Abstract
Poor glycemic control is a risk factor for micro and macrovascular complications of diabetes. The aim of this study was to assess the prevalence and factors related to suboptimal glycemic control and diabetes complications in a group of patients with type 2 diabetes mellitus (T2DM). This cross-sectional descriptive study conducted in Al Qassim region, Saudi Arabia. Two hundred patients with T2DM were enrolled. Demographic, social, and self-care behavior data were collected. A thorough clinical evaluation was done. Glycated hemoglobin, lipid, and kidney profile results were recorded. Mann–Whitney test was used to compare different groups. For comparing categorical data, Chi-square (χ2) test was performed. Multivariate logistic regression analyses used to detect predictors of poor glycemic control and macrovascular and microvascular complications. The median age of patients was 58 years, and 62% of them were males. Only 22.5% of patients had glycated hemoglobin <7%. Forty-four patients (22%) had evidence of macrovascular complications. Retinopathy, neuropathy, and nephropathy were found in 42.5%, 32.5%, and 12%, respectively. Longer diabetes duration was significantly associated with poor glycemic control (OR = 1.006, P < .005). The age of the patients was independently associated with macrovascular complications (OR = 1.050, P = .029). Hyperlipidemia was significantly associated with neuropathy (OR = 0.229, P = .043) and retinopathy (OR = 12.887, P = .003). Although physical activity was lower in patients with suboptimal glycemic levels (P = .024), cardiovascular disease (P = .030), neuropathy (P < .001), retinopathy (P < .001), and nephropathy (P = .019), multivariate analysis showed that it was only independently associated with neuropathy (OR = 0.614, P = .001). The prevalence of suboptimal glycemic control is high in the studied population. Effective health measures are urgently needed to stop diabetes complications, especially retinopathy and neuropathy. Elderly people with long durations of diabetes, and lower physical activity should be the focus of the interventions. Tailored exercise programs are particularly needed for better diabetes control and for the prevention of complications in patients with T2DM.
Keywords: complications, diabetes, glycated hemoglobin
1. Introduction
Diabetes mellitus is one of the main worldwide health problems. In 2021, approximately 537 million individuals had type 2 diabetes (T2DM), and diabetes was accountable for 6.7 million deaths.[1] In Saudi Arabia, diabetes is a major health problem. Saudi Arabia is the seventh-highest country in the world in diabetes prevalence, and approximately 17.7% of the adult Saudi population has diabetes.[1] There has been a 10-fold increase in the prevalence of diabetes in the Saudi population in the last thirteen years.[2] The Saudi population appears to have a unique genetic propensity to T2DM, which is increased by rising obesity rates and changing lifestyles.[3]
Diabetes is a well-known risk factor for both macrovascular and microvascular complications.[4,5] Chronic hyperglycemia induces oxidative stress, promotes the polyol pathway, increases advanced glycation end-product formation, and ultimately alters gene expression.[6] Moreover, there are many other risk factors that are significantly related to diabetes complications, including blood pressure, hyperlipidemia, body weight, age, and diabetes duration.[7,8]
Improving blood sugar control reduces the risk of diabetes complications and prevents death from diabetes-related complications.[9] A 1% reduction in mean glycated hemoglobin (HbA1c) level has been reported to reduce micro- and macrovascular complications by 12% to 43%.[9] Several factors have been reported to influence glycemic control, such as age, diabetes duration, education level, sugar consumption, physical activity, and adherence to medications.[10] However, the degree of diabetes control and factors related to glycemic indices are variable in different countries. Also, the prevalence of T2DM complications and factors related to the development of these complications are variable among different populations.[11] Identifying the different aspects associated with poor diabetes control is essential to institute appropriate interventions to improve glycemic control and prevent different diabetes-associated complications. Therefore, the aim of this study was to assess the prevalence of suboptimal glycemic control and determine the risk factors associated with poor blood sugar control and diabetes complications in a group of Saudi patients with T2DM.
2. Material and methods
2.1. Study design
This was a cross-sectional descriptive study.
2.2. Study population
The study included subjects above 18 years who attended the diabetes center at King Fahad Specialist Hospital, Buraydah, Al Qassim Region, Kingdom of Saudi Arabia, and had a diagnosis of T2DM. Data collection started before COVID-19 pandemic in March 2019 and resumed after it and we were able to finish data collection in June 2022. Patients who had factors affecting HbA1c and cardiovascular complications such as anemia, renal failure, liver cirrhosis, were excluded from the study.
The study was conducted in accordance with the ethical guidelines of the 1975 Declaration of Helsinki. The study was approved by the Al Qasem Regional Research Ethics Committee and registered with the National Biochemistry Commission Ethics with registration number H-04-Q-001. An explanation of the study was given to all participants, and all of them gave voluntary consent.
