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Experimental evidence indicates that exposure to certain pollutants is associated with liver damage. Per- and polyfluoroalkyl substances (PFAS) are persistent synthetic chemicals widely used in industry and consumer products and bioaccumulate in food webs and human tissues, such as the liver. The objective of this study was to conduct a systematic review of the literature and meta-analysis evaluating PFAS exposure and evidence of liver injury from rodent and epidemiological studies. PubMed and Embase were searched for all studies from earliest available indexing year through 1 December 2021 using keywords corresponding to PFAS exposure and liver injury. For data synthesis, results were limited to studies in humans and rodents assessing the following indicators of liver injury: serum alanine aminotransferase (ALT), nonalcoholic fatty liver disease, nonalcoholic steatohepatitis, or steatosis. For human studies, at least three observational studies per PFAS were used to conduct a weighted -score meta-analysis to determine the direction and significance of associations. For rodent studies, data were synthesized to qualitatively summarize the direction and significance of effect. Our search yielded 85 rodent studies and 24 epidemiological studies, primarily of people from the United States. Studies focused primarily on legacy PFAS: perfluorooctanoic acid (PFOA), perfluorooctanesulfonic acid (PFOS), perfluorononanoic acid (PFNA), and perfluorohexanesulfonic acid. Meta-analyses of human studies revealed that higher ALT levels were associated with exposure to PFOA ( 6.20, ), PFOS ( 3.55, ), and PFNA ( 2.27, ). PFOA exposure was also associated with higher aspartate aminotransferase and gamma-glutamyl transferase levels in humans. In rodents, PFAS exposures consistently resulted in higher ALT levels and steatosis. There is consistent evidence for PFAS hepatotoxicity from rodent studies, supported by associations of PFAS and markers of liver function in observational human studies. This review identifies a need for additional research evaluating next-generation PFAS, mixtures, and early life exposures. https://doi.org/10.1289/EHP10092.

Exposure to per- and polyfluoroalkyl substances (PFAS) is ubiquitous and has been associated with an increased risk of several cardiometabolic diseases. However, the metabolic pathways linking PFAS exposure and human disease are unclear. We examined associations of PFAS mixtures with alterations in metabolic pathways in independent cohorts of adolescents and young adults. Three hundred twelve overweight/obese adolescents from the Study of Latino Adolescents at Risk (SOLAR) and 137 young adults from the Southern California Children's Health Study (CHS) were included in the analysis. Plasma PFAS and the metabolome were determined using liquid-chromatography/high-resolution mass spectrometry. A metabolome-wide association study was performed on log-transformed metabolites using Bayesian regression with a g-prior specification and g-computation for modeling exposure mixtures to estimate the impact of exposure to a mixture of six ubiquitous PFAS (PFOS, PFHxS, PFHpS, PFOA, PFNA, and PFDA). Pathway enrichment analysis was performed using Mummichog and Gene Set Enrichment Analysis. Significance across cohorts was determined using weighted -tests. In the SOLAR and CHS cohorts, PFAS exposure was associated with alterations in tyrosine metabolism (meta-analysis ) and fatty acid biosynthesis ( ), among others. For example, when increasing all PFAS in the mixture from low ( th percentile) to high ( th percentile), thyroxine (T4), a thyroid hormone related to tyrosine metabolism, increased by 0.72 standard deviations (SDs; equivalent to a standardized mean difference) in the SOLAR cohort (95% Bayesian credible interval (BCI): 0.00, 1.20) and 1.60 SD in the CHS cohort (95% BCI: 0.39, 2.80). Similarly, when going from low to high PFAS exposure, arachidonic acid increased by 0.81 SD in the SOLAR cohort (95% BCI: 0.37, 1.30) and 0.67 SD in the CHS cohort (95% BCI: 0.00, 1.50). In general, no individual PFAS appeared to drive the observed associations. Exposure to PFAS is associated with alterations in amino acid metabolism and lipid metabolism in adolescents and young adults. https://doi.org/10.1289/EHP11372.

Polygenic risk scores (PRSs) summarize the genetic predisposition of a complex human trait or disease and may become a valuable tool for advancing precision medicine. However, PRSs that are developed in populations of predominantly European genetic ancestries can increase health disparities due to poor predictive performance in individuals of diverse and complex genetic ancestries. We describe genetic and modifiable risk factors that limit the transferability of PRSs across populations and review the strengths and weaknesses of existing PRS construction methods for diverse ancestries. Developing PRSs that benefit global populations in research and clinical settings provides an opportunity for innovation and is essential for health equity.This Review summarizes the genetic and non-genetic factors that impact the transferability of polygenic risk scores (PRSs) across populations, highlighting the technical challenges of existing PRS construction methods for diverse ancestries and the emerging resources for more widespread use of PRSs.

Exposure to per- and polyfluoroalkyl substances (PFAS), a prevalent class of persistent pollutants, may increase the risk of type 2 diabetes. We examined associations between PFAS exposure and glucose metabolism in youth. Overweight/obese adolescents from the Study of Latino Adolescents at Risk of Type 2 Diabetes (SOLAR; ) participated in annual visits for an average of . Generalizability of findings were tested in young adults from the Southern California Children's Health Study (CHS; ) who participated in a clinical visit with a similar protocol. At each visit, oral glucose tolerance tests were performed to estimate glucose metabolism and function via the insulinogenic index. Four PFAS were measured at baseline using liquid chromatography-high-resolution mass spectrometry; high levels were defined as concentrations percentile. In females from the SOLAR, high perfluorohexane sulfonate (PFHxS) levels ( ) were associated with the development of dysregulated glucose metabolism beginning in late puberty. The magnitude of these associations increased postpuberty and persisted through 18 years of age. For example, postpuberty, females with high PFHxS levels had higher 60-min glucose (95% CI: ; ), higher 2-h glucose (95% CI: ; ), and 25% lower function ( ) compared with females with low levels. Results were largely consistent in the CHS, where females with elevated PFHxS levels had higher 60-min glucose (95% CI: ; ) and higher 2-h glucose, which did not meet statistical significance (95% CI: ; ). In males, no consistent associations between PFHxS and glucose metabolism were observed. No consistent associations were observed for other PFAS and glucose metabolism. Youth exposure to PFHxS was associated with dysregulated glucose metabolism in females, which may be due to changes in function. These associations appeared during puberty and were most pronounced postpuberty. https://doi.org/10.1289/EHP9200.

Background Exposure to per- and polyfluoroalkyl substances (PFAS) has been linked with various cancers. Assessment of PFAS in drinking water and cancers can help inform biomonitoring and prevention efforts. Objective To screen for incident cancer (2016–2021) and assess associations with PFAS contamination in drinking water in the US. Methods We obtained county-level age-adjusted cancer incidence (2016–2021) from the Surveillance, Epidemiology, and End Results (SEER) Program. Data on PFAS levels in public drinking water systems were obtained from the Third (UCMR3; 2013–2015) and Fifth (UCMR5; 2023–2024) Unregulated Contaminant Monitoring Rule. UCMR3 measured PFOS, PFOA, PFNA, PFHxS, PFHpA, and PFBS. UCMR5 expanded measurements to include PFBA, PFHxA, PFPeA, and PFPeS. We created indicators of PFAS detection and, for UCMR5, concentrations above Maximum Contaminant Levels (MCLs). MCLs for PFOA and PFOS are 4 ng/L, and for PFNA and PFHxS are 10 ng/L. We used Poisson regression models to assess associations between PFAS detection or MCL violation and cancer incidence, adjusting for potential confounders. We estimated the number of attributable cancer cases. Results PFAS in drinking water was associated with increased cancer incidence in the digestive, endocrine, oral cavity/pharynx, and respiratory systems. Incidence rate ratios (IRRs) ranged from 1.02 to 1.33. The strongest association was observed between PFBS and oral cavity/pharynx cancers (IRR: 1.33 [1.04, 1.71]). Among males, PFAS was associated with cancers in the urinary, brain, leukemia, and soft tissues. Among females, PFAS was associated with cancers in the thyroid, oral cavity/pharynx, and soft tissue. PFAS in drinking water is estimated to contribute to 4626 [95% CI: 1,377, 8046] incident cancer cases per year based on UCMR3 data and 6864 [95% CI: 991, 12,804] based on UCMR5. Impact statement The ecological study examined the associations between PFAS in drinking water measured in two waves (2013–2015 and 2023–2024) and cancer incidence between 2016 and 2021. We found that PFAS in drinking water was associated with cancers in the organ system including the oral cavity/pharynx, lung, digestive system, brain, urinary system, soft tissue, and thyroid. Some cancers have not been widely studied for their associations with PFAS. We also observed sex differences in the associations between PFAS and cancer risks. This is the first ecological study that examined PFAS exposure in drinking water and various cancer risks.

Addressing global warming and adapting to the impacts of climate change is a primary focus of climate change adaptation strategies at both European and national levels. Land surface temperature (LST) is a widely used proxy for investigating climate-change-induced phenomena, providing insights into the surface radiative properties of different land cover types and the impact of urbanization on local climate characteristics. Accurate and continuous estimation across large spatial regions is crucial for the implementation of LST as an essential parameter in climate change mitigation strategies. Here, we propose a deep-learning-based methodology for LST estimation using multi-source data including Sentinel-2 imagery, land cover, and meteorological data. Our approach addresses common challenges in satellite-derived LST data, such as gaps caused by cloud cover, image border limitations, grid-pattern sensor artifacts, and temporal discontinuities due to infrequent sensor overpasses. We develop a regression-based convolutional neural network model, trained on ECOSTRESS (ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station) mission data, which performs pixelwise LST predictions using 5 × 5 image patches, capturing contextual information around each pixel. This method not only preserves ECOSTRESS’s native resolution but also fills data gaps and enhances spatial and temporal coverage. In non-gap areas validated against ground truth ECOSTRESS data, the model achieves LST predictions with at least 80% of all pixel errors falling within a ±3 °C range. Unlike traditional satellite-based techniques, our model leverages high-temporal-resolution meteorological data to capture diurnal variations, allowing for more robust LST predictions across different regions and time periods. The model’s performance demonstrates the potential for integrating LST into urban planning, climate resilience strategies, and near-real-time heat stress monitoring, providing a valuable resource to assess and visualize the impact of urban development and land use and land cover changes.

