What is he best known for?
The fields of study he is best known for:
- Internal medicine
- Gene
- Genetics
Vilmundur Gudnason mainly focuses on Genome-wide association study, Genetics, Internal medicine, Genetic association and Endocrinology.
His Genome-wide association study study is related to the wider topic of Single-nucleotide polymorphism.
His study in Body mass index extends to Genetics with its themes.
His studies deal with areas such as Surgery and Cardiology as well as Internal medicine.
His Cardiology research integrates issues from Blood pressure and Renal function.
His Genetic association study incorporates themes from Human height and Atrial fibrillation.
His most cited work include:
- Biological, clinical and population relevance of 95 loci for blood lipids (2804 citations)
- Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease (2614 citations)
- C-Reactive Protein and Other Circulating Markers of Inflammation in the Prediction of Coronary Heart Disease (2560 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Internal medicine, Genome-wide association study, Genetics, Cardiology and Endocrinology.
His study in Internal medicine focuses on Prospective cohort study, Cohort study, Cohort, Odds ratio and Myocardial infarction.
His Cohort study research incorporates themes from Gerontology and Hazard ratio.
His Genome-wide association study research entails a greater understanding of Single-nucleotide polymorphism.
Genetics is represented through his Gene, Locus, Allele, Genotype and Genetic variation research.
The Cardiology study combines topics in areas such as Framingham Risk Score, Disease and Blood pressure.
He most often published in these fields:
- Internal medicine (40.46%)
- Genome-wide association study (26.59%)
- Genetics (25.55%)
What were the highlights of his more recent work (between 2018-2021)?
- Internal medicine (40.46%)
- Genome-wide association study (26.59%)
- Genetics (25.55%)
In recent papers he was focusing on the following fields of study:
His main research concerns Internal medicine, Genome-wide association study, Genetics, Disease and Cardiology.
His work deals with themes such as Gastroenterology, Endocrinology and Oncology, which intersect with Internal medicine.
His research on Genome-wide association study concerns the broader Single-nucleotide polymorphism.
His studies in Genetics integrate themes in fields like Sleep in non-human animals and Blood pressure.
His Disease research incorporates elements of Blood proteins, Epidemiology, Cohort and Bioinformatics.
His research integrates issues of PR interval and Magnetic resonance imaging in his study of Cardiology.
Between 2018 and 2021, his most popular works were:
- The genetic architecture of the human cerebral cortex. (116 citations)
- World Health Organization cardiovascular disease risk charts: revised models to estimate risk in 21 global regions (108 citations)
- A meta-analysis of genome-wide association studies identifies multiple longevity genes (73 citations)
In his most recent research, the most cited papers focused on:
- Internal medicine
- Gene
- Disease
His scientific interests lie mostly in Genome-wide association study, Internal medicine, Genetics, Pathology and Cohort study.
His Genome-wide association study study is concerned with Single-nucleotide polymorphism in general.
The various areas that Vilmundur Gudnason examines in his Internal medicine study include Diabetes mellitus, Endocrinology and Type 2 diabetes.
Vilmundur Gudnason has researched Genetics in several fields, including Blood lipids, Lipid profile and Lipid metabolism.
His work on Calcification as part of general Pathology study is frequently connected to HDAC9, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
The concepts of his Cohort study study are interwoven with issues in Lower risk, Confidence interval, Ethnically diverse and Time trends.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
