Comparison of maternal venous blood metabolomics collected as dried blood spots, dried blood microsamplers, and plasma for integrative environmental health research.

Use of capillary blood devices for exposome research can deepen our understanding of the intricate relationship between environment and health, and open up new avenues for preventive and personalized medicine, particularly for vulnerable populations. While the potential of these whole blood devices to accurately measure chemicals and metabolites has been demonstrated, how untargeted metabolomics data from these samplers can be integrated with previous and ongoing environmental health studies that have used conventional blood collection approaches is not yet clear. Therefore, we performed a comprehensive comparison between relative-quantitative metabolite profiles measured in venous blood collected with dried whole blood microsamplers (DBM), dried whole blood spots (DBS), and plasma from 54 mothers in an ethnically diverse population. We determined that a majority of the 309 chemicals and metabolites showed similar median intensity rank, moderate correlation, and moderate agreement between participant-quantiled intraclass correlation coefficients (ICCs) for pair-wise comparisons among the three biomatrices. In particular, whole blood sample types, DBM and DBS, were in highest agreement across metabolite comparison metrics, followed by metabolites measured in DBM and plasma, and then metabolites measured in DBS and plasma. We provide descriptive characteristics and measurement summaries as a reference database. This includes unique metabolites that were particularly concordant or discordant in pairwise comparisons. Our results demonstrate that the range of metabolites from untargeted metabolomics data collected with DBM, DBS, and plasma provides biologically relevant information for use in independent exposome investigations. However, before meta-analysis with combined datasets are performed, robust statistical approaches that integrate untargeted metabolomics data collected on different blood matrices need to be developed.

A miniaturized sample preparation method for routine elemental determination in whole blood using volumetric absorptive micro-sampling by ICP-QQQ.

Volumetric absorptive micro-sampling (VAMS) has emerged as a simple and safe tool for collecting and storing blood samples in clinical and bioanalytical fields. This study presents a novel method for determining essential and non-essential trace elements (As, Be, Cd, Cs, Cu, Fe, Mg, P, Pb, S, Sb, Se, Tl, V, U) in VAMS-collected blood samples using microwave-assisted digestion with diluted acid as sample preparation method and an inductively coupled plasma triple quadrupole mass spectrometry (ICP-QQQ) as determination technique. While certain elements posed challenges due to VAMS tip background issues (Al, Ti, Cr, Mn, Co, Ni, Sn, Mo, Ba), the method demonstrated high precision and accuracy for the targeted analytes. It was demonstrated that 4.5 mol L-1 HNO3 plus 100 µL H2O2 30% (w/w) was suitable for an efficiency of digestion for further elemental determination using micro-analysis (spending less than 300 µL analytical solution) by ICP-QQQ, given that the residual carbon content (RCC) after the digestion procedure was lower than 5%. All the results higher than limit of quantification (LOQ) were in agreement with reference values for all analytes. Accuracy was assessed through reference material analysis and recovery tests using spiked samples. Moreover, suitable agreements (p > 0.05) between this method (VAMS-M) and the comparative method (liquid sampling method) were obtained for all analytes >LOQ. Furthermore, all results >LOQ showed good precision according to precision requirements (Horwitz equation). In this way, with the use of dilute acid, low dilution factor (30-fold), and excellent digestion efficiency (>95%), the proposed method was able to achieve an excellent detection limit, precision, and accuracy for 15 elements: As, Be, Cd, Cs, Cu, Fe, Mg, P, Pb, S, Sb, Se, Tl, V, and U using ICP-MS/MS, without the need for matrix-matched calibration curves. This research showcases an innovative analytical approach using VAMS for blood samples, offering biosafety, practicality, sensitivity, versatility, and robustness. This method contributes to the advancement of trace element analysis in biomedical research and clinical applications.

Effects of storage temperature and time on metabolite profiles measured in dried blood spots, dried blood microsamplers, and plasma.

