Should insulin resistance (HOMA-IR), insulin secretion (HOMA-β), and visceral fat area be considered for improving the performance of diabetes risk prediction models
Authors' conclusion
This cohort study, primarily composed of male workers, suggests that a model with BMI, FPG, and HbA1c effectively identifies those at high diabetes risk. However, adding HOMA-IR, HOMA-β, or replacing BMI with VFA does not significantly improve the model. Further studies are needed to confirm our findings.
Study context
- Population
- Cohort of individuals (94.7% male, primarily male workers)943 incident diabetes
- Participants
- 5,578 participants
- Exposure
- Model 1 (baseline) vs models adding fasting plasma glucose (FPG), HOMA-IR and HOMA-β, or replacing body mass index (BMI) with visceral fat area (VFA)
- Outcome
- Model discrimination and calibration for incident diabetes
- Follow-up
- First 10 years
Methodological detailscohort
- Cohort
Main finding
As reported in the abstract
AUC for diabetes prediction
Continuous net reclassification improvement index
0.61
CI 0.56–0.70
Model 2
Integrated discrimination improvement index
0.09
CI 0.08–0.10
Model 2
Model 1
0.79
95% CI 0.78–0.81
Model 2
0.84
CI 0.83–0.85
Observational data: associations, not effects.
Original publication
Should insulin resistance (HOMA-IR), insulin secretion (HOMA-β), and visceral fat area be considered for improving the performance of diabetes risk prediction models. BMJ Open Diabetes Res Care. Published 8 January 2024. doi:10.
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