Appl Clin Inform 2018; 09(01): 221-231
DOI: 10.1055/s-0038-1635115
Research Article
Schattauer GmbH Stuttgart

Nutrigenomic Information in the openEHR Data Set

Priscila Alves Maranhão
1   Faculty of Medicine, Center for Research in Health Technologies and Information Systems (CINTESIS), University of Porto, Porto, Portugal
Gustavo Marísio Bacelar-Silva
1   Faculty of Medicine, Center for Research in Health Technologies and Information Systems (CINTESIS), University of Porto, Porto, Portugal
Duarte Nuno Gonçalves Ferreira
1   Faculty of Medicine, Center for Research in Health Technologies and Information Systems (CINTESIS), University of Porto, Porto, Portugal
Conceição Calhau
1   Faculty of Medicine, Center for Research in Health Technologies and Information Systems (CINTESIS), University of Porto, Porto, Portugal
2   Faculty of Medical Science, Nova de Lisboa University, Nova Medical School, Lisboa, Portugal
Pedro Vieira-Marques
1   Faculty of Medicine, Center for Research in Health Technologies and Information Systems (CINTESIS), University of Porto, Porto, Portugal
Ricardo João Cruz-Correia
1   Faculty of Medicine, Center for Research in Health Technologies and Information Systems (CINTESIS), University of Porto, Porto, Portugal
› Author Affiliations
Further Information

Publication History

23 November 2017

27 January 2018

Publication Date:
28 March 2018 (online)


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.

Protection of Human and Animal Subjects

Not required.

