title: An approach to estimate between- and within-group correlation coefficients in multicenter studies: plasma carotenoids as biomarkers of intake of fruits and vegetables.
abstract: In a multicenter study, the overall correlation between two variables can be broken down into a within- and a between-group correlation reflecting associations at the individual and aggregate levels, respectively. A random-effects model is used to estimate variance components of nutrition-related variables and the within- and between-group correlation coefficients. Using data from the European Prospective Investigation into Cancer and Nutrition (EPIC), the authors analyzed the association between levels of three plasma carotenoids (alpha-carotene, beta-cryptoxanthin, and lycopene) and dietary intake of three items (total fruits, carrots, and tomatoes), assessed through dietary questionnaire and single 24-hour dietary recall measurements, in a cross-sectional study involving 3,089 subjects from nine European countries. Intraclass correlation coefficients were 0.178 for alpha-carotene, 0.216 for beta-cryptoxanthin, and 0.299 for lycopene. The between-group correlation coefficients were higher than the within-group coefficients for all three carotenoids. For beta-cryptoxanthin and fruit intake, the between-group versus the within-group correlations were 0.78 and 0.26 for the dietary questionnaire and 0.85 and 0.19 for the 24-hour dietary recall. Results indicate that variability of exposure is driven mainly by the individual compared with the aggregate variation and that biomarker levels perform fairly accurately in discriminating population-level consumption of fruits and vegetables.
authors.affiliation: Unit of Nutrition and Cancer, International Agency for Research on Cancer, Lyon, France. Ferrari@iarc.fr
authors.forename: Pietro
authors.initials: P
authors.lastname: Ferrari
authors.forename: Wael K
authors.initials: WK
authors.lastname: Al-Delaimy
authors.forename: Nadia
authors.initials: N
authors.lastname: Slimani
authors.forename: Hendriek C
authors.initials: HC
authors.lastname: Boshuizen
authors.forename: Andrew
authors.initials: A
authors.lastname: Roddam
authors.forename: Philippos
authors.initials: P
authors.lastname: Orfanos
authors.forename: Guri
authors.initials: G
authors.lastname: Skeie
authors.forename: Miguel
authors.initials: M
authors.lastname: Rodríguez-Barranco
authors.forename: Anne
authors.initials: A
authors.lastname: Thiebaut
authors.forename: Gerd
authors.initials: G
authors.lastname: Johansson
authors.forename: Domenico
authors.initials: D
authors.lastname: Palli
authors.forename: Heiner
authors.initials: H
authors.lastname: Boeing
authors.forename: Kim
authors.initials: K
authors.lastname: Overvad
authors.forename: Elio
authors.initials: E
authors.lastname: Riboli
mesh_terms: D000328:Adult; D000465:Algorithms; D000704:Analysis of Variance; D015415:Biomarkers / Q000097:blood; D002338:Carotenoids / Q000097:blood*; D015331:Cohort Studies; D004032:Diet; D004034:Diet Surveys; D005260:Female; D005638:Fruit; D006801:Humans; D008297:Male; D015233:Models, Statistical; D012044:Regression Analysis; D014675:Vegetables
chemical_list: D015415:Biomarkers; D002338:Carotenoids