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Zhou YJ, Zheng JN, Liu WY, et al. The NAFL Risk Score: A simple scoring model to predict 4-y risk for non-alcoholic fatty liver. Clin Chim Acta. 2017;468:17-24doi: 10.1016/j.cca.2017.01.021.
Zhou, Y. J., Zheng, J. N., Liu, W. Y., Miele, L., Vitale, A., Van Poucke, S., Zou, T. T., Fang, D. H., Shen, S., Zhang, D. C., & Zheng, M. H. (2017). The NAFL Risk Score: A simple scoring model to predict 4-y risk for non-alcoholic fatty liver. Clinica chimica acta; international journal of clinical chemistry, 46817-24. https://doi.org/10.1016/j.cca.2017.01.021
Zhou, Yu-Jie, et al. "The NAFL Risk Score: A simple scoring model to predict 4-y risk for non-alcoholic fatty liver." Clinica chimica acta; international journal of clinical chemistry vol. 468 (2017): 17-24. doi: https://doi.org/10.1016/j.cca.2017.01.021
Zhou YJ, Zheng JN, Liu WY, Miele L, Vitale A, Van Poucke S, Zou TT, Fang DH, Shen S, Zhang DC, Zheng MH. The NAFL Risk Score: A simple scoring model to predict 4-y risk for non-alcoholic fatty liver. Clin Chim Acta. 2017 May;468:17-24. doi: 10.1016/j.cca.2017.01.021. Epub 2017 Jan 19. PMID: 28111272.
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Clin Chim Acta. 2017 May;468:17-24. doi: 10.1016/j.cca.2017.01.021. Epub 2017 Jan 19.

The NAFL Risk Score: A simple scoring model to predict 4-y risk for non-alcoholic fatty liver.

Clinica chimica acta; international journal of clinical chemistry

Yu-Jie Zhou, Ji-Na Zheng, Wen-Yue Liu, Luca Miele, Alessandro Vitale, Sven Van Poucke, Tian-Tian Zou, Dan-Hong Fang, Shengrong Shen, Dong-Chu Zhang, Ming-Hua Zheng

Affiliations

  1. Department of Hepatology, Liver Research Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China; School of the First Clinical Medical Sciences, Wenzhou Medical University, Wenzhou, China.
  2. Department of Endocrinology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
  3. Department of Surgery, Oncology and Gastroenterology, University of Padua, Padua, Italy; Institute of Internal Medicine, Catholic University of Rome, Rome, Italy.
  4. Gastroenterology Area, Fondazione Policlinico Gemelli, Rome, Italy.
  5. Department of Anesthesiology, Critical Care, Emergency Medicine and Pain Therapy, Ziekenhuis Oost-Limburg, Genk, Belgium.
  6. Department of Hepatology, Liver Research Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China; School of the Second Clinical Medical Sciences, Wenzhou Medical University, Wenzhou, China.
  7. Department of Cardiovascular Medicine, The Heart Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China.
  8. Department of Food Science & Nutrition, Zhejiang University, Hangzhou, China.
  9. Wenzhou Medical Center, Wenzhou People's Hospital, Wenzhou, China.
  10. Department of Hepatology, Liver Research Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China; Institute of Hepatology, Wenzhou Medical University, Wenzhou, China. Electronic address: [email protected].

PMID: 28111272 DOI: 10.1016/j.cca.2017.01.021

Abstract

BACKGROUND: Although several risk factors for non-alcoholic fatty liver (NAFL) have been reported, there are few clinical scores that predict its incidence in the long term. We developed and validate a scoring model for individual prediction of 4-y risk for NAFL.

METHODS: Four-year follow-up data of 8226 initially NAFL-free subjects enrolled for an annual physical examination from Wenzhou Medical Center were analyzed. These subjects are randomly split into the training and the validation cohort. Univariate and multivariable logistic regression models were employed for model development. The selected variables were assigned an integer or half-integer risk score proportional to the estimated coefficient from the logistic model. Risk scores were tested in a validation cohort. We also compared the predictive performance of with that of the NAFLD Index by computing the area under the receiver operating characteristic curve (AUROC).

RESULTS: The NAFL Risk Score was developed as 0 to 18 points comprising of BMI, TG×GGT, ALT/AST, LDL-C/HDL-C and UA in both sexes. Comparison of the observed with the estimated incidence of NAFL at both cohorts showed satisfactory precision. In addition, the NAFL Risk Score showed relatively good discriminative power (AUROC=0.739 for males, 0.823 for females) compared with the NAFLD Index (AUROC=0.661 for males, 0.729 for females) in these Chinese subjects.

CONCLUSIONS: We developed and validated the NAFL Risk Score, a new scoring model to predict 4-y risk for NAFL. The NAFL Risk Score may be clinically simple and useful for assessing individual risk for NAFL.

Copyright © 2017 Elsevier B.V. All rights reserved.

Keywords: General population; Non-alcoholic fatty liver; Prediction model; Risk factors

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