# A specific metabolomic and lipidomic signature reveals the postpartum resolution of gestational diabetes mellitus or its evolution to type 2 diabetes in rat

Paul Bobin, Delphine Mitanchez, Blandine Castellano, Isabelle Grit, Thomas Moyon, Axel Raux, Anne Vambergue, Norbert Winer, Dominique Darmaun, Catherine Michel, et al.

► To cite this version:

Paul Bobin, Delphine Mitanchez, Blandine Castellano, Isabelle Grit, Thomas Moyon, et al. A specific metabolomic and lipidomic signature reveals the postpartum resolution of gestational diabetes mellitus or its evolution to type 2 diabetes in rat. AJP - Endocrinology and Metabolism, 2025, 328 (4), pp.E493-E512. 10.1152/ajpendo.00396.2024. hal-05054470

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RESEARCH ARTICLE

Women's Health Research and Pregnancy, Postnatal Conditions, Endometriosis, Polycystic Ovarian Syndrome, Preeclampsia

A specific metabolomic and lipidomic signature reveals the postpartum resolution of gestational diabetes mellitus or its evolution to type 2 diabetes in rat

Paul Bobin,¹ Delphine Mitanchez,²,³ Blandine Castellano,¹ Isabelle Grit,¹ Thomas Moyon,¹

Axel Raux,⁴ Anne Vambergue,⁵ Norbert Winer,¹,⁶ Dominique Darmaun,¹ Catherine Michel,¹

Gwenola Le Drean,¹ and Marie-Cécile Alexandre-Gouabau¹

¹Nantes Université, INRAE, UMR1280 PhAN, Nantes, France; ²Department of Neonatology, Bretonneau Hospital, François Rabelais University, Tours, France; ³INSERM UMRS_938, Centre de Recherche Saint Antoine, Paris, France; ⁴Oniris, INRAE, LABERCA, Nantes, France; ⁵Department of Diabetology, Hospital Huriez, CHRU de Lille, University of Lille, EGID-UMR 8199, Lille, France; and ⁶Department of Obstetrics and Gynecology, CHU, Nantes University Hospital, Nantes, France

Abstract

Gestational diabetes mellitus (GDM) represents a major public health concern due to adverse maternal postpartum and long-
term outcomes. Current strategies to manage GDM fail to reduce the maternal risk to develop later impaired glucose tolerance
(IGT) and type 2 diabetes (T2D). In a rodent model of diet-induced GDM without obesity, we explored the perinatal metabolic adap-
tations in dams with gestational IGT followed by either persistent or resolved postpartum IGT. Female Sprague-Dawley rats were
fed a high-fat high-sucrose (HFHS) or a chow [control group (CTL)] diet, 1 wk before mating and throughout gestation (G).
Following parturition, HFHS dams were randomized to two subgroups: one switched to a chow diet and the other one main-
tained on an HFHS diet throughout lactation (L). Oral glucose tolerance tests (OGTTs) were performed, and plasma metabo-
lome-lipidome were characterized at G12 and L12. We found that 1) in GDM-pregnant dams, IGT was associated with
incomplete fatty acid oxidation (FAO), enhanced gluconeogenesis, altered insulin signaling, and oxidative stress; 2)
improved glucose tolerance postpartum seemed to restore complete FAO along with elevation of nervonic acid-containing
sphingomyelins, assumed to impart β-cell protection; and 3) persistence of IGT after delivery was associated with metabo-
lites known to predict the early onset of insulin and leptin resistance, with maintained liver dysfunction. Our findings shed
light on the impact of postpartum IGT evolution on maternal metabolic outcome after an episode of GDM. They suggest
innovative strategies, implemented shortly after delivery and targeted on these biomarkers, should be explored to curb or
delay the transition from GDM to T2D in these mothers.

**NEW & NOTEWORTHY** Specific metabolomic/lipidomic features are associated with GDM postpartum outcomes. GDM-pregnant dams exhibit partial fatty acid oxidation and boosted gluconeogenesis. Resolution of postpartum IGT relies on nervonic acid-sphingomyelin, a β-cell protector. Postpartum IGT persistence suggests muscle insulin resistance and liver dysfunction.

biomarkers; gestational diabetes; impaired glucose tolerance; metabolic risk; metabolomic-lipidomic

INTRODUCTION

According to the International Association of Diabetes,
16.7% of women with live births in 2021 had some form of
hyperglycemia in pregnancy, with 80.3% due to gestational
diabetes mellitus (GDM) and the remaining 19.7% due to ei-
ther known or undiagnosed diabetes (type 1 and type 2) before
the beginning of pregnancy (1). Recent epidemiological

evidence has documented the rising incidence of GDM in
women who are neither overweight nor obese, accounting
for around 30% of all GDM cases (2, 3). Such emerging evi-
dence highlights the need for specific approaches to the
management of GDM in pregnant women, whether or not
they have predisposing factors, such as overweight (4). GDM
is defined as an impaired glucose tolerance (IGT) of vari-
able severity diagnosed in the second or third trimester of

<figure><img src="image_2.png" /></figure>

<figure><img src="image_3.png" /></figure>
pregnancy that was not clearly overt diabetes before gesta-
tion (1) and that resolves more or less rapidly after delivery
(5). GDM is suspected to arise from inadequate maternal
β-cell response, with metabolic adaptations mainly in lipid
metabolism (6, 7), worsening the insulin resistance that
occurs in healthy pregnant women, who compensate for it
through an adapted response of β-cell (6).

GDM constitutes a rising public health burden. Antenatal
preventive strategies are relatively efficacious in achieving
gestational glycemic control in GDM mothers (5, 8), reducing
the risk of infant macrosomia and perinatal morbidity (9).
However, they fail to prevent the maternal risk of experienc-
ing GDM in a subsequent pregnancy, with a 7.5-fold higher
relative risk of developing type 2 diabetes (T2D) later in life
(10, 11). A recent Danish study conducted in nonobese women
with GDM showed that insulin sensitivity (IS) improved im-
mediately after delivery but began to deteriorate again within
the first 6 mo postpartum, warranting initiatives shortly
after delivery (12). Moreover, the International Association of
Diabetes' recommendation to test GDM mothers at 4–12 wk
postpartum for T2D risk is poorly implemented (1). Then, cir-
culant biomarkers may be beneficial for identifying a target
population if there are interventions that will prevent progres-
sion to type 2 diabetes.

GDM and T2D are two closely related diseases, with highly
intertwined metabolic alterations, such as insulin resistance
(IR), glucotoxicity and lipotoxicity associated with altered
hepatic mitochondrial function and oxidative stress. Clues
to the early underlying pathways of future GDM and/or T2D
onset are emerging from metabolomics/lipidomics compre-
hensive approaches (13–16). Indeed, in a Chinese GDM case-
control cohort, a higher risk of GDM, diagnosed at 24–28 wk
of gestation, was reported to be associated with a decrease in
the plasma concentration of several lipids of the sphingomy-
elin (SM) family and an increase in one plasmalogen at 6–15
wk of gestation (7). Similarly, in GDM women who developed
T2D within 2 years post-delivery ('Study of Women, Infant
Feeding and Type 2 Diabetes after GDM, SWIFT'), plasma
phenotyping at 6–9 wk postpartum highlighted a decrease in
sphingolipids (SPLs) and glycerophospholipids (PLs), and an
increase in branched-chain amino acids (BCAAs) (13, 16).
Yet, those studies did not take into account whether GDM
mothers were lactating or not (13, 14), even though lactation
is a critical postdelivery period with long-lasting benefits
even in resetting the metabolic processes that occur during
normal pregnancy, with improving glucose homeostasis and
IS (17–19). A more complete metabolomic and lipidomic
analysis at specific stages in the perinatal development of
GDM is clearly needed to search for relevant markers of the
maternal T2D risk according to its management care and
subsequent IGT postpartum evolution during the lactation
period.

To achieve this goal, we used a previously validated rodent
model of GDM induced by a high-fat and high-sugar (HFHS)
diet (20, 21). In this nutritional model, hyperglycemia occurs
only during gestation, with no prediabetic conditions and no
overweight. By stopping or continuing the HFHS diet post-
partum, it also offers the option to either correct or sustain
IGT postpartum. We excluded genetic or pharmacologic
models, which produce more severe, often irreversible
IGT, and/or are associated with obesity (22). Using a

comprehensive plasma lipidomic and metabolomic pheno-
typing in this model of GDM dams, we aimed to investigate
1) how gestational IGT, in the absence of a predisposing fac-
tor, affects the maternal perinatal metabolome and lipi-
dome, particularly regarding biomarkers involved in
insulin signaling that are putatively predictive of the tran-
sition from GDM to T2D, and 2) whether the resolution or
not of IGT postpartum, depending on the change in mater-
nal diet at delivery, affects these metabolic disturbances
during the lactation period.

MATERIALS AND METHODS

Animal Experiments

The experimental protocol was carried out in accordance
with current institutional guidelines on animal experimenta-
tion in the EU (directive 2010/63/EU) and in France (French
Veterinary Department—A44276) and the 3Rs (Replacement,
Reduction, Refinement) principle. All animal procedures
were approved by the Animal Ethics Committee of Pays de
La Loire and with approval from the French Ministry
of Superior Education and Research (APAFIS No. 35043-
2022011317589759v6). Six-week-old, virgin female Sprague
Dawley rats (RGD_70508) weighing 159 ± 9.2 g were ordered
from Janvier Labs (Saint Berthevin, France). Animals were
housed conventionally in a constant temperature of 21°C and
humidity (50%-60%) with a 12-h light-dark cycle with free
access to food and water. After 1 wk of habituation to the
animal facility, virgin females were fed either a regular
chow diet [control group (CTL)] or an HFHS diet (HFHS
group) for 1 wk before overnight mating with 6-wk-old
males of the same strain (2 females to 1 male). Then, dur-
ing the three gestation weeks, the CTL or HFHS dams
were maintained on chow or HFHS diet, respectively.
The HFHS diet was made of 45% kcal lipid, 17% kcal su-
crose, and 20% kcal protein (D12451i from Research Diet,
New Brunswick, NJ), whereas the chow A03 diet (Safe,
Augy, France) was made of 13.5% kcal lipid, 5.2% kcal su-
crose, and 25% kcal protein (Supplemental Table S1).
Upon delivery, litter size was adjusted to 8 pups (4
females and 4 males), and HFHS dams were randomly
split into 2 arms, the 4HFHS and the 7HFHS groups. The
4HFHS group was switched to the chow diet postpartum,
whereas the 7HFHS group was kept under the HFHS diet
throughout the 3 wk of lactation. The experimental
design is illustrated in Fig. 1. Food intake and weight
gain were monitored by weighing pellets and dams three
times a week. Dams and offspring were euthanized at the
end of lactation (LD20).

Glucose Tolerance and Insulin Sensitivity

Oral glucose tolerance test (OGTT) was performed at mid-
and end-gestation (GD12; GD18) and mid- and end-lactation
(LD12; LD18) (Fig. 1). After a 4-h fast, rats received by gavage
a 2 g/kg body wt (BW) load of glucose. Blood samples were
collected from the tail vein at T0, T15, T30, T45, T60, T90,
and T120min (Microvette CB 300 K2E, Sarstedt, Germany).
Plasma glucose and insulin concentrations were measured
using a colorimetric test (DiaSys Glucose GOD FS; Eurobio
Scientific, France) and ELISA (80-INSRT-E01; E10, ALPCO),
<figure><img src="image_4.png" /><figcaption>**Figure 1.** Schematic diagram of the study design. Time schedule used to monitor physiological parameters of dams. Fasting glycemia was checked before mating and throughout gestation, and lactation and response to oral glucose tolerance test (OGTT) were checked at mid- and end-gestation (GD12 and GD18) and mid- and end-lactation (LD12 and LD18). Days are represented as gestational days (GD) and lactation days (LD).</figcaption></figure>

respectively. Concentrations were calculated by using a multimode microplate reader (Varioskan LUX; Thermo Fisher Scientific). Glucose and insulin area under the curve (glucose AUC and insulin AUC, respectively) were then calculated on the basis of fasting glycemia and fasting insulin. The rate of glucose uptake was reflected by the metabolic clearance rate (MCR), expressed in $mL·mmol⁻¹·min⁻¹$, and calculated according to Cederholm and Wibell (23). Whole body IS was estimated by the IS index ($ISI_{Gutt}$) during a 2-h OGTT using the equation: $ISI_{0.120} = m/MPG/log(MSI)$, where MPG is mean plasma glucose concentration (mmol/L, mean of the 0 and 120 glucose values from the OGTT), MSI is the mean serum insulin concentration (mU/L, mean of the 0 and 120 insulin values from the OGTT), and m is the glucose uptake rate (mg/min), calculated as follows: glucose load (mg) + (0 min glucose - 120 min glucose, mg/L) × 0.19 (glucose space expressed in L/kg of BW) × BW (body weight, kg)/120 min, as previously described in humans (24) and rats (25). $ISI_{Gutt}$ is highly correlated to the IS determined with the hyperinsulinemic-euglycemic clamp. The insulin secretory capacity of β-cells was estimated using Homeostasis model assessment (HOMA) of β-cells (HOMA-β index) (26).

## Plasma Metabolic Markers

Plasma was isolated from blood samples collected from dams after a 4-h fast and at several time points: 7 days before mating (GD7), during gestation (GD12; GD18), and lactation (LD12; LD18). Plasma triglycerides (TGs), non-esterified fatty acids (NEFAs), and cholesterol (CHOL) concentrations (mg/dL) were measured using manufactured determination kits (DiaSys; Eurobio Scientific, France). Leptin (ng/mL) was quantified in blood samples collected after a 4-h fast at GD12 ($n = 16–17$) and LD12, respectively ($n = 12–15$), using a leptin ELISA EIA-kit (Sigma-Aldrich, St. Louis, MO). All concentrations were calculated based

on absorbance determined using a multimode microplate reader (Varioskan LUX; Thermo Fisher Scientific).

## High-Throughput Metabolomics/Lipidomic Analysis

A quantitative screen of selected metabolites based on mass spectrometry in tandem (MS/MS) with multiple reaction monitoring, neutral loss, and precursor ion scans was performed on fasting plasma collected from dams at GD12 and LD12, using the MxP500 kit (Biocrates Life Science AG, Innsbruck, Austria). This commercially available assay was originally developed for human plasma, covering 630 metabolites from 14 metabolites and 12 lipid classes. According to the manufacturer, 413 metabolites had previously been detected with the kit in 10 µL of rat plasma, including 40 acyl-carnitines (CN x:y, where x indicates the total number of carbons and y the number of double bonds in the fatty acid side chains), 21 amino acids (19 proteinogenic plus citrulline and ornithine), 21 biogenic amines, total hexoses, 90 glycerophospholipids [14 lyso-phosphatidylcholines (Lyso-PCs) and 76 phosphatidylcholines (PCs)], 85 sphingolipids (SPLs), 44 diglycerides (DGs), and 242 triglycerides (TGs). Metabolites were then quantified by comparison to structurally similar molecules labeled with stable isotopes added to the samples in defined concentrations as internal standards. In parallel with rat samples, the different range points, quality control (QC) Biocrates, and QC rat serum (pool of all 77 samples) were also prepared following the same protocol of the Biocrates kit. The range contained seven different concentrations, enabling quantification of metabolites. The liquid chromatography tandem MS (LC-MS/MS)-based method with positive and negative electrospray ionization was used to quantify, in ng/mL, amino acids and biogenic amines, whereas acyl-carnitines, cholesterol esters, glycerophospholipids, glycerides, sphingolipids, and hexoses (ng/mL) were assessed using flow injection analysis (FIA) tandem MS (FIA-MS/MS) with
positive and negative electrospray ionization, using multiple reaction monitoring. The extracts were analyzed using an EXIONLC Ultra High-Pressure Liquid Chromatography System coupled in line to a 6500 + Q-Trap LC/MS system (Sciex, Framingham, MA). Data were collected utilizing Analyst software v1.7 (Sciex, Framingham, MA) and analyzed with Biocrates MetIDQ software v8.7.1 DB110 Oxygen 2893 (Biocrates Life Sciences AG, Innsbruck, Austria). See Supplementary Materials for more detailed information.

## Statistical Analysis

Univariate and multivariate statistical analyses were performed using GraphPad Prism v10 (RRID:SCR_002798) (La Jolla, CA) and SIMCA P v18 (RRID:SCR_014688) (Umetrics AB, Umeå, Sweden) software, respectively. Univariate statistical analysis was performed using a two-tailed unpaired *t* test (2 groups) or a one-way analysis of variance (ANOVA) (more than 2 groups) followed by Sidak's or Tukey's multiple comparisons

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Log2(FC)
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post hoc tests, respectively, after checking the normality, using Shapiro-Wilk or D'Agostino-Pearson test, and homoscedasticity, using Brown-Forsythe and Barlett's test. Robust regression and outlier removal method was used to identify the putative outliers in our data. In case of no Gaussian distribution of residuals and heteroscedasticity, nonparametric tests were performed: Mann-Whitney (2 groups) and Kruskal-Wallis (more than 2 groups) tests with post hoc Dunn's test for multiple comparisons.

The Biocrates metabolomics/lipidomics datasets underwent several quality control checks. First, data were normalized between plates and then investigated for missingness at the level of each metabolite and individual (i.e., if the sum of analyte concentrations in the QC was zero, the analyte was removed). A second filtration was performed according to the coefficient of variation (CV) of the analytes in the QCs (if CV > 35% in LC-MS/MS and > 50% in FIA-MS/MS in the QC, the analyte was removed). Datasets were next analyzed by multivariate statistical analysis to select variables of importance in the discrimination of dams' groups. Unsupervised principal component analysis (PCA) or partial least square discriminant analysis (PLS-DA) was performed on LC- and FIA-MS/MS metabolomics/lipidomics datasets following normalization, that is, UV scaling (data were centered and reduced) for LC and Log-Pareto scaling for FIA, using SIMCA P v18 software (RRID:SCR_014688). The quality of the PLS-DA model was evaluated by several goodness-of-fit parameters [R2 (X), R2(Y)], and the goodness of prediction parameter Q2 (Y) was calculated by a seven-round internal cross-validation (27). The interaction effect on plasma metabolomics/lipidomics signatures of maternal nutritional factors both during lactation and gestation (i.e., whether dams were switched or not to a chow diet at delivery) were explored more specifically using AoV-PLS, a straightforward extension of PLS-DA combining one-way ANOVA, PLS, and Fisher's linear discriminant analysis (28). The volcano plot was designed with, on the x-axis, the log2 of the fold

change of plasma biomarkers (families or individual metabolites) between two groups, and on the y-axis, the -log10 of the adjusted *P* value [false discovery rate (FDR) approach using the two-stage step-up method of Benjamini, Krieger, and Yekutieli (BKY)] obtained from a multiple unpaired *t* test (*q* value), using GraphPad Prism v10 software (RRID: SCR_002798). BKY-FDR <0.05 and fold change >1.5 (for biomarker indexes) or 2.0 (for individual metabolites) were used as significance cut-offs. In all tests, *P* < 0.05 was considered statistically significant.

## RESULTS

### Perinatal Exposure to Moderate Hypercaloric Diet Did Not Induce Overweight or Obesity in Dams

During the 4 wk of the pregestational and gestational period, maternal weight was similar in both HFHS and CTL dams (Supplemental Fig. S1A). Only occasional differences were observed in maternal weight at specific times during lactation between CTL, 4HFHS, and 7HFHS groups (Supplemental Fig. S1B). During the first 3 days on the HFHS diet, 48-h food intake was significantly higher in HFHS than in CTL (Supplemental Fig. S1C). Overall cumulative food intake (expressed in kcal per kg of BW and per week) was significantly increased in HFHS compared with CTL throughout gestation (Supplemental Fig. S1C), and, for 7HFHS, during the first week of lactation only, compared with 4HFHS and CTL (Supplemental Fig. S1D).

### Gestational Hyperglycemia and IGT Were Associated with a Specific Plasma Metabolome/Lipidome

#### Impaired glucose tolerance setting at mid-gestation (GD12) in the HFHS model.

The maternal glucose tolerance was assessed using OGTT (Figs. 2 and 3). At GD12, fasting plasma glucose (Fig. 2A) and glucose AUC in OGTT [Fig. 2B (inset)] were significantly

**Figure 2.** Pancreatic capacity of insulin secretion increased in HFHS group at GD12, associated with a specific plasma metabolome/lipidome. Fasting glucose (A), glucose tolerance curve during oral glucose tolerance test (OGTT) and the corresponding glucose area under the curve (AUC) (B), glucose metabolic clearance rate (MCR) (C), fasting insulin (D), insulin tolerance curve and the corresponding insulin AUC (E), peripheral insulin sensitivity index (ISI Gutt) (F). A, C, D, F, B and E (insets) are represented as squatter dot plot with individual values and with means ± SD; B and E are represented as means ± SD (CTL: *n* = 12, HFHS: *n* = 31). Significant differences (t test) between HFHS and CTL: \* *P* < 0.05, \*\* *P* < 0.01, \*\*\* *P* < 0.001. G and H: score plot of principal component analysis (PCA), on the two first components C1 and C2, performed on plasma metabolome (G) (R2 = 0.65 and Q2 = 0.23, 5 components) and lipidome (H) (R2 = 0.67 and Q2 = 0.41, 4 components), respectively, with each point representing an individual plasma metabolome/lipidome of CTL (*n* = 8, green) and HFHS (*n* = 20, red) groups. *I*: volcano plot are performed on all plasma biomarker indexes (*n* = 109) quantified in dams with each point representing a biomarker index, as a sum or a ratio of different variables, as detailed in Table 2. Volcano plot biomarkers indexes are labeled in dark blue for phospholipids, magenta for bile acids, brown for amino acids and amines relatives, blue light for acyl-carnitines, red for fatty acids, green for cholesterol esters, yellow for glycerides, gray for sphingolipids, and orange for lyso-phospholipids. Green color for point means that biomarker indexes are lower in HFHS than CTL groups, and red color for point means that biomarker indexes are higher in HFHS than CTL groups. See also Supplemental Fig. S4 for individual variables. 12a-OM BAs (12-alpha-hydroxylated bile acids), CA + DCA + GCA + GDCA + TCA + TDCA; aromatic AAs (phenylalanine + tyrosine + tryptophane); BCAA, branched-chain amino acids (leucine + isoleucine + valine); CA, total cholic acid; CDCa, chenodeoxycholic acid; CE, cholesterol ester; Cer, ceramide; CN, carnitine; Cx-OH-CN, hydroxylated carnitine; DCA, deoxycholic acid; GCA, glycocholic acid; GCDCA, glycochenodeoxycholic acid; GDCA, glycodeoxycholic acid; GLCA, glycol-lithocholic acid; glucogenic AAs (alanine + arginine + asparagine + aspartate + glutamine + glutamate + glycine + histidine + methionine + cysteine + proline + serine + threonine + valine); GSH (glutathione) precursors, (cysteine + cysteine + homocysteine + methionine + betaine + serine + glycine + glutamate); GUDCA, glycol-ursodeoxycholic acid; HFHS, high-fat high-sucrose; indole synthesis [3-Indoleacetic acid (3-IAA) + 3-indolepropionic acid (3-IPA) + indoxyl sulfate (Ind-SO4)]; ketogenic AAs (leucine + lysine); LCFA, long-chain fatty acid (C16, C18, and C20); LCFA-Cer, Cer(d1x:1/14–20:x) + Cer(d18:1/xx:0(OH)); LC-Lyso PC, long-chain lyso-phosphatidylcholine; PC, phosphatidylcholine; PE, phosphatidylethanolamine; primary BAs, CA + CDCA + GCA + GCDCA + TCA + TCDCA; ratio of conjugated/unconjugated primary BAs, (GCA + GCDCA + TCA + TCDCA + TLCA)/(CA + CDCA); ratio of conjugated/unconjugated secondary BAs, (GDCA + GLCA + GUDCA + TDCA + TLCA)/DCA; ratio of secondary BAs/total BAs, (DCA + GDCA + GLCA + GUDCA + TDCA + TLCA)/(CA + CDCA + GCA + GCDCA + GLCA + GUDCA + TCA + TCDCA + TLCA); ratio of secondary/primary BAs, (DCA + GDCA + GLCA + GUDCA + TDCA + TLCA)/(CA + CDCA + GCA + GCDCA + TCA + TCDCA); secondary BAs, DCA + GDCA + GLCA + GUDCA + TDCA + TLCA; SM, sphingomyelin; sulfur AAs (cysteine + cysteine + homocysteine + methionine + taurine); TCA, tauro-cholic acid; TCDA, tauro-chenodeoxycholic acid; TDCA, tauro-deoxycholic acid; TLCA, taurolithocholic acid; TMAO synthesis, trimethyl-amine N-oxide synthesis (TMAO/(Betain + CO-CN + choline); VLFA, very-long-chain fatty acid (C22–C24).
<figure><img src="image_15.png" /><figcaption>**Figure 3.** At GD18, high pancreatic capacity of insulin secretion was maintained in HFHS group. Fasting glucose (A), glucose tolerance curve during oral glucose tolerance test (OGTT) (B), and the corresponding glucose area under the curve (AUC) (C), glucose metabolic clearance rate (MCR) (D), fasting insulin (E), insulin tolerance curve (F), and the corresponding insulin AUC (G), peripheral insulin sensitivity index (ISI Gutt) (H). A, C, D, E, G, and H data are represented as squatter dot plot with individual values and with means ± SD; data are means ± SD (CTL: n = 12, HFHS: n = 31) (B and F). Significant differences (t test) between HFHS and CTL: \*P < 0.05, \*\*P < 0.01, and \*\*\*P < 0.001. HFHS, high-fat high-sucrose.</figcaption></figure>

higher in HFHS versus CTL. A reduced peripheral IS was suggested by the lower glucose metabolic clearance rate (MCR) in the HFHS group (Fig. 2C). Although fasting plasma insulin did not differ significantly (Fig. 2D), insulin response between 60 and 120 min and insulin AUC in OGTT were significantly higher (Fig. 2E) in HFHS as compared with CTL. These changes were associated with a loss of IS, suggested by the lower ISI<sub>Gutt</sub> index (Fig. 2F), along with a likely unchanged insulin secretory capacity of β-cells (similar HOMA-β Index, data not shown). At GD18, the higher fasting plasma glucose (Fig. 3A) and glucose AUC in OGTT (Fig. 3, B and C) persisted in HFHS, as compared with CTL, as well as the decrease in glucose MCR (Fig. 3D). Fasting plasma insulin (Fig. 3E) and insulin AUC (Fig. 3, F and G) were similar in both groups. Nevertheless, insulin concentration was significantly elevated in the second hour of the OGTT in HFHS dams, and the ISI<sub>Gutt</sub> was lower in HFHS (Fig. 3H).

