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Back to Journal »Diabetes, Metabolic Syndrome and Obesity: Goals and Treatment» Volume 14

Correlation between hemoglobin glycosylation index and nerve conduction velocity in patients with type 2 diabetes

Authors: Li Z, Gao Y, Jia Y, Chen S

Published on December 4, 2021, Volume 2021: 14 pages, 4757-4765 pages

DOI https://doi.org/10.2147/DMSO.S334767

Single anonymous peer review

Approved for publication editor: Professor Minghui Zou

Li Zelin,1,2 Gao Yuan,3 Jia Yujiao,1,2 Chen Shuchun1,2,4 1 Graduate School of Hebei Medical University, Shijiazhuang, Hebei Province; 2 Department of Endocrinology, Hebei General Hospital, Shijiazhuang City, Hebei Province; 3 Shijiazhuang Sixteenth, Hebei Military Region Retirement Center for Retired Cadres, Shijiazhuang, Hebei Province; 4 Key Laboratory of Metabolic Diseases, Shijiazhuang City, Hebei Province Corresponding Author: Chen Shuchun, Department of Endocrinology, Hebei General Hospital, No. 348 Heping West Road, Shijiazhuang City, Hebei Province 050051 People's Republic of China Tel/Fax +86 311 8659 [ Email protection] Purpose: This study aims to investigate the relationship between hemoglobin glycation variation index (HGI) and peripheral nerve conduction velocity (NCV) in patients with type 2 diabetes (T2DM). Materials and methods: This is a cross-sectional study, including 324 T2DM patients who were included in this study. Collect basic information and blood indicators. Measure the motor conduction velocity (MCV) of the ulnar nerve, median nerve, and common peroneal nerve on both sides, as well as the sensory conduction velocity (SCV) of the ulnar nerve, median nerve, and superficial peroneal nerve. According to NCV, patients were divided into two groups: normal nerve conduction group (NCVN group) and abnormal nerve conduction group (NCVA group). When NCV is less than a certain normal value, the patients are divided into NCVA group. HGI is calculated as the difference between the measured and predicted values ​​of glycosylated hemoglobin (HbA1c), using the linear relationship between HbA1c levels and fasting blood glucose levels. Finally, using the median of HGI as the boundary, all study participants were divided into high HGI group and low HGI group. The study also analyzed the correlation between HGI and NCV. Results: Compared with the NCVN group, the NCVA group had a higher HGI level (P <0.001). The prevalence of NCVA in the high HGI group was higher than that in the low HGI group (P = 0.004). HGI is negatively correlated with bilateral ulnar nerve MCV, bilateral median nerve MCV, bilateral common peroneal nerve MCV, bilateral median nerve SCV, and left superficial peroneal nerve SCV. The correlation still exists after adjusting for confounding factors. Conclusion: This study found that HGI and NCV in patients with T2DM are negatively correlated, and HGI is highly correlated with peripheral nerve MCV. Keywords: hemoglobin glycosylation variation index, peripheral nerve conduction velocity, type 2 diabetes

Type 2 diabetes (T2DM) is one of the most common metabolic diseases in the world. The International Diabetes Federation estimates that there are approximately 425 million diabetic patients in the world, of which 115 million are in China. 1 T2DM is a disease caused by disorders of carbohydrate metabolism due to insulin resistance and insufficient insulin secretion, and is characterized by chronic hyperglycemia. The most common and complex complication of T2DM is diabetic peripheral neuropathy (DN), especially diabetic polyneuropathy (DPN). 2 Approximately 37-45% of T2DM patients have DN. 3 Despite its high prevalence, it is often missed and approximately 50% of patients are asymptomatic. 4 DN will damage the quality of life and may cause a huge economic burden. Late diagnosis of DN can lead to a significant increase in morbidity and mortality in the form of diabetic foot ulcers and amputations. 5 Therefore, it is recommended that all patients should be screened for DN when they are diagnosed with T2DM. 6 Peripheral nerve conduction velocity (NCV) test is the most sensitive, accurate and reliable method for diagnosing DN. 7

A number of studies have shown that hyperglycemia, vitamin D deficiency, diabetes course, hypertension and hyperlipidemia are all risk factors for DN. 8,9 Among them, poor blood glucose control is considered an important risk factor for DN. 10 Glycated hemoglobin (HbA1c) is an important risk factor for DN. The gold standard for blood sugar control in the last 2-3 months. However, recent studies have found that HbA1c levels are affected by blood sugar levels and biological differences between individuals. 11,12 HbA1c can be affected by many factors, such as age, red blood cell lifespan, anemia, genetic factors, etc. 13 Therefore, the hemoglobin glycosylation index (HGI) was introduced to quantify the individual differences between fasting blood glucose (FBG) and HbA1c. 14 Studies have found that high HGI levels are associated with many chronic complications of diabetes, including diabetic retinopathy and diabetic nephropathy, especially cardiovascular diseases. Disease 13–16

As far as we know, this is the first study to examine the correlation between HGI and NCV in patients with T2DM. The purpose of this study is to investigate whether HGI is an independent factor of NCV. In other words, this study aims to investigate whether high HGI is a risk factor for NCV. Early identification of risk factors for NCV, so that early screening can be carried out.

The study was conducted in the Department of Endocrinology, Hebei General Hospital, using a cross-sectional method. Recruitment period of subjects is from December 2018 to December 2019. This study followed the principles of the Declaration of Helsinki and was approved by the Medical Ethics Committee of Hebei Provincial General Hospital (Ethics Committee Number: NO.202027). All patients signed an informed consent form before enrollment. Inclusion criteria for this study: 1) Patients (age> 18 years old) meet the WHO diagnostic criteria for diabetes, 1999. 2) NCV test was performed.

Study exclusion criteria: type 1 diabetes or other specific types of diabetes; pregnant or breastfeeding women; serious physical diseases; liver or kidney failure; complications of diabetic foot; diseases that affect nerve conduction speed, such as heredity, demyelination Sexual or multifocal neuropathy, radiculopathy, mononeuritis and cerebrovascular disease.

All participants completed a questionnaire to collect basic information, including gender, age, and disease course. The height and weight were measured twice by a professional medical examiner, and the average value was recorded. A blood sample was taken from the patient after 8 hours of fasting. The study tested all blood indicators in the same laboratory. Total cholesterol, triglycerides, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, alanine aminotransferase, aspartate aminotransferase, urea nitrogen, creatinine, uric acid, and FBG are all used by professionals in automatic biochemistry Analyzer measurement. Vitamin D and HbA1c are measured by laboratory doctors using electrochemiluminescence method.

Under standard surface stimulation, NCV was recorded using a Keypoint Evoked Potentiometer (Alpine Biomed ApS; Dantec, Denmark). When measuring, apply conductive glue on the electrode and fix it with tape. Measure the NCV on both sides of the limbs. The test is carried out at room temperature (20-25°C), the stimulation frequency is 1 Hz, the stimulation pulse width is 0.1 ms, and the sensitivity is 5 mV/div. Use a thermal imaging camera to measure the patient's skin temperature and control it between 33 and 34°C. In order to study the motor nerve, we chose bilateral ulnar nerve, median nerve and common peroneal nerve. We choose bilateral ulnar nerve, median nerve and superficial peroneal nerve for sensory nerve inspection.

HGI is calculated using the following formula:

HGI = measured HbA1c value-predicted HbA1c value.

In this study, linear regression was used to analyze the correlation between HbA1c and FBG, and the HbA1c-FBG regression equation was established based on the data of all subjects. The predicted HbA1c value is calculated by inserting the FBG value into the linear regression equation: predicted HbA1c level = 0.325 × FBG (mmol/L) + 5.724 (r = 0.592, P<0.001).

