Predictive value of anthropometric and biochemical indices in non-alcoholic fatty pancreas disease: a cross-sectional study

Abstract

Objectives: Triglyceride (TG), triglyceride-glucose index (TyG), body mass index (BMI), TyG-BMI and triglyceride to high-density lipoprotein ratio (TG/HDL) have been reported to be reliable predictors of non-alcoholic fatty liver disease. However, there are few studies on potential predictors of non-alcoholic fatty pancreas disease (NAFPD). Our aim was to evaluate these and other parameters for predicting NAFPD.

Design: Cross-sectional study design.

Setting: Physical examination centre of a tertiary hospital in China.

Participants: This study involved 1774 subjects who underwent physical examinations from January 2016 to September 2016.

Primary and secondary outcome measures: From each subject, data were collected for 13 basic physical examination and blood biochemical parameters: age, weight, height, BMI, TyG, TyG-BMI, high-density lipoprotein (HDL), low-density lipoprotein, total cholesterol, TG, fasting plasma glucose, TG/HDL and uric acid. NAFPD was diagnosed by abdominal ultrasonography. A logistic regression model with a restricted cubic spline was used to evaluate the relationship between each parameter and NAFPD. The receiver operating characteristic (ROC) curve was used to calculate the area under the curve for each parameter.

Results: HDL was negatively correlated with NAFPD, height was almost uncorrelated with NAFPD and the remaining 11 parameters were positively correlated with NAFPD. ROC curve showed that weight-related parameters (weight, BMI and TyG-BMI) and TG-related parameters (TyG, TG and TG/HDL) had high predictive values for the identification of NAFPD. The combinations of multiple parameters had a better prediction effect than a single parameter. All the predictive effects did not differ by sex.

Conclusions: Weight-related and TG-related parameters are good predictors of NAFPD in all populations. BMI showed the greatest predictive potential. Multiparameter combinations appear to be a good way to predict NAFPD.

Keywords: body mass index; health informatics; pancreatic disease.