Associations between national plant-based vs animal-based protein supplies and age-specific mortality in human populations

Abstract

Transitions to sustainable food systems require shifts in food production and availability, particularly the replacement of animal-based protein with plant-based protein. To explore how this transition may relate to demographic patterns, we undertake an ecological analysis of global associations between age-specific mortality, total national macronutrient distributions, and protein substitution. Our dataset includes per capita daily food supply and demographic data for 101 countries from 1961–2018. After adjusting for time, population size, and economic factors, we find associations between low total protein supplies and higher mortality rates across all age groups. Early-life survivorship improves with higher animal-based protein and fat supplies, while later-life survival improves with increased plant-based protein and lower fat supplies. Here, we show that the optimal balance of protein and fat in national food supplies, which correlates with minimal mortality, varies with age, suggesting that reductions in dietary protein, especially from animal sources, may need to be managed with age-specific redistributions to balance health and environmental benefits.

Full text

Article https://doi.org/10.1038/s41467-025-58475-1
Associations between national plant-based
vs animal-based protein supplies and age-
specific mortality in human populations
Caitlin J. Andrews
1,2,3
, David Raubenheimer
1,2
, Stephen J. Simpson
1,2
&
Alistair M. Senior
1,2,3
Transitions to sustainable food systems require shifts in food production and
availability, particularly the replacement of animal-based protein with plant-
based protein. To explore how this transition may relate to demographic
patterns, we undertake an ecological analysis of global associations between
age-specific mortality, total national macronutrient distributions, and protein
substitution. Our dataset includes per capita daily food supply and demo-
graphic data for 101 countries from 1961–2018. After adjusting for time,
population size, and economic factors, we find associations between low total
protein supplies and higher mortality rates across all age groups. Early-life
survivorship improves with higher animal-based protein and fat supplies, while
later-life survival improves with increased plant-based protein and lower fat
supplies. Here, we show that the optimal balance of protein and fat in national
food supplies, which correlates with minimal mortality, varies with age, sug-
gesting that reductions in dietary protein, especially from animal sources, may
need to be managed with age-specific redistributions to balance health and
environmental benefits.
In an era marked by the pressing need for decarbonization and
environmental sustainability, global food systems, which contribute
a third of all greenhouse gas emissions, are under increasing
scrutiny1. Food systems are complex social-ecological networks that
encompass all processes and infrastructure involved in feeding a
population2. Many have argued that a reduction in the production of
animal-based foods is key to achieving more sustainable food
systems3, however, reduced production of animal-based foods is
likely to result in a substitution of animal-based protein (ABP) for
plant-based protein (PBP) and/or a reduction in total protein supplies
within food systems4. For many populations, this transition therefore
represents a substantial change in the nutritional environment. The
capacity of post-transition PBP-rich nutritional environments to
support the health of different demographics and age-classes has
become a focus of debate5.
In general, the nutritional environment, encompassing factors
such as the quality, quantity, and source of nutrients available for
consumption, has been identified as a critical determinant of health
and survivorship at different ages6. Malnutrition, including ‘under-
nutrition’,‘overnutrition’and imbalanced nutrition, are major risk
factors for numerous age-related chronic diseases7, which remain the
leading cause of death and age-related disability globally8.Further-
more, the health of older adults may be especially sensitive to the
nutritional environment, as this demographic displays elevated risk of
both under- and over-nutrition in a food-insecure environment9.Atthe
other end of life, stunting or chronic malnutrition primarily occurs
during the first 1000 days from conception due to insufficient mater-
nal or child nutrition10. This often results from a food system that
provides limited access to adequate and high-quality foods and is
linked to an increased risk of death.
Received: 9 May 2024
Accepted: 20 March 2025
Check for updates
1
Charles Perkins Centre, The University of Sydney, NSW Sydney 2006, Australia.
2
School of Life and Environmental Sciences, The University of Sydney, NSW
Sydney 2006, Australia.
3
Sydney Centre for Precision Data Science, The University of Sydney, NSW Sydney 2006, Australia.
e-mail: caitlin.andrews@sydney.edu.au
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The availability of macronutrients, and particularly protein, at the
national level is of interest when considering how nutritional envir-
onments affect health and survivorship as a function of age. There is
abundant evidence that at the dietary level (i.e. below that of the
nutritional environment) protein intake affects health. At the level of
diet composition, recent reviews11,12 suggest that protein content, and,
particularly, its amounts relative to the quantity of carbohydrate, plays
a vital role in shaping age-related health outcomes. At the level of
individual intakes, protein appears to exhibit a non-linear relationship
with all-cause mortality, relative to total caloric intake in the diet, with
both low and high protein intakes associated with increased mortality
risk13,14. Furthermore, researchers have advocated for increases in
protein intake recommendations for adults of all ages, but especially
among older individuals15,16.
At the dietary level, the associations between protein and mor-
tality also appear to vary depending on the source, with ABP poten-
tially having a greater impact on mortality than PBP sources17,18.In
particular, high intake of processed meats and—with less certainty—
unprocessed red meats, has been linked to an increased risk of certain
chronic diseases, including cardiovascular disease, type 2 diabetes,
and some types of cancer19,20.On the other hand, plant protein sources,
such as legumes, nuts, and whole grains, have been associated with a
lower risk of chronic diseases and overall mortality18,21. Researchers
have proposed that a predominantly plant-based diet is one of the key
common denominators contributing to the extended vitality and
longevity observed in long-lived communities, also known as “blue
zones“22,23.These“blue zones,”including Okinawa, Japan; Ikaria,
Greece; and Loma Linda, California;among others, have been found to
have diets high in predominantly plant-based foods and low in animal-
based protein. The differential associations of protein sources have
been attributed to various dietary factors, such as nutrient composi-
tion and bioactive compounds24. While the exact causes remain the
focus of ongoing work, there is sufficient evidence to suspect that
transitioning from ABP to PBP sources can, at the very least, lead to a
shift in the overall nutrient profile of the diet. The nutritional variation
reflected in this shift may contribute to differences in health outcomes
among humans.
A tool thatcan be used to parse the differential effects of nutrients
and their interactions on outcomes of interest is the geometric fra-
mework for nutrition (GFN)25. The GFN is a comprehensive multi-
dimensional framework that allows simultaneous examination of
undernutrition, overnutrition, nutrient amounts, and compositions
within a single framework. It enables insights into the combined
macronutrient landscape associated with food environments, and
identifies interactive relationships between nutritional components
and response variables. The aim of the current study is to assess how
the nutritional environment, with special reference to national per
capita supplies of ABP and PBP, associates with patterns of age-specific
mortality (ASM). Within this context, ASM emerges as an indicator of
population-wide, and age-specific, health26. ASM serves as a standar-
dised measure to assess the broader impact of nutritional environ-
ments on the morbidity of different age groups.
In this study, we used the GFN to investigate the associations
between PBP versus ABP supplies on ASM using global demographic
data. Our dataset comprises complete country- and year-specific
entries from 101 different countries, spanning 1961–2018. We used
data on macronutrient supply quantities available for human con-
sumption from food balance sheets (FBS; see Methods for full expla-
nation) compiled by the Food and Agriculture Organization of the
United Nations (FAO). Specifically, these data reflect the average
availability offood and nutrients at the national level, which are crucial
for understanding the nutritional environment’s ability to support
population health. This FBS supply data was combined with sex-
