Desensitization and remission after peanut sublingual immunotherapy in 1- to 4-year-old peanut-allergic children: A randomized, placebo-controlled trial.

BACKGROUND: Prior studies of peanut sublingual immunotherapy (SLIT) have suggested a potential advantage with younger age at treatment initiation. OBJECTIVE: We studied the safety and efficacy of SLIT for peanut allergy in 1- to 4-year-old children. METHODS: Peanut-allergic 1- to 4-year-old children were randomized to receive 4 mg peanut SLIT versus placebo. Desensitization was assessed by double-blind, placebo-controlled food challenge (DBPCFC) after 36 months of treatment. Participants desensitized to at least 443 mg peanut protein discontinued therapy for 3 months and then underwent DBPCFC to assess for remission. Biomarkers were measured at baseline and longitudinally during treatment. RESULTS: Fifty participants (25 peanut SLIT, 25 placebo) with a median age of 2.4 years were enrolled across 2 sites. The primary end point of desensitization was met with actively treated versus placebo participants having a significantly greater median cumulative tolerated dose (4443 mg vs 143 mg), higher likelihood of passing the month 36 DBPCFC (60% vs 0), and higher likelihood of demonstrating remission (48% vs 0). The highest rate of desensitization and remission was seen in 1- to 2-year-olds, followed by 2- to 3-year-olds and 3- to 4-year-olds. Longitudinal changes in peanut skin prick testing, peanut-specific IgG4, and peanut-specific IgG4/IgE ratio were seen in peanut SLIT but not placebo participants. Oropharyngeal itching was more commonly reported by peanut SLIT than placebo participants. Skin, gastrointestinal, upper respiratory, lower respiratory, and multisystem adverse events were similar between treatment groups. CONCLUSION: Peanut SLIT safely induces desensitization and remission in 1- to 4-year-old children, with improved outcomes seen with younger age at initiation.

Efficient computation of high-dimensional penalized generalized linear mixed models by latent factor modeling of the random effects.

Modern biomedical datasets are increasingly high-dimensional and exhibit complex correlation structures. Generalized linear mixed models (GLMMs) have long been employed to account for such dependencies. However, proper specification of the fixed and random effects in GLMMs is increasingly difficult in high dimensions, and computational complexity grows with increasing dimension of the random effects. We present a novel reformulation of the GLMM using a factor model decomposition of the random effects, enabling scalable computation of GLMMs in high dimensions by reducing the latent space from a large number of random effects to a smaller set of latent factors. We also extend our prior work to estimate model parameters using a modified Monte Carlo Expectation Conditional Minimization algorithm, allowing us to perform variable selection on both the fixed and random effects simultaneously. We show through simulation that through this factor model decomposition, our method can fit high-dimensional penalized GLMMs faster than comparable methods and more easily scale to larger dimensions not previously seen in existing approaches.

Multi-response Regression for Block-missing Multi-modal Data without Imputation.

Multi-modal data are prevalent in many scientific fields. In this study, we consider the parameter estimation and variable selection for a multi-response regression using block-missing multi-modal data. Our method allows the dimensions of both the responses and the predictors to be large, and the responses to be incomplete and correlated, a common practical problem in high-dimensional settings. Our proposed method uses two steps to make a prediction from a multi-response linear regression model with block-missing multi-modal predictors. In the first step, without imputing missing data, we use all available data to estimate the covariance matrix of the predictors and the cross-covariance matrix between the predictors and the responses. In the second step, we use these matrices and a penalized method to simultaneously estimate the precision matrix of the response vector, given the predictors, and the sparse regression parameter matrix. Lastly, we demonstrate the effectiveness of the proposed method using theoretical studies, simulated examples, and an analysis of a multi-modal imaging data set from the Alzheimer's Disease Neuroimaging Initiative.

Open-label study of the efficacy, safety, and durability of peanut sublingual immunotherapy in peanut-allergic children.

