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Joseph A Boscarino1,2, Porat M Erlich1,3, Stuart N Hoffman4, Xiaopeng Zhang51Center for Health Research, Geisinger Clinic, Danville, PA, 2Department of Psychiatry, 3Department of Medicine, Temple University School of Medicine, Philadelphia, PA, 4Department of Neurology, 5Department of Anesthesiology, Geisinger Clinic, Danville, PA, USAObjective: The study aim was to assess the cumulative burden of polymorphisms located within four genetic loci previously associated with posttraumatic stress disorder (PTSD) among outpatients at risk for PTSD.Methods: Diagnostic interviews were completed and DNA samples collected among 412 pain patients to determine if FKBP5 (rs9470080), COMT (rs4680), CHRNA5 (rs16969968), and CRHR1 (rs110402) single nucleotide polymorphisms were cumulatively associated with increased risk for PTSD.Results: In bivariate analyses, it was found that a count of specific PTSD risk alleles located within FKBP5, COMT, CHRNA5, and CRHR1 genetic loci (allele range = 0–6, mean count = 2.92, standard deviation = 1.36) was associated with lifetime (t [409] = 3.430, P = 0.001) and early onset PTSD (t [409] = 4.239, P = 0.000028). In logistic regression, controlling for demographic factors, personality traits, and trauma exposures, this risk allele count remained associated with both lifetime (odds ratio = 1.49, P = 0.00158) and early onset PTSD (odds ratio = 2.36, P = 0.000093). Interaction effects were also detected, whereby individuals with higher risk allele counts and higher trauma exposures had an increased risk of lifetime PTSD (allele count × high trauma, P = 0.026) and early onset PTSD (allele count × high trauma, P = 0.016) in these logistic regressions. Those with no or few risk alleles appeared resilient to PTSD, regardless of exposure history.Conclusion: A cumulative risk allele count involving four single nucleotide polymorphisms located within the FKBP5, COMT, CHRNA5, and CRHR1 genes are associated with PTSD. Level of trauma exposure interacts with risk allele count, such that PTSD is increased in those with higher risk allele counts and higher trauma exposures. Since the single nucleotide polymorphisms studied encompass stress circuitry and addiction biology, these findings may have implications for neuropsychiatric research and treatment.Keywords: posttraumatic stress disorder, genetic association study, single nucleotide polymorphism, risk alleles, trauma exposure, neuroticism, childhood adversity

Aims Ourstudysoughttoassesstheprevalenceof andriskfactorsforopioiddrugdependenceamongout-patientson long-term opioid therapy in a large health-care system. Methods Using electronic health records, we identified out-patients receiving 4+ physician orders for opioid therapy in the past 12 months for non-cancer pain within a large US health-care system. We completed diagnostic interviews with 705 of these patients to identify opioid use disorders and assess risk factors. Results Preliminary analyses suggested that current opioid dependence might be as high as 26% (95% confidence interval (CI) = 22.0-29.9) among the patients studied. Logistic regressions indicated that current dependence was associated with variables often in the medical record, including age

Previous studies suggested that low total cholesterol was associated with external mortality, including deaths from suicide, homicide, and accidents. However, this reported association was potentially confounded, since cholesterol was also reported to be associated with alcohol abuse, anti-social personality disorder, and other risk factors for external mortality.We examined external-cause mortality among a national sample of 4462 male, US veterans at baseline in 1985. Using Cox regressions to estimate survival time, we assessed the impact of low baseline total cholesterolor =165 mg/dl, age, race, intelligence, BMI, alcohol abuse, anti-social personality disorder, depression, and other factors at follow-up. Study follow-up continued until December 31, 2000. A total of 55 external mortalities occurred during this approximately 16-year period.Multivariate Cox regressions predicting external-cause mortality suggested that three predictor variables were significant: low total cholesterol, morbid depression, and anti-social personality disorder, with hazard ratios (HRs) of 1.97 (p=0.046), 1.76 (p=0.043), and 2.22 (p=0.006), respectively. In addition, a significant interaction was detected for low cholesterol x morbid depression (p0.005), whereby those with both at baseline were approximately 7 times more likely to die from external mortality (HR=6.5, 95% CI=3.07-13.76).Among a national random sample of community-based men, lower baseline cholesterol predicted external mortality and revealed an interaction with morbid depression. Patients presenting with low cholesterol and morbid depression in clinical practice may warrant clinical attention and surveillance.

