Abstract

   Prenatal stress is known to epigenetically program offspring physiology
   and behaviour, and may become a risk factor for adult complex diseases.
   To gain insight into the underlying environment-gene interactions, we
   used proton nuclear magnetic resonance spectroscopy to analyze urinary
   metabolomes of male and female adolescents who were in utero during the
   1998 Quebec Ice Storm. Metabolomic profiles in adolescent groups were
   found to be significantly different. Higher prenatal stress exposure
   generated alterations in metabolic pathways involved in energy
   metabolism and protein biosynthesis, such as branched-chain amino acid
   synthesis, alanine metabolism, and ketone body metabolism.
   Dysregulation of energy and protein metabolism suggests an increased
   risk of metabolic diseases like insulin resistance, diabetes, and
   obesity. These findings are consistent with prior observations of
   physiological phenotypes from this cohort. Understanding the impact of
   natural disasters on health risks will provide new and improved
   therapeutic strategies to mitigate stress-associated adverse health
   outcomes. Using metabolomic biomarkers may also assist in the
   prediction and prevention of these adverse outcomes.

Introduction

   Stress during pregnancy has a significant long-term impact on offspring
   physiology and behaviour^[36]1–[37]4. Abnormally high levels of
   circulating maternal glucocorticoids and increased placental
   corticotropin-releasing hormone may cross the placenta to reach the
   fetal brain, leading to dysregulation of hypothalamic-pituitary-adrenal
   (HPA) axis activity and altered neuronal development^[38]4,[39]5.
   Growing evidence suggests that these phenomena are associated with the
   pathophysiology of stress-related depression^[40]6. HPA axis
   programming due to fetal glucocorticoid overexposure has further
   potential lifetime consequences as a risk factor for complex adult
   diseases, including cardio-metabolic illnesses^[41]7, altered glucose
   and insulin metabolism, and adiposity^[42]8,[43]9.

   The association between HPA axis function and risk of metabolic
   diseases, such as diabetes and obesity, may be causally linked to
   altered genetic and epigenetic regulation. Epigenetics provides an
   additional layer of variation, mediating the relationship between
   internal and external environments and genotype^[44]10. Accordingly,
   prenatal maternal stress (PNMS) may alter DNA methylation^[45]11,[46]12
   and microRNA signatures in tissues of exposed offspring^[47]13,[48]14.
   Genetic and epigenetic regulation affect cellular functions that
   ultimately will reflect an altered metabolic output. Hence, the
   metabolome, i.e., the sum of metabolites in an organism, arguably
   represents a direct measure of environment-gene interactions and
   associated phenotypes^[49]15.

   Various biological fluids and tissues can be utilized to measure
   systemic metabolic changes. Urination is the primary method for
   excreting water-soluble metabolites from the body^[50]16. Urine sample
   accession is simple and non-invasive, making it ideal for metabolomic
   analyses. As the primary liquid by-product of cellular metabolism in
   humans and animals, urine is made up of numerous breakdown products of
   metabolic processes, such as nitrogenous wastes requiring clearance
   from the bloodstream. As such, urine can be expected to reflect
   metabolic activity from many organ systems. Since the brain accounts
   for up to 20% of metabolic activity in humans^[51]17,[52]18, its waste
   products are significantly reflected in urine profiles.

   Disease states and altered stress responses involve abnormal metabolic
   states that reflect altered cellular processes^[53]10. For example,
   adaptation to chronic stress facilitates visceral fat
   accumulation^[54]19 as adipocytes take up and catabolize
   glucocorticoids as a regulatory effect, justifying body fat gain as a
   response to stress^[55]20. In Project Ice Storm, 5½-year-old children
   of mothers who had experienced high disaster-related stress during
   pregnancy revealed increased risk of obesity^[56]21, which persisted
   into adolescence^[57]22. Studying natural disasters, such as exposure
   to the 1998 Quebec Ice Storm, allows for the isolation of the mother’s
   objective degree of exposure from genetic predispositions,
   socioeconomic biases, and from her own subjective level of
   distress^[58]11.

   The goals of the present study were (a) to detect downstream
   metabolomic effects in male versus female adolescents resulting from
   prenatal maternal stress exposure using proton nuclear magnetic
   resonance (^1H NMR) spectroscopy; (b) to differentiate between
   adolescents prenatally exposed to either low or high levels of PNMS
   based on presence and quantity of small-molecule metabolites in urine;
   and (c) to identify a subset of significantly altered metabolites and
   metabolic pathways that may be used to predict and determine future
   health status and disease risk in the study cohort. Metabolic
   signatures linked to perinatal programming of disease phenotypes may
   provide novel prognostic and diagnostic biomarkers and targets for
   therapeutic strategies.

