Elemental metabolomics in human cord blood: Method validation and trace element quantification

Journal of Trace Elements in Medicine and Biology xxx (xxxx) xxxx

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Analytical methodology

Elemental metabolomics in human cord blood: Method validation and trace element quantification Daniel R. McKeatinga, Joshua J. Fishera, Ping Zhanga, William W. Bennettb, Anthony V. Perkinsa,* a b

School of Medical Science, Griffith University, Gold Coast Campus, Southport, 9726, Queensland, Australia School of Environmental Science, Griffith University, Gold Coast Campus, Southport, 9726, Queensland, Australia

ARTICLE INFO

ABSTRACT

Keywords: Elemental metabolomics Pregnancy Cord blood Gestational disorders

Background: Trace elements are an essential requirement for human health and development and changes in trace element status have been associated with pregnancy complications such as gestational diabetes mellitus (GDM), pre-eclampsia (PE), fetal growth restriction (FGR), and preterm birth. Elemental metabolomics, which involves the simultaneous quantification and characterisation of multiple elements, could provide important insights into these gestational disorders. Methods: This study used an Agilent 7900 inductively coupled plasma mass spectrometer (ICP-MS) to simultaneously measure 68 elements, in 166 placental cord blood samples collected from women with various pregnancy complications (control, hypertensive, PE, GDM, FGR, pre-term, and post-term birth). Results: There were single element differences across gestational outcomes for elements Mg, P, Cr, Ni, Sr, Mo, I, Au, Pb, and U. Hypertensive and post-term pregnancies were significantly higher in Ni concentrations when compared to controls (control = 2.74 μg/L, hypertensive = 6.72 μg/L, post-term = 7.93 μg/L, p < 0.05), iodine concentration was significantly higher in post-term pregnancies (p < 0.05), and Pb concentrations were the lowest in pre-term pregnancies (pre-term = 2.79 μg/L, control = 4.68 μg/L, PE = 5.32 μg/L, GDM = 8.27 μg/L, p < 0.01). Further analysis was conducted using receiver operating characteristic (ROC) curves for differentiating pregnancy groups. The ratio of Sn/Pb showed the best diagnostic power in discriminating between control and pre-term birth with area under the curve (AUC) 0.86. When comparing control and post-term birth, Mg/Cr (AUC = 0.84), and Cr (AUC = 0.83) had the best diagnostic powers. In pre-term and post-term comparisons Ba was the best single element (81.5%), and P/Cu provided the best ratio (91.7%). Conclusions: This study has shown that analysis of multiple elements can enable differentiation between fetal cord blood samples from control, hypertensive, PE, GDM, FGR, pre and post-term pregnancies. This data highlights the power of elemental metabolomics and provides a basis for future gestational studies.

1. Introduction Fetal development and subsequent health requires trace elements and aberrant changes have been associated with a variety of negative health outcomes. Some essential elements play key roles during gestation with deficiencies often resulting in poor perinatal outcomes [1–3]. Essential elements, including Mg, K, Ca, Se, and Zn have been associated with gestational disorders such as preeclampsia (PE), gestational diabetes mellitus (GDM), fetal growth restriction (FGR), and preterm birth [1–3]. Various micronutrients have been shown to have important roles in modulation of maternal and fetal metabolism, oxidative stress, placentation, and the development of key fetal organs and tissues [4–6], ⁎

however the more specific pregnancy related functions of many are poorly understood. The placenta plays a crucial role in mediating the transfer of nutrients via both active and passive transport mechanisms [7]. Previous research indicates that the placenta will preferentially uptake nutrients from the maternal system to prevent fetal deficiency [8,9]. Maternal conditions such as diabetes or obesity can alter the nutrient transporters in the placenta, leading to increased or decreased nutrient transfer, with potential negative outcomes including fetal growth restriction [3]. As the placenta coordinates many aspects of gestational development and maternal blood nourishes the foetus, biological samples from the maternal or fetal circulation provide highly meaningful information relating to micronutrient status, maternal health and fetal development.

Corresponding author at: School of Medical Science, Griffith University, Gold Coast Campus, Parklands Drive, Southport, Queensland, 9726, Australia. E-mail address: [email protected] (A.V. Perkins).

https://doi.org/10.1016/j.jtemb.2019.126419 Received 24 June 2019; Received in revised form 26 August 2019; Accepted 18 October 2019 0946-672X/ Crown Copyright © 2019 Published by Elsevier GmbH. All rights reserved.

Please cite this article as: Daniel R. McKeating, et al., Journal of Trace Elements in Medicine and Biology, https://doi.org/10.1016/j.jtemb.2019.126419

Journal of Trace Elements in Medicine and Biology xxx (xxxx) xxxx

D.R. McKeating, et al.

Table 1 Demographic and clinical characteristics of the study goups. PE, pre-eclamsia; GDM, gestational diabetes mellitus; IUGR, intrauterine growth restriction; BMI, body mass index; PIH, pregnancy induced hypertension.

Age Baby Sex BMI Diabetes mellitus Gestational diabetes PIH PE Smokea Birth weight (g) Gestation period (weeks) Supplements containing metals a b c

M F

Control (n = 64)

Hypertensive (n = 26)

PE (n = 20)

GDM (n = 22)

IUGR (n = 8)

Pre-termb (n = 14)

Post-termc (n = 12)

28.77 ± 6.58 (18 - 41) 26; 40.62% 38; 59.37% 25.65 ± 6.22 (14.56 - 42.51) 0; 0% 0; 0% 0; 0% 0; 0% 16; 25% 3519.09 ± 600.48 (1710 - 4760) 38.79 ± 1.56 (32 - 41) 43; 67.18%

30.06 ± 5.6 (20 - 41) 9; 34.61% 17; 65.38% 31.51 ± 7.55 (23.5 - 49.71) 0; 0% 0; 0% 26; 100% 0; 0% 5; 19.23% 3481.03 ± 527.83 (2520 - 4860) 38.93 ± 1.45 (35 - 41) 9; 34.61%

28.76 ± 6.23 (17 - 40) 12; 60% 8; 40% 27.69 ± 7.53 (16.73 - 47.68) 0; 0% 0; 0% 20; 100% 20; 100% 3; 15% 3211.92 ± 664.78 (2040 - 4845) 37.72 ± 2.63 (32 - 42) 10; 50%

30.96 ± 6.33 (20 - 44) 9; 40.90% 13; 59.09% 28.1 ± 6.54 (21.96 - 47.46) 0; 0% 22; 100% 1; 4.54% 0; 0% 0; 0% 3473.78 ± 516 (2450 - 4575) 38.5 ± 1.19 (36 - 40) 7; 31.81%

24.25 ± 5.47 (18 - 34) 3; 37.5% 5; 62.5% 20.24 ± 4.42 (15.74 - 27.63) 0; 0% 0; 0% 0; 0% 0; 0% 6; 75% 2518 ± 457.11 (1692 - 3090) 37.87 ± 1.24 (35 - 39) 0; 0%

30.15 ± 5.51 (19 - 42) 5; 35.71% 9; 64.28% 24.49 ± 4.28 (18.93 - 32.34) 0; 0% 0; 0% 0; 0% 0; 0% 4; 28.57% 2874.21 ± 665.57 (1692 - 4350) 30.69 ± 9.22 (10 - 35) 4; 28.57%

29.5 ± 7.5 (18 - 45) 5; 41.66% 7; 58.33% 22.79 ± 4.43 (17.92 - 33.51) 0; 0% 0; 0% 1; 8.33% 0; 0% 2; 16.66% 3732.91 ± 484.89 (2930 - 4410) 42 ± 0 (42 - 42) 6; 50%

Smoking during pregnancy at any time. Born prior to 35 weeks gestation. Born at or post 42 weeks.

