Accurately quantifying and characterizing human internal exposure to micro- and nanoplastics are critical for assessing potential health risks. However, the detection of these particles in human tissues, fluids, cell systems, and relevant models remains a major analytical challenge. There is an urgent need for robust, selective, sensitive, and high-throughput methods capable of generating reliable quantitative data. Equally essential is the transparent reporting of methodological limitations and uncertainties, supported by rigorous data collection and standardized practices. These challenges are compounded by the ubiquity of plastic particles, and therefore the risk of sample contamination and their diverse properties (e.g., size, shape, composition), all adding to the complexity of identifying and quantifying them in biological matrices. To address these issues, we propose a framework that integrates orthogonal analytical techniques to enhance the data reliability. Commonly used analytical techniques for the analysis of micro- and nanoplastics are assigned a category based on their specificity when identifying plastic particles. The framework proposes minimum data requirements from orthogonal techniques for the identification of plastic particles at various confidence levels. Clear communication of analytical confidence is vital, and we present a structured approach to support this. We emphasize the importance of scientific integrity, rigorous study design, and transparent reporting in health research. Finally, we call for the universal adoption of harmonized confidence criteria for reporting the presence of plastics in humans, an essential step toward informed decision-making.

Per- and polyfluoroalkyl substances (PFAS) are chemicals linked to obesity and metabolic dysfunction, but their role in bariatric surgery remains poorly understood. This prospective pilot study examined correlations between plasma PFAS concentrations, body composition, and glycemic measures in adults undergoing bariatric surgery. Thirty-two patients (91% female; 66% Black; mean age 43 years) were enrolled preoperatively; twenty-two completed follow-up at a mean 8.6 months post-surgery. Three PFAS (PFHxS, PFNA, and PFOS) were quantified by plasma liquid chromatography-mass spectrometry; body composition and insulin sensitivity were assessed by dual-energy X-ray absorptiometry and intravenous glucose tolerance testing. At baseline, higher plasma PFNA and PFOS concentrations tracked with lower total lean mass (ρs = -0.46 and -0.48, respectively) and lean mass index (ρs = -0.46 and -0.42), and PFNA was inversely correlated with body weight (ρs = -0.40). No baseline associations were observed with adiposity or glycemic indices. Postoperatively, PFHxS concentrations decreased (median = -1.103 ng/mL, p < 0.001), whereas PFNA and PFOS did not change. Average PFNA was positively correlated with postoperative changes in HOMA-IR (ρs = 0.51) and total lean mass (ρs = 0.49). No significant associations were observed for average PFHxS or PFOS. These findings suggest that PFNA and PFOS may be linked to reduced lean tissue at baseline, and that PFNA burden modestly tracks with attenuated metabolic and body composition recovery. In an ANCOVA, baseline PFNA was not significantly associated with postoperative HOMA-IR or total lean mass. Larger, longitudinal studies are needed to clarify how PFAS influence these associations.

