Abstract
The Michigan Neuropathy Screening Instrument (MNSI) is used to screen patients for diabetic neuropathy (DNP). We aimed to translate the MNSI questionnaire into Arabic (MNSIq-Ar) and to assess the validity and diagnostic performance of the MNSI Arabic version (MNSI-Ar).
Cronbach alpha α and the interclass correlation coefficient were used to measure the reliability and reproducibility of the MNSIq-Ar. The instrument's validity was assessed by Spearman correlation with the Utah Early Neuropathy Scale (UENS), the Modified Toronto Neuropathy Score (mTCNS), diabetic neuropathy symptoms (DNS), and sural nerve amplitude (SNA). The construct validity of the MNSI-Ar was assessed by its ability to differentiate the severity of DNP (using the Kruskal–Wallis test). The diagnostic performance was assessed through the receiver operator curve area.
We recruited 89 participants (mean [SD] age, 50.8 [12.3] years; 48% men). The MNSIq-Ar showed an α of 0.81 and intraclass correlation coefficient = 0.94, and the correlation coefficients with UENS, mTCNS, DNS, and sural nerve amplitude were 0.67, 0.83, 0.73, and −0.49, respectively (all P < .0001). The MNSI-Ar was able to differentiate the different severities of DNP. The receiver operator curve area was 0.93 with a high sensitivity of 95.9% and 100% for probable and confirmed DNP, respectively.
MNSI-Ar is a reliable and valid tool to screen for diabetic neuropathy in the Arabic language with a good diagnostic performance and high sensitivity.
Keywords: diabetes, diagnosis, neuropathy, screening, sensitivity
1. Introduction
Peripheral polyneuropathy is a common neurological disease, with a prevalence of 2% to 8%.[1] Diabetes mellitus (DM) is the most common cause of peripheral neuropathy.[2,3] The prevalence of diabetes in the general population is 12% to 14%.[4] Diabetes is more prevalent in Arab countries and in the Middle East. For example, in Saudi Arabia, diabetes affects 20% to 30% of the adult population.[5,6] The prevalence of diabetes is increasing worldwide, particularly in Arab countries.[7] Neuropathy affects 8% of patients with diabetes at the baseline, 42% at 10 years, and 50% at 20 years.[4,8,9] The cost of diabetic neuropathy care exceeds $10 billion annually in the United States, and the difference between the health care costs of mild and severe cases may reach 80%.[3] Early recognition may delay the progression of diabetic neuropathy and reduce the complications of neuropathy.[10] Hence, several guidelines have recommended annual screening for neuropathy among patients with diabetes.[11–13] Additionally, therapeutic clinical trials are targeting early neuropathy cases, which make the availability of a valid and reliable screening tool important[14] Another important reason for validating a neuropathy screening instrument is that early lifestyle intervention could modify the disease course.[15]
There are several screening tools for early diabetic neuropathy, and these include the Michigan Neuropathy Screening Instrument (MNSI),[16] the Utah Early Neuropathy Scale (UENS),[17] the Toronto Clinical Neuropathy Score (TCNS) and its modified version (mTCNS),[18] the lower extremity portion of the Neuropathy Impairment Score (NIS-LL), and the Diabetic Neuropathy Symptoms (DNS) Score.[19] The UENS showed a better profile in diagnosing neuropathy than the NIS-LL and the Michigan Diabetic Neuropathy Scale (MDNS).[17] The UENS was also found to be a sensitive measure for changes that occurred during a one-year follow up period.[17] The mTCNS showed the best screening profile among seven other screening/diagnostic tools.[14] Clinical trials have used these tools as part of outcome assessments.[20] Monofilament testing is another tool that can be used to screen for diabetic neuropathy; however, a recent meta-analysis found it to be an insensitive screening tool.[21] The Neuropathic Pain Diagnostic Questionnaire (DN4) is used to diagnose neuropathic pain and has been translated and validated in the Arabic language. However, it is not specific for painful neuropathy and includes various pain localizations, such as radiculopathy, entrapment neuropathy, and cranial neuropathy. In addition, as many patients with neuropathy do not complain of pain, DN4 may miss this group of patients.[22] Our aim was to translate the MNSI questionnaire into Arabic and to assess the validity and diagnostic performance of the MNSI Arabic version (MNSI-Ar) and describe its sensitivity and specificities as well as its positive and negative predictive values.
