Cold environments and health: proteomic analysis of health impacts

Abstract

Background Cold temperatures cause blood vessels to constrict, shallow breathing, and slight thickening of the blood. Working in extremely cold environments can have negative effects on health, yet there are currently no effective biomarkers to monitor these health conditions. Proteins are important intermediate phenotypes that can provide a theoretical basis for understanding disease pathophysiology. Proteins in the circulatory system reflect the physiological status of individuals, and plasma proteins have significant potential as biomarkers for various health conditions. Methods In this study, we employed the Mendelian randomization (MR) method to analyze the effects of freezing temperatures on over 2900 plasma proteins. Subsequently, the selected plasma proteins were subjected to causal analysis in relation to 55 diseases, including respiratory disorders, cardiovascular diseases, various cancers, and oral diseases. The aim was to identify proteins that could serve as biomarkers for health status. Results Our results indicate that cold environments may affect the concentrations of 78 plasma proteins. Further MR analysis revealed that nine of these plasma proteins are associated with the risk of respiratory disorders, cardiovascular diseases, various cancers, and oral diseases. Conclusion These proteins show promise as biomarkers for monitoring the hazards and risks faced by individuals working in cold environments. These findings provide valuable insights into the biological mechanisms underlying occupational hazards.

Keywords

plasma protein / biomarker / cold environment / Mendelian randomization

Acknowledgments

We thank all the investigators and participants for their contribution to the GWAS and making the summary data publicly available.

Research ethics

This study was based on publicly available datasets. Ethical review and approval was not required for the study, in accordance with the local legislation and institutional requirements.

Informed consent

Informed consent was obtained from all subjects involved in this study.

Author contributions

Conceptualization: Yan J Q, Jiao X H; Methodology: Song H Q, Zhang R; Software: Song H Q, Validation: Lu Z X; Formal analysis: Lu Z X, Zhang R; Investigation: Jiao X H; Resources: Song H Q; Data Curation: Song H Q, Zhang R; Writing, original draft preparation: Song H Q, Zhang R, Lu Z X; Writing, review and editing: Song H Q, Yan J Q, Jiao X H; Visualization: Yan J Q, Jiao X H; Supervision: Song H Q; Project administration: Yan J Q, Jiao X H; Funding acquisition: Yan J Q, Jiao X H.

Use of Large Language Models, AI and Machine Learning Tools

In this study, we utilized the R programming language and its associated packages for data analysis and processing.

Conflict of interest

The authors declare no conflict of interest.

Research funding

This study was funded by the Health Commission of Heilongjiang Province (Project Number: 20230808010517).

Data availability

We declare that the data presented in this study are available.

References

[1] Agache I, Sampath V, Aguilera J, et al. Climate change and global health: A call to more research and more action. Allergy, 2022; 77(5): 1389-1407. doi: 10.1111/all.15229

[2] Ebi K L, Vanos J, Baldwin J W, et al. Extreme weather and climate change: population health and health system implications. Annu Rev Public Health, 2021; 42: 293-315. doi: 10.1146/annurev-publhealth-012420-105026

[3] Ashrafuzzaman M, Furini G L. Climate change and human health linkages in the context of globalization: An overview from global to southwestern coastal region of Bangladesh. Environ Int, 2019; 127: 402-411. doi: 10.1016/j.envint.2019.03.020

[4] Chen R, Yin P, Wang L, et al. Association between ambient temperature and mortality risk and burden: time series study in 272 main Chinese cities. BMJ, 2018; 363: k4306. doi: 10.1136/bmj.k4306

[5] González Hernández E, et al.Cabadés O'Callaghan A, Cebrián Doménech J, Variaciones estacionales en los ingresos por infarto agudo de micardio. El estudio PRIMVAC [Seasonal variations in admissions for acute myocardial infarction. The PRIMVAC study]. Rev Esp Cardiol, 2004; 57(1): 12-19. doi: 10.1157/13056503

