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Horm Metab Res 2023; 55(05): 343-354
DOI: 10.1055/a-2053-2688
Original Article: Endocrine Care

Metabolic-Related Index to Predict Post-Transplantation Diabetes Mellitus After Kidney Transplantation

Ni Xiaojie
1   Department of Urology (Renal Transplantation), Wenzhou Medical University First Affiliated Hospital, Wenzhou, China
,
Chen Bicheng
2   Key Laboratory of Diagnosis and Treatment of Severe Hepato-Pancreatic Diseases of Zhejiang Province, Wenzhou Medical University First Affiliated Hospital, Wenzhou, China
,
Li Yongling
2   Key Laboratory of Diagnosis and Treatment of Severe Hepato-Pancreatic Diseases of Zhejiang Province, Wenzhou Medical University First Affiliated Hospital, Wenzhou, China
,
Huang Tingting
2   Key Laboratory of Diagnosis and Treatment of Severe Hepato-Pancreatic Diseases of Zhejiang Province, Wenzhou Medical University First Affiliated Hospital, Wenzhou, China
,
Zhou Yi
2   Key Laboratory of Diagnosis and Treatment of Severe Hepato-Pancreatic Diseases of Zhejiang Province, Wenzhou Medical University First Affiliated Hospital, Wenzhou, China
,
Zimiao Chen
3   Department of Endocrine and Metabolic Diseases, Wenzhou Medical University First Affiliated Hospital, Wenzhou, China
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Abstract

Metabolic-related markers are novel tools for assessing insulin resistance. Early identification of post-transplantation diabetes mellitus (PTDM) before hyperglycemia can be helpful to attenuate the rapid development of diabetic complications. This article aims to explore the convenient and inexpensive values of metabolic-related markers, including TyG, TyG-BMI, TG/HDL-C, and non-HDL-C/HDL-C for predicting PTDM. The data of 191 kidney transplant recipients in our center were collected retrospectively. The association between TyG, TyG-BMI, TG/HDL-C, non-HDL-C/HDL-C and the risk of PTDM was examined by the area under the curve and logistic regression analyses. During 6 months follow-up, 12.04% of KT recipients developed PTDM, and significantly higher values of TyG-BMI, TyG, and non-HDL-C/HDL-C was found in patients with PTDM than in nondiabetic patients, especially among the recipients taking tacrolimus, regardless of gender. The incidence of PTDM increased along with the values of TyG or TyG-BMI. After adjusting for multiple potential factors, recipients with the highest trisector of TyG or TyG-BMI still had a higher risk of PTDM morbidity. In conclusion, TyG, TyG-BMI, TG/HDL-C and non-HDL-C/HDL-C can be used as cost-effective and promising monitors to identify individuals at high risk of PTDM, and TyG-BMI was the best alternative marker among the four markers.

Key words

insulin resistance  - post transplantation diabetes mellitus - triglyceride - triglyceride-glucose index