2. Methods
2.1. Study design and participants
We conducted a prospective cross-sectional study between June 2018 and February 2020 at King Abdulaziz University Hospital. The institutional review board at the King Abdulaziz University Hospital approved the protocol. All participants provided informed consent. Our inclusion criteria were Arabic language as their first language, the absence of any other neurological diagnosis affecting the sensory and motor system, such as stroke or multiple sclerosis, age ranges between 18 and 75 years, and a history of DM diagnosed by a physician and confirmed by either hemoglobin A1c ≥6.5% or the use of hypoglycemic agents. As diabetic neuropathy is associated with longer disease duration and an older age group, we included a small subset of participants without DM who fulfilled criteria 1 and 2 above and who were aged 50 to 75 years. The purpose in including participants in the older age group without diabetes was to ensure that the Arabic version of the MNSI questionnaire was not measuring symptoms related to age that can be found in persons with no neuropathy. This subset of patients was excluded in a sensitivity analysis (supplementary appendix D).
2.2. Translation and cultural adaptation
The author obtained permission from the MNSI owner to translate the MNSI questionnaire (MNSIq). The translation process followed cross-cultural adaptation guidelines.[23,24] Two neurologists and 1 translator, all fluent in Arabic, independently translated the MNSIq into Arabic (MNSIq-Ar). Consensus was established between the 3 versions, and then the Arabic version was back translated into English independently by 2 persons who speak English as their first language; both of these translations were equivalent to the original MNSIq. The MNSIq-Ar draft was piloted on 10 DM patients, and then the final MNSIq-Ar was produced (supplementary Appendix A).
2.3. Data collection
A training session was held for the neurologists and senior neurology residents (physicians who have finished two years of the neurology residency program) to standardize the data collection procedure and examination techniques. Consecutive patients visiting the neurology and endocrinology clinic who fulfilled our criteria were recruited as participants in the study. They were asked to complete the Arabic version of the MNSI questionnaire (MNSIq-Ar). A trained physician blinded to the MNSIq-Ar administered the following: the MNSI examination (MNSIe), the Utah Early Neuropathy Scale (UENS), the modified Toronto Clinical Neuropathy Score (mTCNS), the DNS Score, and 10-g monofilament examinations.[16,17,19] A blinded, trained electrophysiology technician performed the nerve conduction study (NCS) of bilateral sural and tibial nerves. To check for reproducibility, 49 participants were asked to complete the MNSIq-Ar again 2 weeks after the initial visit.
The patients were classified into diabetic neuropathy versus no neuropathy based on the probable diabetic neuropathy criteria developed by the Toronto diabetic neuropathy expert group for primary analysis; however, the diagnostic performance was evaluated based on the confirmed criteria, in addition to evaluation based on probable criteria.[25] We used 2 methods to grade the severity of the neuropathy among the diabetic neuropathy patients. The first method was NCS, where grade 1 indicated normal bilateral sural nerve sensory potentials (SP) and tibial nerve motor potentials (MP), grade 2 indicated that the SP amplitude was abnormal (SP <6 microVolt), and grade 3 indicated that both the SP and MP amplitudes were abnormal (MP <4 milliVolt). The second method used monofilament testing, where grade 1 indicated normal monofilament sensation (8 correct responses of 8 trials), grade 2 indicated reduced monofilament sensation (1–7 correct responses of 8 trials), and grade 3 indicated absent monofilament sensation.
2.4. Outcomes
We assessed the test reliability and reproducibility of MNSIq-Ar. We looked at the concurrent validity by assessing the correlation between MNSIq-Ar and MNSIe, UENS, mTCNS, DNS, and sural nerve amplitude (SNA). We assessed construct validity by assessing the ability of the MNSI-Ar (MNSIq-Ar + MNSIe) score to differentiate different grades of severity of diabetic neuropathy according to NCS and monofilament testing. We chose to use MNSI-Ar instead of MNSq-Ar when investigating the ability of the test to differentiate grades of diabetic neuropathy severity as this tool was developed and validated as a single tool (MNSI) rather than MNSIq alone. Additionally, the construct validity was assessed though the diagnostic performance of the MNSIq-Ar and MNSI-Ar, which include the receiver operator characteristic (ROC) area under the curve (AUC) for diagnosing diabetic neuropathy as well as the sensitivity, specificity, and positive and negative predictive values.
