Edited by: David Vauzour, University of East Anglia, United Kingdom
Reviewed by: Ramit Ravona-Springer, Sheba Medical Center, Israel; Roberta Zupo, National Institute of Gastroenterology S. de Bellis Research Hospital (IRCCS), Italy
This article was submitted to Nutrition and Brain Health, a section of the journal Frontiers in Nutrition
†These authors share first authorship
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The prevalence of diabetes has reached epidemic proportions in the past few years. There are currently 463 million people living with diabetes, representing 8.5% of the world's adult population (
Diabetes consequences can be avoided or delayed with good glycemic control and the management of cardiovascular risk factors, which can be achieved by following a healthy diet, practicing regular physical activity, and smoking cessation (
As lifestyle and weight management alone often fail to establish and sustain optimal glycemic control, glucose-lowering treatments are also an important component of diabetes management (
Previous studies have shown that metformin use for more than 6 years was associated with lower risk of cognitive impairment (
The objective of this study is to characterize different glycemic trajectories subgroups and to examine the association between metformin use and cognition in subjects with type 2 diabetes that participated in the PREDIMED-Plus MedDiet intervention.
The present study is a prospective cohort study framed in the PREDIMED-Plus-
Four study sites participated in the PREDIMED-Plus-
According to the American Diabetes Association criteria (
Cognitive function was assessed by trained neuropsychologists blinded to the participants' group assignment and included the following cognitive domains: (i)
Composite scores of 3 cognitive domains, namely memory, executive function and global cognition, were calculated for each participant by standardizing raw test scores to z-scores using the mean and standard deviation of baseline data. The memory composite included the mean standardized individual scores of the RAVLT immediate and delayed scores and the RCFT immediate, delayed and recognition scores. The executive function composite included the RCFT copy score, the SDMT direct score, the Stroop interference score, the IGT total score and the CPT omission, commission and hit reaction time scores. Lastly, the global cognition composite included all the tests of memory and executive functions.
The severity of depressive symptomatology was assessed using the Beck's Depression Inventory-II (BDI-II) (
Weight and height were measured by nurses using standardized procedures. BMI (kg/m2) was categorized as normo-weight (BMI 18.5–24.9 kg/m2), overweight (BMI 25.0–29.9 kg/m2), obesity I (BMI 30.0–34.9 kg/m2), and obesity II (BMI 35.0–39.9 kg/m2).
Fasting blood glucose, HbA1c and lipid levels (triglycerides, total cholesterol and HDL cholesterol) were determined using standard methodology after an overnight fast. LDL cholesterol concentrations were calculated using the Friedewald formula whenever triglycerides were lower than 300 mg/dL. Insulin was centrally measured by an electrochemiluminescence immunoassay using an Elecsys immunoanalyzer (Roche Diagnostics, Meylan, France). Insulin resistance was estimated at baseline using the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) index (
Adherence to the er-MedDiet was evaluated with an adapted version of the validated 14-item PREDIMED questionnaire including 17-items, the energy-restricted Mediterranean Diet Adherence Screener (er-MEDAS) (
Covariates were evaluated at baseline through face-to-face interviews by trained staff using self-reported general questionnaires on socio-demographics (gender, age, years of education, employment status), lifestyle (smoking status), medication (use of treatment for high cholesterol, use of tranquilizers, or sedatives for anxiety or sleeping, use of medication for hypertension, use of medication for heart) and history of disease.
Longitudinal finite mixture modeling was applied to explain the between-subject heterogeneity in growth of HbA1c by identifying latent classes or subgroups with different growth trajectories (
Descriptive statistics of study variables stratified by diabetes status (yes/no) and by diabetes subgroups were obtained as mean and standard deviation (SD) or 95% confidence intervals (95%CI) for continuous variables and percentages for categorical variables. Univariate differences were estimated with the unpaired
Given that metformin was not randomly assigned, it was necessary to achieve comparability between groups with regard to pretreatment characteristics to reduce the potential confounding by indication bias and to get better estimates of the treatment effect. This was accomplished using inverse probability of treatment weights (IPTW) (
Missing data was reported as absolute and relative frequencies (
Baseline characteristics of study participants stratified by diabetes status are included in
Baseline characteristics of study participants stratified by type 2 diabetes (T2D) status and univariate differences.
487 (100) | 339 (100) | 148 (100) | |||
Study group | Intervention group | 240 (49.3) | 162 (47.8) | 78 (52.7) | 0.368 |
Study site | IMIM | 116 (23.8) | 65 (19.2) | 51 (34.5) | |
IISPV | 143 (29.4) | 131 (38.6) | 12 (8.1) | ||
UV | 70 (14.4) | 34 (10.0) | 36 (24.3) | ||
HUB | 158 (32.4) | 109 (32.2) | 49 (33.1) | ||
Sex | Women | 246 (50.5) | 173 (51.0) | 73 (49.3) | 0.804 |
Age | 65.2 (4.7) | 64.9 (4.7) | 65.9 (4.7) | ||
Education (years) | 11.7 (5.3) | 12.1 (5.7) | 10.5 (4.0) | ||
Employment status | Employed | 91 (18.7) | 68 (20.1) | 23 (15.5) | 0.611 |
Unemployed | 36 (7.4) | 27 (8.0) | 9 (6.1) | ||
Housework | 50 (10.3) | 36 (10.7) | 14 (9.5) | ||
Retired | 302 (62.1) | 202 (59.8) | 100 (67.6) | ||
Missing | 1 | 1 | |||
Current smoker | 59 (12.1) | 47 (13.9) | 12 (8.11) | 0.101 | |
Physical activity |
Sedentary | 76 (15.6) | 48 (14.2) | 28 (18.9) | 0.082 |
Under-active | 326 (66.9) | 238 (70.2) | 88 (59.5) | ||
Moderately active | 44 (9.03) | 25 (7.37) | 19 (12.8) | ||
Active | 41 (8.42) | 28 (8.26) | 13 (8.78) | ||
Er-MedDiet adherence |
Low | 221 (45.4) | 150 (44.2) | 71 (48.0) | 0.741 |
Medium | 202 (41.5) | 144 (42.5) | 58 (39.2) | ||
High | 64 (13.1) | 45 (13.3) | 19 (12.8) | ||
BMI category | Over-weight | 133 (27.3) | 99 (29.2) | 34 (23.0) | 0.156 |
Obesity I | 236 (48.5) | 164 (48.4) | 72 (48.6) | ||
Obesity II | 118 (24.2) | 76 (22.4) | 42 (28.4) | ||
Depressive symptomatology |
No or minimal | 304 (62.4) | 217 (64.0) | 87 (58.8) | 0.544 |
Mild-to-moderate | 140 (28.7) | 93 (27.4) | 47 (31.8) | ||
Moderate-to-severe | 43 (8.8) | 29 (8.5) | 14 (9.5) | ||
Metformin | 111 (22.7) | 0 (0.00) | 111 (75.0) | ||
Insulin | 10 (2.0) | 0 (0.00) | 10 (6.7) | ||
Other treatments for diabetes |
51 (10.5) | 0 (0.00) | 51 (34.5) | ||
Tranquilizers/sedatives | 112 (23.0) | 72 (21.2) | 40 (27.0) | 0.201 | |
Cholesterol treatment | 245 (50.3) | 153 (45.1) | 92 (62.2) | ||
92.0 (39.6) | 88.7 (39.5) | 99.5 (38.8) | 0.006 | ||
Hba1c (%) | 6.1 (0.8) | 5.8 (0.4) | 7.0 (1.0) | ||
HbA1c (mmol/mol) | 43.5 (9.2) | 39.8 (4.2) | 52.6 (11.2) | ||
Glucose (mg/dL) | 116 (30.9) | 103 (13.2) | 146 (38.7) | ||
HOMA-IR index | 5.6 (3.9) | 5.0 (3.1) | 7.1 (5.2) |
Compared to individuals without diabetes, individuals with type 2 diabetes were older (65.9 vs. 64.9 years), had less years of education (10.5 vs. 12.1 years) and took more treatments for high cholesterol (62.2 vs. 45.1%). Moreover, most individuals with diabetes were being treated with metformin (75.0%), only 6.7% were taking insulin and 34.5% were taking other oral medications for diabetes (alone or in combination with metformin or insulin). As expected, participants with diabetes had poorer glycemic profile than those without diabetes, with higher values of HbA1c (mean of 7.0 vs. 5.8%), fasting plasma glucose (mean of 146 mg/dL vs. 103 mg/dL) and HOMA-IR index (mean of 5.0 vs. 7.1).