BACKGROUND: Studies on the efficacy of peanut sublingual immunotherapy (SLIT) are limited. The durability of desensitization after SLIT has not been well described. OBJECTIVE: We sought to evaluate the efficacy and safety of 4-mg peanut SLIT and persistence of desensitization after SLIT discontinuation. METHODS: Challenge-proven peanut-allergic 1- to 11-year-old children were treated with open-label 4-mg peanut SLIT for 48 months. Desensitization after peanut SLIT was assessed by a 5000-mg double-blind, placebo-controlled food challenge (DBPCFC). A novel randomly assigned avoidance period of 1 to 17 weeks was followed by the DBPCFC. Skin prick test results immunoglobulin levels, basophil activation test results, TH1, TH2, and IL-10 cytokines were measured longitudinally. Safety was assessed through patient-reported home diaries. RESULTS: Fifty-four participants were enrolled and 47 (87%) completed peanut SLIT and the 48-month DBPCFC per protocol. The mean successfully consumed dose (SCD) during the DBPCFC increased from 48 to 2723 mg of peanut protein after SLIT (P < .0001), with 70% achieving clinically significant desensitization (SCD > 800 mg) and 36% achieving full desensitization (SCD = 5000 mg). Modeled median time to loss of clinically significant desensitization was 22 weeks. Peanut skin prick test; peanut-specific IgE, IgG4, and IgG4/IgE ratio; and peanut-stimulated basophil activation test, IL-4, IL-5, IL-13, IFN-γ, and IL-10 changed significantly compared with baseline, with changes seen as early as 6 months. Median rate of reaction per dose was 0.5%, with transient oropharyngeal itching being the most common, and there were no dosing symptoms requiring epinephrine. CONCLUSIONS: In this open-label, prospective study, peanut SLIT was safe and induced clinically significant desensitization in most of the children, lasting more than 17 weeks after discontinuation of therapy.

Decomposition of variation of mixed variables by a latent mixed Gaussian copula model.

Many biomedical studies collect data of mixed types of variables from multiple groups of subjects. Some of these studies aim to find the group-specific and the common variation among all these variables. Even though similar problems have been studied by some previous works, their methods mainly rely on the Pearson correlation, which cannot handle mixed data. To address this issue, we propose a latent mixed Gaussian copula (LMGC) model that can quantify the correlations among binary, ordinal, continuous, and truncated variables in a unified framework. We also provide a tool to decompose the variation into the group-specific and the common variation over multiple groups via solving a regularized M-estimation problem. We conduct extensive simulation studies to show the advantage of our proposed method over the Pearson correlation-based methods. We also demonstrate that by jointly solving the M-estimation problem over multiple groups, our method is better than decomposing the variation group by group. We also apply our method to a Chlamydia trachomatis genital tract infection study to demonstrate how it can be used to discover informative biomarkers that differentiate patients.

An Efficient Greedy Search Algorithm for High-dimensional Linear Discriminant Analysis.

High-dimensional classification is an important statistical problem that has applications in many areas. One widely used classifier is the Linear Discriminant Analysis (LDA). In recent years, many regularized LDA classifiers have been proposed to solve the problem of high-dimensional classification. However, these methods rely on inverting a large matrix or solving large-scale optimization problems to render classification rules-methods that are computationally prohibitive when the dimension is ultra-high. With the emergence of big data, it is increasingly important to develop more efficient algorithms to solve the high-dimensional LDA problem. In this paper, we propose an efficient greedy search algorithm that depends solely on closed-form formulae to learn a high-dimensional LDA rule. We establish theoretical guarantee of its statistical properties in terms of variable selection and error rate consistency; in addition, we provide an explicit interpretation of the extra information brought by an additional feature in a LDA problem under some mild distributional assumptions. We demonstrate that this new algorithm drastically improves computational speed compared with other high-dimensional LDA methods, while maintaining comparable or even better classification performance.

HIGH-DIMENSIONAL FACTOR REGRESSION FOR HETEROGENEOUS SUBPOPULATIONS.

