Usefulness of minimal modelling to assess impaired insulin sensitivity in patients with chronic heart failure

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Tibor Szabo1, Stephan von Haehling, Dirk Habedank

  • 1Applied Cachexia Research, Department of Cardiology, Campus Virchow-Klinikum, Charité-University Medical School, Berlin, Germany. tibor.szabo@charite.de

International Journal of Cardiology
|September 8, 2009
PubMed

Insights

Impaired insulin sensitivity (Si) is common in chronic heart failure (CHF). Minimal modeling provides a superior assessment of Si in CHF patients compared to HOMA, FIRI, and QUICKI indices.

Area of Science:

  • Cardiology
  • Metabolic Health
  • Physiology

Background:

  • Impaired insulin sensitivity (Si) is prevalent in chronic heart failure (CHF), correlating with disease severity and prognosis.
  • Current methods like HOmeostasis Model Assessment (HOMA), Fasting Insulin Resistance Index (FIRI), and QUick Insulin CheCK Index (QUICKI) rely on single-time fasting measurements.
  • The comparative value of these indices against dynamic Si assessment in CHF remains unclear.

Purpose of the Study:

  • To compare the discriminatory power of minimal modeling for Si assessment against HOMA, FIRI, and QUICKI in stable CHF patients.
  • To evaluate the association of different Si assessment methods with clinical parameters and survival in CHF.

Main Methods:

  • Insulin sensitivity (Si) was assessed in 105 stable CHF patients using minimal modeling.
  • HOMA, FIRI, and QUICKI were calculated from fasting glucose and insulin levels.
  • Body composition was analyzed using dual-energy X-ray absorptiometry; peak oxygen uptake and skeletal muscle strength were measured.

Main Results:

  • All methods indicated impaired Si in CHF patients compared to controls.
  • Minimal modeling-derived Si distinguished between NYHA classes and correlated with peak oxygen uptake and muscle strength.
  • Minimal model-derived Si independently predicted survival in CHF patients, unlike HOMA, FIRI, and QUICKI.

Conclusions:

  • HOMA, FIRI, and QUICKI offer semi-quantitative estimates of Si but have limited discriminatory power in CHF.
  • Dynamic assessment of Si using minimal modeling is superior for quantitative evaluation and pathophysiological studies in CHF.
  • Minimal modeling provides a more robust assessment of Si in CHF, correlating with disease severity and prognosis.
Abstract

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