Maternal BMI and infant obesity risk: a lipidomics perspective on the developmental origins of obesity

Maternal BMI and infant obesity risk: a lipidomics perspective on the developmental origins of obesity | 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 Maternal BMI and infant obesity risk: a lipidomics perspective on the developmental origins of obesity Alexandra George, Tingting Wang, Thy Duong, Yvette Schooneveldt, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7089146/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 Background Maternal obesity is a critical determinant of infant health, influencing birth weight and increasing the risk of obesity in early life. Plasma lipids are mechanistically linked to obesity and may mediate the intergenerational transfer. Using the Barwon Infant Study, a longitudinal birth cohort, we aimed to investigate the associations between maternal pre-pregnancy body mass index (pp-BMI), lipidomic profiles of mothers, human milk, and infants, and early life growth. We were particularly interested in ether lipids as they are higher in breastfed infants compared to formula-fed infants, are enriched in human milk compared to infant formula, and are involved in metabolic health and inflammation in adult populations. Methods Linear regression analyses assessed relationships between maternal pp-BMI and lipid profiles across all biospecimens, and infant BMI. A composite plasmalogen score, reflecting ether lipid metabolism, was developed due to its strong associations with maternal BMI and breastfeeding. Causal mediation analysis was performed to quantify the extent to which cord lipids mediated the effect of maternal pp-BMI on infant birth weight. Results Our findings revealed significant associations between maternal pp-BMI and both maternal and cord lipid profiles, as well as obesity risk indicators. Of the cord blood lipids, 6 of them mediated up to 18% of the effect of maternal pp-BMI on birth weight. Maternal plasmalogen score was negatively associated with pp-BMI and positively associated with plasmalogens in human milk and infant plasmalogen scores from birth to four years of age. Conclusions These findings position plasmalogens and ether lipids as potential biomarkers or intervention targets for reducing transmission of obesity from mother to infant. Optimising lipid profiles through reducing maternal pp-BMI and dietary or supplemental ether lipids may represent a novel strategy for mitigating early-life obesity risk. Biological sciences/Biochemistry/Lipidomics Biological sciences/Biochemistry/Lipids Figures Figure 1 Figure 2 Figure 3 Introduction The prevalence of overweight and obesity in children has reached alarming levels, affecting approximately one in four Australian children ( 1 ). Contributing to this public health issue, maternal overweight and obesity are significant risk factors for infant obesity ( 2 ). Maternal pre-conception obesity increases the odds of childhood obesity by up to 264% ( 3 ) and maternal pre-pregnancy body mass index (pp-BMI) is associated with infant birth weight, BMI and overweight status ( 4 , 5 ). Furthermore, high gestational weight gain is associated with increased infant BMI and obesity risk in adulthood ( 6 ). These findings suggest that a healthy maternal metabolic status before and during pregnancy may contribute to favourable metabolic programming and reduced obesity risk for the infant, although the underlying biology remains poorly defined. The role of lipids and lipid metabolism, known to be critical in metabolic health, has been largely overlooked in early life studies. Circulating lipids are known to have critical roles in health and disease and may be involved in health programming ( 7 – 9 ). Adult obesity is associated with lipid dysregulation ( 8 ), and during pregnancy, a time of high metabolic activity, many circulating lipids are elevated ( 7 ). The circulating lipid profile in infancy changes across early life as metabolism develops, and previous studies have reported links between circulating lipids and growth in the first months and years of life ( 10 , 11 ). Early life growth is also associated with breastfeeding, and the plasma lipid profiles of breastfed and formula-fed infants differ substantially ( 12 ). Thus, lipid transfer from mother to infant may occur via the placenta in utero, or postnatally through human milk, providing two critical windows of exposure. While this study considers the total circulating lipidome, we were particularly interested in ether lipids, a subclass of lipids characterised by an ether bond at the sn-1 position of the glycerol backbone. Ether lipids, including plasmalogens, are abundant in human milk, strongly associated with breastfeeding, and highly bioactive ( 12 – 14 ). They play key roles in cellular membrane integrity, oxidative stress regulation, and immune development - functions that are critical during the early pre- and post-natal period ( 15 ). Notably, ether lipids derived from human milk have been shown to directly promote adipose tissue development and thermogenic capacity in early life ( 16 ). Because ether lipids are modifiable through diet, they are compelling candidates for targeted interventions ( 17 , 18 ). Despite their potential significance, ether lipids remain understudied in the context of early life metabolic programming and intergenerational obesity risk ( 19 ). Understanding how maternal lipids contribute to infant lipid profiles and obesity risk is critical. Circulating lipids are modifiable, and identifying key lipid pathways may reveal opportunities for targeted early interventions to reduce intergenerational obesity risk ( 7 , 12 , 20 ). This study aimed to explore the role of maternal and infant lipids in the early-life transmission of obesity, using longitudinal data from the Barwon Infant Study ( 12 , 13 , 21 ). Methods Study Cohort and Data The Barwon Infant Study (BIS) cohort is a population-derived birth cohort from Victoria, Australia ( 22 ). The cohort comprises 1074 mother-infant dyads and includes maternal and infant anthropometric measures (Table 1 ). For this study all BIS mothers and infants with available lipidomics data and covariate data for each time point were utilised. The distribution of maternal pp-BMI was as follows: 2.4% underweight (BMI < 18.5), 55.4% healthy (18.5 ≤ BMI < 25.0), 25.2% overweight (25.0 ≤ BMI < 30.0), and 17.0% obese (BMI ≥ 30.0). The mothers provided written informed consent at recruitment and study ethics was approved by the Barwon Health Human Research Ethics Committee (HREC 10/24). Comprehensive lipidomics profiling using liquid chromatography-mass spectrometry has previously been performed on infant plasma, cord blood, maternal serum, and maternal milk in the BIS. Briefly, 733 lipid species were measured in maternal samples at 28 weeks’ gestation, cord serum, and infant plasma at 6, 12, and 48 months, and > 900 lipid species in maternal milk samples at 1 and 6 months ( 12 , 13 ). Table 1 The basic characteristics of BIS cohort variables utilised in this study Variable Distribution 1 Maternal characteristics Pre-pregnancy BMI (kg/m 2 ) 25.7 (5.6) Age (years) 31.9 (4.5) University education 460 (53.6%) Infant characteristics Sex (female) 495 (48.5%) Gestational age (days) 276.51 (10.2) Birth weight (kg) 3.6 (0.5) BMI (kg/m 2 ) At 6 months At 12 months At 48 months 17.2 (1.7) 17.7 (1.7) 15.6 (1.4) Birth mode Unassisted vaginal birth Unscheduled caesarean section Forceps vaginal birth Scheduled caesarean section Vacuum vaginal birth 510 (50.1%) 144 (14.1%) 86 (8.4%) 160 (15.7%) 119 (11.7%) Breastfeeding (any) duration (weeks) 28.65 (21.1) Breastfeeding (any) status At 6 months At 12 months 549 (60.3%) 286 (31.4%) 1 Values are presented as mean (standard deviation) for continuous variables or number (percentage) for categorical variables; BMI: body mass index Statistical analyses An overview of the analyses in this study is presented in Fig. 1 . Variables of interest and covariates utilised throughout the analyses were maternal pp-BMI (kg/m 2 ), maternal age (years), birth mode (categorical), infant gestational age (weeks), infant birth weight (kg), infant BMI (kg/m 2 ), maternal clinical lipids (high-density lipoprotein, HDL, low-density lipoprotein, LDL, cholesterol, triglycerides at 28 weeks), breastfeeding duration (weeks, defined as any breastfeeding, self-reported), current breastfeeding at 6 and 12 months (binary yes or no at each time point), and education (dichotomised as completed any university level certificate or less than university level education). Sample size differs for each analysis, due to incomplete data and/or samples. Lipidomics measures were log transformed and scaled to unit variance prior to analysis. Associations between pp-BMI and infant birth weight, gestational age, infant BMI at 6, 12, and 48 months, and breastfeeding duration were assessed using univariate linear regression with no covariates. Associations between pp-BMI and maternal lipidome were assessed using univariate linear regression, adjusted for maternal age, education, and clinical lipids (n = 854). The fold change for maternal lipids associated with pp-BMI were included in the supplementary material of a previous publication ( 12 ). Associations between pp-BMI and the cord lipidome were assessed using univariate linear regression, adjusted for maternal age, infant birth weight, delivery mode, infant sex, and gestational age (n = 731). Associations between paired maternal and infant lipids at each time point were assessed using univariate linear regression, adjusted for maternal and infant BMI, infant sex, birth weight, and breastfeeding (n = 501–776). Associations between infant BMI and infant lipidomes at 6, 12, and 48 months were assessed using univariate linear regression, adjusted for infant sex, breastfeeding, and clinical lipids (n = 456–723). All p-values were corrected for multiple comparisons by the method of Benjamini and Hochberg (BH) ( 23 ). Causal mediation analysis was performed using “mediation” R package, to investigate the mediating effect of cord lipids on the relationship between maternal pp-BMI and infant birth weight (adjusted for infant sex, delivery mode, gestational age, and maternal age) ( 24 ). Total effects were estimated from the linear regression analysis. There was limited evidence to support performing other mediation analyses in this study (i.e. mediation was only tested when the exposure was significantly associated with both the mediator and the outcome). For each lipid, two models were constructed: ( 1 ) a mediator model with the lipid as the dependent variable and pp-BMI as the independent variable, and ( 2 ) an outcome model with birth weight as the dependent variable and both pp-BMI and the lipid as independent variables. The “mediate” function from the mediation package was used to quantify the indirect (mediated) effect, direct effect, and total effect of pp-BMI on birth weight. Mediation models were fitted using non-parametric bootstrapping with 10,000 simulations to estimate 95% confidence intervals for the proportion of the effect mediated by each lipid (n = 551). To provide a composite lipid measure and reduce the difficulty in interpreting individual lipid species, we developed a plasmalogen score. This score was derived via principal component analysis (PCA) on the mean-centred and scaled phosphatidylethanolamine plasmalogen (PE(P)) and phosphatidylethanolamine (PE) species, from each expressed as a proportion of total PE(P) and PE. The first principal component (PC1) was used as the plasmalogen score, separately calculated for maternal serum at 28 weeks’ gestation and infant plasma at 48 months ( 18 ). Given these known associations, we hypothesised that a plasmalogen score might represent a meaningful biological signature of early lipid programming. For infant plasma at 6 and 12 months, the 48-month infant PCA model was applied to calculate plasmalogen scores, avoiding the confounding due to breastfeeding at 6- and 12-month timepoints. For human milk samples, a plasmalogen ratio was calculated (total PE(P)/total PE) instead of a PCA-based score due to compositional differences in lipid species between milk and plasma. Linear regression was used to assess associations between maternal pp-BMI and plasmalogen score (adjusted for maternal age, n = 666), maternal plasmalogen score and infant plasmalogen score (adjusted for maternal age, birth weight, breastfeeding, and maternal pp-BMI, n = 514), infant plasmalogen score and infant BMI (adjusted for maternal age, breastfeeding, birth weight, and maternal pp-BMI, n = 511), and maternal plasmalogen score and human milk plasmalogen ratio (adjusted for maternal age and pp-BMI, n = 198). Analyses were performed in RStudio (version 2024.04.1). For each model, statistical significance was defined as p < 0.05, either unadjusted or after BH correction. Results Factors associated with maternal pre-pregnancy BMI We examined associations between maternal pp-BMI and obesity related outcomes in the Barwon Infant Study (BIS) cohort; infant birth weight and age, BMI, and duration of breastfeeding (Table 2 ). Table 2 Associations of maternal pre-pregnancy body mass index (kg/m 2 ) with infant growth and breastfeeding Outcome Beta coefficient [95% CI] p-value 1 Birth weight (kg) 0.013 [0.007, 0.020] 9.12x10 − 5 Gestational age (weeks) 0.001 [-0.133, 0.135] 9.87x10 − 1 BMI at 6 months (kg/m 2 ) 0.052 [0.028, 0.076] 2.99x10 − 5 BMI at 12 months (kg/m 2 ) 0.056 [0.031, 0.081] 9.48x10 − 6 BMI at 48 months (kg/m 2 ) 0.051 [0.028, 0.073] 9.53x10 − 6 Breastfeeding duration (weeks) -0.937 [-1.193, -0.680] 1.95x10 − 12 1 significant p-values (p < 0.05) are in bold; BMI: body mass index. No model adjustments were made We used maternal pp-BMI as a proxy for maternal obesity, with higher pp-BMI representing increased risk of infant obesity. Infant birthweight and BMI at 6, 12, and 48 months were used as indicators of early-life obesity risk, with higher values interpreted as proxies for greater risk. Breastfeeding duration, and associations with breastfeeding, were considered indicators of protection against obesity. These variables were used to investigate how lipid profiles may contribute to the intergenerational transfer of obesity risk or resilience. Maternal pre-pregnancy BMI is associated with gestational lipids and cord blood lipids As BMI and the plasma lipidome are linked in non-pregnant populations ( 8 ), we confirmed that maternal pp-BMI was associated with the plasma lipidome during gestation ( 12 ) (Supp Table 1 ). The maternal 28-week gestational lipidome showed significant associations with pp-BMI after correcting for multiple comparisons (50.1%, 367/733 lipid species, Fig. 2 A). Approximately half (182/367) of the lipids were negatively associated with maternal pp-BMI, including several ether lipids from classes PE(P) and PE(O) (Fig. 2 C). The remaining (185/367) lipids were positively associated with maternal pp-BMI, including many species from the ceramide, sphingomyelin, acylcarnitine, and glycerolipid classes. We also assessed if maternal pp-BMI was linked to cord lipids (Supp Table 2 ). The cord blood lipidome contained 41 lipids significantly associated with maternal pp-BMI after BH correction (Fig. 2 B). These included 41% (17/41) negatively associated (from CE, LPC(O), DE, PC(O), PE(P), Fig. 2 D) and 59% (24/41) positively associated, predominantly from TG, AC, and FFA classes. In contrast, maternal pp-BMI was significantly associated with only one infant lipid (TG(56:8) at 6 months of age). Cord lipids partially mediate the effect of maternal pre-pregnancy BMI on infant birth weight Having established associations between maternal pp-BMI and infant birth weight, and maternal pp-BMI and cord lipids, we performed mediation analysis to investigate the extent to which cord lipids mediate the effect of pp-BMI on infant birth weight (Supp Table 3 ). There were 6 lipids (Fig. 3 ) that mediated between 5.4 and 18.0% of the effect of maternal pp-BMI on birth weight, after BH correction. Associations between lipids, plasmalogen scores, and obesity-related outcomes To explore potential protective lipid mechanisms linked to maternal pp-BMI, breastfeeding, and infant growth, we looked at lipids, focusing on ether lipids, and calculated a composite plasmalogen score. We aimed to identify how lipids may act across the maternal-infant axis to influence obesity risk protectively. We first assessed associations between infant lipid species and BMI z-score at 6, 12, and 48 months. At 6 months of age, 20 lipid species were significantly associated with infant BMI z-score (p < 0.05). In contrast, only one lipid was significantly associated with BMI at 12 and 48 months (Supplementary Tables 4–6, Supplementary Figs. 1–3). Then we checked the association between maternal and infant lipids at all timepoints, which showed frequent significantly positive association of ether lipid species, particularly plasmalogens (Supplementary Tables 7–9). Plasmalogen scores were then calculated in maternal serum and infant plasma, cord blood, and the plasmalogen ratio in maternal milk. Linear regression was used to examine associations between maternal pp-BMI and maternal plasmalogen score, maternal plasmalogen score and milk plasmalogen ratio, maternal and infant plasmalogen scores, and infant plasmalogen score and infant BMI, including appropriate covariates as in the previous models (Table 3 , Supp Table 10). Maternal 28 weeks’ gestation weight and weight gain were also explored as measures for maternal obesity; however, these were not included in the final models (Supp 11). Table 3 Maternal to infant plasmalogen score link Association Effect estimate (β) 95%CI p-value 1 Pre-pregnancy BMI with maternal plasmalogen score -0.116 -0.196, -0.035 4.94x10 − 3 Maternal plasmalogen score with cord blood plasmalogen score 0.789 0.529, 1.049 4.47x10 − 9 Maternal plasmalogen score and milk plasmalogen ratio 0.765 0.044, 1.487 3.77x10 − 2 Maternal plasmalogen score with infant 6-month plasmalogen score 0.218 0.008, 0.427 4.22x10 − 2 Maternal plasmalogen score with Infant 12-month plasmalogen score 0.412 0.226, 0.598 1.69x10 − 5 Maternal plasmalogen score with child 48-month plasmalogen score 0.201 0.096, 0.309 3.77x10- 2 1 significant p-values (p < 0.05) are in bold Discussion Maternal obesity is a major risk factor for infant obesity, yet the biological mechanisms remain unclear ( 25 ). This study reveals that lipid metabolism, including ether lipids, may serve as a key protective factor capable of modifying the intergenerational transmission of obesity risk. Maternal pre-pregnancy BMI and protective lipid signatures Maternal pp-BMI, which is a key determinant of infant obesity risk ( 2 ), was associated with a distinct lipid signature during pregnancy (28 weeks’ gestation, Fig. 2 A). Higher maternal BMI at conception has consistently been linked with increased risk of macrosomia and childhood obesity and was therefore used as a proxy for obesity ( 2 , 3 , 5 , 25 ). Consistent with this, we observed a positive association between maternal pp-BMI and infant birth weight, which is a known risk factor for later obesity ( 26 ). While maternal dysregulation of free fatty acid (FFA) metabolism has been commonly implicated in transfer of obesity risk to the infant ( 27 ), we observed that many other lipid classes were also affected. This included several increased ceramides and sphingolipids, and decreases to hexosylceramides, sulfatides, and many ether lipids and the maternal plasmalogen score. Similar dysregulation is also observed in non-pregnant adults, including the negative relationship between BMI, type 2 diabetes, and cardiovascular disease with plasmalogen score ( 9 , 18 ). The plasmalogen score allows the assessment of relative PE(P) and PE levels, is modifiable, and has been previously validated as a metabolic marker for adult cardiometabolic health ( 18 ). The maternal lipid profile with lower pp-BMI, marked with higher ether lipid and plasmalogen levels, may reflect a protective metabolic signature for pregnancy. This signature may contribute to healthy offspring growth and development (for example a less adverse birth weight) but is diminished with a higher maternal pp-BMI. Overall, these findings suggest that maternal lipid profiles characteristic of obesity have the potential to be passed on to the newborn, via the umbilical cord in utero, and via human milk postnatally, ultimately setting-up metabolism and future obesity risk. Cord blood lipids as early mediators of obesity risk and protection Maternal pp-BMI can alter the cord blood lipids, which represent infant circulating lipids at birth (Fig. 2 B). While transfer of polyunsaturated fatty acids (PUFA) from mother to foetus is tightly regulated ( 28 , 29 ), our findings show that maternal metabolic status is still reflected in the infant birth lipidome. There were 6 cord lipids, comprising LPCs, LPEs, and one PE species, that each significantly mediated up to 18.0% of the relationship between maternal pp-BMI and infant birth weight (Fig. 3 ). Many lysophospholipids have previously been linked to infant growth ( 30 ). This suggests that higher pp-BMI is associated with a shift away from a more protective cord lipid profile, reflective of maternal circulation, and towards a lipid state linked with adversely high infant birth weight ( 26 ). In a prior analysis within the BIS cohort, several ether lipids, including PC(P) and TG(O) species, were negatively associated with birth weight ( 12 ), and studies in placental tissue have shown impaired transfer of ether lipids in pregnancies complicated by obesity ( 31 ). Further exploring lipids in this context will be important to elucidate pathways through which maternal metabolic status programs offspring health. Enhancing maternal plasmalogen score to reduce infant obesity risk Our results highlight that increasing maternal ether lipid levels and plasmalogen score, may offer a novel intervention to promote healthier lipid profiles to influence infant metabolic outcomes: maternal dietary modification and/or reduction of pp-BMI to a healthy range ( 32 , 33 ). Reducing BMI, which is widely recommended for women with overweight or obesity during pregnancy planning, is likely to improve lipid profiles and infant health outcomes ( 34 , 35 ). Additionally, our findings suggest that lipid supplementation of women prior to/during pregnancy, especially in women with high BMI, to normalise lipid profiles may reduce infant obesity risk. Human studies have shown that plasmalogens are modifiable through alkylglycerol precursors or plasmalogens ( 36 – 38 ). We have previously shown in the BIS that the majority of cord blood lipids are significantly positively associated with maternal gestational lipids, implying that modification of the maternal lipidome during