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Open Access 27-06-2025 | Original Article

Impact of Preterm Birth Subtype on Risk of Diagnosis of Autism Spectrum Disorders in the Offspring

Auteurs: Morgan R. Peltier, Michael J. Fassett, Nehaa Khadka, Meiyu Yeh, Vicki Y. Chiu, Yinka Oyelese, Meera Wells, Darios Getahun

Gepubliceerd in: Journal of Autism and Developmental Disorders

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Abstract

Preterm birth (PTB) can result from spontaneous preterm labor (spontaneous PTB, SPTB) or as an intervention by obstetricians where the baby is deliberately delivered preterm (Indicated PTB, IPTB) to get them to neonatal intensive care. The impact of these PTB subtypes on ASD risk is unclear. Therefore, we compared the risk of ASD diagnosis for children born from pregnancies that ended in SPTB or IPTB with those born at term. Electronic Health Record (EHR) data from women delivering singleton pregnancies between 2010 and 2021 were linked to their child’s EHR data to create 337,868 maternal-child dyads. The impact of IPTB and SPTB on risk of ASD diagnosis in the child was evaluated by estimating adjusted hazards ratios (adj. HR) with 95% Confidence Intervals (CI). Both SPTB (adj. HR = 1.69; 95% CI:1.34, 2.12) and IPTB (adj. HR = 2.68; 95% CI: 1.98, 3.63) were significantly increased the risk of being diagnosed with ASD compared with term birth with a larger effect size for IPTB. This trend was observed for both boys and girls; late, as well as, early PTBs, and in all racial groups except non-hispanic Blacks where no association between IPTB or SPTB with ASD was detected. In conclusion, both IPTB and SPTB significantly increase the risk of ASD diagnosis in the offspring, however, the effect may be stronger for IPTB. This may reflect differences in the etiologies of the PTB subtypes. Lack of an association between either PTB subtype with ASD diagnosis in non-Hispanic Blacks suggests that race-ethnicity may be a risk modifier.
Opmerkingen

Supplementary Information

The online version contains supplementary material available at https://​doi.​org/​10.​1007/​s10803-025-06934-5.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Introduction

Autism spectrum disorders (ASD) are a leading cause of lifelong disability with a worldwide prevalence rate of about 100/10,000 children, but rates vary wildly between different regions of the world and are as low as 1.09/10000 in South Korea and as high as 436/10000 in Australia (Zeidan et al., 2022). Although both boys and girls are affected by the condition, boys are more commonly diagnosed than girls by a factor of 4 (Zeidan et al., 2022). Economic analysis suggests that the costs in the United States may exceed 1 trillion dollars by 2025 (Leigh & Du, 2015; Wolff & Piven, 2021). Although the severity of the condition and the extent of the disability varies widely among children, recent economic analyses suggest that the national economic impact of the condition is considerable on patients and societies. Parents of autistic children were far more likely to be unemployed when compared to parents of non-autistic children or those with asthma (Lynch et al., 2023), with mothers taking the brunt of the adverse employment impacts and financial burdens (Liao & Li, 2020). Intellectual disability, a common comorbidity of ASD, further enhanced the financial and emotional strain on the parents (Saunders et al., 2015).
Early studies suggested a strong genetic component to the condition because the twin concordance rate was much higher for monozygotic than dizygotic twins (Bailey et al., 1995; Ritvo et al., 1985). Recurence of the condition amongst siblings and disparities in the prevalence of ASD due to sex and race-ethnicity further suggested that there is a genetic component to the condition. More recent studies, however, have suggested that the heritability (proportion of the disease due to genetics) of the condition is 0.50 (Sandin et al., 2014). This suggests that environmental or non-genetic exposures play an equally important role in the etiology of the condition. Over the past two decades, there has been an increasing appreciation for the role of pregnancy complications and exposures to infectious, pharmacological or toxicological agents as potential risk factors for the condition. Women whose pregnancies were complicated by hyperemesis gravidarum (Fejzo et al., 2019; Getahun et al., 2021), hypothyroidism (Andersen et al., 2018; Getahun et al., 2018; Rotem et al., 2020), preeclampsia (Gardener et al., 2009; Getahun et al., 2017; Wallace et al., 2008), placental abruption (Villamor et al., 2022), infections (Getahun et al., 2023; Zerbo et al., 2015), and exposure to valproic acid (Hernandez-Diaz et al., 2024) were more likely to have their children diagnosed with ASD than women without these conditions. In many of these studies, the risk of ASD disagnosis from the exposures were found to vary due to race-ethnicity and/or sex of the child. This suggests that these factors could modify the risk of environmental exposures as would occur in “2-hit” epidemiological models.
Preterm birth (PTB), the foremost problem in modern obstetrics due to its high prevalence and limited methods of treatment, has also been associated with increased risk for ASD (Agrawal et al., 2018; Haglund & Kallen, 2011; Pritchard et al., 2016). PTB can occur after the spontaneous initiation of contractions that proceed to labor and delivery prior to 37 weeks of gestation (spontaneous PTB, SPTB), or it can happen as an obstetrical intervention where the baby is deliberately delivered prior to 37 completed weeks of gestation (indicated PTB, IPTB) by cesarean section or induction of labor for other pregnancy complications (preeclampsia, placental abruption, fetal distress, or intra-uterine growth restriction) with the aim of providing care to the newborn in the neonatal intensive care unit. Although there is considerable overlap, these different subtypes of PTB, reflect different etiological conditions. SPTB occurs most often due to ascending microbial infections from the lower genital tract through the cervical os. In these infections, microbial growth eventually triggers the production of proinflammatory cytokines, prostaglandins and matrix metalloproteinases that, in turn, lead to premature uterine contractions, cervical ripening and placental detachment. IPTB, however, is often associated with chronic conditions of inadequate placental development/perfusion such as preeclampsia and intrauterine growth restriction. These complications become severe enough that the baby needs to be delivered early in order to avoid worse outcomes should the pregnancy continue any longer.
Rates of IPTB have been increasing over the past 30 years due largely to improvements in fetal heart rate and ultrasound monitoring, while rates of SPTB have been declining due to improvements in prenatal care (Ananth & Vintzileos, 2008; Mensah et al., 2023). The net effect of these changes has been a marked reduction in perinatal mortality for infants (Ananth & Vintzileos, 2008). The extent to which the impact of PTB subtype has on the risk of ASD diagnosis in the offspring and how it could be modified by child sex or race-ethnicity is unclear. Therefore, to test this hypothesis, we performed a retrospective cohort study using electronic health records (EHR) from a large integrated healthcare system.

