Combinational Risk Factors of Metabolic Syndrome Identified by Fuzzy Neural Network Analysis of Health-check Data

Overview

Journal BMC Med Inform Decis Mak

Publisher Biomed Central

Specialty Medical Informatics

Date 2012 Aug 3

PMID 22853735

Citations 6

Authors

Yasunori Ushida

Ryuji Kato

Kosuke Niwa

Daisuke Tanimura

Hideo Izawa

Kenji Yasui

Tomokazu Takase

Yasuko Yoshida

Mitsuo Kawase

Tsutomu Yoshida

Toyoaki Murohara

Hiroyuki Honda

Affiliations

Soon will be listed here.

Abstract

Background: Lifestyle-related diseases represented by metabolic syndrome develop as results of complex interaction. By using health check-up data from two large studies collected during a long-term follow-up, we searched for risk factors associated with the development of metabolic syndrome.

Methods: In our original study, we selected 77 case subjects who developed metabolic syndrome during the follow-up and 152 healthy control subjects who were free of lifestyle-related risk components from among 1803 Japanese male employees. In a replication study, we selected 2196 case subjects and 2196 healthy control subjects from among 31343 other Japanese male employees. By means of a bioinformatics approach using a fuzzy neural network (FNN), we searched any significant combinations that are associated with MetS. To ensure that the risk combination selected by FNN analysis was statistically reliable, we performed logistic regression analysis including adjustment.

Results: We selected a combination of an elevated level of γ-glutamyltranspeptidase (γ-GTP) and an elevated white blood cell (WBC) count as the most significant combination of risk factors for the development of metabolic syndrome. The FNN also identified the same tendency in a replication study. The clinical characteristics of γ-GTP level and WBC count were statistically significant even after adjustment, confirming that the results obtained from the fuzzy neural network are reasonable. Correlation ratio showed that an elevated level of γ-GTP is associated with habitual drinking of alcohol and a high WBC count is associated with habitual smoking.

Conclusions: This result obtained by fuzzy neural network analysis of health check-up data from large long-term studies can be useful in providing a personalized novel diagnostic and therapeutic method involving the γ-GTP level and the WBC count.

Citing Articles

Identifying Metabolic Syndrome Easily and Cost Effectively Using Non-Invasive Methods with Machine Learning Models.

Xu W, Zhang Z, Hu K, Fang P, Li R, Kong D Diabetes Metab Syndr Obes. 2023; 16:2141-2151.

PMID: 37484515 PMC: 10361460. DOI: 10.2147/DMSO.S413829.

Health Data-Driven Machine Learning Algorithms Applied to Risk Indicators Assessment for Chronic Kidney Disease.

Chiu Y, Jhou M, Lee T, Lu C, Chen M Risk Manag Healthc Policy. 2021; 14:4401-4412.

PMID: 34737657 PMC: 8558038. DOI: 10.2147/RMHP.S319405.

The Utility of Artificial Neural Networks for the Non-Invasive Prediction of Metabolic Syndrome Based on Personal Characteristics.

Wang F, Lin C Int J Environ Res Public Health. 2020; 17(24).

PMID: 33322521 PMC: 7763080. DOI: 10.3390/ijerph17249288.

A Data-Driven Assessment of the Metabolic Syndrome Criteria for Adult Health Management in Taiwan.

Chen M, Chen S Int J Environ Res Public Health. 2019; 16(1).

PMID: 30602658 PMC: 6339104. DOI: 10.3390/ijerph16010092.

Identification of an interaction between VWF rs7965413 and platelet count as a novel risk marker for metabolic syndrome: an extensive search of candidate polymorphisms in a case-control study.

Nakatochi M, Ushida Y, Yasuda Y, Yoshida Y, Kawai S, Kato R PLoS One. 2015; 10(2):e0117591.

PMID: 25646961 PMC: 4315519. DOI: 10.1371/journal.pone.0117591.

References

Ando T, Suguro M, Kobayashi T, Seto M, Honda H . Multiple fuzzy neural network system for outcome prediction and classification of 220 lymphoma patients on the basis of molecular profiling. Cancer Sci. 2003; 94(10):906-13. PMC: 11160167. DOI: 10.1111/j.1349-7006.2003.tb01374.x. View

Ando T, Suguro M, Hanai T, Kobayashi T, Honda H, Seto M . Fuzzy neural network applied to gene expression profiling for predicting the prognosis of diffuse large B-cell lymphoma. Jpn J Cancer Res. 2002; 93(11):1207-12. PMC: 5926895. DOI: 10.1111/j.1349-7006.2002.tb01225.x. View

Xu Y, Bi Y, Xu M, Huang Y, Lu W, Gu Y . Cross-sectional and longitudinal association of serum alanine aminotransaminase and γ-glutamyltransferase with metabolic syndrome in middle-aged and elderly Chinese people. J Diabetes. 2011; 3(1):38-47. DOI: 10.1111/j.1753-0407.2010.00111.x. View

