Human milk cortisol and immune factors over the first three postnatal months: Relations to maternal psychosocial distress

Marina Aparicio; Pamela D Browne; Christine Hechler; Roseriet Beijers; Juan Miguel Rodríguez; Carolina de Weerth; Leonides Fernández

doi:10.1371/journal.pone.0233554

. 2020 May 21;15(5):e0233554. doi: 10.1371/journal.pone.0233554

Human milk cortisol and immune factors over the first three postnatal months: Relations to maternal psychosocial distress

Marina Aparicio ^1,^#, Pamela D Browne ^2,^#, Christine Hechler ³, Roseriet Beijers ^2,³, Juan Miguel Rodríguez ¹, Carolina de Weerth ^2,^‡,^*, Leonides Fernández ^4,^‡,^*

Editor: Igor Branchi⁵

PMCID: PMC7241837 PMID: 32437424

Abstract

Background

Many biologically active factors are present in human milk including proteins, lipids, immune factors, and hormones. The milk composition varies over time and shows large inter-individual variability. This study examined variations of human milk immune factors and cortisol concentrations in the first three months post-partum, and their potential associations with maternal psychosocial distress.

Methods

Seventy-seven healthy mothers with full term pregnancies were enrolled, of which 51 mothers collected morning milk samples at 2, 6 and 12 weeks post-delivery. Maternal psychosocial distress was assessed at 6 weeks post-delivery using questionnaires for stress, anxiety, and depressive symptoms. Immune factors were determined using multiplex immunoassays and included innate immunity factors (IL1β, IL6, IL12, IFNγ, TNFα), acquired immunity factors (IL2, IL4, IL10, IL13, IL17), chemokines (IL8, Groα, MCP1, MIP1β), growth factors (IL5, IL7, GCSF, GMCSF, TGFβ2) and immunoglobulins (IgA, total IgG, IgM). Cortisol was quantified using liquid chromatography-tandem mass spectrometry. A linear mixed effects model was fit to test whether stress, anxiety, and depressive symptoms individually predicted human milk cortisol concentrations after accounting for covariates. Repeated measurement analyses were used to compare women with high (n = 13) versus low psychosocial distress (n = 13) for immune factors and cortisol concentrations.

Results

Virtually all immune factors and cortisol, with the exception of the granulocyte-macrophage colony-stimulating factor (GMCSF), were detected in the human milk samples. The concentrations of the immune factors decreased during the first 3 months, while cortisol concentrations increased over time. No correlation was observed between any of the immune factors and cortisol. No consistent relationship between postnatal psychosocial distress and concentrations of immune factors was found, whereas higher psychosocial distress was predictive of higher cortisol concentrations in human milk.

Conclusion

In the current study we found no evidence for an association between natural variations in maternal distress and immune factor concentrations in milk. It is uncertain if this lack of association would also be observed in studies with larger populations, with less uniform demographic characteristics, or with women with higher (clinical) levels of anxiety, stress and/or depressive symptoms. In contrast, maternal psychosocial distress was positively related to higher milk cortisol concentrations at week 2 post-delivery. Further investigation on maternal psychosocial distress in relation to human milk composition is warranted.

Introduction

Many bioactive factors are present in human milk, including immune factors [1] and hormones [2,3]. These factors contribute to optimal infant health and development [4]. The immune factors in human milk complement the infant’s immature immune system [5,6]. In addition to anti-infectious properties, immune factors also demonstrate anti-inflammatory properties and play a role in the establishment of the infant’s gut barrier and gut microbiota. The latter favors the development of the infant’s intestinal and immune functioning [7,8,9]. Concentrations of immune factors tend to be higher in colostrum compared to mature milk, with the decrease occurring during the first months postpartum [10,11]. However, most of the available studies only assessed a narrow panel of immune factors [1,11, 12].

The immunological composition of human milk varies greatly within an individual mother over time, but also between women [1]. This variation seems partly explained by different maternal factors, including maternal postnatal psychosocial distress (henceforth referred to as psychosocial distress) [10,13,14,15,16]. In the present study, psychosocial distress is defined as higher levels of stress, anxiety and depressive symptoms during the postpartum period. It differs from postpartum blues in that it can last for over 3 months instead of the first week after delivery [17]. Moreover, unlike postpartum depression, psychosocial distress is not necessarily diagnosed by clinical evaluation [18]. Psychosocial distress is highly prevalent, with up to 25% of women experiencing symptoms of distress after delivery [19]. Hypothetically, a state of psychological distress may modulate the maternal immune system, including the mucosa-associated lymphoid tissue (MALT) and plasma cells in the mammary gland. Indeed, maternal postpartum depression has been associated with depressed cellular immunity [13]. Modulations in the maternal immune system may consequently lead to shifts of immune factor concentrations in human milk [5]. In line with this, a previous study with 50 women found that maternal perceived stress was correlated with human milk secretory immunoglobulin A (sIgA) concentrations [5], and higher levels of depressive symptoms in 139 mothers have been associated with higher concentrations of transforming growth factor-beta (TGFβ) in human milk [16].

Recently, in the same sample of women as included in the current study, we found that human milk cortisol concentrations increased from week 2 to week 12 [20]. In the present study, we determined whether maternal distress was related to higher cortisol concentrations in human milk. Cortisol is the hormonal end product of the hypothalamic-pituitary-adrenal axis (HPA-axis), the stress control system. Exposure to higher levels of human milk cortisol may influence infant behavior and brain development [3,21]. Animal studies showed that serum cortisol concentrations increased during physical and psychological distress, leading to increased concentrations of milk cortisol [22,23]. In humans, relaxation therapy was effective in lowering milk cortisol at two weeks postpartum [24]. Other observational studies that examined whether cortisol concentrations (i.e. milk, serum or salivary cortisol) were related to maternal distress have shown conflicting results [3,14,24,25,26,27].

The present study sought to shed light on the possible relations between maternal psychosocial distress, immune factors, and cortisol in human milk in the early postpartum period. The first aim was to longitudinally investigate the presence, concentrations, and potential changes over time of a relatively large panel of immune factors in human milk during the first three months postpartum. The immune factors included innate immunity factors (IL1β, IL6, IL12, IFNγ, TNFα), acquired immunity factors (IL2, IL4, IL10, IL13, IL17), chemokines (IL8, Groα, MCP1, MIP1β), growth factors (IL5, IL7, GCSF, GMCSF, TGFβ2), and immunoglobulins (IgA, total IgG, IgM). The second aim was to identify whether maternal postnatal psychosocial distress (i.e. perceived stress due to daily hassles, general anxiety, and depressive symptoms) in a group of 51 healthy mothers was related to changes in the presence and concentration of immune factors and cortisol in human milk.

Materials and methods

Participants and procedures

This project is part of the BINGO study (Dutch acronym for Biological Influences on Baby’s Health and Development), which is a longitudinal study designed to identify prenatal and early postnatal predictors of infant health and development. The Ethics Committee of the Faculty of Social Sciences, Radboud University approved the study (ECSW2014-1003-189). All participants gave written informed consent prior to the start of the study.

Participants were recruited for the BINGO study via the project’s website and folders that were handed out at various locations in the region Nijmegen-Arnhem, including midwife practices, pregnancy courses, and baby stores. Initial exclusion criteria were an unhealthy, complicated pregnancy, insufficient mastery of the Dutch language, excessive alcohol use (i.e., alcohol dependency) and/or drug use. Women who were interested in potential participation received additional study information by mail. Ninety-eight expectant women signed up for the study by providing written informed consent, 88 of these women were eligible for study participation, and participated in this project (S1 Fig). The exclusion criteria after birth were: gestational age at birth <37 weeks, 5-minute Apgar score <7, birth weight <2,500 g, congenital malformations, complications during pregnancy after initial contact, maternal antibiotic use, incomplete milk samples (i.e. not collected at all three time points), and maternal illness. Maternal illness was defined as any general illnesses (i.e. temporary illnesses such as fever during the preceding week, or chronic diseases) and pregnancy-related illnesses that required second line obstetric care. These criteria rendered a final group of 51 mothers for the analyses (S1 Fig).

Mothers filled out paper questionnaires on psychosocial stress, anxiety, and depressive symptoms at home at six weeks postpartum. Milk samples were collected at 2, 6 and 12 weeks post-delivery.

Collection of human milk samples

Mothers collected milk after the infant reached the age of 2 weeks [mean (SD) age of 14.75 (1.84) days], 6 weeks [mean (SD) age of 43.58 (5.02) days], and 12 weeks [mean (SD) age of 85.35 (2.33) days]. Prior to collection, mothers washed their hands, breasts, and nipples, and cleaned the small collection cups with boiling water. Subsequently, prior to feeding the infant, they collected 20 mL of the first foremilk in the morning [mean (SD) time at 08:36 (02:48) am] by hand expression. Additionally, mothers noted the date and time of sample collection as well as maternal illnesses and/or intake of medication the preceding week. After collection, samples were immediately stored in the mothers' freezers (-18 - -20 ºC). When the infant was 13 weeks of age, samples were collected in a portable freezer to be stored at -80 ºC at Radboud University, the Netherlands. Aliquots of the samples were afterwards shipped by temperature-controlled shipment to the Complutense University of Madrid, Spain (for immune factor analyses), and to the Utrecht University Medical Centre, the Netherlands (for cortisol analyses).

Immunological analysis of human milk

Immune factors in the milk samples were determined at the Complutense University of Madrid (Spain) following the procedure described by Ruiz et al. [1]. The concentrations of innate immune factors (IL1β, IL6, IL12, IFNγ, TNFα), acquired immunity factors (IL2, IL4, IL10, IL13, IL17), chemokines (IL8, Groα, MCP1, MIP1β), and growth factors (IL5, IL7, GCSF, GMCSF, TGFβ2) were determined by magnetic beads-based multiplex immunoassays, using a Bio-Plex 200 instrument (Bio-Rad, Hercules, CA) and the Bio-Plex Pro Human Cytokine, Chemokine, and Growth Factor Assays and the Bio-Plex Pro TGF-β 3-plex Immunoassay kits (Bio-Rad). Epidermal growth factor (EGF) was determined by ELISA using the RayBio Human EGF ELISA kit (RayBiotech, Norcross, GA). The concentrations of immunoglobulins (IgA, total IgG, and IgM) in the milk samples were determined using the Bio-Plex Pro Human Isotyping Assay kit (Bio-Rad) in the Bio-Plex system instrument.

Prior to their analysis, the samples (1 mL) were processed and aliquoted as described previously [28]. Every assay was run in duplicate according to the manufacturer's instructions and standard curves were performed for each analyte.

Human milk cortisol concentration analysis

Cortisol concentrations in milk were determined at the Clinical Chemistry and Haematology laboratory (LKCH) of the University Medical Centre in Utrecht, the Netherlands. Cortisol was quantified by Liquid Chromatography-tandem Mass Spectrometry (LC-MS/MS) adding cortisol-D₄ as an internal standard, as described in Hechler et al. [20]. The limit of quantification for cortisol was 1.0 nmol/L.

Stress

General stress was measured with the Alledaagse Problemen Lijst (Everyday Problem Checklist; EPL), which assessed the occurrence and intensity of daily hassles [29]. Participants indicated whether a daily hassle (49 items presented) had occurred in the past two months and how much it had bothered them on a 4-point Likert scale ranging from 1 (“I do not mind at all) to 4 (“I do mind a lot”). The mean intensity rating of daily hassles was computed by dividing the sum of total (negative) valence by the frequency of the events, with higher values indicating more experienced negativity. Internal consistency in this sample was good, with Cronbach's α equal to 0.84.

Anxiety

A Dutch translation of the State Trait Anxiety Inventory (STAI-S) was used to measure maternal postnatal state anxiety [30]. The STAI-S consists of 20 statements related to feelings of anxiety at the present moment. Participants scored on a 4-point scale from 1 (“not at all”) to 4 (“very much”) how they felt at a specific moment and scores were summed up. Higher scores indicated more feelings of anxiety. Results indicated excellent internal consistency in this sample (α = 0.90).

Depressive symptoms

Depressive symptoms were measured with the Edinburgh Postnatal Depression Scale (EPDS) [31]. The EPDS consists of 10 items for which participants indicated whether they experienced depressive symptoms in the past seven days. Responses were scored on a 4-point scale ranging from 0 to 3 in seriousness of symptoms; higher scores indicated more depressive symptoms. Internal consistency was good in this sample with α = 0.87.

Psychosocial distress

Pregnancy psychosocial distress is a multidimensional concept of different pregnancy specific and non specific mood states [32,33]. Therefore, as we did in our previous studies, we aggregated the scores of the three distress questionnaires on stress, anxiety and depressive symptoms into one measure of “maternal psychosocial distress” [34,35,36].

Statistical analyses

Normality of data distribution was examined through visual inspection of histograms and Shapiro-Wilks tests. Variables following a normal distribution were expressed as the mean and standard deviation or 95% confidence interval (CI) of the mean, while those variables that were not normally distributed were expressed as the median and interquartile range (IQR). Cortisol and immune factor concentrations were logarithmically transformed prior to analysis when indicated. Postnatal week and time of milk collection were included in the analyses as potential confounders.

The strength and direction of association between variables (cortisol and immune factor concentrations in milk and the scores of stress, anxiety, and depressive symptoms) was measured using the Spearman’s rank-order correlation analyses. The correlation matrix was visualized using R package “corrplot” [37].

Principal Component Analyses (PCA) with a variable reduction approach (cos²>0.2) was performed to test for similarities among milk samples according to the concentrations of immune factors and cortisol as active variables using “FactoMineR” package in R [38]. Postnatal week and psychosocial distress level were used as supplementary variables to label milk samples in the scatterplot.

Kruskal-Wallis tests were used in non-parametric analysis, followed by post-hoc Nemenyi-test when required, and one-way ANOVA was applied for parametric data. Friedman’s non-parametric repeated measures comparisons followed by post-hoc Nemenyi tests were applied to evaluate differences in the concentrations of immune factors and cortisol at the three postnatal sampling times. Comparisons of cortisol concentrations in milk, time of collection during the day and time-elapse between wake up and milk sampling were performed with t-tests. Differences in the detection frequencies of the immune factors and of the number of samples collected at each of the three sampling points were evaluated by the Chi-squared or Fisher’s exact test, followed by a post-hoc Nemenyi-test adjusted to χ² statistics for pairwise multiple comparison, when required. The PMCMRplus package in R was used to perform these analyses [39].

Linear mixed effects model of log-transformed concentrations of cortisol and immune factors including postnatal week, sample collection hour, time-elapse between wake up and milk sampling, and the scores of stress, anxiety, and depressive symptoms as effects were run using StatGraphics Centurion 18 (version 18.1.06) (Statpoint Technologies, Inc., The Plains, VA, USA).

All statistical analyses were considered significant at the p < 0.05 level.

Results

Participant characteristics

Women participating were between 26 and 40 years of age, healthy, predominantly highly educated (80%), and with an uncomplicated, full term pregnancies (Table 1). The mean (SD) gestational age at delivery was 39.73 (1.66) weeks. Maternal scores on stress, anxiety and depressive symptoms are shown in Table 1.

Table 1. Maternal and infant characteristics of all participants (n = 51) included in the study.

Maternal characteristics
Age (years)
Mean (SD)	32.04 (3.70)
Range (min, max)	26.00–40.00
Educational background
Middle job training, n (%)	8 (16)
Special college, n (%)	15 (29)
University, n (%)	26 (51)
Other, n (%)	2 (4)
Psychosocial distress
Postnatal stress^a, median (IQR)	2.14 (1.83–2.50)
Postnatal general anxiety^b, median (IQR)	27.0 (23.0–32.0)
Postnatal depressive symptoms^c, median (IQR)	5.0 (3.0–7.0)
Infant characteristics
Infant gender
Boy, n (%)	25 (49)
Girl, n (%)	26 (51)
Gestational age at birth (weeks)
Mean (SD)	39.73 (1.66)
Range (min, max)	35.57–42.14

Open in a new tab

^a Alledaagse Problemen Lijst (Everyday Problem Checklist; EPL; scale: 0–6) [29].

