Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/128839
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dc.contributor.authorMartín, Ven_US
dc.contributor.authorDávila Batista, Verónicaen_US
dc.contributor.authorCastilla, Jen_US
dc.contributor.authorGodoy, Pen_US
dc.contributor.authorDelgado-Rodríguez, Men_US
dc.contributor.authorSoldevila, Nen_US
dc.contributor.authorMolina, AJen_US
dc.contributor.authorFernandez-Villa, Ten_US
dc.contributor.authorAstray, Jen_US
dc.contributor.authorCastro, Aen_US
dc.contributor.authorGonzález-Candelas, Fen_US
dc.contributor.authorMayoral, JMen_US
dc.contributor.authorQuintana, JMen_US
dc.contributor.authorDomínguez, Angelaen_US
dc.contributor.authorCIBERESP Cases and Controls in Pandemic Influenza Working Group in Spainen_US
dc.date.accessioned2024-02-07T17:48:49Z-
dc.date.available2024-02-07T17:48:49Z-
dc.date.issued2016en_US
dc.identifier.issn1471-2458en_US
dc.identifier.urihttp://hdl.handle.net/10553/128839-
dc.description.abstractBackground: Obesity is a world-wide epidemic whose prevalence is underestimated by BMI measurements, but CUN-BAE (Clínica Universidad de Navarra - Body Adiposity Estimator) estimates the percentage of body fat (BF) while incorporating information on sex and age, thus giving a better match. Our aim is to compare the BMI and CUN-BAE in determining the population attributable fraction (AFp) for obesity as a cause of chronic diseases. Methods: We calculated the Pearson correlation coefficient between BMI and CUN-BAE, the Kappa index and the internal validity of the BMI. The risks of arterial hypertension (AHT) and diabetes mellitus (DM) and the AFp for obesity were assessed using both the BMI and CUN-BAE. Results: 3888 white subjects were investigated. The overall correlation between BMI and CUN-BAE was R2 = 0.48, which improved when sex and age were taken into account (R2 > 0.90). The Kappa coefficient for diagnosis of obesity was low (28.7 %). The AFp was 50 % higher for DM and double for AHT when CUN-BAE was used. Conclusions: The overall correlation between BMI and CUN-BAE was not good. The AFp of obesity for AHT and DM may be underestimated if assessed using the BMI, as may the prevalence of obesity when estimated from the percentage of BF.en_US
dc.languageengen_US
dc.relation.ispartofBMC Public Healthen_US
dc.sourceBMC Public Health [1471-2458], v. 16:82 (Enero 2016)en_US
dc.subject32 Ciencias médicasen_US
dc.subject3206 Ciencias de la nutriciónen_US
dc.subject.otherObesityen_US
dc.subject.otherBody mass indexen_US
dc.subject.otherBody faten_US
dc.subject.otherCUN-BAEen_US
dc.subject.otherPopulation attributable fractionen_US
dc.subject.otherHypertensionen_US
dc.subject.otherDiabetes mellitusen_US
dc.titleComparison of body mass index (BMI) with the CUN-BAE body adiposity estimator in the prediction of hypertension and type 2 diabetesen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.typeArticleen_US
dc.identifier.doi10.1186/s12889-016-2728-3en_US
dc.identifier.pmid26817835-
dc.identifier.scopus2-s2.0-84955257587-
dc.identifier.isiWOS:000369475800001-
dc.contributor.orcid#NODATA#-
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dc.identifier.issue1-
dc.relation.volume16en_US
dc.investigacionCiencias de la Saluden_US
dc.type2Artículoen_US
dc.description.notasCon la participación de: CIBERESP Cases and Controls in Pandemic Influenza Working Group in Spainen_US
dc.description.numberofpages8en_US
dc.utils.revisionen_US
dc.date.coverdateEnero 2016en_US
dc.identifier.ulpgcen_US
dc.contributor.buulpgcBU-MEDen_US
dc.description.sjr1,328
dc.description.jcr2,265
dc.description.sjrqQ1
dc.description.jcrqQ2
dc.description.scieSCIE
item.grantfulltextopen-
item.fulltextCon texto completo-
crisitem.author.deptGIR IUIBS: Diabetes y endocrinología aplicada-
crisitem.author.deptIU de Investigaciones Biomédicas y Sanitarias-
crisitem.author.deptDepartamento de Ciencias Clínicas-
crisitem.author.deptGIR IUSA-ONEHEALTH 3: Histología y Patología Veterinaria y Forense (Terrestre y Marina)-
crisitem.author.deptIU de Sanidad Animal y Seguridad Alimentaria-
crisitem.author.deptDepartamento de Morfología-
crisitem.author.deptGIR SIANI: Inteligencia Artificial, Robótica y Oceanografía Computacional-
crisitem.author.deptIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.deptDepartamento de Informática y Sistemas-
crisitem.author.orcid0000-0001-8888-395X-
crisitem.author.orcid0000-0002-2243-5449-
crisitem.author.orcid0000-0002-3252-5683-
crisitem.author.parentorgIU de Investigaciones Biomédicas y Sanitarias-
crisitem.author.parentorgIU de Sanidad Animal y Seguridad Alimentaria-
crisitem.author.parentorgIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.fullNameDávila Batista, Verónica-
crisitem.author.fullNameCastro Alonso, Ayoze-
crisitem.author.fullNameDomínguez Brito, Antonio Carlos-
Colección:Artículos
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