Background Whether knowledge of genetic risk for coronary heart disease (CHD) affects health-related outcomes is unknown. We investigated whether incorporating a genetic risk score (GRS) in CHD risk estimates lowers low-density lipoprotein cholesterol (LDL-C) levels. Methods and Results Participants (n=203, 45–65 years old, at intermediate risk for CHD, and not on statins) were randomized to receive their 10-year probability of CHD based either on a conventional risk score (CRS) or CRS + GRS (+GRS). Participants in the +GRS group were stratified as having high (+H-GRS) or average/low (+L-GRS) GRS. Risk was disclosed by a genetic counselor followed by shared decision-making regarding statin therapy with a physician. We compared the primary endpoint of LDL-C levels at 6 months and assessed whether any differences were due to changes in dietary fat intake, physical activity levels or statin use. Participants (mean age 59.4±5 years, 48% men, mean 10-year CHD risk 8.5±4.1%) were allocated to receive either CRS (n=100) or +GRS (n=103). At the end of the study period, the +GRS group had a lower LDL-C than the CRS group (96.5±32.7 vs. 105.9±33.3 mg/dL; P=0.04). +H-GRS participants had lower LDL-C levels (92.3±32.9 mg/dL) than CRS participants (P=0.02) but not +L-GRS participants (100.9±32.2 mg/dL; P=0.18). Statins were initiated more often in the +GRS group than in the CRS group (39% vs. 22%, P<0.01). No significant differences in dietary fat intake and physical activity levels were noted. Conclusions Disclosure of CHD risk estimates that incorporated genetic risk information led to lower LDL-C levels than disclosure of CHD risk based on conventional risk factors alone. Clinical Trial Registration Information ClinicalTrials.gov. Identifier: NCT01936675.

Pitfalls in hormonal assays are commonly seen in clinical practice and may lead to erroneous clinical impressions and treatments. In this article, we address common laboratory pitfalls encountered during evaluation of patients with real or presumed endocrine disorders including high dose hook effect and falsely normal prolactin in cases of macroprolactinomas, macroprolactinemia and falsely elevated prolactin, macrothyrotropinemia and falsely elevated TSH, heterophile antibodies leading to false elevation of hormonal concentration, biotin interference with different hormonal assays, cross-reactivity of steroid hormones immunoassays, and others. We describe the mechanisms of such laboratory pitfalls, review clinical scenarios in which they might occur, and discuss the ways to resolve such conundrums. The aim of this article is to present a learning material for the endocrine trainees and practitioners.

The electronic Medical Records and Genomics (eMERGE) (Phase I) network was established in 2007 to further genomic discovery using biorepositories linked to the electronic health record (EHR). In Phase II, which began in 2011, genomic discovery efforts continue and in addition the network is investigating best practices for implementing genomic medicine, in particular, the return of genomic results in the EHR for use by physicians at point-of-care. To develop strategies for addressing the challenges of implementing genomic medicine in the clinical setting, the eMERGE network is conducting studies that return clinically-relevant genomic results to research participants and their health care providers. These genomic medicine pilot studies include returning individual genetic variants associated with disease susceptibility or drug response, as well as genetic risk scores for common “complex” disorders. Additionally, as part of a network-wide pharmacogenomics-related project, targeted resequencing of 84 pharmacogenes is being performed and select genotypes of pharmacogenetic relevance are being placed in the EHR to guide individualized drug therapy. Individual sites within the eMERGE network are exploring mechanisms to address incidental findings generated by resequencing of the 84 pharmacogenes. In this paper, we describe studies being conducted within the eMERGE network to develop best practices for integrating genomic findings into the EHR, and the challenges associated with such work.