Combining the targeted and untargeted screening of environmental contaminants reveals associations between PFAS exposure and vitamin D metabolism in human plasma

Abstract

We have developed, validated, and applied a method for the targeted and untargeted screening of environmental contaminants in human plasma using liquid chromatography high-resolution mass spectrometry (LC-HRMS). The method was optimized for several classes of environmental contaminants, including PFASs, OH-PCBs, HBCDs, and bisphenols. One-hundred plasma samples from blood donors (19–75 years, men n = 50, women n = 50, from Uppsala, Sweden) were analyzed. Nineteen targeted compounds were detected across the samples, with 18 being PFASs and the 19th being OH-PCB (4-OH-PCB-187). Ten compounds were positively associated with age (in order of increasing p-values: PFNA, PFOS, PFDA, 4-OH-PCB-187, FOSA, PFUdA, L-PFHpS, PFTrDA, PFDoA, and PFHpA; p-values ranging from 2.5 × 10⁻⁵ to 4.67 × 10⁻²). Three compounds were associated with sex (in order of increasing p-values: L-PFHpS, PFOS, and PFNA; p-values ranging from 1.71 × 10⁻² to 3.88 × 10⁻²), all with higher concentrations in male subjects compared with female subjects. Strong correlations (0.56–0.93) were observed between long-chain PFAS compounds (PFNA, PFOS, PFDA, PFUdA, PFDoA, and PFTrDA). In the non-targeted data analysis, fourteen unknown features correlating with known PFASs were found (correlation coefficients 0.48–0.99). Five endogenous compounds were identified from these features, all correlating strongly with PFHxS (correlation coefficients 0.59–0.71). Three of the identified compounds were vitamin D₃ metabolites, and two were diglyceride lipids (DG 24:6;O). The results demonstrate the potential of combining targeted and untargeted approaches to increase the coverage of compounds detected with a single method. This methodology is well suited for exposomics to detect previously unknown associations between environmental contaminants and endogenous compounds that may be important for human health.

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Environmental significance

Combining the targeted and untargeted screening of environmental contaminants in human plasma may detect previously unknown associations between environmental contaminants and endogenous compounds with important implications for human health. Here we demonstrate how the methodology may be used to detect previously unknown associations between known environmental contaminants and endogenous metabolites and lipids. Specifically, we found strong associations between the perfluorohexanesulfonic acid and three metabolites of vitamin D₃ and two diglyceride lipids. The present method is fast and straightforward and applicable for high-throughput exposome profiling of large cohorts.

1 Introduction

It has been estimated that environmental exposures likely have a much more significant impact on chronic diseases than genetic factors.^1–3 To acknowledge this the exposome concept was introduced to describe the totality of exposures of an individual throughout life, considering both exogenous and endogenous exposures.^4–6 To measure the exposome, different techniques are needed,⁷ and an important group of compounds is environmental contaminants. A subset of environmental contaminants has been described as environmental organic acids, defined as environmental organic compounds utilized in commerce with at least one ionizable proton.⁸ This group of compounds includes per- and polyfluoroalkyl substances (PFASs),^9,10 the phenolic metabolites of polybrominated diphenylethers (OH-BDEs)¹¹ and polychlorinated biphenyls (OH-PCBs),¹² and bisphenols.¹³ There are several exposure sources of these substances ranging from food, drinking water, and consumer products to both indoor and outdoor environments.^9,12–15 These compounds are of serious concern because many of them have, or could potentially have, endocrine disrupting effects and other health effects, such as association with an increased risk of cancer and immune, reproductive, and developmental effects, as well as the potential to bioaccumulate.^12,16–20 Because of the typically low abundances of environmental contaminants in blood, about 1000 times lower compared to endogenous compounds and drugs,⁶ highly sensitive methods are required. This may be achieved by using targeted measurements with triple quadrupole mass spectrometry²¹ or untargeted discovery with high-resolution mass spectrometry instruments coupled to liquid chromatography (LC-HRMS). The benefit of using LC-HRMS in the untargeted screening mode is that the data may be used to discover previously unknown environmental contaminants and to find previously unknown associations with both endogenous and exogenous compounds.^21,22

We developed a method for high-throughput combined targeted and untargeted screening of environmental contaminants in human plasma using LC-HRMS. The methodology was inspired by our previous work in untargeted metabolomics and lipidomics,^23–25 applying fast and straightforward workflows for data analysis not typically used for environmental contaminant screening. The result is a method applicable for high-throughput exposome profiling of large cohorts, giving both quantitative measures of environmental contaminants and the possibility to reveal previously unknown associations between environmental exposures and endogenous compounds in the same experiments.

2 Materials and methods

2.1. Chemicals

Methanol (MeOH, ≥99.9%, MS grade) was purchased from Honeywell (Seelze, Germany), acetic acid (100%, anhydrous for analysis) and ammonium acetate (NH₄Ac) from Merck (Darmstadt, Germany), and water was purified using a Milli-Q IQ 7000 system (Merck Millipore, Burlington, MA, USA).

All standards and references were purchased from Wellington Laboratories (Guelph, Ontario, Canada). A detailed summary of all references and standards used with molecular formulae, monitored m/z-values, and retention times is given in the ESI (Table S1†). In summary, the following were used, including Wellington product names within parentheses, representing a total of 37 different compounds; native standards: a mixture of 24 common PFASs (PFAC-24PAR), bisphenol A (BPA), and a mixture containing α-, β-, and γ-HBCD (HBCD-MXA); heavy isotope labeled standards: a mixture of 19 common labeled PFASs (MPFAC-24ES), labeled bisphenol A (MBPA), a mixture containing labeled α-, β-, and γ-HBCD (MHBCD-MXA), labeled 6-OH-BDE47 (M6HBDE47), labeled 6-OH-BDE100 (M6PHBDE100), and a mixture containing seven labeled OH-PCBs (MHPCB-MXA).

