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


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.


TyG Neonates Insulin Glucose HOMA-IR QUICKI TG/HDLc 



Chi-squared automatic interaction detection


Gestational diabetes mellitus


Growth hormone


High density lipoprotein cholesterol


Homeostatic model assessment-insulin resistance


Insulin-like growth factor 1


Impaired glucose tolerance


Insulin resistance


Metabolic syndrome


Quantitative insulin sensitivity check index


Type 2 diabetes mellitus




Triglyceride-glucose index



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.


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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

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