Our study includes two groups. First, the patients were divided into two groups according to the nerve conduction velocity (NCV) (normal value, see Table 1 for specific normal values): the normal nerve conduction group and the abnormal nerve conduction group. Table 1 Specific normal values ​​of nerve conduction velocity

Table 1 Specific normal values ​​of nerve conduction velocity

Secondly, the patients were divided into high HGI group (HGI≥-0.32) and low HGI group (HGI<-0.32).

All analyses were performed using Statistical Product and Service Solutions 25.0 (SPSS 25.0). The study used the Kolmogorov-Smirnov test to test the normality of the distribution. If the data is in a normal distribution, it is expressed as the mean ± standard deviation, and the student's t test is used for comparison between groups. If the data does not fit a normal distribution, it is expressed as the median (25th-75th interquartile range), and Mann-Whitney U analysis is used for comparison between groups. The data are expressed as numbers (percentages) of categorical variables, and the chi-square test is used for comparison between groups. In this study, logistic regression analysis was used to investigate whether HGI is a risk factor for abnormalities in the peripheral nerve conduction group (NCVA), and Spearman or Pearson correlation analysis was used to analyze the correlation between HGI and NCV. We used multiple linear regression analysis to study the independent correlation between HGI and NCV. P values ​​less than 0.05 are considered statistically significant.

A total of 324 patients with T2DM (231 males and 93 females) were included in this study, 150 of which (46.30%) constituted the abnormal peripheral nerve conduction group (NCVA group). The median age of the study population was 56 years, and the average course of disease was 8 years. 162 patients had high HGI values, with an average HGI level of -0.32 (Table 2). Table 2 Clinical characteristics of all participants

Table 2 Clinical characteristics of all participants

Compared with the NCVN group, the HGI level of the NCVA group was significantly increased (P<0.001) (Figure 1). Compared with the NCVN group, patients in the NCVA group had lower levels of vitamin D and a longer course of disease (P=0.010, P=0.006, respectively). (table 3). Table 3 Index comparison between NCVN and NCVA groups Figure 1 Comparison of HGI levels between the normal peripheral nerve conduction group and the abnormal peripheral nerve conduction group in patients with type 2 diabetes.

Table 3 Comparison of NCVN and NCVA group indexes

Figure 1 Comparison of HGI levels in patients with type 2 diabetic patients with normal peripheral nerve conduction group and abnormal peripheral nerve conduction group

The prevalence of NCVA in the high HGI group was higher than that in the low HGI group b (P=0.004) (Figure 2). Compared with the low-HGI group, the high-HGI group had higher HbA1c levels and lower vitamin D levels (P<0.001, P=0.005, respectively) (Table 4). Table 4 Index comparison between high HGI group and low HGI group Figure 2 Comparison of the incidence of abnormal peripheral nerve conduction among all participants in the high HGI group and low HGI group. * Indicates significance when P value<0.05.

Table 4 Index comparison between high HGI group and low HGI group

Figure 2 Comparison of the incidence of abnormal peripheral nerve conduction among all participants in the high-HGI group and the low-HGI group. * Indicates significance when P value<0.05.

The study performed dichotomous logistic regression analysis to analyze the correlation between HGI and NCV. Age, course of disease, diastolic blood pressure, glutamate aminotransferase, vitamin D level, metformin use, insulin use, and HGI are all included in this dichotomous logistic model. The results showed that HGI is a risk factor for NCVA. For every increase in HGI, the incidence of NCVA increased by 1.338 times (P=0.004) (Table 5). Table 5 Binary Logistic regression of risk factors for abnormal peripheral nerve conduction velocity in patients with T2DM

Table 5 Binary Logistic regression of risk factors for abnormal peripheral nerve conduction velocity in patients with T2DM

Correlation analysis showed that HGI was negatively correlated with bilateral ulnar nerve motor conduction velocity (MCV), median nerve MCV, common peroneal nerve MCV, bilateral median nerve sensory conduction velocity (SCV), and left superficial peroneal nerve SCV (Table 6) Table 6 Correlation between HGI and NCV in T2DM patients

Table 6 Correlation between HGI and NCV in T2DM patients

However, there is no correlation between HGI and bilateral ulnar nerve SCV or right superficial peroneal nerve SCV (Table 6).

