Simple Skeletal Muscle Mass Estimation Formulas: What We Can Learn From Them

Abstract

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. Using the Harris-Benedict approach as a template, we systematically examined the relations between body size, body shape, age, and skeletal muscle mass (SM), the main anatomic feature of sarcopenia. The sample consisted of 12,330 non-Hispanic (NH) white and NH black participants in the US National Health and Nutrition Survey who had complete weight, height, waist circumference, age, and dual-energy X-ray (DXA) absorptiometry data. A conversion formula was used to derive SM from DXA-measured appendicular lean soft tissue mass. Weight, height, waist circumference, and age alone and in combination were significantly correlated with SM (all, p < 0.001). Advancing analyses through the aforementioned sequence of predictor variables allowed us to establish how at the anatomic level these body size, body shape, and age measures relate to SM much in the same way the Harris-Benedict equations provide insights into the structural origins of basal heat production. Our composite series of SM prediction equations should prove useful in modeling efforts and in generating hypotheses aimed at understanding how SM relates to body size and shape across the adult lifespan.

Effects of time-restricted feeding in weight loss, metabolic syndrome and cardiovascular risk in obese women

“…For body composition analysis, we used anthropometric prediction equations, which were validated by the National Health and Nutrition Examination Survey (NHANES). We used the equation of Lee et al [13] [SE (R 2 ) = 2.44 (0.93)] and Heymsfied et al [14] [SE (R 2 ) = 1.6 (0.87)] with weight, height, waist circumference (WC) and race/ethnicity data to calculate total body fat mass (FM) and total skeletal muscle mass (MM), respectively. The values of total FM were divided by the total body weight and height 2 , to obtain the body fat percentage (%BF) and fat mass index (FMI), respectively.…”

Nutrition Counseling in an Academic Medical Center According to Socioeconomic Status and Grocery Shopping Consumer Behavior Decreased Relative Fat Mass in Breast Cancer Survivors

“…A decrease in MUAC and TSF, but not in arm muscle area, was observed at the end of the study, meaning that in the arm area, there could have been greater mobilization of fat mass but not of skeletal muscle mass. On the contrary, according to Heymsfield et al recent equation 6 , skeletal muscle mass decreased during the intervention (Table S4), which may partially compromise the quality of life and prognosis of breast cancer survivors 1 .…”

“…Participants FM in kg were obtained from the RFM% and BW resultant. For SM assessment, we used one of the novel equations for women, recently proposed by Heymsfield et al and validated by DXA in 12,330 participants (r 2 = 0.89, p < 0.0001) (Equation S2) 6 . Likewise, we used anthropometric measurements to calculate the arm bone-free muscle area for women (Equation S3) 21 .…”

“…For these reasons, novel and reliable equations validated by dual-energy X-ray absorptiometry (DXA) could be used to assess body composition in adult cancer patients. Such equations have been recently proposed by Heymsfield et al for skeletal muscle mass (SM) 6 , and by Woolcott and Bergman for relative fat mass (RFM) 7 , additionally validated in Northwest Mexican adults by different reference methods along with DXA 5 . During and after cancer treatment, body composition assessment in breast cancer survivors is essential to design strategies and implement lifestyle interventions to prevent sarcopenic obesity 8 .…”

Estimating appendicular muscle mass in older adults with consideration on paralysis

“…Decreased muscle mass is often evaluated using anthropometric measurements in clinical practice during nutritional assessment. Several ASM estimating formulas using anthropometric measurements [12][13][14][15][16][17][18][19][20] have already been reported, but only few studies have focused on older adults. [17][18][19][20] In addition, given that paralyzed limbs have reduced muscle mass 21 and sarcopenia in paralyzed patients is a poor factor for ADL improvement, 22 the presence of paralysis is also an important factor for ASM estimation.…”