Abstract
Background
The recent Maternal Immune Activation (MIA) theory suggests maternal systemic inflammation may serve as a mediator in associations between prenatal maternal adversities and neurodevelopmental diseases in offspring. Given the co-exposure to multiple adversities may be experienced by pregnant person, it is unclear whether a quantitative index can be developed to characterize the inflammation related exposure level, and whether this index is associated with neurodevelopmental delays in offspring.
Methods
Based on Jiangsu Birth Cohort (JBC), a total of 3051 infants were included in the analysis. Inflammation related Prenatal Adversity Index (IPAI) was constructed using maternal data. Neurodevelopmental outcomes were assessed using the Bayley Scales of Infant and Toddler Development, third edition, screening test in one year. Multivariate linear regression and Poisson regression model were performed to analyze the associations between IPAI and neurodevelopment in offspring.
Results
Compared with “low IPAI” group, offspring with “high IPAI” have lower scores of cognition, receptive communication, expressive communication, and fine motor. The adjusted β were − 0.23 (95%CI: -0.42, -0.04), -0.47 (95%CI: -0.66, -0.28), -0.30 (95%CI: -0.49, -0.11), and − 0.20 (95%CI: -0.33, -0.06). Additionally, the elevated risk for noncompetent development of cognition and receptive communication among “high IPAI” group was observed. The relative risk [RR] and 95% confidence interval [CI] were 1.35 (1.01, 1.69) and 1.37 (1.09, 1.72).
Conclusions
Our results revealed a significant association between higher IPAI and lower scores across cognition, receptive communication, expressive communication, and fine motor domains, and an increased risk of noncompetent development in the cognition and receptive communication domains.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12884-024-06839-8.
Keywords: Pregnancy, Adversities, Inflammation, Index, Offspring, Neurodevelopment
Introduction
Neurodevelopmental delays, typically observed in early childhood and encompassing delays in language development, cognitive function, and motor skills [1], affect a significant proportion of children, ranging from 6.4 to 11.5% [2]. These delays are associated with an increased risk of conditions such as autism spectrum disorder and attention-deficit/hyperactivity disorder, which in turn adversely affect their academic performance and future economic status prospects [3]. Evidence is accumulating to suggest that prenatal adversities, encompassing factors such as maternal obesity [4–6], diabetes, hypertensive disorders [7, 8], psychosocial factors [9], socioeconomic status [10], physical activity [11], and diet [12, 13], play a significant role in the development of neurodevelopmental delays in children [14]. Notably, an increasing number of studies have proposed that maternal systemic inflammation may mediate the connection between prenatal exposures and neurodevelopmental delays in offspring [15]. Consequently, the Maternal Immune Activation (MIA) hypothesis has emerged, positing that exposure to an adversity induced dysregulated maternal immune environment during pregnancy can impact fetal neurodevelopment [16]. Thus, various prenatal adversities may affect fetal neurodevelopment through a shared MIA pathway, and the cumulative effect of single adverse exposure on fetal neurodevelopment remains uncertain. However, the integration of co-exposure factors related to the MIA hypothesis into a unified exposure index and their overall impact on fetal neurodevelopment still require further clarification.
During pregnancy, the second trimester emerges as a critical period for fetal brain development because cortical lamination during this phase represents the brain’s largest and most vital information processing network, and is actively progressing [17–19]. Therefore, any adverse exposures experienced in utero during this delicate and sensitive period may lead to neurodevelopmental abnormalities. We hypothesized that co-exposure to multiple maternal adversities during the second trimester of pregnancy could lead to a cumulated negative effect on noncompetent development in offspring.
In this longitudinal cohort study, we aimed to construct an Inflammation related Prenatal Adversity Index (IPAI), and then to examine the associations between IPAI and neurodevelopment of offspring at one year of age using data of the Jiangsu Birth Cohort (JBC).
Methods and materials
Study design and participants
This population-based cohort study was conducted within the JBC, a prospective and longitudinal cohort study in Eastern China [20]. In short, couples were recruited during early pregnancy (≤ 14 weeks) or when they were planning to become pregnant and were followed up in their regularly scheduled prenatal and postnatal visits. Participants were asked to complete the questionnaires including lifestyle, behavioral traits, health and diseases, and medication with face-to-face help from professionally trained staff in the first (10–14 weeks), second (22–26 weeks), and third (30–34 weeks) trimester of gestation. After birth, children were followed up by telephone after 42 days and 6 months. When they reached one year old, they were invited back to the hospital where they were born to undergo systematic physical examination and neurodevelopment assessment by a professional doctor. Every participant provided written informed consent before inclusion.
From April 2014 to June 2020, among 17,854 couples recruited of JBC, a total of 15,670 pregnancies resulted in 16,480 live-born infants. The present study included mother-infant pairs fulfilling the following criteria: (1) live birth; (2) the complete variables in IPAI; (3) infants with complete data of intelligence test result follow-up in the first year (11-12.5 months). Since the Bayley Scales of Infant and Toddler Development, Version-III (Bayley-III) Screening Test has been implemented since November 2018 to assess the neurodevelopment of infants, we included 6,328 pregnancies to reach one year of age in November 2018. Among them, 3,489 pregnancies were excluded for the following reasons: missing values for variables related to inflammation (n = 1,712), failure to follow-up (n = 848), secondary enrollment (n = 1), completion of the Bayley-III screening test beyond 12.5 months of age (n = 928). Finally, 2,839 pregnancies with 3,051 infants were included (Supplementary Fig. 1). Based on this sample size, power is 85.41% and 80.73%, respectively.
Exposure assessment
Based on factors affecting offspring neurodevelopment reported in previous literature [8, 11, 21–25] and data from this cohort, we finally selected 6 variables to use to calculate the IPAI, including socioeconomic status [26], complication of pregnancy [27], psychological state [28, 29], maternal pre-pregnancy body mass index (BMI) [30], physical activity [31] and diet [32, 33] (appendix 1 pp 2–8). The specific scoring rules are shown in Table 1.
