Abstract
Background
Early neurodevelopment results from the interaction between biological conditions at birth and the postnatal caregiving environment. Understanding the relative contribution of these factors during early infancy may improve early identification of children at developmental risk.
Objective
To examine associations between biological, maternal, and environmental factors and neurodevelopmental outcomes at six months of age.
Methods
A cross-sectional study was conducted among 124 healthy term infants aged six months. Neurodevelopment was assessed using the Ages and Stages Questionnaire, Third Edition (ASQ-3) across five developmental domains: communication, gross motor, fine motor, problem solving, and personal–social development. Potential predictors were categorized as biological factors (sex, birth weight, gestational age, breastfeeding status, and prenatal alcohol exposure), maternal factors (maternal educational attainment and early postpartum return to employment), and environmental factors (primary language spoken in the home and daily parent–infant verbal interaction). Hierarchical multiple regression analyses were performed to evaluate the independent contribution of each predictor group to infant neurodevelopmental outcomes.
Results
Biological variables showed the most consistent associations across models, although the proportion of explained variance was modest. Birth weight emerged as the most stable predictor, showing significant positive associations with communication, gross motor, problem-solving, and personal–social development. Prenatal alcohol exposure was associated with lower personal–social scores. Sex differences were observed in communication outcomes, with female infants demonstrating slightly higher scores. Maternal and environmental factors contributed only modestly to explained variance and were not retained as independent predictors in the final regression models.
Conclusion
In this exploratory cross-sectional study of healthy term infants, biological variables, particularly birth weight, showed the most consistent associations with developmental scores at six months. Prenatal alcohol exposure was associated with lower personal–social scores. However, the explained variance was modest, and findings should be interpreted cautiously given the sample size, imbalance in some predictors, and the cross-sectional design.
Supplementary Information
The online version contains supplementary material available at https://doi.org/10.1186/s12887-026-07384-3.
Keywords: Infant neurodevelopment, Birth weight, Prenatal alcohol exposure, ASQ-3, Early development, Developmental screening
Introduction
Early neurodevelopment results from a complex interaction between biological conditions at birth and the postnatal caregiving environment. The sixth month of life represents a critical developmental milestone, as early cognitive, motor, and social trajectories become measurable through observable behavioral markers during this period. According to the Developmental Origins of Health and Disease (DOHaD) framework, biological parameters at birth—such as birth weight and gestational age—serve as important predictors of later neurodevelopmental outcomes [1, 2]. Even within the normative range among term-born infants, higher birth weight is frequently associated with better outcomes in communication and problem-solving abilities, whereas lower gestational age may contribute to subtle developmental differences during early infancy [3, 4].
One of the most significant challenges to optimal neurodevelopment is prenatal alcohol exposure. Large-scale meta-analyses demonstrate that prenatal alcohol exposure is associated with persistent impairments in sensory processing and cognitive functioning [5, 6]. Importantly, these adverse effects may be moderated by the biological sex of the infant. Accumulating evidence suggests greater neurological vulnerability among males, with boys demonstrating lower communicative abilities than girls when exposed to comparable levels of prenatal risk [7, 8].
Despite the impact of biological risk factors, the postnatal environment possesses a substantial capacity to compensate through mechanisms of neuroplasticity. Breastfeeding and adequate early nutrition support myelination and early cognitive development, while parental stimulation functions as a major driver of developmental progress [9, 10]. In particular, longer daily caregiver language stimulation and higher maternal educational attainment have been associated with infant communication abilities and fine motor development [11, 12]. These environmental determinants may therefore play an important role in shaping early developmental outcomes [13–15].
To our knowledge, evidence from Eastern European populations regarding early developmental determinants during infancy remains limited. In addition, the application of the Ages and Stages Questionnaire, Third Edition (ASQ-3), in population-based studies from this region has been relatively scarce. The aim of the present study was to examine associations between biological, maternal, and environmental factors and neurodevelopmental outcomes at six months of age. Predictors were categorized into three conceptual groups: biological factors (including sex, birth weight, gestational age, and prenatal exposures), family-related factors (parental education and employment status), and environmental factors (primary language spoken in the home and daily parent–infant verbal interaction). Infant development was assessed across the five developmental domains of the Ages and Stages Questionnaire (ASQ-3).
Materials and methods
Study design and participants
This observational cross-sectional study was conducted between August and December 2025 at the University Multiprofile Hospital for Active Treatment “Medica Ruse” Ltd., Bulgaria. Infants were recruited during routine 6-month pediatric health examinations.
A total of 159 infants were initially screened. Following the exclusion of four cases due to incomplete developmental domain responses, 155 valid Ages and Stages Questionnaire, Third Edition (ASQ-3) forms were obtained. To ensure a homogeneous study population of full-term infants, a further 31 infants were excluded: 13 due to preterm birth (gestational age < 37 weeks) and/or low birth weight (< 2500 g), and 18 due to incomplete data regarding predictor variables. The final analytic sample consisted of 124 full-term infants (gestational age ≥ 37 weeks; birth weight ≥ 2500 g) with a chronological age of 6 months (± 2 weeks) at the time of assessment.
