There is mounting evidence that in utero and early life exposures may predispose an individual to metabolic disorders in later life; and dysregulation of lipid metabolism is critical in such outcomes. However, there is limited knowledge about lipid metabolism and factors causing lipid dysregulation in early life that could result in adverse health outcomes in later life. We studied the effect of antenatal factors such as gestational age, birth weight, and mode of birth on lipid metabolism at birth; changes in the circulating lipidome in the first 4 years of life and the effect of breastfeeding in the first year of life. From this study, we aim to generate a framework for deeper understanding into factors effecting lipid metabolism in early life, to provide early interventions for those at risk of developing metabolic disorders including cardiovascular diseases.We performed comprehensive lipid profiling of 1074 mother-child dyads in the Barwon Infant Study (BIS), a population-based pre-birth cohort and measured 776 distinct lipid features across 39 lipid classes using ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS). We measured lipids in 1032 maternal serum samples at 28 weeks’ gestation, 893 cord serum samples at birth, 793, 735, and 511 plasma samples at 6, 12 months, and 4 years, respectively. Cord serum was enriched with long chain poly-unsaturated fatty acids (LC-PUFAs), and corresponding cholesteryl esters relative to the maternal serum. We performed regression analyses to investigate the associations of cord serum lipid species with antenatal factors: gestational age, birth weight, mode of birth and duration of labour.The lipidome differed between mother and newborn and changed markedly with increasing child’s age. Alkenylphosphatidylethanolamine species containing LC-PUFAs increased with child’s age, whereas the corresponding lysophospholipids and triglycerides decreased. Majority of the cord serum lipids were strongly associated with gestational age and birth weight, with most lipids showing opposing associations. Each mode of birth showed an independent association with cord serum lipids. Breastfeeding had a significant impact on the plasma lipidome in the first year of life, with up to 17-fold increases in a few species of alkyldiaclylglycerols at 6 months of age.This study sheds light on lipid metabolism in infancy and early childhood and provide a framework to define the relationship between lipid metabolism and health outcomes in early childhood.This work was supported by the A*STAR-NHMRC joint call funding (1711624031). The developmental origins of health and disease paradigm suggests that prenatal, perinatal, and postnatal heritable and environmental influences result in long-term developmental, physiological, and metabolic changes in major tissues and organs . These mThe Barwon Infant Study (BIS) is a longitudinal Australian birth cohort with antenatal recruitment and repeated in-depth data and sample collection that is designed to facilitate a detailed mechanistic investigation of disease development within an epidemiological framework . In this2. Ethics approval for this study was granted by the Barwon Health Human Research and Ethics Committee (HREC 10/24).The BIS is a pre-birth cohort  from the Barwon region of south-eastern Australia . Women wg, 10 min, and 20°C) before transferring the supernatant into sample vials with glass inserts for analysis.Lipid extraction was performed as described previously in Lipidomics was performed as described previously in 2O/30% acetonitrile/20% isopropanol (v/v/v) containing 10 mM ammonium formate and solvent B: 1% H2O/9% acetonitrile/90% isopropanol (v/v/v) containing 10 mM ammonium formate. To avoid peak tailing for acidic phospholipids, we passivised the instrument prior to each batch by running 0.5% phosphoric acid in 90% acetonitrile for 2 hr and subsequently flushing the system with 85% H2O/15% acetonitrile prior to sample run.The running solvent consisted of solvent A: 50% HWe utilised a stepped linear gradient with a 12.9 min cycle time per sample and a 1 µL sample injection. To increase throughput, we used a dual column setup to equilibrate the second column while the first is running a sample. The sample analytical gradient was as follows: starting with a flow rate of 0.4 mL/min at 15% B and increasing to 50% B over 2.5 min, then to 57% over 0.1 min, to 70% over 6.4 min, to 93% over 0.1 min, to 96% over 1.9 min, and finally to 100% over 0.1 min. The solvent was then held at 100% B for 0.9 min . Equilibration was started as follows: solvent was decreased from 100% B to 15% B over 0.2 min and held for an additional 0.8 min for a total run time of 12.9 min per sample. The next sample is injected, and the columns are switched.The following mass spectrometer conditions were used: gas temperature, 150°C, gas flow rate 17 L/min, nebuliser 20 psi, sheath gas temperature 200°C, capillary voltage 3500 V, and sheath gas flow 10 L/min. Isolation widths for Q1 and Q3 were set to ‘unit’ resolution (0.7 amu).Plasma quality control (PQC) samples consisting of a pooled set of plasma samples taken from six healthy individuals and extracted alongside the study samples were incorporated into the analysis at 1 PQC per 20 plasma samples. Technical QC samples (TQC) consisted of PQC extracts which were pooled, then split into individual vials to provide a measure of technical variation from the mass spectrometer only. These were included at a ratio of 1 TQC per 20 plasma samples. TQCs were monitored for changes in peak area, width, and retention time to determine the performance of the UHPLC-MS/MS analysis and were subsequently used to account for differential responses across the analytical batches. To align the results to any future datasets, the NIST 1950 SRM sample (Sigma) was included as a reference plasma sample, at a rate of 1 per 40 samples.Relative quantification of lipid species was determined by comparison to the relevant internal standard. Lipid class total concentrations were calculated as the sum of individual lipid species concentrations, except in the case of triacylglycerol (TG) and alkyldiacylglycerol (TG(O)) species, where we measured both neutral loss (NL) and single ion monitoring (SIM) peaks, and subsequently used the abundant but less structurally resolved (SIM) species concentrations for summation purposes when examining lipid class totals. Any lipid differences based on the matrix must be kept in mind as serum samples were used during gestation, and at birth vs. plasma samples at other time points. Several studies have reported matrix-associated differences in species of lysophospholipids, diacylglycerols, free long chain fatty acids, and oxidised fatty acids . HoweverLipidomic analysis was performed across 11 batches of ~500 samples with quality control samples analysed every 20 samples. The chromatographic peaks were integrated using the Mass Hunter  software and assigned to specific lipid species based on MRM (precursor/product) ion pairs and retention time. Upon completion of quantification, lipidome data for 777 lipid features in 4007 samples was available across 11 batches. Zero values  were replaced by 1/10th the minimum value for the concerned lipids in the corresponding sample types. We then aligned the lipid concentrations across different batches by median centring the PQC samples, and additionally scaled all batch-wise standard deviations for biological samples  to the mean standard deviation. We visually assessed lipids for large-scale technical variations , and re-quantified affected lipids where possible, and excluding them or selectively setting some values to missing where not. Missing values were then imputed using a k-nearest neighbour approach in the samples space. We used two different approaches for outlier sample detection. On one hand, lipid concentrations were log-transformed, and Z-scored, and absolute Z-scores were summed for each sample as a measure of their extremeness. Samples showing sums above the 95th percentile were flagged as potential outliers. On the other hand, the Z-scores were used in a principal components analysis (PCA), and the seven first principal components were retained as explaining the most variance. In the space defined by these components, each sample’s distance to the origin was calculated as a measure of the sample’s extremeness. Samples with a distance greater than the 95th percentile were flagged as potential outliers. Samples flagged by both approaches were declared outliers are removed from further analysis. Further inspection revealed that 16 samples were ‘missed injections’ on the mass spectrometer, 27 cord serum samples were contaminated by maternal blood, these which were excluded, leaving 3964 samples in total. Ultimately, we retained 776 lipid measures across all batches. SIM-based lipid measures were not included in lipid species-level analyses, leaving 733 species.All statistical analyses were carried out in R (3.5.0 or 4.0.3). Plasma and cord serum lipid concentrations were log10-transformed prior to statistical analysis. PCA was performed using package FactoMineR using default settings.Associations between maternal/infant/children characteristics and lipid species were determined using multiple linear regression, adjusting for appropriate covariates in each analysis . All theLongitudinal analyses  were performed using linear regression models of breastfeeding/sex/weight against the log-transformed abundance of lipid species at each time point, adjusted for sex and age. Breastfeeding status (y/n) at 6 and 12 months were taken from questionnaires at these time points. We observed that gestational age influences lipids at 6 months and not at 12 months and hence adjusted for gestational age at the 6-month time point. The effective sample size varied slightly with each time point and between models due to missing samples or missing values for some covariates. Beta-coefficients and 95% confidence intervals were then converted to percentage difference (percentage change = (10^β-coefficient – 1) × 100) to facilitate interpretation