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 Table of Contents  
ORIGINAL ARTICLE
Year : 2015  |  Volume : 6  |  Issue : 1  |  Page : 31-34

Can chronoscopic reading in whole body reaction time predict peripheral neuropathy in type 2 diabetics? A case control study


1 Department of Physiology, SDM College of Medical Sciences, Dharwad, Karnataka, India
2 Department of General Medicine, SDM College of Medical Sciences, Dharwad, Karnataka, India

Date of Web Publication8-Dec-2014

Correspondence Address:
Vitthal Khode
Department of Physiology, SDM College of Medical Sciences, Sattur, Dharwad, Karnataka
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/0975-9727.146420

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  Abstract 

Background: Type 2 diabetes is investigated as a risk factor for peripheral neuropathy. In whole body simple reaction time (WBSRT), the reaction time is split into two chronoscopic readings C1 and C2. C1 measures the time required for central processing which requires cognition and C2 measures total reaction time. C2-C1 measures the time required for peripheral motor response, i.e. the required for conduction of impulse in motor nerve fiber. We hypothesized that WBSRTC2-C1 will be delayed in diabetes and WBSRTC2-C1 will have predictive value in detecting peripheral neural dysfunction. Settings and Design: Hospital-based case control study. Materials and Methods: This study was conducted on 120 subjects using whole body reaction timers with the criteria of age (40-60 years) and type 2 diabetes and the results were compared with an equal number of age- and sex-matched controls. Statistical analysis was done by independent t-test and duration of diabetes was correlated with the time required for motor response (WBSRTC2-C1) using Pearson's correlation. Predictive value of WBSRTC2-C1 was calculated by using receiver operating characteristic (ROC) curve. Results: WBSRTC2-C1 (334 ± 67 ms) in diabetics was more delayed than WBSRTC2-C1 (297 ± 66 ms) in controls, indicating peripheral neural dysfunction in diabetes. There was no significant correlation between HbA1c, diabetic duration with WBSRTC2-C1 in diabetes. ROC curve for WBSRTC2-C1 to predict peripheral neuropathy was insignificant. Conclusion: Though WBSRTC2-C1 is delayed in diabetics, it alone cannot predict peripheral neural dysfunction in diabetics.

Keywords: Peripheral neuropathy, reaction times, type 2 diabetes


How to cite this article:
Khode V, Ruikar K, Sindhur J, Nallulwar S. Can chronoscopic reading in whole body reaction time predict peripheral neuropathy in type 2 diabetics? A case control study. Muller J Med Sci Res 2015;6:31-4

How to cite this URL:
Khode V, Ruikar K, Sindhur J, Nallulwar S. Can chronoscopic reading in whole body reaction time predict peripheral neuropathy in type 2 diabetics? A case control study. Muller J Med Sci Res [serial online] 2015 [cited 2022 Jul 5];6:31-4. Available from: https://www.mjmsr.net/text.asp?2015/6/1/31/146420


  Introduction Top


Type 2 diabetes mellitus, the most common endocrine disorder characterized by metabolic abnormalities, causes micro- and macrovascular complications in the long run resulting in significant morbidity and mortality. [1] Diabetic neuropathy (DN) is one of the most commonly occurring microvascular complications, accounting for 28% of all the complications in diabetics. [2] It is a progressive process that has a long asymptomatic stage. [3] It is important to identify neuropathy in the asymptomatic stages as the disease process progresses to diabetic foot, a highly morbid condition that arises from infection and ulceration of the foot, finally leading to amputation. [4] Early identification and glycemic control are the key factors for preventing DN. The American Academy of Neurology recommends at least one of the following five criteria for diagnosing DN: Symptoms, signs, electrodiagnostic tests, quantitative sensory tests, and autonomic testing. [5] Practically, electrodiagnostic tests are less utilized for the diagnosis or the follow-up of DN. Nerve conduction studies (NCS) are electrodiagnostic tests which are used to evaluate the ability of the motor and the sensory nerves to conduct electricity. Reaction time (RT) is a reliable indicator of the time taken from the onset of stimulus to an appropriate response which includes the rate of processing of sensory stimuli by the central nervous system and its execution by motor response. Investigators have shown that the RTs are delayed in diabetics. [6],[7] Although delayed RTs indicate involvement of peripheral processing, they cannot quantify how much time is required for peripheral processing.

