Leslie A. Brick

Your Path to Transplant: a randomized controlled trial of a tailored computer education intervention to increase living donor kidney transplant.

BackgroundBecause of the deceased donor organ shortage, more kidney patients are considering whether to receive kidneys from family and friends, a process called living donor kidney transplantation (LDKT). Although Blacks and Hispanics are 3.4 and 1.5 times more likely, respectively, to develop end stage renal disease (ESRD) than Whites, they are less likely to receive LDKTs. To address this disparity, a new randomized controlled trial (RCT) will assess whether Black, Hispanic, and White transplant patients’ knowledge, readiness to pursue LDKT, and receipt of LDKTs can be increased when they participate in the Your Path to Transplant (YPT) computer-tailored intervention.Methods/DesignNine hundred Black, Hispanic, and White ESRD patients presenting for transplant evaluation at University of California, Los Angeles Kidney and Pancreas Transplant Program (UCLA-KPTP) will be randomly assigned to one of two education conditions, YPT or Usual Care Control Education (UC). As they undergo transplant evaluation, patients in the YPT condition will receive individually-tailored telephonic coaching sessions, feedback reports, video and print transplant education resources, and assistance with reducing any known socioeconomic barriers to LDKT. Patients receiving UC will only receive transplant education provided by UCLA-KPTP. Changes in transplant knowledge, readiness, pros and cons, and self-efficacy to pursue LDKT will be assessed prior to presenting at the transplant center (baseline), during transplant evaluation, and 4- and 8-months post-baseline, while completion of transplant evaluation and receipt of LDKTs will be assessed at 18-months post-baseline. The RCT will determine, compared to UC, whether Black, Hispanic, and White patients receiving YPT increase in their readiness to pursue LDKT and transplant knowledge, and become more likely to complete transplant medical evaluation and pursue LDKT. It will also examine how known patient, family, and healthcare system barriers to LDKT act alone and in combination with YPT to affect patients’ transplant decision-making and behavior. Statistical analyses will be performed under an intent-to-treat approach.DiscussionAt the conclusion of the study, we will have assessed the effectiveness of an innovative and cost-effective YPT intervention that could be utilized to tailor LDKT discussion and education based on the needs of individual patients of different races in many healthcare settings.Trial registrationClinicalTrials.gov, number NCT02181114.

Sustainable transportation stage of change, decisional balance, and self-efficacy scale development and validation in two university samples

Single occupancy vehicle (SOV) transportation is a key contributor to climate change and air pollution. Sustainable transportation (ST), commuting by any means other than SOV, could both slow climate change and enhance public health. The transtheoretical model (TTM) provides a useful framework for examining how people progress towards adopting ST. Short valid and reliable measures for ST decisional balance, self-efficacy, and climate change doubt were developed and their relationship with stages of change was examined. Two large university-based volunteer samples participated in measurement studies. Using multiple procedures, three brief internally consistent measures were developed: decisional balance, self-efficacy, and climate change doubt. The stages of change correctly discriminated both decisional balance and self-efficacy, as well as replicated hypothesized relationships. Climate change doubt did not vary by stages; however, it may prove useful in future studies. Results support the validation of these measures and the application of the TTM to ST.

Adaptation of the AOAC 2011.25 Integrated Total Dietary Fiber Assay To Determine the Dietary Fiber and Oligosaccharide Content of Dry Edible Beans

Dietary fiber (DF) has important health benefits in the human diet. Developing dry edible bean (Phaseolus vulgaris L.) cultivars with improved DF and reduced nondigestible oligosaccharide content is an important goal for dry bean breeders to increase consumer acceptance. To determine if genetic variation exists among dry bean cultivars for DF, two populations of diverse dry bean cultivars/lines that represent two centers of dry bean domestication were evaluated for dietary fiber using the Integrated Total Dietary Fiber Assay (AOAC 2011.25). This assay was adapted to measure water insoluble dietary fiber, water soluble dietary fiber, oligosaccharides raffinose and stachyose, and the calculated total dietary fiber (TDF) content of cooked dry bean seed. The AOAC 2011.25 protocol was modified by using a quick, simple, and sensitive high-performance liquid chromatography method paired with an electrochemical detection method to separate and quantify specific oligosaccharides, and using duplicate samples as replicates to generate statistical information. The TDF of dry bean entries ranged from 20.0 to 27.0% in population I and from 20.6 to 25.7% in population II. Total oligosaccharides ranged from 2.56 to 4.65% in population I and from 2.36 to 3.84% in population II. The results suggest that significant genetic variation exists among dry bean cultivars/lines to allow for genetic selection for improved DF content in dry beans and that the modifications to the AOAC 2011.25 method were suitable for estimating DF in cooked dry edible beans.

Testing 40 Predictions From the Transtheoretical Model Again, With Confidence.

