Effects of aspirin in combination with EPA and DHA on HDL-C cholesterol and ApoA1 exchange in individuals with type 2 diabetes mellitus

BACKGROUND/SYNOPSIS

Low-dose aspirin is an effective drug for the prevention of cardiovascular disease (CVD) events but individuals with diabetes mellitus can be subject to 'aspirin resistance'. Thus, aspirin's effect in these individuals is controversial. Higher blood levels of seafood-derived omega-3 polyunsaturated fatty acids (ω3) eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) also have beneficial effects in reducing risk of CVD events but few studies have examined the interaction of plasma EPA and DHA with aspirin ingestion.

OBJECTIVE/PURPOSE

Our study examined the combinatory effects of EPA, DHA, and aspirin ingestion on HDL-cholesterol (HDL-C) and apoA-I exchange (shown to be associated with CVD event risk).

METHODS

30 adults with Type 2 diabetes mellitus ingested aspirin (81mg/day) for 7 consecutive days, EPA+DHA (2.6g/day) for 28 days, then both for 7 days. Plasma was collected at baseline and at 5 subsequent visits including 4h after each aspirin ingestion. Mixed model methods were used to determine HDL-C-concentrations and apoA-I exchange compared to the baseline visit values. LOWESS curves were used for non-linear analyses of outcomes to help discern change patterns, which was followed by piecewise linear functions for formal testing of curvilinear relationships.

RESULTS

Significant changes (p < 0.05) compared to baseline in both HDL-C-concentrations and apoA-I exchange were present at different times. After 7 days of aspirin-only ingestion, apoA-I exchange was significantly modified by increasing levels of DHA concentration, with increased apoA-I exchange observed up until log(DHA) of 4.6 and decreased exchange thereafter (p = 0.03). These LOWESS curve effects were not observed for EPA or HDL-C (p > 0.05). Aspirin's effects on apoA-I exchange were the greatest when EPA or DHA concentrations were moderate compared to high or low. Comparison of EPA, DHA, and EPA+DHA LOWESS curves, demonstrated that the majority of the effect is due to DHA.

CONCLUSION

Our results strongly suggest that plasma concentrations of EPA and DHA influence aspirin effects on lipid mediators of CVD event risk where their concentrations are most beneficial when moderate, not high or low. These effects on HDL-C cholesterol and apoA-I exchange are novel. Personalized dosing of DHA in those who take aspirin may be a beneficial option for patients with type 2 diabetes mellitus.

Population metrics for suicide events: A causal inference approach

Large-scale public health prevention initiatives and interventions are a very important component to current public health strategies. But evaluating effects of such large-scale prevention/intervention faces a lot of challenges due to confounding effects and heterogeneity of study population. In this paper, we will develop metrics to assess the risk for suicide events based on causal inference framework when the study population is heterogeneous. The proposed metrics deal with the confounding effect by first estimating the risk of suicide events within each of the risk levels, number of prior attempts, and then taking a weighted sum of the conditional probabilities. The metrics provide unbiased estimates of the risk of suicide events. Simulation studies and a real data example will be used to demonstrate the proposed metrics.

Correlation and agreement: overview and clarification of competing concepts and measures

Summary: Agreement and correlation are widely-used concepts that assess the association between variables. Although similar and related, they represent completely different notions of association. Assessing agreement between variables assumes that the variables measure the same construct, while correlation of variables can be assessed for variables that measure completely different constructs. This conceptual difference requires the use of different statistical methods, and when assessing agreement or correlation, the statistical method may vary depending on the distribution of the data and the interest of the investigator. For example, the Pearson correlation, a popular measure of correlation between continuous variables, is only informative when applied to variables that have linear relationships; it may be non-informative or even misleading when applied to variables that are not linearly related. Likewise, the intraclass correlation, a popular measure of agreement between continuous variables, may not provide sufficient information for investigators if the nature of poor agreement is of interest. This report reviews the concepts of agreement and correlation and discusses differences in the application of several commonly used measures.

Measuring Associations of the Department of Veterans Affairs' Suicide Prevention Campaign on the Use of Crisis Support Services

Campaigns have become popular in public health approaches to suicide prevention; however, limited empirical investigation of their impact on behavior has been conducted. To address this gap, utilization patterns of crisis support services associated with the Department of Veterans Affairs' Veterans Crisis Line (VCL) suicide prevention campaign were examined. Daily call data for the National Suicide Prevention Lifeline, VCL, and 1-800-SUICIDE were modeled using a novel semi-varying coefficient method. Analyses reveal significant increases in call volume to both targeted and broad resources during the campaign. Findings underscore the need for further research to refine measurement of the effects of these suicide prevention efforts.