2.5. Statistical analysis
The demographic features were described using the mean, standard deviation (SD), and frequencies. The reliability of the MNSIq-Ar was assessed using Cronbach α (>0.8 is sufficient), inter-item correlations, and corrected item–total correlations. Reproducibility was assessed using the intraclass correlation coefficients between the baseline MNSIq-Ar and retests. A Bland–Altman plot was used to assess the absolute agreement between the baseline test and retest. The agreement between each item (test–retest) was assessed with Cohen weighted κ coefficients (>0.7 is sufficient). For concurrent validity correlations, we used Spearman correlation coefficients. For construct validity, we used the Kruskal–Wallis test to compare the different MNSI-Ar scores across different grades of severity with Bonferroni-corrected Mann–Whitney U tests for post hoc pairwise comparison. The diagnostic properties of MNSI-Ar were evaluated through the ROC AUC for diagnosing diabetic neuropathy (ROC area >0.8 is a good performance, whereas >0.9 is an excellent performance). The diagnostic properties of MNSIq-Ar and MNSI-Ar included the sensitivity, specificity, and positive and negative predictive values. Statistical analyses were performed using STATA version 13 (Stata-Corp, College Station, TX).
3. Results
Between June 2018 and February 2020, 89 patients (43 men) participated in the study; 76 were patients with diabetes, and 12 were participants without diabetes. There were 49 patients in the neuropathy group and 40 participants in the non-neuropathy group, with the mean ± standard deviation ages at 52.9 ± 11.1 and 48.1 ± 13.3, respectively (P = .06); men represented 42.9% and 55% of these 2 groups, respectively (P = .3). The mean MNSIq-Ar score was 5.7 ± 1.9 among the neuropathy group and 2.4 ± 2.6 among the non-neuropathy group. Forty-nine patients underwent retesting (30 with neuropathy and 19 with no neuropathy), and 65 patients underwent the nerve conduction study (35 with neuropathy and 30 with no neuropathy).
3.1. Reliability and reproducibility
The internal consistency of the MNSIq-Ar was satisfactory, with a Cronbach α of 0.81, a mean inter-item correlation of 0.19, and an item–total correlation of 0.4 to 0.73 in 10 items; the other 5 items (7, 8, 13, 14, and 15), which describe symptoms typically associated with severe neuropathy, ranged from 0.07 to 0.25 (Table 1). The intraclass correlation coefficient was 0.94 with a 95% confidence interval (CI) (range: 0.9–.97), which suggests excellent reproducibility. The Bland–Altman blot showed satisfactory agreement, with a mean difference of 0.27 and 95% limits (−2.12 to 2.66). (supplementary Appendix B, Figure A-B).
Table 1.
The reliability and stability of the Michigan Neuropathy Screening Instrument (MNSI) questionnaire, Arabic version.
