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Background

Obstructive sleep apnea (OSA), characterized by recurrent upper airway collapse during sleep, leads to chronic intermittent hypoxia (CIH), sleep fragmentation, and systemic pathophysiological changes.^1–3 Globally affecting nearly 1 billion people,⁴ OSA poses a growing public health challenge.⁵

In recent years, the bidirectional link between OSA and endocrine/metabolic disorders has gained significant attention,⁶ particularly the nuanced relationship between OSA and thyroid function.⁷ Thyroid hormones, regulated by the hypothalamic-pituitary-thyroid (HPT) axis, play critical roles in metabolism and cardiovascular function.^8–11 Notably, the evidence regarding the association between OSA and thyroid function remains inconsistent. Some studies suggest a subtle connection, such as hypothyroidism exacerbating OSA through airway narrowing⁷ and OSA-induced oxidative stress impairing thyroid hormone synthesis,^12–15 other research, however, indicates no significant associations.¹⁶ Our previous study revealed that the progression of OSA may promote increased levels of TH, especially FT3, in non-elderly individuals.¹⁷ Adding to this inconsistency, several studies have reported an association between hypothyroidism and OSA severity.¹⁸ Together, these divergent findings highlight the need to clarify the contentious relationship between OSA and thyroid function, including its specific mechanisms and bidirectional interactions.

The sleep apnea-specific hypoxic burden (SASHB) and sleep breathing impairment index (SBII) are crucial early indicators for assessing hypoxia in OSA, comprehensively capturing respiratory event frequency, depth, and hypoxia duration.^19,20 They can reflect the physiological impact of CIH more accurately than the traditional AHI.²¹ This focus on oxygen saturation dynamics is particularly relevant for studying thyroid function, as thyroid hormone synthesis and release are highly sensitive to hypoxic stress. By integrating hypoxia intensity and duration, SASHB and SBII offer a more precise tool for dissecting how CIH modulates thyroid hormone levels. Notably, SASHB has already been linked to glucose and lipid metabolism abnormalities²² and cardiovascular risks,^23,24 underscoring its utility in capturing hypoxia-related endocrine and metabolic dysfunction. However, no relevant studies have been conducted on the relationships among the SASHB, the SBII, and thyroid function.

Sleep consists of rapid eye movement (REM) and non-rapid eye movement (NREM) stages,²⁵ with distinct physiological profiles influencing respiratory function. REM sleep, characterized by heightened brain activity and muscle atonia,^25–27 contrasts with NREM sleep, where physiological functions like heart rate and respiration slow, promoting recovery.^25,26 Patients with OSA exhibit more severe airway collapse during REM sleep, likely due to stage-specific neuromuscular regulation.²⁸ Therefore, it is crucial to consider the impact of different sleep stages on physiological functions when studying the relationship between OSA and thyroid function.

Therefore, this study aimed to explore the thyroid function changes in patients with OSA and analyze the intrinsic connections between SASHB, SBII, and thyroid function indicators (such as thyroid hormones and antibodies). Additionally, this study focused on the associations between the SASHB and SBII during different sleep stages and thyroid function, which have never been explored previously. This research is of great theoretical and practical importance for comprehensively understanding the mechanisms by which OSA affects thyroid function, optimizing clinical diagnostic strategies, and developing targeted therapeutic interventions.

Subjects and Methods

Study Design and Participants

This retrospective study included 1681 individuals with suspected OSA who visited the Department of Otorhinolaryngology at Xi’an Jiaotong University Second Affiliated Hospital from August 2017 to March 2024. Participants were included if they (1) were ≥18 years of age; (2) had undergone overnight polysomnography (PSG) and were diagnosed with OSA; and (3) had complete TH and antibody test results. The exclusion criteria were as follows: (1) history of OSA treatment; (2) severe systemic diseases, such as heart, liver, lung, and kidney failure; (3) other non-OSA sleep disorders; (4) severe mental illnesses or malignant tumors; (5) use of sedatives or medications that may interfere with thyroid function; (6) missing clinical PSG data. After stringent screening, 452 participants with complete data were included in this study. The entire recruitment process is illustrated in Figure 1.

This study strictly adhered to the tenets of the Declaration of Helsinki and was approved by the Ethics Committee of Xi’an Jiaotong University Second Affiliated Hospital (Approval No. 2022–1417), with all participants providing informed consent.

Data Elements

A total of 35 relevant clinical parameters were collected in this study, including the following candidate variables: (1) demographic characteristics, including sex and age; (2) anthropometric measures, including body mass index (BMI), neck circumference (NC) and waist circumference (WC), (3) comorbidities, including history of diabetes, coronary heart disease (CHD) and hypertension; (4) lifestyle habits, including smoking and alcohol use; (5) OSA-related history and indicators, including total sleep time (TST) recorded during the overnight PSG study, Epworth Sleepiness Scale (ESS),²⁹ mean apnea duration, maximum apnea duration, apnea–hypopnea index (AHI), lowest nocturnal peripheral oxygen saturation (Lowest SpO₂), time spent with peripheral oxygen saturation <90% (T90), percentage of time spent with peripheral oxygen saturation <90% (CT90), average heart rate during sleep, lowest heart rate during sleep, highest heart rate during sleep, SASHB, SASHB during NREM sleep (NREM-SASHB), SASHB during REM sleep (REM-SASHB); SBII, SBII during NREM sleep (NREM-SBII), and SBII during REM sleep (REM-SBII); (6) thyroid function-related indicators, including serum free triiodothyronine (FT3, pmol/L), serum free thyroxine (FT4, pmol/L), serum total triiodothyronine (TT3, nmol/L), serum total thyroxine (TT4, nmol/L), serum thyroid stimulating hormone (TSH, mIU/L), thyroid peroxidase antibodies (Anti-TPO, IU/mL), thyroid globulin antibodies (Anti-TG, IU/mL), and reverse triiodothyronine (RT3, ng/dL). All fasting venous blood samples were collected between 7:00 and 8:00 AM on the morning immediately following an overnight PSG study, under strict quality control protocols, with thyroid function indicators analyzed using standardized laboratory procedures.

Sleep Evaluation

To obtain accurate and objective sleep parameters, all enrolled patients underwent overnight PSG monitoring at the Sleep Center of the Department of Otorhinolaryngology-Head and Neck Surgery at the Second Affiliated Hospital of Xi’an Jiaotong University. All the records were evaluated by certified clinical PSG experts, who comprehensively analyzed various parameters, including electroencephalography, electrooculography, electromyography, electrocardiography, nasal and oral airflow recordings, oxygen saturation levels, chest movements, and muscle activity.

The Epworth Sleepiness Scale (ESS) used in this study has been authorized by the copyright holder.

