The following projects are in progress. Researchers submitting requests to use the DLW Database are encouraged to check whether their planned analyses overlap with current projects.
Effect of year of measurement on DEE, AEE and DEE/BEE
Expands the analysis Westerterp and Speakman (2008) in IJO
Likely restricted to data from USA and Europe. Normalised for body weight effects and divided by sex. Analysis will be completed using different age groups. Testing hypothesis that energy expenditure and in particular PAL or AEE have declined over the time course of the obesity epidemic. Sagayama and Yamada have conducted preliminary analysis using the Schoeller Lab database.
Proposed by Speakman, Westerterp, Yamada, Sagayama, Wells and Schoeller
Comparison between NHANES food intake 24 recall vs DEE
NHANES generated 5 year estimates of food intake since 1980s The aim of this analysis is to compare the NHANES estimates of total intake with the estimates of DEE from DLW measured at the same times.
Proposed by Yamada, Sagayama and Schoeller
Comparing the actual DEE by DLW to the recommended daily intakes (RDi for energy)
Are the current RDIs for energy correct and useful? Using estimated conversions of DEE to MEI can we make new recommendations or confirm the validity of the existing ones. Analysis will focus on adults and children separately as well as reproductive females.
Proposed by Speakman.
Effect of date of measurement on water turnover
Investigating the elevation of water turnover due to the campaigns to encourage people to obtain water from adequate amounts of food, and to drink more water to prevent dehydration. Involves analysis of water turnover in relation to ambient temperature by date of measurement (seasonal effects and time trends), normalised for body weight and sex. Integration of findings with respect to global water budgets.
Proposed by Speakman, Wells and Yamada
Effects of spatial and seasonal variation across the mainland USA in relation to ambient temperature
This suggestion is prompted by the finding that diabetes but not obesity levels are related to ambient temperature in the USA (Speakman and Heidari bakavoli 2015). The prediction is that DEE will be higher where and when it is colder. Involves merging data on DEE, AEE, BEE and ratios in relation to spatial/temporal aspects with ambient temperature data from oak ridge laboratory that are already available. Data for DEE, AEE, BEE and PAL normalised for body weight in both sexes will be analysed. This analysis may be extended more globally using a method developed by Pontzer for estimating ambient temperature for any point globally based on latitude/longitude and date. Depending on the results this may be part of a single analysis or a separate paper. UCL can provide a range of global climate variables to facilitate this analysis.
Proposed by Speakman, Wells and Pontzer
Factors influencing Nd/No ratio
Expands previous analysis by Sagamaya et al (2016) in J. Appl. Phys. which was based on 2297 adult individuals, and the study of infants by Wells et al 1998 in Pediatric research, based on 281 measurements.
Proposed by Wong and Wells
Impact of age on body composition, DEE and DEE/BMR of adults
Builds on analysis by Speakman and Westerterp (2013) in AJCN and poster by Yamada et al (2016). Will include changes in DEE, AEE, BEE and ratios in relation to body composition (wt, FFM and FM) divided by sex. Probably restricted to US and European data. The analysis will encompass whether the break point (around 52 yrs old identified in Speakman and Westerterp 2013) is the same between lean and obese subjects. Also covered will be the difference of decrease rate of body composition (in particular FFM), PAL, and REE(BMR), and TEE between lean and obese subjects in middle and older age. Calorie restricted monkeys showed attenuated skeletal muscle decrease (Colman et al. 2008) and PAL (Yamada et al. 2013). Establish the best models & equations for predicting DEE and its components (BMR, AEE, and AEE/BMR) from body size and composition among adults. Examine effects of sex and other anthropometrics as well (height, BMI, etc).
Proposed by Westerterp, Yamada, Sagayama, Schoeller, Pontzer, Wells and Speakman
Energy demands of traditional vs modern lifestyles
Analysis of the effects of ecological & lifestyle variables in the database to see how lifestyle (occupation, economic development) and physical environment (eg climate) affects DEE, BMR, AEE and DEE/BMR. The climate (temperature) component might be part of the present analysis or part of the analysis detailed in proposal 5. University College London can provide a range of global climate variables to facilitate this analysis.
Proposed by Pontzer and Wells.
