Exposure Data Research Articles

Meta-Analysis of Exposure-Adverse Event Relationships for Antibody-Drug Conjugates.

Antibody-drug conjugates (ADCs) have become a vital class of therapeutics in oncology because of their ability to selectively deliver potent drug molecules to tumor cells. However, ADC-associated toxicities cause high failure rates in the clinic and hinder their full potential. Due to the complex structure and pharmacokinetics of ADCs, it is challenging to identify the drivers of their toxicities. Here, quantitative analysis was performed to correlate the incidence of clinical adverse events (AEs) with nine different commonly measured exposure parameters collected from study-level summary data. We considered ADC analytes for different classes of ADCs, to identify ADC analytes that are strongly associated with the AEs for ADCs. Published clinical exposure and safety data for any grade and grade ≥3 AEs from 40 publications across six ADCs and three payloads were collected and analyzed. Exposure-AE relationships were quantified using logit models, and the strength of the correlations and rank order were determined. The analysis suggests that deruxtecan ADC-related toxicities correlated most strongly with the exposure of the free payload; monomethyl auristatin E (MMAE) ADC-related toxicities correlated with the free MMAE area under the curve; and pyrrolobenzodiazepine ADC-related toxicities correlated with no specific analyte but the dose. These findings agree with the published literature and support the notion that AE profiles are often shared by ADCs that deliver the same cytotoxic payload. The exposure-AE relationships presented here, together with identification of the most informative ADC analytes, may facilitate more focused mechanistic studies on the drivers of clinical AEs and could support dosing decisions during clinical development of ADCs.

Inundation and evacuation of shoreline populations during landslide-triggered tsunamis: an integrated numerical and statistical hazard assessment

Abstract. The volcanic island of Stromboli (southern Tyrrhenian Sea, Italy) is renowned for its persistent, periodic, low-intensity explosive activity, whose spectacular manifestations attract tens of thousands of tourists every year. However, sporadic more intense major explosive and effusive eruptions and paroxysms pose serious threats to the island. In addition to direct hazards, granular slides of volcanic debris and pyroclastic avalanches, which can rapidly reach the sea and potentially generate tsunamis, are often associated with such unpredictable eruptive activity. Due to the very fast propagation of the tsunami around the island and the consequent short tsunami warning time (ranging from less than a minute to only a few minutes), mitigation efforts and evacuation from the Strombolian coast must be carefully planned. In this paper, we describe a new GIS-assisted procedure that allows us to combine the outputs of an ensemble of 156 pre-computed landslide-generated tsunami hazard scenarios (with variable landslide volume, position, and density), statistical exposure data (i.e. the number of inhabitants and tourists), and digital geographic information to obtain a quantitative (scenario-based) risk analysis. By means of the analysis of the road network and coastal morphology, we develop a model with routes and times to reach a safe area from every pixel in the inundated area and an appraisal of the time needed to escape versus the wave arrival time. This allows us to evaluate and quantify the effectiveness of potential risk mitigation by means of evacuation. The creation of an impact score linking the predicted inundation extent and the tsunami warning signals is intended, in the long term, to be used to predict the intensity of future tsunamis and to adapt evacuation plans accordingly. The model, here applied to Stromboli, is general and can be applied to other volcanic islands. Evacuating an island hosting several thousand tourists every summer with very little warning time underlines the absolute necessity for such mitigation efforts, aimed at informing hazard planners and managers and all other stakeholders.

Ambient air pollution, urban green space and childhood overweight and obesity: A health impact assessment for Barcelona, Spain

BackgroundThe burden of childhood overweight and obesity attributable to ambient air pollution and a lack of urban green spaces (UGS) remains unknown. This study aimed to estimate the attributable cases of childhood overweight and obesity due to air pollution and insufficient UGS exposure in Barcelona, Spain. MethodsWe applied a quantitative health impact assessment approach. We collected childhood overweight and obesity prevalence levels and exposure data from 69 spatial basic health zones in Barcelona. We estimated particulate matter (PM2.5) and nitrogen dioxide (NO2) levels using land use regression models, normalized difference vegetation index (NDVI) levels using remote sensing and percentage of green area (%GA) using land use. We estimated relative risks, population attributable fractions, and preventable overweight/obesity cases in children under following scenarios: Compliance of World Health Organization (WHO) air quality guidelines (AQGs) for (1) PM2.5 and (2) NO2; achieving (3) city-target NDVI levels and (4) 25% green area (%GA) recommendations. The analyses were stratified by socioeconomic deprivation index (in quintiles). Uncertainty was quantified using Monte Carlos simulations. ResultsCompliance of WHO AQGs could prevent 0.4% [253 (95%CI, −604; 1086)] and 4.2% [3000 (95%CI, 1009; 4943)] of childhood overweight/obesity cases due to excess PM2.5 and NO2 levels in Barcelona, respectively. Compliance of NDVI and %GA targeted levels could prevent 6% [4094 (95%CI, 1698; 6379)] and 10% [6853 (95%CI, 1440; 12779)] of childhood overweight/obesity cases respectively. The preventable burdens of childhood overweight/obesity cases were slightly higher in middle-class socioeconomic areas due to the higher adverse exposure levels at baseline (high air pollution, less UGS). DiscussionCompliance with WHO AQGs and achieving UGS targets can reduce childhood overweight and obesity levels in Barcelona, and potentially in other locations as well. This underscores the need for policies that foster healthier urban environments of high environmental quality in order to protect child health.

Is environmental risk assessment possible with the alternatives to acute fish toxicity test? Case study with pharmaceuticals

BackgroundAcute fish toxicity test (AFT) is one of the cornerstones of environmental risk assessment (ERA) of chemicals for the aquatic environment. Despite many efforts to find an alternative able to fully replace the test, there is still lasting pressure from stakeholders for AFT results. ResultsHere, we present the results of a case study with eight pharmaceuticals from various pharmaceutical groups with different levels of expected toxicity to fish. Selected compounds were tested in two validated alternative tests—fish embryo toxicity test with zebrafish (Danio rerio) (zFET) and in vitro RTgill-W1 assay according to their corresponding OECD guidelines TG 236 and TG 249, respectively. Data for AFT were collected from PubMed and ECOTOX knowledgebase databases, and acute toxicity to fish was further predicted in silico by the ECOSAR program. Predicted environmental risks (risk quotients, RQ, calculated using the exposure data from NORMAN) from both zFET and RTgill-W1 well correlated with the average RQs based on AFT LC50s. The strongest and most significant correlation was observed while comparing the AFT results with the median of combined alternative methods (zFET, RTgill-W1, ECOSAR).ConclusionsThis proposed approach combining experimental data with modeling could serve as a reliable tool for predictions of environmental risks promoting the 3R alternatives to acute fish toxicity testing.

Causal relationship between cortical structural changes and onset of anxiety disorder: evidence from Mendelian randomization.

Previous studies have reported a correlation between anxiety disorders and changes in brain structure, yet the specific alterations in brain region volumes remain unclear. This study aimed to infer the causal relationship between anxiety disorders and changes in brain structure volume through Mendelian Randomization analysis. We selected 63 cortical structure volumes from the GWAS database as exposure data and anxiety disorder data from the FinnGen and UK Biobank databases as outcomes. We found a significant correlation between atrophy in the Left precentral volume area (Odds Ratio [OR] = 0.935, 95% Confidence intervals [CI]: 0.891-0.981, P value, P = 0.007) and an increased risk of anxiety disorders. Additionally, changes identified in specific brain regions, such as atrophy in the Right rostral anterior cingulate area (OR = 0.993, 95% CI: 0.987-0.999, P = 0.025) and increased volume in the Left superior parietal area (OR = 1.001, 95% CI: 1.000-1.001, P = 0.028), may correlate with an increased risk of anxiety disorders. Furthermore, both phenotypes demonstrated directional consistency in their respective and overall meta-analyzed OR values pre- and post-merger, enhancing the reliability of the results. This study elucidates the causal relationship between anxiety disorders and specific brain structures, providing new insights for further research into psychiatric disorders.

Analyzing the causal role of blood cells in aging: a Mendelian randomization study.

