A systematic analysis of the contribution of genetics to multimorbidity and comparisons with primary care dataResearch in context
Summary: Background: Multimorbidity, the presence of two or more conditions in one person, is common but studies are often limited to observational data and single datasets. We address this gap by integrating large-scale primary-care and genetic data from multiple studies to interrogate multimorbid...
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Elsevier
2025-03-01
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| author | Olivia Murrin Ninon Mounier Bethany Voller Linus Tata Carlos Gallego-Moll Albert Roso-Llorach Lucía A. Carrasco-Ribelles Chris Fox Louise M. Allan Ruby M. Woodward Xiaoran Liang Jose M. Valderas Sara M. Khalid Frank Dudbridge Sally E. Lamb Mary Mancini Leon Farmer Kate Boddy Jack Bowden David Melzer Timothy M. Frayling Jane A.H. Masoli Luke C. Pilling Concepción Violán João Delgado |
| author_facet | Olivia Murrin Ninon Mounier Bethany Voller Linus Tata Carlos Gallego-Moll Albert Roso-Llorach Lucía A. Carrasco-Ribelles Chris Fox Louise M. Allan Ruby M. Woodward Xiaoran Liang Jose M. Valderas Sara M. Khalid Frank Dudbridge Sally E. Lamb Mary Mancini Leon Farmer Kate Boddy Jack Bowden David Melzer Timothy M. Frayling Jane A.H. Masoli Luke C. Pilling Concepción Violán João Delgado |
| author_sort | Olivia Murrin |
| collection | DOAJ |
| description | Summary: Background: Multimorbidity, the presence of two or more conditions in one person, is common but studies are often limited to observational data and single datasets. We address this gap by integrating large-scale primary-care and genetic data from multiple studies to interrogate multimorbidity patterns and producing digital resources to support future research. Methods: We defined chronic, common, and heritable conditions in individuals aged ≥65 years, using two large primary-care databases [CPRD (UK) N = 2,425,014 and SIDIAP (Spain) N = 1,053,640], and estimated heritability using the same definitions in UK Biobank (N = 451,197). We used logistic regression to estimate the co-occurrence of pairs of conditions in the primary care data. Linkage disequilibrium score regression was used to estimate genetic similarity between pairs of conditions. Meta-analyses were conducted across databases, and up to three sources of genetic data, for each pair of conditions. We classified pairs of conditions as across or within-domain based on the international classification of disease. Findings: We identified 72 chronic conditions, with 43.6% of 2546 pairs showing higher co-occurrence than chance in primary care and evidence of shared genetics. Many across-domain pairs exhibited substantial shared genetics (e.g., iron deficiency anaemia and peripheral arterial disease: genetic correlation Rg = 0.45 [95% Confidence Intervals 0.27:0.64]). 33 pairs displayed negative genetic correlations, such as skin cancer and rheumatoid arthritis (Rg = −0.14 [−0.21:−0.06]), due to potential adverse drug effects. Discordance between genetic and primary care data was also observed, e.g., abdominal aortic aneurysm and bladder cancer co-occurred in primary care but were not genetically correlated (Odds-Ratio = 2.23 [2.09:2.37], Rg = 0.04 [−0.20:0.28]) and schizophrenia and fibromyalgia were less likely to co-occur together in primary care but were positively genetically correlated (OR = 0.84 [0.75:0.94], Rg = 0.20 [0.11:0.29]). Interpretation: Most pairs of chronic conditions show evidence of shared genetics, and co-occurrence in primary care, suggesting shared mechanisms. The identified patterns of shared genetics, negative correlations and discordance between genetic and observational data provide a foundation for future multimorbidity research. Funding: UK Medical Research Council [MR/W014548/1]. |
| format | Article |
| id | doaj-art-ae6a7e720b5b4312aad0ca41582764f5 |
| institution | Kabale University |
| issn | 2352-3964 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
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| series | EBioMedicine |
