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Update on risk factors and biomarkers of sudden unexplained cardiac death
Jiaqian Lou1, Huaiyao Chen2, Shuainan Huang3
1Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai, 200063, PR China; Department of Forensic Medicine, Nanjing Medical University, Nanjing, Jiangsu, 211166, PR China; Yangpu Branch of Shanghai Public Security Bureau, Shanghai, 200090, PR China.
Insights
Sudden unexplained cardiac death (SUCD) poses a forensic challenge due to lack of morphological evidence. Research highlights psychological stress and genetics as risk factors, with micro-RNAs and AI showing promise for diagnosis.
Area of Science:
- Forensic Pathology
- Cardiology
- Molecular Biology
Background:
- Sudden cardiac death (SCD) is a major global cause of mortality, often linked to structural heart disease.
- Sudden unexplained cardiac death (SUCD) accounts for 1-15% of SCD cases and lacks clear postmortem pathological findings.
- Diagnosing SUCD is challenging due to the absence of morphological evidence, especially in cases involving psychological stress or ion channelopathies.
Purpose of the Study:
- To review updated risk factors for SUCD, including genetic predispositions and psychological stress.
- To explore potential molecular biomarkers for the diagnosis and prediction of SUCD.
- To discuss emerging research in novel diagnostic measurements for SUCD.
Main Methods:
- Literature review of recent advancements in understanding SUCD.
- Identification and summary of genetic and psychological risk factors.
- Compilation of potential diagnostic biomarkers such as micro-RNAs, heat shock proteins, Galectin-3, and inflammation factors.
Main Results:
- Psychological stress, exemplified by Takotsubo cardiomyopathy, and inherited ion channelopathies are significant contributors to SUCD.
- Micro-RNAs, heat shock proteins, Galectin-3, and inflammation factors are identified as potential diagnostic biomarkers.
- Novel approaches including chemical analysis, machine learning, and artificial intelligence are being investigated for SUCD prediction.
Conclusions:
- SUCD diagnosis remains a significant challenge in forensic pathology due to the lack of observable evidence.
- Identifying SUCD risk factors and reliable biomarkers is crucial for improving diagnosis and prediction.
- Continued research into molecular biomarkers and advanced analytical techniques is essential for advancing SUCD understanding.
Abstract:
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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