Harrison Quick

High-Density Lipoprotein Particles and Markers of Inflammation and Thrombotic Activity in Patients with Untreated HIV Infection

BACKGROUND Untreated human immunodeficiency virus (HIV) infection is associated with changes in blood lipids, inflammation, thrombotic activity, and increased risk for cardiovascular disease. METHODS We studied high-density lipoprotein particle (HDLp) concentrations and inflammatory (high-sensitivity C-reactive protein [hsCRP] and interleukin [IL] 6), endothelial activation (E-selectin and soluble intercellular adhesion molecule-1 [sICAM-1]), and thrombotic (fibrinogen and D-dimer) biomarkers in a group of 32 untreated HIV-infected and 29 uninfected persons. Differences in the levels of blood lipids and biomarkers by HIV status were examined before and after adjustment for age, sex, race/ethnicity, smoking status, body mass index, and the presence of hepatitis C. RESULTS HIV-infected participants, compared with uninfected participants, had lower HDL cholesterol (HDLc) levels (-26%) and HDLp numbers (-21%), with reductions in large (-50%) and small (-20%) HDLp, specifically (P < or = .01 for all). A trend was present for higher total cholesterol (P = .15 and triglyceride levels (P = .11) among individuals with HIV infection. Levels of IL-6, sICAM-1, and D-dimer were 65%-70% higher in HIV-infected participants (P < or = .02 for all). Covariate adjustment did not diminish these associations. For HIV-infected participants, total and small HDLp (respectively) tended to correlate inversely with levels of IL-6 (P = .08 and P = .02), sICAM-1 (P < .01 for both) and D-dimer (P = .03 and p < .01). CONCLUSIONS Persons with untreated HIV infection have lower HDLp (primarily large and small HDLp) and higher IL-6, sICAM-1, and D-dimer levels, and the relationship of these markers to HIV-mediated atherosclerotic risk requires further study.

The Association Between Density of Alcohol Establishments and Violent Crime Within Urban Neighborhoods

BACKGROUND Numerous studies have found that areas with higher alcohol establishment density are more likely to have higher violent crime rates, but many of these studies did not assess the differential effects of type of establishments or the effects on multiple categories of crime. In this study, we assess whether alcohol establishment density is associated with 4 categories of violent crime and whether the strength of the associations varies by type of violent crime and by on-premise establishments (e.g., bars, restaurants) versus off-premise establishments (e.g., liquor and convenience stores). METHODS Data come from the city of Minneapolis, Minnesota in 2009 and were aggregated and analyzed at the neighborhood level. Across the 83 neighborhoods in Minneapolis, we examined 4 categories of violent crime: assault, rape, robbery, and total violent crime. We used a Bayesian hierarchical inference approach to model the data, accounting for spatial auto-correlation and controlling for relevant neighborhood demographics. Models were estimated for total alcohol establishment density as well as separately for on-premise establishments and off-premise establishments. RESULTS Positive, statistically significant associations were observed for total alcohol establishment density and each of the violent crime outcomes. We estimate that a 3.9 to 4.3% increase across crime categories would result from a 20% increase in neighborhood establishment density. The associations between on-premise density and each of the individual violent crime outcomes were also all positive and significant and similar in strength as for total establishment density. The relationships between off-premise density and the crime outcomes were all positive but not significant for rape or total violent crime, and the strength of the associations was weaker than those for total and on-premise density. CONCLUSIONS Results of this study, combined with earlier findings, provide more evidence that community leaders should be cautious about increasing the density of alcohol establishments within their neighborhoods.

