Rationale and Objectives
Skeletal muscle metabolism is a primary contributor to whole-body energy expenditure. Currently, methods to measure changes in skeletal muscle metabolism in vivo are limited. Our objectives were to characterize the regional variation in skeletal muscle and adipose tissue (AT) FDG uptake as a surrogate for glycolytic metabolism using 18 F-2-fluorodeoxyglucose (FDG)-positron emission tomography (PET)/computed tomography (CT) in healthy men and to correlate these findings to body mass index (BMI).
Materials and Methods
Eighteen healthy men were enrolled and underwent FDG-PET/CT. The mean standardized uptake value of 14 skeletal muscles and two AT regions was measured and linear regression analysis was performed to identify metabolic predictors of BMI.
Results
FDG-PET/CT reliably detected changes in skeletal muscle and AT depot metabolic activity based on location. The most metabolically active muscles were those used for posture and breathing, which have the highest percentage of reported type I muscle myofiber content. Visceral AT tended to have a higher FDG uptake than subcutaneous AT. The mean standardized uptake value of VAT, pectoralis major, and gluteus maximus muscles accounted for 64% of the variance in BMI.
Conclusions
FDG-PET/CT can be used to quantify the regional variation in glucose metabolism of multiple skeletal muscle groups and AT depots.
Introduction
Body mass is maintained by balancing changes in energy intake with energy expenditure. Skeletal muscle is a primary contributor to energy expenditure because of the substantial amount of energy used to maintain posture and to mediate locomotion. For example, skeletal muscle can account for up to 90% of the whole-body energy usage during physical activity . Skeletal muscle generates energy to power muscle contraction mostly via oxidation of glucose. Slow twitch (type I) muscle fibers are rich in mitochondria, rely on oxidative metabolism, and are resistant to fatigue, whereas fast twitch (type II) fibers more readily fatigue and contain more glycolytic enzymes.
Skeletal muscle metabolism is difficult to measure in vivo. Estimates can be made using blood gas exchange, indirect calorimetry, or serum metabolites such as glucose and lactate, but direct nutrient uptake is not routinely measured. Muscle biopsies can be done; however, these biopsies are invasive and only one skeletal muscle is usually assessed (most commonly the vastus lateralis [VL]).
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Materials and Methods
Study Sample
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FDG-PET/CT Image Acquisition
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FDG-PET/CT Image Analysis
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Data Analysis
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Results
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TABLE 1
Variation in Skeletal Muscle and Adipose SUV mean
SUV mean P Value Int 0.68 ± 0.02 — SM 0.67 ± 0.02 0.824 ES 0.67 ± 0.02 0.824 S 0.66 ± 0.03 0.606 SA 0.63 ± 0.02 0.029 GM 0.59 ± 0.02 0.005 IO 0.57 ± 0.02 0.000 MG 0.57 ± 0.03 0.002 PS 0.56 ± 0.02 0.000 PM 0.51 ± 0.02 0.000 RF 0.51 ± 0.02 0.000 AM 0.49 ± 0.02 0.000 VL 0.48 ± 0.02 0.000 RA 0.46 ± 0.02 0.000 SAT 0.24 ± 0.03 — VAT 0.30 ± 0.02 0.058
AM, adductor magnus; ES, erector spinae; GM, gluteus maximus; Int, intercostal; IO, internal oblique; MG, medial gastrocnemius; PM, pectoralis major; PS, psoas; RA, rectus abdominis; RF, rectus femoris; S, soleus; SA, serratus anterior; SAT, subcutaneous adipose tissue; SEM, standard error of the mean; SM, sternocleidomastoid; SUV mean , standardized uptake value; VAT, visceral adipose tissue; VL, vastus lateralis.
Data are mean SUV mean ± SEM for Int, SM, ES, S, SA, GM, IO, MG, PS, PM, RF, AM, VL, and RA muscles, as well as for SAT and VAT. Comparisons were made by Student t test using Int as a reference for muscles and SAT as a reference for adipose tissue depots.
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TABLE 2
Relationship of BMI to FDG-PET Tissue Metabolic Activity
Variable Pearson r Correlation β_b_ PM GM VAT BMI PM 0.149 −0.214 0.596 ** 15.198 ** 0.472 GM −0.331 0.517 * 8.922 0.332 VAT −0.556 * −7.749 −0.344 Intercept = 9.065R 2 = 0.64 **
BMI, body mass index; FDG, 18 F-2-fluorodeoxyglucose; GM, gluteus maximus; PET, positron emission tomography; PM, pectoralis major; VAT, visceral adipose tissue.
Pearson correlation values from a univariate analysis among independent and dependent variables (left) and the results of multivariate linear regression (right) for BMI based on independent variables: PM, GM, and VAT. Pearson r correlation values, unstandardized (β) regression coefficients, and standardized ( b ) regression coefficients are presented. Two-tailed P values were P < 0.05 and P < 0.01.
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Discussion
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Conclusions
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