Publication History

Received: 10 September 2022

Accepted after revision: 06 March 2023

Article published online:
02 May 2023

© 2023. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Sheu A, Depczynski B, O’Sullivan AJ. et al. The effect of different glycaemic states on renal transplant outcomes. J Diabetes Res 2016; 8735782
    PubMedGoogle Scholar
  • 2 Burroughs TE, Swindle J, Takemoto S. et al. Diabetic complications associated with new-onset diabetes mellitus in renal transplant recipients. Transplantation 2007; 83: 1027-1034
    CrossrefPubMedGoogle Scholar
  • 3 Cron DC, Noon KA, Cote DR. et al. Using analytic morphomics to describe body composition associated with post-kidney transplantation diabetes mellitus. Clin Transplant 2017; 31 DOI: 10.1111/ctr.13040.
    CrossrefPubMedGoogle Scholar
  • 4 Sharif A, Baboolal K. Risk factors for new-onset diabetes after kidney transplantation. Nat Rev Nephrol 2010; 6: 415-423
    CrossrefPubMedGoogle Scholar
  • 5 Hjelmesaeth J, Hartmann A, Kofstad J. et al. Glucose intolerance after renal transplantation depends upon prednisolone dose and recipient age. Transplantation 1997; 64: 979-983
    CrossrefPubMedGoogle Scholar
  • 6 Bayer ND, Cochetti PT, Anil Kumar MS. et al. Association of metabolic syndrome with development of new-onset diabetes after transplantation. Transplantation 2010; 90: 861-866
    CrossrefPubMedGoogle Scholar
  • 7 Drew BG, Rye KA, Duffy SJ. et al. The emerging role of HDL in glucose metabolism. Nat Rev Endocrinol 2012; 8: 237-245
    CrossrefPubMedGoogle Scholar
  • 8 Caillard S, Eprinchard L, Perrin P. et al. Incidence and risk factors of glucose metabolism disorders in kidney transplant recipients: role of systematic screening by oral glucose tolerance test. Transplantation 2011; 91: 757-764
    CrossrefPubMedGoogle Scholar
  • 9 Liang S, Cai GY, Chen XM. Clinical and pathological factors associated with progression of diabetic nephropathy. Nephrology (Carlton) 2017; 22: 14-19
    CrossrefPubMedGoogle Scholar
  • 10 DeFronzo RA, Tobin JD, Andres R. Glucose clamp technique: a method for quantifying insulin secretion and resistance. Am J Physiol 1979; 237: E214-E223
    CrossrefPubMedGoogle Scholar
  • 11 Guerrero-Romero F, Simental-Mendía LE, González-Ortiz M. et al. The product of triglycerides and glucose, a simple measure of insulin sensitivity. Comparison with the euglycemic-hyperinsulinemic clamp. J Clin Endocrinol Metab 2010; 95: 3347-3351
    CrossrefPubMedGoogle Scholar
  • 12 Unger G, Benozzi SF, Perruzza F. et al. Triglycerides and glucose index: a useful indicator of insulin resistance. Endocrinol Nutr 2014; 61: 533-540
    PubMedGoogle Scholar
  • 13 Li Z, Bai Y, Guo X. et al. Alcohol consumption and cardiovascular diseases in rural China. Int J Cardiol 2016; 215: 257-262
    CrossrefPubMedGoogle Scholar
  • 14 Sánchez-Íñigo L, Navarro-González D, Fernández-Montero A. et al. The TyG index may predict the development of cardiovascular events. Eur J Clin Invest 2016; 46: 189-197
    CrossrefPubMedGoogle Scholar
  • 15 Finucane FM, Luan J, Wareham NJ. et al. Correlation of the leptin: adiponectin ratio with measures of insulin resistance in non- diabetic individuals. Diabetologia 2009; 52: 2345-2349
    CrossrefPubMedGoogle Scholar
  • 16 American Diabetes Association. Improving care and promoting health in populations: standards of medical care in diabetes-2020. Diabetes Care 2020; 43: S7-S13
    CrossrefPubMedGoogle Scholar
  • 17 Sarafidis PA, Ruilope LM. Insulin resistance, hyperinsulinemia, and renal injury: mechanisms and implications. Am J Nephrol 2006; 26: 232-244
    CrossrefPubMedGoogle Scholar
  • 18 Abbasi F, Reaven GM. Comparison of two methods using plasma triglyceride concentration as a surrogate estimate of insulin action in nondiabetic subjects: triglycerides_glucose versus triglyceride/highdensity lipoprotein cholesterol. Metabolism 2011; 60: 1673-1676
    CrossrefPubMedGoogle Scholar
  • 19 Yu X, Wang L, Zhang W. et al. Fasting triglycerides and glucose index is more suitable for the identification of metabolically unhealthy individuals in the Chinese adult population: A nationwide study. J Diabetes Investig 2019; 10: 1050-1058
    CrossrefPubMedGoogle Scholar
  • 20 Jin JL, Sun D, Cao YX. et al. Triglyceride glucose and hemoglobin glycation index for predicting outcomes in diabetes patients with new-onset, stable coronary artery disease: a nested case-control study. Ann Med 2018; 50: 576-586
    CrossrefPubMedGoogle Scholar
  • 21 Du T, Yuan G, Zhang M. et al. Clinical usefulness of lipid ratios, visceral adiposity indicators, and the triglycerides and glucose index as risk markers of insulin resistance. Cardiovasc Diabetol 2014; 13: 146
    CrossrefPubMedGoogle Scholar
