Non-target time trend screening in human blood - Environmental

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Non-target time trend screening in human blood Merle M. Plassmann, Stellan Fischer, and Jonathan P. Benskin Environ. Sci. Technol. Lett., Just Accepted Manuscript • DOI: 10.1021/acs.estlett.8b00196 • Publication Date (Web): 25 May 2018 Downloaded from http://pubs.acs.org on May 25, 2018

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Environmental Science & Technology Letters

Non-target Time Trend Screening in Human Blood Merle M. Plassmann,1* Stellan Fischer,2 Jonathan P. Benskin1 1. Department of Environmental Science and Analytical Chemistry (ACES), Stockholm University, SE-106 91 Stockholm, Sweden 2. Swedish Chemicals Agency (KemI), SE-172 67 Stockholm, Sweden *Corresponding author: Merle Plassmann Contaminant Chemistry Unit Dept. of Environmental Science and Analytical Chemistry (ACES) Stockholm University, Stockholm, Sweden Phone: +46 8 674 7159 Fax: +46 8 674 7636 [email protected]

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Abstract

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Human biomonitoring (HBM) programs monitor exposure to a limited number of prioritized

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chemicals resulting in some important substances being overlooked. Non-target analysis shows

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promise for capturing novel substances, yet the large quantity of data produced by these methods

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remains challenging. Here we apply a prioritization strategy for temporal non-target HBM data, which

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shortlists features with increasing time trends, possibly representing substances which are

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bioaccumulating or to which humans are increasingly exposed. Human whole blood sampled in

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Germany between 1983 and 2015 was extracted using a modified QuEChERS method, and analyzed

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by UHPLC-Oribtrap-mass spectrometry. Following alignment, peak detection, grouping and gap

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filling, up to 14460 features were obtained. This number was reduced to ≤716 using time trend ratios

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and Spearman’s rank correlation coefficients to identify features which increased over the 32-year

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time series. Increasing features were investigated further using the KemI market list database (which

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prioritizes based on human hazard and/or exposure potential) as well as data-dependent product ion

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scans, followed by MetFrag and mzCloud database searches. Finally, 7 prioritized substances,

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including 1 pharmaceutical, 2 pesticides and 4 performance chemicals, were confirmed using

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standards, demonstrating the potential of time trend screening as a data reduction strategy for non-

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target HBM data.

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Introduction

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Every year humans are exposed to an increasingly complex mixture of anthropogenic substances,1 via

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direct contact with consumer products (e.g. personal care products), or through intake of contaminated

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exposure media (e.g. dust, air, food and drinking water). Human Biomonitoring (HBM) programs

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evaluate exposure within a population through analysis of biological matrices.2 These programs focus

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on characterizing exposure to a limited number of priority substances. For example, since 1999, the

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National Health and Nutrition Examination Survey (NHANES) in the United States has carried out

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biomonitoring of up to 346 environmental chemicals (or their metabolites) in blood, serum, and urine

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samples.3 Similar initiatives such as the Canadian Health Measures Survey4 and the HBM4EU

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biomonitoring initiative5, include even fewer monitored substances.

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Inclusion of a particular target within a biomonitoring program is usually based on existing exposure

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or hazard data.6 For many emerging contaminants - and indeed transformation products - few hazard

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or exposure data are available. Moreover, highly specific, targeted analytical approaches rarely exist

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for emerging substances and their transformation products. As a result, numerous chemicals are

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overlooked or not included in biomonitoring programs. Fortunately, over the last decade non-target

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screening has emerged as a promising new technique for detecting many hundreds of chemicals

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simultaneously in a sample.7-9 Most non-target studies to date have been conducted on water samples10

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but more recently the approach has been applied to biological samples as well.11-13 Non-target

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screening offers some clear advantages for HBM, yet the enormous quantity of data produced by such

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approaches remains a challenge. Data reduction strategies which filter and isolate important features

