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Klug S, Murgaš M, Godbersen GM, Hacker M, Lanzenberger R, Hahn A. Synaptic signaling modeled by functional connectivity predicts metabolic demands of the human brain. Neuroimage 2024; 295:120658. [PMID: 38810891 DOI: 10.1016/j.neuroimage.2024.120658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 04/22/2024] [Accepted: 05/27/2024] [Indexed: 05/31/2024] Open
Abstract
PURPOSE The human brain is characterized by interacting large-scale functional networks fueled by glucose metabolism. Since former studies could not sufficiently clarify how these functional connections shape glucose metabolism, we aimed to provide a neurophysiologically-based approach. METHODS 51 healthy volunteers underwent simultaneous PET/MRI to obtain BOLD functional connectivity and [18F]FDG glucose metabolism. These multimodal imaging proxies of fMRI and PET were combined in a whole-brain extension of metabolic connectivity mapping. Specifically, functional connectivity of all brain regions were used as input to explain glucose metabolism of a given target region. This enabled the modeling of postsynaptic energy demands by incoming signals from distinct brain regions. RESULTS Functional connectivity input explained a substantial part of metabolic demands but with pronounced regional variations (34 - 76%). During cognitive task performance this multimodal association revealed a shift to higher network integration compared to resting state. In healthy aging, a dedifferentiation (decreased segregated/modular structure of the brain) of brain networks during rest was observed. Furthermore, by including data from mRNA maps, [11C]UCB-J synaptic density and aerobic glycolysis (oxygen-to-glucose index from PET data), we show that whole-brain functional input reflects non-oxidative, on-demand metabolism of synaptic signaling. The metabolically-derived directionality of functional inputs further marked them as top-down predictions. In addition, the approach uncovered formerly hidden networks with superior efficiency through metabolically informed network partitioning. CONCLUSIONS Applying multimodal imaging, we decipher a crucial part of the metabolic and neurophysiological basis of functional connections in the brain as interregional on-demand synaptic signaling fueled by anaerobic metabolism. The observed task- and age-related effects indicate promising future applications to characterize human brain function and clinical alterations.
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Affiliation(s)
- Sebastian Klug
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria; Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Austria
| | - Matej Murgaš
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria; Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Austria
| | - Godber M Godbersen
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria; Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Austria
| | - Marcus Hacker
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Austria
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria; Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Austria
| | - Andreas Hahn
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria; Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Austria.
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Zhang Y, Chen H, Zeng M, Guo P, Liu M, Cao B, Wang R, Hao F, Zheng X, Feng W. Futoquinol improves Aβ 25-35-induced memory impairment in mice by inhibiting the activation of p38MAPK through the glycolysis pathway and regulating the composition of the gut microbiota. Phytother Res 2024; 38:1799-1814. [PMID: 38330236 DOI: 10.1002/ptr.8136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 12/18/2023] [Accepted: 01/13/2024] [Indexed: 02/10/2024]
Abstract
Futoquinol (Fut) is a compound extracted from Piper kadsura that has a nerve cell protection effect. However, it is unclear whether Fut has protective effects in Alzheimer's disease (AD). In this study, we aimed to explore the therapeutic effect of Fut in AD and its underlying mechanism. UPLC-MS/MS method was performed to quantify Fut in the hippocampus of mice brain. The cognition ability, neuronal and mitochondria damage, and levels of Aβ1-42, Aβ1-40, p-Tau, oxidative stress, apoptosis, immune cells, and inflammatory factors were measured in Aβ25-35-induced mice. The content of bacterial meta-geometry was predicted in the microbial composition based on 16S rDNA. The protein levels of HK II, p-p38MAPK, and p38MAPK were detected. PC-12 cells were cultured in vitro, and glucose was added to activate glycolysis to further explore the mechanism of action of Fut intervention in AD. Fut improved the memory and learning ability of Aβ25-35 mice, and reduced neuronal damage and the deposition of Aβ and Tau proteins. Moreover, Fut reduced mitochondrial damage, the levels of oxidative stress, apoptosis, and inflammatory factors. Fut significantly inhibited the expression of HK II and p-p38MAPK proteins. The in vitro experiment showed that p38MAPK was activated and Fut action inhibited after adding 10 mM glucose. Fut might inhibit the activation of p38MAPK through the glycolysis pathway, thereby reducing oxidative stress, apoptosis, and inflammatory factors and improving Aβ25-35-induced memory impairment in mice. These data provide pharmacological rationale for Fut in the treatment of AD.
