ORIGINAL RESEARCH published: 13 December 2021 doi: 10.3389/fnagi.2021.782067 Mediterranean, DASH, and MIND Dietary Patterns and Cognitive Function: The 2-Year Longitudinal Changes in an Older Spanish Cohort Stephanie K. Nishi 1,2,3,4,5* , Nancy Babio 1,2,3,6* , Carlos Gómez-Martínez 1,2,3 , Miguel Ángel Martínez-González 3,7,8 , Emilio Ros 3,9 , Dolores Corella 3,10 , Olga Castañer 3,11 , J. Alfredo Martínez 3,12,13 , Ángel M. Alonso-Gómez 3,14 , Julia Wärnberg 3,15 , Jesús Vioque 16,17 , Dora Romaguera 3,18 , José López-Miranda 3,19 , Ramon Estruch 3,20 , Francisco J. Tinahones 3,21 , José Lapetra 3,22 , J. Luís Serra-Majem 3,23 , Aurora Bueno-Cavanillas 16,24,25 , Josep A. Tur 3,26 , Vicente Martín Sánchez 16,27 , Xavier Pintó 3,28 , Miguel Delgado-Rodríguez 16,29 , Pilar Matía-Martín 30 , Josep Vidal 31,32 , Clotilde Vázquez 3,33 , Lidia Daimiel 34 , Cristina Razquin 3,7 , Oscar Coltell 3,35 , Nerea Becerra-Tomás 1,2,3 , Rafael De La Torre Fornell 3,36 , Itziar Abete 3,12 , Carolina Sorto-Sanchez 3,14 , Francisco Javier Barón-López 3,15 , Antonio José Signes-Pastor 16,17 , Jadwiga Konieczna 3,18 , Antonio Garcia-Rios 3,19 , Rosa Casas 3,20 , Ana Maria Gomez-Perez 3,21 , José Manuel Santos-Lozano 3,22 , Ana García-Arellano 3,21 , Patricia Guillem-Saiz 3,10 , Jiaqi Ni 1,2,3 , Maria Trinidad Soria-Florido 37 , M. Ángeles Zulet 3,12 , Jessica Vaquero-Luna 3,14 , Edited by: Estefanía Toledo 3,7 , Montserrat Fitó 3,11 and Jordi Salas-Salvadó 1,2,3,6 Yashi Mi, University of Arizona, United States 1 Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, and Hospital Universitari San Joan de Reus, Unitat Reviewed by: de Nutrició Humana, Reus, Spain, 2 Institut d’Investigació Sanitária Pere Virgili (IISPV), Reus, Spain, 3 Consorcio CIBER, M.P. Thomas Holland, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain, 4 Toronto 3D (Diet, Rush University, United States Digestive Tract and Disease) Knowledge Synthesis and Clinical Trials Unit, Toronto, ON, Canada, 5 Clinical Nutrition and Risk Nazanin Moslehi, Factor Modification Centre, St. Michael’s Hospital, Unity Health Toronto, Toronto, ON, Canada, 6 Nutrition Unit, University Shahid Beheshti University of Medical Hospital of Sant Joan de Reus, Reus, Spain, 7 Department of Preventive Medicine and Public Health, University of Navarra, Sciences, Iran IdiSNA, Pamplona, Spain, 8 Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States, 9 Christy Tangney, Department of Endocrinology and Nutrition, Lipid Clinic, Institut d’Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Rush University Medical Center, Hospital Clínic, Barcelona, Spain, 10 Department of Preventive Medicine, University of Valencia, Valencia, Spain, 11 Unit of United States Cardiovascular Risk and Nutrition, Institut Hospital del Mar de Investigaciones Médicas Municipal d’Investigació Médica (IMIM), Barcelona, Spain, 12 Department of Nutrition, Food Sciences, and Physiology, Center for Nutrition Research, *Correspondence: University of Navarra, IdiSNA, Pamplona, Spain, 13 Precision Nutrition and Cardiometabolic Health Program, IMDEA Food, Stephanie K. Nishi CEI UAM + CSIC, Madrid, Spain, 14 Bioaraba Health Research Institute, Cardiovascular, Respiratory and Metabolic Area, stephanie.nishi@urv.cat Osakidetza Basque Health Service, Araba University Hospital, University of the Basque Country UPV/EHU, Vitoria-Gasteiz, Nancy Babio Spain, 15 EpiPHAAN Research Group, School of Health Sciences, Instituto de Investigación Biomédica de Málaga (IBIMA), nancy.babio@urv.cat University of Málaga, Málaga, Spain, 16 CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain, 17 Instituto de Investigación Sanitaria y Biomédica de Alicante, Universidad Miguel Hernández (ISABIAL-UMH), Specialty section: Alicante, Spain, 18 Health Research Institute of the Balearic Islands (IdISBa), Palma de Mallorca, Spain, 19 Department of This article was submitted to Internal Medicine, Maimonides Biomedical Research Institute of Cordoba (IMIBIC), Reina Sofia University Hospital, University Neurocognitive Aging and Behavior, of Cordoba, Cordoba, Spain, 20 Department of Internal Medicine, Institut d’Investigacions Biomédiques August Pi Sunyer a section of the journal (IDIBAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain, 21 Department of Endocrinology, Virgen de la Victoria Frontiers in Aging Neuroscience Hospital, Instituto de Investigación Biomédica de Málaga (IBIMA), University of Málaga, Málaga, Spain, 22 Department of Received: 23 September 2021 Family Medicine, Research Unit, Distrito Sanitario Atención Primaria Sevilla, Seville, Spain, 23 Research Institute of Biomedical Accepted: 08 November 2021 and Health Sciences (IUIBS), University of Las Palmas de Gran Canaria and Centro Hospitalario Universitario Insular Materno Published: 13 December 2021 Infantil (CHUIMI), Canarian Health Service, Las Palmas de Gran Canaria, Spain, 24 Department of Preventive Medicine and Frontiers in Aging Neuroscience | www.frontiersin.org 1 December 2021 | Volume 13 | Article 782067 Nishi et al. Dietary Patterns and Cognitive Function Public Health, University of Granada, Granada, Spain, 25 Instituto de Investigación Biosanitaria ibs, GRANADA, Granada, Spain, 26 Research Group on Community Nutrition and Oxidative Stress, University of Balearic Islands, Palma de Mallorca, Spain, 27 Institute of Biomedicine (IBIOMED), University of León, León, Spain, 28 Lipids and Vascular Risk Unit, Internal Medicine, Hospital Universitario de Bellvitge, Hospitalet de Llobregat, Barcelona, Spain, 29 Division of Preventive Medicine, Faculty of Medicine, University of Jaén, Jaén, Spain, 30 Department of Endocrinology and Nutrition, Instituto de Investigación Sanitaria Hospital Clínico San Carlos (IdISSC), Madrid, Spain, 31 CIBER Diabetes y Enfermedades Metabólicas (CIBERDEM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain, 32 Department of Endocrinology, Institut d’ Investigacions Biomédiques August Pi Sunyer (IDIBAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain, 33 Department of Endocrinology and Nutrition, Hospital Fundación Jimenez Díaz, Instituto de Investigaciones Biomédicas IISFJD, University Autonoma, Madrid, Spain, 34 Nutritional Control of the Epigenome Group, Precision Nutrition and Obesity Program, IMDEA Food, CEI UAM + CSIC, Madrid, Spain, 35 Department of Computer Languages and Systems, Universitat Jaume I, Castellon, Spain, 36 Integrative Pharmacology and Systems Neurosciences, Instituto Hospital del Mar de Investigaciones Médicas (IMIM), Barcelona, Spain, 37 Division of Pure and Applied Biochemistry, Lund University, Lund, Sweden Background and Aims: Plant-forward dietary patterns have been associated with cardiometabolic health benefits, which, in turn, have been related to cognitive performance with inconsistent findings. The objective of this study was to examine the relationship between baseline adherence to three a priori dietary patterns (Mediterranean, DASH, and MIND diets) with 2-year changes in cognitive performance in older adults with overweight or obesity and high cardiovascular disease risk. Methods: A prospective cohort analysis was conducted within the PREDIMED-Plus trial, involving 6,647 men and women aged 55–75 years with overweight or obesity and metabolic syndrome. Using a validated, semiquantitative 143-item food frequency questionnaire completed at baseline, the dietary pattern adherence scores were calculated. An extensive neuropsychological test battery was administered at baseline and 2-year follow-up. Multivariable-adjusted linear regression models were used to assess associations between 2-year changes in cognitive