Background Growing literature examines the impact of per- and polyfluoroalkyl substances (PFAS) on diabetes risk. We aimed to conduct a comprehensive systematic review and meta-analysis of epidemiological studies to characterize the associations of exposures to PFAS with markers of glycemic control, insulin resistance, pancreatic β-cell function, and diabetes risk. Methods A systematic search of epidemiological articles published through July 21, 2025 was conducted by two researchers in PubMed/MEDLINE and Ovid/EMBASE following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Experimental studies were excluded from our review. Reported findings were extracted from published articles. Risk of bias was evaluated using the Navigation Guide. Random-effects meta-analyses stratified by study design estimated PFAS associations with gestational diabetes mellitus (GDM), type 2 diabetes (T2D), and continuous measures of Homeostatic Model Assessment for Insulin Resistance (HOMA-IR), HOMA-β, fasting insulin, fasting glucose, and hemoglobin A1c (HbA1c). This study was registered in PROSPERO (CRD42022369711). Findings Out of 738 records retrieved, we identified 129 eligible studies. Most studies focused on GDM (n = 25) and/or T2D (n = 36), while three focused on type 1 diabetes (T1D). Participant numbers ranged from n = 40 to n = 1,331,541 in the systematic review and from n = 399 to n = 111,544 in the meta-analyses. We found consistent associations between 8 different PFAS and higher odds of GDM across prospective and other study designs, including PFOS [n = 8, OR (95%CI) per doubling PFOS increase: 1.13 (1.01, 1.26), I2 = 0.0%] among other PFAS. We also found positive associations between several legacy PFAS such as PFOS with HOMA-IR [(n = 8), β (95%CI): 0.06 (0.01, 0.12), I2 = 0.0%] and fasting insulin [n = 5, β (95% CI) in μU/mL: 0.23 (0.06, 0.40), I2 = 0.0%] in prospective studies, and HOMA-β in cross-sectional studies [(n = 6), β (95% CI): 5.93 (1.72, 10.2), I2 = 67.0%], among other. Less consistent or null associations were with T2D, fasting glucose, and HbA1c. The evidence was of low-moderate quality and limited strength. Most studies were categorized as low risk of bias for other criteria, except for study design (cross-sectional). Interpretation Evidence from observational studies supports PFAS associations with higher odds of GDM and increased markers of insulin resistance and secretion. PFAS associations with established T2D or T1D remain to be elucidated, as evidence is still limited and effect sizes for some continuous diabetes markers were small and should be interpreted with caution. Larger life-course prospective studies with greater representation of well-characterized cases and evaluating emerging PFAS and mixtures are needed to fully capture the potential PFAS impacts on diabetes. Funding National Institutes of Health (NIH), National Institute of Environmental Health Sciences (NIEHS).

BACKGROUND:Hepatocellular carcinoma (HCC) is the most common primary liver cancer and often arises in cirrhosis cases. Current surveillance methods, including ultrasonography and α-fetoprotein, have limited sensitivity for early detection. Blood metabolomics may improve HCC risk prediction. We aimed to identify pre-diagnostic plasma metabolites associated with HCC risk and evaluate whether cirrhosis-related metabolites enhance prediction beyond established risk factors in a multiethnic population. METHODS:We analyzed data from a nested case-control study with pre-diagnostic blood samples in the Multiethnic Cohort, including 240 HCC cases, 151 cirrhosis cases, and individually matched controls. Metabolome-wide association studies and pathway enrichment analyses were performed, followed by feature selection in the cirrhosis samples to construct HCC prediction models. RESULTS:Of 294 metabolites analyzed, 53 were significantly associated with HCC after false discovery rate correction (odds ratios: 0.25-3.93). Pathway analyses highlighted perturbations in lipid and amino acid metabolism. Two cirrhosis-associated metabolites, glutamate and glycochenodeoxycholate, were consistently selected and improved HCC prediction. Adding these metabolites to known risk factors (age, sex, race/ethnicity, study area, BMI, smoking, alcohol consumption, and diabetes) increased the AUC from 0.64 to 0.73 (P < 0.001). CONCLUSIONS:Pre-diagnostic metabolomic profiling revealed metabolic alterations linked to HCC risk, emphasizing dysregulated amino acid and bile acid pathways within the cirrhosis context. IMPACT:Glutamate and glycochenodeoxycholate improved HCC risk prediction beyond established factors, supporting biologically plausible links between hepatic metabolic dysfunction and hepatocarcinogenesis. These findings highlight the potential of metabolomic biomarkers to enhance surveillance and risk stratification among patients with cirrhosis.

