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Background: Host and environment early-life risk factors are associated with progression of wheezing symptoms over time; however, their individual contribution is relatively small. We hypothesised that the dynamic interactions of these factors with an infant's developing respiratory system are the dominant factor for subsequent wheeze and asthma., Methods: In this dynamic network analysis we used data from term healthy infants from the Basel-Bern Infant Lung Development (BILD) cohort (435 neonates aged 0-4 weeks recruited in Switzerland between Jan 1, 1999, and Dec 31, 2012) and replicated the findings in the Protection Against Allergy Study in Rural Environments (PASTURE) cohort (498 infants aged 0-12 months recruited in Germany, Switzerland, Austria, France, and Finland between Jan 1, 2002, and Oct 31, 2006). BILD exclusion criteria for the current study were prematurity (<37 weeks), major birth defects, perinatal disease of the neonate, and incomplete follow-up period. PASTURE exclusion criteria were women younger than 18 years, a multiple pregnancy, the sibling of a child was already included in the study, the family intended to move away from the area where the study was conducted, and the family had no telephone connection. Outcome groups were subsequent wheeze, asthma, and healthy. The first outcome was defined as ever wheezed between the age of 2 years and 6 years. Week-by-week correlations of the determining factors with cumulative symptom scores (CSS) were calculated from weeks 2 to 52 (BILD) and weeks 8 to 52 (PASTURE). The complex dynamic interaction between the determining factors and the CSS was assessed via dynamic host-environment correlation network, quantified by a simple descriptor: trajectory function G(t). Wheeze outcomes at age 2-6 years were compared in 335 infants from BILD and 437 infants from PASTURE, and asthma outcomes were analysed at age 6 years in a merged cohort of 783 infants., Findings: CSS was significantly different for wheeze and asthma outcomes and became increasingly important during infancy in direct comparison with all determining factors. Weekly symptoms were tracked for groups of infants, showing a non-linear increase with time. Using logistic regression classification, G(t) distinguished between the healthy group and wheeze or asthma groups (area under the curve>0·97, p<0·0001; sensitivity analysis confirmed significant CSS association with wheeze [BILD p=0·0002 and PASTURE p=0·068]) and G(t) was also able to distinguish between the farming and non-farming exposure groups (p<0·0001)., Interpretation: Similarly to other risk factors, CSS had weak sensitivity and specificity to identify risks at the individual level. At group level however, the dynamic host-environment correlation network properties (G(t)) showed excellent discriminative ability for identifying groups of infants with subsequent wheeze and asthma. Results from this study are consistent with the 2018 Lancet Commission on asthma, which emphasised the importance of dynamic interactions between risk factors during development and not the risk factors per se., Funding: The Swiss National Science Foundation, the Kühne Foundation, the EFRAIM study EU research grant, the FORALLVENT study EU research grant, and the Leibniz Prize., Competing Interests: Declaration of interests PL reports board membership, funding grants, and speaking and lecture fees from Vertex and OM Pharma; speaking and lecture fees from Vifor Pharma Switzerland; and board membership with Polyphor, Santhera Pharmaceuticals Schweiz, Allecra Therapeutics, and Sanofi. AD-C reports consulting fees from Sanofi, Stallergens, ALK, and Aimmune Therapeutics; speaking fees and meeting attendance costs from Novartis and ALK; stock or stock options with Essilor Luxottica; a grant from Novartis and ARAIRLOR for asthma and cough research; received a grant from Don du Souffle, Fondation du Souffle, and ARAIRLOR for the PASTURE research; and received a contract with the French public agency ANSES. AMK reports that payments were made to their institution from the Juho Vanio Foundation, the Päiwikki and Sakari Sohlberg Foundation, the Finnish Cultural Foundation, the Academy of Finland, and the Finnish Institute for Health and Welfare. RL reports a research grant from the Kühne Foundation; and advisory board and lecture fees from ALK, Pfizer, Milupia, VIFOR, and Sanofi. BS reports research grants from Deutsche Forschungsgemeinschaft and consulting fees from