Development of a prediction model for predicting the prevalence of nonalcoholic fatty liver disease in Chinese nurses: the first-year follow data of a web-based ambispective cohort study

Abstract Background Nonalcoholic fatty liver disease (NAFLD) is gradually becoming a huge threat to public health. With complex working characteristics, female nurses had been found with high risk of NAFLD. To develop and validate a prediction model to predict the prevalence of NAFLD based on demographic characteristics, work situation, daily lifestyle and laboratory tests in female nurses. Methods This study was a part of the Chinese Nurse Cohort Study (The National Nurse Health Study, NNHS), and data were extracted from the first-year follow data collected from 1st June to 1st September 2021 by questionnaires and physical examination records in a comprehensive tertiary hospital. The questionnaires included demographic characteristics, work situation and daily lifestyle. Logistic regression and a nomogram were used to develop and validate the prediction model. Results A total of 824 female nurses were included in this study. Living situation, smoking history, monthly night shift, daily sleep time, ALT/AST, FBG, TG, HDL-C, UA, BMI, TBil and Ca were independent risk factors for NAFLD occurance. A prediction model for predicting the prevalence of NAFLD among female nurses was developed and verified in this study. Conclusion Living situation, smoking history, monthly night shift, daily sleep time, ALT/AST, FBG, TG, UA, BMI and Ca were independent predictors, while HDL-C and Tbil were independent protective indicators of NAFLD occurance. The prediction model and nomogram could be applied to predict the prevalence of NAFLD among female nurses, which could be used in health improvement. Trial registration This study was a part of the Chinese Nurse Cohort Study (The National Nurse Health Study, NNHS), which was a ambispective cohort study contained past data and registered at Clinicaltrials.gov ( https://clinicaltrials.gov/ct2/show/NCT04572347 ) and the China Cohort Consortium ( http://chinacohort.bjmu.edu.cn/project/102/ ).