pregnancy may modify cord blood lipids ( 12 ). Maternal dietary supplementation with plasmalogen precursors, such as alkylglycerols, may promote a gestational lipid profile associated with lower infant obesity risk by enhancing protective ether lipid signals in both maternal and cord blood, supporting protective lipid transfer to the infant. The impact of maternal obesity on developing infant lipid metabolism Postnatally, we observed significant correlations between maternal and infant plasmalogen scores, at 6, 12, and 48 months of age, suggesting transmission of ether lipid profiles across maternal and infant contexts. This is a novel finding and, to our knowledge, one of the first demonstrations of maternal-infant lipid tracking across timepoints with a composite lipid (plasmalogen) score. The strong associations between maternal and infant plasmalogen scores up to four years of age, even after adjusting for breastfeeding, suggests that although human milk is a major source of ether lipids in infancy, maternal lipid metabolism may exert a longer-term influence on the infant lipid profile beyond the breastfeeding window. Early life lipid profiling to understand obesity risk Understanding early life lipid metabolism is essential for translating these findings into strategies that support healthy development and reduce obesity risk. While higher BMI in infancy is associated with increased risk of obesity later in life, BMI is a crude measure of metabolic health during infancy ( 39 – 41 ). Our data reveal that associations between lipids and BMI at 6 and 12 months were weak and inconsistent, but by 4 years of age, the lipidomic profile began to reflect adult-like patterns. This suggests a developmental shift in lipid metabolism and highlights the importance of long-term follow-up in birth cohorts to fully capture when and how lipid signatures predict obesity risk. Among the lipid classes, ether lipids, particularly plasmalogens, are emerging as key candidates in early life metabolic priming ( 12 , 13 , 16 , 42 ). Plasmalogens, higher in mothers with lower pp-BMI, may represent a biological link between maternal metabolic status, infant lipid programming, and obesity risk. Mechanistic studies support this role, for example in mice dietary alkylglycerols (plasmalogen precursors) enhance mitochondrial activity, promote beige adipose tissue, and activate lipid-regulating pathways ( 16 ). In humans, early ether lipid intake has been linked to both increased weight gain in infancy and reduced fat mass, underscoring their dynamic influence on growth ( 43 ). Our data suggest that the plasmalogen score could serve as a biologically meaningful lipid marker of protective potential in the context of intergenerational obesity risk. The score reflects maternal metabolic status, is associated with milk composition, persists across infancy, and begins to show metabolic relevance by 4 years of age. Although we were unable to establish clear links between plasmalogen score and infant BMI within the first 4 years, its consistent biological relevance supports its further investigation. These findings underscore the potential for early ether lipid exposure to shape long-term metabolic health. The high ether lipid content in human milk compared to formula may be one mechanism through which breastfeeding confers protection against obesity ( 13 ). Breastfeeding promotion remains a critical public health strategy, especially for populations at higher risk of maternal obesity. At the same time, supplementation strategies (including maternal dietary supplementation during pregnancy or infant formula reformulation) could offer additional ways to optimise ether lipid availability in early life. Plasmalogen score and ether lipids in early life warrant further investigation as potential tools to reduce intergenerational obesity risk by targeting metabolic programming early. Strengths and Limitations The integration of a well-characterised birth cohort (BIS) with comprehensive lipidomics data and advanced statistical methods, including mediation analysis, is a major strength of this study. The mediation analysis included 551 mother-child dyads with complete datasets, larger than other studies to date, and 6 lipids were successfully identified for mediating the relationship between maternal pp-BMI and infant birth weight. Use of pp-BMI and infant BMI are limited in their interpretation; however, they were the most appropriate growth measures in the context of lipidomics. Prior studies indicate that pp-BMI is a strong predictor of offspring obesity risk and that infants with high BMI in early life have higher obesity risk as they age ( 44 – 46 ). While lipid metabolism was the primary focus of this study, we acknowledge that other biological mechanisms, including inflammation and hormonal pathways, may also contribute to intergenerational obesity risk ( 5 ). The cross-sectional nature of some of the analyses limited causal inference. Furthermore, it is not possible to unravel all environmental factors that will also be at play in this study. Our findings need to be validated in additional larger and more diverse cohorts, with later follow-up of participants to ensure that links hold and develop. Conclusion Maternal pre-pregnancy BMI significantly impacts gestational lipid profiles and has downstream effects on infant birth weight and breastfeeding, which are obesity risk factors. Plasmalogen scores appear to reflect a maternal metabolic state that supports protective lipid programming in the infant, offering promising targets for intervention. Strategies to optimise ether lipid levels, including maternal dietary supplementation, breastfeeding promotion, and early life infant dietary supplementation, could provide a practical, modifiable way to enhance metabolic resilience and reduce obesity risk, improving metabolic health outcomes for future generations. Declarations Competing interests PJM declares a potential conflict of interest (PJM consults for Juvenescence Ltd, a biotech company that is developing a plasmalogen supplement product for market. The Baker Institute has a commercialization agreement with the Murdoch Children’s Research Institute relating to the development of infant formula products with plasmalogen precursor supplements. The Baker Institute holds several patents related to increasing plasmalogen levels for improved health outcomes. These have been licensed to Juvenescence Ltd). The remaining authors declare no conflict of interest. Acknowledgments We acknowledge the participation and commitment of all the families in the Barwon Infant Study. Author Contributions Cohort administration: RS, PV, ALP, DB, Barwon Infant Study Investigator Group. Conceptualisation: ADG, SB, PJM. Formal analysis: ADG, TW. Writing - original draft: ADG, TW. Writing - reviewing and editing: ADG, TW, TD, YS, SP, GO, TM, RS, PV, AP, DB, SB, PJM. All authors contributed to the article and approved the submitted version. Competing Interests PM declares a potential conflict of interest (PJM consults for Juvenescence Ltd, a biotech company that is developing a plasmalogen supplement product for market. The Baker Institute has a commercialization agreement with the Murdoch Children’s Research Institute relating to the development of infant formula products with plasmalogen precursor supplements. The Baker Institute holds several patents related to increasing plasmalogen levels for improved health outcomes. These have been licensed to Juvenescence Ltd). The remaining authors declare no conflict of interest. Funding The establishment work and infrastructure for the BIS was provided by the Murdoch Children’s Research Institute (MCRI), Deakin University and Barwon Health. Subsequent funding was secured from the National Health and Medical Research Council of Australia, The Jack Brockhoff Foundation, the Scobie Trust, the Shane O’Brien Memorial Asthma Foundation, the Our Women’s Our Children’s Fund-Raising Committee Barwon Health, The Shepherd Foundation, the Rotary Club of Geelong, the Ilhan Food Allergy Foundation, GMHBA Limited and the Percy Baxter Charitable Trust, Perpetual Trustees and the Minderoo Foundation. In-kind support was provided by the Cotton On Foundation and CreativeForce. This work was supported by the Victorian Government's Operational Infrastructure Support Program, NHMRC Investigator Grants (ALP, DB, PJM), an NHMRC Career Development Fellowship (PV), and The DHB Foundation Fellowship (TM). The funding bodies had no input in design or publication of this study. Data Availability Statement Access to the Barwon Infant Study (BIS) data used in this paper may be requested through the BIS Steering Committee. 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Development and validation of a plasmalogen score as an independent modifiable marker of metabolic health: population based observational studies and a placebo-controlled cross-over study. eBioMedicine. 2024;105:105187. George AD, Burugupalli S, Paul S, Mansell T, Burgner D, Meikle PJ. The Role of Human Milk Lipids and Lipid Metabolites in Protecting the Infant against Non-Communicable Disease. International Journal of Molecular Sciences. 2022;23(14):7490. Huang Y, Sulek K, Stinson SE, Holm LA, Kim M, Trost K, et al. Lipid profiling identifies modifiable signatures of cardiometabolic risk in children and adolescents with obesity. Nature Medicine. 2025;31(1):294–305. Vuillermin P, Saffery R, Allen KJ, Carlin JB, Tang ML, Ranganathan S, et al. Cohort Profile: The Barwon Infant Study. Int J Epidemiol. 2015;44(4):1148–60. Vuillermin P, Saffery R, Allen KJ, Carlin JB, Tang ML, Ranganathan S, et al. Cohort Profile: The Barwon Infant Study. International Journal of Epidemiology. 2015;44(4):1148–60. Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society: Series B (Methodological). 1995;57(1):289–300. Imai K, Keele LJ, Yamamoto T. Identification, Inference and Sensitivity Analysis for Causal Mediation Effects. Statistical Science. 2010;25:51–71. McAuliffe FM, Killeen SL, Jacob CM, Hanson MA, Hadar E, McIntyre HD, et al. Management of prepregnancy, pregnancy, and postpartum obesity from the FIGO Pregnancy and Non-Communicable Diseases Committee: A FIGO (International Federation of Gynecology and Obstetrics) guideline. Int J Gynaecol Obstet. 2020;151 Suppl 1(Suppl 1):16–36. Schellong K, Schulz S, Harder T, Plagemann A. Birth weight and long-term overweight risk: systematic review and a meta-analysis including 643,902 persons from 66 studies and 26 countries globally. PloS one. 2012;7(10):e47776. Álvarez D, Muñoz Y, Ortiz M, Maliqueo M, Chouinard-Watkins R, Valenzuela R. Impact of Maternal Obesity on the Metabolism and Bioavailability of Polyunsaturated Fatty Acids during Pregnancy and Breastfeeding. Nutrients. 2020;13(1). Woodard V, Thoene M, Van Ormer M, Thompson M, Hanson C, Natarajan SK, et al. Intrauterine Transfer of Polyunsaturated Fatty Acids in Mother-Infant Dyads as Analyzed at Time of Delivery. Nutrients. 2021;13(3). Larqué E, Demmelmair H, Gil-Sánchez A, Prieto-Sánchez MT, Blanco JE, Pagán A, et al. Placental transfer of fatty acids and fetal implications. The American journal of clinical nutrition. 2011;94:S1908-S13. Rzehak P, Hellmuth C, Uhl O, Kirchberg FF, Peissner W, Harder U, et al. Rapid Growth and Childhood Obesity Are Strongly Associated with LysoPC(14:0). Annals of Nutrition and Metabolism. 