Combining biomarker and food intake data: calibration equations for citrus intake

DC FieldValueLanguage
dc.contributor.authorD'Angelo, Silvia-
dc.contributor.authorGormley, Isobel Claire-
dc.contributor.authorMcNulty, Breige A.-
dc.contributor.authorBrennan, Lorraine-
dc.contributor.authoret al.-
dc.date.accessioned2019-11-15T15:27:27Z-
dc.date.available2019-11-15T15:27:27Z-
dc.date.copyright2019 American Society for Nutritionen_US
dc.date.issued2019-10-
dc.identifier.citationThe American Journal of Clinical Nutritionen_US
dc.identifier.issn0002-9165-
dc.identifier.urihttp://hdl.handle.net/10197/11209-
dc.description.abstractBACKGROUND:Measurement error associated with self-reported dietary intake is a well-documented issue. Combining biomarkers of food intake and dietary intake data is a high priority. OBJECTIVES:The objective of this study was to develop calibration equations for food intake, illustrated with an application for citrus intake. Further, a simulation-based framework was developed to determine the portion of biomarker data needed for stable calibration equation estimation in large population studies. METHODS:Calibration equations were developed using mean daily self-reported citrus intake (4-d semiweighed food diaries) and biomarker-derived intake (urinary proline betaine biomarker) data from participants (n = 565) as part of a cross-sectional study. Different functional specifications and biomarker transformations were tested to derive the optimal calibration equation specifications. The simulation study was developed using linear regression for the calibration equations. Stability in the calibration equation estimations was investigated for varying portions of biomarker and intake data "qualities." RESULTS:With citrus intake, linear regression on nontransformed biomarker data resulted in the optimal calibration equation specifications and produced good-quality predicted intakes. The lowest mean squared error (14,354) corresponded to a linear regression model, defined with biomarker-derived estimates of intakes on the original scale. Using this model in a subpopulation without biomarker data resulted in an average mean ± SD citrus intake of 81 ± 66 g/d. The simulation study suggested that in large population studies, biomarker data on 20-30% of the subjects are required to guarantee stable estimation of calibration equations. This article is accompanied by a web application ("Bio-Intake"), which was developed to facilitate measurement error correction in self-reported mean daily citrus intake data. CONCLUSIONS:Calibration equations proved to be a useful instrument to correct measurement error in self-reported food intake data. The simulation study demonstrated that the use of food intake biomarkers may be feasible and beneficial in the context of large population studies.en_US
dc.description.sponsorshipEuropean Research Councilen_US
dc.description.sponsorshipScience Foundation Irelanden_US
dc.format.mediumPrint-Electronic-
dc.language.isoenen_US
dc.publisherOxford University Pressen_US
dc.rightsThis is a pre-copyedited, author-produced PDF of an article accepted for publication in The American Journal of Clinical Nutrition following peer review. The definitive publisher-authenticated version Silvia D'Angelo, Isobel Claire Gormley, Breige A McNulty, Anne P Nugent, Janette Walton, Albert Flynn, Lorraine Brennan, Combining biomarker and food intake data: calibration equations for citrus intake, The American Journal of Clinical Nutrition, Volume 110, Issue 4, October 2019, Pages 977–983, is available online at: https://doi.org/10.1093/ajcn/nqz168en_US
dc.subjectBiomarkersen_US
dc.subjectCalibration equationsen_US
dc.subjectCitrusen_US
dc.subjectMeasurement erroren_US
dc.subjectProline betaineen_US
dc.titleCombining biomarker and food intake data: calibration equations for citrus intakeen_US
dc.title.alternativeCalibration equations for citrus intakeen_US
dc.typeJournal Articleen_US
dc.internal.authorcontactotheraoife.ogorman@ucd.ieen_US
dc.statusPeer revieweden_US
dc.identifier.volume110en_US
dc.identifier.issue4en_US
dc.identifier.startpage977en_US
dc.identifier.endpage983en_US
dc.identifier.doi10.1093/ajcn/nqz168-
dc.neeo.contributorD'Angelo|Silvia|aut|-
dc.neeo.contributorGormley|Isobel Claire|aut|-
dc.neeo.contributorMcNulty|Breige A.|aut|-
dc.neeo.contributorBrennan|Lorraine|aut|-
dc.neeo.contributoret al.||aut|-
dc.date.embargo2020-08-20en_US
dc.description.admin12 month embargo - ACen_US
dc.description.adminEmbargo reduced due to funder requirement - JGen_US
dc.date.updated2019-09-25T08:04:39Z-
dc.identifier.grantid647783-
dc.identifier.grantidSFI/12/RC/2289_P2-
item.fulltextWith Fulltext-
item.grantfulltextopen-
Appears in Collections:Mathematics and Statistics Research Collection
Institute of Food and Health Research Collection
Insight Research Collection
Agriculture and Food Science Research Collection
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