Evidence map›Paper›PMID 39606095›Full record

ArticleImmunoTargets and therapy2024

Novel Insights into Causal Effects of Lipid and Lipid-Lowering Targets with Autoimmune Thyroid Disease: A Mendelian Randomization Study.

Chang Su, Juan Tian, Xueqing He, Xiaona Chang, Guang Wang, Jia Liu

Abstract read
In one paragraph

Article in ImmunoTargets and therapy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. 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

6 authors.

Chang SuDepartment of Endocrinology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, 100020, People's Republic of China.
Juan TianDepartment of Endocrinology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, 100020, People's Republic of China.
Xueqing HeDepartment of Endocrinology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, 100020, People's Republic of China.
Xiaona ChangDepartment of Endocrinology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, 100020, People's Republic of China.
Guang WangDepartment of Endocrinology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, 100020, People's Republic of China.
Jia LiuDepartment of Endocrinology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, 100020, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Dyslipidemia has been implicated in the pathogenesis of several diseases, including thyroid dysfunction and immune disorders. However, whether circulating lipids and long-term use of lipid-lowering drugs influence the development of autoimmune thyroid disease (AITD) remains unclear. This study aims to evaluate the effects of lipid-lowering drugs on AITD and explore their potential mechanisms. Methods: Two-sample and two-step Mendelian randomization (MR) studies were performed to assess the causal relationships between circulating lipids (LDL-C, TC, TG, and ApoB) and seven lipid-lowering drug targets ( Results: There was no clear causality between circulating lipids (ApoB, LDL-C, TC, and TG) and AITD ( Conclusion: There was no clear causality between circulating lipids (ApoB, LDL-C, TC, and TG) and AITD. Lipid-lowering drug target gene inhibitors reduced the AITD risk by modulating inflammatory factors.

Indexed as

drug targetinginflammatory factorslipid traitMendelian randomizationthyroid autoimmune disease

Identifiers

PMID39606095
PMCPMC11600951

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.