function z-scores across tertiles of baseline adherence to the a priori dietary patterns. Results: Adherence to the Mediterranean diet at baseline was associated with 2-year changes in the general cognitive screening Mini-Mental State Examination (MMSE, β: 0.070; 95% CI: 0.014, 0.175, P-trend = 0.011), and two executive function-related assessments: the Trail Making Tests Part A (TMT-A, β: −0.054; 95% CI: −0.110, − 0.002, P-trend = 0.047) and Part B (TMT-B, β: −0.079; 95% CI: −0.134, −0.024, P-trend = 0.004). Adherence to the MIND diet was associated with the backward recall Digit Span Test assessment of working memory (DST-B, β: 0.058; 95% CI: 0.002, 0.114, P-trend = 0.045). However, higher adherence to the DASH dietary pattern was not associated with better cognitive function over a period of 2 years. Conclusion: In older Spanish individuals with overweight or obesity and at high cardiovascular disease risk, higher baseline adherence to the Mediterranean dietary pattern may be associated with better cognitive performance than lower adherence over a period of 2 years. Keywords: cognition, dietary pattern, Mediterranean diet (MedDiet), DASH diet, MIND diet INTRODUCTION rise rapidly (United Nations et al., 2020). Globally, dementia affects an estimated 50 million people, and this prevalence Cognitive decline, associated with aging, is a serious is projected to increase over 130 million by 2050 (World public health concern, given the increasing prevalence of Health Organization [WHO], 2019). Epidemiological studies neurodegenerative diseases as people are living longer and further suggest a negative interaction of aging and obesity with the proportion of older persons worldwide continues to cognitive dysfunction (Kanaya et al., 2009; Hassing et al., 2010). Frontiers in Aging Neuroscience | www.frontiersin.org 2 December 2021 | Volume 13 | Article 782067 Nishi et al. Dietary Patterns and Cognitive Function With the prevalence of overweight and obesity affecting an MATERIALS AND METHODS estimated 30% of the adult population and more, there are additional adverse implications for cognition health (Ng et al., Study Design 2014; Balasubramanian et al., 2020). Cognitive decline carries The present analyses were conducted within the framework of the a significant social and economic burden, given cognitive PREvención con DIeta MEDiterránea (PREDIMED)-Plus trial, impairment and dementia are strong predictors of functional as an observational cohort, assessing the longitudinal (2-year) disability and dependence (Petersen et al., 2001). Cognitive associations between baseline adherence to prespecified dietary decline is a normal part of the aging process; however, the rate patterns and cognitive performance. The PREDIMED-Plus study of decline may vary depending on the differences in genetic and is an ongoing 6-year, multicenter, randomized, parallel-group lifestyle-related factors (Wu et al., 2020). and primary prevention trial conducted in Spain. The aim of The potential of modifiable lifestyle factors is important the trial is to assess the effect of an intensive weight loss as there are no effective pharmacological agents identified for intervention program based on an energy-restricted traditional the improvement of cognition or delay of the progression MedDiet and physical activity promotion and behavioral support, of cognitive decline (Petersen et al., 2018). Diet is a key on clinical cardiovascular events, than usual care and dietary lifestyle risk factor. Individual nutrients and foods have been counseling intervention only with an energy unrestricted inconsistently associated with cognitive function, including MedDiet (control group). More detailed information about the some vitamins, carotenoids, long-chain n-3 polyunsaturated study protocol can be found at http://predimedplus.com/ and fatty acids (PUFAs), such as seafood, and whole foods rich elsewhere (Martínez-González et al., 2019). in polyphenols, such as fruits and vegetables, nuts, olive oil, and coffee (Rutjes et al., 2018; Ammar et al., 2020; Brainard et al., 2020). As food is consumed as part of a Participants dietary pattern, it is important to consider the interactions Participants were recruited between October 2013 and December and associations of whole dietary approaches. Three dietary 2016 in 23 Spanish health centers. Eligible participants were patterns, in particular, are hypothesized to have a beneficial community-dwelling adults (55–75 years) with overweight or impact on cognitive function: the Mediterranean diet (MedDiet), obesity (BMI: 27–40 kg/m2 ) who met at least three criteria the Dietary Approaches to Stop Hypertension (DASH), and for metabolic syndrome, namely, without stroke, myocardial the MedDiet-DASH Intervention for Neurodegenerative Delay infarction, or diagnosis of neurodegenerative disease at baseline, (MIND). The MedDiet and DASH are currently promoted for according to the International Diabetes Federation and the their cardiovascular benefits (Arnett et al., 2019) yet may also American Heart Association (Alberti et al., 2009). Participants be advisable to benefit cognition in themselves and because who had not completed the baseline dietary questionnaires or had of the association of vascular risk factors with dementia risk reported energy intakes outside the prespecified limits of ≥800 (Gottesman et al., 2017). to ≤4,000 kcal/day for men and ≥500 to ≤3,500 kcal/day for Epidemiological studies and clinical trials have shown a women were excluded from these analyses (Willett, 2012). If a relationship between adherence to MedDiet and cognitive given cognitive function assessment was missing, this test was not function (Loughrey et al., 2017; Wu and Sun, 2017), and included in the analysis for that participant. the World Health Organization (WHO) has included this dietary pattern in their guidelines for risk reduction of Exposure: Dietary Assessments cognitive decline and dementia; however, the strength of Trained dietitians assessed dietary intake via face-to- the recommendation is considered conditional (World Health face interviews at baseline using a previously validated Organization [WHO], 2019). A hybrid of the MedDiet and semiquantitative 143-item food frequency questionnaire (FFQ) DASH diet, the MIND diet, is also being promoted for brain (Fernández-Ballart et al., 2010). For each item, a portion size was health, albeit it has been less extensively investigated in relation established, and nine consumption frequencies were available, to cognition and other cardiometabolic health outcomes (van ranging from “never or almost never” to “ ≥6 times/day”. Energy den Brink et al., 2019). At any rate, dietary recommendations and nutrient intakes were obtained using data from Spanish for preventing cognitive decline are still not widely accepted food composition tables and by multiplying the frequency by in guidelines due to conflicting and limited evidence. The the portion size and accounting for the duration of the period MedDiet, DASH, and MIND dietary patterns each represent a assessed (Moreiras et al., 2018). modifiable lifestyle practice that could aid cognitive performance, Dietary pattern adherence scores were computed from yet further evidence is needed to inform cognitive guideline responses to the FFQ. In the case of the MedDiet, it was recommendations, as well as assess whether changes may be determined based on a Mediterranean Diet Adherence Screener observed in a period of 2 years. (MEDAS) score, ranging from 0 to 14 points, which has been The aim of this study was to prospectively examine the previously validated (Schröder