Association of prenatal depression and plasma prolactin concentrations with neonatal birthweight

doi:10.21203/rs.3.rs-1460839/v1

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Research Article

Association of prenatal depression and plasma prolactin concentrations with neonatal birthweight

https://doi.org/10.21203/rs.3.rs-1460839/v1

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Background: Accumulating evidence suggests that prenatal depression is associated with altered birthweight in the offspring. The hormone prolactin (PRL) plays an important role in the etiology of mood disorders in women, especially for depression. We tested the hypotheses that maternal prenatal PRL levels can significantly influence neonatal birthweight and adequate PRL levels can alleviate prenatal depression-induced normal neonatal birthweight loss risk.

Methods: The study population consisted of 392 mother-infant dyads. Symptoms of maternal prenatal depression were evaluated through the Edinburgh Postnatal Depression Scale and concentrations of plasma PRL and placental growth hormone (PGH) were measured before the delivery and umbilical cord plasma levels of insulin-like growth factor -1(IGF1) were measured at birth. The associations among maternal depressed symptoms, concentrations of related hormones and infant's birthweight were examined through linear regressions models.

Results: Infants of low maternal PRL had a 0.169 unit (95%CI, -0.488,- 0.130) decrease in the birthweight Z-score compared to infants whose mothers had adequate PRL. Infants of depressed mothers with low PRL had a 0.253 unit(95%CI, -0.853,-0.369) decrease in the birthweight Z score compared to infants whose mothers had adequate PRL and normal emotion. Among infants of depressed mothers, we demonstrated that neonatal birthweight was reduced by a 0.285 unit if the mothers had low PRL concentrations compared to adequate PRL concentrations. As compared with low maternal PRL concentrations, high PRL concentrations were associated with an increase in the concentrations of IGF1 in offspring, but no association with PGH.

Conclusion: We found that prenatal depression and low maternal PRL levels are at increased risk of normal infant’ s birthweight loss as measured by weight Z-score and sufficient maternal PRL levels would mitigate the adverse impact of prenatal depression on birthweight. We also found that prenatal depression and low PRL concentrations were associated with reduced fetal IGF1.

Prenatal depression

birthweight

Prolactin (PRL)

Placental growth hormone (PGH)

Insulin-like growth factor-1 (IGF1)

Depression is an individual’s self-report of low mood and aversion to activities, which affects women approximately twice as often as men[1]. Prenatal depression is a universal problem that affects 5–15% of pregnant women[2]. Untreated depression during pregnancy may increase the risk of fetal growth restriction and preterm birth [3] which are associated with higher infant mortality and long-lasting consequences[4, 5]. Meta-analytic evidence indicates that subclinical maternal prenatal depression may have a detectable impact on offspring socioemotional development[6] and birth outcomes[7]. Although substantial evidence indicates that antenatal depression is related to newborn adverse outcomes, the underlying mechanism remains unclear.

Abnormal neuroendocrine hormones in pregnancy may have major impacts on antenatal depression, maternal behavior, fetal development and long-term effects of offspring, such as metabolic disorders and cardiovascular disease [8–10]. Prolactin (PRL) which is greatly increased during pregnancy is a polypeptide hormone with pleiotropic functional regulation that modulates multiple physiological processes, including reproductive processes, osmotic balance, angiogenesis, and lactogenesis[11]. PRL is related to emotional disorders among women, particularly depression [12]. PRL plays an important role in the initiation and maintenance of maternal parenting ability [13]. Compared to normal mothers, depressed mothers are at higher risk of having inadequate PRL levels[14]. From this perspective, PRL may mediate the influence of maternal depression on offspring physical development and this may offer another possibility to discover maternal depression-induced metabolic risk of offspring via the detection of maternal PRL concentrations.

Reduced physical measurements at birth are a clinical indicator of intrauterine exposure to adverse environment. The birthweight is a surrogate for fetal growth, which is a strong predictor of disease risk across the life course, including metabolic disorders, cardiometabolic disease and cognitive decline[15–17]. The intrauterine growth and development of the fetus is mediated by placental growth hormone(PGH) and insulin-like growth factor − 1(IGF1)[18]. PGH, a variant of pituitary growth hormone(GH), is secreted by placental syncytiotrophoblast cells. Pituitary GH is gradually replaced by PGH during pregnancy[19]. PGH regulates IGF1 to stimulate the anabolism of glycogen and lipids in maternal tissues, affecting fetal growth and maternal adaptation to pregnancy[20, 21].Studies have shown that the overexpression of PGH in transgenic mice can increase the body weight of offspring by 84% and IGF1 concentrations by 56%[22, 23]. Dale E. Bauman proved that GH and PRL to mothers resulted in increased milk production and weight gain to their offspring[24]. The animal experiment revealed that maternal stress resulted in low body weight of male mouse offspring and decreased the serum IGF-1, GH and PRL concentrations in the pups[25]. PRL and GH are pituitary hormones that affect proliferation and growth, but PGH instead of GH during pregnancy, moreover PRL shares an ancestral gene with growth hormone, so we hypothesized that prolactin regulates fetal growth through PGH and IGF1.

This study employed a combination of psychological and hormonal assessments to better explore how prenatal depression combined with prolactin related to infant birthweight. We mainly focused on the birthweight because it is significant to note that birthweight, gestational age, and body length may reflect the different underlying mechanisms as health outcomes. Because we want to understand the differences in physical development of normal newborns and explore the mechanism of the effects of depressed mothers on normal term infant weight, we selected normal full-term newborns weighing more than 2500. We tested umbilical cord blood for IGF1, because umbilical cord blood can be used as a symbol of fetal environment in utero and it mostly reflects the hormone concentrations of fetuses in the third trimester of pregnancy. This is a non-invasive assessment of fetal circulation[26]. We measured maternal plasma PGH, as PGH is detected primarily in the maternal blood[19]. We concentrated on the last trimester because this is a stage of rapid fetal growth as well as development, during which exposure to maternal stressors affects fetal growth development systems and is related to the risk of later psychological problems [27, 28]. We tried to verify the hypotheses that prenatal PRL levels can influence prenatal depression- induced birthweight loss risk as measured by the birthweight Z score and fetal growth development biomarkers (PGH and IGF1).

