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Pashaei et al. Diabetology & Metabolic Syndrome (2024) 16:253
https://doi.org/10.1186/s13098-024-01490-x Diabetology & Metabolic
Syndrome
*Correspondence:
Seyyed Reza Sobhani
Seyyedrezasobhani@gmail.com
1Department of Nutrition, Faculty of Medicine, Mashhad University of
Medical Sciences, Mashhad, Iran
Abstract
Background The metabolic syndrome (MetS) comprises metabolic irregularities, including hypertension and central
obesity, which are inuenced by genetic, metabolic, environmental, and dietary factors. As diet and lifestyle are risk
factors for MetS, it is important to know which diet quality index better predicts MetS. The aim of this study is to
compare the ability of dierent diet quality indices in predicting MetS and to identify the most eective one.
Methods This cross-sectional study involved 5,206 participants aged 35 to 70 engaged in the Prospective
Epidemiological Research Study in Iran (PERSIAN) cohort. Assessment of one year’s food intake via a validated 134-
item semi-quantitative food frequency questionnaire (FFQ) facilitated the calculation of adherence to ve diet quality
indices: Dietary Approaches to Stop Hypertension (DASH), Mediterranean, Mediterranean-DASH Intervention for
Neurodegenerative Delay (MIND), Dietary Inammatory Index (DII), and Diet Quality Indices (DQI). While bivariate
Pearson correlation and binary logistic regression aided in identifying the strongest correlation and predictor for MetS
among the indices.
Results This study showed a signicant association between adhering to the DASH diet score, Mediterranean diet
score, MIND diet score, DII score, and DQI score, and the odds of developing MetS (OR: 0.94, (95% CI: 0.93–0.95),
OR: 0.85, (95% CI: 0.81–0.89), OR: 0.84, (95% CI: 0.80–0.89), OR: 1.22, (95%CI: 1.11–1.34), OR: 0.95, (95%CI 0.94–0.96)
respectively). Therefore, with each unit increase in DASH diet score, Mediterranean diet score, MIND diet score, DII
score, and DQI score, the odds of MetS was reduced by 5.4%, 14.5%, 15.6%, 22%, 5%, respectively. All the indices were
correlated with the intake of most of the micronutrients, with the strongest correlations being observed in the DII.
DASH diet score aligned with the most favourable MetS biomarker risk, while DII score primarily associated with MetS
and could be considered as a predictor for MetS.
Conclusion The present study’s ndings reveal that between all these ve diet quality indices, the DASH diet score
correlates strongly with a favourable biomarker risk prole, while the DII score is predominantly linked to MetS.
Keywords Metabolic syndrome, DASH diet, Mediterranean diet, Healthy diet
Comparison of diet quality indices
for predicting metabolic syndrome in Iran:
cross-sectional ndings from the persian
cohort study
Kimia Haji AliPashaei1, ZahraNamkhah1 and Seyyed RezaSobhani1*
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Page 2 of 13
Pashaei et al. Diabetology & Metabolic Syndrome (2024) 16:253
Introduction
e metabolic syndrome (MetS) is a common meta-
bolic disorder that representing a spectrum of meta-
bolic abnormalities including hypertension, central
obesity, and insulin resistance [1]. Several factors, includ-
ing genetics, metabolism and environment, and diet, can
inuence MetS risk [2, 3]. e global prevalence of MetS
is 12.5 to 31.4 [4–7]. However, in people with type 2 dia-
betes, the percentage can reach almost 80% [8]. In Iran,
the prevalence rate of MetS based on the National Cho-
lesterol Education Program Adult Treatment Panel crite-
ria (NCEP ATP III) was 38.3% [9].
MetS is strongly associated with an almost 2-fold and
a 5-fold increased risk of cardiovascular disease and
new-onset type 2 diabetes mellitus [10–12] and also
other comorbidities including the prothrombotic, pro-
inammatory, non-alcoholic steatosis, polycystic ovary
syndrome (PCOS), obstructive sleep apnea, lipodystro-
phy, reproductive disfunction, and all-cause mortality
multiply [11]. In a study by Gami et al. [13] it was shown
that after adjusting for conventional cardiovascular risk
factors, there was a signicant association between the
MetS and cardiovascular events.
erapeutic options for managing the MetS range
from expensive and invasive methods (surgery and drug
therapy), to cheap and publicly available methods (life-
style modication). Lifestyle recommendations are the
primary way to control the earliest levels of MetS. ese
recommendations include smoking cessation, physical
activity, weight loss, limiting saturated and trans fats,
reducing sugar and salt intake, and eating a healthy diet
[11]. Among dietary factors, high intakes of fruits, veg-
etables, legumes and nuts and also low intakes of high-fat
dairy products, red meat, and processed meat reduce the
risk of MetS and its components [3].
e Diet Quality Indices (DQIs) provides an overall
picture of a person’s dietary intake by assessing food and/
or nutrient intake and lifestyle factors based on how well
they align with dietary guidelines [14]. Indices reecting
overall diet quality are used in research worldwide to pre-
dict the risk for metabolic disorders such as MetS. ese
indices are created to measure adherence to dietary
guidelines or to optimally evaluate diet–illness associa-
tion [15]. Below are some of the dietary quality indices
that have these qualities presented.
e rst score is the DASH (Dietary Approaches to
Stop Hypertension) diet which was designed to reduce
the incidence of hypertension [16, 17]. e second score
represents dietary aspects of Mediterranean lifestyle
consisting of plant-based dishes [18]. Another score is
Mediterranean-DASH Intervention for Neurodegenera-
tive Delay (MIND) diet, initially designed for brain health
[19]. e fourth score is the Dietary Inammatory Index
(DII) which is a nutritional index designed to measure
the potential eect of a diet on inammatory status of
people [20]. At last but not the least, the Diet Quality
Indices (DQIs) are tools to assess the quality of dietary
intake, and lifestyle factors, depending on how close they
are to dietary guidelines [14]. e DQI was devised with
the objective of incorporating the various elements of a
diet that contribute to its overall quality. ese include
diversity, adequacy, moderation and balance [21].
Although all the diet quality indices assess the quality
of dietary intake but they dier not only in the items that
represents healthy and unhealthy diet, but also in the way
they are calculated; for instance, the DASH diet empha-
sizes on high potassium low sodium foods which low-
ers BP [22], the Mediterranean diet focuses on benecial
eects of olive oil like its anti-inammatory eect [23],
brain healthy foods are in MIND diet, micronutrients
are much more focused on in DII score [24] and the DQI
fully concentrated on the overall quality of diet, therefore
all of these diet quality indices measure a MetS related
diet.
e present study attempts to distinguish the eec-
tiveness of various diet quality indices for consideration
of MetS, and to determine the most eective diet qual-
ity index. Although there are studies that investigated
the association between each of these indices and MetS,
according to our knowledge, this is the rst study to com-
pare these dietary indices to predict MetS.
Methods
Subjects and study design
e present cross-sectional study was derived from Pro-
spective Epidemiological Research Study in Iran (PER-
SIAN) cohort study involving 5,206 Mashhad University
of Medical Sciences (MUMS) employees [25, 26].
e inclusion criteria of the study were possessing
Iranian nationality, being residents of Mashhad, aging
between 35 and 70 years old, being employed by Mash-
had University of Medical Sciences, and participating in
the Persian cohort. Individuals who were pregnant and
those with physical or mental disabilities that prevented
them from participating fully in the study were excluded
the research. All participants provided written informed
consent before participating in this study. Further infor-
mation on PERSIAN cohort methods and strategies can
be found in a study by Poustchi. et al. [27]. Information
on age, sex, education, smoking (including those who
have smoked at least 100 cigarettes during lifetime), mar-
ital status, BMI, waist circumference, Wealth Score Index
(WSI), physical activity (MET-h/week), muscle mass, fat
mass were collected for each individual in this study by
self-reported questionnaire [28].
In the present study, MetS was dened by the guide-
lines of the National Cholesterol Education Program
Adult Treatment Panel III (NCEP ATP III). MetS is
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Pashaei et al. Diabetology & Metabolic Syndrome (2024) 16:253
diagnosed when three or more of the following crite-
ria are met: obesity, high blood sugar, low fasting high-
density lipoprotein (HDL), high fasting triglyceride (TG),
and high blood pressure [29]. erefore, according to this
denition, 895 of the total 5206 participants of the study
had MetS.
Study measures
Dietary intakes
Each participant’s food and nutrient intake was assessed
using food-frequency questionnaire (FFQ). People’s food
intake over the past year was assessed using a validated
134-item semi-quantitative food frequency question-
naire [30]. FFQ classies food items into groups of bread
and cereals, legumes, meat and meat products, milk and
dairy products, vegetables, fruits, sugars, oil and oil-
seeds, spices and miscellaneous. is questionnaire has
been completed by trained nutritionists in the Persian
cohort based on face-to-face interviews. e frequency
of consumption of each food over the past year by day,
week, month, and year were rated based on participants’
self-report. en, the measured food portions were con-
verted to grams by using the Iranian Food Composition
Tables and the United States Department of Agriculture
(USDA) [31, 32]. Nutritionist IV software (version 7.0)
was used to determine the nutrient contents and energy
of foods [30].
Dietary quality indices
FFQ-based dietary intakes were used to calculate adher-
ence to the ve diet quality indices.
DASH score
Numerous approaches exist for computing the DASH
diet score [33]. Evaluating a person’s compliance with the
DASH diet could be achieved by basing the DASH diet
score on eight dietary elements, such as fruits, vegetables,
nuts and legumes, dairy products, whole grains, sugar-
sweetened beverages and sweets, sodium, and red and
processed meats. Initially, energy-adjusted consumptions
of these foods and nutrients were computed by using
residual method [34]. Afterwards, people were classied
into deciles based on their energy-adjusted consumption
of various foods. ose in the top decile of fruits, vege-
tables, dairy products, legumes, and nuts were given the
score of 10, while those in the lowest decile were given
the score of 1. Conversely, for red and processed meat,
sugar-sweetened beverages, sweets, and sodium, the
highest decile received a score of 1 and the lowest, a score
of 10. e participant’s DASH diet score was then deter-
mined by totalling the scores for all foods and nutrients.
Consequently, the smallest and greatest scores for the
DASH diet for an individual was between 8 and 80 [3].
Mediterranean dietary score (MDS)
To evaluate adherence to the traditional Mediterranean
diet, the modied version of the Mediterranean Diet
Score (MDS), as developed by Trichopoulou et al. [35],
was chosen as the region-specic diet quality index. It is
characterized by nine food and nutritional components:
high consumption of vegetables, fruits, nuts, legumes,
grains, and sh, as well as high ratio of monounsaturated
fat: saturated fat; low intake of meat and dairy products;
moderate alcohol consumption. For Healthy components
(vegetables, legumes, fruits and nuts, grains, and sh),
intake above-average was assigned a value of 1; other-
wise, the value 0 was assigned. For components deemed
unhealthy (meat, poultry, and dairy products), equal to or
above the average consumption was assigned a value of
0; otherwise, the value 1 was assigned. Overall MDS was
between 0 and 8 (representing minimum to maximum
adherence to this index) [36]. Alcohol intake is unusual
or is perhaps less commonly reported in the Iranian pop-
ulation because of religious reasons, therefore the alcohol
component was excluded from calculations [37].
MIND diet (Mediterranean-DASH Diet intervention for
neurodegenerative Delay)
e MIND diet, which is based on the Mediterranean
and DASH diets, emphasizes on consumption of natu-
ral, plant foods and limits foods with animal origin and
high in saturated fat. However, while the MIND diet spe-
cically mentions intake of berries and green leafy veg-
etables, and does not specify high fruit consumption (as
seen both DASH and Mediterranean), high dairy intake
(DASH), high potato or higher than one sh meal per
week (Mediterranean). e MIND revisions focus on the
foods and nutrients that have been scientically linked
to dementia prevention [38–40]. e MIND diet com-
prises of 9 food groups that are healthy for brain (green
leafy vegetables, other vegetables, nuts, berries, beans,
whole grains, seafood, poultry, olive oil, wine) and 5
unhealthy food groups (red meats, butter and stick mar-
garine, cheese, pastries and sweets, and fried/fast food).
In this research, the modied MIND diet scoring was
applied to align with Iranian dietary habits [41]. Alcohol
consumption is not included as consumption is prohib-
ited. erefore, another 14 food groups were used in the
MIND scoring. Olive oil consumption was scored as 1 if
the participants identied it as the most commonly used
oil at their homes, and as 0 otherwise [42]. For all compo-
nents of this dietary score, the frequency of consumption
of each food item portion was summed associated with
that component and then assigned a concordance score
of 0, 0.5, or 1. e total MIND diet score was calculated
by summing over all 14 of the component scores [43].