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Affiliation(s)
- Yuhan Zhang
- College of pharmacy, Henan University of Chinese Medicine, Zhengzhou, China
- The Engineering and Technology Center for Chinese Medicine Development of Henan Province, Zhengzhou, China
| | - Hui Chen
- College of pharmacy, Henan University of Chinese Medicine, Zhengzhou, China
| | - Mengnan Zeng
- College of pharmacy, Henan University of Chinese Medicine, Zhengzhou, China
- The Engineering and Technology Center for Chinese Medicine Development of Henan Province, Zhengzhou, China
| | - Pengli Guo
- College of pharmacy, Henan University of Chinese Medicine, Zhengzhou, China
- The Engineering and Technology Center for Chinese Medicine Development of Henan Province, Zhengzhou, China
| | - Meng Liu
- College of pharmacy, Henan University of Chinese Medicine, Zhengzhou, China
- The Engineering and Technology Center for Chinese Medicine Development of Henan Province, Zhengzhou, China
| | - Bing Cao
- College of pharmacy, Henan University of Chinese Medicine, Zhengzhou, China
- The Engineering and Technology Center for Chinese Medicine Development of Henan Province, Zhengzhou, China
| | - Ru Wang
- College of pharmacy, Henan University of Chinese Medicine, Zhengzhou, China
- The Engineering and Technology Center for Chinese Medicine Development of Henan Province, Zhengzhou, China
| | - Fengxiao Hao
- College of pharmacy, Henan University of Chinese Medicine, Zhengzhou, China
- The Engineering and Technology Center for Chinese Medicine Development of Henan Province, Zhengzhou, China
| | - Xiaoke Zheng
- College of pharmacy, Henan University of Chinese Medicine, Zhengzhou, China
- The Engineering and Technology Center for Chinese Medicine Development of Henan Province, Zhengzhou, China
| | - Weisheng Feng
- College of pharmacy, Henan University of Chinese Medicine, Zhengzhou, China
- The Engineering and Technology Center for Chinese Medicine Development of Henan Province, Zhengzhou, China
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3
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Volpi T, Silvestri E, Aiello M, Lee JJ, Vlassenko AG, Goyal MS, Corbetta M, Bertoldo A. The brain's "dark energy" puzzle: How strongly is glucose metabolism linked to resting-state brain activity? J Cereb Blood Flow Metab 2024:271678X241237974. [PMID: 38443762 DOI: 10.1177/0271678x241237974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/07/2024]
Abstract
Brain glucose metabolism, which can be investigated at the macroscale level with [18F]FDG PET, displays significant regional variability for reasons that remain unclear. Some of the functional drivers behind this heterogeneity may be captured by resting-state functional magnetic resonance imaging (rs-fMRI). However, the full extent to which an fMRI-based description of the brain's spontaneous activity can describe local metabolism is unknown. Here, using two multimodal datasets of healthy participants, we built a multivariable multilevel model of functional-metabolic associations, assessing multiple functional features, describing the 1) rs-fMRI signal, 2) hemodynamic response, 3) static and 4) time-varying functional connectivity, as predictors of the human brain's metabolic architecture. The full model was trained on one dataset and tested on the other to assess its reproducibility. We found that functional-metabolic spatial coupling is nonlinear and heterogeneous across the brain, and that local measures of rs-fMRI activity and synchrony are more tightly coupled to local metabolism. In the testing dataset, the degree of functional-metabolic spatial coupling was also related to peripheral metabolism. Overall, although a significant proportion of regional metabolic variability can be described by measures of spontaneous activity, additional efforts are needed to explain the remaining variance in the brain's 'dark energy'.
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Affiliation(s)
- Tommaso Volpi
- Padova Neuroscience Center, University of Padova, Padova, Italy
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
| | - Erica Silvestri
- Department of Information Engineering, University of Padova, Padova, Italy
| | | | - John J Lee
- Neuroimaging Laboratories at the Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - Andrei G Vlassenko
- Neuroimaging Laboratories at the Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - Manu S Goyal
- Neuroimaging Laboratories at the Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - Maurizio Corbetta
- Padova Neuroscience Center, University of Padova, Padova, Italy
- Department of Neuroscience, University of Padova, Padova, Italy
| | - Alessandra Bertoldo
- Padova Neuroscience Center, University of Padova, Padova, Italy
- Department of Information Engineering, University of Padova, Padova, Italy
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4
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Strain JF, Rahmani M, Dierker D, Owen C, Jafri H, Vlassenko AG, Womack K, Fripp J, Tosun D, Benzinger TLS, Weiner M, Masters C, Lee JM, Morris JC, Goyal MS. Accuracy of TrUE-Net in comparison to established white matter hyperintensity segmentation methods: An independent validation study. Neuroimage 2024; 285:120494. [PMID: 38086495 DOI: 10.1016/j.neuroimage.2023.120494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 10/23/2023] [Accepted: 12/09/2023] [Indexed: 12/17/2023] Open
Abstract
White matter hyperintensities (WMH) are nearly ubiquitous in the aging brain, and their topography and overall burden are associated with cognitive decline. Given their numerosity, accurate methods to automatically segment WMH are needed. Recent developments, including the availability of challenge data sets and improved deep learning algorithms, have led to a new promising deep-learning based automated segmentation model called TrUE-Net, which has yet to undergo rigorous independent validation. Here, we compare TrUE-Net to six established automated WMH segmentation tools, including a semi-manual method. We evaluated the techniques at both global and regional level to compare their ability to detect the established relationship between WMH burden and age. We found that TrUE-Net was highly reliable at identifying WMH regions with low false positive rates, when compared to semi-manual segmentation as the reference standard. TrUE-Net performed similarly or favorably when compared to the other automated techniques. Moreover, TrUE-Net was able to detect relationships between WMH and age to a similar degree as the reference standard semi-manual segmentation at both the global and regional level. These results support the use of TrUE-Net for identifying WMH at the global or regional level, including in large, combined datasets.