In whole body simple reaction time (WBSRT), the RT is split into two chronoscopic readings, WBSRTC1 and WBSRTC2. WBSRTC1 is the time required from visual stimuli to subject lifts his leg from starting board, which measures the time required for central processing. WBSRTC2 is the total time required from visual stimuli to end task. WBSRTC2-C1 is the time required for lifting his leg from starting board to stepping board i.e., time required for conduction of impulse in motor nerve fiber. The purpose of our study is to measure WBSRTC2-C1 in diabetics without overt cerebrovascular disorder or target organ damage or other vascular risk factors and compare it with that of age- and sex-matched controls to detect peripheral nerve dysfunction in diabetics. Also, it is known that poor glycemic control is responsible for microvasular complications. [8] Glycated hemoglobin (HbA1c) has not only been established as a marker of glycemic control but also indicates the risk of developing small vessel complications. [9] Therefore, we intended to correlate WBSRTC2-C1 with HbA1c to establish the role of WBSRTC2-C1 in diabetes mellitus, so that it can help in identifying the asymptomatic stage of DN and preventive measures can be instituted. We tried to find the predictive value of WBSRTC2-C1 in detecting peripheral neural dysfunction.


  Materials and Methods Top


After getting approval of ethical clearance committee of the institution, this case control study was carried out over 6 months (from August 2010 to January 2011) by purposive sampling with the criteria of age and diabetes. The selection of sample was carried out from the outpatient department of medicine of our institution. One hundred and twenty individuals participated in the study. The study population was divided into two groups. Group 1 consisted of randomly selected patients of age between 40 and 60 years with clinically diagnosed type 2 diabetes of more than 2 years duration. Group 2 consisted of randomly selected sex- and age-matched controls from the college staff and subjects attending medical OPD for routine checkup. Sample size was determined by standard error of test obtained by a pilot study. Each individual was briefed about the study, its importance, and procedural details, and written consent of participants was taken before recording the various RTs. The following class of subjects was excluded from the study: Hypertensives, smokers, subjects with cardiovascular or cerebrovascular disorders, neuropathy, or chronic renal disorders. We also excluded patients having chronic lower back pain or spasms, deformities of the spine, bones, or joints (including advanced arthritis), spinal cord injuries or any other damage to the nervous system, non-healing skin ulcers, and current drug or alcohol dependence. Individuals taking any prescription medicine to prevent dizziness were also excluded. The basic parameters and detailed history were recorded. Data on basic parameters such as pulse, blood pressure, height, weight, food habits, and exercise pattern were recorded. Ophthalmic evaluation was done by using Snellen and Jaeger's chart.

According to ICMR guidelines (2005), Diabetes mellitus was diagnosed on the basis of the following criteria: 1) fasting blood sugar (FBS) >126 mg% and 2) postprandial blood sugar (PPBS) level >200 mg% or patients on anti-diabetic therapy. HbA1c, FBS, and PPBS levels were measured. HbA1c was estimated in the whole blood by ion-exchange resin method. The optical density of each proportion was measured spectrophotometrically on semi-automated chemistry analyzer, Microlab 200, followed by an evaluation of the relative proportion of HbA1c with respect to total HbA. Plasma samples of the same patients were analyzed on fully automated chemical pathology analyzer, Selectra E, for the estimation of plasma glucose levels by enzymatic (glucose oxidase) colorimetric method.

Equipment Used for Reaction Times

The reaction timers: WBSRT (Chronoscope-1, Chronoscope-2, and Chronoscope 2-1)

Anand Agencies, Pune, India is the manufacturer of research tool RT apparatus, with the chronoscope compartment showing time in milliseconds.

Procedure

After giving brief instructions, three trials for WBSRT were given and the individual RT in milliseconds was recorded 5 times in both diabetic patients and controls. An attempt was made to obtain at least five acceptable recordings for each participant. Measurements of the WBSRT were considered reproducible if the difference between maximum and minimum values did not exceed 50 ms. Reliability of the test was calculated based on the data obtained in the pilot study. Coefficient of correlation for WBSRT was 0.927, with α error 0.9844. WBSRT- The subject standing on the starting board is instructed to watch the glowing arrow and to step one leg on the stepping board in that single direction. Chronoscope-1(C1) gives the time taken for lifting the foot from the onset of the stimulus, whereas Chronoscope-2(C2) gives the total time required for placing the foot on the stepping board from the onset of stimulus and C2 minus C1 gives the movement time from starting board to stepping board, which is the time taken for motor activity.

Statistical Analysis

The results were tabulated separately and statistical results are presented as mean ± SD. The software analyzer used was SPSS version 16. The data were analyzed by independent t-test which indicates the level of difference between groups, with significance at 5% level using t Stat, i.e., P values <0.05. Pearson's correlation was performed to find the correlation between duration of diabetes and HbA1c with WBSRTC2-C1. To determine the accuracy and respective best cut-off values of WBSRTC2-C1 for predicting cognitive dysfunction, the receiver operating characteristic (ROC) curves and their corresponding areas under the curve (AUC) were used. A P value of <0.05 was considered statistically significant.


