Dose-response Estimates Research Articles

Assessing the contribution of smoking to the disease burden in Belgium, 2013-2020

Abstract Tobacco use remains a challenge given its substantial role in contributing to disability and premature death globally. Understanding its impact on population health, as an attributable burden, can help monitor progress on the effectiveness of tobacco policies. In this study, we used the Comparative Risk Assessment (CRA) method to estimate the share of the disease burden due to smoking in Belgium from 2013 to 2020. Population attributable fractions (PAF) were computed for risk-outcome pairs by age, sex, and region in Belgium for 2013-2020. Exposure estimates were extracted from a time series of the Belgian Health Interview Survey. Dose-response curves or relative risk estimates were drawn from the Global Burden of Disease Study 2019. In Belgium, smoking-related deaths decreased from 12,851 deaths (PAF=11%) in 2013 to 9,413 deaths (PAF=7%) in 2020. Deaths among men (2013: PAF=18%, n = 9859; 2020: PAF=11%, n = 7041) were three times higher than among women (2013: PAF=5%, n = 2991; 2020: PAF=3%, n = 2341). Over time, larynx cancer (2013: PAF=72%, n = 185; 2020: PAF=63%, n = 125), trachea, bronchus, and lung cancer (2013: PAF=60%, n = 4,899; 2020: PAF=60%, n = 3,682), other pharyngeal and oropharyngeal cancers (2013: PAF=47%, n = 118; 2020: PAF=40%, n = 100), mouth cancer (2013: PAF=37%, n = 172; 2020: PAF=30%, n = 122), and aortic aneurysm (2013: PAF=40%, n = 268; 2020: PAF=33%, n = 209) emerged as the primary contributors to smoking-related deaths in Belgium. While the ranking of contributing causes stayed mostly consistent in both genders over the years, cervical cancer (2013: PAF=23%, n = 53; 2020: PAF=21%, n = 64) ranked fourth instead of mouth cancer (2013: PAF=22%, n = 28; 2020: PAF=14%, n = 15) among women. Despite decreasing trends over time, smoking remains a significant contributor to cancer deaths in Belgium, particularly among men. Results from this study can help inform the development of strategies for smoking prevention and reinforce the need for continued efforts in prevention initiatives. Key messages • Cancers are the primary cause of smoking-related deaths in Belgium, with a decline in the death toll over time. • Understanding the role of smoking on population health as an attributable burden can improve health monitoring.

Efficacy of pharmacological interventions for ADHD: protocol for an updated systematic review and dose–response network meta-analysis

BackgroundAttention-deficit/hyperactivity disorder (ADHD) affects approximately 5% of children globally, with symptoms often persisting into adulthood. While pharmacological interventions are commonly employed for management, understanding the optimal dosing for efficacy and tolerability remains crucial. This study aims to conduct a dose–response network meta-analysis to estimate the efficacy of pharmacological treatments across different doses, aiming to inform clinical decision-making and improve treatment outcomes.MethodsThis updated systematic review will include randomized controlled trials evaluating ADHD medication efficacy in children, adolescents, and adults. An updated search from a 2018 NMA will be conducted across multiple electronic databases with no language restrictions, using specific eligibility criteria focused on randomized controlled trials. The primary outcome will assess the severity of ADHD core symptoms, while secondary outcomes will consider treatment tolerability. A dose–response Bayesian hierarchical model will be used to estimate dose–response curves for each medication, identifying optimal dosing strategies.DiscussionWith this dose–response network meta-analysis, we aim to better understand the dose–response relationship of pharmacological treatment in ADHD, which could help clinician to the identification of optimal doses.Systematic review registrationOSF https://doi.org/10.17605/OSF.IO/3MY4A.

