Advertisement

European Journal of Pediatrics

, Volume 177, Issue 4, pp 513–520 | Cite as

The triglyceride-glucose index, an insulin resistance marker in newborns?

  • Eva Gesteiro
  • Sara Bastida
  • Laura Barrios
  • Francisco J Sánchez-Muniz
Original Article

Abstract

The study aims to assess the utility of the triglyceride-glucose index (TyG) as a marker of insulin resistance (IR) in neonates. TyG and the homeostatic model assessment (HOMA-IR) values were compared in 196 singleton, term normoweight and without distress newborns. A Decision Tree procedure (CHAID) was used to classify cases into groups or predict values of a dependent (Ln HOMA-IR) variable. Three nodes were drawn for TyG: ≤ 6.7, > 6.7–7.8 and > 7.8 (p < 0.0001; F = 20.52). The predictability of those TyG values vs HOMA-IR was statistically significant (p < 0.0001). It was neither affected by gender (p = 0.084), glucose challenge test (p = 0.138) classifications nor by the TyG node* glucose challenge test and TyG node*gender interactions (p = 0.456 and p = 0.209, respectively). Glucose, HOMA-IR, and the triglyceride/HDL cholesterol ratio increased progressively from node 1 to 3 for TyG while QUICKI decreased.

Conclusion: In conclusion, TyG appears to be a suitable tool for identifying IR at birth, justifying the further insulin determination in those neonates. TyG ≥ 7.8 is recommended as cut-off point in neonates. The need for a follow-up study to confirm the TyG as early IR marker is desirable.

What is Known

HOMA-IR and the triglyceride-glucose index (TyG) show a high correlation.

The TyG has been used as an insulin resistance marker in adults.

What is New

This is the first study where TyG has been assessed in neonates.

TyG appears to be a suitable and cheap tool for identifying insulin resistance at birth.

Keywords

TyG Neonates Insulin Glucose HOMA-IR QUICKI TG/HDLc 

Abbreviations

CHAID

Chi-squared automatic interaction detection

GDM

Gestational diabetes mellitus

GH

Growth hormone

HDLc

High density lipoprotein cholesterol

HOMA-IR

Homeostatic model assessment-insulin resistance

IGF-1

Insulin-like growth factor 1

IGT

Impaired glucose tolerance

IR

Insulin resistance

MS

Metabolic syndrome

QUICKI

Quantitative insulin sensitivity check index

T2DM

Type 2 diabetes mellitus

TG

Triglycerides

TyG

Triglyceride-glucose index

Notes

Acknowledgements

We thank the Gynaecology and Obstetrics Department and Laboratory Services of Mérida Hospital (Extremadura, Spain) and participant mothers and neonates.

Authors’ contributions

E Gesteiro contributed to the data acquisition, analysis, discussion and writing of the paper; FJ Sánchez-Muniz contributed to the study design, data discussion, writing of the paper and is the corresponding author and guarantor of the paper; S Bastida contributed to the study design and data discussion; L Barrios has contributed to the design and discussion of the statistical study.

Funding information

Partially supported by the Spanish Project AGL 2014-53207-C2-2-R.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Informed consent

The study performed in accordance with the Helsinki Declaration of 1975 as revised in year 2000, following approval by the Management and Ethical Committee of the Hospital. Data were obtained from an anonymous hospital screening record.

Nonetheless, informed consent is available from 35 mothers whose pregnancy diet was studied and wished to be engaged in a follow-up study.

