Recent studies have demonstrated the use of genomic data, particularly gene expression signatures, as clinical prognostic factors in complex diseases. Such studies herald the future for genomic medicine and the opportunity for personalized prognosis in a variety of clinical contexts that utilize genomescale molecular information. Several key areas represent logical and critical next steps in the use of complex genomic profiling data towards the goal of personalized medicine. First, analyses should be geared toward the development of molecular profiles that predict future events – such as major clinical events or the response, resistance, or adverse reaction to therapy. Secondly, these must move into actual clinical practice by forming the basis for the next generation of clinical trials that will employ these methodologies to stratify patients. Lastly, there remain formidable challenges is in the translation of genomic technologies into clinical medicine that will need to be addressed: professional and public education, health outcomes research, reimbursement, regulatory oversight and privacy protection.
genomic medicine, personalized medicine, human genome.
Background: Variable health literacy and genetic knowledge may pose significant challenges to engaging the general public in personal genomics, specifically with respect to promoting risk comprehension and healthy behaviors. Methods: We are conducting a multistage study of individual responses to genomic risk information for Type 2 diabetes mellitus. A total of 300 individuals were recruited from the general public in Durham, North Carolina: 60% self-identified as White; 70% female; and 65% have a college degree. As part of the baseline survey, we assessed genetic knowledge and attitudes toward genetic testing. Results: Scores of factual knowledge of genetics ranged from 50% to 100% (average=84%), with significant differences in relation to racial groups, the education level, and age. Scores were significantly higher on questions pertaining to the inheritance and causes of disease (mean score 90%) compared to scientific questions (mean score 77.4%). Scores on the knowledge survey were significantly higher than scores from European populations. Participants' perceived knowledge of the social consequences of genetic testing was significantly lower than their perceived knowledge of the medical uses of testing. More than half agreed with the statement that testing may affect a person's ability to obtain health insurance (51.3%) and 16% were worried about the consequences of testing for chances of finding a job. Conclusions: Despite the relatively high educational status and genetic knowledge of the study population, we find an imbalance of knowledge between scientific and medical concepts related to genetics as well as between the medical applications and societal consequences of testing, suggesting that more effort is needed to present the benefits, risks, and limitations of genetic testing, particularly, at the social and personal levels, to ensure informed decision making.
In 2012, the National Cancer Institute (NCI) engaged the scientific community to provide a vision for cancer epidemiology in the 21st century. Eight overarching thematic recommendations, with proposed corresponding actions for consideration by funding agencies, professional societies, and the research community emerged from the collective intellectual discourse. The themes are (i) extending the reach of epidemiology beyond discovery and etiologic research to include multilevel analysis, intervention evaluation, implementation, and outcomes research; (ii) transforming the practice of epidemiology by moving towards more access and sharing of protocols, data, metadata, and specimens to foster collaboration, to ensure reproducibility and replication, and accelerate translation; (iii) expanding cohort studies to collect exposure, clinical and other information across the life course and examining multiple health-related endpoints; (iv) developing and validating reliable methods and technologies to quantify exposures and outcomes on a massive scale, and to assess concomitantly the role of multiple factors in complex diseases; (v) integrating “big data” science into the practice of epidemiology; (vi) expanding knowledge integration to drive research, policy and practice; (vii) transforming training of 21st century epidemiologists to address interdisciplinary and translational research; and (viii) optimizing the use of resources and infrastructure for epidemiologic studies. These recommendations can transform cancer epidemiology and the field of epidemiology in general, by enhancing transparency, interdisciplinary collaboration, and strategic applications of new technologies. They should lay a strong scientific foundation for accelerated translation of scientific discoveries into individual and population health benefits.