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for further identification are vital to the success of these methods. Case/control data designs are the

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most common and have been used, for example, for identification of biomarkers of exposure and

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effect in green sea turtles from the Great Barrier Reef,11 as well as novel fluorosurfactants in the blood

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of firefighters from Australia.13 Recently, we proposed another data reduction strategy involving time-

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trends, specifically for application in temporal trend biomonitoring studies.14 The approach involves

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flagging features which show an increasing temporal trend, while removing those with a random,

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decreasing, valley, or peaking trend. Features displaying an increasing time trend may represent

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emerging bioaccumulative contaminants which should be prioritized for further investigation. Similar

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conceptual approaches have been used for identifying transformation products7, 15 and for identifying

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emerging contaminants in sediment cores,16 but to our knowledge time trend screening in human blood

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has not yet been carried out.

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The objective of the present study was to apply a non-target time trend data reduction strategy to

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identify possible bioaccumulative contaminants in human blood. We performed non-target analysis on

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human whole blood samples from Germany, collected between 1983 – 2015. Features displaying an

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increasing time trend over the time series were subjected to further identification using a combination 3 ACS Paragon Plus Environment


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of data-dependent MS2 (ddMS2) experiments and database mining. Confirmation of a limited number

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of prioritized substances was achieved using authentic standards, thereby demonstrating the

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effectiveness of the workflow.

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Materials and Methods

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Standards and Reagents

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Standards of benzotriazole-d4, caffeine-d9, cotinine-d3, diglyme-d6, sucralose-d6, sulfamethoxazole-d4,

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and triethyl-phosphate-d15 were obtained from Toronto Research Chemicals (Toronto, Canada). 13C8-

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perfluorooctane sulfonate,

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obtained from Wellington Labs (Guelph, ON, Canada) while tonalide-d3 was obtained from Dr.

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Ehrenstorfer (Augsburg, Germany). An internal standard (IS) mixture was prepared using the

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aforementioned standards at a concentration between 5 – 10 µg/mL.

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C8-perfluorooctanoic acid and

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C12-hexabromocyclododecane were

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Sample Collection and Handling

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Anonymized human whole blood samples from female students aged 21-29 years were acquired from

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the German Environmental Specimen Bank (Münster, Germany), where they were stored at -150ºC. It

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is assumed that most contaminants are stable under these conditions, but clearly long-term stability

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studies are needed to confirm this assumption. A total of 6 samples were randomly selected every 4th

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year from 1983 to 2015, resulting in 9 different years and 54 samples in total. To test whether pooling

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affected the performance of our time-trend mining approach (as can occur in targeted time trend

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studies17), we analyzed samples both individually and as yearly pools. The pooled samples were

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prepared by combining 2 mL of blood from each of the individuals sampled on the same year.

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Extractions were carried out using a previously developed method.18 Briefly, after thawing at room

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temperature, blood samples (4 mL) were spiked with 20 µL IS-mixture and then vortexed. Four

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stainless steel beads, 4 mL acetonitrile, 1.6 g MgSO4 and 0.4 g NaCl were added, followed by

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thorough mixing using a bead blender for 2 min at 1500 rpm. After centrifugation (10 min at 3000

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rpm), 2.5 mL of supernatant were removed and concentrated to 300 µL under a stream of N2.

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QA/QC

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Instrumental drift was monitored by analysing a 20-fold dilution of the IS-mixture and a quality

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control (QC) sample (a pooled sample from 2015) repeatedly over the course of the sample sequence.

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Method detection limits (MDLs) for the 11 ISs were derived from repeated analysis of the QC sample

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(see SI for details). IS percent recoveries were determined by comparing their peak area in samples to

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their peak area in a solution prepared in acetonitrile. Finally, each batch of 9 samples was extracted

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together with a method blank consisting of 1 mL MilliQ water in order to monitor for background

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contamination.