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Pashaei et al. Diabetology & Metabolic Syndrome (2024) 16:253
Dietary inammatory index (DII)
e DII is a new non-recommendation-based index
selected for comparison purposes, focusing on the pro-
inammatory aspects of the diet. e eects of 45 food
parameters on 6 inammatory biomarkers (IL-1β, IL-4,
IL-6, IL-10, TNF-α and C-reactive protein) are calculated.
e scores were based on whether the dietary param-
eter increased [1], decreased (-1) or had no eect (0) on
each of the six inammatory biomarkers [18] Unlike the
other nutritional quality indices, higher DII score dem-
onstrate a pro-inammatory unhealthy nutritional status,
while lower score demonstrates an anti-inammatory
healthy dietary condition. e DII score was calculated
based on 30 groups of food including energy, fat, trans
fat, cholesterol, caeine, carbohydrate, protein, saturated
fat, vitamin B12, iron, MUFA, PUFA, n-3 Fatty acids,
n-6 Fatty acids, vitamin D, vitamin B6, ber, vitamin B9,
vitamin C, niacin, thiamin, riboavin, vitamin A, mag-
nesium, vitamin E, β-carotene, onion, garlic, green/black
tea, selenium, and zinc. Consumption of groups such as
alcohol, eugenol, turmeric, saron, ginger, pepper, rose-
mary, thyme/oregano, isoavones, anthocyanidins, a-
vonones, avonols, avones, and avan-3-ol were not
available for to calculate this index. First, the participants’
energy intake of the participants was adjusted based
on a 1000kcal basis. erefore, to calculate the DII, we
subtracted the nutritional parameters from the global
mean and divided it by the “global standard deviation” to
obtain a Z score. e Z-score values were converted to
percentiles. e percentile values were then multiplied
by 2 minus 1. Lastly, the scores obtained for each of the
parameters were multiplied by the overall inammatory
score; then, we added up all food items to calculate the
total DII score [44].
Diet quality index (DQI)
e original DQI had 4 aspects representing variety,
adequacy, moderation, and balance [21]. e main article
discussing DQI from Kim et al. [21] has explained the
calculation of this index thoroughly but here is a brief
explanation of calculating DQI. Variety includes 2 ele-
ments assessing variety in food group and protein source
(score between from 0 to 20). Food group variety encom-
passes meat, poultry, sh, eggs, dairy products, legumes,
grains, fruits and vegetables. Protein source variety com-
prises meat, poultry, sh, dairy products, legumes and
eggs.
Adequacy includes vegetables, fruits, grains, ber, pro-
tein, iron, calcium, and vitamin C (score ranging from
0 to 40). e requirement for adequate protein intake
is fullled when the percentage of total energy intake
derived from protein is > 10%. Moderation incorporates
total fat, Saturated fatty acid (SFA), cholesterol, sodium,
empty calorie foods (score ranging from 0 to 30). One of
the unique components of the DQI is the “empty calo-
rie foods” like sugar, alcohol and oils. Balance refers to
the score ranging from 0 to 10 that represents the ratio
of macronutrient ratio and fatty acid ratio. e cut o
points used in this study were obtained from the DQI
from Kim et al. [21] and have been utilized in previous
studies conducted in Iran [45]. Total DQI score, could be
ranged from 0 to 100 (0 is the minimum and 100 is the
maximum score) [21].
Data analysis
For descriptive objectives, all the diet quality indices
included in this study were separated into quartiles (Q).
e Kolmogorov-Smirnov test was employed to evaluate
the normality of the data. To compare the participants’
demographic, socioeconomic, and lifestyle character-
istics, the data on the rst and fourth quartiles of each
index were presented. Except for the DII, which exhibited
a reverse pattern (i.e., ranging from a more anti-inam-
matory prole to a more pro-inammatory prole), Q1
and Q4 represented the minimum and maximum adher-
ence to each nutritional score respectively. e number
and percentage of participants are reported for categori-
cal variables and means and standard errors are reported
for continuous distributed variables. One-way ANOVA
test and Chi-square test were employed to compare
quantitative variables across groups, and compare fre-
quency distribution of qualitative variables for Table1
respectively.
To assess the correlation between the dietary quality
indices and various micronutrients and vitamins, we uti-
lized the bivariate Pearson’s correlation method. Accord-
ing to the research conducted by Alkerwi et al. [36], the
higher the absolute value of the Pearson’s correlation
coecient (r), the stronger the correlation becomes.
erefore, the diet quality index could be considered a
better index to assess the correlation between food intake
and MetS. Binary logistic regression is a method used to
predict the values of outcome therefore, we utilized it to
predict MetS [46].
is research utilized binary logistic regression in both
crude and adjusted models. e initial model involved
controlling for age and sex. e second model included
additional adjustment for physical activity. Furthermore,
energy, WSI and smoking cigarettes were additionally
controlled in the nal model. Variables with p-values
less than 0.05 were deemed signicant, also odds ratio
(OR) can help in understanding whether that variable
increased or decreased the risk of MetS. e statisti-
cal analysis was conducted by utilizing SPSS version 16
(SPSS Inc., Chicago, IL, USA).
