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Endocrine

, Volume 60, Issue 2, pp 292–300 | Cite as

Association of glycaemic variability evaluated by continuous glucose monitoring with diabetic peripheral neuropathy in type 2 diabetic patients

  • Yu-ming Hu
  • Li-hua Zhao
  • Xiu-lin Zhang
  • Hong-li Cai
  • Hai-yan Huang
  • Feng Xu
  • Tong Chen
  • Xue-qin Wang
  • Ai-song Guo
  • Jian-an Li
  • Jian-bin Su
Original Article

Abstract

Purpose

Diabetic peripheral neuropathy (DPN), a common microvascular complication of diabetes, is linked to glycaemic derangements. Glycaemic variability, as a pattern of glycaemic derangements, is a key risk factor for diabetic complications. We investigated the association of glycaemic variability with DPN in a large-scale sample of type 2 diabetic patients.

Methods

In this cross-sectional study, we enrolled 982 type 2 diabetic patients who were screened for DPN and monitored by a continuous glucose monitoring (CGM) system between February 2011 and January 2017. Multiple glycaemic variability parameters, including the mean amplitude of glycaemic excursions (MAGE), mean of daily differences (MODD), standard deviation of glucose (SD), and 24-h mean glucose (24-h MG), were calculated from glucose profiles obtained from CGM. Other possible risks for DPN were also examined.

Results

Of the recruited type 2 diabetic patients, 20.1% (n = 197) presented with DPN, and these patients also had a higher MAGE, MODD, SD, and 24-h MG than patients without DPN (p < 0.001). Using univariate and multiple logistic regression analyses, MAGE and conventional risks including diabetic duration, HOMA-IR, and hemoglobin A1c (HbA1c) were found to be independent contributors to DPN, and the corresponding odds ratios (95% confidence interval) were 4.57 (3.48–6.01), 1.10 (1.03–1.17), 1.24 (1.09–1.41), and 1.33 (1.15–1.53), respectively. Receiver operating characteristic analysis indicated that the optimal MAGE cutoff value for predicting DPN was 4.60 mmol/L; the corresponding sensitivity was 64.47%, and the specificity was 75.54%.

Conclusions

In addition to conventional risks including diabetic duration, HOMA-IR and HbA1c, increased glycaemic variability assessed by MAGE is a significant independent contributor to DPN in type 2 diabetic patients.

Keywords

Type 2 diabetes Risk factor Glycaemic variability Diabetic peripheral neuropathy 

Abbreviations

BMI

Body mass index

SBP

Systolic blood pressure

DBP

Diastolic blood pressure

TC

Total cholesterol

TG

Triglyceride

HDLC

High-density lipoprotein cholesterol

LDLC

Low-density lipoprotein cholesterol

FPG

Fasting plasma glucose

Serum UA

Serum uric acid

HOMA-IR

Insulin resistance estimated by the HOMA model

HbA1c

Glycosylated hemoglobin A1c

MAGE

Mean amplitude of glycaemic excursions

MODD

Mean of daily differences

SD

Standard deviation of glucose

24-h MG

24-h Mean glucose

Notes

Funding

The study was funded by the Scientific Research Program of Nantong (HS2012028 and MS22015065), the Scientific Research Program of Health and Planning Commission of Jiangsu (H201553) and the Scientific and Educational Program for Prosperity of Health Care of Jiangsu (QNRC2016408).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This study strictly adhered to the principles of the Helsinki Declaration and was approved by the Medical Ethics Committee of Nantong University.

Informed consent

Informed consent was obtained from all individual participants included in the study.