The practical advantages of capillary whole blood collection over venipuncture plasma collection for human exposome research are well known. However, before epidemiologists, clinicians, and public health researchers employ these microvolume sample collections, a rigorous evaluation of pre-analytical storage conditions is needed to develop protocols that maximize sample stability and reliability over time. Therefore, we performed a controlled experiment of dried whole blood collected on 10 µL Mitra microsamplers (DBM), 5-mm punches of whole blood from a dried blood spot (DBS), and 10 µL of plasma, and evaluated the effects of storage conditions at 4 °C, -20 °C, or -80 °C for up to 6 months on the resulting metabolite profiles measured with untargeted liquid chromatography-high resolution mass spectrometry (LC-HRMS). At -80 °C storage conditions, metabolite profiles from DBS, DBM, and plasma showed similar stability. While DBS and DBM metabolite profiles remained similarly stable at -20 °C storage, plasma profiles showed decreased stability at -20 °C compared to -80 °C storage. At refrigerated temperatures (4 °C), metabolite profiles collected on DBM were more stable than plasma or DBS, particularly for lipid classes. These results inform robust capillary blood sample storage protocols for DBM and DBS at potentially warmer temperatures than -80 °C, which may facilitate blood collections for populations outside of a clinical setting.

Per- and Poly-Fluoroalkyl Substances Exposure is Associated With Later Occurrence Of Inflammatory Bowel Disease.

Inflammatory bowel disease (IBD) is an immune-mediated inflammatory disease of the intestinal tract of elusive etiology. Environmental chemical exposures are increasingly acknowledged as a potential IBD risk factor. Per- and poly-fluoroalkyl substances (PFASs), a large class of persistent fluorinated organic chemicals used in industrial applications and consumer products such as paints, food packaging, and nonstick cookware, for over 6 decades, may be implicated in IBD etiology. Yet, epidemiological evidence has so far been scarce. Exposures to a few legacy PFASs, including perfluorooctane sulfonic acid (PFOS), perfluorooctanoic acid (PFOA), perfluorodecanoic (PFDA), and perfluorohexane sulfonate (PFHxS), have been associated with immunotoxicity and increased risk of other immune-mediated diseases,1 but data for their potential association with IBD are conflicting.2,3 Further, the impact of more recently emerging PFAS chemicals on IBD risk has not been studied.

Per- and polyfluoroalkyl substances (PFAS) exposure and thyroid cancer risk.

BACKGROUND: Although per- and polyfluoroalkyl substances (PFAS) exposure is a potential contributor to the increasing thyroid cancer trend, limited studies have investigated the association between PFAS exposure and thyroid cancer in human populations. We therefore investigated associations between plasma PFAS levels and thyroid cancer diagnosis using a nested case-control study of patients with thyroid cancer with plasma samples collected at/before cancer diagnosis. METHODS: 88 patients with thyroid cancer using diagnosis codes and 88 healthy (non-cancer) controls pair-matched on sex, age (±5 years), race/ethnicity, body mass index, smoking status, and year of sample collection were identified in the BioMe population (a medical record-linked biobank at the Icahn School of Medicine at Mount Sinai in New York); 74 patients had papillary thyroid cancer. Eight plasma PFAS were measured using untargeted analysis with liquid chromatography-high resolution mass spectrometry and suspect screening. Associations between individual PFAS levels and thyroid cancer were evaluated using unconditional logistic regression models to estimate adjusted odds ratios (ORadj) and 95% confidence intervals (CI). FINDINGS: There was a 56% increased rate of thyroid cancer diagnosis per doubling of linear perfluorooctanesulfonic acid (n-PFOS) intensity (ORadj, 1.56, 95% CI: 1.17-2.15, P = 0.004); results were similar when including patients with papillary thyroid cancer only (ORadj, 1.56, 95% CI: 1.13-2.21, P = 0.009). This positive association remained in subset analysis investigating exposure timing including 31 thyroid cancer cases diagnosed ≥1 year after plasma sample collection (ORadj, 2.67, 95% CI: 1.59-4.88, P < 0.001). INTERPRETATION: This study reports associations between exposure to PFAS and increased rate of (papillary) thyroid cancer. Thyroid cancer risk from PFAS exposure is a global concern given the prevalence of PFAS exposure. Individual PFAS studied here are a small proportion of the total number of PFAS supporting additional large-scale prospective studies investigating thyroid cancer risk associated with exposure to PFAS chemicals. FUNDING: National Institutes of Health grants and The Andrea and Charles Bronfman Philanthropies.