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Pashaei et al. Diabetology & Metabolic Syndrome (2024) 16:253
Table 1 Demographic characteristics of the participants across quartiles (Q) of ve dierent diet quality indices
Participant
character-
istics
DASH Diet score (n = 5206) MDS
(n = 5206)
MIND score (n = 5206) DII score (n = 5206) DQI score (n = 5206)
Q1 Q4 p£Q1 Q4 p£Q1 Q4 p£Q1 Q4 p£Q1 Q4 p£
Age (years) 42.89 ± 7.14 47.68 ± 9.75 < 0.001 44.76±
8.48
45.98 ± 9.02 < 0.001 43.16 ± 7.37 47.58 ± 9.57 < 0.001 45.50 ± 8.78 44.84 ± 8.78 0.13 43.69 ± 7.96 46.78 ± 9.36 < 0.001
Sex Male
(%)
600 (50.8%) 656 (39.4%) < 0.00 701 (45.9%) 853 (43.7%) 0.45 515
(46.9%)
753
(41.4%)
< 0.001 844
(47.8%)
605
(42.8%)
< 0.001 576
(46.5%)
778
(45.7%)
0.08
Edu-
ca-
tion
Di-
ploma
and
under
Diplo-
ma
188
(15.9%)
416
(25.0%)
< 0.001 357 (23.4%) 337 (17.3%) < 0.001 182
(16.6%)
394
(21.7%)
0.02 321
(18.2%)
340
(24.1%)
< 0.001 268
(21.6%)
358
(21.0%)
0.02
Bach-
elor
and
associ-
ated
627
(53.0%)
853
(51.2%)
808 (52.9%) 1030 (52.8%) 615
(56.1%)
925
(50.9%)
937
(53.0%)
727
(51.5%)
654
(52.8%)
869
(51.1%)
MSc
and
PhD
367
(31.0%)
397
(23.8%)
363 (23.8%) 583 (29.9%) 300
(27.3%)
498
(27.4%)
509
(28.8%)
346
(24.5%)
317
(25.6%)
475
(27.9%)
Non smokers 1113 (94.2%) 1606
(96.4%)
0.02 1458
(95.4%)
1851 (94.9%) 0.29 1042
(95.0%)
1736
(95.5%)
0.57 1657
(93.8%)
1376
(97.4%)
< 0.001 1170
(94.4%)
1636
(96.1%)
0.12
Mar-
ital
Sta-
tus
Single 95 (8.0%) 132 (7.9%) 0.42 123 (8.0%) 167 (8.6%) 0.96 89
(8.1%)
159
)8.8%(
0.11 133
(7.5%)
135
(9.6%)
0.14 102
(8.2%)
141
(8.3%)
0.82
Mar-
ried
1034 (78.5%) 1428
(85.7%)
1319
(86.3%)
1671 (85.7%) 935
(85.2%)
1543
(84.9%)
1543
(87.3%)
1185
(83.9%)
1069
(86.3%)
1454
(85.4%)
Di-
vorced
53 (4.5%) 106 (6.4%) 86 (5.6%) 112 (5.7%) 73
(6.7%)
115 (6.3%) 91
(5.1%)
93
(6.6%)
68
(5.5%)
107
(6.3%)
BMI (Kg/M2)26.74 ± 4.02 27.32 ± 4.12 < 0.001 26.61 ± 3.96 27.10 ± 4.11 < 0.001 26.53 ± 3.92 27.32 ± 4.24 < 0.001 27.51 ± 4.19 26.28 ± 3.98 < 0.001 27.27 ± 4.09 26.70 ± 3.96 < 0.001
Waist Cir-
cumference
(Cm)
96.98 ± 9.61 97.28 ± 9.98 0.02 96.29 ± 9.90 96.98 ± 9.86 0.10 96.07 ± 9.95 97.28 ± 10.26 < 0.001 97.83 ± 9.98 95.47 ± 10.24 < 0.001 97.54 ± 9.87 96.23 ± 9.70 < 0.001
WSI 0.11 ± 0.95 -
0.054 ± 1.02
< 0.001 -0.12 ± 0.99 0.10 ± 0.98 < 0.001 -0.05 ± 0.97 0.07 ± 1.01 < 0.001 0.14 ± 1.00 -0.19 ± 1.00 < 0.001 -0.09 ± 1.02 -0.05 ± 1.01 < 0.001
Physical ac-
tivity (MET-h/
week)
38.00 ± 5.93 38.70 ± 5.21 0.00 38.47 ± 5.92 38.57 ± 5.40 0.62 38.33 ± 5.66 38.61 ± 5.55 0.45 38.83 ± 5.34 38.71 ± 5.45 0.01 38.58 ± 5.73 38.64 ± 5.58 0.96
Muscle mass
(%)
49.50 ± 10.68 46.97 ± 9.58 < 0.001 47.53 ± 9.92 47.99 ± 10.03 0.16 48.49 ± 10.41 47.49 ± 10.00 0.11 49.49 ± 10.32 46.55 ± 9.73 < 0.001 48.78 ± 9.87 47.55 ± 9.69 0.03
Fat mass (%) 24.95 ± 7.42 25.32 ± 7.84 < 0.001 24.40 ± 7.40 25.09 ± 7.80 0.19 24.37 ± 7.54 25.53 ± 7.83 < 0.001 25.55 ± 7.80 24.07 ± 7.29 < 0.001 25.34 ± 7.66 24.07 ± 7.62 < 0.001
BMI, Body Mass Index; DASH, Dietary Approaches to Stop Hypertension; DII, Dietary Inammatory Index; DQI, Diet Quality Index; MDS, Mediterranean Diet Score; MIND, Mediterranean-DASH Intervention for
Neurode generative Delay; WS I, Wealth Score Index. D ata presented as mean ± SD or frequenc y (%). One-way ANOVA test was use d to compare quantitati ve variables bet ween groups like age, BMI , Waist circumference, W SI,
physical ac tivity, muscle mass, fat m ass, and Chi-square tes t was used to compare frequ ency distribution o f qualitative variabl es like sex, education c ategory, nonsmokers , and marital status
£ : P value lower th an 0.05 is considered to be sig nicant
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Pashaei et al. Diabetology & Metabolic Syndrome (2024) 16:253
Results
Characteristics of participants according to diet quality
indices
Table 1 presented an overview of the attributes of the
participants, categorized into quartiles based on each
diet quality index. ere was a notable distinction across
age, education level, BMI, WSI among dierent quartiles
of all ve diet scores. A meaningful dierence was seen
across sex and waist circumference across quartiles of
all dietary scores, except for the MDS. Smoking demon-
strated a signicant dierence across quartiles of DASH
diet and DII score. e quartiles of the DASH and DII
scores revealed a considerable dissimilarity in terms of
physical activity and muscle mass. Also, there was a note-
worthy dierence between muscle mass and quartiles
of the DQI score. e dierence of fat mass was notice-
able across quartiles of the DASH, MIND, DII and DQI
scores.
Correlations between diet quality indices and food groups
Table 2 displays the associations between the ve diet
quality indices and their correlation with food groups.
All of the indices were signicantly correlated with
each other (r ranged between − 0.378 and 0.644 and all
P = 0.01). As anticipated, the DII had negative correla-
tions with the other four indices due to its inverse scor-
ing method. Additionally, all the indices showed strong
correlations with the consumption of food groups except
that the intake of poultry and dairy were not signicantly
correlated with adherence to the DASH diet score and
DQI score at the 0.05 level (-0.005, 0.020) respectively.
DII score showed the maximum correlation with food
groups like vegetables, fruits, nuts, legumes, meat, sh,
poultry and ber which makes DII a great score for eval-
uating dietary intake. As the Pearson’s correlation coe-
cient (r) for DII score was highly signicant (ber= -0.78,
vegetable = 0.62, fruits = 0.49) the correlations were much
more remarkable. DASH diet score had the strongest
correlation with dairy and sugar-sweetened beverages
and sweets group. MIND diet score was highly correlated
with olive oil. For most of the food groups, DQI showed
the lowest correlation except for fruits and sugar-sweet-
ened beverages.
