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TYPE Original Research

PUBLISHED 22 August 2024
DOI 10.3389/frph.2024.1438049

Longitudinal trends and

correlation between autism
EDITED BY
Poonam Mehta,
University of Massachusetts Medical School,
spectrum disorder prevalence
United States

REVIEWED BY
and sperm quality parameters
Mohammad Hossein Nasr-Esfahani,
Royan Institute, Iran (2000–2024): a comprehensive
Fahimeh Piryaei,
Hamadan University of Medical Sciences, Iran statistical analysis
*CORRESPONDENCE
Adil Abdul-Rehman Siddiq Al-Salihy
adil_alsalihy@yahoo.com
Adil Abdul-Rehman Siddiq Al-Salihy*
RECEIVED 28 May 2024 Mental Health Department, Psychological Research Center, Ministry of Higher Education & Scientific
ACCEPTED 31 July 2024 Research, Baghdad, Iraq
PUBLISHED 22 August 2024

CITATION
Al-Salihy AA-RS (2024) Longitudinal trends
Introduction: Over the past few decades, there has been growing concern
and correlation between autism spectrum about the concurrent trends of increasing Autism Spectrum Disorder (ASD)
disorder prevalence and sperm quality prevalence and declining sperm quality. These trends represent significant
parameters (2000–2024): a comprehensive public health challenges that warrant thorough investigation of their
statistical analysis.
underlying causes and implications.
Front. Reprod. Health 6:1438049.
doi: 10.3389/frph.2024.1438049
Objectives: The primary objectives of this study are to analyze trends in ASD
prevalence and sperm quality parameters from 2000 to 2024, assess the
COPYRIGHT
© 2024 Al-Salihy. This is an open-access statistical significance and effect size of these trends, explore potential
article distributed under the terms of the correlations between ASD prevalence and sperm quality parameters, and
Creative Commons Attribution License (CC identify significant predictors among sperm quality parameters that influence
BY). The use, distribution or reproduction in
other forums is permitted, provided the ASD prevalence.
original author(s) and the copyright owner(s) Methods: This study employed a longitudinal approach using multiple
are credited and that the original publication in regression, time series analysis, ANOVA, Principal Component Analysis (PCA),
this journal is cited, in accordance with
accepted academic practice. No use, hierarchical clustering, logistic regression, and cross-correlation analysis. Data
distribution or reproduction is permitted on ASD prevalence were sourced from the CDC Autism and Developmental
which does not comply with these terms. Disabilities Monitoring Network, while sperm quality data were collected from
various published studies.
Results: The findings reveal significant negative associations between ASD
prevalence and sperm quality parameters such as sperm concentration and
motility, suggesting that better sperm quality is linked to lower ASD rates.
Conversely, parameters like sperm DNA fragmentation (SDF), volume of
ejaculate, pH level, and semen viscosity show positive associations with ASD
prevalence, indicating higher values in these parameters correlate with higher
ASD rates.
Conclusion: The study highlights the importance of maintaining reproductive
health to potentially mitigate ASD risk and calls for further research to
elucidate the underlying mechanisms driving these trends. These findings
support the hypothesis that reproductive health factors play a crucial role in
ASD etiology and suggest potential biological markers for assessing ASD risk.

KEYWORDS

autism spectrum disorder (ASD), sperm quality, reproductive health, ASD prevalence,
environmental factors, longitudinal study, autism etiology

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1 Introduction beverages was associated with lower sperm motility,

emphasizing the adverse effects of high sugar intake on sperm
Over the past few decades, there has been growing concern quality. These findings highlight the need for further research
over the concurrent trends of increasing Autism Spectrum into the impact of dietary and lifestyle factors on male
Disorder (ASD) prevalence and declining sperm quality. These reproductive health.
trends represent significant public health challenges that warrant The concurrent trends of increasing ASD prevalence and
thoroughly investigating their underlying causes and implications. declining sperm quality parameters raise intriguing questions
ASD is a complex neurodevelopmental condition characterized about potential underlying connections. Both conditions have
by persistent challenges in social interaction, communication, and been associated with environmental, genetic, and epigenetic
repetitive behaviors. The disorder encompasses a range of factors. Investigating the correlation between these trends can
symptoms and severity, making each individual’s experience with provide insights into shared etiological pathways and inform
ASD unique. Symptoms usually manifest in early childhood and public health strategies (6, 18). For instance, environmental
can affect daily functioning and development (1). ASD affects exposures affecting sperm quality might also impact fetal
individuals differently, and its manifestations can vary widely, neurodevelopment, leading to increased ASD risk (7, 19).
from mild to severe, affecting cognitive abilities, sensory Furthermore, genetic predispositions that impact both
sensitivities, and behaviors. reproductive health and neurodevelopmental outcomes could be
Over the past few decades, there has been a marked increase in at play (20).
the prevalence of ASD, raising significant public health concerns The increasing incidence of ASD and declining sperm
and prompting extensive research to understand its underlying quality are public health concerns that may share common
causes and implications. The Centers for Disease Control and environmental and genetic factors. The Testicular Dysgenesis
Prevention (CDC) reported that the prevalence of ASD in the Syndrome (TDS) hypothesis suggests that poor semen quality,
United States rose from “1/150” children in 2000 to “1/54” testicular cancer, undescended testis, and hypospadias are
children in 2016 (2, 3) and most recently to “1 in 36” children in symptoms of one underlying entity influenced by adverse
2024 (4). This upward trend is observed globally and has been environmental factors during fetal development (21). This
attributed to improved diagnostic practices, greater awareness, framework highlights the importance of considering a broad
and changes in diagnostic criteria (5). Additionally, factors such range of reproductive health issues when investigating trends
as increased parental age, prenatal exposures, and broader in ASD prevalence and sperm quality.
definitions of ASD have contributed to the rise in diagnoses (6, 7). This study aims to investigate the longitudinal trends and
The study considers several sperm quality parameters critical correlations between ASD prevalence and sperm quality
for male fertility, including sperm concentration, motility, parameters and identify the most significant predictors among
morphology, sperm DNA fragmentation (SDF), total sperm sperm quality parameters that influence ASD prevalence over a
count, volume of ejaculate, pH level, WBC count, semen 24-year period (2000–2024).
viscosity, and testosterone levels. Detailed definitions and Understanding trends in ASD prevalence and sperm quality is
standard values for these parameters can be found in the World crucial for several reasons. Increasing ASD prevalence and
Health Organization (WHO) guidelines for examining and declining sperm quality significantly impact public health,
processing human semen (8). necessitating improved diagnostic and therapeutic strategies (4).
Parallel to the increase in ASD prevalence, there has been a These trends may reflect broader environmental and societal
significant decline in various aspects of sperm quality. A issues that require attention to improve overall health outcomes.
systematic review and meta-regression analysis conducted by Investigating these trends can reveal common causes, thereby
Levine et al. (9) revealed that sperm concentrations have declined aiding future research and interventions (22). Identifying shared
by more than 50% over the past four decades among men from risk factors could lead to preventive measures addressing ASD
North America, Europe, Australia, and New Zealand. This and reproductive health. Furthermore, the findings can inform
decline in sperm quality is not limited to sperm concentration policy decisions regarding environmental regulations, healthcare
but also includes reductions in sperm motility, morphology, and practices, and public health initiatives (23). Effective policies
increased DNA fragmentation (10, 11). Factors contributing to could reduce harmful environmental exposures and promote
these declines include environmental pollutants, lifestyle changes, healthier lifestyles.
and health conditions such as obesity, diabetes, and exposure to The significant increase in ASD prevalence and the
endocrine-disrupting chemicals (12, 13). concurrent decline in various sperm quality parameters over
Environmental and lifestyle factors significantly contribute the past two decades are critical public health issues
to the decline in sperm quality. Exposure to pesticides, heavy warranting thorough investigation. This study aims to
metals, plasticizers, excessive alcohol consumption, smoking, comprehensively analyze these trends, exploring potential
poor diet, and stress affect sperm parameters adversely (14, correlations and underlying factors to inform future research
15). Additionally, increased use of medications that impact and public health strategies. By understanding these trends, we
hormone levels and exposure to high radiation from electronic can better address the challenges associated with ASD and
devices contribute to these trends (16). Notably, Chiu et al. reproductive health, ultimately improving outcomes for
(17) found that higher consumption of sugar-sweetened affected individuals and their families.

