Autism Spectrum Disorder and Cardiovascular Risk: The Role of Frontal QRS–T Angle and Systemic Immune-Inflammation Index
Article information
Abstract
Objective
Autism spectrum disorder (ASD) has been linked to autonomic dysregulation and low-grade systemic inflammation, but their expression on routine electrocardiography (ECG) in pediatric samples remains unclear. We examined whether inflammatory indices relate to ECG markers, particularly the frontal QRS–T angle (fQRS–T), in youths with ASD.
Methods
In this case—control study, children and adolescents with Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition— confirmed ASD and age- and sex-matched healthy controls (HC) were enrolled. Sociodemographic data, complete blood counts, biochemistry/hormones, and standard 12-lead ECGs were obtained. Inflammatory composites included the systemic immune-inflammation index (SII), systemic inflammatory response index, and pan-immune-inflammation value. fQRS–T was derived from the automated ECG report as the absolute difference between frontal QRS and T axes (0°–180°) and verified by clinician review.
Results
Eighty-four ASD participants and 82 HC were analyzed, with no age or sex differences. Compared with HC, ASD showed higher heart rate (97.56±21.20 bpm vs. 84.63±17.02 bpm; p=0.004) and a wider fQRS–T (median 23.5° [IQR 5.75–43.25] vs. 18° [IQR 3.5–31.5]; p=0.005). SII was modestly higher in ASD (539.66 vs. 453.14; p=0.030). Autism Behavior Checklist scores correlated with SII (r=0.387; p=0.041) and fQRS–T (r=0.566; p=0.002), but not with heart rate (r=0.117; p=0.546). SII also correlated with fQRS–T (r=0.424; p=0.010).
Conclusion
ASD was associated with increased fQRS–T and SII despite largely normal conventional ECG intervals, and behavioral severity tracked with both inflammatory tone and repolarization heterogeneity, suggesting a possible association between inflammatory, autonomic, and electrophysiological processes.
INTRODUCTION
Autism spectrum disorder (ASD) is a common childhood psychiatric condition with a multifactorial etiology that can lead to substantial functional impairment. Beyond limitations in communication and social interaction, ASD is characterized by restricted and repetitive behaviors and interests. The most frequently implicated etiologic factors include physiological and metabolic abnormalities, immune-related dysregulation, oxidative stress, and mitochondrial dysfunction [1]. ASD often co-occurs with disorders spanning morphological, physiological, and psychiatric systems [2]. As a neurodevelopmental condition, its prevalence has been reported to range from 1% to 2.76% [3].
In addition to its neurobehavioral manifestations, growing evidence suggests that ASD is associated with systemic physiological alterations, including autonomic nervous system dysregulation and low-grade chronic inflammation. These changes may extend beyond the central nervous system and involve cardiovascular regulation. Recent studies have reported altered heart rate variability (HRV), reduced parasympathetic tone, and increased sympathetic activity in individuals with ASD, indicating a state of autonomic imbalance [4,5].
Cardiac electrophysiological markers derived from electrocardiography (ECG) have been increasingly investigated as noninvasive indicators of autonomic and myocardial function. Among these, the frontal QRS–T angle (fQRS–T) reflects the spatial relationship between ventricular depolarization and repolarization and has been associated with myocardial heterogeneity and adverse cardiovascular outcomes in various clinical populations [6-9].
In parallel, systemic inflammation has emerged as a key pathophysiological component in ASD. Composite inflammatory indices such as the systemic immune-inflammation index (SII), systemic inflammatory response index (SIRI), and panimmune-inflammation value (PIV) integrate multiple hematological parameters and provide a more comprehensive assessment of inflammatory status than single biomarkers [10-12]. However, the relationship between these inflammatory markers and cardiac electrophysiological parameters in ASD remains poorly understood.
Current treatment emphasizes individualized, intensive, and early educational interventions, which have shown promising benefits for core symptoms in large-scale studies [13]. Alongside educational approaches, pharmacologic treatments and several experimental modalities offer additional options.
Psychiatric medication use, a major component of ASD management, has risen noticeably in recent years. Owing to behavioral challenges, co-occurring intellectual disability, sleep problems, irritability, and stereotyped movements, approximately two-thirds of children and adolescents with ASD are prescribed psychotropics. Comorbid psychiatric disorders are reported in up to 70% of individuals with ASD, including attention-deficit/hyperactivity disorder (ADHD), mood disorders, and anxiety disorders [13,14]. About 56% use at least one psychotropic medication and ~20% use more than one [15]. Commonly prescribed classes include antidepressants, stimulants, antipsychotics, mood stabilizers, and antihistamines, among others. Reported adverse effects span cardiac (e.g., tachycardia, tachypnea, hypo/hypertension, arrhythmia), hepatic or renal laboratory abnormalities, thyroid dysfunction, and lowered seizure threshold. Given the frequent communication limitations in ASD, regular monitoring with complete blood count, biochemistry, electrocardiogram, and—when epilepsy is present—electroencephalography, is important for the surveillance of medication side effects.
Despite increasing recognition of both inflammatory activation and autonomic dysregulation in ASD, studies integrating these mechanisms with cardiac electrophysiological markers are limited. In particular, the potential link between systemic inflammation and repolarization heterogeneity—as reflected by fQRS–T—has not been sufficiently explored in pediatric ASD populations. Addressing this gap may provide insight into subclinical cardiovascular risk and underlying pathophysiological pathways in ASD.
Therefore, the aim of this study was to evaluate the association between systemic inflammation and cardiac electrophysiological parameters in children and adolescents with ASD. Specifically, we compared ECG indices—including heart rate, QT interval, and fQRS–T—as well as inflammatory markers (SII, SIRI, and PIV) between individuals with ASD and ageand sex-matched healthy controls (HC). Additionally, we examined the relationship between behavioral severity, assessed by the Autism Behavior Checklist (ABC), and both inflammatory and ECG parameters.
METHODS
Study design and sample
This study was designed as a case–control, cross-sectional study. Prior to study initiation, approval was obtained from the Non-Interventional Clinical Research Ethics Committee of Karabük University (Decision No: 2025/2467, Date: September 30, 2025) (Clinical trial number: not applicable). The study was conducted at the Department of Child and Adolescent Health, Karabük Training and Research Hospital. All procedures were carried out in accordance with the principles of the Declaration of Helsinki and the guidelines of Good Clinical Practice (ICH E6, 1996, and its subsequent amendments). Written informed consent was obtained from the parents or legal guardians of all participating children and adolescents. A total of 142 patients with ASD were initially screened. After the application of inclusion and exclusion criteria, 84 ASD patients were enrolled. Additionally, 82 age- and sexmatched HC were included for comparison. Flow-chart of the study group was presented in the Figure 1. In this study, the sociodemographic characteristics, complete blood count, biochemical and hormonal parameters, as well as ECG findings of the participants were recorded. To assess the symptoms of patients with ASD, the ABC was administered.
Flow-chart of the study. ASD, autism spectrum disorder; ECG, electrocardiography.
Inclusion and exclusion criteria
Children and adolescents with a confirmed diagnosis of ASD according to Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria were eligible for inclusion. Participants were between 6 and 18 years of age and had com-plete clinical, laboratory, and ECG data available for analysis. Age- and sex-matched HC without a history of psychiatric, neurological, or systemic medical disorders were also included for comparison.
Exclusion criteria comprised the presence of congenital or acquired heart disease, arrhythmias, or conduction abnormalities; known metabolic or endocrine disorders such as diabetes mellitus or thyroid dysfunction; acute or chronic infection at the time of assessment; autoimmune or systemic inflammatory diseases; and the use of medications known to affect cardiac conduction or systemic inflammation (e.g., antipsychotics, antidepressants, stimulants, antiepileptics, corticosteroids, or immunosuppressants). Patients with incomplete records, poor-quality ECG tracings, or missing laboratory values necessary for inflammatory index calculations were also excluded.
Participants using medications that could affect cardiac conduction or inflammatory parameters were excluded; therefore, all included individuals were medication-free with respect to these agents at the time of assessment. ASD severity was not used as an inclusion criterion; instead, it was assessed using the ABC and analyzed as a continuous variable.
Measures
Blood analysis
Complete blood counts were analyzed using a Mindray BC-600 hematology analyzer (Shenzhen Mindray Bio-Medical Electronics Co., Ltd.). Serum biochemical parameters were assessed with the Atellica system (Siemens Healthineers). Hormonal assays were performed on the ADVIA Centaur XPT platform (Siemens Healthineers). Based on these measurements, the following derived indices were calculated: SII=platelet×neutrophil÷lymphocyte; SIRI=neutrophil×monocyte ÷lymphocyte; and PIV=platelet×neutrophil×monocyte÷ lymphocyte. Serum concentrations of vitamin B12, folate, and vitamin D were measured. In addition, inflammatory markers, including C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR), were also assessed and compared between groups.
Electrocardiogram examination
Electrocardiograms were obtained using a GE Healthcare MAC 2000, 12-lead ECG system (GE Healthcare) under standard conditions (supine position, 25 mm/s paper speed, 10 mm/mV calibration). Heart rate, QT interval, QTc, QRS duration, and fQRS–T were recorded from automated device outputs and verified by clinician review. The fQRS–T was calculated as the absolute difference between the frontal QRS and T axes (0°–180°) [6]. QT interval was measured from the onset of the QRS complex to the end of the T wave using the tangent method. QTc was calculated using Bazett’s formula [7]. QRS duration was defined as the interval from QRS onset to the J-point. Measurements were averaged over three consecutive sinus beats, and tracings with artifacts were excluded. Calculation of fQRS-T was shown in Figure 2.
Calculation of the frontal QRS–T angle.
Autism Behavior Checklist
The ABC is a caregiver-reported screening instrument designed to identify autism-related behaviors and has shown solid discriminative performance in clinical samples. In a validation study, Marteleto and Pedromônico [16] reported that an receiver operating characteristic-based cut-off of 49 yielded high sensitivity (92.1%) and specificity (92.6%), supporting the ABC’s utility for identifying children with autistic disorders in clinical and educational contexts; however, optimal cut-offs may vary by population. In Türkiye, a Modified Turkish ABC has undergone pilot-level validation, demonstrating acceptable internal consistency and discriminant validity; this work was conducted by Özdemir et al. [17] In the present study, the ABC was completed by the primary caregivers of the participants under the supervision of a trained clinician. The total ABC score was used as a continuous variable to reflect behavioral severity, and no categorical cut-off was applied for sample selection.
Statistical analysis
Descriptive statistics were calculated for all study variables. Categorical data are presented as frequency (n) and percentage (%). The distribution of continuous variables was assessed using the Kolmogorov–Smirnov test. Normally distributed variables are expressed as mean±SD, while non-normally distributed variables are expressed as median and interquartile range (IQR, 25th–75th percentile). Between-group comparisons of categorical variables were performed with Pearson’s chi-square test. For continuous variables, independent samples t-test was applied to normally distributed data, and the Mann–Whitney U test was used for data not following a normal distribution. Correlations between variables were evalu-ated using Spearman’s rank correlation coefficient. Effect sizes were calculated for all relevant comparisons (Cohen’s d for parametric tests and r for non-parametric tests), and 95% confidence intervals (CI) were reported where appropriate to enhance interpretability. In addition, multiple linear regression analysis was performed to identify independent predictors of fQRS–T. Variables were selected based on clinical relevance and prior literature. Multicollinearity was assessed using variance inflation factor and tolerance statistics, and no significant collinearity was detected. A p value of <0.05 was considered statistically significant. All analyses were conducted with IBM SPSS Statistics for Mac, version 26.0 (IBM Corp.). A priori sample size calculation was performed using G*Power software (version 3.1.9.7, Heinrich-Heine-Universität Düsseldorf, Germany), based on previously reported differences in fQRS–T in pediatric neurodevelopmental populations [18]. Assuming a medium effect size (d=0.5), α=0.05, and power=80%, the minimum required sample size was calculated as 64 participants per group. The final study population exceeded this threshold.
RESULTS
The ASD (n=84) and HC (n=82) groups were comparable in baseline demographics (Table 1). Mean age did not differ significantly (11.35±3.35 years vs. 11.76±3.44 years, p=0.824; d=0.12, 95% CI -1.29 to 0.88), and sex distribution was similar between groups (p=0.262). The ABC score in the ASD group had a median of 53 (IQR 29.5–85.0), indicating substantial variability in behavioral severity; ABC was not applicable to HC.
Baseline demographic characteristics of the study groups
In ECG analyses, heart rate was significantly higher in the ASD group compared with HCs (97.56±21.20 bpm vs. 84.63± 17.02 bpm, p=0.004; d=0.67, 95% CI 5.00 to 20.86). QT interval tended to be shorter in ASD but did not reach statistical significance (p=0.054; d=0.50, 95% CI -35.78 to 0.52). QTc and QRS duration were comparable between groups (p=0.228 and p=0.552, respectively). The fQRS–T was significantly wider in ASD (median 23.5° [IQR 5.75–43.25] vs. 18° [IQR 3.5– 31.5], p=0.005; r=0.22) (Table 2).
Electrocardiographic parameters in ASD vs. HC
Laboratory parameters were largely similar between groups. Hemoglobin, platelets, leukocyte subsets, metabolic markers, liver enzymes, thyroid function tests, albumin, vitamin levels, CRP, and ESR showed no significant differences (all p>0.05). Lymphocyte counts were slightly lower in ASD, with a trend toward significance (p=0.073; r=0.14). Among inflammatory indices, SII was significantly higher in ASD (median 539.66 vs. 453.14, p=0.030; r=0.17), whereas SIRI and PIV did not differ (p=0.271 and p=0.172, respectively) (Table 3).
Laboratory parameters in ASD vs. HC
Correlation analyses revealed that ABC scores were positively associated with SII (r=0.387, p=0.041) and more strongly with fQRS–T (r=0.566, p=0.002), but not with heart rate (p=0.546). SII was not correlated with heart rate (p=0.196) but showed a significant positive correlation with fQRS–T (r=0.424, p=0.010). Overall, higher behavioral severity and inflammatory burden were associated with greater fQRS–T values (Table 4 and Figure 3).
