Skip to main content
NCBI home page
Preventive Nutrition and Food Science logoLink to Preventive Nutrition and Food Science
. 2026 Feb 28;31(1):pnf.2025.158. doi: 10.3746/pnf.2025.158

Maternal Programming with a Cafeteria Diet Leads to Glucose Intolerance, Increased Insulinemia, Inflammation, and Intestinal Dysbiosis in Rat Offspring

Dayane Correia Gomes 1, Jesús Alejandro Zamora-Briseño 2, Alberto Camacho Morales 3, Marcela Cárdenas Tueme 4, Cynthia Cano Sarmiento 5, Rossana C Zepeda 6, Rubi Viveros Contreras 7,✉
PMCID: PMC12973421  PMID: 41815191

Abstract

The intake of diets high in fat and sugar but low in protein, fiber, and vitamins during pregnancy can contribute to metabolic imbalance, immunological disorders, and intestinal barrier disruption in the offspring. This study investigated the effects of a cafeteria (CAF) diet, high in fat and sugar, consumed throughout pregnancy and lactation, on metabolism, inflammation-related markers, and intestinal microbiota in rat offspring. Maternal programming was conducted for more than 9 weeks in 16 female Wistar rats aged 8－10 weeks. They were divided into two diet groups: control and CAF, each with n=8. On the 21st day after birth, the offspring were weaned, and fecal samples were collected. We conducted a series of glucose and insulin tolerance tests and analyzed serum metabolic markers [triglycerides (TGs), total cholesterol (TC), aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP), and gamma-glutamyl transferase (GGT)], inflammatory cytokines [monocyte chemoattractant protein-1 (MCP-1/CCL2) and interleukin 6 (IL-6)], and hormones (insulin, leptin, and active ghrelin). Gut microbial profiling was performed to assess compositional changes. The CAF offspring exhibited higher levels of serum glucose, insulin, TGs, MCP-1, and IL-6 compared with the control group. Moreover, the CAF diet altered the intestinal bacterial community structure, increasing the abundance of Prevotellaceae and Lachnospiraceae while reducing Lactobacillaceae. Our results suggest that ingesting a CAF diet during maternal programming promotes adverse impacts on the offspring metabolism homeostasis, inflammatory responses, and intestinal microbiota composition.

Keywords: cafeteria diet, gastrointestinal microbiome, inflammation, maternal programming, metabolism disorders

INTRODUCTION

Nutrition during pregnancy and lactation plays a vital role in the development of offspring and may increase their vulnerability to obesity and related disorders (Koletzko et al., 2017; Chaves et al., 2021). In addition to coordinating physiological and structural functions during fetal and neonatal development, maternal nutrition critically influences the growth of organs and organ systems of the progeny (Chen et al., 2020; Chaves et al., 2021). For these reasons, the consumption of a hypercaloric diet during pregnancy and lactation has been associated with excessive fat accumulation in mothers and increased adiposity in the fetus, which affects energy balance regulation and contributes to weight gain in the offspring (Sandler et al., 2017; Wesolowski et al., 2017). Such a diet also triggers inflammatory cytokine activation and induces insulin resistance. In turn, this effect enhances the breakdown of fat tissues and the release of free fatty acids into the maternal blood circulation, which may be vertically transferred (Shrestha et al., 2020; Harmancıoğlu and Kabaran, 2023). Hypercaloric diets during this critical period have further been linked with maternal gut dysbiosis, an imbalance in the microbiota that can impact the host’s physiology (Dominguez-Bello et al., 2010; Murphy et al., 2013). It may lead to an immune imbalance, intestinal barrier disruption, and the translocation of pathogenic bacteria to the uterus, resulting in placental inflammation and complications during pregnancy (Di Gesù et al., 2021; Gorczyca et al., 2022; Sinha et al., 2023).

The maternal diet markedly impacts the health and development of the fetus and offspring (Koletzko et al., 2017; Aguila et al., 2021; Tain and Hsu, 2022; Zeebone et al., 2023). Therefore, examining the effects of maternal diet on the intestinal and metabolic health of offspring is crucial, as it may uncover the underlying mechanisms and the related long-term impacts. It can guide the formulation of nutritional recommendations aimed at improving the well-being of future generations. Such knowledge may support the development of more effective strategies to modulate the intestinal microbial community, thereby enhancing maternal and child health (Selma-Royo et al., 2021; Maher et al., 2023). Based on these considerations, this study aimed to investigate how consuming a cafeteria (CAF) diet during pregnancy and lactation influences metabolic outcomes and gut microbiota composition in the offspring.

MATERIALS AND METHODS

Maternal programming model

Mating and programming tests were conducted with 8 male and 16 virgin female Wistar rats aged 8－10 weeks. Animals were housed in polycarbonate cages at 22°C under a 12/12 h light/dark cycle. Female rats were randomly allocated to two dietary intervention experimental groups of 8 rats each after 6 days of acclimatization: CAF and control (CT). The CT group animals were fed a standard rodent diet with 10% calories from fat and a calorie density of 3.35 kcal/g. This diet consisted of 71% carbohydrates, 11% lipids, and 18% proteins (Cat. D12450B, Research Diets Inc.). The CAF diet included smoked pork bacon, chocolate spread, fries, potatoes, biscuits, and pork liver paté. It consisted of 39% carbohydrates, 12% proteins, 49% lipids, and 513.53 mg sodium, a diet control ratio of 1:1:1:1:1:2, and a calorie density of 3.72 kcal/g (Cardenas-Perez et al., 2018). The CAF diet mimics high-calorie human dietary patterns rich in saturated fats and simple sugars linked to metabolic disorders.

