Plasma amino acid profile associated with fatty liver disease and co-occurrence of metabolic risk factors View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-12

AUTHORS

Minoru Yamakado, Takayuki Tanaka, Kenji Nagao, Akira Imaizumi, Michiharu Komatsu, Takashi Daimon, Hiroshi Miyano, Mizuki Tani, Akiko Toda, Hiroshi Yamamoto, Katsuhisa Horimoto, Yuko Ishizaka

ABSTRACT

Fatty liver disease (FLD) increases the risk of diabetes, cardiovascular disease, and steatohepatitis, which leads to fibrosis, cirrhosis, and hepatocellular carcinoma. Thus, the early detection of FLD is necessary. We aimed to find a quantitative and feasible model for discriminating the FLD, based on plasma free amino acid (PFAA) profiles. We constructed models of the relationship between PFAA levels in 2,000 generally healthy Japanese subjects and the diagnosis of FLD by abdominal ultrasound scan by multiple logistic regression analysis with variable selection. The performance of these models for FLD discrimination was validated using an independent data set of 2,160 subjects. The generated PFAA-based model was able to identify FLD patients. The area under the receiver operating characteristic curve for the model was 0.83, which was higher than those of other existing liver function-associated markers ranging from 0.53 to 0.80. The value of the linear discriminant in the model yielded the adjusted odds ratio (with 95% confidence intervals) for a 1 standard deviation increase of 2.63 (2.14-3.25) in the multiple logistic regression analysis with known liver function-associated covariates. Interestingly, the linear discriminant values were significantly associated with the progression of FLD, and patients with nonalcoholic steatohepatitis also exhibited higher values. More... »

PAGES

14485

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41598-017-14974-w

DOI

http://dx.doi.org/10.1038/s41598-017-14974-w

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1092446434

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/29101348


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