(2023) 21:404
Neuenschwander et al. BMC Medicine
https://doi.org/10.1186/s12916-023-03093-1

BMC Medicine

RESEARCH ARTICLE

Open Access

Substitution of animal‑basedwith plant‑based foods on cardiometabolic
health and all‑cause mortality: a systematic
review and meta‑analysis of prospective studies
Manuela Neuenschwander1,2, Julia Stadelmaier3, Julian Eble3, Kathrin Grummich3, Edyta Szczerba1,2,
Eva Kiesswetter3, Sabrina Schlesinger1,2*†   and Lukas Schwingshackl3†

Abstract
Background There is growing evidence that substituting animal-based with plant-based foods is associatedwith a lower risk of cardiovascular diseases (CVD), type 2 diabetes (T2D), and all-cause mortality. Our aim was to summarize and evaluate the evidence for the substitution of any animal-based foods with plant-based foods on cardiometabolic health and all-cause mortality in a systematic review and meta-analysis.
Methods We systematically searched MEDLINE, Embase, and Web of Science to March 2023 for prospective studies investigating the substitution of animal-based with plant-based foods on CVD, T2D, and all-cause mortality. We
calculated summary hazard ratios (SHRs) and 95% confidence intervals (95% CI) using random-effects meta-analyses.
We assessed the certainty of evidence (CoE) using the GRADE approach.
Results In total, 37 publications based on 24 cohorts were included. There was moderate CoE for a lower risk of CVD
when substituting processed meat with nuts [SHR (95% CI): 0.73 (0.59, 0.91), n = 8 cohorts], legumes [0.77 (0.68, 0.87),
n = 8], and whole grains [0.64 (0.54, 0.75), n = 7], as well as eggs with nuts [0.83 (0.78, 0.89), n = 8] and butter with olive
oil [0.96 (0.95, 0.98), n = 3]. Furthermore, we found moderate CoE for an inverse association with T2D incidence
when substituting red meat with whole grains/cereals [0.90 (0.84, 0.96), n = 6] and red meat or processed meatwith nuts [0.92 (0.90, 0.94), n = 6 or 0.78 (0.69, 0.88), n = 6], as well as for replacing poultry with whole grains [0.87 (0.83,0.90), n = 2] and eggs with nuts or whole grains [0.82 (0.79, 0.86), n = 2 or 0.79 (0.76, 0.83), n = 2]. Moreover, replacing
red meat for nuts [0.93 (0.91, 0.95), n = 9] and whole grains [0.96 (0.95, 0.98), n = 3], processed meat with nuts [0.79(0.71, 0.88), n = 9] and legumes [0.91 (0.85, 0.98), n = 9], dairy with nuts [0.94 (0.91, 0.97), n = 3], and eggs with nuts [0.85(0.82, 0.89), n = 8] and legumes [0.90 (0.89, 0.91), n = 7] was associated with a reduced risk of all-cause mortality.
Conclusions Our findings indicate that a shift from animal-based (e.g., red and processed meat, eggs, dairy, poultry,
butter) to plant-based (e.g., nuts, legumes, whole grains, olive oil) foods is beneficially associated with cardiometabolic
health and all-cause mortality.

†

Sabrina Schlesinger and Lukas Schwingshackl contributed equally to thiswork.
*Correspondence:
Sabrina Schlesinger
sabrina.schlesinger@ddz.de
Full list of author information is available at the end of the article© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
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Neuenschwander et al. BMC Medicine(2023) 21:404

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Keywords Diet, Cardiovascular disease, Type 2 diabetes, Mortality, Substitution, Plant-based food, Animal-based foodBackgroundThe current food system has been shown to be detrimental to planetary health by depleting Earth’s resources
and contributing to climate change, thus decreasing thequality and sufficiency of food [1, 2]. Furthermore, noncommunicable diseases related to dietary choices such as
cardiovascular diseases (CVD) and type 2 diabetes (T2D)
highly contribute to deaths worldwide [3]. Therefore,
the food system and current dietary habits negatively
impact both planetary and human health [2]. Consequently, one of the approaches to address these problems
is to change dietary habits [2, 4]. Plant-based diets are thefocus of recent studies suggesting that increased intakeof plant-based foods is not only beneficial for planetary
health [2, 4], but also reduces the risk of T2D, CVD, andpremature death [5, 6]. In contrast, the production andconsumption of animal-based foods, especially red andprocessed meat, pose a high burden on the environment[2] and have been linked to increased risk for CVD, T2D,
and mortality [7–9]. Such findings contribute to recommendations to reduce the intake of red and processed
meat. However, to keep energy intake constant, otherfoods need to be consumed instead and the association
with disease risks may depend on this substitution [10].
A promising option is the replacement of animal-basedwith plant-based foods. In this context, epidemiological studies investigated the substitution of animal-based
foods with other protein sources and indicated that sucha replacement is associated with reduced risk of CVD,
T2D, and mortality [11–14]. A recent systematic review
and meta-analysis found a lower risk for coronary heartdisease (CHD) when replacing red meat with poultry,
dairy, eggs, nuts, or legumes and a lower risk of all-cause
mortality when substituting red meat with fish/seafood,poultry, eggs, or nuts [15]. However, the authors did notinvestigate further cardiometabolic endpoints, including
CVD or T2D. Furthermore, the substitution of any animal based-foods with only plant-based foods has yet to
be investigated in a systematic review and meta-analysis.
Therefore, to clarify the strength and certainty of theoverall evidence on this topic, there is an urgent needfor a systematic review and meta-analysis examining
the association between substituting animal-based withpurely plant-based foods and cardiometabolic healthoutcomes, including CVD, stroke, myocardial infarction(MI), T2D, and mortality. Thus, it was our aim to summarize and evaluate the meta-evidence on the substitutionof animal-based with plant-based foods regarding cardiometabolic health outcomes and all-cause mortality.

