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Utilizing Life's Crucial 9 for Rheumatoid Arthritis Risk Prediction: A Machine Learning Approach Based on NHANES

Jiasi Zheng¹, Yuanyuan Gao¹, Xuemei Yuan²

¹Guizhou University of Traditional Chinese Medicine Guiyang China.

Food Science & Nutrition

|June 8, 2026

Summary

This summary is machine-generated.

Related Concept Videos

Genome-wide Association Studies-GWAS

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

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Improving cardiovascular health through Life's Crucial 9 (LC9) significantly reduces rheumatoid arthritis (RA) risk. A predictive model using LC9 shows high accuracy for RA risk stratification.

Area of Science:

Cardiovascular Health
Rheumatology
Epidemiology

Background:

Rheumatoid arthritis (RA) is a chronic autoimmune disease with significant morbidity.
Cardiovascular health is increasingly recognized as a factor influencing systemic inflammation and autoimmune conditions.
Life's Crucial 9 (LC9) is a composite metric assessing cardiovascular health, encompassing factors like diet, physical activity, and metabolic parameters.

Purpose of the Study:

To examine the association between the Life's Crucial 9 (LC9) score and the risk of developing rheumatoid arthritis (RA).
To develop and validate a predictive model for RA risk using LC9 and other relevant covariates.
To identify key predictors of RA risk through machine learning analysis.

Main Methods:

Analysis of data from 16,154 adults in the U.S. National Health and Nutrition Examination Survey (NHANES) 2011-2018.

Keywords:

NHANES cardiovascular machine learning rheumatoid arthritis risk assessment

Related Experiment Videos

Weighted multivariable logistic regression and restricted cubic splines to assess the LC9-RA relationship.

Development of RA risk prediction models using machine learning algorithms (XGBoost, LightGBM, Random Forest, etc.) with LC9 and 10 covariates, validated via 7:3 train-test split and 5-fold cross-validation.

Main Results:

Each 1-unit increase in LC9 score was associated with a 27% reduction in RA risk (OR=0.73, P<0.0001).
Individuals in the highest LC9 quartile had a 57% lower RA risk compared to the lowest quartile (OR=0.43, P<0.0001), indicating a linear dose-response relationship.
The XGBoost model achieved the highest performance (AUC=0.987, accuracy=96.20%), with AST, age, ALT, and HDL identified as top predictors, alongside LC9.

Conclusions:

Life's Crucial 9 (LC9) is an independent protective factor against rheumatoid arthritis (RA) risk.
An XGBoost-based predictive model incorporating LC9 demonstrates excellent diagnostic performance for RA risk stratification.
Promoting cardiovascular health via LC9 may offer a novel strategy for RA prevention and risk management.