With your risk of Parkinsons you'll want your doctor to test for this and have removal protocols in place.
Your risk of Parkinsons here:
Parkinson’s Disease May Have Link to Stroke March 2017
How brain iron accumulates here:
Brain Iron Accumulation in Atypical Parkinsonian Syndromes: in vivo MRI Evidences for Distinctive Patterns
Possible brain iron removal here:
The Efficacy of Iron Chelators for Removing Iron from Specific Brain Regions and the Pituitary—Ironing out the Brain
Don't listen to me, I'm not medically trained, your doctors should be intimately knowledgeable about all of this, ask them.
The latest here:
http://orcid.org/0000-0001-6107-0326George Edward Calver Thomas1,
Abstract
Background
Dementia is common in Parkinson’s disease (PD) but measures that track
cognitive change in PD are lacking. Brain tissue iron accumulates with
age and co-localises with pathological proteins linked to PD dementia
such as amyloid. We used quantitative susceptibility mapping (QSM) to
detect changes related to cognitive change in PD.
Methods
We assessed 100 patients with early-stage to mid-stage PD, and 37
age-matched controls using the Montreal Cognitive Assessment (MoCA), a
validated clinical algorithm for risk of cognitive decline in PD,
measures of visuoperceptual function and the Movement Disorders Society
Unified Parkinson’s Disease Rating Scale part 3 (UPDRS-III). We
investigated the association between these measures and QSM, an MRI
technique sensitive to brain tissue iron content.
Results
We found QSM increases (consistent with higher brain tissue iron
content) in PD compared with controls in prefrontal cortex and putamen
(p<0.05 corrected for multiple comparisons). Whole brain regression
analyses within the PD group identified QSM increases covarying: (1)
with lower MoCA scores in the hippocampus and thalamus, (2) with poorer
visual function and with higher dementia risk scores in parietal,
frontal and medial occipital cortices, (3) with higher UPDRS-III scores
in the putamen (all p<0.05 corrected for multiple comparisons). In
contrast, atrophy, measured using voxel-based morphometry, showed no
differences between groups, or in association with clinical measures.
Conclusions
Brain tissue iron, measured using QSM, can track cognitive involvement
in PD. This may be useful to detect signs of early cognitive change to
stratify groups for clinical trials and monitor disease progression.
https://creativecommons.org/licenses/by/4.0/This
is an open access article distributed in accordance with the Creative
Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits
others to copy, redistribute, remix, transform and build upon this work
for any purpose, provided the original work is properly cited, a link to
the licence is given, and indication of whether changes were made.
See: https://creativecommons.org/licenses/by/4.0/.
Statistics from Altmetric.com
Introduction
Dementia affects up to 50% of patients with Parkinson’s disease (PD)1
but patients vary in the timing and severity of cognitive involvement
and useful quantitative tools to track cognitive change in PD are
required. PD dementia is thought to be caused by the combination of
amyloid, tau and α-synuclein, but the reasons for selective
vulnerability of particular brain regions in PD dementia remain unclear.2
Neuroimaging measures sensitive to PD cognition are
important to track change in clinical trials and detect early
neuroanatomical correlates of cognitive involvement. Conventional
neuroimaging, which uses MRI to assess volume loss caused by neuronal
cell death, is poorly sensitive in PD as cell death at a large scale
occurs only at later disease stages.3
Techniques sensitive to brain tissue microstructure are better suited
to detect brain changes linked to cognitive involvement in PD.
A potential mechanism for selective vulnerability in PD dementia is excess brain iron accumulation.4 Iron is ubiquitous in numerous biological processes in normal ageing as well as in neurodegeneration.5 Brain iron accumulation is seen with age, in part due to increased blood-brain-barrier permeability,6 especially affecting the basal ganglia.7–9 The toxic potential of excess iron lies in its ability to generate reactive oxygen species,10 which damage DNA,11 irreversibly modify proteins via highly reactive aldehydes12 and stimulate release of iron from storage proteins leading to generation of further reactive oxygen species.5 This can ultimately end in iron-mediated cell death.13 Excess brain iron is also important in key pathophysiological pathways specific to PD.9 Notably, free radical species generated through iron overload interact with α-synuclein to promote Lewy-related pathology14 and produce neurotoxic by-products via catalysation of dopamine oxidation reactions.15 Increased iron is seen in the substantia nigra at post mortem in PD16 and in vivo using transcranial sonography.17
Of key significance, brain iron co-localises with Alzheimer’s pathology, particularly amyloid and tau,18 which are key predictors of PD dementia.19
Therefore, detecting levels of brain iron could be a sensitive way to
identify brain tissue already affected by the earliest processes that
ultimately lead to PD dementia.20
Quantitative susceptibility mapping (QSM) is an emerging MRI technique which detects local variations in iron content.21 22
QSM is sensitive to magnetic susceptibility differences between
chemical species, which are captured by the signal phase of MRI gradient
echo sequences. QSM recovers local susceptibility sources giving rise
to magnetic field perturbations which are increased in basal ganglia
regions in PD,20 but has never been used across the whole brain to track cognitive changes in PD.
Outcomes
relating to progression of cognitive impairment are of particular
interest. Recently, risk algorithms combined clinical information to
predict cognitive change over time.23 Visual changes are also emerging as early markers of cognitive change in PD.24
Whether structural brain changes are more strongly linked with clinical
risk scores or visual deficits before onset of dementia is not yet
known.
Here, we used QSM to measure cognitive-related
changes in 100 patients with PD without dementia. We hypothesised that
magnetic susceptibility values reflecting brain tissue iron would be
higher (1) in mesial temporal structures in relation to poorer cognitive
ability; (2) in posterior and prefrontal cortical regions in relation
to higher risk of dementia, measured using algorithmic scores and
finally, (3)in basal ganglia regions in relation to motor change.
Abstract
Background
Dementia is common in Parkinson’s disease (PD) but measures that track cognitive change in PD are lacking. Brain tissue iron accumulates with age and co-localises with pathological proteins linked to PD dementia such as amyloid. We used quantitative susceptibility mapping (QSM) to detect changes related to cognitive change in PD.
Methods
We assessed 100 patients with early-stage to mid-stage PD, and 37 age-matched controls using the Montreal Cognitive Assessment (MoCA), a validated clinical algorithm for risk of cognitive decline in PD, measures of visuoperceptual function and the Movement Disorders Society Unified Parkinson’s Disease Rating Scale part 3 (UPDRS-III). We investigated the association between these measures and QSM, an MRI technique sensitive to brain tissue iron content.
Results
We found QSM increases (consistent with higher brain tissue iron content) in PD compared with controls in prefrontal cortex and putamen (p<0.05 corrected for multiple comparisons). Whole brain regression analyses within the PD group identified QSM increases covarying: (1) with lower MoCA scores in the hippocampus and thalamus, (2) with poorer visual function and with higher dementia risk scores in parietal, frontal and medial occipital cortices, (3) with higher UPDRS-III scores in the putamen (all p<0.05 corrected for multiple comparisons). In contrast, atrophy, measured using voxel-based morphometry, showed no differences between groups, or in association with clinical measures.
Conclusions
Brain tissue iron, measured using QSM, can track cognitive involvement in PD. This may be useful to detect signs of early cognitive change to stratify groups for clinical trials and monitor disease progression.
This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.
Statistics from Altmetric.com
Introduction
Dementia affects up to 50% of patients with Parkinson’s disease (PD)1 but patients vary in the timing and severity of cognitive involvement and useful quantitative tools to track cognitive change in PD are required. PD dementia is thought to be caused by the combination of amyloid, tau and α-synuclein, but the reasons for selective vulnerability of particular brain regions in PD dementia remain unclear.2
Neuroimaging measures sensitive to PD cognition are important to track change in clinical trials and detect early neuroanatomical correlates of cognitive involvement. Conventional neuroimaging, which uses MRI to assess volume loss caused by neuronal cell death, is poorly sensitive in PD as cell death at a large scale occurs only at later disease stages.3 Techniques sensitive to brain tissue microstructure are better suited to detect brain changes linked to cognitive involvement in PD.
A potential mechanism for selective vulnerability in PD dementia is excess brain iron accumulation.4 Iron is ubiquitous in numerous biological processes in normal ageing as well as in neurodegeneration.5 Brain iron accumulation is seen with age, in part due to increased blood-brain-barrier permeability,6 especially affecting the basal ganglia.7–9 The toxic potential of excess iron lies in its ability to generate reactive oxygen species,10 which damage DNA,11 irreversibly modify proteins via highly reactive aldehydes12 and stimulate release of iron from storage proteins leading to generation of further reactive oxygen species.5 This can ultimately end in iron-mediated cell death.13 Excess brain iron is also important in key pathophysiological pathways specific to PD.9 Notably, free radical species generated through iron overload interact with α-synuclein to promote Lewy-related pathology14 and produce neurotoxic by-products via catalysation of dopamine oxidation reactions.15 Increased iron is seen in the substantia nigra at post mortem in PD16 and in vivo using transcranial sonography.17
Of key significance, brain iron co-localises with Alzheimer’s pathology, particularly amyloid and tau,18 which are key predictors of PD dementia.19 Therefore, detecting levels of brain iron could be a sensitive way to identify brain tissue already affected by the earliest processes that ultimately lead to PD dementia.20
Quantitative susceptibility mapping (QSM) is an emerging MRI technique which detects local variations in iron content.21 22 QSM is sensitive to magnetic susceptibility differences between chemical species, which are captured by the signal phase of MRI gradient echo sequences. QSM recovers local susceptibility sources giving rise to magnetic field perturbations which are increased in basal ganglia regions in PD,20 but has never been used across the whole brain to track cognitive changes in PD.
Outcomes relating to progression of cognitive impairment are of particular interest. Recently, risk algorithms combined clinical information to predict cognitive change over time.23 Visual changes are also emerging as early markers of cognitive change in PD.24 Whether structural brain changes are more strongly linked with clinical risk scores or visual deficits before onset of dementia is not yet known.
Here, we used QSM to measure cognitive-related changes in 100 patients with PD without dementia. We hypothesised that magnetic susceptibility values reflecting brain tissue iron would be higher (1) in mesial temporal structures in relation to poorer cognitive ability; (2) in posterior and prefrontal cortical regions in relation to higher risk of dementia, measured using algorithmic scores and finally, (3)in basal ganglia regions in relation to motor change.
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