References

  1. 1.
    Abbasi F, Reaven GM (2011) Comparison of two methods using plasma triglyceride concentration as a surrogate estimate of insulin action in nondiabetic subjects: triglycerides × glucose versus triglyceride/high-density lipoprotein cholesterol. Metabolism 60(12):1673–1676.  https://doi.org/10.1016/j.metabol.2011.04.006 CrossRefPubMedGoogle Scholar
  2. 2.
    Al-Hamad D, Raman V (2017) Metabolic syndrome in children and adolescents. Transl Pediatr 6(4):397–407.  https://doi.org/10.21037/tp.2017.10.02 CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Bastida S, Sánchez-Muniz FJ, Cuesta C, Perea S, Aragonés A (1997) Male and female cord blood lipoprotein profile differences throughout the term-period. J Perinat Med 25(2):184–191.  https://doi.org/10.1515/jpme.1997.25.2.184 CrossRefPubMedGoogle Scholar
  4. 4.
    Gesteiro E, Bastida S, Sánchez-Muniz FJ (2009) Insulin resistance markers in term, normoweight neonates. The Mérida cohort. Eur J Pediatr 168:281–288CrossRefPubMedGoogle Scholar
  5. 5.
    Gesteiro E, Bastida S, Sánchez-Muniz FJ (2011) Effects of maternal glucose tolerance, pregnancy diet quality and neonatal insulinemia upon insulin resistance/sensitivity biomarkers in normoweight neonates. Nutr Hosp 26:1447–1455PubMedGoogle Scholar
  6. 6.
    Gesteiro E, Bastida S, Sánchez-Muniz FJ (2013) Cord-blood lipoproteins, homocysteine, insulin sensitivity/resistance marker profile, and concurrence of dysglycaemia and dyslipaemia in full-term neonates of the Merida study. Eur J Ped 172:883–894CrossRefGoogle Scholar
  7. 7.
    Gesteiro E, Rodríguez Bernal B, Bastida S, Sánchez-Muniz FJ (2012) Maternal diets with low healthy eating index or Mediterranean diet adherence scores are associated with high cord-blood insulin levels and insulin resistance markers at birth. Eur J Clin Nutr 66(9):1008–1015.  https://doi.org/10.1038/ejcn.2012.92 CrossRefPubMedGoogle Scholar
  8. 8.
    Gesteiro E, Sánchez-Muniz FJ, Bastida S (2017) Hypercortisolaemia and hyperinsulinaemia interaction and their impact upon insulin resistance/sensitivity markers at birth. In: Mauricio AC (ed) Blood banking for clinical application and regenerative medicine. InTech. Rijeka, Croatia, pp 69–98Google Scholar
  9. 9.
    Gesteiro E, Sánchez-Muniz FJ, Ortega-Azorín C, Guillén M, Corella D, Bastida S (2016) Maternal and neonatal FTO rs9939609 polymorphism affect insulin sensitivity markers and lipoprotein profile at birth in appropriate-for-gestational-age term neonates. J Physiol Biochem 72(2):169–181.  https://doi.org/10.1007/s13105-016-0467-7 CrossRefPubMedGoogle Scholar
  10. 10.
    Guerrero Romero F, Simental Mendia LE, Gonzalez Ortiz M, Martinez Abundis E, Ramos Zavala MG, Hernandez Gonzalez SO et al (2010) The product of triglycerides and glucose, a simple measure of insulin sensitivity. Comparison with the euglycemic-hyperinsulinemic clamp. J Clin Endocrinol Metab 95(7):3347–3351.  https://doi.org/10.1210/jc.2010-0288 CrossRefPubMedGoogle Scholar
  11. 11.
    Higgins V, Adeli K (2017) Pediatric metabolic syndrome: pathophysiology and laboratory assessment. EJIFCC 28(1):25–42PubMedPubMedCentralGoogle Scholar
  12. 12.
    Irace C, Carallo C, Scavelli FB, De Franceschi MS, Esposito T, Tripolino C, Gnasso A (2013) Markers of insulin resistance and carotid atherosclerosis. A comparison of the homeostasis model assessment and triglyceride glucose index. Int J Clin Pract 67(Suppl. 7):665–672.  https://doi.org/10.1111/ijcp.12124 CrossRefPubMedGoogle Scholar
  13. 13.
    Janghorbani M, Almasi SZ, Amini M (2015) The product of triglycerides and glucose in comparison with fasting plasma glucose did not improve diabetes prediction. Acta Diabetol 52(4):781–788.  https://doi.org/10.1007/s00592-014-0709-5 CrossRefPubMedGoogle Scholar
  14. 14.
    de Jong M, Cranendonk A, van Weissenbruch MM (2015) Components of the metabolic syndrome in early childhood in very-low-birth-weight infants and term small and appropriate for gestational age infants. Pediatr Res 78:457–461CrossRefPubMedGoogle Scholar
  15. 15.
