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Open Access Research article

Predicting incident fatty liver using simple cardio-metabolic risk factors at baseline

Ki-Chul Sung18*, Bum-Soo Kim1, Yong-Kyun Cho2, Dong-il Park2, Sookyoung Woo3, Seonwoo Kim3, Sarah H Wild4 and Christopher D Byrne567*

Author affiliations

1 Division of Cardiology, Department of Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea

2 Division of Gastroenterology, Department of Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea

3 Biostatistics Team, Samsung Biomedical Research Institute, Seoul, Republic of Korea

4 Centre for Population Health Sciences, University of Edinburgh, IDS Building, Southampton General Hospital, MP 887, Southampton, UK

5 Nutrition and Metabolism Unit, Faculty of Medicine, University of Southampton, IDS Building, Southampton General Hospital, MP 887, Southampton, UK

6 NIHR Southampton Biomedical Research Centre, University Hospital Southampton, IDS Building, Southampton General Hospital, MP 887, Southampton, UK

7 Endocrinology and Metabolism Unit, University of Southampton, IDS Building, Southampton General Hospital, MP 887, Tremona Road, Southampton, UK, SO166YD

8 Division of Cardiology, Kangbuk Samsung, Hospital, Sungkyunkwan University School of Medicine, #108, Pyung Dong, Jongro-Ku, Seoul, 110-746, Republic of Korea

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Citation and License

BMC Gastroenterology 2012, 12:84  doi:10.1186/1471-230X-12-84

Published: 6 July 2012

Abstract

Background

Non alcoholic fatty liver disease (NAFLD) is associated with increased risk of type 2 diabetes and chronic liver disease but identifying patients who have NAFLD without resorting to expensive imaging tests is challenging. In order to help identify people for imaging investigation of the liver who are at high risk of NAFLD, our aim was to: a) identify easily measured risk factors at baseline that were independently associated with incident fatty liver at follow up, and then b) to test the diagnostic performance of thresholds of these factors at baseline, to predict or to exclude incident fatty liver at follow up.

Methods

2589 people with absence of fatty liver on ultrasound examination at baseline were re-examined after a mean of 4.4 years in a Korean occupational cohort study. Multi-variable logistic regression analyses were used to identify baseline factors that were independently associated with incident fatty liver at follow up. The diagnostic performance of thresholds of these baseline factors to identify people with incident fatty liver at follow-up was assessed using receiver operating characteristic (ROC) curves.

Results

430 incident cases of fatty liver were identified. Several factors were independently associated with incident fatty liver: increased triglyceride (per mmol/l increase) OR 1.378 [95%CIs 1.179, 1.611], p < 0.0001; glucose (per mmol/l increase) OR 1.215 [95%CIs 1.042, 1.416], p = 0.013; waist (per cm increase) OR 1.078 [95%CIs 1.057, 1.099], p < 0.001; ALT (per IU/L increase) OR 1.009 [95%CIs 1.002, 1.017], p = 0.016; and platelets (per 1x109/L increase) OR 1.004 [1.001, 1.006], p = 0.001; were each independently associated with incident fatty liver. Binary thresholds of the five factors were applied and the area under the ROC curve for incident fatty liver was 0.75 (95%CI 0.72–0.78) for the combination of all five factors above these thresholds.

Conclusion

Simple risk factors that overlap considerably with risk factors for type 2 diabetes allow identification of people at high risk of incident fatty liver at who use of hepatic imaging could be targeted.

Keywords:
Non alcoholic fatty liver disease; Fatty liver; Etiology; Risk prediction; Metabolic syndrome