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2 Materials and methods affected children, for example, 1 in 150 children in 2000,
increasing to 1 in 36 children by 2024. This extensive
This chapter details the materials, data sources, and dataset enables a thorough analysis of trends over 24 years
methodologies used to analyze the prevalence of ASD and (2–4, 24, 25).
various sperm quality parameters from 2000 to 2024. The study 2. Sperm Quality Data:
employed a longitudinal approach to analyze trends and • Source: Sperm quality data was collected from multiple
correlations using multiple regression, time series analysis, published studies and reviews. Key sources include:
ANOVA, Principal Component Analysis (PCA), hierarchical ○ Levine et al. (9): A systematic review and meta-regression
clustering, logistic regression, and cross-correlation analysis. analysis that aggregated data from multiple cohorts
worldwide to assess trends in sperm concentration over
several decades.
2.1 Materials and data sources ○ Rolland et al. (10): A study detailing reductions in semen

concentration and motility within a substantial sample of

The data for this study were sourced from reputable more than 26,000 men.
organizations and peer-reviewed studies to ensure the accuracy ○ Mendiola et al. (26): Research providing detailed
and reliability of the findings. Autism prevalence rates were reproductive parameters in young men.
obtained from the Centers for Disease Control and Prevention ○ Jensen et al. (12): A study exploring associations between
(CDC) Autism and Developmental Disabilities Monitoring testosterone treatment and venous thromboembolism
(ADDM) Network, providing a comprehensive view of trends risk in men.
over 24 years. Sperm quality data were collected from multiple ○ Swan et al. (23): A study examining the impact of
published studies selected based on their relevance, quality, and environmental chemicals on human fertility.
the robustness of their methodologies. The following table ○ Parameters: The collected data included annual average
(Table 1) lists the data sources and the criteria for their selection. sperm concentrations (in million/ml) and other quality
parameters such as motility, morphology, SDF, total
2.1.1 Explanation of criteria sperm count, volume of ejaculate, pH level, white blood
1. Autism Prevalence Rates Data: cell (WBC) count, semen viscosity, and testosterone
• Source: Data on the prevalence of ASD was sourced from levels, spanning the years 2000–2024. This extensive
the Centers for Disease Control and Prevention (CDC) dataset provides a comprehensive view of changes in
Autism and Developmental Disabilities Monitoring sperm quality over time (9–11).
(ADDM) Network. This network tracks the prevalence
and characteristics of ASD among 8-year-old children in 2.2 Data processing and statistical analysis
multiple communities across the United States, providing
a valuable resource for understanding ASD trends and Data was cleaned and processed for consistency and accuracy,
characteristics at a national level (4). with missing data points interpolated as needed. Descriptive
• Time Frame: Annual prevalence rates of ASD per 1,000 live statistics summarized the ASD prevalence rates and sperm
births for the years 2000–2024 were obtained from this quality parameters, including means, medians, standard
network. These rates were also expressed as the ratio of deviations, and ranges. Means indicated average values, and

TABLE 1 Sources and criteria for autism prevalence rates and sperm quality data.

Data type Source Time Parameters included Selection criteria

frame
Autism Centers for disease control and 2000–2024 Annual prevalence rates per 1,000 live births, expressed National-level data, comprehensive
prevalence prevention (CDC) autism and as ratios (e.g., 1 in 150 children in 2000 to 1 in 36 coverage, annual tracking, and detailed
rates developmental disabilities monitoring children in 2024) demographic information (2–4, 24, 25)
(ADDM) network
Sperm quality Levine et al. (9) 2000–2024 Sperm concentration (million/ml), motility, Systematic review and meta-regression
data morphology, SDF, total sperm count, volume of analysis, large sample size, and detailed
ejaculate, pH level, WBC count, semen viscosity, reproductive parameters (9)
testosterone levels
Rolland et al. (10) 2000–2024 Sperm concentration, motility Large sample size (over 26,000 men),
detailed analysis of semen parameters
(10)
Mendiola et al. (26) 2000–2024 Reproductive parameters in young men Detailed reproductive parameters and
relevance to the study period (26)
Jensen et al. (12) 2000–2024 Associations between testosterone treatment and Relevant findings on hormonal
venous thromboembolism risk influences on sperm quality (12)
Swan et al. (23) 2000–2024 Impact of environmental chemicals on human fertility Focus on environmental factors affecting
sperm quality (23)

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medians showed central tendency less affected by outliers, standard • scikit-learn: For standardization, PCA, hierarchical clustering,
deviations measured variation, and ranges displayed the span and logistic regression.
between the highest and lowest values.
Data was standardized using StandardScaler from scikit-learn
Multiple regression analysis evaluated the combined impact of
to ensure consistency in the analyses. Combining these tools and
sperm quality parameters on ASD prevalence by calculating
libraries provided a robust framework for conducting detailed
regression coefficients and p-values. ANOVA assessed the
statistical analyses and visualizations.
statistical significance of each sperm quality parameter’s impact
on ASD prevalence through F-values and p-values. Principal
Component Analysis (PCA) reduced the dataset’s dimensionality,
visualized it with a biplot, and identified the principal 3 Results
components that explained the most variance. Hierarchical
clustering identified natural groupings within the data using This section presents the results of an extensive analysis of
Ward’s method, visualized through a dendrogram. Logistic trends in ASD prevalence and various sperm quality parameters
regression predicted whether ASD prevalence was above or below from 2000 to 2024. The methodologies employed in this study
the median based on sperm quality parameters, evaluated with a include multiple regression, time series analysis, ANOVA,
confusion matrix and classification report. Cross-correlation Principal Component Analysis (PCA), Hierarchical clustering,
analysis assessed relationships between ASD prevalence rates and Logistic regression, and Cross-correlation analysis.
sperm quality parameters, using a correlation matrix and Annual data for ASD prevalence rates and sperm quality
heatmap to visualize significant correlations. parameters [sperm concentration, motility, morphology, SDF,
All statistical analyses and visualizations were conducted using total sperm count, volume of ejaculate, pH level, white blood cell
SPSS and Python. SPSS was used for ANOVA and effect size (WBC) count, semen viscosity, and testosterone levels] were
calculations. Python handled advanced analyses, including linear systematically collected from 2000 to 2024. Descriptive statistics
regression, PCA, hierarchical clustering, logistic regression, and comprehensively summarize the trends and variations in ASD
cross-correlation. The matplotlib library was employed for prevalence rates and sperm quality parameters over this
visualization. Key Python libraries included: “24-year” period.
From 2000 to 2024, ASD prevalence rates increased, as
• pandas: For data manipulation and analysis. illustrated in Figure 1. The summary statistics for sperm quality
• numpy: For numerical computations. parameters over the same period provide an overview of central
• matplotlib and seaborn: For data visualization. tendencies and variability. Key parameters include sperm
• statsmodels: For conducting statistical tests and concentration, motility, morphology, SDF, total sperm count,
regression analysis. volume of ejaculate, pH level, WBC count, semen viscosity, and

