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Over the past 20 y, higher-protein diets have been touted as a successful strategy to prevent or treat obesity through improvements in body weight management. These improvements are thought to be due, in part, to modulations in energy metabolism, appetite, and energy intake. Recent evidence also supports higher-protein diets for improvements in cardiometabolic risk factors. This article provides an overview of the literature that explores the mechanisms of action after acute protein consumption and the clinical health outcomes after consumption of long-term, higher-protein diets. Several meta-analyses of shorter-term, tightly controlled feeding studies showed greater weight loss, fat mass loss, and preservation of lean mass after higher-protein energy-restriction diets than after lower-protein energy-restriction diets. Reductions in triglycerides, blood pressure, and waist circumference were also reported. In addition, a review of the acute feeding trials confirms a modest satiety effect, including greater perceived fullness and elevated satiety hormones after higher-protein meals but does not support an effect on energy intake at the next eating occasion. Although shorter-term, tightly controlled feeding studies consistently identified benefits with increased protein consumption, longer-term studies produced limited and conflicting findings; nevertheless, a recent meta-analysis showed persistent benefits of a higher-protein weight-loss diet on body weight and fat mass. Dietary compliance appears to be the primary contributor to the discrepant findings because improvements in weight management were detected in those who adhered to the prescribed higher-protein regimen, whereas those who did not adhere to the diet had no marked improvements. Collectively, these data suggest that higher-protein diets that contain between 1.2 and 1.6 g protein · kg(-1) · d(-1) and potentially include meal-specific protein quantities of at least ∼25-30 g protein/meal provide improvements in appetite, body weight management, cardiometabolic risk factors, or all of these health outcomes; however, further strategies to increase dietary compliance with long-term dietary interventions are warranted. © 2015 American Society for Nutrition.
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The role of protein in weight loss and maintenance
1–5
Heather J Leidy, Peter M Clifton, Arne Astrup, Thomas P Wycherley, Margriet S Westerterp-Plantenga,
Natalie D Luscombe-Marsh, Stephen C Woods, and Richard D Mattes
ABSTRACT
Over the past 20 y, higher-protein diets have been touted as a success-
ful strategy to prevent or treat obesity through improvements in body
weight management. These improvements are thought to be due, in
part, to modulations in energy metabolism, appetite, and energy in-
take. Recent evidence also supports higher-protein diets for improve-
ments in cardiometabolic risk factors. This article provides an
overview of the literature that explores the mechanisms of action af-
ter acute protein consumption and the clinical health outcomes after
consumption of long-term, higher-protein diets. Several meta-analyses
of shorter-term, tightly controlled feeding studies showed greater
weight loss, fat mass loss, and preservation of lean mass after higher-
protein energy-restriction diets than after lower-protein energy-
restriction diets. Reductions in triglycerides, blood pressure, and
waist circumference were also reported. In addition, a review of
the acute feeding trials confirms a modest satiety effect, including
greater perceived fullness and elevated satiety hormones after higher-
protein meals but does not support an effect on energy intake at the
next eating occasion. Although shorter-term, tightly controlled feed-
ing studies consistently identified benefits with increased protein con-
sumption, longer-term studies produced limited and conflicting findings;
nev ertheless, a recent meta-analysis sho wed persistent benefits of
a higher-protein weight-loss diet on body weight and fat mass. Dietary
compliance appears to be the primary contributor to the discrepant find-
ings because improvements in weight management were detected in
those who adhered to the prescribed higher-protein regimen, whereas
those who did not adhere to the diet had no marked improvements. Col-
lectiv ely, these data suggest that higher-protein diets that contain between
1.2 and 1.6 g protein $ kg
21
$ d
21
and potentially include meal-specific
protein quantities of at least w25–30 g protein/meal provide improve-
ments in appetite, body weight management, cardiometabolic risk fac-
tors, or all of these health outcomes; howe ver, further strategies to
increase dietary compliance with long-term dietary interventions are
warranted. Am J Clin Nutr 2015;101(Suppl):1320S–9S.
Keywords: appetite control, compliance, high protein, satiety,
weight management
INTRODUCTION
Substantial evidence exists that supports the consumption of
increased dietary protein (ranging from 1.2 to 1.6 g protein $
kg
21
$ d
21
) as a successful strategy to prevent or treat obesity
through reductions in body weight and fat mass concomitant
with the preservation of lean mass (1–4). The effectiveness of
these diets may be due, in part, to modulations in energy me-
tabolism and appetitive signaling leading to reduced energy
intake. Furthermore, improvements in cardiometabolic risk
factors were also observed with higher-protein diets (1–4).
However, one point of contention is the feasibility of ad-
hering to a higher-protein diet for periods .1 y (5, 6).
The purpose of this article is to provide an overview of the
literature that explores the mechanisms of action after acute
protein consumption a nd the clinical health outcomes a fter
long-term, higher-protein diets. Acceptability and compliance
to the chronic consumption of increased dietary protein are also
considered. Last, novel recommendations for protein quantity
andtimingofconsumptiontoachieveimprovementsinweight
management are discussed.
1
From the Department of Nutrition and Exercise Physiology, School of Med-
icine, University of Missouri; Columbia, MO (HJL); the Sansom Institute for
Health Research, School of Pharmacy and Medical Sciences (PMC) and School
of Population Health (TPW), University of South Australia, Adelaide, Australia;
the Department of Nutrition, Exercise, and Sports, University of Copenhagen ,
Copenhagen, Denmark (AA); the Department of Human Biology, NUTRIM,
Faculty of Health, Medicine, and Life Sciences, Maastricht University, Maas-
tricht, The Netherlands (MSW -P); the Centre of Clinical Research Excellence in
Nutritional Physio logy, Interventions, and Outcomes, Uni ve rsity of Adelaide,
Adelaide, Australia (NDL-M and PMC); Preventati v e Health National Research
Flagship, Commonwealth Scientific and Industrial Research Organization
(CSIRO )–Animal, Food, and Health Sciences, Adelaide, Australia (NDL-M);
the Department of Psychiatry and Behavioral Neuroscience; UC College of
Medicine, University of Cincinnati, Cincinnati, OH (SCW); and the Department
of Nutrition Science, College of Health and Human Sciences, Purdue Universi ty,
West Lafayette, IN (RDM).
2
Presented at the conference “Protein Summit 2.0: Evaluating the Role of
Protein in Public Health,” held in Washington, DC, 2 October 2013.
3
Protein Summit 2.0 wa s hosted by Purdue University, Ingestive Be-
havior Research Center; the University of Missouri, Department of Nu-
trition and Exercise Physiology and Nutritional Center for Health; and
the Reynolds Institute on Aging and University of Arkansas for Medical
Sciences.
4
The Protein Summit 2.0 and this supplement were supported by funding
from The Beef Checkoff, Dairy Research Institute, Egg Nutrition Center,
Global Dairy Platform, Hillshire Brands, and the National Pork Board. Re-
sponsibility for the design, implementation, analysis, and interpretation of
the information presented in this review was that of the authors. This is a free
access article, distributed under terms (http://www.nutrition.org/publications/
guidelines-and-policies/license/) that permit unrestricted noncommercial use,
distribution, and reproduction in any medium, provided the original work is
properly cited.
5
Address correspondence to HJ Leidy, Department of Nutrition and Ex-
ercise Physiology, 307 Gwynn Hall, University of Missouri, Columbia, MO
65201. E-mail: leidyh@health.missouri.edu.
First published online April 29, 2015; doi: 10.3945/ajcn.114.084038.
