Background Insomnia has been associated with mortality risk, but whether this association is different in subjects with persistent versus intermittent insomnia is unclear. Additionally, the role of systemic inflammation in such an association is unknown. Methods We used data from a community-based cohort to determine whether persistent or intermittent insomnia, defined based on persistence of symptoms over a six-year period, were associated with death during the following 20-years of follow-up. We also determined whether changes in serum C-reactive protein (CRP) levels measured over two decades between study initiation and insomnia determination were different for the persistent, intermittent, and never insomnia groups. The results were adjusted for confounders such as age, sex, body mass index, smoking, physical activity, alcohol and sedatives. Results Of the 1409 adult participants, 249 (18%) had intermittent and 128 (9%) had persistent insomnia. During a 20-year follow-up period, 318 participants died (118 due to cardiopulmonary disease). In adjusted Cox proportional-hazards models, participants with persistent insomnia (adjusted Hazards Ratio [HR] 1.58, 95%CI: 1.02-2.45) but not intermittent insomnia (HR 1.22, 0.86-1.74), were more likely to die than participants without insomnia. Serum CRP levels were higher and increased at a steeper rate in subjects with persistent insomnia as compared with intermittent (p=0.04) or never (p=0.004) insomnia. Although CRP levels were themselves associated with increased mortality (adjHR: 1.36, 1.01-1.82, p=0.04), adjustment for CRP levels did not notably change the association between persistent insomnia and mortality. Conclusions In a population-based cohort, persistent, and not intermittent, insomnia was associated with increased risk for all-cause and cardiopulmonary mortality and was associated with a steeper increase in inflammation.

Background Low serum levels of the anti-inflammatory club cell secretory protein (CC16) have been associated with an accelerated FEV1 decline in COPD. Whether low circulating CC16 precedes lung function deficits and incidence of COPD in the general population is unknown. Methods We used longitudinal data from adults who were COPD-free at baseline from the population-based TESAOD (N=960, mean follow-up: 14yrs), ECRHS-Sp (N=514, 11yrs) and SAPALDIA (N=167, 8yrs) studies. CC16 was measured in serum from baseline and associated with subsequent FEV1 decline and incidence of airflow limitation. To evaluate early life CC16 effects, we also measured circulating CC16 in samples from ages 4-6yrs to predict subsequent lung function in childhood in the CRS (N=427), MAAS (N=481), and BAMSE (N=231) birth cohorts. Findings In adults – after adjustment for sex, age, height, smoking status/intensity, pack-years, asthma, and initial FEV1 levels – baseline CC16 was inversely associated with subsequent decline of FEV1 in TESAOD (p=0.0014), ECRHS-Sp (p=0.023), and a similar trend was found in SAPALDIA (p=0.052). Low CC16 at baseline also predicted an increased risk for incident stage 2 airflow limitation (i.e., FEV1/FVC<70% plus FEV1 % predicted < 80%) in TESAOD and ECRHS-Sp. In children, the lowest tertile of CC16 at age 4–6yrs was associated with subsequent FEV1 deficits up to age 16yrs (meta-analyzed estimate from adjusted models on birth cohorts: −68ml, p=0.0001). Results were confirmed among subjects who never smoked by age 16yrs (−71ml, p<0.0001). Interpretation Low serum CC16 is associated with subsequent slower growth and accelerated decline of lung function, and increased risk of developing stage 2 airflow limitation. Funding US National Heart, Lung, and Blood Institute and EU Seventh Framework Programme. For a complete list of other funding agencies, please refer to the acknowledgements section of the paper.

BackgroundThe study of circulating biomarkers and their association with disease outcomes has become progressively complex due to advances in the measurement of these biomarkers through multiplex technologies. The Least Absolute Shrinkage and Selection Operator (LASSO) is a data analysis method that may be utilized for biomarker selection in these high dimensional data. However, it is unclear which LASSO-type method is preferable when considering data scenarios that may be present in serum biomarker research, such as high correlation between biomarkers, weak associations with the outcome, and sparse number of true signals. The goal of this study was to compare the LASSO to five LASSO-type methods given these scenarios.MethodsA simulation study was performed to compare the LASSO, Adaptive LASSO, Elastic Net, Iterated LASSO, Bootstrap-Enhanced LASSO, and Weighted Fusion for the binary logistic regression model. The simulation study was designed to reflect the data structure of the population-based Tucson Epidemiological Study of Airway Obstructive Disease (TESAOD), specifically the sample size (N = 1000 for total population, 500 for sub-analyses), correlation of biomarkers (0.20, 0.50, 0.80), prevalence of overweight (40%) and obese (12%) outcomes, and the association of outcomes with standardized serum biomarker concentrations (log-odds ratio = 0.05–1.75). Each LASSO-type method was then applied to the TESAOD data of 306 overweight, 66 obese, and 463 normal-weight subjects with a panel of 86 serum biomarkers.ResultsBased on the simulation study, no method had an overall superior performance. The Weighted Fusion correctly identified more true signals, but incorrectly included more noise variables. The LASSO and Elastic Net correctly identified many true signals and excluded more noise variables. In the application study, biomarkers of overweight and obesity selected by all methods were Adiponectin, Apolipoprotein H, Calcitonin, CD14, Complement 3, C-reactive protein, Ferritin, Growth Hormone, Immunoglobulin M, Interleukin-18, Leptin, Monocyte Chemotactic Protein-1, Myoglobin, Sex Hormone Binding Globulin, Surfactant Protein D, and YKL-40.ConclusionsFor the data scenarios examined, choice of optimal LASSO-type method was data structure dependent and should be guided by the research objective. The LASSO-type methods identified biomarkers that have known associations with obesity and obesity related conditions.

Background The timing and mechanisms of asthma inception remain imprecisely defined. Although epigenetic mechanisms likely contribute to asthma pathogenesis, little is known about their role in asthma inception. Objective To assess whether the trajectory to asthma begins already at birth and epigenetic mechanisms, specifically DNA methylation, contribute to asthma inception. Methods We used Methylated CpG Island Recovery Assay (MIRA)-chip to survey DNA methylation in cord blood mononuclear cells (CBMC) from 36 children (18 non-asthmatic, 18 asthmatic by age 9) from the Infant Immune Study (IIS), an unselected birth cohort closely monitored for asthma for a decade. SMAD3 methylation in IIS (n=60) and in two replication cohorts (The Manchester Asthma and Allergy Study, n=30, and the Childhood Origins of ASThma Study, n=28) was analyzed by bisulfite sequencing or Illumina 450K arrays. CBMC-derived IL-1β was measured by ELISA. Results Neonatal immune cells harbored 589 differentially methylated regions (DMRs) that distinguished IIS children who did and did not develop asthma by age 9. In all three cohorts, methylation in SMAD3, the most connected node within the network of asthma-associated DMRs, was selectively increased in asthmatic children of asthmatic mothers and was associated with childhood asthma risk. Moreover, SMAD3 methylation in IIS neonates with maternal asthma was strongly and positively associated with neonatal production of IL-1β, an innate inflammatory mediator. Conclusions The trajectory to childhood asthma begins at birth and involves epigenetic modifications in immunoregulatory and pro-inflammatory pathways. Maternal asthma influences epigenetic mechanisms that contribute to the inception of this trajectory.