Demographic criteria of diabetes mellitus burden in the periphery of the Sultanate of Oman: Realizing where we stand is the first step in deciding where to go

Study design and setting

This investigation was conducted as a single-center, retrospective, cross-sectional analysis. The study was carried out at the Sur Specialized Diabetic Centre, which serves as the primary referral center for adult patients with DM in the South AlSharqiyah governorate, Sultanate of Oman. This region represents a significant peripheral area where detailed epidemiological data on diabetes have been limited.

Ethical considerations

The study protocol and design received full ethical approval from the Ministry of Health’s Research and Ethical Review and Approve Committee in Oman (MoH/CSR/21/25320). Patient confidentiality was rigorously maintained throughout the study. All data were collected anonymously from the existing health database system, and no patient identifiers were extracted or used in the analysis, thereby ensuring privacy and compliance with ethical guidelines for research involving patient data.

Study population and period

The study included all adult patients (aged 18 years and older) diagnosed with any DM who were registered and attended the Sur Specialized Diabetic Center for their care between January 1, 2019, and December 31, 2021. Patients with gestational diabetes were excluded. The center does not have a pediatric clinic; therefore, pediatric cases (under 18 years) were not included in this cohort, which primarily reflects the adult diabetic population.

Data collection

Comprehensive data for the included patients were retrospectively collected from the Al Shifaa 3+ electronic health record system, which is the standardized health information system used across Ministry of Health institutions in Oman. The data extracted included:

• Demographic information: Age, gender

• Diabetes-specific information: Type of DM (Type 1, Type 2, or unspecified), duration of diabetes (if available)

• Anthropometric measurements: Body mass index (BMI), categorized as underweight (<18.5 kg/m²), normal (18.5-24.9 kg/m²), overweight (25-29.9 kg/m²), and obese (≥30 kg/m²)

• Risk factors: Documented history of obesity, family history of diabetes, smoking status, and other relevant risk factors as recorded in the patient files

• Comorbidities: Presence of hypertension, dyslipidemia, and other chronic conditions

• Diabetic complications: Documented evidence of microvascular complications (retinopathy, nephropathy, and neuropathy) and macrovascular complications (ischemic heart disease [IHD], peripheral arterial disease, and history of foot ulcers or amputations)

• Glycemic control parameters: Initial glycated hemoglobin (HbA1c) level at the time of registration or first visit within the study period and the latest HbA1c level recorded during the follow-up period up to December 2021.

Study outcomes

The primary outcome was to describe the demographic and clinical characteristics of the diabetic patient population in the South AlSharqiyah governorate. This included distributions by age, gender, type of DM, and prevalence of key risk factors.

Secondary outcomes included (1) assessment of the degree of glycemic control at baseline and at the end of the study period, based on HbA1c levels, and (2) determination of the prevalence of various documented diabetic complications and common comorbidities within the study cohort.

Statistical analysis

All collected data were first entered into a Microsoft Excel spreadsheet for cleaning and organization. Subsequently, the data were imported into the Statistical Package for the Social Sciences version 26.0 (IBM Corp., Armonk, NY, USA) for statistical analysis. Descriptive statistics were used to summarize the data; categorical variables were presented as frequencies and percentages, while continuous variables were presented as means and standard deviations (or medians and interquartile ranges if not normally distributed, though the manuscript primarily uses percentages from categorical groupings). The analysis focused on generating frequencies and proportions to describe the study population’s characteristics, risk factors, complications, and glycemic control status, aligning with the descriptive nature of a cross-sectional study.

Patient demographics and diabetes type

A total of 3306 adult patients with DM were included in this study. The demographic analysis revealed a higher prevalence of females (1879 patients, 56.8%) compared to males (1427 patients, 43.2%). The age distribution of the patients showed that the largest group was those aged 40-59 years, accounting for 1572 patients (47.5%). Patients aged 60 years and above constituted 1267 individuals (38.3%), while those in the 20-39 years age bracket were 428 (12.9%). A small fraction of patients, 34 individuals (1.0%), were under 20 years of age, though our center primarily focuses on adult care. Five patient records had missing age data (0.2%). These demographic characteristics, specifically age and gender distributions, are summarized in Figure 1.

Close

Figure 1:

Distribution of study patients according to gender and age group.

Export to PPT

Regarding the type of diabetes, the vast majority of patients, 3130 individuals (94.7%), were diagnosed with type 2 DM. Type 1 DM was present in 119 patients (3.6%), and for 56 patients (1.7%), the type of diabetes was unspecified. Data on diabetes type were missing for one patient. This distribution is detailed in Figure 2a.

