Cargando…
Explorative study of serum biomarkers of liver failure after liver resection
Conventional biochemical markers have limited usefulness in the prediction of early liver dysfunction. We, therefore, tried to find more useful liver failure biomarkers after liver resection that are highly sensitive to internal and external challenges in the biological system with a focus on liver...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7305107/ https://www.ncbi.nlm.nih.gov/pubmed/32561884 http://dx.doi.org/10.1038/s41598-020-66947-1 |
_version_ | 1783548388910825472 |
---|---|
author | Yoon, Kyung Chul Kwon, Hyung Do Jo, Hye-Sung Choi, Yoon Young Seok, Jin-I Kang, Yujin Lee, Do Yup Kim, Dong-Sik |
author_facet | Yoon, Kyung Chul Kwon, Hyung Do Jo, Hye-Sung Choi, Yoon Young Seok, Jin-I Kang, Yujin Lee, Do Yup Kim, Dong-Sik |
author_sort | Yoon, Kyung Chul |
collection | PubMed |
description | Conventional biochemical markers have limited usefulness in the prediction of early liver dysfunction. We, therefore, tried to find more useful liver failure biomarkers after liver resection that are highly sensitive to internal and external challenges in the biological system with a focus on liver metabolites. Twenty pigs were divided into the following 3 groups: sham operation group (n = 6), 70% hepatectomy group (n = 7) as a safety margin of resection model, and 90% hepatectomy group (n = 7) as a liver failure model. Blood sampling was performed preoperatively and at 1, 6, 14, 30, 38, and 48 hours after surgery, and 129 primary metabolites were profiled. Orthogonal projection to latent structures-discriminant analysis revealed that, unlike in the 70% hepatectomy and sham operation groups, central carbon metabolism was the most significant factor in the 90% hepatectomy group. Binary logistic regression analysis was used to develop a predictive model for mortality risk following hepatectomy. The recommended variables were malic acid, methionine, tryptophan, glucose, and γ-aminobutyric acid. Area under the curve of the linear combination of five metabolites was 0.993 (95% confidence interval: 0.927–1.000, sensitivity: 100.0, specificity: 94.87). We proposed robust biomarker panels that can accurately predict mortality risk associated with hepatectomy. |
format | Online Article Text |
id | pubmed-7305107 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-73051072020-06-22 Explorative study of serum biomarkers of liver failure after liver resection Yoon, Kyung Chul Kwon, Hyung Do Jo, Hye-Sung Choi, Yoon Young Seok, Jin-I Kang, Yujin Lee, Do Yup Kim, Dong-Sik Sci Rep Article Conventional biochemical markers have limited usefulness in the prediction of early liver dysfunction. We, therefore, tried to find more useful liver failure biomarkers after liver resection that are highly sensitive to internal and external challenges in the biological system with a focus on liver metabolites. Twenty pigs were divided into the following 3 groups: sham operation group (n = 6), 70% hepatectomy group (n = 7) as a safety margin of resection model, and 90% hepatectomy group (n = 7) as a liver failure model. Blood sampling was performed preoperatively and at 1, 6, 14, 30, 38, and 48 hours after surgery, and 129 primary metabolites were profiled. Orthogonal projection to latent structures-discriminant analysis revealed that, unlike in the 70% hepatectomy and sham operation groups, central carbon metabolism was the most significant factor in the 90% hepatectomy group. Binary logistic regression analysis was used to develop a predictive model for mortality risk following hepatectomy. The recommended variables were malic acid, methionine, tryptophan, glucose, and γ-aminobutyric acid. Area under the curve of the linear combination of five metabolites was 0.993 (95% confidence interval: 0.927–1.000, sensitivity: 100.0, specificity: 94.87). We proposed robust biomarker panels that can accurately predict mortality risk associated with hepatectomy. Nature Publishing Group UK 2020-06-19 /pmc/articles/PMC7305107/ /pubmed/32561884 http://dx.doi.org/10.1038/s41598-020-66947-1 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Yoon, Kyung Chul Kwon, Hyung Do Jo, Hye-Sung Choi, Yoon Young Seok, Jin-I Kang, Yujin Lee, Do Yup Kim, Dong-Sik Explorative study of serum biomarkers of liver failure after liver resection |
title | Explorative study of serum biomarkers of liver failure after liver resection |
title_full | Explorative study of serum biomarkers of liver failure after liver resection |
title_fullStr | Explorative study of serum biomarkers of liver failure after liver resection |
title_full_unstemmed | Explorative study of serum biomarkers of liver failure after liver resection |
title_short | Explorative study of serum biomarkers of liver failure after liver resection |
title_sort | explorative study of serum biomarkers of liver failure after liver resection |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7305107/ https://www.ncbi.nlm.nih.gov/pubmed/32561884 http://dx.doi.org/10.1038/s41598-020-66947-1 |
work_keys_str_mv | AT yoonkyungchul explorativestudyofserumbiomarkersofliverfailureafterliverresection AT kwonhyungdo explorativestudyofserumbiomarkersofliverfailureafterliverresection AT johyesung explorativestudyofserumbiomarkersofliverfailureafterliverresection AT choiyoonyoung explorativestudyofserumbiomarkersofliverfailureafterliverresection AT seokjini explorativestudyofserumbiomarkersofliverfailureafterliverresection AT kangyujin explorativestudyofserumbiomarkersofliverfailureafterliverresection AT leedoyup explorativestudyofserumbiomarkersofliverfailureafterliverresection AT kimdongsik explorativestudyofserumbiomarkersofliverfailureafterliverresection |