Ethical considerations
The study protocol was approved by the Ethics Committee of the University Multiprofile Hospital for Active Treatment “Medica Ruse” Ltd. (Approval No. A-471/01.08.2025). The research was conducted in strict accordance with the Declaration of Helsinki. Written informed consent was obtained from all parents or legal guardians prior to enrollment.
Developmental assessment
Neurodevelopmental outcomes were evaluated using the 6-month form of the Ages and Stages Questionnaire, Third Edition (ASQ-3), a validated parent-completed developmental screening instrument [14, 15]. The ASQ-3 assesses five developmental domains: Communication, Gross Motor, Fine Motor, Problem Solving, and Personal–Social. Each domain comprises six items scored as 0 (“Not yet”), 5 (“Sometimes”), or 10 (“Yes”), resulting in a maximum score of 60 points per domain. Higher scores indicate more advanced developmental performance. To ensure standardized timing of developmental assessment, parents completed the questionnaire in close temporal proximity to the routine six-month pediatric examination.
Predictor variables
Potential predictors were classified into three conceptual groups: biological, maternal, and environmental factors.
Biological factors included sex of the infant, birth weight, gestational age at birth, exclusive breastfeeding (yes/no), and prenatal alcohol exposure. Gestational age at birth was entered as a reverse-coded ordinal categorical variable, with higher values indicating lower gestational age. Prenatal alcohol exposure was based on maternal self-report and was coded as a binary variable (yes/no). Information regarding the timing, frequency, quantity of alcohol consumption, or co-exposure to tobacco, cannabis, or other substances was not available.
Maternal factors included maternal educational attainment (primary, secondary, or university education) and early postpartum return to employment (yes/no). Early postpartum return to employment was considered a proxy indicator of reduced maternal availability for direct infant caregiving during early infancy.
Environmental factors included language environment (monolingual or bilingual) and the duration of daily caregiver language stimulation. Language stimulation was defined as the average daily time caregivers spent talking, reading, singing, or otherwise verbally interacting with the infant.
Information on biological, maternal, and environmental characteristics was collected using a structured parent questionnaire developed specifically for the present study and administered together with the informed consent procedure during the routine six-month pediatric visit. An English-language version of the questionnaire is provided as Additional file 1.
Statistical analysis
Data were analyzed using IBM SPSS Statistics (Version 19; IBM Corp., Armonk, NY, USA). Data distribution was assessed for normality. Continuous variables (e.g., birth weight and ASQ-3 scores) are presented as mean ± standard deviation (SD). Binary and categorical variables (e.g., sex, gestational age category, prenatal alcohol exposure) were dummy-coded or ordinally coded, as appropriate, for regression analyses and are reported as frequencies and percentages (%).
Pearson correlation coefficients were calculated to examine bivariate associations, with effect sizes interpreted according to Cohen’s guidelines. Confidence intervals were derived using Fisher’s z transformation. Two-tailed p-values were used to determine statistical significance.
Regression analysis: To evaluate the incremental explanatory contribution of biological, maternal, and environmental predictor blocks and the independent associations of individual predictors, hierarchical multiple linear regression models were constructed separately for each ASQ-3 developmental domain. Multicollinearity among predictors was assessed using variance inflation factors (VIF).
Predictors were entered sequentially in three hierarchical blocks (Fig. 1).
Fig. 1.
Hierarchical structure of predictors included in Models 1–3
The hierarchical regression approach was used as an exploratory framework to examine whether maternal and environmental variables added explanatory variance beyond biological variables. The order of entry was theory-driven and should not be interpreted as evidence that earlier blocks are intrinsically more important.
Model 1 – Biological factors: sex, birth weight, gestational age, exclusive breastfeeding, and prenatal alcohol exposure.
Model 2 – Biological + maternal factors: Model 1 variables plus maternal education and early postpartum return to employment.
Model 3 – Biological + maternal + environmental factors: Model 2 variables plus language environment and duration of caregiver language stimulation.
This hierarchical approach allowed assessment of the incremental explanatory contribution of each conceptual group of predictors.
Model fit was evaluated using the coefficient of determination (R²) and adjusted R² values. Standardized regression coefficients (β) were used to assess the strength of individual predictors. Statistical significance was set at p < 0.05. Because only two mothers reported early return to paid employment, analyses involving this variable were considered underpowered and interpreted with caution. To account for multiple testing, statistical significance was additionally evaluated using Bonferroni-adjusted p-values.
Results
Sample characteristics and developmental outcomes
The final analytic sample comprised 124 full-term infants. The mean birth weight was 3.28 ± 0.41 kg, and the mean gestational age was 39.1 ± 1.2 weeks. Males represented 55.6% of the cohort. Regarding early nutrition, 74.2% of infants were exclusively breastfed. Prenatal alcohol exposure was reported in 14.5% of pregnancies. Detailed demographic and clinical characteristics are summarized in Table 1.
Table 1.