of results. All p-values were corrected for multiple comparisons, per model term, using the method of The results from the association analyses are presented as forest plots in Figures 2–4 and 6, where each lipid species (or class) is represented by a marker showing either a positive association (increases with an increase in the outcome) or a negative association (decreases with an increase in the outcome). The colour of the markers relate to the significance of the association as indicated in the figure legends. The lipid species are grouped into lipid class and subclass and ordered into categories  each separated by a grey dotted line.The baseline characteristics of the complete cohort are shown in We measured the plasma lipid species in a total of 3964 samples: 1032 in mothers, 893 at birth, 793 at 6 months, 735 at 12 months, 511 at 4 years . Of the Overall lipid levels increased with age: maternal serum had higher total lipid levels than infant plasma, which in turn had higher levels than cord serum . PCA of 2 = 0.714, Of 733, 461 (63%) cord lipid species were associated with gestational age and 299  lipid species were associated with birth weight . Figure –24 and 1.81 × 10–15 respectively and the lipid classes (red diamonds). The error bars represent 95% confidence intervals. Grey dotted lines separate sphingolipids, phospholipids, and other lipids.Each circle on the plot represents a lipid species, grey open circles show non-significant lipid species (p > 0.05), white closed circles show significant lipid species (p < 0.05), the top 10 lipid species  are shown in blue circles. Each red diamond represents a lipid class. All p-values were corrected for multiple comparisons. The horizontal bars (error bars) are shown for the significant lipid species (white closed circles), the top 10 lipid species (blue circles), corrected p-value < 2.88 × 102 = 0.543, In this study, we compared four different birth modes: assisted vaginal birth  (including both forceps- and vacuum-assisted birth), scheduled caesarean  and unscheduled caesarean , with unassisted vaginal birth  as reference. Unscheduled CS accounted for all the emergency CS including breech births, while individual data on the reason for the emergency was not recorded. Of 733, 214 lipid species (29%) showed evidence of association with at least one mode of birth . Lipid sOf 733, 36 lipid species (5%) showed evidence of association with duration of labour independent of the mode of birth. Similar to assisted VB and unscheduled CS, we observed elevated levels of acylcarnitine, di- and triacylglycerols with longer duration of labour.We then investigated the association of maternal lipids at 28 weeks’ gestation with mode of birth and duration of labour. However, none of the maternal lipids were significantly associated with any of the delivery modes or the duration of labour .We performed paired Wilcoxon tests to investigate the relationship between maternal and newborn lipidome. While 80% of the lipid species were significantly higher in the maternal serum, the lipid species of sphingosine and sphingosine-1-phosphate, phosphatidylserine, acylcarnitine, dehydrocholesteryl ester, glycosphingolipids, and FFAs were higher in the newborn lipidome . Omega-3We performed paired t-tests to study the changes in lipid concentration at different time points . Of 733,Majority of lipid species  were higher at 6 months compared to at birth. We observed the greatest increase in species of alkenylphosphatidylethanolamine and alkyldiacylglycerol and the largest decrease in 20:4 and 20:6 containing lysophosphatidylcholine, lysophosphatidylethanolamine, and cholesteryl esters .Of 733, 363 (50%) lipid species increased in concentration from 6 to 12 months. Odd- and branched-chain phospholipids showed the greatest increase while species of alkyldiacylglycerol and cholesteryl esters showed the greatest decrease .Of 733, 272 (37%) lipid species consistently increased from birth to 6 months and from 6 to 12 months, while 90  lipid species showed a consistent decrease. Of 733, 365 (50%) lipid species had an opposing trend at these time points. Lipid species of dihydroceramide, phospholipids, glycophospholipids, lysophospholipids, FFA, alkyl- and alkenylphospholipids, di- and triacylglycerols, acylcarnitine, sphingomyelin, mono- and trihexosyl ceramides, ceramide-1-phosphate increased from birth to 6 months but decreased from 6 to 12 months. Lipid species of sphingosine decreased from birth to 6 months but increased from 6 to 12 months.Of 733, 284 (37%) of the lipid species were higher at 4 years than at 12 months. We saw the highest increase in CE 20:5 and largest decrease in 22:6 containing di- and triacylglycerols .Some lipid species showed variable changes as the child aged. Lipid species of dehydrocholesterol, alkenyllysophosphatidylcholine that decreased from birth to 6 months, and from 6 to 12 months, increased at 4 years. Lipid species of ceramide, phosphatidylethanolamine, and phosphatidylglycerol that increased from birth to 6 months and 6–12 months, decreased at 4 years. Lipid species of sphingosine decreased from birth to 6 months, increased at 12 months, and decreased again at 4 years .Overall omega-3 and omega-6 LC-PUFAs: 20:3, 20:4, 22:4, 22:5, and 22:6 that were higher at birth, decreased by the time the infant reached 6 months, and continued decreasing further until 4 years. Most of the omega-3 PUFA cholesteryl esters: CE (22:5) (n-3) and 22:6 decreased in the first year but increased at 4 years except for CE (20:5) that decreased at 6 months but increased at 12 months and 4 years. Omega-6 PUFA cholesteryl esters: CE (20:4), CE (22:4), and CE (22:5) (n-6) decreased at 6 months but increased at 12 months and 4 years .Additionally, we performed time-series clustering to capture the changes of all the lipid species across all the time points. We utilised dynamic time warping distance as a dissimilarity measurement . We usedCluster 1, consisted of 63 lipid species, comprising of ceramide species and LC-PUFA containing phospholipid species, all of which increased from birth to 6 months, and then stabilised by the time the child reached 12 months. Cluster 2 consisted of 149 lipid species of which phospholipids such as the odd chain phosphatidylcholine PC(31:0) and branched-chain and LC-PUFA containing phosphatidylethanolamine species: PE(15-MHDA_20:4) and PE(15-MHDA_22:6) decreased at 6 months, increased at 12 months, and decreased at 4 years. These PE species were also positively associated with birth weight in cord serum. Lysophospholipids containing 18:1 and 18:2 fatty acids, increased at 6 months, decreased at 12 months and increased as the child reached 4 years. In adults, these lysophospholipids are generally associated with higher BMI. LC-PUFA containing phospholipids, especially alkenylphosphatidylethanolamine and alkylphosphatidylcholine, increased at 6 months, but decreased at 12 months and 4 years. Similarly, alkyldiacylglycerols increased at 6 months and decreased thereafter. Cluster 3 comprised of 143 lipid species. Species of phospholipids in cluster 3 increased at all time points while ceramide and phospholipid species containing odd chain fatty acids increased until 12 months and decreased by the time the child reached 4 years. Cluster 4 consisted of 41 lipid species. The pattern was similar to cluster 2, with species of LC-PUFA containing phosphatidylethanolamine species increasing from birth to 6 months and decreasing at 12 months and 4 years. Cluster 5 consists of 60 lipid species. Most of the lipid species in cluster 5 increased until the child reached 6 months but decreased as the child reached 12 months and further. Cluster 6 consisted of 38 lipid species, mostly exhibiting mixed patterns. Cluster 7 consisted of 51 lipid species. As seen in cluster 2, the alkyldiacylglcyerols increased at 6 months and decreased at 12 months and 4 years, but the alkenylphosphatidylethanolaminespecies increased until 12 months and decreased at 4 years. Cluster 8 consisted of 33 lipid species, with species of lysophospholipids, cholesteryl esters, and fatty acids showing a consistent decrease across all time points. Cluster 9 consisted of 98 lipid species and showed a similar profile to cluster 1. Ceramide and sphingomyelin species containing 23:0 and 24:0 long chain fatty acids increased across all the time points. Additionally, alkyl- and alkenylphosphatidylethanolamine species containing LC-PUFAs  increased across all the time points. Cluster 10 consisted of 57 lipid species. Fatty acids 20:0 and 22:0 containing hexosylceramides and lysophosphatidylcholines followed a peculiar pattern of increasing until 6 months decreasing at 12 months, and later increasing as the child reaches 4 years. Acylcarnitine and fatty acid species, that were elevated in cord serum, compared to maternal serum, increased until 6 months and later decreased at 12 months and further at 4 years suggesting a change in energy metabolism over the first 4 years.We investigated the association of breastfeeding with plasma lipids at 6 and 12 months. Of the 776 lipid species, 664 lipid species (90%) were significantly associated with breastfeeding at 6 months, and 438  were significantly associated at 12 months . Of partWe investigated the influence of sex on lipid metabolism in early childhood. We found 218 lipid species  to be significantly associated at birth, 214  at 6 months, 62  at 12 months, and 82  at 4 years. We found significant differences in lipid species of ceramide, sphingomyelin, acylcarnitine, lysophosphatidylcholine, lysophosphatidylethanolamine, alkyllysophosphatidylcholine, and triacylglycerols between boys and girls .This is the first and largest study of the lipidome from birth and early childhood in a population-derived cohort. The lipidome was different between mother and child and changed markedly with child’s age. We identified that gestational age and birth weight were associated with cord serum lipids. Most of the lipid associations with gestational age were in the opposing direction to the associations with birth weight. Mode of