An approach to quantifying abnormalities in energy expenditure and lean mass in metabolic disease

Watson, Laura; Raymond-Barker, Philippa; Moran, Carla; Schoenmakers, Nadia; Mitchell, Catherine; Bluck, Les; Chatterjee, Krishna; Savage, DB; Murgatroyd, Peter R.

doi:10.1038/ejcn.2013.237

Cited by 21 publications

(23 citation statements)

References 51 publications

(66 reference statements)

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“…Resting metabolic rate was estimated from anthropometry variables by averaging three prediction equations; one based on age, sex, height, and total bodymass derived in a large database 22 , and two based on smaller studies which also take into account body composition 23,24 . In order to calculate 24-hour resting energy expenditure (REE), we integrated this resting metabolic rate value over time, but with a small adjustment for the 5% lower metabolic rate observed during sleep 25…”

Section: Measurementsmentioning

confidence: 99%

Descriptive epidemiology of energy expenditure in the UK: Findings from the National Diet and Nutrition Survey 2008 – 2015

Brage

Lindsay

Venables

et al. 2019

Preprint

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Background Little is known about population levels of energy expenditure as national surveillance systems typically employ only crude measures. The National Diet and Nutrition Survey (NDNS) in the UK measures energy expenditure in a 10% subsample by goldstandard doubly-labelled water (DLW).Methods DLW-subsample participants from the NDNS (383 males, 387 females) aged 4-91yrs were recruited between 2008 and 2015. Height and weight were measured, and bodyfat percentage was estimated by deuterium dilution. ResultsAbsolute Total Energy Expenditure (TEE) increases steadily throughout childhood, ranging from 6.3 and 7.2 MJ/day in 4-7yr-old to 9.9 and 11.8 MJ/day for 14-16yr-old girls and boys, respectively. TEE peaked in 17-27yr-old women (10.9 MJ/day) and 28-43yr-old men (14.4 MJ/day), before decreasing gradually in old age. Physical Acitivty Energy Expenditure (PAEE) declines steadily with age from childhood (87.7 kJ/day/kg in 4-7yr olds) through to old age (38.9 kJ/day/kg in 71-91yr olds). Bodyfat percentage was strongly inversely associated with PAEE throughout life, irrespective of expressing PAEE relative to bodymass or fat-free mass. Compared to females with <30% bodyfat, females >40% recorded 28 kJ/day/kg and 17 kJ/day/kg fat-free mass less PAEE in analyses adjusted for age, geographical region, and time of assessment. Similarly, compared to males with <25% bodyfat, males >35% recorded 26 kJ/day/kg and 10 kJ/day/kg fat-free mass less PAEE.Conclusions This first nationally representative study reports levels of human energy expenditure as measured by gold-standard methodology; values may serve as reference for other population studies. Age, sex and body composition are main biological determinants of energy expenditure. Key messages:• First nationally representative study of human energy expenditure, covering the UK in the period 2008-2015 • Total Energy Expenditure (MJ/day) increases steadily with age thoughout childhood and adolescence, peaks in the 3 rd decade of life in women and 4 th decade of life in men, before decreasing gradually in old age • Physical Acitivty Energy Expenditure (kJ/day/kg or kJ/day/kg fat-free mass) declines steadily with age from childhood to old age, more steeply so in males • Bodyfat percentage is strongly inversely associated with physical activity energy expenditure

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Section: Measurementsmentioning

confidence: 99%

Descriptive epidemiology of energy expenditure in the UK: Findings from the National Diet and Nutrition Survey 2008 – 2015

Brage

Lindsay

Venables

et al. 2019

Preprint

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“…A seven-breath running median was calculated and the lowest observed average rate over a five minute consecutive window was found, which was scaled down by 6% to compensate for within-day elevation of resting metabolic rates 23 . Basal metabolic rate was also estimated via three different equations which differ in the specific body composition information utilised 2,24,25 . Resting energy expenditure was primarily characterised as the nearest measured value to the mean average estimated value, and a further sensitivity analysis was conducted using exclusively measured values.…”

Section: Methodsmentioning

confidence: 99%

Estimating energy expenditure from wrist and thigh accelerometry in free-living adults: a doubly labelled water study

White

Westgate

Hollidge

et al. 2018

Preprint

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BackgroundMany large studies have implemented wrist or thigh accelerometry to capture physical activity, but the accuracy of these measurements to infer Activity Energy Expenditure (AEE) and consequently Total Energy Expenditure (TEE) has not been demonstrated. The purpose of this study was to assess the validity of acceleration intensity at wrist and thigh sites as estimates of AEE and TEE under free-living conditions using a gold-standard criterion.MethodsMeasurements for 193 UK adults (105 men, 88 women, aged 40-66 years, BMI 20.4-36.6 kg·m-2) were collected with triaxial accelerometers worn on the dominant wrist, non-dominant wrist and thigh in free-living conditions for 9-14 days. In a subsample (50 men, 50 women) TEE was simultaneously assessed with doubly labelled water (DLW). AEE was estimated from non-dominant wrist using an established estimation model, and novel models were derived for dominant wrist and thigh in the non-DLW subsample. Agreement with both AEE and TEE from DLW was evaluated by mean bias, Root Mean Squared Error (RMSE) and Pearson correlation.ResultsMean TEE and AEE derived from DLW was 11.6 (2.3) MJ·day-1 and 49.8 (16.3) kJ·day-1·kg-1. Dominant and non-dominant wrist acceleration were highly correlated in free-living (r=0.93), but less so with thigh (r=0.73 and 0.66, respectively). Estimates of AEE were 48.6 (11.8) kJ·day-1·kg-1 from dominant wrist, 48.6 (12.3) from non-dominant wrist, and 46.0 (10.1) from thigh; these agreed strongly with AEE (RMSE ~12.2 kJ·day-1·kg-1, r ~0.71) with small mean biases at the population level (~6%). Only the thigh estimate bias was statistically significantly different from the criterion. When combining these AEE estimates with estimated REE, agreement was stronger with the criterion (RMSE ~1.0 MJ·day-1, r ~0.90). Conclusions: In UK adults, acceleration measured at either wrist or thigh can be used to estimate population levels of AEE and TEE in free-living conditions with high precision.

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“…Resting metabolic rate (RMR) was measured on two separate days during clinic visits with a fifteen-minute rest test by respired gas analysis (OxyconPro, Jaeger, Germany), and scaled by a factor of 0.94 to account for RMR measurements being conducted in the afternoon rather than the morning (Haugen, Melanson, Tran, Kearney, & Hill, 2003). The closest measurement value (visit 1, visit 2, or their mean) by proximity to the withinperson median of predictions of RMR using three equations (Henry, 2005;Nielsen et al, 2000;Watson et al, 2014) was used in analysis. Total daily REE was calculated, with an additional adjustment of sleeping metabolic rate being 5% lower than awake resting metabolic rate (Goldberg, Prentice, Davies, & Murgatroyd, 1988).…”

Section: Direct Modelmentioning

confidence: 99%

Network Harmonization of Physical Activity Variables Through Indirect Validation

Pearce

Bishop

Sharp

et al. 2020

Journal for the Measurement of Physical Behaviour

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Harmonization of data for pooled analysis relies on the principle of inferential equivalence between variables from different sources. Ideally, this is achieved using models of the direct relationship with gold standard criterion measures, but the necessary validation study data are often unavailable. This study examines an alternative method of network harmonization using indirect models. Starting methods were self-report or accelerometry, from which we derived indirect models of relationships with doubly labelled water (DLW)-based physical activity energy expenditure (PAEE) using sets of two bridge equations via one of three intermediate measures. Coefficients and performance of indirect models were compared to corresponding direct models (linear regression of DLW-based PAEE on starting methods). Indirect model beta coefficients were attenuated compared to direct model betas (10%–63%), narrowing the range of PAEE values; attenuation was greater when bridge equations were weak. Directly and indirectly harmonized models had similar error variance but most indirectly derived values were biased at group-level. Correlations with DLW-based PAEE were identical after harmonization using continuous linear but not categorical models. Wrist acceleration harmonized to DLW-based PAEE via combined accelerometry and heart rate sensing had the lowest error variance (24.5%) and non-significant mean bias 0.9 (95%CI: −1.6; 3.4) kJ·day−1·kg−1. Associations between PAEE and BMI were similar for directly and indirectly harmonized values, but most fell outside the confidence interval of the criterion PAEE-to-BMI association. Indirect models can be used for harmonization. Performance depends on the measurement properties of original data, variance explained by available bridge equations, and similarity of population characteristics.

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An approach to quantifying abnormalities in energy expenditure and lean mass in metabolic disease

Cited by 21 publications

References 51 publications

Descriptive epidemiology of energy expenditure in the UK: Findings from the National Diet and Nutrition Survey 2008 – 2015

Descriptive epidemiology of energy expenditure in the UK: Findings from the National Diet and Nutrition Survey 2008 – 2015

Estimating energy expenditure from wrist and thigh accelerometry in free-living adults: a doubly labelled water study

Network Harmonization of Physical Activity Variables Through Indirect Validation

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