Why In Vitro Fertilization Increases Macrosomia Risk? A New Anti-Cold-Stress Theory Mirroring Bergmann's Evolutionary Adaptation Rule

Why In Vitro Fertilization Increases Macrosomia Risk? A New Anti-Cold-Stress Theory Mirroring Bergmann's Evolutionary Adaptation Rule | 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 Research Article Why In Vitro Fertilization Increases Macrosomia Risk? A New Anti-Cold-Stress Theory Mirroring Bergmann's Evolutionary Adaptation Rule Huamei Jian, Ping Zhu, Qiutong Zheng, Yingying Zhang, Xiaohui Cao, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8284673/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 In vitro fertilization revolutionized reproduction, but its association with macrosomia (overweight) remains unknown. We investigated molecular/metabolic impacts of extreme-cold-environment cryopreservation in frozen-embryo-transfer (FET), proposing new theory to explain increased macrosomia risk. Birth-weight was assessed as growth indicator; tandem mass spectrometry quantified amino-acids (AAs) and carnitine in newborn blood for metabolic profiles; and RNA sequencing analyzed gene expression in umbilical-cord vasculature, a model reflecting fetal circulatory system. Comparisons between fresh-embryo-transfer (ET) and FET revealed that extreme-cold-environment(-196°C) altered AA metabolism and transcriptional profiles linked to protein synthesis and energy metabolism. These changes were associated with higher birth-weight in FET offspring. Our findings bridge phenotypic observations (macrosomia), metabolic disturbances (AA/carnitine alterations), and molecular signatures (differential gene expression), supporting a novel anti-cold-stress mechanism. The new theory firstly suggests embryonic adaptation to cryopreservation environment may program metabolic changes increasing overweight risk, offering insights to optimize ART practices and improve long-term health outcomes of IVF-conceived children. In vitro fertilization Cryopreservation Amino Acids transcriptional profiles Macrosomia Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Infertility remains a global health challenge, and assisted reproductive technologies (ARTs), particularly in vitro fertilization (IVF), have revolutionized reproductive medicine since the first IVF birth in 1978. However, growing evidence indicates IVF-conceived pregnancies carry unique risks, such as an elevated incidence of macrosomia or increased birth-weight compared to natural conception, highlighting the need to understand underlying mechanisms (Yu et al. 2022). These clinical findings indicate that while IVF provides a valuable solution for infertility, it also exposes pregnancies and offspring to certain health challenges in early prevention of overweight obesity. Research on fetal-origin diseases has revealed that early-life environmental exposures can induce lasting molecular changes via epigenetic imprinting and developmental programming, increasing long-term risks of metabolic disorders like diabetes(Zhu et al. 2019). These findings underscore the importance of investigating how IVF procedures affect embryonic development at molecular and metabolic levels. Our study firstly integrates tandem mass spectrometry (MS/MS) and RNA sequencing to address this gap in study of human tissue and blood samples. MS/MS analyzed amino acids (AAs) and carnitine in newborn blood—key markers of protein and energy metabolism—while RNA sequencing explored transcriptional profiles in umbilical cord vasculature. This choice of human tissue avoids ethical issues with fetal samples and reflects fetal circulatory and nutrient exchange vessels, representing a pioneering approach in human fetal development research (Haniffa et al. 2021). Notably, previous IVF studies rarely differentiated outcomes between fresh (ET) and frozen-thawed embryo transfer (FET). Cryopreservation in FET subjects embryos to extreme cold-stress environment (-196°C), halting cell division and inducing physiological stress, yet the mechanisms linking this stress to outcomes remain unclear. Our study compares ET and FET, integrating birth weight (phenotype), AA/carnitine levels (biochemistry), and gene expression (molecular signatures) to bridge gaps between phenotype, metabolism, and transcription. By synthesizing multi-level data, we aim to elucidate how the extreme cold-stress environment impacts fetal and neonate development and propose a new theory explaining the higher overweight risk in FET offspring. Materials and Methods General Information A total of 846 full-term pregnant women (gestational age ≥ 37 weeks) admitted to our hospital between 2018 and 2022 were enrolled. Their neonates were categorized into three groups: 492 neonates from naturally conceived pregnancies (Ctrl, n = 492), 85 from fresh embryo transfer (ET, n = 85), and 287 from frozen embryo transfer (FET, n = 287). Pairwise comparisons of maternal baseline characteristics between the Ctrl group and the two embryo transfer groups (ET and FET) showed no statistical differences (P > 0.05). All procedures in this study were approved by the Hospital Ethical Committee. Inclusion criteria Singleton pregnancy; full-term neonate (gestational age ≥ 37 weeks, birth weight ≥ 2500 g); signed informed consent. Exclusion criteria Twin or multiple pregnancies; preterm neonate (gestational age < 37 weeks, birth weight < 2500 g). Newborn Birth Weight Measurement Newborn weight was measured by trained medical personnel within 1–5 minutes after delivery. Newborns were placed on a pre-warmed radiant warmer with all clothing/coverings removed. Naked weight (in grams) was recorded using a daily-calibrated electronic infant scale (Seca 334, precision ± 5 g) after the reading stabilized, with dual verification by trained staff. Blood Spot (DBS) Sample Collection Within 24 hours post-delivery, DBS samples were collected from the neonatal lateral heel using a sterile lancet (puncture depth ≤ 2.0 mm). The first blood drop was discarded to avoid tissue fluid contamination. The second drop was allowed to form a blood bead ≥ 3 mm in diameter, then gently and perpendicularly applied to the center of a dedicated filter paper card (e.g., Whatman 903®) until complete penetration, forming uniform, concentric blood spots ≥ 8 mm in diameter on both sides. Three independent blood spots were collected per neonate. Cards were dried horizontally at room temperature (15–25°C) in the dark for ≥ 3 hours until dark brown. Dried cards were sealed in moisture-barrier bags with desiccant and transported refrigerated (2–8°C) to the laboratory. Strict precautions were taken to avoid duplicate spotting, slanted contact with filter paper, or stacking before complete drying. Hematocrit (HCT) levels were monitored, and quantitative results were corrected for abnormal HCT. Tandem Mass Spectrometry (MS/MS) Analysis From each DBS sample, a 3.2 mm diameter punch (equivalent to ~ 3.2 µL whole blood) was transferred to a microplate well. Methanol containing stable isotope-labeled internal standards (e.g., ¹³C₆-phenylalanine, d₃-palmitoylcarnitine) was added for oscillatory extraction, with isotope dilution used to correct matrix effects. Extracts were either derivatized with butanol/HCl (65°C, 15 minutes) or injected directly via flow injection analysis (FIA) into a triple quadrupole tandem mass spectrometer, with ionization in positive electrospray mode (ESI+). Target metabolites were detected using multiple reaction monitoring (MRM) of characteristic precursor/product ion pairs: for amino acids, derivatized precursor ions and carboxyl-loss fragment ions (e.g., phenylalanine butyl ester m/z 222→104); for acylcarnitines, acyl chain-specific precursor ions and the common m/z 85 fragment ion (carnitine moiety). Collision energy was optimized (15–25 eV) for maximum signal-to-noise ratio. Metabolite concentrations (µmol/L) were quantified using analyte-to-internal standard peak area ratios, referenced against a six-point calibration curve. Key metabolic ratios were calculated (e.g., phenylalanine/tyrosine [Phe/Tyr] for phenylketonuria risk, propionylcarnitine/acetylcarnitine [C3/C2] for methylmalonic acidemia). Results were compared against laboratory-established cutoffs (99th percentile from 100,000 healthy neonates). Each batch included low-, medium-, and high-concentration quality control (QC) samples, with intra-batch precision (CV) 30 amino acids and acylcarnitines per sample within 3 minutes, screening for > 40 inherited metabolic diseases (e.g., phenylketonuria [PKU], maple syrup urine disease [MSUD], medium-chain acyl-CoA dehydrogenase deficiency [MCAD]) with a false-positive rate < 0.1%. Mass scanning covered all target amino acid molecular