Harnessing machine learning to decode dietary Impacts on cardiometabolic multimorbidity
Ran Liu1, Lei Tang1, Feng Zhang1
1Department of Neurology,Xiangya Hospital,Central South University,Changsha, Hunan 410008 China; Multi-Modal Monitoring Technology for Severe Cerebrovascular Disease of Human Engineering Research Center, Changsha, China; Brain Health Center of Hunan Province, Changsha, China; Human Brain Disease Biological Resources Platform of Hunan Province, Changsha, China; National Clinical Research Center for Geriatric Disorders,Xiangya Hospital,Central South University, Changsha, China; Department of Neurology,Xiangya Hospital, Central South University, Jiangxi 330006, China; FuRong Laboratory, Changsha, Hunan 410078, China; Hunan Provincial Key Laboratory of Neurocritical Care Medicine, China; Hunan Provincial Quality Management Center for Human Brain Injury Assessment, China.
Objective:
The objective of this research is to develop and validate the efficacy of a machine learning model that integrates 13 nutrients with baseline characteristics to predict Cardiometabolic Multimorbidity (CMM). Furthermore, the study aims to examine the role of key nutrients in assessing disease risk and to elucidate the underlying mechanisms involved.
Methods:
Data were synthesized from two population-based databases: the NHANES and the CHNS. The analysis included 13 nutrients and seven demographic and health variables. Binary and gradient logistic regressions were used to assess associations. Six machine learning models were trained and validated for generalizability on both NHANES and CHNS datasets. SHAP values were used to interpret feature contributions and understand variable relationships with prediction outcomes.
Results:
The SVM model demonstrated the best performance, achieving an external validation AUC of 0.874, indicating good predictive ability across populations. SHAP analysis identified age, magnesium, BMI, total fat, vitamin B1, and dietary fiber as important contributors to model predictions. Magnesium, vitamin B1, and dietary fiber showed inverse associations with CMM risk within the modeling framework. While total fat exhibited an inverse association in logistic regression, it played a significant role in model prediction, suggesting that its relationship with CMM may be complex and context-dependent.
Conclusions:
A machine learning model integrating nutritional and baseline characteristics may provide a useful approach for predicting CMM risk, with the SVM model showing the best performance. The findings highlight the relevance of multiple dietary factors in risk prediction; however, these associations should be interpreted with caution. Further longitudinal and interventional studies are needed to clarify potential causal relationships.