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Affiliation(s)
- Jeremy F Strain
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA; Neuroimaging Labs Research Center, Washington University School of Medicine, St. Louis MO, USA.
| | - Maryam Rahmani
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA; Neuroimaging Labs Research Center, Washington University School of Medicine, St. Louis MO, USA
| | - Donna Dierker
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA; Neuroimaging Labs Research Center, Washington University School of Medicine, St. Louis MO, USA
| | - Christopher Owen
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Hussain Jafri
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Andrei G Vlassenko
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA; Neuroimaging Labs Research Center, Washington University School of Medicine, St. Louis MO, USA
| | - Kyle Womack
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Jurgen Fripp
- The Australian e-Health Research Centre, CSIRO Health and Biosecurity, Brisbane, QLD, Australia
| | - Duygu Tosun
- Division of Radiology and Biomedical Imaging, University of California - San Francisco, San Francisco, CA, USA
| | - Tammie L S Benzinger
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA; Knight Alzheimer Disease Research Center, St. Louis, MO, USA
| | - Michael Weiner
- Division of Radiology and Biomedical Imaging, University of California - San Francisco, San Francisco, CA, USA
| | - Colin Masters
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Jin-Moo Lee
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA; Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA; Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - John C Morris
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA; Knight Alzheimer Disease Research Center, St. Louis, MO, USA
| | - Manu S Goyal
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA; Neuroimaging Labs Research Center, Washington University School of Medicine, St. Louis MO, USA
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5
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Liu M, Cui L, Zhao Z, Ren S, Huang L, Guan Y, Guo Q, Xie F, Huang Q, Shen D. Verifying and refining early statuses in Alzheimer's disease progression: a possibility from deep feature comparison. Cereb Cortex 2023; 33:11486-11500. [PMID: 37833708 DOI: 10.1093/cercor/bhad381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 09/25/2023] [Accepted: 09/26/2023] [Indexed: 10/15/2023] Open
Abstract
Defining the early status of Alzheimer's disease is challenging. Theoretically, the statuses in the Alzheimer's disease continuum are expected to share common features. Here, we explore to verify and refine candidature early statuses of Alzheimer's disease with features learned from deep learning. We train models on brain functional networks to accurately classify between amnestic and non-amnestic mild cognitive impairments and between healthy controls and mild cognitive impairments. The trained models are applied to Alzheimer's disease and subjective cognitive decline groups to suggest feature similarities among the statuses and identify informative subpopulations. The amnestic mild cognitive impairment vs non-amnestic mild cognitive impairments classifier believes that 71.8% of Alzheimer's disease are amnestic mild cognitive impairment. And 73.5% of subjective cognitive declines are labeled as mild cognitive impairments, 88.8% of which are further suggested as "amnestic mild cognitive impairment." Further multimodal analyses suggest that the amnestic mild cognitive impairment-like Alzheimer's disease, mild cognitive impairment-like subjective cognitive decline, and amnestic mild cognitive impairment-like subjective cognitive decline exhibit more Alzheimer's disease -related pathological changes (elaborated β-amyloid depositions, reduced glucose metabolism, and gray matter atrophy) than non-amnestic mild cognitive impairments -like Alzheimer's disease, healthy control-like subjective cognitive decline, and non-amnestic mild cognitive impairments -like subjective cognitive decline. The test-retest reliability of the subpopulation identification is fair to good in general. The study indicates overall similarity among subjective cognitive decline, amnestic mild cognitive impairment, and Alzheimer's disease and implies their progression relationships. The results support "deep feature comparison" as a potential beneficial framework to verify and refine early Alzheimer's disease status.
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Affiliation(s)
- Mianxin Liu
- School of Biomedical Engineering, State Key Laboratory of Advanced Medical Materials and Devices, Shanghai Tech University, Shanghai 201210, China
- Shanghai Artificial Intelligence Laboratory, Shanghai 200232, China
| | - Liang Cui
- Department of Gerontology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai 200233, China
| | - Zixiao Zhao
- Department of Laboratory Medicine, Center for Molecular Imaging and Translational Medicine, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian 361102, China
| | - Shuhua Ren
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Lin Huang
- Department of Gerontology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai 200233, China
| | - Yihui Guan
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai 200040, China
- National Center for Neurological Disorders, Shanghai 201112, China
| | - Qihao Guo
- Department of Gerontology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai 200233, China
| | - Fang Xie
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai 200040, China
- National Center for Neurological Disorders, Shanghai 201112, China
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Qi Huang
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Dinggang Shen
- School of Biomedical Engineering, State Key Laboratory of Advanced Medical Materials and Devices, Shanghai Tech University, Shanghai 201210, China
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200230, China
- Shanghai Clinical Research and Trial Center, Shanghai, 201210, China
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6
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Zegarra-Valdivia JA, Pignatelli J, Nuñez A, Torres Aleman I. The Role of Insulin-like Growth Factor I in Mechanisms of Resilience and Vulnerability to Sporadic Alzheimer's Disease. Int J Mol Sci 2023; 24:16440. [PMID: 38003628 PMCID: PMC10671249 DOI: 10.3390/ijms242216440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 11/06/2023] [Accepted: 11/14/2023] [Indexed: 11/26/2023] Open
Abstract
Despite decades of intense research, disease-modifying therapeutic approaches for Alzheimer's disease (AD) are still very much needed. Apart from the extensively analyzed tau and amyloid pathological cascades, two promising avenues of research that may eventually identify new druggable targets for AD are based on a better understanding of the mechanisms of resilience and vulnerability to this condition. We argue that insulin-like growth factor I (IGF-I) activity in the brain provides a common substrate for the mechanisms of resilience and vulnerability to AD. We postulate that preserved brain IGF-I activity contributes to resilience to AD pathology as this growth factor intervenes in all the major pathological cascades considered to be involved in AD, including metabolic impairment, altered proteostasis, and inflammation, to name the three that are considered to be the most important ones. Conversely, disturbed IGF-I activity is found in many AD risk factors, such as old age, type 2 diabetes, imbalanced diet, sedentary life, sociality, stroke, stress, and low education, whereas the Apolipoprotein (Apo) E4 genotype and traumatic brain injury may also be influenced by brain IGF-I activity. Accordingly, IGF-I activity should be taken into consideration when analyzing these processes, while its preservation will predictably help prevent the progress of AD pathology. Thus, we need to define IGF-I activity in all these conditions and develop a means to preserve it. However, defining brain IGF-I activity cannot be solely based on humoral or tissue levels of this neurotrophic factor, and new functionally based assessments need to be developed.
