2005; 54: 3043-8.
18. Guo W, Che Z, Xu A, Zhou Z. Angiopoietin-like protein 3 and adiponectin levels in patients with metabolic syndrome. Zhong Nan Da Xue Xue Bao Yi Xue Ban (China) 2010; 35: 203-8.
19. Bartosch-Harlid A, Andersson R. Diabetes mellitus in pancreatic cancer and the need for diagnosis of asymptomatic disease. Pancreatology 2010; 10: 423-8.
20. Coppedè F, Grossi E, Migheli F, Migliore L. Polymorphisms in folate-metabolizing genes, chromosome damage, and risk of Down syndrome in Italian women: identification of key factors using artificial neural networks. BMC Med. Genomics 2010; 3: 42.
21. Shi H, Lu Y, Du J, Du W, Ye X, Yu X, et al. Application of Back Propagation Artificial Neural Network on Genetic Variants in Adiponectin ADIPOQ, Peroxisome Proliferator-Activated Receptor-gamma, and Retinoid X Receptor-alpha Genes and Type 2 Diabetes Risk in a Chinese Han Population. Diabetes Technol Ther 2011; 14: 293-300.
22. Lee IT, Chiu Y, Hwu C, He C, Chiang F, Lin Y, et al. Central obesity is important but not essential component of the metabolic syndrome for predicting diabetes mellitus in a hypertensive family-based cohort. Results from the Stanford Asia-Pacific Program for Hypertension and Insulin Resistance (SAPPHIRe) Taiwan follow-up study. Cardiovasc Diabetol 2012; 11: 43.
23. Zhuo Q, Wang ZQ, Fu P, Piao JH, Tian Y, Xu J, et al. Association between adiponectin and metabolic syndrome in older adults from major cities of China. Biomed. Environ Sci 2010; 23: 53-61.
24. Yang LL, Hua Q, Liu RK, Yang Z. Association between two common polymorphisms of PPARgamma gene and metabolic syndrome families in a Chinese population. Arch. Med Res 2009; 40: 89-96.
25. Jaziri R, Lobbens S, Aubert R, Pean F, Lahmidi S, Vaxillaire M, et al. The PPARG Pro12Ala polymorphism is associated with a decreased risk of developing hyperglycemia over 6 years and combines with the effect of the APM1 G-11391A single nucleotide polymorphism: the Data From an Epidemiological Study on the Insulin Resistance Syndrome (DESIR) study. Diabetes 2006; 55: 1157-62.
26. Prakash J, Srivastava N, Awasthi S, Agarwal C, Natu S, Rajpal N, et al. Association of PPAR-gamma gene polymorphisms with obesity and obesity-associated phenotypes in north indian population. Am J Hum Biol 2012; 24: 454-9.
27. Dongxia L, Qi H, Lisong L, Jincheng G. Association of peroxisome proliferator-activated receptorgamma gene Pro12Ala and C161T polymorphisms with metabolic syndrome. Circ J 2008; 72: 551-7.
28. Suyama Y, Matsuda C, Isomura M, Hamano T, Karino K, Yamasaki M, et al. Effects of six functional SNPs on the urinary 8-isoprostane level in a general Japanese population; Shimane COHRE Study. Dis Markers 2011; 30: 291-8.
29. Curtis D. Comparison of artificial neural network analysis with other multimarker methods for detecting genetic association. BMC Genet 2007; 8: 49.
30. Eftekhar B, Mohammad K, Ardebili HE, Ghodsi M, Ketabchi E. Comparison of artificial neural network and logistic regression models for prediction of mortality in head trauma based on initial clinical data. BMC Med Inform Decis Mak 2005; 5: 3.
31. Jack V. T. Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. J Clin Epidemiol 1996; 49: 1225-31.