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Am J Primatol. Author manuscript; available in PMC 2017 August 01. Published in final edited form as: Am J Primatol. 2016 August ; 78(8): 838–850. doi:10.1002/ajp.22544.
Bioactive factors in milk across lactation: maternal effects and influence on infant growth in rhesus macaques (Macaca mulatta) Robin Bernstein, Ph.D.1,2 and Katie Hinde, Ph.D.3,4,5 1Department
of Anthropology, University of Colorado Boulder
Author Manuscript
2Health
& Society Program, Institute of Behavioral Science, University of Colorado Boulder
3School
of Human Evolution and Social Change, Arizona State University
4Center
for Evolution and Medicine, Arizona State University
5Brain,
Mind, and Behavior Unit, California National Primate Research Center
Abstract
Author Manuscript Author Manuscript
Among mammals, numerous bioactive factors in milk vary across mothers and influence offspring outcomes. This emerging area of research has primarily investigated such dynamics within rodent biomedical models, domesticated dairy breeds, and among humans in clinical contexts. Less understood are signaling factors in the milk of non-human primates. Here, we report on multiple bioactive components in rhesus macaque (Macaca mulatta) milk and their associations with maternal and infant characteristics. Milk samples were collected from 59 macaques at multiple time points across lactation in conjunction with maternal and infant morphometrics and lifehistory animal records. Milk was assayed for adiponectin (APN), epidermal growth factor (EGF) and its receptor (EGF-R), and transforming growth factor beta 2 (TGF-β2). Regression models were constructed to assess the contributions of maternal factors on variation in milk bioactives, and on the relationship of this variation to infant body mass and growth. Maternal body mass, parity, social rank and infant sex were all predictive of concentrations of milk bioactives. Primiparous mothers produced milk with higher adiponectin, but lower EGF, than multiparous mothers. Heavier mothers produced milk with lower EGF and EGF-R, but higher TGF-β2. Mothers of daughters produced milk with higher TGF-β2. Mid-ranking mothers produced milk with higher mean EGF and adiponectin concentrations than low-ranking mothers. Milk EGF and EGF-R were positively associated with infant body mass and growth rate. Importantly, these signaling bioactives (APN, EGF, EGF-R, TGF-β2) were significantly correlated with nutritional values of milk. The effects of milk signals remained after controlling for the available energy in milk revealing the added physiological role of non-nutritive milk bioactives in the developing infant. Integrating analyses of energetic and other bioactive components of milk yields an important perspective for interpreting the magnitude, sources, and consequences of interindividual variation in milk synthesis.
Correspondence to: Robin Bernstein, Department of Anthropology, University of Colorado, Boulder, 1350 Pleasant Street, Boulder, CO 80309-0233, Phone: 303-492-2547, Fax: 303-492-1871, [email protected].
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Keywords lactation; growth factor; adiponectin; infant development; macaque; maternal investment
INTRODUCTION
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Maternal effects, or the early life organization of integrated physical, physiological, neurobiological, and behavioral systems, shape offspring with potential manifestations across the life course [Russ et al. 2014; Wells 2014; Hinde 2015]. The influence of maternal, ecological, nutritional, and social conditions during reproduction influence infant outcomes, but the vast majority of research effort has been allocated toward physiological investment during pregnancy and behavioral care post-natally [Phillips et al. 2014; Hinde 2015]. Greater recognition of infancy as an important critical developmental window for mammals, sensitive to physiological investment in the form of milk, has motivated investigations of variation in milk composition across species, individuals and, within individuals, across lactation. Exploration of variation in milk constituents within and among species has provided an evolutionary perspective on the role of milk as a critical part of maternal investment, driving many aspects of species-specific life history strategies [Hinde and Milligan 2011].
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Among the few primate taxa reported to date (including humans), inter-individual variation in milk synthesis reflects aspects of both maternal and infant characteristics. In this way milk is a dynamic substance influenced by and responsive to the requirements, resources, and conditions of mother and baby. Dietary restriction of laboratory baboons does not alter milk composition, but reduces milk volume and requires increased mobilization of maternal reserves to sustain lactation [Roberts et al. 1985]. Smaller marmoset mothers rearing twins produce lower fat concentrations in milk (and therefore lower milk energy density) than do larger mothers [Tardif et al. 2001]. Higher parity and heavier macaque mothers produce higher milk volumes and consequently higher available milk energy [Hinde et al. 2009; Hinde and Capitanio 2010; Hinde et al. 2015]. Among humans, parity, body mass, and skinfold thickness have all been associated with milk fat content and/or milk volume [e.g., Danish women, Michaelson et al., 1994; Egyptian women, Soliman et al. 2014; ethnic Tibetan women, Quinn et al. 2015] but not in all populations [Filipino women, Quinn et al. 2012, for further review see Picciano, 2001, Hinde and Milligan 2011]. Such effects are the most marked as the duration of lactation increases and maternal reserves become increasingly depleted [US women, Nommsen et al., 1991]. Nutritional status of mothers during pregnancy and lactation affects both the composition of breast milk as well as the overall volume of milk produced per day [Robyn et al., 1986], although these effects are not necessarily mediated by differences in maternal body mass index [Gambian women, Prentice and Prentice, 1995]. The “biological recipe” of milk synthesized for sons can differ from milk synthesized for daughters [macaques, Hinde 2009; humans, Powe et al 2010; Thakkar et al. 2013; Fujita et al. 2012 but not always - see Quinn 2013] and likely reflects, in part, divergent developmental priorities and trajectories [Hinde et al. 2014].
