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Record W2998765153 · doi:10.1002/oby.22705

Association of Hot Tea Consumption with Regional Adiposity Measured by Dual‐Energy X‐Ray Absorptiometry in NHANES 2003‐2006

2020· article· en· W2998765153 on OpenAlexaff
Justin Roberts, Qinran Liu, Chao Cao, Sarah E. Jackson, Xiaoyu Zong, Gretchen A. Meyer, Lin Yang, W. Todd Cade, Xiaobin Zheng, Guillermo F. López Sánchez, Xiaojian Wu, Lee Smith

Bibliographic record

VenueObesity · 2020
Typearticle
Languageen
FieldMedicine
TopicTea Polyphenols and Effects
Canadian institutionsAlberta Health Services
Fundersnot available
KeywordsMedicineDual-energy X-ray absorptiometryDual energyNational Health and Nutrition Examination SurveyAssociation (psychology)Internal medicineEnvironmental healthPsychologyPopulationBone mineral

Abstract

fetched live from OpenAlex

OBJECTIVE: This study aimed to investigate the potential antiobesity benefits of hot tea consumption at the population level. METHODS: Using data from the National Health and Nutrition Examination Survey (NHANES) 2003-2006, the association between hot tea consumption and dual-energy x-ray-measured body fat was examined in a large representative sample of US adults (n = 5,681, 51.9% women). RESULTS: Compared with non-tea drinkers, men who consumed 0.25 to 1 cup per day of hot tea had 1.5% (95% CI: 0.4% to 2.6%) and 1.7% (95% CI: 0.4% to 3.0%) less total and trunk body fat, respectively. The associations were stronger among men 45 to 69 years old compared with younger men (20-44 years). For men who consumed 1 or more cups per day of hot tea, lower total (-1.2%, 95% CI: -2.3% to -0.2%) and trunk body fat (-1.3%, 95% CI: -2.6 to -0.1%) was observed among men 45 to 69 years old only. In women, those who drank 1 or more cups per day had 1.5% lower (95% CI: -2.7% to -0.3%) trunk body fat compared with non-tea drinkers. CONCLUSIONS: Consumption of hot tea might be considered as part of a healthy diet in order to support parameters associated with metabolic health and may be particularly important in older male age groups in supporting reduced central adiposity.

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)0.000
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 score0.080
Threshold uncertainty score0.443

Codex and Gemma teacher scores by category

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

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 head0.017
GPT teacher head0.228
Teacher spread0.211 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations7
Published2020
Admission routes1
Has abstractyes

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