2014;64(3–4):294–303. Powell TL, Uhlson C, Madi L, Berry KZ, Chassen SS, Jansson T, Ferchaud-Roucher V. Fetal sex differences in placental LCPUFA ether and plasmalogen phosphatidylethanolamine and phosphatidylcholine contents in pregnancies complicated by obesity. Biology of Sex Differences. 2023;14(1):66. Zhang J, Zhang R, Chi J, Li Y, Bai W. Pre-pregnancy body mass index has greater influence on newborn weight and perinatal outcome than weight control during pregnancy in obese women. Archives of Public Health. 2023;81(1):5. Lim S, Harrison C, Callander E, Walker R, Teede H, Moran L. Addressing Obesity in Preconception, Pregnancy, and Postpartum: A Review of the Literature. Curr Obes Rep. 2022;11(4):405–14. Xie D, Yang W, Wang A, Xiong L, Kong F, Liu Z, et al. Effects of pre-pregnancy body mass index on pregnancy and perinatal outcomes in women based on a retrospective cohort. Scientific reports. 2021;11(1):19863. Braddon KE, Keown-Stoneman CDG, Dennis C-L, Li X, Maguire JL, O’Connor DL, et al. Maternal Preconception Body Mass Index and Early Childhood Nutritional Risk. The Journal of Nutrition. 2023;153(8):2421–31. Paul S, Smith AAT, Culham K, Gunawan KA, Weir JM, Cinel MA, et al. Shark liver oil supplementation enriches endogenous plasmalogens and reduces markers of dyslipidemia and inflammation. Journal of Lipid Research. 2021;62:100092. Smith T, Knudsen KJ, Ritchie SA. First-In-Human Safety, Tolerability, and Pharmacokinetics of PPI-1011, a Synthetic Plasmalogen Precursor. Clin Transl Sci. 2025;18(3):e70195. Fujino T, Yamada T, Asada T, Tsuboi Y, Wakana C, Mawatari S, Kono S. Efficacy and Blood Plasmalogen Changes by Oral Administration of Plasmalogen in Patients with Mild Alzheimer's Disease and Mild Cognitive Impairment: A Multicenter, Randomized, Double-blind, Placebo-controlled Trial. EBioMedicine. 2017;17:199–205. Bell KA, Wagner CL, Perng W, Feldman HA, Shypailo RJ, Belfort MB. Validity of Body Mass Index as a Measure of Adiposity in Infancy. J Pediatr. 2018;196:168 – 74.e1. Roy SM, Spivack JG, Faith MS, Chesi A, Mitchell JA, Kelly A, et al. Infant BMI or Weight-for-Length and Obesity Risk in Early Childhood. Pediatrics. 2016;137(5). Agbaje AO. Waist-circumference-to-height-ratio had better longitudinal agreement with DEXA-measured fat mass than BMI in 7237 children. Pediatric Research. 2024;96(5):1369–80. Burugupalli S, Mansell T, Wang T, George A, Paul S, Saffery R, et al. The protective effect of breastfeeding on infant inflammation: a mediation analysis of the plasma lipidome and metabolome. TBC. 2024. Ramadurai S, Andrews C, Cheema S, Thomas R, Wagner CL, Sen S. Maternal Predictors of Breast Milk Plasmalogens and Associations with Infant Body Composition and Neurodevelopment. Clinical Therapeutics. 2022;44(7):998–1009. Yu Z, Han S, Zhu J, Sun X, Ji C, Guo X. Pre-pregnancy body mass index in relation to infant birth weight and offspring overweight/obesity: a systematic review and meta-analysis. PloS one. 2013;8(4):e61627. Simmonds M, Llewellyn A, Owen CG, Woolacott N. Predicting adult obesity from childhood obesity: a systematic review and meta-analysis. Obes Rev. 2016;17(2):95–107. Braun JM, Kalkwarf HJ, Papandonatos GD, Chen A, Lanphear BP. Patterns of early life body mass index and childhood overweight and obesity status at eight years of age. BMC Pediatrics. 2018;18(1):161. Additional Declarations Yes there is potential conflict of interest. Supplementary Files SummaryofSupp.docx Summary of supplementary figures and tables ManuscriptSupplementaryTables.xlsx Supplementary tables ManuscriptSupplementaryFigures.docx Supplementary figures 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. 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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-7089146","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":486938779,"identity":"ecccdf5a-4a31-4a3f-a745-5696e54d7af7","order_by":0,"name":"Alexandra George","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0001-7079-6647","institution":"Baker Heart and Diabetes Institute","correspondingAuthor":true,"prefix":"","firstName":"Alexandra","middleName":"","lastName":"George","suffix":""},{"id":486938780,"identity":"02b9841e-a8f1-4a70-81a2-6a417e95ccbf","order_by":1,"name":"Tingting Wang","email":"","orcid":"","institution":"Baker Heart and Diabetes Institute","correspondingAuthor":false,"prefix":"","firstName":"Tingting","middleName":"","lastName":"Wang","suffix":""},{"id":486938781,"identity":"131e965c-8521-4f61-8ba6-d2ff6c09d875","order_by":2,"name":"Thy Duong","email":"","orcid":"","institution":"Baker Heart and Diabetes Institute","correspondingAuthor":false,"prefix":"","firstName":"Thy","middleName":"","lastName":"Duong","suffix":""},{"id":486938782,"identity":"edcfe821-62ee-4b68-baed-079c6d0f86b0","order_by":3,"name":"Yvette Schooneveldt","email":"","orcid":"","institution":"Baker Heart and Diabetes Institute","correspondingAuthor":false,"prefix":"","firstName":"Yvette","middleName":"","lastName":"Schooneveldt","suffix":""},{"id":486938783,"identity":"f0fc8baf-1ce0-43ca-bbaa-821844c609f3","order_by":4,"name":"Sudip Paul","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Sudip","middleName":"","lastName":"Paul","suffix":""},{"id":486938784,"identity":"593c8090-5ff6-4420-a748-8657884ce17b","order_by":5,"name":"Gavriel Olshansky","email":"","orcid":"","institution":"Baker Heart and Diabetes Institute","correspondingAuthor":false,"prefix":"","firstName":"Gavriel","middleName":"","lastName":"Olshansky","suffix":""},{"id":486938785,"identity":"eba81b2d-5033-4ec8-ad34-478d56e29a98","order_by":6,"name":"Toby Mansell","email":"","orcid":"https://orcid.org/0000-0002-1282-6331","institution":"Murdoch Children's Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Toby","middleName":"","lastName":"Mansell","suffix":""},{"id":486938786,"identity":"706b751a-0f8d-4100-965a-02b1b6e76e57","order_by":7,"name":"Richard Saffery","email":"","orcid":"https://orcid.org/0000-0002-9510-4181","institution":"Royal Children's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Richard","middleName":"","lastName":"Saffery","suffix":""},{"id":486938787,"identity":"e586fe91-deed-4759-be8e-821da23399b6","order_by":8,"name":"Peter Vuillermin","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Peter","middleName":"","lastName":"Vuillermin","suffix":""},{"id":486938788,"identity":"1ac4fd78-e7c4-40e1-ad11-504a2a1384df","order_by":9,"name":"Anne-Louise Ponsonby","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Anne-Louise","middleName":"","lastName":"Ponsonby","suffix":""},{"id":486938789,"identity":"5eeadb81-ff99-446b-9202-70625aa98700","order_by":10,"name":"David Burgner","email":"","orcid":"https://orcid.org/0000-0002-8304-4302","institution":"Murdoch Children's Research Institute","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Burgner","suffix":""},{"id":486938790,"identity":"2ca13e6c-0fc0-4800-b96d-bba574cedd0f","order_by":11,"name":"Satvika Burugupalli","email":"","orcid":"","institution":"Baker Heart and Diabetes Insitute","correspondingAuthor":false,"prefix":"","firstName":"Satvika","middleName":"","lastName":"Burugupalli","suffix":""},{"id":486938791,"identity":"82fcca51-a76b-4c8a-949a-2f2e2042ac53","order_by":12,"name":"Peter Meikle","email":"","orcid":"https://orcid.org/0000-0002-2593-4665","institution":"Baker Heart and Diabetes Institute","correspondingAuthor":false,"prefix":"","firstName":"Peter","middleName":"","lastName":"Meikle","suffix":""}],"badges":[],"createdAt":"2025-07-10 05:31:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7089146/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7089146/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87373012,"identity":"cda72cba-c4e0-4604-8318-1c1f0a94dda6","added_by":"auto","created_at":"2025-07-23 07:27:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":140562,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverview of statistical analyses to interrogate the lipid link between maternal obesity and infant obesity risk in the Barwon Infant Study cohort.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7089146/v1/75d9d33815b41f672e3fb9b4.png"},{"id":87373782,"identity":"2bc5a98c-a072-42f4-86d0-417468d274bd","added_by":"auto","created_at":"2025-07-23 07:35:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":580336,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plots depicting associations between maternal gestational lipids (A) maternal gestational ether lipids (B), cord blood lipids (C), cord blood ether lipids (D) and pre-pregnancy BMI. \u003c/strong\u003eLinear regression analysis between maternal 28-week gestational lipids or cord blood lipids and pp-BMI was performed. For maternal lipids, analysis was adjusted for maternal age, education, and clinical lipids (n=1031). For cord blood lipids, analysis was adjusted for infant birth weight, delivery mode, sex, and gestational age (n=867). Each circle represents an individual lipid species, open circles represent p \u0026gt; 0.05, white closed grey circles represent corrected p \u0026lt; 0.05, the top 10 most significantly associated lipid species are shown in blue and labelled. Purple diamonds represent lipid class totals. All p-values were corrected for multiple comparisons (BH correction). Horizontal bars indicate 95% confidence intervals for lipid class totals and significant lipid species. \u0026nbsp;Abbreviations for lipids are: acylcarnitine (AC), alkenylphosphatidylcholine (PC(P)), alkenylphosphatidylethanolamine (PE(P)), alkylphosphatidylcholine (PC(O)), alkylphosphatidylethanolamine (PE(O)), alkyldiacylglycerol (TG(O)), ceramide (Cer), ceramide-1-phosphate (C1P), cholesterol ester (CE), dehydrocholesterol ester (DE), diacylglycerol (DG), dihexosylceramide (Hex2Cer),\u0026nbsp; cholesterol (COH), free fatty acid (FFA), GM\u003csub\u003e1\u003c/sub\u003e ganglioside (GM1), ganglioside GM\u003csub\u003e3\u003c/sub\u003e (GM3), lysophosphatidylcholine (LPC), lysoalkylphosphatidylcholine (LPC(O)), lysoalkenylphosphatidylcholine (LPC(P)), lysophosphatidylethanolamine (LPE), lysoalkenylphosphatidylethanolamine (LPE(P)), lysophosphatidylinositol (LPI), monohexosylceramide (HexCer), phosphatidic acid (PA), phosphatidylcholine (PC), phosphatidylethanolamine (PE), phosphatidylglycerol (PG), phosphatidylinositol (PI), phosphatidylinositol-1-phosphate (PIP1), phosphatidylserine (PS), sphingomyelin (SM), sphingosine (Sph), sphingosine-1-phosphate (S1P), triacylglycerol (TG), and trihexosylceramide (Hex3Cer). \u0026nbsp;\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7089146/v1/3f5ceadcc5e11fe973262b3b.png"},{"id":87373013,"identity":"43749176-d9bf-4f08-914a-116046e74e19","added_by":"auto","created_at":"2025-07-23 07:27:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":73641,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCord lipids mediate some of the association between maternal pre-pregnancy BMI and infant birth weight.\u003c/strong\u003e A) Proportion mediation was estimated in causal mediation models, where maternal pp-BMI was the exposure, infant birth weight was the outcome, and cord lipids were the mediator (adjusted for maternal age, infant gestational age, infant sex, and delivery mode). Average causal mediation effect (ACME) = product of the associations between the exposure and mediator, and the mediator and outcome; Total effect = average direct effect (ADE)+ACME; Proportion mediated = ACME/ Total effect. B) Cord lipids that significantly mediate the association of maternal pp-BMI on birth weight, proportion mediated is plotted with 95% confidence intervals.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7089146/v1/ddc833de5a35e190a94847ad.png"},{"id":87376584,"identity":"3323d702-bd71-4642-bd76-055e480c8298","added_by":"auto","created_at":"2025-07-23 07:59:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1694720,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7089146/v1/a4fd47fb-fb1b-4dc2-8df3-df198d4a8b2a.pdf"},{"id":87373784,"identity":"a5777383-c771-48da-b995-202d93131ba5","added_by":"auto","created_at":"2025-07-23 07:35:07","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":23044,"visible":true,"origin":"","legend":"Summary of supplementary figures and tables","description":"","filename":"SummaryofSupp.docx","url":"https://assets-eu.researchsquare.com/files/rs-7089146/v1/38fd8e8545d2591b5e7cea2e.docx"},{"id":87373785,"identity":"c23baf24-b845-4cf4-8668-2857c48234d5","added_by":"auto","created_at":"2025-07-23 07:35:07","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":769276,"visible":true,"origin":"","legend":"Supplementary tables","description":"","filename":"ManuscriptSupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7089146/v1/669231cde4b50d08e992e35f.xlsx"},{"id":87373022,"identity":"11a35caa-ecc1-4020-a922-821c3de5fb07","added_by":"auto","created_at":"2025-07-23 07:27:07","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":333712,"visible":true,"origin":"","legend":"Supplementary figures","description":"","filename":"ManuscriptSupplementaryFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-7089146/v1/f3573cfc8009323db4940bca.docx"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e there is potential conflict of interest.","formattedTitle":"Maternal BMI and infant obesity risk: a lipidomics perspective on the developmental origins of obesity","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe prevalence of overweight and obesity in children has reached alarming levels, affecting approximately one in four Australian children (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Contributing to this public health issue, maternal overweight and obesity are significant risk factors for infant obesity (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Maternal pre-conception obesity increases the odds of childhood obesity by up to 264% (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) and maternal pre-pregnancy body mass index (pp-BMI) is associated with infant birth weight, BMI and overweight status (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Furthermore, high gestational weight gain is associated with increased infant BMI and obesity risk in adulthood (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). These findings suggest that a healthy maternal metabolic status before and during pregnancy may contribute to favourable metabolic programming and reduced obesity risk for the infant, although the underlying biology remains poorly defined.\u003c/p\u003e\u003cp\u003eThe role of lipids and lipid metabolism, known to be critical in metabolic health, has been largely overlooked in early life studies. Circulating lipids are known to have critical roles in health and disease and may be involved in health programming (\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Adult obesity is associated with lipid dysregulation (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), and during pregnancy, a time of high metabolic activity, many circulating lipids are elevated (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). The circulating lipid profile in infancy changes across early life as metabolism develops, and previous studies have reported links between circulating lipids and growth in the first months and years of life (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Early life growth is also associated with breastfeeding, and the plasma lipid profiles of breastfed and formula-fed infants differ substantially (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Thus, lipid transfer from mother to infant may occur via the placenta in utero, or postnatally through human milk, providing two critical windows of exposure.\u003c/p\u003e\u003cp\u003eWhile this study considers the total circulating lipidome, we were particularly interested in ether lipids, a subclass of lipids characterised by an ether bond at the sn-1 position of the glycerol backbone. Ether lipids, including plasmalogens, are abundant in human milk, strongly associated with breastfeeding, and highly bioactive (\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). They play key roles in cellular membrane integrity, oxidative stress regulation, and immune development - functions that are critical during the early pre- and post-natal period (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Notably, ether lipids derived from human milk have been shown to directly promote adipose tissue development and thermogenic capacity in early life (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Because ether lipids are modifiable through diet, they are compelling candidates for targeted interventions (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Despite their potential significance, ether lipids remain understudied in the context of early life metabolic programming and intergenerational obesity risk (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eUnderstanding how maternal lipids contribute to infant lipid profiles and obesity risk is critical. Circulating lipids are modifiable, and identifying key lipid pathways may reveal opportunities for targeted early interventions to reduce intergenerational obesity risk (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). This study aimed to explore the role of maternal and infant lipids in the early-life transmission of obesity, using longitudinal data from the Barwon Infant Study (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Cohort and Data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Barwon Infant Study (BIS) cohort is a population-derived birth cohort from Victoria, Australia (\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e). The cohort comprises 1074 mother-infant dyads and includes maternal and infant anthropometric measures (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). For this study all BIS mothers and infants with available lipidomics data and covariate data for each time point were utilised. The distribution of maternal pp-BMI was as follows: 2.4% underweight (BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5), 55.4% healthy (18.5\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;25.0), 25.2% overweight (25.0\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;30.0), and 17.0% obese (BMI\u0026thinsp;\u0026ge;\u0026thinsp;30.0). The mothers provided written informed consent at recruitment and study ethics was approved by the Barwon Health Human Research Ethics Committee (HREC 10/24). Comprehensive lipidomics profiling using liquid chromatography-mass spectrometry has previously been performed on infant plasma, cord blood, maternal serum, and maternal milk in the BIS. Briefly, 733 lipid species were measured in maternal samples at 28 weeks\u0026rsquo; gestation, cord serum, and infant plasma at 6, 12, and 48 months, and \u0026gt;\u0026thinsp;900 lipid species in maternal milk samples at 1 and 6 months (\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eThe basic characteristics of BIS cohort variables utilised in this study\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDistribution\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eMaternal characteristics\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePre-pregnancy BMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25.7 (5.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge (years)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31.9 (4.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUniversity education\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e460 (53.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eInfant characteristics\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSex (female)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e495 (48.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGestational age (days)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e276.51 (10.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBirth weight (kg)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.6 (0.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n\u003cp\u003eAt 6 months\u003c/p\u003e\n\u003cp\u003eAt 12 months\u003c/p\u003e\n\u003cp\u003eAt 48 months\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e17.2 (1.7)\u003c/p\u003e\n\u003cp\u003e17.7 (1.7)\u003c/p\u003e\n\u003cp\u003e15.6 (1.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBirth mode\u003c/p\u003e\n\u003cp\u003eUnassisted vaginal birth\u003c/p\u003e\n\u003cp\u003eUnscheduled caesarean section\u003c/p\u003e\n\u003cp\u003eForceps vaginal birth\u003c/p\u003e\n\u003cp\u003eScheduled caesarean section\u003c/p\u003e\n\u003cp\u003eVacuum vaginal birth\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e510 (50.1%)\u003c/p\u003e\n\u003cp\u003e144 (14.1%)\u003c/p\u003e\n\u003cp\u003e86 (8.4%)\u003c/p\u003e\n\u003cp\u003e160 (15.7%)\u003c/p\u003e\n\u003cp\u003e119 (11.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBreastfeeding (any) duration (weeks)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.65 (21.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBreastfeeding (any) status\u003c/p\u003e\n\u003cp\u003eAt 6 months\u003c/p\u003e\n\u003cp\u003eAt 12 months\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e549 (60.3%)\u003c/p\u003e\n\u003cp\u003e286 (31.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\"\u003e\u003csup\u003e1\u003c/sup\u003eValues are presented as mean (standard deviation) for continuous variables or number (percentage) for categorical variables; BMI: body mass index\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn overview of the analyses in this study is presented in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eVariables of interest and covariates utilised throughout the analyses were maternal pp-BMI (kg/m\u003csup\u003e2\u003c/sup\u003e), maternal age (years), birth mode (categorical), infant gestational age (weeks), infant birth weight (kg), infant BMI (kg/m\u003csup\u003e2\u003c/sup\u003e), maternal clinical lipids (high-density lipoprotein, HDL, low-density lipoprotein, LDL, cholesterol, triglycerides at 28 weeks), breastfeeding duration (weeks, defined as any breastfeeding, self-reported), current breastfeeding at 6 and 12 months (binary yes or no at each time point), and education (dichotomised as completed any university level certificate or less than university level education). Sample size differs for each analysis, due to incomplete data and/or samples.