2. Methods and materials

For this study, the use of elemental metabolomics; the study of multiple elements present within an organism, is proposed as a means of determining the essential role of multiple elements in fetal development. Recent advances in chemical analysis in conjunction with cheminformatics and bioinformatics have allowed the determination of detailed metabolic patterns that can be established in normal and diseased states [10]. This allows the establishment of “elemental signatures” which may prove to be powerful diagnostic aids, providing insight into the pathogenesis of many conditions [11–14]. The majority of techniques used in elemental analysis include inductively coupled plasma mass spectrometry (ICP-MS), which is capable of detecting metals present in concentrations as low as nanograms per litre. Compared to other methodologies, ICP-MS allows for smaller sample volume owing to its greater sensitivity. Currently, ICP-MS has been successfully used for large scale elemental studies in yeast, plants and mammals [15,16]. The research conducted in the medical field to date has yielded promising results for various conditions such as Alzheimer’s disease [11], Parkinson’s disease [12], type 2 diabetes [13], and cancer [14]. A recent study reported elemental profiles in 3416 maternal and cord blood sera from various time points and highlighted changes that occurred over the course of gestation and with seasonal variations [17]. Though no comparison between gestational disorders was made, it was a first step in using this methodology for analysis of fetal development and gestational outcomes. These investigations illustrate the potential of metabolomics to identify new aspects of trace element metabolism and homeostasis, and how such information can be used to develop hypotheses regarding the functions of elements in disease states [3,18,19]. Multi-elemental analysis and the predictive capabilities of this methodology could contribute to further understanding gestational disorders and improving the prediction of pregnancy outcomes. This study aims to analyse the elemental profile of term cord blood plasma samples from the Environments for Healthy Living (EFHL) cohort as a means of determining if elemental metabolomics could be used for the quantification of fetal elements and might differentiate cord blood samples from normally developed babies and those experiencing a gestational disorder. This methodology could provide a means to not only quantify important elements to assess their relationship to gestational disorders, but also provide a means of identifying key elements important to fetal development and placental health.

2.1. Study population Samples were from the Environments for Healthy Living (EFHL) study which collected data from 2879 pregnant women from 2006 to 2010 [20]. Ethical approval for EFHL participants was obtained from Griffith University Human Research Ethics Committee (Ref: MED/23/ 11/HREC), Logan Hospital (HREC/06/QPAH/96), Gold Coast Hospital (HREC/06/GCH/52), and Tweed Hospital (NCAHS HREC 358 N). Enrolment occurred over 4 months of every year, commencing in November 2006; August in 2007 and 2008; and from July 2009 onwards. Umbilical cord blood was collected by a trained midwife at delivery from consenting mothers. Approximately 10–12 ml of cord blood was collected into EDTA vacutainers and stored at −80 °C until analysis. The full EFHL cohort methodology is described in detail elsewhere [20]. Cord blood samples from pregnancies recording a gestational disorder were chosen for analysis and matched to normal control samples. These included: 26 pregnancy induced hypertension, 20 PE, 22 GDM, 8 FGR, 14 pre-term birth, and 12 post-term birth. Control samples (64) were matched for age, BMI, baby sex, ethnicity and were from term babies with clinically normal pregnancy. 2.2. Sample analysis All reagents were analytical grade and deionised water (18.2 MΩ cm−1; Milli-Q Element) was used to prepare all solutions. Custom 1000 mg/L standard solutions were obtained from High Purity Standards (Choice Analytical). All plastics and components were acidcleaned in 10% (v/v) HNO3 (AR grade, Merck) for at least 24 h and rinsed thoroughly with deionised water prior to use. All salts used to prepare experimental solutions were AR grade or higher. All samples, standards and quality controls were prepared for ICPMS in a 1:10 dilution with a solution of 2.8% ammonia, 10% isopropanol, 0.2% triton x solution, 0.1% EDTA, and deionized water [21,22]. Quality control standards at 10–100 μg/L were analyzed every 12 samples. Sc, Y, In, and Tb were added to all samples (final concentration of 10 μg/L) as an internal standard to account for instrument drift. Measurements were performed using an inductively coupled plasma mass spectrometer (ICP-MS, Agilent 7900). The parameters of the 2

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Fig. 1. Principal Component Analysis (PCA) (a) PCA highlights variance in GDM, hypertensive, pre-term and post-term compared to other groups. (b) Loading biplot shows how elements contribute to sample variance.

Agilent 7900 can be seen in supplementary Table 1. The following masses were measured: 7Li, 23Na, 24Mg, 27Al, 31P, 39K, 44Ca, 47Ti, 51V, 52 Cr, 55Mn, 56Fe, 59Co, 60Ni, 63Cu, 66Zn, 71Ga, 72Ge, 75As, 78Se, 88Sr, 90 Zr, 95Mo, 105Pd, 107Ag, 111Cd, 118Sn, 121Sb, 127I, 133Cs, 137Ba, 172Yb, 182 W, 197Au, 201Hg, 205Tl, 208Pb, 232Th, 238U. Standard curves were created through a serial dilution of elements from a custom stock (Highpurity standards batch numbers: HPS-152-100-100: #1826305, ICPMS-68A-B-100: #1823625, ICP-MS-68A-B-100: #1809318). The concentration range for Na, Mg, P, K, Ca, Fe were 10,000-2,500-1,000-10010-5-1-0 μg/L, and for all other elements 1,000-250-100-10-1-0.5-0.10 μg/L. Correlation coefficients (R2) of all elemental calibration curves were above 0.99. Limit of detection (LOD) was determined as three times standard deviation of 20 blank samples. Limit of quantification (LOQ) was determined as ten times the standard deviation of 20 blank samples (supplementary Table 3) [11,17]. ClinCheck trace element controls for plasma (Levels I and II, ref 8883-8885 lot 1286, plasma control lyophilised) were used for internal quality assurance. Relative standard deviation was less than 15%. Elements that were removed from further analysis were either not detectable in the sample set (Li, Ga, Zr, Pd, Cd, Yb, W, Hg, Tl), or did not meet quality control standards (Al, Ti, V, Th). Elements were included for subsequent analysis if the percentage of samples greater than the level of detection was 100%, leaving 26 elements.

