Novel metabolic biomarkers for diagnosis of Alzheimer’s disease
Yingxin Zhao, Alejandro Villasante‐Tezanos, Ernesto Miranda, Miguel A Pappolla, Xiang Fang- Psychiatry and Mental health
- Cellular and Molecular Neuroscience
- Geriatrics and Gerontology
- Neurology (clinical)
- Developmental Neuroscience
- Health Policy
- Epidemiology
Abstract
Background
The need for biomarkers for the etiological identification of dementia syndromes can not be overemphasized. Blood metabolites are attractive targets for identifying biomarkers with predictive values for AD diagnosis and progression. This study aims to identify novel AD blood metabolic biomarkers and develop a high‐sensitivity and specificity blood‐based test for AD.
Method
In the discovery phase, the serum metabolome of AD patients (n = 10) and age, sex‐matched healthy controls (n = 10) were analyzed with a mass spectrometry (MS)‐based approach. Supervised machine learning was used to identify the biomarker candidates and develop a computational model for AD diagnosis. Finally, the computational model and biomarker candidates were verified with a second AD cohort in the verification phase, where the serum metabolome of AD patients (n = 27) and age, sex‐matched healthy controls (n = 30) were analyzed.
Result
In this discovery study, 7075 metabolic features were quantified. Among them, the abundance of 750 unique features significantly differed between AD and Controls (t‐test p‐value <0.05). Support vector machine (SVM) with radial basis function (RBF) kernel was used for features optimization and identified a panel of 14 metabolic features which predicted AD with 0.0% classification error rate (4‐fold cross‐validation). In the verification study, 10595 metabolic features were quantified. Among them, 908 unique features significantly differed between AD and Controls (t‐test with Permutation q‐value <0.05). Then, the computational model and biomarker panel identified in the discovery phase were tested with the verification dataset and confirmed that they could predict AD with high accuracy (0.0% classification error rate, 4‐fold cross‐validation).
Conclusion
This study comprehensively profiled the AD serum metabolome and identified a panel of metabolic biomarkers and a computational model for AD diagnosis, which will serve as a foundation for a high‐performance, blood‐based test for clinical AD screening and diagnosis.