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While these studies aid in understanding some of the sources of variation in milk composition, with manifold potential effects on infants, their targets of measurement (e.g., volume of milk synthesized, concentrations of total fats, carbohydrates, and proteins) represent very “macro” approaches to milk nutrition. Other aspects of nutrition- e.g. vitamins, minerals, fatty acids, amino acids, oligosaccharides, and many others- have not been systematically investigated among non-human primate milks for sources and consequences of individual variation [Hinde and Milligan 2011; Hinde et al. 2013]. Moreover, milk is more than nutrition, milk is also medicine [e.g., Prentice et al., 1984; Ngom et al., 2004; Hassiotou et al. 2013; Breakey et al. 2015] and signal [Power and Schulkin 2013; Hinde et al. 2015]. While the immune factors in milk have long been recognized and studied [Goldman 1993], other maternal-origin, bioactive signals in milk are the least understood [Neville et al. 2013]. The presence and abundance of bioactive signaling factors has been observed and experimentally demonstrated to influence offspring growth, behavior, and reproductive maturation [humans, Savino et al 2009; rodents, Catalani et al. 2011; pigs, Bartol et al. 2013; rhesus macaques Hinde et al. 2015].
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Multiple bioactive factors in mother’s milk - adiponectin (APN), epidermal growth factor (EGF) and its receptor (EGF-R), and transforming growth factor beta 2 (TGF-β2)- have been demonstrated or speculated to reflect maternal characteristics and infant outcomes. These milk bioactives — with the exception of EGF-R, which has not been reported— can covary with maternal health, gestation length, primiparity, and environmental conditions (Table 1). Their physiological functions are expected to exert significant influence on infant immune and gastrointestinal development, as well as infant growth patterns (Table 1). These milk bioactives derive from maternal circulation and local mammary tissue synthesis [Donovan and Odle 1994; Hawkes et al, 2002]. Bioactives can be particularly enriched in milk; EGF concentrations in breast milk are 30–1,000 fold higher than concentrations in circulation (Donovan and Odle 1994). Additionally, during early postnatal life and the development of the adaptive immune response, breast milk is a major source of immune-regulatory cytokines for the infant, including TGF-β2 [Penttila, 2010]. These maternal-origin cytokines ingested through milk bind to receptors within the infant intestinal tract, suggesting resistance to digestion and indicating a direct role in gut development as well as in the activation of other developmental pathways following signaling at the receptor [Zhou et al., 2005; Woo et al., 2009]. To our knowledge, no studies to date have reported on EGF receptor (EGF-R) concentrations in milk. An investigation of bioactive molecules in conjunction with their receptors has been suggested as being particularly informative with regard to understanding how those molecules might be taken up and ultimately used by the infant. The formation of molecule-receptor complexes in the digestive tract have been hypothesized to protect the molecule from degradation, and to facilitate delivery of the molecule at target tissues within the infant [Zhang et al., 2001]. In most studies to date, milk bioactives and nutritive factors have been investigated in isolation from one another – but the interactions between these signaling and nutritional compartments within milk are biologically relevant. For example, in milk the concentrations of leptin are correlated with concentrations of fat [Smith-Kirwin et al. 1998] and similarly cortisol concentrations in milk are associated with concentrations of fat and protein in milk [Sullivan et al. 2010]. Further, maternal energy status, even prior to pregnancy, may affect Am J Primatol. Author manuscript; available in PMC 2017 August 01.
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milk nutrient composition via canalization of hormonal pathways. Insulin, glucocorticoids, prolactin, and leptin orchestrate and regulate maternal metabolism and locally, the morphological and functional development of the mammary gland during puberty and pregnancy [Neville and Morton, 2011; Rasmussen, 2007]. At minimum, as the nutritional and signaling components of milk are expected to be correlated, studies that intend to evaluate sources and consequences of variation of milk signaling bioactives that do not account for the milk energy density and milk yield may be missing or obscuring essential features of this dynamic system [Hinde et al. 2015]. Investigating the presence and abundance of bioactive signaling factors in milk and their relationship to available milk energy is an important next step in disentangling how milk reflects characteristics of the mother-infant dyad and contributes to early life organization of the infant.
Author Manuscript
Notably much of the research reviewed above has been conducted within the framework of experimental models, public health, or clinical medicine, rather than from an evolutionary perspective that emphasizes the mother- her care, body, and milk- as the adaptively relevant environment of the developing mammalian infant [Hinde 2014]. As such, variation in mother’s milk has the potential to provide important cues about maternal conditions for shaping infant developmental priorities and trajectories. Here we leverage the rhesus macaque lactation model to investigate aspects of the energetic and signaling compartments of milk in concert with maternal characteristics and infant growth. The rhesus macaque, having provided the most extensive exploration of non-human primate milk to date [Lönnerdal et al. 1984; Kunz and Lönnerdal 1993; Hinde et al. 2009; Hinde 2009; Hinde and Capitanio 2010; Smith et al. 2014; Hinde et al. 2015] often in direct comparison to human breast milk [Lemay et al., 2013; O’Sullivan et al., 2013; Beck et al. 2015], presents a particularly useful model system. We report for the first time the magnitude and sources of variation in several milk bioactives (adiponectin, EGF, its receptor, EGF-R, and TGF-β2) in rhesus macaque milk, none of which have previously been quantified in nonhuman primate milk [except for gorilla and orangutan milk – see Power et al., in review]. Utilizing a longitudinal sample from a captive colony, we evaluated maternal characteristics on variation in these milk bioactives and assessed the effect on infant growth outcomes, controlling for the contributions of milk nutritive factors. We predicted that mothers characterized by fewer resources as a function of young age, small body mass, primiparity, and/or low social rank would differ in their concentrations of milk bioactives from mothers with greater resources to support lactation and fewer life history tradeoffs. Given that prior studies have not established consistent relationships between sources of variation in these milk bioactives and concentrations of the bioactives, we did not predict the directionality of the differences according to maternal factors. However, we did predict that increased concentrations of milk bioactives would be associated with heavier infant mass and higher estimated daily growth (Table 1).
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METHODS Subjects Mother-infant dyads (N=59) were recruited from 11 different social groups in the outdoor breeding colony at the CNPRC during infant birth seasons in 2010 (N=21) and 2011 (N=38).
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Subjects had produced 1–15 infants in their reproductive career at the time of the study, however 17% were primiparous (10/59). Just over half of the mothers were rearing daughters (34/59, 58%). Subjects were fed a commercial diet twice-daily (Purina Monkey chow) supplemented with fresh produce semi-weekly. Subjects were housed in 0.2 hectare corrals. Linear hierarchies generated by the behavioral management division at the CNPRC, based on standardized observations of social interactions, were used to categorize social rank of mothers. We split the linear hierarchies into thirds to assign “high,” “middle,” and “low” rank to individual mothers, with the exception that study mothers were assigned the rank category that characterized the majority of their matriline if the arbitrary tertile assignment binned them apart from their matrilineal kin [Hinde et al., 2009; Hinde et al., 2015]. This study was approved by the University of California Davis Institutional Animal Care and Use Committee (#15461), adhered to the American Society of Primatologists principles for the ethical treatment of primates, and adhered to the legal requirements of the United States.
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Milk collection
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Milk was collected using standardized methods from mothers (Hinde et al., 2009) at three time points during lactation: between 4–6 weeks (early), 3–4 months (peak), and 5–6 months (late). In the early post-natal period, at one month of infant age (mean±SD=34±3 days), infants and mothers were captured in their outdoor enclosures between 7:30–9:00AM, and were relocated together to temporary housing. To prevent nursing, mothers were placed in mesh jackets and allowed a standardized period of milk accumulation for 3.5–4 hours. This allowed infants to remain in contact with their mother during this period. Between 11:30 and 13:00, mothers were sedated with ketamine hydrochloride (5–10 mg/kg IM) and administered a non-physiological dose of exogenous oxytocin [2IU/kg (0.1ml/kg)IM] to stimulate milk letdown. Milk was collected by hand, and mammary glands were fully evacuated to prevent sampling bias [Oftedal 1984]. Following collection, milk samples were placed directly into wet ice and transported from the procedure room to the wet lab where they were vortexed for 5 seconds, aliquoted into cryovials, and frozen at −80°C. Milk collection procedures used in this study have been described in further detail elsewhere [Hinde et al., 2009]. At the one-month time point, after the mother had recovered from sedation, mothers and infants were returned to their social group the same day. Between 3–4 months (mean±SD=110±11 days) and again between 5–6 months (mean±SD=159±6 days), infants and mothers were again captured between 7:30 and 9:00AM and separated and mothers relocated to temporary housing for milk accumulation. Following 3.5–4 hours of milk accumulation, milk was again collected and handled using identical techniques as described for the earliest timepoint.
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Milk assays We used DuoSet ELISA Development kits to measure adiponectin, EGF, EGF-R, and IGFIIR, and Quantikine Human TGF-β2 Immunoassay kit to measure TGF-β2 (R&D Systems, Inc., Minneapolis, MN). Before assay, all samples were brought to room temperature to thaw, vortexed and centrifuged to separate and remove the fat layer. All validations, recoveries, and assays were run using the skim fraction, and samples were measured in duplicate. We performed parallelism and recovery tests to validate all assays for use in Am J Primatol. Author manuscript; available in PMC 2017 August 01.
