|Year : 2016 | Volume
| Issue : 4 | Page : 232-237
Frequency and determinants of subclinical neuropathy in type 1 diabetes mellitus
Yosria A Al-Taweel1, Rasha M Fahmi MD, PhD 1, Nahed Shehta1, Tamer S Elserafy1, Hala M Allam2, Ahmed F Elsaid3
1 Department of Neurology, Zagazig University, Zagazig, Egypt
2 Department of Internal Medicine, Zagazig University, Zagazig, Egypt
3 Department of Community Medicine and Public Health, Zagazig University, Zagazig, Egypt
|Date of Submission||17-Sep-2016|
|Date of Acceptance||28-Oct-2016|
|Date of Web Publication||17-Mar-2017|
Rasha M Fahmi
Department of Neurology, Zagazig University, Zagazig
Source of Support: None, Conflict of Interest: None
Diabetic neuropathy is the most common complication of diabetes. We hypothesized that uncontrolled diabetes is associated with subclinical diabetic neuropathy that is influenced by duration of disease. Assessment of the prevalence and associated determining factors will be important for the prevention and treatment of neuropathy.
This aim of this study was to assess the frequency and determining factors of subclinical peripheral neuropathy in type 1 diabetic (T1DM) patients.
Patients and methods
The current hospital-based, case–control study was conducted at Zagazig University Hospitals. It included three age-matched and sex-matched groups. Each group comprised 30 participants: group A included diabetic patients with a duration of T1DM of 5 years or less; group B included patients with a duration of T1DM of more than 5 years; and the control group included normal healthy individuals. Clinical assessment was carried out to exclude symptoms and signs of neuropathy. Laboratory investigations including fasting and 2-h postprandial blood glucose level, glycosylated hemoglobin (HbA1c), lipid profile, liver function, kidney function, and nerve conduction studies were carried out for every participant.
The frequency of subclinical neuropathy in group A and group B was 46.6 and 76.6%, respectively, and this difference was statistically significant (P=0.03). Univariate analysis revealed significantly higher levels of HbA1c, dyslipidemia, and nerve conduction parameters in group B compared with group A and the control group. Multivariate logistic regression analysis showed that duration of diabetes (P=0.02) and HbA1c (P=0.02) were the only independent factors associated with subclinical neuropathy.
The high frequency of subclinical neuropathy in diabetic patients highlights the importance of nerve conduction studies for the early detection of neuropathy in T1DM.
Keywords: nerve conduction studies, subclinical neuropathy, type 1 diabetes mellitus
|How to cite this article:|
Al-Taweel YA, Fahmi RM, Shehta N, Elserafy TS, Allam HM, Elsaid AF. Frequency and determinants of subclinical neuropathy in type 1 diabetes mellitus. Egypt J Neurol Psychiatry Neurosurg 2016;53:232-7
|How to cite this URL:|
Al-Taweel YA, Fahmi RM, Shehta N, Elserafy TS, Allam HM, Elsaid AF. Frequency and determinants of subclinical neuropathy in type 1 diabetes mellitus. Egypt J Neurol Psychiatry Neurosurg [serial online] 2016 [cited 2018 Apr 20];53:232-7. Available from: http://www.ejnpn.eg.net/text.asp?2016/53/4/232/202383
| Introduction|| |
Diabetic neuropathy (DN) is recognized as the major long-term complication and is considered the most common type of neuropathies . It affects both type 1 and type 2 diabetes, but in type 1 diabetes mellitus (T1DM) it develops more rapidly with severe manifestations because of the early onset of diabetes ,. Subclinical neuropathy is defined as an electrophysiological abnormality of nerve function with no clinical symptoms of peripheral nerve disease .
Little is known about the prevalence of subclinical neuropathy in T1DM in Egypt. Electrophysiological studies are the most widely used electrodiagnostic tools for the assessment of DN even in the asymptomatic stage. They are sensitive, accurate, objective, reproducible, and strongly correlated with the underlying nerve structural changes ,. Moreover, they were used to detect, localize, and describe the type and severity of the pathophysiological lesion that are not recognized clinically ,.
The aim of this study was to assess the peripheral nervous system using nerve conduction studies (NCS) in patients with T1DM without clinical symptoms or signs of DN and analyze the relation between the demographic as well as clinical risk factors and neurophysiological parameters.
| Patients and methods|| |
The present study was a case–control one. Patients were selected from Internal Medicine Department outpatients and inpatients in Zagazig University Hospitals between March 2015 and December 2015. The study was approved by the local ethics committee. Informed written consent was obtained from each patient or legal guardian and controls.
