Red cell distribution width to albumin ratio (RAR) as a potential biomarker of coronary heart disease (CHD): Insights from a cross-sectional study

While significant evidence has linked various biomarkers to cardiovascular risk, the role of the red cell distribution width to albumin ratio (RAR) in predicting coronary heart disease (CHD) remains underexplored. This cross-sectional study utilized data from 48,928 participants from the National Health and Nutrition Examination Survey (NHANES) 1999 to 2018. The exposure variable was RAR, calculated as the ratio of red cell distribution width to serum albumin levels. CHD was determined based on self-reported data. Multivariate logistic regression was used to examine the relationship between RAR and CHD, adjusting for potential confounders, with stratified analyses by sex, diabetes status, and hypertension status. The mean age of participants was 49.64 ± 18.17 years, with a mean RAR of 3.15 ± 0.51. In the fully adjusted model (Model 3), an increase in RAR was associated with an increased risk of CHD (OR = 1.31, 95% CI: 1.13, 1.51, P < .001). Stratified analysis revealed that higher RAR quartiles were associated with higher CHD risk, especially in females and those with diabetes. But no significant interaction was found between RAR and gender or diabetes status on CHD risk. Our study suggests that RAR is associated with an increased risk of CHD, particularly at higher quartiles. While RAR may help clinicians identify individuals at elevated risk for CHD, further longitudinal studies and mechanistic investigations are needed to better understand its predictive value and clinical applicability.

Authors

Institutions

Publication Details

Journal
Medicine
Published
2025-12-19
DOI
https://doi.org/10.1097/md.0000000000046663
Primary Topic
Inflammatory Biomarkers in Disease Prognosis
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Red cell distribution width to albumin ratio (RAR) as a potential biomarker of coronary heart disease (CHD): Insights from a cross-sectional study

Xumeng Ding, Chaofu Yue, Qi Qiu, Mei Yang et al.
Medicine
Inflammatory Biomarkers in Disease Prognosis
article

Red cell distribution width to albumin ratio (RAR) as a potential biomarker of coronary heart disease (CHD): Insights from a cross-sectional study

Xumeng Ding, Chaofu Yue, Qi Qiu, Mei Yang, Huiling Liang, Qiaolin Li, Wei Bao, Xian Huang, Rong Lei
article en

Abstract

While significant evidence has linked various biomarkers to cardiovascular risk, the role of the red cell distribution width to albumin ratio (RAR) in predicting coronary heart disease (CHD) remains underexplored. This cross-sectional study utilized data from 48,928 participants from the National Health and Nutrition Examination Survey (NHANES) 1999 to 2018. The exposure variable was RAR, calculated as the ratio of red cell distribution width to serum albumin levels. CHD was determined based on self-reported data. Multivariate logistic regression was used to examine the relationship between RAR and CHD, adjusting for potential confounders, with stratified analyses by sex, diabetes status, and hypertension status. The mean age of participants was 49.64 ± 18.17 years, with a mean RAR of 3.15 ± 0.51. In the fully adjusted model (Model 3), an increase in RAR was associated with an increased risk of CHD (OR = 1.31, 95% CI: 1.13, 1.51, P < .001). Stratified analysis revealed that higher RAR quartiles were associated with higher CHD risk, especially in females and those with diabetes. But no significant interaction was found between RAR and gender or diabetes status on CHD risk. Our study suggests that RAR is associated with an increased risk of CHD, particularly at higher quartiles. While RAR may help clinicians identify individuals at elevated risk for CHD, further longitudinal studies and mechanistic investigations are needed to better understand its predictive value and clinical applicability.

MedicineVol. 104(51)
Qujing Normal University (CN)
Openalex Percentile: Top 43%
Inflammatory Biomarkers in Disease Prognosis
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.