| Title: |
Partitioned polygenic scores show mechanistic heterogeneity in type 2 diabetes and hypertension comorbidity |
| Authors: |
Pascat, Vincent; Zudina, Liudmila; Maurin, Lucas; Ulrich, Anna; Maina, Jared G.; Demirkan, Ayse; Balkhiyarova, Zhanna; Pupko, Igor; Sharhorodska, Yevheniya; Pattou, François; Staels, Bart; Kaakinen, Marika; Khamis, Amna; Bonnefond, Amélie; Munroe, Patricia; Froguel, Philippe; Prokopenko, Inga |
| Contributors: |
Institute for Molecular Medicine Finland |
| Publisher Information: |
Nature Publishing Group |
| Publication Year: |
2026 |
| Collection: |
Helsingfors Universitet: HELDA – Helsingin yliopiston digitaalinen arkisto |
| Subject Terms: |
Biomedicine; Genome-wide association; Influencing blood-pressure; Insulin-resistance; Metabolic syndrome; Genetic-variants; Susceptibility; Traits; Target; Common; Risk |
| Description: |
Type 2 diabetes and hypertension are common health conditions that often occur together, suggesting shared biological mechanisms. To explore this relationship, we analyse large-scale multiomic data to uncover genetic factors underlying type 2 diabetes and blood pressure comorbidity. We curate 1304 independent single-nucleotide variants associated with type 2 diabetes and blood pressure, grouping them into five clusters related to metabolic syndrome, inverse type 2 diabetes/blood pressure risk, impaired pancreatic beta-cell function, higher adiposity, and vascular dysfunction. Colocalization with tissue-specific gene expression highlights significant enrichment in pathways related to thyroid function and fetal development. Partitioned polygenic scores derived from these clusters improve risk prediction for type 2 diabetes/hypertension comorbidity, identifying individuals with more than twice the usual susceptibility. These results reveal a mechanistically heterogeneous genetic architecture shared between type 2 diabetes and blood pressure, enhancing comorbidity risk prediction. Partitioned polygenic risk scores offer a promising approach for early risk stratification, personalised prevention, and improved management of these interconnected conditions. ; Peer reviewed |
| Document Type: |
article in journal/newspaper |
| File Description: |
application/pdf |
| Language: |
English |
| Relation: |
This research has been conducted using the UK Biobank Resource under application number 236. This project was in part funded by the Agence Nationale de la Recherche under the Programme d'Investissement d'Avenir (PreciDIAB, ANR-18-IBHU-0001 and RHU PreciNASH ANR-16-RHUS-0006), by the European Union through the "Fonds Europeen de Developpement Regional" (FEDER), by the "Conseil Regional des Hauts-de-France" (Hauts-de-France Regional Council), by the "Metropole Europeenne de Lille" (MEL, European Metropolis of Lille), and by the European Research Council (ERC OpiO - 101043671, to A.B.). I.Pr. and Z.B. were in part funded by the Diabetes UK (BDA number: 20/0006307), UKRI (EP/Z535072/1), European Foundation for the Study of Diabetes (EFSD), and Novo Nordisk A/S Programme for Diabetes Research in Europe-2025. The research of Y.S. was funded by The British Academy (RaR\100084). The authors would like to thank all the investigators from different consortia that built and shared the GWAS meta-analysis, eQTLs, and scATAC-seq atlases used in this study, as well as the UK Biobank participants and dedicated staff.; https://hdl.handle.net/10138/628033; 105029535023; 001685458900002 |
| Availability: |
https://hdl.handle.net/10138/628033 |
| Rights: |
cc_by ; info:eu-repo/semantics/openAccess ; openAccess |
| Accession Number: |
edsbas.B1E13746 |
| Database: |
BASE |