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Updated: May 13, 2025
Calcification of Vascular Smooth Muscle Cells and Imaging of Aortic Calcification and Inflammation
Published on: May 31, 2016
Exploring the influencing factors of abdominal aortic calcification events in chronic kidney disease (CKD) and
Haowen Lin1, Xiaoying Dong2, Yuhe Yin3
1Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangdong, 510000, China.
Insights
Age, smoking, and kidney function are key to calcification in both CKD and non-CKD patients. Inflammatory markers are more critical for calcification risk in those with chronic kidney disease (CKD).
Area of Science:
- Nephrology
- Cardiology
- Data Science
Background:
- Calcification is common in chronic kidney disease (CKD) patients.
- Abdominal aortic calcification (AAC) predicts coronary calcification.
- Identifying calcification drivers in CKD and non-CKD populations is crucial.
Purpose of the Study:
- To identify key factors contributing to calcification.
- To compare calcification determinants between CKD and non-CKD individuals.
- To utilize machine learning for analyzing calcification predictors.
Main Methods:
- Analysis of National Health and Nutrition Examination Survey (NHANES) data.
- Application of machine learning models to predict calcification.
- Utilizing Shapley additive explanations (SHAP) for model interpretability.
Main Results:
- Common calcification factors include age, eGFR, smoking, blood glucose, Ca*P, and UACR.
- Age, smoking, and eGFR are top predictors in both groups.
- Inflammatory markers (MHR, MLR, NLR) are more significant in the CKD group.
Conclusions:
- Age, smoking history, and eGFR are universal calcification determinants.
- Inflammatory markers play a more significant role in calcification among CKD patients.
- Machine learning effectively identified key calcification factors across different populations.
Background:
Calcification is prevalent in CKD patients, with abdominal aortic calcification (AAC) being a strong predictor of coronary calcification. We aimed to identify key calcification factors in CKD and non-CKD populations using machine learning models.
Methods:
Data from the National Health and Nutrition Examination Survey (NHANES), including demographics, blood and urine tests, and AAC scores, were analyzed using machine learning models. The Shapley additive explanations (SHAP) analysis was applied to interpret the models.
Results:
Among 505 CKD and 2,582 non-CKD participants, common key factors for calcification included age, estimated glomerular filtration rate (eGFR), smoking history, blood glucose levels (Glu), Ca*P and the urine albumin-to-creatinine ratio (UACR). Age, smoking history and eGFR were the top-ranking features in the model for both two groups. Inflammatory markers such as monocyte-to-lymphocyte ratio (MHR), monocyte-to-high-density lipoprotein ratio (MLR) and neutrophil-to-lymphocyte ratio (NLR) were more significant in CKD group. Trigger points for AAC events were identified: in CKD, eGFR of 90 mL/min/1.73 m2, MHR values of 0.5 and 0.75, MLR values of 0.25, and SP of 120 mmHg; in non-CKD, eGFR of 105 mL/min/1.73 m2, Ca*P values of 40, UACR values of 10, and TG of 200 mg/dL.
Conclusions:
Regardless of CKD status, age, smoking history, and eGFR are key determinants of calcification. In the CKD population, inflammatory markers are more significant than in the non-CKD group.
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