Background: Asthma is the most common chronic disease in children, yet its causes, environmental links, and underlying mechanisms are still not well understood despite extensive research. Methods: We examined the cross-sectional relationship between asthma and metabolic features in 628 serum samples (165 cases, 463 controls) from children aged 8, 12, and 16 years, in the Prevention and Incidence of Asthma and Mite Allergy birth cohort. Metabolic features were assessed using liquid chromatography with high-resolution mass spectrometry. In a single-feature-at-a-time approach, asthma status (i.e., current asthma) was regressed against the measured intensity of each of the features; this approach alone was further extended to age-stratified analyses. Biological pathways were explored using Mummichog. In addition, we assessed the association of exogenous mixtures exhibiting substantial intercorrelations (i.e., for polyfluoroalkyl substances [PFAS] only) with asthma. Results: Liquid chromatography with high-resolution mass spectrometry detected 55,444 metabolic features, including 38 identified exogenous compounds (15 PFAS, 14 pesticides, 4 phenols, 4 phthalates, and 1 other compound) and 460 identified endogenous metabolites. Overall, we observed limited evidence of robust associations between individual environmental compounds and childhood asthma. Some age-specific signals were observed, including a positive association for monocyclohexyl phthalate and a negative association for monoethyl phosphate in age-stratified analyses, although these findings did not consistently meet multiple-testing thresholds. PFAS as a mixture was not associated with asthma (P = 0.67, odds ratio = 1.00). Pathway analyses indicated potential involvement of the tyrosine metabolism pathway in relation to asthma and several environmental compounds. Conclusion: In this exploratory metabolomics analysis, we found limited evidence for strong associations between measured environmental compounds and childhood asthma. Nevertheless, several age-specific signals and pathway-level patterns, particularly involving tyrosine metabolism, were observed and may help guide future hypothesis-driven studies.

Despite advances in pharmacogenomics that have led to innovative therapeutic strategies, substantial interindividual variability in drug efficacy and toxicity persists. Both endogenous and exogenous factors contribute to this unexplained variation, resulting in suboptimal treatment effectiveness, unnecessary healthcare costs, preventable mortality, and significant human suffering. The emerging field of exposomics is aimed at addressing the complex exposures that impact human health and offers an opportunity to interrogate how these exposures influence drug actions and outcomes. Here, we introduce pharmacoexposomics as a complementary framework that systematically quantifies the body's metabolic response to drug and environmental exposures using high-resolution mass spectrometry. We then present IndiPHARM (Individualized Pharmacology), a multi-institution, Advanced Research Projects Agency for Health–supported platform designed to integrate population-scale pharmacoexposomics with pharmacogenomics and clinical data to elucidate interindividual variability in drug response, identify adverse reactions, and inform tailored, effective treatment strategies that advance precision medicine.

Poor diet quality is a known risk factor for type 2 diabetes and related outcomes, including declines in insulin sensitivity. Biological changes that occur in response to diet may explain this relationship. This study was conducted in a cohort of young adults (the MetaAIR study, n = 77, 52

OBJECTIVE:To evaluate the association between per- and polyfluoroalkyl substances (PFAS) in follicular fluid (FF) and live birth after in vitro fertilization (IVF) and characterize the FF metabolome in relation to both. DESIGN:Retrospective cohort. SUBJECTS:Thirty-six women who underwent IVF treatment at 1 of the 3 centers in Eastern Massachusetts between 1999 and 2003. EXPOSURE:Twenty-four PFAS were measured in FF retrieved from the first follicle aspirated during the first treatment cycle. The 8 PFAS detected in >90% of samples were evaluated. MAIN OUTCOME MEASURES:We analyzed the FF metabolome using untargeted liquid chromatography high-resolution mass spectrometry. We used linear regression to estimate associations between FF PFAS and metabolic feature intensities, and logistic regression to estimate associations of FF PFAS and metabolites with live birth, adjusting for age and infertility type. We subsequently conducted pathway enrichment analyses and used a meet-in-the-middle approach to screen for overlapping metabolic pathways associated with FF PFAS and live birth. RESULTS:Higher FF perfluoroheptanesulfonic acid (PFHpS) was associated with lower odds of live birth (odds ratio = 0.42 [95% confidence interval: 0.15-0.97]). All other FF PFAS, except perfluorobutanesulfonic acid (PFBS), were also inversely associated with live birth, although confidence intervals included the null value. We evaluated 27,903 features detected in >25% of participant samples. For PFAS-feature associations, the top 5% of features for each PFAS were enriched for bile acid biosynthesis. Thirty-eight FF metabolic pathways enriched for features associated with live birth (yes/no) overlapped with pathways enriched for features associated with perfluorooctanoic acid (PFOA), PFHpS, or perfluorohexanesulfonic acid (PFHxS). These included pathways related to lipid (n = 11), carbohydrate (n = 9), and vitamin and cofactor metabolism (n = 6), among others. CONCLUSION:Higher FF PFAS concentrations were associated with lower odds of achieving live birth, although most 95% confidence intervals included the null. Metabolic pathways enriched for features associated with live birth overlapping with those associated with FF PFAS were related to lipid, carbohydrate, vitamin, and cofactor metabolism. Although our sample size was small, our findings align with other studies characterizing environmental exposures in reproductive organs and using the metabolome to assess their impact on fertility.

