High Prevalence of Prediabetes and Cardiometabolic Risk Profiles Among Omani Adults in the Muscat Governorate: Analysis from the National Screening Program

Abstract

Prediabetes is a critical precursor to type 2 diabetes (T2DM) and cardiometabolic diseases, yet its burden in Oman remains understudied. Leveraging data from Oman’s 2023 National Screening Program, this study quantifies the prevalence of prediabetes and its risk profiles among adults in Muscat Governorate—providing urgent evidence to guide diabetes prevention strategies in the Gulf region. Objectives: To estimate the prevalence of prediabetes and identify associated risk factors among Omani adults screened at primary health centers in Muscat Governorate (2023), given its critical role in preventing type 2 diabetes mellitus (T2DM) progression. Methods: This cross-sectional study analyzed data from Oman’s national screening program. Socio-demographics, clinical parameters (blood pressure, body mass index [BMI]), and laboratory results (fasting glucose, lipids, renal function) were extracted from the Al-Shifa electronic health system and National Screening Register. Multivariable logistic regression was performed using SPSS 30.0 (IBM Corp., Armonk, NY, USA). Results: Among 4862 participants (mean age 43.2 ± 6.3 years; 61.7% female), prevalences were: prediabetes 29.0%, T2DM 5.5%, obesity (BMI 30–40 kg/m²) 35.7%, hypertension 42.0%, hypercholesterolemia 48.8%, and renal involvement 51.8%. Males had significantly higher prediabetes prevalence than females (35.4% vs. 24.7%; adjusted odds ratio [aOR] = 1.43; 95% confidence interval [CI]: 1.21–1.70). Independent risk factors included each 1-year age increase (aOR = 1.05; 95% CI: 1.03–1.08), each 1-unit BMI increase (aOR = 1.03; 95% CI: 1.01–1.05), and family history of diabetes (aOR = 1.28; 95% CI: 1.09–1.50). Conclusions: The high burden of prediabetes and comorbid non-communicable diseases in Oman necessitates urgent public health strategies, including enhanced screening, lifestyle interventions, and gender-specific approaches to curb the T2DM epidemic.

1. Introduction

Type 2 diabetes has become an escalating health crisis in numerous countries worldwide. The American Diabetes Association reported that in 2021, approximately 38.4 million Americans, or 11.6% of the population, were living with diabetes, while an additional 97.6 million adults aged 18 and older were classified as having prediabetes. Prediabetes is defined by elevated blood glucose levels that do not meet the criteria for a diabetes diagnosis, yet it significantly increases the likelihood of developing diabetes. Research indicates that nearly 50% of individuals diagnosed with prediabetes may progress to type 2 diabetes within five years [1]. Furthermore, those with impaired fasting glucose (IFG) or impaired glucose tolerance (IGT) face heightened risks of chronic kidney disease (CKD), cardiovascular disease (CVD), and increased mortality rates [2,3]. Intensive lifestyle changes, such as dietary adjustments and increased physical activity, along with pharmacological treatments like metformin, have been shown to effectively lower the risk of progression to type 2 diabetes in individuals with IGT or IFG [4,5,6].

A recent study conducted in Turkey in 2023 revealed that the number of individuals aged 20 to 84 with any form of glucose intolerance increased by over 5.7 million from 2010 to 2021, with 2.4 million diagnosed with diabetes and 3.3 million with prediabetes. This research highlighted a significant rise in diabetes prevalence, particularly among the elderly, over the past 11 years, correlating with population growth and aging. Consequently, the burden of diabetes on social, economic, and healthcare systems is expected to escalate in the coming decades [7].

Numerous studies have explored the prevalence and risk factors associated with prediabetes globally. A 2015 study in Mexico found a prediabetes prevalence of 14.6% among young adults in primary healthcare, with a notable history of obesity, hypertension, and substance use among those affected [8]. Similarly, a systematic review published in 2017 indicated a high prevalence of diabetes and prediabetes in Cameroon, with no significant differences between urban and rural populations or genders, driven primarily by increasing age, overweight, and obesity [9].

