Abstract
BACKGROUND:
Type 2 Diabetes Mellitus is one of the most common metabolic diseases worldwide. The most common complication of DM is diabetic neuropathy (DN), especially diabetic polyneuropathy (DPN). Vitamin D plays an important role in the pathogenesis of DN, thus affecting its severity which can be assessed using nerve conduction study (NCS).
AIM:
This study aimed to develop a predictive model of DPN severity based on vitamin D level.
METHODS:
This was a prospective cohort study involving 50 subjects with DM which was conducted in Haji Adam Malik General Hospital Medan. All subjects were fulfilling inclusion criteria underwent laboratory examination to determine HbA1c and 25 (OH) D levels. Predictive variables were sex, age, duration of DM, smoking status, type and number of anti-diabetic drugs, the presence of metabolic syndrome, HbA1c and vitamin D levels. A scoring system was developed to determine a predictive model. The DPN severity was assessed using NCS and was re-evaluated after 3 months.
RESULTS:
Most of the subjects were female (60%), belonged to ≥ 50 years old age-group (88%), with DM duration < 5 years (56%), were non-smoker (90%), we’re using one anti-diabetic drug (60%), were using insulin (50%), had metabolic syndrome (68%), had HbA1c level > 6.5% (94%), and had vitamin D level < 20 ng/ml (56%). A score of > 4 on this predictive model of DPN severity had a relative risk (RR) of 2.70. The predictive model had a sensitivity of 82.8% and specificity of 61.9%.
CONCLUSION:
A score of higher than 4 on this predictive model showed a 2.7 times higher risk of severe DPN. A predictive model of DPN severity based on vitamin D level had high sensitivity and specificity.
Keywords: Diabetic polyneuropathy, Nerve conduction study, Predictive model, Vitamin D
Introduction
Diabetic neuropathy (DN) is the most common complication of diabetes mellitus (DM) [1], [2]. Its prevalence is high in developed countries. It is related to greater mortality, morbidity and higher economic burden and rate of hospitalisations [3], [4]. Chronic diabetic polyneuropathy (DPN) is the most common type of DN, accounting for about 75% of DN [5], [6]. American Diabetic Association recommends that all diabetic patients should be screened for DN at the time of diagnosis in type 2 DM and five years after diagnosis of type 1 DM [3], [7]. Once established, neuropathy is difficult to reverse [4]. Neurological examinations and careful evaluation of neuropathic signs and symptoms are important in early detection and severity determination [8]. Nerve conduction study (NCS) can detect neural changes even before the sign develops and determine the severity of neuropathy [9].
Vitamin D may have a direct effect on the pathogenesis of DN, particularly vitamin D3 that has been shown the ability to reduce demyelination in an experimental model [10]. Serum 25-hydroxyvitamin D (25 (OH) D) is the major circulating form of vitamin D. Some studies showed vitamin 25 (OH) D deficiency is associated with DPN [11], [12]. In Asian, diabetic patients with vitamin D deficiency are 1.22 times more likely to suffer from DPN compared with those with normal vitamin D level [13].
The aim of this study was mainly to develop a predictive model of DPN severity based on vitamin D level, that can be clinically used for early detection, severity prevention of DPN so the treatment can be optimised and may improve the quality of life of type 2 DM patients.
Material and Methods
This was a prospective cohort study which was conducted in Haji Adam Malik General Hospital Medan and had been approved by the Local Ethical Committee. Inclusion criteria were typed 2 DM patients with DPN. Exclusion criteria were patients with impaired renal or hepar function, consumption of anti-tuberculous drugs or chemotherapy. All subjects signed an informed consent before the examination. We recorded data from the subjects such as sex, age, duration of DM, smoking status, type and number of anti-diabetic drugs, and the presence of the metabolic syndrome. All subjects underwent NCS examination by the same neurologist using Cadwell ENMG (electroneuromyography) machine. HbA1c level was evaluated using the enzyme immunoassay method, and serum vitamin 25 (OH) D status was evaluated using the chemiluminescent immunoassay method. The NCS was repeated after 3 months to assess DPN severity.
The predictive variables consisted of demographic and clinical data, combined with HbA1c and vitamin 25(OH)D levels. Results of NCS after 3 months were classified using Baba’s Diabetic Neuropathy Classification (BDC), consisted of BDC-0: no NCS abnormalities, BDC-1: delay in any MCV, SCV, BDC-2: sural amplitude < 5 µV, BDC-3: plantar muscle-CMAP amplitude to 2 – 5 mV, BDC-4: plantar muscle-CMAP < 2 mV.14 We classified BDC-1 was mild and BDC-2 – 4 was severe. A scoring system was developed; the total value was 0 – 12.