et al., 2011; García-Conesa et al., relationship between baseline adherence to a priori dietary 2020). The DASH diet was defined using the score developed patterns, assessed using the MedDiet, DASH, and MIND dietary by Fung et al. (2008), which ranges from 8 to 40 points. patterns scores, with 2-year changes in cognitive performance For the MIND diet, the score developed by Morris et al. in a large sample of community-dwelling older adults with (2015a; Berendsen et al., 2018), which ranges from 0 to 15 overweight or obesity at high cardiovascular disease risk. points, was used. Frontiers in Aging Neuroscience | www.frontiersin.org 3 December 2021 | Volume 13 | Article 782067 Nishi et al. Dietary Patterns and Cognitive Function Outcome: Cognitive Assessments 2020. Data for dietary adherence scores (exposure variables) are Participants completed a battery of cognitive tasks at baseline presented as median (range). For the covariates and outcome and 2 years of follow-up. This battery of neuropsychological variables, data are shown as percentages and mean ± standard tests included Mini-Mental State Examination (MMSE), a deviation (SD), for qualitative and quantitative descriptive commonly used cognitive screening test (Folstein et al., 1975; variables, respectively, and as β [95% confidence interval (CI)] Blesa et al., 2001); clock-drawing test (CDT) for evaluating for associations. Participants were classified according to tertiles visuospatial and visuo-constructive capacity (del Ser Quijano of dietary pattern adherence, and the lowest tertile was used et al., 2004; Aprahamian et al., 2009; Paganini-Hill and Clark, as the reference category. The chi-squared test and one-way 2011); semantical and phonological verbal fluency tasks (VFT-a ANOVA were used for qualitative and quantitative variables, and VFT-p, respectively) for assessing verbal ability and executive respectively, to compare baseline characteristics according to function (Benton et al., 1994); Trail Making Tests (TMT) parts A dietary pattern adherence score. and B for executive function assessment, where part A assesses Longitudinal associations between adherences to the a priori attention and processing speed and part B further examines dietary patterns of participants who completed each of the cognitive flexibility (Llinàs-Reglà et al., 2017); and forward neuropsychological function tests were analyzed separately using recall and backward recall Digit Span Tests (DST-f and DST-b, multivariate linear regression. All analyses were conducted with respectively) of the Wechsler Adult Intelligence Scale-III (WAIS- robust estimates of the variance to correct for intracluster III), where DST-f evaluates attention and short-term memory correlation. Crude and two adjusted models were assessed. capacity and DST-b tests working memory (Wechsler, 1997; Rossi The first model was minimally adjusted using established non- et al., 2008). Raw scores for each cognitive assessment were modifiable risk factor-related confounders for cognitive function standardized using the mean and standard deviation from the (age, sex) along with intervention arm, study center size, baseline population scores, creating z-scores. Global cognitive respective baseline cognitive function score, and corrected for function (GCF) was determined as a composite score of all eight clusters (to account for couples living in the same household assessments (Shah et al., 2013; Gómez Martínez et al., 2021), being randomized as a single unit). The second model was adding or subtracting each individual test value based on whether further adjusted for baseline education level (i.e., primary school, a higher score indicates higher or lower cognitive performance, secondary school, or college), civil status (i.e., single, divorced respectively, using z-scores according to the following equation: or separated, married, or widower), smoking status (i.e., former smoker, never smoked, or current smoker), BMI (kg/m2 ), GCF = zMMSE + zCDT + zVFTa + zVFTb + (−zTMTA) hypertension (yes/no), hypercholesterolemia (yes/no), diabetes  (yes/no), depressive symptomology (yes/no), baseline physical +(−zTMTB) + zDSTf + zDSTb 8. activity (METs min/day), and total energy intake (kcal/day). The probability P for trend across categories of dietary Covariate Assessment pattern adherence score was calculated using the median value Trained staff collected information about sociodemographic of each category as a continuous variable, and a two-tailed (i.e., age, sex, education level, and civil status) and lifestyle (i.e., P-value < 0.05 was considered statistically significant. Several physical activity, dietary intake, and smoking habits) factors via sensitivity analyses were performed to test the robustness of interviewer-administered questionnaires. Physical activity was the findings and identify significant exposure factors to aid in assessed using a Spanish validated version of the Minnesota developing priorities for risk mitigation. First, the removal of leisure-time physical activity questionnaire (Elosua et al., 1994, participants with a baseline MMSE score ≤ 23 indicated possible 2000). Total daily energy intake was estimated according to mild dementia (Dementia Care Central, 2020). Second, alcohol data from the FFQ. Anthropometric variables, such as weight was added as a potential confounder in the models (despite being and height, were measured by trained staff using calibrated a component of the MedDiet and MIND patterns) as excessive scales and wall-mounted stadiometers, respectively. Body mass alcohol intake is considered a risk factor for cognitive decline index (BMI) was calculated as weight in kilograms divided by and dementia (Livingston et al., 2020). Third, analyses were height in meters squared. Personal history related to chronic conducted assessing the impact of individual food components diseases (e.g., hypertension, hypercholesterolemia, and type of each dietary pattern using linear regression accounting for 2 diabetes) was self-reported or collected from the medical multicollinearity, if present. Statistical analyses were performed records of participants. Depressive symptomology was evaluated using Stata (14.0, StataCorp LP, TX, United States). based on Beck’s Depression Inventory-II (BDI-II) with the threshold for depressive status risk established as a score ≥ 14 (Beck et al., 1996; Sanz et al., 2003). The intervention group RESULTS (treatment or control) and center size (<250, 250 to <300, 300 to <400, ≥400) of the PREDIMED-Plus study were also Figure 1 provides the flow diagram of participants. This study considered as covariates. included a total of 6,647 participants (mean age 65 years, 48% women). Table 1 provides the baseline characteristics of Statistical Analyses the participants overall and shows the categories representing All statistical analyses were performed using the latest the lowest and highest adherence to each of the a priori PREDIMED-Plus study dataset generated on December 22, dietary pattern scores at baseline. The median (range) of dietary Frontiers in Aging Neuroscience | www.frontiersin.org 4 December 2021 | Volume 13 | Article 782067 Nishi et al. Dietary Patterns and Cognitive Function adherence scores for the lowest and highest tertiles of each of the MIND diet was significantly associated with 2-year changes in three assessed dietary patterns were 6 (1–7) and 10 (10–14) for DST-B (β: 0.058; 95% CI: 0.002, 0.114, P-trend = 0.045). No the MedDiet (lowest possible score 0, highest possible score 14), other significant beneficial associations with changes in cognitive respectively; 19 (8–21) and 30 (27–38) for DASH (lowest possible performance measured by the different neuropsychological test score 8, highest possible score 40), respectively; and 8 (2.5–8.5) batteries were observed between adherence to the MedDiet, and 10.5 (10.0–13.5) for MIND (lowest possible score 0, highest MIND, or DASH dietary patterns. Conversely, significant possible score 15), respectively. associations were observed in the crude models with greater A higher percentage of women (P ≤ 0.001), older age 2-year increases in the DASH diet being associated with lower (P < 0.001), higher physical activity (P < 0.001), and a performance in all nine cognitive tests. Sensitivity analyses, tendency toward lower BMI, although not clinically significant which included assessing only participants with baseline MMSE (P ≤ 0.05), were observed in the highest adherence tertiles for scores above 23 (as scores 23 and below suggest possible all three dietary patterns. In the DASH and MIND patterns, mild dementia or worse), or the addition of alcohol as lower percentages of daily energy intake and current smokers a potential confounder in the model, did not significantly (both P < 0.001) were also observed in the highest adherence modify the findings (data not shown). The only modification tertiles of these patterns. Furthermore, higher adherence to the observed was that including total alcohol intake in the DASH and MIND diets were associated with less alcohol intake model slightly, but non-significantly, mitigated any negative (P < 0.001) and depression (P = 0.001), respectively. For the associations observed between adherence to the DASH diet and MedDiet, a lower percentage of participants with depressive changes in cognitive function. Figure 3 shows the impact of symptoms, higher alcohol, and total energy (all P < 0.01) was all 14 food components of the MedDiet on changes of each observed in the highest adherence tertile compared with the individual cognitive test. Of these components, olive oil used lowest adherence tertile. as the primary oil was found to be positively associated with Figure 2 and Supplementary Table 1 show the β coefficients changes in global cognitive function, as well as changes in (95% CIs) associated with 2-year changes in cognitive assessment the two DSTs (both forward recall P = 0.007 and backward z-scores across tertiles of a priori dietary pattern adherence recall P ≤ 0.001). Nut intake was significantly and positively scores. Results of the fully adjusted linear regression models associated with an increase in CDT performance (P = 0.034) show a significant association between highest adherence to and trended toward beneficially impacting changes in MMSE the MedDiet and 2-year changes in MMSE (β: 0.070; 95% score (P = 0.069). Red wine was also significantly associated CI: 0.014, 0.175, P-trend = 0.011), TMT-A (β: −0.054; 95% with changes in various cognitive function assessments, yet CI: −0.110, −0.002, P-trend = 0.047), and TMT-B (β: −0.062; indicating converse findings, where red wine intake may have 95% CI: −0.116, −0.007, P-trend = 0.024). Adherence to the a beneficial relationship with changes in TMT-A (P = 0.004), FIGURE 1 | Flow diagram of participants for the analysis of a priori dietary pattern adherence and cognitive performance in the PREDIMED-Plus trial. *Energy intakes outside pre-specified limits were identified as ≤800 to ≥4,000 Kcal/d for men and ≤500 to ≥3,500 Kcal/d for women. Frontiers in Aging Neuroscience | www.frontiersin.org 5 December 2021 | Volume 13 | Article 782067 Nishi et al. Dietary Patterns and Cognitive Function TABLE 1 | Baseline characteristics of the PREDIMED-Plus participants according to categories of highest and lowest baseline adherence (based on tertile categorization1 ) to a priori dietary patterns. Dietary patterns Total MedDiet (0–14) DASH (8–40) MIND (0–15) (score range) Trait Lowest Highest P-value Lowest Highest P-value Lowest Highest P-value Diet score adherence, 6 (1–7) 10 (10–14) 19 (8–21) 30 (27–38) 8 (2.5–8.5) 10.5 (10.0–13.5) Median (Range) Frequency, n 6,647 2,415 1,621 2,674 2,141 3,021 1,652 Socio-demographic data Age (years) 65.0 ± 4.9 64.5 ± 5.0 65.5 ± 4.8 <0.001 64.1 ± 5.0 65.9 ± 4.6 <0.001 64.7 ± 5.0 65.4 ± 4.9 <0.001 Sex (women) 3,218 (48.4) 1,086 (45.0) 831 (51.3) <0.001 915 (34.2) 1,372 (64.1) <0.001 1,374 (45.5) 854 (51.7) 0.001 Education level Primary school 3,270 (49.2) 1,201 (49.7) 803 (49.5) 1,213 (45.4) 1,138 (53.2) 1,431 (47.4) 822 (49.8) High school 1,918 (28.9) 727 (30.1) 425 (26.2) 849 (31.8) 540 (25.2) 922 (30.5) 447 (27.1) College 1,459 (22.0) 487 (20.2) 393 (24.2) 0.010 612 (22.9) 463 (21.6) <0.001 668 (22.1) 383 (23.2) 0.014 Civil status Single, divorced, or 858 (12.9) 330 (13.7) 203 (12.5) 323 (12.1) 301 (14.1) 390 (12.9) 238 (14.4) separated Married 5,101 (76.7) 1,834 (75.9) 1,270 (78.4) 2,117 (79.2) 1,577 (73.7) 2,328 (77.1) 1,243 (75.2) Widower 688 (10.4) 251 (10.4) 148 (9.1) 0.192 234 (8.8) 263 (12.3) <0.001 303 (10.0) 171 (10.4) 0.151 Disease risk factors Body mass index, 32.5 ± 3.4 32.9 ± 3.5 32.1 ± 3.3 <0.001 32.7 ± 3.4 32.4 ± 3.5 0.012 32.7 ± 3.5 32.4 ± 3.3 0.050 kg/m2 Physical activity (MET 351.9 ± 329.1 316.3 ± 312.7 408.8 ± 368.1 <0.001 325.7 ± 322.0 382.7 ± 339.9 <0.001 317.2 ± 305.1 396.9 ± 362.8 <0.001 min/day) Smoking status Never smoked 2,948 (44.4) 1,052 (43.6) 732 (45.2) 965 (36.1) 1,137 (53.1) 1,320 (43.7) 727 (44.0) Former smoker 2,876 (43.3) 1,036 (42.9) 715 (44.1) 1,286 (48.1) 801 (37.4) 1,267 (41.9) 753 (45.6) Current smoker 823 (12.4) 327 (13.5) 174 (10.7) 0.128 423 (15.8) 203 (9.5) <0.001 434 (14.4) 172 (10.4) <0.001 Diabetes 2,047 (30.8) 772 (32.0) 475 (29.3) 0.194 784 (29.3) 659 (30.8) 0.003 949 (31.4) 524 (31.7) 0.140 Hypertension 5,583 (84.0) 2,064 (85.5) 1,339 (82.6) 0.035 2,254 (84.3) 1,775 (82.9) 0.222 2,553 (84.5) 1,379 (83.5) 0.573 Hypercholesterolemia 4,649 (69.9) 1,679 (69.5) 1,116 (68.9) 0.281 1,829 (68.4) 1,513 (70.7) 0.072 2,069 (68.5) 1,198 (72.5) 0.016 Depressive 1,368 (20.6) 571 (23.6) 304 (18.8) <0.001 533 (19.9) 477 (22.3) 0.059 683 (22.6) 307 (18.6) 0.001 symptoms Dietary intake Alcohol intake (g/day) 11.0 ± 15.0 11.1 ± 15.2 11.9 ± 16.0 0.004 15.2 ± 17.7 7.0 ± 10.7 <0.001 10.7 ± 15.3 11.6 ± 14.5 0.152 Energy intake 2,365.2 ± 551.5 2,364.1 ± 597.2 2,434.4 ± 502.3 <0.001 2,467.3 ± 569.1 2,283.5 ± 506.0 <0.001 2,417.6 ± 578.6 2,310.8 ± 512.8 <0.001 (kcal/day) Cognitive function tests GCF2 0.02 ± 0.65 0.02 ± 0.66 0.03 ± 0.63 0.852 0.10 ± 0.63 -0.08 ± 0.66 <0.001 0.04 ± 0.64 0.01 ± 0.64 0.543 MMSE 28.22 ± 1.91 28.17 ± 1.94 28.29 ± 1.83 0.154 28.38 ± 1.77 28.01 ± 2.02 <0.001 28.25 ± 1.86 28.18 ± 2.00 0.500 CDT 5.93 ± 1.23 5.91 ± 1.28 5.95 ± 1.20 0.578 6.02 ± 1.19 5.80 ± 1.31 <0.001 5.95 ± 1.22 5.91 ± 1.25 0.566 VFT-a 16.01 ± 4.90 15.88 ± 4.96 16.23 ± 4.94 0.080 16.42 ± 4.95 15.45 ± 4.82 <0.001 16.13 ± 4.92 15.95 ± 4.89 0.205 VFT-p 12.18 ± 4.53 11.96 ± 4.55 12.43 ± 4.57 0.004 12.42 ± 4.50 11.75 ± 4.47 <0.001 12.15 ± 4.56 12.14 ± 4.45 0.572 TMT-A 52.80 ± 28.56 53.01 ± 28.86 52.23 ± 27.33 0.649 49.95 ± 26.31 56.33 ± 31.62 <0.001 52.38 ± 28.22 53.55 ± 29.71 0.411 TMT-B 130.05 ± 72.36 131.96 ± 75.05 127.49 ± 68.05 0.158 122.47 ± 68.08 139.77 ± 72.36 <0.001 128.70 ± 71.89 131.59 ± 72.39 0.365 DST-f 8.79 ± 2.46 8.66 ± 2.44 8.89 ± 2.45 0.013 8.95 ± 2.46 8.56 ± 2.40 <0.001 8.79 ± 2.43 8.73 ± 2.50 0.579 DST-b 5.11 ± 2.22 5.13 ± 2.19 5.09 ± 2.26 0.834 5.33 ± 2.24 4.80 ± 2.15 <0.001 5.18 ± 2.19 4.99 ± 2.26 0.034 Data are n (%) or mean ± SD for categorical and quantitative variables, respectively. The exception is the diet score is represented as mean (range). The chi-squared analysis was used to assess categorical variables and one-way ANOVA for quantitative variables. 