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Background Carriers of germline pathogenic variants (PVs) in the BRCA1 and BRCA2 genes are at higher risk of developing breast and ovarian cancer than the general population. It is unclear if these PVs influence other breast or ovarian cancer risk factors, including age at menopause (ANM), age at menarche (AAM), menstrual cycle length, BMI or height. There is a biological rationale for associations between BRCA1 and BRCA2 PVs and reproductive traits, for example involving DNA damage and repair mechanisms. The evidence for or against such associations is limited. Methods We used data on 3,046 BRCA1 and 3,264 BRCA2 PV carriers, and 2,857 non-carrier female relatives of PV carriers from the Epidemiological Study of Familial Breast Cancer (EMBRACE). Associations between ANM and PV carrier status was evaluated using linear regression models allowing for censoring. AAM, menstrual cycle length, BMI, and height in carriers and non-carriers were compared using linear and multinomial logistic regression. Analyses were adjusted for potential confounders, and weighted analyses carried out to account for non-random sampling with respect to cancer status. Results No statistically significant difference in ANM between carriers and non-carriers was observed in analyses accounting for censoring. Linear regression effect sizes for ANM were -0.002 (95%CI: -0.401, 0.397) and -0.172 (95%CI: -0.531, 0.188), for BRCA1 and BRCA2 PV carriers respectively, compared with non-carrier women. The distributions of AAM, menstrual cycle length and BMI were similar between PV carriers and non-carriers, but BRCA1 PV carriers were slightly taller on average than non-carriers (0.5 cm difference, p  = 0.003). Conclusion Information on the distribution of cancer risk factors in PV carriers is needed for incorporating these factors into multifactorial cancer risk prediction algorithms. Contrary to previous reports, we found no evidence that BRCA1 or BRCA2 PV are associated with hormonal or anthropometric factors, except for a weak association with height. We highlight methodological considerations and data limitations inherent in studies aiming to address this question.

This study described a broad clinical characterization of classical homocystinuria (HCU) in Brazil. This was a cross-sectional, observational study including clinical and biochemical data from 72 patients (60 families) from Brazil (South, n = 13; Southeast, n = 37; Northeast, n = 8; North, n = 1; and Midwest, n = 1). Parental consanguinity was reported in 42% of families. Ocular manifestations were the earliest detected symptom (53% of cases), the main reason for diagnostic suspicion (63% of cases), and the most prevalent manifestation at diagnosis (67% of cases). Pyridoxine responsiveness was observed in 14% of patients. Only 22% of nonresponsive patients on treatment had total homocysteine levels <100 mmol/L. Most commonly used treatment strategies were pyridoxine (93% of patients), folic acid (90%), betaine (74%), vitamin B12 (27%), and low-methionine diet + metabolic formula (17%). Most patients diagnosed with HCU in Brazil are late diagnosed, express a severe phenotype, and poor metabolic control. Milder forms of HCU are likely underrepresented due to underdiagnosis.

Childhood obesity poses a significant public health challenge, yet the molecular intricacies underlying its pathobiology remain elusive. Leveraging extensive multi-omics profiling (methylome, miRNome, transcriptome, proteins and metabolites) and a rich phenotypic characterization across two parts of Europe within the population-based Human Early Life Exposome project, we unravel the molecular landscape of childhood obesity and associated metabolic dysfunction. Our integrative analysis uncovers three clusters of children defined by specific multi-omics profiles, one of which characterized not only by higher adiposity but also by a high degree of metabolic complications. This high-risk cluster exhibits a complex interplay across many biological pathways, predominantly underscored by inflammation-related cascades. Further, by incorporating comprehensive information from the environmental risk-scape of the critical pregnancy period, we identify pre-pregnancy body mass index and environmental pollutants like perfluorooctanoate and mercury as important determinants of the high-risk cluster. Overall, our work helps to identify potential risk factors for prevention and intervention strategies early in the life course aimed at mitigating obesity and its long-term health consequences. Obesity encompasses numerous interconnected pathological mechanisms. Here, the authors show that integrating multi-omics data uncovers distinct molecular profiles and prenatal factors linked to childhood obesity and metabolic dysfunction, providing insights for early prevention and intervention strategies.

