Monitoring changes in the healthy female metabolome across the menstrual cycle using GC × GC-TOFMS

Journal of Chromatography B 1121 (2019) 48–57

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Journal of Chromatography B journal homepage: www.elsevier.com/locate/jchromb

Monitoring changes in the healthy female metabolome across the menstrual cycle using GC × GC-TOFMS

T

Jarrett Eshimaa, Stephanie Onga, Trenton J. Davisb, Christopher Mirandaa, Devika Krishnamurthya, Abigael Nachtsheimc, John Stufkenc, Christopher Plaisiera, John Fricksc, ⁎ Heather D. Beanb, Barbara S. Smitha, a

School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ 85287, USA School of Life Sciences, Arizona State University, Tempe, AZ 85287, USA c School of Mathematical and Statistical Sciences, Arizona, State University, Tempe, AZ 85287, USA b

ARTICLE INFO

ABSTRACT

Keywords: Fertility GC × GC-TOFMS Personalized diagnostics Ovulation Urine

Urinary metabolomics offers a non-invasive means of obtaining information about the system-wide biological health of a patient. Untargeted metabolomics approaches using one-dimensional gas chromatography (GC) are limited due to the chemical complexity of urine, which poorly detects co-eluting low-abundance analytes. Metabolite detection and identification can be improved by applying comprehensive two-dimensional GC, allowing for the discovery of additional viable biomarkers of disease. In this work, we applied comprehensive twodimensional GC coupled with time-of-flight mass spectrometry (GC × GC-TOFMS) to the analysis of urine samples collected daily across 28-days from 10 healthy female subjects for a personalized approach to female reproductive health monitoring. Through this analysis, we identified 935 unique volatile metabolites. Two statistical methods, a modified T-statistic and Wilcoxon Rank Sum, were applied to analyze differences in metabolome abundance on ovulation days as compared to non-ovulation days. Four metabolites (2-pentanone, 3penten-2-one, carbon disulfide, acetone) were identified as statistically significant by the modified T-statistic but not the Rank Sum, after a false-discovery rate of 0.1 was set using a Benjamini-Hochberg correction. Subsequent analyses by boxplot indicated that the putative volatile metabolic biomarkers for fertility are expressed in increased or decreased abundance in urine on the day of ovulation. Individual analysis of metabolome expression across 28-days revealed some subject-specific features, which suggest a potential for long-term, personalized fertility monitoring using metabolomics.

1. Introduction Female infertility is often identified by the disruption of endocrine signaling that leads to impaired ovarian function [1]. The current gold standard for diagnosing female infertility involves a laparoscopic inspection of the intra-abdominal cavity, however, due to the invasive nature of the procedure this approach is not used as a large-scale screening tool [2]. At a clinical diagnostic level, the hysterosalpingogram is most frequently used but suffers from poor sensitivity and modest specificity, 53% and 87% respectively [2]. Alternative diagnostic tests, such as the hysterosalpingo contrast sonography and chlamydia antibody titre offer similar accuracies [2]. Clinical ultrasound offers the best accuracy but requires expensive, inconvenient and daily testing of the patient [3]. Therefore, clinical assays show major limitations in widespread adoption due to the time consuming and

⁎

invasive nature of the diagnostic tests. In order to circumvent this issue, point-of-care ovulation kits have been developed. The majority of these kits First Response (Church & Dwight Co., Inc., Ewing, NJ), Answer Quick (Carter-Wallace, New York, NY) and Simple One Step Ovulation (Carter-Wallace, New York, NY) have demonstrated the ability to achieve average to high sensitivity (70–90%) through the measurement of luteinizing hormone (LH) concentration in urine [3]. The most accurate [3] of these ovulation kits in recent publications, ClearBlue®, has demonstrated the ability to achieve a high sensitivity (90 + %) through the combinatorial measurement of luteinizing hormone (LH) and esterone-3-glucuronide concentration in urine. However, it has been shown that commonly used point-of-care test kits for ovulation can be limited in specificity (25%) as a result of abnormal surges in production of LH [4]. Thus, current clinical and point-of-care diagnostic approaches pose a serious gap in female health monitoring because they

Corresponding author. E-mail address: [email protected] (B.S. Smith).

https://doi.org/10.1016/j.jchromb.2019.04.046 Received 7 December 2018; Received in revised form 16 April 2019; Accepted 23 April 2019 Available online 24 April 2019 1570-0232/ © 2019 Published by Elsevier B.V.

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fail to consider the natural systemic changes in reproductive health that occur across time. The advantage of metabolome-based fertility testing over current diagnostics includes the ability to both: i) non-invasively track reproductive health across time and ii) significantly improve diagnostic specificity through a personalized approach. Furthermore, the development of these biomarkers has the potential to work synergistically with currently available point-of-care ovulation kits by expanding the diagnostic biomarker pool to include specific volatile urinary metabolites, in addition to LH, to promote improved specificity. From a system biology perspective, the hormonal changes that lead to infertility have been shown to cause a variety of systematic metabolic alterations, which often generate detectable biological features including increased oxidative stress, increased inflammation, altered energy metabolism and dysregulated molecular cellular function [5]. Among these mechanisms, oxidative stress has been shown to play an important role in modulating age-related fertility, pregnancy, and parturition [6]. This supported link between endocrine reproductive health and metabolic mechanisms provides the potential for improving the accuracy of point-of-care fertility kits and developing non-invasive infertility diagnostic methods using metabolomic biomarkers. Untargeted metabolomics applies a biological system-level analysis as a means to identify biomarkers of disease and predict patient response to treatment [7,8]. The human metabolome comprises downstream products generated through a variety of cell metabolic pathways. Irregularities in the biological system, caused by disease, are translated as changes in metabolite abundance observable in biological samples including: urine, serum, plasma, breath, sweat, feces, and cells [9–13]. Researchers apply a variety of analytical techniques including nuclear magnetic resonance (NMR), liquid and gas chromatography (LC and GC, respectively) and mass spectrometry (MS) to identify thousands of possible endogenous metabolites in biological systems for metabolome analyses [14]. Of the spectral analytical approaches, mass spectrometry has emerged as a powerful tool, driven by advancements in resolution and mass sensitivity [15]. Using GC-MS or LC-MS approaches in combination with multivariate statistics, a number of studies have successfully identified potential biomarkers of cancer [16–19], diabetes [20], and mental illness [21–23]. Promising results from these preliminary studies have generated interest in the capacity to develop clinically translatable panels of biomarkers, through personalized metabolic phenotyping, for use in drug treatment selection, early disease screening and diagnostics [24,25]. In a recent study, hairbased metabolic profiling was used to identify the stage of pregnancy and develop a foundation for differentiating healthy and unhealthy pregnancies [26]. Despite the initial successes of metabolomic diagnostics, resolution limitations of one-dimensional GC translate to poor untargeted metabolome coverage due to loss of coeluting low-abundance metabolites. Two-dimensional (orthogonal) separation offers improvement in the overall resolution through modulated injection of coeluting compounds into the second column [27]. Given the complexity of human metabolism, generating thousands of endogenous compounds [12], orthogonal separation afforded by comprehensive two-dimensional GC (GC × GC) offers significant advancements for untargeted analyses of biological specimens for the discovery of novel biomarkers of disease. In order to bridge the gap between metabolomic applications and personalized female health monitoring, our study describes a timebased analysis for determining normal shifts in urinary metabolome expression. By specifically designing an exploratory study, we aim to provide a foundation for developing personalized tools to monitor fertility through urinary metabolites across time. In this study, the urine volatile metabolome was characterized for ten female subjects over a 28-day period using GC × GC coupled with time-of-flight mass spectrometry (GC × GC-TOFMS). In an effort to detect the greatest number of volatile metabolites, an untargeted approach was utilized. Although work has been done to measure metabolome changes across time [28], no previous studies have applied GC × GC to analyze the healthy