Sixty patients (32 male and 28 female) with a diagnosis of T1DM without clinical symptoms and/or signs of DN and having no other cause for polyneuropathy were selected. Their ages ranged from 15 to 37 years. They were classified into two groups: group A comprised 30 diabetic patients with a duration of T1DM of 5 years or less, and group B comprised 30 patients with a duration of more than 5 years.
The control group comprised 30 healthy age-matched and sex-matched individuals. They did not have any symptoms or signs of systemic, metabolic, or central nervous system affection.
All participants were informed about the details and objectives of this study before the start and informed consent was obtained.
All patients underwent detailed history taking and complete general and neurological examination. The following patients were excluded from the study: diabetic patients with symptomatic DN (those having clinical symptoms or/and signs of peripheral neuropathy), and patients with other obvious causes of neuropathy, such as chronic renal and liver diseases, thyroid diseases, alcohol abuse, use of neurotoxic medication, exposure to toxins, or malignant diseases. In addition, patients with mental retardation or psychiatric diseases were excluded.
Routine laboratory investigations including fasting and 2-h postprandial blood glucose level, glycosylated hemoglobin (HbA1c), lipid profile, liver function, kidney function, erythrocyte sedimentation rate, and thyroid profile were carried out.
Laboratory measurement of HbA1c was carried out using the fast ion exchange resin separation method. This research followed the recommendation of the American Diabetes Association for HbA1c values: values less than 7% is considered well-controlled DM, values ranging from 7 to 9% is considered mildly controlled, and values more than 9% is considered poorly controlled .
NCS in the median, ulnar, peroneal, tibial, and sural nerves were carried out for all participants with the Micromed machine in the neurology outpatient clinic. Motor conduction studies were carried out with surface disk electrodes, with the active electrode inserted on the muscles and supramaximal stimulation of the corresponding nerves. Sensory NCS were performed.
The parameters were compared between patients and controls. Individual values of conduction velocities, latencies, or amplitude were considered abnormal when outside the mean±2 SD of controls . If two or more nerves had at least one abnormal parameter compared with the age-matched controls, it is electrophysiologically considered DN. The association between the electrophysiological data and the clinical variables of patients such as age, disease duration, HbA1c, and lipid profile was analyzed.
Data were coded and analyzed using SPSS statistics (V. 19.0, 2010; IBM Corp., NY, USA) . Quantitative variables were expressed as mean±SD, whereas qualitative variables were expressed as number and percentage. Multiple group comparison was performed using the χ2-test for qualitative variables and analysis of variance was used for quantitative variables. Bonforoni adjustment was utilized to adjust the P-value according to the number of groups. P-value of 0.05 or less was considered significant and less than 0.001 as highly significant .
Multivariable logistic regression analysis was performed, with all significant variables identified using univariate analysis as independent variables and diagnosis of peripheral neuropathy as dependent variable. The model yielded adjusted odds ratios and their 95% confidence intervals, which was used as a measure of association between independent and dependent variables .
Sample size calculation
The prevalence of electrophysiological abnormalities (peripheral neuropathy) in diabetic patients was reported to be in the range from 0 to 93%. Using a prevalence of 66% , a sample size of 30 participants per group was found to achieve a power of more than 80% at a significant α-level of 0.05 in case–control studies. Calculation was performed using Epi Info 7 (CDC, 2015) .
| Results|| |
Ninety participants, 60 patients and 30 controls, were included in this study. All patients were clinically asymptomatic for peripheral neuropathy.
The data were collected, summarized, and statistically analyzed, and the results were presented in tables, graphs, and figures as follows.
The clinical characteristics and laboratory results of patients and controls are shown in [Table 1]. The patients were classified according to the duration of disease into two groups (A and B). There were significant differences with regard to HbA1c, total cholesterol, and triglycerides. There was no statistically significant difference as regards age of onset of diabetes.
|Table 1 Comparison of the studied characteristic of patients and controls|
Click here to view
As regards NCS ([Table 2] and [Table 3]), there were statistically significant differences between controls and patients, especially in group B. The frequency of subclinical neuropathy in diabetic patients with duration greater than 5 years and in patients with duration of 5 years or less was 76.6 and 46.6%, respectively. This difference was statistically significant (P=0.03). Thirty-seven patients (61.7%) had subclinical neuropathy in which the lower limb was more affected and the most frequently affected nerve was the sural nerve ([Figure 1]).
|Figure 1 Percentages of abnormal motor and sensory parameters of different nerves in diabetic patients.|
Click here to view
Multivariable logistic regression analysis revealed that the duration of T1DM and level of HbA1c were the only independent predictors of subclinical neuropathy ([Table 4]).
|Table 4 Multivariable logistic regression analysis for significant variables associated with diabetic neuropathy|
Click here to view
| Discussion|| |
DN is variable, ranging from symptomatic to asymptomatic, but once established as neuropathy it is irreversible and may finally be disabling . Therefore, early diagnosis of DPN is important for immediate interventions to decrease the associated complications and disabilities .
Diagnosis of DPN on clinical ground alone is not accurate due to difficulty in detecting a small alteration of neuropathy . It has been accepted that the ideal diagnosis of DPN could be made by means of both clinical findings and electrophysiological changes . For that reason, NCS was widely used in the evaluation and prediction of DN .
The present study revealed that the frequency of subclinical neuropathy was 61.7% in T1DM patients who were free from symptoms and signs of peripheral neuropathy. The frequency was higher in patients with disease duration more than 5 years (76.6%). This result is in agreement with other studies that reported a prevalence of 66%  and 62.5% . Studies conducted at the time of diagnosis of diabetes reported a low prevalence of 7.5%  and 25% . However, a high prevalence of 80%  and 87%  was reported in other studies. This difference in prevalence could be attributed to the different diagnostic methods and duration of DM.
As a result, the estimation of DN is variable as it tends to be subclinical in these young patients with T1DM . This was supported by the result of Hyllienmark et al. , who reported that subclinical nerve dysfunction with T1DM predicts clinical neuropathy many years later as confirmed by pathological findings in the distal nerves in about one-fourth of young patients . Moreover, two follow-up studies showed a significant association between clinical neuropathy and early abnormalities of electrophysiological parameters ,.
In this study, there were significant changes in most of the parameters, including both motor as well as the sensory nerves of both the upper and lower extremities of patients as compared with controls. NCS in this study revealed that the lower limbs were more affected compared with the upper limbs, especially the sensory part. The sural nerve was the most affected. Various studies suggested that sural nerve was the first to be affected and the most common indicator of peripheral neuropathy ,,. This prominence of peripheral neuropathy in the lower limb could be attributed to the length of these nerves and interruption of axoplasmic flow. In these long nerves, all necessary proteins that are synthesized in cell body are transmitted to the distal parts of the nerves through axoplasmic flow to maintain the nerve anatomic and functional integrity. Any interruption of axoplasmic flow is affected in early period and more prominent in long nerves than in short nerves .
Numerous factors increase the vulnerability to nerve damage in diabetic patients, such as poor glucose control, duration of disease, age, sex, BMI, smoking, and lipid ,,. These factors enhance the atherosclerotic effect and reduce the blood flow to the legs and feet leading to damage of the peripheral nerves , through increased release of cytokines and stimulating protein C kinase and other oxidative stress , which exacerbate with hyperglycemia, and this process is time dependent ,. In the present study, it was found that diabetic patients had poor glucose control as indicated by high HbA1c, especially in group B, dyslipidemia, and increased duration of diabetes. These results are in agreement with other studies ,,,.
Poor metabolic control in diabetes was suggested to be a strongest predictor for the development of both clinical and subclinical neuropathy , especially in newly diagnosed patients . Interestingly, this progression of neuropathy could be prevented or improved during the first 5 years by controlling diabetes ,. HbA1c was used as an index of glycemic control and its role as a risk factor for DPN was confirmed in some studies , but not established by other studies ,.
Further analysis of the result with multivariate logistic regression showed that the main predictors for subclinical peripheral neuropathy were HbA1c and duration of disease. This is in agreement with other studies that concluded that the duration of disease and impaired glycemic control play an important role in the development of neuropathy ,. This was supported by the study by Oguejiofor et al. , who realized that long duration of diabetes of more than 10 years was always associated with peripheral neuropathy.
| Conclusion|| |
The results of the current study demonstrated the high frequency of subclinical neuropathy in diabetic patients. It was more marked in patients with poor metabolic control and longer disease duration (>5 years). This high frequency highlights the importance of NCS for early detection of neuropathy in T1DM. Rigorous control of HbA1c is essential for the prevention and treatment of diabetic peripheral neuropathy.
