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This is a Cross-Sectional Study study.

October 2, 2025Open Access

Non-Traditional Lipid Ratios Predict Cardiovascular-Kidney-Metabolic Syndrome: Insights from Machine Learning Model Using NHANES Data

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Cross-sectional analysis highlights non-traditional lipid ratios as predictors of CKM syndrome, suggesting urgent need for early detection.

Elevated TG/HDL-C and non-HDL-C levels significantly increase CKM syndrome risk, indicating poor metabolic health.
The study observed nonlinear associations among lipid ratios and CKM risk, revealing complex interactions in lipid profiles.
Using NHANES data, a machine learning model identified TG/HDL-C as a key independent predictor of CKM syndrome risk.
Integration of non-traditional lipid markers into risk models may improve detection and prevention strategies for CKM conditions.

Wang et al. (2025) studied this question.

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October 2, 2025Open Access

View Full Paper

Ask AI

Bookmark

Authors

Cross-sectional analysis highlights non-traditional lipid ratios as predictors of CKM syndrome, suggesting urgent need for early detection.

Elevated TG/HDL-C and non-HDL-C levels significantly increase CKM syndrome risk, indicating poor metabolic health.
The study observed nonlinear associations among lipid ratios and CKM risk, revealing complex interactions in lipid profiles.
Using NHANES data, a machine learning model identified TG/HDL-C as a key independent predictor of CKM syndrome risk.
Integration of non-traditional lipid markers into risk models may improve detection and prevention strategies for CKM conditions.

Wang et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark