Prediabetes in adult Saudis: a systematic review & meta-analysis of prevalence studies (2000–2024)

Introduction

Diabetes is becoming increasingly prevalent at an alarming rate. This trend is leading to worsening impacts on individuals, the economy, and society as a whole (Perveen et al., 2016). The healthcare community is showing a growing focus on the prevention and prediction of diabetes (Cho et al., 2018). Recent data from the International Diabetes Federation (IDF) indicate that 415 million adults worldwide have been diagnosed with diabetes, with an additional 318 million individuals experiencing impaired glucose tolerance (Dawish & Robert, 2021). Additionally, these numbers are projected to rise to 642 million for diabetes and 482 million for impaired glucose tolerance by 2040 (Cefalu et al., 2016).

Prediabetes, also known as impaired fasting glucose or impaired glucose tolerance, is a condition in which blood glucose levels are higher than normal but have not yet reached the cutoff for diabetes (Rett & Gottwald-Hostalek, 2019; Amelia et al., 2023). Individuals with prediabetes are at risk of developing diabetes and experiencing both microvascular and macrovascular complications (Nathan et al., 2007; Brannick & Dagogo-Jack, 2018). The Diabetes Prevention Program reported that, on average, about 11% of individuals with prediabetes develop type 2 diabetes each year over a three-year follow-up period (Al-Zahrani et al., 2019). Consequently, timely intervention and management of prediabetes are crucial to prevent its progression to diabetes (Gottwald-Hostalek & Gwilt, 2022). Findings from several randomized controlled studies have shown that individuals at high risk of developing type 2 diabetes can successfully prevent its progression. This success is attributed to early, intensive interventions that are tailored to each individual’s unique needs (Li et al., 2008; Ramachandran et al., 2006; Lindström et al., 2006; Kosaka, Noda & Kuzuya, 2005).

Saudi Arabia has undergone significant lifestyle changes in recent years. Rapid urbanization, shifts in dietary patterns, and increased sedentary behavior are among the most notable changes. Together, these factors have contributed to the rising risk of prediabetes and diabetes (Munawir Alhejely et al., 2023). Both prediabetes and type 2 diabetes are common in Saudi Arabia and have shown a rising trend over time (Aldossari et al., 2018). Historical data on prediabetes prevalence in Saudi Arabia provide valuable context for understanding trends over time. A large survey of 13,177 Saudi subjects over 15 years of age reported IGT prevalence rates of 10% and 8% among urban and rural males, respectively, and 11% and 8% among urban and rural females (Al-Nuaim, 1997). A regional study conducted in Najran found even lower prevalence, with rates of 1.02% in males and 0.693% in females over 14 years old, with a modest increase among individuals over 30 years (EI-Hazmi et al., 1997). Similarly, a national household survey of 23,493 participants reported IGT prevalence rates of 0.717% in males and 1.347% in females aged 14–70 years (El-Hazmi et al., 1996).

Enhancing healthcare services and reducing the prevalence of chronic diseases are key goals of the healthcare initiatives under Saudi Vision 2030 (Mani & Goniewicz, 2024). Tremendous improvements in healthcare services and the Saudi health system have been recognized. These advancements support efforts in health promotion and disease prevention (Marwa Tuffaha, 2015). Although several studies on prediabetes have been conducted in various regions of Saudi Arabia, no systematic review and meta-analysis (SRMA) has been published to specifically estimate the pooled prevalence of prediabetes among the adult Saudi population. Only one SRMA has assessed prediabetes prevalence in Saudi Arabia, as part of a broader study of Eastern Mediterranean Region (EMRO) countries (Mirahmadizadeh et al., 2020). We believe that a comprehensive SRMA on prediabetes prevalence among Saudi adults is essential for accurately assessing the disease burden. Therefore, we expect that this analysis will strengthen public health interventions aligned with the goals of Saudi Vision 2030. Ultimately, it could help reduce the future burden of diabetes. This SRMA aimed to identify, summarize, and estimate the pooled prevalence of prediabetes among adult Saudis living in Saudi Arabia. The analysis included studies published between January 2000 and September 2024.

Material and Methods

Results

Study selection

Figure 1 presents the schematic flow of the identification and inclusion processes for the studies. A total of 730 records were identified through the literature search, with 313 duplicates subsequently removed. The remaining 417 publications were screened by title and abstract, and 370 articles were found irrelevant and excluded. The remaining 47 articles were evaluated for eligibility through the full-text screening, and 30 were excluded. The reasons for excluded articles were categorized as population issues (n = 14); study design (n = 3); diagnostic methods (n = 10); outcome (n = 2); and data collection methods (n = 1) (see Table S3). An additional eligible study was identified by examining reference lists from the included studies, resulting in a final total of 18 included studies (Al-Nozha et al., 2004; Mirza et al., 2013; Al-Rubeaan et al., 2015; Ghoraba et al., 2016; Turki, Hegazy & Abaalkhail, 2016; Aldossari et al., 2018; Aldossari et al., 2020; Al-Ghamdi et al., 2018; Aljabri, 2018; Al Amri et al., 2019; Al-Zahrani et al., 2019; Bahijri et al., 2020; Latif & Rafique, 2020; Fayed et al., 2022; Al Shehri et al., 2022; Alomari & Al Hisnah, 2022; Abu-Almakarem, 2024; Alhomaid & Moin Ahmed, 2024). The eighteen studies were conducted across various regions of Saudi Arabia, encompassing a total population of 47,718 adult Saudis.

The regional distribution of the excluded studies by full-text screening was 46.6% (n = 14) for Central region, 20% (n = 6) for Western, 10% (n = 3) for Southern, 10% (n = 3) for Northern, 6.7% (n = 2) for Eastern, and 6.7% (n = 2) for a nationwide study. None of the three studies conducted in the Northern region (Alreshidi et al., 2023; Sebeh Alhazmi et al., 2017; Ali Moustafa Marzok Elkhateeb, 2018) satisfied the eligibility criteria for the meta-analysis.

Pooled national prevalence of prediabetes among adult Saudis

A total of 18 eligible reports were included in the systematic review, with an overall sample size of 47,718. The pooled national prevalence of prediabetes in the Saudi adult population, as determined by WHO/ADA criteria, was estimated to be 24.1% (95% CI [19.5%–29.4%]). This estimate was calculated using the random-effects model, as shown in Fig. 2. This prevalence indicates that a significant portion of the Saudi adult population is affected; that is, approximately one in four adults in Saudi Arabia may be prediabetic.

A sensitivity analysis was conducted by systematically removing each study from the meta-analysis to assess the robustness of the pooled estimate. Findings from the sensitivity test indicated that the pooled prevalence of prediabetes remained stable at 24.1% (95% CI [19.5%–29.4%]), regardless of which study was removed. The lower and upper confidence limits also remained relatively stable across the sensitivity analysis. Furthermore, excluding the study by Al Shehri et al. (2022) which had one of the highest individual prevalence rates, only slightly reduced the pooled prevalence to 22.7% (95% CI [19.4%–26.3%)]. Figure S1 illustrates the findings from the sensitivity analysis.

Sub-group analyses

Data analyses revealed that studies using FBG as the diagnostic criterion reported the highest prevalence of prediabetes (Fig. 3). The pooled prevalence of prediabetes was 25.7% (95% CI [16.6%–37.6%]) for FBG; 23.7% (95% CI [16.7%–32.5%]) for HbA1c; and 23.3% (95% CI [14.8%–34.6%]) for studies using mixed diagnostic methods (including both HbA1c and FBG).