Methods
Our protocol was pre-registered at PROSPERO (https://​www.​crd.​york.​ac.​uk/​prosp​ero/​displ​ay_​record.​php?​ID=​
CRD42​02230​2982). We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA) statement 2020 [16]. All steps were conductedby at least two investigators, independently. Disagreements were solved by consensus.
Deviations from the protocol

There were no deviations in methods, but we added allcause mortality to our outcomes since cardiovasculardisease and type 2 diabetes highly contribute to deaths
worldwide [3].
Search strategy and selection criteria

A systematic literature search was conducted on MEDLINE via Ovid, Embase via Ovid, and Web of Science(Clarivate) up to December 2021 using predefined searchterms (Additional file 1: Search terms) applying norestrictions or filters. Additionally, we screened the reference lists of relevant publications for further studies andconducted a PubMed Similar Articles search. Moreover, an update of the literature search was performed in
March 2023.
Studies were included if (1) substitution analyses of any
animal-based foods (including red and processed meat,
poultry, fish, shellfish, eggs, dairy, and dairy products)
with any plant-based foods (including legumes, nuts,
whole and refined grains, fruit, vegetables, soy, seeds,
and oils) that specified the substitution were conducted;(2) cardiometabolic health outcomes, including CVD
mortality; incidence of CVD, CHD, stroke, MI, and T2D;
and all-cause mortality, were investigated; (3) they were
prospective observational studies; and (4) the study was
conducted among the general healthy population. The
detailed exclusion criteria are displayed in Additional
file 2: Table S1. If multiple publications reported results
regarding the same association based on data from thesame cohort, the study with more cases and/or a longer
follow-up was included in order to avoid duplication.
Thus, each cohort is only included once in each respective meta-analysis. In addition to the single outcomes,
we investigated composite outcomes, such as total CVD,
including CVD mortality, CVD incidence, CHD incidence, MI, and stroke, as well as total diabetes, including
T2D incidence and mortality.
Relevant study characteristics were extracted (Additional file 2: Table S2).

Neuenschwander et al. BMC Medicine(2023) 21:404

Risk of bias and certainty of evidence assessment

A risk of bias assessment for each study was conductedusing the Risk of Bias in Non-randomized Studies ofInterventions (ROBINS-I) tool [17]. It includes sevendomains of bias due to (1) confounding, (2) selection ofparticipants, (3) exposure assessment, (4) misclassification of exposure during follow-up, (5) missing data, (6)measurement of the outcome, and (7) selective reporting of the results (Additional file 2: Table S3).
The certainty of evidence for each association was
evaluated using the updated Grading of Recommendations, Assessment, Development, and Evaluations(GRADE) approach [18]. In this updated approach, theinitial certainty of evidence level is “high” for observational studies. However, the certainty of evidence isdowngraded (up to three levels) unless the study design
reduces confounding, selection, and information bias,
as evaluated by ROBINS-I. Additionally, indication
for inconsistency (as measured by the similarity of thepoint estimates, overlap of 95% confidence intervals,
and statistical tests, such as I2), indirectness (e.g., substantial differences in population or exposure), imprecision (wide 95% confidence interval and/or small
number of events), and publication bias can lead to adowngrading, while large effects (SHR < 0.5 or > 2.0)
and a dose–response gradient can lead to an upgrading[18, 19]. High and moderate certainty of evidence meanthat it is very likely or probable that the true effect lies
close to the estimated effect. Our confidence in the
result is limited, if the certainty of evidence is rated as
low or very low [19].
Data analysis