1320S Am J Clin Nutr 2015;101(Suppl):1320S–9S. Printed in USA. Ó 2015 American Society for Nutrition
by guest on October 20, 2015ajcn.nutrition.orgDownloaded from
MECHANISTIC OUTCOMES WITH ACUTE PROTEIN
CONSUMPTION
Thermic effect of food and resting energy expenditure
Higher-protein diets have been promoted to increase energy
expenditure through increased postprandial thermogenesis
and resting metabolism. In general, dietary protein requires
20–30% of its usable energy to be expended for metabolism
and/or stora ge, whereas car bohydrates require 5–10% and
dietary fats requir e 0–3% (7). Previous reviews confirmed
that dietary protein consistently elicits a grea ter postprandial
thermic effect of food (TEF)
6
than do carbohydrates or fats
(8, 9). Furthermore, in a recent meta-analysis, protein intake
was shown to be positively associated with TEF after adjustment
for covariates (r =0.43,P = 0.009), such as sex, caffeine intake,
and dinner energy intake (10). Although differences in TEF are
evident after the consumption of lower- compared with higher-
protein meals, the actual energy differential is modest, highly
variable, and difficult to quantify, and hence, probably has mini-
mal impact on weight loss and weight maintenance.
During weight loss, higher-protein diets also prevent a decline in
resting energy expenditure (REE) (8, 9). Wycherley et al. (11)
ev aluated 24 randomized controlled trials comparing higher- with
lower -protein energy-restricted diets. Of the 24 studies, only 4
included REE analysis. Although both diets reduced REE, the
higher-protein diets led to a greater preservation of REE [mean
difference (MD): +142 kcal/d; 95% CI: 16, 269 kcal/d; P , 0.03]
(11). The mechanism by which dietary protein preserves REE
during energy restriction is likely due to the concomitant retention
of lean mass observed with higher-protein diets (discussed in
subsequent sections) (11). These data sho w a significant positi ve
effect of increased protein consumption on energy metabolism.
Appetite
There are 2 dimensions to proteins effects on appetitive
sensations. First, there may be a protein-specific appetite origi-
nating from the hypothesized homeostatic regulation of dietary
protein to meet bodily needs/requirements. Second, dietary
protein has stronger nonspecific satiety properties than do dietary
fat or carbohydrates (12, 13), which may lead to reductions in
daily energy intake (14, 15).
Protein-specific appetite
A protein-specific appetite purportedly exists to maintain
protein requirements and to prevent excess protein consumption
(16). This concept is summarized by the protein leverage hy-
pothesis, which suggests that a protein-specific appetite will
stimulate the drive for increased food intake when the protein
density of the diet is limited but will reduce intake of diets with
higher protein density (17). This hypothesis suggests a mecha-
nism linking dietary protein intake and energy balance. There
have been 3 direct tests of the hypothesis.
Gosby et al. (17) completed a randomized crossove r study in-
volving three 4-d ad libitum diets containing 10%, 15%, or 25% of
energy as protein. When the protein content of the diet was lowered
from 15% to 10%, daily energy intake increased by 12 6 4.5%
(+259 kcal/d; P , 0.05). Ho we v er , despite the additional energy
consumed, dietary protein remained lower than what was con-
sumedinthe15%diet(23% of energy, 275gproteinover4d).
When the protein content of the diet increased from 15% to 25%,
energy intake remained unchanged. By using a similar design,
Martens et al. (18) compared 12-d ad libitum diets consisting of
5%, 15%, or 30% of intake as protein. No change in energy intake
was observed between the 5% and 15% protein diets; how ev er,
energy intake was lower (2576 6 103 kcal/d) after the 30% than
after the 15% protein diet (18). In a second trial of comparable
design, this group used a different predominant protein source (beef
compared with soy or whey with a-lactalbumin) and obtained
similar results (19). Thus, whereas the Martens et al. trials con-
firmed the satiety value of protein, no study in humans has tested
and shown the con v ergence of protein intake from diets with higher
and lower relative protein content. Whether the time course of these
trials was adequate to observe an effect is unclear . In a recent
analysis of 38 ad libitum feeding trials, the percentage of dietary
protein was negativ ely associated with total daily energy intake
(F =6.9,P , 0.0001). This observation was noted only when
protein intakes were between 10% and 20%, and anything above this
amount resulted in no further reductions in daily intake. Taken
together, there is some evidence that supports the concept of
protein-specific appetite; however, the data are inconclusive for
the protein-leverage hypothesis because no single study has
reported data supporting both sides of the protein leverage.
Nonspecific appetite
Ingestive behavior is a complex system composed of homeo-
static, hedonic (i.e., reward), and behavioral/environmental inputs.
Clarification of the interactions between these drivers is just be-
ginning to emerge. From 2000 to the present, much has been
learned about the peripheral hormonal signals and central targets
that influence energy intake (20–22). Dietary protein is an ef-
fective stimulus for the release or inhibition of many of these
peptides (23–25). Ghrelin reportedly enhances hunger, initiates
eating, and increases energy intake (26–28). The responsiveness
of ghrelin release to specific nutrients is still under study, but there
is evidence of an effect of protein (25, 29). Peptide YY (PYY)
and glucagon-like peptide 1 (GLP-1) are associated with satiety
and reduce subsequent food intake (23, 24). Both PYY and GLP-1
are stimulated by the ingestion of various dietary components,
particularly dietary protein (23, 24, 30). There is also evidence of
a dose-response relation between protein quantity and the mag-
nitude of PYY and GLP-1 responses (30).
Importance of satiety
There is substantial industry and consumer interest to identify
specific foods, diets, or both that lead to enhanced satiety as
a mechanism to promote healthy eating and improved weight
management (31). One popular dietary strategy is to increase the
consumption of dietary protein. Although the data are not fully
consistent, compared with dietary carbohydrates or fats, the
consumption of protein has stronger satiety effects (12, 32).
6
Abbreviations used: Diogenes, Diet, Obesity and Genes; GLP-1, glucagon-
like peptide 1; MD, mean difference; PYY, peptide YY; REE, resting energy
expenditure; TEF, thermic effect of feeding; VLCKD, very-low-carbohydrate
(ketogenic) diet; WMD, weighted mean difference.
PROTEIN AND WEIGHT MANAGEMENT 1321S
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Higher-protein ad libitum diets have led to unintentional weight
loss caused from reductions in daily energy intake, which may
have occurred as a result of increased satiety (14, 15).
A challenge with this line of research is determining the “best”
index of satiety (or overall appetite) (33, 34). Although postprandial
appetitiv e sensations and hormonal responses are associated with
and may lead to alterations in subsequent energy intake (23, 24,
35–38), they do not consistently track with each other. More work
is needed to identify the most important indexes of appetite (hun-
ger , satiety) and associated markers (appetitive sensations, gut
hormones) for weight management over the long term.
APPETITE AND SUBSEQUENT FOOD INTAKE AFTER
HIGHER-PROTEIN MEALS
Despite the large number of randomized, acute meal, cross-
over-design studi es pu blished over the past 20 y, to our
knowledge, there are no systematic reviews or meta-analyses to
date comparing the effects of normal-protein with higher-protein
meals on markers of appetite, satiety, and subsequent food intake.
Thus, as a rst step in summarizing the existing data, the fol-
lowing inclusion criteria were applied to the existing literature: 1)
acute feeding trials of $120 min; 2) comparison of lower-fat
(,40% of meal as fat), isocaloric normal-protein with higher-
protein mixed meals with a protein differential of $10 g protein
between meals; and 3) repetitive, postprandial assessments of
appetitive sensations, hormonal responses, and/or subsequent
food intake. Twenty-four studies met the criteria and are sum-
marized in Table 1.
Only 6 (35%) reported greater reductions in postprandial
hunger after the higher-protein meals than after the lower-protein
meals, whereas 11 (55%) showed significant increases in post-
prandial fullness. Seven (37%) reported greater reductions in
postprandial ghrelin, and 7 (47%) showed a greater increase in
either PYY or GLP-1. Although the majority of these studies (17
of 24; 71%) reported at least one beneficial alteration in appetite
indexes after higher- than a fter lower-protein meals, only 3
studies (18%) observed a reduction in subsequent food intake
at the next eating occasion. Although the positive findings were
inconsistent across studies, it is important to note that none of
the studies reported a weakening in appetite control or increased
subsequent meal energy intake after the higher-protein meals
compared with the lower-protein meals. Restated, the studies
found either improvements with higher-protein meals or no
differences between the meals. Several dietary factors might have
contributed to the inconsistent results.
The consumption of beverages generally elicits a weaker
satiety response and less dietary compensation at the next eating
occasion in comparison with solid foods (58, 59). This effect was
also observed when dietary protein was consumed in a beverage
instead of consumed in solid form (60, 61). Thus, it is possible
that the blunted satiety response from beverages might ameliorate
protein-related effects. Of the 24 studies examined, 3 included
beverages; however, 5 studies (45, 47, 50–52) incorporated
semisolids (e.g., custards, yogurts), which might have also
o bscured the findings.
Protein quality (source) varied within and across studies.
Although the impact of protein quality on appetite control and
food intake is poorly characterized, there are data, albeit in-
consistent, that show protein-source effects. In some (62, 63), but
not all (64–66), studies the consumption of whey protein elicited
a greater reduction in postprandial hunger and a greater increase
in postprandial satiety than consumption of casein and/or soy.
The contribution of protein quality on these outcomes is further
supported by the Veldhorst et al. (50, 51) studies that compared
higher- with lower-protein meals but included different types of
protein. In one study, greater reductions in postprandial ghrelin
and increases in postprandial fullness and GLP-1 responses were
observed after the higher-protein whey meals than after the
lower-protein whey meals, whereas the second study found no
differences in postprandial ghrelin or GLP-1 concentrations after
the higher-protein casein meals when compared with the lower-
protein casein meals. Because many studies incorporated a mixture
of proteins and typically vary these proteins within and between
the lower- and higher-protein meals, it is difficult to determine
the actual contribution of protein quantity due to the protein
quality effects.
Another confounding factor concerns the potential for a pro-
tein quantity threshold effect. Several articles reported a specific
meal-related protein threshold of w25–30 g protein that is
necessary to stimulate protein synthesis (67–69). Whether
a similar threshold exists for satiety is not known. As shown
in Table 1, the quantity of protein included within the higher-
protein meals ranges from 20 to 207 g/meal. However, all but 2
of the studies included protein quantities well above the 25–30-g
protein synthesis threshold. Because of the varied experimental
designs and the few studies that contained lower protein quan-
tities, it is not possible to accurately perform a breakpoint
analysis. Nonetheless, neither of the studies that included ,25 g
protein had a satiety effect. Although preliminary, data from
several of Leidy’s previously published acute trials (70) permit
comparison of 15, 20, 25, and 30 g protein/meal interventions.
Postprandial fullness was significantly higher after a 30-g pro-
tein meal than after the other lower-protein versions (70) and
provides support for a potential satiety threshold at this quantity.
Future dose-response research including smaller qua ntities
o f protein is needed to identify an absolute protein threshold
specific to satiety.
The last discussion focuses on whether there is a ceiling effect
for dietary protein such that additional protein consumption in
a meal is not accompanied by further increases in satiety. The
most appropriate study to address this point is that of Belza et al.
(30), which observed dose-dependent increases in postprandial
fullness, GLP-1, and PYY responses and decreased postprandial
hunger and ghrelin responses after the consumption of 24, 44, and
88 g protein/meal. Several other studies (13, 40) compared even
larger quantities of protein (i.e., 58 compared with 185 g; 46
compared with 178 g) and found graded appetitive responses with
the higher-protein versions (Table 1). Although these study de-
signs do not allow for a direct examination for a protein ceiling, it
is clear that fairly large ranges of protein quantity elicit graded
satiety effects after a meal.
In general, these data confirm a modest satiety effect with
protein-rich meals but do not support an effect on energy intake at
the next eating occasion. Because most of these studies did not
assess changes in daily energy intake, it is unclear as to whether
the satiety effects of protein might affect eating behavior across
the entire day or beyond. A recent trial examined the effects of
a higher-protein breakfast on daily intake in habitual breakfast-
skipping young adults (55). Compared with a lower-protein
1322S LEIDY ET AL.
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TABLE 1
Summary of the tightly controlled acute feeding trials comparing lower- with higher-protein meals
1
First author, year
(reference)
Acute meals
Postprandial
period, min
Perceived sensations, (%) Hormonal responses, (%)
Lower-protein Higher-protein
Energy, kcal Meal type Hunger
Fullness/
satiety Ghrelin PYY GLP-1
Subsequent
meal energy
content, (kcal)Protein, g Protein source Protein, g Protein source
Stubbs, 1999 (39) 66 Plant 207 Animal 1400 Solid 120 Y (45) [ (10) Ø
Stubbs, 1996 (40) 58 Mixed 185 Mixed 1400 Solid 240 Ø [ (10) Ø
Batterham, 2006 (13) 46 Mixed 178 Mixed 1100 Solid 180 Y (58) Ø [ (21) Ø
Brennan, 2012 (41) 28 Mixed 127 Mixed 1130 Solid 180 Y (37) [ (49) Y (28) Ø Y (148)
Foster-Schubert, 2008 (25) 13 Dairy (no eggs, whey) 100 Mixed 500 Beverage 180 Ø Ø Y (38)
Belza, 2013 (30) 24 Mixed 88 Mixed 700 Solid 240 Y (25) [ (19) Y (10) [ (14) [ (20) Ø
Barkeling, 1990 (42) 16 Plant 64 Animal 600 Solid 240 Ø Ø Y (38)
van der Klaauw, 2013 (43) 20 Mixed (no pork) 60 Mixed 400 Solid 240 Ø Ø Ø [ (28) [ (20) Ø
Boelsma, 2010 (44) 17 Whey 59 Whey 675 Beverage 240 Ø Ø Y (4) Ø
Blom, 2006 (45) 19 Dairy 57 Dairy 400 Semisolid 180 Ø Ø Y (45) [ (66) Ø
El Khoury, 2010 (46) 14 Mixed 55 Mixed 550 Beverage 240 Ø Ø
Vozzo, 2003 (47) 25 Dairy 51 Dairy 700 Semisolid 180 Ø Ø
Leidy, 2010 (48) 26 Mixed (no pork, eggs) 46 Mixed 700 Solid 600 Ø [ (6) Ø [ (20)
Leidy, 2010 (49) 26 Mixed 46 Mixed 500 Solid 240 Ø Ø Ø Y (131)
Belza, 2013 (30) 24 Mixed 44 Mixed 700 Solid 240 Y (15) [ (6) Ø [ (7) [ (10) Ø
Veldhorst, 2009 (50) 15 Whey 38 Whey 600 Semisolid 240 [ (12) Y (38) [ (60) Ø
Veldhorst, 2009 (51) 15 Casein 38 Casein 600 Semisolid 240 [ (36) Ø Ø Ø
Veldhorst, 2009 (52) 15 Soy 38 Soy 600 Semisolid 240 [ (36) Ø Ø Ø
Al Awar, 2005 (53) 20 Dairy 36 Dairy 400 Solid 180 Ø
Smeets, 2008 (54) 14 Pork 35 Pork 350 Solid 180 [
(10) Ø Ø Ø
Leid
y, 2013 (55) 13 Mixed (no beef, eggs) 35 Mixed 350 Solid 480 Ø [ (9) Ø Ø Ø
Leidy, 2007 (29) 17 Mixed (no pork) 28 Mixed 400 Solid 195 Y (17) Ø Y (8)
Makris, 2011 (56) 12 Mixed 24 Mixed 350 Solid 240 Ø Ø Ø
Karhunen, 2010 (57) 3 Plant 20 Plant 300 Solid 120 Ø Ø Ø Ø Ø Ø
1
n = 24 studies. Higher-protein compared with lower-protein meals: [, increased; Y, reduced; Ø, no difference; —, not assessed. GLP-1, glucagon-like peptide 1; PYY, peptide YY.
