Rationale and design for an investigation to optimize selective serotonin reuptake inhibitor treatment for pregnant women with depression.

The physiological changes of pregnancy can affect the pharmacokinetics of a drug, thereby affecting its dose requirements. Because pharmacokinetic (PK) studies in pregnant women have rarely been conducted, evidence-based dosing adjustments are seldom available. In particular, despite the fact that the use of antidepressants has become increasingly common, pregnancy-associated PK changes of the selective serotonin reuptake inhibitors (SSRIs) are largely unknown.

Genetic variation among 82 pharmacogenes: The PGRNseq data from the eMERGE network.

Genetic variation can affect drug response in multiple ways, although it remains unclear how rare genetic variants affect drug response. The electronic Medical Records and Genomics (eMERGE) Network, collaborating with the Pharmacogenomics Research Network, began eMERGE-PGx, a targeted sequencing study to assess genetic variation in 82 pharmacogenes critical for implementation of "precision medicine." The February 2015 eMERGE-PGx data release includes sequence-derived data from ∼5,000 clinical subjects. We present the variant frequency spectrum categorized by variant type, ancestry, and predicted function. We found 95.12% of genes have variants with a scaled Combined Annotation-Dependent Depletion score above 20, and 96.19% of all samples had one or more Clinical Pharmacogenetics Implementation Consortium Level A actionable variants. These data highlight the distribution and scope of genetic variation in relevant pharmacogenes, identifying challenges associated with implementing clinical sequencing for drug treatment at a broader level, underscoring the importance for multifaceted research in the execution of precision medicine.

Design and anticipated outcomes of the eMERGE-PGx project: a multicenter pilot for preemptive pharmacogenomics in electronic health record systems.

We describe here the design and initial implementation of the eMERGE-PGx project. eMERGE-PGx, a partnership of the Electronic Medical Records and Genomics Network and the Pharmacogenomics Research Network, has three objectives: (i) to deploy PGRNseq, a next-generation sequencing platform assessing sequence variation in 84 proposed pharmacogenes, in nearly 9,000 patients likely to be prescribed drugs of interest in a 1- to 3-year time frame across several clinical sites; (ii) to integrate well-established clinically validated pharmacogenetic genotypes into the electronic health record with associated clinical decision support and to assess process and clinical outcomes of implementation; and (iii) to develop a repository of pharmacogenetic variants of unknown significance linked to a repository of electronic health record-based clinical phenotype data for ongoing pharmacogenomics discovery. We describe site-specific project implementation and anticipated products, including genetic variant and phenotype data repositories, novel variant association studies, clinical decision support modules, clinical and process outcomes, approaches to managing incidental findings, and patient and clinician education methods.

A rigorous algorithm to detect and clean inaccurate adult height records within EHR systems.

BACKGROUND: Height is a critical variable for many biomedical analyses because it is an important component of Body Mass Index (BMI). Transforming EHR height measures into meaningful research-ready values is challenging and there is limited information available on methods for "cleaning" these data. OBJECTIVES: We sought to develop an algorithm to clean adult height data extracted from EHR using only height values and associated ages. RESULTS: The algorithm we developed is sensitive to normal decreases in adult height associated with aging, is implemented using an open-source software tool and is thus easily modifiable, and is freely available. We checked the performance of our algorithm using data from the Northwestern biobank and a replication sample from the Marshfield Clinic biobank obtained through our participation in the eMERGE consortium. The algorithm identified 1262 erroneous values from a total of 33937 records in the Northwestern sample. Replacing erroneous height values with those identified as correct by the algorithm resulted in meaningful changes in height and BMI records; median change in recorded height after cleaning was 7.6 cm and median change in BMI was 2.9 kg/m(2). Comparison of cleaned EHR height values to observer measured values showed that 94.5% (95% C.I 93.8-% - 95.2%) of cleaned values were within 3.5 cm of observer measured values. CONCLUSIONS: Our freely available height algorithm cleans EHR height data with only height and age inputs. Use of this algorithm will benefit groups trying to perform research with height and BMI data extracted from EHR.

A genotype risk score predicts type 2 diabetes from young adulthood: the CARDIA study.