Table 1 Demographics of study subjects. Subject no

Age (years)

Ethnicity

Height (cm)

Weight (kg)

Ovulation confirmation

1 2 3 4

24 21 19 21

163 160 168 155

55 50 68 48

Yes Yes Yes Yes

5 6 7 8 9 10

20 24 19 18 18 27

Latino Latino Asian African American Caucasian Asian Latino Caucasian Asian Caucasian

165 175 152 173 163 165

49 55 45 58 59 68

Yes Yes Yes No No No

female urine metabolome across the menstrual cycle. By establishing a framework for metabolome changes in healthy females during reproductive years, this study aims to provide information to assist in the development of long-term screening panels for fertility through personalized phenotyping. This metabolomic pipeline reflects the potential to improve patient monitoring at the clinical level. 2. Methods and materials 2.1. Human subjects and sample collection Urine samples were collected from ten healthy females, between the ages of 18–28 years (Table 1). Inclusion criteria for the study required all subjects (1) to be medication free (including birth control) and (2) to verify their mental and physical wellness with a detailed questionnaire. A single ethnicity was not specified as an inclusion criterion. By allowing all ethnicities, dietary response was removed as a potential confounding factor that could unintentionally promote strong exogenous VOC correlation with ovulation due to similar dietary and lifestyle habits. Statistical clean-up methods included a filter to remove compounds that were not present in at least 50% of all collected samples. This was done in an effort to focus the pool of putative metabolites around commonly expressed volatiles. Morning urine samples were acquired daily, for 28 consecutive days, with the criterion that subjects had not consumed food or water for at least 2 h prior to collection. All subjects were requested to provide morning first pass urine, prior to consumption of food and water. However, due to the fact that some subject may have provided a sample after consumption of food and water, there exists a possibility for dietary influences. To account for this potential bias, metabolite filtering was applied to remove dietbased low frequency VOCs. Within 1 h after collection, urine samples were aliquoted into 1.5 mL cryogenic vials (VWR International, Radnor, PA) for storage at −80 °C until testing. Ovulation days were confirmed using commercially available testing kits (Clearblue®, Geneva, Switzerland) by randomly testing urine samples around the reported ovulation day until two tests confirmed positive results for the same day. Seven of the ten subjects were validated for a single day of ovulation and were used for statistically significant metabolite identification. Data from the threeremaining ovulation-unconfirmed subjects was removed from this study. The various uses of data subsets within the sampled population are described in Fig. 1. The study was conducted in accordance with the “Urine Specimen Collection Guidelines” [29] and approved by the Institutional Review Board at Arizona State University. Written informed consent was obtained from all subjects. 2.2. Sample preparation and volatile metabolite extraction Urine samples were removed from the −80 °C freezer, brought to room temperature, and mixed by inversion to promote matrix 49

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Fig. 1. Flow diagram for sample collection and analysis.

homogeneity. Then, 1 mL was transferred to 10 mL glass vials with PTFE/ silicone caps (Supelco/Sigma-Aldrich®, St. Louis, MO), which had been heated at 100 °C for 12 h prior to sample analysis in an effort to reduce contamination and background chemical signals. Samples were maintained at 4 °C prior to analysis using a temperature-controlled tray. For analysis, the urine was incubated at 60 °C with agitation at 250 rpm for 5 min. The volatile organic compounds (VOCs) were collected by solidphase microextraction (SPME) using a 1 cm, 50/30 μm divinylbenzene/ carboxen/polydimethylsiloxane (DVB/CAR/PDMS; Supelco/SigmaAldrich) coated fiber. SPME fibers were replaced approximately every 80 injections and conditioned at 270 °C for 1 h prior to use. Successful conditioning was validated by repeated sampling of an empty, conditioned glass vial to check for contaminants prior to use. Fibers were heated at 270 °C for 10 min between sample injections to minimize carryover. During extraction, the SPME fiber was exposed in the headspace of the sample for 60 min with continuous agitation.

thickness); Restek®, Bellefonte, PA) first column and a Stabilwax (1 m × 250 μm × 0.5 μm; Restek) second column, joined together by a press-fit connection. The first column was heated to an initial temperature of 50 °C, held for 2 min, then the temperature was ramped at 5 °C/min to 225 °C and held for 30 min. The secondary oven was maintained at a + 5 °C offset relative to the primary oven. A quad-jet modulator was used with a 2 s modulation period (0.5 s hot, 0.5 s cold pulses) and a + 15 °C offset relative to the secondary oven. The transfer line was maintained at 250 °C. The helium carrier gas flow rate was 2 mL/min (UHP Helium 99.999%). Mass spectra were acquired at 100 Hz over a mass range of 35–550 Da with an ionization energy of −70 eV. Urine volatile metabolite data was collected over two continuous weeks of GC × GC-TOFMS analysis. A PFTBA standard was run at the start of each day to tune the MS in an effort to minimize drift. Empty vials (blanks) were run prior to clinical samples to monitor for system contamination. Column temperatures were ramped at the start of each sampling day to reduce contamination. Kovats Index (KI) alkane standards were sampled twice for use in determination of retention indices. Data acquisition was performed in ChromaTOF® software, Version 4.60.8.0 (Leco® Corp., St. Joseph, MI). Preliminary testing on the GC × GC-TOFMS indicated there was negligible loss (< 10% relative standard deviation) of volatiles after 10 consecutive samples using urine from a single collection time and subject. The preliminary analysis of sample degradation showed that the metabolites lost over repeated sampling of the same urine control sample were primarily low-

2.3. GC × GC-TOFMS analysis Urinary metabolites were tested using comprehensive GC × GCTOFMS (Pegasus 4D®, LECO Corp., St. Joseph, MI), equipped with an autosampler (Multipurpose Sampler Robotic®, Gerstel, Inc., Linthicum Heights, MD). Volatiles were desorbed into the GC inlet for 5 min at 250 °C, using a splitless injection. The instrument was fitted with a twodimensional column set consisting of a Rxi-624Sil MS (60 m × 250 μm × 1.4 μm (length × internal diameter × film 50

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abundance analytes, specifically VOCs close to the S/N cutoff. Filtering by frequency of detection was applied during statistical analysis to minimize variability in the total number of detected analytes. Samples in this study were randomized and run in groups of 10.

2.5. Statistical analysis Prior to the application of statistical models, the data was normalized using Probabilistic Quotient Normalization [32] and analyte abundance was log10 transformed in R, Version 1.0.136 (The R Foundation for Statistical Computing, Vienna, Austria). Subject data sets were aligned, and the confirmed ovulation day was set to day 14. A Pearson's correlation test was performed on 55 VOCs that expressed complete datasets (allowing for one missing value) across all seven ovulation-confirmed subjects, using R. Repeated for each of the 55 VOCs, the first variable matrix, X, was assigned abundance values for a VOC across days 1 to 27. The second variable matrix, Y, was offset by one day, consisting of the same VOC abundances across days 2 to 28. Comparison of sample means was performed using a modified pooled variance T-statistic using MATLAB Version 2018b (Mathworks® Inc., Natick, MA). The two populations comprised the metabolite expression on the day of ovulation and an averaged expression on nonovulation days. The modification in the pooled-variance T-statistic was necessary because a single data point was collected for subject ovulation day (Eq. (2) and (3)). Where i is the day of sample collection, with ovulation aligned to day 14, j is the subject number and k is the VOC number.

2.4. Data processing Data processing and chromatographic alignment were completed using the Statistical Compare package of ChromaTOF® software. For peaks at a given 1st and 2nd dimension retention time, all chromatograms are compared spectrally. The reference peak is determined by the unique mass ion and the overall purity and shape of the peak. Once identified, the reference chromatogram is used for peak identification by comparison to a library. The signal-to-noise (S/N) cutoff for peak picking was set at 50:1 for a minimum of two apexing masses. The baseline signal was drawn through the middle of the noise. Within individual chromatograms, subpeaks in the second dimension were required to meet a S/N ≥ 6 to be combined. For aligning peaks across chromatograms, the first-dimension retention time shift had to be ≤2 s and the second-dimension retention time shift ≤0.2 s from chromatogram to chromatogram, and a minimum spectral similarity match of 600 (60%) was required. A secondary round of peak picking was performed on the aligned chromatograms using a S/N threshold of 5 and a minimum spectral similarity match of 600. The data set of identified volatile metabolites was manually filtered by excluding from data analysis, all peaks eluting prior to 358 s and compounds identified during blank runs. Quantitative values for signal abundance were obtained by integrating peak areas of the unique ions. The resulting aligned peaks were compared to the National Institute of Standards and Technology (NIST) 2011 Mass Spectral Library, and tentative peak names were assigned to mass spectra with similarity scores greater than or equal to 600. Peaks of interest were assigned tentative identification confidence values on a scale of 1–4 (1 highest), using published guidelines [30]. Level 2 was the highest classification in this study, verified with a ≥ 85% mass spectral match on a forward search in the NIST 2011 library and retention index data that are consistent with the midpolar Rxi-624Sil stationary phase. Retention indices (RI) are quantified using published median RIs for non-polar and polar columns using the equation below. A cutoff of 35% is established for a level 2 confidence classification [31].

RIexperimental RIpolar

RInon RInon

polar

polar

100 = 5

35%

Tk =

X14

Ek = 1 +

X(

14)

Ek 7

1 27

(2) 7 j=1

28 i 14 Xi, j, k

27

7

X( 14)

(3)

Once the modified T-statistic had been calculated for all 935 named VOCs, the top 5% of compounds with the largest absolute T-statistic (n = 47) were selected for further analysis. Using bootstrapping, 10,000 new data sets were generated for each of the 47 compounds. pvalues were calculated by finding the number of bootstrapped T-statistics that fell above or below the absolute value of the true T-statistic, divided by the total number of iterations. A Benjamini-Hochberg (BH) correction was applied to control the false discovery rate (FDR), set at 0.10. A second statistical comparison was performed using a two-tailed, non-parametric Wilcoxon Rank Sum statistical test. While the Rank Sum analysis does not take into account the magnitude of variation, this statistical test was used because the limited sample size and amount of missing data requires a more robust method. A standard distribution was generated by randomly summing seven rank values over 100,000 iterations in order to more readily observe an overall effect. Compounds of interest were identified by observing Rank Sum values that fell two standard deviations outside of the mean. p-values were calculated using z-scores and a BH correction was applied with the FDR, set at 0.10. Selected statistical analyses were chosen based on power limitations of this study to investigate urinary metabolite trends across the seven ovulation-confirmed subjects. Numeric values for analyte abundance were obtained by peak integration of the unique ion chromatograms, post-alignment. Metabolites identified as significant in this paper were not preselected for targeted analysis, and further validation was not performed.

(1)

A level 3 confidence classification was assigned for compounds with NIST 2011 library mass spectral match ≥85%, but without confirmatory RI data. Compounds that fell below the 85% matching criteria were assigned a level 4 classification. Compound names with an ID confidence level of 4 were given names based on functional group if the mass spectra and second-dimension retention times supported the functional group classification. Otherwise, compounds with an ID level of 4 were given the name “Unknown”. All identified analytes were assigned a functional group classification in one of the following categories: acids, alcohols, aldehydes, amides, amines, ethers, functionalized aromatics, saturated/unsaturated hydrocarbons, heteroaromatics, ketones, thiols and other (more than one functional group). Metabolites were also classified by their frequency of appearance across the seven ovulation-confirmed subjects according to the following criteria, where: core compounds were detected in all subjects at least once, accessory compounds were detected in 2–6 subjects at least once, and rare compounds were only detected in a single subject.

