Literature DB >> 29590680

Nutrigenomic Information in the openEHR Data Set.

Priscila Alves Maranhão1, Gustavo Marísio Bacelar-Silva1, Duarte Nuno Gonçalves Ferreira1, Conceição Calhau1,2, Pedro Vieira-Marques1, Ricardo João Cruz-Correia1.   

Abstract

BACKGROUND: The traditional concept of personalized nutrition is based on adapting diets according to individual needs and preferences. Discussions about personalized nutrition have been on since the Human Genome Project, which has sequenced the human genome. Thenceforth, topics such as nutrigenomics have been assessed to help in better understanding the genetic variation influence on the dietary response and association between nutrients and gene expression. Hence, some challenges impaired the understanding about the nowadays important clinical data and about clinical data assumed to be important in the future.
OBJECTIVE: Finding the main clinical statements in the personalized nutrition field (nutrigenomics) to create the future-proof health information system to the openEHR server based on archetypes, as well as a specific nutrigenomic template.
METHODS: A systematic literature search was conducted in electronic databases such as PubMed. The aim of this systemic review was to list the chief clinical statements and create archetype and templates for openEHR modeling tools, namely, Ocean Archetype Editor and Ocean Template Design.
RESULTS: The literature search led to 51 articles; however, just 26 articles were analyzed after all the herein adopted inclusion criteria were assessed. Of these total, 117 clinical statements were identified, as well as 27 archetype-friendly concepts. Our group modeled four new archetypes (waist-to-height ratio, genetic test results, genetic summary, and diet plan) and finally created the specific nutrigenomic template for nutrition care.
CONCLUSION: The archetypes and the specific openEHR template developed in this study gave dieticians and other health professionals an important tool to their nutrigenomic clinical practices, besides a set of nutrigenomic data to clinical research. Schattauer GmbH Stuttgart.

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Year:  2018        PMID: 29590680      PMCID: PMC5874138          DOI: 10.1055/s-0038-1635115

Source DB:  PubMed          Journal:  Appl Clin Inform        ISSN: 1869-0327            Impact factor:   2.342


  56 in total

1.  Meta-Analysis of Genes in Commercially Available Nutrigenomic Tests Denotes Lack of Association with Dietary Intake and Nutrient-Related Pathologies.

Authors:  Cristiana Pavlidis; Zoi Lanara; Angeliki Balasopoulou; Jean-Christophe Nebel; Theodora Katsila; George P Patrinos
Journal:  OMICS       Date:  2015-09

2.  Gene-Gene Interplay and Gene-Diet Interactions Involving the MTNR1B rs10830963 Variant with Body Weight Loss.

Authors:  Leticia Goni; Marta Cuervo; Fermin I Milagro; J Alfredo Martínez
Journal:  J Nutrigenet Nutrigenomics       Date:  2015-04-10

3.  Two common single nucleotide polymorphisms in the gene encoding beta-carotene 15,15'-monoxygenase alter beta-carotene metabolism in female volunteers.

Authors:  W C Leung; S Hessel; C Méplan; J Flint; V Oberhauser; F Tourniaire; J E Hesketh; J von Lintig; G Lietz
Journal:  FASEB J       Date:  2008-12-22       Impact factor: 5.191

4.  Mediterranean meal versus Western meal effects on postprandial ox-LDL, oxidative and inflammatory gene expression in healthy subjects: a randomized controlled trial for nutrigenomic approach in cardiometabolic risk.

Authors:  Antonino De Lorenzo; Sergio Bernardini; Paola Gualtieri; Andrea Cabibbo; Marco Alfonso Perrone; Ilio Giambini; Laura Di Renzo
Journal:  Acta Diabetol       Date:  2016-10-05       Impact factor: 4.280

5.  The effect of the apolipoprotein E genotype on response to personalized dietary advice intervention: findings from the Food4Me randomized controlled trial.

Authors:  Rosalind Fallaize; Carlos Celis-Morales; Anna L Macready; Cyril Fm Marsaux; Hannah Forster; Clare O'Donovan; Clara Woolhead; Rodrigo San-Cristobal; Silvia Kolossa; Jacqueline Hallmann; Christina Mavrogianni; Agnieszka Surwillo; Katherine M Livingstone; George Moschonis; Santiago Navas-Carretero; Marianne C Walsh; Eileen R Gibney; Lorraine Brennan; Jildau Bouwman; Keith Grimaldi; Yannis Manios; Iwona Traczyk; Christian A Drevon; J Alfredo Martinez; Hannelore Daniel; Wim Hm Saris; Michael J Gibney; John C Mathers; Julie A Lovegrove
Journal:  Am J Clin Nutr       Date:  2016-08-10       Impact factor: 7.045

6.  A quantitative assessment of plasma homocysteine as a risk factor for vascular disease. Probable benefits of increasing folic acid intakes.

Authors:  C J Boushey; S A Beresford; G S Omenn; A G Motulsky
Journal:  JAMA       Date:  1995-10-04       Impact factor: 56.272

7.  Archetype relational mapping - a practical openEHR persistence solution.

Authors:  Li Wang; Lingtong Min; Rui Wang; Xudong Lu; Huilong Duan
Journal:  BMC Med Inform Decis Mak       Date:  2015-11-05       Impact factor: 2.796

Review 8.  Clinical genomics in the world of the electronic health record.

Authors:  Keith Marsolo; S Andrew Spooner
Journal:  Genet Med       Date:  2013-07-11       Impact factor: 8.822

9.  Intake of red wine in different meals modulates oxidized LDL level, oxidative and inflammatory gene expression in healthy people: a randomized crossover trial.

Authors:  Laura Di Renzo; Alberto Carraro; Roberto Valente; Leonardo Iacopino; Carmen Colica; Antonino De Lorenzo
Journal:  Oxid Med Cell Longev       Date:  2014-04-30       Impact factor: 6.543

10.  Validating archetypes for the Multiple Sclerosis Functional Composite.

Authors:  Michael Braun; Alexander Ulrich Brandt; Stefan Schulz; Martin Boeker
Journal:  BMC Med Inform Decis Mak       Date:  2014-08-03       Impact factor: 2.796

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  3 in total

1.  Sync for Genes: Making Clinical Genomics Available for Precision Medicine at the Point-of-Care.

Authors:  Stephanie J Garcia; Teresa Zayas-Cabán; Robert R Freimuth
Journal:  Appl Clin Inform       Date:  2020-04-22       Impact factor: 2.342

2.  Discovering Clinical Information Models Online to Promote Interoperability of Electronic Health Records: A Feasibility Study of OpenEHR.

Authors:  Lin Yang; Xiaoshuo Huang; Jiao Li
Journal:  J Med Internet Res       Date:  2019-05-28       Impact factor: 5.428

3.  Nutrition Information in Oncology - Extending the Electronic Patient-Record Data Set.

Authors:  Priscila A Maranhão; Ana Margarida Pereira; Conceição Calhau; Paula Ravasco; Federico Bozzetti; Alessandro Laviano; Liz Isenring; Elisa V Bandera; Maureen B Huhmann; Pedro Vieira-Marques; Ricardo J Cruz-Correia
Journal:  J Med Syst       Date:  2020-09-28       Impact factor: 4.460

  3 in total

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