Sample characteristics (N = 124)
| Variable | Value |
|---|---|
| Sex | |
| Male | 69 (55.6) |
| Female | 55 (44.4) |
| Birth weight (kg) | 3.28 ± 0.41 (2.51–4.45) |
| Gestational age (weeks) | 39.1 ± 1.2 (37–41) |
| Exclusive breastfeeding (yes) | 92 (74.2) |
| Prenatal alcohol exposure (yes) | 18 (14.5) |
| Maternal education | |
| Primary | 1 (0.8) |
| Secondary | 47 (37.9) |
| University | 76 (61.3) |
| Early postpartum return to paid employment (yes) | 2 (1.6) |
| Time spent with child ≥ 30 min/day | 102 (82.3) |
| Bilingual environment (yes) | 25 (20.2) |
Data are presented as n (%) unless otherwise indicated. Continuous variables are presented as mean ± SD
Developmental scores across all ASQ-3 domains were within expected normative ranges. The highest mean scores were observed in Problem Solving (53.39 ± 8.50), followed by Fine Motor (50.27 ± 11.89). The Gross Motor domain exhibited the lowest mean score (42.94 ± 11.93) and the highest inter-individual variability. Clinical risk for developmental delay (scores below standardized cut-offs) was low, ranging from 0.8% in Problem Solving to 6.5% in Gross Motor (Table 2).
Table 2.
Developmental outcomes at 6 months (N = 124)
| Domain | Mean ± SD | Range (Min–Max) | 95% CI | Below Cut-off n (%) |
|---|---|---|---|---|
| Communication | 49.76 ± 8.41 | 20–60 | 48.28–51.24 | 2 (1.6) |
| Gross Motor | 42.94 ± 11.93 | 10–60 | 40.85–45.03 | 8 (6.5) |
| Fine Motor | 50.27 ± 11.89 | 10–60 | 48.19–52.35 | 5 (4.0) |
| Problem Solving | 53.39 ± 8.50 | 20–60 | 51.90–54.88 | 1 (0.8) |
| Personal–Social | 48.75 ± 9.38 | 25–60 | 47.11–50.39 | 3 (2.4) |
Correlation analysis
Pearson correlation analysis revealed several significant associations between developmental outcomes and the examined predictors. Table 3 presents univariate correlation analyses only.
Table 3.
Univariate pearson correlations between developmental domains and significant predictors
| Domain | Predictor | r | 95% CI | p | Effect size |
|---|---|---|---|---|---|
| Communication | Sex | −0.181 | −0.343 to − 0.009 | 0.044 | small |
| Birth weight | 0.186 | 0.014 to 0.348 | 0.038 | small | |
| Daily parent–infant verbal interaction | 0.188 | 0.016 to 0.350 | 0.036 | small | |
| Gross Motor | Birth weight | 0.253 | 0.084 to 0.405 | 0.002 | small–moderate |
| Exclusive breastfeeding | 0.177 | 0.004 to 0.339 | 0.025 | small | |
| Fine Motor | Exclusive breastfeeding | 0.216 | 0.047 to 0.373 | 0.016 | small |
| Problem Solving | Birth weight | 0.212 | 0.043 to 0.370 | 0.018 | small |
| Personal–Social | Birth weight | 0.248 | 0.079 to 0.400 | 0.006 | small–moderate |
| Prenatal alcohol exposure | −0.288 | −0.436 to − 0.128 | 0.002 |
upper small (approaching moderate) |
Pearson correlation coefficients (two-tailed)
95% confidence intervals were calculated using Fisher’s z transformation
Only statistically significant associations (p < 0.05) are presented
Birth weight showed consistent positive correlations with several developmental domains, including communication, gross motor, problem-solving, and personal–social development. Exclusive breastfeeding showed modest positive correlations with gross motor and fine motor development. Prenatal alcohol exposure was negatively associated with personal–social development.
Most associations were small in magnitude, with several approaching moderate effect sizes.
Hierarchical regression analyses
Hierarchical multiple regression analyses were conducted separately for each ASQ-3 developmental domain to examine the incremental contributions of biological, maternal, and environmental predictors. Three models were constructed for each domain:
Model 1: biological factors.
Model 2: biological + maternal factors.
Model 3: biological + maternal + environmental factors.
Detailed hierarchical model statistics are presented in Table 4.
Table 4.
Hierarchical regression models across developmental domains (N = 124)
| Domain | Model | k | df | R² | Adj. R² | ΔR² | F | p | f² | Δf² |
|---|---|---|---|---|---|---|---|---|---|---|
| Communication | M1 | 5 | 5,118 | 0.110 | 0.073 | — | 2.93 | 0.016 | 0.124 | — |
| M2 | 7 | 7,116 | 0.120 | 0.067 | 0.010 | 2.25 | 0.035 | 0.136 | 0.011 | |
| M3 | 9 | 9,114 | 0.145 | 0.078 | 0.025 | 2.15 | 0.031 | 0.170 | 0.028 | |
| Gross Motor | M1 | 5 | 5,118 | 0.113 | 0.076 | — | 3.02 | 0.013 | 0.127 | — |
| M2 | 7 | 7,116 | 0.137 | 0.085 | 0.024 | 2.63 | 0.015 | 0.159 | 0.027 | |
| M3 | 9 | 9,114 | 0.140 | 0.072 | 0.003 | 2.06 | 0.039 | 0.163 | 0.003 | |
| Fine Motor | M1 | 5 | 5,118 | 0.077 | 0.038 | — | 1.96 | 0.089 | 0.083 | — |
| M2 | 7 | 7,116 | 0.100 | 0.045 | 0.023 | 1.83 | 0.088 | 0.111 | 0.025 | |
| M3 | 9 | 9,114 | 0.115 | 0.045 | 0.015 | 1.65 | 0.111 | 0.130 | 0.017 | |
| Problem Solving | M1 | 5 | 5,118 | 0.090 | 0.051 | — | 2.32 | 0.047 | 0.099 | — |
| M2 | 7 | 7,116 | 0.095 | 0.040 | 0.005 | 1.73 | 0.108 | 0.105 | 0.006 | |
| M3 | 9 | 9,114 | 0.095 | 0.024 | 0.000 | 1.33 | 0.228 | 0.105 | 0.000 | |
| Personal–Social | M1 | 5 | 5,118 | 0.159 | 0.124 | — | 4.48 | 0.001 | 0.189 | — |
| M2 | 7 | 7,116 | 0.160 | 0.109 | 0.001 | 3.16 | 0.004 | 0.190 | 0.001 | |
| M3 | 9 | 9,114 | 0.167 | 0.101 | 0.007 | 2.54 | 0.011 | 0.200 | 0.008 |
M1 – biological factors, M2 – biological + maternal factors, M3 – biological + maternal + environmental factors. Effect size interpretation (Cohen): f² = 0.02 small, f² = 0.15 medium, f² = 0.35 large
Across domains, biological factors explained a modest proportion of variance in developmental outcomes. The inclusion of maternal and environmental variables resulted in only small increases in explained variance.
