Obesity-like metabolic signatures in females: from birth to weaning in a mammalian model

Obesity-like metabolic signatures in females: from birth to weaning in a mammalian model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Obesity-like metabolic signatures in females: from birth to weaning in a mammalian model Jorge Mateo-López, Mette Skou Hedemann, César Cortés-García, Lola Llobat, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7381399/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Identifying early metabolic signatures and biomarkers is pivotal to understanding the molecular drivers of obesity. This study investigates how genetic variation influences obesity-like metabolic profiles using a mammalian model. Female rabbits from a genetic line selected for growth rate, obtained at two different generations and corresponding to low (LGR) and high (HGR) growth genotypes (GG), were evaluated at first parturition and weaning. Both generations originated from frozen embryos thawed and transferred simultaneously to generate contemporaneous animals. Across 88 animals, we assessed feed intake (n=88) and nutrient digestibility (n=24) and performed untargeted plasma metabolomic profiling (n=48) to characterise systemic metabolic alterations. While no differences were observed in feed intake or digestibility, HGR animals showed significantly elevated plasma levels of Lysophosphatidylethanolamine (0:0/20:4) (LysoPE) at parturition (+131%; P < 0.0001) and weaning (+47%; P < 0.0001) compared to LGR, along with increased betaine at weaning (+16%; P = 0.0189). These findings suggest that genotype-driven metabolic programming may promote nutritional and metabolic adaptations, particularly in membrane remodelling and osmotic stress response. Further targeted studies, including transcriptomic or proteomic integration, are needed to clarify the underlying regulatory pathways and assess possible trade-offs. Ultimately, these insights could advance our understanding of the early metabolic mechanisms contributing to obesity and help to identify biomarkers for early detection and intervention in humans. Biological sciences/Genetics Biological sciences/Molecular biology Biological sciences/Physiology Betaine fat Lysophosphatidylethanolamine Metabolome Figures Figure 1 Figure 2 Figure 3 Introduction Obesity is a significant global health challenge linked to chronic diseases and reduced quality of life 1,2 , with its incidence steadily increasing 3 . This problem affects not only humans but also animals 4 . Traditionally, research on obesity has focused primarily on adults and adolescents 5–7 , and recent studies have explored its relationship with reproductive disorders in females, including its effects on hormonal balance, fertility, and other related functions 8–11 . Metabolomics has enabled the transition from analysing a limited number of metabolites to the comprehensive characterisation of the entire metabolome. This global approach provides a deeper and more detailed understanding of physiological changes across different species 12–14 . In recent years, the application of omics technologies has incorporated metabolomic studies to investigate the causes and consequences of obesity 15–18 . Nonetheless, identifying metabolic signatures associated with obesity remains challenging due to the lack of mammalian models that allow the comparison of different genetic backgrounds under the same physiological conditions. A good mammalian model of obesity should exhibit, under standardised conditions, an increased capacity to accumulate body fat 19 . Various mammalian models have been employed to study obesity, including mice 20 , as well as larger species like pigs 21 . Despite the extensive use of rodent models, these often fail to fully replicate the complex metabolic and genetic features of human obesity, especially regarding maternal metabolic adaptations during critical reproductive stages. The molecular mechanisms driving differential susceptibility to obesity in genetically selected models remain poorly understood, especially in larger mammals with translational relevance. Rabbits represent a valuable model for obesity research due to their metabolic physiology, which is intermediate between rodents and humans, and their amenability to genetic selection, positioning them as a powerful and translationally relevant system to elucidate the genetic and metabolic mechanisms underlying obesity 22 . In this study, we used a rabbit model selectively bred for growth rate (GR) to analyze the effect of obesity in the metabolome. For that, two contemporaneous generations separated by 15 selection cycles have been compared, resulting in two distinct experimental growth genotypes (GG): a low GR line (LGR) and a high GR line (HGR). This design allows for a direct comparison, under fully standardised conditions, between populations differing solely in their GG. Previous findings reveal an intrinsic predisposition to fat accumulation in the selected population, independent of dietary factors, which motivated a detailed research into the molecular mechanisms driving differential obesity susceptibility 23 , 24 . By focusing on these two distinct GGs at different physiological stages, we aim to capture critical maternal metabolic adaptations. Our main goal is to identify early metabolic biomarkers of obesity, providing novel insights into obesity-like metabolism within a controlled mammalian model. Results Figure 1 illustrates how physiological stage impacts the metabolomic profile. Figures 1a and 1c display the first two principal components of the PLS-DA model in positive (1a) and negative (1c) modes, accounting for 37.7% and 36.7% of the total variance, respectively. These components clearly distinguish the experimental groups, with limited overlap observed in both positive and negative mode. Figures 1b and 1d show volcano plots that emphasise the principal metabolites responsible for the separation based on the PLS-DA analysis. Table 1 lists the metabolites that differ between physiological stages. At parturition, animals showed higher plasmatic levels of betaine (+30%; p = 0.0003) and alpha-CEHC (+67%; p < 0.0001), compared to the same animals at weaning. However, at weaning, animals showed higher plasmatic levels of 4-Pyridoxic acid (+290%; p < 0.0001), Proline betaine (+820%; p < 0.0001), Allysine (+940%; p < 0.0001), 2-Acetylpyrazine (+330%; p < 0.0001), Lysophosphatidylcholine (LysoPC) (+880%; p < 0.0001), Lysophosphatidylethanolamine(LysoPE) (18:3/0:0) (+250%; p < 0.0001), LysoPC (+220%; p < 0.0001) and Kaurane diterpenoid (+140%; p < 0.0001) than those at parturition. Feed intake and nutrient digestibility coefficients were similar between the two GG, with average values provided in Supplementary Table 1. The model including GG as the primary factor exhibited signs of overfitting, making its outputs unreliable and therefore unsuitable for interpretation or reporting. This is the main reason why it was not performed. Figure 2 depicts the effect of GG at parturition in a mammalian model on the PLS-DA score plots derived from the non-targeted metabolomics analysis. Figures 2a and 2c show the first two principal components of the PLS-DA model in positive (2a) and negative (2c) ionisation modes, capturing 36.9% and 37.8% of the total variance, respectively. These components effectively distinguish the experimental groups, with no overlap observed. Figures 2b and 2d present volcano plots of the main discriminative metabolites. Table 2 presents the identified metabolites that explain the variations between both groups at the time of parturition. High growth genotypes (HGR) showed higher plasmatic levels of LysoPE (0:0/20:4) (+131%; p < 0.0001) than LGR animals. Figure 3 shows the effect of GG at weaning in a mammalian model on the PLS-DA score plots derived from the non-targeted metabolomics analysis. Figures 3a and 3c show the first two principal components of the PLS-DA model in positive (3a) and negative (3c) ionisation modes, representing 29.0% and 31.8% of the total variance, respectively. These components separate the experimental groups, with no observed overlap. Figures 3b and 3d present volcano plots highlighting the key metabolites driving the PLS-DA-based separation. Table 3 lists the tentatively identified metabolites that explain the disparities between both groups at the time of weaning (28 days after birth). HGR showed higher plasmatic levels of betaine (+16%; p = 0.0189) and LysoPE (+47%; p < 0.0001) than those at weaning. To further investigate the metabolites significantly affected by GG, Figure 4 presents the temporal profiles of LysoPE in the GG groups, revealing distinct metabolic responses. HGR animals maintained relatively stable LysoPE levels from parturition to weaning, whereas LGR animals exhibited marked fluctuations over the same period. Notably, LysoPE levels in HGR animals were consistently higher than those in LGR animals across both time points analysed. Similarly, Figure 5 highlights individual variability at weaning among animals from the two selected lines, with HGR individuals displaying significantly higher levels of this metabolite. Discussion Although genetic selection for GR has been widely implemented in livestock species, its long-term metabolic consequences —particularly during critical physiological stages— remain poorly understood. In this study, we used an untargeted metabolomics approach to assess how GG influenced the metabolomic profile in a mammalian model at two different physiological stages: parturition and weaning. Regarding validation of the metabolomics analysis, R² measures how well the model fits the observed data, whereas Q² assesses its predictive ability. According to the SIMCA user guide, a Q² value above 0.5 indicates good predictability⁸⁴. Given our average R² and Q² values of 0.89 and 0.57, respectively, we conclude that the models used exhibited a good fit and can be regarded as reliable predictors. Physiological stage effect Independent of GG, our results reveal that the physiological stage significantly influenced the metabolome, as evidenced