Beyond the hot flashes: how machine learning is uncovering the complexity of menopause-related depression.

IF 3.4 3区 医学 Q2 CLINICAL NEUROLOGY
Graziella Orrù, Rebecca Ciacchini, Anna Conversano, Ciro Conversano, Angelo Gemignani
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Abstract

Background: The transition into menopause marks a significant stage in a woman's life, indicating the end of reproductive capability. This period, encompassing perimenopause and menopause, is characterized by declining levels of estrogen and progesterone, leading to various symptoms such as hot flashes, sleep disturbances, sexual dysfunction, and mood irregularities. Moreover, cognitive functions, notably memory, may decline during this phase.

Objective: This exploratory study aimed to evaluate psychological factors in a sample of 98 women recruited from a diagnostic-assistance hospital pathway (AOUP).

Methods: Psychological variables, including depression, anxiety, stress, sleep quality, memory, personality traits, and mindfulness, were assessed using psychometric questionnaires. Machine learning techniques were employed to identify independent variables strongly correlated with higher levels of depression measured by BDI-II.

Results: The findings revealed positive associations between depression and anxiety, stress, low mood, poor sleep quality, and memory complaints, while mindfulness showed a negative correlation. Remarkably, the machine learning analysis achieved a high classification accuracy in distinguishing between individuals with different levels of depression (low vs high).

Conclusions: These results underscore the importance of addressing psychological factors during menopause and offer valuable insights for future research and the development of targeted clinical interventions aimed at enhancing mental health and quality of life for women during this transitional phase.

除了潮热:机器学习如何揭示更年期相关抑郁症的复杂性。
背景:过渡到更年期标志着一个重要的阶段在一个女人的生活,表明生殖能力的结束。这一时期包括围绝经期和绝经期,其特点是雌激素和黄体酮水平下降,导致各种症状,如潮热、睡眠障碍、性功能障碍和情绪不规则。此外,认知功能,尤其是记忆力,在这一阶段可能会下降。目的:本探索性研究旨在评估从诊断辅助医院途径(AOUP)招募的98名女性样本的心理因素。方法:采用心理测量问卷对抑郁、焦虑、压力、睡眠质量、记忆、人格特征和正念等心理变量进行评估。使用机器学习技术来识别与BDI-II测量的高抑郁水平强烈相关的自变量。结果:研究结果显示,抑郁与焦虑、压力、情绪低落、睡眠质量差和记忆抱怨呈正相关,而正念与焦虑、压力、情绪低落、睡眠质量差和记忆抱怨呈正相关。值得注意的是,机器学习分析在区分不同抑郁程度(低与高)的个体方面取得了很高的分类准确性。结论:这些结果强调了解决更年期心理因素的重要性,并为未来的研究和有针对性的临床干预措施的发展提供了有价值的见解,旨在提高妇女在这个过渡阶段的心理健康和生活质量。
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来源期刊
CNS Spectrums
CNS Spectrums 医学-精神病学
CiteScore
6.20
自引率
6.10%
发文量
239
审稿时长
>12 weeks
期刊介绍: CNS Spectrums covers all aspects of the clinical neurosciences, neurotherapeutics, and neuropsychopharmacology, particularly those pertinent to the clinician and clinical investigator. The journal features focused, in-depth reviews, perspectives, and original research articles. New therapeutics of all types in psychiatry, mental health, and neurology are emphasized, especially first in man studies, proof of concept studies, and translational basic neuroscience studies. Subject coverage spans the full spectrum of neuropsychiatry, focusing on those crossing traditional boundaries between neurology and psychiatry.
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