White matter hyperintensity volume in pre-diabetes, diabetes and normoglycemia

Berberine improves cognitive impairment by alleviating brain atrophy and promoting white matter reorganization in diabetic db/db mice: a magnetic resonance imaging-based study

Diabetic cognitive impairment is a common complication in type 2 diabetes. Berberine (BBR) is an isoquinoline alkaloid that has been shown to have neuroprotective effects against diabetes. This study aimed to investigate the effect of BBR on the gray and white matter of the brain by using magnetic resonance imaging (MRI) and to explore the underlying mechanisms. The study used diabetic db/db mice and administered BBR (50 and 100 mg/kg) intragastrically for twelve weeks. Morris water maze was applied to examine cognitive function. T2-weighted imaging (T2WI) was performed to assess brain atrophy, and diffusion tensor imaging (DTI) combined with fiber tracking was conducted to monitor the structural integrity of the white matter, followed by histological immunostaining. Furthermore, the protein expressions of the phosphatidylinositol 3-kinase (PI3K)/ protein kinase B (AKT)/ glycogen synthase kinase-3β (GSK-3β) were detected. The results revealed that BBR significantly improved the spatial learning and memory of the db/db mice. T2WI exhibited ameliorated brain atrophy in the BBR-treated db/db mice, as evidenced by reduced ventricular volume accompanied by increased hippocampal volumes. DTI combined with fiber tracking revealed that BBR increased FA, fiber density and length in the corpus callosum/external capsule of the db/db mice. These imaging findings were confirmed by histological immunostaining. Notably, BBR significantly enhanced the protein levels of phosphorylated AKT at Ser473 and GSK-3β at Ser9. Collectively, this study demonstrated that BBR significantly improved the cognitive function of the diabetic db/db mice through ameliorating brain atrophy and promoting white matter reorganization via AKT/GSK-3β pathway.

Tu-Xian Decoction ameliorates diabetic cognitive impairment by inhibiting DAPK-1

Tu-Xian decoction (TXD), a traditional Chinese medicine (TCM) formula, has been frequently administered to manage diabetic cognitive impairment (DCI). Despite its widespread use, the mechanisms underlying TXD's protective effects on DCI have yet to be fully elucidated. As a significant regulator in neurodegenerative conditions, death-associated protein kinase-1 (DAPK-1) serves as a focus for understanding the action of TXD. This study was designed to whether TXD mediates its beneficial outcomes by inhibiting DAPK-1. To this end, a diabetic model was established using Sprague-Dawley (SD) rats through a high-fat, high-sugar (HFHS) diet regimen, followed by streptozotocin (STZ) injection. The experimental cohort was stratified into six groups: Control, Diabetic, TC-DAPK6, high-dose TXD, medium-dose TXD, and low-dose TXD groups. Following a 12-week treatment period, various assessments-including blood glucose levels, body weight measurements, Morris water maze (MWM) testing for cognitive function, brain magnetic resonance imaging (MRI), and histological analyses using hematoxylin-eosin (H&E), and Nissl staining-were conducted. Protein expression in the hippocampus was quantified through Western blotting analysis. The results revealed that TXD significantly improved spatial learning and memory abilities, and preserved hippocampal structure in diabetic rats. Importantly, TXD administration led to a down-regulation of proteins indicative of neurological damage and suppressed DAPK-1 activity within the hippocampal region. These results underscore TXD's potential in mitigating DCIvia DAPK-1 inhibition, positioning it as a viable therapeutic candidate for addressing this condition. Further investigation into TXD's molecular mechanisms may elucidate new pathways for the treatment of DCI.

