A significant contributor to the rising rates of human obesity is an increase in energy intake. The 'protein leverage hypothesis' proposes that a dominant appetite for protein in conjunction with a decline in the ratio of protein to fat and carbohydrate in the diet drives excess energy intake and could therefore promote the development of obesity. Our aim was to test the 'protein leverage hypothesis' in lean humans by disguising the macronutrient composition of foods offered to subjects under ad libitum feeding conditions. Energy intakes and hunger ratings were measured for 22 lean subjects studied over three 4-day periods of in-house dietary manipulation. Subjects were restricted to fixed menus in random order comprising 28 foods designed to be similar in palatability, availability, variety and sensory quality and providing 10%, 15% or 25% energy as protein. Nutrient and energy intake was calculated as the product of the amount of each food eaten and its composition. Lowering the percent protein of the diet from 15% to 10% resulted in higher (+12±4.5%, p = 0.02) total energy intake, predominantly from savoury-flavoured foods available between meals. This increased energy intake was not sufficient to maintain protein intake constant, indicating that protein leverage is incomplete. Urinary urea on the 10% and 15% protein diets did not differ statistically, nor did they differ from habitual values prior to the study. In contrast, increasing protein from 15% to 25% did not alter energy intake. On the fourth day of the trial, however, there was a greater increase in the hunger score between 1-2 h after the 10% protein breakfast versus the 25% protein breakfast (1.6±0.4 vs 25%: 0.5±0.3, p = 0.005). In our study population a change in the nutritional environment that dilutes dietary protein with carbohydrate and fat promotes overconsumption, enhancing the risk for potential weight gain.
... [43] Protein leverage has also been tested in humans and the data suggests that protein prioritization in humans is substantially stronger than in mice. [3,46] This is consistent with observation that protein typically provides ∼15% of energy in humans, and its intake has remained relatively stable across different populations, both as a percentage of total energy and in terms of absolute amounts eaten, indicating tight biological regulation of protein intake. [3,10,11,47] Tight regulation of protein intake requires that a small decline in the protein density of the diet "leverages" a disproportionately large increase in food intake. ...
The global obesity epidemic results from a complex interplay of genetic and environmental factors, with diet being a prominent modifiable element driving weight gain and adiposity. Although excess intake of energetic macronutrients is implicated in causing obesity, ongoing debate centers on whether sugar or fat or both are driving the rising obesity rates. This has led to competing models of obesity such as the “Carbohydrate Insulin Model”, the “Energy Balance Model”, and the “Fructose Survival Hypothesis”. Conflicting evidence from studies designed to focus on individual energetic macronutrients or energy rather than macronutrient mixtures underlies this disagreement. Recent research in humans and animals employing the nutritional geometry framework (NGF) emphasizes the importance of considering interactions among dietary components. Protein interacts with carbohydrates, fats, and dietary energy density to influence both calorie intake (“protein leverage”) and, directly and indirectly, metabolic physiology and adiposity. Consideration of these interactions can help to reconcile different models of obesity, and potentially cast new light on obesity interventions.
... The protein-leverage concept suggests that humans at least partially prioritise protein consumption over other macronutrients. A randomised controlled trial found that lowering dietary protein from 15% to 10% led to a significant increase in total energy intake, but increasing protein to 20% had no effect, indicating low protein levels may trigger overeating of other nutrients [41]. Similar results have been observed in other studies, including among adolescents, where high-protein diets resulted in lower energy intake compared to low-protein diets [42,43]. ...