References

  1. 1.
    P.R.J. Vas, M.E. Edmonds, Early recognition of diabetic peripheral neuropathy and the need for one-stop microvascular assessment. Lancet Diabetes Endocrinol. 4(9), 723–725 (2016).  https://doi.org/10.1016/s2213-8587(16)30063-8 CrossRefPubMedGoogle Scholar
  2. 2.
    B.C. Callaghan, H.T. Cheng, C.L. Stables, A.L. Smith, E.L. Feldman, Diabetic neuropathy: clinical manifestations and current treatments. Lancet Neurol. 11(6), 521–534 (2012).  https://doi.org/10.1016/s1474-4422(12)70065-0 CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    R. Pop-Busui, J. Lu, M.M. Brooks, S. Albert, A.D. Althouse, J. Escobedo, J. Green, P. Palumbo, B.A. Perkins, F. Whitehouse, T.L. Jones, Impact of glycemic control strategies on the progression of diabetic peripheral neuropathy in the bypass angioplasty revascularization investigation 2 diabetes (BARI 2D) cohort. Diabetes Care 36(10), 3208–3215 (2013).  https://doi.org/10.2337/dc13-0012 CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    L. Monnier, C. Colette, D.R. Owens, Integrating glycaemic variability in the glycaemic disorders of type 2 diabetes: a move towards a unified glucose tetrad concept. Diabetes Metab. Res. Rev. 25(5), 393–402 (2009).  https://doi.org/10.1002/dmrr.962 CrossRefPubMedGoogle Scholar
  5. 5.
    N.R. Hill, N.S. Oliver, P. Choudhary, J.C. Levy, P. Hindmarsh, D.R. Matthews, Normal reference range for mean tissue glucose and glycemic variability derived from continuous glucose monitoring for subjects without diabetes in different ethnic groups. Diabetes Technol. Ther. 13(9), 921–928 (2011).  https://doi.org/10.1089/dia.2010.0247 CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    C. Colette, L. Monnier, Acute glucose fluctuations and chronic sustained hyperglycemia as risk factors for cardiovascular diseases in patients with type 2 diabetes. Horm. Metab. Res. 39(9), 683–686 (2007).  https://doi.org/10.1055/s-2007-985157 CrossRefPubMedGoogle Scholar
  7. 7.
    G. Sartore, N.C. Chilelli, S. Burlina, A. Lapolla, Association between glucose variability as assessed by continuous glucose monitoring (CGM) and diabetic retinopathy in type 1 and type 2 diabetes. Acta Diabetol. 50(3), 437–442 (2013).  https://doi.org/10.1007/s00592-013-0459-9 CrossRefPubMedGoogle Scholar
  8. 8.
    F. Xu, L.H. Zhao, J.B. Su, T. Chen, X.Q. Wang, J.F. Chen, G. Wu, Y. Jin, X.H. Wang, The relationship between glycemic variability and diabetic peripheral neuropathy in type 2 diabetes with well-controlled HbA1c. Diabetol. Metab. Syndr. 6(1), 139 (2014).  https://doi.org/10.1186/1758-5996-6-139 CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    H.Y. Jin, K.A. Lee, T.S. Park, The impact of glycemic variability on diabetic peripheral neuropathy. Endocrine 53(3), 643–648 (2016).  https://doi.org/10.1007/s12020-016-1005-7 CrossRefPubMedGoogle Scholar
  10. 10.
    American Diabetes Association Diagnosis and classification of diabetes mellitus. Diabetes Care 34(Suppl 1), S62–S69 (2011). Doi:10.2337/dc11-S062.Google Scholar
  11. 11.
