title: Construction Formula of Biological Age Using the Principal Component Analysis.
abstract: The biological age (BA) equation is a prediction model that utilizes an algorithm to combine various biological markers of ageing. Different from traditional concepts, the BA equation does not emphasize the importance of a golden index but focuses on using indices of vital organs to represent the senescence of whole body. This model has been used to assess the ageing process in a more precise way and may predict possible diseases better as compared with the chronological age (CA). The principal component analysis (PCA) is applied as one of the common and frequently used methods in the construction of the BA formula. Compared with other methods, PCA has its own study procedures and features. Herein we summarize the up-to-date knowledge about the BA formula construction and discuss the influential factors, so as to give an overview of BA estimate by PCA, including composition of samples, choices of test items, and selection of ageing biomarkers. We also discussed the advantages and disadvantages of PCA with reference to the construction mechanism, accuracy, and practicability of several common methods in the construction of the BA formula.
authors.affiliation: Department of Nephrology, The Second Hospital of Jilin University, Ziqiang Street, No. 218, Changchun, Jilin 130041, China.
authors.forename: Linpei
authors.initials: L
authors.lastname: Jia
authors.affiliation: Department of Nephrology, Chinese People's Liberation Army General Hospital, Chinese People's Liberation Army Institute of Nephrology, State Key Laboratory of Kidney Disease, National Clinic Research Center for Kidney Diseases, Fuxing Road, No. 28, Beijing 100853, China.
authors.forename: Weiguang
authors.initials: W
authors.lastname: Zhang
authors.affiliation: Central Hospital of Cangzhou, Xinhua Middle Street, No. 201, Cangzhou, Hebei 061001, China.
authors.forename: Rufu
authors.initials: R
authors.lastname: Jia
authors.affiliation: Department of Life Sciences, The National Natural Science Foundation of China, Shuangqing Road, No. 83, Beijing 100085, China.
authors.forename: Hongliang
authors.identifier: 0000-0001-9205-5559
authors.initials: H
authors.lastname: Zhang
authors.affiliation: Department of Nephrology, The Second Hospital of Jilin University, Ziqiang Street, No. 218, Changchun, Jilin 130041, China; Department of Nephrology, Chinese People's Liberation Army General Hospital, Chinese People's Liberation Army Institute of Nephrology, State Key Laboratory of Kidney Disease, National Clinic Research Center for Kidney Diseases, Fuxing Road, No. 28, Beijing 100853, China.
authors.forename: Xiangmei
authors.identifier: 0000-0001-8774-6021
authors.initials: X
authors.lastname: Chen
mesh_terms: D000375:Aging / Q000502:physiology*; D000465:Algorithms; D015415:Biomarkers / Q000378:metabolism; D006801:Humans; D008954:Models, Biological; D025341:Principal Component Analysis
chemical_list: D015415:Biomarkers