Real-World Evaluation of Demographics, Treatment Pattern, and Economic Burden of Heart Failure and Kidney Disease in Type 2 Diabetes Mellitus Patient Population in Dubai, United Arab Emirates Open Access

Diabetes mellitus is a major public health issue, imposing a substantial disease and economic burden on the healthcare system and affected individuals [1]. Type 2 diabetes mellitus (T2DM) is the most common type of diabetes, accounting for ∼90% of all cases of diabetes [2, 3]. Globally, an estimated 537 million individuals are affected by T2DM, corresponding to 10.5% of the world’s population [3]. As per estimates, in the Middle East and North Africa (MENA) region, approximately 73 million people (23–79 years) had diabetes in 2019, which is projected to rise to 136 million in 2045 [3]. A serious concern is the high proportion of undiagnosed cases in many regions, including the MENA region, which has ∼27 million (38%) people with undiagnosed diabetes [3]. The United Arab Emirates (UAE) is one of the countries with a relatively high prevalence of diabetes (12.3% for adults aged 20–79 years), as reported by International Diabetes Federation statistics in 2021 [2]. Another Northern Emirates study (2018) found the prevalence to be 25.1% among UAE citizens [4]. A study of migrants in the UAE also showed a crude prevalence of 15.5%, with 64.2% of them previously undiagnosed [4].

T2DM is characterized by the co-occurrence of conditions including cardiovascular disease (CVD), renal insufficiency, hypertension, and dyslipidemia [5]. A high prevalence of heart failure (HF) (6–27%) and chronic kidney disease (CKD) (4–20%) in patients with T2DM has been reported in clinical studies [6‒8]. A wealth of evidence suggests that concomitant HF and T2DM are associated with an approximately 2-fold increased risk of cardiovascular (CV) or all-cause mortality [9‒11]. Clinical trials report that there is a 2.2–4.3-fold incremental risk of hospitalization in T2DM patients with HF [12]. On a similar note, patients with T2DM with comorbid CKD are also at an increased risk of mortality due to CV and renal outcomes, often progressing to end-stage kidney disease (ESRD) [13].

Furthermore, the presence of concomitant comorbid conditions, such as HF and CKD, in patients with T2DM mandates a specific patient-centric approach in relation to glucose-lowering therapies. The American Diabetes Association (ADA) 2020 guidelines recommend a sodium-glucose cotransporter-2 inhibitor (SGLT-2i) or glucagon-like peptide-1 receptor agonist (GLP-1 RA) with demonstrated CV benefit for the treatment of patients with T2DM with established atherosclerotic CVD or indicators of high risk, established kidney disease, or HF, independent of glycemic control [14]. However, specialists may prefer different prescription patterns for diabetic medications while treating patients with T2DM with HF or CKD [15].

In addition to the clinical burden, HF and CKD impose a significant economic burden on patients with T2DM. Evidence from a retrospective study with data linked to electronic medical records and claims databases suggested that costs and resource utilization were significantly higher in patients with T2DM with a history of CVD compared with patients without a history of CVD [16]. A systematic review in 13 different countries reported median annual costs per patient were 112%, 107%, 59%, and 322% higher for CVD, coronary artery disease, HF, and stroke, respectively, in T2DM patients with CVD compared to patients with T2DM without CVD [17]. Real-world studies have suggested that patients with CKD experience a significant and incremental increase in their economic burden [18‒20]. The progression of CKD in T2DM also drives medical care costs substantially [21, 22].

There are currently few real-world studies on treatment patterns and the economic burden in patients with T2DM with comorbidities such as HF and CKD. A better understanding of patient characteristics with a tailored therapeutic approach is paramount when treating patients with T2DM with comorbidities, to reduce associated healthcare costs. The objective of the current study is to describe demographic characteristics and evaluate treatment patterns, clinical outcomes, and the economic burden of HF and CKD in patients with T2DM in Dubai, UAE.