Table 1.
List of 8 variables of mothers included in the IPAI
| Variables | Details of variables | Coding of variables |
|---|---|---|
| Household income, CNY |
A. < 50,000 B. 50,000-100,000 C. 100,000-200,000 D. 200,000-300,000 E. ≥ 300,000 |
Options C, D, and E: 0; Options A and B: 1. |
| Maternal education at children birth, years |
A. 0 ~ 6 B. 7 ~ 9 C. 10 ~ 12 D. 13 ~ 16 E. ≥ 17 |
Options C, D, and E: 0; Options A and B: 1. |
| Hypertensive disorders in pregnancy | Information on maternal hypertensive disorder was retrieved from electronic medical records and questionnaire during the 2nd trimester of pregnancy. |
Without maternal hypertensive disorder: 0; With maternal hypertensive disorder: 1. |
| Hyperglycemia in pregnancy | Information on maternal diabetes was retrieved from electronic medical records and questionnaire during the 2nd trimester of pregnancy. |
Without maternal diabetes: 0; With maternal diabetes: 1. |
| Psychological state | The psychological state of pregnant women is assessed by three self-filled scales: SAS, CES-D and PSS-10. |
A normal score on three scales: 0; An abnormal score on one or two scales: 1; An abnormal score on three scales: 2. |
| Maternal pre-pregnancy BMI | Data for height and weight was obtained from a baseline questionnaire, then BMI was calculated by weight (kg) / height (m)2. |
BMI less than 24: 0; BMI between 24-27.9: 1; BMI greater than or equal to 28: 2. |
| Physical activity | The intensity of exercise was evaluated according to the MET value, and then the score is assigned according to the weekly exercise time. |
Moderate-intensity exercise greater than or equal to 150 min/week or high-intensity exercise greater than or equal to 75 min/week: 0; Moderate-intensity exercise less than 150 min/week or high-intensity exercise less than 75 min/week: 1; Never exercise: 2. |
| Diet | We calculated a dietary inflammation score based on the intake of 16 types of food. First, all food types were divided into inflammatory foods and anti-inflammatory foods, and then assigned 0 or 1 points according to the median amount of people consumed. The total score is sixteen points, and the higher the score, the more inflammatory foods are consumed. |
The dietary inflammation score < 7: 0; The dietary inflammation score ≥ 7 and < 9: 1; The dietary inflammation score ≥ 9: 2. |
Abbreviation: IPAI, inflammation related prenatal adversity index; SAS, self-rating anxiety scale; CES-D, center for epidemiological survey, depression scale; PSS-10, perceived stress scale; BMI, body mass index; MET, metabolic equivalent
Socioeconomic status, including household income and maternal education at children birth, was assigned based on the data distribution of the population and previous literature [34]. Psychological state was jointly assessed by three self-filled scales: Center for Epidemiological Survey, Depression Scale (CES-D) [35], Self-Rating Anxiety Scale (SAS) [36] and Perceived Stress Scale (PSS-10) [37], and then assigned points according to the cut-off value. Physical activity was assigned points based on a combination of metabolic equivalent (MET) and weekly exercise time. Diet was assigned based on the inflammatory properties of food and the median intake of the population. Complication of pregnancy included hypertensive disorders in pregnancy (HDP) and hyperglycemia in pregnancy (HIP). Socioeconomic status, psychological state, physical activity and diet were collected by trained staff using a tablet-based questionnaire. Complication of pregnancy was abstracted from medical records. Pre-pregnancy height and weight were each measured twice by trained study personnel using calibrated instruments.
Each inflammation-related adversity was dichotomized or mapped into the 0.00–2.00 interval, with 0.00 indicating the least severe state of inflammation and 2.00 indicating the most severe state of inflammation. The IPAI was calculated for each participant as the 6 inflammation-related adversity are added together.
The IPAI is a continuous variable that ranged from 0.00 to 12.00, with a higher value indicating a worse status of inflammation related exposure. Based on the distribution of score, we further categorized the IPAI into three levels: low IPAI group (IPAI ≤ 2.00), moderate IPAI group (2.00 < IPAI < 5.00), and high IPAI group (IPAI ≥ 5.00).
Neurodevelopment
The Bayley Scales of Infant and Toddler Development, third edition, screening test (Bayley-III Screening Test) was used to evaluate the neurodevelopment of children aged 11–12.5 months in this study. It consists of five domains: cognition, receptive communication, expressive communication, fine motor, and gross motor. The JBC Study have taken a series of measures in order to ensure the validity and reliability of the neurodevelopment evaluation. All the psychologists have been strictly trained and evaluated before taking up their posts, and after parental consent, the whole process was filmed and randomly checked monthly by an appointed developmental neuropsychologist. Corrected for prematurity, each domain could be divided into at risk, emerging, and competent (Table S1). In this study, participants in the category of at-risk and emerging were classified as noncompetent for analysis. The reliability and validity of Bayley-III Screening Test have been shown to be good to excellent.
Covariates
Information on covariates was primarily extracted from the questionnaires and medical records, including maternal age at delivery, parity (nulliparous/multiparous), child sex (male/female), child age at examination (days), and duration of breast feeding (< 6/≥6 months).
Statistical analysis
Baseline characteristics were presented by three categories of inflammation status (i.e., IPAI ≤ 2.00, IPAI > 2.00 to < 5.00, and IPAI ≥ 5.00) as means (SD) for continuous variables or percentages for categorical variables, with adjustment for maternal age at delivery, parity, child sex, child age at examination, and duration of breast feeding.
The association between IPAI and scores in neurodevelopment was estimated by using a linear regression model, and the association between IPAI and noncompetence in neurodevelopment was estimated by using a Poisson regression model. Regression models were fitted with the use of a generalized linear mixed model given the nonindependence of observations from twin pairs.
