UNLABELLED: Skeletal muscle glucose utilization (SMGU) can be measured by dynamic PET imaging with (18)F-FDG to characterize insulin resistance. The aim of this study was to determine the validity of simple methods to quantify SMGU by static PET imaging. METHODS: Ten patients underwent dynamic (18)F-FDG PET of the femoral region during hyperinsulinemic euglycemic clamping. SMGU was determined by Patlak graphical analysis using data from dynamic imaging with frequent arterial blood sampling. Standardized uptake values (SUVs) were calculated at 45 and 55 min after tracer injection. Skeletal muscle-to-background ratio (SM/B ratio), tissue count divided by venous plasma activity, was also computed at 45 and 55 min. These simple indices were compared by linear regression with the SMGU measured as above, and an estimated SMGU was obtained using the regression equation thus generated, together with a simple index. RESULTS: SMGU was highly correlated with SUVs (r = 0.941 at 45 min, r = 0.951 at 55 min) and SM/B ratios (r = 0.968 at 45 min, r = 0.984 at 55 min). Although SMGU was almost proportional to SM/B ratios, the y-intercepts of the regression lines for SUVs significantly differed from zero. The residual in estimating SMGU using the regression equation was marginally smaller for SM/B ratios than for SUVs and for indices at 55 min than at 45 min, but these differences did not reach statistical significance. Correction for plasma glucose level slightly elevated the correlation coefficients between SMGU and these simple indices. CONCLUSION: It is proposed that the simple quantitative indices, SUV and the SM/B ratio, are reliable indicators of SMGU during hyperinsulinemic euglycemic clamping. Static imaging with or without a single venous blood sampling may therefore be able to replace dynamic imaging with frequent arterial blood sampling, offering substantially greater convenience in evaluating insulin resistance.
UNLABELLED: Skeletal muscle glucose utilization (SMGU) can be measured by dynamic PET imaging with (18)F-FDG to characterize insulin resistance. The aim of this study was to determine the validity of simple methods to quantify SMGU by static PET imaging. METHODS: Ten patients underwent dynamic (18)F-FDG PET of the femoral region during hyperinsulinemic euglycemic clamping. SMGU was determined by Patlak graphical analysis using data from dynamic imaging with frequent arterial blood sampling. Standardized uptake values (SUVs) were calculated at 45 and 55 min after tracer injection. Skeletal muscle-to-background ratio (SM/B ratio), tissue count divided by venous plasma activity, was also computed at 45 and 55 min. These simple indices were compared by linear regression with the SMGU measured as above, and an estimated SMGU was obtained using the regression equation thus generated, together with a simple index. RESULTS: SMGU was highly correlated with SUVs (r = 0.941 at 45 min, r = 0.951 at 55 min) and SM/B ratios (r = 0.968 at 45 min, r = 0.984 at 55 min). Although SMGU was almost proportional to SM/B ratios, the y-intercepts of the regression lines for SUVs significantly differed from zero. The residual in estimating SMGU using the regression equation was marginally smaller for SM/B ratios than for SUVs and for indices at 55 min than at 45 min, but these differences did not reach statistical significance. Correction for plasma glucose level slightly elevated the correlation coefficients between SMGU and these simple indices. CONCLUSION: It is proposed that the simple quantitative indices, SUV and the SM/B ratio, are reliable indicators of SMGU during hyperinsulinemic euglycemic clamping. Static imaging with or without a single venous blood sampling may therefore be able to replace dynamic imaging with frequent arterial blood sampling, offering substantially greater convenience in evaluating insulin resistance.
Authors: Kari K Kalliokoski; Robert Boushel; Henning Langberg; Celena Scheede-Bergdahl; Ann Kathrine Ryberg; Simon Døssing; Andreas Kjær; Michael Kjær Journal: Front Physiol Date: 2011-10-25 Impact factor: 4.566
Authors: Bryan Haddock; Søren Holm; Jákup M Poulsen; Lotte H Enevoldsen; Henrik B W Larsson; Andreas Kjær; Charlotte Suetta Journal: Eur J Nucl Med Mol Imaging Date: 2016-09-08 Impact factor: 9.236
Authors: Keunyoung Kim; In-Joo Kim; Kyoungjune Pak; Taewoo Kang; Young Mi Seol; Young Jin Choi; Hyojeong Kim Journal: BMC Cancer Date: 2021-10-27 Impact factor: 4.430