1.

Working title of research project: HbA1c Variability as a Predictor of Disease Severity in Diabetic Peripheral
Neuropathy among type 2 Diabetics
2. DRP No.:

3. Investigators:
i) Name of students:
Dr Thanisha Santhosh, M.B.B.S Intern, Phone:+91 9008727196 Email: thanishasanthosh@gmail.com
Dr Talika Sibal, M.B.B.S Intern, Phone: +91 9769840204, Email: talika@sibal.com

ii) Name of Guide:

Dr Divya Nagabushana, Department of Neurology, MS Ramaiah, Phone: +91 9900974025, Email:
divya.nagabushana@gmail.com

4. Departments involved: Department of Neurology, Ramaiah Medical College

5. Summary of proposed study:

Diabetes affects around 425 million people worldwide.(1) Of these, around 30% of individuals will go on to develop
diabetic neuropathy, a common microvascular complication of diabetes that leads to nerve damage manifesting as loss
of sensation or pain. (2) The level of glycosylated hemoglobin (HbA1c) is used as a diagnostic marker for diabetes,
and as a measure of blood sugar levels it is used to assess the severity of diabetes. Studies have shown that a greater
variability in HbA1c is associated with a greater incidence of diabetic sensorimotor polyneuropathies (DSPN). (3) (4)
(5) (6) However, the literature exploring the association between this variability and the severity of diabetic
neuropathy is limited. In this study, we will explore the correlation between variability in HbA1c levels and the
severity of diabetic sensorimotor polyneuropathy. The severity of DSPN will be quantified by calculating the modified
composite score of derived from amplitude of nerve conduction studies for amplitude as constructed by Dyke et al.
(7) Distal latencies and conduction velocities will also be collected and correlated with HbA1c variability.

6. Justification for study:

Peripheral neuropathy is a debilitating microvascular complication of diabetes mellitus. The percentage of people who
go on to develop diabetic neuropathy is significantly higher among individuals having type 2 DM in comparison to
those having type 1 DM. A systematic review and meta-analysis found that around 30% of individuals with diabetes
will go on to develop diabetic neuropathy. (2) Among these, diabetic sensorimotor polyneuropathy (DSPN) is the
most common presentation. (7) (8) The International Diabetes Federation estimates that 425 million people worldwide
have diabetes- the largest global epidemic of the 21st century- with 73 million in India alone. (1)
DN is one of the commonest causes of peripheral neuropathy. It accounts for hospitalisation more frequently than
other complications of diabetes and also is the most frequent cause of non ‐traumatic amputation.
As discussed below in the review of literature, several recent studies have shown that glycaemic variability,
demonstrated either through blood glucose monitoring (9) (10) (11) (12) or through serial HbA1c measurements (3)
(4) (5) (6) is an important risk factor in the development of DSPN amongst type 2 diabetics. However, there is paucity
in information about the correlation between severity of DSPN and glycaemic excursions with only one study by Lai
et al (13) that has been done on a subset of Taiwanese population that showed a strong association between HbA1c
variability and severity of diabetic neuropathy in type 2 DM by measuring composite scores of nerve conduction
studies. There have been no similar studies done in the Indian subcontinent or elsewhere. Thus there is a pressing need
for more studies investigating the correlation between HbA1c variability and severity of diabetic neuropathy. The
present study aims to show the association between HbA1c variability and severity of DSPN with the Indian
population in mind. Keeping in mind the burgeoning morbidity and disease burden attributed to DSPN as well as the
prevalence of microvascular complications like neuropathy in diabetics, identifying glycaemic variability as an
independent, modifiable disease predictor can help to guide clinical equipoise towards early and rigorous glycaemic
control, ultimately preventing the onset or progression to severe diabetic neuropathy in type 2 diabetics.
8. Aims & Objectives
The aim of the project is to study the relation between HbA1c variability and severity of diabetic sensorimotor
polyneuropathy (DSPN) as determined by composite scores of nerve conduction studies in patients with type 2
diabetes mellitus.

9. Hypotheses
HbA1c variability is associated with severity of diabetic sensorimotor peripheral neuropathy in individuals with type 2
diabetes.

10. Review of Literature:

Several studies have shown that glycemic variability is significantly associated with risk of developing diabetic
sensorimotor polyneuropathy (DSPN) in both type 1 (14) (15) (16) (17) as well as type 2 DM (3) (4) (5) (18). The
aforementioned studies have taken flux in both HbA1c as well as in blood glucose levels in the form of continuous
glucose monitoring (CGM) as indicators of variability.
One retrospective case-control study by Pai et al (18) conducted in a tertiary care hospital in Taiwan on 426 patients
demonstrated, through multivariate analysis, that greater long-term glycemic variability significantly increased the risk
of DSPN, with a corresponding odds ratio of 1.85 and 1.61 (95% confidence intervals of 1.25–2.73 and 1.02–2.55,
respectively), after being adjusted for hypoglycemia and types of diabetes treatment.
In another study by Hu et al (12) it was found that Mean Amplitude of Glucose Excursions (MAGE), calculated using
values from continuous glucose monitoring and taken as an indicator of glycemic variability, independently
contributed to the presence of diabetic neuropathy.
In a cross-sectional study by Su and colleagues (3) HbA1c variability of 563 type 2 diabetic patients with DPN was
assessed by the coefficient of variation of HbA1c (CV-HbA1c), and the mean of HbA1c (M-HbA1c) was calculated.
DPN was confirmed in patients displaying both clinical manifestations of neuropathy and abnormalities in a nerve
conduction evaluation. The study found that among the recruited patients, 18.1% (n = 102) were found to have DPN,
and these patients also presented with a higher CV-HbA1c than the patients without DPN (p < 0.001). The proportion
of patients with DPN increased significantly from 6.9% in the first to 19.1% in the second and 28.5% in the third
tertile of CV-HbA1c (p for trend < 0.001. The study concluded that increased HbA1c variability is closely associated
with DPN in type 2 diabetic patients and could be considered as a potent indicator for DSPN in these patients.
A pilot study by Oyibi et al (11) divided twenty diabetic patients with peripheral neuropathy into painful and painless
and monitored their glucose flux through a continuous glucose monitoring system that the patients were required to
wear for 3 days. Symptomatic patients kept a daily pain score diary. The painful group had a greater mean glucose
(12.1 +/- 2.9 mmol/l vs. 9.3 +/- 1.9 mmol/l, P = 0.02), a greater M-value (68.4 vs. 31.1, P = 0.02) and more glycaemic
excursions (13 vs. 10, P < 0.01)with the study concluding that patients with painful neuropathy have greater glucose
flux.
However, to the best of our knowledge, only one study in literature (Lai et al) has studied the relationship between
HbA1c variability and severity of DSPN. (13)
It is also worthwhile to mention that clinical guidelines on quantifying the severity and intensity of diabetic
neuropathy are still limited with the most noteworthy effort being that of Dyck J et al (7) who, in the Rochester
Diabetic formulated a composite score from 0 to 10, based on the sum of five normal deviates of nerve conduction
consisting of peroneal nerve compound muscle action potential (CMAP), tibial CMAP, ulnar CMAP, sural sensory
nerve action potential (SNAP) and ulnar SNAP This same score was used by Lai et al to quantify severity of DSPN in
patients in their study which enrolled 223 individuals with type 2 diabetes and calculated the intrapersonal mean,
standard deviation (SD), and coefficient of variation of HbA1c for each patient using all measurements obtained for 3
years prior to the study. Patients were then divided into quartiles according to the SD of HbA1c. The study found that
those with higher SD-HbA1c showed lower amplitudes and reduced motor nerve conduction velocity in tested nerves,
and lower sensory nerve conduction velocity in the sural nerve. Furthermore, those with higher SD-HbA1c had higher
composite scores of low extremities. Multiple linear regression analysis revealed that diabetes duration, SD-HbA1c,
and eGFR were independently associated with mean composite scores.
Materials & Methods:
i) Study Design:
Retrospective cohort study.

ii) Study Period:

a) Period which may be needed for collecting the data: 5 months
b) Period that may be required for analyzing the data and writing manuscript: 1 month

iii) Inclusion and Exclusion Criteria:

Inclusion Criteria:

 Male or female patients, 18–75 years of age.