2.3. Clinical measurements and laboratory data
The data were collected through both thorough medical evaluations including history taking, complete physical examination and patient records. The collected data were categorized into 2 parts. First, demographic and general characteristics included age, gender, nationality, marital status, level of education, employment status, socioeconomic status, residence, smoking, overall daily life stress, adherence to medication and medical recommendations, diabetes duration and treatment, presence of hyperlipidemia, presence of diabetes microvascular and macrovascular complications, fasting plasma glucose, HbA1c, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), creatinine, and urinary albumin creatinine ratio. Also, weight, height, calculated body mass index (BMI), and blood pressure readings were recorded. Regarding stressful life style, we initiated a discussion with the patient regarding their overall lifestyle, inquiring about any specific concerns they may have at work or home, among other aspects. Subsequently, we explored whether they perceived their lifestyle as stressful or not. Ultimately, we took great care to adapt our questioning approach, ensuring that the patients fully comprehended the questions being asked.
Second, data about health behavior gathered from answering the following questions derived from the Summary of Diabetes Self-Care Activities questionnaire[12]: How many days per week had the patient followed the dietary recommendation given by the health care provider? How many days per week did the patient do general physical activity for at least 30 minutes (including walking and exercise)? How many days per week did the patient check his or her blood sugar? How many days per week did the participant check his or her feet? How many days per week did the patient check the inside of his or her shoes (to make sure there were no cuts or bumps)?
2.3.1. Definitions of glycemic control and complications.
The diagnosis of diabetes and diabetes complications in the current study was based on ADA guidelines.[13] Optimal glycemic control was defined as HbA1c <7%. Established cardiovascular disease (CVD) was defined as the presence of evidence of cerebrovascular disease, coronary heart disease, heart failure, peripheral artery disease, or carotid artery disease detected by history, examination and medical record. Nephropathy was diagnosed when the median urine sample for an albumin-creatinine ratio was ≥30 mg/g, confirmed in at least 2 of 3 samples. Assessment of neuropathy was based on patient-reported symptoms in the form of abnormal sensations in the lower limbs: tingling, sensations of cold or heat, pain, or restlessness, and assessment of pinprick sensation and vibration sensation using a 128-Hz tuning fork. For the presence of retinopathy, we depended on the patient records, as there is a regular dilated and comprehensive eye examination for all patients by an ophthalmologist. All the studied population possessed these specific details owing to the specialized nature of the hospital in the Al-Qassim and the patient has regular follow up and screening of complications with strict documentation.
2.4. Statistical analysis
Social Sciences (SPSS) version 28 (IBM Corp., Armonk, NY) was used in data analysis. Quantitative variables were summarized using the median and interquartile range, while categorical variables were summarized as relative frequencies (percentages). The Mann–Whitney test was used to compare different groups. For comparing categorical data, the Chi-square (χ2) test was performed. The Exact test was used when the expected frequency was <5. Multivariate logistic regression analyses to detect predictors of poor glycemic control and macrovascular and microvascular complications were performed. P values <.05 were considered statistically significant.
3. Results
3.1. General characteristics of the studied population
Two hundred patients with T2DM participated in this study. About 62% were male and 38% were female, with a median age of 58 years. Most of the patients (96%) held Saudi nationality. The majority of patients were married (94.5%), and more than half of them graduated from middle school (28.4%) or high school (29.4%). A higher percentage was unemployed (43.8%) or retired (33.8%). Most patients were of the middle socioeconomic class, and 87.6% were from urban areas. Few were smokers (13.9%), and the majority (84.1%) reported a non-stressful lifestyle. The majority of patients had suboptimal glycemic control, with only 22.5% having HbA1c <7%. About 81% had dyslipidemia, 22% had a history of CVD diseases, 12% had nephropathy, 32.5% had neuropathy, and 45.7% had diabetic retinopathy.
3.2. Different variables in patients with optimal and suboptimal glycemic control
Patients with HbA1c >7 had a longer duration of diabetes compared to those with optimal glycemic control (P = .004). Five variables were significantly associated with glycemic control: level of education (P = .005), employment status (P = .008), adherence to medical recommendations (P = .001), diabetes medications, and physical activity (P = .024). Physical activity was significantly correlated to HbA1c (r = −0.194, P = .007) and HDL-C (R = 0.171, P = .020) (Tables 1 and 2).
Table 1.
Comparison of demographic and clinical parameters of patients with optimal and suboptimal control of diabetes.
