Lower insulin secretion is associated with hippocampal and parahippocampal gyrus atrophy in elderly patients with type 2 diabetes mellitus

Sequence of episodic memory-related behavioral and brain-imaging abnormalities in type 2 diabetes

BACKGROUND

Episodic memory decline is a common complication of type 2 diabetes (T2D). To comprehensively explore the neural mechanisms underlying it, we aimed to explore the sequence that episodic memory-related behavioral and brain-imaging biomarkers appear abnormal in the progression of T2D.

METHODS

We enrolled 62 healthy controls and 110 patients with T2D. The California Verbal Learning Test, Montreal cognitive assessment, and Stroop color word test was used to assess the episodic memory, general cognitive function, and executive function. Principal component analysis was applied to extract behavioral biomarkers. Imaging biomarkers included structural and functional MRI features of the entorhinal cortex-hippocampus and hippocampus-anterior cingulate cortex pathways. We used a novel discriminative event-based model to determine the sequence that memory-related biomarkers appear abnormal and estimate the stage of memory decline.

RESULTS

T2D patients exhibited poorer memory, general cognitive function, and executive function compared to healthy controls after controlling age, sex, and education level. In the progression of T2D, functional interaction between brain regions showed abnormalities first, followed by memory tests, the cerebral spontaneous neural activity, and finally the gray matter volume. Besides, abnormalities appeared earlier in the entorhinal cortex than in the anterior cingulate cortex. Later stage of memory decline was distributed in older patients with T2D and was associated with higher systolic blood pressure, postprandial blood glucose, and low-density lipoprotein.

CONCLUSIONS

In T2D, behavioral and brain imaging biomarkers of episodic memory appear abnormal in a specific sequence, and the stage of memory decline was closely associated with old age and vascular risk factors.

CLINICAL TRIAL REGISTRATION

NCT02420470, ClinicalTrials.gov ( https://www.

CLINICALTRIALS

gov/ ).

Association of individual-based morphological brain network alterations with cognitive impairment in type 2 diabetes mellitus

Objective

To investigate the altered characteristics of cortical morphology and individual-based morphological brain networks in type 2 diabetes mellitus (T2DM), as well as the neural network mechanisms underlying cognitive impairment in T2DM.

Methods

A total of 150 T2DM patients and 130 healthy controls (HCs) were recruited in this study. The study used voxel- and surface-based morphometric analyses to investigate morphological alterations (including gray matter volume, cortical thickness, cortical surface area, and localized gyrus index) in the brains of T2DM patients. Then two methods, Jensen-Shannon divergence-based similarities (JSDs) and Kullback-Leibler divergence-based similarities (KLDs), were used to construct individual morphometric brain networks based on gray matter volume, to discover altered features of the topological network and extract abnormal key brain regions. Subsequently, partial correlation analyses were performed to explore the relationship between clinical biochemical indices, neuropsychological test scores, and altered cortical morphology and network indices.

Results

Brain regions with reduced gray matter volume and cortical thickness in T2DM patients were mainly concentrated in the frontal lobe, temporal lobe, parietal lobe, anterior cingulate gyrus, insula, lingual gyrus, and cerebellar hemispheres. The global attributes of the Individual-based morphological brain network were significantly reduced (Cp, Eloc, σ), with an increase in the nodal efficiency of the hippocampus and the nodal local efficiency of the anterior cingulate gyrus, and the nodal local efficiency of the parahippocampal gyrus and transverse temporal gyrus were reduced. There was a correlation between these node attributes and cognitive scale scores.

Conclusion

This study demonstrated that patients with T2DM exhibit generalized cortical atrophy and damage to individual morphologic brain networks. It also identified overlapping and cognitively relevant key brain regions, primarily within the limbic/paralimbic network (especially the hippocampus and cingulate gyrus), which may serve as imaging markers for identifying cognitive deficits in T2DM. These findings offer new insights into the neural network mechanisms underlying T2DM-associated brain damage and cognitive impairment.

