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1.  Symptoms of depression and anxiety and adherence to antihypertensive medication 
American Journal of Hypertension  2012;25(4):505-511.
Nonadherence to drug treatment is a major contributor to antihypertensive treatment failure. Mood disorders could impair the patient's desire and ability to follow physician's recommendations. We evaluated the role of symptoms of depression and anxiety on adherence to antihypertensive drug treatment.
We conducted a longitudinal cohort study in 20–70 years old patients starting antihypertensive drug treatment, without other chronic conditions, and not taking mood-modifying drugs. Severity of symptoms of depression and anxiety were evaluated at enrollment and 3, 6, 9, and 12 months of follow-up, using the Beck depression inventory-II (BDI-II) and the psychological general well-being index (PGWB), respectively. Treatment adherence was measured by pill count. Nonadherence was defined as taking <80% of the prescribed number of pills. Poisson regression was used to model the association of the exposures with adherence.
We enrolled 178 patients (58% male; mean age: 50 years; 508 follow-up visits). The risk of nonadherence was 52.6% in 12 months (95% confidence interval (CI): 46.1, 59.1). After adjusting for other risk factors, individuals with at least mild depression (BDI-II ≥14) and those with at least mild anxiety (PGWB anxiety score <22) were 2.48 (95% CI: 1.47, 4.18) and 1.59 (95% CI: 0.99, 2.56) times more likely to become nonadherent in the following 3 months, respectively.
Patients with at least mild anxiety and depression symptoms are at increased risk of becoming nonadherent to antihypertensive medication. Screening for depression and anxiety symptoms could be used to identify high-risk patients. Further evidence is needed to elucidate whether interventions targeting these conditions improve adherence.
PMCID: PMC3588114  PMID: 22258334
anxiety; blood pressure; cohort studies; depression; hypertension; pateint non-adherence
2.  Analysis of human mini-exome sequencing data from Genetic Analysis Workshop 17 using a Bayesian hierarchical mixture model 
BMC Proceedings  2011;5(Suppl 9):S93.
Next-generation sequencing technologies are rapidly changing the field of genetic epidemiology and enabling exploration of the full allele frequency spectrum underlying complex diseases. Although sequencing technologies have shifted our focus toward rare genetic variants, statistical methods traditionally used in genetic association studies are inadequate for estimating effects of low minor allele frequency variants. Four our study we use the Genetic Analysis Workshop 17 data from 697 unrelated individuals (genotypes for 24,487 autosomal variants from 3,205 genes). We apply a Bayesian hierarchical mixture model to identify genes associated with a simulated binary phenotype using a transformed genotype design matrix weighted by allele frequencies. A Metropolis Hasting algorithm is used to jointly sample each indicator variable and additive genetic effect pair from its conditional posterior distribution, and remaining parameters are sampled by Gibbs sampling. This method identified 58 genes with a posterior probability greater than 0.8 for being associated with the phenotype. One of these 58 genes, PIK3C2B was correctly identified as being associated with affected status based on the simulation process. This project demonstrates the utility of Bayesian hierarchical mixture models using a transformed genotype matrix to detect genes containing rare and common variants associated with a binary phenotype.
PMCID: PMC3287935  PMID: 22373180

Results 1-2 (2)