European Journal of Nutrition (2022) 61:357–372 https://doi.org/10.1007/s00394-021-02647-4 ORIGINAL CONTRIBUTION Pro‑vegetarian food patterns and cardiometabolic risk in the PREDIMED‑Plus study: a cross‑sectional baseline analysis Alejandro Oncina‑Cánovas1,2,3 · Jesús Vioque1,2,3,33 · Sandra González‑Palacios1,2,3 · Miguel Ángel Martínez‑González4,5,6 · Jordi Salas‑Salvadó4,7,8,9 · Dolores Corella4,10 · Dolores Zomeño4,11 · J. Alfredo Martínez4,12,13 · Ángel M. Alonso‑Gómez4,14 · Julia Wärnberg4,15 · Dora Romaguera4,16 · José López‑Miranda4,17 · Ramon Estruch4,18 · Rosa M. Bernal‑Lopez4,19 · José Lapetra4,20 · J. Luís Serra‑Majem4,21 · Aurora Bueno‑Cavanillas1,22 · Josep A. Tur4,23 · Vicente Martín‑Sánchez10,24 · Xavier Pintó4,25 · Miguel Delgado‑Rodríguez10,26 · Pilar Matía‑Martín27 · Josep Vidal28,29 · Clotilde Vázquez4,30 · Lidia Daimiel31 · Emili Ros4,32 · Estefanía Toledo4,5 · Nancy Babio4,7,8,9 · Jose V. Sorli4,10 · Helmut Schröder1,11 · María Angeles Zulet4,12 · Carolina Sorto‑Sánchez4,14 · Francisco Javier Barón‑López4,15 · Laura Compañ‑Gabucio1,2,3 · Marga Morey4,16 · Antonio García‑Ríos4,17 · Rosa Casas4,18 · Ana María Gómez‑Pérez4,19 · José Manuel Santos‑Lozano4,20 · Zenaida Vázquez‑Ruiz4,5 · Stephanie K. Nishi4,7,8,9 · Eva M. Asensio4,10 · Núria Soldevila1,11 · Itziar Abete4,12 · Leire Goicolea‑Güemez4,14 · Pilar Buil‑Cosiales4,5 · Jesús F. García‑Gavilán4,7,8,9 · Erik Canals1,11 · Laura Torres‑Collado1,2,3 · Manuela García‑de‑la‑Hera1,2,3 Received: 19 May 2021 / Accepted: 19 July 2021 / Published online: 9 August 2021 © The Author(s) 2021, corrected publication 2021 Abstract Purpose We explored the cross-sectional association between the adherence to three different provegetarian (PVG) food patterns defined as general (gPVG), healthful (hPVG) and unhealthful (uPVG), and the cardiometabolic risk in adults with metabolic syndrome (MetS) of the PREDIMED-Plus randomized intervention study. Methods We performed a cross-sectional analysis of baseline data from 6439 participants of the PREDIMED-Plus rand- omized intervention study. The gPVG food pattern was built by positively scoring plant foods (vegetables/fruits/legumes/ grains/potatoes/nuts/olive oil) and negatively scoring, animal foods (meat and meat products/animal fats/eggs/fish and seafood/dairy products). The hPVG and uPVG were generated from the gPVG by adding four new food groups (tea and cof- fee/fruit juices/sugar-sweetened beverages/sweets and desserts), splitting grains and potatoes and scoring them differently. Multivariable-adjusted robust linear regression using MM-type estimator was used to assess the association between PVG food patterns and the standardized Metabolic Syndrome score (MetS z-score), a composed index that has been previously used to ascertain the cardiometabolic risk, adjusting for potential confounders. Results A higher adherence to the gPVG and hPVG was associated with lower cardiometabolic risk in multivariable models. The regression coefficients for 5th vs. 1st quintile were − 0.16 (95% CI: − 0.33 to 0.01) for gPVG (p trend: 0.015), and − 0.23 (95% CI: − 0.41 to − 0.05) for hPVG (p trend: 0.016). In contrast, a higher adherence to the uPVG was associated with higher cardiometabolic risk, 0.21 (95% CI: 0.04 to 0.38) (p trend: 0.019). Conclusion Higher adherence to gPVG and hPVG food patterns was generally associated with lower cardiovascular risk, whereas higher adherence to uPVG was associated to higher cardiovascular risk. Keywords Dietary food patterns · Cardiometabolic risk · Metabolic syndrome · Pro-vegetarian Abbreviations hPVG Healthful pro-vegetarian PVG Pro-vegetarian uPVG Unhealthful pro-vegetarian gPVG General pro-vegetarian SUN Seguimiento Universidad de Navarra MetS Metabolic syndrome MetS z-score Metabolic syndrome z-score * Jesús Vioque vioque@umh.es CVD Cardiovascular disease DBP/SBP Diastolic and systolic blood pressure Extended author information available on the last page of the article 13 Vol.:(0123456789) 358 European Journal of Nutrition (2022) 61:357–372 BMI Body mass index consumption of plant-based foods, such as fruits, vegeta- MedDiet Mediterranean diet bles, legumes or nuts, which could explain its benefits. Thus, T2D Type 2 diabetes while the animal foods might play a harmful role because FFQ Food frequency questionnaire of their content in certain nutrients (e.g., saturated fat or WHR Waist to hip ratio heme iron), the plant-based foods may have a protective EDTA Ethylene diamine tetraacetic acid role through antioxidant nutrients (e.g., polyphenols) and HDL-c High density lipoprotein cholesterol fiber [13]. Hence the interest in knowing whether a pro-veg- METS Metabolic equivalents etarian (PVG) food pattern could act as an early marker of SD Standard deviation cardiometabolic risk may be well justified, especially in non- Q Quintile vegetarian populations. In a cross-sectional analysis of the CI Confidence interval PREDIMED study, a priori defined PVG index (gPVG) was PDI Plant-based diet index developed based by positively scoring the consumption of uPDI Unhealthful plant-based diet index plant-based foods and negatively the consumption of animal origin foods, in 7216 men and women aged 55–80 at high cardiovascular risk, showing a reduction in total mortality Introduction [14]. Since not all plant-based foods are equally healthy, Satija et al. subsequently proposed to differentiate between a Cardiovascular disease (CVD) is the leading cause of pre- healthful PVG food pattern (hPVG) which positively scores mature death and chronic disability worldwide and increases healthful plant-based foods (fruits, vegetables, legumes, the costs of the healthcare system [1]. Therefore, it is urgent whole grains, nuts, olive oil and coffee), and an unhealthful and a priority to provide solutions based on the best scien- PVG food pattern (uPVG), which positively scores unhealth- tific evidence for early detection and prevention [2]. Cardio- ful plant-based foods, such as juices, chips, refined cereals, metabolic risk indices or equations are a useful tool to early sugary drinks and pastries [15]. A more recently published evaluate CVD risk, and to explore the factors associated with study carried out with more than 70,000 U.S. women, found this early onset, thus helping to respond in the short term and that those women with higher adherence to hPVG were less to avoid the development of CVD in the long term. These likely to develop coronary heart disease, while those with equations take into account the main modifiable risk factors higher adherence to the uPVG showed a higher risk [16]. In for CVD, such as high blood glucose levels, triglycerides, the prospective follow-up study of the University of Nav- diastolic and systolic blood pressure (DBP/SBP), body mass arra (SUN) with 11,554 participants an inverse association index (BMI), waist and hip circumferences and low levels between adherence to a hPVG pattern and overweight and of HDL-c or high levels of LDL-c, to obtain a final score of obesity was shown [17]. cardiometabolic risk for each individual. Therefore, it might be of interest to add evidence about Diet is another modifiable risk factor of particular interest the association of increased adherence to PVG patterns on to public health in relation to cardiometabolic risk [3]. To early cardiovascular risk markers as measured by