Untreated HIV Infection and Large and Small Artery Elasticity

Background:Untreated HIV infection may increase risk for cardiovascular disease, and arterial elasticity is a marker of cardiovascular risk and early disease. Methods:HIV-infected participants not taking antiretroviral therapy (n = 32) were compared with HIV-negative controls (n = 30). Large and small artery elasticity (LAE and SAE) were estimated via analysis of radial pulse waveforms. Differences in LAE and SAE by HIV status were compared using analysis of covariance, with and without adjustment for Framingham risk (model 1); covariates that differed between groups [smoking, injection drug use, hepatitis C, and high-density lipoprotein cholesterol (HDLc); model 2]; or age, sex, race/ethnicity, smoking, injection drug use, hepatitis C, HDLc, and non-HDLc (model 3). Results:HIV infection was associated with impaired LAE (−2.55 mL/mm Hg × 10; P = 0.02) and SAE (−1.50 mL/mm Hg × 100; P = 0.02). Associations with traditional risk factors were often stronger for SAE than LAE, including with Framingham score (per 1% higher; SAE −0.18, P = 0.01; LAE −0.19, P = 0.13). Fasting lipid levels were not significantly associated with LAE and SAE. After adjustment, differences between HIV-infected and HIV-uninfected participants were similar in model 1 (−2.36 for LAE, P = 0.04; −1.31 for SAE, P = 0.04), model 2 (−2.67 for LAE, P = 0.02; −1.13 for SAE, P = 0.07) and model 3 (−2.91 for LAE, P = 0.02; −1.34 for SAE, P = 0.03). CD4 count and HIV RNA level were not associated with LAE and SAE among HIV-infected participants. Conclusions:Untreated HIV infection is associated with impaired arterial elasticity, of both the large and small vasculature, after controlling for additional risk factors. Pulse waveform analysis is a noninvasive technique to assess cardiovascular disease risk that should be evaluated in larger studies of HIV-infected persons.

Modeling temporal gradients in regionally aggregated California asthma hospitalization data

Advances in Geographical Information Systems (GIS) have led to the enormous recent burgeoning of spatial-temporal databases and associated statistical modeling. Here we depart from the rather rich literature in space-time modeling by considering the setting where space is discrete (e.g., aggregated data over regions), but time is continuous. Our major objective in this application is to carry out inference on gradients of a temporal process in our data set of monthly county level asthma hospitalization rates in the state of California, while at the same time accounting for spatial similarities of the temporal process across neighboring counties. Use of continuous time models here allows inference at a finer resolution than at which the data are sampled. Rather than use parametric forms to model time, we opt for a more flexible stochastic process embedded within a dynamic Markov random field framework. Through the matrix-valued covariance function we can ensure that the temporal process realizations are mean square differentiable, and may thus carry out inference on temporal gradients in a posterior predictive fashion. We use this approach to evaluate temporal gradients where we are concerned with temporal changes in the residual and fitted rate curves after accounting for seasonality, spatiotemporal ozone levels and several spatially-resolved important sociodemographic covariates.

Interleukin-6 and d-dimer levels are associated with vascular dysfunction in patients with untreated HIV infection