Screening for T1DM during early years with NCS is important to identify individuals who are at high risk of developing DN. This allows the prediction and prevention of the development of neuropathy. Rigorous control of HbA1c is essential for the prevention and treatment of peripheral DN.
Further prospective studies are needed to verify whether controlling diabetes could improve the neuropathy.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Uzun N, Uluduz D, Mikla S, Aydin A. Evaluation of asymptomatic central neuropathy in type I diabetes mellitus. Electromyogr Clin Neurophysiol 2006; 46:131–137.
Otto-Buczkowska E, Kazibutowska Z, Soltyk J, Machnica L. Neuropathy and type1 diabetes mellitus. Pediatr Endocrinol Diabetes Metab 2008; 14:109–116.
Louraki M, Karayianni C, Kanaka-Gantenbein C, Katsalouli M, Karavanaki K. Peripheral neuropathy in children with type 1 diabetes. Diabetes Metab 2012; 38:281–289.
Albers JW, Herman WH, Pop-Busui R, Martin CL, Cleary P, Waberski B. Subclinical neuropathy among diabetes control and complications trial participants without diagnosable neuropathy at trial completion. Diabetes Care 2007; 30:2613–2618.
Dyck PJ, Overland CJ, Low PA, Litchy WJ, Davies JL, Dyck PJB et al.
Signs and symptoms versus nerve conduction studies to diagnose diabetic sensorimotor polyneuropathy: Cl vs. NPhys trial. Muscle Nerve 2010; 42:157–164.
Dyck PJ, Albers JW, Wolfe J, Bolton CF, Walsh N, Klein CJ et al.
A TRIAL OF Proficiency of nerve conduction: greater standardization still needed. Muscle Nerve 2013; 48:369–374.
Tkac I, Bril V. Glycemic control is related to the electrophysiologic severity of diabetic peripheral sensorimotor polyneuropathy. Diabetes Care 1998; 21:1749–1752.
Meh D, Denislic M. Subclinical neuropathy in type I diabetic children. Electroencephalogr Clin Neurophysiol 1998; 109:274–280.
American Diabetes Association Standards of medical care in diabetes. Diabetes Care 2008; 31:12–54.
Dumitru D, Amato AA, Zwarts MJ. Nerve conduction studies. In: Dumitru D, editors. Electrodiagnostic medicine. 2nd ed. Philadelphia: Hanley and Belfus Inc; 2002. 194–217.
IBM Corp. Released 2010. IBM SPSS Statistics for Windows, Version 19.0. Armonk, NY: IBM Corp; 2010.
Dyck PJ, Kratz KM, Karnes JL, Litchy WJ, Klein R, Pach JM et al.
The prevalence by staged severity of various types of diabetic neuropathy, retinopathy, and nephropathy in a population-based cohort: The Rochester Diabetic Neuropathy Study. Neurology 1993; 43:817–824.
Mojaddidi MA, Aboonq M, AL Nozha OM, Allam A, EL-Bab MF. Early diagnosis of diabetic neuropathy in Almadinah Almunawwarah. J Taibah Univ Med Sci 2011; 6:121–131.
Várkonyi T, Kempler P. Diabetic neuropathy: new strategies for treatment. Diab Obes Metab 2008; 10:99–108.
Perkins BA, Olaleye D, Zinman B, Bril V. Simple screening tests for peripheral neuropathy in the diabetes clinic. Diabetes Care 2001; 24:250–256.
American Diabetes Association American Academy of Neurology. Consensus statement. Report and recommendations of the San Antonio conference on diabetic neuropathy. Diabetes Care 1988; 11:592–597.
Hasani N, Khosrawi S, Hashemipour M, Haghighatiyan M, Javdan Z, Hadi M et al.
Prevalence and related risk-factors of peripheral neuropathy in children with insulin-dependent diabetes mellitus. J Res Med Sci 2013; 18:132–136.
Semiz S, Fişenk F, Akçurin S, Bircan I. Temporary multiple cranial nerve palsies in a patient with type 1 diabetes mellitus. Diabetes Metab 2002; 28:413–416.