Serum lactate/creatinine ratio and acute kidney injury in cardiac arrest patients

ObjectivesSerum lactate and creatinine levels upon admission in cardiac arrest (CA) patients significantly correlate with acute kidney injury (AKI) post-restoration of autonomic circulation. However, the association between serum lactate/creatinine ratio (LCR) and AKI in this population remains unclear. This study aimed to explore the relationship between LCR at admission and cardiac arrest-associated acute kidney injury (CA-AKI). Design and methodsWe conducted a secondary analysis of previously published data on CA patient resuscitation, categorizing them into tertiles based on LCR levels. Univariate and multivariate logistic regression models and subgroup analyses were employed to investigate the association between LCR and CA-AKI. Non-linear correlations were explored using restricted cubic splines, and a two-piece wise logistic proportional hazards model for both sides of the inflection point was constructed. ResultsA total of 374 patients (72.19 % male) were included, with intensive care unit mortality, in-hospital mortality, and neurologic dysfunction rates of 51.87 %, 56.95 %, and 39.57 %, respectively. The overall CA-AKI incidence was 59.09 %. Multivariate logistic proportional hazards analysis revealed a negative association between LCR and CA-AKI incidence (adjusted odds ratio [OR] 0.85, 95 % confidence intervals [CI] = 0.78–0.93, P=0.001). Triple spline restriction analysis depicted an L-shaped correlation between baseline LCR and CA-AKI incidence. Particularly, a baseline LCR<0.051 was negatively associated with CA-AKI incidence (OR 0.494, 95 % CI=0.319–0.764, P=0.002). Beyond the LCR turning point, estimated dose–response curves remained consistent with a horizontal line. ConclusionsBaseline LCR in CA patients exhibits an L-shaped correlation with AKI incidence following restoration of autonomic circulation. The threshold for CA-AKI is 0.051. This finding suggests that LCR may aid in identifying CA patients at high risk of AKI.

Dietary choline intake and health outcomes in U.S. adults: exploring the impact on cardiovascular disease, cancer prevalence, and all-cause mortality

BackgroundCholine, an indispensable nutrient, plays a pivotal role in various physiological processes. The available evidence regarding the nexus between dietary choline intake and health outcomes, encompassing cardiovascular disease (CVD), cancer, and all-cause mortality, is limited and inconclusive. This study aimed to comprehensively explore the relationship between dietary choline intake and the aforementioned health outcomes in adults aged > 20 years in the U.S.MethodsThis study utilized data from the National Health and Nutrition Examination Survey between 2011 and 2018. Dietary choline intake was evaluated using two 24-h dietary recall interviews. CVD and cancer status were determined through a combination of standardized medical status questionnaires and self-reported physician diagnoses. Mortality data were gathered from publicly available longitudinal Medicare and mortality records. The study utilized survey-weighted logistic and Cox regression analyses to explore the associations between choline consumption and health outcomes. Restricted cubic spline (RCS) analysis was used for dose‒response estimation and for testing for nonlinear associations.ResultsIn our study of 14,289 participants (mean age 48.08 years, 47.71% male), compared with those in the lowest quintile (Q1), the adjusted odds ratios (ORs) of CVD risk in the fourth (Q4) and fifth (Q5) quintiles of choline intake were 0.70 (95% CI 0.52, 0.95) and 0.65 (95% CI 0.47, 0.90), respectively (p for trend = 0.017). Each 100 mg increase in choline intake was associated with a 9% reduced risk of CVD. RCS analysis revealed a linear correlation between choline intake and CVD risk. Moderate choline intake (Q3) was associated with a reduced risk of mortality, with an HR of 0.75 (95% CI 0.60–0.94) compared with Q1. RCS analysis demonstrated a significant nonlinear association between choline intake and all-cause mortality (P for nonlinearity = 0.025). The overall cancer prevalence association was nonsignificant, except for colon cancer, where each 100 mg increase in choline intake indicated a 23% reduced risk.ConclusionElevated choline intake demonstrates an inverse association with CVD and colon cancer, while moderate consumption exhibits a correlated reduction in mortality. Additional comprehensive investigations are warranted to elucidate the broader health implications of choline.