Relationships among three popular measures of differential risks: relative risk, risk difference, and odds ratio

The relative risk, risk difference, and odds ratio are the three most commonly used measures for comparing the risk of disease between different groups. Although widely popular in biomedical and psychosocial research, the relationship among the three measures has not been clarified in the literature. Many researchers incorrectly assume a monotonic relationship, such that higher (or lower) values in one measure are associated with higher (or lower) values in the other measures. In this paper we discuss three theorems and provide examples demonstrating that this is not the case; there is no logical relationship between any of these measures. Researchers must be very cautious when implying a relationship between the different measures or when combining results of studies that use different measures of risk.

Variable selection for distribution-free models for longitudinal zero-inflated count responses

Zero-inflated count outcomes arise quite often in research and practice. Parametric models such as the zero-inflated Poisson and zero-inflated negative binomial are widely used to model such responses. Like most parametric models, they are quite sensitive to departures from assumed distributions. Recently, new approaches have been proposed to provide distribution-free, or semi-parametric, alternatives. These methods extend the generalized estimating equations to provide robust inference for population mixtures defined by zero-inflated count outcomes. In this paper, we propose methods to extend smoothly clipped absolute deviation (SCAD)-based variable selection methods to these new models. Variable selection has been gaining popularity in modern clinical research studies, as determining differential treatment effects of interventions for different subgroups has become the norm, rather the exception, in the era of patent-centered outcome research. Such moderation analysis in general creates many explanatory variables in regression analysis, and the advantages of SCAD-based methods over their traditional counterparts render them a great choice for addressing this important and timely issues in clinical research. We illustrate the proposed approach with both simulated and real study data. Copyright © 2016 John Wiley & Sons, Ltd.

On performance of parametric and distribution-free models for zero-inflated and over-dispersed count responses

Zero-inflated Poisson (ZIP) and negative binomial (ZINB) models are widely used to model zero-inflated count responses. These models extend the Poisson and negative binomial (NB) to address excessive zeros in the count response. By adding a degenerate distribution centered at 0 and interpreting it as describing a non-risk group in the population, the ZIP (ZINB) models a two-component population mixture. As in applications of Poisson and NB, the key difference between ZIP and ZINB is the allowance for overdispersion by the ZINB in its NB component in modeling the count response for the at-risk group. Overdispersion arising in practice too often does not follow the NB, and applications of ZINB to such data yield invalid inference. If sources of overdispersion are known, other parametric models may be used to directly model the overdispersion. Such models too are subject to assumed distributions. Further, this approach may not be applicable if information about the sources of overdispersion is unavailable. In this paper, we propose a distribution-free alternative and compare its performance with these popular parametric models as well as a moment-based approach proposed by Yu et al. [Statistics in Medicine 2013; 32: 2390-2405]. Like the generalized estimating equations, the proposed approach requires no elaborate distribution assumptions. Compared with the approach of Yu et al., it is more robust to overdispersed zero-inflated responses. We illustrate our approach with both simulated and real study data.

The Avalanche Hypothesis and Compression of Morbidity: Testing Assumptions through Cohort-Sequential Analysis

Background

The compression of morbidity model posits a breakpoint in the adult lifespan that separates an initial period of relative health from a subsequent period of ever increasing morbidity. Researchers often assume that such a breakpoint exists; however, this assumption is hitherto untested.

Purpose

To test the assumption that a breakpoint exists—which we term a morbidity tipping point—separating a period of relative health from a subsequent deterioration in health status. An analogous tipping point for healthcare costs was also investigated.

Methods

Four years of adults’ (N = 55,550) morbidity and costs data were retrospectively analyzed. Data were collected in Pittsburgh, PA between 2006 and 2009; analyses were performed in Rochester, NY and Ann Arbor, MI in 2012 and 2013. Cohort-sequential and hockey stick regression models were used to characterize long-term trajectories and tipping points, respectively, for both morbidity and costs.

Results

Morbidity increased exponentially with age (P<.001). A morbidity tipping point was observed at age 45.5 (95% CI, 41.3-49.7). An exponential trajectory was also observed for costs (P<.001), with a costs tipping point occurring at age 39.5 (95% CI, 32.4-46.6). Following their respective tipping points, both morbidity and costs increased substantially (Ps<.001).

Conclusions

Findings support the existence of a morbidity tipping point, confirming an important but untested assumption. This tipping point, however, may occur earlier in the lifespan than is widely assumed. An “avalanche of morbidity” occurred after the morbidity tipping point—an ever increasing rate of morbidity progression. For costs, an analogous tipping point and “avalanche” were observed. The time point at which costs began to increase substantially occurred approximately 6 years before health status began to deteriorate.

Adapting interpersonal psychotherapy for older adults at risk for suicide

OBJECTIVE

To pilot a psychological intervention adapted for older adults at risk for suicide.