by changes in 11 metabolites identified between parturition and weaning. Similar metabolic shifts between physiological stages have also been observed in humans and across other mammalian species. Although no studies with a directly comparable design have been identified, existing evidence in humans suggests that significant changes in the metabolic profile occur during both labour and lactation, indicating that the extensive physiological adaptations involved in these processes alter multiple metabolic pathways 25–29 . Comparable metabolic changes have been reported in other mammals. For example NEFA, β-hydroxybutyrate, and insulin increase after parturition in cows, while adiponectin decreases 30 . In goats, β-hydroxybutyrate, enzymes, proteins, triglycerides, and urea increase, whereas NEFA decreases seven days postpartum 31 . In pregnant guinea pigs, prepartum adrenocorticotrophin rises, adrenaline remains stable, and placental lactogen drops vs . non-pregnant controls 32 . In Bama miniature pigs, levels of several metabolite classes (e.g., benzenoids and alkaloids) increase, while others (e.g., lipids and nucleotides) decrease 33 . Finally, in rabbits, postpartum changes include decreased aspartate aminotransferase and increased alkaline phosphatase 34 , along with elevated NEFA 24,35 , and after weaning, reductions in lipids and thyroid hormones, as well as increases in creatinine and lysozyme, have been observed 36 . Genotype effect The effect of GG is particularly relevant when considering obesity-like metabolic signatures. It is estimated that the selection applied in this trial has also influenced GR, resulting in an average increase of +0.84 g/day in daily weight gain per generation 37–41 . Thus, this GG model is considered validated. Since no significant differences were observed regarding daily feed intake (DFI) or digestibility, differences observed in the metabolome cannot be attributed to intake or nutrient availability. The absence of a GG effect on these parameters has also been reported elsewhere 42–46 . Despite identical diets, DFI and nutrient digestibility, the HGR line exhibited significantly greater perirenal fat thickness —a reliable indicator of body fat reserves 23 — both at parturition (+4.7%; p = 0.0079) and at weaning (+3.8%; p = 0.0364) 24 . In this context, exploring how distinct genotypes respond at the molecular level to identical environmental stimuli can provide valuable insight into the genotype–phenotype relationship, offering a more precise understanding of the metabolic mechanisms underlying obesity. GG had a pronounced impact on the metabolome, as evidenced by the clear separation between experimental groups and by significant differences in the concentration of specific metabolites. Due to the novelty of this approach, no studies were found in the reviewed literature that directly examine metabolomic changes resulting from GG through contemporaneous comparisons of individuals from different generations. Nevertheless, a study in Crassostrea gigas reported distinct metabolic profiles between selectively bred and wild individuals, identifying metabolic signatures associated with artificial selection for fast growth. Key altered pathways included CoA biosynthesis, steroid hormone biosynthesis, and amino acid metabolism, suggesting a metabolic basis for the enhanced growth in selected oysters 47 . In a different selective context, research in Drosophila melanogaster showed that selection for stress tolerance induces widespread metabolomic changes, accompanied by correlated expression of general stress response genes. These findings correlated regulation between metabolite levels and gene expression under selective pressure 48 . Additionally, other studies have identified genetic factors influencing the metabolome across different mammals, providing evidence of an underlying genetic basis for the metabolic profile 49 , 50 . Nevertheless, other studies have reported changes in the levels of specific metabolites concerning GR in different species 49,51,52 . Recalling our ultimate objective —to identify early metabolic biomarkers of obesity that provide novel insights into obesity-like metabolism in a controlled mammalian model— we now discuss the two metabolites that showed significant differences between the experimental groups, LysoPE and Betaine. Lysophosphatidylethanolamine (LysoPE) is a phospholipid metabolite from the cephalin group involved in membrane fluidity. It is generated by phospholipase A activity on phosphatidylethanolamine 53 . LysoPE can be further metabolised in various tissues into lecithin and phosphatidylethanolamine, both of which serve as precursors of cellular phosphoglycerides 54 . LysoPE has previously been associated with GR 55 . Our data are consistent with other studies where an increased concentration of most phospholipids and cholesterol was observed in the most selected animals 56 . Thus, the literature indicates that LysoPE plays a key role in glycerophospholipid metabolism, acting as an intermediate in the degradation and remodelling of various phospholipids. In fact, obese adults showed altered circulating LysoPE profiles in human, suggesting dysregulated phospholipid metabolism 57 .Additionally, it functions as a signalling molecule involved in processes such as phagocytosis, apoptosis, cellular stress response, membrane homeostasis, and vesicle formation. The increase in LysoPE may indicate a cellular response to osmotic stress, reflecting membrane remodelling that accompanies the activation of osmoprotective mechanisms to maintain cellular stability and function. Higher levels of LysoPE were shown in humans with metabolically unhealthy overweight 58 . Betaine, also known as trimethylglycine, is a derivative of glycine. Glycine is generally considered a non-essential amino acid. However, its high nutritional demand during pregnancy, along with its critical role in the synthesis of maternal tissues, classifies it as conditionally essential 59,60 . Betaine is a compound that accumulates to protect organisms against osmotic stress 61–63 . Associations have been observed between betaine, lipid metabolism, and reduced body fat 64,65 . Our data may suggest a potential adaptive response in HGR animals. These elevated levels of betaine may be attributed to an increased retention and utilisation of osmoprotective compounds under conditions of osmotic stress 66–68 . The elevated LypoPE levels and betaine in HGR, at both time points (Figure 4), suggest a genetically driven nutritional metabolic adaptation to metabolic and osmotic stress due to higher fat accumulation in the obese animals. This alteration in lipid metabolism could reflect an increased capacity to mobilise and remodel membrane lipids, optimising the use of dietary fats to meet higher energetic and functional demands. Thus, selection might be favouring greater efficiency in lipid nutrient handling, contributing to a metabolic profile that supports improvements in growth or resilience, highlighting the integration of genetic and nutritional processes in the adaptive response 25,54,69–74 . Obesity, particularly when associated with rapid GR, induces significant metabolic and osmotic stress due to excessive lipid accumulation, which disrupts cellular homeostasis 75 . In this work, we observed increased levels of metabolites such as betaine —osmoprotectant— and LysoPE —membrane composition and fluidity, as well as involvement in inflammatory signalling—. The increased presence of these compounds in obese animals indicates an adaptive response to obesity-induced cellular stress, positioning them as potential biomarkers for detecting and monitoring metabolic and cellular alterations associated with obesity in a mammalian model In summary, this study aimed to identify key maternal metabolic adaptations that reveal early biomarkers and mechanisms underlying obesity in a controlled mammalian model. The main conclusions are as follows: (i) There is a clear alteration in the metabolome due to the GG effect. (ii) Betaine and LysoPE are proposed as potential biomarkers associated with obesity. These findings suggest that obesity may promote nutritional and metabolic adaptations —particularly in metabolic and osmotic stress—. Further targeted studies, including transcriptomic or proteomic integration, are needed to clarify the underlying regulatory pathways and assess possible trade-offs. Methods Animal ethics statement This study was carried out in compliance with the ARRIVE guidelines. The following experimental procedure was reviewed and approved by the Animal Welfare Ethics Committee of the Universitat Politècnica de València (UPV) (2015/VSC/PEA/00.061). This project was also carried out in accordance with the recommendations of the European Group on Rabbit Nutrition and the regulations established in Directive 2010/63/EU/EEC and Spanish Royal Decree 53/2013 on the protection of animals used for scientific purposes. Animals In this study, we aim to investigate the effect of growth genotype (GG) on early-life metabolic profiles in females from birth to weaning, so the experimental unit was the rabbit doe. The rabbits belonged to a paternal line, established and selected at the Polytechnic University of Valencia. This line was established in 1989, following two generations of random mating from a pool of animals belonging to three commercial sire lines 39 . This line is a synthetic line developed through individual selection based on daily weight gain from 28 to 63 days of age. For this study, two GG were used: a low GR line (LGR, generation 19) and a high GR line (HGR, generation 37), separated by 18 generations of selection. The LGR population was rederived from 256 embryos collected from 25 donors belonging to eight different families of the 18th generation and vitrified in 2000. Similarly, the HGR population was rederived from 301 embryos obtained from 28 donors belonging to nine different families of the 36th generation, which were vitrified in 2015. The parents of both populations used in this study were stored as frozen embryos, then thawed and transferred simultaneously in 2015 into recipient rabbit females, to obtain contemporaneous populations and avoid the effects of cryopreservation. After one generation without selection, rabbit females from LGR and HGR were obtained simultaneously. Further information regarding cryopreservation and embryo transfer is described 76,77 . Experimental design A total of 88 rabbit does (44 from LGR and 44 from HGR were used). The animals were housed in the experimental rabbit farm of the UPV (Valencia, Spain) and their conditions were fully standardised and distributed evenly throughout the experimental farm (average daily temperatures ranging from 12.2°C to 26.6°C), with 16 hours (h) of light period interspersed with 8 h of darkness period). The intake of animals from each generation was distributed similarly over time. At 63 days of age, females from both generations (LGR and HGR) were housed individually in breeding pens (700 x 500 x 320 mm). They were inseminated at 19 weeks of age (with semen from males of their respective generation) and provided with an external litter nest (220 x 350 x 370 mm) from day 28 of gestation until weaning. Until the first parturition, all females received the same commercial rearing diet for young rabbits of reproductive age, provided ad libitum (9.9 MJ of digestible energy, 120 g of digestible protein and 480 g of neutral detergent fibre per kg of DM). Daily feed intake (DFI) was recorded in all animals throughout the experimental period. Additionally, a randomly selected subset of animals (n=24; 12 from each generation) was used for both a digestibility trial and plasma metabolomic analysis. The digestibility trial was conducted from 14 to 25 days postpartum to determine coefficients of dry matter (DM), organic matter (OM), crude protein (CP), neutral detergent fibre (NDF) and acid detergent fibre (ADF) 78 . The day of the first parturition, the first blood sample was taken (n=24). Blood samples were taken from the central ear artery (1 mL in EDTA vials) and were immediately centrifuged for 5 min at 700 G, and the supernatant plasma was frozen at -80ºC until further analysis. From that point until their first weaning (on day 28 after birth), all females were fed the same commercial rearing diet for adult rabbits of reproductive age, provided ad libitum (12.3 MJ of digestible energy, 148 g of digestible protein and 359 g of NDF per kg of DM). Litter was standardised to six kits at the first birth and seven at subsequent births. This standardisation was used to equalise lactation effort among females, reduce the coefficient of variation and increase the statistical precision of the estimates (Fernández-Carmona et al ., 2010). At weaning, the second blood sample (n=24) was collected again, as previously described. Plasma metabolomic analysis by LC-MS Solvents and chemical standards for metabolomic analysis The 48 plasma samples were analyzed for untargeted metabolomic analysis. The following solvents and eluents were used: HPLC-grade acetonitrile (VWR, West Chester, PA, USA), formic acid (FA, Fluka, Merck KGaA, Darmstadt, Germany), and Mili-Q water. The internal standards included during sample preparation were glycocholic acid (1-13C-Glycine) and 4-chloro-DL-phenylalanine (Sigma, Merck KGaA, Darmstadt, Germany). Sample preparation and LC-MS analysis Plasma from each rabbit was analyzed individually. Plasma preparation was carried out by deproteinizing 150 µL of the sample with 450 µL of ice-cold acetonitrile (100% acetonitrile [ACN]), containing an internal standard mixture of glycocholic acid (glycine-1-13C) and p-chlorophenylalanine at a final concentration of 0.01 mg/mL. These samples were prepared in 1 mL 96-well plates. The contents of each plate were mixed for 1 minute (min), incubated at 4°C for 10 min, and centrifuged for 25 min at 2250 × g at 4°C. Approximately 400 µL of the resulting supernatant was transferred to Phenomenex 96-square-well filter plates. A vacuum was applied to the plates, and the solvent containing plasma metabolites was subsequently collected in a collection plate. The filtered supernatant was transferred to two 200 µL 96-well plates (65 µL per well) and these were centrifuged under vacuum until dry (approximately 2.5 h, 805 × g at 30 °C). The samples were resuspended in a mixture of H₂O:ACN:FA (95:5:0.1) using the same volume as before evaporation. Finally, the plate was sealed with a protective film using a heat sealer and centrifuged at 2250 × g, 4 °C for 25 min. The treated samples were analyzed by ultra-high performance liquid chromatography (UHPLC) using a Nexera X2 LC coupled to an LCMS-9030 Q-TOF MS system (Shimadzu Corporation, Kyoto, Japan) using positive and negative electrospray ionization (ESI). Chromatographic separations were carried out using an Acquity HSS T3 column (1.7 µm, 100 x 2.1 mm, Waters Ltd., Elstree, UK). The column temperature was set to 40 °C, the samples were maintained at 10 °C and 3 µl aliquots were injected onto the column. The chromatographic system used a binary gradient of solvent A (water with 0.1% formic acid) and solvent B (acetonitrile with 0.1% formic acid) at a flow rate of 0.4 ml/min. In detail, a linear gradient from 5% B to 100% B over 12 min was used, with a 1-min hold at 100% before returning to the initial conditions of 5% B for 3 min to achieve column equilibration again. Therefore, the total analysis time per sample was 16 min. The untargeted qualitative analysis of plasma metabolome was performed in positive and negative mode. Data was collected in full scan (MS) and autoMS/MS modes from 50 to 1000 m/z. For MS, parameters set were: ion source temperature: 300 °C, heated capillary temperature: 250 °C, heat block temperature: 400 °C, electrospray voltage: -3.5 kV (ESI-), electrospray nebulizer gas flow: 3 L/min, drying gas flow: 10 L/min, and detector voltage: 2.02 kV. MS/MS spectra of the QC samples were obtained using data-dependent acquisition (DDA) with a TOF MS scan followed by 10 dependent events (precursor ions) that were continuously selected for fragmentation during the run. Precursor ions were fragmented with a collision energy (CE) of 20 eV with a CE spread of ±10 eV. Mass calibration was performed externally using a sodium iodide solution (400 ppm in methanol) of m/z 50–1000. Data acquisition was performed by LabSolutions version 5.114 software (Shimadzu Corporation, Kyoto, Japan). Sample quality Control and metabolomics data pre-processing Quality of the chromatographic analyses, stability of the UPLC system and accuracy of sample preparation were monitored using quality control (QC) samples. Plasma QCs were prepared by aliquoting all samples and following the same preparation protocol as the samples themselves. QCs were injected multiple times during, as well as at the beginning and end of the analysis, and were used in data preprocessing to correct for signal drift. Blanks were also injected during the chromatographic analysis to monitor and detect any potential external contaminants from solvents, eluents and carryover effects. The sample order was randomized for chromatographic analysis to eliminate bias in the results and ensure that each group of samples was examined identically. MS-DIAL software was used for peak detection, alignment, and gap filling of the data files, with the option to identify metabolites using this tool's predefined library. The data matrix generated by MS-DIAL was exported to Excel and filtered to remove peaks present in the blanks. Retention time was narrowed down to present only the portions with chromatographic peaks, discarding masses above 700 m/z. Primary principal component analysis (PCA) was performed using the Metaboanalyst tool to check the quality of the dataset and eliminate potential outliers. Partial least squares discriminant analysis (PLS-DA) models were assembled to determine the metabolites responsible for the differences between the generations. Model validation was carried out using repeated random subsampling. Models were evaluated using the explained variation in Y; graphs represented the actual and predicted values and the proportion of explained variation (R²). Variables for identification were selected using the variable importance scores in projection (VIP). To validate the obtained PLS-DA models, cross-validation was performed using a maximum number of search components of 5, along with the five-fold CV method. For each PLS model generated, R² and Q² were calculated. Metabolite identification Metabolites were identified by searching the online Human Metabolome Database (www.hmdb.ca) for potential chemical structures using precise mass fragmentation patterns and LC-MS mass spectrometry. Statistical analysis of metabolites For DFI, apparent digestibility coefficients and identified metabolites discriminating between physiological stage and growth GG both at parturition and at weaning, data were fitted to a normal distribution and analyzed as dependent variables using a GLM model from SAS, including sex, life-stage class, and their interaction as a main fixed effect. Least-square means comparisons were performed by t-test . Only for LysoPE, since it was the only metabolite that showed significance in both physiological stages, it was analyze using the following model was used: y ijk = GS i |AGE j + p k + e ijk where y ijk represents LysoPE levels; GS i was the effect of generation (two levels: LGR and HGR); AGE j was the age of the females (with two levels: parturition and weaning); and p k and e ijk were the fixed effect and the random residuals of the records, respectively. Fixed effect was to correct for multiple records for the same female and included all interactions. This analysis was performed using the proc MIXED of SAS (2009), in a repeated measure design that considered the variation between animals and the covariation within them. Covariance structures were objectively compared using the most severe criteria (Schwarz Bayesian criterion), as suggested by Littell et al . (1998) with Compound Symmetry Structure selected as the most adequate 79 Declarations Funding This study was supported by the Regional Ministry of Innovation, Universities, Science