Background: Infant growth trajectories reflect current health status and may predict future obesity and metabolic diseases. Human milk is tailored to support optimal infant growth. However, nutrient intake rather than milk composition more accurately predicts growth outcomes. Although the role of protein leverage in infant growth is unclear, protein intake is important for early infancy growth. Materials and methods: This study of exclusively breastfeeding mothers with allergies (n = 161) from the Infant Fish Oil Supplementation Study assessed relationships between intake of human milk macronutrients and infant growth. Human milk fat, protein and lactose concentrations were measured at 3 months postpartum, and infant daily intakes were estimated using an average milk intake of 800 mL/day. Results: Higher human milk protein:energy ratio was associated with higher weight-for-age z-score at 2.5 years compared to 3 months and higher body mass index-for-age z-score change (6 months to 1 year compared to 3–6 months). Maternal atopy and birth season (summer) were negatively associated with human milk lactose concentration. Passive smoke exposure was associated with reduced energy and fat concentrations and increased lactose:energy ratio. Conclusions: Our results indicate that intake of human milk macronutrients may impact early childhood growth.
... ,[84][85][86] These changing eating patterns involve complex links to globalized food systems87 and marketing practices.88 Other established etiological factors include sleep disturbances, ingesting more chemicals and endocrine disruptors ("obesogens"), less variability in ambient temperatures, medications with the side effect of weight gain, for example, some antibiotics and psychotropics, and specific alterations in our gut microbiota.76,89 ...
Despite numerous public health organizations supporting the pathologization of obesity and considering recent obesity rates a health crisis, many researchers in the humanities, social sciences, and even in the health sciences remain unconvinced. In this paper, we address a set of arguments coming from these academic fields that criticize medical models of obesity for their supposedly flawed diagnostic categories that shift focus onto individuals and support moralizing judgements. Clarifying some key claims in these models and explicating the view of obesity in terms of energy dysregulation, we aim to tease apart misunderstandings and argue that not only do these models not say what they are often accused of saying, but their apparent vices may actually be virtues in helping to combat stigma. Building on the social psychology of stigma and disease labeling, we then suggest that current medical models are largely supportive of many moral and political aims promoted by critics of these models.
... From the nutritional and environmental perspectives, proteins have been identified as key macronutrients. On one side, proteins play a significant role in food caloric intake, satiety, delivery of important amino acids as well as delivery of micronutrients and other bioactive moieties (Chen, Remondetto, & Subirade, 2006;Gosby et al., 2011;Livney, 2010;Martinez-Cordero et al., 2012;Raubenheimer & Simpson, 2019). On the other side, proteins may undergo a myriad of physical and chemical changes from the source to the consumer because of their processing which may beneficially or adversely affect their techno-functionality and digestive fate (Loveday, 2019;Mackie & Macierzanka, 2010;Martins, Jongen, & Van Boekel, 2000;van Boekel et al., 2010). ...
... Evidence for PL in humans has been observed in several different contexts, including tightly controlled experiments in lean humans [12][13][14], in a free-living human population [15], in a cohort of youth with obesity [11], and in quantitative syntheses of published data [7,16]. While most studies on PL have focused on younger and active people, investigating PL in older adults is importantby 2050, globally one in six people will be aged 65 years or older [17]. ...
Background
Protein leverage (PL) is the phenomenon of consuming food until absolute intake of protein approaches a ‘target value’, such that total energy intake (TEI) varies passively with the ratio of protein: non-protein energy (fat + carbohydrate) in the diet. The PL hypothesis (PLH) suggests that the dilution of protein in energy-dense foods, particularly those rich in carbohydrates and fats, combines with protein leverage to contribute to the global obesity epidemic. Evidence for PL has been reported in younger adults, children and adolescents. This study aimed to test for PL and the protein leverage hypothesis (PLH) in a cohort of older adults.
Methods
We conducted a retrospective analysis of dietary intake in a cohort of 1699 community-dwelling older adults aged 67–84 years from the NuAge cohort. We computed TEI and the energy contribution (EC) from each macronutrient. The strength of leverage of macronutrients was assessed through power functions (TEI=μ*ECL). Body mass index (BMI) was calculated, and mixture models were fitted to predict TEI and BMI from macronutrients’ ECs.
Results
In this cohort of older adults, 53% of individuals had obesity and 1.5% had severe cases. The mean TEI was 7673 kJ and macronutrients’ ECs were 50.4%, 33.2% and 16.4%, respectively for carbohydrates, fat, and protein. There was a strong negative association (L = −0.37; p < 0.001) between the protein EC and TEI. Each percent of energy intake from protein reduced TEI by 77 kJ on average, ceteris paribus. However, BMI was unassociated with TEI in this cohort.
