Particulate matter exposure during pregnancy is associated with birth weight, but not gestational age, 1962-1992: a cohort study

<p>Abstract</p> <p>Background</p> <p>Exposure to air pollutants is suggested to adversely affect fetal growth, but the evidence remains inconsistent in relation to specific outcomes and exposure windows.</p> <p>Methods</p> <p>Using birth records from the two major maternity hospitals in Newcastle upon Tyne in northern England between 1961 and 1992, we constructed a database of all births to mothers resident within the city. Weekly black smoke exposure levels from routine data recorded at 20 air pollution monitoring stations were obtained and individual exposures were estimated via a two-stage modeling strategy, incorporating temporally and spatially varying covariates. Regression analyses, including 88,679 births, assessed potential associations between exposure to black smoke and birth weight, gestational age and birth weight standardized for gestational age and sex.</p> <p>Results</p> <p>Significant associations were seen between black smoke and both standardized and unstandardized birth weight, but not for gestational age when adjusted for potential confounders. Not all associations were linear. For an increase in whole pregnancy black smoke exposure, from the 1^st(7.4 μg/m³) to the 25^th(17.2 μg/m³), 50^th(33.8 μg/m³), 75^th(108.3 μg/m³), and 90^th(180.8 μg/m³) percentiles, the adjusted estimated decreases in birth weight were 33 g (SE 1.05), 62 g (1.63), 98 g (2.26) and 109 g (2.44) respectively. A significant interaction was observed between socio-economic deprivation and black smoke on both standardized and unstandardized birth weight with increasing effects of black smoke in reducing birth weight seen with increasing socio-economic disadvantage.</p> <p>Conclusions</p> <p>The findings of this study progress the hypothesis that the association between black smoke and birth weight may be mediated through intrauterine growth restriction. The associations between black smoke and birth weight were of the same order of magnitude as those reported for passive smoking. These findings add to the growing evidence of the harmful effects of air pollution on birth outcomes.</p

Inference of an Integrative, Executable Network for Rheumatoid Arthritis Combining Data-Driven Machine Learning Approaches and a State-of-the-Art Mechanistic Disease Map

International audienceRheumatoid arthritis (RA) is a multifactorial, complex autoimmune disease that involves various genetic, environmental, and epigenetic factors. Systems biology approaches provide the means to study complex diseases by integrating different layers of biological information. Combining multiple data types can help compensate for missing or conflicting information and limit the possibility of false positives. In this work, we aim to unravel mechanisms governing the regulation of key transcription factors in RA and derive patient-specific models to gain more insights into the disease heterogeneity and the response to treatment. We first use publicly available transcriptomic datasets (peripheral blood) relative to RA and machine learning to create an RA-specific transcription factor (TF) co-regulatory network. The TF cooperativity network is subsequently enriched in signalling cascades and upstream regulators using a state-of-the-art, RA-specific molecular map. Then, the integrative network is used as a template to analyse patients’ data regarding their response to anti-TNF treatment and identify master regulators and upstream cascades affected by the treatment. Finally, we use the Boolean formalism to simulate in silico subparts of the integrated network and identify combinations and conditions that can switch on or off the identified TFs, mimicking the effects of single and combined perturbations

Association of genetic variants in CDK6 and XRCC1 with the risk of dysplastic nevi in melanoma-prone families

Dysplastic nevi (DN) is a strong risk factor for cutaneous malignant melanoma (CMM), and it frequently occurs in melanoma-prone families. To identify genetic variants for DN, we genotyped 677 tagSNPs in 38 melanoma candidate genes that are involved in pigmentation, DNA repair, cell cycle control, and melanocyte proliferation pathways in a total of 504 individuals (310 with DN, 194 without DN) from 53 melanoma-prone families (23 CDKN2A mutation positive and 30 negative). Conditional logistic regression, conditioning on families, was used to estimate the association between DN and each single-nucleotide polymorphism (SNP) separately, adjusted for age, sex, CMM, and CDKN2A status. P-values for SNPs in the same gene were combined to yield gene-specific P-values. Two genes, CDK6 (cyclin-dependent kinase 6) and XRCC1, were significantly associated with DN after Bonferroni correction for multiple testing (P=0.0001 and 0.00025, respectively), whereas neither gene was significantly associated with CMM. Associations for CDK6 SNPs were stronger in CDKN2A mutation-positive families (rs2079147, Pinteraction=0.0033), whereas XRCC1 SNPs had similar effects in mutation-positive and -negative families. The association for one of the associated SNPs in XRCC1 (rs25487) was replicated in two independent data sets (random-effect meta-analysis: P<0.0001). Our findings suggest that some genetic variants may contribute to DN risk independently of their association with CMM in melanoma-prone families

Features associated with germline CDKN2A mutations: a GenoMEL study of melanoma‐prone families from three continents

Background: The major factors individually reported to be associated with an increased frequency of CDKN2A mutations are increased number of patients with melanoma in a family, early age at melanoma diagnosis, and family members with multiple primary melanomas (MPM) or pancreatic cancer. Methods: These four features were examined in 385 families with [&gt;=]3 patients with melanoma pooled by 17 GenoMEL groups, and these attributes were compared across continents. Results: Overall, 39% of families had CDKN2A mutations ranging from 20% (32/162) in Australia to 45% (29/65) in North America to 57% (89/157) in Europe. All four features in each group, except pancreatic cancer in Australia (p = 0.38), individually showed significant associations with CDKN2A mutations, but the effects varied widely across continents. Multivariate examination also showed different predictors of mutation risk across continents. In Australian families, [&gt;=]2 patients with MPM, median age at melanoma diagnosis [&lt;=]40 years and [&gt;=]6 patients with melanoma in a family jointly predicted the mutation risk. In European families, all four factors concurrently predicted the risk, but with less stringent criteria than in Australia. In North American families, only [&gt;=]1 patient with MPM and age at diagnosis [&lt;=]40 years simultaneously predicted the mutation risk. Conclusions: The variation in CDKN2A mutations for the four features across continents is consistent with the lower melanoma incidence rates in Europe and higher rates of sporadic melanoma in Australia. The lack of a pancreatic cancer-CDKN2A mutation relationship in Australia probably reflects the divergent spectrum of mutations in families from Australia versus those from North America and Europe. GenoMEL is exploring candidate host, genetic and/or environmental risk factors to better understand the variation observe

A comparison of CDKN2A mutation detection within the Melanoma Genetics Consortium (GenoMEL)

CDKN2A is the major melanoma susceptibility gene so far identified, but only 40% of three or more case families have identified mutations. A comparison of mutation detection rates was carried out by “blind” exchange of samples across GenoMEL, the Melanoma Genetics Consortium, to establish the false negative detection rates. Denaturing high performance liquid chromatography (DHPLC) screening results from 451 samples were compared to screening data from nine research groups in which the initial mutation screen had been done predominantly by sequencing. Three samples with mutations identified at the local centres were not detected by the DHPLC screen. No additional mutations were detected by DHPLC. Mutation detection across groups within GenoMEL is carried out to a consistently high standard. The relatively low rate of CDKN2A mutation detection is not due to failure to detect mutations and implies the existence of other high penetrance melanoma susceptibility genes