**Metabolic parameters.**

As expected, at GD7, plasma glucose, insulin, TG, NEFA, and CHOL concentrations were similar in HFHS and CTL (Table 1). At mid-gestation (GD12), both groups displayed similar plasma concentrations in lipid markers (NEFA, TG, and CHOL), whereas higher leptin concentration was measured in the hyperglycemic HFHS dams. At the end of gestation (GD18), only a transient decrease in NEFA concentration was observed in HFHS versus CTL.

**Plasma metabolome/lipidome of HFHS pregnant dams suggested alterations in energy conversion, antioxidant defense, and intestinal microbiota, reflected by secondary bile acid patterns.**

Unsupervised principal components analysis (PCA) of plasma metabolome (Fig. 2G) and lipidome (Fig. 2H) at
<table><caption>**Table 1.** Plasma concentration of selected metabolic variables (glucose, insulin, TG, NEFA, CHOL, and leptin) 7 days before mating, at the middle (GD12) and at the end (GD18) of gestation</caption><thead><tr><th rowspan="2"></th><th colspan="2">Groups</th></tr><tr><th>CTL</th><th>HFHS</th></tr></thead><tbody><tr><td>GD7</td><td></td><td></td></tr><tr><td>Glucose</td><td>120.0 ± 16.3</td><td>112.9 ± 13.9</td></tr><tr><td>Insulin</td><td>0.8029 ± 0.32</td><td>0.753 ± 0.34</td></tr><tr><td>TG</td><td>64.7 ± 16.5</td><td>69.4 ± 13.4</td></tr><tr><td>NEFA</td><td>15.0 ± 5.1</td><td>12.1 ± 5.3</td></tr><tr><td>CHOL</td><td>91.0 ± 13.5</td><td>86.0 ± 14.9</td></tr><tr><td>GD12</td><td></td><td></td></tr><tr><td>TG</td><td>60.0 ± 9.2</td><td>57.9 ± 16.1</td></tr><tr><td>NEFA</td><td>14.7 ± 4.6</td><td>13.3 ± 5.2</td></tr><tr><td>CHOL</td><td>77.1 ± 12.6</td><td>71.4 ± 13.8</td></tr><tr><td>Leptin</td><td>8.4 ± 2.2</td><td>17.8 ± 4.4***</td></tr><tr><td>GD18</td><td></td><td></td></tr><tr><td>TG</td><td>149.3 ± 33.8</td><td>122.7 ± 44.5</td></tr><tr><td>NEFA</td><td>26.8 ± 5.1</td><td>19.9 ± 5.3***</td></tr><tr><td>CHOL</td><td>108.2 ± 23.4</td><td>107.4 ± 26.1</td></tr></tbody></table>

Values are means ± SD (CTL: *n* = 11–12, HFHS: *n* = 30–31). Glucose, triglycerides (TG), non-esterified fatty acids (NEFA), and cholesterol (CHOL) are expressed as mg/dL. Insulin and leptin are expressed as ng/mL. CTL, control; HFHS, high-fat high-sucrose. Significant differences between HFHS and CTL, ***P < 0.0001 (multiple unpaired *t* test).

GD12 revealed in both cases a clear-cut clustering of the two groups. The supervised PLS-DA highlighted substantial differences between HFHS and CTL pregnant dams, with several biomarkers known to be associated with GDM in pregnant women and/or T2D in nonpregnant individuals in epidemiological surveys. Numerous biomarkers discriminating between CTL and HFHS were selected based on univariate analysis (Table 2) and multivariate analysis in volcano plots (Fig. 2I, for biomarker indexes and Supplemental Fig. S4, for individual variables).

A specific lipidome was characterized in HFHS dams, compared with CTL dams, with a decrease in the concentrations of lipid species-containing choline, that is, long chain (C16–C20) (LCFA) or very long chain (VLCFA) (C22–C28) fatty acids-sphingolipids (LCFA or VLCFA-SPLs), more representative of liver SPLs metabolism and in the form of ceramide (Cer), sphingomyelin (SM), or glycosyl-ceramide, particularly those containing the nervonic acid (C24:1) (Table 2F and Fig. 2I). The latter species of SPLs was shown to upregulate glucose-stimulated insulin secretion (29, 30). These findings were associated with lower concentrations of VLCFA phosphatidylcholines (PCs)—breakdown products (e.g., VLCFA Lyso-PCs) (Table 2H and Fig. 2I). In addition, plasma HFHS presented putative alterations in fatty acid oxidation (FAO) and/or amino acid (AA) oxidation, as reflected by lower circulating contents in free-carnitine (C0-CN), an indicator of the cellular carnitine uptake, propionyl-CN (C3-CN), involved in isoleucine and valine catabolism, and butyryl-CN (C4-CN), involved in both AA and FA metabolism (25). These findings were associated with enhanced levels of hydroxylated carnitine (as Cx-OH-CN), and of two ratios (LCFA-CN/C0-CN or LCFA-CN/acetyl (C2-CN) reflecting likely impaired activity of two major enzymes

involved in FAO [i.e., carnitine palmitoyl-transferase-I (CPTI) and II (CPTII)], suggesting impaired FAO (Table 2E and Supplemental Fig. S4B).

Lower contents in lipid species-containing long-chain polyunsaturated fatty acids (LC-PUFA) such as PUFA ω3 [docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA)] also marked the lipidome of HFHS dams (Table 2B). Moreover, HFHS dams displayed a dramatic decrease in bile acids (BAs) {total, primary, and secondary, unconjugated [cholic acid] and taurine-conjugated [taurocholic acid (TCA), taurochenodeoxycholic acid (TCDCA), and taurodeoxycholic acid (TDCA)] BAs} (Table 2A and Supplemental Fig. S4A).

Compared with CTL dams, HFHS dams presented higher plasma contents in several AAs known to be insulin secretagogue [branched-chain amino acids (BCAAs) as valine], glucogenic (proline, lysine, threonine), or ketogenic (lysine) (31, 32) (Table 2I and Supplemental Fig. S4A). Interestingly, plasma BCAA levels correlated with fasting insulinemia (R²: 0.58, *P* value <0.01) and the HOMA-IR index (R²: 0.62, *P* value <0.01) at mid-gestation. These findings were associated with evidence of an upregulation of the kynurenine-tryptophan pathway and a downregulation of aromatic AA degradation, reflected by a decrease in the carboxylic acid, the hippuric acid, and a phenylalanine and tryptophan-derived metabolite (33) (Table 2J). Finally, HFHS dams presented lower contents in key actors of antioxidant systems [sum of sulfur amino acids (SAAs), that is, methionine, cysteine, homocysteine, and taurine; two precursors of glutathione (glycine and betaine); an indicator of cysteine synthesis from serine and methionine; and an indicator of systemic oxidative stress, reflected by the sulfoxide methionine/methionine ratio] (Table 2I).

## Effects of Postpartum Resolution (4HFHS) or Persistence (7HFHS) of IGT on Metabolome/Lipidome of Dams

### At mid-lactation, glucose tolerance and insulin sensitivity were restored in 4HFHS dams.

At mid-lactation (LD12), fasting plasma glucose was not different between the three groups (Fig. 4A). However, glucose AUC in response to OGTT was higher in 7HFHS than in the two other groups (Fig. 4B). The calculated glucose MCR was lower in 7HFHS than in CTL and 4HFHS groups (Fig. 4C). Although fasting insulin concentration did not differ among the three groups (Fig. 4D), insulin AUC was lower in 4HFHS, compared with CTL dams, but not in 7HFHS dams [Fig. 4E (inset)]. ISI<sub>Gutt</sub> indexes also showed a significant decrease in 7HFHS versus CTL and versus 4HFHS group (Fig. 4F). Altogether, these findings suggest that, after delivery, IGT and decreased insulin sensitivity persisted only in nursing 7HFHS dams.

### At the end of lactation, IGT and loss of IS persisted only in 7HFHS dams.

At LD18 (Supplemental Fig. S2), fasting plasma glucose was similar in the three groups despite a trend toward higher glucose in 7HFHS than in 4HFHS (*P* = 0.0506) (Supplemental
<table><caption>**Table 2.** Summary of discriminant maternal circulating metabolites or metabolic indexes at the middle of gestation (GD12) or lactation (LD12) periods</caption><thead><tr><th rowspan="2"></th><th colspan="3">Pregnant Groups (G12)</th><th colspan="3">Lactating Dams (L12)</th><th rowspan="2">Multiple<br>comparison test<br>P value</th></tr><tr><th>CTL (n = 9)</th><th>HFHS (n = 21)</th><th>Two-tailed<br>t test<br>P value</th><th>CTL (n = 8)</th><th>4HFHS (n = 10)</th><th>7HFHS (n = 10)</th></tr></thead><tbody><tr><td><strong>Lipid species</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td><strong>A. Bile acids (BAs) (15 metabolic<br>indexes, 13 individual<br>metabolites)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Total bile acids, µg/mL</td><td>10.4±10.2</td><td>24.1±13.3</td><td><strong>0.0036</strong><sup>c</sup></td><td>6.4±6.4</td><td>11.8±11.2</td><td>5.3±3.7</td><td>0.6827<sup>e</sup></td></tr><tr><td>Unconjugated bile acid,<br>µg/mL</td><td>24.4±25.2</td><td>4.1±2.7</td><td><strong>0.0401</strong><sup>c</sup></td><td>10.0±11.0</td><td>30.1±28.9</td><td>13.9±9.4</td><td>0.3906<sup>f</sup></td></tr><tr><td>Conjugated/unconjugated pri-<br>mary BAs ratio</td><td>0.27±0.29</td><td>0.06±0.04</td><td><strong>0.0401</strong><sup>c</sup></td><td>0.34±0.42</td><td>0.39±0.39</td><td>0.18±0.10</td><td>0.7441<sup>f</sup></td></tr><tr><td>Conjugated/unconjugated sec-<br>ondary BAs ratio</td><td>0.19±0.09</td><td>0.26±0.11</td><td>0.1204<sup>c</sup></td><td>0.29±0.34</td><td>0.25±0.25</td><td>0.15±0.12</td><td>0.9621<sup>f</sup></td></tr><tr><td>Taurine-conjugated bile acid,<br>µg/mL</td><td>2.5±1.3</td><td>0.75±0.55</td><td><strong>&lt;0.0001</strong><sup>c</sup></td><td>1.3±0.7</td><td>1.3±0.8</td><td>0.68±0.23£#</td><td><strong>0.0124</strong><sup>f</sup></td></tr><tr><td>Glycine-conjugated bile acid,<br>ng/mL</td><td>2,350±28</td><td>142±68</td><td>0.5336<sup>c</sup></td><td>177±53</td><td>142±45</td><td>261±126#</td><td><strong>0.0391</strong><sup>f</sup></td></tr><tr><td>Taurine/glycine-conjugated BA<br>ratio</td><td>14.3±7.1</td><td>4.6±2.5</td><td><strong>0.0004</strong><sup>c</sup></td><td>10.4±7.4</td><td>8.7±4.4</td><td>1.6±1.3£##</td><td><strong>0.0044</strong><sup>f</sup></td></tr><tr><td>Cholic acid, µg/mL</td><td>9.1±9.6</td><td>1.9±1.1</td><td><strong>0.0165</strong><sup>c</sup></td><td>5.4±6.5</td><td>11.0±11.0</td><td>4.7±3.5</td><td>0.7321<sup>f</sup></td></tr><tr><td>Tauro-deoxycholic acid (TDCA),<br>ng/mL</td><td>16.6±15.1</td><td>5.4±4.2</td><td><strong>0.0125</strong><sup>c</sup></td><td>13.2±11.9</td><td>16.1±8.9</td><td>7.7±4.2</td><td>0.1334<sup>f</sup></td></tr><tr><td>Tauro-chenodeoxycholic acid<br>(TCDCA), ng/mL</td><td>403±240</td><td>82±33</td><td><strong>&lt;0.0001</strong><sup>c</sup></td><td>292±163</td><td>244±127</td><td>99±32££#</td><td><strong>0.0019</strong><sup>f</sup></td></tr><tr><td>Taurocholic acid (TCA),<br>ng/mL</td><td>1,008±570</td><td>257±119</td><td><strong>&lt;0.0001</strong><sup>c</sup></td><td>287±85</td><td>328±141</td><td>167±61#</td><td><strong>0.0079</strong><sup>f</sup></td></tr><tr><td>Chenodeoxycholic acid (CDCA),<br>µg/mL</td><td>13.7±1.5</td><td>19±6.7</td><td><strong>0.0293</strong><sup>c</sup></td><td>22.3±31.9</td><td>18.9±19.4</td><td>9.0±6.0</td><td>0.8234<sup>f</sup></td></tr><tr><td>12α-Hydroxylated bile acid,<br>ng/mL</td><td>10.4±10.2</td><td>2.3±1.3</td><td><strong>0.0036</strong><sup>c</sup></td><td>10.0±11.8</td><td>11.8±11.2</td><td>5.2±3.4</td><td>0.6845<sup>f</sup></td></tr><tr><td>Primary bile acids, µg/mL</td><td>25.8±25.8</td><td>4.6±2.8</td><td><strong>0.0144</strong><sup>c</sup></td><td>11.0±11.1</td><td>30.6±28.6</td><td>14.2±9.4</td><td>0.5580<sup>f</sup></td></tr><tr><td>Secondary bile acids, ng/mL</td><td>191±82</td><td>113±36</td><td><strong>0.0051</strong><sup>c</sup></td><td>233±108</td><td>228±106</td><td>238±123</td><td>0.9735<sup>f</sup></td></tr><tr><td>Secondary/primary bile acids<br>ratio</td><td>0.02±0.02</td><td>0.03±0.01</td><td><strong>0.0672</strong><sup>c</sup></td><td>0.03±0.04</td><td>0.02±0.01</td><td>0.02±0.01</td><td>0.9348<sup>f</sup></td></tr><tr><td><strong>B. Fatty acids (FAs) (4 metabolic<br>index, 12 individual metabolites)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Saturated fatty acids, µg/mL</td><td>216±71</td><td>181±29</td><td>0.2264<sup>c</sup></td><td>288±91</td><td>1,877±24§§</td><td>201±20££</td><td><strong>0.0008</strong><sup>d</sup></td></tr><tr><td>MUFA, µg/mL</td><td>24.0±6.1</td><td>244±6.3</td><td>0.9845<sup>a</sup></td><td>26.7±7.0</td><td>28.3±6.1</td><td>34.6±4.0£</td><td><strong>0.0150</strong><sup>d</sup></td></tr><tr><td>PUFA omega 3, µg/mL</td><td>10.9±2.8</td><td>5.7±1.8</td><td><strong>&lt;0.0001</strong><sup>a</sup></td><td>9.5±6.1</td><td>4.7±0.9§</td><td>3.8±1.5££</td><td><strong>0.0050</strong><sup>e</sup></td></tr><tr><td>Docosahexaenoic acid (DHA),<br>µg/mL</td><td>9.9±2.7</td><td>5.0±1.7</td><td><strong>&lt;0.0001</strong><sup>a</sup></td><td>8.3±3.1</td><td>3.9±0.8§</td><td>3.0±1.3£££</td><td><strong>0.0005</strong><sup>e</sup></td></tr><tr><td>Eicosapentaenoic acid (EPA),<br>µg/mL</td><td>0.4±0.2</td><td>0.2±0.05</td><td><strong>0.0009</strong><sup>a</sup></td><td>0.6±0.1</td><td>0.3±0.1</td><td>0.2±0.05£££</td><td><strong>&lt;0.0001</strong><sup>e</sup></td></tr><tr><td>PUFA omega 6, µg/mL</td><td>114±29</td><td>95±22</td><td><strong>0.0545</strong><sup>a</sup></td><td>143±42</td><td>111±25</td><td>114±13</td><td><strong>0.0465</strong><sup>d</sup></td></tr><tr><td><strong>C. Glycerides (6 metabolic index,<br>222 individual metabolites)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Diglycerides (DGs) and<br>triglycerides (TGs)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Total DGs, µg/mL</td><td>12.0±4.9</td><td>8.5±3.7</td><td><strong>0.0398</strong><sup>a</sup></td><td>4.0±1.2</td><td>6.6±1.5§</td><td>6.9±2.4£</td><td><strong>0.0087</strong><sup>f</sup></td></tr><tr><td>Saturated DGs, µg/mL</td><td>0.13±0.04</td><td>0.12±0.02</td><td>0.3635<sup>a</sup></td><td>0.11±0.04</td><td>0.14±0.02</td><td>0.16±0.02££</td><td><strong>0.0051</strong><sup>d</sup></td></tr><tr><td>Unsaturated DGs, µg/mL</td><td>11.8±4.9</td><td>8.3±3.7</td><td><strong>0.0416</strong><sup>a</sup></td><td>3.8±1.1</td><td>6.4±1.5§</td><td>6.7±2.5£</td><td><strong>0.0098</strong><sup>d</sup></td></tr><tr><td>Total TGs, µg/mL</td><td>728±350</td><td>796±506</td><td>0.9292<sup>c</sup></td><td>392±180</td><td>627±164</td><td>790±341££</td><td><strong>0.0126</strong><sup>d</sup></td></tr><tr><td>Saturated TGs, µg/mL</td><td>0.13±0.04</td><td>0.12±0.02</td><td>0.3635<sup>a</sup></td><td>0.11±0.04</td><td>0.14±0.02</td><td>0.16±0.02££</td><td><strong>0.0051</strong><sup>d</sup></td></tr><tr><td>Unsaturated TGs, µg/mL</td><td>11.8±4.9</td><td>8.3±3.7</td><td><strong>0.0416</strong><sup>a</sup></td><td>3.8±1.1</td><td>7.5±3.7§§</td><td>6.7±2.5£</td><td><strong>0.0030</strong><sup>f</sup></td></tr><tr><td><strong>D. Cholesterol esters (CEs)<br>(1 metabolic index, 16 individual<br>metabolites)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>LCFA-CE, µg/mL</td><td>517±29</td><td>526±22</td><td>0.9292<sup>c</sup></td><td>271±167</td><td>560±174§§</td><td>673±170£££</td><td><strong>0.0001</strong><sup>d</sup></td></tr><tr><td><strong>E. Acyl-carnitines (CNs)<br>(30 metabolic index built on 14<br>individual metabolites)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>C0-CN, µg/mL: free-carnitine</td><td>3.3±0.8</td><td>2.2±0.3</td><td><strong>&lt;0.0001</strong><sup>a</sup></td><td>1.9±0.6</td><td>2.8±0.4§§</td><td>1.8±0.4###</td><td><strong>0.0001</strong><sup>d</sup></td></tr><tr><td>C2-CN, µg/mL: acetyl-carnitine</td><td>1.7±0.6</td><td>1.3±0.4</td><td><strong>0.0268</strong><sup>a</sup></td><td>1.2±0.6</td><td>1.7±0.3</td><td>0.83±0.24###</td><td><strong>0.0002</strong><sup>e</sup></td></tr><tr><td>C3-CN, µg/mL: propionyl-carni-<br>tine (BCAA oxidation)</td><td>0.10±0.03</td><td>0.05±0.01</td><td><strong>0.0006</strong><sup>b</sup></td><td>0.07±0.04</td><td>0.10±0.02</td><td>0.04±0.01###</td><td><strong>0.0004</strong><sup>e</sup></td></tr></tbody></table>