big data; clinical trials; cohort studies; epidemiology; genomics; medicine; public health; technologies; training; translational research
Sepsis is a common cause of death, but outcomes in individual patients are difficult to predict. Elucidating the molecular processes that differ between sepsis patients who survive and those who die may permit more appropriate treatments to be deployed. We examined the clinical features, and the plasma metabolome and proteome of patients with and without community-acquired sepsis, upon their arrival at hospital emergency departments and 24 hours later. The metabolomes and proteomes of patients at hospital admittance who would die differed markedly from those who would survive. The different profiles of proteins and metabolites clustered into fatty acid transport and β-oxidation, gluconeogenesis and the citric acid cycle. They differed consistently among several sets of patients, and diverged more as death approached. In contrast, the metabolomes and proteomes of surviving patients with mild sepsis did not differ from survivors with severe sepsis or septic shock. An algorithm derived from clinical features together with measurements of seven metabolites predicted patient survival. This algorithm may help to guide the treatment of individual patients with sepsis.
Studies have shown that the quality of family health history (FHH) collection in primary care is inadequate to assess disease risk. To use FHH for risk assessment, collected data must have adequate detail. To address this issue, we developed a patient facing FHH assessment tool, MeTree. In this paper we report the content and quality of the FHH collected using MeTree.
Design: A hybrid implementation-effectiveness study. Patients were recruited from 2009 to 2012. Setting: Two community primary care clinics in Greensboro, NC. Participants: All non-adopted adult English speaking patients with upcoming appointments were invited to participate. Intervention: Education about and collection of FHH with entry into MeTree. Measures: We report the proportion of pedigrees that were high-quality. High-quality pedigrees are defined as having all the following criteria: (1) three generations of relatives, (2) relatives’ lineage, (3) relatives’ gender, (4) an up-to-date FHH, (5) pertinent negatives noted, (6) age of disease onset in affected relatives, and for deceased relatives, (7) the age and (8) cause of death (Prim Care31:479–495, 2004.).
Enrollment: 1,184. Participant demographics: age range 18-92 (mean 58.8, SD 11.79), 56% male, and 75% white. The median pedigree size was 21 (range 8-71) and the FHH entered into MeTree resulted in a database of 27,406 individuals. FHHs collected by MeTree were found to be high quality in 99.8% (N = 1,182/1,184) as compared to <4% at baseline. An average of 1.9 relatives per pedigree (range 0-50, SD 4.14) had no data reported. For pedigrees where at least one relative has no data (N = 497/1,184), 4.97 relatives per pedigree (range 1-50, SD 5.44) had no data. Talking with family members before using MeTree significantly decreased the proportion of relatives with no data reported (4.98% if you talked to your relative vs. 10.85% if you did not, p-value < 0.001.).
Using MeTree improves the quantity and quality of the FHH data that is collected and talking with relatives prior to the collection of FHH significantly improves the quantity and quality of the data provided. This allows more patients to be accurately risk stratified and offered appropriate preventive care guided by their risk level.
Family history; Data quality; Patient-centered
Despite stunning advances in our understanding of the genetics and the molecular basis for cancer, many patients with cancer are not yet receiving therapy tailored specifically to their tumor biology. The translation of these advances into clinical practice has been hindered, in part, by the lack of evidence for biomarkers supporting the personalized medicine approach. Most stakeholders agree that the translation of biomarkers into clinical care requires evidence of clinical utility. The highest level of evidence comes from randomized controlled clinical trials (RCTs). However, in many instances, there may be no RCTs that are feasible for assessing the clinical utility of potentially valuable genomic biomarkers. In the absence of RCTs, evidence generation will require well-designed cohort studies for comparative effectiveness research (CER) that link detailed clinical information to tumor biology and genomic data. CER also uses systematic reviews, evidence-quality appraisal, and health outcomes research to provide a methodologic framework for assessing biologic patient subgroups. Rapid learning health care (RLHC) is a model in which diverse data are made available, ideally in a robust and real-time fashion, potentially facilitating CER and personalized medicine. Nonetheless, to realize the full potential of personalized care using RLHC requires advances in CER and biostatistics methodology and the development of interoperable informatics systems, which has been recognized by the National Cancer Institute's program for CER and personalized medicine. The integration of CER methodology and genomics linked to RLHC should enhance, expedite, and expand the evidence generation required for fully realizing personalized cancer care.