GSK, Novartis, and Sanofi. EvM reports funding grants from the PASTURE study, the EFRAIM study, the FORALLVENT study, and the Leibniz Prize; research grants from the German Federal Ministry of Education and Research, the Bavarian State Ministry of Health and Care, OM Pharma, and the European Research Council; royalties or licences from Elsevier, Georg Thieme Verlag, Springer-Verlag, and Springer Nature Group; consulting fees from the Chinese University of Hong Kong, the European Commission, AstraZeneca, Imperial College London, and OM Pharma; lecture, speaker, or educational fees from ALK-Abello Arzneimittel, Japanese Society of Pediatric Allergy and Clinical Immunology, Klinikum Rechts der Isar, University of Colorado, Paul-Martini-Stiftung, AstraZeneca, Imperial College London, Children's Hospital Research Institute of Manitoba Kompetenzzentrum für Ernährung (Kern), OM Pharma, Swedish Pediatric Society for Allergy and Lung Medicine, Chinese College of Allergy and Asthma, Abbott Laboratories, Deutscher Apotheker Verlag, Socieded Chilena de Enfermedades Respiratorias, Japanese Society of Allergology,British Society for Asthma and Clinical Immunology, American Academy of Allergy, Asthma & Immunology, and the European Respiratory Society (ERS); meeting attendance support from Deutsches Zentrum für Lungenforschung, Fraunhofer ITEM Hannover, MCCA Institut für Immunologie Uni Wien, Karl-Landsteiner Privatuniversität für Gesundheitswissenschaften, Swiss Institute of Allergy and Asthma Research, Davos (Associated Institute of the University of Zurich), Medizinische Hochschule Hannover, Natasha Allergy Research Foundation, Deutsche Forschungsgemeinschaft, Gordon Research Conferences, Socieded Chilena de Enfermedades Respiratorias, Arla, Universität Leiden,OM Pharma, American Academy of Allergy, Asthma & Clinical Immunology, Deutsche Forschungsgemeinschaft, ERS, Deutsche Gesellschaft für Kinder- und Jugendmedizin, World Allergy Organization, and European Parliament; has a patent pending (PCT/EP2019/085016) for barn dust extract for the prevention and treatment of diseases; has royalties paid to ProtectImmun for a patent (EP2361632 for specific environmental bacteria for the protection from and/or the treatment of allergic, chronic inflammatory and/or autoimmune disorders, granted on March 19, 2014); has patents licensed to ProtectImmun (EP1411977 for composition containing bacterial antigens used for the prophylaxis and the treatment of allergic diseases, granted on April 18, 2007; EP1637147 for stable dust extract for allergy protection granted on Dec 10, 2008; and EP1964570 for pharmaceutical compound to protect against allergies and inflammatory diseases, granted on Nov 21, 2012); has a patent (EP21189353.2) for proteins identified from barn dust extract for the prevention and treatment of diseases; has a patent (PCT/US2021/016918) for therapeutic fractions and proteins from asthma-protective farm dust; has a patent (EP21189353.2) for proteins identified from barn dust extract for the prevention and treatment of diseases; and is a member of the EXPANSE (funded by European Commission) Scientific Advisory Board, the BEAMS External Scientific Advisory Board, the Editorial Board of The Journal of Allergy and Clinical Immunology: In Practice, the Scientific Advisory Board of the Children's Respiratory and Environmental Workgroup, the International Scientific & Societal Advisory Board of Utrecht Life Sciences University of Utrecht, the External Review Panel of the Faculty of Veterinary Science (University of Utrecht), the Selection Committee for the Gottfried Wilhelm Leibniz Programme, the International Advisory Board of Asthma UK Centre for Applied Research, the International Advisory Board of The Lancet Respiratory Medicine, the Scientific Advisory Board of the Canadian Healthy Infant Longitudinal Development study (McMaster University, Hamilton, Canada), the Asthma UK Centre for Applied Research, the Pediatric Scientific Advisory Board Iceland, and the Abbott Allergy Risk Reduction Advisory Board. All other authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Aquaporin 1 (AQP1) is one of thirteen known mammalian aquaporins. Its main function is the transport of water across cell membranes. Lately, a role of AQP has been attributed to other physiological and pathological functions including cell migration and peripheral pain perception. AQP1 has been found in several parts of the enteric nervous system, e.g., in the rat ileum and in the ovine duodenum. Its function in the