2.1 Participants

The research participants involved in mother-infant dyads from a prospective survey of infant behavioral observation. The research participants were pregnant women (28–36 weeks of pregnancy) who underwent regular physical examinations and who gave birth in the First Affiliated Hospital of Xi 'an Jiaotong University between November 2020 and July 2021, and we recruited them via postings. All patients had a physical healthy pregnancy at the time of recruitment. All pregnant women completed the Edinburgh Postnatal Depression Scale (EPDS), which has good validity and reliability and is used to assess perinatal depression[29].

Participants were excluded if they met anyone of the following criteria: 1) smoking, drinking, or drug abuse; 2) any serious maternal disease, such as hypertension, heart failure, renal failure, cancer, stroke, or other life-threatening diseases; 3)any hormonal medication during pregnancy, diagnosis of diabetes mellitus or thyroid disease;4) neonatal gestational age < 37 weeks; or gestational age > 42 weeks; or 5) pregnant with polyembryony. Infants with some congenital, metabolic, chronic diseases, Apgar score of 1,5,10 minutes after birth was < 8 or birthweight ≤ 2500g were excluded. Inclusion criteria included maternal age 18–45 years, self-reported good health, and received standard prenatal care.

We recruited 415 pregnant women during pregnancy. According to our infants’ exclusion criteria, 16 participants were excluded because they had complications (neonatal asphyxia, low birthweight and preterm birth). 7 qualified mother-infant pairs were excluded as mothers did not want to draw blood or they didn‘t give birth at this hospital. A score of EPDS > 12 was used to define as showing symptoms of depression[29]. A total of 392 mother-infant pairs were eventually recruited, with 133 pairs in the depressed group (n = 133) and 259 pairs in the control group (n = 259) based on their EPDS scores.

There were no differences in any socio-demographic/maternal variables, infant-related variables, or situational factors among the maternal and infant population involved in or out of the study. This research was conducted in accordance with the Helsinki Declaration, and ethical approval of the study was obtained from the Ethics Committee of Xi’an Jiaotong University. Consent forms for biological samples were collected under the Organic Act on the Human Body. Participants provided written informed consent.

2.2 Procedures

Participants were required to complete baseline data sheets to collect health-related data during pregnant period (such as, any risk/medical disorder/complication, etc.) and demographic characteristics (such as, age/residence/education/income). Gestational age was estimated according to the following methods: the date of an early ultrasound examination in the second trimester (63.8%) or date of last menstrual period (36.2%). Pre-pregnancy body mass index (BMI) was computed by using height measured at the time of antenatal care registration and self-reported weight. Prenatal BMI was computed by using height and weight measured at the time of antenatal care registration. Pre-pregnancy and prenatal BMI (kg/m²) was calculated through dividing weight by height squared.

The EPDS is a ten-item self-report questionnaire (range 0–30) on the extent to which the previous week's feelings were considered depressed. It adopts a 4-point Likert scale from 0(low) to 3(high). Higher scores indicate greater depression. EPDS is a commonly used assessment tool for depression, with high specificity and sensitivity [29, 30]. To obtain antenatal depression scores, the average EPDS score was measured by combining the two prenatal periods, including two assessment sessions at 28 to 36 weeks and 37 to 42 weeks of pregnancy. The first evaluation of EPDS (28–36 weeks) was conducted during volunteer recruitment in an outpatient setting and the second evaluation (37–42 weeks) was conducted before inpatient delivery.

2.2.1 Laboratory measurements

Participants agreed to have blood drawn and 5 mL of maternal peripheral blood was drawn in the morning after they were hospitalized before delivery. After delivery, the umbilical cord was clamped and cut immediately. 10 mL of umbilical vein blood were extracted. Maternal peripheral blood and umbilical vein blood were refrigerated at 4℃until reaching the biological laboratory of the Institute of Xi'an Jiaotong University, where the plasma was centrifuged, labeled, and stored at -80℃.

Plasma concentrations of PRL, PGH and IGF1 were measured by chemiluminescence analysis (CLIA). All sample analyses were tested in the chemistry laboratory of the First Affiliated Hospital of Xi’an Jiaotong University. Sample testers were blinded to mother and infant outcomes. Based on the manufacturer’s instructions, biological assays for maternal PRL, PGH and fetal IGF1 concentrations were run in duplicate using CLIA kits (Siemens Healthcare Diagnostics Products Limited). The intra-assay coefficient of variation (CV) was < 6% for PRL, < 5% for PGH, and < 4% for IGF1 and inter-assay CV was < 10% for all markers.

2.2.2 Neonatal assessment

We extracted information about neonatal health such as gestational age, birthweight, head circumference, body length and any medication or treatment from medical records. According to sex and age, the birthweight for age Z score (BWAZ) was calculated using the WHO Anthro software version 3.2.2[31]. BWAZ were used to assess neonatal physical development.

