Novel Metabolomic Signature as a Biomarker to Detect Subclinical Atherosclerosis in Individuals Traditionally Classified as Low Risk

医学 弗雷明翰风险评分 逻辑回归 队列 内科学 生物标志物 疾病 亚临床感染 代谢物 冠状动脉疾病 急诊分诊台 冠状动脉钙 风险评估 队列研究 肿瘤科 心脏病学 代谢组学 流行病学 一致性 颈动脉疾病 生物信息学 肌钙蛋白 无症状的 风险因素 动脉粥样硬化性心血管疾病 冠心病 代谢组
作者
Yuxin Xue,Joshua Vescovi,Adam S. Chan,Stephen Thomas Vernon,Owen Tang,Dantong Zhu,Katharine A. Kott,Daniel G Brieger,Michael Patrick Gray,Lake-Ee Quek,John O’Sullivan,Stuart M. Grieve,Ellis Patrick,Jean Yee Hwa Yang,Gemma A. Figtree
出处
期刊:Circulation [Wolters Kluwer]
卷期号:: e005501-e005501
标识
DOI:10.1161/circgen.125.005501
摘要

BACKGROUND: Standard modifiable risk factors such as age, hypertension, diabetes, cholesterol, and smoking contribute to coronary artery disease risk. However, up to 27% of first-time heart attacks occur in individuals without standard modifiable cardiovascular risk factors. As medical management can stabilize coronary atherosclerosis, there is a need for biomarkers to identify disease before symptoms or a heart attack. METHODS: We performed targeted metabolomics, identifying 54 metabolites in 1212 adults undergoing coronary artery calcium score (CACS) from the BioHEART-CT Study. Individuals were classified as having clinically actionable CACS positive or not (clinically actionable CACS negative). Metabolite-to-metabolite ratios were then calculated to identify transferable biomarkers. Logistic regression models incorporating age, sex, and metabolite ratios were developed and classified as low risk by traditional scoring. The BioHEART Metabolite Risk Score (BioHEART-MRS) was derived from the best-performing model, trained in a Discovery cohort (n=635), adapted using transfer component analysis (n=266), and validated in an independent External cohort (n=311). RESULT: A model including age, sex, and 5 metabolite ratios: histidine-kynurenic acid, histidine-riboflavin, citrullin-proline, kynurenic acid-phosphocholine, and arginine-dimethylguanidino valerate, referred to as BioHEART-MRS, demonstrated the best performance in younger individuals for predicting CACS. In the External cohort, BioHEART-MRS outperformed the traditional risk score (area under the curve=0.67 versus 0.55). The traditional risk score underperformed in the under-60 cohort, misclassifying 62% of those with clinically actionable CACS as low risk. Within this group, BioHEART-MRS identified 46 additional cases among low-risk individuals (n=495). As a triage tool, BioHEART-MRS reduced the number needed to scan to detect one true positive case to 7, compared with scanning all low-risk individuals. CONCLUSIONS: In the External validation cohort, BioHEART-MRS showed greater discrimination than Multi-Ethnic Study of Atherosclerosis risk score among traditionally low-risk individuals and enriched for clinically actionable CACS positive among those selected for imaging. These findings support prospective evaluation of metabolite-based triage for CACS, particularly in younger and SMuRFless individuals.

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