intestine appears to be multifaceted and is still not completely understood. The aim of the study was to analyze the distribution and localization of AQP1 in the entire intestinal tract of mice. AQP1 expression was correlated with the hypoxic expression profile of the various intestinal segments, intestinal wall thickness and edema, as well as other aspects of colon function including the ability of mice to concentrate stools and their microbiome composition. AQP1 was found in a specific pattern in the serosa, the mucosa, and the enteric nervous system throughout the gastrointestinal tract. The highest amount of AQP1 in the gastrointestinal tract was found in the small intestine. AQP1 expression correlated with the expression profiles of hypoxia-dependent proteins such as HIF-1α and PGK1. Loss of AQP1 through knockout of AQP1 in these mice led to a reduced amount of bacteroidetes and firmicutes but an increased amount of the rest of the phyla, especially deferribacteres, proteobacteria, and verrucomicrobia. Although AQP-KO mice retained gastrointestinal function, distinct changes regarding the anatomy of the intestinal wall including intestinal wall thickness and edema were observed. Loss of AQP1 might interfere with the ability of the mice to concentrate their stool and it is associated with a significantly different composition of the of the bacterial stool microbiome.

Background: In all chronic airway diseases, the dynamics of airway function are influenced by underlying airway inflammation and bronchial hyperresponsiveness along with limitations in reversibility owing to airway and lung remodeling as well as mucous plugging. The relative contribution of each component translates into specific clinical patterns of symptoms, quality of life, exacerbation risk, and treatment success., Objective: We aimed to evaluate whether subgrouping of patients with obstructive airway diseases according to patterns of fluctuation in lung function allows identification of specific phenotypes with distinct clinical characteristics., Methods: We applied the novel method of fluctuation-based clustering (FBC) to twice-daily FEV 1 measurements recorded over a 1-year period in a mixed group of 134 adults with mild-to-moderate asthma, severe asthma, or chronic obstructive pulmonary disease from the European BIOAIR cohort., Results: Independently of clinical diagnosis, FBC divided patients into 4 fluctuation-based clusters with progressively increasing alterations in lung function that corresponded to patterns of increasing clinical severity, risk of exacerbation, and lower quality of life. Clusters of patients with airway disease with significantly elevated levels of biomarkers relating to remodeling (osteonectin) and cellular senescence (plasminogen activator inhibitor-1), accompanied by a loss of airway reversibility, pulmonary hyperinflation, and loss of diffusion capacity, were identified. The 4 clusters generated were stable over time and revealed no differences in levels of markers of type 2 inflammation (blood eosinophils and periostin)., Conclusion: FBC-based phenotyping provides another level of information that is complementary to clinical diagnosis and unrelated to eosinophilic inflammation, which could identify patients who may benefit from specific treatment strategies or closer monitoring., (Copyright © 2021. Published by Elsevier Inc.)

Accurate detection of human respiratory viral infections is highly topical. We investigated how strongly inflammatory biomarkers (FeNO, eosinophils, neutrophils, and cytokines in nasal lavage fluid) and lung function parameters change upon rhinovirus 16 infection, in order to explore their potential use for infection detection. To this end, within a longitudinal cohort study, healthy and mildly asthmatic volunteers were experimentally inoculated with rhinovirus 16, and time series of these parameters/biomarkers were systematically recorded and compared between the pre- and post-infection phases of the study, which lasted two months and one month, respectively. We found that the parameters'/biomarkers' ability to discriminate between the infected and the uninfected state varied over the observation time period. Consistently over time, the concentration of cytokines, in nasal lavage fluid, showed moderate to very good discrimination performance, thereby qualifying for disease progression monitoring, whereas lung function and FeNO, while quickly and non-invasively measurable using cheap portable devices (e.g., at airports), performed poorly.

