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

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 2nd 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 5th and 6th most important factors,as opposed to Ca and BF in the NAFLD+group.

Roles of biochemistry data, lifestyle, and inflammation in identifying abnormal renal function in old Chinese

BACKGROUND The incidence of chronic kidney disease(CKD)has dramatically increased in recent years,with significant impacts on patient mortality rates.Previous studies have identified multiple risk factors for CKD,but they mostly relied on the use of traditional statistical methods such as logistic regression and only focused on a few risk factors.AIM To determine factors that can be used to identify subjects with a low estimated glomerular filtration rate(L-eGFR<60 mL/min per 1.73 m^(2))in a cohort of 1236 Chinese people aged over 65.METHODS Twenty risk factors were divided into three models.Model 1 consisted of demographic and biochemistry data.Model 2 added lifestyle data to Model 1,and Model 3 added inflammatory markers to Model 2.Five machine learning methods were used:Multivariate adaptive regression splines,eXtreme Gradient Boosting,stochastic gradient boosting,Light Gradient Boosting Machine,and Categorical Features+Gradient Boosting.Evaluation criteria included accuracy,sensitivity,specificity,area under the receiver operating characteristic curve(AUC),F-1 score,and balanced accuracy.RESULTS A trend of increasing AUC of each was observed from Model 1 to Model 3 and reached statistical significance.Model 3 selected uric acid as the most important risk factor,followed by age,hemoglobin(Hb),body mass index(BMI),sport hours,and systolic blood pressure(SBP).CONCLUSION Among all the risk factors including demographic,biochemistry,and lifestyle risk factors,along with inflammation markers,UA is the most important risk factor to identify L-eGFR,followed by age,Hb,BMI,sport hours,and SBP in a cohort of elderly Chinese people.