Increased Environmental Temperature Is an Emerging Health Risk That Correlates with Rates of Acute Lymphoblastic Leukemia in Europe
Bibliographic record
Abstract
Background: The World Meteorological Organization (WMO) has calculated that European temperatures have increased at more than double the global average over the last 30 years 1. Besides research in radiation, little research has focused on how this might connect to the mechanisms initiating mutations in ALL. Both latitude and solar irradiance are potential risk factors in the development of ALL 2,3. Latitude covaries with the intensity of solar irradiance and ambient temperature, and both could therefore influence the risk of ALL. Furthermore, high ambient temperature during pregnancy was found to be associated with increased likelihood of childhood ALL 4. Global increases in temperature escalate the likelihood of extreme heat events, which in mammals increases reactive oxygen species, increases DNA damage, and suppresses the immune system 5. Few studies have evaluated changes in ambient temperature as a risk factor for ALL in Europe. We also included chronic lymphocytic leukemia (CLL) for comparison. Objective:To investigate the relationship between population-weighted country centroids (location of greatest population density), average country yearly temperature, solar irradiance and ALL and CLL rates in 40 European countries between the years 2010 and 2019. Design/Method:Incidence and prevalence rates for ALL and CLL (Age-standardized data with males and females) were obtained from the Global Health Data Exchange (GHDx) database. Population-weighted latitude/longitude centroids for Europe 6 were obtained from Baylor University and input into the Global Solar Atlas to obtain the average yearly global horizontal irradiance values. European temperature data was obtained from the Climatic Research Unit at the University of East Anglia (Copernicus Climate Data Store) 7. ALL and CLL incidence and prevalence rates per 100,000 people for 40 European countries were compared by linear regression to each country's temperature or solar irradiance at the population centroids. Statistical analyses were performed using GraphPad Prism and Excel. Results:After2010, we detected a significant positive correlation between ALL incidence and prevalence rates with mean annual temperature across countries for each year of analysis (for example in 2014 the ALL prevalence p-value = 0.01 and R= 0.4, see Figure 1). There was also an increase in the significance of this relationship over time between 2010 and 2019 (Figure 1B). We also detected a significant positive correlation between the increase in mean annual temperature across Europe and an increase in ALL over the past decade (Figure 1B). However, no relationship was established in this study for solar irradiance or population-weighted latitude and CLL prevalence and incidence rates. Conclusions:Increasing mean annual temperatures may increase the risk of developing ALL. We speculate this may be connected through increases in extreme weather events impacting oxidative stress, DNA damage, and/or suppression of the immune system. Considering the global climate crisis, the WMO finding of higher European temperature increases and our finding of significance between temperature and ALL rates, these relationships should be functionally studied. Finally, these studies should be expanded to countries outside of Europe. References 1 World Meteorological Organization. Temperatures in Europe increase more than twice global average. (2022). 2 Pordanjani, S. R., Kavousi, A., Mirbagheri, B., Shahsavani, A. & Etemad, K. Temporal trend and spatial distribution of ALL in Iranian children during 2006-2014: a mixed ecological study. Epidemiol. Health42, (2020). 3 Coste, A. et al. Residential exposure to UV light and risk of precursor B-cell ALL: assessing the role of individual risk factors, the ESCALE and ESTELLE studies. Cancer Causes Control28, 1075-1083 (2017). 4 Rogne, T. et al. High Ambient Temperature in Pregnancy and Risk of Childhood ALL. medRxiv (2023) 5 Chauhan, S. S., Rashamol, V. P., Bagath, M., Sejian, V. & Dunshea, F. R. Impacts of heat stress on immune responses and oxidative stress in farm animals and nutritional strategies for amelioration. Int. J. Biometeorol.65, 1231-1244 (2021). 6 Hamerly, G. Population-weighted European state centers. (2006). 7 Climatic Research Unit, U. of E. A. Temperature and precipitation gridded data for global and regional domains derived from in-situ and satellite observations. (2023).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".