| Questions | Reported positive symptom in no neuropathy (n = 40) | Reported positive symptom in neuropathy (n = 49) | P | Corrected item–total correlation | Cronbach α if item deleted | Test–retest weighted κ |
| 1. Are your legs and/or feet numb? | 27.5% | 89.8% | .00 | 0.725 | 0.771 | 0.91 |
| 2. Do you ever have any burning pain in your legs and/or feet? | 37.5% | 71.4% | .00 | 0.474 | 0.793 | 0.70 |
| 3. Are your feet too sensitive to touch? | 15% | 28.6% | .13 | 0.422 | 0.797 | 0.51 |
| 4. Do you get muscle cramps in your legs and/or feet? | 45.0% | 75.5% | .00 | 0.466 | 0.794 | 0.78 |
| 5. Do you ever have any prickling feelings in your legs or feet? | 35% | 79.6% | .00 | 0.705 | 0.773 | 0.91 |
| 6. Does it hurt when the bed covers touch your skin? | 12.5% | 34.7% | .02 | 0.498 | 0.792 | 0.70 |
| 7. When you get into the tub or shower, are you able to tell the hot water from the cold water? | 5% | 18.4% | .06 | 0.251 | 0.807 | 0.9 |
| 8. Have you ever had an open sore on your foot? | 5% | 8.2% | .55 | 0.121 | 0.812 | 0.65 |
| 9. Has your doctor ever told you that you have diabetic neuropathy? | 7.5% | 42.9% | .00 | 0.4 | 0.799 | 0.60 |
| 10. Do you feel weak all over most of the time? | 35% | 77.6% | .00 | 0.553 | 0.786 | 0.69 |
| 11. Are your symptoms worse at night? | 25% | 69.4% | .00 | 0.471 | 0.793 | 0.80 |
| 12. Do your legs hurt when you walk? | 35% | 79.6% | .00 | 0.603 | 0.782 | 0.78 |
| 13. Are you able to sense your feet when you walk? | 5% | 12.2% | .24 | 0.076 | 0.815 | -0.03 |
| 14. Is the skin on your feet so dry that it cracks open? | 27.5% | 32.7% | .6 | 0.135 | 0.819 | 0.66 |
| 15. Have you ever had an amputation? | 0.0% | 4.1% | .20 | 0.096 | 0.812 | 1.00 |
3.2. Concurrent and construct validity
The mean scores of the MNSIq-Ar, MNSIe, UENS, mTCNS, and DNS were 4.2 ± 2.8, 2.7 ± 2.4, 8 ± 7.96, 11.4 ± 8.6, and 1.96 ± 1.48, respectively. The mean SNA was 9.7 ± 8.6 μV. The Spearman correlation coefficients between the MNSIq-Ar and the MNSIe, UENS, mTCNS, DNS, and SNA were 0.58, 0.67, 0.83, 0.73, and −0.49, respectively (all P < .0001) (supplementary Appendix B, Figure C-G). Among the diabetic neuropathy group, the MNSI-Ar score differentiated between grades of severities stratified by NCS (P = .005) and monofilaments (P < .0001), Table 2. For pairwise comparison, the MNSI-Ar score differentiated between Grades 1 and 3 stratified by both NCS and monofilament (P < .01, P < .01, Bonferroni corrected). The MNSI-Ar differentiated between Grades 1 and 2 stratified by monofilament (P = .006, Bonferroni corrected). The MNSI-Ar did not differentiate between grades 2 and 3 stratified by NCS or monofilament.
Table 2.
The MNSI Arabic scores categorized according to the nerve conduction study and monofilament tests (only patients with neuropathy included).
| Test to Classify the Neuropathy Severity Grades | MNSI-Ar | |||
| Nerve conduction study | Neuropathy Severity Grades | n (%) | Mean (SD) | Median (IQR) |
| Normal | 13 (37.1) | 7.9 (4.2) | 8 (5–11) | |
| Sensory abnormalities only | 13 (37.1) | 10.8 (2.6) | 10 (9.5–13) | |
| Sensory and motor abnormalities | 9 (25.7) | 13.2 (1.9) | 14 (12–14) | |
| ∗ P | .005 | |||
| Monofilaments | ||||
| Normal | 16 (32.7) | 7.2 (3.3) | 7.5 (5.5–9.5) | |
| Reduced | 22 (44.9) | 10.9 (2.5) | 11 (9–13) | |
| Absent | 11 (22.5) | 12.5 (2.6) | 14 (10–14) | |
| ∗∗ P | .000 | |||
IQR = interquartile range, MNSI-Ar = Michigan neuropathy screening instrument-Arabic version, SD = standard deviation.
P ≤ .001 for comparisons between MNSI-Ar scores between normal NCS versus sensory and motor abnormalities. (Bonferroni corrected). There was no different in MNSI-Ar scores between sensory abnormalities and sensory and motor abnormalities or normal versus sensory abnormalities. (Bonferroni corrected).
P ≤ .006 for comparisons between MNSI-Ar scores between normal versus reduced and versus absent. (Bonferroni corrected). There was no difference in the MNSI-Ar scores between reduced versus absent.
