{"doi":"10.1101/2024.08.22.24312440","title":"Integrated clinical risk prediction of type 2 diabetes with a multifactorial polygenic risk score","abstract":"Combining information from multiple GWASs for a disease and its risk factors has proven a powerful approach for development of polygenic risk scores (PRSs). This may be particularly useful for type 2 diabetes (T2D), a highly polygenic and heterogeneous disease where the additional predictive value of a PRS is unclear. Here, we use a meta-scoring approach to develop a metaPRS for T2D that incorporated genome-wide associations from both European and non-European genetic ancestries and T2D risk factors. We evaluated the performance of this metaPRS and benchmarked it against existing genome-wide PRS in 620,059 participants and 50,572 T2D cases amongst six diverse genetic ancestries from UK Biobank, INTERVAL, the All of Us Research Program, and the Singapore Multi-Ethnic Cohort. We show that our metaPRS was the most powerful PRS for predicting T2D in European population-based cohorts and had comparable performance to the top ancestry-specific PRS, highlighting its transferability. In UK Biobank, we show the metaPRS had stronger predictive power for 10-year risk than all individual risk factors apart from BMI and biomarkers of dysglycemia. The metaPRS modestly improved T2D risk stratification of QDiabetes risk scores for 10-year risk prediction, particularly when prioritising individuals for blood tests of dysglycemia. Overall, we present a highly predictive and transferrable PRS for T2D and demonstrate that the potential for PRS to incrementally improve T2D risk prediction when incorporated into UK guideline-recommended screening and risk prediction with a clinical risk score.","journal":"medRxiv","year":2024,"id":485228,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":6,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9579,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":991167,"name":"Henry J. Taylor","orcid":"0000-0003-2088-5240","position":1,"is_corresponding":false},{"id":986222,"name":"Yujian Liang","orcid":null,"position":2,"is_corresponding":false},{"id":289153,"name":"Hasanga D. Manikpurage","orcid":"0000-0002-2365-6956","position":3,"is_corresponding":false},{"id":614371,"name":"Lisa Pennells","orcid":"0000-0002-8594-3061","position":4,"is_corresponding":false},{"id":65492,"name":"Carles Foguet","orcid":"0000-0001-8494-9595","position":5,"is_corresponding":false},{"id":495094,"name":"Gad Abraham","orcid":"0000-0003-4853-0118","position":6,"is_corresponding":false},{"id":1201502,"name":"Joel T. Gibson","orcid":"0000-0002-8967-4188","position":7,"is_corresponding":false},{"id":997585,"name":"Xilin Jiang","orcid":"0000-0001-6773-9182","position":8,"is_corresponding":false},{"id":275952,"name":"Yang Liu","orcid":"0000-0002-5694-8760","position":9,"is_corresponding":false},{"id":985797,"name":"Yu Xu","orcid":"0000-0002-7304-5045","position":10,"is_corresponding":false},{"id":1327792,"name":"Lois G. Kim","orcid":"0000-0002-4552-3820","position":11,"is_corresponding":false},{"id":21799,"name":"Anubha Mahajan","orcid":"0000-0001-5585-3420","position":12,"is_corresponding":false},{"id":1089,"name":"Mark I. McCarthy","orcid":"0000-0002-4393-0510","position":13,"is_corresponding":false},{"id":51463,"name":"Stephen Kaptoge","orcid":"0000-0002-1155-4872","position":14,"is_corresponding":false},{"id":551616,"name":"Samuel A. Lambert","orcid":"0000-0001-8222-008X","position":15,"is_corresponding":false},{"id":50860,"name":"Angela Wood","orcid":"0000-0002-7937-304X","position":16,"is_corresponding":false},{"id":240625,"name":"Xueling Sim","orcid":"0000-0002-1233-7642","position":17,"is_corresponding":false},{"id":19902,"name":"Francis S. Collins","orcid":"0000-0002-1023-7410","position":18,"is_corresponding":false},{"id":22022,"name":"Joshua C. Denny","orcid":"0000-0002-3049-7332","position":19,"is_corresponding":false},{"id":21866,"name":"John Danesh","orcid":"0000-0003-1158-6791","position":20,"is_corresponding":false},{"id":49710,"name":"Adam S. Butterworth","orcid":"0000-0002-6915-9015","position":21,"is_corresponding":false},{"id":51462,"name":"Emanuele Di Angelantonio","orcid":"0000-0001-8776-6719","position":22,"is_corresponding":false},{"id":33743,"name":"Michael Inouye","orcid":"0000-0001-9413-6520","position":23,"is_corresponding":false},{"id":108709,"name":"Scott C. Ritchie","orcid":"0000-0002-8454-9548","position":0,"is_corresponding":true}],"reference_count":60,"raw_metadata":null,"created_at":"2026-07-19T02:07:47.633574Z","pmid":"39228710","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}