The predictive variables were categorical data. The predictive variables as independent variables and DPN severity as a dependent variable. The association of predictive factors and score with DPN severity was assessed using a Chi-Square Test or Fisher’s Test. The results were considered significant at p < 0.05. Risk Ratio (RR) for each group was calculated. The statistical calculations were done using the computerized program.
Results
There were 50 patients included in this study. Age, sex, ethnicity, duration of diabetes and glycemic control are established risk factor for DN [15]. High body mass index, hypertension and smoking are established risk factors for DN [16]. Age, an insulin treatment, longer duration of DM, and higher HbA1c were independently significant risk factors for DPN [17]. Metabolic syndrome, including pre-diabetes, are potential risk factors for neuropathy [18], [19]. Skalli et al.'s study (2012) found diabetic neuropathy was significantly linked to age, diabetes duration, and vitamin D status [12].
Based on previous studies, we selected some risk factors to be the predictive factors. The predictive factors consisted of sex, age, duration of DM, smoking status, type and number of anti-diabetic drugs, the presence of metabolic syndrome, HbA1c and vitamin 25(OH)D levels. A scoring system is shown in Table 1.
Table 1.
The Predictive Score
| Characteristics | Parameters | Score |
|---|---|---|
| Sex | Male | 0 |
| Female | 1 | |
| Age group (years) | < 50 | 0 |
| >50 | 1 | |
| Duration of DM (years) | < 5 | 0 |
| ≥ 5 | 1 | |
| Smoking status | No | 0 |
| Yes | 1 | |
| Number of anti-diabetics | 1 | 0 |
| > 1 | 1 | |
| Type of anti-diabetic | Insulin | 0 |
| Oral | 1 | |
| Combination | 2 | |
| Metabolic syndrome | No | 0 |
| Yes | 1 | |
| HbA1c levels (%) | < 6.5 | 0 |
| ≥ 6.5 | 1 | |
| Vitamin 25 (OH) D levels (ng/ml) | ≥ 30 | 0 |
| 20–29.9 | 1 | |
| 10–19.9 | 2 | |
| < 10 | 3 | |
| Total | 0 – 12 |
As shown in Table 2, most of the subjects were female (30 subjects, 60%), 44 subjects (88%) belonged to ≥ 50 years old age-group, 28 subjects (56%) with DM duration < 5 years, 45 subjects (90%) were non-smoker, 30 subjects (60%) were using one anti-diabetic drug, 25 subjects (50%) were using insulin, 34 subjects (68%) had metabolic syndrome, 47 subjects (94%) had HbA1c level ≥ 6,5%, and 28 subjects (56%) had vitamin 25(OH)D level < 20 ng/ml.
Table 2.
Association of Predictive Factors with DPN Severity
| Predictive Factors | DPN severity | p | RR | 95% CI | |
|---|---|---|---|---|---|
| Mild | Severe | ||||
| Sex | 0.349a | 1.73 | 0.50–6.19 | ||
| Male | 10 | 10 | |||
| Female | 11 | 19 | |||
| Age group (years) | 0.499b | 0.69 | 0.12–3.85 | ||
| < 50 | 3 | 3 | |||
| ≥ 50 | 18 | 26 | |||
| Duration of DM (years) | 0.002a* | 0.14 | 0.03–0.49 | ||
| < 5 | 17 | 11 | |||
| ≥ 5 | 4 | 18 | |||
| Smoking status | 0.056b | 1.88 | 1.47–2.61 | ||
| No | 21 | 24 | |||
| Yes | 0 | 5 | |||
| Number of anti-diabetics | 0.413a | 1.63 | 0.55–6.22 | ||
| 1 | 14 | 16 | |||
| > 1 | 7 | 13 | |||
| Type of anti-diabetic | |||||
| Insulin | 11 | 14 | |||
| Oral | 9 | 12 | 0.483a | 2.36 | 0.21–5.91 |
| Combination | 1 | 3 | 0.938b | 1.05 | 0.33–3.38 |
| Metabolic syndrome | 0.432a | 0.62 | 0.17–2.19 | ||
| No | 8 | 8 | |||
| Yes | 13 | 21 | |||
| HbA1c (%) | 0.621b | 1.48 | 0.19–4.18 | ||
| < 6,5 | 1 | 2 | |||
| ≥ 6,5 | 20 | 27 | |||
| 25 (OH) D (ng/ml) | 0.661a | 0.78 | 0.23–2.59 | ||
| < 20 | 11 | 17 | |||
| ≥ 20 | 10 | 12 | |||
Chi-Square Test;
Fisher’s Test;
p < 0.05; RR = a / (a + b): c / (c + d).
