Please use this identifier to cite or link to this item:
doi:10.22028/D291-35409
Title: | Circulating small non-coding RNAs associated with age, sex, smoking, body mass and physical activity |
Author(s): | Rounge, Trine B. Umu, Sinan U. Keller, Andreas Meese, Eckart Ursin, Giske Tretli, Steinar Lyle, Robert Langseth, Hilde |
Language: | English |
Title: | Scientific Reports |
Volume: | 8 |
Issue: | 1 |
Publisher/Platform: | Springer Nature |
Year of Publication: | 2018 |
Free key words: | piRNAs Janus Serum Bank (JSB) Blood Donor Group (BDg) isomiRs Red Cross Blood Donors (RCBD) |
DDC notations: | 610 Medicine and health |
Publikation type: | Journal Article |
Abstract: | Small non-coding RNAs (sncRNA) are regulators of cell functions and circulating sncRNAs from the majority of RNA classes are potential non-invasive biomarkers. Understanding how common traits influence ncRNA expression is essential for assessing their biomarker potential. In this study, we identify associations between sncRNA expression and common traits (sex, age, self-reported smoking, body mass, self-reported physical activity). We used RNAseq data from 526 serum samples from the Janus Serum Bank and traits from health examination surveys. Ageing showed the strongest association with sncRNA expression, both in terms of statistical significance and number of RNAs, regardless of RNA class. piRNAs were abundant in the serum samples and they were associated to sex. Interestingly, smoking cessation generally restored RNA expression to non-smoking levels, although for some sncRNAs smoking-related expression levels persisted. Pathway analysis suggests that smoking-related sncRNAs target the cholinergic synapses and may therefore potentially play a role in smoking addiction. Our results show that common traits influence circulating sncRNA expression. It is clear that sncRNA biomarker analyses should be adjusted for age and sex. In addition, for specific sncRNAs, analyses should also be adjusted for body mass, smoking, physical activity and technical factors. |
DOI of the first publication: | 10.1038/s41598-018-35974-4 |
Link to this record: | urn:nbn:de:bsz:291--ds-354091 hdl:20.500.11880/32331 http://dx.doi.org/10.22028/D291-35409 |
ISSN: | 2045-2322 |
Date of registration: | 4-Feb-2022 |
Description of the related object: | Electronic supplementary material |
Related object: | https://static-content.springer.com/esm/art%3A10.1038%2Fs41598-018-35974-4/MediaObjects/41598_2018_35974_MOESM1_ESM.docx https://static-content.springer.com/esm/art%3A10.1038%2Fs41598-018-35974-4/MediaObjects/41598_2018_35974_MOESM2_ESM.xlsx |
Faculty: | M - Medizinische Fakultät |
Department: | M - Humangenetik M - Medizinische Biometrie, Epidemiologie und medizinische Informatik |
Professorship: | M - Univ.-Prof. Dr. Andreas Keller M - Prof. Dr. Eckhart Meese |
Collections: | SciDok - Der Wissenschaftsserver der Universität des Saarlandes |
Files for this record:
File | Description | Size | Format | |
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s41598-018-35974-4.pdf | 3,67 MB | Adobe PDF | View/Open |
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