Copyright
©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.
Roles of biochemistry data, lifestyle, and inflammation in identifying abnormal renal function in old Chinese
Chao-Hung Chen, Chun-Kai Wang, Chen-Yu Wang, Chun-Feng Chang, Ta-Wei Chu
Chao-Hung Chen, Chun-Feng Chang, Division of Urology, Department of Surgery, Kaohsiung Armed Forces General Hospital, Kaohsiung 802, Taiwan
Chao-Hung Chen, Division of Urology, Department of Surgery, Chang Gung Memorial Hospital, Keelung 204, Taiwan
Chun-Kai Wang, Department of Obstetrics and Gynecology, Zuoying Branch of Kaohsiung Armed Forces General Hospital, Kaohsiung 813, Taiwan
Chen-Yu Wang, Ta-Wei Chu, Department of Obstetrics and Gynecology, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan
Chun-Feng Chang, Division of Urology, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan
Ta-Wei Chu, Chief Executive Officer's Office, MJ Health Research Foundation, Taipei 114, Taiwan
Author contributions: Chen CH participated in the design and oversight of the study, and was involved in data collection; Wang CK participated in the design of the study and was involved in data collection; Wang CY was involved in data collection and assisted with data analysis; Chang CF drafted the manuscript and assisted with data analysis; Chu TW drafted the manuscript and assisted with data analysis; all authors read and approved the final manuscript.
Supported by the Kaohsiung Armed Forces General Hospital.
Institutional review board statement: The study protocol was approved by the Institutional Review Board of the Tri-Service General Hospital, National Defense Medical Center (IRB No.: KAFGHIRB 109-46).
Informed consent statement: All study participants, or their legal guardian, provided written consent prior to study enrollment.
Conflict-of-interest statement: All the authors of this manuscript have no conflicts of interest to disclose.
Data sharing statement: There is no additional data available.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See:
https://creativecommons.org/Licenses/by-nc/4.0/ Corresponding author: Ta-Wei Chu, MD, PhD, CEO, Chief Physician, Doctor, Department of Obstetrics and Gynecology, Tri-Service General Hospital, National Defense Medical Center, No. 325 Sec. 2, Chenggong Road, Neihu District, Taipei 114, Taiwan. david_chu@mjlife.com
Received: July 5, 2023
Peer-review started: July 5, 2023
First decision: July 18, 2023
Revised: August 1, 2023
Accepted: September 11, 2023
Article in press: September 11, 2023
Published online: October 16, 2023
Processing time: 94 Days and 6.5 Hours
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 m2) 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.
Core Tip: This is a retrospective study that used five machine learning methods to evaluate the impact of lifestyle and chronic inflammation in identifying subjects with abnormal estimated glomerular rates among elderly Chinese subjects. Our results showed that uric acid is the most important risk factor (inflammatory marker), followed by age, hemoglobin, body mass index, sport hours, and systolic blood pressure.