Calculation and Definition

The AHI is defined as the number of apnea and hypopnea events per hour during sleep. In addition, hypopnea is defined as an abnormal respiratory event lasting at least 10s with at least a 30% reduction in thoracoabdominal movement or airflow as compared with baseline and with at least a 4% oxygen desaturation.³⁰ The SASHB is the total area under the baseline SpO₂ curve corresponding to respiratory events per hour during sleep. REM-SASHB and NREM-SASHB refer to the SASHB during REM and NREM sleep, respectively. The SBII is defined as the sum of the duration of breathing events and the corresponding desaturation area per hour during sleep. Accordingly, REM-SBII and NREM-SBII are the SBII during REM and NREM sleep, respectively. In this study, the calculations for SASHB and SBII were based on laboratory test data, including nasal airflow and blood oxygen saturation trend graphs. Using the algorithms developed by Ali Azarbarzin¹⁹ and Wenhao Cao,²⁰ we created calculation codes for SASHB and SBII using Python software (version 3.7), enabling efficient batch processing of the raw data. In this study, when the severity of OSA was evaluated by the AHI, patients were categorized according to the number of events per hour: mild OSA was defined as an AHI of 5 to less than 15, moderate OSA was defined as an AHI of 15 to less than 30, and severe OSA was defined as an AHI of 30 or higher. In addition, when the severity of OSA was assessed by the SASHB and SBII, patients were grouped according to quartiles.

Statistical Analysis

The statistical analyses were conducted using R software (version 4.3.2) and SPSS 26.0 (IBM Corporation, Armonk, NY, USA). If the data were normally distributed, the continuous variables were expressed as the means ± standard deviations; if the data were nonnormally distributed, they were expressed as the medians and interquartile ranges. Categorical variables are presented as counts (n) and percentages (%). First, OSA severity was assessed according to the AHI, SASHB, and SBII, and descriptive statistics were calculated. The Kruskal‒Wallis H-test was used for continuous variables, and the chi-square test was employed for categorical variables to examine intergroup differences in descriptive statistics. Additionally, Spearman correlation analysis was performed to assess the associations between thyroid function parameters and sleep parameters, with Benjamini–Hochberg false discovery rate (FDR) correction applied to account for multiple testing. Furthermore, multiple linear regression analysis was performed to evaluate the independent relationships between sleep parameters and thyroid hormone levels, adjusting for potential confounding factors. Sex-stratified analyses were also performed, with separate multiple linear regression models constructed for male and female subgroups to explore sex-specific associations. Moreover, collinearity diagnostics were conducted before statistical analysis to eliminate potential multicollinearity among the variables. All the statistical tests were two-tailed, with the significance level set at p < 0.05.

Results

Baseline and Sleep Parameter Characteristics

A total of 452 patients with OSA, 395 males and 57 females, were included in this study. Significant baseline differences were observed across AHI severity groups (all p < 0.05, Table 1). The male proportion increased with AHI severity, while BMI, NC, and WC showed an upward trend. Hypertension history, smoking, and alcohol use were more prevalent in the severe OSA group (23.51%, 70.82%, and 81.87%, respectively). Sleep parameters, including ESS, hypoxia duration (T90, CT90), and mean heart rate, worsened with increasing AHI, whereas Lowest SpO₂ decreased progressively.

Table 1 Demographic Characteristics and Sleep Parameters of Patients According to the AHI

Thyroid Function Indicators

The relationship between OSA and thyroid function was explored in depth. When OSA severity was evaluated by the AHI, the results indicated significant differences in FT3, FT4, and TT3 levels among the mild, moderate, and severe OSA groups (all p < 0.05). Specifically, Dunn’s multiple comparison tests revealed significant differences in FT3 between the mild and moderate OSA groups (p < 0.01) and between the mild and severe OSA groups (p < 0.001); FT4 also showed significant differences between the mild and moderate OSA groups (p < 0.01) and between the mild and severe OSA groups (p < 0.01). TT3 differed only between the mild and severe OSA groups (p < 0.05). However, there were no significant differences in TT4, TSH, Anti-TPO, Anti-TG, or RT3 levels (all p > 0.05) (Figure 2 and Table 2).

Table 2 Analysis of Thyroid Indicators According to AHI Severity

Figure 2 Mean values of FT3, FT4, and TT3 at the AHI level. (a) FT3; (b) FT4; (c) TT3. *P < 0.05, **P < 0.01, ****P < 0.0001. Abbreviations: FT3, serum free triiodothyronine; FT4, serum free thyroxine; TT3, serum total triiodothyronine; AHI, apnea–hypopnea index.

In addition to being grouped by the AHI, patients were also classified according to SASHB (≤21.38, 21.38–74.47, 74.47–221.21, and >221.21) and SBII (≤9.53, 9.53–36.05, 36.05–110.34, and >110.34) quartiles. In the analysis of intergroup differences, thyroid function indicators exhibited complex and diverse trends. There were statistically significant differences in the FT3 levels in the SASHB and SBII quartiles (all p < 0.01). With the gradual increase in the SASHB and SBII quartiles, the FT3 levels tended to increase. Although FT4 exhibited a meaningful intergroup difference when grouped by SBII severity (p < 0.05), post hoc multiple comparisons revealed no significant differences in the FT4 levels across groups. Additionally, as the SBII increased, the TSH tended to decrease initially and then increase (Figures 3 and 4, Table 3 and Table 4).

Table 3 Analysis of Thyroid Indicators According to SASHB Severity

Table 4 Analysis of Thyroid Indicators According to SBII Severity

Figure 3 Mean values of FT3 and FT4 at the SASHB level. (a) FT3; (b) FT4. *P < 0.05, **P < 0.01. Abbreviations: FT3, serum free triiodothyronine; FT4, serum free thyroxine; SASHB, sleep apnea-specific hypoxic burden.

Figure 4 Mean values of FT3, FT4, and TSH at the SBII level. (a) FT3; (b) FT4; (c) TSH. *P < 0.05, **P < 0.01. Abbreviations: FT3, serum free triiodothyronine; FT4, serum free thyroxine; TSH, serum thyroid stimulating hormone; SBII, sleep breathing impairment index.

To further explore correlations among the SASHB, the SBII, and thyroid function during the NREM and REM periods, patients were regrouped according to REM-SASHB, NREM-SASHB, REM-SBII, and NREM-SBII severity. Focusing first on NREM sleep, significant differences in FT3, FT4, and TSH levels were found among the groups according to NREM-SASHB and NREM-SBII severity (all p < 0.05), with TSH levels initially decreasing but then increasing. The specific results of the multiple comparison tests are presented in Figure S1. In REM sleep, only FT3 levels differed between the REM-SASHB and REM-SBII groups (all p < 0.05) (Tables S1–S4; Figures S1 and S2).

Exploration of the Correlation of Variables and Regression Analysis

Based on the correlation analysis results after FDR correction, we initially explored the correlations between PSG and thyroid function variables, demonstrating the necessity of regression analysis. FT3 was significantly positively correlated with AHI, mean apnea duration, T90, CT90, REM-SASHB, NREM-SASHB, SASHB, REM-SBII, NREM-SBII and SBII (all q < 0.05), and negatively correlated with lowest SpO₂ (q < 0.05). FT4 was significantly positively correlated with TST and maximum heart rate (both q < 0.05). TT3 was significantly positively correlated with AHI, mean heart rate and maximum heart rate (all q < 0.05). Anti-TPO was significantly positively correlated with TST and T90, and negatively correlated with lowest SpO₂ (all q < 0.05). The negative correlation between TSH and NREM-SBII did not remain significant after correction (q > 0.05), while TT4, Anti-TG and RT3 showed no significant correlations (all q > 0.05). These results further demonstrate the necessity of regression analysis to explore the independent associations (Tables S5 and S6, Figure 5).

Figure 5 Heatmap of the correlations between sleep parameters and thyroid parameters. Abbreviations: TST, total sleep time; AHI, apnea–hypopnea index; Lowest SpO2, lowest oxygen saturation at night; T90, sleep time spent with oxygen saturation below 90%; CT90, the percentage of sleep time with oxygen saturation below 90%; REM-SASHB, sleep apnea-specific hypoxic burden during rapid eye movement sleep; NREM-SASHB, sleep apnea-specific hypoxic burden during non-rapid eye movement sleep; SASHB, sleep apnea-specific hypoxic burden; REM-SBII, sleep breathing impairment index during rapid eye movement sleep; NREM-SBII, sleep breathing impairment index during non-rapid eye movement sleep; SBII, sleep breathing impairment index; FT3, serum free triiodothyronine; FT4, serum free thyroxine; TT3, serum total triiodothyronine; TT4, secretes total thyroxine; TSH, serum thyroid stimulating hormone; Anti-TPO, thyroid peroxidase antibodies; Anti-TG, thyroid globulin antibodies; RT3, reverse triiodothyronine.

After fully adjusting for confounding factors, including age, sex and BMI, SASHB (β = 0.145; p < 0.01), NREM-SASHB (β = 0.127; p < 0.05), REM-SASHB (β = 0.137; p < 0.01), SBII (β = 0.132; p < 0.01), NREM-SBII (β = 0.095; p < 0.05), and REM-SBII (β = 0.145; p < 0.01) were independently associated with elevated FT3 levels, whereas the AHI was not independently associated with FT3 levels (Table 5).

Table 5 Stepwise Multiple Linear Regression for Thyroid Indicators

In the male subgroup, multiple linear regression models were used to evaluate the associations between the AHI, SASHB, SBII, and thyroid function. In the same models, independent correlations were observed between SASHB (β = 0.152; p < 0.01), NREM-SASHB (β = 0.130; p < 0.05), REM-SASHB (β = 0.159; p < 0.01), SBII (β = 0.143; p < 0.01), NREM-SBII (β = 0.103; p < 0.05), and REM-SBII (β = 0.169; p < 0.01) with elevated FT3 levels. However, no significant independent associations were observed between the AHI, SASHB, SBII, and thyroid function indicators in the female subgroup (Tables 6 and 7).

Table 6 Stepwise Multiple Linear Regression Against Thyroid Indicators for Male Subgroups

Table 7 Stepwise Multiple Linear Regression Against Thyroid Indicators for Female Subgroups

Discussion

This study focused on the characteristics of thyroid function in patients with OSA. By analyzing clinical data and PSG results from 452 patients with OSA, we specifically investigated the intrinsic relationships between the SASHB, the SBII, and thyroid function indicators (such as thyroid hormones and antibodies) and how these relationships manifest across different severities of OSA, sleep stages, and sex subgroups. After adjusting for multiple variables, the SASHB, NREM-SASHB, REM-SASHB, SBII, NREM-SBII, and REM-SBII were found to be independently associated with elevated FT3 levels in male patients, whereas no significant associations were observed in female subgroups.

Existing research highlights a bidirectional link between OSA and thyroid dysfunction. Hypothyroidism is prevalent in 25–35% of patients with OSA.³¹ However, the prevalence of OSA in patients with hypothyroidism is also high.^32,33 Our data align with this interplay: thyroid function indicators varied significantly across AHI groups, reinforcing the complex relationship between OSA severity and thyroid hormones. In previous studies conducted by our team, we reported that the progression of OSA in nonelderly individuals might promote an increase in thyroid hormone levels, particularly FT3, driven by oxidative stress and inflammation.¹⁷ This study further confirms this finding. Mechanistically, this interplay might can be explained by distinct responses to varying hypoxia severity. Mild hypoxia triggers compensatory thyroid hormone secretion via HPT axis activation. Conversely, severe hypoxia induces oxidative stress-mediated injury to thyroid follicular cells, impairing thyroid hormone synthesis.^12,13 Notably, most thyroid hormone levels in our cohort remained within normal ranges, potentially explaining discrepancies in prior studies.

The specific associations between SASHB, SBII, and thyroid function found in this study are novel. Existing research suggests that CIH in OSA can alter endocrine function by influencing thyroid hormone synthesis and release via the HPT axis, thereby affecting metabolic state.^14,34 Therefore, incorporating the depth and duration of CIH into thyroid function assessments is meaningful. Our analysis revealed that FT3 levels increased with higher SASHB and SBII quartiles, possibly due to hypoxia stimulating thyroid cells and affecting FT3 secretion. Regarding FT4, although there was a trend of differences in the SBII group comparisons, post hoc multiple comparisons revealed no significant differences, suggesting that the mechanisms by which FT4 is affected by OSA might be more complex, possibly involving other yet unidentified regulatory factors. Additionally, the trend of TSH levels initially decreasing and then increasing with SBII might indicate an early attempt to maintain thyroid stability via negative feedback, which becomes imbalanced as OSA progresses. Notably, a significant correlation was found between FT3 levels, SASHB and SBII, but not with AHI. This result strongly suggests that the SASHB and SBII capture key information that the AHI cannot encompass, thereby providing more accurate and sensitive insight into the intrinsic relationship between OSA and thyroid function.

The interplay between sleep stages and thyroid function was an interesting finding. Previous studies often focused on comparing the AHI during REM and NREM periods, classifying OSA populations into REM phenotypes and NREM phenotypes,^35,36 rather than studying the related sleep indicators for REM and NREM independently as a whole. Recently, more researchers have begun to recognize this, with one study finding a correlation between OSA during REM and NREM sleep and lipid levels.³⁷ These findings suggest that different sleep stages significantly impact endocrine and metabolic functions. In our study, the relationships between thyroid function indicators and OSA-related indicators differed during NREM and REM sleep. In NREM sleep, the FT3, FT4, and TSH levels significantly differed, with TSH initially decreasing but then increasing. This finding may be related to the physiological characteristics of NREM sleep. NREM sleep accounts for the majority of total sleep duration and is predominantly mediated by the parasympathetic nervous system,²⁷ during which metabolic and endocrine functions are relatively stable.²⁵ The deep stages of NREM sleep are particularly associated with the secretion of growth hormone,³⁸ significantly impacting thyroid function. Therefore, a greater SASHB and SBII during NREM sleep suggest that the patient experiences frequent or prolonged hypoxic and low oxygen saturation states during this phase, potentially leading to reduced secretion of thyroid hormones that might otherwise increase. In our study, only FT3 levels differed in relation to the REM-related SASHB and SBII groups, increasing with the severity of OSA. During REM sleep, increased sympathetic nervous system activity and a high metabolic state may suppress thyroid hormone secretion.^26,27 Thus, a higher SASHB and SBII during REM sleep indicate that frequent or prolonged hypoxia overrides typical REM-related thyroid hormone suppression, resulting in increased secretion of TH. In summary, considering the physiological changes associated with different sleep stages and their regulatory effects on thyroid function may provide a basis for personalized treatment for patients with OSA.