Statistics were gathered and analyzed using SPSS25 for Windows (SPSS Inc., Chicago, IL, USA). Quantitative variables were first examined for outliers, skewness and distribution of normality. The Pre-pregnancy BMI, Prenatal BMI, neonatal head circumference and neonatal body length are not normally distributed. They were presented as median (Min, Max). Data normally distributed were presented as mean (standard deviation). First, all collected factors were analyzed by univariate analysis. Comparisons between categorical variables were made using χ2 tests, while Mann–Whitney U tests were employed for continuous variables. Neonatal head circumference and neonatal body length were both significantly positively correlated with birthweight (r = 0.765, r = 0.733, p < .001), thus to address the issue of multicollinearity, they were not included in subsequent analyses. Assessing the potential impact of variables known to affect depression and biomarkers, preliminary Spearman correlations were used to check correlations between BWAZ, EPDS, maternal PRL, PGH and fetal IGF1. The main study outcomes included BWAZ and fetal development related hormone biomarkers in maternal plasma and umbilical cord blood. The major predictors were prenatal plasma PRL levels that were evaluated as categorical variables in quartiles. In order to verify which quartile affects BWAZ of infants, regression for qualitative variables (CATREG—Categorical Regression using Alternating Least Squares) was used[32]. According to the result of CATREG, we devided maternal PRL levels into low (Q1) versus adequate (Q2–Q4), because there are no clinically significant cut-points or standards for its concentrations. Next, the receiver operating characteristic (ROC) curve was used to evaluate the sensitivity of individual and combined effects of PRL to predict neonatal physical development. Then, the linear regression was performed to estimate the separate and combined effect of maternal PRL levels and prenatal depression categories on neonatal BWAZ and fetal growth development biomarkers(PGH and IGF1) Z-scores. As plasma PGH and IGFI had skewed distributions, these biomarkers were log transformed before calculating Z -score. Covariates were selected based on mother-infant dyads univariate analysis results and the published literature. Candidate variables( maternal age, education and income ) with a p value of the significant tests < 0.15 on univariate analysis were included in multivariable model. Besides, different living condition, pregnancy BMI and parity were associated with birthweight[33, 34], so residence, prenatal BMI and parity were included as covariates in subsequent analyses. All statistical tests were two-sided, with P < 0.05 considered to indicate statistical significance.

4.1 Demographic characteristics in mother-infant dyads.

The demographic variables of mother-infant pairs were shown in Table 1 and Univariate analysis of hormone concentrations was shown in Table 2. This study was composed of 392 mother-infants pairs, including 133(33.9%) mothers were depressed in pregnancy. There was no difference in maternal age, income level, education, pre-pregnancy BMI, prenatal BMI, pregnancy will, type of delivery, parity, gestational age, gender, and Apgar scores in 1 min between the depressed group and normal group (P > 0.05). There were significant differences in birthweight and neonatal head circumference between the two groups(p < 0.05). Depressed group had lower birthweight and neonatal head circumference. Normal group had greater maternal PRL and fetal IGF1 than depressed group (p < 0.01), but there was no difference in maternal PGH between the depressed group and the normal group (P > 0.05, Table 2).

Table 1

Demographic characteristics of mother-infant dyads in the total sample and subgroups stratified by maternal depressive symptoms in the our cohort *
Variables	Total (n = 392)	Normal group (N = 259)	Depressed group (N = 133)	z/X ²	p
Maternal characteristic
Maternal age (year) Mean(SD)	29.89(4.01)	30.05(3.86)	29.56(4.29)	-1.749	0.08
Pre-pregnancy BMI(kg/m²) Median(Range)	21.09(15.63,37.32)	21.23(15.63,37.32)	20.9(16.85,31.24)	-0.38	0.704
Prenatal BMI(kg/m²) Median(Range)	27.05(19.52, 42.6)	26.91(19.52,42.6)	27.29(21.05,35.76)	-0.582	0.56
Residence N(%)				2.101	0.152
Rural area	106(27.0)	64(24.7)	42(31.6)
Urban area	286(73.0)	195(75.3)	91(68.4)
Income level N(%)				4.812	0.09
< 3000 RMB/m/person	98(25.0)	61(23.6)	37(27.8)
3000–6000 RMB /m/person	156(39.8)	97(37.5)	59(44.4)
> 6000 RMB /m/person	138(35.2)	101(39.0)	37(27.8)
Education N(%)				4.447	0.108
Lower than high school	97(24.7)	58(22.4)	39(29.3)
Bachelor's degree	135(34.4)	86(33.2)	49(36.8)
Higher than bachelor's degree	160(40.8)	115(44.4)	45(33.8)
Type of delivery N(%)
Vaginal delivery	161(41.1)	105(40.5)	56(42.1)	0.089	0.828
Cesarean delivery	231(58.9)	154(59.5)	77(57.9)
Parity N(%)				0.011	0.915
Primiparity	196(50)	129(49.8)	67(50.4)
Multiparity	196(50)	130(50.2)	66(49.6)
Live with parents N(%)				0.327	0.59
NO	164(41.8)	111(42.9)	53(39.8)
Yes	228(58.2)	148(57.1)	80(60.2)
neonatal characteristic
Gestational age(week) Median(Range)	39.28(37.00,42.14)	39.28(37.00,42.14)	39.28(37.00,41.57)	-0.403	0.687
Gender N(%)				0.116	0.749
Male	211(53.8)	141(54.4)	70(52.6)
Female	181(46.2)	118(45.6)	63(47.4)
Apgar scores in 1 min N(%)				1.615	0.446
8	11(2.8)	6(2.3)	5(3.8)
9	26(6.6)	15(5.8)	11(8.3)
10	355(90.6)	238(91.9)	117(88)
Birth weight (kg) Mean(SD)	3.28(0.38)	3.3(0.39)	3.14(0.33)	-3.817	0.000
Neonatal head circumference (cm) Median(Range)	34(32,36)	34(32.36)	34(32,36)	-4.090	0.000
Neonatal body length(cm) Median(Range)	50(46,53.5)	50(46,53.5)	50(46,53)	-1.579	0.114
Abbreviation: EPDS: Edinburgh Postnatal Depression Scale.
* The research participants involved in mother-infant dyads from a prospective survey of infant behavioral observation from 2020–2021. Subjects were divided into depression group and normal group according to their mothers' EPDS scores. Mothers completed the EPDS to assess depressive symptoms, with depressed group defined as an EPDS score of ≥ 12.P values are for the comparison between mother-infant dyads with depressed group and normal group.

Table 2

Concentrations of maternal PRL, PGH and fetal IGF1 in the total sample and subgroups stratified by maternal depressed status in our cohort*
Variables	Total	Normal group	Depressed group	z	p
Maternal PRL(ng/ml) Mean(SD)	226.86 (84.49)	240.60(83.31)	200.10(80.54)	-4.486	<0.001
Maternal PGH(ng/ml) Median(Range)	8.95(1.7, 40.0)	9.3(1.8,35.7)	8.8(1.7,40.0)	-0.905	0.366
Fetal IGF1(ng/ml) Median(Range)	45(17,107)	50(17,107)	35(18,107)	-4.058	<0.001
Abbreviation: PRL, prolactin; PGH, placental growth hormone; IGF1, insulin-like growth factor-1, EPDS: Edinburgh Postnatal Depression Scale.
* The research participants involved in mother-infant dyads from a prospective survey of infant behavioral observation from 2020–2021. Subjects were divided into depression group and normal group according to their mothers' EPDS scores. Mothers completed the EPDS to assess depressive symptoms, with depressed group defined as an EPDS score of ≥ 12. P values are for the comparison between hormone concentration with depressed group and normal group.

