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Record W2939873653 · doi:10.1002/oby.22453

Metabolic Risk Factors Associated with Visceral and Subcutaneous Adipose Tissue in a Sex‐Specific Manner in Seven‐Year‐Olds

2019· article· en· W2939873653 on OpenAlexfundno aff
Emma Kjellberg, Josefine Roswall, Jonathan Andersson, Stefan Bergman, Ann‐Katrine Karlsson, Pär‐Arne Svensson, Joel Kullberg, Jovanna Dahlgren

Bibliographic record

VenueObesity · 2019
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsnot available
FundersPratt and Whitney CanadaVästra Götalandsregionen
KeywordsAdipose tissueMedicineSubcutaneous adipose tissueSubcutaneous fatInternal medicineObesityEndocrinologyIntra-Abdominal FatPhysiologyVisceral fatInsulin resistance

Abstract

fetched live from OpenAlex

OBJECTIVE: This study aimed to investigate how visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) volumes were associated with metabolic risk factors in 7-year-old children. METHODS: A total of 81 children (52% girls) from a Swedish birth cohort were studied. At 6 years of age, anthropometric data, fasting insulin, glucose, cholesterol, and blood pressure were collected on 53 children with normal weight and 28 children with overweight or obesity, and insulin resistance was estimated. At 7 years of age, magnetic resonance imaging quantified VAT and SAT. Sex and regression analyses were conducted. RESULTS: SAT was more strongly associated with metabolic risk factors than VAT. The associations between VAT and metabolic risk factors were stronger in girls (P < 0.05). When VAT was adjusted for birth weight and maternal BMI and education, it accounted for 51% of insulin variance (β = 11.72; P = 0.001) but only in girls. The key finding of this study was that adjusted SAT accounted for 63% of the fasting insulin variance in girls (β = 2.76; P < 0.001). Waist circumference was the best anthropometric marker for insulin resistance. CONCLUSIONS: Insulin resistance was associated with abdominal adipose tissue and its associated metabolic risk factors in children as young as 7 years old.

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 score0.003
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.001
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 head0.011
GPT teacher head0.235
Teacher spread0.224 · 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

Citations4
Published2019
Admission routes1
Has abstractyes

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