Quantifying posterior effect size distribution of susceptibility loci by common summary statistics.

Testing millions of single nucleotide polymorphisms (SNPs) in genetic association studies has become a standard routine for disease gene discovery. In light of recent re-evaluation of statistical practice, it has been suggested that p-values are unfit as summaries of statistical evidence. Despite this criticism, p-values contain information that can be utilized to address the concerns about their flaws. We present a new method for utilizing evidence summarized by p-values for estimating odds ratio (OR) based on its approximate posterior distribution. In our method, only p-values, sample size, and standard deviation for ln(OR) are needed as summaries of data, accompanied by a suitable prior distribution for ln(OR) that can assume any shape. The parameter of interest, ln(OR), is the only parameter with a specified prior distribution, hence our model is a mix of classical and Bayesian approaches. We show that our method retains the main advantages of the Bayesian approach: it yields direct probability statements about hypotheses for OR and is resistant to biases caused by selection of top-scoring SNPs. Our method enjoys greater flexibility than similarly inspired methods in the assumed distribution for the summary statistic and in the form of the prior for the parameter of interest. We illustrate our method by presenting interval estimates of effect size for reported genetic associations with lung cancer. Although we focus on OR, the method is not limited to this particular measure of effect size and can be used broadly for assessing reliability of findings in studies testing multiple predictors.

DOT: Gene-set analysis by combining decorrelated association statistics.

Historically, the majority of statistical association methods have been designed assuming availability of SNP-level information. However, modern genetic and sequencing data present new challenges to access and sharing of genotype-phenotype datasets, including cost of management, difficulties in consolidation of records across research groups, etc. These issues make methods based on SNP-level summary statistics particularly appealing. The most common form of combining statistics is a sum of SNP-level squared scores, possibly weighted, as in burden tests for rare variants. The overall significance of the resulting statistic is evaluated using its distribution under the null hypothesis. Here, we demonstrate that this basic approach can be substantially improved by decorrelating scores prior to their addition, resulting in remarkable power gains in situations that are most commonly encountered in practice; namely, under heterogeneity of effect sizes and diversity between pairwise LD. In these situations, the power of the traditional test, based on the added squared scores, quickly reaches a ceiling, as the number of variants increases. Thus, the traditional approach does not benefit from information potentially contained in any additional SNPs, while our decorrelation by orthogonal transformation (DOT) method yields steady gain in power. We present theoretical and computational analyses of both approaches, and reveal causes behind sometimes dramatic difference in their respective powers. We showcase DOT by analyzing breast cancer and cleft lip data, in which our method strengthened levels of previously reported associations and implied the possibility of multiple new alleles that jointly confer disease risk.

E-Cigarette Demand: Impact of Commodity Definitions and Test-Retest Reliability.

INTRODUCTION: Behavioral economic demand provides a multidimensional understanding of reinforcement. Commodity purchase tasks are an efficient method for measuring demand in human participants. One challenge in translating these procedures to electronic nicotine delivery systems (ENDS or e-cigarettes) is defining commodity units given the lack of standardization in the e-cigarette marketplace. AIMS AND METHODS: The purpose of this study was to directly compare methods of operationalizinge-cigarette purchases, puffs, cartridges, and mLs liquid, using a within-subject design. Participants (N = 132) reporting past week e-cigarette use were recruited using crowdsourcing. Purchase tasks were completed operationalizing e-cigarette units as puffs or cartridges at baseline and puffs or mLs liquid at a 3-month follow-up. RESULTS: Bivariate associations supported convergent and discriminant validity with the largest effect size correlations for intensity and elasticity observed for the puff version. Interaction models suggested that product preferences moderated the relationship between time-to-first use and cartridge demand with larger effect size correlations among persons reporting a preference for JUULs, but weaker relationships among persons reporting other device preferences. Puff intensity (rxx = .61) and elasticity (rxx = .62) showed good test-retest reliability for participants reporting stable consumption, but poor test-retest reliability for individuals with changed consumption levels (intensity rxx = -.08; elasticity rxx = -.10). CONCLUSIONS: This study highlights the relevance of commodity definitions in the e-cigarette purchase task. Puffs as an experimental commodity may provide flexibility for studying e-cigarette demand in heterogenous or unknown populations, whereas more tailored or personalized approaches like cartridge or mL-based tasks will likely be helpful when studying known subgroups. IMPLICATIONS: The commodity purchase task procedure is widely used for understanding cigarette and e-cigarette demand in nicotine dependence research. This study evaluates the importance of operational definitions of e-cigarette commodities in the purchase task (ie, puffs, cartridges, or mLs liquid). Puffs may provide a more flexible commodity unit when evaluating e-cigarette demand in general or heterogenous populations, whereas device-specific units may prove more valuable when studying populations with consistent and known product use.

Measurement matters: changing penalty calculations under the hospital acquired condition reduction program (HACRP) cost hospitals millions.

BACKGROUND: Since October 2014, the Centers for Medicare and Medicaid Services has penalized 25% of U.S. hospitals with the highest rates of hospital-acquired conditions under the Hospital Acquired Conditions Reduction Program (HACRP). While early evaluations of the HACRP program reported cumulative reductions in hospital-acquired conditions, more recent studies have not found a clear association between receipt of the HACRP penalty and hospital quality of care. We posit that some of this disconnect may be driven by frequent scoring updates. The sensitivity of the HACRP penalties to updates in the program's scoring methodology has not been independently evaluated. METHODS: We used hospital discharge records from 14 states to evaluate the association between changes in HACRP scoring methodology and corresponding shifts in penalty status. To isolate the impact of changes in scoring methods over time, we used FY2018 hospital performance data to calculate total HAC scores using FY2015 through FY2018 CMS scoring methodologies. RESULTS: Comparing hospital penalty status based on various HACRP scoring methodologies over time, we found a significant overlap between penalized hospitals when using FY 2015 and 2016 scoring methodologies (95%) and between FY 2017 and 2018 methodologies (46%), but substantial differences across early vs later years. Only 15% of hospitals were eligible for penalties across all four years. We also found significant changes in a hospital's (relative) ranking across the various years, indicating that shifts in penalty status were not driven by small changes in HAC scores clustered around the penalty threshold. CONCLUSIONS: HACRP penalties have been highly sensitive to program updates, which are generally announced after performance periods are concluded. This disconnect between performance and penalties calls into question the ability of the HACRP to improve patient safety as intended.

Early signatures of bleeding and mortality in patients on left ventricular assist device support: novel methods for personalized risk-stratification.

Background: Left ventricular assist devices (LVADs) provide support for patients with end-stage heart failure. The aims of this study were to determine whether baseline analysis and early trends in routine laboratory data, platelet activity, and thromboinflammatory biomarkers following LVAD implantation reveal trends that predict personalized risks of one-year gastrointestinal (GI) bleeding, stroke, pump thrombosis, drive-line infections and mortality in patients on LVAD support. Methods: We performed an observational study at the University of Kentucky with 61 participants who underwent first-time LVAD implantation. Blood was collected at baseline and post-op days 0, 1, 3 and 6 as well as clinical follow-up. Demographics, clinical characteristics, one-year adverse events and routine laboratory data were collected from electronic medical records. Platelet function and plasma biomarkers were profiled. Results: Evaluation of routine laboratory results revealed that sustained thrombocytopenia and increased mean platelet volume (MPV) were associated with development of GI bleeding and mortality. Platelet function at follow-up visit predicted one-year bleeding events. Thrombotic biomarker sCD40L strongly predicted one-year GI bleeding at baseline before implantation and within the first week following LVAD implant. Conclusions: Early trends in routine bloodwork and platelet function may serve as novel signatures of patients at risk to experience adverse events.

The more you test, the more you find: The smallest P-values become increasingly enriched with real findings as more tests are conducted.

The increasing accessibility of data to researchers makes it possible to conduct massive amounts of statistical testing. Rather than follow specific scientific hypotheses with statistical analysis, researchers can now test many possible relationships and let statistics generate hypotheses for them. The field of genetic epidemiology is an illustrative case, where testing of candidate genetic variants for association with an outcome has been replaced by agnostic screening of the entire genome. Poor replication rates of candidate gene studies have improved dramatically with the increase in genomic coverage, due to factors such as adoption of better statistical practices and availability of larger sample sizes. Here, we suggest that another important factor behind the improved replicability of genome-wide scans is an increase in the amount of statistical testing itself. We show that an increase in the number of tested hypotheses increases the proportion of true associations among the variants with the smallest P-values. We develop statistical theory to quantify how the expected proportion of genuine signals (EPGS) among top hits depends on the number of tests. This enrichment of top hits by real findings holds regardless of whether genome-wide statistical significance has been reached in a study. Moreover, if we consider only those "failed" studies that produce no statistically significant results, the same enrichment phenomenon takes place: the proportion of true associations among top hits grows with the number of tests. The enrichment occurs even if the true signals are encountered at the logarithmically decreasing rate with the additional testing.

