Oxidative stress, inflammation, psychological status, and severity of respiratory infections are negatively affected during the pre-contest period in amateur bodybuilders
Bibliographic record
Abstract
We examined whether off-season (OffS) and pre-contest (PreC) periods affect blood oxidative stress, inflammatory, immunological, and psychological markers in 20 bodybuilders. The athletes recorded their food intake (3-day record), physical activities, mood states (Profile of Mood States, POMS), recovery-stress (Recovery-Stress Questionnaire for Athletes, RESTQ-Sport), and upper respiratory symptoms (Wisconsin Upper Respiratory Symptom Survey, WURSS-21), and blood was obtained for biochemical analysis. Almost all athletes were in positive energy balance during OffS, while bodybuilders presented markedly restricted energy intake (∼45%) leading to loss of weight (-9%) and fat mass (-45%) with preservation of fat-free mass in PreC. Protein intake was high during both periods, while lipid and carbohydrate intakes were reduced ∼50% in PreC. Almost all athletes consumed 100% of the Recommended Dietary Allowance (RDA) for micronutrients in OffS, while 45% and 75% of the athletes had intakes below the RDA for vitamins A and E in PreC. Oxidative damage to lipids (thiobarbituric acid reactive substances, TBARS), protein carbonyls, and the TBARS/total antioxidant capacity ratio increased in PreC (32%, 27%, 60%), as did plasma tumor necrosis factor α (4-fold) and WURSS-21 scores (25%). There were no significant changes in serum catalase, glutathione reductase, and superoxide dismutase activities nor in interleukin 1β and immunoglobulins. In PreC, POMS showed negative changes in vigor (-20%), fatigue (23%), and total mood disturbance (35%), and RESTQ-Sport showed alterations for general and sport stress (34% and 50%, respectively) and sport recovery (-23%). Thus, PreC negatively affects nutrient intake, which may worsen oxidative stress, inflammation, psychological status, and the severity of respiratory infections in bodybuilders.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".