Metabolically active components of fat‐free mass and resting energy expenditure in humans: recent lessons from imaging technologies

Müller, Manfred J.; Bosy‐Westphal, Anja; Kutzner, D; Heller, Martin

doi:10.1046/j.1467-789x.2002.00057.x

Cited by 212 publications

(189 citation statements)

References 36 publications

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“…The second equation uses fat-free mass, which is a more valid predictor than body mass of resting metabolic rate (RMR) because it is associated with a much higher rate of resting EE (Elia, 1992). Sophisticated methods can be used to provide more insight into the metabolically active components of fat-free mass, such as the liver, heart and kidney, in relation to energy metabolism (Muller et al, 2002), but their applicability to epidemiological studies is restricted. Anthropometry, a relatively simpler technique used to predict RMR, has an accuracy rate similar to that of more complicated techniques (Van der Ploeg et al, 2001).…”

Section: Discussionmentioning

confidence: 99%

Interindividual variability in sleeping metabolic rate in Japanese subjects

et al. 2007

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Introduction: Basal metabolic rate (BMR) or sleeping metabolic rate (SMR) is the largest component of total energy expenditure (EE). An accurate prediction of BMR or SMR is needed to accurately predict total EE or physical activity EE for each individual. However, large variability in BMR and SMR has been reported. Objectives: This study was designed to develop prediction equations using body size measurements for the estimation of both SMR and BMR and to compare the prediction errors with those in previous reports. Methods: We measured body size, height, weight and body composition (fat mass and fat-free mass) from skinfold thickness in adult Japanese men (n ¼ 71) and women (n ¼ 66). SMR was determined as the sum of EE during 8 h of sleep (SMR-8h) and minimum EE during 3 consecutive hours of sleep (SMR-3h) measured using two open-circuit indirect human calorimeters. BMR was determined using a human calorimeter or a mask and Douglas bag. Results: The study population ranged widely in age. The SMR/BMR ratio was 1.0170.09 (range 0.82-1.42) for SMR-8h and 0.9470.07 (range 0.77-1.23) for SMR-3h. The prediction equations for SMR accounted for a 3À5% larger variance with 2-3% smaller standard error of estimate (SEE) than the prediction equations for BMR. Discussion: SMR can be predicted more accurately than previously reported, which indicates that SMR interindividual variability is smaller than expected, at least for Japanese subjects. The prediction equations for SMR are preferable to those for BMR because the former exhibits a smaller prediction error than the latter.

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Section: Discussionmentioning

confidence: 99%

Interindividual variability in sleeping metabolic rate in Japanese subjects

et al. 2007

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“…[1][2][3] Fat-free mass (FFM) is considered to be the best single predictor of REE. 4 However, anatomically as well as metabolically, FFM is heterogeneous.…”

Section: Introductionmentioning

confidence: 99%

“…It consists of organs with a high metabolic rate (840-1848 kJ/kg/day) and it also comprises skeletal muscle (MM) or bone mass with a low specific metabolic rate (o60 kJ/kg/day). [1][2][3] The relative proportions of these components are changing with increasing FFM, leaving a higher contribution to low metabolically active MM due to its disproportionate increase. 3 Therefore, the regression line of REE vs FFM has a considerable yintercept, and its slope decreases with increasing FFM.…”

Section: Introductionmentioning

confidence: 99%

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Effect of organ and tissue masses on resting energy expenditure in underweight, normal weight and obese adults

Bosy‐Westphal¹,

Reinecke²,

Schlörke³

et al. 2003

Int J Obes

119

118

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BACKGROUND:In normal-weight subjects, resting energy expenditure (REE) can be accurately calculated from organ and tissue masses applying constant organ-specific metabolic rates. This approach allows a precise correction for between-subjects variation in REE, explained by body composition. Since a decrease in organ metabolic rate with increasing organ mass has been deduced from interspecies comparison including human studies, the validity of the organ-and tissue-specific REE calculation remains to be proved over a wider range of fat-free mass (FFM). DESIGN: In a cross-sectional study on 57 healthy adults (35 females and 22 males, 19-43 y; 14 underweight, 25 intermediate weight and 18 obese), magnetic resonance imaging (MRI) and dual-energy X-ray absorptiometry (DXA) were used to assess the masses of brain, internal organs, skeletal muscle (MM), bone and adipose tissue. REE was measured by indirect calorimetry (REEm) and calculated from detailed organ size determination by MRI and DXA (REEc1), or in a simplified approach exclusively from DXA (REEc2). RESULTS: We found a high agreement between REEm and REEc1 over the whole range of FFM (28-86 kg). REE prediction errors were À177505, À1457514 and À14171058 kJ/day in intermediate weight, underweight and obese subjects, respectively (n.s.). Regressing REEm on FFM resulted in a significant positive intercept of 1.6 MJ/day that could be reduced to 0.5 MJ/day by adjusting FFM for the proportion of MM/organ mass. In a multiple regression analysis, MM and liver mass explained 81% of the variance in REEm. DXA-derived REE prediction showed a good agreement with measured values (mean values for REEm and REEc2 were 5.7271.87 and 5.8271.51 MJ/day; difference n.s.). CONCLUSION: Detailed analysis of metabolically active components of FFM allows REE prediction over a wide range of FFM. The data provide indirect evidence for a view that, for practical purposes within humans, the specific metabolic rate is constant with increasing organ mass. Nonlinearity of REE on FFM was partly explained by FFM composition. A simplified REE prediction algorithm from regional DXA measurements has to be validated in future studies.