1 Highest and lowest dietary pattern adherence categories were determined based on tertiles of baseline data with the highest and lowest adherence groups being tertile 3 and 1, respectively. 2 A composite of z-scores was used to calculate GCF using the formula: GCF = (Z MMSE + ZCDT + ZVFT−a + ZVFT−b + (–ZTMT−A) + (–ZTMT−B ) + ZDST−f + ZDST−b )/8. CDT, clock drawing test; DASH, Dietary Approaches to Stop Hypertension; DST-b, digit span test-backward; DST-f, digit span test -forward; GCF, global cognitive function; MedDiet, Mediterranean dietary pattern; MIND, Mediterranean-DASH Intervention for Neurodegenerative Delay; MMSE, Mini-Mental State Examination; TMT-A, Trail Making Test Part A; TMT-B, Trail Making Test Part B; VFT-a, verbal fluency tasks semantical; VFT-p, verbal fluency tasks phonological. Frontiers in Aging Neuroscience | www.frontiersin.org 6 December 2021 | Volume 13 | Article 782067 Nishi et al. Dietary Patterns and Cognitive Function FIGURE 2 | Cognitive function assessment by dietary pattern adherence [standardized beta-coefficients (95% confidence intervals)]. CDT; clock drawing test; DASH; Dietary Approaches to Stop Hypertension; DST-b, digit span test backward; DST-f, digit span test forward; GCF, global cognitive function; MedDiet, Mediterranean dietary pattern; MIND; Mediterranean-DASH Intervention for Neurodegenerative Delay; MMSE, Mini-Mental State Examination; TMT-A, Trail Making Test Part A; TMT-B, Trail Making Test Part B; VFT-a; verbal fluency tasks semantical; VFT-b; verbal fluency tasks phonological. Model presented adjusted for age (in Years), sex, intervention group, centre size (<250, 250 to <300, 300 to <400, ≥400), respective cognitive test score at baseline, baseline education level (primary school, secondary school collage), civil status (single, divorced, or separated, married, and windower), smoking habits (smoker, former smoker, and never smoked), corrected for clusters (to account for couples living in the same household being randomized as a single unit), BMI (kg/m2 ), hypertension (yes/no), baseline physical activity (MET min/week) and total energy intake (kcal/day). For the neurological tests, a positive beta-coefficient value in the figure indicates better cognitive performance according to the associated test. TMT-B (P = 0.006), and DST-b (P = 0.020), and a negative DISCUSSION association with changes in VFT-a (P = 0.048). Likewise, preferably consuming white meat as opposed to red or processed This study examined the PREDIMED-Plus trial as a longitudinal, meat showed conflicting findings where a beneficial association observational cohort to evaluate the relationship between was observed with changes in DST-f (P = 0.001) and negatively adherence to a priori dietary patterns and changes in associated with changes in CDT (P = 0.001). Fish and cognitive performance in community-dwelling older adults shellfish intake was negatively associated with changes in DST- with overweight or obesity and at high cardiovascular disease b (P = 0.008). Analyses investigating the DASH and MIND risk. Findings suggested that the MedDiet may support cognitive diets showed similar associations with nut consumption and function in older age as significant beneficial associations wine intake. Additionally, when assessed within the context of were observed between greater adherence to the MedDiet the MIND diet, lower consumption of confectionery products with favorable cognitive changes in MMSE, TMT-A, and was associated with improvements in 2-year changes in GCF TMT-B assessments over the follow-up period of 2 years. (P = 0.007), VFT-a (P = 0.019), TMT-a (P = 0.011), DST- This represented better general cognitive function, as well b (P = 0.042), and higher red meat intake was associated as executive function specifically attention and processing with worsening changes in TMT-a (P = 0.001) and TMT-b speed and cognitive flexibility in those with higher adherence (P = 0.014) scores. to a MedDiet. Findings also indicated that the MIND diet Table 2 presents the quantity of intake of various may be associated with better working memory based on dietary components overall and by tertile of dietary higher adherence being related to higher DST-b assessment. pattern adherence score and shows differences between However, the DASH diet was not beneficially associated with the dietary patterns. When assessing the MedDiet 2-year changes in cognitive function in this population with adherence, 94.7% of participants within the highest overweight or obesity at high cardiovascular disease risk. The adherence tertile used olive oil as their primary oil with observed advantageous associations between adherence to the a mean intake level of 47.4 ± 14.7 g/day, nut intake was MedDiet and cognition align with previous findings presented 19.9 ± 18.2 g/day, and red wine consumption was on average in systematic reviews and meta-analyses of observational studies 59.4 ± 98.1 g/day. suggesting associations between MedDiet adherence with slower Frontiers in Aging Neuroscience | www.frontiersin.org 7 December 2021 | Volume 13 | Article 782067 Nishi et al. Dietary Patterns and Cognitive Function FIGURE 3 | Association of specific dietary components of the Mediterranean dietary pattern (MedDiet) on the cognitive function battery of tests. CDT; clock drawing test; DST-b, digit span test backward; DST-f, digit span test forward; GCF, global cognitive function; MMSE, Mini-Mental State Examination; TMT-A, Trail Making Test Part A; TMT-B, Trail Making Test Part B; VFT-a; verbal fluency tasks semantical; VFT-b; verbal fluency tasks phonological. cognitive decline, lower risk of dementia (especially Alzheimer’s functioning, including attention and processing speed (TMT-A) disease), and reduced conversion of mild cognitive impairment and cognitive flexibility (TMT-B). Conversely, a systematic to Alzheimer’s disease (Singh et al., 2014; Loughrey et al., review of randomized controlled trials (nine reports, five unique 2017; Wu and Sun, 2017). Recently, a meta-analysis including trials) showed inconsistent findings when comparing a MedDiet nine prospective cohort studies reported that high adherence with either a waiting list, usual diet, or a low-fat control group to the MedDiet was associated with a 21% risk reduction in for a duration ranging from 10 days to 6.5 years on cognition pooled cognitive disorders, in addition to a dose-response with or brain morphology and function (Radd-Vagenas et al., 2018). positive findings almost exclusively limited to higher MedDiet However, the authors stated that significant and clinically adherence (Wu and Sun, 2017). Furthermore, neuroimaging meaningful effect sizes were found for cognitive composites in evaluations have found evidence in favor of a protective effect the largest and most robust trial, with a duration of 4.1 years, (Karstens et al., 2019). Nevertheless, it is interesting to highlight conducted within the context of the PREDIMED trial (Valls- that previous prospective cohort studies generally had 4 or Pedret et al., 2015). Furthermore, in a more recent analysis of more years of follow-up, many of which were conducted in a smaller sub-cohort of the PREDIMED-Plus trial evaluating relatively healthy, non-Mediterranean populations, with many cognition, higher adherence to an energy-reduced MedDiet was of the cognitive assessments using screening tests based on associated with greater improvements in memory; however, criteria to discriminate overall mild cognitive impairment or interpretation of these findings was related to the interplay