\u003c/p\u003e\n\u003cp\u003eLipidomics measures were log transformed and scaled to unit variance prior to analysis. Associations between pp-BMI and infant birth weight, gestational age, infant BMI at 6, 12, and 48 months, and breastfeeding duration were assessed using univariate linear regression with no covariates. Associations between pp-BMI and maternal lipidome were assessed using univariate linear regression, adjusted for maternal age, education, and clinical lipids (n\u0026thinsp;=\u0026thinsp;854). The fold change for maternal lipids associated with pp-BMI were included in the supplementary material of a previous publication (\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e). Associations between pp-BMI and the cord lipidome were assessed using univariate linear regression, adjusted for maternal age, infant birth weight, delivery mode, infant sex, and gestational age (n\u0026thinsp;=\u0026thinsp;731). Associations between paired maternal and infant lipids at each time point were assessed using univariate linear regression, adjusted for maternal and infant BMI, infant sex, birth weight, and breastfeeding (n\u0026thinsp;=\u0026thinsp;501\u0026ndash;776). Associations between infant BMI and infant lipidomes at 6, 12, and 48 months were assessed using univariate linear regression, adjusted for infant sex, breastfeeding, and clinical lipids (n\u0026thinsp;=\u0026thinsp;456\u0026ndash;723). All p-values were corrected for multiple comparisons by the method of Benjamini and Hochberg (BH) (\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eCausal mediation analysis was performed using \u0026ldquo;mediation\u0026rdquo; R package, to investigate the mediating effect of cord lipids on the relationship between maternal pp-BMI and infant birth weight (adjusted for infant sex, delivery mode, gestational age, and maternal age) (\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e). Total effects were estimated from the linear regression analysis. There was limited evidence to support performing other mediation analyses in this study (i.e. mediation was only tested when the exposure was significantly associated with both the mediator and the outcome). For each lipid, two models were constructed: (\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e) a mediator model with the lipid as the dependent variable and pp-BMI as the independent variable, and (\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e) an outcome model with birth weight as the dependent variable and both pp-BMI and the lipid as independent variables. The \u0026ldquo;mediate\u0026rdquo; function from the mediation package was used to quantify the indirect (mediated) effect, direct effect, and total effect of pp-BMI on birth weight. Mediation models were fitted using non-parametric bootstrapping with 10,000 simulations to estimate 95% confidence intervals for the proportion of the effect mediated by each lipid (n\u0026thinsp;=\u0026thinsp;551).\u003c/p\u003e\n\u003cp\u003eTo provide a composite lipid measure and reduce the difficulty in interpreting individual lipid species, we developed a plasmalogen score. This score was derived via principal component analysis (PCA) on the mean-centred and scaled phosphatidylethanolamine plasmalogen (PE(P)) and phosphatidylethanolamine (PE) species, from each expressed as a proportion of total PE(P) and PE. The first principal component (PC1) was used as the plasmalogen score, separately calculated for maternal serum at 28 weeks\u0026rsquo; gestation and infant plasma at 48 months (\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e). Given these known associations, we hypothesised that a plasmalogen score might represent a meaningful biological signature of early lipid programming. For infant plasma at 6 and 12 months, the 48-month infant PCA model was applied to calculate plasmalogen scores, avoiding the confounding due to breastfeeding at 6- and 12-month timepoints. For human milk samples, a plasmalogen ratio was calculated (total PE(P)/total PE) instead of a PCA-based score due to compositional differences in lipid species between milk and plasma. Linear regression was used to assess associations between maternal pp-BMI and plasmalogen score (adjusted for maternal age, n\u0026thinsp;=\u0026thinsp;666), maternal plasmalogen score and infant plasmalogen score (adjusted for maternal age, birth weight, breastfeeding, and maternal pp-BMI, n\u0026thinsp;=\u0026thinsp;514), infant plasmalogen score and infant BMI (adjusted for maternal age, breastfeeding, birth weight, and maternal pp-BMI, n\u0026thinsp;=\u0026thinsp;511), and maternal plasmalogen score and human milk plasmalogen ratio (adjusted for maternal age and pp-BMI, n\u0026thinsp;=\u0026thinsp;198). Analyses were performed in RStudio (version 2024.04.1). For each model, statistical significance was defined as p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, either unadjusted or after BH correction.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eFactors associated with maternal pre-pregnancy BMI\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe examined associations between maternal pp-BMI and obesity related outcomes in the Barwon Infant Study (BIS) cohort; infant birth weight and age, BMI, and duration of breastfeeding (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociations of maternal pre-pregnancy body mass index (kg/m\u003csup\u003e2\u003c/sup\u003e) with infant growth and breastfeeding\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOutcome\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBeta coefficient [95% CI]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-value\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBirth weight (kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.013 [0.007, 0.020]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e9.12x10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;5\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGestational age (weeks)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.001 [-0.133, 0.135]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.87x10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI at 6 months (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.052 [0.028, 0.076]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e2.99x10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;5\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI at 12 months (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.056 [0.031, 0.081]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e9.48x10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;6\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI at 48 months (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.051 [0.028, 0.073]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e9.53x10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;6\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBreastfeeding duration (weeks)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.937 [-1.193, -0.680]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.95x10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;12\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003csup\u003e1\u003c/sup\u003esignificant p-values (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) are in bold; BMI: body mass index. No model adjustments were made\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWe used maternal pp-BMI as a proxy for maternal obesity, with higher pp-BMI representing increased risk of infant obesity. Infant birthweight and BMI at 6, 12, and 48 months were used as indicators of early-life obesity risk, with higher values interpreted as proxies for greater risk. Breastfeeding duration, and associations with breastfeeding, were considered indicators of protection against obesity. These variables were used to investigate how lipid profiles may contribute to the intergenerational transfer of obesity risk or resilience.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMaternal pre-pregnancy BMI is associated with gestational lipids and cord blood lipids\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAs BMI and the plasma lipidome are linked in non-pregnant populations (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), we confirmed that maternal pp-BMI was associated with the plasma lipidome during gestation (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) (Supp Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The maternal 28-week gestational lipidome showed significant associations with pp-BMI after correcting for multiple comparisons (50.1%, 367/733 lipid species, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Approximately half (182/367) of the lipids were negatively associated with maternal pp-BMI, including several ether lipids from classes PE(P) and PE(O) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). The remaining (185/367) lipids were positively associated with maternal pp-BMI, including many species from the ceramide, sphingomyelin, acylcarnitine, and glycerolipid classes. We also assessed if maternal pp-BMI was linked to cord lipids (Supp Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The cord blood lipidome contained 41 lipids significantly associated with maternal pp-BMI after BH correction (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). These included 41% (17/41) negatively associated (from CE, LPC(O), DE, PC(O), PE(P), Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD) and 59% (24/41) positively associated, predominantly from TG, AC, and FFA classes. In contrast, maternal pp-BMI was significantly associated with only one infant lipid (TG(56:8) at 6 months of age).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eCord lipids partially mediate the effect of maternal pre-pregnancy BMI on infant birth weight\u003c/b\u003e\u003c/p\u003e\u003cp\u003eHaving established associations between maternal pp-BMI and infant birth weight, and maternal pp-BMI and cord lipids, we performed mediation analysis to investigate the extent to which cord lipids mediate the effect of pp-BMI on infant birth weight (Supp Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). There were 6 lipids (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) that mediated between 5.4 and 18.0% of the effect of maternal pp-BMI on birth weight, after BH correction.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eAssociations between lipids, plasmalogen scores, and obesity-related outcomes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo explore potential protective lipid mechanisms linked to maternal pp-BMI, breastfeeding, and infant growth, we looked at lipids, focusing on ether lipids, and calculated a composite plasmalogen score. We aimed to identify how lipids may act across the maternal-infant axis to influence obesity risk protectively. We first assessed associations between infant lipid species and BMI z-score at 6, 12, and 48 months. At 6 months of age, 20 lipid species were significantly associated with infant BMI z-score (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In contrast, only one lipid was significantly associated with BMI at 12 and 48 months (Supplementary Tables\u0026nbsp;4\u0026ndash;6, Supplementary Figs.\u0026nbsp;1\u0026ndash;3). Then we checked the association between maternal and infant lipids at all timepoints, which showed frequent significantly positive association of ether lipid species, particularly plasmalogens (Supplementary Tables\u0026nbsp;7\u0026ndash;9).\u003c/p\u003e\u003cp\u003ePlasmalogen scores were then calculated in maternal serum and infant plasma, cord blood, and the plasmalogen ratio in maternal milk. Linear regression was used to examine associations between maternal pp-BMI and maternal plasmalogen score, maternal plasmalogen score and milk plasmalogen ratio, maternal and infant plasmalogen scores, and infant plasmalogen score and infant BMI, including appropriate covariates as in the previous models (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Supp Table\u0026nbsp;10). Maternal 28 weeks\u0026rsquo; gestation weight and weight gain were also explored as measures for maternal obesity; however, these were not included in the final models (Supp 11).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMaternal to infant plasmalogen score link\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAssociation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEffect estimate (β)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePre-pregnancy BMI with maternal plasmalogen score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.196, -0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e4.94x10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;3\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaternal plasmalogen score with cord blood plasmalogen score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.789\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.529, 1.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e4.47x10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;9\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaternal plasmalogen score and milk plasmalogen ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.765\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.044, 1.487\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e3.77x10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaternal plasmalogen score with infant 6-month plasmalogen score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.218\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.008, 0.427\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e4.22x10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaternal plasmalogen score with Infant 12-month plasmalogen score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.412\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.226, 0.598\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.69x10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;5\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaternal plasmalogen score with child 48-month plasmalogen score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.201\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.096, 0.309\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e3.77x10-\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003e1\u003c/sup\u003esignificant p-values (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) are in bold\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eMaternal obesity is a major risk factor for infant obesity, yet the biological mechanisms remain unclear (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). This study reveals that lipid metabolism, including ether lipids, may serve as a key protective factor capable of modifying the intergenerational transmission of obesity risk.\u003c/p\u003e\u003cp\u003e\u003cem\u003eMaternal pre-pregnancy BMI and protective lipid signatures\u003c/em\u003e\u003c/p\u003e\u003cp\u003eMaternal pp-BMI, which is a key determinant of infant obesity risk (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), was associated with a distinct lipid signature during pregnancy (28 weeks\u0026rsquo; gestation, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Higher maternal BMI at conception has consistently been linked with increased risk of macrosomia and childhood obesity and was therefore used as a proxy for obesity (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Consistent with this, we observed a positive association between maternal pp-BMI and infant birth weight, which is a known risk factor for later obesity (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). While maternal dysregulation of free fatty acid (FFA) metabolism has been commonly implicated in transfer of obesity risk to the infant (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), we observed that many other lipid classes were also affected. This included several increased ceramides and sphingolipids, and decreases to hexosylceramides, sulfatides, and many ether lipids and the maternal plasmalogen score. Similar dysregulation is also observed in non-pregnant adults, including the negative relationship between BMI, type 2 diabetes, and cardiovascular disease with plasmalogen score (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). The plasmalogen score allows the assessment of relative PE(P) and PE levels, is modifiable, and has been previously validated as a metabolic marker for adult cardiometabolic health (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). The maternal lipid profile with lower pp-BMI, marked with higher ether lipid and plasmalogen levels, may reflect a protective metabolic signature for pregnancy. This signature may contribute to healthy offspring growth and development (for example a less adverse birth weight) but is diminished with a higher maternal pp-BMI. Overall, these findings suggest that maternal lipid profiles characteristic of obesity have the potential to be passed on to the newborn, via the umbilical cord in utero, and via human milk postnatally, ultimately setting-up metabolism and future obesity risk.\u003c/p\u003e\u003cp\u003e\u003cem\u003eCord blood lipids as early mediators of obesity risk and protection\u003c/em\u003e\u003c/p\u003e\u003cp\u003eMaternal pp-BMI can alter the cord blood lipids, which represent infant circulating lipids at birth (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). While transfer of polyunsaturated fatty acids (PUFA) from mother to foetus is tightly regulated (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e), our findings show that maternal metabolic status is still reflected in the infant birth lipidome. There were 6 cord lipids, comprising LPCs, LPEs, and one PE species, that each significantly mediated up to 18.0% of the relationship between maternal pp-BMI and infant birth weight (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Many lysophospholipids have previously been linked to infant growth (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). This suggests that higher pp-BMI is associated with a shift away from a more protective cord lipid profile, reflective of maternal circulation, and towards a lipid state linked with adversely high infant birth weight (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). In a prior analysis within the BIS cohort, several ether lipids, including PC(P) and TG(O) species, were negatively associated with birth weight (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), and studies in placental tissue have shown impaired transfer of ether lipids in pregnancies complicated by obesity (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Further exploring lipids in this context will be important to elucidate pathways through which maternal metabolic status programs offspring health.\u003c/p\u003e\u003cp\u003e\u003cem\u003eEnhancing maternal plasmalogen score to reduce infant obesity risk\u003c/em\u003e\u003c/p\u003e\u003cp\u003eOur results highlight that increasing maternal ether lipid levels and plasmalogen score, may offer a novel intervention to promote healthier lipid profiles to influence infant metabolic outcomes: maternal dietary modification and/or reduction of pp-BMI to a healthy range (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Reducing BMI, which is widely recommended for women with overweight or obesity during pregnancy planning, is likely to improve lipid profiles and infant health outcomes (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Additionally, our findings suggest that lipid supplementation of women prior to/during pregnancy, especially in women with high BMI, to normalise lipid profiles may reduce infant obesity risk. Human studies have shown that plasmalogens are modifiable through alkylglycerol precursors or plasmalogens (\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). We have previously shown in the BIS that the majority of cord blood lipids are significantly positively associated with maternal gestational lipids, implying that modification of the maternal lipidome during pregnancy may modify cord blood lipids (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Maternal dietary supplementation with plasmalogen precursors, such as alkylglycerols, may promote a gestational lipid profile associated with lower infant obesity risk by enhancing protective ether lipid signals in both maternal and cord blood, supporting protective lipid transfer to the infant.\u003c/p\u003e\u003cp\u003e\u003cem\u003eThe impact of maternal obesity on developing infant lipid metabolism\u003c/em\u003e\u003c/p\u003e\u003cp\u003ePostnatally, we observed significant correlations between maternal and infant plasmalogen scores, at 6, 12, and 48 months of age, suggesting transmission of ether lipid profiles across maternal and infant contexts. This is a novel finding and, to our knowledge, one of the first demonstrations of maternal-infant lipid tracking across timepoints with a composite lipid (plasmalogen) score. The strong associations between maternal and infant plasmalogen scores up to four years of age, even after adjusting for breastfeeding, suggests that although human milk is a major source of ether lipids in infancy, maternal lipid metabolism may exert a longer-term influence on the infant lipid profile beyond the breastfeeding window.\u003c/p\u003e\u003cp\u003e\u003cem\u003eEarly life lipid profiling to understand obesity risk\u003c/em\u003e\u003c/p\u003e\u003cp\u003eUnderstanding early life lipid metabolism is essential for translating these findings into strategies that support healthy development and reduce obesity risk. While higher BMI in infancy is associated with increased risk of obesity later in life, BMI is a crude measure of metabolic health during infancy (\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Our data reveal that associations between lipids and BMI at 6 and 12 months were weak and inconsistent, but by 4 years of age, the lipidomic profile began to reflect adult-like patterns. This suggests a developmental shift in lipid metabolism and highlights the importance of long-term follow-up in birth cohorts to fully capture when and how lipid signatures predict obesity risk. Among the lipid classes, ether lipids, particularly plasmalogens, are emerging as key candidates in early life metabolic priming (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Plasmalogens, higher in mothers with lower pp-BMI, may represent a biological link between maternal metabolic status, infant lipid programming, and obesity risk. Mechanistic studies support this role, for example in mice dietary alkylglycerols (plasmalogen precursors) enhance mitochondrial activity, promote beige adipose tissue, and activate lipid-regulating pathways (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). In humans, early ether lipid intake has been linked to both increased weight gain in infancy and reduced fat mass, underscoring their dynamic influence on growth (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). Our data suggest that the plasmalogen score could serve as a biologically meaningful lipid marker of protective potential in the context of intergenerational obesity risk. The score reflects maternal metabolic status, is associated with milk composition, persists across infancy, and begins to show metabolic relevance by 4 years of age. Although we were unable to establish clear links between plasmalogen score and infant BMI within the first 4 years, its consistent biological relevance supports its further investigation.