Cold-induced vasodilation response in a Japanese cohort: insights from cold-water immersion and genome-wide association studies
Bibliographic record
Abstract
BACKGROUND: Cold-induced vasodilation (CIVD) occurs after blood vessels in the skin are constricted due to local cold exposure. Although many CIVD studies have been conducted, the underlying molecular mechanisms are yet to be clarified. Therefore, we explored genetic variants associated with CIVD response using the largest-scale dataset reported to date in a CIVD study involving wavelet analysis; thus, the findings improve our understanding of the molecular mechanisms that regulate the CIVD response. METHODS: We performed wavelet analysis of three skin blood flow signals [endothelial nitric oxide (eNO)-independent, eNO-dependent, and neurogenic activities] during finger cold-water immersion at 5 °C in 94 Japanese young adults. Additionally, we conducted genome-wide association studies of CIVD using saliva samples collected from the participants. RESULTS: We found that the mean wavelet amplitudes of eNO-independent and neurogenic activities significantly increased and decreased prior to CIVD, respectively. Our results also implied that as many as ~ 10% of the Japanese subjects did not show an apparent CIVD response. Our genome-wide association studies of CIVD using ~ 4,040,000 imputed data found no apparent CIVD-related genetic variants; however, we identified 10 genetic variants, including 2 functional genes (COL4A2 and PRLR) that are associated with notable blunted eNO-independent and neurogenic activity responses in individuals without CIVD response during local cold exposure. CONCLUSIONS: Our findings indicate that individuals without CIVD response differentiated by genotypes with COL4A2 and PRLR genetic variants exhibited notable blunted eNO-independent and neurogenic activity responses during local cold exposure.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".