Macronutrient (im)balance drives energy intake in an obesogenic food environment: An ecological analysis

Abstract

Objective
The protein leverage hypothesis (PLH) postulates that strong regulation of protein intake drives energy overconsumption and obesity when human diets are diluted by fat and carbohydrates. The two predictions of the PLH are that humans (i) regulate intake to maintain protein within a narrow range and that (ii) energy intake is an inverse function of percentage energy from protein because absolute protein intake is maintained within narrow limits.

Methods
Multidimensional nutritional geometry was used to test the predictions of the PLH using dietary data from the Australian National Nutrition and Physical Activity Survey.

Results
Both predictions of the PLH were confirmed in a population setting: the mean protein intake was 18.4%, and energy intake decreased with increasing energy from protein (L = −0.18, p < 0.0001). It was demonstrated that highly processed discretionary foods are a significant diluent of protein and associated with increased energy but not increased protein intake.

Conclusions
These results support an integrated ecological and mechanistic explanation for obesity, in which low‐protein highly processed foods lead to higher energy intake because of the biological response to macronutrient imbalance driven by a dominant appetite for protein. This study supports a central role for protein in the obesity epidemic, with significant implications for global health.

Full text

OBESITY SYMPOSIUM
Epidemiology/Genetics
Macronutrient (im)balance drives energy intake in an
obesogenic food environment: An ecological analysis
Amanda Grech
1,2
| Zhixian Sui
1,2
| Anna Rangan
1,2
| Stephen J. Simpson
1,2
|
Sean C. P. Coogan
1,2
| David Raubenheimer
1,2
1
Charles Perkins Centre, University of Sydney,
Sydney, New South Wales, Australia
2
School of Life and Environmental Science,
University of Sydney, Sydney, New South
Wales, Australia
Correspondence
Amanda Grech and David Raubenheimer,
Charles Perkins Centre and School of Life and
Environmental Science, University of Sydney,
D17-Charles Perkins Centre, John Hopkins
Drive, Sydney, NSW 2006, Australia.
Email: amanda.grech@sydney.edu.au;
david.raubenheimer@sydney.edu.au
Funding information
The Meat and Livestock Association; The
National Health and Medical Research Council,
Australia, Nutrition and Complexity Program
Grant, Grant/Award Number: GNT1149976
Abstract
Objective: The protein leverage hypothesis (PLH) postulates that strong regulation
of protein intake drives energy overconsumption and obesity when human diets are
diluted by fat and carbohydrates. The two predictions of the PLH are that humans
(i) regulate intake to maintain protein within a narrow range and that (ii) energy
intake is an inverse function of percentage energy from protein because absolute
protein intake is maintained within narrow limits.
Methods: Multidimensional nutritional geometry was used to test the predictions of
the PLH using dietary data from the Australian National Nutrition and Physical Activ-
ity Survey.
Results: Both predictions of the PLH were confirmed in a population setting: the
mean protein intake was 18.4%, and energy intake decreased with increasing energy
from protein (L =0.18, p< 0.0001). It was demonstrated that highly processed dis-
cretionary foods are a significant diluent of protein and associated with increased
energy but not increased protein intake.
Conclusions: These results support an integrated ecological and mechanistic explana-
tion for obesity, in which low-protein highly processed foods lead to higher energy
intake because of the biological response to macronutrient imbalance driven by a
dominant appetite for protein. This study supports a central role for protein in the
obesity epidemic, with significant implications for global health.
INTRODUCTION
With an estimated 11 million premature deaths and 255 million
disability-adjusted life years lost annually because of suboptimal nutri-
tion, there is an urgent need to understand the factors that influence
human diets and their health consequences [1]. A substantial amount
of research has been directed at the problem at multiple levels, from
the physiological pathways by which diet influences health, to the
cognitive mechanisms driving food choice and the roles of food
environments in influencing diets. Yet the problem continues to grow
with no apparent solution in sight. New approaches are needed to
understand and improve human diets.
Human nutrition science may benefit from theory and approaches
from the field that applies evolutionary and ecological frameworks to
the study of animal nutrition, nutritional ecology [2]. “Ecological Social
Models”in public health research provide a framework for examining
the interactive effects of personal and environmental factors on
behavior and health [3]. In this respect, these models share important
Received: 3 March 2022 Revised: 18 May 2022 Accepted: 19 May 2022
DOI: 10.1002/oby.23578
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any
medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
© 2022 The Authors. Obesity published by Wiley Periodicals LLC on behalf of The Obesity Society.
2156 Obesity (Silver Spring). 2022;30:2156–2166.
www.obesityjournal.org