PROTEIN AND WEIGHT MANAGEMENT 1323S
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breakfast, the higher-protein version led to less energy consumed
throughout the day, particularly from high-fat/high-sugar evening
snacks (55).
CLINCIAL OUTCOMES WITH CONTROLLED
HIGHER-PROTEIN DIETS OF £1y
Three recent meta-analyses examined the effects of higher-
protein diets on body weight management and cardiometabolic
outcomes. As previously described, Wyc herley et al. (71)
performed a meta-analysis with 24 tightly controlled feeding
trials that compa red higher-protein with lower-protein energy-
restriction diets of 12 6 9 wk in duration. It included 1063
overweight a nd obese individuals between 18 and 80 y of age.
The higher-protein diets contained between 27% a nd 35% of
daily energy intake as protein (1.07–1.60 g protein $ kg
21
$ d
21
),
whereas the lower-protein diets contained 16–21% protein
(0.55–0.88 g protein $ kg
21
$ d
21
) (71). Despite a similar en-
ergy deficit, the higher-protein diets led to greater weight loss
(MD: 20.79 kg; 95% CI: 21.50, 20.08 kg; P , 0.03) and fat
loss (MD: 20.87 kg; 95% CI: 21.26, 20.48 kg; P , 0.001)
compared with the lower-protein diets (71). The higher-protein
diets also preserved more lean mass during energy restriction
than did the lower-protein diets (MD: +0.43 kg; 95% CI: 0.09,
0.78 kg; P , 0.01) (71). Although fasting glucose, fasting
insulin, blood pressure, and total, LDL, and HDL cholesterol
were not different between diets, fasting triglycerides were
lower in the higher-protein diets than after the lower-protein
diets (MD: 20.23 mmol/L; 95% CI: 20.33, 20.12 mmol/L;
P , 0.0001).
Similar findings were reported in a meta-analysis in in-
dividuals with type 2 diabetes (4). Nine controlled-feeding
studies of 4–24 wk in duration with 418 participants were an-
alyzed (4). The higher-protein diets contained between 25%
and 32% of energy as protein, whereas the lower-protein diets
contained 15–20% of energy as protein. Compared with the
lower-protein diets, the higher-protein versions led to greater
weight loss (MD: 22.08 kg; 95% CI: 23.25, 20.90 kg; P ,
0.05), greater reductions in glycated hemoglobin concentrations
(MD: 20.52%; 95% CI: 20.90%, 2014%; P , 0.05), and
greater reductions in systolic and diastolic blood pressure [MD
(95% CI): 23.13 (26.58, 20.32) mm Hg (P , 0.05) and 21.86
(24.26, 20.56) mm Hg (P , 0.05), respectively].
Last, Santesso et al. (72) extended these findings to include
both energy restriction and ad libitum feeding studies in adults
who varied in age, health status, and daily energy intake. In this
meta-analysis, 74 randomized controlled trials were included
comparing higher-protein (16–45% of energy intake as protein)
with lower-protein (5–23% of energy intake as protein) diets.
The higher-protein diets led to greater weight loss (MD: 20.36 kg;
95% CI: 20.56, 20.17 kg; P ,
0.001), greater reductions
in
BMI (in kg/m
2
; MD: 20.37; 95% CI: 20.56, 20.19; P ,
0.001), and greater reductions in waist circumference (MD:
20.43 cm; 95% CI: 20.69, 20.16 cm; P , 0.001) than the
lower-protein diets.
The magnitude of change in many of these outcomes is modest
but holds possible clinical relevance in light of the increased
prevalence of obesity, type 2 diabetes, metabolic syndrome, and
sarcopenia in the elderly. However, these benefits may be realized
only if the increase in actual protein intake or the increased
percentage of protein consumed within the diet can be sustained
over the long term.
CLINICAL OUTCOMES WITH LONG-TERM
HIGHER-PROTEIN DIETS OF 1y
One critical aspect of body weight management is the pre-
vention of weight regain after weight loss. This section discusses
the current, but limited, evidence exploring whether increased
dietary protein is a significant factor for long-term success with
weight loss and the prevention of weight regain. Cardiometabolic
risk factors are also considered.
The first comparisons include chronic very-low-carbohydrate
(ketogenic) diets (VLCKDs) because these diets typically
contain either higher protein content or a higher percentage of
protein within the die t (even if the absolu te amount remains
unchanged). Bue no et al. (73) completed a meta-analysis
comparing long-term (.12 mo), con ventio nal, low-fat customary-
protein diets (10–15% of intake as protein) with VLCKDs. The
VLCKDs contained w20% of intake as protein. Thirteen studies
were included in the meta-analysis. Compared with conven-
tional low-fat die ts, VLCKDs led to greater weight loss
[weighted MD (WMD): 20.91 kg; 95% CI: 21.65, 20.17 kg;
P = 0.02] and improvements in fasting triglycerides (WMD:
20.18 mmol/L; 95% CI: 20.27, 20.08 mmol/L; P , 0.001),
HDL cholesterol (WMD: +0.09; 95% CI: +0.06, +0.12 mmol/L;
P , 0.001), and diastolic blood pressure (WMD: 21.43 mm Hg;
95% CI: 22.49, 20.37 mm Hg; P = 0.008) (73). These data are
consistent with a view that increased dietary protein, within the
context of very-low-carbohydrate and high-fat intakes, improves
weight management, reduces cardiometabolic risk factors, and
might aid in the treatment of obesity and other disease states
over the long term. However, adherence to these diets is quite
low as evidenced by the w40% dropout rate, the specific role of
protein is uncertain, and the high-fat, low-carbohydrate regimen
does not coincide with current dietary guidelines and may not be
appropriate for all ages, populations, and disease states (73).
Schwingshackl and Hoffmann (74) performed a meta-analysis
to examine the effects of low-fat (,30% of intake as fat) diets
containing either higher protein (.25% of intake as protein) or
lower protein (,20% of intake as protein) on long-term changes
in body weight, body composition, and cardiometabolic risk
factors. The diets in this meta-analysis were very similar to those
in Wycherley et al. (71) but included $12 mo of follow-up. No
differences in weight loss, fat mass loss, or reductions in waist
circumference were observed between diets. No differences in
total, LDL, or HDL cholesterol or triglycerides were detected
(74).
Recently, Clifton et al. (75) performed a more comprehensive
meta-analysis that included 32 studies in 3492 individuals of
.12 mo in duration that contrasted weight-loss diets that dif-
fered in the percentage of protein. VLCKDs and low-fat diets
were permitted and outcomes consisted of changes in body
weight and body composition as well as in fasting glucose, in-
sulin, and lipid concentrations. A recommendation to consume
a lower-carbohydrate, higher-protein diet was associated with
better weight loss, compared with lower-protein diets, but the
effect size was small (standardized MD: 20.14; 95% CI: 20.24,
20.04 ; P = 0.008). Although lean mass did not differ between
diets, fat mass losses were greater after the higher-protein diets
1324S LEIDY ET AL.
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(standardized MD: 20.22; 95% CI: 20.33, 20.11; P , 0.001).
These effects are equivalent to a difference of w0.4 kg. A dif-
ference of $5% in protein intake between diets at 12 mo was
associated with a 3-fold greater reduction in fat mass compared
with ,5% (0.9 compared with 0.3 kg; P = 0.038). Triglyceride
and insulin concentrations were also lower after the higher-
protein diets.
Last, a lthough the previous meta-analyses do not include the
findings from the Diet, Obesity and Genes (Diogenes) Study, it
is the largest trial to date comparing lower- with higher-protein
diets for weight-loss maintenance and thus merits specific
discussion (76–78). This pan-E uropea n multice nter trial i n-
cluded 938 adults and 253 childr en (from the adults in the
study). The parents completed an initial 8-wk energy-restriction
period. In those families in which at least one parent lost 8% of
their initial body weight (average: 211.0 kg), the parents and
children then completed 26 wk of weight maintenance with
eithe r a higher-protein (25% of intake as protein) or lower-
protein (13% of intake as protein) ad libitum diet (76, 77).
