Genetic and Molecular Epidemiology
Paul Franks’s research group
Our research
An assumption that underpins most public health and medicine is that risk profiles and treatment responses of individuals within a target population will approximate the average risk profile for the population. While this will be true for most people, many others deviate from these expectations, such that standard prediction models and interventions are ineffective. By harnessing the inherent heterogeneity that is common in clinical presentation, aetiology and treatment responses of many diseases, precision medicine offers solutions to address major gaps in knowledge, clinical decision-making and health inequities for people either living with or at risk of developing these conditions.
Cardiometabolic diseases (CMDs) are a group of interconnected conditions that include obesity, type 2 diabetes (T2D), metabolic-associated fatty liver disease, hypertension and ischemic heart disease. Multimorbidity of cardiometabolic diseases is common, and shared molecular pathways and clinical risk factors have been identified. These conditions are heterogeneous in aetiology and clinical manifestation with onset and prognosis often challenging to predict at an individual level. Precision medicine offers great potential to help overcome some of the inefficiencies in prevention and treatment of CMDs that face contemporary medicine.
Our team uses machine learning methods applied to complex multimodal datasets (omics, genetics, clinical, behavioural, sociodemographic) to identify hidden structures that are informative for diagnostic subclassification, precision prediction and prognosis, or therapeutic decision making.
Aims
- Develop, test and validate precision medicine algorithms for better prediction, prevention and treatment of cardiometabolic diseases.
- Lead national and international initiatives for the development of consensus, evidence evaluation, and clinical implementation strategies for precision medicine.
Impact
Our team has developed novel analytical approaches to inform evidence-based precision medicine. These include algorithms for diagnostic subclassification, precision prediction of disease events, and therapeutic optimisation. We have also developed translational frameworks for precision medicine research and clinical implementation. Moreover, we have led major international precision medicine initiatives, including global consensus statements, commissions and guidelines committees. We anticipate that these and future initiatives will contribute to optimised interventions for people at risk of or with a range of cardiometabolic disease, thereby reducing costs and unnecessary side-effects.
Research output
Link to a list of research output by the group in Lund University’s research portal
Team members
Link to a list of team members in Lund University’s research portal

Paul Franks
Principal Investigator
Professor of genetic epidemiology
+46 (0)73 04 57 55
paul [dot] franks [at] med [dot] lu [dot] se (paul[dot]franks[at]med[dot]lu[dot]se)
Paul Franks’s profile in Lund University’s research portal
Affiliations
EXODIAB: Excellence of Diabetes Research in Sweden
Link to EXODIAB’s page in Lund University’s research portal
Epihealth