birth was also associated with the cord serum lipidome, and the profile differed from that observed for gestational age and birth weight. There were marked changes in the ontogeny of the plasma lipidome from birth to 4 years of age: LC-PUFA and cholesteryl esters containing LC-PUFAs were enriched in cord serum relative to the maternal serum. LC-PUFA containing lipid species altered between birth and 4 years with an enrichment in alkenylphosphatidylethanolamine species and a reduction in lysophospholipids and triglycerides. Concentrations of free LC-PUFAs decreased at 4 years, but the corresponding cholesteryl esters increased. Alkenylphosphatidylethanolamine species and alkyldiacylglycerol species were significantly elevated in breastfed infants at 6 and 12 months.There has been a 30% increase in the incidence of late preterm births since 1980s, which now account for 75% of all preterm births. There is increasing epidemiological evidence suggesting infants born late preterm (34–36 weeks) and early term (37–38 weeks) are at higher risk of a host of adverse health outcomes including respiratory disorders, increased and prolonged hospitalisations, and developmental delay . On the In our study, we found di- and triacylglycerols were positively associated with gestational age, which reflect an evolutionary adaptation in preparation for the metabolic stress of labour, a period of high energy expenditure . A concoGestational age and birth weight showed overall inverse lipidomic profiles that were largely independent of each other. In infants with higher birth weight, the observed lower level of triglyceride levels could reflect better packaging within the adipose tissue vs. free availability in the circulation for energy mobilisation.The decrease in cholesteryl esters and dehydrocholesterols suggest a decrease in cholesterol synthesis with increasing gestational age. This may relate to increased use of FFAs for energy production and so less for cholesteryl ester production but might also relate to a downregulation of the cholesterol biosynthesis pathway as the dehydrocholesterol is a precursor to cholesterol.There were significant associations with multiple sphingolipid species, but overall these were very small, showing only 2–3% difference for a 10-day difference in gestational age. Thus while these lipids are known to associate with cardiometabolic outcomes in adults, the differences at birth cannot yet be ascribed to specific outcomes or risk. Further analyses will be required to define these relationships.The lipid profile associated with gestational age resembles the lipid profile associated with BMI in adults. For example, in adults, acylcarnitines, di and triacylglycerols are positively associated with BMI and body weight  like thaThese results have also been observed in an ethnically diverse cohort, Growing Up in Singapore Towards healthy Outcomes (GUSTO), revealing that these lipidome changes associated with birth weight may be largely independent of race/ethnicity .There are robust associations between mode of birth and child health outcomes, including risk of future overweight and obesity . ElevateCompared to the mother, we observed elevated levels of LC-PUFAs in cord serum which are critical for in utero brain development as well as the rapid brain development in the first year of life . The elesn-1 cleavage by endothelial lipases in the syncytiotrophoblast producing LC-PUFA lysophosphatidylcholine species which are then transported across the placenta by the major facilitator superfamily domain containing 2A (MFSD2a) transporter proteins explaining the elevated levels of LC-PUFA containing LPCs in cord serum .Understanding changes in the lipidome early on in life provides a window of opportunity for early intervention and decrease the risk of future metabolic disorders.It is well established that breast milk is rich in PUFAs. LC-PUFAs, especially arachidonic acid , eicosapentaenoic acid , and docosahexaenoic acid , have been shown to be critical for brain development and function in early life. While the actual mechanisms of how breastfeeding is associated with neurodevelopment remain unknown, LC-PUFAs in breast milk have been postulated to account for the effect of breastfeeding and improved cognition. We observed elevated levels of multiple lipid species containing these LC-PUFAs in breastfed infants at both 6 and 12 months. In addition, we observed a marked enrichment of alkenylphosphatidylethanolamine and alkyldiacylglycerol species in breastfed, relative to formula-fed, infants. Dysregulation of lipid metabolism is recognised as a primary driver of obesity and more recently of inflammation and immune regulation. Lipids make up 3% of the total breast milk composition. A small but important component of these lipids are alkylglycerols (AKGs), a class of ether lipids that constitute about 1% of total milk lipids. Breast milk represents the most abundant source of alkylglycerols in the human diet. While we see up to 17-fold increase in alkyldiacylglcyerols and alkenylphosphatidylethanolamine species at 6 and 12 months in breastfed infants, we did not observe any associations of breastfeeding at 12 months with 4 years circulating lipids. However, early modulation of these lipids might have long-term health effects, which is still to be understood.–04). In adults, the differential activity of the fatty acid desaturase (FADS3) is responsible for the elevated levels of SM (d18:2/14:0) in women  may limit the understanding of outcomes in different ethnicities and socio-economic strata. It would be valuable to follow the children through adolescence to understand the long-term modulation of lipid metabolism and early markers of future health or disease outcomes. We are currently investigating these results in another large birth cohort, based in Singapore – the GUSTO cohort to investigate whether similar changes are evident in a population of Asian ethnicity. In this study, plasma lipidomics was performed on serum  and plasma . Several studies have pointed out to minor differences in lipid levels of these two matrices, however, these differences do not show any associations with biological functions .We report in-depth lipidomic profiling in both gestation and the earliest stages of life, thereby providing insight into the ontogeny of lipid metabolism through infancy and early childhood. Despite the limitations, the extensive lipidomic profiling gives us insight into perinatal and postnatal factors associated with lipid metabolism during the first 4 years of life. There is now growing evidence of foetal programming emphasising the profound and sustained impact of intrauterine and early life factors to foetal health and development of metabolic diseases in later life. Understanding the baseline characteristics of lipid metabolism at birth and throughout early years provide a resource for further studies to elucidate the clinical implications. The datasets resulting from this study now provide a rich resource to further investigate the relationship between lipid metabolism and health outcomes during early life. Children within this study are continuing to be followed and future outcome data will build on these initial early life findings. The paper is well written and the experimental approach is sound. It contains a large amount of data and addresses an important and an understudied area of science. The authors were very responsive to the reviewers' comments and the reviewers unanimously decided that the paper is significantly improved and is ready for publication. The paper will likely have a high impact on the field, as it identifies changes in plasma lipids at the early stages after birth and its link with factors related to pregnancy. public reviews designed to be posted alongside the preprint for the benefit of readers; ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: i) Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, including Hossein Ardehali as Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Y M Dennis Lo as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Liang Wei Wang (Reviewer #3).Thank you for submitting your article "Ontogeny of plasma lipid metabolism in pregnancy and early childhood: a longitudinal population study" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:The paper has been discussed extensively among the reviewers. The Reviewers found the paper interesting and identified significant merit in the work. However, several major weaknesses were also identified that reduced enthusiasm and prevented the publication of the work in the present form. The main shortcomings of the work are mainly the lack of deep analysis of the data and also presentation of the results. It is important for the author to include the following studies in the revised version:1) Better definition of the significance of the findings, as requested by Reviewers #1 and #3.2) Better analysis of the data that employ other methods besides linear regression analysis to assess high-risk, medium-risk, and low-risk clusters of lipid species.3) Discussion on how various complications e.g., breech births, meconium aspiration, might associate with gestational lipid signatures, with immediate implications for triage and emergency planning;4) Inclusion of discussion on how milk as a source of lipids can influence the ontogeny of the child's lipidome;5) Limiting the paper and the results to lipid data only during labor, delivery and the first year of life.Reviewer #1 (Recommendations for the authors):This is a well written paper and a large amount of data are provided. I have the following comments:1. Figures 2-4 are very difficult to understand. What do each point in the blot represent? What does the line around the data points represent? It is important the authors provide more explanation for readers who do not have familiarity with these plots.2. There are a number of lipids that may have toxic effects on certain cells. For example ceramide has been shown to have cardiotoxic effects in adults. The authors show that ceramide levels change, but do not discuss