
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

71677
10.1038/s41598-024-71677-9
Article
Untargeted metabolomic analysis of human milk from healthy mothers reveals drivers of metabolite variability
Holmes Zachary C.
Koivusaari Katariina
O’Brien Claire E.
Richeson Katherine V.
Strickland Leila I. leila.strickland@biomilq.com

BIOMILQ, Inc., Research Triangle Park, NC USA
6 9 2024
6 9 2024
2024
14 2082717 1 2024
29 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Understanding the human milk metabolome can help inform infant nutrition and health. Untargeted metabolomics was used to study breast milk from 31 healthy participants to assess the shared metabolites in milk from participants with various backgrounds and understand how different demographic, health, and environmental factors impact the milk metabolome. Breast milk samples were analyzed by four separate UPLC-MS/MS methods. Metabolite Set Enrichment Analysis was used to study the most and least variable metabolites. The associations between participant factors and the metabolome were assessed with redundancy analyses. Among all 31 participants and between each untargeted UPLC-MS/MS method, 731 metabolites were detected, of which 389 were shared among all participants. Of the shared metabolites, lactose was the least and lactobionate the most variable metabolite. In the biological super pathway analysis, xenobiotics were the most variable metabolites. Infant age, maternal age, number of live births, and pre-pregnancy BMI were associated with the milk metabolome. In conclusion, the most variable metabolites originate from environmental exposures while the well-conserved core metabolites are linked to cell metabolism or are crucial for infant nutrition and osmoregulation. Understanding the variability of the breast milk metabolome can help identify components that are crucial for infant nutrition, growth, and development.

Subject terms

Metabolomics
Nutrition
Paediatric research
issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

Breast milk is considered the gold standard for infant nutrition1 and it contains thousands of molecules that are nutritionally or functionally important for infant development. However, the full complexity of breast milk is still not well-defined, and the biological roles of many milk components are not fully understood. As such, defining a standard range of concentrations for many breast milk components is challenging due to the high inter- and intra-variability and dynamic nature of human milk1.

Metabolomics methods have been applied over the past decade to study the vast array of small molecules present in breast milk. These small molecules can be synthesized de novo in the mammary gland or originate from maternal blood circulation and can be a result of gut microbial metabolism, environmental exposures, and maternal health and nutrition2. Metabolomics is an important tool for the identification of exogenous chemicals like drugs in milk3. In addition, metabolomics has been used to study the inter-individual variation1,4, changes across lactation stages5, and the impacts of specific maternal health conditions in breast milk composition6,7. As breast milk composition is known to vary geographically8, it is important to study the breast milk metabolome in different populations. Despite efforts to characterize breast milk variability, few studies have concentrated on identifying the core metabolites that are widely shared among milk samples from various, diverse groups of lactating mothers. Understanding what defines human milk consistently across lactating people can help identify or substantiate what is crucial for infant nutrition, growth, and development.

The aim of this study was to investigate the shared metabolites among lactating people with various backgrounds, thus assessing the metabolomic fingerprint of breast milk. We utilized samples collected in North Carolina and Virginia, USA, from healthy women at various lactation stages, from different ethno-racial groups. In particular, metabolites that were the most and least consistent between the study participants and the biological super pathways consistent across all participants were identified. Further, we assessed which maternal and infant characteristics were the main drivers of metabolome differences between breast milk samples. Lastly, we assessed the effect of pooling on the metabolite variability of breast milk as pooling is commonly used in milk banks.

Results

Study population

Of the 280 participants who completed the parent study, breast milk samples from a diversified sub-sample of 31 participants were analyzed for metabolites. Data regarding maternal demographics are presented in Table 1. Maternal age ranged from 23 to 40 years (mean = 31.7 years) and pre-pregnancy body mass index (BMI) ranged from 18.9 to 29.2 (mean = 23.7). Over half of the participants perceived that they produced enough milk to feed their baby.Table 1 Maternal demographics (n = 31).

Characteristics	Mean	SD	
Age (years)	31.7	4.2	
Pre-pregnancy BMI	23.7	3	
Weight gain during pregnancy (kg)	12.5	5.4	
Number of Pregnancies	2	1	
Number of Live Births, % (n)	
 One	54.8% (17)	
 Two	38.7% (12)	
 Three	3.2% (1)	
 Four	3.2% (1)	
Parity, % (n)	
 Primiparous	54.8% (17)	
 Multiparous	45.2% (14)	
Mode of Delivery, % (n)	
 Vaginal	77.4% (24)	
 C-section	22.6% (7)	
Ethnicity, % (n)	
 Not Hispanic or Latino(a)	67.7% (21)	
 Hispanic or Latino(a)	32.3% (10)	
Race, % (n)	
 Asian	16.1% (5)	
 Black or African American	25.8% (8)	
 White	48.4% (15)	
 Other	9.7% (3)	
Education, % (n)	
 Highschool, no degree	3.2% (1)	
 Some college, no degree or AA degree	9.7% (3)	
 Bachelor's degree (BA or BS)	35.5% (11)	
 Master's, Professional, or Doctorate degree	51.6% (16)	
Milk supply and lifestyle characteristics	
 Power Pumping, % (n)	
  Yes	9.7% (3)	
  No	90.3% (28)	
 Supply Perception, % (n)	
  I produce more than enough milk to feed my baby	51.6% (16)	
  I produce just enough milk to satisfy my baby	29.0% (9)	
  I do not produce enough milk to feed my baby	12.9% (4)	
  Missing information	6.5% (2)	
 Use of vitamin supplements during the last month, % (n)	
  Yes	80.6% (25)	
  No	19.4% (6)	
 Use of antibacterial products	
  I often use them, % (n)	25.8% (8)	
  I sometimes use them, % (n)	29.0% (9)	
  I rarely use them, % (n)	41.3% (13)	
  Unsure, % (n)	3.2% (1)	

Thirty-two infants were included in this study because one participant gave birth to twins. Infant demographics are presented in Table 2. Infant age at time of breast milk sample collection ranged from 4.7 to 64.3 weeks (mean = 27.6 weeks). One infant was born extremely preterm (≤ 28 weeks of gestation), two infants were born preterm (28 0/7–36 6/7 weeks of gestation), five infants were born early term (37 0/7–38 6/7 weeks of gestation), twenty infants were born full term (39 0/7–40 6/7 weeks of gestation) and three infants were born later term (41 0/7 weeks–41 6/7 weeks of gestation); as defined by Spong et al.9; gestational age at birth ranged from 26 to 41 weeks (mean = 38.7 weeks). Six infants had diagnosed or suspected food allergies.Table 2 Infant demographics (n = 32).

Characteristics	Mean	SD	
Age (weeks)	27.6	15.6	
Gestational age at birth (weeks) (n = 31)a	38.7	3.0	
Birth weight (g)	3171.2	674.3	
Birth length (cm)	48.6	8.5	
Sex, % (n)			
 Male	43.8% (14)	
 Female	56.2% (18)	
Eczema ever	
 Yes, % (n)	18.8 (6)	
 No, % (n)	81.3 (26)	
Food allergies ever	
 Yes, % (n)	18.8 (6)	
 No, % (n)	81.3 (26)	
aThe information on gestational age was missing for one infant.

Metabolite Variability

Among all 31 participants and between each untargeted UPLC-MS/MS method, 731 metabolites were detected and passed quality control criteria, of which 682 were fully identified. Only the 682 fully identified metabolites are included in subsequent analyses. Of the fully identified metabolites, 389 were detected above the limit of detection in every participant and are considered shared metabolites among all participants. To examine the variability and consistency of these core, universally shared metabolites among this cohort, we visualized the relative coefficient of variation (CV, the ratio of the standard deviation to the mean multiplied by 100) and biological super pathway of all shared metabolites (Fig. 1). The inter-individual variation in the abundance of these shared metabolites varied widely, with relative CV ranging from 8% for lactose to 510% for lactobionate (Fig. 1). We expected the universally shared metabolites to represent the core components of breast milk and lactation, but we also observed many xenobiotics within this shared set.Fig. 1 Metabolite relative coefficient of variation (CV), defined as the ratio of standard deviation to the mean multiplied by 100, and biological class of 389 metabolites shared among all 31 participants. The boxes highlight the least and most variable shared metabolites among all participants. Metabolites are colored by the biological super pathway.

To better understand the impact of biological function on metabolite variability in breast milk, we compared the relative CVs of each of the 682 fully identified metabolites based on their biological super pathway. The biological super pathways these metabolites were assigned impacted the relative CV (p < 0.0001, Kruskal–Wallis test; Supplementary Fig. 1). Xenobiotics had higher relative CVs than any other group (p < 0.0001, Dunn test). Nucleotides had lower relative CVs than amino acids (p = 0.0064), peptides (p = 0.0047), and lipids (p = 0.0029). Additionally, lipids had higher relative CVs than carbohydrates (p = 0.046). The biological sub-pathway analysis showed that of all the metabolites that belonged to a sub-pathway that cumulatively represented ≥ 1% of the relative abundance of all metabolites, long chain polyunsaturated fatty acids had the highest relative CVs, followed by food components from plants (Fig. 2).Fig. 2 The relative coefficients of variation (CVs) for the biological sub-pathways, defined as the ratio of the standard deviation to the mean multiplied by 100. Metabolites that belonged to a sub-pathway that cumulatively represented < 1% of the relative abundance of all metabolites were excluded. Bars represent the median relative CV for each sub-pathway and points represent individual metabolite relative CVs.

We then sought to understand which participant factors drive metabolite variation in breast milk. We used redundancy analysis (RDA) to evaluate relationships between survey responses and the 682 identified metabolites from the untargeted metabolomic data. We first included all the 16 participant factors in the model (Fig. 3A) and discovered a significant correlation between the survey and metabolite data (p = 0.011, pseudo-R2 = 0.156, RDA). This approach identified a correlation within the survey data, where eczema, food allergies, and antibacterial use appeared to cluster together. Subsequently, we used forward variable selection to find the survey responses that were meaningfully associated with the abundance of the metabolites, i.e., were the main drivers of any differences in the metabolite sets between the participants. The four factors retained by forward variable selection were infant age, maternal age, number of live births, and pre-pregnancy BMI. We then repeated the redundancy analysis by including only these four significant factors (p = 0.002, pseudo-R2 = 0.125, RDA; Fig. 3B). This approach identified the survey data that were meaningfully associated with the breast milk metabolomics data to enable further exploration of these factors.Fig. 3 Redundancy analysis (RDA) of metabolites including (a) 16 background factors (p = 0.011, pseudo-R2 = 0.156) and (b) four background factors that were found to be meaningful based on the forward variable selection (p = 0.002, pseudo-R2 = 0.125). Points represent the placement of individual samples on the first two canonical axes, which are linear combinations of both explanatory (survey data) and response (metabolomics data) variables. Points which are closer together represent samples that are more similar in metabolomic composition and survey responses. Arrows represent the direction and strength of the impact of survey data on metabolomic composition, with longer arrows having a stronger association with metabolomic composition.

We conducted Quantitative Metabolite Set Enrichment Analysis (MSEA) using MetaboAnalystR to find differentially regulated metabolic functions within these four factors (infant age, maternal age, number of live births, pre-pregnancy BMI). After false discovery rate (FDR) correction, the abundance of sterol lipids and carbohydrates varied significantly by infant age (Fig. 4A, Supplementary Table 1). The older the infant was, the more cholesterol sulfate, dehydroepiandrosterone sulfate, and cholesterol was present in the milk (Supplementary Table 2). The abundance of several carbohydrates, including 3’-sialyllactose varied by infant age (Supplementary Table 3). Maternal age was associated with the abundance of organic oxygen compounds (Fig. 4B, Supplementary Tables 4 and 5). Number of live births was associated with organic acids, polyketides, organoheterocyclic compounds, nucleic acids, benzenoids, carbohydrates, sterol lipids, and fatty acyls (Fig. 4C, Supplementary Table 6). The results for the single components belonging to these groups are presented in Supplementary Tables 7–14. After FDR correction, no metabolites showed significant variation in abundance by maternal pre-pregnancy BMI (Fig. 4D, Supplementary Table 15).Fig. 4 Quantitative Metabolite Set Enrichment Analysis (MSEA) by (a) infant age, (b) maternal age, (c) number of live births, (d) pre-pregnancy BMI using MetaboAnalystR. Each metabolite set is plotted by − log10(p-value) (the larger the more confident) and enrichment ratio. The top hits for each cluster represent those metabolites that most strongly differ by the background variable (e.g., infant age).

The correlation matrix of the 16 participant factors is presented in Supplementary Fig. 2. Positive correlations (Spearman correlations) were detected between study participant being black or African American and power pumping (correlation coefficient, ρ = 0.56, p = 0.0012), infant age in weeks and infant food allergies (ρ  = 0.42, p = 0.017), infant food allergies and infant eczema (ρ  = 0.38, p = 0.035), infant age and antibacterial use (ρ  = 0.41, p = 0.023), pre-pregnancy BMI and antibacterial use (ρ  = 0.39, p = 0.023), and between being black or African American and gestational age (ρ  = 0.40, p = 0.027). Maternal age and pre-pregnancy BMI (ρ  = − 0.38, p = 0.035) and power pumping and vitamin supplement intake (ρ  =− 0.39, p = 0.029) showed the strongest negative correlations.

We also analyzed the metabolome of pooled human milk to determine the reduction of variability of metabolites due to pooling. Pooled human milk from 10 donors not inclusive of the 31 individual samples was analyzed by the same methods as the individual donor samples. Despite none of the 31 individuals being included in the pool, pooled milk was closer to the centroid of the 31 individuals in metabolite space than any individual sample, based on the 731 detected metabolites (Supplementary Fig. 3).

Discussion

We aimed to study the fingerprint of breast milk by identifying the most consistent metabolites across milk samples from a diverse group of healthy mothers. Of the 731 metabolites detected in total, 389 were shared among all participants’ milk samples. Based on the biological super pathways of all 682 identified metabolites, xenobiotics were the most and nucleotides the least variable between the participants.

Nucleotides are intracellular compounds involved in many cellular functions and metabolic processes important for growth and development in infants, and breast milk nucleotides serve as precursors for nucleic acids10. In addition to being important nutrients for infants, some milk components may also play an important role in the complex biochemistry of milk. In parallel with previous studies, lactose, a sugar synthesized in mammary gland and the most abundant human milk component—was the most consistent metabolite across the samples11. Among other highly stable metabolites were myo-inositol, creatinine, and glutamate, also in line with previous studies5,11. Lactose and myo-inositol are known to be important in the osmotic balance of human milk and determination of milk volume11–13. Their consistency across samples suggests the process of osmotic regulation is conserved across populations and lactation stage.

Of the metabolites shared among all participants, lactobionate and a sweetener, erythritol, were found to be the most variable. Lactobionate is formed in the oxidation of lactose, but it is also present in drugs and cosmetics14. Interestingly, erythritol was detected in all 31 samples, although variable in abundance. Non-nutritive sweeteners are known to enter breast milk through maternal diet15. It is unsurprising that xenobiotics were the most variable group of compounds identified in this study as they arise from maternal environmental exposures. A high degree of variability in xenobiotics originating from plasticizers, cosmetics, and personal care products has been reported by Thomas et al.3, who noted that over 60% of the synthetic compounds identified in untargeted metabolomics of breast milk were known cosmetic ingredients. These results suggest that while the basic cell metabolism is well conserved across participants, there is variation in environmental and dietary exposures between study participants which affects breast milk composition.

The sub-pathway analysis showed high variation in the abundance of omega-3 and omega-6 fatty acids. The concentration of lipids in human milk, including cholesterol and specific fatty acids, is known to differ according to the lactation stage, geographical region, and year of sample collection16. Fish consumption and dietary supplements containing DHA and EPA are associated with increased omega-3 fatty concentration in breast milk17, which is why fish is recommended as part of diet to support child’s brain development. While the focus of this study was not the influence of maternal diet on the human milk metabolome, the results support previous evidence on maternal diet contributing to the variability of the metabolome of milk.

We next used redundancy analyses to study how demographic, health, and environmental factors are associated with the abundance of different metabolites in the samples. Redundancy analysis (RDA) is a multivariate statistical technique that is used to analyze the relationship between explanatory and response variables using regression and ordination. It can also identify associations between explanatory varibles. In our analysis, reports of infant eczema, infant food allergies, and maternal antibacterial use appeared to cluster together. This clustering might suggest that antibacterial use and atopy lead to similar changes in the milk metabolome, or that they are correlated within participants, however, our cross-sectional data does not allow us to examine the causality of such associations. Some mechanistic studies suggest that detergents disrupt the epithelial barrier integrity leading to an increased risk for atopy18. This analysis highlights the utility of RDA to integrate untargeted compositional data, like metabolomics, and sample or participant metadata.

Using RDA, we found a significant association between the explanatory survey data and the response metabolomic data. We next leveraged forward variable selection to identify the explanatory factors that were most associated with the metabolome. This technique iteratively estimates the effect of each survey factor individually and retains only those factors that improve the fit of the RDA analysis. This technique identified infant age, maternal age, pre-pregnancy BMI, and the number of previous live births to be the most determinant of the metabolome of breast milk. To learn more about what metabolomic data these factors are associated with, we conducted metabolite set enrichment analysis (MSEA) on infant age, maternal age, pre-pregnancy BMI, and previous live births. We chose MSEA to leverage prior knowledge of metabolite function and find coordinated changes to the metabolome. Infant age, i.e., lactation stage, has been associated with differences in the milk metabolome in several previous studies as milk composition is known to be highly dynamic and change over the course of lactation 8. Poulsen et al.5 observed higher levels of human milk oligosaccharides (HMOs), cis-aconitate, O-phosphocholine, O-acetylcarnitine, gluconate, and citric acid in early lactation, whereas levels of lactose, 3-fucosyllactose, glutamine, glutamate, and short- and medium-chain fatty acids were increased later in lactation. While the concentrations of several HMOs varied over the course of lactation in Poulsen et al.5, only 3′-sialyllactose was associated with lactation stage in the present study, which may be due to our smaller sample size.

We found that cholesterol sulfate and cholesterol increased with infant age, however, previous studies have reported cholesterol to be lower in mature milk (≥ 15 days postpartum or overall mean of repeated measures across different lactation stages) than in transitional milk (7 or 8–14  days postpartum) 16,19. It should be noted that the youngest infant in the present study was 4.7 weeks at the time of the sample collection. Thus, all the analyzed samples in this study are considered mature milk, i.e., none of the samples were colostrum or transitional milk. This nuanced interpretation between studies highlights the need for additional studies of breast milk composition that incorporate women of diverse backgrounds and from all lactation stages. As these two studies indicate that cholesterol may have a non-linear relationship with infant age, many other components of breast milk may be similarly non-linear and require broad coverage to understand.

