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Article

The Prevalence of and Factors Associated with Prediabetes Among Adolescents in Central Sudan: A Community-Based Cross-Sectional Study

by
Walaa M. Alsafi
1,
Abdullah Al-Nafeesah
2,
Ashwaq AlEed
2,* and
Ishag Adam
3
1
Faculty of Medicine, Gadarif University, Gadarif 32211, Sudan
2
Department of Pediatrics, College of Medicine, Qassim University, Buraydah 52571, Saudi Arabia
3
Department of Obstetrics and Gynecology, College of Medicine, Qassim University, Buraydah 52571, Saudi Arabia
*
Author to whom correspondence should be addressed.
Children 2025, 12(11), 1447; https://doi.org/10.3390/children12111447
Submission received: 13 September 2025 / Revised: 18 October 2025 / Accepted: 23 October 2025 / Published: 24 October 2025
(This article belongs to the Section Pediatric Endocrinology & Diabetes)
Versions Notes

Abstract

Background: Prediabetes is a significant precursor to type 2 diabetes mellitus (T2DM) and its well-known complications. In Sudan, data on the epidemiology of prediabetes among adolescents are scarce, especially in the central region. Thus, this study aimed to determine the prevalence of and factors associated with prediabetes among adolescents in central Sudan. Methods: This community-based cross-sectional study was carried out in East Gezira, central Sudan, from April to June 2025 and included 379 adolescents. Sociodemographic characteristics, anthropometric data (body mass index and BMI-z-score), and clinical information were gathered. Prediabetes was defined as a glycated hemoglobin (HbA1c) level in the range of 5.7% to 6.4%. Multivariate binary analysis was applied to identify the factors associated with prediabetes. Results: The median (interquartile range, IQR) age and BMI-z-score of the 379 enrolled adolescents were 14 (12–16) years and −1.4 (−2.1–−0.39), respectively. Sixty-four (17.0%) adolescents had prediabetes. Univariate analysis did not reveal any significant associations between the investigated factors and prediabetes. However, multivariate binary analysis showed that being female was independently associated with prediabetes among adolescents (adjusted odds ratio, AOR = 1.80, 95% confidence interval, CI 1.01–3.18). Age, BMI z-score, parent education, and occupation were not associated with prediabetes. Conclusions: The prevalence of prediabetes among adolescents in central Sudan is substantial, highlighting a potential future surge in T2DM. The finding that female adolescents are at a higher risk underscores the need for targeted, gender-sensitive screening and preventive health programs to mitigate the progression from prediabetes to T2DM.

1. Introduction

There is an increasing global trend in both prediabetes and diabetes mellitus (DM) in adolescents, including those in sub-Saharan Africa (SSA) [1,2,3,4]. The American Diabetes Association defines prediabetes as glycated hemoglobin (HbA1c) levels ranging from 5.7% to 6.4% [5]. Prediabetes, both in the general population and specifically in adolescents, has recently received increased attention for several reasons, including the global increase in type 2 diabetes mellitus (T2DM) in adolescents, with SSA being no exception [2,3,4]. Ng and Chan reported that youth-onset DM is particularly concerning due to its more aggressive progression than adult-onset diabetes, resulting in a faster decline in the body’s ability to produce insulin and causing accelerated end-organ damage [6]. Unless appropriate preventative measures are taken, this global increase in prediabetes in adolescents will lead to a rise in the incidence and prevalence of T2DM onset in adulthood [6,7,8]. In addition, a key problem of prediabetes arises from its progression to T2DM [9,10] and its well-known complications, including diabetic nephropathy, cardiovascular diseases, microalbuminuria, and diabetic retinopathy [6,11,12,13,14,15]. Unfortunately, such complications, including cardiovascular diseases, may place a significant burden on already fragile health systems, such as those in SSA, including Sudan [3,8]. However, it is possible to slow or halt the progression from prediabetes to frank DM [9,10] or even reverse prediabetes [3,16].
According to the International Diabetes Federation (IDF), approximately 90% of people with undiagnosed DM live in low- and middle-income countries [15]. Therefore, any approach to preventing DM and its associated complications is recommended in these limited-resource settings [3,6]. Different prevalence rates have been reported regarding prediabetes among adolescents in populations such as SSA [1,17], including Sudan [18]. High prevalence rates of prediabetes in adolescents have been reported in India (16.2%) [19] and the United States (18%) [8]. In contrast, Nigeria has a low prevalence rate of prediabetes in adolescents, ranging from 4% to 9.4% [4,20].
The literature reveals several factors associated with prediabetes in adolescents, including age [4,8], sex [8,21], low parental education level [22], parental employment [4,18,23], tobacco use [24], a family history of DM [4,21], and a high body mass index (BMI) [8,20,25].
Although existing research has addressed prediabetes among adults in Sudan [26,27], little is known about the epidemiology of prediabetes in Sudanese adolescents [18]. According to the World Health Organization (WHO), more than 20% of Sudan’s population is composed of adolescents [28]. The ongoing war in Sudan is negatively impacting the health of children and adolescents, especially those with chronic diseases such as DM who need adequate storage conditions for their medications [29,30,31]. In addition, there is low awareness of prediabetes and DM among the Sudanese population, even among healthcare professionals [32,33]. The authors’ prior studies in eastern Sudan reported high prevalence rates of both prediabetes among adolescents and DM and its complications among adults [18,34,35]. To increase proactivity, a clear map of prediabetes prevalence and associated factors is needed across different regions of Sudan, particularly at the community level. Thus, this study aims to investigate the prevalence of and factors associated with prediabetes among adolescents in East Gezira, central Sudan.