these tests, variable transformations such as logarithmic or square were considered. The log of Ca, Co, Zn, Ag, Sn, I, Cs, Ba, Au, Pb, U and the square Mo improved normality of the distribution, however normality could not be achieved for Mg and K. Correlation analysis used Spearman rank order, and power analysis via post hoc using a one-tail test. PCA was performed in order to determine any variance in the database. Univariate ROC curve analysis was used to determine the diagnostic power of each element and their ratios. The data for this analysis was log transformed, then scaled via mean centring, and divided by the standard deviation of each variable [11]. 3. Results 3.1. Characteristics of the participants Demographic characteristics of the participants, and details of gestational pathology are shown in Table 1. The data shows minimal variation in age, baby sex, BMI, diabetes, and smoking status. Birth weight was affected in FGR, pre-term, and post-term pregnancies. 3.2. Concentration of elements in placental cord blood plasma Comparison of elemental concentrations between gestational complication groups showed differences between groups for elements Mg, P, Cr, Ni, Sr, Mo, I, Au, Pb, and U. Hypertensive and post-term pregnancies were statistically higher in Ni concentrations when compared to controls (control = 2.74 μg/L, hypertensive = 6.72 μg/L, preterm = 7.93 μg/L, p < 0.05), iodine concentration was higher in post-term pregnancies (p < 0.05), and Pb concentrations were the lowest in pre-term pregnancies (pre-term = 2.79 μg/L, control = 4.68 μg/L, PE = 5.32 μg/L, GDM = 8.27 μg/L, p < 0.01).

2.3. Statistical analysis Data was analysed using SPSS v.25 statistical software package (SPSS Inc., Chicago, Ill.), and GraphPad Prism v.8.0.2 (GraphPad Software, San Diego, Cal.). Variables were examined for outliers and extreme values by means of box and normal quantile-quantile plots, Shapiro-Wilk’s and Kolmogorov-Smirnov’s tests. Results are reported as mean ± standard deviation and (range). Ordinary one-way ANOVA performed on all samples groups and 26 elements, with Tukey’s posthoc analysis for pairwise comparison, significance classified as p < 0.05. Correlation analysis, Principal component analysis (PCA), and receiver operating curves (ROC) analysis was performed using MetaboAnalyst. When normal distribution could not be accepted for

3.3. Elemental signatures across gestational disorders Principal component analysis (PCA) showed that the first and second principal components (PC1 and PC2) tend to show greater variance in the GDM, hypertensive, pre-term, and post-term samples; whilst control, FGR and PE appear to group relatively tightly, implying less variance (Fig. 1A). The biplot indicated that variance appears to be 3

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D.R. McKeating, et al.

Fig. 2. Correlation analysis for elements across all groups. Essential elements (P, Fe, Ge, Mg and Cu) had strong positive correlations with each other, and negatively correlated with Na, Ca, Sr and I etc. The tables show positive and negative Spearman correlations between the paired elements.(r > 0.4 or r < -0.3 and p < 0.05).

influenced by three elemental groupings, consisting of essential elements such as Ca, Na, K, Se, Zn, and Mn, and toxic elements like U and Pb (Fig. 1B). Elements with strong correlations along PC1 include Au, Sn, Cr, Ag, and U; whilst PC2 is influenced by P, Fe, Mn, Cu, Ge, Zn, Na, and Ca. This is supported by the table of principal component values (Supp table 5). Spearman rank correlation analysis was used to determine the strength and direction of relationships between variables, in this case cord blood elements (Fig. 2). Significant positive and negative correlations were noted between essential elements (spearman rank correlations rI-Ca = 0.58, rCu-Fe = 0.56, rP-Mn = 0.56, rCa-Fe = -0.55, rCa-Ge = -0.49, rI-Fe = -0.36, all p < 0.0001), whilst there were no significant correlations for toxic elements. This indicates that essential elements are more likely to have correlations associated with other essential elements, whereas toxic elements are unlikely to correlate with any other element analysed.

control and FGR, Sn/U (AUC = 0.758) for control versus pregnancy hypertension, P/Cr (AUC = 0.846) for control versus post-term birth, P/Cu (AUC = 0.917) for pre-term and post-term and P/Ge (AUC = 0.763) for pregnancy hypertension and PE (Fig. 3). 4. Discussion Several studies have reported the levels of elements in gestation (cord blood, maternal blood/urine), though most only focus on specific subsets of elements without commenting on further interactions [3,17,23–25]. Studies that measure an overall array of elements (19+) in biological samples from gestation with standardised methodologies are limited, most opting for measuring a smaller subset of elements to answer specific research questions [3,17]. A study by Paglia, G., et al, (2016) on Alzheimer’s disease highlighted the difficulties in comparing results from different studies, citing how often within their field results do not agree with each other. Though several studies have reported the levels of elements in biological fluids and brain regions in Alzheimer’s, they surmised that the difficulties in comparative analysis arises from studies reporting single elements or a small number of them, measured by various methods and technologies [11]. Similar problems can be noted in Parkinson’s, cancer, and type-2 diabetes research, however all areas are beginning to show cohesion in analytical methods [12–14]. This is a relatively new field to pregnancy research, with a limited number of studies reporting the important data that can be obtained. A recent study by Liang, C., et al. (2019) noted seasonal differences in the circulating concentrations of elements, however due to the collection of EFHL samples occurring around the same months every year, this could not be further investigated in this data set [17]. Difficulties associated with sample collection in gestational studies leads to a tendency to work with relatively low sample numbers [3]. The number of complicated samples in this study is considered normal for gestational cohorts,

3.4. Elemental marker validation: predictive capability of elementals Univariate receiver operating characteristics (ROC) curves were used to determine the predictive potential of this method for identifying a pathology based on elemental profiles (Table 3). Markers were selected based on t-test significance, and area under the curve (AUC) of greater than 0.7 which represents the ability to differentiate between samples with a 70% certainty. In the discrimination between sample sets, elements provided different diagnostic powers, based on which two groups were being compared. Cr (AUC = 0.835, post-term), and Ge (AUC = 0.813, FGR) had the best diagnostic power for single elements when compared against controls. Others such as Pb, Ge, Fe, Ba, and Sn showed good statistical power (Table 3). To differentiate between pregnancy groups, Sn/Pb (AUC = 0.859) was highlighted for control versus pre-term birth, Ge (AUC = 0.813) for 4

107.54 ± 22.89 (51.63 - 160.08) 31.66 ± 3.92 (25.71 - 43.19) 450.18 ± 75.62 (274.17 - 645.31) 2.83 ± 0.6 (2.13 - 6.85) 42.5 ± 12.46 (21.26 - 74.94) 1.22 ± 0.55 (0.22 - 2.78) 31.6 ± 12.68 (12.04 - 87.12) 456.83 ± 105.55 (211.84 - 673.61) 0.34 ± 0.13 (0.16 - 0.95) 2.74 ± 4.34 (0.03 - 18.52) 0.53 ± 0.08 (0.38 - 0.86) 2.23 ± 0.53 (1.48 - 4.62) 3.19 ± 0.82 (1.33 - 5.15) 0.9 ± 1.83 (0.12 - 13.69) 88.71 ± 14.26 (60.08 - 118.55) 19.67 ± 14.15 (8.17 - 121.57)