This was a retrospective cohort analysis of patients with T2DM and comorbidities from the Dubai Real-World Claims Database (DRWD), an e-claims database. The DRWD e-claims database is the largest claims database of private insurers in the Emirates of Dubai, and contains information pertaining to patients’ demographics, diagnoses, procedures (medical, surgical, and diagnostic), prescriptions, and other related services. This database captures approximately 100% of the population covered by Dubai’s private health insurance. Approximately, 80% of the population in Dubai is covered by private insurance (predominantly comprising the expatriate community), while the remaining 20% is covered by public funding (comprising the local Emirati population) [23]; Dubai e-claims represent the multiethnic expat population of Dubai.

The data analysis covered the period between January 2014 and December 2019. The index date for each patient was defined as the date on which the first diagnosis for T2DM was made (during the study identification period between January 2014 and December 2019) within the database. This does not necessarily mean that the study represents the data of people with a T2DM diagnosis duration of under 5 years. Since DRWD majorly covers the expatriate population, many patients would have been diagnosed with T2DM before the beginning of the study period (January 2014). But based on the data available in the DRWD claims database, the index date is captured. The pre-index period was defined as 12 months prior to the index date and the post-index period as 12 months following the index date.

All local, legal, and regulatory guidelines were followed, and ethical approval was not sought from any independent review board. The signing of an informed consent was not required, as it was anonymized patient-level data.

Patients ≥18 years of age, who had at least one medical claim with a T2DM diagnosis between January 2014 and December 2019 were eligible for this study. International Classification of Diseases, tenth revision, clinical modification (ICD-10-CM) codes (E 11) were used to identify patients with a diagnosis of T2DM during the study period. Other inclusion criteria included patients >18 years of age and without any claims for pediatric visits with at least one activity claim for medication, procedures (diagnostic procedures and medical/surgical procedures), consumables (medical consumables that comprise heterogeneous objects and materials such as needles, syringes, sutures, staples, surgical gloves, masks, adhesives, sealants of wound dressings, and other devices used in the hospital or surgical environment used for patient care), and services (hospital and administrative services), anytime during the two 6-month periods of the study as a proxy for continuous enrollment (continuous enrollment refers to a patient with at least one claim for any service during the two 6-month periods of the study). Patients with HF, CKD, or CVD without HF diagnosis occurring before T2DM diagnosis and patients with fewer than 2 claims, not necessarily specific to T2DM, anytime during the pre-index or post-index period were excluded from the study (shown in Fig. 1).

Patients were stratified into 5 cohorts based on claims for the diagnosis of T2DM and associated comorbidities: patients with ≥1 claim related to diagnosis of T2DM only (cohort 1); patients with ≥1 claim related to diagnosis of T2DM and ≥1 claim related to diagnosis of CKD (cohort 2); patients with ≥1 claim related to diagnosis of T2DM and ≥1 claim related to diagnosis of CVD but without claims related to HF and CKD (cohort 3); patients with at ≥1 claim related to diagnosis of T2DM and ≥1 claim related to diagnosis of HF (cohort 4); and patients with at least 1 claim related to diagnosis of T2DM, HF, and CKD (cohort 5). The ICD-10 codes used for the identification of patients with diagnoses of HF, CKD, and CVD are provided in online supplementary Table S1 (for all online suppl. material, see https://doi.org/10.1159/000530467). The distribution of patients across cohorts was done based on claims in a specific calendar year and was not mutually exclusive (the type of cohort could change for each patient in the following years based on type of claims, i.e., T2DM, CKD, HF, CVD).

Baseline variables, including demographics and clinical characteristics, were defined for each cohort based on information available during the 12-month pre-index period from the database. Age, gender, the Deyo-Charlson Comorbidity Index (DCCI) score [24], and baseline glycated hemoglobin (HbA1c) levels were among the baseline demographic and clinical characteristics evaluated. The latest available age in the database was considered the age at the index date.

DCCI scores were used to quantify the comorbidities identified in any secondary diagnosis coding fields. For each patient, data were retrieved 1 year prior to the index date to identify comorbidities. Data for baseline or pre-index HbA1c levels were extracted from the HbA1c test results available prior to the patient’s index date.

Study outcomes, including treatment patterns and healthcare resource utilization (HCRU), were reported for each cohort for the 12-month post-index period. Clinical outcomes in terms of glycemic control (assessment of variation in glycemic control trend in patients with T2DM across cohorts) and CKD disease progression (progression of kidney disease in patients with T2DM with CKD) were assessed during the study period.

The progression of CKD from stage 1 to stage 5 and ESRD (patients with CKD requiring chronic dialysis) was assessed in a subgroup of patients with T2DM and CKD. The CKD stage in each patient was determined based on the latest CKD stage available in the database during the 1-year period from the first diagnosis (0–365 days). Similarly, the latest CKD stage was retrieved based on the diagnosis during the second-year period (366–730 days). A similar methodology was used to identify the CKD stages of the patient during the third, fourth, and fifth years of the study period, and the progression of the CKD from stage 1 to ESRD was evaluated for the 5-year study period.

Data on treatment patterns were stratified by specialty (number and percentage of patients by specialty per year for each cohort, as well as prescription pattern for each specialty). Details of prescription patterns for all the specialties are provided in the online supplementary material. However, prescription patterns of antihyperglycemic drugs for specialties, including general practice and endocrinology, across various cohorts during the study period are described in this article (Note: They do not represent treatment guidelines or indications for the various drugs prescribed). Furthermore, the first claim visit by specialty was evaluated. To determine the first claim visit by specialty, primary disease-specific data (T2DM, HF, CVD, and CKD) were selected, and the first claim by specialty was chosen (e.g., if data were available for a patient with only T2DM, the first claim was considered as a T2DM claim; if data pertained to a patient with T2DM and HF, the first claim of HF was considered). Treatment patterns were also assessed based on HbA1c levels, categorized as follows: (1) <7%, (2) ≥7% and <8%, (3) ≥8% and <9%, and (4) 9% and above [24]. Patients with at least 1 HbA1c test every year for 5 continuous years were considered for assessment, and the latest HbA1c test value (the most recent HbA1c test report available in the database after selection of all the claims after the index date) was selected for analysis. HCRU claims and costs were assessed for inpatient, outpatient, and emergency visits and for medications, procedures, consumables, and services.

Of note, HCRU claims and costs were stratified by comorbidities (assessment of HCRU claims and cost for each cohort) and glycemic levels (assessment of HCRU claims and cost based on glycemic levels in each cohort). Data pertaining to HCRU claims and associated gross cost (gross cost is defined as the sum of the amount paid by insurance and the amount paid out of pocket by the patient) by the type of visit were available for 71,357 patients with 1-year continuous enrollment; 16,713 patients with 2-year continuous enrollment; 6,308 patients with 3-year continuous enrollment; 2,851 patients with 4-year continuous enrollment; and 1,356 patients with 5-year continuous enrollment. Data pertaining to HCRU claims and associated net cost (net cost refers to the amount paid by insurance) for medications, consumables, procedures, and services were available for 71,498 patients with at least 1 year of follow-up; 16,753 patients with at least 2 years of follow-up; 6,330 patients with at least 3 years of follow-up; 2,867 patients with at least 4 years of follow-up; and 1,365 patients with at least 5 years of follow-up.

Descriptive statistics were used to analyze the study variables assessed during the pre-index and post-index periods: demographic characteristics and clinical characteristics, HbA1c patterns, treatment patterns, healthcare utilization, and cost. The following were used to calculate continuous variables: mean, median, standard deviation, minimum, and maximum. Categorical variables were calculated by frequency and percentage.

Out of 1,162,393 potentially eligible patients (with at least 1 medical claim for T2DM disease diagnosis anytime during the index period), 374,271 patients with T2DM met all the inclusion criteria and none of the exclusion criteria and were considered for the study. The number of patients across study cohorts was as follows: 116,262 (cohort 1), 2,468 (cohort 2), 14,267 (cohort 3), 1,263 (cohort 4), and 178 patients (cohort 5) (Table 1).

Across all cohorts, the majority of patients were male (72–84%). In cohort 1, ∼90% of patients belonged to the age group of 30–60 years; however, in other cohorts, most patients were above 40 years of age (Table 1).

Patients in cohorts 4 and 5 had a higher risk of mortality, with high DCCI scores of 4.4 and 5.8, respectively. The majority of patients with comorbidities (HF or CKD, CVD, i.e., cohorts 2–5) had higher scores compared to those with T2DM only (cohort 1). Furthermore, patients with T2DM and HF + CKD (cohort 5) were at the highest risk, with 70% of patients having scores above 5. In patients with T2DM, mild liver disease (20–40%), pulmonary disease (12–34%), and myocardial infarction (12–20%) were the most prevalent comorbidities (Table 1). More patients in cohort 4 (30.6%) and cohort 5 (43.9%) had poor glycemic control at baseline compared to other cohorts.

Frequently prescribed antihyperglycemic drugs as monotherapy for the treatment of patients with T2DM across all cohorts were biguanides and sulfonylureas. Other antihyperglycemic drugs prescribed included GLP-1 RA, SGLT-2i, dipeptidyl peptidase-4 inhibitor (DPP-4i), thiazolidinediones, and alpha-glucosidase inhibitors (AGIs). The study noted a higher number of prescriptions for monotherapy with biguanides across cohorts 1–4 (cohort 1: 80.8%; cohort 2: 51%; cohort 3: 69%; cohort 4: 65.3%), whereas the number of prescriptions for monotherapy with DPP-4i was high in cohort 5 (43.2%). The prescribing rates of GLP-1 RA were very low (∼2–8%) even in cohorts with cardiovascular and renal comorbidities (cohort 2: 3.1%; cohort 3: 2.8%; cohort 4: 3.4%; cohort 5: 7.4%). A similar observation was noted with prescribing rates of SGLT-2i (cohort 2: 3.8%; cohort 3: 4.3%; cohort 4:4.0%; cohort 5: 0.6%) (online suppl. Table S2).

Dual antihyperglycemic drug therapy was largely more prescribed (cohort 1:76.5%, cohort 2: 69.7%, cohort 3: 65.8%, cohort 4: 68.5%, cohort 5: 63.3%) compared to triple (cohort 1: 20.1%, cohort 2: 24.2%, cohort 3: 27.4%, cohort 4: 26.6%, cohort 5: 31.6%) and triple+ (cohort 1: 3.2%, cohort 2: 6.1%, cohort 3: 6.8%, cohort 4: 4.9%, cohort 5: 5.1%) antihyperglycemic drug therapy (online suppl. Table S3). A combination of biguanides and DPP-4i was the most prescribed dual combination therapy of antihyperglycemic drugs across all cohorts (cohort 1: 37.5%, cohort 2: 36.1%, cohort 3: 39.2%, cohort 4: 41.6%, cohort 5: 33.0%).

A few patients required insulin in combination with antihyperglycemic drugs across all cohorts. Insulin was commonly prescribed with biguanides in cohort 1 (25.5%); biguanides and DPP-4i in cohort 3 (23.4%) and cohort 4 (26.2%); and with DPP-4i in cohort 2 (29.6%) and cohort 5 (41.8%) (online suppl. Table S4).

Most patients with T2DM (∼50%) visited GPs for consultation, whereas those with T2DM and HF, CVD, or CKD, required cardiac consultation. First claim visits showed a higher number of cardiology visits (∼30%) among CVD, HF, and HF + CKD patients. Nearly 10–11% of patients in cohorts 2 and 5 consulted a nephrologist during the study period (online suppl. Table S5).

