ArticleCurrent genetic medicine reports2016
Using Mendelian Randomization studies to Assess Causality and Identify New Therapeutic Targets in Cardiovascular Medicine.
Article in Current genetic medicine reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
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.
Who cites it
3 citing papers in PubMed, 4 citations in OpenAlex.
- Chemopreventive Agents After Pancreatic Resection for Ductal Adenocarcinoma: Legend or Scientific Evidence?Annals of surgical oncology · 2021Article
- Genetics of Subclinical Coronary Atherosclerosis.Current genetic medicine reports · 2018Article
- Genome-wide mapping of plasma protein QTLs identifies putatively causal genes and pathways for cardiovascular disease.Nature communications · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
Integration of knowledge generated from genetic studies on intermediate biomarkers and CHD can provide a reliable approach to help assess causal pathways in coronary heart disease. Mendelian Randomization (MR) studies are a powerful tool to assess causal relevance of a range of pathways. These analyses use genetic variants as proxies for soluble biomarkers in association studies of disease risk. MR studies can provide unbiased estimates of causal effects and avoid distortions due to confounding factors arising later in life, because genetic variants are fixed at conception. MR studies have provided evidence pointing towards the likelihood of a causal relevance of a range of pathways in CHD, including LDL-C, triglycerides, lipoprotein (a), and interleukin-6 receptor. On the other hand, MR studies have refuted the causal relevance of a number of biomarkers, including C-reactive protein (CRP), fibrinogen, uric acid, LpPLA2 activity, and homocysteine. Carefully conducted MR studies should overcome the limitations that are inherent to other observational studies (e.g., residual confounding and reverse causality) to help assess causal relevance of a range of pathways in CHD.
Indexed as
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What OpenQuestion holds
Registered trials
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.