    S. Tesfaye, A.J. Boulton, P.J. Dyck, R. Freeman, M. Horowitz, P. Kempler, G. Lauria, R.A. Malik, V. Spallone, A. Vinik, L. Bernardi, P. Valensi, Diabetic neuropathies: update on definitions, diagnostic criteria, estimation of severity, and treatments. Diabetes Care 33(10), 2285–2293 (2010).  https://doi.org/10.2337/dc10-1303 CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Chinese Diabetes Society, Chinese clinical guideline for continuous glucose monitoring (2012). Chin. Med. J. 125(23), 4167–4174 (2012).  https://doi.org/10.3760/cma.j.issn.0366-6999.2012.23.002 Google Scholar
  13. 13.
    D. Rodbard, New and improved methods to characterize glycemic variability using continuous glucose monitoring. Diabetes Technol. Ther. 11(9), 551–565 (2009).  https://doi.org/10.1089/dia.2009.0015 CrossRefPubMedGoogle Scholar
  14. 14.
    F.J. Service, Glucose variability. Diabetes 62(5), 1398–1404 (2013).  https://doi.org/10.2337/db12-1396 CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    M. Ohara, T. Fukui, M. Ouchi, K. Watanabe, T. Suzuki, S. Yamamoto, T. Yamamoto, T. Hayashi, K. Oba, T. Hirano, Relationship between daily and day-to-day glycemic variability and increased oxidative stress in type 2 diabetes. Diabetes Res. Clin. Pract. 122, 62–70 (2016).  https://doi.org/10.1016/j.diabres.2016.09.025 CrossRefPubMedGoogle Scholar
  16. 16.
    A. Ceriello, M.A. Ihnat, ‘Glycaemic variability’: a new therapeutic challenge in diabetes and the critical care setting. Diabet. Med. 27(8), 862–867 (2010).  https://doi.org/10.1111/j.1464-5491.2010.02967.x CrossRefPubMedGoogle Scholar
  17. 17.
    L. Monnier, E. Mas, C. Ginet, F. Michel, L. Villon, J.P. Cristol, C. Colette, Activation of oxidative stress by acute glucose fluctuations compared with sustained chronic hyperglycemia in patients with type 2 diabetes. JAMA 295(14), 1681–1687 (2006).  https://doi.org/10.1001/jama.295.14.1681 CrossRefPubMedGoogle Scholar
  18. 18.
    K. Torimoto, Y. Okada, H. Mori, Y. Tanaka, Relationship between fluctuations in glucose levels measured by continuous glucose monitoring and vascular endothelial dysfunction in type 2 diabetes mellitus. Cardiovasc. Diabetol. 12, 1 (2013).  https://doi.org/10.1186/1475-2840-12-1 CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    P.S. Dasari, B.S. Gandomani, A.M. Teague, A. Pitale, M. Otto, K.R. Short, Glycemic variability is associated with markers of vascular stress in adolescents. J. Pediatr. 172, 47–55 e42 (2016).  https://doi.org/10.1016/j.jpeds.2016.01.065 CrossRefPubMedGoogle Scholar
  20. 20.
    G. Su, S. Mi, H. Tao, Z. Li, H. Yang, H. Zheng, Y. Zhou, C. Ma, Association of glycemic variability and the presence and severity of coronary artery disease in patients with type 2 diabetes. Cardiovasc. Diabetol. 10, 19 (2011).  https://doi.org/10.1186/1475-2840-10-19 CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    G. Su, S.H. Mi, H. Tao, Z. Li, H.X. Yang, H. Zheng, Y. Zhou, L. Tian, Impact of admission glycemic variability, glucose, and glycosylated hemoglobin on major adverse cardiac events after acute myocardial infarction. Diabetes Care 36(4), 1026–1032 (2013).  https://doi.org/10.2337/dc12-0925 CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    C.M. Chang, C.J. Hsieh, J.C. Huang, I.C. Huang, Acute and chronic fluctuations in blood glucose levels can increase oxidative stress in type 2 diabetes mellitus. Acta Diabetol. 49(Suppl 1), S171–177 (2012).  https://doi.org/10.1007/s00592-012-0398-x CrossRefPubMedGoogle Scholar
