Associations Between Adherence to the EAT-Lancet Planetary Health Diet and Nutritional Adequacy, and Sociodemographic Factors Among Australian Adults PDF Free Download
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Article
Associations Between Adherence to the EAT-Lancet Planetary
Health Diet and Nutritional Adequacy, and Sociodemographic
Factors Among Australian Adults
Jayden B. Ordner * , Claire Margerison , Linda A. Atkins and Ewa A. Szymlek-Gay
Institute for Physical Activity and Nutrition (IPAN), School of Exercise and Nutrition Sciences, Deakin University,
Melbourne Burwood Campus, 221 Burwood Highway, Burwood, VIC 3125, Australia;
claire.margerison@deakin.edu.au (C.M.); l.atkins@deakin.edu.au (L.A.A.);
ewa.szymlekgay@deakin.edu.au (E.A.S.-G.)
*Correspondence: j.ordner@deakin.edu.au; Tel.: +61-3-9244-6613
Abstract
Background/Objectives: Adherence to the EAT-Lancet Planetary Health Diet (PHD) may
promote human health and environmental sustainability, yet evidence regarding adherence
and nutritional adequacy in Australia is limited. Globally, no research to date has used the
recently updated 2025 PHD guidelines. We benchmarked the compatibility of Australian
adults’ dietary patterns with the 2025 PHD and examined its associations with nutritional
adequacy and sociodemographic factors. Methods: This was a cross-sectional analysis
of dietary data from 5655 adults who participated in the National Nutrition and Physical
Activity Survey. Usual intakes were estimated from two 24 h recalls using the Multiple
Source Method. PHD adherence was measured using the Healthy Reference Diet Score
(0–130 points). Nutrient adequacy was assessed using the full probability method for iron
and the Australian/New Zealand Estimated Average Requirement Cut-Point Method for all
other nutrients. Survey-weighted regression models examined associations with nutritional
adequacy and sociodemographic factors. Results: The mean PHD adherence score was
50 (SE 0.3) points. Higher adherence was associated with lower odds of inadequate in-
takes of several micronutrients, but with higher odds of inadequacy for vitamin B12
(OR: 1.24; 95% CI: 1.06, 1.45) and calcium (OR: 1.09; 95% CI: 1.01, 1.17). PHD adherence
was higher among females, older adults, those with higher educational attainment, those
born in countries where English is not the main language, two-person households and
non-smokers; adherence was non-linearly associated with alcohol and was lower among
those with a Body Mass Index
≥
30 kg/m
2
.Conclusions: PHD adherence in Australia was
low. Higher adherence was associated with improved adequacy for several micronutrients.
Trade-offs for vitamin B12 and calcium warrant consideration. Equity-conscious strate-
gies will be needed to support the adoption of nutritionally adequate, environmentally
sustainable diets.
Keywords: 2025 EAT-Lancet Commission; sustainable food systems; dietary assessment;
dietary patterns; nutritional epidemiology; food sustainability; Australian diets
1. Introduction
The necessity of radical global food system transformation under the duress of climate
change has exposed knowledge gaps in how to ensure both nutritional adequacy and
environmental sustainability [
1
,
2
]. The ‘Planetary Health Diet’ (PHD), developed by the
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Nutrients 2026,18, 340 2 of 21
EAT-Lancet Commission on Food, Planet and Health in 2019 and updated in 2025, is a
landmark framework for a healthy and environmentally sustainable plant-based diet [
3
,
4
].
It predominantly emphasises wholegrains, fruits, vegetables and legumes in favour of
reducing the consumption of red meats and added sugars, while recommending moder-
ate intakes of dairy, poultry and seafood. Adhering to the PHD is proposed to provide
nutritional adequacy, reduce chronic disease burden and match nutrition recommenda-
tions with emerging sustainability and climate-related goals [
4
]. It is well established
that population nutrition in economically developed nations cannot continue to be dom-
inated by the overconsumption of environmentally intensive animal-source foods such
as beef and dairy [
5
,
6
]. However, the degree of animal-source food restriction within the
2019 PHD recommendations has attracted criticism and debate, particularly regarding
potential micronutrient shortfalls, most notably vitamin B12, iron, zinc and calcium [
7
].
Inadequate intakes of nutrients like these, commonly found in red meats, dairy and other
animal-source foods, may increase the risk of deficiency and disease if not adequately
substituted for a diversity of plant-based foods, including those with fortification [8]. The
2025 PHD report has since described calcium, iodine, iron and vitamin B12 as nutrients
requiring further attention and emphasised that the PHD reference ranges are sufficiently
flexible to support the optimisation of food intakes across diverse contexts to ensure nutri-
tional adequacy for all population groups [4].
To date, no research has examined the nutritional implications of the 2025 PHD. Glob-
ally, studies examining associations between 2019 PHD adherence and nutritional adequacy
have produced mixed results [
9
–
14
]. Some research suggests that higher 2019 PHD adher-
ence decreases the risk of inadequacy for vitamins B6, B9 and C, as well as magnesium and
zinc, but increases the risk of inadequacy for vitamin B12 and calcium [
13
]. Others report
that high adherence reduces the probability of iron inadequacy [
9
], while elsewhere, overall
nutrient adequacy improves; however, for some nutrients, there may be no difference
between lower or higher adherence [
11
]. In Australia, evidence is limited. Australians
reportedly exceed the 2019 PHD recommendations for the consumption of meat (by 258%)
and dairy (by 161%), indicating low overall adherence to its guidelines [
15
]. However,
it remains unclear whether greater adherence to the PHD would support nutritionally
adequate diets in the Australian population. Given the various calls to action from nutrition
organisations to consider sustainability during the development of dietary guidelines and
nutrition policy, there is a pressing need to better understand how diets like the PHD
intersect with population nutrition in Australia [16–18].
In addition to understanding nutritional adequacy, it is important to explore the
broader factors that influence PHD adherence. Dietary behaviours are shaped by a range of
sociodemographic factors such as age, sex, education and socioeconomic disadvantage [
19
].
Identifying these associations can help contextualise what healthy and sustainable diets
look like in practice relative to actual dietary intake patterns and the diversity of the
Australian population.
The objectives of this study were to (1) benchmark the compatibility of Australian
adults’ dietary patterns with the 2025 PHD, (2) examine associations between PHD adher-
ence and nutritional adequacy and (3) identify sociodemographic factors associated with
PHD adherence.
2. Materials and Methods
2.1. Study Design and Participants
This study involved a secondary analysis of the 2011–2012 National Nutrition and
Physical Activity Survey (NNPAS) component of the Australian Health Survey, which
has been described in detail elsewhere [
20
]. In brief, the Australian Health Survey was
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a cross-sectional, nationally representative study conducted by the Australian Bureau
of Statistics using random multistage area sampling which identified 9500 dwellings
from all Australian states and territories across both urban and rural areas. Household
and person-level weights were assigned to account for both selection probability and
non-response to enable population-representative estimates for Australia. Computer-
assisted personal interviews with one responsible adult from each dwelling were used to
determine the general characteristics of the household, and from this information, one adult
aged
≥
18 years was selected for inclusion. Data on socioeconomic status, demographic
variables, anthropometric measures and dietary intake were recorded. The final NNPAS
sample contained data from 12,153 participants aged
≥
2 years, of whom 9435 were adults
aged ≥18 years and were eligible for inclusion in the present study (Figure 1).
Figure 1. Flow chart of eligible and excluded participants screened for the present study.
2.2. Sociodemographic Data
The sociodemographic data included in these analyses were age, sex (male/female),
education level (did not complete secondary school [<Year 12], completed secondary
school [Year 12], certificate/trade or diploma, bachelor’s degree, or postgraduate de-
gree), country of birth (Australia, major English-speaking countries (United King-
dom [England, Scotland, Wales, Northern Ireland], Republic of Ireland, New Zealand,
Canada, United States of America and South Africa) or other countries), household size
(categorised as 1, 2, 3 or
≥
4 people per household), Index of Relative Socio-economic
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Disadvantage (IRSD; this index ranked areas by socioeconomic disadvantage using 16 key
socioeconomic indicators (some of which were % of households with low income, % of
people without qualifications and % of people in low skilled occupations) divided into
quintiles ranging from the most disadvantaged [lowest quintile] to the least disadvantaged
[highest quintile]), labour force status (employed, unemployed or not in the labour force)
and current smoking status (yes/no) [20].
2.3. Anthropometric Data
Participants’ height and weight were measured by trained interviewers using a
portable stadiometer and digital scales [
20
]. BMI was calculated as kg/m
2
and categorised
as underweight (<18.5), healthy weight range (18.5 to <25), overweight (25 to <30) or
obese (≥30) [20].
2.4. Dietary Data
Dietary data used in these analyses were collected in the NNPAS between 2011 and
2012. Participants completed two non-consecutive 24 h dietary recalls using the 5-step
United States Department of Agriculture automated multiple-pass method adapted for
the Australian food system [
20
]. The first recall was conducted during the in-person inter-
views, with the second conducted at least 8 days later via a computer-assisted telephone
interview. All NNPAS participants provided at least one 24 h recall, with 3456 adults
providing a second recall. Pamphlets containing depictions of foods, serving sizes and con-
tainers were supplied to assist participants with the recollection of meals and their portion
sizes. The dietary data were coded (name, description, 8-digit code) and analysed (energy
and nutrients) using the AUSNUT 2011–2013 food database, a collaboration between the
Australian Bureau of Statistics and Food Standards Australia and New Zealand [
20
]. The
database contained 5740 foods, 15,847 measures and food preparation methods (e.g., cooked,
raw, fortified) [20].
2.5. Analytic Sample
NNPAS participants aged
≥
18 years were eligible for inclusion in the current study
(n= 9435). Pregnant and lactating individuals were excluded, as the PHD was not specif-
ically developed to meet the nutritional needs of these individuals. Individuals who
reported consuming any supplements were also excluded. Misreporters of energy intake
were then identified and excluded using the method described by Huang et al., which
compares reported energy intake to predicted total energy expenditure [
21
]. This was
estimated using the Institute of Medicine’s sex and age-specific coefficients for predicting
total energy expenditure [22]. The final analytic sample comprised 5655 adults (Figure 1).
2.6. Usual Food and Nutrient Intakes
The dietary data were used with the Multiple Source Method (Version 1.0.1; De-
partment of Epidemiology, German Institute of Human Nutrition, Potsdam-Rehbrücke,
Germany) to estimate the usual intakes of energy, fats (saturated and unsaturated), and all
nutrients reported in the NNPAS that have an Australian/New Zealand Estimated Average
Requirement [
23
,
24
]. This included vitamins A, B1, B2, B3, B6, B9, B12, C, D and E and the
minerals calcium, iodine, iron, magnesium, phosphorus, selenium and zinc. For energy, fats
and nutrients, the Multiple Source Method’s default assumption of habitual consumption
was used in the models. Usual intake estimates of 12 dietary components (wholegrains,
vegetables, fruits, legumes, starchy vegetables, dairy, eggs, poultry, fish and seafood, nuts
and seeds, red meats, and added sugars) were also modelled using the Multiple Source
Method for all participants. For individuals who did not report consumption of a dietary
component on a recall day, a probability value of 0.5 was applied to reflect potential episodic
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consumption, allowing usual intake estimations for 50% of those individuals [
23
]. Usual
alcohol consumption estimates were specified using a consumption probability of 0.81
based on the proportion of adults who reported alcohol consumption in the 2013 National
Drug Strategy Household Survey [
25
]. All usual intake models were adjusted for age, sex,
day type (weekday or weekend) and recall sequence (first or second).
2.7. Estimation of Planetary Health Diet Adherence
Usual intakes of dietary components and fats (g/day) were used to measure 2025 PHD
adherence using an adaptation of the Healthy Reference Diet Score (HRDS) (Table 1) [
26
].
In brief, the HRDS uses each of the 13 dietary recommendations outlined in the PHD, which
are organised into adequacy, optimum, moderation and ratio components. Individuals
receive proportional scores for each dietary component, ranging from 0 to 10 points,
based on how closely their usual intake aligns with the recommended intake targets. For
an adequacy component, such as vegetables, the recommended intake is 300 g/day for
those consuming 2400 kcal/day. Intakes are scored proportionally starting from 0 points
at 0 g/day to 10 points at 300 g/day, with no modification to the score for exceeding
this recommendation.
Table 1. Healthy Reference Diet Score design with target values (g/day) based on the 2025 EAT-Lancet
Planetary Health Diet of 2400 kcal/day 1.
Dietary Components PHD Intake Target Proportional Score
(0–10)
Max Points
(10)
Inverse Score
(10–0)
Adequacy Components
Wholegrains 420 g 20–420 g ≥420 g
Vegetables 300 g 0–300 g ≥300 g
Fruits 200 g 0–200 g ≥200 g
Legumes 375 g 0–75 g ≥75 g
Optimum Components 4
Starchy Vegetables 50 g 0–50 g 50–100 g 100–150 g
Dairy 250 g 0–250 g 250–500 g 500–750 g
Eggs 15 g 0–15 g 15–25 g 25–40 g
Poultry 30 g 0–30 g 30–60 g 60–90 g
Fish and Seafood 30 g 0–30 g 30–100 g 100–130 g
Nuts and Seeds 50 g 0–50 g 50–75 g 75–125 g
Moderation Components
Red Meats 15 g 0 g 15–0 g
Added Sugars 30 g 0 g 30–0 g
Ratio Component 515th Percentile 85th Percentile Proportional Score
(0–10)
Max Points
(10)
Unsaturated/Saturated Fats 1.072 1.829 1.072–1.829 ≥1.829
PHD, Planetary Health Diet.
1
For individuals in the present study, usual energy intakes were divided by the
2400 kcal/day intake targets to obtain a ratio. Each target was multiplied by this ratio to establish personalised
energy-adjusted targets for each dietary component. The ratio component (unsaturated/saturated fats) was
excluded from this adjustment.
2
The PHD recommendation for wholegrains was converted from dry weight,
as described earlier [
27
].
3
Legumes include fortified soy milks. Only the soy component of the beverage
contributed to the estimation of usual intake.
4
Maximum point ranges for optimum components are based
on the 2025 EAT-Lancet PHD intake ranges [
4
]. Inverse scores in this system are symmetrical from 0 g up
to the lower bound of the maximum point range.
5
Due to an absence of disaggregated data for food items
such as palm oil, a ratio of unsaturated to saturated fats was used in line with previous research [
26
,
28
]. The
15th and 85th percentiles of intake were derived from the present sample using methods described earlier [29].
