Epidemiological Study of Parental Obstructive Sleep Apnea and Subsequent Risk of ADHD in Their Children: A Nationwide Population-Based Study

Abstract

Objective:

Obstructive sleep apnea (OSA), with daytime drowsiness, nocturnal hypoxia, could result in systemic inflammation and oxidative damage. We hypothesize that parental OSA, with chronic systemic inflammation and oxidative stress, might contribute to children’s neurodevelopmental disorders, such as ADHD.

Method:

By linking National Birth Registry with the National Health Insurance Research Database, Taiwan, we identified 2006–2015 birth cohort, which comprised 1,723,873 singleton live births, and conducted a nested case-control study. We included children with ADHD and compared them with non-ADHD controls matched with ADHD case on index date. Conditional logistic regression was utilized to calculate adjusted odds ratio (aOR) when investigating the association between parental diseases with risk of ADHD in their offspring.

Results:

The aOR (95% CI) of offspring’s ADHD was 1.758 (1.458–2.119) with paternal OSA and 2.159 (1.442–3.233) with maternal OSA. The subgroup analysis revealed different effects of parental diseases among children’s gender.

Conclusion:

Our study demonstrates an association in parental OSA and offspring ADHD, which could inspire further research to clarify the mechanisms.

Keywords

parental obstructive sleep apnea ADHD case control study gender effect

Introduction

ADHD is the most common neurodevelopmental behavioral disorder in childhood, characterized by inattention, hyperactivity, and impulsivity according to DSM 5 diagnostic criteria (American Psychiatric Association, 2013), which could extend into adolescence and adulthood. It affects many aspects, including physical health, academic, social, or occupational performance (Posner et al., 2020). According to previous worldwide studies, the prevalence of ADHD was estimated as 5.29% (95% CI 5.01–5.56; Polanczyk et al., 2007).

It has raised more concerns due to increasing research about the disease and its pathophysiology over the past several decades. The etiology is multifactorial, including hereditary, environmental exposures, and gene-environment interactions. Previous reports have shown that prenatal and postnatal risk factors, including prematurity, low birth body weight (Nigg & Breslau, 2007), maternal mental disorders, especially depression, anxiety (Vizzini et al., 2019; Wolford et al., 2017), various inflammation status, maternal autoimmune diseases, family history of autoimmune diseases (thyrotoxicosis, type 1 diabetes, gestational diabetes, autoimmune hepatitis, psoriasis, and ankylosing spondylitis), atopic diseases, or asthma are associated with ADHD or ADHD symptoms (Dalsgaard, 2021; Han et al., 2021; P. R. Nielsen et al., 2017; T. C. Nielsen et al., 2021).

The association between maternal diseases and offspring ADHD could be explained through the proposed concept of shared genetic and environmental risk factors or direct effects of maternal autoantibodies/cytokines crossing the placenta and altering the fetal immune response, which in turns leads to changes in the fetus central nervous system (Dalsgaard, 2021). Maternal inflammatory states, which could result from autoimmune diseases or other diseases, might also play a role in immune activation during pregnancy through the placenta and immature blood-brain barrier, rendering the immature fetal brain to be more susceptible to evolve into various neurodevelopmental disorders through microglia activation and epigenetic alterations (Han et al., 2021).

Obstructive sleep apnea (OSA) syndrome is characterized by snoring and/or increased respiratory effort secondary to increased upper airway resistance and pharyngeal collapsibility, with disruption of normal oxygenation, ventilation, and sleep pattern (Kaditis et al., 2016). OSA affects 24% of men and 9% of women of the middle-aged population in the US (Hiestand et al., 2006). It was not until 2019, Vizzini birth cohort study (Vizzini et al., 2019) showed the relative increase in ADHD scores are associated with maternal sleep disorders, which was the first study reporting an association between maternal sleep disorders and offspring ADHD symptoms. OSA, with daytime drowsiness, nocturnal hypoxia, could result in systemic inflammation and oxidative damage (Dehlink & Tan, 2016). We hypothesized the systemic maternal inflammation status and oxidative stress caused by OSA symptoms, might contribute to children’s neurodevelopmental disorders, such as ADHD.

Since numerous observational studies showed various prenatal, perinatal factors, and maternal status that might be associated with offspring ADHD, few studies focused on paternal impacts which might also contribute to children’s outcomes by genetic-environmental interplay. Moreover, previous conclusions regarding associations between offspring ADHD and maternal autoimmune diseases, systemic diseases during pregnancy or through the lifetime were still not consistent. Maternal sleep disorders, to our knowledge, have not yet been studied before in association with children diagnosed as ADHD by physicians and paternal sleep disorders have not been done for association between offspring ADHD.

Understanding the familial risk of OSA, parental autoimmune or systemic diseases and offspring ADHD will help clinicians to identify children at risk and offer opportunities for early intervention and treatment. The present study represents a different perspective, that is, investigating the relationship between parental OSA and offspring ADHD, and also provides more evidence between specific autoimmune diseases and offspring ADHD. Additionally, we assessed whether these risk factors differed by patient’s gender.