The intervention lasted for 9 weeks: 3 weeks prior to mating, followed by gestation and lactation (Fig. 1). All animals had unlimited access to water. Vaginal delivery recorded as confirmation of mating. Female rats were housed in pairs for 48 h with a male rat, weighing 300－350 g. The detection of a vaginal plug the next day marked gestational day 0.5. The females were then individually housed and continued on their assigned diet.

Fig. 1.

Fig. 1

Open in a new tab

The experimental design. OGTT, oral glucose tolerance; ITT, insulin tolerance test.

After birth, we assessed the similarities between ten male and female offspring from each mother in each group. Male offspring of mothers exposed to the CT or CAF diets were weaned at postnatal day 21. From each group, 22 males were randomly selected for a follow-up and fed the standard rodent diet until 2 months of age.

Body weight (BW)

The BWs of the mothers were monitored throughout pregnancy until birth. The offspring’s weight was recorded every week (eight days) until they were 2 months old, using a digital scale and standardized procedures.

Oral glucose tolerance test (OGTT) and insulin tolerance tests (ITTs)

OGTT was conducted on male offspring at 7 and 8 weeks. The male was kept fasting for 8 h and given a glucose solution orally (2 g/kg of fasting BW). For the ITT, the rats were kept on a fast for 6 h and then intraperitoneally injected with insulin at 0.5 U/kg BW (Pharmalife) at 8 weeks. The glucose levels of the tail vein blood for each animal were analyzed at 0, 15, 30, 60, 90, and 120 min after glucose administration with an Accu-Chek ActiveⓇ glucometer. The glucose response curve plotted was utilized to calculate the trapezoidal area under the curve (AUCglucose).

Analysis of biochemical parameters

The male offspring were euthanized at 9 weeks with 120 mg/kg of sodium pentobarbital. The serum from each animal was collected by centrifuging the blood at 3,000 g for 15 min at 5°C. The in vitro diagnostic clinical kit (SPINREACT) was used to measure the total cholesterol (TC) and triglyceride (TG) levels, as well as for aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP), and gamma-glutamyl transferase (GGT) assays.

Analysis of hormone and inflammation-associated factor contents

The offspring serum samples were tested in triplicate using a Bio-Plex Pro Assays Cytokine and Chemokine multiplex bead assay (Bio-Rad Laboratories) to ascertain the levels of the cytokines monocyte chemoattractant protein-1 (MCP-1/CCL2) and interleukin 6 (IL-6), as well as the hormones insulin, leptin, and active ghrelin.

DNA extraction from feces and 16S rRNA gene amplification

Fecal samples (0.1 g each) were collected in sterile microtubes to analyze the microbiota of the offspring’s intestines. DNA was extracted from the fecal samples using the Quick-DNATM Fungal/Bacterial kit (Zymo Research). The DNA purity was determined using a NanodropTM spectrophotometer (Thermo Fisher Scientific). The quality of the gDNA was verified by 0.7% agarose gel electrophoresis. The samples were kept at −70°C until use.

The V3－V4 segment of the bacterial 16S rRNA gene was polymerase chain reaction amplified using the gDNA samples and the 16S-specific primers 341F: 5’-CCTAYGGGRBGCASCAG-3’ and 806R: 5’-GGACTACNNGGGTATCTAAT-3’. All reactions utilized the PhusionⓇ High-Fidelity PCR Master Mix (New England Biolabs). The PCR products were analyzed on a 2% agarose gel and purified with a Gel Extraction Kit (Qiagen).

Library preparation and sequencing

The sequencing libraries were prepared employing the NEBNext UltraTM DNA Library Preparation Kit for Illumina (New England Biolabs). The amplicons were tagged with index codes, and their quality was assessed using the Agilent Bioanalyzer 2100 system and the Qubit@ 2.0 Fluorometer (Thermo Scientific). The MiSeq Illumina platform was used for sequencing in a 250-bp paired-end format.

Bioinformatics analysis

The sequencing data were processed using the “DADA2” package (Callahan et al., 2016). Reads were filtered, accepting only two errors in both reads and not ambiguous (N) bases. The first 18 bases in the sense and 12 bases in the antisense read, as well as truncated forward reads, 280 bases in length, and reverse reads, 265 bases in length, were also trimmed. The filtered sequences were then denoised (Callahan et al., 2016, 2017), and the residual ones were merged. Chimeric and merged sequences were separated using the “removeBimeraDenovo” algorithm with the “consensus” parameter (Callahan et al., 2017).

Amplicon sequence variants (ASVs) were taxonomically annotated using the Bayesian classification approach (Wang et al., 2007) employing the SILVA database version 138 (Quast et al., 2013). The ASVs, metadata, reference sequences, and taxonomic annotations were integrated using the “Phyloseq” package (McMurdie and Holmes, 2013). This phyloseq object was transformed to an MPSE one within the “Microbiota Process” package (Xu et al., 2023) and the rarefaction curves were estimated with the “mp_cal_rarecurve” function. The phyloseq object was also converted into an R6 one using the “file2meco” package (Liu et al., 2022) to feed the “microeco” package (Liu et al., 2021). We then estimated and compared the Shannon diversity index by applying a Wilcoxon test with a significance threshold of 0.05. We employed the random forest function of the “microeco” package (Segata et al., 2011; Liu et al., 2021) to identify differentially accumulated taxa at the genus level, based on linear discriminant analysis and P-value cut-offs of 1 and 0.05, respectively. The 15 most statistically distinct taxa, identified by the mean decrease in the GINI score, were plotted and customized using the “ggplot2” function, with all analyses conducted in R (R Core Team, 2025).

Ethical approval

This study handled and cared for the animals in accordance with the NOM-ZOO-1999 standards for the treatment and use of laboratory animals. The Institutional Committee for the Care and Use of Laboratory Animals (CICUAL) of the Health Sciences Unit, Universidad Veracruzana, approved the experiments (No. CICUAL-UCS-07).

Statistical analysis

Descriptive statistics were performed via the Shapiro-Wilk test to assess normality and the Fligner and Levene tests for the homogeneity of variances. Accordingly, the Student’s t-test with Welch correction (weight, hepatic, lipid, inflammatory profiles, and hormones) and the Two-way analysis of variance (OGTT and ITT) were applied, with a P-value cutoff of <0.05. All analyses were performed in RStudio software (R Core Team, 2025) and plotted using ggplot2.

RESULTS

Effects of a maternal programming diet on food intake and BW

At the beginning of the study, the female rats exhibited an average BW of ∼240 g. They were maintained under consistent breeding conditions throughout the maternal programming phase. At the end of the experiment, mothers on a CAF diet experienced a statistically significant weight gain compared to those in the control group (P<0.01) (Table 1). They also exhibited a substantial increase in food intake, averaging 23.3 g/d/animal, compared to the control group (P<0.01). The offspring of the former reduced their food intake by ∼17 g/d/animal; a difference which was statistically insignificant (P>0.05; Table 1). The birth and final weights of the offspring of the CAF and control groups did not vary with statistical significance (Table 1).

Table 1.

Somatometry and biochemical parameters of the male offspring of the study groups

Parameter Control CAF P-value
Mothers
Final body weight (g) 280.3±27.0 325.6±32.7 0.01
Food intake (g/d) 18.8±6.0 23.3±5.3** 0.01
Offspring
Food intake (g/d) 19.0±1.9 17.0±1.8 0.30
Total food intake (kcal) 62.7±19.8 80.2±22.1** 0.01
Weight at birth 7.1±0.5 6.7±0.2 0.08
Final body weight (g) 202.0±7.7 193.0±9.9 0.21
Serum (mg/dL)
TC 35.6±6.0 35.2±13.2 0.92
TG 34.8±14.0 91.2±30.9** 0.01
AST 37.4±6.1 36.4±6.6 0.75
ALT 36.6±3.7 38.3±1.9 0.31
ALP 18.3±4.5 22.4±11.0 0.35
GGT 12.5±1.2 12.2±1.6 0.65
Open in a new tab

Values are presented as mean±standard deviation.

Student’s t-test with correction by employing Welch two-sample, considering P<0.05.

Values were statistically significant at **P<0.01.

CAF, cafeteria; TC, total cholesterol; TG, triglycerides; AST, aspartate aminotransferase; ALT, alanine aminotransferase; ALP, alkaline phosphatase, GGT, gamma-glutamyl transferase.

A CAF diet maternal programming impacts glucose tolerance in the offspring

To evaluate the impact of CAF diet maternal programming on the progeny, we performed OGTT/ITT in the offspring at 7 and 8 weeks of age, respectively. Baseline glucose concentrations exhibited notable intergroup disparities (Fig. 2A and 2C). The OGTT revealed that the CAF diet group offspring exhibited marked differences in their ability to process glucose (Fig. 2B and 2E) compared to the control group (P<0.001), indicating impaired glucose tolerance, unlike the ITT analysis (Fig. 2D and 2F).

Fig. 2.

Fig. 2

Open in a new tab

Oral glucose tolerance (OGTT) and insulin tolerance test (ITT). (A) Basal glucose levels in the offspring of control and cafeteria (CAF) groups showed statistically significant differences (***P<0.001, Student’s t-test). (B) OGTT curve following glucose administration. (C) Glucose levels were measured before insulin administration, with statistically significant differences observed (***P<0.001, Student’s t-test). (D) ITT curve following insulin administration. (E) Area under the curve (AUC) of the OGTT. (F) AUC of the ITT. Offspring of the CAF group rats exhibited a marked increase in the glucose tolerance AUC. Two-way analysis of variance considering P<0.05. The data are presented as the mean±standard deviation. Values were statistically significant at ***P<0.001.

A CAF diet during maternal programming elevates TG levels in the offspring

The CAF group progeny demonstrated significantly enhanced serum TG levels compared to the control group (P<0.01; Table 1), unlike those of TC, AST, ALT, ALP, and GGT.

A CAF diet maternal programming altered the inflammatory profile of the offspring

We analyzed the serum concentrations of the inflammatory markers (MCP-1 and IL-6) in the male offspring (Fig. 3). The CAF group offspring demonstrated markedly elevated levels of MCP-1 when compared to the control group (P<0.001). The serum insulin concentrations were significantly elevated in the CAF group progeny (P<0.001; Fig. 4A), and no statistically significant variations were found in the ghrelin and leptin levels (Fig. 4B and 4C, respectively).

Fig. 3.

Fig. 3

Open in a new tab

Effects of maternal diet on the inflammatory marker levels in the offspring of both control and cafeteria (CAF) groups. (A) Serum interleukin 6 (IL-6) levels, and (B) serum monocyte chemoattractant protein-1 (MCP-1) levels in the offspring of the two experimental groups. Statistical significance of the differences was ascertained using Student’s t-test (***P<0.001). The data are presented as the mean±standard deviation.

Fig. 4.