For each substitution meta-analysis, we calculated summary hazard ratios (SHR) and 95% confidence intervals(95% CIs) using a random-effects model by DerSimonianand Laird, taking into account both within- and betweenstudy variability [20, 21].
To ensure comparability of the results, we converted
hazard ratios (HRs) and 95% CIs for standardized foodportions, as previously applied, for the substituted foods[7–9]. To ensure that the adaption of the HRs and 95%
CIs was not only correct for the substituted food but also
for the replacement, we recalculated the portion size ofthe substitute according to the conversion of the replaced
food. For example, if a study substituted 100 g/day of redmeat by 30 g nuts/day, we calculated the HR and 95% CI
for a substituted portion of 50 g/day of red meat by 15 gof nuts.
Moreover, we calculated I2 and tau2 (τ2) as measures ofinconsistency and between-study variability, respectively,
as well as 95% prediction intervals (95% PIs), which show

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the range in which the true effect of future studies will lie
with 95% certainty [22, 23].
For some associations, single publications provided
only pooled risk estimates of multiple cohorts and did
not show risk estimates for the single studies separately.
When we did not identify further relevant studies, we
extracted the pooled risk estimates from these publications [11, 14, 24–29]. Furthermore, three studies reported
risk estimates based on changes over time rather than thebaseline consumption or a cumulative average [11, 29,30]. Those risk estimates were not pooled and reported
separately.
We planned to conduct sensitivity analyses, by excluding studies with a high risk of bias as well as leaving out
one study at a time, and subgroup analyses by sex, region,
dietary assessment method, or level of adjustment. However, due to the low number of studies, only the sensitivity analyses excluding one study at a time could be
conducted.
Publication bias and small study effects were assessedusing funnel plots and Egger’s test [31, 32], if at least ten
studies were available [33]. Potential publication bias
was indicated by the asymmetry of the funnel plot and ap-value of < 0.1 for Egger’s test [32].
All statistical analyses were conducted using the
STATA version 14.1.

Results
Of the 1216 studies identified in our search after theremoval of duplicates, 158 fulltext articles were considered for inclusion. Out of these, 126 publications were
excluded, leaving 32 studies to be included in our analyses. A list of the excluded studies with reasons is provided
in Additional file 2: Table S4 [12, 34–157]. Additionally,
five relevant studies were identified via hand search.
Thus, 37 studies were included in our final meta-analyses (Fig. 1). All identified publications were prospective
cohort studies. No randomized controlled trials (RCTs)
analyzed as observational studies were included.
Twenty-two publications including 12 cohorts wereconducted in the USA [11, 13, 14, 24–29, 138, 154,
158–168], ten publications including seven cohorts in
Europe [30, 156, 157, 169–175], and four studies including three cohorts in Asia [155, 176–178]. One publication
included one US and one European cohort [179]. In allcohorts except for three (using consecutive 24-h recalls[173, 177, 178]), diet was assessed using validated foodfrequency questionnaires. The mean follow-up duration
was 19 years. All cohorts except for five included both
men and women. One cohort (Health Professionals’ Follow-up Study (HPFS)) only included men [11, 13, 24–29,
138, 154, 162, 163, 166] and four cohorts (Nurses’ HealthStudy (NHS), NHS II, Women’s Health Initiative (WHI),

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Fig. 1 Study selection

and Black Women’s Health Study (BWHS)) only women[11, 24–29, 138, 154, 158, 159, 161–166, 179] (Additional
file 2: Table S2).
All publications were judged as moderate risk of bias,
except for one [169] (Additional file 3: Fig. S1), which
was judged as serious risk of bias due to insufficientadjustment of confounders (Additional file 3: Fig. S2).
No study was judged as low risk of bias due to the possibility of residual confounding in observational studies
and the possibility of measurement error in the dietaryassessment.
Total CVD

The meta-analyses on total CVD are shown in Fig. 2 andAdditional file 3: Fig. S3.
We observed an association with a lower incidence of
total CVD for the substitution of processed meat (50 g/day) with nuts (28–50 g/day) [SHR (95% CI): 0.73 (0.59,0.91), I2 = 68%, ncohorts = 8], legumes (50 g/day) [SHR (95%
CI): 0.77 (0.68, 0.87) I2 = 0%, ncohorts = 8], or whole grains(30 g/day) [SHR (95% CI): 0.64 (0.54,0.75), I2 = 38%, ncohorts = 7]; one egg/day with nuts (25–28 g/day) [SHR
(95% CI): 0.83 (0.78, 0.89), I2 = 54%, ncohorts = 8]; and butter (5 g/day) with olive oil (5 g/day) [SHR (95% CI): 0.96
(0.95, 0.98), I2 = 0%, ncohorts = 3] with moderate certaintyof evidence. There was also an indication that replacing
red meat with nuts, unprocessed red meat with nuts orlegumes, poultry with nuts, and eggs with legumes was
associated with a lower risk of total CVD; however, thecertainty of evidence for these associations was low.
There were no clear associations for the other meta-analyses, and the certainty of evidence was rated as low andvery low (Additional file 2: Table S5).
Single CVD outcomes

Figure 3 and Additional file 3: Figs. S4 and S5 show thefindings on CVD mortality and CHD incidence.
Regarding CVD mortality, there was moderate certainty of evidence that replacing one egg/day with nuts
(25 g/day) was associated with lower CVD mortality

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Fig. 2 Substitution meta-analyses replacing animal-based foods with plant-based foods regarding total CVD (including incidence of CVD, CHD, MI,
and CVD mortality). Portion sizes: red meat/(un)processed meat/poultry/fish/dairy: 50 g/day; eggs: 1 egg/day; nuts: 10–50 g/day; legumes: 13–50 g/day; whole grains: 10–30 g/day; butter: 5 g/day; olive oil: 5 g/day; and margarine: 5 g/day[SHR (95% CI): 0.84 (0.79, 0.90), I2 = 61%, ncohorts = 2],
as did replacing butter (5 g/day) with olive oil (5 g/day)[SHR (95% CI): 0.96 (0.94, 0.98), I2 = 0%, ncohorts = 3]. The
other associations were rated as low and very low certainty of evidence (Additional file 2: Table S6).
There was moderate certainty of evidence that substituting processed meat (50 g/day) with nuts (28 g/day)
was associated with a lower CHD incidence [SHR (95%
CI): 0.87 (0.80, 0.95), I2 = 4%, ncohorts = 2] and that replacing poultry (50 g/day) with nuts (11–14 g/day), as well as
fish/seafood (50 g/day) with nuts (7 g/day) or legumes(12.5 g/day), was not associated with CHD incidence.
There was an indication that replacing red meat withnuts or legumes, as well as processed meat with legumes,
was associated with a lower CHD risk, but the certaintyof evidence for these associations was low (Additional
file 2: Table S7).
Type 2 diabetes

The meta-analyses regarding total T2D (incidence andmortality combined) and T2D incidence are shown in
Fig. 4 and Additional file 3: Figs. S6 and S7.

There was moderate certainty of evidence for a lower riskof total T2D associated with replacing butter (5 g/day) witholive oil (5 g/day) [SHR (95% CI): 0.94 (0.91, 0.98), I2 = 12%,
ncohorts = 3] (Additional file 2: Table S8). Moreover, weobserved an association with a lower T2D incidence withmoderate certainty of evidence when substituting red meat
(50 g/day) with nuts (10 g/day) [SHR (95% CI): 0.92 (0.90,0.94), I2 = 25%, ncohorts = 6] or whole grains/cereals (11–30 g/day) [SHR (95% CI): 0.90 (0.84, 0.96), I2 = 93%, ncohorts = 6],
as well as processed meat (50 g/day) with nuts (10–28 g/day) [SHR (95% CI): 0.78 (0.69, 0.88), I2 = 88%, ncohorts = 6]
and poultry (50 g/day) with whole grains (30 g/day) [SHR
(95% CI): 0.87 (0.84, 0.91), I2 = 0%, ncohorts = 2]. Furthermore,
replacing one egg/day with nuts (10 g/day) or whole grains(30 g/day) was also inversely associated with T2D incidence[SHR (95% CI): 0.82 (0.79, 0.86), I2 = 0%, ncohorts = 2, or 0.79
(0.76, 0.83), I2 = 4%, ncohorts = 2]. The remaining associations
were rated as low or very low (Additional file 2: Table S9).
All‑cause mortality

The meta-analyses regarding all-cause mortality are
shown in Fig. 5 and Additional file 3: Fig. S8.

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Fig. 3 Substitution meta-analyses replacing animal-based foods with plant-based foods regarding single CVD outcomes. Portion sizes: red meat/
processed meat/poultry/fish and seafood: 50 g/day; eggs: 1 egg/day; nuts: 10–28 g/day; legumes: 12.5–50 g/day; whole grains/cereals: 15–30 g/day; butter: 5 g/day; and olive oil: 5 g/day

We found moderate certainty of evidence for an association with a lower risk of all-cause mortality, whenreplacing red meat (50 g/day) with nuts (10–50 g/day)or whole grains (11–15 g/day) [SHR (95% CI): 0.93
(0.91, 0.95), I2 = 0%, ncohorts = 9, or 0.96 (0.95, 0.98),
I2 = 34%, ncohorts = 3], processed meat (50 g/day) withnuts (28–50 g/day) or legumes (50 g/day) [SHR (95%
CI): 0.79 (0.71, 0.88), I2 = 48%, ncohorts = 9 or 0.91 (0.85,0.98), I2 = 9%, ncohorts = 9], and unprocessed red meat
(50 g/day) with nuts (10–16 g/day) [SHR (95% CI): 0.93
(0.92, 0.94), I2 = 0%, ncohorts = 8]. Furthermore, replacing dairy (50 g/day) with nuts/legumes (7–50 g/day)or nuts only (7–50 g/day) [SHR (95% CI): 0.94 (0.92,0.97), I2 = 65%, ncohorts = 6, or 0.94 (0.91, 0.97), I2 = 67%,
ncohorts = 3], one egg/day with nuts (25–28 g/day) or legumes (25–50 g/day) [SHR (95% CI): 0.85 (0.82, 0.89),
I2 = 67%, ncohorts = 8, or 0.90 (0.89, 0.91), I2 = 0%, ncohorts = 7], and butter (5 g/day) with olive oil (5 g/day)[SHR (95% CI): 0.94 (0.92, 0.97), I2 = 87%, ncohorts = 3]
was also associated with a lower risk of all-cause mortality. The other associations were rated as low or very
low certainty of evidence (Additional file 2: Table S10).

Single study findings

A pooled analysis (based on two cohorts, only the pooled
effect estimate was reported, and new meta-analysis was
not possible) showed that replacing processed meat,
yogurt, cheese, eggs, and butter with avocado was associated with a lower incidence of CVD (Additional file 3:
Fig. S9) and CHD (Additional file 3: Fig. S10) [25]. Furthermore, the substitution of poultry, fish, and eggs
with peanuts and peanut butter or with whole grain was
associated with a lower T2D incidence (pooled analysis,
based on three cohorts) [28]. The estimates on changeover time were pooled from three cohorts, and the findings showed an inverse association with T2D when substituting red meat, as well as unprocessed and processed
red meat with nuts and legumes (Additional file 3: Fig.
S11) [11]. In addition, replacing processed meat, unprocessed red meat, dairy, and eggs with whole grains was
associated with a lower risk of all-cause mortality (pooled
analysis, based on six cohorts) [14, 24]. Furthermore, theestimates on change over time were pooled from twocohorts and showed that replacing red meat, processed
meat, and unprocessed meat with nuts or whole grains

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Fig. 4 Substitution meta-analyses replacing animal-based foods with plant-based foods regarding type 2 diabetes incidence and total diabetes(type 2 diabetes incidence and diabetes mortality). Portion sizes: red and processed meat/red meat/processed meat: 50 g/day; nuts: 10–28 g/day;legumes: 18–50 g/day; whole grains/cereals: 11–30 g/day; butter: 5 g/day; and olive oil: 5 g/day

was associated with a lower risk of all-cause mortality(Additional file 3: Fig. S12) [29].
Furthermore, several substitution analyses were only conducted in one single cohort. In these studies, there was an
association with a lower risk of CVD mortality when substituting butter with canola oil (Additional file 3: Fig. S13)[167]. In addition, replacing butter with margarine wasassociated with an increased incidence of CVD (Additional
file 3: Fig. S13) and CHD (Additional file 3: Fig. S14) [161].
Furthermore, there was an inverse association with T2D
incidence for the substitution of milk with coffee or tea[172], as well as of sweetened milk-beverages with drinking
water [173]. The study estimates based on change over time
showed an inverse association with T2D for the replacement of red meat with whole grains or refined grains (Additional file 3: Fig. S15). Moreover, there were associations
with a lower risk of all-cause mortality when substituting
butter with margarine, corn oil, and canola oil [167], as wellas animal cooking oil with plant oils other than peanut, soybean, canola, or salad oil (Additional file 3: Fig. S16) [177].
Heterogeneity, subgroup, sensitivity and bias analysis

I2 is reported in Figs. 2, 3, 4 and 5 and, together withtau2 and the 95% PIs, in the individual forest plotsin Additional file 3: Figs. S3-S8. The only 95% PI that
excluded the null value was for the association between
the substitution of red meat with nuts on T2D incidence(95% PI: 0.85, 0.98), indicating that the true effects offuture studies are expected to point in the same direction. All the other 95% PIs were wider and indicated thatthe true effect of future studies could be null or point in
the opposite direction.
Since only few publications were available for the metaanalyses, which partly provided pooled risk estimates ofmultiple cohorts, but no single estimates [11, 14, 24, 26,
28, 29, 154, 159, 162, 163, 166], no subgroup analyses orassessment of publication bias was possible.
In sensitivity analyses leaving out one study at a time,
the results remained mainly robust. The direction of theassociations always remained the same; however, for several associations, the risk estimate changed in magnitude
and precision. For the substitution of red and processedmeat with nuts regarding CVD mortality, the SHR (95%
CI) changed from 0.78 (0.53, 1.14) to 0.62 (0.44, 0.88)
when excluding the study by Sun et al. [165] (WHI).
Moreover, the association between the replacement ofeggs with legumes regarding T2D incidence changed
from an SHR (95% CI) of 0.86 (0.72, 1.03) to 0.63 (0.22,