Materials and Methods

Methods

This study followed the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) reporting guideline. The Kaiser-Permanente Southern California (KPSC) Institutional Review Board provided oversight for this study and approved the protocols with a waiver of informed consent.

Study Population

The KPSC is an integrated healthcare system that provides Medicaid and private insurance plans to over 4. million subscribers in the Southern California region and delivers medical care at its 15 hospitals and 236 affiliated medical offices, all of which use a centralized EHR system and standardized protocols for screening for neurodevelopmental conditions across the network.
We performed a retrospective cohort study using individual EHRs of pregnant women and their children (n = 498,484) born between January 1, 2010, and December 31, 2021, at KPSC hospitals. The child’s medical records were liked with their biological mothers using unique identifiers. The linked datasets contained information on maternal sociodemographic and behavioral characteristics, maternal medical and obstetrical history, along with complete child healthcare information. These methods for linking maternal and child data have been previously validated for this patient population (Shi et al., 2024). By using data collected in the provision of routine clinical care for each individual, we are able to reduce the risk of selection, recall, and misclassification biases regarding exposure and outcomes measures that could be introduced into the study.
Records were included only for singleton, live-born children to KPSC members from 280/7 to 426/7 weeks gestation and remained in the program between the ages of 2 and 13 and whose mothers must also have been a KPSC health plan member for ≥ 3 months (Fig. 1). After exclusions, we were left with 337,868 births for analysis. These criteria were necessary because exposures, outcomes, and confounders cannot be adequately measured in patients who were members for < 3 months. Furthermore, children under 2 years of age cannot be reliably diagnosed with ASD. Since these individuals are not at risk of being diagnosed with ASD, they were removed from the risk pool. Children born at less that 28 weeks gestation were excluded because they often have much greater morbidity risk that would complicate the diagnosis of ASD.
Fig. 1
Construction of the retrospective cohort after application of inclusion and exclusion criteria detailed in the methods section
Afbeelding vergroten

Exposures

During the study period, 87.5% of member pregnant women initiated prenatal care in their first trimester, and their pregnancy was confirmed by a first-trimester ultrasound; 9.3% initiated prenatal care during the second trimester of pregnancy, and their gestational age was determined by self-reported LMP date and later confirmed by second-trimester ultrasound with ultrasound as a gold standard. Only 2.3% initiated prenatal care during the third trimester of pregnancy or had no prenatal care, and their gestational age was determined by self-reported LMP date. We used natural language processing (NLP) to extract data on preterm labor triage. Our NLP application’s performance in extracting preterm labor evaluation visits from the unstructured text of the EHR has been previously validated with a 97% positive predictive value (Xie et al., 2022).
To ascertain SPTB, we first identified all preterm labor visits and defined SPTB as a preterm delivery that follows the spontaneous onset of labor, is not indicated by concomitant pregnancy complications, and occurs within 7 days of the last preterm labor visit given that preterm labor that does not result in delivery within 7 days is most likely false labor (Blackwell et al., 2017; Peaceman et al., 1997; Wing et al., 2017). All the remaining PTBs with medical indications, such as preeclampsia/eclampsia, without spontaneous preterm labor were grouped as IPTBs. We recently evaluated the accuracy of the NLP algorithm in identifying PTB subtypes and term births by comparing them with a manual review of randomly-selected medical records (gold standard) by a trained chart abstractor. For each of the PTB subtypes and term births, we randomly sampled 30 pregnancies, a total of 90 pregnancies for chart review. Sensitivity, specificity, positive (PPV), and negative (NPV) predictive values and their corresponding 95% confidence intervals (CI) were calculated. The estimated sensitivity, specificity, PPV, and NPVs ranged between 93 and 100%, with corresponding 95% CI ranging between 83 and 100%.

Outcomes

KPSC employs universal screening for developmental disorders and pediatricians are required to perform a series of behavioral and developmental surveillance at all well-child visits. Beginning at 18 months of age, ASD is screened for using a modified version of the Checklist for Autism in Toddlers [M-CHAT] and developmental screening questionnaires for toddlers and this screening continues with age-appropriate instruments at subsequent well-visits. Positive initial screens are referred for further follow-up with our child/adolescent psychiatrist, developmental/behavioral pediatrician, child psychologist, neurologist, or contracted specialist through the medical plan. Ninety six percent of the children diagnosed with ASD in the KPSC system were diagnosed by KPSC experts and received subsequent consultations by doctors within the KPSC system. The rest were first diagnosed outside KPSC, but the ASD diagnosis was confirmed by KPSC specialists. Given the standardized protocols for screening and diagnosing ASD in this patient population, all children in the cohort have an equal baseline probability of being diagnosed.
Diagnosis of ASD was made using the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR) for any of the following conditions: Autistic Disorder, Childhood Disintegrative Disorder, Rett’s Disorder, Asperger’s Disorder, or Pervasive Developmental Disorder-Not Otherwise Specified (PDD-NOS). Children, ages 2–13 years, with at least one documented DSM-IV-TR code for ASD on any two separate visits during the follow-up period were considered ASD cases.
To identify cases of ASD in our EHRs ICD-9 and ICD-10 (since Oct 1, 2015) codes that included: 299.0, 299.00, 299.01, 299.10, 299.11, 299.80, 299.81, 299.90, 299.91, 299.8, 299.9, F84.0, F84.5, F84.8, and F84.9 at the date of diagnosis were used. The accuracy of these codes was validated by research associates who were highly trained in medical record review and adjudication by experts in the field (MJF and DG)(gold standard). ASD cases were identified with 100% sensitivity, 100% specificity, 100% NPV, and 99.2% PPV (Shi et al., 2024).

Covariates

We considered the child’s race/ethnicity and sex, maternal age (< 25, 25–34, ≥ 35 years), median household income based on census tract of residence, parity, the timing of prenatal care initiation, smoking and/or illicit drug use during pregnancy (yes/no), and maternal medical and obstetrical conditions (chronic hypertension, pre-gestational and gestational diabetes) as potential confounders. Maternal and paternal race/ethnicity defined the child’s race/ethnicity, where a child was categorized as having the same race-ethnicity as their parents. And if the parents were of interracial/interethnic unions, the child was considered to be of Other/Multiple race/ethnicity.

Statistical Analyses

Descriptive statistics regarding our patient population were obtained from the EHRs for women whose child was later diagnosed with ASD and for those in which no ASD diagnosis was reported. The potential association between IPTB and SPTB and the risk of ASD diagnosis in the offspring was evaluated using marginal Cox proportional hazards models. Follow-up of children started from the delivery date until the date of ASD diagnosis or censoring due to health plan disenrollment, 13th birthday, non-ASD-related death, or the end of the study (Dec 31, 2021). Results are presented as crude and adjusted hazard ratios (HR) with 95% confidence intervals (CI). Incidence rates of ASD are also presented as an index of absolute risk.
Since child sex and race-ethnicity are well-established risk factors for ASD (Zeidan et al., 2022), we also stratified the data by these factors in additional analyses. Missing data were handled in the model-building step by creating dummy variables for missing observations. This way, an entire patient’s record would not be removed due to missing data for a variable. All analyses were performed using SAS version 9.4 (SAS Institute, Cary, NC).
A series of sensitivity analyses were also performed to examine how our overall findings may be affected by the mother having a diagnosis of ASD, antepartum depression, psychosocial disordrs, late or no initiation of prenatal care, or who who consumed anti-depressants before and during the pregnancy. Additional sensitivity analyses made further adjuments for year of birth, pre-pregnancy body mass index and maternal comoribidities such as hypertension, autoimmune diseases, and thyroid conditions and pregnancy complications. Finally, we estimated the size that an unknown and unmeasured confounder(s) would have to be (E-value) to account for our observed findings.

Results

Characteristics of the mothers and their pregnancies who gave birth to a child that was later diagnosed with ASD and those whose child was not diagnosed are listed in Table 1. Women whose children were diagnosed with ASD were significantly more likely to: be older, have higher income, be nulliparous, have a PTB, have smoked during pregnancy, or come from non-White or Hispanic race-ethnicity. They were also more likely to have reported drug use during pregnancy, have had gestational or pre-gestational diabetes, and to have given birth to a male infant. Although there was a slight excess in the proportion of women with late or no prenatal care, the results did not reach statistical significance.
Table 1
Baseline Characteristics by Autism Spectrum Disorders (ASD) Status
 Characteristic, n (%)
ASD; N = 14,882 (%)
No ASD; N = 322,986 (%)
Total; N = 337,868 (%)
p-value
Maternal age (years)
   
 < 0.0011
< 20
312 (2.1)
8021 (2.5)
8333 (2.5)
 
20–29
5552 (37.3)
123,929 (38.4)
129,481 (38.3)
 
30–34
4864 (32.7)
111,630 (34.6)
116,494 (34.5)
 
 ≥35
4154 (27.9)
79,406 (24.6)
83,560 (24.7)
 
Household income, USD2
   
 < 0.0011
 < $30,000
397 (2.7)
6335 (2.0)
6732 (2.0)
 
$30,000–$49,999
3647 (24.5)
65,975 (20.4)
69,622 (20.6)
 
$50,000–$69,999
4455 (29.9)
93,075 (28.8)
97,530 (28.9)
 
$70,000–$89,999
3196 (21.5)
73,466 (22.7)
76,662 (22.7)
 
$90,000 + 
3183 (21.4)
83,888 (26.0)
87,071 (25.8)
 
