Identifying modifiable factors associated with neuroimaging markers of brain health

IF 4.8 1区医学 Q1 NEUROSCIENCES

CNS Neuroscience & Therapeutics Pub Date : 2024-10-15 DOI:10.1111/cns.70057

Liang-Yu Huang, Yan Fu, Yi Zhang, He-Ying Hu, Ling-Zhi Ma, Yi-Jun Ge, Yong-Li Zhao, Ya-Ru Zhang, Shi-Dong Chen, Jian-Feng Feng, Wei Cheng, Lan Tan, Jin-Tai Yu

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Abstract

Aims

Brain structural alterations begin long before the presentation of brain disorders; therefore, we aimed to systematically investigate a wide range of influencing factors on neuroimaging markers of brain health.

Methods

Utilizing data from 30,651 participants from the UK Biobank, we explored associations between 218 modifiable factors and neuroimaging markers of brain health. We conducted an exposome-wide association study using the least absolute shrinkage and selection operator (LASSO) technique. Restricted cubic splines (RCS) were further employed to estimate potential nonlinear correlations. Weighted standardized scores for neuroimaging markers were computed based on the estimates for individual factors. Finally, stratum-specific analyses were performed to examine differences in factors affecting brain health at different ages.

Results

The identified factors related to neuroimaging markers of brain health fell into six domains, including systematic diseases, lifestyle factors, personality traits, social support, anthropometric indicators, and biochemical markers. The explained variance percentage of neuroimaging markers by weighted standardized scores ranged from 0.5% to 7%. Notably, associations between systematic diseases and neuroimaging markers were stronger in older individuals than in younger ones.

Conclusion

This study identified a series of factors related to neuroimaging markers of brain health. Targeting the identified factors might help in formulating effective strategies for maintaining brain health.

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确定与大脑健康神经影像标记相关的可改变因素

目的大脑结构的改变早在出现脑部疾病之前就已经开始了；因此，我们旨在系统地研究大脑健康神经影像标志物的各种影响因素。方法我们利用英国生物库中 30651 名参与者的数据，探讨了 218 个可改变因素与脑健康神经影像标志物之间的关联。我们使用最小绝对收缩和选择算子（LASSO）技术进行了全暴露组关联研究。我们还进一步采用了限制性三次样条（RCS）来估计潜在的非线性相关性。根据单个因素的估计值计算神经影像标记物的加权标准化得分。最后，还进行了分层分析，以研究不同年龄段影响大脑健康的因素的差异。结果已确定的与脑健康神经影像标志物相关的因素分为六个领域，包括系统性疾病、生活方式因素、个性特征、社会支持、人体测量指标和生化标志物。加权标准化得分对神经影像标志物的解释方差百分比从 0.5% 到 7% 不等。值得注意的是，与年轻人相比，老年人的系统性疾病与神经影像标志物之间的关联性更强。结论本研究发现了一系列与脑健康神经影像标志物相关的因素。针对所发现的因素，可能有助于制定保持大脑健康的有效策略。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

CNS Neuroscience & Therapeutics 医学-神经科学

CiteScore

7.30

自引率

12.70%

发文量

240

审稿时长

2 months

期刊介绍： CNS Neuroscience & Therapeutics provides a medium for rapid publication of original clinical, experimental, and translational research papers, timely reviews and reports of novel findings of therapeutic relevance to the central nervous system, as well as papers related to clinical pharmacology, drug development and novel methodologies for drug evaluation. The journal focuses on neurological and psychiatric diseases such as stroke, Parkinson’s disease, Alzheimer’s disease, depression, schizophrenia, epilepsy, and drug abuse.