Plant versus animal based diets and insulin resistance, prediabetes and type 2 diabetes: the Rotterdam Study
European Journal of Epidemiology
Plant versus animal based diets and insulin resistance, prediabetes and type 2 diabetes: the Rotterdam Study
Zhangling Chen 0 1
Maria Geertruida Zuurmond 0 1
Niels van der Schaft 0 1
Jana Nano 0 1
Hanneke Anna Hendrikje Wijnhoven 0 1
Mohammad Arfan Ikram 0 1
Oscar Horacio Franco 0 1
Trudy Voortman 0 1
0 Department of Health Sciences, Faculty of Earth and Life Sciences, Vrije Universiteit , Amsterdam , The Netherlands
1 Department of Epidemiology, Erasmus University Medical Center , Office Na-2903, PO Box 2040, 3000 CA Rotterdam , The Netherlands
Vegan or vegetarian diets have been suggested to reduce type 2 diabetes (T2D) risk. However, not much is known on whether variation in the degree of having a plant-based versus animal-based diet may be beneficial for prevention of T2D. We aimed to investigate whether level of adherence to a diet high in plant-based foods and low in animal-based foods is associated with insulin resistance, prediabetes, and T2D. Our analysis included 6798 participants (62.7 ± 7.8 years) from the Rotterdam Study (RS), a prospective population-based cohort in the Netherlands. Dietary intake data were collected with food-frequency questionnaires at baseline of three sub-cohorts of RS (RS-I-1: 1989-1993, RS-II-1: 2000-2001, RSIII-1: 2006-2008). We constructed a continuous plant-based dietary index (range 0-92) assessing adherence to a plantbased versus animal-based diet. Insulin resistance at baseline and follow-up was assessed using homeostasis model assessment of insulin resistance (HOMA-IR). Prediabetes and T2D were collected from general practitioners' records, pharmacies' databases, and follow-up examinations in our research center until 2012. We used multivariable linear mixed models to examine association of the index with longitudinal HOMA-IR, and multivariable Cox proportional-hazards regression models to examine associations of the index with risk of prediabetes and T2D. During median 5.7, and 7.3 years of follow-up, we documented 928 prediabetes cases and 642 T2D cases. After adjusting for sociodemographic and lifestyle factors, a higher score on the plant-based dietary index was associated with lower insulin resistance (per 10 units higher score: b = -0.09; 95% CI: - 0.10; - 0.08), lower prediabetes risk (HR = 0.89; 95% CI: 0.81; 0.98), and lower T2D risk [HR = 0.82 (0.73; 0.92)]. After additional adjustment for BMI, associations attenuated and remained statistically significant for longitudinal insulin resistance [b = -0.05 (- 0.06; - 0.04)] and T2D risk [HR = 0.87 (0.79; 0.99)], but no longer for prediabetes risk [HR = 0.93 (0.85; 1.03)]. In conclusion, a more plant-based and less animal-based diet may lower risk of insulin resistance, prediabetes and T2D. These findings strengthen recent dietary recommendations to adopt a more plant-based diet. Clinical Trial Registry number and website NTR6831, http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=6831.
Cohort study; Epidemiology; Plant-based diet; Insulin resistance; Prediabetes; Type 2 diabetes; Abbreviations; T2D; Type 2 diabetes
Zhangling Chen and Maria Geertruida Zuurmond are shared
Extended author information available on the last page of the article
HOMA-IR BMI CI HR
Homeostatic model assessment of insulin resistance Body mass index Confidence interval
Diet is an important modifiable lifestyle determinant in the
development of type 2 diabetes (T2D) [
]. Among these
dietary determinants, several plant-based foods such as root
vegetables, green leafy vegetables, whole grains, nuts and
peanut butter, have been associated with a lower risk of
]. By contrast, several animal-based foods,
including red meat, processed meat, and daily consumption
of eggs have been associated with an increased risk of T2D
4, 6, 7
Although multiple food groups seem to influence the
risk of T2D, humans generally do not consume single food
items or food groups, and the role of diet in health may be
better described by overall dietary patterns [
studies have observed that vegan or vegetarian diets are
associated with improved glycemic control [
] and lower
T2D risk [
]. However, these previous studies
dichotomously classified participants, and only defined diets as
vegetarian or vegan versus non-vegetarian diets. A
dichotomous classification of vegans or vegetarians versus
their non-vegetarian counterparts might not be an optimal
approach in understanding the effect of a plant-based diet
in Western countries, because it does not reflect dietary
patterns of a large proportion of the population. For public
health advice, it is interesting to know if a more
plantbased and less animal-based diet may also influence insulin
resistance and risk of prediabetes and T2D beyond strict
adherence to a vegetarian or vegan diet. To our knowledge,
only one previous study, a large prospective cohort study in
the US, examined associations between variations in the
degree of adherence to plant-based versus animal-based
diets with T2D risk and observed that a more plant-based
diet was associated with a lower T2D risk [
]. Studies on
the associations of such plant-based dietary patterns with
T2D risk in other populations are needed. In addition, the
association of such plant-based dietary patterns with
intermediate risk factors for T2D, such as insulin resistance
and prediabetes remains unknown.
