Model for predicting metabolic syndrome in postmenopausal women

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Case-control analysis identifies risk factors for metabolic syndrome in early postmenopause, suggesting targeted prevention strategies.

The developed model predicts metabolic syndrome in postmenopausal women and identifies those at increased risk.
Key metrics include HOMA-IR, visceral adiposity, and menopausal index, with significant differences noted in patients.
Using logistic regression, the screening model achieved an AUC of 0.909 with sensitivity of 87.1% and specificity of 78.6%.
The model highlights the importance of early identification for personalized prevention and treatment in this population.

Danilova et al. (2025) studied this question.

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View Full Paper

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Authors

Case-control analysis identifies risk factors for metabolic syndrome in early postmenopause, suggesting targeted prevention strategies.

The developed model predicts metabolic syndrome in postmenopausal women and identifies those at increased risk.
Key metrics include HOMA-IR, visceral adiposity, and menopausal index, with significant differences noted in patients.
Using logistic regression, the screening model achieved an AUC of 0.909 with sensitivity of 87.1% and specificity of 78.6%.
The model highlights the importance of early identification for personalized prevention and treatment in this population.

Danilova et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark