Can Personal Exposures to Higher Nighttime and Early-Morning Temperatures Increase Blood Pressure?
Bibliographic record
Abstract
Environmental temperatures are inversely related to BP; however, the effects of short-term temperature changes within a 24-hour period and measured with high accuracy at the personal level have not been described. Fifty-one nonsmoking patients living in the Detroit area had up to 5 consecutive days of 24-hour personal-level environmental temperature (PET) monitoring along with daily cardiovascular measurements, including BP, performed mostly between 5 pm and 7 pm during summer and/or winter periods. The associations between hour-long mean PET levels during the previous 24 hours with the outcomes were assessed by linear mixed models. Accounting for demographics, environmental factors, and monitoring compliance, systolic and diastolic BP were positively associated with several hour-long PET measurements ending from 10 to 15 hours beforehand. During this time, corresponding mostly to a period starting from between 1 am and 3 am to ending between 7 am and 9 am, an increase of 1°C was associated with a 0.81 mm Hg to 1.44 mm Hg and 0.59 mm Hg to 0.83 mm Hg elevation in systolic and diastolic BP, respectively. Modestly warmer, commonly encountered PET levels posed a clinically meaningful effect (eg, a 6.95 mm Hg systolic pressure increase per interquartile range (4.8°C) elevation at lag hour 10). Community-level outdoor ambient temperatures were not related to BP. The authors provide the first evidence that personal exposure to warmer nighttime and early-morning environmental temperatures might lead to an increase in BP during the ensuing day.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".