Metabolomics : Official journal of the Metabolomic Society · 2025
Multi-marker discovery for mild cognitive impairment in metabolomics using machine learning with a global surrogate model via partial least squares.
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Tatara Yota, Yamamoto Hiroyuki, Terai Kozue, Matsuta Rira, Kasai Shuya, Tamada Yoshinori, Mikami Tatsuya, Murashita Koichi, Itoh Ken
Аннотация
INTRODUCTION: Dementia can be prevented through early intervention; hence, there is an urgent need for biomarkers to help diagnose mild cognitive impairment (MCI).
OBJECTIVES: We aimed to develop a multi-marker panel composed of plasma metabolites to aid in the diagnosis of MCI.
METHODS: We performed an analysis of a multi-marker panel of MCI metabolites using a random forest algorithm with variable selection methods and a global surrogate with principal component analysis and partial least squares (PLS).
RESULTS: By incorporating variable selection methods, we constructed a predictive model that demonstrated robust performance, with an AUC of approximately 0.85 in both cross-validation and test evaluations, using only five metabolites (methionine, quinic acid, hypoxanthine, O-acetylcarnitine, and 2-oxoglutaric acid). However, owing to the limited number of selected metabolites, it was challenging to infer the biological meaning of this multi-marker panel. To interpret this multi-marker panel biologically, we constructed a global surrogate model using PLS. By examining the PLS loadings corresponding to the scores with intergroup differences, we identified a relationship between 14 metabolites involved in neuronal energy metabolism and neurotransmission. This suggests that the multi-marker panel constructed in this study is related to abnormalities in energy metabolism and neurotransmission in patients with MCI.
CONCLUSION: The method used in this study may be broadly applicable for analyzing multi-marker panels of metabolites and their biological interpretation. This study included an independent validation, and further larger-scale studies using additional external cohorts are warranted to confirm the generalizability of this approach.
OBJECTIVES: We aimed to develop a multi-marker panel composed of plasma metabolites to aid in the diagnosis of MCI.
METHODS: We performed an analysis of a multi-marker panel of MCI metabolites using a random forest algorithm with variable selection methods and a global surrogate with principal component analysis and partial least squares (PLS).
RESULTS: By incorporating variable selection methods, we constructed a predictive model that demonstrated robust performance, with an AUC of approximately 0.85 in both cross-validation and test evaluations, using only five metabolites (methionine, quinic acid, hypoxanthine, O-acetylcarnitine, and 2-oxoglutaric acid). However, owing to the limited number of selected metabolites, it was challenging to infer the biological meaning of this multi-marker panel. To interpret this multi-marker panel biologically, we constructed a global surrogate model using PLS. By examining the PLS loadings corresponding to the scores with intergroup differences, we identified a relationship between 14 metabolites involved in neuronal energy metabolism and neurotransmission. This suggests that the multi-marker panel constructed in this study is related to abnormalities in energy metabolism and neurotransmission in patients with MCI.
CONCLUSION: The method used in this study may be broadly applicable for analyzing multi-marker panels of metabolites and their biological interpretation. This study included an independent validation, and further larger-scale studies using additional external cohorts are warranted to confirm the generalizability of this approach.