Study flow diagram including the follow-up in the neuropsychological visits (neurops. visit) after 1 and 3 years of intervention. IMIM, Hospital del Mar Medical Research Institute. IISPV, Pere Virgili Institute for Health Research. UV, University of Valencia. HUB, Bellvitge University Hospital.
First, participants with type 2 diabetes were classified into three distinct latent subgroups based on their HbA1c trajectory from baseline to 1 and 3 years of MedDiet intervention. Subgroup 1 (S1) contained most of the subjects with diabetes (83.1%,
Representation of
As shown in
Finally, metformin was used by 70.7% (
Within the main subgroup of subjects with diabetes with a good-stable glycemic control (S1), those subjects using metformin (
As shown in
Differences in MedDiet adherence and in cognitive composites at each time point and in the mean change from baseline between individuals with type 2 diabetes from subgroup 1 (T2D-S1) treated with metformin and not treated with metformin (matched with inverse probability of treatment weights).
er-MedDiet adherence score | Baseline | 0 (0) | 7.7 (6.8, 8.5) | 0 (0) | 7.7 (7.2, 8.3) | 0.03 (−0.36, 0.41) | VS | 0.752 | |||
1 year | 1 (2.8) | 12.1 (10.9, 13.3) | 3 (3.4) | 11.6 (11, 12.2) | −0.16 (−0.56, 0.23) | S | 0.427 | 4.4 (3.4, 5.5) | 3.9 (3.2, 4.6) | 0.476 | |
3 years | 1 (2.8) | 12.6 (11.8, 13.4) | 8 (9.2) | 11.5 (10.9, 12.1) | −0.44 (−0.84, −0.02) | S | 4.9 (3.9, 5.9) | 3.9 (3.3, 4.4) | 0.145 | ||
Memory composite (z-score) | Baseline | 1 (2.8) | −0.17 (−0.46, 0.12) | 3 (3.4) | 0.1 (−0.03, 0.23) | 0.38 (−0.02, 0.79) | S | 0.115 | |||
1 year | 7 (19.4) | 0.1 (−0.21, 0.41) | 13 (14.9) | 0.18 (0.03, 0.33) | 0.11 (−0.32, 0.54) | VS | 0.795 | 0.2 (−0.03, 0.42) | 0.01 (−0.11, 0.14) | 0.307 | |
3 years | 6 (16.7) | 0.33 (0.04, 0.63) | 16 (18.4) | 0.29 (0.14, 0.44) | −0.06 (−0.49, 0.36) | VS | 0.557 | 0.38 (0.15, 0.62) | 0.1 (−0.05, 0.25) | ||
Executive functions composite ( |
Baseline | 10 (34.5) | −0.14 (−0.42, 0.14) | 21 (34.4) | 0.13 (−0.02, 0.28) | 0.51 (−0.06, 1.08) | M | 0.086 | |||
1 year | 10 (34.5) | −0.13 (−0.47, 0.21) | 14 ( |
0.09 (−0.04, 0.21) | 0.39 (−0.16, 0.93) | S | 0.333 | 0.08 (0.00, 0.16) | −0.02 (−0.17, 0.13) | 0.293 | |
3 years | 14 (48.3) | 0.23 (−0.14, 0.6) | 28 (45.9) | 0.14 (−0.01, 0.28) | −0.18 (−0.79, 0.44) | S | 0.557 | 0.36 (0.13, 0.59) | 0.02 (−0.09, 0.14) | ||
Global cognition composite ( |
Baseline | 10 (34.5) | −0.1 (−0.35, 0.14) | 22 (36.1) | 0.13 (−0.02, 0.27) | 0.48 (−0.1, 1.04) | M | 0.124 | |||
1 year | 10 (34.5) | 0.02 (−0.34, 0.37) | 15 (24.6) | 0.15 (0.02, 0.29) | 0.23 (−0.31, 0.77) | S | 0.676 | 0.12 (−0.05, 0.29) | 0.12 (0, 0.23) | 0.511 | |
3 years | 14 (48.3) | 0.34 (−0.01, 0.69) | 28 (45.9) | 0.19 (0.03, 0.34) | −0.28 (−0.9, 0.34) | S | 0.304 | 0.29 (0.10, 0.49) | −0.02 (−0.11, 0.07) |
Inverse probability of treatment weighting (IPTW) was applied to all analyses to weight each individual with his/her inverse probability of being treated with metformin, generating a pseudo-population with (almost) perfect covariate balance. All models were adjusted by diagnosis of sleep apnoea.
Differences in
Finally, at baseline, both groups presented a moderate adherence to the MedDiet (mean score of 7.7). However, after 3 years of dietary intervention, MedDiet adherence increased to 12.6 points (95% CI 11.8, 13.4) in subjects with type 2 diabetes not treated with metformin and to 11.5 points (95% CI 10.9, 12.1) in subjects treated with metformin, a difference which was statistically significant (
Irrespective of metformin exposure, participants with type 2 diabetes from S1 were compared to participants without diabetes. Participants with diabetes presented lower total cholesterol, LDL-cholesterol, lower HDL-cholesterol and were more physically active (
There were no differences in baseline memory, executive functions and global cognition between subjects with and without type 2 diabetes (as shown in
Differences in MedDiet adherence, memory, executive functions and global cognition between subjects without type 2 diabetes (No-T2D) and all subjects with type 2 diabetes from subgroup 1 (T2D-S1)
Subjects with type 2 diabetes from S1 treated with metformin were compared to subjects without diabetes (
Participants without diabetes were compared to participants with type 2 diabetes from S1 who were not treated with metformin (
This is the first study to date to examine the effect of metformin on cognition in older adults with type 2 diabetes following a MedDiet intervention. We first examined the heterogeneity in HbA1c trajectories after 1 and 3 years of dietary intervention. We identified three different subgroups of individuals with diabetes irrespective of the intervention group. The largest group exhibited good glycemic control that remained stable during the follow-up, while the remaining two subgroups showed poor baseline glycemic control that improved or worsened during the follow-up. Among the group with good glycemic control, we observed that those treated with metformin presented a better baseline performance in memory, executive functions and global cognition than those not treated with metformin. However, those not treated with metformin presented higher adherence to the MedDiet over time as well as greater improvements in memory, executive functions and global cognition, so that baseline differences between individuals with type 2 diabetes treated and not treated with metformin vanished after 1 and 3 years of MedDiet intervention. These results suggest that adherence to a MedDiet intervention could be superior to the potential neuroprotective effects of metformin among older adults with overweight/obesity and metabolic syndrome who have good glycemic control of their type 2 diabetes.
Our results suggest that metformin could have neuroprotective effects. Specifically, we observed that before starting the MedDiet intervention, individuals with type 2 diabetes from a group presenting good glycemic control (S1) treated with metformin presented a higher performance in memory, executive functions and global cognition than those not treated with metformin. These results agree with previous observational studies showing better memory performance (
Metformin mainly acts by reducing liver gluconeogenesis and inhibiting glucagon-mediated signaling in the liver, but it can also cross the blood brain barrier and thus affect the brain more directly (
Our results also suggest that a higher adherence to the MedDiet could reverse the cognitive disadvantage of those subjects with diabetes that were not treated with metformin, since both groups with diabetes achieved similar cognitive scores along the follow-up. Subjects with diabetes from S1 not treated with metformin presented improvements in memory, executive functions and global cognition composites during the 3 years of follow-up, but cognition remained almost stable among those treated with metformin. Moreover, subjects with diabetes who were not exposed to metformin showed greater adherence to the MedDiet after 3 years of follow-up. The reason for this is unknown, but this indicates a group of subjects with a high capacity to make lifestyle changes. In fact, prior to participating in this study, most subjects from this group were able to control their blood glucose without medication and when offered a lifestyle intervention they adhered to it very faithfully, which probably translated into cognitive improvement. Another possibility is that those individuals with type 2 diabetes who take anti-diabetic drugs value lifestyle interventions less than those who do not take any drugs. Moreover, the greater compliance with the MedDiet among individuals with type 2 diabetes not taking metformin at baseline may also explain why they did not require metformin during the 3 years of follow-up. Previous studies have already reported the delayed need of medication for diabetes in patients with a newly diagnosed type 2 diabetes after a MedDiet intervention, compared to a low-fat diet (
The favorable effect of the MedDiet intervention was likely due to the overall composition of the dietary pattern and not to a decreased caloric intake, weight loss or increased physical activity, because the allocation to the intervention or control group was balanced among subjects with diabetes either treated and not treated with metformin. In individuals with type 2 diabetes, the MedDiet has been consistently associated with better glycemic control (reduction of HbA1c by 0.32–0.53%) and a better profile of cardiovascular risk factors, compared to low-fat diets (
When individuals with diabetes were compared to those without diabetes, we did not find baseline differences in memory, executive functions and global cognition composites. These results differ from previous cross-sectional studies in the overall PREDIMED-Plus population (
Nevertheless, in our study subjects with diabetes not treated with metformin experienced a greater increase in their executive functions than subjects without diabetes after 3 years of follow-up. Therefore, their greater adherence to the MedDiet could explain this difference in the rate of change in executive functions. In turn, MedDiet adherence did not differ between subjects with diabetes treated with metformin and subjects without diabetes. However, those using metformin experienced a lower improvement in their memory after 1 and 3 years, and in their global cognition after 3 years of follow-up, compared to subjects without diabetes. Thus, in the face of equivalent adherence to MedDiet, metformin was unable to neutralize the negative impact of type 2 diabetes on cognition.
The strengths of this study include its longitudinal design with 3 years of follow-up and the large number of cognitive tests that are administered to participants, covering 12 different cognitive abilities that are grouped in memory, executive functions and global cognitive composites. Moreover, the methodology used in the analysis of results allowed us to minimize confounding by indication which is not frequently addressed in most studies of metformin and cognitive associations. Finally, we also described the heterogeneity in the response to a MedDiet intervention among individuals with diabetes type 2, which aligns with the current recommendations of more patient-centered research and care in the field of diabetes (
However, this study has several limitations. First, the small sample size of the population with diabetes (
Second, there were losses in the evaluation of the cognitive function during the follow-up (within S1, 14% in the first year and 18% in the third year). They were not unexpected given the burden of neuropsychological visits and the fact that visits of this sub-study were performed on different days to those of the main trial. In addition, executive functions and global cognition composites excluded participants from the UV study site (representing 27% of subjects from S1) since not all the tests that made up the construct of executive functions were administered in this site. Therefore, selection bias cannot be completely excluded from this study.
Third, our methodology was not suitable for investigating causal effects since metformin administration was not randomized, and we did not collect data on the duration of metformin use, specific doses, or patients' adherence to their medication regimens. However, we noted that participants did not change their metformin treatment during the 3 years of follow-up. Moreover, we used IPTW to match treated and untreated subjects in each comparison. This approach allows to account for systematic differences in comorbidities between groups and is used to limit confounding by indication. We also had no information about the
Finally, this study does not have a control group since all subjects were exposed to a MedDiet intervention. However, without any intervention, individuals with metabolic syndrome would have probably presented a cognitive decline over time (
In summary, both metformin and MedDiet seem to have neuroprotective effects in older adults at increased risk of pathological cognitive decline, presenting overweight/obesity, metabolic syndrome and type 2 diabetes. Given the heterogeneity in type 2 diabetes and in the response to lifestyle interventions and glucose-lowering medications, a group-based trajectory analysis was initially performed to stratify the population with diabetes. There were two minor subgroups with high HbA1c levels that did not achieve good glycemic control despite of the intensive MedDiet intervention. Future studies should consider applying more intensive and personalized dietary interventions to subjects with poor glycemic control of their type 2 diabetes. However, the majority subgroup of individuals with type 2 diabetes presented good glycemic control throughout the follow-up. In this subgroup, metformin treatment was associated with better memory, executive functions and global cognition at baseline. Nevertheless, after 1 and 3 years of MedDiet intervention, both metformin-treated and non-metformin-treated subjects achieved similar cognitive function. We postulate that increased adherence to the MedDiet explained the cognitive improvement observed in individuals with type 2 diabetes not treated with metformin. In conclusion, a high adherence to MedDiet seems to at least slow down cognitive decline in the elderly with metabolic syndrome and other chronic diseases. Our results support the hypothesis that both metformin and MedDiet interventions are good candidates for future cognitive decline preventive studies.
The datasets presented in this article are not readily available because there are restrictions on the availability of data for the PREDIMED-Plus trial, due to the signed consent agreements around data sharing. Requestors wishing to access the PREDIMED-Plus dataset generated and/or analyzed during the current study can make a request to the PREDIMED-Plus trial Steering Committee chair. Requests to access the datasets should be directed to Jordi Salas-Salvadó,
The studies involving human participants were reviewed and approved by Parc de Salut Mar Drug Research Ethics Committee, Clinical Research Ethics Committee of Bellvitge University Hospital, Drug Research Ethics Committee of the Institut d'Investigació Sanitària Pere Virgili and Committee of Ethics and Research on Humans of Valencia University. The patients/participants provided their written informed consent to participate in this study.
RT, NS-D, AC-R, and LF contributed to the conception and design of the study, wrote the manuscript, and reviewed/edited the manuscript. NS-D performed the statistical analyses. AC-R, LF, NB, SN, CG-M, RF-C, AA-S, CV-A, SJ-M, and OC contributed to data acquisition. DC, SC, JD-E, OC, MG-G, XP, JS-S, and FF-A contributed to critical revision of the manuscript for key intellectual content. RT, JS-S, and FF-A obtained funding for the study. All authors have read and approved the final manuscript.
Study resulting from the following grants: SLT006/17/00246, SLT002/16/00045 and SLT006/17/00077 funded by the Department of Health of the Generalitat de Catalunya by the calls Acció instrumental de programes de recerca orientats en l'àmbit de la recerca i la innovació en salut and Pla estratègic de recerca i innovació en salut (PERIS). We thank CERCA Programme/Generalitat de Catalunya for institutional support. This project was funded by Instituto de Salud Carlos III (ISCIII), the Spanish Government Official Agency for funding biomedical research-with competitive grants leaded by JS-S and Josep Vidal for the periods 2014–2016, 2015–2017, 2017–2019, and 2018–2020, through the Fondo de Investigación para la Salud (FIS), which is co-funded by the European Regional Development Fund, ERDF, a way to build Europe) [grants: PI13/00233, PI13/00728, PI13/01123, PI13/00462, PI16/00533, PI16/00366, PI16/01094, PI16/00501, PI17/01167, PI19/00017, PI19/00781, PI19/01032, PI19/00576]; the Especial Action Project entitled: Implementación y evaluación de una intervención intensiva sobre la actividad física Cohorte PREDIMED-Plus grant to JS-S; the European Research Council [Advanced Research Grant 2014–2019; agreement #340918] granted to Miguel Ángel Martínez-González; the Recercaixa (number 2013ACUP00194) grant to JS-S. This research was also partially funded by EU-H2020 Grants (Eat2beNICE/H2020-SFS-2016-2; Ref 728018; and PRIME/H2020-SC1-BHC-2018-2020; Ref: 847879), Grant PROMETEO/2017/017 (Generalitat Valenciana) and Grant FEA/SEA 2017 for Primary Care Research. This work is also partially supported by ICREA under the ICREA Academia programme. This work was supported by grants from DIUE de la Generalitat de Catalunya 2017 SGR 138 from the Departament d'Economia i Coneixement de la Generalitat de Catalunya (Spain). NS-D has received the FI 2021 predoctoral grant (FI_B2021/00104) from the Agency for Management of University and Research Grants (AGAUR) of the Generalitat de Catalunya. CV-A was supported by a predoctoral Grant of the Ministerio de Educación, Cultura y Deporte (FPU16/01453). AA-S has received a post-doctoral grant from the Consellería de Innovación, Ciencia y Sociedad Digital, Generalitat Valenciana, Valencia (APOSTD/2020/003). The Physiopathology of Obesity and Nutrition Networking Biomedical Research Center (CIBEROBN) is an initiative of ISCIII.
None of these funding sources plays any role in the design, collection, analysis, or interpretation of the data or in the decision to submit manuscripts for publication. The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.
JS-S reports non-financial support from Nut and Dried Fruit Foundation, personal fees from Danone Institute Spain, other from Danone S.A., other from Font Vella Lanjaron, other from Nuts for Life, other from Eroski Distributors, outside the submitted work. FF-A reports consultation fees from Novo Nordisk and editor-in-chief honorarium from Wiley. JD-E reports honoraria for lectures and presentations from Novo Nordisk, Mundipharma, Lilly, Astra Zeneca, MSD and Boehringer Ingelheim. All these relationships did not influence study design, data collection, data analysis, data interpretation, or writing of the report. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
The authors thank the participants for their enthusiastic collaboration and the PREDIMED-Plus personnel for their invaluable support, as well as all affiliated primary care centers, for their excellent work.
The Supplementary Material for this article can be found online at: