Elevated maternal C-reactive protein and autism in a national birth cohort.

Autism is a complex neuropsychiatric syndrome with a largely unknown etiology. Inflammation during pregnancy may represent a common pathway by which infections and other insults increase risk for the disorder. Hence, we investigated the association between early gestational C-reactive protein (CRP), an established inflammatory biomarker, prospectively assayed in maternal sera, and childhood autism in a large national birth cohort with an extensive serum biobank. Other strengths of the cohort included nearly complete ascertainment of pregnancies in Finland (N=1.2 million) over the study period and national psychiatric registries consisting of virtually all treated autism cases in the population. Increasing maternal CRP levels, classified as a continuous variable, were significantly associated with autism in offspring. For maternal CRP levels in the highest quintile, compared with the lowest quintile, there was a significant, 43% elevated risk. This finding suggests that maternal inflammation may have a significant role in autism, with possible implications for identifying preventive strategies and pathogenic mechanisms in autism and other neurodevelopmental disorders.

Severity and persistence of asthma and mental health: a birth cohort study.

BACKGROUND: The goal of the current study was to investigate asthma and mental health among youth in the community, and to consider the role of asthma severity and persistence in this link. Method Data were drawn from the Raine Study, a population-based birth cohort study in Western Australia. Logistic regression models and generalized estimating equations were used to examine the relationship between asthma at age 5 years and the range of internalizing and externalizing mental health problems at ages 5-17 years. Analyses were stratified by asthma severity and persistence, and adjusted for a range of potential confounders. RESULTS: More severe and persistent asthma at age 5 was associated with significantly increased odds of affective, anxiety, somatic, oppositional defiant and conduct problems at ages 5-17. Mild asthma and remitted asthma were not associated with heightened vulnerability to mental disorders. CONCLUSIONS: Our results suggest that youth with symptomatic asthma are more likely to suffer from a wide range of mental health problems, and that the likelihood of mental health problems appears to increase as a function of asthma severity. Youth with poorly controlled and/or more severe and persistent asthma may be considered a vulnerable group who might benefit from mental health screening in clinical, school and community settings.

The Early Determinants of Adult Health Study.

This issue of the Journal features collaborative follow-up studies of two unique pregnancy cohorts recruited during 1959-1966 in the United States. Here we introduce the Early Determinants of Adult Health (EDAH) study. EDAH was designed to compare health outcomes in midlife (age 40s) for same-sex siblings discordant on birthweight for gestational age. A sufficient sample of discordant siblings could only be obtained by combining these two cohorts in a single follow-up study. All of the subsequent six papers are either based upon the EDAH sample or are related to it in various ways. For example, three papers report results from studies that significantly extended the 'core' EDAH sample to address specific questions. We first present the overall design of and rationale for the EDAH study. Then we offer a synopsis of past work with the two cohorts to provide a context for both EDAH and the related studies. Next, we describe the recruitment and assessment procedures for the core EDAH sample. This includes the process of sampling and recruitment of potential participants; a comparison of those who were assessed and not assessed based on archived data; the methods used in the adult follow-up assessment; and the characteristics at follow-up of those who were assessed. We provide online supplementary tables with much further detail. Finally, we note further work in progress on EDAH and related studies, and draw attention to the broader implications of this endeavor.

Pre- and postnatal determinants of childhood body size: cohort and sibling analyses.

Growing evidence suggests obesity may have its roots in early life but it is still uncertain whether prenatal factors operate primarily though altering early infant growth. It is also still unclear if rapid growth during selected time periods is more important than other time periods in predicting future body size. Using prospectively collected data on 20,523 participants born from 1959 to 1966 (10,327 boys; 10,196 girls) of the Collaborative Perinatal Project, we investigated the associations between pre- and postnatal factors and childhood body size at age 7 years and compared these associations across linear, logistic and quantile regression models. Maternal body mass index (BMI), maternal pregnancy weight gain, birth weight and postnatal weight change for three time periods (birth to 4 months; 4-12 months; 1-4 years) were all positively and independently associated with BMI at age 7 years. Rapid growth during each time period had a similar association BMI at age 7 years. For example, a 10-percentile increase in weight increased the probability of being overweight at age 7 years by approximately two-fold regardless of time period (OR = 1.8-2.2 for boys and girls). Using same-sex siblings (n = 571 boy sets; n = 651 girl sets) from the same cohort, we observed that siblings with higher BMI at age 7 years than their same-sex siblings were more likely to have higher maternal pregnancy weight gain, higher maternal pre-pregnancy BMI, higher birth weight and increased rate of weight gain during the three time periods. These consistent findings both from the overall cohort and the sibling analyses suggest that there are multiple, rather than specific critical periods of influence shaping childhood body size.

Analyzing growth trajectories.

Growth trajectories play a central role in life course epidemiology, often providing fundamental indicators of prenatal or childhood development, as well as an array of potential determinants of adult health outcomes. Statistical methods for the analysis of growth trajectories have been widely studied, but many challenging problems remain. Repeated measurements of length, weight and head circumference, for example, may be available on most subjects in a study, but usually only sparse temporal sampling of such variables is feasible. It can thus be challenging to gain a detailed understanding of growth patterns, and smoothing techniques are inevitably needed. Moreover, the problem is exacerbated by the presence of large fluctuations in growth velocity during early infancy, and high variability between subjects. Existing approaches, however, can be inflexible because of a reliance on parametric models, require computationally intensive methods that are unsuitable for exploratory analyses, or are only capable of examining each variable separately. This article proposes some new nonparametric approaches to analyzing sparse data on growth trajectories, with flexibility and ease of implementation being key features. The methods are illustrated using data on participants in the Collaborative Perinatal Project.

Omnibus tests for comparison of competing risks with adjustment for covariate effects.

This article develops omnibus tests for comparing cause-specific hazard rates and cumulative incidence functions at specified covariate levels. Confidence bands for the difference and the ratio of two conditional cumulative incidence functions are also constructed. The omnibus test is formulated in terms of a test process given by a weighted difference of estimates of cumulative cause-specific hazard rates under Cox proportional hazards models. A simulation procedure is devised for sampling from the null distribution of the test process, leading to graphical and numerical technques for detecting significant differences in the risks. The approach is applied to a cohort study of type-specific HIV infection rates.

Bayesian estimators for conditional hazard functions.

This article introduces a new Bayesian approach to the analysis of right-censored survival data. The hazard rate of interest is modeled as a product of conditionally independent stochastic processes corresponding to (1) a baseline hazard function and (2) a regression function representing the temporal influence of the covariates. These processes jump at times that form a time-homogeneous Poisson process and have a pairwise dependency structure for adjacent values. The two processes are assumed to be conditionally independent given their jump times. Features of the posterior distribution, such as the mean covariate effects and survival probabilities (conditional on the covariate), are evaluated using the Metropolis-Hastings-Green algorithm. We illustrate our methodology by an application to nasopharynx cancer survival data.