Utilizing a biology-driven approach to map the exposome in health and disease:An essential investment to drive the next generation of environmental discovery

BACKGROUND: Recent developments in technologies have offered opportunities to measure the exposome with unprecedented accuracy and scale. However, because most investigations have targeted only a few exposures at a time, it is hypothesized that the majority of the environmental determinants of chronic diseases remain unknown. OBJECTIVES: We describe a functional exposome concept and explain how it can leverage existing bioassays and high-resolution mass spectrometry for exploratory study. We discuss how such an approach can address well-known barriers to interpret exposures and present a vision of next-generation exposomics. DISCUSSION: The exposome is vast. Instead of trying to capture all exposures, we can reduce the complexity by measuring the functional exposome— the totality of the biologically active exposures relevant to disease development—through coupling biochemical receptor-binding assays with affinity purification–mass spectrometry. We claim the idea of capturing exposures with functional biomolecules opens new opportunities to solve critical problems in exposomics, including low-dose detection, unknown annotations, and complex mixtures of exposures. Although novel, biology-based measurement can make use of the existing data processing and bioinformatics pipelines. The functional exposome concept also complements conven-tional targeted and untargeted approaches for understanding exposure-disease relationships. CONCLUSIONS: Although measurement technology has advanced, critical technological, analytical, and inferential barriers impede the detection of many environmental exposures relevant to chronic-disease etiology. Through biology-driven exposomics, it is possible to simultaneously scale up discovery of these causal environmental factors. https://doi.org/10.1289/EHP8327

The urgent need for microbiology literacy in society

Contains fulltext : 204933.pdf (publisher's version ) (Open Access

Dynamics of the 16^{16}O(e,e'p) cross section at high missing energies

We measured the cross section and response functions (R_L, R_T, and R_LT) for the 16O(e,e'p) reaction in quasielastic kinematics for missing energies 25 60 MeV and P_miss > 200 MeV/c, the cross section is relatively constant. Calculations which include contributions from pion exchange currents, isobar currents and short-range correlations account for the shape and the transversity but only for half of the magnitude of the measured cross section

The genetic architecture of the human cerebral cortex

INTRODUCTION The cerebral cortex underlies our complex cognitive capabilities. Variations in human cortical surface area and thickness are associated with neurological, psychological, and behavioral traits and can be measured in vivo by magnetic resonance imaging (MRI). Studies in model organisms have identified genes that influence cortical structure, but little is known about common genetic variants that affect human cortical structure. RATIONALE To identify genetic variants associated with human cortical structure at both global and regional levels, we conducted a genome-wide association meta-analysis of brain MRI data from 51,665 individuals across 60 cohorts. We analyzed the surface area and average thickness of the whole cortex and 34 cortical regions with known functional specializations. RESULTS We identified 306 nominally genome-wide significant loci (P < 5 × 10−8) associated with cortical structure in a discovery sample of 33,992 participants of European ancestry. Of the 299 loci for which replication data were available, 241 loci influencing surface area and 14 influencing thickness remained significant after replication, with 199 loci passing multiple testing correction (P < 8.3 × 10−10; 187 influencing surface area and 12 influencing thickness). Common genetic variants explained 34% (SE = 3%) of the variation in total surface area and 26% (SE = 2%) in average thickness; surface area and thickness showed a negative genetic correlation (rG = −0.32, SE = 0.05, P = 6.5 × 10−12), which suggests that genetic influences have opposing effects on surface area and thickness. Bioinformatic analyses showed that total surface area is influenced by genetic variants that alter gene regulatory activity in neural progenitor cells during fetal development. By contrast, average thickness is influenced by active regulatory elements in adult brain samples, which may reflect processes that occur after mid-fetal development, such as myelination, branching, or pruning. When considered together, these results support the radial unit hypothesis that different developmental mechanisms promote surface area expansion and increases in thickness. To identify specific genetic influences on individual cortical regions, we controlled for global measures (total surface area or average thickness) in the regional analyses. After multiple testing correction, we identified 175 loci that influence regional surface area and 10 that influence regional thickness. Loci that affect regional surface area cluster near genes involved in the Wnt signaling pathway, which is known to influence areal identity. We observed significant positive genetic correlations and evidence of bidirectional causation of total surface area with both general cognitive functioning and educational attainment. We found additional positive genetic correlations between total surface area and Parkinson’s disease but did not find evidence of causation. Negative genetic correlations were evident between total surface area and insomnia, attention deficit hyperactivity disorder, depressive symptoms, major depressive disorder, and neuroticism. CONCLUSION This large-scale collaborative work enhances our understanding of the genetic architecture of the human cerebral cortex and its regional patterning. The highly polygenic architecture of the cortex suggests that distinct genes are involved in the development of specific cortical areas. Moreover, we find evidence that brain structure is a key phenotype along the causal pathway that leads from genetic variation to differences in general cognitive function

The predictive value of polls in a fragmented multi-party system: the Netherlands (1998–2021)

Although vote intention polls are often used in the public debate as forecasts of future election outcomes, their predictive value has been subjected to scholarly inquiry. This research note contributes to the literature by assessing the predictive value of vote intention polls simultaneously at the macro-level (polls), meso-level (parties), and micro-level (voters). We analyse polls presented by the main polling agencies in the Netherlands (covering seven election cycles between 1998 and 2021), as well as micro-level panel data (covering 27,572 respondents and 46 polls between 2006 and 2010, and 35,574 respondents and 31 polls between 2010 and 2012). We reach three main conclusions. First, vote intention polls in the Netherlands generally do not provide more information than the previous election outcome, until the last few weeks of an election cycle. Second, the predictive accuracy of vote intention polls is lower for challenger parties than for non-challenger parties, particularly midway through the election cycle. Third, the predictive value for individual voters is generally very low, until the last few months before the election. Institutions, Decisions and Collective Behaviou

Utilizing a biology-driven approach to map the exposome in health and disease: An essential investment to drive the next generation of environmental discovery

BACKGROUND: Recent developments in technologies have offered opportunities to measure the exposome with unprecedented accuracy and scale. However, because most investigations have targeted only a few exposures at a time, it is hypothesized that the majority of the environmental determinants of chronic diseases remain unknown. OBJECTIVES: We describe a functional exposome concept and explain how it can leverage existing bioassays and high-resolution mass spectrometry for exploratory study. We discuss how such an approach can address well-known barriers to interpret exposures and present a vision of next-generation exposomics. DISCUSSION: The exposome is vast. Instead of trying to capture all exposures, we can reduce the complexity by measuring the functional exposome— the totality of the biologically active exposures relevant to disease development—through coupling biochemical receptor-binding assays with affinity purification–mass spectrometry. We claim the idea of capturing exposures with functional biomolecules opens new opportunities to solve critical problems in exposomics, including low-dose detection, unknown annotations, and complex mixtures of exposures. Although novel, biology-based measurement can make use of the existing data processing and bioinformatics pipelines. The functional exposome concept also complements conven-tional targeted and untargeted approaches for understanding exposure-disease relationships. CONCLUSIONS: Although measurement technology has advanced, critical technological, analytical, and inferential barriers impede the detection of many environmental exposures relevant to chronic-disease etiology. Through biology-driven exposomics, it is possible to simultaneously scale up discovery of these causal environmental factors. https://doi.org/10.1289/EHP8327

Utilizing a biology-driven approach to map the exposome in health and disease: An essential investment to drive the next generation of environmental discovery