Impact of age on body composition, DEE and DEE/BMR, and water turnover in infants and children
Establish the best models & equations for predicting DEE and its components (BMR, AEE, and AEE/BMR) from body size and composition from birth to adulthood. Examine effects of sex and other anthropometrics as well (height, BMI, etc) and where available physical activity records.
Proposed by Yamada, Sagayama, Wells and Pontzer
Limits on energy expenditures
What are the maximum and minimum DEE, AEE, DEE/BMR among humans? This analysis will examine this both for measurements taken in extreme/ competitive conditions, during pregnancy/lactation, and in normal daily life.
Proposed by Pontzer & Speakman
Using the database to forecast changes in energy demands due to global change
Integrating the database with global data on height and weight and physical activity across 195 countries to predict energy and water requirements. Combining data with agricultural energy needs to produce novel insights into actual and predicted future energy use. Also looking at modelling trends in diet composition - more vegetarians, changes in fat/CHO ratio, etc. Using the data to model potential consequences of future developments. For example how much resources would be saved by reversing the obesity epidemic? What happens to global energy demands under different health scenarios – eg cutting calories to reduce obesity, or increasing calorie consumption to make people more active.
Proposed by Wells, Cole, Hall and Speakman
Refining the cut-off points for realistic measurements of food intake based on TEE rather than multiples of BEE
It is already well established that food intake measurements based on self reported intakes under-estimate actual intake. Historo=ically cut-offs have been based on a multiple of BEE (the Goldberg cutoff). The database allows us to refine this cut-off using direct measurements of TEE
Proposed by Speakman
Defining ethnic differences in energy expenditure controlled for sex, body composition and age.
Some ethnic groups show increased adiposity but whether this is linked to lowered energy expenditure remains unclear. This work will compare energy demands of different ethnic groups normalised for the main other factors that affect energy expenditure (sex, body composition and age).
Proposed by Speakman
Anthropometric Predictions of Body Composition in Older Adults
We will investigate which standard anthropometric measures (height, weight, age, gender, BMI) provide the strongest predictions of body composition (fat free mass, fat mass, fat percentage) in adults 50+ years of age. The objective is to identify readily available anthropmetric measures that can be used to determine the degree of age-related sarcopenia in older adults.
Proposed by: William Pan, Herman Pontzer
Colman, R.J., Beasley, T.M., Allison, D.B.,Weindruch, R., 2008. Attenuation of sarcopenia by dietary restriction in rhesus monkeys. J. Gerontol. A Biol. Sci. Med. Sci. 63, 556–559.
Sagayama et al (2016) Dilution space ratio of H-2 and O-18 of doubly labelled water method in humans. J. Appl. Phys. 120 : 1349-54.
Speakman, J.R. and Heidari-Bakavoli, S. (2016) Type 2 diabetes, but not obesity, prevalence is positively associated with ambient temperature. Scientific reports 6: 30409
Speakman, J.R. and Westerterp, K.R. (2010) Associations between energy demands, physical activity and body composition in adult humans between 18 and 96 years of age. American Journal of Clinical Nutrition 92: 826-834
Westerterp, K.R. and Speakman, J.R. (2008) Physical activity energy expenditure has not declined since the 1980s and matches energy expenditure of wild mammals. International Journal of Obesity 32:1256-63
Wells, J.C.K., Ritz, P., Davies, P.S.W. and Coward, W.A, (1998) Factors affecting the 2H to 18O dilution space ratio in children. Pediatric Research 43: 467-471.
Yamada Y, Colman RJ, Kemnitz JW, et al. Long-term calorie restriction decreases metabolic cost of movement and prevents decrease of physical activity during aging in rhesus monkeys. Exp Gerontol. 2013;48:1226–1235.
Yamada Y, Sagayama H, Racine NM, Shiriver TC, Schoeller DA and DLW Study group (2016) Objective energy requirements and water intake in 2297 humans aged 0.25 to 89 years measured by doubly labelled water. International Conference of Obesity 2016. Vancouver, Canada.
Sagayama H, Yamada Y, Racine NM, Shiriver TC, Schoeller DA. and DLW Study group (2016) Comparison of total energy expenditure vs age by doubly labelled water in healthy weight and obese United States adults. The Obesity Society (Obesity Week) (analysing DLW data assembled from 1853 adult participants aged 20-89 years old living in the United States). New Orleans, LA, USA.