[6] Geyer P E, Kulak N A, Pichler G, et al. Plasma proteome profiling to assess human health and disease. Cell Syst, 2016; 2(3): 185-195. doi: 10.1016/j.cels.2016.02.015

[7] Geyer P E, Holdt L M, Teupser D, et al. Revisiting biomarker discovery by plasma proteomics. Mol Syst Biol, 2017; 13(9): 942. doi: 10.15252/msb.20156297

[8] Anderson N L. The clinical plasma proteome: a survey of clinical assays for proteins in plasma and serum. Clin Chem, 2010; 56(2): 177-185. doi: 10.1373/clinchem.2009.126706

[9] Davey Smith G, Hemani G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet, 2014; 23(R1): R89-R98. doi: 10.1093/hmg/ddu328

[10] Sun B B, Maranville J C, Peters J E, et al. Genomic atlas of the human plasma proteome. Nature, 2018; ( 2018) 558: 73-79.

[11] Hemani G, Zheng J, Elsworth B, et al. The MR-Base platform supports systematic causal inference across the human phenome. Elife, 2018; 7: e34408. doi: 10.7554/eLife.34408

[12] Sudlow C, Gallacher J, Allen N, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med, 2015; 12(3): e1001779. doi: 10.1371/journal.pmed.1001779

[13] He Y, Zhang H, Wang T, et al. Impact of serum calcium levels on Alzheimer's disease: A mendelian randomization study. J Alzheimers Dis, 2020; 76(2): 713-724. doi: 10.3233/JAD-191249

[14] Zhang H, Wang T, Han Z, et al. Mendelian randomization study to evaluate the effects of interleukin-6 signaling on four neurodegenerative diseases. Neurol Sci, 2020; 41(10): 2875-2882. doi: 10.1007/s10072-020-04381-x

[15] Li A, Liao W, Xie J, et al. Plasma proteins as occupational hazard risk monitors for populations working in harsh environments: A mendelian randomization study. Front Public Health, 2022; 10: 852572. doi: 10.3389/fpubh.2022.852572

[16] Vicedo-Cabrera A M, Scovronick N, Sera F, et al. The burden of heat-related mortality attributable to recent human-induced climate change. Nat Clim Chang, 2021; 11(6): 492-500. doi: 10.1038/s41558-021-01058-x

[17] GBD 2019 Risk Factors Collaborators. Global burden of 87 risk factors in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet, 2020; 396(10258): 1223-1249. doi: 10.1016/S0140-6736(20)30752-2

[18] Zhao Q, Guo Y, Ye T, et al. Global, regional, and national burden of mortality associated with non-optimal ambient temperatures from 2000 to 2019: a three-stage modelling study. Lancet Planet Health, 2021; 5(7): e415-e425. doi: 10.1016/S2542-5196(21)00081-4

[19] Moholdt T, Afoakwah C, Scuffham P, et al. Excess mortality at Christmas due to cardiovascular disease in the HUNT study prospective population-based cohort in Norway. BMC Public Health, 2021; 21(1): 549. doi: 10.1186/s12889-021-10503-7

[20] Li Y, Zhou Z, Chen N, et al. Seasonal variation in the occurrence of ischemic stroke: A meta-analysis. Environ Geochem Health, 2019; 41(5): 2113-2130. doi: 10.1007/s10653-019-00265-y

[21] Sun H M, Mi Y S, Yu F D, et al. SERPINA 4 is a novel independent prognostic indicator and a potential therapeutic target for colorectal cancer. Am J Cancer Res, 2016; 6(8): 1636-1649.

[22] Hitomi K, Tahara-Hanaoka S, Someya S, et al. An immunoglobulin-like receptor, Allergin-1, inhibits immunoglobulin E-mediated immediate hypersensitivity reactions. Nat Immunol, 2010; 11(7): 601-607. doi: 10.1038/ni.1886

[23] Pazgier M, Hoover D M, Yang D, et al. Human beta-defensins. Cell Mol Life Sci, 2006; 63(11): 1294-1313. doi: 10.1007/s00018-005-5540-2