William V. Rumpler

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.

Identifying sources of reporting error using measured food intake.

Objective:To investigate the magnitude and relative contribution of different sources of measurement errors present in the estimation of food intake via the 24-h recall technique.Design:We applied variance decomposition methods to the difference between data obtained from the USDAs Automated Multiple Pass Method (AMPM) 24-h recall technique and measured food intake (MFI) from a 16-week cafeteria-style feeding study. The average and the variance of biases, defined as the difference between AMPM and MFI, were analyzed by macronutrient content, subject and nine categories of foods.Subjects:Twelve healthy, lean men (age, 39±9 year; weight, 79.9±8.3 kg; and BMI, 24.1±1.4 kg/m2).Results:Mean food intakes for AMPM and MFI were not significantly different (no overall bias), but within-subject differences for energy (EI), protein, fat and carbohydrate intakes were 14, 18, 23 and 15% of daily intake, respectively. Mass (incorrect portion size) and deletion (subject did not report foods eaten) errors were each responsible for about one-third of the total error. Vegetables constituted 8% of EI but represented >25% of the error across macronutrients, whereas grains that contributed 32% of EI contributed only 12% of the error across macronutrients.Conclusions:Although the major sources of reporting error were mass and deletion errors, individual subjects differed widely in the magnitude and types of errors they made.

Effect of reduced dietary intake on energy expenditure, protein turnover, and glucose cycling in man

The effect of a 50% reduction in food intake on energy expenditure, protein metabolism, glucose cycling, and body composition was investigated in eight moderately overweight men. The prestudy mean calorie and protein intake was determined for eight subjects. They were then maintained on this diet for 6 weeks (mean +/- SEM, 3,269 +/- 75 kcal/d, 20.0 +/- 0.5 g N/d, period I), after which the diet was reduced uniformly in the major foodstuffs by 50% for the next 4 weeks (1,555 +/- 38 kcal/d, 9.6 +/- 5 g N/d, period II). At the end of each period we measured (1) body fat and fat free mass by underwater weighing, (2) 24-hour energy expenditure by indirect calorimetry in a calorimeter, (3) whole body protein synthesis and breakdown rates with 15N glycine, and (4) glucose cycling between glucose and glucose-6-phosphate and fructose cycling between fructose-6-phosphate and fructose-1,6 bisphosphate with 6,6-D2- and 2-D1-labeled glucose. The results were subjects lost 4.0 +/- 0.1 kg fat (by underwater weighing) during the 4 weeks on the reduced-energy regimen. Protein turnover and glucose cycling were reduced by 20% and 15%, respectively. Twenty-four-hour energy expenditure was 2,553 +/- 166 kcal/d for period I and 2,369 +/- 69 kcal/d for period II, giving a difference of 184 +/- 34 kcal/d between the two periods. In conclusion, (1) although energy intake was reduced by 50%, the decrease in energy expenditure was small due to the buffering effect of body fat.(ABSTRACT TRUNCATED AT 250 WORDS)

Comparison of two different physical activity monitors

BackgroundUnderstanding 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.Methods56 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).ResultsThe 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 CVs of 2.2 and 11.7%, log-transformed and raw data, respectively.ConclusionAlthough 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.

Three 15-min Bouts of Moderate Postmeal Walking Significantly Improves 24-h Glycemic Control in Older People at Risk for Impaired Glucose Tolerance

OBJECTIVE The purpose of this study was to compare the effectiveness of three 15-min bouts of postmeal walking with 45 min of sustained walking on 24-h glycemic control in older persons at risk for glucose intolerance. RESEARCH DESIGN AND METHODS Inactive older (≥60 years of age) participants (N = 10) were recruited from the community and were nonsmoking, with a BMI <35 kg/m2 and a fasting blood glucose concentration between 105 and 125 mg dL−1. Participants completed three randomly ordered exercise protocols spaced 4 weeks apart. Each protocol comprised a 48-h stay in a whole-room calorimeter, with the first day serving as the control day. On the second day, participants engaged in either 1) postmeal walking for 15 min or 45 min of sustained walking performed at 2) 10:30 a.m. or 3) 4:30 p.m. All walking was on a treadmill at an absolute intensity of 3 METs. Interstitial glucose concentrations were determined over 48 h with a continuous glucose monitor. Substrate utilization was measured continuously by respiratory exchange (VCO2/VO2). RESULTS Both sustained morning walking (127 ± 23 vs. 118 ± 14 mg dL−1) and postmeal walking (129 ± 24 vs. 116 ± 13 mg dL−1) significantly improved 24-h glycemic control relative to the control day (P < 0.05). Moreover, postmeal walking was significantly (P < 0.01) more effective than 45 min of sustained morning or afternoon walking in lowering 3-h postdinner glucose between the control and experimental day. CONCLUSIONS Short, intermittent bouts of postmeal walking appear to be an effective way to control postprandial hyperglycemia in older people.

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.

Oolong tea does not improve glucose metabolism in non-diabetic adults.