| Patients with optimal control of diabetes (N = 45) | Patients with suboptimal control of diabetes (N = 155) | P value | ||
|---|---|---|---|---|
| Gender (no [%]) | Male | 28 (62.2%) | 96 (61.9%) | .830 |
| Female | 17 (37.8%) | 59 (38.1%) | ||
| Age (yr) (median [IQR]) | 57.5 (49–61) | 58.00 (50–63) | .36 | |
| Diabetes duration (mo) (median [IQR]) | 114 (54.04–150) | 156 (92–240) | .004 | |
| BMI (median [IQR]) | 31.39 (27.06–32.93) | 29.5 (26.3–35) | .756 | |
| Nationality (no [%]) | Saudi | 44 (97.8%) | 148 (95.5%) | .685 |
| Non-Saudi | 1 (2.2%) | 7 (4.5%) | ||
| Marital status (no [%]) | Married | 4 (91.2%) | 149 (96.1%) | .205 |
| Unmarried | 4 (8.8%) | 6 (3.9%) | ||
| Level of education (no [%]) | Uneducated | 10 (22.2%) | 36 (23.2%) | .005 |
| Primary school | 1 (2.2%) | 17 (11.0%) | ||
| Middle school | 8 (17.8%) | 49 (31.6%) | ||
| High school | 16 (35.6%) | 43 (27.7%) | ||
| College | 10 (22.2%) | 10 (6.5%) | ||
| Employment status (no [%]) | Unemployed | 15 (33.3%) | 73 (47.1%) | .008 |
| Employed | 18 (40%) | 27 (17.4%) | ||
| Retired | 12 (26.7%) | 55 (35.5%) | ||
| Socioeconomic class (no [%]) | Low | 2 (4.5%) | 6 (4%) | .460 |
| Lower middle | 20 (44.5%) | 81 (52.2%) | ||
| Upper middle | 22 (48.8%) | 67 (43.2%) | ||
| High | 1 (2.2%) | 1 (0.6%) | ||
| Residence (no [%]) | Rural | 8 (17.8%) | 17 (11.0%) | .246 |
| Urban | 37 (82.2%) | 138 (89.0%) | ||
| Hyperlipidemia (no [%]) | Yes | 35 (77.7%) | 127 (81.9%) | .668 |
| No | 10 (22.3%) | 28 (18.1%) | ||
| SBP (mm Hg) (median [IQR]) | 135 (121–144.5) | 135 (120–150) | .675 | |
| Nephropathy (no [%]) | Yes | 4 (8.9%) | 20 (12.9%) | .466 |
| No | 41 (91.1%) | 135 (87.1%) | ||
| CVD (no [%]) | Yes | 7 (15.6%) | 37 (24.1%) | .225 |
| No | 38 (84.4%) | 118 (75.9%) | ||
| Neuropathy (no [%]) | Yes | 8 (17.8%) | 57 (37.1%) | .016 |
| No | 37 (82.2%) | 98 (62.9%) | ||
| Retinopathy (no [%]) | Yes | 15 (33.3%) | 71 (48.9%) | .117 |
| No | 30 (66.7%) | 74 (51.1%) | ||
| HbA1c (%) (median [IQR]) | 6.40 (5.8–6.9) | 9 (8.2–10.5) | <.001 | |
| Fasting plasma glucose (mmol/L) (median [IQR]) | 5.8 (4.59–6.74) | 8.76 (7–12.5) | <.001 | |
| Cholesterol (mmol/L) (median [IQR]) | 3.9 (3.3–4.9) | 4 (3.3–4.9) | .733 | |
| LDL-C (mmol/L) (median [IQR]) | 2.6 (2–3.4) | 2.6 (2–3.3) | .759 | |
| HDL-C (mmol/L) (median [IQR]) | 1 (0.9–1.2) | 1 (0.8–1.1) | .340 | |
| Triglycerides (mmol/L) (median [IQR]) | 1.4 (0.9–1.9) | 1.5 (1.1–2.1) | .205 | |
| Creatinine (umol/L) (median [IQR]) | 77 (65–96) | 79 (66–95) | .977 | |
| Urine ACR (mg/g) (median [IQR]) | 27.9 (20–99.4) | 28 (13.6–42.3) | .661 | |
Values are median and interquartile range (IQR).
ACR = albumin creatinine ratio, BMI = body mass index, CVD = cardiovascular disease, HbA1c = glycated hemoglobin, HDL-C = high density lipoprotein cholesterol, LDL-C = low density lipoprotein cholesterol, SBP = systolic blood pressure.
P value < .05 is considered significant.
Table 2.
Self-care and habits in patients with optimal and suboptimal control of diabetes.
| Patients with optimal control of diabetes (N = 45) |
Patients with suboptimal control of diabetes (N = 155) |
P value | ||
|---|---|---|---|---|
| Smoking status | Yes | 6 (13.4%) | 22 (14.2%) | .843 |
| No | 39 (86.6%) | 133 (85.8%) | ||
| Daily life | Stressful | 5 (11.2%) | 27 (17.4%) | .566 |
| Peaceful | 20 (44.4%) | 65 (41.9%) | ||
| Uncertain | 20 (44.4%) | 63 (40.6%) | ||
| Adherence to medical recommendation | Highly adherent | 35 (79.5%) | 92 (58.9%) | .001 |
| Partially adherent | 9 (20.5%) | 64 (40.1%) | ||
| Diabetes medications | Diet | 2 (4.5%) | 2 (1.3%) | .026 |
| Insulin | 5 (11.1%) | 31 (20%) | ||
| Oral medications | 28 (62.2%) | 67 (43.2%) | ||
| Both | 10 (22.2%) | 55 (35.5%) | ||
| Number of d/wk eating healthy diet (median [IQR]) | 3 (1–5) | 3 (2–4) | .768 | |
| Physical activity in 7 d (median [IQR]) | 2 (0–4) | 0 (0–2) | .024 | |
| Number of d/wk of blood glucose monitoring (median [IQR]) | 3 (3–5) | 3 (2–4) | .251 | |
Values are median and interquartile range (IQR).
P value < .05 is considered significant.