3.3. Diagnostic performance
The diagnostic performance of the MNSI-Ar supported its construct validity (outlined in detail in the supplementary Appendix C). Based on the probable neuropathy criteria, the ROC AUC was excellent at 0.93 (95% CI 0.86–0.97) (supplementary Appendix C, Figure H). However, as MNSI-Ar is primarily a screening tool, a cut-off with higher sensitivity would likely be more appropriate, such as ≥4, which had a 95.9% sensitivity and 62.5% specificity as shown in Table 3. Based on the confirmed neuropathy criteria, the ROC AUC was excellent at 0.91 (95% confidence interval 0.81–0.97).
Table 3.
The sensitivity, specificity and predictive values of the MNSI-Ar and MNSIq-Ar.
| Cut-off | PCC | Sensitivity (95% CI) | Specificity (95% CI) | PPV (95% CI) | NPV (95% CI) |
| MNSI-Ar | |||||
| Probable diabetic neuropathy criteria | |||||
| ≥4 | 80.9% | 95.9% (86%–99.5%) | 62.5% (45.8%–77.3%) | 75.8% (63.3%–85.8%) | 92.6% (75.7%–99.1%) |
| Confirmed diabetic neuropathy criteria | |||||
| ≥6 | 75.4% | 100% (83.9%–100%) | 63.6% (47.8%–77.6%) | 56.8% (39.5%–72.9%) | 100% (87.7%–100%) |
| MNSIq-Ar | |||||
| Probable diabetic neuropathy criteria | |||||
| ≥3 | 80.9% | 93.9% (83.1%–98.7%) | 65% (48.3%–79.4%) | 76.7% (64%–86.6%) | 89.7% (72.6%–97.8%) |
| Confirmed diabetic neuropathy criteria | |||||
| ≥4 | 70.8% | 95.2% (76.2%–99.9%) | 59.1% (43.2%–73.7%) | 52.6% (35.8%–69%) | 96.3% (81%–99.9%) |
CI = confidence interval, MNSI-Ar = Michigan neuropathy screening instrument-Arabic version, MNSIq-Ar = Michigan neuropathy screening instrument questionnaire-Arabic version, NPV = negative predictive value, PCC = percentage correctly classified, PPV = positive predictive value.
4. Discussion
Clinical practice and clinical trials use standard tools for screening, diagnosis, and outcome measurement which, in many instances, are patient-reported measures. As diabetic neuropathy is highly prevalent and annual screening is recommended, it is important to make a screening tool available in many languages, including Arabic. The usefulness of these tools is inherited from its validity. Several previous reports showed appropriate validity of the original MNSI.[26,27] Hence, some clinical trials have incorporated MNSI in the case definition of diabetic neuropathy.[28,29] In the original MNSI study, many patients with no diabetic neuropathy answered positively to questions in the MNSIq.[16] This highlights the importance of validating the MNSIq. In this study, we found that the MNSI-Ar had a satisfactory reliability and validity for use as a screening tool for diabetic neuropathy.
The MNSIq-Ar demonstrated satisfactory reliability overall; however, certain questions did not contribute to its reliability. Five questions (7, 8, 13, 14, and 15) showed low item–total correlation. These items describe symptoms that are more prevalent in advanced neuropathy. The item–total correlation was also low (<0.4) for these 5 questions in a previous validation study in another language.[30] Question 13 “Are you able to sense your feet when you walk?”—showed low item-total correlation and low reproducibility. A previous study that derived data from a randomized trial showed that the prevalence of positive answers to this item was 10% among patients with diabetes without neuropathy and 12% among diabetic neuropathy patients, which is similar to the prevalence in our study as shown in Table 1.[27] Omitting this question did not alter the diagnostic performance of the test, which is likely due to the low prevalence of this symptom among neuropathic patients. Each item's reproducibility was not examined in all previous validation and translation studies.[26,30–32] However, in our study, all the items had satisfactory reproducibility except for Q13. The measures of the test's overall reliability and reproducibility in our study were comparable to the test's previous validation in other languages.[30–32]
The MNSIq-Ar showed good concurrent validity with a strong correlation with other neuropathy screening tools. Of the tools we used, 2 (UENS and mTCNS) have been shown to have the highest AUC compared to other scales at 0.88 to 0.94 for UENS and 0.99 for mTCNS.[14,17] The AUC reported for UENS and mTCNS were examined against criteria that used NCSs, epidermal nerve fiber densities, and the quantitative sudomotor axon reflex.[14,17] The MNSI-Ar correlation with UENS and mTCNS along with the correlation with NCSs and the ability to differentiate different grades of NCSs neuropathy severity adds to the collective evidence suggestive of MNSI-Ar validity.