Therefore, we aimed to investigate whether adherence to
a more plant-based, and less animal-based diet is associated
with insulin resistance, and risk of prediabetes and T2D in
a Dutch middle-aged and older general population.
This study was carried out within three sub-cohorts of the
Rotterdam Study (RS), a prospective cohort study of adult
aged 45 years and older living in the well-defined district
of Ommoord in Rotterdam, the Netherlands. A detailed
description of the Rotterdam Study methodology is
described elsewhere [
]. Briefly, recruitment of
participants for the first sub-cohort (RS-I) started in the period of
1989–1993 among inhabitants aged C 55 years (n = 7983).
In 2000–2001, the study was extended with a second
subcohort (RS-II) of new individuals (n = 3011) who had
become 55 years of age or moved into the study area after
1990. In 2006–2008, a third sub-cohort (RS-III) was
recruited with new individuals aged 45 years and older
(n = 3932). By the end of 2008, the overall study population
contained 14,926 participants. Upon entering the study,
participants underwent home interviews and a series of
examinations in our research center every 3–5 years.
The Rotterdam Study has been approved by the
institutional review board (Medical Ethics Committee) of
Erasmus Medical Center and by the review board of The
Netherlands Ministry of Health, Welfare and Sports. The
approval has been renewed every 5 years. All participants
gave informed consent.
Population for current analyses
For the current study, we used data from all three
subcohorts (Fig. 1). Of the 14,926 participants, we excluded
those without valid dietary data (no dietary data (n = 5141)
or unreliable dietary intake according to a trained
nutritionist or an estimated energy intake of \ 500 or
[ 5000 kcal/day (n = 84) [
]) at baseline (RS-I-1:
1989–1993, RS-II-1: 2000–2001, RS-III-1: 2006–2008),
and those without diabetes information or with prevalent
T2D at baseline (n = 2903), leaving 6798 participants
included as main population for analysis.
From this group of 6798 participants, 6514 participants
had data on HOMA-IR before onset of T2D and were
included in the longitudinal HOMA-IR analyses. For the
analyses on prediabetes risk, we excluded those with
prevalent prediabetes at baseline (n = 1005) or without
follow-up of prediabetes (n = 25), leaving 5768
participants. In the analyses assessing risk of T2D, we excluded
participants without follow-up of T2D (n = 28), leaving
6770 participants. The flow-diagram of the included
participants is presented in Fig. 1.
Dietary intake was assessed at baseline in all three
subcohorts using semi-quantitative food-frequency
questionnaires (FFQ) as described in more detail elsewhere [
We used an FFQ with 170 food items to assess dietary
intake at baseline of RS-I (1989–1993) and RS-II
]; and at baseline of RS-III (2006–2008)
we used an FFQ with 389 food items [
]. The 170-item
FFQ was validated in a subsample of the Rotterdam Study
(n = 80) against fifteen 24-h food records and four 24 h
urinary urea excretion samples [
]; and the 389-item FFQ
was previously validated in other Dutch population against
measurement of biomarkers, against a 9-day dietary record,
and against a 4 week dietary history [
]. In general, the
validation studies demonstrated that the FFQs were able to
adequately rank participants according to their intake [
Food intake data were converted to energy and nutrient
intake based on Dutch Food Composition tables (NEVO).