what the biological significance of these findings are. In general, although the paper provides a large amount of data, it lacks a clear explanation for what these changes mean. The authors need to go beyond simply showing their lipidomic data and provide more physiological relevance to their findings.3. Additional environmental variables and their link to changes in childhood serum lipids need to be studied and included in the paper.Reviewer #2 (Recommendations for the authors):This is an important and well-written manuscript. While the work is largely descriptive, the authors provide important information on the natural biological variation of the circulating lipidome from gestation to up to 4 years of age, along with corresponding data on their mothers. However, there are major concerns:1. The authors show with regression analyses that certain lipid species are increased/decreased in response birth and year 4 BMI and weight. However, it is highly unlikely these individual lipid species are acting in isolation. It is more likely that there are various high-risk, moderate-risk, and low-risk "clusters" of lipid species that change over time with respect to each other which are associated with birth weight and ultimately year 4 BMI. Linear regression is not sufficient to resolve such clusters, and time-series principal component analysis should be used 2. The significance of the work would be significantly strengthened if the authors performed machine learning analysis of the dynamic lipidome to identify particular species associated with birth weight and year 4 BMI to complement traditional statistical approaches.Reviewer #3 (Recommendations for the authors):Cohort description:The description of the cohort can be further improved. Although the study cohort's demographic details have been published in reference 12, those numbers are woefully outdated. It is not clear how the new subjects recruited in the past 6 years have altered the demographic structure of the cohort. Additionally, more pertinent details about the cohort in relation to metabolism should be presented in Supplementary Table 1 e.g., caloric and fat intake during pregnancy, breast milk lipid content.What is the reason for excluding the 1-, 9- and 24-month time points?Language:The language of the manuscript can be changed to improve clarity and ease of reading.– The authors use American and British English words interchangeably e.g., "fetal" and "foetal". I suggest standardization.– For example, "postnatal age" can be substituted with "child's age".– In the Statistical Analysis section, it is not clear who "participant" refers to in paragraph 2.– The manuscript title is somewhat misleading – sera, not plasma, were collected at the gestational and birth time points. Hence, the title should read "blood lipid metabolism", instead of "plasma lipid metabolism". Consequently, the Methods section should include a line on this difference in sample type for avoidance of doubt.– The authors noted a similarity between the β coefficients of lipid species associated with BMI at 4 years and pre-pregnancy BMI in mothers . However, the authors should qualify this as a very weak correlation.– On page 20, the authors say that the lipid profiles of 4-year-old children associated with BMI closely resembled that of adults. It is not clear whether this said adult cohort is the one referred to by reference 8. Even so, the data stated in support of the authors' claims are found in Supplementary Figures5 and 6 and, Supplementary Tables 13-16. However, none of these data actually provide substantive support, since a PCA is the best first step to establishing that fact. On a related note, it will also be quite interesting to find out how the lipidome differs between healthy non-pregnant females in reference 8's cohort and healthy pregnant women in this study.– On page 22, the authors order assisted VB, non-assisted VB, unscheduled CS and a 4th CS. I believe the word, "schedule", is missing here. In the same paragraph, the authors say that [the] "mode of birth also effects the neonate's gut microbiota". Here, "affects" should be used in place of "effects".– On page 23, paragraph 2, there is a missing "as" in line 5. On page 24, the word, "the", is missing in paragraph 2 line 13 and paragraph 3 line 1.– On page 25, the authors assert that FADS3 "starts exhibiting this differential activity at four years". This should be rephrased as "by four years" since the activity difference could have happened any time between the prior time point and 4 years of age.Presentation:The presentation of figures needs to be improved. For example, in the Results section, Figures1b and 1c are referenced after Figure 1d in paragraph 2.In Figure 1b, cord sera are represented as a distinct cluster to other child-derived samples. How do the authors know that this represents a real biological difference that is not due to a difference in sample processing? Figure 1c should be re-plotted with PC3 to achieve better separation.Page 11 presumably refers to Figure 2. But no textual reference is actually made.Figures2 to 4 should have category labels on the y-axis to aid reading. Lipid species are classified by molecular similarity. However, it will be much more intuitive and useful to categorize them according to pathway relatedness or molecular function. If possible, dendrograms should also be drawn to indicate molecular relatedness.Supplementary Figure 2 is very ambiguous – it is not clear to me how certain lipid species have been highlighted/cherry-picked. The authors should define clear cut-offs.Figure legends can be improved for clarity. For example, for p-values, 10-15 should be used, rather than E-15. Red diamonds are said to represent lipid classes – but what does this mean exactly? Is it the mean or median value of all species within the class? In Supplementary Figure 5, the legend title says, "Association of infant BMI with lipid species". However, panel d relates to 4-year-old children i.e., toddlers, rather than infants; the correct word to use is "child".Other Suggestions and Points Requiring Clarification:The authors note the interesting observation that acylcarnitine, DAGs and TAGs were more abundant with longer duration of labor, alluding to a role for fueling the strenuous activity of labor. How does one know that the increased levels of these lipid species are due to the physiology of labor per se , rather than a result of mothers often being prohibited from consuming food and drink  during labor?A few clinical issues related to breastfeeding and child lipidomic changes could have been but were also not addressed by the authors using the present dataset. For example, it would be important to know whether the mode of feeding – exclusive breastfeeding or mixed feeding – influences the development of the child's lipidome. Other questions that can be answered as well include: (a) delineating the influences of different types of formula milk  on the child's lipidome at different time points, (b) definition of particular breast milk lipid signatures that correlate to higher infant/toddler BMI, and (c) determination of breast milk lipid content and compositions for GDM and healthy mothers. Essential revisions:The paper has been discussed extensively among the reviewers. The Reviewers found the paper interesting and identified significant merit in the work. However, several major weaknesses were also identified that reduced enthusiasm and prevented the publication of the work in the present form. The main shortcomings of the work are mainly the lack of deep analysis of the data and also presentation of the results. It is important for the author to include the following studies in the revised version:1) Better definition of the significance of the findings, as requested by Reviewers #1 and #3.While there are large population studies that have established dysregulation of lipid metabolism to be associated with metabolic syndrome in adults, very little is known about lipid metabolism in utero and in early life, nor about the ante-natal factors affecting lipid metabolism in infants and how this manifest in later life or whether these changes persist. This is partly due to the long follow up times required for such studies in order to investigate clinical outcomes in later life. Additionally, in depth lipidomic profiling data of infants and young children are scarse. Accordingly, we have made inferences from adult studies where possible and made changes to the discussion explaining the potential implications of early metabolic changes to cholesteryl esters and sphingolipids. These changes are added to the discussion in the manuscriptDiscussion“There has been an 30% increase in the incidence of late preterm births (LPIs) since 1980s. which now account for 75% of all pre-term births. […] Understanding the changes in lipid metabolism during the final stages of gestation may provide mechanistic insights into the increasingly recognised long-term health sequelae of preterm and late preterm births [11].”2) Better analysis of the data that employ other methods besides linear regression analysis to assess high-risk, medium-risk, and low-risk clusters of lipid species.In addition to the existing analyses to study the changes in lipidome over the first four years, we performed time-series clustering to capture the changes of all the lipid species across all the time points. We utilised Dynamic Time Warping (DTW) distance as a dissimilarity measurement . We used the partitional clustering method with partitions around medoids (PAM) to cluster the lipid species. We identified 10 different lipid clusters, each of them showing unique lipid trajectories over the first four years of life .The results are now included in page 22, line 22 – page 24, line 14 and discussed in page 33, line 19.“Additionally, we performed time-series clustering to capture the changes of all the lipid species across all the time points. […] Acylcarnitine and fatty acid species, that were elevated in cord serum, compared to maternal serum, increased until 6 months and later decreased at 12 months and further at 4 years suggesting a change in energy metabolism over the first 4 years.”3) Discussion on how various complications e.g., breech births, meconium aspiration, might associate with gestational lipid signatures, with immediate implications for triage and emergency planning:In this study, we have studied four different modes of birth: Unassisted vaginal delivery (n = 525), assisted vaginal delivery (vacuum assisted + forceps assisted) (n = 214), scheduled cesarean (n = 178), and unscheduled cesarean births (n = 155). The dataset does not include further classification of unscheduled cesarean births. While it may be informative to have such information and evaluate lipid signatures associated with such complications, we suspect that the low numbers of such cases within a cohort would be underpowered for such analyses. Additionally, they are all, to varying degrees, stress-inducing for the foetus and if they are associated with any changes, they could probably show a similar profile.We have now included this explanation in the Results section “In this study, we compared four different birth modes: assisted vaginal birth  (including both forceps- and vacuum-assisted birth), scheduled cesarean  and unscheduled cesarean , with unassisted vaginal birth  as reference. Unscheduled CS accounted for all the emergency CS including breech births, while individual data on the reason for the emergency was not recorded.”4) Inclusion of discussion on how milk as a source of lipids can influence the ontogeny of the child's lipidome;We have now included an analysis of the association of breast feeding with plasma lipids at 6 and 12 months of age and observed strong associations with most lipid species and classes.This has been included in the results page 26 line 1-15, Figure 6a and 6b, Supplementary Tables 13 and 14. We further discuss how breastfeeding can influence the lipid metabolism in the first year of life page 34, line 3 to page 35, line 7.“Breastfeeding impacts lipid metabolism in the first year of life. We investigated the association of breastfeeding with plasma lipids at 6 months and 12 months. Of the 776 lipid species 664 lipid species (90%) were significantly associated with breastfeeding at 6 months, and 438  were significantly associated at 12 months (Supplementary tables 13 and 14). Of particular note, species of alkyl and alkenylphospholipids  and alkyldiacylglycerides (TG-O) were markedly elevated in breastfed infants. At a class level these elevations were of the order of 2-4 fold, while individual species of alkydiacylglycerols: TG(O-54:2) [NL-17:1], TG(O-54:2) [NL-18:1], TG(O-52:1) [NL-18:1] were elevated 17 – 19 fold at 6 months. However, at 12 months, this effect size was only 2-4 fold, given the introduction of a wide variety of foods along with breast milk . In addition to the dramatic increase in these ether lipid species, we also observed an increase in many species containing odd- and branched-chain fatty acids such as phosphatidylcholine PC(33:0), sphingomyelin SM(d19:1/24:1) and others. These odd- and branched-chain fatty acids represent a novel, potentially bioactive, class of lipids enriched in breast fed infants.”5) limiting the paper and the results to lipid data only during labor, delivery and the first year of life.Based on the suggestions from the editors, we have now limited the manuscript to analyses of the circulating lipids in the first year of life. Additionally, we extended into the study of changes in lipidome over the first four years of life. Based on the suggestions from reviewer 2, we performed time-series clustering to capture the changes of all the lipid species across all the time points. We utilised Dynamic Time Warping (DTW) distance as a dissimilarity measurement. We used the partitional clustering method with partitions around medoids (PAM) to cluster the lipid species. We identified 10 different lipid clusters, each of them showing unique lipid trajectories over the first four years of life .We have also included the analyses on association of breastfeeding on infant lipids in the first year of life .The manuscript now includes the following analyses:1. Analyses of association of gestational age and birthweight with newborn cord serum lipids.2. Analyses of association of mode of birth and duration of labour with the newborn cord serum lipids.3. Enrichment analyses of lipid species in cord serum relative to maternal plasma.4. Analyses of changes in the circulating lipidome over the first four years.5. Analyses of association of sex with circulating lipids at all the time points.6. Analyses of the association of plasma lipids at 6 months and 12 months with breastfeeding in the first year of life.Reviewer #1 (Recommendations for the authors):This is a well written paper and a large amount of data are provided. I have the following comments:1. Figures 2-4 are very difficult to understand. What do each point in the blot represent? What does the line around the data points represent? It is important the authors provide more explanation for readers who do not have familiarity with these plots.The authors agree. In addition to the Figure legends, we have now included a section “data representation” under the methods section “Data Representation: The results from the association analyses are presented as forest plots in Figures 2-4 and 6 where each lipid species (or class) is represented by a marker showing either a positive association (increases with an increase in the outcome) or a negative association (decreases with an increase in the outcome). The colour of the markers relate to the significance of the association as indicated in the figure legends. The lipid species are grouped into lipid class and subclass and ordered into categories  each separated by a grey dotted line.”2. There are a number of lipids that may have toxic effects on certain cells. For example ceramide has been shown to have cardiotoxic effects in adults. The authors show that ceramide levels change, but do not discuss what the biological significance of these findings are. In general, although the paper provides a large amount of data, it lacks a clear explanation for what these changes mean. The authors need to go beyond simply showing their lipidomic data and provide more physiological relevance to their findings.The authors agree with the concerns raised by the reviewer. While there are large population studies that have established dysregulation of lipid metabolism to be associated with metabolic syndrome, very little is known about the lipid metabolism in early life and the in-utero and ante-natal factors affecting lipid metabolism in infants and how its manifested in later life. This is partly, due to the long follow up times required for such studies and most of them have containing sufficient variation in gestational age have begun to reach adulthood allowing other potential outcomes to be investigated. Additionally, in depth lipidomic profiling of infants and young children is only currently emerging and our study is one of such early ones. We have tried our best to bring in inference from the adult population where possible. Accordingly, changes have been made to the discussion explaining changes to cholesteryl esters and sphingolipids additionally.Please refer to Discussion Refer to essential revisions 13. Additional environmental variables and their link to changes in childhood serum lipids need to be studied and included in the paper.The authors recognise that several environmental variables such as exposure to phthalates are known to be associated with neurodevelopment. However, the manuscript as such presents a lot of data, and is now scaled back based on the recommendations from the editors. A separate study by our collaborators Prof. Anne Louise Ponsonby et al., focusses on understanding the effect of environmental factors on lipid metabolism at birth. In this manuscript we focussed on understanding the baseline lipid metabolism in newborns, infants and children at four years of age. We have made attempts to understand the association of antenatal factors with lipid metabolism at birth. The overall goal of this manuscript and the resulting dataset is to provide a framework for future studies to understand and identify several factors in early life that can cause lipid dysregulation.We have now clarified this in the abstract and introduction.Abstract “We studied the effect of antenatal factors such as gestational age, birth weight and mode of birth on lipid metabolism at birth; changes in the circulating lipidome in the first four years of life and the effect of breastfeeding in the first year of life. From this study, we aim to generate a framework for deeper understanding into factors effecting lipid metabolism in early life, to provide early interventions for those at risk of developing metabolic disorders including cardiovascular diseases”Introduction “This manuscript emphasises on understanding the lipid metabolism in newborns and infants in the first year of life, specially focusing on the association of cord serum lipids with antenatal factors: gestational age, birthweight, mode of birth and duration of labour. We also establish the association of breastfeeding with infant plasma lipids at 6 months and 12 months of age. We provide a baseline characterisation of circulating lipids in the mother and newborn and changes in the circulating plasma lipids from birth to four years. In summary, the results from this study provide a framework for future studies/ analyses to understand lipid metabolism in early life and provide early interventions for lipid dysregulation.”Reviewer #2 (Recommendations for the authors):This is an important and well-written manuscript. While the work is largely descriptive, the authors provide important information on the natural biological variation of the circulating lipidome from gestation to up to 4 years of age, along with corresponding data on their mothers. However, there are major concerns:1. The authors show with regression analyses that certain lipid species are increased/decreased in