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Hemoglobin A1c Has Suboptimal Performance to Diagnose and Monitor Diabetes Mellitus in Patients with Cirrhosis

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Abstract

Background

Glycated hemoglobin A1c (HbA1c) is routinely used to diagnose and monitor type 2 diabetes mellitus (T2DM) in cirrhotic patients. Remarkably, HbA1c may be falsely low in such patients.

Aims

We assessed the diagnostic and monitoring yield of HbA1c in cirrhotic patients with T2DM (DM-Cirr) and without T2DM (NoDM-Cirr).

Methods

We conducted a composite study allocating 21 NoDM-Cirr into a cross-sectional module and 16 DM-Cirr plus 13 controls with T2DM only (DM-NoCirr) into a prospective cohort. Oral glucose tolerance test (OGTT) was performed in NoDM-Cirr. DM-Cirr and DM-NoCirr were matched by sex, age, BMI, and T2DM treatment and studied with continuous glucose monitoring (CGM). Percent deviations from target, low/high blood glucose indexes (LBGI/HBGI) were calculated from CGM, as well as the average daily risk range (ADRR) as a marker of glucose variability.

Results

Overall, HbA1c and OGTT diagnostic yield agreed in 12 patients (57%, ρ = 0.45, p < 0.03). CGM captured 3463 glucose determinations in DM-Cirr and 4273 in DM-NoCirr (p = 0.42). Regression analysis showed an inferior association between HbA1c and CGM in DM-Cirr (R2 = 0.52), when compared to DM-NoCirr (R2 = 0.94), and fructosamine did not improve association for DM-Cirr (R2 = 0.31). Interestingly, cirrhosis and Child–Turcotte–Pugh class accounted for HbA1c variance (p < 0.05). Patients in DM-Cirr were less frequently within target glucose (70–180 mg/dL), but at higher risk for hyperglycemia (HBGI > 9) when compared to DM-NoCirr, and they also showed higher glucose variability (ADRR 13.9 ± 2.5 vs. 8.9 ± 1.8, respectively, p = 0.03).

Conclusion

HbA1c inaccurately represents chronic glycemia in patients with cirrhosis, likely in relation to increased glucose variability.

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Abbreviations

T2DM:

Type 2 diabetes mellitus

NoDM-Cirr:

Cirrhosis but no T2DM

DM-Cirr:

Cirrhosis and T2DM

DM-NoCirr:

T2DM without cirrhosis

OGTT:

Oral glucose tolerance test

HbA1c:

Glycated hemoglobin A1c

CGM:

Continuous glucose monitors

ALT:

Alanine aminotransferase

AST:

Aspartate aminotransferase

TIPS:

Transjugular intrahepatic portosystemic shunt

BMI:

Body mass index

INR:

International normalized ratio

NGSP:

National Glycohemoglobin Standardization Program

NPO:

Nothing by mouth

SD:

Standard deviation

IQR:

Interquartile range

MELD:

Model for end-stage liver disease

MELD-Na:

MELD-sodium

LBGI:

Low blood glucose index

HBGI:

High blood glucose index

ADRR:

Average daily risk range

HOMA-IR:

Homeostatic model assessment for insulin resistance

95% CI:

Confidence interval

References

  1. Hashiba M, Ono M, Hyogo H, et al. Glycemic variability is an independent predictive factor for development of hepatic fibrosis in nonalcoholic fatty liver disease. PloS ONE. 2013;8:e76161.

    Article  CAS  Google Scholar 

  2. Schiaffini R, Liccardo D, Alisi A, et al. Early glucose derangement detected by continuous glucose monitoring and progression of liver fibrosis in nonalcoholic fatty liver disease: an independent predictive factor? Horm Res Paediatr. 2016;85:29–34.

    Article  CAS  Google Scholar 

  3. Elkrief L, Rautou PE, Sarin S, Valla D, Paradis V, Moreau R. Diabetes mellitus in patients with cirrhosis: clinical implications and management. Liver Int. 2016;36:936–948.

    Article  CAS  Google Scholar 

  4. Huang JF, Yu ML, Dai CY, et al. Reappraisal of the characteristics of glucose abnormalities in patients with chronic hepatitis C infection. Am J Gastroenterol. 2008;103:1933–1940.

    Article  CAS  Google Scholar 

  5. Garvey P, Murphy N, Flanagan P, et al. Disease outcomes in a cohort of women in Ireland infected by hepatitis C-contaminated anti-D immunoglobulin during 1970s. J Hepatol. 2017;67:1140–1147.

    Article  Google Scholar 

  6. Jepsen P, Watson H, Andersen PK, Vilstrup H. Diabetes as a risk factor for hepatic encephalopathy in cirrhosis patients. J Hepatol. 2015;63:1133–1138.

    Article  Google Scholar 

  7. Singh S, Singh PP, Singh AG, Murad MH, Sanchez W. Anti-diabetic medications and the risk of hepatocellular cancer: a systematic review and meta-analysis. Am J Gastroenterol. 2013;108:881–891.

    Article  CAS  Google Scholar 

  8. Piano S, Morando F, Carretta G, et al. Predictors of early readmission in patients with cirrhosis after the resolution of bacterial infections. Am J Gastroenterol. 2017;112:1575–1583.

    Article  Google Scholar 

  9. Gonzalez HC, Duarte-Rojo A. Virologic cure of hepatitis C: impact on hepatic fibrosis and patient outcomes. Curr Gastroenterol Rep. 2016;18:32.

    Article  Google Scholar 

  10. Nathan DM, Turgeon H, Regan S. Relationship between glycated haemoglobin levels and mean glucose levels over time. Diabetologia. 2007;50:2239–2244.

    Article  CAS  Google Scholar 

  11. D’Amico G, Pasta L, Morabito A, et al. Competing risks and prognostic stages of cirrhosis: a 25-year inception cohort study of 494 patients. Aliment Pharmacol Ther. 2014;39:1180–1193.

    Article  Google Scholar 

  12. American Diabetes A. 2. Classification and diagnosis of diabetes. Diabetes Care. 2017;40:S11–S24.

    Article  Google Scholar 

  13. Wallace TM, Levy JC, Matthews DR. Use and abuse of HOMA modeling. Diabetes Care. 2004;27:1487–1495.

    Article  Google Scholar 

  14. Altman DG, Gardner MJ. Calculating confidence intervals for regression and correlation. Br Med J (Clin Res Ed). 1988;296:1238–1242.

    Article  CAS  Google Scholar 

  15. Clarke W, Kovatchev B. Statistical tools to analyze continuous glucose monitor data. Diabetes Technol Ther. 2009;11:S45–S54.

    Article  CAS  Google Scholar 

  16. Monaghan M, Younge TB, McCarter R, Cogen FR, Streisand R. Average daily risk range (ADRR) in young children with type 1 diabetes. J Diabetes Sci Technol. 2014;8:70–73.

    Article  Google Scholar 

  17. Roglic G, World Health Organization. Global Report on Diabetes. Geneva, Switzerland: World Health Organization; 2016:86.

    Google Scholar 

  18. Mokdad AA, Lopez AD, Shahraz S, et al. Liver cirrhosis mortality in 187 countries between 1980 and 2010: a systematic analysis. BMC Med. 2014;12:145.

    Article  Google Scholar 

  19. Pattullo V, Duarte-Rojo A, Soliman W, et al. A 24-week dietary and physical activity lifestyle intervention reduces hepatic insulin resistance in the obese with chronic hepatitis C. Liver Int. 2013;33:410–419.

    Article  Google Scholar 

  20. Haraguchi M, Miyaaki H, Ichikawa T, et al. Glucose fluctuations reduce quality of sleep and of life in patients with liver cirrhosis. Hepatol Int. 2017;11:125–131.

    Article  Google Scholar 

  21. Ochi T, Kawaguchi T, Nakahara T, et al. Differences in characteristics of glucose intolerance between patients with NAFLD and chronic hepatitis C as determined by CGMS. Sci Rep. 2017;7:10146.

    Article  Google Scholar 

  22. Kishimoto M, Noda M. Verification of glycemic profiles using continuous glucose monitoring: cases with steroid use, liver cirrhosis, enteral nutrition, or late dumping syndrome. J Med Invest. 2015;62:1–10.

    Article  Google Scholar 

  23. Nathan DM, Kuenen J, Borg R, Zheng H, Schoenfeld D, Heine RJ. Translating the A1C assay into estimated average glucose values. Diabetes Care. 2008;31:1473–1478.

    Article  CAS  Google Scholar 

  24. Lahousen T, Hegenbarth K, Ille R, et al. Determination of glycated hemoglobin in patients with advanced liver disease. World J Gastroenterol. 2004;10:2284–2286.

    Article  CAS  Google Scholar 

  25. Kim WR, Biggins SW, Kremers WK, et al. Hyponatremia and mortality among patients on the liver-transplant waiting list. New Engl J Med. 2008;359:1018–1026.

    Article  CAS  Google Scholar 

  26. Gallagher EJ, Le Roith D, Bloomgarden Z. Review of hemoglobin A(1c) in the management of diabetes. J Diabetes. 2009;1:9–17.

    Article  CAS  Google Scholar 

  27. Sartore G, Chilelli NC, Burlina S, Lapolla A. Association between glucose variability as assessed by continuous glucose monitoring (CGM) and diabetic retinopathy in type 1 and type 2 diabetes. Acta Diabetol. 2013;50:437–442.

    Article  CAS  Google Scholar 

  28. Shah AG, Lydecker A, Murray K, et al. Use of the FIB4 index for non-invasive evaluation of fibrosis in nonalcoholic fatty liver disease. Clin Gastroenterol Hepatol. 2009;7:1104–1112.

    Article  CAS  Google Scholar 

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Acknowledgment

We would like to thank Dr. Peter Goulden from the Division of Endocrinology and Metabolism for his kind advice and support during conception and implementation of the study.

Funding

This study was funded in full by a Diabetes Research Grant from the Sturgis Foundation and the University of Arkansas for Medical Sciences College of Medicine Clinician Scientist Program.

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Correspondence to Andres Duarte-Rojo.

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Authors have nothing to disclose

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of our Institutional Review Board (UAMS IRB) and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

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

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Addepally, N.S., George, N., Martinez-Macias, R. et al. Hemoglobin A1c Has Suboptimal Performance to Diagnose and Monitor Diabetes Mellitus in Patients with Cirrhosis. Dig Dis Sci 63, 3498–3508 (2018). https://doi.org/10.1007/s10620-018-5265-3

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  • DOI: https://doi.org/10.1007/s10620-018-5265-3

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