Fig. 4

Open in a new tab

Effect of cafeteria (CAF) diet consumption during maternal programming on the hormone levels of the offspring. (A) Insulin. (B) Ghrelin. (C) Leptin. Offspring of the CAF group animals exhibited a statistically significant enhancement in insulin levels (**P<0.01).

A CAF diet during maternal programming changes the intestinal microbiota profile of the offspring

We conducted 16S rRNA gene-based metataxonomic analyses to evaluate the influence of a CAF diet during pregnancy and lactation on the composition of the gut bacterial communities in the male offspring. At the phylum level, the control group offspring exhibited a higher prevalence of Firmicutes (Bacillota) and a notable reduction in Bacteroidota (Bacteroidetes); however, an opposite trend was observed in the CAF group offspring (Fig. 5A). Fig. 5B indicates that the male offspring of the control group exhibited a significant difference in relative abundance concerning the phylum Firmicutes. Lactobacillaceae members were significantly reduced in the CAF group offspring. In contrast, those of Prevotellaceae, followed by Lachnospiraceae, Muribaculaceae, Erysipelotrichaceae, and Peptostreptococcaceae, showed an increased prevalence (Fig. 5C).

Fig. 5.

Fig. 5

Open in a new tab

A profile of the intestinal microbiota of the male offspring. (A) Relative abundance of the top five dominant phyla in both experimental groups. (B) Statistical comparisons of the relative abundance of Firmicutes between the groups. (C) Hierarchical clustering based on the relative abundance of the ten most dominant families indicates that the samples were clustered per the respective experimental groups. (D) Comparison of the α-diversity (Shannon index) of the bacterial communities of the offspring from mother rats on cafeteria (CAF) and control diets. Values were statistically significant at *P<0.05.

The α-diversity of the intestinal microbiota was elevated in the offspring of mothers on a CAF diet (Fig. 5D). Fig. 6 shows the class-level composition and abundance of the intestinal bacterial community of the offspring from both groups. Bacilli, Bacteroidia, and Clostridia were the predominant taxa identified in both. In the control group offspring, Bacilli were the dominant class, followed by Bacteroides and Clostridia. Nevertheless, the offspring of mothers on a CAF diet exhibited a notable prevalence of the classes Bacteroidia, Bacilli, and Clostridia.

Fig. 6.

Fig. 6

Open in a new tab

Analysis of the relative abundances (%) of the gut microbiota of the offspring of both groups. CAF, cafeteria.

A differential analysis indicated that the CAF group offspring had a lower abundance of the genera Lactobacillus, Candidatus, Stoquefichus, and Anaerovorax (Fig. 7), which belong to Bacillota (Firmicutes). In contrast, the control group offspring were enriched in Rothia (Actinomicetota), Colidextribacter (Bacillota), the Christensenellaceae R-7 group (Bacillota), and Oscillibacter (Bacillota).

Fig. 7.

Fig. 7

Open in a new tab

Comparison of the intestinal bacterial communities of the experimental groups. We applied a random forest method to identify the differentially accumulated taxa at the genus level. Such taxa with a linear discriminant analysis and a P-value cut-off of 1 and 0.05, respectively, were considered statistically different. The 15 most statistically distinct taxa are shown. CAF, cafeteria.

DISCUSSION

The influence of maternal nutrition on the lifelong health of the progeny has generated interest (Raspini et al., 2020; Peng et al., 2024). Such impacts, before and after birth, have received considerable attention (Koletzko et al., 2017), particularly concerning the relationship between hypercaloric diet consumption and the composition of the intestinal microbiota, as well as the emergence of metabolic and immunological disorders (Griffiths et al., 2016; Koos and Gornbein, 2021). A hypercaloric diet during pregnancy and lactation exacerbates adipocyte hypertrophy, leading to adipose tissue dysfunction. This effect contributes to the development of insulin resistance, glucose intolerance, and adverse lipid profiles in the offspring (Ahmed et al., 2021; Arellano-García et al., 2023).

Such a diet during gestation enhances fat deposition within the maternal and fetal adipose tissues (Yamaguchi et al., 2023), which contributes to elevated TG, glucose, insulin, leptin, body fat, and BW in the offspring (Tellechea et al., 2017; Easton and Regnault, 2020). Specific diets during pregnancy can alter glucose metabolism and negatively affect pancreatic β-cell mass in the progeny, which in turn, may result in the structural remodeling of pancreatic islets, including α-cell proliferation in the islet center. Consequently, an increase in α and β cell mass and islet size ultimately leads to a decline in β cell function. Our investigation revealed that the CAF group offspring exhibited remarkably higher serum glucose concentrations, impaired insulin-mediated glucose metabolism, and elevated TG levels compared with the control group. These findings suggest that the progeny of mother rats on a hypercaloric diet exhibit metabolic dysregulation, even after they switch to a standard healthy diet after weaning, and that these changes occur within 2 months of birth. They contradict other studies, which suggest that metabolic disorders develop only in adulthood because of maternal dietary habits (Cardenas-Perez et al., 2018; Zheng et al., 2020; Barbosa et al., 2020; Tain and Hsu, 2022; Vipin et al., 2022; Harmancıoğlu and Kabaran, 2023).