  • References

  • 1 Sales NM, Pelegrini PB, Goersch MC. Nutrigenomics: definitions and advances of this new science. J Nutr Metab 2014; 2014: 202759
  • 2 Joffe YT, Houghton CA. A novel approach to the nutrigenetics and nutrigenomics of obesity and weight management. Curr Oncol Rep 2016; 18 (07) 43
  • 3 Sebat J, Lakshmi B, Troge J. , et al. Large-scale copy number polymorphism in the human genome. Science 2004; 305 (5683): 525-528
  • 4 Hesketh J. Personalised nutrition: how far has nutrigenomics progressed?. Eur J Clin Nutr 2013; 67 (05) 430-435
  • 5 Ferguson LR, De Caterina R, Görman U. , et al. Guide and position of the International Society of Nutrigenetics/Nutrigenomics on Personalised Nutrition: part 1—fields of precision nutrition. J Nutrigenet Nutrigenomics 2016; 9 (01) 12-27
  • 6 Massoud M, Ragozin H, Schmid G, Spalding L. The Future of Nutrition: Consumers Engage with Science. Palo Alto, CA: Institute for the Future; 2001
  • 7 Ferguson JF, Allayee H, Gerszten RE. , et al; American Heart Association Council on Functional Genomics and Translational Biology, Council on Epidemiology and Prevention, and Stroke Council. Nutrigenomics, the microbiome, and gene-environment interactions: new directions in cardiovascular disease research, prevention, and treatment: a scientific statement from the American Heart Association. Circ Cardiovasc Genet 2016; 9 (03) 291-313
  • 8 Peña-Romero AC, Navas-Carrillo D, Marín F, Orenes-Piñero E. The future of nutrition: nutrigenomics and nutrigenetics in obesity and cardiovascular diseases. Crit Rev Food Sci Nutr 2017; DOI: 10.1080/10408398.2017.1349731.
  • 9 Boushey CJ, Beresford SA, Omenn GS, Motulsky AG. A quantitative assessment of plasma homocysteine as a risk factor for vascular disease. Probable benefits of increasing folic acid intakes. JAMA 1995; 274 (13) 1049-1057
  • 10 Ashfield-Watt PA, Pullin CH, Whiting JM. , et al. Methylenetetrahydrofolate reductase 677C-->T genotype modulates homocysteine responses to a folate-rich diet or a low-dose folic acid supplement: a randomized controlled trial. Am J Clin Nutr 2002; 76 (01) 180-186
  • 11 Sterling R. The on-line promotion and sale of nutrigenomic services. Genet Med 2008; 10 (11) 784-796
  • 12 Goddard KA, Robitaille J, Dowling NF. , et al. Health-related direct-to-consumer genetic tests: a public health assessment and analysis of practices related to Internet-based tests for risk of thrombosis. Public Health Genomics 2009; 12 (02) 92-104
  • 13 Cormier H, Tremblay BL, Paradis AM. , et al. Nutrigenomics - perspectives from registered dietitians: a report from the Quebec-wide e-consultation on nutrigenomics among registered dietitians. J Hum Nutr Diet 2014; 27 (04) 391-400
  • 14 Rosen R, Earthman C, Marquart L, Reicks M. Continuing education needs of registered dietitians regarding nutrigenomics. J Am Diet Assoc 2006; 106 (08) 1242-1245
  • 15 Leung WC, Hessel S, Méplan C. , et al. Two common single nucleotide polymorphisms in the gene encoding beta-carotene 15,15′-monoxygenase alter beta-carotene metabolism in female volunteers. FASEB J 2009; 23 (04) 1041-1053
  • 16 Haggarty P. B-vitamins, genotype and disease causality. Proc Nutr Soc 2007; 66 (04) 539-547
  • 17 Goni L, Cuervo M, Milagro FI, Martínez JA. Future perspectives of personalized weight loss interventions based on nutrigenetic, epigenetic, and metagenomic data. J Nutr 2016; jn218354
  • 18 Pavlidis C, Lanara Z, Balasopoulou A, Nebel JC, Katsila T, Patrinos GP. Meta-analysis of genes in commercially available nutrigenomic tests denotes lack of association with dietary intake and nutrient-related pathologies. OMICS 2015; 19 (09) 512-520
  • 19 Castle D, DeBusk R. The electronic health record, genetic information, and patient privacy. J Am Diet Assoc 2008; 108 (08) 1372-1374
  • 20 Vanek VW. Providing nutrition support in the electronic health record era: the good, the bad, and the ugly. Nutr Clin Pract 2012; 27 (06) 718-737
  • 21 Vanek VW, Ayers P, Charney P. , et al. Follow-up survey on functionality of nutrition documentation and ordering nutrition therapy in currently available electronic health record systems. Nutr Clin Pract 2016; 31 (03) 401-415
  • 22 McDonald CJ. The barriers to electronic medical record systems and how to overcome them. J Am Med Inform Assoc 1997; 4 (03) 213-221
  • 23 Delpierre C, Cuzin L, Fillaux J, Alvarez M, Massip P, Lang T. A systematic review of computer-based patient record systems and quality of care: more randomized clinical trials or a broader approach?. Int J Qual Health Care 2004; 16 (05) 407-416
  • 24 Goetz Goldberg D, Kuzel AJ, Feng LB, DeShazo JP, Love LE. EHRs in primary care practices: benefits, challenges, and successful strategies. Am J Manag Care 2012; 18 (02) e48-e54
  • 25 Beale T. Archetypes and the EHR. In: Blobel BGME, Pharow P. , eds. Advanced Health Telematics and Telemedicine: The Magdeburg Expert Summit Textbook. Amsterdam: IOS Press; 2003