In modern scientific research, data heterogeneity is commonly observed owing to the abundance of complex data. We propose a factor regression model for data with heterogeneous subpopulations. The proposed model can be represented as a decomposition of heterogeneous and homogeneous terms. The heterogeneous term is driven by latent factors in different subpopulations. The homogeneous term captures common variation in the covariates and shares common regression coefficients across subpopulations. Our proposed model attains a good balance between a global model and a group-specific model. The global model ignores the data heterogeneity, while the group-specific model fits each subgroup separately. We prove the estimation and prediction consistency for our proposed estimators, and show that it has better convergence rates than those of the group-specific and global models. We show that the extra cost of estimating latent factors is asymptotically negligible and the minimax rate is still attainable. We further demonstrate the robustness of our proposed method by studying its prediction error under a mis-specified group-specific model. Finally, we conduct simulation studies and analyze a data set from the Alzheimer's Disease Neuroimaging Initiative and an aggregated microarray data set to further demonstrate the competitiveness and interpretability of our proposed factor regression model.

Kinetics of basophil hyporesponsiveness during short-course peanut oral immunotherapy.

BACKGROUND: Oral immunotherapy (OIT) leads to suppression of mast cell and basophil degranulation along with changes in the adaptive immune response. OBJECTIVES: This study aimed to determine how rapidly these effects occur during OIT and more broadly, the kinetics of basophil and mast cell suppression throughout the course of therapy. METHODS: Twenty participants, age 4 to 12 years, were enrolled in a peanut OIT trial and assessed for desensitization and sustained unresponsiveness after 9 months of therapy. Blood was collected 5 times in the first month and then intermittently throughout to quantify immunoglobulins and assess basophil activation by CD63, CD203c, and phosphorylated SYK (pSYK). RESULTS: Twelve of 16 participants that completed the trial were desensitized after OIT, with 9 achieving sustained unresponsiveness after discontinuing OIT for 4 weeks. Basophil hyporesponsiveness, defined by lower CD63 expression, was detected as early as day 90. pSYK was correlated with CD63 expression, and there was a significant decrease in pSYK by day 250. CD203c expression remained unchanged throughout therapy. Interestingly, although basophil activation was decreased across the cohort during OIT, basophil activation did not correlate with individual clinical outcomes. Serum peanut-specific IgG4 and IgA increased throughout therapy, whereas IgE remained unchanged. CONCLUSIONS: Suppression of basophil activation occurs within the first 90 days of peanut OIT, ultimately leading to suppression of signaling through pSYK.

Prevalence of traditional, complementary, and alternative medicine (TCAM) among adult cancer patients in Malawi.

PURPOSE: The objective of this study is to document the prevalence of traditional, complementary, and alternative medicine (TCAM) use by adult cancer patients at a national teaching hospital in Malawi. We aim to document the products/therapies used, the reason for use, as well as patient-reported satisfaction with TCAM practitioners and modalities. METHODS: We conducted investigator-administered interviews with adult cancer patients presenting to the Kamuzu Central Hospital (KCH) Cancer Clinic in Lilongwe, Malawi between January and July 2018. The KCH is a national teaching hospital in the capital of Lilongwe, which serves patients with cancer from the northern half of Malawi. Descriptive statistics were used to describe TCAM use and logistic regression was applied to identify predictors of TCAM. RESULTS: A total of 263 participants completed the survey, of which 70% (n = 183) were female and average age was 45 (SD 14) years old. The prevalence of overall TCAM use was 84% (n = 222), and 60% (n = 157) of participants reported combining TCAM with conventional cancer treatment. The majority of patients used TCAM to directly treat their cancer versus for symptom management. Patients reported using faith-based healing (64%, n = 168), herbal medicine (56%, n = 148), diet change (46%, n = 120), and vitamins/minerals (23%, n = 61). Participants reported the highest satisfaction for physicians among practitioners and diet change for modalities. Female gender was found to be a predictor of TCAM with conventional treatment use, no other significant predictors were observed. CONCLUSION: There is a high prevalence of TCAM use among an adult population with cancer in Malawi, and a wide variety in the TCAM modalities used among patients. Additional studies are needed to identify risks and benefits of TCAM use to assist with policy and public health, patient safety, and holistically address the global burden of cancer.

