IntroductionBlood-based biomarkers offer a promising, minimally invasive approach to Alzheimer’s disease (AD) diagnosis, yet validation in admixed populations remains limited. We investigated whether plasma biomarkers predict CSF-defined AD pathology in a Brazilian cohort.MethodsSeventy-eight older adults [including individuals with mild cognitive impairment (MCI), subjective cognitive decline (SCD), and cognitively unimpaired controls] underwent cognitive testing, neuroimaging, and plasma biomarker assessment. CSF data were available for symptomatic participants (MCI and SCD; n = 61), and regression and ROC analyses were performed in the subset with both CSF and APOE genotyping data (n = 53). Plasma Aβ42, p-Tau181, p-Tau217, t-Tau, and derived ratios were quantified. Multivariable logistic regression and ROC analyses evaluated prediction of abnormal CSF p-Tau181/Aβ42 and t-Tau/Aβ42, adjusting for age, sex, and APOE ε4 status.ResultsApproximately 25% of individuals with MCI exhibited abnormal CSF p-Tau181/Aβ42 and t-Tau/Aβ42 ratios. Moderate correlations were observed between plasma and CSF biomarkers (r > 0.4), particularly for Aβ42/p-Tau217 and p-Tau217. In adjusted models, plasma p-Tau217 and the Aβ42/p-Tau217 ratio independently predicted abnormal CSF pathology. Each one standard deviation increase in p-Tau217 was associated with 3.53–4.83-fold higher odds of abnormal CSF (p ≤ 0.003). In contrast, higher Aβ42/p-Tau217 ratios were associated with substantially lower odds of pathology, with each one standard deviation increase corresponding to a 91%–93% reduction in risk (p ≤ 0.002). The ratio showed stronger associations than p-Tau217 alone. ROC analyses demonstrated good discrimination. For CSF p-Tau181/Aβ42, Aβ42/p-Tau217 achieved an AUC of 0.88 (83% sensitivity, 85% specificity), compared with 0.83 for p-Tau217. For CSF t-Tau/Aβ42, both biomarkers yielded AUCs of 0.89.DiscussionPlasma Aβ42/p-Tau217 and p-Tau217 effectively identify CSF-defined AD pathology in an admixed cohort. While higher p-Tau217 levels were associated with increased odds of pathology, higher Aβ42/p-Tau217 ratios were associated with lower pathological burden and demonstrated stronger effect sizes, supporting the added value of combining amyloid and tau biomarkers. These findings provide initial evidence for local validation of blood-based AD biomarkers in Brazil.

BackgroundAlzheimer's disease (AD) is increasingly prevalent in Latin America. Neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) are promising biomarkers of neurodegeneration, but their relationship with white matter (WM) integrity remains unclear.ObjectiveTo investigate associations between fluid neurodegeneration biomarkers and WM microstructure in a Brazilian cohort of individuals across the AD continuum and cognitively healthy controls.MethodsNinety-one participants were included: 27 cognitively healthy controls (mean age = 68.3 ± 5.2 years) and 64 amyloid-positive individuals with mild cognitive impairment or AD dementia (mean age = 70.6 ± 6.9 years). AD participants were characterized by low cerebrospinal fluid (CSF) Aβ42 concentrations (<540 pg/mL) and altered Aβ42/p-Tau and Aβ42/t-Tau ratios. Serum and CSF concentrations of NfL and GFAP were measured using single-molecule array technology and examined in relation to diffusion tensor imaging metrics, including fractional anisotropy, mean diffusivity, radial diffusivity, and axial diffusivity (AxD).ResultsWithin the clinical AD group, higher serum NfL levels were associated with lower AxD in the left cingulum tract (r = -0.372, p = 0.007). In cognitively healthy controls, serum NfL showed positive correlations with AxD and mean diffusivity in the right cingulum (r = 0.650, p = 0.001 and r = 0.607, p = 0.003, respectively). No significant associations were observed between serum or CSF GFAP concentrations and diffusion tensor imaging metrics.ConclusionsSerum NfL was associated with anatomically specific WM microstructural changes, with differing patterns across clinical groups.