Serial peak expiratory flow (PEF) measurements can identify phenotypes in severe adult asthma, enabling more targeted treatment. The feasibility of this approach in children has not been investigated. Overall, 105 children (67% male, median age 12.4 years) with a range of asthma severities were recruited and followed up over a median of 92 days. PEF was measured twice daily. Fluctuation-based clustering (FBC) was used to identify clusters based on PEF fluctuations. The patients' clinical characteristics were compared between clusters. Three PEF clusters were identified in 44 children with sufficient measurements. Cluster 1 (27% of patients: n=12) had impaired spirometry (mean forced expiratory volume in 1 s (FEV 1 ) 71% predicted), significantly higher exhaled nitric oxide (≥35 ppb) and uncontrolled asthma (asthma control test (ACT) score <20 of 25). Cluster 2 (45%: n=20) had normal spirometry, the highest proportion of difficult asthma and significantly more patients on a high dose of inhaled corticosteroids (≥800 µg budesonide). Cluster 3 (27%: n=12) had mean FEV 1 92% predicted, the highest proportion of patients with no bronchodilator reversibility, a low ICS dose (≤400 µg budesonide), and controlled asthma (ACT scores ≥20 of 25). Three clinically relevant paediatric asthma clusters were identified using FBC analysis on PEF measurements, which could improve telemonitoring diagnostics. The method remains robust even when 80% of measurements were removed. Further research will determine clinical applicability., Competing Interests: Conflict of interest: A. Jochmann has nothing to disclose. Conflict of interest: L. Artusio has nothing to disclose. Conflict of interest: H. Sharifian is a full-time employee of AstraZeneca and owns stock/stock options in AstraZeneca. Conflict of interest: A. Jamalzadeh has nothing to disclose. Conflict of interest: L.J. Fleming has nothing to disclose. Conflict of interest: A. Bush has nothing to disclose. Conflict of interest: U. Frey has nothing to disclose. Conflict of interest: E. Delgado-Eckert has nothing to disclose., (Copyright ©ERS 2020.)

Background: Epidemiological evidence on the influence of long-term exposure to traffic-related particulate matter (TPM 10 ) on heart rate variability (HRV) is weak., Objective: To evaluate the association of long-term exposure (10 years) with TPM 10 on the regulation of the autonomic cardiovascular system and heart rate dynamics (HRD) in an aging general population, as well as potential modifying effects by the a priori selected factors sex, smoking status, obesity, and gene variation in selected glutathione S-transferases (GSTs)., Methods: We analyzed data from 1593 SAPALDIA cohort participants aged ≥ 50 years. For each participant, various HRV and HRD parameters were derived from 24-hour electrocardiogram recordings. Each parameter obtained was then used as the outcome variable in multivariable mixed linear regression models in order to evaluate the association with TPM 10 . Potential modifying effects were assessed using interaction terms., Results: No association between long-term exposure to TPM 10 and HRV/HRD was observed in the entire study population. However, HRD changes were found in subjects without cardiovascular morbidity and both HRD and HRV changes in non-obese subjects without cardiovascular morbidity. Subjects without cardiovascular morbidity with homozygous GSTM1 gene deletion appeared to be more susceptible to the effects of TPM 10 ., Conclusion: This study suggests that long-term exposure to TPM 10 triggers adverse changes in the regulation of the cardiovascular system. These adverse effects were more visible in the subjects without cardiovascular disease, in whom the overall relationship between TPM 10 and HRV/HRD could not be masked by underlying morbidities and the potential counteracting effects of related drug treatments., (Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Children with frequent respiratory symptoms