Person:

Vassy, Jason

Background: Hemoglobin A1c (HbA1c) levels diagnose diabetes, predict mortality and are associated with ten single nucleotide polymorphisms (SNPs) in white individuals. Genetic associations in other race groups are not known. We tested the hypotheses that there is race-ethnic variation in 1) HbA1c-associated risk allele frequencies (RAFs) for SNPs near SPTA1, HFE, ANK1, HK1, ATP11A, FN3K, TMPRSS6, G6PC2, GCK, MTNR1B; 2) association of SNPs with HbA1c and 3) association of SNPs with mortality. Methods We studied 3,041 non-diabetic individuals in the NHANES (National Health and Nutrition Examination Survey) III. We stratified the analysis by race/ethnicity (NHW: non-Hispanic white; NHB: non-Hispanic black; MA: Mexican American) to calculate RAF, calculated a genotype score by adding risk SNPs, and tested associations with SNPs and the genotype score using an additive genetic model, with type 1 error = 0.05. Results: RAFs varied widely and at six loci race-ethnic differences in RAF were significant (p < 0.0002), with NHB usually the most divergent. For instance, at ATP11A, the SNP RAF was 54% in NHB, 18% in MA and 14% in NHW (p < .0001). The mean genotype score differed by race-ethnicity (NHW: 10.4, NHB: 11.0, MA: 10.7, p < .0001), and was associated with increase in HbA1c in NHW (β = 0.012 HbA1c increase per risk allele, p = 0.04) and MA (β = 0.021, p = 0.005) but not NHB (β = 0.007, p = 0.39). The genotype score was not associated with mortality in any group (NHW: OR (per risk allele increase in mortality) = 1.07, p = 0.09; NHB: OR = 1.04, p = 0.39; MA: OR = 1.03, p = 0.71). Conclusion: At many HbA1c loci in NHANES III there is substantial RAF race-ethnic heterogeneity. The combined impact of common HbA1c-associated variants on HbA1c levels varied by race-ethnicity, but did not influence mortality.

Abstract Aims/Introduction: The efficacy, dose–response relationship and safety of ginsam, a vinegar extract from Panax ginseng, were evaluated in an 8‐week, double‐blind, randomized, placebo‐controlled study in drug‐naïve patients with type 2 diabetes. Materials and Methods: A total of 72 diabetic patients were randomized to receive 1500, 2000 or 3000 mg of ginsam, or placebo daily for 8 weeks (n = 18 in each group). The primary end‐point was the changes from the baseline HbA1c level. The secondary end‐points were the changes of fasting and postprandial 2‐h glucose concentration, and the proportion of patients achieving a reduction in HbA1c >0.5%. Results: In the intention‐to‐treat analysis, ginsam treatment reduced HbA1c level significantly: −0.56 ± 0.25% in the 1500 mg group, −0.31 ± 0.12% in the 2000 mg group, and −0.29 ± 0.11% in the 3000 mg group (all P < 0.05), with a significant difference between the 1500 mg ginsam and the placebo group (−0.02 ± 0.12%, P = 0.021). The changes in fasting glucose concentration followed the same pattern: −21.40, −14.27 and −6.76 mg/dL for 1500, 2000, and 3000 mg, respectively, vs −2.25 mg/dL for the placebo. The percentage of patients whose HbA1c level decreased by >0.5% differed significantly between the placebo group (11.1%) and the 1500 mg (27.8%) and 2000 mg (27.8%) groups. No severe adverse events were observed in any group. Conclusions: An 8‐week treatment with ginsam, a vinegar extract from P. ginseng, moderately improved HbA1c level and was well tolerated in type 2 diabetic patients with inadequate glycemic control. This trial was registered with ClinicalTrial.Gov (no. NCT01008163). (J Diabetes Invest, doi: 10.1111/j.2040‐1124.2011.00185.x, 2011)

Background: Whole genome sequencing (WGS) is already being used in certain clinical and research settings, but its impact on patient well-being, health-care utilization, and clinical decision-making remains largely unstudied. It is also unknown how best to communicate sequencing results to physicians and patients to improve health. We describe the design of the MedSeq Project: the first randomized trials of WGS in clinical care. Methods/Design This pair of randomized controlled trials compares WGS to standard of care in two clinical contexts: (a) disease-specific genomic medicine in a cardiomyopathy clinic and (b) general genomic medicine in primary care. We are recruiting 8 to 12 cardiologists, 8 to 12 primary care physicians, and approximately 200 of their patients. Patient participants in both the cardiology and primary care trials are randomly assigned to receive a family history assessment with or without WGS. Our laboratory delivers a genome report to physician participants that balances the needs to enhance understandability of genomic information and to convey its complexity. We provide an educational curriculum for physician participants and offer them a hotline to genetics professionals for guidance in interpreting and managing their patients’ genome reports. Using varied data sources, including surveys, semi-structured interviews, and review of clinical data, we measure the attitudes, behaviors and outcomes of physician and patient participants at multiple time points before and after the disclosure of these results. Discussion The impact of emerging sequencing technologies on patient care is unclear. We have designed a process of interpreting WGS results and delivering them to physicians in a way that anticipates how we envision genomic medicine will evolve in the near future. That is, our WGS report provides clinically relevant information while communicating the complexity and uncertainty of WGS results to physicians and, through physicians, to their patients. This project will not only illuminate the impact of integrating genomic medicine into the clinical care of patients but also inform the design of future studies. Trial registration ClinicalTrials.gov identifier NCT01736566

OBJECTIVE To examine whether diabetes genetic risk testing and counseling can improve diabetes prevention behaviors. RESEARCH DESIGN AND METHODS We conducted a randomized trial of diabetes genetic risk counseling among overweight patients at increased phenotypic risk for type 2 diabetes. Participants were randomly allocated to genetic testing versus no testing. Genetic risk was calculated by summing 36 single nucleotide polymorphisms associated with type 2 diabetes. Participants in the top and bottom score quartiles received individual genetic counseling before being enrolled with untested control participants in a 12-week, validated, diabetes prevention program. Middle-risk quartile participants were not studied further. We examined the effect of this genetic counseling intervention on patient self-reported attitudes, program attendance, and weight loss, separately comparing higher-risk and lower-risk result recipients with control participants. RESULTS The 108 participants enrolled in the diabetes prevention program included 42 participants at higher diabetes genetic risk, 32 at lower diabetes genetic risk, and 34 untested control subjects. Mean age was 57.9 ± 10.6 years, 61% were men, and average BMI was 34.8 kg/m2, with no differences among randomization groups. Participants attended 6.8 ± 4.3 group sessions and lost 8.5 ± 10.1 pounds, with 33 of 108 (30.6%) losing ≥5% body weight. There were few statistically significant differences in self-reported motivation, program attendance, or mean weight loss when higher-risk recipients and lower-risk recipients were compared with control subjects (P > 0.05 for all but one comparison). CONCLUSIONS Diabetes genetic risk counseling with currently available variants does not significantly alter self-reported motivation or prevention program adherence for overweight individuals at risk for diabetes.

Purpose To measure the frequency of prescription medication changes following direct-to-consumer personal genomic testing (DTC-PGT) and their association with the pharmacogenomic results received. Methods: New DTC-PGT customers were enrolled in 2012 and completed surveys prior to return of results and 6 months post-results; DTC-PGT results were linked to survey data. ‘Atypical response’ pharmacogenomic results were defined as those indicating an increase or decrease in risk of an adverse drug event or likelihood of therapeutic benefit. At follow-up, participants reported prescription medication changes and health care provider consultation. Results: Follow-up data were available from 961 participants, of which 54 (5.6%) reported changing a medication they were taking, or starting a new medication, due to their DTC-PGT results. Of these, 45 (83.3%) reported consulting with a health care provider regarding the change. Pharmacogenomic results were available for 961 participants, of which 875 (91.2%) received ≥1 atypical response result. For each such result received, the odds of reporting a prescription medication change increased 1.57 times (95% confidence interval = 1.17, 2.11). Conclusion: Receipt of pharmacogenomic results indicating atypical drug response is common with DTC-PGT, and associated with prescription medication changes; however, fewer than 1% of consumers report unsupervised changes at 6 months post-testing.

Background: The MedSeq Project is a randomized clinical trial developing approaches to assess the impact of integrating genome sequencing into clinical medicine. To facilitate the return of results of potential medical relevance to physicians and patients participating in the MedSeq Project, we sought to develop a reporting approach for the effective communication of such findings. Methods: Genome sequencing was performed on the Illumina HiSeq platform. Variants were filtered, interpreted, and validated according to methods developed by the Laboratory for Molecular Medicine and consistent with current professional guidelines. The GeneInsight software suite, which is integrated with the Partners HealthCare electronic health record, was used for variant curation, report drafting, and delivery. Results: We developed a concise 5–6 page Genome Report (GR) featuring a single-page summary of results of potential medical relevance with additional pages containing structured variant, gene, and disease information along with supporting evidence for reported variants and brief descriptions of associated diseases and clinical implications. The GR is formatted to provide a succinct summary of genomic findings, enabling physicians to take appropriate steps for disease diagnosis, prevention, and management in their patients. Conclusions: Our experience highlights important considerations for the reporting of results of potential medical relevance and provides a framework for interpretation and reporting practices in clinical genome sequencing. Electronic supplementary material The online version of this article (doi:10.1186/s12881-014-0134-1) contains supplementary material, which is available to authorized users.

Objective: Precision medicine utilizes genomic and other data to optimize and personalize treatment. Although more than 2,500 genetic tests are currently available, largely for extreme and/or rare phenotypes, the question remains whether this approach can be used for the treatment of common, complex conditions like obesity, inflammation, and insulin resistance, which underlie a host of metabolic diseases. Methods: This review, developed from a Trans-NIH Conference titled “Genes, Behaviors, and Response to Weight Loss Interventions,” provides an overview of the state of genetic and genomic research in the area of weight change and identifies key areas for future research. Results: Although many loci have been identified that are associated with cross-sectional measures of obesity/body size, relatively little is known regarding the genes/loci that influence dynamic measures of weight change over time. Although successful short-term weight loss has been achieved using many different strategies, sustainable weight loss has proven elusive for many, and there are important gaps in our understanding of energy balance regulation. Conclusions: Elucidating the molecular basis of variability in weight change has the potential to improve treatment outcomes and inform innovative approaches that can simultaneously take into account information from genomic and other sources in devising individualized treatment plans.