^b State Trait Anxiety Inventory (STAI-S; scale: 20–80) [30].

^c Edinburgh Postnatal Depression Scale (EPDS; scale: 0–30) [31].

Relationship between cortisol and immune factors in human milk

Given that immune factors and cortisol concentrations were analyzed in parallel, potential correlations between these compounds were tested. Cortisol concentrations were not correlated with the concentrations of the most frequently detected immune factors assayed (Spearman’s rank correlation: -0.19 ≤ r_s ≤ 0.11) (Fig 1). In contrast, most of the immune factors were positively correlated, and the correlation was particularly strong between the pairs IL8 and MIP1β, and IL7 and Groα at weeks 2 and 6 (r_s > 0.75) (Fig 1).

Fig 1 — Spearman’s rank correlation matrix of cortisol and immune factors concentrations in milk samples provided by women participating in this study (n = 51). Blue color indicates positive correlation and red color negative correlation. Color intensity is proportional to the magnitude of correlation coefficients. The actual values of the Spearman’s rank correlation coefficient (r_s) are shown in the lower left corner of the plots.

In order to find potential pattern profiles for immune factors and cortisol concentrations in the samples, an initial PCA exploratory analysis was performed. PCA analysis did not reveal any distinct patterns in the concentrations of cortisol and immune factors in the samples. Samples roughly distributed according to the three postnatal weeks in the scatter plot (S2 Fig). The explained variance using the first two dimensions was about 33%, being IL8 the most influential variable (explaining 21.34% of the variance; S2 Fig). Samples did not cluster when labelled with the distress level (S2 Fig).

Concentrations and changes over time of cortisol

Median (IQR) cortisol concentration in milk was 7.93 (4.22–14.25) μg/dL and ranged from 1.43 μg/dL to 64.20 μg/dL during the first three months postpartum (Fig 2). Although more than half of the samples (n = 85) were obtained between 06:00 and 09:00, there was a large range of collection times (from 03:00 to 23:00) (Fig 2). The association between the time of milk collection and the cortisol concentrations is shown in Fig 2, where samples for which sampling time was registered were grouped at hourly intervals. Most samples (n = 139) were taken between 05:00 and 12:00. The highest mean cortisol concentrations were recorded in samples taken between 06:00 and 06:59. Cortisol concentrations continuously increased in the early morning from 05:00 to 06:00–06:59, and showed a continuous decrease throughout the day from 06:59 until noon.

Mean cortisol concentration in samples taken at week 2 (6.61 μg/dL) was lower than concentrations at weeks 6 and 12 (8.51 and 9.12 μg/dL, respectively) (S3 Fig). The difference in concentrations was statistically significant between weeks 2 and 12 (p = 0.002) (S3 Fig). The number of samples collected at each of the three sampling points (week 2, 6 and 12) were similarly distributed among the different hourly intervals (Fig 2; χ² statistical test for a 3×9 contingency table; χ² = 8.59, p = 0.929). This excludes that the observed increase of cortisol concentrations over time is due to differences in milk sampling times.

The diurnal rhythm of cortisol in milk was also observed when the samples were analyzed separately for the three postnatal weeks (Fig 2). At week 2, the median cortisol concentrations were highest (22.72 μg/dL) in samples collected at 06:00–06:59, although the variability was high (between 5.79 and 35.66 μg/dL). In contrast, at weeks 6 and 12, the highest median cortisol concentration was found in samples collected at 05:00–05:59 (18.55 μg/dL) and 07:00–07:59 (19.04 μg/dL), respectively (Fig 2).

Concentrations and changes over time of immune factors

All the assayed immune factors could be detected among the milk samples provided by the 51 participants, with the only exception of GMCSF (Table 2). In general, the values obtained for all the immune factors showed a high inter-individual variability in detection frequencies (Table 2). Globally, IgA, IgGt, IgM, EGF, TGFβ2, IL8 and Groα displayed the highest detection frequencies (100% of the samples) at week 2, followed by IL1β, TNFα, MCP1 and MIP1β (75–97% of the samples). In contrast, IL2, IL10, IL12, IL17 and IFNγ were found in ≤ 10% of the samples.

Table 2. Frequency and concentration of immune factors in milk samples.

Immune factor		Week 2		Week 6		Week 12
	n (%)^a	Median (IQR)^b	n (%)	Median (IQR)	n (%)	Median (IQR)	p–value^c	p–value^d
Immunoglobulins
IgA (g/L)	51 (100)	1.68 (1.08–2.09)^a	51 (100)	1.26 (0.92–1.67)^b	51 (100)	1.26 (0.83–1.68)^b	1.000	<0.001
IgGt (mg/L)	51 (100)	52.10 (39.10–76.42)^a	51 (100)	43.60 (32.64–57.71)^b	51 (100)	42.00 (31.51–56.02)^b	1.000	<0.001
IgM (mg/L)	51 (100)	64.73 (47.84–97.12)^a	51 (100)	38.19 (21.73–61.92)^b	51 (100)	33.06 (16.93–49.22)^c	1.000	<0.001
Innate immunity
IL1β (ng/L)	44 (86)	0.52 (0.31–1.36)^a	35 (69)	0.51 (0.29–0.87)^b	34 (67)	0.37 (0.18–0.69)^b	0.046	0.065
IL6 (ng/L)	28 (55)	9.36 (5.31–19.63)^a	16 (31)	5.32 (3.09–12.36)^b	11 (22)	2.64 (1.19–7.42)^b	0.002	0.002
IL12(p70) (ng/L)	2 (4)	2.41 (1.44–3.39)	0	–	2 (4)	0.44 (0.42–0.45)	0.547*	–
IFNγ (ng/L)	4 (8)	8.53 (6.81–12.64)	2 (4)	6.65 (5.01–8.29)	2 (4)	2.49 (2.31–2.66)	0.600*	–
TNFα (ng/L)	38 (75)	2.69 (1.38–3.77)	34 (67)	2.24 (1.38–3.77)	33 (65)	2.84 (1.38–4.08)	0.530	0.914
Acquired immunity
IL2 (ng/L)	1 (2)	0.93	2 (4)	0.92 (0.69–1.14)	2 (4)	3.41 (2.70–4.11)	0.873*	–
IL4 (ng/L)	7 (14)	0.22 (0.12–0.65)	2 (4)	0.41 (0.40–0.42)	3 (6)	0.18 (0.13–0.39)	0.211*	–
IL10 (ng/L)	2 (4)	0.71 (0.37–1.04)	2 (4)	5.35 (4.56–6.15)	5 (10)	0.62 (0.53–1.11)	0.433*	–
IL13 (ng/L)	17 (33)	0.21 (0.12–0.40)	13 (25)	0.26 (0.15–0.40)	17 (33)	0.31 (0.12–0.62)	0.613	0.651
IL17 (ng/L)	5 (10)	3.61 (1.01–4.88)	0	–	0	–	0.011*	–
Chemokines
IL8 (ng/L)	51 (100)	15.35 (6.87–47.80)	51 (100)	11.24 (6.56–29.78)	51 (100)	14.11 (8.52–23.99)	1.000	0.111
MCP1 (ng/L)	45 (88)	249.82 (92.32–542.20)^a	30 (59)	84.68 (27.16–179.62)^b	28 (55)	33.51 (11.85–51.99)^b	<0.001	<0.001
MIP1β (ng/L)	49 (96)	13.46 (4.78–39.98)^a	41 (80)	5.75 (2.52–15.67)^b	36 (71)	3.73 (2.03–7.79)^b	0.003	<0.001
GROα (μg/L)	51 (100)	1.69 (0.54–4.01)^a	51 (100)	0.64 (0.23–1.99)^b	51 (100)	0.57 (0.25–1.67)^b	1.000	0.004
Hematopoyetic factors
IL5 (ng/L)	24 (47)	1.47 (0.81–2.02)	24 (47)	1.28 (0.87–1.89)	25 (49)	0.87 (0.65–1.66)	0.975	0.363
IL7 (ng/L)	24 (47)	101.55 (59.67–143.33)	19 (37)	42.92 (12.96–121.21)	19 (37)	82.87 (34.40–126.54)	0.507	0.607
GCSF (ng/L)	15 (29)	1.51 (1.26–3.04)	16 (31)	1.00 (0.54–3.69)	18 (35)	3.04 (1.50–3.90)	0.811	0.505
GMCSF (ng/L)	0	–	0	–	0	–	–	–
EGF (μg/L)	51 (100)	5.51 (4.37–6.78)^a	51 (100)	4.84 (3.87–5.65)^b	51 (100)	4.55 (3.47–5.42)^b	1.000	<0.001
TGFβ₂ (μg/L)	51 (100)	2.23 (1.33–3.30)^a	51 (100)	1.30 (0.73–2.11)^b	51 (100)	1.71 (0.87–2.63)^a ^b	1.000	0.016

Open in a new tab

Abbreviations: EGF, epidermal growth factor; GCSF, granulocyte colony-stimulating factor; GMCSF, granulocyte–macrophage colony-stimulating factor; Groα, growth-related oncogene-α; IFNγ, interferon-γ; Ig, immunoglobulin; IL, interleukin; MCP1, macrophage–monocyte chemoattractant protein-1; MIP1β, macrophage inflammatory protein-1β; TGFβ₂, transforming growth factor-β2; TNFα, tumor necrosis factor-α.

^a n (%): number (percentage) of samples in which the immunological compound was detected.

^b Concentrations are expressed as median and interquartile range (IQR).

^c Chi-squared tests (or Fisher-tests, marked with an asterisk [*]) were used to evaluate differences in expression frequencies of the analyzed parameters across time.

^d Friedman tests were used to evaluate differences in concentration along time. Different caption letters in a row mean statistical differences in the concentration of the immune factor between postnatal weeks when the post hoc pairwise comparison Nemenyi test was done.

When the detection frequencies of all immune factors at week 2 were compared with those obtained at weeks 6 and 12, statistical differences were observed for IL1β (p = 0.046), IL6 (p = 0.002), IL17 (p = 0.011), MCP1 (p < 0.001), and MIP1β (p = 0.003) (Table 2). The detection frequencies of these immune factors decreased from week 2 to week 6, and the reduction was particularly noticeable for MCP1 and IL6 (from 88% to 59% and from 55% to 31%, respectively). A further decrease in the detection frequency of MCP1 (to 55%), IL6 (to 22%), and MIP1β (71%) was observed at week 12. IL17 was not detected in any sample taken at week 6 or 12 (Table 2).

The concentrations of all detected immune factors also showed a high degree of variability between women, as reflected by the large IQR values for some of the analyzed parameters (Table 2). The most abundant immune factor detected at week 2 was IgA (median [IQR] = 1.68 [1.08–2.09] g/L), followed by IgM and IgGt (about 25 and 30 times less abundant, respectively). The second largest group of immune factors included hematopoietic factors EGF and TGFβ2 and the chemokine Groα with median concentrations of 5.51, 2.23 and 1.69 μg/L, respectively. The median levels of other immune factors ranged from 249.82 ng/L (for MCP1) to 0.21 ng/L (for IL13). Despite such inter-individual variability, statistically significant differences between postnatal weeks were found for the concentrations of IgA (p < 0.001), IgM (p < 0.001), IgGt (p < 0.001), IL6 (p = 0.002), MCP1 (p < 0.001), MIP1β (p < 0.001), GROα (p = 0.004), EGF (p < 0.001), and TGFβ2 (p = 0.016) (Table 2). The concentrations of these immune factors decreased over time, and the decline was particularly pronounced between samples taken at weeks 2 and 6. The only exception was TGFβ2: the median value decreased from week 2 (2.23 μg/L) to week 6 (1.30 μg/L) and increased from week 6 to week 12 (1.71 μg/L).

Association between stress, anxiety and depressive symptoms and immune factor concentrations

Spearman’s correlation coefficients between stress, anxiety, and depressive symptoms, and immune factors (only those that were detected in at least half of the samples) concentrations in milk at week 6 were very low, indicating very weak monotonic correlations between these variables (Table 3). Out of total 54 correlations analyzed, only three reached statistical significance: stress and IL8 (r_s = 0.29, p = 0.039), depressive symptoms and IL7 (r_s = 0.29, p = 0.040), and stress and TGFβ₂ (r_s = -0.34, p = 0.014) (Table 3). None of the psychosocial distress variables explained the concentrations of any of the immune factors according to the linear mixed effects models (results not shown).

Table 3. Correlation between immune factors concentrations and the postnatal psychosocial distress variables at week 6.

	Stress			Anxiety			Depressive symptoms
	n^a	r_s^b	p-value	n	r_s	p-value	n	r_s	p-value
Immunoglobulins
IgA	51	0.14	0.332	51	0.07	0.636	51	-0.04	0.760
IgGt	51	0.19	0.187	51	0.07	0.610	51	0.09	0.533
IgM	51	0.25	0.082	51	0.11	0.429	51	0.19	0.177
Innate immunity
IL1β	35	0.03	0.844	35	0.26	0.138	35	0.28	0.103
TNFα	34	0.05	0.777	34	0.23	0.197	34	0.05	0.764
Chemokines
IL8	51	0.29	0.039	51	0.14	0.343	51	0.03	0.820
MCP1	30	0.07	0.718	30	0.04	0.818	30	-0.09	0.640
MIP1β	41	-0.15	0.351	41	0.06	0.704	41	-0.28	0.081
GROα	51	0.03	0.837	51	-0.17	0.221	51	-0.15	0.286
Hematopoyetic factors
IL5	24	-0.07	0.743	24	-0.04	0.838	24	-0.08	0.714
IL7	51	0.23	0.109	51	0.10	0.498	51	0.29	0.040
EGF	51	-0.01	0.939	51	-0.13	0.368	51	-0.04	0.794
TGFβ₂	51	-0.34	0.014	51	0.01	0.938	51	-0.11	0.444

Open in a new tab

Abbreviations: EGF, epidermal growth factor; Groα, growth-related oncogene-α; Ig, immunoglobulin; IL, interleukin; MCP1, macrophage–monocyte chemoattractant protein-1; MIP1β, macrophage inflammatory protein-1β; TGFβ₂, transforming growth factor-β2; TNFα, tumor necrosis factor-α.

^a n: sample size.

^b r_s: Spearman’s correlation coefficient.

Association between stress, anxiety and depressive symptoms and cortisol concentrations

Linear mixed effects model allowed exploring the predictive values of stress, anxiety, and depressive symptoms, as well as postnatal week, sample collection time, and time elapsed between waking up and milk sampling on cortisol concentrations (Table 4). Cortisol concentrations in milk were higher with increasing stress (F = 5.28; p = 0.023) and with increasing anxiety (F = 6.51; p = 0.012), but they were not positively predicted by depressive symptoms. As described previously, cortisol concentrations in milk varied along postnatal weeks (F = 3.23; p = 0.043) and were strongly influenced by the sample collection time (F = 21.37; p = 0.000), but they were not related to the time elapsed between waking up and milk sampling (Table 4).

Table 4. Linear mixed effects model of cortisol concentration (log transformed) in human milk.

Explanatory term	Sum squares	DF	Mean squares		p-value
Postnatal stress	0.502	1	0.502	5.28	0.023
Postnatal anxiety	0.681	1	0.618	6.51	0.012
Postnatal depressive symptoms	0.059	1	0.059	0.62	0.434
Postnatal week	0.614	2	0.307	3.23	0.043
Sample collection hour	2.030	1	2.030	21.37	0.000
Time elapsed between waking up and milk sampling	0.0004	1	0.0004	0.00	0.947

Open in a new tab

Differences in concentrations of immune factors between mothers with low and high maternal psychosocial distress

In line with a previous study by Zijlmans et al. [36], we chose to compare women with highest and lowest psychosocial distress (i.e. compare the extremes within the study population). To this end, a median score for each of the three psychological variables was computed. The group of mothers who scored below the median on all three variables was categorized as ‘low maternal postnatal psychosocial distress group’ (group LOW) (n = 13, 25%). The group of mothers scoring above the median on all three variables was categorized as the ‘high maternal postnatal psychosocial distress group’ (group HIGH) (n = 13, 25%). There were no significant differences between women with LOW and HIGH psychosocial distress for age, educational background, infant sex, and gestational age at delivery (Table 5).

Table 5. Maternal and infant characteristics of participants with lowest (group LOW) and highest (group HIGH) levels of maternal postnatal psychosocial distress.

	Group LOW (n = 13)	Group HIGH (n = 13)	p-value
Maternal characteristics
Age (years)			0.159^d
Mean (SD)	30.50 (3.37)	32.69 (4.09)
Range (min, max)	25–36	28–40
Educational background
Middle job training, n (%)	3 (23)	1 (8)	0.621^e
Special college, n (%)	3 (23)	2 (15)
University, n (%)	7 (54)	9 (69)
Other, n (%)	0 (-)	1 (8)
Psychosocial distress
Postnatal stress^a [median (IQR)]	1.86 (1.60–2.00)	2.50 (2.40–2.67)	< 0.001^f
Postnatal general anxiety^b [median (IQR)]	24 (22–25)	38.0 (32.0–43.0)	< 0.001
Postnatal depressive symptoms^c [median (IQR)]	3.0 (0.0–6.0)	10.0 (8.0–11.0)	< 0.001
Infant characteristics
Infant gender
Boy, n (%)	7 (54)	6 (46)	0.699^g
Girl, n (%)	6 (46)	7 (54)
Gestational age at birth (weeks)
Mean (SD)	39.95 (1.38)	39.93 (1.28)
Range (min, max)	36.71–41.71	37.00–41.71	0.966^d

Open in a new tab

^a Alledaagse Problemen Lijst (Everyday Problem Checklist; EPL; scale: 0–6) [29].

^b State Trait Anxiety Inventory (STAI-S; scale: 20–80) [30].

^c Edinburgh Postnatal Depression Scale (EPDS; scale: 0–30) [31].

^d One-way ANOVA test was used to determine differences in gestational age at birth between groups LOW and HIGH.

^e Fisher exact probability test was used to evaluate differences in mother’s educational background between groups LOW and HIGH.

There were no differences in the detection frequency of any immune factor when both groups LOW and HIGH were compared at each postnatal week (week 2, 6 and 12) (Table 6). Regarding immune factor concentrations, significant differences were only found for IL4 (week 2), EGF (week 2), and IL5 (all three sampling times) (p < 0.05). Of note, these immune factors were only detected in a small percentage of the samples in both groups.

Table 6. Immune factors in milk samples provided by women with lowest (group LOW) and highest (group HIGH) maternal postnatal psychosocial distress.

	Week 2				Week 6				Week 12
	Group LOW (n = 13)		Group HIGH (n = 13)		Group LOW (n = 13)		Group HIGH (n = 13)		Group LOW (n = 13)		Group HIGH (n = 13)
	n^a	Median (IQR)	n	Median (IQR)	n	Median (IQR)	n	Median (IQR)	n	Median (IQR)	N	Median (IQR)
Immunoglobulins
IgA, mg/L (×10³)	13	1.8 (1.3–2.5)	13	1.9 (1.1–2.1)	13	1.5 (1.1–1.8)	13	1.5 (1.0–2.0)	13	1.2 (1.1–1.8)	13	1.6 (1.1–1.7)
IgGt (mg/L)	13	47.7 (34.6–85.9)	13	52.1 (44.0–67.2)	13	43.0 (30.4–58.4)	13	36.4 (32.6–60.7)	13	48.2 (37.3–51.7)	13	42.0 (30.7–52.7)
IgM (mg/L)	13	60.6 (54.7–93.1)	13	78.7 (58.7–100.9)	13	29.1 (18.4–55.3)	13	45.3 (39.8–79.1)	13	33.1 (25.4–39.9)	13	42.9 (23.5–74.4)
Innate immunity
IL1β (ng/L)	12	0.7 (0.2–2.6)	12	0.5 (0.3–2.1)	7	0.6 (0.3–0.79)	10	0.7 (0.6–1.0)	9	0.2 (0.1–0.3)	10	0.4 (0.2–2.4)
IL6 (ng/L)	8^♯	10.0 (6.8–16.3)	7	7.8 (4.0–78.6)	3^♯	4.6 (2.6–22.1)	3	4.5 (3.6–7.5)	3^♯	1.5 (1.2–2.1)	4	2.7 (1.4–5.3)
IL12(p70) (ng/L)	0	-	1	4.4	0	-	0	-	1	0.5	1	0.4
IFNγ (ng/L)	0	-	2	14.9 (11.2–18.5)	0	-	0	-	0	-	1	2.1
TNFα (ng/L)	11	3.1 (1.4–6.1)	10	2.0 (1.0–5.3)	8	2.0 (0.9–3.0)	10	3.0 (1.6–4.0)	8	2.9 (0.7–5.0)	9	2.8 (1.4–3.3)
Acquired immunity
IL2 (ng/L)	0	-	1	0.9	0	-	0	-	0	-	2	3.4 (2.7–4.1)
IL4 (ng/L)	3	0.50 (0.0–0.1)*	2	1.6 (1.2–1.9)*	0	-	0	-	1	0.2	1	0.1
IL10 (ng/L)	0	-	1	0.03	0	-	0	-	3	0.6 (0.3–0.9)	1	0.5
IL13 (ng/L)	5	0.3 (0.3–0.7)	6	0.2 (0.1–0.4)	3	0.5 (0.3–0.7)	6	0.26 (0.22–0.30)	5	0.6 (0.2–0.8)	3	0.6 (0.3–0.9)
IL17 (ng/L)	2	2.6 (1.5–3.8)	2	11.8 (7.7–16.0)	0	-	0	-	0	-	0	-
Chemokines
IL8 (ng/L)	13	25.8 (11.0–83.0)	13	13.3 (5.8–53.8)	13	6.9 (4.5–27.6)	13	8.7 (6.2–48.1)	13	10.2 (9.3–17.1)	13	14.5 (7.4–34.7)
MCP1 (ng/L)	11	267.6 (125.9–700.8)	10	372.2 (121.3–542.2)	7	118.1 (90.2–218.6)	5	110.1 (40.6–123.5)	6	44.5 (35.6–53.2)	8	43.1 (19.5–60.7)
MIP1β (ng/L)	13	34.2 (7.7–68.1)	13	14.7 (2.6–40.0)	9	10.4 (4.5–15.7)	10	5.3 (1.6–14.7)	10	3.6 (1.8–4.0)	9	2.6 (1.2–14.6)
GROα (μg/L)	13	3.8 (1.1–4.1)	13	4.1 (0.4–5.9)	13	0.73 (0.2–6.4)	13	0.3 (0.2–1.0)	13	0.8 (0.5–2.1)	13	0.4 (0.2–0.9)
Hematopoyetic factors
IL5 (ng/L)	4	1.7 (1.1–2.6)*	6	1.6 (0.5–1.8)*	4	3.0 (2.4–3.4)*	6	1.3 (1.0–1.6)*	5	0.9 (0.8–0.9)*	5	1.5 (1.3–1.9)*
IL7 (ng/L)	5	134.0 (79.0–134.6)	9	132.0 (90.7–145.2)	5	119.2 (52.7–151.8)	3	15.2 (13.5–57.7)	4	137.5 (102.5–167.4)	6	85.0 (59.1–162.1)
GCSF (ng/L)	4	1.5 (1.4–3.1)	5	2.3 (1.3–24.4)	4	1.05 (0.7–3.9)	3	0.1 (0.1–0.4)	4	2.0 (1.6–2.6)	6	3.0 (1.3–3.3)
GMCSF (ng/L)	0	-	0	-	0	-	0	-	0	-	0	-
EGF (μg/L)	13	4.5 (3.6–5.2)*	15	5.8 (5.5–6.9)*	13	4.0 (3.6–4.9)	13	5.3 (4.1–6.2)	13	3.8 (2.7–5.4)	13	4.6 (4.0–5.5)
TGFβ₂ (μg/L)	13	2.12 (1.6–2.7)	13	2.4 (1.3–3.7)	13	1.2 (0.8–2.6)	13	1.1 (0.7–1.5)	13	2.2 (1.0–2.6)	13	2.2 (1.7–2.7)

Open in a new tab

^a n: number of samples in which the parameter was detected (relative frequency of detection).

Chi-squared tests (or Fisher exact tests) were used to evaluate differences in the detection of the immune factors between groups LOW and HIGH at each sampling time and within each group across time. There were no differences in the detection frequency of any immune factor when both groups were compared at each sampling time. Statistical differences in the detection frequency of an immune factor within a group (LOW or HIGH) across time are indicated with a hash (^♯ p< 0.05).

Mann-Whitney U tests were used to evaluate differences in the concentration of immune factors between groups LOW and HIGH at each sampling time. Statistical differences of the concentrations between groups LOW and HIGH are indicated with an asterisk (*p < 0.05).

Similar to the total group of women, the frequencies of detection of the immune factors decreased in both groups over time (Table 6). However, the change reached statistical significance only for IL6 in group LOW: IL6 was more frequently found among samples at week 2 (62%) than at weeks 6 and 12 (23%) (p = 0.044).

The concentrations of IgM, MCP1, and MIP1β decreased significantly in both groups over time (S1 Table), which was similar in comparison with the total group of participants (Table 2). There was a significant decrease in IgA (p = 0.002), IL8 (p = 0.012), and EGF (p = 0.023) concentrations in samples of mothers in group LOW, which was not observed in women in group HIGH. In contrast, Groα only showed a significant decrease over time in group HIGH (p < 0.001) but not in group LOW (p = 0.926).

Differences in cortisol concentrations between mothers with low and high maternal psychosocial distress

At week 2, the mean concentrations of cortisol in milk samples of group LOW were approximately less than half of those found in group HIGH [mean (95% CI) concentration of 6.62 (4.36–8.88) μg/dL and 14.10 (6.34–21.80) μg/dL, respectively; p = 0.046] (Table 7). Women in both groups collected their milk samples around the same time at week 2, with only a small gap of 18 min difference in the mean collection time (mean time group LOW 07:56 and group HIGH 08:14 am). However, an increasing trend in the difference in the collection time was observed along time, reaching 2 h and 46 min at week 12 (mean collection time was 06:56 for women in group LOW and 09:42 for women in group HIGH; p = 0.024). Therefore, it is possible that the difference in cortisol concentration in milk between women in group LOW [mean (95% CI) concentration of 12.20 (7.81–16.60) μg/dL] and group HIGH [mean (95% CI) concentration of 18.60 (7.73–29.40) μg/dL] at week 12 would have been greater if samples had been collected at similar day times. Additionally, the time-lapse between waking up and the moment in which women actually collected the sample also tended to increase in women of the HIGH group. Women in group LOW took the samples 33.6 min (mean time for the group) after waking up during the study while women in group H collected milk samples 120 min (mean time for the group) after waking up throughout the study. At week 2, the time passed from waking up to sample collection was approximately twice among women in group HIGH when compared to women in group LOW; this time-lapse increased to about 2 h at week 12 [mean (95% CI) elapsed time of 24.70 (2.05–47.40) min in group LOW and of 153 (27.40–278.00) min in group HIGH; p = 0.046)] (Table 7).

Table 7. Change in the cortisol concentration in milk samples, in the collection time and in the time elapsed between waking up and milk sampling in women with lowest (group LOW) and highest (group HIGH) maternal postnatal psychosocial distress.

	Group LOW (n = 13)	Group HIGH (n = 13)
Postnatal week	Mean (95% CI)	Mean (95% CI)	p-value^a
Cortisol (μg/dL)
Week 2	6.62 (4.36–8.88)	14.10 (6.34–21.80)	0.046
Week 6	13.00 (7.03–19.10)	8.73 (5.08–12.40)	0.194
Week 12	12.20 (7.81–16.60)	18.60 (7.73–29.40)	0.249
Collection time (h)
Week 2	7.93 (6.70–9.16)	8.37 (7.10–9.65)	0.592
Week 6	7.95 (6.89–9.00)	9.35 (7.34–11.40)	0.174
Week 12	6.93 (5.98–7.88)	9.71 (7.37–12.10)	0.024
Time elapsed between waking up and milk sampling (min)
Week 2	30.00 (8.58–51.40)	63.40 (17.10–110.00)	0.178
Week 6	45.40 (15.20–75.60)	154.00 (27.10–281.00)	0.055
Week 12	24.70 (2.05–47.40)	153.00 (27.40–278.00)	0.046

Open in a new tab

^a t-tests were used to evaluate differences between both groups at each postnatal week.

Discussion

The results of this study showed that most immune factors were present in the milk samples at the different stages of lactation and that their concentrations decreased over time. In our study group, which included healthy and mostly highly educated women, there was no apparent relationship between maternal psychosocial distress and immune factor concentrations, while maternal psychosocial distress at three months postpartum was related to higher milk cortisol concentrations. It must be highlighted that the women recruited in this study did not have high levels of psychosocial distress, as shown in Table 1.

Changes in human milk immune factor concentrations over the first three months postpartum

The immune factors IgA, IgG, IgM, EGF, TGFβ2, GROα and IL8, which were present in all milk samples and detectable in all stages of lactation, have been previously described as the “core” immune factors in human milk in healthy individuals [1]. Concentrations of most immune factors decreased over the first three months postpartum, especially between weeks 2 and 6. Previous studies have shown that the concentrations of most immune factors are notably higher in colostrum compared to transient and mature milk [10,11]. Despite the observed decrease over time, the concentrations of all “core” immune factors remained within the previously reported ranges for healthy women [1,40]. The decrease in the concentrations of immune factors over time follows the diminishing needs of the developing infant for immune factors, due to the increased immune memory of the infant’s own immune system (e.g., increased B cell repertoire development to produce IgA, IgM and IgG) [41]. This gradual decline parallels the decrease of neonatal Fc receptors (FcRn) located on enterocytes’ surface (FcRn are receptors responsible for the uptake of Ig from gut lumen to infant blood), the infant’s decreasing intestinal permeability to transfer maternal immune factors, as well as the increased acidity of the infant’s stomach [41,42]. The idea that the composition of milk adjusts to the infant’s needs is further supported by the parallel increase of energy and nutrients (e.g., lipids) in milk and the infant’s increasing need for these components during the first weeks postpartum [43]. Our findings support the existing theory that maternal milk immune factors are especially important during the early postnatal period, compared to the late postpartum period, for optimal priming of the neonatal immune system [41,42].

Human milk immune factor concentrations were not related to psychosocial distress

No significant association was found between psychosocial distress symptoms and the concentrations of immune factors in milk in this study. This finding is in contrast with previous studies that described positive correlations between immune factor concentrations in milk and depressive symptoms and/or anxiety [10,15,16]. Variations in the experimental designs, including sampling at different stages of lactation, may in part explain these conflicting results. For example, Thibeau et al. [10] found a negative association between maternal stress and EGF and MIP-1α in milk collected 3 days post-delivery, while we sampled later in the post-partum period. In addition, differences in maternal distress and characteristics between studies may also potentially explain discrepancies in results. Women in our sample scored lower on psychosocial distress compared to the other studies. Additionally, women included in this study were located in the Netherlands, reported no diseases and were highly educated, while mothers included in previous studies were located in the United States and Japan, reported sickness or allergic diseases, and included many mothers with lower socio-economic status [10,15,16]. Population characteristics, including geographical location, physical health, maternal lifestyle, and nutritional status, have been associated with changes in concentrations of milk immune factors [1,44]. For future research, it would be interesting to study a comparable population of Dutch women with higher levels of distress to enable greater understanding of the possible relation between maternal distress and milk immune factor concentrations.

