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LeVine SM. Exploring Potential Mechanisms Accounting for Iron Accumulation in the Central Nervous System of Patients with Alzheimer's Disease. Cells 2024; 13:689. [PMID: 38667304 PMCID: PMC11049304 DOI: 10.3390/cells13080689] [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: 04/04/2024] [Revised: 04/12/2024] [Accepted: 04/14/2024] [Indexed: 04/28/2024] Open
Abstract
Elevated levels of iron occur in both cortical and subcortical regions of the CNS in patients with Alzheimer's disease. This accumulation is present early in the disease process as well as in more advanced stages. The factors potentially accounting for this increase are numerous, including: (1) Cells increase their uptake of iron and reduce their export of iron, as iron becomes sequestered (trapped within the lysosome, bound to amyloid β or tau, etc.); (2) metabolic disturbances, such as insulin resistance and mitochondrial dysfunction, disrupt cellular iron homeostasis; (3) inflammation, glutamate excitotoxicity, or other pathological disturbances (loss of neuronal interconnections, soluble amyloid β, etc.) trigger cells to acquire iron; and (4) following neurodegeneration, iron becomes trapped within microglia. Some of these mechanisms are also present in other neurological disorders and can also begin early in the disease course, indicating that iron accumulation is a relatively common event in neurological conditions. In response to pathogenic processes, the directed cellular efforts that contribute to iron buildup reflect the importance of correcting a functional iron deficiency to support essential biochemical processes. In other words, cells prioritize correcting an insufficiency of available iron while tolerating deposited iron. An analysis of the mechanisms accounting for iron accumulation in Alzheimer's disease, and in other relevant neurological conditions, is put forward.
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Affiliation(s)
- Steven M LeVine
- Department of Cell Biology and Physiology, University of Kansas Medical Center, 3901 Rainbow Blvd., Mail Stop 3043, Kansas City, KS 66160, USA
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Bian B, Hou L, Chai Y, Jiang Y, Pan X, Sun Y, Wang H, Qiu D, Yu Z, Zhao H, Zhang H, Meng F, Zhang L. Visualizing the Habenula Using 3T High-Resolution MP2RAGE and QSM: A Preliminary Study. AJNR Am J Neuroradiol 2024; 45:504-510. [PMID: 38453416 PMCID: PMC11288573 DOI: 10.3174/ajnr.a8156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Accepted: 12/18/2023] [Indexed: 03/09/2024]
Abstract
BACKGROUND AND PURPOSE The habenula is a key node in the regulation of emotion-related behavior. Accurate visualization of the habenula and its reliable quantitative analysis is vital for the assessment of psychiatric disorders. To obtain high-contrast habenula images and allow them to be compatible with clinical applications, this preliminary study compared 3T MP2RAGE and quantitative susceptibility mapping with MPRAGE by evaluating the habenula segmentation performance. MATERIALS AND METHODS Ten healthy volunteers were scanned twice with 3T MPRAGE and MP2RAGE and once with quantitative susceptibility mapping. Image quality and visibility of habenula anatomic features were analyzed by 3 radiologists using a 5-point scale. Contrast assessments of the habenula and thalamus were also performed. The reproducibility of the habenula volume from MPRAGE and MP2RAGE was evaluated by manual segmentation and the Multiple Automatically Generated Template brain segmentation algorithm (MAGeTbrain). T1 values and susceptibility were measured in the whole habenula and habenula geometric subregion using MP2RAGE T1-mapping and quantitative susceptibility mapping. RESULTS The 3T MP2RAGE and quantitative susceptibility mapping demonstrated clear boundaries and anatomic features of the habenula compared with MPRAGE, with a higher SNR and contrast-to-noise ratio (all P < .05). Additionally, 3T MP2RAGE provided reliable habenula manual and MAGeTbrain segmentation volume estimates with greater reproducibility. T1-mapping derived from MP2RAGE was highly reliable, and susceptibility contrast was highly nonuniform within the habenula. CONCLUSIONS We identified an optimized sequence combination (3T MP2RAGE combined with quantitative susceptibility mapping) that may be useful for enhancing habenula visualization and yielding more reliable quantitative data.