In addition, several sensitivity analyses were performed to assess whether the potential associations could be attributed to the confounding by the risk of twins and preterm birth or structural birth defects.
We did all statistical analyses using of R software (Version 4.1.3, R Foundation for Statistical Computing, http://www.cran.r-project. org/). All p values were two-sided and the level of statistical significance was defined as p less than 0.05.
Results
In this study, a total of 3,051 infants at one year of age from 2,839 families were included. Among them, 1,096 mothers with 1,139 infants were categorized into the “low IPAI” group, 1,094 mothers with 1,176 infants into the “moderate IPAI” group, and 649 mothers with 736 infants into the “high IPAI” group. Compared to mothers in “low IPAI” group, mothers in “moderate and high IPAI” groups were more likely to be older, to be multiparous, to be treated with ART. Compared to infants born to mothers in “low IPAI” group, infants born to mothers with “moderate and high IPAI” were more likely to be caesarean, to be twins, to be male, to be low birth weight and preterm birth (Table 2). Table S2 presented participants’ characteristics of sample excluded and included in the analysis. For all variables, the missing rate were below 5% (Table S3).
Table 2.
Baseline characteristics of offspring exposed in inflammation related prenatal adversity index (IPAI) groups
| Variable | Low IPAI (n = 1139) |
Moderate IPAI (n = 1176) |
High IPAI (n = 736) |
p |
|---|---|---|---|---|
| Maternal age at delivery, yearsa | 29.90 (3.61) | 30.14 (3.91) | 30.34 (4.03) | 0.056 |
| Parityb | 0.218 | |||
| Nulliparous | 860 (80.8) | 825 (77.8) | 482 (79.3) | |
| Multiparous | 204 (19.2) | 236 (22.2) | 126 (20.7) | |
| Mode of conceptionb | < 0.001 | |||
| Spontaneous | 775 (70.7) | 667 (61.0) | 288 (44.4) | |
| ART | 321 (29.3) | 427 (39.0) | 361 (55.6) | |
| Centerb | 0.001 | |||
| Changzhou | 241 (22.0) | 254 (23.2) | 158 (24.3) | |
| Nanjing | 620 (56.6) | 563 (51.5) | 304 (46.8) | |
| Suzhou | 235 (21.4) | 277 (25.3) | 187 (28.8) | |
| Mode of deliveryb | < 0.001 | |||
| Caesarean | 497 (45.6) | 556 (51.0) | 403 (62.3) | |
| Vaginal | 593 (54.4) | 534 (49.0) | 244 (37.7) | |
| Smoking during pregnancyb | 0.026 | |||
| Yes | 2 (0.2) | 1 (0.1) | 5 (0.8) | |
| No | 1094 (99.8) | 1093 (99.9) | 644 (99.2) | |
| Pluralityb | < 0.001 | |||
| Singleton | 1050 (92.2) | 1010 (85.9) | 555 (75.4) | |
| Twins | 89 (7.8) | 166 (14.1) | 181 (24.6) | |
| Infant sexb | 0.186 | |||
| Boy | 587 (51.5) | 622 (52.9) | 411 (55.8) | |
| Girl | 552 (48.5) | 554 (47.1) | 325 (44.2) | |
| Gestational age, weeksa | 39.26 (1.56) | 38.96 (1.64) | 38.38 (2.18) | < 0.001 |
| Preterm birth, < 37 weeksb | < 0.001 | |||
| Yes | 78 (6.8) | 136 (11.6) | 139 (18.9) | |
| No | 1061 (93.2) | 1040 (88.4) | 597 (81.1) | |
| Birth weight, grama | 3306.79 (483.86) | 3256.38 (529.57) | 3173.10 (623.02) | < 0.001 |
| Low birth weight, < 2500gb | < 0.001 | |||
| Yes | 54 (4.8) | 102 (8.7) | 89 (12.1) | |
| No | 1079 (95.2) | 1069 (91.3) | 644 (87.9) | 0.982 |
| Breastfeeding duration, monthsb | < 0.001 | |||
| < 6 | 168 (14.8) | 241 (20.5) | 189 (25.8) | |
| ≥ 6 | 970 (85.2) | 932 (79.5) | 544 (74.2) | |
| Age at assessment, yearsa | 1.00 (0.02) | 1.00 (0.02) | 1.00 (0.02) |
Abbreviation: SD: standard deviation; IPAI: inflammation related prenatal adversity index; ART: assisted reproductive technology
a Expressed as mean (SD)
b Expressed as frequency (percentage)
c Infants include twins
Associations between IPAI and scores of infant neurodevelopment in five domains were presented in Table 3. In the adjusted model, compared with the “low IPAI” group, the scores of the “moderate IPAI” group decreased by 0.30 (95%CI: -0.46, -0.13) points in the receptive communication domain, and 0.20 (95%CI: -0.37, -0.04) points in the expressive communication domain, respectively. The scores of the “high IPAI” group decreased by 0.23 (95%CI: -0.42, -0.04) points in the cognition domain, 0.47 (95%CI: -0.66, -0.28) points in the receptive communication domain, 0.30 (95%CI: -0.49, -0.11) points in the expressive communication domain, and 0.20 (95%CI: -0.33, -0.06) points in the fine motor domain, respectively. There was a dose-response relationship in these four domains.
Table 3.