In the Strategies for Management of AntiRetroviral Therapy (SMART) study, higher baseline levels of the inflammatory cytokine interleukin-6 (IL-6) and the thrombotic marker D-dimer were strongly associated with mortality risk (cardiovascular disease [CVD] and non-CVD related).1 In that report, authors speculated activation of tissue factors and endothelial surfaces may be driving elevations in IL-6 and D-dimer levels. In this pilot study of persons with untreated HIV infection, we explore the relationship between IL-6 and D-dimer with the following measures of vascular dysfunction: large and small artery elasticity (LAE and SAE, respectively) and plasma markers of endothelial function (E-selectin and soluble intercellular adhesion molecule-1 [sICAM-1]). HIV-infected participants had not taken ART in the previous year, and had no known CVD. Arterial elasticity was assessed via pulse waveform analysis of the diastolic decay curve (model HDI/PulseWave CR-2000, Eagan, MN). Biomarker levels were measured at the Laboratory for Clinical Biochemistry Research at the University of Vermont. Details of these methods and the study protocol have been reported.2 3 To consider the combined influence of IL-6 and D-dimer, a joint mortality risk score was also generate according to the weighted contribution of each biomarker for risk of death in SMART.4 The joint mortality risk score was obtained from SMART data using a conditional logistic regression model that considered both IL-6 and D-dimer (each log10 transformed) for outcome of all-cause mortality. The joint mortality risk score was calculated by solving for the logit formed with the estimated parameters from SMART and the log10 transformed values of IL-6 and D-dimer from the current study. Higher values of this score were associated with a higher risk of death in SMART. Data were analyzed by use of R statistical software (version 2.8.1; http://www/cran.r-project.org). Characteristics of the 32 HIV-infected participants who were enrolled have been previously reported.2 3 Mean (SD) age was 40 (9.6) years and body mass index was 26 (5.1) kg/m2. Twenty-eight (88%) were male, 19 (59%) were current smokers, 11 (34%) with hepatitis C co-infection, 2 (6%) with diabetes mellitus, and 2 (7%) had a prior AIDS clinical event. Mean CD4 count was 391 (182) cells/mm3 and HIV RNA level was 4.15 (0.73) log10 copies/mL. The median (IQR) values for IL-6, D-dimer, and the joint mortality risk score were 1.79 (1.34–4.88) pg/mL , 0.39 (0.19–0.60) µg/mL, and 0.47 (0.33–0.74), respectively. Mean values for each surrogate measure of vessel function (untransformed) and HIV RNA level (log10 transformed) are reported by quartile of IL-6 and D-dimer (table1). Higher levels of IL-6 (4th versus 1st quartile and as continuous variable in spearman rank correlations) tended to associate with impaired SAE and higher levels of sICAM-1 and E-selectin. A similar patter was seen when comparing markers of vascular dysfunction with D-dimer levels, though a significant association was only consistently present for E-selectin. LAE and CD4 count (data not shown) did not vary by IL-6 or D-dimer levels. For comparisons using the joint (IL-6/D-dimer) mortality risk score, the associations with markers of vascular dysfunction (SAE, sICAM-1 and E-selectin) became more pronounced. Table 1 Markers of Vascular Dysfunction and HIV RNA by Quartile of IL-6, D-dimer, and Joint (IL-6 / D-dimer) Risk Score In summary, we show that higher IL-6 and D-dimer levels among persons with untreated HIV infection are associated with vascular dysfunction, indicated by higher endothelial biomarkers and impaired small artery elasticity (SAE)—a marker of early vascular disease and future clinical risk. Findings from SMART suggest that non-AIDS related mortality may be a consequence of greater inflammation (IL-6) and thrombotic activity (D-dimer) in persons with HIV infection.1 Levels of IL-6 and D-dimer and estimates of artery elasticity (LAE and SAE) are being ascertained in a subset of participants in the ongoing Strategic Timing of Antiretroviral Therapy (START) trial, and will provide valuable insight into the mechanisms driving vessel dysfunction and early vascular disease in persons with HIV infection. Future research should consider the role of HIV-mediated endothelial injury as a contributor to both CVD- and non-CVD-related mortality in the current era.

A Comparison of the β-Substitution Method and a Bayesian Method for Analyzing Left-Censored Data

