Exploring the relationship between heavy metals and diabetic retinopathy: a machine learning modeling approach

Abstract

Diabetic retinopathy (DR) is one of the leading causes of adult blindness in the United States. Although studies applying traditional statistical methods have revealed that heavy metals may be essential environmental risk factors for diabetic retinopathy, there is a lack of analyses based on machine learning (ML) methods to adequately explain the complex relationship between heavy metals and DR and the interactions between variables. Based on characteristic variables of participants with and without DR and heavy metal exposure data obtained from the NHANES database (2003-2010), a ML model was developed for effective prediction of DR. The best predictive model for DR was selected from 11 models by receiver operating characteristic curve (ROC) analysis. Further permutation feature importance (PFI) analysis, partial dependence plots (PDP) analysis, and SHapley Additive exPlanations (SHAP) analysis were used to assess the model capability and key influencing factors. A total of 1042 eligible individuals were randomly assigned to two groups for training and testing set of the prediction model. ROC analysis showed that the k-nearest neighbour (KNN) model had the highest prediction performance, achieving close to 100% accuracy in the testing set. Urinary Sb level was identified as the critical heavy metal affecting the predicted risk of DR, with a contribution weight of 1.730632 ± 1.791722, which was much higher than that of other heavy metals and baseline variables. The results of the PDP analysis and the SHAP analysis also indicated that antimony (Sb) had a more significant effect on DR. The interaction between age and Sb was more significant compared to other variables and metal pairs. We found that Sb could serve as a potential predictor of DR and that Sb may influence the development of DR by mediating cellular and systemic senescence. The study revealed that monitoring urinary Sb levels can be useful for early non-invasive screening and intervention in DR development, and also highlighted the important role of constructed ML models in explaining the effects of heavy metal exposure on DR.

Keywords: Diabetic retinopathy; Factors; Heavy metal; Machine learning; Prediction.

Figure 1

The results of Pearson's correlation analysis among the metal factors and baseline variables.

Figure 2

The ROC of the 11 machine learning models in testing set.

Figure 3

The contribution of metal factors and baseline variables in predictive model. (A) The forest map based on PFI analysis displays the corresponding contribution weights of heavy metals and baseline variables and their corresponding standard deviations; (B) The SHAP summary plot of all variables and DR risk. The width of the range of the horizontal bars can be interpreted as the effect on the model predictions, with the wider the range, the greater the effect. The direction on the x-axis represented the likelihood of developing DR (right) or not developing (left); (C) The SHAP features importance plot of heavy metals and DR risk. The magnitude of the effect of each feature on the model output was measured by the average of the absolute values of the SHAP values for all samples, ranked from top to bottom by their magnitude of effect; D) The SHAP summary plot of heavy metals and DR risk.

Figure 4

Relationships between key metal including (A) Sb, (B) Ba, (C) Pt, (D) As, (E) Tl, (F) Cd and predictive DR risk. The x-axis of the plot represented the log-transformed values of each metal.

Figure 5

Interaction effects of variables on DR. (A) Interactions between heavy metals and baseline variables on DR; (B) Interactions between Sb levels and other variables on DR. The range of the straight line represents the overall interaction strength, the wider the range, the greater the effect.