Department VI Cardiology, "Victor Babes" University of Medicine and Pharmacy, Timisoara 300041, Romania.
Institute of Cardiovascular Diseases, Timisoara 300310, Romania.
Medicina (Kaunas). 2021 Sep 11;57(9):956. doi: 10.3390/medicina57090956.
Autonomic nervous system (ANS) dysfunction is present in early stages of alcohol abuse and increases the likelihood of cardiovascular events. Given the nonlinear pattern of dynamic interaction between sympathetic nervous system (SNS) and para sympathetic nervous system (PNS) and the complex relationship with lifestyle factors, machine learning (ML) algorithms are best suited for analyzing alcohol impact over heart rate variability (HRV), because they allow the analysis of complex interactions between multiple variables. This study aimed to characterize autonomic nervous system dysfunction by analysis of HRV correlated with cardiovascular risk factors in young individuals by using machine learning. Total of 142 young adults (28.4 ± 4.34 years) agreed to participate in the study. Alcohol intake and drinking patterns were assessed by the AUDIT (Alcohol Use Disorders Identification Test) questionnaire and the YAI (Yearly Alcohol Intake) index. A short 5-min HRV evaluation was performed. Post-hoc analysis and machine learning algorithms were used to assess the impact of alcohol intake on HRV. Binge drinkers presented slight modification in the frequency domain. Heavy drinkers had significantly lower time-domain values: standard deviation of RR intervals (SDNN) and root mean square of the successive differences (RMSSD), compared to casual and binge drinkers. High frequency (HF) values were significantly lower in heavy drinkers ( = 0.002). The higher low-to-high frequency ratio (LF/HF) that we found in heavy drinkers was interpreted as parasympathetic inhibition. Gradient boosting machine learner regression showed that age and alcohol consumption had the biggest scaled impact on the analyzed HRV parameters, followed by smoking, anxiety, depression, and body mass index. Gender and physical activity had the lowest impact on HRV. In healthy young adults, high alcohol intake has a negative impact on HRV in both time and frequency-domains. In parameters like HRV, where a multitude of risk factors can influence measurements, artificial intelligence algorithms seem to be a viable alternative for correct assessment.
自主神经系统（ANS）功能障碍存在于酒精滥用的早期阶段，并增加了心血管事件的可能性。鉴于交感神经系统（SNS）和副交感神经系统（PNS）之间的动态相互作用呈非线性模式，以及与生活方式因素的复杂关系，机器学习（ML）算法最适合分析酒精对心率变异性（HRV）的影响，因为它们允许分析多个变量之间的复杂相互作用。本研究旨在通过使用机器学习分析与心血管危险因素相关的 HRV，来描述年轻人自主神经系统功能障碍。共有 142 名年轻人（28.4 ± 4.34 岁）同意参加这项研究。酒精摄入量和饮酒模式通过 AUDIT（酒精使用障碍识别测试）问卷和 YAI（每年酒精摄入量）指数进行评估。进行了简短的 5 分钟 HRV 评估。事后分析和机器学习算法用于评估酒精摄入对 HRV 的影响。狂饮者在频域略有改变。与偶然饮酒者和狂饮者相比，重度饮酒者的时域值：RR 间期标准差（SDNN）和连续差值的均方根（RMSSD）显著降低。高频（HF）值在重度饮酒者中显著降低（= 0.002）。我们在重度饮酒者中发现的 LF/HF 比值较高被解释为副交感神经抑制。梯度提升机学习者回归显示，年龄和酒精摄入量对分析的 HRV 参数的影响最大，其次是吸烟、焦虑、抑郁和体重指数。性别和体力活动对 HRV 的影响最小。在健康的年轻人中，高酒精摄入对 HRV 产生负面影响，无论是在时域还是频域。在 HRV 等参数中，许多危险因素可能会影响测量结果，人工智能算法似乎是正确评估的一种可行替代方法。