weights, with collision energy optimized for fragment ion signals. Raw data underwent noise reduction and calibration, followed by peak identification and quantification via specialized software. Amino acid presence and concentrations were confirmed by comparing sample chromatograms and mass spectra with reference standards. RNA-seq and Bioinformatics Analysis RNA-seq analysis followed our previously described protocol(Zhang, Zhou, Zheng, Zheng, Zhang, Liu, Tang and Xu 2024).Briefly, clean reads were aligned to the GRCh38 reference genome (ENSEMBL annotation) using Hisat2 (v2.2.1). Differentially expressed genes (DEGs) at the transcript level were identified using the DESeq2 package, with thresholds of log₂ fold change (log₂fc) > 1 (absolute fold change > 2) and false discovery rate (FDR) < 0.05. Functional enrichment analyses (Gene Ontology [GO]: biological processes, cellular components, molecular functions; pathways: Reactome, KEGG(Kanehisa and Goto 2000) were performed using the ToppGene tool. Protein-protein interactions were annotated via the STRING database(Szklarczyk et al. 2019). Statistical Analysis All data are original raw data and available upon request. MS/MS and bioinformatic process and analysis were treated with a double-blind manner. Analyses were performed using SPSS 27.0. Continuous data are presented as mean ± standard deviation (x̄ ± s). Between-group comparisons were analyzed using Student’s t-test, with P ≤ 0.05 indicating statistical significance. Graphs and significance annotations were generated using GraphPad Prism 9.5.0. Results 1. Increased Birth Weight in IVF-Conceived Neonates Fetal birth weights were significantly higher in IVF-conceived neonates than in naturally conceived ones. Specifically, the frozen embryo transfer (FET) group showed the most substantial increase, with a statistically significant elevation compared to the natural conception control group (Fig. 1 ). 2. Metabolomic Profiling–Tandem Mass Spectrometry Tandem mass spectrometry was used to determine the concentration profiles of multiple amino acids in naturally conceived individuals (Ctrl group) and those conceived via assisted reproductive technologies (ART). Multidimensional comparative analysis among the Ctrl, frozen embryo transfer (FET), and fresh embryo transfer (ET) groups revealed the following: Essential Amino Acids Compared to the Ctrl group, both the ET and FET groups exhibited reduced levels of the essential amino acids methionine (MET) and tyrosine (TYR), as well as succinylacetone (AS) (P < 0.05). No significant differences were observed in phenylalanine (PHE), valine (VAL), or leucine (LEU) levels (Fig. 2 ). Non-Essential Amino Acids Compared to the Ctrl group, the FET group showed significantly reduced levels of the non-essential amino acids arginine (ARG), ornithine (ORN), and citrulline (CIT), while the ET group exhibited decreased concentrations of ORN and CIT. No significant differences were detected in proline (PRO), alanine (ALA), or glycine (GLY) levels (Fig. 3 ). Carnitine Carnitine is an endogenous enzyme-like compound critical for fatty acid metabolism. Thirteen acylcarnitines differed significantly among the three groups. Relative to the Ctrl group, the ET group had markedly higher levels of free carnitine (C0), acetylcarnitine (C2), propionylcarnitine (C3), methylmalonylcarnitine (C4DC + C5OH), tetradecanoylcarnitine (C14), and 3-hydroxy-octadecenoylcarnitine (C18:1-OH), but significantly lower octanoylcarnitine (C8). In the FET group compared to the Ctrl group, only free carnitine (C0) was elevated, while methylmalonylcarnitine (C4DC + C5OH), adipylcarnitine (C6DC), octanoylcarnitine (C8), sebacenylcarnitine (C10:1), tetradecenoylcarnitine (C14:1), tetradecadienylcarnitine (C14:2), and octadecadienylcarnitine (C18:2) were all significantly reduced. Direct comparison between the ET and FET groups showed lower concentrations of free carnitine (C0), acetylcarnitine (C2), propionylcarnitine (C3), adipylcarnitine (C6-DC), and octadecadienylcarnitine (C18:2) in the FET group, whereas no significant differences were observed in malonylcarnitine (C3-DC), butyrylcarnitine (C4), valerylcarnitine (C5), or 14 other acylcarnitines between the two ART groups (Figure. 4). 3. RNA-Seq Analysis of Umbilical Cord Vascular Tissues Heatmaps and Volcano Plots of DEGs RNA sequencing identified differentially expressed genes (DEGs) in umbilical cord vascular tissues across group comparisons (FET vs. Ctrl, FET vs. ET, ET vs. Ctrl). Heatmaps illustrated overall gene expression patterns, while volcano plots highlighted DEGs with log₂ fold change > 1 and false discovery rate (FDR) < 0.05 (Fig. 5 ). GO and KEGG Enrichment Results Fig. 6 shows canonical pathway enrichment results for the ET and FET groups, respectively. Subpanels a–d within A, B, and C correspond to Biological Processes, Cellular Components, Molecular Functions, and functional enrichment summaries, respectively. KEGG analysis identified enriched signaling pathways among DEGs, and GO analysis highlighted pathways with significant alterations. Core Genes Associated with Protein Synthesis Processes Four categories of DEGs were identified: genes upregulated in the Ctrl group vs. FET group; genes upregulated in the Ctrl group vs. ET group; genes downregulated in the Ctrl group vs. FET group; and genes downregulated in the Ctrl group vs. ET group. An Upset plot illustrating intersecting genes across these comparisons is shown in Fig. 7 A-C. The top 10 hub genes from the Upset analysis are highlighted in Fig. 7 D. Core interacting genes shared between the Ctrl vs. FET and Ctrl vs. ET comparisons (both upregulated and downregulated) exhibited high similarity and were functionally linked to protein metabolic processes. Key identified genes include: NUP50, USP15, AKR1B1, APAF1, ARHGEF2, ATP1A1, AURKA, BCAR1, BCL2L11, CTNNA1, FCGR3A, FERMT2, FGFR1, FKTN, FYN, GAB2, GATM, GRIK2, HSPA8, IGF1R, LAMA2, NEDD4, NEDD9, PFKP, PHKB, PKM, PTK2, SDC2, PYGL, NOS3, SCRIB, NR3C1, WASF1, YES1, FUT8, LIMS2, SDCBP, ZFP36L1 (Fig. 7 E). A complete list of differentially expressed transcripts is provided in Supplementary Table 1. Discussion 1. Newborn Weight and Amino Acid Profiles This study explored the impacts of cold-stress environment on the development of the embryo. A significant weight increase was observed in the FET (frozen embryo transfer) group, aligning with previous observations that IVF-conceived children have a higher incidence of macrosomia(Yu et al. 2022). This suggests potential effects of frozen-embryo procedures, whereas the ET (fresh embryo transfer) group showed weight similar to natural conception controls. This distinction provides valuable clinical guidance: when clinically permitted, ET may be prioritized for reducing overweight or macrosomia risk. The higher overweight incidence in FET(Litzky et al. 2018; Berntsen and Pinborg 2018; Rosalik et al. 2021) has remained poorly understood, even no any reasonable explanations in clinical reproduction or biology, thus our study points to cryopreservation as a key driver. We propose a novel theory: Cryopreservation alters the embryonic epigenetic landscape or transcriptional profiling, leading to gene expression changes in growth-regulatory pathways. For example, upregulation of placental nutrient uptake genes could enhance fetal nutrient absorption from the mother(Rosario et al. 2015), contributing to increased weight. This theory opens new avenues to investigate how cryopreservation influences fetal growth via specific molecular routes. Essential Amino Acids : Both ET and FET groups exhibited reduced neonatal blood levels of tyrosine (TYR) and methionine (MET) compared to controls. Tyrosine, a precursor for dopamine, norepinephrine, and thyroid hormones(Jongkees et al. 2015), may impact metabolic regulation when depleted(Zhang et al. 2022). Methionine is critical for DNA methylation, a key process in gene regulation and cell function(Espe et al. 2023), making its reduction similarly noteworthy. Beyond shared effects, distinct patterns emerged—our primary focus. Tyrosine’s metabolite, succinylacetone (SA), showed striking group differences: ET neonates had lower SA than controls, suggesting disrupted tyrosine metabolism with potential inhibition of SA production. Conversely, FET neonates exhibited significantly higher SA than both controls and ET, indicating cryopreservation-thawing uniquely modulates tyrosine metabolism. This “over-correction” in FET raises questions about elevated SA’s health implications, as excessive levels may disrupt physiological process(Priestley et al. 2020). To our knowledge, this opposing SA pattern between ET and FET is a pioneering finding, highlighting