For all participants, in the coarse model 1 (Table 7) and model 2 (adjusted for age), HGI and bilateral ulnar nerve MCV, median nerve NCV, common peroneal nerve MCV, bilateral median nerve SCV and left superficial peroneal nerve Nerve SCV is negatively correlated, BMI, disease course, systolic blood pressure, diastolic blood pressure) (Table 8) and model 3 (adjusted according to age, BMI, disease course, systolic blood pressure, diastolic blood pressure, glutamate aminotransferase, glutathione aminotransferase) (Table 9). Table 7 Correlation between HGI and different nerve conduction velocities in T2DM patients in Model 1 Table 8 Correlation between HGI and different nerve conduction velocities in T2DM patients in Model 2 Table 9 Correlation of HGI and different nerve conduction velocities in T2DM patients in model 3

Table 7 Correlation between HGI and different nerve conduction velocities in T2DM patients in Model 1

Table 8 Correlation between HGI and different nerve conduction velocities in T2DM patients in Model 2

Table 9 Correlation between HGI and different nerve conduction velocities in T2DM patients in Model 3

However, regardless of adjusted or unadjusted confounding factors, there is no correlation between HGI and SCV of the bilateral ulnar nerve or SCV of the right superficial peroneal nerve.

HGI is another indicator of blood sugar, which reflects the tendency of an individual to glycosylate. Individuals with high HGI levels have higher HbA1c levels than expected. 17 Previous studies have focused on the relationship between HGI and cardiovascular complications, diabetic nephropathy, and diabetic retinopathy. As far as we know, this is the first study to investigate the relationship between HGI and NCV in patients with T2DM.

DN is the most common complication of type 2 diabetes, and DPN is the most common type of DN. The occurrence and development of DN can be explained by a variety of causes, including increased polyol and hexose channel activity, 1 oxidative stress, complex inflammatory processes, endothelial dysfunction, increased advanced glycation end products (AGEs), and vitamin D deficiency. 9,18-20 The study found that T2DM patients with high HGI levels are more susceptible to NCVA, and HGI levels are independently negatively correlated with NCV. Therefore, we recommend that high HGI levels are a risk factor for DN. The following are some possible mechanisms.

First, HGI was proposed to identify the phenotype of glucose metabolism. Patients with high HGI levels are more sensitive to protein glycosylation and have higher levels of AGEs in their tissues. 21 Previous studies have shown that individuals with high HGI levels have higher levels of AGEs in many tissues, including liver and skin. 16,22 The accumulation of AGE is an important mechanism for the development of DN. I. Misur et al. found that AGEs can be detected on the axons and myelin sheaths of peripheral nerves in patients with DN, and the level of AGEs is positively correlated with the severity of DN. 23 The important mechanism of AGEs causing nerve damage is the formation of cross-links and the interaction between AGEs and AGEs receptors. 24 AGEs can form cross-links with proteins such as basement membrane, cell matrix, blood vessel wall components, and mitochondrial electron transport chain-related proteins. This cross-linking will affect the normal structure and function of the protein. 25 In addition, intracellular AGEs can induce changes in DNA and nuclear proteins, further affecting protein transport and function. 23 The interaction between AGEs and AGE receptors can change intracellular signal transduction, including activation of protein kinase C (PKC) subtypes, induction of oxidative stress, activation of NF-KB signaling pathways, production of pro-inflammatory factors, and free radicals. Release, this change will eventually lead to inflammation and apoptosis. 24, 26, 27 The combination of these mechanisms leads to impaired blood flow to peripheral nerves and impaired nutritional support.