Emerging biomarkers for acute myocardial infarction (AMI) may enhance conventional risk-prediction algorithms if they are informative and associated with risk independently of established predictors. In this study, we constructed a cohort for testing emerging biomarkers for AMI in managed-care populations using existing biospecimen repositories linked to electronic health records (EHR).Electronic health record-based biorepositories collected by healthcare systems can be federated to provide large, methodologically sound testing sets for biomarker validation.Subjects ages 40 to 80 years were selected from 2 existing population-based biospecimen repositories. Incident AMI status and covariates were ascertained from the EHR. An ad hoc model for AMI risk was parameterized and validated. Simulation was used to test incremental gains in performance due to the inclusion of biomarkers in this model. Gains in performance were assessed in terms of area under the receiver operating characteristic curve (ROC-AUC) and case reclassification.A total of 18 329 individuals (57% female) contributed 108 400 person-years of EHR follow-up. The crude AMI incidence was 10.8 and 5.0 per 1000 person-years among males and females, respectively. Compared with the model with risk factors alone, inclusion of a simulated biomarker yielded substantial gains in sensitivity without loss of specificity. Furthermore, a net ROC-AUC gain of 13.3% was observed, as well as correct reclassification of 9.8% of incident cases (79 of 806) that were otherwise not considered statin-indicated at baseline under the National Cholesterol Education Program Adult Treatment Panel III criteria.More research is needed to assess incremental contribution of emerging biomarkers for AMI prediction in managed-care populations.

Our study sought to assess the prevalence of and risk factors for opioid drug dependence among out-patients on long-term opioid therapy in a large health-care system.Using electronic health records, we identified out-patients receiving 4+ physician orders for opioid therapy in the past 12 months for non-cancer pain within a large US health-care system. We completed diagnostic interviews with 705 of these patients to identify opioid use disorders and assess risk factors.Preliminary analyses suggested that current opioid dependence might be as high as 26% [95% confidence interval (CI) = 22.0-29.9] among the patients studied. Logistic regressions indicated that current dependence was associated with variables often in the medical record, including age65 [odds ratio (OR) = 2.33, P = 0.001], opioid abuse history (OR = 3.81, P0.001), high dependence severity (OR = 1.85, P = 0.001), major depression (OR = 1.29, P = 0.022) and psychotropic medication use (OR = 1.73, P = 0.006). Four variables combined (age, depression, psychotropic medications and pain impairment) predicted increased risk for current dependence, compared to those without these factors (OR = 8.01, P0.001). Knowing that the patient also had a history of severe dependence and opioid abuse increased this risk substantially (OR = 56.36, P0.001).Opioid misuse and dependence among prescription opioid patients in the United States may be higher than expected. A small number of factors, many documented in the medical record, predicted opioid dependence among the out-patients studied. These preliminary findings should be useful in future research efforts.

Magnetic resonance imaging (MRI) can aid clinical assessment of brain changes potentially correlated with Alzheimer disease (AD). MRI traits may improve our ability to identify genes associated with AD-outcomes. We evaluated semi-quantitative MRI measures as endophenotypes for genetic studies by assessing their association with AD in families from the Multi-Institutional Research in Alzheimer Genetic Epidemiology (MIRAGE) Study.Discordant siblings from multiple ethnicities were ascertained through a single affected proband. Semi-quantitative MRI measures were obtained for each individual. The association between continuous/ordinal MRI traits and AD were analyzed using generalized estimating equations. Medical history and Apolipoprotein E (APOE)epsilon4 status were evaluated as potential confounders.Comparisons of 214 affected and 234 unaffected subjects from 229 sibships revealed that general cerebral atrophy, white matter hyperintensities (WMH), and mediotemporal atrophy differed significantly between groups (each at P.0001) and varied by ethnicity. Age at MRI and duration of AD confounded all associations between AD and MRI traits. Among unaffected sibs, the presence of at least one APOEepsilon4 allele and MRI infarction was associated with more WMH after adjusting for age at MRI.The strong association between MRI traits and AD suggests that MRI traits may be informative endophenotypes for basic and clinical studies of AD. In particular, WMH may be a marker of vascular disease that contributes to AD pathogenesis.

Previous studies have shown association of single nucleotide polymorphisms (SNPs) in three contiguous genes (PON1, PON2 and PON3) encoding paraoxonase with risk of Alzheimer disease (AD). We evaluated the association of serum paraoxonase activity measured by phenyl acetate (PA) and thiobutyl butyrolactone (TBBL) with risk of AD and with 26 SNPs spanning the PON gene cluster in 266 AD cases and 306 sibling controls from the MIRAGE Study. The odds of AD (adjusted for age, gender and ethnicity) increased 20% for each standard deviation decrease in PA or TBBL activity. There were association signals with activity in all three genes. Haplotypes including SNPs spanning the PON genes were generally more significant than haplotypes comprising SNPs from one gene. Significant interactions were observed between SNP pairs located across the PON cluster with either serum activity measure as the outcome, and between several PON SNPs and PA activity with AD status as the outcome. Our results suggest that low serum paraoxonase activity is a risk factor for AD. Furthermore, multiple variants in PON influence serum paraoxonase activity and their effects may be synergistic.

Background To find specific genes predisposing to heavy alcohol consumption (self-reported consumption of 24 grams or more of alcohol per day among men and 12 grams or more among women), we studied 330 families collected by the Framingham Heart Study made available to participants in the Genetic Analysis Workshop 13 (GAW13). Results Parametric and nonparametric methods of linkage analysis were used. No significant evidence of linkage was found; however, weak signals were identified in several chromosomal regions, including 1p22, 4q12, 4q25, and 11q24, which are in the vicinity of those reported in other similar studies. Conclusion Our study did not reveal significant evidence of linkage to heavy alcohol use; however, we found weak confirmation of studies carried out in other populations.