Deep phenotyping of the unselected COPSAC2010 birth cohort study

BACKGROUND: We hypothesize that perinatal exposures, in particular the human microbiome and maternal nutrition during pregnancy, interact with the genetic predisposition to cause an abnormal immune modulation in early life towards a trajectory to chronic inflammatory diseases such as asthma and others. OBJECTIVE: The aim of this study is to explore these interactions by conducting a longitudinal study in an unselected cohort of pregnant women and their offspring with emphasis on deep clinical phenotyping, exposure assessment, and biobanking. Exposure assessments focus on the human microbiome. Nutritional intervention during pregnancy in randomized controlled trials are included in the study to prevent disease and to be able to establish causal relationships. METHODS: Pregnant women from eastern Denmark were invited during 2008–2010 to a novel unselected ‘COPSAC(2010)’ cohort. The women visited the clinic during pregnancy weeks 24 and 36. Their children were followed at the clinic with deep phenotyping and collection of biological samples at nine regular visits until the age of 3 and at acute symptoms. Randomized controlled trials of high‐dose vitamin D and fish oil supplements were conducted during pregnancy, and a trial of azithromycin for acute lung symptoms was conducted in the children with recurrent wheeze. RESULTS: Seven hundred and thirty‐eight mothers were recruited from week 24 of gestation, and 700 of their children were included in the birth cohort. The cohort has an over‐representation of atopic parents. The participant satisfaction was high and the adherence equally high with 685 children (98%) attending the 1 year clinic visit and 667 children (95%) attending the 2 year clinic visit. CONCLUSIONS: The COPSAC(2010) birth cohort study provides longitudinal clinical follow‐up with highly specific end‐points, exposure assessments, and biobanking. The cohort has a high adherence rate promising strong data to elucidate the interaction between genomics and the exposome in perinatal life leading to lifestyle‐related chronic inflammatory disorders such as asthma

Boosting for high-dimensional two-class prediction

Background In clinical research prediction models are used to accurately predict the outcome of the patients based on some of their characteristics. For high-dimensional prediction models (the number of variables greatly exceeds the number of samples) the choice of an appropriate classifier is crucial as it was observed that no single classification algorithm performs optimally for all types of data. Boosting was proposed as a method that combines the classification results obtained using base classifiers, where the sample weights are sequentially adjusted based on the performance in previous iterations. Generally boosting outperforms any individual classifier, but studies with high-dimensional data showed that the most standard boosting algorithm, AdaBoost.M1, cannot significantly improve the performance of its base classier. Recently other boosting algorithms were proposed (Gradient boosting, Stochastic Gradient boosting, LogitBoost)they were shown to perform better than AdaBoost.M1 but their performance was not evaluated for high-dimensional data. Results In this paper we use simulation studies and real gene-expression data sets to evaluate the performance of boosting algorithms when data are high-dimensional. Our results confirm that AdaBoost.M1 can perform poorly in this setting, often failing to improve the performance of its base classifier. We provide the explanation for this and propose a modification, AdaBoost.M1.ICV, which uses cross-validated estimates of the prediction errors and outperforms the original algorithm when data are high-dimensional. The use of AdaBoost.M1.ICV is advisable when the base classifier overfits the training data: the number of variables is large, the number of samples is small, and/or the difference between the classes is large. To a lesser extent also Gradient boosting suffers from similar problems. Contrary to the findings for the low-dimensional data, shrinkage does not improve the performance of Gradient boosting when data are high-dimensional, however it is beneficial for Stochastic Gradient boosting, which outperformed the other boosting algorithms in our analyses. LogitBoost suffers from overfitting and generally performs poorly. Conclusions The results show that boosting can substantially improve the performance of its base classifier also when data are high-dimensional. However, not all boosting algorithms perform equally well. LogitBoost, AdaBoost.M1 and Gradient boosting seem less useful for this type of data. Overall, Stochastic Gradient boosting with shrinkage and AdaBoost.M1.ICV seem to be the preferable choices for high-dimensional class-prediction

Study group flowchart.