Blood cells are crucial components of the human body, closely linked to the aging process. This study aims to explore the causal relationship between 91 blood cell phenotypes and aging through Mendelian randomization (MR) analysis. Exposure data from genome-wide association studies (GWAS) was extracted from the GWAS of blood cell perturbation phenotypes in 2,600 European individuals. Initial analysis utilized GWAS data related to aging from the GWAS Catalog database GCST90014288, with inverse-variance weighting as the primary method for causal analysis. Sensitivity analyses included Cochran's Q test, MR-Egger intercept test, MR-PRESSO, and leave-one-out analysis. For significant associations, replication and meta-analysis were conducted using independent aging GWAS data from GCST90014300. Initial analysis revealed that environmental peroxide-impacted red blood cells and ciprofloxacin-impacted reticulocytes accelerated aging. Additionally, elevated neutrophil levels were found to accelerate aging, while LiCl-impacted neutrophils reduced aging risk. Replication and meta-analysis showed consistent results: ciprofloxacin-impacted reticulocytes and elevated neutrophil levels increased the risk of aging, while LiCl-impacted neutrophils reduced the risk. RBCs showed no significant impact on aging progression. Sensitivity analyses confirmed the robustness and reliability of these positive findings. Our study provides evidence of a causal relationship between three blood cell disturbance phenotypes and human aging.

Pesticides and prostate cancer incidence and mortality: An environment-wide association study.

Prostate cancer is the most common cancer among men in the United States, yet modifiable risk factors remain elusive. In this study, the authors investigated the potential role of agricultural pesticide exposure in prostate cancer incidence and mortality. For this environment-wide association study (EWAS), linear regression was used to analyze county-level associations between the annual use of 295 distinct pesticides (measured in kg per county) and prostate cancer incidence and mortality rates in the contiguous United States. Data were analyzed in two cohorts: 1997-2001 pesticide use with 2011-2015 outcomes (discovery) and 2002-2006 use with 2016-2020 outcomes (replication). The reported effect sizes highlight how a 1-standard-deviation increase in log-transformed pesticide use (kg per county) corresponds to changes in incidence. Analyses were adjusted for county-level demographics, agricultural data, and multiple testing. Twenty-two pesticides showed consistent, direct associations with prostate cancer incidence across both cohorts. Of these, four pesticides were also associated with prostate cancer mortality. In the replication cohort, each 1-standard-deviation increase in log-transformed pesticide use corresponded to incidence increases per 100,000 individuals (trifluralin, 6.56 [95% confidence interval (CI), 5.04-8.07]; cloransulam-methyl, 6.18 [95% CI, 4.06-8.31]; diflufenzopyr, 3.20 [95% CI, 1.09-5.31]; and thiamethoxam, 2.82 [95% CI, 1.14-4.50]). Limitations included ecological study design, potential unmeasured confounding, and lack of individual-level exposure data. The results of this study suggest a potential link between certain pesticides and increased prostate cancer incidence and mortality. These findings warrant further investigation of these specific pesticides to confirm their role in prostate cancer risk and to develop potential public health interventions.

Multiresolution Analysis of HRRR Meteorological Parameters and GOES-R AOD for Hourly PM2.5 Prediction.

High-resolution, comprehensive exposure data are crucial for accurately estimating the human health impact of PM2.5. In recent years, satellite remote sensing data have been increasingly utilized in PM2.5 models to overcome the limited spatial coverage of ground monitoring stations. However, data gaps in satellite-retrieved parameters such as aerosol optical depth (AOD), the sparsity of regulatory air quality monitors for model training, and nonlinear relationships between PM2.5 and meteorological conditions can affect model performance and cause data gaps in most existing PM2.5 models. In this study, spatial gaps in AOD obtained from Geostationary Operational Environmental Satellite-16 are filled using Goddard Earth Observing System Composition Forecasting AOD estimations. Furthermore, to improve model performance, meteorological predictors such as temperature from the High-Resolution Rapid Refresh model are preprocessed using Daubechies wavelet to extract low and high frequency components. The spatially gap-filled AOD, along with meteorological data, are ingested into a machine learning model to predict hourly PM2.5 at a 1 km spatial resolution in California. The model evaluation metrics (OOB (out-of-bag) R2 = 0.86 and RMSE (root-mean-square error) = 9.27 μg/m3 and 10-fold spatial cross-validation R2 = 0.82 and RMSE = 9.82 μg/m3) demonstrate the model's reliability in predicting ambient PM2.5, especially for states like California that experience frequent episodes of wildfires.