Correlations between diet quality indices and calorie,
essential macronutrients and micronutrients
Table3 presents the associations between the micronu-
trients and the ve diet quality indices. Individuals who
had higher scores on the diet quality indices reported
consuming lower amounts of total energy, except for
those following the Mediterranean dietary pattern. As
the scores increased, the percentage of daily fat derived
energy intake decreased, while the carbohydrates derived
energy increased (except for the DII). All the indi-
ces showed notable correlations with the intake of the
three types of fatty acids and most of the vitamins and
Table 2 Pearson’s correlation coecients (r) across the diet quality indices and food groups
DASH Diet score MDS MIND score DII score DQI score
DASH Diet score 1 0.43** 0.64** -0.40** 0.54**
Mediterranean Diet score 0.43** 1 0.52** -0.52** 0.38**
MIND score 0.64** 0.52** 1 -0.50** 0.46*
DII score -0.40** -0.52** -0.50** 1 -0.37**
DQI score Variety 0.04** 0.02 0.13** -0.47** 0.21**
Adequacy 0.44** 0.47** 0.40** -0.71** 0.65**
Moderation 0.28** 0.05** 0.16** 0.33** 0.54**
Balance 0.03** 0.16** 0.06** -0.02*0.25**
Total 0.54** 0.38** 0.46** -0.37** 1
Vegetables 0.47** 0.35** 0.46** -0.62** 0.25**
Fruits 0.44** 0.36** 0.29** -0.49** 0.39**
Nuts 0.17** 0.36** 0.24** -0.38** 0.10**
Legumes 0.28** 0.28** 0.26** -0.32** 0.18**
Dairy 0.30** -0.12** 0.15** -0.28** 0.02
Poultry -0.01 -0.03** 0.19** -0.27** 0.03*
Fish 0.07** 0.26** 0.25** -0.31** 0.06**
Meat -0.21** 0.01 0.03*-0.35** -0.01**
Sugar-sweetened beverages and sweets -0.32** 0.06** -0.13** -0.23** -0.15**
Fiber 0.52** 0.55** 0.48** -0.78** 0.52**
Olive oil 0.13** 0.20** 0.22** -0.18** 0.07**
DASH, Dietary Approaches to Stop Hypertension; DII, Dietary Inammatory Index; DQI, Diet Quality Index; MDS, Mediterranean Diet Score; MIND, Mediterranean-
DASH Intervention for Neurodegenerative Delay
*. Correlation is s ignicant at the 0.05 level (2-t ailed)
**. Correlation is sig nicant at the 0.01 level (2-taile d)
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 7 of 13
Pashaei et al. Diabetology & Metabolic Syndrome (2024) 16:253
minerals. e strongest correlations were discovered
between nutrient consumption and the DII. Same as
Table2, DII had the strongest correlation with most of
the items and the absolute numbers were signicant (K=
-0.84, Mg= -0.82, Vitamin B5= -0.77) except for carbohy-
drate and fat which were correlated with DQI score and
protein which was correlated with MIND score. ere-
fore, DII seems to be able to demonstrate intake of calo-
rie, essential macronutrients and micronutrients much
better.
Associations between diet quality indices and risk
biomarkers of metabolic syndrome
Table4 provided a summary of the connections between
the chosen diet quality indices and the biomarkers linked
to risk of MetS. Despite considering possible factors
that could aect the results, all the diet quality indices
showed a signicant association with MetS. Furthermore,
the connection between the DASH diet score and MetS
remained signicant even after accounting for other vari-
ables. In fact, each increment in the DASH score resulted
in a 5.4% decrease in the risk of MetS in the thoroughly
modied model (OR = 0.946, CI 95%=0.935–0.956,
P < 0.001). Similarly, the link between the Mediterranean
diet score and MetS was found to be signicant, suggest-
ing that a one-unit increase in the score led to a 14.5%
reduction in the risk of metabolic syndrome in model 3
(OR = 0.855, CI 95%=0.813–0.899, P < 0.001). e MIND
score also demonstrated a signicant association with
metabolic syndrome, with each unit increase in the score
resulting in a 15.6% decrease in the risk of MetS in the
third model (OR = 0.848, CI 95%=0.808–0.890, P < 0.001).
e DII score exhibited a signicant association with
metabolic syndrome, indicating that as the score
increased by one, the risk of MetS incremented by 22%
in model 3 (OR = 1.222, CI 95%=1.111–1.345, P = 0.001).
Lastly, the DQI score was also signicantly associated
with metabolic syndrome, with one-unit increase in
the score potentially lowering the risk of MetS by 5% in
model 3 (OR = 0.955, 0.945–0.966, P < 0.001).
Metabolic markers
e DASH score was associated with TG, HDL
(OR = 0.980, CI 95%=0.972–0.988, P < 0.001; OR = 0.980,
CI 95%=0.971–0.989, P < 0.001 respectively), and Medi-
terranean diet displayed a signicant relationship with
glucose level (OR = 0.921, CI 95%=0.888–0.955, P < 0.001),
indicating that higher compliance to this dietary pat-
tern was linked with a decreased likelihood of elevated
glucose levels. Waist circumference was found to be sig-
nicantly related with DII score, indicating that increased
DII scores were associated with larger waist circumfer-
ences (OR = 0.948, CI 95%=0.907–0.992, P value = 0.020).
Moreover, the Mediterranean diet, MIND, and DQI
score demonstrated signicant associations with HDL
levels, implying that individuals with higher scores in
these dietary patterns were less likely to have low HDL
levels (OR = 0.951 (CI 95%=0.912–0.992) P value = 0.020,
OR = 0.954 (CI 95%= 0.916–0.994) P value = 0.025 and
OR = 0.985 (CI 95%= 0.976–0.994) P value = 0.02 respe c-
tively). Both the DASH diet and MIND diet score dem-
onstrated an association with systolic blood pressure
(SBP), although the association between the DASH diet
and SBP was only marginally signicant, the MIND diet
had a signicant association with SBP (OR = 1.012 (CI
95%=1.000- 1.025) P value = 0.048, and OR = 1.097 (CI
95%=1.037–1.159) P value = 0.001 respectively). Further
association was not observed with diet scores.