Regarding gender-based subgroup analysis, studies involving male-only populations (n = 4) had the highest pooled prevalence of prediabetes (Fig. S3). The pooled prevalence was 34.8% (95% CI [25.4%–45.5%]) for male-only studies, 18.7% (95% CI [10.9%–30.2%]) for female-only studies (n = 2), and 22.2% (95% CI [18.1%–26.8%]) for mixed-gender populations (n = 12). A sensitivity analysis for male-only studies was conducted, excluding the Al Shehri et al. (2022) study. The results showed that the pooled prevalence decreased to 27.9% (95% CI [24.1%–32.1%]) but still remained the highest among the subgroups (Fig. S4).

The region-specific subgroup analysis revealed geographical variations in prediabetes prevalence across Saudi Arabia (Fig. S5). The Central region had the highest pooled prevalence at 26.0% (95% CI [19.0%–34.5%]), followed closely by the Western region at 25.4% (95% CI [16.9%–36.4%]). In contrast, the Southern region reported a lower prevalence of 22.1% (95% CI [11.0%–39.3%]), and the Eastern region had the lowest prevalence at 18.7% (95% CI [6.7%–42.5%]). A sensitivity test was conducted by excluding the Al Shehri et al. (2022) study, which reported a Central region prevalence of 55%. This exclusion reduced the pooled prevalence for the Central region to 22.8% (95% CI [20.8%–24.9%]) (Fig. S6).

We conducted a sensitivity analysis based on the quality of the included studies, categorized as either high or medium quality according to the Joanna Briggs Institute (JBI) assessment tool. The pooled prevalence from high-quality studies (n = 16) was 24.2% (95% CI [19.3%–29.8%]), while the pooled prevalence from medium-quality studies (n = 2) was 23.9% (95% CI [12.1%–41.8%]). These findings suggest that the overall pooled estimate remains consistent regardless of study quality, indicating that study quality did not substantially influence the observed prevalence of prediabetes (Fig. S7).

Results of the meta-regression

The meta-regression analysis indicated a slight upward trend in prediabetes prevalence over time (Fig. 4), with the regression equation −55.633 + (0.027 × Year) and a positive coefficient for ‘Year’ of 0.027. However, with a p-value of 0.2312 (95% CI [−0.0172–0.0712]), this relationship was not statistically significant.

According to the GRADE approach, the certainty was rated as moderate, with a downgrade for inconsistency due to high heterogeneity (I² = 99.1%). Other domains, including risk of bias, indirectness, imprecision, and publication bias, were considered not serious (Table S7).

Discussion

Early detection and treatment of prediabetes can prevent diabetes, reduce healthcare costs, and lead to significant improvements in public health. A clearer understanding of the pooled prevalence of prediabetes is essential for assessing the effectiveness of intervention strategies and guiding future prevention efforts (Tuso, 2014). In this meta-analysis, we synthesized studies from the past 24 years to address knowledge gaps and provide a comprehensive estimate of prediabetes prevalence among adult Saudis in Saudi Arabia. Additionally, this study examined variations in prediabetes prevalence based on diagnostic methods, gender, study quality, and regional distribution. The pooled prevalence of prediabetes across all eligible cross-sectional studies (n = 18) was 24.1% (95% CI [19.5%–29.4%]). Moreover, the sensitivity analysis showed that the pooled prevalence of prediabetes remained stable at 24.1% (95% CI [19.5%–29.4%]), regardless of which study was removed. These results suggest that no single study or outlier significantly skews the overall observed prevalence, supporting the reliability and robustness of the pooled national estimate of prediabetes among adult Saudis. This high prevalence indicates a substantial prediabetic burden among the adult Saudi population, underscoring a serious public health concern.

Although most studies showed relatively narrow confidence intervals, a few—such as Al Shehri et al. (2022)—exhibited notably wide intervals. This may be attributed to factors such as small sample sizes, population-specific characteristics (e.g., young males applying for military colleges), and potential selection bias. While these studies contributed to the pooled estimate, sensitivity analysis confirmed that their influence does not disproportionately affect the overall pooled prevalence.

