Association between animal protein sources and risk of neurodegenerative diseases: a systematic review and dose-response meta-analysis

Asoudeh, Farzaneh; Naeini, Fatemeh; Sadeghi, Erfan; Travica, Nikolaj; Mohammadi, Hamed

doi:10.1093/nutrit/nuac114

Cited by 14 publications

(9 citation statements)

References 77 publications

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“…The relationship between red meat consumption and neurodegeneration and PD is still unclear. Our research identified a significant association between a preference for beef steak and an increased brain iron in motor regions as well as a decreased risk of PD; however, recent systematic reviews have found no association between red meat consumption and cognitive impairment, Alzheimer’s disease, dementia, or PD 100,101 . Results may be so variegated because the actual iron absorbed from a meat will vary based on the cut and cooking method 102 as well as the presence or absence of iron absorption inhibitors or promoters in the meal 103 .…”

Section: Discussionmentioning

confidence: 47%

Dietary and Lifestyle Factors of Brain Iron Accumulation and Parkinson’s Disease Risk

Ahern,

Boyle,

Thompson

et al. 2024

Preprint

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Purpose: Iron is an essential nutrient which can only be absorbed through an individual's diet. Excess iron accumulates in organs throughout the body including the brain. Iron dysregulation in the brain is commonly associated with neurodegenerative diseases like Alzheimer's disease and Parkinson's Disease (PD). Our previous research has shown that a pattern of iron accumulation in motor regions of the brain related to a genetic iron-storage disorder called hemochromatosis is associated with an increased risk of PD. To understand how diet and lifestyle factors relate to this brain endophenotype and risk of PD we analyzed the relationship between these measures, estimates of nutrient intake, and diet and lifestyle preference using data from UK Biobank. Methods: Using distinct imaging and non-imaging samples (20,477 to 28,388 and 132,023 to 150,603 participants, respectively), we performed linear and logistic regression analyses using estimated dietary nutrient intake and food preferences to predict a) brain iron accumulation score (derived from T2-Weighted Magnetic Resonance Imaging) and b) PD risk. In addition, we performed a factor analysis of diet and lifestyle preferences to investigate if latent lifestyle factors explained significant associations. Finally, we performed an instrumental variable regression of our results related to iron accumulation and PD risk to identify if there were common dietary and lifestyle factors that were jointly associated with differences in brain iron accumulation and PD risk. Results: We found multiple highly significant associations with measures of brain iron accumulation and preferences for alcohol (factor 7: t=4.02, pFDR=0.0003), exercise (factor 11: t=-4.31, pFDR=0.0001), and high-sugar foods (factor 2: t=-3.73, pFDR=0.0007). Preference for alcohol (factor 7: t=-5.83, pFDR<1x10-8), exercise (factor 11: t=-7.66, pFDR<1x10-13), and high sugar foods (factor 2: t=6.03, pFDR<1x10-8) were also associated with PD risk. Instrumental variable regression of individual preferences revealed a significant relationship in which dietary preferences associated with higher brain iron levels also appeared to be linked to a lower risk for PD (p=0.004). A similar relationship was observed for estimates of nutrient intake (p=0.0006). Voxel-wise analysis of i) high-sugar and ii) alcohol factors confirmed T2-weighted signal differences consistent with iron accumulation patterns in motor regions of the brain including the cerebellum and basal ganglia. Conclusion: Dietary and lifestyle factors and preferences, especially those related to carbohydrates, alcohol, and exercise, are related to detectable differences in brain iron accumulation and alterations in risk of PD, suggesting a potential avenue for lifestyle interventions that could influence risk.

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Section: Discussionmentioning

confidence: 47%

Dietary and Lifestyle Factors of Brain Iron Accumulation and Parkinson’s Disease Risk

Ahern,

Boyle,

Thompson

et al. 2024

Preprint

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“…This allowed us to determine the effect size for each extra exposure serving. [42] We also implemented a nonlinear dose-dependent analysis applying limited cubic splines with three knots (10, 50, and 90 percentiles of the distribution). [43] We examined the correlation within every group of published RRs and then combined the study-certain estimates through a one-stage mixed effects analysis.…”

Section: Discussionmentioning

confidence: 99%

Fruit and Vegetable Consumption and the Risk of Bone Fracture: A Grading of Recommendations, Assessment, Development, and Evaluations (GRADE)‐Assessed Systematic Review and Dose–Response Meta‐Analysis