Optimum components combine a different proportional and inverse scoring de-
sign. For example, for eggs (based on 2400 kcal/day), scores proportionally scale from
0 to 10 points for intakes between 0 g and 15 g/day. Egg intakes between 15 and 25 g/day
score 10 points, representing an optimum intake range. However, for intakes between
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25 and 40 g/day, scores become inversely proportional, falling from 10 to 0 points. Intakes
above 40 g/day score 0 points; these optimum cut-offs are based on the PHD’s reference
values [
4
]. Moderation components are scored inversely, penalising higher-impact dietary
components. For added sugars (based on 2400 kcal/day), scoring starts at 0 points for
31 g/day and proportionally increases to 10 points for 0 g/day. Due to a lack of disaggre-
gated data for the added fats PHD category (palm oils, lard, tallow, ghee and butter), a ratio
of total unsaturated to total saturated fats was used for the HRDS ratio component, consis-
tent with the approach taken in previous research [
26
,
28
]. Cut-offs for this ratio component
were derived from the 15th percentile (i.e., a ratio of 1.072) and the 85th percentile (i.e., a
ratio of 1.829) of intake values observed in our sample, in line with methods described in
earlier research [
29
]. Ratios of unsaturated to saturated fats at or below 1.072 scored 0 points,
ratios at or above 1.829 scored 10 points, and intermediate values received proportional
scores between these cut-offs. A total of 130 points represents the highest possible PHD
adherence score according to this design. To account for variation in individual energy
intake, we energy-adjusted the gram-based targets for each of the dietary components
(adequacy, optimum and moderation) for each participant by scaling them to each partici-
pant’s usual energy intake. This was performed by dividing each participant’s usual energy
intake by the PHD reference energy intake of 2400 kcal/day and multiplying each indi-
vidual dietary component’s target intake by the resulting ratio; the unsaturated/saturated
fat ratio component of the HRDS was excluded from this adjustment as it was derived
from sample percentiles. The foods included in each dietary component are listed in
Supplementary Table S1.
2.8. Adequacy of Nutrient Intakes
The Estimated Average Cut-Point Method was used to classify individuals as hav-
ing either adequate or inadequate nutrient intakes based on age- and sex-specific Aus-
tralian/New Zealand Estimated Average Requirement values [
24
]. This method is appro-
priate for nutrients where requirements are symmetrically distributed around the mean [
30
].
For iron, the full probability method was used, as the distribution of iron requirements,
particularly among menstruating females, is not symmetrical around the mean [24,30].
2.9. Statistical Analysis
Descriptive statistics were used to summarise sociodemographic characteristics, mean
PHD adherence scores, usual intakes (energy, nutrients and alcohol), and the HRDS di-
etary components, both for the whole sample and per quartile of PHD adherence; these
variables were each assessed for normality. Survey-weighted means, standard errors, me-
dians, interquartile ranges (25th and 75th percentile) and percentages were calculated, as
appropriate, using the survey sampling weights to account for the complex survey design.
Logistic regression was used to assess the associations between PHD adherence scores
and nutritional adequacy (adequate/inadequate). To facilitate interpretation, adherence
scores were rescaled by a factor of 10 (from a range of 0–130 to 0–13), and logistic regression
was used to estimate the odds of being at risk of inadequate intake per 10-point change in
adherence score. Iron was excluded from this analysis due to the requirement for the full
probability method. Instead, linear regression was used to assess the association between
PHD adherence scores and the probability of inadequate iron intake, as estimated using
the full probability method. These probabilities were modelled as a continuous outcome,
and regression analysis was performed to estimate the change in probability per 10-point
increase in adherence score. Nutritional adequacy models were also fitted using unscaled
adherence scores (0–130), which reflected a 1-point increase in adherence. Potential non-
linearity was examined for all nutrients using restricted cubic splines and Wald’s test of
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non-linear components. All nutritional adequacy models were adjusted for the covariates
of age, sex and energy intake. Due to the very low number of individuals with inadequate
intakes of vitamin B3 and phosphorus, regression modelling was not feasible.
Univariable linear regression was used to identify potential sociodemographic factors
associated with PHD adherence scores, and each was adjusted for energy intake. Due to
missing data for BMI (n= 913) and education (n= 77), these models were restricted to
a complete case sample (n= 4678; Figure 1); multiple imputation was not implemented.
Alcohol consumption, which showed a non-linear association with PHD adherence scores,
was modelled using restricted cubic splines. Regression diagnostics were performed on all
univariable regression models to assess model assumptions (linearity, homoscedasticity
and the normality of residuals). Factors with p-values < 0.2 were subsequently included in a
multivariable linear regression model to identify independent associations. Post-estimation
multicollinearity was evaluated using variance inflation factors.
Jackknife survey weights were applied to account for the NNPAS design and enable
inference to the Australian population. All proportions and estimates were calculated, and
regression models were fitted using survey commands. Statistical significance was defined
as p< 0.05. All analyses were conducted between May and October 2025 using STATA 18.0
(StataCorpLLC. College Station, TX, USA).
3. Results
3.1. Population Characteristics
The final analytic sample included 5655 adults (Figure 1) with a median (25th, 75th
percentile) age of 43 (29, 57) years (Table 2). Approximately 60% of participants were
living with overweight or obesity. Most participants were born in Australia (70%), and
11% were from major English-speaking countries. Over half of the participants (58%) had
completed a post-secondary school education. The sample was socioeconomically diverse,
with approximately equal representation across IRSD quintiles. Nearly one-third (32%)
of participants lived in two-person households, and 36% lived in households with four
or more people. Most participants were employed (68%), 21% currently smoked and the
median (25th, 75th percentile) usual alcohol consumption was 3.7 g/day (0.9, 19.9) (Table 2).
Table 2. Population characteristics and their distribution according to the level of adherence
1
to the
2025 EAT-Lancet Planetary Health Diet.
Population Characteristic Total Sample
(n= 5655)
Q1 Lowest
(6–41) 2
(n= 1499)
Q2
(42–50) 2
(n= 1381)
Q3
(51–59) 2
(n= 1365)
Q4 Highest
(60–104) 2
(n= 1410)
Age Median (25th, 75th percentile) 3
Years 43 (29, 57) 40 (26, 53) 42 (28, 57) 43 (30, 57) 47 (33, 59)
2025 PHD Adherence Score Mean (SE) 3
Possible range 0–130 50 (0.3) 33 (0.3) 46 (0.1) 55 (0.1) 67 (0.2)
Sex n(%) 3n(%) 3n(%) 3n(%) 3n(%) 3
Female 2715 (45%) 604 (37%) 660 (45%) 660 (45%) 791 (55%)
Male 2940 (55%) 895 (63%) 721 (55%) 705 (55%) 619 (45%)
Body Mass Index (kg/m2)
Underweight (<18.5) 75 (2%) 21 (2%) 21 (2%) 15 (2%) 18 (2%)
Healthy Weight Range (18.5 to <25) 1602 (35%) 397 (34%) 403 (37%) 374 (34%) 428 (37%)
Overweight (25 to <30) 1703 (35%) 432 (34%) 380 (31%) 432 (38%) 459 (38%)
Obese (≥30) 1362 (27%) 373 (31%) 359 (29%) 337 (26%) 293 (23%)
Missing 913 276 218 207 212
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Table 2. Cont.
