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. 2020 Feb 19;16(3):e12979. doi: 10.1111/mcn.12979

Impact of maternal overweight and obesity on milk composition and infant growth

Lindsay Ellsworth 1, Wei Perng 2,3, Emma Harman 4, Arun Das 5, Subramaniam Pennathur 5,6, Brigid Gregg 4,✉
PMCID: PMC7296794  PMID: 32074402

Abstract

Overweight and obesity (OW/OB) impact half of the pregnancies in the United States and can have negative consequences for offspring health. Studies are limited on human milk alterations in the context of maternal obesity. Alterations in milk are hypothesized to impact offspring development during the critical period of lactation. We aimed to evaluate the relationships between mothers with OW/OB (body mass index [BMI] ≥25 kg/m2), infant growth, and selected milk nutrients. We recruited mother–infant dyads with pre‐pregnancy OW/OB and normal weight status. The primary study included 52 dyads with infant growth measures through 6 months. Thirty‐two dyads provided milk at 2 weeks, which was analysed for macronutrients, long‐chain fatty acids, and insulin. We used multivariable linear regression to examine the association of maternal weight status with infant growth, maternal weight status with milk components, and milk components with infant growth. Mothers with OW/OB had infants with higher weight‐for‐length (WFL) and BMI Z‐scores at birth. Mothers with OW/OB had higher milk insulin and dihomo‐gamma‐linolenic, adrenic, and palmitic acids and reduced conjugated linoleic and oleic acids. N6 long‐chain polyunsaturated fatty acid (LC‐PUFA)‐driven factor 1 was associated with higher WFL, lower length‐for‐age (LFA), and lower head circumference‐for‐age Z‐scores change from 2 weeks to 2 months in human milk‐fed infants, whereas N6 LC‐PUFA‐driven factor 5 was associated with lower LFA Z‐score change. Human milk composition is associated with maternal pre‐pregnancy weight status and composition may be a contributing factor to early infant growth trajectory.

Keywords: breastfeeding, human milk, infant growth, macronutrients, obesity, polyunsaturated fatty acids

Key messages

  • Animal models of maternal obesity have shown roles for milk nutrients, fatty acids, oxidative stress markers, inflammatory markers, hormones, and bioactive factors in programming of offspring growth. Human studies with longitudinal infant outcomes are limited.

  • This study is novel in investigating the impact of pre‐pregnancy overweight and obesity on the early macronutrient, fatty acid profile, and insulin content of human milk during the period of transition from colostrum to mature milk and the association with infant growth through 6 months.

  • This study found associations of higher milk N6 LC‐PUFAs with infant growth trajectory.

1. INTRODUCTION

In the United States, over 50% of pregnancies are complicated by overweight or obesity (American College of Obstetricians and Gynecologists, 2013). Obesity has adverse effects on maternal health, impacts fetal development, and programmes lifelong offspring risk for obesity (Leddy, Power, & Schulkin, 2008; Poston, Harthoorn, & Van Der Beek, 2011). In addition to the in utero environment, rapid weight gain in infancy has been linked to childhood obesity risk, pointing to the neonatal period as a critical window for adipose tissue development (Monteiro & Victora, 2005; Weng, Redsell, Swift, Yang, & Glazebrook, 2012).

The lactation period is a window during which alterations in milk composition impact infant metabolic outcomes (Prentice et al., 2016). In the setting of maternal disease, there are differences in milk nutrients, oxidative stress and inflammatory markers, and bioactive factors, many of which have been implicated in programming offspring growth and appetite (Ellsworth, Harman, Padmanabhan, & Gregg, 2018; Young, Johnson, & Krebs, 2012). Animal models demonstrate that maternal obesity and high‐fat diet influence milk composition, leading to increased insulin and altered fatty acids in milk of obese dams and obesity in adult offspring (Gorski, Dunn‐Meynell, Hartman, & Levin, 2006; Wahlig et al., 2012). Limited human research on maternal weight status during lactation has shown differences in milk composition of nutrients, hormones, bioactives, and microbiota with suggested links to infant growth (Eriksen, Christensen, Lind, & Michaelsen, 2018; Fields et al., 2017; Makela, Linderborg, Niinikoski, Yang, & Lagstrom, 2013; Panagos et al., 2016).

Milk composition has a high degree of variability despite well‐conserved macronutrients, with maternal weight status contributing to this variation (Ballard & Morrow, 2013). Maternal weight status has been associated with increased factors promoting adipogenesis including N6 long‐chain polyunsaturated fatty acids (LC‐PUFAs) and metabolic regulating factors such as insulin (Ahuja et al., 2011; Amaral, Marano, Oliveira, & Moreira, 2019; Chan et al., 2018; Ley, Hanley, Sermer, Zinman, & O'Connor, 2012; Makela et al., 2013; Young et al., 2017). The impact of maternal weight status on milk composition may be an important aspect of developmental programming; however, the degree to which milk composition influences infant growth remains an area for exploration. Recent studies have suggested roles for mature milk growth factors, inflammatory factors, and fatty acids in the development of infant body composition (Fields & Demerath, 2012; Much et al., 2013). The aims of this study were twofold: (a) evaluate the influence of pre‐pregnancy overweight and obesity on infant growth and early milk composition in the first weeks of life and (b) investigate the potential for milk composition to impact infant growth trajectories during the first 6 months of life.

2. METHODS

2.1. Study overview

Mother–infant dyads were recruited after written informed consent was obtained from mothers for their own participation and assent on behalf of their infant. Mothers were given detailed instructions for milk collection. Maternal demographics were obtained through paper survey with health history obtained through medical record review. Infant characteristics, growth measurements, and nutrition source were obtained through medical record review at birth and well child appointments at 2 weeks, 2 months, and 6 months. Participants were given a stipend for their involvement. The Institutional Review Board of the University of Michigan (HUM00107801) and Saint Joseph Mercy Hospital (HSR ‐17‐1686) approved this study protocol.

2.2. Participants

Fifty‐five dyads were enrolled peripartum while admitted for birth at two participating hospitals in Ann Arbor, Michigan, USA. Three dyads opted out of the study. Dyads were categorized on the basis of maternal pre‐pregnancy body mass index (BMI) into normal weight (NW; BMI <25.0 kg/m2) and overweight or obese (OW/OB; BMI ≥25.0 kg/m2). Pre‐pregnancy BMI was obtained through the medical record by obstetrics recorded pre‐pregnancy weight or early first trimester weight with BMI then calculated using recorded height. Inclusion criteria included maternal age > 18, gestational age ≥ 35 weeks, and healthy singleton birth with no maternal diabetes. Dyads were enrolled if the mother intended to breastfeed but not excluded if formula was supplemented. Infant growth trends and milk composition associations with growth were excluded for those receiving exclusive formula feedings.

2.3. Milk collection

The research team verbally instructed mothers on milk sample collection with a written protocol provided. Samples were collected by mothers at home on the morning of their infant's physician visit near 2 weeks of life, on average at postpartum day 16. This time point was selected given expected improvement in milk supply to allow for milk in transition to mature milk to be provided without limiting the infant supply. Mothers collected nonfasting milk between 8:00 a.m. and 10:00 a.m. at least 2 hr after feeding their infant. Mothers provided milk by hand expression or pumping emptying an entire single breast. The method of expression and which breast to empty were based on maternal preference. Milk was expressed into a container then inverted to mix prior to transfer of 25 ml into glass vials in 5‐ml aliquots. Samples were stored in mother's home freezer then transported to clinic on ice for storage at −20°C for less than 1 week before transport on ice for final freezing at −80°C. Prior to analysis, samples were thawed on ice. Milk collection protocol was based on published milk collection with modifications as described for our study design and population (Fields & Demerath, 2012).

2.4. Milk analysis

2.4.1. Macronutrients

Whole milk samples were analysed using a mid‐infrared spectroscopy macronutrient analyser (MIRIS HMA™, Uppsala, Sweden). For macronutrient analysis of carbohydrate, protein, fat, and total energy, 5 ml milk sample was warmed to 40°C in a bead bath then underwent ultrasonic homogenization prior to immediate analysis per manufacturer protocol. Analysis was performed in duplicate for reproducibility, and average value was used in analysis. As control prior to analysis, internal calibration using standards provided by MIRIS HMA™ was completed and verified with the range of targeted values established by the manufacturer. MIRIS HMA™ reported repeatability for fat, crude protein, true protein ≤0.05 g/100 ml, and carbohydrate ≤0.08 g/100 ml and accuracy for fat ±12%, crude protein, true protein, and carbohydrate ±15%. Milk macronutrient analysis was not completed for samples with under 5‐ml volume available.

2.4.2. Fatty acid profiles

Lipids were extracted from milk samples (30 μl) following the method of Bligh and Dyer (1959) with modifications. The volume of the milk was adjusted to 600 μl with water followed by addition of 2.25 ml of a mixture of chloroform‐methanol (1:2) containing 0.01% of butylated hydroxytoluene and 10 μl of 4‐mM heptadecanoic (C17:0) acid as an internal standard. The mixture was homogenized on a vortex mixer. The homogenized sample was further treated with 0.75 ml each of chloroform and aqueous sodium chloride (NaCl 0.9%) solution followed by mixing and centrifugation at 3000 rpm for 5–6 min at 4°C on a tabletop centrifuge. The upper aqueous layer was discarded by aspiration under vacuum, and the lower organic (chloroform) layer containing the lipids was separated out, transferred into a screw‐capped glass tube, and saved at −20°C unless used as follows.

