Precision medicine for managing diabetes

Journal of Diabetes Science and Technology

Volumn 9, Issue 1, 2015, Pages 3-7

  Klonoff, David C ^a  

^a Mills Peninsula Health Services   (United States)

Author keywords

big data; diabetes; genomics; personalized medicine; pharmacogenomics; precision medicine

Indexed keywords

BIOLOGICAL MARKER;

DIABETES CONTROL; DIABETIC PATIENT; FOOD INTAKE; GENOMICS; GLYCEMIC CONTROL; HUMAN; INDIVIDUALIZATION; MEDICAL CARE; MEDICATION COMPLIANCE; MEDICINE; PERSONALIZED MEDICINE; PHYSICAL EXAMINATION; PRECISION MEDICINE; REVIEW; RISK FACTOR; CLASSIFICATION; DIABETES MELLITUS; EXPERIMENTAL THERAPY; GENE EXPRESSION PROFILING; GENETIC DATABASE; GENETIC PREDISPOSITION; GENETICS; HIGH THROUGHPUT SEQUENCING; MOLECULARLY TARGETED THERAPY; NOMENCLATURE; PHARMACOGENETICS; PROCEDURES; TRENDS;

DATABASES, GENETIC; DIABETES MELLITUS; GENE EXPRESSION PROFILING; GENETIC PREDISPOSITION TO DISEASE; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; HUMANS; MOLECULAR TARGETED THERAPY; PHARMACOGENETICS; PRECISION MEDICINE; TERMINOLOGY AS TOPIC; THERAPIES, INVESTIGATIONAL;

EID: 84939600813     PISSN: None     EISSN: 19322968     Source Type: Journal    
DOI: 10.1177/1932296814563643     Document Type: Review

References (24)

1. Mirnezami R, Nicholson J, Darzi A. Preparing for precision medicine. N Engl J Med. 2012;366(6):489-491
2. Klonoff DC. Personalized medicine for diabetes. J Diabetes Sci Technol. 2008;2(3):335-341
3. Timmerman L. Whats in a name? A lot, when it comes to precision medicine. February 4, 2013. Available at: http://www.xconomy.com/national/2013/02/04/whats-in-A-name-Alot-when-it-comes-to-precision-medicine/?single-page=true. Accessed September 9, 2014
4. Khan N, Yaqoob I, Hashem IA, et al. Big data: survey, technologies, opportunities, and challenges. Scientific World Journal. 2014;2014:712826
5. Klonoff DC. Twelve modern digital technologies that are transforming decision making for diabetes and all areas of health care. J Diabetes Sci Technol. 2013;7:291-295
6. Munson ME, Wrobel JS, Holmes CM1, Hanauer DA. Data mining for identifying novel associations and temporal relationships with Charcot foot. J Diabetes Res. 2014;2014:214353. doi:10.1155/2014/214353
7. Heinzel A, Perco P, Mayer G, Oberbauer R, Lukas A, Mayer B. From molecular signatures to predictive biomarkers: modeling disease pathophysiology and drug mechanism of action. Front Cell Dev Biol. 2014;2:37. doi:10.3389/fcell.2014.00037
8. Lemoine C. Precision medicine for nurses: 101. Seminar Oncol Nurs. 2014;30(2):84-89
9. National Research Council Committee on a Framework for Developing a New Taxonomy of Disease. Toward Precision Medicine: Building a Knowledge Network for Biomedical Research and a New Taxonomy of Disease. Washington, DC: National Academies Press; 2011
10. National Human Genome Research Institute. The Human Genome Project completion: frequently asked questions. October 30, 2010. Available at: http://www.genome. gov/11006943. Accessed September 9, 2014
11. Illumina. Systems/HiSeq X Ten. Available at: http://systems. illumina.com/systems/hiseq-x-sequencing-system.ilmn. Accessed September 9, 2014
12. Santin I, Eizirik DL. Candidate genes for type 1 diabetes modulate pancreatic islet inflammation and-cell apoptosis. Diabetes Obes Metab. 2013;15(suppl 3):71-81
13. Abbas S, Raza ST, Ahmed F, et al. Association of genetic polymorphism of PPAR-2, ACE, MTHFR, FABP-2 and FTO genes in risk prediction of type 2 diabetes mellitus. J Biomed Sci. 2013;20(1):80
14. Pawlyk AC, Giacomini KM, McKeon C, et al. Metformin pharmacogenomics: current status and future directions. Diabetes. 2014;63(8):2590-2599
15. Trifonova O, Lokhov P, Archakov A. Postgenomics diagnostics: metabolomics approaches to human blood profiling. OMICS. 2013;17(11):550-559. doi:10.1089/omi.2012.0121
16. Frey LJ. Informatics tools to advance the biology of glycosaminoglycans and proteoglycans. Methods Mol Biol. 2015;1229:271-287. doi:10.1007/978-1-4939-1714-3-23
17. Peer D. Precision medicine-delivering the goods? Cancer Lett. 2014;352(1):2-3. doi:10.1016/j.canlet.2014.04.011
18. Driscoll KA, Johnson SB, Wang Y, et al. Importance of manually entering blood glucose readings when wireless-compatible meters are not being used with an insulin pump. J Diabetes Sci Technol. 2013;7(4):898-903
19. New KiCoPen developed by Cambridge Consultants could make managing diabetes treatment easier. October 6, 2014. Available at: http://www.cambridge-news.co.uk/New-KiCoPen-developed-Cambridge-Consultants-make/story-23050566-detail/story.htm. Accessed November 19, 2014
20. Merelli I, Perez-Sanchez H, Gesing S, DAgostino D. Managing, analysing, and integrating big data in medical bioinformatics: open problems and future perspectives. Biomed Res Int. 2014;2014:134023. doi:10.1155/2014/134023
21. Taha K, Elmasri R. GMB: an efficient query processor for biological data. J Integr Bioinform. 2011;8(2):165. doi:10.2390/biecoll-jib-2011-165
22. Khan N, Yaqoob I, Hashem IA, et al. Big data: survey, technologies, opportunities, and challenges. Scientific World Journal. 2014;2014:712826. doi:10.1155/2014/712826
23. Shah S, Horne A, Capella J. Good data wont guarantee good decisions. Harvard Business Review. April 2012. Available at: https://hbr.org/2012/04/good-data-wont-guarantee-good-decisions/ar/1. Accessed November 19, 2014
24. Herder C, Kowall B, Tabak AG, Rathmann W. The potential of novel biomarkers to improve risk prediction of type 2 diabetes. Diabetologia. 2014;57(1):16-29