Results

   Analyses using objective hardship as the predictor were based on 17
   participants (9 males, 8 females) who were exposed in utero to high
   levels of objective hardship and 15 participants (9 males, 6 females)
   who were exposure in utero to low levels of objective hardship.
   Analyses using subjective distress as the predictor were based on
   samples obtained from 16 participants (9 males, 7 females) exposed in
   utero to high levels of subjective distress and 16 participants (9
   males, 7 females) exposed in utero to low levels of subjective
   distress.

Exploratory statistical analysis

   Two hundred and fifty-five spectral bins were included in the analyses.
   Mann-Whitney U (MW) and Variable Importance Analysis Based on Random
   Variable Combination (VIAVC) tests were applied to each comparison
   group to identify which features (bins) led to observed group
   differences. These analyses resulted in the following subset of bins:
   objective hardship (male) (18 MW, 7 VIAVC, and 4 common bins);
   subjective distress (male) (17 MW, 10 VIAVC, and 2 common bins);
   objective hardship (female) (8 MW, 4 VIAVC, and 1 common bin); and
   subjective distress (female) (33 MW, 2 VIAVC, and 2 common bins).
   Multivariate statistical tests were initially performed using all bins,
   with no separation of groups observed (data not shown). Group
   separation was observed when only the bins identified as significant by
   either VIAVC or MW tests were used in PCA and PLS-DA analyses.

   In males, the unsupervised PCA separation was significant, particularly
   when split by the level of subjective distress exposure
   (Fig. [59]1A,B). Subsequent supervised PLS-DA separations were
   significant and supported the PCA findings (Fig. [60]2A,B). For
   females, significant unsupervised separation was observed when the
   sample was split by in utero levels of both maternal subjective
   distress and objective hardship (Fig. [61]1C,D). Significant supervised
   separation for females was also observed (Fig. [62]2C,D). Double
   cross-validation and permutation tests validated the observed
   supervised separation results as a function of subjective distress
   (male p = 0.034, female p < 0.001) and objective hardship exposure
   levels (male p = 0.01, female p = 0.011).

Figure 1.

   [63]Figure 1
   [64]Open in a new tab

   PCA plots showing statistically significant unsupervised separation for
   male (A,B) and female (C,D) adolescents exposed in utero to high or low
   levels of PNMS. (A,C) Subjective distress measured by the Impact of
   Events Scale – Revised (IES-R) in mothers exposed to stress during
   pregnancy. (B,D) Objective hardship measured by the Storm 32 survey
   (ST32) in mothers exposed to stress during pregnancy. Each triangle or
   cross represents one individual under study, plotted using a list of
   urinary metabolites found to be statistically significant by either MW
   or VIAVC testing. X and Y axes show principal components with brackets
   indicating percent variance.

Figure 2.

   [65]Figure 2
   [66]Open in a new tab

   PLS-DA plots showing statistically significant supervised separation
   for male (A,B) and female (C,D) adolescents exposed to high or low
   levels of PNMS. (A,C) Subjective distress during pregnancy. (B,D)
   Objective hardship during pregnancy. Each triangle or cross represents
   one individual under study, plotted using a list of urinary metabolites
   found to be statistically significant by either MW or VIAVC testing. X
   and Y axes show principal components with brackets indicating the
   percent variance.

   Variable Importance in Projection (VIP) plots (Fig. [67]3) indicate
   which variables contribute most to significant supervised separation.
   In males, 1,3-dimethylurate, malonate, and 2-hydroxybutyrate
   contributed most to the separation when subjective distress was
   included (Fig. [68]3A), with VIP scores of 1.47, 1.35, and 1.15
   (Table [69]1). When objective hardship was included, 3-chlorotyrosine,
   3-hydroxymandelate, and isoleucine contributed most (Fig. [70]3B), with
   VIP scores of 1.39, 1.31, and 1.31, respectively (Table [71]1). In
   females, the top three compounds that contributed to separation when
   subjective distress was included were hypoxanthine, 3-methyladipate,
   and tyramine (Fig. [72]3C), with VIP scores of 1.65, 1.50, and 1.38
   (Table [73]2). The top three compounds contributing to group separation
   when objective hardship was included (Fig. [74]3D) were malonate,
   2-hydroxy-3-methylvalerate, and adenine, with VIP scores of 1.71, 1.18,
   and 1.04, respectively (Table [75]2). In males, 22/27 (81%) unique,
   significant metabolites were up-regulated in high compared to low PNMS,
   with the remaining metabolites being down-regulated (Table [76]1). In
   contrast, in females 27/35 (77%) unique, significant metabolites were
   down-regulated in high compared to low PNMS, with the remainder being
   up-regulated (Table [77]2).