Likewise, our results indicate that the CAF group offspring exhibited elevated levels of the inflammatory markers IL-6 and MCP-1 than the control group, suggesting heightened inflammation. Consuming hypercaloric diets during pregnancy increases maternal fat reserves, which can lead to inflammation of the maternal peripheral tissues and insulin resistance. This effect promotes lipolysis, plasma free fatty acids, and activates pro-inflammatory cytokines (Harmancıoğlu and Kabaran, 2023). Offspring born to mothers on hypercaloric diets during gestation and lactation demonstrate heightened levels of pro-inflammatory cytokines, including MCP-1 and IL-6. Such an inflammatory response is further characterized by an upregulation of the genes associated with inflammation-related pathways and lipid metabolism (Saengnipanthkul et al., 2021; Zheng et al., 2023).

The CAF group offspring also exhibited altered intestinal microbiota, which play a critical role in regulating glucose, insulin, and TG levels (Portela-Cidade et al., 2015; Crudele et al., 2023). Additionally, they influence energy profiles, nutrient availability, as well as metabolic and immunological modulation, while their imbalance contributes to metabolic disorders associated with obesity and diabetes (Portela-Cidade et al., 2015; Cunningham et al., 2021). Notably, the CAF group progeny had a greater abundance of Bacteroidota, accompanied by a decline in Bacillota (Firmicutes). Firmicutes synthesize butyrate, which stimulates insulin sensitivity, has anti-inflammatory properties, and regulates energy-associated metabolism (Mollica et al., 2017), whereas Bacteroidetes produce propionate and acetate. Propionate suppresses appetite by increasing the glucagon-like peptide-1 and YY-like peptide levels in the L-enteroendocrine cells and suppresses hepatic lipogenesis (Demigné et al., 1995; Magne et al., 2020). Acetate, when absorbed, reaches systemic circulation and peripheral organs, stimulating lipogenesis and potentially causing dyslipidemia (Perry et al., 2016).

Gut dysbiosis alters the intestinal barrier integrity, elevating permeability. This change facilitates the translocation of bacterial antigens, such as lipopolysaccharides, from the gut lumen into systemic circulation, thereby inducing a host inflammatory response (Politi et al., 2023). A comparison of the intestinal microbiota of the offspring from the study groups revealed that a hypercaloric maternal diet during programming increases Bacteroidetes (Prevotellaceae and Muribaculaceae) and Clostridia (Firmicutes), but decreases Lactobacillaceae. Enhanced colonization by Prevotellaceae members (i.e., Prevotella) reduces short-chain fatty acid (e.g., acetate) and IL-18 levels, exacerbating inflammation and potentially contributing to the development of systemic autoimmune disease (Iljazovic et al., 2021). The increased prevalence of Clostridia has been associated with inflammatory bowel disease and antibiotic-associated diarrhea. Nonetheless, additional research is warranted to elucidate the mechanisms that associate Clostridia with intestinal microbiota dysbiosis (Alhobayb and Ciorba, 2023).

In conclusion, we found that consuming a CAF diet during maternal programming adversely influenced metabolism, inflammatory marker levels, and the assembly of healthy gut microbiota in male offspring. Our findings suggest that promoting optimal nutrition during pregnancy and breastfeeding is crucial to prevent potential adverse effects on the metabolic health and intestinal microbiota profile of offspring.

Limitations of the study

Our study provides substantial evidence that maternal consumption of a CAF diet during critical developmental periods is associated with alterations in metabolism, inflammation, and the gut microbiota composition in the offspring. We recognize that our study is limited to comparing only two groups and does not necessarily cover the full range of dietary diversity in the general population. Furthermore, restricting the investigations to only a single sex (males) may not fully explain the biological response in females, which needs further investigation. Despite these constraints, our findings provide clues for future, more complex experimental designs.

ACKNOWLEDGEMENTS

The authors gratefully acknowledge the Secretariat for Science, Humanities, Technology, and Innovation (SECIHTI) for awarding a grant to DCG with the registration number (CVU) 1075184.

Footnotes

FUNDING

None.

AUTHOR DISCLOSURE STATEMENT

The authors declare no conflict of interest.

AUTHOR CONTRIBUTIONS

Concept and design: RVC. Data collection: DCG, RVC, MCT. Analysis and interpretation: DCG, ACM, RVC, JAZB. Statistical analysis: DCG, JAZB. Writing the article: DCG. Critical revision of the article: RVC, JAZB, CCS, RCZ. Overall responsibility: RVC. Final approval of the article: All authors.