Table 3 Pearson’s correlation coecients (r) across the diet
quality indices and essential macronutrients and micronutrients
DASH
Diet
score
MDS MIND
score
DII
score
DQI
score
Energy (Kcal/day) 0.14** 0.31** 0.16** -0.69** 0.26**
Carbohydrates (%E) 0.25** 0.19** 0.15** -0.00 0.50**
Protein (%E) 0.05** -0.08** 0.25** -0.15** 0.04**
Fats (%E) -0.19** -0.11** -0.19** -0.01 -0.50**
SFA -0.01 0.01 -0.07** -0.43** -0.19**
MUFA -0.05** 0.22** 0.02*-0.52** -0.11**
PUFA 0.06** 0.37** 0.14** -0.55** 0.06**
Retinol -0.02*0.01 -0.03** -0.36** -0.13**
Vitamin B1 0.25** 0.36** 0.29** -0.64** 0.41**
Vitamin B2 0.21** 0.24** 0.25** -0.73** 0.22**
Vitamin B3 0.08** 0.30** 0.25** -0.64** 0.31**
Vitamin B5 0.32** 0.31** 0.34** -0.77** 0.34**
Vitamin B6 0.19** 0.16** 0.18** -0.40** 0.12**
Vitamin B9 0.34** 0.42** 0.38** -0.76** 0.38**
Vitamin B12 0.05** 0.14** 0.12** -0.49** 0.04**
Vitamin C 0.36** 0.34** 0.32** -0.59** 0.31**
Vitamin D 0.03** 0.21** 0.19** -0.51** -0.03**
Vitamin E 0.18** 0.40** 0.23** -0.62** 0.12**
Ca 0.36** 0.14** 0.28** -0.59** 0.24**
Selenium 0.03** 0.23** 0.15** -0.57** 0.21**
Iron 0.26** 0.41** 0.34** -0.71** 0.39**
Mg 0.38** 0.44** 0.40** -0.82** 0.39**
Na -0.10** 0.15** 0.03** -0.37** -0.01
K 0.44** 0.41** 0.42** -0.84** 0.37**
P 0.29** 0.30** 0.33** -0.76** 0.29**
Zn 0.10** 0.25** 0.20** -0.70** 0.20**
Ca, Calcium; DASH, Dietary Approaches to Stop Hypertension; DII, Dietary
Inammator y Index; DQI, Di et Quality Index ; K, Potassium; MDS, Med iterranean
Diet Score; Mg, Magnesium; MIND, Mediterranean-DASH Intervention for
Neurodegenerative Delay; MUFA, Mono Unsaturated Fatty Acid; Na, Sodium;
P, Phosphorous; PUFA, Poly Unsaturated Fatty Acid; SFA, Saturated Fatty Acid;
Zn, Zinc
**. Correlation is sig nicant at the 0.01 level (2-taile d)
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Page 8 of 13
Pashaei et al. Diabetology & Metabolic Syndrome (2024) 16:253
Discussion
is cross-sectional study aimed to assess the predictive
capability of ve dierent diet quality indices for meta-
bolic syndrome and metabolic markers. e research
found that all ve dietary scores were signicantly asso-
ciated with the odds of MetS, even after accounting for
potential confounding factors. Among the ve indices,
the DII score demonstrated the strongest association
with Mets. Additionally, the DASH score was more likely
to be associated with multiple components of MetS.
Recent studies suggest that the quality of dietary mac-
ronutrients may predict the risk of metabolic disorders
[47]. e relationship between MetS, sleep disorders,
nutrient consumption, and social development factors,
with a focus on gender dierences, has been investigated
[48].
Our study compares and nds the best diet quality
score, and similar studies have been conducted. there
are studies similar to ours. However, their ndings dif-
fer from ours. For instance, Alkerwi et al. [36], conducted
a cross-comparison research to assess the predictive
ability of various diet quality indices for chronic dis-
ease risk. ey determined that the Mediterranean diet
score was the most optimal among the DII, DQI, MDS,
RCI, and DASH indices. Another study by Golzarand et
al. [49] demonstrated that high adherence to the DASH,
MDS and MIND scores was linked with a reduced risk
of metabolically unhealthy normal weight phenotypes.
So as another research by Vahid et al. [50] demonstrated
that DASH, DQI and Alternative Healthy Eating Index
(AHEI), which are food-group-based indices, proved to
be eective in predicting metabolic parameters.
As previously mentioned, our ndings revealed that
the DII score has the strongest association with MetS
compared to other selected indices. Consistent with our
ndings, a systematic review and meta-analysis study
consisting of eighteen cross-sectional and cohort studies
showed that except for low HDL-cholesterol, an elevated
DII, representing an inammation promoting diet, was
linked to greater likelihood of MetS and its various fac-
tors [51]. Same as the ndings of a previous study, our
research also indicates that a diet that promotes inam-
mation may increase the risk of developing MetS [52]. In
another study conducted on Koreans, after adjusting for
various confounders, the DII was signicantly associated
with the odds of MetS. DII was also positively correlated
with the occurrence of hyperglycemia and obesity in men
and postmenopausal women respectively [53]. Consistent
Table 4 Binary logistic regression of the associations between diet quality indices and risk markers
DASH Diet score
(n = 5206)
MDS(n = 5206) MIND score (n = 5206) DII score(n = 5206) DQI score(n = 5206)
OR (CI 95%) P OR (CI
95%)
P OR (CI 95%) P OR (CI
95%)
P OR (CI
95%)
P
Metabolic
syndrome
Crude 0.96
(0.95–0.97)
< 0.001 0.88
(0.84–0.92)
< 0.001 0.90
(0.86–0.94)
< 0.001 1.07
(1.00- 1.14)
0.03 0.96
(0.95–0.97)
< 0.001
Model 1 0.94
(0.93–0.95)
< 0.001 0.87
(0.83–0.91)
< 0.001 0.85
(0.81–0.89)
< 0.001 1.09
(1.02–1.16)
0.01 0.96
(0.95–0.97)
< 0.001
Model 2 0.94
(0.93–0.95)
< 0.001 0.85
(0.81–0.89)
< 0.001 0.84
(0.80–0.88)
< 0.001 1.23
(1.12–1.35)
< 0.001 0.95
(0.94–0.96)
< 0.001
Model 3 0.94
(0.93–0.95)
< 0.001 0.85
(0.81–0.89)
< 0.001 0.84
(0.80–0.89)
< 0.001 1.22
(1.11–1.34)
< 0.001 0.95
(0.94–0.96)
< 0.001
Metabolic
markers
TG (mg/dl) 0.98
(0.97–0.98)
< 0.001 0.96
(0.93–1.07)
0.11 0.99
(0.95–1.03)
0.66 0.98
(0.93–1.03)
0.54 0.99
(0.98- 1.00)
0.09
Glucose
(mg/dl)
1.00
(0.99–1.01)
0.34 0.92
(0.88–0.95)
< 0.001 1.01
(0.97–1.04)
0.53 1.00
(0.95–1.05)
0.99 0.99
(0.98- 1.00)