An unexpected finding was the absence of significant associations in the female subgroup, in contrast to male-specific correlations between SASHB, SBII and FT3. This disparity may stem from sex-specific hormonal influences on thyroid-hypoxia interactions. Estrogen can modulate thyroid hormone-binding globulin levels, subsequently affecting TH’s metabolism and activity.³⁹ Therefore, we speculate that during the pathophysiological changes associated with OSA, the hormonal dynamics could introduce variability that obscures direct OSA-TH associations in female. Additionally, the relatively small number of female patients in this study (57) may have limited the statistical power to detect associations between the AHI, SASHB, SBII, and thyroid function indicators, failing to adequately reflect the true physiological relationships.

This study is the first to investigate the relationships among the SASHB, the SBII, and thyroid function. Compared with the traditional AHI, these two indicators can more comprehensively reflect the characteristics of OSA-related respiratory events, capturing the degree of hypoxia and the duration and frequency of hypoxic events. By clearly establishing the independent correlations among FT3 and the SASHB and SBII, this study enriches our understanding of how OSA affects thyroid function and opens new avenues for research on the relationship between OSA and thyroid function. These findings suggest that future theoretical models should emphasize the impact of comprehensive quantitative indicators of sleep respiratory events on the endocrine system. Furthermore, unlike previous studies, we employed more refined sleep stage-related indicators to analyze their relationships with thyroid function indicators. Additionally, we conducted separate analyses for the male and female subgroups, revealing sex differences in the associations between OSA and thyroid function; specifically, multiple indicators in the male subgroup showed significant independent correlations with thyroid function, whereas no obvious associations were observed in the female subgroup, providing a reference for future targeted research on sex differences.

This study also has several limitations. First, as a retrospective study using historical medical records, information bias may be present. Second, although 452 participants with complete data were included, the sample size is still relatively small for an in-depth exploration of complex relationships, such as sex differences and the intricate relationships between different sleep stages and thyroid function and OSA indicators, which may limit the representativeness of the study results. Third, the single-center design introduces potential selection bias, affecting the generalizability of the findings. Notably, a large proportion of patients had severe OSA, likely reflecting clinical referral bias—patients with severe symptoms from a tertiary hospital sleep clinic were more likely to undergo PSG. Further multicenter, large-sample studies are needed to validate the conclusions of this study and improve its generalizability and reliability. Fourth, despite adjusting for potential confounders, unmeasured factors such as trace element deficiencies and medication history could still influence thyroid function, interfering with accurate assessment of the relationship between OSA and thyroid function. Fifth, this study is correlational and cannot determine the causal relationship between OSA and thyroid function abnormalities. Further prospective studies or animal experiments are needed for deeper exploration.

Conclusion

Overall, this study revealed that the SASHB and SBII are independently correlated with elevated FT3 levels in patients with OSA, with significant associations observed in male but not female subgroups and that this relationship varies across sleep stages. Future research needs to improve in aspects such as larger prospective sample sizes, multicenter collaborations, and optimized study designs to explore further the exact mechanisms of the impact of OSA on thyroid function, providing more valuable theoretical support for clinical diagnosis and treatment.

Abbreviations

AHI, apnea–hypopnea index; Anti-TG, thyroid globulin antibodies; Anti-TPO, thyroid peroxidase antibodies; BMI, body mass index; CHD, coronary heart disease; CT90, the percentage of sleep time with oxygen saturation below 90%; ESS, Epworth Sleepiness Scale; FT3, serum free triiodothyronine; FT4, serum free thyroxine; HPT, hypothalamic‒pituitary‒thyroid; Lowest SpO₂, lowest transcutaneous oxygen saturation at night; NC, neck circumference; NREM, non-rapid eye movement; NREM-SASHB, sleep apnea-specific hypoxic burden during non-rapid eye movement sleep; NREM-SBII, sleep breathing impairment index during non-rapid eye movement sleep; OSA, obstructive sleep apnea; PSG, polysomnography; REM, rapid eye movement; REM-SASHB, sleep apnea-specific hypoxic burden during rapid eye movement sleep; REM-SBII, sleep breathing impairment index during rapid eye movement sleep; RT3, reverse triiodothyronine; SASHB, sleep apnea-specific hypoxic burden; SBII, sleep breathing impairment index; T90, sleep time spent with oxygen saturation below 90%; TSH, serum thyroid stimulating hormone; TST, total sleep time; TT3, serum total triiodothyronine; TT4, secretes total thyroxine; WC, waist circumference.

Data Sharing Statement

The data supporting our findings are available on reasonable request from the corresponding author.

Ethics Approval

This retrospective study was approved by the Ethics Committee of the Second Affiliated Hospital of Xi’an Jiaotong University (approval no. 2022-1417). The procedures used in this study adhered to the tenets of the Declaration of Helsinki.

Acknowledgments

We wish to thank all who volunteered to participate in this study.

Author Contributions

YZ: conceptualization, methodology, software, writing original draft. YS: conceptualization, methodology, writing review and editing. SZ: software, formal analysis, writing original draft. ZC: investigation, resources, data curation, writing original draft. YX: investigation, resources. writing original draft CL: investigation, resources, writing review and editing. XN: investigation, resources, writing review and editing. LM: investigation, resources, writing review and editing. ZW: investigation, resources, writing original draft. YS: formal analysis, validation, writing original draft. ZX: formal analysis, validation, writing original draft. YY: formal analysis, validation, writing original draft. JY: formal analysis, validation, writing original draft. RL: formal analysis, writing original draft. YF: formal analysis, writing original draft. XR: conceptualization, methodology, supervision, funding acquisition, project administration, writing review and editing. WH: conceptualization, methodology, supervision, project administration, resources, writing review and editing. Xiaoyong Ren and Wei Hou are corresponding authors. All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

This work was supported by the National Natural Science Foundation of China (grant no. 82371129) and the Free Exploration and Innovation Teacher Program of Xi’an Jiaotong University (no. xzy012023119). The funding bodies played no role in the study’s design, the collection, analysis, and interpretation of the data, or the writing of this paper.

Disclosure

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Chibuike Alagboso (Lead writer)

Over 5 billion people globally lack access to safe, quality, and affordable medical oxygen services, according to the Lancet Global Health Commission on Medical Oxygen Security published in February 2025. This represents a significant access gap when compared to other essential medicines, the commission found.

Oxygen is not only vital for treating respiratory illnesses but also essential for successful surgical and trauma management. Vulnerable groups including older adults, pregnant women, infants, and newborns are especially at risk when oxygen is unavailable.

In low and middle-income countries, the situation is more worrisome; only about 30% out of the 299 million people who need oxygen for acute medical or surgical conditions receive adequate oxygen therapy.

The gap is most severe in sub-Saharan Africa, where access is critically low. To sustainably improve access, African countries must create the right environment to attract investments and collaborations for improved access to quality medical oxygen.

Medical oxygen: A lifeline for women and children

Medical oxygen is a recognised essential medicine, crucial for a wide range of conditions. For mothers and newborns, it is lifesaving. Strengthening oxygen systems could reduce in-hospital mortality rates from childhood pneumonia by up to 50% and significantly improve maternal and neonatal health outcomes.

Oxygen is a life-sustaining element that is indispensable for surgical procedures, emergency care, and the survival of newborns, children, and mothers. Hypoxemia, or low oxygen levels in the blood, can be a grave threat to life, particularly for young patients. Also, prompt access to oxygen is crucial in mitigating maternal fatalities caused by postpartum hemorrhage (PPH) or hypertension.