Worldwide, the pooled prevalence of prediabetes has been reported across several populations. In the Eastern Mediterranean Region (EMRO), Mirahmadizadeh et al. (2020) reported an overall pooled prevalence of prediabetes of 12.78% (95% CI [10.67–14.89]%). Additionally, the authors reported a prevalence of 14% (95% CI [4.9%–23.1%]) for Saudi Arabia based on seven studies. The discrepancy in the pooled prevalence of prediabetes in Saudi Arabia between the two meta-analyses may be explained by the following factors. Of the seven studies included in the Mirahmadizadeh et al. meta-analysis, only two (Al-Nozha et al., 2004; Al-Rubeaan et al., 2015) met our eligibility criteria and were included in the current meta-analysis. Three studies were excluded due to population issues (El Bcheraoui et al., 2014; Bahijri et al., 2016; Al, Al Osaimi & AL-Gelban, 2007). A fourth study was excluded because of unclear diagnostic methods for prediabetes (Al Baghli et al., 2010); and a fifth study was excluded due to an irrelevant the outcome (Memish et al., 2015). Details on the reasons for excluding studies during full-text screening are provided in Table S3. In a meta-analysis of the adult Cameroonian population residing in Cameroon, Bigna et al. (2018) reported a pooled prevalence of prediabetes of 7.1% (95% CI [3.0%–21.9%]) based on four studies with a combined sample size of 5,872 individuals. In another meta-analysis of the Bangladeshi population, Akhtar et al. (2020) reported a pooled prevalence of prediabetes of 10.1%, based on a combined sample size of 56,452 individuals across 17 studies. Additionally, among populations with similar socioeconomic characteristics, the pooled prevalence of prediabetes was 11.62% (95% CI [7.17%–16.97%]), based on nine studies involving 88,702 individuals from Malaysia (Akhtar et al., 2022). Furthermore, the pooled prevalence of prediabetes in the East African population (n = 43,379) across six countries—Uganda, Tanzania, Ethiopia, Kenya, Rwanda, and Sudan—was 12.58% (95% CI [10.30%–14.86%]) (Asmelash et al., 2023). A separate meta-analysis of the Ethiopian population reported a pooled prevalence of impaired fasting glucose at 8.94%, with a 95% confidence interval (CI) of [2.60%–15.28%] (Yitbarek et al., 2021). A summary of the pooled prevalence of prediabetes across different populations is presented in Tables S9. The Saudi population has the highest prevalence rate, as demonstrated by the comparative analysis of pooled prediabetes prevalence across the populations mentioned above. This may be attributed to increased caloric intake, reduced physical activity, and lifestyle changes linked to urbanization in Saudi Arabia and the Gulf countries overall. A systematic review of 65 studies (Al-Hazzaa, 2018) found that most Saudis do not meet the recommended levels of moderate to vigorous physical activity. The authors identified several barriers to physical activity, including heavy traffic, extreme weather, cultural constraints, limited social support, and lack of time. The Saudi Food and Drug Authority (SFDA)(2024) mandated the display of calorie information on menus in restaurants, hotels, and cafes. This initiative aims to promote health in Saudi Arabia by encouraging healthier food choices in line with international public health standards. Despite these efforts, a recent systematic review found that most Saudis do not adhere to dietary guidelines. Many, especially students and young adults, prefer fast food and processed foods because of their convenience and accessibility (Almutairi et al., 2023). In addition, lack of willpower, limited time, and insufficient social support have been identified as the primary barriers to following a healthy diet (Aldosari et al., 2024). A cross-sectional study also revealed that one-third of adult participants (59.3% male) reported sleeping less than the recommended seven hours per night. This finding highlights the high prevalence of short sleep duration among Saudi adults (Ahmed et al., 2017). Bruckner et al. (2024) emphasized the importance of lifestyle factors in managing insulin resistance and inflammation. They concluded that lower adherence to health-promoting lifestyle habits is associated with increased insulin resistance. A meta-analysis of 30 cohort comparisons reported that strict adherence to multiple low-risk lifestyle behaviors significantly reduces the risk of developing type 2 diabetes by 85%. These behaviors include maintaining a healthy body weight, following a healthy diet, engaging in regular physical activity, and avoiding smoking (Khan et al., 2023).

The high pooled prevalence of prediabetes (24.1%) found in our meta-analysis is consistent with Saudi Arabia’s status as one of the countries with the highest diabetes prevalence worldwide. According to the International Diabetes Federation (IDF), the age-adjusted prevalence of diabetes among Saudi adults (aged 20–79 years) was estimated at 23.1% in 2024, significantly higher than the global average of 11.1% (IDF DIABETES ATLAS, 2015). These findings support the hypothesis that lifestyle factors are key contributors to this burden.

The high heterogeneity (I² = 99.1%) observed in this meta-analysis necessitates cautious interpretation of the pooled prevalence estimate. Several factors may explain for this variability, including regional lifestyle differences, variations in sampling strategies—such as convenience sampling or recruitment from specific subpopulations—which may introduce selection bias, and inconsistencies in diagnostic methods (FBG vs. HbA1c) and criteria (ADA vs. WHO) used across studies. To explore these sources of heterogeneity, we conducted subgroup analyses based on the region of the study, diagnostic criteria (A1c vs. FBG), gender, and quality of the included studies.

Subgroup analysis revealed higher prevalence estimates in studies using FBG (25.7% (95% CI [16.6%–37.6%]) compared to those using HbA1c (23.7%; 95% CI [16.7%–32.5%]) and mixed methods (23.3%; 95% CI [14.8%–34.6%]). The variation in prediabetes prevalence between studies using FBG and HbA1c reflects important methodological differences. These differences may influence prevalence estimates and contribute to the high heterogeneity observed in this meta-analysis. The higher prevalence observed in studies using FBG (n = 5) studies suggests that the FBG method may be more sensitive and capture more individuals in the prediabetic range than HbA1c (n = 8). Moreover, this discrepancy highlights the importance of standardizing diagnostic methods in epidemiological research, as inconsistencies can contribute to variability in prevalence estimates. In this meta-analysis, significant variability was observed among studies (I² = 99.1%, p < 0.001). Consequently, we applied a random-effects model instead of a fixed-effects model, which yielded a pooled prevalence of 24.1% (95% CI [19.5%–29.4%]). From this perspective, Ghazanfari et al. (2010) reported that FBS was a more accurate predictor of HbA1c than HbA1c was of FBS. Nomura, Inoue & Akimoto (2012) reported that FPG was generally the strongest predictor of type 2 diabetes in the general population. However, among individuals with FPG levels between 6.1–6.9 mmol/L, its predictive power was comparable to that of HbA1c in men, while HbA1c was more effective in women. The authors proposed a two-step screening approach that combines HbA1c and FPG to improve the accuracy of diabetes prediction. The majority of studies in this meta-analysis (44.4%; n = 8) used HbA1c as the diagnostic tool for prediabetes, while 27.7% used FPG and another 27.7% employed mixed methods. This finding contrasts with the results reported by Nomura, Inoue & Akimoto (2012), who recommended using FPG or a two-step approach combining HbA1c and FPG for more accurate diabetes prediction. The preference for using HbA1c alone in most studies may reflect its convenience, particularly because it does not require fasting. However, our finding raises concerns about potential variability in accurately identifying individuals at risk.

