Reductions in whole-body fat mass but not increases in lean mass predict changes in cardiometabolic health indices with exercise training among weight-stable adults.

We assessed whether body composition changes with 9 months of exercise training predicted changes in cardiometabolic health indices in weight-stable adults. We hypothesized that within ±5% weight change, changes in whole-body fat and lean masses would predict changes in cardiometabolic health indices with exercise training. Using a randomized parallel design, 152 adults (age: 49 ±â€¯8 year; body mass index: 30.0 ±â€¯2.7 kg/m2; mean ±â€¯SD) performed resistance exercises 2 d/wk and aerobic exercises 1 d/wk for 9 months. Participants consumed isoenergetic supplements with 0, 10, 20, or 30 g whey protein twice daily and remained weight stable within ±5% of baseline weight. Body weight and composition were measured using dual-energy x-ray absorptiometry pre- and postintervention. Multiple linear regression model was applied for data analyses. Independent of whey protein supplementation, reductions in fat mass predicted increases in high-density lipoprotein cholesterol (unstandardized beta-coefficient [ß], -0.03; 95% confidence interval [CI], -0.06 to -0.01; P = .007) and insulin sensitivity index (ß, -0.52; 95% CI, -0.95 to -0.09; P = .018) and decreases in waist circumference (ß, 0.67; 95% CI, 0.17-1.18; P = .009). In contrast, increases in lean mass did not predict changes in any of the measured cardiometabolic health indices. Health improvements with training that emphasize resistance exercises are typically attributed to increases in lean mass; however, these results underscore reducing body fat to predict cardiometabolic health improvements.

Effects of Higher Dietary Protein and Fiber Intakes at Breakfast on Postprandial Glucose, Insulin, and 24-h Interstitial Glucose in Overweight Adults.

Dietary protein and fiber independently influence insulin-mediated glucose control. However, potential additive effects are not well-known. Men and women (n = 20; age: 26 ± 5 years; body mass index: 26.1 ± 0.2 kg/m²; mean ± standard deviation) consumed normal protein and fiber (NPNF; NP = 12.5 g, NF = 2 g), normal protein and high fiber (NPHF; NP = 12.5 g, HF = 8 g), high protein and normal fiber (HPNF; HP = 25 g, NF = 2 g), or high protein and fiber (HPHF; HP = 25 g, HF = 8 g) breakfast treatments during four 2-week interventions in a randomized crossover fashion. On the last day of each intervention, meal tolerance tests were completed to assess postprandial (every 60 min for 240 min) serum glucose and insulin concentrations. Continuous glucose monitoring was used to measure 24-h interstitial glucose during five days of the second week of each intervention. Repeated-measures ANOVA was applied for data analyses. The HPHF treatment did not affect postprandial glucose and insulin responses or 24-h glucose total area under the curve (AUC). Higher fiber intake reduced 240-min insulin AUC. Doubling the amount of protein from 12.5 g to 25 g/meal and quadrupling fiber from 2 to 8 g/meal at breakfast was not an effective strategy for modulating insulin-mediated glucose responses in these young, overweight adults.

The Effects of Increased Protein Intake on Fullness: A Meta-Analysis and Its Limitations.

BACKGROUND: Higher protein intake has been implicated in weight management because of its appetitive properties. However, the effects of protein intake on appetitive sensations such as fullness have not been systematically assessed. Meta-analysis is a useful technique to evaluate evidence of an intervention's effect on testable outcomes, but it also has important limitations. OBJECTIVE: The primary aim of this study was to synthesize the available evidence on the effect of protein intake on fullness using a quantitative meta-analysis and a secondary directional analysis using the vote-counting procedure. A tertiary aim was to address limitations of meta-analyses as they pertain to findings from this meta-analysis. DESIGN: We searched multiple databases for interventional studies that evaluated the effect of increased protein intake on fullness ratings. Inclusion criteria for both analyses were as follows: healthy human participants, preload studies that utilized intact dietary protein, delivery of protein load orally, and studies reporting fullness as an outcome. For the meta-analysis, an additional criterion was that the studies also needed to report 2- to 4-hour area under the curve value for fullness. RESULTS: Five studies met all criteria for the meta-analysis. Twenty-eight studies met all criteria for the directional analysis. The meta-analysis indicated higher protein preloads have a greater effect on fullness than lower protein preloads (overall effect estimate: 2,435.74 mm.240 min, (95% CI 1,375.18 to 3,496.31 mm.240 min; P<0.0001). The directional analysis also revealed a positive effect on fullness with higher protein preloads (P<0.01). Many related scientifically rigorous studies were excluded from the analysis because analytical criteria required a narrowly focused research question. CONCLUSIONS: The present analyses show that higher protein preloads increase fullness ratings more than lower protein preloads under tightly defined conditions. Extrapolation of findings to common conditions outside the specified criteria of this analysis must be made cautiously, as must speculation about the influence of fullness sensations on ingestive behavior, body weight, and various health outcomes.

Effects of Dietary Protein and Fiber at Breakfast on Appetite, ad Libitum Energy Intake at Lunch, and Neural Responses to Visual Food Stimuli in Overweight Adults.

Increasing either protein or fiber at mealtimes has relatively modest effects on ingestive behavior. Whether protein and fiber have additive or interactive effects on ingestive behavior is not known. Fifteen overweight adults (5 female, 10 male; BMI: 27.1 ± 0.2 kg/m²; aged 26 ± 1 year) consumed four breakfast meals in a randomized crossover manner (normal protein (12 g) + normal fiber (2 g), normal protein (12 g) + high fiber (8 g), high protein (25 g) + normal fiber (2 g), high protein (25 g) + high fiber (8 g)). The amount of protein and fiber consumed at breakfast did not influence postprandial appetite or ad libitum energy intake at lunch. In the fasting-state, visual food stimuli elicited significant responses in the bilateral insula and amygdala and left orbitofrontal cortex. Contrary to our hypotheses, postprandial right insula responses were lower after consuming normal protein vs. high protein breakfasts. Postprandial responses in other a priori brain regions were not significantly influenced by protein or fiber intake at breakfast. In conclusion, these data do not support increasing dietary protein and fiber at breakfast as effective strategies for modulating neural reward processing and acute ingestive behavior in overweight adults.

Higher Total Protein Intake and Change in Total Protein Intake Affect Body Composition but Not Metabolic Syndrome Indexes in Middle-Aged Overweight and Obese Adults Who Perform Resistance and Aerobic Exercise for 36 Weeks.

BACKGROUND: Studies assessing the effects of protein supplementation on changes in body composition (BC) and health rarely consider the impact of total protein intake (TPro) or the change in TPro (CTPro) from participants' usual diets. OBJECTIVE: This secondary data analysis assessed the impact of TPro and CTPro on changes in BC and metabolic syndrome (MetS) indexes in overweight and obese middle-aged adults who participated in an exercise training program. METHODS: Men and women [n = 117; age: 50 ± 0.7 y, body mass index (BMI; in kg/m(2)): 30.1 ± 0.3; means ± SEs] performed resistance exercise 2 d/wk and aerobic exercise 1 d/wk and consumed an unrestricted diet along with 200-kcal supplements (0, 10, 20, or 30 g whey protein) twice daily for 36 wk. Protein intake was assessed via 4-d food records. Multiple linear regression model and stratified analysis were applied for data analyses. RESULTS: Among all subjects, TPro and CTPro were inversely associated (P < 0.05) with changes in body mass, fat mass (FM), and BMI. Changes in BC were different (P < 0.05) among groups that consumed <1.0 (n = 43) vs. ≥1.0 to <1.2 (n = 29) vs. ≥1.2 g · kg(-1) · d(-1) (n = 45). The TPro group with ≥1.0 to <1.2 g ·: kg(-1) ·: d(-1) reduced FM and %FM and increased percentage of LM (%LM) compared with the lowest TPro group, whereas the TPro group with ≥1.2 g ·: kg(-1) ·: d(-1) presented intermediate responses on changes in FM, %FM, and %LM. The gain in LM was not different among groups. In addition, MetS indexes were not influenced by TPro and CTPro. CONCLUSIONS: In conjunction with exercise training, higher TPro promoted positive changes in BC but not in MetS indexes in overweight and obese middle-aged adults. Changes in TPro from before to during the intervention also influenced BC responses and should be considered in future research when different TPro is achieved via diet or supplements. This trial was registered at clinicaltrials.gov as NCT00812409.