cryopreservation as the critical variable driving divergent SA regulatory mechanisms. Notably, elevated SA in FET correlated with increased birth weight, suggesting a link between altered SA metabolism and fetal growth. Since reduced SA may indicate impaired growth pathways(Erickson and Action 1969), its rise in FET could reflect cryopreservation-triggered anti-cold stress mechanisms enhancing body mass. This supports our theory: cryopreservation may alter tyrosine-metabolizing enzymes (e.g., tyrosine aminotransferase, fumarylacetoacetate hydrolase), leading to SA accumulation and growth-promoting pathway activation. Whether SA directly mediates growth or reflects broader metabolic adaptations requires further investigation, including longitudinal health tracking and mechanistic studies. Non-essential Amino Acids : Both IVF groups showed reduced arginine (ARG) and citrulline (CIT) versus controls. Arginine is vital for nitric oxide synthesis, supporting vascular and immune function(Martí and Reith 2021), while citrulline’s role in the urea cycle links its reduction to potential nitrogen metabolism disruption(Milner and Visek 1975). Ornithine (ORN), a key urea cycle component and polyamine precursor(Caldovic et al. 2015) with antioxidant-modulating properties, exhibited a unique pattern: FET (but not ET) significantly increased ORN levels. This cryopreservation-specific effect suggests frozen stress may alter ORN transporters or enzymes, with low ORN potentially increasing oxidative stress and metabolic dysregulation(Couchet et al. 2021). Essential amino acids (EAA) depend entirely on maternal-fetal transfer(Battaglia et al. 2001), so their disruption implies IVF-related factors may impair placental transport. Non-essential amino acids (NEAA), endogenously synthesized in the body(Hou et al. 2015), may be affected by IVF factors like cryopreservation inhibiting maternal/fetal synthetic mechanisms. In summary, altered amino acid profiles associated with weight changes indicate both IVF procedures impact fetal metabolism, with cryopreservation uniquely altering tyrosine/SA and ORN pathways. Future research should clarify underlying mechanisms and long-term consequences to optimize IVF protocols. 2 . Carnitines Associated with Amino Acid Metabolism Thirteen of 30 measured carnitines differed across groups, offering insights into metabolic adaptations. Carnitines facilitate mitochondrial fatty acid β-oxidation, regulating energy metabolism(Xiang et al. 2025), with implications for amino acid metabolism and fetal growth. Carnitines and Essential Amino Acids : L-carnitine (C0), critical for fatty acid transport(Virmani et al. 2022), was elevated in both IVF groups but lower in FET than ET. This dynamic may reflect energy demands for EAA metabolism: EAA catabolism/anabolism (e.g., tyrosine-derived neurotransmitter synthesis) requires ATP from fatty acid oxidation(Corsetti et al. 2024). ET’s higher C0 may support increased EAA processing, while FET’s reduction suggests shifted energy pathways potentially impairing EAA utilization. Elevated propionylcarnitine (C3) and tetradecanoylcarnitine (C14) in ET (vs. Ctrl) further support upregulated fatty acid oxidation—odd-chain and long-chain, respectively(Adams et al. 2009)—to fuel EAA-related processes. Carnitines and Non-essential Amino Acids : Acetylcarnitine (C2), convertible to acetyl-CoA (a carbon source for NEAA synthesis)(Scafidi et al. 2010), increased in ET but decreased in FET. Methylmalonylcarnitine (C4DC + C5OH), linked to TCA cycle precursors for NEAA(Violante et al. 2013), showed a similar opposing trend. These IVF-specific differences, attributable to cryopreservation, represent novel findings warranting deeper investigation. Carnitines and Combined Amino Acid Metabolism : 3-hydroxyoctadecenoylcarnitine (C18:1OH), involved in unsaturated fatty acid oxidation(Violante et al. 2013), may support energy-dependent amino acid metabolism. FET’s reduced long-chain acyl-carnitines (C10, C10:1, C14:1, C14:2, C18:2) suggest altered fatty acid metabolism favoring fat storage, contributing to increased weight. Concurrently, carnitine-mediated amino acid changes may enhance tissue growth, as amino acids are building blocks for development. Clinical Significance of Null Findings : Seventeen carnitines (e.g., C3DC, C4) and multiple amino acids showed no group differences, indicating preserved core metabolic pathways. This selectivity—IVF impacts specific carnitine-dependent pathways rather than global disruption—suggests metabolic resilience or compensatory mechanisms, reassuring that IVF does not systemically threaten amino acid homeostasis. 3. Bioinformatics Analysis of Umbilical Cord Vascular Tissue Following metabolic analyses, RNA-seq of umbilical cord vascular tissues revealed molecular mechanisms. To our knowledge, this is the first comprehensive combined MS/MS and RNAseq study comparing human neonatal samples across natural conception, ET, and FET groups. Its novelty lies in: (1) unprecedented focus on fresh vs. frozen IVF subgroups in human neonatal vessels; (2) ethical use of umbilical cord as a fetal tissue surrogate, addressing gaps in fetal research(Sakurai et al. 2019). Biological Processes & Cellular Components : Ctrl vs. IVF comparisons enriched processes like “Growth” and “Regulation of Growth,” linking amino acid-dependent protein synthesis(Suryawan et al. 2007) to weight differences. Ctrl vs. FET highlighted “Circulatory System Development,” critical for nutrient transport(Gualtieri et al. 2013), with amino acids driving vessel-related cell proliferation(Huang et al. 2017).FET-altered cellular components (e.g., “Anchoring Junction,” “Growth Hormone Receptor Complex”) may disrupt cell signaling and nutrient uptake, impacting amino acid metabolism and growth. Molecular Functions & Pathways : Enriched functions in Ctrl vs. FET (e.g.,“Protein Tyrosine Kinase Activity”) support signal transduction roles in nutrient utilization(Gualtieri et al. 2013). Pathways like “Prolactin Receptor Signaling” and “Cell Junction Organization”(Bole-Feysot et al. 1998)may mediate FET’s effects on metabolic regulation and nutrient exchange, contributig to weight gain. Overall, functional enrichment links transcriptional profiles to birth weight and amino acid metabolism, identifying 38 hub genes whose interactions form a metabolic regulatory network. 4. Hub-Genes in Amino Acid Metabolism Essential Amino Acids : HSPA8 (a chaperone ensuring proper protein folding (Bonam et al. 2019) and PKM (glycolytic enzyme supplying AA precursors) (Violante et al. 2013)showed differential expression, potentially disrupting EAA metabolism and development. Non-essential Amino Acids : FGFR1 (regulates serine/glycine)(Hu et al. 2018), AURKA (urea cycle involvement), and GATM (creatine synthesis)(Ingoglia et al. 2021)varied across groups, implicating NEAA imbalance in fetal growth. Carnitine-related Genes : PHKB (modulates carnitine demand via glycogenolysis)(Burwinkel et al. 1997), NOS3 (inhibits fatty acid oxidation)(Dick et al. 2020), and ZFP36L1 (regulates lipid metabolism transcripts)(Tarling et al. 2017)may link carnitine-mediated energy metabolism to growth via IVF procedures. Protein Synthesis and Modification : a) Translation/Folding : Coordinated changes in GATM, ZFP36L1, and HSPA8 may impair protein synthesis(Tarling et al. 2017). b) Ubiquitin-Proteasome System : Altered NEDD4, USP15, and APAF1 could disrupt protein homeostasis(Fan et al. 2015). c) Signal Transduction : Kinases like YES1 and FYN, alongside NR3C1, may dysregulate translation(Lu et al. 2021), with FET upregulating anti-apoptotic factors (e.g., BCL-2) as a stress response. d) Network Interactions : The 38 hub genes form a regulatory network where disruptions in amino acid metabolism ripple through protein synthesis and signaling pathways, influencing neonatal development. These genes offer targets to optimize IVF outcomes. 5. New Theory Integrating findings with evolutionary cold-environment adaptation concepts, we propose: Extreme cryopreservation stress environment (-196°C) awakes conserved anti-cold mechanisms, altering gene expression profile to disrupt amino acid/carnitine metabolism, ultimately increasing body mass/weight. This “Cryopreservation→gene changes→metabolic alterations→phenotypic (weight) changes” hypothesis explains FET’s macrosomia link. Notably, FET’s weight increase aligns with Bergmann’s rule—endotherms in cold environments evolve larger size to minimize heat loss (Tabh and Nord 2023). Cryopreservation (-196°C), an extremely cold-stress environment, may trigger ancient cold-responsive pathways, mirroring evolutionary adaptations. This links extreme cold stress to conserved genetic programs promoting growth, supporting our theory (Fig. 1 B). 6. Limitations A key limitation is the study’s focus on human neonatal samples (blood, umbilical tissue) with observational analyses of phenotypes, metabolites, and transcripts. While strong associations exist between cryopreservation, metabolic/gene expression changes, and birth weight, deep mechanistic validation (e.g., SA manipulation, gene knockouts) in animal models was beyond our scope. Causal relationships—such as whether SA or specific hub genes directly mediate growth—remain to be confirmed. Future preclinical studies should complement these human with obesity data to clarify mechanisms. In conclusion, this study integrates RNAseq and tandem mass spectrometry to systematically compare cryopreserved, fresh IVF, and naturally conceived neonates at the physiological, metabolic, and transcriptional levels. It pioneers insights into amino acid/carnitine perturbations, identifies hub genes, and proposes a novel evolutionary theory explaining frozen environment-associated overweight. These findings underscore the need for long-term metabolic surveillance of cryopreserved IVF offspring, informing safe optimization of assisted reproductive technologies and early prevention of obesity. Declarations Acknowledgements Not applicable. Author Contributors HJ :Writing–original draft, Methodology，Conceptualization. PZ, XZ and YC : Writing–review & editing, Conceptualization. QZ and YY : Writing–review & editing, Supervision, Methodology. YZ , SL and TZ : Writing–review & editing, Formal analysis, Conceptualization. XC, YZ and YZ : Writing–review & editing, Methodology, Conceptualization. ZX : Writing–review & editing, Supervision, Funding acquisition.All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work. Funding The authors declare funding was received for the research work of this article. These fundings are Wuxi Taihu-Rencai Fund (number: 2021-19), the Key Lab of Perinatal Bio-Medicine (2023). Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Ethics statement The studies involving humans were approved by the Research Ethics Committee (Wuxi Maternity and Child Health Care Hospital). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. Consent for publication Not applicable. Conflict of Interest The authors declare that they have no competing interests. References Adams SH, Hoppel CL, Lok KH, Zhao L, Wong SW, Minkler PE, Hwang DH, Newman JW, Garvey WT (2009) Plasma acylcarnitine profiles suggest incomplete long-chain fatty acid beta-oxidation and altered tricarboxylic acid cycle activity in type 2 diabetic african-american women. 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1","display":"","copyAsset":false,"role":"figure","size":190893,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Newborn weight in the frozen embryos, fresh embryos, and natural conception group. (B) New Anti-Cold-Stress Theory: Explaning why FET could cause an increase of birth weight and higher incidence of macrosomia. Cold stress (-196°C) may awake ancient anti-cold related genes evoluted from icy-cold stages long long time ago. Then the expression of those awoken genes could be changed. In turn, the changed transcriptional profile altered mETabolic signals, resulting in an increased body mass and macrosomia, as a response and consequence to the -196°C environment at early stage of human life. **, p \u0026lt; 0.01. Ctrl: naturally conceived pregnancies; ET: Fresh embryo transfer; FET: frozen-thawed embryo transfer.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8284673/v1/a147a09958f2eccb322169e8.png"},{"id":98246008,"identity":"df86e2fe-c10d-47d0-979a-fb9fae50f6d2","added_by":"auto","created_at":"2025-12-15 16:18:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":88291,"visible":true,"origin":"","legend":"\u003cp\u003eEssential amino acid concentrations in newborn blood samples analyzed by tandem mass spectrometry. *P \u0026lt; 0.05; **P \u0026lt; 0.01; ****P \u0026lt; 0.0001. Ctrl: naturally conceived pregnancies; ET: Fresh embryo transfer; FET: frozen-thawed embryo transfer.\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8284673/v1/14d9d6d2986b870886ed3199.png"},{"id":98245963,"identity":"4fb36113-10ea-45e4-9eb1-93e6f85b0f26","added_by":"auto","created_at":"2025-12-15 16:18:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":65175,"visible":true,"origin":"","legend":"\u003cp\u003eTandem mass spectrometry quantified non-essential amino acids in newbone blood samples. *P \u0026lt; 0.05; **P \u0026lt; 0.01; ****P \u0026lt; 0.0001. Ctrl: naturally conceived pregnancies; ET: Fresh embryo transfer; FET: frozen-thawed embryo transfer.\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8284673/v1/4dd90a0bd935c633db601335.png"},{"id":98434538,"identity":"6261b104-807f-47b0-8bbd-f0f532e623dd","added_by":"auto","created_at":"2025-12-17 16:52:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":75597,"visible":true,"origin":"","legend":"\u003cp\u003eTandem mass spectrometry quantified carnitines in newbone blood samples. *P \u0026lt; 0.05; **P \u0026lt; 0.01; ****P \u0026lt; 0.0001. Ctrl: naturally conceived pregnancies; ET: Fresh embryo transfer; FET: frozen-thawed embryo transfer.\u003c/p\u003e","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8284673/v1/31770bef22d3b5afaca096c0.png"},{"id":98246038,"identity":"ab1b4060-e6d8-468c-a724-e9b3df4350e5","added_by":"auto","created_at":"2025-12-15 16:18:45","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":299576,"visible":true,"origin":"","legend":"\u003cp\u003e(A)Volcano plots for the comparison of differentially expressed transcripts. FET versus Ctrl , (B)ET versus Ctrl and (C)FET versus ET. (D)The color gradient represents fold change. The size of each dot is proportional to the significance value (-LOG10 transformation of FDR value). The vertical and horizontal dashed lines represent fold change and FDR cut-off values respectively. Heatmap plots for the differentially expressed transcripts between different paired groups: FET versus Ctrl , (E) ET versus Ctrl and（F）FET versus ET. The columns and rows represent sample and transcripts respectively. The color gradient of the heatmap represents the scaled expression value. Abbreviations: Ctrl (naturally conceived pregnancies), FET (frozen-thawed embryo transfer) and ET (fresh embryo transfer).\u003c/p\u003e","description":"","filename":"figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-8284673/v1/c7ac45f1b6330e36f791fbca.png"},{"id":98245903,"identity":"56f5c9da-772f-4647-ac99-d38602bac5fc","added_by":"auto","created_at":"2025-12-15 16:18:35","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":276129,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Top representative enriched terms for the DE genes of FET versus ET, (B) FET versus Ctrl and (C) ET versus Ctrl. The four sub-plots (from the left to the right) represent results for GO: biological process, GO: cellular component, GO: molecular function, and Reactome pathways respectively. The color gradient represents the significance value (-LOG10 transformation of p value) of the enrichment test.\u003c/p\u003e","description":"","filename":"figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-8284673/v1/d092583a89db2e18101727c0.png"},{"id":98245955,"identity":"fdb51335-4513-4a16-823a-c088504458e5","added_by":"auto","created_at":"2025-12-15 16:18:39","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":251139,"visible":true,"origin":"","legend":"\u003cp\u003eInteracting network among genes associated with frozen stress Representative enriched terms associated with frozen stress at system level. (A) Venn plots for the comparison of the differentially expressed transcripts between and among the classified gene lists (up and down classification): FET versus Ctrl and ET versus Ctrl, (B)FET versus ET and ET versus Ctrl, (C) FET versus ET and FET versus Ctrl. (D)Venn plot for the comparison of the differentially expressed transcripts between and among FET versus Ctrl, ET versus Ctrl and FET versus ET. The color gradient is proportional to the number of transcripts. (E) The lines represent gene interactions annotated by the STRING database. The thickness of the line is proportional to the combined score of interaction. The border size of each gene node is proportional to the degree of interactions.