Communication development
The hierarchical regression models predicting communication development were statistically significant across all three steps. The biological model explained 11.0% of the variance, increasing to 14.5% in the final model.
In the fully adjusted model, sex, birth weight, and gestational age remained significant predictors within the adjusted model. Male sex and lower gestational age were associated with lower communication scores, whereas higher birth weight was associated with better communication performance.
Gross motor development
For gross motor development, all three models reached statistical significance. The biological model explained 11.3% of the variance, with the final model explaining 14.0%.
Across all hierarchical steps, birth weight remained the only significant predictor within the adjusted models. Higher birth weight was consistently associated with improved gross motor performance at six months.
Fine motor development
Hierarchical regression models for fine motor development did not reach statistical significance. Although exclusive breastfeeding showed a modest positive association in the biological model, this effect was not maintained after the inclusion of additional predictors.
Overall, the examined biological, maternal, and environmental variables did not significantly explain variability in fine motor development at six months.
Problem-solving development
The biological model predicting problem-solving development reached statistical significance and explained 9.0% of the variance. However, the addition of maternal and environmental factors did not improve model fit.
In the final model, birth weight and gestational age remained significant predictors. Higher birth weight was associated with better problem-solving performance, while lower gestational age was associated with poorer outcomes.
Personal–social development
Personal–social development demonstrated the highest proportion of explained variance among the examined domains. The biological model explained 15.9% of the variance, increasing slightly to 16.7% in the final model.
In the fully adjusted model, birth weight and prenatal alcohol exposure remained significant predictors within the adjusted model. Higher birth weight was associated with improved personal–social development, whereas prenatal alcohol exposure was associated with lower personal–social scores.
Significant predictors within adjusted models
Significant predictors identified across hierarchical models are summarized in Table 5.
Table 5.
Significant predictors within adjusted hierarchical models
| Domain | Model | Significant Predictor(s) | β | p |
|---|---|---|---|---|
| Communication | M1 | Sex | −0.182 | 0.039 |
| Birth weight | 0.293 | 0.005 | ||
| Gestational age | −0.221 | 0.035 | ||
| M2 | Sex | −0.194 | 0.030 | |
| Birth weight | 0.293 | 0.005 | ||
| Gestational age | −0.227 | 0.034 | ||
| M3 | Sex | −0.194 | 0.030 | |
| Birth weight | 0.278 | 0.008 | ||
| Gestational age | −0.212 | 0.048 | ||
| Gross Motor | M1 | Birth weight | 0.324 | 0.002 |
| M2 | Birth weight | 0.322 | 0.002 | |
| M3 | Birth weight | 0.322 | 0.002 | |
| Fine Motor | M1 | Exclusive breastfeeding | 0.182 | 0.048 |
| Problem Solving | M1 | Birth weight | 0.326 | 0.002 |
| Gestational age | −0.235 | 0.027 | ||
| M2 | Birth weight | 0.328 | 0.002 | |
| Gestational age | −0.249 | 0.022 | ||
| M3 | Birth weight | 0.331 | 0.002 | |
| Gestational age | −0.252 | 0.023 | ||
| Personal–Social | M1 | Birth weight | 0.281 | 0.006 |
| Prenatal alcohol exposure | −0.264 | 0.002 | ||
| M2 | Birth weight | 0.280 | 0.006 | |
| Prenatal alcohol exposure | −0.262 | 0.003 | ||
| M3 | Birth weight | 0.266 | 0.010 | |
| Prenatal alcohol exposure | −0.273 | 0.002 |
Birth weight showed the most consistent associations across developmental domains, while gestational age contributed to communication and problem-solving outcomes and prenatal alcohol exposure showed a specific negative association with personal–social development.
Exclusive breastfeeding was associated with fine motor development only in the initial biological model.
Full coefficients for all predictors included in the fully adjusted models (Model 3) are presented in Supplementary Table S3.
Comparison of explanatory strength across domains
Pairwise comparisons of explanatory strength across developmental domains did not show statistically significant differences after correction for multiple testing. Detailed results are provided in Supplementary Tables S1 and S2.
Summary of key findings
Across the examined developmental domains, biological variables showed the most consistent associations across models, although the overall explained variance remained modest. Birth weight emerged as the most stable predictor across domains, while gestational age and prenatal alcohol exposure showed domain-specific effects.
Maternal and environmental variables contributed minimally to the explanatory power of the models at six months of age.
Discussion
The present study examined the relative contributions of biological, prenatal, and environmental factors to developmental outcomes at six months of age. However, the observed effect sizes and explained variance were modest and should be interpreted cautiously. Birth weight emerged as the most consistent predictor across developmental domains, while prenatal alcohol exposure demonstrated a specific negative association with personal–social development.
Biological risk factors and early development
The findings of the present study suggest that biological variables may contribute to variability in early neurodevelopmental outcomes within this cohort. Risks associated with low birth weight represent a global phenomenon, and neurodevelopmental deficits among these children have been consistently documented across diverse populations [2]. Both low birth weight and lower gestational age are well-established predictors of neurological outcomes and are frequently associated with an increased risk of later cognitive and motor impairments [16]. Although infants with low birth weight were excluded from this cohort, birth weight still varied within the normal term range. The findings should therefore be interpreted as reflecting variation within a healthy term-born population rather than effects of low birth weight pathology.
Importantly, developmental variability is not restricted to extremely preterm or very low birth weight infants. Evidence indicates that even among term-born infants, developmental outcomes follow a gradient pattern related to birth weight [17]. Each additional increment in birth weight and each additional week of gestation contribute to more favorable outcomes in communication, problem solving, and gross motor development [18]. These findings suggest that infants born close to term but with relatively lower birth weight may already exhibit subtle developmental differences by six months of age and may merit further developmental observation in future studies.
Early somatic growth parameters are closely linked to cognitive development. In particular, the rate of head circumference growth during the first six months reflects ongoing neuronal maturation and has been identified as a predictor of future cognitive capacity [19]. Motor development represents another important indicator of neurological maturation. Delays in gross motor development during the first year have been identified as early markers for later difficulties in fine motor skills and academic functioning [20].
Even small differences in gestational age among term infants may be associated with slower maturation of cortical structures and motor milestones during infancy [21]. Taken together, these findings suggest that biological parameters at birth retain prognostic value even among infants born at term. Higher birth weight and longer gestation appear to support more mature neural networks involved in communication and social cognition [4, 18].
Prenatal alcohol exposure
The significant negative association between prenatal alcohol exposure and personal–social development observed in this study is consistent with previous literature. Even low to moderate levels of prenatal alcohol exposure have been associated with poorer early cognitive and behavioral outcomes due to disruptions in neural systems involved in attention, emotional regulation, and social cognition [5].
Prenatal alcohol exposure interferes with mechanisms of sensory processing and behavioral regulation. Effective social interaction during infancy requires the integration of multiple external stimuli, including vocal signals, facial expressions, and tactile input. Disruption of these regulatory processes may therefore limit the development of typical social responses during early infancy [6].
Neuroimaging studies demonstrate that prenatal alcohol exposure is associated with structural alterations in cortical and subcortical brain regions and reduced white matter integrity—areas that are essential for social cognition and rapid processing of social stimuli [22]. These alterations may manifest functionally during infancy as reduced social engagement and impaired self-regulation.
Combined toxic exposures may exert cumulative effects on emotional reactivity and social participation during early development [23]. Even subclinical levels of prenatal alcohol exposure may therefore produce measurable effects on early social neurofunction, emphasizing the importance of early screening and monitoring of exposed infants.
However, the observed association should be interpreted as exploratory and cannot be attributed specifically to dose, timing, or isolated alcohol exposure, since detailed exposure characteristics and co-exposure to other substances were not assessed.
Effect of exclusive breastfeeding
Infant feeding during the first six months is an important component of the early postnatal environment that may influence neurodevelopment. Breast milk provides bioactive nutrients, including long-chain polyunsaturated fatty acids, oligosaccharides, and choline, which support myelination, synaptic development, and brain maturation [10]. Systematic reviews have reported positive associations between breastfeeding and neurodevelopmental outcomes, particularly cognitive performance, although the magnitude of these associations varies across populations and study designs [9].
In the present study, exclusive breastfeeding showed a modest positive association with fine motor development in the initial biological model. However, this association was no longer statistically significant after adjustment for biological, maternal, and environmental variables, indicating that breastfeeding did not emerge as an independent predictor of developmental outcomes at six months. Thus, the observed univariate association may partly reflect the influence of other biological or environmental factors rather than an independent effect of breastfeeding itself.
Although an independent association was not demonstrated in the fully adjusted models, the present findings should not be interpreted as contradicting the established benefits of breastfeeding reported in the literature. Previous studies suggest that optimal maternal and early infant nutrition supports brain maturation and neurodevelopment through multiple biological mechanisms and may contribute to more favorable developmental trajectories [10, 23, 24]. Given the relatively small sample size, the homogeneous cohort of healthy term-born infants, the limited variability in environmental exposures, and the cross-sectional design, the present study may have had limited power to detect these more subtle independent effects.
Sex differences
The present study identified sex-related differences in communication outcomes within the regression models. Previous research indicates that female infants exhibit earlier maturation of social–communication neural networks during the first year of life [8]. These differences may reflect faster cortical organization and earlier integration of frontotemporal connections among girls.
In contrast, male fetal brains appear more sensitive to prenatal stressors and toxic exposures, resulting in more pronounced structural alterations in brain regions associated with social communication [7]. Boys also tend to exhibit slower maturation of language-related cortical networks, which may increase vulnerability to developmental delays when additional prenatal risk factors are present.
These findings highlight the importance of sex-sensitive approaches in early developmental screening and monitoring.
Environmental influences and integrative perspective
Environmental and caregiving factors are widely recognized as important contributors to early neurodevelopment. In the present study, caregiver language stimulation showed a positive association with communication abilities in the univariate analysis, but this relationship did not remain statistically significant after adjustment for biological, maternal, and environmental variables. Therefore, language stimulation did not emerge as an independent predictor in the fully adjusted models. These findings suggest that the contribution of caregiver language stimulation may be less readily detectable at six months after adjustment for biological and maternal factors, rather than indicating an absence of its developmental importance.
Although these variables were not retained as independent predictors in the present study, previous research consistently demonstrates that rich parental linguistic input and responsive verbal interaction support early communication and attentional processes that are fundamental for later language development [25]. Similarly, although maternal education was not independently associated with developmental outcomes in the present study, previous evidence suggests that maternal education may mediate the relationship between maternal characteristics and early motor development, highlighting its importance as a contextual determinant of the early caregiving environment [26]. Responsive parenting behaviors—including shared attention, reading, and sustained verbal interaction—have repeatedly been associated with improved communication and cognitive outcomes during early childhood [27]. Importantly, not only the quantity of caregiver interaction but also its quality appears to be crucial for optimal neurodevelopment [28, 29].
Our findings support contemporary developmental frameworks emphasizing the dynamic interaction between biological vulnerability and environmental context. According to the Developmental Origins of Health and Disease (DOHaD) paradigm, early development emerges through continuous interaction between prenatal biological programming and postnatal environmental influences [1]. Likewise, models of early brain architecture emphasize that responsive caregiver–infant interactions shape neural development by strengthening synaptic connectivity during critical periods of brain maturation [30, 31]. Although the independent contribution of environmental variables was limited in the present analyses, these factors remain central components of early developmental support and may exert cumulative influences that become more apparent later in childhood or in populations with greater environmental variability [32–34].
Strengths and limitations
The present study has several strengths. Developmental outcomes were assessed using the validated screening instrument ASQ-3, which allows reliable identification of early developmental differences across multiple domains. Previous studies have demonstrated good agreement between ASQ-3 and standardized developmental assessments such as the Bayley Scales [35] https://doi.org/10.1016/j.pedneo.2023.03.013.
The hierarchical analytical approach allowed simultaneous examination of biological, prenatal, and environmental determinants of development, providing a comprehensive perspective on early developmental influences.
Several methodological limitations should be emphasized. The sample size was relatively small for multivariable modelling, particularly given the number of predictors and developmental outcomes examined. Some categorical predictors were highly imbalanced, especially early return to paid employment, which limited statistical power. The hierarchical order of variable entry was theory-driven and may have influenced the apparent relative importance of predictor blocks. Birth weight and gestational age were also biologically correlated, which may have affected the stability of individual regression coefficients. In addition, prenatal alcohol exposure was self-reported and lacked information on timing, dose, frequency, and co-exposures. Finally, the cross-sectional design precludes causal inference and does not allow conclusions about long-term developmental trajectories.
Clinical implications
The clinical implications of these findings should be considered cautiously. Most infants in the cohort scored within expected developmental ranges, and the study does not support changes in clinical practice. However, the findings suggest that infants with prenatal or biological risk indicators, such as lower birth weight within the term range or reported prenatal alcohol exposure, may benefit from careful developmental observation within routine pediatric care.
Larger longitudinal studies are needed before definitive clinical recommendations can be made.
Conclusion
In this exploratory cross-sectional study of healthy term infants, birth weight showed the most consistent associations with developmental scores at six months, while prenatal alcohol exposure was associated with lower personal–social scores. Maternal and environmental variables added limited explanatory variance in the present models. Given the modest effect sizes, small sample size, and cross-sectional design, these findings should be interpreted as preliminary and hypothesis-generating.
Supplementary Information
Acknowledgements
The author would like to thank the participating parents for their consent and cooperation. The author also acknowledges Momchil Itov for technical support, including data entry and organization of the study database.
Abbreviations
- ASQ-3
Ages and Stages Questionnaire, Third Edition
- β
Standardized regression coefficient
- CI
Confidence interval
- SD
Standard deviation
Authors’ contributions
The author conceived and designed the study, collected the data, performed the statistical analysis, and drafted the manuscript. The author read and approved the final manuscript.
Funding
This study was conducted under the project “WORKSHOP FOR ADVANCED MEDICAL TECHNOLOGIES”, No. Д-ФВП-03/04.03.2025, and was financed by the European Union – NextGenerationEU, through the National Recovery and Resilience Plan of the Republic of Bulgaria, within the framework of the project “Scientific University of Ruse”, Contract No. BG-RRP-2.013-0001.