There is no criterion standard method to diagnose distal neuropathy, including diabetic neuropathy. Therefore, validity in our study was measured using three different methods with two or more different approaches for each method. We assessed the diagnostic performance and, specifically, the ROC AUC as one of the methods of evaluating validity. To classify the patients into the neuropathy and no neuropathy groups, we used two different diabetic neuropathy diagnosis criteria: probable criteria and definite criteria.[25] We chose to include probable criteria in the analysis as many patients with diabetic neuropathy have normal NCS.[3] The AUCs were stable using these 2 different criteria and were consistent with the previously reported AUC.[26,32]
Our study has several limitations. We did not include other objective measures for neuropathy, such as epidermal nerve fiber densities and quantitative sudomotor axon reflex; however, the MNSIq-Ar showed a good correlation with NCS parameters. The absence of a gold standard test to diagnose diabetic neuropathy is a major limitation in all similar studies. Also, we did not correlate the MNSI-Ar with other outcomes, such as falls, foot ulcers, and amputation. The sensitivity of the MNSI-Ar to change was not explored because the pain may take a long time to improve, and the main use of the instrument is as a screening rather than an outcome assessment tool. In conclusion, the MNSI-Ar was shown to be a reliable and valid screening tool for diabetic neuropathy in Arabic-speaking patients. The diagnostic performance suggests that it is appropriate to use it as a screening tool.
Author contributions
Ahmad R. Abuzinadah: study design, data acquisition and interpretation, statistical analysis, and manuscript preparation
Hussien S. Alkully: data acquisition and study design and manuscript review
Mohammed H. Alanazy: study design, statistical analysis, and manuscript review
Moafaq S. Alrawaili: data acquisition and manuscript review
Haneen Milyani: study design, data acquisition and manuscript review
Bashayr Alamri: data acquisition and manuscript review
Aysha A. AlShareef: study design and manuscript review
Ahmed K. Bamaga: study design, data acquisition, and manuscript review
Conceptualization: Ahmad Rida Abuzinadah, Hussien S. Alkully, Mohammed H. Alanazy, Moafaq S. Alrawaili, Haneen Milyani, Bashayr AlAmri, Ahmed K. Bamaga.
Data curation: Ahmad Rida Abuzinadah, Hussien S. Alkully, Mohammed H. Alanazy, Moafaq S. Alrawaili, Haneen Milyani, Bashayr AlAmri, Ahmed K. Bamaga.
Formal analysis: Ahmad Rida Abuzinadah, Ahmed K. Bamaga.
Investigation: Ahmad Rida Abuzinadah.
Methodology: Ahmad Rida Abuzinadah, Hussein S. Alkully, Mohammed H. Alanazy, Moafaq S. Alrawaili, Haneen Milyani, Bashayr AlAmri, Aysha A. AlShareef, Ahmed K. Bamaga.
Software: Ahmad Rida Abuzinadah.
Supervision: Mohammed H. Alanazy.
Validation: Ahmed K. Bamaga.
Writing – original draft: Ahmad Rida Abuzinadah, Mohammed H. Alanazy, Ahmed K. Bamaga.
Writing – review & editing: Ahmad Rida Abuzinadah, Hussien S. Alkully, Mohammed H. Alanazy, Moafaq S. Alrawaili, Haneen Milyani, Bashayr AlAmri, Aysha A. AlShareef, Ahmed K. Bamaga.
Supplementary Material
Supplementary Material
Supplementary Material
Supplementary Material
Footnotes
Abbreviations: DM = diabetes mellitus, DNP = diabetic neuropathy, DNS = diabetic neuropathy symptoms, MNSI = Michigan Neuropathy Screening Instrument, Arabic version, MNSIq = Michigan Neuropathy Screening Instrument questionnaire, MNSIq = Michigan Neuropathy screening instrument questionnaire, Arabic version, MP = nerve motor potential, mTCNS = Modified Toronto Neuropathy Score, SNA = sural nerve amplitude, SP = sensory potential, UENS = Utah Early Neuropathy Scale.