Zeraattalab‐Motlagh,

Ghoreishy,

Arab

et al. 2023

JBMR Plus

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Researchers have examined the link between consuming fruit and vegetables and the incidence of fractures for many years. Nevertheless, their findings have been unclear. Furthermore, the dose‐dependent relationship has not been examined, and the level of certainty in the evidence was not evaluated. We carried out a dose‐dependent meta‐analysis examining the relation between fruit and vegetables intake and fracture incidence. PubMed, Web of Sciences, and Scopus were searched until April 2023 for cohort studies evaluating the relation between fruit and vegetables and fracture incidence. Summary relative risks (RRs) were computed from complied data by applying random effects analysis. To examine the level of evidence, we utilized the approach called the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE). Ten cohort studies comprising 511,716 individuals were entered. There was a nonsignificant relation between fruit and vegetables, as well as only fruit intake and any fracture risk. In contrast, high versus low analysis presented that vegetables consumption was linked to a 16% decrease in any type of fracture incidence (RR 0.84; 95% confidence interval [CI], 0.75 to 0.95; I2 = 83.1%; n = 6). Also, per one serving/day (200 g/day) increments in vegetables consumption, there was a 14% decline in the fracture risk (RR 0.86; 95% CI, 0.77 to 0.97; I2 = 84.7%; n = 5; GRADE = moderate). With moderate certainty, a greater consumption of only vegetables, but not total fruit and vegetables or only fruit, might reduce the risk of fracture. These associations were also evident in dose–response analysis. Large intervention trials are demanded to approve our findings. © 2023 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.

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“…To cover broad medical fields, we selected three high-quality meta-analyses as validation datasets, which focused on inflammatory bowel diseases (IBD), 15 diabetes mellitus (DM), 16 and sarcopenia, 17 respectively ( Table 2 ). These meta-analyses provided clear search strategies for Medline/PubMed database and complete list of records that remained after screening based on their titles and abstracts.…”

Section: Methodsmentioning

confidence: 99%

Utilizing ChatGPT to select literature for meta-analysis shows workload reduction while maintaining a similar recall level as manual curation

Cai,

Geng,

et al. 2023

Preprint

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Background: Large language models (LLMs) like ChatGPT showed great potential in aiding medical research. A heavy workload in filtering records is needed during the research process of evidence-based medicine, especially meta-analysis. However, no study tried to use LLMs to help screen records in meta-analysis. In this research, we aimed to explore the possibility of incorporating ChatGPT to facilitate the screening step based on the title and abstract of records during meta-analysis. Methods: To assess our strategy, we selected three meta-analyses from the literature, together with a glioma meta-analysis embedded in the study, as additional validation. For the automatic selection of records from curated meta-analyses, a four-step strategy called LARS was developed, consisting of (1) criteria selection and single-prompt (prompt with one criterion) creation, (2) best combination identification, (3) combined-prompt (prompt with one or more criteria) creation, and (4) request sending and answer summary. We evaluated the robustness of the response from ChatGPT with repeated requests. Recall, workload reduction, precision, and F1 score were calculated to assess the performance of LARS. Findings: ChatGPT showed a stable response for repeated requests (robustness score: 0.747 - 0.996). A variable performance was found between different single-prompts with a mean recall of 0.841. Based on these single-prompts, we were able to find combinations with performance better than the pre-set threshold. Finally, with a best combination of criteria identified, LARS showed a 39.5% workload reduction on average with a recall greater than 0.9. In the glioma meta-analysis, we found no prognostic effect of CD8+ TIL on overall survival, progress-free survival, and survival time after immunotherapy. Interpretation: We show here the groundbreaking finding that automatic selection of literature for meta-analysis is possible with ChatGPT. We provide it here as a pipeline, LARS, which showed a great workload reduction while maintaining a pre-set recall. Funding: China Scholarship Council.

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Association between animal protein sources and risk of neurodegenerative diseases: a systematic review and dose-response meta-analysis

Cited by 14 publications

References 77 publications

Dietary and Lifestyle Factors of Brain Iron Accumulation and Parkinson’s Disease Risk

Dietary and Lifestyle Factors of Brain Iron Accumulation and Parkinson’s Disease Risk

Fruit and Vegetable Consumption and the Risk of Bone Fracture: A Grading of Recommendations, Assessment, Development, and Evaluations (GRADE)‐Assessed Systematic Review and Dose–Response Meta‐Analysis

Utilizing ChatGPT to select literature for meta-analysis shows workload reduction while maintaining a similar recall level as manual curation

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