Higher maternal age was associated with higher abundance of nicotinamide riboside (a form of vitamin B3) and pyrraline, and lower abundance of pantothenol (provitamin of B6). Maternal age and gestational age had a negative correlation, and the study included mothers of two preterm infants and one extremely preterm infant. Preterm milk has previously been shown to contain higher levels of B group vitamins than term milk, but only during the second week postpartum20. Pyrraline is an advanced glycation end product, which may originate from foods processed at high temperatures21. There is a lack of studies assessing whether eating foods cooked at higher temperatures influences milk, but acrylamide, a component formed by the Maillard reaction, has been detected in breast milk22.

The number of live births was associated with the abundance of numerous metabolites, including several carnitine fatty acyls and caffeine. Thomas et al.3, previously analyzed the metabolome of ~ 1000 breast milk samples and found caffeine and caffeine metabolites were the most dominant food-derived compounds detected. Our data suggests that mothers with more than one child may consume more caffeine than mothers with one child only. The results suggest that previous lactation experience may modify the lactation process, but as the current knowledge is limited, most of the metabolite differences by the number of live births are challenging to interpret. Poulsen et al.5 found no influence from parity on the metabolome when analyzed using 1H nuclear magnetic resonance (NMR) spectroscopy however, the metabolite coverage of NMR is limited compared to the methods used in the present study8. Overall, in comparison to previous studies23, the analysis method of the present study allows to cover a wide range of metabolites, decreasing the risk of missing relevant metabolites.

While pre-pregnancy BMI was identified as being significantly linked to the milk metabolite profile, no specific individual metabolites were associated with pre-pregnancy BMI after FDR correction. Previous studies have found differences in the unsaturated fatty acids based on pre-pregnancy BMI and weight of the mothers24,25.

Finally, pooling milk was found to reduce the variability of the metabolites in a repeat measurement of one sample made from pooling milk across 10 donors. This supports the common strategy of pooling donor milk to protect at-risk infants from variability of milk composition.

One strength of this study is that participants were instructed to collect milk from a full breast and to empty their breast entirely or to collect at least 2 oz of milk in order to get a sample that contains both foremilk and hindmilk. Thus, we aimed to minimize variability of metabolites due to milk collection. Additionally, all participants were given detailed instructions on how to properly store milk in order to minimize variability due to storage conditions. As participants collected milk in their own homes, using their own equipment, there may be some variability in the milk metabolome due to differences in sample collection and storage. Another limitation of the study is that this cohort is relatively highly educated and, despite efforts to diversify the sub cohort, participants in this study are not representative of the general US population. Education is known to correlate with lifestyle factors like diet which can influence the milk metabolome; including participants of more representative educational backgrounds in breast milk research may provide much needed insight into how environmental factors influence milk composition26. We also analyzed only one sample per participant in this study, thus the metabolic profile only represents a snapshot in time4,23. Because breast milk has such high intra-individual variation, we are not able to capture the full complexity of the milk metabolome and how metabolites might change within a feed, over the course of the day or throughout lactation. The metabolome of breast milk varies over the course of lactation5, and maternal factors like obesity, diet, geographical location, and time of the day, and infections are known to affect the metabolome of breast milk8,27. Our computational methods also bear limitations. Quantitative MSEA is biased by uneven metabolite identification and annotation, both in databases and in the current study28, the varying completeness of pathway annotations29, and the choice of database used29. It will be important to verify the associations apparent in the present study in future cohorts.

An increased understanding of breast milk metabolites is important as milk composition may influence the short- and long-term health of children30. In this study, the most variable metabolites originated from environmental sources, indicating that lifestyle factors such as diet and use of chemicals may play a role in milk composition. Some metabolites that have been previously identified as most crucial for infant nutrition or cell metabolism were consistent across participants. Understanding what metabolites are widely consistent and which are not, can help us better understand the biological function of milk metabolites and their role in infant nutrition.

Methods

Study population

Between October 2021 and July 2022, lactating women from North Carolina, Virginia, and surrounding regions in the USA were recruited and enrolled in an observational study investigating the influence of maternal factors on breast milk composition. This “Evaluation of human milk composition and the biosynthetic output of human mammary epithelial cells derived from breast milk” Study was approved by an independent Institutional Review Board (Advarra, Pro00050454) and conducted in accordance with the Declaration of Helsinki. All participants provided written informed consent prior to participating in any study activities.

Inclusion criteria for this study were as follows: lactating women over the age of 18 who gave birth to infants with no health complications. Exclusion criteria were as follows: use of tobacco products in the past year or during their past pregnancy, excessive alcohol consumption (8 or more drinks per week), positive test for SARS-CoV2 within the past 6 months or experienced symptoms of SARS-CoV2 in the past month, had undergone cancer treatments in the past 3 years, and those who did not qualify to donate breast milk based on the Human Milk Banking Association of North America’s Guidelines for the Establishment and Operation of Donor Human Milk Bank 2020: current use of tobacco products or illegal recreational drugs; received a blood transfusion or blood products in the past 4 months; risk of Creutzfeldt-Jakob disease (CJD) or blood borne illness; received new body piercings, tattoos or permanent makeup in the past 12 months; vegans not supplementing with B12; and having an at-risk sexual partner.

A subset of breast milk samples (N = 31) from participants enrolled in the parent study (N = 280) were analyzed to determine the metabolite profile of breast milk. For the sub-study, we aimed to analyze samples from participants of diverse racial and ethnic backgrounds in order to determine how maternal characteristics drive differences in milk metabolites. Participants in the sub-study were chosen based on the following criteria: absence of key medical diagnoses relating to metabolism, normal or overweight BMI (18.5–30), race, and ethnicity. Specifically, participants who had indicated a prior diagnosis with one of the following conditions were excluded: autoimmune disease, celiac disease, endocrine disease, heart disease, high blood pressure, high cholesterol, high triglycerides, hyperthyroidism, hypothyroidism, and polycystic ovary syndrome. Our decision to focus on participants experiencing healthy metabolism was made so that the metabolomic dataset would represent the typical milk from healthy individuals. Based on the demographics of the parent study cohort, we attempted to include equal representation from the four largest ethno-racial groups in this analysis wherever possible; non-Hispanic or non-Latin White, Hispanic or Latin White, non-Hispanic or non-Latin Black or African American, and non-Hispanic or non-Latin Asian. Where the number of participants in an ethno-racial group exceeded the maximum number of participants for this sub-study in each group, we selected those participants with dietary restrictions that limited animal protein (vegan, lacto-ovo-vegetarian, dairy-free) to diversify typical dietary intake among the study population. Wherever more participants meeting our selection criteria for inclusion in this sub-study existed in the parent study, participants were selected at random.

Sample collection and health questionnaire

Breast milk samples from each breast were collected via manual expression or the participant’s own breast pump. Prior to collecting their milk samples, participants were instructed to wait at least 2 h since last breastfeeding or expressing milk; to wash their hands, breast, areola, and nipple using warm water; and to wash their breast pumps. From each breast, participants were instructed to express at least 2 oz of milk or to empty their breast completely into their own collection bottles, mix the milk gently by swirling the bottle three times, and separate into one 12 mL and three 1 mL aliquots in 15 mL and 2 mL polypropylene collection tubes, respectively. Once collected, milk samples were immediately frozen in participants’ home kitchen freezers (− 20 °C) before being transported on ice to the laboratory and stored at – 80 °C prior to analysis. Milk samples from one breast (1 mL aliquot) were analyzed for each participant. Participants were also asked to complete a health questionnaire about their health, diet, lactation and reproductive history, lifestyle, and their infant’s health and diet. All health information was self-reported.

Pooling milk

Pooling donor human milk is a common strategy employed by milk banks to reduce variability in banked milk31. To empirically validate this approach in metabolite space, we pooled human milk from 10 donors not included in the 31 individual samples sent for metabolomics and analyzed this pool by the same methods. Pooling was done by heating the samples to 38 °C, inverting the sample tubes three times, and pouring equal volumes into a beaker, after which the milk was aliquoted and subsequently frozen until the sample preparation and analyses. The pool was analyzed in four replicates.

Sample preparation and analysis

Samples were prepared and analyzed by Metabolon (Metabolon, Inc., Morrisville, NC, USA) using the automated MicroLab STAR® system from Hamilton Company. Several recovery standards were added prior to the first step in the extraction process for QC purposes and are detailed is Supplementary Table 1. To remove protein, dissociate small molecules bound to protein or trapped in the precipitated protein matrix, and to recover chemically diverse metabolites, proteins were precipitated with methanol under vigorous shaking for 2 min (Glen Mills GenoGrinder 2000) followed by centrifugation for 10 min at 680 g. A 25 μL subaliquot of the sample was used for extraction in 500uL of methanol. The resulting extract was divided into five fractions: two for analysis by two separate reversed-phase (RP)/Ultrahigh Performance Liquid Chromatography-Tandem Mass Spectrometry (UPLC-MS/MS) methods with positive ion mode electrospray ionization (ESI), one for analysis by RP/UPLC-MS/MS with negative ion mode ESI, one for analysis by HILIC/UPLC-MS/MS with negative ion mode ESI, and one sample was reserved for backup. Samples were placed on a TurboVap® (Zymark) for 45 min at 40 °C to remove the organic and aqueous solvents. To ensure complete dryness, the sample extracts were stored overnight under nitrogen before preparation for analysis.

Quality control and quality assurance

Several types of controls were analyzed in concert with the experimental samples. A technical replicate throughout the data set was a pooled matrix sample generated by taking a 25 μL of each experimental sample. Extracted water samples were used as process blanks. Instrument performance monitoring and chromatographic alignment aiding was allowed by a cocktail of quality control standards spiked into every analyzed sample that were carefully chosen not to interfere with the measurement of endogenous compounds. Instrument variability was determined by calculating the median relative standard deviation (RSD) for the standards added to each sample prior to injection into the mass spectrometers. Overall process variability was determined by calculating the median RSD for all endogenous metabolites (i.e., non-instrument standards) present in 100% of the pooled matrix samples. The median RSD for internal standards was 4%, and the median RSD for endogenous metabolites within the quality control standards was 10%.

Ultrahigh performance liquid chromatography-tandem mass spectroscopy (UPLC-MS/MS)

All methods utilized a Waters ACQUITY ultra-performance liquid chromatography (UPLC) and a Thermo Scientific Q-Exactive high resolution/accurate mass spectrometer interfaced with a heated electrospray ionization (HESI-II) source and Orbitrap mass analyzer operated at 35,000 mass resolution. The sample extract was dried then reconstituted in solvents compatible to each of the four methods. Each reconstitution solvent contained a series of standards at fixed concentrations to ensure injection and chromatographic consistency. One aliquot was analyzed using acidic positive ion conditions, chromatographically optimized for more hydrophilic compounds. In this method, the extract was gradient eluted from a C18 column (Waters UPLC BEH C18-2.1 × 100 mm, 1.7 µm) using water and methanol, containing 0.05% perfluoropentanoic acid (PFPA) and 0.1% formic acid (FA). Another aliquot was also analyzed using acidic positive ion conditions, however it was chromatographically optimized for more hydrophobic compounds. In this method, the extract was gradient eluted from the same afore mentioned C18 column using methanol, acetonitrile, water, 0.05% PFPA and 0.01% FA and was operated at an overall higher organic content. Another aliquot was analyzed using basic negative ion optimized conditions using a separate dedicated C18 column. The basic extracts were gradient eluted from the column using methanol and water, however with 6.5 mM Ammonium Bicarbonate at pH 8. The fourth aliquot was analyzed via negative ionization following elution from a HILIC column (Waters UPLC BEH Amide 2.1 × 150 mm, 1.7 µm) using a gradient consisting of water and acetonitrile with 10 mM Ammonium Formate, pH 10.8. The MS analysis alternated between MS and data-dependent MSn scans using dynamic exclusion. The scan range varied slighted between methods but covered 70–1000 m/z. Chromatography and mass spectrometry conditions were as previously published32. Raw data files are archived by Metabolon and extracted as described below. Raw data has also been deposited at MetaboLights under study identifier MTBLS241931.

Data extraction and compound identification

Metabolon, Inc. (Morrisville, NC, USA) extracted, peak-identified, and processed the data. Metabolites were identified by comparing them to library entries based on purified standards or recurrent unknown entities, using retention time, mass to charge ratio, and chromatographic data (including MS/MS spectra). The complete methods supplied by Metabolon are included in the Supplemental Methods.

Statistical analyses

Statistical analyses were conducted using R (R Development Core Team, 2013; http://www.R-project.org) and MetaboAnalyst 5.0 (https://www.metaboanalyst.ca, accessed on May 23, 2023) for pathway and enrichment analyses. Means and standard deviations were calculated for maternal and infant characteristics. Correlations between demographic, health, and environmental factors were studied using the Pearson correlation. For the enrichment analyses, metabolite values were median scaled, normalized to extraction volume, and log-transformed. Where missing value imputation was necessary, quantile regression imputation of left-censored data (QRILC) was performed using the impute.QRILC() function from the imputeLCMD R package (version 2.0)33,34.

We studied how distinct characteristics and lifestyle factors were associated with the abundance of metabolites in breast milk using redundancy analyses. Before conducting the redundancy analyses, we selected demographic, health, and environmental factors to be included. Considering the sample size, we chose to include the predetermined 16 factors to avoid overfitting. The factors were chosen based on existing literature and the impact these factors may have on breast milk composition; these factors were: maternal age, race (black/Asian/white), ethnicity (Hispanic or Latin/not Hispanic or Latin), pre-pregnancy BMI, weight gain during pregnancy, number of live births, power pumping (yes/no), and supply perception (I do/I do not produce enough breast milk to satisfy my baby). Use of antibacterial products (not including hand sanitizer or regular soap; rarely/sometimes/often), as cleaning and disinfecting compounds are known to enter human milk35, and use of any vitamin supplements during the last month (yes/no) were also included, as supplementation is known to affect the milk composition36. We also included infant sex, infant age in weeks, infant’s eczema (ever/never), and infant’s food allergies (suspected or diagnosed ever/never) in the model.

Supplementary Information

Supplementary Information.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-71677-9.

Acknowledgements

The authors warmly thank all the study participants and acknowledge the contribution of Charlotte Fron and the other members of the Breast Milk Study team at BIOMILQ.

Author contributions

All authors were involved in the planning of this study. CO managed IRB compliance, sample collection, and survey data collection, storage, and cleaning. KR and LIS supervised the data collection and analyses. ZH managed collection of the metabolomic data and conducted all data processing and statistical analyses, including figure generation. All authors were involved in interpreting the results. CO drafted the methods section and KK wrote the manuscript. All authors reviewed and accepted the final version of the manuscript.

Data availability

The data is available from the corresponding author upon reasonable request. The survey data are not publicly available due to the protection of the identity of the study participants and their personal data. The raw metabolomics data has been deposited at MetaboLights under study identifier MTBLS2419.

Competing interests

The authors declare no competing interests.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

These authors contributed equally: Zachary C. Holmes and Katariina Koivusaari.
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References

1. Garwolińska D Namieśnik J Kot-Wasik A Hewelt-Belka W Chemistry of human breast milk—a comprehensive review of the composition and role of milk metabolites in child development J. Agric. Food Chem. 2018 66 11881 11896 10.1021/acs.jafc.8b04031 30247884
Garwolińska, D., Namieśnik, J., Kot-Wasik, A. & Hewelt-Belka, W. Chemistry of human breast milk—a comprehensive review of the composition and role of milk metabolites in child development. J. Agric. Food Chem. 66, 11881–11896. 10.1021/acs.jafc.8b04031 (2018).30247884 10.1021/acs.jafc.8b04031
2. Stinson LF George AD Human milk lipids and small metabolites: Maternal and microbial origins Metabolites 2023 13 422 10.3390/metabo13030422 36984862
Stinson, L. F. & George, A. D. Human milk lipids and small metabolites: Maternal and microbial origins. Metabolites 13, 422 (2023).36984862 10.3390/metabo13030422
3. Thomas S An untargeted metabolomics analysis of exogenous chemicals in human milk and transfer to the infant Clin. Transl. Sci. 2022 15 2576 2582 10.1111/cts.13393 36043481
Thomas, S. et al. An untargeted metabolomics analysis of exogenous chemicals in human milk and transfer to the infant. Clin. Transl. Sci. 15, 2576–2582. 10.1111/cts.13393 (2022).36043481 10.1111/cts.13393
4. Dekker PM Boeren S van Goudoever JB Vervoort JJM Hettinga KA Exploring human milk dynamics: Interindividual variation in milk proteome, peptidome, and metabolome J. Proteome Res. 2022 21 1002 1016 10.1021/acs.jproteome.1c00879 35104145
Dekker, P. M., Boeren, S., van Goudoever, J. B., Vervoort, J. J. M. & Hettinga, K. A. Exploring human milk dynamics: Interindividual variation in milk proteome, peptidome, and metabolome. J. Proteome Res. 21, 1002–1016. 10.1021/acs.jproteome.1c00879 (2022).35104145 10.1021/acs.jproteome.1c00879
5. Poulsen KO Dynamic changes in the human milk metabolome over 25 weeks of lactation Front. Nutr. 2022 2022 9 10.3389/fnut.2022.917659
Poulsen, K. O. et al. Dynamic changes in the human milk metabolome over 25 weeks of lactation. Front. Nutr. 2022, 9. 10.3389/fnut.2022.917659 (2022).10.3389/fnut.2022.917659
6. Bardanzellu F Puddu M Fanos V The human breast milk metabolome in preeclampsia, gestational diabetes, and intrauterine growth restriction: Implications for child growth and development J. Pediatr. 2020 221 20 28 10.1016/j.jpeds.2020.01.049
Bardanzellu, F., Puddu, M. & Fanos, V. The human breast milk metabolome in preeclampsia, gestational diabetes, and intrauterine growth restriction: Implications for child growth and development. J. Pediatr. 221, 20–28. 10.1016/j.jpeds.2020.01.049 (2020).10.1016/j.jpeds.2020.01.049
7. Bardanzellu F Puddu M Peroni DG Fanos V The human breast milk metabolome in overweight and obese mothers Front. Immunol. 2020 11 1533 10.3389/fimmu.2020.01533 32793208
Bardanzellu, F., Puddu, M., Peroni, D. G. & Fanos, V. The human breast milk metabolome in overweight and obese mothers. Front. Immunol. 11, 1533. 10.3389/fimmu.2020.01533 (2020).32793208 10.3389/fimmu.2020.01533
8. Ten-Doménech I Current practice in untargeted human milk metabolomics Metabolites 2020 2020 10 10.3390/metabo10020043
Ten-Doménech, I. et al. Current practice in untargeted human milk metabolomics. Metabolites 2020, 10. 10.3390/metabo10020043 (2020).10.3390/metabo10020043
9. Spong CY Defining, “term” pregnancy: Recommendations from the defining “term” pregnancy workgroup JAMA 2013 309 2445 2446 10.1001/jama.2013.6235 23645117
Spong, C. Y. Defining, “term” pregnancy: Recommendations from the defining “term” pregnancy workgroup. JAMA 309, 2445–2446. 10.1001/jama.2013.6235 (2013).23645117 10.1001/jama.2013.6235
10. Hodgkinson A Nucleotides: An updated review of their concentration in breast milk Nutr. Res. 2022 99 13 24 10.1016/j.nutres.2021.09.004 35081503
Hodgkinson, A. et al. Nucleotides: An updated review of their concentration in breast milk. Nutr. Res. 99, 13–24. 10.1016/j.nutres.2021.09.004 (2022).35081503 10.1016/j.nutres.2021.09.004
11. Smilowitz JT The human milk metabolome reveals diverse oligosaccharide profiles J. Nutr. 2013 143 1709 1718 10.3945/jn.113.178772 24027187
Smilowitz, J. T. et al. The human milk metabolome reveals diverse oligosaccharide profiles. J. Nutr. 143, 1709–1718. 10.3945/jn.113.178772 (2013).24027187 10.3945/jn.113.178772
12. Beck FX Schmolke M Guder WG Osmolytes Curr. Opin. Nephrol. Hypertens. 1992 1 43 52 10.1097/00041552-199210000-00007 1365830
Beck, F. X., Schmolke, M. & Guder, W. G. Osmolytes. Curr. Opin. Nephrol. Hypertens. 1, 43–52 (1992).1365830 10.1097/00041552-199210000-00007
13. Romero-Velarde E The importance of lactose in the human diet: Outcomes of a Mexican consensus meeting Nutrients 2019 2019 11 10.3390/nu11112737
Romero-Velarde, E. et al. The importance of lactose in the human diet: Outcomes of a Mexican consensus meeting. Nutrients 2019, 11. 10.3390/nu11112737 (2019).10.3390/nu11112737
14. Sáez-Orviz S Marcet I Rendueles M Díaz M The antimicrobial and bioactive properties of lactobionic acid J. Sci. Food Agric. 2022 102 3495 3502 10.1002/jsfa.11823 35174887
Sáez-Orviz, S., Marcet, I., Rendueles, M. & Díaz, M. The antimicrobial and bioactive properties of lactobionic acid. J. Sci. Food Agric. 102, 3495–3502. 10.1002/jsfa.11823 (2022).35174887 10.1002/jsfa.11823
15. Sylvetsky AC Nonnutritive sweeteners in breast milk J. Toxicol. Environ. Health A 2015 78 1029 1032 10.1080/15287394.2015.1053646 26267522
Sylvetsky, A. C. et al. Nonnutritive sweeteners in breast milk. J. Toxicol. Environ. Health A 78, 1029–1032. 10.1080/15287394.2015.1053646 (2015).26267522 10.1080/15287394.2015.1053646
16. Zhang Z Human milk lipid profiles around the world: A systematic review and meta-analysis Adv. Nutr. 2022 13 2519 2536 10.1093/advances/nmac097 36083999
Zhang, Z. et al. Human milk lipid profiles around the world: A systematic review and meta-analysis. Adv. Nutr. 13, 2519–2536. 10.1093/advances/nmac097 (2022).36083999 10.1093/advances/nmac097
17. Bravi F Impact of maternal nutrition on breast-milk composition: A systematic review1,2 Am. J. Clin. Nutr. 2016 104 646 662 10.3945/ajcn.115.120881 27534637
Bravi, F. et al. Impact of maternal nutrition on breast-milk composition: A systematic review1,2. Am. J. Clin. Nutr. 104, 646–662. 10.3945/ajcn.115.120881 (2016).27534637 10.3945/ajcn.115.120881
18. Wright BL Allergies come clean: The role of detergents in epithelial barrier dysfunction Curr. Allergy Asthma Rep. 2023 23 443 451 10.1007/s11882-023-01094-x 37233851
Wright, B. L. et al. Allergies come clean: The role of detergents in epithelial barrier dysfunction. Curr. Allergy Asthma Rep. 23, 443–451. 10.1007/s11882-023-01094-x (2023).37233851 10.1007/s11882-023-01094-x
19. Yang Z Human milk cholesterol is associated with lactation stage and maternal plasma cholesterol in Chinese populations Pediatr. Res. 2022 91 970 976 10.1038/s41390-021-01440-7 33846555
Yang, Z. et al. Human milk cholesterol is associated with lactation stage and maternal plasma cholesterol in Chinese populations. Pediatr. Res. 91, 970–976. 10.1038/s41390-021-01440-7 (2022).33846555 10.1038/s41390-021-01440-7
20. Redeuil K Vitamins and carotenoids in human milk delivering preterm and term infants: Implications for preterm nutrient requirements and human milk fortification strategies Clin. Nutr. 2021 40 222 228 10.1016/j.clnu.2020.05.012 32534950
Redeuil, K. et al. Vitamins and carotenoids in human milk delivering preterm and term infants: Implications for preterm nutrient requirements and human milk fortification strategies. Clin. Nutr. 40, 222–228. 10.1016/j.clnu.2020.05.012 (2021).32534950 10.1016/j.clnu.2020.05.012
21. Foerster A Henle T Glycation in food and metabolic transit of dietary AGEs (advanced glycation end-products): Studies on the urinary excretion of pyrraline Biochem. Soc. Trans. 2003 31 1383 1385 10.1042/bst0311383 14641068
Foerster, A. & Henle, T. Glycation in food and metabolic transit of dietary AGEs (advanced glycation end-products): Studies on the urinary excretion of pyrraline. Biochem. Soc. Trans. 31, 1383–1385. 10.1042/bst0311383 (2003).14641068 10.1042/bst0311383
22. Mojska H Gielecińska I Winiarek J Sawicki W Acrylamide content in breast milk: The evaluation of the impact of breastfeeding women's diet and the estimation of the exposure of breastfed infants to acrylamide in breast milk Toxics 2021 2021 9 10.3390/toxics9110298
Mojska, H., Gielecińska, I., Winiarek, J. & Sawicki, W. Acrylamide content in breast milk: The evaluation of the impact of breastfeeding women’s diet and the estimation of the exposure of breastfed infants to acrylamide in breast milk. Toxics 2021, 9. 10.3390/toxics9110298 (2021).10.3390/toxics9110298
23. Baumgartel K The human milk metabolome: A scoping literature review J. Hum. Lact. 2023 39 255 277 10.1177/08903344231156449 36924445
Baumgartel, K. et al. The human milk metabolome: A scoping literature review. J. Hum. Lact. 39, 255–277. 10.1177/08903344231156449 (2023).36924445 10.1177/08903344231156449
24. Grote V Breast milk composition and infant nutrient intakes during the first 12 months of life Eur. J. Clin. Nutr. 2016 70 250 256 10.1038/ejcn.2015.162 26419197
Grote, V. et al. Breast milk composition and infant nutrient intakes during the first 12 months of life. Eur. J. Clin. Nutr. 70, 250–256. 10.1038/ejcn.2015.162 (2016).26419197 10.1038/ejcn.2015.162
25. Mäkelä J Linderborg K Niinikoski H Yang B Lagström H Breast milk fatty acid composition differs between overweight and normal weight women: The STEPS Study Eur. J. Nutr. 2013 52 727 735 10.1007/s00394-012-0378-5 22639073
Mäkelä, J., Linderborg, K., Niinikoski, H., Yang, B. & Lagström, H. Breast milk fatty acid composition differs between overweight and normal weight women: The STEPS Study. Eur. J. Nutr. 52, 727–735. 10.1007/s00394-012-0378-5 (2013).22639073 10.1007/s00394-012-0378-5
26. Ojo-Okunola A Cacciatore S Nicol MP Du-Toit E The determinants of the human milk metabolome and its role in infant health Metabolites 2020 2020 10 10.3390/metabo10020077
Ojo-Okunola, A., Cacciatore, S., Nicol, M. P. & Du-Toit, E. The determinants of the human milk metabolome and its role in infant health. Metabolites 2020, 10. 10.3390/metabo10020077 (2020).10.3390/metabo10020077
27. Arias-Borrego A Metallomic and untargeted metabolomic signatures of human milk from SARS-CoV-2 positive mothers Mol. Nutr. Food Res. 2022 66 e2200071 10.1002/mnfr.202200071 35687731
Arias-Borrego, A. et al. Metallomic and untargeted metabolomic signatures of human milk from SARS-CoV-2 positive mothers. Mol. Nutr. Food Res. 66, e2200071. 10.1002/mnfr.202200071 (2022).35687731 10.1002/mnfr.202200071
28. Xia J Wishart DS MSEA: A web-based tool to identify biologically meaningful patterns in quantitative metabolomic data Nucl. Acids Res. 2010 38 W71 77 10.1093/nar/gkq329 20457745
Xia, J. & Wishart, D. S. MSEA: A web-based tool to identify biologically meaningful patterns in quantitative metabolomic data. Nucl. Acids Res. 38, W71-77. 10.1093/nar/gkq329 (2010).20457745 10.1093/nar/gkq329
29. Marco-Ramell A Evaluation and comparison of bioinformatic tools for the enrichment analysis of metabolomics data BMC Bioinf. 2018 19 1 10.1186/s12859-017-2006-0
Marco-Ramell, A. et al. Evaluation and comparison of bioinformatic tools for the enrichment analysis of metabolomics data. BMC Bioinf. 19, 1. 10.1186/s12859-017-2006-0 (2018).10.1186/s12859-017-2006-0
30. Li C Li M Hu M Zhang T Metabolic engineering of de novo pathway for the production of 2′-fucosyllactose in Escherichia coli Mol. Biotechnol. 2023 10.1007/s12033-023-00657-7 38159171
Li, C., Li, M., Hu, M. & Zhang, T. Metabolic engineering of de novo pathway for the production of 2′-fucosyllactose in Escherichia coli. Mol. Biotechnol.10.1007/s12033-023-00657-7 (2023).38159171 10.1007/s12033-023-00657-7
31. Haiden N Ziegler EE Human milk banking Ann. Nutr. Metabol. 2017 69 7 15 10.1159/000452821
Haiden, N. & Ziegler, E. E. Human milk banking. Ann. Nutr. Metabol. 69, 7–15. 10.1159/000452821 (2017).10.1159/000452821
32. Ford L Precision of a clinical metabolomics profiling platform for use in the identification of inborn errors of metabolism J. Appl. Lab. Med. 2020 5 342 356 10.1093/jalm/jfz026 32445384
Ford, L. et al. Precision of a clinical metabolomics profiling platform for use in the identification of inborn errors of metabolism. J. Appl. Lab. Med. 5, 342–356. 10.1093/jalm/jfz026 (2020).32445384 10.1093/jalm/jfz026
33. Wei R Missing value imputation approach for mass spectrometry-based metabolomics data Sci. Rep. 2018 8 663 10.1038/s41598-017-19120-0 29330539
Wei, R. et al. Missing value imputation approach for mass spectrometry-based metabolomics data. Sci. Rep. 8, 663. 10.1038/s41598-017-19120-0 (2018).29330539 10.1038/s41598-017-19120-0
34. imputeLCMD. A Collection of Methods for Left-Censored Missing Data Imputation v. 2.0 (Springer, 2015).
35. Zheng G Schreder E Sathyanarayana S Salamova A The first detection of quaternary ammonium compounds in breast milk: Implications for early-life exposure J. Expo Sci. Environ. Epidemiol. 2022 32 682 688 10.1038/s41370-022-00439-4 35437305
Zheng, G., Schreder, E., Sathyanarayana, S. & Salamova, A. The first detection of quaternary ammonium compounds in breast milk: Implications for early-life exposure. J. Expo Sci. Environ. Epidemiol. 32, 682–688. 10.1038/s41370-022-00439-4 (2022).35437305 10.1038/s41370-022-00439-4
36. Keikha M Shayan-Moghadam R Bahreynian M Kelishadi R Nutritional supplements and mother’s milk composition: A systematic review of interventional studies Int. Breastfeed. J. 2021 16 1 10.1186/s13006-020-00354-0 33397426
Keikha, M., Shayan-Moghadam, R., Bahreynian, M. & Kelishadi, R. Nutritional supplements and mother’s milk composition: A systematic review of interventional studies. Int. Breastfeed. J. 16, 1. 10.1186/s13006-020-00354-0 (2021).33397426 10.1186/s13006-020-00354-0