4.2 Correlations among prenatal EPDS, maternal PRL, PGH, fetal IGF1 and BWAZ of mother-infant dyads.

As shown in Table 3, we assessed the relationships among maternal depressive symptoms (EPDS), BWAZ and hormone concentrations (maternal plasma PRL, PGH and umbilical cord blood IGF1). In bivariate analyses, prenatal maternal EPDS scores were significantly negatively associated with BWAZ, maternal PRL and fetal IGF1 concentrations, and no significant correlation with PGH. However, BWAZ was positively associated with maternal plasma PRL, PGH and fetal IGF1.

Table 3

The correlation analysis of maternal PRL, PGH and fetal IGF1 concentrations and EPDS scores on Z score of neonatal birth weight in our Cohort*
	EPDS	Maternal PRL	Maternal PGH	Fetal IGF1	BWAZ
EPDS	-	.168^**	− .085	− .184^**	− .219^**
Maternal PRL	.168**	-	.091	.171^**	.319^**
Maternal PGH	− .085	.091	-	.050	.388^**
Fetal IGF1	− .184^**	.171^**	.050	-	.182^**
BWAZ	− .219^**	.319^**	.388^**	.182^**	-
Abbreviation: PRL, prolactin; PGH, placental growth hormone; IGF1, insulin-like growth factor-1; EPDS: Edinburgh Postnatal Depression Scale. BWAZ, birth weight for age and sex Z score
* The research participants involved in mother-infant dyads from a prospective survey of infant behavioral observation from 2020–2021. Birth weight for age and sex Z score = (weight measurement - median weight in the standard population)/weight standard deviation in the standard population. The outcome variable was the Z score of the weight for age and sex.
p < 0 .05; *p < 0 .01; p > 0.05 was considered non-significant.

4.3 ROC curve analysis

ROC curves of maternal PRL concentrations predicted neonatal weight Z-score categories. To determine the best cutoff values for more than 50% normal birthweight newborn, we select Z score of birthweight 50% of the birthweight adjusted by gender and age as the cut-off point, and ROC curves were plotted and analyzed (Fig. 1 and Table S1). The AUC was 0.70 (95% CI: 0.648–0.752,p<0.05) for PRL. Youden index was used to determine the cutoff value. The maximum Youden index is 0.328, and the corresponding PRL concentration is 227.16ng/ml. The sensitivity and specificity are 68.7% and 35.8%. It can be considered that a PRL concentration of 227.16ng/ml is the critical concentration of a full-term newborn whose weight is equal to 50% for sex at birth. The combined probability of ROC curve was formed by the combined action of prolactin, growth hormone and IGF1. The AUC was 0.836(CI 95%: 0.797–0.876 for the combined probability. The maximum Youden index is 0.554, and the sensitivity and specificity are 81.9% and 73.5%. These results indicate that the combination of PRL, PGH and IGF1 has high sensitivity and specificity in predicting the newborn birthweight.

4.4 Maternal PRL concentrations, prenatal depressed status, and BWAZ

As shown in Table 4, through linear regression, independent and combined effects of prenatal depressed status and maternal PRL concentration on BWAZ were tested separately, while controlling for covariates. The median (interquartile range [IQR]) for maternal PRL concentration was 226.91 (167.60-283.74)ng/mL. Maternal PRL concentrations and maternal prenatal depression had significant effects on the offspring BWAZ. Infants of low maternal PRL had a 0.169 unit (95% CI, -0.488, − 0.130) decrease in BWAZ compared to those infants whose mothers had adequate PRL. And infants of depressed mothers had a 0.188 unit (95%CI, -0.478, -0.150) decrease in BWAZ compared to those infants of normal mothers.

Table 4

The separate and combined effect of maternal PRL concentrations and prenatal depression categories on Z score of neonatal birth weight in our Cohort*
Maternal			Model1			Model2
EPDS	PRL	N(%)	β	95%CI	p	β	95%CI	p
	Q1	98 (25)	-0.176	-0.500,-0.142	< 0.001	-0.169	-0.488,- 0.130	0.001
	Q2-Q4	294 (75)	Ref	Ref	Ref	Ref	Ref	Ref
Depressed		132 (33.7)	-0.195	-0.489,-0.163	< 0.001	-0.188	-0.478, -0.150	< 0.001
Normal		260 (66.3)	Ref	Ref	Ref	Ref	Ref	Ref
Combined effect
Depressed	Q1	48(12.2)	-0.259	-0.867,-0.383	< 0.001	-0.253	-0.853,-0.369	< 0.001
	Q2-Q4	84(21.4)	-0.101	-0.39,0.001	0.051	-0.094	-0.377,0.015	0.07
Normal	Q1	50(12.8)	-0.058	-0.376,0.100	0.255	-0.050	-0.356,0.118	0.323
	Q2-Q4	210(53.6)	Ref	Ref	Ref	Ref	Ref	Ref
Stratified by depression categories
Depressed	Q1	48(36.4)	-0.293	-0.671,-0.187	0.001	-0.285	-0.666,-0.168	0.001
	Q2-Q4	84(63.6)	Ref	Ref	Ref	Ref	Ref	Ref
Normal	Q1	50(19.2)	-0.068	-0.391,0.112	0.276	-0.062	-0.379,0.126	0.325
	Q2-Q4	210(80.8)	Ref	Ref	Ref	Ref	Ref	Ref
Abbreviation: PRL: prolactin; EPDS: Edinburgh Postnatal Depression Scale;
*The research participants involved in mother-infant dyads from a prospective survey of infant behavioral observation from 2020–2021. Q2–Q4 PRL concentration range: 167.61–465.32ng/mL, Q1 PRL concentration range:20.35–167.60ng/mL. The outcome variable was the Z score of the weight for age and sex.
Model1 was unadjusted.
Model2 was adjusted for prenatal BMI, maternal age, residence, education, income, and parity. p < 0 .05, *p<0.01