Our study found that it was a significant association between duration of DM and DPN severity. Most of the subjects with a duration of DM < 5 years had mild DPN (17 subjects, 60.7%), but most of them with a duration of DM ≥ 5 years had severe DPN (18 subjects, 62.1%).
In Table 3, almost all of the subjects had vitamin D deficiency (49 subjects, 98%). Most of them (26 subjects, 52%) had vitamin 25 (OH) D level 10 – 19.9 ng/ml (deficient).
Table 3.
Proportion Vitamin 25 (OH) D Level
| Vitamin 25(OH)D Level (ng/ml) | Frequency | Percentage |
|---|---|---|
| <10 (severe deficient) | 2 | 4 |
| 10–19.9 (deficient) | 26 | 52 |
| 20–29.9 (insufficient) | 21 | 42 |
| >30 (adequate) | 1 | 2 |
In 3rd month, approximately 29 subjects (58%) had severe DPN, which 14 subjects (28%) had BDC 2. The proportion of DPN severity is shown in Table 4.
Table 4.
Proportion of DPN Severity
| DPN Severity | Frequency | Percentage |
|---|---|---|
| Mild | ||
| BDC 1 | 21 | 42 |
| Severe | ||
| BDC 2 | 14 | 28 |
| BDC 3 | 9 | 18 |
| BDC 4 | 6 | 12 |
The predictive model analysed by logistic regression and cutoff point on the ROC curve was 4 (Figure 1). After 3 months, 24 subjects (75%) with predictive score > 4 had severe DPN and 13 subjects (72%) with predictive score ≤ 4 had mild DPN. The combination of predictive factors had strongly basic to estimate DPN severity after 3 months. The predictive model was more accurate than a predictive variable alone.
Figure 1.
ROC Curve for Predictive Model
In Table 5, the predictive model had a sensitivity of 82.8% and specificity of 61.9%. Positive predictive value of 75%. Negative predictive value of 72.2%. Subjects with a score > 4 on this predictive model of DPN severity had relative risk (RR) of 2.70, that showed 2.7 times higher risk of severe DPN after 3 months.
Table 5.
Comparison between Predictive Score and DPN Severity
| DPN Severity | Total | p | RR | 95% CI | |||
|---|---|---|---|---|---|---|---|
| Severe | Mild | ||||||
| Predictive Score | ≥ 4 | 24 | 8 | 32 | 0.001* | 2.70 | 0.171–0.801 |
| ≤ 4 | 5 | 13 | 18 | ||||
| Total | 29 | 21 | 50 | ||||
Chi-Square Test;
p < 0.05; RR = a / (a + b): c / (c + d).
Discussion
In this study, most of the subjects were female (60%) and belonged to ≥ 50 years old age-group (88%), with DM duration < 5 years (56%), had metabolic syndrome (68%) and had HbA1c level ≥ 6.5% (94%). Willer et al.'s study (2016) found risk factor of type 2 DM and its complications which is obesity and psychosocial stress appears to have a greater impact on women rather than on men [20]. The DN is commonest after 5th decade. Middle age / elderly diabetic was generally more affected [21]. Neuropathic symptoms increase with duration of DM [16], [21]. Some studies found a lower prevalence of DPN in those with duration < 5 years and the highest in those with duration > 15 years [7], [22]. Accumulating evidence suggests that the prevalence of DPN markedly elevated at the time of diabetes diagnosis [15]. Hyperglycemia, dyslipidemia, and metabolic syndrome have all been shown to initiate neuropathy through a common mechanism oxidative stress [15]. HbA1c as an indicator of glycemic control [22]. Poor glycemic control is regarded as the most important contributor to the mechanism of DN [11].
We found most of the subjects had vitamin 25 (OH) D level < 20 ng/ml (56%). Vitamin 25 (OH) D might play a functional role in glucose homeostasis. Vitamin D has a potential impact on insulin secretion, insulin sensitivity, and subsequently on the incidence of DM [11]. Patients with vitamin D deficiency (25 (OH) D < 20 ng/ml) had higher odds of having symptomatic DN than individuals with 25 (OH) D of 30 – 40 ng/ml [11], [12]. Vitamin D might be implicated in DPN’s pathophysiology via its potential influence on nerve function [12].