Close

Figure 2:

Distribution of study patients according to (a) type of diabetes, (b) body mass index, and (c) risk factors.

Export to PPT

Risk factors and anthropometric measures

Analysis of risk factors for DM highlighted a significant prevalence of overweight and obesity within the study population. Based on BMI, 1298 patients (53% of those with BMI data) were classified as obese (BMI ≥30 kg/m²), and 790 patients (32%) were overweight (BMI 25–29.9 kg/m²). Normal weight (BMI 18.5–24.9 kg/m²) was observed in 355 patients (14%), while 25 patients (1%) were underweight (BMI <18.5 kg/m²). BMI data were available for 2468 patients, with 838 records missing this information. The distribution according to BMI is presented in Figure 2b.

Among other documented risk factors (data available for 2103 patients, 1203 missing), obesity itself (as a distinct risk factor entry, potentially overlapping with BMI classification but reported separately in the source data) was noted in 794 patients (38%). A positive family history of diabetes was reported by 643 patients (31%). The combination of family history and obesity was present in 264 patients (13%). Multiple risk factors (excluding the aforementioned specific combinations) were identified in 232 patients (11%), and smoking was reported by 120 patients (6%). Other miscellaneous risk factors accounted for 50 patients (2%). These risk factor distributions are illustrated in Figure 2c.

Associated comorbidities and diabetic complications

Hypertension was the most frequently documented comorbidity among the diabetic patients in this cohort. Out of 3301 patients with available data, 1829 (55%) were hypertensive, while 1472 (45%) were normotensive. Data on hypertension status were missing for 5 patients. The prevalence of hypertension is shown in Figure 3a.

Close

Figure 3:

Prevalence of (a) hypertension and (b) diabetic complications among study patients.

Export to PPT

Regarding diabetic complications, data were available for all 3306 patients. Diabetic retinopathy was the most common complication, affecting 725 patients (22%). This was followed by kidney disease (diabetic nephropathy), which was present in 423 patients (13%). IHD was documented in 312 patients (9%), and peripheral arterial disease in 61 patients (2%). Foot ulcers were recorded in 30 patients (1%), and foot amputations in 41 patients (1%). The spectrum of diabetic complications is detailed in Figure 3b.

Glycemic control

Assessment of glycemic control was based on glycated hemoglobin (HbA1c) levels. The initial HbA1c levels, typically recorded at the time of registration or first visit to the center within the study period, were available for 2990 patients. These initial levels indicated generally poor glycemic control: 995 patients (33%) had an HbA1c ≥11%, 876 patients (29%) had HbA1c levels between 9% and 11%, and 1119 patients (38%) had HbA1c levels between 7% and 9%. This distribution of initial HbA1c levels is presented in Figure 4.

Close

Figure 4:

Distribution of study patients according to initial glycated hemoglobin levels.

Export to PPT

Significant improvement in glycemic control was observed when examining the latest HbA1c levels recorded during the study period (data available for 3191 patients). At this later time point, 1305 patients (41%) achieved an HbA1c level <7%, which is a common target for good glycemic control. HbA1c levels between 7% and 8% were observed in 1060 patients (33%), while 826 patients (26%) still had HbA1c levels >9%. Data on the last HbA1c were missing for 115 patients. The distribution of the latest HbA1c levels is illustrated in Figure 5.

Close

Figure 5:

Distribution of study patients according to last glycated hemoglobin (HbA1c) levels.

Export to PPT

Demographic profile and diabetes type

Our cohort demonstrated a higher prevalence of DM in females (56.8%) compared to males (43.2%), and the largest proportion of patients (47.5%) fell within the 40–59 age group. This contrasts with some regional data, such as from Kuwait, where DM prevalence was higher in males^[7,8] but aligns with general observations in Oman, where national data previously indicated a higher incidence rate in females.^[9] The predominance of patients in middle age (40–59 years) underscores the significant impact of DM on the economically active population. The overwhelming prevalence of type 2 DM (94.7%) in our study is consistent with global trends and expectations in adult diabetic populations, particularly where pediatric cases are excluded.^[1,6]

Risk factors and high prevalence of obesity

A striking finding of this study is the exceptionally high prevalence of overweight and obesity, with 32% of patients being overweight and a further 53% classified as obese. Collectively, 85% of the diabetic patients in our cohort had a BMI indicative of being overweight or obese. This alarming figure is comparable to findings in other Gulf countries, such as Saudi Arabia, where a combined prevalence of 85.6% for overweight and obesity was reported among type 2 diabetic patients.^[10] Obesity is a well-established and critical modifiable risk factor for type 2 DM,^[11] and its high prevalence in our Omani cohort points to an urgent need for aggressive public health campaigns focusing on lifestyle modification, including dietary changes and increased physical activity, to curb this epidemic. The second most common risk factor identified was a positive family history of diabetes (31%), which is a recognized non-modifiable risk factor. This is lower than a previous Omani study by Al Sinani et al. which reported a 95% positive family history in type 2 DM patients;^[12] this discrepancy might be due to differences in data collection methods or patient populations. The relatively low reported rate of smoking (6%) in our study compared to some Western populations^[13] might be influenced by underreporting due to social desirability bias, especially among females, as acknowledged in the original manuscript. This suggests that more objective measures or culturally sensitive questioning techniques might be needed to ascertain the true prevalence of smoking.

Comorbidities and diabetic complications

Hypertension was a highly prevalent comorbidity, affecting 55% of our patients. This co-occurrence of diabetes and hypertension significantly amplifies cardiovascular risk and underscores the importance of integrated management strategies for both conditions. The rate of diabetic complications observed in our study is a cause for concern. Diabetic retinopathy was the most common complication (22%), a figure similar to studies from India (24.5%) and Australia (26.8%).^[14,15] Diabetic nephropathy affected 13% of our patients, comparable to findings in China (10.7%).^[16] The prevalence of IHD at 9% in our cohort is notably higher than the 3.54% reported among diabetic patients in Thailand,^[17] suggesting a potentially higher cardiovascular burden in our Omani population, although differences in diagnostic criteria and study populations could contribute. The rates of foot ulcers (1%) and amputations (1%) also highlight the burden of diabetic foot disease. These complication rates, particularly retinopathy and nephropathy, emphasize the critical need for regular screening programs, early detection, and proactive management to prevent or delay the progression of these debilitating and costly complications.

Glycemic control – Initial challenges and subsequent improvements

A significant proportion of patients (over 60%) presented with very poor glycemic control at their initial registration (HbA1c >9%). This indicates either a late diagnosis of diabetes, delayed referral to specialized care, or initial challenges in achieving glycemic targets. However, the study also demonstrated a commendable improvement in glycemic control over the follow-up period, with 41% of patients achieving the target HbA1c of <7% by their latest measurement. This level of control is comparable to achievements in the UAE, where 40% of patients reached similar targets.^[18] This improvement likely reflects the impact of specialized diabetic care, including patient education, structured management plans, and access to a wider range of therapeutic options. Despite this progress, a substantial proportion of patients (59%) still had HbA1c levels above the target, with 26% remaining in very poor control (HbA1c >9%). This highlights an ongoing need to intensify efforts to optimize glycemic management, potentially through personalized approaches, addressing barriers to adherence, and utilizing newer therapeutic agents where appropriate.

Study limitations

While this study provides valuable baseline data for the South AlSharqiyah governorate, several limitations must be acknowledged, as also noted by the reviewers. First, being a retrospective, cross-sectional study based on existing health records, it is susceptible to information bias, missing data (as seen with BMI and some risk factors), and potential inaccuracies in documentation. The reliance on recorded diagnoses for complications might underestimate the true prevalence if screening was not universally applied or if early-stage complications were not documented. Second, the study was single center, which, while providing specialized care data, may not fully represent all diabetic patients in the governorate, particularly those managed solely in primary care or those undiagnosed. Third, the cross-sectional design limits the ability to infer causality or track the progression of complications over time definitively for individuals. The assessment of glycemic control improvement is based on initial and latest HbA1c values within the study window, not a formal longitudinal follow-up of a fixed cohort with predefined intervention protocols. Furthermore, the study did not delve into socioeconomic factors, educational levels, or specific treatment regimens and their impact on the observed outcomes, which are important areas for future research. The lack of detailed data on the duration of diabetes for many patients also makes it challenging to correlate complication rates with disease longevity accurately.

Future directions

Building upon these findings, future research should focus on prospective longitudinal studies to better understand the incidence and progression of diabetic complications and the long-term effectiveness of management strategies in this Omani population. Investigating the specific barriers to achieving optimal glycemic control, including patient-related factors (e.g., health literacy, socioeconomic status, adherence) and system-related factors (e.g., access to care, availability of resources), is crucial. Studies evaluating the impact of specific interventions, such as structured diabetes education programs, lifestyle modification initiatives tailored to the local culture, and the introduction of newer antidiabetic medications, would be highly beneficial. Furthermore, exploring the psychosocial aspects of living with diabetes in this community and the impact of DM on quality of life are important avenues for future investigation. Expanding data collection to include primary healthcare centers would provide a more comprehensive epidemiological picture of DM across the entire governorate.