Relationships between cerebral structure and cognitive function in African Americans with type 2 diabetes

A. SD. A diagnosis model for brain atrophy using deep learning and MRI of type 2 diabetes mellitus

Objective

Type 2 Diabetes Mellitus (T2DM) is linked to cognitive deterioration and anatomical brain abnormalities like cerebral brain atrophy and cerebral diseases. We aim to develop an automatic deep learning-based brain atrophy diagnosis model to detect, segment, classify, and predict the survival rate.

Methods

Two hundred thirty-five MRI images affected with brain atrophy due to prolonged T2DM were acquired. The dataset was divided into training and testing (80:20%; 188, 47, respectively). Pre-processing is done through a novel convolutional median filter, followed by segmentation of atrophy regions, i.e., the brain shrinkage, white and gray matter is done through the proposed TRAU-Net model (Transfer Residual Attention U-Net), classification with the proposed Multinomial Logistic regression with Attention Swin Transformer (MLAST), and prediction of chronological age is determined through Multivariate CoX Regression model (MCR). The classification of Brain Atrophy (BA) types is determined based on the features extracted from the segmented region. Performance measures like confusion matrix, specificity, sensitivity, accuracy, F1-score, and ROC-AUC curve are used to measure classification model performance, whereas, for the segmentation model, pixel accuracy and dice similarity coefficient are applied.

Results

The pixel accuracy and dice coefficient for segmentation were 98.25 and 96.41, respectively. Brain atrophy multi-class classification achieved overall training accuracy is 0.9632 ± 1.325, 0.9677 ± 1.912, 0.9682 ± 1.715, and 0.9521 ± 1.877 for FA, PA, R-MTA, and L-MTA, respectively. The overall AUC-ROC curve for the classification model is 0.9856. The testing and validation accuracy obtained for the proposed model are 0.9379 and 0.9694, respectively. The prediction model's performance is measured using correlation coefficient (r), coefficient determination r2, and Mean Square Error (MSE) and recorded 0.951, 0.904, and 0.5172, respectively.

Conclusion

The brain atrophy diagnosis model consists of sub-models to detect, segment, and classify the atrophy regions using novel deep learning and multivariate mathematical models. The proposed model has outperformed the existing models regarding multi-classification and segmentation; therefore, the automated diagnosis model can be deployed in healthcare centers to assist physicians.

Neuroactive Steroid-Gut Microbiota Interaction in T2DM Diabetic Encephalopathy

The pathological consequences of type 2 diabetes mellitus (T2DM) also involve the central nervous system; indeed, T2DM patients suffer from learning and memory disabilities with a higher risk of developing dementia. Although several factors have been proposed as possible contributors, how neuroactive steroids and the gut microbiome impact brain pathophysiology in T2DM remain unexplored. On this basis, in male Zucker diabetic fatty (ZDF) rats, we studied whether T2DM alters memory abilities using the novel object recognition test, neuroactive steroid levels by liquid chromatography-tandem mass spectrometry, hippocampal parameters using molecular assessments, and gut microbiome composition using 16S next-generation sequencing. Results obtained reveal that T2DM worsens memory abilities and that these are correlated with increased levels of corticosterone in plasma and with a decrease in allopregnanolone in the hippocampus, where neuroinflammation, oxidative stress, and mitochondrial dysfunction were reported. Interestingly, our analysis highlighted a small group of taxa strictly related to both memory impairment and neuroactive steroid levels. Overall, the data underline an interesting role for allopregnanolone and microbiota that may represent candidates for the development of therapeutic strategies.

Late-life depressive symptoms and white matter structural integrity within older Black adults

Introduction

Older Black adults experience a high burden of depressive symptoms and cerebrovascular disease but the specific neurobiological substrates underlying the association between late-life depressive symptoms and brain integrity are understudied, particularly in within-group designs.

Methods

Using the Center for Epidemiologic Studies Depression Scale and diffusion-tensor imaging, within-Black variation in the association between late-life depressive symptoms and white matter structural integrity was examined in 297 older Black participants without dementia that were enrolled across three epidemiological studies of aging and dementia. Linear regression models were used to test associations with DTI metrics (fractional anisotropy, trace of the diffusion tensor) as the outcomes and depressive symptoms as the predictor, while adjusting for age, sex, education, scanner, serotonin-reuptake inhibitor use, total volume of white-matter hyperintensities normalized by intracranial volume, and presence of white-matter hyperintensities at the voxel level.

Results

Higher level of self-reported late-life depressive symptoms was associated with greater diffusion-tensor trace (reduced white matter integrity) in connections between commissural pathways and contralateral prefrontal regions (superior and middle frontal/dorsolateral prefrontal cortex), association pathways connecting dorsolateral prefrontal cortex with insular, striatal and thalamic regions, and association pathways connecting the parietal, temporal and occipital lobes and the thalamus.

