Bone lead level prediction models and their application to examine the relationship of lead exposure and hypertension in the Third National Health and Nutrition Examination Survey

Abstract

Objective: We developed prediction models for bone lead using blood lead levels and other standard covariates in a community-based cohort of older men.

Methods: Participants having bone lead levels measured by K X-ray fluorescence were included in the model selection process (n = 825). Predictors of each tibia and patella lead were identified in three quarters of the population and then predicted the bone lead levels in the remaining one quarter and in the Community Lead Study.

Results: Eighteen predictors were selected for tibia (blood lead, age, education, occupation, smoking status, pack-years of cigarette, serum levels of phosphorus, uric acid, calcium, creatinine and total and high-density lipoprotein cholesterols, hematocrit, body mass index, systolic and diastolic blood pressure, and diagnoses of cancer and diabetes; R2 = 0.32) and 16 for patella lead (among the predictors included in the tibia model diagnosis of cancer, serum levels of calcium, and total cholesterol were not included in patella lead model, but diagnosis of hypertension was included; R2 = 0.34), respectively. The correlation coefficients between the observed and predicted values were 0.43 to 0.50 for tibia and 0.52 to 0.58 for patella lead in internal and external validation. We applied these predicted bone lead models to the Third National Health and Nutrition Examination Survey (NHANES-III) to examine associations with hypertension and found relatively more significant associations compared with blood lead.

Conclusions: This study suggests that the prediction equations may be used to predict bone lead levels in other community-based cohorts with reasonable accuracy.

Figure 1

Procedures of the model development and validation.

Figure 2

LASSO model selection for the tibia bone lead model. We performed the LASSO with the residuals corresponding to a linear regression model of tibia bone lead on the three pre-selected variables, age, education, and job-type (fitted on the training set) as response and using the remaining set of 25 potential predictors (smoking status was represented by two dummy variables (former and current smokers), thus, leading to a total of 26 LASSO iterations). The arrows on the graph point to the models selected by the LASSO operator. AIC, Akaike Information Criterion; BIC, Bayes information criterion; Cp, Mallows' Cp.

Figure 3

Validation of the prediction models (see Table 3) in test set 1. The numbers of subjects are 188 for tibia lead and 186 for patella lead. Figures in the upper panel show relationships of the observed versus the predicted values. The lines drawn on the plots indicate the equiangular observed=predicted line. The correlations of the observed versus the predicted are 0.454 for tibia lead and 0.524 for patella lead. The lower panels are scatterplots of the errors in prediction (observed – predicted).

Figure 4

Validation of the prediction models (see Table 5) in the Community Lead Study. The number of subjects is 84 for each tibia lead or patella lead. Figures in the upper panel show relationships of the observed versus the predicted values. The lines drawn on the plots indicate the equiangular observed=predicted line. The correlations of the observed versus the predicted are 0.431 for tibia lead and 0.575 for patella lead. The lower panels are scatterplots of the errors in prediction (observed – predicted).