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Affiliation(s)
- Jonathan A. Zegarra-Valdivia
- Achucarro Basque Center for Neuroscience, 48940 Leioa, Spain;
- Biomedical Research Networking Center on Neurodegenerative Diseases (CIBERNED), 28029 Madrid, Spain;
- School of Medicine, Universidad Señor de Sipán, Chiclayo 14000, Peru
| | - Jaime Pignatelli
- Biomedical Research Networking Center on Neurodegenerative Diseases (CIBERNED), 28029 Madrid, Spain;
- Cajal Institute (CSIC), 28002 Madrid, Spain
| | - Angel Nuñez
- Department of Anatomy, Histology and Neuroscience, Universidad Autónoma de Madrid, 28049 Madrid, Spain;
| | - Ignacio Torres Aleman
- Achucarro Basque Center for Neuroscience, 48940 Leioa, Spain;
- Biomedical Research Networking Center on Neurodegenerative Diseases (CIBERNED), 28029 Madrid, Spain;
- Ikerbasque, Basque Foundation for Science, 48009 Bilbao, Spain
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7
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Ramezani M, Fernando M, Eslick S, Asih PR, Shadfar S, Bandara EMS, Hillebrandt H, Meghwar S, Shahriari M, Chatterjee P, Thota R, Dias CB, Garg ML, Martins RN. Ketone bodies mediate alterations in brain energy metabolism and biomarkers of Alzheimer's disease. Front Neurosci 2023; 17:1297984. [PMID: 38033541 PMCID: PMC10687427 DOI: 10.3389/fnins.2023.1297984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 10/30/2023] [Indexed: 12/02/2023] Open
Abstract
Alzheimer's disease (AD) is the most common form of dementia. AD is a progressive neurodegenerative disorder characterized by cognitive dysfunction, including learning and memory deficits, and behavioral changes. Neuropathology hallmarks of AD such as amyloid beta (Aβ) plaques and neurofibrillary tangles containing the neuron-specific protein tau is associated with changes in fluid biomarkers including Aβ, phosphorylated tau (p-tau)-181, p-tau 231, p-tau 217, glial fibrillary acidic protein (GFAP), and neurofilament light (NFL). Another pathological feature of AD is neural damage and hyperactivation of astrocytes, that can cause increased pro-inflammatory mediators and oxidative stress. In addition, reduced brain glucose metabolism and mitochondrial dysfunction appears up to 15 years before the onset of clinical AD symptoms. As glucose utilization is compromised in the brain of patients with AD, ketone bodies (KBs) may serve as an alternative source of energy. KBs are generated from the β-oxidation of fatty acids, which are enhanced following consumption of ketogenic diets with high fat, moderate protein, and low carbohydrate. KBs have been shown to cross the blood brain barrier to improve brain energy metabolism. This review comprehensively summarizes the current literature on how increasing KBs support brain energy metabolism. In addition, for the first time, this review discusses the effects of ketogenic diet on the putative AD biomarkers such as Aβ, tau (mainly p-tau 181), GFAP, and NFL, and discusses the role of KBs on neuroinflammation, oxidative stress, and mitochondrial metabolism.
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Affiliation(s)
- Matin Ramezani
- Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Macquarie, NSW, Australia
| | - Malika Fernando
- Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Macquarie, NSW, Australia
| | - Shaun Eslick
- Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Macquarie, NSW, Australia
| | - Prita R. Asih
- Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Macquarie, NSW, Australia
- Flinders Health and Medical Research Institute, College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia
| | - Sina Shadfar
- Motor Neuron Disease Research Centre, Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW, Australia
| | | | - Heidi Hillebrandt
- Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Macquarie, NSW, Australia
| | - Silochna Meghwar
- Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Macquarie, NSW, Australia
| | - Maryam Shahriari
- Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Macquarie, NSW, Australia
| | - Pratishtha Chatterjee
- Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Macquarie, NSW, Australia
| | - Rohith Thota
- Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Macquarie, NSW, Australia
| | - Cintia B. Dias
- Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Macquarie, NSW, Australia
| | - Manohar L. Garg
- Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Macquarie, NSW, Australia
| | - Ralph N. Martins
- Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Macquarie, NSW, Australia
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
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8
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Lee JJ, Earnest T, Ha SM, Bani A, Kothapalli D, Liu P, Sotiras A. Patterns of Glucose Metabolism in [ 18 F]FDG PET Indicate Regional Variability and Neurodegeneration in the Progression of Alzheimer's Dementia. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.10.23298396. [PMID: 38116031 PMCID: PMC10729728 DOI: 10.1101/2023.11.10.23298396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
In disorders of cognitive impairment, such as Alzheimer's disease, neurodegeneration is the final common pathway of disease progression. Modulating, reversing, or preventing disease progression is a clinical imperative most likely to succeed following accurate and explanatory understanding of neurodegeneration, requiring enhanced consistency with quantitative measurements and expanded interpretability of complex data. The on-going study of neurodegeneration has robustly demonstrated the advantages of accumulating large amounts of clinical data that include neuroimaging, motiving multi-center studies such as the Alzheimer's Disease Neuroimaging Initiative (ADNI). Demonstrative advantages also arise from highly multivariate analysis methods, and this work reports advances provided by non-negative matrix factorization (NMF). NMF revealed patterns of covariance for glucose metabolism, estimated by positron emission tomography of [ 18 F]fluorodeoxyglucose, in 243 healthy normal participants of ADNI. Patterns for glucose metabolism provided cross-sectional inferences for 860 total participants of ADNI with and without cerebral amyloidosis and clinical dementia ratings (CDR) ranging 0-3. Patterns for glucose metabolism were distinct in number and topography from patterns identified in previous studies of structural MRI. They were also distinct from well-establish topographies of resting-state neuronal networks mapped by functional magnetic resonance imaging. Patterns for glucose metabolism identified significant topographical landmarks relating age, sex, APOE ε4 alleles, amyloidosis, CDR, and neurodegeneration. Patterns involving insular and orbitofrontal cortices, as well as midline regions of frontal and parietal lobes demonstrated the greatest neurodegeneration with progressive Alzheimer's dementia. A single pattern for the lateral parietal and posterior superior temporal cortices demonstrated preserved glucose metabolism for all diagnostic groups, including Alzheimer's dementia. Patterns correlated significantly with topical terms from the Neurosynth platform, thereby providing semantic representations for patterns such as attention, memory, language, fear/reward, movement and motor planning. In summary, NMF is a data-driven, principled, supervised statistical learning method that provides interpretable patterns of neurodegeneration. These patterns can help inform the understanding and treatment of Alzheimer's disease. Highlights ▪ Data-driven non-negative matrix factorization (NMF) identified 24 canonical patterns of spatial covariance of cerebral glucose metabolism. The training data comprised healthy older participants (CDR = 0 without amyloidosis) cross-sectionally drawn from ADNI. ▪ In healthy participants, mean SUVRs for specific patterns in precuneus, lateral parietal cortex, and subcortical areas including superficial white matter and striatum, demonstrated increasing glucose metabolism with advancing age. ▪ In asymptomatic participants with amyloidosis , glucose metabolism increased compared to those who were asymptomatic without amyloid , particularly in medial prefrontal cortex, frontoparietal cortex, occipital white, and posterior cerebellar regions. ▪ In symptomatic participants with amyloidosis , insular cortex, medial frontal cortex, and prefrontal cortex demonstrated the most severe losses of glucose metabolism with increasing CDR. Lateral parietal and posterior superior temporal cortices retained glucose metabolism even for CDR > 0.5. ▪ NMF models of glucose metabolism are consistent with models arising from principal components, or eigenbrains, while adding additional regional interpretability. ▪ NMF patterns correlated with regions catalogued in Neurosynth. Following corrections for spatial autocorrelations, NMF patterns revealed meta-analytic identifications of patterns with Neurosynth topics of fear/reward, attention, memory, language, and movement with motor planning. Patterns varied with degrees of cognitive impairment.
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Volpi T, Vallini G, Silvestri E, Francisci MD, Durbin T, Corbetta M, Lee JJ, Vlassenko AG, Goyal MS, Bertoldo A. A new framework for metabolic connectivity mapping using bolus [ 18F]FDG PET and kinetic modeling. J Cereb Blood Flow Metab 2023; 43:1905-1918. [PMID: 37377103 PMCID: PMC10676136 DOI: 10.1177/0271678x231184365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Revised: 04/11/2023] [Accepted: 06/03/2023] [Indexed: 06/29/2023]
Abstract
Metabolic connectivity (MC) has been previously proposed as the covariation of static [18F]FDG PET images across participants, i.e., across-individual MC (ai-MC). In few cases, MC has been inferred from dynamic [18F]FDG signals, i.e., within-individual MC (wi-MC), as for resting-state fMRI functional connectivity (FC). The validity and interpretability of both approaches is an important open issue. Here we reassess this topic, aiming to 1) develop a novel wi-MC methodology; 2) compare ai-MC maps from standardized uptake value ratio (SUVR) vs. [18F]FDG kinetic parameters fully describing the tracer behavior (i.e., Ki, K1, k3); 3) assess MC interpretability in comparison to structural connectivity and FC. We developed a new approach based on Euclidean distance to calculate wi-MC from PET time-activity curves. The across-individual correlation of SUVR, Ki, K1, k3 produced different networks depending on the chosen [18F]FDG parameter (k3 MC vs. SUVR MC, r = 0.44). We found that wi-MC and ai-MC matrices are dissimilar (maximum r = 0.37), and that the match with FC is higher for wi-MC (Dice similarity: 0.47-0.63) than for ai-MC (0.24-0.39). Our analyses demonstrate that calculating individual-level MC from dynamic PET is feasible and yields interpretable matrices that bear similarity to fMRI FC measures.
