Impact of Chronic Kidney Disease on Brain Structure and Function

Steinbach, Emily J.; Harshman, Lyndsay A.

doi:10.3389/fneur.2022.797503

Cited by 14 publications

(9 citation statements)

References 65 publications

(82 reference statements)

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“…An intricate network of mutual interdependencies exists between brain-age and other bodily (patho)physiological aging phenomena (Cole et al, 2019;Tian et al, 2022). Brain health is shaped by other systems in the body, with cardiovascular and renal (mal)functioning known to have a potentially major impact (Cherbuin et al, 2021;Steinbach & Harshman, 2022;Wagen et al, 2022) atrophy of specific regions, and a greater WMH burden as the main neuroimaging correlates of higher brain-PAD values (Leonardsen et al, 2022;Wagen et al, 2022;Wood et al, 2022), with no FDspecific determinants emerging from the interaction analysis. Nevertheless, it should be noted that, while the results of the interpretability analysis, the voxel-wise correlations, and the sensitivity analysis on skull-stripped images all show that age predictions are driven by brain features, a minor contribution of non-brain tissues of the skull/scalp cannot be fully excluded (Cali et al, 2023).…”

Section: Discussionmentioning

confidence: 99%

Assessing brain involvement in Fabry disease with deep learning and the brain‐age paradigm

Montella,

Tranfa,

Scaravilli

et al. 2024

Human Brain Mapping

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While neurological manifestations are core features of Fabry disease (FD), quantitative neuroimaging biomarkers allowing to measure brain involvement are lacking. We used deep learning and the brain‐age paradigm to assess whether FD patients' brains appear older than normal and to validate brain‐predicted age difference (brain‐PAD) as a possible disease severity biomarker. MRI scans of FD patients and healthy controls (HCs) from a single Institution were, retrospectively, studied. The Fabry stabilization index (FASTEX) was recorded as a measure of disease severity. Using minimally preprocessed 3D T1‐weighted brain scans of healthy subjects from eight publicly available sources (N = 2160; mean age = 33 years [range 4–86]), we trained a model predicting chronological age based on a DenseNet architecture and used it to generate brain‐age predictions in the internal cohort. Within a linear modeling framework, brain‐PAD was tested for age/sex‐adjusted associations with diagnostic group (FD vs. HC), FASTEX score, and both global and voxel‐level neuroimaging measures. We studied 52 FD patients (40.6 ± 12.6 years; 28F) and 58 HC (38.4 ± 13.4 years; 28F). The brain‐age model achieved accurate out‐of‐sample performance (mean absolute error = 4.01 years, R2 = .90). FD patients had significantly higher brain‐PAD than HC (estimated marginal means: 3.1 vs. −0.1, p = .01). Brain‐PAD was associated with FASTEX score (B = 0.10, p = .02), brain parenchymal fraction (B = −153.50, p = .001), white matter hyperintensities load (B = 0.85, p = .01), and tissue volume reduction throughout the brain. We demonstrated that FD patients' brains appear older than normal. Brain‐PAD correlates with FD‐related multi‐organ damage and is influenced by both global brain volume and white matter hyperintensities, offering a comprehensive biomarker of (neurological) disease severity.

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Section: Discussionmentioning

confidence: 99%

Assessing brain involvement in Fabry disease with deep learning and the brain‐age paradigm

Montella,

Tranfa,

Scaravilli

et al. 2024

Human Brain Mapping

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“…(27,28) Brain health is shaped by other systems in the body, with cardiovascular and renal (mal)functioning known to have a potentially major impact. (7,29,30) Likewise, common, genetically-determined, mechanisms may underlie the development of FD-related damage in different organs in parallel.…”

Section: Discussionmentioning

confidence: 99%

Assessing Brain Involvement in Fabry Disease with Deep Learning and the Brain-Age Paradigm

Montella

Tranfa

Scaravilli

et al. 2023

Preprint

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Background While neurological manifestations are core features of Fabry disease (FD), quantitative neuroimaging biomarkers allowing to measure brain involvement are lacking. We used deep learning and the brain-age paradigm to assess whether FD patients brains appear older than normal and to validate brain-predicted age difference (brain-PAD) as a possible disease severity biomarker. Methods MRI scans of FD patients and healthy controls (HC) from a single Institution were retrospectively studied. The Fabry stabilization index (FASTEX) was recorded as a measure of disease severity. Using minimally preprocessed 3D T1-weighted brain scans of healthy subjects from 8 publicly available sources (N=2160; mean age=33y[range 4-86]), we trained a model predicting chronological age based on a DenseNet architecture and used it to generate brain-age predictions in the internal cohort. Within a linear modeling framework, brain-PAD was tested for age/sex-adjusted associations with diagnostic group (FD vs HC), FASTEX score, and both global and voxel-level neuroimaging measures. Results We studied 52 FD patients (mean age: 40.6 years, SD: 12.6; 28F) and 58 HC (mean age: 38.4 years, SD: 13.4; 28F). The brain-age model achieved accurate out-of-sample performance (mean absolute error=4.01y, R2=0.90). FD patients had significantly higher brain-PAD than HC (estimated marginal means: 3.1 vs -0.1, p=0.01). Brain-PAD was associated with FASTEX score (B=0.10, p=0.02), brain parenchymal fraction (B=-153.50, p=0.001), white matter hyperintensities load (B=0.85, p=0.01), and tissue volume reduction throughout the brain. Conclusions We demonstrated that the brains of FD patients appear older than normal. Brain-PAD correlates with FD-related multi-organ damage and is influenced by both global brain volume and white matter hyperintensities, offering a comprehensive biomarker of (neurological) disease severity.

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“…55 Proinflammatory factors have been reported to decrease serum NO in the brain vasculature which may contribute to cerebral hypoperfusion. 56 Uremic toxins that affect the nervous system include uric acid, indoxyl sulfate, TMAO (trimethylamine N-oxide), and inflammatory proteins. 57 Certain protein-bound toxins and large middle compounds that are poorly removed during dialysis cause neurotoxicity through vascular effects.…”

Section: A Man Is As Old As His Arteries—thomas Sydenham (1624–1689)mentioning

confidence: 99%

Accelerated Vascular Aging in Chronic Kidney Disease: The Potential for Novel Therapies

Hobson

Arefin

Witasp

et al. 2023

Circulation Research

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The pathophysiology of vascular disease is linked to accelerated biological aging and a combination of genetic, lifestyle, biological, and environmental risk factors. Within the scenario of uncontrolled artery wall aging processes, CKD (chronic kidney disease) stands out as a valid model for detailed structural, functional, and molecular studies of this process. The cardiorenal syndrome relates to the detrimental bidirectional interplay between the kidney and the cardiovascular system. In addition to established risk factors, this group of patients is subjected to a plethora of other emerging vascular risk factors, such as inflammation, oxidative stress, mitochondrial dysfunction, vitamin K deficiency, cellular senescence, somatic mutations, epigenetic modifications, and increased apoptosis. A better understanding of the molecular mechanisms through which the uremic milieu triggers and maintains early vascular aging processes, has provided important new clues on inflammatory pathways and emerging risk factors alike, and to the altered behavior of cells in the arterial wall. Advances in the understanding of the biology of uremic early vascular aging opens avenues to novel pharmacological and nutritional therapeutic interventions. Such strategies hold promise to improve future prevention and treatment of early vascular aging not only in CKD but also in the elderly general population.

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Impact of Chronic Kidney Disease on Brain Structure and Function

Cited by 14 publications

References 65 publications

Assessing brain involvement in Fabry disease with deep learning and the brain‐age paradigm

Assessing brain involvement in Fabry disease with deep learning and the brain‐age paradigm

Assessing Brain Involvement in Fabry Disease with Deep Learning and the Brain-Age Paradigm

Accelerated Vascular Aging in Chronic Kidney Disease: The Potential for Novel Therapies

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