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macaque milk. All parallelisms showed a correlation between slopes of serially diluted pooled samples and the standard curve of R2 ≥ 0.98. All recoveries were ≥ 96%. Sample dilutions and alterations to kit protocols were informed by tests of parallelism and were as follows. For measurement of adiponectin, we diluted samples from early lactation 1:5, and used dilutions of 1:8 for peak and late lactation samples. For the EGF assay, we diluted early milk samples 1:50, and peak and late lactation samples 1:60. To measure EGF-R, we incorporated the following alterations to the kit protocol: we used the capture antibody at 0.5 ug/ml, the detection antibody at 150 ng/ml, and following the initial reconstitution of standard and detection antibody, we added 0.05% Tween 20 to the reagent diluent recipe. For EGF-R assay, we diluted early milk samples 1:6, and peak and late samples 1:8. We followed the manufacturer’s protocol for measurement of TGF-β2. Milk composition and yield
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Milk samples were assayed for nutritional composition in duplicate on the MIRIS milk analyzer in concert with >500 additional samples for other on-going studies calibrated specifically for Old World monkey milk [Hinde et al. 2015]. The mean±SD coefficients of variation (CV) of duplicate determinations in macaque milk for fat, protein, and sugar were 0.9±1.6%, 2.0±6.5%, and 1.1±2.0%, respectively. The gross energy of milk, also referred to as energy density, was calculated as 9.11 kcal/g for fat, 3.95 kcal/g for sugars, and 5.86 kcal/g for protein, which allowed for an estimate of the total energy density of milk (kcal/g) [Hinde and Milligan 2011]. Pooled bovine whole milk samples were used as controls at the beginning and end of each assay run. Based on these bovine samples, the inter-assay CV (calculated as the standard deviation divided by the mean value of all replicates) for fat was 2.52%, for protein was 1.64%, and for sugar was 4.11%. The intra-assay CV (calculated as the average of the individual CVs generated by dividing the standard deviation by the mean value of each days internal standard duplicates) for fat was 1.34%, for protein was 1.05%, and for sugar was 1.86%. Milk yield was measured gravimetrically as total sample (g) obtained by full evacuation of milk from both mammary glands after a standard period of milk accumulation (3.5–4 hours). This method of estimating relative differences in milk production among individuals has been used for other primates, validated with doubly labeled water [Tardif et al. 2001], and has been associated with infant mass and growth in the CNPRC population [Hinde et al. 2009; Hinde et al. 2015]. Available milk energy (AME) was calculated by multiplying kcal/g by milk yield (g). Statistical analysis
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All analyses were conducted using JMP Pro 11.2.0 (© 2013 SAS Institute). Milk bioactive values were log10-transformed to achieve normal distribution for multivariate analyses. Descriptive statistics were calculated for milk bioactives (mean, standard deviation) and reported as raw values (pg/ml). Differences in bioactives across stages of lactation were determined using repeated measures ANOVA with infant sex as a factor and maternal ID as a covariate. Milk energetic content (percent fat, protein, and sugars) and milk bioactives (APN, EGF, EGF-R, and TGF-β2) were assessed for correlations in which maternal ID nested by timepoint was included as a random effect. APN, EGF, EGF-R, and TGF-β2 were significantly correlated with milk macro-constituents; see results below (Table 2). Given this correlation between the energetic and bioactive concentrations in milk, available milk energy Am J Primatol. Author manuscript; available in PMC 2017 August 01.
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was included in regression models as an index to control for potential confounds with milk energy and milk volume.
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We constructed multiple regression models to examine 1) the degree to which variation in maternal and infant characteristics predicted variation in milk bioactives, and 2) the degree to which variation in infant growth outcomes could be predicted by variation in milk bioactives. We used generalized linear models to determine significant predictors of variation in milk bioactives. We built separate models for each bioactive, with maternal ID nested within time point (e.g., early, mid, late lactation) included as random parameters in the model in order to control for the effects of repeated measures. Our models included maternal mass, social rank (high, mid, low), parity (dichotomously as primiparous, multiparous), infant sex, infant age, and available milk energy (AME). We included available milk energy as a covariate, as prior studies have demonstrated significant associations between milk yield, caloric content, and hormone content in rhesus monkey milk [Hinde et al., 2015, Sullivan et al. 2010]. We report False Discovery Rate LogWorth (FDR LogWorth) and FDR LogWorth p-values for each model effect, calculated using the Benjamini-Hochberg technique (Benjamini and Hochberg, 1995), in order to assess the influence of effect size upon significance for each model parameter. Daily infant growth rate was calculated by subtracting the mass at early lactation from the mass at late lactation and then divided by the number of days between the two time points. We constructed models that initially included all milk bioactives, as well as the interaction between bioactives and infant sex, as predictors for infant mass and infant daily growth rate. Infant age and infant mass at the one-month measurement were included in the models to control for potential confounding effects associated with variation in absolute age among infants at each time point, and the scaling effect of body size on growth rate. For both sets of model analysis, we also used the corrected Akaike’s Information Criterion (AICc) value to aid in assessment of best fit [Akaike 1974]. The alpha level was set at 0.05. All analyses were performed using JMP, version 12.0.1 (SAS Institute Inc.).
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RESULTS Variation in milk composition across lactation
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Concentrations of all bioactives in milk increased across lactation (Table 2), with significant differences among early, peak, and late lactation in adiponectin (ANOVA: F (2,176)=21.39, pHeavier, p=0.0015), but higher concentrations of TGF-β2 than did mothers of lower mass (Heavier>Lighter, p = 0.0003: Fig. 3). Mothers of daughters produced milk with significantly high higher concentrations of TGF-β2 than did mothers of sons (475 ± SE 41 pg/ml and 390 ± SE 49 pg/ml, respectively, p = 0.0026: Fig. 4). AICc model selection retained AME as a predictor in models that appreciably explained variation, although this predictor was not statistically significant. Variation in infant size in relationship to milk bioactives and available milk energy Bioactive concentrations in milk predicted infant mass and growth rate during peak lactation, but not early or late lactation (Table 3). In both male and female infants, EGF and EGF-R were positively associated with mass after controlling for infant age (Model adj. R2 = 0.76; EGF: 0.10 ± SE 0.03, t=3.06, p=0.0026; EGF-R: −0.09 ± SE 0.03, t=−3.25, p=0.0014), as was AME (0.15 ± SE 0.03, t=5.79, p
Am J Primatol. Author manuscript; available in PMC 2017 August 01. Published in final edited form as: Am J Primatol. 2016 August ; 78(8): 838–850. doi:10.1002/ajp.22544.
Bioactive factors in milk across lactation: maternal effects and influence on infant growth in rhesus macaques (Macaca mulatta) Robin Bernstein, Ph.D.1,2 and Katie Hinde, Ph.D.3,4,5 1Department
of Anthropology, University of Colorado Boulder
Author Manuscript
2Health
& Society Program, Institute of Behavioral Science, University of Colorado Boulder
3School
of Human Evolution and Social Change, Arizona State University
4Center
for Evolution and Medicine, Arizona State University
5Brain,
Mind, and Behavior Unit, California National Primate Research Center
Abstract
Author Manuscript Author Manuscript
Among mammals, numerous bioactive factors in milk vary across mothers and influence offspring outcomes. This emerging area of research has primarily investigated such dynamics within rodent biomedical models, domesticated dairy breeds, and among humans in clinical contexts. Less understood are signaling factors in the milk of non-human primates. Here, we report on multiple bioactive components in rhesus macaque (Macaca mulatta) milk and their associations with maternal and infant characteristics. Milk samples were collected from 59 macaques at multiple time points across lactation in conjunction with maternal and infant morphometrics and lifehistory animal records. Milk was assayed for adiponectin (APN), epidermal growth factor (EGF) and its receptor (EGF-R), and transforming growth factor beta 2 (TGF-β2). Regression models were constructed to assess the contributions of maternal factors on variation in milk bioactives, and on the relationship of this variation to infant body mass and growth. Maternal body mass, parity, social rank and infant sex were all predictive of concentrations of milk bioactives. Primiparous mothers produced milk with higher adiponectin, but lower EGF, than multiparous mothers. Heavier mothers produced milk with lower EGF and EGF-R, but higher TGF-β2. Mothers of daughters produced milk with higher TGF-β2. Mid-ranking mothers produced milk with higher mean EGF and adiponectin concentrations than low-ranking mothers. Milk EGF and EGF-R were positively associated with infant body mass and growth rate. Importantly, these signaling bioactives (APN, EGF, EGF-R, TGF-β2) were significantly correlated with nutritional values of milk. The effects of milk signals remained after controlling for the available energy in milk revealing the added physiological role of non-nutritive milk bioactives in the developing infant. Integrating analyses of energetic and other bioactive components of milk yields an important perspective for interpreting the magnitude, sources, and consequences of interindividual variation in milk synthesis.
Correspondence to: Robin Bernstein, Department of Anthropology, University of Colorado, Boulder, 1350 Pleasant Street, Boulder, CO 80309-0233, Phone: 303-492-2547, Fax: 303-492-1871, [email protected].
Bernstein and Hinde
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Keywords lactation; growth factor; adiponectin; infant development; macaque; maternal investment
INTRODUCTION
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Maternal effects, or the early life organization of integrated physical, physiological, neurobiological, and behavioral systems, shape offspring with potential manifestations across the life course [Russ et al. 2014; Wells 2014; Hinde 2015]. The influence of maternal, ecological, nutritional, and social conditions during reproduction influence infant outcomes, but the vast majority of research effort has been allocated toward physiological investment during pregnancy and behavioral care post-natally [Phillips et al. 2014; Hinde 2015]. Greater recognition of infancy as an important critical developmental window for mammals, sensitive to physiological investment in the form of milk, has motivated investigations of variation in milk composition across species, individuals and, within individuals, across lactation. Exploration of variation in milk constituents within and among species has provided an evolutionary perspective on the role of milk as a critical part of maternal investment, driving many aspects of species-specific life history strategies [Hinde and Milligan 2011].
Author Manuscript Author Manuscript
Among the few primate taxa reported to date (including humans), inter-individual variation in milk synthesis reflects aspects of both maternal and infant characteristics. In this way milk is a dynamic substance influenced by and responsive to the requirements, resources, and conditions of mother and baby. Dietary restriction of laboratory baboons does not alter milk composition, but reduces milk volume and requires increased mobilization of maternal reserves to sustain lactation [Roberts et al. 1985]. Smaller marmoset mothers rearing twins produce lower fat concentrations in milk (and therefore lower milk energy density) than do larger mothers [Tardif et al. 2001]. Higher parity and heavier macaque mothers produce higher milk volumes and consequently higher available milk energy [Hinde et al. 2009; Hinde and Capitanio 2010; Hinde et al. 2015]. Among humans, parity, body mass, and skinfold thickness have all been associated with milk fat content and/or milk volume [e.g., Danish women, Michaelson et al., 1994; Egyptian women, Soliman et al. 2014; ethnic Tibetan women, Quinn et al. 2015] but not in all populations [Filipino women, Quinn et al. 2012, for further review see Picciano, 2001, Hinde and Milligan 2011]. Such effects are the most marked as the duration of lactation increases and maternal reserves become increasingly depleted [US women, Nommsen et al., 1991]. Nutritional status of mothers during pregnancy and lactation affects both the composition of breast milk as well as the overall volume of milk produced per day [Robyn et al., 1986], although these effects are not necessarily mediated by differences in maternal body mass index [Gambian women, Prentice and Prentice, 1995]. The “biological recipe” of milk synthesized for sons can differ from milk synthesized for daughters [macaques, Hinde 2009; humans, Powe et al 2010; Thakkar et al. 2013; Fujita et al. 2012 but not always - see Quinn 2013] and likely reflects, in part, divergent developmental priorities and trajectories [Hinde et al. 2014].
