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Record W1992541500 · doi:10.2105/ajph.2005.080614

Prevalence of Obesity and Its Metabolic Correlates Among the Circumpolar Inuit in 3 Countries

2007· article· en· W1992541500 on OpenAlexaboutno aff
T. Kue Young, Peter Bjerregaard, Éric Dewailly, Patricia Markham Risica, Marit E. Jørgensen, Sven E. O. Ebbesson

Bibliographic record

VenueAmerican Journal of Public Health · 2007
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsWaistOverweightBody mass indexObesityMedicineDemographyCircumpolar starMetabolic syndromeCircumferencePopulationEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: We investigated the prevalence of obesity and the metabolic correlates of different levels of body mass index (BMI) and waist circumference among the Inuit in 3 countries. METHODS: Data from 4 surveys of Inuit in Canada, Greenland, and Alaska conducted during 1990-2001 were pooled, with a total sample size of 2545 participants. These data were compared with data from a Canadian population of predominantly European origin. RESULTS: Using the World Health Organization criteria for overweight and obesity, we found that the crude prevalence of overweight among Inuit men and women was 36.6% and 32.5%, respectively, and obesity was 15.8% and 25.5%, respectively. Inuit prevalences were similar to those of the highly developed countries of Europe and North America. As levels of obesity increased, as measured by BMI or waist circumference, the mean values of various metabolic indicators-lipid, glucose, and insulin levels and blood pressure-also increased. However, at each level of BMI or waist circumference, the Inuit had lower blood pressure and lipid levels than did Euro-Canadians. CONCLUSIONS: Our data indicate that universal criteria for obesity may not reflect the same degree of metabolic risk for populations such as the Inuit and suggest that ethnic-specific criteria are needed.

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)0.013
metaresearch head score (Gemma)0.001
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 score0.048
Threshold uncertainty score0.852

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.032
GPT teacher head0.361
Teacher spread0.329 · 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

Citations92
Published2007
Admission routes1
Has abstractyes

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