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“…76 Those who recovered from MetS following weight loss had a significantly lower adjusted BMR when compared with those who still had MetS and those who never had the MetS. While the contribution of high-activity organ tissue mass 77 to low-activity skeletal mass 78 were not controlled for, these data provide some argument for a role of energy metabolism in the etiopathogenesis of the syndrome. 76 Interestingly in that analysis, those who showed the greatest decrease in the number of MetS components also showed the greatest decline in BMR.…”

Section: Vitamin D Pth Mets and Adiponectinmentioning

confidence: 89%

Factors determining the risk of the metabolic syndrome: is there a central role for adiponectin?

2013

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EK Calton, VS Miller and MJ SoaresBACKGROUND AND OBJECTIVES: The pathogenesis of the metabolic syndrome (MetS) is not well understood. This review is based on the hypothesis that both traditional and emerging risk factors act through adiponectin. SUBJECTS AND METHODS:We conducted a search of the literature using prominent electronic databases and search terms that included in combination: adiponectin, diet, dietary patterns, exercise, metabolic rate, MetS and testosterone. Articles were restricted to studies conducted on adult humans, reported in English and within the time period 2000-2012. RESULTS AND CONCLUSIONS: Both traditional and emerging risk factors associated with the MetS show some evidence of exerting their influence through adiponectin. High-quality randomized controlled trials that alter adiponectin levels are required to further corroborate this hypothesis. INTRODUCTIONThe metabolic syndrome (MetS) represents a clustering of risk factors for cardiovascular disease and type 2 diabetes mellitus (T2DM), which include: hypertension, low high-density lipoproteincholesterol (HDL-C), high triglycerides (TG), high fasting blood glucose (FBG) and abdominal obesity. 1 Obesity and insulin resistance (IR) have long been accepted as having a pivotal role in the etiopathogenesis of the syndrome. 2,3 However, emerging evidence indicates that even after controlling for these variables, there remains a substantial unexplained risk. 2,[4][5][6] Adiponectin, a collagen-like protein was first discovered in 1995 by Dcherer and Lodish, who called it adipocyte complementrelated protein of 30 kDa (Acrp30). 7 At a similar time, three other independent groups discovered adiponectin, calling it adipose most abundant gene transcript 1 (apM1), AdipoQ and gelatinbinding protein of 28 kDa (GBP28). 8 Adiponectin is secreted primarily from adipose tissue into the circulation where highmolecular-weight, medium-molecular-weight and low-molecularweight oligomeric forms exist. 8,9 Total adiponectin as well as the individual oligomeric forms have been associated with health benefits. 9 Consistent links between low adiponectin levels and increased TG, FBG, waist circumference, blood pressure (BP) levels and decreased HDL-C explain its potential role in the etiology of MetS. 2,10 Furthermore, genetic studies have linked adiponectin and MetS in individuals of European descent. 11 Specifically, overall adiposity, abdominal adiposity and IR were influenced by gene loci, which also encode adiponectin. 12 This review discusses both traditional and emerging factors that may influence the prevalence of MetS. Furthermore, the possibility that adiponectin may have an important role in the pathogenesis of the syndrome was evaluated. As there is inconsistency in the literature whether total, high-molecular-weight, mediummolecular-weight or low-molecular-weight adiponectin was measured, 4,13,14 this review discusses adiponectin in general terms. We present a model that emphasizes the flow-through effects of several factors on MetS risk through adi...

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Metabolically active components of fat‐free mass and resting energy expenditure in humans: recent lessons from imaging technologies

Cited by 212 publications

References 36 publications

Interindividual variability in sleeping metabolic rate in Japanese subjects

Interindividual variability in sleeping metabolic rate in Japanese subjects

Effect of organ and tissue masses on resting energy expenditure in underweight, normal weight and obese adults

Factors determining the risk of the metabolic syndrome: is there a central role for adiponectin?

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