Zhang Xueli, Huang Yu, Liu Shunming, Ma Shuo, Li Min, Zhu Zhuoting, Wang Wei, Zhang Xiayin, Liu Jiahao, Tang Shulin, Hu Yijun, Ge Zongyuan, Yu Honghua, He Mingguang, Shang Xianwen
Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China.
Medical Research Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
J Transl Med. 2024 Dec 3;22(1):1098. doi: 10.1186/s12967-024-05868-3.
It is unclear regarding the association between metabolomic state/genetic risk score(GRS) and brain volumes and how much of variance of brain volumes is attributable to metabolomic state or GRS.
Our analysis included 8635 participants (52.5% females) aged 40-70 years at baseline from the UK Biobank. Metabolomic profiles were assessed using nuclear magnetic resonance at baseline (between 2006 and 2010). Brain volumes were measured using magnetic resonance imaging between 2014 and 2019. Machine learning was used to generate metabolomic state and GRS for each of 21 brain phenotypes.
Individuals in the top 20% of metabolomic state had 2.4-35.7% larger volumes of 21 individual brain phenotypes compared to those in the bottom 20% while the corresponding number for GRS ranged from 1.5 to 32.8%. The proportion of variance of brain volumes (R [2]) explained by the corresponding metabolomic state ranged from 2.2 to 19.4%, and the corresponding number for GRS ranged from 0.8 to 8.7%. Metabolomic state provided no or minimal additional prediction values of brain volumes to age and sex while GRS provided moderate additional prediction values (ranging from 0.8 to 8.8%). No significant interplay between metabolomic state and GRS was observed, but the association between metabolomic state and some regional brain volumes was stronger in men or younger individuals. Individual metabolomic profiles including lipids and fatty acids were strong predictors of brain volumes.
In conclusion, metabolomic state is strongly associated with multiple brain volumes but provides minimal additional prediction value of brain volumes to age + sex. Although GRS is a weaker contributor to brain volumes than metabolomic state, it provides moderate additional prediction value of brain volumes to age + sex. Our findings suggest metabolomic state and GRS are important predictors for multiple brain phenotypes.
代谢组学状态/遗传风险评分（GRS）与脑容量之间的关联尚不清楚，且脑容量的多少变异可归因于代谢组学状态或GRS也不明确。
我们的分析纳入了英国生物银行中8635名基线年龄在40 - 70岁的参与者（52.5%为女性）。在基线时（2006年至2010年之间）使用核磁共振评估代谢组学谱。在2014年至2019年之间使用磁共振成像测量脑容量。使用机器学习为21种脑表型中的每一种生成代谢组学状态和GRS。
代谢组学状态处于前20%的个体与处于后20%的个体相比，21种个体脑表型的体积大2.4% - 35.7%，而GRS的相应数值范围为1.5%至32.8%。相应代谢组学状态解释的脑容量方差比例（R[2]）范围为2.2%至19.4%，GRS的相应数值范围为0.8%至8.7%。代谢组学状态对脑容量的额外预测值相对于年龄和性别而言没有或极少，而GRS提供了中等程度的额外预测值（范围为0.8%至8.8%）。未观察到代谢组学状态与GRS之间有显著的相互作用，但代谢组学状态与某些区域脑容量之间的关联在男性或较年轻个体中更强。包括脂质和脂肪酸在内的个体代谢组学谱是脑容量的强预测指标。
总之，代谢组学状态与多种脑容量密切相关，但对脑容量相对于年龄+性别的额外预测价值极小。虽然GRS对脑容量的贡献比代谢组学状态弱，但它对脑容量相对于年龄+性别的额外预测价值中等。我们的研究结果表明代谢组学状态和GRS是多种脑表型的重要预测指标。