2.2. Study samples

The use of surplus sample volumes from routine samples was approved by the Uppsala Regional Ethics Committee, Uppsala, Sweden (01/367). The blood donor plasma samples (n = 100) were collected in 2021 and in agreement with the ethical permit, only age and sex were gathered for the subjects. The samples were stored at −80 °C until sample preparation. Prepared samples were stored at −80 °C until analysis. The Declaration of Helsinki and its subsequent revisions were followed. The demographic data of the subjects are summarized in Table 1. Newborn bovine serum (USA) was purchased from Sigma-Aldrich and stored at −20 °C until analysis.

Table 1 Demographic data of the studied subjects

Sex	n	Age in years, mean (±SD)	Median age in years	Age range in years
Female	50	44.9 (±14.0)	47	19–71
Male	50	45.6 (±14.0)	48	21–75

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2.3. Sample preparation

The samples were prepared in randomized order. Fifty μL plasma was added to 140 μL MeOH in 0.6 mL Eppendorf tubes, followed by adding 10 μL internal standards (IS) in MeOH (final concentration 2.5 ng mL⁻¹ of each IS). The samples were then vortexed for 15 s and centrifuged at 4 °C with 14 800 rpm. One hundred μL of the supernatants were transferred to plastic HPLC vials (Thermo Scientific) with polyimide-lined caps (Macherey-Nagel, Dueren, Germany) and stored at −80 °C until analysis. Samples were prepared in six batches with each batch containing up to 28 samples. Ten μL of each prepared sample were pooled and used for quality control throughout the experiments. For MS/MS (MS2) data collection experiments, a pooled prepared sample was concentrated about four times. The sample was evaporated under a gentle stream of nitrogen and reconstituted in 3 : 1 MeOH : H₂O.

2.4. Calibration curves and quality controls

Internal standard calibration was performed for all compounds where both native and heavy isotope labeled standards were available. For compounds for which only heavy isotope labeled standards were available, one-point calibration compared with the corresponding internal standard was performed. Calibration curves, quality controls, and blanks were prepared using newborn bovine serum filtered through ENVI-Carb SPE tubes (250 mg, Merck). The used filters contain graphitized non-porous carbon and were used for the purpose of obtaining a calibration matrix with decreased background levels of, e.g., PFASs.²⁶ Calibrators and QCs were prepared the same way as the authentic samples, but the total 150 μL MeOH used for precipitation was made up of 130 μL MeOH, 10 μL IS MeOH solution (the same as for samples described above), and 10 μL native reference MeOH solution (of varying concentrations).

The calibration curves were prepared at seven calibrator levels, spanning the plasma concentration range of 0.02–80 ng mL⁻¹. As most of the reference standards used were provided as mixtures, the same concentrations were used for all targeted compounds, i.e., not considering individual reference intervals for each compound. The approximate limit of detection (LOD) and lowest limit of quantification (LLOQ) for each specific compound were estimated by visual evaluation of the corresponding peaks in the calibrator samples, considering a peak with height >2 × 10³ that matches the corresponding internal standard as a clearly detected peak. For estimations of LODs and matrix effects, background (blank) subtraction was performed for the compounds for which background levels were observed. The QCs were prepared at two concentration levels: 2 ng mL⁻¹ (QCL) and 20 ng mL⁻¹ (QCH). The calibration curve was prepared once with the first batch of samples. Three of each QC (QCL and QCH) were prepared with all batches. Also prepared with each batch of samples was one blank sample without IS and one blank sample with IS using the same newborn bovine serum used for calibration curves and quality controls.

The calibration curve was also prepared in 3 : 1 MeOH : H₂O (without matrix) to estimate matrix effects. Two corresponding calibration curves were also prepared for the heavy isotope-labeled standards to control their linearity. Solvent blanks (3 : 1 MeOH : H₂O) with and without IS were prepared to control possible carry-over. The bovine serum calibration curves were used for estimating sample concentrations and 1/x² weighting was used for each curve to acknowledge the large range of concentrations covered.

2.5. Liquid chromatography-mass spectrometry analysis

Samples were injected in a randomized order. After every tenth sample, an injection of the pooled sample followed by a newborn bovine serum blank injection was done. Prior to injecting the study samples, all calibrator samples and QC samples were injected. Six repeated newborn bovine serum blank injections conditioned the column before any runs, and before the injection of samples, six repeated injections of the pooled sample were done.

Twenty μL of each sample was injected on a reversed-phase HPLC column (Accucore C18, 100 × 2.1 mm, 2.6 μm, Thermo Scientific) using an Ultimate 3000 HPLC system (Thermo Scientific) interfaced to a high-resolution hybrid quadrupole Q Exactive Orbitrap MS (Thermo Scientific). A 17.5 min long chromatographic program including a gradient was applied using the mobile phases H₂O with 0.1% acetic acid and 10 mM NH₄Ac (mobile phase A) and MeOH with 0.1% acetic acid and 10 mM NH₄Ac (mobile phase B); 5% B for 1 min, 5–100% B for 9.5 min, hold at 100% B for 3 min, return to 5% B over 0.1 min followed by re-equilibration at 5% B for 3.9 min. The flow rate was 0.6 mL min⁻¹, and the column temperature was 55 °C.

HRMS was operated in negative ionization mode with the following source parameters: spray voltage: 2.5 kV, capillary temperature: 275 °C, sheath gas flow rate: 55, auxiliary gas flow rate: 15, sweep gas flow rate: 3, S-lens RF level: 50, auxiliary gas heater: 450 °C. The HRMS analysis was performed in the full scan mode, collecting data in the profile mode with a resolution of 70 000 in the range of m/z 212.5–750.

Following the full-scan experiments of all samples, MS2 experiments were performed using a solution of all standards and references (both native and heavy isotope-labeled) at a concentration of 50 ng mL⁻¹ as well as a concentrated pooled sample (four-fold concentration). The MS2 experiments were performed in six m/z windows: (1) 212.5–300 m/z, (2) 290–390, (3) 380–480, (4) 470–570 m/z, (5) 560–660 m/z, (6) 650–750 m/z. For each separate m/z window, ten experiments (with separate injections) were done, both for the reference solution and the pooled sample: one full scan (MS1) followed by nine data-dependent MS2 scans at nine different collision energies (normalized collision energy, NCE): 10, 15, 20, 22.5, 25, 27.5, 30, 35, and 40 (in total 120 experiments). Apart from varying NCEs the settings were the same for all data-dependent MS2 experiments: resolution: 35 000, loop count: 10 (TopN), maximum injection time: 200 ms, and isolation window: 1.9 m/z, with data collected in the profile mode. Chromatography was performed in the same way as the main MS1 experiments. Twenty μL was injected for each experiment.