Quantifying residents' exposure to agricultural pesticides using new geospatial approaches

A better understanding of environmental exposure to agricultural pesticides is crucial for public health, regulatory and management purposes. Residents in close vicinity to agricultural fields are likely to be more exposed to pesticides. In that context, an innovative geospatial approach for mapping estimates of agricultural pesticide exposure was developed in this study.Data on pesticide application rates, high-resolution annual datasets of the geographic distribution of crops, and the 100x100m grid population dataset were utilized to complete this analysis in Wallonia (Belgium) over the period 2015-2019. Pesticide exposure metrics were estimated using a buffer-based exposure model by conducting neighborhood analysis within a 1000m buffer radius. Subsequently, a population weighted method was used to ‘up-scale’ the exposure data to administrative levels. In a limited validation effort, model estimates were compared with pesticide measurements in air collected at 12 stations during the period 2015-2016.The results present the first modeling map of environmental exposure to agricultural pesticides for the whole of the Walloon region. The northern part of the Sambre-Meuse axis demonstrates more intensive agriculture and the highest pesticide weighted exposure indices. The majority of the population resides near agricultural areas, with only 4% living in regions that lack crops within a 1000m radius, primarily in the central areas of large cities. A positive trend association between pesticide measures in air and the index was observed at the different stations, nevertheless further validation efforts are needed to accurately compare the same active ingredients.This work gives a valuable basis for research and environmental health actions in Belgium. Maps highlight areas where human biomonitoring and epidemiological studies should be implemented to investigate the impact of potential environmental exposure to pesticides. This information helps policy-makers to take measures to control and reduce the load of agricultural pesticides in the environment.

Factors Associated with Impact of Event Scores Among Ontario Education Workers During the COVID-19 Pandemic

There is limited information regarding factors related to education workers’ responses to traumatic stress during the COVID-19 pandemic. The study goal was to determine whether personal factors, behaviours that mitigate viral spread, and work-related factors were associated with post-traumatic symptoms. This observational study, embedded within a cohort study, recruited Ontario education workers from February 2021 to June 2023. Exposure data were collected at enrollment and updated annually. Participants completed the Impact of Event Scale (IES) at withdrawal/study completion. Modified Poisson regression was used to build hierarchical models of dichotomized IES scores (≥26: moderate/severe post-traumatic symptoms). Of the 1518 education workers who submitted an IES between September 2022 and December 2023, the incidence rate ratio of IES scores ≥26 was significantly higher among participants who usually/always wore a mask at work (1.48; 95% confidence interval 1.23, 1.79), usually/always practiced physical distancing (1.31; 1.06, 1.62), lived in larger households (1.06; 1.01, 1.12), and reported poor/fair/good health (1.27; 1.11, 1.46). However, models accounted for little of the variance in IES scores, suggesting the need for future studies to collect data on other factors associated with the development of PTSD, such as pre-existing mental health challenges. Early identification of those experiencing traumatic stress and the implementation of stress reduction strategies are needed to ensure the ongoing health of education workers.

Causal Mediation Analysis for Integrating Exposure, Genomic, and Phenotype Data

Causal mediation analysis provides an attractive framework for integrating diverse types of exposure, genomic, and phenotype data. Recently, this field has seen a surge of interest, largely driven by the increasing need for causal mediation analyses in health and social sciences. This article aims to provide a review of recent developments in mediation analysis, encompassing mediation analysis of a single mediator and a large number of mediators, as well as mediation analysis with multiple exposures and mediators. Our review focuses on the recent advancements in statistical inference for causal mediation analysis, especially in the context of high-dimensional mediation analysis. We delve into the complexities of testing mediation effects, especially addressing the challenge of testing a large number of composite null hypotheses. Through extensive simulation studies, we compare the existing methods across a range of scenarios. We also include an analysis of data from the Normative Aging Study, which examines DNA methylation CpG sites as potential mediators of the effect of smoking status on lung function. We discuss the pros and cons of these methods and future research directions.

Role of double-negative autoreactive cells (CD4−CD8−) in phosphatidylcholine-mediated rheumatoid arthritis: A Mendelian randomization study

ObjectiveMendelian randomization (MR) was employed to explore the potential causal relationship between liposomes (LP) and rheumatoid arthritis (RA), with a focus on the mediating roles of immune cells (IC). MethodsBy screening public GWAS data, LP were used as exposure data, RA as outcome data, and IC as mediating factors. The Inverse Variance Weighted (IVW) method was the main analytical technique used in this paper to evaluate causal effects. Additional techniques included the MR-Egger, weighted median, weighted mode, and simple mode methods. Cochran's Q and MR-Egger were utilized for heterogeneity and multi-effect analysis. ResultsPhosphatidylcholine was revealed to enhance the risk of RA by MR analysis (P = 0.013, OR = 1.073, 95%CI = 1.015–1.136). There was no strong evidence that RA could affect liposome changes (IVW: P = 0.705, OR = 0.992, 95%CI = 0.952–1.034). The IVW method showed that increased levels of phosphatidylcholine were notably linked to higher levels of Double-Negative Autoreactive Cells (CD4−CD8−, DNAC) (P = 0.006, OR = 1.152, 95%CI = 1.041–1.276). The IVW approach showed that increased levels of DNAC were substantially linked to a higher risk of RA (P = 0.001, OR = 1.105, 95%CI = 1.041–1.173). Of the genetically predicted liposomes mediated by DNAC, 19.2 % were found. ConclusionThe present work established a causal association between LP and RA, and identified a potential mediating influence of IC. However, the specific mechanism of LP triacyl-glycerol and IC on RA is still unclear, and further research is needed.

Source attribution of Listeria monocytogenes in the Netherlands

The aim of this study was to determine the relative contributions of various potential food sources of human listeriosis and to identify source-specific risk factors, at exposure level, for human Listeria monocytogenes (Lm) infection. To achieve this, available Lm isolates from human cases (n = 756) and food/animal sources (n = 950) from national surveillance systems in the Netherlands (2010−2020) were whole genome sequenced. Additionally, questionnaire-based exposure data for human cases was collected. Source attribution analysis was performed using a Random Forest model based on core-genome multilocus sequence typing (cgMLST). Risk factors for human Lm infection of cattle, chicken and seafood origin were determined using beta regression analysis on the cgMLST-based attribution estimates. Results indicated that the 756 human Lm isolates were mainly attributed to cattle (62.3 %), chicken (19.4 %), and seafood (16.9 %). Specifically, fresh meat (86.2 %), including fresh bovine meat (43.7 %) and fresh chicken meat (39.3 %), accounted for most cases. These attributions stemmed from Lm contamination of either the food products or their production environments. Consumption of steak tartare and smoked salmon was associated with an increased risk of human Lm infections attributed to cattle and seafood, respectively, while no specific risk factors for chicken-borne listeriosis were identified. This study indicated that Lm isolates of cattle origin, particularly those from fresh bovine meat and associated production environments, are estimated to be the primary cause of human listeriosis in the Netherlands. This aligns with several other European source attribution studies on Lm. Moreover, the identified risk factors for human Lm infection from cattle (i.e. steak tartare) and seafood (i.e. smoked salmon) clearly indicated their attributable sources. This joint analysis of core genome and epidemiological data provided novel insights into the origins and transmission pathways of human listeriosis.

Novel R package to quantify and monetize burden of disease attributable to air pollution and noise

Abstract Background Ambient air pollution and noise substantially impact public health and the economy. Different methods to quantify these impacts are described in the literature. Objectives Within the European project BEST-COST (Burden of disease-based methods for estimating the socio-economic cost of environmental stressors), we aim to develop an open-source R package to quantify and monetise the disease burden attributable to air pollution and noise, including the impact of social inequalities. Methods The developed code will allow for different calculation pathways including: single baseline health data vs. age-specific estimates from life tables; relative and absolute risk; scenario comparison based on the population attributable fraction vs. population impact fraction; single-pollutant vs. correlated exposures; and outdoor vs. indoor air pollution. Moreover, the package can digest a variety of input data formats for the exposure-response function (e.g. relative risk with fixed shape vs. user-defined function) and exposure data (e.g. population-weighted mean vs. categorical distribution, spatial data). Eligible impact metrics will include morbidity incidence and prevalence, number of deaths, years of life lost, and years lived with disability. Finally, the R package will enable quantifying social inequities (using a novel multiple deprivation index) and monetising the attributable disease burden (using a state-of-the-art approach). Results During development, the R package will be tested in BEST-COST case studies in 5 European countries (BE, EE, FR, NO, PT), compared with existing tools, and discussed with key users in a workshop. The final R package will be available on GitHub in 2026. Conclusions This tool will allow the quantification and monetisation of the health impact of air pollution and noise. The R package will be a flexible programming resource that can be used as a standalone or combined with existing tools to build a more solid basis for evidence-based policy making. Key messages • This open-source tool will allow the quantification and monetisation of the health impact of air pollution and noise, including the impact of social inequalities. • The R package will be a flexible programming resource that can be used as a standalone or combined with existing tools to build a more solid basis for evidence-based policy making.

