Update on risk factors and biomarkers of sudden unexplained cardiac death

心源性猝死 医学 病态的 法医病理学 心肌病 猝死 死因 重症监护医学 生物信息学 尸检 病理 疾病 内科学 心力衰竭 生物
作者
Jiaqian Lou,Huaiyao Chen,Shuainan Huang,Peng Chen,Youjia Yu,Feng Chen
出处
期刊:Journal of Forensic and Legal Medicine [Elsevier BV]
卷期号:87: 102332-102332 被引量:6
标识
DOI:10.1016/j.jflm.2022.102332
摘要

Sudden cardiac death (SCD) accounts for approximately 15%-20% of all deaths worldwide, the causes of which are mainly structural heart diseases. However, SCD also occurs in patients without major cardiac structural abnormalities due to electrophysiological abnormalities or other unexplained causes which account for about 1%-15% cases of SCD, namely sudden unexplained cardiac death (SUCD). The causes of SUCD cannot be explained by routine forensic pathological examination. Hence, identifying the causes of SUCD remains a major challenge in forensic fields. Consequently, it is significant to discover the risk factors and molecular biomarkers for diagnosis and prediction of SUCD. SUCD can be at least partially explained by acute and chronic psychological stress, such as Takotsubo cardiomyopathy (TCM), which is increasingly recognized as a potentially modifiable risk factor for SUCD. Additionally, the inherited lethal cardiac arrhythmia due to ion channelopathies is one of the causes of SUCD. However, SCD resulted from psychological stress or lethal cardiac arrhythmia lacks postmortem observable morphological evidence. Therefore, the postmortem diagnosis of SUCD remains a primary challenge in forensic pathology because of the absence of recognized diagnostic biomarkers. Recent advances in knowledge give us additional possibility in understanding the cause of SUCD. In this review, we summarized the updated risk factors of SUCD including genetics and psychological stress, as well as the potential diagnostic biomarkers of SUCD, such as micro-RNAs, heat shock proteins, Galectin-3 and inflammation factors. Meanwhile, we concluded the ongoing researches on novel measurements including chemical analysis, machine learning and artificial intelligence for predicting the cause of SCD.
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