2. Materials and Methods

2.1. Study Design and Setting

This community-based cross-sectional study was conducted from April to June 2025 among adolescents in Elrikieb, East Gezira, central Sudan. Gezira State is located in east-central Sudan, lying between the Blue and White Nile Rivers. Elrikieb was chosen because it is composed of four sub-villages whose inhabitants have similarities with those of the overall Gezira State. The authors strictly adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines [36].

2.2. Study Population and Sampling

A total of 379 adolescents were recruited from the community using a systematic approach. A multistage stratified random sampling approach was employed to select potential participants. Within each selected sub-village, households were randomly assigned (lottery method) in proportion to the sub-village’s population and the desired sample size; one adolescent from each household was invited (lottery method) to participate. If there were no adolescents in the selected household or if they refused to participate, the next household was chosen.

2.3. Inclusion and Exclusion Criteria

Before data collection, informed consent was obtained from the parents or guardians. Based on the WHO definition of adolescents [28], all individuals aged 10–19 years were included in the present study, with those younger than 10 or older than 19 years being excluded, along with those who did not consent to participate in the study. All recruited participants were apparently healthy (based on self- and parental report), and adolescents who were sick, were known diabetics, or were pregnant as well as nursing adolescent girls were excluded from the study.

2.4. Sample Size Calculation

The desired sample size was computed using OpenEpi Menu software Version 3.01 [37]. The authors assumed that 35% of adolescents would have prediabetes. This assumption was based on the authors’ prior study in Eastern Sudan, which revealed that 32.6% of adolescents had prediabetes or DM [20]. Thus, a sample size of 379 adolescents was computed for this study to detect a 5% difference at α = 0.05 with 80% power. Moreover, the authors assumed that 10% of adolescents would not respond or would have incomplete data.

2.5. Study Variables and Measures

Following a thorough review of the literature, including research in Sudan [4,8,18,20,21,25], we developed a questionnaire designed to collect sociodemographic data, including age in years, sex (male or female), parental educational levels (<secondary or ≥secondary), mother’s occupational status (housewife, employed, or none), father’s occupational status (working or none), tobacco use (yes or no), family history of DM (yes or no), and presence of a family member who smokes (yes or no). Anthropometric measurements such as weight and height were collected. The WHO reference was used to compute the body mass index (BMI) z-score [38].
After the participants and their guardians agreed to participate and signed an informed consent form, the selected adolescents were approached by trained research assistants. The adolescents were informed about the study’s objectives and all necessary information, including the voluntary nature of their participation, their right to withdraw at any time without having to provide a reason, and the measures taken to ensure their privacy, confidentiality, and safety, such as excluding personal identifiers during data collection. In this study, the primary outcome was prediabetes, and the independent variables were sociodemographic data and BMI z-score. To achieve the study’s objective, a few newly diagnosed cases of T2DM (n = 12) were excluded from the model.

2.6. Blood Sample Processing

Under septic conditions, 3 mL of blood was taken in an ethylenediaminetetraacetic acid tube for HbA1c analysis. An Ichroma machine was used to measure HbA1c according to the manufacturer’s instructions (Republic of Korea), as detailed in the authors’ prior work [34]. Prediabetes can be defined as an HbA1c level in the range of 5.7–6.4%, and T2DM can be defined as an HbA1c level of 6.5% or greater [5]. HbA1c has been used as a diagnostic tool for prediabetes/DM among children and adolescents in different contexts, including Sudan [18,22,39]. In this study, prediabetes and DM were diagnosed using HbA1c as it is not influenced by the last meal, which makes it more suitable in limited-resource settings. Furthermore, Ghaddar et al. reported that analyses including fasting plasma glucose and HbA1c yielded similar findings in children and adolescents [22]. It is worth mentioning that other studies have used the WHO’s definition for prediabetes, namely, impaired fasting blood glucose (110–125 mg/dL) and impaired glucose tolerance (2 h glucose 140–199 mg/dL) [2,21].

2.7. Statistical Analysis

The data were entered into SPSS statistical software for Windows (version 22.0; SPSS Inc., New York, NY, USA) for analysis. Continuous data, such as age and BMI z-score, were tested for normality using the Kolmogorov–Smirnov test and were found to be non-normally distributed. Consequently, they are expressed as medians (interquartile ranges, IQRs). Initially, univariate binary analysis was performed with HbA1c as the categorical dependent variable (prediabetes vs. nondiabetes) and sociodemographic data—such as age, sex, parents’ education, parents’ occupation, tobacco use, and family history of DM—as the independent variables. All variables from the univariate binary analysis were included in a multivariate analysis to adjust for covariates (backward stepwise selection using likelihood ratio). Multicollinearity was checked using the Variance Inflation Factor (VIF, more than 5) and was not detected. Adjusted odds ratios (AORs) and 95% CIs were computed as they were applied. A two-sided p-value less than 0.05 was considered statistically significant.

3. Results

A total of 379 adolescents were enrolled in this study, including 194 females (51.2%) and 185 males (48.8%). The median (IQR) age and BMI z-score were 14.0 (12.0–16.0) years and −1.4 (−2.1–−0.39), respectively. In total, 58 mothers (15.3%) and 103 fathers (27.2%) had secondary education or above. Most of the mothers were housewives (n = 340, 89.7%), and 333 (87.9%) of the fathers were employed. Nearly one-fifth (17.9%) of the adolescents had a family history of DM in a first-degree relative. The median (IQR) HbA1c was 5.4% (5.1–5.5%). One-fifth (n = 76, 20%) of these adolescents had prediabetes or T2DM. Among the 379 participants, 303 (80.0%) had no DM, 64 (17.0%) had prediabetes, and 12 (3.0%) had DM. The prevalence of prediabetes was higher in female adolescents than in male adolescents (n = 37, 57.8% vs. n = 27, 42.2%); however, this difference was not significant (Table 1). Females had a higher median BMI z-score (IQR) of −0.8 (−1.8–0.3) compared to their male counterparts at −1.6 (−2.4–−1.0), p < 0.001.
In the univariate binary analysis, none of the investigated factors (age, gender, BMI z-score, tobacco use, parent education and occupation, and family history of DM), were found to be associated with prediabetes. However, in the multivariate binary analysis, being female was found to be a risk factor for prediabetes (AOR = 1.80, 95% CI 1.01–3.18) (Table 2).

4. Discussion

This study is the first to provide community-based data on the prevalence of prediabetes and its associated factors in adolescents in central Sudan. This prevalence (17.0%) represents a significant public health concern, highlighting the substantial burden of this precursor condition among adolescents in central Sudan, and it is comparable to the high rates observed in eastern Sudan (30.0%) [18] and other countries such as India (16.2%) [19] and the United States (18.0%) [8]. This high prevalence of prediabetes and DM supports the increasing global trend of prediabetes and DM among adolescents, including in SSA [2,3,4]. This suggests that a considerable number of adolescents in central Sudan are at risk of developing T2DM and its related complications, underscoring the urgent need for targeted preventive interventions. In addition, the high prevalence of prediabetes (17.0%) observed in this study may explain the elevated prevalence of T2DM among adolescents and adults in different regions [18,26,34,35]. The observed prevalence of T2DM among adolescents in central Sudan (3.0%) was higher than that observed in the United Arab Emirates (0.9%) [23], Cote d’Ivoire (0.4%) [2], and the United States (1.1%) [40].
The most compelling finding of this study is the independent association between the female sex and the approximately twofold higher risk of prediabetes. However, the confidence interval is wide, and the p-value is borderline (AOR = 1.80, 95% CI = 1.01–3.18, p = 0.045). This finding is noteworthy as previous studies have either found no sex-based difference or have reported higher rates among males [8,18,21]. However, this finding aligns with other studies reporting a higher prevalence of prediabetes in female children and adolescents in specific populations [41,42,43]. The reasons for this gender disparity among adolescents are likely complex and may be rooted in a combination of biological, social, and cultural factors. For instance, specific hormonal changes during puberty, unique dietary practices, physical inactivity, and psychosocial stressors related to gender roles in this cultural context could contribute to increased risk. This finding highlights the critical need for gender-sensitive public health programs that specifically target female adolescents through screening programs and educational initiatives.
Interestingly, this study did not find any significant association between prediabetes and several other factors commonly implicated in the literature, such as age [4,8], low parental education level [22], parental employment [4,18,23], tobacco use [24], family history of DM [4,21], or high BMI [8,20,25]. The findings regarding BMI, which is widely considered a key risk factor for prediabetes globally [8,20,25], are particularly striking. The median BMI z-score in this study population was low (−1.4), which may indicate that obesity is not the primary driver of prediabetes in this specific community. However, in this study, females had a higher median (IQR) BMI z-score than their male counterparts. Such differences in BMI z-score between sexes and the influence of gender on prediabetes require further exploration. In addition, the ongoing war in Sudan and the associated socioeconomic instability may have altered the etiological landscape, with other factors—such as psychosocial stress, dietary shifts toward more processed foods, and disruptions to regular metabolic function—playing a leading role. These factors, which were not comprehensively captured in this study, represent a crucial area for future research. The lack of association with DM family history—a well-established genetic risk factor—was unexpected and may have been due to the underreporting of this condition in a population with low health literacy and in a fragmented healthcare system. Moreover, the absence of an association between these factors and prediabetes may be due to a genuinely low influence in this context, or it may reflect limitations in measurement (e.g., self-reported family history and relatively low BMI z-score overall).

4.1. Study Implications

The findings of this study address the urgent need for community-based interventions in central Sudan. With 17% of adolescents having prediabetes in the region, health initiatives should prioritize early screening programs, particularly for females, who were identified as a high-risk group. The findings underscore the importance of promoting healthy lifestyles, including physical activity and balanced nutrition, to prevent the progression from prediabetes to T2DM in this vulnerable population. One practical implication of this study was that the 12 adolescents identified with frank T2DM were advised to seek medical care at the nearest healthcare facility for further evaluation and management.

4.2. Strengths and Limitations

This study has several strengths, including its community-based design, which provides a more representative picture of the population than hospital-based studies. This research can add value to the limited number of studies on prediabetes, especially those of adolescents in Sudan [26,27]. Regarding central Sudan, this study provides a vital baseline in a region with no prior data on this topic, enabling future trend analysis and the evaluation of intervention effectiveness. However, this study has some limitations that should be noted to inform future research. The cross-sectional design precludes any inference of causality, and the study was conducted over a short period, meaning it may not capture seasonal variations in glucose regulation. Furthermore, while the authors collected data on several key risk factors, other important variables—such as dietary habits [4], physical activity levels [4,25], and psychological stress—were not assessed in detail. The reliance on self-reported data for some variables, such as family history of DM, is a potential source of bias.
Fasting plasma glucose or oral glucose tolerance test (OGTT) was not assessed. Using HbA1c as the sole diagnostic tool is practical in low-resource settings, but it has its limitations. Anemia, hemoglobinopathies, or nutritional deficiencies may influence HbA1c, which could lead to over- or under-estimation of prevalence.
The calculation assumes a prevalence of 35% based on a previous study in eastern Sudan, but the observed prevalence was much lower (17%). This discrepancy may affect the statistical power of this study.

5. Conclusions

This study highlights the high prevalence of prediabetes among adolescents in central Sudan, with the female sex being identified as a significant independent risk factor. These findings are crucial for informing targeted public health interventions aimed at this vulnerable group. Future research is warranted, including longitudinal studies that explore the complex interplay of environmental, social, and biological factors in this unique setting, to better understand the drivers of prediabetes and to develop effective strategies to prevent its progression to type 2 diabetes mellitus (T2DM).

Author Contributions

W.M.A. and I.A.: conception, design, acquisition of data, analysis, interpretation of data, and drafting the article. A.A.-N. and A.A.: conception, design, acquisition of data, analysis, interpretation of data, and revising the manuscript critically for important intellectual content. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Ethical approval for this study was obtained from the Institutional Review Board of the Faculty of Medicine and Health Sciences at the University of Gadarif in eastern Sudan (ref. #2025, 04; approved 23 April 2025). Informed written consent was obtained from all participants and their legal guardians before data collection. Participants were assured of confidentiality and anonymity. They were told that participation was voluntary and that they had the right to withdraw at any time without penalty. Those adolescents identified with prediabetes and DM were advised to seek further evaluation and management.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data of this study will be available from the corresponding author upon reasonable request.

Acknowledgments

The authors would like to thank the participants and their guardians for their cooperation in the present study. The researchers would like to thank the Deanship of Graduate Studies and Scientific Research at Qassim University for providing financial support (QU-APC-2025).

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
AORadjusted odds ratio
CIconfidence interval
DMdiabetes mellitus
IQRinterquartile range
HbA1cglycated hemoglobin
USUnited States
WHOWorld Health Organization

References

  1. Han, C.; Song, Q.; Ren, Y.; Chen, X.; Jiang, X.; Hu, D. Global prevalence of prediabetes in children and adolescents: A systematic review and meta-analysis. J. Diabetes 2022, 14, 434–441. [Google Scholar] [CrossRef]
  2. Agbre-Yace, M.L.; Oyenusi, E.E.; Oduwole, A.O.; Ake, M.D.; Abodo, J.R. Prevalence of diabetes mellitus among children and adolescents in the district of Abidjan in Cote d’Ivoire: A population-based study. J. Diabetes Metab. Disord. 2016, 15, 38. [Google Scholar] [CrossRef]
  3. Nwatu, C.; Young, E. Prediabetes in Sub-Saharan Africa: Pathophysiology, Predictors, and Prevalence. Niger. J. Med. 2020, 29, 343–350. [Google Scholar] [CrossRef]
  4. Akintayo-Usman, N.O.; Okanlawon, F.A.; Usman, S.O. Prevalence of pre-diabetes and risk factors among secondary school adolescents in osogbo local government area, Osun State, Nigeria. Afr. Health Sci. 2021, 21, 1301–1309. [Google Scholar] [CrossRef]
  5. The American Diabetes Association Releases the Standards of Care in Diabetes—2024. Available online: https://diabetes.org/newsroom/press-releases/american-diabetes-association-releases-standards-care-diabetes-2024 (accessed on 16 October 2025).
  6. Ng, H.Y.; Chan, L.T.W. Prediabetes in children and adolescents: An updated review. World J. Clin. Pediatr. 2023, 12, 263–272. [Google Scholar] [CrossRef]
  7. Lynch, J.L.; Barrientos-Pérez, M.; Hafez, M.; Jalaludin, M.Y.; Kovarenko, M.; Rao, P.V.; Weghuber, D. Country-Specific Prevalence and Incidence of Youth-Onset Type 2 Diabetes: A Narrative Literature Review. Ann. Nutr. Metab. 2021, 76, 289–296. [Google Scholar] [CrossRef] [PubMed]
  8. Andes, L.J.; Cheng, Y.J.; Rolka, D.B.; Gregg, E.W.; Imperatore, G. Prevalence of Prediabetes among Adolescents and Young Adults in the United States, 2005–2016. JAMA Pediatr. 2020, 174, e194498. [Google Scholar] [CrossRef] [PubMed]
  9. Zuniga, R.E.; Deboer, M.D. Prediabetes in adolescents: Prevalence, management and diabetes prevention strategies. Diabetes Metab. Syndr. Obes. 2021, 14, 4609–4619. [Google Scholar] [CrossRef] [PubMed]
  10. Weiner, A.; Zhang, M.; Ren, S.; Tchang, B.; Gandica, R.; Murillo, J. Progression from prediabetes to type 2 diabetes mellitus in adolescents: A real world experience. Front. Clin. Diabetes Healthc. 2023, 4, 1181729. [Google Scholar] [CrossRef]
  11. Ma, Y.; Li, S.; Zhang, A.; Ma, Y.; Wan, Y.; Han, J.; Cao, W.; Xu, G. Association between Red Blood Cell Distribution Width and Diabetic Retinopathy: A 5-Year Retrospective Case-Control Study. J. Ophthalmol. 2021, 2021, 6653969. [Google Scholar] [CrossRef]
  12. Wang, L.; Lv, Y. Construction of a Prediction Model for the Mortality of Elderly Patients with Diabetic Nephropathy. J. Healthc. Eng. 2022, 2022, 5724050. [Google Scholar] [CrossRef]
  13. Assulyn, T.; Khamisy-Farah, R.; Nseir, W.; Bashkin, A.; Farah, R. Neutrophil-to-lymphocyte ratio and red blood cell distribution width as predictors of microalbuminuria in type 2 diabetes. J. Clin. Lab. Anal. 2020, 34, e23259. [Google Scholar] [CrossRef]
  14. Dai, H.; Su, X.; Li, H.; Zhu, L. Association between red blood cell distribution width and mortality in diabetic ketoacidosis. J. Int. Med. Res. 2020, 48, 300060520911494. [Google Scholar] [CrossRef]
  15. Magliano, D.J.; Boyko, E.J. IDF Diabetes Atlas 2021, 10th ed.; International Diabetes Federation: Brussels, Belgium, 2021; Volume 102, ISBN 9782930229980. [Google Scholar]