In this study, most of the predictive factors were not significant association with DPN severity. But the combination of that variable in the predictive model was a significant association with DPN severity.
On this predictive model of DPN severity, subjects with a score of higher than 4 had RR of 2.70, showed that had 2.7 times higher risk of severe DPN than a score of lower as 4 after 3 months.
The predictive model had a sensitivity of 82.8% and specificity of 61.9%. The sensitivity of 82.8% was probability subjects had severe DPN with predictive score > 4 as 82.8%. The specificity of 61.9% was probability subjects had mild DPN with predictive score ≤ 4 as 61.9%. Positive predictive value of 75%, was probability subjects with predictive score > 4 had severe DPN as 75%. Negative predictive value of 72.2%, was probability subjects with predictive score ≤ 4 had mild DPN as 72.2%.
In conclusion, a score of higher than 4 on this predictive model showed 2.7 times higher risk of severe DPN. A predictive model with using a scoring system in predicting DPN severity based on vitamin D level had high sensitivity and specificity.
Footnotes
Funding: This research was funded by Lembaga Penelitian, Universitas Sumatera Utara (TALENTA’s research program, No: 4167/UN5.1.R/PPM/2019), Indonesia
Competing Interests: The authors have declared that no competing interests exist
References
- 1.Thakur S. Rachana. Diabetic neuropathy:its pathogenesis and therapeutic drug targets. Journal of Cell Science & Molecular Biology. 2014;1:1. [Google Scholar]
- 2.Wang F, Zhang J, Yu J, Liu S, Zhang R, Ma X, Yang Y, Wang P. Diagnostic accuracy of monofilament tests for detecting diabetic peripheral neuropathy:a systematic review and meta-analysis. Journal of diabetes research 2017. 2017 doi: 10.1155/2017/8787261. https://doi.org/10.1155/2017/8787261 PMid:29119118 PMCid:PMC5651135. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Vinik AI, Nevoret ML, Casellini C, Parson H. Diabetic neuropathy. Endocrinology and Metabolism Clinics. 2013;42(4):747–87. doi: 10.1016/j.ecl.2013.06.001. https://doi.org/10.1016/j.ecl.2013.06.001 PMid:24286949. [DOI] [PubMed] [Google Scholar]
- 4.Stino AM, Smith AG. Peripheral neuropathy in prediabetes and the metabolic syndrome. Journal of diabetes investigation. 2017;8(5):646–55. doi: 10.1111/jdi.12650. https://doi.org/10.1111/jdi.12650 PMid:28267267 PMCid:PMC5583955. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Pop-Busui R, Boulton AJ, Feldman EL, Bril V, Freeman R, Malik RA, Sosenko JM, Ziegler D. Diabetic neuropathy:a position statement by the American Diabetes Association. Diabetes care. 2017;40(1):136–54. doi: 10.2337/dc16-2042. https://doi.org/10.2337/dc16-2042 PMid:27999003. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Oyenihi AB, Ayeleso AO, Mukwevho E, Masola B. Antioxidant strategies in the management of diabetic neuropathy. BioMed Research International 2015. 2015 doi: 10.1155/2015/515042. https://doi.org/10.1155/2015/515042 PMid:25821809 PMCid:PMC4363503. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Soheilykhah S, Rashidi M, Dehghan F, Shojaoddiny-Ardekani A, Rahimi-saghand S. Prevalence of peripheral neuropathy in diabetic patients. Iranian Journal of Diabetes and Obesity. 2013;5(3):107–13. [Google Scholar]
- 8.Jin HY, Park TS. Can nerve conduction studies detect earlier and predict clinical diabetic neuropathy? J Diabetes Invest. 2015;6(1):18–20. doi: 10.1111/jdi.12236. https://doi.org/10.1111/jdi.12236 PMid:25621128 PMCid:PMC4296698. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Kakrani AL, Gokhale VS, Vohra KV, Chaudhary N. Clinical and nerve conduction study correlation in patients of diabetic neuropathy. J Assoc Physicians India. 2014 Jan;62(1):24–7. [PubMed] [Google Scholar]
- 10.Alam U. Vitamin D and diabetic neuropathy [Disertasi] University of Manchester, Doctor of Philosophy in Medicine; 2013. [Google Scholar]
