Predicting superagers: a machine learning approach utilizing gut microbiome features.

Objective: Cognitive decline is often considered an inevitable aspect of aging; however, recent research has identified a subset of older adults known as "superagers" who maintain cognitive abilities comparable to those of younger individuals. Investigating the neurobiological characteristics associated with superior cognitive function in superagers is essential for understanding "successful aging." Evidence suggests that the gut microbiome plays a key role in brain function, forming a bidirectional communication network known as the microbiome-gut-brain axis. Alterations in the gut microbiome have been linked to cognitive aging markers such as oxidative stress and inflammation. This study aims to investigate the unique patterns of the gut microbiome in superagers and to develop machine learning-based predictive models to differentiate superagers from typical agers. Methods: We recruited 161 cognitively unimpaired, community-dwelling volunteers aged 60 years or from dementia prevention centers in Seoul, South Korea. After applying inclusion and exclusion criteria, 115 participants were included in the study. Following the removal of microbiome data outliers, 102 participants, comprising 57 superagers and 45 typical agers, were finally analyzed. Superagers were defined based on memory performance at or above average normative values of middle-aged adults. Gut microbiome data were collected from stool samples, and microbial DNA was extracted and sequenced. Relative abundances of bacterial genera were used as features for model development. We employed the LightGBM algorithm to build predictive models and utilized SHAP analysis for feature importance and interpretability. Results: The predictive model achieved an AUC of 0.832 and accuracy of 0.764 in the training dataset, and an AUC of 0.861 and accuracy of 0.762 in the test dataset. Significant microbiome features for distinguishing superagers included Alistipes, PAC001137_g, PAC001138_g, Leuconostoc, and PAC001115_g. SHAP analysis revealed that higher abundances of certain genera, such as PAC001138_g and PAC001115_g, positively influenced the likelihood of being classified as superagers. Conclusion: Our findings demonstrate the machine learning-based predictive models using gut-microbiome features can differentiate superagers from typical agers with a reasonable performance.

Elevated cerebral blood flow proxy with increased beta-amyloid burden in Alzheimer's disease preclinical phase evaluated by dual-phase 18F-florbetaben positron emission tomography.

This study investigated the earliest change of cerebral blood flow (CBF) and its relationship with ß-amyloid (Aß) burden in preclinical Alzheimer's disease (AD) employing dual-phase 18F-florbetaben (FBB) PET. Seventy-one cognitively normal (NC) individuals were classified as Aß negative (Aß-NC) or positive (Aß+NC) based on two different cutoff values: an SUVR of > 1.08 and a Centiloid scale of > 20. The PET scans were acquired in two phases: an early phase (0-10 min, eFBB) and a delayed phase (90-110 min, dFBB), which were averaged to generate single-frame images for each phase. Furthermore, an R1 parametric map was generated from the early phase data using a simplified reference tissue model. We conducted regional and voxel-based analyses to compare the eFBB, dFBB, and R1 images between the Aß positive and negative groups. In addition, the correlations between the CBF proxy R1 and the dFBB SUVR were analyzed. The Aß+NC group showed significantly higher dFBB SUVR in both the global cerebral cortex and target regions compared to the Aß-NC group, while no significant differences were observed in eFBB SUVR between the two groups. Furthermore, the Aß+NC group exhibited significantly higher R1 values, a proxy for cerebral perfusion, in both the global cerebral cortex and target regions compared to the Aß-NC group. Significant positive correlations were observed between R1 and dFBB SUVR in both the global cerebral cortex and target regions, which remained significant after controlling for demographics and cognitive profiles, except for the medial temporal and occipital cortices. The findings reveal increased CBF in preclinical AD and a positive correlation between CBF and amyloid pathology. The positive correlation between R1 and amyloid burden may indicate a compensatory mechanism in the preclinical stage of Alzheimer's disease, but to elucidate this hypothesis, further longitudinal observational studies are necessary.

