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Record W2022615539 · doi:10.1159/000327147

The Trends of Metabolic Syndrome in Normal-Weight Tehranian Adults

2011· article· en· W2022615539 on OpenAlexfundno aff
Farhad Hosseinpanah, Maryam Barzin, Parisa Amiri, Fereidoun Azizi

Bibliographic record

VenueAnnals of Nutrition and Metabolism · 2011
Typearticle
Languageen
FieldMedicine
TopicDiabetes, Cardiovascular Risks, and Lipoproteins
Canadian institutionsnot available
FundersPratt and Whitney Canada
KeywordsWaistMetabolic syndromeMedicineAbdominal obesityObesityBody mass indexCohortPopulationNormal weightDemographyDiabetes mellitusInternal medicineEndocrinologyOverweightEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: In recent decades, more attention has been directed to the clustering of cardiometabolic risk factors in normal-weight adults. This study investigates trends of the prevalence of metabolic syndrome (MetS) in a normal-weight Tehranian adult population during 6.6 years of follow-up. METHODS: In this population-based cohort study of 5,269 participants aged ≥ 20 years during the 3 phases (1999-2001, 2002-2005 and 2005-2008) of the Tehran Lipid and Glucose Study, we selected 390 males and 358 females with a normal body mass index (18.5-24.9) during all 3 periods of follow-up. MetS was defined according to the International Diabetes Federation criteria, and waist circumference (WC) cut points were ≥ 89 cm for males and ≥ 91 cm for females. RESULTS: The overall prevalence of MetS increased from 2.3 and 4.0% in phases I and II to 9.6% in phase III. This trend was significant in males (p < 0.001) but not in females (p = 0.6). No significant changes in components of MetS were seen, except in WC among males. In the 3 study phases, prevalence of abdominal obesity was 3.1, 18.5 and 36.2% in males, respectively. CONCLUSIONS: There was a dramatic 4-fold increase in the prevalence of MetS in the Tehranian normal-weight adult population, highlighting the importance of MetS components, especially of WC, in normal-weight adult males.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.982
Threshold uncertainty score0.411

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.031
GPT teacher head0.266
Teacher spread0.234 · 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 designOther design
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

Citations14
Published2011
Admission routes1
Has abstractyes

Explore more

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