机器学习衍生的腹主动脉钙化与社区成年人的身体虚弱有关：英国生物库成像研究
Machine learning derived abdominal aortic calcification is associated with physical frailty in community-dwelling adults: the UK Biobank Imaging Study.
摘要(中文译文)
临床心血管疾病（CVD）常见于衰弱个体。然而，亚临床CVD，例如腹主动脉钙化（AAC），是否与衰弱相关仍不明确。本研究探讨了使用经验证的机器学习模型（ML-AAC24）评分的AAC与身体衰弱之间的横断面关系。研究纳入来自英国生物银行影像研究的49，081名无动脉粥样硬化性心血管疾病（ASCVD）诊断的参与者。根据既定的严重程度分类，ML-AAC24程度分为低、中、高三个等级。身体衰弱基于改良的Fried衰弱表型，包括握力减弱、步速减慢、体重减轻、疲乏和体力活动不足。存在三项或以上缺陷的个体被视为衰弱，而一项或两项缺陷被视为衰弱前期。采用多变量校正的多项逻辑回归模型检验ML-AAC24程度与衰弱状态之间的关联。五分之一的人存在中度或高度ML-AAC24。与低ML-AAC24个体相比，中度和高度ML-AAC24个体在多变量校正后处于衰弱前期（OR分别为1.06，95%CI 1.00-1.12中度；1.14，95%CI 1.04-1.26高度）或衰弱（OR分别为1.27，95%CI 1.12-1.44中度；1.58，95%CI 1.31-1.91高度）的几率更高。按性别分层后，衰弱的結果相似。在人群中，中度和高度ML-AAC24者更可能表现为衰弱前期和衰弱。从常规骨密度检测时获得的侧位脊柱图像中识别的AAC可作为衰弱早期检测的有用标志物，凸显了多模态诊疗的重要性。
Abstract
Clinical cardiovascular disease (CVD) is often present in frail individuals. However, it remains unclear whether subclinical CVD, e.g., abdominal aortic calcification (AAC), is associated with frailty. This study investigated the cross-sectional relationship between AAC scored using a validated machine learning model (ML-AAC24) and physical frailty. 49,081 participants from the UK Biobank Imaging Study without atherosclerotic CVD (ASCVD) diagnosis were included. ML-AAC24 extent was categorised as low, moderate and high, based on established severity categories. Physical frailty was based on a modified Fried's frailty phenotype comprising weak hand grip strength, slow walking speed, weight loss, exhaustion, and physical inactivity. Individuals with three or more deficits were considered frail, while one or two deficits was considered pre-frail. Multivariable-adjusted multinominal logistic regression models were used to test the associations between ML-AAC24 extent and frailty status. One in five individuals had moderate or high ML-AAC24. Compared to individuals with low ML-AAC24, those with moderate and high ML-AAC24 had greater odds of being pre-frail (ORs 1.06 95%CI 1.00-1.12 moderate; 1.14 95%CI 1.04-1.26 high) or frail (ORs 1.27 95%CI 1.12-1.44 moderate; 1.58 95%CI 1.31-1.91 high), adjusted for multiple covariates. When stratified by sex, similar results for frailty were recorded. In a population, those with moderate and high ML-AAC24 were more likely to present as pre-frail and frail. AAC identified from lateral spine images obtained during routine bone density testing, could serve as a useful marker for the early detection of frailty, highlighting the importance of multimodality care.
如何引用
AMA
Abadi K Gebre, Marion Mundt, Elsa Dent, James Webster, Afsah Saleem, Syed Zulqarnain Gilani, et al. Machine learning derived abdominal aortic calcification is associated with physical frailty in community-dwelling adults: the UK Biobank Imaging Study.. GeroScience. 2026; doi:10.1007/s11357-026-02375-9.
APA
Abadi K Gebre, Marion Mundt, Elsa Dent, James Webster, Afsah Saleem, Syed Zulqarnain Gilani, et al (2026). Machine learning derived abdominal aortic calcification is associated with physical frailty in community-dwelling adults: the UK Biobank Imaging Study.. GeroScience. https://doi.org/10.1007/s11357-026-02375-9
GB/T 7714
Abadi K Gebre, Marion Mundt, Elsa Dent, James Webster, Afsah Saleem, Syed Zulqarnain Gilani, et al. Machine learning derived abdominal aortic calcification is associated with physical frailty in community-dwelling adults: the UK Biobank Imaging Study.[J]. GeroScience, 2026 doi:10.1007/s11357-026-02375-9.
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