Magnetic resonance imaging detects cerebral gray and white matter injury correlated with cognitive impairments in diabetic db/db mice

Type-2 diabetes not only causes gray matter injury but also induces widespread white matter damages, which may contribute the cognitive impairments. This study aimed to assess the structural alterations of the gray and white matter in 20-week-old diabetic db/db mice using magnetic resonance imaging including T2-weighted imaging (T2WI) and diffusion tensor imaging (DTI), and to correlate them with the cognitive performance detected by Morris water maze (MWM). The results revealed impaired spatial learning and memory in db/db mice. T2WI detected severe brain atrophy involving the hippocampus and cortex after diabetes. DTI showed reduced fractional anisotropy (FA) in the cortex, hippocampus, corpus callosum/external capsule, and increased radial diffusivity in the corpus callosum/external capsule of the db/db mice. The immunostaining confirmed the MRI findings showing decreased cell density in the cortex, hippocampus, and reduced integrated optical density of Luxol fast blue staining in the corpus callosum/external capsule. The correlational analysis revealed that the T2WI-derived tissue atrophy and DTI-derived FA in the relevant gray matter and white matter significantly correlated with the behavior performance in the MWM test. Collectively, the present in vivo MRI detected varying degrees of structural abnormalities in the gray and white matter of db/db mice, which might be favorable predictors of diabetic cognitive dysfunction. Our findings might provide new clues for identifying gray and white matter damages associated with cognitive decline, which is imperative for the evaluation of potential pharmacological therapies in preclinical phase.

Predicting the severity of white matter lesions among patients with cerebrovascular risk factors based on retinal images and clinical laboratory data: a deep learning study

Background and purpose

As one common feature of cerebral small vascular disease (cSVD), white matter lesions (WMLs) could lead to reduction in brain function. Using a convenient, cheap, and non-intrusive method to detect WMLs could substantially benefit to patient management in the community screening, especially in the settings of availability or contraindication of magnetic resonance imaging (MRI). Therefore, this study aimed to develop a useful model to incorporate clinical laboratory data and retinal images using deep learning models to predict the severity of WMLs.

Methods

Two hundred fifty-nine patients with any kind of neurological diseases were enrolled in our study. Demographic data, retinal images, MRI, and laboratory data were collected for the patients. The patients were assigned to the absent/mild and moderate-severe WMLs groups according to Fazekas scoring system. Retinal images were acquired by fundus photography. A ResNet deep learning framework was used to analyze the retinal images. A clinical-laboratory signature was generated from laboratory data. Two prediction models, a combined model including demographic data, the clinical-laboratory signature, and the retinal images and a clinical model including only demographic data and the clinical-laboratory signature, were developed to predict the severity of WMLs.

Results

Approximately one-quarter of the patients (25.6%) had moderate-severe WMLs. The left and right retinal images predicted moderate-severe WMLs with area under the curves (AUCs) of 0.73 and 0.94. The clinical-laboratory signature predicted moderate-severe WMLs with an AUC of 0.73. The combined model showed good performance in predicting moderate-severe WMLs with an AUC of 0.95, while the clinical model predicted moderate-severe WMLs with an AUC of 0.78.

Conclusion

Combined with retinal images from conventional fundus photography and clinical laboratory data are reliable and convenient approach to predict the severity of WMLs and are helpful for the management and follow-up of WMLs patients.

Diabetes and dementia: Clinical perspective, innovation, knowledge gaps

The world faces a pandemic-level prevalence of type 2 diabetes. In parallel with this massive burden of metabolic disease is the growing prevalence of dementia as the population ages. The two health issues are intertwined. The Lancet Commission on dementia prevention, intervention, and care was convened to tackle the growing global concern of dementia by identifying risk factors. It concluded, along with other studies, that diabetes as well as obesity and the metabolic syndrome more broadly, which are frequently comorbid, raise the risk of developing dementia. Type 2 diabetes is a modifiable risk factor; however, it is uncertain whether anti-diabetic drugs mitigate risk of developing dementia. Reasons are manifold but constitute a critical knowledge gap in the field. This review outlines studies of type 2 diabetes on risk of dementia, illustrating key concepts. Moreover, it identifies knowledge gaps, reviews strategies to help fill these gaps, and concludes with a series of recommendations to mitigate risk and advance understanding of type 2 diabetes and dementia.

Genetic and Environmental Effects on the Development of White Matter Hyperintensities in a Middle Age Twin Population

Introduction: White matter hyperintensities (WMH) indicate white matter brain lesions in magnetic resonance imaging (MRI), which can be used as a marker for brain aging and cerebrovascular and neurodegenerative disorders. Twin studies revealed substantial but not uniform WMH heritability in elderly twins. The objective of our study was to investigate the genetic and environmental components of WMH, as well as their importance in a healthy twin population, utilizing 3T MRI scanners in a middle-aged twin population. Methods: Brain MRI was performed on 120 healthy adult twins from the Hungarian Twin Registry on a 3T scanner (86 monozygotic, MZ and 34 dizygotic, DZ twins; median age 50 ± 26.5 years, 72.5% female and 27.5% male). The count of WMH on FLAIR images was calculated using an automated volumetry pipeline (volBrain) and human processing. The age- and sex-adjusted MZ and DZ intra-pair correlations were determined and the total variance was decomposed into genetic, shared and unique environmental components using structural equation modeling. Results: Age and sex-adjusted MZ intrapair correlations were higher than DZ correlations, indicating moderate genetic influence in each lesion (rMZ = 0.466, rDZ = −0.025 for total count; rMZ = 0.482, rDZ = 0.093 for deep white matter count; rMZ = 0.739, rDZ = 0.39 for infratentorial count; rMZ = 0.573, rDZ = 0.372 for cerebellar count and rMZ = 0.473, rDZ = 0.19 for periventricular count), indicating a moderate heritability (A = 40.3%, A = 45%, A = 72.7% and A = 55.5%and 47.2%, respectively). The rest of the variance was influenced by unique environmental effects (E between 27.3% and 59.7%, respectively). Conclusions: The number of WMH lesions is moderately influenced by genetic effects, particularly in the infratentorial region in middle-aged twins. These results suggest that the distribution of WMH in various brain regions is heterogeneous.

Rheumatoid Arthritis, Cognitive Impairment, and Neuroimaging Biomarkers: Results from the Mayo Clinic Study of Aging

BACKGROUND

Observational studies suggested that dementia risk in patients with rheumatoid arthritis (RA) is higher than in the general population.

OBJECTIVE

To examine the associations of RA with cognitive decline and dementia, and neuroimaging biomarkers of aging, Alzheimer's disease, and vascular pathology in adult participants in the Mayo Clinic Study of Aging (MCSA).

METHODS

Participants with RA were matched 1:3 on age, sex, education, and baseline cognitive diagnosis to participants without RA. RA cases with MRI were also matched with non-cases with available MRI. All available imaging studies (i.e., amyloid and FDG PET, sMRI, and FLAIR) were included. The study included 104 participants with RA and 312 without RA (mean age (standard deviation, SD) 75.0 (10.4) years, 33% male and average follow-up (SD) 4.2 (3.8) years).

RESULTS

Groups were similar in cognitive decline and risk of incident dementia. Among participants with neuroimaging, participants with RA (n = 33) and without RA (n = 98) had similar amyloid burden and neurodegeneration measures, including regions sensitive to aging and dementia, but greater mean white matter hyperintensity volume relative to the total intracranial volume (mean (SD)% : 1.12 (0.57)% versus 0.76 (0.69)% of TIV, p = 0.01), and had higher mean (SD) number of cortical infarctions (0.24 (0.44) versus 0.05 (0.33), p = 0.02).

CONCLUSION

Although cognitive decline and dementia risk were similar in participants with and without RA, participants with RA had more abnormal cerebrovascular pathology on neuroimaging. Future studies should examine the mechanisms underlying these changes and potential implications for prognostication and prevention of cognitive decline in RA.