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Article

Interactive Effects of Genetic Susceptibility and Early-Life Tobacco Smoke Exposure on the Asthma–Eczema Complex Phenotype in Children: 6-Year Follow-Up Case-Control Study

by
Anna Dębińska
*,
Hanna Danielewicz
,
Anna Drabik-Chamerska
and
Barbara Sozańska
Department and Clinic of Paediatrics, Allergology and Cardiology, Wrocław Medical University, ul. Chałubińskiego 2a, 50-368 Wrocław, Poland
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2026, 27(1), 346; https://doi.org/10.3390/ijms27010346
Submission received: 29 November 2025 / Revised: 21 December 2025 / Accepted: 27 December 2025 / Published: 29 December 2025
(This article belongs to the Special Issue Molecular Research in Asthma and Allergy)
Versions Notes

Abstract

Atopic eczema and asthma frequently co-occur, forming a distinct complex phenotype that likely arises from shared genetic pathways and early-life environmental influences. We aimed to investigate whether variants in TNS1 and NRXN1—previously identified in a genome-wide interaction study—influence susceptibility to atopic eczema and the asthma–eczema phenotype and whether early-life environmental tobacco smoke (ETS) exposure modifies these genetic effects. A total of 188 Caucasian children under 2 years at recruitment were prospectively followed up to 6 years of age. Eligibility of all participants for the study or control group was based on a questionnaire and a physician-confirmed diagnosis of eczema and asthma. Early-life ETS exposure was assessed by parental questionnaire. All participants were genotyped for TNS1 and NRXN1 SNPs. The TNS1 rs918949 [T] allele was associated with the combined asthma–eczema phenotype but not with eczema alone. Synergistic gene–environment interactions were identified for both TNS1 and NRXN1, with the highest risk of the combined asthma–eczema phenotype observed among ETS-exposed carriers of risk alleles. Our findings provide the first independent replication of evidence suggesting that TNS1 and NRXN1 may contribute to the asthma–eczema comorbidity through mechanisms that could be substantially modified by early-life ETS exposure.

1. Introduction

Atopic diseases, such as eczema and asthma, represent the most common chronic conditions in children and are closely associated with one another [1,2,3,4]. This co-occurrence suggests shared pathogenic mechanisms and overlapping genetic determinants, as evidenced by their strong familial aggregation and frequent coexistence of both conditions within the same individuals [5,6,7,8]. Atopic eczema often predates the onset of other allergic diseases and is a well-established risk factor for subsequent progression to asthma, particularly in cases with early-onset atopic eczema [9,10,11]. One of the most compelling objectives of current research is to identify risk factors associated with atopic eczema that may also serve as predictors of subsequent atopic disease progression. However, the mechanisms underlying the development of atopic eczema and its subsequent progression to asthma remain incompletely understood. Linkage and association studies have identified multiple shared inherited susceptibility variants that contribute to the increased risk of both eczema and asthma [12,13,14,15]. Nonetheless, these variants account for only a fraction of the observed heritability, as most studies have focused on broad allergic disease phenotypes defined as the presence of any one of the allergic conditions, without addressing the complex phenotype of asthma–eczema comorbidity, which may better reflect the natural history of allergic disease progression.
Given that the co-occurrence of asthma and eczema appears to represent a distinct phenotype with a unique pathogenic pathway, it was of particular interest to investigate genetic variants specifically associated with this comorbidity. The first meta-analysis of genome-wide association studies (GWAS) addressing the comorbidity of asthma and eczema within the context of atopic march reported several genes already implicated in either asthma or eczema, along with two new loci associated with allergic diseases for the first time [16]. More recently, a second GWAS meta-analysis aimed to detect genes uniquely contributing to the combined asthma–eczema phenotype, resulting in the discovery of six new genes not previously linked to allergic diseases [17]. Nevertheless, genetic predisposition alone cannot fully account for the susceptibility and phenotypic heterogeneity of multifactorial diseases such as eczema and asthma, which result from complex interactions between multiple genetic and environmental factors.
Among the environmental factors, early-life exposure to environmental tobacco smoke (ETS) warrants particular attention due to its well-documented effects on airway inflammation, lung function, and immune regulation [18,19,20]. A substantial body of evidence demonstrates that both prenatal and postnatal exposure to ETS represents a significant risk factor for wheezing and asthma [21,22] and, to a lesser extent, for eczema, although studies have yielded conflicting results regarding the latter association [23,24,25]. Importantly, children with a genetic predisposition to atopy may exhibit heightened susceptibility to the detrimental effects of tobacco smoke, suggesting potential gene–environment interactions [26,27,28]. However, the full extent to which ETS influences genetic susceptibility to increase disease risk is not yet completely understood. Although individual associations between genetic variants and atopic diseases [29,30,31,32,33,34], as well as between ETS exposure and allergic outcomes [21,24,35], are well established, research exploring their combined effects, particularly in relation to the asthma–eczema comorbidity, remains limited. To our knowledge, only one genome-wide interaction study (GWIS) investigating the combined asthma–eczema phenotype in the context of ETS exposure has been published to date. This analysis identified two previously unreported candidate genes, NRXN1 (2p16) and TNS1 (2q35), showing significant interactions with environmental tobacco smoke (ETS) exposure [36]. These findings highlight the importance of investigating distinct phenotypes, such as comorbid conditions and gene–environment interactions, to identify new genetic susceptibility loci, as well as the need for replication analysis in independent populations [36]. Understanding whether genetic predisposition and early-life ETS exposure interact in the development of the asthma–eczema phenotype may provide insights into the shared pathophysiological mechanisms underlying these conditions and inform strategies for early prevention in genetically predisposed populations.
Therefore, the present study aimed to investigate the role of SNPs in the NRXN1 and TNS1 genes in susceptibility to atopic eczema and to the eczema-associated asthma complex phenotype in children. Furthermore, we examined the interactive effects between these genetic variants and early-life exposure to ETS on the development of the combined asthma–eczema phenotype in an independent population of Polish children.

2. Results

A summary of the characteristics of patients with atopic eczema and control subjects, including genotyping results for the TNS1 rs918949 and NRXN1 rs10194978 SNPs, as well as information on ETS exposure status, is presented in Table 1. No significant differences were observed between cases and controls with respect to age or sex.

2.1. The Association of TNS1 rs918949 and NRXN1 rs10194978 with Eczema

In the overall study population, no significant associations were found between the TNS1 rs918949 and NRXN1 rs10194978 polymorphisms and the prevalence of atopic eczema. The eczema and control groups did not differ significantly in genotype frequencies for either TNS1 rs918949 (p = 0.441; χ2 = 0.592) or NRXN1 rs10194978 (p = 0.730; χ2 = 0.119). In genotype and allele models adjusted for potential confounders, the risk alleles of both SNPs did not significantly increase the risk of atopic eczema (Table 2 and Table 3).

2.2. The Association of TNS1 rs918949 and NRXN1 rs10194978 with the Asthma–Eczema Complex Phenotype

Associations between TNS1 rs918949 and tested phenotypes are summarized in Table 2. Analysis of the TNS1 rs918949 variant demonstrated that the risk allele [T] was associated with an increased predisposition to the asthma–eczema comorbidity compared with controls without asthma or eczema (p = 0.023; χ2 = 5.132). This association remained significant when compared with an expanded control group comprising all participants who did not belong to the combined phenotype group (p = 0.016; χ2 = 5.737). Individuals carrying at least one [T] allele exhibited more than a twofold higher risk of having eczema-associated asthma (OR = 2.00, 95% CI 1.08–3.71; p = 0.031) in the allelic analysis. In genotype models adjusted for relevant confounders, individuals homozygous for the effect allele demonstrated a significantly increased risk of the combined asthma–eczema phenotype compared with wild-type homozygotes under the codominant model. This association was also evident—and even stronger—when these individuals were compared with controls without asthma or eczema in the recessive model (OR = 6.75, 95% CI 2.37–19.2; p < 0.001). Next, to verify the specificity of the observed association with comorbidity, the analysis was restricted to children with atopic eczema. First, we examined the association between TNS1 rs918949 and eczema-associated asthma by comparing participants with eczema plus asthma to those with eczema only. A significant association of the TNS1 rs918949 risk allele with eczema-associated asthma was confirmed at both the genotype and allele levels. In the additive model, the presence of the T allele was associated with an approximately twofold increase in risk (OR = 2.08, 95% CI 1.11–3.88, p = 0.028), while in the recessive model, the risk was further increased. The strongest effect was observed in the codominant model, where carriers of the TT genotype showed an increased risk of the asthma–eczema phenotype compared with wild-type homozygotes. Then, the association between TNS1 rs918949 and eczema was evaluated by comparing participants with eczema only to controls without asthma or eczema. Notably, in the absence of asthma, no significant association with eczema was observed in any of the tested genetic models (Table 2).
No significant associations were observed between the NRXN1 rs10194978 variant and the asthma–eczema comorbidity in any of the analyses performed. Specifically, genotype and allele frequencies did not differ significantly across the tested phenotypic groups, nor between the subgroups with eczema plus asthma (p = 0.548; χ2 = 0.360) and eczema only (p = 0.991; χ2 = 0.018), when compared with the respective control groups. This lack of differences in allelic and genotypic distributions persisted when the control group was expanded to include all participants not belonging to the combined phenotypic group (p = 0.558; χ2 = 0.342). In the allelic model, carriers of the G allele did not show a significantly increased risk of eczema-associated asthma compared with controls. Similarly, genotype-based analyses under dominant, recessive, and codominant models revealed no significant associations between NRXN1 rs10194978 and the combined asthma–eczema phenotype after adjustment for relevant confounders. No significant associations were detected in further analysis restricted to children with atopic eczema or in the absence of asthma. (Table 3).

2.3. Interaction Analysis of TNS1 rs918949 and NRXN1 rs10194978 with ETS Exposure in Relation to Asthma–Eczema Complex Phenotype

In our study population, exposure to ETS during the first two years of life was significantly more frequent among children with the asthma–eczema comorbidity compared to the control group (p = 0.006, χ2 = 8.434). In contrast, the frequency of parent-reported early-life ETS exposure did not differ significantly between children with eczema and healthy controls (p = 0.272, χ2 = 1.347). In logistic regression analysis, early-life ETS exposure was significantly associated with the combined eczema–asthma phenotype (OR = 3.59; 95% CI 1.48–8.74), whereas no such association was observed for eczema alone (OR = 1.46; 95% CI 0.89–2.49).
Next, we explored whether the early-life ETS exposure interacts with TNS1 and NRXN1 variants to modulate the risk of developing the combined asthma–eczema phenotype. Table 4 presents additional analyses for the risk of eczema-associated asthma after stratification by ETS exposure status. Among children exposed to ETS, carriers of the TNS1 rs918949 T allele showed a markedly increased risk of the asthma–eczema comorbidity compared with carriers of the C allele (RR = 8.50, 95% CI 3.02–23.95; p < 0.001). In contrast, no association was observed for TNS1 in the absence of ETS exposure (RR = 0.71, 95% CI 0.29–1.73; p = 0.509). Similarly, for NRXN1 rs10194978, the A allele was associated with an increased risk of eczema-associated asthma among ETS-exposed children (RR = 4.23, 95% CI 1.62–11.52; p = 0.005), whereas no association was detected in non-exposed children (RR = 1.02, 95% CI 0.41–2.59; p = 1.000). Therefore, ETS exposure was included as a secondary predictor in a logistic regression model to evaluate its potential confounding effect. After adjustment for ETS exposure, the TNS1 rs918949 risk allele remained significantly associated with the asthma–eczema phenotype (OR = 2.20, 95% CI 1.15–4.21; p = 0.016), indicating little evidence of confounding by ETS exposure. For the NRXN1 rs10194978 variant, the association of the risk allele [A] also remained significant after adjustment (OR = 2.14, 95% CI 1.10–4.28; p = 0.031). However, early-life ETS exposure appeared to confound this relationship, as the adjusted OR was higher than the crude OR for the NRXN1–asthma–eczema association. Furthermore, early-life ETS exposure emerged as an important independent predictor of the combined asthma–eczema phenotype, even after controlling for both TNS1 rs918949 and NRXN1 rs10194978 risk alleles with adjusted OR = 3.81, 95% CI 2.00–7.25; p < 0.001 and OR = 4.45, 95% CI 2.27–8.72; p < 0.001, respectively (Table 4).
To investigate potential gene–environment interactions, we examined the combined effects of early-life ETS exposure and TNS1 and NRXN1 variants on the risk of developing the asthma–eczema phenotype. Significant interactions were observed for both TNS1 rs918949 and NRXN1 rs10194978, indicating that early-life ETS exposure enhanced the genetic risk associated with the respective effect alleles. Specifically, carriers of at least one risk allele of either TNS1 rs918949 or NRXN1 rs10194978 who were exposed to ETS had the highest risk of developing the combined asthma–eczema phenotype compared with nonexposed children carrying the wild-type genotype (Table 5).
The measures of interaction (RERI, AP, and synergy index) indicated a significant positive interaction on the additive scale, suggesting that the combined effect of both risk factors was significantly greater than the sum of their individual effects. Furthermore, the risk of the eczema-associated asthma phenotype among exposed children carrying the risk alleles fitted a multiplicative model, with ratios of relative risks of 4.70 for TNS1 and 2.16 for NRXN1, respectively (Table 5).
A logistic regression model including an interaction term between genotype and ETS exposure in relation to the asthma–eczema comorbidity confirmed a significant gene–environment interaction for both TNS1 rs918949 (p for interaction < 0.001) and NRXN1 rs10194978 (p for interaction = 0.037), consistent with their synergistic effect on the risk of developing the combined asthma–eczema phenotype.
Post hoc power analysis indicated moderate power to detect the main genetic effect of TNS1 rs918949 (achieved power ≈ 0.59), whereas power to detect the main effect of NRXN1 rs10194978 was low (achieved power ≈ 0.20). In contrast, power to detect genotype × ETS interaction effects was high for TNS1 (achieved power > 0.90) and moderate for NRXN1 (achieved power ≈ 0.55), likely driven by the relatively large observed effect sizes for the interaction terms (Table S1).

3. Discussion

In this study, we evaluated whether early-life exposure to ETS interacts with genetic variants at two candidate loci, TNS1 and NRXN1, to impact the development of the combined asthma–eczema phenotype in children. These loci were previously identified in the only existing genome-wide interaction study of this phenotype, where both demonstrated significant interaction signals with ETS [36]. In addition, we extended these analyses by evaluating the associations of TNS1 and NRXN1 variants with susceptibility to atopic eczema and to the eczema-associated asthma phenotype in children, irrespective of ETS exposure. We found that TNS1 rs918949 was associated with an increased risk of the asthma–eczema phenotype, and gene–environment interaction analyses indicated that both TNS1 and NRXN1 variants conferred the greatest risk among children exposed to ETS in early life.
To the best of our knowledge, there are no published data linking the TNS1 rs918949 variant to asthma–eczema comorbidity, a phenotype that has been rarely investigated and is generally overlooked in most genetic studies. To date, only three GWAS meta-analyses focusing on this combined phenotype have been reported. The first, examining eczema followed by asthma within the framework of the atopic march, identified five loci previously associated with at least one allergic disease (FLG, IL4/KIF3A, AP5B1/OVOL1, C11orf30/LRRC32, and IKZF3) and two novel loci specific to the eczema–asthma phenotype (EFHC1 and TMTC2) [16]. A subsequent GWAS meta-analysis designed to detect genes uniquely contributing to this comorbidity phenotype revealed six new loci not previously linked to allergic disease, including two SNPs in the OAC2 gene that reached genome-wide significance [17]. Most recently, a GWAS conducted in a Korean pediatric population identified CACNA2D3 (3p14.3) as a novel locus, with the lead variant showing an association with reduced risk of asthma and atopic dermatitis multimorbidity [37]. Together, these findings support the view that the asthma–eczema comorbidity represents a distinct biological phenotype, defined by genetic determinants that only partially overlap with those involved in isolated asthma or eczema. Our results are broadly consistent with this concept, as we demonstrate that the TNS1 rs918949 variant is specifically associated with the combined asthma–eczema phenotype but not with eczema alone. The increased asthma risk associated with the rs918949 [T] allele observed among children with eczema argues against the interpretation that the association with the asthma–eczema comorbidity merely reflects susceptibility to eczema alone. This is further supported by the absence of any association between TNS1 rs918949 and eczema in the absence of asthma. Together, these findings suggest that TNS1 may act as a susceptibility locus predominantly in the context of the combined asthma–eczema phenotype. However, due to the study design, all asthmatic participants in our cohort also presented with eczema, which precluded the evaluation of genetic associations with asthma in the absence of eczema. Consequently, we cannot determine whether the observed association is specific to the asthma–eczema comorbidity or only reflects a more general effect on asthma risk. Therefore, extrapolation of these findings to asthma phenotypes without concomitant eczema should be made with caution. Taken together, these results are consistent with the hypothesis that asthma–eczema comorbidity may reflect a distinct immunobiological phenotype rather than a simple coexistence of two independent conditions.
Although the precise biological mechanisms linking TNS1 to allergic multimorbidity have not yet been elucidated, several lines of evidence suggest that this gene may play a role in pathways relevant to epithelial barrier integrity and tissue remodeling—key processes in both eczema and asthma pathophysiology [38,39,40]. TNS1 encodes tensin-1, a focal adhesion protein that links the extracellular matrix components to the actin cytoskeleton, participates in various signaling pathways, and thereby influences cell adhesion, migration, and proliferation, as well as epithelial integrity and cytoskeletal remodeling [41]. In the human lung, TNS1 has been detected in airway smooth muscle, the lamina propria, and the airway epithelium, and its expression is markedly upregulated in response to TGF-β (Transforming growth factor beta), a key cytokine involved in inflammation and fibrotic airway remodeling of asthmatic patients [42,43]. Functional studies further demonstrated that TNS1 regulates α-smooth muscle actin expression and contractile responses in human airway smooth muscle cells, directly implicating this protein in asthma-related airway pathology [43]. In line with these functional observations, previous GWASs have identified significant associations between TNS1 genetic variants and spirometric measures, including post-bronchodilator FEV1 and the FEV1/FVC ratio (Forced expiratory volume in one second/Forced vital capacity), suggesting a potential role for TNS1 in pulmonary function and respiratory pathophysiology [44,45]. In skin-related contexts, TNS1 is expressed in human dermal fibroblasts, where it is thought to contribute to the stability, tension, and structural organization of the extracellular matrix mediated by fibroblasts [46,47,48]. Experimental studies showed that reduced TNS1 expression impairs cell–matrix interactions at both focal and fibrillar adhesions, supporting a functional role for TNS1 in maintaining extracellular matrix architecture and overall dermal structural integrity [47,49]. Against this background, the observed association of the TNS1 rs918949 variant with the asthma–eczema comorbidity appears biologically plausible, suggesting that genetic variation within TNS1 may modulate barrier-related pathways, particularly relevant in multimorbid allergic disease.
A key finding of this study is the significant interaction observed between the TNS1 rs918949 and NRXN1 rs10194978 variants and early-life ETS exposure in relation to the asthma–eczema comorbidity phenotype. Children exposed to ETS during the first two years of life who carried risk alleles at either locus exhibited the highest susceptibility to the combined phenotype. The observed interactions suggest a synergistic model in which genetic predisposition and ETS exposure jointly increase disease risk beyond what would be expected from their individual contributions. These findings are consistent with the only GWIS meta-analysis of asthma and eczema published to date, which considers ETS exposure in four independent populations. This study resulted in the detection of four new SNPs, among which TNS1 and NRXN1 showed significant evidence for their interaction with ETS exposure in relation to the comorbidity of asthma and eczema [36]. Our study provides the first independent replication of these findings in a case-control setting, further supporting the hypothesis that TNS1 and NRXN1 contribute to the susceptibility underlying the asthma–eczema phenotype and that early-life environmental exposures modulate their effects. Additional support for this interpretation comes from GWAS and association studies demonstrating interactions between active or passive smoking and TNS1 variants in relation to pulmonary function in adults, as well as transient wheezing and lung function in early childhood [50,51,52]. Likewise, genetic variation in NRXN1 has previously been associated with lung-related phenotypes and spirometric measures, particularly in current and former smokers, suggesting a potential role of this locus in pulmonary function and environmentally responsive respiratory pathways [53]. In the present study, NRXN1 did not show a significant main effect in unstratified analyses, and the observed association emerged primarily in the context of ETS exposure. This pattern is consistent with, but does not definitively establish, a context-dependent genetic effect in which genetic susceptibility may become apparent only under specific environmental conditions [54]. However, given the absence of a main genetic effect, the moderate statistical power for the NRXN1 × ETS interaction, and the relatively wide confidence intervals, it cannot be ruled out that factors such as exposure misclassification or limited sample size within exposure strata contributed to these findings. Although neurexins encoded by the NRXN1 gene are traditionally recognized for their roles in neurotransmission and synaptic function, emerging evidence indicates that they are also expressed in epithelial cells and participate in cell adhesion and developmental signaling pathways, which are known to be susceptible to environmental influences and may therefore underlie the ETS-dependent effects [55,56,57,58,59]. Nevertheless, these results should be interpreted as preliminary, and the biological relevance of NRXN1 and ETS exposure on the risk of eczema-asthma comorbidity remains speculative at this stage. Accordingly, our findings should be interpreted as hypothesis-generating rather than confirmatory, warranting replication in larger cohorts with more detailed exposure assessment, as well as functional studies to elucidate the underlying molecular mechanisms. Despite these considerations, these observations highlight the importance of considering environmental modulators when evaluating genetic risk, particularly in complex atopic conditions.
More broadly, our findings are consistent with the established concept of gene–environment interaction, in which genetic susceptibility and environmental exposures jointly influence disease risk by modifying each other’s effects [54]. In this context, it is conceivable that early-life ETS enhances the effect size of the risk alleles at TNS1 and NRXN1, while conversely, these genetic variants may shape the extent to which ETS exposure increases the risk of eczema-associated asthma. From a pathophysiological perspective, it is plausible that ETS interacts with underlying DNA sequence variation through mechanisms such as modulation of enhancer or promoter activity, chromatin remodeling, and activation of specific transcription factors, ultimately leading to differential gene expression [60,61,62,63,64,65]. For completeness, beyond DNA sequence-dependent interactions, components of ETS are also known to induce a range of epigenetic modifications—including changes in DNA methylation, post-translational histone modifications, and regulation of non-coding RNA expression—which can influence gene expression and phenotype without altering the DNA sequence [66,67,68]. Within the broader framework of gene–environment interaction, it can be assumed that genetic variants in TNS1 and NRXN1, involved in epithelial integrity and cell adhesion, may heighten susceptibility to the detrimental effects of early-life ETS exposure. It is well established that chemicals present in tobacco smoke can trigger the immune system, modulate innate and adaptive immune responses, and promote inflammatory processes, including allergic inflammation and sensitization [18,19,20,68,69,70,71]. The tobacco-related toxins disrupt airway epithelial differentiation and integrity, impair mucociliary function, enhance mucous production, and increase oxidative stress, leading to activation of the inflammatory response, further lung tissue damage, and airway remodeling [72,73,74,75,76,77,78,79,80,81]. Chemical constituents of tobacco smoke also exert multiple detrimental effects on the skin barrier and extracellular matrix [82,83]. Experimental studies demonstrate that ETS exposure increases transepidermal water loss, promotes degeneration of dermal connective tissue, and induces the upregulation of matrix metalloproteinases—particularly MMP-1 and MMP-3 (Matrix metalloproteinases)—which accelerate the degradation of collagen and elastic fibers [83,84]. Additionally, compounds present in tobacco smoke have been shown to facilitate epicutaneous sensitization by promoting Langerhans cell migration, inducing the release of epithelial alarmins such as TSLP (Thymic stromal lymphopoietin) and IL-33, and enhancing Th2- and Th17-skewed immune responses, suggesting a plausible mechanism through which ETS exposure may contribute to the development of atopic eczema [85,86].
Taken together, these mechanistic insights align with epidemiological observations demonstrating that early-life ETS exposure is a robust risk factor for asthma-related outcomes and may also contribute to the development of atopic eczema, particularly in genetically susceptible children. Numerous cohort and observational studies consistently show that both prenatal and postnatal ETS exposure are associated with impaired lung development, increased wheezing, reduced lung function, and a higher risk of developing asthma [21,22,87,88,89,90,91,92,93]. In contrast, the evidence linking ETS exposure to atopic eczema is more heterogeneous: although several studies report elevated risk with active or passive smoking, the overall findings remain less consistent [23,24,25,34]. In our study, early-life ETS exposure emerged as an independent predictor of the combined asthma–eczema phenotype, even after adjustment for genetic variation. By contrast, ETS exposure did not significantly increase the risk of eczema alone, suggesting that environmental exposures may preferentially influence developmental pathways leading to the expression of specific phenotypes. It can be assumed that ETS may act as a “second hit” that facilitates the transition from skin inflammation to respiratory manifestations, consistent with models of sequential allergic disease progression.
The early-life timing of ETS exposure is particularly noteworthy. The first two years of life represent a critical window for gene–environment interactions, as the lungs, skin, epithelial barrier, immune, and neuroendocrine systems still undergo rapid maturation, rendering infants especially vulnerable to the adverse effects of ETS that may exert long-lasting impact [94,95,96]. These considerations highlight the need for further research focusing on early-life gene–environment interactions in pediatric populations, an area that remains insufficiently explored despite persistently high levels of tobacco smoke exposure in children. Another implication of our work is that studying precise phenotypes and comorbidities is a critical feature in genetic research, especially when investigating gene–environment interactions, which often manifest in phenotype-specific ways.
The strengths of this study include its prospective design, which enabled repeated monitoring of environmental exposures and clinical outcomes within the same cohort; detailed phenotypic characterization; and the use of well-matched healthy controls recruited from the general population. The independent replication of interaction signals previously reported in a GWIS further adds credibility to our findings and demonstrates the utility of targeted replication in hypothesis-driven gene–environment research. However, several limitations should be acknowledged. The relatively small sample size reduces statistical power and increases the likelihood of both false-negative findings and spurious false-positive associations, particularly for interaction analyses. Statistical power in gene–environment analyses is typically lower than in main-effect genetic analyses, particularly when environmental exposures are dichotomized and unevenly distributed across subgroups, as was the case for early-life ETS in our cohort. Consistent with this, post hoc power analysis indicated moderate-to-low power for detecting main genetic effects, particularly for NRXN1, which should be considered when interpreting null findings. ETS exposure was assessed by parental self-report and may therefore affect the precision of interaction effect estimates by introducing measurement error, including recall and social desirability bias. Moreover, detailed information on exposure intensity, duration, and prenatal timing was unavailable. While this approach is commonly used in pediatric epidemiological studies and enables the inclusion of environmental factors when more detailed exposure data are lacking, it may introduce non-differential exposure misclassification. Such misclassification is expected to bias effect estimates toward the null and further reduce statistical power, particularly for detecting gene–environment interactions. Consequently, the observed genotype × ETS interactions may represent conservative estimates of the true effects. At the same time, the use of a dichotomous exposure variable precludes evaluation of dose–response relationships and limits inference regarding critical exposure windows. In addition, other environmental factors that may interact with genetic susceptibility were not captured. Despite these limitations, post hoc analyses suggested comparatively higher power for detecting genotype × ETS interaction effects, likely driven by the relatively large interaction effect sizes observed in the present sample. Nevertheless, given the limited number of children with the asthma–eczema phenotype, the magnitude of these effects should be interpreted with caution. However, these considerations do not diminish the biological plausibility of the observed interactions. Future studies in larger, independent cohorts incorporating quantitative, time-resolved, and prenatal exposure measures, ideally supported by objective biomarkers, as well as functional studies aimed at elucidating the underlying biological mechanisms, are warranted to confirm and refine these findings.
In conclusion, this study provides evidence that genetic variation in TNS1 and NRXN1 may interact synergistically with early-life ETS exposure to increase susceptibility to the combined asthma–eczema phenotype in children. The context-dependent nature of these genetic effects highlights the importance of including gene–environment interactions in studies of complex allergic diseases. We identified TNS1 rs918949 as a locus specifically linked to the comorbid phenotype but not with eczema alone, supporting the idea that asthma–eczema multimorbidity is a distinct biological phenotype rather than the coexistence of two separate conditions. Early-life ETS exposure also emerged as an independent predictor of the combined phenotype, underscoring the importance of considering environmental factors when assessing genetic susceptibility in complex atopic conditions. From a prevention standpoint, our findings suggest that early childhood is a critical developmental period during which ETS avoidance may be particularly crucial, especially for children with a genetic predisposition. Future replication studies in larger groups, detailed exposure assessments, and functional analyses of the involved pathways will be necessary to confirm these results and better understand how early-life exposures interact with genetic factors to influence asthma–eczema comorbidity.

4. Materials and Methods

4.1. Study Population

A total of 188 unrelated children (107 males), all younger than 2 years at the time of recruitment, were enrolled in the study. This group included 103 patients with atopic eczema (mean age: 13.2 ± 6.7 months) and 85 healthy control subjects (mean age: 15.3 ± 5.6 months). All participants were prospectively followed at yearly intervals until reaching 6 years of age. The entire study population was of Caucasian ethnicity. The study subjects were recruited from patients attending the Outpatient Clinic for Children at Wroclaw Medical University Hospital and from the general population. Recruitment of the control group was community-based and involved the dissemination of flyers in local nurseries, pediatric and family medicine practices, and during health fairs. Parents who expressed interest were invited to contact the research coordinator by phone. Eligibility of all participants, both cases and controls, was assessed using a structured questionnaire designed to collect comprehensive information on general health status, symptoms of atopic eczema or other allergic diseases, sociodemographic data, exposure to environmental factors including tobacco smoke, and family history of allergic conditions. The subjects with atopic eczema were examined and diagnosed in accordance with the diagnostic criteria proposed by Hanifin and Rajka [97]. The mean (±SD) age at disease onset was 4.6 ± 3.5 months. Asthma was defined as physician-diagnosed asthma ever by the age of 6 years. Asthma at 6 years of age was defined by the presence of a previous asthma diagnosis made by a doctor during follow-up visits and one or more wheezing episodes during the 12 months preceding the analysis. Eczema-associated asthma at 6 years of age was defined by the presence of a previous physician’s diagnosis of eczema according to the criteria established by Hanifin and Rajka, visible eczema at the time of follow-up together with the presence of a previous asthma diagnosis made by a doctor during the follow-up visits, and 1 or more wheezing episodes during the 12 months before the analysis. The control group, matched to the case group for age and sex, consisted of healthy children who met the following inclusion criteria: absence of any clinical symptoms of atopic eczema and asthma, and no reported family history of allergic diseases.
Exposure to environmental tobacco smoke (ETS) during early childhood was defined as a positive response by the child’s mother or father to the question, “Did you or your child’s other parent smoke when your child was younger than 2 years of age?”
Serum samples obtained from all participants were analyzed for total and allergen-specific IgE concentrations. The panel of specific IgE measurements included the 10 most common inhalant and 10 most common food allergens. Total serum IgE concentrations were determined using a commercial chemiluminescent immunoassay (IMMULITE 2000 Total IgE; Diagnostic Products Corporation, Los Angeles, CA, USA). Allergen-specific IgE levels were assessed using a standardized enzyme immunoassay (Polycheck; BIOCHECK, Munster, Germany). Allergic sensitization was defined as the presence of specific IgE to at least one of the tested allergens at a concentration ≥ 0.7 kU/L (class II or higher).

4.2. Genotyping

Genotyping for the single-nucleotide polymorphisms (SNPs) rs918949 and rs10194978, located in the TNS1 (2q35) and NRXN1 (2p16) genes, respectively, was performed in 188 subjects. Genomic DNA was extracted from EDTA-anticoagulated whole blood samples using the QIAamp DNA Blood Mini Kit (QIAGEN GmbH, Hilden, Germany) according to the manufacturer’s instructions. All genotyping assays were conducted using the LightSNiP assay (TibMolbiol, Berlin, Germany). Polymerase chain reaction (PCR) amplification was carried out in a final volume of 10 µL containing 1 µL of genomic DNA (15–60 ng/µL), 0.5 µL of reagent mix containing specific primers and SimpleProbe® probes at optimized concentrations, 0.8 µL of MgCl2, and 1 µL of LightCycler® FastStart DNA Master HybProbe (Roche Applied Science, Mannheim, Germany). Reactions were performed on a LightCycler 1.5 platform (Roche Applied Science, Mannheim, Germany). For quality assurance, positive controls representing each genotype, as well as negative (no-template) controls, were included in every run.

4.3. Statistical Analysis

The Hardy–Weinberg equilibrium was assessed among control subjects using the χ2 goodness-of-fit test to compare observed and expected genotype frequencies. Differences in genotype distributions and demographic characteristics between case and control groups were analyzed using the χ2 test or Fisher’s exact test, as appropriate. Associations between genotypes or alleles and disease status were evaluated by calculating odds ratios (ORs) with corresponding 95% confidence intervals (CIs) and p-values, using logistic regression analysis. Both crude and adjusted ORs were estimated, with adjustments made for age, sex, and family history of atopy. Statistical significance was defined as a p-value < 0.05. Associations between SNPs and disease phenotypes were evaluated under additive, dominant, recessive, and codominant models of inheritance using logistic regression analyses adjusted for potential confounders. The χ2 test or Fisher’s exact test was applied to assess the combined effect of genotype pairs. Gene–environment interactions between the tested SNPs and early-life exposure to tobacco smoke in relation to the combined asthma–eczema were assessed using logistic regression models including interaction terms (SPSS). To determine whether an interaction between two risk factors (A and B) was present, the relative excess risk due to interaction (RERI), the attributable proportion due to interaction (AP), the synergy index (S), and the ratio of relative risks (RRs) were calculated, as recommended by Knol et al. [98,99]. Interaction was defined as a deviation from either the additive or multiplicative model. On the additive scale, RERI and AP values greater than zero indicated a superadditive (positive) interaction, whereas values less than zero indicated a subadditive (negative) interaction. Similarly, an S value greater than 1 indicated a superadditive effect, while an S value less than 1 indicated a subadditive effect. On the multiplicative scale, a ratio of ORs greater than 1 suggested a positive interaction, whereas a ratio less than 1 indicated a negative interaction. Post hoc power calculations were performed for both main genetic effects and gene–environment interaction effects using Wald-based normal approximations derived from the logistic regression models. Odds ratios were transformed to the log-odds scale (β = ln [OR]), and standard errors were obtained from the reported 95% confidence intervals or directly from the regression output. Achieved power for two-sided tests at α = 0.05 was approximated as Φ (|β|/SE − 1.96), where Φ denotes the standard normal cumulative distribution function. All statistical analyses were performed using STATISTICA software, version 9.0 (StatSoft Inc., Tulsa, OK, USA), and SPSS Statistics software, version 11.1 (SPSS Inc., Chicago, IL, USA).
The study received approval from the Ethics Committee of Wroclaw Medical University, Wroclaw, Poland (protocol codes: 392/11, 631/21, and 264/24), and informed written consent, including consent for genetic studies, was obtained from all subjects before they participated in the testing.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms27010346/s1.

Author Contributions

Conceptualization, A.D. and B.S.; Data curation, A.D. and H.D.; Formal analysis, A.D., H.D. and A.D.-C.; Funding acquisition, A.D.; Investigation, A.D., H.D. and A.D.-C.; Methodology, A.D.; Project administration, A.D.; Resources, A.D. and H.D.; Supervision, B.S.; Validation, A.D. and A.D.-C.; Visualization, A.D. and H.D.; Writing—original draft, A.D.; Writing—review and editing, B.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding. The presented research results, carried out within the project, according to the records in the Wroclaw Medical University Simple system, with the number SUBZ.A220.24.057.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of Wroclaw Medical University, Wroclaw, Poland (protocol codes: 392/11; date of approval 8 September 2011 and 631/21; date of approval 27 July 2021; protocol codes: 264/24; date of approval 18 April 2024).

Informed Consent Statement

Informed consent, including consent for genetic studies, was obtained from all subjects involved in the study.

Data Availability Statement

The original contributions presented in this study are included in the article/Supplementary Materials. Further inquiries can be directed to the corresponding author.

Acknowledgments

We would like to thank all the individuals who generously shared their time to participate in this research.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
ETSEnvironmental tobacco smoke
TNS1Tensin-1
NRXN1Neurexin 1
GWASGenome-wide association study
GWISGenome-wide interaction study
SNPSingle nucleotide polymorphism
OROdds ratio
CIConfidence interval
RERIRelative excess risk due to interaction
APAttributable proportion due to interaction
SSynergy index
RRRatio of relative risk
FLGFilaggrin—filament aggregating protein
IL-Interleukins
KIF3AKinesin Family Member 3A
AP5B1Adaptor-Related Protein Complex 5 Subunit Beta 1
OVOL1Ovo-like transcriptional repressor 1
LRRC32Leucine-Rich Repeat Containing 32
IKZF3IKAROS Family Zinc Finger 3
EFHC1EF-hand domain containing 1
TMTC2Transmembrane O-Mannosyltransferase Targeting Cadherins 2
OAC2Aconitase-2
CACNA2D3Gene encoding a member of the alpha-2/delta subunit family
TGF-βTransforming growth factor beta
FEV1Forced expiratory volume in one second
FVCForced vital capacity
DNADeoxyribonucleic acid
MMP Matrix metalloproteinases
TSLPThymic stromal lymphopoietin

References

  1. Leshem, Y.A.; Weil, C.; Busse, W.W.; Beck, L.A.; Chodick, G.; Cyr, S.L.; Bosman, K.; Lubwama, R. Real-world onset of atopic comorbidities relative to atopic dermatitis in pediatric patients. Dermatol. Ther. 2025, 15, 3425–3436. [Google Scholar] [CrossRef]
  2. Narla, S.; Silverberg, J.I. Current updates in the epidemiology and comorbidities of atopic dermatitis. Ann. Allergy Asthma Immunol. 2025, 135, 511–520. [Google Scholar] [CrossRef]
  3. Zhou, W.; Tang, J. Prevalence and risk factors for childhood asthma: A systematic review and meta-analysis. BMC Pediatr. 2025, 25, 50. [Google Scholar] [CrossRef] [PubMed]
  4. Paller, A.S.; Spergel, J.M.; Mina-Osorio, P.; Irvine, A.D. The atopic march and atopic multimorbidity: Many trajectories, many pathways. J. Allergy Clin. Immunol. 2019, 143, 46–55. [Google Scholar] [CrossRef]
  5. Schoettler, N. Advances in asthma and allergic disease genetics. Curr. Opin. Allergy Clin. Immunol. 2025, 25, 58–65. [Google Scholar] [CrossRef]
  6. Lawson, L.P.; Parameswaran, S.; Panganiban, R.A.; Constantine, G.M.; Weirauch, M.T.; Kottyan, L.C. Update on the genetics of allergic diseases. J. Allergy Clin. Immunol. 2025, 155, 1738–1752. [Google Scholar] [CrossRef] [PubMed]
  7. Maggi, E.; Parronchi, P.; Azzarone, B.G.; Moretta, L. A pathogenic integrated view explaining the different endotypes of asthma and allergic disorders. Allergy 2022, 77, 3267–3292. [Google Scholar] [CrossRef]
  8. Falcon, R.M.G.; Caoili, S.E.C. Immunologic, genetic, and ecological interplay of factors involved in allergic diseases. Front. Allergy 2023, 4, 1215616. [Google Scholar] [CrossRef] [PubMed]
  9. Mrkić Kobal, I.; Plavec, D.; Vlašić Lončarić, Ž.; Jerković, I.; Turkalj, M. Atopic march or atopic multimorbidity—Overview of current research. Medicina 2024, 60, 21. [Google Scholar] [CrossRef]
  10. Zheng, T.; Yu, J.; Oh, M.H.; Zhu, Z. The atopic march: Progression from atopic dermatitis to allergic rhinitis and asthma. Allergy Asthma Immunol. Res. 2011, 3, 67–73. [Google Scholar] [CrossRef]
  11. Maiello, N.; Comberiati, P.; Giannetti, A.; Ricci, G.; Carello, R.; Galli, E. New directions in understanding atopic march starting from atopic dermatitis. Children 2022, 9, 450. [Google Scholar] [CrossRef]
  12. Ferreira, M.A.; Vonk, J.M.; Baurecht, H.; Marenholz, I.; Tian, C.; Hoffman, J.D.; Helmer, Q.; Tillander, A.; Ullemar, V.; van Dongen, J.; et al. Shared genetic origin of asthma, hay fever and eczema elucidates allergic disease biology. Nat. Genet. 2017, 49, 1752–1757. [Google Scholar] [CrossRef]
  13. Zhu, Z.; Lee, P.H.; Chaffin, M.D.; Chung, W.; Loh, P.R.; Lu, Q.; Christiani, D.C.; Liang, L. A genome-wide cross-trait analysis from UK Biobank highlights the shared genetic architecture of asthma and allergic diseases. Nat. Genet. 2018, 50, 857–864. [Google Scholar] [CrossRef]
  14. Wang, D.; Liu, S.; Wu, Q.; Jiang, Y.; Zhang, C.; Ye, W.; Peng, B.; Xie, H.; Li, W.; Wang, Y.; et al. Identification of shared genetic loci for asthma, allergic rhinitis, and pollinosis in East Asians. Sci. Rep. 2025, 15, 6068. [Google Scholar] [CrossRef]
  15. Johansson, Å.; Rask-Andersen, M.; Karlsson, T.; Ek, W.E. Genome-wide association analysis of 350,000 Caucasians from the UK Biobank identifies novel loci for asthma, hay fever and eczema. Hum. Mol. Genet. 2019, 28, 4022–4041. [Google Scholar] [CrossRef]
  16. Marenholz, I.; Esparza-Gordillo, J.; Rüschendorf, F.; Bauerfeind, A.; Strachan, D.P.; Spycher, B.D.; Baurecht, H.; Margaritte-Jeannin, P.; Sääf, A.; Kerkhof, M.; et al. Meta-analysis identifies seven susceptibility loci involved in the atopic march. Nat. Commun. 2015, 6, 8804. [Google Scholar] [CrossRef] [PubMed]
  17. Margaritte-Jeannin, P.; Budu-Aggrey, A.; Ege, M.; Madore, A.M.; Linhard, C.; Mohamdi, H.; von Mutius, E.; Granell, R.; Demenais, F.; Laprise, C.; et al. Identification of OCA2 as a novel locus for the co-morbidity of asthma-plus-eczema. Clin. Exp. Allergy 2022, 52, 70–81. [Google Scholar] [CrossRef]
  18. Strzelak, A.; Ratajczak, A.; Adamiec, A.; Feleszko, W. Tobacco smoke induces and alters immune responses in the lung triggering inflammation, allergy, asthma and other lung diseases: A mechanistic review. Int. J. Environ. Res. Public Health 2018, 15, 1033. [Google Scholar] [CrossRef] [PubMed]
  19. Arnson, Y.; Shoenfeld, Y.; Amital, H. Effects of tobacco smoke on immunity, inflammation and autoimmunity. J. Autoimmun. 2010, 34, J258–J265. [Google Scholar] [CrossRef]
  20. Lee, J.; Taneja, V.; Vassallo, R. Cigarette smoking and inflammation: Cellular and molecular mechanisms. J. Dent. Res. 2012, 91, 142–149. [Google Scholar] [CrossRef] [PubMed]
  21. Lu, W.; Rylance, S.; Schotte, K.; Aarsand, R.; Lebedeva, E.; Bill, W.; Han, J.; Lam, D.C.; Soriano, J.B.; Yorgancioglu, A.; et al. Tobacco and asthma: Presenting the World Health Organization (WHO) tobacco knowledge summary. Subst. Abus. Treat. Prev. Policy 2025, 20, 34. [Google Scholar] [CrossRef]
  22. Agache, I.; Ricci-Cabello, I.; Canelo-Aybar, C.; Annesi-Maesano, I.; Cecchi, L.; Biagioni, B.; Chung, K.F.; D’Amato, G.; Damialis, A.; Del Giacco, S.; et al. The impact of exposure to tobacco smoke and e-cigarettes on asthma-related outcomes: Systematic review informing the EAACI guidelines on environmental science for allergic diseases and asthma. Allergy 2024, 79, 2346–2365. [Google Scholar] [CrossRef]
  23. Al-Alusi, N.A.; Ramirez, F.D.; Chan, L.N.; Ye, M.; Langan, S.M.; McCulloch, C.; Abuabara, K. Atopic dermatitis and tobacco smoke exposure during childhood and adolescence. J. Allergy Clin. Immunol. Glob. 2024, 4, 100345. [Google Scholar] [CrossRef]
  24. Kantor, R.; Kim, A.; Thyssen, J.P.; Silverberg, J.I. Association of atopic dermatitis with smoking: A systematic review and meta-analysis. J. Am. Acad. Dermatol. 2016, 75, 1119–1125.e1. [Google Scholar] [CrossRef]
  25. Lau, H.X.; Lee, J.W.; Yap, Q.V.; Chan, Y.H.; Samuel, M.; Loo, E.X.L. Smoke exposure and childhood atopic eczema and food allergy: A systematic review and meta-analysis. Pediatr. Allergy Immunol. 2023, 34, e14010. [Google Scholar] [CrossRef] [PubMed]
  26. Koppelman, G.H.; Pino-Yanes, M.; Melén, E.; Powell, P.; Bracke, K.R.; Celedón, J.C.; Brusselle, G.G. Genetic and environmental risk factors for asthma: Towards prevention. Lancet Respir. Med. 2025, 13, 1011–1025. [Google Scholar] [CrossRef]
  27. Turner, S. Gene–environment interactions—What can these tell us about the relationship between asthma and allergy? Front. Pediatr. 2017, 5, 118. [Google Scholar] [CrossRef]
  28. Custovic, A.; Marinho, S.; Simpson, A. Gene–environment interactions in the development of asthma and atopy. Expert Rev. Respir. Med. 2012, 6, 301–308. [Google Scholar] [CrossRef]
  29. Martin, M.J.; Estravís, M.; García-Sánchez, A.; Dávila, I.; Isidoro-García, M.; Sanz, C. Genetics and epigenetics of atopic dermatitis: An updated systematic review. Genes 2020, 11, 442. [Google Scholar] [CrossRef] [PubMed]
  30. Tamari, M.; Hirota, T. Genome-wide association studies of atopic dermatitis. J. Dermatol 2014, 41, 213–220. [Google Scholar] [CrossRef]
  31. Stemmler, S.; Hoffjan, S. Trying to understand the genetics of atopic dermatitis. Mol. Cell. Probes 2016, 30, 374–385. [Google Scholar] [CrossRef]
  32. Herrera-Luis, E.; Martin-Almeida, M.; Pino-Yanes, M. Asthma—Genomic advances toward risk prediction. Clin. Chest Med. 2024, 45, 599–610. [Google Scholar] [CrossRef]
  33. Kim, K.W.; Ober, C. Lessons learned from GWAS of asthma. Allergy Asthma Immunol. Res. 2019, 11, 170–187. [Google Scholar] [CrossRef]
  34. Moffatt, M.F.; Gut, I.G.; Demenais, F.; Strachan, D.P.; Bouzigon, E.; Heath, S.; von Mutius, E.; Farrall, M.; Lathrop, M.; Cookson, W.O.C.M.; et al. A large-scale, consortium-based genomewide association study of asthma. N. Engl. J. Med. 2010, 363, 1211–1221. [Google Scholar] [CrossRef] [PubMed]
  35. Kim, S.Y.; Sim, S.; Choi, H.G. Atopic dermatitis is associated with active and passive cigarette smoking in adolescents. PLoS ONE 2017, 12, e0187453. [Google Scholar] [CrossRef]
  36. Margaritte-Jeannin, P.; Vernet, R.; Budu-Aggrey, A.; Ege, M.; Madore, A.M.; Linhard, C.; Mohamdi, H.; von Mutius, E.; Granell, R.; Demenais, F.; et al. TNS1 and NRXN1 genes interacting with early-life smoking exposure in asthma-plus-eczema susceptibility. Allergy Asthma Immunol. Res. 2023, 15, 779–794. [Google Scholar] [CrossRef] [PubMed]
  37. Kim, D.Y.; Lee, S.; Jung, J.H.; Sub, Y.; Lee, S.; Kim, E.G.; Kim, M.N.; Kim, S.Y.; Kim, Y.H.; Sohn, M.H.; et al. GWAS identifies CACNA2D3 associated with asthma and atopic dermatitis multimorbidity in children. Allergy 2025, 80, 1776–1781. [Google Scholar] [CrossRef]
  38. Lu, H.F.; Zhou, Y.C.; Yang, L.T.; Zhou, Q.; Wang, X.J.; Qiu, S.Q.; Cheng, B.H.; Zeng, X.H. Involvement and repair of epithelial barrier dysfunction in allergic diseases. Front. Immunol. 2024, 15, 1348272. [Google Scholar] [CrossRef] [PubMed]
  39. Zeyneloglu, C.; Babayev, H.; Ogulur, I.; Ardicli, S.; Pat, Y.; Yazici, D.; Zhao, B.; Chang, L.; Liu, X.; D’Avino, P.; et al. The epithelial barrier theory proposes a comprehensive explanation for the origins of allergic and other chronic noncommunicable diseases. FEBS Lett. 2025, 599, 3208–3243. [Google Scholar] [CrossRef]
  40. Losol, P.; Sokolowska, M.; Hwang, Y.K.; Ogulur, I.; Mitamura, Y.; Yazici, D.; Pat, Y.; Radzikowska, U.; Ardicli, S.; Yoon, J.E.; et al. Epithelial barrier theory: The role of exposome, microbiome, and barrier function in allergic diseases. Allergy Asthma Immunol. Res. 2023, 15, 705–724. [Google Scholar] [CrossRef]
  41. Wang, Z.; Ye, J.; Dong, F.; Cao, L.; Wang, M.; Sun, G. TNS1: Emerging insights into its domain function, biological roles, and tumors. Biology 2022, 11, 1571. [Google Scholar] [CrossRef]
  42. Bernau, K.; Torr, E.E.; Evans, M.D.; Aoki, J.K.; Ngam, C.R.; Sandbo, N. Tensin 1 is essential for myofibroblast differentiation and extracellular matrix formation. Am. J. Respir. Cell Mol. Biol. 2017, 56, 465–476. [Google Scholar] [CrossRef]
  43. Stylianou, P.; Clark, K.; Gooptu, B.; Smallwood, D.; Brightling, C.E.; Amrani, Y.; Roach, K.M.; Bradding, P. Tensin1 expression and function in chronic obstructive pulmonary disease. Sci. Rep. 2019, 9, 18942. [Google Scholar] [CrossRef]
  44. Wain, L.V.; Shrine, N.; Artigas, M.S.; Erzurumluoglu, A.M.; Noyvert, B.; Bossini-Castillo, L.; Obeidat, M.; Henry, A.P.; Portelli, M.A.; Hall, R.J.; et al. Genome-wide association analyses for lung function and chronic obstructive pulmonary disease identify new loci and potential druggable targets. Nat. Genet. 2017, 49, 416–425. [Google Scholar] [CrossRef] [PubMed]
  45. Shrine, N.; Guyatt, A.L.; Erzurumluoglu, A.M.; Jackson, V.E.; Hobbs, B.D.; Melbourne, C.A.; Batini, C.; Fawcett, K.A.; Song, K.; Sakornsakolpat, P.; et al. New genetic signals for lung function highlight pathways and chronic obstructive pulmonary disease associations across multiple ancestries. Nat. Genet. 2019, 51, 481–493, Erratum in Nat. Genet. 2024, 56, 1032–1033. [Google Scholar] [CrossRef] [PubMed]
  46. Brooks, S.; Mittler, S.; Hamilton, D.W. Contact guidance of connective tissue fibroblasts on submicrometer anisotropic topographical cues is dependent on tissue of origin, β1 integrins, and tensin-1 recruitment. ACS Appl. Mater. Interfaces 2023, 15, 19817–19832. [Google Scholar] [CrossRef] [PubMed]
  47. Saintigny, G.; Bernard, F.X.; Juchaux, F.; Pedretti, N.; Mahé, C. Reduced expression of the adhesion protein tensin1 in cultured human dermal fibroblasts affects collagen gel contraction. Exp. Dermatol 2008, 17, 788–789. [Google Scholar] [CrossRef]
  48. Ng, H.Y.; Wu, Y.S.; Biswas, M.; Sim, M.S. Deciphering the molecular clock: Exploring molecular mechanisms and genetic influences on skin ageing. Biogerontology 2025, 26, 153. [Google Scholar] [CrossRef]
  49. Pankov, R.; Cukierman, E.; Katz, B.Z.; Matsumoto, K.; Lin, D.C.; Lin, S.; Hahn, C.; Yamada, K.M. Integrin dynamics and matrix assembly: Tensin-dependent translocation of α5β1 integrins promotes early fibronectin fibrillogenesis. J. Cell Biol. 2000, 148, 1075–1090. [Google Scholar] [CrossRef]
  50. Wain, L.V.; Shrine, N.; Miller, S.; Jackson, V.E.; Ntalla, I.; Soler Artigas, M.; Billington, C.K.; Kheirallah, A.K.; Allen, R.; Cook, J.P.; et al. Novel insights into the genetics of smoking behaviour, lung function, and chronic obstructive pulmonary disease (UK BiLEVE): A genetic association study in UK Biobank. Lancet Respir. Med. 2015, 3, 769–781, Erratum in Lancet Respir. Med. 2016, 4, e4. [Google Scholar] [CrossRef]
  51. Hancock, D.B.; Artigas, M.S.; Gharib, S.A.; Henry, A.; Manichaikul, A.; Ramasamy, A.; Loth, D.W.; Imboden, M.; Koch, B.; McArdle, W.L.; et al. Genome-wide joint meta-analysis of SNP and SNP-by-smoking interaction identifies novel loci for pulmonary function. PLoS Genet. 2012, 8, e1003098. [Google Scholar] [CrossRef] [PubMed]
  52. Kerkhof, M.; Boezen, H.M.; Granell, R.; Henry, A.; Manichaikul, A.; Ramasamy, A.; Loth, D.W.; Imboden, M.; Koch, B.; McArdle, W.L.; et al. Transient early wheeze and lung function in early childhood associated with chronic obstructive pulmonary disease genes. J. Allergy Clin. Immunol. 2014, 133, 68–76.e1–4. [Google Scholar] [CrossRef] [PubMed]
  53. Lutz, S.M.; Cho, M.H.; Young, K.; Hersh, C.P.; Castaldi, P.J.; McDonald, M.L.; Regan, E.; Mattheisen, M.; DeMeo, D.L.; Parker, M.; et al. A genome-wide association study identifies risk loci for spirometric measures among smokers of European and African ancestry. BMC Genet. 2015, 16, 138. [Google Scholar] [CrossRef] [PubMed]
  54. Herrera-Luis, E.; Benke, K.; Volk, H.; Ladd-Acosta, C.; Wojcik, G.L. Gene–environment interactions in human health. Nat. Rev. Genet. 2024, 25, 768–784. [Google Scholar] [CrossRef]
  55. Reissner, C.; Runkel, F.; Missler, M. Neurexins. Genome Biol. 2013, 14, 213. [Google Scholar] [CrossRef]
  56. Gomez, A.M.; Traunmüller, L.; Scheiffele, P. Neurexins: Molecular codes for shaping neuronal synapses. Nat. Rev. Neurosci. 2021, 22, 137–151. [Google Scholar] [CrossRef]
  57. Fuccillo, M.V.; Pak, C. Copy number variants in neurexin genes: Phenotypes and mechanisms. Curr. Opin. Genet. Dev. 2021, 68, 64–70. [Google Scholar] [CrossRef]
  58. Guzman, C.; Mohri, K.; Nakamura, R.; Miyake, M.; Tsuchiya, Y.; Tomii, K.; Watanabe, H. Neuronal and non-neuronal functions of the synaptic cell adhesion molecule neurexin in Nematostella vectensis. Nat. Commun. 2024, 15, 6495. [Google Scholar] [CrossRef]
  59. Boucard, A.A.; Ko, J.; Südhof, T.C. High affinity neurexin binding to cell adhesion G-protein-coupled receptor CIRL1/latrophilin-1 produces an intercellular adhesion complex. J. Biol. Chem. 2012, 287, 9399–9413. [Google Scholar] [CrossRef]
  60. Hernandez-Pacheco, N.; Kere, M.; Melén, E. Gene–environment interactions in childhood asthma revisited; expanding the interaction concept. Pediatr. Allergy Immunol. 2022, 33, e13780. [Google Scholar] [CrossRef]
  61. Morales, E.; Duffy, D. Genetics and gene–environment interactions in childhood and adult onset asthma. Front. Pediatr. 2019, 7, 499. [Google Scholar] [CrossRef]
  62. London, S.J.; Melén, E. Genomic interactions with exposure to inhaled pollutants. J. Allergy Clin. Immunol. 2019, 143, 2011–2013.e1. [Google Scholar] [CrossRef]
  63. Morissette, M.C.; Lamontagne, M.; Bérubé, J.C.; Gaschler, G.; Williams, A.; Yauk, C.; Couture, C.; Laviolette, M.; Hogg, J.C.; Timens, W.; et al. Impact of cigarette smoke on the human and mouse lungs: A gene-expression comparison study. PLoS ONE 2014, 9, e92498. [Google Scholar] [CrossRef]
  64. Brody, J.S. Transcriptome alterations induced by cigarette smoke. Int. J. Cancer 2012, 131, 2754–2762. [Google Scholar] [CrossRef] [PubMed]
  65. Zhou, Z.; Chen, P.; Peng, H. Are healthy smokers really healthy? Tob. Induc. Dis. 2016, 14, 35. [Google Scholar] [CrossRef] [PubMed]
  66. Vlachou, M.; Kyrkou, G.; Georgakopoulou, V.E.; Kapetanaki, A.; Vivilaki, V.; Spandidos, D.A.; Diamanti, A. Smoke signals in the genome: Epigenetic consequences of parental tobacco exposure (Review). Biomed. Rep. 2025, 23, 146. [Google Scholar] [CrossRef]
  67. Kaur, G.; Begum, R.; Thota, S.; Batra, S. A systematic review of smoking-related epigenetic alterations. Arch. Toxicol. 2019, 93, 2715–2740. [Google Scholar] [CrossRef] [PubMed]
  68. Zong, D.; Liu, X.; Li, J.; Ouyang, R.; Chen, P. The role of cigarette smoke-induced epigenetic alterations in inflammation. Epigenetics Chromatin 2019, 12, 65. [Google Scholar] [CrossRef]
  69. Pappas, R.S. Toxic elements in tobacco and in cigarette smoke: Inflammation and sensitization. Metallomics 2011, 3, 1181–1198. [Google Scholar] [CrossRef]
  70. Rom, O.; Avezov, K.; Aizenbud, D.; Reznick, A.Z. Cigarette smoking and inflammation revisited. Respir. Physiol. Neurobiol. 2013, 187, 5–10. [Google Scholar] [CrossRef]
  71. Shiels, M.S.; Katki, H.A.; Freedman, N.D.; Purdue, M.P.; Wentzensen, N.; Trabert, B.; Kitahara, C.M.; Furr, M.; Li, Y.; Kemp, T.J.; et al. Cigarette smoking and variations in systemic immune and inflammation markers. J. Natl. Cancer Inst. 2014, 106, dju294. [Google Scholar] [CrossRef] [PubMed]
  72. Liu, Y.; Di, Y.P. Effects of second hand smoke on airway secretion and mucociliary clearance. Front. Physiol. 2012, 3, 342. [Google Scholar] [CrossRef] [PubMed]
  73. Bhalla, D.K.; Hirata, F.; Rishi, A.K.; Gairola, C.G. Cigarette smoke, inflammation, and lung injury: A mechanistic perspective. J. Toxicol. Environ. Health B Crit. Rev. 2009, 12, 45–64. [Google Scholar] [CrossRef]
  74. Gangl, K.; Reininger, R.; Bernhard, D.; Campana, R.; Pree, I.; Reisinger, J.; Kneidinger, M.; Kundi, M.; Dolznig, H.; Thurnher, D.; et al. Cigarette smoke facilitates allergen penetration across respiratory epithelium. Allergy 2009, 64, 398–405. [Google Scholar] [CrossRef]
  75. Lugg, S.T.; Scott, A.; Parekh, D.; Naidu, B.; Thickett, D.R. Cigarette smoke exposure and alveolar macrophages: Mechanisms for lung disease. Thorax 2022, 77, 94–101. [Google Scholar] [CrossRef]
  76. Tamimi, A.; Serdarevic, D.; Hanania, N.A. The effects of cigarette smoke on airway inflammation in asthma and COPD: Therapeutic implications. Respir. Med. 2012, 106, 319–328. [Google Scholar] [CrossRef]
  77. Wylam, M.E.; Sathish, V.; VanOosten, S.K.; Freeman, M.; Burkholder, D.; Thompson, M.A.; Pabelick, C.M.; Prakash, Y.S. Mechanisms of cigarette smoke effects on human airway smooth muscle. PLoS ONE 2015, 10, e0128778. [Google Scholar] [CrossRef]
  78. Amatngalim, G.D.; Broekman, W.; Daniel, N.M.; van der Vlugt, L.E.; van Schadewijk, A.; Taube, C.; Hiemstra, P.S. Cigarette smoke modulates repair and innate immunity following injury to airway epithelial cells. PLoS ONE 2016, 11, e0166255. [Google Scholar] [CrossRef]
  79. Tilp, C.; Bucher, H.; Haas, H.; Duechs, M.J.; Wex, E.; Erb, K.J. Effects of conventional tobacco smoke and nicotine-free cigarette smoke on airway inflammation, airway remodelling and lung function in a triple allergen model of severe asthma. Clin. Exp. Allergy 2016, 46, 957–972. [Google Scholar] [CrossRef]
  80. Lin, H.; Li, H. How does cigarette smoking affect airway remodeling in asthmatics? Tob. Induc. Dis. 2023, 21, 13. [Google Scholar] [CrossRef]
  81. Huang, Q.; Li, Y.; Li, C.; Zhang, X.; Du, X.; Chen, Y.; Corrigan, C.J.; Wang, W.; Ying, S. Cigarette smoke aggravates asthma via altering airways inflammation phenotypes and remodelling. Clin. Respir. J. 2023, 17, 1316–1327. [Google Scholar] [CrossRef]
  82. Roberts, W. Air pollution and skin disorders. Int. J. Womens Dermatol 2020, 7, 91–97. [Google Scholar] [CrossRef]
  83. Puri, P.; Nandar, S.K.; Kathuria, S.; Ramesh, V. Effects of air pollution on the skin: A review. Indian J. Dermatol. Venereol. Leprol. 2017, 83, 415–423. [Google Scholar] [CrossRef]
  84. Percoco, G.; Patatian, A.; Eudier, F.; Grisel, M.; Bader, T.; Lati, E.; Savary, G.; Picard, C.; Benech, P. Impact of cigarette smoke on physical-chemical and molecular properties of human skin in an ex vivo model. Exp. Dermatol 2021, 30, 1610–1618. [Google Scholar] [CrossRef]
  85. Hong, C.H.; Lee, C.H.; Yu, H.S.; Huang, S.K. Benzopyrene, a major polyaromatic hydrocarbon in smoke fume, mobilizes Langerhans cells and polarizes Th2/17 responses in epicutaneous protein sensitization through the aryl hydrocarbon receptor. Int. Immunopharmacol. 2016, 36, 111–117. [Google Scholar] [CrossRef]
  86. Rajagopalan, P.; Nanjappa, V.; Raja, R.; Jain, A.P.; Mangalaparthi, K.K.; Sathe, G.J.; Babu, N.; Patel, K.; Cavusoglu, N.; Soeur, J.; et al. How does chronic cigarette smoke exposure affect human skin? A global proteomics study in primary human keratinocytes. Omics A J. Integr. Biol. 2016, 20, 615–626. [Google Scholar] [CrossRef] [PubMed]
  87. Ferrante, G.; Antona, R.; Malizia, V.; Montalbano, L.; Corsello, G.; La Grutta, S. Smoke exposure as a risk factor for asthma in childhood: A review of current evidence. Allergy Asthma Proc. 2014, 35, 454–461. [Google Scholar] [CrossRef] [PubMed]
  88. Accordini, S.; Calciano, L.; Johannessen, A.; Portas, L.; Benediktsdóttir, B.; Bertelsen, R.J.; Bråbäck, L.; Carsin, A.E.; Dharmage, S.C.; Dratva, J.; et al. Ageing lungs in European cohorts (ALEC) study. A three-generation study on the association of tobacco smoking with asthma. Int. J. Epidemiol. 2018, 47, 1106–1117. [Google Scholar] [CrossRef]
  89. den Dekker, H.T.; Voort, A.; de Jongste, J.C.; Reiss, I.K.; Hofman, A.; Jaddoe, V.W.V.; Duijts, L. Tobacco smoke exposure, airway resistance, and asthma in school-age children: The Generation R study. Chest 2015, 148, 607–617. [Google Scholar] [CrossRef] [PubMed]
  90. Fernández-Plata, R.; Rojas-Martínez, R.; Martínez-Briseño, D.; García-Sancho, C.; Pérez-Padilla, R. Effect of passive smoking on the growth of pulmonary function and respiratory symptoms in schoolchildren. Rev. Investig. Clin. 2016, 68, 119–127. [Google Scholar] [CrossRef]
  91. Gibbs, K.; Collaco, J.M.; McGrath-Morrow, S.A. Impact of tobacco smoke and nicotine exposure on lung development. Chest 2016, 149, 552–561. [Google Scholar] [CrossRef]
  92. Neuman, Å.; Hohmann, C.; Orsini, N.; Pershagen, G.; Eller, E.; Kjaer, H.F.; Gehring, U.; Granell, R.; Henderson, J.; Heinrich, J.; et al. Maternal smoking in pregnancy and asthma in preschool children: A pooled analysis of eight birth cohorts. Am. J. Respir. Crit. Care Med. 2012, 186, 1037–1043. [Google Scholar] [CrossRef]
  93. Zheng, K.; Wang, X. Early-life risk factors for recurrent wheezing in preschool children: A meta-analysis of 15 cohort studies. Allergy Asthma Proc. 2025, 46, e98–e109. [Google Scholar] [CrossRef]
  94. Schrott, R.; Song, A.; Ladd-Acosta, C. Epigenetics as a biomarker for early-life environmental exposure. Curr. Environ. Health Rep. 2022, 9, 604–624. [Google Scholar] [CrossRef]
  95. Amine, I.; Guillien, A.; Philippat, C.; Anguita-Ruiz, A.; Casas, M.; de Castro, M.; Dedele, A.; Garcia-Aymerich, J.; Granum, B.; Grazuleviciene, R.; et al. Environmental exposures in early life and general health in childhood. Environ. Health 2023, 22, 53. [Google Scholar] [CrossRef]
  96. Mocelin, H.T.; Fischer, G.B.; Bush, A. Adverse early-life environmental exposures and their repercussions on adult respiratory health. J. Pediatr. 2022, 98, S86–S95. [Google Scholar] [CrossRef]
  97. Hanifin, J.M.; Rajka, G. Diagnostic features of atopic dermatitis. Acta Derm. Venereol. 1980, 92, 44–47. [Google Scholar] [CrossRef]
  98. Knol, M.J.; VanderWeele, T.J.; Groenwold, R.H.; Klungel, O.H.; Rovers, M.M.; Grobbee, D.E. Estimating measures of interaction on an additive scale for preventive exposures. Eur. J. Epidemiol. 2011, 26, 433–438. [Google Scholar] [CrossRef]
  99. Knol, M.J.; VanderWeele, T.J. Recommendations for presenting analyses of effect modification and interaction. Int. J. Epidemiol. 2012, 41, 514–520. [Google Scholar] [CrossRef]
Table 1. Characteristics of the study group.
Table 1. Characteristics of the study group.
VariableAtopic EczemaControl
Age at the time of recruitment, month (mean ± SD)13.6 ± 6.715.9 ± 5.6
Gender (male/female)63/4044/41
Allergic sensitization (%)55 (53.4%)11 (12.9%)
Asthma (%)28 (27.2%)0
Atopic hereditary (%)57 (55.3%)0
Serum Total IgE, IU/mL,
geometric mean, 95% CI		24.6 (27.8 ÷ 53.4)17.7 (14.3 ÷ 22.2)
TNS1 rs918949
CC33 (32.0%)24 (28.2%)
CT42 (40.8%)48 (56.5%)
TT28 (27.2%)13 (15.3%)
NRXN1 rs10194978
GG21 (20.4%)20 (23.5%)
GA45 (43.7%)35 (41.2%)
AA37 (35.9%)30 (35.3%)
Tobacco smoke exposure:
YES36 (34.9%)23 (27.1%)
NO67 (65.1%)62 (72.9%)
Table 2. Associations between TNS1 rs918949 genotype and allergic diseases. The control * group comprises all participants who did not belong to the combined phenotype group.
Table 2. Associations between TNS1 rs918949 genotype and allergic diseases. The control * group comprises all participants who did not belong to the combined phenotype group.
TNS1 rs918949
Genotype Status		Genotype/
Risk Allele Frequency
Total n (%)		Phenotype
Eczema
vs. Control		Asthma Plus Eczema vs. ControlAsthma Plus Eczema vs. Control *Asthma Plus Eczema vs. Eczema OnlyEczema Without Asthma vs. Control
Total n (%) 103/188 (54.8%)28/113 (24.8%)28/188 (14.9%)28/103 (27.2%)75/160 (46.9%)
CC

CT


TT		57/188 (30.3%)

90/188 (47.9%)


41/188 (21.8%)
1.0 Reference

p = 0.236
0.64 (0.32 ÷ 1.24)

p = 0.398
1.56 (0.68 ÷ 3.64)
1.0 Reference

p = 0.776
0.83 (0.27 ÷ 2.56)

p = 0.043
3.69 (1.12 ÷ 12.1)
1.0 Reference

p = 1.000
1.06 (0.36 ÷ 3.10)

p = 0.032
3.52 (1.19 ÷ 10.36)
1.0 Reference

p = 0.586
1.41 (0.45 ÷ 4.37)

p = 0.050
3.75 (1.08 ÷ 10.7)
1.0 Reference

p = 0.155
0.59 (0.29 ÷ 1.20)

p = 1.000
1.09 (0.44 ÷ 2.73)
CC vs.
CT + TT		131/188 (69.7%)p = 0.634
0.83 (0.44 ÷ 1.56)		p = 0.623
1.44 (0.52 ÷ 3.99)		p = 0.373
1.72 (0.66 ÷ 4.49)		p = 0.235
2.06 (0.74 ÷ 5.71)		p = 0.312
0.69 (0.36 ÷ 1.36)
CC + CT
vs. TT		41/188 (21.8%)p = 0.072
2.07 (0.99 ÷ 4.30)		p < 0.001
6.75 (2.37 ÷ 19.2)		p = 0.006
3.39 (1.45 ÷ 7.92)		p = 0.045
2.76 (1.10 ÷ 7.01)		p = 0.411
1.50 (0.67 ÷ 3.72)
C vs. T172/376 (45.7%)p = 0.467
1.18 (0.78 ÷ 1.77)		p = 0.031
2.00 (1.08 ÷ 3.71)		p = 0.020
2.04 (1.14 ÷ 3.64)		p = 0.028
2.08 (1.11 ÷ 3.88)		p = 0.910
0.96 (0.62 ÷ 1.50)
Table 3. Associations between NRXN1 rs10194978 genotype and allergic diseases. The control * group comprises all participants who did not belong to the combined phenotype group.
Table 3. Associations between NRXN1 rs10194978 genotype and allergic diseases. The control * group comprises all participants who did not belong to the combined phenotype group.
NRXN1 rs10194978
Genotype Status		Genotype/
Risk Allele Frequency
Total n (%)		Phenotype
Eczema vs.
Control		Asthma Plus Eczema vs. ControlAsthma Plus Eczema vs. Control *Asthma Plus Eczema vs. Eczema OnlyEczema Without Asthma vs. Control
Total n (%) 103/188 (54.8%)28/113 (24.8%)28/188 (14.9%)28/103 (27.2%)75/160 (46.9%)

GG

GA


AA

41/188(21.8%)

80/188(42.6%)


67/188(35.6%)

1.0 Reference

p = 0.700
1.22 (0.57 ÷ 2.61)

p = 0.696
1.17 (0.54 ÷ 2.56)
1.0 Reference

p = 0.388
2.00 (0.58 ÷ 6.91)

p = 0.541
1.67 (0.46 ÷ 6.06)
1.0 Reference

p = 0.295
1.96 (0.60 ÷ 6.39)

p = 0.561
1.62 (0.47 ÷ 5.59)
1.0 Reference

p = 0.383
1.92 (0.54 ÷ 6.76)

p = 0.544
1.57 (0.43 ÷ 5.83)
1.0 Reference

p = 1.000
1.04 (0.46 ÷ 2.34)

p = 1.000
1.06 (0.46 ÷ 2.43)
GG vs.
GA + AA 		147/188 (78.2%)p = 0.723
1.20 (0.60 ÷ 2.40)		p = 0.426
1.85 (0.57 ÷ 5.95)		p = 0.456
1.81 (0.59 ÷ 5.43)		p = 0.420
1.76 (0.54 ÷ 5.77)		p = 1.000
1.05 (0.50 ÷ 2.19)
GG + GA
vs. AA 		67/188 (35.6%)
p = 1.000
1.03 (0.56 ÷ 1.87)		p = 1.000
1.02 (0.42 ÷ 2.48)		p = 1.000
1.01 (0.43 ÷ 2.32)		p = 1.000
0.99 (0.36 ÷ 2.44)		p = 1.000
1.03 (0.54 ÷ 1.97)
G vs. A211/376 (56.1%)p = 0.834
1.05 (0.69 ÷ 1.58)		p = 0.280
1.42 (0.76 ÷ 2.65)		p = 0.306
1.40 (0.78 ÷ 2.52)		p = 0.344
1.38 (0.73 ÷ 2.59)		p = 0.910
1.03 (0.66 ÷ 1.61)
Table 4. Eczema-associated asthma risk after stratification for early-life ETS exposure.
Table 4. Eczema-associated asthma risk after stratification for early-life ETS exposure.
GenotypeETS Exposure (Yes)
p-Value
RR (95% CI)		ETS Exposure (No)
p-Value
RR (95% CI)
TNS1 rs918949
TNS1 rs918949 allele C1.00 (Reference)
-		1.00 (Reference)
-
TNS1 rs918949 allele Tp < 0.001
8.50 (3.02 ÷ 23.95)		p = 0.509
0.71 (0.29 ÷ 1.73)
NRXN1 rs10194978
NRXN1 rs10194978 allele G1.00 (Reference)
-		1.00 (Reference)
-
NRXN1 rs10194978 allele Ap = 0.005
4.23 (1.62 ÷ 11.52)		p = 1.000
1.02 (0.41 ÷ 2.59)
Table 5. Interaction between TNS1 rs918949 and NRXN1 rs10194978 and early-life ETS exposure in eczema-associated asthma. RERI (the relative excess risk due to interaction), AP (the attributable proportion due to interaction), S (the synergy index).
Table 5. Interaction between TNS1 rs918949 and NRXN1 rs10194978 and early-life ETS exposure in eczema-associated asthma. RERI (the relative excess risk due to interaction), AP (the attributable proportion due to interaction), S (the synergy index).
Genotype and Exposure CombinationsEczema-Associated Asthma
n (%)		Control
n (%)		p-ValueRR (95% CI)
TNS1 rs918949 allele C
ETS exposure (No)		14
(25.0%)		62
(36.5%)		-----1.00 (Reference)RERI = 2.84; AP = 0.78;
S = 52.4
ratio of RRs = 4.70
TNS1 rs918949 allele T
ETS exposure (No)		10
(17.8%)		62
(36.5%)		p = 0.5090.75
(0.33 ÷ 1.69)
TNS1 rs918949 allele C
ETS exposure (Yes)		8
(14.3%)		34
(20.0%)		p = 1.0001.03
(0.42 ÷ 2.40)
TNS1 rs918949 allele T
ETS exposure (Yes)		24
(42.9%)		12
(7.0%)		p < 0.0013.62
(2.10 ÷ 6.07)
NRXN1 rs10194978 allele G
ETS exposure (No)		8
(14.3%)		42
(24.7%)		-----1.00 (Reference)RERI = 2.07; AP = 0.55;
S = 2.38
ratio of RRs = 2.16
NRXN1 rs10194978 allele A
ETS exposure (No)		16
(28.6%)		82
(48.2%)		p = 1.0001.05
(0.46 ÷ 2.56)
NRXN1 rs10194978 allele G
ETS exposure (Yes)		12
(21.4%)		33
(19.4%)		p = 0.2201.67
(0.69 ÷ 4.16)
NRXN1 rs10194978 allele A
ETS exposure (Yes)		20
(35.7%)		13
(7.7%)		p < 0.0013.79
(1.85 ÷ 8.14)
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Dębińska, A.; Danielewicz, H.; Drabik-Chamerska, A.; Sozańska, B. Interactive Effects of Genetic Susceptibility and Early-Life Tobacco Smoke Exposure on the Asthma–Eczema Complex Phenotype in Children: 6-Year Follow-Up Case-Control Study. Int. J. Mol. Sci. 2026, 27, 346. https://doi.org/10.3390/ijms27010346

AMA Style

Dębińska A, Danielewicz H, Drabik-Chamerska A, Sozańska B. Interactive Effects of Genetic Susceptibility and Early-Life Tobacco Smoke Exposure on the Asthma–Eczema Complex Phenotype in Children: 6-Year Follow-Up Case-Control Study. International Journal of Molecular Sciences. 2026; 27(1):346. https://doi.org/10.3390/ijms27010346

Chicago/Turabian Style

Dębińska, Anna, Hanna Danielewicz, Anna Drabik-Chamerska, and Barbara Sozańska. 2026. "Interactive Effects of Genetic Susceptibility and Early-Life Tobacco Smoke Exposure on the Asthma–Eczema Complex Phenotype in Children: 6-Year Follow-Up Case-Control Study" International Journal of Molecular Sciences 27, no. 1: 346. https://doi.org/10.3390/ijms27010346

APA Style

Dębińska, A., Danielewicz, H., Drabik-Chamerska, A., & Sozańska, B. (2026). Interactive Effects of Genetic Susceptibility and Early-Life Tobacco Smoke Exposure on the Asthma–Eczema Complex Phenotype in Children: 6-Year Follow-Up Case-Control Study. International Journal of Molecular Sciences, 27(1), 346. https://doi.org/10.3390/ijms27010346

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