We propose a mixture model for text data designed to capture underlying structure in the history of present illness section of electronic medical records data. Additionally, we propose a method to induce bias that leads to more homogeneous sets of diagnoses for patients in each cluster. We apply our model to a collection of electronic records from an emergency department and compare our results to three other relevant models in order to assess performance. Results using standard metrics demonstrate that patient clusters from our model are more homogeneous when compared to others, and qualitative analyses suggest that our approach leads to interpretable patient sub-populations when applied to real data. Finally, we demonstrate an example of our patient clustering model to identify adverse drug events.
Capturing the host response by using genomic technologies such as transcriptional profiling provides a new paradigm for classifying and diagnosing infectious disease and for potentially distinguishing infection from other causes of serious respiratory illness. This strategy has been used to define a blood-based RNA signature as a classifier for pandemic H1N1 influenza infection that is distinct from bacterial pneumonia and other inflammatory causes of respiratory disease. To realize the full potential of this approach as a diagnostic test will require additional independent validation of the results and studies to examine the specificity of this signature for viral versus bacterial infection or co-infection.
It is anticipated that as the range of drugs for which pharmacogenetic testing becomes available expands, primary care physicians (PCPs) will become major users of these tests. To assess their training, familiarity, and attitudes toward pharmacogenetic testing in order to identify barriers to uptake that may be addressed at this early stage of test use, we conducted a national survey of a sample of PCPs. Respondents were mostly white (79%), based primarily in community-based primary care (81%) and almost evenly divided between family medicine and internal medicine. The majority of respondents had heard of PGx testing and anticipated that these tests are or would soon become a valuable tool to inform drug response. However, only a minority of respondents (13%) indicated they felt comfortable ordering PGx tests and almost a quarter reported not having any education about pharmacogenetics.
Our results indicate that primary care practitioners envision a major role for themselves in the delivery of PGx testing but recognize their lack of adequate knowledge and experience about these tests. Development of effective tools for guiding PCPs in the use of PGx tests should be a high priority.
Family health history (FHH) is the single strongest predictor of disease risk and yet is significantly underutilized in primary care. We developed a patient facing FHH collection tool, MeTree©, that uses risk stratification to generate clinical decision support for breast cancer, colorectal cancer, ovarian cancer, hereditary cancer syndromes, and thrombosis. Here we present data on the experience of patients and providers after integration of MeTree© into 2 primary care practices.
This was a Type 2 hybrid controlled implementation-effectiveness study in 3 community-based primary care clinics in Greensboro, NC. All non-adopted adult English speaking patients with upcoming routine appointments were invited. Patients were recruited from December 2009 to the present and followed for one year. Ease of integration of MeTree© into clinical practice at the two intervention clinics was evaluated through patient surveys after their appointment and at 3 months post-visit, and physician surveys 3 months after tool integration.
Total enrollment =1,184. Average time to complete MeTree© = 27 minutes. Patients found MeTree©: easy to use (93%), easy to understand (97%), useful (98%), raised awareness of disease risk (85%), and changed how they think about their health (86%). Of the 26% (N = 311) asking for assistance to complete the tool, age (65 sd 9.4 vs. 57 sd 11.8, p-value < 0.00) and large pedigree size (24.4 sd 9.81 vs. 22.2 sd 8.30, p-value < 0.00) were the only significant factors; 77% of those requiring assistance were over the age of 60. Providers (N = 14) found MeTree©: improved their practice (86%), improved their understanding of FHH (64%), made practice easier (79%), and worthy of recommending to their peers (93%).
Our study shows that MeTree© has broad acceptance and support from both patients and providers and can be implemented without disruption to workflow.
Family health history; Cancer screening; Clinical decision support; Health services
Pharmacogenetic (PGx) testing is one of the primary drivers of personalized medicine. The use of PGx testing may provide a lifetime of benefits through tailoring drug dosing and selection of multiple medications to improve therapeutic outcomes and reduce adverse responses. We aimed to assess public interest and concerns regarding sharing and storage of PGx test results that would facilitate the re-use of PGx data across a lifetime of care.
We conducted a random-digit-dial phone survey of a sample of the U.S. public.
We achieved an overall response rate of 42% (n=1,139). Most respondents indicated they were extremely or somewhat comfortable allowing their PGx test results to be shared with other doctors involved in their care management (90% ± 2.18%); significantly fewer respondents (74% ± 3.27%) indicated they were extremely or somewhat comfortable sharing results with their pharmacist (p<0.0001).
Patients, pharmacists, and physicians will all be critical players in the pharmacotherapy process. Patients are supportive of sharing PGx test results with physicians and pharmacists as well as personally maintaining their test results. However, further study is needed to understand which options are needed for sharing, appropriate storage and patient education about the relevance of PGx test results to promote consideration of this information by other prescribing practitioners.
Venous thromboembolism may recur in up to 30% of patients with a spontaneous venous thromboembolism after a standard course of anticoagulation. Identification of patients at risk for recurrent venous thromboembolism would facilitate decisions concerning the duration of anticoagulant therapy.
In this exploratory study, we investigated whether whole blood gene expression data could distinguish subjects with single venous thromboembolism from subjects with recurrent venous thromboembolism.
40 adults with venous thromboembolism (23 with single event and 17 with recurrent events) on warfarin were recruited. Individuals with antiphospholipid syndrome or cancer were excluded. Plasma and serum samples were collected for biomarker testing, and PAXgene tubes were used to collect whole blood RNA samples.
D-dimer levels were significantly higher in patients with recurrent venous thromboembolism, but P-selectin and thrombin-antithrombin complex levels were similar in the two groups. Comparison of gene expression data from the two groups provided us with a 50 gene probe model that distinguished these two groups with good receiver operating curve characteristics (AUC 0.75). This model includes genes involved in mRNA splicing and platelet aggregation. Pathway analysis between subjects with single and recurrent venous thromboembolism revealed that the Akt pathway was up-regulated in the recurrent venous thromboembolism group compared to the single venous thromboembolism group.
In this exploratory study, gene expression profiles of whole blood appear to be a useful strategy to distinguish subjects with single venous thromboembolism from those with recurrent venous thromboembolism. Prospective studies with additional patients are needed to validate these results.
genomics; risk factors; deep vein thrombosis
There is often interest in predicting an individual’s latent health status based on high-dimensional biomarkers that vary over time. Motivated by time-course gene expression array data that we have collected in two influenza challenge studies performed with healthy human volunteers, we develop a novel time-aligned Bayesian dynamic factor analysis methodology. The time course trajectories in the gene expressions are related to a relatively low-dimensional vector of latent factors, which vary dynamically starting at the latent initiation time of infection. Using a nonparametric cure rate model for the latent initiation times, we allow selection of the genes in the viral response pathway, variability among individuals in infection times, and a subset of individuals who are not infected. As we demonstrate using held-out data, this statistical framework allows accurate predictions of infected individuals in advance of the development of clinical symptoms, without labeled data and even when the number of biomarkers vastly exceeds the number of individuals under study. Biological interpretation of several of the inferred pathways (factors) is provided.
Bayesian nonparametrics; Dynamic factor analysis; High-dimensional; Infectious disease; Joint model; Multidimensional longitudinal data; Multivariate functional data; Predictive model
To develop an integrated metric of non COX-1 dependent platelet function (NCDPF) to measure the temporal response to aspirin in healthy volunteers and diabetics.
