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. 2026 Jan 26;52:101184. doi: 10.1016/j.bbih.2026.101184

Immune system up-regulation in securely attached infants during early childhood

Jorge González-Puelma a,b,1, Lindybeth Sarmiento Varón b,1, Jessica Vidal c, Constanza Ceroni b, Sebastián Escobedo b,d, Roberto Uribe-Paredes d,e, David Medina-Ortiz d,e, Rodrigo A Cárcamo c,⁎, Marcelo A Navarrete a,b,⁎⁎
PMCID: PMC12882713  PMID: 41660369

Abstract

Early caregiving relationships shape the coordination of stress and immune systems, yet their biological correlates in early infancy remain insufficiently understood. This study examined whether attachment relationships are associated with mucosal immune function and stress physiology. Thirty-five infants (mean age = 16.6 months) were classified as securely or insecurely attached using the Strange Situation Procedure. Salivary secretory IgA (SIgA) was collected in the morning and afternoon at two time points, and cumulative cortisol was quantified from hair samples.

Securely attached infants showed higher morning SIgA concentrations and more stable intra-day immune profiles compared with insecurely attached children. No group-level differences were observed for cumulative cortisol, but immune–endocrine associations revealed that higher cortisol was linked to lower morning SIgA and greater intra-day fluctuation. Bayesian regression models supported consistent directional effects, and machine-learning analyses confirmed that SIgA-based features accurately predict attachment type.

Our findings support the idea that secure attachment fosters stable coordination between immune and endocrine systems during a critical stage of early development. These effects could be readily captured at very early stages of life, identifying SIgA as a potential biomarker of early socioemotional environments. By integrating behavioral, immunological, and computational approaches, this study provides evidence for the biological embedding of attachment and highlights the potential of non-invasive biomarkers to support early identification of psychosocial vulnerability.

Keywords: Infant attachment, Hair cortisol, Secretory IgA, Psychoneuroimmunology

1. Introduction

Human infants exhibit an intrinsic predisposition to form selective attachments to a small number of primary caregivers, a foundational process in early socioemotional development that lays the groundwork for future emotional and relational functioning (Feldman, 2017). These early attachment relationships constitute the core of the infant's relational environment, holding a lasting influence on emotional regulation, stress responsiveness, and biopsychosocial development (Diamond, 2015). The quality of these early attachment relationships serves as a critical determinant of health trajectories across the lifespan (Thompson, 2015).

Infant attachment is commonly classified as secure or insecure (Ainsworth et al., 1978). Secure attachment typically arises from consistent, emotionally attuned, and responsive caregiving, whereas insecure attachment is often linked to patterns of inconsistency, emotional unavailability, or neglect (Waters and Waters, 2024). The attachment system is closely intertwined with the regulation of the hypothalamic–pituitary–adrenal (HPA) axis, a central mediator of physiological stress responses (Quirin et al., 2008). Cortisol, the primary effector hormone of this system, is modulated by attachment quality (Brooks et al., 2011; Tops et al., 2007). Secure attachment is generally associated with lower basal cortisol levels and more adaptive stress responses, while insecure attachment has been correlated with elevated cortisol levels and heightened reactivity to stressors (Waynforth, 2007; Kidd et al., 2013; Pietromonaco and Powers, 2015; Sahin et al., 2023).

According to the Learning Theory of Attachment (Bosmans et al., 2020), the development of secure attachment is derived from repeated experiences of support during stressful events that become safety signals. When the child perceives a threat and the parent provides contingent support, the child experiences endocrinological and emotional responses, including a decrease in cortisol and increase in emotional comfort (Hostinar et al., 2015). Over time, the mere presence of the caregiver can inhibit fear and stress responses and become a dispositional sense of felt security.

In parallel, early infancy represents a critical stage for the maturation of the immune system, which is shaped not only by genetic and developmental programming but also by environmental factors such as microbial exposure, nutritional inputs, and caregiver–infant interactions (Nash et al., 2017; Reindl et al., 2022). Secretory immunoglobulin A (SIgA), the predominant antibody at mucosal surfaces, plays a key role in protecting epithelial barriers, regulating microbial composition, and modulating immune responses (Phillips et al., 2006). During the first months of life, SIgA is largely derived from maternal breast milk, conferring passive immune protection, while endogenous production typically begins between four and six months of age (Mulyani et al., 2023). As mucosal immune tissues mature, SIgA concentrations steadily rise, reaching more stable levels around 16 months of life (Gleeson and Cripps, 2004). Given its central role in mucosal immunity and its sensitivity to developmental and environmental factors, SIgA is emerging as a non-invasive biomarker of immunological maturation and early-life immune regulation (Marques-Feixa et al., 2022).

While the link between early attachment and neuroendocrine stress regulation is well established, its relationship to immune development remains insufficiently understood. Chronic psychosocial stress induced by insensitive caregiving has been shown to suppress mucosal immunity, including SIgA secretion, potentially increasing susceptibility to infection. Vermeer et al. (2012) demonstrated that toddlers exposed to low caregiver sensitivity exhibited lower salivary SIgA levels, highlighting the lasting impact of relational stress on immune function (Vermeer et al., 2012).

This study investigates, in human infants, whether attachment classification is associated with SIgA levels and explores the relationship between SIgA and cumulative cortisol concentrations. We hypothesize that secure attachment supports both neuroendocrine and immune system functioning during early development, potentially through buffering effects on stress-related biological systems.

Insecure attachment in infancy has been linked to a heightened risk of mental health disorders, immune dysregulation, and long-term health disparities, underscoring the public health relevance of developing accessible tools for early screening. More broadly, identifying non-invasive, biologically grounded indicators of psychosocial vulnerability, such as salivary SIgA and cortisol, could open new avenues for early detection and intervention (Kushner et al., 2016; Reindl et al., 2022). The integration of such biomarkers into pediatric or psychological assessments may enhance the identification of children at risk for affective dysregulation, immune dysfunction, or broader developmental difficulties (Kushner et al., 2016; Scribante et al., 2024).

Ultimately, our work contributes to a growing framework that considers how attachment-related experiences in early life become biologically embedded, shaping trajectories of health and well-being across the lifespan.

2. Material and methods

2.1. Study design and ethical considerations

This study was designed to investigate the relationship between infant–mother attachment patterns and biomarkers of chronic stress and mucosal immunity (Fig. 1). Ethical approval was granted by the Ethics Committee of the Universidad de Magallanes. Written informed consent was obtained from all participating caregivers prior to data collection, in accordance with the principles of the Declaration of Helsinki.

Fig. 1.

Fig. 1

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Study design and data collection workflow. Infants were recruited from public health and childcare centers in Punta Arenas city and screened for eligibility based on age (14–21 months), absence of chronic illness or developmental delay, and caregiver-reported sociodemographic information. Attachment quality was evaluated during a laboratory session between 14 and 21 months using the Strange Situation Procedure with video-coded behavioral observations. Biological samples were collected during the same developmental period and at a subsequent follow-up (21–30 months): salivary secretory IgA (SIgA) was obtained at two time points (11:00 a.m. and 3:00 p.m.) and quantified via ELISA, while cumulative cortisol was measured from ∼100 hair strands collected from the posterior vertex of the scalp following methanol extraction and ELISA. The final stage comprised statistical and Bayesian analyses, including descriptive and distributional assessments, group comparisons, correlation tests, and probabilistic modeling, complemented by machine learning approaches and explainability methods to investigate immune–endocrine contributions to attachment classification.

2.2. Participants and recruitment

Participants were recruited through public health services and government-subsidized childcare centers in Punta Arenas, Chile, using printed flyers and direct outreach conducted by trained staff. Inclusion criteria required that infants were between 14 and 21 months of age at baseline, that mothers identified themselves as the primary caregiver, and that infants had no diagnosed developmental delays or chronic medical conditions. Dyads were excluded if they were unable to attend scheduled laboratory assessments or declined participation in biological sampling procedures.

2.3. Study procedure

2.3.1. Home visit and sociodemographic assessment

An initial home visit was conducted to establish confidence with caregivers and to collect sociodemographic information through standardized self-report questionnaires. Variables assessed included maternal age, education level, employment status, and household composition. These visits also served to coordinate the scheduling of subsequent assessments, taking into account the family's availability and preferences.

2.3.2. Laboratory-based assessment of attachment

Attachment quality was assessed using the Strange Situation Procedure (SSP), conducted in a university-affiliated research laboratory (Ainsworth et al., 1978). The SSP is a validated observational paradigm involving a sequence of separations and reunions between the infant and caregiver, designed to elicit attachment-related behaviors (Simonelli et al., 2014). All sessions were video-recorded and later coded by trained observers following standardized criteria. Infants were classified into the typical attachment categories Avoidant (A), Secure (B), or Resistant (C). This ABC system of classification is commonly used to summarize SSP outcomes, based on behavioral indices such as proximity-seeking, contact maintenance, avoidance, and resistance during the reunions.

2.3.3. Sampling protocols for SIgA and hair cortisol

Salivary SigA concentration were assessed in a subsample of 35 children. The sample were collected in duplicate at two standardized points: 11:00 a.m. and 3:00 p.m. to account for diurnal variation. Saliva samples were obtained using nitrocellulose microsponges (®Salimetrics) and stored at −20 °C until analysis. SIgA levels were quantified in duplicate using an indirect ELISA kit (Salivary Secretory IgA ®Salimetrics). Saliva samples were diluted 1:5 according to the manufacturer's instructions. The standard curve was generated using six measurement points, ranging from 600 μ g/mL to 2,5 μ g/mL. Absorbance was measured at 450/492 nm using the NanoQuant module on a Tecan Infinite M200 Pro plate reader. The average intra-assay and inter-assay coefficients of variation were 5.6 % and 8.8 %, respectively. Additionally, hair cortisol concentrations (HCC) were obtained from a subset of eight children. Approximately 3–5 cm of hair (about 100 strands) was cut from the posterior vertex of the scalp. Cortisol was extracted using a standardized methanol-based protocol and quantified in triplicate with the Cortisol ELISA Kit (®Cayman). Absorbance was recorded at 405 nm at two dilutions (FD1 and FD2) using a Tecan Infinite 200 Pro spectrophotometer. The intra- and inter-assay coefficients of variation for HCC were 8.5 % and 12.5 %, respectively.

2.4. Statistical analysis

Logarithmic transformations were applied to both SIgA and cortisol values to stabilize variance and reduce the influence of extreme values. Subsequently, non-parametric methods, including the Mann–Whitney U test, Cliff's Delta, Rank-Biserial Correlation, and Pearson's correlation coefficient, were applied to assess relationships between transformed SIgA and cortisol levels. To evaluate the combined influence of neuroendocrine and immune markers, a Bayesian modeling framework was employed to estimate the dependency structure between log-transformed SIgA, cumulative cortisol, and attachment categories. Machine learning models based on Random Forest classifiers were implemented to predict attachment classifications from SIgA variables, and model interpretability was explored using SHAP (SHapley Additive exPlanations) AI methods (Panda and Mahanta, 2023). Further methodological details are provided in Supplementary Information, sections S1 and S5.

3. Results

3.1. Sample retention and attachment classification

From an initial cohort of 66 Chilean infant–mother dyads, 35 were retained based on complete participation across all study phases, which included laboratory-based attachment assessments and biological sample collection. Salivary SIgA was obtained from all 35 infants, while hair samples for cortisol analysis were available for a subset (n = 8). At the time of the attachment evaluation, infants were on average 16.6 months old (SD = 1.96; range: 14–21 months), with a balanced sex distribution (57.1 % female). To ensure the measurement of endogenous secretion, salivary biomarker sampling was conducted at a later developmental stage, when infants were, on average, 25.5 months old (SD = 2.34; range: 21–30 months). Attachment classifications were derived using the ABC system. Among the 35 infants, 63 % were classified as securely attached to their primary caregiver, 31 % as insecure-avoidant, and 6 % as insecure-resistant. Given the small number of insecure-resistant cases, attachment was analyzed as a dichotomous variable (secure vs. insecure). In this classification, the majority of infants were securely attached (21 of 35; 60 %), while a smaller proportion were classified as insecure (14 of 35; 40 %) (see section S2 of Supplementary Information for a complementary statistical description of the collected sample).

3.2. Association between attachment and secretory IgA in infancy

Infants with secure attachment displayed significantly higher morning SIgA levels than those with insecure attachment (p = 0.01), with large effect sizes (Cliff's Delta = −0.51; Rank-Biserial Correlation = −0.51) (Fig. 2A). In contrast, no significant differences were observed in afternoon SIgA concentrations (p = 0.58), with negligible effect sizes (Cliff's Delta = −0.12; RBC = −0.12) (Fig. 2A). Analysis of intra-day variation measured as the difference between afternoon and morning SIgA revealed a significant distinction between groups (p = 0.03), with medium effect sizes (Cliff's Delta = 0.43; RBC = 0.43) (Fig. 2A). To further characterize these group-level patterns, we applied t-distributed Stochastic Neighbor Embedding (t-SNE) to the log-transformed SIgA values (Fig. 2B). This non-linear dimensionality reduction technique revealed more apparent spatial separation between securely and insecurely attached infants, with the emergence of distinct local clusters (see section S3 of Supplementary Information for more details and complementary descriptions).

Fig. 2.

Fig. 2

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Secretory immunoglobulin A (sIgA) and cortisol levels by attachment group. Boxplots show log-transformed mean levels of sIgA measured in the morning, afternoon, and their difference (Δ Afternoon–Morning) stratified by attachment classification (Insecure vs. Secure). B. Two-dimensional t-SNE projection of participants based on immunological and physiological features, illustrating separation patterns by attachment. C. Boxplots of log-transformed salivary cortisol levels (pg/mL) across attachment groups. Boxes indicate the interquartile range (IQR), horizontal lines the median, and whiskers extend to 1.5 × IQR; points represent outliers.

3.3. Attachment-related regulation of immune and endocrine systems in infancy

To further examine the biological correlates of early attachment quality, we analyzed cumulative hair cortisol levels and SIgA concentrations in a subsample of infants with complete biomarker and attachment data. No significant differences in cortisol levels were found between securely and insecurely attached infants (p = 1.0), and effect sizes were negligible and consistent across metrics (Cliff's Delta = −0.067; Rank-Biserial Correlation = 0.067) (Fig. 2C). To explore immune–endocrine interactions, we assessed the relationship between cortisol and three SIgA-derived variables: morning levels, afternoon levels, and intra-day change (Fig. 3). Pearson correlations revealed a modest inverse association between cortisol and morning SIgA concentrations (r = −0.34). This association further weakened in the afternoon (r = −0.12). A positive correlation emerged between cortisol and intra-day fluctuation in SIgA (r = 0.34).

Fig. 3.

Fig. 3

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Associations between salivary sIgA and hair cortisol levels by attachment classification. Scatter plots display the relationship between log-transformed salivary secretory IgA (sIgA) and log-transformed cumulative cortisol levels (pg/mL) across attachment groups (Insecure, orange; Secure, teal). A. Morning sIgA, B. Afternoon sIgA, and C. The difference between afternoon and morning sIgA (Δ) is shown in relation to cortisol concentrations. Each point represents one participant, with attachment classification indicated by color. Pearson's correlation coefficient (r) is reported for each panel. The plots illustrate differential associations between immune (sIgA) and endocrine (cortisol) markers across various measurement contexts, offering insight into immune–endocrine regulation in early childhood attachment. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

3.4. Predictive modeling of attachment status using immune and endocrine biomarkers

To examine whether immune and endocrine biomarkers jointly predict attachment status in infancy, we implemented a series of Bayesian logistic regression models (see Section S4 of the Supplementary Information for methodological details). The first model included salivary secretory IgA (sIgA) measures (morning, afternoon, and intra-day difference) as predictors of insecure attachment probability. Posterior distributions for all coefficients (βmorning, βafternoon, Δβafternoon–morning) exhibited wide 94 % highest density intervals (HDI94 %​) overlapping zero, reflecting modest parameter uncertainty (Fig. 4A). Nevertheless, the directionality of effects remained consistent across estimates. Higher morning sIgA levels were associated with lower probabilities of insecure attachment, while greater intra-day differences were linked to higher probabilities of insecure attachment. The afternoon sIgA measure showed smaller and more variable effects, which suggests that its predictive strength was limited compared with morning values.

Fig. 4.

Fig. 4

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Bayesian parameter estimates, model performance, and feature contribution for salivary IgA and stress physiology. A. Posterior distributions of regression coefficients (β) describing log-transformed secretory IgA (sIgA) levels across sampling times (morning, afternoon) and in relation to cortisol concentrations. Each density plot shows the posterior mean, 94 % Highest Density Interval (HDI94 %​), and Region of Practical Equivalence (ROPE). The afternoon–morning contrast (Δβ) quantifies the diurnal change in sIgA secretion. Bayesian estimates indicate a moderate reduction from morning to afternoon, with low overlap within the ROPE, suggesting credible temporal variation. B. Mean Receiver Operating Characteristic (ROC) curve across six stratified folds (mean AUC = 0.98 ± 0.01), demonstrating high classification accuracy. C. SHAP (Shapley Additive exPlanations) values ranking the relative contribution of predictors (morning, afternoon, and differential sIgA levels, and gender) to model output. Higher SHAP values indicate greater influence on prediction, highlighting the predominant role of morning and afternoon sIgA measures in discriminating stress-related immune patterns.

A second extended Bayesian model incorporated cumulative hair cortisol level to assess the covariation between immune and endocrine systems. The inclusion of cortisol did not alter the general pattern of sIgA effects. Still, it introduced a modest negative coefficient (βcortisol​) with an HDI encompassing zero (Fig. 4A), indicating limited precision in estimating endocrine contributions within this sample.

To complement the Bayesian inference framework with a predictive perspective, we trained a Random Forest classifier using the same immunological features (see Section S5 of the Supplementary Information). The model achieved a mean area under the receiver operating characteristic curve (AUC = 0.98 ± 0.01) across six stratified folds, demonstrating robust discriminative performance (Fig. 4B). SHAP (Shapley Additive Explanations) analysis further revealed that morning and afternoon sIgA levels contributed most strongly to model output, followed by intra-day variability. At the same time, gender exhibited minimal impact (Fig. 4C). These results indicate that early-life immune markers, particularly morning and afternoon sIgA concentrations, carry predictive information relevant to attachment classification within this cohort.

4. Discussion

Attachment is a deep emotional relationship formed between infants and their caregivers. Its biological implications remain insufficiently understood; however, stress regulation has been one of the most studied effects, consistently linked to cortisol levels (Costa-Martins et al., 2016; de Mendonça Filho et al., 2022). Stress regulation is also closely linked to immune system maturation, which unfolds rapidly during infancy. Secretory IgA (SIgA), the predominant mucosal antibody, offers a non-invasive marker of immune development and is sensitive to psychosocial influences (Ulmer-Yaniv et al., 2018). In this study, we investigated the association between attachment and SIgA levels in infants, and further examined the relationship between SIgA and cumulative stress indexed by hair cortisol concentration (HCC). Most of the children analyzed developed secure attachment (66.7 %). This aligns with previous studies suggesting that secure attachment is the most common attachment type among infants (Granqvist et al., 2017; Madigan et al., 2023). Secretory IgA is a fundamental mucous membrane antibody that acts as a first-line defense against infections; thus, SIgA was determined to assess the adaptive immune response by attachment type. The secure attachment group presented higher levels of SIgA compared to the insecure group, mainly in the morning. The difference in SIgA level was not significant in the afternoon. These findings likely reflect the effects of SIgA circadian rhythm, with higher levels in the morning and a gradual decrease in the afternoon (Shirakawa et al., 2010; Vermeer et al., 2012; Cajochen et al., 2021) This chronobiological effect operates under the influence of the hypothalamic-pituitary-adrenal axis and cortisol secretion, which is also higher when awake and decreases throughout the day, potentially masking more subtle relationships with psychosocial variables as the day progresses. Secure attachment allows better emotional self-regulation, allowing lower levels of basal stress (Pallini et al., 2018) and is associated with lower activation of the HPA axis and increased immune function (Dunn, 2007; Fulford and Harbuz, 2005). In the morning, when basal SIgA levels are naturally higher, this positive relationship may be readily detectable. As the day progresses, there is more exposure to social events. Therefore, individual differences in attachment can be diluted or modulated by other contextual variables, such as school stress, social interactions, and fatigue (Klimes–Dougan et al., 2001; Hasebe et al., 2023). Therefore, it is plausible that the modulatory effects of attachment are more pronounced at resting phases, but they do not necessarily buffer against all daily stressors.

Subsequently, we evaluated the clustering of SIgA data according to attachment using t-SNE (t-Distributed Stochastic Neighbor Embedding), which enables the visualization of complex and nonlinear patterns in multivariate data, to assess whether children with secure attachment form distinct groups in a multidimensional space. We identified clusters predominantly characterized by a single attachment classification, indicating that early immune signatures associated with secure attachment are not only statistically distinguishable but also exhibit structured organization in multidimensional space, supporting the existence of latent immune phenotypes shaped by early relational experiences (Ehrlich, 2019). Next, we measured HCC to assess cumulative stress and its relationship with SIgA levels. We observed an inverse association between cortisol and morning SIgA, consistent with the downregulatory effects of cortisol. Elevated stress activity may therefore suppress mucosal immune function, potentially compromising the infant's first line of defense against pathogens. (Drummond and Hewson-Bower, 1997). Insecure attachment, often characterized by inconsistent or inadequate care, can predispose infants to greater fluctuation in immune function, as evidenced by the positive association between cortisol and intraday variability of SIgA (Waynforth, 2007). These results highlight the relevance of exploring the immune-endocrine dynamics in relation to early attachment, even in the absence of significant differences at the group level in cortisol. Although securely and insecurely attached infants did not differ in cumulative cortisol concentrations, the observed correlations between cortisol and sIgA suggest that relational experiences may still modulate the coordination between stress and immune systems (van Bakel and Riksen-Walraven, 2004). These neuroendocrine mechanisms are closely linked to mucosal immune regulation. Cortisol, the end product of HPA axis activation, exerts immunosuppressive effects by binding to glucocorticoid receptors (GR) on immune cells and epithelial tissues. Elevated cortisol levels inhibit key processes involved in IgA production, including class-switch recombination mediated by TGF-β and cytokines essential for plasma cell differentiation in mucosa-associated lymphoid tissue (Stavnezer and Kang, 2009). Additionally, cortisol may downregulate the expression of the polymeric immunoglobulin receptor (pIgR), reducing IgA transport across epithelial barriers (Turula and Wobus, 2018). This pathway offers a plausible molecular explanation for the observed inverse association between hair cortisol and morning SIgA levels and deserves further investigation. Securely attached infants, through more efficient regulation of HPA axis activity, may preserve mucosal immune function and preserve IgA secretion stability. These findings support the idea that attachment quality influences both endocrine and immune systems through coordinated molecular mechanisms, contributing to the biological embedding of early caregiving experiences.

The inverse association between cortisol and morning SIgA, together with the positive correlation with intraday variability, points to a pattern in which a greater stress load can disrupt mucosal immune stability. These findings contribute to emerging psychoneuroimmunological models that posit that early caregiving environments influence not only individual physiological systems but also their interaction. In this context, SIgA complements cortisol as a sensitive biomarker of attachment-related biological regulation during childhood. However, the reduced hair cortisol concentration dataset and the small sample size (n = 8) constitute a limitation of the present study. Notably, low availability of endocrine data is a common challenge in infancy research (Martins-Silva et al., 2025) and most likely reflects practical difficulties in obtaining sufficient hair samples from infants, rather than selective sampling bias (Serdar et al., 2021). In this context we are confident that our study provides information on the feasibility and directionality of immune–endocrine associations, however we are cautious about definitive evidence of group effects.

To evaluate whether SIgA could be a biological marker, reflecting the influence of attachment beyond stress regulation, we developed a predictive machine learning model of attachment status, using immune and endocrine biomarkers. The results suggest that early-day immune profiles and daily immune stability may serve as sensitive indicators of relational regulation during childhood, even when differences at the group level are subtle. These findings underscore the importance of multidimensional biological modeling in understanding the early origins of attachment (Gagliardi, 2022; Schore, 2002). While the posterior intervals of the Bayesian models highlight the uncertainty linked to small sample sizes, the consistent directional effects across models are noteworthy. The observed patterns indicate that securely attached infants may show more stable SIgA levels (Paquette et al., 2024). The positive coefficient for intra-day SIgA fluctuation again suggested that greater immune instability may signal heightened stress exposure or diminished regulatory capacity. The effect of cortisol was negative but comparatively weaker, indicating that hormonal measures alone may not fully capture the relational modulation of physiological systems in early life. The strong performance of the machine learning classifier offers complementary evidence that SIgA carries predictive utility, even in the absence of substantial differences at the group level. The ability to classify attachment status based on a small set of immune characteristics opens promising avenues for the development of physiological screening tools to enhance behavioral assessments in developmental contexts (Picardi et al., 2013). The small sample size can also affect the robustness of the machine-learning analyses. Although the classifier trained on SIgA-derived features demonstrated high discriminative performance, models trained on limited datasets are susceptible to overfitting. They may capture idiosyncratic patterns rather than stable, generalizable structures. Even though cross-validation and regularization strategies can mitigate these risks, they cannot fully compensate for the inherent variability introduced by the small sample. Thus, the predictive value observed here should be interpreted as a proof-of-concept, highlighting the potential of mucosal immune markers for early relational assessment and requires validation in larger and more diverse cohorts.

Our data suggest that integrating machine learning with probabilistic modeling may offer a powerful strategy for identifying early life biological signatures of relational health, particularly in settings where traditional metrics may lack sensitivity or where sample sizes are restricted (Chen et al., 2021; Tewari et al., 2021).

5. Conclusions

This work provides novel evidence that the quality of early attachment is reflected not only in behavioral patterns but also in physiological systems central to stress and immune regulation. By examining SIgA and cumulative hair cortisol in human infants, we demonstrate that securely attached infants exhibit greater mucosal immune stability, particularly in the early morning, and reduced intraday immune variability.

The correlations between cortisol and SIgA revealed a meaningful immune-endocrine coordination that varied depending on the status of attachment. The inverse relationship between cortisol and morning SIgA, as well as the positive association between cortisol and intra-day immune fluctuation, suggests that chronic stress may compromise mucosal immunity, particularly in vulnerable contexts of infant-caregiver interactions.

Predictive modeling approaches further underscore the discriminative value of immune biomarkers. Our Random Forest classifier, trained exclusively on SIgA-derived features, achieved high accuracy in classifying attachment status, supporting the feasibility of non-invasive, biomarker-based screening tools for healthy early attachment relationships. These results highlight the potential of combining computational and biological methods to identify early-life risk profiles, even in small sample sizes or low-resource settings.

Our findings suggest that the infant's caregiving environment may become biologically embedded through changes in immune function and its coordination with endocrine responses. Secretory IgA represents a sensitive marker of this process, reflecting both baseline readiness and regulatory dynamics. Although these results should be interpreted with caution given the small sample size, the evidence indicates that secure attachment is associated with greater immune stability, consistent with psychoneuroimmunological models in which security promotes integration across stress and immune systems. In contrast, insecure attachment may be associated with increased physiological variability, potentially indicating early vulnerability in immune development.

CRediT authorship contribution statement

Jorge González-Puelma: Writing – original draft, Investigation, Conceptualization. Lindybeth Sarmiento Varón: Writing – original draft, Investigation. Jessica Vidal: Investigation, Conceptualization. Constanza Ceroni: Investigation. Sebastián Escobedo: Investigation. Roberto Uribe-Paredes: Funding acquisition, Conceptualization. David Medina-Ortiz: Writing – original draft, Software, Formal analysis, Data curation. Rodrigo A. Cárcamo: Writing – original draft, Funding acquisition, Conceptualization. Marcelo A. Navarrete: Writing – original draft, Funding acquisition, Conceptualization.

Codes and data statement

All analyses were performed using Python v3.12, employing standard scientific libraries including Pandas, NumPy, SciPy, and Seaborn. The complete analysis pipeline including all preprocessing scripts, statistical routines, and visualization code is openly available at the following GitHub repository: https://github.com/dMedinaO/IgA_attachment_analysis. The repository also contains raw and processed data, along with detailed documentation to facilitate full reproducibility of the results. All resources are released under the MIT license for non-commercial academic use. Additional information regarding software dependencies, environment configuration, and execution instructions can be found in the README.md and pyproject.toml files within the repository.

AI statement

As non-native English speakers, the authors used ChatGPT (OpenAI, GPT-4.0) during content was subsequently reviewed and edited by the authors, who take full responsibility for the final version of the manuscript.

Funding

This work was supported by the National Agency for Research and Development of Chile (ANID) [Fondecyt 11140663, Fondecyt 1230298, Anillo ATE220016, Subvención Instalación en la Academia 85220004, Fondecyt Iniciación 11250295, and Fondecyt 1252183], by Ministry of Education [MAG, 2095], and by the Centre for Biotechnology and Bioengineering – CeBiB [PIA projects FB0001 and AFB240001, ANID, Chile].

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

We thank the Laboratory of Molecular Biology at the Centro Asistencial Docente e Investigación (CADI-UMAG) for institutional support and the staff for their technical assistance. We also thank the participants, as well as the public health services and government-subsidized childcare centers in Punta Arenas, Chile, for making this study possible.

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.bbih.2026.101184.

Contributor Information

Rodrigo A. Cárcamo, Email: rodrigo.carcamol@uss.cl.

Marcelo A. Navarrete, Email: marcelo.navarrete@umag.cl.

Appendix A. Supplementary data

The following is/are the supplementary data to this article:

Multimedia component 1
mmc1.pdf (509.6KB, pdf)

Data availability

The link to the code is provided within the manuscript

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