The heart of Detroit study: a window into urban middle-aged and older African Americans' daily lives to understand psychosocial determinants of cardiovascular disease risk.

BACKGROUND: Cardiovascular disease disproportionately affects African Americans. Psychosocial factors, including the experience of and emotional reactivity to racism and interpersonal stressors, contribute to the etiology and progression of cardiovascular disease through effects on health behaviors, stress-responsive neuroendocrine axes, and immune processes. The full pathway and complexities of these associations remain underexamined in African Americans. The Heart of Detroit Study aims to identify and model the biopsychosocial pathways that influence cardiovascular disease risk in a sample of urban middle-aged and older African American adults. METHODS: The proposed sample will be composed of 500 African American adults between the ages of 55 and 75 from the Detroit urban area. This longitudinal study will consist of two waves of data collection, two years apart. Biomarkers of stress, inflammation, and cardiovascular surrogate endpoints (i.e., heart rate variability and blood pressure) will be collected at each wave. Ecological momentary assessments will characterize momentary and daily experiences of stress, affect, and health behaviors during the first wave. A proposed subsample of 60 individuals will also complete an in-depth qualitative interview to contextualize quantitative results. The central hypothesis of this project is that interpersonal stressors predict poor cardiovascular outcomes, cumulative physiological stress, poor sleep, and inflammation by altering daily affect, daily health behaviors, and daily physiological stress. DISCUSSION: This study will provide insight into the biopsychosocial pathways through which experiences of stress and discrimination increase cardiovascular disease risk over micro and macro time scales among urban African American adults. Its discoveries will guide the design of future contextualized, time-sensitive, and culturally tailored behavioral interventions to reduce racial disparities in cardiovascular disease risk.

Toxicity mechanisms of biomass burning aerosols in in vitro hepatic steatosis models.

Nonalcoholic fatty liver disease (NAFLD) is a common chronic liver disease that contributes to the global rise in liver-related morbidity and mortality. Wood tar (WT) aerosols are a significant fraction of carbonaceous aerosol originating from biomass smoldering, contributing to air pollution particles smaller than 2.5 mm (PM2.5). Mechanistic biological associations exist between exposure to PM2.5 and increased NAFLD phenotypes in both cell and animal models. Therefore, this study examines whether an existing NAFLD-like condition can enhance the biological susceptibility of liver cells exposed to air pollution in the form of WT material. Liver cells were incubated with lauric or oleic acid (LA, OA, respectively) for 24 h to accumulate lipids and served as an in vitro hepatic steatosis model. When exposed to 0.02 or 0.2 g/L water-soluble WT aerosols, both steatosis model cells showed increased cell death compared to the control cells (blank-treated cells with or without pre-incubation with LA or OA) or compared to WT-treated cells without pre-incubation with LA or OA. Furthermore, alterations in oxidative status included variations in reactive oxygen species (ROS) levels, elevated levels of lipid peroxidation adducts, and decreased expression of antioxidant genes associated with the NRF2 transcription factor. In addition, steatosis model cells exposed to WT had a higher degree of DNA damage than the control cells (blank-treated cells with or without pre-incubation with LA or OA). These results support a possible systemic effect through the direct inflammatory and oxidative stress response following exposure to water-soluble WT on liver cells, especially those predisposed to fatty liver. Furthermore, the liver steatosis model can be influenced by the type of fatty acid used; increased adverse effects of WT on metabolic dysregulation were observed in the LA model to a higher extent compared to the OA model.

Maternal steroids during pregnancy and their associations with exposure to lifetime stressful life events, prenatal stress appraisal and psychological functioning.

BACKGROUND: During pregnancy, steroids enable physiological adaptations in response to many factors, including maternal stress or psychological functioning. While stress and psychological dysfunction can have endocrine-disrupting effects beyond cortisol disruption, associations between prenatal maternal stress or related psychological dysfunction and the broader steroid milieu remain understudied. AIM: To assess associations between independent and joint maternal stress and psychological functioning measures and steroid profiles in pregnancy (22-40 gestational weeks) in the Programming of Intergenerational Stress Mechanisms (PRISM) birth cohort (n = 334). METHODS: Serum metabolomics detected 42 steroids and their metabolites, which were grouped into five classes (pregnenolone, androgens, estrogens, progestin, and corticosteroids). The Perceived Stress Scale, Life Stressor Checklist-Revised, and Edinburgh Postnatal Depression Scale indexed lifetime traumatic/non-traumatic stressors, global prenatal stress appraisal, and depressive symptoms during pregnancy, respectively. Exposures were categorized as high-low using the corresponding 3rd quartiles. We assessed associations between both individual and joint stress exposures with steroid classes using linear mixed effect models and with individual steroids using linear regressions. We also examined fetal sex-specific effects. RESULTS: High prenatal perceived stress was independently associated with lower levels of androgens and estrogens in the overall sample [ß (95%CI): androgens: -0.13 (-0.25;-0.01); estrogens: -0.16 (-0.31;-0.01)], particularly among women carrying males [androgens: -0.22 (-0.39;-0.05); estrogens: -0.28 (-0.50;-0.07)]. Results on estrogens were consistent when considering joint exposure to both greater lifetime stressors and higher prenatal perceived stress. We also found a single testosterone metabolite-5alpha-androstan-3alpha,17alpha-diol disulfate-negatively associated with both individual high perceived stress and joint exposure to high lifetime stressors and high perceived stress among women carrying males. CONCLUSIONS: Increased maternal perceived stress experienced in pregnancy was independently associated with lower maternal androgen and estrogen levels during pregnancy in the overall sample, particularly among women carrying males. Results on estrogens were consistent when we considered the joint exposure of increased lifetime stressors and higher prenatal perceived stress.

Salivary Metabolomic Signatures and Body Mass Index in Italian Adolescents: A Pilot Study.

Context: Obesity surveillance is scarce in adolescents, and little is known on whether salivary metabolomics data, emerging minimally invasive biomarkers, can characterize metabolic patterns associated with overweight or obesity in adolescents. Objective: This pilot study aims to identify the salivary molecular signatures associated with body mass index (BMI) in Italian adolescents. Methods: Saliva samples and BMI were collected in a subset of n = 74 young adolescents enrolled in the Public Health Impact of Metal Exposure study (2007-2014). A total of 217 untargeted metabolites were identified using liquid chromatography-high resolution mass spectrometry. Robust linear regression was used to cross-sectionally determine associations between metabolomic signatures and sex-specific BMI-for-age z-scores (z-BMI). Results: Nearly 35% of the adolescents (median age: 12 years; 51% females) were either obese or overweight. A higher z-BMI was observed in males compared to females (P = .02). One nucleoside (deoxyadenosine) and 2 lipids (18:0-18:2 phosphatidylcholine and dipalmitoyl-phosphoethanolamine) were negatively related to z-BMI (P < .05), whereas 2 benzenoids (3-hydroxyanthranilic acid and a phthalate metabolite) were positively associated with z-BMI (P < .05). In males, several metabolites including deoxyadenosine, as well as deoxycarnitine, hyodeoxycholic acid, N-methylglutamic acid, bisphenol P, and trigonelline were downregulated, while 3 metabolites (3-hydroxyanthranilic acid, theobromine/theophylline/paraxanthine, and alanine) were upregulated in relation to z-BMI (P < .05). In females, deoxyadenosine and dipalmitoyl-phosphoethanolamine were negatively associated with z-BMI while deoxycarnitine and a phthalate metabolite were positively associated (P < .05). A single energy-related pathway was enriched in the identified associations in females (carnitine synthesis, P = .04). Conclusion: Salivary metabolites involved in nucleotide, lipid, and energy metabolism were primarily altered in relation to BMI in adolescents.