the stand- date, a multitude of studies have focused on exploring the ardized Metabolic Syndrome score (MetS z-score) and its role of diet in CVD from a macronutrient-focused approach, components, in the context of the PREDIMED-Plus ran- such as low-fat or low-carb diets [4]. However, there is less domized intervention study, which includes participants evidence on the role of a food pattern as a whole, focusing at high cardiovascular risk and with a low prevalence of on the consumption of foods and their interactions, and the vegetarians. This would help to broaden our knowledge of relationship it could have with cardiovascular risk [5, 6]. the possible protective role of these food patterns and to The Mediterranean diet (MedDiet) pattern has been one of propose more healthy dietary recommendations. Thus, the the most studied food patterns up to now. In a review of aim of this study was to explore the cross-sectional associa- 27 studies published by Martínez-González et al., a higher tion between three plant-based diet patterns (gPVG, hPVG adherence to the MedDiet pattern as measured by the and uPVG) and MetS z-score, in the adult population of the Trichopoulou’s index showed an 11% reduction in the risk PREDIMED-Plus study. of cardiovascular events [7]. The vegetarian diet is another food pattern that has also been recognized for its beneficial effects on numerous health Material and methods events, such as reducing morbidity including less risk of obesity, hypertension, or type 2 diabetes (T2D), among Study population others, and mortality from chronic diseases [8–12]. This food pattern is characterized by the absence of some ani- The present study is a cross-sectional assessment conducted mal foods, such as red and processed meats, and a high within the PREDIMED-Plus project (Spain) (www.​predi​ 13 European Journal of Nutrition (2022) 61:357–372 359 medpl​us.​es). This intervention study aims to evaluate the Participants randomized in effect of an intensive intervention with weight loss objec- PREDIMED-Plus study (n= 6874) tives based on the consumption of a low-calorie MedDiet, promotion of physical activity and behavioral therapy in the primary prevention of CVD and has been described in detail 50 participants without dietary information elsewhere [18]. Briefly, the participants included in this pro- ject were men (55–75 years) and women (60–75 years) with overweight or obesity (BMI 27–40 kg/m2) who meet at least n= 6824 participants three criteria of the Metabolic Syndrome (MetS) according to the updated criteria of the International Diabetes Fed- 188 participants with total eration and the American Heart Association and National energy intake out of predefine limits Heart, Lung and Blood Institute [19] and without prior car- diovascular events. n= 6636 participants Recruitment of participants took place between Septem- ber 2013 and December 2016 including 6874 participants who were randomized. After excluding participants with 3 participants without physical activity information missing data for the dietary baseline information, for the parameters necessary to the calculation of MetS z-score and those with implausible values for the mean daily n= 6633 participants energy intake (< 500 and > 3500 kcal/day for women, < 800 and > 4000 kcal/day for men) [20], 6439 participants were 194 participants with included in the present study (Fig. 1). All participants signed incomplete information for the the informed consent, and the project protocol was approved cardiometabolic risk score by the Research Ethics Committees from all recruiting cent- ers according to the ethical standards of the Declaration of Final sample (n= 6439) Helsinki. The trial was registered at the International Stand- ard Randomized Controlled Trial (ISRCTN:http://​www.​ Fig. 1  Flowchart of participants included in the present analysis from isrctn.​com/​ISRCT​N8989​8870). the PREDIMED-Plus Study Dietary assessment and pro‑vegetarian food In short, to create the different PVG food patterns, con- patterns sumption in grams of the 18 food groups was adjusted for total energy intake following the residual method [23]. To obtain the final score of the different PVG patterns, the After that, calorie-adjusted consumption in grams was cat- dietary information was evaluated using a semi-quantitative egorized into quintiles giving values of 1–5 according to food frequency questionnaire (FFQ) previously validated in the consumption quintile of each food group. In the case of Spain [21, 22]. The FFQ was completed at a baseline visit the gPVG food pattern seven components, belonging to the with the help of a trained interviewer. The FFQ includes a plant food groups, scored positively: vegetables, fruits, leg- list of 143 foods specifying the standard size or ration of umes, grains (whole and refined), potatoes (cooked, roasted consumption over a period of the previous year including 9 and/or fried), nuts and olive oil, and five components (meat possible responses to determine the frequency of consump- and other products, animal fats, eggs, seafood, and dairy), tion ranging from “never or < 1 month” to “ ≥ 6 times a day”. belonging to animal food groups were scored reversely For the creation of the gPVG pattern, the methodology (a value of 5 for lowest consumption). For the hPVG and proposed by Martínez-González [14] was followed. For uPVG, the grain group was separated into whole and refined healthful and unhealthful PVG versions, the method pro- grains and the potatoes group in fried or chips and cooked posed by Satija et al. [15] was the reference. Dietary infor- or roasted. Four new groups (tea and coffee, natural fruit mation from 18 food groups (Vegetables, Fruits, Legumes, juices, sweetened drinks and desserts or sweets) were also Whole Grains, Refined Grains, Cooked or Roasted Potatoes, introduced in both, hPVG and uPVG. To obtain the score Chips, Nuts, Olive Oil, Tea and Coffee, Fruit Juices, Sug- of each participant, the points for the 12 components, in the ary Drinks, Sweets and Desserts, Meat and Meat Products, case of the gPVG pattern, and for the 18 groups, in the case Animal Fats, Eggs, Fish and Seafood and Dairy) was used. of the hPVG and uPVG patterns, were be sum. So, the pos- Table 1 specifies the items included in the 18 food groups sible results ranged from 12 points (minimum adherence) to and the scoring criteria for each pattern. 60 points (maximum adherence) for the gPVG pattern, and 13 360 European Journal of Nutrition (2022) 61:357–372 Table 1  Scoring criteria for the PVG food ­patternsa Component Included foods gPVGc hPVG uPVG Plant food g­ roupsb 1. Vegetables Swiss chard, spinach, cauliflower, broccoli, lettuce, tomatoes, carrot, Positive Positive Reverse green beans, zucchini, eggplant, cucumber, peppers, asparagus, onion, other fresh vegetables 2. Fruits Citrus, banana, apple, pear, strawberry, cherry, peach, fig, melon, Positive Positive Reverse watermelon, grapes, kiwi, canned fruit 3. Legumes Lentils, beans, chickpeas, peas Positive Positive Reverse 4. Whole grains Whole-grain bread, muesli, brown rice, whole-grain pasta Positive Positive Reverse 5. Refined grains White bread, breakfast cereals, white rice, white pasta Positive Reverse Positive 6. Potatoes* Potato chips, French fries, boiled potatoes Positive Reverse Positive 7. Nuts Almonds, pistachios, walnuts, other nuts Positive Positive Reverse 8. Olive oil Refined olive oil, extra-virgin olive oil, olive pomace oil Positive Positive Reverse 9. Tea and coffee Caffeinated coffee, decaffeinated coffee, tea Not scored Positive Reverse 10. Fruit juices Orange juice, other natural fruits juice Not scored Reverse Positive 11. Sugar-sweetened beverages Regular soft drinks, low calorie soft drinks, fruit flavored punch or Not scored Reverse Positive noncarbonated beverages 12. Sweets and desserts Cookies, chocolate cookies, whole-grain cookies, home-made cakes Not scored Reverse Positive and biscuits, croissant, tea pastries, industrial cakes, donuts, cup- cake, muffin, chocolate, cocoa powder, nougat, marzipan, sugar Animal food groups 13. Meat/meat products Chicken or turkey with or without skin, beef, pork, lamb, rabbit, Reverse Reverse Reverse liver, viscera, Parma ham, cooked ham, cured meats, salami, mor- tadella, spicy pork sausage, hot dogs, foie gras, hamburger, bacon 14. Animal fats for cooking or as a spread Butter, lard Reverse Reverse Reverse 15. Eggs Eggs Reverse Reverse Reverse 16. Fish and other seafood White fish, blue fish, salad or smoked fish, clams, squid, shrimp, oil Reverse Reverse Reverse canned fish, natural canned fish 17. Dairy products Whole milk, skim or low-fat milk, condensed milk, cream, milk Reverse Reverse Reverse shake, full fat yogurt, low-fat yogurt, cheese, custard, ice cream a Positive indicates that higher consumption of this food group received higher scores. Reverse indicates that higher consumption of this food group received lower scores b In the hPVG food pattern, whole grains, fruits, vegetables, nuts, legumes, potatoes* (boiled), tea, and coffee were considered “healthy plant foods.” Refined grains, French fries and chips*, fruit juices, sugar-sweetened and artificially sweetened beverages, and sweets and desserts were considered “unhealthy plant foods.” The gPVG food pattern did not differentiate plant foods as healthy or unhealthy c In the gPVG food pattern, consumption of whole grains and refined grains was aggregated as the “grains” food group from 18 points (minimum adherence) to 90 points (maxi- Weight, height, waist and hip circumference were meas- mum adherence) for the hPVG and uPVG patterns. ured by duplicated with light clothing and no shoes using a calibrate scale, a wall-mounted stadiometer, and a non- MetS z‑score and its components elastic tape, respectively. Waist circumference was meas- ured midway between the lowest rib and the iliac crest. The continuous cardiometabolic risk scale that we used was Hip circumference was measured at the widest part. BMI the MetS z-score proposed by Franks [24]. Prior to the cal- was calculated as weight (kg) divided by height (meters) culation of this scale, all variables were standardized for the squared, and WHR as waist circumference (in cm) divided total number of participants, except for HDL and waist to by hip circumference (in cm). Blood pressure was meas- hip ratio (WHR) which were standardized by sex using sex- ured three times with a validated semiautomatic oscillometer specific cut-off points. The original version of MetS z-score after 5 min of rest in-between measurement (Omron HEM- includes fasting insulin in the formula, but we exclude that 705CP, Hoofddorp, The Netherlands), and the mean of the parameter from the calculation since it was not measured three measurements was used. After an overnight fast, blood and determined. We also calculated standardized compo- samples were collected at baseline and aliquots of serum nents of MetS z-score (BMI, WHR, SBP/DBP, HDL-c, and ethylene diamine tetraacetic acid (EDTA) plasma were plasma triglycerides and plasma glucose). immediately processed, coded and stored at − 80 °C in a 13 European Journal of Nutrition (2022) 61:357–372 361 central laboratory until analysis. High Density Lipoprotein smoker), alcohol intake (grams/day) and total physical activ- (HDL), serum glucose and triglyceride levels were deter- ity per day (METS-min/day). mined by standard enzymatic methods in automatic analyz- Statistical analyses were carried out with R 3.5.1 (R ers in local laboratories. Foundation for Statistical Computing, Vienna, Austria; The MetS z-score for each participant was obtained using http://​www.R-​proje​ct.​org). For robust linear regression the following formula: analyses, we also used a robust base package of statistical (BMI + WHR)/2 + (SBP + DBP)/2 + hyperglycemia software R. We used the database version of the PRED- (plasma fasting glucose)—HDLc + triglycerides IMED-Plus dated March 2019. Covariates Results Other sociodemographic variables, lifestyles and previous Baseline characteristics of participants according to quintiles history of various diseases, as well as assigned intervention, of the three PVG food patterns are presented in Table 2. was also collected at baseline. Information about total physi- Participants with a higher adherence to gPVG and hPVG cal activity in Metabolic Equivalents (METS) min/day was patterns were more likely to be older, more physically active, measured using the validated Regicor Short Physical Activ- have a lower BMI and better adhere to the MedDiet pattern. ity Questionnaire [25]. Adherence to MedDiet was valued Inversely, those participants with a higher adherence to the with a 17-item questionnaire, a modified version of a previ- uPVG pattern were more likely to be younger, smoker, less ously validated 14-item questionnaire [26], for an energy- physically active and less adherent to the MedDiet. Lower restricted version. education and lower alcohol consumption were observed in those participants with higher adherence to the gPVG Statistical analysis pattern, and a higher alcohol consumption in more adhered participants to hPVG and uPVG patterns. Diabetes preva- Descriptive analysis of participants’ characteristics accord- lence was lower in participants with a higher adherence to ing to quintiles of each PVG food pattern adherence was uPVG pattern. shown as mean and standard deviation (SD) for quantita- The results of the multiple robust linear regression analy- tive traits, and percentage for categorical variables. We sis for the association between the different PVG patterns performed the ANOVA test for quantitative variables and (in quintiles of adherence and in continuous for every 5 the Chi-square test for qualitative variables to compare the points) and the score of MetS with its components sepa- characteristics of the sample between adherence quintiles. rately are presented in Tables 3, 4 and 5. Reduction in MetS Multiple robust linear regression models were performed score and its components separately is shown in units of SD using an MM-type estimator by adjusting for possible con- according to quintiles of adherence for the PVG food pat- founders to explore the association between adherence to terns (p trend < 0.001). After adjusting for energy intake, each PVG food pattern (in quintiles and per 5 points incre- sex, age, educational level, smoking status, alcohol intake ment in adherence) and MetS z-score, along and with its and total physical activity per day, we observed a reduc- components separately [27]. Regression coefficients repre- tion in the global MetS z score (regression coefficient, ‘β’ sent the change in each outcome, where 1 unit