Population Characteristic Total Sample
(n= 5655)
Q1 Lowest
(6–41) 2
(n= 1499)
Q2
(42–50) 2
(n= 1381)
Q3
(51–59) 2
(n= 1365)
Q4 Highest
(60–104) 2
(n= 1410)
Education Status
Did Not Complete Secondary School 1631 (26%) 504 (31%) 439 (29%) 368 (25%) 320 (21%)
Completed Secondary School 736 (15%) 194 (16%) 175 (15%) 173 (15%) 194 (15%)
Certificate, Trade or Diploma 1922 (35%) 559 (40%) 488 (36%) 436 (32%) 439 (33%)
Bachelor’s Degree 894 (16%) 166 (11%) 195 (15%) 251 (19%) 282 (20%)
Postgraduate Degree 395 (7%) 54 (3%) 71 (5%) 113 (8%) 157 (11%)
Missing or Not Determined 77 22 13 24 18
Country of Birth
Australia 4110 (70%) 1189 (76%) 1023 (72%) 990 (71%) 908 (61%)
Major English-Speaking 4673 (11%) 171 (12%) 167 (12%) 158 (10%) 177 (12%)
Other 872 (19%) 139 (12%) 191 (16%) 217 (19%) 325 (28%)
Household Size
1 Person 1470 (13%) 420 (14%) 351 (12%) 358 (13%) 341 (12%)
2 Persons 1832 (32%) 428 (27%) 443 (31%) 441 (31%) 520 (37%)
3 Persons 917 (20%) 243 (19%) 243 (21%) 237 (21%) 194 (17%)
≥4 Persons 1436 (36%) 408 (39%) 344 (36%) 329 (34%) 355 (34%)
IRSD 5
Lowest Quintile (Most Disadvantage)
1155 (20%) 371 (24%) 307 (22%) 242 (17%) 235 (16%)
Second Quintile 1220 (21%) 326 (21%) 332 (23%) 284 (21%) 278 (21%)
Third Quintile 1093 (20%) 275 (20%) 276 (21%) 261 (19%) 281 (21%)
Fourth Quintile 947 (18%) 243 (17%) 210 (16%) 242 (20%) 252 (18%)
Highest Quintile (Least
Disadvantage) 1240 (21%) 284 (18%) 256 (18%) 336 (23%) 364 (23%)
Labour Force Status
Employed 3732 (68%) 984 (68%) 888 (67%) 900 (68%) 960 (70%)
Unemployed 147 (3%) 56 (4%) 42 (4%) 25 (3%) 24 (2%)
Not in the Labour Force 1776 (28%) 459 (28%) 451 (29%) 440 (30%) 426 (28%)
Smoking Status
Yes 1295 (21%) 496 (29%) 336 (23%) 260 (17%) 203 (14%)
No 4360 (79%) 1003 (71%) 1045 (77%) 1105 (83%) 1207 (86%)
Alcohol Consumption 6Median (25th, 75th percentile) 3
Usual Intake (g/day) 3.7 (0.9, 19.9) 3.7 (1.0, 20.7) 3.4 (0.7, 18.1) 3.7 (1.0, 20.4) 3.6 (0.7, 20.4)
PHD, Planetary Health Diet; IRSD, Index of Relative Socio-economic Disadvantage.
1
Planetary Health Diet
adherence was measured using the Healthy Reference Diet Score [
26
]. Possible scores range from 0 (lowest) to
130 (highest) points; quartiles (Q1 Lowest, Q4 Highest) were derived from the observed score range (6–104 points).
2
A range (min–max) of PHD adherence scores per quartile.
3
Mean (SE) and median (25th, 75th percentile) were
calculated using the survey sampling weights to account for the complex survey design; n(% = survey-weighted
proportions); values may not sum to 100% due to rounding.
4
Major English-speaking countries include Canada,
Ireland, New Zealand, South Africa, the United Kingdom and the United States of America, as specified in the
Standard Australian Classification of Countries [
20
].
5
The Index of Relative Socio-economic Disadvantage reflects
the relative level of disadvantage in an area based on variables such as income, education level, unemployment
and occupation. Lower quintiles indicate greater disadvantage [
20
].
6
10 g of ethanol is equivalent to one standard
drink [31]. Usual intakes of alcohol were estimated using the Multiple Source Method [23].
3.2. Usual Dietary Intakes
Usual dietary component intakes are reported in Table 3. The lowest scoring di-
etary components (median [25th, 75th percentile]) were legumes (0.6 [0.0, 2.0] points),
nuts and seeds (0.6 [0.0, 1.7] points), wholegrains (0.4 [0.0, 1.4] points), added sugars
(0.0 [0.0, 0.0] points) and red meat (0.0 [0.0, 0.0] points), while the highest scoring were
dairy (8.9 [4.7, 10.0] points), starchy vegetables (7.7 [1.3, 10.0] points), fruit (6.8 [2.7, 10.0]
points) and vegetables (6.4 [4.4, 8.6] points). Usual energy and nutrient intakes are reported
in Table 4.
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Table 3. Median
1
(25th, 75th percentile) usual dietary component intakes (g/day)
2
and their distribution according to the level of adherence
3
to the 2025 EAT-Lancet
Planetary Health Diet.
Dietary Components
Usual Population Intake
Q1 (Lowest)
(6–41) 4
n= 1530
Q2
(42–50) 4
n= 1302
Q3
(51–59) 4
n= 1412
Q4 (Highest)
(60–104) 4
n= 1411
Median Component
Scores 5
(0–10)
Adequacy Components Median (25th, 75th Percentile)
Wholegrains 14.8 (0.0, 48.2) 11.3 (0.0, 36.9) 13.4 (0.0, 41.8) 16.9 (6.1, 52.7) 28.1 (8.0, 60.6) 0.4 (0.0, 1.4)
Vegetables 157.1 (111.3, 210.9) 129.6 (86.8, 175.1) 151.1 (105.3, 203.0) 166.1 (123.2, 222.4) 183.7 (139.9, 233.9) 6.4 (4.4, 8.6)
Fruits 109.6 (44.9, 202.4) 49.9 (25.6, 112.2) 92.3 (41.6, 178.0) 128.4 (59.4, 212.6) 183.6 (109.4, 264.3) 6.8 (2.7, 10.0)
Legumes 3.6 (0.0, 11.6) 0.0 (0.0, 6.6) 2.8 (0.0, 9.2) 4.3 (0.0, 12.6) 7.6 (0.0, 29.6) 0.6 (0.0, 2.0)
Optimum Components
Starchy Vegetables 60.6 (32.5, 97.0) 64.4 (20.7, 118.6) 66.5 (33.5, 101.5) 58.3 (34.8, 87.8) 56.9 (36.9, 79.5) 7.7 (1.3, 10.0)
Dairy 308.2 (191.0, 454.1) 365.7 (194.6, 552.0) 321.9 (199.3, 464.8) 292.5 (183.6, 428.8) 273.4 (185.9, 383.7) 8.9 (4.7, 10.0)
Eggs 12.5 (5.4, 24.0) 10.1 (0.0, 24.0) 11.7 (5.3, 24.7) 13.6 (6.9, 24.8) 13.3 (8.5, 22.8) 5.5 (0.0, 9.4)
Poultry 46.4 (18.7, 73.0) 47.2 (0.0, 82.4) 47.7 (16.2, 76.8) 46.9 (25.4, 70.5) 43.0 (27.5, 64.9) 1.3 (0.0, 9.8)
Fish and Seafood 15.3 (0.0, 34.0) 0.0 (0.0, 15.3) 13.9 (0.0, 25.9) 17.4 (4.9, 36.8) 29.3 (14.0, 53.7) 5.6 (0.0, 10.0)
Nuts and Seeds 2.7 (0.0, 7.3) 1.0 (0.0, 3.9) 2.2 (0.0, 5.5) 3.3 (0.0, 8.7) 5.5 (0.8, 14.7) 0.6 (0.0, 1.7)
Moderation Components
Red Meats 82.0 (51.3, 115.2) 89.2 (60.4, 120.1) 83.8 (56.8, 117.5) 85.4 (53.0, 118.4) 69.3 (31.6, 103.4) 0.0 (0.0, 0.0)
Added Sugars 42.1 (24.8, 69.1) 52.3 (29.1, 80.4) 44.2 (26.8, 75.3) 41.0 (24.4, 65.1) 34.2 (20.3, 52.5) 0.0 (0.0, 0.0)
Ratio Component 6
Unsaturated Fats 36.4 (28.5, 45.6) 34.7 (26.8, 43.5) 36.3 (27.7, 46.4) 37.0 (29.2, 46.3) 37.3 (30.0, 46.6) 4.1 (1.3, 8.0)
Saturated Fats 26.2 (19.7, 33.5) 28.8 (22.0, 36.9) 27.1 (20.7, 34.8) 25.4 (19.3, 32.7) 23.0 (18.0, 29.5)
PHD, Planetary Health Diet.