Infants of depressed mothers with low PRL had a 0.253 unit ( 95% CI, -0.853, -0.369) decrease in BWAZ compared to those whose mothers had adequate PRL and normal emotion. Furthermore data analysis was stratified according to maternal emotions status, in depressed group, children of mothers with low PRL concentrations were associated with a 0.285 reduction in BWAZ, compared to children of mothers with adequate PRL concentrations (β = 0.285 [95%CI, -0.666,-0.168], p = 0.001). However, in the normal group, there was no significant interaction between PRL concentration and BWAZ. Pregnancy BMI, maternal age, residence, education, income and parity were adjusted. These results suggested that maternal PRL played an important role in birthweight following maternal prenatal depressive symptoms.

4.5 Maternal PRL concentrations, prenatal depressed status, and growth development biomarkers ( PGH and IGF1)

As compared with maternal low PRL levels, high PRL levels were related to an increase in the levels of IGF1 in offspring, and no association with PGH. There was no significant combined effect of maternal plasma PRL levels and different emotion on maternal PGH. However, newborns of normal mothers with high PRL had a positive correlation with the fetal plasma IGF1 Z-score compared to infants whose mothers had low PRL and depressed symptoms (β = 0.314, p < 0.01). And analysis stratified based on maternal emotions status, newborns with high PRL concentrations had a positive correlation with the plasma IGF1 Z –score (β = 0.206, p = 0.019) compared to low maternal PRL concentrations in the depressed group, but there was no significant correlation in the normal group (Table 5).

Table 5

The individual and combined effect of maternal PRL concentrations and prenatal depression categories on neonatal physical growth metabolic biomarkers in our Cohort*
Maternal		PGH z-score				IGF1 z-score
		Model1		Model2		Model1		Model2
EPDS	PRL	β	p	β	p	β	p	β	p
	Q1	Ref	Ref	Ref	Ref	Ref	Ref	Ref	Ref
	Q2-Q4	0.010	0.838	0.010	0.842	0.121	0.017	0.124	0.013
Normal		Ref	Ref	Ref	Ref	Ref	Ref	Ref	Ref
Depressed		-0.063	0.217	-0.045	0.383	-0.200	<0.001	-0.183	<0.001
Combined effect
Depressed	Q1	Ref	Ref	Ref	Ref	Ref	Ref	Ref	Ref
	Q2-Q4	0.044	0.558	0.046	0.544	0.142	0.052	0.158	0.030
Normal	Q1	0.088	0.193	0.075	0.271	0.182	0.006	0.179	0.007
	Q2-Q4	0.090	0.264	0.074	0.357	0.32	<0.001	0.314	<0.001
Stratified by depression categories
Depressed	Q2-Q4	0.047	0.592	0.039	0.669	0.161	0.063	0.206	0.019
	Q1	Ref	Ref	Ref	Ref	Ref	Ref	Ref	Ref
Normal	Q2-Q4	0.034	0.591	0.027	0.671	0.042	0.501	0.042	0.499
	Q1	Ref	Ref	Ref	Ref	Ref	Ref	Ref	Ref
Abbreviation: PRL, prolactin; PGH, placental growth hormone; IGF1, insulin-like growth factor-1; EPDS, Edinburgh postnatal depression scale.
The research participants involved in mother-infant dyads from a prospective survey of infant behavioral observation from 2020–2021. Q2–Q4 PRL concentration range: 167.61–465.32ng/mL, Q1 PRL concentration range:20.35–167.60ng/mL.
As plasma PGH and IGFI had positively skewed distributions, these biomarkers were log transformed before calculating z-score.
Model1 was unadjusted.
Model2 was adjusted for prenatal BMI, maternal age, residence, education, income, and parity. p < 0 .05, *<0.01

Our study scope was quite a normal population, so our findings were appropriate for the general population, and increased the literature on biological molecular mechanisms underlying fetal programming, by highlighting distinct associations between variations in maternal PRL level and neonatal birthweight. This is a prospective cohort research to assess the separate and combined effects of prenatal emotional status as well as plasma PRL levels on normal full-term offspring birthweight outcomes and growth development biomarkers. Prenatal depression may play an important role in difference in weight development of normal birthweight infants and it is further supported by our findings.

Our research showed that sufficient maternal PRL levels would mitigate the adverse impact of prenatal depression on infant birthweight. We proved that, given prenatal depression, neonatal birthweight was reduced by a 0.285 unit if the mothers had low PRL levels. And simultaneously we found that infants of depressed mothers who had lower PRL levels had the most risk to have low weight at birth. In animal models of chronic unpredictable mild stress (CMS), which is one of the best validated animal model of depression, the stress-none reactive group had an increased level of PRL in plasma and an increase in PRL binding to PRL receptors in the choroid plexus [35]. This result indicated that PRL had a resilience to stress thereby protecting rodent models against hazard of depression. Zhang H et al. indicated mothers exposed to maternal prenatal depressive symptoms decreased maternal serum PRL [14]. There is an evidence suggesting that a one standard deviation increase in birthweight was linked to an 18% reduction in the risk of psychosis-like symptoms [36]. Wiles et al. observed that for each additional standard deviation in fetal growth is related to a 14% decrease in the child's risk of behavioral problems[37]. Some studies indicate that birthweight is associated with the frequency and severity of depression as well as negative emotional reactions (fear, distress, sadness, anger) in childhood [38, 39]. An analysis of Scandinavian registry data indicated a relationship between birthweight and adult psychiatric disorders, but there was no indication that the effect was specific to birthweight below 2,500 g [40]. Therefore, our results suggest that even maternal depression, if the level of maternal prolactin is sufficient, it could mitigate the adverse effects of depression on offspring physical development.

