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散发性、迟发性和多阶段疾病。

Sporadic, late-onset, and multistage diseases.

作者信息

Webster Anthony J, Clarke Robert

机构信息

Nuffield Department of Population Health, Big Data Institute, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK.

出版信息

PNAS Nexus. 2022 Jun 22;1(3):pgac095. doi: 10.1093/pnasnexus/pgac095. eCollection 2022 Jul.

Abstract

Multistage disease processes are often characterized by a linear relationship between the log of incidence rates and the log of age. Examples include sequences of somatic mutations, that can cause cancer, and have recently been linked with a range of non-malignant diseases. Using a Weibull distribution to model diseases that occur through an ordered sequence of stages, and another model where stages can occur in any order, we characterized the age-related onset of disease in UK Biobank data. Despite their different underlying assumptions, both models accurately described the incidence of over 450 diseases, demonstrating that multistage disease processes cannot be inferred from this data alone. The parametric models provided unique insights into age-related disease, that conventional studies of relative risks cannot. The rate at which disease risk increases with age was used to distinguish between "sporadic" diseases, with an initially low and slowly increasing risk, and "late-onset" diseases whose negligible risk when young rapidly increases with age. "Relative aging rates" were introduced to quantify how risk factors modify age-related risk, finding the effective age-at-risk of sporadic diseases is strongly modified by common risk factors. Relative aging rates are ideal for risk-stratification, allowing the identification of ages with equivalent-risk in groups with different exposures. Most importantly, our results suggest that a substantial burden of sporadic diseases can be substantially delayed or avoided by early lifestyle interventions.

摘要

多阶段疾病过程通常以发病率的对数与年龄的对数之间的线性关系为特征。例如包括体细胞突变序列,其可导致癌症,并且最近已与一系列非恶性疾病相关联。使用威布尔分布对通过有序阶段序列发生的疾病进行建模,以及另一种阶段可按任何顺序发生的模型,我们在英国生物银行数据中表征了与年龄相关的疾病发病情况。尽管它们的潜在假设不同,但两种模型都准确描述了450多种疾病的发病率,表明仅从这些数据无法推断多阶段疾病过程。参数模型提供了与年龄相关疾病的独特见解,这是传统的相对风险研究无法做到的。疾病风险随年龄增长的速率被用于区分“散发性”疾病(其风险最初较低且缓慢增加)和“迟发性”疾病(其年轻时可忽略不计的风险随年龄迅速增加)。引入“相对衰老率”以量化风险因素如何改变与年龄相关的风险,发现散发性疾病的有效风险年龄受到常见风险因素的强烈影响。相对衰老率非常适合风险分层,可在不同暴露组中识别具有等效风险的年龄。最重要的是,我们的结果表明,通过早期生活方式干预,散发性疾病的大量负担可以大幅延迟或避免。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc65/9896956/beaff6c4d341/pgac095fig1.jpg

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