specificmortalitydata(specifically, the proportion of individuals
within a cohort that survive to each age) from life tables provided at
the HumanMortality Database, economic statistics from theMaddison
Project Database, and age, sex specific population estimates from the
population division of the United Nations. We explored interactions
between macronutrient supplies, time,country,andwealth,withsta-
tistical corrections to control for possible confounding by such fac-
tors. Using the best approximating model, we predicted the effects of
ABP, PBP, fat and carbohydrate supplies on human life table para-
meters. Additionally, we considered age-specific macronutrient and
energy availabilities for optimising survival over the human life course.
Results
Animal-based vs plant-based protein
The collated data comprised complete entries for macronutrient
supplies from national food balance sheets (FBSs), survivorship at ages
5 and 60 (l5and l60, i.e., the proportion of a cohort still alive at ages 5
and 60) from national life tables, and gross domestic product per
capita (GDP) across both sexes, for 101 countries. Briefly, per capita
food supply data provided by FBSs refers to the average amount of
food available for consumption per person within a nation for a given
year, a foundational element of the nutritional environments con-
tributing to health outcomes across different populations. Quantities
of food items available for consumption are calculated based on the
total quantities produced and imported, adjusting for any changes in
stocks including exports. FBSs also give daily per capita supplies in
terms of total energy, and specific energy sources, based on compo-
sition factors27. Here we have analysed daily per capita protein supplies
from animal and vegetal sources (i.e., animal-based protein and plant-
based protein; ABP andPBP), as well as daily percapita suppliesof total
fat supplies and carbohydrate (note that carbohydrate was estimated
as total available energy less energy from alcohol, ABP, PBP and fat).
Our final dataset spanned from 1961 to 2018 with data from 4000
country-,year-,andsex-specific life tables. We begin by summarising
the trends and global coverage of daily per capita protein supplies in
our dataset. Figure 1A and B show the ratio of ABP to PBP supplies
across different countries in historic (1961–1990) and more recent
(1991–2018) data, respectively. The geographic distributionof supplies
shows a temporal trend towards convergence, where areas with higher
ratios of ABP to PBP supplies in earlier years, such as northern America,
north and west Europe, Argentina,Hong Kong, and NewZealand, tend
to show a reduction in later years. In contrast, areas where supply is
predominantly PBP in early years, including most parts of central and
south America, Japan, South Korea, and Taiwan, show an increase in
the ratio of ABP to PBP supplies during more recent years. Figure 1C
illustrates an average convergence of total ABP and PBP supply
quantities, toward parity around the mid 1990’s and remaining rela-
tively similar thereafter. Notably, the total protein supplies available
within countries has tended to increase over time, largely attributable
to increased ABP (Fig. 1C). Within the data there is a positive associa-
tion between total protein supplies and economic wealth, with a
greater proportion of PBP to ABP where GDP is lower and the ratio of
ABP to PBP increases up to GDP values of ~2011 $ 12,000 per capita
(Fig. 1D). Additionally, Fig. 1Eand1F show a greater proportion of PBP
supplies relative to ABP where fat supplies are lower and carbohydrate
supplies are higher, and vice versa.
Age-specific mortality
Using the model life table approach proposed by the World Health
Organisation (WHO)28, a comprehensive picture of ASM in human
populations can be built by taking survivorship to ages 5 (l
5
)and60
(l
60
) as markers of early and late life survival, respectively. Hence, we
assessed the l
5
and l
60
data using separate analyses. We further stra-
tified the data into male and female survivorship, yielding four subsets
for statistical modelling (i.e., l
5
male; l
60
male; l
5
female; l
60
female).
Our primary tool was the generalised additive mixed model (GAMM).
To each subset we fitted several models exploring a range of different
Article https://doi.org/10.1038/s41467-025-58475-1
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Fig. 1 | Summary of global protein supplies (kcal/capita/day) and correlations
within the available data. A Average log ratio of animal-based to plant-based
protein supplies for each country, from 1961–1990, and B1991 –2018, using all
available data; grey shows an absence of data. (C) Supplies of plant- and animal-
based protein as a function of year, DGDP per capita, (E) fat supplies (kcal/capita/
day), and (F) carbohydrate supplies (kcal/capita/day). C–FThe individual data
pointsshow the raw data values. The red,green, and bluesmoothed lines represent
the predicted values for ABP,PBP, and totalprotein, respectively,based on year (C),
GDP (D), fat supplies (E), and carbohydrate supplies (F).Predictions were obtained
using generalized additive mixed models, with random effects for countries. The
shaded areas around the lines represent the 95% confidence intervals centred on
these predictions. The smooth term significance is detailed in Supplementary
Table 1 to Supplementary Table 12. GDP gross domestic product per capita
(expressed in international-$ at 2011 prices). A, B Map data from Natural Earth,
facilitated by rnaturalearth Rpackage
94.
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potential predictors of survivorship including daily per capita nutrient
supplies, time, and wealth. All models accounted for differences in
relative precision of estimates of survivorship coming from countries
with different total population sizes. The best fit model for all four
stratifications of the dataset was selected based on lowest AIC score. In
all four stratifications, the favoured model captured the 3D interactive
effects of ABP, PBP and fat supply varying across time, with the addi-
tive effects of GDP, and carbohydrate supply (Supplementary Table 13
and Supplementary Table 14). This indicates that within our dataset,
ASM is best predicted by nutritional supplies in a time-dependent
manner. Furthermore, there are interactive effects of ABP, PBP, and fat
supplies on survival in both early and late life and in both sexes.
Figure 2uses the surfaces-based approach from the GFN to
visualise the associations between ABP, PBP, fat supplies and l₅,as
estimated by the AIC-favoured GAMM. Note here and below, model
predictions were made for 2017, this being the most recent year for
which comprehensive data were available. These figures show the
associations of survivorship with ABP and PBP supply, at low, medium,
and high fat supplies (while assuming a median carbohydrate supply,
and GDP for 2017). For both sexes our model predicted a strong
positive association between l₅and absolute amount of ABP, while any
beneficial effects of PBP were found to be much weaker (Fig. 2). The
grey dashed isolines on Fig. 2at 400 kcal/capita/day total protein
energy (PE) supplies, show the effects of isocalcorically substituting
ABP for PBP (and vice versa) while holding total energy supplies con-
stant at 3023 (non-protein energy (NPE) + PE = 2623 + 400), 3120
(NPE + PE = 2720 + 400) and 3228 (NPE + PE = 2828 + 400) kcal/capita/
day (Fig. 2AandD,Fig.2B and E, and Fig. 2Cand2F,respectively).
Moving in the direction of the isolines illustrates improvements in l₅
associated with the substitution of ABP from PBP without changing the
amounts of total protein and total energy supplies. l₅also demon-
strated positive association with total fat supplies. At low fat supplies,
l₅showed similar improvements as protein supplies were increased in
the direction of either ABP or PBP, however l₅was ultimately max-
imised where both fat and ABP supplies were high (Fig. 2AvsCand
Fig. 2D vs F). There was a non-linear association between l₅and car-
bohydrate supplies, where l₅appeared to be maximised with lower
carbohydrate supplies (~1200 kcal/cap/day) in both males and females
(Supplementary Fig. 4).
Figure 3shows the associations estimated between nutrient-
supplies and l₆₀.Hereweseebeneficial associations when elevating
average daily protein supplies in the animal or plant-based dimension,
with low levels of either being associated with poor survival. However,
per unit of energy, PBP was predicted to improve l₆₀ more than ABP,
and peak l₆₀ was predicted at high PBP and lower ABP supplies
(Fig. 3A–F). While there were positive associations between fat and l₅,
this appeared to reverse at older age with l₆₀ peaking at low fat
(Fig. 2A–FvsFig.3A–F). For both sexes, moderate carbohydrate sup-
plies (~1500 kcal/cap/day) maximised later-life survival (Supplemen-
tary Fig. 4), though survivorship in males appeared more sensitive to
carbohydrate supplies than in females.
Using the l₅and l₆₀ values predicted in Figs. 2and 3,wederived
entire life tables using the WHO-proposed model life table method
(see Methods). We visualised the effects of macronutrient supplies on
the rederived life table functions, such as: life-expectancy atbirth e0

and the age-specific risk of mortality (qx). Put another way, for every
predicted point in the nutrient space shown in Figs. 2and 3we gen-
erated a complete life table, with mortality patterns of age specific
Fig. 2 | Predicted effects of macronutrient supplies (in terms of kcal/capita/
day) on life table survivorship to age 5 (l_5). Survivorship outcomes were pre-
dicted based on 2017 global nutrient supply, GDP, and population data, using the
GAMM selected as the best approximating model for the entire dataset.A–Cshows
the effects of animal-based protein, plant-based protein, and fat supplies on l5in
females. D–Fshows the effects of animal-based protein, plant-based protein, and
fat supplies on l5in males.The colour map and contour lineson each surface shows
predicted l5associated with the aligning proportions of protein supplies on the x
and yaxes. Survivorship values increase from blue to red, as shown by the colour
bar scale. Moving from (A–C) and from (D–F) across each row of surfaces, the
quantity of fat supplies increases. The surfaces for low (A,D), median (B,E)and
high (C,F) fat show predicted effects at the first, second, and third quartiles of
global fat supplies in 2017, respectively. A–Fassume the median carbohydrate
supply and GDP for 2017 (1571 kcal/capita/day; 2011-Intl$30902). Total non-protein
energy (NPE) is the sum of energy supplies from fat and carbohydrate for each
surface. The grey dashed isoline indicates a constant total protein energy (PE)
supply at 400 kcal/capita/day on all surfaces (A–F). The smooth terms for macro-
nutrient supply were statistically significant in both sexes (Supplementary
Table 16). GAMM generalized additive mixed model, GDP gross domestic product
per capita (in international-$ at 2011 prices).
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survivorship.Predicted effects of nutrient supply on the derived e0are
shown in Fig. 4. For both sexes, e0appeared to decrease when com-
bined total protein energy from both ABP and PBP supplies are low
(Fig. 4). A beneficial association between PBP and e0wasseenwithlow
to moderate ABP and fat supplies (Fig. 4A, B and Fig. 4D, E), such that
e0is maximised where PBP is high and ABP and fat supplies are low.
Additionally, with low fat supplies, improvements in l60 were asso-
ciated with the isocaloric substitution of PBP from ABP (grey-dashed
lines in Fig. 4A, B).
Finally, Fig. 5shows the logged hazard ratio (based on the pre-
dicted q
x
) at each age for an ABP-rich vs PBP-rich nutritional envir-
onment. A negative value for a given age class indicated better
survival under an ABP-rich supply vs a PBP-rich supply and vice versa.
A higher proportion of ABP supplies was found to support a lower qx
during early life. Whereas, from early adulthood, qxwas lower
with a higher proportionof PBP supplies. For both sexes, beyond age
70 there was a clear reduction in qxsuggesting that differences
between survival in the two nutritional environments becomes less
prominent.
Discussion
We investigated associations between national daily per capita PBP
and ABP supplies and patterns of ASM across countries and time, after
accounting for international variation in national prosperity, popula-
tion size, and the supplies of fat and carbohydrate. Robust disparities
existed between the association of ABP and PBP with early and late-life
mortality. Higher ABP supplies were associated with enhanced survi-
vorship during infancy and early childhood, independent of total
calorie supplies, while PBP-rich environments were associated with
improved survivorship in later life and overall life expectancy at birth.
Our results align with previous ecological studies, indicating
negative effects of energy-deficient nutritional environments on
population survivorship across age groups26,29.Inouranalysis,low
total protein supplies were consistently associated with reduced sur-
vivorship and diminished life expectancy. These results mirror epide-
miological studies at both the ecological and cohort level, linking
protein-energy malnutrition (PEM) to various mortality risk
factors30–32. After mitigating undernutrition by ensuring an adequate
availability of energy and total protein, an age-specific association with
different protein sources (specifically, the balance between animal-
and plant-based) emerged in our analysis, prompting a consideration
of potential nutritional imbalances between PBP and ABP.
We have leveraged nutrient supply data from FBSs as a metric of
the nutritional environment. This method offers a complementary, but
different, perspective relative to assessments of individual dietary
habits and nutrient intake. Firstly, FBS data facilitate extensive tem-
poral and trans-national comparisons of food availability and con-
sumption patterns, which can reveal long-term trends and regional
differences; such extensive coverage is not currently available for
intra-national data on diet pattern. Secondly, the FBS data capture the
nutritional environment, which is likely to be highly responsive to
policy interventions (e.g., agricultural, trade, and environmental poli-
cies affecting food systems), where individual diets can prove hard to
affect.
Food and nutrient supplies, as captured by FBSs, are an upstream
and key determinant of individual diet, and population-wide dietary
Fig. 3 | Predicted effects of macronutrient supplies (in terms of kcal/capita/
day) on life table survivorship to age 60 (l
60
). Survivorship outcomes were
predicted across the 2017 global nutrient supply space, as described in Fig. 2.
A–Cand D–Fshow female and male l60 outcomes, respectively. The colour map
and contour lines show predicted l60 survivorship, as described in Fig. 2.Survi-
vorship values increase from blue to red, as specified by the colour bar legend
(min = 0.849, max = 0.959). Fat supplies are fixed at low, median and high for sur-
faces (A,D), (B,E)andC,F, respectively, based on the first, second and third
quartiles of global fat supplies in 2017. Carbohydrate supplies and GDP are held
constant across all surfaces (A–F), as specified in Fig. 2. Total non-protein energy
(NPE) held constant as indicated on each surface, as specified in Fig. 2.Thegrey
dashed isoline on each surface indicates total protein energy (PE) supplies of
400 kcal/capita/day. The smooth terms for macronutrient supply were statistically
significant in both sexes (Supplementary Table 17). GDP gross domestic product
per capita (in international-$ at 2011 prices).
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patterns; the food supply influencesthe accessibility, affordability, and
distribution of various food groups and nutrients, as well as setting a
hard upper limit on average intake33. Indeed, several studies have
shown that at the level of nutrient composition and food patterns,
average national diet is correlated with nutrient supply34,35.Despite
these correlations, nutrient supply cannot be considered a direct
surrogate for intake, the latter being influenced by myriad intervening
factors including inefficiencies in the food system such as food waste.
Hence, our results should not be interpreted as evidencefor the effects
of nutrient intake on health. We can, however, look to the literature on
dietary intake and health to provide potential nutritional explanations
for the differential associations between ABP and PBP supplies and
mortality that we have observed. We pay particular attention to both
deficiencies of specific nutrients and how the consumption of certain
nutritional compounds has been linked to chronic disease. Finally, we
consider the limitations of this study including residual confounding.
Early-life survivorship
Our findings suggest that the composition of the nutritional environ-
ment may play a role in determining early life survival outcomes. In
low-fat environments, early life survivorship was reduced when overall
proteinsupplieswerelow,withnosignificant distinction between ABP
and PBP sources. This result points to the general importance of pro-
tein supplies in energy-deficit environments36. Conversely, in higher fat
environments (i.e., with sufficient net calories), increasing ABP sup-
plies showed a distinct advantage for early life survivorship over PBP.
This suggests a possible link between essential nutrient availability and
survival up to age 5.
A possible explanation for this result is that ABP may offer some
advantages over PBP in terms of bioavailability, digestibility, and
essential animo acid content. ABPs provide a complete and balanced
profile of indispensable amino acids37, which are the building blocks of
proteins. They also have higher bioavailability and digestibility than
most PBPs10,38, thus are more easily absorbed and utilised by the body.
Moreover, ABPs are rich in other bioactive compounds including iron,
zinc, and vitamin A, which are essential for growth and development.
These nutrients are often lacking indeveloping regions, where under-5
mortality is high39. For example, growth and development increase the
demand for iron in pregnant and lactating women, and children, put-
ting them at higher risk of rapidly developing iron deficiency anaemia
and increasing the risk of child mortality40.Notingthatirondeficiency
has been reported to be less frequent in countries where meat con-
stitutes a significant part of the diet41,itispossiblethattheincreased
content and bioavailability of micronutrients, particularly iron, present
in ABP may contribute to the stronger association between improved
early life survivorship and increased ABP supplies, compared to PBP
supplies42. Additionally, complementary consumption of ABPs with
PBPs has been found to counteract the negative effects of some anti-
nutrients commonly produced in many PBP, such as phytic acids,
tannins and lectins, which can impair the absorption of iron and zinc
from PBP sources10,43. Furthermore, nutritional environments may
indirectly impact infant and early childhood survivorship due to
maternal undernutrition and nutritional deficiencies, which have pre-
viously been connected to major causes of child mortality44.Therefore,
increased ABP supplies may enhance early life survivorship by
addressing maternal undernutrition, reducing nutritional deficiencies,
Fig. 4 | Predicted effects of macronutrient supplies (in terms of kcal/capita/
day) on life expectancy at birth (e
0
). e
0
values were derived from pairs of corre-
sponding l₅and l₆₀, as shown in Figs. 2,3, and visualised across the same nutrient
supply space. A–Cand (D–F) showfemale and male e0outcomes, respectively. The
colour map and contour lines in (A–F)showthee0in years, corresponding to the
aligning proportions of protein supplies given by the ðx,yÞcoordinates. In (A–F),
life expectancy values increase from blue to red, as specified by the colour bars
legend (min= 70 years, max = 85 years).Moving from (A–C)andfrom(D–F)across
each rowof surfaces, thequantity of fatand total non-protein energy (NPE) supplies
increase. The surfaces for low(A,D), median (B,E) and high (C,F) fatsupplies show
predicted effects at the first, second, and third quartiles of global fat supplies in
2017, respectively. (A–F) assume the median carbohydrate supply and GDPfor 2017
(1571 kcal/capita/day; $30902). Total non-proteinenergy (NPE) is the sum of energy
supplies from fat and carbohydrate for each surface. Total protein energy (PE)
supplies are held constant at 400kcal/capita/day along the grey dashed isoline
shown on each surface. l5= survivorship to age 5; l60 = survivorship to age 60. GDP
gross domestic product per capita (in international-$ at 2011 prices).
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improving breast milk quality and quantity, and lowering the risk of
infant health problems39.
While our findings reinforce the benefits of ABP for early life
survivorship, it is crucial to acknowledge the potential role of PBP,
particularly in resource-constrained environments. In these settings –
specifically, regions with limited ABP and fat supplies –our results
indicated that adding energy from any protein source, plant or
otherwise, was beneficial. This supports the idea that our findings do
not refute the advantage of incorporating PBP in a malnourished
environment8,45. In resource-constrained environments where there is
limited access to diverse and protein-rich foods, heightened energy
demands, and a reliance on less nutrient-dense diets, vegetarian diet-
ary patterns show promise if they are balanced and complete46.PBPs
may be tailored to mimic the AA profile of ABPs37 and can also be