Human milk cortisol was related to maternal distress

In the present study, psychosocial distress was positively associated with increased milk cortisol concentrations. When investigating the relations between individual psychosocial distress measures and milk cortisol concentrations, it appeared that while daily hassles and general anxiety were predictive of increased cortisol concentrations, depressive symptoms were not. Other studies that measured cortisol concentrations from the first days up till 12 weeks postpartum found a positive association between postpartum blues and higher levels of salivary and serum cortisol concentrations [25,27]. More specifically, Taylor et al. [27] reported a positive association for postpartum blues, but not for higher depressive symptoms, and serum cortisol concentrations. However, in contrast to our findings, two other studies reported that women with depressive symptoms, compared to women without symptoms, had significantly higher salivary and serum cortisol 14 days and 7 weeks post-partum [45,46]. Yet, several other studies indicated non-significant or negative associations between postpartum blues, depressive symptoms or anxiety and serum, salivary, or milk cortisol concentrations [3,47,48,49,50,51,52,53,54,55]. Finally, a recent study found no relation between a construct of maternal distress (i.e. depressive symptoms and anxiety) and milk cortisol concentrations at 3 months postpartum [3]. The large variability in experimental designs, methods, and outcomes of these studies hampers comparison of findings, making it difficult to draw definitive conclusions on possible associations between stress, anxiety, depressive symptoms, and milk cortisol concentrations. In order to compare studies and draw definitive conclusions, it would be worthwhile to initiate an international consensus expert meeting on the principles of milk research among researchers and clinicians in this field in order to create guidelines on milk collection methods, analyses techniques, and measurement outcomes that should be applied in future research.

Distinct cortisol concentrations in human milk, associated to different levels of psychosocial distress, could influence infant outcomes through breastfeeding. In line with the Lactational Programming hypothesis (i.e. the theory that biological components of milk can affect offspring neurobiological and metabolic development and in turn behavior and phenotype), these higher cortisol concentrations may have potential implications for infant health and behavior [56,57]. Milk cortisol is likely to be absorbed in the infant’s gut [57], enter the systemic circulation, and cross the blood-brain barrier [58]. In the brain, cortisol can bind to glucocorticoid receptors in the limbic region, including the hippocampus and amygdala [59]. These brain areas are involved in, amongst others, behavior and emotion regulation [60]. Previous studies indeed suggest that exposure to higher levels of milk cortisol, together with other biologically active components and parenting behavior, may influence infant behavior [3,21]. For instance, milk cortisol has been related to infant experimentally induced emotional fear reactivity, although this behavior was only observed in girls. Next to the possible effects on infant behavior, milk cortisol might also influence maturation of the infant microbiome composition [61]. In this way, milk cortisol may indirectly affect the development of the infant’s innate and adaptive immune systems, and contribute to the creation of a physical barrier to prevent pathogens from entering the body [62]. Moreover, milk cortisol is involved in the metabolism of carbohydrates, proteins and fats [63], and it may thus ensure sufficient energy uptake and weight gain in infants [2].

Better understanding of the biological significance of milk cortisol and its effects on both infant behavior and health is warranted, including estimations of the actual amount of milk cortisol that reaches the infant gut, blood circulation, and brain. Identifying the amount of cortisol that diffuses through the gut may help to further examine the potential effects of this hormone on infant health and behavior. Candidate methods to obtain this information would be to use in vitro evaluation methods to assess the passage of cortisol through the intestinal barrier [64], or in vivo rodent models with labelled cortisol to assess the amount of cortisol that reaches offspring blood circulation and brain [65,66].

Strengths of this study are its longitudinal design, the wide range of assayed milk immune factors, and the combined assessment of milk immune factors and cortisol concentrations. The main limitation of this study is that maternal psychosocial distress was measured only at one time point, while distress may change over time. Although some studies indicated that anxiety and depressive symptoms in the postpartum period are relatively constant [67,68], other studies reported that postpartum psychosocial distress in the late postpartum period is lower compared to the early postpartum period [69,70,71,72]. A next step in research would be to assess psychosocial distress at several time points throughout the postpartum period when investigating potential relations with milk immune factors and cortisol concentrations, because cortisol in human milk has a distinct circadian rhythm [73]. Although our study controlled for sample collection hour, women collected only one sample at each outcome assessment moment. Future studies should consider collecting multiple daily samples to provide more reliable information on the potential association between milk cortisol concentrations and psychosocial distress and to improve accuracy of results. These future studies should also standardize the daily time of collection. In this study, mothers were instructed to collect milk after waking up and before the first feed of infant. These instructions may have been too imprecise as reflected by the significant difference for time elapsed between waking up and sample collection between mothers with low and high psychosocial distress. Women with higher psychosocial distress reported later waking times, as well as longer periods between waking and sampling, over the weeks. As a result, and because milk cortisol concentrations are generally higher early in the morning, milk cortisol concentrations in women having high maternal psychosocial distress may have been underestimated in this study. Finally, women in our sample were predominantly highly educated and potential maternal confounders such as parity, atopy, BMI, health behavior (i.e. smoking, alcohol use, diet) were not accounted for, which may hamper the generalizability of the results. Future studies should consider including women clinically diagnosed with a stress or anxiety disorder, or with clinical depression, as well as increasing the sample size. When working with women with clinical diagnoses, extra attention should be given to instructions and reminders, in order to standardize sample collection times. Moreover, it would be relevant also to consider analyzing maternal intestinal microbiota since it may influence milk immune factors and bacterial composition [8,74]. Finally, research is needed to clarify whether varying concentrations of milk cortisol leave an imprint on infant health and development.

Conclusions

In this study, we found a previously described “core” of milk immune factors that is naturally present in healthy mothers throughout the first three months postpartum. Previous reports have shown that immune factors in milk seem influenced by several factors, including maternal physical health, nutritional status, geographical location, and intestinal microbiota [1,42,74]. The current study added to this emerging literature by showing that immune factor concentrations in milk are not always associated to natural variations in maternal distress, at least in healthy populations such as the current one. This result should be confirmed using study populations with larger sample sizes, less uniformity of demographic characteristics, or higher (clinical) levels of anxiety, stress and/or depressive symptoms. Maternal psychosocial distress was positively related to higher cortisol concentrations in milk, which highlights the importance of further investigating this and other maternal factors in relation to human milk composition, as well as the potential effects of milk bioactive factors on infant health and behavior.

Supporting information

S1 Fig. Flowchart of the participants included in the study.

(TIF)

Click here for additional data file.^{(9.4MB, tif)}

S2 Fig. Principal component analysis for the concentration of cortisol and immunological compounds in human milk samples (n = 152).

One sample was excluded because it determined the strong aggregation of the rest of the samples. Individual factor maps for milk samples, where each point represents a milk sample: (A) samples taken at week 2 postpartum are colored in green, at week 6 postpartum are colored in blue, and at week 12 postpartum are colored in red; (B) samples from women with low (0, red), medium (1, green) and high (2, blue) maternal postnatal psychosocial distress. (C) Correlation circle showing the active (cos²>0.2) variables (arrows) contributing to the spatial distribution of the milk samples represented in the upper graph (A). Arrows pointing in the same direction are positively correlated, in the opposite direction are negatively correlated, and unrelated when they are orthogonal (90º).

(TIF)

Click here for additional data file.^{(6.3MB, tif)}

S3 Fig. Cortisol concentrations in human milk samples at week 2 (n = 50), week 6 (n = 51) and week 12 (n = 51) postpartum.

White circles represent outliers (>1.5× IQR). The bar on the top indicates significant differences between the cortisol concentration in milk samples taken at weeks 2 and 12 (Friedman’s non parametric repeated measures comparison followed by post-hoc Nemenyi tests).

(TIF)

Click here for additional data file.^{(83.5KB, tif)}

S1 Table. Change in the concentrations of immune factors in milk samples of women categorized as having low (group LOW, n = 13) or high (group HIGH, n = 13) maternal postnatal psychosocial distress along time.

(DOCX)

Click here for additional data file.^{(34KB, docx)}

S1 File. Dataset.

(XLSX)

Click here for additional data file.^{(41.8KB, xlsx)}

Acknowledgments

The authors acknowledge and thank the contribution of all the participants who generously shared their time and samples for this study.

Data Availability

All relevant data are within the manuscript and its Supporting Information files.

Funding Statement

LF and JR’s work was funded by project AGL2016-75476-R from the Ministerio de Economía y Competitividad (Spain). RB’s work was funded by a VENI grant from the Netherlands Organization for Scientific Research (016.Veni.195.197). A Jacobs Foundation Advanced Research Fellowship and a VICI grant from the Netherlands Organization for Scientific Research (016.Vici. 185.038) supported CdW’s work. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