Methods

Ethical Statements

This study was approved by the Institutional Review Board of The Institutional Review Board of Chung Shan Medical University Hospital in Taiwan. IRB, CS 19009. Need for informed consent was waived because the data we used consisted of a de-identified secondary data set which had been released for research purposes and only de-identified data. The protocol for the research project is conformed to the provisions of the Declaration of Helsinki.

Data Source

This study is based on record linkage of the datasets between the National Birth Registry with National Health Insurance Research Database (Hsieh et al., 2019) and we selected the birth cohort that involved children were born from 2006 to 2015 for analysis. National Birth Registry was initiated in 2003 under the auspices of the Health Promotion Administration in Taiwan and has routinely collected perinatal data in Taiwan. These data include demographic characteristics, infants’ gender, birth weight, gestational week, mode of delivery, and parental age (at delivery). Taiwan’s National Health Insurance (NHI) program was established in 1995 and covers up to 99.99% of Taiwan’s population. The National Health Insurance Research Database (NHIRD) was released for research purposes and thoroughly provided population-based evidence to support clinical decisions. The NHIRD comprised comprehensive demographic characteristics, and the insurance claim records including diagnosis, procedures, expenses, as well as prescription in outpatient, inpatient, and emergency service.

Selection of ADHD Cases and Non-ADHD Controls

Initially, there were 1,939,449 infants (the birth cohort) were born from 2006 and 2015. The exclusion criteria include missing birth data from National Birth Registry (n = 42,396), stillbirth (n = 610), infants from multiple birth (n = 57,976), and mothers with foreign nationality (n = 114,594). A total of 1,723,873 singleton live births were selected after exclusion.

We obtain independently paired mother–child data (mother–child dyads) and associated medical information regarding relevant variables of parents and children. The nested case-control study was conducted. The ADHD children were identified by International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) code 314.x from birthdate to the 31th, December 2017. In each ADHD case, the first date of ADHD diagnosis was defined as index date, then the 1:4 ratio of non-ADHD controls were individually matched by the birth year and sex on the index date.

In order to ensure the accuracy, diagnosis of ADHD would need to be documented at least three times by board-certified psychiatrists during the follow-up period. We finally identified 60,367 mother-child dyads when the children were diagnosed with ADHD and compared them with 241,468 control mother-child dyads (Figure 1). Supplemental Figure 1 showed the age distribution of newly diagnosed ADHD, which identified from birthdate until the end of 2017, in the 2006 to 2015 birth cohort.

Figure 1.

Flowchart of enrolment of study subjects.

Exposure of Parental Diseases Before Birth

The records of parental diseases were obtained from the NHIRD and the diagnosis was identified within 3 years before the child birth. The various kinds of parental autoimmune diseases that have ever been reported in previous comprehensive studies (Nielsen et al., 2017, 2021; Vizzini et al., 2019), including rheumatoid arthritis (ICD-9-CM code 714.0), systemic lupus erythematosus (SLE, ICD-9-CM code 710.0), Sjogren’s syndrome (ICD-9-CM code 710.2), ankylosing spondylitis (AS, ICD-9-CM code 720.0), psoriasis (ICD-9-CM code 696.0, 696.1), hyperthyroidism (ICD-9-CM code 240.9) were selected to clarify if the link to offspring ADHD is universal.

In addition, the parental systemic diseases include anemia (ICD-9-CM code 285.9), chronic obstructive pulmonary disease (COPD, ICD-9-CM code 490–492, 493–496), and sleep apnea (ICD-9-CM code 327.2), and the parental allergic disease of asthma (ICD-9-CM code 493) and atopic dermatitis (ICD-9-CM code 691) were identified to verify our hypothesis.

Other Infants and Parental Characteristics During Perinatal Period

Baseline data including birth weight, gestational weeks, infant’s sex, gestational age, mode of delivery, insurance characteristics (which was assumed to be related to socioeconomic status), parental age during delivery, maternal preeclampsia, or gestational diabetes were collected and adjusted.

Statistical Analysis

This study had nationwide and large-scale sample size, it is likely to reveal a statistically significant difference of p < .05, even if the effect size is negligible or small (Sullivan & Feinn, 2012). The absolute standardized difference (ASD) was used to compare the statistical values, including the means and proportion, of baseline covariates between groups in this large-sample observational study. When the ASD less than 0.1, the characteristic was small or no difference between ADHD and non-ADHD groups. We applied inverse probability of treatment weighting (IPTW) to minimize the potential confounding effect due to the difference on the baseline characteristics between two groups. The average treatment effect (ATE) of ADHD was estimated by using the logistic regression that contains the variables including infant’s sex, birth weight, gestational age, mode of delivery, maternal age, maternal preeclampsia, gestational diabetes, and paternal age during pregnancy. The primary results, odds ratios, were inverse weighted by the treatment weighting.

In this study, the birth year and sex matched ADHD cases and non-ADHD controls were selected for analysis, therefore, a conditional logistic regression model was conducted to estimate the adjusted odds ratio (aOR) and 95% confidence interval (CI) of children’s ADHD after adjustment for infant’s sex, birth weight, gestational age, mode of delivery, maternal age, maternal preeclampsia, gestational diabetes, and paternal age during pregnancy. Sensitivity analysis was further conducted to evaluate whether the influences of parental diseases on offspring ADHD differed between genders.

