Results
This study thoroughly analyzed 356 children with lead poisoning and an equal number of healthy controls matched by age and gender. The median age for both groups was 4.0 years, with an inter-quartile range of 6.0 years. Males were the majority, comprising 72.11% (257) of each group. The mean blood lead level in the poisoned group was significantly higher at 186.47 µg/L, compared to 40.26 µg/L in the control group. Notably, 78.65% of the lead-exposed children exhibited neurological symptoms such as irritability, hyperactivity, headaches, and memory impairments. The sources of lead exposure were varied, including contaminated water (17.98%), traditional skin care products containing lead (23.88%), environmental pollution (21.07%), and parental occupational exposure (17.13%). In 17.98% of cases, exposure came from multiple sources, and 32.86% of the cases had unidentified sources of lead exposure.

A comparative analysis of hematology parameters indicated that children exposed to lead had significantly lower values of monocytes, basophil, hemoglobin, red blood cell count (RBC), hematocrit (HCT), MCH, MCHC, mean platelet volume (MPV), and platelet volume distribution width (PDW), and higher white blood cell count (WBC), neutrophils, eosinophil, red cell distribution width coefficient of variation (RDW-CV), RDW-SD, platelet, and plateletcrit (PCT) levels compared to healthy children ( Table 1 ). These hematological differences, combined with biochemical indicators showing significant differences from another study previously published by our authors ( Ye et al., 2021 ), which also utilized the same cohort of participants, were used to develop predictive models. The results from three different machine learning models indicated that the generalized linear model (GLM) algorithm was the most effective for classifying the presence of neurological impairment (Accuracy 0.84, Kappa 0.43, Table 2 ), incorporating variables such as MCHC, RDW-SD, RDW-CV, globulin, types of lead exposure, indirect bilirubin, and total bilirubin. Further assessment of the association between biomarkers and outcome variables revealed that the coefficients for MCHC, RDW-SD, and types of lead exposure were statistically significant ( P -value < 0.05), suggesting a strong association of these variables with neurological damage in children with lead poisoning Table 3 .

Notes:

Data are mean ± standard deviation.

Abbreviations WBC white blood cell count RBC red blood cell count HCT hematocrit MCV mean corpuscular volume MCH mean corpuscular hemoglobin MCHC mean corpuscular hemoglobin concentration RDW red blood cell distribution width CV Coefficient of variation SD standard deviation PCT Plateletcrit MPV mean platelet volume PDW platelet volume distribution width

white blood cell count

red blood cell count

hematocrit

mean corpuscular volume

mean corpuscular hemoglobin

mean corpuscular hemoglobin concentration

red blood cell distribution width

Coefficient of variation

standard deviation

Plateletcrit

mean platelet volume

platelet volume distribution width

Notes:

Abbreviations WBC white blood cell count MCHC mean corpuscular hemoglobin RDW red blood cell distribution width CV Coefficient of variation SD standard deviation

white blood cell count

mean corpuscular hemoglobin

red blood cell distribution width

Coefficient of variation

standard deviation

Notes:

Abbreviations MCHC mean corpuscular hemoglobin RDW red blood cell distribution width CV Coefficient of variation SD standard deviation

mean corpuscular hemoglobin

red blood cell distribution width

Coefficient of variation

standard deviation

To evaluate the effectiveness of various indicators in the model for detecting neurological damage in children with lead poisoning, receiver operating characteristic (ROC) analysis was performed. The results are graphically illustrated in Figure 1 . The values of each indicator along with their corresponding sensitivity and specificity are as follows: The highest performance was observed in the interactions model of MCHC, RDW-SD, and types of lead exposure, with an AUC of 0.80 (sensitivity 76%, specificity 76%). The interactions model of MCHC and RDW-SD showed a slightly lower AUC of 0.76 (sensitivity 80%, specificity 67%). For individual indicators, the AUC for MCHC was 0.70 (sensitivity 54%, specificity 81%), and for RDW-SD, it was 0.68 (sensitivity 55%, specificity 78%).

According to the ROC analysis, a criterion value of 1.046094 for the Interactions Model of MCHC and RDW-SD was identified to indicate neurological damage in children with lead poisoning. It was observed that 89.4% (220/246) of the lead-poisoned children with a higher MCHC*RDW-SD Interactions value (above 1.046094) exhibited neurological symptoms, significantly more than 53.7% (58/108) of those with a lower value ( χ 2 = 54.72, p < 0.001, Figure 2 ).

Spearman correlation analysis indicated that the MCHC*RDW-SD Interactions value, apart from showing strong correlation with its parent indicators (MCHC and RDW-SD), exhibited no or weak correlation (less than 0.5) with other variables ( Figure 3 ).

Our analysis primarily focused on the interactions model of MCHC and RDW-SD. The rationale behind this focused approach, including the decision to exclude ‘Types of lead exposure’ from this stage of the analysis, is further elaborated in the discussion section.