Estimation of metabolic energy expenditure from core temperature using a human thermoregulatory model.

Human metabolic energy expenditure is critical to many scientific disciplines but can only be measured using expensive and/or restrictive equipment. The aim of this work is to determine whether the SCENARIO thermoregulatory model can be adapted to estimate metabolic rate (M) from core body temperature (TC). To validate this method of M estimation, data were collected from fifteen test volunteers (age = 23 ± 3yr, height = 1.73 ± 0.07m, mass = 68.6 ± 8.7kg, body fat = 16.7 ± 7.3%; mean ± SD) who wore long sleeved nylon jackets and pants (Itot,clo = 1.22, Im = 0.41) during treadmill exercise tasks (32 trials; 7.8 ± 0.5km in 1h; air temp. = 22°C, 50% RH, wind speed = 0.35ms-1). Core body temperatures were recorded by ingested thermometer pill and M data were measured via whole room indirect calorimetry. Metabolic rate was estimated for 5min epochs in a two-step process. First, for a given epoch, a range of M values were input to the SCENARIO model and a corresponding range of TC values were output. Second, the output TC range value with the lowest absolute error relative to the observed TC for the given epoch was identified and its corresponding M range input was selected as the estimated M for that epoch. This process was then repeated for each subsequent remaining epoch. Root mean square error (RMSE), mean absolute error (MAE), and bias between observed and estimated M were 186W, 130 ± 174W, and 33 ± 183W, respectively. The RMSE for total energy expenditure by exercise period was 0.30 MJ. These results indicate that the SCENARIO model is useful for estimating M from TC when measurement is otherwise impractical.

The metabolizable energy of dietary resistant maltodextrin is variable and alters fecal microbiota composition in adult men.

Resistant maltodextrin (RM) is a novel soluble, nonviscous dietary fiber. Its metabolizable energy (ME) and net energy (NE) values derived from nutrient balance studies are unknown, as is the effect of RM on fecal microbiota. A randomized, placebo-controlled, double-blind crossover study was conducted (n = 14 men) to determine the ME and NE of RM and its influence on fecal excretion of macronutrients and microbiota. Participants were assigned to a sequence consisting of 3 treatment periods [24 d each: 0 g/d RM + 50 g/d maltodextrin and 2 amounts of dietary RM (25 g/d RM + 25 g of maltodextrin/d and 50 g/d RM + 0 g/d maltodextrin)] and were provided all the foods they were to consume to maintain their body weight. After an adaptation period, excreta were collected during a 7-d period. After the collection period, 24-h energy expenditure was measured. Fluorescence in situ hybridization, quantitative polymerase chain reaction, and 454 titanium technology-based 16S rRNA sequencing were used to analyze fecal microbiota composition. Fecal amounts of energy (544, 662, 737 kJ/d), nitrogen (1.5, 1.8, 2.1 g/d), RM (0.3, 0.6, 1.2 g/d), and total carbohydrate (11.1, 14.2, 16.2 g/d) increased with increasing dose (0, 25, 50 g) of RM (P < 0.0001). Fat excretion did not differ among treatments. The ME value of RM was 8.2 and 10.4 kJ/g, and the NE value of RM was -8.2 and 2.0 kJ/g for the 25 and 50 g/d RM doses, respectively. Both doses of RM increased fecal wet weight (118, 148, 161 g/d; P < 0.0001) and fecal dry weight (26.5, 32.0, 35.8 g/d; P < 0.0001) compared with the maltodextrin placebo. Total counts of fecal bacteria increased by 12% for the 25 g/d RM dose (P = 0.17) and 18% for the 50 g/d RM dose (P = 0.019). RM intake was associated with statistically significant increases (P < 0.001) in various operational taxonomic units matching closest to ruminococcus, eubacterium, lachnospiraceae, bacteroides, holdemania, and faecalibacterium, implicating RM in their growth in the gut. Our findings provide empirical data important for food labeling regulations related to the energy value of RM and suggest that RM increases fecal bulk by enhancing the excretion of nitrogen and carbohydrate and the growth of specific microbial populations.

Whey protein but not soy protein supplementation alters body weight and composition in free-living overweight and obese adults.