Statistical considerations in model-based dose finding for binary responses under model uncertainty.

The statistical methodology for model-based dose finding under model uncertainty has attracted increasing attention in recent years. While the underlying principles are simple and easy to understand, developing and implementing an efficient approach for binary responses can be a formidable task in practice. Motivated by the statistical challenges encountered in a phase II dose finding study, we explore several key design and analysis issues related to the hybrid testing-modeling approaches for binary responses. The issues include candidate model selection and specifications, optimal design and efficient sample size allocations, and, notably, the methods for dose-response testing and estimation. Specifically, we consider a class of generalized linear models suited for the candidate set and establish D-optimal designs for these models. Additionally, we propose using permutation-based tests for dose-response testing to avoid asymptotic normality assumptions typically required for contrast-based tests. We perform trial simulations to enhance our understanding of these issues.

Contrastive Balancing Representation Learning for Heterogeneous Dose-Response Curves Estimation

Estimating the individuals' potential response to varying treatment doses is crucial for decision-making in areas such as precision medicine and management science. Most recent studies predict counterfactual outcomes by learning a covariate representation that is independent of the treatment variable. However, such independence constraints neglect much of the covariate information that is useful for counterfactual prediction, especially when the treatment variables are continuous. To tackle the above issue, in this paper, we first theoretically demonstrate the importance of the balancing and prognostic representations for unbiased estimation of the heterogeneous dose-response curves, that is, the learned representations are constrained to satisfy the conditional independence between the covariates and both of the treatment variables and the potential responses. Based on this, we propose a novel Contrastive balancing Representation learning Network using a partial distance measure, called CRNet, for estimating the heterogeneous dose-response curves without losing the continuity of treatments. Extensive experiments are conducted on synthetic and real-world datasets demonstrating that our proposal significantly outperforms previous methods.

The Use of Joint Models in Analysis of Aggregate Endpoints in RERF Cohort Studies.

In radiation risk estimation based on the Radiation Effects Research Foundation (RERF) cohort studies, one common analysis is Poisson regression on radiation dose and background and effect modifying variables of an aggregate endpoint such as all solid cancer incidence or all non-cancer mortality. As currently performed, these analyses require selection of a surrogate radiation organ dose, (e.g., colon dose), which could conceptually be problematic since the aggregate endpoint comprises events arising from a variety of organs. We use maximum likelihood theory to compare inference from the usual aggregate endpoint analysis to analyses based on joint analysis. These two approaches are also compared in a re-analysis of RERF Life Span Study all cancer mortality. We show that, except for a trivial difference, these two analytic approaches yield identical inference with respect to radiation dose response and background and effect modification when based on a single surrogate organ radiation dose. When repeating the analysis with organ-specific doses, an interesting issue of bias in intercept parameters arises when dose estimates are undefined for one sex when sex-specific outcomes are included in the aggregate endpoint, but a simple correction will avoid this issue. Lastly, while the joint analysis formulation allows use of organ-specific doses, the interpretation of such an analysis for inference regarding an aggregate endpoint can be problematic. To the extent that analysis of radiation risk for an aggregate endpoint is of interest, the joint-analysis formulation with a single surrogate dose is an appropriate analytic approach, whereas joint analysis with organ-specific doses may only be interpretable if endpoints are considered separately for estimating dose response. However, for neither approach is inference about dose response well defined.

Quantitative Integration of Mode of Action Information in Dose-Response Modeling and POD Estimation for Nonmutagenic Carcinogens: A Case Study of TCDD.

Traditional dose-response assessment applies different low-dose extrapolation methods for cancer and noncancer effects and assumes that all carcinogens are mutagenic unless strong evidence suggests otherwise. Additionally, primarily focusing on one critical effect, dose-response modeling utilizes limited mode of action (MOA) data to inform low-dose risk. We aimed to build a dose-response modeling framework that continuously extends the curve into the low-dose region via a quantitative integration of MOA information and to estimate MOA-based points of departure (PODs) for nonmutagenic carcinogens. 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) was used as an example to demonstrate the new dose-response modeling framework. There were three major steps included: a) identifying and extracting key quantifiable events (KQEs), b) calculating essential doses that sequentially activate KQEs using the benchmark dose (BMD) methodology, and c) characterizing pathway dose-response relationship for MOA-based POD estimation. We identified and extracted six KQEs and corresponding essential events composing the MOA of TCDD-induced liver tumors. With the essential doses estimated from the BMD method using various settings, three link functions were applied to model the pathway dose-response relationship. Given a toxicologically plausible definition of adversity, an MOA-based POD was derived from the pathway dose-response curve. The estimated MOA-based PODs were generally comparable with traditional PODs and can be further used to calculate reference doses (RfDs). The proposed framework quantitatively integrated mechanistic information in the modeling process and provided a promising strategy to harmonize cancer and noncancer dose-response assessment through pathway dose-response modeling. However, the framework can also be limited by data availability and the understanding of the underlying mechanism. https://doi.org/10.1289/EHP12677.

REAP-2: An interactive quantitative tool for robust and efficient dose-response curve estimation.

REAP-2 is an interactive dose-response curve estimation tool for Robust and Efficient Assessment of drug Potency. It provides user-friendly dose-response curve estimation for in vitro studies and conducts statistical testing for model comparisons with a redesigned user interface. We also make a major update of the underlying estimation method with penalized beta regression, which demonstrates great reliability and accuracy in dose estimation and uncertainty quantification. In this note, we describe the method and implementation of REAP-2 with a highlight on potency estimation and drug comparison.

Spinal manipulation characteristics: a scoping literature review of force-time characteristics

BackgroundSpinal manipulation (SM) is a recommended and effective treatment for musculoskeletal disorders. Biomechanical (kinetic) parameters (e.g. preload/peak force, rate of force application and thrust duration) can be measured during SM, quantifying the intervention. Understanding these force-time characteristics is the first step towards identifying possible active ingredient/s responsible for the clinical effectiveness of SM. Few studies have quantified SM force-time characteristics and with considerable heterogeneity evident, interpretation of findings is difficult. The aim of this study was to synthesise the literature describing force-time characteristics of manual SM.MethodsThis scoping literature review is reported following the Preferred Reporting Items for Scoping Reviews (PRISMA-ScR) statement. Databases were searched from inception to October 2022: MEDLINE (Ovid), Embase, CINAHL, ICL, PEDro and Cochrane Library. The following search terms and their derivatives were adapted for each platform: spine, spinal, manipulation, mobilization or mobilisation, musculoskeletal, chiropractic, osteopathy, physiotherapy, naprapathy, force, motor skill, biomechanics, dosage, dose-response, education, performance, psychomotor, back, neck, spine, thoracic, lumbar, pelvic, cervical and sacral. Data were extracted and reported descriptively for the following domains: general study characteristics, number of and characteristics of individuals who delivered/received SM, region treated, equipment used and force-time characteristics of SM.ResultsOf 7,607 records identified, 66 (0.9%) fulfilled the eligibility criteria and were included in the analysis. Of these, SM was delivered to the cervical spine in 12 (18.2%), the thoracic spine in 40 (60.6%) and the lumbopelvic spine in 19 (28.8%) studies. In 6 (9.1%) studies, the spinal region was not specified. For SM applied to all spinal regions, force-time characteristics were: preload force (range: 0-671N); peak force (17-1213N); rate of force application (202-8700N/s); time to peak thrust force (12-938ms); and thrust duration (36-2876ms).ConclusionsConsiderable variability in the reported kinetic force-time characteristics of SM exists. Some of this variability is likely due to differences in SM delivery (e.g. different clinicians) and the measurement equipment used to quantify force-time characteristics. However, improved reporting in certain key areas could facilitate more sophisticated syntheses of force-time characteristics data in the future. Such syntheses could provide the foundation upon which dose-response estimates regarding the clinical effectiveness of SM are made.

Ebct: Using entropy balancing for continuous treatments to estimate dose–response functions and their derivatives

Interest in evaluating dose–response functions of continuous treatments has been increasing recently. To facilitate the estimation of causal effects in this setting, I introduce the ebct command for the estimation of dose–response functions and their derivatives using entropy balancing for continuous treatments. First, balancing weights are estimated by numerically solving a globally convex optimization problem. These weights eradicate Pearson correlations between covariates and the treatment variable. Because simple uncorrelatedness may be insufficient to yield consistent estimates in the next step, higher moments of the treatment variable can be rendered uncorrelated with covariates. Second, the weights are used in local linear kernel regressions to estimate the dose–response function or its derivative. To perform statistical inference, I use a bootstrap procedure. The command also provides the option of producing publication-quality graphs for the estimated relationships.

A comparative study of in vitro dose-response estimation under extreme observations.

Quantifying drug potency, which requires an accurate estimation of dose-response relationship, is essential for drug development in biomedical research and life sciences. However, the standard estimation procedure of the median-effect equation to describe the dose-response curve is vulnerable to extreme observations in common experimental data. To facilitate appropriate statistical inference, many powerful estimation tools have been developed in R, including various dose-response packages based on the nonlinear least squares method with different optimization strategies. Recently, beta regression-based methods have also been introduced in estimation of the median-effect equation. In theory, they can overcome nonnormality, heteroscedasticity, and asymmetry and accommodate flexible robust frameworks and coefficients penalization. To identify a reliable estimation method(s) to estimate dose-response curves even with extreme observations, we conducted a comparative study to review 14 different tools in R and examine their robustness and efficiency via Monte Carlo simulation under a list of comprehensive scenarios. The simulation results demonstrate that penalized beta regression using the mgcv package outperforms other methods in terms of stable, accurate estimation, and reliable uncertainty quantification.

An investigation of non-informative priors for Bayesian dose-response modeling

Toxicology analyses are built around dose-response modeling, and increasingly these methodologies utilize Bayesian estimation techniques. Bayesian estimation is unique because it includes prior distributional information in the analysis, which may impact the dose-response estimate meaningfully. As such analyses are often used for human health risk assessment, the practitioner must understand the impact of adding prior information to the dose-response study. One proposal in the literature is the use of the flat uniform prior distribution, which places a uniform prior probability over the dose-response model's parameters for a chosen range of values. Though the motivation of such a prior distribution is laudable in that it is most like maximum likelihood estimation seeking unbiased estimates of the dose-response, one can show that such priors add information and may introduce unexpected biases into the analysis. This manuscript shows through numerous empirical examples why prior distributions that are non-informative across all endpoints of interest do not exist for dose-response models; that is, other quantities of interest will be informed by choosing one inferential quantity not informed.

The Effect of the Locally Hired Teacher Ratio on School Outcomes: the Dose-Response Function Estimation Evidence from Kenya

The Effect of the Locally Hired Teacher Ratio on School Outcomes: the Dose-Response Function Estimation Evidence from Kenya

Robust and Efficient Assessment of Potency (REAP) as a quantitative tool for dose-response curve estimation.

The median-effect equation has been widely used to describe the dose-response relationship and identify compounds that activate or inhibit specific disease targets in contemporary drug discovery. However, the experimental data often contain extreme responses, which may significantly impair the estimation accuracy and impede valid quantitative assessment in the standard estimation procedure. To improve the quantitative estimation of the dose-response relationship, we introduce a novel approach based on robust beta regression. Substantive simulation studies under various scenarios demonstrate solid evidence that the proposed approach consistently provides robust estimation for the median-effect equation, particularly when there are extreme outcome observations. Moreover, simulation studies illustrate that the proposed approach also provides a narrower confidence interval, suggesting a higher power in statistical testing. Finally, to efficiently and conveniently perform common lab data analyses, we develop a freely accessible web-based analytic tool to facilitate the quantitative implementation of the proposed approach for the scientific community.

Epidemiology of Thyroid Cancer.

Epidemiology of Thyroid Cancer.

Association of Habitual Alcohol Intake With Risk of Cardiovascular Disease

Observational studies have consistently proposed cardiovascular benefits associated with light alcohol consumption, while recent genetic analyses (ie, mendelian randomization studies) have suggested a possible causal link between alcohol intake and increased risk of cardiovascular disease. However, traditional approaches to genetic epidemiology assume a linear association and thus have not fully evaluated dose-response estimates of risk across different levels of alcohol intake. To assess the association of habitual alcohol intake with cardiovascular disease risk and to evaluate the direction and relative magnitude of cardiovascular risk associated with different amounts of alcohol consumption. This cohort study used the UK Biobank (2006-2010, follow-up until 2016) to examine confounding in epidemiologic associations between alcohol intake and cardiovascular diseases. Using both traditional (ie, linear) and nonlinear mendelian randomization, potential associations between alcohol consumption and cardiovascular diseases (eg, hypertension and coronary artery disease) as well as corresponding association shapes were assessed. Data analysis was conducted from July 2019 to January 2022. Genetic predisposition to alcohol intake. The association between alcohol consumption and cardiovascular diseases, including hypertension, coronary artery disease, myocardial infarction, stroke, heart failure, and atrial fibrillation. This study included 371 463 participants (mean [SD] age, 57.0 [7.9] years; 172 400 [46%] men), who consumed a mean (SD) 9.2 (10.6) standard drinks per week. Overall, 121 708 participants (33%) had hypertension. Light to moderate alcohol consumption was associated with healthier lifestyle factors, adjustment for which attenuated the cardioprotective epidemiologic associations with modest intake. In linear mendelian randomization analyses, a 1-SD increase in genetically predicted alcohol consumption was associated with 1.3-fold (95% CI, 1.2-1.4) higher risk of hypertension (P < .001) and 1.4-fold (95% CI, 1.1-1.8) higher risk of coronary artery disease (P = .006). Nonlinear mendelian randomization analyses suggested nonlinear associations between alcohol consumption and both hypertension and coronary artery disease: light alcohol intake was associated with minimal increases in cardiovascular risk, whereas heavier consumption was associated with exponential increases in risk of both clinical and subclinical cardiovascular disease. In this cohort study, coincident, favorable lifestyle factors attenuated the observational benefits of modest alcohol intake. Genetic epidemiology suggested that alcohol consumption of all amounts was associated with increased cardiovascular risk, but marked risk differences exist across levels of intake, including those accepted by current national guidelines.

Chromosome aberrations among atomic-bomb survivors exposed in utero: updated analysis accounting for revised radiation doses and smoking

A previous study of peripheral blood lymphocyte translocations around age 40 among atomic-bomb survivors exposed in utero revealed no overall association with radiation dose—despite a clear association between translocations and dose among their mothers—but the data suggested an increase at doses below 100 mGy with a definite peak. That analysis of the in utero-exposed survivors did not adjust for their subsequent smoking behavior, an established cause of chromosomal aberrations, or their subsequent exposures to medical irradiation, a potential mediator. In addition, atomic-bomb survivor radiation dose estimates have subsequently been updated and refined. We therefore re-estimated the dose response using the latest DS02R1 dose estimates and adjusting for smoking as well as for city and proximal–distal location at the time of exposure to the atomic bomb. Sex of the survivor, mother’s age around the time of conception, and approximate trimester of gestation at the time of exposure were also considered as explanatory variables and modifiers. Precision of the estimated dose response was slightly lower due to greater variability near zero in the updated dose estimates, but there was little change in evidence of a low-dose increase and still no suggestion of an overall increase across the entire dose range. Adjustment for smoking behavior led to a decline in background number of translocations (the dose–response intercept), but smoking did not interact with dose overall (across the entire dose range). Adjustment for medical irradiation did not alter the association between dose and translocation frequency. Sex, mother’s age, and trimester were not associated with number of translocations, nor did they interact with dose overall. Interactions with dose in the low-dose range could not be evaluated because of numerical instability.

A computational system for Bayesian benchmark dose estimation of genomic data in BBMD.

Background:Existing studies have revealed that the benchmark dose (BMD) estimates from short-term in vivo transcriptomics studies can approximate those from long-term guideline toxicity assessments. Existing software applications follow this trend by analyzing omics data through the maximum likelihood estimation and choosing the “best” model for BMD estimates. However, this practice ignores the model uncertainty and may result in over-confident inferences and predictions, leading to an inadequate decision.Objective:By generally following the National Toxicology Program Approach to Genomic Dose-Response Modeling, we developed a web-based dose–response modeling and BMD estimation system, Bayesian BMD (BBMD), for genomic data to quantitatively address uncertainty from various sources. The performances of BBMD are compared with BMDExpress.Methods:The system is primarily based on the previously developed BBMD system and further developed in a genomic perspective. Bayesian model averaging method is applied to BMD estimation and pathways analyses. Generally, the system is unique regarding the flexibility in preparing/storing data and in characterizing uncertainties.Results:This system was tested and validated versus 24 previously published in-vivo microarray dose–response datasets (GSE45892) and 64 molecules data from the Open TG-Gates database. Short term transcriptional BMD values for the median pathway in BBMD are highly correlated with the long-term apical BMD values (R = 0.78–0.91). The BMD estimates obtained by BBMD were compared to those by BMDExpress. The results indicate that BBMD provides more adequate results in terms of less extreme values and no failure in BMD and BMDL calculations. Also, the pathway analysis in BBMD provides a conservative estimate because a broader confidence interval is established.Discussion:Overall, this study demonstrates that dose–response modeling using genomic data can play a substantial role in support of chemical risk assessment. BBMD represents a robust and user-friendly alternative for genomic dose–response data analysis with outstanding functionalities to quantify uncertainty from various sources.

The Effect of Seasonal Priming on Specific Inhalation Challenges With Birch and Grass Allergen Among Persons With Allergic Rhinitis.

Objectives: Allergic diseases are prevalent in the working population, and work-related airborne pollen exposure might be substantial, especially among outdoor workers, resulting in work-exacerbated effects. Seasonal exposure to pollen may induce a priming effect on the allergic bronchial response resulting in exaggerated effects at the end of the natural pollen season. This was previously observed among people with asthma but may also be of importance for persons with allergic rhinitis. In this study, we examined the effect of seasonal priming on bronchial responsiveness among young adults with allergic rhinitis and no or mild asthma. In addition, we explored the association between the baseline characteristics of participants and the severity of bronchoconstriction. Finally, we evaluated the application of a novel non-linear regression model to the log-dose-response curves.Material and methods: In a crossover design, 36 participants underwent specific inhalation challenges (SICs) with either grass or birch allergen outside and at the end of the pollen season. The differences in bronchial response were evaluated by comparing the dose-response profiles and PD20 estimates derived by applying a non-linear regression model.Results: The results showed that 12 of the 19 grass pollen-exposed participants had a lower PD20 at the end of the season compared with the outside season. For birch, this was true for nine out of the 17 participants. However, no statistically significant effects of the seasonal pollen exposure were found on neither the shape nor the magnitude of the modeled dose-response curves for either birch allergen, p = 0.77, or grass allergen, p = 0.45. The model depicted a good fit for the data. Among the baseline characteristics, only the size of the skin prick test for grass allergen was associated with PD20.Conclusion: This study does not support a priming effect of pollen exposure on the bronchial response from the natural seasonal exposure levels of grass or birch allergens among young adults with allergic rhinitis.