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| Reference Man and four phenotypic variations in height and waist circumference (WC) that relate to skeletal muscle mass (SM) differences when weight and age are held constant. The SM values were generated from the Series 2 model for NH white men, waist circumferences from a NHANES model based on weight, height, and age, and the images from a software program provided to the authors by Dr. Brian Curless at the University of Washington.
Source publication
One century ago Harris and Benedict published a short report critically examining the relations between body size, body shape, age, and basal metabolic rate. At the time, basal metabolic rate was a vital measurement in diagnosing diseases such as hypothyroidism. Their conclusions and basal metabolic rate prediction formulas still resonate today. Us...
Context in source publication
Context 1
... circumference adds to weight, height, and age to define body shape and body composition that relate to SM. To visualize an example of these effects, a human avatar was generated with the three measures of body size (weight, height, and waist circumference) and age of Reference Man (Figure 1, left). The SM shown in the figure was calculated using the Series 2 regression model for NH white men. ...
Similar publications
BACKGROUND
Sarcopenia is the loss of skeletal muscle mass (SMM) and is a sign of cancer
cachexia. Patients with advanced renal cell carcinoma (RCC) may show cachexia.
AIM
To evaluate the amount of SMM in male clear cell RCC (ccRCC) patients with and
without collateral vessels.
METHODS
In this study, we included a total of 124 male Caucasian patient...
Citations
... Body fat percent were classified according to the tables from Suverza Fernandez et al. [11], whereas muscle mass was according to the following procedure. From the Tanita results, total muscle mass (kg) was converted to total skeletal muscle mass (kg) using the equations by Heymsfield et al. [12]: for men: skeletal muscle mass = 0.46(weight in kg) + 0.03(height in cm) + 0.013(age in years) -0.0006(age 2 ) -0.28(waist circumference in cm) + 13.8; for women: skeletal muscle mass = 0.24(weight in kg) + 0.09(height in cm) + 0.097(age in years) + 0.0004(age 2 ) -0.06(waist circumference in cm) -3.9. Afterwards, the relative skeletal muscle mass (%) was calculated by taking skeletal muscle mass divided by body weight multiplied by 100. ...
... SMM can be evaluated using an estimation equation involving appendicular lean soft tissue mass estimated using DXA [2,4]. It can also be assessed using MRI or CT [5]. According to recent validation studies, SMM can also be estimated qualitatively and accurately using the creatine (methyl-d 3 ) dilution (D 3 -creatine) method [6,7]. ...
Background:
Bioelectrical impedance analysis (BIA) is a minimally invasive, safe, easy, and quick technology used to determine body composition.
Objective:
We compared the relationship among impedance indices obtained using single-frequency BIA, multi-frequency BIA, bioelectrical impedance spectroscopy (BIS), and skeletal muscle mass (SMM) of physically active young men and athletes using the creatine (methyl-d3) dilution method. We also compared the SMM and intracellular water (ICW) of athletes and active young men measured using a reference stable isotope dilution and BIS method, respectively.
Methods:
We analyzed data of 28 men (mean age, 20 ± 2 years) who exercised regularly. Single-frequency BIA at 5 kHz and 50 kHz (R5 and R50), multi-frequency BIA (R250-5), and BIS (RICW) methods of determining the SMM were compared. The deuterium and bromide dilution methods of obtaining the total body water, ICW, and extracellular water measurements were also used, and the results were compared to those acquired using bioimpedance methods.
Results:
The correlation coefficients between SMM and L2/R5, L2/R50, L2/R250-5, and L2/RICW were 0.738, 0.762, 0.790, and 0.790, respectively (p < 0.01). The correlation coefficients between ICW and L2/R5, L2/R50, L2/R250-5, and L2/RICW were 0.660, 0.687, 0.758, and 0.730, respectively (p < 0.001). However, the correlation coefficients of L2/R50, L2/R250-5, and L2/RICW for SMM and ICW were not significantly different.