The early years of a child’s life are especially vulnerable. Birth complications such as asphyxia- a condition where the body is deprived of enough oxygen, leading to breathing impairment and potentially unconsciousness or death- and trauma are among the leading causes of neonatal deaths according to the World Health Organization (WHO). Reliable and quality medical oxygen make a lot of difference in improving their outcomes.

Recent trends report by WHO show that while progress has been made, reduction in maternal mortality rate remains insufficient to meet the global target by 2030. The report highlights the urgent need for consistent availability of essential medicines, diagnostics, and devices in poor countries, where 90% of maternal deaths occurred in 2023. It further notes that all maternal deaths are preventable, and strengthening health systems to address shortages of essential supplies is crucial

Why the gap?

Despite being an essential medicine with no substitute, access to medical oxygen remains inequitable. The story of Dr. Rosemary Chukwudebe’s death in 2018 due to lack of oxygen access in the facility she works is a sad reminder of this reality. Nigeria has taken steps to improve oxygen production and distribution since COVID-19 exposed Africa’s vulnerability, but sustained, long-term investment remains essential.

Dr Bamidele, a resident doctor in a Nigerian public hospital, said that oxygen is sometimes unavailable and often unaffordable, costing between NGN 1,000 and NGN 2,000 per hour.

The Lancet Commission also identified major contributors to the oxygen coverage gap as health facilities lacking basic oxygen service capacity; failure to identify oxygen need due to the unavailability of pulse oximetry; interrupted, unsafe, or otherwise low-quality oxygen care; and high costs for patients. Pulse oximeters, which help measure blood oxygen levels, are available in only 54% general hospital and 83% of tertiary hospitals across low-income countries. Primary healthcare centres (PHCs) rarely have them at all.

Mu’azu Muhammad is the Nigeria Country Champion of Oxygen CoLab. He pointed out that while significant investments were made during COVID-19, many facilities are now inactive.

Efforts are underway to transfer these investments to the private sector, but without dedicated budget lines for medical oxygen security, subnational prioritisation remains low. Over-reliance on international partners and lack of regulatory oversight on technical specifications for oxygen production further undermine sustainability, he noted.

For better quality control, the oxygen desk office at the Federal Ministry of Health and Social Welfare, the National Agency for Food and Drug Administration and Control (NAFDAC) and the Standard Organisation of Nigeria (SON) must align through the United for Oxygen Coalition, Muhammad said.

Closing the gap

The Lancet Commission calls on governments to develop National Oxygen Plan as was done by Nigeria and invest in local oxygen manufacturing and maintenance. Nigeria’s Presidential Initiative for Unlocking the Healthcare Value Chain (PVAC) is an example of national-level initiative that could provide the policy and investment environment needed to strengthen oxygen ecosystems.

Collaboration with the private sector is essential to maximise the value of oxygen plant installations. Oxygen CoLab’s “oxygen-as-a-service” model focuses on delivering oxygen concentrators for underserved health facilities. Can this be further scaled up? Numerous partners- including the Canadian government, Global Fund, and UNICEF, have supported Nigeria to set up oxygen plants. However, ongoing maintenance and sustainable financing are critical to prevent these facilities from falling into neglect.

Introduction

Refractive errors, primarily including myopia (nearsightedness), hyperopia (farsightedness), and astigmatism, are among the most prevalent ocular disorders affecting populations globally.¹ These visual impairments result from irregularities in the eye’s ability to focus light on the retina, leading to blurred vision and, if uncorrected, can cause functional limitations and educational disadvantages in children.² Among these, myopia has emerged as a major global public health concern due to its rapidly increasing prevalence and potential for sight-threatening complications such as myopic maculopathy, retinal detachment, and glaucoma.² The global prevalence of myopia is projected to rise dramatically, from approximately 22.9% in 2020 to nearly 50% by 2050, highlighting the urgent need for early detection and effective management strategies.^1,3

This trend is not merely a biological inevitability; rather, it reflects a complex interplay of genetic predispositions and environmental exposures, particularly lifestyle factors.^1,3,4 Modern risk factors, such as increased screen time, excessive near-work activities (eg, prolonged use of digital devices), reduced time spent outdoors, and early educational pressures, have been strongly associated with the onset and progression of myopia in children.^5,6 These risk factors are particularly concerning in high-income countries undergoing rapid urbanization, where behavioral and environmental patterns have shifted significantly in recent decades.⁷

Epidemiological studies reveal that the prevalence and distribution of refractive errors vary considerably by geographic region, ethnicity, and socioeconomic conditions. For instance, cross-sectional studies from Australia have demonstrated that myopia affects 42.7% of 12-year-old Asian children compared to 8.3% in their European Caucasian peers. By age 17, these figures rise to 59.1% and 17.7%, respectively, reflecting both ethnic and environmental influences.^5,6 Similar findings are reported in the U.S.-based Multi-Ethnic Pediatric Eye Disease Study, which documented a myopia prevalence of 3.98% in Asian children compared to 1.2% in Non-Hispanic White children. Hyperopia was more prevalent in NHW children (25.65%) than in Asian children (13.47%), while astigmatism showed less variation (6.33% vs 8.29%).⁸

However, these international comparisons, while informative, may not directly translate to the Saudi Arabian context due to differences in population structure, educational systems, urbanization rates, and cultural practices.^9,10 While global studies are valuable in identifying overarching trends, localized data are crucial for tailoring public health strategies that address specific risk profiles and healthcare infrastructure.

In Saudi Arabia, the current literature on refractive errors in children and adolescents remains fragmented and inconsistent. Previous studies vary widely in methodology, sampling strategies, diagnostic criteria, and regional coverage, making it difficult to draw reliable national-level conclusions.¹¹ Moreover, few reviews have attempted to consolidate existing findings or assess the heterogeneity in reported prevalence rates across Saudi regions.¹¹ The lack of a comprehensive synthesis of data not only limits our understanding of the burden of refractive errors in the Kingdom but also hampers efforts to implement standardized screening and early intervention programs.

Given these gaps, a systematic review and meta-analysis focused specifically on Saudi school-aged children and adolescents is warranted. This review aims to provide a pooled estimate of the prevalence of myopia, hyperopia, and astigmatism in this demographic and to explore regional differences, temporal trends, and methodological inconsistencies in the available literature.

Methods

Study Design

This research employed a systematic review and meta-analysis to synthesize available evidence on the prevalence of refractive errors among children and adolescents aged 3 to 18 years in Saudi Arabia. The study was structured using the PICO framework as follows:

1. Population (P): School-aged children and adolescents residing in Saudi Arabia.
2. Intervention/Exposure (I): Diagnosis of refractive errors (myopia, hyperopia, or astigmatism).
3. Comparison (C): Not applicable, as the objective was to assess prevalence without comparative analysis.
4. Outcome (O): Reported prevalence rates of the specified refractive errors.

The methodology followed the standards set by the Cochrane Handbook for Systematic Reviews of Interventions and conformed to the PRISMA 2020 reporting guidelines, ensuring a transparent and rigorous review process.^12,13 This review was prospectively registered in PROSPERO (registration number: CRD420251006138).

