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Record W4416851263 · doi:10.1186/s12916-025-04488-y

Mediterranean diet, gut microbiota, and cognitive decline in older adults with obesity/overweight and metabolic syndrome: a prospective cohort study

2025· article· en· W4416851263 on OpenAlexafffund
Jiaqi Ni, Adrián Hernández-Cacho, Stephanie Nishi, Nancy Babio, Clara Belzer, Prokopis Konstati, Jesús Vioqué, Dolores Corella, Olga Castañer, Josep Juan Vidal, Isabel Moreno‐Indias, Laura Torres‐Collado, Óscar Coltell, Montserrat Fitó, Miguel Ruiz‐Canela, Dong D. Wang, Francisco J. Tinahones, Jordi Salas-Salvadó

Bibliographic record

VenueBMC Medicine · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institute of Nursing ResearchNational Institute on AgingInstituto de Salud Carlos IIICanadian Institutes of Health ResearchCentro de Investigación Biomédica en Red Diabetes y Enfermedades Metabólicas AsociadasMinisterio de Ciencia, Innovación y UniversidadesAlmond Board of California
KeywordsCognitive declineProspective cohort studyMediterranean dietCognitionCohort studyWestern dietPsychological interventionCohortAffect (linguistics)

Abstract

fetched live from OpenAlex

BACKGROUND: Emerging evidence highlights that diet dynamically shapes the gut microbiome, which in turn influences cognitive function through bidirectional gut-brain communication, offering a promising target for mitigating cognitive decline and neurodegenerative disorders. While the Mediterranean diet (MedDiet) is a well-established dietary pattern with demonstrated neuroprotective benefits, the interplay between MedDiet adherence, gut microbiota, and longitudinal cognitive trajectories remains poorly understood. We aimed to identify a gut microbial signature of the MedDiet adherence and prospectively examine the associations of MedDiet adherence and MedDiet gut microbial signature (MedDiet-GMS) with cognitive changes over time in older adults at high risk of cognitive decline. METHODS: This study included 746 participants (mean age 65 ± 5 years, 48% women) with overweight/obesity and metabolic syndrome. Adherence to the MedDiet was assessed using a validated 14-item Mediterranean Diet Adherence Screener (MEDAS). Baseline gut microbiota composition was profiled via 16S rRNA sequencing. Cognitive function was evaluated at baseline, 2, 4, and 6 years using a comprehensive neuropsychological battery. Elastic net regressions were applied to derive a MedDiet-GMS, and linear mixed models were used to assess associations of both MEDAS and MedDiet-GMS with trajectories of cognitive function, adjusting for potential confounders. RESULTS: Higher adherence to the MedDiet was associated with greater gut microbial diversity (p < 0.05) and distinct microbial composition (PERMANOVA, p = 0.001). The MedDiet-GMS comprised 20 taxa, including short-chain fatty acid-producers (e.g., Barnesiella, Butyricicoccus) positively weighted and pro-inflammatory taxa (e.g., Eggerthella) negatively weighted. Both higher MEDAS scores (p = 0.007) and MedDiet-GMS (p = 0.036) were independently associated with slower global cognitive decline. The MedDiet-GMS was additionally linked to preserved executive function (p = 0.049), while MEDAS was associated with attenuated general cognitive decline (p = 0.028). Eggerthella, inversely associated with MedDiet adherence, was linked to greater executive function decline (FDR < 0.05). CONCLUSIONS: Greater adherence to the MedDiet was associated with a favorable gut microbiota profile and slower cognitive decline over 6-year of follow-up. A microbiome-derived signature of MedDiet adherence was prospectively associated with favorable cognitive trajectories in older adults at risk of cognitive decline. External validation and experimental research are warranted to translate these findings into targeted microbiome-based dietary interventions for healthy cognitive aging.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.713

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.005
GPT teacher head0.262
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2025
Admission routes2
Has abstractyes

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