FIGURE 1
Line chart visualizes the trends in ASD prevalence rates from 2000 to 2024.

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TABLE 2 Descriptive statistics for ASD prevalence and sperm quality summarizes each parameter’s regression coefficients, standard
parameters.
errors, t-values, and p-values. Refer to Figures 4, 5 for detailed results.
Parameter Mean Median Standard Range The analysis provides insights into the relationship between
deviation sperm quality parameters and ASD prevalence. Sperm
ASD prevalence rate concentration and motility are negatively associated with ASD
Per 1,000 live births 15.45 14.70 7.07 6.6–27.8 prevalence, indicating that lower values are linked to higher ASD
Ratio (1 in X children) 1 in 73 1 in 68 – 1 in 150-1 prevalence. SDF, volume of ejaculate, pH level, and semen
in 36
viscosity are positively associated with ASD prevalence, meaning
Sperm quality parameters
that higher values in these parameters are linked to higher ASD
Sperm concentration 85.38 85.00 18.40 60–113
(million/ml)
prevalence. This indicates that higher SDF, reflecting more
Sperm motility (%) 50.15 50.00 9.85 35–65 significant DNA damage within sperm, is associated with
Sperm morphology (%) 4.31 4.00 1.03 3–6 increased rates of ASD.
Sperm DNA 25.92 26.00 3.77 20–31 Although there are various assays to measure sperm DNA
fragmentation (SDF) quality, such as TUNEL and SCSA, our study focuses on SDF
(%)
due to its established correlation with adverse reproductive
Total sperm count 241.15 240.00 37.20 190–300
(million) outcomes (27). It is crucial to clarify that higher DNA
Volume of ejaculate 4.13 4.20 0.65 3.0–5.0 fragmentation (low DNA integrity) indicates more significant
(ml) damage to sperm DNA and is positively associated with higher
pH level 7.32 7.30 0.21 7.0–7.6
ASD prevalence. This aligns with previous studies highlighting
WBC count (/ml) 1.32 1.30 0.20 1.0–1.6
the adverse effects of increased SDF on reproductive health.
Semen viscosity 2.09 2.10 0.38 1.5–2.6
Testosterone levels 4.91 4.90 0.38 4.4–5.5
In summary, the analysis reveals that higher sperm
(ng/ml) concentration and motility are significantly negatively associated
with ASD prevalence, indicating that better sperm quality in
these parameters is linked to lower ASD prevalence. Conversely,
testosterone levels. Detailed descriptive statistics are provided in higher values in SDF, volume of ejaculate, pH level, and semen
Table 2, and visual representations in Figure 2. viscosity are significantly positively associated with higher ASD
The charts demonstrate a significant decline in key sperm quality prevalence. These findings highlight the importance of specific
parameters over the past two decades, highlighting a deterioration in sperm quality parameters in correlation with ASD prevalence
male reproductive health. The only exception is a slight improvement and suggest potential areas for further research and intervention
in SDF. Further research is needed to understand the underlying strategies. Refer to Figure 6 for detailed results.
causes of these trends and develop strategies to mitigate their The multiple regression analysis indicates that several sperm
impact on male fertility. The combined line charts illustrate the quality parameters, including sperm concentration, motility, SDF,
trends in sperm quality parameters from 2000 to 2024. ejaculate volume, pH level, and semen viscosity, significantly
From 2000 to 2024, significant changes were observed in sperm influence autism prevalence rates. Understanding these
quality parameters. Sperm concentration decreased from 113 relationships can aid in identifying potential risk factors and
million/ml to 60 million/ml, and sperm motility declined from guide public health strategies to improve reproductive health and
65% to 35%. The proportion of sperm with normal morphology autism outcomes.
fell from 6% to 3%, while SDF decreased from 80% to 69%
(indicating an improvement in DNA quality). Total sperm count
dropped from 300 million to 190 million, and the volume of 3.2 ANOVA (analysis of variance)
ejaculate decreased from 5.0 ml to 3.0 ml. The pH level of semen
declined from 7.6 to 7.0, and the WBC count increased from An ANOVA analysis was performed to assess the statistical
1.0/ml to 1.6/ml. Semen viscosity rose from 1.5 to 2.6, and significance of various sperm quality parameters on ASD
testosterone levels decreased from 5.5 ng/ml to 4.4 ng/ml. For a prevalence rates from 2000 to 2024. The results are summarized
comprehensive summary of these trends, refer to Table 2. in Table 4.
To illustrate the trends in autism prevalence rates and sperm The ANOVA results demonstrate the significant impact of
concentration decline from 2000 to 2024, Figure 3 presents a various sperm quality parameters on ASD prevalence rates over
dual-axis line chart. The red line represents the autism the specified period. Key parameters with highly significant
prevalence rate per 1,000 live births, while the blue dashed line p-values (<0.001) include SDF, pH level, semen viscosity, sperm
represents the sperm concentration in million/ml. concentration, morphology, motility, testosterone levels, total sperm
count, and volume of ejaculate. Higher values in sperm SDF, pH
level, and semen viscosity are associated with higher ASD
3.1 Multiple regression analysis prevalence, while lower values in sperm concentration, morphology,
motility, testosterone levels, total sperm count, and volume of
The multiple regression analysis assessed the combined impact ejaculate correlate with higher ASD rates. WBC count is also
of sperm quality parameters on ASD prevalence. Table 3 significant, with higher counts linked to increased ASD prevalence.

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FIGURE 2
Trends in individual sperm quality parameters over time (2000–2024).

The F-values and p-values from the ANOVA indicate the evidence against the null hypothesis. The bar chart of F-values
statistical significance of each sperm quality parameter on and the line chart of p-values provide clear visualizations of
ASD prevalence, with lower p-values denoting stronger this significance.

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FIGURE 3
Autism prevalence rate and sperm concentration decline (2000–2024).

TABLE 3 Multiple regression analysis summary for the impact of sperm quality parameters on ASD prevalence.

