Received date: February 23, 2014; Accepted date: March 24, 2014; Published date: March 28, 2014
Citation: Al-Kaabi JM, Maskari FAl, Zoubeidi T, Abdulle A, Shah SM, et al. (2014) Prevalence and Determinants of Peripheral Neuropathy in Patients with Type 2 Diabetes Attending a Tertiary Care Center in the United Arab Emirates. J Diabetes Metab 5:346 doi: 10.4172/2155-6156.1000346
Copyright: © 2014 Al-Kaabi JM, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Visit for more related articles at Journal of Diabetes & Metabolism
Methods: Adults with type 2 diabetes mellitus (394 patients, age = 57 ± 12 yr, 67% females, duration of diabetes = 10.9 ± 7.9 yr) were randomly selected from the Diabetes Center at Tawam Hospital (Al Ain City, United Arab Emirates). The Michigan Neuropathy Screening Instrument (MNSI) history and sign scores were used to assess neuropathy. Stepwise logistic regression analysis was used to assess independent predictors of peripheral neuropathy on the Michigan score.
Results: Prevalence of peripheral neuropathy was 10.4% based on the MNSI-history score of ≥7 and 25.6% based on the MNSI-sign score of ≥ 3. Logistic regression analysis revealed that HbA1c [OR=3.41, 95% CI; 1.15– 10.16] and physical activity [OR=4.99, 95% CI; 2.21–11.29] were significant predictors of the MNSI-history score. Age [OR=1.06, 95% CI; 1.03–1.09], height [OR=1.06, 95% CI; 1.02–1.10], systolic blood pressure [OR=1.03, 95% CI; 1.01–1.06] and duration of diabetes [OR=1.08, 95% CI; 1.04–1.13] were significant predictors of the MNSI-sign score.
Conclusion: Peripheral neuropathy was common in the studied diabetic patients and was associated with modifiable risk factors, such as glycemic control, blood pressure control and physical activity.
Type 2 diabetes; Peripheral neuropathy; Prevalence; Michigan neuropathy screening instrument; United Arab Emirates
BMI: Body Mass Index; MNSI: Michigan Neuropathy Screening Instrument; GFR: Glomerular Filtration Rate; HbA1c: Glycosylated Hemoglobin
United Arab Emirates (UAE) is ranked among the world’s top fifteen countries with the highest prevalence of type 2 diabetes (International Diabetes Federation report, 2013) . Previously reported frequency of diabetic peripheral neuropathy in the UAE is about 35% [2,3]. The entity diabetic peripheral neuropathy is defined as symptoms and signs of nerve dysfunction in diabetic patients after other causes have been excluded . This complication is common and may result in muscle atrophy, joint deformity, foot ulceration and limb amputation . Its severity is usually linked to patient age, duration and control of diabetes and other risk factors, such as hypertension, dyslipidemia, obesity and smoking [2,3]. In addition, type 2 diabetes has been associated with adverse micro- and macro-vascular outcomes . A complex array of metabolic, vascular and perhaps hormonal factors shifts the balance between nerve fiber damage and repair, favoring the former [5,7]. Risk factors related to diabetic neuropathy have not been adequately addressed in the Peninsular Arabs [2,3,8-10].
Distal symmetric polyneuropathy is the most common form of diabetic neuropathy. The disease is frequently insidious and may be asymptomatic. Prompt medical care and improved glycemic control may reduce its severity and progression .
This study was designed to investigate peripheral neuropathy in UAE adult patients with type 2 diabetes. The objectives were to estimate prevalence and identify relevant determinants of neuropathy in this population.
The study was approved by Al Ain Medical District Human Research Ethics Committee. Subjects signed a written consent; a verbal consent was obtained from illiterate subjects. The questionnaire and clinical examination were piloted on ten patients to ensure feasibility of the study and clear understanding of the inquiries.
The inclusion criteria were adults (> 18 years) with type 2 diabetes for at least 12-months. Subjects with the followings conditions were excluded: type 1 diabetes, pregnancy, alcohol intake, malignancy, medication-induced neuropathy, liver disease, hypothyroidism, collagen vascular disease, vasculitis and other known causes of neuropathy.
Four hundred and twenty-two patients with type 2 diabetes were recruited from the Diabetes Centre at Tawam-Johns Hopkins Hospital (Al Ain, Abu Dhabi) between May 2007 and May 2008. This tertiary care facility accommodated about 20,000 diabetic visits per year. Twenty-eight patients were subsequently found to have type 1 diabetes and were excluded due to the known variation in the natural history of peripheral neuropathy between the two types of diabetes. Thus, the statistical analysis was performed on the 394 patients (264 females, 67%) with type 2 diabetes. Regular visits to the Center were defined as clinic consultations at least once every three months. A systematic random sampling approach was used to select these patients from the appointments list. Every third patient on the list was recruited. Of the 500 recruited patients, 422 consented to the study resulting in a response rate of 84%.
The Michigan Neuropathy Screening Instrument (MNSI) was used and the responses were added to attain a score . A trained nurse (fluent in English and Arabic) facilitated completing the questionnaire that covered socio-demographics, medical history and physical activity. ‘Yes’ responses to questions 1–3, 5–6, 8–9, 11–12, and 14–15 were each counted as one point. ‘No’ responses to questions 7 and 13 were each counted as one point. Question 4 was a measure of impaired circulation and question 10 was a measure of asthenia. A score of ≥ 7 was considered abnormal .
Patients were asked to wear light clothing without shoes. A portable digital scale and stadiometer were used to measure weight and height, respectively. Patients were asked to stand straight with their heads, backs and buttocks vertically aligned to the height gauge; their heights were then taken and rounded to the nearest 0.5 cm. Percent body fat was estimated by bioelectric impedance, using Tanita Body Composition Analyzer; model TBF-410 (Tanita Corporation, Tokyo, Japan). Abnormal fat composition on bioelectric impedance was defined in the instrument’s protocol as >23% for males and >27% for females. Obesity was defined as body mass index(BMI) ≥ 30 kg/m2. Blood pressure (systolic and phase-V diastolic) measurements recorded were the mean of three separate readings in a sitting position after 10 minutes of resting, using validated electronic sphygmomanometers (Omron Hem 907, Omron Healthcare, Kyoto, Japan). Hypertension was defined as a mean systolic blood pressure ≥130 mm Hg or diastolic blood pressure ≥ 80 mm Hg.
Neuropathy was assessed by MNSI history and examination. MNSI investigates aspects of both small (pain and hyperesthesia) and large (numbness and muscular) nerve fiber patency. Neuropathy was defined as MNSI-history score of ≥ 7 of a total possible score of 13, or MNSIsign score of ≥ 3 of a total possible score of 10 . A podiatrist assessed the feet for ulcers, callus, dry skin, fissures, infection, deformity, footwear, foot care and gait.
Both feet were examined for signs of peripheral neuropathy. Briefly, each foot with any abnormality received a score of one. An ulcer received a score of one. Ankle reflexes were elicited. If absent, the patient was asked to perform the Jendrassic manoeuver; if present, the reflex was designated present with reinforcement and scored 0.5. If absent with the Jendrassic manoeuver, the reflex was designated absent and scored 1.0. Vibration sensation was tested in the great toe, using a 128-Hz tuning fork (Rydel-Seiffer) exactly as per the MNSI protocol. The 10- gram Semmes-Weinstein monofilament was done in accordance with the MNSI .
An ophthalmologist conducted eye examinations for cataract and retinopathy. Retinopathy was graded as none, non-proliferative (microaneurysms, hemorrhage and hard exudates) or proliferative (new blood vessels and/or scar tissue growing into the vitreous).
