Immune inflammation markers and physical fitness during a congested match play period in elite male soccer players
Bibliographic record
Abstract
Abstract Background/Objective Cellular immune markers of inflammation such as neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) and systemic immune inflammation index (SII) are frequently used in patient care. The adoption of these markers to elite sports, e.g. soccer could be beneficial when monitoring training and aiming to maximize physical fitness. This study investigated cellular immune inflammation markers and physical fitness in elite male soccer players in relation to changes in training and match exposure during a congested match play period. Methods Fifteen elite male soccer players were evaluated three times (T1, T2, and T3) over 12 weeks (T1–T2: six weeks uncongested period of match play and T2–T3: six weeks congested period of match play). Players performed vertical jump tests (squat jumps [SJ], countermovement jumps [CMJ]), the 20-meter sprint test, and the Yo-Yo intermittent recovery test (YYIRL1) at T1, T2 and T3. Measurements included counts of leucocytes and its subtypes, as well as platelets. Cellular immune inflammation markers (NLR, PLR and SII) were calculatedat T1, T2, and T3. Training session rating of perceived exertion was also recorded on a daily basis. Results Significant increases in leucocyte, neutrophil, eosinophil, basophil and monocyte counts occurred at T3 compared with T2 (0.002 < p < 0.04, -0.56 < ES < -0.40) and T1 (-0.78 < ES < -0.49). Lymphocyte counts were lower at T3 as compared to T2 and T1 (p = 0.038, -0.48 < ES <-0.25), while NLR, PLR and SII were greater at T3 compared to T2 (0.001 < p < 0.015, -1.01 < ES < -0.44) and T1 (-0.99 < ES < -0.21). There was a negative correlation between YYIRL1 performance with NLR (r= -0.56; p = 0.02), PLR (r=-0,44, p = 0.015), and SII (r= -0.63; p = 0.01) after the congested period of match play (i.e., T3). Values for maximal oxygen uptake (VO2max), estimated from the YYIRL1 test, negatively correlated with NLR (r= -0.56; p = 0.02), PLR (r=-0,44, p = 0.015), and SII (p = 0.01; r= -0.63). There was a positive correlation between NLR, and SII with workload parameters. In addition, a clear positive correlation was observed between NLR and SII with competitive loadinstead (r= [0.59–0.64; p˂ 0.001), training load (TL) (r= [0.65–0.68]; p˂ 0.001), session rating of perceived exertion (S-RPE) (r= [0.65–0.68]; p = 0.001), and training volume (r= [0.60–0.61; p = 0.001). Conclusion An intensive period of congested match play significantly alterated immune cell counts and cellular markers of inflammation (NLR, PLR and SII). Changes in NLR and SII were related to workload parameters, suggesting the usefulness of these markers in regulating training intensity and competitive load. An association between physical fitness (YYIRL1, VO2max) and NLR, PLR and SII suggests that these biomarkers are promising tools to monitor aerobic physical fitness of elite soccer players during congested periods of match play.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".