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Instrumental Analysis

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Sample extracts were analysed using a Dionex UltiMate 3000 ultra-high performance liquid

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chromatograph (UHPLC) coupled to a Q Exactive HF hybrid Quadrupole-Orbitrap mass spectrometer

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(Thermo Scientific), equipped with a heated electrospray ionization source. A detailed description of

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instrumental conditions can be found in the supporting information (SI). Two different MS

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experiments were carried out using separate runs in positive and negative mode. First, all samples

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were run in full scan mode (100 – 1000 Da), referred to herein as the “screening experiment”. After

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data processing, an inclusion list was created based on features increasing over the time series (see

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Data Handling section). A second experiment was then performed on selected samples (the

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“identification experiment”) using full scan (100 – 1000 Da) with ddMS2 fragmentation on masses

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present in the aforementioned inclusion list. In both experiments, a resolution of 120,000 Full Width at

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Half Maximum (FWHM) at 200 m/z was used in full scan mode while the resolution of the ddMS2

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scan was set to 15,000 FWHM at 200 m/z with a stepped normalized collision energy of 30, 70 and

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120%.

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Data Handling

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Data from the screening experiment were processed using Compound Discoverer 2.0 (Thermo

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Scientific) using a workflow involving alignment, unknown compound detection, grouping and gap

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filling (exact parameters see Table S1, SI). All detected features were exported to Excel where they

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were split into individual (6 from each year) and pooled samples. A blank subtraction was conducted

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by deleting all features with peak areas 3 or ρ>0.5 for individual samples; TTR>5 or ρ>0.6 for pooled samples) were combined in an

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inclusion list to record ddMS2 scans during the identification experiment.

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mzCloud and MetFrag search

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Data from the identification experiment were processed in Compound Discoverer 2.0 using a

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workflow which included additional mass list search of an internal suspect list (i.e. features with

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increasing trends in the screening experiment), and an mzCloud search (parameters see Table S1). All

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features in the resulting list with recorded MS2 spectra were selected for further processing using

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MetFrag, which combines database searching and fragmentation prediction for small molecules.19

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Prioritization using KEMI market list



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Features displaying an increasing trend in the screening experiment were also subjected to further

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investigation using the KEMI market list (including ~30000 substances, available on the NORMAN

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Suspect Exchange webpage20), which contains chemicals expected to be on the European market. This

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database facilitates prioritization of suspects based on a human chronic hazard score (1-9; 9 represents

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the greatest hazard) and a human exposure score (0-27; 27 represents the highest exposure) which are

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calculated according to existing hazard data and confidential data about use pattern, tonnage, and

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hazards in a similar manner as described previously.20 The monoisotopic mass derived from the m/z of

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features with increasing time trends were matched with the database via an in-house script using a 5

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ppm tolerance. Out of these matches all halogen containing compounds and those with human

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exposure score ≥15 and/or a human chronic hazard score of ≥3 were selected for further investigation.

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These thresholds were found to effectively reduce the number of potential matches while prioritizing

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substances with higher potential of human hazard and/or exposure.

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Confirmation using authentic standards

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Standards were obtained for 11 substances tentatively identified by either the ddMS2 +

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mzCloud/MetFrag workflow or KEMI market list search. These substances were subjected to

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additional UHPLC-HRMS analyses, using exact mass, isotopic pattern, retention time and MS2

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fragmentation for confirmation.

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Results and Discussion

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QA/QC

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MDLs ranged from 0.01 – 1.6 ng/mL whole blood (Table S2) which are similar to traditional targeted

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methods, demonstrating the suitability of the method for biomonitoring of environmental pollutants in

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blood. IS recoveries ranged from 61.9 – 101.7% for most targets (Figure S1, total range 36.3 –

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101.7%), demonstrating reasonable accuracy of the method. The exceptions were for sucralose-d6 and

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cotinine-d3 which displayed lower (albeit reproducible) recoveries of 36.3 and 36.8%, respectively.