and Digital Society of the Generalitat Valenciana (CIGE/2023/12) and by Spanish Ministry of Science, Innovation, and University “Tailor-made feeding strategies and precision livestock farming technology for cage-free rabbit farming” FREE-RAB project (PID2022-143036OB-I00). Author contributions Conceptualization and experimental design were performed by P.J.M.G, M.C.L, L.L, J.J.P and D.P.R. Data were collected by P.J.M.G and analysed by P.J.M.G, C.C.G, J.M.L and M.S.H. The manuscript was written by P.J.M.G and J.M.L and reviewed by all authors, who approved the final manuscript. Abbreviations LC−MS liquid chromatography−mass spectrometry; PCA principal component analysis; PLS-DA partial least-squares discriminant analysis; ACN acetonitrile; IS internal standards; HPLC high-performance liquid chromatography; m/z mass to charge ratio; QC quality control. Data Availability statement All data generated or analysed during this study are included in this published article (and its Supplementary Information files). Competing interests The authors declare no competing interests. References Stephenson, J., Smith, C. M., Kearns, B., Haywood, A. & Bissell, P. The association between obesity and quality of life: a retrospective analysis of a large‑scale population‑based cohort study. BMC Public Health 21 , 1990 (2021). Egger, G. & Dixon, J. Beyond obesity and lifestyle: a review of 21st century chronic disease determinants. BioMed Res. Int. 2014 , 1–12 (2014). Masood, B. & Moorthy, M. Causes of obesity: a review. Clin. Med. 23 , 284–291 (2023). 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Successful re‑establishment of a rabbit population from embryos vitrified 15 years ago: the importance of biobanks in livestock conservation. PLoS One 13 , e0199234 (2018). Vicente, J. S. & García‑Ximénez, F. Effects of strain and embryo‑transfer model (embryos from one versus two donor does/recipient) on results of cryopreservation in rabbit. Reprod. Nutr. Dev. 33 , 1–11 (1993). Pérez, J. M. et al. European reference method for in vivo determination of diet digestibility in rabbits. World Rabbit Sci. 3 , 41–43 (2010). Littell, R. C., Henry, P. R. & Ammerman, C. B. Statistical analysis of repeated measures data using SAS procedures. J. Anim. Sci. 76 , 1216–1224 (1998). Tables Tables are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Tables.docx PPabJorgepositive.xlsx PPabJorgenegative.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7381399","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":587622038,"identity":"0f1694d8-285a-4ac8-9dfc-c72ab5b711f8","order_by":0,"name":"Jorge Mateo-López","email":"","orcid":"","institution":"Universitat Politècnica de València","correspondingAuthor":false,"prefix":"","firstName":"Jorge","middleName":"","lastName":"Mateo-López","suffix":""},{"id":587622039,"identity":"5d9a8599-d24b-4f36-8d18-69d88172f6a4","order_by":1,"name":"Mette Skou Hedemann","email":"","orcid":"","institution":"Aarhus University","correspondingAuthor":false,"prefix":"","firstName":"Mette","middleName":"Skou","lastName":"Hedemann","suffix":""},{"id":587622040,"identity":"ce278cfc-5f8d-42a0-889f-857e81af70e3","order_by":2,"name":"César Cortés-García","email":"","orcid":"","institution":"Universidad Cardenal Herrera-CEU, CEU Universities","correspondingAuthor":false,"prefix":"","firstName":"César","middleName":"","lastName":"Cortés-García","suffix":""},{"id":587622041,"identity":"d90370ee-3805-4e1e-87e0-1df5e2c18d81","order_by":3,"name":"Lola Llobat","email":"","orcid":"","institution":"Universidad Cardenal Herrera-CEU, CEU Universities","correspondingAuthor":false,"prefix":"","firstName":"Lola","middleName":"","lastName":"Llobat","suffix":""},{"id":587622042,"identity":"4beb7a69-5929-4fd7-baf7-484d987431e2","order_by":4,"name":"María Cambra-López","email":"","orcid":"","institution":"Universitat Politècnica de València","correspondingAuthor":false,"prefix":"","firstName":"María","middleName":"","lastName":"Cambra-López","suffix":""},{"id":587622043,"identity":"da6aec0c-3857-4403-8706-ee90bb40377f","order_by":5,"name":"Juan José Pascual","email":"","orcid":"","institution":"Universitat Politècnica de València","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"José","lastName":"Pascual","suffix":""},{"id":587622044,"identity":"76ae19b1-ae06-4a8f-a392-36bc49d5b6a9","order_by":6,"name":"Diego Páez-Rosas","email":"","orcid":"","institution":"Universidad San Francisco de Quito","correspondingAuthor":false,"prefix":"","firstName":"Diego","middleName":"","lastName":"Páez-Rosas","suffix":""},{"id":587622045,"identity":"a4d4ca68-823a-4223-a928-99f6871b0cd1","order_by":7,"name":"Pablo Jesús Marín-García","email":"data:image/png;base64,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","orcid":"","institution":"Universidad Cardenal Herrera-CEU, CEU Universities","correspondingAuthor":true,"prefix":"","firstName":"Pablo","middleName":"Jesús","lastName":"Marín-García","suffix":""}],"badges":[],"createdAt":"2025-08-15 12:38:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7381399/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7381399/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102351675,"identity":"5ddb3261-a4e9-4af1-89c3-7441321eab1e","added_by":"auto","created_at":"2026-02-10 19:08:06","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":117009,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSummary of untargeted metabolomics results showing the effect of physiological stage in a mammalian model. \u003c/strong\u003e(\u003cstrong\u003ea\u003c/strong\u003e and \u003cstrong\u003ec\u003c/strong\u003e). PLS-DA score plot of plasma in positive \u003cstrong\u003e(a) \u003c/strong\u003eand negative mode \u003cstrong\u003e(c)\u003c/strong\u003e. The colours and shapes correspond to the two plasma extraction times: ● : parturition time, \u003cstrong\u003e△\u003c/strong\u003e : weaning time. (\u003cstrong\u003eb\u003c/strong\u003e and \u003cstrong\u003ed\u003c/strong\u003e) Volcano plots show significantly different abundant metabolites between both plasma extraction times (two-sided Wilcoxon rank tests with the value adjusted by false discovery rate, FDR \u0026lt; 0.05) are shown; fold change threshold \u0026gt; 2.0 in the volcano plots. Volcano plots are in positive \u003cstrong\u003e(b)\u003c/strong\u003e and in negative mode \u003cstrong\u003e(d)\u003c/strong\u003e. Source: own elaboration.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7381399/v1/8af39b12cad5c39d654d80c7.jpg"},{"id":102351671,"identity":"752ebd8d-8e6d-4dc2-a2c3-4c7631cabfbe","added_by":"auto","created_at":"2026-02-10 19:08:06","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":101944,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSummary of untargeted metabolomics results showing the effect of growth genotype (GG) at parturition in a mammalian model. \u003c/strong\u003e(\u003cstrong\u003ea and c\u003c/strong\u003e) PLS-DA score plot of plasma in positive \u003cstrong\u003e(a)\u003c/strong\u003e and negative mode \u003cstrong\u003e(c)\u003c/strong\u003e. The colours and shapes correspond to the two experimental groups: ●: LGR, ● : HGR. \u003cstrong\u003e(b and d)\u003c/strong\u003eVolcano plots show significantly different abundant metabolites between both experimental groups (two-sided Wilcoxon rank tests with the value adjusted by false discovery rate, FDR \u0026lt; 0.05) are shown; fold change threshold \u0026gt; 2.0 in the volcano plots. Volcano plots are in positive \u003cstrong\u003e(b) \u003c/strong\u003eand in negative mode \u003cstrong\u003e(d)\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7381399/v1/bfc044e45240f947cfac11df.jpg"},{"id":102351672,"identity":"f0c9472b-f1aa-4332-aca8-02d8f653e251","added_by":"auto","created_at":"2026-02-10 19:08:06","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":109041,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSummary of untargeted metabolomics results showing the effect of growth genotype (GG) at weaning in a mammalian model. \u003c/strong\u003e(\u003cstrong\u003ea and c\u003c/strong\u003e)\u003cstrong\u003e.\u003c/strong\u003ePLS-DA score plot of plasma in positive \u003cstrong\u003e(a)\u003c/strong\u003e and negative mode \u003cstrong\u003e(c)\u003c/strong\u003e. The colours and shapes correspond to the two experimental groups: \u003cstrong\u003e△\u003c/strong\u003e LGR, \u003cstrong\u003e△\u003c/strong\u003e: HGR. \u003cstrong\u003e(b \u003c/strong\u003eand\u003cstrong\u003e d)\u003c/strong\u003e Volcano plots show significantly different abundant metabolites between both experimental groups (two-sided Wilcoxon rank tests with the value adjusted by false discovery rate, FDR \u0026lt; 0.05) are shown; fold change threshold \u0026gt; 2.0 in the volcano plots. Volcano plots are in positive \u003cstrong\u003e(b) \u003c/strong\u003eand in negative mode \u003cstrong\u003e(d)\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7381399/v1/e8329302916e9b31dbf79cd9.jpg"},{"id":104305214,"identity":"30f8bbc5-aa7f-4bff-be61-d6d402e43bc1","added_by":"auto","created_at":"2026-03-10 09:42:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1345877,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7381399/v1/8618fdeb-d47e-4657-9d7d-007a963e815a.pdf"},{"id":102351670,"identity":"6b4af292-8966-45f2-aa7a-258e51f9b158","added_by":"auto","created_at":"2026-02-10 19:08:06","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":26868,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-7381399/v1/d3e2fcc6c3d01e0052089775.docx"},{"id":102397532,"identity":"72febe5d-baef-47c2-8b23-1e92cc97d00d","added_by":"auto","created_at":"2026-02-11 10:17:47","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":559031,"visible":true,"origin":"","legend":"","description":"","filename":"PPabJorgepositive.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7381399/v1/62e6140ed85c9e02cbc47427.xlsx"},{"id":102351674,"identity":"36e184ad-3241-405a-aea1-c82b527165cf","added_by":"auto","created_at":"2026-02-10 19:08:06","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1436306,"visible":true,"origin":"","legend":"","description":"","filename":"PPabJorgenegative.