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. 2025 Mar 18;15(1):9384.
doi: 10.1038/s41598-025-92777-0.

Life's Crucial 9 and NAFLD from association to SHAP-interpreted machine learning predictions

Jianxin Xi  1 , Yuguo Chen  1 , Chen Jie  2 , Jason Chi Shing Law  3 , Zhongqi Fan  1 , Guoyue Lv  4
Affiliations

Life's Crucial 9 and NAFLD from association to SHAP-interpreted machine learning predictions

Jianxin Xi et al. Sci Rep. .

Abstract

Non-alcoholic fatty liver disease (NAFLD) is the most prevalent chronic liver disease worldwide. Cardiovascular disease (CVD) and NAFLD share multiple common risk factors. Life's Crucial 9 (LC9), a novel indicator for comprehensive assessment of cardiovascular health (CVH), has not yet been studied in terms of its association with or predictive value for NAFLD. This study analyzed data from 10,197 participants in the National Health and Nutrition Examination Survey (NHANES) from 2007 to 2018. The association between LC9 and NAFLD was assessed using weighted logistic regression, while weighted Cox proportional hazards models were applied to evaluate the relationship between LC9 and all-cause mortality among NAFLD patients. Restricted cubic spline (RCS) analysis was conducted to explore dose-response relationships, and Kaplan-Meier survival curves were utilized to examine differences in survival outcomes. Machine learning (ML) approaches were employed to construct predictive models, with the optimal model further interpreted using SHapley Additive exPlanations (SHAP). An increase of 10 points in LC9 was negatively associated with the risk of NAFLD (model 3: OR = 0.39, 95% CI = 0.36 - 0.42, P < 0.001) and all-cause mortality in NAFLD patients (model 3: HR = 0.78, 95% CI = 0.67 - 0.91, P < 0.001). A non-linear relationship was observed between LC9 and NAFLD (P < 0.0001 for nonlinearity). Among the eight ML models, the Support Vector Machine (SVM) demonstrated the best predictive performance (AUC = 0.873). SHAP analysis indicated that LC9 was the most significant predictor in the model. LC9 demonstrated a nonlinear negative association with NAFLD and a linear negative association with all-cause mortality in NAFLD patients. Maintaining a higher LC9 score may reduce the risk of NAFLD and all-cause mortality among NAFLD patients. The predictive model developed using Support Vector Machine (SVM) exhibited strong clinical predictive value, with LC9 being the most critical factor in the model, facilitating self-risk assessment and targeted intervention.

Keywords: Life’s Crucial 9; Machine learning; NAFLD; NHANES; SHAP.

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Conflict of interest statement

Competing interests: The authors declare no competing interests. Ethics approval: The present study utilizes publicly accessible data from the National Health and Nutrition Examination Survey (NHANES). Given that the utilized data are anonymized and publicly available, ethical approval was not deemed necessary for this research. Consent to participate: This study used publicly available data from the National Health and Nutrition Examination Survey (NHANES). NHANES is a program conducted by the Centers for Disease Control and Prevention (CDC) that collects health and nutritional data from a representative sample of the U.S. population. The NHANES data used in this research are de-identified and publicly available, so no individual consent was required for this study. The study was conducted in accordance with ethical standards and institutional guidelines for research using publicly available data.

Figures

Fig. 1
Fig. 1
Flowchart illustrating selection of the study population and analysis in NHANES from 2007 to 2018.
Fig. 2
Fig. 2
Association of LC9 with NAFLD and all-cause mortality among individuals with NAFLD. In Model 1, the odds ratio (OR) and hazard ratio is presented without adjusting for any variables. In Model 2, the OR and HR are adjusted for age, gender, race and ethnicity, educational level, PIR, and marital status. In Model 3, the OR and HR are additionally adjusted for Alb, ALP, GGT, ALT, AST, TB, SCr, and UA. Shaded areas represent 95% CIs. The LC9 value corresponding to an OR of 1 is 74.83 and an HR of 1 is 67.33.
Fig. 3
Fig. 3
Subgroup analysis of the association of LC9 with NAFLD and all-cause mortality among individuals with NAFLD.
Fig. 4
Fig. 4
Image of Boruta algorithm for selecting ML model variables.
Fig. 5
Fig. 5
(A) ROC curves of the training set. (B) ROC curves of the testing set.
Fig. 6
Fig. 6
SHAP diagram of SVM model. (A) SHAP value ranking of the variables in the model. (B) SHAP honeycomb diagram of the SVM model. (C) SHAP waterfall plot for the second sample in the study population. (D) the dependency relationships between SHAP values and different variables.
See this image and copyright information in PMC

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