Figure 3.

   [78]Figure 3
   [79]Open in a new tab

   VIP plots of subjective distress and objective hardship, showing the
   relative contribution of metabolites. (A) Male subjective distress. (B)
   Male objective hardship. (C) Female subjective distress. (D) Female
   objective hardship. High VIP values indicate greater contribution of
   these metabolites to group separation, shown in PLS-DA plots. Green and
   red boxes indicate relative metabolite concentration. A VIP score of
   1.0 is considered able to discriminate between two phenotypes.

Table 1.

   P-values of urinary metabolites found to be significant in male
   subjective distress and objective hardship groups (IESR and ST32,
   respectively) in either a Mann-Whitney U test, the Variable Importance
   Analysis based on random Variable Combination (VIAVC), or both.
   Group Metabolite NMR Chemical Shift Range of bin (ppm) Mann-Whitney U
   Test VIAVC VIP Score Regulation
   Male Subjective Distress (IESR) 1,3-Dimethylurate.2^‡ 3.317–3.2958 —
   2.94E-88 1.47 Down
   Malonate^‡ 3.120–3.104 — 2.17E-53 1.35 Down
   2-Hydroxyisobutyrate.1^‡ 1.368–1.354 3.99E-03 1.68E-09 1.15 Up
   Valine.1 1.005–0.998 1.85E-02 5.19E-01 1.1 Up
   Isoleucine.1^† 0.998–0.993 1.88E-02 2.37E-04 1.09 Up
   Leucine.1^‡ 0.965–0.958 2.76E-03 2.27E-07 1.08 Up
   N-Isovaleroylglycine^‡ 0.958–0.948 3.99E-03 5.23E-06 1.07 Up
   Threonine, Lactate^‡ 1.354–1.338 3.99E-03 1.59E-10 1.02 Up
   Trimethylamine N-Oxide, Betaine.2^‡ 3.254–3.240 4.00E-02 4.99E-17 1.01
   Up
   2,6-Dihydroxybenzoate 6.495–6.439 1.88E-02 1.78E-03 0.96 Up
   Leucine.2^† 0.971–0.965 1.42E-02 2.96E-03 0.96 Up
   5-Hydroxyindole-3-Acetate 7.047–7.024 — 3.54E-01 0.94 Up
   Leucine.3 0.979–0.971 2.43E-02 1.93E-04 0.94 Up
   Valine.2^† 1.054–1.044 3.15E-02 2.01E-02 0.92 Up
   1,3-Dimethylurate.1^‡ 3.349–3.317 4.00E-02 4.24E-13 0.89 Down
   Alanine 1.476–1.460 — 1.35E-01 0.88 Up
   3-Hydroxy-3-Methylglutarate 1.338–1.313 3.15E-02 2.53E-12 0.87 Up
   Pantothenate.2^‡ 0.912–0.903 1.06E-02 1.26E-03 0.84 Up
   Valine.3^‡ 1.013–1.005 1.42E-02 1.04E-03 0.8 Up
   3-Methyladipate.6 0.919–0.912 2.44E-02 1.35E-02 0.76 Up
   2-Hydroxybutyrate.2^‡ 1.388–1.368 4.00E-02 1.89E-02 0.75 Up
   3-Hydroxyisovalerate.2^† 1.285–1.265 — 1.49E-03 0.66 Up
   Betaine.1 3.905–3.881 — 8.35E-15 0.64 Up
   Male Objective Hardship (ST32) 3-Chlorotyrosine 6.964–6.953 3.99E-03
   1.40E-05 1.39 Down
   3-Hydroxymandelate 6.925–6.913 3.99E-03 1.27E-04 1.31 Up
   Isoleucine.1^† 0.998–0.993 5.64E-03 2.04E-03 1.31 Up
   Arabinose.1 4.528–4.511 1.42E-02 1.52E-03 1.19 Up
   2-Hydroxyisobutyrate.1^‡ 1.368–1.354 1.42E-02 7.71E-06 1.12 Up
   3-Hydroxyisovalerate.2^† 1.285–1.265 3.15E-02 3.63E-08 1.08 Up
   Valine.2^† 1.054–1.044 3.15E-02 1.50E-06 1.08 Up
   N-Isovalerylglycine^‡ 0.958–0.948 1.88E-02 1.50E-07 1.04 Up
   Pantothenate.2^‡ 0.912–0.903 5.64E-03 6.54E-04 1.04 Up
   Arabinose.2 4.540–4.528 1.42E-02 4.31E-05 1.01 Up
   Leucine.1^‡ 0.965–0.958 3.15E-02 3.01E-04 0.96 Up
   Isoleucine.2^‡ 1.038–1.013 2.44E-02 4.87E-07 0.9 Up
   Leucine.2^† 0.971–0.965 3.99E-03 7.65E-02 0.88 Up
   Pyruvate, Succinate, Oxalacetic Acid 2.402–2.366 3.15E-02 3.30E-07 0.84
   Up
   Valine.3^‡ 1.013–1.005 2.44E-02 3.27E-02 0.79 Up
   N6-Acetyllysine 1.973–1.955 3.99E-03 1.29E-03 0.65 Up
   Tryptophan 7.746–7.711 — 3.10E-03 0.36 Up
   Methylsuccinate 1.067–1.054 3.99E-03 1.00E + 00 0.25 Down
   [80]Open in a new tab

