Combined Effect of Ambient Temperature and Relative Humidity on Skin Aging Phenotypes in the Era of Climate Change: Results from an Indian Cohort Study
Bibliographic record
Abstract
Abstract: Background: There is no doubt that global warming, with its extreme heat events, is having an increasing impact on human health. Heat is not independent of ambient temperature but acts synergistically with relative humidity (RH) to increase the risk of several diseases, such as cardiovascular and pulmonary diseases. Although the skin is the organ in direct contact with the environment, it is currently unknown whether skin health is similarly affected. Objective: While mechanistic studies have demonstrated the mechanism of thermal aging, this is the first epidemiological study to investigate the effect of long-term exposure to heat index (HI) as a combined function of elevated ambient temperature and RH on skin aging phenotypes in Indian women. Methods: The skin aging phenotypes of 1510 Indian women were assessed using the Score of Intrinsic and Extrinsic Skin Aging (SCINEXA™) scoring tool. We used data on ambient temperature and RH, combined into an HI with solar ultraviolet radiation (UVR), and air pollution (particulate matter <2.5 µm [PM 2.5] ; nitrogen dioxide [NO 2 ]) from secondary data sources with a 5-year mean residential exposure window. An adjusted ordinal multivariate logistic regression model was used to assess the effects of HI on skin aging phenotypes. Results: HI increased pigmentation such as hyperpigmented macula on the forehead (odds ratios [OR]: 1.31, 95% confidence interval [CI]: 1.12, 1.54) and coarse wrinkles such as crow’s feet (OR: 1.17, 95% CI: 1.05, 1.30) and under-eye wrinkles (OR: 1.3, 95% CI: 1.15, 1.47). These associations were robust to the confounding effects of solar UVR and age. Prolonged exposure to extreme heat, as indicated by high HI, contributes to skin aging phenotypes. Conclusion: Thus, ambient temperature and RH are important factors in assessing the skin aging exposome.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".