Data availability
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
The study protocol was approved by the Ethics Committee of the University Multiprofile Hospital for Active Treatment “Medica Ruse” Ltd. (Approval No. A-471/01.08.2025). The research was conducted in strict accordance with the Declaration of Helsinki. Written informed consent was obtained from all parents or legal guardians prior to enrollment.
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.
References
- 1.Shi Y, Zhang Y, Wei Q, Ma X, Zhang Y, Shi H. Longitudinal association between maternal psychological stress during pregnancy and infant neurodevelopment: the moderating effects of responsive caregiving. Front Pediatr. 2022;10:1007507. 10.3389/fped.2022.1007507. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Wang LW, Wang ST, Huang CC. Neurodevelopmental outcomes at 6, 12, and 24 months of age in very-low-birth-weight preterm infants. J Formos Med Assoc. 2022;121(11):2345–52. 10.1016/j.jfma.2022.04.015. [DOI] [PubMed] [Google Scholar]
- 3.Chan JYY, Leung RCC, Ho FKW, Ip P, Chan KL. Early life neurodevelopment of infants across a wide gestational age spectrum. J Dev Behav Pediatr. 2025;46(8):e454–61. 10.1097/DBP.0000000000001411. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Boyle EM, Poulsen G, Field DJ, Kurinczuk JJ, Wolke D, Alfirevic Z, et al. Effects of gestational age at birth on health outcomes at 3 and 5 years of age: population based cohort study. BMJ. 2012;344:e896. 10.1136/bmj.e896. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Akison LK, Sabadia S, Akter S, Gardiner KM, Moritz KM, Reid N. Prenatal alcohol exposure and associations with physical size, dysmorphology, functional neurodevelopment and structural/neurological outcomes: a systematic review and meta-analysis. BMC Med. 2024;22:467. 10.1186/s12916-024-03656-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Pruner M, Jirikowic T, Yorkston KM, Olson EM, Astley Hemingway SJ. Developmental, sensory and behavioral outcomes among infants and toddlers with prenatal alcohol exposure. Res Dev Disabil. 2024;146:104677. 10.1016/j.ridd.2024.104677. [DOI] [PubMed] [Google Scholar]
- 7.Thomas JC, Goldstein JM, Cherkerzian S, Seidman LJ, Buka SL, Hornig M, et al. Sex-specific effects of prenatal adversity on timing of birth and early infant neurodevelopment. Mol Psychiatry. 2024;29(4):1042–51. 10.1038/s41380-023-02311-x. [DOI] [Google Scholar]
- 8.Chilosi AM, Podda I, Fiori S, Brizzolara D. Sex differences in early language delay and in developmental language disorder. J Clin Med. 2021;10(3):562. 10.3390/jcm10030562. [DOI] [Google Scholar]
- 9.Zhang Y, Jin Y, Chen X, Liang X, Zhang T. A systematic review and meta-analysis of breastfeeding and neurodevelopmental outcomes in preterm infants. Front Public Health. 2024;12:1401250. 10.3389/fpubh.2024.1401250. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Gilbreath D, Bailey S, Azad MB, Reynolds LC. A systematic review over the effect of early infant diet on neurodevelopment: insights from neuroimaging. Nutrients. 2024;16(11):1703. 10.3390/nu16111703. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Tamis-LeMonda CS, Shannon JD, Cabrera NJ, Ryan RE. The role of early cognitive stimulation in the language development of children. Infancy. 2012;17(4):412–41. 10.1111/j.1532-7078.2011.00079.x. [DOI] [Google Scholar]
- 12.Al-Zoubi AM, Al-Dababneh BF. Association between parental education level and intelligence quotient (IQ) of children. Med J Armed Forces India. 2025;81(2):142–9. 10.1016/j.mjafi.2024.11.002. [DOI] [Google Scholar]
- 13.Kim S, Shin JY, Kim JI, Myung SK. Association between parental age and risk of neurodevelopmental disorders in offspring: a systematic review and meta-analysis. JAMA Netw Open. 2020;3(7):e2010413. 10.1001/jamanetworkopen.2020.10413. [DOI] [Google Scholar]
- 14.Boateng JA, Appiah F, Amankwaa I, Oduro MS. Rural-urban differentials in early childhood education and child development: Evidence from Multiple Indicator Cluster Survey (MICS) in Ghana. PLOS Glob Public Health. 2023;3(8):e0002171. 10.1371/journal.pgph.0002171. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Chung SH, Lim JW, Kim TH, Lee SM, Shim JW, Lee JH, et al. Impact of birth weight on long-term growth and development in children: evidence from a nationwide cohort study. BMC Pediatr. 2026;26:292. 10.1186/s12887-026-06670-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Hua J, Barnett AL, Lin Y, Guan H, Sun Y, Williams GJ, et al. Association of gestational age at birth with subsequent neurodevelopment in early childhood: a national retrospective cohort study in China. Front Pediatr. 2022;10:860192. 10.3389/fped.2022.860192. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Hartjen B, Kakon S, Rahman N, Greaves G, Jensen SKG, Haque R, et al. Associations between infant growth and cognitive development: insights from EEG functional connectivity and power spectral density. Child Dev. 2026. 10.1093/chidev/aacag006. [DOI] [PubMed] [Google Scholar]
- 18.Strijker M, Tseng LA, van Avezaath LK, Oude Luttikhuis MAM, Ketelaar T, Coughlin CR, et al. Cognitive and neurological outcome of patients in the Dutch pyridoxine-dependent epilepsy (PDE-ALDH7A1) cohort: a cross-sectional study. Eur J Paediatr Neurol. 2021;33:112–20. 10.1016/j.ejpn.2021.06.001. [DOI] [PubMed] [Google Scholar]
- 19.Dimitrova R, Pietsch M, Ciarrusta J, Fitzgibbon SP, Zhang H, Jbabdi S, et al. Preterm birth alters the development of cortical microstructure and gyrification at term equivalent age. NeuroImage. 2021;243:118488. 10.1016/j.neuroimage.2021.118488. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.De Asis-Cruz J, Andescavage N, Limperopoulos C. Adverse prenatal exposures and fetal brain development: Insights from advanced fetal magnetic resonance imaging. Biol Psychiatry Cogn Neurosci Neuroimaging. 2022;7(5):480–90. 10.1016/j.bpsc.2021.11.009. [DOI] [PubMed] [Google Scholar]
- 21.Sansavini A, Savini S, Guarini A, Broccoli S, Alessandroni R, Faldella G. The effect of gestational age on developmental outcomes: a longitudinal study in the first 2 years of life. Child Care Health Dev. 2011;37(1):26–36. 10.1111/j.1365-2214.2010.01143.x. [DOI] [PubMed] [Google Scholar]
- 22.Hendrikse CJ, Joshi SH, Ringshaw JE, Wedderburn CJ, Bodkin MR, Woods RP, et al. Prenatal alcohol exposure alters brain structure and neurocognitive outcomes for 6- to 7-year-old children in a South African birth cohort. Alcohol Clin Exp Res (Hoboken). 2025;49(5):1028–41. 10.1111/acer.70048. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Reis AEDM, Teixeira IS, Maia JM, Luciano LAA, Brandiao LM, Silva MLS, et al. Maternal nutrition and its effects on fetal neurodevelopment. Nutrition. 2024;125:112483. 10.1016/j.nut.2024.112483. [DOI] [PubMed] [Google Scholar]
- 24.Serwatka CA, Griebel-Thompson AK, Eiden RD, Kong KL. Nutrient supplementation during the prenatal period in substance-using mothers: a narrative review of the effects on offspring development. Nutrients. 2023;15(13):2990. 10.3390/nu15132990. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Chen CH, Castellanos I, Yu C, Houston DM. Parental Linguistic Input and Its Relation to Toddlers’ Visual Attention in Joint Object Play: A Comparison Between Children with Normal Hearing and Children With Hearing Loss. Infancy. 2019;24(4):589–612. 10.1111/infa.12291. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Ye A, Yan S, Huang K, Mao L, Ge X, Weng T, et al. Maternal intelligence quotient and motor development in early childhood: The mediating role of mother’s education. J Paediatr Child Health. 2019;55(1):75–81. 10.1111/jpc.14123. [DOI] [PubMed] [Google Scholar]
- 27.Bornstein MH, Rothenberg WA, Putnick DL, Lansford JE. Multiple child caregivers and multiple cognitive caregiving practices: Associations with early childhood development in 51 low- and middle-income countries. Int J Behav Dev. 2025. 10.1177/01650254251336145. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Center on the Developing Child at Harvard University. Serve and return interaction shapes early brain development. https://developingchild.harvard.edu/science/key-concepts/serve-and-return/. Accessed 14 May 2026.
- 29.Heckman JJ. Skill formation and the economics of investing in disadvantaged children. Science. 2006;312(5782):1900–2. 10.1126/science.1128898. [DOI] [PubMed] [Google Scholar]
- 30.Feldman R. The neurobiology of human attachments. Trends Cogn Sci. 2017;21(2):80–99. 10.1016/j.tics.2016.11.007. [DOI] [PubMed] [Google Scholar]
- 31.Romeo RR, Segaran J, Leonard JA, Robinson ST, West MR, Mackey AP, et al. Language Exposure Relates to Structural Neural Connectivity in Childhood. J Neurosci. 2018;38(36):7870–7. 10.1523/JNEUROSCI.0484-18.2018. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Giraldo-Huertas J, Schafer G. Agreement and Reliability of Parental Reports and Direct Screening of Developmental Outcomes in Toddlers at Risk. Front Psychol. 2021;12:725146. . 10.3389/fpsyg.2021.725146 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Glascoe FP, Leew S. Parenting behaviors, perceptions, and psychosocial risk: impacts on young children’s development. Pediatrics. 2010;125(2):313–9. 10.1542/peds.2008-3129. [DOI] [PubMed] [Google Scholar]
- 34.Crosnoe R, Prickett KC, Smith C, Cavanagh S. Changes in young children’s family structures and child care arrangements. J Marriage Fam. 2014;76(2):318–33. 10.1007/s13524-013-0258-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Agarwal PK, Xie H, Sathapalan Rema AS, Mesney MJ, Godfrey KM, Rajadurai VS, Daniel LM. Concurrent validity of the ages and stages qestionnares with Bayley Scales of infant Development-II at 2 years - Singapore cohort study. Pediatr Neonatol. 2024;65(1):48–54. 10.1016/j.pedneo.2023.03013. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.