Advances in Exercise, Fitness, and Performance Genomics in 2012
Bibliographic record
Abstract
A small number of excellent articles on exercise genomics issues were published in 2012. A new PYGM knock-in mouse model will provide opportunities to investigate the exercise intolerance and very low activity level of people with McArdle disease. New reports on variants in ACTN3 and ACE have increased the level of uncertainty regarding their true role in skeletal muscle metabolism and strength traits. The evidence continues to accumulate on the positive effects of regular physical activity on body mass index or adiposity in individuals at risk of obesity as assessed by their FTO genotype or by the number of risk alleles they carry at multiple obesity-susceptibility loci. The serum levels of triglycerides and the risk of hypertriglyceridemia were shown to be influenced by the interactions between a single nucleotide polymorphism (SNP) in the NOS3 gene and physical activity level. Allelic variation at nine SNPs was shown to account for the heritable component of the changes in submaximal exercise heart rate induced by the HERITAGE Family Study exercise program. SNPs at the RBPMS, YWHAQ, and CREB1 loci were found to be particularly strong predictors of the changes in submaximal exercise heart rate. The 2012 review ends with comments on the importance of relying more on experimental data, the urgency of identifying panels of genomic predictors of the response to regular exercise and particularly of adverse responses, and the exciting opportunities offered by recent advances in our understanding of the global architecture of the human genome as reported by the Encyclopedia of DNA Elements project.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".