Background:Studies of the influence of tea on glucose metabolism have produced inconsistent results, possibly because of the lack of dietary control and/or unclear characterization of tea products.Methods:Therefore, a double-blind crossover study was conducted in which healthy males (n=19) consumed each of three oolong tea products or a control beverage as part of a controlled diet. Treatment beverages (1.4 l/day) were consumed for 5 days, followed by assessment of fasting plasma glucose, fasting serum insulin and an oral glucose tolerance test. Tea products included oolong tea, oolong tea with added catechins and oolong tea with added oolong tea polyphenols, and control beverages included caffeinated water and unsupplemented water. On the fifth day of each treatment period, treatment beverages were consumed with a standardized meal, and glucose and insulin responses were assessed for 240 min.Results:No significant differences were detected for fasting plasma glucose, fasting serum insulin, incremental plasma glucose area under the concentration time curve (AUC), total plasma glucose AUC or total serum insulin AUC.Conclusions:Neither oolong tea nor oolong tea supplemented with catechins or other polyphenols produced improved glucose metabolism in healthy adult volunteers on the basis of this highly controlled dietary intervention trial.

Energy expenditure by indirect calorimetry in premenopausal women: variation within one menstrual cycle☆☆☆

Energy expenditure in relation to the menstrual cycle was determined by indirect calorimetry in premenopausal women. For each subject, three measurements were made within a single menstrual cycle. Energy expenditure measurements coincided with the subjects expected hormonal fluxes of estradiol and progesterone: menstrual phase-both hormones at basal levels; follicular phase-elevated estradiol; and luteal phase-elevated progesterone. In experiment I, resting energy expenditure of 14 women was determined for 1 hour using a canopy system for calorimetry; in experiment 2, 24-hour energy expenditures of 12 subjects were measured in a room-size calorimeter. Blood from fasted (12 hours) subjects was collected following measurements of energy expenditure and analyzed for serum estradiol-17B and progesterone by radioimmunoassay. In experiment 1, resting energy expenditure did not differ within one menstrual cycle; neither estradiol nor progesterone affected resting energy expenditure. In experiment 2, 24-hour energy expenditure was significantly lower (P < 0.013) during the follicular phase when compared with the menstrual (−3.8%) and luteal (−4.9%) phases. Lowered 24-hour energy expenditure during the follicular phase may in part be due to a decrease in spontaneous activity and exercise. Energy expenditure during sleep, an indicator of metabolic energy expenditure, was significantly greater (P < 0.0001) during the luteal phase than during the menstrual (+6.7%) and follicular (+5.4%) phases; this was a reflection of increased progesterone (P < 0.0001). Twenty-four hour energy expenditure (mean ± SEM) during the menstrual, follicular, and luteal phases was 8.86 ± 0.26, 8.52 ± 0.22, and 8.96 ± 0.21 MJ/d, respectively. Corresponding values for energy expenditure during sleep were 5.49 ± 0.09, 5.56 ± 0.10, and 5.86 ± 0.11 MJ/d. The menstrual cycle is a significant contributor to variation in energy expenditure through progesterone-mediated increases in metabolic rate. Variation in metabolic energy expenditure was detectable when the contributory components of 24-hour energy expenditure were measured.

Diet Interviews of Subject Pairs: How Different Persons Recall Eating the Same Foods

OBJECTIVE To compare qualitative descriptions of the same food items eaten by different persons using 24-hour dietary recall interviews. DESIGN Eleven pairs of subjects were interviewed twice using 24-hour dietary recalls such that each member of the pair described the same days foods. Each pair shared a home and ate at least 2 meals together daily. After each interview, subjects were asked to identify the foods reported during the interview that they observed the other member of their pair consuming and to note when a particular food was the only item of that type available in the house. Qualitative descriptions of the foods were compared, differences in descriptions were noted, and calculations were made of the potential energy error produced if a subject erred in reporting a food item. SUBJECTS/SETTING Subjects were randomly selected from a database of persons who have participated in other studies at the Beltsville Human Nutrition Research Center. Ten pairs were husbands and wives and 1 pair was sisters. Each pair reported eating at least 2 meals per day together. Dietary recall interviews were done at the Research Center and were conducted by a trained dietitian in a quiet room free of distractions. RESULTS Discrepancies in qualitative food descriptions were identified for every subject pair interviewed. Men were found to be more likely to omit food items than women, snack items were more likely to be omitted than meal items, meat items were likely to be described inaccurately, and first interviews were likely to contain more errors than second interviews. APPLICATIONS/CONCLUSIONS This analysis shows which types of food items are most likely to be omitted or inaccurately described, and that dietetics professionals may improve the accuracy of dietary intake interviews by asking questions related to meat, milk, and snacks very carefully. The analysis also showed reductions in recall inconsistencies from the first recall to the second recall, suggesting that the learning associated with repeated interviews may be helpful in accurately identifying what a person consumes.

Accuracy and precision of dual‐energy X‐ray absorptiometry for body composition measurements in rhesus monkeys*

Accuracy of body composition measurements by dual‐energy X‐ray absorptiometry (DXA) was compared with direct chemical analysis in 10 adult rhesus monkeys. DXA was highly correlated (r‐values >0.95) with direct analyses of body fat mass (FM), lean mass (LM) and lumbar spine bone mineral content (BMC). DXA measurements of total body BMC were not as strongly correlated (r‐value=0.58) with total carcass ash content. DXA measurements of body FM, LM and lumbar spine BMC were not different from data obtained by direct analyses (P‐values >0.30). In contrast, DXA determinations of total BMC (TBMC) averaged 15% less than total carcass ash measurements (P=0.002). In conclusion, this study confirms the accurate measurement of fat and lean tissue mass by DXA in rhesus monkeys. DXA also accurately measured lumbar spine BMC but underestimated total body BMC as compared with carcass ash determinations.