   VIP scores, shown in descending order, correspond to Fig. [81]3A,B.
   Metabolite regulation is shown as a function of relative concentration
   in high-PNMS individuals. Metabolites for which more than one NMR
   resonance peak was identified as significant are represented as
   metabolite.1, metabolite.2, … metabolite.n. ^†Indicates metabolites
   that were present in both male comparisons while ^‡indicates
   metabolites that were present in both sexes and multiple comparisons.

Table 2.

   P-values of urinary metabolites found to be significant in female
   subjective distress and objective hardship groups (IESR and ST32,
   respectively) in either a Mann-Whitney U test, the Variable Importance
   Analysis based on random Variable Combination (VIAVC), or both. VIP
   scores, shown in descending order, correspond to Fig. [82]3C,D.
   Group Metabolite NMR Chemical Shift range of bin (ppm) Mann-Whitney U
   Test VIAVC VIP Score Regulation
   Female Subjective Distress (IESR) Hypoxanthine 8.200–8.165 2.62E-02
   3.73E-83 1.65 Up
   3-Methyladipate.1 0.876–0.823 5.83E-04 1.65E-80 1.5 Down
   Tyramine 7.206–7.154 1.11E-02 1.44E-17 1.38 Up
   Threonine, Lactate^‡ 1.354–1.338 4.08E-03 5.10E-59 1.27 Down
   1,3-Dimethylurate.1^‡ 3.349–3.317 2.62E-02 1.87E-42 1.24 Up
   Pantothenate.1 0.902–0.876 2.33E-03 3.30E-62 1.17 Down
   Citrate^† 2.545–2.520 3.79E-02 5.04E-02 1.1 Down
   N-Isovalerylglycine^‡ 0.958–0.948 1.11E-02 1.98E-13 1.05 Down
   Formate, Quinolinate, NAD+/NADP+ 8.457–8.370 1.75E-02 7.69E-05 1.03
   Down
   Acetone 2.225–2.209 1.11E-02 2.50E-14 1.03 Up
   Leucine.1^‡ 0.965–0.958 2.62E-02 5.45E-08 0.99 Down
   Valine.3^‡ 1.013–1.005 6.99E-03 1.80E-07 0.95 Down
   2-Hydroxyisobutyrate.2^‡ 1.388–1.368 6.99E-03 4.04E-12 0.91 Down
   Pantothenate.2^‡ 0.912–0.903 6.99E-03 1.20E-02 0.9 Down
   Acetoacetate 2.256–2.225 1.11E-02 1.66E-18 0.89 Down
   UDP-Galactose, UDP-Glucose, UDP-Glucuronate 4.396–4.365 4.08E-03
   2.26E-18 0.85 Down
   3-Hydroxyisovalerate.1 2.366–2.359 2.62E-02 1.40E-06 0.82 Down
   3-Methyladipate.2 2.319–2.310 1.75E-02 1.50E-01 0.79 Down
   3-Methyladipate.3 0.928–0.919 6.99E-03 2.35E-03 0.79 Down
   2-Hydroxyisobutyrate.1^‡ 1.368–1.354 1.11E-02 1.26E-05 0.77 Down
   Isoleucine.2^‡ 1.038–1.013 1.75E-02 1.19E-01 0.76 Down
   Trimethylamine N-Oxide, Betaine.2^‡ 3.254–3.240 3.79E-02 2.17E-05 0.74
   Up
   5-Aminolevulinate^† 2.814–2.793 2.62E-02 1.78E + 00 0.74 Down
   Unknown Singlet 1.671–1.648 1.11E-02 2.91E-02 0.74 Down
   3-Methyladipate.4 1.909–1.835 3.79E-02 9.09E-08 0.71 Down
   Arginine 1.817–1.671 6.99E-03 5.80E-29 0.69 Down
   3-Methyladipate.5 2.017–2.010 2.62E-02 3.57E-03 0.62 Down
   Female Objective Hardship (ST32) Malonate^‡ 3.120–3.104 1.27E-02
   5.05E-138 1.71 Down
   2-Hydroxy-3-Methylvalerate 1.188–1.178 2.93E-02 1.31E-07 1.18 Down
   Adenine 8.072–8.044 2.94E-02 3.66E-04 1.04 Up
   2′-Deoxyinosine 8.319–8.292 — 1.07E-05 1.03 Up
   4-Hydroxybenzoate 7.772–7.746 2.00E-02 1.51E-02 0.97 Down
   Xanthine 7.817–7.772 4.26E-02 1.29E + 00 0.88 Down
   Imidazole 7.009–6.995 2.93E-02 1.99E-02 0.81 Up
   5-Aminolevulinate^† 2.814–2.793 4.26E-02 1.52E+00 0.8 Down
   Dimethylamine, Sarcosine 2.714–2.637 — 7.86E-08 0.53 Down
   Citrate^† 2.545–2.520 — 1.73E-02 0.48 Down
   [83]Open in a new tab

   Metabolite regulation is shown as a function of relative concentration
   in high-PNMS individuals. Metabolites for which more than one NMR
   resonance peak was identified as significant are represented as
   metabolite.1, metabolite.2, … metabolite.n. ^†Indicates metabolites
   that were present in both female comparisons while ^‡indicates
   metabolites that were present in both sexes.

Functional analysis

   Metabolic sets were predicted to be changed in the case of
   dysfunctional enzymes, using a genome-scale network model of human
   metabolism (Figs [84]4 and [85]5). High PNMS exposure in male
   adolescents significantly affected branched chain amino acid (BCAA;
   leucine, isoleucine, and valine) biosynthesis (p < 0.01) (Fig. [86]4A).
   Additionally, numerous energy metabolism systems were altered,
   including pathways in the citric acid cycle (p < 0.001) and
   gluconeogenesis (p < 0.01) (Fig. [87]4B). High PNMS exposure in females
   also had the greatest impact on protein biosynthesis (p < 0.01), as
   well as nucleotide sugar metabolism (p < 0.01) and ketone body
   metabolism (p = 0.016) (Fig. [88]5A). This is supported by the pathway
   topology analysis (Fig. [89]5B), showing significant effects on valine,
   leucine, and isoleucine (BCAA) biosynthesis/degradation (p < 0.01),
   propanoate metabolism (p < 0.001), and ketone body
   biosynthesis/degradation (p > 0.01).

Figure 4.

   [90]Figure 4
   [91]Open in a new tab

   (A) MSEA plot in male offspring of stressed mothers. (B) Metabolomic
   Pathway Analysis showing all matched pathways according to p-values
   from pathway enrichment analysis and pathway impact values in males. A
   higher value on the y-axis indicates a lower p-value. The x-axis gives
   the Pathway Impact. Only metabolic pathways with p < 0.015 are labeled.
   This figure was created using the lists of metabolites identified as
   significant for either MW or VIAVC testing, for both male comparison
   groups.

Figure 5.

   [92]Figure 5
   [93]Open in a new tab

   (A) MSEA plot in female offspring of stressed mothers. (B) Metabolomic
   Pathway. Higher value on the y-axis gives a lower p-value. The x-axis
   gives the Pathway Impact, which correlates to the number of metabolite
   hits in a particular pathway. Only metabolic pathways with p < 0.05 are
   labeled. This figure was created using the lists of metabolites
   identified as significant for either MW or VIAVC testing, for both
   female comparison groups.

Discussion

   Stress is one of the most critical determinants of lifetime health.
   Here we show (i) that adolescents whose mothers experienced high or low
   objective or subjective distress during pregnancy can be clearly
   identified by profiling small-molecule metabolites in urine; and (ii)
   that urinary metabolites are significantly altered in adolescents born
   to mothers who experienced high objective and/or subjective stress
   during a natural disaster, compared to those who experienced low
   objective and/or subjective stress. The majority of the metabolites
   found to be significantly altered belong to metabolic pathways involved
   in energy metabolism and protein biosynthesis, supporting a link
   between early adverse life events and risk of metabolic illness later
   in life^[94]23.

   Forty-nine metabolites known to be present in urine^[95]15 were
   identified as significantly related to the level and type of PNMS
   experienced in utero. The class separation may reflect underlying
   epigenetic changes and associated cellular functions that manifest as
   downstream alterations to the metabolome. Gender-related fetal
   differences in genetics, physiology or metabolism may also lead to
   differential downstream effects manifesting in the metabolome. While
   our results show opposite effects in protein metabolism among males and
   females, particularly in regulation of branched-chain amino acids
   (BCAAs) and protein biosynthesis, the present results also suggest
   gender differences in lipid and ketone body metabolism, nucleotide
   sugar metabolism, and energy metabolism. These findings are further
   supported by exercise physiology studies showing that females oxidize
   proportionately more fat and less carbohydrate than
   males^[96]24,[97]25.

