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Record W4394612579 · doi:10.1177/15598276241244725

Nutritional Supplements for Healthy Aging: A Critical Analysis Review

2024· review· en· W4394612579 on OpenAlexaff
Matthew Kaufman, Sarah DeParis, Marily Oppezzo, Cheri D. Mah, Megan Roche, Levi Frehlich, Michael Fredericson

Bibliographic record

VenueAmerican Journal of Lifestyle Medicine · 2024
Typereview
Languageen
FieldMedicine
TopicNutritional Studies and Diet
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineSarcopeniaGerontologyVitamin D and neurologyMalnutritionCognitive declineDiseasePhysical therapyInternal medicineDementia

Abstract

fetched live from OpenAlex

Background: Healthy aging is defined as survival to advanced age while retaining autonomy in activities of daily living, high societal participation, and good quality of life. Sarcopenia, insomnia, cognitive impairment, and changes in sensation can be key hinderances to healthy aging, but nutritional supplements may abate their impact. As research advances, an updated review on their efficacy on age-related conditions is warranted. Results: Sarcopenia can be mitigated through proper protein intake, supplements like creatine, and in certain situations Branched-Chain Amino Acids and Vitamin D, in adults over 65. Melatonin supplementation has moderate evidence for improving sleep, while valerian root lacks evidence. Magnesium, tart cherry, and kiwifruits have shown promising impacts on sleep in limited articles. Magnesium, Vitamin D, and B vitamin supplementation have been shown to improve cognition in those with mild cognitive impairment and Alzheimer’s disease but require further study prior to recommendation. The Age-Related Eye Disease Study supplement combination is routinely recommended to reduce risk of progression to advance stages of age-related macular degeneration. Alpha-Lipoic Acid and Folate have been investigated for their roles in mitigating age-related hearing losses. Conclusions: Nutritional supplements and lifestyle changes may mitigate disabilities across multiple domains of age-related illnesses and promote healthy 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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.225
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0070.003
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.072
GPT teacher head0.475
Teacher spread0.403 · 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.

Study designNot applicable
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

Citations9
Published2024
Admission routes1
Has abstractyes

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