How to cite this article: Abuzinadah AR, Alkully HS, Alanazy MH, Alrawaili MS, Milyani HA, AlAmri B, AlShareef AA, Bamaga AK. Translation, validation, and diagnostic accuracy of the Arabic version of the Michigan neuropathy screening instrument. Medicine. 2021;100:44(e27627).
Author disclosures:
The authors report no conflicts of Interest.
Financial disclosures: This research did not receive any funding from public, commercial, or not-for-profit funding agencies.
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
Supplemental digital content is available for this article.
References
- [1].Beghi E, Monticelli ML. Chronic symmetric symptomatic polyneuropathy in the elderly: a field screening investigation in two Italian regions. I. Prevalence and general characteristics of the sample. Italian General Practitioner Study Group (IGPSG). Neurology 1995;45:1832–6. [DOI] [PubMed] [Google Scholar]
- [2].Bharucha NE, Bharucha AE, Bharucha EP. Prevalence of peripheral neuropathy in the Parsi community of Bombay. Neurology 1991;41:1315–7. [DOI] [PubMed] [Google Scholar]
- [3].Smith AG, Singleton JR. Diabetic neuropathy. Continuum (Minneap Minn) 2012;18:60–84. [DOI] [PubMed] [Google Scholar]
- [4].Pasnoor M, Dimachkie MM, Kluding P, Barohn RJ. Diabetic neuropathy part 1: overview and symmetric phenotypes. Neurol Clin 2013;31:425–45. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [5].Al Dawish MA, Robert AA, Braham R, et al. Diabetes mellitus in Saudi Arabia: a review of the recent literature. Curr Diabetes Rev 2016;12:359–68. [DOI] [PubMed] [Google Scholar]
- [6].Alotaibi A, Perry L, Gholizadeh L, Al-Ganmi A. Incidence and prevalence rates of diabetes mellitus in Saudi Arabia: An overview. J Epidemiol Glob Health 2017;7:211–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [7].Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4.4 million participants. Lancet 2016;387:1513–30. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [8].Partanen J, Niskanen L, Lehtinen J, Mervaala E, Siitonen O, Uusitupa M. Natural history of peripheral neuropathy in patients with non-insulin-dependent diabetes mellitus. N Engl J Med 1995;333:89–94. [DOI] [PubMed] [Google Scholar]
- [9].Nathan DM. Long-term complications of diabetes mellitus. N Engl J Med 1993;328:1676–85. [DOI] [PubMed] [Google Scholar]
- [10].Rubio JA, Aragon-Sanchez J, Jimenez S, et al. Reducing major lower extremity amputations after the introduction of a multidisciplinary team for the diabetic foot. Int J Low Extrem Wounds 2014;13:22–6. [DOI] [PubMed] [Google Scholar]
- [11].Boulton AJ, Vinik AI, Arezzo JC, et al. Diabetic neuropathies: a statement by the American Diabetes Association. Diabetes Care 2005;28:956–62. [DOI] [PubMed] [Google Scholar]
- [12].Standards of medical care in diabetes--2012. Diabetes Care 2012;35: (suppl 1): S11–63. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [13].Pérez-Panero AJ, Ruiz-Muñoz M, Cuesta-Vargas AI, Gónzalez-Sánchez M. Prevention, assessment, diagnosis and management of diabetic foot based on clinical practice guidelines: A systematic review. Medicine (Baltimore) 2019;98:e16877. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [14].Zilliox LA, Ruby SK, Singh S, Zhan M, Russell JW. Clinical neuropathy scales in neuropathy associated with impaired glucose tolerance. J Diabetes Complications 2015;29:372–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [15].Smith AG, Russell J, Feldman EL, et al. Lifestyle intervention for pre-diabetic neuropathy. Diabetes Care 2006;29:1294–9. [DOI] [PubMed] [Google Scholar]
- [16].Feldman EL, Stevens MJ, Thomas PK, Brown MB, Canal N, Greene DA. A practical two-step quantitative clinical and electrophysiological assessment for the diagnosis and staging of diabetic neuropathy. Diabetes Care 1994;17:1281–9. [DOI] [PubMed] [Google Scholar]