1
Survey-weighted medians (25th, 75th percentile) were calculated using the survey sampling weights to account for the complex survey design.
2
Usual food group intakes were estimated using the Multiple Source Method [
23
].
3
Planetary Health Diet adherence was measured using the Healthy Reference Diet Score [
26
]. Possible
scores ranged from 0 points (lowest) to a theoretical maximum of 130 points (highest); quartiles (Q1–Q4) were derived from the observed score range (6–104 points).
4
A range (min–max)
of the PHD adherence scores per quartile.
5
Median (25th, 75th percentile) dietary component scores reflect individual contributions made by each dietary component towards the
overall PHD adherence scores.
6
The ratio component uses the 15th and 85th percentiles of intake (sample-derived) to establish cut-offs for proportional scoring, using the methods
described earlier [29].
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Table 4. Usual
1
daily population nutrient intakes
2
and their distribution according to the level of
adherence 3to the 2025 EAT-Lancet Planetary Health Diet.
Usual Population
Intake
Q1 (Lowest)
(6–41) 4
n= 1530
Q2
(42–50) 4
n= 1302
Q3
(51–59) 4
n= 1412
Q4 (Highest)
(60–104) 4
n= 1411
Nutrient Units Mean (SE)
Energy kcal/day 2037.2 (8.5) 2063.4 (20.6) 2060.9 (20.2) 2051.1 (19.4) 1973.1 (17.9)
Fibre g/day 21.7 (0.1) 18.9 (0.3) 21.1 (0.2) 22.6 (0.3) 24.5 (0.3)
Vitamin B1 mg/day 1.5 (0.0) 1.5 (0.0) 1.5 (0.0) 1.5 (0.0) 1.5 (0.0)
Vitamin B2 mg/day 1.8 (0.0) 1.9 (0.0) 1.9 (0.0) 1.8 (0.0) 1.7 (0.0)
Vitamin B3 mg/day 41.3 (0.2) 42.0 (0.4) 41.9 (0.5) 41.1 (0.4) 40.1 (0.4)
Vitamin B6 mg/day 1.5 (0.0) 1.4 (0.0) 1.5 (0.0) 1.5 (0.0) 1.5 (0.0)
Vitamin B9 µg/day 606.2 (4.1) 605.2 (10.3) 605.6 (7.6) 608.1 (8.1) 606.2 (6.4)
Vitamin B12 µg/day 4.5 (0.0) 4.7 (0.1) 4.6 (0.1) 4.4 (0.1) 4.1 (0.1)
Vitamin A µg/day 790.5 (8.8) 747.1 (12.7) 797.8 (23.8) 802.8 (16.0) 817.2 (10.9)
Vitamin C mg/day 98.5 (1.1) 83.3 (1.7) 94.4 (2.4) 104.2 (2.7) 113.4 (2.3)
Vitamin E mg/day 9.8 (0.1) 8.6 (0.1) 9.6 (0.1) 10.1 (0.1) 11.0 (0.1)
Calcium mg/day 776.3 (5.3) 816.6 (13.2) 779.5 (11.3) 766.7 (9.9) 739.9 (8.0)
Iodine µg/day 170.4 (0.9) 178.1 (2.2) 173.4 (2.1) 168.6 (2.1) 161.1 (1.7)
Iron mg/day 10.7 (0.1) 10.3 (0.1) 10.7 (0.1) 10.9 (0.1) 11.1 (0.1)
Magnesium mg/day 321.0 (1.7) 302.5 (3.6) 313.2 (3.2) 329.7 (3.2) 340.0 (3.5)
Phosphorus mg/day 1430.4 (5.9) 1448.8 (13.8) 1441.1 (14.1) 1437.1 (12.9) 1393.9 (13.9)
Selenium µg/day 88.1 (0.4) 84.9 (1.0) 87.3 (1.0) 90.1 (1.1) 90.3 (1.2)
Zinc mg/day 10.7 (0.1) 10.8 (0.1) 10.8 (0.1) 10.9 (0.1) 10.4 (0.1)
PHD, Planetary Health Diet.
1
Survey-weighted means and standard errors were calculated using the survey
sampling weights to account for the complex survey design.
2
Usual nutrient intakes were estimated using
the Multiple Source Method [
23
].
3
Planetary Health Diet (PHD) adherence was measured using the Healthy
Reference Diet Score [
26
]. Possible scores range from 0 points (lowest) to a theoretical maximum of 130 points
(highest); quartiles were derived from the observed score range (6–104 points).
4
A range (min–max) of the PHD
adherence scores per quartile.
3.3. Nutritional Adequacy
Inadequate nutrient intakes were common, with prevalence estimates highest for
calcium (70%), magnesium (48%), vitamin E (42%), vitamin B6 (39%), zinc (34%), vitamin A
(26%), vitamin B1 (15%), iron (14%) and vitamin B2 (11%). Inadequacy of intake for iodine,
vitamin B9, selenium, vitamin C, vitamin B12, B3 and phosphorus was less common, with
prevalence below 10% (Table 5).
Table 5. Prevalence of inadequate nutrient intake and odds
1
of inadequate nutrient intake per
10-point increase in adherence to 2025 EAT-Lancet Planetary Health Diet.
Nutrient Adequacy Category 2n(%) 3Odds Ratio 95% CI p-Value
Vitamin A Adequate (reference) 4204 (74%) 1
Inadequate 1451 (26%) 0.803 0.749, 0.861 p< 0.001
Vitamin B1 Adequate (reference) 4803 (85%) 1
Inadequate 852 (15%) 0.909 0.837, 0.987 0.024
Vitamin B2 Adequate (reference) 5009 (89%) 1
Inadequate 646 (11%) 0.915 0.830, 1.010 0.076
Vitamin B6 Adequate (reference) 3423 (61%) 1
Inadequate 2232 (39%) 0.807 0.742, 0.878 p< 0.001
Vitamin B9 Adequate (reference) 5281 (93%) 1
Inadequate 374 (7%) 0.870 0.776, 0.976 0.018
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Table 5. Cont.
Nutrient Adequacy Category 2n(%) 3Odds Ratio 95% CI p-Value
Vitamin B12 Adequate (reference) 5474 (97%) 1
Inadequate 181 (3%) 1.239 1.062, 1.446 0.007
Vitamin C Adequate (reference) 5307 (94%) 1
Inadequate 348 (6%) 0.526 0.464, 0.595 p< 0.001
Vitamin E Adequate (reference) 3269 (58%) 1
Inadequate 2386 (42%) 0.605 0.560, 0.653 p< 0.001
Calcium Adequate (reference) 1715 (30%) 1
Inadequate 3940 (70%) 1.088 1.009, 1.173 0.029
Iodine Adequate (reference) 5231 (93%) 41
Inadequate 424 (8%) 40.978 0.880, 1.088 0.680
Magnesium Adequate (reference) 2962 (52%) 1
Inadequate 2693 (48%) 0.676 0.626, 0.730 p< 0.001
Selenium Adequate (reference) 5253 (93%) 1
Inadequate 402 (7%) 0.824 0.745, 0.912 p< 0.001
Zinc Adequate (reference) 3722 (66%) 1
Inadequate 1933 (34%) 0.950 0.884, 1.020 0.151
Iron 5% of inadequate
intakes 14% Coefficient 95% CI p-value
−0.017 −0.022, −0.011 p< 0.001
1
The logistic regression models were each survey-weighted and adjusted for age, sex and usual energy intake.
