The Social Determinants of Infant Mortality and Birth Outcomes in Western Developed Nations: A Cross-Country Systematic Review

Infant mortality (IM) and birth outcomes, key population health indicators, have lifelong implications for individuals, and are unequally distributed globally. Even among western industrialized nations, striking cross-country and within-country patterns are evident. We sought to better understand these variations across and within the United States of America (USA) and Western Europe (WE), by conceptualizing a social determinants of IM/birth outcomes framework, and systematically reviewing the empirical literature on hypothesized social determinants (e.g., social policies, neighbourhood deprivation, individual socioeconomic status (SES)) and intermediary determinants (e.g., health behaviours). To date, the evidence suggests that income inequality and social policies (e.g., maternal leave policies) may help to explain cross-country variations in IM/birth outcomes. Within countries, the evidence also supports neighbourhood SES (USA, WE) and income inequality (USA) as social determinants. By contrast, within-country social cohesion/social capital has been underexplored. At the individual level, mixed associations have been found between individual SES, race/ethnicity, and selected intermediary factors (e.g., psychosocial factors) with IM/birth outcomes. Meanwhile, this review identifies several methodological gaps, including the underuse of prospective designs and the presence of residual confounding in a number of studies. Ultimately, addressing such gaps including through novel approaches to strengthen causal inference and implementing both health and non-health policies may reduce inequities in IM/birth outcomes across the western developed world

Characteristics and Predictive Value of Blood Transcriptome Signature in Males with Autism Spectrum Disorders

Autism Spectrum Disorders (ASD) is a spectrum of highly heritable neurodevelopmental disorders in which known mutations contribute to disease risk in 20% of cases. Here, we report the results of the largest blood transcriptome study to date that aims to identify differences in 170 ASD cases and 115 age/sex-matched controls and to evaluate the utility of gene expression profiling as a tool to aid in the diagnosis of ASD. The differentially expressed genes were enriched for the neurotrophin signaling, long-term potentiation/depression, and notch signaling pathways. We developed a 55-gene prediction model, using a cross-validation strategy, on a sample cohort of 66 male ASD cases and 33 age-matched male controls (P1). Subsequently, 104 ASD cases and 82 controls were recruited and used as a validation set (P2). This 55-gene expression signature achieved 68% classification accuracy with the validation cohort (area under the receiver operating characteristic curve (AUC): 0.70 [95% confidence interval [CI]: 0.62–0.77]). Not surprisingly, our prediction model that was built and trained with male samples performed well for males (AUC 0.73, 95% CI 0.65–0.82), but not for female samples (AUC 0.51, 95% CI 0.36–0.67). The 55-gene signature also performed robustly when the prediction model was trained with P2 male samples to classify P1 samples (AUC 0.69, 95% CI 0.58–0.80). Our result suggests that the use of blood expression profiling for ASD detection may be feasible. Further study is required to determine the age at which such a test should be deployed, and what genetic characteristics of ASD can be identified

Cluster analysis of the 55 genes used in the prediction model (ASD55).

<p>The dendrogram and heatmap on top show hierarchical clustering (average linkage) of the 99 samples in the training set (P1) and the 55 genes used in our prediction model. The first 2 lines in the graph on bottom indicate whether each sample is from the patient group or the control group. Finally, the bottom line shows the distribution of Fisher's linear discriminant scores (dots) based on ASD55 with moving average (line). The distributions of linear discriminant scores are shown on the right (blue solid line for controls and black broken line for patients). ASD cases and controls are well separated using linear discriminant analysis on the ASD55 genes.</p

Heterogeneous subgroups in dysregulated pathways.

<p>For immune response and synaptic gene sets, robust Mahalanobis distances (RDs) were calculated for all P1 samples. The outlier cutoff was set at the 97.5% quantile of the chi-squared distribution for each gene set (dotted green lines). When all samples were plotted in the 2-dimensional plane of Pathway Cluster 1 (x axis) by RDs in the Pathway Cluster 2 (y axis) (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0049475#pone-0049475-t004" target="_blank">Table 4</a>), four subgroups of samples were distinct. Both gene sets were perturbed for the samples in quadrant I; however, the samples in quadrants II and IV were significant for one gene set but not the other. A majority of samples were in quadrant III where no significant perturbation was found. The marginal density plots show the RD distributions for each gene set. Twenty-three out of 66 ASD samples (34.8%) were outliers for the synaptic gene set compared to 4 of 33 for controls (12.1%) (Fisher's exact test <i>P</i> = 0.017). For the immune response gene set, outliers were not biased towards case or control (Fisher's exact test <i>P</i> = 0.36).</p

Performance of the ASD55 prediction model.

<p>Receiver operating characteristic (ROC) curve analysis was performed to evaluate the prediction accuracy. The dotted diagonal line represents random classification accuracy (AUC 0.5). <b>A</b>. The accuracy of ASD55 within P1 was unsurprisingly high (AUC 0.98, 95% confidence interval (CI), 0.965–1.000, black ROC curve). The ASD55 model was trained with P1 to predict the diagnosis of each sample in an independently collected dataset P2 (dark blue ROC curve). The performance measured by AUC was 0.70 (95% CI, 0.62–0.77). ASD55 genes showed similar performance when the training and testing datasets were switched (AUC 0.69, 95% CI 0. 58–0.80, brown ROC curve). <b>B</b>. P2 male samples were accurately predicted (dark green) while female samples (red) were not (AUC 0.73 and 0.51 respectively) when the ASD55 model was trained with P1.</p

Characteristics of patients with autism spectrum disorders and controls in the training set (P1) and in the validation set (P2).

<p>Characteristics of patients with autism spectrum disorders and controls in the training set (P1) and in the validation set (P2).</p

Prediction performance of ASD55 trained with P1.

<p>Abbreviations: ASD55, the genes in a classifier developed on P1 with 55 genes listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0049475#pone.0049475.s008" target="_blank">Table S4</a>; AUC, area under the receiver operating characteristic curve.</p

Differentially expressed genes in CNV regions previously linked to ASD.

<p>Differentially expressed genes in CNV regions previously linked to ASD.</p

Quantitative RT-PCR validations of 12 differentially expressed genes.

<p>We selected 12 significantly differentially expressed genes that had average fold change greater than 1.5 and mean expression levels greater than 150 in the P1 dataset, and validated changes using quantitative RT-PCR. A total of 30 ASD and 30 control samples from the P1 population were run in replicates of four on the Biomark real time PCR system (Fluidigm, CA) using nanoliter reactions and the Taqman system (Applied Biosystems, CA). We were limited to 60 samples because the other 39 samples did not have enough RNA for qRT-PCR. The housekeeping gene used for qRT-PCR normalization was <i>GAPDH</i> (Hs9999905_m1). The values shown are for 30 ASD and 30 control samples from the P1 population, and fold changes refer to ASD/Control. P-values were calculated using Welch's t-test. For microarray data, p-values and fold changes were recalculated using the available samples. Eleven of 12 genes (all except <i>ZMAT1</i>) were successfully validated.</p