Am J Primatol. Author manuscript; available in PMC 2017 August 01.
Bernstein and Hinde
Page 3
Author Manuscript Author Manuscript
While these studies aid in understanding some of the sources of variation in milk composition, with manifold potential effects on infants, their targets of measurement (e.g., volume of milk synthesized, concentrations of total fats, carbohydrates, and proteins) represent very “macro” approaches to milk nutrition. Other aspects of nutrition- e.g. vitamins, minerals, fatty acids, amino acids, oligosaccharides, and many others- have not been systematically investigated among non-human primate milks for sources and consequences of individual variation [Hinde and Milligan 2011; Hinde et al. 2013]. Moreover, milk is more than nutrition, milk is also medicine [e.g., Prentice et al., 1984; Ngom et al., 2004; Hassiotou et al. 2013; Breakey et al. 2015] and signal [Power and Schulkin 2013; Hinde et al. 2015]. While the immune factors in milk have long been recognized and studied [Goldman 1993], other maternal-origin, bioactive signals in milk are the least understood [Neville et al. 2013]. The presence and abundance of bioactive signaling factors has been observed and experimentally demonstrated to influence offspring growth, behavior, and reproductive maturation [humans, Savino et al 2009; rodents, Catalani et al. 2011; pigs, Bartol et al. 2013; rhesus macaques Hinde et al. 2015].
Author Manuscript Author Manuscript
Multiple bioactive factors in mother’s milk - adiponectin (APN), epidermal growth factor (EGF) and its receptor (EGF-R), and transforming growth factor beta 2 (TGF-β2)- have been demonstrated or speculated to reflect maternal characteristics and infant outcomes. These milk bioactives — with the exception of EGF-R, which has not been reported— can covary with maternal health, gestation length, primiparity, and environmental conditions (Table 1). Their physiological functions are expected to exert significant influence on infant immune and gastrointestinal development, as well as infant growth patterns (Table 1). These milk bioactives derive from maternal circulation and local mammary tissue synthesis [Donovan and Odle 1994; Hawkes et al, 2002]. Bioactives can be particularly enriched in milk; EGF concentrations in breast milk are 30–1,000 fold higher than concentrations in circulation (Donovan and Odle 1994). Additionally, during early postnatal life and the development of the adaptive immune response, breast milk is a major source of immune-regulatory cytokines for the infant, including TGF-β2 [Penttila, 2010]. These maternal-origin cytokines ingested through milk bind to receptors within the infant intestinal tract, suggesting resistance to digestion and indicating a direct role in gut development as well as in the activation of other developmental pathways following signaling at the receptor [Zhou et al., 2005; Woo et al., 2009]. To our knowledge, no studies to date have reported on EGF receptor (EGF-R) concentrations in milk. An investigation of bioactive molecules in conjunction with their receptors has been suggested as being particularly informative with regard to understanding how those molecules might be taken up and ultimately used by the infant. The formation of molecule-receptor complexes in the digestive tract have been hypothesized to protect the molecule from degradation, and to facilitate delivery of the molecule at target tissues within the infant [Zhang et al., 2001]. In most studies to date, milk bioactives and nutritive factors have been investigated in isolation from one another – but the interactions between these signaling and nutritional compartments within milk are biologically relevant. For example, in milk the concentrations of leptin are correlated with concentrations of fat [Smith-Kirwin et al. 1998] and similarly cortisol concentrations in milk are associated with concentrations of fat and protein in milk [Sullivan et al. 2010]. Further, maternal energy status, even prior to pregnancy, may affect Am J Primatol. Author manuscript; available in PMC 2017 August 01.
Bernstein and Hinde
Page 4
Author Manuscript
milk nutrient composition via canalization of hormonal pathways. Insulin, glucocorticoids, prolactin, and leptin orchestrate and regulate maternal metabolism and locally, the morphological and functional development of the mammary gland during puberty and pregnancy [Neville and Morton, 2011; Rasmussen, 2007]. At minimum, as the nutritional and signaling components of milk are expected to be correlated, studies that intend to evaluate sources and consequences of variation of milk signaling bioactives that do not account for the milk energy density and milk yield may be missing or obscuring essential features of this dynamic system [Hinde et al. 2015]. Investigating the presence and abundance of bioactive signaling factors in milk and their relationship to available milk energy is an important next step in disentangling how milk reflects characteristics of the mother-infant dyad and contributes to early life organization of the infant.