    Kim JW, Park SH, Kim Y, Im M, Han HS (2016) The cutoff values of indirect indices for measuring insulin resistance for metabolic syndrome in Korean children and adolescents. Ann Pediatr Endocrinol Metab 21(3):143–148.  https://doi.org/10.6065/apem.2016.21.3.143 CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Lissau I, Overpeck MD, Ruan WJ, Due P, Holstein BE, Hediger ML, Health behaviour in school-aged children obesity Working Group (2004) Body mass index and overweight in adolescents in 13 European countries, Israel, and the United States. Arch Pediatr Adolesc Med 158(1):27–33.  https://doi.org/10.1001/archpedi.158.1.27 CrossRefPubMedGoogle Scholar
  17. 17.
    Mohd Nor NS, Lee S, Bacha F, Tfayli H, Arslanian S (2016) Triglyceride glucose index as a surrogate measure of insulin sensitivity in obese adolescents with normoglycemia, prediabetes, and type 2 diabetes mellitus: comparison with the hyperinsulinemic-euglycemic clamp. Pediatr Diabetes 17(6):458–465.  https://doi.org/10.1111/pedi.12303 CrossRefPubMedGoogle Scholar
  18. 18.
    Muniyappa R, Lee S, Chen H, Quon MJ (2008) Current approaches for assessing insulin sensitivity and resistance in vivo: advantages, limitations, and appropriate usage. Am J Physiol Endocrinol Metab 294(1):E15–E26.  https://doi.org/10.1152/ajpendo.00645.2007 CrossRefPubMedGoogle Scholar
  19. 19.
    Navarro-González D, Sánchez-Íñigo L, Pastrana-Delgado J, Fernández-Montero A, Martinez JA (2016) Triglyceride-glucose index (TyG index) in comparison with fasting plasma glucose improved diabetes prediction in patients with normal fasting glucose: the vascular-metabolic CUN cohort. Prev Med 86:99–105CrossRefPubMedGoogle Scholar
  20. 20.
    Nielsen JH, Haase TN, Jaksch C, Nalla A, Søstrup B, Nalla AA, Larsen L, Rasmussen M, Dalgaard LT, Gaarn LW, Thams P, Kofod H, Billestrup N (2014) Impact of fetal and neonatal environment on beta cell function and development of diabetes. Acta Obstet Gynecol Scand 93(11):1109–1122.  https://doi.org/10.1111/aogs.12504 CrossRefPubMedGoogle Scholar
  21. 21.
    O’Sullivan BA, Henderson ST, Davis JM (1998) Gestational diabetes. JAm Pharm Assoc (Wash) 38(3):364–371; quiz 372-373.  https://doi.org/10.1016/S1086-5802(16)30332-1 CrossRefGoogle Scholar
  22. 22.
    Plagemann A (2005) Perinatal programming and functional teratogenesis: impact on body weight regulation and obesity. Physiol Behav 86(5):661–668.  https://doi.org/10.1016/j.physbeh.2005.08.065 CrossRefPubMedGoogle Scholar
  23. 23.
    Rodríguez-Morán M, Simental-Mendía LE, Guerrero-Romero F (2017) The triglyceride and glucose index is useful for recognising insulin resistance in children. Acta Paediatr 106(6):979–983.  https://doi.org/10.1111/apa.13789 CrossRefPubMedGoogle Scholar
  24. 24.
    Sánchez-Íñigo L, Navarro-González D, Fernández-Montero A, Pastrana-Delgado J, Martínez JA (2016) The TyG index may predict the development of cardiovascular events. Eur J Clin Investig 46(2):189–197.  https://doi.org/10.1111/eci.12583 CrossRefGoogle Scholar
  25. 25.
    Sánchez-Muniz FJ, Gesteiro E, Espárrago Rodilla M, Rodríguez Bernal B, Bastida S (2013) Maternal nutrition during pregnancy conditions the fetal pancreas development, hormonal status and diabetes mellitus and metabolic syndrome biomarkers at birth. Nutr Hosp 28:250–274PubMedGoogle Scholar
  26. 26.
    Singh B, Saxena A (2010) Surrogate markers of insulin resistance: a review. World J Diabetes 1(2):36–47.  https://doi.org/10.4239/wjd.v1.i2.36 CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Unger G, Benozzi SF, Perruzza F, Pennacchiotti GL (2014) Triglycerides and glucose index: a useful indicator of insulin resistance. Endocrinol Nutr 61(10):533–540.  https://doi.org/10.1016/j.endonu.2014.06.009 CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Eva Gesteiro
    • 1
    • 2
  • Sara Bastida
    • 1
  • Laura Barrios
    • 3
  • Francisco J Sánchez-Muniz
    • 1
  1. 1.Departamento de Nutrición y Ciencia de los Alimentos (Nutrición). Facultad de FarmaciaUniversidad Complutense de Madrid e Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC)MadridSpain
  2. 2.Departamento de Salud y Rendimiento Humano, Facultad de Ciencias de la Actividad Física y del Deporte-INEFUniversidad Politécnica de MadridMadridSpain
  3. 3.Centro de CálculoCientífico de la SGAI. Investigación Operativa y Estadística Aplicada. CSICMadridSpain

Personalised recommendations