\u003c/p\u003e\u003cp\u003eThese findings underscore the potential for early ether lipid exposure to shape long-term metabolic health. The high ether lipid content in human milk compared to formula may be one mechanism through which breastfeeding confers protection against obesity (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Breastfeeding promotion remains a critical public health strategy, especially for populations at higher risk of maternal obesity. At the same time, supplementation strategies (including maternal dietary supplementation during pregnancy or infant formula reformulation) could offer additional ways to optimise ether lipid availability in early life. Plasmalogen score and ether lipids in early life warrant further investigation as potential tools to reduce intergenerational obesity risk by targeting metabolic programming early.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStrengths and Limitations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe integration of a well-characterised birth cohort (BIS) with comprehensive lipidomics data and advanced statistical methods, including mediation analysis, is a major strength of this study. The mediation analysis included 551 mother-child dyads with complete datasets, larger than other studies to date, and 6 lipids were successfully identified for mediating the relationship between maternal pp-BMI and infant birth weight. Use of pp-BMI and infant BMI are limited in their interpretation; however, they were the most appropriate growth measures in the context of lipidomics. Prior studies indicate that pp-BMI is a strong predictor of offspring obesity risk and that infants with high BMI in early life have higher obesity risk as they age (\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). While lipid metabolism was the primary focus of this study, we acknowledge that other biological mechanisms, including inflammation and hormonal pathways, may also contribute to intergenerational obesity risk (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). The cross-sectional nature of some of the analyses limited causal inference. Furthermore, it is not possible to unravel all environmental factors that will also be at play in this study. Our findings need to be validated in additional larger and more diverse cohorts, with later follow-up of participants to ensure that links hold and develop.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eMaternal pre-pregnancy BMI significantly impacts gestational lipid profiles and has downstream effects on infant birth weight and breastfeeding, which are obesity risk factors. Plasmalogen scores appear to reflect a maternal metabolic state that supports protective lipid programming in the infant, offering promising targets for intervention. Strategies to optimise ether lipid levels, including maternal dietary supplementation, breastfeeding promotion, and early life infant dietary supplementation, could provide a practical, modifiable way to enhance metabolic resilience and reduce obesity risk, improving metabolic health outcomes for future generations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePJM declares a potential conflict of interest (PJM consults for Juvenescence Ltd, a biotech company that is developing a plasmalogen supplement product for market. The Baker Institute has a commercialization agreement with the Murdoch Children\u0026rsquo;s Research Institute relating to the development of infant formula products with plasmalogen precursor supplements. The Baker Institute holds several patents related to increasing plasmalogen levels for improved health outcomes. These have been licensed to Juvenescence Ltd). The remaining authors declare no conflict of interest. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge the participation and commitment of all the families in the Barwon Infant Study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCohort administration: RS, PV, ALP, DB, Barwon Infant Study Investigator Group. Conceptualisation: ADG, SB, PJM. Formal analysis: ADG, TW. Writing - original draft: ADG, TW. Writing - reviewing and editing: ADG, TW, TD, YS, SP, GO, TM, RS, PV, AP, DB, SB, PJM. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePM declares a potential conflict of interest (PJM consults for Juvenescence Ltd, a biotech company that is developing a plasmalogen supplement product for market. The Baker Institute has a commercialization agreement with the Murdoch Children\u0026rsquo;s Research Institute relating to the development of infant formula products with plasmalogen precursor supplements. The Baker Institute holds several patents related to increasing plasmalogen levels for improved health outcomes. These have been licensed to Juvenescence Ltd). The remaining authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe establishment work and infrastructure for the BIS was provided by the Murdoch Children\u0026rsquo;s Research Institute (MCRI), Deakin University and Barwon Health. Subsequent funding was secured from the National Health and Medical Research Council of Australia, The Jack Brockhoff Foundation, the Scobie Trust, the Shane O\u0026rsquo;Brien Memorial Asthma Foundation, the Our Women\u0026rsquo;s Our Children\u0026rsquo;s Fund-Raising Committee Barwon Health, The Shepherd Foundation, the Rotary Club of Geelong, the Ilhan Food Allergy Foundation, GMHBA Limited and the Percy Baxter Charitable Trust, Perpetual Trustees and the Minderoo Foundation. In-kind support was provided by the Cotton On Foundation and CreativeForce. This work was supported by the Victorian Government's Operational Infrastructure Support Program, NHMRC Investigator Grants (ALP, DB, PJM), an NHMRC Career Development Fellowship (PV), and The DHB Foundation Fellowship (TM). The funding bodies had no input in design or publication of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccess to the Barwon Infant Study (BIS) data used in this paper may be requested through the BIS Steering Committee. Requests to access cohort data are considered on scientific and ethical grounds and, if approved, provided under collaborative research agreements.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAustralian Institute of Health and Welfare. Overweight and obesity among Australian children and adolescents. Canberra: AIHW; 2020.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGodfrey KM, Reynolds RM, Prescott SL, Nyirenda M, Jaddoe VW, Eriksson JG, Broekman BF. Influence of maternal obesity on the long-term health of offspring. 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Pediatrics. 2016;137(5).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAgbaje AO. Waist-circumference-to-height-ratio had better longitudinal agreement with DEXA-measured fat mass than BMI in 7237 children. Pediatric Research. 2024;96(5):1369\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBurugupalli S, Mansell T, Wang T, George A, Paul S, Saffery R, et al. The protective effect of breastfeeding on infant inflammation: a mediation analysis of the plasma lipidome and metabolome. TBC. 2024.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRamadurai S, Andrews C, Cheema S, Thomas R, Wagner CL, Sen S. Maternal Predictors of Breast Milk Plasmalogens and Associations with Infant Body Composition and Neurodevelopment. Clinical Therapeutics. 2022;44(7):998\u0026ndash;1009.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYu Z, Han S, Zhu J, Sun X, Ji C, Guo X. Pre-pregnancy body mass index in relation to infant birth weight and offspring overweight/obesity: a systematic review and meta-analysis. PloS one. 2013;8(4):e61627.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSimmonds M, Llewellyn A, Owen CG, Woolacott N. Predicting adult obesity from childhood obesity: a systematic review and meta-analysis. Obes Rev. 2016;17(2):95\u0026ndash;107.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBraun JM, Kalkwarf HJ, Papandonatos GD, Chen A, Lanphear BP. Patterns of early life body mass index and childhood overweight and obesity status at eight years of age. BMC Pediatrics. 2018;18(1):161.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"","lastPublishedDoi":"10.21203/rs.3.rs-7089146/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7089146/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eMaternal obesity is a critical determinant of infant health, influencing birth weight and increasing the risk of obesity in early life. Plasma lipids are mechanistically linked to obesity and may mediate the intergenerational transfer. Using the Barwon Infant Study, a longitudinal birth cohort, we aimed to investigate the associations between maternal pre-pregnancy body mass index (pp-BMI), lipidomic profiles of mothers, human milk, and infants, and early life growth. We were particularly interested in ether lipids as they are higher in breastfed infants compared to formula-fed infants, are enriched in human milk compared to infant formula, and are involved in metabolic health and inflammation in adult populations.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eLinear regression analyses assessed relationships between maternal pp-BMI and lipid profiles across all biospecimens, and infant BMI. A composite plasmalogen score, reflecting ether lipid metabolism, was developed due to its strong associations with maternal BMI and breastfeeding. Causal mediation analysis was performed to quantify the extent to which cord lipids mediated the effect of maternal pp-BMI on infant birth weight.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eOur findings revealed significant associations between maternal pp-BMI and both maternal and cord lipid profiles, as well as obesity risk indicators. Of the cord blood lipids, 6 of them mediated up to 18% of the effect of maternal pp-BMI on birth weight. Maternal plasmalogen score was negatively associated with pp-BMI and positively associated with plasmalogens in human milk and infant plasmalogen scores from birth to four years of age.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThese findings position plasmalogens and ether lipids as potential biomarkers or intervention targets for reducing transmission of obesity from mother to infant. Optimising lipid profiles through reducing maternal pp-BMI and dietary or supplemental ether lipids may represent a novel strategy for mitigating early-life obesity risk.\u003c/p\u003e","manuscriptTitle":"Maternal BMI and infant obesity risk: a lipidomics perspective on the developmental origins of obesity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-23 07:27:02","doi":"10.21203/rs.3.rs-7089146/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":"f10cb65d-4332-4ec2-916a-564cdd073035","owner":[],"postedDate":"July 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":51698030,"name":"Biological sciences/Biochemistry/Lipidomics"},{"id":51698031,"name":"Biological sciences/Biochemistry/Lipids"}],"tags":[],"updatedAt":"2025-07-23T07:27:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-23 07:27:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7089146","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7089146","identity":"rs-7089146","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}