  Results Top


[Table 1] shows that there was no significant difference in age. The mean age of controls was 51.3 years and of diabetics was 52.5 years. There were 18 females in group 1 and group 2. There was no significant difference in RTs between males and females. [Table 1] also shows the mean of measured values of FBS, PPBS, and body mass index (BMI). There was a significant difference in these parameters [Table 1]. The mean value of HbA1c in diabetics was 8.18 ± 0.93%.
Table 1: Demographic variables of diabetics and controls

Click here to view


WBSRTC2 and WBSRTC2-C1 were delayed in diabetics compared to controls and were statistically insignificant (P = 0.010 and 0.003, respectively) [Table 2]. There was no significant correlation between duration of diabetes (average 4.78 years) and WBSRTC2-C1 (r = −0.231, P = 0.076, n = 60). There was no significant correlation between HbA1c and WBSRTC2-C1 (r = −0.199, P = 0.131, n = 59) in diabetics. There was significant correlation between duration of diabetes and HbA1c (r = 0.515, P = 0.000). ROC curve of WBSRTC2-C1 was constructed for predicting peripheral nerve dysfunction in diabetic patients and the AUC was found to be 0.571 (95% CI, lower bound 0.424; upper bound 0.719) and was statistically insignificant (P = 0.349).
Table 2: Comparison of reaction times among diabetics
and controls


Click here to view



  Discussion Top


We observed that the RTs are delayed in diabetics. WBSRTC2-C1 was significantly delayed in diabetes, which indicates peripheral neural dysfunction. We found that WBSRTC2-C1 alone cannot predict peripheral neuropathy in diabetes.

An intensive treatment of neuropathy at the subclinical level decreases the risk of neuropathy. [10] Therefore, there is a need of methods to identify the at-risk diabetic patients for neuropathy. NCS are one of the important methods for assessing nerve functions in DN. Therefore, monitoring of diabetic patients with NCS may help in predicting the onset of DN. Numerous NCS have been done to prove that diabetes causes conduction abnormalities secondary to peripheral neuropathy. [11],[12],[13],[14],[15] RT measurement includes latency in the sensory neural code traversing the peripheral and central pathways, and perceptive, cognitive, and volitional processing. In peripheral neuropathy, the time required for central processing does not change unless there are cognitive deficits. Whereas the time required for peripheral response changes if there is delayed conduction of the impulses in motor nerve fiber. At this point, we require a tool which clearly measures the time required for central processing and peripheral processing in total RT. Many studies have shown delay in visual and auditory simple and choice RTs in diabetes, [6],[7] but they have failed to explain whether the delay was because of central processing or is the time taken for peripheral response. In our study, we measured the WBSRT in which WBSRTC1 apparently measures the time required for perception and cognition, which is the time taken for lifting the foot from the onset of stimulus from the starting board. WBSRTC2 measures apparently motor signal traversing both the central and peripheral neuronal structures, which is the total time required for placing the foot on the stepping board from the onset of stimulus. WBSRTC2-C1 measures the time required for conduction of impulse in the motor nerve fiber. Therefore, it becomes easy to tease out central effects versus peripheral effects when RTs are slowed down. Our focus of the study was to measure WBSRTC2-C1 in diabetics and compare it with controls, which approximately gives the difference in peripheral nerve dysfunction between these two groups. We hypothesized that measurement of WBSRTC2-C1 can be used as a screening tool to detect peripheral neuropathy. Though there was an association between diabetes and WBSRTC2-C1, we could not establish that WBSRTC2-C1 can predict diabetes mellitus. Probably large sample size will reveal the fact.

There was no significant correlation between HbA1c levels and WBSRTC2-C1 in diabetics. The reason could be that all diabetic patients were on anti-diabetic therapy, and HbA1c indicates the level of glucose present in blood in the past 3 months. There was a significant difference in the BMI in these two groups. Since prevalence of diabetes increases with body weight, high BMI was expected in diabetes, and this was one of the limitations of our study. There was no significant correlation between WBSRTC2-C1 with duration of diabetes because most of the patients' diabetic duration was less than 5 years and all were receiving anti-diabetic therapy.

One fallacy about our study was that we did not perform the gold standard test which can identify peripheral neural dysfunction so that we could compare our findings and assess the sensitivity and specificity of the test. However, these tests are time consuming and require skilled staff. On the contrary, RTs can be easily performed on OPD basis. They can be sensitive indicators of peripheral neural dysfunction. Therefore, the strength of our study is we can use RT as a screening tool for early detection of peripheral neural dysfunction in diabetes. There are no systemic reviews implicating that RTs, especially WBSRTC2-C1, can detect peripheral neural dysfunction. WBSRTC2-C1 can apparently measure the time required for conduction of impulse in motor nerve fiber, if not accurately. This study may provide a platform for further studies in this direction.


  Conclusion Top


From this study we can conclude that diabetes does affect RT; severity of slowing may be related to the difficulty of the task and prevalence of central and peripheral nerve deficits seen as side effects of diabetes. In whole body simple RT with chronoscopic reading C1 and C2-C1, probably it is possible to say how much of time is required for motor response to detect peripheral neuropathy. WBSRTC2-C1 cannot alone be predictive of peripheral neuropathy; further studies with properly matched controls will elucidate the facts.

 
  References Top

1.
Zargar AH, Wani AI, Masoodi SR, Laway BA, Bashir MI. Mortality in diabetes mellitus - data from a developing region of the world. Diabetes Res Clin Pract 1999;43:67-74.  Back to cited text no. 1
    
2.
Tesfaye S, Stevens LK, Stephenson JM, Fuller JH, Plater M, Ionescu-Tirgoviste C, et al. Prevalence of diabetic peripheral neuropathy and its relation to glycaemic control and potential risk factors: The EURODIAB IDDM Complications Study. Diabetologia 1996;39:1377-84.  Back to cited text no. 2
    
3.
Perkins BA, Bril V. Diagnosis and management of diabetic neuropathy. Curr Diab Rep 2002;2:495-500.  Back to cited text no. 3
    
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Consensus statement: Report and recommendations of the San Antonio conference on diabetic neuropathy. American Diabetic Association American Academy of Neurology. Diabetes Care 1988;11:592-7.  Back to cited text no. 5
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Sanchez-Marin FJ, Padilla-Medina JA. Simple reaction times and performance in the detection of visual stimuli of patients with diabetes. Comput Biol Med 2010;40:591-6.   Back to cited text no. 6
    
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Mohan M, Thombre DP, Das AK, Subramanian N, Chandrasekar S. Reaction time in clinical diabetes mellitus. Indian J Physiol Phramacol 1984;28:311-4.  Back to cited text no. 7
    
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Mayurasakorn K, Somthip N, Caengow S, Chulkarat N, Wanichsuwan M. Glycemic control and microvascular complications among type 2 diabetes at primary care units. J Med Assoc Thai 2009;92:1094-101.  Back to cited text no. 8
    
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Schellhase KG, Koepsell TD, Weiss NS. Glycemic control and the risk of multiple microvascular diabetic complications. Fam Med 2005;37:125-30.  Back to cited text no. 9
    
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Dahl-Jørgensen K, Brinchmann-Hansen O, Hanssen KF, Ganes T, Kierulf P, Smeland E, et al. Effect of near normoglycaemia for two years on progression of early diabetic retinopathy, nephropathy, and neuropathy: The Oslo Study. Br Med J (Clin Res Ed) 1986;293:1195-9.  Back to cited text no. 10
    
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Carrington AL, Shaw JE, Van Schie CH, Abbott CA, Vileikyte L, Boulton AJ. Can motor nerve conduction velocity predict foot problems in diabetic subjects over a 6-year outcome period? Diabetes Care 2002;25:2010-5.  Back to cited text no. 11
    
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Hemmi S, Inoue K, Murakami T, Sunada Y. Comparison of the sensitivities of plantar nerve conduction techniques for early detection of diabetic sensory polyneuropathy. Electromyogr Clin Neurophysiol 2010;50:269-75.  Back to cited text no. 12
    
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Mondal A, Sen S, Chanda D, Kundu S, Chatterjee M, Mukherjee S. Evaluation of diabetic polyneuropathy in Type 2 diabetes mellitus by nerve conduction study and association of severity of neuropathy with serum sFasL level. Indian J Endocrinol Metab 2012;16(Suppl 2):S465-7.  Back to cited text no. 13
    
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Kiziltan ME, Gunduz A, Kiziltan G, Akalin MA, Uzun N. Peripheral neuropathy in patients with diabetic foot ulcers: Clinical and nerve conduction study. J Neurol Sci 2007;258:75-9.  Back to cited text no. 14
    
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Bertora P, Valla P, Dezuanni E, Osio M, Mantica D, Bevilacqua M, et al. Prevalence of subclinical neuropathy in diabetic patients: Assessment by study of conduction velocity distribution within motor and sensory nerve fibres. J Neurol 1998;245:81-6.  Back to cited text no. 15
    



 
 
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