Dataset Information

Predicting energy intake in adults who are dieting and exercising.

ABSTRACT:

Background

When a lifestyle intervention combines caloric restriction and increased physical activity energy expenditure (PAEE), there are two components of energy balance, energy intake (EI) and physical activity energy expenditure (PAEE), that are routinely misreported and expensive to measure. Energy balance models have successfully predicted EI if PAEE is known. Estimating EI from an energy balance model when PAEE is not known remains an open question.

Objective

The objective was to evaluate the performance of an energy balance differential equation model to predict EI in an intervention that includes both calorie restriction and increases in PAEE.

Design

The Antonetti energy balance model that predicts body weight trajectories during weight loss was solved and inverted to estimate EI during weight loss. Using data from a calorie restriction study that included interventions with and without prescribed PAEE, we tested the validity of the Antonetti weight predictions against measured weight and the Antonetti EI model against measured EI using the intake-balance method at 168 days. We then evaluated the predicted EI from the model against measured EI in a study that prescribed both calorie restriction and increased PAEE.

Results

Compared with measured body weight at 168 days, the mean (±SD) model error was 1.30 ± 3.58 kg. Compared with measured EI at 168 days, the mean EI (±SD) model error in the intervention that prescribed calorie restriction and did not prescribe increased PAEE, was -84.9 ± 227.4 kcal/d. In the intervention that prescribed calorie restriction combined with increased PAEE, the mean (±SD) EI model error was -155.70 ± 205.70 kcal/d.

Conclusion

The validity of the newly developed EI model was supported by experimental observations and can be used to determine EI during weight loss.

SUBMITTER: Gerving C

PROVIDER: S-EPMC9691568 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Publications

Predicting energy intake in adults who are dieting and exercising.

Gerving Corey C Lasater Robert R Starling James J Ostendorf Danielle M DM Redman Leanne M LM Estabrooks Chad C Cummiskey Kevin K Antonetti Vincent V Thomas Diana M DM

International journal of obesity (2005) 20220820 12

<h4>Background</h4>When a lifestyle intervention combines caloric restriction and increased physical activity energy expenditure (PAEE), there are two components of energy balance, energy intake (EI) and physical activity energy expenditure (PAEE), that are routinely misreported and expensive to measure. Energy balance models have successfully predicted EI if PAEE is known. Estimating EI from an energy balance model when PAEE is not known remains an open question.<h4>Objective</h4>The objective ...[more]

PMID: 35987955

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Project description:BackgroundEnergy deficiency can result in menstrual disturbances and compromised bone health in women, a condition known as the Female Athlete Triad.ObjectivesThe REFUEL randomized controlled trial assessed the impact of increased energy intake on bone health and menstrual function in exercising women with menstrual disturbances.MethodsExercising women with oligomenorrhea/amenorrhea (Oligo/Amen) were randomly assigned to an intervention group (Oligo/Amen + Cal, n = 40, mean ± SEM age: 21.3 ± 0.5 y; weight: 55.0 ± 1.0 kg; BMI: 20.4 ± 0.3 kg/m2) who increased energy intake 20%-40% above baseline energy needs for 12 mo or a control group (Oligo/Amen Control, n = 36; mean ± SEM age: 20.7 ± 0.5 y; weight: 59.1 ± 1.3 kg; BMI: 21.3 ± 0.4 kg/m2). Energy intake and expenditure, metabolic and reproductive hormones, body composition, and areal bone mineral density (aBMD) were assessed.ResultsOligo/Amen + Cal improved energy status [increased body mass (2.6 ± 0.4 kg), BMI (0.9 ± 0.2 kg/m2), fat mass (2.0 ± 0.3 kg), body fat percentage (2.7% ± 0.4%), and insulin-like growth factor 1 (37.4 ± 14.6 ng/mL)] compared with Oligo/Amen Control and experienced a greater likelihood of menses (P < 0.05). Total body and spine aBMD remained unchanged (P > 0.05). Both groups demonstrated decreased femoral neck aBMD at month 6 (-0.006 g/cm2; 95% CI: -0.011, -0.0002 g/cm2 ; time main effect P = 0.043) and month 12 (-0.011 g/cm2; 95% CI: -0.021, -0.001 g/cm2; time main effect P = 0.023). Both groups demonstrated a decrease in total hip aBMD at month 6 (-0.006 g/cm2; 95% CI: -0.011, -0.002 g/cm2; time main effect P = 0.004).ConclusionsAlthough higher dietary energy intake increased weight, body fat, and menstrual frequency, bone mineral density was not improved, compared with the control group. The 12-mo intervention may have been too short and the increase in energy intake (∼352 kcal/d), although sufficient to increase menstrual frequency, was insufficient to increase estrogen or improve aBMD. Future research should refine the optimal nutritional and/or pharmacological interventions for the recovery of bone health in athletes and exercising women with Oligo/Amen.This trial was registered at clinicaltrials.gov as NCT00392873.

Project description:Study questionDoes increased daily energy intake lead to menstrual recovery in exercising women with oligomenorrhoea (Oligo) or amenorrhoea (Amen)?Summary answerA modest increase in daily energy intake (330 ± 65 kcal/day; 18 ± 4%) is sufficient to induce menstrual recovery in exercising women with Oligo/Amen.What is known alreadyOptimal energy availability is critical for normal reproductive function, but the magnitude of increased energy intake necessary for menstrual recovery in exercising women, along with the associated metabolic changes, is not known.Study design, size, durationThe REFUEL study (trial # NCT00392873) is the first randomised controlled trial to assess the effectiveness of 12 months of increased energy intake on menstrual function in 76 exercising women with menstrual disturbances. Participants were randomised (block method) to increase energy intake 20-40% above baseline energy needs (Oligo/Amen + Cal, n = 40) or maintain energy intake (Oligo/Amen Control, n = 36). The study was performed from 2006 to 2014.Participants/materials, setting, methodsParticipants were Amen and Oligo exercising women (age = 21.0 ± 0.3 years, BMI = 20.8 ± 0.2 kg/m2, body fat = 24.7 ± 0.6%) recruited from two universities. Detailed assessment of menstrual function was performed using logs and measures of daily urinary ovarian steroids. Body composition and metabolic outcomes were assessed every 3 months.Main results and the role of chanceUsing an intent-to-treat analysis, the Oligo/Amen + Cal group was more likely to experience menses during the intervention than the Oligo/Amen Control group (P = 0.002; hazard ratio [CI] = 1.91 [1.27, 2.89]). In the intent-to-treat analysis, the Oligo/Amen + Cal group demonstrated a greater increase in energy intake, body weight, percent body fat and total triiodothyronine (TT3) compared to the Oligo/Amen Control group (P < 0.05). In a subgroup analysis where n = 22 participants were excluded (ambiguous baseline menstrual cycle, insufficient time in intervention for menstrual recovery classification), 64% of the Oligo/Amen + Cal group exhibited improved menstrual function compared with 19% in the Oligo/Amen Control group (χ2, P = 0.001).Limitations, reasons for cautionWhile we had a greater than expected dropout rate for the 12-month intervention, it was comparable to other shorter interventions of 3-6 months in duration. Menstrual recovery defined herein does not account for quality of recovery.Wider implications of the findingsExpanding upon findings in shorter, non-randomised studies, a modest increase in daily energy intake (330 ± 65 kcal/day; 18 ± 4%) is sufficient to induce menstrual recovery in exercising women with Oligo/Amen. Improved metabolism, as demonstrated by a modest increase in body weight (4.9%), percent body fat (13%) and TT3 (16%), was associated with menstrual recovery.Study funding/competing interest(s)This research was supported by the U.S. Department of Defense: U.S. Army Medical Research and Material Command (Grant PR054531). Additional research assistance provided by the Penn State Clinical Research Center was supported by the National Center for Advancing Translation Sciences, National Institutes of Health, through Grant UL1 TR002014. M.P.O. was supported in part by the Loretta Anne Rogers Chair in Eating Disorders at University of Toronto and University Health Network. All authors report no conflict of interest.Trial registration numberNCT00392873.Trial registration dateOctober 2006.Date of first patient’s enrolmentSeptember 2006.

Project description:BackgroundVarious recommendations exist for total water intake (TWI), yet it is seldom reported in dietary surveys. Few studies have examined how real-life consumption patterns, including beverage type, variety and timing relate to TWI and energy intake (EI).MethodsWe analysed weighed dietary records from the National Diet and Nutrition Survey of 1724 British adults aged 19-64 years (2000/2001) to investigate beverage consumption patterns over 24 hrs and 7 days and associations with TWI and EI. TWI was calculated from the nutrient composition of each item of food and drink and compared with reference values.ResultsMean TWI was 2.53 L (SD 0.86) for men and 2.03 L (SD 0.71) for women, close to the European Food Safety Authority "adequate Intake" (AI) of 2.5 L and 2 L, respectively. However, for 33% of men and 23% of women TWI was below AI and TWI:EI ratio was <1 g/kcal. Beverages accounted for 75% of TWI. Beverage variety was correlated with TWI (r 0.34) and more weakly with EI (r 0.16). Beverage consumption peaked at 0800 hrs (mainly hot beverages/ milk) and 2100 hrs (mainly alcohol). Total beverage consumption was higher at weekends, especially among men. Overall, beverages supplied 16% of EI (men 17%, women 14%), alcoholic drinks contributed 9% (men) and 5% (women), milk 5-6%, caloric soft drinks 2%, and fruit juice 1%.In multi-variable regression (adjusted for sex, age, body weight, smoking, dieting, activity level and mis-reporting), replacing 100 g of caloric beverages (milk, fruit juice, caloric soft drinks and alcohol) with 100 g non-caloric drinks (diet soft drinks, hot beverages and water) was associated with a reduction in EI of 15 kcal, or 34 kcal if food energy were unchanged. Using within-person data (deviations from 7-day mean) each 100 g change in caloric beverages was associated with 29 kcal change in EI or 35 kcal if food energy were constant. By comparison the calculated energy content of caloric drinks consumed was 47 kcal/100 g.ConclusionsTWI and beverage consumption are closely related, and some individuals appeared to have low TWI. Compensation for energy from beverages may occur but is partial. A better understanding of interactions between drinking and eating habits and their impact on water and energy balance would give a firmer basis to dietary recommendations.

Project description:BackgroundThe fecal metabolome is affected by diet and includes metabolites generated by human and microbial metabolism. Advances in -omics technologies and analytic approaches have allowed researchers to identify metabolites and better utilize large data sets to generate usable information. One promising aspect of these advancements is the ability to determine objective biomarkers of food intake.ObjectivesWe aimed to utilize a multivariate, machine learning approach to identify metabolite biomarkers that accurately predict food intake.MethodsData were aggregated from 5 controlled feeding studies in adults that tested the impact of specific foods (almonds, avocados, broccoli, walnuts, barley, and oats) on the gastrointestinal microbiota. Fecal samples underwent GC-MS metabolomic analysis; 344 metabolites were detected in preintervention samples, whereas 307 metabolites were detected postintervention. After removing metabolites that were only detected in either pre- or postintervention and those undetectable in ≥80% of samples in all study groups, changes in 96 metabolites relative concentrations (treatment postintervention minus preintervention) were utilized in random forest models to 1) examine the relation between food consumption and fecal metabolome changes and 2) rank the fecal metabolites by their predictive power (i.e., feature importance score).ResultsUsing the change in relative concentration of 96 fecal metabolites, 6 single-food random forest models for almond, avocado, broccoli, walnuts, whole-grain barley, and whole-grain oats revealed prediction accuracies between 47% and 89%. When comparing foods with one another, almond intake was differentiated from walnut intake with 91% classification accuracy.ConclusionsOur findings reveal promise in utilizing fecal metabolites as objective complements to certain self-reported food intake estimates. Future research on other foods at different doses and dietary patterns is needed to identify biomarkers that can be applied in feeding study compliance and clinical settings.

Dataset Information

Predicting energy intake in adults who are dieting and exercising.

Background

Objective

Design

Results

Conclusion

Publications

Predicting energy intake in adults who are dieting and exercising.

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