Continued
<table><caption>Table 2.— Continued</caption><thead><tr><th rowspan="2"></th><th colspan="3">Pregnant Groups (G12)</th><th colspan="3">Lactating Dams (L12)</th><th rowspan="2">Multiple<br>comparison test<br>P value</th></tr><tr><th>CTL (n = 9)</th><th>HFHS (n = 21)</th><th>Two-tailed<br>t test<br>P value</th><th>CTL (n = 8)</th><th>4HFHS (n = 10)</th><th>7HFHS (n = 10)</th></tr></thead><tbody><tr><td>C4-CN, ng/mL: butyryl-carnitine (AA and FA oxidation)</td><td>82.8 ± 19.2</td><td>52.0 ± 10.3</td><td><strong>0.0215<sup>b</sup></strong></td><td>79.1 ± 32.5</td><td>92.5 ± 15.5</td><td>60.0 ± 9.0###</td><td><strong>0.0221<sup>e</sup></strong></td></tr><tr><td>C14:2 OH CN, ng/mL: hydroxy-lated carnitine (Cx-OH)</td><td>6.0 ± 1.6</td><td>8.2 ± 1.2</td><td><strong>0.0003<sup>a</sup></strong></td><td>5.3 ± 2.5</td><td>6.2 ± 1.8</td><td>8.9 ± 1.9££#</td><td><strong>0.00229<sup>d</sup></strong></td></tr><tr><td>(C2 + C3)-CN/C0-CN ratio: indicator of beta-oxidation rate</td><td>0.56 ± 0.17</td><td>0.62 ± 0.19</td><td>0.3874<sup>a</sup></td><td>0.64 ± 0.21</td><td>0.67 ± 0.10</td><td>0.50 ± 0.12#</td><td><strong>0.0338<sup>d</sup></strong></td></tr><tr><td>Cx-DC CN/Cx-OH CN ratio</td><td>8.8 ± 1.4</td><td>7.0 ± 1.4</td><td><strong>0.0020<sup>a</sup></strong></td><td>10.9 ± 2.9</td><td>10.9 ± 4.3</td><td>6.5 ± 1.5££#</td><td><strong>0.0079<sup>e</sup></strong></td></tr><tr><td>C2-CN/C0-CN ratio: indicator of complete beta-oxidation rate</td><td>0.52 ± 0.17</td><td>0.59 ± 0.19</td><td>0.3461<sup>a</sup></td><td>0.60 ± 0.21</td><td>0.63 ± 0.11</td><td>0.48 ± 0.12</td><td><strong>0.0518<sup>d</sup></strong></td></tr><tr><td>C4-CN/C2-CN ratio: indicator of isobutyryl-CoA dehydrogenase (IBD) deficiency involved in breakdown valine</td><td>0.05 ± 0.02</td><td>0.05 ± 0.02</td><td>0.3343<sup>a</sup></td><td>0.08 ± 0.03</td><td>0.05 ± 0.01</td><td>0.08 ± 0.02#</td><td><strong>0.0168<sup>f</sup></strong></td></tr><tr><td>LCFA (C16 + C18:1)-CN/C0-CN ratio: indicator of carnitine palmitoyl-transferase-I (CPTI) deficiency</td><td>0.02 ± 0.01</td><td>0.03 ± 0.01</td><td><strong>&lt;0.0001<sup>c</sup></strong></td><td>0.02 ± 0.01</td><td>0.02 ± 0.01</td><td>0.04 ± 0.01££##</td><td><strong>0.0012<sup>f</sup></strong></td></tr><tr><td>LCFA (C16 + C18:1)-CN/C2-CN ratio: indicator of carnitine palmitoyl-transferase-II (CPTII) deficiency</td><td>0.03 ± 0.01</td><td>0.06 ± 0.01</td><td><strong>&lt;0.0001<sup>a</sup></strong></td><td>0.04 ± 0.02</td><td>0.04 ± 0.01</td><td>0.09 ± 0.02£££###</td><td><strong>&lt;0.0001<sup>d</sup></strong></td></tr><tr><td>C16-CN/C16-OH CN ratio: indicator of 3-hydroxyacyl-CoA dehydrogenase (HADH) and mitochondrial trifunctional protein (MTP) deficiency</td><td>7.4 ± 2.6</td><td>7.6 ± 2.6</td><td>0.9446<sup>c</sup></td><td>4.9 ± 0.9</td><td>6.0 ± 1.4</td><td>7.0 ± 1.9£</td><td><strong>0.0231<sup>d</sup></strong></td></tr><tr><td>LCFA-CN (C14-18), ng/mL: long-chain fatty acid-acylcarnitine, indicator of 3-hydroxyacyl-CoA dehydrogenase (HADH) deficiency</td><td>70.1 ± 26.0</td><td>85.4 ± 15.7</td><td>0.0562<sup>a</sup></td><td>45.3 ± 21.1</td><td>72.5 ± 22.4</td><td>87.2 ± 16.4££</td><td><strong>0.0018<sup>f</sup></strong></td></tr><tr><td>(C0 + C2 + C3 + C16 + C18 + C18:1)-CN/citrulline ratio: indicator of the cellular carnitine uptake</td><td>0.42 ± 0.12</td><td>0.31 ± 0.06</td><td><strong>0.0213<sup>b</sup></strong></td><td>0.25 ± 0.13</td><td>0.37 ± 0.09</td><td>0.17 ± 0.03###</td><td><strong>0.0016<sup>e</sup></strong></td></tr><tr><td colspan="8"><strong>Lipid species-containing choline</strong></td></tr><tr><td colspan="8"><strong>F. Sphingolipids (SPLs)</strong></td></tr><tr><td colspan="8">(23 metabolic index, 60 individual metabolites)</td></tr><tr><td colspan="8"><strong>Sphingomyelin (SM), glucosyl- and ceramides (Cer)</strong></td></tr><tr><td>LCFA (C16-C20)-SM, µg/mL</td><td>18.0 ± 5.8</td><td>21.1 ± 3.9</td><td><strong>0.0945<sup>a</sup></strong></td><td>12.4 ± 5.6</td><td>18.6 ± 2.3$</td><td>18.1 ± 2.7</td><td><strong>0.0103<sup>e</sup></strong></td></tr><tr><td>VLCFA (C22-C24)-SM, µg/mL</td><td>32.3 ± 10.1</td><td>21.3 ± 4.7</td><td><strong>0.0017<sup>b</sup></strong></td><td>28.7 ± 12.6</td><td>42.7 ± 7.3$</td><td>24.7 ± 2.9###</td><td><strong>0.0016<sup>e</sup></strong></td></tr><tr><td>Hydroxylated-SM, µg/mL</td><td>6.1 ± 1.9</td><td>4.6 ± 0.94</td><td><strong>0.0030<sup>b</sup></strong></td><td>5.3 ± 2.2</td><td>7.9 ± 1.6$</td><td>5.5 ± 0.7##</td><td><strong>0.0063<sup>e</sup></strong></td></tr><tr><td>LCFA-Cer, µg/mL</td><td>0.78 ± 0.26</td><td>0.61 ± 1.2</td><td><strong>0.0499<sup>a</sup></strong></td><td>0.65 ± 0.34</td><td>0.83 ± 0.20</td><td>0.80 ± 0.27</td><td>0.3624<sup>d</sup></td></tr><tr><td>VLCFA-Cer, µg/mL</td><td>4.0 ± 1.1</td><td>3.2 ± 0.6</td><td><strong>0.0050<sup>b</sup></strong></td><td>4.3 ± 2.3</td><td>5.7 ± 0.8</td><td>5.2 ± 0.7</td><td>0.2592<sup>e</sup></td></tr><tr><td>Hexo-Cer, µg/mL</td><td>0.68 ± 0.18</td><td>0.85 ± 0.16</td><td><strong>0.0132<sup>a</sup></strong></td><td>0.54 ± 0.25</td><td>0.69 ± 0.1</td><td>0.89 ± 0.15££##</td><td><strong>0.0066<sup>e</sup></strong></td></tr><tr><td>Glucosyl-Cer, µg/mL</td><td>6.2 ± 1.9</td><td>4.7 ± 0.9</td><td><strong>0.0051<sup>a</sup></strong></td><td>6.1 ± 4.0</td><td>8.5 ± 1.2</td><td>8.1 ± 1.1</td><td>0.1686<sup>e</sup></td></tr><tr><td>SM/Cer, ng/mL</td><td>11.9 ± 1.4</td><td>12.4 ± 1.8</td><td>0.4764<sup>a</sup></td><td>9.2 ± 1.4</td><td>10.4 ± 1.2</td><td>7.6 ± 1.0£###</td><td><strong>0.0002<sup>d</sup></strong></td></tr><tr><td>SPLs containing C24:1, µg/mL</td><td>18.8 ± 5.8</td><td>11.8 ± 2.5</td><td><strong>0.0010<sup>c</sup></strong></td><td>16.0 ± 7.8</td><td>24.5 ± 3.5$</td><td>13.9 ± 1.2##</td><td><strong>0.0009<sup>f</sup></strong></td></tr><tr><td>SM C24:1, µg/mL</td><td>15.3 ± 4.8</td><td>9.5 ± 2.1</td><td><strong>0.0016<sup>c</sup></strong></td><td>12 ± 6.4</td><td>19 ± 2.9$</td><td>10 ± 1.1###</td><td><strong>0.0008<sup>f</sup></strong></td></tr><tr><td>Cer C24:1, ng/mL</td><td>532 ± 158</td><td>402 ± 91</td><td><strong>0.0042<sup>c</sup></strong></td><td>515 ± 300</td><td>726 ± 99</td><td>612 ± 102</td><td><strong>0.0476<sup>f</sup></strong></td></tr><tr><td colspan="8"><strong>G. Phospholipids (PLs)</strong></td></tr><tr><td colspan="8">(7 metabolic index, 75 individual metabolites)</td></tr><tr><td colspan="8"><strong>Phosphatidylcholine (PC or diacyl phosphatidylcho line) and phosphatidylethanolamine (PE or acyl-alkyl-phosphatidylcholine)</strong></td></tr><tr><td>PC, µg/mL</td><td>565 ± 154</td><td>529 ± 103</td><td>0.4593<sup>a</sup></td><td>608 ± 254</td><td>839 ± 100</td><td>1,047 ± 113££##</td><td><strong>0.0008<sup>e</sup></strong></td></tr><tr><td>PE, µg/mL</td><td>33.5 ± 8.7</td><td>25.2 ± 4.0t</td><td><strong>0.0014<sup>c</sup></strong></td><td>34.2 ± 13.4</td><td>53.2 ± 8.4$$</td><td>46.7 ± 5.8£</td><td><strong>0.0010<sup>d</sup></strong></td></tr><tr><td>Total PC, µg/mL</td><td>599 ± 163</td><td>555 ± 107</td><td>0.3833<sup>a</sup></td><td>642 ± 267</td><td>893 ± 104$$</td><td>1,094 ± 118££##</td><td><strong>0.0009<sup>e</sup></strong></td></tr></tbody></table>