Na (mg/L)

5

3.07 ± 1.02 (1.16 - 5.89)

Cs (μg/l)

I (μg/l)

Sb (μg/l)

Sn (μg/l)

Ag (μg/l)

0.58 ± 0.22 (0.23 - 1.44) 1.05 ± 1.06 (0.2 - 5.46) 0.66 ± 0.31 (0.22 - 1.8) 5.01 ± 3.7 (1.98 - 25.16) 58.62 ± 16.95 (35.6 - 112.6)

Mo (μg/l)

Sr (μg/l)

Se (μg/l)

As (μg/l)

Ge (μg/l)

Zn (mg/L)

Cu (mg/L)

Ni (μg/l)

Co (μg/l)

Fe (mg/L)

Mn (μg/l)

Cr (μg/l)

Ca (mg/L)

K (g/L)

P (mg/L)

Mg (mg/L)

Control a (n = 64)

Elements

b

3.21 ± 1 (1.62 - 6.4)

0.61 ± 0.39 (0.27 - 2.41) 1.51 ± 1.16 (0.27 - 4.73) 0.78 ± 0.48 (0.23 - 1.82) 5.47 ± 2.12 (2.84 - 13.06) 52.82 ± 14.68 (29.48 - 84.54)

93.04 ± 18.4 (62.3 - 134.56) 30.41 ± 4.32 (16.04 - 37.74) 462.79 ± 97.13 (218.75 - 606.58) 2.9 ± 0.52 (1.6 - 3.82) 37.29 ± 11 (21.64 - 63.97) 1.55 ± 0.73 (0.4 - 2.94) 28.09 ± 11.84 (9.47 - 66.56) 466.93 ± 102.16 (211.44 - 626.75) 0.32 ± 0.11 (0.14 - 0.58) 6.72 ± 7.22 (0.22 - 25.38) 0.52 ± 0.09 (0.26 - 0.74) 2.36 ± 0.56 (1.15 - 3.54) 3.37 ± 0.84 (1.7 - 4.9) 1 ± 2.05 (0.15 - 10.69) 88.27 ± 15.37 (40.37 - 122.31) 15.93 ± 4.27 (9.78 - 28.01)

Hypertensive (n = 26)

3.15 ± 0.66 (1.79 - 4.45)

0.57 ± 0.32 (0.15 - 1.45) 1.05 ± 0.64 (0.26 - 2.91) 0.64 ± 0.21 (0.32 - 1.11) 4.49 ± 1.54 (1.93 - 8.38) 57.86 ± 29.4 (28.26 - 164.66)

99.7 ± 22.66 (66.83 - 155.6) 33.41 ± 4.68 (24.33 - 43.02) 497.89 ± 92.8 (255.41 - 636.82) 2.97 ± 0.34 (2.33 - 3.51) 39.42 ± 13.6 (24.85 - 78.35) 1.33 ± 0.39 (0.65 - 1.92) 36.48 ± 15.81 (12.94 - 67.58) 490.12 ± 130.2 (206.39 - 690.7) 0.29 ± 0.1 (0.15 - 0.49) 4.98 ± 8.78 (0.16 - 34.61) 0.56 ± 0.1 (0.37 - 0.8) 2.16 ± 0.54 (1.45 - 3.44) 3.31 ± 0.88 (1.44 - 4.91) 1.15 ± 1.16 (0.15 - 3.75) 94.77 ± 20.74 (55.63 - 144.87) 18.5 ± 6.31 (9.29 - 31.67)

PE c (n = 20)

3.36 ± 1.03 (1.15 - 6.22)

0.65 ± 0.28 (0.2 - 1.63) 1.31 ± 1.25 (0.26 - 4.04) 0.89 ± 0.53 (0.27 - 2.31) 3.93 ± 1.32 (2.7 - 7.53) 62.06 ± 25.12 (37.87 - 155.21)

114.92 ± 27.41 (72.45 - 197.12) 33.24 ± 8.56 (21.8 - 67.7) 455.21 ± 136.43 (238.47 - 932.12) 2.86 ± 0.62 (1.69 - 5.14) 46.79 ± 14.9 (27.98 - 95.28) 1.6 ± 0.76 (0.65 - 3.36) 34.45 ± 13.1 (12.94 - 61.71) 484.16 ± 147.69 (180.83 - 802.43) 0.33 ± 0.14 (0.12 - 0.65) 4.44 ± 5.92 (0.04 - 18.79) 0.51 ± 0.12 (0.28 - 0.92) 2.14 ± 0.56 (1.31 - 4.18) 3.39 ± 1.09 (1.07 - 5.91) 0.85 ± 1.5 (0.14 - 7.4) 95.34 ± 29.38 (52.82 - 200.46) 19.43 ± 6.54 (12.88 - 40.37)

GDM d (n = 22)

3.53 ± 0.75 (2.52 - 4.91)

0.47 ± 0.14 (0.25 - 0.61) 1.03 ± 1 (0.3 - 3.32) 0.73 ± 0.33 (0.28 - 1.33) 4.27 ± 1.39 (2.78 - 6.72) 59.79 ± 52.58 (33 - 188.34)

101.33 ± 15 (84.93 - 123.77) 33.01 ± 1.57 (31.18 - 35.99) 500.61 ± 52.36 (417.46 - 580.65) 3.00 ± 0.27 (2.73 - 3.48) 34.65 ± 5.52 (26.42 - 41.79) 1.43 ± 0.46 (0.85 - 2.22) 31.73 ± 7.38 (21.12 - 40.7) 557.08 ± 58.93 (466.97 - 657.83) 0.27 ± 0.1 (0.12 - 0.42) 2.35 ± 3.51 (0.16 - 9.82) 0.56 ± 0.04 (0.48 - 0.63) 2.14 ± 0.32 (1.88 - 2.87) 4.04 ± 0.5 (3.26 - 4.72) 0.55 ± 0.42 (0.17 - 1.49) 92.6 ± 13.17 (74.73 - 113.04) 13.47 ± 4.35 (8.47 - 21.41)

IUGR e (n = 8)

f

2.69 ± 0.55 (1.88 - 3.48)

0.41 ± 0.07 (0.26 – 0.53) 1.45 ± 1.29 (0.32 - 3.79) 0.92 ± 0.4 (0.44 - 1.85) 5.79 ± 2.99 (3.29 - 15.21) 67.49 ± 42.46 (33.06 - 206.52)

109.07 ± 13.53 (78.41 - 127.71) 30.09 ± 1.85 (27.49 - 33.06) 432.9 ± 62.62 (354 - 558.72) 2.8 ± 0.45 (2.25 - 4.05) 42.59 ± 7.5 (27.05 - 55.39) 1.89 ± 0.68 (1.18 - 3.34) 28.99 ± 10.13 (13.55 - 45.13) 454.05 ± 82.7 (344.17 - 610.03) 0.32 ± 0.11 (0.14 - 0.5) 6.52 ± 6.71 (0.03 - 18.28) 0.48 ± 0.06 (0.38 - 0.58) 2.03 ± 0.26 (1.53 - 2.46) 3.18 ± 0.51 (2.39 - 4.05) 0.55 ± 0.44 (0.19 - 1.77) 85.23 ± 13.43 (62.79 - 113.67) 16.81 ± 5.99 (10.56 - 33.76)