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Notably much of the research reviewed above has been conducted within the framework of experimental models, public health, or clinical medicine, rather than from an evolutionary perspective that emphasizes the mother- her care, body, and milk- as the adaptively relevant environment of the developing mammalian infant [Hinde 2014]. As such, variation in mother’s milk has the potential to provide important cues about maternal conditions for shaping infant developmental priorities and trajectories. Here we leverage the rhesus macaque lactation model to investigate aspects of the energetic and signaling compartments of milk in concert with maternal characteristics and infant growth. The rhesus macaque, having provided the most extensive exploration of non-human primate milk to date [Lönnerdal et al. 1984; Kunz and Lönnerdal 1993; Hinde et al. 2009; Hinde 2009; Hinde and Capitanio 2010; Smith et al. 2014; Hinde et al. 2015] often in direct comparison to human breast milk [Lemay et al., 2013; O’Sullivan et al., 2013; Beck et al. 2015], presents a particularly useful model system. We report for the first time the magnitude and sources of variation in several milk bioactives (adiponectin, EGF, its receptor, EGF-R, and TGF-β2) in rhesus macaque milk, none of which have previously been quantified in nonhuman primate milk [except for gorilla and orangutan milk – see Power et al., in review]. Utilizing a longitudinal sample from a captive colony, we evaluated maternal characteristics on variation in these milk bioactives and assessed the effect on infant growth outcomes, controlling for the contributions of milk nutritive factors. We predicted that mothers characterized by fewer resources as a function of young age, small body mass, primiparity, and/or low social rank would differ in their concentrations of milk bioactives from mothers with greater resources to support lactation and fewer life history tradeoffs. Given that prior studies have not established consistent relationships between sources of variation in these milk bioactives and concentrations of the bioactives, we did not predict the directionality of the differences according to maternal factors. However, we did predict that increased concentrations of milk bioactives would be associated with heavier infant mass and higher estimated daily growth (Table 1).
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METHODS Subjects Mother-infant dyads (N=59) were recruited from 11 different social groups in the outdoor breeding colony at the CNPRC during infant birth seasons in 2010 (N=21) and 2011 (N=38).
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Subjects had produced 1–15 infants in their reproductive career at the time of the study, however 17% were primiparous (10/59). Just over half of the mothers were rearing daughters (34/59, 58%). Subjects were fed a commercial diet twice-daily (Purina Monkey chow) supplemented with fresh produce semi-weekly. Subjects were housed in 0.2 hectare corrals. Linear hierarchies generated by the behavioral management division at the CNPRC, based on standardized observations of social interactions, were used to categorize social rank of mothers. We split the linear hierarchies into thirds to assign “high,” “middle,” and “low” rank to individual mothers, with the exception that study mothers were assigned the rank category that characterized the majority of their matriline if the arbitrary tertile assignment binned them apart from their matrilineal kin [Hinde et al., 2009; Hinde et al., 2015]. This study was approved by the University of California Davis Institutional Animal Care and Use Committee (#15461), adhered to the American Society of Primatologists principles for the ethical treatment of primates, and adhered to the legal requirements of the United States.
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Milk collection
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Milk was collected using standardized methods from mothers (Hinde et al., 2009) at three time points during lactation: between 4–6 weeks (early), 3–4 months (peak), and 5–6 months (late). In the early post-natal period, at one month of infant age (mean±SD=34±3 days), infants and mothers were captured in their outdoor enclosures between 7:30–9:00AM, and were relocated together to temporary housing. To prevent nursing, mothers were placed in mesh jackets and allowed a standardized period of milk accumulation for 3.5–4 hours. This allowed infants to remain in contact with their mother during this period. Between 11:30 and 13:00, mothers were sedated with ketamine hydrochloride (5–10 mg/kg IM) and administered a non-physiological dose of exogenous oxytocin [2IU/kg (0.1ml/kg)IM] to stimulate milk letdown. Milk was collected by hand, and mammary glands were fully evacuated to prevent sampling bias [Oftedal 1984]. Following collection, milk samples were placed directly into wet ice and transported from the procedure room to the wet lab where they were vortexed for 5 seconds, aliquoted into cryovials, and frozen at −80°C. Milk collection procedures used in this study have been described in further detail elsewhere [Hinde et al., 2009]. At the one-month time point, after the mother had recovered from sedation, mothers and infants were returned to their social group the same day. Between 3–4 months (mean±SD=110±11 days) and again between 5–6 months (mean±SD=159±6 days), infants and mothers were again captured between 7:30 and 9:00AM and separated and mothers relocated to temporary housing for milk accumulation. Following 3.5–4 hours of milk accumulation, milk was again collected and handled using identical techniques as described for the earliest timepoint.
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Milk assays We used DuoSet ELISA Development kits to measure adiponectin, EGF, EGF-R, and IGFIIR, and Quantikine Human TGF-β2 Immunoassay kit to measure TGF-β2 (R&D Systems, Inc., Minneapolis, MN). Before assay, all samples were brought to room temperature to thaw, vortexed and centrifuged to separate and remove the fat layer. All validations, recoveries, and assays were run using the skim fraction, and samples were measured in duplicate. We performed parallelism and recovery tests to validate all assays for use in Am J Primatol. Author manuscript; available in PMC 2017 August 01.