Testing Theory-based Quantitative Predictions (TTQP) represents an alternative to traditional Null Hypothesis Significance Testing (NHST) procedures and is more appropriate for theory testing. The theory generates explicit effect size predictions and these effect size estimates, with related confidence intervals, are used to test the predictions. The focus of a study is shifted to a quantitative approach in contrast to the NHST dyadic decision centered on testing a prediction not based on the theory. This article describes the TTQP as an alternative approach by replicating and extending a test of 40 a priori predictions based on the Transtheoretical Model (TTM). Specific quantitative predictions were made about the magnitude of the effect size (ω2). The predictions involved movement from 1 of 3 initial stages (Precontemplation, Contemplation, and Preparation) to stage membership 12 months later. In the initial study, 36 of the 40 predictions were confirmed. The same 40 predictions are evaluated on a sample (N = 3,923) of smokers recruited from a large New England HMO for a smoking cessation study. The predictions were recalibrated based on the first study and 99% confidence intervals were employed to test the predictions. Thirty-two of the 40 predictions were confirmed. Of the 8 failures, 4 were judged to reflect a need for further recalibration, 1 was attributed to sampling fluctuation, and 3 suggested revisions of the theory are needed. The results provide overall support for the TTM. The study also illustrates some of the challenges of testing quantitative predictions.

Replicating cluster subtypes for the prevention of adolescent smoking and alcohol use.

INTRODUCTION Substance abuse interventions tailored to the individual level have produced effective outcomes for a wide variety of behaviors. One approach to enhancing tailoring involves using cluster analysis to identify prevention subtypes that represent different attitudes about substance use. This study applied this approach to better understand tailored interventions for smoking and alcohol prevention. METHODS Analyses were performed on a sample of sixth graders from 20 New England middle schools involved in a 36-month tailored intervention study. Most adolescents reported being in the Acquisition Precontemplation (aPC) stage at baseline: not smoking or not drinking and not planning to start in the next six months. For smoking (N=4059) and alcohol (N=3973), each sample was randomly split into five subsamples. Cluster analysis was performed within each subsample based on three variables: Pros and Cons (from Decisional Balance Scales), and Situational Temptations. RESULTS Across all subsamples for both smoking and alcohol, the following four clusters were identified: (1) Most Protected (MP; low Pros, high Cons, low Temptations); (2) Ambivalent (AM; high Pros, average Cons and Temptations); (3) Risk Denial (RD; average Pros, low Cons, average Temptations); and (4) High Risk (HR; high Pros, low Cons, and very high Temptations). CONCLUSIONS Finding the same four clusters within aPC for both smoking and alcohol, replicating the results across the five subsamples, and demonstrating hypothesized relations among the clusters with additional external validity analyses provide strong evidence of the robustness of these results. These clusters demonstrate evidence of validity and can provide a basis for tailoring interventions.

Shared additive genetic influences on DSM-IV criteria for alcohol dependence in subjects of European ancestry.

BACKGROUND AND AIMS Genetic studies of alcohol dependence (AD) have identified several candidate loci and genes, but most observed effects are small and difficult to reproduce. A plausible explanation for inconsistent findings may be a violation of the assumption that genetic factors contributing to each of the seven DSM-IV criteria point to a single underlying dimension of risk. Given that recent twin studies suggest that the genetic architecture of AD is complex and probably involves multiple discrete genetic factors, the current study employed common single nucleotide polymorphisms in two multivariate genetic models to examine the assumption that the genetic risk underlying DSM-IV AD is unitary. DESIGN, SETTING, PARTICIPANTS, MEASUREMENTS AD symptoms and genome-wide single nucleotide polymorphism (SNP) data from 2596 individuals of European descent from the Study of Addiction: Genetics and Environment were analyzed using genomic-relatedness-matrix restricted maximum likelihood. DSM-IV AD symptom covariance was described using two multivariate genetic factor models. FINDINGS Common SNPs explained 30% (standard error=0.136, P=0.012) of the variance in AD diagnosis. Additive genetic effects varied across AD symptoms. The common pathway model approach suggested that symptoms could be described by a single latent variable that had a SNP heritability of 31% (0.130, P=0.008). Similarly, the exploratory genetic factor model approach suggested that the genetic variance/covariance across symptoms could be represented by a single genetic factor that accounted for at least 60% of the genetic variance in any one symptom. CONCLUSION Additive genetic effects on DSM-IV alcohol dependence criteria overlap. The assumption of common genetic effects across alcohol dependence symptoms appears to be a valid assumption.