3. Results and discussion The impact of hormonal and metabolic changes during the time of ovulation result in modifications to volatile expression, leading to a significant shift in the number of high-quality detectable features by GC × GC-TOFMS. Among the 16 significant compounds identified by the modified T-statistic, most were detected in lower abundance at the time of ovulation. Metabolome analysis from seven healthy, ovulationconfirmed, female subjects took account of natural shifts present during 51

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ovulation and non-ovulation days, within a 28-day period.

classifications across the core, accessory, and rare volatile metabolite categories. Given that the sampling method biases towards highly volatile compounds, no inferences can be made about biology related to frequency of detection. Rather, this functional group breakdown is intended to serve as a guide for future targeted metabolome analyses. Similarly, core analytes provide a reliable source for metabolome data, while rare analytes are more useful for identifying characteristics of a unique condition, specific to an individual or subgroup. Each of these categories of analytes have the potential to provide useful information when identifying target biomarkers. A subset of the core metabolome are compounds that are detectable in all samples of all subjects, representing a universal urine volatile metabolome for this study. Analyzing the urine volatiles of the seven ovulation-confirmed subjects, 35 non-redundant compounds were identified in all urine samples, as displayed in Fig. 3. In this subset of metabolites, ketones are overrepresented with > 30% of the identified volatiles (Supplementary Table S1). The consistent presence of these metabolites across time highlights their potential for use as a baseline reference in screening for disease abnormalities, including infertility, and were further evaluated as potential biomarkers of ovulation.

3.1. Method optimization Traditionally, the testing of human biological samples by GC–MS have been performed to determine the subject's volatile metabolic profile. Many studies have compared VOC abundance in biological samples from individuals across a variety of healthy and diseased states to develop a panel of predictive biomarkers for disease [22,33,34]. Furthermore, successful translation of metabolomic diagnostics to the clinic will allow for personalized phenotyping of disease to improve patient outcomes [25]. The literature suggests a strong potential for biomarker discovery through non-invasive means. Thus, this study was optimized around the intended goal of identifying ovulation-related volatile metabolites via non-invasive urinalysis. SPME extraction was selected due to its high throughput capabilities and ability to enrich the extraction of volatile headspace metabolites in urine. After reviewing published literature, the 50/30 μm DVB/CAR/ PDMS fiber was identified as the most effective coating to maximize urinary volatile headspace extraction coverage and sensitivity for the widest range of analytes [35]. Extraction time is known to greatly influence the number of volatiles retained by the fiber [36]. The SPME extraction time was tested at 30, 45, and 60 min, using 1 mL urine samples in 10 mL vials. Extraction times longer than 60 min were not considered due to the likelihood of oversaturation of high abundance urinary volatiles and loss of potentially predictive low abundance metabolites below the signal-to-noise threshold. Results indicated that a sampling time of 60 min would be optimal for maximizing the total area of volatile metabolome coverage and is supported by previous studies [18,37]. Prior to extraction, each sample was agitated at 60 °C for 5 min to promote the equilibration of volatiles in the headspace for SPME sampling. The ratio between liquid volume and headspace impact the concentration of volatiles in the headspace [38]. Thus, by increasing the urine volume, analytes with a lower vapor pressure will be more readily detectable in the headspace. Preliminary testing indicated 1 mL of sample was sufficient to accurately capture changes in the healthy female urinary metabolome across the menstrual cycle [39].

3.3. Statistical analysis for identified metabolites The human body is a complex system that responds to internal and external signals that can influence metabolite production over time. Dependency in metabolite expression was considered given the sequential nature of sampling the subjects over 28 days. Using the Pearson correlation coefficient, the 55 metabolites with zero or one missing values across each ovulation-confirmed subject's collection period were tested for dependence. Metabolites with missing values were removed because these compounds interfered with the calculation of the correlation coefficient. The distribution of correlation coefficients can be seen in Fig. 4. The results suggest that a majority of the metabolites can be safely assumed to be independent of collection day, given that 85.5% of the 55 metabolites fell within three standard deviations of the mean (centered at 0). Based on the definition of standard deviations in a normal distribution, 99% of metabolites should fall within three standard deviations. Therefore, we recognize that there may be mild dependency across some metabolites. For the purpose of the T-statistic and Rank Sum statistical tests, data independence was assumed. In our statistical analysis, volatile expression was grouped into two classifications: ovulation (aligned to day 14; n = 4 chromatograms) and non-ovulation (all other days; n = 189 chromatograms). Phenotypes were assigned in this way to maximize detectable differences between the two groups. In an effort to identify compounds that were statistically relevant on the day of ovulation, we utilized both a modified pooled-variance T-statistic and Wilcoxon Rank Sum test. The parametric and non-parametric approaches were both applied due to the limited power in the study. The modified pooled variance T-statistic test was performed because the test accounts for information about the magnitude of variance when comparing ovulation to non-ovulation days. The Wilcoxon Rank Sum test was applied due to the improved robustness for our ovulation sample size and lower susceptibility to outliers. The modified T-statistic was calculated for all detected metabolites. The top 5% (n = 47) of VOCs with the largest absolute T-statistic were selected for p-value analysis. VOCs in the top 5% with > 50% missing data from all measurements were filtered from the list (8 removed for a total of 39). Bootstrapping was applied to each of the 39 VOCs to generate distributions of potential T-statistic values, given the assumption that the original dataset is reflective of the entire population. p-values were calculated by counting the number of bootstrap T-statistics that fell outside both tails, defined by the magnitude of the original T-statistic, and divided by the total number of iterations (10,000). Of the top 5% metabolites identified as having the largest variance between ovulation and non-ovulation days, 16 were found to remain

3.2. Metabolite identification and classification For the metabolite breakdown by functional group, a total of 278 GC × GC chromatograms, including 248 clinical samples (32 total missing urine samples), 28 blanks, and two n-alkane standards mix were aligned and processed using the statistical compare function in ChromaTOF®, resulting in the detection of 2232 total non-redundant peaks after cleanup. After removing known contaminants and peaks that fell below the naming S/N ratio, a total of 935 unique peaks were attributed to the human volatile metabolome (from the seven ovulation-confirmed subjects). The orthogonal separation capability of GC × GC-TOFMS used in this study allowed for the detection of a greater number of metabolites given superior chromatographic resolving power for separation of low-mass analytes in complex matrices [40] as demonstrated in previous studies [41]. Numeric values for VOC abundance were assigned by ChromaTOF® after performing peak integration. Volatiles that were not identified (due to poor S/N ratio and/ or poor spectral matching) were assigned a value of 0, at that time point, for the purpose of statistical analyses. Metabolite frequency of appearance and classification by functional group was performed to further segment the human volatile metabolome. Results indicated that 454 VOCs were identified as core metabolites, observed in at least one urine sample in the 7 subjects, 451 as accessory, observed in at least one urine sample in 2–6 subjects, and 30 as rare urine metabolites, detected in only one subject (Fig. 2). Aside from the “other” classification, hydrocarbons (29.2% of total) and ketones (18.7% of total) were the two highest-represented functional 52

