Summary
Objectives:
This study aimed to identify the baseline demographic characteristics, clinical profile and cardiovascular risk factors of newly registered diabetic patients in Muscat Governorate, Oman, in 2022.
Methods:
This cross-sectional study included newly registered diabetic patients attending all primary care centres in Muscat Governorate in 2022. Data regarding sociodemographic characteristics and clinical and laboratory parameters were collected from the national diabetic registry and Al-Shifa health information system.
Results:
A total of 1,309 patients were included and their data analysed, of which 51% were male and the mean age was 53.89 ± 12.47 years. Approximately 50% of the participants were obese and physically inactive and 14.7% were smokers. Uncontrolled glycosylated haemoglobin levels were reported in 60.6%, 53.8% were hypertensive and 62.1% had dyslipidaemia. Chronic kidney disease, ischaemic heart disease and stroke constituted 5.7%, 6.3% and 2%, respectively. High albumin-to-creatinine ratio was found in 46.9% of the participants and 18.7% had proteinuria. Multivariate analysis showed that female and older patients were less likely to have poor glycaemic control. The odds of poor glycaemic control among males were 1.439 (95% confidence interval [CI]: 1.126–1.838) compared to females. Uncontrolled blood pressure and high low-density lipoprotein levels were significantly associated with poor glycaemic control (odds ratio [OR]: 1.421, 95% CI: 1.105–1.828 and OR: 1.540, 95% CI: 1.196–1.984, respectively).
Conclusion:
Cardiovascular risk factors were common in this study's sample, with a high prevalence of poor control and complications. This underscores the importance of diabetes screening programmes for high-risk populations and well-structured preventive programmes for newly registered diabetic patients. Prospective and interventional studies are highly recommended.
Keywords: Type 2 Diabetes Mellitus, Cardiovascular Risk Factors, Diabetes Complications, Oman
Advances in Knowledge
Cardiovascular risk factors were common in diabetic patients attending primary health centres in Oman in 2022.
Poor glycaemic control was highly prevalent.
Age, gender, blood pressure and low-density lipoprotein levels were highly associated with poor glycaemic control.
Nephropathy was the dominant complication, primarily characterised by a high albumin-to-creatinine ratio.
Application to Patient Care
The high prevalence of uncontrolled blood sugar levels necessitates aggressive intervention.
Targeting modifiable risk factors for diabetes is a necessity.
Screening and early interventions for diabetic complications such as nephropathy should be initiated promptly at the time of diagnosis.
1. Introduction
Type 2 diabetes mellitus (T2DM) is a chronic disease characterised by high levels of blood sugar which can lead to serious long-term complications. Both the number of cases and the prevalence of diabetes have been steadily increasing over the past few decades.1 According to recent data, approximately 422 million people worldwide are living with T2DM, most of whom live in low-and middle-income countries, with an estimated 1.5 million deaths directly attributable to diabetes each year.1 Being pre-diabetic, overweight, age ≥45 years, a family history of diabetes, physical inactivity, a previous history of gestational diabetes, non-alcohol fatty liver disease and ethnic background are risk factors for T2DM.2 In addition, modifiable risk factors include untreated high blood pressure (BP), smoking, alcohol consumption, stress, sleep deprivation, low high-density lipoprotein (HDL) and high triglyceride (TG) levels.3
Analysis of the baseline characteristics of patients newly diagnosed with T2DM is crucial to identify important risk factors associated with T2DM and to predict subsequent complications. For instance, a population-based cross-sectional study showed that the mean body mass index (BMI) of newly diagnosed T2DM patients was 32.28 kg/m2, 19.8% were smokers, mean systolic BP was 134 mmHg and mean diastolic BP was 81 mmHg. The mean total cholesterol was 5 mmol/L with a mean glycosylated haemoglobin (HbA1C) value of 7.94%.4 The same study found that male gender were independently associated with microvascular disease (odds ratio [OR] = 1.69, 95% confidence interval [CI]: 1.26–2.28).4 Another cross-sectional study of newly diagnosed T2DM patients in Ghana reported an increase in the prevalence of obesity among newly diagnosed diabetic patients, with female gender significantly associated with the increased risk of obesity.5 Furthermore, a recent prospective cohort study showed that diabetic patients with microvascular complications tended to be younger and smokers, and were more likely to have higher BMI, lower physical activity and lower dietary quality compared to patients with no microvascular complications.6
Oman is ranked eighth among the top 10 countries of the Middle East and North Africa region in terms of diabetes mellitus prevalence.7 In 2010, diabetes mellitus was the fourth leading cause of premature mortality in the country and the third leading cause of disability-adjusted life years lost.8 As per a national study of non-communicable diseases in Oman in 2017, the prevalence of T2DM was 17.5%, with an annual increase of more than 7,000 patients.9 The STEPS survey, a large community-based study in Oman, showed that the overall prevalence of diabetes among the population was 15.7% (95% CI: 14–17.5), whereas the prevalence of pre-diabetes was 11.8% (95% CI: 11.4–12.2).9
A previous study in Oman compared HbA1c levels between young and elderly diabetic patients and factors associated with poor glycaemic control.10 However, no study has specifically evaluated the characteristics and risk factor profiles of newly registered patients with T2DM in Oman. Such a study could help identify modifiable and non-modifiable risk factors for diabetes mellitus in the country, in turn focusing resources and efforts on T2DM and informing modifications to current diabetes screening tools and protocols. This study aimed to identify the baseline demographic characteristics and clinical profiles of newly registered Omani diabetic patients at the diabetes clinics of primary care centres in Muscat Governorate in 2022. It also aimed to establish associations between various risk factors present at the time of diagnosis and HbA1C levels.
2. Methods
This retrospective cross-sectional study involved all primary care health centres in Muscat Governorate. All Omani patients with T2DM who were newly registered in the diabetic national register of the specified health centres from January to December 2022 were included in the study. The diabetic national register is a hardcopy book in which all diabetic patients attending diabetic clinics are registered, with each patient assigned a serial number. Patients who were non-Omani, those diagnosed with other type of diabetes (i.e., type I diabetes, gestational diabetes) and patients with major missing data were excluded from the study.
The sample size was calculated using poor glycaemic control as the primary outcome, with an estimated prevalence of around 13% among newly diagnosed patients based on previous studies.9 A 2% margin of error and 95% confidence interval yielded a calculated sample size of 1,031.9 However, this study included all available patients in order to strengthen the study.
Data were collected using a well-designed data collection sheet. Collected demographic and clinical characteristics included age, gender, BMI, HbA1c, total cholesterol, low-density lipoprotein level (LDL), estimated glomerular filtration rate (mL/min/1.73 m2), albumin-to-creatinine ratio (ACR), smoking status and level of physical activity. Data were obtained from the diabetic register of each health centre, as well as the electronic health information system (Al-Shifa). Where needed, the data from the register was cross-checked with the health information system. This study relied on the results of laboratory tests conducted during the registration visit.
Levels of HbA1c were categorised as either controlled (≤7%) or uncontrolled (>7%). BMI was categorised as underweight (<18.5 kg/m2), normal (18.5–25 kg/m2), obese class I (25–30 kg/m2), obese class II (30–35 kg/m2) or obese class III (>40 kg/ m2). Total cholesterol was considered normal (<5.17 mmol/L), high (>6.21 mmol/L) or borderline (5.17–6.20 mmol/L). LDL level was considered either very high (>4.1 mmol/L) or optimal (<2.5 mmol/L). eGFR was classified into 5 stages: stage 1 (>90 mL/min/1.73 m2), stage 2 (60–89 mL/min/1.73 m2), stage 3 (30–59 mL/min/1.73 m2), stage 4 (15–29 mL/min/1.73 m2) or stage 5 (<15 mL/min/1.73 m2). ACR was considered abnormal at >3 mg/mmol. Information regarding physical activity and smoking status was obtained from the diabetic registry based on patients' self-reports.
Collected data were analysed using the Statistical Package for the Social Sciences (SPSS), Version 26.0 (IBM Corp., Armonk, New York, USA). Categorical variables were described as percentages and frequencies, while continuous variables were presented as means and standard deviations, medians and interquartile ranges and minimum and maximum values. Crude associations between independent and dependent variables were assessed using a Chi-squared test. Independent variables included gender, age, BMI, smoking status, physical activity, dyslipidaemia and cardiovascular diseases, all of which were categorised as categorical variables. The dependent variable was glycaemic control, with poor glycaemic control considered the outcome in comparison to good glycaemic control in the logistic regression analysis. Variables with a P value of <0.25 in the crude analysis were included in the multivariate analysis. A P value of <0.05 was considered statistically significant.
3. Results
A total of 1,309 newly registered diabetic patients were included in the study. Approximately half (51%) were male and the mean age was 53.89 ± 12.47 years (range: 21–100 years). The majority (87.9%) were 40 years or older. The mean BMI was 30.94 ± 6.60, with almost one-third (32.9%) of the sample being overweight and half (50.4%) obese. Smokers constituted 14.7% of the sample and 50.5% were physically inactive [Table 1].
Table 1.
Characteristics of newly registered diabetic patients (N = 1,309).
| Characteristic | n (%) |
|---|---|
| Gender (n = 1,309) | |
| Male | 668 (51) |
| Female | 641 (49) |
| Mean age ± SD, median (IQR), min–max | 53.89 ± 12.47, 53.00 (44.0–63.0), 21.0–100.0 |
| Age group in years (n = 1,309) | |
| <40 | 159 (12.1) |
| 40–60 | 716 (54.7) |
| ≥60 | 434 (33.2) |
| Mean BMI in kg/m2 ± SD, median (IQR), min–max | 30.94 ± 6.60, 30.02 (26.35–34.60), 13.85–68.73 |
| BMI categories in kg/m2 (n = 1,235) | |
| Underweight (BMI <18.5) | 11 (0.9) |
| Normal weight (BMI 18.5–25) | 196 (15.9) |
| Overweight (BMI 25–30) | 406 (32.9) |
| Class 1 obesity (BMI 30–35) | 342 (27.7) |
| Class 2 obesity (BMI 35–40) | 167 (13.5) |
| Class 3 obesity (BMI >40) | 113 (9.1) |
| Smoking status (n = 607) | |
| Smoker | 89 (14.7) |
| Non-smoker | 518 (85.3) |
| Physical activity (n = 489) | |
| Active | 242 (49.5) |
| Inactive | 247 (50.5) |
SD = standard deviation; IQR = interquartile range; BMI = body mass index.
In terms of comorbidity profile, 53.8% were hypertensive, among whom 24.5% had controlled BP. The majority of the study sample had dyslipidaemia (62.1%). Chronic kidney disease, ischemic heart disease and stroke were present in 5.7%, 6.3%, and 2.0% of the study sample, respectively. In addition, 8.4% had other cardiovascular diseases. Retinopathy and neuropathy were observed in 0.2% and 0.3% of patients. Furthermore, hypothyroidism was present in 5.9% of the sample.
Mild anaemia was found in 5.9% and 3.1% presented with moderate-severe anaemia. The mean HbA1c level was elevated (8.20 ± 2.32%) and over 60% had uncontrolled glycaemia. Stage 3, 4 and 5 kidney disease was present in 5.9%, 0.8% and 0.4% of the sample. Approximately 40% had borderline or high cholesterol levels and only 36.9% had optimal LDL readings. High HDL levels were observed in only 11.6% of patients, while 16.6% had high or very high TG levels; 46.9% presented with increased ACR and 18.7% with proteinuria [Table 2].
Table 2.
Biochemical profile of the study's sample.
| Characteristic | n (%) |
|---|---|
| Mean Hb in g/dL ± SD, median (IQR), min–max | 13.01 ± 1.63, 12.90 (11.90–14.10), 6.00–18.50 |
| Hb categories (n = 1,219) | |
| Normal (>11) | 1109 (91) |
| Mild anaemia (10–10.9) | 72 (5.9) |
| Moderate/severe anaemia (<10) | 38 (3.1) |
| Mean HbA1c in % ± SD, median (IQR), min–max | 8.20 ± 2.32, 7.50 (6.50–9.40), 4.3–19.2 |
| Glycaemic control (n = 1,284) | |
| Controlled (HbA1c ≤7) | 506 (39.4) |
| Uncontrolled (HbA1c >7) | 778 (60.6) |
| eGFR in mL/min/1.73 m2 (n = 1,270) | |
| Stage 1 | 760 (59.8) |
| Stage 2 | 420 (33.1) |
| Stage 3 | 75 (5.9) |
| Stage 4 | 10 (0.8) |
| Stage 5 | 5 (0.4) |
| Cholesterol in mmol/L (n = 1,254) | |
| Normal (<5.17) | 753 (60) |
| Borderline high (5.17–6.20) | 322 (25.7) |
| High (>6.21) | 179 (14.3) |
| LDL in mmol/L (n = 1,180) | |
| Optimal (<2.59) | 435 (36.9) |
| Near optimal (2.59–3.35) | 348 (29.5) |
| Borderline high (3.36–4.13) | 212 (18) |
| High (4.14–4.91) | 128 (10.8) |
| Very high (>4.91) | 57 (4.8) |
| HDL in mmol/L (n = 1,245) | |
| Low (<1.03) | 475 (38.2) |
| Normal (1.04–1.55) | 626 (50.3) |
| High (>1.55) | 144 (11.6) |
| TG in mmol/L (n = 1,260) | |
| Normal (<1.70) | 815 (64.7) |
| Borderline (1.70–2.25) | 235 (18.7) |
| High (2.26–5.64) | 193 (15.3) |
| Very high (>5.65) | 17 (1.3) |
| ACR in mg/mmol (n = 828) | |
| Normal (≤3) | 440 (53.1) |
| Increased (>3) | 388 (46.9) |
| ALT in u/dL (n = 1,219) | |
| Normal (<33) | 968 (79.4) |
| High (>34) | 251 (20.6) |
| ALP in u/dL (n = 1,137) | |
| Normal (<105) | 948 (83.4) |
| High (>106) | 189 (16.6) |
| Proteinuria (n = 954) | |
| Absent | 776 (81.3) |
| Present | 178 (18.7) |
Hb = haemoglobin; SD = standard deviation; IQR = interquartile range; HbA1c = glycated haemoglobin; eGFR = estimated glomerular filtration rate; LDL = low-density lipoprotein; HDL = high-density lipoprotein; TG = triglycerides; ACR = albumin-to-creatinine ratio; ALT = alanine transaminase; ALP = alkaline phosphatase.
Gender, age, BP control and LDL levels showed significant associations with glycaemic control. Female patients were less likely to have poor glycaemic control compared to males (OR = 0.703, 95% CI: 0.562–0.880; P = 0.002) and older patients were less likely to exhibit poor glycaemic control (P <0.001). In turn, both uncontrolled BP (OR = 1.388, 95% CI: 1.098–1.753; P = 0.006) and high LDL levels (OR = 1.636, 95% CI: 1.283–2.086; P <0.001) were significantly associated with poor glycaemic control [Table 3]. All 4 of these factors remained significant in the multivariate regression analysis. Female patients and those of older age were less likely to have poor glycaemic control. The odds of poor glycaemic control among male patients was 1.439 (95% CI: 1.126–1.838) compared to females. Uncontrolled BP and high LDL levels were significantly associated with poor glycaemic control (OR: 1.421, 95% CI: 1.105–1.828 and OR: 1.540, 95% CI: 1.196–1.984, respectively) [Table 4].
Table 3.
Crude association results between risk factors and glycaemic control.
| n (%) | |||
|---|---|---|---|
|
|
|||
| Factor | Controlled HbA1c | Uncontrolled HbA1c | P value* |
| Gender (n = 1,284) | |||
| Male | 232 (45.8) | 425 (54.6) | 0.002 |
| Female | 274 (54.2) | 353 (45.4) | |
| Age groups in years (n = 1,284) | |||
| <40 | 41 (8.1) | 116 (14.9) | <0.001 |
| 40–60 | 268 (53) | 436 (56) | |
| >60 | 197 (38.9) | 226 (29) | |
| BMI categories in kg/m2 (n = 1,214) | |||
| BMI <25 | 88 (18.4) | 118 (16) | 0.502 |
| BMI 25–30 | 152 (31.8) | 250 (34) | |
| BMI >30 (obese) | 238 (49.8) | 368 (50) | |
| Smoking status (n = 600) | |||
| Smoker | 31 (13.4) | 57 (15.4) | 0.495 |
| Non-smoker | 200 (86.6) | 312 (84.6) | |
| Physical activity (n = 483) | |||
| Active | 96 (47.3) | 143 (51.1) | 0.412 |
| Inactive | 107 (52.7) | 137 (48.9) | |
| BP control (n = 1,279) | |||
| Controlled | 199 (39.4) | 247 (31.9) | 0.006 |
| Uncontrolled | 306 (60.6) | 527 (68.1) | |
| LDL categories in mmol/L (n = 1,158) | |||
| <2.6 | 202 (43.8) | 225 (32.3) | <0.001 |
| ≥2.6 | 259 (56.2) | 472 (67.7) | |
| CVD (n = 1,284) | |||
| Present | 41 (8.1) | 66 (8.5) | 0.809 |
| Absent | 465 (91.9) | 712 (91.5) | |
| Dyslipidaemia (n = 1,284) | |||
| Present | 316 (62.5) | 482 (62) | 0.858 |
| Absent | 190 (37.5) | 296 (38) | |
HbA1c = glycated haemoglobin; BP = blood pressure; LDL = low-density lipoprotein; CVD = cardiovascular disease.
Using Chi-squared test.
Table 4.
Multivariate analysis results for different factors and glycaemic control (n = 100).
| Factor | OR (95% CI) | P value |
|---|---|---|
| Gender (reference group: female) | 0.004 | |
| Male | 1.439 (1.126–1.838) | |
| Age (reference group: >60 years) | 0.003 | |
| <40 | 2.134 (1.370–3.324) | |
| 40–60 | 1.244 (0.955–1.620) | |
| BP control (reference group: controlled) | 0.001 | |
| Uncontrolled | 1.421 (1.105–1.828) | |
| LDL (reference group: <2.6 mmol/L) | 0.002 | |
| ≥2.6 mmol/L | 1.540 (1.196–1.984) | |
OR = odds ratio; CI = confidence interval; BP = blood pressure; LDL = low-density lipoprotein.
4. Discussion
This is the first local analysis of newly registered patients with T2DM in primary health centres in Oman. Obesity, smoking, and physical inactivity were common among the study's sample. A high prevalence of poor glycaemic control, hypertension, dyslipidaemia and kidney complications was observed. Older age, male gender, uncontrolled BP and high LDL levels at the time of registration were all significantly associated with poor glycaemic control.
Obesity was highly prevalent, with nearly half of the sample study classified as obese. Similar findings have been reported in other studies.4,5 For instance, a multi-ethnic, population-based study from South London in 2013 reported mean BMI values of 32.79 in the white population, 32.39 in the black population and 29.59 in the South Asian population.4 Another study in Ghana found a significantly higher prevalence of obesity among female compared to male participants (85.7% versus 14.3%).5 Previous studies in Oman have also reported similar trends.9,10 While waist-to-hip ratio has been previously examined in Omani diabetic patients, showing a high prevalence, it was not included in the current study since this information is not routinely documented in the Al-Shifa health information system.9
The prevalence of smoking in the current sample was low (14.7%), consistent with findings from other local studies.9 The reason for this could be due to poor documentation of smoking status or under-reporting by patients. Similar findings were reported in a previous study conducted in Malaysia.11 However, other studies have shown a wide variation in the prevalence of smoking among diabetic patients, including a study conducted in Africa.12
A high percentage (60.6%) of the current population had uncontrolled HbA1c levels at the time of registration. This result was consistent with other local studies.9,10,13,14 Uncontrolled blood sugar levels were more prevalent among younger patients. Similar results were reported by Al-Lawati et al. who assessed differences in HbA1c level between young and elderly diabetic patients.10 Similar results were also seen in another study conducted in Philadelphia, USA.16 However, conflicting results were observed in studies conducted in Al-Buraimi Governorate and Iran, in which older patients were found to have higher HbA1c levels.14,15
With regards to gender, a strong association was found between gender and glycaemic control; female patients were less likely to exhibit poor glycaemic control compared to male patients. Other studies from Oman have shown similar results.9,14
There was also a high prevalence of hypertension (53.8%) among the studied population. In Uganda, the prevalence of hypertension was also high in newly diagnosed diabetics (61.9%), with few patients aware of their hypertensive status at the time of their diabetes diagnosis.17 Similar results were seen in the UK Prospective Diabetes Study.18 In Oman, similar results were seen when studying glycaemic control among T2DM patients attending primary care centres.13 However, a multi-ethnic cohort population-based study conducted in South London in 2013 showed conflicting results between black and Asian/other groups, with the former group having a higher prevalence of hypertension.4
Similar to the current study, previous studies conducted in Oman found that dyslipidaemia was highly prevalent among diabetic patients.9,10,13,14 The significance was mainly found with high TG level, high LDL level and high total cholesterol level. The current study showed that LDL levels had a significant association with poor glycaemic control (OR = 1.636, 95% CI: 1.283–2.086); P <0.001).