NCDPF on aspirin demonstrates wide variability, despite suppression of COX-1. Although a variety of NCDPF assays are available, no standard exists and their reproducibility is not established.
We administered 325mg/day aspirin to two cohorts of volunteers (HV1, n = 52, and HV2, n = 96) and diabetics (DM, n = 74) and measured NCDPF using epinephrine, collagen, and ADP aggregometry and PFA100 (collagen/epi) before (Pre), after one dose (Post), and after several weeks (Final). COX-1 activity was assessed with arachidonic acid aggregometry (AAA). The primary outcome of the study, the platelet function score (PFS), was derived from a principal components analysis of NCDPF measures.
The PFS strongly correlated with each measure of NCDPF in each cohort. After two or four weeks of daily aspirin the Final PFS strongly correlated (r > 0.7, p<0.0001) and was higher (p < 0.01) than the Post PFS. The magnitude and direction of the change in PFS (Final - Post) in an individual subject was moderately inversely proportional to the Post PFS in HV1 (r = −0.45), HV2 (r = −0.54), DM (r = −0.68), p<0.0001 for all. AAA remained suppressed during aspirin therapy.
The PFS summarizes multiple measures of NCDPF. Despite suppression of COX-1 activity, NCDPF during aspirin therapy is predictably dynamic: those with heightened NCDPF continue to decline whereas those with low/normal NCDPF return to pre-aspirin levels over time.
aspirin; platelets; light transmittance aggregometry; PFA100; principal components analysis
This paper introduces a new constrained model and the corresponding algorithm, called unsupervised Bayesian linear unmixing (uBLU), to identify biological signatures from high dimensional assays like gene expression microarrays. The basis for uBLU is a Bayesian model for the data samples which are represented as an additive mixture of random positive gene signatures, called factors, with random positive mixing coefficients, called factor scores, that specify the relative contribution of each signature to a specific sample. The particularity of the proposed method is that uBLU constrains the factor loadings to be non-negative and the factor scores to be probability distributions over the factors. Furthermore, it also provides estimates of the number of factors. A Gibbs sampling strategy is adopted here to generate random samples according to the posterior distribution of the factors, factor scores, and number of factors. These samples are then used to estimate all the unknown parameters.
Firstly, the proposed uBLU method is applied to several simulated datasets with known ground truth and compared with previous factor decomposition methods, such as principal component analysis (PCA), non negative matrix factorization (NMF), Bayesian factor regression modeling (BFRM), and the gradient-based algorithm for general matrix factorization (GB-GMF). Secondly, we illustrate the application of uBLU on a real time-evolving gene expression dataset from a recent viral challenge study in which individuals have been inoculated with influenza A/H3N2/Wisconsin. We show that the uBLU method significantly outperforms the other methods on the simulated and real data sets considered here.
The results obtained on synthetic and real data illustrate the accuracy of the proposed uBLU method when compared to other factor decomposition methods from the literature (PCA, NMF, BFRM, and GB-GMF). The uBLU method identifies an inflammatory component closely associated with clinical symptom scores collected during the study. Using a constrained model allows recovery of all the inflammatory genes in a single factor.
Staphylococcus aureus causes a spectrum of human infection. Diagnostic delays and uncertainty lead to treatment delays and inappropriate antibiotic use. A growing literature suggests the host’s inflammatory response to the pathogen represents a potential tool to improve upon current diagnostics. The hypothesis of this study is that the host responds differently to S. aureus than to E. coli infection in a quantifiable way, providing a new diagnostic avenue. This study uses Bayesian sparse factor modeling and penalized binary regression to define peripheral blood gene-expression classifiers of murine and human S. aureus infection. The murine-derived classifier distinguished S. aureus infection from healthy controls and Escherichia coli-infected mice across a range of conditions (mouse and bacterial strain, time post infection) and was validated in outbred mice (AUC>0.97). A S. aureus classifier derived from a cohort of 94 human subjects distinguished S. aureus blood stream infection (BSI) from healthy subjects (AUC 0.99) and E. coli BSI (AUC 0.84). Murine and human responses to S. aureus infection share common biological pathways, allowing the murine model to classify S. aureus BSI in humans (AUC 0.84). Both murine and human S. aureus classifiers were validated in an independent human cohort (AUC 0.95 and 0.92, respectively). The approach described here lends insight into the conserved and disparate pathways utilized by mice and humans in response to these infections. Furthermore, this study advances our understanding of S. aureus infection; the host response to it; and identifies new diagnostic and therapeutic avenues.