is equivalent for fifth quintile (Q5) vs. first quintile (Q1) =− 0.16; 95% to a 1-SD difference in z scores, or a 1-unit difference in the CI: − 0.33 to 0.01; p-trend: 0.015), the BMI (β for Q5 vs MetS z-score or its components, per one point of dietary Q1 =− 0.14; 95% CI: − 0.22 to − 0.06; p trend: < 0.001) adherence to PVG food patterns, either in the continuous and the WHR (β for Q5 vs Q1 =− 0.16; 95% CI: − 0.23 (per each 5 points of adherence) or quintiles form of the to − 0.09; p trend: < 0.001) in those participants with “very different PVG food patterns. high” adherence (> 40 points) to the gPVG pattern (Table 3). Possible confounder selection was based on a previ- Also, we observed direct associations with DBP (β for Q5 vs ous review of the literature. It was also adjusted by those Q1 = 0.11; 95% CI: 0.03 to 0.18; p trend: 0.009) and HDL- variables that when estimating the effect of exposure, the cholesterol (β for Q5 vs Q1 = 0.07; 95% CI: 0.00 to 0.14; p effect changed by ≥ 10% when excluding the variable from trend: 0.046) in the fully adjusted gPVG food pattern model. the model. Crude model was minimally adjusted for energy When the gPVG pattern was considered as continuous vari- intake. Model 1 was additionally adjusted for age (continu- able (every 5 points of increment) we observed inverse asso- ous) and sex. Model 2 was additionally adjusted for edu- ciations with BMI β = − 0.06 (95% CI: − 0.09; − 0.04) and cational level (illiterate or primary education, secondary WHR β = − 0.06 (95% CI: − 0.08; − 0.03). On the other education, academic or graduate, and missing information), hand, we observed a direct association with DBP β = 0.03 smoking status (current smoker, former smoker, and never (95% CI: 0.01; 0.06). 13 362 European Journal of Nutrition (2022) 61:357–372 Table 2  Baseline characteristics of participants according to quintiles of the three PVG food patterns: the PREDIMED-Plus Study (n = 6439) gPVG food ­patterna Very low: < 33 Low: 33–35 Moderate: 36–37 High: 38–40 Very High: > 40 (n = 1589) (n = 1345) (n = 980) (n = 1297) (n = 1228) Sex, male (%) 52.1 52.2 51.0 50.9 52.1 Age (y) 64.4 (5.0)b 64.7 (4.8) 65.2 (4.9) 65.4 (4.9) 65.5 (4.9) Illiterate or primary education (%) 44.6 48.0 50.3 52.4 52.6 Hypertension (%) 83.8 82.8 84.4 82.7 84.0 High blood cholesterol (%) 69.4 70.3 67.6 70.0 69.3 Diabetes (%) 31.4 32.6 32.4 28.0 29.7 BMI (Kg/m2) 32.8 (3.5) 32.8 (3.5) 32.4 (3.4) 32.3 (3.5) 32.3 (3.3) Smoking (% current smokers) 14.7 11.6 11.6 12.6 12.7 Alcohol intake (g/d) 11.8 (15.6) 11.5 (15.1) 10.6 (15.0) 11.0 (15.1) 10.3 (14.4) Physical activity (MET-min/d)c 326.3 (311.3) 332.3 (330.4) 357.6 (314.8) 360.6 (335.5) 394.0 (353.4) Adherence to Mediterranean diet 7.8 (2.6) 8.3 (2.6) 8.6 (2.5) 8.8 (2.6) 9.3 (2.7) (0–17 points)d hPVG food pattern Very low: < 49 Low: 49–52 Moderate: 53–56 High: 57–60 Very high: > 60 (n = 1454) (n = 1231) (n = 1391) (n = 1241) (n = 1122) Sex, male (%) 52.3 49.3 51.8 54.4 50.6 Age (y) 64.5 (5.0) 64.7 (4.9) 65.1 (4.8) 65.4 (4.9) 65.5 (4.8) Illiterate or primary education (%) 49.1 50.0 50.1 48.5 48.7 Hypertension (%) 84.4 84.0 83.9 83.5 81.2 High blood cholesterol (%) 70.2 69.5 69.7 67.8 69.9 Diabetes (%) 32.1 29.7 29.2 32.2 30.8 BMI (Kg/m2) 32.8 (3.5) 32.6 (3.4) 32.5 (3.4) 32.3 (3.4) 32.4 (3.4) Smoking (% current smokers) 14.1 13.7 11.4 12.3 12.2 Alcohol intake (g/d) 9.9 (13.3) 10.4 (14.6) 11.6 (15.8) 11.6 (15.2) 12.3 (16.6) Physical activity (MET-min/d) 303.9 (307.1) 332.5 (315.0) 348.1 (319.6) 379.1 (335.0) 411.4 (367.8) Adherence to Mediterranean diet 7.2 (2.4) 8.2 (2.5) 8.5 (2.4) 9.1 (2.5) 10.0 (2.6) (0–17 points)d uPVG food pattern Very low: < 49 Low: 49–52 Moderate: 53–56 High: 57–60 Very high: > 60 (n = 1504) (n = 1197) (n = 1288) (n = 1192) (n = 1258) Sex, male (%) 51.1 51.5 51.6 53.9 50.7 Age (y) 65.7 (4.9) 64.7 (4.9) 65.1 (4.9) 64.7 (4.8) 64.6 (5.0) Illiterate or primary education (%) 48.1 48.0 49.5 49.8 51.2 Hypertension (%) 82.2 84.0 84.6 83.7 83.0 High blood cholesterol (%) 69.9 67.8 69.8 70.1 69.2 Diabetes (%) 37.3 30.2 31.1 26.8 27.0 BMI (Kg/m2) 32.4 (3.4) 32.5 (3.5) 32.6 (3.4) 32.6 (3.4) 32.6 (3.5) Smoking (% current smoker) 10.4 11.7 13.5 13.2 15.6 Alcohol intake (g/d) 8.6 (11.8) 10.8 (14.0) 11.6 (15.8) 12.3 (16.4) 12.7 (17.0) Physical activity (MET-min/d) 396.7 (344.6) 374.6 (356.3) 352.5 (320.1) 330.5 (306.6) 297.7 (307.1) Adherence to Mediterranean diet 10.0 (2.5) 9.1 (2.5) 8.5 (2.5) 7.7 (2.3) 7.0 (2.4) (0–17 points)d a Comparisons of characteristics across quintiles of the PVG food patterns were performed using 1-factor ANOVA for quantitative variables or chi-square tests for categorical variables b Mean (SD) (all such values) c MET-min metabolic equivalent task minutes d Adherence to an energy-restricted MedDiet was assessed using a 17-item questionnaire, a modified version of a validated 14-item questionnaire [26] 13 Table 3  Association between adherence to general PVG food pattern (βa and 95% confidence intervals for pattern in quintiles and continuous per 5-units) and metabolic syndrome z-score and its components at baseline in participants PREDIMED-Plus Study (n = 6439) gPVG food pattern quintile Very Low: 33–35 Moderate: 36–37 High: 38–40 Very high: > 40 p trend Per 5 points increment in low: < 33 (n = 1345) (n = 980) (n = 1297) (n = 1228) adherence (n = 1589) Metabolic syndrome z-scoreb Crude Ref − 0.02 (− 0.19; 0.14) − 0.06 (− 0.24; 0.13) − 0.23 (− 0.40; − 0.06) − 0.23 (− 0.40; − 0.06) 0.001 − 0.08 (− 0.14; − 0.03) Multiple adjusted 1 Ref 0.00 (− 0.17; 0.17) − 0.01 (− 0.20; 0.17) − 0.18 (− 0.35; − 0.01) − 0.16 (− 0.33; 0.01) 0.016 − 0.06 (− 0.11; 0.00) Multiple adjusted 2 Ref − 0.02 (− 0.19; 0.15) − 0.02 (− 0.20; 0.17) − 0.20 (− 0.37; − 0.03) − 0.16 (− 0.33; 0.01) 0.015 − 0.06 (− 0.12; 0.00) Body mass i­ndexb European Journal of Nutrition (2022) 61:357–372 Crude Ref 0.01 (− 0.07; 0.09) − 0.10 (− 0.19; − 0.02) − 0.14 (− 0.22; − 0.06) − 0.15 (− 0.23; -0.07) < 0.001 − 0.07 (− 0.09; − 0.04) Multiple adjusted 1 Ref 0.01 (− 0.07; 0.09) − 0.10 (− 0.19; − 0.02) − 0.14 (− 0.22; − 0.06) − 0.15 (− 0.23; − 0.07) < 0.001 − 0.07 (− 0.09; − 0.04) Multiple adjusted 2 Ref 0.00 (− 0.08; 0.08) − 0.11 (− 0.19; − 0.02) − 0.14 (− 0.22; − 0.06) − 0.14 (− 0.22; − 0.06) < 0.001 − 0.06 (− 0.09; − 0.04) Waist-to-hip ­ratioc Crude Ref − 0.04 (− 0.12; 0.03) − 0.07 (− 0.14; 0.01) − 0.10 (− 0.18; − 0.03) − 0.15 (− 0.22; − 0.07) < 0.001 − 0.05 (− 0.07; − 0.03) Multiple adjusted 1 Ref − 0.05 (− 0.12; 0.03) − 0.08 (− 0.15; 0.00) − 0.11 (− 0.19; − 0.04) − 0.16 (− 0.23; − 0.09) < 0.001 − 0.06 (− 0.08; − 0.03) Multiple adjusted 2 Ref − 0.05 (− 0.12; 0.02) − 0.08 (− 0.15; 0.00) − 0.12 (− 0.19; − 0.04) − 0.16 (− 0.23; − 0.09) < 0.001 − 0.06 (− 0.08; − 0.03) Systolic blood pressure Crude Ref 0.03 (− 0.05; 0.10) 0.05 (− 0.03; 0.13) 0.01 (− 0.06; 0.08) 0.03 (− 0.05; 0.10) 0.588 0.01 (− 0.01; 0.03) Multiple adjusted 1 Ref 0.02 (− 0.05; 0.09) 0.04 (− 0.04; 0.11) 0.00 (− 0.08; 0.07) 0.01 (− 0.06; 0.09) 0.910 0.00 (− 0.02; 0.03) Multiple adjusted 2 Ref 0.02 (− 0.05; 0.09) 0.05 (− 0.03; 0.13) 0.00 (− 0.07; 0.08) 0.03 (− 0.05; 0.10) 0.606 0.01 (− 0.02; 0.03) Diastolic blood ­pressureb Crude Ref 0.05 (− 0.02; 0.13) 0.02 (− 0.06; 0.10) 0.02 (− 0.06; 0.09) 0.01 (− 0.06; 0.09) 0.935 0.00 (− 0.02; 0.03) Multiple adjusted 1 Ref 0.08 (0.00; 0.15) 0.07 (− 0.01; 0.15) 0.08 (0.00; 0.15) 0.09 (0.02; 0.17) 0.023 0.03 (0.00; 0.05) Multiple