equivalences with nutritional ecology models, which conceptualize
diets as a link within a system that comprises the animal and its envi-
ronment. Key parameters of nutritional ecology models include con-
straints and other influences on dietary intake set by the
environment, the strategies the animal deploys to deal with these con-
straints and influences, and the consequences for both the animal and
the environment of the interaction [2]. To deal with the complexity of
nutritional interactions between animals and food environments,
which involve many nutrients and other food components (e.g., fiber,
toxins), as well as several levels in the dietary hierarchy (e.g., foods,
meals, diets), a multidimensional modeling framework called nutri-
tional geometry was developed in nutritional ecology [2].
Nutritional geometry studies conducted on many species, both
in the laboratory and in the field, have identified the interaction of
nutrient-specific appetites to be a powerful determinant of how ani-
mals respond to the opportunities and constraints set by nutritional
environments and the consequences of these responses. Specific
appetites for the macronutrients protein, fat, and carbohydrates, as
well as for some micronutrients [4], have proven to be a central
mechanism that guides the selection of diets that provide the
required levels of multiple nutrients simultaneously, i.e., balanced
diets. When caused to eat nutritionally imbalanced diets
(e.g., experimentally or because of natural fluctuations in food avail-
ability), however, animals are unable to achieve the target intakes for
all nutrients and are confronted with a trade-off between undereat-
ing some nutrients and overeating others. In many species studied,
several nonhuman primates included, protein intake is regulated
more strongly than fat and carbohydrates, and consequently protein
intake remains relatively invariant while the intakes of fat and carbo-
hydrates (and total energy) vary more widely with variation in dietary
macronutrient balance [5, 6].
This pattern of macronutrient regulation, termed “protein
prioritization,”underpins a novel hypothesis for human obesity, the
protein leverage hypothesis (PLH). According to the PLH, fat, carbohy-
drates, and total energy overconsumption is a passive outcome of
humans strongly regulating protein intake within narrow boundaries
when dietary protein is diluted by fat and carbohydrates [5, 7]. There
now exists significant experimental evidence in support of the key
components of the PLH, namely that nutrient-specific appetites inter-
act to direct dietary intake toward a particular dietary macronutrient
balance, and with macro-nutritionally imbalanced diets, protein regula-
tion dominates fat and carbohydrates. Campbell et al. [8] demon-
strated under randomized controlled conditions that adult human
subjects consistently selected a diet of 14.7% protein when the
options ranged from 10% to 25% protein, confirming the results of an
earlier small-scale study [9]. Several randomized controlled trials in
which humans have been experimentally constrained to imbalanced
diets have demonstrated the protein prioritization pattern of macro-
nutrient regulation and confirmed that it leads to energy overcon-
sumption on protein-dilute diets [5, 6]. One study also found a
negative relationship between change in body weight during the trial
and the percentage contribution of protein to energy intake [8]. The
two clinical trials that have failed to show increased intake on low-
protein diets [10, 11] used experimental diets that were very low in
protein (5%), beyond the regulatory limits of the system [5].
An important priority is to determine whether protein leverage
plays a role in driving energy overconsumption and obesity in free-
living humans, and if so, what the ecological causes of dietary protein
dilution are. Studies have demonstrated that percentage energy from
protein in American diets has decreased coincident with the rise of
obesity, as evidenced both in data from the National Health and
Nutrition Examination Survey (NHANES) and the Food and Agricul-
ture Organization Food Balance Sheets [12, 13]. A retrospective study
of a cohort of youth likewise found the predicted negative relation-
ship between dietary percentage protein and energy consumption.
One analysis of the NHANES data highlighted a category of highly
processed foods, ultraprocessed foods, as a likely diluent of protein in
American diets [14]. This is consistent with an experimental study that
found inpatient adults exposed to ultraprocessed diets ingested more
Study Importance
What is already known?
•Fundamental disagreement exists over the drivers and
mechanisms underlying the obesity epidemic. The pro-
tein leverage hypothesis (PLH) proposes that in macro-
nutritionally imbalanced food environments, strong human
regulation of protein intake drives energy overconsump-
tion and obesity (“protein leverage”) on protein-dilute
highly processed diets. Protein leverage has support from
several randomized controlled trials, and here we test the
PLH in realistic, ecological settings.
What does this study add?
•We show using a large national diet survey that, as pre-
dicted by the PLH, macronutrient intake is regulated
within narrow limits, energy intake is a negative function
of dietary protein concentration, and that the food cate-
gory principally associated with dilution of dietary protein
is highly processed discretionary foods.
How might these results change the direction of
research or the focus of clinical practice?
•That energy overconsumption is driven by a strong human
protein appetite interacting with imbalanced food environ-
ments highlights the importance of interventions focused on
food environments. Methodologically, our analysis addresses
the controversy over diet recall data by showing that a phe-
nomenon established in randomized controlled trials (protein
leverage) is also detectable in dietary surveillance data, thus
establishing both causality (in experimental settings) and rel-
evance (in population settings) of protein leverage.
MACRONUTRIENT (IM)BALANCE 2157

carbohydrates, fat, and total energy than those on unprocessed diets
and gained weight during the 14-day trial, whereas protein intake did
not differ across the diets [15]. However, there has been no inte-
grated study testing in the same population data for macronutrient
balancing, protein leveraging on imbalanced protein-dilute diets, or
the ecological cause of dietary protein dilution.
We applied nutritional geometry to perform an integrated test of
key predictions of the PLH using diet surveillance population data.
Using the Australian Health Survey data: (i) respondents regulate daily
macronutrient intake toward a range of approximately 15% to 25%
energy from protein (macronutrient balancing); (ii) daily dietary protein
intake is more constant across the survey respondents than daily car-
bohydrate and fat intakes (protein prioritization pattern of macronutri-
ent regulation), and therefore daily energy intake is an inverse
function of dietary percentage protein (protein leverage); and
(iii) highly processed industrial foods are generally low in protein rela-
tive to fat and carbohydrates and thus predispose to dietary protein
dilution and excess energy intake via protein leverage. All predictions
were supported, with important implications for nutritional research
and for focusing policy and other measures for transforming obeso-
genic food environments.
METHODS
Respondents and dietary data collection
This study used data from the cross-sectional 2011 to 2012 National
Nutrition and Physical Activity Survey (NNPAS), undertaken by the
Australian Bureau of Statistics (ABS). The survey was designed to col-
lect national benchmark data on nutrition and physical activity of the
Australian population using a multistaged area sample of private
households. It was conducted between May 2011 and June 2012 in
adults and children aged 2 years and older. Ethics approval for the sur-
vey was granted by the Australian Government Department of Health
and Aging Departmental Ethics Committee in 2011. Further details
about the scope and the methodology of the survey are available from
the NNPAS Users’Guide by the ABS [16].
Details on data collection of the 24-hour recall used to assess
diets and definitions of all outcomes, exposures, predictors, potential
confounders, and effect modifiers are provided in online Supporting
Information Methods.
Data analysis
To test the prediction that people regulate the balance of macronutri-
ents eaten over the time scale of a single day, the day interview was
divided into Eating Period 1 (EP1) (≥00:00 to <11:00), EP2 (≥11:00 to
<16:00), and EP3 (≥16:00) (Supporting Information Figure S1).
Respondents’proportion of energy intake from protein in EP1 was
categorized as below the Acceptable Macronutrient Distribution
Range (AMDR) range (<15%), within the AMDR (15%–25%), or above
the AMDR (>25%). Comparisons between time points were assessed
using repeated measures ANOVA with between-subject factors as
adjustments. A sensitivity analysis was conducted to adjust for poten-
tial confounding from regression to the mean, adjusting for baseline
protein energy at EP1 (percentage) minus the baseline mean protein
energy at EP1 (percentage) [17]. This did not alter our conclusions.
Estimated regression coefficients for change in energy and food
intakes with the proportion of energy from protein at EP1 across dif-
ferent eating periods and between tertiles of discretionary food were
identified using generalized linear regression analysis. The analysis
was adjusted for sex, age, country of birth (Australia, English-speaking
countries, or other countries), energy intake versus basal metabolic
rate, physical activity level as defined by the ABS (High, Moderate,
Low, Sedentary [very low], Sedentary [no exercise], or Not Stated)
[16], and season of interview. All regressions were computed in SAS
9.4 (SAS Institute Inc.).
Ratios between dietary macronutrients reported by the respon-
dents were analyzed and displayed in the right-angled mixture triangle
surface plots [2]. Mixture models, also known as Scheffe’polynomials,
were fitted to the data with macronutrients as repeated measures
over total energy intake or individual nutrient intake. To prepare
graphs and run associated tests, R packages required xtable, mgcv, sp,
lattice, ellipse, survival, nlme, mixexp, plyr, ggplot2, scales, directlabels,
and lsmeans. Data analysis and graphics were performed using R soft-
ware [31].
FIGURE 1 Cumulative proportion of macronutrients from Eating
Period 1 (EP1) to EP3 by reported protein density below, within, or
above the AMDR at EP1. The day was divided into three intervals;
EP1 between midnight and 11 AM; EP2 between 11:00 AM and
4:00 PM; and EP3 after 4:00 PM, indicated as 1, 2, 3 on the figure.
Positions at 3 indicate the macronutrient proportions over the day,
that is, from the start of the day to the end of the day. Shaded
polygon area: Acceptable Macronutrient Distribution Range (AMDR)
for Australians and New Zealanders [Color figure can be viewed at
wileyonlinelibrary.com]
2158 MACRONUTRIENT (IM)BALANCE

To test for protein prioritization, power regression was conducted
(SPSS Statistics version 25, IBM Corp.) for modeling dietary percent-
age energy from protein to total macronutrient energy intake for the
specified macronutrients (p) [5, 12]. In this test, if protein intake
(P) remains constant and nonprotein energy varies exponentially with
changing dietary proportion of energy from protein (p), the exponent
(L) in the equation Pp
L
takes a value of 1. In the case of partial pro-
tein prioritization, absolute energy intake from protein increases with
increased percentage energy from protein, but to a lesser degree than
total energy intake decreases (1<L< 0), indicating that factors
other than protein have an influence on the variance in total energy
intake. If L> 0, then total energy intake increases with an increase in
percentage energy from protein.
Where stated, the NNPAS weights were applied to data to pro-
vide estimates for the Australian population accounting for nonre-
sponse and the complex survey design including replicate weights. For
all tests, p< 0.05 was considered statistically significant.
RESULTS
Respondents and timing of food intake
The study population included 9341 adults (≥19 years) with a mean age
of 46.3 years. The mean (SE) energy intake was 8671 kJ (52.2) and the
mean (SE) percentage energy from protein was 18.4% (0.1%), from carbo-
hydrates 43.5% (0.2%), from fat 30.9% (0.1%), from fiber 2.2 (0.1%), and
from alcohol 4.3% (0.1%) (Table 1). Respondents’energy intake in relation
to time of consumption is plotted in Supporting Information Figure S1.
Energy intake accumulated within the three peak eating periods around
8:00 AM,1:00PM,and7:00PM, with small snack intakes in between. EP1
was the smallest eating period of the recorded day with the least amount
of energy and foods consumed, whereas EP3 was the largest.
Macronutrient balancing
The cumulative change of macronutrient proportions over the day is
showninFigure1, with respondents separated by percentage energy
from protein below, within, or above the AMDR at EP1. The figure
shows that respondents categorized according to whether above, below,
or within the AMDR at EP1 tracked across diet space toward a common
position by the end of the day (EP3), as predicted by the macronutrient
balancing model. As predicted, respondents with percentage energy from
protein below the AMDR at EP1 increased the ratio of protein in subse-
quent eating periods, those who started within the AMDR for protein
maintained this, and respondents with percentage energy from protein
above the AMDR at EP1 showed a decline through the day (Figure 1).
There was, nonetheless, a statistically significant difference between
groups for cumulative intake to EP3 (i.e., total intake across the day)
(F
2,4
=860.0, p< 0.0001). This suggests that the compensation through
the day was not complete for those with a lower proportion of energy at
the start of the day. Participants with a higher proportion of energy from
protein at the start of the day (i.e., EP1) had lower daily energy intake
(18.6 [5.7], p=0.0017, Table 2).
Protein leverage
Figure 2A–Gplots the relationships between the proportion of energy
from macronutrients and absolute intakes of various dietary compo-
nents. The combined intakes of carbohydrates and fat (panel A) and
TABLE 1 Characteristics and macronutrient intakes of
participants in the National Nutrition and Physical Activity Survey
Value
Male gender, % 49.4
Age (y), %
18–50 58.4
51–70 29.9
71+11.7
Country of birth, %
Australia 68.8
Canada, Ireland, NZ, South Africa, UK, US 11.6
Other 19.6
SEIFA, %
Lowest—quintile 1 18.1
Middle—quintile 2–3 59.7
Highest—quintile 5 22.2
Tertiary education, %
Not known 1.5
No tertiary education 38.2
Vocational college 34.9
University 25.3
BMI, %
Underweight (<18.5 kg/m
2
) 1.8
Normal (≥18.5 to <25 kg/m
2
) 35.5
Overweight (≥25.0 to <30.0 kg/m
2
) 36.4
Obesity (≥30.0 kg/m
2
) 26.3
Energy reporting status, %
Low energy (energy intake: basal metabolic rate
ratio <0.87)
16.8
Unknown 14.0
Plausible 69.2
Energy and macronutrients, mean
Energy (kJ) 8671.6
Protein (% total energy) 18.4
Carbohydrates (% total energy) 43.5
Fat (% total energy) 30.9
Fiber (% total energy) 2.2
Alcohol (% total energy) 4.3
Note: Survey weights applied.
Abbreviations: NZ, New Zealand; SEIFA, Socio-Economic Indexes for Areas.
MACRONUTRIENT (IM)BALANCE 2159

total energy (panel B) increased with decreasing proportion of macro-
nutrient energy from protein, as predicted by the PLH. The same rela-
tionships were observed in the sensitivity analysis using the first and
the second day of the survey (Supporting Information Figure S2).
Increasing energy intake on low-protein diets was due both to carbo-
hydrates (panel C) and fat (panel D) intakes being high on low-protein
diets. As would be expected, dietary energy density was highest for
diets with a high proportional fat content (panel E), corresponding
with high absolute fat intake (panel D). Because fat has twice the
energy density of carbohydrates and protein, energy density per se
might thus contribute to high energy intake. It cannot, however, on its
own explain the observed variation in energy intake, because both
the dry weight of food (panel F) and carbohydrates eaten (panel C)
were high in regions where energy density was low. In contrast,
intakes of all components except protein increased with decreasing
dietary protein, as expected under the protein leverage model.
Table 3shows that all these relationships were highly significant, and
the strength of leverage (the Lvalue) varied among components. For
none was leverage complete (indicated by L=1), with the strongest
leverage occurring for carbohydrates (L=0.59) and the weakest
being for dry weight intake (L=0.14). Incomplete protein leverage
indicates that in addition to the intake of nonprotein dietary compo-
nents decreasing with increasing dietary percentage protein, absolute
protein intake increased but to a lesser extent [5], as shown in
Figure 2G.
Superimposed on Figure 2B,G are contours representing the
population-level recommendations for energy and protein intakes
[18], respectively. The contours delineate the dietary macronutrient
TABLE 2 Estimated regression coefficients for change in energy and food intakes with the proportion of energy from protein at different
eating periods
EP Dietary component Estimate (SE) pvalue
a
EP1 (midnight to 11 AM) Energy (kJ) 7.8 (3.3) 0.0208
Meat and alternatives (g) 1.9 (0.1) <0.0001
Grain (g) 0.2 (0.2) 0.9136
Vegetables (g) 0.04 (0.1) 0.7313
Fruit (g) 2.7 (0.2) <0.0001
Dairy products (g) 5.9 (0.4) <0.0001
Discretionary (servings)
b
0.03 (0.0) <0.0001
EP2 (11 AM to 4 PM) Energy (kJ) 18.4 (4.0) <0.0001
Meat and alternatives (g) 0.1 (0.2) 0.5789
Grain (g) 0.9 (0.1) <0.0001
Vegetables (g) 0.2 (0.2) 0.2835
Fruit (g) 0.1 (0.3) 0.7468
Dairy products (g) 0.03 (0.2) 0.8626
Discretionary (servings) 0.02 (0.0) 0.0006
EP3 (4 PM to midnight) Energy (kJ) 8.0 (4.0) 0.0507
Meat and alternatives (g) 0.2 (0.2) 0.4440
Grain (g) 0.5 (0.2) 0.0214
Vegetables (g) 0.6 (0.3) 0.0409
Fruit (g) 1.0 (0.4) 0.0153
Dairy products (g) 0.02 (0.3) 0.9381
Discretionary (servings) 0.01 (0.0) 0.0227
Total (midnight to midnight) Energy (kJ) 18.6 (5.7) 0.0017
Meat and alternatives (g) 11.0 (0.4) <0.0001
Grain (g) 1.3 (0.3) 0.0002
Vegetables (g) 0.2 (0.3) 0.8420
Fruit (g) 3.2 (0.4) <0.0001
Dairy products (g) 6.0 (0.5) <0.0001
Discretionary (servings) 0.1 (0.0) <0.0001
Abbreviation: EP, eating period.
a
Generalized linear model adjusted for gender, age, country of birth, energy intake vs. basal metabolic rate, physical activity level, and season of interview.
Survey weights applied.
b
1 serving =600 kJ.
2160 MACRONUTRIENT (IM)BALANCE

compositions that the data predict to be associated with positive
(to the left) and negative (to the right) energy balance and negative
(to the left) and positive (to the right) protein balance.
Food macronutrient composition and protein leverage
Figure 3shows the macronutrient composition of foods from the five
food groups and discretionary foods in the Australian food nutrient
database. Five food group foods predominantly have a protein con-
tent of greater than 15% of energy, whereas discretionary foods are
comparatively protein dilute with <15% of energy from protein and
variable carbohydrate and fat content. Dietary dilution of protein was
evident for participants consuming more discretionary foods relative
to five food group foods, as evidenced by the fact that percentage
energy from protein decreased with increasing discretionary food
intake (protein dilution) (Figure 4A). In contrast, intake of five food
group foods decreased with decreasing percentage energy from
FIGURE 2 Surface plots showing the relationship between daily dietary macronutrient distributions and energy intake from different dietary
components for adults. (A) Carbohydrates and fat (kJ). (B) Total energy (kJ). (C) Total carbohydrates (kJ). (D) Total fat (kJ). (E) Energy density (kJ/g).
(F) Dry weight of food (g). (G) Protein energy (kJ). For any point on the colored surface, the point represents the average energy for that
contribution of protein (%E), fat (%E), and carbohydrates (%E) from the dietary component. As percentage energy from protein increases along
the xaxis, total energy decreases (red to blue), and total protein increases (blue to red). Carbohydrates (%E) are deterministically implied as the
proportion from macronutrients =100%, and the value is shown as diagonal lines with slope =1. The polygon represents the Australian/New
Zealand Acceptable Macronutrient Distribution Range (AMDR). The dashed line in panel (B) represents the estimated energy requirements based
on the basal metabolic rate of the average adult from the survey assuming equilibrium and a physical activity level of 1.4 (9510 kJ); the data
indicate that diets within 15%–20% energy from protein correspond to equilibrium energy intake, whereas dietary protein densities below and
above this level are associated with positive and negative energy balance, respectively. The dashed contour in panel (F) represents an
approximate average protein requirement for the survey population (1597 kJ), based on an average person’s weight of 78.3 kg (95% CI: 77.8–
78.8) and the maximum population-safe requirements estimated by Elango et al. (1.2 g/kg) [18]
TABLE 3 The exponent (L) from power regression testing protein prioritization of adults in the NNPAS
Protein range
Dry weight (g) Total energy Fat Carbohydrates
LpLpLpLp
Full range of protein, %E 0.14 <0.0001 0.18 <0.0001 0.20 <0.0001 0.59 <0.0001
10%–30% energy
a
0.16 <0.0001 0.20 <0.0001 0.22 <0.0001 0.58 <0.0001
Note: Survey weight applied. An exponent of 1 indicates complete protein prioritization where absolute protein intake remains constant, and
carbohydrates and fat differ with the proportion of dietary protein intake.
Abbreviations: NNPAS, National Nutrition and Physical Activity Survey; %E, percentage energy.
a
10%–30% protein indicates the usual variation in human protein intake.
MACRONUTRIENT (IM)BALANCE 2161

FIGURE 3 Macronutrient composition for discretionary foods and the five food groups. Percentage energy from protein, carbohydrates, and
fat for discretionary foods and the five food groups as consumed by participants in the National Nutrition and Physical Activity Survey (2011–
2012) including from left to right, top to bottom: discretionary foods; grains and cereals; meat including poultry, fish, eggs, tofu, nuts, seeds,
legumes, and beans; fruit; vegetables; and dairy products including milk, yogurt, cheese, and/or alternatives. The circled data points indicate the
mean nutrient composition of the food group. The dashed line represents the estimated energy requirements based on the basal metabolic rate
ratio of the average adult from the NNPAS assuming equilibrium and a physical activity level of 1.4 (9510 kJ). Polygon area: Acceptable
Macronutrient Distribution Range (AMDR) for Australians and New Zealanders [Color figure can be viewed at wileyonlinelibrary.com]
FIGURE 4 Surface plots showing the relationship between daily dietary macronutrients and total daily energy (kJ) from (A) discretionary
foods and (B) five food group foods plotted on percentage energy from macronutrients (lower intake represented with cooler colors, i.e., blue, and
higher intake represented with warmer colors, i.e., red). Polygon area: Acceptable Macronutrient Distribution Range (AMDR) for Australians and
New Zealanders [Color figure can be viewed at wileyonlinelibrary.com]
2162 MACRONUTRIENT (IM)BALANCE