0.07
Waist Cir-
cumference
(Cm)
1.00
(1.00- 1.01)
0.02 1.00
(0.97–1.03)
0.66 1.02
(0.99–1.06)
0.07 0.94
(0.90–0.99)
0.02 0.99
(0.98–0.99)
< 0.001
HDL-choles-
terol (mg/dl)
0.98
(0.97–0.98)
< 0.001 0.95
(0.91–0.99)
0.02 0.95
(0.91–0.99)
0.02 1.01
(0.95–1.07)
0.69 0.98
(0.97–0.99)
< 0.001
SBP (mmHg) 1.01
(1.00- 1.02)
0.04 1.00
(0.94–1.06)
0.85 1.09
(1.03–1.15)
< 0.001 1.01
(0.93–1.10)
0.70 1.00
(0.99–1.02)
0.25
DBP (mmHg) 0.98
(0.97- 1.00)
0.09 0.98
(0.91–1.05)
0.62 0.95
(0.88–1.01)
0.15 1.05
(0.95–1.16)
0.29 0.99
(0.98–1.01)
0.68
DASH, Dietary Approaches to Stop Hypertension; DBP, Diastolic Blood Pressure; DII, Dietary Inammatory Index; DQI, Diet Quality Index; HDL-cholesterol: High
Density Lipoprotein Cholesterol; LDL-cholesterol: Low-Density Lipoprotein Cholesterol; MDS, Mediterranean Diet Score; MIND, Mediterranean-DASH Intervention
for Neurod egenerative Delay; SB P, Sys tolic Blood Pressure; TG , Triglyceride; WC , Waist Circumference
Model 1: adjuste d for age and sex
Model 2: ad ditionally adjusted fo r physical activit y
Model 3: fur ther adjustment was m ade for energy, WSI and smoki ng cigarettes
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Pashaei et al. Diabetology & Metabolic Syndrome (2024) 16:253
with another study, our ndings suggest that the DII may
be a valuable clinical tool for identifying dietary sources
of inammation that should be minimized to decrease
the risk of metabolic diseases, including MetS [54]. Simi-
lar to a research showing that an inammation promot-
ing diet was linked to increased C-reactive protein (CRP)
and hyperglycemia, both of which are aspect of MetS
[55].
e potential reason for the eectiveness of DII’s
strength may stem from the fact that individuals adher-
ing closely to the DII tend to have a healthier overall diet
characterized by consuming more vegetables, fruits, nuts,
legumes, sh, poultry, while consuming less meat. is
correlation between the DII and a healthy diet was also
detected in a research by Vahid et al. [50], which supports
our ndings related to food groups such as vegetables,
fruits, and non-caloric beverages. erefore, a high com-
pliance to DII, which involves consuming a high amount
of vegetables and fruits, ensures a high intake of vitamins
C, E, and ber [56]. Additionally, consuming nuts leads to
an increased intake of potassium, magnesium, calcium,
MUFA, PUFA, and vitamin E [57, 58]. Legumes provide
ber, B vitamins, iron, magnesium, zinc, and phosphorus
[59].
Our results, which revealed a strong association
between the DII score and the consumption of various
nutrients such as retinol, MUFA, PUFA, vitamins D, E, C,
B1, B2, B3, B5, B6, B9, B12, calcium, selenium, iron, mag-
nesium, sodium, potassium, phosphorus, and zinc. Same
correlation is supported by another study [50]. A study
discovered that women who consumed twice the recom-
mended daily amounts of vitamin B2, B3, total vitamin
A, retinol, monounsaturated fatty acids, polyunsaturated
fatty acids, potassium, phosphorus, calcium, protein, n-3
fatty acids, and n-6 fatty acids had a reduced occurrence
of MetS. Additionally, individuals with underlying health
conditions who raised their consumption of vitamin B2,
retinol, fruits, and white and red vegetables also expe-
rienced a lessened risk of MetS [60]. Another research
demonstrated the benecial impact of calcium in pre-
venting the accumulation of fat, likely due to the presence
of uncoupled protein (UCP2) in white adipose tissue that
promotes thermogenesis and diminishes waist circumfer-
ence [61, 62]. ese ndings support our results, which
suggest a signicant relationship between the DII score
and waist circumference. Another study showed that ele-
vated DII scores may contribute to abnormal character-
istics, which could result in elevated WC and TG levels
among overweight and obese women [63].
Based on our ndings, it was evident that the DASH
diet score exhibited a stronger correlation with vari-
ous aspects of the MetS. is was demonstrated by
the opposite relationship between alignment with the
DASH diet and elevated blood pressure and TG, along
with decreased levels of HDL-C. Similar to our nd-
ings, research also found that higher diet quality, accord-
ing to alignment with the DASH score, was linked with
reduced odds of MetS, and also correlated with higher
HDL-c and lower TG [64]. In women, there was evidence
of a signicant protective relation between the DASH
diet and MetS and its components [65, 66]. One research
found that compliance with the DASH diet score was
oppositely associated with the likelihood of MetS and its
certain components, such as increased blood pressure,
decreased HDL-C and elevated TG [3]. erefore, pro-
moting healthy dietary patterns such as the DASH diet
can eectively lower the risk of MetS [67].
One proposed mechanism is that DASH diet is high in
fruits and vegetables, low-fat dairy, and unsaturated veg-
etable oils, with reduced sugar-sweetened products and
red and processed meat [68]. Our ndings suggest that
sugar-sweetened beverages, sweets, and dairy group were
found to have the strongest correlation with adherence to
the DASH diet in our ndings. To be precise, there was
a direct relation between consuming added sugar and
having higher LDL and triglycerides levels, while having
lower levels of mean HDL cholesterol because of their
high glycemic load [69, 70]. Regularly consuming dairy
products has been found to be linked with a lower occur-
rence of hypertriglyceridemia [71].