adjusted 2 Ref 0.08 (0.01; 0.15) 0.07 (− 0.01; 0.15) 0.08 (0.01; 0.16) 0.11 (0.03; 0.18) 0.009 0.03 (0.01; 0.06) HDL-cholesterolc Crude Ref 0.03 (− 0.04; 0.10) 0.01 (− 0.07; 0.08) 0.04 (− 0.03; 0.12) 0.05 (− 0.02; 0.12) 0.137 0.01 (− 0.02; 0.03) Multiple adjusted 1 Ref 0.03 (− 0.04; 0.09) 0.00 (− 0.07; 0.08) 0.03 (− 0.04; 0.11) 0.04 (− 0.03; 0.11) 0.277 0.00 (− 0.02; 0.02) Multiple adjusted 2 Ref 0.03 (− 0.03; 0.10) 0.01 (− 0.06; 0.09) 0.06 (− 0.02; 0.13) 0.07 (0.00; 0.14) 0.046 0.01 (− 0.01; 0.04) Plasma ­triglyceridesb Crude Ref 0.01 (− 0.05; 0.06) 0.00 (− 0.06; 0.06) 0.01 (− 0.04; 0.06) − 0.01 (− 0.06; 0.04) 0.805 0.00 (− 0.01; 0.02) Multiple adjusted 1 Ref 0.01 (− 0.04; 0.06) 0.01 (− 0.05; 0.07) 0.02 (− 0.03; 0.08) 0.01 (− 0.05; 0.06) 0.688 0.01 (− 0.01; 0.03) Multiple adjusted 2 Ref 0.01 (− 0.04; 0.07) 0.02 (− 0.04; 0.08) 0.03 (− 0.02; 0.09) 0.02 (− 0.04; 0.07) 0.379 0.01 (0.00; 0.03) Plasma ­glucoseb 13 363 364 European Journal of Nutrition (2022) 61:357–372 When we specifically assessed the hPVG version, the MM-type estimators for linear robust regression models, the betas represent the change in each outcome, where 1 unit is equivalent to a 1-SD difference in z scores, or a 1-unit difference in the p trend test for linear trend were conducted using the adherence to a gPVG food pattern quintile, Crude adjusted for energy intake, multiple adjusted 1 additionally adjusted for sex and age, MetS z-score or its components, per one point of dietary adherence to PVG food patterns, either in the continuous (per each 5 points of adherence) or quintiles form of the different PVG food Per 5 points increment in associations were stronger. For the fully adjusted mod- − 0.01 (− 0.03; 0.01) − 0.01 (− 0.02; 0.01) − 0.01 (-0.03; 0.01) els (Table 4), the hPVG was associated with lower MetS z-scores (β for Q5 vs Q1 = − 0.23; 95% CI: − 0.41 to − 0.05; p trend: 0.016), BMI (β for Q5 vs Q1 = − 0.07; 95% CI: adherence − 0.15 to 0.02; p trend: 0.043) and WHR (β for Q5 vs Q1 = − 0.14; 95% CI: − 0.22 to − 0.07; p trend: < 0.001). We also observed inverse associations between the adher- ence to hPVG (per 5 points of increment) and MetS z-score, 0.228 0.150 0.174 β = − 0.06 (95% CI: − 0.10; − 0.02) and WHR β = − 0.04 p trend (95% CI: − 0.05; − 0.02). By contrast, in the fully adjusted models for the uPVG (Table 5) we observed a significant positive association with − 0.01 (− 0.07; 0.04) − 0.02 (− 0.07; 0.03) − 0.02 (− 0.07; 0.03) the MetS z-score (β for Q5 vs Q1 = 0.21; 95% CI: 0.04 to Very high: > 40 0.38; p trend: 0.019), DBP (β for Q5 vs Q1 = 0.08; 95% CI: 0.00 to 0.15; p trend: 0.042) and plasma triglycerides (β for (n = 1228) Q5 vs Q1 = 0.08; 95% CI: 0.02 to 0.13; p trend: 0.003). In addition, we observed an inverse association with HDL-cho- lesterol (β for Q5 vs Q1 = − 0.11; 95% CI: − 0.18 to − 0.04; multiple adjusted 2 additionally adjusted for educational level, smoking status alcohol intake, and total physical activity per day p trend: 0.001) and plasma glucose (β for Q5 vs Q1 = − 0.07; − 0.06 (− 0.10; − 0.01) − 0.06 (− 0.10; − 0.01) − 0.05 (− 0.10; − 0.01) 95% CI: − 0.12 to − 0.02; p trend: 0.002). In the models with continuous variables, we also observed inverse associa- tions per each 5 points increases in adherence to the uPVG High: 38–40 of β = − 0.02 (95% CI: − 0.04; − 0.01) for HDL-cholesterol (n = 1297) and β = − 0.02 (95% CI: − 0.03; − 0.01) plasma glucose, and direct associations of β = 0.02 (95% CI: 0.01; 0.03) for plasma triglycerides. − 0.01 (− 0.06; 0.04) − 0.01 (− 0.06; 0.04) − 0.01 (− 0.06; 0.04) Discussion Moderate: 36–37 The results of this study suggest that adults with MetS and (n = 980) a higher adherence to the general and hPVG food patterns showed more favorable cardiometabolic markers as meas- ured by the MetS z-score and several of its components. On the contrary, those participants with higher adherence to the − 0.01 (− 0.06; 0.04) − 0.01 (− 0.06; 0.04) − 0.01 (− 0.06; 0.04) uPVG food pattern showed worse cardiometabolic markers. Although the research about these food patterns is rela- tively recent, several studies have shown consistent results. gPVG food pattern quintile Low: 33–35 (n = 1345) A prospective cohort study in South Korean investigated the role of being adherent to four plant-based diet indi- ces (PDI) and found a positive linear association between HDL-c high-density lipoprotein-cholesterol higher adherence to an unhealthful plant-based diet (uPDI) (n = 1589) and the incidence of MetS [28]. In the Adventist Health low: < 33 Study 2, a prospective study with 96,000 participants of Very Ref Ref Ref the Seventh-day Adventist church mostly following vege- tarian diets, positive associations were found between veg- Data were standardized etarian diets and all components of metabolic syndrome Multiple adjusted 1 Multiple adjusted 2 Table 3  (continued) (triglycerides, DBP, SBP, waist circumference, BMI and glucose), although not so for HDL-c [29]. Two other pro- spective cohort studies in the USA and Spain have also patterns Crude found beneficial associations for weight change in the case b a c 13 Table 4  Association between adherence for healthful PVG food pattern (βa and 95% confidence intervals for pattern in quintiles and continuous, per 5-units) and metabolic syndrome z-score and its components at baseline in participants PREDIMED-Plus Study (n = 6439) hPVG food pattern quintile Very low: < 49 Low: 49–52 Moderate: 53–56 High: 57–60 Very high: > 60 p trend Per 5 points increment in (n = 1454) (n = 1231) (n = 1391) (n = 1241) (n = 1122) adherence Metabolic syndrome z-scoreb Crude Ref − 0.25 (− 0.43; − 0.08) − 0.32 (− 0.49; − 0.15) − 0.29 (− 0.47; − 0.12) − 0.40 (− 0.58; − 0.22) < 0.001 − 0.10 (− 0.14; − 0.06) Multiple adjusted 1 Ref − 0.23 (− 0.41; − 0.06) − 0.28 (− 0.45; − 0.11) − 0.24 (− 0.42; − 0.07) − 0.32 (− 0.50; − 0.14) < 0.001 − 0.08 (− 0.12; − 0.04) Multiple adjusted 2 Ref − 0.22 (− 0.39; − 0.04) − 0.24 (− 0.41; − 0.07) − 0.21 (− 0.39; − 0.03) − 0.23 (− 0.41; − 0.05) 0.016 − 0.06 (− 0.10; − 0.02) Body mass i­ndexb Crude Ref − 0.07 (− 0.15; 0.01) − 0.10 (− 0.18; − 0.02) − 0.15 (− 0.23; − 0.07) − 0.11 (− 0.20; − 0.02) 0.002 − 0.03 (− 0.05; − 0.01) Multiple adjusted 1 Ref − 0.08 (− 0.16; 0.00) − 0.11 (− 0.18; − 0.03) − 0.14 (− 0.23; − 0.06) − 0.12 (− 0.20; − 0.03) 0.002 − 0.03 (− 0.05; − 0.01) European Journal of Nutrition (2022) 61:357–372 Multiple adjusted 2 Ref − 0.07 (− 0.15; 0.01) − 0.10 (− 0.17; − 0.02) − 0.12 (− 0.20; − 0.04) − 0.07 (− 0.15; 0.02) 0.043 − 0.02 (− 0.04; 0.00) Waist-to-hip ­ratioc Crude Ref − 0.06 (− 0.14; 0.01) − 0.07 (− 0.15; 0.00) − 0.10 (− 0.17; − 0.02) − 0.15 (− 0.22; − 0.07) < 0.001 − 0.04 (− 0.05; − 0.02) Multiple adjusted 1 Ref − 0.06 (− 0.14; 0.01) − 0.08 (− 0.16; − 0.01) − 0.11 (− 0.18; − 0.03) − 0.16 (− 0.24; − 0.09) < 0.001 − 0.04 (− 0.06; − 0.03) Multiple adjusted 2 Ref − 0.06 (− 0.14; 0.01) − 0.08 (− 0.15; 0.00) − 0.10 (− 0.18; − 0.03) − 0.14 (− 0.22; − 0.07) < 0.001 − 0.04 (− 0.05; − 0.02) Systolic blood ­pressureb Crude Ref − 0.02 (− 0.10; 0.05) 0.03 (− 0.05; 0.11) 0.01 (− 0.06; 0.09) 0.01 (− 0.07; 0.09) 0.564 0.00 (− 0.02; 0.02) Multiple adjusted 1 Ref − 0.02 (− 0.09; 0.06) 0.02 (− 0.05; 0.10) 0.00 (− 0.08; 0.07) 0.01 (− 0.07; 0.08) 0.818 0.00 (− 0.02; 0.01) Multiple adjusted 2 Ref − 0.01 (− 0.09; 0.06) 0.02 (− 0.05; 0.10) 0.00 (− 0.07; 0.08) 0.01 (− 0.07; 0.09) 0.721 