The fatty acid portion of the lipids were converted into their methyl esters with boron trifluoride‐methanol (BF3‐methanol) as described before with slight modification (Morrison & Smith, 1964). The solvents of the lipid extracts were removed under nitrogen. To the dry residues left in the screw‐capped glass tube, 2 ml of BF3‐methanol (14% solution from Sigma, Saint Louis, MO, USA) was added; the mixtures were incubated at 60°C for 2–2.5 hr. The fatty acid methyl esters (FAMEs) were extracted in hexane by adding 2 ml of hexane and 1 ml of water, mixing and centrifuging as above followed by transferring the upper hexane layer into a separate glass tube. The volume of the solvent was reduced under nitrogen, and the crude FAMEs were purified by applying on thin‐layer chromatographic plate (20 × 20 cm, silica gel 60, Merck KGaA, Darmstadt, Germany, now operates as Millipore Sigma, Burlington, MA, USA). The plate was developed with a solvent mixture of hexane–diethyl ether–acetic acid (80 ml–20 ml–1.5 ml, respectively). Methyl ester bands were identified by comparing the retention flow of a commercial standard FAME (methyl heptadecanoate) applied on the same thin‐layer chromatographic plate on a side. The powder of the identified methyl ester was scrapped out into a test tube and extracted with 2 ml of chloroform by mild sonication on a bath sonicator followed by centrifugation at room temperature (22°C) and then transferring the clear chloroform layer into a separate tube. The solvents were removed completely under nitrogen, purified methyl esters redissolved in 100 μl of hexane, and the fatty acid compositions of the lipids analysed by gas chromatography (GC).

GC analyses were performed on an Agilent GC (Santa Clara, CA, USA) model 6890N equipped with flame ionization detector, an auto sampler model 7693, and Chemstation software (version C.01.04) for data analysis. The GC column used was Agilent HP 88, 30‐m, 0.25‐mm I.D., and 0.20‐μm film thickness. Hydrogen was used as a carrier gas and for the flame ionization detector, and nitrogen was used as a makeup gas. Analyses were carried out injecting 1 μl of sample via autoinjector mode. Temperature programmes of 125°C for 0 min, 125–145°C at 39.52°C/min, holding 145°C for 5.3 min, and 145–220°C at 9°C/min, holding 220°C for 5 min were used. Each sample run was completed in 19.14 min. A calibration curve was prepared using known amounts of methyl heptadecanoate and other commercially available standard methyl ester mixtures containing saturated and unsaturated carbon chain length from 12 through 22 carbons on GC and using the peak area ratio response of each methyl ester with respect to methyl heptadecanoate. The fatty acid methyl ester components in the sample were identified comparing the retention time of the commercial standards and quantified with respect to the amount of methyl heptadecanoate internal standard added in each sample during liquid extraction and the peak area ratio response as in the calibration curve prepared. The quality control was done by analysing a pooled sample (indistinguishable from the study samples) periodically and also before analysis of any study samples to ensure GC performances with respect to separation, retention times, and peak response. The coefficient of variation was determined after multiple runs of the sample injection volume of the same pooled sample and calculating area percentages of methyl esters, their mean (average) values, and standard deviations and then evaluating the ratio of the standard deviation to the mean. The coefficient of variation for GC analysis was found to be within 2.3% to 3.7%. Individual long‐chain fatty acid (LCFA) concentrations were calculated as a percentage of total LCFAs for 33 saturated, monounsaturated, and polyunsaturated LCFAs. N6:N3 LC‐PUFA ratio was calculated on the basis of total N6 and total N3 concentrations.

2.4.3. Insulin

Whole milk samples were delipidated to isolate the skim milk layer by centrifugation at 720 relative centrifugal force for 20 min at 4°C, followed by removal of the bottom skim milk layer using a 21‐gauge needle with 1‐ml syringe which is done twice. Using 25 μl of undiluted skim milk, insulin was analysed using Mercodia Insulin ELISA 10‐1113‐01 according to the manufacturer protocol with Mercodia Diabetes Antigen Control Human Low and High 10‐1134‐01 (Mercodia Ab, Uppsala, Sweden; Whitmore, Trengove, Graham, & Hartmann, 2012). Analysis was performed in duplicate, and the average value was used in analysis. Milk insulin analysis was not completed for all samples; if only a minimal volume is available, then milk was prioritized for lipid analysis.

2.5. Infant growth measurements

Infant anthropometrics were obtained by medical record review using birth and paediatric well‐child routine visits at birth, 2 weeks, 2 months and 6 months. Age‐ and sex‐specific Z‐scores for weight‐for‐age (WFA), length‐for‐age (LFA), head circumference‐for‐age (HCA), weight‐for‐length (WFL), and BMI using the World Health Organization (WHO) growth standard for infants 0 to 2 years of age were extracted (WHO Multicentre Growth Reference Study Group, 2006).

2.6. Statistical analysis

We described participant demographics including 52 dyads for growth and 32 dyads for which milk analysis was completed using descriptive statistics (Table 1). In all multivariable models, we included covariates on the basis of prior knowledge of variables that are associated with the independent variable of interest and could be a potential determinant of the dependent variable assessed in each model (Hernan, Hernandez‐Diaz, Werler, & Mitchell, 2002; Schriger, 2008).

Table 1.

Participant characteristics

Participant characteristics (n = 52)

Participant characteristics with human milk (n = 32)
NW (n = 22) OW/OB (n = 30) NW (n = 16) OW/OB (n = 16)
Maternal age (years) 31.1 (3.9) 29.1 (4.7) 30.9 (4.1) 29.9 (3.8)
Maternal pre‐pregnancy BMI (kg/m2) 21.1 (2.2) 31.4 (4.8) 21.3 (2.0) 30.2 (4.5)
Maternal race or ethnicity (%)
Caucasian 12 (54) 22 (73) 9 (56) 14 (88)
African American 1 (5) 5 (17) 0 (0) 1 (6)
Hispanic 1 (5) 0 (0) 1 (6) 0 (0)
Asian 4 (18) 0 (0) 4 (25) 1 (6)
Other/not available 4 (18) 3 (10) 2 (13) 0 (0)
Gestational age 40 weeks 0 days (7 days) 39 weeks 6 days (8 days) 40 weeks 0 days (7 days) 39 weeks 5 days (10 days)
Mode of birth (%)
Vaginal 14 (64) 17 (57) 10 (62) 12 (75)
C‐section 8 (36) 13 (43) 6 (38) 4 (25)
Infant sex (%)
Male 10 (45) 13 (43) 7 (44) 8 (50)
Female 12 (55) 17 (57) 9 (56) 8 (50)
Infant birthweight (kg) 3.5 (0.4) 3.6 (0.4) 3.5 (0.3) 3.5 (0.4)
Infant age at 2 week collection (days) 16.1 (2.9) 16.2 (3.0) 16.1 (3.2) 16.7 (3.3)
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Note. Data presented as mean (standard deviation) or mean (percentage) as appropriate. Bolded font indicates statistical significance at p < .05 based on a t test.

Abbreviations: BMI, body mass index; NW, mothers with normal weight; OW/OB, mothers with overweight or obesity.

2.6.1. Maternal weight status and infant growth

We examined associations of maternal weight status with infant growth at birth, 2 weeks, 2 months, and 6 months of age using multivariable linear regression based on 52 mother–infant dyads. In these models, OW/OB versus NW (referent) was the independent variable of interest, and infant WFA Z‐score, WFL Z‐score, BMI‐Z‐score, LFA Z‐score, and HCA Z‐score at each of the time points were the outcomes of interest. In these models, we adjusted for the following covariates: infant sex and feeding type (milk only or milk + formula) at the time of anthropometric assessment. For time points beyond birth, we also adjusted for the anthropometric indicator of interest at birth; by doing so, the estimate of association can be interpreted as the average change in anthropometry from birth, while accounting for size at birth. In addition to evaluating maternal weight status as OW/OB versus NW, we also examined it as a continuous variable multiplied by 1 standard deviation (SD) of pre‐pregnancy BMI for the study sample to enhance biological relevance, as we have previously done in other studies (Centers for Disease Control and Prevention, 2019; Perng, Gillman, Mantzoros, & Oken, 2014).

2.6.2. Maternal weight status and human milk composition

We examined the relationship between maternal weight status and milk composition, including macronutrients, LCFAs, N6:N3 LC‐PUFA ratio, and insulin for the 32 mother–infant dyads from which milk samples were provided. Here, we explored the ratio of N6:N3 as well as the percentage of fatty acid compared with the total LCFAs using Student's t tests and Mann‐Whitney U test as appropriate on the basis of normality of data distribution.

To process the LCFA data for regression analysis, we used principal components analysis (PCA) to consolidate the 33 lipids into five factors on the basis of the Scree plot and eigenvalues >1 similar our previously published work (Perng et al., 2014; Perng et al., 2017). Use of PCA to derive factors results in clusters of lipid metabolites that are correlated with one another and have been shown to reflect true biological processes (e.g., the clustering of branched chain amino acids and acylcarnitines in a metabolomics analysis of childhood obesity reflects the fact that acylcarnitines are downstream catabolites of branched chain amino acid catabolism; Perng, Gillman, Fleisch, et al., 2014). These factors each have a continuous and normally distributed score that represents distinct lipid metabolite patterns. A higher and positive score for a given factor means that a woman's milk lipid composition more closely represents that particular factor. Conversely, a lower and negative score for a pattern suggests that a woman's milk lipid composition does not reflect the pattern captured by a given factor. We examined associations of maternal weight status with milk composition, we used linear regression models where OW/OB versus NW was the dichotomous independent variable, and each of the milk components were assessed as separate continuous outcomes.

2.6.3. Human milk composition and infant growth

We explored associations of milk composition with infant growth from 2 weeks to 2 months and 2 to 6 months of age for the 32 mother–infant dyads from which milk samples were provided. Here, we used multivariable linear regression models where each milk component was entered as a continuous independent variable and change in the anthropometric indicators were the outcomes. In these models, we also accounted for infant sex. Given the variation in infant fluid status at birth, the 2‐week time point was used to assess change in growth. We evaluated associations with infant growth for infants receiving any type of nutrition (which included any degree of human milk feedings at 2 weeks, 2 months, or 6 months) with additional analysis including only those infants receiving exclusive human milk (EHM) feedings at 2 weeks, 2 months, and 6 months.

In the multivariable analyses, we focused our interpretation on the direction and magnitude of beta estimates and the precision of the 95% confidence intervals (CIs), while also considering statistical significance as p < .05. All models met standard assumptions of multivariate normality.

All analyses were carried out in SAS version 9.4 (Cary, NC, USA) with the exception of demographics and individual LCFA analysis, which was carried out using GraphPad Prism 8.0 (GraphPad Software, San Diego, CA, USA).

3. RESULTS

Characteristics of the 52 dyads enrolled from which the 32 milk samples were collected are presented in Table 1. Birth was at term gestational age with an equal percent of male and female infants of primarily Caucasian race/ethnicity. Of the 52 infants, 17.3% were large for gestational age, defined as birthweight percentile greater than the 90th percentile, and no infants were small for gestational age. For 32 dyads with milk available for analysis, six mothers with OW/OB provided supplemental formula plus human milk at 2 weeks. At 2 months, three mothers with OW/OB provided supplemental formula plus human milk. By 6 months of age in our cohort of 52 dyads, one infant of NW and seven infants of OW/OB had transitioned to exclusive formula feedings, whereas two infants of NW and eight infants of OW/OB received supplemental formula plus human milk.

3.1. Maternal weight status and infant growth

The associations of maternal pre‐pregnancy BMI category (NW versus OW/OB) with infant WHO Z‐score anthropometry from birth to 6 months are shown in Table 2. Maternal OW/OB was associated with higher infant WFL Z‐score and BMI Z‐score only at birth. There were no associations at the later time points.

Table 2.

Adjusted associations of maternal pre‐pregnancy BMI with infant anthropometry at birth, 2 weeks, 2 months, and 6 months of age among mother–infant dyads

Time Point Mean ± SE β (95% CI) Per 1 SD (6.4 kg/m2) BMIc
NW (n = 22) OW/OB (n = 30) OW/OB vs. NW
Birth a
WFA z‐score 0.41 ± 0.17 0.52 ± 0.15 0.11 [‐0.31, 0.54] 0.03 [‐0.19, 0.24]
WFL z‐score −1.48 ± 0.24 −0.53 ± 0.21 0.95 [0.34, 1.56] 0.40 [0.09, 0.71]
BMI z‐score −0.79 ± 0.20 −0.23 ± 0.17 0.56 [0.05, 1.06] 0.24 [−0.02, 0.49]
LFA z‐score 1.51 ± 0.23 1.02 ± 0.19 −0.50 [−1.06, 0.07] −0.24 [−0.52, 0.05]
HCA z‐score 0.49 ± 0.21 0.20 ± 0.18 −0.29 [−0.81, 0.24] −0.08 [−0.34, 0.19]
2 weeks of age b
WFA z‐score −0.01 ± 0.31 −0.27 ± 0.25 −0.26 [−0.64, 0.13] −0.05 [−0.26, 0.14]
WFL z‐score −0.94 ± 0.43 −0.88 ± 0.35 0.07 [−0.49, 0.63] −0.07 [−0.36, 0.22]
BMI z‐score −0.56 ± 0.39 −0.58 ± 0.32 −0.02 [−0.52, 0.47] 0.06 [−0.20, 0.32]
LFA z‐score 0.74 ± 0.47 0.44 ± 0.38 −0.30 [−0.88, 0.29] −0.05 [−0.37, 0.26]
HCA z‐score 0.78 ± 0.47 0.38 ± 0.38 −0.40 [−0.99, 0.19] −0.21 [−0.53, 0.11]
2 months of age b
WFA z‐score −0.43 ± 0.23 −0.43 ± 0.17 0.00 [−0.35, 0.35] 0.08 [−0.10, 0.25]
WFL z‐score −0.59 ± 0.42 −0.77 ± 0.31 −0.18 [−0.86, 0.50] 0.03 [−0.30, 0.37]
BMI z‐score −0.60 ± 0.35 −0.79 ± 0.26 −0.19 [−0.74, 0.36] 0.03 [−0.25, 0.30]
LFA z‐score −0.05 ± 0.29 0.23 ± 0.22 0.28 [−0.17, 0.74] 0.12 [−0.11, 0.35]
HCA z‐score 0.14 ± 0.38 0.20 ± 0.28 0.06 [−0.52, 0.65] 0.05 [−0.25, 0.35]
6 months of age b
WFA z‐score 0.45 ± 0.30 0.34 ± 0.23 −0.11 [−0.59, 0.37] 0.11 [−0.14, 0.36]
WFL z‐score −0.09 ± 0.33 −0.19 ± 0.25 −0.11 [−0.67, 0.45] 0.12 [−0.16, 0.41]
BMI z‐score −0.18 ± 0.33 −0.30 ± 0.24 −0.13 [−0.67, 0.42] 0.10 [−0.17, 0.38]
LFA z‐score 0.79 ± 0.41 0.97 ± 0.31 0.17 [−0.50, 0.85] 0.17 [−0.18, 0.52]
HCA z‐score −1.01 ± 2.69 1.54 ± 2.01 2.55 [−1.80, 6.90] 2.04 [−0.17, 4.25]
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Note. Bolded font indicates statistical significance at p < .05 based on a Wald Chi‐squared test.

Abbreviations: BMI, body mass index; HCA, head circumference‐for‐age; LFA, length‐for‐age; NW, mothers with normal weight; OW/OB, mothers with overweight or obesity; WFA, weight‐for‐age; WFL, weight‐for‐length.

a

Estimates are adjusted for infant sex.

b

Estimates are adjusted for infant sex, anthropometric indicator of interest at birth, and feeding preference at the time of anthropometric assessment.

c

All estimates are multiplied by 1‐standard deviation of pre‐pregnancy BMI (6.4 kg/m2) for population‐level relevance.

3.2. Maternal weight status and human milk composition

Milk samples were analysed for LCFA profiles as presented in Table 3. The distributions of milk components are presented in Figure S1. We observed no significant difference in N6:N3 LC‐PUFA ratio by maternal weight status (NW 12.82 vs OW/OB 12.98). However, individual LCFAs were statistically higher in OW/OB including palmitic acid, dihomo‐gamma‐linolenic acid (DGLA), and adrenic acid. Oleic acid and conjugated linoleic acid were lower in OW/OB. Linear regression analysis of maternal pre‐pregnancy weight status and LCFA profiles demonstrated positive association with palmitic acid, adrenic acid, DGLA and negative association with oleic acid as shown in Figure S2. Milk LCFAs were further assessed using PCA to create latent variables representing metabolite patterns (“factors”). We retained five factors from the PCA, which together accounted for 66.8% of variance in the original LCFA data set. This is compared with other analyses using PCA to reduce high‐dimensional data into factor scores (Perng et al., 2017; Perng, Gillman, Fleisch, et al., 2014). LCFA composition and factor loadings for key metabolites within each factor (factor loading >|0.4|) are shown in Table S1. Lipid factor 1 is primarily composed of N6 LC‐PUFAs including DGLA, bosseopentaenoic acid, and arachidonic acid (AA) in addition to gadoleic acid. Saturated LCFAs comprise lipid factor 2, including stearic acid, arachidic acid, pentadecyclic acid, and palmitic acid. Lipid factor 3 is derived from mainly N3 LC‐PUFAs including docosahexaenoic acid (DHA), docosapenaenoic acid (DPA) and well as a N6 LC‐PUFA docosadienoic acid which is an elongation product of DGLA. Lipid factor 4 contains very long‐chain fatty acids with 22‐24 carbons including lignoceric acid, nervonic acid, brassidic acid, and behenic acid. The top lipid factor 5 metabolites include N6 LC‐PUFAs, eicosadienoic acid, linoleic acid, adrenic acid, and AA. Lipid factor 2 score was higher in OW/OB as shown in Table 4.

Table 3.

Human milk long‐chain fatty acid profiles

Fatty Acid Component NW (n = 16) OW/OB (n = 16) p
N6:N3 LC‐PUFA ratio 12.82 (3.04) 12.98 (3.08) .78
%nmol of total LCFA
Saturated LCFA
14:0, myristic acid 3.75 (0.99) 4.63 (1.76) .09
15:0, pentadecylic acid 0.26 (0.09) 0.28 (0.07) .37
16:0, palmitic acid 21.96 (2.96) 24.34 (2.54) .02
18:0, stearic acid 7.44 (1.86) 7.82 (1.27) .50
19:0, nonadecylic acid 0.14 (0.11) 0.16 (0.09) .29
20:0, arachidic acid 0.22 (0.05) 0.22 (0.03) .95
21:0, heneicosylic acid 0.08 (0.03) 0.10 (0.03) .09
22:0, behenic acid 0.09 (0.04) 0.08 (0.03) .93
24:0, lignoceric acid 0.08 (0.02) 0.09 (0.04) .62
Mono‐unsaturated LCFA
14:1 (n‐5), myristoleic acid 0.18 (0.08) 0.22 (0.08) .19
16:1 (n‐7)c, palmitoleic acid (cis) 2.03 (0.78) 2.40 (0.59) .14
16:1 (n‐7)t, palmitoleic acid (trans) 0.24 (0.05) 0.24 (0.05) .87
18:1 (n‐7), vaccenic acid 2.83 (0.62) 3.04 (0.63) .34
18:1 (n‐9), oleic acid 40.81 (5.16) 36.37 (3.37) .01
20:1, gadoleic acid 0.10 (0.12) 0.2 (0.23) .60
22:1, brassidic acid 0.09 (0.02) 0.10 (0.03) .44
24:1, nervonic acid 0.09 (0.04) 0.10 (0.03) .72
Poly‐unsaturated LCFA
18:2 (n‐6)cc, linoleic acid (cis) 16.08 (2.93) 15.82 (4.06) .83
18:2 (n‐6)tt, linoleic acid (trans) 0.07 (0.04) 0.10 (0.04) .06
18:2 (n‐7,9) Conjugate, conjugated linoleic acid 0.45 (0.18) 0.34 (0.18) .02
18:3 (n‐3), alpha‐linolenic acid 1.02 (0.31) 1.06 (0.59) .52
18:3 (n‐6), gamma‐linolenic 0.08 (0.03) 0.09 (0.03) .41
18:4 (n‐3), stearidonic acid 0.05 (0.03) 0.04 (0.04) .78
20:2 (n‐6), eicosadienoic acid 0.38 (0.07) 0.42 (0.10) .21
20:3 (n‐6), dihomo‐gamma‐linolenic acid 0.38 (0.09) 0.54 (0.20) .01
20:4 (n‐6), arachidonic acid 0.50 (0.14) 0.58 (0.24) .29
20:5 (n‐3), eicosapentaenoic acid 0.07 (0.03) 0.09 (0.07) .62
20:5 (n‐6), bosseopentaenoic acid 0.06 (0.01) 0.07 (0.04) .17
22:2 (n‐6), docosadienoic acid 0.05 (0.07) 0.03 (0.02) .98
22:4 (n‐6), adrenic acid 0.10 (0.04) 0.14 (0.06) .02
22:5 (n‐3), docosapentaenoic acid 0.14 (0.08) 0.12 (0.04) .35
22:5 (n‐6), osbond acid 0.006 (0.004) 0.006 (0.005) .45
22:6 (n‐3), docosahexaenoic acid (DHA) 0.19 (0.16) 0.16 (0.09) .66
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Note. Data presented as mean (standard deviation). Values for fatty acids are given as percentage nmol of the fatty acid/total long‐chain fatty acid. Bolded font indicates statistical significance at p < .05 based on a t test or Mann‐Whitney U test as appropriate based on distribution.