Associations between the Gut Microbiota, Urinary Metabolites, and Diet in Women during the Third Trimester of Pregnancy.

Background: Pregnancy causes many metabolic and physiologic changes. However, associations between gut microbiota, dietary intake, and urinary metabolites are poorly characterized in pregnant women. Objectives: The research objective was to identify dietary and microbial associations with urinary metabolites during pregnancy to elucidate potential biomarkers and microbial targets to improve maternal-fetal health. This is a secondary outcome of the study. Methods: Pregnant women (n = 27) in the Pregnancy EAting and POstpartum Diapers pilot study provided dietary intake information in addition to fecal and urine samples at 36 wk gestation. The gut microbiota was characterized following fecal DNA extraction and 16S rRNA gene sequencing. Urinary metabolites were identified using liquid chromatography high-resolution mass spectrometry. Results: Urinary glycocholate was consistently and negatively correlated with α-carotene intake. There were 9 significant correlations between microbial taxa and urinary metabolites and 13 significant correlations between microbial taxa and dietary intake. On average, Bacteroides were the most abundant taxon in the participants' gut microbiotas. Notably, the gut microbiotas of some pregnant women were not dominated by this taxon. Bacteroides-dominant women consumed more protein, fat, and sodium, and their gut microbiotas had lower alpha diversity than those of nondominant participants. Conclusions: Several urinary metabolites and microbial taxa were associated with maternal diet and gastrointestinal community composition during the third trimester of pregnancy. Future work should determine the mechanisms underlying the associations identified herein.

One-Carbon (Folate) Metabolism Pathway at Birth and Risk of Childhood Acute Lymphoblastic Leukemia: A Biomarker Study in Newborns.

Leukemia is the most common cancer in children in industrialized countries, and its initiation often occurs prenatally. Folic acid is a key vitamin in the production and modification of DNA, and prenatal folic acid intake is known to reduce the risk of childhood leukemia. We characterized the one-carbon (folate) metabolism nutrients that may influence risk of childhood acute lymphoblastic leukemia (ALL) among 122 cases diagnosed at age 0-14 years during 1988-2011 and 122 controls matched on sex, age, and race/ethnicity. Using hydrophilic interaction chromatography (HILIC) applied to neonatal dried blood spots, we evaluated 11 folate pathway metabolites, overall and by sex, race/ethnicity, and age at diagnosis. To conduct the prediction analyses, the 244 samples were separated into learning (75%) and test (25%) sets, maintaining the matched pairings. The learning set was used to train classification methods which were evaluated on the test set. High classification error rates indicate that the folate pathway metabolites measured have little predictive capacity for pediatric ALL. In conclusion, the one-carbon metabolism nutrients measured at birth were unable to predict subsequent leukemia in children. These negative findings are reflective of the last weeks of pregnancy and our study does not address the impact of these nutrients at the time of conception or during the first trimester of pregnancy that are critical for the embryo's DNA methylation programming.

AI/ML-driven advances in untargeted metabolomics and exposomics for biomedical applications.

Metabolomics describes a high-throughput approach for measuring a repertoire of metabolites and small molecules in biological samples. One utility of untargeted metabolomics, unbiased global analysis of the metabolome, is to detect key metabolites as contributors to, or readouts of, human health and disease. In this perspective, we discuss how artificial intelligence (AI) and machine learning (ML) have promoted major advances in untargeted metabolomics workflows and facilitated pivotal findings in the areas of disease screening and diagnosis. We contextualize applications of AI and ML to the emerging field of high-resolution mass spectrometry (HRMS) exposomics, which unbiasedly detects endogenous metabolites and exogenous chemicals in human tissue to characterize exposure linked with disease outcomes. We discuss the state of the science and suggest potential opportunities for using AI and ML to improve data quality, rigor, detection, and chemical identification in untargeted metabolomics and exposomics studies.

Prenatal alcohol exposure can be determined from baby teeth: Proof of concept.

