“隐蔽青年”的血液代谢特征,病理性社交退缩。

IF 8.3 2区 医学 Q1 Medicine
Dialogues in Clinical Neuroscience Pub Date : 2022-06-01 eCollection Date: 2021-01-01 DOI:10.1080/19585969.2022.2046978
Daiki Setoyama, Toshio Matsushima, Kohei Hayakawa, Tomohiro Nakao, Shigenobu Kanba, Dongchon Kang, Takahiro A Kato
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引用次数: 3

摘要

背景:“蛰居族”是一种严重的病态社会退缩症,在日本已经得到承认,并在世界范围内蔓延,成为一个全球性的健康问题。“隐蔽青年”的病理生理尚不清楚,其生物学特性也未被探索。方法:招募无药物的“隐蔽青年”患者42例和健康对照41例。对“隐蔽青年”和抑郁症的严重程度进行了心理评估。进行血液生化试验和血浆代谢组分析。基于整合的信息,创建了机器学习模型,以区分隐蔽青年和健康对照,预测隐蔽青年的严重程度,对病例进行分层,并识别有助于每种模型的代谢特征。结果:长链酰基肉碱水平明显高于隐蔽青年;男性“隐蔽青年”患者的胆红素、精氨酸、鸟氨酸和血清精氨酸酶有显著差异。判别随机森林模型表现良好,其ROC曲线下面积为0.854(置信区间为0.648-1.000)。为预测“隐蔽青年”的严重程度,成功建立了线性度高、精度高的偏最小二乘pls -回归模型。此外,血清尿酸和血浆胆固醇酯有助于病例的分层。结论:这些发现揭示了“隐蔽青年”的血液代谢特征,这是阐明“隐蔽青年”病理生理的关键,也可作为监测康复治疗进程的指标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Blood metabolic signatures of hikikomori, pathological social withdrawal.

Blood metabolic signatures of hikikomori, pathological social withdrawal.

Blood metabolic signatures of hikikomori, pathological social withdrawal.

Blood metabolic signatures of hikikomori, pathological social withdrawal.

Background: A severe form of pathological social withdrawal, 'hikikomori,' has been acknowledged in Japan, spreading worldwide, and becoming a global health issue. The pathophysiology of hikikomori has not been clarified, and its biological traits remain unexplored.

Methods: Drug-free patients with hikikomori (n = 42) and healthy controls (n = 41) were recruited. Psychological assessments for the severity of hikikomori and depression were conducted. Blood biochemical tests and plasma metabolome analysis were performed. Based on the integrated information, machine-learning models were created to discriminate cases of hikikomori from healthy controls, predict hikikomori severity, stratify the cases, and identify metabolic signatures that contribute to each model.

Results: Long-chain acylcarnitine levels were remarkably higher in patients with hikikomori; bilirubin, arginine, ornithine, and serum arginase were significantly different in male patients with hikikomori. The discriminative random forest model was highly performant, exhibiting an area under the ROC curve of 0.854 (confidential interval = 0.648-1.000). To predict hikikomori severity, a partial least squares PLS-regression model was successfully created with high linearity and practical accuracy. In addition, blood serum uric acid and plasma cholesterol esters contributed to the stratification of cases.

Conclusions: These findings reveal the blood metabolic signatures of hikikomori, which are key to elucidating the pathophysiology of hikikomori and also useful as an index for monitoring the treatment course for rehabilitation.

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来源期刊
Dialogues in Clinical Neuroscience
Dialogues in Clinical Neuroscience Medicine-Psychiatry and Mental Health
CiteScore
19.30
自引率
1.20%
发文量
1
期刊介绍: Dialogues in Clinical Neuroscience (DCNS) endeavors to bridge the gap between clinical neuropsychiatry and the neurosciences by offering state-of-the-art information and original insights into pertinent clinical, biological, and therapeutic aspects. As an open access journal, DCNS ensures accessibility to its content for all interested parties. Each issue is curated to include expert reviews, original articles, and brief reports, carefully selected to offer a comprehensive understanding of the evolving landscape in clinical neuroscience. Join us in advancing knowledge and fostering dialogue in this dynamic field.
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