During method development, several parameters were evaluated and optimized. For mobile phase additives, three options were evaluated: (1) 0.1% acetic acid, (2) 2 mM ammonium acetate (NH₄Ac), and (3) 0.1% acetic acid with 10 mM NH₄Ac, with the third option resulting in optimal responses and peak shapes. The following ion source parameters were evaluated for the targeted compounds, with the tested and optimal values within parentheses: capillary temperature (250 °C, 275 °C, 300 °C, 325 °C, and 350 °C; optimal: 275 °C), S-lens RF level (0, 25, 50, 75, and 100; optimal: 50), and spray voltage (2.00 kV, 2.25 kV, 2.50 kV, 2.75 kV, and 3.00 kV; optimal: 2.50 kV).

2.6. Statistical analysis

The LC-HRMS data for targeted compounds were processed using TraceFinder 4.1 software (Thermo Scientific) using ten ppm mass tolerance to extract ion chromatograms. The concentration of targeted compounds was estimated using 1/x² weighted calibration curves with internal standard calibration. For some of the compounds, a background was detected in the newborn calf serum used as the matrix for calibration curves and QCs (in order of increasing background level: PFDA, 6:2FTS, PFOS, PFTrDA, PFNA, and PFUdA). For these compounds, the concentration in the calf serum was calculated using the standard addition method, and the estimated concentrations were adjusted using this information. Injections of the precipitation solvent showed negligible background of these compounds, thus identifying the calf serum as the background source.

To investigate the associations of the targeted compounds with age and sex, linear models were fitted on log-transformed concentration data from TraceFinder, including compounds present in ≥15% of the samples. The linear models used age, sex and the interaction age: sex as independent variables and compound concentrations as dependent variables, according to the following equation: y = b₀ + b₁ × age + b₂ × sex + b₃ × age × sex. The variables were evaluated individually with p < 0.05 as the limit for significance. The correlations of the concentrations between compounds were estimated using Pearson correlation. The resulting correlation coefficients were used for distance calculation between the compounds and the obtained distance matrix was subjected to hierarchical clustering. This is plotted as heatmaps, where the compounds are clustered based on the correlation between them.

For the untargeted data the raw data was converted to mzML using MSConvert, peak picked using FeatureFinder, and linked using FeatureLinker,²⁷ as previously described by us.²⁴ FeatureFinder and FeatureLinker were run using in-house nf-core-metabolinden workflow²⁸ to quantify and link features in this data, using non-default parameters (available at https://github.com/caramba-uu/env-con).²⁴ All statistical analyses and plots were done using python-3.10.4,²⁹ scipy-1.7.3,³⁰ sklearn-1.0.2,³¹ seaborn-0.11.2,³² numpy-1.23.1 (ref. 33) and pandas-1.4.3.³⁴

2.7. Identification of unknown compounds

In the untargeted evaluation, features present in >90% of the samples and a correlation coefficient of absolutely >0.6 were used to obtain unknown features having at least a moderate correlation with targeted compounds. The m/z of the resulting features was compared against the NORMAN database.³⁵ To improve our identification of unknown compounds, we made a linear model to predict log K_ow values for the unknown compounds using the retention times and listed log K_ow values in the NORMAN database for the targeted compounds (estimated by EPISuite³⁶). When multiple matches were found, the candidates were evaluated based on the listed log K_ow values compared to our predicted log K_ow values. A similar log K_ow was deemed to increase the likelihood of the matching compound. The NORMAN database focuses on exogenous environmental compounds. For unknowns deemed to correspond to endogenous substances not found in the NORMAN database, we used HMDB,³⁷ Metlin,³⁸ and Lipid Maps³⁹ to find likely candidates within 5 ppm of the observed m/z. Furthermore, the available MS2 spectra were compared with molecular structure databases using CSI:FingerID.⁴⁰ Finally, the matching MS2 spectra were manually evaluated to strengthen the identification.

3 Results

We developed a combined targeted and untargeted method for screening environmental contaminants using LC-HRMS. The method was optimized for a total of 37 environmental contaminants across different chemical classes: PFASs (24 compounds), OH-PBDEs (two compounds), OH-PCBs (seven compounds), HBCDs (three isomers), and bisphenol A. One hundred plasma samples from blood donors ranging in age from 19 to 75, with 50 females and 50 males, were screened with the method. Nineteen of the monitored compounds were detected across the samples. Their concentrations were estimated based on calibration curves. Full names and abbreviations for the 19 compounds are given in Table 2. The method was validated by evaluating repeated injections of pooled and spiked QC samples. The MS1 and MS2 data were further investigated for untargeted compounds associated with the detected targeted compounds.

Table 2 Concentrations for the 19 environmental contaminants detected in the 100 analyzed plasma samples from blood donors^a

