White matter fiber-specific degeneration in older adults with metabolic syndrome

Andica, Christina; Kamagata, Koji; Uchida, Wataru; Takabayashi, Kaito; Shimoji, Keigo; Someya, Yuki; Tamura, Yasuaki; Kawamori, Ryuzo; Watada, Hirotaka; Hori, Masaaki; Aoki, Shigeki

doi:10.1016/j.molmet.2022.101527

Cited by 11 publications

(5 citation statements)

References 76 publications

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“…Moreover, our results showed that in patients with CSF volumes over 2012.82 cm 3 , the presence of concomitant metabolic syndrome can be assumed with high specificity. The potential underlying mechanism of the influence of HIV infection and metabolic syndrome on CSF would include persistent low-level inflammation in the brain at the level of the hematoencephalic barrier, multiplied by the effect of the metabolic syndrome on the small vessels in the white matter [ 33 ]. This finding should induce additional evaluation of the patient in order to reduce potential complications of both disorders.…”

Section: Discussionmentioning

confidence: 99%

Additive Effect of Metabolic Syndrome on Brain Atrophy in People Living with HIV–Magnetic Resonance Volumetry Study

Andric,

Boban,

Maric

et al. 2024

Metabolites

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With people living with HIV (PLWH) reaching the senium, the importance of aging-related comorbidities such as metabolic syndrome (MS) becomes increasingly important. This study aimed to determine the additive effect of MS on brain atrophy in PLWH. This prospective study included 43 PLWH, average age of 43.02 ± 10.93 years, and 24 healthy controls, average age of 36.87 ± 8.89 years. PLWH were divided into two subgroups: without MS and with MS, according to NCEP ATP III criteria. All patients underwent brain magnetic resonance imaging (MRI) on a 3T clinical scanner with MR volumetry, used for defining volumes of cerebrospinal fluid (CSF) spaces and white and grey matter structures, including basal ganglia. A Student’s t-test was used to determine differences in brain volumes between subject subgroups. The binary classification was performed to determine the sensitivity and specificity of volumetry findings and cut-off values. Statistical significance was set at p < 0.05. PLWH presented with significantly lower volumes of gray matter, putamen, thalamus, globus pallidus, and nc. accumbens compared to healthy controls; cut-off values were: for gray matter 738.130 cm3, putamen 8.535 cm3, thalamus 11.895 cm3, globus pallidus 2.252 cm3, and nc. accumbens 0.715 cm3. The volumes of CSF and left lateral ventricles were found to be higher in PLWH with MS compared to those without MS, where, with a specificity of 0.310 and sensitivity of 0.714, it can be assumed that PLWH with a CSF volume exceeding 212.83 cm3 are likely to also have MS. This suggests that PLWH with metabolic syndrome may exhibit increased CSF volume above 212.83 cm3 as a consequence of brain atrophy. There seems to be an important connection between MS and brain volume reduction in PLWH with MS, which may add to the accurate identification of persons at risk of developing HIV-associated cognitive impairment.

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Section: Discussionmentioning

confidence: 99%

Additive Effect of Metabolic Syndrome on Brain Atrophy in People Living with HIV–Magnetic Resonance Volumetry Study

Andric,

Boban,

Maric

et al. 2024

Metabolites

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“…The term “fixel” refers to a specific individual fiber population within a voxel (Raffelt et al 2015 , 2017 ) and is derived from white matter FODs as computed by the CSD method (Jeurissen et al 2014 ). Recently, TractSeg was utilized to perform fixel-based white matter bundle segmentation in children with attention-deficit hyperactivity disorder (Fuelscher et al 2021 ; Hyde et al 2021 ) and older adults with metabolic syndrome (Andica et al 2022 ).…”

Section: Tractsegmentioning

confidence: 99%

Automated three-dimensional major white matter bundle segmentation using diffusion magnetic resonance imaging

2023

Self Cite

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White matter bundle segmentation using diffusion magnetic resonance imaging fiber tractography enables detailed evaluation of individual white matter tracts three-dimensionally, and plays a crucial role in studying human brain anatomy, function, development, and diseases. Manual extraction of streamlines utilizing a combination of the inclusion and exclusion of regions of interest can be considered the current gold standard for extracting white matter bundles from whole-brain tractograms. However, this is a time-consuming and operator-dependent process with limited reproducibility. Several automated approaches using different strategies to reconstruct the white matter tracts have been proposed to address the issues of time, labor, and reproducibility. In this review, we discuss few of the most well-validated approaches that automate white matter bundle segmentation with an end-to-end pipeline, including TRActs Constrained by UnderLying Anatomy (TRACULA), Automated Fiber Quantification, and TractSeg.

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“…In recent years, studies generally believe that white matter fiber tracts are damaged in T2DM patients 13 – 15 , and related to the metabolic disorder 16 , microvascular dysfunction, and decreased cognitive function 17 . Commissural, projection and association fibers are the three major classes of white matter fiber tracts 15 .…”

Section: Introductionmentioning

confidence: 99%

White matter microstructure alterations in type 2 diabetes mellitus and its correlation with cerebral small vessel disease and cognitive performance

Liu,

Jiang,

et al. 2024

Sci Rep

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Microstructural abnormalities of white matter fiber tracts are considered as one of the etiology of diabetes-induced neurological disorders. We explored the cerebral white matter microstructure alteration accurately, and to analyze its correlation between cerebral small vessel disease (CSVD) burden and cognitive performance in type 2 diabetes mellitus (T2DM). The clinical-laboratory data, cognitive scores [including mini-mental state examination (MMSE), Montreal cognitive assessment (MoCA), California verbal learning test (CVLT), and symbol digit modalities test (SDMT)], CSVD burden scores of the T2DM group (n = 34) and healthy control (HC) group (n = 21) were collected prospectively. Automatic fiber quantification (AFQ) was applied to generate bundle profiles along primary white matter fiber tracts. Diffusion tensor images (DTI) metrics and 100 nodes of white matter fiber tracts between groups were compared. Multiple regression analysis was used to analyze the relationship between DTI metrics and cognitive scores and CSVD burden scores. For fiber-wise and node-wise, DTI metrics in some commissural and association fibers were increased in T2DM. Some white matter fiber tracts DTI metrics were independent predictors of cognitive scores and CSVD burden scores. White matter fiber tracts damage in patients with T2DM may be characterized in specific location, especially commissural and association fibers. Aberrational specific white matter fiber tracts are associated with visuospatial function and CSVD burden.

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White matter fiber-specific degeneration in older adults with metabolic syndrome

Cited by 11 publications

References 76 publications

Additive Effect of Metabolic Syndrome on Brain Atrophy in People Living with HIV–Magnetic Resonance Volumetry Study

Additive Effect of Metabolic Syndrome on Brain Atrophy in People Living with HIV–Magnetic Resonance Volumetry Study

Automated three-dimensional major white matter bundle segmentation using diffusion magnetic resonance imaging

White matter microstructure alterations in type 2 diabetes mellitus and its correlation with cerebral small vessel disease and cognitive performance

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