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“Interpretation and researches”
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PROGNOSTIC CRITERIA FOR AUTISM SPECTRUM DISORDER: A
COMPARATIVE ANALYSIS ACROSS SYNDROMES
F.A. Doniyorova
Associate Professor, Department of Neurology and Traditional Medicine Tashkent
State Dental Institute Tashkent, Uzbekistan
Abstract:
This article explores the prognostic criteria for Autism Spectrum
Disorder (ASD) based on a clinical study involving 80 children aged 3 to 7 years.
The children were categorized into four subgroups: Kanner's syndrome, Asperger's
syndrome, Atypical Autism, and Pervasive Developmental Disorder-Not Otherwise
Specified (PDD-NOS). Statistical analysis was used to identify the most significant
factors influencing disease prognosis, with data visualized in tables and figures. The
study reveals syndrome-specific prognostic features and recommends individualized
approaches for diagnosis and intervention.
Keywords:
autism, prognostic criteria, biomarkers, early diagnosis, ASD
syndromes
Introduction
Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition
characterized by difficulties in social communication and restricted, repetitive
behaviors. Early diagnosis and prognosis are crucial for developing effective
therapeutic strategies. Recent studies have increasingly focused on identifying
prognostic biomarkers and risk factors that contribute to the trajectory of ASD
development [1, 2]. Autism Spectrum Disorder (ASD) is a multifactorial
neurodevelopmental condition characterized by persistent deficits in social
interaction and communication, as well as restricted, repetitive patterns of behavior,
interests, or activities. The global prevalence of ASD has steadily increased over the
past two decades, now estimated at 1 in 100 children worldwide, with significant
variation across countries and socioeconomic backgrounds (WHO, 2023). This rise
underscores
the
urgent
need
for
early
detection
and
evidence-based
intervention.Although the core features of ASD are well-established, its presentation
is highly heterogeneous, both across and within diagnostic subtypes such as Kanner’s
syndrome (classic autism), Asperger’s syndrome, Atypical Autism, and Pervasive
Developmental Disorder-Not Otherwise Specified (PDD-NOS). These subtypes vary
in terms of language development, cognitive functioning, adaptive behaviors, and
comorbidities, complicating diagnosis and long-term managementRecent advances in
neuroscience and genetics have emphasized the importance of identifying reliable
prognostic criteria that can predict the developmental trajectory and treatment
International scientific journal
“Interpretation and researches”
Volume 1 issue 5 (51) | ISSN: 2181-4163 | Impact Factor: 8.2
134
responsiveness of children with ASD. Prognostic indicators may include a
combination of clinical symptoms, cognitive profiles, biological markers
(biomarkers), neuroimaging findings, and family history. Studies suggest that certain
patterns — such as early language ability, nonverbal IQ, and degree of social
reciprocity — are closely linked with later functional outcomes (Lord et al., 2020;
Vivanti et al., 2022). Therefore, establishing clear prognostic frameworks for children
with different ASD syndromes is essential for personalizing early intervention
strategies, optimizing therapeutic windows, and improving long-term quality of life.
The current study aims to contribute to this goal by analyzing a sample of 80 children
diagnosed with ASD, categorized by syndrome, and assessing key clinical and
developmental variables.
Materials and Methods
This study analyzed clinical data from 80 children aged 3–7 years diagnosed
with ASD, subdivided into four groups: Kanner (n=18), Asperger (n=25), Atypical
(n=17), and PDD-NOS (n=20). Each child was assessed using standardized scales for
language delay, social interaction, repetitive behaviors, cognitive level, and family
history of ASD.
Results
Syndrome
Age
Language
Delay
Social
Interaction
Repetitive
Behavior
Cognitive
Level
Family
History
Asperger
4.38
2.12
3.25
2.75
2.88
0.56
Atypical
4.62
2.62
2.81
2.88
3.31
0.23
Kanner
5.11
2.78
3.61
3.11
2.78
0.50
PDD-
NOS
4.50
2.80
2.50
2.80
3.35
0.45
Table 1 presents the average clinical scores by syndrome type:
International scientific journal
“Interpretation and researches”
Volume 1 issue 5 (51) | ISSN: 2181-4163 | Impact Factor: 8.2
135
Figure 1. Language Delay by Syndrome
Children with Asperger’s syndrome exhibited the least severe language delays
(mean = 2.12), consistent with diagnostic profiles. Kanner’s syndrome and PDD-
NOS presented with moderately higher scores, indicating greater need for early
speech-language intervention.
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“Interpretation and researches”
Volume 1 issue 5 (51) | ISSN: 2181-4163 | Impact Factor: 8.2
136
Figure 2. Social Interaction Difficulties by Syndrome
Interpretation:
Kanner’s group demonstrated the most severe difficulties in social interaction (mean
= 3.61), reflecting core deficits. This contrasts with PDD-NOS (2.50), suggesting a
milder form of social dysfunction.
Figure 3. Repetitive Behavior by Syndrome
Repetitive behaviors were most pronounced in Kanner’s syndrome (mean =
3.11), typical of stereotypic behavior. Atypical cases (2.88) also showed significant
traits, while Asperger and PDD-NOS had less severe expressions.
Figure 4. Cognitive Level by Syndrome
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Volume 1 issue 5 (51) | ISSN: 2181-4163 | Impact Factor: 8.2
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PDD-NOS and Atypical groups exhibited relatively higher cognitive scores
(3.35 and 3.31), associated with more favorable prognosis. In contrast, Kanner’s
group (2.78) indicated lower cognitive capacity, suggesting the need for tailored
educational interventions.
Discussion
Children with Kanner’s syndrome showed the most significant impairments in
social interaction and repetitive behaviors, aligning with earlier findings [3]. In
contrast, children with Asperger’s syndrome showed lower language delays but had
persistent social challenges. These observations support the need for a personalized,
syndrome-specific diagnostic framework [4].
Recent research emphasizes the role of biomarkers in predicting ASD
progression, enhancing early detection [5]. Machine learning models have been
shown to predict ASD with high accuracy using developmental profiles [6].
Moreover, rapid increases in early-life growth markers like head circumference and
weight gain are linked to ASD risk [7].
Conclusion
This study confirms the necessity of a multifaceted approach to assessing
prognostic indicators in ASD. Recognizing syndrome-specific profiles allows for
more targeted interventions and supports early, individualized care strategies.
Recommendations
Utilize biological and behavioral markers for early ASD detection and
prognosis.
Integrate machine learning tools into clinical assessment protocols.
Develop intervention programs tailored to specific ASD syndromes.
References:
1. Frontiers in Neuroscience. (2024). Predictive markers in autism.
https://www.frontiersin.org/articles/10.3389/fnins.2024.1514678
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https://www.cas.org/resources/cas-insights/autism-diagnosis-biomarkers
3. Sage Journals. (2023). Prognostic analysis of autism subtypes.
https://journals.sagepub.com/doi/10.1177/02537176231210063
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