No significant difference was observed between patients with controlled and uncontrolled diabetes regarding socioeconomic status, daily life stress, BMI (P = .756), blood pressure (P = .675), or hyperlipidemia (P = .668). The multivariate logistic regression analysis showed that longer diabetes duration was the only independent factor related to suboptimal glycemic control (OR 1.006, 95% CI 1.002–1.010, P = .005).
3.3. Variables associated with the presence of macrovascular complications in patients with diabetes
Twenty-two percent of patients with diabetes in this study had a history of macrovascular diseases. Compared to patients without CVD, subjects with CVD were older (P = .015), had a longer duration of diabetes (P = .047), had a higher BMI (P = .012), and were less physically active (P = .03) (Fig. 1A).
Figure 1.
Frequency of diabetes complications according to the number of d of walking per wk. (A) Macrovascular complications; (B) Diabetic neuropathy; (C) Diabetic retinopathy; (D) Diabetic nephropathy. The number of d of walking per wk was significantly lower in patients with macrovascular complications (P = .03), diabetic neuropathy (P < .001), retinopathy (P < .001) and nephropathy (P = .019) compared to those without these complications.
There is no significant difference between patients with and without macrovascular complications regarding smoking status (P = .201), daily life stress (P = .707), diabetes medications (P = .125), healthy diet (P = .786), HbA1c (P = .137), LDL-C (P = .819), and HDL-C (P = .539) (Table 3).
Table 3.
Comparison of demographic and clinical parameters of patients with and without cardiovascular diseases.
| Cardiovascular diseases | P value | |||
|---|---|---|---|---|
| Yes (N = 44) | No (N = 156) | |||
| Gender (no [%]) | Male | 32 (72.7%) | 92 (58.9%) | .175 |
| Female | 12 (27.3%) | 64 (41.1%) | ||
| Age (yr) (median [IQR]) | 59.5 (51.5–62.5) | 57 (50–62) | .015 | |
| Diabetes duration (mo) (median [IQR]) | 168 (60–240) | 144 (92–216) | .047 | |
| BMI (median [IQR]) | 31.6 (27.15–35.2) | 30.14 (27–34) | .012 | |
| Marital status (no [%]) | Married | 41 (91.2%) | 149 (95.5%) | 1 |
| Unmarried | 3 (6.8%) | 7 (3.6%) | ||
| Level of education (no [%]) | Uneducated | 9 (20.5%) | 36 (23.2%) | .343 |
| Primary school | 6 (13.6%) | 14 (8.9%) | ||
| Middle school | 16 (36.4%) | 40 (25.7%) | ||
| High school | 9 (20.4%) | 50 (32 %) | ||
| College | 4 (9.1%) | 16 (10.2%) | ||
| Employment status (no [%]) | Unemployed | 18 (40.90%) | 74 (47.4%) | .767 |
| Employed | 9 (20.45%) | 32 (20.5%) | ||
| Retired | 17 (38.65%) | 50 (32.1 %) | ||
| Socioeconomic classes (no [%]) | low | 4 (9.5%) | 4 (2.5%) | .139 |
| lower middle | 23 (52.4%) | 78 (50%) | ||
| upper middle | 17 (38.1%) | 72 (46%) | ||
| high | 0 (0.0%) | 2 (1.5%) | ||
| Residence (no [%]) | Rural | 7 (16 %) | 18 (11.6%) | .446 |
| Urban | 37 (84 %) | 138 (88.4%) | ||
| Hyperlipidemia (no [%]) | Yes | 38 (86.3%) | 129 (82.6%) | .687 |
| No | 6 (13.7%) | 27 (17.4%) | ||
| SBP (mm Hg) (median [IQR]) | 135 (132–147) | 135 (120–149) | 1.00 | |
| Nephropathy (no [%]) | Yes | 7 (15.9%) | 17 (10.9%) | .410 |
| No | 37 (84.1%) | 139 (89.1%) | ||
| Neuropathy (no [%]) | Yes | 21 (47.7%) | 44 (28.2%) | .018 |
| No | 23 (52.3%) | 112 (71.8%) | ||
| Retinopathy (no [%]) | Yes | 31 (70.4%) | 60 (38.5%) | <.001 |
| No | 13 (29.6%) | 96 (61.5%) | ||
| HbA1c (%) (median [IQR]) | 8.5 (8.1–10.3) | 8.4 (7.3–10.1) | .137 | |
| Cholesterol (mmol/L) (median [IQR]) | 4.8 (3.9–5.5) | 4 (3.3–4.8) | .707 | |
| LDL-C (mmol/L) (median [IQR]) | 3.4 (2.5–4) | 2.6 (2–3.25) | .819 | |
| HDL-C (mmol/L) (median [IQR]) | 1 (0.8–1) | 1 (0.8–1.20) | .539 | |
| Triglycerides (mmol/L) (median [IQR]) | 1.9 (1.6–2.5) | 1.5 (1.1–2.08) | .689 | |
Values are median and interquartile range (IQR).
BMI = body mass index; HbA1c = glycated hemoglobin, HDL-C = high density lipoprotein cholesterol, LDL-C = low density lipoprotein cholesterol, SBP = systolic blood pressure.