DESIGN

A focused, uncontrolled, pre-to-post-treatment psychotherapy trial. All eligible participants were offered the study intervention.

SETTING

Outpatient mental health care provided in the psychiatry department of an academic medical center in a mid-sized Canadian city.

PARTICIPANTS

Seventeen English-speaking adults 60 years or older, at risk for suicide by virtue of current suicide ideation, death ideation, and/or recent self-injury.

INTERVENTION

A 16-session course of Interpersonal Psychotherapy (IPT) adapted for older adults at risk for suicide who were receiving medication and/or other standard psychiatric treatment for underlying mood disorders.

MEASUREMENTS

Participants completed a demographics form, screens for cognitive impairment and alcohol misuse, a semi-structured diagnostic interview, and measures of primary (suicide ideation and death ideation) and secondary study outcomes (depressive symptom severity, social adjustment and support, psychological well-being), and psychotherapy process measures.

RESULTS

Participants experienced significant reductions in suicide ideation, death ideation, and depressive symptom severity, and significant improvement in perceived meaning in life, social adjustment, perceived social support, and other psychological well-being variables.

CONCLUSIONS

Study participants experienced enhanced psychological well-being and reduced symptoms of depression and suicide ideation over the course of IPT adapted for older adults at risk for suicide. Larger, controlled trials are needed to further evaluate the impact of this novel intervention and to test methods for translating and integrating focused interventions into standard clinical care with at-risk older adults.

Two Studies of Connectedness to Parents and Suicidal Thoughts and Behavior in Children and Adolescents

We tested hypotheses that greater connectedness to parent(s) is associated with lower risk for nonlethal suicidal thoughts and behavior (STB), termed direct protective effects, and that parent connectedness serves to moderate (lower) the risk for STB associated with psychopathology including major depressive episode (MDE), termed moderating protective effects. Independent samples of children and adolescents recruited for a multicenter study of familial alcoholism were studied. Generalized estimating equation models were used that adjusted for age, sex, and youth psychopathology variables. The sample for Study 1 was assessed at baseline and about 2- and 4-year follow-ups, with baseline characteristics of n = 921, M age = 14.3 ± 1.8 years, and 51.8% female. The sample for Study 2 was assessed at baseline and about 5-year follow-up, with baseline characteristics of n = 867, M age = 12.0 ± 3.2 years, and 51.0% female. In both studies, increased perceived connectedness to father but not mother was associated with lower risk for measures of STB, consistent with direct protective effects. In Study 1, measures of parent connectedness were associated with lower risk for STB but only for youth that did not experience MDE (or alcohol use disorder), inconsistent with moderating protective effects. Study 2 showed that connectedness to fathers was associated with lower risk for suicide plans or attempts (severe STB) but not frequent thoughts of death or dying (nonsevere STB). Improved connectedness to fathers may lower risk for STB in children and adolescents, consistent with direct protective effects. Hypotheses about moderating protective effects were not supported.

A class of distribution-free models for longitudinal mediation analysis

Mediation analysis constitutes an important part of treatment study to identify the mechanisms by which an intervention achieves its effect. Structural equation model (SEM) is a popular framework for modeling such causal relationship. However, current methods impose various restrictions on the study designs and data distributions, limiting the utility of the information they provide in real study applications. In particular, in longitudinal studies missing data is commonly addressed under the assumption of missing at random (MAR), where current methods are unable to handle such missing data if parametric assumptions are violated.In this paper, we propose a new, robust approach to address the limitations of current SEM within the context of longitudinal mediation analysis by utilizing a class of functional response models (FRM). Being distribution-free, the FRM-based approach does not impose any parametric assumption on data distributions. In addition, by extending the inverse probability weighted (IPW) estimates to the current context, the FRM-based SEM provides valid inference for longitudinal mediation analysis under the two most popular missing data mechanisms; missing completely at random (MCAR) and missing at random (MAR). We illustrate the approach with both real and simulated data.

Posttraumatic stress disorder and suicide in 5.9 million individuals receiving care in the veterans health administration health system

BACKGROUND

Post-traumatic stress disorder (PTSD) confers risk for suicidal ideation and suicide attempts but a link with suicide is not yet established. Prior analyses of users of the Veterans health administration (VHA) Health System suggest that other mental disorders strongly influence the association between PTSD and suicide in this population. We examined the association between PTSD and suicide in VHA users, with a focus on the influence of other mental disorders.

METHODS

Data were based on linkage of VA National Patient Care Database records and the Centers for Disease Control and Prevention׳s National Death Index, with data from fiscal year 2007-2008. Analyses were based on multivariate logistic regression and structural equation models.