Our study revealed that MDS was linked with lowered
blood glucose and levels of HDL-C. Researchers found
that compliance with Mediterranean diet can aect MetS
and all its parameters oxidative and inammatory status
[72–74]. e Mediterranean diet’s unique characteristics
lies in its ability to maintain diabetes balance, through
promoting anti-inammatory and antioxidant eects and
enhancing insulin sensitivity [75].
e usefulness of the Mediterranean diet in regulating
blood glucose and promoting healthy HDL cholesterol
levels can be attributed to several mechanisms. Our nd-
ings showed that MDS had the strongest correlation with
nut intake and ber which may aect blood glucose and
HDL levels. Consuming more nuts was linked to signi-
cantly decreased levels of all biomarkers related to diabe-
tes, including blood glucose [76]. Furthermore, a diet rich
in ber slows down the emptying of the stomach, result-
ing in reduced indicators of metabolic abnormalities [64,
66, 77].
is study presented that the MIND diet was related to
incremented SBP also lowered HDL-c. A research found
that greater compliance with the MIND diet was asso-
ciated with the reduced HDL-c [62]. A cross-sectional
study by Aminifar et al. [78] on Iranian adults showed
that the MIND diet score was contrarily correlated with
decreased HDL and obesity [79]. Tsartsou et al. [80]
reported that the main eect of polyphenol-rich olive
oil was to increase circulating HDL-C. A recent study
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Pashaei et al. Diabetology & Metabolic Syndrome (2024) 16:253
has claimed that, for the rst time, uncovered a relation-
ship between stronger adherence to the MIND diet and a
reduction in SBP [81].
e mechanism of the eectiveness of the MIND diet
on HDL and SBP can be attributed to its emphasis on
specic nutrient-rich foods like vegetables and healthy
fats especially olive oil which has the strongest correla-
tion with MIND diet. ese foods have been shown to
have favourable eects on HDL levels and BP because
of their high antioxidant and polyphenol content which
relieves inammation [62, 82, 83]. Additionally, the
MIND diet restricts the consumption of red meat, butter,
and high-fat dairy products, which are recognised con-
tributors to elevated BP and decreased HDL levels [42].
e ndings of this research indicated that there was
an association between the DQI score and reduced levels
of HDL-c and waist circumference. A related prospective
study conducted by Funtikova et al. [84] revealed that, a
higher DQI score at the beginning of the study was linked
to a decrease in waist circumference during a 10-year fol-
low-up. Similarly, the study by Alkerwi et al. [36] found
a negative relationship between DQI and HDL. Another
research indicated a link between DQI and factors con-
tributing to cardiometabolic risk. It also suggested that
changes in diet could potentially forecast a reduction in
Waist-Hip Ratio (WHR) [85].
Our results have shown that DQI had the strongest cor-
relation with vegetables, fruits and ber intake. ese ele-
ments, along with vitamin C, are commonly found in this
diet score and serve as primary sources of dietary anti-
oxidants [86]. Higher fruit and vegetable consumption
was associated with increased HDL-C levels [87]. Fur-
thermore, increased total antioxidant capacity, specially
vitamin C was linked to higher HDL-C [86]. Increased
HDL-C levels may also be attributed to increased con-
sumption of ber [88, 89], which is consistent with the
ndings of our study. Fiber could also have benecial role
in prevention of waist circumference gain which supports
our ndings [90].
As a result, these ve diet quality indices were signi-
cantly associated with MetS due to their high content of
fruits, vegetables, legumes, and unsaturated vegetable
oils, as well as their low content of saturated fat, red and
processed meat. Among the study population, the DASH
diet score was found to have the most favourable MetS
biomarker risk prole, while the DII score was mainly
linked to MetS. e research’s primary strength was its
use of all ve of these diet quality indices. ese indices
were derived from existing literature, based on popula-
tion data, and standardized to allow quantitative compar-
isons within the same study [36]. As far as we know, this
is one of the initial population-based studies to examine
the predictive ability of diet quality indices for MetS.
Moreover, the study’s strength also lies in its signicant
sample size.
As with the majority of dietary studies conducted on
populations, there are certain constraints involved in
this research that prevent us from drawing causal con-
clusions due to its cross-sectional design. Furthermore,
this study has limitations because it did not take into
account certain components of the Dietary Inammatory
Index (DII), namely saron, eugenol, ginger, turmeric,
pepper, rosemary, and thyme, when conducting calcula-
tions. Alcohol and red wine, on the other hand, were not
included in calculation of this study’s diet quality scores
because there was no information about wine in the used
FFQ and also due to religious reasons, alcohol consump-
tion is unusual or less commonly reported in the Iranian
population.
Conclusion
In conclusion, our study provides compelling evidence
that the DASH diet score is correlated with a highly
favourable risk biomarker prole. Also, the DII score is
primarily linked to MetS within the study population.
ese ndings underscore the importance of dietary
patterns in inuencing overall health and accentuate
the possible advantages of choosing the DASH diet to
improve biomarker proles and reduce the risk of MetS.
is research revealed that the DII is a valuable tool for
predicting the risk of MetS. e strong link between the
DII score and MetS observed in our study suggests that
dietary inammation plays a crucial role in the develop-
ment of MetS. By considering the intake of various pro-
inammatory and anti-inammatory nutrients, the DII
provides a comprehensive assessment of dietary patterns
and their eect on metabolic health. Incorporating the
DII into clinical practice and public health interventions
may enable targeted interventions to prevent or manage
MetS more eectively. Further RCT and clinical studies
are needed to be done in the future on a more diverse
population to conclude cause and eect outcomes.
Abbreviations
BMI Body Mass Index
BP Blood Pressure
DASH Dietary Approaches to Stop Hypertension
DII Dietary Inammatory Index
DM Diabetes Mellitus
DQIs Diet Quality Indices
FFQ Food-Frequency Questionnaire
HDL-C High Density Lipoprotein Cholesterol
MDS Mediterranean Dietary Score
MET Metabolic Equivalent of Task
MetS Metabolic syndrome
MIND Mediterranean-DASH Intervention for Neurodegenerative
Delay
MUFA Monounsaturated Fatty Acids
NCEP ATP III National Cholesterol Education Program Adult Treatment
Panel
PCOS Polycystic Ovarian Syndrome
PUFA Polyunsaturated Fatty Acids
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Page 11 of 13
Pashaei et al. Diabetology & Metabolic Syndrome (2024) 16:253
SBP Systolic Blood Pressure
TC Total Cholesterol
TG Triglyceride
UC2 Uncoupled Protein
WSI Wealth Score Index
Acknowledgements
We sincerely thank all patients participating in this study in advance, because
this study would not be possible without their cooperation. We also express
our appreciation to the colleagues at the Department of Nutrition, Mashhad
University of Medical, and also the PERSIAN Cohort Study Department,
Mashhad, for help and providing data for help in this epidemiological study.
Author contributions
KHAP and SRS designed the study. KHAP and SRS performed all statistical
analyses and wrote the rst draft of the manuscript. ZN contributed to draft
editing and revisions. All authors read and approved the nal manuscript.
Funding
No funding was received to assist with the preparation of this manuscript.
Data availability
No datasets were generated or analysed during the current study.
Declarations
Competing interests
The authors declare no competing interests.
Received: 8 February 2024 / Accepted: 18 October 2024
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