Opinion: How will advances in aerosol science inform our understanding of the health impacts of outdoor particulate pollution?

Abstract. Air pollution, characterized by high levels of particulate matter (PM), poses the greatest environmental threat to human health, causing an estimated 7 million deaths annually and accounting for 5 % of the global gross domestic product (GDP). While the health impacts of PM are influenced by the toxicity of its individual chemical constituents, the mortality burden of PM is solely based on its total mass concentration. This is because of a lack of large-scale, high-resolution data on PM chemical composition, needed for epidemiological assessments. Identifying which PM constituents are harmful to health has been the “holy grail” of atmospheric science since the landmark 1993 study on six US cities established a definitive link between PM and mortality. Ever since, atmospheric scientists have focused on understanding aerosol composition, emission sources, and formation pathways, while longitudinal epidemiological studies have required individual-level exposure data, employing land use regression models for the prediction of exposures at fine resolutions. In this opinion article, we argue that the time has come to shift the focus towards incorporating PM chemical composition into epidemiological health assessments, laying the foundation for the development of new regulatory metrics. This shift will enable the creation of targeted guidelines and subsequent regulations, prioritizing mitigation efforts against the most harmful anthropogenic emissions. Central to this shift is the availability of global, long-term, high-resolution data on PM chemical composition that are obtained through field observations and modelling outputs. In the article, we underscore key milestones within aerosol science that have been integral for advancing this foundational shift. Specifically, we examine emerging modelling tools for estimating exposure to individual PM components, present the type of ambient observations needed for model developments, identify key gaps in our fundamental understanding of emissions and their atmospheric transformation, and propose advancing cross-disciplinary collaboration between aerosol scientists and epidemiologists to understand the health impacts of individual PM components. We contend that aerosol science has now reached a pivotal moment in elucidating the differential health impacts of PM components, representing a first step towards their incorporation into air quality guidelines.

Shared and unique lifetime stressor characteristics and brain networks predict adolescent anxiety and depression.

Exposure to major life stressors and aberrant brain functioning have been linked to anxiety and depression, especially during periods of heighted functional brain plasticity, such as adolescence. However, it remains unclear if specific characteristics of major life stressors and functional network disruptions differentially predict anxiety and depression symptoms over time and, if so, whether they act independently or jointly. We collected baseline lifetime stressor exposure data and resting-state functional magnetic resonance imaging data in a longitudinal sample of 107 adolescents enriched for anxiety and depressive disorders. We examined five stressor characteristics: physical danger, interpersonal loss, humiliation, entrapment, and role change/disruption. Anxiety and depression symptoms were assessed at baseline, 6-month and 12-month follow-ups. Linear mixed effect models tested if these stressor characteristics, functional connectivity within and between frontoparietal, default, and ventral attention networks, and their interactions differentially predicted anxiety and depression symptoms at 6-month and 12-month follow-ups. Greater lifetime severity of physical danger and humiliation prospectively predicted increased anxiety symptoms at both follow-ups, whereas greater lifetime entrapment severity prospectively predicted higher anxiety and depression symptoms. Only the effects of lifetime entrapment severity were robust to including within- and between-network functional connectivity metrics and other significantly predictive stressor characteristics. Lifetime entrapment severity more strongly predicted anxiety symptoms in youth with higher default network connectivity. Greater functional connectivity between frontoparietal and default networks prospectively predicted increased depression symptoms. Taken together, these results underscore the critical importance of using stressor characteristics and functional connectivity jointly to study predictors for adolescent anxiety and depression.

Mendelian randomization and multi-omics approach analyses reveal impaired glucose metabolism and oxidative phosphorylation in visceral adipose tissue of women with polycystic ovary syndrome.

What is the significance of visceral adipose tissue (VAT) in the pathogenesis of polycystic ovary syndrome (PCOS) and its impact on the regulation of metabolic disorders in women with PCOS? We revealed a potentially causal relationship between increased genetically predicted VAT and PCOS-related traits, and found that VAT exhibited impaired glucose metabolism and mitochondrial oxidative phosphorylation (OXPHOS) in women with PCOS. PCOS is a common reproductive endocrine disorder accompanied by many metabolic abnormalities. Adipose tissue is a metabolically active endocrine organ that regulates multiple physiological processes, and VAT has a much stronger association with metabolism than subcutaneous adipose tissue does. Mendelian randomization (MR) analysis was used to investigate the potential causal association between genetically predicted VAT and the risk of PCOS. Data for MR analysis were extracted from European population cohorts. VAT samples from sixteen PCOS patients and eight control women who underwent laparoscopic surgery were collected for proteomics and targeted metabolomics analyses. PCOS was diagnosed according to the 2003 Rotterdam Criteria. The control subjects were women who underwent laparoscopic investigation for infertility or benign indications. Proteomics was performed by TMT labeling and liquid chromatography-tandem mass spectrometry analysis, and targeted metabolomics was performed by ultra-performance liquid chromatography-tandem mass spectrometry analysis. The key differentially expressed proteins (DEPs) were validated by immunoblotting. MR analysis revealed a potentially causal relationship between increased genetically predicted VAT and PCOS, as well as related traits, such as polycystic ovaries, total testosterone, bioavailable testosterone, and anti-Müllerian hormone, while a negative relationship was found with sex hormone-binding globulin. Enrichment pathway analysis of DEPs indicated the inhibition of glycolysis and activation of mitochondrial OXPHOS in the VAT of PCOS patients. MR analysis revealed that key DEPs involved in glycolysis and OXPHOS were significantly linked to PCOS and its related traits. Dot blot assay confirmed a significant decrease in glycolysis enzymes PKM2 and HK1, and an increase in mitochondrial Complex I and III subunits, NDUFS3 and UQCR10. Moreover, metabolomics analysis confirmed down-regulated metabolites of energy metabolic pathways, in particular glycolysis. Further analysis of PCOS and control subjects of normal weight revealed that dysregulation of glucose metabolism and OXPHOS in VAT of women with PCOS was independent of obesity. The mass spectrometry proteomics data have been deposited to the iProX database (http://www.iprox.org) with the iProX accession: IPX0005774001. There may be an overlap in some exposure and outcome data, which might affect the results in the MR analysis. The changes in protein expression of key enzymes affect their activities and disrupt the energy metabolic homeostasis in VAT, providing valuable insight for identifying potential intervention targets of PCOS. This work was supported by the National Key Research and Development Project of China (2021YFC2700402), the National Natural Science Foundation of China (82071608, 82271665), the Key Clinical Projects of Peking University Third Hospital (BYSY2022043), and the CAMS Innovation Fund for Medical Sciences (2019-I2M-5-001). All authors report no conflict of interest. N/A.

Identifying cardiovascular disease risk in the U.S. population using environmental volatile organic compounds exposure: A machine learning predictive model based on the SHAP methodology

BackgroundCardiovascular disease (CVD) remains a leading cause of mortality globally. Environmental pollutants, specifically volatile organic compounds (VOCs), have been identified as significant risk factors. This study aims to develop a machine learning (ML) model to predict CVD risk based on VOC exposure and demographic data using SHapley Additive exPlanations (SHAP) for interpretability. MethodsWe utilized data from the National Health and Nutrition Examination Survey (NHANES) from 2011 to 2018, comprising 5098 participants. VOC exposure was assessed through 15 urinary metabolite metrics. The dataset was split into a training set (70 %) and a test set (30 %). Six ML models were developed, including Random Forest (RF), Light Gradient Boosting Machine (LightGBM), Decision Tree (DT), Extreme Gradient Boosting (XGBoost), Multi-Layer Perceptron (MLP), and Support Vector Machines (SVM). Model performance was evaluated using the Area Under the Receiver Operating Characteristic Curve (AUROC), accuracy, balanced accuracy, F1 score, J-index, kappa, Matthew's correlation coefficient (MCC), positive predictive value (PPV), negative predictive value (NPV), sensitivity (sens), specificity (spec) and SHAP was applied to interpret the best-performing model. ResultsThe RF model exhibited the highest predictive performance with an ROC of 0.8143. SHAP analysis identified age and ATCA as the most significant predictors, with ATCA showing a protective effect against CVD, particularly in older adults and those with hypertension. The study found a significant interaction between ATCA levels and age, indicating that the protective effect of ATCA is more pronounced in older individuals due to increased oxidative stress and inflammatory responses associated with aging. E-values analysis suggested robustness to unmeasured confounders. ConclusionsThis study is the first to utilize VOC exposure data to construct an ML model for predicting CVD risk. The findings highlight the potential of combining environmental exposure data with demographic information to enhance CVD risk prediction, supporting the development of personalized prevention and intervention strategies.

Use of renin-angiotensin system blockers and posttraumatic stress disorder risk in the UK Biobank: a retrospective cohort study

BackgroundPrevious research has shown that the use of renin-angiotensin system (RAS) blockers is linked to a lower prevalence of posttraumatic stress disorder (PTSD), but longitudinal studies are scarce. We aimed to estimate the association between the use of RAS blockers and the risk of PTSD among individuals taking antihypertensive medications.MethodsThis longitudinal study included participants aged 40–69 from the UK Biobank. Exposure data were obtained from the initial assessment (2006–10), while outcome data were obtained from the online mental health questionnaire administered 6–11 years later (2016–17). We included participants who were under antihypertensive treatment and did not have a prior diagnosis of PTSD before the initial assessment. Use of RAS blockers was defined as self-reported regular use, at the initial assessment, of angiotensin-converting enzyme inhibitor (ACEi) or angiotensin receptor blocker (ARB). Among participants who experienced adverse life experiences, cases of probable PTSD were defined with the six-item PTSD Checklist-Civilian version score ≥ 14. Logistic regression with inverse probability of treatment weighting was used to estimate the odds ratios (ORs) and 95% confidence interval (CI) for the association between RAS blocker use and the risk of probable PTSD.ResultsOf the 15,954 participants (mean age = 59.9 years; 42.6% women) under antihypertensive treatment with no prior history of PTSD at the initial assessment, 64.5% were taking RAS blockers. After a mean follow-up of 7.5 years, 1,249 (7.8%) were newly identified with probable PTSD. RAS blocker users had a lower risk of probable PTSD than RAS blocker non-users (OR = 0.84 [95% CI: 0.75–0.94]), whereas the use of other antihypertensive medications showed no such association (users vs. non-users; calcium channel blockers, OR = 0.99 [95% CI: 0.88–1.11]; beta-blockers, 1.20 [1.08–1.34]; and thiazide-related diuretics, 1.15 [1.03–1.29]). The association between probable PTSD risk and the use of ACEi vs. ARB showed no significant difference (p = 0.96).ConclusionsAmong individuals under antihypertensive treatment, the use of RAS blockers was associated with a decreased risk of probable PTSD. This added benefit of RAS blockers should be considered in the selection of antihypertensive medications.

Global warming level indicators of climate change and hotspots of exposure

Abstract In the 21st century, a growing population will be exposed to various hazards caused by a warming climate. Here we present a new database of 12 climate change indicators with a total of 42 variants at different global warming levels (GWLs) (1.2 °C–3.5 °C), which is based on global climate and hydrological model data from the Coupled Model Intercomparison Project round 6 and the Inter-Sectoral Impact Model Intercomparison Project round 3b at 0.5° spatial resolution. It comprises of indicators relating to temperature and precipitation extremes, heatwave events, and hydrological variability. To facilitate the comparison of hazards from different indicators, including for an audience without a scientific background, we have developed a bivariate hazard score which is applied on the grid cell level and incorporates statistics on both the absolute hazard (e.g. low or high precipitation) and the relative change under global warming compared to the reference period (e.g. a large change from low to high precipitation). Additionally, we calculate exposed land area and population through the 21st century for a large set of countries and regions by combining this score with gridded projections of population from the Shared Socioeconomic Pathways (SSPs). The datasets are intended for use by the wider research community and analysts seeking digestible climate hazard and exposure data summarized by GWLs. To illustrate potential uses of the data, in a preliminary analysis we find that even at 1.5 °C large parts of the land area and population face substantial unavoidable risks from multiple indicators, with 86% of the world’s population exposed to at least three indicators with at least medium hazard using the population projections for SSP2 in the year 2050. This picture only worsens with increasing warming, as the land areas facing the highest number of impacts coincide with some of the most densely populated parts of the world.