  16. Nwosu, B.U. The Progression of Prediabetes to Type 2 Diabetes in Children and Adolescents in the United States: Current Challenges and Solutions. Endocrines 2022, 3, 545–551. [Google Scholar] [CrossRef]
  17. Partap, U.; Nyundo, A.; Manu, A.; Regan, M.; Ismail, A.; Chukwu, A.; Dessie, Y.; Njau, T.; Kaaya, S.F.; Fawzi, W.W. Depressive symptoms among adolescents in six sub-Saharan African countries: A pooled analysis of associated factors. Prev. Med. Rep. 2023, 36, 102499. [Google Scholar] [CrossRef] [PubMed]
  18. Alsafi, W.M.; Eed, A.A.; Hassan, A.A.; Nafeesah, A.A.; Alfaifi, J.; Adam, I. Prevalence of and factors associated with pre-diabetes among adolescents in Eastern Sudan: A community-based cross-sectional study. BMJ Open 2024, 14, e086197. [Google Scholar] [CrossRef]
  19. Kushwaha, S.; Srivastava, R.; Bhadada, S.K.; Khanna, P. Prevalence of pre-diabetes and diabetes among school-age children and adolescents of India: A brief report. Diabetes Res. Clin. Pract. 2023, 202, 110738. [Google Scholar] [CrossRef]
  20. Arigbede, O.; Adeoye, I.; Jarrett, O.; Yusuf, O. Prediabetes among Nigerian adolescents: A School-based study of the prevalence, risk factors and pattern of fasting blood glucose in Ibadan, Nigeria. Int. J. Diabetes Dev. Ctries. 2017, 37, 437–445. [Google Scholar] [CrossRef]
  21. Musa, A.Z.; Umar, U.I.; Obiagwu, P.N.; Ibrahim, M. School-based Study of the Prevalence and Associated Factors of Prediabetes Among Adolescents in Kano, Nigeria. Niger. Med. J. 2023, 64, 43–53. [Google Scholar] [PubMed]
  22. Ghaddar, R.; Hudson, E.A.; Jeans, M.R.; Vandyousefi, S.; Landry, M.J.; Davis, J.N. Ethnicity/race, parent educational attainment, and obesity associated with prediabetes in children. Nutr. Diabetes 2023, 13, 15. [Google Scholar] [CrossRef] [PubMed]
  23. Amiri, E.A.; Abdullatif, M.; Abdulle, A.; Bitar, N.A.; Afandi, E.Z.; Parish, M.; Darwiche, G. The prevalence, risk factors, and screening measure for prediabetes and diabetes among Emirati overweight/obese children and adolescents. BMC Public Health 2015, 15, 1298. [Google Scholar] [CrossRef] [PubMed]
  24. Barakat, C.; Yousufzai, S.J.; Booth, A.; Benova, L. Prevalence of and risk factors for diabetes mellitus in the school-attending adolescent population of the United Arab Emirates: A large cross-sectional study. BMJ Open 2021, 11, e046956. [Google Scholar] [CrossRef]
  25. Kumar, P.; Srivastava, S.; Mishra, P.S.; Mooss, E.T.K. Prevalence of pre-diabetes/type 2 diabetes among adolescents (10–19 years) and its association with different measures of overweight/obesity in India: A gendered perspective. BMC Endocr. Disord. 2021, 21, 146. [Google Scholar] [CrossRef]
  26. Eltom, M.A.; Babiker Mohamed, A.H.; Elrayah-Eliadarous, H.; Yassin, K.; Noor, S.K.; Elmadhoun, W.M.; Ahmed, M.H. Increasing prevalence of type 2 diabetes mellitus and impact of ethnicity in north Sudan. Diabetes Res. Clin. Pract. 2018, 136, 93–99. [Google Scholar] [CrossRef] [PubMed]
  27. Elfaki, F.A.; Mukhayer, A.I.G.; Moukhyer, M.E.; Chandika, R.M.; Kremers, S.P.J. Prevalence of metabolic syndrome among early adolescents in Khartoum State, Sudan. Int. J. Environ. Res. Public Health 2022, 19, 14876. [Google Scholar] [CrossRef]
  28. World Health Organization. Sudan Takes Action to Improve the Health of Its Youth—AA-HA! World Health Organization: Geneva, Switzerland, 2019. [Google Scholar]
  29. Siddig, E.E.; Eltigani, H.F.; Ahmed, A. Urgent call to protect children and their health in Sudan. BMJ 2023, 382, 1799. [Google Scholar] [CrossRef] [PubMed]
  30. Osman, S.H.M.; Nashwan, A.J. Prioritizing children’s mental health amidst Sudan’s humanitarian crisis: Policy recommendations for immediate action. Child Adolesc. Psychiatry Ment. Health 2023, 17, 97. [Google Scholar] [CrossRef]
  31. Khan, Y.; Albache, N.; Almasri, I.; Gabbay, R.A. The Management of Diabetes in Conflict Settings: Focus on the Syrian Crisis. Diabetes Spectr. 2019, 32, 264–269. [Google Scholar] [CrossRef]
  32. Teoh, K.W.; Ng, C.M.; Chong, C.W.; Bell, J.S.; Cheong, W.L.; Lee, S.W.H. Knowledge, attitude, and practice toward pre-diabetes among the public, patients with pre-diabetes and healthcare professionals: A systematic review. BMJ Open Diabetes Res. Care 2023, 11, e003203. [Google Scholar] [CrossRef]
  33. Saleh, A.M.; Almobarak, A.O.; Badi, S.; Siddiq, S.B.; Tahir, H.; Suliman, M.; Ahmed, M.H. Knowledge, Attitudes and Practice Among Primary Care Physicians in Sudan Regarding Prediabetes: A Cross—Sectional Survey. Int. J. Prev. Med. 2021, 12, 80. [Google Scholar] [CrossRef]
  34. Omar, S.M.; Musa, I.R.; ElSouli, A.; Adam, I. Prevalence, risk factors, and glycaemic control of type 2 diabetes mellitus in eastern Sudan: A community-based study. Ther. Adv. Endocrinol. Metab. 2019, 10, 2042018819860071. [Google Scholar] [CrossRef] [PubMed]
  35. Omar, S.M.; Musa, I.R.; Idrees, M.B.; Adam, I. Prevalence of depression and associated factors among patients with type 2 diabetes mellitus in eastern Sudan. BMC Psychiatry 2021, 21, 336. [Google Scholar] [CrossRef] [PubMed]
  36. Cuschieri, S. The STROBE guidelines. Saudi J. Anaesth. 2019, 13 (Suppl. 1), S31–S34. [Google Scholar] [CrossRef] [PubMed]
  37. Dean, A.G.; Sullivan, K.M.; Soe, M.M. OpenEpi: Open Source Epidemiologic Statistics for Public Health, Version. Available online: www.openepi.com (accessed on 6 April 2013).
  38. BMI-for-Age (5–19 Years). Available online: https://www.who.int/toolkits/growth-reference-data-for-5to19-years/indicators/bmi-for-age (accessed on 29 January 2023).
  39. Spurr, S.; Bally, J.; Bullin, C.; Allan, D.; McNair, E. The prevalence of undiagnosed Prediabetes/type 2 diabetes, prehypertension/hypertension and obesity among ethnic groups of adolescents in Western Canada. BMC Pediatr. 2020, 20, 31. [Google Scholar] [CrossRef] [PubMed]
  40. Ouyang, A.; Hu, K.; Chen, L. Trends and Risk Factors of Diabetes and Prediabetes in US Adolescents, 1999–2020. Diabetes Res. Clin. Pract. 2023, 207, 111022. [Google Scholar] [CrossRef]
  41. Di Bonito, P.; Licenziati, M.R.; Corica, D.; Wasniewska, M.G.; Di Sessa, A.; del Giudice, E.M.; Morandi, A.; Maffeis, C.; Faienza, M.F.; Mozzillo, E.; et al. Phenotypes of prediabetes and metabolic risk in Caucasian youths with overweight or obesity. J. Endocrinol. Investig. 2022, 45, 1719–1727. [Google Scholar] [CrossRef]
  42. Balamurugan, K.; Ponprabha, R.; Sivashankari, V. Prevalence of pre-diabetes in adolescents aged 11–17 years with high risk factors. Int. J. Res. Med. Sci. 2021, 9, 3648. [Google Scholar] [CrossRef]
  43. Ranjani, H.; Sonya, J.; Anjana, R.M.; Mohan, V. Prevalence of glucose intolerance among children and adolescents in urban South India (ORANGE-2). Diabetes Technol. Ther. 2013, 15, 13–19. [Google Scholar] [CrossRef]
Table 1. Sociodemographic and clinical characteristics of the studied adolescents in central Sudan (n = 379), 2025.
Table 1. Sociodemographic and clinical characteristics of the studied adolescents in central Sudan (n = 379), 2025.
VariableTotal (n = 379)
MedianInterquartile range
Age (years)14.012.0–16.0
Body mass index z-score−1.4−2.1–−0.39
Glycated hemoglobin %5.45.1–5.5
FrequencyPercentage
SexMale19451.2
Female18548.8
Mother’s education≥secondary5815.3
<secondary32184.7
Father’s education≥secondary10327.2
<secondary27672.8
Mother’s occupationHousewife34089.7
Employed256.6
None143.7
Father’s occupationWorking33387.9
None4612.1
Tobacco useYes143.7
No36596.3
Family history of diabetesNo31182.1
Yes6817.9
Presence of a smoker in the familyNo26369.4
Yes11630.6
Diabetes statusNone30380.0
Prediabetes6417.0
Diabetic123.0
Table 2. Univariate and multivariate binary analyses of the factors associated with prediabetes among adolescents in central Sudan (n = 367), 2025.
Table 2. Univariate and multivariate binary analyses of the factors associated with prediabetes among adolescents in central Sudan (n = 367), 2025.
Variable Adolescents with Prediabetes (n = 64)Adolescents Without Prediabetes (n = 303)Univariate AnalysisMultivariate Analysis
Median (Interquartile Range)Non–Adjusted Odds Ratio (95% Confidence Interval, CI)pAdjusted Odds Ratio (95% CI)p
Age, years 14.0 (11.2–16.0)14.0 (12.0–16.0)1.01 (0.97–1.05)0.5590.93 (0.83–1.05)0.296
Body mass index z-score −1.14 (−1.9–−0.3)−1.45 (−2.1–−0.4)1.09 (0.96–1.25)0.1721.11 (0.96–1.18)0.155
Frequency(proportion)
SexMale27 (42.2)163 (53.8)Reference
Female37 (57.8)140 (46.2)1.60 (0.93–2.75)0.0931.80 (1.01–3.18)0.045
Mother’s education level≥Secondary level10 (15.6)48 (15.8)Reference
˂Secondary level54 (84.4)255 (84.2)1.01 (0.48–2.13)0.9661.90 (0.75–4.82)0.174
Mother’s occupation statusHousewife55 (85.9)273 (90.1)Reference
Employed7 (10.9)18 (5.9)1.93 (0.76–4.84)0.1612.52 (0.82–7.71)0.105
None2 (3.1)12 (4.0)0.82 (0.18–3.80)0.8070.77 (0.16–3.69)0.747
Father’s education level≥Secondary level23 (35.9)77 (25.4)Reference
˂Secondary level41 (64.1)226 (74.6)0.60 (0.34–1.07)0.0880.58 (0.31–1.05)0.075
Father’s occupation statusEmployed59 (92.2)264 (87.1)Reference
None5 (7.8)39 (12.9)0.57 (0.21–1.51)0.2630.56 (0.21–1.55)0.270
Family history of diabetes mellitusNo53 (82.8)249 (82.20Reference
Yes11 (17.2)54 (17.8)0.95 (0.46–1.95)0.9040.85 (0.40–1.83)0.688
Presence of a smoker in the familyNo46 (71.9)207 (68.3)Reference
Yes18 (28.1)96 (31.7)0.84 (0.46–1.53)0.5770.73 (0.38–1.37)0.331
Tobacco useNo61 (95.3)292 (96.4)Reference
Yes3 (4.7)11 (3.6)1.30 (0.35–4.82)0.6891.89 (0.45–7.79)0.378
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Alsafi, W.M.; Al-Nafeesah, A.; AlEed, A.; Adam, I. The Prevalence of and Factors Associated with Prediabetes Among Adolescents in Central Sudan: A Community-Based Cross-Sectional Study. Children 2025, 12, 1447. https://doi.org/10.3390/children12111447

AMA Style

Alsafi WM, Al-Nafeesah A, AlEed A, Adam I. The Prevalence of and Factors Associated with Prediabetes Among Adolescents in Central Sudan: A Community-Based Cross-Sectional Study. Children. 2025; 12(11):1447. https://doi.org/10.3390/children12111447

Chicago/Turabian Style

Alsafi, Walaa M., Abdullah Al-Nafeesah, Ashwaq AlEed, and Ishag Adam. 2025. "The Prevalence of and Factors Associated with Prediabetes Among Adolescents in Central Sudan: A Community-Based Cross-Sectional Study" Children 12, no. 11: 1447. https://doi.org/10.3390/children12111447

APA Style

Alsafi, W. M., Al-Nafeesah, A., AlEed, A., & Adam, I. (2025). The Prevalence of and Factors Associated with Prediabetes Among Adolescents in Central Sudan: A Community-Based Cross-Sectional Study. Children, 12(11), 1447. https://doi.org/10.3390/children12111447

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