response birth and year 4 BMI and weight. However, it is highly unlikely these individual lipid species are acting in isolation. It is more likely that there are various high-risk, moderate-risk, and low-risk "clusters" of lipid species that change over time with respect to each other which are associated with birth weight and ultimately year 4 BMI. Linear regression is not sufficient to resolve such clusters, and time-series principal component analysis should be used Upon the recommendation from the editors, we have now excluded the analyses of association of lipids with BMI. Based on reviewer 2’s comments, we performed time-series clustering to capture the changes of all the lipid species across all the time points. We utilised Dynamic Time Warping (DTW) distance as a dissimilarity measurement. We used the partitional clustering method with partitions around medoids (PAM) to cluster the lipid species. We identified 10 different lipid clusters, each of them showing unique lipid trajectories over the first four years of life .The results are included in page 22, line 22 – page 24, line 12 and discussed page 33, line 18Refer to essential revisions 22. The significance of the work would be significantly strengthened if the authors performed machine learning analysis of the dynamic lipidome to identify particular species associated with birth weight and year 4 BMI to complement traditional statistical approaches.Validity of BMI as a measure of obesity in Australian white Caucasian and Australian Sri Lankan children. Annals of Human Biology, 2005.; Himes, J.H., Challenges of accurately measuring and using BMI and other indicators of obesity in children. Pediatrics, 2009)The authors agree that analysis of the dynamic lipidome is required. We also recognise the limitations of the use of BMI as a measure for obesity in children. A child’s weight status is different from adult BMI categories. Children’s body composition varies as they age and varies between boys and girls. Therefore, BMI levels among children and teens need to be expressed relative to other children of the same age and sex. Hence, growth trajectories developed using BMI are widely used to track child’s growth 9 :Cohort description:The description of the cohort can be further improved. Although the study cohort's demographic details have been published in reference 12, those numbers are woefully outdated. It is not clear how the new subjects recruited in the past 6 years have altered the demographic structure of the cohort. Additionally, more pertinent details about the cohort in relation to metabolism should be presented in Supplementary Table 1 e.g., caloric and fat intake during pregnancy, breast milk lipid content.The authors have now made necessary changes to the cohort description. Recruitment was limited to 2010-13, so there has been no change in baseline demographics over the ensuing years. This has now been clarified We have now added additional details in Supplementary Table (S1) including– Pre-pregnancy BMI– Gestational weight gain– Dietary caloric and fat intake for the mothers, and– Duration of breastfeeding for the childrenWhat is the reason for excluding the 1-, 9- and 24-month time points?Plasma/ serum samples at these time points are not available.Clarified under the Research design and cohort in the Methods section :“Maternal serum samples were collected at 28 weeks gestation. Child serum/ plasma samples were available only at birth, 6 months, 12 months and 4 years.”Language:The language of the manuscript can be changed to improve clarity and ease of reading.– The authors use American and British English words interchangeably e.g., "fetal" and "foetal". I suggest standardization.This is now corrected– For example, "postnatal age" can be substituted with "child's age".This is now corrected– In the Statistical Analysis section, it is not clear who "participant" refers to in paragraph 2.This is now corrected.Page 9, line 12“Associations between maternal/ infant/ children characteristics and lipid species were determined using multiple linear regression, adjusting for appropriate covariates in each analysis”– The manuscript title is somewhat misleading – sera, not plasma, were collected at the gestational and birth time points. Hence, the title should read "blood lipid metabolism", instead of "plasma lipid metabolism". Consequently, the Methods section should include a line on this difference in sample type for avoidance of doubt.The authors agree to the reviewer’s comments. However, blood lipid metabolism could also be misleading as we have utilised serum and plasma, derived from blood, rather than whole blood.Hence, we have changed the title to “Ontogeny of circulating lipid metabolism in pregnancy and early childhood: a longitudinal population study”.We have also clarified this in the methods section “Any lipid differences based on the matrix must be kept in mind as serum samples were used during gestation, and at birth vs plasma samples at other time points. Several studies have reported matrix-associated differences in species of lysophospholipids, diacylglycerols, free long chain fatty acids, and oxidised fatty acids. However, these differences are unlikely to reflect biological processes in the body. These differences could be reflected in change in lipid levels observed at birth and 6 months, but not affect the association with outcomes.”– The authors noted a similarity between the β coefficients of lipid species associated with BMI at 4 years and pre-pregnancy BMI in mothers . However, the authors should qualify this as a very weak correlation.At the request of the editor, we have now excluded the analyses.– On page 20, the authors say that the lipid profiles of 4-year-old children associated with BMI closely resembled that of adults. It is not clear whether this said adult cohort is the one referred to by reference 8. Even so, the data stated in support of the authors' claims are found in Supplementary Figures5 and 6 and, Supplementary Tables 13-16. However, none of these data actually provide substantive support, since a PCA is the best first step to establishing that fact. On a related note, it will also be quite interesting to find out how the lipidome differs between healthy non-pregnant females in reference 8's cohort and healthy pregnant women in this study.At the request of the editor, we have now excluded the analyses.Developmental and Intergenerational Landscape of Human Circulatory Lipidome and its Association with Obesity Risk. bioRxiv, 2021). However, in the BIS cohort, we don’t have the follow up samples available.A similar analysis has been performed in the Growing up in Singapore towards healthy outcomes (GUSTO) cohort, where they have followed up the pregnant women in the cohort 6 years post pregnancy. In the analyses, they have compared the lipid profiles associated with BMI in pregnant women to the lipid profiles associated with BMI in the same women, 6 years post pregnancy In the same paragraph, the authors say that [the] "mode of birth also effects the neonate's gut microbiota". Here, "affects" should be used in place of "effects".This is now corrected Additionally, mode of birth also affects the neonate’s gut microbiota.– On page 23, paragraph 2, there is a missing "as" in line 5. On page 24, the word, "the", is missing in paragraph 2 line 13 and paragraph 3 line 1.These are now correctedPage 32, line 7“The elevated levels of di- and tri-acylglycerols, and phospholipids observed in the maternal circulation most likely act as a source of fatty acids for the foetus.”Page 34, line 1“Understanding changes in the lipidome early on in life provides a window of opportunity for early intervention and decrease the risk of future metabolic disorders”Page 35, line 8“There is evidence of sex-differences in the lipidome from late gestation onwards…”– On page 25, the authors assert that FADS3 "starts exhibiting this differential activity at four years". This should be rephrased as "by four years" since the activity difference could have happened any time between the prior time point and 4 years of age.This is now corrected “Our results suggest that FADS3 starts exhibiting this differential activity by four years.”Presentation:The presentation of figures needs to be improved. For example, in the Results section, Figures1b and 1c are referenced after Figure 1d in paragraph 2.This is now corrected “Overall lipid levels increased with age: maternal serum had higher total lipid levels than infant plasma, which in turn had higher levels than cord serum . Principal Component Analysis (PCA) of the lipidomic data from all participants at all the time points revealed a clear separation of maternal, newborn, and infant samples across the first and second principal components . A PCA of the infant samples showed further separation of the 6, 12- and 4-year time points .”In Figure 1b, cord sera are represented as a distinct cluster to other child-derived samples. How do the authors know that this represents a real biological difference that is not due to a difference in sample processing?The authors acknowledge that samples at birth and other child time points are different matrices, serum and plasma.We have also clarified this in the methods section “Any lipid differences based on the matrix must be kept in mind as serum samples were used during gestation, and at birth vs plasma samples at other time points. Several studies have reported matrix-associated differences in species of lysophospholipids, diacylglycerols, free long chain fatty acids, and oxidised fatty acids. However, these differences are unlikely to reflect biological processes in the body. These differences could be reflected in change in lipid levels observed at birth and 6 months, but not affect the association with outcomes”Figure 1c should be re-plotted with PC3 to achieve better separation.The authors have now included a PC2 vs PC3 plot  in Figure 1Page 11 presumably refers to Figure 2. But no textual reference is actually made.This is now corrected 461  cord lipid species were associated with gestational age and 299  lipid species were associated with birth weight .Figures 2 to 4 should have category labels on the y-axis to aid reading. Lipid species are classified by molecular similarity. However, it will be much more intuitive and useful to categorize them according to pathway relatedness or molecular function. If possible, dendrograms should also be drawn to indicate molecular relatedness.The lipid species are arranged according to the pathway relatedness. All the ceramides and sphingomyelins are under one section, followed by all phospholipids and lyso-phospholipids under one section, cholesterols, triglycerides and diacylglycerides are under one section, each divided by a grey dotted line. We have also included a supplementary figure 1, to interpret the lipid pathways. This information has now been included in methods  to aid better understanding.