The objective of this study was to use available data on the prevalence of COVID-19 risk factors in subpopulations and epidemic dynamics at the population level to estimate probabilities of severe illness and the case and infection fatality rates (CFR and IFR) stratified across subgroups representing all combinations of the risk factors age, comorbidities, obesity, and smoking status. We focus on the first year of the epidemic in Los Angeles County (LAC) (March 1, 2020–March 1, 2021), spanning three epidemic waves. A relative risk modeling approach was developed to estimate conditional effects from available marginal data. A dynamic stochastic epidemic model was developed to produce time-varying population estimates of epidemic parameters including the transmission and infection observation rate. The epidemic and risk models were integrated to produce estimates of subpopulation-stratified probabilities of disease progression and CFR and IFR for LAC. The probabilities of disease progression and CFR and IFR were found to vary as extensively between age groups as within age categories combined with the presence of absence of other risk factors, suggesting that it is inappropriate to summarize epidemiological parameters for age categories alone, let alone the entire population. The fine-grained subpopulation-stratified estimates of COVID-19 outcomes produced in this study are useful in understanding disparities in the effect of the epidemic on different groups in LAC, and can inform analyses of targeted subpopulation-level policy interventions.

Background Child blood pressure (BP) is predictive of future cardiovascular risk. Prenatal exposure to metals has been associated with higher BP in childhood, but most studies have evaluated elements individually and measured BP at a single time point. We investigated impacts of prenatal metal mixture exposures on longitudinal changes in BP during childhood and elevated BP at 11 years of age. Methods The current study included 176 mother-child pairs from the Rhea Study in Heraklion, Greece and focused on eight elements (antimony, arsenic, cadmium, cobalt, lead, magnesium, molybdenum, selenium) measured in maternal urine samples collected during pregnancy (median gestational age at collection: 12 weeks). BP was measured at approximately 4, 6, and 11 years of age. Covariate-adjusted Bayesian Varying Coefficient Kernel Machine Regression and Bayesian Kernel Machine Regression (BKMR) were used to evaluate metal mixture impacts on baseline and longitudinal changes in BP (from ages 4 to 11) and the development of elevated BP at age 11, respectively. BKMR results were compared using static versus percentile-based cutoffs to define elevated BP. Results Molybdenum and lead were the mixture components most consistently associated with BP. J-shaped relationships were observed between molybdenum and both systolic and diastolic BP at age 4. Similar associations were identified for both molybdenum and lead in relation to elevated BP at age 11. For molybdenum concentrations above the inflection points (~ 40–80 μg/L), positive associations with BP at age 4 were stronger at high levels of lead. Lead was positively associated with BP measures at age 4, but only at high levels of molybdenum. Potential interactions between molybdenum and lead were also identified for BP at age 11, but were sensitive to the cutoffs used to define elevated BP. Conclusions Prenatal exposure to high levels of molybdenum and lead, particularly in combination, may contribute to higher BP at age 4. These early effects appear to persist throughout childhood, contributing to elevated BP in adolescence. Future studies are needed to identify the major sources of molybdenum and lead in this population.

Prostate-specific antigen (PSA) screening for prostate cancer remains controversial because it increases overdiagnosis and overtreatment of clinically insignificant tumors. Accounting for genetic determinants of constitutive, non-cancer-related PSA variation has potential to improve screening utility. In this study, we discovered 128 genome-wide significant associations ( P  < 5 × 10 −8 ) in a multi-ancestry meta-analysis of 95,768 men and developed a PSA polygenic score (PGS PSA ) that explains 9.61% of constitutive PSA variation. We found that, in men of European ancestry, using PGS-adjusted PSA would avoid up to 31% of negative prostate biopsies but also result in 12% fewer biopsies in patients with prostate cancer, mostly with Gleason score <7 tumors. Genetically adjusted PSA was more predictive of aggressive prostate cancer (odds ratio (OR) = 3.44, P  = 6.2 × 10 −14 , area under the curve (AUC) = 0.755) than unadjusted PSA (OR = 3.31, P  = 1.1 × 10 −12 , AUC = 0.738) in 106 cases and 23,667 controls. Compared to a prostate cancer PGS alone (AUC = 0.712), including genetically adjusted PSA improved detection of aggressive disease (AUC = 0.786, P  = 7.2 × 10 −4 ). Our findings highlight the potential utility of incorporating PGS for personalized biomarkers in prostate cancer screening. Analyses of large population-based cohorts and clinical trials show that using polygenic scores to account for variability in PSA levels improves detection of prostate cancer, suggesting an approach for enhancing screening accuracy.