Prevention: an achievable goal in personalized medicine.
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In the past 15 years a considerable number of studies have found evidence that it may be possible to prevent the onset of some mental disorders. Most evidence is available for depressive disorders, but a growing number of studies have focused on anxiety disorders and psychotic disorders. This paper reviews the studies which have examined the effects of preventive interventions on the incidence of mental disorders in people who do not meet criteria for a mental disorder at baseline. More than 20 studies have examined prevention of depressive disorders, and they have found an overall reduction in the incidence of about 25% compared with control groups. The problem of identifying the most optimal target groups for preventive interventions is also illustrated. This is a problem because most risk indicators have a low specificity, and most people with a risk indicator do not develop a mental disorder. Finally, this paper will show how other statistics, such as the exposure rate, the attributable fraction, and the number needed to treat can help in identifying the most optimal target groups for preventive interventions. (+info)
Shared decision making in mental health: prospects for personalized medicine.
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This paper describes the shared decision-making model, reviews its current status in the mental health field, and discusses its potential impact on personalized medicine. Shared decision making denotes a structured process that encourages full participation by patient and provider. Current research shows that shared decision making can improve the participation of mental health patients and the quality of decisions in terms of knowledge and values. The impact of shared decision making on adherence, illness self-management, and health outcomes remains to be studied. Implementing shared decision making broadly will require re-engineering the flow of clinical care in routine practice settings and much greater use of information technology. Similar changes will be needed to combine genomic and other biological data with patients' values and preferences and with clinicians' expertise. The future of personalized medicine is clearly linked with our ability to create the infrastructure and cultural receptivity to these changes. (+info)
Personalized medicine: selected web resources.
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Information about personalized medicine abounds, yet it is difficult to comprehensively search for information on this topic due to the broadness of the term "personalized medicine," the variety of terms that are used to describe this concept, the vast amount of pertinent journal articles and Web sites, and the fast pace of developments in this field. A selected list of Web sites is provided as a starting place for information about concepts, terminology, projects, databases, tools, and stakeholders related to personalized medicine. (+info)
Pharmacogenomics: paving the path to personalized medicine.
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The personalized medicine for diabetes meeting summary report.
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Personalized medicine for diabetes is a potential method to specifically identify people who are at high risk of developing type 2 diabetes based on a combination of personal history, family history, physical examination, circulating biomarkers, and genome. High-risk individuals can then be referred to lifestyle programs for risk reduction and disease prevention. Using a personalized medicine approach, a patient with already-diagnosed type 2 diabetes can be treated individually based on information specific to that individual. The field of personalized medicine for diabetes is rapidly exploding. Diabetes Technology Society convened the Personalized Medicine for Diabetes (PMFD) Meeting March 19-20, 2009 in San Francisco. The meeting was funded through a contract from the US Air Force. Diabetes experts from the military, government, academic, and industry communities participated. The purpose was to reach a consensus about PMFD in type 2 diabetes to (a) establish screening programs, (b) diagnose cases at an early stage, and (c) monitor and treat the disease with specific measures. The group defined what a PMFD program should encompass, what the benefits and drawbacks of such a PMFD program would be, and how to overcome barriers. The group reached six conclusions related to the power of PMFD to improve care of type 2 diabetes by resulting in (1) better prediction, (2) better prophylactic interventions, (3) better treatments, and (4) decreased cardiovascular disease burden. Additional research is needed to demonstrate the benefits of this approach. The US Air Force is well positioned to conduct research and develop clinical programs in PMFD. (+info)
The case for personalized medicine.
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Personalized medicine may be considered an extension of traditional approaches to understanding and treating disease, but with greater precision. Physicians may now use a patient's genetic variation or expression profile as well as protein and metabolic markers to guide the selection of certain drugs or treatments. In many cases, the information provided by molecular markers predicts susceptibility to conditions. The added precision introduces the possibility of a more preventive, effective approach to clinical care and reductions in the duration and cost of clinical trials. Here, we make the case, through real-world examples, that personalized medicine is delivering significant value to individuals, to industry, and to the health care system overall and that it will continue to grow in importance if we can lift the barriers that impede its adoption and build incentives to encourage its practice. (+info)
Genetics factors contributing to type 2 diabetes across ethnicities.
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Type 2 diabetes mellitus (T2DM) is among the many common diseases with a strong genetic component, but until recently, the variants causing this disease remained largely undiscovered. With the ability to interrogate most of the variation in the genome, the number of genetic variants has grown from 2 to 19 genes, many with multiple variants. An additional three genes are associated primarily with fasting glucose rather than T2DM. Despite the plethora of new markers, the individual effect is uniformly small, and the cumulative effect explains little of the genetic risk for T2DM. Furthermore, the success is largely restricted to European populations. Despite success in mapping genes in Asian populations, success in United States minorities, particularly African Americans, has been limited. The genetic findings highlight the role of the beta cell in diabetes pathogenesis, but much remains to be discovered before genetic prediction and individualized medicine can become a reality for this disease. (+info)
Genetic susceptibility to type 2 diabetes and implications for therapy.
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Since 2000, we have witnessed an explosion of known genetic determinants of type 2 diabetes risk. These findings have seeded the expectation that our ability to make personalized, predictive, therapeutic clinical decisions is imminent. However, the loci discovered to date explain only a small fraction of overall inheritable risk for this disease. In many cases, the reported associations merely signal regions of the genome that are overrepresented in disease versus health but do not identify the causal variants. Well-powered cohort studies have shown that the set of markers detected thus far does not significantly improve individual risk prediction or stratification over common clinical variables, with the possible exception of younger subjects. On the other hand, risk genotypes may help target subgroups for more intensive surveillance or prevention efforts, although whether such a strategy improves patient outcomes and/or is cost-effective should be examined. Similarly, whether genetic information will help guide therapeutic decisions must be tested in adequately designed and rigorously conducted clinical trials. (+info)