Associations of Long-Term Exposure to Temperature Variability with Glucose Metabolism: Results from KORA F4 and FF4
Bibliographic record
Abstract
The impact of rising temperature variability driven by climate change on metabolic health remains understudied, especially considering the global increase in diabetes prevalence, with long-term effects on glucose metabolism unexplored. This study investigated associations between long-term temperature variability exposure and glucose metabolism in a population-based cohort of 2997 participants (4954 observations) over a 7-year period from KORA F4 and FF4 cohorts in Augsburg, Germany. Long-term exposure to temperature variability was estimated as the standard deviation of the daily mean air temperature over the 365-day period preceding each examination. We applied generalized estimating equations to examine the longitudinal associations between long-term exposure to temperature variability and multiple glucose metabolism biomarkers: fasting glucose, 2h glucose, fasting insulin, homeostasis model assessment of insulin resistance (HOMA-IR), homeostasis model assessment of β-cell function (HOMA-B), quantitative insulin sensitivity check index (QUICKI), and glycated hemoglobin (HbA1c). We found that a 1 °C higher temperature variability was significantly associated with higher fasting insulin, HOMA-IR, and HbA1c with % changes (95% CI) of 2.62 (0.79; 4.49), 2.81 (0.79; 4.87), and 2.38 (1.97; 2.79), respectively, and lower QUICKI (-0.41 [-0.70; -0.11]). These findings suggest that increasing temperature variability exposure may contribute to metabolic dysfunction, potentially accelerating the global diabetes epidemic.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".