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Record W2965841642 · doi:10.4103/1673-5374.262599

Relationship between high dietary fat intake and Parkinson’s disease risk: a meta-analysis

2019· article· en· W2965841642 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueNeural Regeneration Research · 2019
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineOdds ratioParkinson's diseasePolyunsaturated fatMeta-analysisDiseaseInternal medicineConfidence intervalSaturated fatPhysiologyCholesterol

Abstract

fetched live from OpenAlex

OBJECTIVE: To assess whether dietary fat intake influences Parkinson's disease risk. DATA SOURCES: We systematically surveyed the Embase and PubMed databases, reviewing manuscripts published prior to October 2018. The following terms were used: ("Paralysis agitans" OR "Parkinson disease" OR "Parkinson" OR "Parkinson's" OR "Parkinson's disease") AND ("fat" OR "dietary fat" OR "dietary fat intake"). DATA SELECTION: Included studies were those with both dietary fat intake and Parkinson's disease risk as exposure factors. The Newcastle-Ottawa Scale was adapted to investigate the quality of included studies. Stata V12.0 software was used for statistical analysis. OUTCOME MEASURES: The primary outcomes included the relationship between high total energy intake, high total fat intake, and Parkinson's disease risk. The secondary outcomes included the relationship between different kinds of fatty acids and Parkinson's disease risk. RESULTS: Nine articles met the inclusion criteria and were incorporated into this meta-analysis. Four studies scored 7 and the other five studies scored 9 on the Newcastle-Ottawa Scale, meaning that all studies were of high quality. Meta-analysis results showed that high total energy intake was associated with an increased risk of Parkinson's disease (P = 0.000, odds ratio (OR) = 1.49, 95% confidence interval (CI): 1.26-1.75); in contrast, high total fat intake was not associated with Parkinson's disease risk (P = 0.123, OR = 1.07, 95% CI: 0.91-1.25). Subgroup analysis revealed that polyunsaturated fatty acid intake (P = 0.010, OR = 1.03, 95% CI: 0.88-1.20) reduced the risk of Parkinson's disease, while arachidonic acid (P = 0.026, OR = 1.15, 95% CI: 0.97-1.37) and cholesterol (P = 0.002, OR = 1.09, 95% CI: 0.92-1.29) both increased the risk of Parkinson's disease. Subgroup analysis also demonstrated that, although the results were not significant, consumption of n-3 polyunsaturated fatty acids (P = 0.071, OR = 0.88, 95% CI: 0.73-1.05), α-linolenic acid (P = 0.06, OR = 0.86, 95% CI: 0.72-1.02), and the n-3 to n-6 ratio (P = 0.458, OR = 0.89, 95% CI: 0.75-1.06) were all linked with a trend toward reduced Parkinson's disease risk. Monounsaturated fatty acid (P = 0.450, OR = 1.06, 95% CI: 0.91-1.23), n-6 polyunsaturated fatty acids (P = 0.100, OR = 1.15, 95% CI: 0.96-1.36) and linoleic acid (P = 0.053, OR = 1.11, 95% CI: 0.94-1.32) intakes were associated with a non-significant trend toward higher PD risk. Saturated fatty acid (P = 0.619, OR = 1.01, 95% CI: 0.87-1.18) intake was not associated with Parkinson's disease. CONCLUSION: Dietary fat intake affects Parkinson's disease risk, although this depends on the fatty acid subtype. Higher intake of polyunsaturated fatty acids may reduce the risk of Parkinson's disease, while higher cholesterol and arachidonic acid intakes may elevate Parkinson's disease risk. However, further studies and evidence are needed to validate any link between dietary fat intake and Parkinson's disease.

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.

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)0.001
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 score0.035
Threshold uncertainty score0.738

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.215
GPT teacher head0.392
Teacher spread0.177 · 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