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Fig. 5 Substitution meta-analyses replacing animal-based foods with plant-based foods regarding all-cause mortality. Portion sizes: red meat/(un)processed meat/poultry/fish/dairy: 50 g/day; eggs: 1 egg/day; nuts: 10–50 g/day; legumes: 13–50 g/day; whole grains: 11–30 g/day; butter: 5 g/day; and olive oil: 5 g/day1.84) and 0.60 (0.24, 1.55) when leaving out the study
by Li et al. [179] (WHI) and Li et al. [179] (UK Biobank
study (UKB)), respectively (data not shown).

Discussion
This is the first systematic review and meta-analysis thatsummarized the associations between the substitution ofanimal-based with plant-based foods with a wide rangeof cardiometabolic outcomes, such as CVD mortality;
incidence of CVD, CHD, and T2D; diabetes mortality,
and all-cause mortality, and evaluated the risk of bias andcertainty of evidence of the meta-findings. Our results
indicate that replacing animal-based with plant-based
foods is beneficially associated with cardiometabolic
health. The substitution of red and processed meat withnuts, legumes, and whole grains reduced the risk of total
CVD, CHD, and T2D and all-cause mortality with moderate certainty of evidence. We also observed moderate
certainty of evidence for the association between thereplacement of eggs with nuts and a lower incidence of
total CVD and all-cause mortality. Additionally, replacing
butter with olive oil lowered the incidence of total CVD,
CVD mortality, total diabetes, and all-cause mortality
with moderate certainty of evidence.

Furthermore, we found moderate certainty of evidence
for an inverse association between the substitution ofpoultry with whole grains and T2D incidence and for the
replacement of dairy products with nuts and legumes
regarding all-cause mortality. For the other associations
regarding the replacement of dairy products, poultry, or
fish/seafood with plant-based foods, the certainty of evidence was low. However, our results indicated an inverse
association for the substitution of poultry with nuts orlegumes in relation to T2D incidence but no clear association for replacing dairy products, fish/seafood, orpoultry with nuts or legumes regarding total CVD. In
addition, no association was observed for the substitution of fish/seafood or poultry with nuts or legumes
regarding all-cause mortality.
Our results are in agreement with a previous systematic
review and meta-analysis investigating the replacement of
red meat with other protein sources on CHD and all-cause
mortality [15]. The authors observed a lower risk of CHD,
when replacing red meat, processed red meat, and unprocessed red meat with nuts or legumes, and a lower risk of
all-cause mortality when substituting red meat with nuts
and processed red meat with nuts or legumes [15]. The
certainty of evidence was assessed using NutriGrade [180]Neuenschwander et al. BMC Medicine(2023) 21:404

and was also rated as moderate for these associations [15].
However, they did not investigate further cardiometabolic endpoints, such as CVD mortality, CVD incidence,or T2D. Previous meta-analyses also support our findingsthat higher meat intake was associated with a risk of T2D[8], CHD, and stroke [7], as well as all-cause mortality [9].
This also applies to the associations between olive oil and
CVD, T2D, and all-cause mortality [181, 182]. In contrast, a higher intake of eggs and butter was not associatedwith these outcomes in previous studies [7–9]. However,these studies included no substitution analyses. This suggests that not only the amount of these foods consumed isimportant but that it also plays a role what foods they are
replaced with. Furthermore, our results indicate that the
replacement of dairy products with nuts and legumes is
associated with a lower risk of CVD or all-cause mortality.
However, dairy includes a wide range of different products(e.g., milk, yogurt, cheese) with different associations withcardiometabolic outcomes. For example, contrary to our
results, a higher intake of yogurt was inversely associatedwith risk for all-cause and CVD mortality [183]. Thus,
associations for the replacement of different subtypes ofdairy products with plant-based foods might differ, andmore research is needed on this aspect. A network metaanalysis of randomized controlled trials comparing different food groups and their effect on intermediate diseases
markers, such as blood lipids, blood pressure, and glycaemic control parameters, also found that plant-based
foods, such as nuts, legumes, and whole grains, were mostbeneficial for all outcomes combined [184]. Given theseresults, stronger associations for the replacement of animal-based foods with legumes might have been expected.
However, the network meta-analysis focussed on intermediate endpoints and rather reflects short-term effects
due to the inclusion of RCTs with shorter follow-up times(mainly around 8–12 weeks). Therefore, long-term effects
on hard endpoints might differ. However, the certainty ofevidence for the majority of the findings on legumes was
low or very low, limiting our confidence in the results.
Thus, future studies might provide more insight into theseassociations. Moreover, in line with our results, a network
meta-analysis comparing different types of oils and solidfats on blood lipids confirmed a more beneficial effect ofolive oil compared to butter [185].
There are different mechanisms that might explain theobserved associations. First, it is likely that persons favoring plant-based foods follow a more health-conscious
lifestyle in general. However, the included studies all
adjusted for important lifestyle confounders such as totalenergy intake, physical activity, alcohol intake, and smoking, and the associations persisted. Nevertheless, residual
confounding cannot be ruled out.

Page 9 of 16

Furthermore, processed meat contains saturated fatty
acids, such as stearic and palmitic acid, which potentially
increase the risk of CVD and T2D [108, 116]. Moreover,
red and processed meats contain compounds promoting oxidative stress and chronic low-grade inflammation, such as heme iron, sodium, nitrates, and nitrites
that might be associated with increased risk of CVD,
T2D, and mortality [54, 186]. In contrast, plant-based
foods such as nuts, legumes, and whole grains, as wellas olive oil, contain high amounts of anti-oxidant andanti-inflammatory compounds, including fiber, phytochemicals, vitamins and minerals, and polyphenols thatshow beneficial associations with cardiovascular healthand, in relation to cardiovascular health, obesity [187,188]. Thus, both the reduction of animal-based foods,
especially meat, and the increase of plant-based foods
simultaneously contribute to the observed beneficial
associations regarding cardiometabolic health.
Our findings also highlight the need for future research.
First, some of the results still show some inconsistencies. For example, both the substitution of processed
and unprocessed red meat with legumes were associatedwith a lower risk of total CVD, while the association for
the replacement of red meat with legumes was less clear.
This was mainly due to an imprecise estimate of the latterassociation also resulting in a very low level of the certainty of evidence. Therefore, more studies on this association are needed in order to provide further insights
into the consistency of these associations and to improve
our confidence in the findings. In general, to increase thecertainty of evidence, well-conducted prospective observational studies on the associations with low and very
low certainty of evidence and on the associations only
investigated in single studies are needed. This includes
for example the substitution of dairy products, especially subtypes of dairy products, poultry, or fish/seafood
with plant-based products, or the replacement of animal-based foods with further plant-based foods, such as
fruit and vegetables. These studies should minimize therisk of bias, e.g., by using models with a sufficient levelof adjustment, and use standardized portion sizes (e.g.,
50 g for meat/poultry/fish/seafood, 30 g for whole grains/
legumes, 5 g for butter/olive oil) for both the substituted
food and the replacement in order to improve the comparability between the results and to decrease heterogeneity between the studies. Furthermore, the majority ofthe studies investigated a theoretical substitution. Futurestudies should focus more on an observed change in foodintake, as was done in three recent studies [11, 29, 30].
Moreover, more research is needed on the substitution ofanimal-based products with meat and dairy replacement
foods, which strongly gained popularity in recent years.

Neuenschwander et al. BMC Medicine(2023) 21:404

Strengths and limitations

This is the first systematic review and meta-analysis
focussing on the association between the substitution ofanimal-based with plant-based foods on multiple cardiovascular outcomes, including 37 studies on CVD, CHD,
T2D, related mortality, and all-cause mortality. We followed the recommended procedures (pre-registeredprotocol and reporting guidelines) and applied validated
tools for the risk of bias and certainty of evidence assessments. Moreover, we used standardized portions for thesubstituted foods, which increased the comparability of
the results.
However, our work also has several limitations. First,
only few studies were available for some of the metaanalyses, and these findings should be interpreted withcaution and more studies on this topic are needed.
While some studies provided pooled risk estimates ofmultiple cohorts, no separate estimates for these single
cohorts were available, and thus, subgroup and sensitivity analyses excluding studies with a high risk of bias
or assessment of publication bias could not be conducted. Second, several substitution analyses were only
investigated in one study. Therefore, no meta-analyses
were possible. More prospective studies with substitution analyses for these associations are needed in orderto further investigate this evidence. Third, while we
increased the comparability of the results, using standardized portions for the substituted foods, this was notpossible for the substitute simultaneously. We adapted
the portion sizes of the replacement according to theconversion of the substituted foods. However, given thevarying portion sizes between studies, the portions of
the replacement still varied, leading to the comparisonof different amounts of the replacement. This was considered in the indirectness domain in GRADE, contributing to the downgrading of the certainty of evidence
for the respective associations. Fourth, due to theobservational design of the included studies, unknown
confounding cannot be ruled out. Therefore, no study
could be judged as low risk of bias in ROBINS-I, leading to a downgrade in the GRADE risk of bias domain.
Furthermore, given that the substitution models
adjusted for total energy intake while controlling fordifferent foods in grams per day, a residual energy
substitution was introduced [10]. However, we only
included prospective observational studies thus minimizing the risk of selection or recall bias. Fifth, to date,no study is available regarding meat and dairy replacement products, which have rapidly increased in popularity in recent years. Sixth, dairy products were mostly
treated as one group, and more research is needed on
the substitution of subtypes of dairy products withplant-based foods.

