Sociodemographic and clinical risk factors associated with suboptimal glycemic control among patients with type 2 diabetes attending Mansoura Specialized Medical Hospital, Egypt


 Background/Aim: Diabetes is a major contributor to global mortality. Poor glycemic control is the major risk factor of diabetes-related complications and deaths. The implicated risk factors of poor glycemic control vary between countries and different ethnic groups. In Egypt, limited data are currently available therefore, the present study was conducted to evaluate the frequency and predictors of suboptimal glycemic control in patients with T2DM attending Outpatient Diabetes Clinic at Mansoura Specialized Medical Hospital, Mansoura, Egypt.Methods: This cross-sectional study was conducted on 250 patients with T2DM. Glycemic status was detected via glycated hemoglobin (HbA1c) and suboptimal glycemic control” was defined as HbA1c level ≥7%. Predictors of suboptimal glycemic control were identified using a multivariate regression analysis.Results: Of the total T2DM participants, 80.4% had suboptimal glycemic control. Irregular anti-diabetic drug intake was detected in 52.9% of patients. With multivariate analysis, earlier age at diabetes diagnosis, inadequate physical activity and increased body mass index (BMI) were the independent predictors of suboptimal glycemic control. Conclusion: A high proportion of the analyzed study population has suboptimal glycemic control. Among all the studied sociodemographic and clinical risk factors, earlier age at diabetes diagnosis, inadequate physical activity and increased BMI are the independent predictors of suboptimal glycemic control.


Background
Diabetes mellitus is a global pandemic; it affects 415 million people worldwide, of whom 90% have type 2 diabetes mellitus (T2DM) [1]. In Middle East and North Africa region, the prevalence of diabetes is raised to greater than 34.6 million patients and this is expected to rich 67.9 million by 2035. In Egypt, 7.5 million patients with diabetes have been reported in 2013 and this is expected to rich 13.1 million by 2035 [2] and 16.9 million by 2045 [3]. Diabetes is a major contributor to global mortality [1] with poor glycemic control is the major risk factor for diabetes-related complications and deaths. This is greatly increases healthcare costs, reduces life expectancy and quality of life [4,5]. Therefore, glycemic control is the ultimate goal of diabetes management [6].
Good glycemic control is di cult to achieve and remains challenging all over the world [7][8][9]. Poor glycemic control is complex and multi-factorial [10]. Factors in uencing glycemic status vary between countries and different ethnic groups; previous reports outside Egypt have been illustrated different predictors for poor glycemic control in patients with T2DM including age, level of education, weight, marital status, duration of diabetes, anti-diabetic agents and numerous other factors [11][12][13]. In Egypt, limited data are currently available which is highly important for achievement of tailored intervention and prevention program. Therefore, this study was conducted to evaluate the frequency and predictors of suboptimal glycemic control in patients with T2DM attending Outpatient Diabetes Clinic at Mansoura Specialized Medical Hospital, Mansoura, Egypt.

Methods
This cross sectional study was conducted on 250 patients with T2DM. Inclusion criteria were patients with con rmed diagnosis of T2DM receiving treatment as outpatients for a minimum of 1 year. Exclusion criteria were diagnosed mental and psychological illness, anemia, haemoglobinopathies, pregnancy, renal failure, hepatic failure, connective tissue disorders and malignancy.
A thorough medical history, a clinical examination and anthropometric measurements including weight, height and body mass index (BMI) [calculated as weight/height 2 (kg/m 2 )] were assessed.
Sociodemographic status and clinical factors were evaluated by a questionnaire specially designed for the study which included age, gender, marital status, residency, smoking, alcohol consumption, education level, occupation, income, number of children/room, duration of diabetes, age at diabetes diagnosis, family history of T2DM, hospitalization due to diabetes complications, self-monitoring of blood glucose, known hypertension, other self-reported diabetes-related complications, treatment characteristics and physical activity. Adequate physical activity was de ned as > 150 minutes of aerobic moderateintensity physical activity per week. Glycated hemoglobin (HbA1c) was measured as an index of metabolic control on a DCA 2000 analyzer, fast ion exchange resin (Roche Diagnostic, Germany). Suboptimal glycemic control was de ned as HbA1c ≥ 7% [14].

