Open AccessStructural Equation Modeling Highlights the Potential of Kim-1 as a Biomarker for Chronic Kidney Disease
Author(s) -
Lesley Gardiner,
Adebayo D. Akintola,
Gang Chen,
Jeffrey M. Catania,
Vishal S. Vaidya,
Robert C. Burghardt,
Joseph V. Bonventre,
Jerome P. Trzeciakowski,
Alan R. Parrish
Publication year - 2012
Publication title -
american journal of nephrology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 85
eISSN - 1421-9670
pISSN - 0250-8095
DOI - 10.1159/000335579
Subject(s) - kidney disease , biomarker , renal function , medicine , kidney , structural equation modeling , disease , endocrinology , physiology , biology , statistics , mathematics , biochemistry
Chronic kidney disease (CKD) is a major public health problem, and despite continued research in the field, there is still a need to identify both biomarkers of risk and progression, as well as potential therapeutic targets. Structural equation modeling (SEM) is a family of statistical techniques that has been utilized in the fields of sociology and psychology for many years; however, its utilization in the biological sciences is relatively novel. SEM's ability to investigate complex relationships in an efficient, single model could be utilized to understand the progression of CKD, as well as to develop a predictive model to assess kidney status in the patient.