Abbreviations: LCFA, long‐chain fatty acid; N6:N3 LC‐PUFA, N6:N3 long‐chain polyunsaturated fatty acid; NW, mothers with normal weight; OW/OB, mothers with overweight or obesity.

Table 4.

Associations of maternal pre‐pregnancy weight status with human milk composition near 2 weeks postpartum

Milk Composition Component Pre‐pregnancy weight status p
NW (mean ± SD) OW/OB (mean ± SD)
Macronutrients n = 15 n = 15
Protein (g/100 ml) 1.2 ± 0.2 1.2 ± 0.4 .89
Carbohydrate (g/100 ml) 7.0 ± 0.3 6.8 ± 0.5 .24
Fat (g/100 ml) 3.7 ± 1.1 3.5 ± 0.9 .43
Lipids n = 16 n = 16
Factor 1 −0.23 ± 1.00 0.23 ± 0.98 .18
Factor 2 −0.41 ± 0.87 0.41 ± 0.97 .01
Factor 3 −0.22 ± 0.72 0.22 ± 1.20 .19
Factor 4 0.18 ± 1.34 −0.18 ± 0.46 .29
Factor 5 0.14 ± 0.93 −0.14 ± 1.08 .41
N6:N3 LC‐PUFA ratio 12.8 ± 3.0 13.0 ± 3.1 .88
Metabolic components n = 16 n = 13
Insulin (mU/L) 14.5 ± 15.7 29.5 ± 21.3 .02
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Note. Data presented as mean ± standard deviation. Bolded font indicates statistical significance at p < .05 based on linear regression models.

Abbreviations: N6:N3 LC‐PUFA, N6:N3 long‐chain polyunsaturated fatty acid; NW, mothers with normal weight; OW/OB, mothers with overweight or obesity.

Milk macronutrient analysis revealed no significant difference between maternal groups as shown in Table 4. Milk from mothers with OW/OB had higher insulin levels.

3.3. Human milk composition and infant growth

We then evaluated milk macronutrient, LCFA factors, N6:N3 LC‐PUFA ratio, and insulin associations with infant growth by WHO Z‐score change from 2 weeks to 2 months and 2 weeks to 6 months as presented in Table 5 for infants receiving any type of nutrition (which included any degree of human milk feedings at 2 weeks, 2 months, or 6 months) and infants receiving EHM at 2 weeks, 2 months, and 6 months. Milk fat was positively associated with WFA Z‐score change from 2 weeks to 2 months in EHM‐fed infants only with no other associations between milk macronutrients and infant growth. N6 LC‐PUFA‐driven lipid factor 1 and 5 were associated with infant growth. Lipid factor 1 was associated with higher infant WFL, lower LFA, and lower HCA Z‐score change from 2 weeks to 2 months with lower HCA Z‐score also noted from 2 weeks to 6 months. N6 LC‐PUFA‐driven factor 5 was also associated with lower LFA Z‐score change from 2 weeks to 2 months in EHM‐fed infants. Milk N6:N3 LC‐PUFA ratio was positively associated with infant WFA, WFL, and BMI Z‐score change from 2 weeks to 6 months in infants receiving all types of nutrition; however, this trend persisted but was no longer significant when evaluating infants receiving EHM feedings. Milk insulin was positively associated with HCA and WFA Z‐score changes in infants receiving any type of nutrition; however, this association was small and not significant when evaluating EHM‐fed infants.

Table 5.

Associations of human milk composition near 2 weeks postpartum with change in offspring WFAZ, WFLZ, BMIZ, LFAZ, and HCAZ between 2 weeks and 2 months, and 2 weeks and 6 months of age