P value < .05 is considered significant.
The multivariate logistic regression analysis showed the only independent factors related to the presence of CVD were the age of the patients (OR = 1.050, 95% CI 1.005–1.097, P = .029) and the presence of retinopathy (OR = 3.745, 95% CI 1.606–8.734, P = .002).
3.4. Factors associated with the presence of neuropathy in patients with T2DM
Most of the patients with neuropathy were male (81.5%). Patients with neuropathy were older (P = .016) and had longer diabetes duration (P = .032), a lower BMI (P = .032), less physical activity (P < .001) (Fig. 1B), and suboptimal glycemic control compared to those without neuropathy. Smoking was more common among patients with neuropathy (23.1%) compared to those without neuropathy (9.6%) (P = .015). Compared to those without neuropathy, patients with neuropathy had a higher prevalence of nephropathy (P = .003) and macrovascular complications (P = .018). There is no significant difference between patients with and without neuropathy regarding LDL-C, HDL-C, or TG levels (Table 4).
Table 4.
Comparison of demographic and clinical parameters of patients with and without neuropathy.
| Diabetic neuropathy | P value | |||
|---|---|---|---|---|
| Yes (N = 65) | No (N = 135) | |||
| Gender (no [%]) | Male | 53 (81.5%) | 71 (52.6%) | <.001 |
| Female | 12 (18.5%) | 64 (47.4%) | ||
| Age (yr) (median [IQR]) | 60 (52–65) | 56 (49–61) | .016 | |
| Diabetes duration (mo) (median [IQR]) | 180 (120–240) | 120 (60–216) | .013 | |
| BMI (median [IQR]) | 28.7 (26.5–31.2) | 31.39 (27–35.2) | .032 | |
| Marital Status (no [%]) | Married | 65 (100.0%) | 125 (92.6%) | .102 |
| Unmarried | 0 (0.0%) | 10 (7.4%) | ||
| Level of education (no [%]) | Uneducated | 8 (13.1%) | 38 (28.3%) | .157 |
| Primary school | 7 (11.5%) | 11 (8.7%) | ||
| Middle school | 20 (31.1%) | 37 (26.8%) | ||
| High school | 23 (34.4%) | 36 (27.6%) | ||
| College | 7 (8.2%) | 13 (8.6%) | ||
| Employment status (no [%]) | Unemployed | 18 (27.7%) | 70 (51.8%) | <.001 |
| Employed | 12 (18.5%) | 33 (24.4%) | ||
| Retired | 35 (53.8%) | 32 (23.8%) | ||
| Socioeconomic status (no [%]) | low | 1 (1.5%) | 7 (5.1%) | .753 |
| lower middle | 33 (51%) | 68 (50.4%) | ||
| upper middle | 31 (47.5%) | 58 (42.9%) | ||
| high | 0 (0.0%) | 2 (1.6%) | ||
| Residence (no [%]) | Rural | 8 (12.3%) | 17 (12.6%) | .610 |
| Urban | 57 (87.7%) | 118 (87.4%) | ||
| Hyperlipidemia (no [%]) | Yes | 57 (87.7%) | 105 (77.8%) | .299 |
| No | 8 (12.3%) | 30 (22.2%) | ||
| SBP (mm Hg) (median [IQR]) | 131.5 (120–139) | 135.5 (120–150) | .132 | |
| Nephropathy (no [%]) | Yes | 15 (23.1%) | 9 (6.7%) | .003 |
| No | 50 (76.9%) | 126 (93.3%) | ||
| CVD (no [%]) | Yes | 21 (32.3%) | 23 (17%) | .018 |
| No | 44 (67.7%) | 112 (83%) | ||
| Retinopathy (no [%]) | Yes | 49 (75.3%) | 41 (30.4%) | <.001 |
| No | 16 (24.7%) | 94 (69.6%) | ||
| HbA1c (%) (median [IQR]) | 8.76 (8.00–10.5) | 8.33 (7.05–10) | .035 | |
| Cholesterol (mmol/L) (median [IQR]) | 4 (3.25–4.85) | 4.1 (3.40–4.9) | .494 | |
| LDL-C (mmol/L) (median [IQR]) | 2.55 (2–3.2) | 2.6 (2.10–3.4) | .640 | |
| HDL-C (mmol/L) (median [IQR]) | 1 (0.8–1.1) | 1 (0.80–1.20) | .065 | |
| Triglycerides (mmol/L) (median [IQR]) | 1.5 (1.2–2) | 1.5 (1.05–2.1) | .853 | |
| Creatinine (umol/L) (median [IQR]) | 82 (68–98) | 74 (64–92) | .113 | |
| Urine ACR (median [IQR]) | 37.75 (23.25–63.27) | 27.9 (16.74–42.3) | .571 | |
Values are median and interquartile range.
ACR = albumin creatinine ratio, BMI = body mass index, CVD = cardiovascular disease, HbA1c: = glycated hemoglobin, HDL-C = high density lipoprotein cholesterol, LDL-C = low density lipoprotein cholesterol, SBP = systolic blood pressure.