Compound	Abbreviation	Median conc. (ng mL^−1)	Min conc. (ng mL^−1)	Max conc. (ng mL^−1)	CV (%)	Coverage (%)	p age	p sex
a Bold: p < 0.05.
1H,1H,2H,2H-perfluoro-1-decanesulfonic acid (8 : 2)	8:2FTS	<LLOQ	<LLOQ	0.02	13	53	2.64 × 10^−1	7.42 × 10^−1
1H,1H,2H,2H-perfluoro-1-octanesulfonic acid (6 : 2)	6:2FTS	0.27	0.27	0.33	3	100	3.95 × 10^−1	1.09 × 10^−1
2,2′,3,4′,5,5′,6-Heptachloro-4-biphenylol	4-OH-PCB-187	<LLOQ	<LLOQ	<LLOQ	132	15	7.12 × 10^− ^4	6.90 × 10^−1
N-ethylperfluoro-1-octanesulfonamidoacetic acid	N-EtFOSAA	0.06	0.06	0.14	34	10	4.08 × 10^−1	2.83 × 10^−1
N-methylperfluoro-1-octanesulfonamidoacetic acid	N-MeFOSAA	0.08	0.08	2.15	201	49	2.83 × 10^−1	5.05 × 10^−1
Perfluoro-1-octanesulfonamide	FOSA	<LLOQ	<LLOQ	0.06	46	93	1.58 × 10^− ^3	2.46 × 10^−1
Perfluorobutanesulfonic acid	L-PFBS	<LLOQ	<LLOQ	0.07	72	79	7.24 × 10^−1	3.20 × 10^−1
Perfluoroheptanesulfonic acid	L-PFHpS	0.10	0.02	0.42	53	100	4.13 × 10^− ^3	1.71 × 10^− ^2
Perfluorohexanesulfonic acid	PFHxS	1.50	0.23	31.82	140	100	9.63 × 10^−1	5.06 × 10^−1
Perfluoro-n-decanoic acid	PFDA	0.19	0.05	0.83	59	100	2.30 × 10^− ^4	1.11 × 10^−1
Perfluoro-n-dodecanoic acid	PFDoA	<LLOQ	<LLOQ	0.07	87	61	3.30 × 10^− ^2	5.44 × 10^−1
Perfluoro-n-heptanoic acid	PFHpA	0.03	<LLOQ	0.13	66	94	4.67 × 10^− ^2	8.61 × 10^−1
Perfluoro-n-hexanoic acid	PFHxA	<LLOQ	<LLOQ	0.02	39	55	5.13 × 10^−2	6.27 × 10^−1
Perfluoro-n-nonanoic acid	PFNA	0.37	0.10	2.15	70	100	2.50 × 10^− ^5	3.88 × 10^−2
Perfluoro-n-octanoic acid	PFOA	0.85	0.29	2.96	54	100	8.83 × 10^−2	2.56 × 10^−1
Perfluoro-n-tridecanoic acid	PFTrDA	<LLOQ	<LLOQ	0.11	121	49	6.78 × 10^− ^3	8.03 × 10^−1
Perfluoro-n-undecanoic acid	PFUdA	0.15	<LLOQ	0.76	74	98	3.96 × 10^− ^3	2.41 × 10^−1
Perfluorooctanesulfonic acid	PFOS	3.39	0.82	15.16	58	100	2.89 × 10^− ^5	2.36 × 10^−2
Perfluoropentanesulfonic acid	L-PFPeS	0.04	<LLOQ	0.45	104	99	1.87 × 10^−1	5.92 × 10^−1

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3.1. Method validation

The developed method displayed desirable robustness and selectivity for the targeted analytes. The CV in concentration and retention time for the detected targeted analytes was calculated based on the repeated injections of the pooled sample (containing equal volumes of all 100 samples, total number of injections, n = 22). The CV in concentration ranged from 0.9% to 12.3%, with a mean CV of 5.9%. The CV in retention time ranged from 0.1% to 0.4%, with a mean CV of 0.2%. This demonstrates the high instrumental reproducibility of the method. A representative chromatogram from an injection of the pooled sample is presented in the ESI (Fig. S1†). Validation results for spiked QC samples and calibrators are summarized in the ESI (Table S2†).

The calibration curves for the targeted analytes, using 1/x² weighting, generally exhibited good linearity with a mean coefficient of determination (R²) of 0.959 (range 0.594–0.995). Except for 6:2FTS (R² = 0.594), BPA (R² = 0.612), and PFUdA (R² = 0.881), the remaining compounds exhibited R²-values in the range 0.974–0.995. Carry-over was evaluated by investigating the presence of the targeted compounds in the solvent blank injected directly after the highest calibrator (80 ng mL⁻¹). For the majority of compounds, no carry-over was observed, and for the two compounds with observed carry-over (FOSA and PFHpA) the carry-over was very low considering the concentrations observed in actual samples (corresponding to <40% of the lowest calibrator, i.e., <0.01 ng mL⁻¹ after injection of 80 ng mL⁻¹).

For 21 of the 37 targeted compounds, including 16 of the 19 compounds detected in the samples under study (see Section 3.2. below), the response was good with clearly detected peaks in the lowest calibrator sample (0.02 ng mL⁻¹) with average peak heights 7 × 10³ (ranging 2 × 10³–2 × 10⁴; adjusted for background concentrations when relevant) and matching the corresponding IS (where IS was available). By using 10 ppm filtering all noise was virtually removed from the extracted ion chromatograms, resulting in easily interpretable data. For the 21 compounds with good response at the lowest calibrator, LODs were estimated to be approximately 0.01 ng mL⁻¹, i.e., half of the lowest calibrator with the corresponding LLOQ (lowest limit of quantification) at 0.02 ng mL⁻¹. For these compounds the calibration curves spanned the plasma concentration range of 0.02–80 ng mL⁻¹. For the remaining 16 compounds, the response was not as good with large variations in response between compounds. The majority of these compounds, with the exception of N-MeFOSAA, N-EtFOSAA, and 4-OH-PCB-187 (that had acceptably low LODs), were not detected in the samples under study. By visually evaluating the peak height at the different calibration levels we estimated LODs and LLOQs as follows: 4:2FTS, N-MeFOSAA, N-EtFOSAA, 6-OH-BDE47, 4-OH-PCB-187, and 4-OH-PCB-61, LOD at 0.04 ng mL⁻¹ and LLOQ at 0.08 ng mL⁻¹ (calibration curves spanning the plasma concentration range 0.08–80 ng mL⁻¹); 4-OH-PCB-172, 4-OH-PCB-159, 4-OH-PCB-29, and 4-OH-PCB-12, LOD at 0.16 ng mL⁻¹ and LLOQ at 0.32 ng mL⁻¹ (calibration curves spanning the plasma concentration range 0.3–80 ng mL⁻¹); 6-OH-BDE100, PFBA, and HBCD (including all three isomers), LOD at 0.63 ng mL⁻¹ and LLOQ at 1.25 ng mL⁻¹ (calibration curves spanning the plasma concentration range 1.25–80 ng mL⁻¹); BPA, LOD at 2.5 ng mL⁻¹ and LLOQ at 5 ng mL⁻¹ (calibration curve spanning the plasma concentration range 5–80 ng mL⁻¹). The matrix effect was on average 82% (range 43–168%; adjusted for background concentrations when relevant), indicating low matrix effects with slightly more suppression (<100%) than enhancement (>100%) on average.

Spiked QC samples at two concentration levels (2 ng mL⁻¹ and 20 ng mL⁻¹) were used to evaluate within- and between-batch accuracy and precision (detailed results in ESI†). The precision measured as CVs of the targeted analytes was low, ranging from 1.4% to 9.0% within-batch (mean 3.2%) and 4.8–7.9% between-batch (mean 6.0%). The bias was acceptable, ranging from −8–33.6% within-batch (mean 1.3%) to −4.9–53.7% between-batch (mean 6.0%), compared to the nominal values. Only four compounds had bias values over >10% (6:2FTS, PFNA, PFTrDA, and PFUdA). The high bias values for PFNA, PFTrDA, and PFUdA were caused by the high background concentrations in the calf serum used for calibration curves. Overall, these results demonstrate a generally accurate and precise method.

3.2. Results of targeted analysis

The developed method was applied to analyze 100 plasma samples from blood donors. Out of the 37 targeted compounds, 19 were detected across the samples including 18 PFAS compounds and one hydroxylated PCB metabolite (4-OH-PCB-187) (Table 2).

Eleven of the 19 compounds were detected in ≥93% of the studied samples, whereas some were only detected in a subset. The least detected compounds were N-EtFOSAA and 4-OH-PCB-187, which were detected in 10 and 15 samples, respectively. The estimated concentrations for all compounds were within the low end of the calibration curve, with the highest single concentrations observed across the samples for PFHxS at 31.8 ng mL⁻¹ and PFOSs at 15.2 ng mL⁻¹ (Table 2).

Ten compounds (in order of increasing p-values: PFNA, PFOS, PFDA, 4-OH-PCB-187, FOSA, PFUdA, L-PFHpS, PFTrDA, PFDoA, and PFHpA) were positively associated with age (p-values ranging from 2.5 × 10⁻⁵ to 4.67 × 10⁻²) (Table 2). Three compounds (in order of increasing p-values: L-PFHpS, PFOS, and PFNA) were associated with sex (p-values ranging from 1.71 × 10⁻² to 3.88 × 10⁻²), all with higher concentrations in male subjects compared with female subjects (Table 2). No significant age × sex interactions were found. Four illustrative examples (PFNA, PFOS, PFDoA, and 4-OH-PCB-187) of the observed differences regarding age and sex are presented in Fig. 1.

Fig. 1 Examples of compounds that exhibited significant differences associated with age: (A) PFNA (p = 2.5 × 10⁻⁵); (B) PFOS (p = 2.89 × 10⁻⁵); (C) 4-OH-PCB-187 (p = 7.12 × 10⁻⁴); (D) PFDA (p = 2.3 × 10⁻⁴); and sex: (A) PFNA (p = 3.88 × 10⁻²) and (B) PFOS (p = 2.36 × 10⁻²). The regression line is not shown for 4-OH-PCB-187 because of low coverage and concentrations <LLOQ for this compound (estimated LLOQ = 0.08 ng mL⁻¹); the concentrations in the figure were estimated using one-point calibration compared with the corresponding internal standard only considering samples with clearly detected peaks. Male subjects are labeled with blue circles and female subjects with orange triangles.

Moderate to strong correlations (0.56–0.93) were observed between the concentrations of long-chain PFAS compounds with high coverage: PFNA, PFOS, PFDA, PFUdA, PFDoA, and PFTrDA. L-PFHpS correlated with PFOS, PFOA, and PFHxS (0.48–0.69). The structurally similar FOSA and N-MeFOSAA were strongly correlated (0.93). The short-chain L-PFBS correlated with the other short-chain L-PFPeS (0.78) and PFHpA (0.68). PFHxS correlated with L-PFPeS (0.70) and L-PFHpS (0.68). The non-PFAS compound, 4-OH-PCB-187, demonstrated weak to moderate correlations to PFAS compounds (≤0.53). A single compound, the short-chain 6:2FTS, exhibited virtually no correlation with any of the other investigated compounds (≤0.10) (Fig. 2).

Fig. 2 Correlation heatmap for the compounds observed in the targeted analysis (n = 18, coverage cutoff ≥ 15%). The percentage values within parentheses next to compound names indicate compound coverage. The stars (*) below correlation coefficients indicate significance: ***: p ≤ 0.001; **: p ≤ 0.01; *: p ≤ 0.05. A detailed heatmap with the specific p-values is found in the ESI (Fig. S2†).

3.3. Results of untargeted screening

In the untargeted data analysis, 39 538 features were detected and quantified. Eleven out of the 18 compounds found in the targeted approach were also quantified in the untargeted data. The compounds not detected in the untargeted data analysis were compounds with low concentrations or low responses, e.g., 4-OH-PCB-187 and 6:2FTS. For the eleven compounds detected using both methods, there was a good correlation between the concentrations estimated using the targeted approach and the same data analyzed using the untargeted approach (ESI, Fig. S5†). The quantitative results of the untargeted approach were similar to the targeted approach with strong correlations between long-chain PFAS compounds (Fig. 3).

Fig. 3 Correlation heatmap for the compounds from the target list observed in the untargeted analysis (n = 11, coverage cutoff ≥ 15%). The percentage values within parentheses next to compound names indicate compound coverage. The stars (*) below correlation coefficients indicate significance: ***: p ≤ 0.001; **: p ≤ 0.01; *: p ≤ 0.05. A detailed heatmap with the specific p-values is found in the ESI (Fig. S3†).

Including all features detected in ≥90% of the samples (4939 features), we found fourteen features with statistically significant associations (correlation coefficients 0.48–0.99) to seven of the confirmed environmental contaminants (Fig. 4). Based on the available MS1 and MS2 data, we identified four compounds at confidence level 2 (putative identifications, supported by MS2 spectra) and three at confidence level 3 (tentative identifications). The confidence levels for identification used here were introduced and described by Schymanski et al. to communicate the confidence of HRMS identifications.⁴¹

Fig. 4 Fourteen unknown features were found to be associated with seven confirmed environmental contaminants. The percentage values within parentheses next to compound names indicate compound coverage. The stars (*) below correlation coefficients indicate significance: ***: p ≤ 0.001; **: p ≤ 0.01; *: p ≤ 0.05. A detailed heatmap with the specific p-values is found in the ESI (Fig. S4†).