<p>Study group flowchart.</p

Altered Response to A(H1N1)pnd09 Vaccination in Pregnant Women: A Single Blinded Randomized Controlled Trial

<div><p>Background</p><p>Pregnant women were suspected to be at particular risk when H1N1pnd09 influenza became pandemic in 2009. Our primary objective was to compare the immune responses conferred by MF59®-adjuvanted vaccine (Focetria®) in H1N1pnd09-naïve pregnant and non-pregnant women. The secondary aims were to compare influences of dose and adjuvant on the immune response.</p><p>Methods</p><p>The study was nested in the Copenhagen Prospective Studies on Asthma in Childhood (COPSAC₂₀₁₀) pregnancy cohort in 2009-2010 and conducted as a single-blinded block-randomised [1∶1∶1] controlled clinical trial in pregnant women after gestational week 20: (1) 7.5 µg H1N1pnd09 antigen with MF59-adjuvant (Pa7.5 µg); (2) 3.75 µg antigen half MF59-adjuvanted (Pa3.75 µg); (3) 15 µg antigen unadjuvanted (P15 µg); and in non-pregnant women receiving (4) 7.5 µg antigen full adjuvanted (NPa7.5 µg). Blood samples were collected at baseline, 3 weeks, 3 and 10 months after vaccination, adverse events were recorded prospectively.</p><p>Results</p><p>58 pregnant women were allocated to Pa7.5 µg and 149 non-pregnant women were recruited to NPa7.5 µg. The sero-conversion rate was significantly increased in non-pregnant (NPa7.5 µg) compared with pregnant (Pa7.5 µg) women (OR = 2.48 [1.03–5.95], p = 0.04) and geometric mean titers trended towards being higher, but this difference was not statistically significant (ratio 1.27 [0.85–1.93], p = 0.23). The significant titer increase rate showed no difference between pregnant (Pa7.5 µg) and non-pregnant (NPa7.5 µg) groups (OR = 0.49 [0.13–1.85], p = 0.29).</p><p>Conclusion</p><p>Our study suggests the immune response to the 7.5 µg MF59-adjuvanted Focetria® <i>H1N1pnd09</i> vaccine in pregnant women may be diminished compared with non-pregnant women.</p><p>Trial Registration</p><p>ClinicalTrials.gov<a href="http://www.clinicaltrials.gov/ct2/show/NCT01012557?term=NCT01012557&rank=1" target="_blank"> NCT01012557</a>.</p></div

Antibody responses according to vaccine groups.

<p>P: Pregnant.</p><p>NP: Non-pregnant.</p><p>GMT: Geometric mean titer.</p><p>GMR: Geometric mean ratio.</p><p>CI: Confidence interval.</p><p>Sero-protection: Titer ≥40.</p><p>Sero-conversion: Pre titer<10, Post titer ≥40.</p><p>Significant increase: Pre titer ≥10, Post titer 4 fold Pre titer.</p><p>Sero-conversion or significant increase: Percentage of total number of women in each vaccine group.</p>†<p>At day 0 GMT were the same in all groups (ANOVA, p = 0.51).</p>‡<p>The number of women with sero-protection did not differ in any of the groups compared over time (GEE).</p>*<p>GMT at 3 weeks, 3 months and 10 months were adjusted for baseline titer.</p

Evolution of sero-conversion rate (as percentage) in women in the four vaccine groups over time.

<p>The trends over time for the number of women sero-converted were not significantly different. The number of non-pregnant women (NPa7.5 µg) who sero-converted was 2.48-fold higher than among pregnant (Pa7.5 µg) women using general estimating equations.</p

Evolution of geometric mean titers in the four vaccine groups over time.

<p>The trends over time for geometric mean titers in the vaccine groups were not significantly different using general estimating equations.</p

Definitions.

<p>Definitions.</p

Baseline characteristic of the study subjects according to vaccine group.

A<p>ANOVA.</p>F<p>Fisher's exact test.</p>*<p>The gestational age at time of vaccination is different in the Pa3.75 µg group compared with the Pa7.5 µg group (P = 0.01). There is no significant difference between Pa7.5 µg and P15 µg (P = 0.82). Wilcoxon Rank Sum Test (t approximation).</p