Comparative Informativity of Computing Methods of Insulin Resistance Assessment

S I Kseneva; E V Borodulina; O Yu Trifonova; V P Fisenko; V V Udut

doi:10.1007/s10517-022-05398-2

Comparative Informativity of Computing Methods of Insulin Resistance Assessment

Bull Exp Biol Med. 2022 Jan;172(3):385-389. doi: 10.1007/s10517-022-05398-2. Epub 2022 Jan 10.

Authors

S I Kseneva¹, E V Borodulina², O Yu Trifonova², V P Fisenko³, V V Udut^{2

4}

Affiliations

¹ E. D. Goldberg Research Institute of Pharmacology and Regenerative Medicine, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, Russia. viksbest@mail.ru.
² E. D. Goldberg Research Institute of Pharmacology and Regenerative Medicine, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, Russia.
³ I. M. Sechenov First Moscow State Medical University, Ministry of Health of the Russian Federation (Sechenov University), Moscow, Russia.
⁴ National Research Tomsk State University, Tomsk, Russia.

PMID: 35001303
DOI: 10.1007/s10517-022-05398-2

Abstract

We conducted a comparative study of the calculated indices of insulin resistance HOMA-R, Caro, FGIR, and QUICKI in 29 healthy volunteers (mean age 26.21±0.93 years) with normal body mass index (23.34±0.55 kg/m2). Among the used methods for insulin resistance assessment, QUICKI is the only method that has characteristics required for the diagnostic criterium: low variability coefficient, 100% reproducibility, and minimum coefficient of variation.

Keywords: Caro; FGIR; HOMA-R; QUICKI; insulin resistance.

MeSH terms

Adult
Blood Glucose
Humans
Insulin
Insulin Resistance*
Reproducibility of Results

Substances

Blood Glucose
Insulin