protein and peaked with high fat intake (Figure 4B). Because the PLH
concerns the role of dietary percentage protein in influencing the
intakes of other dietary components, we also compared the dietary
intakes for participants with different percentage energy from pro-
tein (Supporting Information Table S1). The distribution of dietary
protein as a proportion of total energy is shown in Supporting Infor-
mation Figure S3. Participants with a lower proportion of energy
from protein (below the AMDR) consumed more discretionary foods
and less of the five food groups (Supporting Information Table S1).
Similarly, those with lower energy from protein at EP1 also had an
overall poorer diet quality at each mealtime, consuming more dis-
cretionary foods and less meat and alternatives (Table 2).
Figure 5shows the dietary macronutrient composition for
respondents separated into low, intermediate, or high levels (tertiles)
of discretionary food intake. Total energy intake increased with
increasing discretionary food intake and it was 7638 kJ and 9772 kJ
for the lowest and highest tertile consumers, respectively. Absolute
protein intake was maintained almost constant at 1500 kJ across all
tertiles of discretionary foods. Those with the greater consumption of
five food groups foods (i.e., lowest percentage energy from discretion-
ary foods) had higher percentage energy from protein, lower nonpro-
tein energy, and the lowest total energy intake. In comparison, those
who consumed the most discretionary foods had the lowest percent-
age energy from protein and higher absolute nonprotein energy and
total energy intakes.
DISCUSSION
The persistent rise of obesity and diabetes despite immense research
effort to find solutions has stimulated robust debate around the rela-
tive merits of different kinds of evidence used in nutritional research
[19]. A key finding from our analysis of dietary surveillance data is that
absolute energy intake varied inversely with dietary percentage
energy from protein, as predicted (our prediction ii), if a strong human
appetite for protein drove the overconsumption of fat and carbohy-
drates in protein-dilute diets (protein leverage). This on its own is not
definitive evidence for the PLH, because there are alternative plausi-
ble explanations. For example, the hyperpalatability of aggressively
marketed low-protein, energy-dense, industrially processed foods
could account for the correlation independent of protein appetite or
indeed for the opposite reason to PLH, namely that excess energy
intake is driven not by a strong appetite for protein but strong appe-
tites for fat and carbohydrates. Additionally, relationships in popula-
tion data could be affected by the degree of variance and covariance
among estimated measures of nutrient and energy intakes [20].
Although such alternative explanations may contribute to patterns
seen in population data, many sources of evidence independently point
to a dominant protein appetite interacting with dietary protein dilution
as a driver of energy overconsumption [5]. In addition to several random-
ized controlled trials in human diets,thishasbeenobservedinexperi-
ments in laboratory animals and in nonhuman primates in the wild,
FIGURE 5 Protein and nonprotein energy intakes by respondent group of discretionary food intake. If the respondents prioritized total
energy intake, regardless of its macronutrient source, the data would align along a negative-sloped diagonal representing constant energy intake
(x+y=constant); if nonprotein energy was prioritized, the data would align along a horizontal line (y=constant); and if protein was prioritized,
the data would align along a vertical line (x=constant). The analysis shows that the respondents maintained absolute protein intake relatively
tightly, with nonprotein energy intake varying more passively with dietary macronutrient ratios. Protein density decreased with larger intake of
discretionary foods (ranging from 20.5% for respondents categorized as discretionary food consumption tertile 1 to 15.1% for respondents from
tertile 3), and total energy intake increased (from 7638 kJ for tertile 1 to 9772 kJ for tertile 3). Data are adjusted for age, sex, Socio-Economic
Indexes for Areas, country of birth, energy intake to basal metabolic rate ratio.
MACRONUTRIENT (IM)BALANCE 2163

