Non-invasive markers of liver fibrosis in fatty liver disease are unreliable in people of South Asian descent. Issue 2 (16th November 2017) - De Silva, Sampath; Li, Wenhao; Kemos, Polychronis; Brindley, James H; Mecci, Jibran; Samsuddin, Salma; Chin-Aleong, Joanne; Feakins, Roger M; Foster, Graham R; Syn, Wing-Kin; Alazawi, William
Abstract : Objective: Liver biopsy is the most accurate method for determining stage and grade of injury in non-alcoholic fatty liver disease (NAFLD). Given risks and limitations of biopsy, non-invasive tests such as NAFLD fibrosis score, aspartate transaminase (AST) to platelet ratio index, Fib-4, AST/alanine transaminase ratio and BARD are used. Prevalence and severity of NAFLD and metabolic syndrome vary by ethnicity, yet tests have been developed in largely white populations. We tested our hypothesis that non-invasive tests that include metabolic parameters are less accurate in South Asian compared with white patients. Design: Retrospective cross-sectional. Setting: Specialist liver centre. Patients: Patients with histologically confirmed NAFLD. Interventions: Scores calculated using clinical data taken within 1 week and compared with histology (Kleiner). Main outcome measures: Diagnostic test characteristics. Results: 175 patients were identified. South Asians (n=90) were younger, had lower body mass index and lower proportion of obesity compared with white patients (n=79), with comparable rates of diabetes and liver injury. Tests are less sensitive at detecting advanced fibrosis in South Asian compared with white patients. Relative risk of correct diagnosis in white patients compared with South Asians is 1.86 (95% CI 1.4 to 2.6). In binary logistic regression models, ethnicity and platelet count predicted accuracy. Transient elastography was equally and highly accurate in both ethnicities. Conclusions: Blood test-based non-invasive scores are less accurate in South Asian patients, irrespective of metabolic parameters. Ethnicity should be considered when devising risk-stratification algorithms for NAFLD.