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Record W4313584979 · doi:10.3389/fnagi.2022.1007629

Association between dietary inflammatory index and cognitive impairment: A meta-analysis

2023· review· en· W4313584979 on OpenAlexaboutno aff
Yuxi Jia, Shoumeng Yan, Mengzi Sun, Yixue Yang, Ling Wang, Caihong Wu, Ping Li

Bibliographic record

VenueFrontiers in Aging Neuroscience · 2023
Typereview
Languageen
FieldMedicine
TopicNutritional Studies and Diet
Canadian institutionsnot available
FundersPeople's Government of Jilin Province
KeywordsMedicineMeta-analysisCochrane LibraryChecklistObservational studyCognitive impairmentCognitionSubgroup analysisMEDLINECognitive declineCohort studyInternal medicineGerontologyDementiaPsychiatryPsychologyDiseaseBiology

Abstract

fetched live from OpenAlex

Aims: Cognitive impairment is an increasingly urgent global public health challenge. Dietary Inflammatory Index (DII) is a literature-derived score that links diet to inflammation. The relationship between DII and cognitive impairment remains controversial. Therefore, our study aimed to analysis the role of DII on the risk of cognitive impairment by meta-analysis. Methods: PubMed, Cochrane Library, MEDLINE, Web of Science and EMBASE databases were searched up to July 2022. Newcastle-Ottawa scale (NOS) and Joanna Briggs Institute (JBI) Checklist were performed to estimate the quality of studies. Results: Nine observational studies with 19,379 subjects were included. Our study found that higher DII could elevate the risk of cognitive impairment (OR = 1.46, 95%CI = 1.26, 1.69). Meanwhile, the OR of cognitive impairment was 1.49 (95%CI = 1.21, 1.83) for cross-sectional studies and 1.42 (95%CI = 1.12, 1.79) for cohort studies, respectively. Conclusion: Our meta-analysis indicated that higher DII (indicating a more pro-inflammatory diet) is related to increased risk of cognitive impairment.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.016
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.016
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0160.032
Bibliometrics0.0060.006
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.097
GPT teacher head0.351
Teacher spread0.254 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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

Citations17
Published2023
Admission routes1
Has abstractyes

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