Research across 15 Indian states reported an overall prediabetes prevalence of 10.3%, with variations ranging from 6.0% in Mizoram to 14.7% in Tripura. Factors such as age, male gender, obesity, hypertension, and family history of diabetes were identified as independent risk factors in both urban and rural settings [10]. A 2022 study in Bahah City, Saudi Arabia, found that 20% of adults attending primary healthcare were prediabetic, with obesity, particularly central obesity, hypertension, and family history of diabetes being significant associations [11].

In Nepal, a study estimated a prediabetes prevalence of 5% among participants, with a majority being aged 45–64 years, exhibiting central obesity, and having high waist–hip ratios [12].

In Oman, a 2017 community survey revealed a diabetes prevalence of 15.7% and a prediabetes prevalence of 11.8%, identifying age, education level, hypertension, family history of diabetes, abnormal waist-to-hip ratio, and hypertriglyceridemia as key risk factors [13]. In response to the growing issue, Oman introduced a national screening program in 2007, initially targeting individuals over 40, which was later adjusted in 2023 to include those from 35 years of age, focusing on screening for diabetes, hypertension, hyperlipidemia, obesity and chronic kidney disease [14].

Critically, prediabetes is not merely a metabolic warning sign but is itself a state of heightened systemic risk. Beyond dysglycemia, it is characterized by subclinical vascular alterations that precede clinical cardiovascular disease. Evidence indicates that individuals with prediabetes exhibit early markers of vascular dysfunction, including increased arterial stiffness, endothelial impairment, and microvascular damage. These alterations contribute to a significantly elevated risk of atherosclerosis, coronary artery disease, and stroke, establishing prediabetes as a key intervention point for preventing both diabetes and its cardiovascular sequelae [15,16,17,18].

The increasing prevalence of type 2 diabetes mellitus poses significant morbidity and mortality risks, placing a substantial burden on healthcare systems globally. Early screening and diagnosis of type 2 diabetes can lead to improved health outcomes. Intervening during the prediabetic stage can delay or prevent the onset of type 2 diabetes and its associated complications. This study aims to provide a more precise estimate of prediabetes prevalence in the Muscat governorate and identify its associated risk factors, enabling healthcare stakeholders to enhance screening programs and establish effective pathways for early intervention and management of prediabetes.

2. Materials and Methods

This cross-sectional study utilized data from Oman’s 2023 National Screening Program conducted across all primary healthcare centers in Muscat Governorate.

The study included all Omani nationals aged >35 years who completed screening at Muscat primary health centers between January and December 2023. Ethical approval was obtained from the regional research committee, Directorate of General Health Service, Muscat.

Those individuals with previously diagnosed diabetes or prediabetes and those with incomplete laboratory results or screening documentation were excluded from the study. Results are based on an estimated prediabetes prevalence of 20% (regional reference), a margin of error of 2% and a confidence level of 95%.

The minimum required sample size was 1428. However, all eligible records from the total screened population (N = 5820) were analyzed to enhance statistical power.

The definitions and thresholds for glycemic status, cardiometabolic risk factors, and lifestyle variables used in this analysis are presented in

Table 1. Definition of key study variables and diagnostic thresholds.

Category	Parameters	Thresholds/Categories
Glycemic Status	Fasting glucose (mmol/L)	<5.6 (Normal), 5.6–6.9 (Prediabetes), ≥7 (Diabetes)
	Random glucose (mmol/L)	<7.8 (Normal), 7.8–11 (Prediabetes), ≥11.1 (Diabetes)
Cardiometabolic	Blood pressure (mmHg)	≥130/80 (Abnormal)
	BMI (kg/m2)	Underweight (<18.5), Normal (18.5–24.9), Overweight (25–29.9), Obese (30–39.9), Severely obese (≥40)
	Total cholesterol (mmol/L)	≥5.2 (High)
	eGFR (mL/min/1.73 m2)	>90 (Normal), 60–90 (Mild reduction), <60 (Significant reduction)
Lifestyle	Physical activity	<200 min/week leisure activity
	Tobacco use	Any form/quantity

BMI, Body Mass Index; eGFR, estimated Glomerular Filtration Rate. Trained primary care physicians collected data using a standardized electronic health record (Al-Shifa system) and the national screening program documentation.

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All laboratory analyses were performed at centralized facilities (Al-Seeb and Bowsher Polyclinics) using standardized protocols. Data was analyzed using IBM SPSS Statistics 30.0. (IBM Corp. Released 2024. IBM SPSS Statistics for Windows, Version 30.0, IBM Corp, Armonk, NY, USA). For the descriptive statistics, categorical variables were presented as frequencies and percentages, and continuous variables were described as mean ± SD, median and range. Prevalence of various NCDs was reported as a proportion with a 95% confidence interval (CI). The chi-square test, Fisher’s exact test, and the Mann–Whitney U test were performed to assess the association between various factors and pre-diabetes. In addition, the odds ratio (OR) with a 95% CI was addressed. For the multivariate analysis, binary logistic regression was performed by including factors showing crude p-values of < 0.25. A p-value of < 0.05 was considered statistically significant.

During the preparation of this work, the authors used AI tool (DeepSeek) to check grammar and improve language clarity.

3. Results

A total of 4862 participants were included in this study. Females dominated the sample (61.7%). The mean age was 43.21 ± 6.30, with a minimum of 35.0 and a maximum of 83.0 years. Most (70.8%) of the included participants were physically inactive, and 56.2% reported a family history of diabetes. Only 0.6% reported alcohol intake, and 3.0% were smokers.

Table 2. Sociodemographic and clinical characteristics of the study sample.

Characteristics	Categories	n (%)/Mean ± SD, Median, Range
Gender (n = 4796)	Male	1822 (38.3)
	Female	2941 (61.7)
Age (n = 4833) (Mean ± SD, Median, Range)		43.12 ± 6.35, 42.0, 35.0–83.0
BMI (n = 3105) (Mean ± SD, Median, Range)		29.34 ± 6.26, 28.4, 13.9–74.0
Physical activity (n = 2348)	No	1663 (70.8)
	Yes	685 (29.2)
Alcohol (n = 2853)	No	2836 (99.4)
	Yes	17 (0.6)
Smoking (n = 2921)	No	2833 (97.0)
	Yes	88 (3.0)
Family history of diabetes (n = 2646)	No	1159 (43.8)
	Yes	1487 (56.2)

Data are presented as n (%) for categorical variables and as mean ± standard deviation (SD) with median and range for continuous variables. The n-value for each variable indicates the number of participants with available data.

details the sociodemographic and general features of the sample studied.

Regarding the prevalence of pre-diabetes and other non-communicable diseases (NCDs), the sample showed a prevalence of pre-diabetes of 29.0% (95% CI: 27.7–30.3), and 5.5% had diabetes mellitus (95% CI: 4.9–6.1). Obesity was also highly prevalent among the study sample, with 35.7% (95% CI: 34.0–37.4) having a BMI of 30.0–40.0, and 6.2% (95% CI: 5.4–7.0) having a BMI of ≥ 40. In addition, the prevalence of hypertension (HTN) and hypercholesterolemia was 42.0% (95% CI: 40.6–43.4) and 48.8% (95% CI: 47.4–50.2), respectively. Furthermore, CKD prevalence was 51.8% (95% CI: 49.9–53.7). Among the CKD patients, 638 (48.67%) cases had decreased eGFR, 349 (26.62%) cases had proteinuria, and 565 (43.10%) cases had hematuria.

Table 3. Prevalence of prediabetes and other non-communicable diseases (NCDs).

Characteristics	Categories	Prevalence % (95% CI)
Diabetes status (n = 4772)	Pre-diabetes	29.0 (27.7–30.3)
	DM	5.5 (4.9–6.1)
Obesity (n = 3105)	BMI 25–30	37.0 (35.3–38.7)
	BMI 30–40	35.7 (34.0–37.4)
	BMI ≥ 40	6.2 (5.4–7.0)
HTN (n = 4746)	Present	42.0 (40.6–43.4)
CKD (n = 2533)	Present	51.8 (49.9–53.7)
Hypercholesterolemia (n = 4722)	Present	48.8 (47.4–50.2)

Prevalence is presented as a percentage with its 95% Confidence Interval (CI). Abbreviations: DM, Diabetes Mellitus; BMI, Body Mass Index; HTN, Hypertension; CKD, Chronic Kidney Disease.

gives the details of the prevalence of NCDs among the study sample.

With regard to risk factors of pre-diabetes, gender, age, BMI, and family history of DM showed significant association with prediabetes in the crude testing. In this regard, 43.3% of patients with pre-diabetes were males, compared to 35.4% among participants with no pre-diabetes, with a p-value of < 0.001. The odds of pre-diabetes among females were 0.718 (95% CI: 0.630–0.817) times less than the odds among males. Participants with pre-diabetes reported significantly higher median age (43.0 years) compared to 41.0 years among participants with no pre-diabetes (p < 0.001). In addition, the median BMI was significantly higher among pre-diabetes patients (29.05 vs. 28.0, p < 0.001). Furthermore, 59.0% of patients with pre-diabetes had a family history of diabetes, compared to 54.1% among participants with no pre-diabetes (p =0.023, OR 1.221 (95% CI: 1.027–1.451). The parameters of the crude association testing are given in

Table 4. Crude association between studied factors and pre-diabetes.

Variables	Categories	Pre-Diabetes No	Pre-Diabetes Yes	p-Value
Gender (n = 4418)	Male	1078 (35.4%)	592 (43.3%)	<0.001 *
	Female	1970 (64.6%)	776 (56.7%)
Age (n = 4449)	Median (IQR)	41.0 (38.0–46.0)	43.0 (40.0–48.0)	<0.001 ^
BMI (n = 2888)	Median (IQR)	28.0 (25.0–32.0)	29.05 (26.0–33.0)	<0.001 ^
Family history (n = 2446)	No	768 (45.9%)	317 (41.0%)	0.023 *
	Yes	905 (54.1%)	456 (59.0%)
Physical activity (n = 2181)	No	1062 (70.8%)	486 (71.5%)	0.732 *
	Yes	439 (29.2%)	194 (28.5%)
Alcohol intake (n = 2658)	No	1820 (99.5%)	822 (99.2%)	0.286 #
	Yes	9 (0.5%)	7 (0.8%)
Smoking (n = 2721)	No	1810 (97.1%)	831 (97.0%)	0.844 *
	Yes	54 (2.9%)	26 (3.0%)

Comparisons were made using the Chi-square test (), Fisher’s Exact test (#), or the Mann–Whitney U test (^). The Odds Ratio (OR) with its 95% CI is presented for categorical variables. Abbreviations: IQR, Interquartile Range *.

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The multivariate logistic regression revealed similar results. Gender, age, BMI and family history showed independent significant association with pre-diabetes. The odds of pre-diabetes were 0.806 times lower among females compared to males. For each one-unit increase in age, the odds of pre-diabetes increased by 1.052, and for each one-unit increase in BMI, the odds increased by 1.029. In addition, the odds of pre-diabetes among participants with a family history of DM were 1.279 times the odds among participants with no family history of DM. The parameters of the multivariate analysis are given in

Table 5. Results of the multivariable logistic regression analysis for factors associated with prediabetes.

Variable (Reference)	β Coefficient	p-Value	Odds Ratio	95% CI for OR
				Lower Upper
Gender (Female vs. Male)	−0.215	0.039	0.806	0.657 0.990
Age (per year increase)	0.051	<0.001	1.052	1.035 1.069
BMI (per unit increase)	0.029	<0.001	1.029	1.013 1.045
Family history (Yes vs. No)	0.246	0.016	1.279	1.047 1.562

The model presents Adjusted Odds Ratios (aOR) with 95% CIs. The reference categories for analysis are: Female for Gender, and ‘No’ for Family history of diabetes. β, beta coefficient.