2
Nutritional adequacy was determined using usual nutrient intakes and the age- and sex-specific Estimated
Average Requirement Cut-Point Method as per the Australian/New Zealand Nutrient Reference Values for all
nutrients except iron, for which the full probability method was used [
24
].
3
Proportions reflect survey-weighted
estimates.
4
Values that exceed 100% are due to rounding.
5
For iron, a linear regression was used to model the
probability of inadequate intakes, as is necessary when using the full probability method [32].
Higher 2025 PHD adherence was significantly associated with lower odds of inad-
equate intakes for several micronutrients (Table 5). For each 10-point increase in the
PHD adherence score, the odds of inadequacy decreased significantly for vitamins A
(OR: 0.803; 95% CI: 0.749, 0.861; p< 0.001), B1 (OR: 0.909; 95% CI: 0.837, 0.987; p= 0.024), B6
(OR: 0.807; 95% CI: 0.742, 0.878; p< 0.001), B9 (OR: 0.870; 95% CI: 0.776, 0.976; p= 0.018),
C (OR: 0.526; 95% CI: 0.464, 0.595; p< 0.001) and E (OR: 0.605; 95% CI: 0.560, 0.653;
p< 0.001). Protective associations were also observed for magnesium (OR: 0.676;
95% CI: 0.626, 0.730; p< 0.001) and selenium (OR: 0.824; 95% CI: 0.745, 0.912; p< 0.001). For
iron, for each 10-point increase in the PHD adherence score, the probability of inadequate
intakes reduced by 1.7 percentage points (coefficient =
−
0.017; 95% CI:
−
0.022,
−
0.011;
p< 0.001). By contrast, for each 10-point change in the PHD adherence score, the odds of
inadequacy increased significantly for both vitamin B12 (OR: 1.239; 95% CI: 1.062, 1.446;
p= 0.007) and calcium (OR: 1.088; 95% CI: 1.009, 1.173; p= 0.029) (Table 5). No significant
associations were observed for vitamin B2, iodine and zinc (all p> 0.05). The odds for each
1-point increase in adherence score are reported in Supplementary Table S2.
3.4. Sociodemographics
All sociodemographic variables were associated with PHD adherence scores in uni-
variable models (all p< 0.01) (Table 6). In the multivariable regression model, several
sociodemographic factors were independently and positively associated with PHD ad-
herence (Table 7). Higher adherence was observed among older compared to younger
individuals (
β
per each additional year = 0.135; 95% CI: 0.089, 0.180; p< 0.001), females
compared to males (
β
= 3.931; 95% CI: 2.579, 5.284; p< 0.001), those born in other countries
compared to those born in Australia (
β
= 4.993; 95% CI: 3.398, 6.587; p< 0.001) and non-
smokers compared to smokers (
β
= 2.891; 95% CI: 1.500, 4.283; p< 0.001). PHD adherence
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was significantly higher among individuals with higher levels of educational attainment
compared to those who did not complete secondary school. The largest differences were
observed among those with postgraduate degrees (
β
= 6.465; 95% CI: 4.521, 8.409; p< 0.001)
and bachelor’s degrees (
β
= 4.936; 95% CI: 2.935, 6.936; p< 0.001). Additionally, living in
a two-person household was associated with greater PHD adherence compared to living
alone (
β
= 1.784; 95% CI: 0.537, 3.032; p= 0.006). A non-linear association was observed
between alcohol consumption and adherence (p< 0.001) characterised by a modest positive
trend at 20–30 g/day, with a progressive decline beyond consumption of 40 g/day. In
contrast, individuals with obesity had significantly lower PHD adherence scores compared
to those with BMIs in the healthy weight range (
β
=
−
1.620; 95% CI:
−
2.828,
−
0.413;
p= 0.009) (Table 7). No significant associations were detected between household socioeco-
nomic position (as measured by IRSD or labour force status) and PHD adherence scores
(all overall p-values > 0.05). Linear regression post-estimation indicated no evidence of
problematic multicollinearity, with a mean variance inflation factor of 1.45 (max 1.82).
Table 6. Univariable regression
1
assessing associations between adherence to the 2025 EAT-Lancet
Planetary Health Diet Scores and sociodemographic factors.
Population Characteristic Coefficient 95% CI p-Value 2
Age 0.090 0.059, 0.121 p< 0.001
Sex p< 0.001
Male
Female 3.331 2.042, 4.621
BMI (kg/m2)0.005
Normal (18.5 to <25)
Underweight (<18.5) 0.738 −4.770, 6.245
Overweight (25 to <30) 0.560 −0.713, 1.832
Obese (≥30) −2.013 −3.238, −0.787
Education p< 0.001
Did Not Complete Secondary School
Completed Secondary School 1.784 0.148, 3.419
Certificate, Trade or Diploma 0.278 −1.016, 1.573
Bachelor’s Degree 5.379 3.489, 7.268
Postgraduate Degree 7.983 5.987, 9.980
Country of Birth p< 0.001
Australia
Major English-Speaking 30.818 −1.132, 2.768
Other 5.605 4.073, 7.137
Household Size 0.008
1 person
2 persons 2.044 0.761, 3.326
3 persons 0.371 −1.215, 1.958
≥4 persons 0.253 −1.005, 1.511
IRSD 0.002
Lowest Quintile
Second Quintile 1.665 0.261, 3.068
Third Quintile 2.231 0.479, 3.982
Fourth Quintile 2.473 0.798, 4.149
Fifth Quintile 3.514 1.779, 5.249
Labour Force Status 0.009
Employed
Unemployed −4.658 −7.583, −1.733
Not in the labour force 0.461 −0.762, 1.685
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Table 6. Cont.
Population Characteristic Coefficient 95% CI p-Value 2
Smoking Status p< 0.001
Yes
No 4.798 3.436, 6.160
Alcohol Consumption 4p< 0.001
Usual Intakes (g/day)
IRSD, Index of Relative Socio-economic Disadvantage.
1
Linear regression on complete cases (n= 4678) excluded
977 adults missing BMI or education and was adjusted for energy intake.
2
Overall p-values are derived from the
adjusted Wald’s Test assessing the joint significance of all categories within each categorical variable. Continuous
and binary variables are not included in these tests.
3
Major English-speaking countries include Canada, Ireland,
New Zealand, South Africa, the United Kingdom and the United States of America, as specified in the Standard
Australian Classification of Countries [
33
].
4
Alcohol consumption was modelled using restricted cubic splines to
account for non-linearity. The reported p-value reflects the joint significance of all spline terms from Wald’s test.