Conclusions
Findings indicate clear evidence for PL on TEI, but not on BMI, likely because of aging, body composition, sarcopenia, or protein wasting.
Various scientists and philosophers have heavily criticized what they see as problematic forms of ‘nutritional reductionism’ or ‘nutritionism’ whereby studying food-health interactions at the level of isolated food components produces largely misguided science and misleading interpretations. However, the exact target of these diverse criticisms remains elusive, and its implications are overstated, which may hinder scientific understanding. To better identify the types of flaws supposedly hindering reductionist research, we disentangle three types of reductionist claims to better determine what the debate is about and to propose ways to move it forward. We then present a qualified defense of the reductionist program that hinges on the ability to identify nutritional causes that make a difference, which we illustrate through central examples in the history of nutrition. This defense is taken further by using insights from the philosophy of mechanisms to analyze how the field of nutritional ecology offers a synthetic framework to explain, generate, and test predictions about nutrient-organism interactions, which is premised on the biological mechanisms of nutrient-specific appetite regulation. The result, which we call ‘synthetic reductionism,’ avoids many, though perhaps not all, of the challenges raised by antireductionists, and also highlights the potential of reductionism to identify nutritional difference makers.
Summary 1. Primates meet their nutritional goals by prioritizing certain nutritional parameters when choosing the types and quantities of different foods. 2. There are five major models applied in primate nutritional ecology, each of which proposes that diet selection subserves a different primary nutritional goal: (i) energy maximization; (ii) nitrogen (protein) maximization; (iii) avoidance or regulation of intake of plant secondary metabolites; (iv) limitations on the intake of dietary fibre; and (v) nutrient balancing. 3. Here, we review the evidence in support of each of these nutritional goals as drivers of primate diet selection. We discuss some of the costs and benefits associated with different methodological approaches used in primate nutritional ecology. 4. New approaches developed outside of primatology have provided better frameworks for understanding the nutritional goals of some primate species. We suggest that the field of primate nutritional ecology needs to take greater advantage of the techniques developed by nutritional ecologists working in other fields. 5. Specifically, we recommend (i) the increased application of the Geometric Framework for nutrition, (ii) the application of methodological approaches that enable the estimation of nutrient and energy availability from food sources, and (iii) continuous follows of individual primates in the wild for determining primary nutritional goals.
Both anthropologists and nutritionists have long recognized that the diets of modern-day hunter-gatherers may represent a reference standard for modern human nutrition and a model for defense against certain diseases of affluence. Because the hunter-gatherer way of life is now probably extinct in its purely un-Westernized form, nutritionists and anthropologists must rely on indirect procedures to reconstruct the traditional diet of preagricultural humans. In this analysis, we incorporate the most recent ethnographic compilation of plant-to-animal economic subsistence patterns of hunter-gatherers to estimate likely dietary macronutrient intakes (% of energy) for environmentally diverse hunter-gatherer populations. Furthermore, we show how differences in the percentage of body fat in prey animals would alter protein intakes in hunter-gatherers and how a maximal protein ceiling influences the selection of other macronutrients. Our analysis showed that whenever and wherever it was ecologically possible, hunter-gatherers consumed high amounts (45-65% of energy) of animal food. Most (73%) of the worldwide hunter-gatherer societies derived >50% (> or =56-65% of energy) of their subsistence from animal foods, whereas only 14% of these societies derived >50% (> or =56-65% of energy) of their subsistence from gathered plant foods. This high reliance on animal-based foods coupled with the relatively low carbohydrate content of wild plant foods produces universally characteristic macronutrient consumption ratios in which protein is elevated (19-35% of energy) at the expense of carbohydrates (22-40% of energy).