Milk Composition Component WFAZ WFLZ BMIZ LFAZ HCAZ
Difference (β [95% CI]) in Δanthropometry in offspring between two weeks and two months of age
Any feeding preference EHM feeding preference Any feeding preference EHM feeding preference Any feeding preference EHM feeding preference Any feeding preference EHM feeding preference Any feeding preference EHM feeding preference
Macronutrients (AFP n = 29, EHM n = 21)
Fat 0.06 [−0.17, 0.30] 0.27 [0.05, 0.50] −0.01 [−0.39, 0.37] 0.35 [−0.07, 0.77] 0.08 [−0.43, 0.28] 0.20 [−0.21, 0.61] 0.12 [−0.09, 0.33] 0.09 [−0.16, 0.35] −0.13 [−0.41, 0.14] −0.02 [−0.36, 0.33]
Protein 0.43 [−0.31, 1.17] 0.27 [−0.41, 0.95] 0.77 [−0.43, 1.97] 0.58 [−0.62, 1.77] 0.65 [−0.48, 1.78] 0.40 [−0.73, 1.53] −0.05 [−0.74, 0.65] −0.06 [−0.77, 0.64] −0.05 [−0.97, 0.86] −0.15 [−1.09, 0.80]
Carbohydrate 0.17 [−0.40, 0.74] 0.08 [−0.69, 0.86] −0.26 [−1.19, 0.68] 0.14 [−1.23, 1.50] −0.02 [−0.89, 0.86] 0.26 [−1.01, 1.53] 0.38 [−0.12, 0.90] −0.11 [−0.90, 0.68] 0.00 [−0.71, 0.71] −0.42 [−1.51, 0.67]
Lipids (AFP n = 31, EHM n = 23)
Factor 1 −0.06 [−0.26, 0.13] 0.04 [−0.19, 0.26] 0.02 [−0.35, 0.39] 0.38 [0.01, 0.75] −0.03 [−0.34, 0.29] 0.21 [−0.15, 0.57] −0.12 [−0.33, 0.09] −0.30 [−0.52, −0.08] −0.21 [−0.47, 0.05] −0.32 [−0.60, −0.03]
Factor 2 0.15 [−0.05, 0.34] 0.13 [−0.06, 0.33] 0.28 [−0.09, 0.64] 0.22 [−0.14, 0.58] 0.24 [−0.07, 0.56] 0.20 [−0.13, 0.53] −0.05 [−0.27, 0.18] −0.02 [−0.25, 0.22] 0.28 [0.02, 0.54] 0.22 [−0.05, 0.49]
Factor 3 −0.11 [−0.31, 0.09] −0.03 [−0.26, 0.20] −0.11 [−0.49, 0.27] 0.11 [−0.30, 0.53] −0.31 [−0.62, −0.00] −0.28 [−0.65, 0.08] 0.03 [−0.19, 0.26] 0.02 [−0.23, 0.29] −0.10 [−0.37, 0.17] 0.07 [−0.24, 0.38]
Factor 4 −0.10 [−0.29, 0.10] −0.05 [−0.24, 0.14] −0.15 [−0.52, 0.21] −0.07 [−0.42, 0.28] −0.05 [−0.37, 0.26] 0.01 [−0.31, 0.33] −0.02 [−0.24, 0.19] −0.03 [−0.25, 0.18] −0.13 [−0.40, 0.14] −0.03 [−0.30, 0.25]
Factor 5 −0.03 [−0.23, 0.16] −0.07 [−0.28, 0.14] 0.27 [−0.09, 0.63] 0.14 [−0.24, 0.51] 0.20 [−0.11, 0.51] 0.12 [−0.22, 0.47] −0.29 [−0.48, −0.10] −0.24 [−0.45, −0.02] 0.04 [−0.22, 0.31] −0.06 [−0.35, 0.22]
N6:N3 PUFA 0.03 [−0.03, 0.09] 0.01 [−0.07, 0.09] 0.08 [−0.04, 0.20] −0.01 [−0.15, 0.13] 0.06 [−0.05, 0.16] 0.00 [−0.13, 0.13] −0.02 [−0.09, 0.05] 0.02 [−0.07, 0.10] 0.04 [−0.05, 0.13] −0.04 [−0.15, 0.08]
Metabolic components (AFP n = 28, EHM n = 28)
Insulin (mU/L) 0.01 [−0.01, 0.02] 0.01 [−0.01, 0.02] 0.00 [−0.02, 0.02] −0.01 [−0.03, 0.02] 0.00 [−0.02, 0.02] 0.00 [−0.03, 0.02] 0.01 [−0.00, 0.02] 0.01 [0.00, 0.03] 0.02 [0.00, 0.03] 0.01 [−0.01, 0.03]
Difference (β [95% CI]) in Δanthropometry in offspring between two weeks and six months of age
Macronutrients (AFP n = 26, EHM n = 19)
Fat −0.03 [−0.37, 0.32] 0.28 [−0.13, 0.69] −0.06 [−0.49, 0.38] 0.34 [−0.18, 0.86] −0.11 [−0.50, 0.28] 0.22 [−0.27, 0.71] −0.01 [−0.35, 0.33] 0.05 [−0.39, 0.49] −2.18 [−5.26, 0.89] 0.06 [−0.38, 0.49]
Protein 0.42 [−0.67, 1.52] 0.17 [−0.92, 1.27] 1.29 [−0.01, 2.58] 1.16 [−0.12, 2.45] 0.91 [−0.29, 2.10] 0.69 [−0.56, 1.95] −0.42 [−1.49, 0.65] −0.66 [−1.76, 0.45] 2.80 [−7.34, 12.95] −0.30 [−1.41, 0.83]
Carbohydrate 0.40 [−0.43, 1.23] 0.14 [−1.08, 1.36] −0.12 [−1.18, 0.94] −0.31 [−1.85, 1.22] 0.10 [−0.85, 1.06] −0.07 [−1.51, 1.36] 0.66 [−0.13, 1.45] 0.46 [−0.80, 1.72] 2.00 [−5.97, 9.97] −0.41 [−1.71, 0.90]
Lipids (AFP n = 28, EHM n = 21)
Factor 1 −0.17 [−0.51, 0.17] −0.18 [−0.55, 0.19] −0.17 [−0.61, 0.27] 0.05 [−0.42, 0.52] −0.13 [−0.53, 0.26] −0.05 [−0.49, 0.39] −0.17 [−0.51, 0.17] −0.35 [−0.72, 0.01] −0.24 [−0.59, 0.10] −0.47 [−0.81, −0.13]
Factor 2 −0.06 [−0.43, 0.30] −0.18 [−0.54, 0.17] −0.03[−0.50, 0.44] −0.26 [−0.70, 0.18] 0.02 [−0.40, 0.43] −0.16 [−0.58, 0.26] −0.09 [−0.46, 0.27] −0.07 [−0.45, 0.31] 0.19 [−0.18, 0.55] 0.16 [−0.21, 0.53]
Factor 3 −0.07 [−0.39, 0.25] 0.08 [−0.28, 0.44] −0.11 [−0.52, 0.31] 0.14 [−0.31, 0.58] −0.28 [−0.63, 0.07] −0.24 [−0.65, 0.17] 0.07 [−0.25, 0.39] 0.16 [−0.21, 0.53] −0.14 [−0.46, 0.17] 0.00 [−0.36, 0.36]
Factor 4 −0.60 [−1.08, −0.12] −0.47 [−0.96, 0.01] −0.60 [−1.24, 0.03] −0.36 [−0.99, 0.27] −0.38 [−0.96, 0.21] −0.22 [−0.82, 0.38] −0.35 [−0.86, 0.16] −0.34 [−0.87, 0.19] −0.14 [−0.70, 0.42] 0.05 [−0.53, 0.63]
Factor 5 0.06 [−0.28, 0.40] −0.02 [−0.39, 0.35] 0.40 [−0.01, 0.81] 0.27 [−0.18, 0.71] 0.33 [−0.04, 0.70] 0.24 [−0.18, 0.65] −0.30 [−0.62, 0.02] −0.31 [−0.67, 0.05] −0.08 [−0.41, 0.26] −0.12 [−0.49, 0.24]
N6:N3 PUFA 0.13 [0.03, 0.22] 0.10 [−0.02, 0.22] 0.18 [0.06, 0.31] 0.12 [−0.04, 0.27] 0.16 [0.05, 0.27] 0.12 [−0.02, 0.26] 0.01 [−0.10, 0.12] 0.02 [−0.12, 0.15] 0.03 [−0.08, 0.14] −0.02 [−0.16, 0.12]
Metabolic components (AFP n = 25, EHM n = 18)
Insulin (mU/L) 0.02 [0.00, 0.04] 0.01 [−0.02, 0.03] 0.02 [−0.01, 0.04] 0.00 [−0.03, 0.03] 0.02 [−0.00, 0.04] 0.00 [−0.02, 0.03] 0.01 [−0.01, 0.03] 0.01 [−0.02, 0.03] 0.02 [−0.00, 0.04] 0.01 [−0.02, 0.04]
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Notes. Estimates are adjusted for infant sex. Bolded font indicates statistical significance at p < .05 based on a Wald Chi‐square test. Any feeding preference (AFP) includes infants receiving a degree of human milk at 2 weeks, 2 months, and 6 months. Exclusive human milk (EHM) feeding preference includes infants receiving only human milk at 2 weeks, 2 months, and 6 months.

Abbreviations: Δ, change; BMIZ, body mass index z‐score; CI, confidence interval; LFAZ, length‐for‐age z‐score; HCAZ, head circumference‐for‐age z‐score; N6:N3 LC‐PUFA, N6:N3 long‐chain polyunsaturated fatty acid; PUFA, polyunsaturated fatty acid; WFAZ, weight‐for‐age z‐score; WFLZ, weight‐for‐length z‐score.

4. DISCUSSION

The purpose of this study was to evaluate for associations between maternal weight status and infant growth, the impact of maternal weight status on the composition of human milk at 2 weeks post‐partum, and the role of specific milk components in early infant growth. We found that maternal pre‐pregnancy weight status was positively associated with infant birth WFL and BMI Z‐scores. Mothers with OW/OB were found to have higher LCFAs that may be obesity promoting but no overall difference in the N6:N3 LC‐PUFA ratio. Milk composition of N6 LC‐PUFAs were associated with infant growth changes over the first months of life in exclusively human milk‐fed infants. Because maternal pre‐pregnancy weight status was not associated with infant growth beyond birth, these associations highlight the contribution of milk composition in the early postnatal period to infant growth.

Our study supports prior associations between increased fetal growth based on birth anthropometrics and maternal pre‐pregnancy BMI (Sewell, Huston‐Presley, Super, & Catalano, 2006; Starling et al., 2015). In this cohort, we did not find an association between maternal weight status and infant growth beyond birth. Our ability to detect this difference may be limited by a comparatively small amount of data. With this lack of association, our study identifies the importance of alterations in milk as a significant factor in programming of infant growth outcomes. Studies have shown that rapid early infant growth by increased WFL Z‐score over the first 6 months of life, independent of maternal BMI, predicted obesity of children at 3 years old (Taveras et al., 2009). Additional evaluation with a larger cohort and longer follow‐up is necessary to broaden conclusions on the role of milk composition differences in infant programming.

On the basis of animal models of maternal obesity, altered mammary gland lipogenesis and decreased fatty acid synthesis have been shown (Saben et al., 2014). Limited human studies on the impact of maternal obesity on milk lipid composition in mature milk have shown higher total saturated fatty acids with increased N6:N3 LC‐PUFA ratio, although others have shown increased PUFAs. These outcomes highlight the need for additional research examining specific maternal populations and periods of lactation (Makela et al., 2013; Marin, Sanjurjo, Rodrigo, & de Alaniz, 2005). Our study did not find a difference in the overall milk N6:N3 LC‐PUFA ratio. Many human milk LC‐PUFAs have been shown to be influenced by maternal dietary intake; however, maternal diet was not assessed in this study.

Fatty acid profile analysis in this study was novel for increased percentages of potentially obesity‐promoting and inflammation‐associated LCFAs, particularly palmitic acid, DGLA, and adrenic acid in mothers with OW/OB when assessed individually. Although the mechanisms of action of these fatty acids ingested in milk need further elucidation, studies on serum fatty acid level and dietary intervention models give insight. Pro‐inflammatory palmitic acid, as seen in lipid factor 2, has been shown to stimulate inflammatory and metabolic responses through the toll‐like receptor signalling pathways, and diets high in palmitic acid have been associated with lowered fatty acid oxidation, impaired insulin signalling, and increased fat mass (Kien, Bunn, & Ugrasbul, 2005; Rogero & Calder, 2018). Studies have shown that increased insulin results in increased conversion of gamma linolenic acid to DGLA and decreased conversion to AA (metabolites in lipid factor 1), which may alter metabolic regulation (Vessby, Gustafsson, Tengblad, Boberg, & Andersson, 2002). This unique association with insulin may contribute to the findings in our cohort. DGLA has been identified as a biomarker for obesity with maternal plasma levels during pregnancy associated with increased childhood BMI at 7 years (de Vries et al., 2014). Elevated adrenic acid, as seen in lipid factor 5, has been found in the colostrum of mothers with OW/OB and plays a protective role in infant brain development (Hadley, Ryan, Forsyth, Gautier, & Salem, 2016; Sinanoglou et al., 2017). Oleic acid and conjugated linoleic acid were lower in mothers with OW/OB in our study. Oleic acid has been shown to reduce obesity and improve metabolic and circulating lipid profiles in adults on high oleic acid diets (Liu et al., 2016). Although conjugated linoleic acid found in milk in our diet protects against obesity and diabetes in animal models, adult human studies have not shown consistent benefits (Fuke & Nornberg, 2017).

