Abstract
Objective
Maternal diabetes is a known driver of fetal metabolic programming, yet its impact on offspring cutaneous health remains poorly characterized. We investigated the impact of intrauterine exposure to various maternal diabetes subtypes on the risk of offspring seborrheic dermatitis (SD), and to explore whether this risk is modulated by maternal glycemic control or treatment modality.
Methods
This large-scale, population-based cohort study included 331,335 mother–child pairs. Maternal diabetes subtypes—type 1 (T1DM), type 2 (T2DM), and gestational diabetes mellitus (GDM)—were assessed, along with glycemic control (HbA1c) and pharmacological treatment. SD was identified using physician-documented diagnoses and medication records. Multivariable logistic regression models adjusted for maternal and perinatal factors were used to estimate adjusted odds ratios (aORs).
Results
The incidence of SD was 4.5%. SD was significantly more frequent among offspring of mothers with diabetes (4.9% vs. 4.4%, p < 0.001), primarily driven by GDM. T1DM and T2DM showed similar trends but weren't statistically significant. SD risk did not differ by treatment modality or glycemic control. While overall associations for T1DM and T2DM did not reach significance in the full cohort, age-stratified analysis revealed that maternal diabetes was significantly associated with increased SD risk across all subtypes within the first year of life (T1DM: p = 0.033, T2DM: p = 0.024, GDM: p = 0.019). Multivariable analysis showed maternal diabetes was independently associated with increased SD risk (aOR 1.16, 95% CI 1.08–1.24).
Conclusion
Maternal diabetes is associated with a significantly increased risk of infantile SD across all diabetes subtypes, suggesting that the intrauterine diabetic environment may influence early-life cutaneous homeostasis through metabolic programming of the pilosebaceous unit.
Supplementary Information
The online version contains supplementary material available at 10.1007/s00404-026-08398-z.
Keywords: Gestational diabetes mellitus, Seborrheic dermatitis, Fetal programming, Intrauterine environment, Metabolic imprinting
What does this study add to the clinical work
| Maternal diabetes is associated with an increased risk of infantile seborrheic dermatitis. This risk is most pronounced during the first year of life across all maternal diabetes subtypes, suggesting a time-limited ‘metabolic priming’ effect on the fetal pilosebaceous unit’. |
Introduction
Seborrheic dermatitis (SD) is a prevalent chronic inflammatory skin disorder, affecting sebaceous gland-rich regions. Its global prevalence follows a bimodal distribution, with a significant peak in infants under three months and another during adolescence [1–3]. Beyond its physical symptoms, the visible nature of SD—characterized by erythema and flaking—can lead to psychological distress and significantly impact the quality of life for both affected individuals and their families.
The pathogenesis of SD is multifactorial, involving a complex interplay between sebocyte activity, skin microbiome dysbiosis (particularly Malassezia species), genetic predisposition, and immune dysregulation [2, 4–7]. Recent advances highlight the critical roles of immune-mediated inflammation and skin barrier dysfunction in the onset of the disease [4, 8–11]. Notably, infantile SD emerges during a period of rapid maturation of the skin barrier and innate immune system. Given that fetal sebaceous glands are highly sensitive to maternal hormonal and metabolic signals, early-life metabolic alterations—such as those associated with maternal diabetes—may fundamentally influence disease susceptibility.
Accumulating evidence suggests that maternal diabetes during pregnancy exerts a long-term influence on offspring health through fetal immune and metabolic programming [12–22]. While maternal diabetes has been linked to an increased risk of other inflammatory skin conditions, such as atopic dermatitis [16, 23], its association with offspring SD remains unexplored. The objective of this study is to elucidate the association between pregestational and gestational maternal diabetes and the risk of SD in offspring, utilizing a large population-based cohort to address this significant knowledge gap.
Materials and methods
Data source
This retrospective cohort study was conducted using the Clalit Health Services (CHS) data-sharing platform, powered by MDClone (https://www.mdclone.com). The platform employs advanced algorithms to extract and de-identify data from electronic medical records, thereby ensuring both patient privacy and data integrity. The study protocol was approved by the Ethics Committee of Soroka University Medical Center (approval No. 0049–25-SOR).
Study population
CHS is the largest health organization in Israel, providing care for approximately 4.8 million members—about 51% of the national population. The study population included all live births recorded between January 1, 2010, and December 31, 2023. Children were followed until December 31, 2024, to guarantee at least one year of follow-up for each child.
