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Record W2060982303 · doi:10.1139/h06-092

Characteristics of metabolically obese normal-weight (MONW) subjects

2007· review· en· W2060982303 on OpenAlexaffvenue
Florence Conus, Rémi Rabasa‐Lhoret, François Péronnet

Bibliographic record

VenueApplied Physiology Nutrition and Metabolism · 2007
Typereview
Languageen
FieldMedicine
TopicDiabetes, Cardiovascular Risks, and Lipoproteins
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsHyperinsulinemiaMedicineInternal medicineHypertriglyceridemiaBlood pressureBody mass indexInsulin resistanceObesityEndocrinologyMetabolic syndromeAbdominal obesityNormal weightOverweightCholesterolTriglyceride

Abstract

fetched live from OpenAlex

The existence of a subgroup of normal-weight individuals displaying obesity-related phenotypic characteristics was first proposed in 1981. These individuals were identified as metabolically obese but normal weight (MONW). It was hypothesized that these individuals might be characterized by hyperinsulinemia and (or) insulin resistance, as well as by hypertriglyceridemia and high blood pressure despite having a body mass index (BMI) < 25 kg/m2. Such characteristics could confer upon MONW subjects a higher cardiovascular risk; however, scientific data on MONW subjects are scarce since only 9 publications are directly related to this topic. Despite differences in the criteria for identifying MONW subjects and the small number of subjects involved in most of these studies, their consistent results indicate that: (i) the prevalence of the MONW syndrome ranges between 5% and 45%, depending on the criteria used, age, BMI, and ethnicity; (ii) when compared with control subjects, MONW subjects display an altered insulin sensitivity, a higher abdominal and visceral adiposity, a more atherogenic lipid profile, a higher blood pressure, and a lower physical activity energy expenditure; and (iii) MONW subjects are at higher risks for type 2 diabetes and cardiovascular diseases.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.980
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.023
GPT teacher head0.284
Teacher spread0.262 · 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

Citations203
Published2007
Admission routes2
Has abstractyes

Explore more

Same venueApplied Physiology Nutrition and MetabolismSame topicDiabetes, Cardiovascular Risks, and LipoproteinsFrench-language works237,207