Ruth Loos

Ruth Loos


  • Loos Group

    Blegdamsvej 3B, Mærsk Tårnet, 7. sal

    2200 København N.

    Phone: +4535337781Mobile: +4530589681

My research focuses on the etiology of obesity and the identification of genes and genetic loci contributing to the risk of obesity and related traits. By identifying genes, my team aims to gain insight into the biology that underlies body weight regulation and the mechanisms that link adiposity to its comorbidities. With the GIANT (Genetic Investigation of ANTropometric traits) consortium, my team has contributed to most large-scale gene-discovery efforts that thus far have revealed many obesity-associated loci.

Furthermore, my team studies more refined adiposity phenotypes and biomarkers to target the deeper layers that define “obesity” and to fully capture all aspects of the biology involved. Besides gene discovery, I use epidemiological methods to assess the role of genetic information in precision medicine of common obesity by examining its value in identifying subtypes of obesity, predicting who is at risk of gaining weight, and in tailoring prevention and treatment strategies.


The following three projects are the current focus of our research:

In the first project, we aim to gain insight into the deeper layers of biology that underlie body weight regulation and fat distribution. We do this through the discovery of genes and genetic variations that are associated with obesity and other adiposity traits.

In the second project, we aim to determine the genetic and non-genetic determinants of body weight through deep-phenotyping of individuals at high vs. low genetic risk for obesity in recall-by-genotype studies. This project requires insight into the role of rare genetic variations in obesity.

In the third project, we will build a precision health cohort to identify key predictors of individuals’ metabolic responses to diet and exercise, to improve the precision of lifestyle recommendations for optimal health. This project requires experience with wearable, mobile, and sensor technologies and expertise in generating and integrating multi-modal data to better understand complex disease.

ID: 183694311