Eligibility required both the infant and mother to be CHS members during the study period. The initial dataset contained 352,299 live births. After excluding 20,964 multiple gestations, the final analytic sample consisted of 331,335 singleton births.
Variable definitions
The principal exposure of interest was maternal diabetes during pregnancy, defined according to International Classification of Diseases, 10th Revision (ICD-10) codes documented prior to delivery or by a recorded prescription for anti-diabetic medication before delivery. The diabetes-exposed group was subsequently categorized into type 1 diabetes (T1DM), type 2 diabetes (T2DM), and gestational diabetes mellitus (GDM) based on ICD diagnoses recorded in the medical file. The primary study outcome was a diagnosis of SD in offspring, identified through ICD-10 codes available in the CHS database. Diagnoses were recorded by board-certified pediatricians, family physicians, or dermatologists during outpatient or inpatient encounters. To ensure clinical relevance, cases were identified based on physician-documented diagnoses. In the context of the CHS database, such coding reflects a clinical consultation where the severity of the condition warranted medical attention, often resulting in relevant prescriptions for topical treatments.
Maternal prescriptions for anti-diabetic medication were identified through Anatomical Therapeutic Chemical (ATC) classification system codes at the fifth level (ATC-5), capturing prescriptions dispensed within the nine months preceding delivery.
Additional maternal and perinatal covariates included maternal age at delivery, maternal smoking status, maternal history of allergic rhinitis, and maternal glycated hemoglobin (HbA1C) levels measured between 18 and 6 months prior to delivery as indicators of glycemic control and proxies for disease severity. Pregnancy and infant related covariates included mode of delivery, gestational age, infant birth weight, child sex, SES, and follow-up duration. SES was derived from a municipality-level index developed by the Israeli Central Bureau of Statistics, based on 14 socioeconomic indicators. Municipalities were classified into deciles (1–10) and further grouped into low (1–3), medium (4–6), and high (7–10) categories.
Statistical analysis
Descriptive statistics summarized baseline maternal and infant characteristics. Continuous variables are reported as means, medians, standard deviations (S.D.), interquartile ranges (IQR), and ranges, while categorical variables are presented as counts and percentages. Comparisons of continuous variables were performed using Student’s t-test when normally distributed and the Mann–Whitney U test otherwise. Proportions were compared using Chi-square tests, with Fisher’s exact test applied in cases of sparse data.
Logistic regression models were used to evaluate associations between maternal diabetes during pregnancy and S.D., with results expressed as odds ratios (ORs) and 95% confidence intervals (CIs). Multivariable models adjusted for child sex, maternal age, SES, ethnicity, gestational age at birth, maternal smoking status, and follow-up duration.
All analyses were two-sided, and statistical significance was defined as P < 0.05. Data processing and analyses were performed using R software, version 4.2.3.
Results
The cohort included a total of 331,335 mother–child pairs, comprising 310,600 offspring to mothers without diabetes and 20,735 offspring to mothers with any type of diabetes. Clinical and demographic characteristics of the participants are listed in Table 1.
Table 1.
Patient characteristics
| Characteristic | Overall N = 331,335 | Without diabetes N = 310,600 [Reference] | With any diabetes N = 20,735 | p-value |
|---|---|---|---|---|
| Maternal age at delivery | < 0.001 | |||
| Mean ± SD | 30.3 ± 5.6 | 30.1 ± 5.5 | 33.5 ± 5.2 | |
| Median (IQR) | 30.3 (26.2–34.3) | 30.1 (26.0–34.1) | 33.6 (29.8–37.2) | |
| Range | 15.2–50.0 | 15.2–50.0 | 16.4–50.0 | |
| Maternal parity, | ||||
| Mean ± SD | 1.68 ± 1.77 | 1.66 ± 1.76 | 1.95 ± 1.92 | < 0.001 |
| Gestational week | < 0.001 | |||
| Mean ± SD | 38.89 ± 1.72 | 38.93 ± 1.71 | 38.25 ± 1.66 | |
| Median (IQR) | 39.00 (38.00–40.00) | 39.00 (38.00–40.00) | 38.00 (38.00–39.00) | |
| Range | 24.00–44.00 | 24.00–44.00 | 24.00–44.00 | |
| Preterm birth (< 36 weeks), n (%) | 19,305 (5.8%) | 17,501 (5.6%) | 1804 (8.7%) | < 0.001 |
| Delivery type, n (%) | < 0.001 | |||
| Cesarean | 50,568 (17%) | 45,056 (17%) | 5512 (31%) | |
| Vaginal | 239,960 (83%) | 227,633 (83%) | 12,327 (69%) | |
| Maternal allergic rhinitis, n (%) | 43,588 (13%) | 40,309 (13%) | 3279 (16%) | < 0.001 |
| Maternal atopic dermatitis, n (%) | 28,394 (8.6%) | 26,458 (8.5%) | 1936 (9.3%) | < 0.001 |
| Maternal other atopic state, n (%) | 65,551 (20%) | 60,843 (20%) | 4708 (23%) | < 0.001 |
| Maternal smoking status, n (%) | < 0.001 | |||
| Non-smoker | 294,953 (89%) | 277,395 (90%) | 17,558 (85%) | |
| Past smoker | 4,046 (1.2%) | 3,694 (1.2%) | 352 (1.7%) | |
| Current smoker | 31,238 (9.5%) | 28,439 (9.2%) | 2799 (14%) | |
| Ethnicity, n (%) | < 0.001 | |||
| Arabic | 36,552 (11%) | 34,478 (11%) | 2074 (10%) | |
| Bedouin | 64,517 (20%) | 62,061 (20%) | 2456 (12%) | |
| Jewish | 225,034 (69%) | 209,194 (68%) | 15,840 (78%) | |
| Socioeconomic score, n (%) | < 0.001 | |||
| Low | 91,960 (31%) | 87,690 (32%) | 4270 (22%) | |
| Medium | 150,852 (51%) | 139,774 (50%) | 11,078 (58%) | |
| High | 53,435 (18%) | 49,694 (18%) | 3741 (20%) | |
| Offspring follow-up duration | < 0.001 | |||
| Mean ± SD | 6.1 ± 3.3 | 6.2 ± 3.3 | 5.5 ± 3.2 | |
| Median (IQR) | 6.0 (3.0–9.0) | 6.0 (3.0–9.0) | 5.0 (3.0–8.0) | |
| Range | 1.0–14.0 | 1.0–14.0 | 1.0–14.0 | |
| Offspring gender, n (%) | 0.004 | |||
| Female | 160,855 (49%) | 150,992 (49%) | 9863 (48%) | |
| Male | 170,477 (51%) | 159,605 (51%) | 10,872 (52%) |
SD Standard deviation; IQR Interquartile range
Reference category: Without diabetes (N = 310,600). p-values represent comparison between with-diabetes and without-diabetes groups
The overall incidence of SD in the study population was 4.5%. Children born to mothers with any type of diabetes had an incidence of 4.9%, compared to 4.4% in offspring of mothers without diabetes (p = 0.003) (Table 2). This corresponds to an absolute risk increase (ARI) of 0.44% and a number needed to harm (NNH) of 229. Among the different types of maternal diabetes, multivariable analysis showed that T2DM exhibited the strongest association with offspring SD (aOR 1.38, 95% CI 1.17–1.62, p < 0.001), followed by GDM (aOR 1.11, 95% CI 1.02–1.20, p = 0.013). Crucially, when stratified by age at diagnosis, the association was driven entirely by infantile SD (diagnosed at < 1 year of age), where any maternal diabetes was associated with a significantly higher risk (3.2% vs. 2.8%, p < 0.001). This trend was consistent across all subtypes, including T1DM (3.8% vs 2.8%, p = 0.033), T2DM (3.4% vs 2.8%, p = 0.024), and GDM (3.1% vs 2.8%, p = 0.019). In contrast, no significant association was observed for SD diagnosed after one year of age (1.7% vs. 1.7%, p = 0.7). The mean age at SD diagnosis was similar across all groups (1.80 ± 2.64 years for diabetes-negative vs. 1.64 ± 2.46 years for any diabetes; p = 0.6).
Table 2.
Incidence and Timing of Offspring Seborrheic Dermatitis by Maternal Diabetes Status
| Characteristic | Diabetes-Negative (N = 310,600) [Reference] | Any Diabetes Positive (N = 20,735) | p-value |
|---|---|---|---|
| Seborrheic dermatitis diagnosis, n (%) | 13,789 (4.4%) | 1011 (4.9%) | 0.003 |
| SD before age 1 year, n (%) | 8553 (2.8%) | 655 (3.2%) | < 0.001 |
| SD after age 1 year, n (%) | 5236 (1.7%) | 356 (1.7%) | 0.7 |
| Age at SD diagnosis (years) | 0.6 | ||
| Mean ± SD | 1.80 ± 2.64 | 1.64 ± 2.46 | |
| Median (IQR) | 0.44 (0.19–2.38) | 0.43 (0.21–2.21) | |
| Range | 0.00–14.77 | 0.04–14.63 |
SD Seborrheic dermatitis; IQR Interquartile range
Reference category: Diabetes-negative children (N = 310,600). Breakdown by diabetes subtype (T1DM, T2DM, GDM) is presented in Supplementary Table S1
Detailed breakdown of SD incidence by maternal diabetes subtype is provided in Supplementary Table S1.
When SD incidence was examined according to maternal treatment modality (Table 3), no significant differences were observed. Children of mothers who did not receive pharmacological treatment had an incidence of 4.9%, compared to 5.0% for insulin-treated mothers, 4.5% for those treated with sulfonylurea, and 4.4% for metformin-treated mothers (all p > 0.2). The mean age at diagnosis of SD was also comparable across all groups, with no significant differences observed.
Table 3.
Offspring morbidity by diabetic treatment
| Characteristic | Overall N = 20,735 | Insulin treatment | Sulfonylurea treatment | Metformin treatment | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Lifestyle changes N = 14,729 | Pharmacologic N = 6006 | p-value | Negative [Ref] N = 16,728 | Positive N = 4007 | p-value | Negative [Ref] N = 19,829 | Positive N = 906 | p-value | Negative [Ref] N = 17,937 | Positive N = 2798 | p-value | |
| Seborrheic dermatitis diagnosis, n (%) | 721 (4.9%) | 290 (4.8%) | 0.8 | 809 (4.8%) | 202 (5.0%) | 0.6 | 970 (4.9%) | 41 (4.5%) | 0.6 | 887 (4.9%) | 124 (4.4%) | 0.2 |
| SD before age 1 year, n (%) | 461 (3.1%) | 194 (3.2%) | 0.7 | 524 (3.1%) | 131 (3.3%) | 0.7 | 630 (3.2%) | 25 (2.8%) | 0.5 | 563 (3.1%) | 92 (3.3%) | 0.7 |
| SD after age 1 year, n (%) | 260 (1.8%) | 96 (1.6%) | 0.4 | 285 (1.7%) | 71 (1.8%) | 0.8 | 340 (1.7%) | 16 (1.8%) | > 0.9 | 324 (1.8%) | 32 (1.1%) | 0.012 |
| Age at SD diagnosis (years) | 0.5 | 0.8 | 0.2 | 0.028 | ||||||||
| Mean ± SD | 1.67 ± 2.45 | 1.54 ± 2.48 | 1.62 ± 2.39 | 1.70 ± 2.72 | 1.63 ± 2.46 | 1.86 ± 2.48 | 1.73 ± 2.56 | 0.98 ± 1.43 | ||||
| Median (IQR) | 0.43 (0.21–2.31) | 0.41 (0.20–1.91) | 0.42 (0.21–2.29) | 0.43 (0.22–1.94) | 0.42 (0.20–2.18) | 0.60 (0.27–3.02) | 0.43 (0.21–2.34) | 0.35 (0.19–1.05) | ||||
| Range | 0.04–12.69 | 0.07–14.63 | 0.04–12.69 | 0.07–14.63 | 0.04–14.63 | 0.08–10.41 | 0.04–14.63 | 0.09–8.74 | ||||
SD Seborrheic dermatitis; IQR Interquartile range; Ref Reference category
Reference categories: Insulin-negative (N = 16,728); Sulfonylurea-negative (N = 19,829); Metformin-negative (N = 17,937). p-values represent comparison between treatment-positive and treatment-negative groups within the diabetes-positive cohort
SD incidence by maternal glycemic control (HbA1c < 6.5 vs. ≥ 6.5) is shown in Table 4. Among offspring of mothers with HbA1c < 6.5, the incidence was 4.4% (1,664/37,723), whereas among offspring of mothers with HbA1c ≥ 6.5, the incidence was 5.0% (76/1,527; p = 0.3).
Table 4.
Offspring morbidity by maternal glycemic control (HbA1c > 6.5%)
| Characteristic | HbA1c < 6.5% (N = 37,723) [Reference] | HbA1c > 6.5% (N = 1,527) | p-value |
|---|---|---|---|
| Seborrheic dermatitis diagnosis, n (%) | 1664 (4.4%) | 76 (5.0%) | 0.3 |
| SD before age 1 year, n (%) | 1107 (2.9%) | 54 (3.5%) | 0.2 |
| SD after age 1 year, n (%) | 557 (1.5%) | 22 (1.4%) | > 0.9 |
| Age at SD diagnosis (years) | 0.7 | ||
| Mean ± SD | 1.51 ± 2.33 | 1.40 ± 2.51 | |
| Median (IQR) | 0.38 (0.18–1.85) | 0.38 (0.19–1.32) | |
| Range | 0.03–12.66 | 0.09–14.39 |
SD Seborrheic dermatitis; IQR Interquartile range
Reference category: HbA1c < 6.5% (N = 37,723). Overall N = 39,250
Results of the multivariate logistic regression analysis are presented in Table 5. After adjustment for child gender, maternal age, gestational age, socioeconomic status, ethnicity, allergic rhinitis, maternal smoking, and child follow-up duration, maternal diabetes was associated with an increased risk of SD in offspring. The crude odds ratio (OR) was 1.10 (95% CI 1.03–1.18; p = 0.003), and the adjusted OR was 1.16 (95% CI 1.08–1.24; p < 0.001).
Table 5.
Multivariate regression: association between maternal diabetes and offspring seborrheic dermatitis
| Exposure | Crude OR | 95% CI | p-value | Adjusted OR | 95% CI | p-value |
|---|---|---|---|---|---|---|
| Any maternal diabetes | 1.10 | 1.03–1.18 | 0.003 | 1.16 | 1.08–1.24 | < 0.001 |
| Maternal T1DM | 1.14 | 0.87–1.49 | 0.327 | 1.15 | 0.86–1.53 | 0.353 |
| Maternal T2DM | 1.19 | 1.03–1.38 | 0.022 | 1.38 | 1.17–1.62 | < 0.001 |
| Maternal GDM | 1.08 | 1.00–1.16 | 0.051 | 1.11 | 1.02–1.20 | 0.013 |
| Reference: No maternal diabetes | Ref | — | — | Ref | — | — |
OR Odds ratio; CI Confidence interval; Ref Reference category
Reference category: No maternal diabetes (N = 310,600)
*Adjusted for: offspring gender, maternal age, gestational age, socioeconomic scale, ethnicity, allergic rhinitis, maternal smoking status, child follow-up duration
Discussion:
In this large population-based study, we found that maternal diabetes mellitus was associated with a modest but statistically significant increase in the risk of infantile SD, a finding consistent across all diabetes subtypes in the first year of life. While the absolute risk difference was small (ARI 0.44%, NNH 229), the association was specifically localized to the first year of life.
Crucially, our age-stratified analysis (Table 2) demonstrated that this risk was strictly confined to infancy. While the incidence of SD before age 1 was significantly higher among offspring of mothers with diabetes (3.2% vs. 2.8%, p < 0.001), no such difference was observed for diagnoses made after the first year. This temporal specificity—where the risk dissipates as the child’s own metabolic milieu matures—is highly suggestive of an intrauterine 'metabolic priming' effect rather than shared genetic or postnatal environmental factors.
The pathogenesis of SD is multifactorial, involving an interplay between sebocyte activity, skin microbiome dysbiosis (particularly Malassezia species) and immune dysregulation [4–11]. We hypothesize that fetal hyperinsulinemia—a hallmark of maternal diabetes—acts as a growth stimulus for neonatal sebaceous glands. Insulin and IGF-1 receptors are highly expressed in human sebocytes, stimulating cellular proliferation and lipogenesis. This in-utero priming may lead to transient sebaceous gland hyperplasia and altered sebum production, providing a favorable substrate for Malassezia colonization and the subsequent inflammatory responses characteristic of SD [24]. Furthermore, maternal diabetes is known to induce broader fetal immune dysregulation and microbial imprinting [25–32]. Infants born to mothers with GDM often exhibit reduced microbial diversity and pro-inflammatory signatures, which could further increase vulnerability to the inflammatory cascades involved in SD, such as NLRP3 inflammasome activation [25].
These observations align with the Developmental Origins of Health and Disease (DOHaD) framework [28], which posits that intrauterine exposures can fundamentally re-program neonatal physiological systems. Our findings mirror evidence linking maternal diabetes to other inflammatory conditions, such as atopic dermatitis [16, 23]. Furthermore, previous large-scale studies from our center have demonstrated that intrauterine exposure to maternal diabetes is associated with a wide spectrum of long-term offspring morbidities, including respiratory, neuropsychiatric, and endocrine disorders [20–22]. Since these conditions share overlapping mechanisms of skin barrier impairment and immune dysregulation, the elevated SD risk likely reflects a broader impact of maternal metabolic status on early-life cutaneous health.
Notably, while significant associations emerged for all subtypes in the age-stratified analysis, GDM exhibited the most robust association in our adjusted models. This may be attributed to the timing of the metabolic insult; GDM typically manifests during the third trimester, coinciding precisely with the peak development of fetal sebaceous glands. The lack of association with maternal glycemic control (HbA1c) or treatment modality may suggest a 'threshold effect,' where the presence of a diabetic environment itself triggers the priming effect. Although HbA1c was assessed 6–18 months prior to delivery to maximize data coverage, these baseline values may not capture the transient postprandial glucose spikes critical to fetal skin development.
Strengths, limitations, and future directions
To our knowledge, this is the first large-scale population-based study to evaluate the association between maternal diabetes and seborrheic dermatitis in offspring. While prior studies have focused on metabolic and allergic outcomes, cutaneous manifestations have received relatively little attention in this context. The use of a large, non-selected national cohort minimizes referral bias and ensures that our findings are representative of the general population. The study benefits from a large sample size, comprehensive adjustment for potential confounders, and rigorous outcome assessment. Additionally, it utilizes long-term data from a unified health maintenance organization (HMO) database, ensuring reliable coding and consistent data quality over time. The ability to distinguish between types of diabetes further strengthens the study by allowing detailed subgroup analyses.
However, several limitations exist: First, SD was identified via physician-recorded ICD-10 codes. While some diagnostic overlap with physiologic cradle cap is possible, our reliance on physician-documented coding serves as an inherent clinical filter, as such coding is linked to a formal medical encounter. This ensures that even in infancy, the cases identified were clinically significant enough to warrant medical consultation and were not merely transient symptoms. Furthermore, any potential misclassification is expected to be non-differential, which typically biases results toward the null, suggesting our findings may be conservative estimates of the true biological effect.
Second, while the absolute risk difference was small (ARI 0.44%), it reflects a meaningful population-level impact given the high prevalence of both conditions.
Third, the relatively small sample size for pre-gestational diabetes, particularly T1DM (n = 1104), may have limited our statistical power in the non-stratified analysis. The wider confidence intervals in these subgroups reflect lower statistical precision rather than a definitive absence of biological effect. However, the emergence of significant associations across all diabetes types in our age-stratified model (SD before age 1) suggests a consistent biological trend. We therefore emphasize that our results represent a conservative estimate, where the degree of uncertainty in specific point estimates is largely a function of the available sample size in those strata.
Fourth, our reliance on HbA1c values from 6–18 months prior to delivery may introduce non-differential misclassification. This window was chosen to maximize data availability, as HbA1c is more consistently recorded during the pre-conception and early pregnancy periods compared to later stages. Since glycemic control fluctuates, this lack of proximal data likely biased our findings toward the null, potentially underestimating the true association.
Finally, data on residual confounders such as breastfeeding, infant skin care practices, and antibiotic exposure were unavailable. While these postnatal factors influence the neonatal microbiome and skin barrier, the 'metabolic priming' of the fetal pilosebaceous unit—driven by maternal hyperglycemia and fetal hyperinsulinemia—occurs in utero, prior to these exposures. Thus, while unmeasured factors like dysbiosis may modulate SD severity, they are unlikely to fully account for the robust association observed during the infantile peak of the disease. Nevertheless, future prospective studies should incorporate these variables to better isolate the independent effect of maternal diabetes.
Conclusion
In summary, our findings demonstrate that maternal diabetes, particularly gestational diabetes, is associated with a small but statistically significant increase in the risk of infantile seborrheic dermatitis. These results support the Developmental Origins of Health and Disease (DOHaD) framework [25], suggesting that the intrauterine diabetic environment may induce lasting alterations in the offspring's cutaneous metabolic and inflammatory pathways. While the absolute risk difference is modest, this study highlights the fetal skin as a relevant target for developmental imprinting. These findings underscore the importance of a holistic approach to prenatal care and suggest that the long-term dermatological health of the offspring may be influenced by maternal metabolic status during critical gestational windows.
Supplementary Information
Below is the link to the electronic supplementary material.
Author contributions
Eliya Honig: Conceptualization, Data curation, Writing—original draft preparation Nir Amitai: Methodology, Statistical analysis, Writing—methods Sarah Weissmann: Data validation, Clinical interpretation, Writing Amir Horev: Supervision, Writing—review and editing Tamar Eshkoli: Conceptualization, Methodology, Project administration, Writing—review and editing.
Funding
Open access funding provided by Ben-Gurion University. Open access funding provided by Ben-Gurion University.
Data availability
The data that support the findings of this study are available from the corresponding author upon reasonable request, subject to the approval of Clalit Health Services and the relevant Institutional Review Board.
Declarations
Conflict of interest
The authors declare no competing interests.
Ethical approval
Reviewed and approved by the Institutional Review Board of Soroka University Medical Center; approval No. 0049-25-SOR.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Eliya Honig and Nir Amitai have contributed equally as first authors.
Amir Horev and Tamar Eshkoli have contributed equally as last authors.
References
- 1.Polaskey MT, Chang CH, Daftary K et al (2024) The global prevalence of seborrheic dermatitis: a systematic review and meta-analysis. JAMA Dermatol 160(8):846–855. 10.1001/jamadermatol.2024.1987 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Naldi L, Rebora A (2009) Seborrheic dermatitis. N Engl J Med 360(4):387–396. 10.1056/NEJMcp0806464 [DOI] [PubMed] [Google Scholar]
- 3.Victoire A, Magin P, Coughlan J, van Driel ML (2019) Interventions for infantile seborrhoeic dermatitis (including cradle cap). Cochrane Database Syst Rev. 10.1002/14651858.CD011380.pub2 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Wikramanayake TC, Borda LJ, Miteva M, Paus R (2019) Seborrheic dermatitis—looking beyond Malassezia. Exp Dermatol 28(9):991–1001. 10.1111/exd.14006 [DOI] [PubMed] [Google Scholar]
- 5.Tao R, Li R, Wang R (2021) Skin microbiome alterations in seborrheic dermatitis and dandruff: a systematic review. Exp Dermatol 30(10):1546–1553. 10.1111/exd.14450 [DOI] [PubMed] [Google Scholar]
- 6.Sanders MGH, Nijsten T, Verlouw J, Kraaij R, Pardo LM (2021) Composition of cutaneous bacterial microbiome in seborrheic dermatitis patients: a cross-sectional study. PLoS ONE 16(5):e0251136. 10.1371/journal.pone.0251136 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Adalsteinsson JA, Kaushik S, Muzumdar S, Guttman-Yassky E, Ungar J (2020) An update on the microbiology, immunology and genetics of seborrheic dermatitis. Exp Dermatol 29(5):481–489. 10.1111/exd.14091 [DOI] [PubMed] [Google Scholar]
- 8.Chang CH, Chovatiya R (2024) More yeast, more problems? Reevaluating the role of Malassezia in seborrheic dermatitis. Arch Dermatol Res 316(4):100. 10.1007/s00403-024-02830-7 [DOI] [PubMed] [Google Scholar]
- 9.Piacentini F, Camera E, Di Nardo A, Dell’Anna ML (2025) Seborrheic dermatitis: exploring the complex interplay with Malassezia. Int J Mol Sci 26(6):2650. 10.3390/ijms26062650 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Xu Y, Tong X (2025) The dual roles of T cells and keratinocytes in seborrheic dermatitis: a narrative review. Eur J Med Res 30(1):853. 10.1186/s40001-025-03005-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Ungar B, Manson M, Kim M et al (2025) Tape-strip profiling identifies unique immune and lipid dysregulation in patients with seborrheic dermatitis. J Am Acad Dermatol 92(6):1277–1287. 10.1016/j.jaad.2025.01.082 [DOI] [PubMed] [Google Scholar]
- 12.Li T, Ma X, Ma L (2025) Effects of gestational diabetes mellitus on offspring: a literature review. Int J Gynaecol Obstet. 10.1002/ijgo.70185 [DOI] [PubMed] [Google Scholar]
- 13.Burlina S, Dalfrà MG, Lapolla A (2019) Short- and long-term consequences for offspring exposed to maternal diabetes: a review. J Matern Fetal Neonatal Med 32(4):687–694. 10.1080/14767058.2017.1387893 [DOI] [PubMed] [Google Scholar]
- 14.Eletri L, Mitanchez D (2022) How do the different types of maternal diabetes during pregnancy influence offspring outcomes? Nutrients 14(18):3870. 10.3390/nu14183870 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Guan J, Qiu J, Li L et al (2025) A meta-analysis of adverse offspring health outcomes in patients with gestational diabetes mellitus. Diabetes Obes Metab 27(7):3555–3567. 10.1111/dom.16341 [DOI] [PubMed] [Google Scholar]
- 16.Li Z, Yu M, Wang P et al (2021) Association between maternal diabetes mellitus and allergic diseases in children: a systematic review and meta-analysis. Pediatr Allergy Immunol 32(5):880–891. 10.1111/pai.13498 [DOI] [PubMed] [Google Scholar]
- 17.Hivert MF, Backman H, Benhalima K et al (2024) Pathophysiology from preconception, during pregnancy, and beyond. Lancet 404(10448):158–174. 10.1016/S0140-6736(24)00827-4 [DOI] [PubMed] [Google Scholar]
- 18.Rodolaki K, Pergialiotis V, Iakovidou N et al (2023) The impact of maternal diabetes on the future health and neurodevelopment of the offspring: a review of the evidence. Front Endocrinol 14:1125628. 10.3389/fendo.2023.1125628 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Dalrymple KV, El-Heis S, Godfrey KM (2022) Maternal weight and gestational diabetes impacts on child health. Curr Opin Clin Nutr Metab Care 25(3):203–208. 10.1097/MCO.0000000000000826 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Abokaf H, Shoham-Vardi I, Sergienko R, Landau D, Sheiner E (2018) In utero exposure to gestational diabetes mellitus and long term endocrine morbidity of the offspring. Diabetes Res Clin Pract 144:231–235. 10.1016/j.diabres.2018.09.003 [DOI] [PubMed] [Google Scholar]
- 21.Zamstein O, Sheiner E, Wainstock T, Landau D, Walfisch A (2018) Maternal gestational diabetes and long-term respiratory related hospitalizations of the offspring. Diabetes Res Clin Pract 140:200–207. 10.1016/j.diabres.2018.03.050 [DOI] [PubMed] [Google Scholar]
- 22.Nahum Sacks K, Friger M, Shoham-Vardi I, Abokaf H, Spiegel E, Sergienko R, Landau D, Sheiner E (2016) Prenatal exposure to gestational diabetes mellitus as an independent risk factor for long-term neuropsychiatric morbidity of the offspring. Am J Obstet Gynecol 215(3):380.e1–7. 10.1016/j.ajog.2016.03.030 [DOI] [PubMed] [Google Scholar]
- 23.Kumar R, Ouyang F, Story RE et al (2009) Gestational diabetes, atopic dermatitis, and allergen sensitization in early childhood. J Allergy Clin Immunol 124(5):1031-1038.e1-4. 10.1016/j.jaci.2009.06.052 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Melnik B (2012) Dietary intervention in acne: attenuation of increased mTORC1 signaling promoted by Western diet. Dermatoendocrinol 4(1):20–32. 10.4161/derm.19828 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Onori M, Beneduce G, Colella F et al (2025) Placental inflammation is increased in gestational diabetes mellitus: the role of inflammasome NLRP-3 and chemokine scavenger decoy receptor D6. PLoS ONE 20(6):e0326087. 10.1371/journal.pone.0326087 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Zgutka K, Tkacz M, Tomasiak P et al (2024) Gestational diabetes mellitus-induced inflammation in the placenta via IL-1β and toll-like receptor pathways. Int J Mol Sci 25(21):11409. 10.3390/ijms252111409 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Jawerbaum A, González E (2006) Diabetic pregnancies: the challenge of developing in a pro-inflammatory environment. Curr Med Chem 13(18):2127–2138. 10.2174/092986706777935302 [DOI] [PubMed] [Google Scholar]
- 28.Barker DJ (2007) The origins of the developmental origins theory. J Intern Med 261(5):412–417. 10.1111/j.1365-2796.2007.01809.x [DOI] [PubMed] [Google Scholar]
- 29.Ponzo V, Ferrocino I, Zarovska A et al (2019) The microbiota composition of the offspring of patients with gestational diabetes mellitus (GDM). PLoS ONE 14(12):e0226545. 10.1371/journal.pone.0226545 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Wang J, Zheng J, Shi W et al (2018) Dysbiosis of maternal and neonatal microbiota associated with gestational diabetes mellitus. Gut 67(9):1614–1625. 10.1136/gutjnl-2018-315988 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Singh S, Karagas MR, Mueller NT (2017) Charting the maternal and infant microbiome: what is the role of diabetes and obesity in pregnancy? Curr Diab Rep 17(2):11. 10.1007/s11892-017-0836-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Hu Y, Zheng S, Xu J et al (2025) Gestational diabetes mellitus alters neonatal gut microbiota and increases infection susceptibility. Front Microbiol 16:1600325. 10.3389/fmicb.2025.1600325 [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
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Supplementary Materials
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request, subject to the approval of Clalit Health Services and the relevant Institutional Review Board.