Statistical analysis
Data were fed to the computer and analyzed using IBM SPSS Corp. Released 2013, IBM SPSS Statistics for Windows and Version 22, Armonk, NY: IBM Corp. Qualitative data were described as numbers and percentages. Quantitative data were described as median (minimum -maximum) for non-parametric data and M ± SD for parametric data after testing normality by Kolmogrov-Smirnov test. A chi-square test was performed to compare categorical data. Monte Carlo and Fischer Exact tests were used as corrections for chi-square test when more than 25% of cells have count less than 5 in tables >2x2 and 2x2, respectively. Student t-test was performed to compare 2 independent groups. Binary stepwise logistic regression analysis was performed to detect independent predictor variables of binary outcome. Signi cant predictors in the univariate analysis were entered into regression model using forward Wald method/Enter. Adjusted odds ratios (OR) and 95% con dence interval (CI) were calculated. P ≤ 0.05 was considered to be signi cant. Table 1 illustrates the sociodemographic characteristics of the analyzed study population. The mean age was 54.52 (55.6% aged from 33 to 56 years and 44.4% aged from 57 to 80 years), 66% were females, 66.8% were married and 58% were of rural residence, 10% were smokers, 52.8% were illiterate and 70.8% were unemployed. The median income was 800 LE. None of the studied patients were alcoholic.  Data are expressed as means ± standard deviation, numbers or percentages.

Results
Compared with optimal glycemic control, patients with suboptimal glycemic control had signi cantly lower age, earlier age at T2DM diagnosis (≤ 45 years), higher frequency of urban residence, family history of T2DM, obesity, retinopathy, nephropathy, neuropathy, lower physical activity and irregular antidiabetic drug intake (all cases with suboptimal glycemic control have history of irregular anti-diabetic drug intake). No signi cant differences between optimal and suboptimal glycemic control states with regard to all other sociodemographic and clinical characteristics. Table 3   Table 3 Comparison between optimal and suboptimal glycemic state with regard to socio-demographic and clinical characteristics identi ed race as an independent variable of glycemic control after adjusting for sociodemographic status. Differences in studies with regard to sample size, methods of data collection and assay for de ning glycemic control should be considered. It should also be noted that our study population was recruited from a tertiary referral diabetes clinic where moderate to severe disease is expected.
Of the current study population, 58.4% had reported irregular anti-diabetic drug intake and as expected all of those patients had suboptimal glycemic control. In agreement, Kassahun et al. [16] and Demoz et al.
[18] found an inverse association between medications adherence and poor glycemic control. Poor compliance might be owing to the low monthly income and disease unawareness among the majority of patients. Accordingly, counseling, improving nancial challenges and medications adherence have been suggested [22].
With multivariate analysis, earlier age at diabetes diagnosis, inadequate physical activity and increased BMI were the independent predictors of suboptimal glycemic control in our study participants. Similarly, many studies found an association between younger age in patients with T2DM and poor glycemic control [12,19,23] whereas, Souliotis et al.
[13] did not report any association. The potential explanations are the nancial challenges, work and family responsibilities and diabetes-related distress in adults with DM [24,25].
We found more than half of patients with T2DM (59.6%) did not practice adequate physical activity. This is in parallel with Fiseha et al. [17] who found that 66.9% of diabetic participants were physically inactive.
Tekalegn et al. [26] also estimated that only 54.4% of the total respondents were performing adequate physical activity. Moreover, the lack of physical activity was a potential risk factor for poor glycemic control [15]. On the other hand, the association between physical activity and improvement in glycemic control had been reported by several studies [27][28][29]. The underlying mechanism is attributed to increase insulin sensitivity We did not observe any independent association between sociodemographic factors and suboptimal glycemic control. This is in accordance with Tan et al.
[37] but in contrast with Cheng et al.
and Kassahun et al. [12,16] Although achievement of good glycemic control is well-known to be associated with reduced microvascular and macrovascular diabetic complications in the long-term, a signi cant proportion of patients with poor glycemic control still reported all over the world. From the previous discussion, we found 2 modi able risk factors in uencing suboptimal glycemic control which are the physical inactivity and obesity thus, the need to design strategies encouraging physical activity and body weight reduction are recommended in order to improve glycemic control and hence delay diabetes-related complications.

Conclusion
A high proportion of the analyzed study population has suboptimal glycemic control. Among all the studied sociodemographic and clinical risk factors, earlier age at diabetes diagnosis, inadequate physical activity and increased BMI are the independent predictors of suboptimal glycemic control. Our ndings highlight the importance of lifestyle intervention targeting physical inactivity and obesity in patients with T2DM.

Declarations
Ethics approval and consent to participate All procedures performed in the study were in accordance with Mansoura university institution and the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This study complies with current research ethics standards and was approved by the institutional Research Ethics Board of the University of Mansoura. A written informed consent was obtained from literate participants and legal guardian for illiterate participants.

Consent for publication
Not applicable.

Availability of data and materials
All data generated or analyzed during this study are included in this published article.

Competing interests
The authors declare that they have no competing interests.

Funding
This research did not receive any speci c grant from any funding agency in the public, commercial or notfor-pro t sector.
Author's contributions MME, GS, HM, AAN, and NM contributed to the study concept, design, drafting and critical revision of the manuscript. All authors have read and approved the nal version of manuscript.

Figure 1
Frequency of optimal and suboptimal glycemic control in patients with type 2 diabetes