Discussion

This study demonstrated a discernable pattern of compromised white matter structural integrity underlying late-life depressive symptoms within older Black adults.

Effect of Dapagliflozin Combined with Cognitive Behavior Training on Quality of Life and Cognitive Function in Elderly Patients with Type 2 Diabetes Mellitus Complicated with Mild Cognitive Impairment

BACKGROUND

To investigate the effect of dapagliflozin combined with cognitive behavior training on quality of life and cognitive function in elderly patients with type 2 diabetes mellitus (T2DM) complicated with mild cognitive impairment.

METHODS

Ninety-six elderly patients with T2DM and mild cognitive impairment treated in Shenzhen People's Hospital, Guangdong Province China from June 2019 to December 2020 were selected and equally randomized into control group (CG) and experimental group (EG). The CG received conventional intervention, while the EG received dapagliflozin combined with cognitive behavior training based on the CG. The clinical observation indexes of both groups were compared to assess the clinical intervention effect of dapagliflozin combined with cognitive behavior training on elderly patients with T2DM complicated with mild cognitive impairment.

RESULTS

After intervention, the blood glucose levels of both groups decreased, and compared with the CG, the blood glucose level of the EG was markedly lower (P<0.05). The scores of C-DMSES, ADL and MMSE of both groups were higher than those before intervention, and compared with the CG, the scores of the EG were obviously higher (P<0.05). The QOL-AD scores of both groups gradually increased, and compared with the CG, the QOL-AD scores of the EG were higher at 3 and 6 months after intervention (P<0.05).

CONCLUSION

For elderly patients with T2DM complicated with mild cognitive impairment, dapagliflozin combined with cognitive behavior training intervention can obviously improve their cognitive function, self-efficacy of diabetes management and quality of life, which should be promoted in clinic.

Associations of deformation-based brain morphometry with cognitive level and decline within older Blacks without dementia

Blacks are at higher risk of developing cognitive impairment with age than non-Hispanic Whites, yet most brain morphometry and cognition research is performed with White samples or with mixed samples that control for race or compare across racial groups. A deeper understanding of the within-group variability in associations between brain structure and cognitive decline in Blacks is critically important for designing appropriate outcomes for clinical trials, predicting adverse outcomes, and developing interventions to preserve cognitive function, but no studies have examined these associations longitudinally within Blacks. We performed deformation-based morphometry in 376 older Black participants without dementia and examined associations of deformation-based morphometry with cognitive level and decline for global cognition and five cognitive domains. After correcting for widespread age-associated effects, there remained regions with less tissue and more cerebrospinal fluid associated with level and rate of decline in global cognition, memory, and perceptual speed. Further study is needed to examine the moderators of these associations, identify adverse outcomes predicted by brain morphometry, and deepen knowledge of underlying biological mechanisms.

Diagnosis of TCM symptoms and analysis of risk factors of mild cognitive impairment in patients with type 2 diabetes mellitus

OBJECTIVE

To explore the diagnosis of traditional Chinese medicine (TCM) syndrome and analyze the risk factors of mild cognitive impairment (MCI) in patients with type 2 diabetes mellitus (T2DM).

METHODS

141 T2DM patients, who were hospitalized in department of endocrinology of our hospital from February 2020 to December 2020, were chosen as research subjects. The patients were divided into an observation group (n=65, T2DM with MCI) and a control group (n=76, T2DM with normal cognitive function) according to the Montreal Cognitive Assessment (MoCA) score and diagnostic criteria of MCI. Pearson correlation analysis was used to study the correlation between MoCA score and influencing factors, and multiple logistic regression analyses were applied to analyze the risk factors of T2DM patients.

RESULTS

Deficiency of kidney essence (34/65, 52.31%) and phlegm obstructing orifices (16/65, 2.62%) were common in T2DM patients with MCI. The observation group had apparently lower MoCA scores than the control group (23.46±3.12 points vs. 27.39±2.56 points, t=8.2150, P=0.0000). According to the results of multivariate logistic regression analysis, age, course of diabetes, homocysteine (HCY) and glycosylated hemoglobin (HbAlc) were the independent risk factors of MCI, and the education level was a protective factor.

CONCLUSION

Mental deficiency and phlegm obstruction are common in T2DM patients complicated with MCI. The factors such as age, diabetes course, education degree, HCY and HbAlc are closely related to MCI. The occurrence of MCI in T2DM patients can be prevented by improving the education degree of patients, effective control of blood glucose and reduction of HCY level.

ZiBuPiYin Recipe Prevented and Treated Cognitive Decline in ZDF Rats With Diabetes-Associated Cognitive Decline via Microbiota-Gut-Brain Axis Dialogue

Gut microbiota is becoming one of the key determinants in human health and disease. Shifts in gut microbiota composition affect cognitive function and provide new insights for the prevention and treatment of neurological diseases. Diabetes-associated cognitive decline (DACD) is one of the central nervous system complications of type 2 diabetes mellitus (T2DM). ZiBuPiYin recipe (ZBPYR), a traditional Chinese medicine (TCM) formula, has long been used for the treatment of T2DM and prevention of DACD. However, the contribution of ZBPYR treatment to the interaction between the gut microbiota and metabolism for preventing and treating DACD remains to be clarified. Here, we investigate whether the gut microbiota plays a key role in ZBPYR-mediated prevention of DACD and treatment of T2DM via incorporating microbiomics and metabolomics, and investigate the links between the microbiota-gut-brain axis interaction and the efficacy of ZBPYR in ZDF rats. In the current study, we found that ZBPYR treatment produced lasting changes in gut microbiota community and metabolites and remotely affected hippocampus metabolic changes, thereby improving memory deficits and reversing β-amyloid deposition and insulin resistance in the brain of ZDF rats from T2DM to DACD. This may be related to a series of metabolic changes affected by gut microbiota, including alanine, aspartic acid, and glutamic acid metabolism; branched-chain amino acid metabolism; short-chain fatty acid metabolism; and linoleic acid/unsaturated fatty acid metabolism. In summary, this study demonstrates that prevention and treatment of DACD by ZBPYR partly depends on the gut microbiota, and the regulatory effects of bacteria-derived metabolites and microbiota-gut-brain axis are important protective mechanisms of ZBPYR.

The Contribution of Kidney Disease to Cognitive Impairment in Patients with Type 2 Diabetes

PURPOSE OF REVIEW

This review focuses on the relationships between diabetes, cognitive impairment, and the contribution of kidney disease.

RECENT FINDINGS

We review the independent contributions of parameters of kidney disease, including albuminuria, glomerular filtration, bone/mineral metabolism, and vitamin D synthesis, on cognitive performance in patients with diabetes. Potential pathophysiologic mechanisms underlying these associations are discussed highlighting gaps in existing knowledge. Finally, effects of the dialysis procedure on the brain and cognitive performance are considered. Emphasis is placed on novel non-invasive screening tools with the potential to preserve cerebral perfusion during hemodialysis and limit cognitive decline in patients with diabetic ESKD. Patients with type 2 diabetes and advanced chronic kidney disease suffer a higher prevalence of cognitive impairment. This is particularly true in patients with diabetes and end-stage kidney disease (ESKD).

Potential Biochemical Mechanisms of Brain Injury in Diabetes Mellitus

The goal of this review was to summarize current biochemical mechanisms of and risk factors for diabetic brain injury. We mainly summarized mechanisms published in the past three years and focused on diabetes induced cognitive impairment, diabetes-linked Alzheimer's disease, and diabetic stroke. We think there is a need to conduct further studies with increased sample sizes and prolonged period of follow-ups to clarify the effect of DM on brain dysfunction. Additionally, we also think that enhancing experimental reproducibility using animal models in conjunction with application of advanced devices should be considered when new experiments are designed. It is expected that further investigation of the underlying mechanisms of diabetic cognitive impairment will provide novel insights into therapeutic approaches for ameliorating diabetes-associated injury in the brain.

Relationship Between Type 2 Diabetes and White Matter Hyperintensity: A Systematic Review

White matter (WM) disease is recognized as an important cause of cognitive decline and dementia. White matter lesions (WMLs) appear as white matter hyperintensities (WMH) on T2-weighted magnetic resonance imaging (MRI) scans of the brain. Previous studies have shown that type 2 diabetes (T2DM) is associated with WMH. In this review, we reviewed the literature on the relationship between T2DM and WMH in PubMed and Cochrane over the past five years and explored the possible links among the presence of T2DM, the course or complications of diabetes, and WMH. We found that: (1) Both from a macro- and micro-scopic point of view, most studies support the relationship of a larger WMH and a decrease in the integrity of WMH in T2DM; (2) From the relationship between brain structural changes and cognition in T2DM, the poor performance in memory, attention, and executive function tests associated with abnormal brain structure is consistent; (3) Diabetic microangiopathy or peripheral neuropathy may be associated with WMH, suggesting that the brain may be a target organ for T2DM microangiopathy; (4) Laboratory markers such as insulin resistance and fasting insulin levels were significantly associated with WMH. High HbA1c and high glucose variability were associated with WMH but not glycemic control.

Understanding Structure and Function as a Model for 21st-Century Health Care

Medicine is now at a turning point. The important role of a broader application of the structure-function model in health care is gaining recognition and it may, in fact, be a defining principle that can guide the development of future health care systems. This concept can be applied to the search for solutions to complex health-related issues that span across multiple domains: molecules, cells, tissues, organs, and organ systems. I believe it can equally be applied to a search for solutions to the social issues of our modern era: individual empowerment, relationships between and among people, relationships between and among nations, and population-based global concerns.