AIMS/HYPOTHESIS: Genotype does not change over the life course and may thus facilitate earlier identification of individuals at high risk for type 2 diabetes. We hypothesised that a genotype score predicts incident type 2 diabetes from young adulthood and improves diabetes prediction models based on clinical risk factors alone. METHODS: The Coronary Artery Risk Development in Young Adults (CARDIA) study followed young adults (aged 18-30 years, mean age 25) serially into middle adulthood. We used Cox regression to build nested prediction models for incident type 2 diabetes based on clinical risk factors assessed in young adulthood (age, sex, race, parental history of diabetes, BMI, mean arterial pressure, fasting glucose, HDL-cholesterol and triacylglyercol), without and with a 38-variant genotype score. Models were compared with C statistics and continuous net reclassification improvement indices (NRI). RESULTS: Of 2,439 participants, 830 (34%) were black and 249 (10%) had a BMI ≥ 30 kg/m(2) at baseline. Over a mean 23.9 years of follow-up, 215 (8.8%) participants developed type 2 diabetes. The genotype score significantly predicted incident diabetes in all models, with an HR of 1.08 per risk allele (95% CI 1.04, 1.13) in the full model. The addition of the score to the full model modestly improved reclassification (continuous NRI 0.285; 95% CI 0.126, 0.433) but not discrimination (C statistics 0.824 and 0.829 in full models with and without score). Race-stratified analyses were similar. CONCLUSIONS/INTERPRETATION: Knowledge of genotype predicts type 2 diabetes over 25 years in white and black young adults but may not improve prediction over routine clinical measurements.

Development of associations among central adiposity, adiponectin and insulin sensitivity from adolescence to young adulthood.

OBJECTIVE: To examine associations of central adiposity, serum adiponectin and clamp-derived insulin sensitivity in a single longitudinal cohort from early adolescence to young adulthood. METHODS: The cohort was examined three times at mean ages 15 years (n = 308), 19 years (n = 218) and 22 years (n = 163). Insulin sensitivity was measured with the euglycaemic hyperinsulinaemic clamp. Circulating adiponectin was measured by enzyme-linked immunosorbent assay. Computed tomography scans were used at mean age 22 to compute subcutaneous and visceral abdominal fat volume. Partial Pearson correlations and linear regression were used to examine cross-sectional associations at each examination. RESULTS: The moderate negative correlation between waist circumference and adiponectin was significant and essentially unchanged from mean age 15 (-0.32, P < 0.0001) to mean age 22 (-0.29, P < 0.002), whereas the negative correlation between waist circumference and insulin sensitivity and the positive correlation between adiponectin and insulin sensitivity increased steadily in magnitude to mean age 22 (-0.29, P = 0.0002; and 0.32, P < 0.0001, respectively). In regression models including both visceral and subcutaneous fat, only visceral fat was significantly associated with insulin sensitivity, while only subcutaneous fat was nearly significantly associated with adiponectin. CONCLUSIONS: This study shows that the significant negative relationship between waist circumference and adiponectin predated the development of significant relationships between insulin sensitivity and both waist circumference and adiponectin. It also shows that adiponectin is more closely related to subcutaneous fat and insulin sensitivity is more closely related to visceral fat in young adults.

Heritability and genetic correlations of insulin sensitivity measured by the euglycaemic clamp.

AIMS: Studies investigating genetic factors influencing insulin sensitivity/insulin resistance have measured this phenotype using a variety of methods. In this study, genetic correlations and heritability of insulin sensitivity measured using the euglycaemic hyperinsulinaemic clamp and related phenotypes were examined. METHODS: The study population included 818 non-diabetic individuals from 297 nuclear families. Genetic correlations and heritability estimates were calculated using variance components methods. RESULTS: Homeostasis model of insulin resistance (HOMA-IR) and fasting insulin were very highly phenotypically and genetically correlated (r = 0.99 and r = 0.99). HOMA-IR and insulin sensitivity measured with the euglycaemic clamp were only moderately genetically correlated (r = -0.53), suggesting that the two traits may be influenced, at least in part, by different genes. Heritabilities for fasting insulin (h2 = 0.36) and HOMA-IR (h2 = 0.38) were consistent with the published literature, but heritability for insulin sensitivity measured by the euglycaemic clamp was slightly lower than other published estimates (h(2) = 0.24). CONCLUSIONS: Because HOMA-IR (or fasting insulin) and insulin sensitivity measured with the euglycaemic clamp are not highly genetically correlated, they should not be used interchangeably in genetic studies. Given the very high correlations between fasting insulin and HOMA-IR, HOMA-IR does not offer any advantage over fasting insulin in analyses of insulin sensitivity in this population.