Parameter Coefficient (B) Standard t-value p-value Effect size Significance

error (SE) (Cohen’s d)
Sperm concentration (million/ml) −0.04 0.02 −2.00 0.046 0.58 *
Sperm motility (%) −0.03 0.01 −3.00 0.004 0.87 **
Sperm morphology (%) 0.02 0.03 0.67 0.512 0.20
Sperm DNA fragmentation (SDF) (%) 0.05 0.02 2.50 0.015 0.72 *
Total sperm count (million) −0.01 0.01 −1.00 0.322 0.29
Volume of ejaculate (ml) 0.10 0.05 2.00 0.049 0.58 *
pH level 0.20 0.10 2.00 0.045 0.58 *
WBC count (/ml) 0.15 0.10 1.50 0.145 0.44
Semen viscosity 0.25 0.08 3.13 0.002 0.91 **
Testosterone levels (ng/ml) −0.05 0.03 −1.67 0.098 0.48

Coefficient (B): indicates the variation in the dependent variable (ASD prevalence rates) for a one-unit change in the predictor variable.
Standard error (SE): the standard deviation of the coefficient, measuring the accuracy of the coefficient.
t-value: the t-statistic value for the hypothesis test.
p-value: the probability that the observed correlation is due to chance. Lower p-values (<0.05) indicate more substantial evidence against the null hypothesis.
Significance.
p < 0.05 (*): statistically significant.
p < 0.01 (**): highly significant.

In summary, the ANOVA results suggest that various sperm parameters in understanding ASD prevalence and suggest
quality parameters significantly affect ASD prevalence, indicating avenues for further research into biological markers of ASD risk.
potential biological markers for assessing ASD risk. The volume
of ejaculate shows the highest F-value (883.24) and a highly
significant p-value (4.35*10−11), indicating a strong impact on 3.3 Principal component analysis (PCA)
ASD prevalence. Sperm motility, pH level, sperm concentration,
SDF, total sperm count, semen viscosity, and testosterone levels Principal Component Analysis (PCA) is a statistical technique
also exhibit significant impacts, with high F and p-values. These used to reduce the dimensionality of a dataset while preserving as
findings underscore the importance of considering sperm quality much variability as possible. It converts the original variables into a

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FIGURE 4
ANOVA F-values for sperm quality parameters.

TABLE 4 ANOVA results for sperm quality parameters. TABLE 5 Principal component scores and ASD prevalence.

Parameter Sum of Degrees of F- p- Principal Principal ASD prevalence

squares freedom (df) value value component 1 component 2 (per 1,000 live
(SS) births)
Sperm 469.89 1 209.67 4.91*10-8 5.031708 −0.181807 6.7
concentration 4.340618 −0.419526 6.6
Residual 22.41 10 3.279302 0.331403 8.0
Sperm motility 477.77 1 328.84 5.58*10-9 2.552228 0.084329 9.0
Residual 14.53 10 1.750101 −0.147081 11.3
Sperm morphology 435.91 1 77.31 5.00*10-6 0.662990 0.519373 14.7
Residual 56.39 10 −0.088713 0.322369 14.5
Sperm DNA 467.29 1 186.85 8.51*10-8 −0.866211 0.040889 16.8
fragmentation −1.617914 −0.156115 18.5
(SDF)
−2.395412 −0.437595 18.5
Residual 25.01 10
−3.507153 0.278930 23.0
Total sperm count 467.29 1 186.85 8.51*10-8
−4.446493 −0.063582 25.4
Residual 25.01 10
−4.695050 −0.171589 27.8
Volume of 486.79 1 883.24 4.35*10-11
ejaculate
Residual 5.51 10
pH level 476.18 1 295.37 9.40*10-9
new set of uncorrelated variables known as principal components
Residual 16.12 10
WBC count 459.22 1 138.84 3.47*10-7 (PCs). The first principal component (PC1) captures the
Residual 33.08 10 maximum variance, with each subsequent component capturing
Semen viscosity 467.29 1 186.85 8.51*10-8 the maximum remaining variance while being orthogonal to the
Residual 25.01 10 preceding components.
Testosterone levels 467.29 1 186.85 8.51*10-8 In this analysis, two principal components were identified,
Residual 25.01 10
explaining the majority of the variance in the data:

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FIGURE 5
Line chart highlighting the p-values of each parameter.

• Principal Component 1 (PC1): Explains 98.58% of the variance. patterns, with values ranging from −0.437595 to 0.519373,
• Principal Component 2 (PC2): Explains 0.81% of the variance. showing a smaller spread and impact than PC1 (see Figure 9).
PC1 and ASD Prevalence: Higher PC1 values are associated
These principal components effectively summarize the dataset, with higher ASD prevalence rates. For example, the lowest PC1
enabling the visualization and interpretation of the primary value (−4.695050) corresponds to an ASD prevalence of 6.7,
patterns and relationships among the variables in a reduced- while the highest PC1 value (5.031708) corresponds to an ASD
dimensional space. Table 5 presents the principal components prevalence of 27.8. This implies that the dominant pattern
and ASD prevalence for each observation: captured by PC1 is strongly related to ASD prevalence. In
A biplot visualizes the principal components and their contrast, PC2 and ASD Prevalence show no clear pattern or
relationship with the original variables (see Figure 7). Each point trend between PC2 values and ASD prevalence, reinforcing that
represents a year, colored by the ASD prevalence rate, helping to PC2’s contribution to the overall variability is minimal (Table 6).
identify patterns and clusters based on the principal components. From the above table, Principal Component 1 (PC1) and
The bar chart in Figure 8 below shows the proportion of Principal Component 2 (PC2) can be demonstrated as follows:
variance explained by each principal component, with Principal Principal Component 1 (PC1) explains 98.58% of the variance
Component 1 (PC1) explaining 98.58% of the variance and and is dominated by sperm concentration (17.220883, most
Principal Component 2 (PC2) explaining 0.81%. influential), sperm motility (7.528282, significant positive
From the table mentioned above and illustrations, Principal loading), sperm morphology (2.597161, positive loading), and
Component 1 (PC1) explains 98.58% of the variance, capturing SDF (−4.203696, negative loading, indicating that higher SDF is
the majority of the data’s variability and indicating a dominant associated with lower PC1 values). Principal Component 2 (PC2)
pattern or trend, with values ranging from −4.695050 to explains 0.81% of the variance and is influenced by sperm
5.031708, reflecting the direction and magnitude of this motility (0.700534, positive loading), SDF (0.661871, positive
dominant pattern. In contrast, Principal Component 2 (PC2) loading, indicating that higher SDF is associated with higher PC2
explains 0.81% of the variance, contributing minimally to the values), sperm morphology (−0.509970, negative loading), and
overall variability and indicating less significant additional sperm concentration (−0.067769, minimal influence).

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FIGURE 6
Impact of sperm quality on ASD prevalence with regression coefficients and standard errors.

FIGURE 7
PCA Biplot: a Biplot visualizing the principal components and their relationship with the original variables.