Glucose metabolism and smaller hippocampal volume in elderly people with normal cognitive function

We investigated associations of glycemic measures, and insulin resistance and secretion measures with hippocampal and subfield volumes. In this cross-sectional study, 7400 community-dwelling participants underwent brain MRI and health checkups between 2016 and 2018. Hemoglobin A1c (HbA1c), glycated albumin (GA), homeostasis model assessment for insulin resistance (HOMA-IR), and HOMA of percent β-cell function (HOMA-β) were evaluated. The associations of each measure with a smaller volume of the hippocampus and twelve hippocampal subfields were investigated. As a result, higher HbA1c or GA and lower HOMA-β levels were significantly associated with smaller volumes in multiple hippocampal subfields. Furthermore, even when we analyzed non-diabetic individuals, substantial associations remained between higher GA or lower HOMA-β levels and smaller volumes of the whole hippocampus or the fimbria. Our findings indicate that postprandial glucose fluctuations, postprandial hyperglycemia, and low insulin secretion have a specific effect on the development of smaller hippocampal volume, suggesting that primary prevention of diabetes and/or sufficient glucose control are important for the prevention of dementia.

Associations of Dental Health With the Progression of Hippocampal Atrophy in Community-Dwelling Individuals: The Ohasama Study

BACKGROUND AND OBJECTIVES

Although tooth loss and periodontitis have been considered risk factors of Alzheimer disease, recent longitudinal researches have not found a significant association with hippocampal atrophy. Therefore, this study aimed to clarify a longitudinal association between the number of teeth present (NTP) and hippocampal atrophy dependent on the severity of periodontitis in a late middle-aged and older adult population.

METHODS

This study included community-dwelling individuals aged 55 years or older who had no cognitive decline and had undergone brain MRI and oral and systemic data collection twice at 4-year intervals. Hippocampal volumes were obtained from MRIs by automated region-of-interest analysis. The mean periodontal probing depth (PD) was used as a measure of periodontitis. Multiple regression analysis was performed with the annual symmetric percentage change (SPC) of the hippocampal volume as the dependent variable and including an interaction term between NTP and mean PD as the independent variable. The interaction details were examined using the Johnson-Neyman technique and simple slope analysis. The 3-way interaction of NTP, mean PD, and time on hippocampal volume was analyzed using a linear mixed-effects model, and the interaction of NTP and time was examined in subgroups divided by the median mean PD. In all models, dropout bias was adjusted by inverse probability weighting.

RESULTS

Data of 172 participants were analyzed. The qualitative interaction between NTP and the mean PD was significant for the annual SPC in the left hippocampus. The regression coefficient of the NTP on the annual SPC in the left hippocampus was positive (B = 0.038, p = 0.026) at the low-level mean PD (mean -1 SD) and negative (B = -0.054, p = 0.001) at the high-level mean PD (mean +1 SD). Similar results were obtained in the linear mixed-effects model; the interaction of NTP and time was significant in the higher mean PD group.

DISCUSSION

In a late middle-aged and older cohort, fewer teeth were associated with a faster rate of left hippocampal atrophy in patients with mild periodontitis, whereas having more teeth was associated with a faster rate of atrophy in those with severe periodontitis. The importance of keeping teeth healthy is suggested.

Voxel-based morphometry reveals the correlation between gray matter volume and serum P-tau-181 in type 2 diabetes mellitus patients with different HbA1c levels

Introduction

Emerging evidence suggested widespread decreased gray matter volume (GMV) and tau hyperphosphorylation were associated with type 2 diabetes mellitus (T2DM). Insulin resistance is one of the mechanisms of neuron degeneration in T2DM; it can decrease the activity of protein kinase B and increase the activity of glycogen synthesis kinase-3β, thus promoting the hyperphosphorylation of tau protein and finally leading to neuronal degeneration. However, the association between GMV and serum tau protein phosphorylated at threonine 181 (P-tau-181) in T2DM patients lacks neuroimaging evidence. We aimed to investigate the difference in brain GMV between T2DM patients with different glycated hemoglobin A1c (HbA1c) levels and healthy control (HC) subjects and the correlation between serum P-tau-181 and GMV in T2DM patients.

Methods

Clinical parameters, biochemical indicators, and MRI data were collected for 41 T2DM patients with high glycosylated hemoglobin level (HGL), 17 T2DM patients with normal glycosylated hemoglobin level (NGL), and 42 HC subjects. Voxel-based morphometry (VBM) method was applied to investigate GMV differences among groups, and multiple regression analysis was used to examine the correlation between serum P-tau-181 and GMV.

Results

Compared with HC subjects, the T2DM patients with HGL or NGL all showed significantly decreased GMV. Briefly, the GMV decreased in T2DM patients with HGL was mainly in the bilateral parahippocampal gyrus (PHG), right middle temporal gyrus (MTG), temporal pole (TPOmid), hippocampus (HIP), and left lingual gyrus. The GMV reduction in T2DM patients with NGL was in the right superior temporal gyrus (STG), and there was no significant difference in GMV between the two diabetic groups. The GMV values of bilateral PHG, right MTG, TPOmid, HIP, and STG can significantly (p < 0.0001) distinguish T2DM patients from HC subjects in ROC curve analysis. In addition, we found that serum P-tau-181 levels were positively correlated with GMV in the right superior and middle occipital gyrus and cuneus, and negatively correlated with GMV in the right inferior temporal gyrus in T2DM patients.

