Author information
11Université Paris Cité, UMR1149 (CRI), INSERM, Paris, France.
22Service d'hépatologie, Assistance Publique-Hôpitaux de Paris, Hôpital Beaujon, Clichy-la-Garenne, France.
33Université Paris Cité, UMR1137 (IAME), INSERM, Paris, France.
44DEBRC, Assistance Publique-Hôpitaux de Paris, Hôpital Bichat, Paris, France.
55Service d'hépatologie, Assistance Publique-Hôpitaux de Paris, Groupe hospitalier Cochin, Paris, France.
66Centre Universitaire du Diabète et de ses Complications, Assistance Publique-Hôpitaux de Paris, Hôpital Lariboisière, Paris, France.
77Service d'anatomie et de cytologie pathologiques, Assistance Publique-Hôpitaux de Paris, Hôpital Beaujon, Clichy-la-Garenne, France.
88Université Paris-Est, U955, INSERM, Créteil, France.
99Unité d'hépatologie, Assistance Publique-Hôpitaux de Paris, Hôpital Avicenne, Bobigny, France.
1010Université Paris Cité, Institut Cochin, U1016, INSERM, Paris, France.
1111Service de diabétologie, Assistance Publique-Hôpitaux de Paris, Groupe hospitalier Cochin, Paris, France.
1212Service d'anatomie et de cytologie pathologiques, Assistance Publique-Hôpitaux de Paris, Groupe hospitalier Cochin, Paris, France.
1313Université Paris 13, EA 3412, Bobigny, France.
1414Service Endocrinologie, Diabète, Nutrition, Assistance Publique-Hôpitaux de Paris, Hôpital Avicenne, Bobigny, France.
1515Université Paris Cité, Institut Necker Enfants Malades (INEM), INSERM U1151, CNRS UMR 8253, IMMEDIAB Laboratory, INSERM, Paris, France.
1616Service de nutrition, centre spécialisé Obésité, Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, Paris, France.
1717Sorbonne Université, UMR938 and Institute of Cardiometabolism and Nutrition, INSERM, Paris, France.
1818Service d'hépato-gastroentérologie, Groupe hospitalier Pitié Salpêtrière, Paris, France.
1919BioPredictive, Paris, France.
2020Délégation régionale Paris 5/Paris 7, Paris, France.
2121Université Paris Cité, UMR1153 (METHODS Team, CRESS), INSERM, Paris, France.
2222Liverpat, Paris, France.
Abstract
Objective: Most people with type 2 diabetes (T2DM) and nonalcoholic steatohepatitis (NASH) or advanced fibrosis (AF) remain undiagnosed, resulting in missed opportunities for early intervention. This multicenter, prospective study assessed the yield of using routinely available data to identify these patients.
Research design and methods: A total of 713 outpatients with T2DM, screened in four diabetology clinics for nonalcoholic fatty liver disease according to American Diabetes Association criteria, were referred to hepatologists for further work-up (Fibrosis-4 and vibration-controlled transient elastography [VCTE]). A liver biopsy was proposed when ALT levels were persistently >20 IU/L in female patients or >30 IU/L in male patients, in the absence of other liver disease.
Results: Liver biopsies were performed in 360 patients and considered adequate for reading after central review for 330 specimens (median patient age, 59 years; male patients, 63%; median BMI and HbA1c values, 32 and 7.5%, respectively). Prevalence of NASH, AF, and cirrhosis were 58%, 38%, and 10%, respectively. Liver lesions were independently associated with the components of metabolic syndrome but not with the micro- and macrovascular complications of T2DM. Models based on routinely available data with or without VCTE had good accuracy to predict AF (respectively: area under the receiver operating characteristic curve [AUROC], 0.84 and 0.77; and correctly classified 59% and 45%) and NASH (respectively: AUROC, 0.82 and 0.81; 44% and 42%).
Conclusions: Despite the use of a low ALT threshold, prevalence of NASH (58%) or AF (38%) was high. Routinely available data had a high yield in identifying patients with T2DM with AF and/or NASH requiring further liver assessment.