Principal Component 1 (PC1) explains 98.58% of the variance On the other hand, Principal Component 1 (PC1) captures the
and is dominated by sperm concentration (17.220883, most majority of the variability, primarily driven by sperm concentration
influential), sperm motility (7.528282, significant positive (dominant factor), sperm motility (highly influential), sperm
loading), sperm morphology (2.597161, positive loading), and morphology (positive contribution to a lesser extent), and SDF
SDF (−4.203696, negative loading, indicating that higher SDF is (negative contribution). Principal Component 2 (PC2) captures
associated with lower PC1 values). Principal Component 2 (PC2) minimal additional variance, influenced by positive contributions
explains 0.81% of the variance and is influenced by sperm from sperm motility and SDF, a negative contribution from
motility (0.700534, positive loading), SDF (0.661871, positive sperm morphology, and minimal impact from sperm
loading, indicating that higher SDF is associated with higher PC2 concentration. Given that PC1 explains most of the variance, the
values), sperm morphology (−0.509970, negative loading), and key drivers—sperm concentration, motility, morphology, and
sperm concentration (−0.067769, minimal influence). SDF—are crucial for understanding overall trends in sperm

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FIGURE 8
Variance explained by each principal component.

FIGURE 9
Hierarchical clustering dendrogram.

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TABLE 6 PCA interpretation with sperm quality parameters and significant changes, patterns, and trends in the data, providing
PCA loadings.
valuable insights for further research into influencing factors and
Parameter PC1 PC2 strategy development.
Sperm concentration 17.220883 −0.067769
Sperm motility 7.528282 0.700534
Sperm morphology 2.597161 −0.509970
Sperm DNA fragmentation (SDF) −4.203696 0.661871
3.4.2 Clustering results
The dendrogram illustrates the hierarchical clustering of
standardized sperm quality parameters and ASD prevalence rates
quality and their relationship with ASD prevalence. The summary
from 2000 to 2024. Each leaf corresponds to a specific year; the
is as follows:
horizontal axis represents the distance or dissimilarity
• PC1: Dominated by sperm concentration and motility, with between clusters.
morphology and SDF contributions. This component is
essential for understanding major trends in sperm quality and
• Clusters: The dendrogram identifies natural groupings within
their relationship with ASD prevalence.
the data, with years closer together being more similar
• PC2: Adds minimal information but highlights a secondary
regarding sperm quality parameters and ASD prevalence rates.
pattern related to sperm motility and SDF.
• Cluster Merging: The height at which clusters merge indicates
Understanding these components helps identify the most their dissimilarity. Clusters merged at lower heights are more
influential sperm quality parameters and their potential impact similar than those merged at higher heights.
on ASD prevalence.
This hierarchical clustering analysis provides insights into how
different years group based on the given parameters, helping to
3.4 Hierarchical clustering analysis identify trends and patterns over time.

The hierarchical clustering dendrogram visualizes the natural

groupings or clusters within the data based on sperm quality • Natural Groupings: The dendrogram reveals natural groupings
parameters and ASD prevalence. Each leaf represents a specific within the data, highlighting patterns and similarities in ASD
year, and the horizontal axis indicates the distance or prevalence and sperm quality parameters across the years.
dissimilarity between clusters (Figure 9). This can indicate periods of stability or significant changes.
The hierarchical clustering dendrogram visualizes the natural • Temporal Trends: By examining the clusters, specific periods
groupings or clusters within the data based on sperm quality with significant shifts in sperm quality parameters or ASD
parameters and ASD prevalence from 2000 to 2024. Each leaf prevalence can be identified. Clusters of consecutive years
represents a specific year, and the horizontal axis indicates the suggest stable trends, while isolated years may indicate
distance or dissimilarity between clusters. The dendrogram anomalies or shifts.
illustrates how different years group together based on the • Parameter Correlation: The clustering might suggest a
similarity of their sperm quality parameters and ASD prevalence. correlation between sperm quality parameters and ASD
The closer the branches are, the more similar the data points. prevalence. Years grouped likely share similar characteristics,
The clustering results can be summarized as follows: potentially indicating a relationship between the two variables.
• Policy Impact: Changes in clusters over time could reflect the
• Clusters: The dendrogram identifies clusters of years that share impact of public health policies or environmental factors
similar sperm quality characteristics and ASD prevalence rates. affecting sperm quality and ASD prevalence. For example, a
• Distance: The horizontal axis represents the Euclidean distance, policy implemented in a specific year might show its effects in
indicating dissimilarity between clusters. subsequent clusters.
• Predictive Insights: Understanding these clusters can provide
3.4.1 Key observations predictive insights, allowing for anticipating future trends
• Early 2000s (2000–2004): These years exhibit similar sperm based on past patterns. This can be valuable for public health
quality parameters and lower ASD prevalence, forming a planning and resource allocation.
distinct cluster. • Anomaly Detection: The dendrogram can help detect anomalies
• Mid 2000s to Early 2010s (2006–2012): This period shows or outliers. Years that do not cluster with others may indicate
changes in sperm quality parameters and an increasing ASD unique events or factors that significantly influenced the data
prevalence, creating another cluster. in those years.
• Mid 2010s to Early 2020s (2014–2024): These years cluster
together, indicating further changes in sperm quality The hierarchical clustering analysis offers a comprehensive
parameters and higher ASD prevalence.
view of how sperm quality parameters and ASD prevalence rates
The dendrogram visually represents the evolution of sperm have evolved, helping uncover underlying patterns and potential
quality parameters and ASD prevalence over time. It highlights causative factors.

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3.5 Logistic regression analysis 3.5.2 Classification report

The classification report presents detailed performance metrics
A logistic regression model was used to predict whether ASD illustrated in Table 8 and Figure 10.
prevalence exceeds the median value based on sperm quality
parameters. A confusion matrix and a classification report
assessed the model’s performance. 3.5.3 Model coefficients
The dataset was divided into training and testing sets, and the The coefficients of the logistic regression model indicate the
logistic regression model was subsequently trained and evaluated. relationship between each sperm quality parameter and the
Key results of the analysis include: likelihood of ASD prevalence. Sperm concentration (−0.232684),
sperm motility (−0.093286), sperm morphology (−0.000043),
total sperm count (−0.465383), volume of ejaculate (−0.009337),
3.5.1 Confusion matrix pH level (−0.000009), and testosterone levels (−0.004654) are
The model attained perfect accuracy on the small test set, negatively associated with ASD prevalence, suggesting that higher
correctly classifying all samples. However, because of the small values of these parameters decrease the likelihood of ASD.
size of the test set, further validation with a larger dataset is Conversely, SDF (0.046527), WBC count (0.004653), and semen
recommended to ensure the robustness of these results. The viscosity (0.004653) are positively associated with ASD
confusion matrix in Table 7 illustrates the model’s performance prevalence, indicating that higher values of these parameters
on the test data. increase the likelihood of ASD. Negative coefficients indicate that
higher values of the corresponding parameter are linked to a
lower likelihood of increased ASD prevalence, while positive
TABLE 7 Confusion matrix for logistic regression model.
coefficients suggest that higher values are associated with a
Predicted Predicted greater likelihood of increased ASD prevalence.
negative (0) positive (1)
Actual negative (0) 1 0
Actual positive (1) 0 2
3.5.4 Interpretation
True negatives (TN): 1, true positives (TP): 2, false positives (FP): 0, false negatives (FN): 0. • Performance: The logistic regression model achieved perfect
accuracy on the test set, with precision, recall, and F1-scores of
1.00 for both classes. However, it is essential to note that the
TABLE 8 Logistic regression model performance evaluation.
test set size was very small (only three samples), which may not
Precision Recall F1-score Support provide a robust evaluation of the model’s performance.
Negative (0) 1.00 1.00 1.00 1 • Key Influencers: Parameters such as sperm concentration, total
Positive (1) 1.00 1.00 1.00 2 sperm count, and sperm motility have negative coefficients,
Accuracy 1.00 3
indicating that higher values are associated with a lower
Macro Avg 1.00 1.00 1.00 3
likelihood of higher ASD prevalence. Conversely, SDF, WBC
Weighted Avg 1.00 1.00 1.00 3
count, and semen viscosity have positive coefficients,

FIGURE 10
Logistic regression analysis of ASD prevalence based on sperm quality parameters: model performance and key metrics (including confusion matrix
and classification report).

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suggesting that higher values of these parameters are associated

Testosterone
with a higher likelihood of higher ASD prevalence.

levels
−0.98

−1.00

−0.99
−1.00
1.00

1.00
0.96

1.00

0.99

0.99

1.00
The logistic regression analysis provides insights into the
relationship between sperm quality parameters and ASD
prevalence, highlighting key factors that may influence ASD risk.
Further validation with a larger dataset is recommended to
confirm these findings.

viscosity
Semen

−1.00

−1.00
−0.96

−1.00

−0.99

−0.99

−1.00
0.98

1.00

0.99
1.00
3.6 Cross-correlation analysis

count
WBC

−0.99

−0.99
−0.94

−0.98

−0.98

−0.97

−0.99
0.97

0.99

1.00
0.99
The cross-correlation matrix offers a detailed view of the
relationships between various variables in the dataset. Key
observations are presented in Table 9.

level
−0.99

−0.99

−0.97
−0.99
pH

0.99

0.99
0.95

0.99

0.99

1.00

0.99
The cross-correlation matrix reveals significant relationships
between ASD prevalence rates and various sperm quality
parameters. Strong negative correlations exist between ASD

Volume of
ejaculate
prevalence and sperm concentration (−0.98), sperm motility

−1.00

−0.99

−0.98
−0.99
0.99

0.99
0.94

0.98

1.00

0.99

0.99
(−0.99), sperm morphology (−0.95), total sperm count (−0.98),
the volume of ejaculate (−0.99), pH level (−0.99), and
testosterone levels (−0.98). This indicates that decreases in these
SDF Total sperm
sperm quality parameters are associated with increases in ASD
count

prevalence. Conversely, there are strong positive correlations

−0.98

−1.00

−0.99
−1.00
1.00

1.00
0.96

1.00

0.98

0.99

1.00
between ASD prevalence and SDF (0.98), WBC count (0.97), and
semen viscosity (0.98), suggesting that higher values in these
parameters are associated with higher ASD prevalence.
−1.00

−1.00
−0.96

−1.00

−0.99

−0.99

−1.00
0.98

1.00

0.99
1.00
The heatmap visually represents the correlation matrix, making
identifying strong positive or negative correlations among the
morphology

variables easier. This cross-correlation analysis provides a

Sperm

comprehensive overview of the interrelationships between sperm

−0.95

−0.96

−0.94
−0.96
0.95

0.95
1.00

0.96

0.94

0.95

0.96
quality parameters and ASD prevalence rates, aiding in
identifying key factors influencing ASD.
Volume of Ejaculate as the Most Significant Predictor: The
TABLE 9 Cross-correlation matrix of ASD prevalence and sperm quality parameters.

volume of ejaculate shows the strongest negative correlation with

motility
Sperm

ASD prevalence (−0.99), suggesting it is a highly influential

−0.99

−1.00

−0.99
−1.00
1.00

1.00
0.95

1.00

0.99

0.99

1.00

factor. Its consistency over time and significant impact on ASD

prevalence make it a reliable predictor. The volume of ejaculation
is a comprehensive measure that can reflect overall male
concentration

reproductive health, including aspects like hydration, prostate

Sperm

function, and seminal vesicle function, all of which may be

−0.98

−1.00

−0.99
−1.00
1.00

1.00
0.95

1.00

0.99

0.99

1.00

linked to broader health issues impacting ASD prevalence.

These correlations underscore the significant relationships
between sperm quality parameters and ASD prevalence. Better
sperm quality is typically associated with lower ASD prevalence,
prevalence

while adverse parameters are linked to higher ASD prevalence.

ASD

−0.98

−0.99
−0.95

−0.98

−1.00

−0.99

−0.98

This suggests potential biological markers for assessing ASD risk

1.00

0.98

0.97
0.98

and highlights the importance of maintaining good sperm quality

for reproductive health. Further research and intervention
strategies should focus on these key parameters to better
Year

−1.00

−1.00
−0.95

−1.00

−0.99

−0.99

−1.00
0.99

1.00

0.99
1.00

understand and potentially mitigate ASD prevalence.

The matrix highlights the significant relationships between
ASD prevalence

Semen viscosity
Parameter

Sperm motility

sperm quality parameters and ASD prevalence. Better sperm

concentration

Testosterone
Total sperm
morphology

WBC count
Volume of

quality (higher sperm concentration, motility, morphology, etc.)

ejaculate
pH level
Sperm

Sperm

count

is generally associated with lower ASD prevalence, while adverse

levels
SDF

parameters (higher SDF, WBC count, semen viscosity) are linked

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FIGURE 11
Cross-correlation matrix of ASD prevalence and sperm quality parameters. Negative correlations (blue): higher values of sperm concentration, motility,
morphology, total sperm count, volume of ejaculate, pH level, and testosterone levels are associated with lower ASD prevalence. Positive correlations
(red): higher values of SDF, WBC count, and semen viscosity are associated with higher ASD prevalence.

to higher ASD prevalence. This suggests potential biological volume of ejaculate, pH level, white blood cell count, semen
markers for assessing ASD risk and emphasizes the importance viscosity, and testosterone levels. Descriptive statistics summarize
of maintaining good sperm quality for reproductive health. trends over this period.
The cross-correlation analysis reveals significant relationships ASD prevalence rates have increased over time, averaging 15.45
between sperm quality parameters and ASD prevalence. per 1,000 live births. Sperm quality parameters have generally
Improved sperm quality (higher counts, motility, morphology, declined, with decreases in sperm concentration, motility,
and testosterone levels) is generally linked to lower ASD morphology, total sperm count, volume of ejaculate, pH level,
prevalence, while adverse parameters (higher SDF, WBC count, and testosterone levels, though SDF has slightly improved.
and semen viscosity) correlate with higher ASD prevalence. Multiple regression and ANOVA analyses reveal significant
These findings offer valuable insights into potential biological negative associations between ASD prevalence and sperm
markers for ASD risk and highlight areas for further research concentration, motility, morphology, total sperm count, volume
and intervention strategies. Refer to Figure 11 for details. of ejaculate, pH level, and testosterone levels. Positive
This section examines trends in ASD prevalence and sperm associations were found with SDF, WBC count, and semen
quality parameters from 2000 to 2024 using multiple regression, viscosity. PCA identifies two principal components, with the
ANOVA, PCA, hierarchical clustering, logistic regression, and first strongly related to ASD prevalence through sperm
cross-correlation analysis. The study collected annual data on concentration, motility, morphology, and SDF. Hierarchical
ASD prevalence and sperm quality parameters, including sperm clustering highlights periods of stability and change in sperm
concentration, motility, morphology, SDF, total sperm count, the quality and ASD prevalence.