  23. 23.
    C. Gorst, C.S. Kwok, S. Aslam, I. Buchan, E. Kontopantelis, P.K. Myint, G. Heatlie, Y. Loke, M.K. Rutter, M.A. Mamas, Long-term glycemic variability and risk of adverse outcomes: a systematic review and meta-analysis. Diabetes Care 38(12), 2354–2369 (2015).  https://doi.org/10.2337/dc15-1188 CrossRefPubMedGoogle Scholar
  24. 24.
    A.O. Luk, R.C. Ma, E.S. Lau, X. Yang, W.W. Lau, L.W. Yu, F.C. Chow, J.C. Chan, W.Y. So, Risk association of HbA1c variability with chronic kidney disease and cardiovascular disease in type 2 diabetes: prospective analysis of the Hong Kong Diabetes Registry. Diabetes Metab. Res. Rev. 29(5), 384–390 (2013).  https://doi.org/10.1002/dmrr.2404 CrossRefPubMedGoogle Scholar
  25. 25.
    J.E. Jun, S.M. Jin, J. Baek, S. Oh, K.Y. Hur, M.S. Lee, M.K. Lee, J.H. Kim, The association between glycemic variability and diabetic cardiovascular autonomic neuropathy in patients with type 2 diabetes. Cardiovasc. Diabetol. 14, 70 (2015).  https://doi.org/10.1186/s12933-015-0233-0 CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    C.C. Lin, C.C. Chen, F.N. Chen, C.I. Li, C.S. Liu, W.Y. Lin, S.Y. Yang, C.C. Lee, T.C. Li, Risks of diabetic nephropathy with variation in hemoglobin A1c and fasting plasma glucose. Am. J. Med. 126(11), 1017. e1011–1010 (2013).  https://doi.org/10.1016/j.amjmed.2013.04.015 CrossRefGoogle Scholar
  27. 27.
    C.C. Lin, C.P. Yang, C.I. Li, C.S. Liu, C.C. Chen, W.Y. Lin, K.L. Hwang, S.Y. Yang, T.C. Li, Visit-to-visit variability of fasting plasma glucose as predictor of ischemic stroke: competing risk analysis in a national cohort of Taiwan Diabetes Study. BMC Med. 12, 165 (2014).  https://doi.org/10.1186/s12916-014-0165-7 CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    H.T. Chiu, T.C. Li, C.I. Li, C.S. Liu, W.Y. Lin, C.C. Lin, Visit-to-visit glycemic variability is a strong predictor of chronic obstructive pulmonary disease in patients with type 2 diabetes mellitus: Competing risk analysis using a national cohort from the Taiwan diabetes study. PLoS One 12(5), e0177184 (2017).  https://doi.org/10.1371/journal.pone.0177184 CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    T.C. Li, C.P. Yang, S.T. Tseng, C.I. Li, C.S. Liu, W.Y. Lin, K.L. Hwang, S.Y. Yang, J.H. Chiang, C.C. Lin, Visit-to-visit variations in fasting plasma glucose and HbA1c associated with an increased risk of alzheimer disease: Taiwan Diabetes Study. Diabetes Care 40(9), 1210–1217 (2017).  https://doi.org/10.2337/dc16-2238 CrossRefPubMedGoogle Scholar
  30. 30.
    J.I. Chiang, T.C. Li, C.I. Li, C.S. Liu, N.H. Meng, W.Y. Lin, S.Y. Yang, H.J. Chen, C.C. Lin, Visit-to-visit variation of fasting plasma glucose is a predictor of hip fracture in older persons with type 2 diabetes: the Taiwan Diabetes Study. Osteoporos. Int. 27(12), 3587–3597 (2016).  https://doi.org/10.1007/s00198-016-3689-1 CrossRefPubMedGoogle Scholar
  31. 31.
