Machine learning based metabolomic and genetic profiles for predicting multiple brain phenotypes
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 b...
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BMC
2024-12-01
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| Series: | Journal of Translational Medicine |
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| Online Access: | https://doi.org/10.1186/s12967-024-05868-3 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Xueli Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-cbad88030c824862a96ebfebd8e7fb0e |
| institution | Kabale University |
| issn | 1479-5876 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Translational Medicine |
| spelling | doaj-art-cbad88030c824862a96ebfebd8e7fb0e2024-12-08T12:44:34ZengBMCJournal of Translational Medicine1479-58762024-12-0122111310.1186/s12967-024-05868-3Machine learning based metabolomic and genetic profiles for predicting multiple brain phenotypesXueli Zhang0Yu Huang1Shunming Liu2Shuo Ma3Min Li4Zhuoting Zhu5Wei Wang6Xiayin Zhang7Jiahao Liu8Shulin Tang9Yijun Hu10Zongyuan Ge11Honghua Yu12Mingguang He13Xianwen Shang14Department of Ophthalmology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical UniversityDepartment of Ophthalmology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical UniversityDepartment of Ophthalmology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical UniversityMedical Big Data Center, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences, Southern Medical University)Medical Research Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical UniversityDepartment of Ophthalmology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical UniversityState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen UniversityDepartment of Ophthalmology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical UniversityCentre for Eye Research AustraliaDepartment of Ophthalmology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical UniversityDepartment of Ophthalmology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical UniversityMonash e-Research Center, Faculty of Engineering, Airdoc Research, Nvidia AI Technology Research Center, Monash UniversityDepartment of Ophthalmology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical UniversityDepartment of Ophthalmology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical UniversityDepartment of Ophthalmology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical UniversityAbstract 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.https://doi.org/10.1186/s12967-024-05868-3Metabolomic profilesMetabolomic stateGenetic risk scoreBrain phenotypePrediction valueModeration analysis |
| spellingShingle | 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 Machine learning based metabolomic and genetic profiles for predicting multiple brain phenotypes Journal of Translational Medicine Metabolomic profiles Metabolomic state Genetic risk score Brain phenotype Prediction value Moderation analysis |
| title | Machine learning based metabolomic and genetic profiles for predicting multiple brain phenotypes |
| title_full | Machine learning based metabolomic and genetic profiles for predicting multiple brain phenotypes |
| title_fullStr | Machine learning based metabolomic and genetic profiles for predicting multiple brain phenotypes |
| title_full_unstemmed | Machine learning based metabolomic and genetic profiles for predicting multiple brain phenotypes |
| title_short | Machine learning based metabolomic and genetic profiles for predicting multiple brain phenotypes |
| title_sort | machine learning based metabolomic and genetic profiles for predicting multiple brain phenotypes |
| topic | Metabolomic profiles Metabolomic state Genetic risk score Brain phenotype Prediction value Moderation analysis |
| url | https://doi.org/10.1186/s12967-024-05868-3 |
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