[This corrects the article DOI: 10.3389/fnagi.2026.1773606.].

Mild cognitive impairment (MCI) refers to cognitive alterations with preservation of functionality. Individuals with this diagnosis have a higher risk of developing dementia. Non-pharmacological interventions, such as physical exercise, are beneficial for the cognition of this population. However, the impact of resistance training (RT) on the brain anatomy of older adults with MCI has not yet been clarified. This study aimed to investigate the effects of RT on cognition and brain anatomy in MCI. Forty-four older adults with MCI, 22 in the training group and 22 in the control group, were evaluated in neuropsychological tests and magnetic resonance imaging at the beginning and end of the study, which lasted 24 weeks. We used repeated measures ANOVA. The training group showed better performance in verbal episodic memory after intervention. The control group showed a decrease in gray matter volume in the hippocampus and precuneus, while the training group showed no reduction in the right hippocampus and precuneus. However, it showed a decrease in the volume of these regions on the left side and in the left superior frontal gyrus. In the analysis of white matter integrity, fractional anisotropy increased in the training group and decreased in the control group. Axial diffusivity decreased in the training group, while radial diffusivity increased in the control group, and mean diffusivity varied according to the tract evaluated. RT improves memory performance, positively influences white matter integrity parameters, and plays a protective role against atrophy of the hippocampus and precuneus in MCI.

Caspase-cleaved tau can critically be involved in the pathogenesis and progression of Alzheimer’s disease (AD), due to its ability to promote both misfolding and neurodegeneration, which ultimately leads to progressive cognitive impairment. Neuropathological studies show that caspase-cleaved tau species are abundant in AD brain neurons, yet show only a modest degree of co-occurrence with phospho-tau. This suggests that caspase-cleaved tau is an overlooked form of tau pathology that is related to the vulnerability and pathogenesis of AD. We developed a homebrew Simoa immunoassay for detecting N-terminal D13 caspase-6-cleaved tau in the cerebrospinal fluid (CSF). For developing the assay, we used a neo-epitope monoclonal antibody developed and validated by our group and used in previously published histological work. After optimization, we tested this assay in a pilot study with a discovery cohort comprising CSF samples from 20 subjects with mild to moderate dementia and positive for AD biomarkers and 20 well-characterized healthy controls (HC). The signal translated in the mean average enzymes per bead was significantly higher in Alzheimer’s disease cases compared to healthy controls (0.01640 vs. 0.01189, ρ < 0.001). There were no significant differences in demographics such as age (ρ = 0.072) or gender (ρ = 0.723) between the two groups. We introduce the initial D13 caspase-6-cleaved tau biomarker immunoassay for quantitatively assessing caspase-cleaved tau pathology in CSF. This assay offers the chance to identify pathological tau that conventional p-tau assays may overlook, potentially enhancing the assessment of clinical trials. We are currently expanding the study cohort and correlating the findings with metrics from p-tau biomarkers.

Introduction: Mild cognitive impairment (MCI) refers to the transition zone between preserved cognition and dementia. With aging and the decrease in muscle tissue, there tends to be an increase in inflammatory agents and cytokines that are often related to cognitive decline. C-reactive protein (CRP), for example, has high levels in elderly people with MCI. Physical exercise is a determining factor for the increase in muscle mass, aerobic capacity, and improvement of cognitive functions in the elderly population; in addition to promoting the reduction of inflammatory processes, it also increases muscle mass and tends to significantly improve activities of daily living. Objective: The aim of this study was to investigate whether there is a correlation between CRP serum concentration and aerobic fitness measured by performance in the VO2 Max test. Methods: In total, 19 participants with MCI diagnosed according to the NIAA criteria were evaluated according to serum CRP and VO2 max values. For statistical analysis, we used Kendall’s tau correlation, due to the non-parametric distribution of the data. Significance was set at p < 0.05. Results: We found a weak negative correlation (r = -359*) between serum CRP levels and VO2 max test performance in elderly patients with MCI. Discussion: Our findings corroborate the literature indicating that aerobic fitness influences the decrease in CRP concentrations in elderly patients with MCI, which had also been previously shown in cognitively healthy elderly and young adults. Conclusion: Our findings suggest that the higher the performance in the V02 max test, the lower the serum CRP concentration. Aerobic fitness seems to have a relevant potential effect on inflammation in elderly patients with MCI, and its increase may favor the reduction of inflammatory processes related to cognitive decline.

Individuals with Mild Cognitive Impairment (MCI) are at greater risk of developing Alzheimer's disease (AD). Previous studies have shown that physical exercise is a protective factor against the clinical evolution of dementia in MCI. Lower muscle strength levels are associated with a greater risk of AD incidence. Physical exercises can also promote improvements in brain networks' functional connectivity (FC). However, the influence of resistance exercise, which significantly impacts the development of muscular strength and cognition, remains unknown concerning FC in MCI. We aimed to investigate the FC of brain networks after 24 weeks of resistance training. 37 older adults with MCI were investigated. Nineteen performed the resistance training protocol, and 18 constituted the control group, not performing the exercises. Participants were evaluated before and after the intervention using a magnetic resonance imaging device (3 Tesla) for functional magnetic resonance imaging. FC was assessed in Matlab software using the uf2c program to investigate intra-network connectivity, considering a p-value of 0.05 with an FDR-corrected comparison. We evaluated 12 networks: AnteSalience, PostSalience, Auditory, BasalGanglia, Dorsal Default Mode Network, Ventral Default Mode Network, Language, Left executive control network, Right executive control network, Sensorimotor, Visual and Visuospatial networks. AnteSalience connectivity decreased in the control group (T-score > 3.1473) and increased in the exercise group in the PostSalience (T-score > 3.7114). The results suggest that the training intervention also increased the FC of the visuospatial network (uncorrected results T-score > 2.7195), while the control group showed no changes. None of the other networks shows differences between the pre-and post-intervention moments. The intervention time may have influenced the results, as changes in FC tend to occur over extended periods; even so, resistance exercise proved influential. FC increased or tended to increase in some networks in the exercise intervention group, while in the control group, it decreased or remained stable, suggesting that exercise may be a beneficial modulator of brain connectivity in specific networks such as AnteSalience, PostSalience, and the Visuospatial network. More studies are suggested, especially those that monitor FC changes over extended periods.

Alzheimer’s disease (AD) pathophysiology is complex and not completely known. Emerging new biomarkers that evaluate synaptic function (VILIP-1, neurogranin), co-pathology (alpha-synuclein), and neurodegeneration (NFL) are potential candidates to be incorporated into the early AD diagnosis. To better understand the relevance of these biomarkers, we evaluated the correlations between their CSF concentrations with whole-brain grey matter volumes in SCD and MCI, according to their amyloid status (A- or A+). 75 participants diagnosed with SCD or MCI were included, 30 A- and 45 A+. They all underwent comprehensive neuropsychological assessment, CSF analyses (Roche Elecsys), and volumetric 3T MRI (Philips Achieva). Voxel-based morphometry was used to quantify brain volumes through CAT12 running on MATLAB 2019b. Partial correlation analysis between CSF biomarkers and cortical volumes was performed with SPSS 22, adjusting for age and sex. Significant correlations were found in the A- but not in the A+ group. NFL and hippocampus (r = 0.40, p = 0.028); VILIP-1 and superior temporal gyrus (r = -0.38, p = 0.035), and superior frontal gyrus (r = -0.40, p = 0.025); alpha-synuclein and hippocampus (r = -0.39, p = 0.036), middle frontal gyrus (r = -0.375, p = 0.049), and superior temporal gyrus (r = -0.478, p = 0.01). We found different patterns of correlations between brain anatomy and emerging biomarkers, according to amyloid status, in the very early stage of the neurodegenerative process. Interestingly, these correlations were found only in the amyloid-negative group, which might suggest that different pathological processes involving synaptic function, neurodegeneration, and co-pathology occur in suspected non-Alzheimer pathophysiology cases.