\u003c/p\u003e","description":"","filename":"figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-8284673/v1/a99fb94a4f54fff5c8348131.png"},{"id":99316343,"identity":"f7d618ff-490f-4765-97a6-30ce8cbf2eb3","added_by":"auto","created_at":"2025-12-31 16:28:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2170328,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8284673/v1/d63f493c-a6ca-4db3-b112-f78db655f35c.pdf"},{"id":98246025,"identity":"32a54038-60ca-4bbb-97cf-54659c150548","added_by":"auto","created_at":"2025-12-15 16:18:45","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":17725,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydata1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8284673/v1/ed44a48491d1c0b36c25deb5.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Why In Vitro Fertilization Increases Macrosomia Risk? A New Anti-Cold-Stress Theory Mirroring Bergmann's Evolutionary Adaptation Rule","fulltext":[{"header":"Introduction","content":"\u003cp\u003eInfertility remains a global health challenge, and assisted reproductive technologies (ARTs), particularly in vitro fertilization (IVF), have revolutionized reproductive medicine since the first IVF birth in 1978. However, growing evidence indicates IVF-conceived pregnancies carry unique risks, such as an elevated incidence of macrosomia or increased birth-weight compared to natural conception, highlighting the need to understand underlying mechanisms (Yu et al. 2022). These clinical findings indicate that while IVF provides a valuable solution for infertility, it also exposes pregnancies and offspring to certain health challenges in early prevention of overweight obesity.\u003c/p\u003e\u003cp\u003eResearch on fetal-origin diseases has revealed that early-life environmental exposures can induce lasting molecular changes via epigenetic imprinting and developmental programming, increasing long-term risks of metabolic disorders like diabetes(Zhu et al. 2019). These findings underscore the importance of investigating how IVF procedures affect embryonic development at molecular and metabolic levels. Our study firstly integrates tandem mass spectrometry (MS/MS) and RNA sequencing to address this gap in study of human tissue and blood samples. MS/MS analyzed amino acids (AAs) and carnitine in newborn blood\u0026mdash;key markers of protein and energy metabolism\u0026mdash;while RNA sequencing explored transcriptional profiles in umbilical cord vasculature. This choice of human tissue avoids ethical issues with fetal samples and reflects fetal circulatory and nutrient exchange vessels, representing a pioneering approach in human fetal development research (Haniffa et al. 2021).\u003c/p\u003e\u003cp\u003eNotably, previous IVF studies rarely differentiated outcomes between fresh (ET) and frozen-thawed embryo transfer (FET). Cryopreservation in FET subjects embryos to extreme cold-stress environment (-196\u0026deg;C), halting cell division and inducing physiological stress, yet the mechanisms linking this stress to outcomes remain unclear. Our study compares ET and FET, integrating birth weight (phenotype), AA/carnitine levels (biochemistry), and gene expression (molecular signatures) to bridge gaps between phenotype, metabolism, and transcription.\u003c/p\u003e\u003cp\u003eBy synthesizing multi-level data, we aim to elucidate how the extreme cold-stress environment impacts fetal and neonate development and propose a new theory explaining the higher overweight risk in FET offspring.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cb\u003eGeneral Information\u003c/b\u003e A total of 846 full-term pregnant women (gestational age\u0026thinsp;\u0026ge;\u0026thinsp;37 weeks) admitted to our hospital between 2018 and 2022 were enrolled. Their neonates were categorized into three groups: 492 neonates from naturally conceived pregnancies (Ctrl, n\u0026thinsp;=\u0026thinsp;492), 85 from fresh embryo transfer (ET, n\u0026thinsp;=\u0026thinsp;85), and 287 from frozen embryo transfer (FET, n\u0026thinsp;=\u0026thinsp;287). Pairwise comparisons of maternal baseline characteristics between the Ctrl group and the two embryo transfer groups (ET and FET) showed no statistical differences (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). All procedures in this study were approved by the Hospital Ethical Committee.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eInclusion criteria\u003c/strong\u003e\u003cp\u003eSingleton pregnancy; full-term neonate (gestational age\u0026thinsp;\u0026ge;\u0026thinsp;37 weeks, birth weight\u0026thinsp;\u0026ge;\u0026thinsp;2500 g); signed informed consent.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eExclusion criteria\u003c/strong\u003e\u003cp\u003eTwin or multiple pregnancies; preterm neonate (gestational age\u0026thinsp;\u0026lt;\u0026thinsp;37 weeks, birth weight\u0026thinsp;\u0026lt;\u0026thinsp;2500 g).\u003c/p\u003e\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eNewborn Birth Weight Measurement\u003c/h2\u003e\u003cp\u003eNewborn weight was measured by trained medical personnel within 1\u0026ndash;5 minutes after delivery. Newborns were placed on a pre-warmed radiant warmer with all clothing/coverings removed. Naked weight (in grams) was recorded using a daily-calibrated electronic infant scale (Seca 334, precision\u0026thinsp;\u0026plusmn;\u0026thinsp;5 g) after the reading stabilized, with dual verification by trained staff.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eBlood Spot (DBS) Sample Collection\u003c/h3\u003e\n\u003cp\u003eWithin 24 hours post-delivery, DBS samples were collected from the neonatal lateral heel using a sterile lancet (puncture depth\u0026thinsp;\u0026le;\u0026thinsp;2.0 mm). The first blood drop was discarded to avoid tissue fluid contamination. The second drop was allowed to form a blood bead\u0026thinsp;\u0026ge;\u0026thinsp;3 mm in diameter, then gently and perpendicularly applied to the center of a dedicated filter paper card (e.g., Whatman 903\u0026reg;) until complete penetration, forming uniform, concentric blood spots\u0026thinsp;\u0026ge;\u0026thinsp;8 mm in diameter on both sides. Three independent blood spots were collected per neonate. Cards were dried horizontally at room temperature (15\u0026ndash;25\u0026deg;C) in the dark for \u0026ge;\u0026thinsp;3 hours until dark brown. Dried cards were sealed in moisture-barrier bags with desiccant and transported refrigerated (2\u0026ndash;8\u0026deg;C) to the laboratory. Strict precautions were taken to avoid duplicate spotting, slanted contact with filter paper, or stacking before complete drying. Hematocrit (HCT) levels were monitored, and quantitative results were corrected for abnormal HCT.\u003c/p\u003e\n\u003ch3\u003eTandem Mass Spectrometry (MS/MS) Analysis\u003c/h3\u003e\n\u003cp\u003eFrom each DBS sample, a 3.2 mm diameter punch (equivalent to ~\u0026thinsp;3.2 \u0026micro;L whole blood) was transferred to a microplate well. Methanol containing stable isotope-labeled internal standards (e.g., \u0026sup1;\u0026sup3;C₆-phenylalanine, d₃-palmitoylcarnitine) was added for oscillatory extraction, with isotope dilution used to correct matrix effects. Extracts were either derivatized with butanol/HCl (65\u0026deg;C, 15 minutes) or injected directly via flow injection analysis (FIA) into a triple quadrupole tandem mass spectrometer, with ionization in positive electrospray mode (ESI+). Target metabolites were detected using multiple reaction monitoring (MRM) of characteristic precursor/product ion pairs: for amino acids, derivatized precursor ions and carboxyl-loss fragment ions (e.g., phenylalanine butyl ester m/z 222\u0026rarr;104); for acylcarnitines, acyl chain-specific precursor ions and the common m/z 85 fragment ion (carnitine moiety). Collision energy was optimized (15\u0026ndash;25 eV) for maximum signal-to-noise ratio. Metabolite concentrations (\u0026micro;mol/L) were quantified using analyte-to-internal standard peak area ratios, referenced against a six-point calibration curve. Key metabolic ratios were calculated (e.g., phenylalanine/tyrosine [Phe/Tyr] for phenylketonuria risk, propionylcarnitine/acetylcarnitine [C3/C2] for methylmalonic acidemia). Results were compared against laboratory-established cutoffs (99th percentile from 100,000 healthy neonates). Each batch included low-, medium-, and high-concentration quality control (QC) samples, with intra-batch precision (CV)\u0026thinsp;\u0026lt;\u0026thinsp;10% and accuracy (recovery) 90\u0026ndash;110%. Background interference was eliminated using blank filter paper and double-blank samples. This method enables quantitative analysis of \u0026gt;\u0026thinsp;30 amino acids and acylcarnitines per sample within 3 minutes, screening for \u0026gt;\u0026thinsp;40 inherited metabolic diseases (e.g., phenylketonuria [PKU], maple syrup urine disease [MSUD], medium-chain acyl-CoA dehydrogenase deficiency [MCAD]) with a false-positive rate\u0026thinsp;\u0026lt;\u0026thinsp;0.1%.