Targeted SNP Interrogation to Determine if Select Polymorphisms are Associated with Skeletal Muscle Hypertrophy Following 12 Weeks of Resistance Training
Bibliographic record
Abstract
Introduction We aimed to determine if candidate genetic polymorphisms were associated with resistance training‐induced changes in skeletal muscle hypertrophy variables. Methods Two cohorts of predominantly Caucasian college‐aged male participants (N=109; n=66: Auburn, AL, USA; n=43: Hamilton, Ontario, Canada) performed 12 weeks of progressive full‐body resistance training (3–4 days/week). Vastus lateralis muscle biopsies and dual x‐ray absorptiometry (DXA) scans were performed prior to the intervention (Pre), and 72 hours following the last training bout (Post). Immunohistochemistry was performed to assess mean fiber cross sectional area (fCSA), DXA scans were analyzed to assess whole‐body (fat‐ and bone‐free) lean soft tissue mass (LSTM), and over 800,000 genetic polymorphisms were interrogated from muscle tissue using DNA microarrays. Select polymorphisms from a systematic literature review were examined in relation to Pre‐to‐Post changes in mean fCSA as well as changes in DXA LSTM. Results There were no genotype*time interactions for ACTN3 (rs1815739), ACE (rs4343), ADRB2 (rs1042714), FTO (rs9939609, rs1421085, rs8050136), IL15RA (rs2296135), VDR (rs1544410), LEPR (rs113710182), FST (rs7229102), IGF1 (rs5742692), or MSTN (rs72909336) with regard to training‐induced changes in DXA LSTM or mean fCSA. Interestingly, when participants were clustered in tertiles according to percent changes in mean fCSA and DXA LSTM, Pre mean fCSA and Pre DXA LSTM were inversely correlated. Pre mean fCSA values were greater in the lower (5607±1195 μm 2 ) versus middle (4673±1154 μm 2 , p=0.007) and upper tertiles (4558±895 μm 2 , p<0.001), while Pre DXA LSTM values were greater in the lower (63.3±7.0 kg) versus middle (59.6±6.9 kg, p=0.043) upper tertiles (57.5±5.8 kg, p<0.001). Stepwise linear regression was performed using baseline DXA LSTM and mean fCSA along with gene scores from the candidate polymorphisms to predict percent changes in DXA LSTM as well as mean fCSA with training, respectively. The only significant predictor of percent DXA LSTM change to training was Pre DXA LSTM (β=−0.327, model r 2 =0.11, p=0.001). Likewise, the only significant predictor of percent mean fCSA change to training was Pre mean fCSA (β=−0.310, model r 2 =0.09, p=0.001). Conclusions Collectively, our data suggest that pre‐training DXA LSTM or fCSA values (rather than the genetic influence of select polymorphisms) are better predictors of change scores in these variables with resistance training. Support or Funding Information Funding for this project on the Auburn Campus was provided by Hilmar Ingredients, Bionutritional Research Group, and discretionary lab funds by M.D.R. Funding for the McMaster Campus project was provided through an operating grant provided to S.M.P through the Natural Science and Engineering Research Council of Canada. Figure 1
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".