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Editorial for "Diffusion Tensor Imaging Reveals Altered Topological Efficiency of Structural Networks in Type-2 Diabetes Patients With and Without Mild Cognitive Impairment"
Abstract
Type 2 diabetes mellitus (T2DM) is the most frequent category of diabetes, consisting of a chronic metabolic disease in a response for the deficit in insulin secretion and/or action, which results in hyperglycemia 1 and in long term is associated with the dysfunction of several organs such as kidney, heart, eyes, and the vascular system. These dysfunc-tions and failures are enhanced by coexisting comorbidities, including obesity, hypertension, and dyslipidemia. 2 The severity of T2DM increases when it affects the vascular system , with the flow reaching its final target in the cerebrovas-cular and brain tissues. While hyperglycemia and glucose intolerance are the main manifestation for T2DM patients, the neurological impact of type 2 diabetes, whose main outcome is the cognitive impairment, 3 is also an important condition for these patients. Although evaluating the direct impact of T2DM in the occurrence of cognitive impairment is hampered due to the coexisting cerebrovascular diseases, emerging evidence are being carried out to elucidate the relationship between T2DM and cognitive impairment, which may increase the risk of developing dementia. 4 Neuroimaging studies arise as an important field in the understanding of the pathways, mechanisms, and the preferred target regions in the brain that are related to the development of cognitive impairment. In especial, magnetic resonance imaging provides a set of tools to investigate the alterations in the T2DM patient's brain. Among all the methods available, structural analysis of volumetry and cortical thickness, 5 functional integration, 6 and metabolic evaluation 7 has already resulted in alterations when compared to healthy subjects. Furthermore, diffusion-weighted imaging has been well explored to evaluate the macro and microstructural integrity of white matter (WM) for specific regions and brain topology. 8,9 However, there is still lacking a deeper understanding on the specific brain organization in T2DM that differs from patients with and without cognitive impairment. In this issue of JMRI, Xiong et al assessed the differences between T2DM patients with and without mild cogni-tive impairment (MCI) regarding the alterations in WM network topology at global and nodal levels. 10 The network topological study was based on the use of diffusion-weighted images to perform a deterministic tractography and graph theory analysis followed by the appropriate statistical analysis to identify the differences between the two target groups of T2DM patients. For the regions that resulted in differences between the groups, they performed a correlation analysis to investigate the relationship between topological brain networks and traditional markers for diabetes severity. Xiong et al revealed that T2DM-MCI patients presented more damaged global topological organization of WM than T2DM patients without MCI when compared to healthy subjects and decreased network efficiency when straight compared to T2DM without MCI. It was also reported that despite decreased nodal efficiency was found for both MCI and not-MCI T2DM patients in the frontal, parie-tal, temporal, and occipital cortices than in healthy controls, other regions showed decreased efficiency only for MCI-T2DM patients in comparison to healthy subjects, suggesting advanced disease stages for this group. In that sense, the outcomes of this study included the correlation of the severity of diabetes and reduced cognitive capacity to the alterations in several network parameters. Finally, the authors suggested a possible early bio-marker of cognitive impairment for T2DM patients by reporting reduced nodal efficiency in brain regions straight related to cognitive functions (posterior cingulate, insula, and the orbital part of the inferior frontal gyrus) for the group of diabetes patients with normal cognition
... For instance, the blood oxygen level-dependent (BOLD) signal in fMRI, which reveals hemodynamic changes associated with neural activities, has been used to detect altered functional connectivity (FC) in patients with T2DM-MCI 5,10-12 . Diffusion tensor imaging (DTI), which quantifies the diffusion anisotropy of water molecules in white matter (WM), has been used to explore disruptions of structural network connectivity [13][14][15][16] . PET has been used to capture metabolic changes in the brain for early diagnosis 17 . ...
Type 2 diabetes mellitus (T2DM) is closely linked to cognitive decline and alterations in brain structure and function. Resting-state functional magnetic resonance imaging (rs-fMRI) is used to diagnose neurodegenerative diseases, such as cognitive impairment (CI), Alzheimer’s disease (AD), and vascular dementia (VaD). However, whether the functional connectivity (FC) of patients with T2DM and mild cognitive impairment (T2DM-MCI) is conducive to early diagnosis remains unclear. To answer this question, we analyzed the rs-fMRI data of 37 patients with T2DM and mild cognitive impairment (T2DM-MCI), 93 patients with T2DM but no cognitive impairment (T2DM-NCI), and 69 normal controls (NC). We achieved an accuracy of 87.91% in T2DM-MCI versus T2DM-NCI classification and 80% in T2DM-NCI versus NC classification using the XGBoost model. The thalamus, angular, caudate nucleus, and paracentral lobule contributed most to the classification outcome. Our findings provide valuable knowledge to classify and predict T2DM-related CI, can help with early clinical diagnosis of T2DM-MCI, and provide a basis for future studies.