Table 7. Multivariable Regression
1
assessing independent associations between adherence to the
2025 EAT-Lancet Planetary Health Diet Scores and sociodemographic factors.
Population Characteristic Coefficient 95% CI p-Value Overall p-Value 2
Age 0.135 0.089, 0.180 p< 0.001
Sex
Male
Female 3.931 2.579, 5.284 p< 0.001
BMI (kg/m2)
Healthy Weight Range (18.5 to <25)
Underweight (<18.5) 2.342 −2.693, 7.378 0.356
0.018
Overweight (25 to <30) 0.473 −0.709, 1.655 0.427
Obese (≥30) −1.620 −2.828, −0.413 0.009
Education
Did Not Complete Secondary School (<Year 12)
Completed Secondary School (Year 12) 3.060 1.397, 4.722 0.001
p< 0.001
Certificate, Trade or Diploma 1.432 0.142, 2.723 0.030
Bachelor’s Degree 4.936 2.935, 6.936 p< 0.001
Postgraduate Degree 6.465 4.521, 8.409 p< 0.001
Country of Birth
Australia
Major English-Speaking 3−0.060 −1.879, 1.758 0.947 p< 0.001
Other 4.993 3.398, 6.587 p< 0.001
Household Size
1 person
2 persons 1.784 0.537, 3.032 0.006
0.029
3 persons 0.801 −0.720, 2.322 0.296
≥4 persons 0.397 −0.907, 1.701 0.545
IRSD
Lowest Quintile–Most Disadvantaged
Second Quintile 1.155 0.011, 2.298 0.048
0.219
Third Quintile 1.169 −0.459, 2.798 0.156
Fourth Quintile 1.223 −0.309, 2.756 0.115
Fifth Quintile–Least Disadvantaged 1.414 −0.040, 2.868 0.056
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Table 7. Cont.
Population Characteristic Coefficient 95% CI p-Value Overall p-Value 2
Labour Force Status
Employed
Unemployed −2.567 −5.789, 0.654 0.116 0.178
Not in the Labour Force −0.721 −2.278, 0.835 0.357
Smoking Status
Yes
No 2.891 1.500, 4.283 p< 0.001
Alcohol Consumption
Usual Intakes (g/day) 4p< 0.001
IRSD, Index of Relative Socio-economic Disadvantage.
1
Linear regression on complete cases (n= 4678) excluded
977 adults missing BMI or education and was adjusted for energy intake.
2
The overall p-values are derived
from the adjusted Wald’s Test assessing the joint significance of all categories within each categorical variable.
Continuous and binary variables are not included in these tests.
3
Major English-speaking countries include
Canada, Ireland, New Zealand, South Africa, the United Kingdom and the United States of America as specified
in the Standard Australian Classification of Countries [
20
].
4
Alcohol consumption was modelled using restricted
cubic splines to account for non-linearity. The reported p-value reflects the joint significance of all spline terms
from Wald’s test.
For sensitivity analyses, we also used the 2019 version of the PHD dietary recommen-
dations, and there were no appreciable changes to any of the associations in the nutrient
adequacy and sociodemographic models.
4. Discussion
To our knowledge, this is the first study to examine associations between 2025 PHD
adherence and nutritional adequacy, as well as sociodemographic factors, in a nationally
representative sample of Australian adults. Our findings demonstrate that low overall
PHD adherence is driven by underconsumption of wholegrains, legumes, nuts and seeds
and overconsumption of added sugars and red meats. Our analyses show that greater PHD
adherence may reduce the odds of inadequate intake of several vitamins and minerals while
increasing the odds of inadequate vitamin B12 and calcium intake. Our results suggest
that PHD adherence may be shaped by a complex interplay of demographic, cultural and
behavioural factors, with higher adherence observed among older adults, females, those
with higher educational attainment, migrants born in countries where English is not the
main language and non-smokers, and lower adherence among those with obesity and those
with higher alcohol consumption.
4.1. Nutritional Adequacy
Our results largely support the EAT-Lancet Commission’s conclusion that improved
PHD adherence can support adequate intake of most nutrients [
4
], but with important
distinctions. Our findings closely mirror those reported across eight Latin American
countries, where greater 2019 PHD adherence was associated with increased risks of
calcium and vitamin B12 inadequacy, despite reduced risks of deficiency for the other
nutrients assessed [
13
]. Similar trends were reported in the United States, where the
probability of inadequate calcium intake appeared to be higher and the probability of
inadequate iron lower among individuals in higher 2019 PHD adherence quintiles in a
representative sample [
9
]. Although both studies used methods similar to those employed
in the present study, comparisons are limited by differing statistical approaches and by
dietary index modifications that are tailored to the available dietary data, which shape
how adherence is scored and interpreted [
34
]. Regardless, there may be trade-offs for
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a small number of critical nutrients with increasing PHD adherence, unless diets are
appropriately optimised.
The prevalence of inadequate calcium intake was high (70%). The higher odds of
inadequate intake among those with greater PHD adherence likely reflect reduced dairy
consumption, as recommended by the PHD, together with generally low intakes of other
calcium-containing foods (wholegrains, legumes, nuts and seeds and fortified plant-based
alternatives). In this context, lower dairy consumption is not being offset by increased
consumption of these non-dairy calcium sources, resulting in low overall calcium intake [
4
].
Comparatively, higher odds of inadequate vitamin B12 intake with greater PHD adherence
were expected, given the PHD’s emphasis on reducing animal-source foods and our exclu-
sion of supplement users, although the overall prevalence of inadequate B12 intake was
low (3%). These findings support the 2025 EAT-Lancet Commission’s acknowledgement
that these two nutrients require particular attention in some contexts. Should dietary shifts
increasingly align with PHD recommendations, an emphasis on strategic food choices will
be important to improve nutritional adequacy. In particular, vitamin B12 supplementa-
tion/fortification, alongside calcium fortification, should be prioritised to offset reductions
in animal-source foods and dairy [3].
For minerals like iron and zinc, the adequacy of intake is more complex. In our
sample, the prevalence of inadequate intake was high for zinc (34%) and moderate for iron
(14%). Since total intake is not directly proportional to absorption, these estimates may
not reflect the proportion of the nutrient that is bioavailable [35]. For example, vegetarian
diets exclude animal tissue and do not contain highly bioavailable haem iron, instead
containing only non-haem iron, which is less bioavailable. As a result, iron requirements
for vegetarians are up to 80% higher to achieve an equivalent absorption of iron [
24
]. This
reduced iron bioavailability is due to intakes of non-haem iron combined with higher
intakes of compounds such as phytates and polyphenols that are found in plant foods,
which inhibit iron absorption [
35
,
36
]. Zinc absorption is similarly affected by phytates, with
requirements up to 50% greater in high-phytate vegetarian diets [
32
]. Calcium absorption
may also be reduced by oxalates found in some plant foods, though the magnitude of this
effect and the amount consumed in PHDs remain poorly quantified [
37
]. Future research
should quantify the intake of haem and non-haem iron, zinc and calcium derived from
different sources, as well as the presence of known absorption inhibitors in the PHD. Such
data could clarify whether the PHD provides sufficient amounts of bioavailable iron, zinc
and calcium to meet physiological requirements [
38
]. Standard assumptions about mineral
bioavailability may not apply within this dietary context; without accounting for potential
differences in absorption, nutrient adequacy may be overestimated, even when reported
intakes appear sufficient.
4.2. Demographic Factors
We found several strong associations that contribute to understanding the sociode-
mographic context of PHDs in Australia. The positive association between age and PHD
adherence, also observed elsewhere [
9
,
13
,
39
], is likely explained by overlapping sociode-
mographic and behavioural mechanisms rather than intentional alignment with healthy or
sustainable eating principles. Young adults likely face more barriers to adherence, despite
higher motivation, including lower food literacy and cooking skills, financial barriers,
more frequent exposure to health and nutrition misinformation online and higher ultra-
processed food intakes that displace core PHD foods [
40
–
45
]. In contrast, older adults may
have more stable, traditional or routine eating patterns, which contribute to more home-
cooked meals using whole foods, underpinned by differing views about healthy eating
compared with young adults [
46
–
50
]. Though the effect size for age appears small, we
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estimate a 35-year age gap to produce a ~4.7-point adherence score increase, equivalent to a
~100 g/day increase in wholegrains and ~50 g/day of fruit (at 2400 kcal/day). This
association, however, is best interpreted alongside other correlates of adherence.
