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Record W2171791669 · doi:10.1152/jappl.2000.88.5.1571

No association between the angiotensin-converting enzyme ID polymorphism and elite endurance athlete status

2000· article· en· W2171791669 on OpenAlexaff
Tuomo Rankinen, Bernd Wolfarth, Jean‐Aimé Simoneau, Dirk Maier-Lenz, Rainer Rauramaa, Miguel A. Rivera, Marcel R. Boulay, Louis Përusse, Joseph Keul, Claude Bouchard

Bibliographic record

VenueJournal of Applied Physiology · 2000
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics and Physical Performance
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsEndurance trainingInternal medicineAngiotensin-converting enzymeMedicineEliteEndocrinologyPolymorphism (computer science)Physical therapyBiologyGeneticsGenotypeGeneBlood pressure

Abstract

fetched live from OpenAlex

Several studies have reported that the insertion (I) allele of the angiotensin-converting enzyme (ACE) I/deletion (D) polymorphism is associated with enhanced responsiveness to endurance training and is more common in endurance athletes than in sedentary controls. We tested the latter hypothesis in a cohort of 192 male endurance athletes with maximal oxygen uptake >/=75 ml. kg(-1). min(-1) and 189 sedentary male controls. The ACE ID polymorphism in intron 16 was typed with the three-primer polymerase chain reaction method. Both the genotype (P = 0.214) and allele (P = 0.095) frequencies were similar in the athletes and the controls. Further analyses in the athletes revealed no excess of the I allele among the athletes within the highest quartile (> 80 ml. kg(-1). min(-1)) or decile (>83 ml. kg(-1). min(-1)) of maximal oxygen uptake. These data from the GENATHLETE cohort do not support the hypothesis that the ACE ID polymorphism is associated with a higher cardiorespiratory endurance performance level.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0030.000

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 head0.005
GPT teacher head0.206
Teacher spread0.201 · 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 source (direct Gemma or distilled Codex), 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

Citations190
Published2000
Admission routes1
Has abstractyes

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