in infancy have an increased risk for later wheezing, but the association with symptom dynamics is unknown. We developed an observer-independent method to characterise symptom dynamics and tested their association with subsequent respiratory morbidity. In this birth-cohort of healthy neonates, we prospectively assessed weekly respiratory symptoms during infancy, resulting in a time series of 52 symptom scores. For each infant, we calculated the transition probability between two consecutive symptom scores. We used these transition probabilities to construct a Markov matrix, which characterised symptom dynamics quantitatively using an entropy parameter. Using this parameter, we determined phenotypes by hierarchical clustering. We then studied the association between phenotypes and wheezing at 6 years. In 322 children with complete data for symptom scores during infancy (16 864 observations), we identified three dynamic phenotypes. Compared to the low-risk phenotype, the high-risk phenotype, defined by the highest entropy parameter, was associated with an increased risk of wheezing (odds ratio (OR) 3.01, 95% CI 1.15-7.88) at 6 years. In this phenotype, infants were more often male (64%) and had been exposed to environmental tobacco smoke (31%). In addition, more infants had siblings (67%) and attended childcare (38%). We describe a novel method to objectively characterise dynamics of respiratory symptoms in infancy, which helps identify abnormal clinical susceptibility and recovery patterns of infant airways associated with persistent wheezing., Competing Interests: Conflict of interest: P. Latzin reports receiving personal fees from Vertex, Novartis, Roche, Polyphor, Vifor and Gilead outside the submitted work.

Rationale: Asthma is characterised by inflammation and reversible airway obstruction. However, these features are not always closely related. Fluctuations of daily lung function contain information on asthma phenotypes, exacerbation risk and response to long-acting β-agonists., Objectives: In search of subgroups of asthmatic participants with specific lung functional features, we developed and validated a novel clustering approach to asthma phenotyping, which exploits the information contained within the fluctuating behaviour of twice-daily lung function measurements., Methods: Forced expiratory volume during the first second (FEV 1 ) and peak expiratory flow (PEF) were prospectively measured over 4 weeks in 696 healthy and asthmatic school children (Protection Against Allergy - Study in Rural Environments (PASTURE)/EFRAIM cohort), and over 1 year in 138 asthmatic adults with mild-to-moderate or severe asthma (Pan-European Longitudinal Assessment of Clinical Course and BIOmarkers in Severe Chronic AIRway Disease (BIOAIR) cohort). Using enrichment analysis, we explored whether the method identifies clinically meaningful, distinct clusters of participants with different lung functional fluctuation patterns., Measurements and Main Results: In the PASTURE/EFRAIM dataset, we found four distinct clusters. Two clusters were enriched in children with well-known clinical characteristics of asthma. In cluster 3, children from a farming environment predominated, whereas cluster 4 mainly consisted of healthy controls. About 79% of cluster 3 carried the asthma-risk allele rs7216389 of the 17q21 locus. In the BIOAIR dataset, we found two distinct clusters clearly discriminating between individuals with mild-to-moderate and severe asthma., Conclusions: Our method identified dynamic functional asthma and healthy phenotypes, partly independent of atopy and inflammation but related to genetic markers on the 17q21 locus. The method can be used for disease phenotyping and possibly endotyping. It may identify participants with specific functional abnormalities, potentially needing a different therapeutic approach., Competing Interests: Competing interests: None declared., (© Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.)