Type 2 diabetes mellitus (T2DM) is a metabolic disorder characterized by chronic hyperglycemia associated with alterations in carbohydrate, lipid, and protein metabolism. The prognosis of T2DM patients is highly dependent on the development of complications, and therefore the identification of biomarkers of T2DM progression, with minimally invasive techniques, is a huge need. In the present study, we applied a 1H-Nuclear Magnetic Resonance (1H-NMR)-based metabolomic approach coupled with multivariate data analysis to identify serum metabolite profiles associated with T2DM development and progression. To perform this, we compared the serum metabolome of non-diabetic subjects, treatment-naïve non-complicated T2DM patients, and T2DM patients with complications in insulin monotherapy. Our analysis revealed a significant reduction of alanine, glutamine, glutamate, leucine, lysine, methionine, tyrosine, and phenylalanine in T2DM patients with respect to non-diabetic subjects. Moreover, isoleucine, leucine, lysine, tyrosine, and valine levels distinguished complicated patients from patients without complications. Overall, the metabolic pathway analysis suggested that branched-chain amino acid (BCAA) metabolism is significantly compromised in T2DM patients with complications, while perturbation in the metabolism of gluconeogenic amino acids other than BCAAs characterizes both early and advanced T2DM stages. In conclusion, we identified a metabolic serum signature associated with T2DM stages. These data could be integrated with clinical characteristics to build a composite T2DM/complications risk score to be validated in a prospective cohort.
Krabbe disease (KD) is a neurodegenerative disorder caused by the lack of galactosylceramidase enzymatic activity and by widespread accumulation of the cytotoxic galactosyl-sphingosine in neuronal, myelinating and endothelial cells. Despite the wide use of Twitcher mice as experimental model for KD, the ultrastructure of this model is partial and mainly addressing peripheral nerves. More details are requested to elucidate the basis of the motor defects, which are the first to appear during KD onset. Here we use transmission electron microscopy (TEM) to focus on the alterations produced by KD in the lower motor system at postnatal day 15 (P15), a nearly asymptomatic stage, and in the juvenile P30 mouse. We find mild effects on motorneuron soma, severe ones on sciatic nerves and very severe effects on nerve terminals and neuromuscular junctions at P30, with peripheral damage being already detectable at P15. Finally, we find that the gastrocnemius muscle undergoes atrophy and structural changes that are independent of denervation at P15. Our data further characterize the ultrastructural analysis of the KD mouse model, and support recent theories of a dying-back mechanism for neuronal degeneration, which is independent of demyelination.
Type 2 diabetes mellitus (T2DM) is associated with cognitive impairment. We investigated whether alterations of intranetwork and internetwork functional connectivity with T2DM progression exist, by using resting-state functional MRI. MRI data were analysed from 19 T2DM patients with normal cognition (DMCN) and 19 T2DM patients with cognitive impairment (DMCI), 19 healthy controls (HC). Functional connectivity among 36 previously well-defined brain regions which consisted of 5 resting-state network (RSN) systems [default mode network (DMN), dorsal attention network (DAN), control network (CON), salience network (SAL) and sensorimotor network (SMN)] was investigated at 3 levels (integrity, network and connectivity). Impaired intranetwork and internetwork connectivity were found in T2DM, especially in DMCI, on the basis of the three levels of analysis. The bilateral posterior cerebellum, the right insula, the DMN and the CON were mainly involved in these changes. The functional connectivity strength of specific brain architectures in T2DM was found to be associated with haemoglobin A1c (HbA1c), cognitive score and illness duration. These network alterations in intergroup differences, which were associated with brain functional impairment due to T2DM, indicate that network organizations might be potential biomarkers for predicting the clinical progression, evaluating the cognitive impairment, and further understanding the pathophysiology of T2DM.