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More importantly, IS recoveries in the sequence QCs (run every 10 samples) displayed an absence of

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trends over the course of the run for most substances, with the exception of tonalide-d3, which

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displayed a weak yet significant (p=0.0038, r=-0.79) declining trend, and cotinine-d3 which displayed

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a positive trend (p=0.0012, r=0.84) (Figure S2). Considering that all samples were randomized

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throughout the run, the slight instrumental drift observed here is unlikely to have a significant effect

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on the observed time trends. Therefore, sequence correction, as described in some metabolomics

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studies,21 was not performed.

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Method precision was also acceptable, with IS RSDs in all samples ranging from 13.4 – 33.5 % in

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positive mode and 13.1 – 26.1 % in negative mode, except for sucralose-d6 which displayed greater 6 ACS Paragon Plus Environment

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variability (63.8 %) due to low peak intensities. The sequence QC sample displayed much lower

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variability (6.5 – 28.2 % RSD in positive and 3.2 – 18.1 % in negative mode; 32.4 % for sucralose-d6),

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indicating that random error from the instrument only accounts for a small part of the overall

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variability. Overall, method performance was comparable to other non-target studies and suitable for

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the non-target time trend screening performed here.

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Time trend filtering

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The screening experiment produced 14460 features in individual samples and 13525 features in pooled

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samples (sum of features in positive and negative mode; Figure 1). These values reflect removal of

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isotopes and adduct peaks, but may include substances which ionize by both positive and negative

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mode. Time trend filtering reduced the number of features to 281 in individual samples (TTR>3 or

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ρ>0.5) and to 716 in pooled samples (TTR>5 or ρ>0.6). The larger number of features in pooled

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samples (despite using higher thresholds in pooled samples) might reflect both greater variability in

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individual samples, resulting in lower TTRs or Spearman’s ρ-values, as well as a higher frequency of

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low abundance features in pooled versus individual samples. The features with positive trends were

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subjected to further investigation via a) the identification experiment + mzCloud and MetFrag search,

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and b) searching the KEMI market list.

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Identification experiment + mzCloud and MetFrag search

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The identification experiment was carried out using an inclusion list based on features shortlisted from

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the time trend filtering step (i.e. 281 in individual and 716 in pooled samples). Of these, the

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identification experiment resulted in MS2 spectra for only 115 of these features in individuals and 134

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in pooled samples. Other features in the inclusion list were below the set threshold for recording MS2

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data. Targets for which MS2 spectra were obtained were subjected to database mining using mzCloud

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and MetFrag.

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A total of 17 features in individuals and 16 features in pooled samples produced matches in MetFrag

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or mzCloud (mzCloud match score ≥80, Table S3). Of these, 8 substances were identified in both

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individuals and pools and 1 substance (18-β-Glycyrrhetinic acid) was observed in both positive and

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negative mode. Notably, 7 pharmaceutical substances were only present in a single sample between

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2007 and 2015 (individual samples), resulting in a high TTR value, but very low or even negative

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Spearman’s ρ values. Thus, caution is warranted when applying a TTR to individuals. This

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phenomena may be controlled either by using pooled samples or by removing samples producing the

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highest peak in the time series and recalculating TTR and ρ. However, to maximize the number of

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features included in database mining, this was not carried out in the present work. Overall, a total of

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17 unique substances were tentatively identified in >1 sample (individual or pooled samples).

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KEMI market list

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A comparison of the monoisotopic mass of positive time trend features in the screening experiment to

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the KEMI market list resulted in 287 matches for individuals (209 unmatched / 72 with an average of

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4 matches/feature) and 833 matches for pools (544 unmatched / 172 with an average of 4.8

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matches/feature). From this, we prioritized 19 halogen-containing compounds and 31 substances with

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exposure scores ≥15 and/or hazard scores ≥3 (see Table S4). Overall, 50 unique substances were

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tentatively identified (individual or pooled samples) of which 3 overlapped with the ddMS2 +

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mzCloud/MetFrag workflow (desloratidine, oxazepam and diazepam).

