Suppr超能文献

常见神经精神障碍个体的脑-体健康评估。

Evaluation of Brain-Body Health in Individuals With Common Neuropsychiatric Disorders.

机构信息

Department of Psychiatry, Melbourne Neuropsychiatry Centre, Melbourne Medical School, the University of Melbourne, Melbourne, Victoria, Australia.

Clinical Brain Networks Group, Queensland Institute of Medical Research Berghofer Medical Institute, Brisbane, Queensland, Australia.

出版信息

JAMA Psychiatry. 2023 Jun 1;80(6):567-576. doi: 10.1001/jamapsychiatry.2023.0791.

Abstract

IMPORTANCE

Physical health and chronic medical comorbidities are underestimated, inadequately treated, and often overlooked in psychiatry. A multiorgan, systemwide characterization of brain and body health in neuropsychiatric disorders may enable systematic evaluation of brain-body health status in patients and potentially identify new therapeutic targets.

OBJECTIVE

To evaluate the health status of the brain and 7 body systems across common neuropsychiatric disorders.

DESIGN, SETTING, AND PARTICIPANTS: Brain imaging phenotypes, physiological measures, and blood- and urine-based markers were harmonized across multiple population-based neuroimaging biobanks in the US, UK, and Australia, including UK Biobank; Australian Schizophrenia Research Bank; Australian Imaging, Biomarkers, and Lifestyle Flagship Study of Ageing; Alzheimer's Disease Neuroimaging Initiative; Prospective Imaging Study of Ageing; Human Connectome Project-Young Adult; and Human Connectome Project-Aging. Cross-sectional data acquired between March 2006 and December 2020 were used to study organ health. Data were analyzed from October 18, 2021, to July 21, 2022. Adults aged 18 to 95 years with a lifetime diagnosis of 1 or more common neuropsychiatric disorders, including schizophrenia, bipolar disorder, depression, generalized anxiety disorder, and a healthy comparison group were included.

MAIN OUTCOMES AND MEASURES

Deviations from normative reference ranges for composite health scores indexing the health and function of the brain and 7 body systems. Secondary outcomes included accuracy of classifying diagnoses (disease vs control) and differentiating between diagnoses (disease vs disease), measured using the area under the receiver operating characteristic curve (AUC).

RESULTS

There were 85 748 participants with preselected neuropsychiatric disorders (36 324 male) and 87 420 healthy control individuals (40 560 male) included in this study. Body health, especially scores indexing metabolic, hepatic, and immune health, deviated from normative reference ranges for all 4 neuropsychiatric disorders studied. Poor body health was a more pronounced illness manifestation compared to brain changes in schizophrenia (AUC for body = 0.81 [95% CI, 0.79-0.82]; AUC for brain = 0.79 [95% CI, 0.79-0.79]), bipolar disorder (AUC for body = 0.67 [95% CI, 0.67-0.68]; AUC for brain = 0.58 [95% CI, 0.57-0.58]), depression (AUC for body = 0.67 [95% CI, 0.67-0.68]; AUC for brain = 0.58 [95% CI, 0.58-0.58]), and anxiety (AUC for body = 0.63 [95% CI, 0.63-0.63]; AUC for brain = 0.57 [95% CI, 0.57-0.58]). However, brain health enabled more accurate differentiation between distinct neuropsychiatric diagnoses than body health (schizophrenia-other: mean AUC for body = 0.70 [95% CI, 0.70-0.71] and mean AUC for brain = 0.79 [95% CI, 0.79-0.80]; bipolar disorder-other: mean AUC for body = 0.60 [95% CI, 0.59-0.60] and mean AUC for brain = 0.65 [95% CI, 0.65-0.65]; depression-other: mean AUC for body = 0.61 [95% CI, 0.60-0.63] and mean AUC for brain = 0.65 [95% CI, 0.65-0.66]; anxiety-other: mean AUC for body = 0.63 [95% CI, 0.62-0.63] and mean AUC for brain = 0.66 [95% CI, 0.65-0.66).

CONCLUSIONS AND RELEVANCE

In this cross-sectional study, neuropsychiatric disorders shared a substantial and largely overlapping imprint of poor body health. Routinely monitoring body health and integrated physical and mental health care may help reduce the adverse effect of physical comorbidity in people with mental illness.

摘要

重要性

在精神病学中,身体健康和慢性合并症被低估、治疗不足且经常被忽视。对神经精神障碍的大脑和身体的多个器官、系统性特征进行描述,可能使我们能够对患者的大脑-身体健康状况进行系统评估,并有可能确定新的治疗靶点。

目的

评估常见神经精神障碍中大脑和 7 个身体系统的健康状况。

设计、设置和参与者:在美国、英国和澳大利亚的多个基于人群的神经影像学生物库中,对脑影像表型、生理测量以及血液和尿液生物标志物进行了协调,这些生物库包括英国生物库;澳大利亚精神分裂症研究银行;澳大利亚成像、生物标志物和生活方式旗舰研究老龄化;阿尔茨海默病神经影像学倡议;前瞻性成像研究衰老;人类连接组计划-年轻成年人;以及人类连接组计划-老龄化。使用横断面数据研究器官健康,数据采集时间为 2006 年 3 月至 2020 年 12 月。纳入的参与者为 18 至 95 岁,有 1 种或多种常见神经精神障碍的终生诊断,包括精神分裂症、双相情感障碍、抑郁症、广泛性焦虑症和健康对照组。

主要结局和测量指标

大脑和 7 个身体系统的健康和功能复合评分与正常参考范围的偏差。次要结局包括使用接收者操作特征曲线下面积(AUC)来衡量分类诊断(疾病与对照)和区分诊断(疾病与疾病)的准确性。

结果

这项研究纳入了 85748 名有预选定神经精神障碍的患者(36324 名男性)和 87420 名健康对照个体(40560 名男性)。与研究的所有 4 种神经精神障碍相比,身体的健康状况,特别是代谢、肝脏和免疫健康评分,都偏离了正常参考范围。与大脑变化相比,在精神分裂症中,较差的身体状况是一种更为明显的疾病表现(身体的 AUC 为 0.81 [95%CI,0.79-0.82];大脑的 AUC 为 0.79 [95%CI,0.79-0.79])、双相情感障碍(身体的 AUC 为 0.67 [95%CI,0.67-0.68];大脑的 AUC 为 0.58 [95%CI,0.58-0.58])、抑郁症(身体的 AUC 为 0.67 [95%CI,0.67-0.68];大脑的 AUC 为 0.58 [95%CI,0.58-0.58])和焦虑症(身体的 AUC 为 0.63 [95%CI,0.63-0.63];大脑的 AUC 为 0.57 [95%CI,0.57-0.58])。然而,与大脑健康相比,大脑健康能够更准确地区分不同的神经精神诊断(精神分裂症与其他:身体的平均 AUC 为 0.70 [95%CI,0.70-0.71]和大脑的平均 AUC 为 0.79 [95%CI,0.79-0.80];双相情感障碍与其他:身体的平均 AUC 为 0.60 [95%CI,0.59-0.60]和大脑的平均 AUC 为 0.65 [95%CI,0.65-0.65];抑郁症与其他:身体的平均 AUC 为 0.61 [95%CI,0.60-0.63]和大脑的平均 AUC 为 0.65 [95%CI,0.65-0.66];焦虑症与其他:身体的平均 AUC 为 0.63 [95%CI,0.62-0.63]和大脑的平均 AUC 为 0.66 [95%CI,0.65-0.66])。

结论和相关性

在这项横断面研究中,神经精神障碍具有明显且广泛重叠的身体不健康特征。常规监测身体的健康状况以及进行身体和精神健康的综合治疗,可能有助于减轻精神疾病患者的身体合并症的不良影响。

相似文献

引用本文的文献

7
Human lifespan changes in the brain's functional connectome.人类寿命在大脑功能连接组中的变化。
Nat Neurosci. 2025 Apr;28(4):891-901. doi: 10.1038/s41593-025-01907-4. Epub 2025 Apr 3.

本文引用的文献

2
The multiple roles of life stress in metabolic disorders.生活压力在代谢紊乱中的多重作用。
Nat Rev Endocrinol. 2023 Jan;19(1):10-27. doi: 10.1038/s41574-022-00746-8. Epub 2022 Oct 12.
6
The normative modeling framework for computational psychiatry.计算精神病学的规范建模框架。
Nat Protoc. 2022 Jul;17(7):1711-1734. doi: 10.1038/s41596-022-00696-5. Epub 2022 Jun 1.
8
Brain charts for the human lifespan.人类寿命的大脑图谱。
Nature. 2022 Apr;604(7906):525-533. doi: 10.1038/s41586-022-04554-y. Epub 2022 Apr 6.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验