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Affiliation(s)
- BingYang Bian
- From the Department of Radiology (B.B., L.H., Y.C., X.P., Y.S., H.W., D.Q., H. Zhang, F.M., L.Z.), Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, The First Hospital of Jilin University, Changchun, Jilin, China
| | - Lin Hou
- From the Department of Radiology (B.B., L.H., Y.C., X.P., Y.S., H.W., D.Q., H. Zhang, F.M., L.Z.), Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, The First Hospital of Jilin University, Changchun, Jilin, China
| | - YaTing Chai
- From the Department of Radiology (B.B., L.H., Y.C., X.P., Y.S., H.W., D.Q., H. Zhang, F.M., L.Z.), Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, The First Hospital of Jilin University, Changchun, Jilin, China
| | - YueLuan Jiang
- MR Scientific Marketing, Diagnostic Imaging (Y.J.), Siemens Healthineers Ltd, Beijing, China
| | - XingChen Pan
- From the Department of Radiology (B.B., L.H., Y.C., X.P., Y.S., H.W., D.Q., H. Zhang, F.M., L.Z.), Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, The First Hospital of Jilin University, Changchun, Jilin, China
| | - Yang Sun
- From the Department of Radiology (B.B., L.H., Y.C., X.P., Y.S., H.W., D.Q., H. Zhang, F.M., L.Z.), Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, The First Hospital of Jilin University, Changchun, Jilin, China
| | - HongChao Wang
- From the Department of Radiology (B.B., L.H., Y.C., X.P., Y.S., H.W., D.Q., H. Zhang, F.M., L.Z.), Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, The First Hospital of Jilin University, Changchun, Jilin, China
| | - DongDong Qiu
- From the Department of Radiology (B.B., L.H., Y.C., X.P., Y.S., H.W., D.Q., H. Zhang, F.M., L.Z.), Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, The First Hospital of Jilin University, Changchun, Jilin, China
| | - ZeChen Yu
- Siemens Healthineers Digital Technology (Shanghai) Co Ltd (Z.Y.), Shanghai, China
| | - Hua Zhao
- Department of Physiology (H. Zhao), College of Basic Medical Sciences, Jilin University, Changchun, Jilin, China
| | - HuiMao Zhang
- From the Department of Radiology (B.B., L.H., Y.C., X.P., Y.S., H.W., D.Q., H. Zhang, F.M., L.Z.), Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, The First Hospital of Jilin University, Changchun, Jilin, China
| | - FanYang Meng
- From the Department of Radiology (B.B., L.H., Y.C., X.P., Y.S., H.W., D.Q., H. Zhang, F.M., L.Z.), Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, The First Hospital of Jilin University, Changchun, Jilin, China
| | - Lei Zhang
- From the Department of Radiology (B.B., L.H., Y.C., X.P., Y.S., H.W., D.Q., H. Zhang, F.M., L.Z.), Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, The First Hospital of Jilin University, Changchun, Jilin, China
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Bjørklund G, Đorđević AB, Hamdan H, Wallace DR, Peana M. Metal-induced autoimmunity in neurological disorders: A review of current understanding and future directions. Autoimmun Rev 2024; 23:103509. [PMID: 38159894 DOI: 10.1016/j.autrev.2023.103509] [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: 12/08/2023] [Accepted: 12/21/2023] [Indexed: 01/03/2024]
Abstract
Autoimmunity is a multifaceted disorder influenced by both genetic and environmental factors, and metal exposure has been implicated as a potential catalyst, especially in autoimmune diseases affecting the central nervous system. Notably, metals like mercury, lead, and aluminum exhibit well-established neurotoxic effects, yet the precise mechanisms by which they elicit autoimmune responses in susceptible individuals remain unclear. Recent studies propose that metal-induced autoimmunity may arise from direct toxic effects on immune cells and tissues, coupled with indirect impacts on the gut microbiome and the blood-brain barrier. These effects can activate self-reactive T cells, prompting the production of autoantibodies, inflammatory responses, and tissue damage. Diagnosing metal-induced autoimmunity proves challenging due to nonspecific symptoms and a lack of reliable biomarkers. Treatment typically involves chelation therapy to eliminate excess metals and immunomodulatory agents to suppress autoimmune responses. Prevention strategies include lifestyle adjustments to reduce metal exposure and avoiding occupational and environmental risks. Prognosis is generally favorable with proper treatment; however, untreated cases may lead to autoimmune disorder progression and irreversible organ damage, particularly in the brain. Future research aims to identify genetic and environmental risk factors, enhance diagnostic precision, and explore novel treatment approaches for improved prevention and management of this intricate and debilitating disease.
