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

IF 9.7 1区 医学 Q1 MEDICINE, RESEARCH & EXPERIMENTAL
EBioMedicine Pub Date : 2025-03-01 Epub Date: 2025-02-06 DOI:10.1016/j.ebiom.2025.105584
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
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引用次数: 0

Abstract

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].

对遗传学对多病的贡献进行系统分析,并与初级保健数据进行比较。
背景:多重发病,即一个人同时出现两种或两种以上的疾病,是很常见的,但研究往往局限于观察性数据和单一数据集。我们通过整合来自多个研究的大规模初级保健和遗传数据来询问多发病模式,并制作数字资源来支持未来的研究,从而解决了这一差距。方法:我们使用两个大型初级保健数据库[CPRD(英国)N = 2,425,014和SIDIAP(西班牙)N = 1,053,640]定义≥65岁个体的慢性、常见和遗传性疾病,并使用英国生物银行(N = 451,197)中相同的定义估计遗传率。我们使用逻辑回归来估计初级保健数据中成对条件的共现性。用连锁不平衡评分回归估计条件对之间的遗传相似性。对每一对条件进行了跨数据库和多达三个遗传数据来源的荟萃分析。根据国际疾病分类,我们将一对对疾病分为跨域或域内。研究结果:我们确定了72种慢性疾病,2546对中有43.6%的人在初级保健中显示出更高的共发率,并有共同遗传的证据。许多跨结构域对表现出大量的共同遗传(例如,缺铁性贫血和外周动脉疾病:遗传相关Rg = 0.45[95%置信区间0.27:0.64])。33对基因呈负相关,如皮肤癌和类风湿关节炎(Rg = -0.14[-0.21:-0.06]),由于潜在的药物不良反应。遗传和初级保健数据之间也存在不一致,如腹主动脉瘤和膀胱癌在初级保健中同时发生,但没有遗传相关性(比值比= 2.23 [2.09:2.37],Rg = 0.04[-0.20:0.28]),精神分裂症和纤维肌痛在初级保健中同时发生的可能性较小,但遗传正相关(OR = 0.84 [0.75:0.94], Rg = 0.20[0.11:0.29])。解释:大多数对慢性病显示出共同的遗传证据,并在初级保健中共同发生,表明共同的机制。共同遗传模式的确定,遗传和观测数据之间的负相关和不一致,为未来的多病研究奠定了基础。资助:英国医学研究理事会[MR/W014548/1]。
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来源期刊
EBioMedicine
EBioMedicine Biochemistry, Genetics and Molecular Biology-General Biochemistry,Genetics and Molecular Biology
CiteScore
17.70
自引率
0.90%
发文量
579
审稿时长
5 weeks
期刊介绍: eBioMedicine is a comprehensive biomedical research journal that covers a wide range of studies that are relevant to human health. Our focus is on original research that explores the fundamental factors influencing human health and disease, including the discovery of new therapeutic targets and treatments, the identification of biomarkers and diagnostic tools, and the investigation and modification of disease pathways and mechanisms. We welcome studies from any biomedical discipline that contribute to our understanding of disease and aim to improve human health.
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