ArticleHuman genetics2016
Harnessing publicly available genetic data to prioritize lipid modifying therapeutic targets for prevention of coronary heart disease based on dysglycemic risk.
Article in Human genetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
What it found
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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
12 citing papers in PubMed, 2 syntheses or guidelines pooled it, 20 citations in OpenAlex.
- The landscape of the methodology in drug repurposing using human genomic data: a systematic review.Briefings in bioinformatics · 2024Pooled it
- Mendelian randomization studies on coronary artery disease: a systematic review and meta-analysis.Systematic reviews · 2024Pooled it
- Long Non-coding RNA Based Therapy for Cardiovascular Disease.Journal of cardiovascular translational research · 2025Review
- Causal relationship between drug target genes of LDL-cholesterol and coronary artery disease: drug target Mendelian randomization study.Lipids in health and disease · 2025Article
- Targeting the Liver with Nucleic Acid Therapeutics for the Treatment of Systemic Diseases of Liver Origin.Pharmacological reviews · 2023Review
- Common and RareInternational journal of molecular sciences · 2022Article
- Genetics Insights in the Relationship Between Type 2 Diabetes and Coronary Heart Disease.Circulation research · 2020Review
- The Study of rs693 and rs515135 in APOB in People with Familial Hypercholestrolemia.Cell journal · 2019Article
- Druggability of Coronary Artery Disease Risk Loci.Circulation. Genomic and precision medicine · 2018Article
- Gene Set Enrichment Analyses: lessons learned from the heart failure phenotype.BioData mining · 2017Article
- Association of Lipid Fractions With Risks for Coronary Artery Disease and Diabetes.JAMA cardiology · 2016Article
- 3-Hydroxy-3-methylglutaryl-CoA reductase variants strongly associated with low-density lipoprotein cholesterol levels and diabetes mellitus risk in a Taiwanese population: A Mendelian randomization study.Tzu chi medical journalArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 7 institutions in 4 countries.
Funding
Abstract
Therapeutic interventions that lower LDL-cholesterol effectively reduce the risk of coronary artery disease (CAD). However, statins, the most widely prescribed LDL-cholesterol lowering drugs, increase diabetes risk. We used genome-wide association study (GWAS) data in the public domain to investigate the relationship of LDL-C and diabetes and identify loci encoding potential drug targets for LDL-cholesterol modification without causing dysglycemia. We obtained summary-level GWAS data for LDL-C from GLGC, glycemic traits from MAGIC, diabetes from DIAGRAM and CAD from CARDIoGRAMplusC4D consortia. Mendelian randomization analyses identified a one standard deviation (SD) increase in LDL-C caused an increased risk of CAD (odds ratio [OR] 1.63 (95 % confidence interval [CI] 1.55, 1.71), which was not influenced by removing SNPs associated with diabetes. LDL-C/CAD-associated SNPs showed consistent effect directions (binomial P = 6.85 × 10(-5)). Conversely, a 1-SD increase in LDL-C was causally protective of diabetes (OR 0.86; 95 % CI 0.81, 0.91), however LDL-cholesterol/diabetes-associated SNPs did not show consistent effect directions (binomial P = 0.15). HMGCR, our positive control, associated with LDL-C, CAD and a glycemic composite (derived from GWAS meta-analysis of four glycemic traits and diabetes). In contrast, PCSK9, APOB, LPA, CETP, PLG, NPC1L1 and ALDH2 were identified as "druggable" loci that alter LDL-C and risk of CAD without displaying associations with dysglycemia. In conclusion, LDL-C increases the risk of CAD and the relationship is independent of any association of LDL-C with diabetes. Loci that encode targets of emerging LDL-C lowering drugs do not associate with dysglycemia, and this provides provisional evidence that new LDL-C lowering drugs (such as PCSK9 inhibitors) may not influence risk of diabetes.
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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 Socratic graph.