\u003c/p\u003e\u003cp\u003eMass scanning covered all target amino acid molecular weights, with collision energy optimized for fragment ion signals. Raw data underwent noise reduction and calibration, followed by peak identification and quantification via specialized software. Amino acid presence and concentrations were confirmed by comparing sample chromatograms and mass spectra with reference standards.\u003c/p\u003e\n\u003ch3\u003eRNA-seq and Bioinformatics Analysis\u003c/h3\u003e\n\u003cp\u003eRNA-seq analysis followed our previously described protocol(Zhang, Zhou, Zheng, Zheng, Zhang, Liu, Tang and Xu 2024).Briefly, clean reads were aligned to the GRCh38 reference genome (ENSEMBL annotation) using Hisat2 (v2.2.1). Differentially expressed genes (DEGs) at the transcript level were identified using the DESeq2 package, with thresholds of log₂ fold change (log₂fc)\u0026thinsp;\u0026gt;\u0026thinsp;1 (absolute fold change\u0026thinsp;\u0026gt;\u0026thinsp;2) and false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Functional enrichment analyses (Gene Ontology [GO]: biological processes, cellular components, molecular functions; pathways: Reactome, KEGG(Kanehisa and Goto 2000) were performed using the ToppGene tool. Protein-protein interactions were annotated via the STRING database(Szklarczyk et al. 2019).\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eAll data are original raw data and available upon request. MS/MS and bioinformatic process and analysis were treated with a double-blind manner. Analyses were performed using SPSS 27.0. Continuous data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (x̄ \u0026plusmn; s). Between-group comparisons were analyzed using Student\u0026rsquo;s t-test, with P\u0026thinsp;\u0026le;\u0026thinsp;0.05 indicating statistical significance. Graphs and significance annotations were generated using GraphPad Prism 9.5.0.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003e1. Increased Birth Weight in IVF-Conceived Neonates\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFetal birth weights were significantly higher in IVF-conceived neonates than in naturally conceived ones. Specifically, the frozen embryo transfer (FET) group showed the most substantial increase, with a statistically significant elevation compared to the natural conception control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e2. Metabolomic Profiling\u0026ndash;Tandem Mass Spectrometry\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTandem mass spectrometry was used to determine the concentration profiles of multiple amino acids in naturally conceived individuals (Ctrl group) and those conceived via assisted reproductive technologies (ART). Multidimensional comparative analysis among the Ctrl, frozen embryo transfer (FET), and fresh embryo transfer (ET) groups revealed the following:\u003c/p\u003e\u003cp\u003e\u003cb\u003eEssential Amino Acids\u003c/b\u003e Compared to the Ctrl group, both the ET and FET groups exhibited reduced levels of the essential amino acids methionine (MET) and tyrosine (TYR), as well as succinylacetone (AS) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). No significant differences were observed in phenylalanine (PHE), valine (VAL), or leucine (LEU) levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eNon-Essential Amino Acids\u003c/b\u003e Compared to the Ctrl group, the FET group showed significantly reduced levels of the non-essential amino acids arginine (ARG), ornithine (ORN), and citrulline (CIT), while the ET group exhibited decreased concentrations of ORN and CIT. No significant differences were detected in proline (PRO), alanine (ALA), or glycine (GLY) levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eCarnitine\u003c/b\u003e Carnitine is an endogenous enzyme-like compound critical for fatty acid metabolism. Thirteen acylcarnitines differed significantly among the three groups. Relative to the Ctrl group, the ET group had markedly higher levels of free carnitine (C0), acetylcarnitine (C2), propionylcarnitine (C3), methylmalonylcarnitine (C4DC\u0026thinsp;+\u0026thinsp;C5OH), tetradecanoylcarnitine (C14), and 3-hydroxy-octadecenoylcarnitine (C18:1-OH), but significantly lower octanoylcarnitine (C8). In the FET group compared to the Ctrl group, only free carnitine (C0) was elevated, while methylmalonylcarnitine (C4DC\u0026thinsp;+\u0026thinsp;C5OH), adipylcarnitine (C6DC), octanoylcarnitine (C8), sebacenylcarnitine (C10:1), tetradecenoylcarnitine (C14:1), tetradecadienylcarnitine (C14:2), and octadecadienylcarnitine (C18:2) were all significantly reduced. Direct comparison between the ET and FET groups showed lower concentrations of free carnitine (C0), acetylcarnitine (C2), propionylcarnitine (C3), adipylcarnitine (C6-DC), and octadecadienylcarnitine (C18:2) in the FET group, whereas no significant differences were observed in malonylcarnitine (C3-DC), butyrylcarnitine (C4), valerylcarnitine (C5), or 14 other acylcarnitines between the two ART groups (Figure. 4).\u003c/p\u003e\u003cp\u003e\u003cb\u003e3. RNA-Seq Analysis of Umbilical Cord Vascular Tissues\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eHeatmaps and Volcano Plots of DEGs\u003c/b\u003e RNA sequencing identified differentially expressed genes (DEGs) in umbilical cord vascular tissues across group comparisons (FET vs. Ctrl, FET vs. ET, ET vs. Ctrl). Heatmaps illustrated overall gene expression patterns, while volcano plots highlighted DEGs with log₂ fold change\u0026thinsp;\u0026gt;\u0026thinsp;1 and false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eGO and KEGG Enrichment Results\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows canonical pathway enrichment results for the ET and FET groups, respectively. Subpanels a\u0026ndash;d within A, B, and C correspond to Biological Processes, Cellular Components, Molecular Functions, and functional enrichment summaries, respectively. KEGG analysis identified enriched signaling pathways among DEGs, and GO analysis highlighted pathways with significant alterations.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eCore Genes Associated with Protein Synthesis Processes\u003c/b\u003e Four categories of DEGs were identified: genes upregulated in the Ctrl group vs. FET group; genes upregulated in the Ctrl group vs. ET group; genes downregulated in the Ctrl group vs. FET group; and genes downregulated in the Ctrl group vs. ET group. An Upset plot illustrating intersecting genes across these comparisons is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA-C. The top 10 hub genes from the Upset analysis are highlighted in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD. Core interacting genes shared between the Ctrl vs. FET and Ctrl vs. ET comparisons (both upregulated and downregulated) exhibited high similarity and were functionally linked to protein metabolic processes. Key identified genes include: NUP50, USP15, AKR1B1, APAF1, ARHGEF2, ATP1A1, AURKA, BCAR1, BCL2L11, CTNNA1, FCGR3A, FERMT2, FGFR1, FKTN, FYN, GAB2, GATM, GRIK2, HSPA8, IGF1R, LAMA2, NEDD4, NEDD9, PFKP, PHKB, PKM, PTK2, SDC2, PYGL, NOS3, SCRIB, NR3C1, WASF1, YES1, FUT8, LIMS2, SDCBP, ZFP36L1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE). A complete list of differentially expressed transcripts is provided in Supplementary Table\u0026nbsp;1.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cb\u003e1. Newborn Weight and Amino Acid Profiles\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study explored the impacts of cold-stress environment on the development of the embryo. A significant weight increase was observed in the FET (frozen embryo transfer) group, aligning with previous observations that IVF-conceived children have a higher incidence of macrosomia(Yu et al. 2022). This suggests potential effects of frozen-embryo procedures, whereas the ET (fresh embryo transfer) group showed weight similar to natural conception controls. This distinction provides valuable clinical guidance: when clinically permitted, ET may be prioritized for reducing overweight or macrosomia risk.\u003c/p\u003e\u003cp\u003eThe higher overweight incidence in FET(Litzky et al. 2018; Berntsen and Pinborg 2018; Rosalik et al. 2021) has remained poorly understood, even no any reasonable explanations in clinical reproduction or biology, thus our study points to cryopreservation as a key driver. We propose a novel theory: Cryopreservation alters the embryonic epigenetic landscape or transcriptional profiling, leading to gene expression changes in growth-regulatory pathways. For example, upregulation of placental nutrient uptake genes could enhance fetal nutrient absorption from the mother(Rosario et al. 2015), contributing to increased weight. This theory opens new avenues to investigate how cryopreservation influences fetal growth via specific molecular routes.