  • 26 Wang L, Min L, Wang R, Lu X, Duan H. Archetype relational mapping - a practical openEHR persistence solution. BMC Med Inform Decis Mak 2015; 15: 88
  • 27 openEHR Foundation: openEHR. An open domain-driven platform for developing flexible e-health systems. Welcome to openEHR - Homepage. Available at: . Accessed August 13, 2017
  • 28 Beale SH, Kalra D, Lloyd D, Schloeffel P. Introducing openEHR. In: openEHR; 2006
  • 29 openEHR Foundation. Archetype Definitions and Principles: openEHR Release 1.0.1. London: Revision:1.0; 2007
  • 30 Leslie H, Heard S. Archetypes 101. Health Informatics Conference, Sydney, Australia, 2006. Available at:
  • 31 Braun M, Brandt AU, Schulz S, Boeker M. Validating archetypes for the Multiple Sclerosis Functional Composite. BMC Med Inform Decis Mak 2014; 14: 64
  • 32 Gustavo M, Bacelar-Silva RC, Cruz-Correia RJ. From clinical guideline to openEHR: converting JNC7 into archetypes and template. XIII Congresso Brasileiro em Informática em Saúde – CBIS ; 2012: 1-6
  • 33 openEHR foundation. EHR Information Model. Release 1.0.2. London, August 2008
  • 34 openEHR Foundation: Clinical knowledge manager. Available at: . Accessed August 21, 2017
  • 35 Marcos M, Martínez-Salvador B. Towards the interoperability of computerised guidelines and electronic health records: an experiment with openEHR archetypes and a chronic heart failure guideline. In: Riaño D, ten Teije A, Miksch S, Peleg M. , eds. Knowledge Representation for Health-Care: ECAI 2010 Workshop KR4HC 2010, Lisbon, Portugal, August 17, 2010, Revised Selected Papers. Berlin, Heidelberg: Springer Berlin Heidelberg; 2011: 101-113
  • 36 Bacelar-Silva G, Cruz-Correia R. Manual de introdução à norma openEHR, 2015. Available at:
  • 37 Archetype Definitions and Principles. OpenEHR Release 1.0.1. London: Revision: 1.0; 2007
  • 38 German JB, Zivkovic AM, Dallas DC, Smilowitz JT. Nutrigenomics and personalized diets: what will they mean for food?. Annu Rev Food Sci Technol 2011; 2: 97-123
  • 39 Mascia C, Uva P, Leo S, Zanetti G. OpenEHR modeling for genomics in clinical practice. bioRxiv; 2017. Available at:
  • 40 Marsolo K, Spooner SA. Clinical genomics in the world of the electronic health record. Genet Med 2013; 15 (10) 786-791
  • 41 Priscila Maranhão GB, Gonçalves D, Vieira-Marques P, Cruz-Correia RJ. Relevant lifelong nutrition information for the prevention and treatment of childhood obesity - design and creation of new openEHR archetype set. IEEE International Symposium on Computer-Based Medical Systems: 23/06/2017. Greece; 2017
  • 42 Spigolon DN, Moro CM. Essential data sets archetypes for nursing care of endometriosis patients. Rev Gaúcha Enferm 2012; 33 (04) 22-33
  • 43 Bosca D, Marco L, Burriel V. , et al. Genetic testing information standardization in HL7 CDA and ISO13606. Stud Health Technol Inform 2013; 192: 338-342
  • 44 Electronic Medical Records and Genomics (eMERGE) Network. Available at: . Accessed October 20, 2017
  • 45 Claustres M, Kožich V, Dequeker E. , et al; European Society of Human Genetics. Recommendations for reporting results of diagnostic genetic testing (biochemical, cytogenetic and molecular genetic). Eur J Hum Genet 2014; 22 (02) 160-170
  • 46 Ortega-Azorín C, Sorlí JV, Asensio EM. , et al. Associations of the FTO rs9939609 and the MC4R rs17782313 polymorphisms with type 2 diabetes are modulated by diet, being higher when adherence to the Mediterranean diet pattern is low. Cardiovasc Diabetol 2012; 11: 137
  • 47 Lockyer S, Tzanetou M, Carvalho-Wells AL, Jackson KG, Minihane AM, Lovegrove JA. SATgenε dietary model to implement diets of differing fat composition in prospectively genotyped groups (apoE) using commercially available foods. Br J Nutr 2012; 108 (09) 1705-1713
  • 48 Mottaghi A, Salehi E, Keshvarz A, Sezavar H, Saboor-Yaraghi AA. The influence of vitamin A supplementation on Foxp3 and TGF-β gene expression in atherosclerotic patients. J Nutrigenet Nutrigenomics 2012; 5 (06) 314-326
  • 49 Farràs M, Valls RM, Fernández-Castillejo S. , et al. Olive oil polyphenols enhance the expression of cholesterol efflux related genes in vivo in humans. A randomized controlled trial. J Nutr Biochem 2013; 24 (07) 1334-1339
  • 50 Castañer O, Corella D, Covas MI. , et al; PREDIMED study investigators. In vivo transcriptomic profile after a Mediterranean diet in high-cardiovascular risk patients: a randomized controlled trial. Am J Clin Nutr 2013; 98 (03) 845-853
  • 51 Lang JE, Mougey EB, Allayee H. , et al; Nemours Network for Asthma Research. Nutrigenetic response to omega-3 fatty acids in obese asthmatics (NOOA): rationale and methods. Contemp Clin Trials 2013; 34 (02) 326-335