| spelling | doaj-art-ae6a7e720b5b4312aad0ca41582764f52025-02-07T04:47:47ZengElsevierEBioMedicine2352-39642025-03-01113105584A systematic analysis of the contribution of genetics to multimorbidity and comparisons with primary care dataResearch in contextOlivia Murrin0Ninon Mounier1Bethany Voller2Linus Tata3Carlos Gallego-Moll4Albert Roso-Llorach5Lucía A. Carrasco-Ribelles6Chris Fox7Louise M. Allan8Ruby M. Woodward9Xiaoran Liang10Jose M. Valderas11Sara M. Khalid12Frank Dudbridge13Sally E. Lamb14Mary Mancini15Leon Farmer16Kate Boddy17Jack Bowden18David Melzer19Timothy M. Frayling20Jane A.H. Masoli21Luke C. Pilling22Concepción Violán23João Delgado24Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, UKDepartment of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, UKDepartment of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, UKResearch Software Engineering Group, University of Exeter, UKUnitat de Suport a la Recerca Metropolitana Nord, Fundacio Institut Universitari per a la recerca a l’Atencio Primaria de Salut Jordi Gol I Gurina (IDIAPJGol), Barcelona, 08007, Spain; Grup de REcerca en Impacte de les Malalties Cròniques i les seves Trajectòries (GRIMTra) (2021 SGR 01537), Institut Universitari d’Investigació en Atenció Primaria Jordi Gol (IDIAPJGol), Mare de Déu de Guadalupe, 2, Barcelona, 08303, SpainUnitat de Suport a la Recerca Metropolitana Nord, Fundacio Institut Universitari per a la recerca a l’Atencio Primaria de Salut Jordi Gol I Gurina (IDIAPJGol), Barcelona, 08007, Spain; Grup de REcerca en Impacte de les Malalties Cròniques i les seves Trajectòries (GRIMTra) (2021 SGR 01537), Institut Universitari d’Investigació en Atenció Primaria Jordi Gol (IDIAPJGol), Mare de Déu de Guadalupe, 2, Barcelona, 08303, SpainUnitat de Suport a la Recerca Metropolitana Nord, Fundacio Institut Universitari per a la recerca a l’Atencio Primaria de Salut Jordi Gol I Gurina (IDIAPJGol), Barcelona, 08007, Spain; Grup de REcerca en Impacte de les Malalties Cròniques i les seves Trajectòries (GRIMTra) (2021 SGR 01537), Institut Universitari d’Investigació en Atenció Primaria Jordi Gol (IDIAPJGol), Mare de Déu de Guadalupe, 2, Barcelona, 08303, Spain; Red de Investigación en Cronicidad, Atención Primaria y Prevención y Promoción de la Salut (RICAPPS), Instituto de Salud Carlos III (ISCIII), Avenida Monforte de Lemos, 5, Madrid, 28029, SpainDepartment of Health and Community Sciences, Faculty of Health and Life Sciences, University of Exeter, UKDepartment of Health and Community Sciences, Faculty of Health and Life Sciences, University of Exeter, UKDepartment of Population Health Sciences, University of Leicester, UKDepartment of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, UKDepartment of Health and Community Sciences, Faculty of Health and Life Sciences, University of Exeter, UK; Department of Family Medicine, National University Health System, 1E Kent Ridge Road, 119228, SingaporeCentre for Statistics in Medicine, Nuffield of Orthopaedics Rheumatology and Musculoskeletal Sciences, University of Oxford, UKDepartment of Population Health Sciences, University of Leicester, UKDepartment of Health and Community Sciences, Faculty of Health and Life Sciences, University of Exeter, UKPublic and Patient involvement representative, UKPublic and Patient involvement representative, UKDepartment of Health and Community Sciences, Faculty of Health and Life Sciences, University of Exeter, UKDepartment of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, UKDepartment of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, UKDepartment of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, UK; Department of Genetic Medicine and Development, Faculty of Medicine, 1 rue Michel-Servet, CH-1211, Genève 4, Switzerland; Corresponding author. Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, UK.Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, UK; Royal Devon University Healthcare NHS Foundation Trust, Barrack Road, Exeter, EX2 5DW, UKDepartment of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, UKGrup de REcerca en Impacte de les Malalties Cròniques i les seves Trajectòries (GRIMTra) (2021 SGR 01537), Institut Universitari d’Investigació en Atenció Primaria Jordi Gol (IDIAPJGol), Mare de Déu de Guadalupe, 2, Barcelona, 08303, Spain; Red de Investigación en Cronicidad, Atención Primaria y Prevención y Promoción de la Salut (RICAPPS), Instituto de Salud Carlos III (ISCIII), Avenida Monforte de Lemos, 5, Madrid, 28029, Spain; Unitat de Suport a la Recerca Metropolitana Nord, Institut Universitari d’Investigació en Atenció Primaria Jordi Gol (IDIAP Jordi Gol), Mare de Déu de Guadalupe, 2, Mataró, 08303, State, Spain; Germans Trias i Pujol Research Institute (IGTP), Street, Badalona, 08916, State, Spain; Department of Medicine, Universitat