Factors influencing variation in basal metabolic rate include fat-free mass, fat mass, age, and circulating thyroxine but not sex, circulating leptin, or triiodothyronine

Johnstone, Alexandra; Murison, Sandra D; Duncan, Jackie; Rance, K. A.; Speakman, John R.

doi:10.1093/ajcn/82.5.941

Cited by 397 publications

(348 citation statements)

References 43 publications

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“…The equations of Elia and Livesey (26) were used to derive RMR. Details of 98 calibration burns and repeatability testing have been described previously (27). 99…”

Section: Resting Metabolic Rate 92mentioning

confidence: 99%

Modelling the associations between fat-free mass, resting metabolic rate and energy intake in the context of total energy balance

et al. 2015

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“…The equations of Elia and Livesey (26) were used to derive RMR. Details of 98 calibration burns and repeatability testing have been described previously (27). 99…”

Section: Resting Metabolic Rate 92mentioning

confidence: 99%

Modelling the associations between fat-free mass, resting metabolic rate and energy intake in the context of total energy balance

et al. 2015

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“…BMR was calculated from minute-by-minute data, using the equations of Elia and Livesey (1992), using the mean of 15 min of stable measurements, with the first and last 5 min excluded. Details of calibration burns and repeatability testing have been described previously (Johnstone et al, 2005).…”

Section: Measurement Of Baseline Anthropometry and Bmrmentioning

confidence: 99%

Additional anthropometric measures may improve the predictability of basal metabolic rate in adult subjects

Johnstone¹,

Rance²,

Murison³

et al. 2006

Eur J Clin Nutr

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Background:The most commonly used predictive equation for basal metabolic rate (BMR) is the Schofield equation, which only uses information on body weight, age and sex to derive the prediction. However, because body composition is a key influencing factor, there will be error in calculating an individual's basal requirements based on this prediction. Objective: To investigate whether adding additional anthropometric measures to the standard measures can enhance the predictability of BMR and to cross-validate this within a separate subgroup. Design: Cross-sectional study of 150 Caucasian adults from Scotland, with a body mass index range of 16.7-49.3 kg/m 2 . All subjects underwent measurement of BMR, body composition, and 148 also had basic skinfold and circumference measures taken. The resultant equation was tested in a subgroup of 39 obese males. Results: The average difference between the predicted (Schofield equation) and measured BMR was 502 kJ/day. There was a slight systematic bias in this error, with the Schofield equation underestimating the lowest values. The average discrepancy between predicted and actual BMR was reduced to 452 kJ/day, with the addition of fat mass, fat-free mass, an overall 10% improvement on the Schofield equation (P ¼ 0.054). Using an equation derived from principal components analysis of anthropometry measurements similarly decreased the difference to 458 kJ/day (P ¼ 0.039). Testing the equation in a separate group indicated a 33% improvement in predictability of BMR, compared to the Schofield equation. Conclusions: In the absence of detailed information on body composition, utilizing anthropometric data provides a useful alternative methodology to improve the predictability of BMR beyond that achieved from the standard Schofield prediction equation. This should be confirmed in more individuals, both within the obese and normal weight category.

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“…This increase is mainly explained by an increase in fat mass and fat-free mass as well. In obese, the resting energy expenditure can be influenced mainly by fat-free mass, which is the largest contributing factor of variance of the basal metabolic rate (50-60%), and fat mass which represents (5-6%) of influence on the variation basal metabolic rate (27). The respiratory quotient of the two groups decreased after 12 months follow-up, indicating a change in metabolism of energy substrates.…”

Section: Discussionmentioning

confidence: 98%

Body composition, resting energy expenditure and inflammatory markers: impact in users of depot medroxyprogesterone acetate after 12 months follow-up

Batista

Souza

Marin

et al. 2017

Arch. Endocrinol. Metab.

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Objective: The aim of this study was to evaluate for 12 months the changes of body weight using Depot Medroxyprogesterone Acetate (DMPA) and if these changes are related to inflammatory markers. Subjects and methods: Twenty women of childbearing age who chose the DMPA, without previous use of this method, BMI < 30 kg/m², and 17 women using IUD TCu 380A, participated in the study. At the baseline and after one year, changes in weight gain, body composition by the bioimpedance electric method, resting energy expenditure (REE) by the indirect calorimetry method, inflammatory markers and HOMA-IR were assessed. Results: After 12 months of evaluation, we could observe a significant increase in the DMPA group in weight (3,01 kg) and BMI, while the IUD group's only significant increase was observed in the BMI. Relative to REE there was an increase of basal metabolic rate (BMR) in both groups after one year. The sub-group DMPA that gained < 3 kg had increased significant weight, BMI and body surface (BS) with respiratory quotient (RQ) reduction, while the sub-group that gained ≥ 3 kg had a significant increase in weight, BMI, BS, fat-free mass, fat mass, BMR, Leptin, HOMA-IR and waist circumference, with RQ significantly reduced. Conclusion: Our study found significant changes in weight, body composition and metabolic profile of the population studied in the first 12 months of contraceptive use. These changes mainly increased body weight, leptin levels and HOMA-IR which can contribute to the development of some chronic complications, including obesity, insulin resistance and diabetes mellitus. Arch Endocrinol Metab. 2017;61(1):70-5

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Factors influencing variation in basal metabolic rate include fat-free mass, fat mass, age, and circulating thyroxine but not sex, circulating leptin, or triiodothyronine

Cited by 397 publications

References 43 publications

Modelling the associations between fat-free mass, resting metabolic rate and energy intake in the context of total energy balance

Modelling the associations between fat-free mass, resting metabolic rate and energy intake in the context of total energy balance

Additional anthropometric measures may improve the predictability of basal metabolic rate in adult subjects

Body composition, resting energy expenditure and inflammatory markers: impact in users of depot medroxyprogesterone acetate after 12 months follow-up

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