BACKGROUND: Prenatal alcohol exposure (PAE), leading to fetal alcohol spectrum disorders (FASD), is a serious public health issue in the United States and globally. Diagnosis of FASD is crucial in obtaining appropriate care, but it is not always possible when PAE cannot be documented. METHODS: Deciduous teeth from a child with known PAE and a child with known absence of PAE were analyzed using liquid chromatography-isotope dilution tandem mass spectrometry (LC-IDMS/MS) in a multiple-reaction monitoring mode for direct markers and LC-high resolution MS in positive and negative mode with hydrophilic interaction liquid chromatography and reverse-phase chromatography, respectively, for indirect markers. RESULTS: Direct markers of PAE (ethyl glucuronide and ethyl sulfate) were detected in prenatal and postnatal dentine from a case tooth but not from a control tooth. Indirect biomarker analysis indicated a dysregulation of amino acids and an increase in cholesterol sulfate in the case compared to the control tooth. CONCLUSIONS: This proof-of-concept study demonstrates for the first time that direct biomarkers of PAE are detectable and measurable in deciduous teeth which begin forming in utero and are typically naturally shed between 5 and 12 years of age. Further examination of these novel biomarkers may allow diagnosis of FASD where documentation of PAE is otherwise unavailable. Furthermore, because teeth grow incrementally, defined growth zones can be sampled allowing for identification of gestational timing of PAE to help better understand mechanisms underlying alcohol's disruption of perinatal development.

Maternal steroids during pregnancy and their associations with ambient air pollution and temperature during preconception and early gestational periods.

Hormones play critical roles in facilitating pregnancy progression and the onset of parturition. Several classes of environmental contaminants, including fine particulate matter (PM2.5) and ambient temperature, have been shown to alter hormone biosynthesis or activity. However, epidemiologic research has not considered PM2.5 in relation to a broader range of steroid hormones, particularly in pregnant women. Using metabolomics data collected within 20-40 weeks of gestation in an ethnically diverse pregnancy cohort study, we identified 42 steroid hormones that we grouped into five classes (pregnenolone, androgens, estrogens, progestin, and corticosteroids) based on their biosynthesis type. We found that exposure to PM2.5 during the pre-conception and early prenatal periods was associated with higher maternal androgen concentrations in late pregnancy. We also detected a positive association between early pregnancy PM2.5 exposure and maternal pregnenolone levels and a marginal positive association between early pregnancy PM2.5 exposure and progestin levels. When considering each hormone metabolite individually, we found positive associations between early pregnancy PM2.5 exposure and five steroids, two of which survived multiple comparison testing: 11beta-hydroxyandrosterone glucuronide (a pregnenolone steroid) and adrosteroneglucuronide (a progestin steroid). None of the steroid classes were statistically significant associated with ambient temperature. In sex-stratified analyses, we did not detect any sex differences in our associations. This is the first study showing that exposure to fine particulate matter during the pre-conception and early prenatal periods can lead to altered steroid adaptation during the state of pregnancy, which has been shown to have potential consequences on maternal and child health.

Molecular Gatekeeper Discovery: Workflow for Linking Multiple Exposure Biomarkers to Metabolomics.

The exposome reflects multiple exposures across the life-course that can affect health. Metabolomics can reveal the underlying molecular basis linking exposures to health conditions. Here, we explore the concept and general data analysis framework of "molecular gatekeepers"─key metabolites that link single or multiple exposure biomarkers with correlated clusters of endogenous metabolites─to inform health-relevant biological targets. We performed untargeted metabolomics on plasma from 152 adolescent girls participating in the Growing Up Healthy Study in New York City. We then performed network analysis to link metabolites to exposure biomarkers including five trace elements (Cd, Mn, Pb, Se, and Hg) and five perfluorinated chemicals (PFCs; n-PFOS, Sm-PFOS, n-PFOA, PFHxS, and PFNA). We found 144 molecular gatekeepers and annotated 22 of them. Lysophosphatidylcholine (16:0) and taurodeoxycholate were correlated with both n-PFOA and n-PFOS, suggesting a shared dysregulation from multiple xenobiotic exposures. Sphingomyelin (d18:2/14:0) was significantly associated with age at menarche; yet, no direct association was detected between any exposure biomarkers and age at menarche. Thus, molecular gatekeepers can also discover molecular linkages between exposure biomarkers and health outcomes that may otherwise be obscured by complex interactions in direct measurements.