Conclusion

Our study shows that GMV atrophy can be used as a potential biological indicator of T2DM and also emphasizes the important role of P-tau-181 in diabetic brain injury, providing new insights into the neuropathological mechanism of diabetic encephalopathy.

The changes in cognitive function following bariatric surgery considering the function of gut microbiome

Background

There is a correlation between gut microbiota and cognitive function. The mechanisms and pathways explain why the incidence of Alzheimer's disease in subjects undergoing bariatric surgery is lower than in other people with obesity.

Methods

In this review article, we aim to discuss the association of obesity, cognitive impairment, and physiological changes after bariatric surgery.

Results

Bariatric surgery has a series of physiological benefits which may lead to an improvement in cognitive functions in individuals who are prone to later developing Alzheimer's disease. Also, taxonomical change in the gut microbiome profile provides a healthy condition for living with better levels of cognition without neuropathological damages in older ages.

Conclusion

It can be concluded that there is a possible correlation between cognitive dysfunction and increased risk of cognitive dysfunction in people with a BMI higher than 40 kg/m2. Bariatric surgery may increase neurotransmitters and improve the gut bacteria, leading to a significant reduction in the risk of Alzheimer's disease.

Altered Functional Network Connectivity of Precuneus and Executive Control Networks in Type 2 Diabetes Mellitus Without Cognitive Impairment

In epidemiological studies, type 2 diabetes mellitus (T2DM) has been associated with cognitive impairment and dementia, but studies about functional network connectivity in T2DM without cognitive impairment are limited. This study aimed to explore network connectivity alterations that may help enhance our understanding of damage-associated processes in T2DM. MRI data were analyzed from 82 patients with T2DM and 66 normal controls. Clinical, biochemical, and neuropsychological assessments were conducted in parallel with resting-state functional magnetic resonance imaging, and the cognitive status of the patients was assessed using the Montreal Cognitive Assessment-B (MoCA-B) score. Independent component analysis revealed a positive correlation between the salience network and the visual network and a negative connection between the left executive control network and the default mode network in patients with T2DM. The differences in dynamic brain network connectivity were observed in the precuneus, visual, and executive control networks. Internal network connectivity was primarily affected in the thalamus, inferior parietal lobe, and left precuneus. Hemoglobin A1c level, body mass index, MoCA-B score, and grooved pegboard (R) assessments indicated significant differences between the two groups (p < 0.05). Our findings show that key changes in functional connectivity in diabetes occur in the precuneus and executive control networks that evolve before patients develop cognitive deficits. In addition, the current study provides useful information about the role of the thalamus, inferior parietal lobe, and precuneus, which might be potential biomarkers for predicting the clinical progression, assessing the cognitive function, and further understanding the neuropathology of T2DM.

Research on Voxel-Based Features Detection and Analysis of Alzheimer’s Disease Using Random Survey Support Vector Machine

Alzheimer’s disease (AD) is a degenerative disease of the central nervous system characterized by memory and cognitive dysfunction, as well as abnormal changes in behavior and personality. The research focused on how machine learning classified AD became a recent hotspot. In this study, we proposed a novel voxel-based feature detection framework for AD. Specifically, using 649 voxel-based morphometry (VBM) methods obtained from MRI in Alzheimer’s Disease Neuroimaging Initiative (ADNI), we proposed a feature detection method according to the Random Survey Support Vector Machines (RS-SVM) and combined the research process based on image-, gene-, and pathway-level analysis for AD prediction. Particularly, we constructed 136, 141, and 113 novel voxel-based features for EMCI (early mild cognitive impairment)-HC (healthy control), LMCI (late mild cognitive impairment)-HC, and AD-HC groups, respectively. We applied linear regression model, least absolute shrinkage and selection operator (Lasso), partial least squares (PLS), SVM, and RS-SVM five methods to test and compare the accuracy of these features in these three groups. The prediction accuracy of the AD-HC group using the RS-SVM method was higher than 90%. In addition, we performed functional analysis of the features to explain the biological significance. The experimental results using five machine learning indicate that the identified features are effective for AD and HC classification, the RS-SVM framework has the best classification accuracy, and our strategy can identify important brain regions for AD.