Continued
<table><caption>**Table 2.**— Continued</caption><thead><tr><th rowspan="2"></th><th colspan="3">Pregnant Groups (G12)</th><th colspan="3">Lactating Dams (L12)</th><th rowspan="2">Multiple<br>comparison test<br>P value</th></tr><tr><th>CTL (n = 9)</th><th>HFHS (n = 21)</th><th>Two-tailed<br>t test<br>P value</th><th>CTL (n = 8)</th><th>4HFHS (n = 10)</th><th>7HFHS (n = 10)</th></tr></thead><tbody><tr><td>Unsaturated PC, µg/mL</td><td>557 ± 153</td><td>522 ± 103</td><td>0.4694<sup>a</sup></td><td>601 ± 252</td><td>831 ± 99</td><td>1,037 ± 112££##</td><td><strong>0.0008</strong><sup>e</sup></td></tr><tr><td>Unsaturated PE, µg/mL</td><td>31.7 ± 8.3</td><td>24.0 ± 3.9</td><td><strong>0.0021</strong><sup>c</sup></td><td>31.6 ± 12.5</td><td>49.6 ± 8.0$$</td><td>44.2 ± 5.5£</td><td><strong>0.0008</strong><sup>d</sup></td></tr><tr><td><strong>H. Lyso-phospholipids (Lyso-PLs)</strong><br>(8 metabolic index, 13 individual<br>metabolites)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>LC-Lyso-PC, µg/mL</td><td>197 ± 54</td><td>207 ± 37</td><td>0.5445<sup>a</sup></td><td>141 ± 53</td><td>181 ± 19.8</td><td>205 ± 24.4£</td><td><strong>0.0110</strong><sup>e</sup></td></tr><tr><td>LysoPC Cxx:1, µg/mL: index of<br>mono-unsaturated PCs-degradation</td><td>10.3 ± 3.2</td><td>10.6 ± 2.1</td><td>0.6892<sup>b</sup></td><td>12.7 ± 4.8</td><td>15.3 ± 1.9</td><td>19.7 ± 2.6£##</td><td><strong>0.0026</strong><sup>e</sup></td></tr><tr><td>LysoPC Cxx:2-4, µg/mL: index<br>of polyunsaturated PCs-degradation</td><td>58.7 ± 20.1</td><td>54.2 ± 11.4</td><td>0.5402<sup>b</sup></td><td>36.9 ± 15.2</td><td>50.0 ± 3.9</td><td>65.0 ± 7.7££###</td><td><strong>0.0004</strong><sup>e</sup></td></tr><tr><td>LysoPC Cxx:0, µg/mL: index of<br>saturated PCs-degradation</td><td>128 ± 32</td><td>142 ± 25</td><td>0.1841<sup>a</sup></td><td>92 ± 34</td><td>116. ± 16</td><td>121 ± 17£</td><td><strong>0.0344</strong><sup>d</sup></td></tr><tr><td>LysoPC C24-28:x, µg/mL: index<br>of VLCFA-PCs degradation</td><td>0.40 ± 0.10</td><td>0.33 ± 0.06</td><td><strong>0.0175</strong><sup>a</sup></td><td>0.39 ± 0.13</td><td>0.50 ± 0.1</td><td>0.41 ± 0.04#</td><td><strong>0.0528</strong><sup>e</sup></td></tr><tr><td>Saturated/unsaturated Lyso-PCs<br>ratio</td><td>1.9 ± 0.2</td><td>2.2 ± 0.2</td><td><strong>0.0010</strong><sup>a</sup></td><td>1.9 ± 0.2</td><td>1.8 ± 0.2</td><td>1.4 ± 0.2££###</td><td><strong>0.0001</strong><sup>d</sup></td></tr><tr><td>LysoPC Cxx:x / PC ax Cxx:x ra-<br>tio: index of PCs-degradation<br>(sum of Lyso-PCs to sum of<br>total PCs and PEs)</td><td>0.33 ± 0.04</td><td>0.38 ± 0.03</td><td><strong>0.0029</strong><sup>a</sup></td><td>0.22 ± 0.02</td><td>0.24 ± 0.02</td><td>0.19 ± 0.03££</td><td><strong>0.0124</strong><sup>d</sup></td></tr><tr><td><strong>Polar compounds</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td><strong>I. Amino acids (AAs) and amines</strong><br>relative (14 metabolic index,<br>49 individual metabolites)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td><strong>Insulinotropic and glucogenic<br>amino acids</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Branched amino acids<br>(BCAAs), µg/mL</td><td>43.8 ± 2.5</td><td>49.7 ± 7.6</td><td><strong>0.0424</strong><sup>a</sup></td><td>46.9 ± 6.0</td><td>46.1 ± 6.8</td><td>59.2 ± 8.8££##</td><td><strong>0.0008</strong><sup>d</sup></td></tr><tr><td>Valine, µg/mL</td><td>18.1 ± 1.2</td><td>21.3 ± 3.6</td><td><strong>0.0139</strong><sup>a</sup></td><td>19.3 ± 2.9</td><td>19.0 ± 2.8</td><td>27.2 ± 3.4££###</td><td><strong>&lt;0.0001</strong><sup>d</sup></td></tr><tr><td>Aromatic AAs, µg/mL</td><td>46.3 ± 4.3</td><td>53.1 ± 8.0</td><td><strong>0.0069</strong><sup>c</sup></td><td>39.9 ± 4.5</td><td>38.1 ± 4.4</td><td>51.1 ± 12.8#</td><td><strong>0.0067</strong><sup>e</sup></td></tr><tr><td>Tyrosine, µg/mL</td><td>13.5 ± 2.7</td><td>17.4 ± 4.2</td><td><strong>0.0151</strong><sup>a</sup></td><td>11.9 ± 2.4</td><td>11.8 ± 2.4</td><td>21.7 ± 7.6££###</td><td><strong>0.0001</strong><sup>d</sup></td></tr><tr><td>Glucogenic AAs, µg/mL</td><td>315 ± 18</td><td>345 ± 45</td><td><strong>0.0143</strong><sup>b</sup></td><td>383 ± 50</td><td>356 ± 45</td><td>548 ± 106££###</td><td><strong>&lt;0.0001</strong><sup>e</sup></td></tr><tr><td>Proline, µg/mL</td><td>13.7 ± 1.5</td><td>21.7 ± 5.2</td><td><strong>&lt;0.0001</strong><sup>c</sup></td><td>19.1 ± 3.8</td><td>19.7 ± 5.4</td><td>47.9 ± 16.4££###</td><td><strong>0.0001</strong><sup>e</sup></td></tr><tr><td>Threonine, µg/mL</td><td>27.2 ± 3.3</td><td>60.8 ± 17.7</td><td><strong>&lt;0.0001</strong><sup>c</sup></td><td>40.7 ± 7.9</td><td>38.9 ± 1.1</td><td>168 ± 41££##</td><td><strong>0.0002</strong><sup>e</sup></td></tr><tr><td>Alanine, µg/mL</td><td>25 ± 2.8</td><td>29 ± 5.2</td><td>0.1255<sup>b</sup></td><td>49 ± 10</td><td>47 ± 13</td><td>68 ± 13£##</td><td><strong>0.0048</strong><sup>e</sup></td></tr><tr><td>Ketogenic AAs, µg/mL</td><td>105 ± 7.1</td><td>128 ± 18</td><td><strong>&lt;0.0001</strong><sup>b</sup></td><td>113 ± 17</td><td>98 ± 14</td><td>170 ± 23££###</td><td><strong>&lt;0.0001</strong><sup>d</sup></td></tr><tr><td>Lysine, µg/mL</td><td>89 ± 6.7</td><td>110 ± 17</td><td><strong>0.0008</strong><sup>b</sup></td><td>96 ± 15</td><td>82 ± 11</td><td>161 ± 9£###</td><td><strong>0.0001</strong><sup>d</sup></td></tr><tr><td><strong>Antioxidant defense system</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Sulfur AAs, µg/mL</td><td>67.2 ± 10.5</td><td>56.5 ± 9.3</td><td><strong>0.0096</strong><sup>a</sup></td><td>70.0 ± 17.9</td><td>58.2 ± 14.4</td><td>61.5 ± 8.7</td><td>0.1875<sup>f</sup></td></tr><tr><td>Cysteine/(serine +<br>methionine) ratio: indicator<br>of cysteine synthesis</td><td>0.36 ± 0.08</td><td>0.20 ± 0.08</td><td><strong>0.0026</strong><sup>b</sup></td><td>0.17 ± 0.04</td><td>0.32 ± 0.42</td><td>0.20 ± 0.15</td><td>0.337<sup>f</sup></td></tr><tr><td>GSH precursors, µg/mL</td><td>173 ± 20</td><td>162 ± 18</td><td>0.1556<sup>a</sup></td><td>192 ± 23</td><td>183 ± 24</td><td>173 ± 35</td><td>0.3754<sup>d</sup></td></tr><tr><td>Glycine, µg/mL</td><td>19 ± 2.3</td><td>13 ± 1.9</td><td><strong>&lt;0.0001</strong><sup>b</sup></td><td>33 ± 7.6</td><td>29 ± 7.8</td><td>21 ± 4.1££#</td><td><strong>0.0014</strong><sup>f</sup></td></tr><tr><td>Betaine, µg/mL</td><td>10 ± 1.6</td><td>6 ± 1.0</td><td><strong>&lt;0.0001</strong><sup>b</sup></td><td>10 ± 1.9</td><td>10 ± 2.3</td><td>7 ± 0.9££#</td><td><strong>0.0009</strong><sup>f</sup></td></tr><tr><td>Me-SO/Met ratio: sulfoxide<br>methionine/unmodified<br>methionine pool, indicator<br>of methionine sulfoxide<br>reductase activity and<br>systemic oxidative stress</td><td>0.04 ± 0.01</td><td>0.02 ± 0.01</td><td><strong>0.0134</strong><sup>b</sup></td><td>0.04 ± 0.02</td><td>0.04 ± 0.01</td><td>0.03 ± 0.01#</td><td><strong>0.0218</strong><sup>f</sup></td></tr><tr><td>Betaine and related metabo-<br>lites, µg/mL</td><td>1.1 ± 0.6</td><td>0.33 ± 0.11</td><td><strong>&lt;0.0001</strong><sup>c</sup></td><td>0.35 ± 0.07</td><td>0.38 ± 0.14</td><td>0.25 ± 0.07#</td><td><strong>0.0246</strong><sup>d</sup></td></tr><tr><td>Betaine/choline ratio</td><td>338 ± 265</td><td>168 ± 137</td><td><strong>0.0609</strong><sup>b</sup></td><td>122 ± 101</td><td>85.4 ± 45.9</td><td>125 ± 87#</td><td>0.6137<sup>f</sup></td></tr><tr><td><strong>Kynurenine/tryptophan<br>pathway</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Kynurenine/tryptophan ratio</td><td>0.03 ± 0.00</td><td>0.04 ± 0.01</td><td><strong>0.0207</strong><sup>a</sup></td><td>0.01 ± 0.00</td><td>0.01 ± 0.00</td><td>0.01 ± 0.00</td><td>0.2181<sup>d</sup></td></tr><tr><td><strong>NO synthesis</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Citrulline/arginine ratio</td><td>0.43 ± 0.06</td><td>0.45 ± 0.05</td><td>0.1757<sup>a</sup></td><td>0.47 ± 0.09</td><td>0.46 ± 0.08</td><td>0.57 ± 0.09#</td><td><strong>0.0157</strong><sup>d</sup></td></tr></tbody></table>

