{
  "id": 15787427,
  "title": "Plasma proteomic signatures of subclinical atherosclerosis progression for cardiovascular risk stratification: A cohort study",
  "url": "https://urgent.news/2026/10/20/plasma-proteomic-signatures-of-subclinical-atherosclerosis",
  "topic": "health",
  "section": "Health & Medicine",
  "published": "2026-10-20T14:00:00.000Z",
  "source": {
    "name": "PLOS Medicine",
    "slug": "plos-medicine",
    "url": "https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005265"
  },
  "original_language": "en",
  "account": "A research study investigated the potential of protein biomarkers in predicting subclinical atherosclerosis (SA) progression and its impact on cardiovascular disease risk. The study involved 903 participants who underwent repeated assessments of SA over a span of six years, from 2008 to 2014. Researchers utilized a technique called LASSO regression to identify 117 proteins as potential biomarkers for SA progression, ultimately constructing protein risk scores (ProtRSs) for both coronary artery calcium (CAC) and carotid plaque progression. These ProtRSs were then evaluated for their ability to predict incident atherosclerotic cardiovascular disease (ASCVD) risk in a separate cohort of 1,835 individuals, with an average follow-up period of 10.4 years. Each ProtRS comprised 14 proteins and accounted for 17.1% and 18.4% of the variance in CAC and plaque progression, respectively. The findings revealed a strong correlation between protein biomarkers affecting SA progression and those impacting incident ASCVD risk. An increase in CAC and plaque ProtRSs by one standard deviation was associated with a 1.36 and 1.38 higher hazard ratio for ASCVD risk, respectively. The researchers concluded that incorporating SA ProtRSs into traditional risk assessment could provide extra value in risk stratification, potentially guiding targeted interventions for individuals with subclinical atherosclerosis.",
  "summary": "by Yani Huang, Ruixin Gao, Jianxin Li, Bin Yang, Keyong Huang, Shufeng Chen, Jie Cao, Xiaoqing Liu, Chong Shen, Ling Yu, Liancheng Zhao, Ying Li, Dongsheng Hu, Jianfeng Huang, Bin Lu, Dongfeng Gu, Fangchao Liu, Xiangfeng Lu Background Existing risk stratification tools inadequately identify individuals with subclinical atherosclerosis (SA) who could benefit from early intervention. This study…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}