title: Structural equation modeling highlights the potential of Kim-1 as a biomarker for chronic kidney disease.
abstract: BACKGROUND
  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.
  
  
  METHODS
  Fischer 344 rats were fed either an ad libitum diet or a calorically restricted diet, and a time-course study of kidney structure and function was performed. EQS, a SEM software package, was utilized to generate five CKD models of the Fisher 344 rat and identify relationships between measured variables and estimates of kidney damage and kidney function.
  
  
  RESULTS
  All models identified strong relationships between a biomarker for CKD, kidney injury molecule-1 (Kim-1) and kidney damage, in the Fischer 344 rat CKD model. Models also indicate a strong relationship between age and renal damage and dysfunction.
  
  
  CONCLUSIONS
  SEM can be used to model CKD and could be useful to examine biomarkers in CKD patients.
authors.affiliation: College of Medicine, Texas A&M Health Science Center, Bryan, TX, USA.
authors.forename: Lesley
authors.initials: L
authors.lastname: Gardiner
authors.forename: Adebayo
authors.initials: A
authors.lastname: Akintola
authors.forename: Gang
authors.initials: G
authors.lastname: Chen
authors.forename: Jeffrey M
authors.initials: JM
authors.lastname: Catania
authors.forename: Vishal
authors.initials: V
authors.lastname: Vaidya
authors.forename: Robert C
authors.initials: RC
authors.lastname: Burghardt
authors.forename: Joseph V
authors.initials: JV
authors.lastname: Bonventre
authors.forename: Jerome
authors.initials: J
authors.lastname: Trzeciakowski
authors.forename: Alan R
authors.initials: AR
authors.lastname: Parrish
mesh_terms: D000367:Age Factors; D000818:Animals; D015415:Biomarkers / Q000378:metabolism; D001806:Blood Urea Nitrogen; D015815:Cell Adhesion Molecules / Q000378:metabolism*; D002908:Chronic Disease; D003404:Creatinine / Q000097:blood; D007668:Kidney / Q000378:metabolism* / Q000473:pathology / Q000503:physiopathology; D007674:Kidney Diseases / Q000378:metabolism* / Q000473:pathology / Q000503:physiopathology; D008297:Male; D015233:Models, Statistical; D051381:Rats; D011916:Rats, Inbred F344; D016037:Single-Blind Method
chemical_list: D015415:Biomarkers; D015815:Cell Adhesion Molecules; D003404:Creatinine; C110936:Havcr1 protein, rat