Plant-based dietary index
We constructed an overall plant-based dietary index, which
was a modified version of two previously created indices
]. More specifically, our index is similar to the
‘‘provegetarian food pattern’’ of Mart´ınez-Gonza´les et al.
 and to the ‘‘overall plant-based diet index’’ of Satija
et al. [
], but was adapted to include slightly different
types and numbers of food categories.
First, the food items as measured by the FFQs were
divided into 23 food categories (Supplemental Table 1), on
the basis of the main food groups in the Dutch diet and the
Dutch food-based dietary guidelines [
]. Twelve of
the categories were plant-based and eleven were
animalbased. Food items that were not clearly animal-based or
plant-based, such as pizza, as well dietary supplements,
were not included in the food categories for the index.
Dietary intake for each of the 23 food categories (g/day)
was calculated for each participant. Subsequently, for each
category, the intake was divided into cohort-specific
quintiles. Each quintile was assigned a value between 0 and
4. For the twelve plant-based food categories, consumption
within the highest quintile was scored a 4, consumption
within the second highest quintile was scored a 3, and so
on, ending with consumption within the lowest quintile
receiving a score of 0. The eleven animal-based food
categories were scored reversely: consumption within the
highest quintile was scored a 0 consumption within the
second highest quintile was scored a 1, ending with
consumption within the lowest quintile receiving a score of 4.
Furthermore, we ensured that all participants with
nullconsumption were given the score belonging to the lowest
quintile by re-scoring when necessary.
Finally, these category quintile-scores were added up for
per participant to create their overall score on the
plantbased dietary index. The resulting index yielded a score for
each participant that measured adherence to a plant-based
versus animal-based diet on a continuous scale, with a
lowest possible score of 0 (low adherence to a plant-based
diet) and a highest possible score of 92 (high adherence:
high plant-based and low animal-based). Information on
intake of each food category across quintiles of scores on
the plant-based dietary index is shown in Supplemental
Assessment of insulin resistance
Fasting blood samples were collected at RS-I (RS-I-3:
1997–1999, RS-I-5: 2009–2010), RS-II (RS-II-1:
2000–2001, RS-II-3: 2010–2011), and RS-III (RS-III-1:
2006–2008, RS-III-2: 2011–2012). Glucose levels were
examined with the glucose hexokinase method. Serum
insulin was measured by electro chemiluminescence
immunoassay technology. Insulin resistance was calculated
using the homeostasis model assessment of insulin
resistance (HOMA-IR). The following formula was used:
fasting insulin (mU/L) 9 fasting glucose (mmol/L)/22.5.
Assessment of prediabetes and type 2 diabetes
Information on prediabetes and T2D was collected from
general practitioners’ records, pharmacies’ databases, and
follow-up examinations in our research center. Data of
prediabetes and T2D in our analyses were collected until
January 1, 2012. Prediabetes and T2D were identified
according to WHO criteria: prediabetes was defined as a
fasting blood glucose concentration of [ 6.0 and
\ 7.0 mmol/L, or a non-fasting blood glucose
concentration of [ 7.7 mmol/L and \ 11.1 mmol/L; T2D was
defined as a fasting blood glucose concentration of
C 7.0 mmol/L, a non-fasting blood glucose concentration
of C 11.1 mmol/L (when fasting samples were
unavailable), or the use of blood glucose-lowering drugs or dietary
treatment and registration of the diagnosis diabetes. All
possible cases of prediabetes and T2D were formally
judged by two independently working study physicians or,
in case of disagreement, by an endocrinologist [
Assessment of covariates
Information on age, sex, smoking status, educational level,
medication use, food supplement use, and family history of
diabetes, was obtained from questionnaires at baseline.
Information on physical activity was obtained using the
adapted version of the Zutphen Physical Activity
Questionnaire at RS-I-3 and RS-II-1, and using the LASA
Physical Activity Questionnaire at RS-III-1. Physical
activities were weighted according to intensity with
Metabolic Equivalent of Task (MET), from the
Compendium of Physical Activities version 2011. To account
for differences between the two questionnaires,
questionnaire-specific z-scores of MET-hours per week were
calculated. At our research center at baseline, body weight
was measured using a digital scale and body height was
measured using a stadiometer, while participants wore light
clothing and no shoes, and BMI was calculated (kg/m2).