1.Ruiz L, Espinosa-Martos I, García-Carral C, Manzano S, McGuire MK, Meehan CL, et al. What’s normal? Immune profiling of human milk from healthy women living in different geographical and socioeconomic settings. Front Immunol. 2017;8: 696 10.3389/fimmu.2017.00696 [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Hahn-Holbrook J, Le TB, Chung A, Davis EP, Glynn LM. Cortisol in human milk predicts child BMI. Obesity. 2016;24: 2471–2474. 10.1002/oby.21682 [DOI] [PMC free article] [PubMed] [Google Scholar]
3.Nolvi S, Uusitupa H.-M, Bridgett DJ, Pesonen H, Aatsinki A.-K, Kataja E.-L, et al. Human milk cortisol concentration predicts experimentally induced infant fear reactivity: moderation by infant sex. Developmental Sci. 2018;21(4): e12625 10.1111/desc.12625 [DOI] [PubMed] [Google Scholar]
4.Victora CG, Bahl R, Barros AJD, França GVA, Horton S, Krasevec J, et al. Breastfeeding in the 21st century: epidemiology, mechanisms, and lifelong effect. Lancet. 2016;387(10017): 475–490. 10.1016/S0140-6736(15)01024-7 [DOI] [PubMed] [Google Scholar]
5.Groer M, Davis M, Steele K. Associations between human milk SIgA and maternal immune, infectious, endocrine, and stress variables. J Hum Lact. 2004;20(2): 153–158. 10.1177/0890334404264104 [DOI] [PubMed] [Google Scholar]
6.Kainonen E, Rautava S, Isolauri E. Immunological programming by breast milk creates an anti-inflammatory cytokine milieu in breast-fed infants compared to formula-fed infants. Br J Nutr. 2013;109(11): 1962–1970. 10.1017/S0007114512004229 [DOI] [PubMed] [Google Scholar]
7.Berdi M, de Lauzon-Guillain B, Forhan A, Castelli FA, Fenaille F, Charles M.-A, et al. Immune components of early breastmilk: Association with maternal factors and with reported food allergy in childhood. Pediatr Allergy Immunol. 2019;30(1): 107–116. 10.1111/pai.12998 [DOI] [PubMed] [Google Scholar]
8.Brandtzaeg P. The mucosal immune system and its integration with the mammary glands. J Pediatr. 2010;156(2): S8–S15. 10.1016/J.JPEDS.2009.11.014 [DOI] [PubMed] [Google Scholar]
9.Sitarik AR, Bobbitt KR, Havstad SL, Fujimura KE, Levin AM, Zoratti EM, et al. Breast Milk transforming growth factor β is associated with neonatal gut microbial composition. J Pediatr Gastr Nutr. 2017;65(3): e60–e67. 10.1097/MPG.0000000000001585 [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Thibeau S, D’Apolito K, Minnick AF, Dietrich MS, Kane B, Cooley S, et al. Relationships of maternal stress with milk immune components in African American mothers of healthy term infants. Breastfeed Med. 2016;11(1): 6–14. 10.1089/bfm.2015.0117 [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Trend S, Strunk T, Lloyd ML, Kok CH, Metcalfe J, Geddes DT, et al. Levels of innate immune factors in preterm and term mothers’ breast milk during the 1st month postpartum. Br J Nutr. 2016;115(07): 1178–1193. 10.1017/S0007114516000234 [DOI] [PubMed] [Google Scholar]
12.Tomičić S, Johansson G, Voor T, Björkstén B, Böttcher MF, Jenmalm MC. Breast milk cytokine and IgA composition differ in Estonian and Swedish mothers—relationship to microbial pressure and infant allergy. Pediatr Res. 2010;68(4): 330–334. 10.1203/00006450-201011001-00646 [DOI] [PubMed] [Google Scholar]
13.Groer MW, Morgan K. Immune, health and endocrine characteristics of depressed postpartum mothers. Psychoneuroendocrinology. 2007;32(2): 133–139. 10.1016/j.psyneuen.2006.11.007 [DOI] [PubMed] [Google Scholar]
14.Hart S, Boylan LM, Border B, Carroll SR, McGunegle D, Lampe RM. Breast milk levels of cortisol and secretory Immunoglobulin A (SIgA) differ with maternal mood and infant neuro-behavioral functioning. Infant Behav Dev. 2004;27(1): 101–106. 10.1016/J.INFBEH.2003.06.002 [DOI] [Google Scholar]
15.Kawano A, Emori Y. The relationship between maternal postpartum psychological state and breast milk secretory immunoglobulin A level. J Am Psychiatr Nurses Assoc. 2015;21(1): 23–30. 10.1177/1078390314566882 [DOI] [PubMed] [Google Scholar]
16.Kondo N, Suda Y, Nakao A, Oh-Oka K, Suzuki K, Ishimaru K, et al. Maternal psychosocial factors determining the concentrations of transforming growth factor-beta in breast milk. Pediatr Allergy Immunol. 2011;22(8): 853–861. 10.1111/j.1399-3038.2011.01194.x [DOI] [PubMed] [Google Scholar]
17.Pop VJM, Truijens SEM, Spek V, Wijnen HA, van Son MJM, Bergink V. A new concept of maternity blues: Is there a subgroup of women with rapid cycling mood symptoms? J Affect Dis. 2015;177: 74–79. 10.1016/j.jad.2015.02.015 [DOI] [PubMed] [Google Scholar]
18.Trimbos-instituut, 2013. Multidisciplinaire Richtlijn Depressie (3e Revisie, 2013) Versie 1.0. Retrieved from https://assets-sites.trimbos.nl/docs/8af6d324-8514-40a6-b943-34d1b434b33a.pdf.
19.Kingston D, Heaman M, Fell D, Dzakpasu S, Chalmers B. Factors associated with perceived stress and stressful life events in pregnant women: findings from the Canadian maternity experiences survey. Matern Child Health J. 2012;16(1): 158–168. 10.1007/s10995-010-0732-2 [DOI] [PubMed] [Google Scholar]
20.Hechler C, Beijers R, Riksen-Walraven JM, de Weerth C. Are cortisol concentrations in human breast milk associated with infant crying? Dev Psychobiol. 2018;60(6): 639–650. 10.1002/dev.21761 [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Grey KR, Davis EP, Sandman CA, Glynn LM. Human milk cortisol is associated with infant temperament. Psychoneuroendocrinology. 2013;38(7): 1178–1185. 10.1016/j.psyneuen.2012.11.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Termeulen SB, Butler WR, Natzke RP. Rapidity of cortisol transfer between blood and milk following adrenocorticotropin injection. J Dairy Sci. 1981;64(11): 2197–2200. 10.3168/jds.S0022-0302(81)82829-9 [DOI] [PubMed] [Google Scholar]
23.Verkerk G, Phipps A, Matthews L. Milk cortisol concentrations as an indicator of stress in lactating dairy cows. Proc New Zeal Soc An. 1996;56: 77–79. Available from: http://www.nzsap.org/proceedings/1996/milk-cortisol-concentrations-indicator-stress-lactating-dairy-cows. [Google Scholar]
24.Mohd Shukri NH, Wells J, Eaton S, Mukhtar F, Petelin A, Jenko-Pražnikar Z, et al. Randomized controlled trial investigating the effects of a breastfeeding relaxation intervention on maternal psychological state, breast milk outcomes, and infant behavior and growth. Am J Clin Nutr. 2019;110(1): 121–130. 10.1093/ajcn/nqz033 [DOI] [PubMed] [Google Scholar]
25.Ehlert U, Patalla U, Kirschbaum C, Piedmont E, Hellhammer DH. Postpartum blues: Salivary cortisol and psychological factors. J Psychosom Res. 1990;34(3): 319–325. 10.1016/0022-3999(90)90088-l [DOI] [PubMed] [Google Scholar]
26.Mohd Shukri NH, Wells JCK, Fewtrell M. The effectiveness of interventions using relaxation therapy to improve breastfeeding outcomes: A systematic review. Matern Child Nutr. 2018;e12563 10.1111/mcn.12563 [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Taylor A, Littlewood J, Adams D, Doré C, Glover V. Serum cortisol levels are related to moods of elation and dysphoria in new mothers. Psychiatry Res. 1994;54(3): 241–247. 10.1016/0165-1781(94)90018-3 [DOI] [PubMed] [Google Scholar]
28.Espinosa-Martos I, Jiménez E, de Andrés J, Rodríguez-Alcalá LM, Tavárez S, et al. Milk and blood biomarkers associated to the clinical efficacy of a probiotic for the treatment of infectious mastitis. Benef Microbes 2016;7(3): 305–318. 10.3920/BM2015.0134 [DOI] [PubMed] [Google Scholar]
29.Vingerhoets AJJM, Jeninga AJ, Menges LJ. The measurement of daily hassles and chronic stressors-the development of the everyday problem checklist (EPCL, Dutch-APL). Gedrag Gezondheid 1989;1(17): 10–17. [Google Scholar]
30.Spielberger CD, Gorsuch RL, Lushene R, Vagg PR, Jacobs GA. Manual for the State-Trait Anxiety Inventory. Palo Alto, CA: Consulting Psychologists Press; 1983 [Google Scholar]
31.Cox JL, Holden JM, Sagovsky R. Detection of postnatal depression. Development of the 10-item Edinburgh Postnatal Depression Scale. Br J Psychiatry. 1987;150: 782–786. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/3651732. 10.1192/bjp.150.6.782 [DOI] [PubMed] [Google Scholar]
32.Beijers R, Buitelaar JK, de Weerth C. Mechanisms underlying the effects of prenatal psychosocial stress on child outcomes: beyond the HPA axis. Eur Child Adolesc Psychiatry. 2014;23(10): 943–956. 10.1007/s00787-014-0566-3 [DOI] [PubMed] [Google Scholar]
33.Nast I, Bolten M, Meinlschmidt G, Hellhammer DH. How to measure prenatal stress? A systematic review of psychometric instruments to assess psychosocial stress during pregnancy. Paediatr Perinat Epidemiol. 2013;27(4): 313–322. 10.1111/ppe.12051 [DOI] [PubMed] [Google Scholar]
34.Browne PD, Aparicio M, Alba C, Hechler C, Beijers R, Rodríguez JM, et al. Human milk microbiome and maternal postnatal psychosocial distress. Front Microbiol. 2019;10: 2333 10.3389/fmicb.2019.02333 [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Hechler C, Borewicz K, Beijers R, Saccenti E, Riksen-Walraven M, Smidt H, et al. Association between psychosocial stress and fecal microbiota in pregnant women. Sci Rep. 2019;9(1): 4463 10.1038/s41598-019-40434-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Zijlmans MAC, Korpela K, Riksen-Walraven JM, de Vos WM, de Weerth C. Maternal prenatal stress is associated with the infant intestinal microbiota. Psychoneuroendocrinology. 2015;53: 233–245. 10.1016/j.psyneuen.2015.01.006 [DOI] [PubMed] [Google Scholar]
37.Wei T, Simko V. R package "corrplot": Visualization of a Correlation Matrix (Version 0.84); 2017. Available from: https://github.com/taiyun/corrplot.
38.Lê S, Josse J, Rennes A, Husson F. FactoMineR: An R package for multivariate analysis. J Stat Softw. 2008;25(1): 1–18. Available from: http://www.jstatsoft.org/. [Google Scholar]
39.Pohlert T. The pairwise multiple comparison of mean ranks package (PMCMR). R Package. 2014;27: 1–9. Available from: http://cran.uni-muenster.de/web/packages/PMCMR/PMCMR.pdf. [Google Scholar]
40.Agarwal S, Karmaus W, Davis S, Gangur V. Review: Immune markers in breast milk and fetal and maternal body fluids: a systematic review of perinatal concentrations. J Hum Lact. 2011;27(2): 171–186. 10.1177/0890334410395761 [DOI] [PubMed] [Google Scholar]
41.Kollmann TR, Kampmann B, Mazmanian SK, Marchant A, Levy O. Protecting the newborn and young infant from infectious diseases: lessons from immune ontogeny. Immunity. 2017;46(3): 350–363. 10.1016/j.immuni.2017.03.009 [DOI] [PubMed] [Google Scholar]
42.Molès JP, Tuaillon E, Kankasa C, Bedin AS, Nagot N, Marchant A, et al. Breastmilk cell trafficking induces microchimerism-mediated immune system maturation in the infant. Pediatr Allergy Immunol 2018;29: 133–143. 10.1111/pai.12841 [DOI] [PubMed] [Google Scholar]
43.Gidrewicz DA, Fenton TR. A systematic review and meta-analysis of the nutrient content of preterm and term breast milk. BMC Pediatr. 2014; 14(1): 216 10.1186/1471-2431-14-216 [DOI] [PMC free article] [PubMed] [Google Scholar]
44.Munblit D, Treneva M, Peroni D, Colicino S, Chow L, Dissanayeke S, et al. Colostrum and mature human milk of women from London, Moscow, and Verona: determinants of immune composition. Nutrients. 2016;8(11): 695 10.3390/nu8110695 [DOI] [PMC free article] [PubMed] [Google Scholar]
45.Corwin EJ, Pajer K, Paul S, Lowe N, Weber M, McCarthy DO. Bidirectional psychoneuroimmune interactions in the early postpartum period influence risk of postpartum depression. Brain Behav Immun. 2015;49: 86–93. 10.1016/j.bbi.2015.04.012 [DOI] [PMC free article] [PubMed] [Google Scholar]
46.Taylor A, Glover V, Marks M, Kammerer M. Diurnal pattern of cortisol output in postnatal depression. Psychoneuroendocrinology. 2009;34(8): 1184–1188. 10.1016/j.psyneuen.2009.03.004 [DOI] [PubMed] [Google Scholar]
47.Abou-Saleh MT, Ghubash R, Karim L, Krymski M, Bhai I. Hormonal aspects of postpartum depression. Psychoneuroendocrinology. 1998;23(5): 465–475. 10.1016/s0306-4530(98)00022-5 [DOI] [PubMed] [Google Scholar]
48.Castral TC, Warnock F, dos Santos CB, Daré MF, Moreira AC, Antonini SRR, et al. Maternal mood and concordant maternal and infant salivary cortisol during heel lance while in kangaroo care. Eur J Pain. 2015;19(3): 429–438. 10.1002/ejp.566 [DOI] [PubMed] [Google Scholar]
49.de Rezende MG, Garcia-Leal C, de Figueiredo FP, Cavalli Rde C, Spanghero MS, Barbieri MA, et al. Altered functioning of the HPA axis in depressed postpartum women. J Affect Disord. 2016;193: 249–256. 10.1016/j.jad.2015.12.065 [DOI] [PubMed] [Google Scholar]
50.Feksi A, Harris B, Walker RF, Riad-Fagmy D, Newcombe RG. “Maternity blues” and hormone levels in saliva. J Affect Disord. 1984;6(3–4): 351–355. 10.1016/s0165-0327(84)80013-0 [DOI] [PubMed] [Google Scholar]
51.Figueiredo B, Costa R. Mother’s stress, mood and emotional involvement with the infant: 3 months before and 3 months after childbirth. Arch Womens Ment Health 2009;(12): 143–153. 10.1007/s00737-009-0059-4 [DOI] [PubMed] [Google Scholar]
52.Harris B, Lovett L, Newcombe RG, Read GF, Walker R, Riad-Fahmy D. Maternity blues and major endocrine changes: Cardiff puerperal mood and hormone study II. BMJ. 1994;308(6934): 949–953. 10.1136/bmj.308.6934.949 [DOI] [PMC free article] [PubMed] [Google Scholar]
53.O’Keane V, Lightman S, Patrick K, Marsh M, Papadopoulos AS, Pawlby S, et al. Changes in the maternal hypothalamic-pituitary-adrenal axis during the early puerperium may be related to the postpartum “blues”. J Neuroendocrinol. 2011;23(11): 1149–1155. 10.1111/j.1365-2826.2011.02139.x [DOI] [PubMed] [Google Scholar]
54.Okun ML, Luther J, Prather AA, Perel JM, Wisniewski S, Wisner KL. Changes in sleep quality, but not hormones predict time to postpartum depression recurrence. J Affect Disord. 2011;130(3): 378–384. 10.1016/j.jad.2010.07.015 [DOI] [PMC free article] [PubMed] [Google Scholar]
55.Urizar GG, Muñoz RF, Muñoz RF. Impact of a prenatal cognitive-behavioral stress management intervention on salivary cortisol levels in low-income mothers and their infants. Psychoneuroendocrinology. 2011;36(10): 1480–1494. 10.1016/j.psyneuen.2011.04.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
56.Hinde K. Lactational programming of infant behavioral phenotype In: Clancy K, Hinde K, Rutherford J, editors. Building Babies. New York: Springer New York; 2013. pp. 187–207. 10.1007/978-1-4614-4060-4_9 [DOI] [Google Scholar]
57.Hinde K, Skibiel AL, Foster AB, Del Rosso L, Mendoza SP, Capitanio JP. Cortisol in mother’s milk across lactation reflects maternal life history and predicts infant temperament. Behav Ecol. 2015;26(1): 269–281. 10.1093/beheco/aru186 [DOI] [PMC free article] [PubMed] [Google Scholar]
58.Karssen AM, Meijer OC, van der Sandt ICJ, Lucassen PJ, de Lange ECM, de Boer AG, et al. Multidrug resistance P-glycoprotein hampers the access of cortisol but not of corticosterone to mouse and human brain. Endocrinology. 2001;142(6): 2686–2694. 10.1210/endo.142.6.8213 [DOI] [PubMed] [Google Scholar]
59.Joëls M, Sarabdjitsingh RA, Karst H. Unraveling the time domains of corticosteroid hormone influences on brain activity: rapid, slow, and chronic modes. Pharmacol Rev. 2012;64(4): 901–938. 10.1124/pr.112.005892 [DOI] [PubMed] [Google Scholar]
60.Richardson MP, Strange BA, Dolan RJ. Encoding of emotional memories depends on amygdala and hippocampus and their interactions. Nat Neurosci. 2004;7(3): 278–285. 10.1038/nn1190 [DOI] [PubMed] [Google Scholar]
61.Hollanders JJ, Heijboer AC, van der Voorn B, Rotteveel J, Finken MJJ. Nutritional programming by glucocorticoids in breast milk: Targets, mechanisms and possible implications. Best Pract Res Clin Endocrinol Metab. 2017;31(4): 397–408. 10.1016/j.beem.2017.10.001 [DOI] [PubMed] [Google Scholar]
62.Sharma R, Young C, Neu J. Molecular modulation of intestinal epithelial barrier: contribution of microbiota. J Biomed Biotechnol. 2010;2010: 305879 10.1155/2010/305879 [DOI] [PMC free article] [PubMed] [Google Scholar]
63.Sapolsky RM, Romero LM, Munck AU. How do glucocorticoids influence stress responses? Integrating permissive, suppressive, stimulatory, and preparative actions. Endocr Rev. 2000;21(1): 55–89. 10.1210/edrv.21.1.0389 [DOI] [PubMed] [Google Scholar]
64.Vancamelbeke M, Vermeire S. The intestinal barrier: a fundamental role in health and disease. Expert Rev Gastroenterol Hepatol. 2017;11(9): 821–834. 10.1080/17474124.2017.1343143 [DOI] [PMC free article] [PubMed] [Google Scholar]
65.Aydın MŞ, Yiğit EN, Vatandaşlar E, Erdoğan E, Öztürk G. Transfer and integration of breast milk stem cells to the brain of suckling pups. Sci Rep. 2018;8(1): 14289 10.1038/s41598-018-32715-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
66.Pourfarzaneh M, White GW, Landon J, Smith DS. Cortisol directly determined in serum by fluoroimmunoassay with magnetizable solid phase. Clin Chem. 1980;26(6): 730–733. [PubMed] [Google Scholar]
67.Heron J, O’Connor TG, Evans J, Golding J, Glover V. The course of anxiety and depression through pregnancy and the postpartum in a community sample. J Affect Disord. 2004;80(1): 65–73. 10.1016/j.jad.2003.08.004 [DOI] [PubMed] [Google Scholar]
68.Paulson JF, Bazemore SD, Goodman JH, Leiferman JA. The course and interrelationship of maternal and paternal perinatal depression. Arch Womens Ment Health 2016;19(4): 655–663. 10.1007/s00737-016-0598-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
69.Andersson L, Sundström-Poromaa I, Wulff M, Åström M, Bixo M. Depression and anxiety during pregnancy and six months postpartum: a follow-up study. Acta Obstet Gynecol Scand. 2006;85(8): 937–944. 10.1080/00016340600697652 [DOI] [PubMed] [Google Scholar]
70.Josefsson A, Berg G, Nordin C, Sydsjö G. Prevalence of depressive symptoms in late pregnancy and postpartum. Acta Obstet Gynecol Scand. 2001;80(3): 251–5. 10.1034/j.1600-0412.2001.080003251.x [DOI] [PubMed] [Google Scholar]
71.Lee AM, Lam SK, Sze Mun Lau SM, Chong CSY, Chui HW, Fong DYT. Prevalence, course, and risk factors for antenatal anxiety and depression. Obstet Gynecol 2007;110(5): 1102–1112. 10.1097/01.AOG.0000287065.59491.70 [DOI] [PubMed] [Google Scholar]
72.Tendais I, Costa R, Conde A, Figueiredo B. Screening for depression and anxiety disorders from pregnancy to postpartum with the EPDS and STAI. Span J Psychol. 2014;17: E7 10.1017/sjp.2014.7 [DOI] [PubMed] [Google Scholar]
73.van der Voorn B, de Waard M, van Goudoever JB, Rotteveel J, Heijboer AC, Finken MJ. Breast-milk cortisol and cortisone concentrations follow the diurnal rhythm of maternal hypothalamus-pituitary-adrenal axis activity. J Nutr. 2016;146(11): 2174–2179. 10.3945/jn.116.236349 [DOI] [PubMed] [Google Scholar]
74.Macpherson AJ, de Agüero MG, Ganal-Vonarburg SC. How nutrition and the maternal microbiota shape the neonatal immune system. Nat Rev Immunol. 2017;17(8): 508–517. 10.1038/nri.2017.58 [DOI] [PubMed] [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0233554.r001

Decision Letter 0

Igor Branchi

16 Jan 2020

PONE-D-19-31037

Human milk cortisol and immune factors over the first three postnatal months: relations to maternal psychosocial distress

PLOS ONE

Dear Dr Fernández,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Based on the comments of experts in the field, the manuscript presents several major weaknesses. These concern mainly the experimental design and methods -- especially statistical approach -- and undermine the relevance of the results obtained. A profound revision is needed to make the study more solid and reliable.

We would appreciate receiving your revised manuscript by Mar 01 2020 11:59PM. When you are ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter.

To enhance the reproducibility of your results, we recommend that if applicable you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). This letter should be uploaded as separate file and labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. This file should be uploaded as separate file and labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. This file should be uploaded as separate file and labeled 'Manuscript'.

Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.

We look forward to receiving your revised manuscript.

Kind regards,

Igor Branchi, Ph.D.