Pre-term (n = 14)

g

3.4 ± 1.76 (1.83 - 7.68)

0.67 ± 0.29 (0.23 - 1.11) 1.17 ± 1.1 (0.47 - 4.6) 1 ± 0.41 (0.43 - 2) 5.05 ± 1.72 (2 - 8.47) 82.31 ± 40.44 (35.33 - 174.94)

112.04 ± 18.49 (90.02 - 150.86) 29.92 ± 5.17 (23.4 - 42.77) 386.29 ± 110.51 (205.37 - 588.96) 2.75 ± 0.43 (1.9 - 3.28) 47.07 ± 13.78 (31.39 - 66.34) 1.86 ± 0.5 (1.39 - 3.07) 27.45 ± 11.92 (16.98 - 58.59) 391.88 ± 169.02 (130.99 - 693.6) 0.32 ± 0.13 (0.14 - 0.69) 7.93 ± 6.68 (0.19 - 18.09) 0.56 ± 0.13 (0.43 - 0.81) 2.33 ± 0.58 (1.62 - 3.3) 2.75 ± 1.07 (1.37 - 4.74) 1.75 ± 3.51 (0.19 - 12.51) 82.49 ± 20.62 (54.28 - 111.48) 22.6 ± 6.3 (12.17 - 35.84)

Post-term (n = 12)

1.439

5.051

1.826

3.057

1.736

2.284

3.540

1.024

1.723

2.004

1.535

2.276

3.759

0.738

1.936

2.479

0.2029

< 0.0001

0.0975

0.0074

0.1161

0.0385

0.0026

0.4120

0.1192

0.0682

0.1701

0.0391

0.0016

0.6201

0.0781

0.0256

0.0038

0.0218

2.554 3.357

0.1522

0.0076

0.0200

1.594

3.045

2.596

0.0252

(continued on next page)

a, g = 0.0016 b, g = 0.0003 c, g = 0.0004 d, g = 0.0066 e, g = 0.0002 f, g = 0.0154 n.s.

n.s.

n.s.

n.s.

b, g = 0.0098 e, g = 0.0054 f, g = 0.0242 f, g = 0.0320

n.s.

n.s.

n.s.

n.s.

a, b = 0.0217 a, g = 0.0367 n.s.

n.s.

n.s.

n.s.

a, f = 0.0162

n.s.

n.s.

c, g = 0.0079

c, g = 0.0195

n.s.

(p value)

F 2.484

Pairwise comparison

ANOVA p value

Table 2 Element concentrations in plasma of placental cord blood of gestational disorders and control patients. Analysis by one-way ANOVA of individual elements; Tukey’s post-hoc analysis for pairwise comparison; n.s., not significant; PE, pre-eclamsia; GDM, gestational diabetes mellitus; IUGR, intrauterine growth restriction. F is the F-value of the ANOVA with statistical significance shown in the p value. Tukey’s post-hoc analysis used for pairwise comparison conducted between acontrol, bhypertensive, cPE, dGDM, eIUGR, fPre-term, gPost-term. Post-hoc analysis only shows the groups that were statistically significant, displayed as the comparison groups followed by the p value.

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0.15 ± 0.07 (0.05 - 0.37)

0.21 ± 0.16 (0.06 - 0.67)

0.13 ± 0.07 (0.05 - 0.28)

0.22 ± 0.13 (0.06 - 0.52)

0.22 ± 0.11 (0.1 - 0.57)

4.489

0.0003

a, f = 0.0059 c, f = 0.0065 d, f = 0.0007 a, b = 0.0111 a, d = 0.0070 a, f = 0.0309 0.0020 3.656

a, b = 0.0344 0.0172 2.668

n.s. 0.0370 2.306

4.27 ± 5.26 (1.16 - 20.62) 1.47 ± 1.52 (0.5 - 6.21) 4.59 ± 2.89 (1.37 - 9.31) 1.82 ± 1.28 (0.77 - 5.87) 1.73 ± 1.66 (0.34 - 4.85) 2.79 ± 1.52 (0.63 - 5.89) 2.73 ± 2.07 (0.67 - 6.46) 1.99 ± 1.87 (0.52 - 5.44) 5.51 ± 3.51 (2.24 - 12.38) 3.66 ± 4.12 (0.95 - 20.66) 1.54 ± 1.49 (0.34 - 5.91) 8.27 ± 12.46 (1.63 - 63.13) 2.12 ± 1.11 (0.81 - 4.32) 1.32 ± 0.93 (0.35 - 3.45) 5.32 ± 2.8 (0.96 - 12.58)

(p value) F

however there is always the goal of conducting larger sample cohorts in the future. In the current body of work, 68 elements in human cord blood plasma were measured using ICP-MS from control and complicated pregnancies (pregnancy induced hypertension, PE, GDM, FGR, pre-term birth, and post-term birth) with ICP-MS; of these 26 were present in sufficient quantity to allow analysis after accounting for quality control parameters. Statistical analysis between controls and abnormal pregnancies revealed that 9 elements were significantly altered in cord blood plasma from gestational disorders (Table 2). This study showed that in instances of hypertension in pregnancy, concentrations of Ni, Au, and U increase. Though there is little literature reporting the concentration of Ni and U in pregnancy induced hypertension, a study by Suliburska, J., et al. (2016) showed an increase in both Ni and U in women with high blood pressure [26]. Ni has been shown to increase in concentration with hypertension in other fields, a study by Wang et al. (2002) analysed the effect of nickel exposure on rats, noting that with exposure, blood pressure increased [27]. Literature on PE and GDM patients has highlighted a number of variations in intake and maternal concentrations for elements. For PE, this primarily involves decreased intakes and circulating levels of Ca, Mn, Se, and Zn when compared to healthy normal pregnancies [3,23,24,28]. In gestational diabetes these include intake and maternal circulating levels of Se, Cr, Zn, and Fe which all tend to decrease when compared to controls [3,25,29–32]. These trends did not follow through to the placenta in this study, where all elements were nonsignificant from normals (Table 2). This could possibly imply some level of increased placental preference for these elements, much the same as folate when it is deficient in maternal systems [8,9]. Two studies have correlated low Se concentration in maternal and cord blood to pre-term birth, and whilst the concentration of Se is lower in the EFHL cohort, the results are not significant (Table 2). Cord blood plasma levels of Cr were significantly higher in pre-term samples when compared to controls; prenatal exposure to Cr has been associated with an increased risk of pre-term birth [33]. Increased maternal urine Pb concentrations have been linked to pre-term birth also, however our data shows that there is a significant decrease in cord blood plasma Pb, suggesting that the placenta is possibly working to prevent the transfer of toxic nutrients into the fetal system [34]. There was an increase in circulating U in GDM, and preterm pregnancies when compared to controls that has not been noted in the literature previously, however, this result will require further investigation in separate cohorts to ensure its validity. Similarly, there is very little published information surrounding circulating elements in cases of post-term births, so no information was available to support or refute the elemental profiles reported here. Differences in concentrations between gestational disorders have not been fully characterised, especially in maternal samples across gestation, and will require further investigation before further conclusions can be drawn [3]. In most studies that assess elemental differences, comparisons are made between control groups and the abnormal pregnancy groups, with few analysing the differences between disorders. For this study it was important to conduct exploratory analysis between gestational disorder groups to note any elemental differences, prior to further statistical analysis. The elements Mg, P, Sr, Mo, I, and Pb had significant differences across the range of gestational disorders (Table 2). When comparing PE and post-term cord blood plasma samples, there was an increase in Mg and P with PE samples (+10%, and +28% respectively). Concentrations of Pb in PE samples when compared to pre-term were almost double (+90.6%). Pre-term samples saw almost half the concentration of Pb when compared to GDM samples (-66.2%). Post-term pregnancies had a 63% greater Mo concentration when compared to pre-term samples. Strontium was lower in hypertensive, IUGR, and preterm when compared to post-terms (-18%, -40%, -26% respectively). Concentrations of iodine were also increased in post term pregnancy by an average of 38% across all groups. The data not only highlighted