    C.C. Lin, C.I. Li, C.S. Liu, W.Y. Lin, C.C. Chen, S.Y. Yang, C.C. Lee, T.C. Li, Annual fasting plasma glucose variation increases risk of cancer incidence and mortality in patients with type 2 diabetes: the Taichung Diabetes Study. Endocr. Relat. Cancer 19(4), 473–483 (2012).  https://doi.org/10.1530/erc-12-0038 CrossRefPubMedGoogle Scholar
  32. 32.
    C.C. Lin, C.I. Li, S.Y. Yang, C.S. Liu, C.C. Chen, M.M. Fuh, W. Chen, T.C. Li, Variation of fasting plasma glucose: a predictor of mortality in patients with type 2 diabetes. Am. J. Med. 125(4), 416. e419–418 (2012).  https://doi.org/10.1016/j.amjmed.2011.07.027 CrossRefGoogle Scholar
  33. 33.
    P. Katulanda, P. Ranasinghe, R. Jayawardena, G.R. Constantine, M.H. Sheriff, D.R. Matthews, The prevalence, patterns and predictors of diabetic peripheral neuropathy in a developing country. Diabetol. Metab. Syndr. 4(1), 21 (2012).  https://doi.org/10.1186/1758-5996-4-21 CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    J.C. Won, H.S. Kwon, C.H. Kim, J.H. Lee, T.S. Park, K.S. Ko, B.Y. Cha, Prevalence and clinical characteristics of diabetic peripheral neuropathy in hospital patients with Type 2 diabetes in Korea. Diabet. Med. 29(9), e290–296 (2012).  https://doi.org/10.1111/j.1464-5491.2012.03697.x CrossRefPubMedGoogle Scholar
  35. 35.
    D. Selvarajah, I.D. Wilkinson, C.J. Emery, N.D. Harris, P.J. Shaw, D.R. Witte, P.D. Griffiths, S. Tesfaye, Early involvement of the spinal cord in diabetic peripheral neuropathy. Diabetes Care 29(12), 2664–2669 (2006).  https://doi.org/10.2337/dc06-0650 CrossRefPubMedGoogle Scholar
  36. 36.
    C. Clair, M.J. Cohen, F. Eichler, K.J. Selby, N.A. Rigotti, The effect of cigarette smoking on diabetic peripheral neuropathy: a systematic review and meta-analysis. J. Gen. Intern. Med 30(8), 1193–1203 (2015).  https://doi.org/10.1007/s11606-015-3354-y CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    X. Qiao, H. Zheng, S. Zhang, S. Liu, Q. Xiong, F. Mao, Z. Zhang, J. Wen, H. Ye, Y. Li, B. Lu, C-peptide is independent associated with diabetic peripheral neuropathy: a community-based study. Diabetol. Metab. Syndr. 9, 12 (2017).  https://doi.org/10.1186/s13098-017-0208-2 CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    E.S. Kim, S.W. Lee, E.Y. Mo, S.D. Moon, J.H. Han, Inverse association between serum total bilirubin levels and diabetic peripheral neuropathy in patients with type 2 diabetes. Endocrine 50(2), 405–412 (2015).  https://doi.org/10.1007/s12020-015-0583-0 CrossRefPubMedGoogle Scholar
  39. 39.
    Y. Hu, F. Liu, J. Shen, H. Zeng, L. Li, J. Zhao, J. Zhao, F. Lu, W. Jia, Association between serum cystatin C and diabetic peripheral neuropathy: a cross-sectional study of a Chinese type 2 diabetic population. Eur. J. Endocrinol. 171(5), 641–648 (2014).  https://doi.org/10.1530/eje-14-0381 CrossRefPubMedGoogle Scholar
  40. 40.
    R. He, Y. Hu, H. Zeng, J. Zhao, J. Zhao, Y. Chai, F. Lu, F. Liu, W. Jia, Vitamin D deficiency increases the risk of peripheral neuropathy in Chinese patients with type 2 diabetes. Diabetes Metab. Res. Rev. 33(2), e2820 (2017).  https://doi.org/10.1002/dmrr.2820 CrossRefGoogle Scholar
  41. 41.
    W. Zhao, H. Zeng, X. Zhang, F. Liu, J. Pan, J. Zhao, J. Zhao, L. Li, Y. Bao, F. Liu, W. Jia, A high thyroid stimulating hormone level is associated with diabetic peripheral neuropathy in type 2 diabetes patients. Diabetes Res. Clin. Pract. 115, 122–129 (2016).  https://doi.org/10.1016/j.diabres.2016.01.018 CrossRefPubMedGoogle Scholar
  42. 42.
    S. Yu, Y. Chen, X. Hou, D. Xu, K. Che, C. Li, S. Yan, Y. Wang, B. Wang, Serum uric acid levels and diabetic peripheral neuropathy in type 2 diabetes: a systematic review and meta-analysis. Mol. Neurobiol. 53(2), 1045–1051 (2016).  https://doi.org/10.1007/s12035-014-9075-0 CrossRefPubMedGoogle Scholar
  43. 43.
    Y. Zhang, Y. Jiang, X. Shen, S. Yan, Can both normal and mildly abnormal albuminuria and glomerular filtration rate be a danger signal for diabetic peripheral neuropathy in type 2 diabetes mellitus? Neurol. Sci. 38(8), 1381–1390 (2017).  https://doi.org/10.1007/s10072-017-2946-1 CrossRefPubMedGoogle Scholar
  44. 44.
    A. Tentolouris, I. Eleftheriadou, P. Grigoropoulou, A. Kokkinos, G. Siasos, I. Ntanasis-Stathopoulos, N. Tentolouris, The association between pulse wave velocity and peripheral neuropathy in patients with type 2 diabetes mellitus. J. Diabetes Complicat. 31(11), 1624–1629 (2017).  https://doi.org/10.1016/j.jdiacomp.2017.07.010 CrossRefPubMedGoogle Scholar
  45. 45.
    S. Liu, H. Zheng, X. Zhu, F. Mao, S. Zhang, H. Shi, Y. Li, B. Lu, Neutrophil-to-lymphocyte ratio is associated with diabetic peripheral neuropathy in type 2 diabetes patients. Diabetes Res. Clin. Pract. 130, 90–97 (2017).  https://doi.org/10.1016/j.diabres.2017.05.008 CrossRefPubMedGoogle Scholar
  46. 46.
    T. Zhu, Q. Meng, J. Ji, X. Lou, L. Zhang, Toll-like receptor 4 and tumor necrosis factor-alpha as diagnostic biomarkers for diabetic peripheral neuropathy. Neurosci. Lett. 585, 28–32 (2015).  https://doi.org/10.1016/j.neulet.2014.11.020 CrossRefPubMedGoogle Scholar
  47. 47.
    C. Herder, I. Schamarek, B. Nowotny, M. Carstensen-Kirberg, K. Strassburger, P. Nowotny, J.M. Kannenberg, A. Strom, S. Puttgen, K. Mussig, J. Szendroedi, M. Roden, D. Ziegler, Inflammatory markers are associated with cardiac autonomic dysfunction in recent-onset type 2 diabetes. Heart 103(1), 63–70 (2017).  https://doi.org/10.1136/heartjnl-2015-309181 CrossRefPubMedGoogle Scholar
  48. 48.
    M. Brownlee, Biochemistry and molecular cell biology of diabetic complications. Nature 414(6865), 813–820 (2001).  https://doi.org/10.1038/414813a CrossRefPubMedGoogle Scholar

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Authors and Affiliations

  1. 1.Department of RehabilitationThe First Affiliated Hospital of Nanjing Medical UniversityNanjingChina
  2. 2.Department of RehabilitationThe Affiliated Hospital of Nantong UniversityNantongChina
  3. 3.Department of EndocrinologyThe Second Affiliated Hospital of Nantong UniversityNantongChina
  4. 4.Department of Clinical LaboratoryThe Second Affiliated Hospital of Nantong UniversityNantongChina
  5. 5.Department of GeriatricsThe Second Affiliated Hospital of Nantong UniversityNantongChina

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