Examining the role of common genetic variants on alcohol, tobacco, cannabis, and illicit drug dependence

BACKGROUND AND AIMS Twin and family studies suggest that genetic influences are shared across substances of abuse. However, despite evidence of heritability, genome-wide association and candidate gene studies have indicated numerous markers of limited effects, suggesting that much of the heritability remains missing. We estimated (1) the aggregate effect of common single nucleotide polymorphisms (SNPs) on multiple indicators of comorbid drug problems that are typically employed across community and population-based samples, and (2) the genetic covariance across these measures. PARTICIPANTS A total of 2596 unrelated subjects from the Study of Addiction: Genetics and Environment provided information on alcohol, tobacco, cocaine, cannabis and other illicit substance dependence. Phenotypic measures included: (1) a factor score based on DSM-IV drug dependence diagnoses (DD), (2) a factor score based on problem use (PU; i.e. 1+ DSM-IV symptoms) and (3) dependence vulnerability (DV; a ratio of DSM-IV symptoms to the number of substances used). FINDINGS Univariate and bivariate genome-wide complex trait analyses of this selected sample indicated that common SNPs explained 25-36% of the variance across measures, with DD and DV having the largest effects [h(2) SNP (standard error) = 0.36 (0.13) and 0.33 (0.13), respectively; PU = 0.25 (0.13)]. Genetic effects were shared across the three phenotypic measures of comorbid drug problems [rDD-PU = 0.92 (0.08), rDD-DV = 0.97 (0.08) and rPU-DV = 0.96 (0.07)]. CONCLUSION At least 20% of the variance in the generalized vulnerability to substance dependence is attributable to common single nucleotide polymorphisms. The additive effect of common single nucleotide polymorphisms is shared across important indicators of comorbid drug problems.

Inhibitory control in siblings discordant for exposure to maternal smoking during pregnancy.

Maternal smoking during pregnancy (SDP) has been linked to poorer offspring executive function across development, but SDP does not occur independent of other familial risk factors. As such, poor and inconsistent control for potential confounds, notably shared familial (i.e., genetic and environmental) confounds, preclude concluding causal effects of SDP on child outcomes. We examined the within-family association between SDP and one component of executive function, inhibitory control, in a sample of families (N = 173) specifically selected for sibling pairs discordant for exposure to SDP. Thus, the present study examines if the SDP-inhibitory control association withstands rigorous control for potential child and familial confounds. 79% of the variation in child inhibitory control was attributable to within-family differences and 21% was attributable to differences between families, indicating that the variability in inhibitory control was primarily a function of differences between siblings rather than differences across families. Further, the association between SDP and inhibitory control was fully attenuated when confounds were considered. These findings suggest that co-occurring vulnerabilities act as more salient risk factors for poorer child inhibitory control than SDP and may serve as effective targets of interventions seeking to improve children’s inhibitory control in families with nicotine dependent mothers.

Additive genetic contribution to symptom dimensions in major depressive disorder.

Major depressive disorder (MDD) is a phenotypically heterogeneous disorder with a complex genetic architecture. In this study, genomic-relatedness-matrix restricted maximum-likelihood analysis (GREML) was used to investigate the extent to which variance in depression symptoms/symptom dimensions can be explained by variation in common single nucleotide polymorphisms (SNPs) in a sample of individuals with MDD (N = 1,558) who participated in the National Institute of Mental Health Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study. A principal components analysis of items from the Hamilton Rating Scale for Depression (HRSD) obtained prior to treatment revealed 4 depression symptom components: (a) appetite, (b) core depression symptoms (e.g., depressed mood, anhedonia), (c) insomnia, and (d) anxiety. These symptom dimensions were associated with SNP-based heritability (hSNP2) estimates of 30%, 14%, 30%, and 5%, respectively. Results indicated that the genetic contribution of common SNPs to depression symptom dimensions were not uniform. Appetite and insomnia symptoms in MDD had a relatively strong genetic contribution whereas the genetic contribution was relatively small for core depression and anxiety symptoms. While in need of replication, these results suggest that future gene discovery efforts may strongly benefit from parsing depression into its constituent parts. (PsycINFO Database Record

Extending Theory-Based Quantitative Predictions to New Health Behaviors

BackgroundTraditional null hypothesis significance testing suffers many limitations and is poorly adapted to theory testing.PurposeA proposed alternative approach, called Testing Theory-based Quantitative Predictions, uses effect size estimates and confidence intervals to directly test predictions based on theory.MethodThis paper replicates findings from previous smoking studies and extends the approach to diet and sun protection behaviors using baseline data from a Transtheoretical Model behavioral intervention (N = 5407). Effect size predictions were developed using two methods: (1) applying refined effect size estimates from previous smoking research or (2) using predictions developed by an expert panel.ResultsThirteen of 15 predictions were confirmed for smoking. For diet, 7 of 14 predictions were confirmed using smoking predictions and 6 of 16 using expert panel predictions. For sun protection, 3 of 11 predictions were confirmed using smoking predictions and 5 of 19 using expert panel predictions.ConclusionExpert panel predictions and smoking-based predictions poorly predicted effect sizes for diet and sun protection constructs. Future studies should aim to use previous empirical data to generate predictions whenever possible. The best results occur when there have been several iterations of predictions for a behavior, such as with smoking, demonstrating that expected values begin to converge on the population effect size. Overall, the study supports necessity in strengthening and revising theory with empirical data.