- 11.Esteghamati A, Fotouhi A, Faghihi-Kashani S, Hafezi-Nejad N, Heidari B, Sheikhbahaei S, Zandieh A, Nakhjavani M. Non-linear contribution of serum vitamin D to symptomatic diabetic neuropathy:A case-control study. Diabetes research and clinical practice. 2016;111:44–50. doi: 10.1016/j.diabres.2015.10.018. https://doi.org/10.1016/j.diabres.2015.10.018 PMid: 26548603. [DOI] [PubMed] [Google Scholar]
- 12.Skalli S, Muller M, Pradines S, Halimi S, Wion-Barbot N. Vitamin D deficiency and peripheral diabetic neuropathy. European journal of internal medicine. 2012;23(2):e67–8. doi: 10.1016/j.ejim.2011.11.008. https://doi.org/10.1016/j.ejim.2011.11.008 PMid:22284260. [DOI] [PubMed] [Google Scholar]
- 13.Qu GB, Wang LL, Tang X, Wu W, Sun YH. The association between vitamin D level and diabetic peripheral neuropathy in patients with type 2 diabetes mellitus:an update systematic review and meta-analysis. Journal of clinical & translational endocrinology. 2017;9:25–31. doi: 10.1016/j.jcte.2017.04.001. https://doi.org/10.1016/j.jcte.2017.04.001 PMid:29067266 PMCid:PMC5651294. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Baba M, Suzuki C, Ogawa Y. Severity grading system of diabetic neuropathy in type-2 diabetes by nerve conduction study:Five-year prospective study on occurrence of diabetic foot, macroangiopathic events, and eventual death. Japanese Journal of Clinical Neurophysiology. 2018;46(2):71–77. [Google Scholar]
- 15.Lee C, Perkins B, Kayaniyil S, Harris S, Retnakaran R, Gerstein H, et al. Peripheral neuropathy and nerve dysfunction in individuals at high risk for type 2 diabetes:the PROMISE cohort. Diabetes Care. 2015;38:793–800. doi: 10.2337/dc14-2585. https://doi.org/10.2337/dc14-2585 PMid:25665810. [DOI] [PubMed] [Google Scholar]
- 16.Soderstrom L, Johnson S, Diaz V, Mainous A. Association between vitamin D and diabetic neuropathy in a nationally representative sample:results from 2001-2004 NHANES. Diabet. Med. 2012;29(1):50–55. doi: 10.1111/j.1464-5491.2011.03379.x. https://doi.org/10.1111/j.1464-5491.2011.03379.x PMid:21726279 PMCid:PMC3461835. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Morkrid K, Ali L, Hussain A. The prevalence and risk factors for diabetic peripheral neuropathy among type 2 diabetic outpatients in Bangladesh [Thesis] University of Oslo, Master of Philosophy Degree in International Community Health. 2007 [Google Scholar]
- 18.Callaghan B, Cheng H, Stables C, Smith A, Feldman E. Diabetic neuropathy:clinical manifestations and current treatments. Lancet Neurol. 2012;11(6):521–534. doi: 10.1016/S1474-4422(12)70065-0. https://doi.org/10.1016/S1474-4422(12)70065-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Eberhardt O, Topga H. Neurological outcomes of antidiabetic therapy:what the neurologist should know? Clinical Neurology and Neurosurgery. 2017;158:60–66. doi: 10.1016/j.clineuro.2017.04.014. https://doi.org/10.1016/j.clineuro.2017.04.014 PMid:28477558. [DOI] [PubMed] [Google Scholar]
- 20.Willer A, Harreiter J, Pacini G. Sex and gender differences in risk, pathophysiology and complications of type 2 diabetes mellitus. Endocrine reviews. 2016;37(3):278–316. doi: 10.1210/er.2015-1137. https://doi.org/10.1210/er.2015-1137 PMid:27159875 PMCid:PMC4890267. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Adgaonkar A, Dawange A, Adgaonkar S, Kale V, Shekokar P. Clinical profile of peripheral neuropathy in diabetes mellitus by nerve conduction study. Sch. J App. Med. Sci. 2014;2(6A):1973–1977. [Google Scholar]
- 22.Nisar M, Asad A, Waqas A, Ali N, Nisar A, Qayyum M, et al. Association of diabetic neuropathy with duration of type 2 diabetes and glycemic control. Cureus. 2015;7(8):e302. doi: 10.7759/cureus.302. https://doi.org/10.7759/cureus.302 PMid:26430576 PMCid:PMC4571902. [DOI] [PMC free article] [PubMed] [Google Scholar]