Missing
4 (0.0)
247 (0.1)
251 (0.1)
 
Parity
   
 < 0.0011
Missing
2154 (14.5)
41,043 (12.7)
43,197 (12.8)
 
Multiparous
7474 (50.2)
185,495 (57.4)
192,969 (57.1)
 
Nulliparous
5254 (35.3)
96,448 (29.9)
101,702 (30.1)
 
Gestational age(week)
   
 < 0.0011
28–32
2895 (0.9)
264 (1.8)
3159 (0.9)
 
33–36
1208 (8.1)
19,307 (6.0)
20,515 (6.1)
 
Term Birth
13,410 (90.1)
300,784 (93.1)
314,194 (93.0)
 
Smoking during pregnancy
   
0.0061
No
14,492 (97.4)
315,641 (97.7)
330,133 (97.7)
 
Yes
390 (2.6)
7345 (2.3)
7735 (2.3)
 
Initiation of prenatal care
   
0.1811
≤3 months
13,303 (89.4)
290,224 (89.9)
303,527 (89.8)
 
Late/No care
1492 (10.0)
30,988 (9.6)
32,480 (9.6)
 
Missing
87 (0.6)
1774 (0.5)
1861 (0.6)
 
Child's race/ethnicity
   
 < 0.0011
Non-Hispanic White
1655 (11.1)
50,240 (15.6)
51,895 (15.4)
 
Non-Hispanic Black
886 (6.0)
15,433 (4.8)
16,319 (4.8)
 
Hispanic
6100 (41.0)
123,642 (38.3)
129,742 (38.4)
 
Asian/Pacific Islander
1430 (9.6)
28,984 (9.0)
30,414 (9.0)
 
Other/Multiple
3369 (22.6)
71,400 (22.1)
74,769 (22.1)
 
Unknown
1442 (9.7)
33,287 (10.3)
34,729 (10.3)
 
Child sex
   
 < 0.0011
Female
3598 (24.2)
161,335 (50.0)
164,933 (48.8)
 
Male
11,284 (75.8)
161,651 (50.0)
172,935 (51.2)
 
Drug during pregnancy
   
 < 0.0011
No
14,244 (95.7)
311,253 (96.4)
325,497 (96.3)
 
Yes
638 (4.3)
11,733 (3.6)
12,371 (3.7)
 
Type I/II Diabetes
   
 < 0.0011
No
14,368 (96.5)
316,459 (98.0)
330,827 (97.9)
 
Yes
514 (3.5)
6527 (2.0)
7041 (2.1)
 
Gestational Diabetes Mellitus
   
 < 0.0011
No
12,458 (83.7)
278,747 (86.3)
291,205 (86.2)
 
Yes
2424 (16.3)
44,239 (13.7)
46,663 (13.8)
 
Statistically significant associations are indicated in bold type
1Chi-Square p-value;
2Median household income based on census tract information with Inflation Adjustment
As expected, children from women whose pregnancies ended in PTB were at significantly greater risk of being diagnosed with ASD (adjusted HR: 1.96, 95% CI: 1.62, 2.36). Survival curves demonstrate a more rapid accumulation of ASD cases in children born preterm than at term (Fig. 2, Panel A). A series of sensitivity analyses demonstrated that this effect persisted after excluding women diagnosed with ASD, antepartum depression, psychosocial disorders in pregnancy, who were prescribed anti-depressants prior to or during pregnancy, or who had no or late (3rd trimester) initiation of prenatal care (Table S1). Additional adjustments for possible confounding by preeclampsia/eclampsia, neonatal sepsis, maternal comorbidities (chronic hypertension, autoimmune disease, and thyroid conditions) as well as year of delivery and maternal BMI did not affect the observed association (Table S1). It was estimated that an unknown, unmeasured confounding variable would have to be rather large (E-value = 3.33) in order to explain the observed associations between PTB and risk of ASD.
Fig. 2
Survival curves for risk of ASD in children born with and without PTB (Panel A) and with IPTB, SPTB or No PTB (Panel B)
Afbeelding vergroten
Increased risk of ASD was present for children born of both spontaneous and indicated subtypes (Table 2). However, the association appeared to be much stronger for IPTB given the higher point estimates for both the hazards and incidence rates (Table 2) and a more rapid accumulation of ASD cases for pregnancies that ended in IPTB than SPTB, which was still much higher than term birth (Fig. 2, Panel B).
Table 2
Association Between Maternal Status and Autism Spectrum Disorders (ASD) Incidence and Risk
Status
Births (N)
ASD N
Person-years
IR (‰)
HR (95% Confidence intervals)
Adjusted p-value
Crude
Adjusted
Term Birth
314,194
13,410
1,853,239
7.24
1.00 (Reference)
1.00 (Reference)
PTB
23,674
1,472
141,202
10.42
1.45 (1.38, 1.53)
1.96 (1.62, 2.36)
 < 0.001
SPTB
16,435
967
100,826
9.59
1.35 (1.27, 1.44)
1.69 (1.34, 2.12)
 < 0.001
IPTB
7,239
505
40,376
12.51
1.70 (1.55, 1.85)
2.68 (1.98, 3.63)
 < 0.001
Statistically significant associations are indicated in bold type
Abbreviations: IR, incidence rates per 1000 person-years; HR, hazard ratio; CI, confidence intervals; PTB, preterm birth; SPTB, spontaneous PTB; IPTB, indicated preterm birth
Adjustments were made for maternal age, Child’s race/ethnicity, median household income, parity, prenatal care, smoking during pregnancy, child’s sex, drug during pregnancy, Type I/II Diabetes, Gestational Diabetes Mellitus, interaction term of PTB and child’s sex (p = 0.0025), and interaction term of PTB and child’s race (p = 0.0013)
We found a getational age-dependent relationship between gestational age at delivery for all types of PTB and an increased risk of ASD (Table 3). The incidence rates, however, were higher at each gestational age of delivery for IPTBs than they were for SPTBs, suggesting a higher level of risk (Table 3).
Table 3
Association Between Maternal Status and Autism Spectrum Disorders (ASD) Incidence and Risk by Gestational Age
Birth (weeks)
Group
Births (N)
ASD (N)
Person-Years
IR (‰)
HR (95% Confidence intervals)
Adjusted p-Value
Crude
Adjusted
 ≥ 37
No PTB
314,194
13,410
1,853,239
7.24
1.00 (Reference)
1.00 (Reference)
28–32
PTB
3,159
264
18,505
14.27
1.98 (1.75, 2.23)
2.56 (2.06, 3.20)
 < 0.001
33–36
PTB
20,515
1,208
122,697
9.85
1.37 (1.30, 1.46)
1.89 (1.56, 2.27)
 < 0.001
28–32
SPTB
1,724
131
10,285
12.74
1.78 (1.50, 2.12)
2.08 (1.56, 2.77)
 < 0.001
33–36
SPTB
14,711
836
90,541
9.23
1.30 (1.22, 1.40)
1.66 (1.32, 2.08)
 < 0.001
28–32
IPTB
1435
133
8220
16.18
2.22 (1.88, 2.63)
3.52 (2.52, 4.92)
 < 0.001
33–36
IPTB
5,804
372
32,156
11.57
1.56 (1.41, 1.73)
2.49 (1.83, 3.39)
 < 0.001
Statistically significant associations are indicated in bold type
Abbreviations: IR, incidence rates per 1000 person-years; HR, hazard ratio; CI, confidence intervals; PTB, preterm birth; SPTB, spontaneous PTB; IPTB, indicated preterm birth
Adjustments were made for maternal age, Child’s race/ethnicity, median household income, parity, prenatal care, smoking during pregnancy and child’s sex, drug during pregnancy, Type I/II Diabetes, Gestational Diabetes Mellitus, interaction term of PTB and child’s sex, and interaction term of PTB and child’s race
Further analysis and stratification of the data by sex suggested statistically significant sex by PTB subtype interaction (P = 0.003). Both SPTB and IPTB increased the risk of ASD for both boys and girls. However, the association appeared to be stronger for girls than for boys, as suggested by the larger hazard ratios for each PTB subtype (Table 4). Examination of the incidence rate ratios for IPTB vs. SPTB suggested a similar trend. Girls had a 1.40-fold increased incidence rate, but for boys, it was only 1.33-fold higher for IPTB vs. SPTB. Boys had high incidence rates of ASD for both types of PTB, suggesting that they are at increased absolute risk for the diagnosis (Table 4).
Table 4
Association Between Maternal Status and Autism Spectrum Disorders (ASD) Incidence and Risk by Child’s Sex
Child’s Sex
Group
Births (N)
ASD (N)
Person-years
IR (‰)
HR (95% Confidence intervals
Adjusted
p-value
Crude
Adjusted
Female
No PTB
154,486
3,243
920,867
3.52
1.00 (Reference)
1.00 (Reference)
 
 
PTB
10,447
355
63,005
5.63
1.61 (1.45, 1.80)
2.17 (1.58, 2.96)
 < 0.001
 
SPTB
7,038
219
43,694
5.01
1.45 (1.27, 1.66)
1.72 (1.14, 2.59)
0.009
 
IPTB
3,409
136
19,311
7.04
1.97 (1.66, 2.33)
3.35 (2.13, 5.29)
 < 0.001
 Male
 No PTB
159,708 
10,167 
932,372 
 10.90
 1.00 (Reference)
 1.00 (Reference)
 
 
PTB
13,227
1,117
78,197
14.28
1.32 (1.24, 1.41)
1.56 (1.30, 1.87)
 < 0.001
 
SPTB
9,397
748
57,132
13.09
1.22 (1.14, 1.32)
1.43 (1.15, 1.77)
0.001
 
IPTB
3,830
369
21,065
17.52
1.57 (1.42, 1.74)
1.95 (1.43, 2.66)
 < 0.001
Statistically significant associations are indicated in bold type
Abbreviations: IR, incidence rates per 1000 person-years; HR, hazard ratio; CI, confidence intervals; PTB, preterm birth; SPTB, spontaneous PTB; IPTB, indicated preterm birth
Adjustments were made for maternal age, Child’s race/ethnicity, median household income, parity, prenatal care, smoking during pregnancy, drug during pregnancy, Type I/II Diabetes, Gestational Diabetes Mellitus, and interaction term of PTB and child’s race
Significant interactions between PTB and race-ethnicity were also found (P < 0.001), suggesting that the impact of PTB on ASD risk differs by child’s race-ethnicity. Both SPTB and IPTB subtypes were found to increase the risk of ASD diagnosis for children of non-Hispanic White, Hispanic, and Other/Multiple race-ethnicity (Table 5). No association was found for any PTB subtype for non-Hispanic Black and children of unknown race-ethnicity. Asians/Pacific Islanders were at increased risk of ASD diagnosis if they were born from an IPTB, but not an SPTB (Table 5). Comparisons of incidence rates suggest that the incidence for ASD was higher for any PTB, IPTB and SPTB than it was for no PTB, regardless of race-ethnicity. Similarly, the incidence of ASD was higher for pregnancies born from IPTBs than it was for SPTBs for all race-ethnicities (Table 5).
Table 5
Association Between Maternal Status and Autism Spectrum Disorders (ASD) Incidence and Risk by Child’s Race/Ethnicity
Child’s Race/Ethnicity
Group
Births
ASD (N)
Person-years
IR (‰)
HR (95% Confidence Interval)
Adj. P-value
Crude
Adjusted
Non-Hispanic White
No PTB
48,915
1,484
303,776
4.89
1.00 (Reference)
1.00 (Reference)
 
 
PTB
2,980
171
18,780
9.11
1.88 (1.61, 2.20)
2.12 (1.55, 2.90)
 < 0.001
 
SPTB
2,217
112
14,162
7.91
1.64 (1.36, 1.99)
1.69 (1.12, 2.54)
0.012
 
IPTB
763
59
4,618
12.78
2.60 (2.01, 3.36)
3.29 (2.09, 5.20)
 < 0.001
Non-Hispanic Black
No PTB
14,928
799
93,501
8.55
1.00 (Reference)
1.00 (Reference)
 
 
PTB
1,391
87
8,793
9.89
1.17 (0.94, 1.45)
1.19 (0.78, 1.81)
0.417
 
SPTB
922
56
5,841
9.59
1.13 (0.87, 1.48)
1.07 (0.61, 1.87)
0.812
 
IPTB
469
31
2,952
10.50
1.24 (0.87, 1.76)
1.38 (0.76, 2.51)
0.289
Hispanic
No PTB
120,406
5,527
746,224
7.41
1.00 (Reference)
1.00 (Reference)
 
 
PTB
9,336
573
58,207
9.84
1.34 (1.23, 1.46)
1.35 (1.13, 1.61)
0.001
 
SPTB
6,504
380
41,154
9.23
1.26 (1.14, 1.40)
1.33 (1.07, 1.65)
0.012
 
IPTB
2,832
193
17,053
11.32
1.51 (1.31, 1.74)
1.38 (1.03, 1.86)
0.031
Asian/PI
No PTB
28,213
1,281
178,599
7.17
1.00 (Reference)
1.00 (Reference)
 
 
PTB
2,201
149
14,299
10.42
1.48 (1.25, 1.75)
1.58 (1.10, 2.26)
0.012
 
SPTB
1,637
98
10,825
9.05
1.30 (1.06, 1.59)
1.17 (0.72, 1.90)
0.524
 
IPTB
564
51
3,474
14.68
2.02 (1.54, 2.67)
2.49 (1.50, 4.13)
 < 0.001
Other/Multiple
No PTB
69,576
3,012
424,077
7.10
1.00 (Reference)
1.00 (Reference)
 
 
PTB
5,193
357
32,023
11.15
1.58 (1.42, 1.76)
1.71 (1.37, 2.13)
 < 0.001
 
SPTB
3,675
235
23,173
10.14
1.46 (1.28, 1.66)
1.65 (1.26, 2.15)
 < 0.001
 
IPTB
1,518
122
8,850
13.79
1.90 (1.59, 2.28)
1.84 (1.27, 2.66)
0.001
Unknown
No PTB
32,156
1,307
107,062
12.21
1.00 (Reference)
1.00 (Reference)
 
 
PTB
2,573
135
9,100
14.84
1.25 (1.05, 1.48)
1.32 (0.95, 1.84)
0.099
 
SPTB
1,480
86
5,671
15.16
1.31 (1.05, 1.63)
1.29 (0.84, 1.98)
0.242
 
IPTB
1,093
49
3,429
14.29
1.15 (0.87, 1.52)
1.35 (0.83, 2.22)
0.231
Statistically significant associations are indicated in bold type
Abbreviations: IR, incidence rates per 1000 person-years; HR, hazard ratio; CI, confidence intervals; PI, Pacific Islander; PTB, preterm birth; SPTB, spontaneous PTB; IPTB, indicated preterm birth
Adjustments were made for maternal age, median household income, parity, prenatal care, smoking during pregnancy, child’s sex, drug during pregnancy, Type I/II Diabetes, Gestational Diabetes Mellitus, and interaction term of PTB and child’s sex