BACKGROUND: Recent developments in technologies have offered opportunities to measure the exposome with unprecedented accuracy and scale. However, because most investigations have targeted only a few exposures at a time, it is hypothesized that the majority of the environmental determinants of chronic diseases remain unknown. OBJECTIVES: We describe a functional exposome concept and explain how it can leverage existing bioassays and high-resolution mass spectrometry for exploratory study. We discuss how such an approach can address well-known barriers to interpret exposures and present a vision of next-generation exposomics. DISCUSSION: The exposome is vast. Instead of trying to capture all exposures, we can reduce the complexity by measuring the functional exposome— the totality of the biologically active exposures relevant to disease development—through coupling biochemical receptor-binding assays with affinity purification–mass spectrometry. We claim the idea of capturing exposures with functional biomolecules opens new opportunities to solve critical problems in exposomics, including low-dose detection, unknown annotations, and complex mixtures of exposures. Although novel, biology-based measurement can make use of the existing data processing and bioinformatics pipelines. The functional exposome concept also complements conven-tional targeted and untargeted approaches for understanding exposure-disease relationships. CONCLUSIONS: Although measurement technology has advanced, critical technological, analytical, and inferential barriers impede the detection of many environmental exposures relevant to chronic-disease etiology. Through biology-driven exposomics, it is possible to simultaneously scale up discovery of these causal environmental factors. https://doi.org/10.1289/EHP8327

Sex- and age-specific associations between cardiometabolic risk and white matter brain age in the UK Biobank cohort

Cardiometabolic risk (CMR) factors are associated with accelerated brain aging and increased risk for sex-dimorphic illnesses such as Alzheimer's disease (AD). Yet, it is unknown how CMRs interact with sex and apolipoprotein E-ϵ4 (APOE4), a known genetic risk factor for AD, to influence brain age across different life stages. Using age prediction based on multi-shell diffusion-weighted imaging data in 21,308 UK Biobank participants, we investigated whether associations between white matter Brain Age Gap (BAG) and body mass index (BMI), waist-to-hip ratio (WHR), body fat percentage (BF%), and APOE4 status varied (i) between males and females, (ii) according to age at menopause in females, and (iii) across different age groups in males and females. We report sex differences in associations between BAG and all three CMRs, with stronger positive associations among males compared to females. Independent of APOE4 status, higher BAG (older brain age relative to chronological age) was associated with greater BMI, WHR, and BF% in males, whereas in females, higher BAG was associated with greater WHR, but not BMI and BF%. These divergent associations were most prominent within the oldest group of females (66–81 years), where greater BF% was linked to lower BAG. Earlier menopause transition was associated with higher BAG, but no interactions were found with CMRs. In conclusion, the findings point to sex- and age-specific associations between CMRs and brain age. Incorporating sex as a factor of interest in studies addressing CMR may promote sex-specific precision medicine, consequently improving health care for both males and females

Progressive diastolic dysfunction in survivors of pediatric differentiated thyroid carcinoma

BackgroundPediatric differentiated thyroid cancer (DTC) has an excellent prognosis but unknown late effects of treatment. The initial cardiac evaluation showed subclinical diastolic dysfunction in 20% of adult survivors. The objective of this follow-up study was to determine the clinical course of this finding. MethodsThis multicenter study, conducted between 2018 and 2020, re-evaluated survivors after 5 years. The primary endpoint was echocardiographic diastolic cardiac function (depicted by the mean of the early diastolic septal and early diastolic lateral tissue velocity (e' mean)). Secondary endpoints were other echocardiographic parameters and plasma biomarkers. ResultsFollow-up evaluation was completed in 47 (71.2%) of 66 survivors who had completed their initial evaluation. Of these 47 survivors, 87.2% were women. The median age was 39.8 years (range: 18.8-60.3), and the median follow-up after the initial diagnosis was 23.4 years (range: 10.2-48.8). Between the first and second evaluation, the e' mean significantly decreased by 2.1 cm/s (s.d. 2.3 cm/s, P < 0.001). The median left ventricular ejection fraction did not significantly change (58.0% vs 59.0%, P= NS). In the best explanatory model of e' mean, multivariate linear regression analysis showed that BMI and age were significantly associated with e' mean (beta coefficient: -0.169, 95% CI: -0.292; -0.047, P = 0.008 and beta coefficient: -0.177, 95% CI: -0.240; -0.113, P < 0.001, respectively). Conclusions and relevanceIn these relatively young survivors of pediatric DTC, diastolic function decreased significantly during 5-year follow-up and is possibly more pronounced than in normal aging. This finding requires further follow-up to assess clinical consequences