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Logistic regression predicts ASD prevalence based on sperm Additional parameters such as sperm concentration and
quality parameters, achieving perfect accuracy on a small test set. motility, which showed negative associations with ASD prevalence,
Key influencers include sperm concentration, total sperm count, have been widely reported to impact fertility positively (9). The
and motility, which have negative coefficients, while SDF, WBC positive association with WBC count and semen viscosity indicates
count, and semen viscosity have positive coefficients. potential inflammatory or infectious processes that might affect
Cross-correlation analysis supports these findings, showing reproductive health and neurodevelopment.
significant negative correlations between ASD prevalence and Our analysis also considered the phenotypes of infertility,
sperm quality parameters and positive correlations with SDF, including teratoasthenospermia, oligoasthenospermia, and OAT
WBC count, and semen viscosity. These results suggest syndrome. These conditions, characterized by impaired sperm
potential biological markers for assessing ASD risk and parameters, were separately analyzed in relation to ASD
emphasize the importance of maintaining good sperm quality prevalence. We found that individuals with these phenotypes had
for reproductive health. a higher prevalence of ASD, suggesting a significant link between
severe forms of male infertility and neurodevelopmental
disorders. This aligns with findings from previous research
4 Discussion indicating that compromised sperm parameters are associated
with broader health implications (32).
The findings from this extensive analysis of ASD prevalence ANOVA results further emphasize the significance of sperm
and sperm quality parameters over 24 years provide significant quality parameters on ASD prevalence, with highly significant p-
insights into the potential relationships between reproductive values for most parameters. PCA identified principal components
health and neurodevelopmental outcomes. The study offers a related to ASD prevalence, echoing findings from Levine et al. (9).
comprehensive understanding of these trends using multiple Hierarchical clustering highlighted periods of stability and
regression, ANOVA, PCA, hierarchical clustering, logistic significant change, providing insights into potential environmental
regression, and cross-correlation analysis. or policy impacts on reproductive health and ASD rates.
From 2000 to 2024, ASD prevalence increased significantly, Despite its limited test set size, the logistic regression model
averaging 15.45 per 1,000 live births, consistent with global effectively predicted ASD prevalence based on sperm quality
studies (2–4). This rise mirrors global trends noted by Elsabbagh parameters. Key influencers identified included sperm
et al. (28) and similar findings in the United States by the CDC concentration, total sperm count, motility (negative coefficients),
and Baio et al. The increased prevalence of ASD can be SDF, WBC count, and semen viscosity (positive coefficients).
attributed to enhanced diagnostic practices with improved tools These findings align with the cross-correlation analysis, which
and broader criteria (5), increased awareness leading to more showed significant relationships between sperm quality
diagnoses (29), environmental factors like pollutant exposure and parameters and ASD prevalence.
maternal health issues (6, 19), and significant genetic and Our study corroborates Behdarvandian et al. (33), who
epigenetic influences suggesting crucial gene-environment demonstrated that SDF increases with age in a large cohort study
interactions (22, 30). Simultaneously, sperm quality parameters involving approximately 10,000 semen samples. Their findings
such as sperm concentration, motility, morphology, total sperm indicated that both SCSA® and TUNEL assays provide
count, the volume of ejaculate, pH level, and testosterone levels concordant data, though TUNEL yields lower percentages due to
have declined, aligning with global data (9, 10). This decline is its detecting existing DNA breaks, while SCSA® detects both
attributed to exposure to environmental pollutants like existing and potential breaks. These insights support our findings
endocrine-disrupting chemicals (23), lifestyle factors such as poor on the relationship between SDF and ASD prevalence. The
diet, obesity, and increased stress (12), and the rising prevalence observed increase in SDF with age supports the hypothesis that
of health conditions like diabetes and hypertension that affect environmental and lifestyle factors contributing to increased SDF
reproductive health (31). may also be linked to rising ASD rates. Future research should
The multiple regression analysis revealed significant negative continue to explore these associations using standardized SDF
associations between ASD prevalence and several sperm quality testing methods for consistency.
parameters, notably sperm concentration and motility, indicating Additionally, our study found a positive association between
that lower sperm quality is associated with higher ASD SDF and ASD prevalence, emphasizing the impact of sperm
prevalence. This is supported by studies linking poor sperm DNA fragmentation on reproductive and neurodevelopmental
quality to adverse health outcomes (12, 23). outcomes. This aligns with Mohammadi et al. (34), who
Interestingly, SDF showed a positive association with ASD examined the reliability of High DNA Stainability (HDS) using
prevalence, which contradicts the generally accepted view that SCSA®, TUNEL, and CMA3 assays. They found weak
normal SDF positively affects pregnancy rates (27). This finding correlations between HDS and other nuclear integrity markers,
suggests that other underlying mechanisms may be at play, suggesting that HDS may not reliably indicate nuclear
potentially involving genetic or epigenetic factors that influence immaturity or DNA fragmentation. Mohammadi et al. (34)
SDF and neurodevelopmental outcomes. Previous studies have highlighted the complexity of assessing sperm nuclear integrity
shown that increased SDF is associated with adverse reproductive and the need for standardized approaches. The variability in
outcomes (27). DNA fragmentation and nuclear condensation tests indicates that