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4. Discussion

This study reveals a concerning 29.0% prevalence of prediabetes among Omani adults screened in Muscat Governorate, significantly higher than the 11.8% reported in Oman’s 2017 national survey and comparable to rates observed in neighboring Gulf countries (20–35%) [13,19]. The findings underscore a rapidly evolving metabolic health crisis in Oman, likely driven by urbanization and lifestyle changes accompanying economic development.

Our prediabetes prevalence exceeds rates reported in Mexico (14.6%), India (10.3%) and Nepal (5%) [8,10,12]. However, it aligns closely with Saudi Arabia’s estimates (20–39.8%), suggesting shared regional risk profiles [11,19].

The male predominance (43.3% vs. 35.4% female) contrasts with some Asian studies but matches Gulf patterns where male obesity rates approach 40% [9,20]. This may be due to higher visceral fat deposition in males [21].

Consistent with the global literature, we identified that each year increased prediabetes odds by 5.2% (p < 0.001), mirroring findings from Turkey’s aging population study [7]. BMI showed a dose–response relationship, with 2.9% increased odds per unit—which is particularly alarming given the 35.7% rate of obesity and 6.2% rate of severe obesity in our sample. Family history as a risk factor give 27.9% higher odds (p = 0.016), supporting genetic–epidemiologic patterns seen in Bahrain (20.1%) [22].

Notably, traditional risks like smoking (3.0%) and alcohol (0.6%) were less prevalent than in Western cohorts, but physical inactivity (70.8%) emerged as a critical modifiable factor even though it was not statistically significant, contrary to meta-analyses [23].

This may reflect measurement limitations (e.g., self-reported activity).

The concurrent high prevalence of hypertension (42.0%), hypercholesterolemia (48.8%) and CKD (51.8%) suggests a metabolic syndrome epidemic. This clustering reinforces ADA guidelines emphasizing integrated NCD screening [19]. Our findings strongly support Oman’s 2023 policy shift to screen from age 35 rather than 40 [14].

Regarding CKD and prediabetes, studies show that even mild deglycation can damage renal microvasculature [24]. Our findings show that 48.7% of CKD cases had reduced eGFR, mirroring U.S. data linking prediabetes to 40% higher CKD risk [25]. Hypercholesterolemia exacerbates insulin resistance, creating a vicious cycle [26].

While the cross-sectional design of our study does not allow us to test interventions, the high prevalence and clear risk profiles we identify define a substantial target population within Oman’s primary care system. This evidence underscores the critical and urgent need to implement and evaluate evidence-based prevention programs, such as intensive lifestyle modification, at a national scale.

This study benefits from several strengths, including a large and representative sample (n = 4862) derived from Oman’s national screening program, standardized data collection protocols across primary health centers ensuring diagnostic consistency, and a comprehensive assessment of cardiometabolic risk factors using objective laboratory measures. However, limitations must be acknowledged: the cross-sectional design precludes causal inferences; potential underrepresentation of working-age males may introduce selection bias; self-reported physical activity and substance use (alcohol: 0.6%, smoking: 3.0%) likely reflect cultural underreporting; and the urban focus on Muscat Governorate limits generalizability to rural populations, where healthcare access and lifestyle determinants may differ substantially.

Although BMI was a significant independent risk factor, our analysis was constrained by the lack of anthropometric measures of central obesity (e.g., waist circumference). Evidence suggests that waist circumference and ratios like waist-to-height may be superior to BMI for predicting insulin resistance and diabetes risk, as they better capture visceral adipose tissue. Consequently, our study may not fully characterize the adiposity-related risk profile, particularly regarding sex-specific patterns of fat distribution that could explain the observed male predominance in prediabetes.

5. Conclusions

This study highlights a critical public health juncture for Oman, with nearly one-third of screened adults having prediabetes—a preventable precursor to diabetes and its complications. By integrating evidence-based screening, lifestyle interventions, and policy reforms, Oman can curb this epidemic. Future research should explore cost-effective interventions tailored to GCC populations, such as mobile health reminders or community-based peer coaching.