Increased Environmental Temperature Is an Emerging Health Risk That Correlates with Rates of Acute Lymphoblastic Leukemia in Europe
Notice bibliographique
Résumé
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).
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».