Alterations in Gut Microbiota and Their Correlation with Brain Beta-Amyloid Burden Measured by 18F-Florbetaben PET in Mild Cognitive Impairment Due to Alzheimer's Disease.

(1) Background: This study investigated changes in the gut microbial composition of individuals with mild cognitive impairment (MCI) due to Alzheimer's disease (AD) and their relationship with positron emission tomography (PET) amyloid accumulation. (2) Methods: In total, 17 cognitively normal individuals without amyloid-beta (Aß) accumulation (Aß-NC) and 24 with Aß-positive mild cognitive impairment (Aß+MCI) who underwent 18F-florbetaben PET and fecal bacterial 16S ribosomal RNA gene sequencing were enrolled. The taxonomic compositions of the Aß-NC and Aß+MCI groups were compared. The abundance of taxa was correlated with the standardized uptake value ratio (SUVR), using generalized linear models. (3) Results: There were significant differences in microbiome richness (ACE, p = 0.034 and Chao1, p = 0.024), alpha diversity (Shannon, p = 0.039), and beta diversity (Bray-Curtis, p = 0.018 and Generalized UniFrac, p = 0.034) between the Aß-NC and Aß+MCI groups. The global SUVR was positively correlated with the genus Intestinibacter (q = 0.006) and negatively correlated with the genera Roseburia (q = 0.008) and Agathobaculum (q = 0.029). (4) Conclusions: In this study, we identified significant changes in the gut microbiota composition that occur in individuals with MCI due to AD. In particular, the correlation analysis results between PET amyloid burden and gut microbial abundance showed that amyloid deposition is associated with a reduction in specific taxa involved in butyrate production.

The Moderating Role of Sleep Quality on the Association between Neuroticism and Frontal Executive Function in Older Adults.

OBJECTIVE/BACKGROUND: Personality traits are regarded as risk factors for cognitive impairment in older adults, while sleep disturbance and physical inactivity are also considered as modifiable risk factors. Therefore, it could be beneficial to investigate the effects of those modifiable risk factors on the relationship between personality traits and cognitive functions, to prepare appropriate strategies for mitigating cognitive impairment. PARTICIPANTS: A total of 155 cognitively unimpaired older adults were included. METHODS: All participants underwent cognitive function tests using the Seoul Neuropsychological Screening Battery and examinations for personality traits using the Big Five Inventory. Individual physical activity and sleep quality were assessed using the International Physical Activity Questionnaire and Pittsburgh Sleep Quality Index, respectively. A hierarchical linear multiple regression analysis was performed to demonstrate the direct association between personality traits and cognitive functions, and the multiple moderator analysis was used to analyze the moderating effects of lifestyle factors on this association. RESULTS: Among the five personality traits, only neuroticism was negatively associated with the frontal executive and visuospatial functions after controlling age, sex, and years of education. Interestingly, the negative relationship between neuroticism and frontal executive function was alleviated in older adults with higher sleep quality. CONCLUSIONS: Our findings demonstrated that higher sleep quality has significant moderating effects on the negative association between neuroticism and frontal executive functions in older adults, which suggests intervention for improving sleep quality such as cognitive behavioral therapy can be considered in older adults who have personality traits associated with a high risk of cognitive impairment.

Preoperative axillary nodal staging of invasive lobular breast cancer with ultrasound guided fine needle aspiration in patients with suspicious ultrasound findings versus aspiration in all patients - A retrospective single institutional analysis.

INTRODUCTION: - At present, surgical strategies for breast cancer patients with >2 lymph nodes (LN) involved differ from those with no or lower degree of nodal involvement. Preoperative assessment of the axilla is less sensitive in patients with lobular carcinoma (ILC) than patients with other histological tumour types. MATERIALS AND METHODS: - A retrospective analysis of axillary staging by palpation, axillary ultrasound (AXUS) and AXUS-guided fine-needle aspiration cytology (FNAC) of 153 patients with ILC diagnosed and operated on between January 2013 and December 2020 was performed. Patients had either sentinel node biopsy or axillary lymph node dissection according to current practice. In period 1, patients had FNAC only when AXUS suggested nodal involvement (n = 106), and in period 2, all ILC patients had axillary FNAC (n = 47). RESULTS: - Of the factors associated with >2LNs involvement, logistic regression suggested only AXUS/FNAC based staging as independent variable for all patients. Patients with AXUS-guided FNAC had a significantly higher proportion of true negative and lower proportion of true positive cases in the P2 period (0 vs 55% and 72% vs 11% for >2 LNs involvement, respectively; both p < 0.0001). CONCLUSIONS: - AXUS-guided FNAC of all ILC patients did not result in improved preoperative identification of patients with >2 metastatic LNs but increased the false-negative rate of the assessment by producing false-negative results in patients who would not have undergone a biopsy due to negative AXUS findings.

Predicting superagers by machine learning classification based on the functional brain connectome using resting-state functional magnetic resonance imaging.

Superagers are defined as older adults who have youthful memory performance comparable to that of middle-aged adults. Classifying superagers based on the brain connectome using machine learning modeling can provide important insights on the physiology underlying successful aging. We aimed to investigate the unique patterns of functional brain connectome of superagers and develop predictive models to differentiate superagers from typical agers based on machine learning methods. We obtained resting-state functional magnetic resonance imaging (rsfMRI) data and cognitive measures from 32 superagers and 58 typical agers. The accuracies of three machine learning methods including the linear support vector machine classifier (SV), the random forest classifier (RF), and the logistic regression classifier (LR) in predicting superagers were comparable (SV = 0.944, RF = 0.944, LR = 0.944); however, RF achieved the highest area under the curve (AUC; 0.979). An ensemble learning method combining the three classifiers achieved the highest AUC (0.986). The most discriminative nodes for predicting superagers encompassed areas in the precuneus; posterior cingulate gyrus; insular cortex; and superior, middle, and inferior frontal gyrus, which were located in default, salient, and multiple-demand networks. Thus, rsfMRI data can provide high accuracy for predicting superagers, thereby capturing and describing the unique characteristics of their functional brain connectome.

Aberrantly higher functional connectivity in the salience network is associated with transient global amnesia.

Triple intrinsic brain networks including the salience network (SN), default mode network (DMN), and central executive network (CEN), are known to be important in human cognition. Therefore, investigating those intrinsic brain networks in transient global amnesia (TGA) may offer novel insight useful for the pathophysiology of TGA. Fifty TGA patients underwent the resting state functional magnetic resonance imaging (rsfMRI) within 24 h, at 72 h, and 3 months after TGA onset. Twenty-five age, gender matched controls also underwent rsfMRI. Within 24 h of TGA onset, TGA patients showed greater functional connectivity in the SN and lower functional connectivity in the DMN, while relatively preserved functional connectivity was observed in the CEN. Interestingly, TGA patients continued to show decreased connectivity in the DMN, while no alterations were shown in the SN 72 h after illness onset. Three months after TGA onset, alterations of functional connectivity in the SN or the DMN were normalized. Our findings suggest that TGA is associated with transient greater functional connectivity in the SN and lower connectivity in the DMN.

White Matter Integrity Is Associated With the Amount of Physical Activity in Older Adults With Super-aging.