All statistical analysis was conducted using Statistical Analysis Software (SAS) version 9.4 (Cary, NC: SAS Institute Inc.). A p-value less than .05 was considered statistically significant.

Results

Demographic Characteristics and Prevalence of Comorbidities

Table 1 shows the baseline characteristics of the ADHD group and control group. According to the absolute standardized difference (ASD) of baseline characteristics between ADHD and control group, the ADHD group had a higher prevalence of small gestational week (defined as <36 weeks) than the control group. After IPTW, there were small or no difference in the infant’s sex, birth weight, gestational age, mode of delivery, maternal age, preeclampsia, gestational diabetes, employment status, and paternal age at delivery between ADHD cases and non-ADHD controls.

Table 1.

Baseline Characteristics Between ADHD and Control Groups From 2006 to 2015 Birth Cohort in Taiwan.

	ADHD group (N = 60,367)	Control group (N = 241,468)	Original ASD	IPTW ASD
Infant’s sex			0.003	0.000
Male	46,419 (76.89%)	185,676 (76.89%)
Female	13,948 (23.11%)	55,792 (23.11%)
Birth weight (g)			0.079	0.000
<2,500	5,055 (8.37%)	13,308 (5.51%)
2,500–3,499	45,048 (74.62%)	185,155 (76.68%)
≥3,500	10,264 (17.00%)	43,005 (17.81%)
Gestational weeks			0.102	0.054
<36	2,964 (4.91%)	7,618 (3.15%)
36–41	55,293 (91.59%)	226,315 (93.72%)
≥41	2,110 (3.50%)	7,535 (3.12%)
Mode of delivery			0.044	0.000
Vaginal delivery	38,006 (62.96%)	157,150 (65.08%)
Cesarean delivery	22,361 (37.04%)	84,318 (34.92%)
Maternal age at delivery (years)			0.063	0.000
<25	7,860 (13.02%)	27,128 (11.23%)
25–34	41,386 (68.56%)	171,712 (71.11%)
35–45	11,043 (18.29%)	42,438 (17.57%)
≥45	78 (0.13%)	190 (0.08%)
Maternal obstetric complications
Preeclampsia	2,291 (3.80%)	6,786 (2.81%)	0.055	0.000
Gestational diabetes	4,862 (8.05%)	17,574 (7.28%)	0.029	0.000
Paternal age at delivery (years)	Missing = 4,362	Missing = 12,339	0.056	0.000
<25	2,552 (4.56%)	9,747 (4.25%)
25–34	33,489 (59.80%)	141,338 (61.68%)
35–45	18,664 (33.33%)	74,066 (32.33%)
≥45	1,300 (2.32%)	3,978 (1.74%)

Note. ASD = absolute standardized difference, the small difference of characteristic appeared between study groups when ASD < 0.1; IPTW = inverse probability of treatment weighting.

Comparison of Adjusted Odds Ratios of Parental Diseases Between ADHD Group and Control Group

Table 2 (the exposure as maternal diseases) and Table 3 (the exposure as paternal diseases) show the odds ratios of ADHD estimated by conditional logistic regression analysis and adjusted for infant’s sex, birth weight, gestational age, mode of delivery, maternal age, preeclampsia, gestational diabetes, employment status, and paternal age during pregnancy. The following factors were associated with a higher risk of ADHD, including parental OSA, COPD, hyperthyroidism, maternal rheumatoid arthritis, psoriasis, atopic dermatitis, and asthma. The odds ratio (95% CI) of developing ADHD was 1.413 (1.285–1.553) for paternal OSA and 1.654 (1.347–2.031) for maternal OSA.

Table 2.

Adjusted Odds Ratios of Offspring’s ADHD When Maternal Diseases Exposed Within 3 years Before Birth by Using Multiple Variable Logistic Regression Analysis and IPTW Model.

Maternal diseases	ADHD group (N = 5,6005)	Control group (N = 22,9129)	Adjusted or (95% CI)	IPTW OR (95% CI)	Maternal diseases during pregnancy
Autoimmune disease
RA	271 (0.48%)	806 (0.35%)	1.380 (1.201–1.586)	1.190 (0.962–1.474)	1.351 (1.060–1.722)
SLE	222 (0.40%)	644 (0.28%)	1.357 (1.163–1.582)	0.853 (0.661–1.101)	1.197 (0.979–1.465)
SS	552 (0.99%)	1,887 (0.82%)	1.235 (1.120–1.362)	1.301 (1.167–1.450)	1.335 (1.141–1.562)
AS	143 (0.26%)	467 (0.20%)	1.259 (1.040–1.524)	1.758 (1.458–2.119)	1.497 (1.095–2.047)
Psoriasis	261 (0.47%)	887 (0.39%)	1.208 (1.050–1.389)	1.072 (1.001–1.147)	1.074 (0.871–1.325)
Hyperthyroidism	1,858 (3.32%)	6,198 (2.71%)	1.226 (1.162–1.293)	1.301 (1.167–1.450)	1.214 (1.130–1.304)
Systemic diseases
Anemia	8,563 (15.2%)	34,751 (15.1%)	1.001 (0.974–1.028)	1.015 (0.959–1.074)	0.964 (0.935–0.994)
COPD	1,355 (2.42%)	4,403 (1.92%)	1.274 (1.193–1.360)	1.183 (0.814–1.719)	1.164 (1.101–1.232)
OSA	136 (0.24%)	333 (0.15%)	1.654 (1.347–2.031)	2.159 (1.442–3.233)	1.413 (0.937–2.131)
Allergic disease
Asthma	2,550 (4.55%)	8,324 (3.63%)	1.242 (1.184–1.303)	1.195 (1.082–1.320)	1.157 (1.076–1.245)
AD	5,356 (9.56%)	20,653 (9.01%)	1.073 (1.039–1.108)	1.072 (1.001–1.147)	1.104 (1.061–1.149)

Note. AD = atopic dermatitis; AS = ankylosing spondylitis; COPD = chronic obstructive pulmonary disease; IPTW = inverse probability of treatment weighting; OSA = obstructive sleep apnea; RA = rheumatoid arthritis; SLE = systemic lupus erythematosus; SS = Sjogren’s syndrome.

This analysis excluded the missing data in paternal information.

Both Adjusted OR and IPTW OR were adjusted for the co-variates including infant’s sex, birth weight, gestational age, mode of delivery, maternal age, maternal preeclampsia, gestational diabetes, and paternal age during pregnancy.

Table 3.

Adjusted Odds Ratios of Offspring’s ADHD When Paternal Diseases Exposed Within 3 years Before Birth by Using Multiple Variable Logistic Regression Analysis and IPTW Model.

Paternal diseases	ADHD group (N = 56,005)	Control group (N = 229,129)	Adjusted OR (95% CI)	IPTW model (95% CI)	Paternal diseases during pregnancy
Autoimmune disease
RA	147 (0.26%)	588 (0.26%)	1.035 (0.862–1.243)	0.976 (0.696–1.369)	1.003 (0.762–1.320)
SLE	29 (0.05%)	120 (0.05%)	1.027 (0.683–1.545)	1.172 (0.882–1.557)	1.002 (0.570–1.763)
SS	209 (0.37%)	885 (0.39%)	0.978 (0.834–1.148)	1.068 (0.852–1.339)	0.963 (0.767–1.210)
AS	405 (0.72%)	1,604 (0.70%)	1.035 (0.927–1.156)	1.195 (1.082–1.320)	0.968 (0.832–1.126)
Psoriasis	294 (0.52%)	1,142 (0.50%)	1.069 (0.939–1.217)	1.025 (0.887–1.183)	1.043 (0.882–1.234)
Hyperthyroidism	430 (0.77%)	1,556 (0.68%)	1.134 (1.016–1.267)	1.068 (0.852–1.339)	1.125 (0.974–1.300)
Systemic diseases
Anemia	1,765 (3.15%)	7,515 (3.28%))	0.960 (0.897–1.027)	0.935 (0.812–1.076)	0.984 (0.909–1.065)
COPD	1,679 (3.00%)	5,922 (2.58%)	1.166 (1.100–1.235)	1.580 (1.145–2.181)	1.176 (1.077–1.285)
OSA	591 (1.06%)	1,715 (0.75%)	1.413 (1.285–1.553)	1.758 (1.458–2.119)	1.552 (1.365–1.765)
Allergic disease
Asthma	1,946 (3.47%)	7,435 (3.24%)	1.054 (0.999–1.113)	1.123 (1.005–1.255)	1.112 (1.033–1.197)
AD	1,287 (2.30%)	5,021 (2.19%)	1.046 (0.977–1.121)	1.025 (0.887–1.183)	1.016 (0.919–1.124)

Note. AD = atopic dermatitis; AS = ankylosing spondylitis; COPD = chronic obstructive pulmonary disease; IPTW = inverse probability of treatment weighting; OSA = obstructive sleep apnea; RA = rheumatoid arthritis; SLE = systemic lupus erythematosus; SS = Sjogren’s syndrome.

This analysis excluded the missing data in paternal information.

In the IPTW models, the results show parental AS significant increased the risk of offspring ADHD. The aOR (95% CI) of developing ADHD was 1.195 (1.082–1.320) in children exposed to paternal AS and 1.758 (1.458–2.119) in children exposed to maternal AS. Moreover, the odds ratio (95% CI) were 1.758 (1.458–2.119) for paternal OSA and 2.159 (1.442–3.233) for maternal OSA.

Subgroup analysis of ADHD patients stratified by gender

Figure 2a (for maternal diseases) and Figure 2b (for paternal diseases) demonstrated the odds ratio of children ADHD in parental autoimmune, systemic, and allergic diseases between gender subgroups. The subgroup analysis revealed the different effects of parental diseases on the risk of ADHD between children’s gender. Table 4 provides more detail results of subgroup analysis.

Figure 2.

(a) Children’s gender-stratified analysis for odds ratio of ADHD in maternal autoimmune, systemic, and allergic disease during pregnancy and (b) Children’s gender-stratified analysis for odds ratio of ADHD in paternal autoimmune, systemic, and allergic disease during pregnancy.

Table 4.

Children’s Gender-Stratified Analysis for Odds Ratio of ADHD in Parental Autoimmune, Systemic, and Allergic Disease During Pregnancy.

	Odds ratio (95% CI) of ADHD in children
Parents’ diseases	All children	Boys	Girls
Autoimmune disease
RA
Maternal	1.351 (1.060–1.722)	1.339 (1.142–1.569)	1.532 (1.147–2.048)
Paternal	1.003 (0.762–1.320)	0.993 (0.804–1.225)	1.183 (0.814–1.719)
SLE
Maternal	1.197 (0.979–1.465)	1.301 (1.092–1.551)	1.580 (1.145–2.181)
Paternal	1.002 (0.570–1.763)	0.835 (0.516–1.352)	2.124 (0.941–4.791)
SS
Maternal	1.335 (1.141–1.562)	1.248 (1.118–1.393)	1.190 (0.962–1.474)
Paternal	0.963 (0.767–1.210)	0.979 (0.816–1.175)	0.976 (0.696–1.369)
AS
Maternal	1.497 (1.095–2.047)	1.228 (0.984–1.531)	1.366 (0.931–2.005)
Paternal	0.968 (0.832–1.126)	1.088 (0.962–1.229)	0.853 (0.661–1.101)
Psoriasis
Maternal	1.074 (0.871–1.325)	1.220 (1.038–1.433)	1.172 (0.882–1.557)
Paternal	1.043 (0.882–1.234)	1.077 (0.927–1.251)	1.049 (0.808–1.363)
Hyperthyroidism
Maternal	1.214 (1.130–1.304)	1.203 (1.132–1.279)	1.301 (1.167–1.450)
Paternal	1.125 (0.974–1.300)	1.157 (1.020–1.313)	1.068 (0.852–1.339)
Systemic diseases
Anemia
Maternal	0.964 (0.935–0.994)	0.997 (0.967–1.028)	1.015 (0.959–1.074)
Paternal	0.984 (0.909–1.065)	0.968 (0.896–1.046)	0.935 (0.812–1.076)
COPD
Maternal	1.148 (1.015–1.299)	1.311 (1.218–1.413)	1.156 (1.007–1.327)
Paternal	1.176 (1.077–1.285)	1.171 (1.097–1.250)	1.148 (1.016–1.296)
OSA
Maternal	1.413 (0.937–2.131)	1.512 (1.190–1.921)	2.159 (1.442–3.233)
Paternal	1.552 (1.365–1.765)	1.316 (1.179–1.470)	1.758 (1.458–2.119)
Allergic disease
Asthma
Maternal	1.157 (1.076–1.245)	1.257 (1.190–1.327)	1.195 (1.082–1.320)
Paternal	1.112 (1.033–1.197)	1.034 (0.971–1.100)	1.123 (1.005–1.255)
AD
Maternal	1.104 (1.061–1.149)	1.073 (1.035–1.114)	1.072 (1.001–1.147)
Paternal	1.016 (0.919–1.124)	1.053 (0.973–1.139)	1.025 (0.887–1.183)

Note. AD = atopic dermatitis; AS = ankylosing spondylitis; COPD = chronic obstructive pulmonary disease; OSA = obstructive sleep apnea; RA = rheumatoid arthritis; SLE = systemic lupus erythematosus; SS = Sjogren’s syndrome.

Odds ratios were adjusted for the co-variates including infant’s sex, birth weight, gestational age, mode of delivery, maternal age, maternal preeclampsia, gestational diabetes, and paternal age during pregnancy.

Figure 2a (for maternal diseases) shows the sex stratified analysis showed aORs (95% CI) of ADHD were 1.512 (1.190–1.921) in boys and 2.159 (1.442–3.233) in girls when exposure to maternal OSA before birth. Maternal Sjogren’s syndrome showed aOR (95% CI) 1.248 (1.118–1.393) in boys, while maternal Sjogren’s syndrome showed 1.190 (0.962–1.474) in girls with ADHD. However, there was no significant interaction (maternal disease × infant’s sex) observed in this study.

Figure 2b (for paternal diseases) shows the paternal OSA contribute the different effect on the boys ADHD (aOR = 1.316, 95% CI = 1.179–1.470) and girl ADHD (aOR = 1.758, 95% CI = 1.458–2.119), the significant interaction effect (paternal OSA × infant’s sex) on children ADHD was observed.

Discussion

This large nationwide case-control study is the first study, to our knowledge, displayed positive associations of parental obstructive sleep apnea and ADHD diagnosis in children. Two of our novel findings deserve further investigation. First, both maternal and paternal OSA are associated with offspring with ADHD. Second, it seems that the association differed by child sex. The subgroup analysis revealed the different effects of parental diseases among children’s gender. Paternal OSA shows aOR (95% CI) 1.316 (1.179–1.470) in boys with ADHD, while maternal OSA showed 1.758 (1.458–2.119) in girls with ADHD. We also found out that maternal diagnosis of ankylosing spondylitis during pregnancy was associated with higher risk of children with ADHD.

Vizzini birth cohort study in 2019 showed the relative increase in ADHD scores are associated with maternal sleep disorders, which was the first study reporting an association between maternal sleep disorders and offspring ADHD. The outcome was ADHD scores (ADHD symptoms) assessed by children’s mothers through questionnaires (Vizzini et al., 2019). On the contrary, our study used ADHD diagnosis by physicians, which requires more detailed evaluation in at least two different settings. OSA symptoms, including daytime drowsiness, nocturnal hypoxia is postulated to result in systemic inflammation and oxidative damage, which could affect neurocognitive, cardiovascular, and metabolic systems from previous studies (Esposito et al., 2013). Developing large studies have been conducted to support the mechanism that maternal inflammatory states might play a role in immune activation during pregnancy through the placenta and immature blood-brain barrier, rendering the immature fetal brain to be more susceptible to evolve into various neurodevelopmental disorders through microglia activation and epigenetic alterations (Han et al., 2021). We speculate these mechanisms might explain why maternal OSA, which results in maternal inflammation status and oxidative change caused by chronic hypoxia, is associated with children’s ADHD. On the other hand, paternal OSA has not shown association with offspring ADHD before. The shared vulnerability between OSA and ADHD may suggest genetic mechanisms behind both diseases, superimposed with gene–environment interactions, causing epigenetic alterations including DNA methylation, histone modifications, and chromatin remodeling which are highly sensitive to environmental stimuli. Dysregulations of Wingless-INT (Wnt)-signaling pathway, which is known to orchestrate cellular proliferation, polarity, and differentiation; and mammalian target of rapamycin (mTOR)-signaling pathway, which is involved in several significant processes of neurodevelopment and synaptic plasticity, might have an important role in the pathophysiology of ADHD (Yde Ohki et al., 2020) . However, future investigation is needed to clarify the mechanism between paternal OSA and offspring ADHD.

Our findings also showed generally consistent results with those reported by previous studies that maternal autoimmune diseases, especially rheumatoid arthritis and ankylosing spondylitis especially during pregnancy, were associated with increased ADHD among children. Nielsen et al. (2017) conducted the first nationwide cohort study that investigated the association between parental history of autoimmune disease and risk of offspring ADHD. Instanes et al. (2017) performed a population-based nested case-control study which also searched for the relationship between maternal diseases with immune components and offspring ADHD. It defined individuals receiving ADHD medications as patients with ADHD, concerning the more severe disease requiring medications that would affect one’s daily life most (Instanes et al., 2017). T. C. Nielsen et al. (2021) conducted a hybrid study which combined a birth cohort study and systematic review, which was the most recent comprehensive study related to association of maternal autoimmune disease with ADHD children from our search. It included one or more maternal autoimmune diagnoses out of 35 conditions linking with hospital admission records. Besides the large-scale studies mentioned above, many observational studies regarding this aspect showed associations between maternal autoimmune diseases and ADHD in children, but the influence of individual diseases varied through different research designs. Further comprehensive research is required to clarify the associations and increase understanding of the mechanisms.

Hypothesis models had been postulated that exaggerated cytokines, inflammatory responses, increase in oxidative stress, and neuroinflammation might contribute to the etiology of ADHD (Buske-Kirschbaum et al., 2013; Corona, 2020). There are also developing studies within this field regarding the association between maternal autoimmune diseases and neurodevelopmental disorders. It has been suggested maternal inflammatory conditions, such as infections, hypoxia, allergy, autoimmune disease, may cause an exaggerated fetal central nervous system inflammatory response (Strickland, 2014). These speculated mechanisms could be summarized as shared genetic and environmental risk factors or direct effects of maternal autoantibodies or cytokines crossing the placenta and altering the fetal immune response, which in turns leads to impairment in the central nervous system (Dalsgaard, 2021). Previous studies have been conducted to investigate the association between maternal autoimmune diseases and neurodevelopmental diseases, including autism spectrum disorder (Chen et al., 2016; Croen et al., 2005), obsessive-compulsive disorder, and Tourette’s/chronic tic disorders (Mataix-Cols et al., 2018). It was not until recent 10 years that large studies focused more on offspring ADHD.

Strengths and Limitations

Our findings provide further evidence that maternal autoimmune diseases (especially for AS) during perinatal period are positively associated with offspring ADHD diagnosis, and extend the existing evidence to maternal OSA and paternal OSA, which could also cause general inflammatory conditions through genetic or environmental factors. Meanwhile, our findings suggest the importance of the sleep disorders assessment in parents of reproductive age.

However, our study has some limitations that should be considered when interpreting the results. First, we assessed only doctor-diagnosed obstructive sleep apnea, therefore, the effect of less severe sleep disturbances, which have much higher prevalence in the general population and among pregnant women, requires future research. Second, our nationwide data bank did not include smoking, alcohol drinking conditions which could be important confounding factors to ADHD. Third, we lack information on parental ADHD diagnosis that potentially could act as a confounding factor. However, given the relatively low ADHD prevalence in the general population compared with sleep disorders, the relatively strong associations that we found, might be unlikely that confounding by maternal ADHD could entirely explain the findings of our study. Last, previous study has shown that children diagnosed with OSA could be associated with the development of ADHD (Wu et al, 2017). Considering the genetic predisposition of OSA, we did not further discuss about the children diagnosed with OSA, which could be a relevant factor of mediation between OSA in the parents and ADHD.

Conclusion

Our study suggested positive associations of parental obstructive sleep apnea and ADHD diagnosis in children. It could provide clinicians more considerations about sleep disorders in reproductive parents. Further research should be done to clarify the result and mechanism.

Supplemental Material

sj-tiff-1-jad-10.1177_10870547221120695 – Supplemental material for Epidemiological Study of Parental Obstructive Sleep Apnea and Subsequent Risk of ADHD in Their Children: A Nationwide Population-Based Study

Supplemental material, sj-tiff-1-jad-10.1177_10870547221120695 for Epidemiological Study of Parental Obstructive Sleep Apnea and Subsequent Risk of ADHD in Their Children: A Nationwide Population-Based Study by Iwen Chen, Jing-Yang Huang, Renin Chang, Yao-Min Hung and James Cheng-Chung Wei in Journal of Attention Disorders

Footnotes

Contributor Information

Study conception and design: Chen, Chang, Hung, and Wei JC; Acquisition of data: Huang; Analysis and interpretation of data: Chen, Hung, Chang, Huang, and Wei JC; Writing (original draft preparation): Chen, Huang, and Hung; Writing (review and editing): Hung and Wei.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The study was supported by Chung Shan Medical University Hospital (Grant Number CSH-2019-C-004).

Patient and Public Involvement

Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

Patient Consent for Publication

Not required because the data we used was a de-identified secondary data set which had been released for research purposes and analyzed anonymously.

ORCID iD

Yao-Min Hung

Data availability statement

All data relevant to the study are included in the article or uploaded as online supplementary information.

Author Biographies

Iwen Chen is in fellowship in the Department of Pediatrics, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan.

Jing-Yang Huang is the director of Center for Health Data Science, Chung Shan Medical University Hospital, Taiwan. He also joined the Institute of Medicine, College of Medicine, Chung Shan Medical University, Taiwan in 2017. He has interests in epidemiology, statistics, real-world evidence research, estimating the disease incidence or prevalence in population, medical utilization in patients, the methodology of the observational study design. His research focuses on real-world datasets including the Taiwan National Health Insurance Research Datasets.

Renin Chang is an assistant professor of TaJen University in Taiwan. His work focuses specifically on the Emergency Medicine, Intensive Care Medicine, Respiratory Medicine, and epidemiological study regarding infection, autoimmune disease, vascular disease, and cancer. His recent publication can be found in eClinicalMedicine, Stroke, Gastric Cancer, and Journal of Infection.

Yao-Min Hung is the medical director of the Department of Medical Education at Kaohsiung Municipal United Hospital. He is also assistant professor of College of Health and Nursing, Meiho University, Pingtung, Taiwan. He is currently an associate editor for the International Journal of Rheumatic Diseases and guest editor for the special issue of “Epidemiology of Human Papillomavirus Infection” for the journal “Pathogens.” His research interests include the epidemiology and pathogenesis of systemic autoimmune and infectious diseases, especially in Human Papillomavirus, Salmonellosis and COVID-19

James Cheng-Chung Wei is the professor of the Division of Allergy, Immunology and Rheumatology and Director of the Clinical Trial Center at Chung Shan Medical University Hospital. He also serves as the vice-superintendent of CSMUH. He is currently the editor-in-chief of the International Journal of Rheumatic Diseases and is an associate editor for Frontiers in Immunology and Frontiers in Pharmacology. His research interests include translational research in ankylosing spondylitis, clinical trials and big data research in rheumatology.

References

American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Author.

Buske-Kirschbaum

Schmitt

Plessow

Romanos

Weidinger

Roessner

(2013). Psychoendocrine and psychoneuroimmunological mechanisms in the comorbidity of atopic eczema and attention deficit/hyperactivity disorder. Psychoneuroendocrinology, 38(1), 12–23.

Chen

S. W.

Zhong

X. S.

Jiang

L. N.

Zheng

X. Y.

Xiong

Y. Q.

S. J.

Qui

Huo

S. T.

Chen

(2016). Maternal autoimmune diseases and the risk of autism spectrum disorders in offspring: A systematic review and meta-analysis. Behavioural Brain Research, 296, 61–69.

Corona

J. C.

(2020). Role of oxidative stress and neuroinflammation in attention-deficit/hyperactivity disorder. Antioxidants (Basel), 9(11), 1039.

Croen

L. A.

Grether

J. K.

Yoshida

C. K.

Odouli

Van de Water

(2005). Maternal autoimmune diseases, asthma and allergies, and childhood autism spectrum disorders: A case-control study. Archives of Pediatrics & Adolescent Medicine, 159(2), 151–157.

Dalsgaard

(2021). More evidence linking autoimmune diseases to attention-deficit/hyperactivity disorder. JAMA Pediatrics, 175(3), e205502.

Dehlink

Tan

H. L.

(2016). Update on paediatric obstructive sleep apnoea. Journal of Thoracic Disease, 8(2), 224–235.

Esposito

Antinolfi

Gallai

Parisi

Roccella

Marotta

Lavano

S. M.

Mazzotta

Precenzano

Carotenuto

(2013). Executive dysfunction in children affected by obstructive sleep apnea syndrome: An observational study. Neuropsychiatric Disease and Treatment, 9, 1087–1094.

Han

V. X.

Patel

Jones

H. F.

Nielsen

T. C.

Mohammad

S. S.

Hofer

M. J.

Gold

Fabienne

Lain

S. J.

Nassar

Dale

R. C.

(2021). Maternal acute and chronic inflammation in pregnancy is associated with common neurodevelopmental disorders: A systematic review. Translation Psychiatry, 11(1), 71.

10.

Hiestand

D. M.

Britz

Goldman

Phillips

(2006). Prevalence of symptoms and risk of sleep apnea in the US population: Results from the national sleep foundation sleep in America 2005 poll. Chest, 130(3), 780–786.

11.

Hsieh

C. Y.

C. C.

Shao

S. C.

Sung

S. F.

Lin

S. J.

Kao Yang

Y. H.

Lai

E. C.

(2019). Taiwan’s national health insurance research database: Past and future. Clinical Epidemiology, 11, 349–358.

12.

Instanes

J. T.

Halmøy

Engeland

Haavik

Furu

Klungsøyr

(2017). Attention-deficit/hyperactivity disorder in offspring of mothers with inflammatory and immune system diseases. Biological Psychiatry, 81(5), 452–459.

13.

Kaditis

A. G.

Alonso Alvarez

M. L.

Boudewyns

Alexopoulos

E. I.

Ersu

Joosten

Larramona

Silvia

Narang

Trang

Tsaoussoglou

Vandenbussche

Villa

M. P.

Waardenburg

S. W.

Verhulst

(2016). Obstructive sleep disordered breathing in 2- to 18-year-old children: Diagnosis and management. The European Respiratory Journal, 47(1), 69–94.

14.

Mataix-Cols

Frans

Pérez-Vigil

Kuja-Halkola

Gromark

Isomura

Fernández de la

C. L.

Serlachius

Leckman

J. F.

Crowley

J. J.

Rück

Almqvist

Lichtenstein

Larsson

(2018). A total-population multigenerational family clustering study of autoimmune diseases in obsessive-compulsive disorder and Tourette's/chronic tic disorders. Molecular Psychiatry, 23(7), 1652–1658.

15.

Nielsen

P. R.

Benros

M. E.

Dalsgaard

(2017). Associations between autoimmune diseases and attention-deficit/hyperactivity disorder: A nationwide study. Journal of the American Academy of Child and Adolescent Psychiatry, 56(3), 234–240.e1.

16.

Nielsen

T. C.

Nassar

Shand

A. W.

Jones

Guastella

A. J.

Dale

R. C.

Lain

S. J.

(2021). Association of maternal autoimmune disease with attention-deficit/hyperactivity disorder in children. JAMA Pediatrics, 175(3), e205487.

17.

Nigg

J. T.

Breslau

(2007). Prenatal smoking exposure, low birth weight, and disruptive behavior disorders. Journal of the American Academy of Child and Adolescent Psychiatry, 46(3), 362–369.

18.

Polanczyk

de Lima

M. S.

Horta

B. L.

Biederman

Rohde

L. A.

(2007). The worldwide prevalence of ADHD: A systematic review and metaregression analysis. The American Journal of Psychiatry, 164(6), 942–948.

19.

Posner

Polanczyk

G. V.

Sonuga-Barke

(2020). Attention-deficit hyperactivity disorder. The Lancet, 395(10222), 450–462.

20.

Strickland

A. D.

(2014). Prevention of cerebral palsy, autism spectrum disorder, and attention deficit-hyperactivity disorder. Medical Hypotheses, 82(5), 522–528.

21.

Sullivan

G. M.

Feinn

(2012). Using effect size-or why the p value is not enough. Journal of Graduate Medical Education, 4(3), 279–282. https://doi.org/10.4300/JGME-D-12-00156.1

22.

Vizzini

Popovic

Zugna

Vitiello

Trevisan

Pizzi

Rusconi

Gagliardi

Merletti

Richiardi

(2019). Maternal anxiety, depression and sleep disorders before and during pregnancy, and preschool ADHD symptoms in the NINFEA birth cohort study. Epidemiology and Psychiatric Sciences, 28(5), 521–531.

23.

Wolford

Lahti

Tuovinen

Lahti

Lipsanen

Savolainen

Heinonen

Hämäläinen

Kajantie

Pesonen

A. K.

Villa

P. M.

Laivuori

Reynolds

R. M.

Räikkönen

(2017). Maternal depressive symptoms during and after pregnancy are associated with attention-deficit/hyperactivity disorder symptoms in their 3- to 6-year-old children. PLoS One, 12(12), e0190248.

24.

Chen

(2017). Factors related to pediatric obstructive sleep apnea-hypopnea syndrome in children with attention deficit hyperactivity disorder in different age groups. Medicine, 96(42), e8281.

25.

Yde Ohki

C. M.

Grossmann

Alber

Dwivedi

Berger

Werling

A. M.

Walitza

Grünblatt

. (2020). The stress-Wnt-signaling axis: A hypothesis for attention-deficit hyperactivity disorder and therapy approaches. Translationalal Psychiatry, 10(1), 315.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.05 MB