Evidence mapPaperPMID 39619395Full record

ArticleJACC. Asia2024

Genetic Associations of Lipids and Lipid-Modifying Drug Targets With Type 2 Diabetes in the Chinese Population.

Jiawen Lu, Yang Liu, Zhenqian Wang, Kaixin Zhou, Ying Pan, Shao Zhong, Guozhi Jiang

Abstract read
In one paragraph

Article in JACC. Asia, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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

1 citing paper in PubMed.

  1. 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

7 authors.

Jiawen LuSchool of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China.
Yang LiuCollege of Life Sciences, University of Chinese Academy of Sciences, Beijing, China.
Zhenqian WangSchool of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China.
Kaixin ZhouGuangzhou Laboratory, Guangzhou, China.
Ying PanDepartment of Endocrinology, Kunshan Hospital Affiliated to Jiangsu University, Kunshan, Jiangsu, China.
Shao ZhongDepartment of Endocrinology, Kunshan Hospital Affiliated to Jiangsu University, Kunshan, Jiangsu, China.
Guozhi JiangSchool of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Dyslipidemia is a recognized risk factor for type 2 diabetes (T2D), yet the genetic basis and causal nature remain unclear, particularly in Chinese populations. Objectives: The authors investigated the causal effects of genetically predicted lipid levels on T2D risk and explored the potential effects of lipid-modifying drugs. Methods: Leveraging data from the Kunshan Community cohort in China, we analyzed the associations between low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol, and triglycerides (TGs) with T2D risk using genetic risk scores, 1-sample univariable, multivariable, and nonlinear Mendelian randomization (MR) analyses. Two-sample MR using summary-level data from Global Lipid Genetics Consortium and Biobank Japan was used for validation. Drug-target MR was used to examine the impact of lipid-modifying drug targets on T2D. Results: Lower genetic risk scores of LDL-C (OR per SD: 0.97 [95% CI: 0.95-0.99]; Conclusions: Our findings suggested potential adverse effects of lower LDL-C, TG levels, as well as long-term use of APOC3 inhibitors on T2D risk in Chinese populations. These findings highlight the need for cautious lipid management strategies in T2D prevention.

Indexed as

Chinese populationlipid-modifying drugslipid traitsMendelian randomizationtype 2 diabetes

Identifiers

PMID39619395
PMCPMC11604551

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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 Socratic graph.