AIMS: To determine whether lean body mass (LBM), a possible surrogate of liver and kidney volumes, correlates with hepatic and renal drug clearances. METHODS: Twenty-one disease-free patients with a history of cancer and with normal hepatic and renal function were studied. Salivary pharmacokinetics of oral antipyrine (1200 mg) and 24 h creatinine clearance were determined following the determination of LBM by dual energy X-ray absorptiometry and the determination of liver and kidney volumes by helical CT scanning. RESULTS: Liver volume correlated with LBM (r2=0.21, P=0.04), body surface area (BSA) (r2=0.54, P<0.001), and total body weight (TBW) (r2=0.61, P<0.001). Kidney volume correlated with LBM (r2=0.49, P<0.001), BSA (r2=0.43, P=0.002) and TBW (r2=0.24, P=0.03). Stepwise multiple regression analysis, incorporating the independent variables of age, height, weight, sex, BSA, LBM, alcohol consumption, smoking status and liver volume and the dependent variable antipyrine clearance, indicated that LBM was the only independent correlate of antipyrine clearance. A stepwise multiple regression analysis with kidney volume in the independent variables, and creatinine clearance as dependent variable, showed that kidney volume and age were the only independent correlates of creatinine clearance. A nomogram using serum creatinine and LBM was comparable with the Cockcroft and Gault nomogram in calculating creatinine clearance. CONCLUSIONS: Of the anthropometric variables tested, LBM was the only determinant of antipyrine clearance, but this was not due to a relationship between LBM and liver volume. By contrast, the relationship between creatinine clearance and LBM appeared to be due to a relationship between LBM and kidney volume.
AIMS: To determine whether lean body mass (LBM), a possible surrogate of liver and kidney volumes, correlates with hepatic and renal drug clearances. METHODS: Twenty-one disease-free patients with a history of cancer and with normal hepatic and renal function were studied. Salivary pharmacokinetics of oral antipyrine (1200 mg) and 24 h creatinine clearance were determined following the determination of LBM by dual energy X-ray absorptiometry and the determination of liver and kidney volumes by helical CT scanning. RESULTS: Liver volume correlated with LBM (r2=0.21, P=0.04), body surface area (BSA) (r2=0.54, P<0.001), and total body weight (TBW) (r2=0.61, P<0.001). Kidney volume correlated with LBM (r2=0.49, P<0.001), BSA (r2=0.43, P=0.002) and TBW (r2=0.24, P=0.03). Stepwise multiple regression analysis, incorporating the independent variables of age, height, weight, sex, BSA, LBM, alcohol consumption, smoking status and liver volume and the dependent variable antipyrine clearance, indicated that LBM was the only independent correlate of antipyrine clearance. A stepwise multiple regression analysis with kidney volume in the independent variables, and creatinine clearance as dependent variable, showed that kidney volume and age were the only independent correlates of creatinine clearance. A nomogram using serum creatinine and LBM was comparable with the Cockcroft and Gault nomogram in calculating creatinine clearance. CONCLUSIONS: Of the anthropometric variables tested, LBM was the only determinant of antipyrine clearance, but this was not due to a relationship between LBM and liver volume. By contrast, the relationship between creatinine clearance and LBM appeared to be due to a relationship between LBM and kidney volume.
Authors: T Hallynck; H H Soep; J Thomis; J Boelaert; R Daneels; J P Fillastre; F De Rosa; E Rubinstein; M Hatala; J Spousta; L Dettli Journal: Clin Pharmacol Ther Date: 1981-09 Impact factor: 6.875
Authors: Kathleen A Meeske; Lingyun Ji; David R Freyer; Paul Gaynon; Kathleen Ruccione; Anna Butturini; Vassilios I Avramis; Stuart Siegel; Yousif Matloub; Nita L Seibel; Richard Sposto Journal: Pediatr Blood Cancer Date: 2015-07-14 Impact factor: 3.167
Authors: Sophie Cousin; A Hollebecque; S Koscielny; O Mir; A Varga; V E Baracos; J C Soria; S Antoun Journal: Invest New Drugs Date: 2013-12-17 Impact factor: 3.850