酒精中毒的预测风险标志物。

Advances in clinical chemistry Pub Date : 2023-01-01 Epub Date: 2023-06-14 DOI:10.1016/bs.acc.2023.05.002
Onni Niemelä
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引用次数: 1

摘要

酒精中毒的医学障碍是世界范围内主要的公共卫生问题之一,对评估酒精使用障碍(AUD)的预测和预后风险标志物的需求已得到广泛认可。早期发现问题饮酒和相关组织毒性是及时开始适当治疗和提高患者对减少饮酒目标的承诺的重要先决条件。临床化学的最新进展为通过检测独特的乙醇代谢产物磷脂酰乙醇(PEth)或乙基葡糖苷酸(EtG)来特异性检测重度饮酒提供了新的方法。碳水化合物缺乏转铁蛋白(CDT)测量可用于指示严重的酒精问题。危险饮酒通常表现为大量的偶发性饮酒,或与其他不利的生活方式因素相结合,如吸烟、身体不活动、不良饮食或肥胖,这些因素以超加性的方式加剧了酒精摄入的代谢后果。这种相互作用也反映在多种疾病的结果以及肝功能、炎症和氧化应激生物标志物的明显异常中。单独使用预测性生物标志物或将其作为专门设计的生物算法的一部分,有助于预测具有此类风险因素的个体的肝脏和肝外发病率。评估纤维化进展(AUD预后的主要决定因素)的新方法也已问世。基于生物标志物和临床观察的联合使用的预测算法可能会对临床决策产生重大影响,这些决策包括在症状前期早期检测AUD,根据患者明显不同的疾病风险对患者进行分层,并预测个体对治疗的反应。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predictive risk markers in alcoholism.

The medical disorders of alcoholism rank among the leading public health problems worldwide and the need for predictive and prognostic risk markers for assessing alcohol use disorders (AUD) has been widely acknowledged. Early-phase detection of problem drinking and associated tissue toxicity are important prerequisites for timely initiations of appropriate treatments and improving patient's committing to the objective of reducing drinking. Recent advances in clinical chemistry have provided novel approaches for a specific detection of heavy drinking through assays of unique ethanol metabolites, phosphatidylethanol (PEth) or ethyl glucuronide (EtG). Carbohydrate-deficient transferrin (CDT) measurements can be used to indicate severe alcohol problems. Hazardous drinking frequently manifests as heavy episodic drinking or in combinations with other unfavorable lifestyle factors, such as smoking, physical inactivity, poor diet or adiposity, which aggravate the metabolic consequences of alcohol intake in a supra-additive manner. Such interactions are also reflected in multiple disease outcomes and distinct abnormalities in biomarkers of liver function, inflammation and oxidative stress. Use of predictive biomarkers either alone or as part of specifically designed biological algorithms helps to predict both hepatic and extrahepatic morbidity in individuals with such risk factors. Novel approaches for assessing progression of fibrosis, a major determinant of prognosis in AUD, have also been made available. Predictive algorithms based on the combined use of biomarkers and clinical observations may prove to have a major impact on clinical decisions to detect AUD in early pre-symptomatic stages, stratify patients according to their substantially different disease risks and predict individual responses to treatment.

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