Glyce mic index was also included within these diets but is not
included in this discussion. In the adults who compl eted the
study, the higher-prot ein diet le d to less weight regain (MD:
20.93 kg; 95% CI : 20.31, 21.55 kg; P = 0.003) than the
lower-protein diet did (66). The children who followed the
higher-protein regimen had reductions in wais t circumference
(22.7 cm; 95% CI: 0.9, 5.1 cm; P , 0.007) and LDL choles-
terol (20.25 mmol/L; 95% CI: 0.09, 0.41 mmol/L; P , 0.007)
compared with those following the lower-protein ver sion (78).
Collectively, these data suggest that a modest increase in di-
etary protei n leads to long-term maintenance of weight loss
and/or imp rovements in cardiometabolic risk factors in adults
and young people.
LIMITATIONS
There was variability in the dropout rates across the studies
included in the meta-analyses (ranging from 7% to 55%; average:
30 6 12%). This contributed to large amounts of missing data,
and only approximately half of these studies reported intention-
to-treat analyses along with completer results. These factors
contributed to a high risk of bias in the interpretation of data
(74).
In addition, regardless of the protein content of the diets,
most of the studies showed poor compliance with the pre-
scribed diets. According to the dietary food records collected in
most of the stu dies, no gr oups met their prescribed pr otein
content for either diet. The higher-protein diet groups reduced
their protein content throughout the study, whereas the lower-
protein diet groups increased their protein content, both po-
tentially returning back to their habitual protein intakes at
baseline (74). This led to several studies having similar protein
contents between diets (79–82). The lack of protein d iffer-
ential between diets was a lso supported by th e validated uri-
nary biomarke r data, particularly urinary nitrogen, urea, or the
urea:creatinine ratio from 24-h ur ine collections (83) mea-
sured in 9 of the 15 studies included in the Schwingshackl and
Hoffmann meta-analysis (74) and 8 of the 32 in the Clifton
et al. meta-analysis (75). H owever, although the partic ipants
in these studies were una ble to reach the prescribed prote in
content, the Diogenes study (76, 77) a nd the Clifton et al.
meta-analysis (75) indicated that only an a bsolute difference
in protein intake of 5% was required for a clinic ally significant
effect on weight management.
COMPLIANCE WITH HIGH-PROTEIN DIETS
There are myriad behavioral and environmental factors that
contribute to the lack of compliance with and/or adherence to
dietary (protein) interventions (84). One factor that is strongly
associated with weight loss and prevention of weight regain is
attendance to dietary counseling sessions (5). This might be even
more critical with higher-protein diets (5). For example, Layman
et al. (85) incorporated weekly dietary counseling sessions over
a 12-mo period and reported a lower dropout rate in the higher-
protein group (36%) than in the lower-protein group (55%).
Furthermore, those who completed the study attended 75% of the
counseling sessions (85).
The incorporation of family-based dietary strategies also
improved adherence to long-term higher-protein diets. As shown
in the Diogenes studies (76–78), the parents exhibited relatively
low dropout rates, regardless of protein intake; however, the
higher-protein diet group had a lower dropout rate than the
lower-protein diet group (26% compared with 37%; P = 0.02).
Another point is whether increase d protein consumption
reduces acceptability to diets over the long term. Several
studies refute this argument by reporting greater overall sat-
isfaction (i.e., greater palatability, pleasure, enjoyment) and/or
motivation with higher-protein diets than with lower-protein
diets (42, 86, 87). Although not definitive, potential reasons fo r
the increased acceptability may be due to the sa tiating effects of
protein and anticipated improvements in body we ight man-
agement (31).
As described above, the lack of adherence to higher-protein
diets is typically attributed to behavioral and/or environmental
factors. However, the return of protein intakes to habitual
quantities has been postulated to be due to a physiologic (nu-
tritional) regulation of protein intake, as proposed by the protein
leverage hypothesis previously discussed (17). Although this
concept is worth exploring, the existing evidence from high-
quality studies in humans fails to support the protein leverage
hypothesis (17–19).
PROTEIN QUANTITY
The last remaining question is “How much protein is required
to elicit the improvements in body weight management?” The
meta-analyses including shorter-term energy restriction and
longer-term weight maintenance studies indicate that the quan-
tity of protein necessary to promote improved weight manage-
ment and cardiometabolic outcomes lies somewhere between
1.2and1.6gprotein$ kg
21
$ d
21
(which is w89–119 g protein/d
for women or 104–138 g protein/d for men) (86, 88). Howev er,
recent evidence suggests that lower protein quantities (i.e., 0.8 g
protein $ kg
21
$ d
21
) during energy restriction might be suf fi cient
for body weight and fat mass losses, whereas higher protein
quantities (i.e., 1.2 g protein $ kg
21
$ d
21
) are required for the
preservation of lean mass (89).
To further support a specific protein quantity that is required to
elicit improvements in weight management, Bosse and Dixon
(90) categorized 25 higher-protein weight-loss studies on the
PROTEIN AND WEIGHT MANAGEMENT 1325S
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basis of those who showed successful weight loss compared with
those who did not. The change in protein intake (from habitual
intake) was compared between groups. An average increase in
protein consumption of 28.6% g protein $ kg
21
$ d
21
beyond
habitual protein intake was needed to elicit significant weight
loss (90). Thus, if habitual protein intake in US adults (ages 19–
70 y) is, on average, 88 g/d (1.07 g protein $ kg
21
$ d
21
), then
the addition of only w25–30 g protein/d [up to 113–118 g/d
(w1.38 g protein $ kg
21
$ d
21
)] would potentially be sufficient
to elicit long-term improvements in weight management (90). In
addition, under isoenergetic conditions, the increase in protein
appears to be the critical component, not the reduction in car-
bohydrates or fat (91).
The protein quantities proposed above are within the ac-
ceptable macronutrient range for protein and allow for the ability
to meet the dietary guidelines for other requirements including
fruit, vegetables, dairy, and fiber. However, a 2-y study by
Jesudason et al. (92) prescribed a 20-g increase in protein intake
but achieved a difference of only 16 g/d at 1 y and 13 g/d at 2 y,
suggesting a 25–30-g/d increase might be a difficult target to
sustain over the long term.
Last, although the current dietary guidelines state the
recommendations in terms of daily protein intake, the
mechanistic data, particularly with regard to energy metab-
olism, protein synthesis, and appetite control, examine meal-
specific quantities, not daily intake. In these studies, w25–30 g
protein/eating occasion was required to elicit protein-related
benefits (29, 55, 67–69). Theoretically, if 4 meals containing
25–30 g protein/meal are consumed throughout the day, the total
amount of protein would equate to the quantities shown to elicit
body weight/body composition changes described above.
Although many Americans consume $25 g protein at lunch
and dinner, the average consumption of protein at breakfast
is well under the 25-g quantity (93). There is evidence that
supports unique benefits with increased protein consumption
at breakfast for improved satiety and reductions in unhealthy
snacking in the evening (55, 94). Future research that ex-
plores meal-specific protein quantity and timing of consumption
is warranted.
CONCLUSIONS
Higher-protein diets that contain be tween 1.2 and 1.6 g
protein $ kg
21
$ d
21
and potentially include meal-specific
protein quantities of at least w25–30 g protein/meal provide
improvements in appetite, body weight management, and/or
cardiometabolic risk factors compared with lower-protein diets
(Table 2). Although greater satiety, weight loss, fat mass loss,
and/or the preservation of lean mass are often observed with
increased protein consumption in controlled feeding studies, the
lack of dietary compliance with prescribed diets in free-living
adults makes it challenging to confirm a sustained protein effect
over the long term.