Thoracic endovascular aortic repair (TEVAR) has been shown to lead to increased aortic stiffness. The aim of this study was to investigate the effect of stent graft type and stent graft length on aortic stiffness in a controlled, experimental setting. Twenty porcine thoracic aortas were connected to a pulsatile mock loop system. Intraluminal pressure was recorded at two sites in order to measure pulse wave velocity (PWV) for each aorta: before stent graft deployment (t1); after deployment of a 100-mm long stent graft (t2); and after distal extension through deployment of a second 100-mm long stent graft (t3). Four different types of stent grafts (Conformable Gore® TAG® Device, Bolton Relay® Device, Cook Zenith Alpha™, and Medtronic Valiant®) were evaluated. For the total cohort of 20 aortas, PWV increased by a mean 0.6 m/s or 8.9% of baseline PWV after deployment of a 100-mm proximal stent graft (P<0.001), and by a mean 1.4 m/s or 23.0% of baseline PWV after distal extension of the stent graft (P<0.001). Univariable regression analysis showed a significant correlation between aortic PWV and extent of stent graft coverage, (P<0.001), but no significant effect of baseline aortic length, baseline aortic PWV, or stent graft type on the percentual increase in PWV at t2 or at t3. In this experimental set-up, aortic stiffness increased significantly after stent graft deployment with each of the four types of stent graft, with the increase in aortic stiffness depending on the extent of stent graft coverage.

Urinary metabolic profiling is a promising powerful tool to reflect dietary intake and can help understand metabolic alterations in response to diet quality. Here, we used 1 H NMR spectroscopy in a multicountry study in European children (1147 children from 6 different cohorts) and identified a common panel of 4 urinary metabolites (hippurate, N -methylnicotinic acid, urea, and sucrose) that was predictive of Mediterranean diet adherence (KIDMED) and ultra-processed food consumption and also had higher capacity in discriminating children’s diet quality than that of established sociodemographic determinants. Further, we showed that the identified metabolite panel also reflected the associations of these diet quality indicators with C-peptide, a stable and accurate marker of insulin resistance and future risk of metabolic disease. This methodology enables objective assessment of dietary patterns in European child populations, complementary to traditional questionary methods, and can be used in future studies to evaluate diet quality. Moreover, this knowledge can provide mechanistic evidence of common biological pathways that characterize healthy and unhealthy dietary patterns, and diet-related molecular alterations that could associate to metabolic disease.

[This corrects the article DOI: 10.1371/journal.pone.0186080.].

Brain imaging genomics has manifested considerable potential in illuminating the genetic determinants of human brain structure and function. This has propelled us to develop the GIANT (Genetically Informed brAiN aTlas) that accounts for genetic and neuroanatomical variations simultaneously. Integrating voxel-wise heritability and spatial proximity, GIANT clusters brain voxels into genetically informed regions, while retaining fundamental anatomical knowledge. Compared to conventional (non-genetics) brain atlases, GIANT exhibits smaller intra-region variations and larger inter-region variations in terms of voxel-wise heritability. As a result, GIANT yields increased regional SNP heritability, enhanced polygenicity, and its polygenic risk score explains more brain volumetric variation than traditional neuroanatomical brain atlases. We provide extensive validation to GIANT and demonstrate its neuroanatomical validity, confirming its generalizability across populations with diverse genetic ancestries and various brain conditions. Furthermore, we present a comprehensive genetic architecture of the GIANT regions, covering their functional annotation at the molecular levels, their associations with other complex traits/diseases, and the genetic and phenotypic correlations among GIANT-defined imaging endophenotypes. In summary, GIANT constitutes a brain atlas that captures the complexity of genetic and neuroanatomical heterogeneity, thereby enhancing the discovery power and applicability of imaging genomics investigations in biomedical science. GIANT, a genetically informed brain atlas, integrates genetic heritability with neuroanatomy. It shows strong neuroanatomical validity and surpasses traditional atlases in discovery power for brain imaging genomics.

Over the years, ComBAT has become the standard method for harmonizing MRI-derived measurements, with its ability to compensate for site-related additive and multiplicative biases while preserving biological variability. However, ComBAT relies on a set of assumptions that, when violated, can result in flawed harmonization. In this paper, we thoroughly review ComBAT’s mathematical foundation, outlining these assumptions, and exploring their implications for the demographic composition necessary for optimal results. Through a series of experiments involving a slightly modified version of ComBAT called Pairwise-ComBAT tailored for normative modeling applications, we assess the impact of various population characteristics, including population size, age distribution, the absence of certain covariates, and the magnitude of additive and multiplicative factors. Based on these experiments, we present five essential recommendations that should be carefully considered to enhance consistency and supporting reproducibility, two essential factors for open science, collaborative research, and real-life clinical deployment.

Correction to: Intensive Care Med (2016) 42:1865–1876 DOI 10.1007/s00134-016-4571-5