International guidelines recommend that severe asthma can only be diagnosed after contributory factors, including adherence, have been addressed. Accurate assessment of adherence is difficult in clinical practice. We hypothesised that electronic monitoring in children would identify nonadherence, thus delineating the small number with true severe asthma.Asthmatic children already prescribed inhaled corticosteroids were prospectively recruited and persistence of adherence assessed using electronic monitoring devices. Spirometry, airway inflammation and asthma control were measured at the start and end of the monitoring period.93 children (62 male; median age 12.4 years) were monitored for a median of 92 days. Median (range) monitored adherence was 74% (21-99%). We identified four groups: 1) good adherence during monitoring with improved control, 24% (likely previous poor adherence); 2) good adherence with poor control, 18% (severe therapy-resistant asthma); 3) poor adherence with good control, 26% (likely overtreated); and 4) poor adherence with poor control, 32%. No clinical parameter prior to monitoring distinguished these groups.Electronic monitoring is a useful tool for identifying children in whom a step up in treatment is indicated. Different approaches are needed in those who are controlled when adherent or who are nonadherent. Electronic monitoring is essential in a paediatric severe asthma clinic., Competing Interests: Conflict of interest: None declared., (Copyright ©ERS 2017.)

The concept of diffusional screening implies that breath-to-breath variations in CO 2 clearance, when related to the variability of breathing, may contain information on the quality and utilization of the available alveolar surface. We explored the validity of the above hypothesis in a cohort of young infants of comparable postmenstrual age but born at different stages of lung maturity, namely, in term-born infants ( n = 128), preterm-born infants without chronic lung disease of infancy (CLDI; n = 53), and preterm infants with moderate/severe CLDI ( n = 87). Exhaled CO 2 volume (V E,CO2 ) and concentration (F E,CO2 ) were determined by volumetric capnography, whereas their variance was assessed by linear and nonlinear variability metrics. The relationship between relative breath-to-breath change of V E,CO2 (ΔV E,CO2 ) and the corresponding change of tidal volume (ΔV T ) was also analyzed. Nonlinear F E,CO2 variability was lower in CLDI compared with term and non-CLDI preterm group ( P < 0.001 for both comparisons). In CLDI infants, most of the V E,CO2 variability was attributed to the variability of V T ( r 2 = 0.749), whereas in term and healthy preterm infants this relationship was weaker ( r 2 = 0.507 and 0.630, respectively). The ΔV E,CO2 - ΔV T slope was less steep in the CLDI group (1.06 ± 0.07) compared with non-CLDI preterm (1.16 ± 0.07; P < 0.001) and term infants (1.20 ± 0.10; P < 0.001), suggesting that the more dysmature the infant lung, the less efficiently it eliminates CO 2 under tidal breathing conditions. We conclude that the temporal variation of CO 2 clearance may be related to the degree of lung dysmaturity in early infancy. NEW & NOTEWORTHY Young infants exhibit appreciable breath-to-breath CO 2 variability that can be quantified by nonlinear variability metrics and may reflect the degree of lung dysmaturity. In infants with moderate/severe chronic lung disease of infancy (CLDI), the variability of the exhaled CO 2 is mainly driven by the variability of breathing, whereas in term-born and healthy preterm infants this relationship is less strong. The slope of the relative CO 2 -to-volume change is less steep in CLDI infants, suggesting that dysmature lungs are less efficient in eliminating CO 2 under tidal breathing conditions.

Background: Poor control of body temperature is associated with mortality and major morbidity in preterm infants. We aimed to quantify its dynamics and complexity to evaluate whether indices from fluctuation analyses of temperature time series obtained within the first five days of life are associated with gestational age (GA) and body size at birth, and presence and severity of typical comorbidities of preterm birth., Methods: We recorded 3h-time series of body temperature using a skin electrode in incubator-nursed preterm infants. We calculated mean and coefficient of variation of body temperature, scaling exponent alpha (Talpha) derived from detrended fluctuation analysis, and sample entropy (TSampEn) of temperature fluctuations. Data were analysed by multilevel multivariable linear regression., Results: Data of satisfactory technical quality were obtained from 285/357 measurements (80%) in 73/90 infants (81%) with a mean (range) GA of 30.1 (24.0-34.0) weeks. We found a positive association of Talpha with increasing levels of respiratory support after adjusting for GA and birth weight z-score (p<0.001; R2 = 0.38)., Conclusion: Dynamics and complexity of body temperature in incubator-nursed preterm infants show considerable associations with GA and respiratory morbidity. Talpha may be a useful marker of autonomic maturity and severity of disease in preterm infants.