Conclusions:
Our findings suggest that single-frequency BIA at L2/R50, multi-frequency BIA, and BIS are valid for assessing the SMM of athletes and active young men. Additionally, we confirmed that the SMM and ICW were correlated with single-frequency BIA, multi-frequency BIA, and BIS. Bioimpedance technologies may be dependable and practical means for assessing SMM and hydration compartment status of active young adult males, however, cross-validation is needed.
... It's worth noting that lean mass from DXA includes both non-bone and non-fat tissue. ASM is defined as the sum of the lean mass of arms and legs (17). HGS was measured by a handgrip dynamometer. ...
Background
The muscle quality index (MQI), as an important component of sarcopenia, is defined as the ratio of muscle strength to muscle mass. Lung function, is a clinical indicator to assess ventilation and air exchange function. This study investigated the relationship between lung function indices and MQI in the NHANES database from 2011 to 2012.
Methods
This study included 1,558 adults from the National Health and Nutrition Examination Survey from 2011 to 2012. Muscle mass and muscle strength were assessed using DXA and handgrip strength, and all participants underwent pulmonary function measurements. Multiple linear regression and multivariable logistic regression were used to assess the correlation between the MQI and lung function indices.
Results
In the adjusted model, MQI was significantly correlated with FVC% and PEF%. And, after quartiles of MQI in Q3, where FEV1%, FVC%, and PEF% were all associated with MQI, in Q4, a lower relative risk of a restrictive spirometry pattern was linked to increased MQI. Compared to the lower age group, the relationship between the MQI and lung function indices was more significant in the higher age group.
Conclusion
There was an association between the MQI and lung function indices. Furthermore, in the middle-aged and older adult populations, lung function indicators and restrictive ventilation impairment were significantly associated with MQI. This implies that improving lung function through muscle training may be beneficial to this group.
... The inclusion of each component present in our models is reported in the literature as determinants for ASM prediction. Body weight and height are important predictors of SMM, as a function of body dimensions (Heymsfield et al., 2020). When height and body weight change, SMM changes. ...
... When height and body weight change, SMM changes. Therefore, taller people tend to have greater body weight and higher SMM values than those less tall (Heymsfield et al., 2020). Lipodystrophy was included in our models considering that it has been identified as an independent risk factor for ASM alterations, negatively influencing the SMM Foss-Freitas et al., 2020). ...
... Low values for lower limb fat percentage and lower FMR are commonly reported in PWH with lipodystrophy (Foss-Freitas et al., 2020). Thus, greater ASM losses may be explained, in part, by the lower distribution of adipose tissue in these regions (Foss-Freitas et al., 2020;Heymsfield et al., 2020). Furthermore, when combined with a longer time of HIV diagnosis (female sex), it can be indicated that these losses result from the chronic effects of this disease (Brown et al., 2009). ...
People living with HIV (PWH) experience an accelerated aging process. There is no anthropometric predictive model for appendicular skeletal muscle mass (ASM) in PWH. This study develops anthropometric models to predict and validate ASM measured by dual energy x-ray absorptiometry (DXA) in PWH; DXA scans were obtained for 125 PWH (male = 74; age >18 years) on antiretroviral therapy. Fat mass ratio was used for lipodystrophy diagnosis. A multiple stepwise linear regression considered ASMDXA as the dependent variable and validated by PRESS method. A high power of determination and low standard estimate error were found for ASMDXA-predicted (adjusted r2 = 0.84 to 0.87, standard estimate error = 1.7-1.6 kg) and high PRESS validation coefficients (Q2PRESS = 0.84-0.86, SPRESS = 1.7-1.6 kg). The variables included were lipodystrophy diagnosis, medial calf circumference, sex, and total body weight. We present novel, reliable, and validated anthropometric models to predict ASMDXA in PWH.
... Matiegka models were developed using information on skeletal muscle mass proportions obtained from human cadavers. The development of noninvasive reference methods for quantifying skeletal muscle mass in vivo during the 1970s and 1980s [4], such as computed tomography, MRI, and DXA, led to further development of anthropometric approaches for predicting a person's muscle mass [5][6][7][8][9]. These reference methods were used to estimate regional or whole-body skeletal muscle mass that were set as dependent variables in anthropometric prediction 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 models. ...