with dementia. Our study also found a positive association between weight loss (Soldevila-Domenech et al., 2021). In this study higher adherence to MedDiet and a screening test (MMSE) population, we did not find an association for the GCF, which but also showed specific beneficial associations for executive may be explained by the short duration (2 years) and by the broad Frontiers in Aging Neuroscience | www.frontiersin.org 8 December 2021 | Volume 13 | Article 782067 Nishi et al. TABLE 2 | Quantity of dietary intake by tertile of dietary pattern adherence score. Dietary pattern Total MedDiet DASH MIND Baseline intake Lowest Moderate Highest Lowest adherence Moderate Highest Lowest adherence Moderate Highest adherence adherence adherence adherence adherence adherence adherence Score, median (range) 6 (1–7) 8 (8–9) 10 (10–14) 19 (8–21) 24 (22–26) 30 (27–38) 8 (2.5–8.5) 9 (9.0–9.5) 10.5 (10.0–13.5) Olive oil as the primary 5,266 (79.2) 1,516 (62.8) 2,214 (84.8) 1,536 (94.8) 2,050 (76.7) 1,454 (79.4) 1,762 (82.3) 2,299 (76.1) 1,578 (79.9) 1,389 (84.1) oil used, n (%) Olive oil, g/d 39.9 ± 17.0 33.9 ± 16.3 40.7 ± 17.0 47.3 ± 14.7 41.4 ± 16.8 39.3 ± 16.9 38.5 ± 17.2 39.2 ± 17.7 40.1 ± 16.5 40.7 ± 16.4 Vegetables, g/d 328.1 ± 139.9 295.2 ± 132.5 332.1 ± 137.4 370.8 ± 142.5 268.6 ± 111.0 329.1 ± 125.7 401.7 ± 148.4 280.0 ± 123.1 342.9 ± 128.7 398.7 ± 147.7 Frontiers in Aging Neuroscience | www.frontiersin.org Green leafy vegetables, 77.8 ± 42.5 68.4 ± 39.3 78.5 ± 42.4 90.5 ± 44.0 64.1 ± 35.4 78.0 ± 40.4 94.6 ± 46.2 63.7 ± 35.4 81.0 ± 39.9 99.7 ± 47.1 g/d Other vegetables, g/d 301.4 ± 125.8 276.3 ± 121.0 303.2 ± 123.4 335.9 ± 128.3 253.8 ± 104.7 303.3 ± 115.5 359.1 ± 133.8 264.4 ± 116.7 314.8 ± 117.5 352.9 ± 130.2 Sofrito, n (%) 3,772 (56.8) 985 (40.8) 1,531 (58.6) 1,256 (77.5) 1,528 (57.1) 1,027 (56.1) 1,217 (56.8) 1,707 (56.5) 1,136 (57.6) 929 (56.2) Fruit, g/d 359.2 ± 206.8 315.2 ± 190.5 365.9 ± 203.8 414.0 ± 220.2 271.5 ± 163.2 365.7 ± 191.6 463.3 ± 218.2 323.7 ± 190.8 367.2 ± 196.1 414.6 ± 232.6 Fruit juice (natural), g/d 59.2 ± 94.5 61.8 ± 101.5 56.6 ± 91.7 59.6 ± 87.6 55.5 ± 92.7 55.6 ± 94.6 66.9 ± 96.1 57.9 ± 94.4 57.9 ± 87.4 63.2 ± 102.3 Berries, g/d 6.0 ± 8.6 5.6 ± 8.8 5.8 ± 8.1 7.0 ± 8.9 4.1 ± 5.7 6.0 ± 8.4 8.4 ± 10.8 4.7 ± 6.7 6.1 ± 7.9 8.3 ± 11.4 Whole grains, g/d 41.3 ± 63.4 33.4 ± 57.1 43.8 ± 64.2 49.0 ± 69.4 19.3 ± 49.2 38.4 ± 60.4 71.2 ± 69.5 22.6 ± 44.3 41.4 ± 62.1 75.3 ± 78.6 Legumes, g/d 20.7 ± 11.2 19.0 ± 10.3 20.8 ± 11.3 23.1 ± 12.1 17.8 ± 9.0 20.3 ± 10.5 24.7 ± 13.1 18.4 ± 9.9 21.3 ± 11.0 24.3 ± 12.7 9 Nuts, g/d 14.9 ± 17.1 11.1 ± 15.2 15.5 ± 16.8 20.2 ± 18.8 9.8 ± 13.1 14.0 ± 15.8 22.1 ± 20.0 10.5 ± 14.0 15.9 ± 17.3 21.8 ± 19.6 Red Wine, g/d 46.9 ± 90.2 40.4 ± 85.5 44.9 ± 87.2 59.9 ± 100.0 61.5 ± 108.6 43.1 ± 82.0 32.1 ± 65.5 38.3 ± 84.6 49.1 ± 91.7 60.1 ± 96.5 Low-fat dairy, g/d 256.6 ± 201.6 244.0 ± 195.7 258.7 ± 202.1 271.9 ± 208.1 189.3 ± 171.7 267.3 ± 201.1 331.4 ± 208.3 249.2 ± 200.0 256.6 ± 202.0 269.9 ± 203.2 Cheese, g/d 346.2 ± 201.3 348.2 ± 194.3 343.8 ± 202.4 347.0 ± 209.7 300.4 ± 179.4 351.8 ± 203.9 399.5 ± 211.4 355.4 ± 197.1 338.8 ± 203.8 338.0 ± 205.3 Fish and seafood, g/d 102.2 ± 47.7 89.9 ± 46.0 104.1 ± 46.7 117.5 ± 46.8 95.4 ± 46.2 103.0 ± 48.0 110.0 ± 48.1 94.4 ± 47.0 105.6 ± 45.2 112.5 ± 49.5 Poultry, g/d 51.0 ± 28.7 48.3 ± 29.9 51.5 ± 27.2 54.3 ± 28.8 48.0 ± 28.2 51.7 ± 29.0 54.1 ± 28.7 42.3 ± 28.9 54.6 ± 26.6 62.5 ± 25.4 Red meat, g/d 47.4 ± 33.7 55.3 ± 37.8 44.9 ± 31.6 39.8 ± 27.3 60.2 ± 35.9 45.1 ± 30.8 33.4 ± 26.2 52.2 ± 35.2 45.9 ± 32.4 40.5 ± 30.9 Processed meat, g/d 36.4 ± 24.1 40.4 ± 27.7 34.7 ± 22.3 33.3 ± 19.9 44.9 ± 27.4 34.4 ± 21.7 27.5 ± 17.0 39.8 ± 24.2 35.8 ± 23.0 30.9 ± 24.0 Added fats1 , g/d 2.5 ± 6.4 3.9 ± 9.0 2.0 ± 4.3 1.4 ± 3.4 1.7 ± 3.9 1.5 ± 3.5 1.2 ± 2.9 1.9 ± 4.3 1.2 ± 2.9 0.9 ± 2.3 Sugar-sweetened 39.1 ± 89.3 60.6 ± 118.3 29.5 ± 68.9 22.4 ± 55.3 66.3 ± 114.1 30.9 ± 79.2 12.0 ± 37.4 48.9 ± 101.8 33.9 ± 79.2 27.3 ± 72.7 beverages, ml/d Confectionary/Pastries, 13.7 ± 17.6 18.3 ± 20.9 12.3 ± 16.2 9.0 ± 11.7 15.9 ± 19.5 13.1 ± 16.3 11.5 ± 15.6 18.0 ± 20.8 11.6 ± 14.5 8.5 ± 11.5 g/d Fast/fried foods, g/d 23.8 ± 24.7 28.8 ± 28.4 21.8 ± 22.5 19.5 ± 20.7 30.5 ± 28.4 21.9 ± 22.2 16.9 ± 19.1 31.2 ± 28.6 20.5 ± 20.3 14.1 ± 16.5 Sodium, mg/d 3,281.9 ± 1,012.9 3,350.98 ± 3,237.8 ± 990.4 3,250.3 ± 964.1 3,637.4 ± 1,046.4 3,190.1 ± 957.6 2,916.5 ± 857.5 3,407.1 ± 1,050.5 3,247.2 ± 973.2 3,094.6 ± 1,064.2 956.2 1 Added fats include butter, margarine, lard, and cream. Cells filled in gray represent food groupings that are not a main component of the specified dietary pattern. Cells with no fill (i.e., have a white background) represent food groupings that are a main component of the specified dietary pattern. DASH, Dietary Approaches to Stop Hypertension; MedDiet, Mediterranean dietary pattern; MIND, Mediterranean-DASH Intervention for Neurodegenerative delay. December 2021 | Volume 13 | Article 782067 Dietary Patterns and Cognitive Function Nishi et al. Dietary Patterns and Cognitive Function neuropsychological battery utilized compared with other studies. such effects given the observed associations for MedDiet However, the present findings further support the MedDiet for with MMSE and TMT-A and TMT-B or the MIND diet better cognition while suggesting that beneficial associations may with DST-b did not appear to be fully explained by any be observable within a shorter timeframe and have applicability one component of the dietary pattern; however, further for populations at greater risk of cognitive decline (older, investigation is warranted. with overweight or obesity, and at high risk of cardiovascular disease), which could have implications for improving quality Potential Mechanisms of life. In particular, obesity and its comorbidities are associated The antioxidant, vitamin, probiotic, plant protein, and with accelerated cognitive decline and impaired cognitive unsaturated fatty acid content along with low glycemic performance including neurodegenerative pathologies, such as index/load components of the a priori dietary patterns dementia, in later life (Dye et al., 2017). studied have been proposed to possibly affect biological While a significant association was seen in the present mechanisms of neurocognitive aging (Frisardi et al., 2010). analyses with the MedDiet and MIND diet within a shorter These factors are thought to potentially lead to improved follow-up duration with some cognitive function assessments cognition through influencing vascular health and direct compared with other prospective cohort studies that have promotion of neuroprotection via anti-inflammatory been conducted, this relationship was not found with the mechanisms and reducing oxidative stress, ameliorating DASH diet. While MedDiet has been associated with a glycemic control, and supporting a favorable microbiome lower risk of cognitive impairment, it has not always been (Caracciolo et al., 2014). Specifically, the observations associated with a slower decline in cognitive function (Keenan with the MedDiet and changes in cognitive function et al., 2020). Inconsistent findings have previously been may be related to synergistic or individual associations observed with the DASH diet (van den Brink et al., 2019). of specific foods, such as olive oil and nuts, due to However, MIND dietary adherence has been associated associations of these foodstuffs with the above-mentioned with better cognitive function across various domains in a mechanisms (Viguiliouk et al., 2014; Marcelino et al., 2019; systematic review of 13 MIND studies (9 cohorts, 3 cross- Creedon et al., 2020). sectional, and 1 RCT) evaluating cognitive functioning in older adults (Kheirouri and Alizadeh, 2021). The observed Limitations and Strengths discrepancies with present findings may be related to differences There are several limitations to this research. First, the in the types of foods consumed by the study population, demographic profile of the PREDIMED-Plus cohort, which is and a potential threshold effect related to the amount of composed of predominantly white, older Spanish individuals each food component consumed, as well as the cognitive with metabolic syndrome and overweight or obesity, may tests performed. limit the generalizability of the results to other populations. The analyzed three dietary patterns each have plant-based However, the homogeneity and the large sample size of foundations, with moderate to high amounts of fish and dairy the cohort increase the internal validity of the findings by products, yet they differ in the types and amounts of each avoiding potential confounding effects of socioeconomic status, dietary component. The MedDiet is typically characterized educational level, and access to health care. Second, FFQs by high consumption of olive oil, fruits, vegetables, legumes, tend to be limited concerning the variety of foods assessed, nuts, cereals, and unsaturated fatty acids; low consumption of as compared to 24-h recalls and food records but are often meat and saturated fatty acids; low to moderate consumption more likely to reflect usual intake (Willett, 2012). The certainty of dairy products; moderate to high consumption of fish; in the dietary pattern scoring systems utilized may also be and a regular, but moderate, intake of wine (Sánchez-Sánchez limited and experience restrictions due to a potential lack of et al., 2020). The DASH diet shares many similarities yet direct alignment of food items and questions noted in the differs in recommending low-fat dairy and low sodium, besides FFQ with each of the diet score components. FFQs are also having fewer specifications (Fung et al., 2008). Based on prone to misclassification and recall bias as they rely on the these two dietary patterns, the MIND diet was developed memory of participants. This is particularly important in a combining Mediterranean and DASH aspects and incorporating study of cognition when there could be a decline in cognitive purported neuroprotective foods such as green leafy vegetables function and memory deficits in the population. However, due and berries (Morris et al., 2015a,b). A potential explanation to the prospective nature of the study, baseline diet recall for our discordant findings may be due to differences in is unlikely to have been influenced by cognitive outcomes the use of olive oil as the primary oil between tertiles over the follow-up period, and baseline cognitive function of adherence. In our sample, the use of olive oil was was considered as a confounding variable. Another limitation clearly linked with a beneficial association in the GCF is that the categorization of dietary pattern adherence was composite, as well epidemiological and clinical evidence based on sample-specific cutoffs, and there are methodological have suggested improved cognition with olive oil (Millman differences among the various dietary scoring systems available, et al., 2021; Theodore et al., 2021). With any dietary limiting comparability (van den Brink et al., 2019). Likewise, pattern, in addition to observing associations with specific differences in the types of foods consumed by the study food components, there is also the potential for synergistic population, and the narrow range in scores between lowest effects (Schulz et al., 2021). The present findings suggest and highest tertiles of intake, may have limited the ability to Frontiers in Aging Neuroscience | www.frontiersin.org 10 December 2021 | Volume 13 | Article 782067 Nishi et al. Dietary Patterns and Cognitive Function discern a difference, especially for the MIND pattern. Given DATA AVAILABILITY STATEMENT that optimal or absolute minimum amounts of key foods for cognitive performance are still unclear, threshold amounts of Data described in the manuscript, code book, and analytic code foods for a neuroprotective effect may not have been reached will be made available upon request pending application and in the present analyses especially in those consuming lower approval of the PREDIMED-Plus Steering Committee. There are overall energy intake levels. In terms of cognitive assessments, restrictions on the availability of data for the PREDIMED-Plus while the use of a composite domain z-score may provide an trial, due to the signed consent agreements around data sharing, overall global assessment of cognitive function, the component which only allow access to external researchers for studies tests used to create these scores in other studies vary, thus following the project purposes. Requestors wishing to access making comparisons difficult. Also, a global screening tool the PREDIMED-Plus trial data used in this study can make may be less sensitive to detect possible associations due to a request to the PREDIMED-Plus trial Steering Committee a potential ceiling effect (Franco-Marina et al., 2010), hence chair: (JS-S, jordi.salas@urv.cat). The request will then be passed potentially explaining the null associations observed with GCF. to members of the PREDIMED-Plus Steering Committee for Presenting this composite score in addition to the individual deliberation. cognitive test assessments in this study provides a broader picture of the relationship of these dietary patterns with overall cognition and specific cognitive functions. Additionally, as an observational study, our analysis may be limited by the ETHICS STATEMENT relatively short duration and is prone to residual confounding from factors not assessed in our models. Specifically, possible The study was conducted in accordance with the genetic interactions, such as with the apolipoprotein E E4 principles of the Declaration of Helsinki. The respective (ApoEε4) genotype, which has been associated with cognition, Institutional Review Board (IRB) of all study centres especially in the presence of hypercholesterolemia (Perna et al., approved the study protocol. The trial was registered at 2016), was not able to be accounted for in the current the International Standard Randomized Controlled Trial in analyses. Finally, a cause-effect relationship could not be 2014 (ISRCTNwww.isrctn.com/ISRCTN8989887089898870). All determined due to the nature of the study design, as an participants provided written informed consent. observational cohort. Nonetheless, this study is strengthened by the longitudinal analysis conducted in a large cohort using a comprehensive and thoroughly measured battery of cognitive tests that AUTHOR CONTRIBUTIONS assess various function areas, as well as the use of an FFQ MAM-G, DC, JS-S, MF, JAM, AMA-G, JW, JV, DR, JL-M, RE, FJT, developed and validated for an older Spanish population. JL, JLS-M, AB-C, JAT, VMS, XP, MD-R, PM-M, JV, CV, LD, and The statistical models were also adjusted for multiple ER (all the principal PREDIMED-Plus investigators) contributed sociodemographic, economic, anthropometric, lifestyle, to the study concept and design and to data extraction from and biological confounders of the association between the participants in the PREDIMED-Plus trial, with SKN, NB, diet and cognition, while evaluation of the three distinct and JS-S, contributing to the study concept and design of the a priori dietary patterns within the same study cohort present analyses. SKN, NB, CG-M, NB-T and JS-S performed the minimizes the effects of population-specific confounders or statistical analyses. SKN, NB and JS-S drafted the manuscript. effect modifiers. All authors reviewed the manuscript for important intellectual content and approved the final version to be published. Future Directions Considering the limitations of the present analyses and inconsistencies observed in the literature, studies, especially randomized controlled trials, accounting for relevant FUNDING genotypes, use of dietary compliance biomarkers such as via the development of diet-specific metabolomes, and This work was supported by the official Spanish Institutions for undertaking standardized neuropsychological assessments funding scientific biomedical research, CIBER Fisiopatología de including biomarkers and neuroimaging would be useful for la Obesidad y Nutrición (CIBEROBN) and Instituto de Salud future research. Carlos III (ISCIII), through the Fondo de Investigación para la Salud (FIS), which was co-funded by the European Regional Development Fund (six coordinated FIS projects leaded by JS-S CONCLUSION and JV, including the following projects: PI13/00673, PI13/00492, PI13/00272, PI13/01123, PI13/00462, PI13/00233, PI13/02184, In older Spanish adults with overweight or obesity, higher PI13/00728, PI13/01090, PI13/01056, PI14/01722, PI14/00636, adherence to the MedDiet may help mitigate the risk of cognitive PI14/00618, PI14/00696, PI14/01206, PI14/01919, PI14/00853, decline, specifically as it relates to general and executive cognitive PI14/01374, PI14/00972, PI14/00728, PI14/01471, PI16/00473, functioning, even over a short (2-year) period. PI16/00662, PI16/01873, PI16/01094, PI16/00501, PI16/00533, Frontiers in Aging Neuroscience | www.frontiersin.org 11 December 2021 | Volume 13 | Article 782067 Nishi et al. Dietary Patterns and Cognitive Function PI16/00381, PI16/00366, PI16/01522, PI16/01120, PI17/00764, MFE-171207). None of the funding sources took part in PI17/01183, PI17/00855, PI17/01347, PI17/00525, PI17/01827, the design, collection, analysis, interpretation of the data, or PI17/00532, PI17/00215, PI17/01441, PI17/00508, PI17/01732, writing the report, or in the decision to submit the manuscript PI17/00926, PI19/00957, PI19/00386, PI19/00309, PI19/01032, for publication. 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, and PI20/01158); the ACKNOWLEDGMENTS Especial Action Project entitled: Implementation y evaluación de una intervención intensiva sobre la actividad física Cohorte The authors thank all PREDIMED-Plus participants and PREDIMED-Plus grant to JS-S; the European Research Council investigators. CIBEROBN, CIBERESP, and CIBERDEM are (Advanced Research Grant 2014–2019; agreement #340918) initiatives of the Instituto de Salud Carlos III (ISCIII), granted to MAM-G; the Recercaixa (number 2013ACUP00194) Madrid, Spain. The Hojiblanca (Lucena, Spain) and Patrimonio grant to JS-S; grants from the Consejería de Salud de la Junta Comunal Olivarero (Madrid, Spain) food companies donated de Andalucía (PI0458/2013, PS0358/2016, and PI0137/2018); the extra-virgin olive oil. The Almond Board of California PROMETEO/2017/017 grant from the Generalitat Valenciana; (Modesto, CA), American Pistachio Growers (Fresno, CA), and the SEMERGEN grant; Juan de la Cierva-Incorporación Paramount Farms (Wonderful Company, LLC, Los Angeles, research grant (IJC2019-042420-I) of the Spanish Ministry CA) donated nuts. of Economy, Industry and Competitiveness and European Social Funds to JK. This research was also partially funded by EU-H2020 Grants [Eat2beNICE/H2020-SFS-2016-2; and SUPPLEMENTARY MATERIAL the Horizon 2020 PRIME study (Prevention and Remediation of Insulin Multimorbidity in Europe); grant agreement The Supplementary Material for this article can be found #847879]. SKN was supported by a postdoctoral fellowship online at: https://www.frontiersin.org/articles/10.3389/fnagi. from the Canadian Institutes of Health Research (CIHR, 2021.782067/full#supplementary-material REFERENCES Brainard, J. S., Jimoh, O. F., Deane, K. H. O., Biswas, P., Donaldson, D., Maas, K., et al. (2020). Omega-3, Omega-6, and polyunsaturated fat for cognition: Alberti, K. G. M. M., Eckel, R. H., Grundy, S. M., Zimmet, P. Z., Cleeman, systematic review and meta-analysis of randomized trials. J. Am. Med. Direct. J. I., Donato, K. A., et al. (2009). 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Med. 175, The remaining authors declare that the research was conducted in the absence of 1094–1103. any commercial or financial relationships that could be construed as a potential van den Brink, A. C., Brouwer-Brolsma, E. M., Berendsen, A. A. M., and van de conflict of interest. Rest, O. (2019). The Mediterranean, Dietary Approaches to Stop Hypertension (DASH), and Mediterranean-DASH Intervention for Neurodegenerative Delay Publisher’s Note: All claims expressed in this article are solely those of the authors (MIND) diets are associated with less cognitive decline and a lower risk of and do not necessarily represent those of their affiliated organizations, or those of Alzheimer’s disease-a review. Adv. Nutrition 10, 1040–1065. doi: 10.1093/ the publisher, the editors and the reviewers. Any product that may be evaluated in advances/nmz054 this article, or claim that may be made by its manufacturer, is not guaranteed or Viguiliouk, E., Kendall, C. W. C., Mejia, S. B., Cozma, A. 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Aging Neurosci. cognitive trajectories in late life and associated predictors and outcomes: a 13:782067. doi: 10.3389/fnagi.2021.782067 systematic review. J. Alzheimer’s Dis. Rep. 4, 459–478. doi: 10.3233/ADR- 200232 Copyright © 2021 Nishi, Babio, Gómez-Martínez, Martínez-González, Ros, Corella, Castañer, Martínez, Alonso-Gómez, Wärnberg, Vioque, Romaguera, López- Conflict of Interest: SKN was a volunteer member of the not-for profit group Miranda, Estruch, Tinahones, Lapetra, Serra-Majem, Bueno-Cavanillas, Tur, Plant Based Canada. JS-S reported receiving research support from the Instituto de Martín Sánchez, Pintó, Delgado-Rodríguez, Matía-Martín, Vidal, Vázquez, Daimiel, Salud Carlos III, Ministerio de Educación y Ciencia, Departament de Salut Pública Razquin, Coltell, Becerra-Tomás, De La Torre Fornell, Abete, Sorto-Sanchez, Barón- de la Generalitat de Catalunya, the European Commission, the United States López, Signes-Pastor, Konieczna, Garcia-Rios, Casas, Gomez-Perez, Santos-Lozano, National Institutes of Health; receiving consulting fees or travel expenses from García-Arellano, Guillem-Saiz, Ni, Trinidad Soria-Florido, Zulet, Vaquero-Luna, Danone, California Walnut Commission, Eroski Foundation, Instituto Danone, Toledo, Fitó, Salas-Salvadó. This is an open-access article distributed under the Nestle, and Abbott Laboratories, receiving non-financial support from Hojiblanca, terms of the Creative Commons Attribution License (CC BY). The use, distribution Patrimonio Comunal Olivarero, the California Walnut Commission, Almond or reproduction in other forums is permitted, provided the original author(s) and Board of California, La Morella Nuts, Pistachio Growers, and Borges SA; serving the copyright owner(s) are credited and that the original publication in this journal on the board of and receiving grant support through his institution from the is cited, in accordance with accepted academic practice. No use, distribution or International Nut and Dried Foundation and the Eroski Foundation; and grants reproduction is permitted which does not comply with these terms. Frontiers in Aging Neuroscience | www.frontiersin.org 14 December 2021 | Volume 13 | Article 782067