Associations of prenatal inflammation related adversity index with infant’s neurodevelopmental scores
| Outcome | No.a | Crude Model | Adjusted Modelb | ||
|---|---|---|---|---|---|
| β (95%CI) | P | β (95%CI) | P | ||
| Cognition | |||||
| Low IPAI | 16.00 (1.87) | Ref. | Ref. | ||
| Moderate IPAI | 15.85 (2.03) | -0.14 (-0.30, 0.02) | 0.089 | -0.15 (-0.31, 0.02) | 0.078 |
| High IPAI | 15.71 (1.91) | -0.28 (-0.46, -0.09) | 0.003 | -0.23 (-0.42, -0.04) | 0.018 |
| P-trend | 0.004 | 0.020 | |||
| Receptive communication | |||||
| Low IPAI | 11.47 (1.97) | Ref. | Ref. | ||
| Moderate IPAI | 11.13 (1.98) | -0.33 (-0.49, -0.17) | < 0.001 | -0.30 (-0.46, -0.13) | < 0.001 |
| High IPAI | 10.93 (1.94) | -0.52 (-0.71, -0.34) | < 0.001 | -0.47 (-0.66, -0.28) | < 0.001 |
| P-trend | < 0.001 | < 0.001 | |||
| Expressive communication | |||||
| Low IPAI | 11.98 (1.93) | Ref. | Ref. | ||
| Moderate IPAI | 11.74 (1.99) | -0.23 (-0.39, -0.06) | 0.006 | -0.20 (-0.37, -0.04) | 0.017 |
| High IPAI | 11.68 (1.94) | -0.30 (-0.48, -0.11) | 0.002 | -0.30 (-0.49, -0.11) | 0.002 |
| P-trend | 0.002 | 0.003 | |||
| Fine motor | |||||
| Low IPAI | 13.09 (1.42) | Ref. | Ref. | ||
| Moderate IPAI | 12.96 (1.44) | -0.12 (-0.24, -0.01) | 0.040 | -0.11 (-0.23, 0.01) | 0.065 |
| High IPAI | 12.83 (1.38) | -0.25 (-0.38, -0.12) | < 0.001 | -0.20 (-0.33, -0.06) | 0.005 |
| P-trend | < 0.001 | 0.006 | |||
| Gross motor | |||||
| Low IPAI | 14.64 (1.59) | Ref. | Ref. | ||
| Moderate IPAI | 14.55 (1.59) | -0.07 (-0.20, 0.06) | 0.298 | -0.04 (-0.18, 0.09) | 0.530 |
| High IPAI | 14.46 (1.65) | -0.17 (-0.32, -0.01) | 0.033 | -0.10 (-0.26, 0.05) | 0.191 |
| P-trend | 0.034 | 0.192 | |||
a Expressed as mean (SD)
b Adjusted for parity, breast-feeding duration, maternal age at delivery, child sex and child age at examination
Associations between IPAI and noncompetent development of infant’s neurodevelopment in five domains were presented in Table 4. In addition to the expressive communication domain, the proportion of noncompetent development in four domains (cognition, receptive communication, fine motor and gross motor) increased with increasing IPAI. In particular, the prevalence of noncompetent development in receptive communication was higher, ranging from 14.2 to 20.1%. The elevated risk was observed for noncompetent development of cognition, receptive communication and expressive communication among “moderate IPAI” group. The RR and 95%CI were 1.30 (1.01, 1.69), 1.26 (1.02, 1.55) and 1.43 (1.02, 2.01), respectively. The elevated risk was observed for noncompetent development of cognition and receptive communication among “high IPAI” group. The RR and 95%CI were 1.35 (1.01, 1.80) and 1.37(1.09, 1.72), respectively.
Table 4.
Associations of prenatal inflammation related adversity index with the risk of non-competent in five domains of neurodevelopment in infants
| Outcome | No.a | Crude Model | Adjusted Modelb | ||
|---|---|---|---|---|---|
| RR (95%CI) | P | RR (95%CI) | P | ||
| Cognition | |||||
| Low IPAI | 108 (9.5) | Ref. | Ref. | ||
| Moderate IPAI | 140 (11.9) | 1.26 (0.98, 1.61) | 0.076 | 1.30 (1.01, 1.69) | 0.042 |
| High IPAI | 97 (13.2) | 1.39 (1.06, 1.83) | 0.019 | 1.35 (1.01, 1.80) | 0.039 |
| P-trend | 0.024 | 0.056 | |||
| Receptive communication | |||||
| Low IPAI | 162 (14.2) | Ref. | Ref. | ||
| Moderate IPAI | 216 (18.4) | 1.29 (1.05, 1.58) | 0.014 | 1.26 (1.02, 1.55) | 0.032 |
| High IPAI | 148 (20.1) | 1.41 (1.13, 1.77) | 0.002 | 1.37 (1.09, 1.72) | 0.007 |
| P-trend | 0.004 | 0.011 | |||
| Expressive communication | |||||
| Low IPAI | 57 (5.0) | Ref. | Ref. | ||
| Moderate IPAI | 87 (7.4) | 1.48 (1.06, 2.06) | 0.022 | 1.43 (1.02, 2.01) | 0.036 |
| High IPAI | 44 (6.0) | 1.19 (0.81, 1.77) | 0.376 | 1.20 (0.81, 1.79) | 0.363 |
| P-trend | 0.506 | 0.472 | |||
| Fine motor | |||||
| Low IPAI | 28 (2.5) | Ref. | Ref. | ||
| Moderate IPAI | 41 (3.5) | 1.42 (0.88, 2.29) | 0.154 | 1.41 (0.87, 2.30) | 0.163 |
| High IPAI | 28 (3.8) | 1.55 (0.92, 2.61) | 0.102 | 1.37 (0.79, 2.37) | 0.261 |
| P-trend | 0.126 | 0.326 | |||
| Gross motor | |||||
| Low IPAI | 99 (8.7) | Ref. | Ref. | ||
| Moderate IPAI | 109 (9.3) | 1.07 (0.81, 1.40) | 0.643 | 1.06 (0.80, 1.40) | 0.682 |
| High IPAI | 80 (10.9) | 1.25 (0.93, 1.68) | 0.137 | 1.20 (0.89, 1.63) | 0.231 |
| P-trend | 0.131 | 0.226 | |||
a Expressed as frequency (percentage)
b Adjusted for parity, breast-feeding duration, maternal age at delivery and child sex
Associations were robust to all sensitivity analyses, including [1] exclusion of preterm birth and twins [2], exclusion of offspring with structural birth defects. See Table S4-S7 for details.