Continued
<table><caption>**Table 2.**— Continued</caption><thead><tr><th rowspan="2"></th><th colspan="3">Pregnant Groups (G12)</th><th colspan="3">Lactating Dams (L12)</th></tr><tr><th>CTL (n = 9)</th><th>HFHS (n = 21)</th><th>Two-tailed<br>t test<br>P value</th><th>CTL (n = 8)</th><th>4HFHS (n = 10)</th><th>7HFHS (n = 10)</th><th>Multiple<br>comparison test<br>P value</th></tr></thead><tbody><tr><td><strong>TMAO and indoles pathway</strong><br>TMAO/(betaine + CO-CN +<br>choline) ratio: indicator of<br>TMAO synthesis</td><td>0.03±0.01</td><td>0.01±0.003</td><td><strong>0.0001</strong><sup>b</sup></td><td>0.05±0.02</td><td>0.04±0.01</td><td>0.02±0.00£££##</td><td><strong>&lt;0.0001</strong><sup>f</sup></td></tr><tr><td>3-IAA + 3-IPA + Ind-SO4,<br>ng/mL: indole synthesis</td><td>2.7±1.3</td><td>1.3±0.4</td><td><strong>&lt;0.0001</strong><sup>c</sup></td><td>4.0±1.6</td><td>3.4±1.0</td><td>1.7±0.5£###</td><td><strong>0.0007</strong><sup>e</sup></td></tr><tr><td><strong>J. Carboxylic acids (1 biomarker,<br>5 individual metabolites)</strong><br>Hippuric acid, ng/mL</td><td>1,011±579</td><td>365±236</td><td><strong>0.0010</strong><sup>c</sup></td><td>1,834±1,314</td><td>1,560±833</td><td>307±191£###</td><td><strong>0.0005</strong><sup>f</sup></td></tr></tbody></table>

Metabolic indexes were calculated, based on 413 individual metabolites measured using targeted metabolomic/lipidomic phenotyping. For each metabolite category, the number of individual metabolites is specified in italic as well as the numbers of metabolic indexes calculated. Only discriminant metabolic indexes between groups were reported with absolute (LC) or semiquantitative (FIA) data or ratio expressed as means ± SD. Two-tailed unpaired *t* test with<sup>b</sup> or without<sup>a</sup> Welch's correction or a Mann-Whitney test<sup>c</sup>, in case of no Gaussian distribution of residuals and heteroscedasticity, were used to compare HFHS vs. CTL dams at GD12. One-way ANOVA with<sup>e</sup> or without<sup>d</sup> Welch's correction, with post hoc Tukey's multiple comparisons test, or a Kruskal-Wallis test, with post hoc Dunn's multiple comparisons test<sup>f</sup>, in case no Gaussian distribution of residuals and heteroscedasticity, were used to compare CTL, 4HFHS, and 7HFHS dams at LD12. $\$Significant difference between 4HFHS vs. CTL ($P < 0.05; \$\$P < 0.01; \$\$\$P < 0.001$); £Significant difference between 7HFHS vs. CTL (£P < 0.05; ££P < 0.01; £££P < 0.001); and #Significant difference between 7HFHS vs. 4HFHS (#P < 0.05; ##P < 0.01; ###P < 0.001). Bold value means a significant *P* value < 0.05, and bold italic value means a trend, 0.05 < *P* value < 0.1. 3-IAA, 3-Indoleacetic acid; 3-IPA, 3-Indolepropionic acid; 12a-OH BAs (12-alpha-hydroxylated bile acids), CA + DCA + GCA + GDCA + TCA + TDCA; Ala, alanine; Arg, arginine; aromatic AAs, Phe + Tyr + Tryp; Asn, aspartate; Asp, aspartate; BCAAs, branched-chain amino acids (Leu + Ileu + Val); CA, total cholic acid; CDCA, chenodeoxycholic acid; CE, cholesterol esters; Cer, ceramides; CEs, cholesterol esters; CN, carnitine; CTL, control; Cys, cysteine; DCA, deoxycholic acid; GCA, glyco-cholic acid; GCDCA, glycochenodeoxycholic acid; GDCA, glycodeoxycholic acid; GLCA, glycol-lithocholic acid; Gln, glutamine; glucogenic AA, Ala + Arg + Asn + Asp + Gln + Glu + Gly + His + Met + Cys + Pro + Ser + Thr + Val; Gly, glycine; GSH (glutathione) precursors, Cys + Cystine + HCys + Met + Betaine + Ser + Gly + Glu; GUDCA, glycol-ursodeoxycholic acid; HCys, homocysteine; HFHS, high-fat high-sucrose; His, histidine; Ileu, isoleucine; Ind-SO4, indoxyl sulfate; IPA, 3-indolepropionic acid; ketogenic AA, Leu + Lys; LCFA-Cer = Cer(d1x,1/14-20,x) + Cer(d18,1/xx,0(OH); LCFA, long-chain fatty acids (C16, C18, and C20); LC-Lyso PCs, long-chain lyso-phosphatidylcholines; Leu, leucine; Lys, lysine; Met, methionine; PC, phosphatidylcholines; PCs (aa) or PCs, diacyl-phosphatidylcholines; PCs (ae) or PEs, acyl-alkyl-phosphatidylcholines; Phe, phenylalanine; primary BAs, CA + CDCA + GCA + GCDCDA + TCA + TCDCA; Pro, proline; ratio of conjugated/unconjugated secondary BAs, (GDCA + GLCA + GUDCA + TDCA + TLCA)/DCA; ratio of secondary BAs/total BAs, (DCA + GDCA + GLCA + GUDCA + TDCA + TLCA)/(CA + CDCA + DCA + GCA + GCDCDA + GDCA + GLCA + GUDCA + TCA + TCDCA + TDCA + TLCA); ratio of secondary/primary BAs, (DCA + GDCA + GLCA + GUDCA + TDCA + TLCA)/(CA + CDCA + GCA + GCDCDA + TCA + TCDCA); secondary BAs, DCA + GDCA + GLCA + GUDCA + TDCA + TLCA; Ser, serine; SMs, sphingomyelins; sulfur AA, Cys + Cystine + HCys + Met + Taur; Taur, taurine; TCA, tauro-cholic acid; TCDCA, tauro-chenodeoxycholic acid; TDCA, tauro-deoxycholic acid; Thr, threonine; TLCA, taurolicholic acid; TMAO, trimethylamine N-oxide; Tryp, tryptophan; Tyr, tyrosine; Val, valine; VLFA, very-long-chain fatty acids (C22-C24).

Fig. S2A). A significantly higher glucose AUC was observed in the 7HFHS as compared with the CTL and 4HFHS groups (Supplemental Fig. S2, B and C). Fasting insulin, insulin AUC, and ISI<sub>Gutt</sub> were not significantly different in 4- or 7HFHS, as compared with CTL (Supplemental Fig. S2, E-H). However, plasma insulin peak at 15 min was much lower in 7HFHS than in 4HFHS dams (Supplemental Fig. S2F), suggesting a lower capacity to secrete insulin. Moreover, IGT was confirmed in 7HFHS with a lower glucose MCR value versus 4HFHS, but just a trend versus CTL (*P* = 0.0845) (Supplemental Fig. S2D).

**Higher plasma leptin persisted in 7HFHS dams during lactation.**

No significant differences were observed concerning classical metabolic parameters between 4HFHS and CTL both at mid (LD12) and end (LD18) of lactation (Table 3). Only 7HFHS dams presented a transient elevation in NEFA and CHOL concentrations at LD12, compared with CTL, along with persistent higher leptin concentration, compared with both CTL and 4HFHS (Table 3).

**IGT-persistent dams (7HFHS) presented enhanced insulin signaling, and imbalanced oxidative status during lactation compared with IGT-corrected dams (4HFHS).**

The PLS-DA built with both plasma metabolome (Fig. 4G) or lipidome (Fig. 4H) data revealed a clear-cut clustering of CTL and 7HFHS dams but did not allow separating 4HFHS from CTL on components 1 and 2. At LD12, compared with insulin-sensitive lactating (CTL and 4HFHS) dams, IGT dams (7HFHS) displayed: 1) a persistent elevation of the biomarkers, previously observed during gestation and involved in insulin secretion (BCAAs such as valine), gluconeogenesis (proline, lysine, threonine, and alanine), and ketogenesis (lysine) (Table 2I); incomplete FAO or mitochondrial dysfunction (indicators of CPTI and CPTII deficiencies, LCFA-acyl- and hydroxylated-carnitines) (Table 2E); and associated with insulin resistance and/or inflammatory status (i.e., Lyso-PCs/PCs ratio and unsaturated Lyso-PCs) (Table 2H); 2) a persistent depletion of biomarkers of oxidative defense system (betaine and glycine) (Table 2I); and finally 3) a postpartum switch to enhanced biomarkers involved in lipids mobilization (i.e., total unsaturated and saturated TGs and DGs) (Table 2C) and insulin resistance
A

<figure><img src="image_16.png" /></figure>

B

<figure><img src="image_17.png" /></figure>

D

<figure><img src="image_18.png" /></figure>

E

<figure><img src="image_19.png" /></figure>

C

<figure><img src="image_20.png" /></figure>

F

<figure><img src="image_21.png" /></figure>

G

Metabolome of 7HFHS vs 4HFHS vs CTL dams
(PLS-DA C1-C2)

<figure><img src="image_22.png" /></figure>

H

Lipidome of 7HFHS vs 4HFHS vs CTL dams
(PLS-DA C1-C2)

<figure><img src="image_23.png" /></figure>

I

Biomarker indexes between 7 HFHS and CTL dams

<figure><img src="image_24.png" /></figure>
<figure><img src="image_25.png" /></figure>

(i.e., accumulation of ceramides, reflected by the decreased SM/Cer ratio) (Table 2F) (Fig. 4, I and J and Supplemental Fig. S5, A-D). The sustained decrease in indexes of trimethylamine N-oxide (TMAO) and indole synthesis (Table 2I) as well as in the total level of taurine-conjugated BAs (such as TCDCA and TCA) (Table 2A) suggests persistent alterations in the intestinal microbiota postpartum in IGT-7 HFHS, as opposed to both CTL and 4HFHS lactating dams.

**Postpartum resolution of IGT (4HFHS) was associated with an increase in energy-metabolism products and insulin-sensitivity regulators, along with lower insulin secretagogue or glucogenic amino acids, compared with dams with persistent IGT (7HFHS).**

The supervised PLS-DA model built on metabolome/lipidome signatures highlighted a clustering of 4HFHS, clearly separated from both 7HFHS and CTL dams, on components 1 and 3 (Supplemental Fig. S3A). This clustering was further validated by the AoV-PLS discriminant model (Supplemental Fig. S3B). In addition, the linear discriminant analysis, performed on the C1-C2 components derived from the AoV-PLS models, showed highly significant effects of both gestation (Supplemental Fig. S3C) and lactation (Supplemental Fig. S3D) alone and, importantly, highlighted the interaction of both periods “that is, postpartum IGT-correction or not” with dietary switch or not at delivery (Supplemental Fig. S3E). Globally, and compared with IGT-persistent dams (7HFHS), insulin-sensitive 4HFHS dams displayed a cluster of indicators reflecting: 1) enhanced cellular carnitine uptake, FAO, and BCAA oxidation [i.e., higher short (C2-C5) carnitine, along with indicators of higher activity of CPTI and CPTII, and isobutyryl-CoA dehydrogenase, involved in long-chain fatty acid and valine catabolism, respectively] (Table 2); 2) decrease in metabolites associated

with mitochondrial dysfunction and insulin resistance (i.e., PCs, Hexo-Cer saturated, and hydroxylated carnitine) (Table 2, respectively); 3) enhanced SM/Cer conversion, driven by higher content in VLCFA-SMs, and particularly those containing the nervonic acid (C24:1) (Table 2), but also 4) enhanced mediators of “anti-inflammatory” lipids, likely driven by a dramatic drop in the levels of their degradation products (i.e., Lyso-PCs of essentially mono- and polyunsaturated and VLCFA-PCs) (Table 2) (Fig. 4J and Supplemental Fig. S5, B and D). The plasma levels of AAs involved in insulin secretion and gluconeogenesis were lower in 4HFHS dams as compared with 7HFHS dams, but similar to those of CTL dams. The citrulline/arginine ratio, likely reflecting lower synthesis of nitric oxide (NO) (34), an important regulator of insulin secretion, behaved similarly (Table 2). In addition, 4HFHS dams displayed higher plasma content of taurine-conjugated BAs (Table 2) and of biomarkers of indole and TMAO synthesis (Table 2) than 7HFHS dams. Finally, compared with CTL dams, 4HFHS presented higher circulating content in SPLs, particularly VLCFA-SPLs containing C24:1; and in the CE family, containing VLCFA; in PE, DG, and TG families, specifically containing unsaturated FAs; but lower content in free saturated FAs; and that, along with similar profiles of insulinotropic and sulfur AAs, BAs, Lyso-PCs, and acyl-CN (Table 2; Fig. 4K and Supplemental Fig. S5E).

