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Record W4396685978 · doi:10.1017/s0029665124001277

A systematic review of diet, nutrition, and medication use among centenarians and near centenarians worldwide

2024· review· en· W4396685978 on OpenAlexaboutno aff
Cong Dai, So‐Yeon Lee, Sanjay Sharma, Edwin C.K. Tan, Saif Ullah, H. Bodady, Pooja Sachdev

Bibliographic record

VenueProceedings of The Nutrition Society · 2024
Typereview
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCINAHLUnderweightGerontologyOverweightConfoundingMeta-analysisMEDLINEScopusDemographyDemographicsPsychological interventionObesityInternal medicine

Abstract

fetched live from OpenAlex

Centenarians represent a phenomenon of successful aging, yet little is known about their lifestyle and health practices, including diet/nutrition, medication use, and health conditions. A protocol for this systematic review was registered previously (1) . We systematically searched Medline, CINAHL, Scopus, and grey literature from 2000 to 2022, limited to quantitative studies published in English among adults aged 95 years or above. Two reviewers independently screened 3,392 records and identified and extracted data from 34 eligible studies. Additionally, they independently assessed the study quality using the Modified Newcastle-Ottawa Scale (mNOS) (2) . Any disagreement was discussed and resolved with a third reviewer. In analysis, pooled prevalence was provided for categorical variables on demographics, lifestyles, medications, and diseases using % (95%CI); mean or median was provided for continuous variables. Due to study heterogeneity, we conducted a narrative synthesis for the associations between the exposures and outcomes. Over 70% of the included studies met 6/8 criteria based on the mNOS; nearly half did not mention or control for confounders in statistical analyses. The age ranged from 95-118y (32 studies: 100y+; 2 studies: mean age 97-98 y); the majority were females (75%; 95%CI: 71%,78%). Most centenarians did not smoke or drink [current smokers: 7% (5%, 9%); former smokers:16% (12%, 19%); daily drinkers: 27% (20%, 34%); former drinkers:21% (13%, 30%)]. Most centenarians were physically inactive (23%; 20%, 26%). Over 50% had normal weight (52%; 42%, 61%), 33% (14%, 52%) underweight, and 14% (8%, 20%) overweight. Regarding nutrition, the narrative synthesis suggests that centenarians had normal levels of albumin (3.8g/dL), total triglycerides (111mg/dL), total (188mg/dL), and HDL cholesterol (54mg/dL) but high levels of LDL cholesterol (109mg/dL). Regarding medications, nearly 50% took antihypertensive medications (49%; 14%, 84%) or other cardiovascular drugs (48%; 24%, 71%); they took a median of 5 (range: 2-7) drugs. Common conditions included impairment of basic activities of daily living (ADL) (54%; 33%, 74%), hypertension (43%; 21%, 65%), and diabetes (22%; 9%, 52%). In regression analyses among centenarians, high dietary diversity, lower salt preference, and weight status were significant factors for more independence in basic ADL, lower mortality, and greater longevity. For example, a high dietary diversity score was associated with a low mortality risk [0.93 (0.92, 0.94) per unit increase]; those who preferred salty food versus those who did not had a 3.6-fold risk of impaired ADL [adj.OR: 3.59 (1.14, 11.25)]. Being overweight vs. normal weight reduced the risk of ADL impairment [adj.OR: 0.84 (0.78, 0.91] while underweight increased this risk [adj.OR:1.34 (1.28, 1.41)]. Also, overweight [adj.OR: 0.92 (0.90, 0.94)] or abdominal obesity [adj.OR: 0.72 (0.52, 0.996)] reduced the likelihood of longevity per kg increase. This systematic review suggests a healthy lifestyle, good nutrition, and normal body weight may contribute to extreme longevity. Interpreting these summary findings should be cautious due to potential recall bias and heterogeneity of the included studies.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.095
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.042
GPT teacher head0.337
Teacher spread0.295 · 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 designSystematic review
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

Citations2
Published2024
Admission routes1
Has abstractyes

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