Horikawa S, Furuhashi T, Uchikawa Y . On fuzzy modeling using fuzzy neural networks with the back-propagation algorithm. IEEE Trans Neural Netw. 1992; 3(5):801-6. DOI: 10.1109/72.159069. View

van Oostrom A, Sijmonsma T, Verseyden C, Jansen E, de Koning E, Rabelink T . Postprandial recruitment of neutrophils may contribute to endothelial dysfunction. J Lipid Res. 2003; 44(3):576-83. DOI: 10.1194/jlr.M200419-JLR200. View

Kato R, Nakano H, Konishi H, Kato K, Koga Y, Yamane T . Novel strategy for protein exploration: high-throughput screening assisted with fuzzy neural network. J Mol Biol. 2005; 351(3):683-92. DOI: 10.1016/j.jmb.2005.05.026. View

Tomida S, Hanai T, Ueda N, Honda H, Kobayashi T . Construction of COD simulation model for activated sludge process by fuzzy neural network. J Biosci Bioeng. 2005; 88(2):215-20. DOI: 10.1016/s1389-1723(99)80205-9. View

Honda H, Kobayashi T . Fuzzy control of bioprocess. J Biosci Bioeng. 2005; 89(5):401-8. DOI: 10.1016/s1389-1723(00)89087-8. View

Kaga C, Okochi M, Tomita Y, Kato R, Honda H . Computationally assisted screening and design of cell-interactive peptides by a cell-based assay using peptide arrays and a fuzzy neural network algorithm. Biotechniques. 2008; 44(3):393-402. DOI: 10.2144/000112693. View

10.

Tanimura D, Shibata R, Izawa H, Hirashiki A, Asano H, Murase Y . Relation of a common variant of the adiponectin gene to serum adiponectin concentration and metabolic traits in an aged Japanese population. Eur J Hum Genet. 2010; 19(3):262-9. PMC: 3062002. DOI: 10.1038/ejhg.2010.201. View

11.

Oda E, Kawai R, Watanabe K, Sukumaran V . Prevalence of metabolic syndrome increases with the increase in blood levels of gamma glutamyltransferase and alanine aminotransferase in Japanese men and women. Intern Med. 2009; 48(16):1343-50. DOI: 10.2169/internalmedicine.48.2094. View

12.

Panagiotakos D, Pitsavos C, Skoumas J, Chrysohoou C, Toutouza M, Stefanadis C . Importance of LDL/HDL cholesterol ratio as a predictor for coronary heart disease events in patients with heterozygous familial hypercholesterolaemia: a 15-year follow-up (1987-2002). Curr Med Res Opin. 2003; 19(2):89-94. View

13.

Takeshita T, Okochi M, Kato R, Kaga C, Tomita Y, Nagaoka S . Screening of peptides with a high affinity to bile acids using peptide arrays and a computational analysis. J Biosci Bioeng. 2011; 112(1):92-7. DOI: 10.1016/j.jbiosc.2011.03.002. View

14.

McCurry J . Japanese people warned to curb unhealthy lifestyles. Health experts urge a return to dietary basics to prevent future health problems. Lancet. 2004; 363(9415):1126. DOI: 10.1016/s0140-6736(04)15942-4. View

15.

Ishizaka N, Ishizaka Y, Toda E, Nagai R, Yamakado M . Association between cigarette smoking, white blood cell count, and metabolic syndrome as defined by the Japanese criteria. Intern Med. 2007; 46(15):1167-70. DOI: 10.2169/internalmedicine.46.0136. View

16.

Nakanishi N, Suzuki K, Tatara K . Serum gamma-glutamyltransferase and risk of metabolic syndrome and type 2 diabetes in middle-aged Japanese men. Diabetes Care. 2004; 27(6):1427-32. DOI: 10.2337/diacare.27.6.1427. View

17.

Ryu S, Chang Y, Woo H, Yoo S, Choi N, Lee W . Longitudinal increase in gamma-glutamyltransferase within the reference interval predicts metabolic syndrome in middle-aged Korean men. Metabolism. 2009; 59(5):683-9. DOI: 10.1016/j.metabol.2009.08.024. View

18.

Lao X, Neil Thomas G, Jiang C, Zhang W, Adab P, Lam T . White blood cell count and the metabolic syndrome in older Chinese: the Guangzhou Biobank Cohort Study. Atherosclerosis. 2008; 201(2):418-24. DOI: 10.1016/j.atherosclerosis.2007.12.053. View

19.

Nakatochi M, Miyata S, Tanimura D, Izawa H, Asano H, Murase Y . The ratio of adiponectin to homeostasis model assessment of insulin resistance is a powerful index of each component of metabolic syndrome in an aged Japanese population: results from the KING Study. Diabetes Res Clin Pract. 2011; 92(3):e61-5. DOI: 10.1016/j.diabres.2011.02.029. View

20.

Haren M, Li M, Petkov J, McDermott R . Alcohol, metabolic risk and elevated serum gamma-glutamyl transferase (GGT) in Indigenous Australians. BMC Public Health. 2010; 10:454. PMC: 2920283. DOI: 10.1186/1471-2458-10-454. View