The second possible mechanism is that high HGI levels affect vitamin D levels and ultimately NCV. Studies have also found that vitamin D deficiency is related to the occurrence of DN. 28,29 Vitamin D supplementation can also improve DN. 9,30 Previously, a study of ours found that T2DM patients with high HGI levels are more likely to lack vitamin D. HGI levels are negatively correlated with vitamin D levels. 31 This study also found that compared with the NCVA group, the NCVN group had a lower vitamin D level and a higher prevalence of vitamin D deficiency. Vitamin D is an effective neurotrophic factor and is involved in the formation of neurotransmitters. 30 In addition, vitamin D can also reduce insulin resistance by binding to vitamin D receptors and reducing the production of inflammatory factors. 32,33 Therefore, when vitamin D is lacking, it will reduce neurotrophic support, affect glucose metabolism, and ultimately lead to a decrease in NCV. Therefore, we suggest that high HGI levels can affect NCV by affecting vitamin D levels.

In addition, HGI can reflect high blood glucose fluctuations, and patients with high HGI levels have higher blood glucose levels after meals. 34 Postprandial blood glucose fluctuations can damage NCV by affecting endothelial function, increasing inflammation and oxidative stress. 35 Liu et al. found that high HGI levels are associated with higher inflammation markers, 36 may lead to DN.

This study has some limitations. First, the study used a cross-sectional method and could not confirm the negative correlation between NCV and HGI. Secondly, this is a single-center clinical study. In addition, there is no correlation between HGI and bilateral ulnar nerve SCV or right superficial peroneal nerve SCV. More extensive clinical and basic research is needed to clarify the specific relationship between the difference between HGI and NCV and its underlying mechanism.

In summary, this study found that regardless of adjusted or unadjusted confounding factors, HGI is negatively correlated with NCV. The results show that high HGI is a risk factor for NCV in patients with T2DM.

The authors report no conflicts of interest in this work.

1. Feldman EL, Callaghan BC, Pop-Busui R, etc. Diabetic neuropathy. Nat Rev Dis Prim. 2019;5(1):42.

2. Fitri A, Sjahrir H, Bachtiar A, Ichwan M, Fitri FI, Rambe AS. A model for predicting the severity of diabetic polyneuropathy based on vitamin D levels. Open Access Macedonia j Med Sci. 2019;7(16):2626–2629. doi:10.3889/oamjms.2019.454

3. Yokoyama H, Tsuji T, Hayashi S, Kabata D, Shintani A. Factors associated with sensory symptoms and signs associated with diabetic polyneuropathy in patients with polyneuropathy: A cross-sectional Japanese study using a nonlinear model (JDDM 52). J Diabetes Survey. 2020; 11(2): 450–457. doi:10.1111/jdi.13117

4. Pop-Busui R, Boulton AJ, Feldman EL, etc. Diabetic Neuropathy: Position Statement of the American Diabetes Association. Diabetes care. 2017; 40(1): 136–154. doi:10.2337/dc16-2042

5. Ponikis G, Elhadd T, Chinnaiyan S, etc. The prevalence and risk factors of diabetic neuropathy and painful diabetic neuropathy in primary and secondary health care in Qatar. J Diabetes investment. 2021; 12(4): 592–600. doi:10.1111/jdi.13388

6. Stino AM, Smith AG. Peripheral neuropathy of prediabetes and metabolic syndrome. J Diabetes Survey. 2017; 8(5): 646–655. doi:10.1111/jdi.12650

7. Won JC, Park TS. The latest developments in diagnostic strategies for diabetic peripheral neuropathy. Endocrine metabolism. 2016;31(2):230–238. doi:10.3803/EnM.2016.31.2.230

8. Sánchez-Pozos K, Monroy-Escutia J, Jaimes-Santoyo J, Granados-Silvestre M, Menjivar M, Ortiz-López MG. Risk factors associated with diabetic neuropathy in Mexican patients. Cir Cir. 2021;89(2):189-199. doi:10.24875/CIRU.20000243

9. Karonova T, Stepanova A. High-dose vitamin D supplementation can improve microcirculation and reduce inflammation in patients with diabetic neuropathy. Nutrients. 2020; 12:9.