References

  1. Aguila MB, Ornellas F, Mandarim-de-Lacerda CA. Nutritional research and fetal programming: Parental nutrition influences the structure and function of the organs. Int J Morphol. 2021. 39:327-334. 10.4067/S0717-95022021000100327 [DOI]
  2. Ahmed B, Sultana R, Greene MW. Adipose tissue and insulin resistance in obese. Biomed Pharmacother. 2021. 137:111315. https://doi.org/10.1016/j.biopha.2021.111315 10.1016/j.biopha.2021.111315 [DOI] [PubMed]
  3. Alhobayb T, Ciorba MA. Clostridium difficile in inflammatory bowel disease. Curr Opin Gastroenterol. 2023. 39:257-262. https://doi.org/10.1097/mog.0000000000000949 10.1097/MOG.0000000000000949 [DOI] [PMC free article] [PubMed]
  4. Arellano-García L, Trepiana J, Martínez JA, Portillo MP, Milton-Laskibar I. Beneficial effects of viable and heat-inactivated Lactobacillus rhamnosus GG administration on oxidative stress and inflammation in diet-induced NAFLD in rats. Antioxidants. 2023. 12:717. https://doi.org/10.3390/antiox12030717 10.3390/antiox12030717 [DOI] [PMC free article] [PubMed]
  5. Barbosa CM, Figueiredo VP, Barbosa MA, Cardoso LM, Alzamora AC. Maternal high-fat diet triggers metabolic syndrome disorders that are transferred to first and second offspring generations. Br J Nutr. 2020. 123:59-71. https://doi.org/10.1017/s0007114519002708 10.1017/S0007114519002708 [DOI] [PubMed]
  6. Callahan BJ, McMurdie PJ, Holmes SP. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 2017. 11:2639-2643. https://doi.org/10.1038/ismej.2017.119 10.1038/ismej.2017.119 [DOI] [PMC free article] [PubMed]
  7. Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJ, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016. 13:581-583. https://doi.org/10.1038/nmeth.3869 10.1038/nmeth.3869 [DOI] [PMC free article] [PubMed]
  8. Cardenas-Perez RE, Fuentes-Mera L, de la Garza AL, Torre-Villalvazo I, Reyes-Castro LA, Rodriguez-Rocha H, et al. Maternal overnutrition by hypercaloric diets programs hypothalamic mitochondrial fusion and metabolic dysfunction in rat male offspring. Nutr Metab. 2018. 15:38. https://doi.org/10.1186/s12986-018-0279-6 10.1186/s12986-018-0279-6 [DOI] [PMC free article] [PubMed]
  9. Chaves WF, Pinheiro IL, da Silva JM, Manhães-de-Castro R, da Silva Aragão R. Repercussions of maternal exposure to high-fat diet on offspring feeding behavior and body composition: a systematic review. J Dev Orig Health Dis. 2021. 12:220-228. https://doi.org/10.1017/s2040174420000318 10.1017/S2040174420000318 [DOI] [PubMed]
  10. Chen J, Zhao X, Cui L, He G, Wang X, Wang F, et al. Genetic regulatory subnetworks and key regulating genes in rat hippocampus perturbed by prenatal malnutrition: implications for major brain disorders. Aging. 2020. 12:8434-8458. https://doi.org/10.18632/aging.103150 10.18632/aging.103150 [DOI] [PMC free article] [PubMed]
  11. Crudele L, Gadaleta RM, Cariello M, Moschetta A. Gut microbiota in the pathogenesis and therapeutic approaches of diabetes. EBioMedicine. 2023. 97:104821. https://doi.org/10.1016/j.ebiom.2023.104821 10.1016/j.ebiom.2023.104821 [DOI] [PMC free article] [PubMed]
  12. Cunningham AL, Stephens JW, Harris DA. Intestinal microbiota and their metabolic contribution to type 2 diabetes and obesity. J Diabetes Metab Disord. 2021. 20:1855-1870. https://doi.org/10.1007/s40200-021-00858-4 10.1007/s40200-021-00858-4 [DOI] [PMC free article] [PubMed]
  13. Demigné C, Morand C, Levrat MA, Besson C, Moundras C, Rémésy C. Effect of propionate on fatty acid and cholesterol synthesis and on acetate metabolism in isolated rat hepatocytes. Br J Nutr. 1995. 74:209-219. https://doi.org/10.1079/bjn19950124 10.1079/BJN19950124 [DOI] [PubMed]
  14. Di Gesù CM, Matz LM, Buffington SA. Diet-induced dysbiosis of the maternal gut microbiome in early life programming of neurodevelopmental disorders. Neurosci Res. 2021. 168:3-19. https://doi.org/10.1016/j.neures.2021.05.003 10.1016/j.neures.2021.05.003 [DOI] [PMC free article] [PubMed]
  15. Dominguez-Bello MG, Costello EK, Contreras M, Magris M, Hidalgo G, Fierer N, et al. Delivery mode shapes the acquisition and structure of the initial microbiota across multiple body habitats in newborns. Proc Natl Acad Sci U S A. 2010. 107:11971-11975. https://doi.org/10.1073/pnas.1002601107 10.1073/pnas.1002601107 [DOI] [PMC free article] [PubMed]
  16. Easton ZJW, Regnault TRH. The impact of maternal body composition and dietary fat consumption upon placental lipid processing and offspring metabolic health. Nutrients. 2020. 12:3031. https://doi.org/10.3390/nu12103031 10.3390/nu12103031 [DOI] [PMC free article] [PubMed]
  17. Gorczyca K, Obuchowska A, Kimber-Trojnar Ż, Wierzchowska-Opoka M, Leszczyńska-Gorzelak B. Changes in the gut microbiome and pathologies in pregnancy. Int J Environ Res Public Health. 2022. 19:9961. https://doi.org/10.3390/ijerph19169961 10.3390/ijerph19169961 [DOI] [PMC free article] [PubMed]