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multiple factors contribute to sperm DNA damage, reinforcing the with significant DNA damage and epigenetic alterations, which
importance of a multifaceted assessment in reproductive can increase the risk of ASD and other genetic disorders in their
health studies. children (37). This is further supported by Xavier et al. (38), who
Our findings align with the Testicular Dysgenesis Syndrome highlight that mutations in sperm increase with paternal age,
(TDS) hypothesis, which posits that various male reproductive impacting offspring health across generations (37).
disorders, such as poor semen quality, testicular cancer, Moreover, Mohammadi et al. (34) discussed the complexities in
undescended testis, and hypospadias, are symptoms of a single assessing sperm nuclear integrity, emphasizing that multiple
condition influenced by environmental factors during fetal factors, including paternal age, contribute to sperm DNA
development (21). This suggests that the decline in sperm quality damage, reinforcing the importance of a multifaceted assessment
and the increase in ASD prevalence may be interconnected in reproductive health studies. These studies collectively
through shared environmental exposures that disrupt embryonic underline the necessity for further research to explore the
development. Animal studies indicate that exposure to endocrine mechanisms by which paternal age influences ASD risk and to
disruptors like diethylstilbestrol and bisphenol A during critical develop targeted interventions.
periods of fetal development can lead to reproductive
abnormalities (21). Additionally, Chiu et al. (17) found that higher
consumption of sugar-sweetened beverages (SSB) was associated
with lower sperm motility among healthy young men, highlighting
5 Conclusions and suggestions
the adverse effects of high sugar intake on sperm quality. These
5.1 Conclusions
findings underscore the need for further research into the
environmental and lifestyle factors contributing to ASD and
Based on the findings from this comprehensive analysis, there
reproductive health issues. Over recent decades, the rapid increase
is a clear indication of an increasing trend in ASD prevalence from
in these conditions suggests that lifestyle and environmental
2000 to 2024, along with a decline in sperm quality parameters
changes, rather than genetic factors alone, drive these trends.
over the same period. The observed associations between lower
Understanding the TDS framework can guide future research and
sperm quality and higher ASD prevalence suggest potential
public health strategies to address these interconnected health issues.
underlying factors that contribute to these trends. The analysis
The World Health Organization (WHO) guidelines also note
highlights the importance of maintaining reproductive health as
inconsistencies across different laboratories and SDF testing
a potential strategy to mitigate the risk of ASD.
methods, recommending further studies with larger populations to
establish SDF as a reliable diagnostic test and to identify the most
valid testing approach. Given the variability observed in the sperm
DNA fragmentation test findings and the acknowledgment in the 5.2 Suggestions
latest WHO guidelines that more research is needed, it is crucial for
future studies to adopt standardized methodologies for SDF To enhance our understanding of the relationships between
sperm quality parameters and ASD prevalence, the following key
assessment to ensure consistency and reliability across studies (8, 35).
This comprehensive analysis underscores the complex interplay recommendations are suggested:
between reproductive health and neurodevelopmental outcomes. 1. Enhanced Data Collection: Future studies should collect more
The consistent negative associations between ASD prevalence and detailed data on sperm quality and ASD prevalence,
sperm quality parameters, particularly sperm concentration and including genetic and environmental factors, to provide
motility, suggest that deteriorating sperm quality may contribute deeper insights into observed trends and associations.
to increasing ASD rates. The positive association with SDF 2. Longitudinal Studies: Implement long-term, longitudinal
implies complex genetic interactions warranting further studies to understand the causative factors and temporal
investigation. These findings highlight the importance of relationships between changes in sperm quality and increases
maintaining good reproductive health to potentially mitigate in ASD prevalence.
ASD risk and suggest areas for further research and intervention 3. Interdisciplinary Research: Encourage interdisciplinary
strategies. The study supports the hypothesis that reproductive research combining reproductive health, neurodevelopmental
health factors play a crucial role in ASD etiology, marking a studies, and environmental sciences to provide a holistic view
potential discovery in identifying biological markers that could of the factors contributing to ASD.
predict ASD risk. Further research is needed to confirm these 4. Public Health Interventions: Develop targeted public health
causal links and develop targeted prevention strategies. interventions focusing on improving reproductive health and
The relationship between paternal age and the risk of ASD has mitigating environmental risk factors to reduce ASD
been well-documented in recent studies. Advanced paternal age is prevalence potentially.
associated with an increase in sperm DNA damage, the emergence 5. Identify Underlying Causes: Conduct further studies to
of epigenetic changes in the germ line, and a higher mutational elucidate the genetic, environmental, and epigenetic factors
load in offspring, leading to a higher incidence of contributing to these trends.
neurodevelopmental disorders, including ASD. Aitken and Baker 6. Develop Preventive Strategies: Use insights from etiology
(36) explain that older fathers are more likely to produce sperm research to inform preventive measures and interventions.

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7. Improve Diagnostic and Therapeutic Approaches: Enhance 8. Public Access to Data: Ensure data on ASD prevalence and
screening, early diagnosis, and treatment for ASD and sperm quality is publicly accessible to support transparency
reproductive health issues. and ongoing research, facilitating a collaborative approach to
addressing these public health challenges.
By following these suggestions, future research can build on
the current findings, leading to more effective strategies for 9. Community-Based Initiatives: Develop community-based
addressing the rising prevalence of ASD and the decline in initiatives to support families affected by ASD and
reproductive health. reproductive health issues, providing tailored resources and
raising awareness in local communities.

Data availability statement

6 Recommendations
The original contributions presented in the study are included
In light of the findings, the following recommendations aim to in the article/Supplementary Material, further inquiries can be
improve research practices, public health strategies, and policy directed to the corresponding author.
formulations related to ASD and reproductive health:
1. Policy Development and Implementation: Enforce stricter
regulations to reduce exposure to environmental pollutants, Ethics statement
particularly endocrine-disrupting chemicals and industrial
pollutants. Develop policies supporting healthy reproductive and All documents, materials, data, and information related to this
developmental outcomes, including access to reproductive health research study were kept confidential in accordance with all
services and early ASD screening and intervention programs. relevant national, international, and local regulations and laws.
2. Healthcare Initiatives: Encourage routine monitoring of sperm Data used in this study were anonymized by assigning
quality in men, especially those planning to start families, to identification numbers for research and statistical analysis
enable early interventions that may reduce ASD risk. Train purposes. All information was only accessible by the researcher
healthcare providers to recognize and address factors and stored securely. No identifying information was included in
contributing to declining sperm quality and ASD prevalence, any presentations or publications resulting from this research.
offering better guidance on reducing risk factors and Ethical approval was not required for this study as it did not
managing related conditions. involve human or animal subjects.
3. Public Awareness and Health Campaigns: Launch public
awareness campaigns on the importance of reproductive health
and its impact on neurodevelopmental outcomes. Promote Author contributions
healthy lifestyles, including balanced diets, regular physical
activity, and smoking cessation, to improve reproductive health AA-s: Writing – original draft, Writing – review & editing.
by addressing factors like poor diet, obesity, and smoking.
4. Research and Funding: Increase funding for research on the
links between reproductive health and ASD, focusing on Funding
genetic, environmental, and lifestyle factors. Allocate more
funding for research into environmental, genetic, and The author(s) declare that no financial support was received for
epigenetic factors contributing to ASD and declining sperm the research, authorship, and/or publication of this article.
quality to understand underlying mechanisms and develop
targeted interventions.
5. Educational Programs: Implement programs in schools and Acknowledgments
communities to educate about maintaining good reproductive
health. Develop programs to improve sperm quality through I extend my deepest gratitude to the CDC Autism
lifestyle changes such as diet, exercise, and reducing exposure and Developmental Disabilities Monitoring Network for
to harmful substances. providing crucial data. Special thanks to my colleagues and
6. Interdisciplinary and International Collaboration: Promote students for their insightful discussions and feedback. Finally,
collaboration among specialists in reproductive health, heartfelt thanks to my family and friends for their
neurology, environmental science, and public health to develop unwavering support.
comprehensive strategies. Encourage international collaboration
through shared research initiatives and data pooling to address
global trends in ASD prevalence and sperm quality. Conflict of interest
7. Longitudinal Studies: Conduct long-term studies to monitor
trends in sperm quality and ASD prevalence, helping to The author declares that the research was conducted in the
establish causal relationships and identify long-term effects of absence of any commercial or financial relationships that could
environmental and lifestyle changes. be construed as a potential conflict of interest.

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Al-Salihy 10.3389/frph.2024.1438049

Publisher’s note organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or
All claims expressed in this article are solely those of the claim that may be made by its manufacturer, is not guaranteed
authors and do not necessarily represent those of their affiliated or endorsed by the publisher.

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