: The therapeutic advance in hereditary transthyretin amyloidosis (ATTRv amyloidosis) requires quantitative biomarkers of nerve involvement in order to foster early diagnosis and monitor therapy response. We aimed at quantitatively assessing Magnetic Resonance Neurography (MRN) and Diffusion Tensor Imaging (DTI) properties of the sciatic nerve in subjects with ATTRv-amyloidosis-polyneuropathy (ATTRv-PN) and pre-symptomatic carriers (ATTRv-C). Twenty subjects with pathogenic variants of the TTR gene (mean age 62.20 ± 12.04 years), 13 ATTRv-PN, and 7 ATTRv-C were evaluated and compared with 20 healthy subjects (mean age 60.1 ± 8.27 years). MRN and DTI sequences were performed at the right thigh from the gluteal region to the popliteal fossa. Cross-sectional-area (CSA), normalized signal intensity (NSI), and DTI metrics, including fractional anisotropy (FA), mean (MD), axial (AD), and radial diffusivity (RD) of the right sciatic nerve were measured. Increased CSA, NSI, RD, and reduced FA of sciatic nerve differentiated ATTRv-PN from ATTRv-C and healthy subjects at all levels (p < 0.01). NSI differentiated ATTRv-C from controls at all levels (p < 0.05), RD at proximal and mid-thigh (1.04 ± 0.1 vs 0.86 ± 0.11 p < 0.01), FA at mid-thigh (0.51 ± 0.02 vs 0.58 ± 0.04 p < 0.01). According to receiver operating characteristic (ROC) curve analysis, cutoff values differentiating ATTRv-C from controls (and therefore identifying subclinical sciatic involvement) were defined for FA, RD, and NSI. Significant correlations between MRI measures, clinical involvement and neurophysiology were found. In conclusion, the combination of quantitative MRN and DTI of the sciatic nerve can reliably differentiate ATTRv-PN, ATTRv-C, and healthy controls. More important, MRN and DTI were able to non-invasively identify early subclinical microstructural changes in pre-symptomatic carriers, thus representing a potential tool for early diagnosis and disease monitoring.

Magnetic resonance neurography and diffusion tensor imaging of the sciatic nerve in hereditary transthyretin amyloidosis polyneuropathy

Gasparotti, Roberto;Corbo, Daniele;Agazzi, Giorgio;
2023-01-01

Abstract

: The therapeutic advance in hereditary transthyretin amyloidosis (ATTRv amyloidosis) requires quantitative biomarkers of nerve involvement in order to foster early diagnosis and monitor therapy response. We aimed at quantitatively assessing Magnetic Resonance Neurography (MRN) and Diffusion Tensor Imaging (DTI) properties of the sciatic nerve in subjects with ATTRv-amyloidosis-polyneuropathy (ATTRv-PN) and pre-symptomatic carriers (ATTRv-C). Twenty subjects with pathogenic variants of the TTR gene (mean age 62.20 ± 12.04 years), 13 ATTRv-PN, and 7 ATTRv-C were evaluated and compared with 20 healthy subjects (mean age 60.1 ± 8.27 years). MRN and DTI sequences were performed at the right thigh from the gluteal region to the popliteal fossa. Cross-sectional-area (CSA), normalized signal intensity (NSI), and DTI metrics, including fractional anisotropy (FA), mean (MD), axial (AD), and radial diffusivity (RD) of the right sciatic nerve were measured. Increased CSA, NSI, RD, and reduced FA of sciatic nerve differentiated ATTRv-PN from ATTRv-C and healthy subjects at all levels (p < 0.01). NSI differentiated ATTRv-C from controls at all levels (p < 0.05), RD at proximal and mid-thigh (1.04 ± 0.1 vs 0.86 ± 0.11 p < 0.01), FA at mid-thigh (0.51 ± 0.02 vs 0.58 ± 0.04 p < 0.01). According to receiver operating characteristic (ROC) curve analysis, cutoff values differentiating ATTRv-C from controls (and therefore identifying subclinical sciatic involvement) were defined for FA, RD, and NSI. Significant correlations between MRI measures, clinical involvement and neurophysiology were found. In conclusion, the combination of quantitative MRN and DTI of the sciatic nerve can reliably differentiate ATTRv-PN, ATTRv-C, and healthy controls. More important, MRN and DTI were able to non-invasively identify early subclinical microstructural changes in pre-symptomatic carriers, thus representing a potential tool for early diagnosis and disease monitoring.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11379/579265
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