   Almost all metabolites that obtained a significant VIP score for both
   males and females are involved in energy metabolism. Abnormal levels of
   metabolites involved in the Krebs cycle, such as citrate and malonate,
   can lead to energy deficiency, which is an important factor in fatigue,
   one of the most frequently represented symptoms in major depressive
   disorder^[98]26. Interestingly, BCAAs, which are critical to human life
   and particularly involved in stress responses, energy, immunity, and
   muscle metabolism^[99]27, are highly represented in the VIP plots for
   males, and in pathway analyses for both males and females.

   In both genders, malonate, which is a competitive inhibitor of
   succinate dehydrogenase in the electron transport chain (ETC)^[100]28,
   was highly significant in contributing to unsupervised and supervised
   separations. In females, the purine derivative and adenosine reaction
   intermediate hypoxanthine, as well as the purine base adenine, were
   found to significantly contribute to group separation. Both metabolites
   are involved in purine and energy metabolism, supporting gender
   differences in these systems^[101]29. Hypoxanthine has been found to
   increase significantly in blood serum of obese individuals during
   exercise^[102]30. In contrast, 3-hydroxymandelate and 3-chlorotyrosine
   were identified as highly important metabolites in the unsupervised and
   supervised separation seen in males. These appear to be breakdown
   products or intermediates in major oxidation pathways^[103]31,[104]32.
   Interestingly, 3-chlorotyrosine has been shown to be markedly elevated
   in low-density lipoprotein isolated from atherosclerotic
   intima^[105]33, suggesting a link to atherogenesis in prenatally
   stressed male offspring.

   MSEA was used to identify patterns of metabolite concentration changes
   in a biologically meaningful framework^[106]34. The most significant
   and consistent pathway was protein biosynthesis, with 6 metabolite hits
   in males and 5 metabolite hits in females. Thus, the formation and
   breakdown of both proteins and amino acids are involved in the response
   to high PNMS exposure. Significant pathways in the female MSEA, apart
   from protein biosynthesis, included ketone body metabolism, nucleotide
   sugar metabolism, and nicotinate and nicotinamide metabolism.
   Nicotinate and nicotinamide play essential roles in respiration,
   glycolysis, and fatty acid synthesis, acting as coenzymes and
   ADP-ribose donors^[107]35,[108]36. Disruption in protein biosynthesis
   and energy metabolism supports the finding that adolescents who were
   exposed in utero to high degrees of objective hardship during the 1998
   Quebec Ice Storm are at higher risk for developing obesity, independent
   of size at birth and maternal characteristics such as height or body
   mass index^[109]21,[110]22. Furthermore, diabetes is the most common
   pathological process known to cause ketone body dysregulation in the
   blood^[111]9,[112]37 and the present data support a causal role for
   PNMS.

   Consistent with MSEA results and VIP scores, both the male and female
   pathway topology analyses reveal changes in valine, leucine, and
   isoleucine synthesis and degradation. In combination with up-regulation
   of BCAAs in males, this draws attention to a possible metabolic
   dysregulation of BCAAs in male adolescents of high PNMS. Circulating
   levels of BCAAs have been shown to be increased in obese individuals
   and are associated with poor metabolic health and future insulin
   resistance, suggesting the possibility that these amino acids
   contribute to pathogenesis of obesity and diabetes^[113]38,[114]39.
   Previous research has also shown that pathways related to energy and
   lipid metabolism are consistently associated with Bipolar Disorder,
   Major Depressive Disorder, and Schizophrenia^[115]40.

   The alanine pathway, which involves the breakdown of pyruvate and some
   dipeptides, was significantly altered between high and low stress male
   adolescents. Alanine is one of the most important amino acids released
   by muscle tissue, acting as a major energy source and an important
   regulator in glucose metabolism, lymphocyte production, and
   immunity^[116]41,[117]42. Alterations in the alanine cycle that
   increase levels of serum alanine aminotransferase (ALT) have been
   linked to the development of type II diabetes^[118]43. Altogether, the
   results of this study suggest that adolescents whose mothers
   experienced high stress during pregnancy, particularly males, may be at
   an increased risk for developing obesity and diabetes later in life.

   The present findings indicate that metabolomic signatures assessed by
   ^1H NMR spectroscopy serve as clinically accessible predictive and
   diagnostic biomarkers of disease. Compared to other methods, NMR
   spectroscopy appears to be the method of choice for global, untargeted
   metabolomic analysis of urine^[119]15, as it permits measurement of the
   largest number of metabolites (209, compared to 179 in gas
   chromatography - mass spectrometry and 127 in liquid chromatography -
   mass spectrometry), and yields the largest chemical diversity.
   Furthermore, NMR is non-destructive, so samples can be saved and
   re-used for further analysis. Lastly, urine is an ideal biofluid for
   stress metabolomic studies since it is very easy to obtain,
   non-invasive, does not easily transmit infectious diseases, and it
   contains many clearly identifiable metabolites (209 compared to 53 in
   cerebrospinal fluid and 49 in blood).

   On a cautionary note, the biomarker potential of urinary metabolomics
   analyses may be limited by varying metabolite concentration (scaled to
   creatinine) of the average compound in normal human urine by ± 50%,
   with some varying by as much as 350%^[120]15. These ranges are
   determined by gender, age, genetic background, diet, and activity level
   of the subject^[121]44–[122]46. In this study, however, adolescents
   were separated by gender, were approximately the same age, and were
   from the same geographical region, mitigating much of the normal
   variance in metabolite quantities seen across individuals.

   Using an isolated traumatic stressor, the 1998 Quebec Ice Storm,
   experience-dependent biomarker signatures resulting from PNMS were
   detected as downstream metabolomic changes using ^1H NMR spectroscopy.
   Here, we accurately differentiate pathologically high and low prenatal
   stress groups based on a subset of significantly altered metabolites
   and/or metabolic pathways, which are potentially linked to metabolic
   illness, such as insulin resistance, diabetes, and obesity. The results
   support long-lasting metabolomic differences in males and females, such
   as ketone body production and energy metabolism. Some similarities
   between high-stress male and female adolescents were also identified,
   which include alterations in BCAA biosynthetic pathways. Possibilities
   exist to correlate future work, such as body composition analyses and
   other health outcomes, with the findings of this study.

Methods and Materials

Study design

Sample development

   During the first week of January 1998, the Southern region of the
   Canadian province of Quebec experienced a severe ice storm that
   decimated the power grid, leaving more than 1.5 million households
   without electricity for up to 45 days. Damage from the storm made it
   one of the costliest disaster in Canadian history and was responsible
   for 27 deaths.

   To identify and recruit women who met criteria for inclusion into the
   study (pregnant on January 9, 1998 or became pregnant during the
   following 3 months after the storm, at least 18 years of age, and spoke
   fluent French), physicians who delivered babies from four regional
   hospitals were asked to mail out an initial recruitment questionnaire
   to their eligible patients on June 1, 1998. Mothers and their children
   have subsequently been assessed several times: at the ages of 6 months,
   2, 4, 5½, 8½, 9½, 11½, 13½, 16½, and 18½ years. The participants in the
   present study included 18 males and 14 females at the age of 16–17
   years.

   This study was approved by the Research Ethics Boards of the Douglas
   Hospital Research Center and the University of Lethbridge in accordance
   to the Canadian Best Practices for Health Research Involving Children
   and Adolescents. We obtained written informed consent from parents and
   written informed assent from adolescents.

Assessment of objective hardship and subjective distress

   Objective hardship was estimated using the mothers’ responses to
   questions about their ice storm experiences from categories of
   exposure: Threat, Loss, Scope, and Change^[123]47. A total objective
   stress score (STORM32) was calculated by summing scores from all four
   dimensions using McFarlane’s approach^[124]48. The mothers’ subjective
   distress related to the ice storm was assessed using a validated French
   adaptation^[125]49 of the widely used Impact of Event Scale – Revised
   (IES-R)^[126]50. The 22-item instrument provides scores for symptoms in
   three scales relevant to post-traumatic stress disorder: Intrusive
   Thoughts, Hyperarousal, and Avoidance^[127]50. The total score was
   used. High and Low Objective and Subjective PNMS groups were obtained
   by splitting the distributions at the median.

Metabolomics assessment

Sample collection and preparation

   Thirty-two male (n = 18) and female (n = 14) participants were
   instructed to obtain a urine sample midstream at their first passage of
   the day, which was subsequently stored at −80 °C until further
   processing. One female sample was removed after discovering elevated
   levels of glucose in the urine using Chemstrip 9 (Roche Diagnostics,
   Indianapolis, IN, USA). In total, 450 µL of urine and 150 µL of
   phosphate buffer were transferred into 1.5 mL centrifuge tubes. The
   sample/buffer solution was vortexed and then centrifuged at 12,000 g
   for 5 minutes at 4 °C to precipitate and pellet any particulate matter.
   Following centrifugation, 550 µL of supernatant was transferred to a
   5-mm NMR tube to be analyzed immediately. Phosphate buffer was prepared
   as a 4:1 ratio of KH[2]PO[4]:K[2]HPO[4] in a 4:1 H[2]O:D[2]O solution
   to obtain a final concentration of 0.5 M^[128]51. The D[2]O contained
   0.05% v/v trimethylsilylpropanoic acid (TSP) as a chemical shift
   reference for ^1H NMR spectroscopy. Sodium azide (NaN[3], 0.03% w/v)
   was added as an antimicrobial agent, and total buffer pH was titrated
   to 7.4 using 3 M HCl.

