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.

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.

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.

Using primary teeth and archived dried spots for exposomic studies in children: Exploring new paths in the environmental epidemiology of pediatric cancer.

It is estimated that 300,000 children 0-14 years of age are diagnosed with cancer worldwide each year. While the absolute risk of cancer in children is low, it is the leading cause of death due to disease in children in high-income countries. In spite of this, the etiologies of pediatric cancer are largely unknown. Environmental exposures have long been thought to play an etiologic role. However, to date, there are few well-established environmental risk factors for pediatric malignancies, likely due to technical barriers in collecting biological samples prospectively in pediatric populations for direct measurements. In this review, we propose the use of novel or underutilized biospecimens (dried blood spots and teeth) and molecular approaches for exposure assessment (epigenetics, metabolomics, and somatic mutational profiles). Future epidemiologic studies of pediatric cancer should incorporate novel exposure assessment methodologies, data on molecular features of tumors, and a more complete assessment of gene-environment interactions.

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.

The use of dried blood spots for characterizing children's exposure to organic environmental chemicals.

Biomonitoring is a commonly used tool for exposure assessment of organic environmental chemicals with urine and blood samples being the most commonly used matrices. However, for children's studies, blood samples are often difficult to obtain. Dried blood spots (DBS) represent a potential matrix for blood collection in children that may be used for biomonitoring. DBS are typically collected at birth to screen for several congenital disorders and diseases; many of the states that are required to collect DBS archive these spots for years. If the archived DBS can be accessed by environmental health researchers, they potentially could be analyzed to retrospectively assess exposure in these children. Furthermore, DBS can be collected prospectively in the field from children ranging in age from newborn to school-aged with little concern from parents and minimal risk to the child. Here, we review studies that have evaluated the measurement of organic environmental toxicants in both archived and prospectively collected DBS, and where available, the validation procedures that have been performed to ensure these measurements are comparable to traditional biomonitoring measurements. Among studies thus far, the amount of validation has varied considerably with no studies systematically evaluating all parameters from field collection, shipping and storage contamination and stability to laboratory analysis feasibility. These validation studies are requisite to ensure reliability of the measurement and comparability to more traditional matrices. Thus, we offer some recommendations for validation studies and other considerations before DBS should be adopted as a routine matrix for biomonitoring.

Lead exposure and serum metabolite profiles in pregnant women in Mexico City.

BACKGROUND: Lead (Pb) exposure is a global health hazard causing a wide range of adverse health outcomes. Yet, the mechanisms of Pb toxicology remain incompletely understood, especially during pregnancy. To uncover biological pathways impacted by Pb exposure, this study investigated serum metabolomic profiles during the third trimester of pregnancy that are associated with blood Pb and bone Pb. METHODS: We used data and specimens from 99 women enrolled in the Programming Research in Obesity, Growth, Environment, and Social Stressors birth cohort in Mexico City. Maternal Pb exposure was measured in whole blood samples from the third trimester of pregnancy and in the tibia and patella bones at 1 month postpartum. Third-trimester serum samples underwent metabolomic analysis; metabolites were identified based on matching to an in-house analytical standard library. A metabolome-wide association study was performed using multiple linear regression models. Class- and pathway-based enrichment analyses were also conducted. RESULTS: The median (interquartile range) blood Pb concentration was 2.9 (2.6) µg/dL. Median bone Pb, measured in the tibia and patella, were 2.5 (7.3) µg/g and 3.6 (9.5) µg/g, respectively. Of 215 total metabolites identified in serum, 31 were associated with blood Pb (p < 0.05). Class enrichment analysis identified significant overrepresentation of metabolites classified as fatty acids and conjugates, amino acids and peptides, and purines. Tibia and patella Pb were associated with 14 and 8 metabolites, respectively (p < 0.05). Comparing results from bone and blood Pb, glycochenodeoxycholic acid, glycocholic acid, and 1-arachidonoylglycerol were positively associated with blood Pb and tibia Pb, and 7-methylguanine was negatively associated with blood Pb and patella Pb. One metabolite, 5-aminopentanoic acid, was negatively associated with all three Pb measures. CONCLUSIONS: This study identified serum metabolites in pregnant women associated with Pb measured in blood and bone. These findings provide insights on the metabolic profile around Pb exposure in pregnancy and information to guide mechanistic studies of toxicological effects for mothers and children.

MetaClean: a machine learning-based classifier for reduced false positive peak detection in untargeted LC-MS metabolomics data.