combined to achieve a balanced indispensable AA profile, by exploit-
ing the complementarity between different sources, such as cereals
and legumes47. Ensuring completeness in these dietary patterns for
early life may require addressing potential deficiencies, such as vitamin
Bdeficiency, which can be detrimental during infant development48.
Consistent with previous ecological analyses, our results demon-
strated positive associations between fat supplies and early life
survivorship26,29, highlighting potentialbenefits associated with dietary
fat in early life. Further research is needed to fully understand this
association. However, a plausible nutritional explanation is that fat,
particularly from animal based sources, serves as a rich energy source
and a carrier of indispensable fatty acids and essential fat-soluble
vitamins38,49,50. Essential fatty acids, required for growth, development,
and immune function, must be obtained from the diet, supporting the
observed benefit of increasing fat-energy supplies during early life51.
Specific fat subtypes and ABP are often correlated in certain animal-
based foods, such as red meats and full-fat dairy products, which can
complicate attributing health effects to specific nutrients52. The GFN
can help to disentangle these effects by considering their relative
balance and interactions with other nutrients, provided sufficient data
are available.
Later-life survivorship
Our investigation revealed distinct associations with nutrient supplies
for later-life survivorship compared to early-life survivorship. Maximal
survivorship to age 60 and overall life expectancy at birth were
observed in nutritional environments with lower fat supplies and a
higher proportion of protein from PBP sources. This finding does
mirror existing research at the level of individual diets, which show the
long-term health benefits and reduced mortality risks associated with
PBP- over ABP-based foods. These benefits include a reduced risk of
type 2 diabetes, all-cause mortality, and cardiovascular disease and
dementia mortality18,53,54. One possible explanation for the positive
association between PBP and increased later-life survivorship is the
higher dietary fibre and wholegrain content associated with PBP food
sources55. High intake of whole grains has been linked to decreased
risks of mortality from all causes, cardiovascular disease, and cancer in
the general population56. Furthermore, the observed advantage of low
ABP combined with low-fat supplies for later life survivorship is con-
sistent with studies that emphasise the potential mid-life harms
resulting from overconsumption of saturated fatty acids and choles-
terol found in red and processed meat57.
Whilst the reduced benefit to later life survivorship in ABP-rich
environments relative to PBP-rich environments may stem from dif-
ferences in nutritional components, it may also be indicative of poorer
overall dietary trends influenced by factors such as income, urbani-
sation, and increased access to processed foods. Specifically, as
income and urbanisationrise, so does theconsumption ofmeat, sugar,
fats, oils, and processed foods58,59. This dietary pattern is particularly
pronounced in countries with a higher Socio-Demographic Index
(SDI), where the abundance of ABP –largely sourced from modern
supermarkets –reflects greater access to ultra-processed and ready-
made foods60. Numerous studies have consistently linked high intake
of processed foods to an increased risk of age-related ill health, and
premature death, providing a plausible explanation for the negative
impact on later-life survivorship observed in ABP-rich environments,
compared to PBP-rich ones19,61–63.
Several pre-clinical studies have suggested that reducing the ratio
of total protein to carbohydrate in the diet may extend longevity56,64,65.
It is important to note that findings from animal models may not
always directly translate to human health outcomes, especially at the
population/ecological level. However, our analyses, which statistically
control for total carbohydrates, correlate with these findings. Further,
we see additional independent benefits of replacing ABP with PBP,
within human populations, irrespective of the protein-carbohydrate
ratio. Some researchers have suggested that the observed benefits of
protein restriction during mid and early-late life maybe driven, in part,
by reduced intake of branched chain amino acids (BCAAs), of which
excess consumption is associated with several age-related chronic
health problems66. Such a mechanism may explain our result, showing
that iso-calorically substituting ABP supplies, generally higher in
BCAAs, with PBP, generally lower in BCAAs67, is associated with gains in
population life-expectancy without any restriction of total protein
supplies or altering the ratio of protein to carbohydrates in the food
supply.
Additionally, we observed unfavourable associations between
higher fat and survivorship in later life, potentially explained by
overnutrition resulting from the higher energy density of dietary fat
compared to carbohydrate and protein68. This detrimental
Fig. 5 | Effects of proportion animal- vs plant- based protein supplies on
probability of mortality (q
x
), across different age classes, in 2017. Optimal
macronutrient supply ratios for sex-specific survival at ages 5 and 60 were com-
puted using a general-purpose optimization and selected as animal- and plant-
based model environments (detailed in Supplementary Table18). The qxschedules
from birth to 95years in each model environment were then derived. The log
hazard ratio for the animal- based nutritional environment was taken as the log of
the ratios of qxfor animal- vs plant-based nutritional environments. The total
energy for the primarily animal-based composition is 3150kcal for males and
3110 kcal for females. Total energy for the primarily plant-based composition is
3710 kcal for males and 3700 kcal for females. Within model environments, the
percentage of carbohydrates and fats differed by <0.5% across sexes (Supple-
mentary Table 18). qx= probability of mortality at age x.
Article https://doi.org/10.1038/s41467-025-58475-1
Nature Communications | (2025) 16:3431 7
Content courtesy of Springer Nature, terms of use apply. Rights reserved

relationship was particularly pronounced when combined with ABP
consumption, indicating a synergistic negative influence. The invol-
vement of fat-rich processed foods in this association is also con-
ceivable, particularly when considering westernised dietary
patterns69,70.
As an ecological study, our findings overlook intra-population
variations in both the nutritional environment and dietary patterns and
are based on national food supply data rather than individual dietary
intake. This distinction is important as it highlights the broader avail-
ability of nutrients rather than specific consumption behaviours, and
the possibility of unaccounted for confounding variables should be
considered. Causal relationships and the exact mechanisms underlying
the observed associations between protein sources and mortality
cannot be established based on this study alone. National -level data are
unable to capture within country heterogeneity, potentially over-
looking regional and inter-individual variations in access to nutrients,
dietary habits and health outcomes71. Additionally, the ecological fal-
lacy limitation must be acknowledged; based on the relationships
illustrated by our findings, it cannot be concluded that every individual
who replaces ABP with PBP sources will or has experienced an
increased likelihood of survival to age 60. While our results are based
on indicative supplies available for human consumption, they are not a
direct measure of dietary intake. As detailed elsewhere and above72,
discrepancies can arise between foods supplies and habitual diets due
to factors such as food wastage, processing losses and variations in
individual consumption patterns. Additionally, to maintain the inter-
pretability of our models, our analyses do not account for the het-
erogeneity within the carbohydrate and fat supplies, such as the
differences between refined carbohydrates, fibre, and sugar, as well as
between saturated and polyunsaturated fats. The current analyses
focus on the impacts of protein sources. Specifically, we examine the
potential effects of reduced animal-based food production, resulting
in the substitution of ABP for PBP and/or a reduction in total protein
supplies on age-specific mortality. It should therefore be noted that
our results may not fully capture the true impact of variations in car-
bohydrate and fat supplies on mortality outcomes. While these issues
make assigning the effect of the nutritional environment directly to
diet a vexed issue, our findings emphasise the potential side effects of
shifts to more sustainable food systems on population survivorship.
With regards to confounding, despite adjusting for time, GDP,
other nutritional supplies, and population size, we acknowledge that
other potential confounding factors may exist. For example, GDP only
provides a partial reflection of the disparities between countries that
influence survival. Nutritional factors that are potentially unaccounted
for include levels of foodborne illness and food safety, which may be
more influential in lower income countries73. Further confounding
factors that have not been accounted for may include broader socio-
economic factors such as food access, affordability, and cultural
dietary preferences74,75.
Finally, while we have undertaken broad corrections, there
remains the possibility of residual confounding associated with the
type of fat or carbohydrates. Our analyses incorporated statistical
adjustments for total fat and carbohydrate supplies. However, limita-
tions in the data prevented us from further separating out the effects
of different fat and carbohydrate types, as well as other nutrients such
as fibre. It is important to note such nutritional covariances represent
potential confounders. Considering fat, for instance, it has been shown
that ABP and PBP sources tend to be enriched with saturated and
unsaturated fat, respectively76–78. At the dietary level, saturated fats are
associated with increased rates of mortality, while unsaturated fats
appear protective79. These complexities are not unique to our study
system, even representing a challenge to the most granular level of
clinical trials. One 12week clinical trial in healthy Finnish adults
replacing ABP with PBP sources resulted in improved plasma lipo-
protein levels55. However, the dietary transition was also associated
with improved overall dietary fat quality and increased intake of
dietary fibre and complex carbohydrates55. All of these dietary
dimensions have been linked, variably, to decreased risks of chronic
diseases and mortality from all causes, cardiovascular diseases, and
cancer in the general human population56,80,81, making it hard to pin-
point if any one individual dietary change was causal.
Our analysis of historical associations between protein supplies
and age-specific mortality patterns predicts the potential impact of
transitioning from ABP to PBP. Our results indicate that low overall
protein supplies negatively associate with survivorship across all age
intervals. In the context of energy sufficiency, higher proportions of
ABP supplies associate with greater improvement in early life survi-
vorship, while nutritional environments richer in PBP maximise later
life survivorship and overall life expectancy. We also note, similarly,
high fat supplies may be beneficial in early life, but detrimental later
throughout life. However, it is important to note that while national
food supplies can highlight potential trends and associations, they are
not definitive in establishing the direct biological effects of specific
nutrient levels on survival. Although we cannot establish causality,
these patterns align with existing literature linking diet and healthspan
in humans, particularly the long-term advantages of consuming a
plant-based diet. In the context of advocating for decarbonisation of
food systems, especially the shift from animal-based to plant-based
products, our findingsstresstheimportanceage-specific
considerations.
Methods
Data
This study uses only publicly available data and therefore compliance
from an ethics committee was not deemed applicable. To build our
dataset we combined data from several sources, incorporating ecolo-
gical design principles to ensure a comprehensive analysis of the
environmental impacts of dietary shifts. Macronutrient supply data
was obtained from the United Nations’Food and Agriculture Organi-
sation Statistical Database (FAOSTAT)82. FAOSTAT provides Food
Balance Sheet (FBS) data which present global, regional, and national
statistics of food availability. This includes the total food supply allo-
cated for potential human consumption as per capita estimates of
dietary energy supply, specific to a country and year. For all available
(country, year) combinations, average per capita food supplies were
extracted in terms of fat and protein content (g/person/day).Giventhe
current goal to distinguish the effects of plant and animal-based pro-
tein supplies, we took separate values forprotein supply–expressed in
terms of protein content from vegetal and animal products –provided
by the database. Protein, fat, and alcohol supply quantities were sub-
tracted from the reportedtotal food supplies per capita, given in terms
of caloric value (kcal/person/day) for each (country, year) population.
The difference was used to represent the average per capita nutritional
contribution of energy from carbohydrate, for the specified (country,
year) combination.
For each (country, year) combination in our dataset we included a
value for GDP, obtained from the 2020 update of the MaddisonProject
Database83. This was found to be the most historically and globally
comprehensive dataset available for worldwide, economic statistics.
Lastly, our dataset included ASM patterns from life tables in the
Human Lifetable Database (HLD)84. The HLD has been developed as
part of the Human Mortality Database project. It hosts a collection of
individual life tables for numerous developed and developing, national
and subnational populations around the world. The HLD has been
prepared from a range of sources, including official reports, as well as
statistical and scientific publications and datasets. The database
assembles and recalculates standardized complete and abridged life
tables that cover many years, providing a quantitative description of
the evolution of human mortality. More specifically, each life table
contains a series of inter-related age-specific mortality statistics,
Article https://doi.org/10.1038/s41467-025-58475-1
Nature Communications | (2025) 16:3431 8
Content courtesy of Springer Nature, terms of use apply. Rights reserved