Uncovering Local Trends in Genetic Effects of Multiple Phenotypes via Functional Linear Models.

Recent technological advances equipped researchers with capabilities that go beyond traditional genotyping of loci known to be polymorphic in a general population. Genetic sequences of study participants can now be assessed directly. This capability removed technology-driven bias toward scoring predominantly common polymorphisms and let researchers reveal a wealth of rare and sample-specific variants. Although the relative contributions of rare and common polymorphisms to trait variation are being debated, researchers are faced with the need for new statistical tools for simultaneous evaluation of all variants within a region. Several research groups demonstrated flexibility and good statistical power of the functional linear model approach. In this work we extend previous developments to allow inclusion of multiple traits and adjustment for additional covariates. Our functional approach is unique in that it provides a nuanced depiction of effects and interactions for the variables in the model by representing them as curves varying over a genetic region. We demonstrate flexibility and competitive power of our approach by contrasting its performance with commonly used statistical tools and illustrate its potential for discovery and characterization of genetic architecture of complex traits using sequencing data from the Dallas Heart Study.

Inter-relationships Linking Probability of Becoming a Case of Nicotine Dependence With Frequency of Tobacco Cigarette Smoking.

INTRODUCTION: Once smoking starts, some tobacco cigarette smokers (TCS) can make very rapid transitions into tobacco dependence syndromes (TCD). With adjustment for smoking frequency, we posit female excess risk for this rapid-onset TCD. In a novel application of functional analysis for tobacco research, we estimate four Hill function parameters and plot TCD risk against a gradient of smoking frequency, as observed quite soon after smoking onset. METHODS: In aggregate, the National Surveys of Drug Use and Health, 2004-2013, identified 1546 newly incident TCS in cross-sectional research, each with standardized TCD assessment. RESULTS: Hill function estimates contradict our apparently over-simplistic hypothesis. Among newly incident TCS males with only 1-3 recent smoking days, an estimated 1%-3% had become rapid-onset TCD cases; non-overlapping confidence intervals show lower TCD risk for females. In contrast, among daily smokers, closer to 50% of female TCS showed rapid-onset TCD, versus under 20% of male TCS, but a larger sample will be needed to confirm the apparent female excess risk at the daily smoking frequency level. CONCLUSIONS: Smoking frequency and TCD onset become inter-dependent quite soon after TCS onset. Feedback loops are expected, and might explain a potential reversal of male-female differences across smoking frequency gradients. These novel epidemiological estimates prompt new thinking and questions about interventions. IMPLICATIONS: In this large sample epidemiological study, with a nationally representative sample of newly incident TCS assessed cross-sectionally, we see a quite rapid onset of tobacco dependence, with an early male excess that fades out at higher levels of smoking frequency. Next steps include development of outreach and intervention for this very rapid-onset tobacco dependence.

A weighted U-statistic for genetic association analyses of sequencing data.

With advancements in next-generation sequencing technology, a massive amount of sequencing data is generated, which offers a great opportunity to comprehensively investigate the role of rare variants in the genetic etiology of complex diseases. Nevertheless, the high-dimensional sequencing data poses a great challenge for statistical analysis. The association analyses based on traditional statistical methods suffer substantial power loss because of the low frequency of genetic variants and the extremely high dimensionality of the data. We developed a Weighted U Sequencing test, referred to as WU-SEQ, for the high-dimensional association analysis of sequencing data. Based on a nonparametric U-statistic, WU-SEQ makes no assumption of the underlying disease model and phenotype distribution, and can be applied to a variety of phenotypes. Through simulation studies and an empirical study, we showed that WU-SEQ outperformed a commonly used sequence kernel association test (SKAT) method when the underlying assumptions were violated (e.g., the phenotype followed a heavy-tailed distribution). Even when the assumptions were satisfied, WU-SEQ still attained comparable performance to SKAT. Finally, we applied WU-SEQ to sequencing data from the Dallas Heart Study (DHS), and detected an association between ANGPTL 4 and very low density lipoprotein cholesterol.

Using publicly available data to predict recreational cannabis legalization at the county-level: A machine learning approach.

BACKGROUND: There is substantial geographic variability in local cannabis policies within states that have legalized recreational cannabis. This study develops an interpretable machine learning model that uses county-level population demographics, sociopolitical factors, and estimates of substance use and mental illness prevalences to predict the legality of recreational cannabis sales within each U.S. county. METHODS: We merged data and selected 14 model inputs from the 2010 Census, 2012 County Presidential Data from the MIT Elections Lab, and Small Area Estimates from the National Surveys on Drug Use and Health (NSDUH) from 2010 to 2012 at the county level. County policies were labeled as having recreational cannabis legal (RCL) if the sale of recreational cannabis was allowed anywhere in the county in 2014, resulting in 92 RCL and 3002 non-RCL counties. We used synthetic data augmentation and minority oversampling techniques to build an ensemble of 1000 logistic regressions on random sub-samples of the data, withholding one state at a time and building models from all remaining states. Performance was evaluated by comparing the predicted policy conditions with the actual outcomes in 2014. RESULTS: When compared to the actual RCL policies in 2014, the ensemble estimated predictions of counties transitioning to RCL had a macro f1 average score of 0.61. The main factors associated with legalizing county-level recreational cannabis sales were the prevalences of past-month cannabis use and past-year cocaine use. CONCLUSION: By leveraging publicly available data from 2010 to 2012, our model was able to achieve appreciable discrimination in predicting counties with legal recreational cannabis sales in 2014, however, there is room for improvement. Having demonstrated model performance in the first handful of states to legalize cannabis, additional testing with more recent data using time to event models is warranted.

Identifying Under- And Overutilization Patterns For Idaho Youth With Serious Emotional Disturbance.

Children and adolescents with serious emotional disturbance represent 7-12 percent of all youth in the United States. In 2017, the State of Idaho implemented the Youth Empowerment Service program, which allows youth with serious emotional disturbance who are younger than age eighteen living in households with income up to 300 percent of the federal poverty level to qualify for Medicaid and receive intensive, community-based treatment. A uniquely detailed method was used to assess the need for services: the Child and Adolescent Needs and Strengths tool, a ninety-seven-indicator instrument administered by a clinician. We used these indicators and Idaho's 2018-22 administrative Medicaid claims data to study the association between children and adolescents' clinical needs complexity and their actual Medicaid behavioral and mental health service use. Our findings show that there was a substantial proportion of youth who were underusing Medicaid behavioral and mental health care services, and there were virtually no overusers. Our findings have implications for the appropriateness of Medicaid utilization management in behavioral health care and program efforts to maintain families with youth having serious emotional disturbance in the Youth Empowerment Service program.

Lower Emotional Exhaustion among Employees Is Associated with Intentional Incorporation of Animals into Residential Care Settings.

Secondary effects of animal-integrated programming on residential care center (RCC) staff and organizational culture are not well understood. We explored emotional exhaustion among RCC employees both in facilities that incorporated animals and those that did not incorporate animals into the therapeutic environment. We conducted a survey throughout a large midwestern RCC system in the United States to determine relationships between organizational culture, emotional exhaustion, and the intentionality by which animals were incorporated into programming. Data were analyzed by examining associations between variables of interest using chi-square or t-tests, and linear mixed-effects modeling was used to identify potential confounding effects due to differences in children served within RCCs. Staff from RCCs that used animals intentionally reported lower emotional exhaustion (p = 0.006), and higher average workplace safety (p = 0.024) and psychological safety (p < 0.001). Integrating animals into RCC programming is associated with elements of a strong organizational culture. It is possible that animal-integrated programming has a positive impact on the facility culture and workforce, and/or that RCCs with strong pre-existing cultures are more likely to use animal-integrated programming.

Tris(1,3-dichloro-2-propyl) phosphate is a metabolism-disrupting chemical in male mice.

Tris(1,3-dichloro-2-propyl) phosphate (TDCPP) is an organophosphate flame retardant. The primary TDCPP metabolite, bis(1,3-dichloro-2-propyl) phosphate (BDCPP), is detectable in the urine of over 90 % of Americans. Epidemiological studies show sex-specific associations between urinary BDCPP levels and metabolic syndrome, which is an established risk factor for type 2 diabetes, heart disease, and stroke. We used a mouse model to determine whether TDCPP exposure disrupts glucose homeostasis. Six-week old male and female C57BL/6J mice were given ad libitum access to diets containing vehicle (0.1 % DMSO) and TDCPP resulting in the following treatment groups: 0 mg/kg/day, 0.02 mg/kg/day, 1 mg/kg/day, or 100 mg/kg/day. After being on the experimental diet for five weeks without interruption, body composition was analyzed, glucose and insulin tolerance tests were performed, and fasting glucose and insulin levels were quantified. TDCPP at 100 mg/kg/day caused male sex-specific adiposity, fasting hyperglycemia, and insulin resistance. TDCPP-induced modulation of nuclear receptor activation was investigated using an in vitro screen to identify potential mechanisms of metabolic disruption. TDCPP activated farnesoid X receptor (FXR) and pregnane X receptor (PXR), and inhibited the androgen receptor (AR). PXR target genes, but not FXR target genes, were upregulated in livers from mice exposed to 100 mg TDCPP/kg/day. Interestingly, PXR target genes were differentially expressed in livers from both males and females. It remains to be determined whether TDCPP-induced metabolic disruption occurs via modulation of nuclear receptor activity. Taken together, these studies build upon the association of TDCPP exposure and metabolic syndrome in humans by identifying sex-specific effects of TDCPP on glucose homeostasis in mice.

Effects of a digital cognitive behavioral therapy for insomnia on sleep and alcohol consumption in heavy drinkers: A randomized pilot study.

BACKGROUND: Insomnia is a well-established, prospective risk factor for Alcohol Use Disorder. Thus, targeting sleep problems could serve as a novel and efficacious means of reducing problematic drinking. Here, we examined the potential utility of a well-validated, interactive, easy to use, self-paced digital cognitive behavioral therapy for insomnia program. In a randomized, single-blind pilot study, we examined the impact of treatment with Sleep Healthy Using the Internet (SHUTi) on drinking and sleep outcomes in a sample of heavy drinkers with insomnia. METHODS: Heavy drinking men (n = 28) and women (n = 42) with insomnia were randomly assigned to complete either the SHUTi program or a control patient education program. Subjective measures of sleep and alcohol use were administered at baseline, immediately following completion of the intervention, 3 months post-intervention, and 6 months post-intervention. Sleep outcomes were assessed using the Insomnia Severity Index and Pittsburgh Sleep Quality Index. Drinking outcomes were assessed using the 30-Day Timeline Follow-Back calendar. We used linear mixed effects models to compare groups on both insomnia and drinking outcomes. RESULTS: Data from all 70 subjects (SHUTI: n = 40; control: n = 30) were analyzed. Linear mixed effects models showed that SHUTi significantly reduced insomnia symptoms (p = 0.01) and drinking outcomes (ps < 0.05) more than the control condition over time. Trend-level effects on sleep quality (p = 0.06) were also observed. No adverse events were reported. CONCLUSIONS: Improving sleep may be an effective treatment intervention for reducing hazardous drinking in at-risk individuals. Further, findings provide preliminary support for the implementation of an easily accessible health behavior intervention with significant public health impact in a high-risk population.

Evaluation of Telehealth in Child Behavioral Health Services Delivery During the COVID-19 Pandemic.

OBJECTIVE: The authors quantified the impact of the use of telehealth services on patient-level clinical outcomes among children with complex behavioral and emotional needs in Idaho during the COVID-19 pandemic by comparing data collected in 2020 with data for the same months in 2019. METHODS: Longitudinal statewide data of Child and Adolescent Needs and Strengths (CANS) assessments were extracted from Idaho's mental and behavioral health system. Prepandemic assessments were matched to midpandemic assessments. A linear mixed-effect model was used to explore four child-level outcomes: psychosocial strengths-building rate, rate of need resolution within a life-functioning domain, rate of need resolution within a behavior-emotional domain, and rate of need resolution within a high-risk behaviors domain. RESULTS: The number of new patients admitted to Idaho's state-funded mental and behavioral health program decreased almost twofold from April-December 2019 to April-December 2020 (N=4,458 vs. 2,794). For most children with complex needs, the use of telehealth was as effective in terms of strengths building and needs resolution as in-person services; for children whose caregivers had issues with access to transportation, availability of telehealth services improved outcomes for the children. CONCLUSIONS: The COVID-19 pandemic in 2020 was associated with a dramatic drop in the number of children served by Idaho's mental health program. Telehealth may effectively bridge mental health service delivery while patients and providers work toward the resolution of transportation issues or may serve as a more acceptable permanent format of service delivery for some populations.

A functional polymorphism in the ATP-Binding Cassette B1 transporter predicts pharmacologic response to combination of nortriptyline and morphine in neuropathic pain patients.

Many genetic markers have been associated with variations in treatment response to analgesics, but none have been assessed in the context of combination therapies. In this study, the treatment effects of nortriptyline and morphine were tested for an association with genetic markers relevant to pain pathways. Treatment effects were determined for single and combination therapies. A total of 24 functional single nucleotide polymorphisms were tested within the gene loci of mu-opioid receptor (OPRM1) gene locus, ATP-Binding Cassette B1 Transporter (ABCB1), Cytochrome P450 gene family (CYP2C19 and CYP2D6), catecholamine inactivator Catechol-O-Methyl Transferase (COMT), and serotonin receptor 2A (HTR2A). Genotyping was performed in a population of neuropathic pain patients who previously participated in a clinical trial. For monotherapy, neither nortriptyline nor morphine responses were associated with single nucleotide polymorphisms. However, for nortriptyline + morphine combination therapy, the single nucleotide polymorphism rs1045642 within the drug efflux pump ABCB1 transporter significantly predicted analgesic response. The presence of the C allele accounted for 51% of pain variance in this subgroup in response to combination treatment. The T-allele homozygotes demonstrated only 20% improvement in pain scores, whereas the C-allele homozygotes 88%. There was no significant contribution of rs1045642 to the medication side effects under all treatment conditions. The UK Biobank data set was then used to validate this genetic association. Here, patients receiving similar combination therapy (opioid + tricyclic antidepressant) carrying the C allele of rs1045642 displayed 33% fewer body pain sites than patients without that allele, suggesting better pain control. In all, our results show a robust effect of the rs1045642 polymorphism in response to chronic pain treatment with a nortriptyline + morphine combination.

Hypercholesterolemia Accelerates Both the Initiation and Progression of Angiotensin II-induced Abdominal Aortic Aneurysms.

OBJECTIVE: This study determined whether hypercholesterolemia would contribute to both the initiation and progression of angiotensin (Ang)II-induced abdominal aortic aneurysms (AAAs) in mice. METHODS AND RESULTS: To determine whether hypercholesterolemia accelerates the initiation of AAAs, male low-density lipoprotein (LDL) receptor -/- mice were either fed one week of Western diet prior to starting AngII infusion or initiated Western diet one week after starting AngII infusion. During the first week of AngII infusion, mice fed normal diet had less luminal expansion of the suprarenal aorta compared to those initiated Western diet after the first week of AngII infusion. The two groups achieved comparable luminal dilation on week 2 through week 6 of AngII infusion as monitored by ultrasound. To determine whether hypercholesterolemia contributed to the progression of established AAAs, male LDL receptor -/- mice were fed Western diet and infused with AngII for 4 weeks. Mice with established AAAs were then stratified into two groups based on luminal diameters measured by ultrasound. While AngII infusion was continued for another 8 weeks in both groups, mice in one group were continuously fed Western diet, but diet in the other group was switched to normal laboratory diet. In the latter group, plasma cholesterol concentrations were reduced rapidly to approximately 500 mg/dl within one week after the diet was switched from Western diet to normal laboratory diet. Luminal expansion progressed constantly in mice continuously fed Western diet, whereas no continuous expansion was detected in mice that were switched to normal laboratory diet. CONCLUSION: Hypercholesterolemia accelerates both the initiation of AAAs and progression of established AAAs in AngII-infused male LDL receptor -/- mice. CLINICAL RELEVANCE: Hypercholesterolemia is modestly associated with AAAs in observational or retrospective clinical studies. It is not feasible to study whether hypercholesterolemia contributes to the initiation of AAAs or progression of established AAAs in human. This study using AngII-induced AAA mouse model provides solid evidence that hypercholesterolemia contributes to both the initiation and progression of AAAs, supporting that statin therapy at any stage of AAA development may be beneficial to hypercholesterolemic patients with AAAs.

Inflammatory properties of diet mediate the effect of depressive symptoms on Framingham risk score in men and women: Results from the National Health and Nutrition Examination Survey (2007-2014).

Depression is common in patients with cardiovascular disease (CVD) and associated with inflammation. Inflammation contributes to the development of CVD and can be modulated by diet. However, the role of inflammatory properties of diet in the relationship between depressive symptoms and CVD risk is not well understood. We hypothesized that the inflammatory properties of diet mediate the relationship between depressive symptoms and CVD risk in men and women. Cross-sectional data collected by the National Health and Nutrition Examination Survey (2007-2014) were used for the study. Depressive symptoms scores, inflammatory properties of diet, and CVD risk were measured by the Patient Health Questionnaire-9 (PHQ-9), the Dietary Inflammatory Index (DII), and the Framingham risk score (FRS), respectively. Generalized linear models were used for the mediation analysis. There were significant differences in the proportions of men and women in the depressed group (PHQ-9 ≥ 10; 5.24 ±â€¯0.65% vs 9.36 ±â€¯0.87%, P < .001) and high CVD risk group (FRS >20%; 16.47 ±â€¯0.79% vs 6.03 ±â€¯0.32%, P < .001). The DII partially mediated the relationship between depressive symptoms and CVD risk in men (indirect effect: 0.06, P = .010) but fully mediated the relationship between depressive symptoms and CVD risk in women (indirect effect: 0.10, P < .001). These findings confirmed our hypothesis that inflammatory properties of diet at least partially mediate the relationship between depressive symptoms and CVD risk in men and women. Our findings suggest that interventions designed to reduce depressive symptoms should contain strategies to reduce pro-inflammatory and increase anti-inflammatory properties of diet to decrease CVD risk.

Detecting Weak Signals by Combining Small P-Values in Genetic Association Studies.

We approach the problem of combining top-ranking association statistics or P-values from a new perspective which leads to a remarkably simple and powerful method. Statistical methods, such as the rank truncated product (RTP), have been developed for combining top-ranking associations, and this general strategy proved to be useful in applications for detecting combined effects of multiple disease components. To increase power, these methods aggregate signals across top ranking single nucleotide polymorphisms (SNPs), while adjusting for their total number assessed in a study. Analytic expressions for combined top statistics or P-values tend to be unwieldy, which complicates interpretation and practical implementation and hinders further developments. Here, we propose the augmented rank truncation (ART) method that retains main characteristics of the RTP but is substantially simpler to implement. ART leads to an efficient form of the adaptive algorithm, an approach where the number of top ranking SNPs is varied to optimize power. We illustrate our methods by strengthening previously reported associations of µ-opioid receptor variants with sensitivity to pain.

Earlier tracheostomy and percutaneous endoscopic gastrostomy in patients with hemorrhagic stroke: associated factors and effects on hospitalization.

OBJECTIVE: Existing literature supports benefits of early tracheostomy and percutaneous endoscopic gastrostomy (PEG) in certain patient populations. The aim of this study was to review tracheostomy and PEG placement data in patients with hemorrhagic stroke in order to identify factors associated with earlier placement and to evaluate outcomes. METHODS: The authors performed a retrospective review of consecutive patients treated for hemorrhagic stroke between June 1, 2011, and June 1, 2015. Data were analyzed by logistic and multiple linear regression. RESULTS: Of 240 patients diagnosed with hemorrhagic stroke, 31.25% underwent tracheostomy and 35.83% underwent PEG tube placement. Factors significantly associated with tracheostomy and PEG included the presence of pneumonia on admission and subarachnoid hemorrhage. Earlier tracheostomy was significantly associated with shorter ICU length of stay; earlier tracheostomy and PEG placement were associated with shorter overall hospitalization. Timing of tracheostomy and PEG was not significantly associated with patient survival or the incidence of complications in this population. CONCLUSIONS: This study identified patient risk factors associated with increased likelihood of tracheostomy and PEG in patients with hemorrhagic stroke who were critically ill. Additionally, we found that the timing of tracheostomy was associated with length of ICU stay and overall hospital stay, and that the timing of PEG was associated with overall length of hospitalization. Complication rates related to tracheostomy and PEG in this population were minimal. This retrospective data set supports some benefit to earlier tracheostomy and PEG placement in this population and justifies the need for further prospective study.