0.00 (− 0.02; 0.02) Diastolic blood ­pressureb Crude Ref − 0.08 (− 0.16; 0.00) − 0.06 (− 0.14; 0.02) − 0.07 (− 0.15; 0.00) − 0.10 (− 0.18; − 0.02) 0.020 − 0.03 (− 0.05; − 0.01) Multiple adjusted 1 Ref − 0.05 (− 0.13; 0.03) − 0.01 (− 0.09; 0.06) − 0.01 (− 0.09; 0.06) − 0.01 (− 0.09; 0.07) 0.966 0.00 (− 0.02; 0.01) Multiple adjusted 2 Ref − 0.05 (− 0.12; 0.03) − 0.01 (− 0.08; 0.07) − 0.01 (− 0.09; 0.07) 0.00 (− 0.08; 0.08) 0.785 0.00 (− 0.02; 0.02) HDL− ­cholesterolc Crude Ref − 0.02 (− 0.09; 0.05) 0.06 (− 0.01; 0.12) 0.07 (− 0.01; 0.14) 0.08 (0.01; 0.16) 0.006 0.02 (0.01; 0.04) Multiple adjusted 1 Ref − 0.02 (− 0.10; 0.05) 0.05 (− 0.02; 0.12) 0.06 (− 0.02; 0.13) 0.07 (− 0.01; 0.15) 0.021 0.02 (0.00; 0.04) Multiple adjusted 2 Ref − 0.03 (− 0.10; 0.04) 0.03 (− 0.03; 0.10) 0.04 (− 0.03; 0.12) 0.05 (− 0.02; 0.13) 0.068 0.01 (0.00; 0.03) b Plasma ­triglycerides Crude Ref − 0.06 (− 0.11; 0.00) − 0.07 (− 0.13; − 0.02) − 0.04 (− 0.10; 0.02) − 0.09 (− 0.15; − 0.04) 0.004 − 0.02 (− 0.03; − 0.01) Multiple adjusted 1 Ref − 0.05 (− 0.11; 0.01) − 0.06 (− 0.12; − 0.01) − 0.03 (− 0.08; 0.03) − 0.08 (− 0.14; − 0.02) 0.029 − 0.01 (− 0.03; 0.00) Multiple adjusted 2 Ref − 0.05 (− 0.11; 0.01) − 0.05 (− 0.11; 0.00) − 0.02 (− 0.07; 0.04) − 0.06 (− 0.11; 0.00) 0.165 − 0.01 (− 0.02; 0.00) 13 365 366 European Journal of Nutrition (2022) 61:357–372 of the gPVG and hPVG food patterns [17, 30], in line with MM-type estimators for linear robust regression models, the betas represent the change in each outcome, where 1 unit is equivalent to a 1-SD difference in z scores, or a 1-unit difference in the MetS z-score or its components, per one point of dietary adherence to PVG food patterns, either in the continuous (per each 5 points of adherence) or quintiles form of the different PVG food p trend test for linear trend were conducted using the adherence to a hPVG food pattern quintile, Crude adjusted for energy intake, multiple adjusted 1 additionally adjusted for sex and age, Per 5 points increment in our findings for BMI. The beneficial effect of a PVG food pattern might extend 0.01 (0.00; 0.02) 0.01 (0.00; 0.02) 0.01 (0.00; 0.02) beyond the improvement in cardiometabolic markers. In a previous research of 12,168 middle-aged adults in South adherence Korea (45–64 years of age at baseline), a higher adherence to a healthful PDI index and PVG patterns was associated to lower risk of cardiovascular morbidity and mortality, and lower all-cause mortality [31]. A lower all-cause mortality 0.250 0.189 0.110 p trend was also reported for those with a gPVG food pattern in an older population of the PREDIMED study [14]. Participants with better adherence to uPVG food pattern showed lower plasma glucose concentrations in our study. 0.01 (− 0.04; 0.07) 0.02 (− 0.03; 0.07) 0.03 (− 0.02; 0.08) This type of inverse association between uPDI food pattern and risk of T2D has been also shown previously [15]. An Very high: > 60 explanation for this unexpected association could be some (n = 1122) reverse causation, in the sense that those subjects with a T2D diagnosis were more aware of sugar content in different food groups and for this reason we observed lower prevalence multiple adjusted 2 additionally adjusted for educational level, smoking status, alcohol intake, and total physical activity per day of T2D among those better adhering a uPVG food pattern. 0.03 (− 0.02; 0.08) 0.03 (− 0.02; 0.09) 0.04 (− 0.01; 0.09) The mechanisms by which PVG food patterns could have cardiometabolic beneficial effects are multiple, likely related to the high content of plant-based foods with low glycemic High: 57–60 index [32]. A higher intake of plant foods like fruits, veg- (n = 1241) etables, nuts, legumes or whole grains, leads to a higher intake of different bioactive compounds such as fiber which has been associated with greater satiation and consequently, a lower energy intake and body weight [33]. Moreover, the 0.00 (− 0.05; 0.05) 0.00 (− 0.04; 0.05) 0.01 (− 0.04; 0.05) consumption of different types of fiber can modulate and improve glucose homeostasis by different mechanisms such Moderate: 53–56 as a delay of gastric emptying with consequent reduction in glucose absorption or via its fermentation in the colon, (n = 1391) that produces short-chain fatty acids, which may reduce glu- cose formation in hepatocytes [34, 35]. Other components of plant foods such as polyphenols or stanols, can reduce the endogen pathways of lipids formation. As shown in a − 0.02 (− 0.07; 0.03) − 0.02 (− 0.07; 0.03) − 0.02 (− 0.07; 0.03) systematic review and meta-analysis of observational and intervention studies, plant-based diets have been consist- Very low: < 49 Low: 49–52 ently associated with lower blood lipid levels such as total (n = 1231) hPVG food pattern quintile cholesterol, c-LDL and c-HDL [36]. Nitric oxide is another substance that we produce when take a sunlight bath or with the ingestion of some nutrients present in plant foods, like HDL-c high-density lipoprotein-cholesterol the amino acid L-arginine present in seeds and nuts [37] or (n = 1454) nitrates present in various vegetables including beets [38], which could improve blood pressure and endothelial and Ref Ref Ref platelet function through different mechanism. Conversely, uPVG may increase cardiometabolic risk Data were standardized because some of its components, such as chips, sugar-sweet- Multiple adjusted 1 Multiple adjusted 2 Table 3  (continued) ened beverages, sugary desserts, sweets, are rich in added Plasma ­glucoseb sugars, sodium, poor quality fats, refined starches and flavor enhancers. Many of these foods usually belong to ultra-pro- patterns Crude cessed food groups that could damage our internal systems, b a c 13 Table 5  Association between adherence for unhealthful PVG food pattern (βa and 95% confidence intervals for pattern in quintiles and continuous, per 5-units) and metabolic syndrome z-score and its components at baseline in participants PREDIMED-Plus Study (n = 6439) uPVG food pattern quintile Very Low: 49–52 Moderate: 53–56 High: 57–60 Very high: > 60 p trend Per 5 points increment in low: < 49 (n = 1197) (n = 1288) (n = 1192) (n = 1258) adherence (n = 1504) Metabolic syndrome z-scoreb Crude Ref − 0.03 (− 0.20; 0.14) 0.21 (0.04; 0.38) 0.07 (− 0.10; 0.24) 0.27 (0.10; 0.44) 0.001 0.06 (0.02; 0.09) Multiple adjusted 1 Ref − 0.05 (− 0.22; 0.12) 0.20 (0.03; 0.37) 0.05 (− 0.13; 0.22) 0.26 (0.09; 0.43) 0.002 0.05 (0.02; 0.09) Multiple adjusted 2 Ref − 0.04 (− 0.21; 0.13) 0.19 (0.02; 0.36) 0.01 (− 0.16; 0.19) 0.21 (0.04; 0.38) 0.019 0.04 (0.00; 0.07) Body mass i­ndexb European Journal of Nutrition (2022) 61:357–372 Crude Ref 0.03 (− 0.05; 0.11) 0.06 (− 0.02; 0.14) 0.05 (− 0.03; 0.13) 0.04 (− 0.04; 0.12) 0.299 0.01 (− 0.01; 0.03) Multiple adjusted 1 Ref 0.02 (− 0.06; 0.10) 0.04 (− 0.03; 0.12) 0.04 (− 0.04; 0.12) 0.02 (− 0.07; 0.10) 0.586 0.01 (− 0.01; 0.02) Multiple adjusted 2 Ref 0.01 (− 0.07; 0.09) 0.02 (− 0.06; 0.10) 0.00 (− 0.08; 0.08) − 0.02 (− 0.11; 0.06) 0.558 − 0.01 (− 0.02; 0.01) Waist-to-hip ­ratioc Crude Ref − 0.04 (− 0.12; 0.03) 0.06 (− 0.01; 0.14) 0.04 (− 0.03; 0.12) 0.06 (− 0.01; 0.14) 0.028 0.02 (0.00; 0.03) Multiple adjusted 1 Ref − 0.03 (− 0.11; 0.04) 0.07 (0.00; 0.14) 0.05 (− 0.03; 0.12) 0.07 (0.00; 0.15) 0.017 0.02 (0.00; 0.04) Multiple adjusted 2 Ref − 0.04 (− 0.11; 0.04) 0.05 (− 0.02; 0.12) 0.03 (− 0.05; 0.10) 0.05 (− 0.03; 0.12) 0.104 0.01 (0.00; 0.03) Systolic blood ­pressureb Crude Ref 0.03 (− 0.04; 0.11) 0.04 (− 0.03; 0.12) 0.02 (− 0.06; 0.10) − 0.02 (− 0.09; 0.06) 0.649 − 0.01 (− 0.02; 0.01) Multiple adjusted 1 Ref 0.06 (− 0.02; 0.13) 0.06 (− 0.02; 0.13) 0.04 (− 0.03; 0.12) 0.02 (− 0.06; 0.09) 0.713 0.00 (− 0.01; 0.02) Multiple adjusted 2 Ref 0.06 (− 0.02; 0.13) 0.06 (− 0.02; 0.13) 0.03 (− 0.05; 0.10) 0.01 (− 0.07; 0.08) 0.957 0.00 (− 0.02; 0.02) Diastolic blood ­pressureb Crude Ref 0.10 (0.03; 0.17) 0.07 (0.00; 0.14) 0.12 (0.04; 0.19) 0.12 (0.04; 0.20) 0.002 0.03 (0.01; 0.04) Multiple adjusted 1 Ref 0.07 (0.00; 0.15) 0.06 (− 0.01; 0.13) 0.09 (0.01; 0.16) 0.09 (0.01; 0.16) 0.020 0.02 (0.00; 0.04) Multiple adjusted 2 Ref 0.07 (− 0.01; 0.14) 0.06 (− 0.01; 0.13) 0.08 (0.00; 0.15) 0.08 (0.00; 0.15) 0.042 0.02 (0.00; 0.03) HDL-cholesterolc Crude Ref − 0.02 (− 0.09; 0.05) − 0.06 (− 0.12; 0.01) − 0.06 (− 0.13; 0.01) − 0.10 (− 0.17; − 0.03) 0.002 − 0.02 (− 0.04; 0.00) Multiple adjusted 1 Ref − 0.02 (− 0.09; 0.05) − 0.05 (− 0.12; 0.02) − 0.05 (− 0.12; 0.02) − 0.10 (− 0.17; − 0.03) 0.005 − 0.02 (− 0.03; 0.00) Multiple adjusted 2 Ref − 0.03 (− 0.10; 0.04) − 0.07 (− 0.14; 0.00) − 0.07 (− 0.14; 0.00) − 0.11 (− 0.18; − 0.04) 0.001 − 0.02 (− 0.04; − 0.01) Plasma ­triglyceridesb Crude Ref 0.00 (− 0.06; 0.05) 0.06 (0.01; 0.11) 0.06 (0.00; 0.11) 0.10 (0.05; 0.15) < 0.001 0.03 (0.01; 0.04) Multiple adjusted 1 Ref − 0.01 (− 0.07; 0.04) 0.05 (0.00; 0.11) 0.05 (0.00; 0.11) 0.09 (0.04; 0.15) < 0.001 0.02 (0.01; 0.04) Multiple adjusted 2 Ref − 0.01 (− 0.07; 0.04) 0.05 (0.00; 0.10) 0.04 (− 0.02; 0.09) 0.08 (0.02; 0.13) 0.003 0.02 (0.01; 0.03) 13 367 368 European Journal of Nutrition (2022) 61:357–372 worsening our glucose homeostasis, increasing our blood MM-type estimators for linear robust regression models, the betas represent the change in each outcome, where 1 unit is equivalent to a 1-SD difference in z scores, or a 1-unit difference in the MetS z-score or its components, per one point of dietary adherence to PVG food patterns, either in the continuous (per each 5 points of adherence) or quintiles form of the different PVG food p trend test for linear trend were conducted using the adherence to a uPVG food pattern quintile, Crude adjusted for energy intake multiple adjusted 1 additionally adjusted for sex and age, mul- Per 5 points increment in − 0.02 (− 0.03; − 0.01) − 0.02 (− 0.03; − 0.01) − 0.02 (− 0.03; − 0.01) pressure and modifying the ratio of blood lipids to a pattern of increased cardiometabolic risk, regardless of whether they come from plants or animals [39–41]. Apart from the potential beneficial effects of PVG food pat- adherence terns, we should consider their environmental consequences. Thus, in one analysis performed in the SUN Project which compared this pattern with other options as MedDiet or West- ern Diet, despite the fact that MedDiet presents the relatively lowest environmental footprint, gPVG food pattern was the eco- 0.005 0.015 0.002 p trend friendliest pattern and with the additional advantage of being more affordable when compared to MedDiet [42]. We acknowledge that our study has several limitations. − 0.06 (− 0.11; − 0.01) − 0.06 (− 0.11; − 0.01) − 0.07 (− 0.12; − 0.02) First, cross-sectional studies have a limited capacity to estab- lish causality, and may be prone to reverse causation as that mentioned above for the inverse association between uPVG Very high: > 60 and T2D. However, there are previous studies, some of them (n = 1258) with prospective design, that showed results in the same direction as our findings. Second, we took into account in the analyses several confounders such as sociodemographic tiple adjusted 2 additionally adjusted for educational level, smoking status alcohol intake, and total physical activity per day or lifestyle variables, but there may be other potential con- − 0.10 (− 0.15; − 0.05) − 0.09 (− 0.14; − 0.04) − 0.11 (− 0.16; − 0.05) founders not accounted for that may influence cardiometa- bolic risk. Another limitation is our diet measurement instru- ment. Although it was a validated instrument, it refers to the High: 57–60 (n = 1192) usual intake over the previous year and therefore makes it difficult to draw conclusions about the longer-term effects of the diet on cardiometabolic risk. Another limitation of our study concerns the study population, elderly people with metabolic syndrome and without prior cardiovascular − 0.04 (− 0.09; 0.01) − 0.04 (− 0.09; 0.02) − 0.04 (− 0.09; 0.01) events, which make it difficult to extrapolate results to other Moderate: 53–56 healthy or dissimilar populations. Thus, it is desirable to rep- licate our results in future studies with different populations. (n = 1288) Our study has also strengths. The quality and quantity of information that we measured is high thanks to our trained personal and the robustness of our findings, that were main- − 0.07 (− 0.12; − 0.02) − 0.07 (− 0.12; − 0.02) − 0.08 (− 0.13; − 0.03) tained after adjusted the models for possible confounders. Additionally, the use of three plant-based dietary patterns with a better assignment of several specific foods helped us uPVG food pattern quintile to distinguish that not all vegetarian patterns are as beneficial Low: 49–52 (n = 1197) as supposed to be. Our findings may also help to clarify some inconsistencies in the literature and to determine which type of dietary recommendations may be most beneficial when fol- HDL-c high-density lipoprotein-cholesterol lowing a PVG pattern to reduce the overall MetS risk. (n = 1504) low: < 49 In conclusion, this study suggests that among older adults at high cardiometabolic risk, a greater adherence to general Very Ref Ref Ref and hPVG food patterns are associated with lower cardio- metabolic risk, while a greater adherence to uPVG food pat- Data were standardized tern is associated with a higher cardiometabolic risk. Further Multiple adjusted 1 Multiple adjusted 2 Table 3  (continued) studies are recommended to investigate if these associations Plasma ­glucoseb are also observed in other healthy populations. patterns Crude Acknowledgements We thank all Predimed-Plus participants and investigators. CIBEROBN, CIBERESP, and CIBERDEM are initiatives b a c 13 European Journal of Nutrition (2022) 61:357–372 369 of the Instituto de Salud Carlos III (ISCIII), Madrid, Spain. The Hoji- Instituto Cervantes in Albuquerque, Milano and Tokyo, Pernod Ricard, blanca (Lucena, Spain) and Patrimonio Comunal Olivarero (Madrid, Fundación Dieta Mediterránea (Spain), Wine and Culinary Internation- Spain) food companies donated extra-virgin olive oil. The Almond al Forum and Lilly Laboratories; non-financial support from Sociedad Board of California (Modesto, CA), American Pistachio Growers Española de Nutrición and Fundación Bosch y Gimpera; and grants (Fresno, CA), and Paramount Farms (Wonderful Company, LLC, Los from Uriach Laboratories. E.R. reports grants, personal fees, non-finan- Angeles, CA) donated nuts. cial support and other from California Walnut Commission, during the conduct of the study; grants, personal fees, non-financial support and Author’s contributions AOC and JV conducted the statistical analy- other from Alexion; grants from Amgen and Pfizer; grants, personal ses and drafted the article. All authors contributed substantially to the fees and other from Sanofi Aventis; personal fees, non-financial sup- acquisition of data or analysis and interpretation of data. All authors port and other from Ferrer International, Danone and Merck Sharp & revised the article critically for important intellectual content. All Dohme, personal fees and other from Amarin, outside the submitted authors approved the final version to be published. work. S.K.N. received research support from a Canadian Institutes of Heath Research and is a volunteer member of the non-for-profit group Plant Based Canada. The rest of the authors have declared that no com- Funding Open Access funding provided thanks to the CRUE-CSIC peting interests exist. The funders had no role in the design of the study; agreement with Springer Nature. This work was supported by the offi- in the collection, analyses, or interpretation of data; in the writing of the cial Spanish Institutions for funding scientific biomedical research, manuscript, or in the decision to publish the results. 