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macaque milk. All parallelisms showed a correlation between slopes of serially diluted pooled samples and the standard curve of R2 ≥ 0.98. All recoveries were ≥ 96%. Sample dilutions and alterations to kit protocols were informed by tests of parallelism and were as follows. For measurement of adiponectin, we diluted samples from early lactation 1:5, and used dilutions of 1:8 for peak and late lactation samples. For the EGF assay, we diluted early milk samples 1:50, and peak and late lactation samples 1:60. To measure EGF-R, we incorporated the following alterations to the kit protocol: we used the capture antibody at 0.5 ug/ml, the detection antibody at 150 ng/ml, and following the initial reconstitution of standard and detection antibody, we added 0.05% Tween 20 to the reagent diluent recipe. For EGF-R assay, we diluted early milk samples 1:6, and peak and late samples 1:8. We followed the manufacturer’s protocol for measurement of TGF-β2. Milk composition and yield
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Milk samples were assayed for nutritional composition in duplicate on the MIRIS milk analyzer in concert with >500 additional samples for other on-going studies calibrated specifically for Old World monkey milk [Hinde et al. 2015]. The mean±SD coefficients of variation (CV) of duplicate determinations in macaque milk for fat, protein, and sugar were 0.9±1.6%, 2.0±6.5%, and 1.1±2.0%, respectively. The gross energy of milk, also referred to as energy density, was calculated as 9.11 kcal/g for fat, 3.95 kcal/g for sugars, and 5.86 kcal/g for protein, which allowed for an estimate of the total energy density of milk (kcal/g) [Hinde and Milligan 2011]. Pooled bovine whole milk samples were used as controls at the beginning and end of each assay run. Based on these bovine samples, the inter-assay CV (calculated as the standard deviation divided by the mean value of all replicates) for fat was 2.52%, for protein was 1.64%, and for sugar was 4.11%. The intra-assay CV (calculated as the average of the individual CVs generated by dividing the standard deviation by the mean value of each days internal standard duplicates) for fat was 1.34%, for protein was 1.05%, and for sugar was 1.86%. Milk yield was measured gravimetrically as total sample (g) obtained by full evacuation of milk from both mammary glands after a standard period of milk accumulation (3.5–4 hours). This method of estimating relative differences in milk production among individuals has been used for other primates, validated with doubly labeled water [Tardif et al. 2001], and has been associated with infant mass and growth in the CNPRC population [Hinde et al. 2009; Hinde et al. 2015]. Available milk energy (AME) was calculated by multiplying kcal/g by milk yield (g). Statistical analysis
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All analyses were conducted using JMP Pro 11.2.0 (© 2013 SAS Institute). Milk bioactive values were log10-transformed to achieve normal distribution for multivariate analyses. Descriptive statistics were calculated for milk bioactives (mean, standard deviation) and reported as raw values (pg/ml). Differences in bioactives across stages of lactation were determined using repeated measures ANOVA with infant sex as a factor and maternal ID as a covariate. Milk energetic content (percent fat, protein, and sugars) and milk bioactives (APN, EGF, EGF-R, and TGF-β2) were assessed for correlations in which maternal ID nested by timepoint was included as a random effect. APN, EGF, EGF-R, and TGF-β2 were significantly correlated with milk macro-constituents; see results below (Table 2). Given this correlation between the energetic and bioactive concentrations in milk, available milk energy Am J Primatol. Author manuscript; available in PMC 2017 August 01.
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was included in regression models as an index to control for potential confounds with milk energy and milk volume.
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We constructed multiple regression models to examine 1) the degree to which variation in maternal and infant characteristics predicted variation in milk bioactives, and 2) the degree to which variation in infant growth outcomes could be predicted by variation in milk bioactives. We used generalized linear models to determine significant predictors of variation in milk bioactives. We built separate models for each bioactive, with maternal ID nested within time point (e.g., early, mid, late lactation) included as random parameters in the model in order to control for the effects of repeated measures. Our models included maternal mass, social rank (high, mid, low), parity (dichotomously as primiparous, multiparous), infant sex, infant age, and available milk energy (AME). We included available milk energy as a covariate, as prior studies have demonstrated significant associations between milk yield, caloric content, and hormone content in rhesus monkey milk [Hinde et al., 2015, Sullivan et al. 2010]. We report False Discovery Rate LogWorth (FDR LogWorth) and FDR LogWorth p-values for each model effect, calculated using the Benjamini-Hochberg technique (Benjamini and Hochberg, 1995), in order to assess the influence of effect size upon significance for each model parameter. Daily infant growth rate was calculated by subtracting the mass at early lactation from the mass at late lactation and then divided by the number of days between the two time points. We constructed models that initially included all milk bioactives, as well as the interaction between bioactives and infant sex, as predictors for infant mass and infant daily growth rate. Infant age and infant mass at the one-month measurement were included in the models to control for potential confounding effects associated with variation in absolute age among infants at each time point, and the scaling effect of body size on growth rate. For both sets of model analysis, we also used the corrected Akaike’s Information Criterion (AICc) value to aid in assessment of best fit [Akaike 1974]. The alpha level was set at 0.05. All analyses were performed using JMP, version 12.0.1 (SAS Institute Inc.).
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RESULTS Variation in milk composition across lactation
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Concentrations of all bioactives in milk increased across lactation (Table 2), with significant differences among early, peak, and late lactation in adiponectin (ANOVA: F (2,176)=21.39, pHeavier, p=0.0015), but higher concentrations of TGF-β2 than did mothers of lower mass (Heavier>Lighter, p = 0.0003: Fig. 3). Mothers of daughters produced milk with significantly high higher concentrations of TGF-β2 than did mothers of sons (475 ± SE 41 pg/ml and 390 ± SE 49 pg/ml, respectively, p = 0.0026: Fig. 4). AICc model selection retained AME as a predictor in models that appreciably explained variation, although this predictor was not statistically significant. Variation in infant size in relationship to milk bioactives and available milk energy Bioactive concentrations in milk predicted infant mass and growth rate during peak lactation, but not early or late lactation (Table 3). In both male and female infants, EGF and EGF-R were positively associated with mass after controlling for infant age (Model adj. R2 = 0.76; EGF: 0.10 ± SE 0.03, t=3.06, p=0.0026; EGF-R: −0.09 ± SE 0.03, t=−3.25, p=0.0014), as was AME (0.15 ± SE 0.03, t=5.79, p
