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Single nucleotide polymorphisms in the myostatin (<i>MSTN</i>) and muscle creatine kinase (<i>CKM</i>) genes are not associated with elite endurance performance

2010· article· en· W1523645164 on OpenAlexaff
Frank Döring, Simone Onur, Claudia Kürbitz, M. R. Boulay, Louis Përusse, Tuomo Rankinen, Rainer Rauramaa, Bernd Wolfarth, Claude Bouchard

Bibliographic record

VenueScandinavian Journal of Medicine and Science in Sports · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics and Physical Performance
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMyostatinSingle-nucleotide polymorphismBiologyGeneticsEndurance trainingCreatine kinaseVO2 maxInternational HapMap ProjectHaplotypeGeneEndocrinologyGenotype

Abstract

fetched live from OpenAlex

Maximal oxygen uptake (VO2max) is one of the most important determinants of elite endurance performance. VO2max is determined by a whole range of genetic and environmental factors. Single nucleotide polymorphisms (SNPs) in muscle myostatin (MSTN) and creatine kinase (CKM) genes are candidates for VO2max and skeletal muscle performance phenotypes. Common MSTN (rs3791783, rs11681628 and rs7570532) and CKM (rs344816, rs10410448, rs432979, rs1133190, rs7260359, rs7260463 and rs4884) SNPs, selected from HapMap CEU data in order to tag the genetic variability of the proteins, were genotyped in 316 male Caucasian elite endurance athletes and 304 sedentary controls from the Genathlete study. Association with elite endurance performance was determined by logistic regression analysis. The P-value for statistical significance was set at <0.01. None of the SNPs or haplotypes showed a significant association with elite endurance status. We conclude that common variants of MSTN and CKM genes do not play a role in attaining high-level endurance performance in Caucasian populations.

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)0.001
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
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.233
Teacher spread0.222 · 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

Citations17
Published2010
Admission routes1
Has abstractyes

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Same venueScandinavian Journal of Medicine and Science in SportsSame topicGenetics and Physical PerformanceFrench-language works237,207