Information on hypertension, hypercholesterolemia,
coronary heart disease (CHD), cancers, and stroke was obtained
from general practitioners, pharmacies’ databases,
Nationwide Medical Register, or follow-up examinations
in our research center.
To obtain a normal distribution for HOMA-IR, we applied
a natural-log transformation. Non-linearity of associations
of score on the plant-based dietary index with all outcomes
were explored using natural cubic splines (degrees of
freedom = 3). As no indications for non-linear associations
for the main models were found, all primary analyses were
performed using models assuming linearity. We examined
the association between score on the plant-based dietary
index with longitudinal HOMA-IR using linear mixed
models, with a random-effects structure including a
random intercept and slope (for time of repeated
measurements of HOMA-IR). We examined the association
between score on the plant-based dietary index and risk of
prediabetes and risk of T2D using Cox
proportional-hazards regressions. Hazard ratios (HRs) and regression
coefficients (bs) were presented per 10 units higher score
on the plant-based dietary index, along with the
corresponding 95% confidence intervals (CIs). All analyses
were performed in participants of the three sub-cohorts
combined and in the three sub-cohorts separately.
All analyses were adjusted for energy intake, age, sex
and RS sub-cohort in model 1, and for the analyses of
longitudinal HOMA-IR we additionally adjusted for the
time of repeated measurements of HOMA-IR. In model 2,
we additionally adjusted for smoking status, educational
level, physical activity, food supplement use, and family
history of diabetes. Baseline BMI was added to model 3 to
examine its potential mediating effect.
We examined effect modification by including
interactions of the plant-based index with age, sex, or BMI for all
outcomes in model 2.
Several sensitivity analyses were performed based on
model 2. First, to check if the associations were driven by
any specific components of the plant-based dietary index,
we repeated our main analyses by excluding each one of
the 23 components from the plant-based dietary index one
by one at a time, and additionally adjusting for the
excluded component. Second, to check if the associations were
mainly driven by plant-based beverages combined, we
examined the associations by excluding all plant-based
beverages combined (category ‘‘coffee and tea’’, category
‘‘alcoholic beverages’’, and category ‘‘sugary beverages’’)
from the plant-based dietary index at a time, and
additionally adjusting for them. Third, we examined the
associations by excluding less healthy plant-based foods
combined (category ‘‘sweets’’, category ‘‘sugary
beverages’’, category ‘‘potatoes’’, and category ‘‘refined grains’’)
from the plant-based dietary index at a time, and
additionally adjusting for them. To further examine whether
these less healthy plant foods contributed to the association
of the plant-based dietary index; we created a less healthy
plant foods score, for which, positive scores were given to
these four types of less healthy plant-based food groups;
and reverse scores were given to healthy plant food groups
and animal food groups [
]. Fourth, to examine if
potential associations of the plant-based dietary score with
outcomes were independent of overall quality of the diet
based on adherence to dietary guidelines, we examined the
correlation between the plant-based dietary score and the
dietary guidelines score; and we repeated analyses with
additional adjustment for dietary guidelines score. Fifth,
we additionally adjusted for hypertension and
hypercholesterolemia. Sixth, we excluded the participants with
chronic diseases at baseline, such as participants with
CHD, cancers, or stroke, to exclude the possibility of a
significant change of diet and life style at follow-up. Last,
we excluded the participants who developed prediabetes
and T2D in the first 2 years of follow-up in the analyses for
risk of prediabetes and T2D, respectively.
Missing values on covariates (ranging from 0.3 to 3.9%)
were accounted for using multiple imputations (n = 10
imputations). We used SPSS version 21 (IBM Corp.,
Armonk, NY, USA) and R version 3.1.2 (R Foundation for
Statistical Computing, Vienna, Austria) to perform these
Baseline characteristics of the study population are shown
in Table 1. In our population of 6798 participants, baseline
scores on the plant-based dietary index (with a theoretical
range from 0 to 92) ranged from 24 to 75, with a
mean ± SD score of 49.3 ± 7.1. Mean age of the study
population was 62.0 ± 7.8 years and 41.3% of the
participants were male. Mean BMI was 26.6 ± 3.9 kg/m2.
Characteristics were similar before and after multiple
imputation (Supplemental Table 3). Supplemental Table 4
shows baseline characteristics of the participants not
included in our analyses.
Plant-based dietary index and insulin resistance
After adjustment for confounders in model 2, a higher
score on the plant-based dietary index was associated with
lower longitudinal HOMA-IR [per 10 units higher score on
the index: b = -0.09; (95% CI: - 0.10; - 0.08)]
(Table 2). Adding BMI to the model (model 3), attenuated
the association, but it remained statistically significant
[b = -0.05; (- 0.06; - 0.04)].
Plant-based dietary index and incidence of prediabetes
During 43,773 person-years of follow-up amongst 5768
participants (median follow-up 5.7 years), 928 participants
developed prediabetes. After adjustment for confounders in
model 2 (Table 2), a higher score on the plant-based
dietary index was associated with a lower incidence of
prediabetes [per 10 units higher score on the index: HR =
0.89; (95% CI 0.81; 0.98)]. After additional adjustment for
BMI (model 3) the association was attenuated, and no
longer statistically significant [HR = 0.93 (0.85; 1.03)].
Plant-based dietary index and incidence of type
During 54,024 person-years of follow-up amongst 6770
participants (median follow-up 7.3 years), 642 participants
developed T2D. In model 2, a higher score on the
plantbased dietary index was associated with a lower incidence
of T2D [per 10 units higher score on the index: HR = 0.82;
(95% CI 0.73; 0.92)] (Table 2). Additional adjustment for
BMI (model 3) attenuated this association, but it was still
statistically significant [HR = 0.87 (0.79; 0.99)].
The associations between the plant-based dietary index
with longitudinal insulin resistance, and risk of prediabetes
Table 1 Baseline characteristics
of study participants (n = 6798)
Mean (SD) or %
Plant-based dietary index: a higher score indicates a higher adherence to a plant-based diet (theoretical
range from 0 to 92). Values shown are based on pooled results of imputed data
MET metabolic equivalent of task, SD standard deviation
aValues shown for MET-hours are un-imputed; imputation was performed on z-scores of physical activity
bVariables expressed as median (IQR) because of their skewed distributions
Effect estimates are regression coefficients (b) for ln HOMA-IR or hazard ratios (HRs) for incidence of prediabetes or type 2 diabetes with their
95%-confidence intervals (95% CIs), per 10 units higher score on the plant-based dietary index. Estimates are based on pooled results of imputed
Model 1 is adjusted for energy intake (kcal), sex (male or female), age (years) and RS sub-cohort (RS-I, -II, or -III); and only for the HOMA
analyses additionally for the time measurements of longitudinal HOMA
Model 2 is additionally adjusted for education (primary, lower/intermediate, intermediate, or higher), smoking status (never, ever, current);
family history of diabetes (yes, no, or unknown); physical activity (z-score of MET-hours/week); and food supplement use (yes or no)
Model 3 is additionally adjusted for BMI
BMI body mass index, CI confidence interval, HR hazard ratio, MET metabolic equivalent of task, RS Rotterdam-Study
*p \ 0.05; **p \ 0.01; ***p \ 0.001
and T2D were similar in three sub-cohorts (Supplemental
Tables 5–7). Associations did not differ by age, sex or
baseline BMI (p-values for all interaction terms were
The exclusion of each one of 23 foods from the index one
by one at a time did not substantially change the estimates
(Supplemental Table 8). Excluding all plant-based
beverages combined at a time (coffee and tea, alcoholic
beverages and sugary beverages) did not substantially change the
estimates [per 10 units higher score on the index, insulin
resistance: b = -0.06 (- 0.10; - 0.03), prediabetes risk:
HR = 0.93 (0.84; 1.02), and T2D risk: HR = 0.85 (0.80;
0.96)]. The estimates also remained similar after excluding
these less healthy plant-based foods combined at a time
(sweets, sugary beverages, potatoes, and refined grains)
[per 10 units higher score on the index, insulin resistance:
b = -0.09 (- 0.10; - 0.07), prediabetes risk: HR = 0.90
(0.84; 0.98), and T2D risk: HR = 0.83 (0.74; 0.94)], and
the less healthy plant foods score was not associated with
insulin resistance or with risk of prediabetes or type 2
diabetes [insulin resistance: b = -0.002 (- 0.01; 0.006),
risk of prediabetes: HR = 1.00 (- 0.99; 1.01), and risk of
type 2 diabetes: HR = 0.99 (0.98; 1.00)]. The Pearson’s
correlation coefficient between the plant-based dietary
score with the dietary guidelines score was 0.16
(P \ 0.05); and controlling for the dietary guidelines score
did not substantially affect the estimates [per 10 units
higher score on the index, insulin resistance: b = -0.09
(- 0.10; - 0.08), prediabetes risk: HR = 0.91 (0.82; 1.00),
and T2D risk: HR = 0.81 (0.71; 0.91)].
Additional adjustment for hypertension and
hypercholesterolemia did not change effect estimates [per 10 units
higher score on the index, insulin resistance: b = -0.08
(- 0.10; - 0.07), risk of prediabetes: HR = 0.90 (0.82;
0.99), and risk of T2D: HR = 0.84 (0.75; 0.94)], and
estimates remained similar after excluding participants with
chronic diseases at baseline [per 10 units higher score on
the index, insulin resistance: b = -0.09 (- 0.11; - 0.07),
prediabetes risk: HR = 0.88 (0.79; 0.97), and T2D risk:
HR = 0.81 (0.72; 0.92)]. Finally, excluding participants
who developed T2D or prediabetes in the first 2 years of
follow-up modestly attenuated the associations for
prediabetes [per 10 units higher score on the index, HR = 0.91
(0.83; 1.01)], and T2D [HR = 0.82 (0.73; 0.92)].
In this large population-based cohort, we observed that a
diet higher in plant-based foods and lower in animal-based
foods was associated with lower insulin resistance, and a
lower risk of prediabetes and T2D, suggesting a protective
role of a more plant-based opposed to a more animal-based
diet in the development to T2D, beyond strict adherence to
a vegetarian or vegan diet.
Comparison with other studies
The inverse association between plant-based diets and T2D
risk is in agreement with previous research showing lower
T2D risk for vegans or vegetarians, compared to
]. Moreover, our observed associations
confirmed the observations of Satija and colleagues in a US
], the only other prospective study examining
adherence to plant-based diets in a continuous graduation
with risk of T2D. Compared to this previous study in the
US population, we have extended this evidence by also
showing associations between plant-based diets in a
continuous graduation with earlier stages of the development
of T2D: insulin resistance, and prediabetes in a European
Our results imply a beneficial effect of adherence to a
diet higher in plant-based foods and lower in animal-based
foods on the development of T2D, irrespective of general
healthfulness of the specific plant-based and animal-based
foods. With these results, we provide a different view on
what a healthy diet may entail. However, we acknowledge
that our plant-based diet included positive scoring for some
components that are not necessarily healthy choices for
prevention of T2D, or a healthy diet in general. Sugary
beverages, for example, have been associated with adverse
effects for T2D in other studies [
To further clarify whether these less healthy plant foods
contributed to the observed associations, we examined the
associations between less healthy plant-based diet score
with insulin resistance, and risk of prediabetes and T2D in
our sensitivity analyses, and observed null associations;
suggesting beneficial associations were mainly driven by
higher intake of healthy plant-based food groups and lower
intake of animal-based food groups. This emphasizes that it
is important to also consider the quality of plant-based
foods consumed, which has important public health
implications. Furthermore, the estimates for the
plantbased dietary index remained similar after excluding these
plant-based beverages combined, or after excluding the less
healthy plant-based foods combined, which indicated that
our results were stable in diverse versions of plant-based
diets, thus increased our confidence in the validity of the
findings. We also observed that excluding each one of 23
components one by one at a time resulted in similar
associations as observed for the total plant-based index,
indicating that the associations were not mainly explained by
any one specific food group, which supports the importance
of recognizing overall plant-based diet. Finally, we
extended our analyses to examine if adherence to a plant-based
diet was independent of adherence to current Dutch dietary
guidelines. In line with results from the large prospective
cohort study in the US which examined if adherence to a
plant-based diet was independent of general healthy dietary
patterns that have been linked to prevention of T2D, such
as the Mediterranean diet, the alternative Healthy Eating
Index (aHEI), and the Dietary approaches to stop
hypertension (DASH) diet [
]. We observed that
associations of the plant-based dietary index with outcomes
remained similar after additional adjustment for adherence
to current Dutch dietary guidelines. This lends support to
novelty of the plant-based dietary index.
Taken together, a more plant-based, less animal-based
diet may help prevent the development of T2D. Still more
important, a more plant-based diet, does not require a
radical change in diet or a total elimination of meat or
animal products but instead can be achieved in various
ways, increasing the potential for population-wide health
recommendations. For example, if a participant in our
cohort would increase fruits intake from 95 to 200 g/day,
increase vegetables intake from 100 to 260 g, and at the
same time decrease red meat intake from 129 to 55 g/day,
this would improve the plant-based dietary index by 10
units, which may decrease risk of T2D by 13%, assuming
other covariates remain stable.
Potential biological mechanisms
Several mechanisms behind the inverse associations could
involve the intermediate conditions of T2D, such as obesity
and inflammation, can offer explanations for the observed
protection and T2D. On the one hand, a plant-based diet
usually has more fiber, chlorogenic acids, certain amino
acids, unsaturated fatty acids, and anti-oxidants. For
example, vegetables and fruits are the main sources of
fiber, anti-oxidants, and chlorogenic acids; nuts are rich in
poly-unsaturated fatty acids; soy and beans are main
sources of plant protein; whole grains are rich in fiber and
plant protein; and coffee and tea are rich in anti-oxidants
and phenol chlorogenic acid. These beneficial components
may influence the development of T2D through impact on
the potential intermediate conditions, such as obesity and
inflammation. Fiber is known to lower gastric emptying
and thereby glycemic responsiveness [
], and might
improve inflammation [
], and obesity .
Chlorogenic acids can improve inflammation, glucose tolerance
and glucose levels, and improve increasing insulin
]. Soy protein contains high amounts of the amino
acids arginine and glycine, which have been associated
with a decrease in cholesterol levels [
]. High intake of
unsaturated fatty acids has also been associated to lower
inflammation and less obesity [
]. Phenol chlorogenic
acid was reported to reduce insulin resistance . On the
other hand, a plant-based diet, usually has less animal
protein, saturated fatty acids, and heme iron. Animal
protein is rich in branched-chain amino acids and aromatic
amino acids and may impair glucose metabolisms and
increase T2D risk [
]; animal protein is also rich in
heme iron, which has been suggested to increase risk of
cardio-metabolic diseases [
]. Higher saturated fatty
acids have been suggested to be associated higher
inflammation , higher risk of obesity [
] and T2D [
Besides, other nutrients from processed red meat, such as
sodium and nitrites, may increase risk of cardio-metabolic
diseases . More research is needed to explore whether
the mechanisms also involve an effect of plant foods on gut
microbiome. Finally, these different mechanisms may
influence each other because of inter-relations between
different food components. This also highlights the
relevance of examining overall diets in additional to isolated
food items, as this enables capturing of the combined
effects of the potential pathways.
Strengths and limitations
This study has several strengths. First, to our knowledge,
we are the first to investigate the associations between
plant-based diets with longitudinal insulin resistance and
prediabetes, for which we had longitudinal data from long
follow-up available. Studying these early risk stages help
minimize reverse causation, understand how plant-based
diet influences the development of T2D. Second, we
observed that the potential beneficial effect of a more
plant-based diet was independent of less healthy plant
foods, such as sweets, sugary beverages and refined grains,
emphasizing the importance of considering the quality of
plant-based foods consumed. We also observed
associations of the plant-based dietary score independent of
overall adherence to dietary guidelines, indicating that the
plant-based diet score may reflect more than only a
healthful dietary pattern as reflected by current dietary
guidelines. Other strengths also included the
populationbased nature of the study, the detailed and thorough data
collected on the outcomes and the assessment of the extent
to which diets were plant-based and animal based, based
upon overall dietary intake patterns of the general
Nevertheless, there are several limitations we should
consider. First, the assessment of a plant-based diet with
this index has its limitations as several sometimes arbitrary
decisions had to be made. A decision was, for example, to
add up food items within categories based on the intake in
grams per day. As a result, products that were high in
water-content will have contributed less energy or nutrients
compared to products containing less water in the same
category. However, using grams per day reflects intake of
foods as they are consumed and recommended [
decisions had to be made for the categorization of foods
and the number of categories. We chose categories
reflecting those used in the Dutch dietary guidelines, which
are based on similarities of the food items in (botanical)
origin, nutrient composition, and nutrient density [
thereby reducing nutritional differences between food
items within one category. Furthermore, in our main
analyses, we treated all plant-based foods equally by giving
all plant-based foods positive scores, and all animal-based
foods equally by giving all animal-based foods reverse
scores, irrespective of their nutrient-density or previous
evidence for a role in T2D prevention and general health.
For example, less healthy plant-based foods, such as sugary
beverages and refined grains, were included as positive
scores, although sugary beverages [
], and refined grains
] have been linked to higher T2D risk; by contrast,
healthy animal-based foods, such as dairy and fish, were
included as reverse scores, although dairy [
] and fish [
have been linked to lower T2D risk or mortality risk. That
is because our study aimed to emphasize an overall
plantbased diet including various increased plant-based foods
consumption and decreased animal-based foods
consumption, which would increase the potential for
populationwide recommendation. However, in our sensitivity
analyses, excluding any one of alcoholic beverages, sugary
beverages, sweets, potatoes, refined grains, fish, and dairy
did not substantially change our estimates.
In addition to the choices we had to make in the
construction of the index, this study has some other limitations.
First, dietary data were derived from self-reported diet
measured with FFQs, making measurement-errors likely.
However, because we used relative scores (quintiles) of
intake and the FFQs were shown in several validation
studies to adequately rank subjects according to intake
], we do not expect these measurement-errors to
have largely affected our results. Second, we did not have
dietary data for many of the participants of the original
cohort, which might have resulted in selection bias if
associations of plant-based diets with T2D risk differed in
those included and those not included in our current
analyses. Third, we assumed stable diets over time. However,
the estimates were similar after excluding the participants
who were likely to change their diet during follow-up, such
as participants with CHD, stroke, and cancers at baseline.
Last, our results may be generalizable only to people of
similar age and race.
In this large population-based cohort, higher adherence to
an overall plant-based diet is associated with lower
longitudinal insulin resistance, and lower risk of prediabetes and
T2D, indicating a protective role of diets high in
plantbased foods and low in animal-based foods in the
development to T2D beyond strict adherence to a vegetarian or
vegan diet. These promising findings call for further
exploration of overall plant-based dietary
recommendations aimed at T2D prevention.
Acknowledgements We gratefully acknowledge the dedication,
commitment, and contribution of inhabitants, general practitioners,
and pharmacists of the Ommoord district who took part in the
Rotterdam Study. We thank dr. E.A.L de Jonge and dr. J.C. Kiefte-de
Jong for their help with processing the dietary data.
Authors’ contributions The authors’ contributions to this study
were as follows: TV, ZC, and MGZ designed the research; ZC, and
MGZ conducted the analyses; TV, and HAW provided consultation
regarding the analyses and interpretation of the data; MAI, and OHF
were involved in the design and planning of the study and data
collection; NS, and JN were involved in data collection; MGZ, ZC, and
TV wrote the manuscript. All authors critically reviewed and
approved the final manuscript. None of the authors declare a financial
or personal conflict of interest related to this work. The corresponding
author had full access to all the data in the study and had final
responsibility for the decision to submit for publication.
Funding The Rotterdam Study is supported by Erasmus University
Medical Center and Erasmus University Rotterdam; The Netherlands
Organization for Health Research and Development; the Research
Institute for Diseases in the Elderly; The Netherlands Genomics
Initiative; the Ministry of Education, Culture and Science; the
Ministry of Health, Welfare and Sports; the European Commission (DG
XII); and the Municipality of Rotterdam. The funders had no role in
design or conduct of the study; collection, management, analysis, or
interpretation of the data; or preparation, review or approval of the
manuscript. The authors declare no conflicts of interest relevant to
Compliance with ethical standards
Conflict of interest No conflict of interest.
Ethical approval The Rotterdam Study has been approved by the
institutional review board (Medical Ethics Committee) of the
Erasmus Medical Center and by the review board of The Netherlands
Ministry of Health, Welfare and Sports. The approval has been
renewed every 5 years. All participants gave informed consent.
Open Access This article is distributed under the terms of the Creative
Commons Attribution 4.0 International License (http://creative
commons.org/licenses/by/4.0/), which permits unrestricted use,
distribution, and reproduction in any medium, provided you give
appropriate credit to the original author(s) and the source, provide a
link to the Creative Commons license, and indicate if changes were
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