- [17].Singleton JR, Bixby B, Russell JW, et al. The Utah Early Neuropathy Scale: a sensitive clinical scale for early sensory predominant neuropathy. J Peripher Nerv Syst 2008;13:218–27. [DOI] [PubMed] [Google Scholar]
- [18].Bril V, Tomioka S, Buchanan RA, Perkins BA, m TSG. Reliability and validity of the modified Toronto Clinical Neuropathy Score in diabetic sensorimotor polyneuropathy. Diabet Med 2009;26:240–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [19].Meijer JW, Smit AJ, Sonderen EV, Groothoff JW, Eisma WH, Links TP. Symptom scoring systems to diagnose distal polyneuropathy in diabetes: the Diabetic Neuropathy Symptom score. Diabet Med 2002;19:962–5. [DOI] [PubMed] [Google Scholar]
- [20].Brown MJ, Bird SJ, Watling S, et al. Natural progression of diabetic peripheral neuropathy in the Zenarestat study population. Diabetes Care 2004;27:1153–9. [DOI] [PubMed] [Google Scholar]
- [21].Wang F, Zhang J, Yu J, et al. Diagnostic accuracy of monofilament tests for detecting diabetic peripheral neuropathy: a systematic review and meta-analysis. J Diabetes Res 2017;2017:8787261. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [22].Chatila N, Pereira B, Maarrawi J, Dallel R. Validation of a New Arabic Version of the Neuropathic Pain Diagnostic Questionnaire (DN4). Pain Pract 2017;17:78–87. [DOI] [PubMed] [Google Scholar]
- [23].Guillemin F, Bombardier C, Beaton D. Cross-cultural adaptation of health-related quality of life measures: literature review and proposed guidelines. J Clin Epidemiol 1993;46:1417–32. [DOI] [PubMed] [Google Scholar]
- [24].Beaton DE, Bombardier C, Guillemin F, Ferraz MB. Guidelines for the process of cross-cultural adaptation of self-report measures. Spine (Phila Pa 1976) 2000;25:3186–91. [DOI] [PubMed] [Google Scholar]
- [25].Tesfaye S, Boulton AJ, Dyck PJ, et al. Diabetic neuropathies: update on definitions, diagnostic criteria, estimation of severity, and treatments. Diabetes Care 2010;33:2285–93. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [26].Moghtaderi A, Bakhshipour A, Rashidi H. Validation of Michigan neuropathy screening instrument for diabetic peripheral neuropathy. Clin Neurol Neurosurg 2006;108:477–81. [DOI] [PubMed] [Google Scholar]
- [27].Herman WH, Pop-Busui R, Braffett BH, et al. Use of the Michigan Neuropathy Screening Instrument as a measure of distal symmetrical peripheral neuropathy in Type 1 diabetes: results from the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications. Diabet Med 2012;29:937–44. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [28].Pop-Busui R, Lu J, Lopes N, Jones TL. Prevalence of diabetic peripheral neuropathy and relation to glycemic control therapies at baseline in the BARI 2D cohort. J Peripher Nerv Syst 2009;14:01–13. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [29].Pop-Busui R, Evans GW, Gerstein HC, et al. Effects of cardiac autonomic dysfunction on mortality risk in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial. Diabetes Care 2010;33:1578–84. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [30].Barbosa M, Saavedra A, Severo M, Maier C, Carvalho D. Validation and reliability of the Portuguese version of the Michigan Neuropathy Screening Instrument. Pain Pract 2017;17:514–21. [DOI] [PubMed] [Google Scholar]
- [31].Oliveira FB, Botelho KK, Bezerra AR, Azevedo DI, Santos-Couto-Paz CC, Fachin-Martins E. Cross-cultural adaptation to Brazilian Portuguese of the Michigan Neuropathy Screening Instrument: MNSI-Brazil. Arq Neuropsiquiatr 2016;74:653–61. [DOI] [PubMed] [Google Scholar]
- [32].Kaymaz S, Alkan H, Karasu U, Çobankara V. Turkish version of the Michigan Neuropathy Screening Instrument in the assessment of diabetic peripheral neuropathy: a validity and reliability study. Diabetol Int 2020;11:283–92. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.