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Affiliation(s)
- Geir Bjørklund
- Council for Nutritional and Environmental Medicine, Mo i Rana, Norway.
| | | | - Halla Hamdan
- Department of Pharmacology, Oklahoma State University Center for Health Sciences, Tulsa, OK, United States
| | - David R Wallace
- Department of Pharmacology, Oklahoma State University Center for Health Sciences, Tulsa, OK, United States
| | - Massimiliano Peana
- Department of Chemical, Physical, Mathematical and Natural Sciences, University of Sassari, Italy.
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Adamczyk-Sowa M, Sowa P, Mucha S, Zostawa J, Mazur B, Owczarek M, Pierzchała K. Changes in Serum Ceruloplasmin Levels Based on Immunomodulatory Treatments and Melatonin Supplementation in Multiple Sclerosis Patients. Med Sci Monit 2016; 22:2484-91. [PMID: 27420299 PMCID: PMC4958372 DOI: 10.12659/msm.895702] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND The cause of multiple sclerosis (MS) is currently unknown, but it is thought that oxidative damage and iron metabolism mechanisms are involved. The aim of this study was to examine ceruloplasmin concentration in MS patients based on various immunomodifying therapies and to test the effect of antioxidative melatonin on ceruloplasmin levels. MATERIAL AND METHODS This prospective study included 102 MS patients and 15 healthy controls. Patients were divided into groups according to different immunomodifying therapies: interferons beta 1a, interferons beta 1b, glatiramer acetate, mitoxantrone, and immunomodifying pre-treatment (A, B, G, Mx, and P groups, respectively), and the relapse R group. MS patients were supplemented with melatonin for 3 months. Serum ceruloplasmin concentrations, EDSS, brain MRI, serum C-reactive protein level, and white blood cell count were examined. RESULTS The results indicated significantly increased levels of ceruloplasmin in MS patients. No differences in ceruloplasmin concentrations between the relapse group and controls were observed. In A and G groups, ceruloplasmin levels before and after melatonin were similar to levels in controls. In group B, ceruloplasmin concentration was significantly higher vs. control and relapse groups. After melatonin administration in group B, ceruloplasmin levels decreased. Ceruloplasmin concentrations in the Mx group were significantly higher compared to controls. CONCLUSIONS We found for the first time that ceruloplasmin concentration in MS patients varies depending on different immunomodulatory treatment and decrease after 3 months of melatonin administration. Ceruloplasmin could be a valuable serum marker for the chronic demyelinating process participating in oxidative stress mechanisms, as well as a neurodegenerative marker, but not a marker of acute-phase MS.
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Affiliation(s)
- Monika Adamczyk-Sowa
- Department of Neurology in Zabrze, Medical University of Silesia in Katowice, Zabrze, Poland
| | - Paweł Sowa
- ENT Department, Medical University of Silesia in Katowice, Zabrze, Poland
| | - Sebastian Mucha
- Department of Neurology in Zabrze, Medical University of Silesia in Katowice, Zabrze, Poland
| | - Jacek Zostawa
- Department of Neurology in Zabrze, Medical University of Silesia in Katowice, Zabrze, Poland
| | - Bogdan Mazur
- Department of Microbiology and Immunology, Medical University of Silesia in Katowice, Zabrze, Poland
| | - Maciej Owczarek
- Department of Neurology in Zabrze, Medical University of Silesia in Katowice, Zabrze, Poland
| | - Krystyna Pierzchała
- Department of Neurology in Zabrze, Medical University of Silesia in Katowice, Zabrze, Poland
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