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7381399/v1/b905e7a04940f830091e64a5.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Obesity-like metabolic signatures in females: from birth to weaning in a mammalian model","fulltext":[{"header":"Introduction","content":"\u003cp\u003eObesity is a significant global health challenge linked to chronic diseases and reduced quality of life\u003csup\u003e1,2\u003c/sup\u003e, with its incidence steadily increasing\u003csup\u003e3\u003c/sup\u003e. This problem affects not only humans but also animals\u003csup\u003e4\u003c/sup\u003e. Traditionally, research on obesity has focused primarily on adults and adolescents\u003csup\u003e5–7\u003c/sup\u003e, and recent studies have explored its relationship with reproductive disorders in females, including its effects on hormonal balance, fertility, and other related functions\u003csup\u003e8–11\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eMetabolomics has enabled the transition from analysing a limited number of metabolites to the comprehensive characterisation of the entire metabolome. This global approach provides a deeper and more detailed understanding of physiological changes across different species\u003csup\u003e12–14\u003c/sup\u003e. In recent years, the application of omics technologies has incorporated metabolomic studies to investigate the causes and consequences of obesity\u003csup\u003e15–18\u003c/sup\u003e. Nonetheless, identifying metabolic signatures associated with obesity remains challenging due to the lack of mammalian models that allow the comparison of different genetic backgrounds under the same physiological conditions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA good mammalian model of obesity should exhibit, under standardised conditions, an increased capacity to accumulate body fat\u003csup\u003e19\u003c/sup\u003e. Various mammalian models have been employed to study obesity, including mice\u003csup\u003e20\u003c/sup\u003e, as well as larger species like pigs\u003csup\u003e21\u003c/sup\u003e. Despite the extensive use of rodent models, these often fail to fully replicate the complex metabolic and genetic features of human obesity, especially regarding maternal metabolic adaptations during critical reproductive stages. The molecular mechanisms driving differential susceptibility to obesity in genetically selected models remain poorly understood, especially in larger mammals with translational relevance. Rabbits represent a valuable model for obesity research due to their metabolic physiology, which is intermediate between rodents and humans, and their amenability to genetic selection, positioning them as a powerful and translationally relevant system to elucidate the genetic and metabolic mechanisms underlying obesity\u003csup\u003e22\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this study, we used a rabbit model selectively bred for growth rate (GR) to analyze the effect of obesity in the metabolome. For that, two contemporaneous generations separated by 15 selection cycles have been compared, resulting in two distinct experimental growth genotypes (GG): a low GR line (LGR) and a high GR line (HGR). This design allows for a direct comparison, under fully standardised conditions, between populations differing solely in their GG. Previous findings reveal an intrinsic predisposition to fat accumulation in the selected population, independent of dietary factors, which motivated a detailed research into the molecular mechanisms driving differential obesity susceptibility\u003csup\u003e23\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e24\u003c/sup\u003e. By focusing on these two distinct GGs at different physiological stages, we aim to capture critical maternal metabolic adaptations. Our main goal is to identify early metabolic biomarkers of obesity, providing novel insights into obesity-like metabolism within a controlled mammalian model.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eFigure 1 illustrates how physiological stage impacts the metabolomic profile. Figures 1a and 1c display the first two principal components of the PLS-DA model in positive (1a) and negative (1c) modes, accounting for 37.7% and 36.7% of the total variance, respectively. These components clearly distinguish the experimental groups, with limited overlap observed in both positive and negative mode. Figures 1b and 1d show volcano plots that emphasise the principal metabolites responsible for the separation based on the PLS-DA analysis. Table 1 lists the metabolites that differ between physiological stages. At parturition, animals showed higher plasmatic levels of betaine (+30%; \u003cem\u003ep\u003c/em\u003e = 0.0003) and alpha-CEHC (+67%; \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001), compared to the same animals at weaning. However, at weaning, animals showed higher plasmatic levels of 4-Pyridoxic acid (+290%; \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001), Proline betaine (+820%; \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001), Allysine (+940%; \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001), 2-Acetylpyrazine (+330%; \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001), Lysophosphatidylcholine (LysoPC) (+880%; \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001), Lysophosphatidylethanolamine(LysoPE) (18:3/0:0) (+250%; \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001), LysoPC (+220%; \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001) and Kaurane diterpenoid (+140%; \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001) than those at parturition.\u003c/p\u003e\n\u003cp\u003eFeed intake and nutrient digestibility coefficients were similar between the two GG, with average values provided in Supplementary Table 1.\u003c/p\u003e\n\u003cp\u003eThe model including GG as the primary factor exhibited signs of overfitting, making its outputs unreliable and therefore unsuitable for interpretation or reporting. This is the main reason why it was not performed.\u003c/p\u003e\n\u003cp\u003eFigure 2 depicts the effect of GG at parturition in a mammalian model on the PLS-DA score plots derived from the non-targeted metabolomics analysis. Figures 2a and 2c show the first two principal components of the PLS-DA model in positive (2a) and negative (2c) ionisation modes, capturing 36.9% and 37.8% of the total variance, respectively. These components effectively distinguish the experimental groups, with no overlap observed. Figures 2b and 2d present volcano plots of the main discriminative metabolites. Table 2 presents the identified metabolites that explain the variations between both groups at the time of parturition. High growth genotypes (HGR) showed higher plasmatic levels of LysoPE (0:0/20:4) (+131%; \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001) than\u0026nbsp;LGR\u0026nbsp;animals.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 3 shows the effect of GG at weaning in a mammalian model on the PLS-DA score plots derived from the non-targeted metabolomics analysis. Figures 3a and 3c show the first two principal components of the PLS-DA model in positive (3a) and negative (3c) ionisation modes, representing 29.0% and 31.8% of the total variance, respectively. These components separate the experimental groups, with no observed overlap. Figures 3b and 3d present volcano plots highlighting the key metabolites driving the PLS-DA-based separation. Table 3 lists the tentatively identified metabolites that explain the disparities between both groups at the time of weaning (28 days after birth). HGR showed higher plasmatic levels of betaine (+16%; \u003cem\u003ep\u003c/em\u003e = 0.0189) and LysoPE (+47%; \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001) than those at weaning.\u003c/p\u003e\n\u003cp\u003eTo further investigate the metabolites significantly affected by GG, Figure 4 presents the temporal profiles of LysoPE in the\u0026nbsp;GG\u0026nbsp;groups, revealing distinct metabolic responses. HGR animals maintained relatively stable\u0026nbsp;LysoPE\u0026nbsp;levels from parturition to weaning, whereas\u0026nbsp;LGR\u0026nbsp;animals exhibited marked fluctuations over the same period. Notably,\u0026nbsp;LysoPE\u0026nbsp;levels in HGR animals were consistently higher than those in\u0026nbsp;LGR\u0026nbsp;animals across both time points analysed. Similarly, Figure 5 highlights individual variability at weaning among animals from the two selected lines, with HGR individuals displaying significantly higher levels of this metabolite.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAlthough genetic selection for GR has been widely implemented in livestock species, its long-term metabolic consequences —particularly during critical physiological stages— remain poorly understood. In this study, we used an untargeted metabolomics approach to assess how GG influenced the metabolomic profile in a mammalian model at two different physiological stages: parturition and weaning. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRegarding validation of the metabolomics analysis, R² measures how well the model fits the observed data, whereas Q² assesses its predictive ability. According to the SIMCA user guide, a Q² value above 0.5 indicates good predictability⁸⁴. Given our average R² and Q² values of 0.89 and 0.57, respectively, we conclude that the models used exhibited a good fit and can be regarded as reliable predictors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePhysiological stage effect\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIndependent of GG, our results reveal that the physiological stage significantly influenced the metabolome, as evidenced by changes in 11 metabolites identified between parturition and weaning. Similar metabolic shifts between physiological stages have also been observed in humans and across other mammalian species. Although no studies with a directly comparable design have been identified, existing evidence in humans suggests that significant changes in the metabolic profile occur during both labour and lactation, indicating that the extensive physiological adaptations involved in these processes alter multiple metabolic pathways\u003csup\u003e25–29\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eComparable metabolic changes have been reported in other mammals. For example NEFA, β-hydroxybutyrate, and insulin increase after parturition in cows, while adiponectin decreases\u003csup\u003e30\u003c/sup\u003e. In goats, β-hydroxybutyrate, enzymes, proteins, triglycerides, and urea increase, whereas NEFA decreases seven days postpartum\u003csup\u003e31\u003c/sup\u003e. In pregnant guinea pigs, prepartum adrenocorticotrophin rises, adrenaline remains stable, and placental lactogen drops \u003cem\u003evs\u003c/em\u003e. non-pregnant controls\u003csup\u003e32\u003c/sup\u003e. In Bama miniature pigs, levels of several metabolite classes (e.g., benzenoids and alkaloids) increase, while others (e.g., lipids and nucleotides) decrease\u003csup\u003e33\u003c/sup\u003e. Finally, in rabbits, postpartum changes include decreased aspartate aminotransferase and increased alkaline phosphatase\u003csup\u003e34\u003c/sup\u003e, along with elevated NEFA\u0026nbsp;\u003csup\u003e24,35\u003c/sup\u003e, and after weaning, reductions in lipids and thyroid hormones, as well as increases in creatinine and lysozyme, have been observed\u003csup\u003e36\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGenotype effect\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe effect of GG is particularly relevant when considering obesity-like metabolic signatures. It is estimated that the selection applied in this trial has also influenced GR, resulting in an average increase of +0.84 g/day in daily weight gain per generation\u003csup\u003e37–41\u003c/sup\u003e. Thus, this GG model is considered validated. Since no significant differences were observed regarding\u0026nbsp;daily feed intake (DFI)\u0026nbsp;or digestibility, differences observed in the metabolome cannot be attributed to intake or nutrient availability. The absence of a GG effect on these parameters has also been reported elsewhere\u003csup\u003e42–46\u003c/sup\u003e. Despite identical diets, DFI and nutrient digestibility, the HGR line exhibited significantly greater perirenal fat thickness —a reliable indicator of body fat reserves\u003csup\u003e23\u003c/sup\u003e— both at parturition (+4.7%; \u003cem\u003ep\u003c/em\u003e = 0.0079) and at weaning (+3.8%; \u003cem\u003ep\u003c/em\u003e = 0.0364)\u0026nbsp;\u003csup\u003e24\u003c/sup\u003e. In this context, exploring how distinct genotypes respond at the molecular level to identical environmental stimuli can provide valuable insight into the genotype–phenotype relationship, offering a more precise understanding of the metabolic mechanisms underlying obesity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGG had a pronounced impact on the metabolome, as evidenced by the clear separation between experimental groups and by significant differences in the concentration of specific metabolites. Due to the novelty of this approach, no studies were found in the reviewed literature that directly examine metabolomic changes resulting from GG through contemporaneous comparisons of individuals from different generations. Nevertheless, a study in \u003cem\u003eCrassostrea gigas\u003c/em\u003e reported distinct metabolic profiles between selectively bred and wild individuals, identifying metabolic signatures associated with artificial selection for fast growth. Key altered pathways included CoA biosynthesis, steroid hormone biosynthesis, and amino acid metabolism, suggesting a metabolic basis for the enhanced growth in selected oysters\u003csup\u003e47\u003c/sup\u003e. In a different selective context, research in \u003cem\u003eDrosophila melanogaster\u003c/em\u003e showed that selection for stress tolerance induces widespread metabolomic changes, accompanied by correlated expression of general stress response genes. These findings correlated regulation between metabolite levels and gene expression under selective pressure\u003csup\u003e48\u003c/sup\u003e. Additionally, other studies have identified genetic factors influencing the metabolome across different mammals, providing evidence of an underlying genetic basis for the metabolic profile\u003csup\u003e49\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e50\u003c/sup\u003e. Nevertheless, other studies have reported changes in the levels of specific metabolites concerning GR in different species\u003csup\u003e49,51,52\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRecalling our ultimate objective —to identify early metabolic biomarkers of obesity that provide novel insights into obesity-like metabolism in a controlled mammalian model— we now discuss the two metabolites that showed significant differences between the experimental groups, LysoPE and Betaine.\u003c/p\u003e\n\u003cp\u003eLysophosphatidylethanolamine (LysoPE) is a phospholipid metabolite from the cephalin group involved in membrane fluidity. It is generated by phospholipase A activity on phosphatidylethanolamine\u003csup\u003e53\u003c/sup\u003e. LysoPE can be further metabolised in various tissues into lecithin and phosphatidylethanolamine, both of which serve as precursors of cellular phosphoglycerides\u003csup\u003e54\u003c/sup\u003e. LysoPE has previously been associated with GR\u003csup\u003e55\u003c/sup\u003e. Our data are consistent with other studies where an increased concentration of most phospholipids and cholesterol was observed in the most selected animals\u003csup\u003e56\u003c/sup\u003e. Thus, the literature indicates that LysoPE plays a key role in glycerophospholipid metabolism, acting as an intermediate in the degradation and remodelling of various phospholipids. In fact, obese adults showed altered circulating LysoPE profiles in human, suggesting dysregulated phospholipid metabolism\u0026nbsp;\u003csup\u003e57\u003c/sup\u003e.Additionally, it functions as a signalling molecule involved in processes such as phagocytosis, apoptosis, cellular stress response, membrane homeostasis, and vesicle formation. The increase in LysoPE may indicate a cellular response to osmotic stress, reflecting membrane remodelling that accompanies the activation of osmoprotective mechanisms to maintain cellular stability and function. Higher levels of LysoPE were shown in humans with metabolically unhealthy overweight\u003csup\u003e58\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eBetaine, also known as trimethylglycine, is a derivative of glycine. Glycine is generally considered a non-essential amino acid. However, its high nutritional demand during pregnancy, along with its critical role in the synthesis of maternal tissues, classifies it as conditionally essential\u003csup\u003e59,60\u003c/sup\u003e. Betaine is a compound that accumulates to protect organisms against osmotic stress\u003csup\u003e61–63\u003c/sup\u003e. Associations have been observed between betaine, lipid metabolism, and reduced body fat\u0026nbsp;\u003csup\u003e64,65\u003c/sup\u003e. Our data may suggest a potential adaptive response in HGR animals. These elevated levels of betaine may be attributed to an increased retention and utilisation of osmoprotective compounds under conditions of osmotic stress\u0026nbsp;\u003csup\u003e66–68\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe elevated LypoPE levels and betaine in HGR, at both time points (Figure 4), suggest a genetically driven nutritional metabolic adaptation to metabolic and osmotic stress due to higher fat accumulation in the obese animals. This alteration in lipid metabolism could reflect an increased capacity to mobilise and remodel membrane lipids, optimising the use of dietary fats to meet higher energetic and functional demands. Thus, selection might be favouring greater efficiency in lipid nutrient handling, contributing to a metabolic profile that supports improvements in growth or resilience, highlighting the integration of genetic and nutritional processes in the adaptive response\u003csup\u003e25,54,69–74\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eObesity, particularly when associated with rapid GR, induces significant metabolic and osmotic stress due to excessive lipid accumulation, which disrupts cellular homeostasis\u003csup\u003e75\u003c/sup\u003e. In this work, we observed increased levels of metabolites such as betaine —osmoprotectant— and LysoPE —membrane composition and fluidity, as well as involvement in inflammatory signalling—. The increased presence of these compounds in obese animals indicates an adaptive response to obesity-induced cellular stress, positioning them as potential biomarkers for detecting and monitoring metabolic and cellular alterations associated with obesity in a mammalian model\u003c/p\u003e\n\u003cp\u003eIn summary, this study aimed to identify key maternal metabolic adaptations that reveal early biomarkers and mechanisms underlying obesity in a controlled mammalian model. The main conclusions are as follows: (i)\u0026nbsp;There is a clear alteration in the metabolome due to the GG effect. (ii) Betaine and LysoPE are proposed as potential biomarkers associated with obesity. These findings suggest that obesity may promote nutritional and metabolic adaptations —particularly in metabolic and osmotic stress—. Further targeted studies, including transcriptomic or proteomic integration, are needed to clarify the underlying regulatory pathways and assess possible trade-offs.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eAnimal ethics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was carried out in compliance with the ARRIVE guidelines. The following experimental procedure was reviewed and approved by the Animal Welfare Ethics Committee of the Universitat Politècnica de València (UPV) (2015/VSC/PEA/00.061). This project was also carried out in accordance with the recommendations of the European Group on Rabbit Nutrition and the regulations established in Directive 2010/63/EU/EEC and Spanish Royal Decree 53/2013 on the protection of animals used for scientific purposes.