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PI19/01032, PI19/00576, PI19/00017, PI19/01226, PI19/00781, PI19/01560, PI19/01332, PI20/01802, PI20/00138, PI20/01532, PI20/00456, PI20/00339, PI20/00557, PI20/00886, PI20/01158); the Especial Action Project entitled: Implementación y evaluación de References una intervención intensiva sobre la actividad física Cohorte PRED- IMED-Plus grant to JS-S; the European Research Council (Advanced 1. Roth GA, Mensah GA, Johnson CO, GBD-NHLBI-JACC Global Research Grant 2014–2019; agreement #340918) granted to MÁM- Burden of Cardiovascular Diseases Writing Group (2020) Global G.; the Recercaixa (number 2013ACUP00194) grant to JS-S; grants burden of cardiovascular diseases and risk factors, 1990–2019: from the Consejería de Salud de la Junta de Andalucía (PI0458/2013, update from the GBD 2019 study. 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BMJ Open 9:e021541. https://​doi.​org/​10.​1136/​ bmjop​en-​2018-​021541 13 European Journal of Nutrition (2022) 61:357–372 371 Authors and Affiliations Alejandro Oncina‑Cánovas1,2,3 · Jesús Vioque1,2,3,33 · Sandra González‑Palacios1,2,3 · Miguel Ángel Martínez‑González4,5,6 · Jordi Salas‑Salvadó4,7,8,9 · Dolores Corella4,10 · Dolores Zomeño4,11 · J. Alfredo Martínez4,12,13 · Ángel M. Alonso‑Gómez4,14 · Julia Wärnberg4,15 · Dora Romaguera4,16 · José López‑Miranda4,17 · Ramon Estruch4,18 · Rosa M. Bernal‑Lopez4,19 · José Lapetra4,20 · J. Luís Serra‑Majem4,21 · Aurora Bueno‑Cavanillas1,22 · Josep A. Tur4,23 · Vicente Martín‑Sánchez10,24 · Xavier Pintó4,25 · Miguel Delgado‑Rodríguez10,26 · Pilar Matía‑Martín27 · Josep Vidal28,29 · Clotilde Vázquez4,30 · Lidia Daimiel31 · Emili Ros4,32 · Estefanía Toledo4,5 · Nancy Babio4,7,8,9 · Jose V. Sorli4,10 · Helmut Schröder1,11 · María Angeles Zulet4,12 · Carolina Sorto‑Sánchez4,14 · Francisco Javier Barón‑López4,15 · Laura Compañ‑Gabucio1,2,3 · Marga Morey4,16 · Antonio García‑Ríos4,17 · Rosa Casas4,18 · Ana María Gómez‑Pérez4,19 · José Manuel Santos‑Lozano4,20 · Zenaida Vázquez‑Ruiz4,5 · Stephanie K. Nishi4,7,8,9 · Eva M. Asensio4,10 · Núria Soldevila1,11 · Itziar Abete4,12 · Leire Goicolea‑Güemez4,14 · Pilar Buil‑Cosiales4,5 · Jesús F. García‑Gavilán4,7,8,9 · Erik Canals1,11 · Laura Torres‑Collado1,2,3 · Manuela García‑de‑la‑Hera1,2,3 1 16 CIBER de Epidemiología y Salud Pública (CIBERESP), Health Research Institute of the Balearic Islands (IdISBa), Instituto de Salud Carlos III, Madrid, Spain Palma de Mallorca, Spain 2 17 Instituto de Investigación Sanitaria y Biomédica de Alicante, Department of Internal Medicine, Maimonides Biomedical Universidad Miguel Hernández (ISABIAL-UMH), Alicante, Research Institute of Cordoba (IMIBIC), Reina Sofia Spain University Hospital, University of Cordoba, Cordoba, Spain 3 18 Nutritional Epidemiology Unit, University Miguel Department of Internal Medicine, Institut D’Investigacions Hernandez, Alicante, Spain Biomèdiques August Pi Sunyer (IDIBAPS), Hospital Clinic, 4 University of Barcelona, Barcelona, Spain Centro de Investigación Biomédica en Red Fisiopatología 19 de La Obesidad y La Nutrición (CIBEROBN), Institute Department of Endocrinology, Instituto de Investigación of Health Carlos III, Madrid, Spain Biomédica de Málaga (IBIMA), Virgen de La Victoria 5 Hospital, University of Málaga, Málaga, Spain Department of Preventive Medicine and Public Health, 20 IDISNA, University of Navarra, Pamplona, Spain Department of Family Medicine, Research Unit, Distrito 6 Sanitario Atención Primaria Sevilla, Sevilla, Spain Department of Nutrition, Harvard T.H. Chan School 21 of Public Health, Boston, MA, USA Research Institute of Biomedical and Health Sciences 7 (IUIBS), University of Las Palmas de Gran Canaria Departament de Bioquímica I Biotecnologia, Universitat and Centro Hospitalario Universitario Insular Rovira I Virgili, Unitat de Nutrició, Reus, Spain Materno Infantil (CHUIMI), Canarian Health Service, 8 Nutrition Unit, University Hospital of Sant Joan de Reus, Las Palmas de Gran Canaria, Spain Reus, Spain 22 Department of Preventive Medicine and Public Health, 9 Institut D’Investigació Sanitària Pere Virgili (IISPV), Reus, University of Granada, Granada, Spain Spain 23 Research Group on Community Nutrition and Oxidative 10 Department of Preventive Medicine, University of Valencia, Stress, University of Balearic Islands, Palma de Mallorca, Valencia, Spain Spain 11 24 Unit of Cardiovascular Risk and Nutrition, Institut Institute of Biomedicine (IBIOMED), University of León, Hospital del Mar de Investigaciones Médicas Municipal León, Spain D’Investigació Médica (IMIM), Barcelona, Spain 25 Lipids and Vascular Risk Unit, Internal Medicine, Hospital 12 Department of Nutrition, Food Sciences, and Physiology, Universitario de Bellvitge, Hospitalet de Llobregat, University of Navarra, Pamplona, Spain Barcelona, Spain 13 26 Precision Nutrition Program, IMDEA Food, CEI UAM + Division of Preventive Medicine, Faculty of Medicine, CSIC, Madrid, Spain University of Jaén, Jaén, Spain 14 27 Bioaraba Health Research Institute, Cardiovascular, Department of Endocrinology and Nutrition, Instituto de Respiratory and Metabolic Area, Osakidetza Basque Health Investigación Sanitaria Hospital Clínico San Carlos (IdISSC), Service, Araba University Hospital, University of the Basque Madrid, Spain Country UPV/EHU, Vitoria‑Gasteiz, Spain 28 CIBER Diabetes y Enfermedades Metabólicas 15 EpiPHAAN Research Group, School of Health Sciences, (CIBERDEM), Instituto de Salud Carlos III (ISCIII), Madrid, Instituto de Investigación Biomédica de Málaga (IBIMA), Spain University of Málaga, 29010 Málaga, Spain 13 372 European Journal of Nutrition (2022) 61:357–372 29 32 Department of Endocrinology, Institut D’Investigacions Lipid Clinic, Department of Endocrinology and Nutrition, Biomédiques August Pi Sunyer (IDIBAPS), Hospital Clinic, Institut D’Investigacions Biomèdiques August Pi Sunyer University of Barcelona, Barcelona, Spain (IDIBAPS), Hospital Clínic, Barcelona, Spain 30 33 Department of Endocrinology and Nutrition, Hospital Dpto. Salud Pública, Facultad de Medicina, Hª de La Fundación Jimenez Díaz, Instituto de Investigaciones Ciencia y Ginecología, Avda. Ramón y Cajal s/n, Sant Joan Biomédicas IISFJD, University Autonoma, Madrid, Spain d’Alacant, 03550 Alicante, Spain 31 Nutritional Control of the Epigenome Group, IMDEA Food, CEI UAM + CSIC, Madrid, Spain 13