Grouped lipid metabolites identified through PCA provides insight into potential biological processes impacting infant growth trends. Lipid factors 1 and 5 highlight distinct lipid metabolite patterns of primarily N6 LC‐PUFAs, many of which have been linked to obesity and adiposity in both children and adults. These factor 5 LC‐PUFAs include linoleic acid‐derived eicosadienoic acid, DGLA, and downstream byproduct AA, which has pro‐inflammatory properties, as does gadoleic acid (de Vries et al., 2014; Kaska et al., 2020; Vidakovic et al., 2016). In our cohort, lipid factors 1 and 5 were negatively associated with early infant LFA Z‐score change and factor 1 positively associated with WFL Z‐score change, which may be driven by reduced length growth. These may be related to the biological impacts of N6 LC‐PUFAs on obesity risk. Additional potentially protective LC‐PUFAs are highlighted in lipid factor 3 including the N3 LC‐PUFAs docosahexaenoic acid, docosapenataenoic acid, which have been shown to be associated with lower body fat in children when plasma levels are high in their mothers (Vidakovic et al., 2016). However, our cohort found no significant impact of this factor on growth in EHM infants and no overall differences in these top metabolites between NW and OW/OB mothers. Lipid factor 2 was composed of saturated fatty acids including palmitic acid, which alone was higher in OW/OB mothers. The higher palmitic acid may reflect an overall higher concentration of saturated fatty acids related to dietary intake in mothers with increased weight status and leads to mixed infant outcomes (Jenkins, West, & Koulman, 2015; Panagos et al., 2016; Petersen et al., 2019; Pfeuffer & Jaudszus, 2016; Vidakovic et al., 2015). This is consistent with a lack of relationship of lipid factor 2 with infant weight and length growth in our cohort. Very long‐chain fatty acids including lignoceric acid and nervonic acid, identified as top metabolites contributing to factor 4, have also been linked to adolescent obesity and metabolic syndrome. There was, however, no significant impact of factor 4 on growth in our cohort (Gunes et al., 2014; Karlsson et al., 2006). Studies specifically evaluating the impact of the lactation period and human milk lipid composition on infant growth are limited and an area of need for future research.

With the rising levels of N6:N3 LC‐PUFA ratio in the western diet and epidemic levels of childhood obesity, adipogenic promoting effects of N6 LC‐PUFAs have been hypothesized (Ailhaud & Guesnet, 2004). Our study showed a positive association between milk N6:N3 LC‐PUFA ratio and infant growth from 2 weeks to 6 months with higher infant WFA, WFL, and BMI Z‐score change in infants receiving any degree of human milk with continued positive trends in EHM‐fed infants. Panagos et al. (2016) showed that increased N6:N3 LC‐PUFA ratio was associated with increased WFL Z‐score at 2 months. On the other hand, a human supplementation study showed that milk N6 LC‐PUFAs were negatively associated with infant BMI and lean body mass at 4 months and increased N3 LC‐PUFAs in milk stimulated early fat accumulation (Much et al., 2013). Given these conflicting results, research is necessary to arrive at a consensus on the impact of milk fatty acid profiles on infant growth with studies differentiating early versus late time frames of lactation.

In this cohort, we found no significant differences in milk macronutrient composition based on maternal weight status. This conservation of macronutrients, particularly in the initial stages of lactation, is consistent with milk literature (Ballard & Morrow, 2013). Pre‐pregnancy OW/OB may result in whole body insulin resistance, thus contributing to altered milk insulin (Badillo‐Suarez, Rodriguez‐Cruz, & Nieves‐Morales, 2017). Our study showed higher milk insulin in mothers with OW/OB for milk obtained near 2 weeks postpartum. Published studies on mature milk insulin in relation to maternal BMI have shown a positive association with maternal pre‐pregnancy BMI at later time points in lactation (Ahuja et al., 2011; Ley et al., 2012; Young et al., 2017).

We detected higher insulin levels in milk at 2 weeks in mothers with elevated BMI and found milk insulin to be positively associated with higher infant WFA from 2 weeks to 6 months and HCA Z‐score change from 2 weeks to 2 months in infants receiving any type of nutrition. Given the minimal increases in growth and the lack of association when infants were receiving human milk exclusively, the clinical significance is uncertain. Studies examining associations between milk insulin and infant weight gain are limited with conflicting results. Fields et al. reported lower infant weight gain with higher milk insulin at 1 month; however, further studies by Fields et al. did not show significant associations at 6 months of age (Fields et al., 2017; Fields & Demerath, 2012). A Canadian cohort showed infant WFL Z‐score with milk insulin having a U‐shaped association with growth at age 4 (Chan et al., 2018). Whether ingested milk insulin has biological effect in infants beyond a role in gut maturation remains controversial, with some evidence to support that ingested insulin in infants is resistant to degradation in the stomach and may have systemic effects (Shehadeh, Sukhotnik, & Shamir, 2006). Further research on the role of insulin in regulation of infant metabolic health is needed.

This study has several strengths including the analysis of milk from an early time point when milk is transitioning from colostrum to mature milk, which has not been included in many milk studies. This time point allows for unique assessment of lactational programming influences on growth outcomes at a time when mothers may be using less formula supplementation and when milk supply has been achieved. However, this early‐stage milk provides a limited period of nutrition for infants, and it is important to recognize that mature milk composition will also contribute to the pattern of ongoing organ development. This study included milk samples stored at −80°C with analysis within 12 months after collection and minimization of repetitive freeze‐thaw cycles, as these factors may impact milk composition to an unknown degree (George, Gay, Trengove, & Geddes, 2018; Nessel, Khashu, & Dyall, 2019).

Limitations of this study include a smaller sample size from a middle‐class primarily Caucasian midwestern population, thus the results may not be applicable to a more diverse population. Other factors influencing milk composition including maternal diet, vitamin supplementation, and milk volume were not measured in this study. As the amount of formula supplementation was not quantified, the impact on infant growth trajectory may vary on the basis of the degree of supplementation. When comparing infant growth associations in Table 5, loss of statistical significance between infants receiving all feeding types and EHM feeds as well as lack of growth associations beyond 2 months of age may be in part due to reduced sample size or additional contributing factors to infant growth including genetics and environmental influences. Infant growth assessments were performed by nonresearch medical staff with weight, length, and head circumference documented in the medical record. The lack of trained research staff obtaining growth measurements may introduce inconsistency in infant measurements. Maternal pre‐pregnancy weight similarly was obtained through medical record review. An assessment of infant body composition would be of great importance for future work and offer additional insight than WFL or BMI Z‐score alone. Finally, although we used PCA to consolidate milk LCFAs into lipid metabolite patterns that likely reflect the clustering of lipids in biochemical pathways, we assessed other components of milk (macronutrients and insulin) individually given differences in the unit of measurement for each. This approach may not accurately represent the interactions among milk components. Future studies with more sophisticated analytical techniques to model joint effects of multiple milk components with health outcomes are warranted.

5. CONCLUSION

Our study has described early alterations in milk composition with increases in potentially obesity‐promoting fatty acids, reduction in protective fatty acids, and elevated insulin levels in milk from mothers with OW/OB. We found associations of higher milk N6 LC‐PUFAs with altered infant length growth trajectory through 2 months of age. Although we have shown implications of maternal increased BMI on milk composition and milk composition on infant growth, our study did not show an overall association between maternal weight status and infant growth after birth. The importance of human milk as the primary source of nutrition for infants cannot be understated; however, as we gain more knowledge regarding the multifaceted and complex changes in milk over the period of lactation, we must appreciate that factors in milk may impact early developmental programming. Future research to understand the complex relationships between maternal health, milk, and early indicators of programmed infant metabolic disease risk is important to design interventions to optimize beneficial aspects of maternal milk for at risk infants.

CONFLICTS OF INTEREST

The authors declare that they have no conflicts of interest.

CONTRIBUTIONS

LE and BG performed the study design, cohort recruitment, sample and data analysis, and manuscript writing. EH performed the cohort recruitment, sample analysis, and manuscript writing. WP analysed the data with contribution to manuscript writing. AR performed LCFA analysis with contribution to manuscript writing. SP assisted with data interpretation and study design.

Supporting information

Table S1. Composition of long chain fatty acid factor scores retained from principal components analysis

Click here for additional data file. (14.3KB, docx)

Figure S1. Significant differences in human milk composition profiles comparing normal weight (NW) and overweight & obesity (OW/OB) for insulin, N6:N3 LC‐PUFA ratio, palmitic acid [16:0], oleic acid [18‐1 (n‐9)], conjugated linoleic acid [18:2 (n‐7,9)], dihomo‐gamma‐linolenic acid [20:3 (n‐6)], adrenic acid [22:4 (n‐6)]. LCFAs represented as percentage (%) of total LCFAs. *P < 0.05

Click here for additional data file. (418.6KB, tif)

Figure S2. Linear regression of maternal pre‐pregnancy BMI and human milk composition: A. N6:N3 LC‐PUFA ratio, B. Palmitic acid [16:0], C. Oleic acid [18‐1 (n‐9)], D. Conjugated linoleic acid [18:2 (n‐7,9)], E. Dihomo‐gamma‐linolenic acid [20:3 (n‐6)], F. Adrenic acid [22:4 (n‐6)], G. Insulin. LCFAs represented as percentage (%) of total LCFAs. *P < 0.05

Click here for additional data file. (26KB, pdf)

ACKNOWLEDGMENTS

Thank you to the mothers and infants participating in this study. We appreciate Dr. Judith Ivacko's assistance with this multisite collaborative effort. Funding sources are the following: University of Michigan T32 Developmental Origins of Metabolic Disease (5T32DK071212‐13), Michigan Nutrition Obesity Research Center from NIDDK (P30DK089503), Michigan Regional Comprehensive Metabolomics Research Core through NIH (U24DK097153), and the Percy J. Murphy and Mary C. Murphy Endowed Children's Research Fund.