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Fig. 2. Volatile Analyte Classification. Analytes were classified as core, accessory, or rare, and each one was assigned to a functional group. Core compounds were detected in the 7 subjects in at least one sample, accessory compounds were detected in 2–6 of the subjects, and rare compounds were detected in a single subject. Fig. 3. Representative GC × GC chromatogram of the human urine volatile metabolome. The 35 compounds detected in all samples are labeled. Relative abundance is displayed on a redblue color scale, where blue indicates baseline signal and red indicates a maximum abundance of ~1 ∗ 105. Chromatographic regions of 1tR < 200, 1tR > 2500, 2tR < 0.5 and 2tR > 1.5 were removed for visual clarity. Additional information about the 35 ubiquitous VOCs can be found in Supplementary Table S1. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

in metabolite abundance on the day of ovulation. Individual metabolite abundances from all clinical samples were then ordered from smallest to largest and assigned a rank between 1 and 174, respectively. As a result, compounds that were found in higher abundance on the day of ovulation should express a larger Rank Sum value. The four ovulation day ranks were summed and reported as a histogram for all metabolites (Fig. 5b). As seen in this figure, the average Rank Sum value for the detected metabolites was approximately equal to the purely theoretical Rank Sum distribution, however, many metabolites were seen to be detected in lower abundance on the day of ovulation. This information suggests that there was systematic downregulation of metabolite abundance on the day of ovulation across the four subjects with ovulation day urine samples. Additionally, metabolites of interest were identified by selecting VOCs that fell at least two standard deviations outside of the mean, at either tail (Fig. 5c, d). After filtering out VOCs with > 50% missing values (n = 24), 38 metabolites were identified, as listed in Table 3. Metabolites that retained an ID level of 4 were not shown in the table. Functional group classifications revealed hydrocarbons and ketones were the most represented among the metabolites that fell greater than two standard deviations from the mean (18.4% and 15.8%, respectively). p-values were calculated using z-scores and a BH correction was applied with an FDR, set at 0.10. After performing the correction, no metabolites retained their significance. We attribute the different results between the T-statistic and the Rank Sum tests to be caused by the more conservative nature of the Rank Sum test. Since the magnitude of variance is not captured in the Rank Sum test, we anticipate that insufficient sample size and high inter-subject variance weakened the significance of the metabolites. While exploratory, these results suggest the importance of individualized phenotyping for the

Fig. 4. Histogram displaying the Pearson correlation coefficient values for 55 VOCs with zero or one missing values from the seven ovulation confirmed subjects.

significant after the BH correction. The majority of significant VOCs resulted in a negative T-statistic, indicating that the compound was less abundant on ovulation day as compared to all other days (Table 2). Breakdown by functional group showed that ketones were the most represented, comprising 37.5% of significant metabolites. Our results suggest that metabolite downregulation may provide useful information about ovulation capability in healthy reproductive years. Prior to the application of the Wilcoxon Rank Sum statistical test to the clinical data set, a theoretical standard distribution was generated by repeatedly sampling 4 random out of 174 possible ranks (four ovulation urine samples; 174 samples analyzed), as shown in Fig. 5a. This distribution was used to determine if there was an overall change 53

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development of more accurate long-term fertility screening tests. It is worth noting that four metabolites were identified by both of the statistical analyses, including 2-pentanone, 3-penten-2-one, carbon disulfide, and acetone. This overlap further supports the claim that metabolite expression is correlated with ovulation. Ketones represent three of the four overlapping metabolites and have previously been shown to play key biological roles as signaling metabolites [42] and epigenetic modifiers associated with lifespan [43]. In a recent review, researchers proposed biological metabolites, such as ketones produced in fatty acid metabolism, can act as signaling molecules to prevent reproductive success during energetically dysregulated conditions [44]. Analysis of the underlying biological mechanism in previous work demonstrated the activation of adenyl cyclase in adipose tissue in response to reproductive hormones, including LH and epinephrine [45]. Adenyl cyclase can subsequently activate the AMPK pathway as a result of increased concentration of cAMP in the cytosol [46]. This pathway is known to regulate energy metabolism through glucose uptake/glycolysis, lipolysis, fatty acid oxidation, and inhibition of lipogenesis [47]. The authors hypothesize that excess acetyl-CoA produced by systemic activation of fatty acid oxidation in adipose tissue led to the formation of ketone bodies through the process of ketogenesis in the liver. Acetone, one of the products of ketogenesis, was shown to be upregulated in the subjects on the day of ovulation, suggesting metabolism may be linked to ovulation-based systemic changes. Among the four overlapping metabolites, carbon disulfide is recognized as a known exogenous compound, originating in the environment. Consequentially, the abundance of carbon disulfide is not linked to any biological phenomenon. Compounds that were identified by only one of the statistical analyses still provide potentially significant markers of ovulation. Future studies should consider a targeted approach for monitoring shifts in abundance from overlapping metabolites for the ultimate objective of moving this research into practical use.

Table 2 List of urine VOCs identified as significantly different at the time of ovulation, after the FDR correction. Extrapolated retention indices are denoted with *. Chemical Class abbreviations are as follows: ACI = Acids; ALC = Alcohols; ALD = Aldehydes; AMI = Amides; AMN = Amines; ETH = Ethers; FUNC AROM = Functionalized Aromatics; HC = Saturated/Unsaturated Hydrocarbons; HET AROM = Heteroaromatics; KET = Ketones; OTH = OTHER; THI = Thiols). 1 tR and 2tR are used to indicate the first- and second-dimension retention times, respectively. Retention Index (RI) was calculated for each volatile metabolite using the KI standards. ID level is displayed in the final column. T-statistic VOC name

Chemical Class

p-Value

1

Unknown 1 Unknown 2 4-methyl-2Heptanone Nonanal Acetophenone

– – KET

< 1.00E-04 1.00E-04 3.00E-04

1540 628 1258

ALD FUNC AROM KET – KET OTH KET HET AROM – KET ALD KET AROM

3.00E-04 4.00E-04 4.00E-04 4.00E-04 5.00E-04 5.00E-04 6.00E-04 7.00E-04 8.00E-04 8.00E-04 9.00E-04 1.00E-03 1.50E-03

2-Pentanone Unknown 3 2-Hexanone Carbon disulfide Acetone Pyrrole Unknown 4 3-Penten-2-one Octanal Ketone 1 Toluene

a)

tR (s)

2

tR (s)

RI

ID Level

1.01 0.83 0.83

1173 746 1041

4 4 2

1602 1584

0.83 1.28

1203 1194

2 2

688 1796 922 368 346 924 1370 824 1396 1364 836

0.85 0.83 0.85 0.70 0.79 0.55 0.78 1.01 0.84 0.90 0.86

766 1303 893 561* 552* 894 1091 825 1104 1089 833

2 4 2 3 2 2 4 2 3 4 2

Mean: 348.50 StDev: 99.52

b)

Mean: 349.90 StDev: 68.01 68.01

c)

d)

