Whole-Body Cryotherapy Reduces Systemic Inflammation in Healthy Adults: Pilot Cohort Study
Bibliographic record
Abstract
Background Chronically elevated inflammation is implicated in many conditions, including obesity, metabolic syndrome, and cardiovascular disease, and has been associated with increased mortality risk. Whole-body cryotherapy (W-BC) is a promising modality to treat inflammation with demonstrated benefits for clinical subpopulations including those with arthritis, obesity, and type 2 diabetes. However, it is unclear whether the benefit from W-BC extends to healthy individuals prior to chronic disease–related inflammation. In addition, the long-term durability of W-BC effect is unknown. Objective This study investigates the inflammatory response to W-BC in healthy adults with a biomarker of inflammation, high-sensitivity C-reactive protein (hsCRP), and clinical biomarkers of metabolism including fasting glucose, hemoglobin A1c (HbA1c), low-density lipoprotein (LDL) and high-density lipoprotein (HDL), and triglycerides. Methods Fifteen individuals (n=9 female) participated in frequent recreational W-BC (3 minutes of cold exposure at –110 ℃) over approximately 9 months and had blood draws at baseline plus follow-up visits. Biomarkers were modeled as linear functions of W-BC sessions received in the month prior to blood draw. Results The mean amount of W-BC received was 6.78 (SD 4.26) times per month with the cumulative total ranging from 13 to 157 W-BC sessions over the course of the study. On average, participants completed 1-2 sessions per week throughout the intervention. The number of W-BC sessions were associated with decreased hsCRP (–0.14 mg/L in hsCRP per W-BC session; P<.01) and with durability of up to 9 months. Increased W-BC was also associated with a downward trend in fasting glucose. This trend failed to reach significance at 1 month (–0.73 mg/dL in fasting glucose per W-BC session; P<.10) but was significant for 2- and 3-month windows (P<.05). HbA1c was increased significantly after 9 months (P<.01); however, the change occurred within normal ranges (difference=0.13% and <5.7%) and was not clinically significant. There was no association between W-BC and LDL cholesterol, HDL cholesterol, or triglycerides (P>.10), although LDL trended lower over the time period examined (P=.07). Conclusions These results suggest that W-BC beneficially impacts systemic inflammation by lowering hsCRP levels in healthy individuals and may also have some modulating effect on fasting glucose.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".