Machine learning-based comparison of factors influencing estimated glomerular filtration rate in Chinese women with or without non-alcoholic fatty liver

Abstract

BACKGROUND

The prevalence of non-alcoholic fatty liver (NAFLD) has increased recently. Subjects with NAFLD are known to have higher chance for renal function impairment. Many past studies used traditional multiple linear regression (MLR) to identify risk factors for decreased estimated glomerular filtration rate (eGFR). However, medical research is increasingly relying on emerging machine learning (Mach-L) methods. The present study enrolled healthy women to identify factors affecting eGFR in subjects with and without NAFLD (NAFLD+, NAFLD-) and to rank their importance.

AIM

To uses three different Mach-L methods to identify key impact factors for eGFR in healthy women with and without NAFLD.

METHODS

A total of 65535 healthy female study participants were enrolled from the Taiwan MJ cohort, accounting for 32 independent variables including demographic, biochemistry and lifestyle parameters (independent variables), while eGFR was used as the dependent variable. Aside from MLR, three Mach-L methods were applied, including stochastic gradient boosting, eXtreme gradient boosting and elastic net. Errors of estimation were used to define method accuracy, where smaller degree of error indicated better model performance.

RESULTS

Income, albumin, eGFR, High density lipoprotein-Cholesterol, phosphorus, forced expiratory volume in one second (FEV1), and sleep time were all lower in the NAFLD+ group, while other factors were all significantly higher except for smoking area. Mach-L had lower estimation errors, thus outperforming MLR. In Model 1, age, uric acid (UA), FEV1, plasma calcium level (Ca), plasma albumin level (Alb) and T-bilirubin were the most important factors in the NAFLD+ group, as opposed to age, UA, FEV1, Alb, lactic dehydrogenase (LDH) and Ca for the NAFLD- group. Given the importance percentage was much higher than the 2^nd important factor, we built Model 2 by removing age.

CONCLUSION

The eGFR were lower in the NAFLD+ group compared to the NAFLD- group, with age being was the most important impact factor in both groups of healthy Chinese women, followed by LDH, UA, FEV1 and Alb. However, for the NAFLD- group, TSH and SBP were the 5^th and 6^th most important factors, as opposed to Ca and BF in the NAFLD+ group.

Keywords: Non-alcoholic fatty liver; Estimated glomerular filtration rate; Machine learning; Chinese women

Core Tip: We examined influential factors affecting the estimated glomerular filtration rate in healthy women with and without non-alcoholic fatty liver disease (NAFLD) by multiple linear regression and machine learning methods, with machine learning methods providing better performance and showing that age was the most important determining factor in both groups, followed by lactic dehydrogenase, uric acid, forced expiratory volume in one second, and albumin. However, for the NAFLD- group, the 5^th and 6^th most important impact factors were thyroid-stimulating hormone and systolic blood pressure, as compared to plasma calcium and body fat for the NAFLD+ group.