Search Strategy

A comprehensive search was performed in PubMed, Scopus, Web of Science, and ScienceDirect for articles published from January 2000 to January 2025. The search was performed using Medical Subject Headings (MeSH) terms and relevant keywords, including “myopia”, “hyperopia”, “astigmatism”, “refractive errors”, “prevalence”, “children”, “adolescents”, “students”, “school”, and “Saudi Arabia”, combined using Boolean operators (AND/OR) to optimize the retrieval of relevant studies. Although our search was limited to studies published in English, this approach is justified by the context of healthcare research in Saudi Arabia. English is the official language of medical education and scientific publication in the country, and nearly all peer-reviewed medical and epidemiological research, including that indexed in major databases, is published in English. As such, excluding non-English sources is unlikely to have significantly impacted the comprehensiveness of our review.

Eligibility Criteria

We included observational studies (cross-sectional, prospective, or retrospective) reporting the prevalence of refractive errors in children and adolescents aged 3–18 years in Saudi Arabia. Studies were included if prevalence data were explicitly stated or could be inferred from raw data (eg, numerator and denominator provided). Studies reporting a broader age range were included only if data for the 3–18 age group were separately reported or could be extracted.

Inclusion and Exclusion Criteria

Studies were included if they met the following criteria: (1) involved school-aged children and adolescents within the specified age range; (2) employed any diagnostic method for refractive error, including cycloplegic and non-cycloplegic refraction; and (3) reported prevalence data on myopia, hyperopia, or astigmatism. All levels of refractive error severity were eligible for inclusion, and no restrictions were placed based on participant gender, school setting (public or private), or geographical region within Saudi Arabia.

Exclusion criteria encompassed non-primary research articles such as reviews, editorials, commentaries, case series, and conference abstracts. Studies were also excluded if they did not report refractive error prevalence or if such data could not be derived from the presented results. Articles that were not accessible in full text or published in languages other than English were omitted.

Study Selection and Screening

The selection of studies followed a structured and methodical approach. All identified records were imported into Rayyan, a web-based platform designed to facilitate the screening process in systematic reviews.¹⁴ Duplicate entries were automatically identified and removed. Title and abstract screening was independently conducted by eight reviewers based on the predefined eligibility criteria. To assess the consistency of reviewer judgments during this phase, inter-rater reliability was evaluated using Cohen’s kappa statistic, which yielded a value of 0.78, indicating substantial agreement.¹⁵ Disagreements were resolved through discussion. Articles deemed eligible or requiring further evaluation underwent full-text screening, which was performed by four reviewers. References were then managed using EndNote for full-text handling and citation organization. Final inclusion decisions were made by consensus to ensure that all selected studies adhered to the established criteria.

Quality Assessment

To evaluate the methodological quality of the included studies, the Newcastle-Ottawa Scale (NOS) was applied. This tool assesses non-randomized studies across three key domains: selection of study participants, comparability of groups, and outcome assessment.¹⁶ Each study received a score between 0 and 9. Based on these scores, studies were classified as high quality (scores of 7 to 9), moderate quality (scores of 4 to 6), or low quality (scores of 0 to 3). The NOS has been widely adopted in large-scale systematic reviews on epidemiological studies, supporting its validity and reliability in assessing methodological rigor.¹⁷ Four reviewers independently assessed the studies. In cases where their evaluations differed, the reviewers first discussed the discrepancies in an attempt to reach agreement. If a consensus could not be achieved, two additional reviewers were consulted to provide a final judgment. This process ensured consistency and rigor in the quality assessment.

Data Extraction and Management

To maintain consistency throughout the data collection process, a standardized extraction form was developed and implemented. Four reviewers independently extracted relevant information from each included study. This included general study details such as author names, year of publication, study design, geographical location, and sample size. Information related to participant demographics, including age range and gender distribution, was also recorded. Additionally, the extracted data included prevalence estimates for myopia, hyperopia, and astigmatism, details on myopia severity (mild, moderate, high), hyperopia classification, and astigmatism thresholds, and information on diagnostic methodologies (cycloplegic vs non-cycloplegic refraction, auto-refractometer, subjective refraction tests). All extracted data were compiled and organized using Microsoft Excel to prepare for statistical analysis. The extraction form was pilot tested to ensure it captured all necessary variables comprehensively and consistently. Any discrepancies between reviewers were addressed through discussion. If consensus could not be reached, two additional authors were consulted to ensure the reliability and accuracy of the final dataset.

Statistical Analysis

All statistical analyses were conducted using R software (version 4.2.2), utilizing the metafor and meta packages. To synthesize prevalence estimates for myopia, hyperopia, and astigmatism, random-effects models were employed, accounting for between-study variability due to expected clinical and methodological heterogeneity. Proportion estimates were presented with 95% confidence intervals (CIs), and the Freeman–Tukey double arcsine transformation was applied to stabilize variance, particularly in studies reporting very high or low prevalence values. Heterogeneity was assessed using both Cochran’s Q test and the I² statistic. A Q test p-value of <0.10 or an I² value exceeding 50% was considered indicative of substantial heterogeneity.

Where applicable, subgroup analyses were conducted to explore sources of heterogeneity, including geographic region, refractive error classification methods (cycloplegic vs non-cycloplegic), and age group stratifications. Meta-regression analyses were pre-specified to evaluate the influence of continuous variables such as publication year and sample size on prevalence estimates.¹⁸ In cases where overlapping datasets from similar populations were identified, the study with the larger sample size or more comprehensive data was prioritized to avoid duplication. To assess the robustness of the pooled estimates, leave-one-out sensitivity analyses were performed. Publication bias was evaluated using contour-enhanced funnel plots and Egger’s regression test. In addition, Doi plots were generated and examined using the Luis Furuya-Kanamori (LFK) index, where LFK values between –1 and +1 were interpreted as indicating symmetry, values between ±1 and ±2 as minor asymmetry, and values exceeding ±2 as evidence of major asymmetry.^19,20

Results

Search Results

A comprehensive search of four electronic databases (PubMed, Scopus, Web of Science, and ScienceDirect) yielded 260 records published between January 2000 and January 2025 (Figure 1). After eliminating 32 duplicate entries, 228 unique records remained for screening. Titles and abstracts were reviewed, leading to the exclusion of 212 records based on criteria such as irrelevance to the research question or setting (n = 196), review articles (n = 9), and protocols or editorials (n = 7). The full texts of the remaining 16 articles were then assessed in detail. Following this evaluation, 7 articles were excluded due to the absence of specific prevalence data on refractive errors. As a result, 9 studies were deemed eligible and included in the final systematic review and meta-analysis.^21–29

Description of Included Studies

The nine studies included in this review were published between 2010 and 2023 and investigated the prevalence of myopia among school-aged children across different regions in Saudi Arabia (Table 1). All studies utilized a cross-sectional design. Sample sizes varied, ranging from 360 participants in the study by Alkhathami et al (2023) to 5,176 participants in the study by Aldebasi (2014). Altogether, these studies encompassed more than 15,000 children, making this one of the most extensive systematic reviews to date on the prevalence of refractive errors among Saudi schoolchildren.