including our closest living relatives, chimpanzees (reviewed in [4, 5]). In
laboratory model systems, the mechanisms of protein appetite are
increasingly understood, both in invertebrates [21, 22]andmammals,in
whichithasbeenshownthatfibroblastgrowthfactor21isthecirculat-
ing signal of low protein status in humans and rodents, acting in the brain
to stimulate protein appetite [23, 24].
Our analysis of daily dietary trajectories (Figure 1)isalsoconsistent
with a specific appetite for protein driving the regulation of protein
intake (our prediction i), which is the key mechanistic component of pro-
tein leverage [5, 7]. Respondents who reported proportional protein
intakes lower or higher than the AMDR range of 15% to 25% at subse-
quent eating periods showed a compensatory intake of higher and lower
protein, respectively, whereas those who started within the AMDR range
remained there throughout the day. As with the protein leverage effect,
this too is potentially subject to confounds butit has independently been
demonstrated in randomized controlled trials. In one study, participants
consistently selected a diet of approximately 15% energy from protein
[8], a value that corresponds closely with results of a recent comparative
analysis using national survey data which showed consistency of protein
intake at approximately 15% of energy across US demographic groups as
well as 13 countries with gross domestic products >$10,000 per capita
per annum [25]. A recent experimental study demonstrated that higher
protein intake reduced subsequent protein intake and that it was regu-
lated meal by meal, supporting our hypothesis [26].
There is thus significant evidence that, firstly, humans regulate the
percentage of dietary energy contributed by protein to within a rela-
tively narrow range and, secondly, that low-protein diets are associated
with increased energy consumption via protein leverage. This set of
observations raises important questions concerning how and why the
diet balancing mechanisms are overridden to cause humans to select
protein-dilute diets in obesogenic food environments. In addressing this
question, it is important to bear in mind that macronutrient balancing
and ingestion of low-protein diets that lead to energy hyperphagia are
not mutually exclusive, because homeostatic regulation can be signifi-
cant but incomplete owing to other factors that influence dietary intake.
A potential illustration of this is our finding that respondents whose
diets at the first eating period were below the AMDR and above the
AMDR both compensated by increasing and decreasing proportional
protein intake, respectively, at subsequent eating periods, which as dis-
cussed previously is consistent with nutrient balancing. That homeo-
static response was, however, incomplete, as the cumulative intakes
over the full day of the two groups remained lower and higher than the
average, respectively, despite the compensatory response. This finding
raises the possibility that even transient diversions from a macronutrient
balanced diet could have lasting and cumulative effects on energy
intake, suggesting an important avenue for experimental research. It also
raises the ecological question of which factors might be associated with
the ingestion, either transient or sustained, of protein-dilute diets.
A first step toward addressing that question is identifying the catego-
ries of foods associated with dietary protein dilution. As predicted (predic-
tion iii), our analysis implicates highly processed discretionary foods as a
likely cause of protein dilution. This category of foods clustered dispro-
portionately within the low-protein region of macronutrient space
(Figure 3), and their contribution to the daily diet correlated positively
with total fat, carbohydrates, and total energy intakes. That there was, in
contrast, no effect of these foods on absolute protein intake is consistent
with the mechanism through which processed foods translate into excess
energy intake being protein leverage. The same pattern was observed
independently in an analysis of the US NHANES diet data [14]. This result
is also consistent with a recent randomized controlled trial that found that
inpatients who were provided ultraprocessed diets showed no difference
in absolute protein intake relative to a control group on an unprocessed
diet, but they ingested significantly more fat, carbohydrates, and total
energy and gained more weight during the 14-day trial [15].
Several factors have been identified that predispose to the con-
sumption of highly processed industrial foods, including their hyperpa-
latability, relatively cheap price, convenience, aggressive marketing, their
ubiquity in food environments, and corporate political activity interfering
with public health policy [27]. A particularly insidious proposed mecha-
nism is the “protein decoy effect,”in which homeostatic protein seeking
responses are diverted by cheap, abundant, fat- and carbohydrate-rich,
umami-flavored, savory snack foods, which exacerbate rather than ame-
liorate the protein deficiency they are selected to redress [28].
Our analysis thus suggests a model in which characteristics of
industrial manufactured foods such as their low cost and hyperpalatabil-
ity influence the selection of these foods over whole-food alternatives,
with the result that their high fat and carbohydrate content dilutes die-
tary protein. This triggers a combination of protein seeking and compen-
satory intake, in which fat and carbohydrates are overingested as a
homeostatic response to maintain protein intake at the target level in
the face of a protein-dilute diet (protein leverage). The effect is exacer-
bated by other dimensions of industrial food manufacturing, including
their high energy density due to low-fiber and umami-flavored protein
decoys subverting the selection of high-protein alternatives.
As noted earlier, like any analysis of population data, our study is
susceptible to confounds and artifacts. However, there are several fac-
tors that suggest the results and conclusions are credible. First, our study
was strongly prediction driven and not based on a posteriori rationaliza-
tion of statistically significant patterns. Second, all our key predictions
(macronutrient balancing, protein leverage, and the role of highly pro-
cessed foods) are consistent with results of previous randomized con-
trolled trials and mechanistic evidence. Third, many of the results have
been observed separately in other population studies and other contexts.
Finally, the results fit into a coherent model proposing a plausible mecha-
nism to explain the well-established association between ultraprocessed
foods and energy overconsumption, obesity, and poor health [29].
We stress that our findings should not be interpreted as an indict-
ment of low-protein diets per se or as an endorsement of habitual
diets that exceed the recommended proportion of dietary energy
from protein. High-protein diets were predicted by our analysis
(Figure 2B), and demonstrated in many other studies, to be associated
with low energy intake, and they could play an important role in
weight loss [30]. However, these diets were also associated with
excess protein intake (Figure 2G), and several sources of evidence
suggest that chronic exposure to high protein intake (especially when
paired with low carbohydrates) accelerate the rate of aging (notably
2164 MACRONUTRIENT (IM)BALANCE

