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Zeidan RS, Martenson M, Tamargo JA, McLaren C, Ezzati A, Lin Y, Yang JJ, Yoon HS, McElroy T, Collins JF, Leeuwenburgh C, Mankowski RT, Anton S. Iron homeostasis in older adults: balancing nutritional requirements and health risks. J Nutr Health Aging 2024; 28:100212. [PMID: 38489995 DOI: 10.1016/j.jnha.2024.100212] [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: 12/13/2023] [Revised: 03/05/2024] [Accepted: 03/06/2024] [Indexed: 03/17/2024]
Abstract
Iron plays a crucial role in many physiological processes, including oxygen transport, bioenergetics, and immune function. Iron is assimilated from food and also recycled from senescent red blood cells. Iron exists in two dietary forms: heme (animal based) and non-heme (mostly plant based). The body uses iron for metabolic purposes, and stores the excess mainly in splenic and hepatic macrophages. Physiologically, iron excretion in humans is inefficient and not highly regulated, so regulation of intestinal absorption maintains iron homeostasis. Iron losses occur at a steady rate via turnover of the intestinal epithelium, blood loss, and exfoliation of dead skin cells, but overall iron homeostasis is tightly controlled at cellular and systemic levels. Aging can have a profound impact on iron homeostasis and induce a dyshomeostasis where iron deficiency or overload (sometimes both simultaneously) can occur, potentially leading to several disorders and pathologies. To maintain physiologically balanced iron levels, reduce risk of disease, and promote healthy aging, it is advisable for older adults to follow recommended daily intake guidelines and periodically assess iron levels. Clinicians can evaluate body iron status using different techniques but selecting an assessment method primarily depends on the condition being examined. This review provides a comprehensive overview of the forms, sources, and metabolism of dietary iron, associated disorders of iron dyshomeostasis, assessment of iron levels in older adults, and nutritional guidelines and strategies to maintain iron balance in older adults.
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Affiliation(s)
- Rola S Zeidan
- Department of Physiology and Aging, College of Medicine, University of Florida, Gainesville, FL, USA; Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Matthew Martenson
- Department of Physiology and Aging, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Javier A Tamargo
- Department of Physiology and Aging, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Christian McLaren
- Department of Clinical and Health Psychology, College of Health and Health Professions, University of Florida, Gainesville, Florida, USA
| | - Armin Ezzati
- Department of Physiology and Aging, College of Medicine, University of Florida, Gainesville, FL, USA; Department of Food, Nutrition, Dietetics and Health, Kansas State University, Manhattan, KS, USA
| | - Yi Lin
- Department of Physiology and Aging, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Jae Jeong Yang
- UF Health Cancer Center, Gainesville, FL, USA; Department of Surgery, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Hyung-Suk Yoon
- UF Health Cancer Center, Gainesville, FL, USA; Department of Surgery, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Taylor McElroy
- Department of Physiology and Aging, College of Medicine, University of Florida, Gainesville, FL, USA; Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - James F Collins
- Department of Food Science & Human Nutrition, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL, USA
| | - Christiaan Leeuwenburgh
- Department of Physiology and Aging, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Robert T Mankowski
- Department of Physiology and Aging, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Stephen Anton
- Department of Physiology and Aging, College of Medicine, University of Florida, Gainesville, FL, USA; Department of Clinical and Health Psychology, College of Health and Health Professions, University of Florida, Gainesville, Florida, USA.
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Madden DJ, Merenstein JL. Quantitative susceptibility mapping of brain iron in healthy aging and cognition. Neuroimage 2023; 282:120401. [PMID: 37802405 PMCID: PMC10797559 DOI: 10.1016/j.neuroimage.2023.120401] [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: 07/31/2023] [Revised: 09/14/2023] [Accepted: 09/30/2023] [Indexed: 10/10/2023] Open
Abstract
Quantitative susceptibility mapping (QSM) is a magnetic resonance imaging (MRI) technique that can assess the magnetic properties of cerebral iron in vivo. Although brain iron is necessary for basic neurobiological functions, excess iron content disrupts homeostasis, leads to oxidative stress, and ultimately contributes to neurodegenerative disease. However, some degree of elevated brain iron is present even among healthy older adults. To better understand the topographical pattern of iron accumulation and its relation to cognitive aging, we conducted an integrative review of 47 QSM studies of healthy aging, with a focus on five distinct themes. The first two themes focused on age-related increases in iron accumulation in deep gray matter nuclei versus the cortex. The overall level of iron is higher in deep gray matter nuclei than in cortical regions. Deep gray matter nuclei vary with regard to age-related effects, which are most prominent in the putamen, and age-related deposition of iron is also observed in frontal, temporal, and parietal cortical regions during healthy aging. The third theme focused on the behavioral relevance of iron content and indicated that higher iron in both deep gray matter and cortical regions was related to decline in fluid (speed-dependent) cognition. A handful of multimodal studies, reviewed in the fourth theme, suggest that iron interacts with imaging measures of brain function, white matter degradation, and the accumulation of neuropathologies. The final theme concerning modifiers of brain iron pointed to potential roles of cardiovascular, dietary, and genetic factors. Although QSM is a relatively recent tool for assessing cerebral iron accumulation, it has significant promise for contributing new insights into healthy neurocognitive aging.
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Affiliation(s)
- David J Madden
- Brain Imaging and Analysis Center, Duke University Medical Center, Box 3918, Durham, NC 27710, USA; Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC 27710, USA; Center for Cognitive Neuroscience, Duke University, Durham, NC 27708, USA.
| | - Jenna L Merenstein
- Brain Imaging and Analysis Center, Duke University Medical Center, Box 3918, Durham, NC 27710, USA
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Li N, Duan YH, Chen L, Zhang K. Iron metabolism: An emerging therapeutic target underlying the anti-Alzheimer's disease effect of ginseng. J Trace Elem Med Biol 2023; 79:127252. [PMID: 37418790 DOI: 10.1016/j.jtemb.2023.127252] [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] [Received: 08/16/2022] [Revised: 06/05/2023] [Accepted: 06/22/2023] [Indexed: 07/09/2023]
Abstract
Finding neuroprotective drugs with fewer side effects and more efficacy has become a major problem as the global prevalence of Alzheimer's disease (AD) rises. Natural drugs have risen to prominence as potential medication candidates. Ginseng has a long history of use in China, and it has a wide range of pharmacological actions that can help with neurological issues. Iron loaded in the brain has been linked to AD pathogenesis. We reviewed the regulation of iron metabolism and its studies in AD and explored how ginseng might regulate iron metabolism and prevent or treat AD. Researchers utilized network pharmacology analysis to identify key factive components of ginseng that protect against AD by regulating ferroptosis. Ginseng and its active ingredients may benefit AD by regulating iron metabolism and targeting ferroptosis genes to inhibit the ferroptosis process. The results present new ideas for ginseng pharmacological studies and initiatives for further research into AD-related drugs. To provide comprehensive information on the neuroprotective use of ginseng to modulate iron metabolism, reveal its potential to treat AD, and provide insights for future research opportunities.
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Affiliation(s)
- Nan Li
- Department of Clinical Laboratory, The Second Hospital of Jilin University, Changchun, China
| | - Yu-Han Duan
- Department of Clinical Laboratory, The Second Hospital of Jilin University, Changchun, China
| | - Lei Chen
- Department of Clinical Laboratory, The Second Hospital of Jilin University, Changchun, China
| | - Kun Zhang
- Department of Medical Research Center, The Second Hospital of Jilin University, Changchun, China.
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Adams AR, Li X, Byanyima JI, Vesslee SA, Nguyen TD, Wang Y, Moon B, Pond T, Kranzler HR, Witschey WR, Shi Z, Wiers CE. Peripheral and Central Iron Measures in Alcohol Use Disorder and Aging: A Quantitative Susceptibility Mapping Pilot Study. Int J Mol Sci 2023; 24:4461. [PMID: 36901892 PMCID: PMC10002495 DOI: 10.3390/ijms24054461] [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: 02/05/2023] [Revised: 02/18/2023] [Accepted: 02/21/2023] [Indexed: 02/26/2023] Open
Abstract
Chronic excessive alcohol use has neurotoxic effects, which may contribute to cognitive decline and the risk of early-onset dementia. Elevated peripheral iron levels have been reported in individuals with alcohol use disorder (AUD), but its association with brain iron loading has not been explored. We evaluated whether (1) serum and brain iron loading are higher in individuals with AUD than non-dependent healthy controls and (2) serum and brain iron loading increase with age. A fasting serum iron panel was obtained and a magnetic resonance imaging scan with quantitative susceptibility mapping (QSM) was used to quantify brain iron concentrations. Although serum ferritin levels were higher in the AUD group than in controls, whole-brain iron susceptibility did not differ between groups. Voxel-wise QSM analyses revealed higher susceptibility in a cluster in the left globus pallidus in individuals with AUD than controls. Whole-brain iron increased with age and voxel-wise QSM indicated higher susceptibility with age in various brain areas including the basal ganglia. This is the first study to analyze both serum and brain iron loading in individuals with AUD. Larger studies are needed to examine the effects of alcohol use on iron loading and its associations with alcohol use severity, structural and functional brain changes, and alcohol-induced cognitive impairments.
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Affiliation(s)
- Aiden R. Adams
- Center for Studies of Addiction, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, 3535 Market St Ste 500, Philadelphia, PA 19104, USA
| | - Xinyi Li
- Center for Studies of Addiction, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, 3535 Market St Ste 500, Philadelphia, PA 19104, USA
| | - Juliana I. Byanyima
- Center for Studies of Addiction, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, 3535 Market St Ste 500, Philadelphia, PA 19104, USA
| | - Sianneh A. Vesslee
- Center for Studies of Addiction, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, 3535 Market St Ste 500, Philadelphia, PA 19104, USA
| | - Thanh D. Nguyen
- Department of Radiology, Weill Cornell Medicine, 525 E 68th St, New York, NY 10065, USA
| | - Yi Wang
- Department of Radiology, Weill Cornell Medicine, 525 E 68th St, New York, NY 10065, USA
| | - Brianna Moon
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd, South Pavilion, Room 11-155, Philadelphia, PA 19104, USA
| | - Timothy Pond
- Center for Studies of Addiction, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, 3535 Market St Ste 500, Philadelphia, PA 19104, USA
| | - Henry R. Kranzler
- Center for Studies of Addiction, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, 3535 Market St Ste 500, Philadelphia, PA 19104, USA
| | - Walter R. Witschey
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd, South Pavilion, Room 11-155, Philadelphia, PA 19104, USA
| | - Zhenhao Shi
- Center for Studies of Addiction, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, 3535 Market St Ste 500, Philadelphia, PA 19104, USA
| | - Corinde E. Wiers
- Center for Studies of Addiction, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, 3535 Market St Ste 500, Philadelphia, PA 19104, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd, South Pavilion, Room 11-155, Philadelphia, PA 19104, USA
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Lumsden AL, Mulugeta A, Mäkinen V, Hyppönen E. Metabolic profile-based subgroups can identify differences in brain volumes and brain iron deposition. Diabetes Obes Metab 2023; 25:121-131. [PMID: 36053807 PMCID: PMC10946804 DOI: 10.1111/dom.14853] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 08/16/2022] [Accepted: 08/28/2022] [Indexed: 12/14/2022]
Abstract
AIMS To evaluate associations of metabolic profiles and biomarkers with brain atrophy, lesions, and iron deposition to understand the early risk factors associated with dementia. MATERIALS AND METHODS Using data from 26 239 UK Biobank participants free from dementia and stroke, we assessed the associations of metabolic subgroups, derived using an artificial neural network approach (self-organizing map), and 39 individual biomarkers with brain MRI measures: total brain volume (TBV), grey matter volume (GMV), white matter volume (WMV), hippocampal volume (HV), white matter hyperintensity (WMH) volume, and caudate iron deposition. RESULTS In metabolic subgroup analyses, participants characterized by high triglycerides and liver enzymes showed the most adverse brain outcomes compared to the healthy reference subgroup with high-density lipoprotein cholesterol and low body mass index (BMI) including associations with GMV (βstandardized -0.20, 95% confidence interval [CI] -0.24 to -0.16), HV (βstandardized -0.09, 95% CI -0.13 to -0.04), WMH volume (βstandardized 0.22, 95% CI 0.18 to 0.26), and caudate iron deposition (βstandardized 0.30, 95% CI 0.25 to 0.34), with similar adverse associations for the subgroup with high BMI, C-reactive protein and cystatin C, and the subgroup with high blood pressure (BP) and apolipoprotein B. Among the biomarkers, striking associations were seen between basal metabolic rate (BMR) and caudate iron deposition (βstandardized 0.23, 95% CI 0.22 to 0.24 per 1 SD increase), GMV (βstandardized -0.15, 95% CI -0.16 to -0.14) and HV (βstandardized -0.11, 95% CI -0.12 to -0.10), and between BP and WMH volume (βstandardized 0.13, 95% CI 0.12 to 0.14 for diastolic BP). CONCLUSIONS Metabolic profiles were associated differentially with brain neuroimaging characteristics. Associations of BMR, BP and other individual biomarkers may provide insights into actionable mechanisms driving these brain associations.
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Affiliation(s)
- Amanda L. Lumsden
- Australian Centre for Precision Health, Unit of Clinical and Health SciencesUniversity of South AustraliaAdelaideAustralia
- South Australian Health and Medical Research InstituteAdelaideAustralia
| | - Anwar Mulugeta
- Australian Centre for Precision Health, Unit of Clinical and Health SciencesUniversity of South AustraliaAdelaideAustralia
- South Australian Health and Medical Research InstituteAdelaideAustralia
- Department of Pharmacology and Clinical PharmacyCollege of Health SciencesAddis AbabaEthiopia
| | - Ville‐Petteri Mäkinen
- South Australian Health and Medical Research InstituteAdelaideAustralia
- Computational Systems Biology Program, Precision Medicine ThemeSouth Australian Health and Medical Research InstituteAdelaideAustralia
| | - Elina Hyppönen
- Australian Centre for Precision Health, Unit of Clinical and Health SciencesUniversity of South AustraliaAdelaideAustralia
- South Australian Health and Medical Research InstituteAdelaideAustralia
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Spence H, McNeil CJ, Waiter GD. Cognition and brain iron deposition in whole grey matter regions and hippocampal subfields. Eur J Neurosci 2022; 56:6039-6054. [PMID: 36215153 PMCID: PMC10092357 DOI: 10.1111/ejn.15838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 09/26/2022] [Accepted: 10/03/2022] [Indexed: 12/29/2022]
Abstract
Regional brain iron accumulation is observed in many neurodegenerative diseases, including Alzheimer's disease and Parkinson's disease, and is associated with cognitive decline. We explored associations between age, cognition and iron content in grey matter regions and hippocampal subfields in 380 participants of the Aberdeen children of the 1950s cohort and their first-generation relatives (aged 26-72 years). Participants underwent cognitive assessment at the time of MRI scanning. Quantitative susceptibility mapping of these MRI data was used to assess iron content in grey matter regions and in hippocampal subfields. Principle component analysis was performed on cognitive test scores to create a general cognition score. Spline analysis was used with the Akaike information criterion to determine if order 1, 2 or 3 natural splines were optimal for assessing non-linear relationships between regional iron and age. Multivariate linear models were used to assess associations between regional iron and cognition. Higher iron correlated with older age in the left putamen across all ages and in the right putamen of only participants over 58. Whereas a decrease in iron with older age was observed in the right thalamus and left pallidum across all ages. Right amygdala iron levels were associated with poorer general cognition scores and poorer immediate recall scores. Iron was not associated with any measures of cognitive performance in other regions of interest. Our results suggest that, whilst iron in some regions was associated with cognitive performance, there is an overall lack of association between regional iron content and cognitive ability in cognitively healthy individuals.
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Affiliation(s)
- Holly Spence
- Aberdeen Biomedical Imaging Centre, Institute of Medical Sciences, University of Aberdeen, Aberdeen, UK
| | - Chris J McNeil
- Aberdeen Biomedical Imaging Centre, Institute of Medical Sciences, University of Aberdeen, Aberdeen, UK
| | - Gordon D Waiter
- Aberdeen Biomedical Imaging Centre, Institute of Medical Sciences, University of Aberdeen, Aberdeen, UK
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Bauer CE, Zachariou V, Maillard P, Caprihan A, Gold BT. Multi-compartment diffusion magnetic resonance imaging models link tract-related characteristics with working memory performance in healthy older adults. Front Aging Neurosci 2022; 14:995425. [PMID: 36275003 PMCID: PMC9581239 DOI: 10.3389/fnagi.2022.995425] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 09/09/2022] [Indexed: 11/25/2022] Open
Abstract
Multi-compartment diffusion MRI metrics [such as metrics from free water elimination diffusion tensor imaging (FWE-DTI) and neurite orientation dispersion and density imaging (NODDI)] may reflect more specific underlying white-matter tract characteristics than traditional, single-compartment metrics [i.e., metrics from Diffusion Tensor Imaging (DTI)]. However, it remains unclear if multi-compartment metrics are more closely associated with age and/or cognitive performance than single-compartment metrics. Here we compared the associations of single-compartment [Fractional Anisotropy (FA)] and multi-compartment diffusion MRI metrics [FWE-DTI metrics: Free Water Eliminated Fractional Anisotropy (FWE-FA) and Free Water (FW); NODDI metrics: Intracellular Volume Fraction (ICVF), Orientation Dispersion Index (ODI), and CSF-Fraction] with both age and working memory performance. A functional magnetic resonance imaging (fMRI) guided, white matter tractography approach was employed to compute diffusion metrics within a network of tracts connecting functional regions involved in working memory. Ninety-nine healthy older adults (aged 60-85) performed an in-scanner working memory task while fMRI was performed and also underwent multi-shell diffusion acquisition. The network of white matter tracts connecting functionally-activated regions was identified using probabilistic tractography. Diffusion metrics were extracted from skeletonized white matter tracts connecting fMRI activation peaks. Diffusion metrics derived from both single and multi-compartment models were associated with age (p s ≤ 0.011 for FA, FWE-FA, ICVF and ODI). However, only multi-compartment metrics, specifically FWE-FA (p = 0.045) and ICVF (p = 0.020), were associated with working memory performance. Our results suggest that while most current diffusion metrics are sensitive to age, several multi-compartment metrics (i.e., FWE-FA and ICVF) appear more sensitive to cognitive performance in healthy older adults.
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Affiliation(s)
- Christopher E. Bauer
- Department of Neuroscience, University of Kentucky, Lexington, KY, United States
| | - Valentinos Zachariou
- Department of Neuroscience, University of Kentucky, Lexington, KY, United States
| | - Pauline Maillard
- Department of Neurology, University of California at Davis, Davis, CA, United States
- Center for Neuroscience, University of California at Davis, Davis, CA, United States
| | | | - Brian T. Gold
- Department of Neuroscience, University of Kentucky, Lexington, KY, United States
- Sanders-Brown Center on Aging, Lexington, KY, United States
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Zachariou V, Bauer CE, Pappas C, Gold BT. High cortical iron is associated with the disruption of white matter tracts supporting cognitive function in healthy older adults. Cereb Cortex 2022; 33:4815-4828. [PMID: 36182267 PMCID: PMC10110441 DOI: 10.1093/cercor/bhac382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 08/29/2022] [Accepted: 08/30/2022] [Indexed: 01/25/2023] Open
Abstract
Aging is associated with brain iron accumulation, which has been linked to cognitive decline. However, how brain iron affects the structure and function of cognitive brain networks remains unclear. Here, we explored the possibility that iron load in gray matter is associated with disruption of white matter (WM) microstructure within a network supporting cognitive function, in a cohort of 95 cognitively normal older adults (age range: 60-86). Functional magnetic resonance imaging was used to localize a set of brain regions involved in working memory and diffusion tensor imaging based probabilistic tractography was used to identify a network of WM tracts connecting the functionally defined regions. Brain iron concentration within these regions was evaluated using quantitative susceptibility mapping and microstructural properties were assessed within the identified tracts using neurite orientation dispersion and density imaging. Results indicated that high brain iron concentration was associated with low neurite density (ND) within the task-relevant WM network. Further, regional associations were observed such that brain iron in cortical regions was linked with lower ND in neighboring but not distant WM tracts. Our results provide novel evidence suggesting that age-related increases in brain iron concentration are associated with the disruption of WM tracts supporting cognitive function in normal aging.
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Affiliation(s)
- Valentinos Zachariou
- Department of Neuroscience, University of Kentucky, Lexington, KY 40536-0298, United States.,College of Medicine, University of Kentucky, Lexington, KY 40536-0298, United States
| | - Christopher E Bauer
- Department of Neuroscience, University of Kentucky, Lexington, KY 40536-0298, United States.,College of Medicine, University of Kentucky, Lexington, KY 40536-0298, United States
| | - Colleen Pappas
- Department of Neuroscience, University of Kentucky, Lexington, KY 40536-0298, United States.,College of Medicine, University of Kentucky, Lexington, KY 40536-0298, United States
| | - Brian T Gold
- Department of Neuroscience, University of Kentucky, Lexington, KY 40536-0298, United States.,College of Medicine, University of Kentucky, Lexington, KY 40536-0298, United States.,Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY 40536-0298, United States.,Magnetic Resonance Imaging and Spectroscopy Center, University of Kentucky, Lexington, KY 40536-0298, United States
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9
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Hofer E, Pirpamer L, Langkammer C, Tinauer C, Seshadri S, Schmidt H, Schmidt R. Heritability of R2* iron in the basal ganglia and cortex. Aging (Albany NY) 2022; 14:6415-6426. [PMID: 35951362 PMCID: PMC9467397 DOI: 10.18632/aging.204212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 07/12/2022] [Indexed: 11/25/2022]
Abstract
BACKGROUND While iron is essential for normal brain functioning, elevated concentrations are commonly found in neurodegenerative diseases and are associated with impaired cognition and neurological deficits. Currently, only little is known about genetic and environmental factors that influence brain iron concentrations. METHODS Heritability and bivariate heritability of regional brain iron concentrations, assessed by R2* relaxometry at 3 Tesla MRI, were estimated with variance components models in 130 middle-aged to elderly participants of the Austrian Stroke Prevention Family Study. RESULTS Heritability of R2* iron ranged from 0.46 to 0.82 in basal ganglia and from 0.65 to 0.76 in cortical lobes. Age and BMI explained up to 12% and 9% of the variance of R2* iron, while APOE ε4 carrier status, hypertension, diabetes, hypercholesterolemia, sex and smoking explained 5% or less. The genetic correlation of R2* iron among basal ganglionic nuclei and among cortical lobes ranged from 0.78 to 0.87 and from 0.65 to 0.97, respectively. R2* rates in basal ganglia and cortex were not genetically correlated. CONCLUSIONS Regional brain iron concentrations are mainly driven by genetic factors while environmental factors contribute to a certain extent. Brain iron levels in the basal ganglia and cortex are controlled by distinct sets of genes.
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Affiliation(s)
- Edith Hofer
- Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Styria, Austria.,Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Styria, Austria
| | - Lukas Pirpamer
- Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Styria, Austria
| | | | - Christian Tinauer
- Department of Neurology, Medical University of Graz, Styria, Austria
| | - Sudha Seshadri
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX 78229, USA.,Department of Neurology, Boston University School of Medicine, Boston, MA 02118, USA
| | - Helena Schmidt
- Research Unit-Genetic Epidemiology, Gottfried Schatz Research Centre for Cell Signalling, Metabolism and Aging, Molecular Biology and Biochemistry, Medical University of Graz, Graz, Styria, Austria
| | - Reinhold Schmidt
- Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Styria, Austria
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Zachariou V, Bauer CE, Powell DK, Gold BT. Ironsmith: An Automated Pipeline for QSM-based Data Analyses. Neuroimage 2021; 249:118835. [PMID: 34936923 PMCID: PMC8935985 DOI: 10.1016/j.neuroimage.2021.118835] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 11/27/2021] [Accepted: 12/17/2021] [Indexed: 12/13/2022] Open
Abstract
Quantitative susceptibility mapping (QSM) is an MRI-based, computational method for anatomically localizing and measuring concentrations of specific biomarkers in tissue such as iron. Growing research suggests QSM is a viable method for evaluating the impact of iron overload in neurological disorders and on cognitive performance in aging. Several software toolboxes are currently available to reconstruct QSM maps from 3D GRE MR Images. However, few if any software packages currently exist that offer fully automated pipelines for QSM-based data analyses: from DICOM images to region-of-interest (ROI) based QSM values. Even less QSM-based software exist that offer quality control measures for evaluating the QSM output. Here, we address these gaps in the field by introducing and demonstrating the reliability and external validity of Ironsmith; an open-source, fully automated pipeline for creating and processing QSM maps, extracting QSM values from subcortical and cortical brain regions (89 ROIs) and evaluating the quality of QSM data using SNR measures and assessment of outlier regions on phase images. Ironsmith also features automatic filtering of QSM outlier values and precise CSF-only QSM reference masks that minimize partial volume effects. Testing of Ironsmith revealed excellent intra- and inter-rater reliability. Finally, external validity of Ironsmith was demonstrated via an anatomically selective relationship between motor performance and Ironsmith-derived QSM values in motor cortex. In sum, Ironsmith provides a freely-available, reliable, turn-key pipeline for QSM-based data analyses to support research on the impact of brain iron in aging and neurodegenerative disease.
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Affiliation(s)
- Valentinos Zachariou
- Department of Neuroscience, College of Medicine, University of Kentucky, Lexington, KY 40536-0298 United States.
| | - Christopher E Bauer
- Department of Neuroscience, College of Medicine, University of Kentucky, Lexington, KY 40536-0298 United States
| | - David K Powell
- Department of Neuroscience, Magnetic Resonance Imaging and Spectroscopy Center, College of Medicine, University of Kentucky, Lexington, KY 40536-0298 United States
| | - Brian T Gold
- Department of Neuroscience, Sanders-Brown Center on Aging, Magnetic Resonance Imaging and Spectroscopy Center, College of Medicine, University of Kentucky, Lexington, KY 40536-0298 United States.
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