0.21 ± 0.13 (0.05 - 0.5) 0.14 ± 0.1 (0.05 - 0.51) U (μg/l)

Pb (μg/l)

Au (μg/l)

2.97 ± 5.15 (0.72 - 40.8) 1.33 ± 1.69 (0.19 - 11.13) 4.68 ± 2.23 (1.51 - 10.62) Ba (μg/l)

7.03 ± 16.5 (0.8 - 79.46) 1.85 ± 1.31 (0.29 - 4.64) 4.18 ± 1.82 (0.76 - 8.75)

Control a (n = 64) Elements

Table 2 (continued)

Hypertensive (n = 26)

b

PE c (n = 20)

GDM d (n = 22)

IUGR e (n = 8)

Pre-term (n = 14)

f

Post-term (n = 12)

g

p value

Pairwise comparison ANOVA

D.R. McKeating, et al.

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Table 3 Univariate ROC curve analysis. AUC = Area under the curves. AUC gives a predictive capability between 2 sets of samples. An AUC of > 0.7 (70%) is good, > 0.8 is great (80%), > 0.9 (90%) is excellent. Data ordered by highest AUC to lowest AUC in each group. (A) Control v Pre-term

(D) Control v Post-term

Elements

AUC

t-test

log 2 FC

Elements

AUC

t-test

log 2 FC

Sn/Pb Cr/Pb Pb/U Fe/Pb Sb/Pb Ge/Pb Cr/Cu K/Pb Cr/Mo Pb Ag/Pb Au/Pb Cu/Sn Se/Pb P/Pb Cr Mo/U Mg/Pb I/Pb Ni/Pb Cl/Pb U Sn

0.859 0.853 0.844 0.83 0.829 0.816 0.813 0.791 0.791 0.777 0.771 0.771 0.768 0.768 0.766 0.752 0.747 0.738 0.733 0.719 0.715 0.713 0.708

< 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 0.002 < 0.0001 0.003 < 0.0001 0.001 0.001 0.003 < 0.0001 < 0.0001 0.011 0.003 0.001 0.002 0.001 0.003 0.016 0.019

−3.45 −2.367 6.829 −0.458 −6.121 −0.526 −7.149 −0.73 −4.76 0.316 −6.252 −6.939 6.799 −0.525 −0.499 −1.213 8.565 −0.49 −1.278 −6.066 −0.834 −0.644 −0.593

P/Cr Mg/Cr Cr P/Ni P/U Cr/Fe Fe/U Ge/U Cr/Se Fe/Ni P/Sn Cr/Ge P/Cu Sn U Ge/Sn Se/U Fe/Sn Ni/Ge Ni/Se P/Zn Ni

0.846 0.842 0.835 0.828 0.822 0.811 0.81 0.809 0.807 0.807 0.803 0.802 0.797 0.793 0.792 0.789 0.789 0.788 0.78 0.776 0.764 0.74

< 0.0001 0.001 0.009 < 0.0001 < 0.0001 < 0.0001 < 0.0001 0.001 0.001 < 0.0001 < 0.0001 0.001 < 0.0001 0.006 0.004 0.001 0.001 < 0.0001 0.001 0.001 < 0.0001 0.003

7.882 8.523 −0.963 11.596 8.167 −9.443 5.799 6.418 −9.014 6.786 9.271 −8.075 5.000 −0.797 −0.739 9.173 7.292 6.448 −6.235 −7.003 7.802 −0.834

(B) Control v IUGR

(E) Pre-term v Post-term

Elements

AUC

t-test

log 2 FC

Elements

AUC

t-test

log 2 FC

Ge Sr/Cs Fe Co/Ge Ge/Sr Na/Sr K/Sr Cl/Sr Fe/Sr Cu/Sr Fe/Co Mg/Sr Co/Pb P/Co Cr/Co Co/Cs K/Co Sr Co/Au Cu Co/Cu

0.813 0.795 0.789 0.787 0.781 0.779 0.777 0.773 0.766 0.752 0.75 0.748 0.748 0.746 0.746 0.74 0.729 0.725 0.719 0.707 0.705

0.019 0.002 0.026 < 0.0001 0.006 0.002 0.005 0.001 0.007 0.005 0.001 0.006 0.005 0.001 0.002 0.004 0.001 0.012 0.007 0.117 0.001

−0.761 1.967 −0.668 4.675 −1.604 −1.02 −1.985 −0.92 −1.566 −0.962 −3.998 −1.085 4.668 −5.548 −4.684 5.913 −6.043 0.529 7.139 −0.208 3.363

P/Cu P/Ba K/Ba Sb/Ba P/Zn Fe/Ba Ge/Ba Fe/Pb Ba Mg/Ba Cu/Ge Ge/Pb Fe/Zn Zn/Ge Na/Sr Cl/Sr Zn/Se Sr Mg/Cu Ge/Sr Ge/Cs

0.917 0.863 0.857 0.845 0.839 0.839 0.839 0.833 0.815 0.815 0.815 0.815 0.81 0.804 0.792 0.792 0.792 0.762 0.75 0.75 0.738

0.012 0.006 0.011 0.004 0.01 0.009 0.005 0.015 0.012 0.014 0.006 0.002 0.015 0.003 0.007 0.013 0.004 0.014 0.013 0.011 0.006

−8.008 −3.374 −0.852 −3.348 −9.142 −3.407 −2.017 −2.177 0.308 −0.666 6.796 −1.969 −7.048 6.906 −0.524 −0.55 4.63 0.339 −4.11 −3.745 −9.158