Classical statistical methods for analyzing exposure data with values below the detection limits are well described in the occupational hygiene literature, but an evaluation of a Bayesian approach for handling such data is currently lacking. Here, we first describe a Bayesian framework for analyzing censored data. We then present the results of a simulation study conducted to compare the β-substitution method with a Bayesian method for exposure datasets drawn from lognormal distributions and mixed lognormal distributions with varying sample sizes, geometric standard deviations (GSDs), and censoring for single and multiple limits of detection. For each set of factors, estimates for the arithmetic mean (AM), geometric mean, GSD, and the 95th percentile (X0.95) of the exposure distribution were obtained. We evaluated the performance of each method using relative bias, the root mean squared error (rMSE), and coverage (the proportion of the computed 95% uncertainty intervals containing the true value). The Bayesian method using non-informative priors and the β-substitution method were generally comparable in bias and rMSE when estimating the AM and GM. For the GSD and the 95th percentile, the Bayesian method with non-informative priors was more biased and had a higher rMSE than the β-substitution method, but use of more informative priors generally improved the Bayesian methods performance, making both the bias and the rMSE more comparable to the β-substitution method. An advantage of the Bayesian method is that it provided estimates of uncertainty for these parameters of interest and good coverage, whereas the β-substitution method only provided estimates of uncertainty for the AM, and coverage was not as consistent. Selection of one or the other method depends on the needs of the practitioner, the availability of prior information, and the distribution characteristics of the measurement data. We suggest the use of Bayesian methods if the practitioner has the computational resources and prior information, as the method would generally provide accurate estimates and also provides the distributions of all of the parameters, which could be useful for making decisions in some applications.

Do neighborhood attributes moderate the relationship between alcohol establishment density and crime

Although numerous studies have found a positive association between the density of alcohol establishments and various types of crime, few have examined how neighborhood attributes (e.g., schools, parks) could moderate this association. We used data from Minneapolis, MN with neighborhood as the unit of analysis (n = 83). We examined eight types of crime (assault, rape, robbery, vandalism, nuisance crime, public alcohol consumption, driving while intoxicated, underage alcohol possession/consumption) and measured density as the total number of establishments per roadway mile. Neighborhood attributes assessed as potential moderators included non-alcohol businesses, schools, parks, religious institutions, neighborhood activism, neighborhood quality, and number of condemned houses. Using Bayesian techniques, we created a model for each crime outcome (accounting for spatial auto-correlation and controlling for relevant demographics) with an interaction term (moderator × density) to test each potential moderating effect. Few interaction terms were statistically significant. The presence of at least one college was the only neighborhood attribute that consistently moderated the density–crime association, with the presence of a college attenuating the association between the density and three types of crime (assaults, nuisance crime, and public consumption). However, caution should be used when interpreting the moderating effect of college presence because of the small number of colleges in our sample. The lack of moderating effects of neighborhood attributes, except for presence of a college, suggests that the addition of alcohol establishments to any neighborhood, regardless of its other attributes, could result in an increase in a wide range of crime.

HIV antiretroviral therapy reduces circulating surfactant protein-D levels

Untreated HIV infection is associated with abnormal lung inflammation, even when CD4+ counts are relatively well preserved. Compared to patients without HIV infection, patients with HIV infection demonstrate increases in CD8+ T cells in bronchoalveolar lavage (BAL) fluid1 that normalize within 6 months of antiretroviral therapy (ART) initiation2. BAL allows direct sampling of lung inflammatory cells, but requires an invasive procedure and specialized personnel to perform. Therefore, we investigated the effects of ART on a non-invasive blood marker of lung inflammation, surfactant protein D (SP-D). Study participants were required to have HIV infection and not have taken ART for at least 3 months prior to enrollment. Exclusion criteria included age 65 years, pregnancy, concurrent self-limited bacterial infection, and active illicit drug or alcohol use. The protocol was registered at ClinicalTrials.gov (NCT 00783614) and approved by the local ethics committee. Plasma was collected at baseline, then again at month 3 and month 5 following ART initiation. Plasma SP-D levels were determined using a commercially available sandwich ELISA assay (BioVendor, Candler, NC). Paired differences in SP-D were compared from baseline to month 3 and baseline to month 5. Statistical analyses were performed using STATA 9 (College Station, TX) Stored specimens from 15 patients were available: 12 and 13 had plasma specimens at month 3 and 5, respectively, to characterize changes from baseline. Mean age was 37 years, 80% were male, and 33% were Caucasian. Baseline median CD4+ cell count was 320 cells/mm3, increased to 412 cells/mm3 at month 3 (p=0.01 vs. baseline) and was 466 cells/mm3 at month 5 (p=0.007 vs. baseline). Median baseline viral load was 17,970 copies/mL and was fully suppressed at <75 copies/mL among all participants at month 3 and at month 5 visits (both p<0.001 vs. baseline). Eleven participants started a protease inhibitor and 4 participants started a non-nucleoside reverse transcriptase inhibitor; all participants started nucleoside reverse transcriptase inhibitors. No patients had known lung disease. Median baseline SP-D was 64.1 ng/mL (interquartile range 49.2 – 73.6 ng/mL). Smoking is known to increase blood SP-D levels3, and our sample of smokers (n=9, 60%) had a higher baseline median SP-D level compared to non-smokers that was not statistically significant (64.3 ng/mL vs. 53.2 ng/mL, respectively, p=0.19). At month 3, there was a non-significant reduction in median SP-D level to 51.6 ng/mL (p=0.10) and at month 5, the reduction became significant to a median SP-D level of 47.3 ng/mL (p=0.01) (Figure). A random effects regression model test for trend showed a slope of −2.7 ng/mL change in SP-D per month (p=0.009). Figure Boxplots of paired plasma surfactant protein D (SP-D) comparisons from baseline to 3 months after ART initiation (n=12), and baseline to 5 months after ART initiation (n=13). p-values derived using Wilcoxon signed rank testing. Random effects regression ... We have demonstrated for the first time that ART initiation and suppression of HIV replication appears to be associated with a reduction in blood SP-D levels. Studies in non-HIV populations have suggested a relationship between SP-D blood levels and mortality in pulmonary fibrosis4, lung function in cystic fibrosis5, and respiratory health status in chronic obstructive pulmonary disease6. Thus, while our study was a small pilot study, we believe our study provides rationale for expanding research into pulmonary outcomes among patients with HIV infection. The ongoing Strategic Timing of AntiRetrovital Therapy (START) trial will evaluate early (CD4+ cell counts >500 cells/mm3) vs. deferred ART initiation in randomized fashion. Lung function, respiratory health status, and respiratory medication use will be ascertained in a subset of 1000 participants (ClinicalTrials.gov NCT00867048). Such studies are required to better understand HIV-specific consequences for pulmonary disease, and whether treatment with ART will improve pulmonary outcomes.

0300 The NIEHS GuLF STUDY: A comparison of the β-substitution method and a Bayesian approach for handling highly censored measurement data

Objectives Over 150 000 measurements taken on workers responding to the 2010 Deepwater Horizon oil spill are being used to develop exposure estimates for the participants in the GuLF STUDY. A large portion of the measurements, however, has values below the limit of detection (left-censored). The β-substitution method has been shown to provide accurate estimates for handling censored data, but a comparison to a Bayesian method, which permits the estimation of uncertainty and accounts for prior information, is currently lacking. The goal of this research was to compare the two methods. Method Each method was challenged with computer-generated datasets drawn from lognormal distributions with the geometric mean (GM) = 1, sample sizes = 5–100, geometric standard deviation (GSD) = 2–5, and percent censoring = 10–90%. Percent bias and coverage (the percentage of 95% uncertainty intervals containing the truth) were used as evaluation metrics. Results For most of our simulation scenarios, estimates of bias from the β-substitution and Bayesian methods were generally comparable for the AM and GM. The β-substitution was generally less biassed in estimating the GSD and the 95th percentile than the Bayesian method. The Bayesian method provided consistently better coverage for the AM than β-substitution. It also provided uncertainty estimates the GM, GSD, and the 95th percentile while β-substitution does not. Conclusions The β-substitution method generally was observed to have little bias but it only allows the calculation of uncertainty estimates around the AM. The Bayesian approach provided reasonably accurate point and interval estimates (i.e., coverage), but this comes with the cost of additional computation.