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Association of Genetic and Environmental Factors with Non-Alcoholic Fatty Liver Disease in a Chinese Han Population

Author

Listed:
  • Zheng Li

    (Medical School, Hangzhou Normal University, Hangzhou 310000, China)

  • Cheng-Yin Ye

    (Medical School, Hangzhou Normal University, Hangzhou 310000, China)

  • Li Wang

    (Medical School, Hangzhou Normal University, Hangzhou 310000, China)

  • Jin-Mei Li

    (Medical School, Hangzhou Normal University, Hangzhou 310000, China)

  • Lei Yang

    (Medical School, Hangzhou Normal University, Hangzhou 310000, China)

Abstract

Lifestyle choices such as the intake of sweets, history of diseases, and genetic variants seem to play a role in the pathogenesis of non-alcoholic fatty liver disease (NAFLD). To explore which genetic and environmental factors are associated with NAFLD in a Chinese Han population, we conducted this study. We collected the medical reports, lifestyle details, and blood samples of individuals and used the polymerase chain reaction-ligase detection reaction method to genotype the single-nucleotide polymorphism (SNPs) from the 2113 eligible people. The GG genotype of the additive model of rs7493 in the PON2, the CC genotype of the additive and recessive models of rs7593130 in the ADCY3, together with dyslipidemia, regular intake of egg and sweets and hypertension, increased the risk of NAFLD (adjusted OR > 1, p < 0.05). The TT genotype of the additive and dominant models of rs11583680 in the PCSK9, together with the regular intake of vegetable, reduced the risk of NAFLD (adjusted OR < 1, p < 0.05). In addition, interactions between some variables were found. Eventually, we identified three SNPs and six environmental factors associated with NAFLD. These results provide the theoretical basis for gene and other risk factors screening to prevent NAFLD.

Suggested Citation

  • Zheng Li & Cheng-Yin Ye & Li Wang & Jin-Mei Li & Lei Yang, 2020. "Association of Genetic and Environmental Factors with Non-Alcoholic Fatty Liver Disease in a Chinese Han Population," IJERPH, MDPI, vol. 17(14), pages 1-14, July.
  • Handle: RePEc:gam:jijerp:v:17:y:2020:i:14:p:5217-:d:386787
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    References listed on IDEAS

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    1. Yi Zhang & Tatiana G. Kutateladze, 2018. "Diet and the epigenome," Nature Communications, Nature, vol. 9(1), pages 1-3, December.
    2. Shuang Huang & Chengcheng Hu & Melanie L. Bell & Dean Billheimer & Stefano Guerra & Denise Roe & Monica M. Vasquez & Edward J. Bedrick, 2018. "Regularized continuous‐time Markov Model via elastic net," Biometrics, The International Biometric Society, vol. 74(3), pages 1045-1054, September.
    3. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    4. Tian-Yu Zhao & Song Lei & Liu Huang & Yi-Nan Wang & Xiao-Ni Wang & Ping-Pu Zhou & Xiao-Jun Xu & Long Zhang & Liang-Wen Xu & Lei Yang, 2019. "Associations of Genetic Variations in ABCA1 and Lifestyle Factors with Coronary Artery Disease in a Southern Chinese Population with Dyslipidemia: A Nested Case-Control Study," IJERPH, MDPI, vol. 16(5), pages 1-13, March.
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