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J. Pers. Med., Volume 5, Issue 1 (March 2015) – 5 articles , Pages 1-49

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Open AccessArticle
Common Genetic Risk for Melanoma Encourages Preventive Behavior Change
J. Pers. Med. 2015, 5(1), 36-49; https://doi.org/10.3390/jpm5010036 - 17 Feb 2015
Cited by 15 | Viewed by 5245
Abstract
There is currently great interest in using genetic risk estimates for common disease in personalized healthcare. Here we assess melanoma risk-related preventive behavioral change in the context of the Coriell Personalized Medicine Collaborative (CPMC). As part of on-going reporting activities within the project, [...] Read more.
There is currently great interest in using genetic risk estimates for common disease in personalized healthcare. Here we assess melanoma risk-related preventive behavioral change in the context of the Coriell Personalized Medicine Collaborative (CPMC). As part of on-going reporting activities within the project, participants received a personalized risk assessment including information related to their own self-reported family history of melanoma and a genetic risk variant showing a moderate effect size (1.7, 3.0 respectively for heterozygous and homozygous individuals). Participants who opted to view their report were sent an optional outcome survey assessing risk perception and behavioral change in the months that followed. Participants that report family history risk, genetic risk, or both risk factors for melanoma were significantly more likely to increase skin cancer preventive behaviors when compared to participants with neither risk factor (ORs = 2.04, 2.79, 4.06 and p-values = 0.02, 2.86 × 10−5, 4.67 × 10−5, respectively), and we found the relationship between risk information and behavior to be partially mediated by anxiety. Genomic risk assessments appear to encourage positive behavioral change in a manner that is complementary to family history risk information and therefore may represent a useful addition to standard of care for melanoma prevention. Full article
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Open AccessCommunication
Saving More Teeth—A Case for Personalized Care
J. Pers. Med. 2015, 5(1), 30-35; https://doi.org/10.3390/jpm5010030 - 16 Feb 2015
Cited by 12 | Viewed by 4920
Abstract
Background: Certain risk factors such as tobacco use, diabetes, genetic variations on the IL1 gene, and other inflammatory conditions are hypothesized to predict tooth loss in patients treated in a large medical center. Tooth loss trends are hypothesized to be greater in patients [...] Read more.
Background: Certain risk factors such as tobacco use, diabetes, genetic variations on the IL1 gene, and other inflammatory conditions are hypothesized to predict tooth loss in patients treated in a large medical center. Tooth loss trends are hypothesized to be greater in patients with more risk factors. Methods: DNA samples for 881 individuals were taken from the Dental Registry and DNA Repository at University of Pittsburgh School of Dental Medicine. Clinical data for all 4137 subjects in the registry were also available. SNP genotyping was performed on the samples for IL1α (rs1800587) and IL1β (rs1143634). IL1 positive status was determined as having one or more of the recessive alleles for either SNP. Tooth loss status was determined based on dental records and data gathered for age, sex, ethnicity, and self-reported medical history. Various statistical analyses were performed on the data including genetic association analysis by the PLINK software, chi-square, Mann-Whitney U, and ANOVA tests to determine significance. Results: Tooth loss averages increased with age by all risk factors (smoking, diabetes, hypertension, and interleukin genotypes; p = 4.07E-13) and by number of risk factors (p = 0.006). Increased tooth loss is associated with age and number of risk factors including diabetes, tobacco use, IL1+, and cardiovascular disease. Conclusion: These trends suggest that older patients and those with more risk factors should seek further preventive care to reduce future tooth loss. Full article
Open AccessCommentary
The Genotype-Tissue Expression (GTEx) Project: Linking Clinical Data with Molecular Analysis to Advance Personalized Medicine
J. Pers. Med. 2015, 5(1), 22-29; https://doi.org/10.3390/jpm5010022 - 05 Feb 2015
Cited by 40 | Viewed by 6144
Abstract
Evaluation of how genetic mutations or variability can directly affect phenotypic outcomes, the development of disease, or determination of a tailored treatment protocol is fundamental to advancing personalized medicine. To understand how a genotype affects gene expression and specific phenotypic traits, as well [...] Read more.
Evaluation of how genetic mutations or variability can directly affect phenotypic outcomes, the development of disease, or determination of a tailored treatment protocol is fundamental to advancing personalized medicine. To understand how a genotype affects gene expression and specific phenotypic traits, as well as the correlative and causative associations between such, the Genotype-Tissue Expression (GTEx) Project was initiated The GTEx collection of biospecimens and associated clinical data links extensive clinical data with genotype and gene expression data to provide a wealth of data and resources to study the underlying genetics of normal physiology. These data will help inform personalized medicine through the identification of normal variation that does not contribute to disease. Additionally, these data can lead to insights into how gene variation affects pharmacodynamics and individualized responses to therapy. Full article
(This article belongs to the Special Issue Biobanking and EHR/EMR)
Open AccessArticle
Public Trust in Health Information Sharing: Implications for Biobanking and Electronic Health Record Systems
J. Pers. Med. 2015, 5(1), 3-21; https://doi.org/10.3390/jpm5010003 - 03 Feb 2015
Cited by 22 | Viewed by 5579
Abstract
Biobanks are made all the more valuable when the biological samples they hold can be linked to health information collected in research, electronic health records, or public health practice. Public trust in such systems that share health information for research and health care [...] Read more.
Biobanks are made all the more valuable when the biological samples they hold can be linked to health information collected in research, electronic health records, or public health practice. Public trust in such systems that share health information for research and health care practice is understudied. Our research examines characteristics of the general public that predict trust in a health system that includes researchers, health care providers, insurance companies and public health departments. We created a 119-item survey of predictors and attributes of system trust and fielded it using Amazon’s MTurk system (n = 447). We found that seeing one’s primary care provider, having a favorable view of data sharing and believing that data sharing will improve the quality of health care, as well as psychosocial factors (altruism and generalized trust) were positively and significantly associated with system trust. As expected, privacy concern, but counterintuitively, knowledge about health information sharing were negatively associated with system trust. We conclude that, in order to assure the public’s trust, policy makers charged with setting best practices for governance of biobanks and access to electronic health records should leverage critical access points to engage a diverse public in joint decision making. Full article
(This article belongs to the Special Issue Biobanking and EHR/EMR)
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Open AccessEditorial
Acknowledgement to Reviewers of the Journal of Personalized Medicine in 2014
J. Pers. Med. 2015, 5(1), 1-2; https://doi.org/10.3390/jpm5010001 - 08 Jan 2015
Viewed by 3788
Abstract
The editors of the Journal of Personalized Medicine would like to express their sincere gratitude to the following reviewers for assessing manuscripts in 2014:[...] Full article
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