Table 1 Summary of Included Studies

The age range of participants spanned from 3 to 18 years, encompassing children in kindergarten, primary, and secondary school. All studies used visual screening techniques, but only 2 studies employed cycloplegic refraction, the gold standard for pediatric refractive error assessment, while 7 studies used non-cycloplegic techniques, such as autorefraction, visual acuity tests, or retinoscopy.

Prevalence of Myopia

The analysis estimated the overall prevalence of myopia among school-aged children in Saudi Arabia at 6.7% (95% CI: 3.0% to 14.2%). This estimate was accompanied by a substantial heterogeneity among the included studies, as indicated by an I² value of 99.5% and a τ² value of 1.59 (Figure 2). The reported prevalence varied considerably, ranging from 0.7% (Alrahili et al, 2017) to 33.3% (AlThomali et al, 2022). The lowest prevalence was reported by Alrahili et al (2017) in a sample of 1,893 children, while the highest prevalence was documented by AlThomali et al (2022) in a large cohort of 3,678 participants. Notably, Alkhathami et al (2023) also reported a high prevalence of 20% among 360 children.

Figure 2 Forest plot of myopia prevalence among school-age children and adolescents in Saudi Arabia.

The severity of myopia varied between studies (Table 1). Mild myopia was the most frequently observed category, with Aldebasi (2014) reporting that 87% of myopic children had mild myopia, while AlThomali et al (2022) reported a slightly lower percentage of 28.6%. Moderate myopia accounted for 3.7% to 10.9% of cases, while high myopia was less common, ranging from 0.3% to 2.1% in the included studies.

Prevalence of Hyperopia

The pooled prevalence of hyperopia across the included studies was 3.6% (95% CI: 1.3–9.8%), with marked heterogeneity (I²=99.2%, τ²=2.3062) (Figure 3). The reported prevalence varied substantially, ranging from 0.7% (Aldebasi, 2014) to 21.1% (Alkhathami et al, 2023).

Figure 3 Forest plot of hyperopia prevalence among school-age children and adolescents in Saudi Arabia.

The prevalence and severity of hyperopia varied across studies (Table 1). Mild hyperopia was the most frequently observed category, with Aldebasi (2014) reporting that 36.4% of hyperopic children had mild hyperopia, while AlThomali et al (2022) noted that low and moderate hyperopia accounted for 16.3% of cases. High hyperopia was less common, ranging from 1.3% to 11.4% in the studies included. The overall prevalence of hyperopia ranged from 0.7% to 17.63%, with some studies reporting higher rates in females compared to males.

Prevalence of Astigmatism

The pooled prevalence of astigmatism was 7.7% (95% CI: 2.5–20.9%), with extremely marked heterogeneity (I²=99.8%, τ²=2.4258) (Figure 4). The reported prevalence varied widely, with AlThomali et al (2022) documenting the highest prevalence at 50.1%, while Al-Rowaily (2010) and Al Wadaani et al (2012) reported the lowest prevalences (2.5% and 1.7%, respectively).

Figure 4 Forest plot of astigmatism prevalence among school-age children and adolescents in Saudi Arabia.

AlThomali et al (2022) reported the highest prevalence, with low and moderate astigmatism accounting for 47% of cases, while severe astigmatism was less common at 3.1% (Table 1). Myopic astigmatism was frequently observed, with rates ranging from 2.7% to 16.6%, while hyperopic astigmatism and mixed astigmatism were less prevalent, ranging from 1.7% to 10.3%. Some studies, such as Alrahili et al (2017), noted similar astigmatism rates between boys and girls, while others, like AlThomali et al (2022), reported slightly higher rates in females (52.6%) compared to males (47.7%).

Sensitivity and Subgroup Analyses

A meta-influence analysis was conducted to examine whether any single study significantly affected the pooled prevalence of myopia, It is provided in Table S1. The results show that removing individual studies did not lead to major changes in the overall prevalence estimates, indicating that no single study disproportionately influenced the meta-analysis findings.

Subgroup analysis results are presented in Table 2, highlighting variations in the estimated prevalence of myopia based on specific study characteristics. While studies utilizing cycloplegic refraction reported a marginally higher pooled prevalence (7.21%, 95% CI: 4.7–11) than those using non-cycloplegic techniques (6.72%, 95% CI: 2.4–17.5), this difference was not statistically significant (p = 0.9). Notable differences were observed across geographic regions. Taif and Bisha reported the highest prevalence estimates at 33.28% and 22.50%, respectively, while Medina had the lowest prevalence at 1.59%. The test for subgroup differences across regions was statistically significant (p < 0.001), indicating meaningful regional variation. Studies published after 2018 showed a significantly higher pooled prevalence (16.36%, 95% CI: 8.0–30.5) compared to those before 2018 (3.27%, 95% CI: 1.4–7.5) (p < 0.001). No significant difference was found when comparing studies with sample sizes below 1500 to those with larger samples (p = 0.9), though both groups showed substantial heterogeneity.

Table 2 Subgroup Analysis of Pooled Prevalence of Myopia Among School-Aged Children and Adolescents in Saudi Arabia

Meta-Regression

Meta-regression analysis revealed that the year of publication significantly contributed to the observed heterogeneity in myopia prevalence (p = 0.038), suggesting that studies published more recently tended to report higher prevalence rates. In contrast, sample size did not appear to account for a meaningful portion of the heterogeneity (p = 0.624), indicating that variations in the number of participants across studies did not significantly influence the differences in reported prevalence estimates (see Table 3).

Table 3 Meta-Regression Analysis Results for Pooled Prevalence of Myopia Among School-Age Children and Adolescents in Saudi Arabia

Publication Bias

To assess potential publication bias, both Doi plots and funnel plots were examined for asymmetry in the prevalence estimates of myopia, hyperopia and astigmatism (Figures S1– S6, respectively). For myopia, the funnel plot appeared symmetrical, and the Doi plot showed an LFK index of 0.09, indicating no evidence of publication bias. The distribution of studies was balanced around the pooled prevalence estimate, suggesting that smaller studies did not systematically report higher or lower prevalence rates. The plot shape and central clustering reinforce the robustness of the pooled estimate.

In contrast, the funnel plot for hyperopia showed noticeable asymmetry, with a skewed distribution of studies. This visual asymmetry was confirmed by the Doi plot, which had an LFK index of 3.31 indicating major asymmetry and potential publication bias. For astigmatism, the Doi plot showed no asymmetry (LFK = 0.47), and similar funnel plot assessments suggested a balanced distribution of prevalence estimates, reinforcing confidence in the pooled findings.

Quality Assessment of Included Studies

The quality of the studies included in this review was evaluated using the Newcastle-Ottawa Scale (NOS), as summarized in Table 4. Seven studies achieved scores between 7 and 9, indicating high methodological quality. These studies demonstrated rigorous participant selection processes, appropriate comparison groups, and reliable methods for measuring outcomes. Notable examples include those conducted by Al-Rowaily (2010), Al Wadaani (2012), Aldebasi (2014), Alrahili (2017), Alemam (2018), and AlThomali (2022), all of which employed sound research designs that support the credibility of their findings. The remaining three studies were rated as having moderate quality, each receiving a score of 6. These ratings were primarily due to issues such as relatively small sample sizes, use of non-cycloplegic refraction methods, or potential selection bias, which may affect the generalizability or precision of their results.