A double-blind, randomized clinical trial was conducted to determine the effect of consumption of supplemental whey protein (WP), soy protein (SP), and an isoenergetic amount of carbohydrate (CHO) on body weight and composition in free-living overweight and obese but otherwise healthy participants. Ninety overweight and obese participants were randomly assigned to 1 of 3 treatment groups for 23 wk: 1) WP; 2) SP (each providing ~56 g/d of protein and 1670 kJ/d); or 3) an isoenergetic amount of CHO. Supplements were consumed as a beverage twice daily. Participants were provided no dietary advice and continued to consume their free-choice diets. Participants' body weight and composition data were obtained monthly. Dietary intake was determined by 24-h dietary recalls collected every 10 d. After 23 wk, body weight and composition did not differ between the groups consuming the SP and WP or between SP and CHO; however, body weight and fat mass of the group consuming the WP were lower by 1.8 kg (P < 0.006) and 2.3 kg (P < 0.005), respectively, than the group consuming CHO. Lean body mass did not differ among any of the groups. Waist circumference was smaller in the participants consuming WP than in the other groups (P < 0.05). Fasting ghrelin was lower in participants consuming WP compared with SP or CHO. Through yet-unknown mechanisms, different sources of dietary protein may differentially facilitate weight loss and affect body composition. Dietary recommendations, especially those that emphasize the role of dietary protein in facilitating weight change, should also address the demonstrated clinical potential of supplemental WP.

Estimates of adherence and error analysis of physical activity data collected via accelerometry in a large study of free-living adults.

BACKGROUND: Activity monitors (AM) are small, electronic devices used to quantify the amount and intensity of physical activity (PA). Unfortunately, it has been demonstrated that data loss that occurs when AMs are not worn by subjects (removals during sleeping and waking hours) tend to result in biased estimates of PA and total energy expenditure (TEE). No study has reported the degree of data loss in a large study of adults, and/or the degree to which the estimates of PA and TEE are affected. Also, no study in adults has proposed a methodology to minimize the effects of AM removals. METHODS: Adherence estimates were generated from a pool of 524 women and men that wore AMs for 13 - 15 consecutive days. To simulate the effect of data loss due to AM removal, a reference dataset was first compiled from a subset consisting of 35 highly adherent subjects (24 HR; minimum of 20 hrs/day for seven consecutive days). AM removals were then simulated during sleep and between one and ten waking hours using this 24 HR dataset. Differences in the mean values for PA and TEE between the 24 HR reference dataset and the different simulations were compared using paired t-tests and/or coefficients of variation. RESULTS: The estimated average adherence of the pool of 524 subjects was 15.8 +/- 3.4 hrs/day for approximately 11.7 +/- 2.0 days. Simulated data loss due to AM removals during sleeping hours in the 24 HR database (n = 35), resulted in biased estimates of PA (p < 0.05), but not TEE. Losing as little as one hour of data from the 24 HR dataset during waking hours results in significant biases (p < 0.0001) and variability (coefficients of variation between 7 and 21%) in the estimates of PA. Inserting a constant value for sleep and imputing estimates for missing data during waking hours significantly improved the estimates of PA. CONCLUSION: Although estimated adherence was good, measurements of PA can be improved by relatively simple imputation of missing AM data.

Blackberry Feeding Increases Fat Oxidation and Improves Insulin Sensitivity in Overweight and Obese Males.

Berries and other anthocyanin-rich treatments have prevented weight gain and adiposity in rodent models of diet-induced obesity. Their efficacy may be explained by modulation of energy substrate utilization. However, this effect has never been translated to humans. The objective of this study was to evaluate the effects of berry intake on energy substrate use and glucoregulation in volunteers consuming a high-fat diet. Twenty-seven overweight or obese men were enrolled in a randomized, placebo-controlled crossover study with two treatment periods. Subjects were fed an investigator controlled, high-fat (40% of energy from fat) diet which contained either 600 g/day blackberries (BB, 1500 mg/day flavonoids) or a calorie and carbohydrate matched amount of gelatin (GEL, flavonoid-free control) for seven days prior to a meal-based glucose tolerance test (MTT) in combination with a 24 h stay in a room-sized indirect calorimeter. The washout period that separated the treatment periods was also seven days. The BB treatment resulted in a significant reduction in average 24 h respiratory quotient (RQ) (0.810 vs. 0.817, BB vs. GEL, p = 0.040), indicating increased fat oxidation. RQ during the MTT was significantly lower with the BB treatment (0.84) compared to GEL control (0.85), p = 0.004. A 4 h time isolation during dinner showed similar treatment effects, where RQ was reduced and fat oxidation increased with BB (0.818 vs. 0.836, 28 vs. 25 g, respectively; BB vs. GEL treatments). The glucose AUC was not different between the BB and GEL treatments during the MTT (3488 vs. 4070 mg·min/dL, respectively, p = 0.12). However, the insulin AUC was significantly lower with the BB compared to the GEL control (6485 vs. 8245 µU·min/mL, p = 0.0002), and HOMA-IR improved with BB (p = 0.0318). Blackberry consumption may promote increased fat oxidation and improved insulin sensitivity in overweight or obese males fed a high fat diet.

A controlled trial of reduced meal frequency without caloric restriction in healthy, normal-weight, middle-aged adults.

BACKGROUND: Although consumption of 3 meals/d is the most common pattern of eating in industrialized countries, a scientific rationale for this meal frequency with respect to optimal health is lacking. A diet with less meal frequency can improve the health and extend the lifespan of laboratory animals, but its effect on humans has never been tested. OBJECTIVE: A pilot study was conducted to establish the effects of a reduced-meal-frequency diet on health indicators in healthy, normal-weight adults. DESIGN: The study was a randomized crossover design with two 8-wk treatment periods. During the treatment periods, subjects consumed all of the calories needed for weight maintenance in either 3 meals/d or 1 meal/d. RESULTS: Subjects who completed the study maintained their body weight within 2 kg of their initial weight throughout the 6-mo period. There were no significant effects of meal frequency on heart rate, body temperature, or most of the blood variables measured. However, when consuming 1 meal/d, subjects had a significant increase in hunger; a significant modification of body composition, including reductions in fat mass; significant increases in blood pressure and in total, LDL-, and HDL-cholesterol concentrations; and a significant decrease in concentrations of cortisol. CONCLUSIONS: Normal-weight subjects are able to comply with a 1 meal/d diet. When meal frequency is decreased without a reduction in overall calorie intake, modest changes occur in body composition, some cardiovascular disease risk factors, and hematologic variables. Diurnal variations may affect outcomes.

Impact of reduced meal frequency without caloric restriction on glucose regulation in healthy, normal-weight middle-aged men and women.

An unresolved issue in the field of diet and health is if and how changes in meal frequency affect energy metabolism in humans. We therefore evaluated the influence of reduced meal frequency without a reduction in energy intake on glucose metabolism in normal-weight, healthy male and female subjects. The study was a randomized crossover design, with two 8-week treatment periods (with an intervening 11-week off-diet period) in which subjects consumed all of their calories for weight maintenance distributed in either 3 meals or 1 meal per day (consumed between 4:00 pm and 8:00 pm). Energy metabolism was evaluated at designated time points throughout the study by performing morning oral glucose tolerance tests and measuring levels of glucose, insulin, glucagon, leptin, ghrelin, adiponectin, resistin, and brain-derived neurotrophic factor (BDNF). Subjects consuming 1 meal per day exhibited higher morning fasting plasma glucose levels, greater and more sustained elevations of plasma glucose concentrations, and a delayed insulin response in the oral glucose tolerance test compared with subjects consuming 3 meals per day. Levels of ghrelin were elevated in response to the 1-meal-per-day regimen. Fasting levels of insulin, leptin, ghrelin, adiponectin, resistin, and BDNF were not significantly affected by meal frequency. Subjects consuming a single large daily meal exhibit elevated fasting glucose levels and impaired morning glucose tolerance associated with a delayed insulin response during a 2-month diet period compared with those consuming 3 meals per day. The impaired glucose tolerance was reversible and was not associated with alterations in the levels of adipokines or BDNF.

Comparison of two different physical activity monitors.

BACKGROUND: Understanding the relationships between physical activity (PA) and disease has become a major area of research interest. Activity monitors, devices that quantify free-living PA for prolonged periods of time (days or weeks), are increasingly being used to estimate PA. A range of different activity monitors brands are available for investigators to use, but little is known about how they respond to different levels of PA in the field, nor if data conversion between brands is possible. METHODS: 56 women and men were fitted with two different activity monitors, the Actigraph (Actigraph LLC; AGR) and the Actical (Mini-Mitter Co.; MM) for 15 days. Both activity monitors were fixed to an elasticized belt worn over the hip, with the anterior and posterior position of the activity monitors randomized. Differences between activity monitors and the validity of brand inter-conversion were measured by t-tests, Pearson correlations, Bland-Altman plots, and coefficients of variation (CV). RESULTS: The AGR detected a significantly greater amount of daily PA (216.2 +/- 106.2 vs. 188.0 +/- 101.1 counts/min, P < 0.0001). The average difference between activity monitors expressed as a CV were 3.1 and 15.5% for log-transformed and raw data, respectively. When a conversion equation was applied to convert datasets from one brand to another, the differences were no longer significant, with CV's of 2.2 and 11.7%, log-transformed and raw data, respectively. CONCLUSION: Although activity monitors predict PA on the same scale (counts/min), the results between these two brands are not directly comparable. However, the data are comparable if a conversion equation is applied, with better results for log-transformed data.

Preprandial ghrelin is not affected by macronutrient intake, energy intake or energy expenditure.

BACKGROUND: Ghrelin, a peptide secreted by endocrine cells in the gastrointestinal tract, is a hormone purported to have a significant effect on food intake and energy balance in humans. The influence of factors related to energy balance on ghrelin, such as daily energy expenditure, energy intake, and macronutrient intake, have not been reported. Secondly, the effect of ghrelin on food intake has not been quantified under free-living conditions over a prolonged period of time. To investigate these effects, 12 men were provided with an ad libitum cafeteria-style diet for 16 weeks. The macronutrient composition of the diets were covertly modified with drinks containing 2.1 MJ of predominantly carbohydrate (Hi-CHO), protein (Hi-PRO), or fat (Hi-FAT). Total energy expenditure was measured for seven days on two separate occasions (doubly labeled water and physical activity logs). RESULTS: Preprandial ghrelin concentrations were not affected by macronutrient intake, energy expenditure or energy intake (all P > 0.05). In turn, daily energy intake was significantly influenced by energy expenditure, but not ghrelin. CONCLUSION: Preprandial ghrelin does not appear to be influenced by macronutrient composition, energy intake, or energy expenditure. Similarly, ghrelin does not appear to affect acute or chronic energy intake under free-living conditions.

Effects of the interaction of sex and food intake on the relation between energy expenditure and body composition.

BACKGROUND: The relation between physical activity energy expenditure (PAEE) and percentage body fat (%BF) is not very strong in the general population. It is possible that variables such as sex, food intake, or both may in part explain this poor coupling. OBJECTIVE: This study was designed to show the relation between PAEE and %BF and to determine whether sex, food intake, or both influence the strength of the relation. DESIGN: We used doubly labeled water or energy intake balance, indirect calorimetry, dietary interview, and dual-energy X-ray absorptiometry to measure total energy expenditure (TEE), resting energy expenditure (REE), food intake, and %BF, respectively, in 91 healthy persons (women: aged 48 y, 38.6%BF, n = 47; men: aged 47 y, 24.1%BF, n = 44). RESULTS: TEE, PAEE, and REE were significantly lower in women than in men. TEE was related to %BF in women (r = 0.53, P < 0.0001) but not in men (r = -0.22, P > 0.05). The relation between PAEE and %BF was significant in men (r = -0.34, P < 0.03) but not in women. PAL was also significantly related to %BF in men (r = -0.36, P < 0.02) but not in women. Macronutrient intake (% of total energy) did not differ significantly between the sexes, but carbohydrate (r = -0.44, P < 0.003) and fat (r = 0.31, P < 0.04) intakes were significantly related to %BF in women. CONCLUSIONS: These results suggest that the relation between PAEE and %BF is stronger in men than in women. Macronutrient composition seems have a stronger influence on %BF in women than in men.

Commercial portion-controlled foods in research studies: how accurate are label weights?

OBJECTIVE: The purpose of this study was to evaluate the reliability of label weights as surrogates for actual weights in commercial portion-controlled foods used in a research setting. DESIGN: Actual weights of replicate samples of 82 portion-controlled food items and 17 discrete units of food from larger packaging were determined over time. Comparison was made to the package label weights for the portion-controlled food items and the per-serving weights for the discrete units. SETTING: The study was conducted at the US Department of Agriculture's Beltsville Human Nutrition Research Center's Human Study Facility, which houses a metabolic kitchen and human nutrition research facility. MAIN OUTCOME MEASURES: The primary outcome measures were the actual and label weights of 99 food items consumed by human volunteers during controlled feeding studies. Statistical analyses performed The difference between label and actual weights was tested by the paired t test for those data that complied with the assumptions of normality. The Wilcoxon signed rank test was used for the remainder of the data. Compliance with federal guidelines for packaged weights was also assessed. RESULTS: There was no statistical difference between actual and label weights for only 37 food items. The actual weights of 15 portion-controlled food items were 1% or more less than label weights, making them potentially out of compliance with federal guidelines. CONCLUSIONS: With advance planning and continuous monitoring, well-controlled feeding studies could incorporate portion-controlled food items and discrete units, especially beverages and confectionery products. Dietetics professionals should encourage individuals with diabetes and others on strict dietary regimens to check actual weights of portion-controlled products carefully against package weights.

Personality characteristics as predictors of underreporting of energy intake on 24-hour dietary recall interviews.

OBJECTIVE: To identify characteristics associated with misreporting of energy intake during 24-hour dietary recalls (24 HR). DESIGN: Ninety-eight subjects were administered two 24 HRs. Energy expenditure was determined by doubly labeled water (44 subjects) or intake balance (54 subjects). Data on subjects' physical, lifestyle, and psychosocial characteristics were also collected. Subjects/setting At the Beltsville Human Nutrition Research Center 52 women and 46 men were administered 24HR and completed lifestyle and personality questionnaires and a memory test. Physical characteristics such as weight, percent body fat, and total energy expenditure were measured. Statistical analysis The influences of subject parameters on energy misreporting were assessed by linear regression and Pearson product-moment correlation analysis for continuous variables and by ANOVA for discrete variables. Stepwise regression was used to identify key factors in underreporting. RESULTS: Factors particularly important in predicting underreporting of energy intake include factors indicating dissatisfaction with body image; for example, a 398 kcal/day underreport in subjects attempting weight loss during the past year with a nearly 500 kcal/day underreport in women. Overall, women underreported by 393 kcal/day relative to men and women evinced a social desirability bias amounting to a 26 kcal underreport for each point on the social desirability scale. Gender differences also were evident in the effect of percent body fat (with men underreporting about 16 kcal/day/percent body fat) and in departure from self-reported ideal body weight (with women underreporting about 21 kcal/day/kg). APPLICATIONS/CONCLUSIONS: Body image and fatness are key factors on which health professionals should focus when seeking predictors of underreporting of dietary intake. Dietary interviews must be conducted to minimize bias related to subjects' tendencies to win approval and avoid censure by the interviewer. In addition, dissatisfaction with body image may lead to underestimation of food intake, therefore reducing likelihood of success in weight loss. Thus, health care professionals involved in weight loss counseling may achieve better success if treatment includes generating a more positive body image.

The effect of daily alcohol intake on breath alcohol concentrations of postmenopausal women after a bolus dose.

OBJECTIVE: This study was designed to test whether daily alcohol intake can influence parameters related to rate of alcohol clearance and systemic alcohol exposure. METHOD: Postmenopausal women (N = 14) completed a study in which they consumed an alcohol treatment daily for 8 weeks. In a three-period crossover design, women consumed 0, 15 or 30 g/day ethanol, with each subject completing each treatment level. Following the 8-week adaptation period, the subjects consumed a single dose of 15 g ethanol, and breath samples collected to assess breath alcohol concentration (BrAC) every 5 minutes until the BrAC declined to zero. RESULTS: Adaptation to daily alcohol intake of 30 g/day resulted in reduced breath alcohol response compared to adaptation to 0 g/day. Specifically, area under the BrAC time curve was lower after women had consumed 30 g ethanol per day compared to that after daily consumption of 0 grams per day. Also, the time required for BrAC to decline to 0.01% after the bolus dose was reduced when subjects were adapted to 30g/day compared to 0 g/day. CONCLUSIONS: Daily intake of alcohol at a level of 30 g/day appears to be sufficient to alter the parameters related to systemic alcohol exposure.

Controlled meal frequency without caloric restriction alters peripheral blood mononuclear cell cytokine production.

BACKGROUND: Intermittent fasting (IF) improves healthy lifespan in animals by a mechanism involving reduced oxidative damage and increased resistance to stress. However, no studies have evaluated the impact of controlled meal frequency on immune responses in human subjects. OBJECTIVE: A study was conducted to establish the effects of controlled diets with different meal frequencies, but similar daily energy intakes, on cytokine production in healthy male and female subjects. DESIGN: In a crossover study design with an intervening washout period, healthy normal weight middle-age male and female subjects (n = 15) were maintained for 2 months on controlled on-site one meal per day (OMD) or three meals per day (TMD) isocaloric diets. Serum samples and peripheral blood mononuclear cells (PBMCs) culture supernatants from subjects were analyzed for the presence of inflammatory markers using a multiplex assay. RESULTS: There were no significant differences in the inflammatory markers in the serum of subjects on the OMD or TMD diets. There was an increase in the capacity of PBMCs to produce cytokines in subjects during the first month on the OMD or TMD diets.Lower levels of TNF-α, IL-17, MCP-1 and MIP-1ß were produced by PBMCs from subjects on the OMD versus TMD diet. CONCLUSIONS: PBMCs of subjects on controlled diets exhibit hypersensitivities to cellular stimulation suggesting that stress associated with altered eating behavior might affect cytokine production by immune cells upon stimulation. Moreover, stimulated PBMCs derived from healthy individuals on a reduced meal frequency diet respond with a reduced capability to produce cytokines.

The US Department of Agriculture Automated Multiple-Pass Method reduces bias in the collection of energy intakes.

BACKGROUND: The US Department of Agriculture Automated Multiple-Pass Method (AMPM) is used for collecting 24-h dietary recalls in What We Eat In America, the dietary interview component of the National Health and Nutrition Examination Survey. Because the data have important program and policy applications, it is essential that the validity of the method be tested. OBJECTIVE: The accuracy of the AMPM was evaluated by comparing reported energy intake (EI) with total energy expenditure (TEE) by using the doubly labeled water (DLW) technique. DESIGN: The 524 volunteers, aged 30-69 y, included an equal number of men and women recruited from the Washington, DC, area. Each subject was dosed with DLW on the first day of the 2-wk study period; three 24-h recalls were collected during the 2-wk period by using the AMPM. The first recall was conducted in person, and subsequent recalls were over the telephone. RESULTS: Overall, the subjects underreported EI by 11% compared with TEE. Normal-weight subjects [body mass index (in kg/m(2)) < 25] underreported EI by <3%. By using a linear mixed model, 95% CIs were determined for the ratio of EI to TEE. Approximately 78% of men and 74% of women were classified as acceptable energy reporters (within 95% CI of EI:TEE). Both the percentage by which energy was underreported and the percentage of subjects classified as low energy reporters (<95% CI of EI:TEE) were highest for subjects classified as obese (body mass index > 30). CONCLUSIONS: Although the AMPM accurately reported EIs in normal-weight subjects, research is warranted to enhance its accuracy in overweight and obese persons.

Validation of a food frequency questionnaire by direct measurement of habitual ad libitum food intake.

Food frequency questionnaires are commonly used to assess habitual food intake. Although food frequency questionnaires are known to produce measurement error, the amount of error and effectiveness of correction methods are poorly understood. Twelve men from the Baltimore, MD/Washington, DC, area consumed an ad libitum diet for 16 weeks during the spring of 2001. At the end of the study period, subjects reported their food intakes with a food frequency questionnaire (Block 98). During weeks 8 and 16, subjects were dosed with doubly labeled water and maintained physical activity logs. Absolute and relative macronutrient intakes were poorly predicted by the food frequency questionnaire. The application of a single, group mean energy adjustment (using doubly labeled water or physical activity) reduced the variance of carbohydrate intake and increased the variance of fat and protein intakes, but none significantly (p>0.05). Subject-specific energy adjustments reduced the variance for carbohydrate and protein intakes (p<0.05). Including a body weight adjustment reduced the variance in fat intake (p<0.05) when doubly labeled water was used to first correct energy intake. The application of correction methods based on energy expenditure and body weight can be used to reduce measurement error, improving the ability of the food frequency questionnaire to measure food intake.