HbA1c/HDL-C Ratio Association with Heart Failure in NHANES and Clinical Cohorts

102,676 participant records from the National Health and Nutrition Examination Survey (1999–2018) and a clinical inpatient sample from 2020 investigate a novel biomarker. The study identifies a nonlinear positive association between the HbA1c/HDL-C ratio and heart failure risk, with an inflection point at 6.21. Author Miao Li published this analysis under a CC-BY-4.0 license on figshare.

Use Cases

• Predicting heart failure risk based on the HbA1c/HDL-C ratio threshold identified in the study
• Training models to identify nonlinear relationships between composite biomarkers and clinical outcomes
• Benchmarking predictive power of the HbA1c/HDL-C ratio against individual biomarkers like HbA1c and HDL-C
• Conducting subgroup analyses on heart failure prevalence across different populations

Strengths

• Analysis combines a large public health survey (101,316 NHANES participants) with a focused clinical sample (1,360 inpatients)
• Study employs multiple statistical methods including multivariate logistic regression, restricted cubic spline regression, and ROC curve analysis
• Specific inflection point (6.21) for the HbA1c/HDL-C ratio is identified, providing a concrete threshold for risk assessment

Limitations

• The dataset is a single 87.4 KB DOCX file, suggesting it contains summary results rather than raw participant-level data
• Column-level documentation is absent; field semantics must be inferred after download
• Row count for the underlying data is unknown, which may limit suitability assessment for direct ML training

Provenance

Source

National Health and Nutrition Examination Survey (NHANES) and a cardiology department inpatient sample.

Collection Method

Cross-sectional study utilizing samples from two sources.

Time Range

NHANES data spans 1999–2018; clinical sample is from 2020.

Freshness

Last updated 2026-06-03 04:47:27; freshness should be verified.

Geography

United States (NHANES) and a specific clinical inpatient population (location unknown).