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A simple clinical model predicts incident hepatic steatosis in a community‐based cohort: The Framingham Heart Study
Author(s) -
Long Michelle T.,
Pedley Alison,
Massaro Joseph M.,
Hoffmann Udo,
Ma Jiantao,
Loomba Rohit,
Chung Raymond T.,
Benjamin Emelia J.
Publication year - 2018
Publication title -
liver international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.873
H-Index - 110
eISSN - 1478-3231
pISSN - 1478-3223
DOI - 10.1111/liv.13709
Subject(s) - steatosis , medicine , framingham heart study , body mass index , framingham risk score , stepwise regression , logistic regression , cohort , incidence (geometry) , fatty liver , gastroenterology , disease , physics , optics
Background and Aims The factors associated with incident hepatic steatosis are not definitively known. We sought to determine factors associated with incident hepatic steatosis, as measured on computed tomography, in the community. Methods We studied Framingham Heart Study participants without heavy alcohol use or baseline hepatic steatosis who underwent computed tomography scans between 2002‐2005 (baseline) and 2008‐2011 (follow‐up). We performed a stepwise logistic regression procedure to determine the predictors associated with incident hepatic steatosis. Results We included 685 participants (mean age: 45.0 ± 6.2 years, 46.8% women). The incidence of hepatic steatosis in our sample was 17.1% over a mean 6.3 years of follow‐up. Participants who developed hepatic steatosis had more adverse cardiometabolic profiles at baseline compared to those free of hepatic steatosis at follow‐up. Multivariable stepwise regression analysis showed that a simple clinical model including age, sex, body mass index, alcohol consumption and triglycerides was predictive of incident hepatic steatosis (C statistic = 0.791, 95% CI : 0.748‐0.834). A complex clinical model, which included visceral adipose tissue volume and liver phantom ratio added to the simple clinical model, and had improved discrimination for predicting incident hepatic steatosis (C statistic = 0.826, 95% CI : 0.786‐0.866, P < .0001). Conclusions The combination of demographic, clinical and imaging characteristics at baseline was predictive of incident hepatic steatosis. The use of our predictive model may help identify those at increased risk for developing hepatic steatosis who may benefit from risk factor modification although further investigation is warranted.