Discussion
In this prospective cohort, we constructed the IPAI based on six maternal adversities factors to analyze the associations between prenatal inflammation related adversities and neurodevelopment of infants at one year of age. We found that higher IPAI was associated with lower scores across cognition, receptive communication, expressive communication, and fine motor domains. In particularly, increased IPAI was also associated with an increased risk of noncompetent development in cognition and receptive communication domains.
Our findings gain some support from several previous studies which focused on neurodevelopmental disorders. A birth cohort in Japan analyzed maternal dietary inflammatory index (DII) scores one year before pregnancy, and evaluated effects on the neurodevelopment of offspring aged three years. They found that delayed development in communication, fine motor, problem-solving, and social skills at age three years increased along with the DII category [38]. A population-based study in Sweden reported that offspring of parents with lower socioeconomic status had a 40% elevated risk of autism spectrum disorder [39]. A meta-analysis indicated that compared with children of mothers with normal weight, those mothers were overweight or obese prior to pregnancy were at an 17–51% increased risk for compromised neurodevelopmental outcomes [24]. In a cohort study in Denmark, prenatal exposure to maternal type 2 diabetes during pregnancy was associated with an 33% increased risks of overall and type-specific mental disorders in offspring [40]. A South African study found that maternal symptoms of prenatal depression were associated with lower language development scores in their offspring at age 2 years [41]. A randomized controlled trial nested into the 2015 Pelotas (Brazil) Birth Cohort found that compared with the control group, children from women in the exercise group had higher language score at age 2 years [42]. However, these studies only evaluated associations between single exposure and neurodevelopmental outcomes. Although each study reported an effect size, the total effect of multiple factors is still unclear. In our prospective cohort study, we observed an 30% increased risk of adverse receptive communication outcome in moderate IPAI group and an 40% increased risk in high IPAI group, compared with low IPAI group. It’s important to note that the effect size of the increased IPAI score is not much higher than the effect size of a single exposure, though the high IPAI means a co-exposure of multiple adversities factors. It may be suggested that the assessment of a single exposure may have limitations in controlling for confounding bias. The IPAI may reflect an overall comprehensive effect of combined exposure.
The biological mechanisms underlying prenatal inflammation related adversities and neurodevelopment in offspring are not completely understood. MIA has been a hot hypothesis in recent years [16]. Evidence from human and animal studies indicates that maternal immune activation programmes the fetal brain and immune system through inflammatory and epigenetic mechanisms during key periods of central nervous system (CNS), microglial and immune system development [43]. Maternal inflammatory factors induce the release of pathogen-associated molecular patterns (PAMPs) and damage-associated molecular patterns (DAMPs), which activate Toll-like receptors on maternal peripheral innate immune cells and placental cells, leading to cytokine production [44, 45]. It is worth noting that elevated levels of cytokine IL-1β may play a mechanistic role in the cross-generational effects of children’s language development [23]. Temporal cortex and inferior frontal cortex play an important role in language function [46]. A study in a mouse model of maternal immune activation demonstrated that Il-1 receptor (Il1r) and Interleukin 1 Receptor Accessory Protein Like 1 (Il1rapl1) mRNA levels in the offspring’s frontal cortex initially rose during early synaptogenesis but decreased during its peak [47]. Microglia play an important role in synaptic transmission, information processing and the homeostatic landscape of the CNS [48]. Defects in microglial synaptic pruning, result in pathological malformation of neuronal circuits and contribute to the pathophysiology of several cognitive impairments including autistic spectrum and psychiatric disorders [49].
The rate of poor receptive communication development was significantly higher than in the other four domains. A view, strongly favored by evidence accumulating over several decades, is that poor receptive communication results from damage to a cortical region separate from both sensory speech perception and speech articulation systems. The function of this region, which includes the posterior Superior Temporal Gyrus (pSTG) and adjacent cortex in the superior temporal sulcus and supramarginal gyrus, is to store and mentally activate phonological (speech sound) forms [50]. A rat study revealed that MIA induces presynaptic protein deficits and down-regulation of postsynaptic scaffolding proteins in the pSTG region in the adolescent rat offspring, in addition to elevated blood cytokine levels, microglial activation, increased pro-inflammatory cytokines expression and increased oxidative stress in the cerebral cortex. Thus, this may affect receptive communication development [51].
Our study has strength in two aspects. Firstly, this is the first study to establish an IPAI and to report a significant association between this index and neurodevelopmental outcomes at one year of age. In addition, we used data of well-designed large-scale birth cohort with long-term follow-up, which provided comprehensive variable acquisition and high data quality. Two limitations also should be noted. First, we lacked biological indicators that reflected maternal inflammatory states, such as cytokines and c-reaction protein (CRP), and could not be analyzed further. Second, despite the good design and strict quality control measures of our study, we were unable to fully consider all confounding factors.
In this prospective birth cohort study, we, for the first time, formulated the IPAI based on six maternal adversities factors. Our results revealed a significant association between higher IPAI and lower scores across cognition, receptive communication, expressive communication, and fine motor domains, and an increased risk of noncompetent development in the cognition and receptive communication domains. These findings not only contribute substantial population evidence supporting the Maternal Immune Activation (MIA) hypothesis but, more significantly, highlighted the crucial role of avoiding maternal inflammation-related adversities in preventing infant neurodevelopmental delays.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Additional file 1: Supplementary methods. Questionnaire used in the Jiangsu Birth Cohort (JBC). Supplementary Table 1. Bayley grading table. Supplementary Table 2. Participant characteristics of sample excluded and included in the analysis. Supplementary Table 3. The characteristics of missing data. Supplementary Table 4. Associations of IPAI with infant’s neurodevelopmental scores excluding infant with preterm birth and twins. Supplementary Table 5. Associations of IPAI with the risk of non-competent in five domains of neurodevelopment in infants excluding infant with preterm birth and twins. Supplementary Table 6. Associations of IPAI with infant’s neurodevelopmental scores excluding infant with structural birth defects. Supplementary Table 7. Associations of IPAI with the risk of non-competent in five domains of neurodevelopment in infants excluding infant with structural birth defects.
Supplementary Fig. 1. Flowchart of included and excluded pregnancies.
Acknowledgements
The authors would like to acknowledge all the families for participating this study, and the whole Jiangsu Birth Cohort team, including Jiahao Sha, Jiayin Liu, Yankai Xia, Feng Chen, Feiyang Diao, Yang Zhao, Di Wu, Wei Wu, Chuncheng Lu, Minjian Chen, Bo Xu, Kun Zhou, Rong Shen, Xuemei Jia, Zhengfeng Xu, Xiufeng Ling, Meiling Tong, Xia Chi, Ting Chen, Zhiliang Ding, Hong Li, Qingxia Meng, Liping Zhu, Boxian Huang, Yanan Wang, Xiaoyan Wang, Zhonghua Shi, Bin Yu, Li Chen, Lingmin Hu, Haiting Hu.
Abbreviations
- MIA
Maternal Immune Activation
- IPAI
Inflammation related Prenatal Adversity Index
- JBC
Jiangsu Birth Cohort
- Bayley-III
Bayley Scales of Infant and Toddler Development, Version-III
- BMI
Body mass index
- CES-D
Center for Epidemiological Survey, Depression Scale
- SAS
Self-Rating Anxiety Scale
- PSS-10
Perceived Stress Scale
- MET
Metabolic equivalent
- HDP
Hypertensive disorders in pregnancy
- HIP
Hyperglycemia in pregnancy
- DII
Dietary inflammatory index
- CNS
Central nervous system
- PAMPs
Pathogen-associated molecular patterns
- DAMPs
Damage-associated molecular patterns
- Il1rapl1
Interleukin 1 Receptor Accessory Protein Like 1
- pSTG
posterior Superior Temporal Gyrus
- CRP
C-reaction protein
Author contributions
MG, X-XZ and W-TW contributed to conceptualization, investigation, formal analysis, data curation and writing - original draft. KY, Y-QJ, TJ, HL, QL and RQ contributed to investigation and data curation. S-YT, LH, XX, CL, Y-YD, KK, T-YS, Y-XL contributed to investigation. YJ and X-MH contributed to validation and funding acquisition. G-FJ, H-XM, H-BS and B-ZH contributed to conceptualization, project administration and supervision. Y-CG, YL and J-BD contributed to conceptualization, writing - review & editing and supervision. All authors approved the final version.
Funding
The study was funded by the National Key Research & Development (R&D) Program of China (2021YFC2700705), the National Nature Science Foundation of China (82103854), the Natural Science Foundation of Jiangsu Province (BK20210533), the China National Key Research & Development (R&D) Plan (2021YFC2700600) and the Foundation for Innovative Research Groups of the National Natural Science Foundation of China (82221005).
Data availability
The datasets generated and/or analysed during the current study are not publicly available due to our containing information that could compromise the privacy of research participants but are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
All methods were carried out in accordance with relevant guidelines and regulations under Ethics approval and consent to participate. All procedures were approved by the institutional review board of Nanjing Medical University, China NJMUIRB (2017) 002. All participants gave their written informed consent at the time of recruitment. Additionally, prior to administering the Bayley-III Screening Test, we secured an additional informed consent specifically for this assessment from each child’s guardian.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Ming Gan, Xianxian Zhu and Weiting Wang contributed equally to this work.
Contributor Information
Yichun Guan, Email: lisamayguan@163.com.
Yuan Lin, Email: yuanlin@njmu.edu.cn.
Jiangbo Du, Email: dujiangbo@njmu.edu.cn.
References
- 1.Wang HX, Yin WJ, Ma SS, Wang P, Zhang L, Li PP et al. Prenatal environmental adversity and child neurodevelopmental delay: the role of maternal low-grade systemic inflammation and maternal anti-inflammatory diet. Eur Child Adoles Psy. 2023. [DOI] [PubMed]
- 2.Su X, Zhang SY, Lin QM, Wu YL, Yang Y, Yu H et al. Prenatal exposure to air pollution and neurodevelopmental delay in children: a birth cohort study in Foshan, China. Sci Total Environ. 2022;816. [DOI] [PubMed]
- 3.Parenti I, Rabaneda LG, Schoen H, Novarino G. Neurodevelopmental disorders: from Genetics to Functional pathways. Trends Neurosci. 2020;43(8):608–21. [DOI] [PubMed] [Google Scholar]
- 4.Pugh SJ, Richardson GA, Hutcheon JA, Himes KP, Brooks MM, Day NL, et al. Maternal obesity and excessive Gestational Weight Gain are Associated with Components of Child Cognition. J Nutr. 2015;145(11):2562–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Huang LS, Yu XD, Keim S, Li L, Zhang L, Zhang J. Maternal prepregnancy obesity and child neurodevelopment in the collaborative Perinatal Project. Int J Epidemiol. 2014;43(3):783–92. [DOI] [PubMed] [Google Scholar]
- 6.Yeung EH, Sundaram R, Ghassabian A, Xie YL, Louis GB. Parental obesity and early Childhood Development. Pediatrics. 2017;139(2). [DOI] [PMC free article] [PubMed]
- 7.Nielsen J, Hulman A, Cunningham SA. Parental type 2 diabetes and Obesity Development from Childhood to Early Adulthood. Diabetes. 2018;67.