Autònoma de Barcelona, Plaça Cívica, 1, Cerdanyola de Vallès, 08193, State, SpainDepartment of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, UK; Corresponding author.Summary: Background: Multimorbidity, the presence of two or more conditions in one person, is common but studies are often limited to observational data and single datasets. We address this gap by integrating large-scale primary-care and genetic data from multiple studies to interrogate multimorbidity patterns and producing digital resources to support future research. Methods: We defined chronic, common, and heritable conditions in individuals aged ≥65 years, using two large primary-care databases [CPRD (UK) N = 2,425,014 and SIDIAP (Spain) N = 1,053,640], and estimated heritability using the same definitions in UK Biobank (N = 451,197). We used logistic regression to estimate the co-occurrence of pairs of conditions in the primary care data. Linkage disequilibrium score regression was used to estimate genetic similarity between pairs of conditions. Meta-analyses were conducted across databases, and up to three sources of genetic data, for each pair of conditions. We classified pairs of conditions as across or within-domain based on the international classification of disease. Findings: We identified 72 chronic conditions, with 43.6% of 2546 pairs showing higher co-occurrence than chance in primary care and evidence of shared genetics. Many across-domain pairs exhibited substantial shared genetics (e.g., iron deficiency anaemia and peripheral arterial disease: genetic correlation Rg = 0.45 [95% Confidence Intervals 0.27:0.64]). 33 pairs displayed negative genetic correlations, such as skin cancer and rheumatoid arthritis (Rg = −0.14 [−0.21:−0.06]), due to potential adverse drug effects. Discordance between genetic and primary care data was also observed, e.g., abdominal aortic aneurysm and bladder cancer co-occurred in primary care but were not genetically correlated (Odds-Ratio = 2.23 [2.09:2.37], Rg = 0.04 [−0.20:0.28]) and schizophrenia and fibromyalgia were less likely to co-occur together in primary care but were positively genetically correlated (OR = 0.84 [0.75:0.94], Rg = 0.20 [0.11:0.29]). Interpretation: Most pairs of chronic conditions show evidence of shared genetics, and co-occurrence in primary care, suggesting shared mechanisms. The identified patterns of shared genetics, negative correlations and discordance between genetic and observational data provide a foundation for future multimorbidity research. Funding: UK Medical Research Council [MR/W014548/1].http://www.sciencedirect.com/science/article/pii/S2352396425000283ComorbidityChronic diseaseMultiple long-term conditionsObservationalGenotype |
| spellingShingle | Olivia Murrin Ninon Mounier Bethany Voller Linus Tata Carlos Gallego-Moll Albert Roso-Llorach Lucía A. Carrasco-Ribelles Chris Fox Louise M. Allan Ruby M. Woodward Xiaoran Liang Jose M. Valderas Sara M. Khalid Frank Dudbridge Sally E. Lamb Mary Mancini Leon Farmer Kate Boddy Jack Bowden David Melzer Timothy M. Frayling Jane A.H. Masoli Luke C. Pilling Concepción Violán João Delgado A systematic analysis of the contribution of genetics to multimorbidity and comparisons with primary care dataResearch in context EBioMedicine Comorbidity Chronic disease Multiple long-term conditions Observational Genotype |
| title | A systematic analysis of the contribution of genetics to multimorbidity and comparisons with primary care dataResearch in context |
| title_full | A systematic analysis of the contribution of genetics to multimorbidity and comparisons with primary care dataResearch in context |
| title_fullStr | A systematic analysis of the contribution of genetics to multimorbidity and comparisons with primary care dataResearch in context |
| title_full_unstemmed | A systematic analysis of the contribution of genetics to multimorbidity and comparisons with primary care dataResearch in context |
| title_short | A systematic analysis of the contribution of genetics to multimorbidity and comparisons with primary care dataResearch in context |
| title_sort | systematic analysis of the contribution of genetics to multimorbidity and comparisons with primary care dataresearch in context |
| topic | Comorbidity Chronic disease Multiple long-term conditions Observational Genotype |
| url | http://www.sciencedirect.com/science/article/pii/S2352396425000283 |
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