(C) Control v Hypertensive

(F) Hypertensive v PE

Elements

AUC

t-test

log 2 FC

Elements

AUC

t-test

log 2 FC

Sn/U Ca/Au Cl/Ni Na/Ni Ca/U Cl/Au Cl/U Ca/Ni Cl/Sb Ni/Sr Ni/I Mg/Ni Sn/Au

0.758 0.728 0.727 0.724 0.719 0.718 0.715 0.713 0.71 0.71 0.707 0.704 0.704

0.001 0.004 0.001 0.002 0.003 0.003 0.003 0.001 0.003 0.004 0.003 0.001 0.004

3.777 4.96 5.266 4.164 5.434 2.484 4.907 4.889 1.877 −4.322 −6.313 6.553 3.39

P/Ge Mn/Ge Mg/Zn Mn/Zn Mg/Sb Cu/Zn Mn/Fe

0.763 0.748 0.744 0.721 0.712 0.712 0.704

0.01 0.005 0.008 0.008 0.007 0.007 0.016

−2.934 −5.414 −5.946 −5.47 −2.114 −3.416 −1.916

7

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Fig. 3. Univariate ROC curves analaysis for each comparison indicating that there are different possible biomarkers for discrimination between gestational disorders and control samples. AUC = Area under the curves. Data displayed as AUC (95% Confidence intervals). AUC gives a predictive capability between 2 sets of samples. All data shown as ROC curve: AUC (5–95% CI), and a box and whisker plot of the sample sets. A. Control and Pre-term; B. Control and IUGR; C. Control and Hypertensive; D. Control and Post-term; E. Pre-term and Post-term; F. Hypertensive and PE.

some key elemental differences between gestational groups, but also provided confidence in the ability to separate groups in subsequent bioinformatic analysis. To understand any variance between the groups based on their elemental concentrations, non-discriminant PCA was performed. Sample from groups of hypertensives, GDM, pre-term, and post-term showed a greater amount of variance in the dataset than FGR, PE, and control (Fig. 1A). In the biplot of elements (Fig. 1B), it was noted that

there was no trend towards essential or toxic elements providing a difference in contribution to the variation in the dataset. Essential elements that were significantly different in ANOVA analysis did not lead to greater variance as much as other metals such as Ca, Na, K, Se, Fe, Zn, and Mn; the exceptions to this being P, and Cr. Whilst toxic elements Pb, and U that were significant in ANOVA testing, tended to cause high levels of variance in the data. Correlation analysis showed toxic elements were weakly correlated between each other, suggesting a 8

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References

variable and inconsistent role regarding their interactions in development of gestational disorders. Dietary essential and beneficial elements showed strong positive and negative correlations with each other; implying that the importance of essential elements, and their interactions with each other, may play a much more significant role in the pathogenesis of gestational disorders than toxic elements. The diagnostic power of these elements was also evaluated using ROC curves. There was no significance, or AUC > 0.7 for control-PE and control-GDM comparisons, as a result they were excluded from further analysis. As a general trend across all comparisons, the ratio of two elements led to better AUC and p values when compared to single element analysis. In addition, the best ratios between the highest correlated elements were also determined. Pb, Cr, and the ratio of Sn/Pb showed the best diagnostic power in discriminating between control and pre-term with predictive capabilities of 77%, 75%, and 86% respectively; the ratio of Cr/Pb performed second best with a power of 85% (table 5). ROC analysis showed the possibility to discriminate between control and IUGR samples by the levels of Ge (AUC = 0.813), control and hypertensive with the ratio of Sn/U (AUC = 0.758), finally hypertensive and PE with the P/Ge (AUC = 0.763). In comparing control and post-terms P/Cr (AUC = 0.85), Mg/Cr (AUC = 0.84), and Cr (AUC = 0.83) had the best diagnostic powers. In pre-term and postterm comparisons Ba was the best single element (81.5%), and P/Cu was the best ratio (91.7%). The best AUCs for all comparisons are visualised in Fig. 3 as ROC curves and data distribution. The spectrum of elements had high discrimination potentials; with a mixture of essential, beneficial and toxic providing the best means of prediction. The power of this model has demonstrated a significant ability to predict a pregnancy group based on cord blood plasma element concentrations. These findings indicate that essential and toxic elements may play a role in the pathogenesis of gestational disorders. Whether these changes are the cause of, or result of the condition, is indeterminant due to a number of factors; the samples are from cord blood at the time of birth, and the data surrounding exposure to environmental and nutritional factors is limited. This study does however highlight the strength of this technique for further use in other cohort analysis where environmental, and nutritional factors, are better characterised. The utilisation of elemental metabolomics to generate an ‘elemental signature’ to identify gestational disorders holds promise. This study has shown that analysis of elements can enable discernment between fetal cord blood samples from control, hypertensive, PE, GDM, FGR, pre and post term pregnancies. The data collected from this study will influence future investigations regarding the function of the placenta in gestational disorders. This study demonstrates the power of this methodology for predicting gestational outcomes however, further investigation is required in more defined sample sets, at earlier gestation time points, using other samples such as maternal blood and urine. Furthermore, dietary data and timing of collection should be taken into consideration when dealing with future maternal samples.