P value < .05 is considered significant.
The multivariate logistic regression analysis showed the independent factors related to the presence of neuropathy were hyperlipidemia (OR = 0.229, 95% CI 0.055–0.952, P = .043), adherence to recommendations (OR = 3.492, 95% CI 1.409–8.653, P = .007), and physical activity (OR = 0.614, 95% CI 0.456–0.829, P = .001).
3.5. Factors associated with the presence of retinopathy in patients with T2DM
Most of the patients with retinopathy were male (70.6%). They were older (P = .028), had longer diabetes duration (P = .001), a higher prevalence of hyperlipidemia (P = .015), and were less physically active (P < .001) compared to those without retinopathy (Fig. 1C). There was no significant difference between patients with and without retinopathy regarding BMI (P = .148), smoking (P = .077), SBP (P = .644), or HbA1c (P = .111). Patients with retinopathy compared to those without had a higher prevalence of nephropathy (P = .007), neuropathy (P < .001), and macrovascular complications (P < .001) (Table 5).
Table 5.
Comparison of demographic and clinical parameters of patients with and without retinopathy.
| Patient with retinopathy N = 85 |
Patient without retinopathy N = 115 |
P value | ||
|---|---|---|---|---|
| Gender (no [%]) | Male | 60 (70.6%) | 56 (49.5%) | .004 |
| Female | 25 (29.4%) | 58 (50.5%) | ||
| Age (yr) (median [IQR]) | 59 (50–65) | 56 (49–60) | .028 | |
| Diabetes duration (mo) (median [IQR]) | 174 (120–240) | 120 (60–204) | .001 | |
| BMI (median [IQR]) | 28.7 (25.74–33.7) | 31.1 (27.4–34) | .148 | |
| Marital status (no [%]) | Married | 83 (97.6%) | 106 (92.2%) | .286 |
| Unmarried | 2 (2.4%) | 9 (7.8%) | ||
| Level of education (no [%]) | Uneducated | 22 (25.9%) | 26 (22.8%) | .392 |
| Primary school | 11 (12.9%) | 8 (6.9%) | ||
| Middle school | 24 (28.2%) | 31 (26.7%) | ||
| High school | 22 (25.9%) | 38 (32.7%) | ||
| College | 6 (7.1%) | 12 (10.9%) | ||
| Employment status (no [%]) | Unemployed | 34 (40.0%) | 54 (47%) | .008 |
| Employed | 13 (15.3%) | 20 (17.4%) | ||
| Retired | 38 (44.7%) | 41 (35.6%) | ||
| Socioeconomic classes (no [%]) | Low | 4 (4.7%) | 5 (4.3%) | .228 |
| Lower middle | 49 (57.6%) | 60 (52.2%) | ||
| Upper middle | 32 (37.6%) | 50 (43.5%) | ||
| High | 0 (0.0%) | 1 (0.6%) | ||
| Residence (no [%]) | Rural | 10 (11.8%) | 13 (11.3%) | .539 |
| Urban | 75 (88.2%) | 102 (88.7%) | ||
| Hyperlipidemia (no [%]) | Yes | 77 (90.6%) | 89 (77.4%) | .015 |
| No | 8 (9.4%) | 26 (22.6%) | ||
| SBP (mm Hg) (median [IQR]) | 135 (120–148.5) | 133 (119–147) | .644 | |
| Nephropathy (no [%]) | Yes | 15 (19.2%) | 7 (6.0%) | .007 |
| No | 63 (80.8%) | 108 (94.0%) | ||
| Neuropathy (no [%]) | Yes | 44 (53.7%) | 17 (14.8%) | <.001 |
| No | 38 (46.3%) | 98 (85.2%) | ||
| CVD (no [%]) | Yes | 28 (33.7%) | 14 (12.2%) | <.001 |
| No | 55 (66.3%) | 101 (87.8%) | ||
| HbA1c (%) (median [IQR]) | 8.60 (7.4–10.6) | 8.3 (7–10) | .111 | |
| Fasting plasma glucose (mmol/L) (median [IQR]) | 7.46 (6.25–12.04) | 7.91 (6.31–11.5) | .992 | |
| Cholesterol (mmol/L) (median [IQR]) | 3.9 (3.20–5) | 4.1 (3.50–4.9) | .382 | |
| LDL-C (mmol/L) (median [IQR]) | 2.45 (1.9–3.4) | 2.7 (2.3–3.4) | .078 | |
| HDL-C (mmol/L) (median [IQR]) | 1 (0.8–1.1) | 1 (0.8–1.2) | .469 | |
| Triglycerides (mmol/L) (median [IQR]) | 1.5 (1.2–2) | 1.5 (1–2.2) | .836 | |
| Creatinine (umol/L) (median [IQR]) | 82 (68–105) | 72 (64–9) | .011 | |
| Urine ACR (mg/g) (median [IQR]) | 35 (27.90–83.94) | 26.45 (12.8–42.45) | .491 | |
Values are median and interquartile range.