Venous blood (fasting) was collected to measure serum lipids, glycosylated hemoglobin (HbA1c), albumin and creatinine. A spot urine sample was collected for albumin and creatinine. The tests were performed on Beckman Coulter auto-analyzer DX800 (Beckman Instruments, Inc. Fullerton, CA, USA). Patients with HbA1c ≥ 7% were considered having uncontrolled diabetes . Dyslipidemia was defined as triglycerides >150 mg/dl (1.7 mmol/l), low density lipoproteincholesterol (LDL-c) >100 mg/dl (2.6 mmol/L) or HDL cholesterol <40 mg/dl (1.0 mmol/l) in men and <50 mg/dl (1.3 mmol/l) in women.
Glomerular filtration rate (GFR) was estimated using the Cockcroft-Gault formula [eGFR = (140 – age) x mass (kg) × (0.85% if female)] / [72 x serum creatinine (mg/dL)] . Diabetic nephropathy was considered if the GFR was <60 ml/min/1.73 m2 or if the urinary albumin: creatinine ratio was ≥ 2.5 mg/mmol in males or ≥ 3.5 mg/ mmol in females .
The Statistical Package for Social Sciences (SPSS) software version 19.0 for Windows was used. Logistic regression analysis was used to identify independent predictors of neuropathy. A p-value of <0.05 was significant.
The clinical and laboratory values in all studied male and female patients are shown in Table 1. Sixty seven percent of the patients were females. Blood pressure and HbA1C were similar in both groups.
|Age (yr)||57 ± 12||52 ± 11|
|Height (cm)||167 ± 8||155 ± 6|
|Weight (kg)||81 ± 16||78 ± 17|
|Body mass index (kg/m2)||29 ± 5||32 ± 6|
|Systolic blood pressure (mm Hg)||133 ± 15||133 ± 17|
|Diastolic blood pressure (mm Hg)||78 ± 9||76 ± 9|
|Body fat percentage||28.0 ± 7.9||40.3 ± 7.6|
|Hemoglobin A1c (%)||8.2 ± 1.9||8.2 ± 2.1|
|Total cholesterol (mmol/L)||4.4 ± 1.1||4.8 ± 1.1|
|Triglycerides (mmol/L)||1.5 ± 1.3||1.4 ± 1.1|
|HDL-cholesterol (mmol/L)||0.9 ± 0.2||1.1 ± 0.3|
|LDL-cholesterol (mmol/L)||2.9 ± 0.9||3.1 ± 0.9|
|Total cholesterol/HDL ratio||5.2 ± 1.5||4.6 ± 1.4|
|Serum creatinine (mmol/L)||96.7 ± 64.7||62.9 ± 21.6|
|Urinary albumin: creatinine ratio (mg/mmoL)||1.4 ± 0.6||1.3 ± 0.5|
|Estimated glomerular filtration rate (mL/min)||82.8 ± 37.9||102.3 ± 39.8|
Table 1: The clinical and laboratory values (mean ± SD) in studied patients (n=394).
Based on MNSI-history score of ≥ 7, the prevalence of neuropathy in males was 8.5% and in females was 11.4% (p=0.381). Based on MNSIsign score of ≥ 3, the prevalence of neuropathy in males was 37.0% and in females was 20.1% (p<0.001), (Table 2). Smoking was more common in males (p<0.001) and body fat percentage was significantly elevated in females (p<0.001), (Table 2).