  18. Griffiths PS, Walton C, Samsell L, Perez MK, Piedimonte G. Maternal high-fat hypercaloric diet during pregnancy results in persistent metabolic and respiratory abnormalities in offspring. Pediatr Res. 2016. 79:278-286. https://doi.org/10.1038/pr.2015.226 10.1038/pr.2015.226 [DOI] [PMC free article] [PubMed]
  19. Harmancıoğlu B, Kabaran S. Maternal high fat diets: impacts on offspring obesity and epigenetic hypothalamic programming. Front Genet. 2023. 14:1158089. https://doi.org/10.3389/fgene.2023.1158089 10.3389/fgene.2023.1158089 [DOI] [PMC free article] [PubMed]
  20. Iljazovic A, Roy U, Gálvez EJC, Lesker TR, Zhao B, Gronow A, et al. Perturbation of the gut microbiome by Prevotella spp. enhances host susceptibility to mucosal inflammation. Mucosal Immunol. 2021. 14:113-124. https://doi.org/10.1038/s41385-020-0296-4 10.1038/s41385-020-0296-4 [DOI] [PMC free article] [PubMed]
  21. Koletzko B, Brands B, Grote V, Kirchberg FF, Prell C, Rzehak P, et al. Long-term health impact of early nutrition: The power of programming. Ann Nutr Metab. 2017. 70:161-169. https://doi.org/10.1159/000477781 10.1159/000477781 [DOI] [PubMed]
  22. Koos BJ, Gornbein JA. Early pregnancy metabolites predict gestational diabetes mellitus: implications for fetal programming. Am J Obstet Gynecol. 2021. 224:215.e1-215.e7. https://doi.org/10.1016/j.ajog.2020.07.050 10.1016/j.ajog.2020.07.050 [DOI] [PubMed]
  23. Liu C, Cui Y, Li X, Yao M. microeco: an R package for data mining in microbial community ecology. FEMS Microbiol Ecol. 2021. 97:fiaa255. https://doi.org/10.1093/femsec/fiaa255 10.1093/femsec/fiaa255 [DOI] [PubMed]
  24. Liu C, Li X, Mansoldo FRP, An J, Kou Y, Zhang X, et al. Microbial habitat specificity largely affects microbial co-occurrence patterns and functional profiles in wetland soils. Geoderma. 2022. 418:115866. https://doi.org/10.1016/j.geoderma.2022.115866 10.1016/j.geoderma.2022.115866 [DOI]
  25. Magne F, Gotteland M, Gauthier L, Zazueta A, Pesoa S, Navarrete P, et al. The Firmicutes/Bacteroidetes ratio: A relevant marker of gut dysbiosis in obese patients? Nutrients. 2020. 12:1474. https://doi.org/10.3390/nu12051474 10.3390/nu12051474 [DOI] [PMC free article] [PubMed]
  26. Maher SE, O'Brien EC, Moore RL, Byrne DF, Geraghty AA, Saldova R, et al. The association between the maternal diet and the maternal and infant gut microbiome: a systematic review. Br J Nutr. 2023. 129:1491-1499. https://doi.org/10.1017/s0007114520000847 10.1017/S0007114520000847 [DOI] [PubMed]
  27. McMurdie PJ, Holmes S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One. 2013. 8:e61217. https://doi.org/10.1371/journal.pone.0061217 10.1371/journal.pone.0061217 [DOI] [PMC free article] [PubMed]
  28. Mollica MP, Mattace Raso G, Cavaliere G, Trinchese G, De Filippo C, Aceto S, et al. Butyrate regulates liver mitochondrial function, efficiency, and dynamics in insulin-resistant obese mice. Diabetes. 2017. 66:1405-1418. https://doi.org/10.2337/db16-0924 10.2337/db16-0924 [DOI] [PubMed]
  29. Murphy EF, Cotter PD, Hogan A, O'Sullivan O, Joyce A, Fouhy F, et al. Divergent metabolic outcomes arising from targeted manipulation of the gut microbiota in diet-induced obesity. Gut. 2013. 62:220-226. https://doi.org/10.1136/gutjnl-2011-300705 10.1136/gutjnl-2011-300705 [DOI] [PubMed]
  30. Peng LH, Tan Y, Bajinka O. The influence of maternal diet on offspring's gut microbiota in early life. Arch Gynecol Obstet. 2024. 309:1183-1190. https://doi.org/10.1007/s00404-023-07305-0 10.1007/s00404-023-07305-0 [DOI] [PubMed]
  31. Perry RJ, Peng L, Barry NA, Cline GW, Zhang D, Cardone RL, et al. Acetate mediates a microbiome-brain-β-cell axis to promote metabolic syndrome. Nature. 2016. 534:213-217. https://doi.org/10.1038/nature18309 10.1038/nature18309 [DOI] [PMC free article] [PubMed]
  32. Politi C, Mobrici M, Parlongo RM, Spoto B, Tripepi G, Pizzini P, et al. Role of gut microbiota in overweight susceptibility in an adult population in Italy. Nutrients. 2023. 15:2834. https://doi.org/10.3390/nu15132834 10.3390/nu15132834 [DOI] [PMC free article] [PubMed]
  33. Portela-Cidade JP, Borges-Canha M, Leite-Moreira AF, Pimentel-Nunes P. Systematic review of the relation between intestinal microbiota and toll-like receptors in the metabolic syndrome: what do we know so far? GE Port J Gastroenterol. 2015. 22:240-258. https://doi.org/10.1016/j.jpge.2015.06.001 10.1016/j.jpge.2015.06.001 [DOI] [PMC free article] [PubMed]
  34. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013. 41:D590-D596. https://doi.org/10.1093/nar/gks1219 10.1093/nar/gks1219 [DOI] [PMC free article] [PubMed]
  35. R Core Team. R: A language and environment for statistical computing. 2025 [cited 2025 Mar 13]. Available from: https://cran.r-project.org/doc/manuals/r-release/fullrefman.pdf