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEssential Amino Acids\u003c/b\u003e: Both ET and FET groups exhibited reduced neonatal blood levels of tyrosine (TYR) and methionine (MET) compared to controls. Tyrosine, a precursor for dopamine, norepinephrine, and thyroid hormones(Jongkees et al. 2015), may impact metabolic regulation when depleted(Zhang et al. 2022). Methionine is critical for DNA methylation, a key process in gene regulation and cell function(Espe et al. 2023), making its reduction similarly noteworthy.\u003c/p\u003e\u003cp\u003eBeyond shared effects, distinct patterns emerged\u0026mdash;our primary focus. Tyrosine\u0026rsquo;s metabolite, succinylacetone (SA), showed striking group differences: ET neonates had lower SA than controls, suggesting disrupted tyrosine metabolism with potential inhibition of SA production. Conversely, FET neonates exhibited significantly higher SA than both controls and ET, indicating cryopreservation-thawing uniquely modulates tyrosine metabolism. This \u0026ldquo;over-correction\u0026rdquo; in FET raises questions about elevated SA\u0026rsquo;s health implications, as excessive levels may disrupt physiological process(Priestley et al. 2020). To our knowledge, this opposing SA pattern between ET and FET is a pioneering finding, highlighting cryopreservation as the critical variable driving divergent SA regulatory mechanisms.\u003c/p\u003e\u003cp\u003eNotably, elevated SA in FET correlated with increased birth weight, suggesting a link between altered SA metabolism and fetal growth. Since reduced SA may indicate impaired growth pathways(Erickson and Action 1969), its rise in FET could reflect cryopreservation-triggered anti-cold stress mechanisms enhancing body mass. This supports our theory: cryopreservation may alter tyrosine-metabolizing enzymes (e.g., tyrosine aminotransferase, fumarylacetoacetate hydrolase), leading to SA accumulation and growth-promoting pathway activation. Whether SA directly mediates growth or reflects broader metabolic adaptations requires further investigation, including longitudinal health tracking and mechanistic studies.\u003c/p\u003e\u003cp\u003e\u003cb\u003eNon-essential Amino Acids\u003c/b\u003e: Both IVF groups showed reduced arginine (ARG) and citrulline (CIT) versus controls. Arginine is vital for nitric oxide synthesis, supporting vascular and immune function(Mart\u0026iacute; and Reith 2021), while citrulline\u0026rsquo;s role in the urea cycle links its reduction to potential nitrogen metabolism disruption(Milner and Visek 1975).\u003c/p\u003e\u003cp\u003eOrnithine (ORN), a key urea cycle component and polyamine precursor(Caldovic et al. 2015) with antioxidant-modulating properties, exhibited a unique pattern: FET (but not ET) significantly increased ORN levels. This cryopreservation-specific effect suggests frozen stress may alter ORN transporters or enzymes, with low ORN potentially increasing oxidative stress and metabolic dysregulation(Couchet et al. 2021).\u003c/p\u003e\u003cp\u003eEssential amino acids (EAA) depend entirely on maternal-fetal transfer(Battaglia et al. 2001), so their disruption implies IVF-related factors may impair placental transport. Non-essential amino acids (NEAA), endogenously synthesized in the body(Hou et al. 2015), may be affected by IVF factors like cryopreservation inhibiting maternal/fetal synthetic mechanisms.\u003c/p\u003e\u003cp\u003eIn summary, altered amino acid profiles associated with weight changes indicate both IVF procedures impact fetal metabolism, with cryopreservation uniquely altering tyrosine/SA and ORN pathways. Future research should clarify underlying mechanisms and long-term consequences to optimize IVF protocols.\u003c/p\u003e\u003cp\u003e\u003cb\u003e2\u003c/b\u003e. \u003cb\u003eCarnitines Associated with Amino Acid Metabolism\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThirteen of 30 measured carnitines differed across groups, offering insights into metabolic adaptations. Carnitines facilitate mitochondrial fatty acid β-oxidation, regulating energy metabolism(Xiang et al. 2025), with implications for amino acid metabolism and fetal growth.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCarnitines and Essential Amino Acids\u003c/b\u003e: L-carnitine (C0), critical for fatty acid transport(Virmani et al. 2022), was elevated in both IVF groups but lower in FET than ET. This dynamic may reflect energy demands for EAA metabolism: EAA catabolism/anabolism (e.g., tyrosine-derived neurotransmitter synthesis) requires ATP from fatty acid oxidation(Corsetti et al. 2024). ET\u0026rsquo;s higher C0 may support increased EAA processing, while FET\u0026rsquo;s reduction suggests shifted energy pathways potentially impairing EAA utilization.\u003c/p\u003e\u003cp\u003eElevated propionylcarnitine (C3) and tetradecanoylcarnitine (C14) in ET (vs. Ctrl) further support upregulated fatty acid oxidation\u0026mdash;odd-chain and long-chain, respectively(Adams et al. 2009)\u0026mdash;to fuel EAA-related processes.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCarnitines and Non-essential Amino Acids\u003c/b\u003e: Acetylcarnitine (C2), convertible to acetyl-CoA (a carbon source for NEAA synthesis)(Scafidi et al. 2010), increased in ET but decreased in FET. Methylmalonylcarnitine (C4DC\u0026thinsp;+\u0026thinsp;C5OH), linked to TCA cycle precursors for NEAA(Violante et al. 2013), showed a similar opposing trend. These IVF-specific differences, attributable to cryopreservation, represent novel findings warranting deeper investigation.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCarnitines and Combined Amino Acid Metabolism\u003c/b\u003e: 3-hydroxyoctadecenoylcarnitine (C18:1OH), involved in unsaturated fatty acid oxidation(Violante et al. 2013), may support energy-dependent amino acid metabolism. FET\u0026rsquo;s reduced long-chain acyl-carnitines (C10, C10:1, C14:1, C14:2, C18:2) suggest altered fatty acid metabolism favoring fat storage, contributing to increased weight. Concurrently, carnitine-mediated amino acid changes may enhance tissue growth, as amino acids are building blocks for development.\u003c/p\u003e\u003cp\u003e\u003cb\u003eClinical Significance of Null Findings\u003c/b\u003e: Seventeen carnitines (e.g., C3DC, C4) and multiple amino acids showed no group differences, indicating preserved core metabolic pathways. This selectivity\u0026mdash;IVF impacts specific carnitine-dependent pathways rather than global disruption\u0026mdash;suggests metabolic resilience or compensatory mechanisms, reassuring that IVF does not systemically threaten amino acid homeostasis.\u003c/p\u003e\u003cp\u003e\u003cb\u003e3. Bioinformatics Analysis of Umbilical Cord Vascular Tissue\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFollowing metabolic analyses, RNA-seq of umbilical cord vascular tissues revealed molecular mechanisms. To our knowledge, this is the first comprehensive combined MS/MS and RNAseq study comparing human neonatal samples across natural conception, ET, and FET groups. Its novelty lies in: (1) unprecedented focus on fresh vs. frozen IVF subgroups in human neonatal vessels; (2) ethical use of umbilical cord as a fetal tissue surrogate, addressing gaps in fetal research(Sakurai et al. 2019).\u003c/p\u003e\u003cp\u003e\u003cb\u003eBiological Processes \u0026amp; Cellular Components\u003c/b\u003e: Ctrl vs. IVF comparisons enriched processes like \u0026ldquo;Growth\u0026rdquo; and \u0026ldquo;Regulation of Growth,\u0026rdquo; linking amino acid-dependent protein synthesis(Suryawan et al. 2007) to weight differences. Ctrl vs. FET highlighted \u0026ldquo;Circulatory System Development,\u0026rdquo; critical for nutrient transport(Gualtieri et al. 2013), with amino acids driving vessel-related cell proliferation(Huang et al. 2017).FET-altered cellular components (e.g., \u0026ldquo;Anchoring Junction,\u0026rdquo; \u0026ldquo;Growth Hormone Receptor Complex\u0026rdquo;) may disrupt cell signaling and nutrient uptake, impacting amino acid metabolism and growth.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMolecular Functions \u0026amp; Pathways\u003c/b\u003e: Enriched functions in Ctrl vs. FET (e.g.,\u0026ldquo;Protein Tyrosine Kinase Activity\u0026rdquo;) support signal transduction roles in nutrient utilization(Gualtieri et al. 2013). Pathways like \u0026ldquo;Prolactin Receptor Signaling\u0026rdquo; and \u0026ldquo;Cell Junction Organization\u0026rdquo;(Bole-Feysot et al. 1998)may mediate FET\u0026rsquo;s effects on metabolic regulation and nutrient exchange, contributig to weight gain. Overall, functional enrichment links transcriptional profiles to birth weight and amino acid metabolism, identifying 38 hub genes whose interactions form a metabolic regulatory network.