|Variables||Male (n=130)||Female (n=264)||p-value1|
|Duration of diabetes (yr), mean ± SD||10.9 ± 7.9||9.4 ± 7.1||0.062|
|Duration of smoking (yr), mean ± SD||30.4 ± 13.7||10.3 ± 4.5||0.022|
|n (%)||n (%)||p-value 2|
|Illiterate||45 (34.6)||171 (64.8)|
|Primary school||32 (24.6)||39 (14.8)|
|Secondary school||39 (30.0)||35 (13.3)|
|University||14 (10.8)||19 (7.2)|
|Retired||73 (56.2)||2 (0.8)|
|Employed||34 (26.2)||21 (8.0)|
|Unemployed||23 (27.7)||14 (5.3)|
|Smoking||24 (16.2)||3 (1.1)||<0.001|
|Regularly visiting the diabetes center||119 (93.7)||249 (95.8)||0.377|
|Active||84 (65.1)||154 (58.6)||0.210|
|Inactive||45 (34.9)||109 (41.4)|
|Abnormal percentage body fat||94 (73%)||254 (95%)||<0.001|
|Retinopathy||25 (20.7)||28 (10.8)||0.008|
|Mode of diabetes diagnosis||0.005|
|Screening||81 (63.3)||204 (78.8)|
|Symptomatic||34 (24.2)||34 (13.1)|
|Incidental||16 (12.5)||21 (8.1)|
|Lifestyle measures||5 (3.8)||10 (3.8)|
|Oral hypoglycaemic agents||98 (75.4)||185 (70.1)|
|Insulin added||27 (20.8)||69 (26.1)|
|Score <7||118 (91.5)||233 (88.6)|
|Score ≥ 7||11 (8.5)||30 (11.4)||0.381|
|Score <3||75 (61.0)||202 (61.0)|
|Score ≥ 3||48 (37.0)||53 (20.1)||<0.001|
Table 2: Summary of the clinical variables in studied patients (n=394).
MNSI-history score ≥ 7 was significantly associated with being physically inactive (p<0.001), longer duration of diabetes (p=0.006), HbA1c ≥ 7 (p=0.014), abnormal body fat and higher BMI (p=0.042), (Table 3). The odds of abnormal MNSI-history score increased with each unit of increase in physically inactivity (6.36 fold) and HbA1c ≥7 (2.79 fold), (Table 3).
|Variables||Coefficient||p-value||Odds Ratio||95%CI for OR|
|Age (yr)||0.02||0.229||1.02||0.99 – 1.05|
|Female||0.32||0.374||1.38||0.67 – 2.85|
|Height (cm)||-0.016||0.436||0.98||0.94 – 1.03|
|Weight (kg)||0.01||0.311||1.01||0.99 – 1.03|
|Body mass index (kg/cm2)||0.05||0.042||1.06||1.00 – 1.11|
|Systolic blood pressure (mm Hg)||0.01||0.296||1.01||0.99 – 1.03|
|Diastolic blood pressure (mm Hg)||-0.01||0.767||1.00||0.96 – 1.03|
|Total cholesterol (mmol/L)||0.17||0.271||1.18||0.88 – 1.58|
|Triglyceride (mmol/L)||0.12||0.359||1.13||0.88 – 1.45|
|LDL-cholesterol (mmol/L)||0.05||0.799||1.05||0.73 – 1.50|
|HDL-cholesterol (mmol/L)||0.42||0.492||1.52||0.47 – 4.88|
|≥7%||1.03||0.014||2.79||1.13 – 6.85|
|Illiterate||0.40||0.527||1.50||0.43 – 5.23|
|Primary school||-0.26||0.731||0.77||0.17 – 3.43|
|Secondary school||-0.32||0.673||0.73||0.16 – 3.23|
|Unemployed||0.12||0.882||1.13||0.24 – 5.35|
|Retired||0.57||0.366||1.77||0.51 – 6.06|
|Housewife||0.46||0.411||1.59||0.53 – 4.76|
|Yes||-0.27||0.717||0.77||0.17 – 3.39|
|Duration of smoking||0.03||0.556||1.03||0.93 – 1.15|
|Duration of diabetes (yr)||0.06||0.006||1.06||1.02 – 1.10|
|Family history of diabetes|
|Present||0.16||0.654||1.17||0.59 – 2.32|
|Regularly visit of diabetes center|
|Yes||-0.51||0.457||0.60||0.17 – 2.16|
|Mode of diabetes diagnosis||0.989|
|Incidental||0.04||0.995||1.00||0.27 – 3.69|
|Screening||-0.06||0.901||0.95||0.40 – 2.27|
|Treatment of diabetes|
|Oral hypoglycaemic agents||-0.84||0.017||0.43||0.22 – 0.85|
|Inactive||1.85||<0.001||6.36||2.94 – 13.78|
|Body fat composition|
|Normal||-0.93||0.024||0.40||0.17 – 0.94|
|Controlled||-0.004||0.994||1.00||0.37 – 2.70|
|Estimated glomerular filtration rate|
|<60||0.594||0.193||1.81||0.77 – 4.29|
|Yes||0.59||0.193||1.81||0.77 – 4.29|
|Yes||0.53||0.265||1.69||0.70 – 4.13|
Table 3: Simple logistic regression of MNSI-history score vs. selected predictors.
