Evaluating the potential of phenotypic age to enhance cardiovascular risk prediction over chronological age in the UK Biobank.

IF 3.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Kristine J S Kwan, Shi-Shuai Xie, Hai-Lei Li, Xue-Guang Lin, Yi-Jie Lu, Bo Chen, Kai-Xin Ge, Shu-Ya Tang, Hui Zhang, Shuai Jiang, Jing-Dong Tang
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Abstract

Phenotypic age acceleration (PhenoAgeAccel) is a novel biological indicator estimates an individual's mortality risk. The primary aim of this study was to evaluate the association between PhenoAge and PhenoAgeAccel with incident cardiovascular diseases (CVD) in the UK Biobank cohort. We analyzed data from 114,517 UK Biobank participants free of CVD history at baseline. PhenoAgeAccel of was obtained by regressing PhenoAge on chronological age (ChronoAge). We applied a Cox regression model with time-dependent variables to assess the association between PhenoAgeAccel and incident CVD. The predictive value of PhenoAge and PhenoAgeAccel was evaluated with reference to the Framingham Risk Score (FRS) model using Kaplan-Meier curves, receiver operating characteristic curves (AUC), and Harrel's C-index. The positive PhenoAgeAccel comprised of 36.5% of the cohort. The mean ChronoAge and PhenoAge of participants in the positive PhenoAgeAccel group was 57.5 years and 61.7 years, respectively. The mean ChronoAge and PhenoAge of participants in the negative PhenoAgeAccel group was 56.1 years and 52.5 years, respectively. Incident CVD occurred at a higher rate in the positive PhenoAgeAccel group (44.8% vs. 33.1%) at a comparatively shorter period (11.2 years vs. 12.4 years). The AUC of PhenoAge in predicting incident CVD was lower than the FRS but higher than ChronoAge (69.3% vs. 70.9% vs. 68.1%, respectively). Discriminative performance was assessed using Harrell's C-index. The model including established cardiovascular risk factors yielded a C-index of 0.670, compared to 0.674 for the model incorporating PhenoAgeAccel (difference = 0.0049, p < 0.001). Separately, the Framingham Risk Score (FRS) model achieved a higher C-index of 0.697 versus 0.674 for the PhenoAgeAccel model (difference = 0.022, p < 0.001). Kaplein-Meier survival patterns of the positive PhenoAgeAccel group was similar to the high-risk group of FRS level. At time points year 4, 8, 12, and 16, the freedom-from-CVD probability for positive PhenoAgeAccel groups versus FRS high risk groups were 86.2% vs. 85.7%, 72.6% vs. 71.1%, 60.0% vs. 57.4%, and 54.8% vs. 51.7% respectively. Positive PhenoAgeAccel was associated with higher 10-year CVD risk, suggesting its potential as an adjunct in CVD risk assessment. PhenoAge, by incorporating biological aging markers, may offer more nuanced risk insights compared to ChronoAge. These findings are primarily applicable to men, given the male predominance in the cohort, and should be interpreted with caution for women.

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在英国生物银行评估表型年龄的潜力,以提高心血管风险预测超过实足年龄。
表型年龄加速(PhenoAgeAccel)是一种估算个体死亡风险的新型生物学指标。本研究的主要目的是评估在英国生物银行队列中,PhenoAge和PhenoAgeAccel与心血管疾病(CVD)发生率之间的关系。我们分析了来自114,517名英国生物银行参与者的数据,他们在基线时没有心血管疾病病史。通过对实足年龄(ChronoAge)的回归得到表型加速。我们应用Cox回归模型与时间相关变量来评估PhenoAgeAccel与CVD事件之间的关系。参照Framingham风险评分(FRS)模型,采用Kaplan-Meier曲线、受试者工作特征曲线(AUC)和Harrel’s c指数对PhenoAge和PhenoAgeAccel的预测价值进行评价。PhenoAgeAccel阳性占队列的36.5%。PhenoAgeAccel阳性组参与者的平均ChronoAge和PhenoAge分别为57.5岁和61.7岁。阴性组参与者的平均ChronoAge和PhenoAge分别为56.1岁和52.5岁。在相对较短的时间内(11.2年对12.4年),阳性的PhenoAgeAccel组CVD发生率更高(44.8%对33.1%)。表型年龄预测CVD发生的AUC低于FRS,但高于ChronoAge(分别为69.3%、70.9%和68.1%)。采用Harrell’s C-index对判别性能进行评价。包含已建立的心血管危险因素的模型的c指数为0.670,而包含PhenoAgeAccel的模型的c指数为0.674(差异= 0.0049,p
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来源期刊
Scientific Reports
Scientific Reports Natural Science Disciplines-
CiteScore
7.50
自引率
4.30%
发文量
19567
审稿时长
3.9 months
期刊介绍: We publish original research from all areas of the natural sciences, psychology, medicine and engineering. You can learn more about what we publish by browsing our specific scientific subject areas below or explore Scientific Reports by browsing all articles and collections. Scientific Reports has a 2-year impact factor: 4.380 (2021), and is the 6th most-cited journal in the world, with more than 540,000 citations in 2020 (Clarivate Analytics, 2021). •Engineering Engineering covers all aspects of engineering, technology, and applied science. It plays a crucial role in the development of technologies to address some of the world''s biggest challenges, helping to save lives and improve the way we live. •Physical sciences Physical sciences are those academic disciplines that aim to uncover the underlying laws of nature — often written in the language of mathematics. It is a collective term for areas of study including astronomy, chemistry, materials science and physics. •Earth and environmental sciences Earth and environmental sciences cover all aspects of Earth and planetary science and broadly encompass solid Earth processes, surface and atmospheric dynamics, Earth system history, climate and climate change, marine and freshwater systems, and ecology. It also considers the interactions between humans and these systems. •Biological sciences Biological sciences encompass all the divisions of natural sciences examining various aspects of vital processes. The concept includes anatomy, physiology, cell biology, biochemistry and biophysics, and covers all organisms from microorganisms, animals to plants. •Health sciences The health sciences study health, disease and healthcare. This field of study aims to develop knowledge, interventions and technology for use in healthcare to improve the treatment of patients.
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