In contrast to our study, which found a significantly higher prevalence of prediabetes among Saudi males (34.8%) compared to females (18.7%), meta-analyses studies from Bangladesh and Malaysia (Akhtar et al., 2020; Akhtar et al., 2022) reported no significant gender differences. Furthermore, a sensitivity analysis, excluding the Al Shehri et al. (2022) study from the male-only subgroup revealed a slight decrease in the pooled prevalence to 27.9% (95% CI [24.1%–32.1%]), yet it remained the highest among the subgroups. The higher prevalence among males suggests gender-related risk factors for prediabetes, including obesity, sedentary lifestyles, and unhealthy dietary habits. Supporting our findings, a cross-sectional study of 968 men and 2,029 women aged 30–75 in Riyadh found that men were 1.49 times more likely than women to report an unhealthy lifestyle. Men commonly cited a lack of enjoyment in physical activity, insufficient social support, and limited knowledge about healthy eating as key barriers. The authors concluded that younger men and older women are at higher risk for unhealthy lifestyle habits (Alquaiz et al., 2021). Likewise, a recent systematic review found that unhealthy dietary practices were widespread, largely driven by the taste and convenience of processed foods. Unhealthy eating and poor glycemic control were particularly associated with male gender, older age, and lower-income (Almutairi et al., 2023).

The gender-based difference observed in this meta-analysis—specifically, the higher prevalence of prediabetes among males—aligns with findings from a recent study involving 3,928 Saudi adults, where 67% consumed soft drinks and 30% consumed energy drinks weekly (Aljaadi et al., 2023). These secondary findings provide valuable context for understanding behavioral risk factors associated with sugary drink consumption in Saudi Arabia. The higher consumption observed among males, younger adults, and individuals with lower income may suggest greater susceptibility to marketing, peer influence, and lifestyle factors. Interestingly, an inverse association was found between energy drink intake and overweight/obesity. In addition, education level was not associated with soft or energy drink consumption. These insights highlight the need for gender-specific public health strategies to address prediabetes. Saudi males may benefit from targeted interventions such as lifestyle modification programs, early screening, or tailored education.

Rapid urban growth, infrastructure development, and societal transitions have significantly improved living standards in Saudi Arabia’s urban centers (Almulhim & Cobbinah, 2023). Almost all studies included in this meta-analysis were conducted in urban settings, which may have contributed to the high prevalence of prediabetes observed across different regions. The Central region reported the highest pooled prevalence of prediabetes at 26.0% (95% CI [17.5%–36.8%]) with a prediction interval of 5.1%–69.7%, indicating high variability in prevalence across studies. However, a sensitivity analysis for the Central region, excluding the Al Shehri et al. (2022) study, revealed a lower pooled prevalence of 22.8% (95% CI [20.8%–24.9%]) with a prediction interval of 17.1%–29.7%, indicating moderate variability across studies.

The significant impact of the Al Shehri et al. study on the pooled prevalence in the Central region may be related to specific characteristics of the study population. The participants in the Al Shehri et al. (2022) study were young males (aged 18–30 years) applying for military colleges. The high reported prevalence of prediabetes (55%) among this group may be attributed to lifestyle changes during recruitment preparation, such as altered dietary habits, reduced physical activity, and increased stress. The variation observed in the regional subgroup analysis highlights the need for further investigation into specific factors that may influence prediabetes prevalence. The highest pooled prevalence of prediabetes in the Central and Western regions may call for urgent interventions targeting lifestyle factors and healthcare availability or resources. Conversely, the lower pooled prevalence of prediabetes in the Eastern region may be attributed to the inclusion of only the Latif et al. study (Latif & Rafique, 2020). Consistent with findings among Saudis, Akhtar et al. (2022) observed variability in prediabetes prevalence across different states in Malaysia, suggesting that regional factors and socioeconomic diversity play a role. Similarly, Yitbarek et al. (2021) highlighted substantial heterogeneity in diabetes prevalence across regions within Ethiopia, especially among urban populations.

A sensitivity analysis based on study quality showed similar pooled prevalence estimates for high- and medium-quality studies (24.2% vs. 23.9%). The close alignment between these estimates suggests that study quality had minimal influence on the overall findings, reinforcing the robustness and reliability of the pooled prevalence reported in this meta-analysis (Fig. S7).

According to the GRADE methodology, the overall quality of evidence was considered moderate. Although study methodologies were sound and findings broadly consistent, the high heterogeneity observed among studies necessitated a cautious interpretation (Table S7).