In addition, ROC curve analysis suggested that combined effect of maternal plasma PRL、PGH and fetal IGF1 had a good predictive effect on the occurrence of neonatal weight more than 50% at birth. Besides, maternal plasma PRL also played an important role in newborn birthweight. PRL have positive effects on adipose tissue mass accumulation during pregnancy [41]. Animal studies demonstrated that injections of ectopic pituitary transplants or exogenous prolactin could result in growing in body weight in both female[42] and male animals[43]. The high PRL level during pregnancy is associated with impaired leptin-induced activation and causes the attenuation of suppression of food intake [44]. PRL promotes lipogenic effects during pregnancy, which is beneficial for the extra energy supply to fetus. Therefore, these beneficial metabolic effects of PRL on white adipose tissues those are supposed to be adaptive in pregnancy, to enhance nutrition for the offspring. During pregnancy, PRL plays roles in the regulation of β-cell mass expansion and insulin resistance in order to transfer nutrients, particularly glucose, to the fetus promoting a passive glucose transport across the placenta. PRL mediates insulin synthesis and release, induces β-cell replication[45], further enhances STAT5 tyrosine phosphorylation[46], as well as increases glucose transporter 2 (GLUT2) expression thus facilitating glucose entry into the β-cells. PRL could improve hepatic insulin sensitivity at normal concentrations during pregnancy and higher PRL levels tend to increase serum insulin concentrations and β-cell function[47].

Biological plausibility was further supported by our findings of growth development biomarkers. Prior mechanistic researches suggested that partial genetic mutations (< 1%), primarily coding with proteins that involved in the GH–IGF-I axis, has affected in infants physical development at birth[48]. PGH resembles pituitary GH, which has a lipolytic and somatotrophic action. Other studies have shown that PGH is a potent lipolytic hormone and insulin antagonist, which stimulates production of IGF1 in pregnancy. The emergence of PGH was the predominant determinant of insulin resistance during pregnancy, through transplacental nutrient delivery promoted IGF1, which promoted fatal liver glycogen storage, fat deposition and fetal growth[9]. PGH and IGF1 may lead to a placenta that is more efficient in nutrient transfer to the fetus, thereby contributing to fetal growth. In animal experiment, models of tissue specific IGF1 knockout manifested the role of circulating IGF1 in regulation of growth[49]. GH deficiency/insensitivity and IGF1 deficiency play an important role in intrauterine linear and cranial growth[50]. The primary clinical features of a homozygous deficient IGF1 include perinatal growth restriction, severe microcephaly as well as severe global developmental delay[51].

We found that PRL, PGH and IGF1 were all correlated with birthweight, and the relationship between birthweight and PGH was higher than other two hormones. However, prenatal depression was not significantly associated with PGH concentration. We explored the effect of PRL mediated maternal depression on PGH and IGF1 by linear regression analysis. We found analysis stratified based on maternal depressed status, newborns with PRL concentrations had a positive correlation with plasma IGF1, but no association was found in the control group. Therefore, we believe that prenatal depression may affect IGF1 through prolactin and further affect fetal physical development. Prenatal depression could impair maternal PRL concentration, and then would be likely to impede the mobilization of maternal nutrient reserves as well as reduce fetal IGF1 levels, thereby reducing uteroplacental blood flow, nutrient transport and limiting fetal growth. Therefore, according to our results, it can be thought that maternal PRL shortage is an important mechanism of prenatal depression lessening offspring growth and development. However, their precise roles in fetal programming remain largely unknown. It is unclear how maternal PRL mediate fetal IGF1 related to neonatal birthweight in different aspects of maternal emotional status.

The major impact of maternal PRL on glucose homeostasis during pregnancy is advantageous for increasing glucose transfer to the fetus. Therefore, maternal PRL of the pituitary gland and IGF1 of fetuses acted synergistically on integrating the metabolic response to pregnancy with the demands of fetal and neonatal development. In short the changes in PRL concentrations have strong impact on fetal growth as well as long-term metabolic consequences. We found that reduced prolactin concentrations in depressed mothers were an adverse factor for lower birthweight in newborns. This association appears to be related to IGF1. Our data suggest that low PRL levels in maternal depression can have harmful influences on neonatal birthweight, and that sufficient PRL levels can counteract the harmful influences of maternal prenatal depression on offspring.

Limitations

Although this study has many advantages, such as prospective design, neonatal physical development expressed by Z-scores of age and sex and maternal and fetal hormone testing, due to some limitations, the interpretation of the results should be made with caution. First, these data were based on single-center hospital samples assessed at last trimester, and self-reported depression degrees were comparatively low, limiting the applicability of our findings to high-risk groups as well as different gestational periods. We focused on the third trimester of pregnancy, because it is a time of fast fetal physical growth and brain development, and the rapid changes make fetus particularly vulnerable to environmental effects. Second, we can‘t pinpoint whether the observation of association was specific to the last trimester or was the consequence of continued exposure during pregnancy. Our findings suggest that exposure to prenatal depression in the late trimester of pregnancy versus the earlier trimester of pregnancy has a stronger and more specific effect on offspring outcomes, but we cannot confirm this hypothesis without performing assessments at multiple time points during pregnancy. Our volunteers were women with healthy, singleton pregnancies, so findings may not be generalize to women with higher-risk pregnancies. Finally, since we did not assess the relevant genetic information, we cannot exclude the possibility that shared genes influence maternal depression and infant outcomes, and the study was observational, so we cannot conclude a causal relationship. Therefore, the conclusions drawn from our research must be considered preliminary and require replicating with larger sample and different cohorts.

In conclusion, we found that prenatal depression and low maternal PRL levels during last trimester are associated with increased risk of normal infant birthweight loss as measured by the birthweight Z-score and the benefit of sufficient PRL concentrations among depressed mothers. Prenatal depression could impair maternal PRL concentration, and then would be likely to impede the mobilization of maternal nutrient reserves as well as reduce fetal IGF-1 levels, thereby reducing uteroplacental blood flow, nutrient transport and limiting fetal growth. The observational nature of our study precludes causal inferences but the present findings may have implications for later health.

Prolactin (PRL); Placental growth hormone (PGH); Insulin-like growth factor-1 (IGF1); growth hormone(GH); Edinburgh Postnatal Depression Scale (EPDS); body mass index (BMI); chemiluminescence analysis (CLIA); birthweight for age Z score (BWAZ); operating characteristic (ROC); chronic unpredictable mild stress (CMS); glucose transporter 2 (GLUT2)

Ethics approval and consent to participate

This research was conducted in accordance with the Helsinki Declaration, and ethical approval of the study was obtained from the Ethics Committee of Xi’an Jiaotong University. Consent forms for biological samples were collected under the Organic Act on the Human Body. All participants provided written informed consent.

Consent for publication

Not applicable

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare that we have no competing interests.

Funding

National Natural Science Foundation of China (No.81873805)

Authors' contributions

Author Shan Wang designed the study and wrote the manuscript. Infant behavioral development assessments were assessed by authors Shan Wang and Hui Li. Author Xiaowen Jia provided the writing assistance and author Dan Zhang provided the proof reading this article. Authors Shengquan Chen, Meina Xu, Xi Jin and Zhongliang Zhu managed the literature analyses and undertook the statistical analysis. Corresponding author Hui Li revised the draft of the manuscript. All authors contributed to and approved the final manuscript.

Acknowledgements

We are extremely thankful to all the families that participated in this study, the pediatrics graduate students for their help in recruiting volunteers. Our team includes laboratory technicians, pediatricians, obstetricians, basic research professor and nurses. This publication is the work of the authors and the named authors will serve as guarantors for the contents of this paper.