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Affiliation(s)
- Tommaso Volpi
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
- Padova Neuroscience Center, University of Padova, Padova, Italy
| | - Giulia Vallini
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Erica Silvestri
- Department of Information Engineering, University of Padova, Padova, Italy
| | | | - Tony Durbin
- Neuroimaging Laboratories at the Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - Maurizio Corbetta
- Padova Neuroscience Center, University of Padova, Padova, Italy
- Department of Neuroscience, University of Padova, Padova, Italy
| | - John J Lee
- Neuroimaging Laboratories at the Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - Andrei G Vlassenko
- Neuroimaging Laboratories at the Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - Manu S Goyal
- Neuroimaging Laboratories at the Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - Alessandra Bertoldo
- Padova Neuroscience Center, University of Padova, Padova, Italy
- Department of Information Engineering, University of Padova, Padova, Italy
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10
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Guo G, Fan L, Yan Y, Xu Y, Deng Z, Tian M, Geng Y, Xia Z, Xu Y. Shared metabolic shifts in endothelial cells in stroke and Alzheimer's disease revealed by integrated analysis. Sci Data 2023; 10:666. [PMID: 37775708 PMCID: PMC10542331 DOI: 10.1038/s41597-023-02512-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 08/30/2023] [Indexed: 10/01/2023] Open
Abstract
Since metabolic dysregulation is a hallmark of both stroke and Alzheimer's disease (AD), mining shared metabolic patterns in these diseases will help to identify their possible pathogenic mechanisms and potential intervention targets. However, a systematic integration analysis of the metabolic networks of the these diseases is still lacking. In this study, we integrated single-cell RNA sequencing datasets of ischemic stroke (IS), hemorrhagic stroke (HS) and AD models to construct metabolic flux profiles at the single-cell level. We discovered that the three disorders cause shared metabolic shifts in endothelial cells. These altered metabolic modules were mainly enriched in the transporter-related pathways and were predicted to potentially lead to a decrease in metabolites such as pyruvate and fumarate. We further found that Lef1, Elk3 and Fosl1 may be upstream transcriptional regulators causing metabolic shifts and may be possible targets for interventions that halt the course of neurodegeneration.
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Affiliation(s)
- Guangyu Guo
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- NHC Key Laboratory of Prevention and treatment of Cerebrovascular Diseases, Zhengzhou, China
- Clinical Systems Biology Laboratories, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Liyuan Fan
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Academy of Medical Sciences of Zhengzhou University, Zhengzhou, China
| | - Yingxue Yan
- Clinical Systems Biology Laboratories, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Academy of Medical Sciences of Zhengzhou University, Zhengzhou, China
| | - Yunhao Xu
- Clinical Systems Biology Laboratories, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Academy of Medical Sciences of Zhengzhou University, Zhengzhou, China
| | - Zhifen Deng
- Clinical Systems Biology Laboratories, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Miaomiao Tian
- Clinical Systems Biology Laboratories, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yaoqi Geng
- Clinical Systems Biology Laboratories, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Department of Endocrinology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Zongping Xia
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
- NHC Key Laboratory of Prevention and treatment of Cerebrovascular Diseases, Zhengzhou, China.
- Clinical Systems Biology Laboratories, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
| | - Yuming Xu
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
- NHC Key Laboratory of Prevention and treatment of Cerebrovascular Diseases, Zhengzhou, China.
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11
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Voronkova MA, Hansen HL, Cooper MP, Miller J, Sukumar N, Geldenhuys WJ, Robart AR, Webb BA. Cancer-associated somatic mutations in human phosphofructokinase-1 reveal a critical electrostatic interaction for allosteric regulation of enzyme activity. Biochem J 2023; 480:1411-1427. [PMID: 37622331 PMCID: PMC10586780 DOI: 10.1042/bcj20230207] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 08/23/2023] [Accepted: 08/25/2023] [Indexed: 08/26/2023]
Abstract
Metabolic reprogramming, including increased glucose uptake and lactic acid excretion, is a hallmark of cancer. The glycolytic 'gatekeeper' enzyme phosphofructokinase-1 (PFK1), which catalyzes the step committing glucose to breakdown, is dysregulated in cancers. While altered PFK1 activity and expression in tumors have been demonstrated, little is known about the effects of cancer-associated somatic mutations. Somatic mutations in PFK1 inform our understanding of allosteric regulation by identifying key amino acid residues involved in the regulation of enzyme activity. Here, we characterized mutations disrupting an evolutionarily conserved salt bridge between aspartic acid and arginine in human platelet (PFKP) and liver (PFKL) isoforms. Using purified recombinant proteins, we showed that disruption of the Asp-Arg pair in two PFK1 isoforms decreased enzyme activity and altered allosteric regulation. We determined the crystal structure of PFK1 to 3.6 Å resolution and used molecular dynamic simulations to understand molecular mechanisms of altered allosteric regulation. We showed that PFKP-D564N had a decreased total system energy and changes in the electrostatic surface potential of the effector site. Cells expressing PFKP-D564N demonstrated a decreased rate of glycolysis, while their ability to induce glycolytic flux under conditions of low cellular energy was enhanced compared with cells expressing wild-type PFKP. Taken together, these results suggest that mutations in Arg-Asp pair at the interface of the catalytic-regulatory domains stabilizes the t-state and presents novel mechanistic insight for therapeutic development in cancer.