Weintrob N, Amitay I, Lilos P, Shalitin S, Lazar L, Josefsberg Z. Bedside neuropathy disability score compared to quantitative sensory testing for measurement of diabetic neuropathy in children, adolescents, and young adults with type 1 diabetes. J Diabetes Complications 2007; 21:13–19.
Sagdeo MM, Pitale SU, Sahasrabuddhe AV. Nerve conduction study in patients with type 1 diabetes. Vidarbha J Intern Med 2014; 16:17–22.
Hyllienmark L, Alstrand N, Jonsson B, Ludvigsson J, Cooray G, Toppet JW. Early electrophysiological abnormalities and clinical neuropathy: a prospective study in patients with type 1 diabetes. Diabetes Care 2013; 36:3187–3194.
Trotta D, Verrotti A, Salladini C, Chiarelli F. Diabetic neuropathy in children and adolescents. Pediatr Diabetes 2004; 5:44–57.
Weisman A, Bril V, Ngo M, Lovblom LE, Halpern EM, Orszag A et al.
Identification and prediction of diabetic sensorimotor polyneuropathy using individual and simple combinations of nerve conduction study parameters. Plos One 2013; 8:e58783.
Karsidag S, Moralı S, Sargın M, Salman S, Karsidag K, Us O. The electrophysiological findings of subclinical neuropathy in patients with recently diagnosed type 1 diabetes mellitus. Diabetes Res Clin Pract 2005; 67:211–219.
Dyck PJ, Karnes JL, Daube J, O’Brien P, Service FJ. Clinical and neuropathological criteria for the diagnosis and staging of diabetic polyneuropathy. Brain 1985; 108:861–880.
Wiggin TD, Sullivan KA, Pop-Busui R, Amato A, Sima AA, Feldman EL. Elevated triglycerides correlate with progression of diabetic neuropathy. Diabetes 2009; 58:1634–1640.
Booya F, Bandarian F, Larijani B, Pajouhi M, Nooraei M, Lotfi J. Potential risk factors for diabetic neuropathy: a case control study. BMC Neurol 2005; 10:5–24.
Genuth S, Eastman R, Kahn R, Klein R, Lachin J, Lebovitz H et al.
Implications of the United Kingdom Prospective Diabetes Study. Diabetes Care 2003; 26:S28–S32.
Yagihashi S, Mizukami H, Sugimoto K. Mechanism of diabetic neuropathy: where are we now and where to go? J Diabetes Invest 2011; 2:18–32.
Kitsios K, Tsapas A, Karagianni P. Glycemia and cardiovascular risk: challenging evidence based medicine. Hippokratia 2011; 15:199–204.
Searls Y, Smirnova IV, Vanhoose L, Fegley B, Loganathan R, Stehno-Bittel L. Time-dependent alterations in rat macrovessels with type 1 diabetes. Exp Diabetes Res 2012; 2012:278620.
Lee SS, Han HS, Kim H. A 5-yr follow-up nerve conduction study for the detection of subclinical diabetic neuropathy in children with newly diagnosed insulin-dependent diabetes mellitus. Pediatr Diabetes 2010; 11:521–528.
Pirart J. Diabetes mellitus and its degenerative complications. A prospective study of 4,400 patients observed between 1947 and 1973. Diabetes Care 1978; 1:168–188.
Martin CL, Albers J, Herman WH, Cleary P, Waberski B, Greene DA et al.
Neuropathy among the diabetes control and complications trial cohort 8 years after trial completion. Diabetes Care 2006; 29:340–344.
Ghavami H, Ahmadi F, Mehin S, Meamarian R, Entezami H. Assessment of the relation between diabetic neuropathy and HbA1C concentration. Razi J Med Sci 2007; 13:141–147.
El-Salem K, Ammari F, Khader Y, Dhaimat O. Elevated glycosylated hemoglobin is associated with subclinical neuropathy in neurologically asymptomatic diabetic patients: a prospective study. J Clin Neurophysiol 2009; 26:50–53.
Oguejiofor OC, Odenigbo CU, Oguejiofor CB. Evaluation of the effect of duration of diabetes mellitus on peripheral neuropathy using the United Kingdom screening test scoring system, biothesiometry and aesthesiometry. Niger J ClinPract 2010; 13:240–247.
[Table 1], [Table 2], [Table 3], [Table 4]