INTRODUCTION: Despite the availability of several pre-processing software, poor peak integration remains a prevalent problem in untargeted metabolomics data generated using liquid chromatography high-resolution mass spectrometry (LC-MS). As a result, the output of these pre-processing software may retain incorrectly calculated metabolite abundances that can perpetuate in downstream analyses. OBJECTIVES: To address this problem, we propose a computational methodology that combines machine learning and peak quality metrics to filter out low quality peaks. METHODS: Specifically, we comprehensively and systematically compared the performance of 24 different classifiers generated by combining eight classification algorithms and three sets of peak quality metrics on the task of distinguishing reliably integrated peaks from poorly integrated ones. These classifiers were compared to using a residual standard deviation (RSD) cut-off in pooled quality-control (QC) samples, which aims to remove peaks with analytical error. RESULTS: The best performing classifier was found to be a combination of the AdaBoost algorithm and a set of 11 peak quality metrics previously explored in untargeted metabolomics and proteomics studies. As a complementary approach, applying our framework to peaks retained after filtering by 30% RSD across pooled QC samples was able to further distinguish poorly integrated peaks that were not removed from filtering alone. An R implementation of these classifiers and the overall computational approach is available as the MetaClean package at https://CRAN.R-project.org/package=MetaClean . CONCLUSION: Our work represents an important step forward in developing an automated tool for filtering out unreliable peak integrations in untargeted LC-MS metabolomics data.

Metabolomics and adductomics of newborn bloodspots to retrospectively assess the early-life exposome.

PURPOSE OF REVIEW: Exposomics studies can measure health-relevant chemical exposures during a lifetime and estimate the 'internal' environment. However, sampling limitations make these features difficult to capture directly during the critical neonatal time period. RECENT FINDINGS: We review the use of newborn dried bloodspots (DBS) archived from newborn screening programs for exposomic analysis in epidemiological children's health studies. Emerging 'omics technologies such as adductomics and metabolomics have been adapted for DBS analysis, and these technologies can now provide valuable etiological information on the complex interplay between exposures, biological response, and population phenotypes. SUMMARY: Adductomics and metabolomics of DBS can provide robust measurements for retrospective epidemiological investigations. With extensive bioarchiving programs in the United States and other countries, DBS are poised to substantially aid epidemiological studies, particularly for rare and low-frequency childhood diseases and disorders.

Filtering procedures for untargeted LC-MS metabolomics data.

BACKGROUND: Untargeted metabolomics datasets contain large proportions of uninformative features that can impede subsequent statistical analysis such as biomarker discovery and metabolic pathway analysis. Thus, there is a need for versatile and data-adaptive methods for filtering data prior to investigating the underlying biological phenomena. Here, we propose a data-adaptive pipeline for filtering metabolomics data that are generated by liquid chromatography-mass spectrometry (LC-MS) platforms. Our data-adaptive pipeline includes novel methods for filtering features based on blank samples, proportions of missing values, and estimated intra-class correlation coefficients. RESULTS: Using metabolomics datasets that were generated in our laboratory from samples of human blood, as well as two public LC-MS datasets, we compared our data-adaptive filtering method with traditional methods that rely on non-method specific thresholds. The data-adaptive approach outperformed traditional approaches in terms of removing noisy features and retaining high quality, biologically informative ones. The R code for running the data-adaptive filtering method is provided at https://github.com/courtneyschiffman/Metabolomics-Filtering . CONCLUSIONS: Our proposed data-adaptive filtering pipeline is intuitive and effectively removes uninformative features from untargeted metabolomics datasets. It is particularly relevant for interrogation of biological phenomena in data derived from complex matrices associated with biospecimens.

Untargeted adductomics of Cys34 modifications to human serum albumin in newborn dried blood spots.

Metabolism of chemicals from the diet, exposures to xenobiotics, the microbiome, and lifestyle factors (e.g., smoking, alcohol intake) produce electrophiles that react with nucleophilic sites in circulating proteins, notably Cys34 of human serum albumin (HSA). To discover potential risk factors resulting from in utero exposures, we are investigating HSA-Cys34 adducts in archived newborn dried blood spots (DBS) that reflect systemic exposures during the last month of gestation. The workflow includes extraction of proteins from DBS, measurement of hemoglobin (Hb) to normalize for blood volume, addition of methanol to enrich HSA by precipitation of Hb and other interfering proteins, digestion with trypsin, and detection of HSA-Cys34 adducts via nanoflow liquid chromatography-high-resolution mass spectrometry. As proof-of-principle, we applied the method to 49 archived DBS collected from newborns whose mothers either actively smoked during pregnancy or were nonsmokers. Twenty-six HSA-Cys34 adducts were detected, including Cys34 oxidation products, mixed disulfides with low molecular weight thiols (e.g., cysteine, homocysteine, glutathione, cysteinylglycine), and other modifications. Data were normalized with a novel method ("scone") to remove unwanted technical variation arising from HSA digestion, blood volume, DBS age, mass spectrometry analysis, and batch effects. Using an ensemble of linear and nonlinear models, the Cys34 adduct of cyanide was found to consistently discriminate between newborns of smoking and nonsmoking mothers with a mean fold change (smoking/nonsmoking) of 1.31. These results indicate that DBS adductomics is suitable for investigating in utero exposures to reactive chemicals and metabolites that may influence disease risks later in life.

Untargeted lipidomic features associated with colorectal cancer in a prospective cohort.

BACKGROUND: Epidemiologists are beginning to employ metabolomics and lipidomics with archived blood from incident cases and controls to discover causes of cancer. Although several such studies have focused on colorectal cancer (CRC), they all followed targeted or semi-targeted designs that limited their ability to find discriminating molecules and pathways related to the causes of CRC. METHODS: Using an untargeted design, we measured lipophilic metabolites in prediagnostic serum from 66 CRC patients and 66 matched controls from the European Prospective Investigation into Cancer and Nutrition (Turin, Italy). Samples were analyzed by liquid chromatography-high-resolution mass spectrometry (LC-MS), resulting in 8690 features for statistical analysis. RESULTS: Rather than the usual multiple-hypothesis-testing approach, we based variable selection on an ensemble of regression methods, which found nine features to be associated with case-control status. We then regressed each selected feature on time-to-diagnosis to determine whether the feature was likely to be either a potentially causal biomarker or a reactive product of disease progression (reverse causality). CONCLUSIONS: Of the nine selected LC-MS features, four appear to be involved in CRC etiology and merit further investigation in prospective studies of CRC. Four other features appear to be related to progression of the disease (reverse causality), and may represent biomarkers of value for early detection of CRC.

compMS2Miner: An Automatable Metabolite Identification, Visualization, and Data-Sharing R Package for High-Resolution LC-MS Data Sets.

A long-standing challenge of untargeted metabolomic profiling by ultrahigh-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) is efficient transition from unknown mass spectral features to confident metabolite annotations. The compMS2Miner (Comprehensive MS2 Miner) package was developed in the R language to facilitate rapid, comprehensive feature annotation using a peak-picker-output and MS2 data files as inputs. The number of MS2 spectra that can be collected during a metabolomic profiling experiment far outweigh the amount of time required for pain-staking manual interpretation; therefore, a degree of software workflow autonomy is required for broad-scale metabolite annotation. CompMS2Miner integrates many useful tools in a single workflow for metabolite annotation and also provides a means to overview the MS2 data with a Web application GUI compMS2Explorer (Comprehensive MS2 Explorer) that also facilitates data-sharing and transparency. The automatable compMS2Miner workflow consists of the following steps: (i) matching unknown MS1 features to precursor MS2 scans, (ii) filtration of spectral noise (dynamic noise filter), (iii) generation of composite mass spectra by multiple similar spectrum signal summation and redundant/contaminant spectra removal, (iv) interpretation of possible fragment ion substructure using an internal database, (v) annotation of unknowns with chemical and spectral databases with prediction of mammalian biotransformation metabolites, wrapper functions for in silico fragmentation software, nearest neighbor chemical similarity scoring, random forest based retention time prediction, text-mining based false positive removal/true positive ranking, chemical taxonomic prediction and differential evolution based global annotation score optimization, and (vi) network graph visualizations, data curation, and sharing are made possible via the compMS2Explorer application. Metabolite identities and comments can also be recorded using an interactive table within compMS2Explorer. The utility of the package is illustrated with a data set of blood serum samples from 7 diet induced obese (DIO) and 7 nonobese (NO) C57BL/6J mice, which were also treated with an antibiotic (streptomycin) to knockdown the gut microbiota. The results of fully autonomous and objective usage of compMS2Miner are presented here. All automatically annotated spectra output by the workflow are provided in the Supporting Information and can alternatively be explored as publically available compMS2Explorer applications for both positive and negative modes ( https://wmbedmands.shinyapps.io/compMS2_mouseSera_POS and https://wmbedmands.shinyapps.io/compMS2_mouseSera_NEG ). The workflow provided rapid annotation of a diversity of endogenous and gut microbially derived metabolites affected by both diet and antibiotic treatment, which conformed to previously published reports. Composite spectra (n = 173) were autonomously matched to entries of the Massbank of North America (MoNA) spectral repository. These experimental and virtual (lipidBlast) spectra corresponded to 29 common endogenous compound classes (e.g., 51 lysophosphatidylcholines spectra) and were then used to calculate the ranking capability of 7 individual scoring metrics. It was found that an average of the 7 individual scoring metrics provided the most effective weighted average ranking ability of 3 for the MoNA matched spectra in spite of potential risk of false positive annotations emerging from automation. Minor structural differences such as relative carbon-carbon double bond positions were found in several cases to affect the correct rank of the MoNA annotated metabolite. The latest release and an example workflow is available in the package vignette ( https://github.com/WMBEdmands/compMS2Miner ) and a version of the published application is available on the shinyapps.io site ( https://wmbedmands.shinyapps.io/compMS2Example ).