BACKGROUND:Female firefighters face elevated risks for cancer and reproductive disorders, but the underlying metabolic mechanisms remain unclear. OBJECTIVES:This study aimed to identify urinary metabolites and metabolic processes associated with training fire exposure among female municipal firefighters. METHODS:High-resolution metabolomics (HRM) was conducted on urine samples collected before and after live-fire training from female firefighters enrolled in the Fire Fighter Cancer Cohort Study. Linear mixed-effects models, adjusting for age, education, and Hispanic ethnicity, were used to identify differentially expressed metabolites (DEMs) with false discovery rate correction. Functional enrichment analysis (FEA) via metabolite-set enrichment analysis (MSEA) from MetaboAnalyst was performed to identify enriched metabolic processes. A stratified analysis examined the influence of fire types on post-fire metabolic profiles. RESULTS:One hundred female firefighters donated a total of 200 urine samples (100 pre-, 100 post-fire). HRM was performed in four modes including HILIC(+), HILIC(-), C18(+), and C18(-). We identified 200, 300, 280, and 306 metabolites and 10, 9, 23, and 19 post-training fire DEMs from the four modes, respectively. FEA highlighted enrichment of glycerophospholipid metabolism (p < 0.05). Stratified analysis identified 11 DEMs by fire type with greater changes observed following burn room/tower exposures compared to flashover fires. CONCLUSION:Training fire exposure induced widespread metabolic alterations in female firefighters, particularly in pathways related to oxidative stress and cell damage. These findings suggest potential biological pathways linking repeated fire exposure to chronic inflammation and disease risk. Burn room/tower burn exercises elicited more pronounced metabolic shifts than flashover fires.

Despite advances in pharmacogenomics that have led to innovative therapeutic strategies, substantial interindividual variability in drug efficacy and toxicity persists. Both endogenous and exogenous factors contribute to this unexplained variation, resulting in suboptimal treatment effectiveness, unnecessary healthcare costs, preventable mortality, and significant human suffering. The emerging field of exposomics is aimed at addressing the complex exposures that impact human health and offers an opportunity to interrogate how these exposures influence drug actions and outcomes. Here, we introduce pharmacoexposomics as a complementary framework that systematically quantifies the body's metabolic response to drug and environmental exposures using high-resolution mass spectrometry. We then present IndiPHARM (Individualized Pharmacology), a multi-institution, Advanced Research Projects Agency for Health-supported platform designed to integrate population-scale pharmacoexposomics with pharmacogenomics and clinical data to elucidate interindividual variability in drug response, identify adverse reactions, and inform tailored, effective treatment strategies that advance precision medicine.

Metabolic signals identifying pregnancies at risk for preterm birth (<37 weeks) and early term birth (37–38 weeks) remain limited. Here we used high-resolution metabolomics to characterize metabolic features associated with spontaneous and medically indicated early birth. We conducted metabolome-wide association studies among 279 (discovery) and 251 (internal validation) pregnant women from the Atlanta African American Maternal-Child Cohort (2014–2018), with serum collected in early (8–14 weeks) and later (24–30 weeks) pregnancy. Distinct metabolic profiles differentiated spontaneous and medically indicated preterm birth and early term birth across pregnancy windows. Perturbations in amino acid pathways, including arginine, proline, aspartate, glutamate, methionine and cysteine metabolism, were observed. Valine, leucine and tyrosine were inversely associated with spontaneous early birth, while acylcarnitines and aldosterone were associated with medically indicated early birth. Thirteen metabolites were validated, supporting their potential as markers of pregnancies at elevated risk. Metabolites associated with preterm and early term birth were identified in a cohort of African American women, 13 of which were validated in an independent internal dataset, with the potential to be used as markers of at-risk women.

This study investigates independent and joint effects of fine particulate matter (PM2.5) components on early childhood neurodevelopment and explores emission sources of key toxic components. We included 165 mother-infant dyads from Southern California. Annual average concentrations of 15 PM2.5 components, including carbonaceous components, secondary inorganic salts, and trace elements, were estimated for the birth year. Neurodevelopment across cognitive, language, motor, social-emotional, and adaptive behavior domains was assessed at age 2 using Bayley-III Scales. Mixture effects and key contributors were evaluated using weighted quantile sum (WQS) and Bayesian kernel machine regression (BKMR). Source inference was conducted through inter-component clustering and spatial analysis. Linear regression showed PM2.5, sulfate (SO42−), nitrate (NO3−), ammonium (NH4+), copper (Cu), nickel (Ni), lead (Pb), and vanadium (V) were inversely, while calcium (Ca) and zinc (Zn) were positively, associated with adaptive behavior scores (p < 0.05). WQS showed negative associations between the mixture and adaptive behavior (p = 0.02–0.06), with Ni, Cu, V, and SO₄²⁻ as key contributors. BKMR showed similar trends. Ni, V, and SO42− likely originate from heavy oil combustion, and Cu from brake wear. Findings suggest that PM2.5 components, particularly from traffic and marine fuel combustion, may adversely affect adaptive behavior in early childhood.

Air pollution is a leading environmental cause of lung cancer, yet the underlying biological pathways remain poorly understood. Identifying circulating biomarkers that capture early molecular responses may clarify how air pollutants contribute to carcinogenesis and help identify individuals at elevated risk. We conduct a prospective nested case-control study within two Cancer Prevention Study cohorts, profiling more than 1100 metabolites in pre-diagnostic plasma samples from 1357 participants. Residential concentrations of six major air pollutants are estimated at the time of blood draw. Here we show that eight circulating metabolites are associated with both air pollution exposure and subsequent lung cancer risk. Four metabolites, including γ-glutamylglutamine, phenylacetylglutamate, N-(2-furoyl)glycine, and 4-vinylguaiacol glucuronide, significantly mediate associations for particulate matter and ozone (adjusted q-value < 0.2). These findings suggest that air pollution may promote lung cancer partly through metabolic pathways related to inflammatory and oxidative processes, providing key insights into potential mechanisms and targets for prevention.

PNPLA3-I148M genotype is the strongest predictive single-nucleotide polymorphism for liver fat. We examine whether PNPLA3-I148M modifies associations between oxidative gaseous air pollutant exposure (O-x(wt)) with i) liver fat and ii) multi-omics profiles of miRNAs and metabolites linked to liver fat. Participants were 69 young adults (17-22 years) from the Meta-AIR cohort. Prior-month residential O-x(wt) exposure (redox-weighted oxidative capacity of nitrogen dioxide and ozone) was spatially interpolated from monitoring stations via inverse-distance-squared weighting. Liver fat fraction was assessed by MRI. Serum miRNAs and metabolites were assayed via NanoString nCounter and LC-HRMS, respectively. Multi-omics factor analysis (MOFA) was used to identify latent factors with shared variance across omics layers. Multivariable linear regression models adjusted for age, sex, body mass index, and genotype with liver fat or MOFA factors as an outcome and examined PNPLA3 (rs738409; CC/CG vs. GG) as a multiplicative interaction term. Overall, a standard deviation difference in O-x(wt) exposure was associated with 8.9% relative increase in liver fat (p = 0.04) and this relationship differed by PNPLA3 genotype (p-value for interaction term: p(intx)<0.001), whereby relative increases in liver fat for GG and CC/CG participants were 71.8% and 2.4%, respectively. There was no main effect of O-x(wt) on MOFA Factor 1 expression (p = 0.85), but there was an interaction with PNPLA3 genotype (p(intx) = 0.01), whereby marginal slopes were 0.211 and -0.017 for GG and CC/CG participants, respectively. MOFA Factor 1 in turn was associated with liver fat (p = 0.006). MOFA Factor 1 miRNAs targeted genes in Fatty Acid Biosynthesis and Metabolism and Lysine Degradation pathways. MOFA Factor 9 was also associated with liver fat and was comprised of branched-chain keto acid and amino acid metabolites. The effects of O-x(wt) exposure on liver fat is exacerbated in young adults with two PNPLA3 risk alleles, potentially through differential effects on miRNA and/or metabolite profiles.

