Evidence map›Paper›PMID 38882679›Full record

ArticleComputational and structural biotechnology journal2024

Causal association and shared genetics between telomere length and COVID-19 outcomes: New evidence from the latest large-scale summary statistics.

Jingwei Zhang, Jie Wen, Ziyu Dai, Hao Zhang, Nan Zhang, Ruoyan Lei, Zhixiong Liu, Luo Peng, Quan Cheng

Abstract read
In one paragraph

Article in Computational and structural biotechnology journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Effect of SARS-CoV-2 infection on sperm telomere length.Journal of assisted reproduction and genetics · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Jingwei ZhangDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.
Jie WenDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.
Ziyu DaiDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.
Hao ZhangDepartment of Neurosurgery, The Second Affiliated Hospital, Chongqing Medical University, Chongqing, China.
Nan ZhangCollege of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China.
Ruoyan LeiXiangya School of Public Health, Central South University, Changsha, China.
Zhixiong LiuDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.
Luo PengDepartment of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
Quan ChengDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Observational studies suggested that leukocyte telomere length (LTL) is shortened in COVID-19 patients. However, the genetic association and causality remained unknown. Methods: Based on the genome-wide association of LTL (N = 472,174) and COVID-19 phenotypes (N = 1086,211-2597,856), LDSC and SUPERGNOVA were used to estimate the genetic correlation. Cross-trait GWAS meta-analysis, colocalization, fine-mapping analysis, and transcriptome-wide association study were conducted to explore the shared genetic etiology. Mendelian randomization (MR) was utilized to infer the causality. Upstream and downstream two-step MR was performed to investigate the potential mediating effects. Results: LDSC identified a significant genetic association between LTL and all COVID-19 phenotypes (rG < 0, Conclusions: Our findings suggested that shorter LTL was genetically associated with a higher risk of COVID-19 phenotypes, with shared genetic etiology and potential causality.

Indexed as

COVID-19Genetic correlationMediation analysisMendelian randomizationTelomere length

Identifiers

PMID38882679
PMCPMC11176559

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.