including survivorship to age x,denotedlx; life expectancy at age x,
denoted ex; probability of mortality at age x, denoted q
x
. For our
analysis we required the values for survivorship (i.e., proportion of a
cohort surviving at a given age) from each life table. For this reason, we
used the complete (unabridged) lifetables provided by the HLD, since
they are constructed based on single year age intervals. Furthermore,
we calculated the standard error for each estimated survival value by
adding population estimates to the HLD life table data. This data was
obtained by age and sex from the United Nations Population Division85.
The Kaplan-Meier product-limit estimator was used to re-estimate the
survival values and confirm their consistency with the HLD life table
data. Standard error was then calcula ted using Greenwood’sformula
86,
which considers the number of people at risk of death in each age
interval. This allowed us to assess stochasticity in, and sampling var-
iance of, the estimated survival values (i.e., estimates from small
populations are expected to varyyear-to-year, more thanthose in large
populations). Following this, the HLD lifetable values and standard
errors for male and female survivorship to ages 5 (l₅)and60(l₆₀)were
added to the already collated nutrient supply and wealth data for all
matching (country, year) combinations. Briefly, we focus on these
parameters, l₅and l₆₀, because they give critical information about
early and late-age mortality and can be used with model life tables to
rederive the complete set of life table parameters across all ages, for
downstream interpretation. We discuss this in further detail below.
Statistical analysis
The complete dataset was stratified by sex, giving country by year
specificvaluesforl₅andl₆₀ in both males and females. Each of the four
stratified datasets was then analysed separately. To quantify covar-
iances between demographics and macronutrient supplies, with cor-
rections for potential confounders, a series of generalised additive
mixed models (GAMMs) were used to estimate the response variables:
l₅and l₆₀. In short, GAMMs combine the flexibility of Generalized
Additive Models, in terms of including conventional linear parametric,
and non-parametric non-linear smoothed terms, with the ability to
account for both within-group correlations and between-group varia-
bility using random effects (e.g., as in generalised linear-mixed
models)87. Additionally, GAMMs can handle non-normal responses
by using a distributional transformation (i.e., link-functions) of the
response variable to make it more appropriate for modelling. In the
current case, since l₅and l₆₀ are bounded response variables (i.e.,
between 0 and 1), we chose to specify the Beta distribution family and
logit link transformationfunction. In summary, by using GAMMs as our
chosen statistical tool we have been able to model complex, non-linear
relationships between the response variables, l₅and l₆₀, and multiple
predictor variables, including macronutrient supply, time, and eco-
nomic wealth, while also accounting for random effects of country. All
GAMMs have been fitted in R using the gam function from the mcgv
package88.
To explore the effects of macronutrient supply, time, and eco-
nomic wealth, we constructed a comprehensive series of models
containing subsets of these predictor variables and their interactions
(Supplementary Table 13). All models accounted for differences in
typical survival among countries as a random effect. The macro-
nutrient supplies considered were ABP, PBP, carbohydrate and fat in
kcal/capita/day. We restricted our most complex terms for nutrient
supply to a 3-dimensional (3D) smooth term. This was because higher-
dimensional models, such as 4-dimensional nutritional effects, are
overly complex and difficult to interpret. We tested a series of models
to determine the effects of PBP and ABP supplies on survival. The
models included 3D terms comprising ABP, PBP, and carbohydrate or
ABP, PBP, and fat. The other nutrient dimension outside the 3D term
was then included as an additional additive smoothed term. Supple-
mentary Table 13 and Supplementary Table 14 list the full set of can-
didate models that we explored. Inclusion of the GDP value, to reflect
economic wealth, aimed to control for the potential confounding and/
or interacting effect of wealth.
Akaike Information Criterion (AIC) was used for model scoring
and selection. The set of models was tested and scored using AIC
values, for each outcome and sex. To rank the relative fitandparsi-
mony of each model to the dataset, differences (Δ) from the lowest AIC
value (AIC
min
) were computed over all candidate models in the set (i.e.,
for model i:Δ=AIC
i
–AIC
min
). Based on a conventionally accepted
threshold, all models with AIC differences >2 (Δ>2) werediscarded.
Among models with AIC differences of Δ≤2 we selected the model
with the lowest parameters using the effective degrees of freedom for
smooth terms (EDF, i.e., the least complex model).
Finally, to improve the generalizability of our models and mitigate
model bias from outliers, survivorship observations were weighted
based on their standard errors, considering lower uncertainties to be
better representations of the true long-term population survival rates.
Weights were calculated by scaling the standard errors of survivorship
using the inverse of the squared standard errors and normalisation,
relative to the mean. New weights were predicted for each observation
as a function of year (to account for the fact thatthe global population
has grownover the time-coursecovered by our data), while accounting
for variance in uncertainty levels between countries. These weights
were then applied to each GAMM using Weighted Least Squares to
providemorereliablemodelestimatesofsurvivorshipprobabilities
and accommodate heteroscedasticity in the data.
Model life tables and derived mortality statistics
In several instances, we used the l₅and l₆₀ values predicted by AIC-
favoured GAMMs to re-derive entire life tables including life-
expectancy (e
x
) and hazards of mortality (q
x
), for all age classes
between 0 and 110. This was done using the modified version of the
Brass-relational model life table system89 proposed by Murray et al.
(2003)90. In the context of generating model life tables representative
of human mortality, the modified system has been shown to accurately
capture the extent to which mortality varies across populations with
respect to age and sex. Specifically, survivorship to age xfor life table k
(lk
x) was calculated using the inverse logit of the transformation shown
in Eq. (1), where (lk
5,lk
60)arethepairsofl₅and l₆₀ values predicted by
the GAMM for life table kand (ls
5,ls
60) come from a sex-specific global
standard life table (s).
Logit lk
x