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Affiliation(s)
- Maria A. Voronkova
- Department of Biochemistry, West Virginia University School of Medicine, Morgantown, WV 26506, U.S.A
| | - Heather L. Hansen
- Department of Biochemistry, West Virginia University School of Medicine, Morgantown, WV 26506, U.S.A
| | - Madison P. Cooper
- Department of Biochemistry, West Virginia University School of Medicine, Morgantown, WV 26506, U.S.A
| | - Jacob Miller
- Department of Biochemistry, West Virginia University School of Medicine, Morgantown, WV 26506, U.S.A
| | - Narayanasami Sukumar
- Northeastern Collaborative Access Team Center for Advanced Macromolecular Crystallography, Argonne National Laboratory, Lemont, IL 60439, U.S.A
| | - Werner J. Geldenhuys
- Department of Pharmaceutical Sciences, West Virginia University School of Pharmacy, Morgantown, WV 26506, U.S.A
| | - Aaron R. Robart
- Department of Biochemistry, West Virginia University School of Medicine, Morgantown, WV 26506, U.S.A
| | - Bradley A. Webb
- Department of Biochemistry, West Virginia University School of Medicine, Morgantown, WV 26506, U.S.A
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12
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Sousa T, Moreira PI, Cardoso S. Current Advances in Mitochondrial Targeted Interventions in Alzheimer's Disease. Biomedicines 2023; 11:2331. [PMID: 37760774 PMCID: PMC10525414 DOI: 10.3390/biomedicines11092331] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 08/16/2023] [Accepted: 08/18/2023] [Indexed: 09/29/2023] Open
Abstract
Alzheimer's disease is the most prevalent neurodegenerative disorder and affects the lives not only of those who are diagnosed but also of their caregivers. Despite the enormous social, economic and political burden, AD remains a disease without an effective treatment and with several failed attempts to modify the disease course. The fact that AD clinical diagnosis is most often performed at a stage at which the underlying pathological events are in an advanced and conceivably irremediable state strongly hampers treatment attempts. This raises the awareness of the need to identify and characterize the early brain changes in AD, in order to identify possible novel therapeutic targets to circumvent AD's cascade of events. One of the most auspicious targets is mitochondria, powerful organelles found in nearly all cells of the body. A vast body of literature has shown that mitochondria from AD patients and model organisms of the disease differ from their non-AD counterparts. In view of this evidence, preserving and/or restoring mitochondria's health and function can represent the primary means to achieve advances to tackle AD. In this review, we will briefly assess and summarize the previous and latest evidence of mitochondria dysfunction in AD. A particular focus will be given to the recent updates and advances in the strategy options aimed to target faulty mitochondria in AD.
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Affiliation(s)
- Tiago Sousa
- Faculty of Medicine, University of Coimbra, 3000-370 Coimbra, Portugal;
| | - Paula I. Moreira
- CNC—Center for Neuroscience and Cell Biology, University of Coimbra, 3004-504 Coimbra, Portugal;
- CIBB—Center for Innovative Biomedicine and Biotechnology, University of Coimbra, 3004-504 Coimbra, Portugal
- Institute of Physiology, Faculty of Medicine, University of Coimbra, 3000-370 Coimbra, Portugal
| | - Susana Cardoso
- CNC—Center for Neuroscience and Cell Biology, University of Coimbra, 3004-504 Coimbra, Portugal;
- CIBB—Center for Innovative Biomedicine and Biotechnology, University of Coimbra, 3004-504 Coimbra, Portugal
- IIIUC—Institute for Interdisciplinary Research, University of Coimbra, 3030-789 Coimbra, Portugal
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13
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Murai T, Matsuda S. Metabolic Reprogramming toward Aerobic Glycolysis and the Gut Microbiota Involved in the Brain Amyloid Pathology. BIOLOGY 2023; 12:1081. [PMID: 37626967 PMCID: PMC10452252 DOI: 10.3390/biology12081081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 08/02/2023] [Accepted: 08/02/2023] [Indexed: 08/27/2023]
Abstract
Alzheimer's disease (AD) is characterized by the formation of senile plaques consisting of fibrillated amyloid-β (Aβ), dystrophic neurites, and the neurofibrillary tangles of tau. The oligomers/fibrillar Aβ damages the neurons or initiates an intracellular signaling cascade for neuronal cell death leading to Aβ toxicity. The Aβ is a 4 kDa molecular weight peptide originating from the C-terminal region of the amyloid precursor protein via proteolytic cleavage. Apart from the typical AD hallmarks, certain deficits in metabolic alterations have been identified. This study describes the emerging features of AD from the aspect of metabolic reprogramming in the main pathway of carbohydrate metabolism in the human brain. Particularly, the neurons in patients with AD favor glycolysis despite a normal mitochondrial function indicating a Warburg-like effect. In addition, certain dietary patterns are well known for their properties in preventing AD. Among those, a ketogenic diet may substantially improve the symptoms of AD. An effective therapeutic method for the treatment, mitigation, and prevention of AD has not yet been established. Therefore, the researchers pursue the development and establishment of novel therapies effective in suppressing AD symptoms and the elucidation of their underlying protective mechanisms against neurodegeneration aiming for AD therapy in the near future.
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Affiliation(s)
- Toshiyuki Murai
- Graduate School of Medicine, Osaka University, 2-2 Yamada-oka, Suita 565-0871, Japan;
| | - Satoru Matsuda
- Department of Food Science and Nutrition, Nara Women’s University, Kita-Uoya Nishimachi, Nara 630-8506, Japan
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14
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Palanca BJA, Kafashan M, Guay CS. In Response. Anesth Analg 2023; 136:e37-e38. [PMID: 37205821 PMCID: PMC10721118 DOI: 10.1213/ane.0000000000006492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Affiliation(s)
- Ben Julian A Palanca
- Department of Anesthesiology, Washington University School of Medicine in St. Louis, St. Louis, Missouri, Center on Biological Rhythms and Sleep, Washington University School of Medicine in St. Louis, St. Louis, Missouri, Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, Missouri, Division of Biology and Biomedical Sciences, Washington University School of Medicine in St. Louis, St. Louis, Missouri, Department of Biomedical Engineering, Washington University School of Medicine in St. Louis, St. Louis, Missouri,
| | - MohammadMehdi Kafashan
- Department of Anesthesiology, Washington University School of Medicine in St. Louis, St. Louis, Missouri, Center on Biological Rhythms and Sleep, Washington University School of Medicine in St. Louis, St. Louis, Missouri
| | - Christian S Guay
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts
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15
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Millar PR, Gordon BA, Luckett PH, Benzinger TLS, Cruchaga C, Fagan AM, Hassenstab JJ, Perrin RJ, Schindler SE, Allegri RF, Day GS, Farlow MR, Mori H, Nübling G, Bateman RJ, Morris JC, Ances BM. Multimodal brain age estimates relate to Alzheimer disease biomarkers and cognition in early stages: a cross-sectional observational study. eLife 2023; 12:e81869. [PMID: 36607335 PMCID: PMC9988262 DOI: 10.7554/elife.81869] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 12/30/2022] [Indexed: 01/07/2023] Open
Abstract
Background Estimates of 'brain-predicted age' quantify apparent brain age compared to normative trajectories of neuroimaging features. The brain age gap (BAG) between predicted and chronological age is elevated in symptomatic Alzheimer disease (AD) but has not been well explored in presymptomatic AD. Prior studies have typically modeled BAG with structural MRI, but more recently other modalities, including functional connectivity (FC) and multimodal MRI, have been explored. Methods We trained three models to predict age from FC, structural (S), or multimodal MRI (S+FC) in 390 amyloid-negative cognitively normal (CN/A-) participants (18-89 years old). In independent samples of 144 CN/A-, 154 CN/A+, and 154 cognitively impaired (CI; CDR > 0) participants, we tested relationships between BAG and AD biomarkers of amyloid and tau, as well as a global cognitive composite. Results All models predicted age in the control training set, with the multimodal model outperforming the unimodal models. All three BAG estimates were significantly elevated in CI compared to controls. FC-BAG was significantly reduced in CN/A+ participants compared to CN/A-. In CI participants only, elevated S-BAG and S+FC BAG were associated with more advanced AD pathology and lower cognitive performance. Conclusions Both FC-BAG and S-BAG are elevated in CI participants. However, FC and structural MRI also capture complementary signals. Specifically, FC-BAG may capture a unique biphasic response to presymptomatic AD pathology, while S-BAG may capture pathological progression and cognitive decline in the symptomatic stage. A multimodal age-prediction model improves sensitivity to healthy age differences. Funding This work was supported by the National Institutes of Health (P01-AG026276, P01- AG03991, P30-AG066444, 5-R01-AG052550, 5-R01-AG057680, 1-R01-AG067505, 1S10RR022984-01A1, and U19-AG032438), the BrightFocus Foundation (A2022014F), and the Alzheimer's Association (SG-20-690363-DIAN).
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Affiliation(s)
- Peter R Millar
- Department of Neurology, Washington University in St. LouisSt LouisUnited States
| | - Brian A Gordon
- Department of Radiology, Washington University in St. LouisSt LouisUnited States
| | - Patrick H Luckett
- Department of Neurosurgery, Washington University in St. LouisSt LouisUnited States
| | - Tammie LS Benzinger
- Department of Radiology, Washington University in St. LouisSt LouisUnited States
- Department of Neurosurgery, Washington University in St. LouisSt LouisUnited States
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University in St. LouisSt LouisUnited States
| | - Anne M Fagan
- Department of Neurology, Washington University in St. LouisSt LouisUnited States
| | - Jason J Hassenstab
- Department of Neurology, Washington University in St. LouisSt LouisUnited States
| | - Richard J Perrin
- Department of Neurology, Washington University in St. LouisSt LouisUnited States
- Department of Pathology and Immunology, Washington University in St. LouisSt LouisUnited States
| | - Suzanne E Schindler
- Department of Neurology, Washington University in St. LouisSt LouisUnited States
| | - Ricardo F Allegri
- Department of Cognitive Neurology, Institute for Neurological Research (FLENI)Buenos AiresArgentina
| | - Gregory S Day
- Department of Neurology, Mayo Clinic FloridaJacksonvilleUnited States
| | - Martin R Farlow
- Department of Neurology, Indiana University School of MedicineIndianapolisUnited States
| | - Hiroshi Mori
- Department of Clinical Neuroscience, Osaka Metropolitan University Medical School, Nagaoka Sutoku UniversityOsakaJapan
| | - Georg Nübling
- Department of Neurology, Ludwig-Maximilians UniversityMunichGermany
- German Center for Neurodegenerative DiseasesMunichGermany
| | - Randall J Bateman
- Department of Neurology, Washington University in St. LouisSt LouisUnited States
| | - John C Morris
- Department of Neurology, Washington University in St. LouisSt LouisUnited States
| | - Beau M Ances
- Department of Neurology, Washington University in St. LouisSt LouisUnited States
- Department of Radiology, Washington University in St. LouisSt LouisUnited States
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