Associations between exposure to heavy metal and sarcopenia prevalence: a cross-sectional study using NHANES data.

BACKGROUND

Sarcopenia is a condition that adversely affects individuals' quality of life and physical health. Exposure to heavy metals poses a significant risk to human health; however, the impact of heavy metal exposure on sarcopenia remains unclear. Therefore, this study expects to construct a risk prediction machine model of heavy metal exposure on sarcopenia and to interpret and analyze it.

METHODS

Model construction was based on data from the NHANES database, covering the years 2011 to 2018. The predictor variables included BA, CD, CO, CS, MN, MO, PB, SB, SN, TL, and W. Additionally, demographic characteristics and health factors were included in the study as confounders. After identifying the core variables, optimal machine learning models were constructed, and SHAP analyses were performed.

RESULTS

We found that the LGBM model exhibited the best predictive performance. SHAP analysis revealed that TL, SN, and CS negatively influenced the prediction of sarcopenia, while CD positively contributed to it. Additionally, le8 BMI was the covariate that had the most significant positive impact on the prediction of sarcopenia in our model.

CONCLUSION

For the first time, we have developed a machine learning (ML) model to predict sarcopenia based on indicators of heavy metal exposure. This model has accurately identified a series of key factors that are strongly associated with sarcopenia induced by heavy metal exposure. We can now identify individuals at an early stage who are suffering from sarcopenia due to heavy metal exposure, thereby reducing the physical and economic burden on public health.