The authors’ responsibilities were as follows—All of the authors partic-
ipated in Protein Summit 2.0 and were involved in the writing and editing of
the manuscript; HJL: developed the draft of the manuscript; HJL, PMC, AA,
TPW, MSW-P, NDL-M, SCW, and RDM: reviewed and revised the manu-
script accordingly; and all authors: substantially contributed to the comple-
tion of the manuscript and read and approved the final version. HJL has
current funding from The Beef Checkoff, Egg Nutrition Center, and DuPont
Nutrition & Health. She is also on the speaker’s bureau for the National
Cattlemen’s Beef Association. PMC has conducted protein-related research
funded by Meal and Livestock Australia and Dairy Australia and published
a number of high-protein cookbooks. AA is a consultant/member of advisory
boards for the Dutch Beer Knowledge Institute, Global Dairy Platform,
Jenny Craig, McCain Foods Ltd, McDonald’s, and the Gerson Lehrman
Group (ad hoc consultant for clients). He is a recipient of honoraria and
travel grants as speaker for a wide range of Danish and international con-
cerns. He has conducted research funded by a number of organizations
with interests in the food pro duction and marketing sector. TPW, MSW-P,
NDL-M, SCW, and RDM had no conflicts of interest to report. None of the
TABLE 2
Summary of review
Conclusions Limitations and/or gaps in the current literature
·
Higher-protein energy-restriction diets lead to greater
weight loss, fat mass loss, and preservation of lean mass
along with greater improvements in select
cardiometabolic health outcomes, over the shorter term,
compared with lower-protein diets. Potential mechanisms
of action include the marginal increase in thermogenesis
and satiety after the consumption of protein-rich meals.
·
Research is needed to examine whether the satiety effects
of protein promote voluntary reductions in energy intake
and improved body weight management over the long
term.
·
Although the long-term data are less consistent, persistent
effects of increased protein consumption are evident with
respect to weight maintenance and/or the prevention of
weight re(gain).
·
Dietary compliance appears to be the primary contributor
to discrepant findings related to energy balance because
improvements in weight management were detected in
those who adhered to the prescribed higher-protein
regimen, whereas those who did not adhere to the diet
had no marked improvements.
·
Future long-term research including family-based
interventions with dietary counseling and meal-specific
quantities of protein are warranted.
·
Higher-protein diets that contain between 1.2 and 1.6 g
protein $ kg
21
$ d
21
and potentially include meal-
specific protein quantities of at least w25–30 g protein/
meal provide improvements in appetite, body weight
management, and/or cardiometabolic risk factors.
·
Future research exploring meal-specific protein quantity
and timing of consumption are warranted.
1326S LEIDY ET AL.
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sponsors were involved in the design, implementation, analysis, or interpre-
tation of data.
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PROTEIN AND WEIGHT MANAGEMENT 1329S
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... Protein intake elicits a rise in circulating amino acid concentration, the extent of which may influence satiety [3]. The increased feelings of fullness following protein intake are accompanied by increases in the anorexigenic hormones glucagon-like peptide 1 (GLP-1) and peptide YY (PYY) [4] which are released from the gastrointestinal tract in response to the presence of amino acids [5]. Ingestion of~25-30 g protein has been suggested to represent a within-meal protein threshold to elicit increases in satiety [4,6]. ...
... The increased feelings of fullness following protein intake are accompanied by increases in the anorexigenic hormones glucagon-like peptide 1 (GLP-1) and peptide YY (PYY) [4] which are released from the gastrointestinal tract in response to the presence of amino acids [5]. Ingestion of~25-30 g protein has been suggested to represent a within-meal protein threshold to elicit increases in satiety [4,6]. Sources of dietary protein differ in their amino acid composition and digestibility, and therefore may have different effects on appetite, satiety, and/or meal energy intake [7,8]. ...
... However, at 25% energy from protein (38 g) there was no difference in hunger between protein sources. In the present study, participants ingested 25 g of protein, a quantity suggested to represent a within-meal protein threshold to elicit increases in satiety [4,6]. It is possible that at this dose, both protein sources elicited a sufficient increase in amino acids and/or the anorexigenic hormones GLP-1 and PYY to support appetite control, as evidenced by the similar changes in appetite sensations and ad libitum energy intake. ...
Article
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Background/Objectives The purpose of this study was to evaluate the acute effects of ingesting beef- and insect-derived protein on postprandial plasma amino acid and appetite hormone concentrations, appetite sensations, and ad libitum energy intake. Subjects/Methods In a randomized, double-blind, crossover study, 20 young men (23 (SD: 4) y) completed two trials during which arterialized blood samples and VAS questionnaires were collected at baseline, and over 300-min after ingestion of beverages with similar energy and macronutrient content containing 25 g beef- or insect-derived (cricket) protein. Blood samples were analyzed for plasma amino acid and appetite hormone concentrations, while VAS questionnaires were applied to assess appetite sensations. After each trial, an ad libitum meal was immediately provided to assess energy intake. Results Adjusted mean postprandial incremental area under the curve (iAUC) was greater for cricket vs. beef-derived protein for plasma leucine, branched-chain amino acid, and essential amino acid concentrations (all P < 0.0001). Adjusted mean postprandial iAUC for hunger was lower following beef (−3030 (SE: 860)) vs. cricket-derived (−1197 (SE: 525)) protein (Difference: −1833 (95% CI: −3358, −308); P = 0.02), but was not different for other appetite sensations or appetite hormones (all P > 0.05). Adjusted mean ad libitum energy intake was 4072 (SE: 292) and 4408 (SE: 316) kJ following beef- and cricket-derived protein (Difference: −336 (95% CI: −992, 320); P = 0.30). Conclusion Acute ingestion of cricket and beef-derived protein leads to differences in postprandial plasma amino acid concentrations, but elicits similar effects on appetite hormones, appetite sensations, and ad libitum energy intake in young men.
... A decreased carbohydrate intake typically results in increased protein and/or fat intake, which may contribute to greater satiety and reduction in hunger [2•, 6, 18, 21]. The effect of protein on appetite appears to be influenced by the amount of protein consumed with the most consistent impact observed when at least 30 g of protein is consumed during an eating occasion [22][23][24]. KDs often limit protein since higher protein intakes can increase gluconeogenesis and thus interfere with the development of ketosis [2•, 6, 11, 18]. Results from studies that compared high-protein, ketogenic diets to high-protein, non-ketogenic diets demonstrated greater appetite suppression with the high-protein, ketogenic diet, supporting an independent effect of ketosis on appetite [2•, 18]. ...
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Purpose of Review Very-low-carbohydrate (VLC) and ketogenic diets (KDs) have been used for weight loss and more recently in patients with insulin resistance and type 2 diabetes. The impact of VLC and KDs on lipids/lipoproteins is a concern. The purpose of this review is to discuss the impact of KDs on body weight and lipids/lipoproteins. Recent Findings VLC/KDs contribute to greater weight loss in the short term (< 6 months) compared to higher carbohydrate diets, but there is typically no difference between the diets by 12 months. Triglyceride and high-density lipoprotein cholesterol levels generally improve, but there is a variable response in low-density lipoprotein cholesterol levels, with some individuals experiencing a dramatic increase, particularly those with latent genetic dyslipidemias. Summary Healthcare professionals should educate patients on the risks and benefits of following VLC/KDs and encourage the consumption of carbohydrate-rich foods associated with positive health outcomes.
... High protein vs. low-protein diets applied in long-term randomized controlled trials on body-weight loss and subsequent body-weight maintenance show a larger body-weight loss, including a larger fat oxidation, loss of fat mass, and sparing of fat free body mass, sustained satiety and energy expenditure [158][159][160][161][162][163]. Such results are partly different from studies comparing relatively high-protein with mediumprotein diets [125,163]. ...
... Single regression analyses (Figure 3) confirmed this, with slopes being different from 0 for CP level (−0.25, 95%-CI −0.46 to −0.05; P = 0.014) but not for NSP (−0.12, 95%-CI −0.37 to 0.14; P = 0.360). Dietary protein content, in the range of 12.0 to 27.4 g CP/MJ ME, reduced food motivation 5 h postprandial and this satiating potential is in line with findings in humans (Veldhorst et al., 2008;Leidy et al., 2015). The effect was detectable irrespective of the considerable variation among the 12 commercial foods in ingredients and nutrients other than protein and fiber. ...
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Overweight and obesity are common in global pet cat populations which makes it important to understand how properties of food affect appetite (food motivation). In four experiments, we studied this by using a model of operant conditioning for assessing appetite in which cats could press a lever for food rewards. There was no effect of protein status on motivation for protein, when evaluated in a cross-over design with cats receiving low protein (LP) or high protein (HP) foods for 14 days. Cats obtained similar numbers of HP and LP rewards, irrespective of whether their daily food was HP or LP (mixed-effects model, P=0.550 for food × reward, P=0.151 for reward). High dietary protein reduced food motivation when we regressed protein levels in 12 commercial foods (12.0 to 27.4 g crude protein/MJ metabolizable energy; P=0.022) fed for 2 days and tested at 5 h postprandially on the third day whereas fiber levels were without effect (3.8 to 17.8 g non-starch polysaccharides/MJ; P=0.992). Dietary fiber may reduce appetite depending on its physicochemical properties and we tested the effect of a gelling fiber (alginate), viscous fiber (psyllium) and a fermentable fiber (inulin). Cats received test foods as well as control foods for 3 days and were tested on the third day at 3 h (alginate), 5 h (psyllium) or 8 h (inulin) postprandially. Enriching the food with alginate (P=0.379) or psyllium (P=0.153) did not affect the number of rewards obtained, but the feeding of the inulin-enriched food did make the cats obtain fewer rewards than when they received the control food (P=0.001). Finally, cooking or grinding of dietary meat increased the number of rewards obtained by cats, on day 3 at 3 h postprandial, without evidence for additive effects of these treatments (P=0.014 for grinding × cooking). This study shows that dietary content of protein or fiber, and the grinding or cooking of meat, all affect appetite in cats as expected, though some predicted effects remained undetected and clearly details regarding food properties matter. These and future findings can guide the designing of foods that promote satiety and prevent over-eating in meal-fed cats.
... Non responders (decreased phosphocreatine (PCr) recovery rate) showed no significant changes with exercise while responders (increased PCr) showed an improved insulin sensitivity and insulin signaling genes such as microfibril associated protein 3 like (MFAP3L), phosphofructokinase, muscle (PFKM), and phosphoinositide-3-kinase regulatory subunit 3 (PIK3R3).Responders shown an upregulation of genes involved in mitochondrial metabolism such as NADH: ubiquinone oxidoreductase subunit A2 (NDUFA2), growth arrest and DNA damage inducible alpha (GADD45A), ubiquinol-cytochrome c reductase hinge protein like (UQCRHL), and pyruvate dehydrogenase kinase 4 (PDK4) and improved VO 2max by ~12% metabolic state [64]. Interestingly, documented literature already supports the notion that weight loss is associated with improved metabolism and skeletal muscle function [61,70]. Consistently, in support to this hypothesis, information from this current review showed that caloric restriction has great potential in improving insulin sensitivity while modulating mitochondrial respiratory capacity in obese individuals and those with T2D [63,81]. ...
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Skeletal muscle insulin resistance and mitochondrial dysfunction are some of the major pathological defects implicated in the development of type 2 diabetes (T2D). Therefore, it has become necessary to understand how common interventions such as physical exercise and caloric restriction affect metabolic function, including physiological processes that implicate skeletal muscle dysfunction within a state of T2D. This review critically discusses evidence on the impact of physical exercise and caloric restriction on markers of insulin resistance and mitochondrial dysfunction within the skeletal muscle of patients with T2D or related metabolic complications. Importantly, relevant information from clinical studies was acquired through a systematic approach targeting major electronic databases and search engines such as PubMed, Google Scholar, and Cochrane library. The reported evidence suggests that interventions like physical exercise and caloric restriction, within a duration of approximately 2 to 4 months, can improve insulin sensitivity, in part by targeting the phosphoinositide 3-kinases/protein kinase B pathway in patients with T2D. Furthermore, both physical exercise and caloric restriction can effectively modulate markers related to improved mitochondrial function and dynamics. This was consistent with an improved modulation of mitochondrial oxidative capacity and reduced production of reactive oxygen species in patients with T2D or related metabolic complications. However, such conclusions are based on limited evidence, additional clinical trials are required to better understand these interventions on pathological mechanisms of T2D and related abnormalities.
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Background and Aims Severe obesity (BMI ≥ 60kg/m²) in multimorbid patients can be acutely life-threatening. While emergency weight-loss surgery is urgently needed to preserve life, most patients are in an inoperable state. Pre-surgical bridging therapy is required to achieve technical operability through weight reduction. Standard bridging using an intragastric balloon (IB) can achieve operability in 6 months but is unsuitable for some patients in a critical condition. A non-invasive fast-track rescue therapy to achieve very rapid operability is urgently needed. We investigated whether a rescue weight reduction therapy (RWR) consisting of liraglutide, a glucagon-like peptide-1 (GLP-1) receptor agonist, a leucine-rich amino acid infusion and a hypocaloric diet, can accelerate readiness for emergency surgery in patients with acutely life-threatening severe obesity. Methods In this proof-of-concept study, prospective data from patients treated with RWR (intervention group 1, n=26) were mathematically matched with retrospective biometric data of 26 patients with severe obesity (historic control group 2) who underwent standard 6-month bridging with IB. A rating scale was developed to identify patients needing urgent fast-track bridging. Results Rapid weight loss was observed in all patients on the RWR therapy. All achieved operability after a mean RWR bridging duration of 20.7±6.9 days. Baseline weight was 236.3±35.8 kg in group 1 compared with 230.1±32.7 kg in group 2. Mean body weight loss during RWR was 27.5±14.1 kg, compared with 20.9±10.5 kg in group 2 (P= 0.0629). Conclusions Pre-operative bridging using liraglutide in combination with a leucine-rich amino acid infusion and hypocaloric diet was effective in all cases of acutely life-threatening severe obesity, achieving technical operability within only ca. 2-4 weeks. This therapy has potential as a life-saving rescue therapy for multimorbid patients with severe obesity who were previously untreatable. This study is registered at ClinicalTrials.gov (identifier: NCT02616003).
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ABSTRACT Dietary manipulation with high-protein or high-carbohydrate content are frequently employed during elite athletic training, aiming to enhance athletic performance. Such interventions are likely to impact upon gut microbial content. This study explored the impact of acute high-protein or high-carbohydrate diets on measured endurance performance and associated gut microbial community changes. In a cohort of well-matched, highly trained endurance runners, we measured performance outcomes, as well as gut bacterial, viral (FVP), and bacteriophage (IV) communities in a double-blind, repeated-measures design randomized control trial (RCT) to explore the impact of dietary intervention with either high-protein or high-carbohydrate content. High-dietary carbohydrate improved time-trial performance by +6.5% (P
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Objective: Higher protein intake during weight loss is associated with better health outcomes, but whether this is because of improved diet quality is not known. The purpose of this study was to examine how the change in self-selected protein intake during caloric restriction (CR) alters diet quality and lean body mass (LBM). Methods: In this analysis of pooled data from multiple weight loss trials, 207 adults with overweight or obesity were examined before and during 6 months of CR (approximately 10 food records/person). Body composition was measured by dual-energy x-ray absorptiometry. Diet quality was assessed using the Healthy Eating Index in 2 groups: lower (LP) and higher (HP) protein intake. Results: Participants (mean [SD], 54 [11] years; 29 [4] kg/m2 ) lost 5.0% (5.4%) of weight. Protein intake was 79 (9) g/d (1.0 [0.2] g/kg/d) and 58 (6) g/d (0.8 [0.1] g/kg/d) in the HP and LP groups, respectively (p < 0.05), and there was an attenuated LBM (kilograms) loss in the HP (-0.6% [1.5%]) compared with the LP (-1.2% [1.4%]) group (p < 0.01). The increased Healthy Eating Index score in the HP compared with the LP group was attributed to greater total protein and green vegetable intake and reduced refined grain and added-sugar intake (p < 0.05). Conclusions: Increasing dietary protein during CR improves diet quality and may be another reason for reduced LBM, but it requires further study.