\u003c/p\u003e\u003cp\u003e\u003cb\u003e4. Hub-Genes in Amino Acid Metabolism\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eEssential Amino Acids\u003c/b\u003e: HSPA8 (a chaperone ensuring proper protein folding (Bonam et al. 2019) and PKM (glycolytic enzyme supplying AA precursors) (Violante et al. 2013)showed differential expression, potentially disrupting EAA metabolism and development.\u003c/p\u003e\u003cp\u003e\u003cb\u003eNon-essential Amino Acids\u003c/b\u003e: FGFR1 (regulates serine/glycine)(Hu et al. 2018), AURKA (urea cycle involvement), and GATM (creatine synthesis)(Ingoglia et al. 2021)varied across groups, implicating NEAA imbalance in fetal growth.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCarnitine-related Genes\u003c/b\u003e: PHKB (modulates carnitine demand via glycogenolysis)(Burwinkel et al. 1997), NOS3 (inhibits fatty acid oxidation)(Dick et al. 2020), and ZFP36L1 (regulates lipid metabolism transcripts)(Tarling et al. 2017)may link carnitine-mediated energy metabolism to growth via IVF procedures.\u003c/p\u003e\u003cp\u003e\u003cb\u003eProtein Synthesis and Modification\u003c/b\u003e:\u003c/p\u003e\u003cp\u003e\u003cb\u003ea) Translation/Folding\u003c/b\u003e: Coordinated changes in GATM, ZFP36L1, and HSPA8 may impair protein synthesis(Tarling et al. 2017).\u003c/p\u003e\u003cp\u003e\u003cb\u003eb) Ubiquitin-Proteasome System\u003c/b\u003e: Altered NEDD4, USP15, and APAF1 could disrupt protein homeostasis(Fan et al. 2015).\u003c/p\u003e\u003cp\u003e\u003cb\u003ec) Signal Transduction\u003c/b\u003e: Kinases like YES1 and FYN, alongside NR3C1, may dysregulate translation(Lu et al. 2021), with FET upregulating anti-apoptotic factors (e.g., BCL-2) as a stress response.\u003c/p\u003e\u003cp\u003e\u003cb\u003ed) Network Interactions\u003c/b\u003e: The 38 hub genes form a regulatory network where disruptions in amino acid metabolism ripple through protein synthesis and signaling pathways, influencing neonatal development. These genes offer targets to optimize IVF outcomes.\u003c/p\u003e\u003cp\u003e\u003cb\u003e5. New Theory\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIntegrating findings with evolutionary cold-environment adaptation concepts, we propose: Extreme cryopreservation stress environment (-196\u0026deg;C) awakes conserved anti-cold mechanisms, altering gene expression profile to disrupt amino acid/carnitine metabolism, ultimately increasing body mass/weight. This \u0026ldquo;Cryopreservation\u0026rarr;gene changes\u0026rarr;metabolic alterations\u0026rarr;phenotypic (weight) changes\u0026rdquo; hypothesis explains FET\u0026rsquo;s macrosomia link. Notably, FET\u0026rsquo;s weight increase aligns with Bergmann\u0026rsquo;s rule\u0026mdash;endotherms in cold environments evolve larger size to minimize heat loss (Tabh and Nord 2023). Cryopreservation (-196\u0026deg;C), an extremely cold-stress environment, may trigger ancient cold-responsive pathways, mirroring evolutionary adaptations. This links extreme cold stress to conserved genetic programs promoting growth, supporting our theory (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003cb\u003e6. Limitations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA key limitation is the study\u0026rsquo;s focus on human neonatal samples (blood, umbilical tissue) with observational analyses of phenotypes, metabolites, and transcripts. While strong associations exist between cryopreservation, metabolic/gene expression changes, and birth weight, deep mechanistic validation (e.g., SA manipulation, gene knockouts) in animal models was beyond our scope. Causal relationships\u0026mdash;such as whether SA or specific hub genes directly mediate growth\u0026mdash;remain to be confirmed. Future preclinical studies should complement these human with obesity data to clarify mechanisms.\u003c/p\u003e\u003cp\u003eIn conclusion, this study integrates RNAseq and tandem mass spectrometry to systematically compare cryopreserved, fresh IVF, and naturally conceived neonates at the physiological, metabolic, and transcriptional levels. It pioneers insights into amino acid/carnitine perturbations, identifies hub genes, and proposes a novel evolutionary theory explaining frozen environment-associated overweight. These findings underscore the need for long-term metabolic surveillance of cryopreserved IVF offspring, informing safe optimization of assisted reproductive technologies and early prevention of obesity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHJ\u003c/strong\u003e:Writing\u0026ndash;original draft, Methodology，Conceptualization. \u003cstrong\u003ePZ, XZ and YC\u003c/strong\u003e: Writing\u0026ndash;review \u0026amp; editing, Conceptualization.\u003cstrong\u003e\u0026nbsp;QZ and YY\u003c/strong\u003e: Writing\u0026ndash;review \u0026amp; editing, Supervision, Methodology. \u003cstrong\u003eYZ\u003c/strong\u003e, \u003cstrong\u003eSL and TZ\u003c/strong\u003e: Writing\u0026ndash;review \u0026amp; editing, Formal analysis, Conceptualization. \u003cstrong\u003eXC, YZ and YZ\u003c/strong\u003e: Writing\u0026ndash;review \u0026amp; editing, Methodology, Conceptualization. \u003cstrong\u003eZX\u003c/strong\u003e: Writing\u0026ndash;review \u0026amp; editing, Supervision, Funding acquisition.All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare funding was received for the research work of this article. These fundings are\u0026nbsp;Wuxi Taihu-Rencai Fund (number: 2021-19), the Key Lab of Perinatal Bio-Medicine (2023).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe studies involving humans were approved by the Research Ethics Committee (Wuxi Maternity and Child Health Care Hospital). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdams SH, Hoppel CL, Lok KH, Zhao L, Wong SW, Minkler PE, Hwang DH, Newman JW, Garvey WT (2009) Plasma acylcarnitine profiles suggest incomplete long-chain fatty acid beta-oxidation and altered tricarboxylic acid cycle activity in type 2 diabetic african-american women. J Nutr 139 (6):1073-1081 https://doi.org/DOI\u003c/li\u003e\n\u003cli\u003eBattaglia FC, Regnault TR (2001) Placental transport and metabolism of amino acids. 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Front Endocrinol (Lausanne) 10:764 https://doi.org/DOI\u003c/li\u003e\n\u003c/ol\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":"In vitro fertilization, Cryopreservation, Amino Acids, transcriptional profiles, Macrosomia","lastPublishedDoi":"10.21203/rs.3.rs-8284673/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8284673/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn vitro fertilization revolutionized reproduction, but its association with macrosomia (overweight) remains unknown. We investigated molecular/metabolic impacts of extreme-cold-environment cryopreservation in frozen-embryo-transfer (FET), proposing new theory to explain increased macrosomia risk. Birth-weight was assessed as growth indicator; tandem mass spectrometry quantified amino-acids (AAs) and carnitine in newborn blood for metabolic profiles; and RNA sequencing analyzed gene expression in umbilical-cord vasculature, a model reflecting fetal circulatory system. Comparisons between fresh-embryo-transfer (ET) and FET revealed that extreme-cold-environment(-196\u0026deg;C) altered AA metabolism and transcriptional profiles linked to protein synthesis and energy metabolism. These changes were associated with higher birth-weight in FET offspring. Our findings bridge phenotypic observations (macrosomia), metabolic disturbances (AA/carnitine alterations), and molecular signatures (differential gene expression), supporting a novel anti-cold-stress mechanism. The new theory firstly suggests embryonic adaptation to cryopreservation environment may program metabolic changes increasing overweight risk, offering insights to optimize ART practices and improve long-term health outcomes of IVF-conceived children.\u003c/p\u003e","manuscriptTitle":"Why In Vitro Fertilization Increases Macrosomia Risk? A New Anti-Cold-Stress Theory Mirroring Bergmann's Evolutionary Adaptation Rule","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-15 16:13:54","doi":"10.21203/rs.3.rs-8284673/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":"4858e64b-5200-48ce-9a00-bece1d03c4f0","owner":[],"postedDate":"December 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-29T19:53:30+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-15 16:13:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8284673","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8284673","identity":"rs-8284673","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}