Classification of disease recurrence using transition likelihoods with expectation-maximization algorithm.

When an infectious disease recurs, it may be due to treatment failure or a new infection. Being able to distinguish and classify these two different outcomes is critical in effective disease control. A multi-state model based on Markov processes is a typical approach to estimating the transition probability between the disease states. However, it can perform poorly when the disease state is unknown. This article aims to demonstrate that the transition likelihoods of baseline covariates can distinguish one cause from another with high accuracy in infectious diseases such as malaria. A more general model for disease progression can be constructed to allow for additional disease outcomes. We start from a multinomial logit model to estimate the disease transition probabilities and then utilize the baseline covariate's transition information to provide a more accurate classification result. We apply the expectation-maximization (EM) algorithm to estimate unknown parameters, including the marginal probabilities of disease outcomes. A simulation study comparing our classifier to the existing two-stage method shows that our classifier has better accuracy, especially when the sample size is small. The proposed method is applied to determining relapse vs reinfection outcomes in two Plasmodium vivax treatment studies from Cambodia that used different genotyping approaches to demonstrate its practical use.

Chronic E-Cigarette Exposure Alters Human Alveolar Macrophage Morphology and Gene Expression.

INTRODUCTION: Alveolar macrophages (AMs) are lung-resident immune cells that phagocytose inhaled particles and pathogens, and help coordinate the lung's immune response to infection. Little is known about the impact of chronic e-cigarette use (ie, vaping) on this important pulmonary cell type. Thus, we determined the effect of vaping on AM phenotype and gene expression. AIMS AND METHODS: We recruited never-smokers, smokers, and e-cigarette users (vapers) and performed research bronchoscopies to isolate AMs from bronchoalveolar lavage fluid samples and epithelial cells from bronchial brushings. We then performed morphological analyses and used the Nanostring platform to look for changes in gene expression. RESULTS: AMs obtained from smokers and vapers were phenotypically distinct from those obtained from nonsmokers, and from each other. Immunocytochemistry revealed that vapers AMs had significantly elevated inducible nitric oxide synthase (M1) expression and significantly reduced CD301a (M2) expression compared with nonsmokers or smokers. Vapers' AMs and bronchial epithelia exhibited unique changes in gene expression compared with nonsmokers or smokers. Moreover, vapers' AMs were the most affected of all groups and had 124 genes uniquely downregulated. Gene ontology analysis revealed that vapers and smokers had opposing changes in biological processes. CONCLUSIONS: These data indicate that vaping causes unique changes to AMs and bronchial epithelia compared with nonsmokers and smokers which may impact pulmonary host defense. IMPLICATIONS: These data indicate that normal "healthy" vapers have altered AMs and may be at risk of developing abnormal immune responses to inflammatory stimuli.

Immediate and long-term effects of the COVID-19 pandemic and lockdown on physical activity in patients with implanted cardiac devices.

BACKGROUND: Physical activity (PA) is an important determinant of cardiovascular health that may be affected the COVID-19 pandemic. Therefore, we examined the immediate and long-term effects of the pandemic and lockdown on PA in patients with established cardiovascular risk. METHODS: Objectively-measured daily PA data was obtained from cardiovascular implantable electronic devices (CIEDs) from 3453 U.S patients (mean and standard deviations [SD] age, 72.65 [13.24] years; 42% women). Adjusted mixed-effects models stratified by device type were used to compare daily PA from periods in 2020: pre-lockdown (March 1-14), lockdown (March 15 to May 8), and the reopening phase of the pandemic (May 9 to December 31) versus 2019. Patient characteristics and events associated with inactivity during lockdown and the proportion of patients who returned to their 2019 PA-level by the end of reopening phase (December 31, 2020) were examined. RESULTS: Daily PA was significantly lower during the lockdown compared to the same period in 2019 (-15%; p < .0001), especially for pacemaker patients, adults aged <65, and patients more active prior to lockdown. Non-COVID hospitalization and ICD shock were similarly associated with low PA during lockdown (p = .0001). In the reopening phase of the pandemic, PA remained 14.4% lower in the overall sample and only 23% of patients returned to their 2019 PA level by the end of follow-up. CONCLUSIONS: In this large cohort of patients with CIEDs, PA was markedly lower during the lockdown and remained lower for months after restrictions were lifted. Strategies to maintain PA during a national emergency are urgently needed.

Hypothermia and Prolonged Time From Procedure End to Extubation After Endovascular Thoracic Aortic Surgery.

OBJECTIVE: Perioperative hypothermia (core temperature <36°C) occurs in 50%-to-80% of patients recovering from thoracic aortic surgery, though its effects have not been described fully in this context. The authors, therefore, sought to characterize the incidence of perioperative hypothermia and its association with time from procedure end to extubation in endovascular aortic surgical patients. DESIGN: A retrospective cohort study. SETTING: At a single academic tertiary center. PARTICIPANTS: Patients recovering from thoracic aortic surgery with lumbar drains. INTERVENTION: None. MEASUREMENTS AND MAIN RESULTS: A total of 196 patients were included in this study, 55 of whom were hypothermic with temperatures <35.0°C at the end of surgery. Though the unadjusted time to extubation was not statistically different in the hypothermic group (median 8 minutes, IQR 5-13.5 minutes) compared to the normothermic group (median 7 minutes, IQR 4-12 minutes; p = 0.062), multivariate predictors of increased time from procedure end to extubation included hypothermia (p = 0.011), age (p = 0.009), diabetes (p = 0.015), history of carotid disease (p = 0.040), and crystalloid volume (p = 0.019). CONCLUSIONS: Hypothermia in patients recovering from endovascular aortic surgery was associated with prolonged time from procedure end to extubation. Because of the retrospective observational nature of the authors' analysis, it was not possible to determine the extent to which prolonged mechanical ventilation was influenced by low temperature.

Predicting need for pacemaker implantation early and late after transcatheter aortic valve implantation.

OBJECTIVES: To identify associations with either early or late permanent pacemaker (PPM) implantation after transcatheter aortic valve replacement (TAVR) in order to develop an easily interpretable management algorithm. BACKGROUND: Injury to the conduction system after TAVR occasionally requires PPM. There is limited data on how to identify which patients will require PPM, particularly after discharge from index hospitalization after TAVR. METHODS: All patients having undergone TAVR at the University of North Carolina through August 2019 were identified and records were manually reviewed. Multivariable analyses were performed to identify associations with post-TAVR PPM due to high-degree atrioventricular block (HAVB). Comparisons were made between patients with no PPM (n = 304) and PPM required, stratified into early (during index hospitalization, n = 32) and late (during subsequent hospitalization, n = 11) PPM cohorts. RESULTS: Of the 347 patents included for analysis, 43 (12.4%) underwent post-TAVR PPM. In multivariable regression models, early PPM was associated with baseline bifascicular block (OR: 42.16; p < .001), requiring any pacing on first post-TAVR electrocardiogram (ECG) (OR: 31.55; p < .001), and valve oversizing >15% (OR: 3.61; p < .05). Late PPM was associated with baseline right bundle branch block (RBBB) (OR 12.62; p < .001) and history of atrial fibrillation/flutter (OR 4.83; p < .05). CONCLUSIONS: Bifascicular block, any pacing on first post-TAVR ECG, and >15% valve oversizing are associated with early PPM, while RBBB and history of atrial fibrillation/flutter are associated with late PPM. We suggest a management strategy for post-TAVR surveillance and management of HAVB.

Relapse or reinfection: Classification of malaria infection using transition likelihoods.

In patients with Plasmodium vivax malaria treated with effective blood-stage therapy, the recurrent illness may occur due to relapse from latent liver-stage infection or reinfection from a new mosquito bite. Classification of the recurrent infection as either relapse or reinfection is critical when evaluating the efficacy of an anti-relapse treatment. Although one can use whether a shared genetic variant exists between baseline and recurrence genotypes to classify the outcome, little has been suggested to use both sharing and nonsharing variants to improve the classification accuracy. In this paper, we develop a novel classification criterion that utilizes transition likelihoods to distinguish relapse from reinfection. When tested in extensive simulation experiments with known outcomes, our classifier has superior operating characteristics. A real data set from 78 Cambodian P. vivax malaria patients was analyzed to demonstrate the practical use of our proposed method.

Time-varying Hazards Model for Incorporating Irregularly Measured, High-Dimensional Biomarkers.

Clinical studies with time-to-event outcomes often collect measurements of a large number of time-varying covariates over time (e.g., clinical assessments or neuroimaging biomarkers) to build time-sensitive prognostic model. An emerging challenge is that due to resource-intensive or invasive (e.g., lumbar puncture) data collection process, biomarkers may be measured infrequently and thus not available at every observed event time point. Lever-aging all available, infrequently measured time-varying biomarkers to improve prognostic model of event occurrence is an important and challenging problem. In this paper, we propose a kernel-smoothing based approach to borrow information across subjects to remedy infrequent and unbalanced biomarker measurements under a time-varying hazards model. A penalized pseudo-likelihood function is proposed for estimation, and an efficient augmented penalization minimization algorithm related to the alternating direction method of multipliers (ADMM) is adopted for computation. Under some regularity conditions to carefully control approximation bias and stochastic variability, we show that even in the presence of ultra-high dimensionality, the proposed method selects important biomarkers with high probability. Through extensive simulation studies, we demonstrate superior performance in terms of estimation and selection performance compared to alternative methods. Finally, we apply the proposed method to analyze a recently completed real world study to model time to disease conversion using longitudinal, whole brain structural magnetic resonance imaging (MRI) biomarkers, and show a substantial improvement in performance over current standards including using baseline measures only.

Generalized Regression Estimators with High-Dimensional Covariates.

Data from a large number of covariates with known population totals are frequently observed in survey studies. These auxiliary variables contain valuable information that can be incorporated into estimation of the population total of a survey variable to improve the estimation precision. We consider the generalized regression estimator formulated under the model-assisted framework in which a regression model is utilized to make use of the available covariates while the estimator still has basic design-based properties. The generalized regression estimator has been shown to improve the efficiency of the design-based Horvitz-Thompson estimator when the number of covariates is fixed. In this study, we investigate the performance of the generalized regression estimator when the number of covariates p is allowed to diverge as the sample size n increases. We examine two approaches where the model parameter is estimated using the weighted least squares method when p < n and the LASSO method when the model parameter is sparse. We show that under an assisted model and certain conditions on the joint distribution of the covariates as well as the divergence rates of n and p, the generalized regression estimator is asymptotically more efficient than the Horvitz-Thompson estimator, and is robust against model misspecification. We also study the consistency of variance estimation for the generalized regression estimator. Our theoretical results are corroborated by simulation studies and an example.

Long-term sublingual immunotherapy for peanut allergy in children: Clinical and immunologic evidence of desensitization.

BACKGROUND: Peanut sublingual immunotherapy (SLIT) for 1 year has been shown to induce modest clinical desensitization in allergic children. Studies of oral immunotherapy, epicutaneous immunotherapy, and SLIT have suggested additional benefit with extended treatment. OBJECTIVE: We sought to investigate the safety, clinical effectiveness, and immunologic changes with long-term SLIT in children with peanut allergy. METHODS: Children with peanut allergy aged 1 to 11 years underwent extended maintenance SLIT with 2 mg/d peanut protein for up to 5 years. Subjects with peanut skin test wheals of less than 5 mm and peanut-specific IgE levels of less than 15 kU/L were allowed to discontinue therapy early. Desensitization was assessed through a double-blind, placebo-controlled food challenge (DBPCFC) with up to 5000 mg of peanut protein after completion of SLIT dosing. Sustained unresponsiveness was further assessed by using identical DBPCFCs after 2 to 4 weeks without peanut exposure. RESULTS: Thirty-seven of 48 subjects completed 3 to 5 years of peanut SLIT, with 67% (32/48) successfully consuming 750 mg or more during DBPCFCs. Furthermore, 25% (12/48) passed the 5000-mg DBPCFC without clinical symptoms, with 10 of these 12 demonstrating sustained unresponsiveness after 2 to 4 weeks. Side effects were reported with 4.8% of doses, with transient oropharyngeal itching reported most commonly. Side effects requiring antihistamine treatment were uncommon (0.21%), and no epinephrine was administered. Peanut skin test wheals, peanut-specific IgE levels, and basophil activation decreased significantly, and peanut-specific IgG4 levels increased significantly after peanut SLIT. CONCLUSION: Extended-therapy peanut SLIT provided clinically meaningful desensitization in the majority of children with peanut allergy that was balanced with ease of administration and a favorable safety profile.

High- and low-dose oral immunotherapy similarly suppress pro-allergic cytokines and basophil activation in young children.

BACKGROUND: Mechanisms underlying oral immunotherapy (OIT) are unclear and the effects on immune cells at varying maintenance doses are unknown. OBJECTIVE: We aimed to determine the immunologic changes caused by peanut OIT in preschool aged children and determine the effect on these immune responses in groups ingesting low or high-dose peanut OIT (300 mg or 3000 mg, respectively) as maintenance therapy. METHODS: Blood was drawn at several time-points throughout the OIT protocol and PBMCs isolated and cultured with peanut antigens. Secreted cytokines were quantified via multiplex assay, whereas Treg and peanut-responsive CD4 T cells were studied with flow cytometry. Basophil activation assays were also conducted. RESULTS: Th2-, Th1-, Th9- and Tr1-type cytokines decreased over the course of OIT in groups on high- and low-dose OIT. There were no significant differences detected in cytokine changes between the high- and low-dose groups. The initial increase in both the number of peanut-responsive CD4 T cells and the number of Tregs was transient and no significant differences were found between groups. Basophil activation following peanut stimulation was decreased over the course of OIT and associated with increased peanut-IgG4/IgE ratios. No differences were found between high- and low-dose groups in basophil activation at the time of desensitization or sustained unresponsiveness oral food challenges. CONCLUSIONS AND CLINICAL RELEVANCE: Peanut OIT leads to decreases in pro-allergic cytokines, including IL-5, IL-13, and IL-9 and decreased basophil activation. No differences in T cell or basophil responses were found between subjects on low or high-dose maintenance OIT, which has implications for clinical dosing strategies.

Race-Specific Patterns of Treatment Intensification Among Hypertensive Patients Using Home Blood Pressure Monitoring: Analysis Using Defined Daily Doses in the Heart Healthy Lenoir Study.

BACKGROUND: Racial disparities in blood pressure (BP) control persist, but whether differences by race in antihypertensive medication intensification (AMI) contribute is unknown. OBJECTIVE: To compare AMI by race for patients with elevated home BP readings. METHODS: This prospective cohort study followed adult patients from 6 rural primary care practices who used home BP monitoring (HBPM) and recorded/reported values. For providers, AMI was encouraged when mean HBPM systolic blood pressure (SBP) values were ⩾135 mm Hg; patients received phone-based coaching on HBPM technique and sharing HBPM findings. AMI was assessed between baseline and 12 months using defined daily dose (DDD) and summed to create a total antihypertensive DDD value. RESULTS: A total of 217 patients (mean age = 61.4 ± 10.2 years; 66% female; 57% black) provided usable HBPM data. Among 90 (41%) intensification-eligible hypertensive patients (ie, mean HBPM SBP values for 6-months ⩾135 mm Hg), mean total antihypertensive DDD was increased in 61% at 12 months. Blacks had significantly higher mean DDD at baseline and 12 months, but intensification (+0.72 vs +0.65; P = 0.83) was similar by race. However, intensification was greater in males than females (+1.1 vs +0.39; P = 0.031). Reduction in mean SBP following intensification was greater in white versus black patients (-8.2 vs -3.9 mm Hg; P = 0.14). Conclusion/Relevance: Treatment intensification in HBPM users was similar by race, differed significantly by gender, and may produce a greater response in white patients. Differential AMI in HBPM users does not appear to contribute to persistent racial disparities in BP control.