Broader attitudes toward PHDs in Australia remain poorly understood [
51
,
52
]. Gen-
dered beliefs regarding meat, masculinity and identity may partly explain females’ signifi-
cantly higher adherence scores, consistent with findings from the United States [
9
]. Females
may face fewer identity and gender-based barriers to reducing meat intake in favour of
substitution with plant-based foods, and have elsewhere been shown to exhibit greater
pro-environmental values and behaviours than males [
53
,
54
]. This is supported by national
data showing that females are less likely to consume meat, poultry and game foods [
55
]. In
Australia, a greater proportion of females also report being on a diet, and females are more
likely to meet guidelines for fruit and vegetable intake and consume fewer discretionary
foods than males [
55
,
56
]. The positive association between educational attainment and
PHD adherence found in our study aligns with previous research linking higher education
to both PHD adherence [
9
,
13
,
14
,
57
] and overall diet quality [
58
]. This may reflect a greater
awareness of nutrition and sustainability-related issues among individuals with higher
education across both sexes.
4.3. Intersecting Sociodemographic and Cultural Factors
Although no consistent association was observed between adherence and socioeco-
nomic status, as measured by the IRSD in our study, PHD adherence may not map neatly
onto area-level disadvantage. Nonetheless, dietary patterns are shaped by broader struc-
tural inequities [
59
]. Although the PHD may be less expensive than a typical Australian
diet [
60
], recommended diets remain unaffordable for individuals in disadvantaged ar-
eas [
61
]. Moreover, we also observed a negative association between obesity and PHD
adherence, consistent with data linking lower diet quality to obesity [
62
]. Nationally, obe-
sity and smoking prevalence are higher in regional and disadvantaged areas [
63
], where
cumulative disadvantages make highly processed, less sustainable foods more financially
accessible, and where fast-food outlets disproportionately concentrate [
64
,
65
]. Various
structural influences mediate the relationship between disadvantage, dietary behaviours,
diet quality and obesity, which merit further investigations to support healthier, more
equitable dietary shifts across Australia [66].
We found that only individuals in two-person households (who may include de
facto or childless couples and cohabiting adults) had higher PHD adherence scores than
those living alone. Shared food purchasing responsibilities, improved meal motivation,
companionship, the benefit of dual incomes shared between two people or fewer competing
household demands that increase the complexity of the within-house food dynamics may
be supportive of healthier food choices in this dynamic [
67
]. Lastly, higher adherence was
found amongst migrants born in countries where English is not the main language, where
culturally distinct dietary habits and food traditions, such as vegetarianism, emphasise
foods found in the PHD [
68
]. Shifts toward the PHD should consider the persistence of
traditional eating habits and preserve sustainable food practices embedded within migrant
or other indigenous food cultures [69].
Our initial framing of Australian PHD adherence in this study highlights the complex
web of interconnected sociodemographic factors which influence food literacy, access and
affordability as well as dietary habits [
70
]. Future research should continue to explore these
barriers and enablers of PHD transitions—transitions that must also navigate the broader
Australian socio-political landscape, influenced by dominant food and industry sectors,
cultural norms and competing policy priorities [71,72].
https://doi.org/10.3390/nu18020340
Nutrients 2026,18, 340 17 of 21
4.4. Strengths and Limitations
This research used the most recently available national dataset, a large representative
sample, with dietary data collected using a gold-standard method [
73
,
74
]. Population-
level dietary patterns are unlikely to have shifted substantially since data collection, as
demonstrated elsewhere [
9
]. Our usual dietary intake estimations were conducted using
a validated method that accounts for within-person variation [
23
]. Moreover, the propor-
tional scoring approach used in the HRDS with those intakes has been shown to provide
greater precision for epidemiological investigations assessing nutritional adequacy than bi-
nary approaches [
34
]. Our energy-adjustment modification to the HRDS further improved
its applicability by avoiding static calorie targets and fixed intake distributions; additional
adjustment for energy intake during modelling further controlled for any residual con-
founding. In addition, our inclusion of numerous sociodemographic variables enabled the
identification of equity-relevant disparities in dietary adherence.
Limitations of this study must be noted. The cross-sectional design of this study
precludes causal inference between predictors and dietary adherence. Additionally, the
updated 2025 PHD included a target for sodium intake (<2 g/day); however, sodium was
not incorporated into the 2025 EAT-Lancet Commission’s modelling due to measurement
limitations. The same limitation applied to the NNPAS dataset, which did not capture
discretionary salt use and therefore underestimated true sodium intake. Accordingly, we
excluded sodium from our analyses.
5. Conclusions
We contribute a new geographical context to the emerging field of planetary health
nutrition which highlights the urgent need to bridge the wide gap between evolving healthy
and sustainable dietary recommendations and current consumption patterns in Australia.
While greater PHD adherence was generally associated with improvements in achieving
nutritional adequacy, specific risks for inadequate intake of calcium and vitamin B12 high-
light the importance of sustainability-aligned dietary patterns that emphasise strategic food
substitution, fortified foods and possibly supplementation. Observed sociodemographic
disparities in adherence further emphasise the need to consider sex, education levels, cul-
tural dietary traditions and broader structural factors in shaping effective interventions for
sustainable dietary shifts. As Australia confronts the dual challenge of improving nutri-
tional adequacy and environmental sustainability, targeted, equity-conscious strategies will
be critical for reducing nutrition-related health risks while advancing environmental goals.
Supplementary Materials: The following supporting information can be downloaded at https:
//www.mdpi.com/article/10.3390/nu18020340/s1, Table S1. Healthy Reference Diet Score
Food Group Inclusions and Exclusions. Foods Matched and Included According to the Available
NNPAS Data in Alignment with the 2025 EAT-Lancet Planetary Health Dietary Recommendations.
Table S2. Prevalence of Inadequate Nutrient Intake and Odds of Inadequate Nutrient Intake per
1-point Increase in Adherence to the 2025 EAT-Lancet Planetary Health Diet.
Author Contributions: E.A.S.-G. conceived the idea for the research. J.B.O. and E.A.S.-G. designed
the research. J.B.O. analysed the data and wrote the manuscript with ongoing input, analysis planning
and feedback from E.A.S.-G. and C.M., E.A.S.-G. contributed analytic code and methodological input.
J.B.O., E.A.S.-G. and L.A.A. interpreted the results and critically reviewed the manuscript. E.A.S.-G.
had primary responsibility for the final content. All authors have read and agreed to the published
version of the manuscript.
Funding: J.B.O. was supported by the Commonwealth through an Australian Government Research
Training Program scholarship [DOI: https://doi.org/10.82133/C42F-K220].
https://doi.org/10.3390/nu18020340
Nutrients 2026,18, 340 18 of 21
Institutional Review Board Statement: Exemption from ethical review in accordance with the
National Statement of Ethical Conduct in Human Research 2023 was granted for the present study by
the Deakin University Health Research Ethics Committee (2024-116; 10 April 2024).
Informed Consent Statement: This study was a secondary analysis of de-identified microdata from
the Australian Bureau of Statistics. Written informed consent was obtained from all participants by
the ABS during primary data collection; consent forms were not available to external users. The
authors of the present study had no direct contact with participants.
Data Availability Statement: Data described in the manuscript, code book, and analytic code may
be made available upon request pending application to and approval by the Australian Bureau of
Statistics and payment of any necessary data fees: https://www.abs.gov.au/statistics/microdata-ta
blebuilder/available-microdata-tablebuilder/australian-health-survey-nutrition-and-physical-acti
vity (accessed on 3 November 2025).
Conflicts of Interest: The authors declare no conflicts of interest.
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https://doi.org/10.3390/nu18020340
