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Precision Medicine Consultation

Personalised Medicine Consultation

Every person has a unique variation of the human genome `{`7`}`. Although most of the variation between individuals has no effect on health, an individual's health stems from genetic variation with behaviors and influences from the environment `{`8`}``{`9`}`.

Modern advances in personalized medicine rely on technology that confirms a patient's fundamental biology, DNA, RNA, or protein, which ultimately leads to confirming disease. For example, personalised techniques such as genome sequencing can reveal mutations in DNA that influence diseases ranging from cystic fibrosis to cancer. Another method, called RNA-seq, can show which RNA molecules are involved with specific diseases. Unlike DNA, levels of RNA can change in response to the environment. Therefore, sequencing RNA can provide a broader understanding of a person's state of health. Recent studies have linked genetic differences between individuals to RNA expression `{`10`}`, translation `{`11`}`, and protein levels `{`12`}`.

The concepts of personalised medicine can be applied to new and transformative approaches to health care. Personalised health care is based on the dynamics of systems biology and uses predictive tools to evaluate health risks and to design personalised health plans to help patients mitigate risks, prevent disease and to treat it with precision when it occurs. The concepts of personalised health care are receiving increasing acceptance with the Veterans Administration committing to personalised, proactive patient driven care for all veterans `{`13`}`. In some instances personalised health care can be tailored to the markup of the disease causing agent instead of the patient's genetic markup; examples are drug resistant bacteria or viruses `{`14`}`.

In order for physicians to know if a mutation is connected to a certain disease, researchers often do a study called a “genome-wide association study” (GWAS). A GWAS study will look at one disease, and then sequence the genome of many patients with that particular disease to look for shared mutations in the genome. Mutations that are determined to be related to a disease by a GWAS study can then be used to diagnose that disease in future patients, by looking at their genome sequence to find that same mutation. The first GWAS, conducted in 2005, studied patients with age-related macular degeneration (ARMD) `{`15`}`. It found two different mutations, each containing only a variation in only one nucleotide (called single nucleotide polymorphisms, or SNPs), which were associated with ARMD. GWAS studies like this have been very successful in identifying common genetic variations associated with diseases. As of early 2014, over 1,300 GWAS studies have been completed `{`16`}`.

Multiple genes collectively influence the likelihood of developing many common and complex diseases `{`8`}`. Personalised medicine can also be used to predict a person's risk for a particular disease, based on one or even several genes. This approach uses the same sequencing technology to focus on the evaluation of disease risk, allowing the physician to initiate preventive treatment before the disease presents itself in their patient. For example, if it is found that a DNA mutation increases a person's risk of developing Type 2 Diabetes, this individual can begin lifestyle changes that will lessen their chances of developing Type 2 Diabetes later in life.

References:
1. Stratified, personalised or P4 medicine: a new direction for placing the patient at the centre of healthcare and health education (Technical report). Academy of Medical Sciences. May 2015. Archived from the original on 27 October 2016. Retrieved 6 January 2016.
2.``Many names for one concept or many concepts in one name?``. PHG Foundation. Retrieved 6 January 2015.
3. Egnew, Thomas (1 March 2009). ``Suffering, Meaning, and Healing: Challenges of Contemporary Medicine``. Annals of Family Medicine. 7 (2): 170–175. doi:10.1370/afm.943. PMC 2653974. PMID 19273873.
4. ``The Case for Personalized Medicine`` (PDF). Personalized Medicine Coalition. 2014. Retrieved 6 January 2016.
5. Smith, Richard (15 October 2012). ``Stratified, personalised, or precision medicine``. British Medical Journal. Retrieved 6 January 2016.
6. ``Personalized Medicine 101``. Personalized Medicine Coalition. Retrieved 26 April 2014.
7. Dudley, J; Karczewski, K. (2014). Exploring Personal Genomics. Oxford: Oxford University Press.
8.``Personalized Medicine 101: The Science``. Personalized Medicine Coalition. Retrieved 26 April 2014.
9. Lu, YF; Goldstein, DB; Angrist, M; Cavalleri, G (24 July 2014). ``Personalized medicine and human genetic diversity``. Cold Spring Harbor Perspectives in Medicine. 4 (9): a008581. doi:10.1101/cshperspect.a008581. PMC 4143101. PMID 25059740.
10. Battle A, Mostafavi S, Zhu X, Potash JB, Weissman MM, McCormick C, et al. (2014). ``Characterizing the genetic basis of transcriptome diversity through RNA-sequencing of 922 individuals``. Genome Research. 24 (1): 14–24. doi:10.1101/gr.155192.113. PMC 3875855. PMID 24092820.
11. Cenik, Can; Cenik, Elif Sarinay; Byeon, Gun W; Candille, Sophie P.; Spacek, Damek; Araya, Carlos L; Tang, Hua; Ricci, Emiliano; Snyder, Michael P. (November 2015). ``Integrative analysis of RNA, translation, and protein levels reveals distinct regulatory variation across humans``. Genome Research. 25 (11): 1610–21. doi:10.1101/gr.193342.115. PMC 4617958. PMID 26297486.
12. Linfeng Wu; Sophie I. Candille; Yoonha Choi; Dan Xie; Lihua Jiang; Jennifer Li-Pook-Than; Hua Tang; Michael Snyder (2013). ``Variation and genetic control of protein abundance in humans``. Nature. 499 (7456): 79–82. Bibcode:2013Natur.499...79W. doi:10.1038/nature12223. PMC 3789121. PMID 23676674.
13. Snyderman, R. Personalized Health Care from Theory to Practice, Biotechnology J. 2012, 7
14. Altmann, Andre; Beerenwinkel, Niko; Sing, Tobias; Savenkov, Igor; Doumer, Martin; Kaiser, Rolf; Rhee, Soo-Yon; Fessel, W. Jeffrey; Shafer, Robert W. (2007). ``Improved prediction of response to antiretroviral combination therapy using the genetic barrier to drug resistance``. Antiviral Therapy. 12 (2): 169–178. ISSN 1359-6535. PMID 17503659.
15. Haines, J.L. (April 15, 2005). ``Complement Factor H Variant Increases the Risk of Age-Related Macular Degeneration``. Science. 308 (5720): 419–21. Bibcode:2005Sci...308..419H. doi:10.1126/science.1110359. PMID 15761120.
16. ``A Catalog of Published Genome-Wide Association Studies``. Retrieved 28 June 2015.