NMR data acquisition and processing

   Spectra were collected on a 700 MHz Bruker Avance III HD spectrometer
   (Bruker, ON, Canada) as described previously^[129]52. All measurements
   were recorded using a Bruker triple resonance TBO-Z probe with the
   outer coil tuned to the nuclei of ^1H, ^31P and ^2H and the inner coil
   tuned to the ^13C nucleus. The 1-D NOESY gradient water suppression
   pulse sequence noesygpr1d with 10 ms mixing time was used (Bruker).
   Samples were run for 128 scans, with a total acquisition size (TD) of
   128k, a spectral window (SW) of 20.5 ppm, a transmitter offset (o1p) of
   4.7 ppm, and a recycle delay of 4 seconds. The Bruker automation (au)
   program “pulsecal” was utilized on each sample prior to data
   acquisition to guarantee that the 90-degree pulse was calibrated
   correctly, which ensures quantitative and comparable data across
   samples. Spectra were zero filled to 256k, automatically phased and
   baseline corrected, and line-broadened by 0.3 Hz. Processed spectra
   were exported to MATLAB (MathWorks, MA, USA) and binned using Dynamic
   Adaptive Binning^[130]53. The dataset was then normalized using the
   Constant Sum method, where each spectrum is set to have a unit total
   area and each data point (bin) is a fraction of the total spectral
   integral (with the regions corresponding to water and urea removed).
   The data set was then Pareto-scaled (mean-centered and divided by the
   square root of each variable’s standard deviation) to reduce the
   influence of intense peaks, while emphasizing weaker ones. All peaks
   were referenced to TSP (0.00δ).

Statistical analyses

   Two hundred and fifty-five spectral bins were first analyzed for all
   comparison groups and deemed significant or non-significant using a
   decision tree algorithm^[131]54 and Mann-Whitney U test. All p-values
   from this algorithm were Bonferroni-Holm corrected for multiple
   comparisons. Data visualization was conducted using Principal Component
   Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA)
   using Metaboanalyst^[132]34,[133]55–[134]63. Double cross-validation
   and permutation testing (minimum 1,000 iterations) were performed to
   verify and support all statistically significant PLS-DA
   results^[135]64,[136]65. Variable Importance in the Projection (VIP)
   plots were made using the weighted sum of squares of the PLS loadings,
   based on explained Y-variance in each dimension.

   Variable Importance Analysis based on random Variable Combination
   (VIAVC) was used^[137]66 as an additional feature/bin selection method.
   VIAVC systematically resamples variables to reveal any synergistic
   effects that may exist between seemingly unimportant variables^[138]66,
   which is unaccounted for in univariate statistical tests. By combining
   random permutations of variable inclusions with ten-fold double cross
   validation, and by using the Area Under the Curve (AUC) of a
   Receiver-Operator Characteristic (ROC), VIAVC determines an optimal
   subset of variables that have led to the most substantial group
   differences. The optimal subset of variables is determined using the
   complete cross validation methods outlined by Westerhuis et
   al.^[139]64. This method sets aside one set of samples as the
   independent test (validation) set and the other samples are included in
   the calibration set. The calibration set is used to form the
   multivariate model which is tested for predication accuracy against the
   samples held back in the validation set. This procedure is repeated
   multiple times until all samples have been chosen to be a part of the
   validation set at least once. The reported p-values for the optimal
   subset are calculated from a t-test of distribution scores based on
   whether a particular metabolite was included in the model.

   Biological significance of important metabolites was investigated using
   the Metabolite Set Enrichment Analysis (MSEA) and Pathway Topology
   Analysis through Metaboanalyst. Metabolic pathway analyses identified
   the most relevant pathways^[140]62 based on the Human Pathway Library
   and the Over Representation Analysis algorithm selected, using a
   hypergeometric test.

Metabolite identification

   The Human Metabolome Database^[141]42,[142]67,[143]68 and the Chenomx
   8.2 NMR Suite (Chenomx Inc., Edmonton, Alberta, Canada) were used for
   spectral deconvolution of biofluid samples into individual components.

Acknowledgements