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Dietary strategies to improve early cardiovascular markers in overweight children are needed. We investigated the effect of dietary protein and glycemic index (GI) on cardiovascular markers and metabolic syndrome (MetS) scores in 5- to 18-y-old children of overweight/obese parents from 8 European centers. Families were randomized to 1 of 5 diets consumed ad libitum: high protein (HP) or low protein (LP) combined with high GI (HGI) or low GI (LGI), or a control diet. At 6 centers, families received dietary instruction (instruction centers); at 2 centers, free foods were also provided (supermarket centers). Diet, anthropometry, blood pressure, and serum cardiovascular markers (lipid profile, glucose regulation, and inflammation) were measured in 253 children at baseline, 1 mo, and/or 6 mo. Protein intake was higher in the HP groups (19.9 ± 1.3% energy) than in the LP groups at 6 mo (16.8 ± 1.2% energy) (P = 0.001). The GI was 4.0 points lower (95% CI: 2.1, 6.1) in the LGI compared with the HGI groups (P < 0.001). In the supermarket centers, the HP and LP groups differed more in protein intake than did the groups in the instruction centers (P = 0.009), indicating better compliance. The HP diets evoked a 2.7-cm (95% CI: 0.9, 5.1) smaller waist circumference and a 0.25-mmol/L (95% CI: 0.09, 0.41) lower serum LDL cholesterol compared with the LP diets at 6 mo (P < 0.007). In a separate supermarket center analysis, the HP compared with LP diets reduced waist circumference (P = 0.004), blood pressure (P < 0.01), serum insulin (P = 0.013), and homeostasis model of assessment-insulin resistance (P = 0.016). In the instruction centers, the HP compared with the LP diets reduced LDL cholesterol (P = 0.004). No consistent effect of GI was seen and the MetS scores were not affected. In conclusion, increased protein intake improved cardiovascular markers in high-risk children, particularly in those undergoing most intensive intervention.
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The protein leverage hypothesis requires specific evidence that protein intake is regulated more strongly than energy intake. The objective was to determine ad libitum energy intake, body weight changes, appetite profile, and nitrogen balance in response to 3 diets with different protein-to-carbohydrate + fat ratios over 12 consecutive days, with beef as a source of protein. A 3-arm, 12-d randomized crossover study was performed in 30 men and 28 women [mean ± SD age: 33 ± 16 y; body mass index (in kg/m(2)): 24.4 ± 4.0] with the use of diets containing 5%, 15%, and 30% of energy (En%) from protein, predominantly from beef. Energy intake was significantly lower in the 30En%-protein condition (8.73 ± 1.93 MJ/d) than in the 5En%-protein (9.48 ± 1.67 MJ/d) and 15En%-protein (9.30 ± 1.62 MJ/d) conditions (P = 0.001), stemming largely from lower energy intake during meals (P = 0.001). Hunger (P = 0.001) and desire to eat (P = 0.001) ratings were higher and fullness ratings were lower (P = 0.001) in the 5En%-protein condition than in the 15En%-protein and 30En%-protein conditions. Nitrogen excretion was lower in the 5En%-protein condition (4.7 ± 1.5 g/24 h; P = 0.001) and was higher in the 30En%-protein condition (15.3 ± 8.7 g/24 h; P = 0.001) compared with the 15En%-protein condition (10.0 ± 5.2 g/24 h). Nitrogen balance was maintained in the 5En%-protein condition and was positive in the 15En%- and 30En%-protein conditions (P = 0.001). Complete protein leverage did not occur because subjects did not consume to a common protein amount at the expense of energy balance. Individuals did underconsume relative to energy requirements from high-protein diets. The lack of support for protein leverage effects on a low-protein diet may stem from the fact that protein intake was sufficient to maintain nitrogen balance over the 12-d trial. This trial was registered at clinicaltrials.gov as NCT01646749.
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Background: Ad libitum, low-carbohydrate diets decrease caloric intake and cause weight loss. It is unclear whether these effects are due to the reduced carbohydrate content of such diets or to their associated increase in protein intake. Objective: We tested the hypothesis that increasing the protein content while maintaining the carbohydrate content of the diet lowers body weight by decreasing appetite and spontaneous caloric intake. Design: Appetite, caloric intake, body weight, and fat mass were measured in 19 subjects placed sequentially on the following diets: a weight-maintaining diet (15% protein, 35% fat, and 50% carbohydrate) for 2 wk, an isocaloric diet (30% protein, 20% fat, and 50% carbohydrate) for 2 wk, and an ad libitum diet (30% protein, 20% fat, and 50% carbohydrate) for 12 wk. Blood was sampled frequently at the end of each diet phase to measure the area under the plasma concentration versus time curve (AUC) for insulin, leptin, and ghrelin. Results: Satiety was markedly increased with the isocaloric high-protein diet despite an unchanged leptin AUC. Mean (±SE) spontaneous energy intake decreased by 441 ± 63 kcal/d, body weight decreased by 4.9 ± 0.5 kg, and fat mass decreased by 3.7 ± 0.4 kg with the ad libitum, high-protein diet, despite a significantly decreased leptin AUC and increased ghrelin AUC. Conclusions: An increase in dietary protein from 15% to 30% of energy at a constant carbohydrate intake produces a sustained decrease in ad libitum caloric intake that may be mediated by increased central nervous system leptin sensitivity and results in significant weight loss. This anorexic effect of protein may contribute to the weight loss produced by low-carbohydrate diets.
Conference Paper
The gut hormone peptide YY (PYY) belongs to the pancreatic polypeptide (PP) family along with PP and neuropeptide Y (NPY). These peptides mediate their effects through the NPY receptors of which there are several subtypes (Y1, Y2, Y4, and Y5). The L cells of the gastrointestinal tract are the major source of PYY, which exists in two endogenous forms: PYY1-36 and PYY3-36. The latter is produced by the action of the enzyme dipeptidyl peptidase-IV (DPP-IV). PYY1-36 binds to and activates at least three Y receptor subtypes (Y1, Y2, and Y5), whereas PYY3-36 is more selective for Y2 receptor (Y2R). The hypothalamic arcuate nucleus, a key brain area regulating appetite, has access to nutrients and hormones within the peripheral circulation. NPY neurons within the arcuate nucleus express the Y2R. In response to food ingestion plasma PYY3-36 concentrations rise within 15 min and plateau by approximately 90 min. The peak PYY3-36 level achieved is proportional to the calories ingested, suggesting that PYY3-36 May signal food ingestion from the gut to appetite-regulating circuits within the brain. We found that peripheral administration of PYY3-36 inhibited food intake in rodents and increased C-Fos immunoreactivity in the arcuate nucleus. Moreover, direct intra-arcuate administration of PYY3-36 inhibited food intake. We have shown that Y2R null mice are resistant to the anorectic effects of peripherally administered PYY3-36, suggesting that PYY3-36 inhibits food intake through the Y2R. In humans, peripheral infusion of PYY3-36, at a dose which produced normal postprandial concentrations, significantly decreased appetite and reduced food intake by 33% over 24 h. These findings suggest that PYY3-36 released in response to a meal acts via the Y2R in the arcuate nucleus to physiologically regulate food intake.