“The lipid species are grouped into lipid class and subclass and ordered into categories  each separated by a grey dotted line.”Supplementary Figure 2 is very ambiguous – it is not clear to me how certain lipid species have been highlighted/cherry-picked. The authors should define clear cut-offs.The phospholipids associated with gestational age and those associated with birth weight showed clear opposing trends. We chose odd chain containing phospholipids to highlight that difference in directionality.Modified the figure legend :Supplementary Figure 3 a, Volcano plot showing newborn phospholipids associated with gestational age. b, Volcano plot showing newborn plasma phospholipids associated with birth weight. Open circles represent all phospholipids (PL) and lysophospholipid (LPL) species. Colored circles represent PL and LPL containing odd and branched chain fatty acids. Red circles represent non-significant associations, blue circles represent significant lipids (p < 0.05), green circles represent 10 most significant lipids Figure legends can be improved for clarity. For example, for p-values, 10-15 should be used, rather than E-15.Figure legends have now been modified to explain the forest plots. P-values have been changed as recommendedas a. Each circle on the plot represents a lipid species, grey open circles show non-significant lipid species (p>0.05), white closed circles show significant lipid species (p < 0.05), the top 10 lipid species  are shown in blue circles. Each red diamond represents a lipid class. All p-values were corrected for multiple comparisons. The horizontal bars (error bars) are shown for the significant lipid species (white closed circles), the top ten lipid species (blue circles), corrected p-value < 2.88 x 10-24 and 1.81 x 10-15 respectively and the lipid classes (red diamonds). The Error bars represent 95% confidence intervals. Grey dotted lines separate sphingolipids, phospholipids and other lipids.Figure 2 The newborn cord serum lipidome is influenced by gestational age (GA) and birth weight (BW). a, Estimated percentage difference of the lipid species per day increase in gestational age, determined using linear regression, adjusted for weight, sex, mode of birth, duration of labor, maternal pre-pregnancy BMI, GDM, maternal education, birth order and lipidomics run batch. b, Estimated percentage difference of the individual lipid species per kilogram increase in birthweight, determined using the same model Red diamonds are said to represent lipid classes – but what does this mean exactly? Is it the mean or median value of all species within the class?Red diamonds represent lipid class totals, calculated as the sum of lipid species in that particular lipid class. This has been explained in the methods section .Lipid class total concentrations were calculated as the sum of individual lipid species concentrations within the class, except in the case of triacylglycerol (TGs) and alkyl-diacylglycerol (TG-Os), where we measured both neutral loss [NL] and single ion monitoring [SIM] peaks, and subsequently used the more accurate, but less structurally resolved, [SIM] species concentrations for summation purposes when examining lipid class totalsIn Supplementary Figure 5, the legend title says, "Association of infant BMI with lipid species". However, panel d relates to 4-year-old children i.e., toddlers, rather than infants; the correct word to use is "child".This figure has been deletedOther Suggestions and Points Requiring Clarification:The authors note the interesting observation that acylcarnitine, DAGs and TAGs were more abundant with longer duration of labor, alluding to a role for fueling the strenuous activity of labor. How does one know that the increased levels of these lipid species are due to the physiology of labor per se , rather than a result of mothers often being prohibited from consuming food and drink  during labor?The reviewer has made a valid point and the fasted state of the mother may also contribute to the response of the infant, potentially as a result of decreased circulating fatty acids available for transfer across the placenta.We have included this possibility in the discussion (page 31 line 16-19)“However, a decrease in circulating free fatty acids and triacylglycerols in scheduled CS may also be a result of mothers often being prohibited from consuming food and drink before the procedure leading to a fasting state and so decreased availability of free fatty acids for transfer across the placenta.”A few clinical issues related to breastfeeding and child lipidomic changes could have been but were also not addressed by the authors using the present dataset. For example, it would be important to know whether the mode of feeding – exclusive breastfeeding or mixed feeding – influences the development of the child's lipidome. Other questions that can be answered as well include: (a) delineating the influences of different types of formula milk  on the child's lipidome at different time points, (b) definition of particular breast milk lipid signatures that correlate to higher infant/toddler BMI, and (c) determination of breast milk lipid content and compositions for GDM and healthy mothers.The authors agree that breast feeding is an important factor in the infant lipidome. We have now included an analysis of the association of breast feeding with plasma lipids at 6 and 12 months of age and observed strong associations with most lipid species and classes.This has been included in the results page 26 line 1-15, Figure 6a and 6b, Supplementary Tables 13 and 14. We further discuss how breastfeeding can influence the lipid metabolism in the first year of life page 34, line 3 to page 35, line 7.Please refer to Essential revisions 4We also agree that the analyses proposed above are important and we are currently analysing breast milk samples and performing additional analyses with these data to address these issues. However this is outside the scope of this manuscript.


Multiple studies have shown that antibiotic utilization increased during the COVID-19 pandemic. However, the impact of this increased utilization has not been well established. The aim of this study is to describe the trends in minimum inhibitory concentrations for various antibiotics against common gram-negative pathogens observed since the start of the COVID-19 pandemic as compared to previous years.This retrospective study was conducted at the Memphis VA. All respiratory, urine, and blood culture MicroScan results run from October 2017-March 2021 were analyzed. Only inpatient and emergency department data was included. The MIC50 and MIC90 of seven antibiotics for four of the most common pathogens were trended by quarterly intervals.Pseudomonas aeruginosa was noted to have the most sustained increase in MIC90 across various antibiotics. In the last 3 quarters of the study time frame, piperacillin-tazobactam mean MIC90 increased from 32 to 64, cefepime from 8 to > 16, and meropenem from 4 to > 8. Escherichia coli had a sustained increase in ceftriaxone MIC90 from < 1 to > 8 in the final quarter of 2020 and beginning of 2021. Klebsiella pneumonia was also found to have a sustained increase in cefepime mean MIC90 from < 1 to > 16 during the year of 2020, with return to previous MIC90 the following quarters.MIC50 and MIC90 were compared using standardized breakpoints. As compared to previous years, Previous studies have clearly demonstrated a widespread increase in antibiotic utilization during the COVID era. Our study demonstrates how even short-term increases in antibiotic use can lead to shifts in MIC, if not outright resistance. This was demonstrated across multiple common gram-negative pathogens and to various broad-spectrum antibiotics which were commonly used more frequently during COVID-19. Further analysis will be needed to determine whether these trends continue or whether the decrease in antibiotic utilization in the recent months will lead to similar decrease in MIC.All Authors: No reported disclosures


