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Record W2141832279 · doi:10.1152/jappl.2001.90.5.1777

Genomic scan for genes affecting body composition before and after training in Caucasians from HERITAGE

2001· article· en· W2141832279 on OpenAlexaff
Treva Rice, Louis Përusse, Ingrid B. Borecki, My-Anh Ho-Kim, Michel Lacaille, Chantal Paré, Luigi Bouchard, Jacques Gagnon, Arthur S. Leon, James S. Skinner, Jack H. Wilmore, D. C. Rao, Claude Bouchard

Bibliographic record

VenueJournal of Applied Physiology · 2001
Typearticle
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsUniversité Laval
FundersNational Heart, Lung, and Blood Institute
KeywordsBody mass indexEndocrinologyLeptinInternal medicineFat massGeneGenetic linkageComposition (language)BiologyBody fat percentageGeneticsObesityMedicine

Abstract

fetched live from OpenAlex

An autosomal genomewide search for genes related to body composition and its changes after a 20-wk endurance-exercise training program has been completed in the HERITAGE Family Study. Phenotypes included body mass index (BMI), sum of eight skinfold thicknesses, fat mass (FM), fat-free mass, percent body fat (%Fat), and plasma leptin levels. A maximum of 364 sib-pairs from 99 Caucasian families was studied with the use of 344 markers with single-point and multipoint linkage analyses. Evidence of significant linkage was observed for changes in fat-free mass with the S100A and the insulin-like growth factor I genes (P = 0.0001). Suggestive evidence (2.0 < or = Lod < 3.0; 0.0001 < P < or = 0.001) was also observed for the changes in FM and %Fat at 1q31 and 18q21-q23, in %Fat with the uncoupling protein 2 and 3 genes, and in BMI at 5q14-q21. At baseline, suggestive evidence was observed for BMI at 8q23-q24, 10p15, and 14q11; for FM at 14q11; and for plasma leptin levels with the low-density lipoprotein receptor gene. This is the first genomic scan on genes involved in exercise-training-induced changes in body composition that could provide information on the determinants of weight loss.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.973
Threshold uncertainty score0.339

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 head0.013
GPT teacher head0.267
Teacher spread0.253 · 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

Citations115
Published2001
Admission routes1
Has abstractyes

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