Maternal tobacco smoking in the perinatal period increases the risk for adverse outcomes in offspring. To better understand the biological pathways through which maternal tobacco use may have long-term impacts on child metabolism, we performed a high-resolution metabolomics (HRM) analysis in newborns, following an untargeted metabolome-wide association study workflow. The study population included 899 children without cancer diagnosis before age 6 and born between 1983 and 2011 in California. Newborn dried blood spots were collected by the California Genetic Disease Screening Program between 12 and 48 h after birth and stored for later research use. Based on HRM, we considered mothers to be active smokers if they were self- or provider-reported smokers on birth certificates or if we detected any cotinine or high hydroxycotinine intensities in newborn blood. We used partial least squares discriminant analysis and Mummichog pathway analysis to identify metabolites and metabolic pathways associated with maternal tobacco smoking. A total of 26,183 features were detected with HRM, including 1003 that were found to be associated with maternal smoking late in pregnancy and early postpartum (Variable Importance in Projection (VIP) scores > = 2). Smoking affected metabolites and metabolic pathways in neonatal blood including vitamin A (retinol) metabolism, the kynurenine pathway, and tryptophan and arachidonic acid metabolism. The smoking-associated metabolites and pathway perturbations that we identified suggested inflammatory responses and have also been implicated in chronic diseases of the central nervous system and the lung. Our results suggest that infant metabolism in the early postnatal period reflects smoking specific physiologic responses to maternal smoking with strong biologic plausibility.

Alzheimer's disease (AD) is a complex neurodegenerative disorder characterized by progressive cognitive decline and neuropathological hallmarks. Despite extensive research, the molecular mechanisms linking metabolic dysregulation to AD neuropathology remain poorly understood. Metabolomics, a powerful tool for profiling small molecules, provides an opportunity to identify metabolic signatures and pathways implicated in disease progression. To address this gap, we conducted a comprehensive high-resolution brain metabolomics study, profiling metabolic perturbations associated with AD neuropathology. Using untargeted high-resolution metabolomics, we analyzed 162 frontal cortex samples from the Emory Goizueta Alzheimer's Disease Research Center brain bank with comprehensive neuropathological evaluations, including Braak stage, CERAD, and ABC scores. We conducted a metabolome-wide association study of AD neuropathology adjusting for confounders and multiple testing (FDR 5%). Pathway enrichment analysis was performed to uncover biological processes implicated in AD, and chemical annotation confirmed key metabolites with Level 1 evidence. We also examined potential effects of APOE ε4 genotype in modifying the associations between significant metabolic features and AD neuropathology markers. Our analysis identified 155 metabolic features, and 36 metabolic pathways significantly associated with the AD neuropathology markers, spanning ten metabolic classes such as energy metabolism, nucleotide metabolism, and amino acid metabolism. ABC score was linked to the largest number of pathways, while Braak stage and CERAD exhibited distinct metabolic associations, including nucleotide metabolism with tau pathology and fatty acid-related pathways with neuritic plaque deposition. Further, 18 unique metabolites were confirmed with level 1 evidence, implicating their involvement in amino acid metabolism, lipid metabolism, carbohydrate metabolism, nucleotide metabolism, and metabolism of cofactors and vitamins in AD neuropathology. Genetic variability influenced these associations, with non-carriers of the APOE ε4 allele showing stronger perturbations in metabolites including glucose, mannose, myo-inositol, and adenosine 5'-diphosphoribose. This study demonstrates the potential of high-resolution metabolomic profiling in brain tissues to elucidate molecular mechanisms underlying AD pathology. Our findings provide critical insights into metabolic dysregulation in AD and its interplay with genetic factors. Future longitudinal studies are needed to validate these findings and explore the functional significance of the identified metabolites in AD progression.

INTRODUCTION:This study aimed to identify specific biological pathways and molecules involved in Alzheimer's disease (AD) neuropathology. METHODS:We conducted cutting-edge high-resolution metabolomics profiling of 162 human frontal cortex samples from the Emory Alzheimer's Disease Research Center (ADRC) brain bank with comprehensive neuropathological evaluations. RESULTS:We identified 155 unique metabolic features and 36 pathways associated with three well-established AD neuropathology markers. Of these, 18 novel metabolites were confirmed with level 1 evidence, implicating their involvement in amino acid metabolism, lipid metabolism, carbohydrate metabolism, nucleotide metabolism, and metabolism of cofactors and vitamins in AD neuropathology. Genetic variability influenced these associations, with non-carriers of the apolipoprotein E (APOE) ε4 allele showing stronger perturbations in metabolites including glucose and adenosine 5'-diphosphoribose. DISCUSSION:This study demonstrates the potential of high-resolution metabolomic profiling in brain tissues to elucidate molecular mechanisms underlying AD pathology. Our findings provide critical insights into metabolic dysregulation in AD and its interplay with genetic factors. HIGHLIGHTS:This is one of the largest untargeted metabolomics studies of human brain tissue. 155 metabolic features, and 36 metabolic pathways were linked to Alzheimer's disease (AD) neuropathology. Of these, 18 unique metabolites were confirmed with level 1 evidence. Glucose and adenosine 5'-diphosphoribose identified as key metabolic alterations in AD.

BACKGROUND:Despite advances in understanding genetic susceptibility to cancer, much of cancer heritability remains unidentified. At the same time, the makeup of industrial chemicals in our environment only grows more complex. This gap in knowledge on cancer risk has prompted calls to expand cancer research to the comprehensive, discovery-based study of nongenetic environmental influences, conceptualized as the "exposome." METHODS:Our scoping review aimed to describe the exposome and its application to cancer epidemiology and to study design limitations, challenges in analytical methods, and major unmet opportunities in advanced exposome profiling methods that allow the quantification of complex chemical exposure profiles in biological matrices. To evaluate progress on incorporating measurements of the exposome into cancer research, we performed a review of such "cancer exposome" studies published through August 2023. RESULTS:We found that only 1 study leveraged untargeted chemical profiling of the exposome as a method to measure tens of thousands of environmental chemicals and identify prospective associations with future cancer risk. The other 13 studies used hypothesis-driven exposome approaches that targeted a set of preselected lifestyle, occupational, air quality, social determinant, or other external risk factors. Many of the included studies could only leverage sample sizes with less than 400 cancer cases (67% of nonecologic studies) and exposures experienced after diagnosis (29% of studies). Six cancer types were covered, most commonly blood (43%), lung (21%), or breast (14%) cancer. CONCLUSION:The exposome is underutilized in cancer research, despite its potential to unravel complex relationships between environmental exposures and cancer and to inform primary prevention.

Studies on prenatal exposure to per- and polyfluoroalkyl substances (PFAS) and cardiometabolic health in childhood have produced inconsistent results. In this study, we evaluated associations between prenatal PFAS exposures, individually and as a mixture, and cardiometabolic outcomes including insulin resistance, beta cell function, blood lipids, blood pressure and central adiposity during middle childhood (7-9 years of age) in a Canadian maternal-child cohort (n = 281). We also explored effect measure modification based on child sex and physical activity. We quantified maternal second trimester plasma concentrations of six PFAS and measured 11 offspring cardiometabolic outcomes at a 7-9-year follow-up. In single-exposure models, ten-fold higher prenatal PFDA (β: -0.82, 95% CI: -1.36, -0.28), PFNA (β: -0.8, 95% CI: -1.41, -0.19), and PFOA (β: -0.69, 95% CI: -1.18, -0.19) concentrations were associated with lower diastolic blood pressure z-scores. This association did not persist when considering PFAS exposures as a mixture using quantile g-computation. Associations between PFAS exposures, individually or as a mixture, and other cardiometabolic outcomes were null. We observed no effect measure modification by child sex or physical activity (p-values for interaction ≥0.2). Our results contradict existing studies that suggest prenatal PFAS exposures are associated with adverse childhood cardiometabolic outcomes. Future studies should consider alternative markers of cardiometabolic health, trajectories in cardiometabolic health throughout childhood, and further explore potentially protective health behaviors.