... These reference methods were used to estimate regional or whole-body skeletal muscle mass that were set as dependent variables in anthropometric prediction 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 models. Predictor variables in these models typically included race/ethnicity, sex, age, weight, height, and multiple body circumferences [5][6][7][8][9]. The recent introduction and refinement of 3-dimensional (3D) optical imaging methods has stimulated new interest in digital anthropometric prediction of body components such as total body fat mass [2,[10][11][12]. ...
Background:
Recent three-dimensional optical (3DO) imaging advancements have provided more accessible, affordable, and self-operating opportunities for assessing body composition. 3DO is accurate and precise with respect to clinical measures made by dual-energy X-ray absorptiometry (DXA). However, the sensitivity for monitoring body composition change over time with 3DO body shape is unknown.
Objective:
To evaluate 3DO's ability to monitor body composition changes across multiple intervention studies.
Methods:
A retrospective analysis was performed using intervention studies on healthy adults that were complimentary to the cross-sectional study, Shape Up! Adults. Each participant received a DXA (Hologic Discovery/A system) and 3DO (Fit3D ProScanner) scan at baseline and follow-up. 3DO meshes were digitally registered and reposed using Meshcapade to standardize the vertices and pose. Using an established statistical shape model, each 3DO mesh was transformed into principal components (PCs), which were used to predict whole-body and regional body composition values using published equations. Body composition changes (follow-up minus baseline) were compared to DXA with linear regression.
Results:
The analysis included 133 participants (45 females) in six studies. The mean (SD) length of follow-up was 13 (5) weeks, range 3-23 weeks. Agreement between 3DO and DXA (R2) for changes in total fat mass (FM), total fat-free mass (FFM), and appendicular lean mass, respectively, were 0.86, 0.73, and 0.70 with RMSEs of 1.98 kg, 1.58 kg, and 0.37 kg in females, and 0.75, 0.75, and 0.52 with RMSEs of 2.31 kg, 1.77 kg, and 0.52 kg in males. Further adjustment with demographic descriptors improved the 3DO change agreement to changes observed with DXA.
Conclusions:
As compared to DXA, 3DO was highly sensitive in detecting body shape changes over time. The 3DO method was sensitive enough to detect even small changes in body composition during intervention studies. The safety and accessibility of 3DO allows users to self-monitor on a frequent basis throughout interventions.
Clinical trials number:
Shape Up! Adults (NCT03637855, https://clinicaltrials.gov/ct2/show/NCT03637855)Macronutrients and Body Fat Accumulation: A Mechanistic Feeding Study (NCT03394664, https://clinicaltrials.gov/ct2/show/NCT03394664)Resistance Exercise and Low-Intensity Physical Activity Breaks in Sedentary Time to Improve Muscle and Cardiometabolic Health (NCT03771417, https://clinicaltrials.gov/ct2/show/NCT03771417)Time Restricted Eating on Weight Loss (NCT03393195, https://clinicaltrials.gov/ct2/show/NCT03393195)Trial of Testosterone Undecanoate for Optimizing Performance During Military Operations (NCT04120363, https://clinicaltrials.gov/ct2/show/NCT04120363).
... Matiegka models were developed using information on skeletal muscle mass proportions obtained from human cadavers. The development of noninvasive reference methods for quantifying skeletal muscle mass in vivo during the 1970s and 1980s [4], such as computed tomography, MRI, and DXA, led to further development of anthropometric approaches for predicting a person's muscle mass [5][6][7][8][9]. These reference methods were used to estimate regional or whole-body skeletal muscle mass that were set as dependent variables in anthropometric prediction models. ...
... These reference methods were used to estimate regional or whole-body skeletal muscle mass that were set as dependent variables in anthropometric prediction models. Predictor variables in these models typically included race/ethnicity, sex, age, weight, height, and multiple body circumferences [5][6][7][8][9]. ...
Background:
Skeletal muscle is a large and clinically relevant body component that has been difficult and impractical to quantify outside of specialized facilities. Advances in smartphone technology now provide the opportunity to quantify multiple body surface dimensions such as circumferences, lengths, surface areas, and volumes.
Objectives:
This study aimed to test the hypothesis that anthropometric body measurements acquired with a smartphone application can be used to accurately estimate an adult's level of muscularity.
Methods:
Appendicular lean mass (ALM) measured by DXA served as the reference for muscularity in a sample of 322 adults. Participants also had digital anthropometric dimensions (circumferences, lengths, and regional and total body surface areas and volumes) quantified with a 20-camera 3D imaging system. Least absolute shrinkage and selection operator (LASSO) regression procedures were used to develop the ALM prediction equations in a portion of the sample, and these models were tested in the remainder of the sample. Then, the accuracy of the prediction models was cross-validated in a second independent sample of 53 adults who underwent ALM estimation by DXA and the same digital anthropometric estimates acquired with a smartphone application.
Results:
LASSO models included multiple significant demographic and 3D digital anthropometric predictor variables. Evaluation of the models in the testing sample indicated respective RMSEs in women and men of 1.56 kg and 1.53 kg and R2's of 0.74 and 0.90, respectively. Cross-validation of the LASSO models in the smartphone application group yielded RMSEs in women and men of 1.78 kg and 1.50 kg and R2's of 0.79 and 0.95; no significant differences or bias between measured and predicted ALM values were observed.
Conclusions:
Smartphone image capture capabilities combined with device software applications can now provide accurate renditions of the adult muscularity phenotype outside of specialized laboratory facilities. Am J Clin Nutr 2023;x:xx. This trial was registered at clinicaltrials.gov as NCT03637855 (https://clinicaltrials.gov/ct2/show/NCT03637855), NCT05217524 (https://clinicaltrials.gov/ct2/show/NCT05217524), and NCT03771417 (https://clinicaltrials.gov/ct2/show/NCT03771417).
... Further, reference data for some populations is available (Buffa et al. 2014). Anthropometric equations with enhanced validity (R 2 over 0.90 and standard errors of the estimate as low as 1-2 kg compared to DXA or NMR) have been developed for total and appendicular mass measurements in some populations, including middle-aged adults (Al-Gindan et al. 2014;Lee et al. 2017;Heymsfield et al. 2020;Kawakami et al. 2021). These equations rely on simple measurements such as height, body weight, and a few circumferences and skinfolds. ...
Given their importance in predicting clinical outcomes, cardiorespiratory fitness (CRF) and muscle status can be considered new vital signs. However, they are not routinely evaluated in healthcare settings. Here, we present a comprehensive review of the epidemiological, mechanistic, and practical bases of the evaluation of CRF and muscle status in adults in primary healthcare settings. We highlight the importance of CRF and muscle status as predictors of morbidity and mortality, focusing on their association with cardiovascular and metabolic outcomes. Notably, adults in the best quartile of CRF and muscle status have as low as one-fourth the risk of developing some of the most common chronic metabolic and cardiovascular diseases than those in the poorest quartile. The physiological mechanisms that underlie these epidemiological associations are addressed. These mechanisms include the fact that both CRF and muscle status reflect an integrative response to the body function. Indeed, muscle plays an active role in the development of many diseases by regulating the body’s metabolic rate and releasing myokines, which modulate metabolic and cardiovascular functions. We also go over the most relevant techniques for assessing peak oxygen uptake as a surrogate of CRF and muscle strength, mass, and quality as surrogates of muscle status in adults. Finally, a clinical case of a middle-aged adult is discussed to integrate and summarize the practical aspects of the information presented throughout. Their clinical importance, the ease with which we can assess CRF and muscle status using affordable techniques, and the availability of reference values, justify their routine evaluation in adults across primary healthcare settings.
... Also, other five calculated scores were recorded: BMI, waist/hip ratio (W/H), Relative Fat Mass (RFM), 16 waist-to-height ratio (W/Ht), and Muscle Mass estimation (MMest). 17 The measurements were taken according to the Official Mexican Norm by a single researcher (LOC), who is a qualified expert for Clinical Nutrition, trained in anthropometry procedures. All anthropometric measurements were obtained the same day as the clinical interview, using a portable device body composition monitor and scale (OMRON Healthcare Co. LTD. ...
Introduction
The type of body composition modulates the severity of some musculoskeletal conditions, in fibromyalgia syndrome (FMS), this type of association remains relatively unexplored.
Objective
To analyze the association between the type of body composition and FMS using Principal Component Analysis (PCA). The FMS clinical outcome measures were: Symptom Severity Scale (SSS), Widespread Pain Index (WPI; and Fibromyalgia Impact Questionnaire (FIQ).
Methods
Forty-three women with FMS (ACR 2010 criteria) were clinically and anthropometrically evaluated. The anthropometric data were integrated into two indicators using a PCA methodology (PCA-Fat and PCA-muscle). Additionally, the patients were classified into high and low categories for each clinical indicator, which were used as dependent variables in binomial logistic regression (BLR) models.
Results
We found a positive correlation between PCA-Fat with WPI (r = 0.326, P = .043) and FIQ (r = 0.325, P = .044), and negative correlation (r = −0.384, P = .013) between PCA-muscle and SSS. In the BLR analysis, PCA-Fat was a significant predictor for high WPI (OR = 2.477, P = .038); while for high SSS, PCA-muscle (OR = 0.303, P = .009) was an inversely significant predictor.
Conclusions
The results suggest that the volume of fat mass can negatively modulate the severity of FMS. We propose that the evaluation of body composition should be a basic element for the clinical approach of patients with FMS.
... Therefore, Bozeman et al. [9] have previously proposed a model (Table 1, first row) for the waist circumference estimation (from body mass index and demographic covariates such as sex, age, and ethnicity) that can be used to identify individuals at risk for cardiometabolic disease when waist circumference data are unavailable. The estimated and measured values of the waist circumference are also useful for the anthropometric prediction of lean, fat [10,11], and muscle mass [12,13]. Table 1 also reports recently published equations [10][11][12][13] for the anthropometric prediction (from waist circumference, weight, height, sex, age, and ethnicity) of lean mass, fat mass, and muscle mass: these equations are practical to apply in clinical settings and should be systematically incorporated into the routine management of patients with different health risks or conditions. ...
... The estimated and measured values of the waist circumference are also useful for the anthropometric prediction of lean, fat [10,11], and muscle mass [12,13]. Table 1 also reports recently published equations [10][11][12][13] for the anthropometric prediction (from waist circumference, weight, height, sex, age, and ethnicity) of lean mass, fat mass, and muscle mass: these equations are practical to apply in clinical settings and should be systematically incorporated into the routine management of patients with different health risks or conditions. ...
This review summarizes body circumference-based anthropometrics that are in common use for research and in some cases clinical application. These include waist and hip circumference-based central body indices to predict cardiometabolic risk: waist circumference, waist-to-hip ratio, waist-to-height ratio, waist-to-thigh ratio, body adiposity index, a body shape index (ABSI), hip index (HI), and body roundness index (BRI). Limb circumference measurements are most often used to assess sarcopenia and include: thigh circumference, calf circumference, and mid-arm circumference. Additionally, this review presents fascinating recent developments in optic-based imaging technologies that have elucidated changes over the last decades in average body size and shape in European populations. The classical apple and pear shape concepts of body shape difference remain useful, but novel and exciting 3-D optical “e-taper” measurements provide a potentially powerful new future vista in anthropometrics.
... There has also been research to systematically examine the relationship between body size, body shape, age, and SMM. [5] found that body weight, height, waist circumference, and age alone and in combination were significantly correlated with SMM (all, p < 0.001). ...
In addition to body weight and Body Mass Index (BMI), body composition is an essential data point that allows people to understand their overall health and body fitness. However, body composition is largely made up of muscle, fat, bones, and water, which makes estimation not as easy and straightforward as measuring body weight. In this paper, we introduce a multimodal multi-task deep neural network to estimate body fat percentage and skeletal muscle mass by analyzing facial images in addition to a person's height, gender, age, and weight information. Using a dataset representative of demographics in Japan, we confirmed that the proposed approach performed better compared to the existing methods. Moreover, the multi-task approach implemented in this study is also able to grasp the negative correlation between body fat percentage and skeletal muscle mass gain/loss.