Obesity interventions can result in weight loss, but accurate prediction of the bodyweight time course requires properly accounting for dynamic energy imbalances. In this report, we describe a mathematical modelling approach to adult human metabolism that simulates energy expenditure adaptations during weight loss. We also present a web-based simulator for prediction of weight change dynamics. We show that the bodyweight response to a change of energy intake is slow, with half times of about 1 year. Furthermore, adults with greater adiposity have a larger expected weight loss for the same change of energy intake, and to reach their steady-state weight will take longer than it would for those with less initial body fat. Using a population-averaged model, we calculated the energy-balance dynamics corresponding to the development of the US adult obesity epidemic. A small persistent average daily energy imbalance gap between intake and expenditure of about 30 kJ per day underlies the observed average weight gain. However, energy intake must have risen to keep pace with increased expenditure associated with increased weight. The average increase of energy intake needed to sustain the increased weight (the maintenance energy gap) has amounted to about 0·9 MJ per day and quantifies the public health challenge to reverse the obesity epidemic.
The prevalence of obesity in the United States has increased dramatically.
The aim of this study was to determine trends in carbohydrate, fat, and protein intakes in adults and their association with energy intake by using data from the National Health and Nutrition Examination Survey (NHANES)-a representative sample of the US population.
Data on adults aged 20-74 y from the first NHANES (NHANES I, 1971-1975; n = 13,106) were compared with data from NHANES 2005-2006 (n = 4381). Normal weight was defined as a body mass index (BMI; in kg/m(2)) of 19 to <25, overweight as a BMI of 25 to <30, and obese as a BMI of ≥30. Carbohydrate, fat, and protein intakes were obtained by dietary recall. Regression analyses were adjusted for potential confounders.
The prevalence of obesity increased from 11.9% to 33.4% in men and from 16.6% to 36.5% in women. The percentage of energy from carbohydrates increased from 44.0% to 48.7%, the percentage of energy from fat decreased from 36.6% to 33.7%, and the percentage of energy from protein decreased from 16.5% to 15.7%. Trends were identical across normal-weight, overweight, and obese groups. Energy intake increased substantially in all 3 BMI groups. In NHANES 2005-2006, a 1% increase in the percentage of energy from protein was associated with a decrease in energy intake of 32 kcal (substituted for carbohydrates) or 51 kcal (substituted for fat). Similar findings were seen across all BMI categories, in men and women, and in NHANES I.
Energy intake and the prevalence of obesity have increased dramatically. Dietary interventions should focus on decreasing energy intake and potentially by substituting protein for fat or carbohydrates.
Studies of weight-control diets that are high in protein or low in glycemic index have reached varied conclusions, probably owing to the fact that the studies had insufficient power.
We enrolled overweight adults from eight European countries who had lost at least 8% of their initial body weight with a 3.3-MJ (800-kcal) low-calorie diet. Participants were randomly assigned, in a two-by-two factorial design, to one of five ad libitum diets to prevent weight regain over a 26-week period: a low-protein and low-glycemic-index diet, a low-protein and high-glycemic-index diet, a high-protein and low-glycemic-index diet, a high-protein and high-glycemic-index diet, or a control diet.
A total of 1209 adults were screened (mean age, 41 years; body-mass index [the weight in kilograms divided by the square of the height in meters], 34), of whom 938 entered the low-calorie-diet phase of the study. A total of 773 participants who completed that phase were randomly assigned to one of the five maintenance diets; 548 completed the intervention (71%). Fewer participants in the high-protein and the low-glycemic-index groups than in the low-protein-high-glycemic-index group dropped out of the study (26.4% and 25.6%, respectively, vs. 37.4%; P=0.02 and P=0.01 for the respective comparisons). The mean initial weight loss with the low-calorie diet was 11.0 kg. In the analysis of participants who completed the study, only the low-protein-high-glycemic-index diet was associated with subsequent significant weight regain (1.67 kg; 95% confidence interval [CI], 0.48 to 2.87). In an intention-to-treat analysis, the weight regain was 0.93 kg less (95% CI, 0.31 to 1.55) in the groups assigned to a high-protein diet than in those assigned to a low-protein diet (P=0.003) and 0.95 kg less (95% CI, 0.33 to 1.57) in the groups assigned to a low-glycemic-index diet than in those assigned to a high-glycemic-index diet (P=0.003). The analysis involving participants who completed the intervention produced similar results. The groups did not differ significantly with respect to diet-related adverse events.