Discussion

In this large retrospective cohort study, we found PTB is a strong risk factor for the diagnosis of ASD in the offspring that persists after adjustment for confounding variables and after exclusion of patients with mental health disorders. This sugges that PTB is not simply a biomarker for maternal mental illness that is passed onto her offspring in the form of ASD. Any unknown, unmeasured confounding variable(s) would have to be particularly large (E-value = 3.33) to account for the observed findings (Table S1).
Both SPTB and IPTBs are associated with increased risk of ASD in a dose-dependent manner; however, the relative and absolute risk may be higher for IPTBs than they are for SPTBs. These findings are consistent with previous studies where any type of PTB (Buchmayer et al., 2009; Curran et al., 2018; D'Onofrio et al., 2013) as well as the pregnancy complications that are often comorbid with PTB such as preeclampsia (Buchmayer et al., 2009; Wallace et al., 2008), hypertension in pregnancy (Curran et al., 2018; Wallace et al., 2008), small for gestational age (Lampi et al., 2012; Moore et al., 2012), maternal hypothyroidism (Andersen et al., 2018; Ge, 2020; Getahun et al., 2018) and placental abruption (Villamor et al., 2022) are associated with ASD diagnosis in the offspring. Our finding of a dose-dependent relationship between the degree of PTB and the risk of ASD diagnosis in the offspring is consistent with previous studies where very high rates of ASD were found in very early PTBs (< 32 weeks) (Chen et al., 2019). By comparing SPTB with IPTB, we found that IPTB may be a stronger predictor for ASD diagnosis in the offspring. This occurred at all degrees of PTB with the dose-dependent association between reduced gestational length and the incidence of ASD in the offspring, demonstrating parallel trends with higher rates for the IPTBs (Table 3).
The reason for a stronger association with IPTB than SPTB is that conditions often resulting in IPTB may have more opportunity to adversely affect neurodevelopment. Conditions such as preeclampsia, placental abruption, and inadequate fetal growth are likely to emerge during placentation and continue throughout the pregnancy and have recently been associated with an increased risk of ASD in the offspring (Villamor et al., 2022). Most SPTBs, by contrast, are associated with ascending infections from the lower genital tract, where the pathogens spread to the maternal–fetal interface, grow, and reach a threshold that activates the labor mechanism. This reflects a sudden revocation of the immunological privileges that the fetus has enjoyed up until that time, and the woman’s body pushes out the fetus before the infection can spread further (Peltier, 2003). Further research is needed to determine which biomarkers may be responsible for the observed differences between IPTB and SPTB on the risk of ASD.
One potential biomarker for pregnancies at higher risk for later child ASD may be interleukin (IL)−6, which is produced at higher levels in women with pregnancies complicated by preeclampsia (Kauma et al., 1995; Silver et al., 1993), placental abruption, intrauterine growth restriction (Silver et al., 1993), thyroid dysfunction (Oztas et al., 2015), and gestational diabetes (Jafarzade et al., 2023) -pregnancy complications that have all been previously associated with increased risk of ASD in the offspring (Andersen et al., 2018; Buchmayer et al., 2009; Ge et al. 2020; Getahun et al., 2018; Wallace et al., 2008; Xiang et al., 2015). IL-6 is also detected at higher levels in cord blood (Galazios et al., 2002) as well as the maternal (Oztas et al., 2015) circulation in cases complicated by PTB. Although IL-6 has been conclusively shown to not be in the causal pathway for infection-mediated PTB (Sadowsky et al., 2006; Yoshimura & Hirsch, 2003), prenatal exposure to this cytokine does lead to ASD-like conditions in the offspring (Smith et al., 2007). Additional studies have suggested that the placenta is the source of IL-6 in animal models of ASD (Hsiao & Patterson, 2011), where it may function by altering myelination (Han et al., 2021), synaptogenesis (Ohki et al., 2024; Russo et al., 2018) and differentiation of neurons (Wu et al., 2017) that are disrupted in ASD. Given that IL-6 is associated with pregnancy complications that can lead to both SPTB and IPTB, higher levels of production of this cytokine could be a point of convergence between these different etiologies. Further studies are needed to determine if the observed differences between the risk of ASD from IPTB and SPTB are mediated through relative differences in IL-6 production.
Our finding that the risk of ASD was greater for females delivering preterm than males delivering preterm is unlikely to reflect differences in neuronal fragility. Estradiol is thought to have neuroprotective effects on the developing brain (Pansiot et al., 2017), and female fetuses have higher amniotic fluid concentrations of estradiol than male fetuses do (Robinson et al., 1977). Furthermore, amniotic fluid estradiol concentrations are increased in pregnancies that are complicated by PTB (Mazor et al., 1994) that may be part of a compensatory mechanism. These higher levels of estradiol would be expected to protect the developing brain from the neurological injury that results from conditions that result in PTB or the PTB process itself. Although there is strong evidence of IL-6 as a biomarker of PTB, most studies have suggested that male placentas produce more IL-6 than female ones do in a setting of infection (Osman et al., 2024), preeclampsia (Muralimanoharan et al., 2013), or environmental toxins (Miller et al., 2010). Therefore, additional research is needed to determine what biological factors could be responsible for sex differences in ASD risk in the setting of PTB.
Our finding of a higher incidence of ASD in non-White children is consistent with recent studies that have documented large increases for non-White children since 2010, that may be due to improvements in screening, diagnosis, and insurance coverage for minority populations (Gallin et al., 2024). The variation in race-ethnicities on the impact of PTB, where we found both subtypes of PTBs are associated with increased risk of ASD diagnosis in the offspring of only non-Hispanic White, Hispanic, and Other/mixed race/ethnicities may be a function of sample size as we did not employ any form of stratified sampling. Women of non-Hispanic Black or Asian/Pacific Islander race/ethnicities were a much smaller proportion of the patient population. Even with much smaller sample sizes, we observed that IPTB increased the risk of ASD in Asian-Pacific Islanders. Further studies that employ stratified sampling to over-sample individuals of minority race-ethnicites are needed to fully test the hypothesis that race-ethnicity may be a risk-modifier for the impact of PTB on ASD risk. These studies would also need to determine the role of the disparities in prevalence of pregnancy complications (e.g., preeclampsia) that can lead to IPTB, as well as the screening and interventions for them have in any observed racial-ethnic disparities.
Strengths of our study include the large patient population of diverse socio-economic background in the United States with long-term follow-up that allowed for complete adjustment for potential confounders, and stratified analyses by sex and race-ethnicity. Since the KPSC model of health care is based around vertical integration of the health care resources into insurance, all member women and children in the study have access to health care, and implementation of standardized protocols for screening and diagnosing children for ASD across the network ensures that each child is at equal baseline probability for being diagnosed for the condition. Validation of our ASD-related ICD-10 codes minimizes the risk of patient misclassification. In this study, we characterized PTB by its subtypes using NLP algorithm with remarkable performance (96% PPV) in ascertaining PTB subtypes (Xie et al., 2022).
An additional strength of this study was the extensive sensitivity analyses performed to scrutinize the finding that PTB is a strong risk factor for ASD diagnosis. ASD has a strong genetic component, and women with ASD are at greater risk of having an IPTB, cesarean section, and preeclampsia (Sundelin et al., 2018). Women with higher degrees of autistic traits (as measured by the Autism Spectrum Quotient Japanese Version 10) were found to be more likely to deliver preterm, very early preterm, or to give birth to a baby that was small for gestational age (Hosozawa et al., 2024). We removed cases from our study where the mothers had diagnosis of ASD, depression or any psychosocial disorder in pregnancy or who consumed antidepressants and found that the overall results and conclusions were not affected (Table S1).
Limitations include limited information about exposure to environmental factors, such as exposure to ambient air pollution levels that are known risk factors for both PTB and ASD. Although Race-Ethnicity is commonly used to look at distinct groups of people withing a population, more comprehensive measures of social determinants of health may be more appropriate that take into account other factors such as food insecurity, employment and residential neighborhood. These, however, require additional individual-level data that we do not currently have access to.
Subtype of preterm labor is unlikely a cause of ASD but is rather a marker for underlying pregnancy complications that correlate with ASD. The underlying pregnancy complications that result in SPTB (eg. infection); however, are often quite different from those that result in IPTB (eg. preeclampsia, GDM, placental abruption and PPROM). Therefore, we expected that there would be different impacts of IPTB and SPTB on risk of ASD diagnosis in the offspring as was observed in our study. This suggests that future studies on specific pregnancy complications such as preeclampsia need to be performed with mediation analysis to explore whether IPTB or SPTB is in the causal pathway between pregnancy complications and ASD diagnosis.
We also do not have ready access to the results of screening tests that could be useful to determine if there is a dose-dependent relationship between the severity of PTB and severity of ASD and its impact on the quality of life that can vary widely from child to child. Furthermore, we are also unable to account for the possible effects of tocolytics or other medications received during pregnancy to prevent PTB (e.g., progesterone) that could also affect the risk of ASD and would be confounded with PTB. Nonetheless, the strong association observed in our study suggests that any confounding factor would have to be very strong (E-value > 3.0; Table S1) to account for the observed associations in this study.