The GPs routinely prescribed biguanides for patients in cohorts 1–4 (24–38%), whereas insulin was prescribed more commonly for patients in cohort 5 (27.7%). Endocrinologists frequently prescribed biguanides for patients in cohort 1 (31.9%), whereas insulin (12.9–36.9%) followed by sulfonylureas (9.9–18.4%) and biguanides (13.5–19.7%) was commonly prescribed for patients in cohorts 2–5. Also, endocrinologists preferred prescribing novel antihyperglycemic drugs, including GLP-1 RA (cohort 2: Endo 5.1% vs. GP 1.3%; cohort 3: Endo 7.3% vs. GP 1.3%; cohort 4: Endo 5.5% vs. GP 1.9%; cohort 5: Endo 7.8% vs. GP 2.7%) and SGLT-2i (cohort 2: Endo 8.4% vs. 3.0%; cohort 3: Endo 11.3% vs. 4.9%; cohort 4: Endo 7.7% vs. GP 4.5%; cohort 5: Endo 4.3% vs. GP 2.3%), in patients with T2DM with comorbidities (Table 2).

The number of patients with T2DM and CKD who progressed to ESRD during the study period was 246 (4%) out of 6,623 patients during the first year (0–365 days), 58 (11%) out of 539 patients during the second year (366–730 days), 30 (15%) out of 194 patients during the third year (730–1,095 days), 15 (15%) out of 99 patients during the fourth year (1,096–1,460 days), and 9 (25%) out of 36 patients during the fifth year (1,461–1,825 days) (Fig. 2; online suppl. Table S6).

Nearly 80–90% of patients in cohort 1–4 and nearly 75% of patients in cohort 5 had HbA1c levels less than 8%. According to the latest HbA1c levels, ∼20–25% of patients with HbA1c levels ≥8% belonged to cohorts 4 and 5 alone. A few patients showed HbA1c values of 9% and above (cohort 1: 10.4%; cohort 2: 9.6%; cohort 3: 9.5%; cohort 4: 12.4%; cohort 5: 14.4%) (online suppl. Table S7). A slight variation was noted between HbA1c levels measured during the pre-index and post-index periods in all cohorts (online suppl. Table S8).

Outpatient claims were higher compared to inpatient and emergency claims across all cohorts, and an increasing trend was observed over the years (Table 3). During the 5-year study period, median outpatient claims were highest in patients in cohort 5 (8.0 [range, 1.0–168.0]), followed by patients in cohort 2 (5.5 [range, 1.0–52.0]) as compared to other cohorts (cohort 1: 5.0 [range, 1.0–26.0]; cohort 3: 6.0 [range, 1.0–37.0]; cohort 4: 5.0 [range, 1.0–25.0]) (Table 3). Most claims pertained to drugs and services across all cohorts. Claims for consumables and procedures were highest in patient cohort 5 (online suppl. Table S9). The highest cost was incurred due to inpatient claims (Table 4) and was attributed to consumables and procedures across cohorts during the study period (online suppl. Table S10). The cost of inpatient claims was higher in patients with T2DM with comorbidities (cohorts 2–4) compared to patients with T2DM without any comorbidities (cohort 1) (Table 4). The median cost incurred for drugs and procedures was much higher in patients in cohort 5 compared to patients in other cohorts during the 5-year study period (online suppl. Table S11).

Outpatient claims were the most common type of claim across all cohorts, irrespective of glycemic control. Emergency claims were more commonly noted in patients in cohort 1 (∼2–7 times a year on average) and patients in cohort 3 with HbA1c levels above 9% (online suppl. Table S12). High costs were incurred due to outpatient and inpatient claims for patients in cohort 2 with poor glycemic control (online suppl. Table S10). Costs incurred due to procedures, consumables, drugs, and services in patients with poor glycemic control in T2DM comorbidity cohorts were higher compared to patients in the T2DM cohort without any comorbidities (online suppl. Table S13).

To the best of our knowledge, this is one of the first studies to describe demographic and clinical characteristics, including treatment patterns and the economic burden, in patients with T2DM with HF, CVD, and CKD in Dubai, UAE. In the study, the majority of patients (72–84%) across cohorts were male. Nearly 90% of T2DM patients without any comorbidities belonged to the age-group of 30–60 years, while most (>80%) of T2DM patients with comorbidities were aged above 40 years, which is consistent with previous literature [25]. Patients with T2DM with HF and CKD had high DCCI scores, indicative of an increased mortality risk. Evidence from a large multinational cohort study of patients with T2DM suggested that patients with either HF or CKD were associated with markedly increased mortality risks, and patients with both HF and CKD were associated with the highest CV and all-cause mortality risks (hazard ratio: 3.91 [95% confidence interval: 3.02–5.07]; and 3.14 [95% CI: 2.90–3.40], respectively) [26].

The current analyses observed that nearly 80–90% of patients in cohorts 1–4 and nearly 75% of patients in cohort 5 had HbA1c levels below 8%. This could be attributed to the reason that the study sample had patients who were on long-term control or well-treated patients. Most patients with T2DM consulted GPs or family medicine practitioners for diabetes-related concerns and advice, whereas patients with T2DM with comorbidities such as HF and CKD predominantly consulted cardiologists.

In our study, general medicine/family medicine practitioners mostly prescribed biguanides and sulfonylureas to patients with T2DM, irrespective of comorbidities, whereas endocrinologists frequently prescribed insulin, DPP-4i, SGLT-2i inhibitors, and other second-line therapies to patients with T2DM with CVD/HF/CKD. Similar to published literature, variations in T2DM prescription patterns were observed across specialties. In a retrospective study that evaluated prescription patterns of diabetes medications in patients with T2DM with HF, cardiologists preferred prescribing SGLT-2i and biguanides, whereas endocrinologists commonly prescribed DPP-4i, biguanides, and sulfonylureas [15]. In a retrospective study from a hospital-based database and electronic medical records conducted in Japan, biguanides, DPP-4i, and AGIs were most frequently prescribed as first-choice oral antihyperglycemic agents in patients with T2DM with and without cardiovascular comorbidities [27].

To improve the standard of care for people with diabetes in the region, in the year 2020, the Emirates Diabetes Society updated the existing guidelines on diabetes care with international management recommendations. The updated guideline recommends risk-based pharmacotherapy (based on cardiovascular, renal, weight, and hypoglycemia risks) for patients with T2DM. In T2DM patients with very high risk (≥2 cardiovascular risk factors [hypertension, dyslipidemia, smoking, obesity, CVD]), guidelines recommend drugs with cardiovascular and/or renal benefits (GLP-1 RA or SGLT-2i) as the preferred second choice of treatment after metformin, irrespective of the level of glycemic control [28].

In the current study, an incremental rise in healthcare resource claims and cost was observed in patients with T2DM with comorbidities. These findings are in line with those reported in earlier retrospective studies [29‒31].

In a retrospective study, annual costs and disease-specific costs increased in patients with T2DM with CKD, with worsening kidney function and suboptimal glycemic control (HbA1c >7.9%). Costs increased fivefold for people with an estimated glomerular filtration rate < 15 mL/min/m² compared to those with an estimated glomerular filtration rate >90 mL/min/m² ($115,348 vs. $25,316); patients with poor glycemic control ($32,629 for HbA1c >9%) had 20% higher costs than those with good control ($27,064 for HbA1c <7%) [29]. In yet another study that used administrative claims data, the mean healthcare cost per patient per month was higher in the CV, HF, and renal cohorts of patients with T2DM than in patients with the no-CV, no-HF, and no-renal cohorts ($936.70, $2,046.30, and $1,582.10 vs. $548.90, $948.70, and $809.70, respectively) [30]. In a study from a claims database, patients with comorbid diabetes and HF had higher annual average total costs ($32,676) compared with patients with diabetes and without HF ($10,566) [31].

To our knowledge, this is the first study to look at the outcomes of HF, CKD, and CVD in patients with T2DM using the DRWD database and can provide useful insights into treatment patterns and healthcare utilization in patients with T2DM in the UAE (Dubai). The DRWD data are representative of the private sector in Dubai, and the current study sample primarily covered only the private insured expatriates’ population (representative of the population who were from a different country of origin but settled in Dubai either temporarily or permanently for an extended period). As a result, this database excludes the local population covered by public funding who were not covered by Dubai’s Private Insurance, and thus data may not be generalizable to patients treated in government hospitals.

There are a few limitations associated with claims data use. First, the presence of a claim for a filled prescription does not indicate that the medication was consumed or that it was taken as prescribed. Second, medications filled over the counter or provided as samples by the physician, or medications obtained directly from the patient out of pocket, are not accounted for in claims data. Third, the presence of a diagnosis code on a medical claim is not indicative of the presence of disease, as the diagnosis code may be incorrectly coded rather than the actual disease. Another limitation is the insurance plan coverage of healthcare expenses. Based on the insurance plan coverage, healthcare expenses are covered, and if the cost exceeds the limits allowed by the insurance policy, it may have to be settled by the patients. Therefore, the cost of treatment may act as a limitation on treatment selection for patients with basic insurance plans. Issues related to accuracy of HbA1c values in CKD patients are key points of consideration and act as a limitation in the accurate interpretation of HbA1c levels [32]. Also, the study included patients with T2DM with at least 2 claims, not necessarily specific to T2DM and, thereby, it might have induced selection bias by restricting the study population to patients with advanced disease or comorbidities. Finally, certain information is not readily available in claims data that could influence study outcomes, such as certain clinical and disease-specific parameters. Furthermore, the results may be limited as the sample size was lower in cohorts with comorbidities.

To conclude, the current real-world study findings elucidate the patient characteristics, treatment patterns, and economic burden of T2DM patients with cardiovascular and renal comorbidities in the UAE. The study noted that males largely outnumbered females (nearly three-fourths of the affected patients were male) across all treatment cohorts. A higher use of conventional antidiabetic medications such as biguanides and sulfonylureas compared to novel antidiabetic agents, including SGLT-2i and GLP-1 RA, was observed in patients with T2DM and comorbidities. An incremental increase in healthcare costs was noted in T2DM patients with comorbidities. Continued and increased usage of drugs such as SGLT-2i and GLP-1 RA with proven cardiorenal benefits could improve long-term outcomes and reduce associated healthcare costs in patients with T2DM and comorbidities, in Dubai, UAE.

We would like to acknowledge Dr. Kavitha Ganesha from IQVIA for providing medical writing assistance.

Ethics Committee approval or obtaining informed consent from patients was not required for this study, as this study did not involve the collection, use, or transmission of individually identifiable data. Also, DRWD contains anonymized structured insurance e-claims data for the patients. The patients’ identity or medical records were not disclosed for the purposes of this study, except where disclosure was allowed as per applicable law. The study conformed to the ethical principles outlined in the Good Pharmacoepidemiology Practices Guidelines, the Declaration of Helsinki 1964 and its later amendments, Good Epidemiological Practice Guidelines issued by the International Epidemiological Association, Good Practices for Outcomes Research issued by the International Society for Pharmacoeconomics and Outcomes Research, and other applicable guidelines. Both the dataset and the security of the office where the dataset was kept met the requirements of the Health Insurance Portability and Accountability Act of 1996. IQVIA had the accessibility to DRWD owned by Dubai Health authority and was used only for the analysis purpose.

The authors have no conflicts of interest to declare.

Funding for this study was provided by AstraZeneca Gulf FZ LLC, Dubai, UAE, and had a substantial role in the study design, study methodology, and decision to publish.

Alaaeldin Bashier, Mohamed Farghaly, Juwairia Yousif Alali, Amna Khalifa AlHadari, Yasmeen Ajaz, Vani Krishna Warrier, Elamin Abdelgadir, and Muhammad Hamed Farooqi contributed to the concept and design of the paper. Sana Qamar, Mohamed Alsayed, Mohamed Samir Fahmy, Dali Tannouri, Arun Jayarame Gowda, Nancy Awad, Badarinath Chickballapur Ramachandrachar, and Ashok Natarajan contributed to the design of the study, analysis of the data, and preparation of the manuscript. All authors approved the final version of the manuscript prior to submission.

All data relevant to the study are included in the article and in the supplementary information material.