Ellsworth L, Perng W, Harman E, Das A, Pennathur S, Gregg B. Impact of maternal overweight and obesity on milk composition and infant growth. Matern Child Nutr. 2020;16:e12979 10.1111/mcn.12979

REFERENCES

  1. Ahuja, S. , Boylan, M. , Hart, S. L. , Román‐Shriver, C. , Spallholz, J. E. , & Sawyer, B. G. (2011). Glucose and insulin levels are increased in obese and overweight mothers' breast‐milk. Food and Nutrition Sciences, 2(3), 201–206. 10.4236/fns.2011.23027 [DOI] [Google Scholar]
  2. Ailhaud, G. , & Guesnet, P. (2004). Fatty acid composition of fats is an early determinant of childhood obesity: A short review and an opinion. Obesity Reviews, 5(1), 21–26. 10.1111/j.1467-789x.2004.00121.x [DOI] [PubMed] [Google Scholar]
  3. Amaral, Y. , Marano, D. , Oliveira, E. , & Moreira, M. E. (2019). Impact of pre‐pregnancy excessive body weight on the composition of polyunsaturated fatty acids in breast milk: A systematic review. International Journal of Food Sciences and Nutrition, 71, 1–7. 10.1080/09637486.2019.1646713 [DOI] [PubMed] [Google Scholar]
  4. American College of Obstetricians and Gynecologists (2013). ACOG committee opinion no. 549: Obesity in pregnancy. Obstetrics and Gynecology, 121(1), 213–217. 10.1097/01.AOG.0000425667.10377.60 [DOI] [PubMed] [Google Scholar]
  5. Badillo‐Suarez, P. A. , Rodriguez‐Cruz, M. , & Nieves‐Morales, X. (2017). Impact of metabolic hormones secreted in human breast milk on nutritional programming in childhood obesity. Journal of Mammary Gland Biology and Neoplasia, 22(3), 171–191. 10.1007/s10911-017-9382-y [DOI] [PubMed] [Google Scholar]
  6. Ballard, O. , & Morrow, A. L. (2013). Human milk composition: Nutrients and bioactive factors. Pediatric Clinics of North America, 60(1), 49–74. 10.1016/j.pcl.2012.10.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Bligh, E. G. , & Dyer, W. J. (1959). A rapid method of total lipid extraction and purification. Canadian Journal of Biochemistry and Physiology, 37(8), 911–917. 10.1139/o59-099 [DOI] [PubMed] [Google Scholar]
  8. Centers for Disease Control and Prevention . About adult BMI|Healthy Weight|. (2019, 2019‐03‐01 T04:12:29Z/). Retrieved from https://www.cdc.gov/healthyweight/assessing/bmi/adult_bmi/index.html
  9. Chan, D. , Goruk, S. , Becker, A. B. , Subbarao, P. , Mandhane, P. J. , Turvey, S. E. , & Azad, M. B. (2018). Adiponectin, leptin and insulin in breast milk: Associations with maternal characteristics and infant body composition in the first year of life. International Journal of Obesity, 42(1), 36–43. 10.1038/ijo.2017.189 [DOI] [PubMed] [Google Scholar]
  10. de Vries, P. S. , Gielen, M. , Rizopoulos, D. , Rump, P. , Godschalk, R. , Hornstra, G. , & Zeegers, M. P. (2014). Association between polyunsaturated fatty acid concentrations in maternal plasma phospholipids during pregnancy and offspring adiposity at age 7: The MEFAB cohort. Prostaglandins, Leukotrienes, and Essential Fatty Acids, 91(3), 81–85. 10.1016/j.plefa.2014.04.002 [DOI] [PubMed] [Google Scholar]
  11. Ellsworth, L. , Harman, E. , Padmanabhan, V. , & Gregg, B. (2018). Lactational programming of glucose homeostasis: A window of opportunity. Reproduction, 156(2), R23–r42. 10.1530/rep-17-0780 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Eriksen, K. G. , Christensen, S. H. , Lind, M. V. , & Michaelsen, K. F. (2018). Human milk composition and infant growth. Current Opinion in Clinical Nutrition and Metabolic Care, 21(3), 200–206. 10.1097/mco.0000000000000466 [DOI] [PubMed] [Google Scholar]
  13. Fields, D. A. , & Demerath, E. W. (2012). Relationship of insulin, glucose, leptin, IL‐6 and TNF‐alpha in human breast milk with infant growth and body composition. Pediatric Obesity, 7(4), 304–312. 10.1111/j.2047-6310.2012.00059.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Fields, D. A. , George, B. , Williams, M. , Whitaker, K. , Allison, D. B. , Teague, A. , & Demerath, E. W. (2017). Associations between human breast milk hormones and adipocytokines and infant growth and body composition in the first 6 months of life. Pediatric Obesity, 12(Suppl 1), 78–85. 10.1111/ijpo.12182 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Fuke, G. , & Nornberg, J. L. (2017). Systematic evaluation on the effectiveness of conjugated linoleic acid in human health. Critical Reviews in Food Science and Nutrition, 57(1), 1–7. 10.1080/10408398.2012.716800 [DOI] [PubMed] [Google Scholar]
  16. George, A. D. , Gay, M. C. L. , Trengove, R. D. , & Geddes, D. T. (2018). Human milk lipidomics: Current techniques and methodologies. Nutrients, 10 10.3390/nu10091169 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Gorski, J. N. , Dunn‐Meynell, A. A. , Hartman, T. G. , & Levin, B. E. (2006). Postnatal environment overrides genetic and prenatal factors influencing offspring obesity and insulin resistance. American Journal of Physiology. Regulatory, Integrative and Comparative Physiology, 291(3), R768–R778. 10.1152/ajpregu.00138.2006 [DOI] [PubMed] [Google Scholar]
  18. Gunes, O. , Tascilar, E. , Sertoglu, E. , Tas, A. , Serdar, M. A. , Kaya, G. , & Ozcan, O. (2014). Associations between erythrocyte membrane fatty acid compositions and insulin resistance in obese adolescents. Chemistry and Physics of Lipids, 184, 69–75. 10.1016/j.chemphyslip.2014.09.006 [DOI] [PubMed] [Google Scholar]
  19. Hadley, K. B. , Ryan, A. S. , Forsyth, S. , Gautier, S. , & Salem, N. (2016). The essentiality of arachidonic acid in infant development. Nutrients, 8 10.3390/nu8040216 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Hernan, M. A. , Hernandez‐Diaz, S. , Werler, M. M. , & Mitchell, A. A. (2002). Causal knowledge as a prerequisite for confounding evaluation: An application to birth defects epidemiology. American Journal of Epidemiology, 155(2), 176–184. 10.1093/aje/155.2.176 [DOI] [PubMed] [Google Scholar]
  21. Jenkins, B. , West, J. A. , & Koulman, A. (2015). A review of odd‐chain fatty acid metabolism and the role of pentadecanoic acid (c15:0) and heptadecanoic acid (c17:0) in health and disease. Molecules, 20(2), 2425–2444. 10.3390/molecules20022425 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Karlsson, M. , Marild, S. , Brandberg, J. , Lonn, L. , Friberg, P. , & Strandvik, B. (2006). Serum phospholipid fatty acids, adipose tissue, and metabolic markers in obese adolescents. Obesity (Silver Spring), 14(11), 1931–1939. 10.1038/oby.2006.225 [DOI] [PubMed] [Google Scholar]
  23. Kaska, L. , Mika, A. , Stepnowski, P. , Proczko, M. , Ratnicki‐Sklucki, K. , Sledzinski, T. , & Swierczynski, J. (2020). The relationship between specific fatty acids of serum lipids and serum high sensitivity C‐reactive protein levels in morbidly obese women. Cellular Physiology and Biochemistry, 34(4), 1101–1108. 10.1159/000366324 [DOI] [PubMed] [Google Scholar]
  24. Kien, C. L. , Bunn, J. Y. , & Ugrasbul, F. (2005). Increasing dietary palmitic acid decreases fat oxidation and daily energy expenditure. The American Journal of Clinical Nutrition, 82(2), 320–326. 10.1093/ajcn.82.2.320 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Leddy, M. A. , Power, M. L. , & Schulkin, J. (2008). The impact of maternal obesity on maternal and fetal health. Reviews in Obstetrics and Gynecology, 1(4), 170–178. [PMC free article] [PubMed] [Google Scholar]
  26. Ley, S. H. , Hanley, A. J. , Sermer, M. , Zinman, B. , & O'Connor, D. L. (2012). Associations of prenatal metabolic abnormalities with insulin and adiponectin concentrations in human milk. The American Journal of Clinical Nutrition, 95(4), 867–874. 10.3945/ajcn.111.028431 [DOI] [PubMed] [Google Scholar]
  27. Liu, X. , Kris‐Etherton, P. M. , West, S. G. , Lamarche, B. , Jenkins, D. J. , Fleming, J. A. , & Jones, P. J. (2016). Effects of canola and high‐oleic‐acid canola oils on abdominal fat mass in individuals with central obesity. Obesity (Silver Spring), 24(11), 2261–2268. 10.1002/oby.21584 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Makela, J. , Linderborg, K. , Niinikoski, H. , Yang, B. , & Lagstrom, H. (2013). Breast milk fatty acid composition differs between overweight and normal weight women: The STEPS Study. European Journal of Nutrition, 52(2), 727–735. 10.1007/s00394-012-0378-5 [DOI] [PubMed] [Google Scholar]
  29. Marin, M. C. , Sanjurjo, A. , Rodrigo, M. A. , & de Alaniz, M. J. (2005). Long‐chain polyunsaturated fatty acids in breast milk in La Plata, Argentina: Relationship with maternal nutritional status. Prostaglandins, Leukotrienes, and Essential Fatty Acids, 73(5), 355–360. 10.1016/j.plefa.2005.07.005 [DOI] [PubMed] [Google Scholar]
  30. Monteiro, P. O. , & Victora, C. G. (2005). Rapid growth in infancy and childhood and obesity in later life—A systematic review. Obesity Reviews, 6(2), 143–154. 10.1111/j.1467-789X.2005.00183.x [DOI] [PubMed] [Google Scholar]
  31. Morrison, W. R. , & Smith, L. M. (1964). Preparation of fatty acid methyl esters and dimethylacetals from lipids with boron fluoride–methanol. Journal of Lipid Research, 5, 600–608. [PubMed] [Google Scholar]
  32. Much, D. , Brunner, S. , Vollhardt, C. , Schmid, D. , Sedlmeier, E. M. , Brüderl, M. , & Hauner, H. (2013). Breast milk fatty acid profile in relation to infant growth and body composition: Results from the INFAT study. Pediatric Research, 74(2), 230–237. 10.1038/pr.2013.82 [DOI] [PubMed] [Google Scholar]
  33. Nessel, I. , Khashu, M. , & Dyall, S. C. (2019). The effects of storage conditions on long‐chain polyunsaturated fatty acids, lipid mediators, and antioxidants in donor human milk—A review. Prostaglandins, Leukotrienes, and Essential Fatty Acids, 149, 8–17. 10.1016/j.plefa.2019.07.009 [DOI] [PubMed] [Google Scholar]
  34. Panagos, P. G. , Vishwanathan, R. , Penfield‐Cyr, A. , Matthan, N. R. , Shivappa, N. , Wirth, M. D. , & Sen, S. (2016). Breastmilk from obese mothers has pro‐inflammatory properties and decreased neuroprotective factors. Journal of Perinatology, 36(4), 284–290. 10.1038/jp.2015.199 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Perng, W. , Gillman, M. W. , Fleisch, A. F. , Michalek, R. D. , Watkins, S. M. , Isganaitis, E. , & Oken, E. (2014). Metabolomic profiles and childhood obesity. Obesity (Silver Spring), 22(12), 2570–2578. 10.1002/oby.20901 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Perng, W. , Gillman, M. W. , Mantzoros, C. S. , & Oken, E. (2014). A prospective study of maternal prenatal weight and offspring cardiometabolic health in midchildhood. Annals of Epidemiology, 24(11), 793–800.e791. 10.1016/j.annepidem.2014.08.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Perng, W. , Rifas‐Shiman, S. L. , McCulloch, S. , Chatzi, L. , Mantzoros, C. , Hivert, M. F. , & Oken, E. (2017). Associations of cord blood metabolites with perinatal characteristics, newborn anthropometry, and cord blood hormones in project viva. Metabolism, 76, 11–22. 10.1016/j.metabol.2017.07.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Petersen, K. , Sullivan, V. , Fulgoni, V. , Eren, F. , Cassens, M. , Bunczek, M. , & Kris‐Etherton, P. (2019). Plasma linoleic acid is associated with less adiposity and lower risk of metabolic syndrome: An NHANES analysis (P08‐121‐19). Curr Dev Nutr, 3 10.1093/cdn/nzz044.P08-121-19 [DOI] [Google Scholar]
  39. Pfeuffer, M. , & Jaudszus, A. (2016). Pentadecanoic and heptadecanoic acids: Multifaceted odd‐chain fatty acids12. Advances in Nutrition, 7, 730–734. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Poston, L. , Harthoorn, L. F. , & Van Der Beek, E. M. (2011). Obesity in pregnancy: Implications for the mother and lifelong health of the child. A consensus statement. Pediatric Research, 69(2), 175–180. 10.1203/PDR.0b013e3182055ede [DOI] [PubMed] [Google Scholar]
  41. Prentice, P. , Ong, K. K. , Schoemaker, M. H. , van Tol, E. A. , Vervoort, J. , Hughes, I. A. , & Dunger, D. B. (2016). Breast milk nutrient content and infancy growth. Acta Paediatrica, 105(6), 641–647. 10.1111/apa.13362 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Rogero, M. M. , & Calder, P. C. (2018). Obesity, inflammation, toll‐like receptor 4 and fatty acids. Nutrients, 10 10.3390/nu10040432 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Saben, J. L. , Bales, E. S. , Jackman, M. R. , Orlicky, D. , MacLean, P. S. , & McManaman, J. L. (2014). Maternal obesity reduces milk lipid production in lactating mice by inhibiting acetyl‐CoA carboxylase and impairing fatty acid synthesis. PLoS ONE, 9(5), e98066 10.1371/journal.pone.0098066 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Schriger, D. L. (2008). Annals of emergency medicine. Annals of Emergency Medicine, 52(4), 480 10.1016/j.annemergmed.2008.06.461 [DOI] [PubMed] [Google Scholar]
  45. Sewell, M. F. , Huston‐Presley, L. , Super, D. M. , & Catalano, P. (2006). Increased neonatal fat mass, not lean body mass, is associated with maternal obesity. American Journal of Obstetrics and Gynecology, 195(4), 1100–1103. 10.1016/j.ajog.2006.06.014 [DOI] [PubMed] [Google Scholar]
  46. Shehadeh, N. , Sukhotnik, I. , & Shamir, R. (2006). Gastrointestinal tract as a target organ for orally administered insulin. Journal of Pediatric Gastroenterology and Nutrition, 43(3), 276–281. 10.1097/01.mpg.0000226377.03247.fb [DOI] [PubMed] [Google Scholar]
  47. Sinanoglou, V. J. , Cavouras, D. , Boutsikou, T. , Briana, D. D. , Lantzouraki, D. Z. , Paliatsiou, S. , & Zoumpoulakis, P. (2017). Factors affecting human colostrum fatty acid profile: A case study. PLoS ONE, 12(4), e0175817 10.1371/journal.pone.0175817 [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Starling, A. P. , Brinton, J. T. , Glueck, D. H. , Shapiro, A. L. , Harrod, C. S. , Lynch, A. M. , & Dabelea, D. (2015). Associations of maternal BMI and gestational weight gain with neonatal adiposity in the Healthy Start study. The American Journal of Clinical Nutrition, 101(2), 302–309. 10.3945/ajcn.114.094946 [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Taveras, E. M. , Rifas‐Shiman, S. L. , Belfort, M. B. , Kleinman, K. P. , Oken, E. , & Gillman, M. W. (2009). Weight status in the first 6 months of life and obesity at 3 years of age. Pediatrics, 123(4), 1177–1183. 10.1542/peds.2008-1149 [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Vessby, B. , Gustafsson, I. B. , Tengblad, S. , Boberg, M. , & Andersson, A. (2002). Desaturation and elongation of fatty acids and insulin action. Annals of the New York Academy of Sciences, 967, 183–195. [DOI] [PubMed] [Google Scholar]
  51. Vidakovic, A. J. , Gishti, O. , Voortman, T. , Felix, J. F. , Williams, M. A. , Hofman, A. , . Gaillard, R. (2016). Maternal plasma PUFA concentrations during pregnancy and childhood adiposity: The Generation R Study. The American Journal of Clinical Nutrition , 103(4), 1017‐1025. 10.3945/ajcn.115.112847 [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Vidakovic, A. J. , Jaddoe, V. W. V. , Gishti, O. , Felix, J. F. , Williams, M. A. , Hofman, A. , & Gaillard, R. (2015). Body mass index, gestational weight gain and fatty acid concentrations during pregnancy: The Generation R Study. European Journal of Epidemiology, 30(11), 1175–1185. 10.1007/s10654-015-0106-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Wahlig, J. L. , Bales, E. S. , Jackman, M. R. , Johnson, G. C. , McManaman, J. L. , & Maclean, P. S. (2012). Impact of high‐fat diet and obesity on energy balance and fuel utilization during the metabolic challenge of lactation. Obesity (Silver Spring), 20(1), 65–75. 10.1038/oby.2011.196 [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Weng, S. F. , Redsell, S. A. , Swift, J. A. , Yang, M. , & Glazebrook, C. P. (2012). Systematic review and meta‐analyses of risk factors for childhood overweight identifiable during infancy. Archives of Disease in Childhood, 97(12), 1019–1026. 10.1136/archdischild-2012-302263 [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Whitmore, T. J. , Trengove, N. J. , Graham, D. F. , & Hartmann, P. E. (2012). Analysis of insulin in human breast milk in mothers with type 1 and type 2 diabetes mellitus. International Journal of Endocrinology, 2012, 296368 10.1155/2012/296368 [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. WHO Multicentre Growth Reference Study Group (2006). WHO Child Growth Standards based on length/height, weight and age. Acta Paediatrica. Supplement, 450, 76–85. [DOI] [PubMed] [Google Scholar]
  57. Young, B. E. , Johnson, S. L. , & Krebs, N. F. (2012). Biological determinants linking infant weight gain and child obesity: Current knowledge and future directions. Advances in Nutrition, 3(5), 675–686. 10.3945/an.112.002238 [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Young, B. E. , Patinkin, Z. , Palmer, C. , de la Houssaye, B. , Barbour, L. A. , Hernandez, T. , & Krebs, N. F. (2017). Human milk insulin is related to maternal plasma insulin and BMI: But other components of human milk do not differ by BMI. European Journal of Clinical Nutrition, 71(9), 1094–1100. 10.1038/ejcn.2017.75 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Table S1. Composition of long chain fatty acid factor scores retained from principal components analysis

Click here for additional data file. (14.3KB, docx)

Figure S1. Significant differences in human milk composition profiles comparing normal weight (NW) and overweight & obesity (OW/OB) for insulin, N6:N3 LC‐PUFA ratio, palmitic acid [16:0], oleic acid [18‐1 (n‐9)], conjugated linoleic acid [18:2 (n‐7,9)], dihomo‐gamma‐linolenic acid [20:3 (n‐6)], adrenic acid [22:4 (n‐6)]. LCFAs represented as percentage (%) of total LCFAs. *P < 0.05

Click here for additional data file. (418.6KB, tif)

Figure S2. Linear regression of maternal pre‐pregnancy BMI and human milk composition: A. N6:N3 LC‐PUFA ratio, B. Palmitic acid [16:0], C. Oleic acid [18‐1 (n‐9)], D. Conjugated linoleic acid [18:2 (n‐7,9)], E. Dihomo‐gamma‐linolenic acid [20:3 (n‐6)], F. Adrenic acid [22:4 (n‐6)], G. Insulin. LCFAs represented as percentage (%) of total LCFAs. *P < 0.05

Click here for additional data file. (26KB, pdf)

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