Table 4 Quality Assessment of Included Studies Using the Newcastle-Ottawa Scale (NOS)

Discussion

This systematic review and meta-analysis offers a detailed overview of the prevalence of refractive errors among school-aged children and adolescents in Saudi Arabia, drawing on data from nine studies that collectively involved more than 15,000 participants. The findings identify astigmatism as the most prevalent refractive error, with a pooled estimate of 7.7%, followed by myopia at 6.7% and hyperopia at 3.6%. These results highlight the substantial burden of uncorrected refractive errors in this population and point to an important area for public health intervention. Although all included studies reported data on myopia, the prevalence varied widely across regions, ranging from 0.7% to 33.3%. The consistently higher prevalence of astigmatism, however, may indicate that this condition has not received adequate attention in either clinical practice or research agendas.

The pooled prevalence of myopia in Saudi Arabia (6.7%) is higher than that reported in Ethiopia (5.26%)³⁰ and other African countries (4.7%)³¹ but lower than rates observed in East Asia (31%).³² This disparity may be attributed to differences in genetic predisposition, lifestyle factors, and levels of urbanization.³³ For instance, increased near-work activities, reduced outdoor time, and technological advancements have been linked to higher myopia prevalence globally.^34–37 In Saudi Arabia, the rising prevalence may also reflect improved diagnostic capabilities and increased awareness of refractive errors.³⁸

The diagnostic methodology significantly influenced the prevalence estimates of myopia across the included studies. Studies employing cycloplegic refraction, such as Al Wadaani et al (2012)²² and Aldebasi (2014),²³ reported higher myopia prevalence rates of 9% and 5.8%, respectively. In contrast, studies using non-cycloplegic methods, such as Al-Rowaily (2010)²¹ and Alrahili et al (2017),²⁴ reported lower prevalence rates ranging from 0.7% to 7.7%. This discrepancy suggests that cycloplegic refraction, which paralyzes the ciliary muscle and eliminates accommodation, may provide a more accurate detection of myopia, particularly in younger children.³⁹ Non-cycloplegic methods, on the other hand, may underestimate myopia due to the influence of accommodation.⁴⁰ These findings underscore the importance of standardized diagnostic approaches, with cycloplegic refraction remaining the gold standard for reliable pediatric refractive error assessment.

The pooled prevalence of astigmatism in this review was 7.7%, though individual study estimates ranged widely from 1.7% to 50.1%. Myopic astigmatism emerged as the most frequently reported subtype, aligning with global trends documented in previous research.^41–43 Several factors may contribute to the relatively high prevalence of astigmatism observed in Saudi Arabia, including hereditary influences, environmental exposures, and lifestyle changes such as increased screen time among children.⁴⁴ One study conducted in Jeddah among participants attending an amblyopia awareness campaign reported a notably high prevalence of astigmatism at 41.5%. Within this group, 40.6% of children without a prior diagnosis were found to have astigmatism incidentally.⁴⁵ However, because the study recruited attendees from a vision health event, the findings may reflect a degree of selection bias, potentially inflating the prevalence estimate. This underscores the importance of conducting well-designed, population-based studies to generate more representative data and to clarify the factors contributing to the burden of astigmatism across different regions and age groups in Saudi Arabia.

Although hyperopia had the lowest pooled prevalence (3.6%), its detection is especially important in early childhood, when uncorrected farsightedness can lead to amblyopia and delayed visual development.^46,47 Mild hyperopia was the most common form, but even low levels can significantly affect reading fluency and school performance. Current findings reinforce the importance of incorporating hyperopia detection into routine early childhood vision screening to support cognitive and educational development.

Gender-specific analysis revealed a slightly higher prevalence of both myopia and astigmatism among females. This aligns with global literature, where differences may stem from hormonal, anatomical, or behavioral factors, including higher rates of near-work activity among girls.⁴⁸ Policymakers and educators could consider targeted interventions for girls, such as vision-friendly classroom environments and awareness campaigns that emphasize early screening and eye health education.

The findings of this study align with regional and global trends in the prevalence of refractive errors. For instance, a meta-analysis conducted in the Middle East reported a myopia prevalence of 5.2%, which is slightly lower than our estimate of 10%.⁴⁹ Similarly, our results are consistent with studies from India (5.3%) and Nepal (7.1%),^50,51 indicating shared risk factors such as urbanization, increased educational demands, and lifestyle changes. These parallels suggest that environmental and socio-cultural factors, including prolonged near-work activities and limited outdoor exposure, may contribute to the rising burden of refractive errors across diverse populations.^37,52 The consistency in findings underscores the importance of addressing these modifiable risk factors through targeted public health interventions to mitigate the growing prevalence of refractive errors among children globally.⁵³

Given the rising prevalence and consequences of uncorrected refractive errors, particularly myopia and astigmatism, school-based screening programs should be expanded and standardized. Programs should prioritize the use of cycloplegic refraction, especially for younger children, and integrate follow-up pathways to ensure timely correction. Public health campaigns should also raise awareness among parents and educators about the importance of limiting near-work activities and encouraging outdoor play.

This review has several notable limitations. First, none of the studies included reported the prevalence of high myopia, a severe and vision-threatening condition. The absence of this data limits the understanding of the full spectrum of refractive errors and the potential long-term burden of uncorrected high myopia in the pediatric population. Second, many studies did not clearly report the refraction techniques used, particularly whether cycloplegic or non-cycloplegic methods were applied. This may have contributed to variability and potential bias in prevalence estimates. Future research should consistently specify the refraction methodology to improve comparability and diagnostic accuracy. Third, the analysis focused solely on prevalence and did not include multivariate analysis to explore risk factors such as age, gender, urban or rural residence, screen time, or genetic predisposition. This limits the ability to identify significant predictors of refractive errors. Finally, the substantial heterogeneity across studies reflects differences in sampling methods, diagnostic protocols, and population characteristics. This underscores the need for consistent and standardized protocols in future epidemiological vision research.

Conclusion

This study demonstrates that refractive errors are a significant public health issue among school-aged children and adolescents in Saudi Arabia. Astigmatism emerged as the most common refractive error, followed by myopia and hyperopia. Notable regional variations were observed, with especially high rates of myopia in cities such as Taif and Bisha. Additionally, studies conducted after 2018 reported markedly higher myopia prevalence compared to earlier research, reflecting an upward trend over time. These findings highlight the urgent need for standardized diagnostic methods, particularly the consistent use of cycloplegic refraction, to improve accuracy in screening and diagnosis. To address the growing burden of refractive errors, national school-based vision screening programs should be implemented. These programs should include regular eye examinations by trained personnel, integration with school health services, and referral pathways for children needing corrective treatment. Policymakers should also promote preventive strategies such as increasing outdoor activities and managing screen time as part of broader child health initiatives. Implementing these measures is essential to reducing visual impairment and supporting the academic and developmental success of Saudi children.

Data Sharing Statement

The datasets analyzed during this study are not publicly available; however, they can be obtained from the corresponding author upon reasonable request.

Author Contributions

All authors made a significant contribution to the work reported, whether in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas. All authors took part in drafting, revising, or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

There is no funding to report.

Disclosure

The authors declare that they have no competing interests in this work.

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