Page 10 of 16

Conclusions
Our findings suggest that a shift in diet from a highconsumption of animal-based foods, especially red andprocessed meat, to plant-based foods (e.g., nuts, legumes, and whole grains) is associated with a lower riskof all-cause mortality, CVD, and T2D. Thus, a change indietary habits towards an increment of plant-based products appears to be important for cardiometabolic health.
However, more research is needed in order to strengthen
the existing evidence and to investigate new associations,especially with a focus on meat and dairy replacement
products.
Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12916-​023-​03093-1.
Additional file 1. Search terms.
Additional file 2: Table S1. Eligibility criteria by the PICOS statement.
Table S2. Study characteristics. Table S3. Description and decision criteria
for each domain in ROBINS-I. Table S4. List of excluded studies. Table S5.
GRADE assessment for the substitution analyses regarding total CVD.
Table S6. GRADE assessment for the substitution analyses regarding
CVD mortality. Table S7. GRADE assessment for the substitution analyses
regarding CHD incidence. Table S8. GRADE assessment for the substitution analyses regarding total diabetes. Table S9. GRADE assessment forthe substitution analyses regarding type 2 diabetes incidence. Table S10.
GRADE assessment for the substitution analyses regarding all-cause
mortality.
Additional file 3: Fig. S1. Risk of bias of each study for each domain andoverall. Fig. S2. Risk of bias of judgements within each bias domain. Fig.
S3. Forest plots for the substitution analyses regarding total CVD. Fig. S4.
Forest plots for the substitution analyses regarding CVD mortality. Fig.
S5. Forest plots for the substitution analyses regarding CHD incidence.
Fig. S6. Forest plot for the substitution analyses regarding total diabetes.
Fig. S7. Forest plots for the substitution analyses regarding incidenceof type 2 diabetes. Fig. S8. Forest plots for the substitution analyses
regarding all-cause mortality. Fig. S9. Forest plot showing the results fromextracted pooled analyses regarding CVD mortality and CVD incidence.
Fig. S10. Forest plot showing the results from extracted pooled analyses
regarding CHD and stroke incidence. Fig. S11. Forest plot showing theresults from extracted pooled analyses regarding diabetes. Fig. S12.
Forest plot showing the results from extracted pooled analyses regarding
all-cause mortality. Fig. S13. Forest plot showing the results from single
cohorts regarding CVD mortality and CVD incidence. Fig. S14. Forest plot
showing the results from single cohorts regarding CHD, MI and stroke
incidence. Fig. S15. Forest plot showing the results from single cohorts
regarding diabetes. Fig. S16. Forest plot showing the results from single
cohorts regarding all-cause mortality.
Authors’ contributions
SS and LS designed the research. KG conducted the literature search. MN, SS,
LS, JE, and EK screened the studies for eligibility. MN, JE, and LS extracted thedata. MN, JS, ES, and EK assessed the risk of bias. MN conducted the metaanalyses. MN and LS assessed the certainty of evidence. MN and SS drafted
the first version of the manuscript. All authors interpreted the data. MN and SS
are guarantors. The corresponding author attests that all listed authors meet
the authorship criteria and that no others meeting the criteria were omitted.
All authors read and approved the final manuscript.
Funding
Open Access funding enabled and organized by Projekt DEAL. This study
received financial support from the Alpro Foundation. The German Diabetes
Center (DDZ) is funded by the German Federal Ministry of Health and theNeuenschwander et al. BMC Medicine(2023) 21:404

Ministry of Innovation, Science, Research and Technology of the State North
Rhine-Westphalia. The funding source has no role in the decisions about thedata collection, analysis, interpretation of the data, preparation, review, or
approval of the manuscript.
Availability of data and materials
This manuscript makes use of publicly available data from published studies;
therefore, no original data are available for sharing.

Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1
Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz
Center for Diabetes Research at Heinrich Heine University Düsseldorf, Auf’m
Hennekamp 65, 40225 Düsseldorf, Germany. 2 German Center for Diabetes
Research (DZD), Partner Düsseldorf, Düsseldorf, Germany. 3 Institute for Evidence in Medicine, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Received: 25 April 2023 Accepted: 25 September 2023

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