  • 52 Ribeiro IF, Miranda-Vilela AL, Klautau-Guimarães MdeN, Grisolia CK. The influence of erythropoietin (EPO T → G) and α-actinin-3 (ACTN3 R577X) polymorphisms on runners' responses to the dietary ingestion of antioxidant supplementation based on pequi oil ( Caryocar brasiliense Camb.): a before-after study. J Nutrigenet Nutrigenomics 2013; 6 (06) 283-304
  • 53 Al-Ghnaniem Abbadi R, Emery P, Pufulete M. Short-term folate supplementation in physiological doses has no effect on ESR1 and MLH1 methylation in colonic mucosa of individuals with adenoma. J Nutrigenet Nutrigenomics 2012; 5 (06) 327-338
  • 54 Kawakami Y, Yamanaka-Okumura H, Sakuma M. , et al. Gene expression profiling in peripheral white blood cells in response to the intake of food with different glycemic index using a DNA microarray. J Nutrigenet Nutrigenomics 2013; 6 (03) 154-168
  • 55 Konings E, Timmers S, Boekschoten MV. , et al. The effects of 30 days resveratrol supplementation on adipose tissue morphology and gene expression patterns in obese men. Int J Obes 2014; 38 (03) 470-473
  • 56 Nielsen DE, El-Sohemy A. Disclosure of genetic information and change in dietary intake: a randomized controlled trial. PLoS One 2014; 9 (11) e112665
  • 57 Kang R, Kim M, Chae JS, Lee SH, Lee JH. Consumption of whole grains and legumes modulates the genetic effect of the APOA5 -1131C variant on changes in triglyceride and apolipoprotein A-V concentrations in patients with impaired fasting glucose or newly diagnosed type 2 diabetes. Trials 2014; 15: 100
  • 58 Gahete MD, Luque RM, Yubero-Serrano EM. , et al. Dietary fat alters the expression of cortistatin and ghrelin systems in the PBMCs of elderly subjects: putative implications in the postprandial inflammatory response. Mol Nutr Food Res 2014; 58 (09) 1897-1906
  • 59 Di Renzo L, Carraro A, Valente R, Iacopino L, Colica C, De Lorenzo A. Intake of red wine in different meals modulates oxidized LDL level, oxidative and inflammatory gene expression in healthy people: a randomized crossover trial. Oxid Med Cell Longev 2014; 2014: 681318
  • 60 Ouellette C, Cormier H, Rudkowska I. , et al. Polymorphisms in genes involved in the triglyceride synthesis pathway and marine omega-3 polyunsaturated fatty acid supplementation modulate plasma triglyceride levels. J Nutrigenet Nutrigenomics 2013; 6 (4-5): 268-280
  • 61 Goni L, Cuervo M, Milagro FI, Martínez JA. Gene-gene interplay and gene-diet interactions involving the MTNR1B rs10830963 variant with body weight loss. J Nutrigenet Nutrigenomics 2014; 7 (4-6): 232-242
  • 62 García-Calzón S, Martínez-González MA, Razquin C. , et al. Pro12Ala polymorphism of the PPARγ2 gene interacts with a Mediterranean diet to prevent telomere shortening in the PREDIMED-NAVARRA randomized trial. Circ Cardiovasc Genet 2015; 8 (01) 91-99
  • 63 Shab-Bidar S, Neyestani TR, Djazayery A. Vitamin D receptor Cdx-2-dependent response of central obesity to vitamin D intake in the subjects with type 2 diabetes: a randomised clinical trial. Br J Nutr 2015; 114 (09) 1375-1384
  • 64 Di Renzo L, Marsella LT, Carraro A. , et al. Changes in LDL oxidative status and oxidative and inflammatory gene expression after red wine intake in healthy people: a randomized trial. Mediators Inflamm 2015; 2015: 317348
  • 65 Hietaranta-Luoma HL, Tahvonen R, Iso-Touru T, Puolijoki H, Hopia A. An intervention study of individual, apoE genotype-based dietary and physical-activity advice: impact on health behavior. J Nutrigenet Nutrigenomics 2014; 7 (03) 161-174
  • 66 Tremblay BL, Rudkowska I, Couture P, Lemieux S, Julien P, Vohl MC. Modulation of C-reactive protein and plasma omega-6 fatty acid levels by phospholipase A2 gene polymorphisms following a 6-week supplementation with fish oil. Prostaglandins Leukot Essent Fatty Acids 2015; 102-103: 37-45
  • 67 Ahn HY, Kim M, Chae JS. , et al. Supplementation with two probiotic strains, Lactobacillus curvatus HY7601 and Lactobacillus plantarum KY1032, reduces fasting triglycerides and enhances apolipoprotein A-V levels in non-diabetic subjects with hypertriglyceridemia. Atherosclerosis 2015; 241 (02) 649-656
  • 68 Fallaize R, Celis-Morales C, Macready AL. , et al; Food4Me Study. The effect of the apolipoprotein E genotype on response to personalized dietary advice intervention: findings from the Food4Me randomized controlled trial. Am J Clin Nutr 2016; 104 (03) 827-836
  • 69 Mansoori A, Sotoudeh G, Djalali M. , et al. Docosahexaenoic acid-rich fish oil supplementation improves body composition without influence of the PPARγ Pro12Ala polymorphism in patients with type 2 diabetes: a randomized, double-blind, placebo-controlled clinical trial. J Nutrigenet Nutrigenomics 2015; 8 (4-6): 195-204
  • 70 Pu S, Eck P, Jenkins DJ. , et al. Interactions between dietary oil treatments and genetic variants modulate fatty acid ethanolamides in plasma and body weight composition. Br J Nutr 2016; 115 (06) 1012-1023
  • 71 De Lorenzo A, Bernardini S, Gualtieri P. , et al. 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. Acta Diabetol 2017; 54 (02) 141-149