during middle and early late-life) and reduce health span [31, 32], an
effect that might be associated particularly with animal-derived pro-
teins [33]. Conversely, several populations with exceptionally good
health and long life-spans, such as the Traditional Okinawan, Blue
Zone Mediterranean, and Kitivan Islander populations, have diets with
low proportional protein, in the order of 10% [2].
Congruently, despite low protein levels, these populations do not
have a high incidence of obesity, which seemingly contradicts the predic-
tion of our model. A key difference, however, is that in obesogenic food
environments such as Australia and the US [14], protein is diluted by
industrially manufactured foods that are low in fiber and high in refined
carbohydrates [34]. In contrast, traditional low-protein diets are rich in
whole foods, and the principal diluent of protein is complex carbohydrates,
including resistant starches, derived from fiber-rich plant foods [35]. It is
likely that, in these diets, the satiating effect of fiber mitigates reduced
satiation of low protein, and emerging evidence suggests that complex
carbohydrates are metabolically healthier and less obesogenic than refined
simple carbohydrates [36]. On close inspection, our analysis is consistent
with this. Figure 2B shows that when dietary percentage protein was low,
energy hyperphagia was particularly pronounced midway up the fat axis,
in the region corresponding to the composition of processed discretionary
foods (Figure 3A), compared with the region closer to the origin corre-
sponding to high-carbohydrate plant foods (grains, fruit, and some vegeta-
bles; Figure 3). Indeed, in this latter region, diets with protein content of
10% intersected with the energy equilibrium line, suggesting that these
diets would not be associated with energy overconsumption.
In addition to its direct relevance for understanding the causes of
obesity, our study addresses an important issue regarding the use of evi-
dence in nutrition science. Nutritional epidemiology has been criticized
on several grounds, including the accuracy of dietary measures, bias, its
correlative nature, and its vulnerability to confounds, even considered
by some as “pseudoscience”[37]. Others have noted that many of these
criticisms arise from a misunderstanding of the role of diet surveillance
data, and alternative sources of evidence, such as randomized controlled
trials and meta-analyses, often considered the gold standard in evidence,
introduce problems of their own. Randomized controlled trials have
been criticized for framing diets within a drug trial paradigm, which is
inappropriate because nutritional exposures are substantially more com-
plex than pharmaceutical treatments [38]. Meta-analysis has been criti-
cized based on variation in the design, context, and populations across
nutritional studies and as being “weighted averages of expert opinions”
[39]. Our analysis supports the view that observational and experimental
evidence should not be regarded as competing but as complementary
sources of evidence, and the greatest confidence is provided when
there is congruence in the results from several sources [40–42]. This
approach is common in the evolutionary and ecological sciences, where
direct evidence can be difficult to obtain, and related views have previ-
ously been expressed for human nutrition science [43–45].
Our application of an ecological approach to analyze observational
diet surveillance data shows tight congruence with experimental studies
and other sources of evidence. While this provides supporting evidence
for protein leverage, its primary value is that it suggests the protein
leverage mechanism is not only real (as established in experimental
studies and corroborated here), but also relevant in free-living context;
that is, it directly tests the PLH [5]. Together, causation and relevance
provide a strong foundation for evidence-based nutrition.O
AUTHOR CONTRIBUTIONS
Conceptualization: Zhixian Sui, Sean C P Coogan, David Raubenheimer.
Methodology: Amanda Grech, Zhixian Sui, Anna Rangan, Stephen J
Simpson, Sean C P Coogan, David Raubenheimer. Investigation: Amanda
Grech, Zhixian Sui, Anna Rangan, Stephen J Simpson, Sean C P Coogan,
David Raubenheimer. Visualization: Amanda Grech, Zhixian Sui. Funding
acquisition: Anna Rangan, Stephen J Simpson, David Raubenheimer.
Supervision: Anna Rangan, David Raubenheimer. Writing (original draft):
Zhixian Sui, Sean C P Coogan. Writing (review and editing): Amanda
Grech, Zhixian Sui, Anna Rangan, Stephen J Simpson, Sean C P Coogan,
David Raubenheimer.
ACKNOWLEDGMENTS
The authors thank the Australian Bureau of Statistics for providing
access to the Australian National Nutrition and Physical Activity Sur-
vey data set. Open access publishing facilitated by The University of
Sydney, as part of the Wiley - The University of Sydney agreement
via the Council of Australian University Librarians.
FUNDING INFORMATION
This research was funded by grants from the National Health and Med-
ical Research Council (NHMRC) Nutrition and Complexity Program
Grant (GNT1149976) and by Meat and Livestock Australia. The funding
bodies had no input into the results presented in the current analysis.
CONFLICT OF INTEREST
The authors declared no conflict of interest.
DATA AVAILABILITY STATEMENT
All data are available upon request from the Australian Bureau of
Statistics. Code and materials used in the analysis are available from
the researchers.
ORCID
Amanda Grech https://orcid.org/0000-0002-1734-9212
Stephen J. Simpson https://orcid.org/0000-0003-0256-7687
David Raubenheimer https://orcid.org/0000-0001-9050-1447
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SUPPORTING INFORMATION
Additional supporting information can be found online in the Support-
ing Information section at the end of this article.
How to cite this article: Grech A, Sui Z, Rangan A, Simpson SJ,
Coogan SCP, Raubenheimer D. Macronutrient (im)balance
drives energy intake in an obesogenic food environment: An
ecological analysis. Obesity (Silver Spring). 2022;30(11):
2156‐2166. doi:10.1002/oby.23578
2166 MACRONUTRIENT (IM)BALANCE