Table 4 shows the results of multivariate stepwise logistic regression analysis to determine the best subset of predictors of MNSI-history score ≥ 7. The six significant predictors in Table 3 were entered in the model based on a forward Likelihood ratio method. HbA1c and physical activity were the only significant predictors of abnormal MNSI-history score (Table 4).
|HbA1c (ref. = normal)||1.23||3.41||1.15 – 10.16||0.028|
|Physical activity (ref. = active)||1.61||4.99||2.21 – 11.29||<0.001|
Table 4: Subset of significant predictors of the odds of abnormal MNSI-history score.
MNSI-sign score ≥ 3 was significantly associated with several variables including advanced patient age, increased height, male gender, higher systolic blood pressure, longer duration of diabetes, albuminuria and retinopathy (Table 5, p<0.001 for each). The odds of MNSI-sign score ≥ 3 increased with each unit of increase in duration of diabetes (12%), age (8%), height (3%) and systolic blood pressure (3%), (Table 5).
|Variables||Coefficient||p-value||Odds Ratio||95% CI for OR|
|Age (yr)||0.08||<0.001||1.08||1.06 – 1.11|
|Female||-0.89||<0.001||0.410||0.26 – 0.66|
|Height (cm)||0.03||0.032||1.03||1.00 – 1.06|
|Weight (kg)||-0.004||0.548||1.00||0.98 – 1.01|
|Body mass index (kg/m2)||-0.03||0.194||0.97||0.94 – 1.01|
|Systolic blood pressure (mm Hg)||0.03||<0.001||1.03||1.01 – 1.05|
|Diastolic blood pressure (mm Hg)||0.01||0.548||1.01||0.98 – 1.03|
|Total cholesterol (mmol/L)||-0.03||0.771||0.97||0.78 – 1.20|
|Triglyceride (mmol/L)||0.03||0.787||1.03||0.84 – 1.26|
|LDL-cholesterol (mmol/L)||0.02||0.864||1.02||0.79 – 1.32|
|HDL-cholesterol (mmol/L)||0.20||0.640||1.23||0.53 – 2.85|
|≥7||0.52||0.055||1.68||0.98 – 2.88|
|Single||-1.61||0.156||0.20||0.02 – 1.85|
|Married||0.12||0.807||1.13||0.43 – 2.93|
|Divorced||0.92||0.232||2.50||0.56 – 11.25|
|Illiterate||0.47||0.301||1.60||0.77 – 4.44|
|Primary school||-0.01||0.988||0.99||0.44 – 3.14|
|Secondary school||-0.23||0.959||0.97||0.41 – 2.98|
|Unemployed||1.88||0.001||6.53||2.24 – 19.10|
|Retired||1.80||<0.001||6.07||2.29 – 16.10|
|Housewife||0.88||0.056||2.42||0.98 – 5.97|
|Yes||0.54||0.233||1.71||0.723 – 4.04|
|Duration of smoking (yr)||0.04||0.197||1.04||0.98 – 1.11|
|Duration of diabetes (yr)||0.11||<0.001||1.12||1.08 – 1.16|
|Family history of diabetes|
|Present||-0.44||0.064||0.64||0.40 – 1.03|
|Regularly visiting diabetes center|
|Yes||-1.31||0.008||0.27||0.10 – 0.71|
|Mode of diabetes diagnosis|
|Incidental||0.04||0.932||1.04||0.42 – 2.60|
|Screening||-0.01||0.969||0.99||0.53 – 1.83|
|Treatment of diabetes|
|Oral hypoglycaemic agents||-0.59||0.057||0.61||0.36 – 1.01|
|Inactive||1.02||<0.001||2.76||1.73 – 4.42|
|Body fat composition|
|Normal||0.59||0.018||1.80||1.11 – 2.93|
|Controlled||-0.06||0.861||0.94||0.48 – 1.86|
|Estimated glomerular filtration rate|
|<60||1.14||<0.001||3.13||1.73 – 5.65|
|Yes||1.14||<0.001||3.13||1.73 – 5.65|
|Yes||0.98||0.003||2.65||1.41 – 5.00|
Table 5: Simple logistic regression of MNSI-sign score vs. selected predictors.
