Rapid detection of depression by volatile organic compounds from exhalation.

IF 3.7 4区 医学 Q1 BIOCHEMICAL RESEARCH METHODS Journal of breath research Pub Date : 2024-10-07 DOI:10.1088/1752-7163/ad7eef
Tao Chen, Mengqi Jin, Liqing Chen, Xi Xuan Cai, Yilin Huang, Keqing Shen, Yi Li, Xing Chen, Liying Chen
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Abstract

Depression is a pervasive and often undetected mental health condition, which poses significant challenges for early diagnosis due to its silent and subtle nature. To evaluate exhaled volatile organic compounds (VOCs) as non-invasive biomarkers for the detection of depression using a virtual surface acoustic wave sensors array (VSAW-SA). A total of 245 participants were recruited from the Hangzhou Community Health Service Center, including 38 individuals diagnosed with depression and 207 control subjects. Breath samples were collected from all participants and subjected to analysis using VSAW-SA. Univariate and multivariate analyses were employed to assess the relationship between VOCs and depression. The findings revealed that the responses of virtual sensor ID 14, 44, 59, and 176, which corresponded respectively to ethanol, trichloroethylene or isoleucine, octanoic acid or lysine, and an unidentified compound, were sensitive to depression. Taking into account potential confounders, these sensor responses were utilized to calculate a depression detection indicator. It has a sensitivity of 81.6% and a specificity of 81.6%, with an area under the curve of 0.870 (95% CI = 0.816-0.923). Conclusions: exhaled VOCs as non-invasive biomarkers of depression could be detected by a VSAW-SA. Large-scale cohort studies should be conducted to confirm the potential ability of the VSAW-SA to diagnose depression.

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从呼气中快速检测挥发性有机化合物的抑制作用。
摘要: 背景:抑郁症是一种普遍存在且经常未被发现的精神疾病,由于其无声和微妙的性质,给早期诊断带来了巨大挑战:目的:利用虚拟表面声波传感器阵列(VSAW-SA)评估呼出的挥发性有机化合物(VOCs)作为检测抑郁症的非侵入性生物标志物:方法:从杭州市社区卫生服务中心招募了 245 名参与者,其中包括 38 名确诊抑郁症患者和 207 名对照组受试者。收集所有参与者的呼吸样本,并使用 VSAW-SA 进行分析。采用单变量和多变量分析评估挥发性有机化合物(VOCs)与抑郁症之间的关系。研究结果表明,虚拟传感器 ID 14、44、59 和 176(分别对应乙醇、三氯乙烯或异亮氨酸、辛酸或赖氨酸以及一种不明化合物)的反应对抑郁敏感。考虑到潜在的混杂因素,利用这些传感器反应计算出抑郁检测指标:结果:该指标的灵敏度为 81.6%,特异度为 81.6%,曲线下面积(AUC)为 0.870(95% CI=0.816-0.923):结论:VSAW-SA 可以检测出作为抑郁症非侵入性生物标志物的呼出挥发性有机化合物。应开展大规模队列研究,以证实 VSAW-SA 诊断抑郁症的潜在能力。
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来源期刊
Journal of breath research
Journal of breath research BIOCHEMICAL RESEARCH METHODS-RESPIRATORY SYSTEM
CiteScore
7.60
自引率
21.10%
发文量
49
审稿时长
>12 weeks
期刊介绍: Journal of Breath Research is dedicated to all aspects of scientific breath research. The traditional focus is on analysis of volatile compounds and aerosols in exhaled breath for the investigation of exogenous exposures, metabolism, toxicology, health status and the diagnosis of disease and breath odours. The journal also welcomes other breath-related topics. Typical areas of interest include: Big laboratory instrumentation: describing new state-of-the-art analytical instrumentation capable of performing high-resolution discovery and targeted breath research; exploiting complex technologies drawn from other areas of biochemistry and genetics for breath research. Engineering solutions: developing new breath sampling technologies for condensate and aerosols, for chemical and optical sensors, for extraction and sample preparation methods, for automation and standardization, and for multiplex analyses to preserve the breath matrix and facilitating analytical throughput. Measure exhaled constituents (e.g. CO2, acetone, isoprene) as markers of human presence or mitigate such contaminants in enclosed environments. Human and animal in vivo studies: decoding the ''breath exposome'', implementing exposure and intervention studies, performing cross-sectional and case-control research, assaying immune and inflammatory response, and testing mammalian host response to infections and exogenous exposures to develop information directly applicable to systems biology. Studying inhalation toxicology; inhaled breath as a source of internal dose; resultant blood, breath and urinary biomarkers linked to inhalation pathway. Cellular and molecular level in vitro studies. Clinical, pharmacological and forensic applications. Mathematical, statistical and graphical data interpretation.
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