\u003c/p\u003e\n\u003cp id=\"_Toc202728923\"\u003e\u003cstrong\u003eAnimals\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, we aim to investigate the effect of growth genotype (GG) on early-life metabolic profiles in females from birth to weaning, so the experimental unit was the rabbit doe. The rabbits belonged to a paternal line, established and selected at the Polytechnic University of Valencia. This line was established in 1989, following two generations of random mating from a pool of animals belonging to three commercial sire lines\u003csup\u003e39\u003c/sup\u003e. This line is a synthetic line developed through individual selection based on daily weight gain from 28 to 63 days of age. For this study, two GG were used: a low GR line (LGR, generation 19) and a high GR line (HGR, generation 37), separated by 18 generations of selection. The LGR population was rederived from 256 embryos collected from 25 donors belonging to eight different families of the 18th generation and vitrified in 2000. Similarly, the\u0026nbsp;HGR\u0026nbsp;population was rederived from 301 embryos obtained from 28 donors belonging to nine different families of the 36th generation, which were vitrified in 2015. The parents of both populations used in this study were stored as frozen embryos, then thawed and transferred simultaneously in 2015 into recipient rabbit females, to obtain contemporaneous populations and avoid the effects of cryopreservation. After one generation without selection, rabbit females from LGR and\u0026nbsp;HGR\u0026nbsp;were obtained simultaneously. Further information regarding cryopreservation and embryo transfer is described\u003csup\u003e76,77\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp id=\"_Toc202728924\"\u003e\u003cstrong\u003eExperimental design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 88 rabbit does (44 from LGR and 44 from\u0026nbsp;HGR\u0026nbsp;were used). The animals were housed in the experimental rabbit farm of the UPV (Valencia, Spain) and their conditions were fully standardised and distributed evenly throughout the experimental farm (average daily temperatures ranging from 12.2°C to 26.6°C), with 16 hours (h) of light period interspersed with 8 h of darkness period). The intake of animals from each generation was distributed similarly over time. At 63 days of age, females from both generations (LGR and\u0026nbsp;HGR) were housed individually in breeding pens (700 x 500 x 320 mm). They were inseminated at 19 weeks of age (with semen from males of their respective generation) and provided with an external litter nest (220 x 350 x 370 mm) from day 28 of gestation until weaning. Until the first parturition, all females received the same commercial rearing diet for young rabbits of reproductive age, provided \u003cem\u003ead libitum\u003c/em\u003e (9.9 MJ of digestible energy, 120 g of digestible protein and 480 g of neutral detergent fibre per kg of DM). Daily feed intake (DFI) was recorded in all animals throughout the experimental period. Additionally, a randomly selected subset of animals (n=24; 12 from each generation) was used for both a digestibility trial and plasma metabolomic analysis. The digestibility trial was conducted from 14 to 25 days postpartum to determine coefficients of dry matter (DM), organic matter (OM), crude protein (CP), neutral detergent fibre (NDF) and acid detergent fibre (ADF)\u0026nbsp;\u003csup\u003e78\u003c/sup\u003e.\u0026nbsp;The day of the first parturition, the first blood sample was taken (n=24). Blood samples were taken from the central ear artery (1 mL in EDTA vials) and were immediately centrifuged for 5 min at 700 G, and the supernatant plasma was frozen at -80ºC until further analysis. From that point until their first weaning (on day 28 after birth), all females were fed the same commercial rearing diet for adult rabbits of reproductive age, provided \u003cem\u003ead libitum\u003c/em\u003e (12.3 MJ of digestible energy, 148 g of digestible protein and 359 g of NDF per kg of DM). Litter was standardised to six kits at the first birth and seven at subsequent births. This standardisation was used to equalise lactation effort among females, reduce the coefficient of variation and increase the statistical precision of the estimates (Fernández-Carmona \u003cem\u003eet al\u003c/em\u003e., 2010). \u0026nbsp;At weaning, the second blood sample (n=24) was collected again, as previously described. \u0026nbsp;\u003c/p\u003e\n\u003cp id=\"_Toc202728925\"\u003e\u003cstrong\u003ePlasma metabolomic analysis by LC-MS\u003c/strong\u003e\u003c/p\u003e\n\u003cp id=\"_Toc202728926\"\u003e\u003cstrong\u003e\u003cem\u003eSolvents and chemical standards for metabolomic analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 48 plasma samples were analyzed for untargeted metabolomic analysis. The following solvents and eluents were used: HPLC-grade acetonitrile (VWR, West Chester, PA, USA), formic acid (FA, Fluka, Merck KGaA, Darmstadt, Germany), and Mili-Q water. The internal standards included during sample preparation were glycocholic acid (1-13C-Glycine) and 4-chloro-DL-phenylalanine (Sigma, Merck KGaA, Darmstadt, Germany).\u003c/p\u003e\n\u003cp id=\"_Toc202728927\"\u003e\u003cstrong\u003e\u003cem\u003eSample preparation and LC-MS analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePlasma from each rabbit was analyzed individually. Plasma preparation was carried out by deproteinizing 150 µL of the sample with 450 µL of ice-cold acetonitrile (100% acetonitrile [ACN]), containing an internal standard mixture of glycocholic acid (glycine-1-13C) and p-chlorophenylalanine at a final concentration of 0.01 mg/mL. These samples were prepared in 1 mL 96-well plates. The contents of each plate were mixed for 1 minute (min), incubated at 4°C for 10 min, and centrifuged for 25 min at 2250 × g at 4°C. Approximately 400 µL of the resulting supernatant was transferred to Phenomenex 96-square-well filter plates. A vacuum was applied to the plates, and the solvent containing plasma metabolites was subsequently collected in a collection plate. The filtered supernatant was transferred to two 200 µL 96-well plates (65 µL per well) and these were centrifuged under vacuum until dry (approximately 2.5 h, 805 × g at 30 °C). The samples were resuspended in a mixture of H₂O:ACN:FA (95:5:0.1) using the same volume as before evaporation. Finally, the plate was sealed with a protective film using a heat sealer and centrifuged at 2250 × g, 4 °C for 25 min.\u003c/p\u003e\n\u003cp\u003eThe treated samples were analyzed by ultra-high performance liquid chromatography (UHPLC) using a Nexera X2 LC coupled to an LCMS-9030 Q-TOF MS system (Shimadzu Corporation, Kyoto, Japan) using positive and negative electrospray ionization (ESI). Chromatographic separations were carried out using an Acquity HSS T3 column (1.7 µm, 100 x 2.1 mm, Waters Ltd., Elstree, UK). The column temperature was set to 40 °C, the samples were maintained at 10 °C and 3 µl aliquots were injected onto the column. The chromatographic system used a binary gradient of solvent A (water with 0.1% formic acid) and solvent B (acetonitrile with 0.1% formic acid) at a flow rate of 0.4 ml/min. In detail, a linear gradient from 5% B to 100% B over 12 min was used, with a 1-min hold at 100% before returning to the initial conditions of 5% B for 3 min to achieve column equilibration again. Therefore, the total analysis time per sample was 16 min. The untargeted qualitative analysis of plasma metabolome was performed in positive and negative mode.\u003c/p\u003e\n\u003cp\u003eData was collected in full scan (MS) and autoMS/MS modes from 50 to 1000 m/z. For MS, parameters set were: ion source temperature: 300 °C, heated capillary temperature: 250 °C, heat block temperature: 400 °C, electrospray voltage: -3.5 kV (ESI-), electrospray nebulizer gas flow: 3 L/min, drying gas flow: 10 L/min, and detector voltage: 2.02 kV. MS/MS spectra of the QC samples were obtained using data-dependent acquisition (DDA) with a TOF MS scan followed by 10 dependent events (precursor ions) that were continuously selected for fragmentation during the run. Precursor ions were fragmented with a collision energy (CE) of 20 eV with a CE spread of ±10 eV. Mass calibration was performed externally using a sodium iodide solution (400 ppm in methanol) of m/z 50–1000. Data acquisition was performed by LabSolutions version 5.114 software (Shimadzu Corporation, Kyoto, Japan).\u003c/p\u003e\n\u003cp id=\"_Toc202728928\"\u003e\u003cstrong\u003e\u003cem\u003eSample quality Control and metabolomics data pre-processing\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQuality of the chromatographic analyses, stability of the UPLC system and accuracy of sample preparation were monitored using quality control (QC) samples. Plasma QCs were prepared by aliquoting all samples and following the same preparation protocol as the samples themselves. QCs were injected multiple times during, as well as at the beginning and end of the analysis, and were used in data preprocessing to correct for signal drift. Blanks were also injected during the chromatographic analysis to monitor and detect any potential external contaminants from solvents, eluents and carryover effects. The sample order was randomized for chromatographic analysis to eliminate bias in the results and ensure that each group of samples was examined identically.\u003c/p\u003e\n\u003cp\u003eMS-DIAL software was used for peak detection, alignment, and gap filling of the data files, with the option to identify metabolites using this tool's predefined library. The data matrix generated by MS-DIAL was exported to Excel and filtered to remove peaks present in the blanks. Retention time was narrowed down to present only the portions with chromatographic peaks, discarding masses above 700 m/z.