A systematic analysis of the contribution of genetics to multimorbidity and comparisons with primary care data

Abstract

BackgroundMultimorbidity, 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 identify novel multimorbidity patterns and producing digital resources to support future research. MethodsWe 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. FindingsWe identified 72 chronic conditions, with 43{middle dot}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{middle dot}45[95% Confidence Intervals 0{middle dot}27:0{middle dot}64]). 33 pairs displayed negative genetic correlations, such as skin cancer and rheumatoid arthritis (Rg =-0{middle dot}14[-0{middle dot}21:-0{middle dot}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{middle dot}23[2{middle dot}09:2{middle dot}37], Rg =0{middle dot}04[-0{middle dot}20:0{middle dot}28]) and schizophrenia and fibromyalgia were less likely to co-occur together in primary care but were positively genetically correlated (OR=0{middle dot}84[0{middle dot}75:0{middle dot}94], Rg =0{middle dot}20[0{middle dot}11:0{middle dot}29]). InterpretationMost 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. FundingUK Medical Research Council [MR/W014548/1]. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe. Searched PUBMED, Embase, Global Health for articles published in English, since 6. Date >2012 (after the definitive Barnett et al. multimorbidity review paper was published) until 01 May 2024., using the search terms: (((multidisease*[tiab] OR multi-disease*[tiab] OR 3ultimorbidity*[tiab] OR multi-morbidit*[tiab] OR multipatholog*[tiab] OR multi-patholog*[tiab] OR pluripatholog*[tiab] OR polypatholog*[tiab] OR poly-pathology*[tiab] OR "multiple long-term conditions"[tiab])) OR (multimorbidity[MeSH Terms])) AND ("genetic*"[tiab] OR "genomic*"[tiab] OR ("genome wide association stud*"[tiab] OR "genome wide association analysis"[tiab] OR "GWAS"[tiab])) NOT (animals[mh] NOT humans[mh]) AND NOT ("retracted publication"[pt]). We excluded studies investigating monogenic mutations and case studies. 243 studies met the primary inclusion criteria, where 68 presented original research addressing multimorbidity and incorporating genetic analysis. Multimorbidity is predominantly characterised by counts or clusters of conditions, with limited exploration of underlying mechanisms. Only 11 investigated multimorbidity beyond the analysis of comorbidities of single conditions, with five reporting systematic investigations of genetics across multiple long-term conditions. None of these studies compare data from more than one genetic database with real-world data. This comparison is necessary to identify correlations of diseases and to be able to carry out preventive actions that facilitate better health care. Added value of this studyWe describe a large-scale analyses into the genetics of multimorbidity informed by representative primary care data, starting from a systematic standardisation of relevant diagnostic codes, and the ascertainment of conditions harmonised across multiple databases. Importantly, we include the analysis of multiple sources of data from both observational and genetic databases, whereas previous studies have usually been limited to one study, such as UK Biobank. We also produced a detailed interactive interface for 72 common long-term and heritable conditions and 2546 pairs of conditions across multiple sources of data: https://gemini-multimorbidity.shinyapps.io/atlas. Together we describe the parallel analysis of specific co-occurring pairs in primary care and genetic data, involving 100,000s to millions of patients and 2546 pairs of conditions. These comparisons identified novel pairs of conditions, and pairs of conditions that shared genetics but did not co-occur in primary care, or vice versa. Implications of available evidenceLarge-scale, digital databases of electronic medical records linked to genetic information involving 100,000s of patients can significantly enhance our comprehension of multiple long-term conditions (multimorbidity). This advancement builds on previous work by providing evidence of potential shared biology between 100s of specific pairs of conditions.