10. Esteghamati A, Fotouhi A, Faghihi-Kashani S, etc. The nonlinear 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

11. Chalew SA, McCarter RJ, Hempe JM. Biological variants and hemoglobin A1c: relevance to diabetes management and complications. Diabetes in children. 2013; 14(6): 391–398. doi:10.1111/pedi.12055

12. Li Ke, Song Wenjie, Wu Xu, etc. The relationship between serum glucagon levels and blood glucose variability in type 1 diabetes with different course of disease. endocrine. 2018;61(3):473–481. doi:10.1007/s12020-018-1641-1

13. Kim W, Go T, Kang DR, Lee EJ, Huh JH. The hemoglobin glycation index is associated with the occurrence of chronic kidney disease in subjects with impaired glucose metabolism: a 10-year longitudinal cohort study. J Diabetes complications. 2021;35(1):107760. doi:10.1016/j.jdiacomp.2020.107760

14. van Steen SC, Woodward M, Chalmers J, etc. The hemoglobin glycation index in diabetes and vascular disease and the risk of diabetes-related complications: the preterax and Diamicron modified release control assessment (ADVANCE) trial. Diabetes. 2018; 61(4): 780–789. doi:10.1007/s00125-017-4539-1

15. McCarter RJ, Hempe JM, Gomez R, Chalew SA. Biological variants of HbA1c can predict the risk of type 1 diabetic retinopathy and nephropathy. Diabetes care. 2004;27(6):1259–1264. doi:10.2337/diacare.27.6.1259

16. Hempe JM, Liu S, Myers L, McCarter RJ, Buse JB, Fonseca V. In the ACCORD trial, the hemoglobin glycation index identifies a subgroup of harm or benefit from intensive treatment. Diabetes care. 2015; 38(6): 1067–1074. doi:10.2337/dc14-1844

17. Soros AA, Chalew SA, McCarter RJ, Shepard R, Hempe JM. Hemoglobin Glycation Index: A powerful measure of hemoglobin A1c bias in children with type 1 diabetes. Diabetes in children. 2010; 11(7): 455-461. doi:10.1111/j.1399-5448.2009.00630.x

18. Oates PJ. Aldose reductase remains an attractive target for diabetic neuropathy. Current Drug Targets January 2008; 9(1):14–36. doi:10.2174/138945008783431781

19. Van Dam PS, Cotter MA, Bravenboer B, Cameron NE. Pathogenesis of diabetic neuropathy: focus on neurovascular mechanisms. European Journal of Pharmacy. 2013;719(1–3):180–186. doi:10.1016/j.ejphar.2013.07.017

20. Jende JME, Groener JB, Oikonomou D, etc. Diabetic neuropathy is different between type 1 and type 2 diabetes: insights from magnetic resonance neuroimaging. Ann Newer. 2018;83(3):588-598. doi:10.1002/ana.25182

21. Kim MK, Jeong JS, Yun JS, etc. The hemoglobin glycation index predicts cardiovascular disease in patients with type 2 diabetes: a 10-year longitudinal cohort study. J Diabetes complications. 2018;32(10):906-910. doi:10.1016/j.jdiacomp.2018.08.007

22. Felipe DL, Hempe JM, Liu S, etc. Intrinsic skin fluorescence is related to hemoglobin A (1c) and hemoglobin glycation index, but not to the average blood sugar of children with type 1 diabetes. Diabetes care. 2011;34(8):1816-1820. doi:10.2337/dc11-0049

23. Misur I, Zarkovic K, Barada A, Batelja L, Milicević Z, Turk Z. Advanced glycation end products of peripheral nerves in type 2 diabetes with neuropathy. Journal of Diabetes. 2004;41(4):158-166. doi:10.1007/s00592-004-0160-0

24. Chillelli NC, Burlina S, Lapolla A. AGEs, not hyperglycemia, are responsible for the microvascular complications of diabetes: the "centered on sugar oxidation" view. Nutri Metabol Cardiovasc Dis. 2013;23(10):913-919. doi:10.1016/j.numecd.2013.04.004

25. Vincent AM, Karabek B, Roberts L, Feldman EL. The biology of diabetic neuropathy. Handb clinical nerve. 2013; 115: 591-606.

26. Yeh CH, Sturgis L, Haidacher J, etc. RAGE-mediated nuclear factor-κB transcriptional activation and cytokine secretion require p38 and p44/p42 mitogen-activated protein kinases. diabetes. 2001;50(6):1495–1504. doi:10.2337/diabetes.50.6.1495

27. Yan SF, Ramasamy R, Naka Y, Schmidt AM. Glycation, inflammation and RAGE: stents for diabetes and other macrovascular complications. Circ Res. 2003;93(12):1159-1169. doi:10.1161/01.RES.0000103862.26506.3D

28. Shehab D, Al-Jarallah K, Mojiminiyi OA, Al Mohamedy H, Abdella NA. Does vitamin D deficiency play a role in type 2 diabetic peripheral neuropathy? . Diabetes medicine. 2012;29(1):43-49.

29. Soderstrom LH, Johnson SP, Diaz VA, Mainous AG 3rd. Association between vitamin D and diabetic neuropathy in a nationally representative sample: NHANES results from 2001 to 2004. Diabetes medicine. 2012;29(1):50–55. doi:10.1111/j.1464-5491.2011.03379.x

30. Ghadiri-Anari A, Mozafari Z, Gholami S, etc. Can vitamin D supplements improve diabetic peripheral neuropathy? Before and after clinical trials. Diabetic metabolic syndrome. 2019; 13(1): 890–893. doi:10.1016/j.dsx.2018.12.014

31. Li Z, Wang F, Jia Y, Guo F, Chen S. The relationship between hemoglobin glycation variation index and vitamin D in type 2 diabetes. Diabetic metabolic syndrome obesity. 2021; 14: 1937-1948. doi:10.2147/DMSO.S310672

32. Bourlon PM, Billaudel B, Faure-Dussert A. The effect of vitamin D3 deficiency and 1,25 dihydroxyvitamin D3 on de novo insulin biosynthesis in rat endocrine pancreatic islets. J endocrine. 1999;160(1):87-95. doi:10.1677/joe.0.1600087

33. Zeitz U, Weber K, Soegiarto DW, Wolf E, Balling R, Erben RG. Mice lacking functional vitamin D receptors have impaired insulin secretion. FASEB J. 2003;17(3):509-511. doi:10.1096/fj.02-0424fje

34. Riddle MC, Gerstein HC, Hempe, etc. In the ACCORD trial, the hemoglobin glycation index identifies a subgroup of harm or benefit from intensive treatment. Diabetes Care 2015;38:1067-1074. Diabetes care. 2015;38(10):e170–171. doi:10.2337/dc15-1073

35. Pai YW, Lin CH, Lee IT, Chang MH. The variability of fasting blood glucose and the risk of painful diabetic peripheral neuropathy in patients with type 2 diabetes. Diabetes metabolism. 2018;44(2):129–134. doi:10.1016/j.diabet.2018.01.015

36. Liu S, Hempe JM, McCarter RJ, Li S, Fonseca VA. Association between inflammation and A1c hemoglobin biological variation in non-diabetic adults in the United States. J Clin Journal of Endocrinology and Metabolism. 2015;100(6):2364–2371. doi:10.1210/jc.2014-4454

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