Previous studies have introduced the concept of "SuperAgers," defined as older adults with youthful memory performance associated with the increased cortical thickness of the anterior cingulate cortex. Given that age-related structural brain changes are observed earlier in the white matter (WM) than in the cortical areas, we investigated whether WM integrity is different between the SuperAgers (SA) and typical agers (TA) and whether it is associated with superior memory performance as well as a healthy lifestyle. A total of 35 SA and 55 TA were recruited for this study. Further, 3.0-T magnetic resonance imaging (MRI), neuropsychological tests, and lifestyle factors related to cognitive function, such as physical activity and duration of sleep, were evaluated in all participants. SA was defined as individuals demonstrating the youthful performance of verbal and visual memory, as measured by the Seoul Verbal Learning Test (SVLT) and the Rey-Osterrieth Complex Figure Test (RCFT), respectively. Tract-based spatial statistics (TBSS) analysis was used to compare the diffusion values such as fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD) and axial diffusivity (AD) between the SA and TA. SA exhibited better performance in memory, attention, visuospatial, and frontal executive functions than the TA did. SA also exhibited greater amounts of physical activity than the TA did. As compared to TA, SA demonstrated higher FA with lower MD, RD, and AD in the corpus callosum and higher FA and lower RD in the right superior longitudinal fasciculus (SLF), which is significantly associated with memory function. Interestingly, FA values of the body of corpus callosum were correlated with the amount of physical activity. Our findings suggest that WM integrity of the corpus callosum is associated with superior memory function and a higher level of physical activities in SA compared to TA.

Four-Week, Home-Based, Robot Cognitive Intervention for Patients with Mild Cognitive Impairment: a Pilot Randomized Controlled Trial.

BACKGROUND AND PURPOSE: Previous studies suggest that cognitive intervention can mitigate the development of dementia in patients with mild cognitive impairment (MCI). However, the previous cognitive intervention was mostly provided as a group session, in which MCI patients sometimes had difficulty in regularly attending sessions or were reluctant to participate in group-based classes. Additionally, experienced instructors for traditional cognitive intervention may be unavailable in some chronic-care facilities or community centers. Considering these reasons, we have developed 5 programs for home-based cognitive intervention using a personal robot for MCI patients. In this preliminary study, we aimed to demonstrate the effects of our newly developed home-based cognitive intervention with robots on cognitive function in MCI patients. METHODS: We conducted a single-blind randomized controlled trial enrolling 46 MCI patients. Participants were randomized into 2 groups: the robot cognitive intervention (robot) (n=24) group and without cognitive intervention (control) (n=22) group. The interventions comprised 60-min sessions per day for 4 weeks. The primary outcome was the change in cognitive function measured using the Cambridge Neuropsychological Test Automated Battery. RESULTS: There were no significant baseline demographic or clinical differences between the robot and control groups. After the 4-week cognitive intervention, the robot group showed greater improvement in working memory than did the control group. CONCLUSIONS: Our home-based cognitive intervention with a personal robot improved the working memory in MCI patients. Further studies with larger samples and longer study periods are required to demonstrate the effects of these programs in other cognitive domains in MCI patients.

Four-Week, Home-Based, Robot Cognitive Intervention for Patients with Mild Cognitive Impairment: a Pilot Randomized Controlled Trial

Background@#and Purpose: Previous studies suggest that cognitive intervention can mitigate the development of dementia in patients with mild cognitive impairment (MCI). However, the previous cognitive intervention was mostly provided as a group session, in which MCI patients sometimes had difficulty in regularly attending sessions or were reluctant to participate in group-based classes. Additionally, experienced instructors for traditional cognitive intervention may be unavailable in some chronic-care facilities or community centers. Considering these reasons, we have developed 5 programs for home-based cognitive intervention using a personal robot for MCI patients. In this preliminary study, we aimed to demonstrate the effects of our newly developed home-based cognitive intervention with robots on cognitive function in MCI patients. @*Methods@#We conducted a single-blind randomized controlled trial enrolling 46 MCI patients. Participants were randomized into 2 groups: the robot cognitive intervention (robot) (n=24) group and without cognitive intervention (control) (n=22) group. The interventions comprised 60-min sessions per day for 4 weeks. The primary outcome was the change in cognitive function measured using the Cambridge Neuropsychological Test Automated Battery. @*Results@#There were no significant baseline demographic or clinical differences between the robot and control groups. After the 4-week cognitive intervention, the robot group showed greater improvement in working memory than did the control group. @*Conclusions@#Our home-based cognitive intervention with a personal robot improved the working memory in MCI patients. Further studies with larger samples and longer study periods are required to demonstrate the effects of these programs in other cognitive domains in MCI patients.