Silicon dioxide nanoparticles increase macrophage atherogenicity: Stimulation of cellular cytotoxicity, oxidative stress, and triglycerides accumulation.

Nanoparticle research has focused on their toxicity in general, while increasing evidence points to additional specific adverse effects on atherosclerosis development. Arterial macrophage cholesterol and triglyceride (TG) accumulation and foam cell formation are the hallmark of early atherogenesis, leading to cardiovascular events. To investigate the in vitro atherogenic effects of silicon dioxide (SiO2 ), J774.1 cultured macrophages (murine cell line) were incubated with SiO2 nanoparticle (SP, d = 12 nm, 0-20 µg/mL), followed by cellular cytotoxicity, oxidative stress, TG and cholesterol metabolism analyses. A significant dose-dependent increase in oxidative stress (up to 164%), in cytotoxicity (up to 390% measured by lactate dehydrogenase (LDH) release), and in TG content (up to 63%) was observed in SiO2 exposed macrophages compared with control cells. A smaller increase in macrophage cholesterol mass (up to 22%) was noted. TG accumulation in macrophages was not due to a decrease in TG cell secretion or to an increased TG biosynthesis rate, but was the result of attenuated TG hydrolysis secondary to decreased lipase activity and both adipose triglyceride lipase (ATGL) and hormone-sensitive lipase (HSL) protein expression (by 42 and 25%, respectively). Overall, SPs showed pro-atherogenic effects on macrophages as observed by cytotoxicity, increased oxidative stress and TG accumulation. © 2014 Wiley Periodicals, Inc. Environ Toxicol 31: 713-723, 2016.

In vitro effects of exogenous carbon monoxide on oxidative stress and lipid metabolism in macrophages.

Carbon monoxide (CO) is a major constituent of traffic-related air pollution and is also produced endogenously under conditions of oxygen-mediated stress. It has been shown to affect both oxidative stress and inflammation. However, its role in lipid metabolism has been neglected. Using short exposure times, the effect of CO on J774A.1 macrophage atherogenic functions was investigated up to 16 h after exposure. Exposure of macrophages was found to be pro-atherogenic as it significantly increased triglyceride mass, up to 60%, and decreased high-density lipoprotein-mediated cholesterol efflux, up to 27%. In contrast, paraoxonase 2 lactonase activity was increased, up to 65%, and cellular oxidative stress was attenuated by 29%, compared with the control cells. The above results on lipid metabolism may lead to arterial macrophage foam cell formation, the hallmark of early atherogenesis.

Active Molecular Network Discovery Links Lifestyle Variables to Breast Cancer in the Long Island Breast Cancer Study Project.

A healthy lifestyle has been associated with decreased risk of developing breast cancer. Using untargeted metabolomics profiling, which provides unbiased information regarding lifestyle choices such as diet and exercise, we aim to identify the molecular mechanisms connecting lifestyle and breast cancer through network analysis. A total of 100 postmenopausal women, 50 with breast cancer and 50 cancer-free controls, were selected from the Long Island Breast Cancer Study Project (LIBCSP). We measured untargeted plasma metabolomics using liquid chromatography-high-resolution mass spectrometry (LC-HRMS). Using the "enet" package, we retained highly correlated metabolites representing active molecular network (AMN) clusters for analysis. LASSO was used to examine associations between cancer status and AMN metabolites and covariates such as BMI, age, and reproductive factors. LASSO was then repeated to examine associations between AMN metabolites and 10 lifestyle-related variables including smoking, physical activity, alcohol consumption, meat consumption, fruit and vegetable consumption, and supplemental vitamin use. Results were displayed as a network to uncover biological pathways linking lifestyle factors to breast cancer status. After filtering, 851 "active" metabolites out of 1797 metabolomics were retained in 197 correlation AMN clusters. Using LASSO, breast cancer status was associated with 71 "active" metabolites. Several of these metabolites were associated with lifestyle variables including meat consumption, alcohol consumption, and supplemental ß-carotene, B12, and folate use. Those metabolites could potentially serve as molecular-level biological intermediaries connecting healthy lifestyle factors to breast cancer, even though direct associations between breast cancer and the investigated lifestyles at the phenotype level are not evident. In particular, DiHODE, a metabolite linked with inflammation, was associated with breast cancer status and connected to ß-carotene supplement usage through an AMN. We found several plasma metabolites associated with lifestyle factors and breast cancer status. Future studies investigating the mechanistic role of inflammation in linking supplement usage to breast cancer status are warranted.

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.