Examining the correlation between SII, heart rate, fQRS–T, and ABC scores
Correlation matrix. NA, not reported; ABC, Autism Behavior Checklist; SII, systemic immune-inflammation index; fQRS–T, frontal QRS–T angle.
DISCUSSION
In this case–control study of children and adolescents with ASD, we observed 1) a higher resting heart rate; 2) a significantly wider fQRS–T; and 3) modestly higher SII compared with HC, with ABC scores correlating positively with both SII and fQRS–T, but not with heart rate. These findings support an association between behavioral severity, inflammatory markers, and repolarization characteristics, rather than establishing a causal relationship.
Elevated resting heart rate in ASD is consistent with prior studies showing altered autonomic regulation, including reduced parasympathetic activity and altered HRV dynamics [4,5]. This autonomic imbalance may be associated with differences in cardiac electrophysiological parameters; however, this relationship remains speculative in the absence of direct autonomic measurements in the present study. Pediatric ECG standards emphasize age- and sex-related variability, underscoring the importance of cautious interpretation of ECG-derived metrics [19].
A wider fQRS–T has been associated with myocardial electrical heterogeneity and adverse outcomes in various clinical settings [8,9]. However, in pediatric populations—particularly in individuals without known cardiac disease—its clinical sig-nificance remains uncertain due to limited normative data. Our findings indicate that fQRS–T may capture subtle repolarization differences even when conventional ECG intervals are within normal ranges.
Mounting evidence suggests that low-grade systemic inflammation plays a role in ASD [20-22]. Recent studies have further highlighted dysregulation of neuroinflammatory pathways, including alterations in complement system components and regulatory proteins such as Sirtuin-1, supporting the presence of immune imbalance in ASD [23,24]. SII represents a composite marker reflecting multiple components of the inflammatory response [10-12]. In our study, SII was higher in ASD and correlated with fQRS–T. While this finding raises the possibility of an interaction between inflammatory status and cardiac electrophysiology, it should be interpreted as a hypothesis-generating observation rather than evidence of a direct mechanistic pathway.
The comparison with ADHD provides contextual insight but should be interpreted cautiously [18]. Unlike our study, the referenced ADHD cohort included both medicated and drugnaïve participants, which may influence electrophysiological parameters and limit direct comparability. Therefore, while both conditions may show alterations in repolarization metrics, it cannot be concluded that they share a common pathophysiological mechanism.
From a clinical perspective, fQRS–T is easily obtainable from routine ECG recordings and may offer additional information beyond conventional parameters. However, given that no clinical outcomes (e.g., arrhythmia, syncope, or cardiovascular events) were assessed in this study, and considering the lack of established pediatric reference ranges, its role should be regarded as exploratory. Similarly, SII is an accessible marker but lacks disease-specific specificity.
Future research should aim to clarify these relationships using longitudinal designs, incorporating autonomic measurements such as HRV, repeated inflammatory assessments, and clinically relevant cardiovascular outcomes.
Limitations
Several limitations should be acknowledged. This was a single-center, cross-sectional study, limiting causal inference. Residual confounding factors (e.g., body mass index, sleep, physical activity) may have influenced results. HRV and other autonomic measures were not assessed, and ECG recordings were based on a single time point. Inflammatory indices such as SII may be affected by transient physiological conditions. Finally, the absence of longitudinal follow-up precludes evaluation of clinical outcomes.
Conclusions
In this case–control study, children and adolescents with ASD showed higher resting heart rate, wider fQRS–T, and modestly elevated SII compared with HC. Behavioral severity was associated with both inflammatory markers and fQRS–T. These findings suggest a potential relationship between inflammation and cardiac electrophysiological features in ASD; however, causal mechanisms cannot be inferred. fQRS–T may provide complementary information beyond conventional ECG parameters, but its clinical utility requires further validation in longitudinal studies.
Notes
Availability of Data and Material
The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.
Conflicts of Interest
The authors have no potential conflicts of interest to disclose.
Author Contributions
Conceptualization: Umut Balatacı, Bari Ay. Data curation: Umut Balatacı, Bari Ay. Formal analysis: Umut Balatacı, Bari Ay. Investigation: Umut Balatacı, Bari Ay. Methodology: Umut Balatacı, Bari Ay. Project administration: Umut Balatacı. Resources: Umut Balatacı, Bari Ay. Software: Umut Balatacı, Bari Ay. Supervision: Umut Balatacı. Validation: Umut Balatacı, Bari Ay. Visualization: Umut Balatacı, Bari Ay. Writing—original draft: Umut Balatacı, Bari Ay. Writing—review & editing: Umut Balatacı, Bari Ay.
Funding Statement
None
Acknowledgments
None