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

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

doi:10.1186/s12967-024-05868-3

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

J Transl Med. 2024 Dec 3;22(1):1098. doi: 10.1186/s12967-024-05868-3.

Authors

Xueli Zhang^#^{1

2

3}, Yu Huang^#^{1

4}, Shunming Liu¹, Shuo Ma⁵, Min Li², Zhuoting Zhu^{1

4

6}, Wei Wang⁷, Xiayin Zhang^{1

4}, Jiahao Liu⁶, Shulin Tang¹, Yijun Hu¹, Zongyuan Ge⁸, Honghua Yu⁹, Mingguang He^{10

11

12

13}, Xianwen Shang^{14

15

16

17

18

19}

Affiliations

¹ Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China.
² Medical Research Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
³ Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China.
⁴ Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China.
⁵ Medical Big Data Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences, Southern Medical University), Guangzhou, China.
⁶ Centre for Eye Research Australia, Melbourne, VIC, 3002, Australia.
⁷ State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, 510060, China.
⁸ Monash e-Research Center, Faculty of Engineering, Airdoc Research, Nvidia AI Technology Research Center, Monash University, Melbourne, VIC, 3800, Australia.
⁹ Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China. yuhonghua@gdph.org.cn.
¹⁰ Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China. mingguang.he@polyu.edu.hk.
¹¹ School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China. mingguang.he@polyu.edu.hk.
¹² Research Centre for SHARP Vision (RCSV), The Hong Kong Polytechnic University, Kowloon, Hong Kong, China. mingguang.he@polyu.edu.hk.
¹³ Centre for Eye and Vision Research (CEVR), 17W Hong Kong, Science Park, Hong Kong, China. mingguang.he@polyu.edu.hk.
¹⁴ Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China. andy243@126.com.
¹⁵ Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China. andy243@126.com.
¹⁶ Centre for Eye Research Australia, Melbourne, VIC, 3002, Australia. andy243@126.com.
¹⁷ Department of Medicine (Royal Melbourne Hospital), University of Melbourne, Melbourne, VIC, 3050, Australia. andy243@126.com.
¹⁸ School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China. andy243@126.com.
¹⁹ Research Centre for SHARP Vision (RCSV), The Hong Kong Polytechnic University, Kowloon, Hong Kong, China. andy243@126.com.

^# Contributed equally.

PMID: 39627804
DOI: 10.1186/s12967-024-05868-3

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Keywords: Brain phenotype; Genetic risk score; Metabolomic profiles; Metabolomic state; Moderation analysis; Prediction value.

MeSH terms

Adult
Aged
Brain* / diagnostic imaging
Brain* / metabolism
Female
Humans
Machine Learning*
Magnetic Resonance Imaging
Male
Metabolome
Metabolomics*
Middle Aged
Organ Size
Phenotype*

Grants and funding

32200545/National Natural Science Foundation of China