title: Predicting urinary creatinine excretion and its usefulness to identify incomplete 24 h urine collections.
abstract: Studies using 24 h urine collections need to incorporate ways to validate the completeness of the urine samples. Models to predict urinary creatinine excretion (UCE) have been developed for this purpose; however, information on their usefulness to identify incomplete urine collections is limited. We aimed to develop a model for predicting UCE and to assess the performance of a creatinine index using para-aminobenzoic acid (PABA) as a reference. Data were taken from the European Food Consumption Validation study comprising two non-consecutive 24 h urine collections from 600 subjects in five European countries. Data from one collection were used to build a multiple linear regression model to predict UCE, and data from the other collection were used for performance testing of a creatinine index-based strategy to identify incomplete collections. Multiple linear regression (n 458) of UCE showed a significant positive association for body weight (β = 0·07), the interaction term sex × weight (β = 0·09, reference women) and protein intake (β = 0·02). A significant negative association was found for age (β = -0·09) and sex (β = -3·14, reference women). An index of observed-to-predicted creatinine resulted in a sensitivity to identify incomplete collections of 0·06 (95 % CI 0·01, 0·20) and 0·11 (95 % CI 0·03, 0·22) in men and women, respectively. Specificity was 0·97 (95 % CI 0·97, 0·98) in men and 0·98 (95 % CI 0·98, 0·99) in women. The present study shows that UCE can be predicted from weight, age and sex. However, the results revealed that a creatinine index based on these predictions is not sufficiently sensitive to exclude incomplete 24 h urine collections.
authors.affiliation: Department of Nutrition and Dietetics, Faculty of Health Care Vesalius, University College Ghent, Keramiekstraat 80, B-9000 Ghent, Belgium. willem.dekeyzer@hogent.be
authors.forename: Willem
authors.initials: W
authors.lastname: De Keyzer
authors.forename: Inge
authors.initials: I
authors.lastname: Huybrechts
authors.forename: Arnold L M
authors.initials: AL
authors.lastname: Dekkers
authors.forename: Anouk
authors.initials: A
authors.lastname: Geelen
authors.forename: Sandra
authors.initials: S
authors.lastname: Crispim
authors.forename: Paul J M
authors.initials: PJ
authors.lastname: Hulshof
authors.forename: Lene F
authors.initials: LF
authors.lastname: Andersen
authors.forename: Irena
authors.initials: I
authors.lastname: Řehůřková
authors.forename: Jiří
authors.initials: J
authors.lastname: Ruprich
authors.forename: Jean-Luc
authors.initials: JL
authors.lastname: Volatier
authors.forename: Georges
authors.initials: G
authors.lastname: Van Maele
authors.forename: Nadia
authors.initials: N
authors.lastname: Slimani
authors.forename: Pieter
authors.initials: P
authors.lastname: van't Veer
authors.forename: Evelien
authors.initials: E
authors.lastname: de Boer
authors.forename: Stefaan
authors.initials: S
authors.lastname: De Henauw
mesh_terms: D000367:Age Factors; D000368:Aged; D015415:Biomarkers / Q000652:urine; D001835:Body Weight; D003404:Creatinine / Q000652:urine*; D004032:Diet / Q000208:ethnology; D005060:Europe; D005260:Female; D006801:Humans; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D010349:Patient Compliance / Q000208:ethnology; D012727:Sex Characteristics; D059349:Urine Specimen Collection
chemical_list: D015415:Biomarkers; D003404:Creatinine