=αk+βkLogit ls
x

+γx
1
Logit lk
5

Logit ls
5

0
@1
A
0
@1
A+θx1
Logit lk
60

Logit ls
60

0
@1
A
0
@1
A
ð1Þ
αk=Logit lk
5

Logit ls
60

Logit ls
5

Logit lk
60

Logit ls
60

Logit ls
5
 ð2Þ
βk=Logit lk
60

Logit ls
5

Logit ls
60

Logit ls
5
 ð3Þ
In the above functions, Logit is the standard logit transformation,
and ls
xis the survivorship to age x, obtained from s.Theγₓand θₓ
parameters make age-specific corrections and are also taken from life
table s. Here, srefers to the global standard life table provided in
Wilmoth et al. (2012)91,specific to the sex in question (Supplementary
Table 15). Having derived the pattern of survivorship from age x=0to
110, for lifetable k, we calculated the related mortality statistics (e.g.,
life-expectancy, e
x
, and probability of death, q
x
) using the LifeTable
function from the R package, MortalityLaws92.
Article https://doi.org/10.1038/s41467-025-58475-1
Nature Communications | (2025) 16:3431 9
Content courtesy of Springer Nature, terms of use apply. Rights reserved

Sensitivity analysis
To assess the sensitivity of our results to biases in the available data we
conduced analyses with imputed values for missing data in certain
countries and years. The imputation was performed using the ‘mice’
function from the R packages mice and miceadds53,54. The variables
subjected to imputation included: (1) protein supply (1.7% missing), (2)
animal-based protein supply (1.7% missing), (3) plant-based protein
supply (1.7% missing), (4) carbohydrate supply (1.7% missing), (5) fat
supply (1.7% missing), and (6) log of GDP per capita as given by the
Maddison project27 (14.95% missing). We employed a two-level impu-
tation approach, treating countries as a clustering factor at the second
level. The imputation method used was ‘2 l.pmm’where ‘pmm’refers
to ‘predictive mean matching’. This semi-parametric method selects
the closest observed values based on prediction to impute missing
values. The choice of 50 imputed datasets was made to account for
uncertainty in the imputation process. The convergence of the impu-
tation process was assessed over 20 iterations. While common in
multiple imputation, the standard approach of fitting independent
models to each imputed dataset before averaging was not suitable for
our analysis due to the presence of GAMM smooth terms. Instead, the
average of the imputed datasets was used to refit selected GAMM as
described in methods. It’s worth noting that this imputation strategy
provides a robust method for handling missing data and enhances the
reliability of our analyses.
This study does not constitute human-subject research and meets
the criteria for IRB exemption under 45 CFR 46.102. The data used
were obtained from sources that ensure the information is de-
identified and publicly accessible therefore, no IRB review was
required.
Reporting summary
Further information on research design is available in the Nature
Portfolio Reporting Summary linked to this article.
Data availability
All data used in this study were obtained from publicly accessible
databases. We have complied with all data usage conditions and
licensing agreements associated with these databases. Population
estimates were sourced from the United Nations Population Division
[available at https://population.un.org/wpp/ and downloaded on 02
Feb 2023]. Life table data were sourced from the Human Lifetable
Database [available at https://www.lifetable.de/ anddownloadedon03
August 2023]. Food supply data were sourced from the Food and
Agriculture Statistical Database [available at https://www.fao.org/
faostat/en/ and downloaded on 11 August 2023]. Historical economic
data were sourced from the Maddison Project Database [available at
https://www.rug.nl/ggdc/historicaldevelopment/maddison/releases/
and downloaded on 21 March 2022]. For any future analyses, it is
recommended to obtain current data directly from the referenced
sources for the most up-to-date information.
Code availability
All code for the current analyses can be found at GitHub, https://doi.
org/10.5281/zenodo.1496353893.
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Acknowledgements
C.J.A. discloses funding support for the research of this work from the
University of Sydney, Faculty of Science. AMS received support from the
University of Sydney Horizon Program and the Australian Research
Council Future Fellowship scheme.
Author contributions
C.J.A., A.M.S., S.J.S., and D.R. designed research and contributed to
interpretation of results; C.J.A. performed analysis. C.J.A., and A.M.S.
wrote the first draft of the paper. C.J.A., A.M.S., S.J.S., and D.R. con-
tributed to revisions.
Competing interests
The authors declare no competing interests.
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