Energy-sparing adaptations in human pregnancy assessed by whole-body calorimetry

Prentice, Andrew M.; Goldberg, Gail; Davies, H. L.; Murgatroyd, Peter R.; Scott, W.M.

doi:10.1079/bjn19890004

Cited by 122 publications

(91 citation statements)

References 18 publications

(19 reference statements)

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“…where CV Iw is the within-subject variation in energy intake (taken as 23%) (Bingham, 1987), k is the number of days of diet assessment, CV B is the variation in repeated BMR measurements (taken as 2.5%) (Prentice et al, 1989) or the precision of estimated compared with measured BMR (taken as 8.0%) (Scho®eld et al, 1985), and CV P is the between-subject variation in PAL (taken as 12.5% here) (FAO/WHO/UNU, 1985). The Goldberg cut-off was calculated for n 1 using both BMR meas and BMR est and three alternatives for PAL: ®rst, the mean population PAL for a sedentary lifestyle of 1.55, second, a study-speci®c mean PAL for women (1.66), men (1.86) and post-obese (1.59) as measured, and third, the subject-speci®c PAL as measured.…”

Section: Validation Of Dietary Intakes Of Protein and Energy Ae Blackmentioning

confidence: 99%

Validation of dietary intakes of protein and energy against 24 hour urinary N and DLW energy expenditure in middle-aged women, retired men and post-obese subjects: comparisons with validation against presumed energy requirements

et al. 1997

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Objectives: To compare validation of reported dietary intakes from weighed records against urinary nitrogen excretion and energy expenditure measured by DLW, and to examine the utility of the Goldberg cut-off for EI:BMR in the identi®cation of under-reporters. Design: Energy (EI) and nitrogen (protein) intake (NI) were measured by 16 d of weighed diet records collected over 1 y. They were validated against urinary nitrogen excretion in 5±8 (mean 6.0) 24 h urine collections and total energy expenditure (EE) measured by doubly labelled water (DLW). Basal metabolic rate (BMR) as measured by whole body calorimetry in women or bedside ventilated hood (Deltatrac) in men. Individual subjects were identi®ed as under-reporters if Urine N:NI was b 1.00 or if EI:EE was`0.79. The agreement between the two ratios in detecting under-reporting was examined. The results from the direct validation by DLW were also compared with validation using the Goldberg cut-off for EI:BMR . Subjects: Eighteen women aged 50±65 y and 27 men aged 55±87 y were selected from participants in two larger dietary surveys as representing the full range of dietary reporting as measured by Urine N:NI. Data from a previous study of 11 post-obese subjects were also included. Results: The two ratios, Urine N:NI and EI:EE, were signi®cantly related (r 70.48, P`0.01). Using the above cut-offs, seven (4F, 3M) subjects were identi®ed as under-reporters by both methods, one (1M) by Urine N:NI only and 8 (3F, 5M) by EI:EE only. There was close agreement in post-obese subjects where 6 subjects showed a substantial degree of under-reporting by both methods (r 70.87, P`0.001). The correlation between direct validation by DLW and EI:BMR est was 0.65 (P`0.001). Some limitations of the Goldberg cutoff for identifying individual under-reporters were demonstrated. Conclusions: EI:EE provides an estimate of the degree of under-reporting of energy at the group and individual level. Urine N:NI identi®es under-reporting of protein intake and the most obvious under-reporters of energy, but is probably of lesser value in estimating the overall degree of under-reporting of energy at group level. Good validation by EI:BMR depends on knowledge of physical activity at both group and individual level. However, the correlation of 0.65 between EI:EE and EI:BMR est suggests that EI:BMR could be usefully incorporated into analysis of data from epidemiological studies. Validation measures consisting of at least predicted EI:BMR ratios and urinary measures should be incorporated into dietary surveys.

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Section: Validation Of Dietary Intakes Of Protein and Energy Ae Blackmentioning

confidence: 99%

Validation of dietary intakes of protein and energy against 24 hour urinary N and DLW energy expenditure in middle-aged women, retired men and post-obese subjects: comparisons with validation against presumed energy requirements

et al. 1997

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“…This value was derived from 279 subjects measured in a whole body calorimeter on successive nights on which the antecedent diet and activity had been the same. 27 However, in community studies subjects are sampled from the population and variation includes that due to antecedent diet and activity, natural weight¯uctua-tions, menstrual cycle and methodological error. Table 3 shows results from studies that have speci®-cally investigated within-subject variation in BMR; the majority were conducted in young men.…”

Section: Factors For Substitution Into the Goldberg Equationmentioning

confidence: 99%

Critical evaluation of energy intake using the Goldberg cut-off for energy intake:basal metabolic rate. A practical guide to its calculation, use and limitations

2000

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OBJECTIVES: To re-state the principles underlying the Goldberg cut-off for identifying under-reporters of energy intake, re-examine the physiological principles and update the values to be substituted into the equation for calculating the cut-off, and to examine its use and limitations. RESULTS: New values are suggested for each element of the Goldberg equation. The physical activity level (PAL) for comparison with energy intake : basal metabolic rate (EI:BMR) should be selected to re¯ect the population under study; the PAL value of 1.55 x BMR is not necessarily the value of choice. The suggested value for average withinsubject variation in energy intake is 23% (unchanged), but other sources of variation are increased in the light of new data. For within-subject variation in measured and estimated BMR, 4% and 8.5% respectively are suggested (previously 2.5% and 8%), and for total between-subject variation in PAL, the suggested value is 15% (previously 12.5%). The effect of these changes is to widen the con®dence limits and reduce the sensitivity of the cut-off. CONCLUSIONS: The Goldberg cut-off can be used to evaluate the mean population bias in reported energy intake, but information on the activity or lifestyle of the population is needed to choose a suitable PAL energy requirement for comparison. Sensitivity for identifying under-reporters at the individual level is limited. In epidemiological studies information on home, leisure and occupational activity is essential in order to assign subjects to low, medium or high PAL levels before calculating the cut-offs. In small studies, it is desirable to measure energy expenditure, or to calculate individual energy requirements, and to compare energy intake directly with energy expenditure.

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“…Total energy expenditure by respiratory calorimetry or doubly labelled water method Whole room respiration calorimetry has been performed in well-nourished women 31,34,38,39 and marginally-nourished women 40 during pregnancy (Table 5). These shortterm 24-hour studies can demonstrate changes in energy expenditure under standardised protocols, but make no allowance for free-living physical activity.…”

Section: Basal Metabolismmentioning

confidence: 99%

Energy requirements during pregnancy and lactation

Butte

King²

2005

Public Health Nutr.

381

313

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Objective: To estimate the energy requirements of pregnant and lactating women consistent with optimal pregnancy outcome and adequate milk production. Design: Total energy cost of pregnancy was estimated using the factorial approach from pregnancy-induced increments in basal metabolic rate measured by respiratory calorimetry or from increments in total energy expenditure measured by the doubly labelled water method, plus energy deposition attributed to protein and fat accretion during pregnancy. Setting: Database on changes in basal metabolic rate and total energy expenditure during pregnancy, and increments in protein based on measurements of total body potassium, and fat derived from multi-compartment body composition models was compiled. Energy requirements during lactation were derived from rates of milk production, energy density of human milk, and energy mobilisation from tissues. Subjects: Healthy pregnant and lactating women. Results: The estimated total cost of pregnancy for women with a mean gestational weight gain of 12.0 kg, was 321 or 325 MJ, distributed as 375, 1200, 1950 kJ day 21 , for the first, second and third trimesters, respectively. For exclusive breastfeeding, the energy cost of lactation was 2.62 MJ day 21 based on a mean milk production of 749 g day 21, energy density of milk of 2.8 kJ g 21, and energetic efficiency of 0.80. In well-nourished women, this may be subsidised by energy mobilisation from tissues on the order of 0.72 MJ day 21, resulting in a net increment of 1.9 MJ day 21 over nonpregnant, non-lactating energy requirements. Conclusions: Recommendations for energy intake of pregnant and lactating women should be updated based on recently available data.

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Energy-sparing adaptations in human pregnancy assessed by whole-body calorimetry

Cited by 122 publications

References 18 publications

Validation of dietary intakes of protein and energy against 24 hour urinary N and DLW energy expenditure in middle-aged women, retired men and post-obese subjects: comparisons with validation against presumed energy requirements

Validation of dietary intakes of protein and energy against 24 hour urinary N and DLW energy expenditure in middle-aged women, retired men and post-obese subjects: comparisons with validation against presumed energy requirements

Critical evaluation of energy intake using the Goldberg cut-off for energy intake:basal metabolic rate. A practical guide to its calculation, use and limitations

Energy requirements during pregnancy and lactation

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