Reproducible untargeted metabolomics workflow for exhaustive MS2 data acquisition of MS1 features.

Unknown features in untargeted metabolomics and non-targeted analysis (NTA) are identified using fragment ions from MS/MS spectra to predict the structures of the unknown compounds. The precursor ion selected for fragmentation is commonly performed using data dependent acquisition (DDA) strategies or following statistical analysis using targeted MS/MS approaches. However, the selected precursor ions from DDA only cover a biased subset of the peaks or features found in full scan data. In addition, different statistical analysis can select different precursor ions for MS/MS analysis, which make the post-hoc validation of ions selected following a secondary analysis impossible for precursor ions selected by the original statistical method. Here we propose an automated, exhaustive, statistical model-free workflow: paired mass distance-dependent analysis (PMDDA), for reproducible untargeted mass spectrometry MS2 fragment ion collection of unknown compounds found in MS1 full scan. Our workflow first removes redundant peaks from MS1 data and then exports a list of precursor ions for pseudo-targeted MS/MS analysis on independent peaks. This workflow provides comprehensive coverage of MS2 collection on unknown compounds found in full scan analysis using a "one peak for one compound" workflow without a priori redundant peak information. We compared pseudo-spectra formation and the number of MS2 spectra linked to MS1 data using the PMDDA workflow to that obtained using CAMERA and RAMclustR algorithms. More annotated compounds, molecular networks, and unique MS/MS spectra were found using PMDDA compared with CAMERA and RAMClustR. In addition, PMDDA can generate a preferred ion list for iterative DDA to enhance coverage of compounds when instruments support such functions. Finally, compounds with signals in both positive and negative modes can be identified by the PMDDA workflow, to further reduce redundancies. The whole workflow is fully reproducible as a docker image xcmsrocker with both the original data and the data processing template.

Comparison of untargeted and targeted perfluoroalkyl acids measured in adolescent girls.

Quantitative biomonitoring (e.g., targeted analysis) has served as the gold standard for environmental exposure biomonitoring for several decades. Recent advancements to broaden exposomic research brought new semi-quantitative untargeted assays that capture a wide range of endogenous metabolites and exogenous exposures in a single assay for discovery, though usually at the expense of absolute quantitation. The high-resolution mass spectrometers (HRMS) typically used in untargeted workflows are sensitive and robust, but there do not yet exist comprehensive comparisons between environmental chemicals at population exposure levels measured using targeted and untargeted assays. Using liquid chromatography (LC)-HRMS, we measured per- and polyfluoroalkyl substances (PFAS) including perfluorohexane sulfonate (PFHxS), n-perfluorooctanoic acid (PFOA), n-perfluorooctanesulfonic acid (PFOS), and perfluorononanoic acid (PFNA) in plasma of 180 girls from New York City, and compared them to previously obtained targeted measures using correlation and rank order methods. We showed high agreement between the methods with Spearman Rhos ranging from 0.69 to 0.92 and weighted Kappa's from 0.62 to 0.82 for tertiles among the PFAS. This finding demonstrates that semi-quantitative data from untargeted assays designed for exposomics can be reliably used to estimate environmental exposures occurring in the general population, providing an economic alternative to targeted assays. We also describe an approach that can be used to compare relative quantitation measurements from an untargeted assay to traditional targeted measures to establish fit-for-purpose usability and validation. These results suggest that environmental exposure measures from untargeted assays can serve as reliable inputs into statistical analysis for discovery and for determining their resultant biological impacts. Future efforts to develop new statistical approaches for standardization and merging with targeted measures-toward harmonization-will further enhance the utility of untargeted assays in environmental epidemiology.