In this large European study, a modest increase in protein content and a modest reduction in the glycemic index led to an improvement in study completion and maintenance of weight loss. (Funded by the European Commission; ClinicalTrials.gov number, NCT00390637.).
Snacking is common in adolescents; however, it is unclear if there is an association between snacking and overweight or obesity within the context of the overall diet.
This study examined the associations of snacking with weight status and abdominal obesity in adolescents 12-18 y of age (n = 5811).
We conducted secondary analyses of 24-h diet recalls and anthropometric data from the National Health and Nutrition Examination Survey (NHANES) 1999-2004. We classified adolescents by frequency of snack consumption (0, 1, 2, 3, and > or =4 snacks/d) and by the percentage of energy intake from snacks (0%, <10%, 10-19%, 20-29%, 30-39%, and > or =40%). We classified adolescents who had a body mass index (BMI) > or =85th percentile of BMI-for-age as overweight or obese. We defined abdominal obesity as a waist circumference > or =90th percentile. We determined covariate-adjusted prevalences of overweight or obesity and abdominal obesity and odds ratios with SUDAAN software (release 9.0.1; Research Triangle Institute, Research Triangle Park, NC).
Mean values of all obesity indicators studied were inversely associated with snacking frequency and percentage of energy from snacks. The prevalence of overweight or obesity and of abdominal obesity decreased with increased snacking frequency and with increased percentage of energy from snacks. Odds ratios (95% CIs) for overweight or obesity and for abdominal obesity ranged from 0.63 (0.48, 0.85) to 0.40 (0.29, 0.57) and from 0.61 (0.43, 0.86) to 0.36 (0.21, 0.63) for 2 to > or =4 snacks/d, respectively. Reduced risks of overweight or obesity and abdominal obesity were associated with snacking.
Snackers, compared with nonsnackers, were less likely to be overweight or obese and less likely to have abdominal obesity.
We used combined field and laboratory studies to investigate the relationships among foraging, food selection, and macronutrient intake and retention by the New Zealand marine reef fish, Girella tricuspidata (F. Girellidae). Gut analyses of field-caught fish revealed a diet comprising significant proportions both of algal and non-algal (copepods, hydroids, and organic detritus) foods, with the relative proportions of algal and non-algal food items varying from 65% of non-algal foods in the winter to 34% in autumn. Observations of the diurnal feeding pattern of free-ranging wild fish showed an increase in bite rate from sunrise to late afternoon, followed by a decrease toward sunset. In laboratory studies we recorded (a) gut throughput rates, (b) macronutrient intake, and (c) macronutrient assimilation by captive G. tricuspidata fed one of three algal species. The algae were Enteromorpha intestinalis and Ulva lactuca, both of which are preferred foods of G. tricuspidata, and Gracilaria chilensis, which has not been recorded in the stomach contents of the fish despite its co-occurrence in some habitats. The two species of preferred algae were found to have a higher starch and lower protein content than the non-dietary G. chilensis. The fish regulated their intake and utilization of the algae such that similar amounts of protein were ingested and assimilated from all three species, but less starch was ingested and assimilated from the non-dietary G. chilensis than from the preferred species. Gut throughput times correlated positively with starch availability in the algae but were lower for all treatments than previous studies would suggest are associated with fermentative digestion. The data suggest that omnivory in G. tricuspidata is likely based on complementarity, rather than substitutability of algal and animal foods.
The publisher regrets that this article is an accidental duplication of an article that has already been published, doi: 10.1016/j.appet.2006.03.001 . The duplicate article has therefore been withdrawn.
Herbivore damage is generally detrimental to plant fitness, and the evolu- tionary response of plant populations to damage can involve either increased resistance or increased tolerance. While characters that contribute to resistance, such as secondary chem- icals and trichomes, are relatively well understood, characters that contribute to a plant's ability to tolerate damage have received much less attention. Using Helianthus annuus (wild sunflower) and simulated damage of Haplorhynchites aeneus (head-clipping weevil) as a model system, we examined morphological characters and developmental processes that contribute to compensatory ability. We performed a factorial experiment that included three levels of damage (none, the first two, or the first four inflorescences were clipped with scissors) and eight sires each mated to four dams. We found that plants compensated fully for simulated head-clipper damage and that there was no variation among plant families in compensatory ability: seed production and mean seed mass did not vary among treat- ments, and sire X treatment interactions were not significant. Plants used four mechanisms to compensate for damage: (1) Clipped plants produced significantly more inflorescences than unclipped plants. Plants produced these additional inflorescences on higher order branches at the end of the flowering season. (2) Clipped plants filled significantly more seeds in their remaining heads than did unclipped plants. (3) Clipped plants, because they effectively flowered later than unclipped plants, were less susceptible to damage by seed- feeding herbivores other than Haplorhynchites. (4) In later heads, seed size was greater on clipped plants, which allowed mean seed size to be maintained in clipped plants. Although there was genetic variation among the families used in this experiment for most of the characters associated with compensation for damage (seed number, mean seed size, mean flowering date, length of the flowering period, and branching morphology), in analyses of these characters, no sire X treatment interactions were significant indicating that all of the families relied on similar mechanisms to compensate for damage.
Our aim was to design a selection of foods with differing proportions of protein but equal palatability in two settings, Sydney Australia and Kingston Jamaica. The foods were manipulated to contain 10, 15 or 25% E as protein with reciprocal changes in carbohydrate to 60, 55 or 45% E and dietary fat was kept constant at 30%. Naïve participants did not identify a difference in protein between the versions. On average, the versions were rated equal in pleasantness (Sydney-10%: 44±2, 15%: 49±2 and 25%: 49±2 Kingston-10%: 41±3, 15%: 41±3 and 25%: 37±3).
![Lean humans increase energy consumption on a lower percent protein diet. ( A ) Cumulative daily bi-coordinate means for protein and non-protein macronutrient (carbohydrate and fat) intake (MJ) for participants during the 4-day 10% (white circles), 15% (grey triangles) and 25% (black squares) ad libitum study periods. The dashed lines represent the nutrient rails participants were restricted to during the 10%, 15% and 25% study periods. The dotted lines represent intakes that may occur on the 10%, 15% and 25% foods if intake was regulated to energy requirements (calculated as 1.6 6 basal energy requirements as derived from the Schofields equation [23] assuming a light to moderate level of physical activity) and that protein and carbohydrate were interchangeable. The inset shows total energy intake (MJ) for participants over the 4-day 10% (white), 15% (grey) and 25% (black) ad libitum study periods. The same letter above the bars in the insert indicates that the means did not differ significantly in Bonferroni post hoc comparisons, whereas different letters indicate differences at p , 0.05. Refer to Figure S1 for individual total energy intake data points. ( B ) Daily protein (triangles) and total (circles) intake (MJ) for participants during the 4-day 10% (white), 15% (grey) and 25% (black) ad libitum study periods. Intake did not change statistically across days within each treatment. Note that the appearance of an increase in intake (of all nutrients, due to fixed diet compositions) from days 1 to 2 and a decline from day 3 to 4 reflected commencement of the study after breakfast on day 1 and fasting overnight on day 4 in readiness for a meal test on day 5 (data not reported). ( C ) Cumulative protein (triangles) and total (circles) intake (MJ) for participants during the 4-day 10% (white), 15% (grey) and 25% (black) ad libitum study periods. doi:10.1371/journal.pone.0025929.g001](https://www.researchgate.net/profile/Miguel-Iglesias-3/publication/51737804/figure/fig1/AS:305961303592965@1449958267472/Lean-humans-increase-energy-consumption-on-a-lower-percent-protein-diet-A-Cumulative_Q320.jpg)