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements:

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at http://www.plosone.org/attachments/PLOSOne_formatting_sample_main_body.pdf and http://www.plosone.org/attachments/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. We note that you have included the phrase “data not shown” in your manuscript. Unfortunately, this does not meet our data sharing requirements. PLOS does not permit references to inaccessible data. We require that authors provide all relevant data within the paper, Supporting Information files, or in an acceptable, public repository. Please add a citation to support this phrase or upload the data that corresponds with these findings to a stable repository (such as Figshare or Dryad) and provide and URLs, DOIs, or accession numbers that may be used to access these data. Or, if the data are not a core part of the research being presented in your study, we ask that you remove the phrase that refers to these data.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: No

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: No

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors performed an interesting longitudinal investigation concerning how several active biological factors fluctuate throughout lactation (first three months) in human milk. The authors also attempted to correlate the fluctuations in these factors with perceived stress. The authors observed that while immune factors tended to decrease throughout lactation, cortisol concentrations increased over time and where correlated with self-reported psychological distress. The latter did not relate to maternal distress.

The manuscript is very well written and the authors are to be commended for being so transparent and honest with respect to the limitations of the study. I only have few minor issues that the authors may decide to take into account.

- The authors conclude that immune factors do not seem to correlate with maternal distress. This lack of effect may be due to a number of factors (many of which are also cogently discussed by the authors) that the experimental design did not allow taking into account. Whilst this does not devalue the study, I would simply suggest the authors to hedge this conclusion (see for example the abstract) and maybe clarify that the lack of association between distress and immune factors in milk may still be observed under other conditions (e.g. increased sample size, more uniform experimental population).

- Along the same considerations, I would suggest the authors to revise the second aim described in the introduction (lines 102-105). The part related to cortisol is fine, but maybe the part related to distress and immune functions shall be revised.

- Sometimes the authors did not specify what the meaning of numbers in parentheses. For example, on line 133, the authors report "... weeks [mean age of 14.75 (1.84)...]" what is 1.84? Is it standard error or standard deviation? Please specify.

- Still related to my principal concern, looking at data on distress (table 1) it seems to me that the subjects of the study were not particularly stressed. Could it be another reason why there does not seem to be a difference in immune response dependent on psychological distress? Please comment this aspect if deemed appropriate.

- I would suggest to put Fig S3 in the main text and not in the supplementary information.

- Line 463: the word postpartum reads odd. The font of the m is smaller that the other letters.

Reviewer #2: This manuscript assesses changes in breast milk immune factor and cortisol concentrations at postnatal weeks of 2, 6, and 12. Additionally, associations between maternal reports of psychological distress at 6weeks and the biological measures have been studied. The study population comprises of 55 healthy women. The topic is interesting and scarcely studied and the mechanisms via which maternal distress is transferred to the offspring also postnatally warrant attention. No child outcomes are included.

1. Considering the large number of biological measures that present with considerable normal variance, the study population is small. There is no power analysis, but there it is likely that the study has no sufficient power to answer the main questions.

2. Additional variance results by the fact that the sample collection time of the milk samples varies. This issue has been discussed and taken into account in the analyses but as the population is small, I wonder if this overcomes the acknowledged limitation of not having standardised sample collection times especially when it comes to cortisol concentrations.

3. The fact that psychological distress is actually a composite measure of conceptually different domains and there really are no subjects with actual high levels of symptoms is not solid enough. Investigating the influence of distress in a population of no distress is a limitation. The authors discuss this and describe the phenomenon as "normal variation in distress" but as the population is small, the milk samples come from three measurement points with varying sampling times and the distress symptoms have been only measured once at 6 weeks and then combined into this composite measure, I find it difficult to believe that the design is really strong in answering this particular question.

4. Distress symptoms ar measured cross-sectionally and inferences are made also backwards so that symptoms at 6 weeks are linked with milk cortisol at 2 weeks. Variation in symptom levels or persistence at a certain level in relation to variation in cortisol or immune factor levels would be more appropriate?

5. There is significant fluctuation in how cortisol levels seems to associate with high and low distress groups (where the numbers are 13/group, which appears small) depending by the time point of milk sampling. How do the authors interpret this (i.e. first the concentrations are higher, then lower and then again higher in high distress group vs the low distress group and all this happens within 10 weeks; Table 7). This, for example, underlines in my mind unreliability of the findings the unreliability being related to the underlying design and low number of subjects.

6. The authors should consider correction for multiple comparisons. In Table 7, for example, the result that is reported as one of the main outcomes is that cortisol levels in breast milk would be related to distress. However, the p value is close to 0.05, the groups are small, several tests have been conducted across the process and the direction of the associations changes between the three measurement points (#5) with only one measurement of the distress symptoms.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Simone Macrì

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files to be viewed.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org. Please note that Supporting Information files do not need this step.

PLoS One. 2020 May 21;15(5):e0233554. doi: 10.1371/journal.pone.0233554.r002

Author response to Decision Letter 0

6 Mar 2020

1. The authors conclude that immune factors do not seem to correlate with maternal distress. This lack of effect may be due to a number of factors (many of which are also cogently discussed by the authors) that the experimental design did not allow taking into account. Whilst this does not devalue the study, I would simply suggest the authors to hedge this conclusion (see for example the abstract) and maybe clarify that the lack of association between distress and immune factors in milk may still be observed under other conditions (e.g. increased sample size, more uniform experimental population).

Answer: We agree with the reviewer and, therefore, the abstract and the conclusions sections have been modified as follows:

Abstract (conclusion subsection, lines 52-55 in the manuscript with tracked changes): “Immune factor concentrations in milk do not seem influenced by natural variations in maternal distress, at least in healthy populations. It is uncertain if this lack of association between immune factors in milk and maternal distress would also be observed in studies with larger populations, with less uniform demographic characteristics, or with women with higher (clinical) levels of anxiety, stress and/or depressive symptoms.”

Conclusions (lines 616-618): “The current study added to this emerging literature by showing that immune factor concentrations in milk do not seem influenced by natural variations in maternal distress, at least in healthy populations such as the current one. This result should be confirmed using study populations with larger sample sizes, less uniformity of demographic characteristics, or higher (clinical) levels of anxiety, stress and/or depressive symptoms.”

2. Along the same considerations, I would suggest the authors to revise the second aim described in the introduction (lines 102-105). The part related to cortisol is fine, but maybe the part related to distress and immune functions shall be revised.

Answer: The sentence has been modified as follows (lines108-109):

“The second aim was to identify whether maternal postnatal psychosocial distress (i.e. perceived stress due to daily hassles, general anxiety, and depressive symptoms) in a group of 51 healthy mothers was related to changes in the presence and concentration of immune factors and cortisol in human milk”.

3. Sometimes the authors did not specify what the meaning of numbers in parentheses. For example, on line 133, the authors report "... weeks [mean age of 14.75 (1.84)...]" what is 1.84? Is it standard error or standard deviation? Please specify.

Answer: We apologize for the omission. It refers to the standard deviation (SD) and it has been indicated in the revised manuscript (lines137-142):

“Mothers collected milk after the infant reached the age of 2 weeks [mean (SD) age of 14.75 (1.84) days], 6 weeks [mean (SD) age of 43.58 (5.02) days], and 12 weeks [mean (SD) age of 85.35 (2.33) days]. Prior to collection, mothers washed their hands, breasts, and nipples, and cleaned the small collection cups with boiling water. Subsequently, prior to feeding the infant, they collected 20 mL of the first foremilk in the morning [mean (SD) time at 08:36 (02:48) am] by hand expression.”

4. Still related to my principal concern, looking at data on distress (table 1) it seems to me that the subjects of the study were not particularly stressed. Could it be another reason why there does not seem to be a difference in immune response dependent on psychological distress? Please comment this aspect if deemed appropriate.

Answer: We are aware of this limitation, and, in fact, we had indicated this concern in some parts of the original manuscript (for example, in the discussion section within the subsection "Human milk immune factor concentrations were not related to psychosocial distress”). However, we agree that this issue is very relevant and, as a consequence (and to further clarify this point), we have highlighted this more in the revised manuscript, as follows:

Abstract (conclusion) (lines 51-55): “Immune factor concentrations in milk do not seem influenced by natural variations in maternal distress, at least in healthy populations. It is uncertain if this lack of association between immune factors in milk and maternal distress would also be observed in studies with larger populations, with less uniform demographic characteristics, or with women with higher (clinical) levels of anxiety, stress and/or depressive symptoms.”

Discussion (lines 470-475): “In our study group, which included healthy and mostly highly educated women, there was no apparent relationship between maternal psychosocial distress and immune factor concentrations, while maternal psychosocial distress at three months postpartum was related to higher milk cortisol concentrations. It must be highlighted that the women recruited in this study did not have high levels of psychosocial distress, as shown in Table 1.”

Conclusions (lines 613-618): “The current study added to this emerging literature by showing that immune factor concentrations in milk do not seem influenced by natural variations in maternal distress, at least in healthy populations such as the current one. This result should be confirmed using study populations with larger sample sizes, less uniformity of demographic characteristics, or higher (clinical) levels of anxiety, stress and/or depressive symptoms.”

5. I would suggest to put Fig S3 in the main text and not in the supplementary information.

Answer: We included that figure as supplementary material because of the fact that the cortisol concentration increases with the lactation time had been already reported (ref. 20. Hechler C, Beijers R, Riksen-Walraven JM, de Weerth C. Are cortisol concentrations in human breast milk associated with infant crying? Dev Psychobiol. 2018;60(6): 639–650. doi: 10.1002/dev.21761). We therefore think it is more adequate to present it as supplementary material but if the reviewer considers that it is a requisite for manuscript approval, then we would have no problem to include it in the revised manuscript.

6. Line 463: the word postpartum reads odd. The font of the m is smaller that the other letters.

Answer: We have corrected the mistake (line 477).

Answer: We thank you for your comment on the innovative aspect of this study. Some child outcomes in this study group, such as the measurement of infant fussing and crying, are described in ref. 20 (Hechler C, Beijers R, Riksen-Walraven JM, de Weerth C. Are cortisol concentrations in human breast milk associated with infant crying? Dev Psychobiol. 2018;60(6): 639–650. doi: 10.1002/dev.21761).

Answer: We agree that it would have been desirable to have a larger group of participants, but our final experimental group comprises 51 women (out of an initial number of 88) who provided samples of milk during the postnatal period and filled out a questionnaire on psychosocial stress, anxiety and depressive symptoms. However, because we have 3 assessment moments for each of the 51 participants, we consider the main results of our work to be valid. These results may be seen as first evidence or preliminary results that will be very valuable to design future studies related to the influence of maternal psychosocial distress and, mainly, cortisol.

One of the main results is the relationship between milk cortisol concentration and maternal psychosocial distress, that it is summarized in Table 4. This table also includes other factors that may have influence in the cortisol concentration (such as the postnatal week, the sample collection hour, and time elapsed between waking up and milk sampling). This relationship was explored using a linear mixed effects model, as indicated in the Statistical analysis section.

Additionally, and in accordance with the suggestion of the reviewer, we conducted a power analysis calculation for the main results presented in the manuscript. The power analysis of the linear mixed effects model of cortisol concentration in human milk given in Table 4 was performed using the G*Power version 3.1.9.2 (Faul, F; Universität Kiel, Germany). The squared multiple correlation R2 = 0.224 gives an effect size f2 = 0.289 (medium-large), resulting in a power of 0.9997 for a sample size of 153. Therefore, it can be inferred that the study has enough power.

Answer: We are aware of the importance of the sampling time when determining cortisol concentrations in a biological sample, a fact that it is explicitly acknowledged in Figure 2. Since differences were not found when the distribution of the samples taken at the three sampling points (week 2, 6, and 12) was analyzed, we ruled out that the increase of cortisol concentration in milk over time was due to differences in the collection time.

Participants were instructed to take the milk sample after getting up in the morning and before feeding the baby. However, inspection of the sampling times revealed that some women had taken samples at later times. When we compared mothers with lower and higher maternal psychosocial distress (always taking into account that all of them were healthy mothers), we realized that the group of women with the highest maternal psychosocial distress collected their samples increasingly late while the opposite was observed for the group of women with the lowest maternal psychosocial distress. This difference may possibly be related to their psychosocial distress, e.g. with women with more psychosocial complaints getting up later or organizing themselves less efficiently. As a result, and as it has been pointed out in the discussion, cortisol concentrations in milk from the group of women with the highest maternal psychosocial distress may have been underestimated. This may also be a useful observation for future studies in this field and that is why we recommend that “future studies should also standardize the daily time of collection” (line 592).

Answer: Yes, we agree that this is a complex condition, with a variety of levels, ranging from a mild to a very severe clinical state. This study reflects such complexity, including some difficulties found during the course of the study. Although the design is far from perfect, we sincerely think that it provides valuable information in the field: (a) by showing the lack of an association between a large set of immune markers and variations in maternal distress in a healthy population; (b) by showing an association between maternal distress and milk cortisol in samples from healthy women; and (c) by providing useful information about the evolution of immune parameters in human milk along time. In addition, and as stated by the reviewer, it contains useful information to improve the design of future studies and to avoid that other scientists may face similar problems. As usually reminded by ethical committees, the results of clinical studies are useful even when they fail to find clear correlations or when they show design inconsistencies that should be improved in future works.

4. Distress symptoms are measured cross-sectionally and inferences are made also backwards so that symptoms at 6 weeks are linked with milk cortisol at 2 weeks. Variation in symptom levels or persistence at a certain level in relation to variation in cortisol or immune factor levels would be more appropriate?

Answer: We agree with the reviewer that it would be preferable to have information about the evolution of the maternal psychosocial distress during the whole study period. Unfortunately, we only had access to information at that specific moment (1.5 months postpartum). However, we do not expect large intra-individual variations in psychosocial distress from 2 to 12 weeks postpartum. Intra-individual levels of psychosocial distress measures have been found to be stable over longer periods of time, from pregnancy to 6 weeks postpartum, and from 6 weeks postpartum till 2 years postpartum (Dipietro JA1, Costigan KA, Sipsma HL. Continuity in self-report measures of maternal anxiety, stress, and depressive symptoms from pregnancy through two years postpartum. J Psychosom Obstet Gynaecol. 2008 Jun;29(2):115-24).

Answer: It is true that there is a wide variation in the cortisol levels, and that the two selected groups with low and high maternal psychosocial distress have a low number of individuals. Nevertheless, even with those drawbacks the information shown in Table 7 shows a tendency to different behavior (collection time and time elapsed between waking up and milk sampling) in both groups of women. In order to cope with the reviewer's observation, the whole paragraph has been rewritten in the revised manuscript (lines 432-453):

“At week 2, the mean concentrations of cortisol in milk samples of group LOW were approximately less than half of those found in group HIGH [mean (95% CI) concentration of 6.62 (4.36-8.88) μg/dL and 14.10 (6.34-21.80) μg/dL, respectively; p = 0.046] (Table 7). Women in both groups collected their milk samples around the same time at week 2, with only a small gap of 18 min difference in the mean collection time (mean time group LOW 07:56 and group HIGH 08:14 am). However, an increasing trend in the difference in the collection time was observed along time, reaching 2 h and 46 min at week 12 (mean collection time was 06:56 for women in group LOW and 09:42 for women in group HIGH; p = 0.024). Therefore, it is possible that the difference in cortisol concentration in milk between women in group LOW [mean (95% CI) concentration of 12.20 (7.81-16.60) μg/dL] and group HIGH [mean (95% CI) concentration of 18.60 (7.73-29.40) μg/dL] at week 12 would have been greater if samples had been collected at similar day times. Additionally, the time-lapse between waking up and the moment in which women actually collected the sample also tended to increase in women of the HIGH group. Women in group LOW took the samples 33.6 min (mean time for the group) after waking up during the study while women in group H collected milk samples 120 min (mean time for the group) after waking up throughout the study. At week 2, the time passed from waking up to sample collection was approximately twice among women in group HIGH when compared to women in group LOW; this time-lapse increased to about 2 h at week 12 [mean (95% CI) elapsed time of 24.70 (2.05-47.40) min in group LOW and of 153 (27.40-278.00) min in group HIGH; p = 0.046)] (Table 7).”