ACR = albumin creatinine ratio, BMI = body mass index, CVD = cardiovascular disease, HbA1c: = glycated hemoglobin, HDL-C = high density lipoprotein cholesterol, LDL-C = low density lipoprotein cholesterol, SBP = systolic blood pressure
P value < .05 is considered significant.
Multivariate logistic regression showed that hyperlipidemia (OR = 12.887, 95% CI 2.366–70.200, P = .003), creatinine (OR = 1.010, 95% CI 1.003–1.017, P = .007), and neuropathy (OR = 5.508, 95% CI 2.244–13.523, P = .001) were independently associated with retinopathy.
3.6. Factors associated with diabetic nephropathy in the studied population
Patients without nephropathy were more likely to adhere to medical recommendations (P = .032), had higher education levels (P = .01), and were more physically active than those with nephropathy (P = .019) (Fig. 1D). There is no significant difference between patients with and without nephropathy regarding age (P = .471), gender (P = .17), marital status (P = 1), smoking (P = .48), BMI (P = .409), HbA1c (P = .546), SBP (P = .444), LDL-C (P = .219), HDL-C (P = .308), or TG (P = .445). Patients with diabetic kidney disease had a higher prevalence of neuropathy (58.3% vs 29%; P = .003) and retinopathy (71.4% vs 40.1%; P = .007) than those without.
4. Discussion
In the current study, the rate of suboptimal glycemic control in patients with diabetes was high (72.5%), similar to studies performed in Saudi Arabia and the Gulf area.[14–16] Despite the high-quality health care system and the wide range of anti-diabetic drugs provided to people with diabetes, most people with T2DM in Saudi Arabia still have inadequate diabetes control. This could be related to poor adherence to medical recommendations and the lack of physical activity found in our study.
In this study, physical activity was a unique factor associated with better glycemic control and a reduction in all macro- and microvascular complications, similar to previous studies that reported a significant association between a sedentary lifestyle and suboptimal glycemic control.[14,17] The presence of a higher percentage of unemployed or retired subjects with uncontrolled diabetes confirms the role of an active lifestyle in glycemic control. The protective mechanism of physical activity is well known, as exercise improves insulin resistance, lowers blood pressure, and improves plasma lipids.[18,19] The current work showed a significant correlation between HbA1c and HDL-C and the number of days of 30-minute walking per week.
Healthy eating habits are vital in managing T2DM; however, we found no significant difference between patients with and without controlled diabetes regarding eating a healthy diet. A recent review study concluded that evidence of the benefit of long-term consumption of low-carbohydrate diets on individuals with T2DM is inconclusive.[20] Moreover, the link between diet and glycemic control could be related to the effect of diet on obesity and not only the composition and quality of the diet.[21] BMI did not significantly differ between patients with and without controlled diabetes, which partially explains the non-significant association between dietary habits and glycemic control observed in our study.
In this study, there was no significant association between age and suboptimal glycemic control. Similarly, a study conducted in Ghana[22] and eastern Sudan[23] failed to show a significant link between age and poor glycemic control. A study conducted in the city of Abha, Saudi Arabia,[24] found that poor glycemic control was related to advanced age. The possible reason for the lack of age effect on blood sugar levels in the current study could be that almost all our patients were in middle age and not elderly population.
In the current study, longer diabetes duration was the only independent factor associated with glycemic control. Studies in Africa and Asia have reported that patients with diabetes diagnosed for more than ten years have a higher risk of suboptimal glycemic control than patients diagnosed for no more than ten years.[25,26] Patients taking insulin were more likely to have poor glycemic control compared to patients taking oral medications. Similarly, a previous study showed that insulin therapy patients had poor glycemic control.[27] This may be because patients prescribed insulin may experience more severe and longer-lasting diabetes, and the fear of low blood sugar and weight gain can have negative consequences for adherence and glycemic control.[28]
Stressful lifestyles, marital status, and socioeconomic class have been reported as factors associated with glycemic control.[23,29] However, these variables had no significant association with the level of diabetes control in our study, which could be related to the fact that most of the patients in this study were middle-class and married, and only a small number of them had stressful lifestyles. On the other hand, diabetes control was related to the level of patient education.
The prevalence of macrovascular complications in our study was 22%, which is lower than the prevalence of CVDs in T2DM (32% in a previous meta-analysis) and in a study involving 13 countries (34.8%).[30,31] However, this was closer to that reported in Saudi Arabia (18.0%).[32] Differences between the studies could be related to potential genetic variability, subject selection, and the timing of CVD diagnosis. Cardiovascular events are more common in older adults, even in those without diabetes, which increases the risk of CVD. This could explain our finding that age was the only significant predictor of CVD.
Although diabetes is associated with a high-risk cardiometabolic profile, this study found no statistically significant differences between patients with macrovascular or microvascular complications and those without these complications regarding blood glucose levels, atherogenic lipid profiles, or BMI. This is not uncommon; as such variations have been reported in previous studies.[26,33] Also, the cross-sectional study design could contribute to this, as these risk factors were assessed once.