Carrier, N., et al., Sex-Specific and Estrous Cycle-Dependent Antidepressant-Like Effects and Hippocampal Akt Signaling of Leptin. Endocrinology, 2015. 156(10): p. 3695–705.
Okagbue, H.I., et al., Systematic Review of Prevalence of Antepartum Depression during the Trimesters of Pregnancy. Open Access Maced J Med Sci, 2019. 7(9): p. 1555–1560.
Jarde, A., et al., Neonatal Outcomes in Women With Untreated Antenatal Depression Compared With Women Without Depression: A Systematic Review and Meta-analysis. JAMA Psychiatry, 2016. 73(8): p. 826–37.
D'Onofrio, B.M., et al., Preterm birth and mortality and morbidity: a population-based quasi-experimental study. JAMA Psychiatry, 2013. 70(11): p. 1231–40.
Goyal, D., S.W. Limesand, and R. Goyal, Epigenetic responses and the developmental origins of health and disease. J Endocrinol, 2019. 242(1): p. T105-t119.
Madigan, S., et al., A Meta-Analysis of Maternal Prenatal Depression and Anxiety on Child Socioemotional Development. J Am Acad Child Adolesc Psychiatry, 2018. 57(9): p. 645–657.e8.
Accortt, E.E., A.C. Cheadle, and C. Dunkel Schetter, Prenatal depression and adverse birth outcomes: an updated systematic review. Matern Child Health J, 2015. 19(6): p. 1306–37.
Burton, G.J., A.L. Fowden, and K.L. Thornburg, Placental Origins of Chronic Disease. Physiol Rev, 2016. 96(4): p. 1509–65.
Newbern, D. and M. Freemark, Placental hormones and the control of maternal metabolism and fetal growth. Curr Opin Endocrinol Diabetes Obes, 2011. 18(6): p. 409–16.
Napso, T., et al., The Role of Placental Hormones in Mediating Maternal Adaptations to Support Pregnancy and Lactation. Front Physiol, 2018. 9: p. 1091.
Freeman, M.E., et al., Prolactin: structure, function, and regulation of secretion. Physiol Rev, 2000. 80(4): p. 1523–631.
Sjoeholm, A., et al., Region-, neuron-, and signaling pathway-specific increases in prolactin responsiveness in reproductively experienced female rats. Endocrinology, 2011. 152(5): p. 1979–88.
Larsen, C.M. and D.R. Grattan, Prolactin, neurogenesis, and maternal behaviors. Brain Behav Immun, 2012. 26(2): p. 201–9.
Zhang, H., et al., Prolactin, a potential mediator of reduced social interactive behavior in newborn infants following maternal perinatal depressive symptoms. J Affect Disord, 2017. 215: p. 274–280.
Hann, M., et al., The growth of assisted reproductive treatment-conceived children from birth to 5 years: a national cohort study. BMC Med, 2018. 16(1): p. 224.
O'Donnell, K.J. and M.J. Meaney, Fetal Origins of Mental Health: The Developmental Origins of Health and Disease Hypothesis. Am J Psychiatry, 2017. 174(4): p. 319–328.
Gluckman, P.D., et al., Effect of in utero and early-life conditions on adult health and disease. N Engl J Med, 2008. 359(1): p. 61–73.
Velegrakis, A., M. Sfakiotaki, and S. Sifakis, Human placental growth hormone in normal and abnormal fetal growth. Biomed Rep, 2017. 7(2): p. 115–122.
Freemark, M., Placental hormones and the control of fetal growth. J Clin Endocrinol Metab, 2010. 95(5): p. 2054–7.
Vatten, L.J., et al., Changes in circulating level of IGF-I and IGF-binding protein-1 from the first to second trimester as predictors of preeclampsia. Eur J Endocrinol, 2008. 158(1): p. 101–5.
Lacroix, M.C., et al., Stimulation of human trophoblast invasion by placental growth hormone. Endocrinology, 2005. 146(5): p. 2434–44.
Barbour, L.A., et al., Human placental growth hormone increases expression of the p85 regulatory unit of phosphatidylinositol 3-kinase and triggers severe insulin resistance in skeletal muscle. Endocrinology, 2004. 145(3): p. 1144–50.
Barbour, L.A., et al., Human placental growth hormone causes severe insulin resistance in transgenic mice. Am J Obstet Gynecol, 2002. 186(3): p. 512–7.
Bauman, D.E., J.H. Eisemann, and W.B. Currie, Hormonal effects on partitioning of nutrients for tissue growth: role of growth hormone and prolactin. Fed Proc, 1982. 41(9): p. 2538–44.
Gao, P., et al., Maternal stress affects postnatal growth and the pituitary expression of prolactin in mouse offspring. J Neurosci Res, 2011. 89(3): p. 329–40.
Alawad, Z.M. and H.L. Al-Omary, Maternal and cord blood prolactin level and pregnancy complications. Pak J Med Sci, 2019. 35(4): p. 1122–1127.
Grossman, A.W., et al., Experience effects on brain development: possible contributions to psychopathology. J Child Psychol Psychiatry, 2003. 44(1): p. 33–63.
Davis, E.P., et al., Prenatal maternal stress programs infant stress regulation. J Child Psychol Psychiatry, 2011. 52(2): p. 119–29.
Cox, J.L., J.M. Holden, and R. Sagovsky, Detection of postnatal depression. Development of the 10-item Edinburgh Postnatal Depression Scale. Br J Psychiatry, 1987. 150: p. 782–6.
Smith-Nielsen, J., et al., Validation of the Edinburgh Postnatal Depression Scale against both DSM-5 and ICD-10 diagnostic criteria for depression. BMC Psychiatry, 2018. 18(1): p. 393.
Abitew, D.B., et al., Rural children remain more at risk of acute malnutrition following exit from community based management of acute malnutrition program in South Gondar Zone, Amhara Region, Ethiopia: a comparative cross-sectional study. PeerJ, 2020. 8: p. e8419.
Bień, A., et al., Quality of life in women with endometriosis: a cross-sectional survey. Qual Life Res, 2020. 29(10): p. 2669–2677.
Burris, H.H. and M.R. Hacker, Birth outcome racial disparities: A result of intersecting social and environmental factors. Semin Perinatol, 2017. 41(6): p. 360–366.
Nazari, M., et al., Comparison of maternal characteristics in low birth weight and normal birth weight infants. East Mediterr Health J, 2013. 19(9): p. 775–81.
Faron-Górecka, A., et al., Involvement of prolactin and somatostatin in depression and the mechanism of action of antidepressant drugs. Pharmacol Rep, 2013. 65(6): p. 1640–6.
Thomas, K., et al., Association of measures of fetal and childhood growth with non-clinical psychotic symptoms in 12-year-olds: the ALSPAC cohort. Br J Psychiatry, 2009. 194(6): p. 521–6.
Wiles, N.J., et al., Fetal growth and childhood behavioral problems: results from the ALSPAC cohort. Am J Epidemiol, 2006. 163(9): p. 829–37.
Costello, E.J., et al., Prediction from low birth weight to female adolescent depression: a test of competing hypotheses. Arch Gen Psychiatry, 2007. 64(3): p. 338–44.
Schlotz, W. and D.I. Phillips, Fetal origins of mental health: evidence and mechanisms. Brain Behav Immun, 2009. 23(7): p. 905–16.
Abel, K.M., et al., Birth weight, schizophrenia, and adult mental disorder: is risk confined to the smallest babies? Arch Gen Psychiatry, 2010. 67(9): p. 923–30.
Lopez-Vicchi, F., et al., Metabolic functions of prolactin: Physiological and pathological aspects. J Neuroendocrinol, 2020. 32(11): p. e12888.
Noel, M.B. and B. Woodside, Effects of systemic and central prolactin injections on food intake, weight gain, and estrous cyclicity in female rats. Physiol Behav, 1993. 54(1): p. 151–4.
Matsuda, M., et al., Chronic effect of hyperprolactinemia on blood glucose and lipid levels in mice. Life Sci, 1996. 58(14): p. 1171–7.
Augustine, R.A., et al., Impaired hypothalamic leptin sensitivity in pseudopregnant rats treated with chronic prolactin to mimic pregnancy. J Neuroendocrinol, 2019. 31(9): p. e12702.
Brelje, T.C., et al., Distinctive roles for prolactin and growth hormone in the activation of signal transducer and activator of transcription 5 in pancreatic islets of langerhans. Endocrinology, 2004. 145(9): p. 4162–75.
LeBaron, M.J., et al., In vivo response-based identification of direct hormone target cell populations using high-density tissue arrays. Endocrinology, 2007. 148(3): p. 989–1008.
Huang, C., F. Snider, and J.C. Cross, Prolactin receptor is required for normal glucose homeostasis and modulation of beta-cell mass during pregnancy. Endocrinology, 2009. 150(4): p. 1618–26.
van der Steen, M., et al., ACAN Gene Mutations in Short Children Born SGA and Response to Growth Hormone Treatment. J Clin Endocrinol Metab, 2017. 102(5): p. 1458–1467.
Yakar, S., H. Werner, and C.J. Rosen, Insulin-like growth factors: actions on the skeleton. J Mol Endocrinol, 2018. 61(1): p. T115-t137.
Forbes, B.E., A.J. Blyth, and J.M. Wit, Disorders of IGFs and IGF-1R signaling pathways. Mol Cell Endocrinol, 2020. 518: p. 111035.
Walenkamp, M.J.E. and J.M. Wit, A homozygous mutation in the highly conserved Tyr60 of the mature IGF1 peptide broadens the spectrum of IGF1 deficiency. Eur J Endocrinol, 2019. 181(6): p. C29-c33.

No competing interests reported.

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Association of prenatal depression and plasma prolactin concentrations with neonatal birthweight

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Abstract

Figures

1. Introduction

2. Method

2.1 Participants

2.2 Procedures

2.2.1 Laboratory measurements

2.2.2 Neonatal assessment

3. Statistical Analysis

4. Results

4.1 Demographic characteristics in mother-infant dyads.

4.2 Correlations among prenatal EPDS, maternal PRL, PGH, fetal IGF1 and BWAZ of mother-infant dyads.

4.3 ROC curve analysis

4.4 Maternal PRL concentrations, prenatal depressed status, and BWAZ

4.5 Maternal PRL concentrations, prenatal depressed status, and growth development biomarkers ( PGH and IGF1)

5. Discussion

6. Conclusions

List Of Abbreviations

Declarations

References

Competing Interests

Supplementary Files

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