-2σ

+2σ

Fig. 5. Histogram distributions for Wilcoxon Rank Sum test a) Standard distribution for a rank sum test consisting of 174 observations (seven subjects over 28 days with 24 missing values) b) Rank Sum distribution for all identified VOCs with generalized downregulation of metabolites c) Enhanced image of the left tail in the Rank Sum distribution d) Enhanced image of the right tail in the Rank Sum distribution. The solid red line indicates a normal fit to the data. The dotted red line is used to indicate where the two standard deviation cut-off fell within the distribution. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) 54

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Table 3 Volatile metabolites identified as having a Rank Sum value greater than two standard deviations from the mean. Extrapolated retention indices (RI) are denoted with *. Compounds with an ID confidence level of 4 were not displayed in this table. Final column displays the VOC abundance on the day of ovulation compared to the average of all non-ovulation days. Rank sum VOCs

Chemical class

P-value

1

2,4-dimethyl-1-heptene 2-pentanone 1-ethyl-4-methyl-benzene 3,7-dimethyl-1,6-octadien-3-ol, 2-aminobenzoate 1,3,5-cycloheptatriene, 7-ethyl Carbon disulfide 2-methyl-1-pentene 3-penten-2-one Acetaldehyde Acetone 4-methyl-heptane

HC KET FUNC AROM OTH OTH OTH HC KET ALD KET HC

8.06E-04 1.45E-03 3.98E-03 4.94E-03 5.15E-03 8.44E-03 8.78E-03 9.14E-03 1.51E-02 1.56E-02 1.56E-02

958 688 1286 1592 1666 368 426 824 248 346 778

3.4. Review of selected metabolites

tR (s)

2

tR (s)

0.66 0.85 0.84 0.96 0.83 0.70 0.65 1.01 0.71 0.79 0.64

RI

ID level

Up/down regulation

911 766 1054 1198 1236 561* 585* 825 512* 552* 797

3 2 3 3 3 3 2 2 3 2 3

↓ ↑ ↑ ↑ ↑ ↓ ↓ ↓ ↓ ↑ ↓

findings potentially indicate that some metabolites are regulated within the body to maintain specific concentrations and that systematic disruptions, such as ovulation, produce detectable differences in abundance. These boxplots demonstrate the potential to use ovulation-respondent metabolites within the female human metabolome to monitor fertility in women. Further targeted analyses, at the individual level, can help to implement metabolomic diagnostics for use in clinical applications. Each of the four overlapping metabolites were further analyzed on an individual subject basis. This analysis was intended to provide additional detail about personalized urinary metabolic phenotype. As shown in Fig. 7, metabolite abundance across the 27 non-ovulation days were graphed on a per-subject basis as a boxplot with the ovulation day abundance shown as a red point. It should be noted that some

Representative boxplots for 2-pentanone, 3-penten-2-one, carbon disulfide, and acetone abundance across the 189 non-ovulation samples (from the seven ovulation-confirmed subjects) were plotted against ovulation days for the four validated subjects (Fig. 6). This analysis revealed the generalized upregulation of 2-pentanone and acetone and generalized downregulation of 3-penten-2-one and carbon disulfide on the day of ovulation. To better visualize ovulation day response, boxplots were generated for the four overlapping metabolites across the 28day timeframe, with ovulation day aligned to day 14 for all seven subjects (Supplemental Fig. 1). Although the overall abundance of these four metabolites remain relatively consistent across the month, downor up-regulation can be observed on the day of ovulation. These

Fig. 6. Representative boxplots comparing the four overlapping metabolite abundances on non-ovulation and ovulation days. Subjects with metabolite abundances that fell below the detectable limit of the instrument are denoted with a “¤” next to the x-axis classification. Statistical significance was identified by the modified Tstatistic after the false discovery rate correction. 55

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Ovula on Abundance

Fig. 7. Individual boxplots for the four overlapping metabolites on non-ovulation days. Ovulation abundance is plotted as a red point for the four subjects with ovulation day measurements. Ovulation abundance data could not be collected for Subjects 1, 2, and 4. Subjects with metabolite abundances that fell below the detectable limit of the instrument are denoted with an asterisk (*) next to the subject number. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

subjects had observable features in their metabolite expression that could be used to develop a more personalized diagnostic test for fertility. For example, in subjects 3 and 6, all four volatile biomarkers are detected in abundances that fall outside of the 25th–75th percentile for expression over the sampling period. For subjects 5 and 7, three and two of the overlapping volatile biomarkers were significantly changed on the day of ovulation, respectively. For subjects 2 and 4, higher metabolite abundance was observed across the four overlapping volatile biomarkers, suggesting that even a pooled panel of fertility markers will not reach 100% sensitivity and specificity in a larger population containing individuals with similarly variable urine biomarker concentrations. As a result, it is understood that no single metabolic biomarker will likely predict infertility with sufficient accuracy, but panels of volatile biomarkers may be predictive. A secondary analysis of these metabolites was performed by grouping days 12–14 to form the basis of the ovulation classification, given significant hormonal changes can be observed days before ovulation. Results from the grouped analysis for 2-pentanone, 3-penten-2-one, carbon disulfide, and acetone can be found in Supplemental Fig. 2a. Predictive power and significance of the metabolites is seen to decrease as a result of averaged abundance values. Further exploration of the grouped ovulation classification, using an adjusted T-Statistic and Rank Sum statistical approach, led to the identification of a second set of four overlapping metabolites significantly different during ovulation. Results from the repeated statistical analysis can be found in Supplemental Figs. 2b and 2c. In short, metabolites identified via this classification were worse predictors for ovulation because individual subject abundances did not show uniformity in upregulation or downregulation on the days of ovulation. One ideal approach to fertility diagnostics could establish baseline urinary metabolite profiles for females during healthy reproductive years and measure deviations of personalized biomarkers to monitor for

changes in fertility. Current clinical diagnostic tests are limited to single time point analyses, poor specificity and do not address the issue from a personalized, system-biology approach [48]. The predictive value of currently used fertility tests can vary depending on the frequency of disease occurrence within a population. Therefore, even diagnostic tests with high sensitivity and specificity will vary considerably in accuracy from one population to the next [48]. By taking a personalized approach to infertility, limitations in diagnostic accuracy can be overcome. The non-invasive and relatively inexpensive nature of this untargeted analytical approach promotes the potential adoption of urinary metabolomics for use in long-term fertility screening. The findings of the study emphasize the importance of developing a personalized approach for long-term health monitoring. Global untargeted studies, such as this one, help to identify potential biological compounds that adapt reliably with changing health status across an entire population. 4. Conclusion The goal of this work was to record the healthy female urinary metabolome across a menstrual cycle to identify potential metabolic biomarkers for fertility. Additionally, the study was intended to demonstrate the diagnostic capacity of metabolomics for the tracking of reproductive health across time, from a personalized approach. By applying a comprehensive two-dimensional GC approach, low-abundance VOCs were detected in higher numbers as a result of improved instrument resolution and reduced co-elutions. The increased number of metabolites in untargeted studies provide additional candidates for predicting abnormalities in biological health. Using the modified T-statistic and Wilcoxon Rank Sum statistical tests, four metabolites (2-pentanone, 3-penten-2-one, carbon disulfide, acetone) were identified as putative biomarkers of fertility. Further 56

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analysis at the individual subject level indicated the necessity to develop predictive models via system biology phenotyping. Future research will work to develop these findings into a clinically translatable method for non-invasive fertility monitoring using a targeted metabolomic approach with predictive modeling.

[21] M. An, Y. Gao, Urinary biomarkers of brain diseases, Genomics Proteomics Bioinformatics 13 (2015) 345–354. [22] P. Zheng, J.J. Chen, T. Huang, M.J. Wang, Y. Wang, M.X. Dong, Y.J. Huang, L.K. Zhou, P. Xie, A novel urinary metabolite signature for diagnosing major depressive disorder, J. Proteome Res. 12 (2013) 5904–5911. [23] E. Trushina, T. Dutta, X.M.T. Persson, M.M. Mielke, R.C. Petersen, Identification of altered metabolic pathways in plasma and CSF in mild cognitive impairment and Alzheimer's disease using metabolomics, PLoS One 8 (2013), https://doi.org/10. 1371/journal.pone.0063644. [24] D.S. Wishart, Emerging applications of metabolomics in drug discovery and precision medicine, Nat. Rev. Drug Discov. 15 (2016) 473–484. [25] J.K. Nicholson, E. Holmes, J.M. Kinross, A.W. Darzi, Z. Takats, J.C. Lindon, Metabolic phenotyping in clinical and surgical environments, Nature 491 (2012) 384–392. [26] T.D.J. Delplancke, J.V.D. Seymour, C. Tong, K. Sulek, Y. Xia, H. Zhang, T.L. Han, P.N. Baker, Analysis of sequential hair segments reflects changes in the metabolome across the trimesters of pregnancy, Sci. Rep. 8 (2018) 36. [27] S. Zhu, W. Zhang, W. Dai, T. Tong, P. Guo, S. He, Z. Chang, X. Gao, A simple model for separation prediction of comprehensive two-dimensional gas chromatography and its applications in petroleum analysis, Anal. Methods 6 (2014) 2608–2620. [28] J.E. Ang, V. Revell, A. Mann, S. Mantele, D.T. Otway, J.D. Johnston, A.E. Thumser, D.J. Skene, F. Raynaud, Identification of human plasma metabolites exhibiting time-of-day variation using an untargeted liquid chromatography-mass spectrometry metabolomic approach, Chronobiol. Int. 29 (2012) 868–881. [29] United States Department of Transportation, Urine Specimen Collection Guidelines, (2008). [30] L.W. Sumner, A. Amberg, D. Barrett, M.H. Beale, R. Beger, C.A. Daykin, T.W.M. Fan, O. Fiehn, R. Goodacre, J.L. Griffin, T. Hankemeier, N. Hardy, J. Harnly, R. Higashi, J. Kopka, A.N. Lane, J.C. Lindon, P. Marriott, A.W. Nicholls, M.D. Reily, J.J. Thaden, M.R. Viant, Proposed minimum reporting standards for chemical analysis: chemical analysis working group (CAWG) metabolomics standards initiative (MSI), Metabolomics 3 (2007) 211–221. [31] H.D. Bean, C.A. Rees, J.E. Hill, Comparative analysis of the volatile metabolomes of Pseudomonas aeruginosa clinical isolates, J. Breath Res. 10 (2016). [32] F. Dieterle, A. Ross, G. Schlotterbeck, H. Senn, Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in 1H NMR metabonomics, Anal. Chem. 78 (2006) 4281–4290. [33] L.Z. Yi, J. He, Y.Z. Liang, D.L. Yuan, F.T. Chau, Plasma fatty acid metabolic profiling and biomarkers of type 2 diabetes mellitus based on GC/MS and PLS-LDA, FEBS Lett. 580 (2006) 6837–6845. [34] X. Chen, F. Xu, Y. Wang, Y. Pan, D. Lu, P. Wang, K. Ying, E. Chen, W. Zhang, A study of the volatile organic compounds exhaled by lung cancer cells in vitro for breath diagnosis, Cancer 110 (2007) 835–844. [35] S. Risticevic, J. Pawliszyn, Solid-phase microextraction in targeted and nontargeted analysis: displacement and desorption effects, Anal. Chem. 85 (2013) 8987–8995. [36] V. Abbasi, A. Sarafraz-Yazdi, A. Amiri, H. Vatani, Determination of aromatic amines using solid-phase microextraction based on an ionic liquid-mediated sol-gel technique, J. Chromatogr. Sci. 54 (2016) 677–681. [37] I. Calejo, N. Moreira, A.M. Araujo, M. Carvalho, M.L. Bastos, P.G. Pinho, Optimisation and validation of a HS-SPME-GC-IT/MS method for analysis of carbonyl volatile compounds as biomarkers in human urine: application in a pilot study to discriminate individuals with smoking habits, Talanta 148 (2016) 486–493. [38] S. Rocha, V. Ramalheira, A. Barros, I. Delgadillo, M. Coimbra, Headspace solid phase microextraction (SPME) analysis of flavor compounds in wines. Effect of the matrix volatile composition in the relative response factors in a wine model, J. Agric. Food Chem. 49 (2001) 5142–5151. [39] P. Emond, S. Mavel, N. Aidoud, L. Nadal-Desbarates, F. Montigny, F. BonnetBrilhault, C. Barthelemy, M. Merten, P. Sarda, F. Laumonnier, P. Vourc'h, H. Blasco, C.R. Andres, GC-MS-based urine metabolic profiling of autism spectrum disorders, Anal. Bioanal. Chem. 405 (2013) 5291–5300. [40] M.F. Almstetter, P.J. Oefner, K. Dettmer, Comprehensive two-dimensional gas chromatography in metabolomics, Anal. Bioanal. Chem. 402 (2012) 1993–2013. [41] M. Mieth, J.K. Schubert, T. Groger, B. Sabel, S. Kischkel, P. Fuchs, D. Hein, R. Zimmermann, W. Miekisch, Automated needle trap heart-cut GC/MS and needle trap comprehensive two-dimensional GC/TOF-MS for breath gas analysis in the clinical environment, Anal. Chem. 82 (2010) 2541–2551. [42] N. John, V. Eric, Ketone bodies as signaling metabolites, Trends Endocrinol. Metab. 25 (2014) 42–52. [43] S. Katada, A. Imhof, P. Sassone-Corsi, Connecting threads: epigenetics and metabolism, Cell 148 (2012) 24–28. [44] R. Fontana, S. Della Torre, The deep correlation between energy metabolism and reproduction: A view on the effects of nutrition for women fertility, Nutrients 8 (2016). [45] L. Birnbaumer, M. Rodbell, Adenyl cyclase in fat cells II. Hormone receptors, J. Biol. Chem. 244 (1969) 3477–3482. [46] J.S. Davis, Mechanisms of hormone action: luteinizing hormone receptors and second-messenger pathways, Curr. Opin. Obstet. Gynecol. 6 (1994) 254–261. [47] Y.C. Long, J.R. Zierath, AMP-activated protein kinase signaling in metabolic regulation, J. Clin. Invest. 116 (2006) 1776–1783. [48] K. Barnhart, J. Osheroff, Follicle stimulating hormone as a predictor of fertility, Curr. Opin. Obstet. Gynecol. 10 (1998) 227–232.

Acknowledgements This work was supported by the Arizona Biomedical Research Commission (Grant No. ADHS16-162403). We would like to thank Vi Nguyen for her time collecting subject samples, Joel Lusk for his project input and assistance, and Emily Hanzlick for her assistance in the calculation of metabolite retention indices. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.jchromb.2019.04.046. References [1] D. Unuane, H. Tournaye, B. Velkeniers, K. Poppe, Endocrine disorders & female infertility, Best Pract. Res. Clin. Endocrinol. Metab. 25 (2011) 861–873. [2] C.P. Lim, Z. Hasafa, S. Bhattacharya, A. Maheshwari, Should a hysterosalpingogram be a first-line investigation to diagnose female tubule subfertility in the modern subfertility workup? Hum. Reprod. 26 (2011) 967–971. [3] P.R. Brezina, E. Haberl, E. Wallach, At home testing: optimizing management for the infertility physician, Fertil. Steril. 95 (2011) 1867–1878. [4] E. Guermandi, W. Vegetti, M.M. Bianchi, A. Uglietti, G. Ragni, P. Crosignani, Obstet. Gynecol. 97 (2001) 92–96. [5] V. Pudata, J. Konduru, Metabolic syndrome in endocrine system, J. Diabetes Metab. Disord. 2 (2011) 1–5. [6] A. Agarwal, S. Gupta, R. Sharma, Role of oxidative stress in female reproduction, Reprod. Biol. Endocrinol. 28 (2005) 1–21. [7] J.C. Lindon, E. Holmes, J.K. Nicholson, Metabonomics, FEBS J. 274 (2007) 1140–1151. [8] K.J. Karczewski, M.P. Snyder, Integrative omics for health and disease, Nat. Rev. Genet. 19 (2018) 299–310. [9] S. Bouatra, F. Aziat, R. Mandal, A.C. Guo, M.R. Wilson, C. Knox, T.C. Bjorndahl, R. Krishnamurthy, F. Saleem, P. Liu, Z.T. Dame, J. Poelzer, J. Huynh, F.S. Yallou, N. Psychogios, E. Dong, R. Bogumil, C. Roehring, D.S. Wishart, The human urine metabolome, PLoS One 8 (2013), https://doi.org/10.1371/journal.pone.0073076. [10] N. Psychogios, D.D. Hau, J. Peng, A.C. Guo, R. Mandal, S. Bouatra, I. Sinelnikov, R. Krishnamurthy, R. Eisner, B. Gautam, N. Young, J. Xia, C. Knox, E. Dong, P. Huang, Z. Hollander, T.L. Pedersen, S.R. Smith, F. Bamforth, R. Greiner, B. McManus, J.W. Newman, T. Goodfriend, D.S. Wishart, The Human Serum Metabolome, PLoS One 6 (2011), https://doi.org/10.1371/journal.pone.0016957. [11] J. A, J. Trygg, J. Gullberg, A.I. Johansson, R. Jonsson, H. Antti, S.L. Marklund, T. Moritz, Extraction and GC/MS analysis of the human blood plasma metabolome, Anal. Chem. 77 (2005) 8086–8094. [12] A. Amann, B.D.L. Costello, W. Miekisch, J. Schubert, B. Buszewski, J. Peil, N. Ratcliffe, T. Risby, The human volatilome: volatile organic compounds (VOCs) in exhaled breath, skin emanations, urine, feces and saliva, J. Breath Res. 8 (2014). [13] D. Zimmermann, M. Hartmann, M.P. Moyer, J. Nolte, J.I. Baumbach, Determination of volatile products of human colon cell line metabolism by GC/MS analysis, Metabolomics 3 (2007) 13–17. [14] A. Alonso, S. Marsal, A. Julià, Analytical methods in untargeted metabolomics: state of the art in 2015, Front. Bioeng. Biotechnol. 3 (2015) 1–20. [15] J.L. Ren, A.H. Zhang, L. Kong, X.J. Wang, Advances in mass spectrometry-based metabolomics for investigation of metabolites, RSC Adv. 8 (2018) 22335–22350. [16] I. Horváth, Z. Lázár, N. Gyulai, M. Kollai, G. Losonczy, Exhaled biomarkers in lung cancer, Eur. Respir. J. 34 (2009) 261–275. [17] S. Hori, S. Nishiumi, K. Kobayashi, M. Shinohara, Y. Hatakeyama, Y. Kotani, N. Hatano, Y. Maniwa, W. Nishio, T. Bamba, E. Fukusaki, T. Azuma, T. Takenawa, T. Nishimura, M. Yoshida, A metabolomic approach to lung cancer, Lung Cancer 74 (2011) 284–292. [18] C.L. Silva, M. Passos, J.S. Câmara, Solid phase microextraction, mass spectrometry and metabolomic approaches for detection of potential urinary cancer biomarkers A powerful strategy for breast cancer diagnosis, Talanta 89 (2012) 360–368. [19] M. Sugimoto, D.T. Wong, A. Hirayama, T. Soga, M. Tomita, Capillary electrophoresis mass spectrometry-based saliva metabolomics identified oral, breast and pancreatic cancer-specific profiles, Metabolomics 6 (2010) 78–95. [20] A. Floegel, N. Stefan, Z. Yu, K. Muhlenbruch, D. Drogan, H.G. Joost, A. Fritsche, H.U. Haring, M. Hrabe de Angelis, A. Peters, M. Roden, C. Prehn, R. Wang-Sattler, T. Illig, M.B. Schulze, J. Adamski, H. Boeing, T. Pischon, Identification of serum metabolites associated with risk of type 2 diabetes using a targeted metabolomic approach, Diabetes 62 (2013) 639–648.

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