In this study, a high rate of microvascular complications was observed in patients with T2DM. The most common microvascular complication in the studied population was retinopathy (42.5%), followed by neuropathy (32.5%), and, lastly, nephropathy (12%). This goes with a study from Saudi Arabia, where diabetic retinopathy was the most frequent microvascular complication, followed by neuropathy and, lastly, nephropathy.[33] Nevertheless, different results from the Indian population showed that peripheral neuropathy (44.9%) was the most frequent complication, followed by nephropathy (12.1%).[34] A Chinese study in patients with diabetes found the prevalence of neuropathy, retinopathy, and kidney disease to be 23.5%, 17.4%, and 10.8%, respectively.[35] This difference could be explained by the fact that susceptibility to diabetes complications varies between different ethnic races and among different individuals due to different genetic predispositions.[36]
Age is an important predictor of vascular and neurological damage in diabetic patients.[37] Our data showed that patients with diabetic retinopathy and neuropathy were more likely to be older and retired. Similar results were recently obtained in a United States review and a prospective observational study in South Asia.[38,39] This is most likely due to the long-term effects of hyperglycemia and microvascular damage.[8] In the current study, the incidence of diabetic retinopathy was consistently higher in men than in women. Similarly, a large multicenter retrospective study found that the male gender may be a risk factor for developing diabetic retinopathy.[40] However, a national study on diabetic retinopathy conducted in Poland argued that women are more likely to develop diabetic retinopathy.[41] Smoking is a source of free radicals and oxidants that lead to cell damage and cell death. The results of the current study showed an increased risk of neuropathy in smokers but not of retinopathy or nephropathy. Most previous studies have shown that smoking significantly increases the risk of developing retinopathy, neuropathy, and nephropathy.[42,43] However, other studies showed a significant reduction or found the association to be lost.[44,45]
This study has limitations that need to be mentioned. Firstly, this study cannot be generalized over the entire population of people with diabetes, so further multicenter studies from different regions in Saudi Arabia are needed. Secondly, the study cross-sectional design assesses these risk factors at only 1 given point, and prospective studies with extended follow-up are required. Thirdly, because this study was conducted shortly after the COVID-19 pandemic, the impact of COVID-19 on patient follow-up during the pandemic might have affected the study results. Fourthly, the study population was relatively small and further larger study is needed.
5. Conclusion
There is an alarming prevalence of suboptimal glycemic control in patients with T2DM in our community that is associated with diabetes duration, level of education, physical activity, and adherence to medical recommendations. Diabetes retinopathy is the most commonly encountered diabetes complication in the studied population, particularly in male patients. Age is the most important factor related to macrovascular complications. Physical activity is uniquely associated with better blood sugar levels and a lower incidence of macrovascular and microvascular complications. An educational program that emphasizes the role of lifestyle changes in glycemic control would greatly benefit people with T2DM in Saudi Arabia.
Acknowledgments
All authors acknowledge their appreciation to the staff members of the diabetes clinic in the Diabetes Center at King Fahad Specialist Hospital, Buraidah for their help and support. The abstract of this paper was presented as poster [AACE Communities MENA Conference 2022] with interim findings. The abstract was published in AACE Endocrine Practice.
Author contributions
Conceptualization: Mohammed Ewid, Abdullah Saleh Algoblan, Elzaki M. Elzaki, Mervat Naguib.
Data curation: Abdullah Saleh Algoblan, Mohamad Ayham Muqresh, Albaraa Muad Alshargabi, Shahad Abdullah Alotaibi, Abdullah Saleh Alfayez, Ahmad Riad Al Khalifa.
Formal analysis: Mervat Naguib.
Investigation: Mohamad Ayham Muqresh, Albaraa Muad Alshargabi, Shahad Abdullah Alotaibi, Abdullah Saleh Alfayez.
Methodology: Elzaki M. Elzaki, Mohammed Ewid.
Writing – original draft: Mervat Naguib.
All authors approved final manuscript.
Abbreviations:
- BMI
- body mass index
- CVD
- cardiovascular disease
- HbA1c
- glycated hemoglobin
- HDL-C
- high-density lipoprotein cholesterol
- LDL-C
- low-density lipoprotein cholesterol
- T2DM
- type 2 diabetes
- TG
- triglycerides
The authors have no funding and conflicts of interest to disclose.
The datasets generated during and/or analyzed during the current study are not publicly available, but are available from the corresponding author on reasonable request.
How to cite this article: Ewid M, Algoblan AS, Elzaki EM, Muqresh MA, Al Khalifa AR, Alshargabi AM, Alotaibi SA, Alfayez AS, Naguib M. Factors associated with glycemic control and diabetes complications in a group of Saudi patients with type 2 diabetes. Medicine 2023;102:38(e35212).
Contributor Information
Mohammed Ewid, Email: m.mahmoudewid@sr.edu.sa.
Abdullah Saleh Algoblan, Email: Abd.384@hotmail.com.
Elzaki M. Elzaki, Email: zmz70@hotmail.com.
Mohamad Ayham Muqresh, Email: Ayham.muqresh@gmail.com.
Albaraa Muad Alshargabi, Email: shargabi.b@gmail.com.
Shahad Abdullah Alotaibi, Email: Shahadabdullah096@gmail.com.
Abdullah Saleh Alfayez, Email: Abd.384@hotmail.com.
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