J. Pers. Med.2015, 5(1), 36-49; doi:10.3390/jpm5010036 - published 17 February 2015 Show/Hide Abstract
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, 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.
J. Pers. Med.2015, 5(1), 30-35; doi:10.3390/jpm5010030 - published 16 February 2015 Show/Hide Abstract
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 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.
J. Pers. Med.2015, 5(1), 22-29; doi:10.3390/jpm5010022 - published 5 February 2015 Show/Hide Abstract
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 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.
J. Pers. Med.2015, 5(1), 3-21; doi:10.3390/jpm5010003 - published 3 February 2015 Show/Hide Abstract
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 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.
J. Pers. Med.2014, 4(4), 489-507; doi:10.3390/jpm4040489 - published 22 December 2014 Show/Hide Abstract
Abstract: The Center for Health Discovery and Wellbeing (CHDWB) is an academic program designed to evaluate the efficacy of clinical self-knowledge and health partner counseling for development and maintenance of healthy behaviors. This paper reports on the change in health profiles for over 90 traits, measured in 382 participants over three visits in the 12 months following enrolment. Significant changes in the desired direction of improved health are observed for many traits related to cardiovascular health, including BMI, blood pressure, cholesterol, and arterial stiffness, as well as for summary measures of physical and mental health. The changes are most notable for individuals in the upper quartile of baseline risk, many of whom showed a positive correlated response across clinical categories. By contrast, individuals who start with more healthy profiles do not generally show significant improvements and only a modest impact of targeting specific health attributes was observed. Overall, the CHDWB model shows promise as an effective intervention particularly for individuals at high risk for cardiovascular disease.