The meta-regression analysis revealed an upward trend in prediabetes prevalence over time; however, this finding should be interpreted cautiously due to a Q-value of 1.43 (p = 0.2312). Additional factors, such as changes in diagnostic criteria and regional differences, may also contribute to the variability in prevalence. Lopez-Ruiz, Blazquez & Hasanov (2019) estimated that the Eastern Province, Riyadh, and Makkah together account for approximately 60% of Saudi Arabia’s gross domestic product (GDP). These may reflect differences in economic development, healthcare access, and early-life risk exposures. For example, Al-Hazzaa et al. (2012) examined lifestyle factors among adolescents in three major Saudi cities—Riyadh, Jeddah, and Al-Khobar—and found that lower physical activity levels and unhealthy dietary habits were significantly associated with overweight and obesity. Similarly, Alquaiz et al. (2021) found that men in Riyadh were more likely to engage in unhealthy lifestyle behaviors.

Overall, publication year did not significantly predict prediabetes prevalence in this meta-analysis. In contrast to our findings, Akhtar et al. (2020) reported an increase in prediabetes prevalence over time. They observed a progressive rise in diabetes prevalence in Bangladesh over recent decades. This may reflect increased longevity and possibly improved detection and diagnostic capabilities (Sanyaolu et al., 2023; Chan et al., 2025). Additionally, Akhtar et al. (2022) reported an increase in prediabetes prevalence in Malaysia between 1995–2010 and 2011–2020, suggesting that lifestyle shifts and dietary changes over time may have contributed.

Study Strengths

To the best of our knowledge, there are no national data are available estimating the pooled prevalence of prediabetes among adult Saudis in Saudi Arabia. This paper is the first systematic review and meta-analysis to synthesize data on the prevalence of prediabetes among adult Saudis living in Saudi Arabia. The strengths of this study include the use of a predefined and registered protocol and a detailed search strategy. Additionally, rigorous methodological and statistical approaches were applied, with two or more independent investigators involved at each stage of the review process.

We believe that these strengths enhance the reliability of the pooled prevalence estimate of prediabetes among adult Saudis in Saudi Arabia.

Study Limitations

Despite the strengths of this meta-analysis, several limitations should be acknowledged. First, the high level of heterogeneity (I² = 99.1%) among the included studies may reflect significant variation in study populations, methodologies, and diagnostic approaches. As a result, the pooled prevalence estimate from this review should be interpreted with caution. Second, most included studies were conducted in urban settings, and no eligible studies specifically from the Northern region of Saudi Arabia were available. This may also limit the national representativeness of the meta-analysis findings. Third, the exclusion of non-English studies may have introduced language bias. However, this is unlikely to have significantly impacted the findings, as most academic research in Saudi Arabia is published in English. Lastly, selection bias in some studies—such as those recruiting from student populations, healthcare settings, or specific occupational groups—may limit the generalizability of their prevalence estimates.

Despite these limitations, this study offers the first comprehensive national estimate of prediabetes prevalence among Saudi adults. It highlights a significant public health concern and provides valuable guidance for healthcare planners and future research focused on diabetes prevention and early intervention.

Conclusion

Findings from the current meta-analysis show a high pooled prevalence of prediabetes among the adult Saudi population, with a particularly higher prevalence among males. The outcomes of this study emphasize the need for early lifestyle interventions, optimized screening programs, and effective resource allocation, all of which could help prevent the progression to type 2 diabetes.

Recommendations

In light of the high heterogeneity, we recommend that future population-based epidemiological studies standardize diagnostic criteria according to approved ADA or WHO guidelines and place greater focus on population sampling. Researchers should employ sampling methods that fairly represent all regions, ages, and genders, with particular attention to the underrepresented Northern province. Furthermore, clear reporting of study settings, diagnostic methods, and population details is essential for accurate interpretation and future meta-analysis. Together, these steps could reduce variability in prevalence estimates and improve comparability across studies.

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