RESULTS

Among users of VHA services studied (N=5,913,648), 0.6% (N=3620) died by suicide, including 423 who had had been diagnosed with PTSD. In unadjusted analysis, PTSD was associated with increased risk for suicide, with odds ratio, OR (95% confidence interval, 95% CI)=1.34 (1.21, 1.48). Similar results were obtained after adjustment for demographic variables and veteran characteristics. After adjustment for multiple other mental disorder diagnoses, PTSD was associated with decreased risk for suicide, OR (95% CI)=0.77 (0.69, 0.86). Major depressive disorder (MDD) had the largest influence on the association between PTSD and suicide.

LIMITATIONS

The analyses were cross-sectional. VHA users were studied, with unclear relevance to other populations.

CONCLUSION

The findings suggest the importance of identifying and treating comorbid MDD and other mental disorders in VHA users diagnosed with PTSD in suicide prevention efforts.

Log-transformation and its implications for data analysis

The log-transformation is widely used in biomedical and psychosocial research to deal with skewed data. This paper highlights serious problems in this classic approach for dealing with skewed data. Despite the common belief that the log transformation can decrease the variability of data and make data conform more closely to the normal distribution, this is usually not the case. Moreover, the results of standard statistical tests performed on log-transformed data are often not relevant for the original, non-transformed data.We demonstrate these problems by presenting examples that use simulated data. We conclude that if used at all, data transformations must be applied very cautiously. We recommend that in most circumstances researchers abandon these traditional methods of dealing with skewed data and, instead, use newer analytic methods that are not dependent on the distribution the data, such as generalized estimating equations (GEE).

On the implication of structural zeros as independent variables in regression analysis: applications to alcohol research

In alcohol studies, drinking outcomes such as number of days of any alcohol drinking (DAD) over a period of time do not precisely capture the differences among subjects in a study population of interest. For example, the value of 0 on DAD could mean that the subject was continually abstinent from drinking such as lifetime abstainers or the subject was alcoholic, but happened not to use any alcohol during the period of interest. In statistics, zeros of the first kind are called structural zeros, to distinguish them from the sampling zeros of the second type. As the example indicates, the structural and sampling zeros represent two groups of subjects with quite different psychosocial outcomes. In the literature on alcohol use, although many recent studies have begun to explicitly account for the differences between the two types of zeros in modeling drinking variables as a response, none has acknowledged the implications of the different types of zeros when such modeling drinking variables are used as a predictor. This paper serves as the first attempt to tackle the latter issue and illustrate the importance of disentangling the structural and sampling zeros by using simulated as well as real study data.

Causal inference for community-based multi-layered intervention study

Estimating causal treatment effect for randomized controlled trials under post-treatment confounding, that is, noncompliance and informative dropouts, is becoming an important problem in intervention/prevention studies when the treatment exposures are not completely controlled. When confounding is present in a study, the traditional intention-to-treat approach could underestimate the treatment effect because of insufficient exposure of treatment. In the recent two decades, many papers have been published to address such confounders to investigate the causal relationship between treatment and outcome of interest based on different modeling strategies. Most of the existing approaches, however, are suitable only for standard experiments. In this paper, we propose a new class of structural functional response model to address post-treatment confounding in complex multi-layered intervention studies within a longitudinal data setting. The new approach offers robust inference and is readily implemented. We illustrate and assess the performance of the proposed structural functional response model using both real and simulated data.

Parent and child psychopathology and suicide attempts among children of parents with alcohol use disorder

Parents with psychopathology such as alcohol use disorder (AUD) that confers risk for suicide attempt (SA) may have children who are more likely to develop such psychopathology and to attempt suicide, suggesting that risk may be "transmitted" from parents to children. We examined this phenomenon during the transition from childhood to adolescence, when risk for SA increases dramatically. A cohort of 418 children were examined at average age 9.4 (range 7-14) years at enrollment (Time 1, childhood) and approximately 5 years later, prior to reaching age 18 (Time 2, adolescence). One or both biological parents, oversampled for AUD, were also interviewed. Structural equation models (SEM) examined father-child, mother-child, and either/both parent-child associations. The primary outcome was SA over follow-up among offspring, assessed at Time 2. As hypothesized, parental antisocial personality disorder predicted conduct disorder symptoms in offspring both during childhood and adolescence (parent-child model, father-child model) and maternal AUD predicted conduct disorder symptoms during childhood (mother-child model). However, we did not find evidence to support transmission of depression from parents to offspring either during childhood or adolescence, and parent psychopathology did not show statistically significant associations with SA during adolescence. In conclusion, we conducted a rare study of parent-to-child "transmission" of risk for SA that used a prospective research design, included diagnostic interviews with both parents and offspring, and examined the transition from childhood to adolescence, and the first such study in children of parents with AUD. Results provided mixed support for hypothesized parent-child associations.