\u003c/p\u003e\n\u003cp\u003ePrimary principal component analysis (PCA) was performed using the Metaboanalyst tool to check the quality of the dataset and eliminate potential outliers. Partial least squares discriminant analysis (PLS-DA) models were assembled to determine the metabolites responsible for the differences between the generations. Model validation was carried out using repeated random subsampling. Models were evaluated using the explained variation in Y; graphs represented the actual and predicted values\u0026nbsp;and the proportion of explained variation (R²). Variables for identification were selected using the variable importance scores in projection (VIP). To validate the obtained PLS-DA models, cross-validation was performed using a maximum number of search components of 5, along with the five-fold CV method. For each PLS model generated, R² and Q² were calculated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMetabolite identification\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMetabolites were identified by searching the online Human Metabolome Database (www.hmdb.ca) for potential chemical structures using precise mass fragmentation patterns and LC-MS mass spectrometry.\u003c/p\u003e\n\u003cp id=\"_Toc202728930\"\u003e\u003cstrong\u003eStatistical analysis of metabolites\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor DFI, apparent digestibility coefficients and identified metabolites discriminating between physiological stage and growth GG both at parturition and at weaning, data were fitted to a normal distribution and analyzed as dependent variables using a GLM model from SAS, including sex, life-stage class, and their interaction as a main fixed effect. Least-square means comparisons were performed by \u003cem\u003et-test\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eOnly for LysoPE, since it was the only metabolite that showed significance in both physiological stages, it was analyze using the following model was used:\u003c/p\u003e\n\u003cp\u003ey\u003csub\u003eijk\u003c/sub\u003e = GS\u003csub\u003ei\u003c/sub\u003e|AGE\u003csub\u003ej\u003c/sub\u003e + p\u003csub\u003ek\u003c/sub\u003e + e\u003csub\u003eijk\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003ewhere y\u003csub\u003eijk\u003c/sub\u003e represents LysoPE levels; GS\u003csub\u003ei\u003c/sub\u003e was the effect of generation (two levels: LGR and\u0026nbsp;HGR); AGE\u003csub\u003ej\u003c/sub\u003e was the age of the females (with two levels: parturition and weaning); and p\u003csub\u003ek\u003c/sub\u003e and e\u003csub\u003eijk\u003c/sub\u003e were the fixed effect and the random residuals of the records, respectively. Fixed effect was to correct for multiple records for the same female and included all interactions. This analysis was performed using the proc MIXED of SAS (2009), in a repeated measure design that considered the variation between animals and the covariation within them. Covariance structures were objectively compared using the most severe criteria (Schwarz Bayesian criterion), as suggested by Littell \u003cem\u003eet al\u003c/em\u003e. (1998) with Compound Symmetry Structure selected as the most adequate\u0026nbsp;\u003csup\u003e79\u003c/sup\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Regional Ministry of Innovation, Universities, Science and Digital Society of the \u003cem\u003eGeneralitat Valenciana\u003c/em\u003e (CIGE/2023/12) and by Spanish Ministry of Science, Innovation, and University “Tailor-made feeding strategies and precision livestock farming technology for cage-free rabbit farming” FREE-RAB project (PID2022-143036OB-I00).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization and experimental design were performed by P.J.M.G, M.C.L, L.L, J.J.P and D.P.R. Data were collected by P.J.M.G and analysed by P.J.M.G, C.C.G, J.M.L and M.S.H. The manuscript was written by P.J.M.G and J.M.L and reviewed by all authors, who approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLC−MS liquid chromatography−mass spectrometry; PCA principal component analysis; PLS-DA partial least-squares discriminant analysis; ACN acetonitrile; IS internal standards; HPLC high-performance liquid chromatography; m/z mass to charge ratio; QC quality control.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article (and its Supplementary Information files).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eStephenson, J., Smith, C. 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Res.\u003c/em\u003e \u003cstrong\u003e42\u003c/strong\u003e, 3490–3503 (2017).\u003c/li\u003e\n \u003cli\u003ePetronini, P. G., De Angelis, E. M., Borghetti, P., Borghetti, A. F. \u0026amp; Wheeler, K. P. Modulation by betaine of cellular responses to osmotic stress. \u003cem\u003eBiochem. J.\u003c/em\u003e \u003cstrong\u003e282\u003c/strong\u003e, 69–73 (1992).\u003c/li\u003e\n \u003cli\u003eGao, X. \u003cem\u003eet al.\u003c/em\u003e Effect of betaine on reducing body fat—a systematic review and meta‑analysis of randomized controlled trials. \u003cem\u003eNutrients\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 2480 (2019).\u003c/li\u003e\n \u003cli\u003eDu, J. \u003cem\u003eet al.\u003c/em\u003e Dietary betaine prevents obesity through gut microbiota‑driven microRNA‑378a family. \u003cem\u003eGut Microbes\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 1862612 (2021).\u003c/li\u003e\n \u003cli\u003eKappes, R. M., Kempf, B. \u0026amp; Bremer, E. Three transport systems for the osmoprotectant glycine betaine operate in \u003cem\u003eBacillus subtilis\u003c/em\u003e: characterization of OpuD. \u003cem\u003eJ. Bacteriol.\u003c/em\u003e \u003cstrong\u003e178\u003c/strong\u003e, 5071–5080 (1996).\u003c/li\u003e\n \u003cli\u003eBoch, J., Kempf, B. \u0026amp; Bremer, E. Osmoregulation in \u003cem\u003eBacillus subtilis\u003c/em\u003e: synthesis of the osmoprotectant glycine betaine from exogenously provided choline. \u003cem\u003eJ. Bacteriol.\u003c/em\u003e \u003cstrong\u003e176\u003c/strong\u003e, 5364–5371 (1994).\u003c/li\u003e\n \u003cli\u003eFelitsky, D. J. \u003cem\u003eet al.\u003c/em\u003e The exclusion of glycine betaine from anionic biopolymer surface: why glycine betaine is an effective osmoprotectant but also a compatible solute. \u003cem\u003eBiochemistry\u003c/em\u003e \u003cstrong\u003e43\u003c/strong\u003e, 14732–14743 (2004).\u003c/li\u003e\n \u003cli\u003eAkhtar, R. A. \u0026amp; Abdel‑Latif, A. A. 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Biochem.\u003c/em\u003e \u003cstrong\u003e40\u003c/strong\u003e, 1–9 (2021).\u003c/li\u003e\n \u003cli\u003eMarco‑Jiménez, F., Baselga, M. \u0026amp; Vicente, J. S. Successful re‑establishment of a rabbit population from embryos vitrified 15 years ago: the importance of biobanks in livestock conservation. \u003cem\u003ePLoS One\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, e0199234 (2018).\u003c/li\u003e\n \u003cli\u003eVicente, J. S. \u0026amp; García‑Ximénez, F. Effects of strain and embryo‑transfer model (embryos from one versus two donor does/recipient) on results of cryopreservation in rabbit. \u003cem\u003eReprod. Nutr. Dev.\u003c/em\u003e \u003cstrong\u003e33\u003c/strong\u003e, 1–11 (1993).\u003c/li\u003e\n \u003cli\u003ePérez, J. M. \u003cem\u003eet al.\u003c/em\u003e European reference method for in vivo determination of diet digestibility in rabbits. \u003cem\u003eWorld Rabbit Sci.\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, 41–43 (2010).\u003c/li\u003e\n \u003cli\u003eLittell, R. C., Henry, P. R. \u0026amp; Ammerman, C. B. Statistical analysis of repeated measures data using SAS procedures. \u003cem\u003eJ. Anim. Sci.\u003c/em\u003e \u003cstrong\u003e76\u003c/strong\u003e, 1216–1224 (1998).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Betaine, fat, Lysophosphatidylethanolamine, Metabolome","lastPublishedDoi":"10.21203/rs.3.rs-7381399/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7381399/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Identifying early metabolic signatures and biomarkers is pivotal to understanding the molecular drivers of obesity. This study investigates how genetic variation influences obesity-like metabolic profiles using a mammalian model. Female rabbits from a genetic line selected for growth rate, obtained at two different generations and corresponding to low (LGR) and high (HGR) growth genotypes (GG), were evaluated at first parturition and weaning. Both generations originated from frozen embryos thawed and transferred simultaneously to generate contemporaneous animals. Across 88 animals, we assessed feed intake (n=88) and nutrient digestibility (n=24) and performed untargeted plasma metabolomic profiling (n=48) to characterise systemic metabolic alterations. While no differences were observed in feed intake or digestibility, HGR animals showed significantly elevated plasma levels of Lysophosphatidylethanolamine (0:0/20:4) (LysoPE) at parturition (+131%; P \u003c 0.0001) and weaning (+47%; P \u003c 0.0001) compared to LGR, along with increased betaine at weaning (+16%; P = 0.0189). These findings suggest that genotype-driven metabolic programming may promote nutritional and metabolic adaptations, particularly in membrane remodelling and osmotic stress response. Further targeted studies, including transcriptomic or proteomic integration, are needed to clarify the underlying regulatory pathways and assess possible trade-offs. Ultimately, these insights could advance our understanding of the early metabolic mechanisms contributing to obesity and help to identify biomarkers for early detection and intervention in humans.","manuscriptTitle":"Obesity-like metabolic signatures in females: from birth to weaning in a mammalian model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-10 19:07:58","doi":"10.21203/rs.3.rs-7381399/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1f2cc2e3-c42a-43f3-9c0a-17749065bf16","owner":[],"postedDate":"February 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":62520976,"name":"Biological sciences/Genetics"},{"id":62520977,"name":"Biological sciences/Molecular biology"},{"id":62520978,"name":"Biological sciences/Physiology"}],"tags":[],"updatedAt":"2026-03-10T09:40:35+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-10 19:07:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7381399","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7381399","identity":"rs-7381399","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}