  • 22 Hameed EK. TyG index a promising biomarker for glycemic control in type 2 Diabetes Mellitus. Diabetes Metab Syndr 2019; 13: 560-563
    CrossrefPubMedGoogle Scholar
  • 23 Liu XZ, Xu X, Zhu JQ. et al. Association between three non-insulin-based indexes of insulin resistance and hyperuricemia. Clin Rheumatol 2019; 38: 3227-3233
    CrossrefPubMedGoogle Scholar
  • 24 Su WY, Chen SC, Huang YT. et al. Comparison of the effects of fasting glucose, hemoglobin A1c, and triglyceride-glucose index on cardiovascular events in type 2 diabetes mellitus. Nutrients 2019; 11: 2838
    CrossrefPubMedGoogle Scholar
  • 25 Zhang S, Du T, Zhang J. et al. The triglyceride and glucose index (TyG) is an effective biomarker to identify nonalcoholic fatty liver disease. Lipids Health Dis 2017; 16: 15
    CrossrefPubMedGoogle Scholar
  • 26 Bickerton AS, Roberts R, Fielding BA. et al. Preferential uptake of dietary Fatty acids in adipose tissue and muscle in the postprandial period. Diabetes 2017; 16: 15
    PubMedGoogle Scholar
  • 27 Delgado P, Diaz JM, Silva I. et al. Unmasking glucose metabolism alterations in stable renal transplant recipients: a multicenter study. Clin J Am Soc Nephrol 2008; 3: 808-813
    CrossrefPubMedGoogle Scholar
  • 28 Chang S, Jiang J. Association of body mass index and the risk of new-onset diabetes after kidney transplantation: a meta-analysis. Transplant Proc 2018; 50: 1316-1325
    CrossrefPubMedGoogle Scholar
  • 29 Er LK, Wu S, Chou HH. et al. Triglyceride glucose-body mass index is a simple and clinically useful surrogate marker for insulin resistance in nondiabetic individuals. PLoS One 2016; 11: e0149731
    CrossrefPubMedGoogle Scholar
  • 30 Lim J, Kim J, Koo SH. et al. Comparison of triglyceride glucose index, and related parameters to predict insulin resistance in Korean adults: an analysis of the 2007-2010 Korean national health and nutrition examination survey. PLoS One 2019; 14: e0212963
    CrossrefPubMedGoogle Scholar
  • 31 Tuo X, Yuan J, Wang XH. et al. Identifying the insulin resistance index in nondiabetic Chinese subjects. Medicine (Baltimore) 2020; 99: e19023
    CrossrefPubMedGoogle Scholar
  • 32 Ramírez-Vélez R, Pérez-Sousa MÁ, González-Ruíz K. et al. Obesity- and lipid-related parameters in the identification of older adults with a high risk of prediabetes according to the American diabetes association: an analysis of the 2015 health, well-being, and aging study. Nutrients 2019; 11: 2654
    CrossrefPubMedGoogle Scholar
  • 33 Netto MC, Alves-Filho G, Mazzali M. Nutritional status and body composition in patients early after renal transplantation. Transplant Proc 2012; 44: 2366-2368
    CrossrefPubMedGoogle Scholar
  • 34 Stalder M, Pometta D, Suenram A. Relationship between plasma insulin levels and high density lipoprotein cholesterol levels in healthy men. Diabetologia 1981; 21: 544-548
    CrossrefPubMedGoogle Scholar
  • 35 Quispe R, Martin SS, Jones SR. Triglycerides to high-density lipoprotein-cholesterol ratio, glycemic control and cardiovascular risk in obese patients with type 2 diabetes. Curr Opin Endocrinol Diabetes Obes 2016; 23: 150-156
    CrossrefPubMedGoogle Scholar
  • 36 González-Chávez A, Simental-Mendía LE, Elizondo-Argueta S. Elevated triglycerides/HDL-cholesterol ratio associated with insulin resistance. Cir Cir 2011; 79: 126-131
    PubMedGoogle Scholar
  • 37 Zonszein J, Lombardero M, Ismail-Beigi F. et al. Triglyceride high-density lipoprotein ratios predict glycemia-lowering in response to insulin sensitizing drugs in type 2 diabetes: a post hoc analysis of the BARI 2D. J Diabetes Res 2015; 2015: 129891
    CrossrefPubMedGoogle Scholar
  • 38 Palamaner Subash Shantha G, Kumar AA, Kahan S. et al. Triglyceride/HDL ratio as a screening tool for predicting success at reducing anti-diabetic medications following weight loss. PLoS One 2013; 8: e69285
    CrossrefPubMedGoogle Scholar
  • 39 Porrini E, Delgado P, Alvarez A. et al. The combined effect of pre-transplant triglyceride levels and the type of calcineurin inhibitor in predicting the risk of new onset diabetes after renal transplantation. Nephrol Dial Transplant 2008; 23: 1436-1441
    CrossrefPubMedGoogle Scholar
  • 40 Porrini E, Moreno JM, Osuna A. et al. Prediabetes in patients receiving tacrolimus in the first year after kidney transplantation: a prospective and multicenter study. Transplantation 2008; 23: 1436-1441
    PubMedGoogle Scholar
  • 41 McLaughlin T, Abbasi F, Cheal K. et al. Use of metabolic markers to identify overweight individuals who are insulin resistant. Ann Intern Med 2003; 139: 802-809
    CrossrefPubMedGoogle Scholar
  • 42 Lamprea-Montealegre JA, Sharrett AR, Matsushita K. et al. Chronic kidney disease, lipids and apolipoproteins, and coronary heart disease: the ARIC study. Atherosclerosis 2014; 234: 42-46
    CrossrefPubMedGoogle Scholar