- 8.Gumusoglu SB, Chilukuri ASS, Santillan DA, Santillan MK, Stevens HE. Neurodevelopmental outcomes of prenatal Preeclampsia exposure. Trends Neurosci. 2020;43(4):253–68. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Tuovinen S, Lahti-Pulkkinen M, Girchenko P, Lipsanen J, Lahti J, Heinonen K, et al. Maternal depressive symptoms during and after pregnancy and child developmental milestones. Depress Anxiety. 2018;35(8):732–41. [DOI] [PubMed] [Google Scholar]
- 10.Lu YC, Kapse K, Andersen N, Quistorff J, Lopez C, Fry A et al. Association between Socioeconomic Status and in Utero fetal brain development. Jama Netw Open. 2021;4(3). [DOI] [PMC free article] [PubMed]
- 11.Alvarez-Bueno C, Cavero-Redondo I, Sánchez-López M, Garrido-Miguel M, Martínez-Hortelano JA, Martínez-Vizcaíno V. Pregnancy leisure physical activity and children’s neurodevelopment: a narrative review. Bjog-Int J Obstet Gy. 2018;125(10):1235–42. [DOI] [PubMed] [Google Scholar]
- 12.Lv SY, Qin R, Jiang YQ, Lv H, Lu Q, Tao SY et al. Association of Maternal Dietary Patterns during gestation and offspring neurodevelopment. Nutrients. 2022;14(4). [DOI] [PMC free article] [PubMed]
- 13.Mandy W, Lai MC. Annual Research Review: the role of the environment in the developmental psychopathology of autism spectrum condition. J Child Psychol Psyc. 2016;57(3):271–92. [DOI] [PubMed] [Google Scholar]
- 14.Sandin S, Lichtenstein P, Kuja-Halkola R, Larsson H, Hultman CM, Reichenberg A. The familial risk of Autism. Jama-J Am Med Assoc. 2014;311(17):1770–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Bodnar TS, Raineki C, Wertelecki W, Yevtushok L, Plotka L, Granovska I et al. Immune network dysregulation associated with child neurodevelopmental delay: modulatory role of prenatal alcohol exposure. J Neuroinflamm. 2020;17(1). [DOI] [PMC free article] [PubMed]
- 16.Estes ML, McAllister AK. Maternal immune activation: implications for neuropsychiatric disorders. Science. 2016;353(6301):772–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Stiles J, Jernigan TL. The basics of Brain Development. Neuropsychol Rev. 2010;20(4):327–48. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Karagyaur M, Primak A, Bozov K, Sheleg D, Arbatsky M, Dzhauari S, et al. Novel missense variants in brain morphogenic genes associated with depression and schizophrenia. Front Psychiatry. 2024;15:1338168. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Chen X, Liu H, Li Y, Zhang W, Zhou A, Xia W, et al. First-trimester fetal size, accelerated growth in utero, and child neurodevelopment in a cohort study. BMC Med. 2024;22(1):181. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Du J, Lin Y, Xia Y, Ma H, Jiang Y, Lu C et al. Cohort Profile: the Jiangsu Birth Cohort. Int J Epidemiol. 2023. [DOI] [PubMed]
- 21.Rakesh D, Whittle S, Sheridan MA, Mclaughlin KA. Childhood socioeconomic status and the pace of structural neurodevelopment: accelerated, delayed, or simply different? Trends Cogn Sci. 2023;27(9):833–51. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Rodolaki K, Pergialiotis V, Iakovidou N, Boutsikou T, Iliodromiti Z, Kanaka-Gantenbein C. The impact of maternal diabetes on the future health and neurodevelopment of the offspring: a review of the evidence. Front Endocrinol. 2023;14. [DOI] [PMC free article] [PubMed]
- 23.Naude PJW, Pariante C, Hoffman N, Koopowitz SM, Donald KA, Zar HJ, et al. Antenatal maternal depression, early life inflammation and neurodevelopment in a South African birth cohort. Brain Behav Immun. 2022;105:160–8. [DOI] [PubMed] [Google Scholar]
- 24.Sanchez CE, Barry C, Sabhlok A, Russell K, Majors A, Kollins SH, et al. Maternal pre-pregnancy obesity and child neurodevelopmental outcomes: a meta-analysis. Obes Rev. 2018;19(4):464–84. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Bordeleau M, de Cossío LF, Chakravarty MM, Tremblay ME. From maternal Diet to Neurodevelopmental disorders: A Story of Neuroinflammation. Front Cell Neurosci. 2021;14. [DOI] [PMC free article] [PubMed]
- 26.Lundström S, Chang Z, Kerekes N, Gumpert CH, Råstam M, Gillberg C, et al. Autistic-like traits and their association with mental health problems in two nationwide twin cohorts of children and adults. Psychol Med. 2011;41(11):2423–33. [DOI] [PubMed] [Google Scholar]
- 27.Donno R, Parker G, Gilmour J, Skuse DH. Social communication deficits in disruptive primary-school children. Br J Psychiatry: J Mental Sci. 2010;196(4):282–9. [DOI] [PubMed] [Google Scholar]
- 28.Umamaheswaran S, Dasari SK, Yang P, Lutgendorf SK, Sood AK. Stress, inflammation, and eicosanoids: an emerging perspective. Cancer Metastasis Rev. 2018;37(2–3):203–11. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Milaneschi Y, Kappelmann N, Ye Z, Lamers F, Moser S, Jones PB, et al. Association of inflammation with depression and anxiety: evidence for symptom-specificity and potential causality from UK Biobank and NESDA cohorts. Mol Psychiatry. 2021;26(12):7393–402. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Fulton S, Décarie-Spain L, Fioramonti X, Guiard B, Nakajima S. The menace of obesity to depression and anxiety prevalence. Trends Endocrinol Metab. 2022;33(1):18–35. [DOI] [PubMed] [Google Scholar]
- 31.Mordre M, Groholt B, Knudsen AK, Sponheim E, Mykletun A, Myhre AM. Is long-term prognosis for pervasive developmental disorder not otherwise specified different from prognosis for autistic disorder? Findings from a 30-year follow-up study. J Autism Dev Disord. 2012;42(6):920–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Christ A, Lauterbach M, Latz E. Western Diet and the Immune System: an inflammatory connection. Immunity. 2019;51(5):794–811. [DOI] [PubMed] [Google Scholar]