Three compounds correlated strongly (correlation coefficients 0.76–0.99) with six targeted PFASs (PFOA, PFNA, PFOS, PFDA, PFUdA, and L-PFHpS). By evaluating the mass spectra and retention times compared to the known targeted compounds, these three were identified at confidence level 2 (Table S3†) as branched PFOS and two in-source fragments of PFOA and PFNA. The branched PFOS was identified based on exhibiting an m/z within 2 ppm of the theoretical m/z and a retention time 0.1 minutes before linear PFOS. Various branched PFOS variants were included in the used reference mix, matching the untargeted compound(s) observed here, although it is not possible to determine which specific branched forms were observed here based on the data. The two in-source fragments can be formed from loss of carboxylic groups from the parent compounds (loss of CO₂). They also share the exact retention times and exhibited high correlations (0.98–0.99) with the parent compounds, making in-source fragmentation their most probable source.

Five endogenous compounds, all strongly correlating with PFHxS, were identified (Table 3). The identification process for these endogenous compounds started from the observed m/z of the respective molecular features. Using the databases HMDB,³⁷ Metlin,³⁸ and Lipid Maps³⁹ we searched for compounds within 5 ppm from the observed m/z to find likely candidates with matching molecular formulae. When several possible candidates were present we evaluated and compared the different candidates to select the most likely compound to be observed in human plasma (e.g., excluding plant metabolites). When available we compared the log P values presented in the databases between the different candidates for matching with the observed retention times. Finally, the MS2 spectra (available for four of the five compounds, identified at confidence level 2) were carefully evaluated to identify characteristic fragments to further strengthen the proposed identities. Annotated MS2 spectra for these compounds are presented in the ESI.†

Table 3 Identified endogenous compounds correlating with PFHxS observed in untargeted analysis

Unknown ID	Observed m/z	rt (min)	Correlation coefficient towards PFHxS (p-value)^a	Identity (confidence level)^b	Molecular formula	Mass error (ppm)
a p-values for correlation coefficients. All p-values are presented in the ESI. b Identification confidence levels according to Schymanski et al. 2014.^41
314	621.3294	8.07	0.71 (p = 2.4 × 10^−16)	Vitamin D3 metabolite: 24-oxo-1α,23,25-trihydroxyvitamin D3-(glucuronide) (level 2)	C33H50O11	2.25
3143 & 3842	445.2965/505.3175	9.07	0.7 (p = 7.6 × 10^−16)/0.69 (p = 5.4 × 10^−15)	Vitamin D3 metabolite: 24-oxo-1α,23,25-trihydroxyvitamin D3 (level 3)	C27H42O5 (loss of hydrogen and acetate adduct respectively)	1.38
4534 & 2991	443.2803/503.3019	8.87	0.59 (p = 1.2 × 10^−10)/0.59 (p = 4.8 × 10^−10)	Vitamin D3 metabolite: 1,25-dihydroxyvitamin D3-23,26-lactone (level 2)	C27H40O5 (loss of hydrogen and acetate adduct respectively)	0.17
3613 & 4862	459.2753/459.2764	8.08/8.24	0.69 (p = 9.3 × 10^−15)/0.66 (p = 1.7 × 10^−12)	Diglyceride lipid: DG 24:6;O (level 3)	C27H40O6	0.11

---

Three metabolites of vitamin D₃ were strongly positively correlated with PFHxS (correlation coefficients 0.59–0.71) and with moderate correlation towards branched PFOS and L-PFHpS (correlation coefficients 0.24–0.38). The vitamin D₃ metabolites are as follows: 1α,25-dihydroxyvitamin D₃-26,23-lactone (1,25(OH)₂D₃-26,23-lactone; level 2), 24-oxo-1α,23,25-trihydroxyvitamin D₃ (24(O)-1,23,25(OH)₃D₃; level 3) and, 24-oxo-1α,23,25-trihydroxyvitamin D₃-glucuronide (24(O)-1,23,25(OH)₃D₃-glucuronide; level 2). The observed m/z for the compounds were all within 2.25 ppm from the theoretical m/z values, giving high confidence to the proposed molecular formulae. For 1,25(OH)2D3-26,23-lactone and 24(O)-1,23,25(OH)₃D₃ two adducts were observed (with two corresponding features): loss of hydrogen ([M − H]⁻; the most abundant adduct for both) and acetate adduct ([M + CH₃COO⁻]⁻). For both compounds, both adducts correlated with PFHxS with highly similar correlation coefficients (Table 3). The observation of two adducts exhibiting the same correlation for these compounds gives further confidence to the proposed identities. For 1,25(OH)₂D₃-26,23-lactone and 24(O)-1,23,25(OH)₃D₃-glucuronide, the concentration in the pooled sample used for MS2 data collection was sufficiently high to collect qualitative MS2 spectra supporting the proposed structures (ESI†). For 24(O)-1,23,25(OH)₃D₃ no useful MS2 spectrum was collected; therefore, this compound is labeled as tentatively identified (confidence level 3). However, the observation of the glucuronide conjugate (24(O)-1,23,25(OH)₃D₃-glucuronide) of the same compound, also correlated with PFHxS, supports the assigned identity of the non-conjugated form.⁴² Following the observations of vitamin D₃ metabolites correlating with PFHxS, we extracted the mass traces for the central vitamin D metabolites 25-hydroxyvitamin D₃ and 1,25-hydroxyvitamin D₃ from the data and investigated possible associations with PFHxS and the other targeted PFASs. No statistically significant associations were found (ESI, Fig. S6†).

Finally, two forms of the diglyceride lipid DG 24:6;O (identified at confidence level 3) also strongly correlated with PFHxS (correlation coefficients 0.66 and 0.69) and with moderate correlation towards branched PFOS and L-PFHpS (correlation coefficients 0.24–0.41) were identified. The observed m/z for these lipids deviated only 0.11 ppm from the theoretical m/z, making the assigned molecular formula, typical of diglyceride lipids, highly probable. Annotated MS2 spectra supporting the proposed identities are found in the ESI. Several structural isomers of these lipids are present in plasma, and here two separate peaks (corresponding to two features) were observed and identified. The annotated identity DG 24:6;O refers to the total number of carbons, double bonds and modifications (six double bonds and an OH-group) of the two fatty acids. In the HMBD, 20 different structural isomers for these DG lipids are reported, and authentic references would be required to specifically identify which isomers are found to correlate with PFHxS in the present study.

4 Discussion

As outlined by Vermeulen et al., several challenges lie ahead for exploiting the full potential of exposome research.⁷ One major point is to improve the available technologies to screen for exogenous chemicals at higher-throughput rates and lower costs and to further develop the chemical and spectral data resources to identify these chemicals in samples. With that in mind, we developed a combined targeted and untargeted LC-HRMS method that is fast, simple, and highly applicable for high-throughput analysis of large cohorts. To our knowledge, the metabolomics-inspired workflows used here have not been previously used for the screening of environmental contaminants.

High-resolution mass spectrometry has been described as a prime example of a technique suited for assessing the exposome.⁷ Using HRMS, thousands to tens of thousands of chemical features may be measured in a single analytical run. Data analysis tools developed for, e.g., metabolome profiling may be used to map the exposome from untargeted HRMS data. A wide range of analytical methodologies are available for different compound classes. For example, several classes of common environmental contaminants, e.g., PCBs and BDEs, typically require gas chromatography-based methods, whereas their hydroxylated metabolites (OH-PCBs and OH-BDEs, respectively) can be measured by LC-based methods with electrospray ionization.^43,44 Here we focused our methodology on compounds that form ions during negative electrospray ionization, a group of compounds that have been labeled environmental organic acids and include, e.g., PFASs, OH-PCBs, OH-BDEs, bisphenols, and HBCDs.⁸ The developed method was optimized with regard to the response and chromatographic performance of all targets from the different compound classes. The targeted compounds were chosen based on environmental relevance and commercial availability. We decided to limit the required sample volume to 50 μL, which is relatively low in environmental analysis²⁶ so the method should apply to the analysis of cohorts where sample volume is limited.

Across the 100 studied samples from blood donors, we detected 19 of the 37 targeted compounds, with 18 of those being PFASs. PFASs are a large group of synthetic compounds with more than 10 000 different PFAS chemicals listed by the US Environmental Protection Agency.⁴⁵ PFASs were detected in all 100 studied samples, with similar concentrations to those reported recently in similar study populations.^46,47 The unusually high concentration of PFHxS in a single sample may be related to PFAS contamination of the drinking water in certain areas of Uppsala, which was discovered and mitigated in 2012. The contamination has been sourced to PFHxS-based aqueous film-forming foam used for firefighting during military training at a nearby airport until at least 2003, which keeps leaking to the environment today.^48,49

During the last 20 years, the industrial production of PFASs has changed significantly internationally due to increased awareness of the associated risks and regulatory pressures.⁵⁰ Some specific PFASs have been phased out, most notably PFOS and PFOA, while short-chain alternatives have seen increased use as replacements. Short-chain PFASs are known to be eliminated faster in animals and humans than the long-chain legacy PFASs that bioaccumulate and are therefore expected to be less toxic. However, there is growing evidence that the toxicity of some short-chain PFASs has been severely underestimated, which raises new concerns.^51,52 In the present study, nine PFASs showed a positive association with increasing age. Most of these were long-chained,⁹ except for short-chain PFHpA. The higher concentrations in older subjects could be related to the bioaccumulating properties of these PFAS, and similar associations have been reported previously.^53,54 We also found higher concentrations of L-PFHpS, PFOS, and PFNA in male compared to female subjects, which agrees with previous observations for several PFASs.^53,55 It has been proposed that this sex difference could be related to occupational exposure to PFASs in male-dominated occupations.^53,56

When investigating the correlation between targeted analytes, we found strong correlations of the concentrations between long-chain PFASs across the samples, potentially related to the bioaccumulating properties of these compounds and possibly indicating common sources of exposures.⁵⁷ Structurally related PFASs such as FOSA and N-MeFOSAA, and short-chained L-PFBS and L-PFPeS were also found to correlate. Among the targeted compounds, short-chained 6:2FTS exhibited virtually no correlation among the targeted compounds. This compound has been increasingly used as a substitute for various industrial applications that formerly required the use of PFOS or PFOA, and is less toxic and bioaccumulative compared to those more fluorinated compounds.^9,58,59 Decreased accumulation together with differing manufacturing trends could explain why no correlations were observed for this compound. As could be expected, structurally related compounds with similar chemical properties were found to correlate in concentrations.

The final targeted compound detected in the samples was 4-OH-PCB-187. This OH-PCB had low coverage at 15% and was mainly detected in subjects >50 years old, showing a positive association with increasing age. Hydroxylated PCBs are strongly retained in human blood and may cause endocrine-related toxicity.^60,61 The 4-OH-PCB-187 is one of the predominant OH-PCB congeners in human plasma, and in several studies, the most abundant OH-PCB.^43,60 That this specific OH-PCB is detected in the studied samples is therefore not surprising, and the association with increasing age is in line with its accumulating properties. PCBs were phased out in the 1970s but are still present in our environment, with the main exposure source being the diet, in Sweden, particularly from the consumption of fatty fish.^60,62,63

Having evaluated the method's performance on targeted analytes, we then explored the untargeted data. An initial challenge was optimizing our peak-picking methods to detect the typically low-level environmental contaminants amongst the much more abundant endogenous compounds also detected with the untargeted method. With too liberal criteria for peak-picking, noise will be included as features, and with too strict criteria, low-level compounds will not be detected. Considering this, we found a balanced set of parameters that could extract and integrate most of the targeted compounds from the untargeted data. We compared the output from the untargeted data with that from the targeted approach and found high agreement and the same general trends with strong correlations between several long-chain PFASs. The targeted approach was, however, better for detecting compounds of very low concentrations or with low responses. The best choice may be to use a combined approach, collecting untargeted data that is evaluated using a list of targets or suspects and with a separate evaluation of the untargeted data.

It is challenging to identify features when data is collected in an untargeted mode. To facilitate feature identification, we therefore used a detailed strategy to collect high-quality MS2 spectra. The complete m/z range of the primary full scan method was divided into six overlapping m/z windows, and nine data-dependent MS2 scans, with varying collision energies, were collected using a pooled sample of all individuals included in the study. This improved our success rate for identification. As an example of this, linear perfluorocarboxylic acids are almost fully fragmented even at low collision energies, while perfluoroalkylsulfonates and the vitamin D₃ metabolites require high collision energies for sufficient fragmentation (example MS2 spectra in ESI†).

We identified three metabolites of vitamin D₃ with strong positive association with PFHxS: 1α,25-dihydroxyvitamin D₃-26,23-lactone (1,25(OH)₂D₃-26,23-lactone), 24-oxo-1α,23,25-trihydroxyvitamin D₃ (24(O)-1,23,25(OH)₃D₃) and 24-oxo-1α,23,25-trihydroxyvitamin D₃-glucuronide (24(O)-1,23,25(OH)₃D₃-glucuronide). Vitamin D₃ is a type of vitamin D made in the human skin under UV light, as opposed to vitamin D₂, which humans get from the diet.⁶⁴ Associations between vitamin D₃ biomarkers and PFAS have previously been reported,⁶⁵ and recently, the vitamin D receptor has been identified as a potential target for the toxic effects of PFASs.⁶⁶ Previously Etzel et al. observed PFHxS to be associated with higher vitamin D concentrations and lower odds of being vitamin D deficient.⁶⁵ This observed association was not modified by age and sex. In that study, PFAS concentrations were compared with concentrations of the main vitamin D metabolite 25-hydroxyvitamin D₃.⁶⁵ We also found 25-hydroxyvitamin D₃ and the additional active vitamin D₃ metabolite 1,25-hydroxyvitamin D₃ in our untargeted data but found no statistically significant associations with PFHxS or other measured PFAS compounds. It has been reported that other endocrine-disrupting chemicals may affect circulating concentrations of vitamin D,⁶⁷ and vitamin D status has been associated with several diseases, including cancer, cardiovascular disease, multiple sclerosis,⁶⁸ and other autoimmune diseases.^64,65,69 Two forms of the diglyceride lipid DG 24:6;O were also strongly associated with PFHxS. To our knowledge, no reports on associations between PFASs and diglycerides have been made; however, there are several examples of how PFAS exposures alter the concentrations of other blood lipids.^70–73 Based on this, the interaction between the lipidome and PFASs is important to investigate in human health.

5 Conclusions

We here present a combined targeted and untargeted method suited for high-throughput analysis of environmental contaminants in plasma. This application of metabolomics-inspired workflows for the screening of environmental contaminants has a high potential for exposome profiling. Nineteen targeted environmental contaminants were detected in the study population, with ten associated with age and three with sex. In the evaluation of the untargeted data, three vitamin D₃ metabolites and two diglyceride lipids were shown to associate with PFHxS strongly. The results demonstrate the potential of combining targeted and untargeted approaches that may be important in understanding the effects of environmental contaminants on human health.

Data availability

The parameters and code used are available at https://github.com/caramba-uu/env-con. The raw and processed data is available upon reasonable request to the corresponding author.

Author contributions

Henrik Carlsson: conceptualization, methodology, investigation, writing – original draft, funding acquisition. A. Parakkal Sreenivasan: methodology, investigation, formal analysis, visualization, writing – original draft. I. Erngren: methodology, investigation, writing – original draft. A. Larsson: resources, supervision, writing – review & editing. K. Kultima: conceptualization, resources, supervision, writing – original draft, funding acquisition.

Conflicts of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Abbreviations

4-OH-PCB-187	2,2′,3,4′,5,5′,6-Heptachloro-4-biphenylol
6:2FTS	1H,1H,2H,2H-perfluoro-1-octanesulfonic acid (6 : 2)
8:2FTS	1H,1H,2H,2H-perfluoro-1-decanesulfonic acid (8 : 2)
FOSA	Perfluoro-1-octanesulfonamide
HBCD	Hexabromocyclododecane
IS	Internal standard
LC-HRMS	High-resolution mass spectrometry
L-PFBS	Perfluorobutanesulfonic acid
L-PFHpS	Perfluoroheptanesulfonic acid
L-PFPeS	Perfluoropentanesulfonic acid
MeOH	Methanol
MS	Mass spectrometry
N-EtFOSAA	N-ethylperfluoro-1-octanesulfonamidoacetic acid
NH4Ac	Ammonium acetate
N-MeFOSAA	N-methylperfluoro-1-octanesulfonamidoacetic acid
OH-BDE	Phenolic metabolites of polybrominated diphenylethers
OH-PCB	Phenolic metabolites of polychlorinated biphenyls
PFAS	Per- and polyfluoroalkyl substances
PFDA	Perfluoro-n-decanoic acid
PFDoA	Perfluoro-n-dodecanoic acid
PFHpA	Perfluoro-n-heptanoic acid
PFHxA	Perfluoro-n-hexanoic acid
PFHxS	Perfluorohexanesulfonic acid
PFNA	Perfluoro-n-nonanoic acid
PFOA	Perfluoro-n-octanoic acid
PFOS	Perfluorooctanesulfonic acid
PFTrDA	Perfluoro-n-tridecanoic acid
PFUdA	Perfluoro-n-undecanoic acid
RT	Retention time

Acknowledgements

This work was supported by FORMAS (2020–01267 and 2022-00488), the Swedish Research Council (2021-02189), Region Uppsala (ALF-grant and R&D funds), Neuro Sweden, Åke Wiberg foundation and Lars Hierta Memorial Foundation. We also acknowledge the anonymous reviewers for their careful reading and constructive criticism, which improved the manuscript.

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Footnote

† Electronic supplementary information (ESI) available. See DOI: https://doi.org/10.1039/d3em00060e

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