There is great potential for host-based gene expression analysis to impact the early diagnosis of infectious diseases. In particular, the influenza pandemic of 2009 highlighted the challenges and limitations of traditional pathogen-based testing for suspected upper respiratory viral infection. We inoculated human volunteers with either influenza A (A/Brisbane/59/2007 (H1N1) or A/Wisconsin/67/2005 (H3N2)), and assayed the peripheral blood transcriptome every 8 hours for 7 days. Of 41 inoculated volunteers, 18 (44%) developed symptomatic infection. Using unbiased sparse latent factor regression analysis, we generated a gene signature (or factor) for symptomatic influenza capable of detecting 94% of infected cases. This gene signature is detectable as early as 29 hours post-exposure and achieves maximal accuracy on average 43 hours (p = 0.003, H1N1) and 38 hours (p-value = 0.005, H3N2) before peak clinical symptoms. In order to test the relevance of these findings in naturally acquired disease, a composite influenza A signature built from these challenge studies was applied to Emergency Department patients where it discriminates between swine-origin influenza A/H1N1 (2009) infected and non-infected individuals with 92% accuracy. The host genomic response to Influenza infection is robust and may provide the means for detection before typical clinical symptoms are apparent.
Current understanding of chronic diseases is based on crude clinical characterization, imaging studies, and laboratory testing that has evolved over decades. The Measurement to Understand Reclassification of Disease of Cabarrus/Kannapolis (MURDOCK) Study is a multi-tiered, longitudinal study designed to enable classification of chronic diseases using clinically annotated biospecimen collections, -omic technologies, electronic health records, and standard epidemiological methods. We expect that detailed molecular classification will improve mechanistic understanding of chronic diseases, augmenting discovery and testing of new treatments, and allowing refined selection of prevention and treatment strategies. The MURDOCK Study Community Registry and Biorepository will serve as a bridge for validation of initial exploratory studies, a platform for future prospective studies in targeted populations, and a resource of both data (analytical and clinical) and samples for cross-registry meta-analyses and comparative population studies. Participation of local health care providers and the Cabarrus County/Kannapolis, NC, community will facilitate future medical research and provide the opportunity to educate and inform the public about genomic research, actively engaging them in shaping the future of medical discovery and treatment of chronic diseases. We present the rationale and study design for the MURDOCK Community Registry and Biorepository and baseline characteristics of the first 6000 participants.
Disease reclassification; community registry; biorepository
Identify SNPs associated with mild statin-induced side effects.
Statin-induced side effects can interfere with therapy. SNPs in cytochrome P450 enzymes impair statin metabolism; the reduced function SLCO1B1*5 allele impairs statin clearance and is associated with simvastatin-induced myopathy with CK elevation.
The STRENGTH study was a pharmacogenetics study of statin efficacy and safety. Subjects (n=509) were randomized to atorvastatin 10mg, simvastatin 20mg, or pravastatin 10mg followed by 80mg, 80mg, and 40mg, respectively. We defined a composite adverse event (CAE) as discontinuation for any side effect, myalgia, or CK>3× baseline during follow-up. We sequenced CYP2D6, CYP2C8, CYP2C9, CYP3A4, and SLCO1B1 and tested seven reduced function alleles for association with the CAE.
The CAE occurred in 99 subjects (54 discontinuations, 49 myalgias, and nine CK elevations). Sex was associated with CAE (percent female in CAE vs. no CAE groups, 66% vs. 50%, p<0.01). SLCO1B1*5 was associated with CAE (percent with ≥ 1 allele in CAE vs. no CAE groups, 37% vs. 25%, p=0.03) and those with CAE with no significant CK elevation (p≤ 0.03). Furthermore, there was evidence for a gene-dose effect (percent with CAE in those with 0, 1, or 2 alleles: 19%, 27%, and 50%, trend p = 0.01). Finally, the CAE risk appeared to be highest in those carriers assigned to simvastatin.
SLCO1B1*5 genotype and female sex were associated mild statin-induced side effects. These findings expand the results of a recent genome wide association study of statin myopathy with CK > 3 times normal to milder, statin-induced, muscle side effects.
hydroxymethylglutaryl-CoA Reductase Inhibitors; pharmacogenetics; single nucleotide polymorphisms; muscular diseases; clinical trial; myopathy
Genomic risk profiling involves the analysis of genetic variations linked through statistical associations to a range of disease states. There is considerable controversy as to how, and even whether, to incorporate these tests into routine medical care.
To assess physician attitudes and uptake of genomic risk profiling among an ‘early adopter’ practice group.
We surveyed members of MDVIP, a national group of primary care physicians (PCPs), currently offering genomic risk profiling as part of their practice.
All physicians in the MDVIP network (N = 356)
We obtained a 44% response rate. One third of respondents had ordered a test for themselves and 42% for a patient. The odds of having ordered personal testing were 10.51-fold higher for those who felt well-informed about genomic risk testing (p < 0.0001). Of those who had not ordered a test for themselves, 60% expressed concerns for patients regarding discrimination by life and long-term/disability insurers, 61% about test cost, and 62% about clinical utility. The odds of ordering testing for their patients was 8.29-fold higher among respondents who had ordered testing for themselves (p < 0.0001). Of those who had ordered testing for patients, concerns about insurance coverage (p = 0.014) and uncertain clinical utility (p = 0.034) were associated with a lower relative frequency of intention to order testing again in the future.
Our findings demonstrate that respondent familiarity was a key predictor of physician ordering behavior and clinical utility was a primary concern for genomic risk profiling. Educational and interpretive support may enhance uptake of genomic risk profiling.
Electronic supplementary material
The online version of this article (doi:10.1007/s11606-011-1651-7) contains supplementary material, which is available to authorized users.
primary care; genetic testing; risk; education
We describe the study design, procedures, and development of the risk counseling protocol used in a randomized controlled trial to evaluate the impact of genetic testing for diabetes mellitus (DM) on psychological, health behavior, and clinical outcomes.
Eligible patients are aged 21 to 65 years with body mass index (BMI) ≥27 kg/m2 and no prior diagnosis of DM. At baseline, conventional DM risk factors are assessed, and blood is drawn for possible genetic testing. Participants are randomized to receive conventional risk counseling for DM with eye disease counseling or with genetic test results. The counseling protocol was pilot tested to identify an acceptable graphical format for conveying risk estimates and match the length of the eye disease to genetic counseling. Risk estimates are presented with a vertical bar graph denoting risk level with colors and descriptors. After receiving either genetic counseling regarding risk for DM or control counseling on eye disease, brief lifestyle counseling for prevention of DM is provided to all participants.
A standardized risk counseling protocol is being used in a randomized trial of 600 participants. Results of this trial will inform policy about whether risk counseling should include genetic counseling.
ClinicalTrials.gov Identifier NCT01060540
Genetic testing; Type II diabetes; Weight loss
Facing critically low return per dollar invested on clinical research and clinical care, the American biomedical enterprise is in need of a significant transformation. A confluence of high-throughput “omic” technologies and increasing adoption of the electronic health record has fueled excitement for a new paradigm for biomedical research and practice. The ability to simultaneously measure thousands of molecular variables and assess their relationships with clinical data collected during the course of care could enable reclassification of disease not only by gross phenotypic observation but according to underlying molecular mechanism and influence of social determinants.In turn, this reclassification could enable development of targeted therapeutic interventions as well as disease prevention strategies at the individual and population levels.
The MURDOCK Study consists of distinct project “horizons” or stages. Horizon 1 entailed the generation and analysis of molecular data for existing large,clinically well-annotated cohorts in four disease areas. Horizon 1.5 involves creating and maintaining a 50,000-person,community volunteer registry for biomarker signature validation and prospective studies, including integration of environmental and social data. Horizon 2 leverages and prospectively recruits Horizon 1.5 volunteers, and extends the study to additional disease areas of interest. Horizon 3 will expand the study through regional, national,and international partnerships.
The MURDOCK Study embodies a new model of team science investigation and represents a significant resource for translational research. The study team invites inquiries to form new collaborations to exploit the rich resources provided by these biospecimens and associated study data.
Stratified medicine; personalized medicine; biomarkers; disease reclassification; community registry; biorepository
The biomedical research community relies on a diverse set of resources, both within their own institutions and at other research centers. In addition, an increasing number of shared electronic resources have been developed. Without effective means to locate and query these resources, it is challenging, if not impossible, for investigators to be aware of the myriad resources available, or to effectively perform resource discovery when the need arises. In this paper, we describe the development and use of the Biomedical Resource Ontology (BRO) to enable semantic annotation and discovery of biomedical resources. We also describe the Resource Discovery System (RDS) which is a federated, inter-institutional pilot project that uses the BRO to facilitate resource discovery on the Internet. Through the RDS framework and its associated Biositemaps infrastructure, the BRO facilitates semantic search and discovery of biomedical resources, breaking down barriers and streamlining scientific research that will improve human health.
Ontology; Biositemaps; Resources; Biomedical research; Resource annotation; Resource discovery; Search; Semantic web; Web 2.0; Clinical and Translational Science Awards
Type 2 diabetes is a prevalent chronic condition globally that results in extensive morbidity, decreased quality of life, and increased health services utilization. Lifestyle changes can prevent the development of diabetes, but require patient engagement. Genetic risk testing might represent a new tool to increase patients' motivation for lifestyle changes. Here we describe the rationale, development, and design of a randomized controlled trial (RCT) assessing the clinical and personal utility of incorporating type 2 diabetes genetic risk testing into comprehensive diabetes risk assessments performed in a primary care setting.
Patients are recruited in the laboratory waiting areas of two primary care clinics and enrolled into one of three study arms. Those interested in genetic risk testing are randomized to receive either a standard risk assessment (SRA) for type 2 diabetes incorporating conventional risk factors plus upfront disclosure of the results of genetic risk testing ("SRA+G" arm), or the SRA alone ("SRA" arm). Participants not interested in genetic risk testing will not receive the test, but will receive SRA (forming a third, "no-test" arm). Risk counseling is provided by clinic staff (not study staff external to the clinic). Fasting plasma glucose, insulin levels, body mass index (BMI), and waist circumference are measured at baseline and 12 months, as are patients' self-reported behavioral and emotional responses to diabetes risk information. Primary outcomes are changes in insulin resistance and BMI after 12 months; secondary outcomes include changes in diet patterns, physical activity, waist circumference, and perceived risk of developing diabetes.
The utility, feasibility, and efficacy of providing patients with genetic risk information for common chronic diseases in primary care remain unknown. The study described here will help to establish whether providing type 2 diabetes genetic risk information in a primary care setting can help improve patients' clinical outcomes, risk perceptions, and/or their engagement in healthy behavior change. In addition, study design features such as the use of existing clinic personnel for risk counseling could inform the future development and implementation of care models for the use of individual genetic risk information in primary care.
genetic information clinical utility; genetic testing; preventive health behavior; RCT protocol; risk perception; type 2 diabetes