Process C (internal clock) and Process S (sleep-wake homeostasis) are the basis of sleep-wake regulation. In the last trimester of pregnancy, foetal heart rate is synchronized with the maternal circadian rhythm. At birth, this interaction fails and an ultradian rhythm appears. Light exposure is a strong factor influencing the synchronization of sleep-wake processes. However, little is known about the effects of phototherapy on the sleep rhythm of premature babies. It was hypothesized that sleep in preterm infants would not differ during phototherapy, but that a maturation effect would be seen. Sleep states were studied in 38 infants born < 32 weeks gestational age and/or < 1 500 g birth weight. Videos of 3 h were taken over the first 5 days of life. Based on breathing and movement patterns, behavioural states were defined as: awake; active sleep; or quiet sleep. Videos with and without phototherapy were compared for amounts of quiet sleep and active states (awake + active sleep). No significant association between phototherapy and amount of quiet sleep was found (P = 0.083). Analysis of videos in infants not under phototherapy revealed an increase in time spent awake with increasing gestational age. The current data suggest that the ultradian rhythm of preterm infants seems to be independent of phototherapy, supporting the notion that sleep rhythm in this population is mainly driven by their internal clock., (© 2016 European Sleep Research Society.)

Background: To investigate air pollution effects during pregnancy or in the first weeks of life, models are needed that capture both the spatial and temporal variability of air pollution exposures., Methods: We developed a time-space exposure model for ambient NO2 concentrations in Bern, Switzerland. We used NO2 data from passive monitoring conducted between 1998 and 2009: 101 rural sites (24,499 biweekly measurements) and 45 urban sites (4350 monthly measurements). We evaluated spatial predictors (land use; roads; traffic; population; annual NO2 from a dispersion model) and temporal predictors (meteorological conditions; NO2 from continuous monitoring station). Separate rural and urban models were developed by multivariable regression techniques. We performed ten-fold internal cross-validation, and an external validation using 57 NO2 passive measurements obtained at study participant's homes., Results: Traffic related explanatory variables and fixed site NO2 measurements were the most relevant predictors in both models. The coefficient of determination (R(2)) for the log transformed models were 0.63 (rural) and 0.54 (urban); cross-validation R(2)s were unchanged indicating robust coefficient estimates. External validation showed R(2)s of 0.54 (rural) and 0.67 (urban)., Conclusions: This approach is suitable for air pollution exposure prediction in epidemiologic research with time-vulnerable health effects such as those occurring during pregnancy or in the first weeks of life.

Aim: To evaluate the long-term influence of smoking cessation on the regulation of the autonomic cardiovascular system in an aging general population, using the subpopulation of lifelong non-smokers as control group., Methods: We analyzed 1481 participants aged ≥50 years from the SAPALDIA cohort. In each participant, heart rate variability and heart rate dynamics were characterized by means of various quantitative analyzes of the inter-beat interval time series generated from 24-hour electrocardiogram recordings. Each parameter obtained was then used as the outcome variable in multivariable linear regression models in order to evaluate the association with smoking status and time elapsed since smoking cessation. The models were adjusted for known confounding factors and stratified by the time elapsed since smoking cessation., Results: Our findings indicate that smoking triggers adverse changes in the regulation of the cardiovascular system, even at low levels of exposure since current light smokers exhibited significant changes as compared to lifelong non-smokers. Moreover, there was evidence for a dose-response effect. Indeed, the changes observed in current heavy smokers were more marked as compared to current light smokers. Furthermore, full recovery was achieved in former smokers (i.e., normalization to the level of lifelong non-smokers). However, while light smokers fully recovered within the 15 first years of cessation, heavy former smokers might need up to 15-25 years to fully recover., Conclusion: This study supports the substantial benefits of smoking cessation, but also warns of important long-term alterations caused by heavy smoking., (Copyright © 2015 Elsevier Inc. All rights reserved.)