Machine learning based metabolomic and genetic profiles for predicting multiple brain phenotypes

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. Question: What is the relationship between metabolomic/genetic profiles and brain volumes? Finding: Metabolomic profiles show a robust association with various brain volumes, while genetic profiles have a less significant impact on brain volumes. Meaning: This study elucidated the landscape of the relationship between metabolomic/genetic profiles and brain volumes.

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Journal
Journal of Translational Medicine
Published
2024-12-03
DOI
https://doi.org/10.1186/s12967-024-05868-3
Primary Topic
Metabolomics and Mass Spectrometry Studies
Type
article
Field-Weighted Citation Impact
0.00

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article

Machine learning based metabolomic and genetic profiles for predicting multiple brain phenotypes

Zongyuan Ge, Zhuoting Zhu, Shunming Liu, Mingguang He et al.
Journal of Translational Medicine
Metabolomics and Mass Spectrometry Studies
article

Machine learning based metabolomic and genetic profiles for predicting multiple brain phenotypes

Zongyuan Ge, Zhuoting Zhu, Shunming Liu, Mingguang He, Honghua Yu, Xiayin Zhang, Xianwen Shang, Shulin Tang, Jiahao Liu, Wei Wang, Shuo Ma, Xueli Zhang, Yu Huang, Min Li, Yijun Hu
article en

Abstract

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. Question: What is the relationship between metabolomic/genetic profiles and brain volumes? Finding: Metabolomic profiles show a robust association with various brain volumes, while genetic profiles have a less significant impact on brain volumes. Meaning: This study elucidated the landscape of the relationship between metabolomic/genetic profiles and brain volumes.

Journal of Translational MedicineVol. 22(1)
The Royal Melbourne Hospital (AU), Hong Kong Polytechnic University (HK), Sun Yat-sen University (CN), The University of Melbourne (AU), Hong Kong Eye Hospital (CN), Key Laboratory of Guangdong Province (CN), Centre for Eye Research Australia (AU), Guangdong Academy of Medical Sciences (CN), Guangdong Provincial People's Hospital (CN), Monash University (AU), Southern Medical University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 80%
Metabolomics and Mass Spectrometry Studies
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