Answer: We agree that some p-values shown in Table 7 are close to 0.05, which is an arbitrary set point for significance. We did not correct for multiple comparisons because data in Table 7 correspond to two independent groups and three different variables that had been examined at three different sampling times. According to the reviewer’s suggestion, we lowered the intensity of the association and indicated that the results just show a tendency, and instead of pointing out to the statistically significant differences, we indicated the raw values (mean and 95% confidence interval) to show their wide variation of values, and the p-value obtained in the contrast, as indicated in the previous question.

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file.^{(29.8KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0233554.r003

Decision Letter 1

Igor Branchi

8 Apr 2020

PONE-D-19-31037R1

Human milk cortisol and immune factors over the first three postnatal months: relations to maternal psychosocial distress

PLOS ONE

Dear Dr Fernández,

The manuscript has been improved by the authors. However, there are still some minor points that need to be revised according to the Reviewers' suggestion. These concern mainly the Discussion section and the conclusions drawn.

We would appreciate receiving your revised manuscript by May 23 2020 11:59PM. When you are ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). This letter should be uploaded as separate file and labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. This file should be uploaded as separate file and labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. This file should be uploaded as separate file and labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Igor Branchi, Ph.D.

Academic Editor

PLOS ONE

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: (No Response)

Reviewer #2: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: (No Response)

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: (No Response)

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: (No Response)

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: (No Response)

Reviewer #2: The authors have sufficiently acknowledged most of the comments.

I still have two suggestions to complete the revision:

1. Absence of evidence is not evidence of absence. Considering all the limitations related to the sample size and the actual design, I would like the conclusions to be phrased differentially. Instead of stating that "Immune factor concentrations in milk do not seem influenced by natural variations in maternal distress" I would like to see a comment statin that "We found no evidence on ... " or "Our data did not support ...." There are so many limitations and restrictions underlying this paper, I would like the conclusion to comprise more directly the many uncertainties related to the absence of the expected association.

2. The implications of my point #5 should also be discussed in the discussion section in addition to the revised results section. the fact that the important variation in sampling time was biased by the maternal condition of psychological symptoms should be discussed clearly.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Simone Macrì

Reviewer #2: Yes: Linnea Karlsson

PLoS One. 2020 May 21;15(5):e0233554. doi: 10.1371/journal.pone.0233554.r004

Author response to Decision Letter 1

7 May 2020

We are glad the reviewers found our revised manuscript (almost) ready for publication.

Below we answer Reviewer 2’s remaining two points.

Reviewer #2: The authors have sufficiently acknowledged most of the comments.

I still have two suggestions to complete the revision:

We agree with the reviewer and replaced our concluding sentence in the Abstract by: “In the current study we found no evidence for an association between natural variations in maternal distress and immune factor concentrations in milk.” (lines 51-52)

We additionally adapted the sentence starting on line 607 of the Discussion as follows: “The current study added to this emerging literature by showing that immune factor concentrations are not always associated to natural variations in maternal distress, at least in healthy populations such as the current one.” This sentence replaces the sentence: “The current study added to this emerging literature by showing that immune factor concentrations in milk do not seem influenced by natural variations in maternal distress, at least in healthy populations such as the current one.”

We now adapted the sentence starting on line 590 to “Women with higher psychosocial distress reported later waking times, as well as longer periods between waking and sampling, over the weeks. As a result, and because milk cortisol concentrations are generally higher early in the morning, milk cortisol concentrations in women having high maternal psychosocial distress may have been underestimated in this study.”

We additionally added the following recommendation for future studies on line 597: “When working with women with clinical diagnoses, extra attention should be given to instructions and reminders, in order to standardize sample collection times.”

Attachment

Submitted filename: Response to Reviewers_31037R1.docx

Click here for additional data file.^{(15.3KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0233554.r005

Decision Letter 2

Igor Branchi

8 May 2020

Human milk cortisol and immune factors over the first three postnatal months: relations to maternal psychosocial distress

PONE-D-19-31037R2

Dear Dr. Fernández,

We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.

Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication.

Shortly after the formal acceptance letter is sent, an invoice for payment will follow. To ensure an efficient production and billing process, please log into Editorial Manager at https://www.editorialmanager.com/pone/, click the "Update My Information" link at the top of the page, and update your user information. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, you must inform our press team as soon as possible and no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

With kind regards,

Igor Branchi, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0233554.r006

Acceptance letter

Igor Branchi

13 May 2020

PONE-D-19-31037R2

Human milk cortisol and immune factors over the first three postnatal months: relations to maternal psychosocial distress

Dear Dr. Fernández:

I am pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

For any other questions or concerns, please email plosone@plos.org.

Thank you for submitting your work to PLOS ONE.

With kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Igor Branchi

Academic Editor

PLOS ONE

Associated Data

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

Supplementary Materials

S1 Fig. Flowchart of the participants included in the study.

(TIF)

Click here for additional data file.^{(9.4MB, tif)}

S2 Fig. Principal component analysis for the concentration of cortisol and immunological compounds in human milk samples (n = 152).

(TIF)

Click here for additional data file.^{(6.3MB, tif)}

S3 Fig. Cortisol concentrations in human milk samples at week 2 (n = 50), week 6 (n = 51) and week 12 (n = 51) postpartum.

(TIF)

Click here for additional data file.^{(83.5KB, tif)}

(DOCX)

Click here for additional data file.^{(34KB, docx)}

S1 File. Dataset.

(XLSX)

Click here for additional data file.^{(41.8KB, xlsx)}

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file.^{(29.8KB, docx)}

Attachment

Submitted filename: Response to Reviewers_31037R1.docx

Click here for additional data file.^{(15.3KB, docx)}

Data Availability Statement

All relevant data are within the manuscript and its Supporting Information files.

[pone.0233554.ref001] 1.Ruiz L, Espinosa-Martos I, García-Carral C, Manzano S, McGuire MK, Meehan CL, et al. What’s normal? Immune profiling of human milk from healthy women living in different geographical and socioeconomic settings. Front Immunol. 2017;8: 696 10.3389/fimmu.2017.00696 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0233554.ref002] 2.Hahn-Holbrook J, Le TB, Chung A, Davis EP, Glynn LM. Cortisol in human milk predicts child BMI. Obesity. 2016;24: 2471–2474. 10.1002/oby.21682 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0233554.ref003] 3.Nolvi S, Uusitupa H.-M, Bridgett DJ, Pesonen H, Aatsinki A.-K, Kataja E.-L, et al. Human milk cortisol concentration predicts experimentally induced infant fear reactivity: moderation by infant sex. Developmental Sci. 2018;21(4): e12625 10.1111/desc.12625 [DOI] [PubMed] [Google Scholar]

[pone.0233554.ref004] 4.Victora CG, Bahl R, Barros AJD, França GVA, Horton S, Krasevec J, et al. Breastfeeding in the 21st century: epidemiology, mechanisms, and lifelong effect. Lancet. 2016;387(10017): 475–490. 10.1016/S0140-6736(15)01024-7 [DOI] [PubMed] [Google Scholar]

[pone.0233554.ref005] 5.Groer M, Davis M, Steele K. Associations between human milk SIgA and maternal immune, infectious, endocrine, and stress variables. J Hum Lact. 2004;20(2): 153–158. 10.1177/0890334404264104 [DOI] [PubMed] [Google Scholar]

[pone.0233554.ref006] 6.Kainonen E, Rautava S, Isolauri E. Immunological programming by breast milk creates an anti-inflammatory cytokine milieu in breast-fed infants compared to formula-fed infants. Br J Nutr. 2013;109(11): 1962–1970. 10.1017/S0007114512004229 [DOI] [PubMed] [Google Scholar]

[pone.0233554.ref007] 7.Berdi M, de Lauzon-Guillain B, Forhan A, Castelli FA, Fenaille F, Charles M.-A, et al. Immune components of early breastmilk: Association with maternal factors and with reported food allergy in childhood. Pediatr Allergy Immunol. 2019;30(1): 107–116. 10.1111/pai.12998 [DOI] [PubMed] [Google Scholar]

[pone.0233554.ref008] 8.Brandtzaeg P. The mucosal immune system and its integration with the mammary glands. J Pediatr. 2010;156(2): S8–S15. 10.1016/J.JPEDS.2009.11.014 [DOI] [PubMed] [Google Scholar]

[pone.0233554.ref009] 9.Sitarik AR, Bobbitt KR, Havstad SL, Fujimura KE, Levin AM, Zoratti EM, et al. Breast Milk transforming growth factor β is associated with neonatal gut microbial composition. J Pediatr Gastr Nutr. 2017;65(3): e60–e67. 10.1097/MPG.0000000000001585 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0233554.ref010] 10.Thibeau S, D’Apolito K, Minnick AF, Dietrich MS, Kane B, Cooley S, et al. Relationships of maternal stress with milk immune components in African American mothers of healthy term infants. Breastfeed Med. 2016;11(1): 6–14. 10.1089/bfm.2015.0117 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0233554.ref011] 11.Trend S, Strunk T, Lloyd ML, Kok CH, Metcalfe J, Geddes DT, et al. Levels of innate immune factors in preterm and term mothers’ breast milk during the 1st month postpartum. Br J Nutr. 2016;115(07): 1178–1193. 10.1017/S0007114516000234 [DOI] [PubMed] [Google Scholar]

[pone.0233554.ref012] 12.Tomičić S, Johansson G, Voor T, Björkstén B, Böttcher MF, Jenmalm MC. Breast milk cytokine and IgA composition differ in Estonian and Swedish mothers—relationship to microbial pressure and infant allergy. Pediatr Res. 2010;68(4): 330–334. 10.1203/00006450-201011001-00646 [DOI] [PubMed] [Google Scholar]

[pone.0233554.ref013] 13.Groer MW, Morgan K. Immune, health and endocrine characteristics of depressed postpartum mothers. Psychoneuroendocrinology. 2007;32(2): 133–139. 10.1016/j.psyneuen.2006.11.007 [DOI] [PubMed] [Google Scholar]

[pone.0233554.ref014] 14.Hart S, Boylan LM, Border B, Carroll SR, McGunegle D, Lampe RM. Breast milk levels of cortisol and secretory Immunoglobulin A (SIgA) differ with maternal mood and infant neuro-behavioral functioning. Infant Behav Dev. 2004;27(1): 101–106. 10.1016/J.INFBEH.2003.06.002 [DOI] [Google Scholar]

[pone.0233554.ref015] 15.Kawano A, Emori Y. The relationship between maternal postpartum psychological state and breast milk secretory immunoglobulin A level. J Am Psychiatr Nurses Assoc. 2015;21(1): 23–30. 10.1177/1078390314566882 [DOI] [PubMed] [Google Scholar]

[pone.0233554.ref016] 16.Kondo N, Suda Y, Nakao A, Oh-Oka K, Suzuki K, Ishimaru K, et al. Maternal psychosocial factors determining the concentrations of transforming growth factor-beta in breast milk. Pediatr Allergy Immunol. 2011;22(8): 853–861. 10.1111/j.1399-3038.2011.01194.x [DOI] [PubMed] [Google Scholar]

[pone.0233554.ref017] 17.Pop VJM, Truijens SEM, Spek V, Wijnen HA, van Son MJM, Bergink V. A new concept of maternity blues: Is there a subgroup of women with rapid cycling mood symptoms? J Affect Dis. 2015;177: 74–79. 10.1016/j.jad.2015.02.015 [DOI] [PubMed] [Google Scholar]

[pone.0233554.ref018] 18.Trimbos-instituut, 2013. Multidisciplinaire Richtlijn Depressie (3e Revisie, 2013) Versie 1.0. Retrieved from https://assets-sites.trimbos.nl/docs/8af6d324-8514-40a6-b943-34d1b434b33a.pdf.

[pone.0233554.ref019] 19.Kingston D, Heaman M, Fell D, Dzakpasu S, Chalmers B. Factors associated with perceived stress and stressful life events in pregnant women: findings from the Canadian maternity experiences survey. Matern Child Health J. 2012;16(1): 158–168. 10.1007/s10995-010-0732-2 [DOI] [PubMed] [Google Scholar]

[pone.0233554.ref020] 20.Hechler C, Beijers R, Riksen-Walraven JM, de Weerth C. Are cortisol concentrations in human breast milk associated with infant crying? Dev Psychobiol. 2018;60(6): 639–650. 10.1002/dev.21761 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0233554.ref021] 21.Grey KR, Davis EP, Sandman CA, Glynn LM. Human milk cortisol is associated with infant temperament. Psychoneuroendocrinology. 2013;38(7): 1178–1185. 10.1016/j.psyneuen.2012.11.002 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0233554.ref022] 22.Termeulen SB, Butler WR, Natzke RP. Rapidity of cortisol transfer between blood and milk following adrenocorticotropin injection. J Dairy Sci. 1981;64(11): 2197–2200. 10.3168/jds.S0022-0302(81)82829-9 [DOI] [PubMed] [Google Scholar]

[pone.0233554.ref023] 23.Verkerk G, Phipps A, Matthews L. Milk cortisol concentrations as an indicator of stress in lactating dairy cows. Proc New Zeal Soc An. 1996;56: 77–79. Available from: http://www.nzsap.org/proceedings/1996/milk-cortisol-concentrations-indicator-stress-lactating-dairy-cows. [Google Scholar]

[pone.0233554.ref024] 24.Mohd Shukri NH, Wells J, Eaton S, Mukhtar F, Petelin A, Jenko-Pražnikar Z, et al. Randomized controlled trial investigating the effects of a breastfeeding relaxation intervention on maternal psychological state, breast milk outcomes, and infant behavior and growth. Am J Clin Nutr. 2019;110(1): 121–130. 10.1093/ajcn/nqz033 [DOI] [PubMed] [Google Scholar]

[pone.0233554.ref025] 25.Ehlert U, Patalla U, Kirschbaum C, Piedmont E, Hellhammer DH. Postpartum blues: Salivary cortisol and psychological factors. J Psychosom Res. 1990;34(3): 319–325. 10.1016/0022-3999(90)90088-l [DOI] [PubMed] [Google Scholar]

[pone.0233554.ref026] 26.Mohd Shukri NH, Wells JCK, Fewtrell M. The effectiveness of interventions using relaxation therapy to improve breastfeeding outcomes: A systematic review. Matern Child Nutr. 2018;e12563 10.1111/mcn.12563 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0233554.ref027] 27.Taylor A, Littlewood J, Adams D, Doré C, Glover V. Serum cortisol levels are related to moods of elation and dysphoria in new mothers. Psychiatry Res. 1994;54(3): 241–247. 10.1016/0165-1781(94)90018-3 [DOI] [PubMed] [Google Scholar]

[pone.0233554.ref028] 28.Espinosa-Martos I, Jiménez E, de Andrés J, Rodríguez-Alcalá LM, Tavárez S, et al. Milk and blood biomarkers associated to the clinical efficacy of a probiotic for the treatment of infectious mastitis. Benef Microbes 2016;7(3): 305–318. 10.3920/BM2015.0134 [DOI] [PubMed] [Google Scholar]

[pone.0233554.ref029] 29.Vingerhoets AJJM, Jeninga AJ, Menges LJ. The measurement of daily hassles and chronic stressors-the development of the everyday problem checklist (EPCL, Dutch-APL). Gedrag Gezondheid 1989;1(17): 10–17. [Google Scholar]

[pone.0233554.ref030] 30.Spielberger CD, Gorsuch RL, Lushene R, Vagg PR, Jacobs GA. Manual for the State-Trait Anxiety Inventory. Palo Alto, CA: Consulting Psychologists Press; 1983 [Google Scholar]

[pone.0233554.ref031] 31.Cox JL, Holden JM, Sagovsky R. Detection of postnatal depression. Development of the 10-item Edinburgh Postnatal Depression Scale. Br J Psychiatry. 1987;150: 782–786. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/3651732. 10.1192/bjp.150.6.782 [DOI] [PubMed] [Google Scholar]

[pone.0233554.ref032] 32.Beijers R, Buitelaar JK, de Weerth C. Mechanisms underlying the effects of prenatal psychosocial stress on child outcomes: beyond the HPA axis. Eur Child Adolesc Psychiatry. 2014;23(10): 943–956. 10.1007/s00787-014-0566-3 [DOI] [PubMed] [Google Scholar]

[pone.0233554.ref033] 33.Nast I, Bolten M, Meinlschmidt G, Hellhammer DH. How to measure prenatal stress? A systematic review of psychometric instruments to assess psychosocial stress during pregnancy. Paediatr Perinat Epidemiol. 2013;27(4): 313–322. 10.1111/ppe.12051 [DOI] [PubMed] [Google Scholar]

[pone.0233554.ref034] 34.Browne PD, Aparicio M, Alba C, Hechler C, Beijers R, Rodríguez JM, et al. Human milk microbiome and maternal postnatal psychosocial distress. Front Microbiol. 2019;10: 2333 10.3389/fmicb.2019.02333 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0233554.ref035] 35.Hechler C, Borewicz K, Beijers R, Saccenti E, Riksen-Walraven M, Smidt H, et al. Association between psychosocial stress and fecal microbiota in pregnant women. Sci Rep. 2019;9(1): 4463 10.1038/s41598-019-40434-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0233554.ref036] 36.Zijlmans MAC, Korpela K, Riksen-Walraven JM, de Vos WM, de Weerth C. Maternal prenatal stress is associated with the infant intestinal microbiota. Psychoneuroendocrinology. 2015;53: 233–245. 10.1016/j.psyneuen.2015.01.006 [DOI] [PubMed] [Google Scholar]

[pone.0233554.ref037] 37.Wei T, Simko V. R package "corrplot": Visualization of a Correlation Matrix (Version 0.84); 2017. Available from: https://github.com/taiyun/corrplot.

[pone.0233554.ref038] 38.Lê S, Josse J, Rennes A, Husson F. FactoMineR: An R package for multivariate analysis. J Stat Softw. 2008;25(1): 1–18. Available from: http://www.jstatsoft.org/. [Google Scholar]

[pone.0233554.ref039] 39.Pohlert T. The pairwise multiple comparison of mean ranks package (PMCMR). R Package. 2014;27: 1–9. Available from: http://cran.uni-muenster.de/web/packages/PMCMR/PMCMR.pdf. [Google Scholar]

[pone.0233554.ref040] 40.Agarwal S, Karmaus W, Davis S, Gangur V. Review: Immune markers in breast milk and fetal and maternal body fluids: a systematic review of perinatal concentrations. J Hum Lact. 2011;27(2): 171–186. 10.1177/0890334410395761 [DOI] [PubMed] [Google Scholar]

[pone.0233554.ref041] 41.Kollmann TR, Kampmann B, Mazmanian SK, Marchant A, Levy O. Protecting the newborn and young infant from infectious diseases: lessons from immune ontogeny. Immunity. 2017;46(3): 350–363. 10.1016/j.immuni.2017.03.009 [DOI] [PubMed] [Google Scholar]

[pone.0233554.ref042] 42.Molès JP, Tuaillon E, Kankasa C, Bedin AS, Nagot N, Marchant A, et al. Breastmilk cell trafficking induces microchimerism-mediated immune system maturation in the infant. Pediatr Allergy Immunol 2018;29: 133–143. 10.1111/pai.12841 [DOI] [PubMed] [Google Scholar]

[pone.0233554.ref043] 43.Gidrewicz DA, Fenton TR. A systematic review and meta-analysis of the nutrient content of preterm and term breast milk. BMC Pediatr. 2014; 14(1): 216 10.1186/1471-2431-14-216 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0233554.ref044] 44.Munblit D, Treneva M, Peroni D, Colicino S, Chow L, Dissanayeke S, et al. Colostrum and mature human milk of women from London, Moscow, and Verona: determinants of immune composition. Nutrients. 2016;8(11): 695 10.3390/nu8110695 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0233554.ref045] 45.Corwin EJ, Pajer K, Paul S, Lowe N, Weber M, McCarthy DO. Bidirectional psychoneuroimmune interactions in the early postpartum period influence risk of postpartum depression. Brain Behav Immun. 2015;49: 86–93. 10.1016/j.bbi.2015.04.012 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0233554.ref046] 46.Taylor A, Glover V, Marks M, Kammerer M. Diurnal pattern of cortisol output in postnatal depression. Psychoneuroendocrinology. 2009;34(8): 1184–1188. 10.1016/j.psyneuen.2009.03.004 [DOI] [PubMed] [Google Scholar]

[pone.0233554.ref047] 47.Abou-Saleh MT, Ghubash R, Karim L, Krymski M, Bhai I. Hormonal aspects of postpartum depression. Psychoneuroendocrinology. 1998;23(5): 465–475. 10.1016/s0306-4530(98)00022-5 [DOI] [PubMed] [Google Scholar]

[pone.0233554.ref048] 48.Castral TC, Warnock F, dos Santos CB, Daré MF, Moreira AC, Antonini SRR, et al. Maternal mood and concordant maternal and infant salivary cortisol during heel lance while in kangaroo care. Eur J Pain. 2015;19(3): 429–438. 10.1002/ejp.566 [DOI] [PubMed] [Google Scholar]

[pone.0233554.ref049] 49.de Rezende MG, Garcia-Leal C, de Figueiredo FP, Cavalli Rde C, Spanghero MS, Barbieri MA, et al. Altered functioning of the HPA axis in depressed postpartum women. J Affect Disord. 2016;193: 249–256. 10.1016/j.jad.2015.12.065 [DOI] [PubMed] [Google Scholar]

[pone.0233554.ref050] 50.Feksi A, Harris B, Walker RF, Riad-Fagmy D, Newcombe RG. “Maternity blues” and hormone levels in saliva. J Affect Disord. 1984;6(3–4): 351–355. 10.1016/s0165-0327(84)80013-0 [DOI] [PubMed] [Google Scholar]

[pone.0233554.ref051] 51.Figueiredo B, Costa R. Mother’s stress, mood and emotional involvement with the infant: 3 months before and 3 months after childbirth. Arch Womens Ment Health 2009;(12): 143–153. 10.1007/s00737-009-0059-4 [DOI] [PubMed] [Google Scholar]

[pone.0233554.ref052] 52.Harris B, Lovett L, Newcombe RG, Read GF, Walker R, Riad-Fahmy D. Maternity blues and major endocrine changes: Cardiff puerperal mood and hormone study II. BMJ. 1994;308(6934): 949–953. 10.1136/bmj.308.6934.949 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0233554.ref053] 53.O’Keane V, Lightman S, Patrick K, Marsh M, Papadopoulos AS, Pawlby S, et al. Changes in the maternal hypothalamic-pituitary-adrenal axis during the early puerperium may be related to the postpartum “blues”. J Neuroendocrinol. 2011;23(11): 1149–1155. 10.1111/j.1365-2826.2011.02139.x [DOI] [PubMed] [Google Scholar]

[pone.0233554.ref054] 54.Okun ML, Luther J, Prather AA, Perel JM, Wisniewski S, Wisner KL. Changes in sleep quality, but not hormones predict time to postpartum depression recurrence. J Affect Disord. 2011;130(3): 378–384. 10.1016/j.jad.2010.07.015 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0233554.ref055] 55.Urizar GG, Muñoz RF, Muñoz RF. Impact of a prenatal cognitive-behavioral stress management intervention on salivary cortisol levels in low-income mothers and their infants. Psychoneuroendocrinology. 2011;36(10): 1480–1494. 10.1016/j.psyneuen.2011.04.002 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0233554.ref056] 56.Hinde K. Lactational programming of infant behavioral phenotype In: Clancy K, Hinde K, Rutherford J, editors. Building Babies. New York: Springer New York; 2013. pp. 187–207. 10.1007/978-1-4614-4060-4_9 [DOI] [Google Scholar]

[pone.0233554.ref057] 57.Hinde K, Skibiel AL, Foster AB, Del Rosso L, Mendoza SP, Capitanio JP. Cortisol in mother’s milk across lactation reflects maternal life history and predicts infant temperament. Behav Ecol. 2015;26(1): 269–281. 10.1093/beheco/aru186 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0233554.ref058] 58.Karssen AM, Meijer OC, van der Sandt ICJ, Lucassen PJ, de Lange ECM, de Boer AG, et al. Multidrug resistance P-glycoprotein hampers the access of cortisol but not of corticosterone to mouse and human brain. Endocrinology. 2001;142(6): 2686–2694. 10.1210/endo.142.6.8213 [DOI] [PubMed] [Google Scholar]

[pone.0233554.ref059] 59.Joëls M, Sarabdjitsingh RA, Karst H. Unraveling the time domains of corticosteroid hormone influences on brain activity: rapid, slow, and chronic modes. Pharmacol Rev. 2012;64(4): 901–938. 10.1124/pr.112.005892 [DOI] [PubMed] [Google Scholar]

[pone.0233554.ref060] 60.Richardson MP, Strange BA, Dolan RJ. Encoding of emotional memories depends on amygdala and hippocampus and their interactions. Nat Neurosci. 2004;7(3): 278–285. 10.1038/nn1190 [DOI] [PubMed] [Google Scholar]

[pone.0233554.ref061] 61.Hollanders JJ, Heijboer AC, van der Voorn B, Rotteveel J, Finken MJJ. Nutritional programming by glucocorticoids in breast milk: Targets, mechanisms and possible implications. Best Pract Res Clin Endocrinol Metab. 2017;31(4): 397–408. 10.1016/j.beem.2017.10.001 [DOI] [PubMed] [Google Scholar]

[pone.0233554.ref062] 62.Sharma R, Young C, Neu J. Molecular modulation of intestinal epithelial barrier: contribution of microbiota. J Biomed Biotechnol. 2010;2010: 305879 10.1155/2010/305879 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0233554.ref063] 63.Sapolsky RM, Romero LM, Munck AU. How do glucocorticoids influence stress responses? Integrating permissive, suppressive, stimulatory, and preparative actions. Endocr Rev. 2000;21(1): 55–89. 10.1210/edrv.21.1.0389 [DOI] [PubMed] [Google Scholar]

[pone.0233554.ref064] 64.Vancamelbeke M, Vermeire S. The intestinal barrier: a fundamental role in health and disease. Expert Rev Gastroenterol Hepatol. 2017;11(9): 821–834. 10.1080/17474124.2017.1343143 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0233554.ref065] 65.Aydın MŞ, Yiğit EN, Vatandaşlar E, Erdoğan E, Öztürk G. Transfer and integration of breast milk stem cells to the brain of suckling pups. Sci Rep. 2018;8(1): 14289 10.1038/s41598-018-32715-5 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0233554.ref066] 66.Pourfarzaneh M, White GW, Landon J, Smith DS. Cortisol directly determined in serum by fluoroimmunoassay with magnetizable solid phase. Clin Chem. 1980;26(6): 730–733. [PubMed] [Google Scholar]

[pone.0233554.ref067] 67.Heron J, O’Connor TG, Evans J, Golding J, Glover V. The course of anxiety and depression through pregnancy and the postpartum in a community sample. J Affect Disord. 2004;80(1): 65–73. 10.1016/j.jad.2003.08.004 [DOI] [PubMed] [Google Scholar]

[pone.0233554.ref068] 68.Paulson JF, Bazemore SD, Goodman JH, Leiferman JA. The course and interrelationship of maternal and paternal perinatal depression. Arch Womens Ment Health 2016;19(4): 655–663. 10.1007/s00737-016-0598-4 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0233554.ref069] 69.Andersson L, Sundström-Poromaa I, Wulff M, Åström M, Bixo M. Depression and anxiety during pregnancy and six months postpartum: a follow-up study. Acta Obstet Gynecol Scand. 2006;85(8): 937–944. 10.1080/00016340600697652 [DOI] [PubMed] [Google Scholar]

[pone.0233554.ref070] 70.Josefsson A, Berg G, Nordin C, Sydsjö G. Prevalence of depressive symptoms in late pregnancy and postpartum. Acta Obstet Gynecol Scand. 2001;80(3): 251–5. 10.1034/j.1600-0412.2001.080003251.x [DOI] [PubMed] [Google Scholar]

[pone.0233554.ref071] 71.Lee AM, Lam SK, Sze Mun Lau SM, Chong CSY, Chui HW, Fong DYT. Prevalence, course, and risk factors for antenatal anxiety and depression. Obstet Gynecol 2007;110(5): 1102–1112. 10.1097/01.AOG.0000287065.59491.70 [DOI] [PubMed] [Google Scholar]

[pone.0233554.ref072] 72.Tendais I, Costa R, Conde A, Figueiredo B. Screening for depression and anxiety disorders from pregnancy to postpartum with the EPDS and STAI. Span J Psychol. 2014;17: E7 10.1017/sjp.2014.7 [DOI] [PubMed] [Google Scholar]

[pone.0233554.ref073] 73.van der Voorn B, de Waard M, van Goudoever JB, Rotteveel J, Heijboer AC, Finken MJ. Breast-milk cortisol and cortisone concentrations follow the diurnal rhythm of maternal hypothalamus-pituitary-adrenal axis activity. J Nutr. 2016;146(11): 2174–2179. 10.3945/jn.116.236349 [DOI] [PubMed] [Google Scholar]

[pone.0233554.ref074] 74.Macpherson AJ, de Agüero MG, Ganal-Vonarburg SC. How nutrition and the maternal microbiota shape the neonatal immune system. Nat Rev Immunol. 2017;17(8): 508–517. 10.1038/nri.2017.58 [DOI] [PubMed] [Google Scholar]

PERMALINK

Human milk cortisol and immune factors over the first three postnatal months: Relations to maternal psychosocial distress

Marina Aparicio

Pamela D Browne

Christine Hechler

Roseriet Beijers

Juan Miguel Rodríguez

Carolina de Weerth

Leonides Fernández

Roles

Abstract

Background

Methods

Results

Conclusion

Introduction

Materials and methods

Participants and procedures

Collection of human milk samples

Immunological analysis of human milk

Human milk cortisol concentration analysis

Stress

Anxiety

Depressive symptoms

Psychosocial distress

Statistical analyses

Results

Participant characteristics

Table 1. Maternal and infant characteristics of all participants (n = 51) included in the study.

Relationship between cortisol and immune factors in human milk

Fig 1. Correlation of cortisol and immune factors concentrations in milk samples.

Concentrations and changes over time of cortisol

Fig 2. Cortisol concentration in milk samples of participants during the day at all postnatal times (all samples), and 2, 6 and 12 weeks postpartum.

Concentrations and changes over time of immune factors

Table 2. Frequency and concentration of immune factors in milk samples.

Association between stress, anxiety and depressive symptoms and immune factor concentrations

Table 3. Correlation between immune factors concentrations and the postnatal psychosocial distress variables at week 6.

Association between stress, anxiety and depressive symptoms and cortisol concentrations

Table 4. Linear mixed effects model of cortisol concentration (log transformed) in human milk.

Differences in concentrations of immune factors between mothers with low and high maternal psychosocial distress

Table 5. Maternal and infant characteristics of participants with lowest (group LOW) and highest (group HIGH) levels of maternal postnatal psychosocial distress.

Table 6. Immune factors in milk samples provided by women with lowest (group LOW) and highest (group HIGH) maternal postnatal psychosocial distress.

Differences in cortisol concentrations between mothers with low and high maternal psychosocial distress

Table 7. Change in the cortisol concentration in milk samples, in the collection time and in the time elapsed between waking up and milk sampling in women with lowest (group LOW) and highest (group HIGH) maternal postnatal psychosocial distress.

Discussion

Changes in human milk immune factor concentrations over the first three months postpartum

Human milk immune factor concentrations were not related to psychosocial distress

Human milk cortisol was related to maternal distress

Conclusions

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Igor Branchi

Roles

Author response to Decision Letter 0

Decision Letter 1

Igor Branchi

Roles

Author response to Decision Letter 1

Decision Letter 2

Igor Branchi

Roles

Acceptance letter

Igor Branchi

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases