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Body characteristics, [corrected] dietary protein and body weight regulation. Reconciling conflicting results from intervention and observational studies?

ABSTRACT: Physiological evidence indicates that high-protein diets reduce caloric intake and increase thermogenic response, which may prevent weight gain and regain after weight loss. Clinical trials have shown such effects, whereas observational cohort studies suggest an association between greater protein intake and weight gain. In both types of studies the results are based on average weight changes, and show considerable diversity in both directions. This study investigates whether the discrepancy in the evidence could be due to recruitment of overweight and obese individuals into clinical trials.Data were available from the European Diet, Obesity and Genes (DiOGenes) post-weight-loss weight-maintenance trial and the Danish Diet, Cancer and Health (DCH) cohort. Participants of the DCH cohort were matched with participants from the DiOGenes trial on gender, diet, and body characteristics. Different subsets of the DCH-participants, comparable with the trial participants, were analyzed for weight maintenance according to the randomization status (high or low protein) of the matched trial participants.Trial participants were generally heavier, had larger waist circumference and larger fat mass than the participants in the entire DCH cohort. A better weight maintenance in the high-protein group compared to the low protein group was observed in the subgroups of the DCH cohort matching body characteristics of the trial participants.This modified observational study, minimized the differences between the RCT and observational data with regard to dietary intake, participant characteristics and statistical analysis. Compared with low protein diet the high protein diet was associated with better weight maintenance when individuals with greater body mass index and waist circumference were analyzed. Selecting subsets of large-scale observational cohort studies with similar characteristics as participants in clinical trials may reconcile the otherwise conflicting results.

SUBMITTER: Ankarfeldt MZ

PROVIDER: S-EPMC4081118 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Body characteristics, [corrected] dietary protein and body weight regulation. Reconciling conflicting results from intervention and observational studies?

Ankarfeldt Mikkel Z MZ Ängquist Lars L Stocks Tanja T Jakobsen Marianne U MU Overvad Kim K Halkjær Jytte J Saris Wim H M WH Astrup Arne A Sørensen Thorkild I A TI

PloS one 20140703 7

<h4>Background/objectives</h4>Physiological evidence indicates that high-protein diets reduce caloric intake and increase thermogenic response, which may prevent weight gain and regain after weight loss. Clinical trials have shown such effects, whereas observational cohort studies suggest an association between greater protein intake and weight gain. In both types of studies the results are based on average weight changes, and show considerable diversity in both directions. This study investigat ...[more]

PMID: 24992329

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Project description:BACKGROUND: Previous studies have focused on weight maintenance following weight loss, i.e. secondary weight maintenance (SWM). The long-term results of SWM have been rather modest and it has been suggested that preventing initial weight gain, i.e. primary weight maintenance (PWM), may be more successful. Therefore, developing a prevention strategy focused on PWM, enabling normal weight or overweight individuals to maintain their weight, would be of great interest. The aim of this study was to identify attitudes, strategies, and behaviors that are predictive of PWM in different age, sex and BMI groups in Northern Sweden. METHODS: A questionnaire was mailed to 3497 individuals in a Swedish population that had two measured weights taken ten years apart, as participants in the Västerbotten Intervention Programme. Subjects were between 41-63 years of age at the time of the survey, had a baseline BMI of 20-30, and a ten year percent change in BMI greater than -3%. The respondents were divided into twelve subgroups based on baseline age (30, 40 and 50), sex and BMI (normal weight and overweight). Analysis of variance (ANOVA), correlation, and linear regression were performed to identify independent predictors of PWM. RESULTS: Of the 166 predictors tested, 152 (91.6%) were predictive of PWM in at least one subgroup. However, only 7 of these 152 variables (4.6%) were significant in 6 subgroups or more. The number of significant predictors of PWM was higher for male (35.8) than female (27.5) subgroups (p=0.044). There was a tendency (non significant) for normal weight subgroups to have a higher number of predictors (35.3) than overweight subgroups (28.0). Adjusted R-squared values ranged from 0.1 to 0.420. CONCLUSIONS: The large number of PWM predictors identified, and accompanying high R-squared values, provide a promising first step towards the development of PWM interventions. The large disparity in the pattern of significant variables between subgroups suggests that these interventions should be tailored to the person's demographic (age, sex and BMI). The next steps should be directed towards evaluation of these predictors for causal potential.

Project description:BackgroundSmart scales are increasingly used at home by patients to monitor their body weight and body composition, but scale accuracy has not often been documented.ObjectiveThe goal of the research was to determine the accuracy of 3 commercially available smart scales for weight and body composition compared with dual x-ray absorptiometry (DEXA) as the gold standard.MethodsWe designed a cross-sectional study in consecutive patients evaluated for DEXA in a physiology unit in a tertiary hospital in France. There were no exclusion criteria except patient declining to participate. Patients were weighed with one smart scale immediately after DEXA. Three scales were compared (scale 1: Body Partner [Téfal], scale 2: DietPack [Terraillon], and scale 3: Body Cardio [Nokia Withings]). We determined absolute error between the gold standard values obtained from DEXA and the smart scales for body mass, fat mass, and lean mass.ResultsThe sample for analysis included 53, 52, and 48 patients for each of the 3 tested smart scales, respectively. The median absolute error for body weight was 0.3 kg (interquartile range [IQR] -0.1, 0.7), 0 kg (IQR -0.4, 0.3), and 0.25 kg (IQR -0.10, 0.52), respectively. For fat mass, absolute errors were -2.2 kg (IQR -5.8, 1.3), -4.4 kg (IQR -6.6, 0), and -3.7 kg (IQR -8.0, 0.28), respectively. For muscular mass, absolute errors were -2.2 kg (IQR -5.8, 1.3), -4.4 kg (IQR -6.6, 0), and -3.65 kg (IQR -8.03, 0.28), respectively. Factors associated with fat mass measurement error were weight for scales 1 and 2 (P=.03 and P<.001, respectively), BMI for scales 1 and 2 (P=.034 and P<.001, respectively), body fat for scale 1 (P<.001), and muscular and bone mass for scale 2 (P<.001 for both). Factors associated with muscular mass error were weight and BMI for scale 1 (P<.001 and P=.004, respectively), body fat for scales 1 and 2 (P<.001 for both), and muscular and bone mass for scale 2 (P<.001 and P=.002, respectively).ConclusionsSmart scales are not accurate for body composition and should not replace DEXA in patient care.Trial registrationClinicalTrials.gov NCT03803098; https://clinicaltrials.gov/ct2/show/NCT03803098.

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Body characteristics, [corrected] dietary protein and body weight regulation. Reconciling conflicting results from intervention and observational studies?

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Body characteristics, [corrected] dietary protein and body weight regulation. Reconciling conflicting results from intervention and observational studies?

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