OBJECTIVE: We sought to compare associations of patella lead, which may represent a unique cumulative and bioavailable lead pool, with other lead measures in models of renal function. METHODS: Renal function measures included blood urea nitrogen, serum creatinine, measured and calculated creatinine clearances, and urinary N-acetyl-beta-D-glucosaminidase (NAG) and retinol-binding protein. RESULTS: In 652 lead workers, mean (SD) blood, patella, and tibia lead were 30.9 (16.7) microg/dL, 75.1 (101.1) and 33.6 (43.4) microg Pb/g bone mineral, respectively, and were correlated (Spearman's r = 0.51-0.74). Patella lead was associated (P < 0.05) with NAG in all lead workers. In models of effect modification by age, higher patella lead also was associated with higher serum creatinine in older participants. Similar associations were observed for blood and tibia lead. CONCLUSIONS: Associations between patella lead and adverse renal outcomes were not unique; this may be due, in part, to high correlations among the lead biomarkers in this study.
OBJECTIVE: We sought to compare associations of patella lead, which may represent a unique cumulative and bioavailable lead pool, with other lead measures in models of renal function. METHODS: Renal function measures included blood ureanitrogen, serum creatinine, measured and calculated creatinine clearances, and urinary N-acetyl-beta-D-glucosaminidase (NAG) and retinol-binding protein. RESULTS: In 652 lead workers, mean (SD) blood, patella, and tibia lead were 30.9 (16.7) microg/dL, 75.1 (101.1) and 33.6 (43.4) microg Pb/g bone mineral, respectively, and were correlated (Spearman's r = 0.51-0.74). Patella lead was associated (P < 0.05) with NAG in all lead workers. In models of effect modification by age, higher patella lead also was associated with higher serum creatinine in older participants. Similar associations were observed for blood and tibia lead. CONCLUSIONS: Associations between patella lead and adverse renal outcomes were not unique; this may be due, in part, to high correlations among the lead biomarkers in this study.
Authors: Fellipe Augusto Tocchini de Figueiredo; Junia Ramos; Erika R Hashimoto Kawakita; Alina S Bilal; Frederico B de Sousa; William D Swaim; Joao P Mardegan Issa; Raquel F Gerlach Journal: Environ Sci Pollut Res Int Date: 2016-08-10 Impact factor: 4.223
Authors: Melanie C Buser; Susan Z Ingber; Nathan Raines; David A Fowler; Franco Scinicariello Journal: Int J Hyg Environ Health Date: 2016-01-28 Impact factor: 5.840
Authors: Virginia M Weaver; Lenworth R Ellis; Byung-Kook Lee; Andrew C Todd; Weiping Shi; Kyu-Dong Ahn; Brian S Schwartz Journal: Am J Ind Med Date: 2008-05 Impact factor: 2.214