Table 6 shows the results of multivariate stepwise logistic regression analysis to determine the best subset of predictors of MNSI-sign score ≥ 3. The significant predictors in Table 5 were entered in the model based on a forward likelihood ratio method. Age, height, systolic blood pressure and duration of diabetes were the only significant predictors of abnormal MNSI-sign score (Table 6).
|Age||0.06||1.06||1.03 – 1.09||<0.001|
|Height||0.05||1.06||1.02 – 1.10||0.005|
|Systolic blood pressure||0.03||1.03||1.01 – 1.06||0.002|
|Duration of diabetes (yr)||0.08||1.08||1.04 – 1.13||<0.001|
Table 6: Subset of significant predictors of the odds of abnormal MNSI-sign score.
Table 7 summarizes the patients’ responses to the MNSI-history questionnaire. The most prevalent leg/foot complaints were numbness, burning pain, prickling feeling and pain with walking (each occurring in ≥ 40% of the patients).
|Percent of Patients with Abnormal Symptom|
|Are your legs/feet numb?||44%|
|Do you have burning pain in your legs/feet?||47%|
|Are your feet too sensitive to touch?||14%|
|Do you have prickling feelings in your legs/feet?||40%|
|Does it hurt when the bed covers touch your legs/feet?||11%|
|Can you tell hot water from cold water in the tub/shower?||5%|
|Have you had open sore on your foot?||12%|
|Has your doctor ever told you that you have neuropathy?||7%|
|Are your symptoms worse at night?||43%|
|Do your legs/feet hurt when you walk?||42%|
|Are you able to sense your legs/feet when you walk?||28%|
|Is the skin on your legs/feet so dry that it cracks open?||12%|
|Have you had an amputation?||<1%|
Table 7: Summary of patients’ responses to the MNSI-history questionnaire.
The reported prevalence of diabetic peripheral neuropathy varies with the diagnostic tools used and the criteria applied. In this study, the MNSI history (questionnaire) and sign (lower extremity examination) scores were used, yielding prevalences of 10.4% and 25.6%, respectively. The sign score value is similar to previous reports from UAE (35%), Saudi Arabia (38%), Bahrain (37%), Egypt (20%), and United Kingdom (29%) [2,3,8,15-17]. In a prospective study in Finland involving newly diagnosed patients with type 2 diabetes, the prevalence of definite or probable neuropathy increased from 8.3% at baseline to 41.9% at 10 years; the corresponding values for healthy individuals were 2.1% and 5.8%, respectively .
A definite diagnosis of peripheral neuropathy requires nerve conduction studies . These gold standard tests, however, are not readily available in most outpatient settings. For routine clinical practice, simpler approaches have been used including the United Kingdom Screening Test and the Michigan Neuropathy Screening Instrument [12,17]. A more detailed testing is also available (e.g., Toronto criteria, San Antonio Consensus and Mayo Clinic criteria) [4,20,21].