  36. Raspini B, Porri D, De Giuseppe R, Chieppa M, Liso M, Cerbo RM, et al. Prenatal and postnatal determinants in shaping offspring's microbiome in the first 1000 days: study protocol and preliminary results at one month of life. Ital J Pediatr. 2020. 46:45. https://doi.org/10.1186/s13052-020-0794-8 10.1186/s13052-020-0794-8 [DOI] [PMC free article] [PubMed]
  37. Saengnipanthkul S, Noh HL, Friedline RH, Suk S, Choi S, Acosta NK, et al. Maternal exposure to high-fat diet during pregnancy and lactation predisposes normal weight offspring mice to develop hepatic inflammation and insulin resistance. Physiol Rep. 2021. 9:e14811. https://doi.org/10.14814/phy2.14811 10.14814/phy2.14811 [DOI] [PMC free article] [PubMed]
  38. Sandler V, Reisetter AC, Bain JR, Muehlbauer MJ, Nodzenski M, Stevens RD, et al. Associations of maternal BMI and insulin resistance with the maternal metabolome and newborn outcomes. Diabetologia. 2017. 60:518-530. https://doi.org/10.1007/s00125-016-4182-2 10.1007/s00125-016-4182-2 [DOI] [PMC free article] [PubMed]
  39. Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, et al. Metagenomic biomarker discovery and explanation. Genome Biol. 2011. 12:R60. https://doi.org/10.1186/gb-2011-12-6-r60 10.1186/gb-2011-12-6-r60 [DOI] [PMC free article] [PubMed]
  40. Selma-Royo M, García-Mantrana I, Calatayud M, Parra-Llorca A, Martínez-Costa C, Collado MC. Maternal diet during pregnancy and intestinal markers are associated with early gut microbiota. Eur J Nutr. 2021. 60:1429-1442. https://doi.org/10.1007/s00394-020-02337-7 10.1007/s00394-020-02337-7 [DOI] [PubMed]
  41. Shrestha N, Sleep SL, Cuffe JSM, Holland OJ, Perkins AV, Yau SY, et al. Role of omega-6 and omega-3 fatty acids in fetal programming. Clin Exp Pharmacol Physiol. 2020. 47:907-915. https://doi.org/10.1111/1440-1681.13244 10.1111/1440-1681.13244 [DOI] [PubMed]
  42. Sinha T, Brushett S, Prins J, Zhernakova A. The maternal gut microbiome during pregnancy and its role in maternal and infant health. Curr Opin Microbiol. 2023. 74:102309. https://doi.org/10.1016/j.mib.2023.102309 10.1016/j.mib.2023.102309 [DOI] [PubMed]
  43. Tain YL, Hsu CN. Maternal high-fat diet and offspring hypertension. Int J Mol Sci. 2022. 23:8179. https://doi.org/10.3390/ijms23158179 10.3390/ijms23158179 [DOI] [PMC free article] [PubMed]
  44. Tellechea ML, Mensegue MF, Pirola CJ. The association between high fat diet around gestation and metabolic syndrome-related phenotypes in rats: A systematic review and meta-analysis. Sci Rep. 2017. 7:5086. https://doi.org/10.1038/s41598-017-05344-7 10.1038/s41598-017-05344-7 [DOI] [PMC free article] [PubMed]
  45. Vipin VA, Blesson CS, Yallampalli C. Maternal low protein diet and fetal programming of lean type 2 diabetes. World J Diabetes. 2022. 13:185-202. https://doi.org/10.4239/wjd.v13.i3.185 10.4239/wjd.v13.i3.185 [DOI] [PMC free article] [PubMed]
  46. Wang Q, Garrity GM, Tiedje JM, Cole JR. Naïve Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol. 2007. 73:5261-5267. https://doi.org/10.1128/aem.00062-07 10.1128/AEM.00062-07 [DOI] [PMC free article] [PubMed]
  47. Wesolowski SR, El Kasmi KC, Jonscher KR, Friedman JE. Developmental origins of NAFLD: a womb with a clue. Nat Rev Gastroenterol Hepatol. 2017. 14:81-96. https://doi.org/10.1038/nrgastro.2016.160 10.1038/nrgastro.2016.160 [DOI] [PMC free article] [PubMed]
  48. Xu S, Zhan L, Tang W, Wang Q, Dai Z, Zhou L, et al. MicrobiotaProcess: A comprehensive R package for deep mining microbiome. Innovation. 2023. 4:100388. https://doi.org/10.1016/j.xinn.2023.100388 10.1016/j.xinn.2023.100388 [DOI] [PMC free article] [PubMed]
  49. Yamaguchi M, Mori J, Nishida N, Miyagaki S, Kawabe Y, Ota T, et al. High-fat diet during pregnancy lowers fetal weight and has a long-lasting adverse effect on brown adipose tissue in the offspring. J Dev Orig Health Dis. 2023. 14:261-271. https://doi.org/10.1017/s2040174422000551 10.1017/S2040174422000551 [DOI] [PubMed]
  50. Zeebone YY, Bóta B, Halas V, Libisch B, Olasz F, Papp P, et al. Gut-faecal microbial and health-marker response to dietary fumonisins in weaned pigs. Toxins. 2023. 15:328. https://doi.org/10.3390/toxins15050328 10.3390/toxins15050328 [DOI] [PMC free article] [PubMed]
  51. Zheng J, Zhang L, Wang Z, Zhang J. Maternal high-fat diet regulates glucose metabolism and pancreatic β cell phenotype in mouse offspring at weaning. PeerJ. 2020. 8:e9407. https://doi.org/10.7717/peerj.9407 10.7717/peerj.9407 [DOI] [PMC free article] [PubMed]
  52. Zheng S, Yin J, Yue H, Li L. Maternal high-fat diet increases the susceptibility of offspring to colorectal cancer via the activation of intestinal inflammation. Front Nutr. 2023. 10:1191206. https://doi.org/10.3389/fnut.2023.1191206 10.3389/fnut.2023.1191206 [DOI] [PMC free article] [PubMed]

Articles from Preventive Nutrition and Food Science are provided here courtesy of Korean Society of Food Science and Nutrition (KFN)

RESOURCES