- 33.Razquin C, Martinez-Gonzalez MA. A traditional Mediterranean Diet effectively reduces inflammation and improves Cardiovascular Health. Nutrients. 2019;11(8). [DOI] [PMC free article] [PubMed]
- 34.Bucholz EM, Sleeper LA, Sananes R, Brosig CL, Goldberg CS, Pasquali SK, et al. Trajectories in Neurodevelopmental, Health-Related Quality of Life, and functional status outcomes by Socioeconomic Status and Maternal Education in children with single ventricle heart disease. J Pediatr-Us. 2021;229:289–. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.El-Ammari A, El Malki H, Moutawakkil SG, El Hilaly J, El Houari F, El Gnaoui S et al. Validation of the Center for Epidemiologic Studies Depression Scale (CES-D) in a Moroccan sample with substance use disorder. BMC Psychiatry. 2023;23(1). [DOI] [PMC free article] [PubMed]
- 36.Dunstan DA, Scott N. Norms for Zung’s Self-rating Anxiety Scale. BMC Psychiatry. 2020;20(1). [DOI] [PMC free article] [PubMed]
- 37.Nielsen MG, Ornbol E, Vestergaard M, Bech P, Larsen FB, Lasgaard M, et al. The construct validity of the perceived stress scale. J Psychosom Res. 2016;84:22–30. [DOI] [PubMed] [Google Scholar]
- 38.Kyozuka H, Murata T, Fukuda T, Yamaguchi A, Kanno A, Yasuda S, et al. Association between preconception dietary inflammatory index and neurodevelopment of offspring at 3 years of age: the Japan Environment and Children’s study. Nutrition. 2022;102:111708. [DOI] [PubMed] [Google Scholar]
- 39.Rai D, Lewis G, Lundberg M, Araya R, Svensson A, Dalman C, et al. Parental socioeconomic status and risk of offspring Autism Spectrum disorders in a Swedish Population-based study. J Am Acad Child Psy. 2012;51(5):467–76. [DOI] [PubMed] [Google Scholar]
- 40.Xiang AH. Association of Maternal Diabetes with Autism in offspring. Jama-J Am Med Assoc. 2017;317(5):537–8. [DOI] [PubMed] [Google Scholar]
- 41.Bednarz HM, Kana RK. Advances, challenges, and promises in pediatric neuroimaging of neurodevelopmental disorders. Neurosci Biobehav Rev. 2018;90:50–69. [DOI] [PubMed] [Google Scholar]
- 42.Leão OAA, Domingues MR, Bertoldi AD, Ricardo LIC, Müller WA, Tornquist L, et al. Effects of regular Exercise during pregnancy on early childhood neurodevelopment: the physical activity for mothers enrolled in longitudinal analysis Randomized Controlled Trial. J Phys Act Health. 2022;19(3):203–10. [DOI] [PubMed] [Google Scholar]
- 43.Kwon HK, Choi GB, Huh JR. Maternal inflammation and its ramifications on fetal neurodevelopment. Trends Immunol. 2022;43(3):230–44. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Liu JJ, Buisman-Pijlman F, Hutchinson MR. Toll-like receptor 4: innate immune regulator of neuroimmune and neuroendocrine interactions in stress and major depressive disorder. Front Neurosci-Switz. 2014;8. [DOI] [PMC free article] [PubMed]
- 45.Afkham A, Eghbal-Fard S, Heydarlou H, Azizi R, Aghebati-Maleki L, Yousefi M. Toll-like receptors signaling network in pre-eclampsia: an updated review. J Cell Physiol. 2019;234(3):2229–40. [DOI] [PubMed] [Google Scholar]
- 46.Friederici AD. The brain basis of Language Processing: from structure to function. Physiol Rev. 2011;91(4):1357–92. [DOI] [PubMed] [Google Scholar]
- 47.Wu YH, Zhang H, Wang CQ, Broekman BFP, Chong YS, Shek LP, et al. Inflammatory modulation of the associations between prenatal maternal depression and neonatal brain. Neuropsychopharmacol. 2021;46(2):470–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Chen ZH, Jalabi W, Hu WW, Park HJ, Gale JT, Kidd GJ et al. Microglial displacement of inhibitory synapses provides neuroprotection in the adult brain. Nat Commun. 2014;5. [DOI] [PMC free article] [PubMed]
- 49.Frost JL, Schafer DP, Microglia. Architects of the developing nervous system. Trends Cell Biol. 2016;26(8):587–97. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50.Binder JR. Current Controversies on Wernicke’s Area and its Role in Language. Curr Neurol Neurosci. 2017;17(8). [DOI] [PubMed]
- 51.Cieslik M, Gassowska-Dobrowolska M, Jesko H, Czapski GA, Wilkaniec A, Zawadzka A et al. Maternal Immune activation induces neuroinflammation and cortical synaptic deficits in the adolescent rat offspring. Int J Mol Sci. 2020;21(11). [DOI] [PMC free article] [PubMed]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Additional file 1: Supplementary methods. Questionnaire used in the Jiangsu Birth Cohort (JBC). Supplementary Table 1. Bayley grading table. Supplementary Table 2. Participant characteristics of sample excluded and included in the analysis. Supplementary Table 3. The characteristics of missing data. Supplementary Table 4. Associations of IPAI with infant’s neurodevelopmental scores excluding infant with preterm birth and twins. Supplementary Table 5. Associations of IPAI with the risk of non-competent in five domains of neurodevelopment in infants excluding infant with preterm birth and twins. Supplementary Table 6. Associations of IPAI with infant’s neurodevelopmental scores excluding infant with structural birth defects. Supplementary Table 7. Associations of IPAI with the risk of non-competent in five domains of neurodevelopment in infants excluding infant with structural birth defects.
Supplementary Fig. 1. Flowchart of included and excluded pregnancies.
Data Availability Statement
The datasets generated and/or analysed during the current study are not publicly available due to our containing information that could compromise the privacy of research participants but are available from the corresponding author on reasonable request.