The neuropathy complaints are subjective and a constellation of symptoms is often more reliable than a single complaint (Table 7). In this study, 10.4% of the patients were abnormally symptomatic (MSNIhistory score ≥ 7). In these patients, use of sandals as normal daily footwear might result in more skin dryness, callus formation and cracks over of heals (~72% of the patients); these findings might lead to a score of 2 out of 10 on the MNSI-sign. Any additional foot examination abnormality would raise the MNSI-sign score to the neuropathy range (defined ≥ 3). Nevertheless, the prevalence of neuropathy based on MNSI-sign score (25.6%) was relatively more objective and, thus, more reliable than that based on subjective MNSI-history score (10.4%). Furthermore, the prevalence of neuropathy based on MNSI-sign score was more consistent with previous studies (20-38%) [2,3,8,15-17,22,23]. Future studies are needed to assess the most appropriate MNSI-history and sign cutoff scores for defining neuropathy in the UAE population.
Limited amount of studies have addressed diabetes-associated symptoms of neuropathy in the Peninsular Arabs. Forty-seven percent of our patients had symptoms of painful neuropathy (Table 7). Higher prevalences were reported in a study from Saudi Arabia (65%) and in a multi-center study from Middle East (54%) [10,24]. This value, however, was less than reported in Australian patients (26%) .
Typical determinants of diabetic neuropathy include patient age, duration of diabetes, glycemic control, hypertension, smoking, obesity and dyslipidemia. In this study, the subset of significant predictors for odds of MSNI-history score ≥ 7 was abnormal HbA1C (odds ratio, 3.41) and inactive physical activity (odds ratio, 3.41), (Table 4). The subset of significant predictors for odds of MSNI-sign score ≥ 3 was patients age (odds ratio, 1.06), height (odds ratio, 1.06), systolic blood pressure (odds ratio, 1.03) and duration of diabetes (odds ratio, 1.08), (Table 6). The results confirm the need for optimal glycemic control. In the European Diabetes Perspective Complications Study, the rate of deterioration of glycemic control contributed markedly to the risk of neuropathy, independently of the glycosylated hemoglobin value at baseline . Previous cross-sectional and cohort studies have also reported hypertension as a significant risk factor for diabetic neuropathy [26,27]. In rodents, the additive effect of hypertension on diabetic neuropathy was attributed to damages in Schwann cells and myelin sheaths around exons. These results endorse the relationship between diabetic neuropathy and its microvascular complications [28,29].
Lack of physical activity was also found to be associated with neuropathy in other studies [30-32]. Height may act in concert with poor glycemic control, which is postulated to contribute to capillary basement membrane thickening, possibly due to greater pressure and decreased capillary blood flow in the lower extremities [23,33-35]. Consistent with previous studies, male gender was a risk factor for the MSNI-sign score (p<0.001, Table 5) [36,37].
Association between abnormal body fat percentage and neuropathy has been shown in previous studies [48-40]. This association was noted here (Tables 3 and 5); however, it disappeared in the regression (Tables 4 and 6). Current evidence supports that the metabolic syndrome and obesity are risks factor of neuropathy. Proposed mechanisms for this nerve damage include fat deposition, extracellular protein glycation, mitochondrial dysfunction, oxidative stress and activation of counter-regulatory signaling pathways leading to chronic metabolic inflammation [38-40].
In this study, 95% of the patients regularly attended the diabetes center. The presence of high illiteracy rate (55%, Table 2) may have contributed to less health education and suboptimal foot care practices. This report addressed the extent of diabetic neuropathy in UAE patients. The study, however, has limitations that include lack of nerve conduction studies (the definite tool for diagnosing neuropathy), causality determination (e.g., vitamin B12 or folic acid deficiency) and lack of patients from other centers in the country. These issues are logical goals in subsequent follow-up studies. Sixteen percent of patients have declined to participate. This problem may produce a selection bias, especially since patients with relevant symptoms are more likely to be included than asymptomatic patients. Thus, the prevalence of neuropathy may be somewhat lower than that shown here.
Diabetic neuropathy is common in UAE patients. This complication is shown here to be associated with poor glycemic control, hypertension and lack of physical activity. The results highlight the need for intensive programs aiming at early detection and prompt implementation of health education.
This work was supported by a fund from the College of Medicine and Health Sciences - UAE University. The authors are in debt of Tawam Hospital staff for their valued support.