 Patients who satisfy the American Diabetes Association (ADA) criteria for diagnosis of type 2 diabetes
mellitus and who were diagnosed at least 1 year ago.
 Patients diagnosed with clinical or subclinical DSPN by the minimum criteria of abnormality in nerve
conduction study with or without symptoms, after having ruled out other causes of sensorimotor peripheral
neuropathy.
 Patients with at least 4 serial measurements of HbA1c over the course of study period.
Exclusion Criteria:

 Subjects with less than 4 serial measurements of HbA1c over the course of study period.
 Subjects suffering from diseases other than diabetes known to cause peripheral neuropathy.

iv) Sample size with justification:

From the literature review, in a study by Yun-Ru Lai et al it has been observed that the correlation between variability
of HbA1c and composite scores of nerve conduction studies was r = 0.304, p < 0.001.
In the present study, expecting similar results with 80% power, 95% confidence level and considering population
coefficient as 0.58, the study requires a minimum of 68 subjects.
v) Procedure:
68 patients with type 2 diabetes mellitus who have come for treatment to M.S. Ramaiah Hospitals for diabetic
sensorimotor polyneuropathy (DSPN) will be enrolled.
Type 2 diabetics will be considered according to the American Diabetes Association (ADA) criteria. DSPN will be
diagnosed based on the diagnostic criteria given by Peter J Dyck & Tesfaye et al., which requires abnormalities in
nerve conduction studies and clinical manifestations in the form of symptoms or signs. Data on glycemic variability
will be collected through past serial HbA1c measurements.
Serial HbA1c measurements will be used to calculate the patients’ Standard Deviation (SD) & Coefficient of
Variation (CV), and this will be used to express their glycemic variability. The SDs will then be divided into quartiles,
which will then be used for statistical analysis.

Severity of diabetic neuropathy will be quantified based on the modified composite score of nerve conduction as
constructed by Dyke et al. The modified composite scores for use in DSPN will be the sum of five normal deviates of
nerve conduction consisting of peroneal nerve compound muscle action potential (CMAP), tibial CMAP, ulnar
CMAP, sural sensory nerve action potential (SNAP) and ulnar SNAP.
Distal latencies and conduction velocities will also be collected and correlated with HbA1c variability.

Descriptive statistics of HbA1c variability and composite scores of nerve conduction will be analyzed and
summarized in terms of mean with standard deviation. The Pearson correlation coefficient will be used to find the
correlation between HbA1c variability and composite scores of nerve conduction. An ANOVA test will be done to
compare nerve conduction scores between different quartiles of HbA1c.
vi) Potential risks and benefits:
-

vii) Place of the study:

MS Ramaiah Memorial Hospital, Mathikere, Bangalore, India

viii) Biological materials required:

-

ix) Investigations:
.

x) Outcome measures:
-

xi) Statistical Methods:

Descriptive statistics of HbA1c variability and composite scores of nerve conduction will be analyzed and
summarized in terms of mean with standard deviation. The Pearson correlation coefficient will be used to find the
correlation between HbA1c variability and composite scores of nerve conduction. An ANOVA test will be done to
compare nerve conduction scores between different quartiles of HbA1c.

12. Ethical considerations and methods to address issues:

-
13. Implications of the study:
Identifying glycaemic variability as an important modifiable disease predictor can help to guide clinical practice
towards early and aggressive glycaemic control. Ultimately this could prevent DSPN or in established cases, prevent
progression to severe DSPN in type 2 diabetics, thus preventing one of the most common, serious and debilitating
microvascular complication of diabetes and improving patient quality of life.

14. Budget and proposed funding source:

15. References:

1. Feldman EL, Callaghan BC, Pop-Busui R, Zochodne DW, Wright DE, Bennett DL, et al. Diabetic neuropathy.
Nat Rev Dis Primer. 2019 Jun 13;5(1):1–18.

2. Sun J, Wang Y, Zhang X, Zhu S, He H. Prevalence of peripheral neuropathy in patients with diabetes: A
systematic review and meta-analysis. Prim Care Diabetes. 2020 Oct 1;14(5):435–44.

3. Su JB, Zhao LH, Zhang XL, Cai HL, Huang HY, Xu F, et al. HbA1c variability and diabetic peripheral
neuropathy in type 2 diabetic patients. Cardiovasc Diabetol. 2018 Mar 29;17(1):47.

4. Cardoso CRL, Leite NC, Moram CBM, Salles GF. Long-term visit-to-visit glycemic variability as predictor of
micro- and macrovascular complications in patients with type 2 diabetes: The Rio de Janeiro Type 2 Diabetes
Cohort Study. Cardiovasc Diabetol. 2018 Feb 24;17(1):33.

5. Scott ES, Januszewski AS, O’Connell R, Fulcher G, Scott R, Kesaniemi A, et al. Long-Term Glycemic
Variability and Vascular Complications in Type 2 Diabetes: Post Hoc Analysis of the FIELD Study. J Clin
Endocrinol Metab. 2020 Oct 1;105(10):e3638–49.

6. Firouzabadi MD, Poopak A, Sheikhy A, Samimi S, Nakhaei P, Firouzabadi FD, et al. Glycemic profile
variability: An independent risk factor for diabetic neuropathy in patients with type 2 diabetes. Prim Care
Diabetes. 2023 Feb;17(1):38–42.
7. Dyck PJ, Albers JW, Andersen H, Arezzo JC, Biessels GJ, Bril V, et al. Diabetic polyneuropathies: update on
research definition, diagnostic criteria and estimation of severity. Diabetes Metab Res Rev. 2011 Oct;27(7):620–

8. Albers JW, Pop-Busui R. Diabetic neuropathy: mechanisms, emerging treatments, and subtypes. Curr Neurol
Neurosci Rep. 2014 Aug;14(8):473.

9. Akaza M, Akaza I, Kanouchi T, Sasano T, Sumi Y, Yokota T. Nerve conduction study of the association between
glycemic variability and diabetes neuropathy. Diabetol Metab Syndr. 2018 Sep 12;10(1):69.

10. Zhang X, Yang X, Sun B, Zhu C. Perspectives of glycemic variability in diabetic neuropathy: a comprehensive
review. Commun Biol. 2021 Dec 7;4:1366.

11. Oyibo SO, Prasad YDM, Jackson NJ, Jude EB, Boulton AJM. The relationship between blood glucose excursions
and painful diabetic peripheral neuropathy: a pilot study. Diabet Med J Br Diabet Assoc. 2002 Oct;19(10):870–3.

12. Hu YM, Zhao LH, Zhang XL, Cai HL, Huang HY, Xu F, et al. Association of glycaemic variability evaluated by
continuous glucose monitoring with diabetic peripheral neuropathy in type 2 diabetic patients. Endocrine. 2018
May;60(2):292–300.

13. Lai YR, Chiu WC, Huang CC, Tsai NW, Wang HC, Lin WC, et al. HbA1C Variability Is Strongly Associated
With the Severity of Peripheral Neuropathy in Patients With Type 2 Diabetes. Front Neurosci. 2019 Feb 13;13:90.

14. Christensen MMB, Hommel EE, Jørgensen ME, Fleischer J, Hansen CS. Glycemic Variability and Diabetic
Neuropathy in Young Adults With Type 1 Diabetes. Front Endocrinol. 2020;11:644.

15. Pinto MV, Rosa LCGF, Pinto LF, Dantas JR, Salles GF, Zajdenverg L, et al. HbA1c variability and long-term
glycemic control are linked to peripheral neuropathy in patients with type 1 diabetes. Diabetol Metab Syndr. 2020
Oct 6;12(1):85.

16. Mao Y, Zhong W. HbA1c Variability as an Independent Risk Factor for Microvascular Complications in Type 1
Diabetes. J Diabetes Sci Technol. 2022 Jun 2;19322968221100830.

17. Kwai NCG, Arnold R, Poynten AM, Krishnan AV. Association between glycemic variability and peripheral nerve
dysfunction in type 1 diabetes. Muscle Nerve. 2016;54(5):967–9.

18. Pai YW, Lin CH, Lin SY, Lee IT, Chang MH. Reconfirmation of newly discovered risk factors of diabetic
peripheral neuropathy in patients with type 2 diabetes: A case-control study. PLOS ONE. 2019 Jul
29;14(7):e0220175.

19. Bansal, V et al. “Diabetic neuropathy.” Postgraduate medical journal vol. 82,964 (2006): 95-100.

16. Enclosures:
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