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Type II Diabetes Mellitus III: Clinical Manifestations and Diagnosis01:25

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Updated: Jun 20, 2026

Study of In Vivo Glucose Metabolism in High-fat Diet-fed Mice Using Oral Glucose Tolerance Test (OGTT) and Insulin Tolerance Test (ITT)
08:13

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Published on: January 7, 2018

Identifying prediabetes using fasting insulin levels.

Jennal L Johnson1, Daniel S Duick, Michelle A Chui

  • 1Endocrinology Associates, Phoenix, Arizona 85013, USA. jennalj@yahoo.com

Endocrine Practice : Official Journal of the American College of Endocrinology and the American Association of Clinical Endocrinologists
|October 1, 2009
PubMed
Summary
This summary is machine-generated.

A predictive model using fasting insulin levels can accurately identify prediabetes in patients. Higher fasting insulin levels significantly increase the likelihood of detecting prediabetes, offering a valuable clinical tool.

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Area of Science:

  • Endocrinology
  • Metabolic Disorders
  • Clinical Diagnostics

Background:

  • Prediabetes affects a significant portion of the population.
  • Early identification of prediabetes is crucial for timely intervention.
  • Current methods for identifying prediabetes may not be universally accessible or easy to implement in clinical settings.

Purpose of the Study:

  • To develop and validate a predictive model for identifying prediabetes in clinical practice.
  • To assess the accuracy and ease of use of a clinical and laboratory model for prediabetes detection.
  • To explore the utility of fasting insulin levels and HOMA-IR in predicting prediabetes.

Main Methods:

  • Retrospective analysis of demographic and laboratory data from 965 patients undergoing glucose testing.
  • Exclusion of patients diagnosed with diabetes mellitus.
  • Definition of prediabetes based on fasting glucose and 2-hour postprandial glucose concentrations.
  • Multivariate logistic regression to identify predictors including fasting insulin quartiles, HOMA-IR, and quantitative insulin sensitivity check index.

Main Results:

  • 29.7% of the study population (287 patients) were diagnosed with prediabetes.
  • Higher quartiles of fasting insulin significantly increased the odds of identifying prediabetes (ORs ranging from 2.076 to 5.035).
  • Increased HOMA-IR index (OR=1.303) and older age (OR=1.037) also predicted prediabetes.
  • A fasting insulin level > 9.0 microIU/mL correctly identified 80% of patients with prediabetes.

Conclusions:

  • A predictive model utilizing fasting insulin levels demonstrates significant utility for identifying prediabetes in clinical settings.
  • Higher fasting insulin levels are strongly associated with an increased likelihood of prediabetes.
  • This model offers a robust and potentially more accessible tool for prediabetes screening and diagnosis.