Brain metabolite level differences between young and middle-aged adults assessed using 7T MRS
Poster No:
2388
Submission Type:
Abstract Submission
Authors:
Flavie Detcheverry1,2,3,4, Ikrame Housni1,2,3,4, Sneha Senthil5,6, Ali Filali-Mouhim4, Rozie Arnaoutelis5,6, Samson Antel5,6, Douglas Arnold5,6, Jamie Near7, Sridar Narayanan5,6, AmanPreet Badhwar1,2,3,4
Institutions:
1Multiomics Investigation of Neurodegenerative Diseases (MIND) lab, Montreal, Quebec, Canada, 2Department of Pharmacology and Physiology, Faculty of Medicine, University of Montreal, Montreal, Quebec, Canada, 3Institute of Biomedical Engineering, University of Montreal, Montreal, Quebec, Canada, 4Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal (CRIUGM), Montreal, Quebec, Canada, 5McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Quebec, Canada, 6Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada, 7Sunnybrook Research Institute, Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
First Author:
Flavie Detcheverry
Multiomics Investigation of Neurodegenerative Diseases (MIND) lab|Department of Pharmacology and Physiology, Faculty of Medicine, University of Montreal|Institute of Biomedical Engineering, University of Montreal|Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal (CRIUGM)
Montreal, Quebec, Canada|Montreal, Quebec, Canada|Montreal, Quebec, Canada|Montreal, Quebec, Canada
Multiomics Investigation of Neurodegenerative Diseases (MIND) lab|Department of Pharmacology and Physiology, Faculty of Medicine, University of Montreal|Institute of Biomedical Engineering, University of Montreal|Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal (CRIUGM)
Montreal, Quebec, Canada|Montreal, Quebec, Canada|Montreal, Quebec, Canada|Montreal, Quebec, Canada
Co-Author(s):
Ikrame Housni
Multiomics Investigation of Neurodegenerative Diseases (MIND) lab|Department of Pharmacology and Physiology, Faculty of Medicine, University of Montreal|Institute of Biomedical Engineering, University of Montreal|Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal (CRIUGM)
Montreal, Quebec, Canada|Montreal, Quebec, Canada|Montreal, Quebec, Canada|Montreal, Quebec, Canada
Multiomics Investigation of Neurodegenerative Diseases (MIND) lab|Department of Pharmacology and Physiology, Faculty of Medicine, University of Montreal|Institute of Biomedical Engineering, University of Montreal|Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal (CRIUGM)
Montreal, Quebec, Canada|Montreal, Quebec, Canada|Montreal, Quebec, Canada|Montreal, Quebec, Canada
Sneha Senthil
McConnell Brain Imaging Centre, Montreal Neurological Institute|Department of Neurology and Neurosurgery, McGill University
Montreal, Quebec, Canada|Montreal, Quebec, Canada
McConnell Brain Imaging Centre, Montreal Neurological Institute|Department of Neurology and Neurosurgery, McGill University
Montreal, Quebec, Canada|Montreal, Quebec, Canada
Ali Filali-Mouhim
Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal (CRIUGM)
Montreal, Quebec, Canada
Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal (CRIUGM)
Montreal, Quebec, Canada
Rozie Arnaoutelis
McConnell Brain Imaging Centre, Montreal Neurological Institute|Department of Neurology and Neurosurgery, McGill University
Montreal, Quebec, Canada|Montreal, Quebec, Canada
McConnell Brain Imaging Centre, Montreal Neurological Institute|Department of Neurology and Neurosurgery, McGill University
Montreal, Quebec, Canada|Montreal, Quebec, Canada
Samson Antel
McConnell Brain Imaging Centre, Montreal Neurological Institute|Department of Neurology and Neurosurgery, McGill University
Montreal, Quebec, Canada|Montreal, Quebec, Canada
McConnell Brain Imaging Centre, Montreal Neurological Institute|Department of Neurology and Neurosurgery, McGill University
Montreal, Quebec, Canada|Montreal, Quebec, Canada
Douglas Arnold
McConnell Brain Imaging Centre, Montreal Neurological Institute|Department of Neurology and Neurosurgery, McGill University
Montreal, Quebec, Canada|Montreal, Quebec, Canada
McConnell Brain Imaging Centre, Montreal Neurological Institute|Department of Neurology and Neurosurgery, McGill University
Montreal, Quebec, Canada|Montreal, Quebec, Canada
Jamie Near
Sunnybrook Research Institute, Department of Medical Biophysics, University of Toronto
Toronto, Ontario, Canada
Sunnybrook Research Institute, Department of Medical Biophysics, University of Toronto
Toronto, Ontario, Canada
Sridar Narayanan
McConnell Brain Imaging Centre, Montreal Neurological Institute|Department of Neurology and Neurosurgery, McGill University
Montreal, Quebec, Canada|Montreal, Quebec, Canada
McConnell Brain Imaging Centre, Montreal Neurological Institute|Department of Neurology and Neurosurgery, McGill University
Montreal, Quebec, Canada|Montreal, Quebec, Canada
AmanPreet Badhwar
Multiomics Investigation of Neurodegenerative Diseases (MIND) lab|Department of Pharmacology and Physiology, Faculty of Medicine, University of Montreal|Institute of Biomedical Engineering, University of Montreal|Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal (CRIUGM)
Montreal, Quebec, Canada|Montreal, Quebec, Canada|Montreal, Quebec, Canada|Montreal, Quebec, Canada
Multiomics Investigation of Neurodegenerative Diseases (MIND) lab|Department of Pharmacology and Physiology, Faculty of Medicine, University of Montreal|Institute of Biomedical Engineering, University of Montreal|Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal (CRIUGM)
Montreal, Quebec, Canada|Montreal, Quebec, Canada|Montreal, Quebec, Canada|Montreal, Quebec, Canada
Introduction:
Neurochemical changes take place in the brain as it ages, and readouts of brain metabolite levels from in vivo magnetic resonance spectroscopy (MRS) can provide key insight into the metabolic fingerprint of healthy aging. Our recent systematic review (Detcheverry et al., 2023) indicates that brain metabolite changes in healthy aging have not been fully characterized, especially in middle-aged adults (age range: 40-59 years (yrs)), an age where risk factors associated with age-related dementia are known to appear. In addition, in the literature to date, only five papers investigated healthy adult group differences using 7T MRS, and mostly focused on N-acetylaspartate (NAA; a marker of neuroaxonal integrity, and the most prominent peak in the proton MRS spectrum) and glutamate (Glu; main excitatory neurotransmitter). Therefore, our study aims to characterize the concentration differences of 16 brain metabolites between young and middle-aged adults using 7T MRS, a magnet strength considered ideal for the detection of low-concentration, yet biologically relevant, metabolites.
Methods:
Included in our age group comparison analysis were 23 adults (N=10 young, 20-29 yrs; N=13 middle-aged, 40-59 yrs; Figure 1A) from our ongoing healthy adult cohort study collecting 7T magnetic resonance imaging (MRI)/MRS and blood data. MRI/MRS data were acquired using a 7T Siemens Terra scanner (Siemens, Erlangen, Germany). The MRI protocol included a whole brain 3D T1-weighted MP2RAGE for localization. Metabolite levels were measured in posterior cingulate cortex (PCC) and centrum-semiovale white matter (CSWM) voxels using STimulated Echo Acquisition Mode (STEAM) MRS with the following parameters: TR/TE=5000/8ms, 64/96 averages for PCC/CSWM, 4096 points, and 5000 Hz spectral bandwidth (Figure 1B). Raw MRS data was preprocessed using the FID Appliance (FID-A) (Simpson et al., 2017), before spectral fitting, and Eddy current correction and spectral analysis were performed in LCModel (Provencher, 2001). We report metabolite concentrations in arbitrary units referenced to water. A series of linear models, controlled for brain region and sex, and adjusted for multiple comparisons (alpha=0.05), were applied to assess group differences between middle-aged and young adults. In addition, we performed linear regressions of metabolite concentrations with age for metabolites from all 32 participants (20-65 yrs) studied so far (Figure 1C).
Results:
In middle-aged adults compared to young adults, we found (a) higher levels of myo-inositol (p=0.01 in PCC; p<0.01 in CSWM) and total creatine (tCr; p=0.02 in PCC; p=0.01 in CSWM) in both PCC and CSWM, (b) higher levels of aspartate (Asp; p=0.04) and phosphocreatine (PCr; p=0.04) in PCC, and (c) lower Glu (p=0.02) levels in CSWM (Figure 1B). In CSWM, while NAA and Glx (Glu + glutamine (Gln)) levels showed a trend for lower levels with increasing age, this finding did not survive multiple comparisons.
Conclusions:
Overall, we demonstrate perturbations in brain metabolite levels in middle-aged adults, which may indicate alterations in the neuro-glial metabolite cycles, including the Glu-Gln, Cr-PCr, and NAA-Asp cycles (Figure 2) (Dingledine and McBain, 1999; Sherry, Lee and Choi, 2015). Proper functioning of these cycles, especially the Glu-Gln, ensures neuron-astrocyte integrity, preventing excitotoxicity caused by excess Glu levels in the extracellular space (Xu et al., 2016). Our future directions are to investigate these pathways in older adults using (a) 7T MRS and (b) plasma metabolomics data using mass spectrometry from our database.
Lifespan Development:
Aging 2
Novel Imaging Acquisition Methods:
MR Spectroscopy 1
Physiology, Metabolism and Neurotransmission :
Cerebral Metabolism and Hemodynamics
Physiology, Metabolism and Neurotransmission Other
Keywords:
ADULTS
Aging
HIGH FIELD MR
Magnetic Resonance Spectroscopy (MRS)
MR SPECTROSCOPY
1|2Indicates the priority used for review
Provide references using author date format
Dingledine, R. and McBain, C.J. (1999) Glutamate and Aspartate Are the Major Excitatory Transmitters in the Brain. Lippincott-Raven.
Provencher, S.W. (2001) ‘Automatic quantitation of localized in vivo 1H spectra with LCModel’, NMR in biomedicine, 14(4), pp. 260–264.
Sherry, E.B., Lee, P. and Choi, I.-Y. (2015) ‘In Vivo NMR Studies of the Brain with Hereditary or Acquired Metabolic Disorders’, Neurochemical research, 40(12), pp. 2647–2685.
Simpson, R. et al. (2017) ‘Advanced processing and simulation of MRS data using the FID appliance (FID-A)-An open source, MATLAB-based toolkit’, Magnetic resonance in medicine: official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine, 77(1), pp. 23–33.
Xu, H. et al. (2016) ‘Evaluation of neuron-glia integrity by in vivo proton magnetic resonance spectroscopy: Implications for psychiatric disorders’, Neuroscience and biobehavioral reviews, 71, pp. 563–577.