Following the price reduction, patients who were prescribed a PCSK9 inhibitor were younger and more likely to be female, but less likely to have Medicare insurance. Conclusions: Despite the reduction in the cost of PCSK9 inhibitors, most eligible patients are not prescribed one. The reduction in cost has improved adherence, primarily in patients with commercial insurance. Older patients and those on Medicare still face significant barriers in accessing a PCSK9 inhibitor. Further reductions in the price of the PCSK9 inhibitors are needed as is further study of the barriers that exist in prescribing one.Background: Proprotein convertase subtilisin kexin type 9 (PCSK9) inhibitors reduce low-density lipoprotein (LDL) cholesterol and cardiovascular event rates, yet due to their high price remain underutilized and difficult to prescribe in clinical practice. In March 2018, their price was significantly reduced. We evaluated whether the price reduction would improve prescribing patterns of PCSK9 inhibitors in eligible patients with atherosclerotic cardiovascular disease (ASCVD). Methods: We identified the number of eligible ASCVD patients and those prescribed a PCSK9 inhibitor for each year between July 2015 and December 2019. Patient demographics and clinical characteristics for those prescribed a PCSK9 inhibitor were extracted from their electronic health record. Results: In total 1059 patients of eligible patients received a new prescription for a PCSK9 inhibitor. From 2015 to 2019, the rate of new prescriptions among eligible patients increased from 0.5 to 3.3% ( In 2015, the United States (US) Food and Drug Administration approved the proprotein convertase subtilisin kexin type 9 (PCSK9) inhibitors alirocumab and evolocumab for the treatment of hypercholesterolemia in patients with atherosclerotic cardiovascular disease (ASCVD) or familial hypercholesterolemia, who need additional low-density lipoprotein (LDL) cholesterol lowering. When added to a maximally tolerated statin alone or in combination with ezetimibe, PCSK9 inhibitors significantly lower LDL cholesterol by up to 60% and do so safely and with few adverse effects  have demAt the 2015 prices, the PCSK9 inhibitors were determined not to be cost efficient and it was estimated that the cost would need to be reduced to an average of 4500 USD per year to meet the threshold of cost-effectiveness . In MarcThe study population was selected from a large single-specialty cardiology practice, Cardiology Consultants of Philadelphia (CCP). CCP is the largest independent cardiac care practice in the US and has 32 cardiology centers and 97 cardiologists across the Delaware Valley and Philadelphia regions. All the cardiologists practice adult cardiology and manage general cardiology patients, and hence are expected to follow guideline-based therapy for the secondary prevention of ASCVD. The centers provide care in both urban and suburban locations and have cardiologists based in both academic and community settings. Patients were included in the study if they had a diagnosis of coronary artery disease, cerebrovascular disease, or peripheral arterial disease in their electronic health record (EHR) problem list, as defined by the 9th and 10th International Classification of Diseases (ICD) codes. EHR data were analyzed from July 2015  to December 2019. Data were queried from the practice EHR database, not by individual chart review.The patient cohort comprised the 1059 patients who were prescribed a PCSK9 inhibitor between July 2015 and December 2019. The study was approved by the independent review board at Thomas Jefferson University Hospital. Patient demographics and clinical characteristics were extracted from the EHR data. A diagnosis of hypertension or diabetes was ascertained from the ICD codes in the problem list. In addition, a patient was considered to have diabetes if they were on a medication used to treat diabetes. Cigarette smoking was extracted from the EHR and was self-reported. For patients prescribed a PCSK9 inhibitor, the LDL-cholesterol level at baseline before being prescribed a PCSK9 inhibitor was recorded. Lipid lowering medications were recorded from the patient’s EHR medication list. When a PCSK9 inhibitor was added for the first time in a patient’s medication list it was considered a new prescription. Patients were considered to have continued their PCSK9 inhibitor prescription if it remained on their active medication list and have discontinued it if it had been removed from their active medication list. The statin dose was recorded from the patient’s last visit. High-intensity statin was defined as 40 or 80 mg of atorvastatin or 20 or 40 mg of rosuvastatin. For each year, from 2015 to 2019, we identified the number of patients who were eligible for a PCSK9 inhibitor based on a diagnosis code for ASCVD, an LDL-cholesterol ≥ 70 mg/dL, and the patient having at least one office visit within the year of interest.Chi-squared tests were used to compare patient characteristics between PCSK9 inhibitor users who initiated their PCSK9 inhibitor before 1 March 2018 to those initiating after 1 March 2018, the date assigned to the price reduction for both PCSK9 inhibitors. We constructed a multivariable logistic regression model to compare those receiving their first PCSK9 inhibitor prescription after the price change versus those receiving their first PCSK9 inhibitor before the price change. The predictor variables included age, gender, insurance type, diabetes, ezetimibe use, smoking, hypertension, statin use, and LDL cholesterol level. The numbers of PCSK9 inhibitor eligible patients, numbers of physicians in the practice, and the numbers of new PCSK9 inhibitor prescriptions per year from 2015 to 2019 were collected and tested for a time trend using a Poisson regression model of the prescription counts, offset by the log of the number of patients. The nominal significance level for each test was set in advance to α = 0.05. All statistical analyses were conducted using SAS v9.4 .p < 0.001) from 45 (0.26%) in 2015 to 316 (1.6%) in 2019. The number of newly prescribed patients who continued or discontinued PCSK9 inhibitor therapy from 2015 to 2019 is presented in p < 0.001). Continuation rates of PSCK9 inhibitor prescriptions significantly increased after the price reduction from 18 to 60% (p < 0.001).From 2015 to 2019, 1059 patients with ASCVD received a new prescription for a PCSK9 inhibitor. The characteristics of the patients are presented in After the price reduction for the PCSK9 inhibitors in March 2018, patients prescribed a PCSK9 inhibitor were younger, on average with an increase in the number of patients under the age of 65 (39% post-price reduction compared with 29% pre-price reduction), and more likely to be female. In addition, after the price reduction, fewer Medicare patients were prescribed a PCSK9 inhibitor (51% post-price reduction compared with 61% pre-price reduction). Multivariable analysis suggested the odds of receiving a prescription for a PCSK9 inhibitor after the price reduction increased in younger patients and those with an LDL cholesterol of between 100 and 130 mg/dL .Our study provides a current analysis of PCSK9 inhibitor use among real-world patients with ASCVD. During the study period from 2015 to the end of 2019, there was only a small increase in the number of eligible patients prescribed a PSCK9 inhibitor. The trend in the increase in new prescriptions for PCSK9 inhibitors predated the price reduction. In March of 2017, one year before the price reduction in alirocumab was announced, the results of the FOURIER trial of evolocumab were presented and demonstrated a 15 to 20% relative reduction in major cardiovascular events compared with the placebo in patients with ASCVD and other cardiovascular risk factors ,12. It iFollowing the price reduction, patients prescribed a PCSK9 inhibitor were younger, more likely to be female, and more likely to have commercial insurance. However, following the price reduction, fewer older patients and those on Medicare were prescribed a PCSK9 inhibitor. An analysis of nationwide Part D Medicare drug plans showed that most plans cover PCSK9 inhibitors . AlthougThis study has several limitations. Medication prescriptions were identified from the patient’s EHR, and we could not determine medication adherence. We could not determine whether patients abandoned versus discontinued their PCSK9 inhibitor prescription or the reasons why. Among patients with ASCVD there is heterogeneity of risk. The 2018 AHA/ACC/Multisociety cholesterol guidelines recommend a PCSK9 inhibitor for very-high-risk ASCVD patients whose LDL-cholesterol is ≥70 mg/dL on maximally tolerated statin therapy, with or without ezetimibe. We could not categorize the ASCVD patients in our study as high compared with very high risk, and it is possible that physicians may have not prescribed a PCSK9 inhibitor in a patient who was eligible based on their treated LDL cholesterol level because they felt the patient was not particularly high risk. In addition, we did not collect data on Lp(a) which if elevated may have influenced a physician’s decision to prescribe a PCSK9 inhibitor. Despite these limitations, this study has several strengths. The study included a large cohort of ASCVD patients treated by a cardiologist and provide up-to-date findings regarding PCSK9 prescriptions since the price reduction. Although the study was conducted from a single practice, the large number of cardiologists and their diversity in practice location and type likely represent a wider group of cardiologists.Despite the reduction in the cost of PCSK9 inhibitors, most eligible ASCVD patients are not prescribed one. The reduction in cost has improved adherence, primarily in patients with commercial insurance; however, most eligible patients including older patients and those on Medicare still face significant barriers in accessing a PCSK9 inhibitor. New LDL-cholesterol agents will compete with the currently available monoclonal PCSK9 inhibitors and potentially lead to further price reductions. Bempedoic acid, which inhibits ATP-citrate lyase, lowers LDL cholesterol by approximately 20% and is currently approved for high-risk patients both in Europe and in the US. Inclisiran, which is a small interfering RNA LDL cholesterol-lowering therapy, has been shown to be as effective in lowering LDL cholesterol and with a similar safety profile as the currently available monoclonal PCSK9 inhibitors. In contrast to the monoclonal PCSK9 inhibitors which are administered every 2 weeks or monthly, inclisiran is dosed every 6 months and if it is administered in a clinic or physician’s office as planned it will improve compliance and make it more cost-effective than the monoclonal PCSK9 inhibitors. Based on a series of phase III trials, inclisiran has been approved in Europe and is awaiting approval in the US. Irrespective of the impact of these new LDL-cholesterol lowering agents on the cost of the currently available monoclonal PCSK9 inhibitors, further reductions in the price of the currently available monoclonal PCSK9 inhibitors are needed, as is further study of the barriers that exist to improve access to the many patients who would benefit from these agents to further lower their LDL cholesterol and cardiovascular risk.