Conclusions

Our findings suggest that both IPTB and SPTB increase the risk of ASD in a dose-dependent manner but that the association may be stronger for IPTB. Although the association was observed in both girls and boys, the impact of PTB on the diagnosis of ASD may be stronger for girls. Race-ethnicity may also be a risk modifier, with PTB increasing the risk largely in non-Hispanic White and Hispanic populations.

Acknowledgments

The authors thank the patients of Kaiser Permanente for helping to improve care through the use of information collected from our electronic health record systems.

Declarations

Conflict of interest

The authors report no conflicts of interest.
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Literatuur
go back to reference Ge, G. M., Leung, M. T., Man, K. K., Leung, W. C., Ip, P., Li, G. H., Wong, I. C., Kung, A. W., & Cheung, C. L. (2020). Maternal Thyroid Dysfunction During Pregnancy and the Risk of Adverse Outcomes in the Offspring: A Systematic Review and Meta-Analysis. The Journal of Clinical Endocrinology & Metabolism, 105(12), 3821–4384. https://doi.org/10.1210/clinem/dgaa555CrossRef Ge, G. M., Leung, M. T., Man, K. K., Leung, W. C., Ip, P., Li, G. H., Wong, I. C., Kung, A. W., & Cheung, C. L. (2020). Maternal Thyroid Dysfunction During Pregnancy and the Risk of Adverse Outcomes in the Offspring: A Systematic Review and Meta-Analysis. The Journal of Clinical Endocrinology & Metabolism, 105(12), 3821–4384. https://​doi.​org/​10.​1210/​clinem/​dgaa555CrossRef
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Metagegevens
Titel
Impact of Preterm Birth Subtype on Risk of Diagnosis of Autism Spectrum Disorders in the Offspring
Auteurs
Morgan R. Peltier
Michael J. Fassett
Nehaa Khadka
Meiyu Yeh
Vicki Y. Chiu
Yinka Oyelese
Meera Wells
Darios Getahun
Publicatiedatum
27-06-2025
Uitgeverij
Springer US
Gepubliceerd in
Journal of Autism and Developmental Disorders
Print ISSN: 0162-3257
Elektronisch ISSN: 1573-3432
DOI
https://doi.org/10.1007/s10803-025-06934-5