Type 2 diabetes mellitus (T2DM) is associated with a twofold increased risk of dementia and can affect many cognitive abilities, but its underlying cause is still unclear. In this study, we used a combination of a battery of neuropsychological tests and diffusion tensor images (DTI) to explore how T2DM impacts white matter (WM) integrity and cognition in 38 T2DM patients and 34 age-, gender- and education-matched normal controls. A battery of neuropsychological tests was used to assess a wide range of cognitive functions. Tract-based spatial statistics (TBSS) combined with ROI-wise analysis of mean values of DTI metrics in ROIs was utilized to compare group differences of DTI metrics on WM skeletons to identify severely disrupted WM tracts in T2DM. We found that T2DM patients showed (1) various cognitive impairments, including executive function, spatial processing, attention and working memory deficits; (2) widespread WM disruptions, especially in the whole corpus callosum, the left anterior limb of the internal capsule and external capsule; and (3) a positive correlation between executive function and WM integrity in ALIC.L and EC.L. In conclusion, T2DM patients show various cognitive impairments and widespread WM integrity disruptions, which we attribute by demyelination. Moreover, executive dysfunction closely correlates with WM abnormalities.
Vascular risk factors, such as type 2 diabetes, hypertension, obesity and dyslipidaemia often co-occur. Each of these factors has been associated with an increased risk of dementia, but it is uncertain which factor imposes the greatest risk. Moreover, the effect of age at time of exposure may differ across factors. This paper systematically reviews the evidence for the association of each of these risk factors with dementia. Longitudinal population-based studies that assessed the incidence of dementia in relation to diabetes (n=14), hypertension (n=13), dyslipidaemia (n=8) or obesity (n=9) were included. All four risk factors were indeed associated with an increased risk of dementia, but the results of studies on diabetes and obesity were most consistent. The magnitude of the effects was comparable across the risk factors, with odds ratios for 'any dementia' around 1.5. For hypertension, obesity and dyslipidaemia age appeared to modulate the association: the risk of dementia was generally largest in studies that measured the risk factor in midlife (compared to late life) and had a long follow-up time. At midlife, the population attributable risk of dementia was highest for hypertension, up to 30% of cases of late life dementia. Later in life diabetes appears to convey the highest risk of dementia. This review shows that vascular risk factors should be regarded as a major target for preventive measures, but that timing of such measures appears to be critical.
Background
Some patients with type 2 diabetes mellitus (T2DM) progress towards mild cognitive impairment (MCI), while some patients can always maintain normal cognitive function. Network topologic alterations at global and nodal levels between T2DM individuals with and without cognitive impairment may underlie the difference.
Purpose
To investigate the topological alterations of the whole-brain white matter (WM) structural connectome in T2DM patients with and without MCI and characterize its relationship with disease severity.
Study Type
Cross-sectional and prospective study.
Subjects
Forty-four (63.6% females) T2DM patients, 22 with mild cognitive impairment (DM-MCI) and 22 with normal cognition (DM-NC), and 34 (58.8% females) healthy controls (HC).
Field Strength/Sequence
3 T/diffusion tensor imaging.
Assessment
Graph theoretical analysis was used to investigate the topological organization of the structural networks. The global topological properties and nodal efficiency were investigated and compared. Relationship between network metrics and clinical measurements was characterized.
Statistical Tests
Student's t-test, chi-square test, ANOVA, partial correlation analyses, and multiple comparisons correction.
Results
The global topological organization of WM networks was significantly disrupted in T2DM patients with cognitive impairment (reduced global and local efficiency and increased shortest path length) but not in those with normal cognition, compared with controls. The DM-MCI group had significantly decreased network efficiency compared with the DM-NC group. Compared with controls, decreased nodal efficiency was detected in three regions in DM-NC group. More regions with decreased nodal efficiency were found in the DM-MCI group. Altered global network properties and nodal efficiency of some regions were correlated with diabetic duration, HbA1c levels, and cognitive assessment scores.
Data Conclusion
The more disrupted WM connections and weaker organized network are found in DM-MCI patients relative to DM-NC patients and controls. Network analyses provide information for the neuropathology of cognitive decline in T2DM patients. Altered nodal efficiency may act as potential markers for early detection of T2DM-related MCI.
Level of Evidence
1
Technical Efficacy
Stage 2
Objectives
To investigate brain microstructural changes in white matter and gray matter of type 2 diabetes mellitus (T2DM) patients using diffusion kurtosis imaging.
Methods
Diffusion kurtosis imaging (b values = 0, 1250, and 2500 s/mm²) was performed for 30 T2DM patients and 28 controls. FMRIB Software Library with tract-based spatial statistics was used to analyze intergroup differences in fractional anisotropy (FA), mean diffusivity (MD), mean kurtosis (MK), axial kurtosis (K∥), and radial kurtosis (K⊥) of multiple white matter regions. Atlas-based ROI analysis was conducted in gray matter structures and some fiber tracts. Correlations between MK changes and clinical measurements were determined.
Results
In whole-brain tract-based spatial statistics analysis, T2DM patients exhibited abnormalities in 29.6%, 30.4%, 35.4%, 10.5%, and 26.0% of white matter regions as measured by FA, MD, MK, K∥, and K⊥, respectively, when compared to the controls. MK reduction was contributed more by the decreased K⊥. In atlas-based analysis, MK detected more ROIs (27/48) with white matter microstructural changes than FA (13/48) and MD (17/48). MK decreased in bilateral thalamus and caudate, while FA showed statistically significant difference only in the left caudate. MK values negatively correlated with disease duration in the genu of corpus callosum and anterior corona radiata (R = -0.512 and -0.459) and positively correlated with neuropsychological scores in the cingulum (hippocampus) (R = 0.466 and 0.440).
Conclusions
Diffusion kurtosis imaging detects more brain regions with white matter and gray matter microstructural alterations of T2DM patients than DTI metrics. It provides valuable information for studying the pathology of diabetic encephalopathy and may lead to better imaging biomarkers for monitoring disease progression.
Key Points
• Diffusion kurtosis imaging detects more brain regions with microstructural alterations in white matter and gray matter of T2DM patients than DTI.
• Mean kurtosis changes are associated with disease severity and impaired neuropsychological function in T2DM.
• Diffusion kurtosis imaging demonstrates potential to assess cognitive impairment in T2DM patients and predict disease progression.
Aims:
We aimed to identify alterations in global gray matter volumes (GMV) and consistent regional abnormalities in T2DM patients via meta-analysis.
Methods:
A systematic search for relevant studies indexed in the PubMed and Embase databases was conducted. A quantitative meta-analysis of volumetric and whole-brain VBM data was conducted using STATA v.12.0 and AES-SDM software packages, respectively.
Results:
A total of 15 volumetric studies and five VBM studies of GM in T2DM patients vs. healthy controls (HCs) were identified. The volumetric meta-analysis showed that the GMV of patients with T2DM is lower than in HCs (SMD = -0.56, 95% CI = -0.81 to -0.31, P 0.01). The whole-brain VBM meta-analysis revealed GM reductions in the left superior temporal gyrus, the right middle temporal gyrus, the right rolandic operculum, and the left fusiform gyrus in T2DM patients compared with HCs. Meta-regression analysis showed that Mini-Mental State Examination (MMSE) scores have a positive relationship with GMV in the right insula.
Conclusions:
The results showed a reduced volume of whole and regional GM in T2DM patients, which may indicate a risk for dementia. Further longitudinal research is needed to confirm GM changes, cognitive dysfunction, and their relationship in T2DM.
Cognitive dysfunction in type 1 and type 2 diabetes share many similarities, but important differences do exist. A primary distinguishing feature of type 2 diabetes is that people with this disorder often (but not invariably) do poorly on measures of learning and memory, whereas deficits in these domains are rarely seen in people with type 1 diabetes. Chronic hyperglycaemia and microvascular disease contribute to cognitive dysfunction in both type 1 and type 2 diabetes, and both disorders are associated with mental and motor slowing and decrements of similar magnitude on measures of attention and executive functioning. Additionally, both types are characterised by neural slowing, increased cortical atrophy, microstructural abnormalities in white matter tracts, and similar, but not identical, changes in concentrations of brain neurometabolites. Disconcertingly, the rapid rise in obesity and type 2 diabetes in all age groups might result in a substantial increase in prevalence of diabetes-related cognitive dysfunction.
The deleterious effects of diabetes mellitus on the retinal, renal, cardiovascular, and peripheral nervous systems are widely acknowledged. Less attention has been given to the effect of diabetes on cognitive function. Both type 1 and type 2 diabetes mellitus have been associated with reduced performance on numerous domains of cognitive function. The exact pathophysiology of cognitive dysfunction in diabetes is not completely understood, but it is likely that hyperglycemia, vascular disease, hypoglycemia, and insulin resistance play significant roles. Modalities to study the effect of diabetes on the brain have evolved over the years, including neurocognitive testing, evoked response potentials, and magnetic resonance imaging. Although much insightful research has examined cognitive dysfunction in patients with diabetes, more needs to be understood about the mechanisms and natural history of this complication in order to develop strategies for prevention and treatment.