Renal complication was the most frequently reported complication in this study (5.7%) compared to other complications. Overall, 46.9% of the studied population had a high ACR and 18.7% presented with proteinuria. A previous study from Al-Buraimi Governorate showed that nephropathy was the second most common complication, manifesting as proteinuria in 8% of the study's sample.14 A systematic review and meta-analysis of 20 studies conducted in 13 countries found that the pooled prevalence of chronic kidney disease among T2DM patients was 27%; however, this prevalence differed between countries, with the highest reported in the USA and the lowest in the United Arab Emirates.19
The baseline characteristics of diabetic patients and their relationship to glycaemic control at the time of registration have not been previously studied in Oman, despite the high prevalence of these risk factors. As such, the current study presents valuable information which may inform future preventive efforts. However, a key limitation was missing data, which resulted in a decrease in the sample size from over 1,300 in the crude analysis to 100 in the logistic regression analysis. Furthermore, this study focused on a single governorate, limiting the generalisability of the findings to other regions. Inadequate documentation of smoking status and physical activity also presented a limitation, as these data are not mandatory in the Al-Shifa information system. This highlights the need for additional quality assurance efforts by the Ministry of Health to enhance the reliability of the local health information system. To address Oman's critical gaps in diabetes documentation and uncontrolled comorbidities, digitising health records with mandatory structured fields and artificial intelligence-driven analytics can enable real-time risk prediction, while national reimbursement policies for validated mHealth apps could enhance self-management and prevent complications.
5. Conclusion
Baseline risk factors, including obesity, smoking and physical inactivity, were common in this study's sample. Moreover, the prevalence of poor glycaemic control, as well as comorbidities such as hypertension, dyslipidaemia and kidney complications, was high. Factors such as age, gender, BP and LDL levels at the time of registration were significantly associated with poor glycaemic control. These findings highlight the need for screening programmes targeting high-risk individuals, as well as well-structured preventive programmes for patients newly diagnosed with diabetes. Prospective research and interventional studies in this area are highly recommended.
Authors' Contribution
Fathiya Al-Shariqi: Conceptualization, Methodology, Formal analysis, Writing - Review & editing. Shaima Al-Mazrooi: Methodology, Investigation, Writing - Original draft. Zaher Al-Kharusi: Investigation, Formal analysis, Writing - Original draft. Mohammed Al-Hinai: Data Curation, Investigation. Samira Al-Maimani: Investigation.
Acknowledgement
We gratefully acknowledge the MOICs across all Muscat Governorate health centres for facilitating data access. Our sincere thanks extend to the research team for their diligent data collection efforts and to the patients whose records made this study possible.
Ethics Statement
Ethical approval for this study was obtained from the Centre of Studies and Research of the Ministry of Health (Approval code #27543).
Conflict of Interest
The authors declare no conflicts of interest.
Funding
No funding was received for this study.
Data Availability
Data is available upon reasonable request from the corresponding author.
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Data Availability Statement
Data is available upon reasonable request from the corresponding author.