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Structural confirmation

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Of the 11 substances for which standards were obtained, structures were confirmed for 4 per-

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/polyfluoroalkyl substances (PFASs), 2 pesticides and 1 pharmaceutical. Time trends for these

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substances are illustrated in Figure 2 and a comparison of MS2 spectra and chromatograms in samples

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versus standards is provided in Figures S3-S11. Ibuprofen and dibenzepin (identified using MetFrag),

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as well as quinmerac and 1-octanamine (identified using the KEMI market list) were all confirmed

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negative (Tables S3+S4). Considering that the KEMI list relies on exact masses of the parent ion,

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while MetFrag utilizes in silico MS2 data, a lack of experimentally-derived MS2 data probably

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contributed to the misidentification. Such false positives could be reduced by extending

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experimentally-derived MS2 databases like mzCloud and Massbank with data for environmental

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pollutants. Increasing PFAS time trends in human serum in samples from the German specimen bank

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have been previously reported22, 23 and their detection here serves as a positive control for the entire

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workflow. The pharmaceutical metabolite triclosan-sulfate has been detected in human serum

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previously,24 but to the best of our knowledge this is the first report of an increasing time trend for this

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substance. Hydroxychlorothalonil, a transformation product of the EU-approved fungicide

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chlorothalonil25 was also confirmed. Previously this substance was tentatively identified in human

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breast milk samples, but time trends were not reported.26 Finally, haloxyfop is an approved pesticide in

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several European Countries, including Germany.25 To the best of our knowledge, this is the first time

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this compound has been detected in human blood.

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Perspectives on the use of non-target time trend screening in HBM

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The three databases employed here have clear advantages and disadvantages for non-target studies

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focusing on identification of xenobiotics. While MetFrag searches are time consuming, they can lead

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to identification of unique substances (e.g. transformation products) which are not present in the

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KEMI market list or mzCloud (e.g. hydroxychlorothalonil and triclosan-sulfate). While the KEMI

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market list is the only database in the present study to prioritize substances based on hazard and/or

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likelihood of human exposure (similar approaches have been described using other databases27, 28), it

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relies exclusively on exact mass searches based on the parent ion, which can result in false positives. 8 ACS Paragon Plus Environment

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MzCloud was by far the most accessible database in the present work, but this is not surprising given

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its development by Thermo Scientific, the manufacturer of the Compound Discoverer software used in

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the present work. However, mzCloud currently contains very few environmental pollutants, thereby

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severely limiting its utility in the present work. Considering the advantages and disadvantages of each

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of the databases used here, we recommend a combined approach for future non-target HBM studies.

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Overall, this study demonstrated that a non-target approach, combined with time-trend data reduction

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and database mining can lead to the detection of novel substances which display increasing

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concentrations over decades in human blood. While we limited ourselves to confirming substances

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with positive time trends where standards were readily available, there remain 38 of as-of-yet

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unidentified features with very high TTR or Spearman’s ρ (TTR≥10 or ρ≥0.7, see Table S5). These

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features may represent contaminants to which humans are increasingly exposed or which are

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bioaccumulating in human blood over time, and should be prioritized for further identification.

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Supporting Information

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The supporting information contains figures and tables about method details, identified substances and

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confirmed compounds and is available free of charge on the ACS Publications website.



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Figure 1: Schematic workflow including the number of peaks at each step and confirmed compounds. ‘+’: analysed in positive mode and ‘-‘: analysed in negative mode.

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Figure 2: Trends of confirmed substances detected in individual samples (coloured circles) and pooled samples (black circles except for 8:2 FTS with blue circles), including smoothed trend lines (loess regression) and 90% confidence intervals (coloured for individual and grey for pooled samples)

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Non-target time trend screening in human blood - Environmental

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