[1] J. Vanderlelie, R. Scott, R. Shibl, J. Lewkowicz, A. Perkins, P.A. Scuffham, First trimester multivitamin/mineral use is associated with reduced risk of pre‐eclampsia among overweight and obese women, Matern. Child Nutr. 12 (2) (2016) 339–348. [2] A.D. Gernand, K.J. Schulze, C.P. Stewart, K.P. West Jr, P. Christian, Micronutrient deficiencies in pregnancy worldwide: health effects and prevention, Nat. Rev. Endocrinol. 12 (5) (2016) 274–289. [3] D. McKeating, J. Fisher, A. Perkins, Elemental metabolomics and pregnancy outcomes, Nutrients 11 (1) (2019) 73. [4] K. Abu-Saad, D. Fraser, Maternal nutrition and birth outcomes, Epidemiol. Rev. 32 (1) (2010) 5–25. [5] M.L. Blumfield, A.J. Hure, L. Macdonald‐Wicks, R. Smith, C.E. Collins, A systematic review and meta‐analysis of micronutrient intakes during pregnancy in developed countries, Nutr. Rev. 71 (2) (2013) 118–132. [6] P. Hofstee, D.R. McKeating, A.V. Perkins, J.S. Cuffe, Placental adaptations to micronutrient dysregulation in the programming of chronic disease, Clin. Exp. Pharmacol. Physiol. (2018). [7] F. Gaccioli, S. Lager, Placental nutrient transport and intrauterine growth restriction, Front. Physiol. 7 (2016) 40. [8] A.C. Antony, Folate receptors, Annu. Rev. Nutr. 16 (1) (1996) 501–521. [9] N. Solanky, A.R. Jimenez, S. D’Souza, C. Sibley, J. Glazier, Expression of folate transporters in human placenta and implications for homocysteine metabolism, Placenta 31 (2) (2010) 134–143. [10] P. Zhang, C.A. Georgiou, V. Brusic, Elemental metabolomics, Brief. Bioinformatics (2017) bbw131. [11] G. Paglia, O. Miedico, A. Cristofano, M. Vitale, A. Angiolillo, A.E. Chiaravalle, G. Corso, A. Di Costanzo, Distinctive pattern of serum elements during the progression of Alzheimer’s disease, Sci. Rep. 6 (2016). [12] H.-W. Zhao, J. Lin, X.-B. Wang, X. Cheng, J.-Y. Wang, B.-L. Hu, Y. Zhang, X. Zhang, J.-H. Zhu, Assessing plasma levels of selenium, copper, iron and zinc in patients of Parkinson’s disease, PLoS One 8 (12) (2013) e83060. [13] G. Liu, L. Sun, A. Pan, M. Zhu, Z. Li, Z. Wang, X. Liu, X. Ye, H. Li, H. Zheng, Nickel exposure is associated with the prevalence of type 2 diabetes in Chinese adults, Int. J. Epidemiol. 44 (1) (2014) 240–248. [14] M. Golasik, W. Jawień, A. Przybyłowicz, W. Szyfter, M. Herman, W. Golusiński, E. Florek, W. Piekoszewski, Classification models based on the level of metals in hair and nails of laryngeal cancer patients: diagnosis support or rather speculation? Metallomics 7 (3) (2015) 455–465. [15] D. Yu, J.M. Danku, I. Baxter, S. Kim, O.K. Vatamaniuk, O. Vitek, M. Ouzzani, D.E. Salt, High-resolution genome-wide scan of genes, gene-networks and cellular systems impacting the yeast ionome, BMC Genomics 13 (1) (2012) 623. [16] X.-Y. Huang, D.E. Salt, Plant ionomics: from elemental profiling to environmental adaptation, Mol. Plant 9 (6) (2016) 787–797. [17] C.-M. Liang, X. Wu, K. Huang, S. Yan, Z. Li, X. Xia, W. Pan, J. Sheng, Y. Tao, H. Xiang, Trace element profiles in pregnant women’s sera and umbilical cord sera and influencing factors: repeated measurements, Chemosphere 218 (2019) 869–878. [18] S. Ma, S.-G. Lee, E.B. Kim, T.J. Park, A. Seluanov, V. Gorbunova, R. Buffenstein, J. Seravalli, V.N. Gladyshev, Organization of the mammalian ionome according to organ origin, lineage specialization, and longevity, Cell Rep. 13 (7) (2015) 1319–1326. [19] M. Malinouski, N.M. Hasan, Y. Zhang, J. Seravalli, J. Lin, A. Avanesov, S. Lutsenko, V.N. Gladyshev, Genome-wide RNAi ionomics screen reveals new genes and regulation of human trace element metabolism, Nat. Commun. 5 (2014) 3301. [20] C.M. Cameron, P.A. Scuffham, A. Spinks, R. Scott, N. Sipe, S. Ng, A. Wilson, J. Searle, R.A. Lyons, E. Kendall, Environments for Healthy living (EFHL) Griffith birth cohort study: background and methods, Matern. Child Health J. 16 (9) (2012) 1896–1905. [21] M.P. Barnett, V.S. Chiang, A.M. Milan, S. Pundir, T.A. Walmsley, S. Grant, J.F. Markworth, S.-Y. Quek, P.M. George, D. Cameron-Smith, Plasma elemental responses to red meat ingestion in healthy young males and the effect of cooking method, Eur. J. Nutr. (2018) 1–8. [22] I.D.B. Bravo, R.S. Castro, N.L. Riquelme, C.T. Díaz, D.A. Goyenaga, Optimization of the trace element determination by ICP-MS in human blood serum, J. Trace Elem. Med. Biol. 21 (2007) 14–17. [23] M. Williams, G. Todd, N. Roney, J. Crawford, C. Coles, P. McClure, J. Garey, K. Zaccaria, M. Citra, Toxicological Profile for Manganese, Agency for Toxic Substances and Disease Registry, Atlanta, GA, USA, 2012 2012. [24] S. Jain, P. Sharma, S. Kulshreshtha, G. Mohan, S. Singh, The role of calcium, magnesium, and zinc in pre-eclampsia, Biol. Trace Elem. Res. 133 (2) (2010) 162–170. [25] M. Basaki, M. Saeb, S. Nazifi, H. Shamsaei, Zinc, copper, iron, and chromium concentrations in young patients with type 2 diabetes mellitus, Biol. Trace Elem. Res. 148 (2) (2012) 161–164. [26] J. Suliburska, R. Kocyłowski, I. Komorowicz, M. Grzesiak, P. Bogdański, D. Barałkiewicz, Concentrations of mineral in amniotic fluid and their relations to selected maternal and fetal parameters, Biol. Trace Elem. Res. 172 (1) (2016) 37–45. [27] S. Wang, D. Paek, B. Cha, Variation of systolic blood pressure in rats exposed to cadmium and nickel, Environ. Res. 88 (2) (2002) 116–119. [28] G.J. Hofmeyr, T.A. Lawrie, A.N. Atallah, L. Duley, Calcium supplementation during pregnancy for preventing hypertensive disorders and related problems, Cochrane Database Syst. Rev. 8 (8) (2010). [29] H.D. Mistry, F.B. Pipkin, C.W. Redman, L. Poston, Selenium in reproductive health,

Declaration of Competing Interest There are no conflicts of interest to declare. Acknowledgements The authors would like to thank the Environments for Healthy Living team for providing access to the data and biological samples and acknowledge the contribution made by the mothers and families who took part in this study. Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.jtemb.2019.126419. 9

Journal of Trace Elements in Medicine and Biology xxx (xxxx) xxxx

D.R. McKeating, et al. Am. J. Obstet. Gynecol. 206 (1) (2012) 21–30. [30] A. El-Yazigi, N. Hannan, D.A. Raines, Effect of diabetic state and related disorders on the urinary excretion of magnesium and zinc in patients, Diabetes Res. (Edinburgh, Scotland) 22 (2) (1993) 67–75. [31] J. Jansen, E. Rosenkranz, S. Overbeck, S. Warmuth, E. Mocchegiani, R. Giacconi, R. Weiskirchen, W. Karges, L. Rink, Disturbed zinc homeostasis in diabetic patients by in vitro and in vivo analysis of insulinomimetic activity of zinc, J. Nutr. Biochem. 23 (11) (2012) 1458–1466.

[32] C. Zhang, S. Rawal, Dietary iron intake, iron status, and gestational diabetes, Am. J. Clin. Nutr. 106 (suppl_6) (2017) 1672S–1680S. [33] X. Pan, J. Hu, W. Xia, B. Zhang, W. Liu, C. Zhang, J. Yang, C. Hu, A. Zhou, Z. Chen, Prenatal chromium exposure and risk of preterm birth: a cohort study in Hubei, China, Sci. Rep. 7 (1) (2017) 3048. [34] B. Zhang, W. Xia, Y. Li, B.A. Bassig, A. Zhou, Y. Wang, Z. Li, Y. Yao, J. Hu, X. Du, Prenatal exposure to lead in relation to risk of preterm low birth weight: a matched case–control study in China, Reprod. Toxicol. 57 (2015) 190–195.

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