Multiomics Analysis of Structural Magnetic Resonance Imaging of the Brain and Cerebrospinal Fluid Metabolomics in Cognitively Normal and Impaired Adults

• Authors: Ronald Eldridge, Emory University; Karan Uppal, Emory University; Mahsa Shokouhi, Emory University; Matthew Smith, Emory University; Xin Hu, Emory University; Zhaohui Qin, Emory University; Dean Jones, Emory University; Ihab Hajjar, Emory University
• Journal Title: FRONTIERS IN AGING NEUROSCIENCE
• Volume: Volume 13
• Publisher: FRONTIERS MEDIA SA | 2022-01-25, Pages 796067-796067
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.3389/fnagi.2021.796067
• Subjects: Biology, Biostatistics; Health Sciences, Nursing
• Keywords: Science & Technology; Life Sciences & Biomedicine; Geriatrics & Gerontology; Neurosciences; Neurosciences & Neurology; MRI imaging; metabolomics (OMICS); multiomics analysis; mild cognition impairment; amino acids (AA); Alzheimer's disease; integrative "omics; "; gray matter atrophy; HIGH-RESOLUTION METABOLOMICS; PARTIAL LEAST-SQUARES; VOXEL-BASED MORPHOMETRY; ALZHEIMERS-DISEASE; DISCRIMINANT-ANALYSIS; MRI; DYSFUNCTION; BIOMARKERS; PATHWAYS; HEALTH

Abstract

Introduction: Integrating brain imaging with large scale omics data may identify novel mechanisms of mild cognitive impairment (MCI) and early Alzheimer’s disease (AD). We integrated and analyzed brain magnetic resonance imaging (MRI) with cerebrospinal fluid (CSF) metabolomics to elucidate metabolic mechanisms and create a “metabolic map” of the brain in prodromal AD. Methods: In 145 subjects (85 cognitively normal controls and 60 with MCI), we derived voxel-wise gray matter volume via whole-brain structural MRI and conducted high-resolution untargeted metabolomics on CSF. Using a data-driven approach consisting of partial least squares discriminant analysis, a multiomics network clustering algorithm, and metabolic pathway analysis, we described dysregulated metabolic pathways in CSF mapped to brain regions associated with MCI in our cohort. Results: The multiomics network algorithm clustered metabolites with contiguous imaging voxels into seven distinct communities corresponding to the following brain regions: hippocampus/parahippocampal gyrus (three distinct clusters), thalamus, posterior thalamus, parietal cortex, and occipital lobe. Metabolic pathway analysis indicated dysregulated metabolic activity in the urea cycle, and many amino acids (arginine, histidine, lysine, glycine, tryptophan, methionine, valine, glutamate, beta-alanine, and purine) was significantly associated with those regions (P < 0.05). Conclusion: By integrating CSF metabolomics data with structural MRI data, we linked specific AD-susceptible brain regions to disrupted metabolic pathways involving nitrogen excretion and amino acid metabolism critical for cognitive function. Our findings and analytical approach may extend drug and biomarker research toward more multiomics approaches.