## DISCUSSION

The findings of the current study demonstrate that postpartum resolution of IGT is associated with a specific plasma metabolome/lipidome in a rat model of GDM. When IGT is resolved postpartum (4HFHS dams), alterations in energy conversion, antioxidant defense,

**Figure 4.** At LD12, hyperglycemia and hyperinsulinemia disappeared in 4HFHS dams, whereas IGT persisted in 7HFHS. Fasting glucose (A), glucose tolerance curve during oral glucose tolerance test (OGTT) and the corresponding glucose area under the curve (AUC) (B), glucose metabolic clearance rate (MCR) (C), fasting insulin (D), insulin tolerance curve and the corresponding insulin AUC (E), peripheral insulin sensitivity index (ISI Gutt) (F). A, C, D, F, B and E (insets) are represented as squatter dot plot with individual values and with means ± SD, *B* and *E* are means ± SD (CTL: *n* = 12, 4HFHS: *n* = 19 and 7HFHS: *n* = 12). Global significant differences are indicated as follows: \* *P* < 0.05, \*\* *P* < 0.01, \*\*\* *P* < 0.001 (one-way ANOVA), followed by Sidak's multiple comparisons post hoc test: \$ *P* < 0.05; \$\$ *P* < 0.01; \$\$\$ *P* < 0.001; \£ Significant difference between 7HFHS vs. CTL (\$ *P* < 0.05; \$\$ *P* < 0.01; \$\$\$ *P* < 0.001); \# Significant difference between 7HFHS vs. 4HFHS (\#\$ *P* < 0.01). Score plot of partial least square discriminant analysis (PLS-DA), on the two first components C1 and C2, performed on plasma metabolome (R2X = 0.56, R2Y = 0.81 and Q2 = 0.57, 3 components) (G) and lipidome (R2X = 0.67, R2Y = 0.98 and Q2 = 0.72, 5 components) (H), respectively. Each point represents an individual plasma metabolome/lipidome of CTL (green); 4HFHS (orange); and 7HFHS (red) groups (CTL: *n* = 8, 4HFHS: *n* = 10, and 7HFHS: *n* = 10). Volcano plot are performed on all plasma biomarker indexes (*n* = 109) quantified in all dams, to compare 7HFHS and CTL (*I*); 7HFHS and 4HFHS (*J*); and 4HFHS and CTL (*K*), respectively. Each point represents a biomarker index as a sum or ratio of different variables, as detailed in Table 2. Volcano plot biomarkers indexes are labeled in dark blue for phospholipids, magenta for bile acids, brown for amino acids and amines relatives, blue light for acyl-carnitines, red for fatty acids, green for cholesterol esters, yellow for glycerides, gray for sphingolipids and orange for lyso-phospholipids. Green color point means that biomarker indexes are lower, and red color point means that biomarkers indexes are higher in 7HFHS than CTL (*I*), 7HFHS than 4HFHS (*J*), and 4HFHS than CTL (*K*). See also Supplemental Fig. S5 for individual variables. 12a-OH BAs (12-alpha-hydroxylated bile acids), CA + DCA + GCA + GDCA + TCA + TDCA; aromatic AAs, (phenylalanine + tyrosine + tryptophane); BCAA, branched-chain amino acids (leucine + isoleucine + valine); CA, total cholic acid; CDCA, chenodeoxycholic acid; CE, cholesterol ester; Cer, ceramide; CN, carnitine; Cx-OH-CN, hydroxylated carnitine; DCA, deoxycholic acid; GCA, glycocholic acid; GCDCBA, glycochenodeoxycholic acid; GDCA, glycodeoxycholic acid; GLCA, glycol-lithocholic acid; glucogenic AAs, (alanine + arginine + asparagine + aspartate + glutamine + glutamate + glycine + histidine + methionine + cysteine + proline + serine + threonine + valine); GSH (glutathione) precursors, (cysteine + cysteine + homocysteine + methionine + betaine + serine + glycine + glutamate); GUDCA, glycol-ursodeoxycholic acid; HFHS, high-fat high-sucrose; indole synthesis, [3-Indoleacetic acid (3-IAA) + 3-indolepropionic acid (3-IPA) + indoxyl sulfate (Ind-SO4)]; ketogenic AAs, (leucine + lysine); LCFA, long-chain fatty acid (C16, C18, and C20); LCFA-Cer = Cer(d16:1/14-20:x) + Cer(d18:1/xx:0(OH)); LC-Lyso PC, long-chain lyso-phosphatidylcholine; PC, phosphatidylcholine; PC, phosphatidylcholine; PE, phosphatidylethanolamine; primary BAs, CA + CDCA + GCA + GDCA + TCA + TCDCBA; ratio of conjugated/unconjugated primary BAs, (GCA + GCDCBA + TCA + TCDCA + TLCA)/(CA + CDCA); ratio of conjugated/unconjugated secondary BAs, (GDCA + GLCA + GUDCA + TDCA + TLCA)/DCA; ratio of secondary BAs/total BAs, (DCA + GDCA + GLCA + GUDCA + TDCA + TLCA)/ (CA + CDCA + DCA + GCA + GCDCBA + GDCA + GLCA + GUDCA + TCA + TCDCBA + TDCA + TLCA); ratio of secondary/primary BAs, (DCA + GDCA + GLCA + GUDCA + TDCA + TLCA)/(CA + CDCA + GCA + GCDCBA + TCA + TCDCA); secondary BAs, DCA + GDCA + GLCA + GUDCA + TDCA + TLCA; SM, sphingomyelin; sulfur AAs, (cysteine + cysteine + homocysteine + methionine + taurine); TCA, taurocholic acid; TCDCBA, tauro-chenodeoxycholic acid; TCDA, tauro-deoxycholic acid; TLCA, tauro lithocholic acid; TMAO synthesis, trimethylamine N-oxide synthesis (TMAO/(Betain + C0-CN + choline)); VLFA, very-long-chain fatty acid (C22-C24).
<figure><img src="image_26.png" /></figure>

J

Biomarker indexes between 7 HFHS and 4 HFHS dams

<figure><img src="image_27.png" /></figure>

K

Biomarker indexes between 4 HFHS and CTL dams

<figure><img src="image_28.png" /><figcaption>**Figure 4.** *Continued*</figcaption></figure>

and secondary bile acid patterns observed during gesta-
tion are reversed during lactation. Conversely, main-
tained IGT during lactation (7HFHS dams) is associated
with imbalanced oxidative status. Interestingly, post-
partum resolution of IGT (4HFHS) is concomitant with
an increase in energy-metabolism products and insulin-
sensitivity regulators, along with lower insulin secreta-
gogue or glucogenic amino acids, compared with persis-
tent IGT. Those specific signatures provide an extremely

accurate metabolic description of the GDM postpartum
transition that should be considered in the management
of GDM.

HFHS Diet Restricted to the Gestational Window
Exacerbates the Pregnancy-Related Insulin Resistance
and Hyperleptinemia, as in GDM

Despite a higher caloric intake throughout gestation and
the first week of lactation, HFHS dams did not show any
<table><caption>**Table 3.** Plasma concentration of selected metabolic variables (leptin, TG, NEFA, and CHOL) at the middle (LD12) or at the end (LD18) of lactation period</caption><thead><tr><th></th><th>CTL</th><th>4HFHS</th><th>7HFHS</th></tr></thead><tbody><tr><td>LD12</td><td></td><td></td><td></td></tr><tr><td>TG</td><td>49.8±6.0</td><td>52.5±10.8</td><td>52.7±10.4</td></tr><tr><td>NEFA</td><td>15.3±6.6</td><td>17.9±5.5</td><td>22.5±4.8£</td></tr><tr><td>CHOL</td><td>95.5±9.7</td><td>98.1±12.9</td><td>109.1±11.2£</td></tr><tr><td>Leptin</td><td>4.0±1.2</td><td>4.4±1.6</td><td>6.4±1.7£££##</td></tr><tr><td>LD18</td><td></td><td></td><td></td></tr><tr><td>TG</td><td>48.4±6.2</td><td>53.3±14.7</td><td>55.5±11.8</td></tr><tr><td>NEFA</td><td>17.7±5.8</td><td>15.0±6.1</td><td>19.9±10.6</td></tr><tr><td>CHOL</td><td>106.2±12.3</td><td>102.9±11.3</td><td>107.1±18.3</td></tr></tbody></table>

Values are means ± SD, (CTL: *n* = 10–12, 4HFHS: *n* = 17–19, 7HFHS: *n* = 8–12). Triglycerides (TG), non-esterified fatty acids (NEFA), and cholesterol (CHOL) are expressed as mg/dL. Leptin is expressed as ng/mL. Significant differences: £P < 0.05 and £££P < 0.0001 for 7HFHS vs. CTL and ##P < 0.01 for 7HFHS vs. 4HFHS (Sidak's test). HFHS, high-fat high-sucrose.

significant difference in body weight or weight gain, compared with those on the chow diet. This absence of overweight or obesity may be attributed to the short duration of HFHS exposure—just 1 wk before mating and during gestation, as previously shown (35, 36). As expected, HFHS dams presented fasting hyperglycemia and IGT at mid-gestation (G12), which corresponds to the second trimester of pregnancy in women, when GDM is most commonly diagnosed (between 24 and 28 wk of amenorrhea) (6). The lack of fasting hyperinsulinemia in HFHS pregnant dams, as previously reported in rats (20), aligns with observations that only about half of women with GDM exhibit adequate or enhanced insulin secretion (6, 37). Impaired IS in HFHS dams was supported by the higher insulin AUC and greater insulin response to OGTT during the second phase of the test, together with a lower ISI<sub>Gutt</sub> index. This higher second phase response in insulin secretion has also been reported in studies conducted in GDM women, but not in the euglycemic pregnant women (12).

Despite normal pregnancy weight gain, IGT dams exhibited hyperleptinemia. In humans as well as in rats, the placenta is able to produce and secrete leptin and thought to be a major contributor to the increased circulating maternal leptin concentration occurring during gestation (6, 38). Even though an increased adipose and placental leptin production in response to an HFHS diet cannot be ruled out (39), it remains that hyperleptinemia is suggested to exacerbate pregnancy-related IR and the onset of GDM (6, 12, 37, 39).

## Impaired Insulin Sensitivity in IGT-Pregnant Dams Is Associated with Modulation of Circulating Biomarkers of Gluconeogenesis and Antioxidant Defense

### Hepatic sphingolipid metabolism and fatty acid oxidation seemed to be altered in IGT-pregnant dams.

IGT-pregnant dams showed an overall decrease in circulating concentrations of VLCFA-containing sphingolipids (SPLs) (both Cer and SM forms), particularly SPLs containing nervonic acid (C24:1), combined with an increase in Hexo-Cer species and also VLCFA-containing phosphatidylcholines-breakdown products (Lyso-PCs), compared with CTL pregnant dams.

Circulating ceramides are prognostic biomarkers for IR, dyslipidemia, T2D, and metabolic dysfunction-associated liver disease in overweight or diabetic nonpregnant subjects (40). This property of ceramides is nevertheless dependent on SPL form (Cer, SM, glucosyl-Cer) and acyl chain configuration (length and saturation) (41). For example, lower levels of VLCFA-containing SPLs were reported to be associated with hyperglycemia in the S-PRESTO cohort of unselected pregnant women (42). Notably, the decrease in SPLs containing nervonic acid, highlighted here in IGT dams, was also evidenced in nonobese pregnant mothers with GDM (41), which is associated with impaired IS and enhanced insulin secretion (43, 44). In particular, the nervonic acid-containing sphingomyelin was reported to induce mitochondrial ATP and reactive oxygen species production and insulin secretion (44). The effects of ceramide in regulating insulin signaling have been suggested to be tissue-specific: medium-chain fatty acid-containing Cer is linked to IR in skeletal muscle, whereas VLCFA-containing Cer and/or SM in the liver may have a beneficial effect on IS (45, 46). VLCFA-Cer species in glycosylated form were shown to stimulate insulin secretion in vivo in Cohen diabetes-sensitive rats fed a high-sucrose diet (47). Downregulation of SMs, as observed in the present study, was shown to impair the glucose-stimulated insulin secretion of islets in a pharmacological non-pregnant mice T2D model (13). Taken together, our data suggest that IGT-pregnant dams have an altered sphingolipid metabolism, particularly in the liver, associated with impaired IS. Notice, lower plasma levels of LCFA-containing unsaturated-diacyl and triacyl-glycerides, observed here, are in line with higher susceptibility to T2D in IGT dams, as shorter and saturated fatty acid residues trigger its development, whereas longer and unsaturated ones provide protection (43).

In addition to their numerous functions not always clearly defined, such as the generation of lipid second messengers and the regulation of inflammatory responses, Lyso-PCs have been reported to increase cardiovascular risk by promoting IR and reducing hepatic FAO (48, 49). Indeed, the higher index of PCs-degradation into Lyso-PCs, combined with enhanced hydroxyl-acylcarnitine levels and higher LCFA-acylcarnitine/C0 or C2-CN ratios (reflecting CPT1 and II deficiencies), but lower levels of short-chain acylcarnitine, support an impaired mitochondrial capacity of the liver to completely metabolize the LCFAs in IGT-pregnant dams. Our findings are in line with the accumulation of acylcarnitine associated with IGT in the S-PRESTO cohort (42).

### Alterations in bile acids and other microbiota-related metabolites in IGT-pregnant dams.

Unlike the high circulating BAs reported in healthy pregnancy-induced cholestasis (50, 51), IGT-pregnant dams displayed reduced plasma levels of taurine-conjugated BAs. These components are negatively associated with the glycemic status in GDM women and T2D population (41, 50). These changes are likely due to the impact of hypercaloric diet on the gut microbiota, similar to alterations observed in diabetic subjects (52) and GDM women (41, 53). The decreased abundance of metabolites synthesized by microbiota, such as TMAO and indole, in IGT-pregnant dams supports alteration in their gut microbiota.
Notably, the combination of low BAs and TMAO levels was shown to greatly amplify the risk of promoting GDM associated with the low concentration of VLCFA-Cer, as ceramide 24:0 or ceramide 24:1, likely through altered glucose metabolism (53).

**Upregulation of gluconeogenesis and the kynurenine/tryptophan pathway along with downregulation of the redox defense system in IGT-pregnant dams.**

The lower plasma levels of BAs observed in IGT-pregnant dams compared with CTL dams may trigger enhanced gluconeogenesis and impaired IS in a “BAs-liver farnesoid X receptor-intestinal Takeda G-protein receptor 5-glucagon-like peptide 1”-dependent manner (41, 50, 54). This potential upregulation of gluconeogenesis is supported by enhanced circulating gluconeogenic amino acids, such as proline and lysine, as well as increased plasma levels in valine and tyrosine in IGT and hyperglycemic dams. Our findings are thus consistent with previous reports showing that circulating lysine, tyrosine, and valine are positively associated with HOMA-IR or HOMA-β indexes in GDM cohorts (55). The enhanced valine has been suggested to be predictive of T2D in the Framingham Offspring cohort (15), possibly via disruption of insulin signaling in skeletal muscle (31) or pancreatic cell exhaustion (15, 56). Similarly, low plasma glycine levels, as observed here in IGT-pregnant dams, are considered a marker of IGT and T2D in human cohorts (41). IGT-pregnant dams also exhibited increased kynurenine/tryptophan conversion, an accepted clinical marker of indoleamine 2,3-dioxygenase activity, which is associated with IR (33, 57). The sulfur amino acids depletion, combined with upregulation of PCs-degradation into proinflammatory Lyso-PCs in HFHS dams, is consistent with the activating role of proinflammatory mediators on the tryptophan-kynurenine pathway (33).

In summary, we showed here that IGT during gestation was associated with early predictive biomarkers of IR and/or T2D development that are related to incomplete fatty acid oxidation, enhanced gluconeogenesis, altered insulin signaling, and antioxidant defense system. We then wondered how these biomarkers evolved during the postpartum transition from gestational IGT.

**The Postpartum Resolution (4HFHS) or Persistence (7HFHS) of Gestational IGT Is Associated with Specific Metabolomic and Lipidomic Biosignatures in Lactating GDM Dams**

**Persistent gluconeogenesis and incomplete fatty acid oxidation in 7HFHS dams.**

Lactating 7HFHS dams exhibited persistent IGT and hyperleptinemia, unlike insulin-sensitive CTL and 4HFHS dams. Hyperleptinemia was consistent with the increased leptin levels observed in GDM women between delivery and 6 mo postpartum (12). This finding was combined, at mid-lactation, with persistent high plasma levels of tyrosine that correlate with GDM-associated IR (55), and low levels of hippuric acid, a phenylalanine-tryptophan metabolite associated with impaired IS (33), suggesting persistent impaired IS in 7HFHS dams, as opposed to 4HFHS dams with postpartum resolution of IGT. The combined persistent higher plasma levels of both valine and alanine may reflect a persistent upregulation of gluconeogenesis in 7HFHS lactating dams, but neither hyperglycemia nor hyperinsulinemia

could be observed. The latter may be partly explained by a predominant mammary diversion of glucose due to the interference of leptin resistance with mammary lipid mobilization, which has been reported to be induced by the high-calorie diet maintained during the lactation period in rodent models (58). The higher levels of glucogenic amino acids such as alanine in 7HFHS dams may also implicate the glucose-alanine cycle between skeletal muscle and liver, which was reported to play a direct regulatory role in FAO (43). Notice, the latter seemed upregulated in the context of maintained IGT postpartum, as attested by persistent circulating elevation of LCFA-acylcarnitine in 7HFHS compared with CTL and 4HFHS dams. This acylcarnitine form is representative of FAO in skeletal muscle, which is considered an early stage of diet-induced IR, that is, when IGT is present long before IR is clearly established. Muscle FAO is also consistent with exacerbated lipo-toxicity, indicated by a continuous decrease in plasma levels of precursors of “anti-inflammatory lipids” (PUFA ω 3), an increase in saturated fatty acid-containing di- and triglycerides, and a shift toward higher phosphatidylcholines (PCs) and their degradation products (Lyso-PCs). The latter, combined with a greater accumulation of Hexo-Cer, lower levels of sphingomyelin, and a lower conversion of ceramide into sphingomyelin, is consistent with T2D-onset in 7HFHS dams. Indeed, a positive association between Lyso-PCs and T2D-onset was previously reported in two Chinese cohorts (59, 60), and the decrease of sphingomyelin was strongly associated with the risk of transition from GDM to T2D in the SWIFT cohort (13).

**Improved glucose tolerance postpartum in 4HFHS dams may be associated with complete FAO as well as enhanced taurine-conjugated bile acids and nervonic acid-containing sphingomyelins, which have protective effects on β-cell.**

The global normalization of bile acid patterns in 4HFHS compared with CTL dams, and particularly taurine-conjugated BAs, suggests an upregulation of bile flow, bile acid synthesis, and bile secretion. This promotes the intestinal absorption of lipids to meet the energy requirements of lactation (61) and seems likely unaffected by a short-time hypercaloric diet. The metabolic phenotyping of 4HFHS dams is consistent with the fact that BAs inhibit gluconeogenesis and improve IS. In particular, taurochenodeoxycholic acid (TCDCA), which is normalized in 4HFHS compared with CTL dams but remained decreased in 7HFHS dams, has been shown to stimulate sphingosine-1-phosphate receptor 2, which in turn activates insulin signaling through the PI3K/Akt pathway (62). This was observed along with a specific distribution in 4HFHS dams of two phosphatidylcholine subclasses, that is, increased circulating phosphatidylethanolamines, but lower levels of phosphatidylcholines as compared with 7HFHS, which was associated with improved IS and reduced insulin secretion (43). This is further supported by a higher concentration in 4HFHS versus 7HFHS dams in acetyl-carnitine, a hallmark of preserved glucose tolerance and IS (41). Furthermore, in contrast to 7HFHS dams, 4HFHS dams displayed higher fuel energy utilization with complete FAO and valine catabolism, reflected by higher propionyl-, acetyl-, and butyryl-carnitine, respectively.

The similar circulating LCFA- and VLCFA-ceramide patterns between lactating dams, regardless of their postpartum
glucose tolerance, likely reflect the physiological hepatic de novo synthesis of ceramide required for the transition of energy metabolism from gestation to lactation (40). Interestingly, 4HFHS dams presented specifically enhanced circulating levels of VLCFA-sphingomyelins, including the nervonic acid-containing sphingomyelin, as compared with CTL and 7HFHS groups. These VLCFA-SMs are representative of liver SPLs metabolism, associated with a protective action on insulin response driven by improved glucose tolerance.

Taken together, our findings show that lactating dams that experienced hyperglycemia during gestation but followed by a postpartum resolution (switch to a healthy diet) exhibited normalization of their glucose tolerance, upregulation of emerging hallmarks of insulin sensitivity and β-cell protection, combined with complete fatty acid oxidation. In contrast, the lactating GDM dams with persistent postpartum IGT (maintenance to HFHS diet) displayed early warning signs of the onset of insulin and leptin resistance, in addition to the liver dysfunction already present during gestation.

The metabolomic and lipidomic signatures of this GDM model, achieved through precise measurements of more than 400 predefined metabolites using only 10 µL of plasma, provide deeper insights into the etiopathogenesis of the phenotype associated with the postpartum resolution—or lack thereof—of impaired glucose tolerance. Some of these metabolites may serve as ideal indicators of metabolic pathways implicated in T2D or metabolic dysfunction-associated liver disease. For instance, specific sphingolipid or phospholipid subclasses exhibit distinct effects on insulin metabolism depending on their acyl chain configuration.

In this study, we identified specific metabolic patterns indicative of incomplete fatty acid oxidation, gluconeogenesis, and altered redox balance. These patterns reflect the unique pathophysiological context of GDM and have also been observed in other metabolomic-based studies. Similar imbalances were noted in nonobese pregnant women with GDM or populations with IGT but without insulin resistance (41). Notably, these findings strongly correlate with those previously evidenced in the risk of transition from GDM to T2D in the SWIFT cohort (13).

These holistic approaches not only highlight new bio-markers of GDM and its T2D transition, but also deepen our understanding of the underlying disease mechanisms. This improved understanding has the potential to enhance GDM-diagnostic procedures, as well as inform disease trajectory and prognosis when integrated with clinical data (63). Despite existing recommendations, there is no consensus on a standardized approach for GDM diagnosis nor for monitoring the onset of diabetic complications in women with a history of GDM, who are at high risk of developing postpartum T2D—especially in the presence of obesity. This lack of consensus is rooted in the observation that glucose intolerance often remits immediately after delivery, especially in lean women without any other risk factors. Future projects aim to validate these metabolic fingerprints, targeting sphingomyelins and phospholipids, both during pregnancy and in the 6 wk following delivery in an ongoing human GDM cohort.

However, there are several limitations to our study. The plasma metabolome and lipidome were measured once in mid-gestation and once in mid-lactation, leaving the dynamic change of these biomarkers throughout gestation and lactation incomplete. This is particularly important as recent studies indicate that IS improves in GDM women immediately after delivery but later deteriorates within the first 6 mo postpartum (12). The predictive power of these biomarkers for maternal T2D risk is difficult to establish with single time-point measurements rather than more comprehensive temporal analysis.

To conclude, our findings provide further insight into the perinatal course of GDM, particularly regarding postdelivery maternal metabolic adaptations, which are under-investigated in human GDM cohorts. This study is the first to use a very in-depth holistic approach in a diet-induced GDM rat. Notably, this experimental study highlights novel lipid biomarkers associated with insulin metabolism, beyond classical risk factors and clinical markers. These biomarkers showed differential evolution in dams with a history of GDM, depending on whether or not their IGT was corrected postpartum.

## ETHICAL APPROVALS

The experimental protocol was carried out in accordance with guidance on the operation of the animals (Act 1986) and the current institutional guidelines on animal experimentation in the EU (directive 2010/63/EU) and in France (French Veterinary Department—A44276). All animal procedures were approved by the Animal Ethics Committee of Pays de La Loire (CEEA 592 PDL) and with approval from the French Ministry of Superior Education and Research (MESR) (APAFIS No. 35043-2022011317589759v6).

## DATA AVAILABILITY

All animal experiments samples are listed in an additional file in accordance with ARRIVE guidelines, and available in the link: https://doi.org/10.57745/SMUJEC.

## SUPPLEMENTAL MATERIAL

Supplemental Figs. S1–S5, Supplemental Tables S1, and Supplemental Material: https://doi.org/10.57745/SMUJEC.

## ACKNOWLEDGMENTS

The authors thank Biogenouest (Western France life science and environment core facility network supported by the Conseil Régional des Pays de la Loire) for supporting MELISA platform (UMR1329), NANTES, France. The work of Justine MASSIAS was supported by a grant from Biogenouest and the Conseil Régional des Pays de la Loire. The authors from the MELISA platform acknowledge the French National Infrastructure for Metabolomics and Fluxomics MetaboHUB-ANR-11-INBS-0010 (www.metabohub.fr), and the Corsaire metabolomics core facility (Biogenouest). The doctoral student's salary (Paul Bobin) was supported by INRAE and the Région Pays de La Loire council. The Graphical Abstract has been designed using BioRender (Agreement No.: LM270ADUMZ, https://biorender.com/).

## GRANTS

This work was supported by the French National Research Agency (ANR-22-CE17-0039-GDM-MILK, 2022–2026); the French National
<figure><img src="image_29.png" /></figure>

Research Institute for Agriculture, Food and environment (INRAe- AlimH department, 2021–2024); the Région Pays de la Loire— Trajectory project-Health Risk assessment and biomarkers (Regional National Trajectory—GDM-MILK—2022–2023); and the Nantes Institute of Digestive System Diseases supported by the SantéDige Foundation (https://www.imad-nantes.org/) (SanteDige, 2019).

## DISCLAIMER

The funders had no role in study design, data collection, analy- sis and interpretation of data, writing of the report, or the decision to submit the article for publication.

## DISCLOSURES

No conflicts of interest, financial or otherwise, are declared by the authors.

## AUTHOR CONTRIBUTIONS

P.B., D.M., A.V., N.W., D.D., C.M., G.L.D., and M.-C.A.-G. con- ceived and designed research; P.B., B.C., I.G., A.R., G.L.D., and M.-C.A.-G. performed experiments; P.B., T.M., A.R., and M.-C.A.-G. analyzed data; P.B., G.L.D., and M.-C.A.-G. interpreted results of experiments; P.B. and T.M. prepared figures; P.B. and M.-C.A.-G. drafted manuscript; P.B., D.M., D.D., C.M., G.L.D., and M.-C.A.-G. edited and revised manuscript; and P.B., D.M., B.C., I.G., T.M., A.R., A.V., N.W., D.D., C.M., G.L.D., and M.-C.A.-G. approved final ver- sion of manuscript.

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