Alzheimer’s disease (AD) is a neurodegenerative disorder that has become increasingly prevalent around the world and can be characterized in vivo by amyloid-beta peptide and hyperphosphorylated tau protein. Subjective cognitive decline (SCD) and mild cognitive impairment (MCI) are potential previous stages of AD dementia. Behavioral and psychological symptoms are common in SCD and MCI, but their biological basis is still not clarified. This study aims to investigate the potential correlations between neuropsychiatric symptoms and AD biomarkers in patients with SCD and MCI. 71 subjects were recruited for a medical and psychological evaluation in the Clinical Research Center of UNICAMP, Brazil. They were diagnosed according to NIA-AA criteria and underwent neuropsychiatric scales, including the Mild Behavioral Impairment Checklist (MBI-C), a helpful questionnaire to measure behavioral and neuropsychiatric symptoms in the AD continuum. The subjects also underwent CSF collection to measure the biomarkers in the samples using immunoassay kits. We used Pearson correlation for the statistical analyses. We found statistically relevant correlations between the MBI-C C domain and the CSF amyloid-beta (p = 0.026, r = -0.302, n = 54) and a correlation between the MBI-C E domain and the CSF total-tau protein (p = 0.026, r = 0.295, n = 57). The MBI-C C domain approaches symptoms of impulse dyscontrol, and the MBI-C E domain comes with symptoms of abnormal perception or thought content. We found that psychological symptoms imply an association of these symptoms with the neurodegenerative process due to the correlation with total-tau protein, a more specific biomarker.

Several studies have aimed at identifying biomarkers in the initial phases of Alzheimer's disease (AD). Conversely, texture features, such as those from gray-level co-occurrence matrices (GLCMs), have highlighted important information from several types of medical images. More recently, texture-based brain networks have been shown to provide useful information in characterizing healthy individuals. However, no studies have yet explored the use of this type of network in the context of AD. This work aimed to employ texture brain networks to investigate the distinction between groups of patients with amnestic mild cognitive impairment (aMCI) and mild dementia due to AD, and a group of healthy subjects. Magnetic resonance (MR) images from the three groups acquired at two instances were used. Images were segmented and GLCM texture parameters were calculated for each region. Structural brain networks were generated using regions as nodes and the similarity among texture parameters as links, and graph theory was used to compute five network measures. An ANCOVA was performed for each network measure to assess statistical differences between groups. The thalamus showed significant differences between aMCI and AD patients for four network measures for the right hemisphere and one network measure for the left hemisphere. There were also significant differences between controls and AD patients for the left hippocampus, right superior parietal lobule, and right thalamus-one network measure each. These findings represent changes in the texture of these regions which can be associated with the cortical volume and thickness atrophies reported in the literature for AD. The texture networks showed potential to differentiate between aMCI and AD patients, as well as between controls and AD patients, offering a new tool to help understand these conditions and eventually aid early intervention and personalized treatment, thereby improving patient outcomes and advancing AD research.

Background: Detection of amyloid status in the central nervous system, based on a non-invasive blood test, is essential, especially in patients in potentially pre-dementia stages and candidates for receiving new anti-amyloid drugs. Objective: To evaluate if plasma measurements of Aβ42/Aβ40, Aβ42/p-Tau181, and Aβ42/T-tau ratios can predict cerebrospinal fluid (CSF) amyloid alteration in patients with Subjective Cognitive Decline (SCD) and Mild Cognitive Impairment (MCI). Methods: We evaluated 51 patients (31 women) diagnosed with SCD and MCI (NIA/AA criteria). All participants underwent neuropsychological assessment, magnetic resonance imaging, blood and cerebrospinal fluid tests. Roche’s Elecsys immunoassay measured the quantification of CSF Aβ42 peptide (normal > 1000 pg/mL). Plasma biomarkers Aβ42, Aβ40, p-Tau 181, and t-Tau were measured on an automated SIMOA HD-X immunoassay equipment. Logistic regression models considering Aβ42/Aβ40, Aβ42/p-Tau181and Aβ42/t-Tau ratios in plasma, age, and sex were performed, as well as ROC curve analysis to evaluate sensitivity and specificity. Results: 29 subjects had altered Aβ42 in the CSF. The full model was significant, χ2 (5.51) =11.53, p=0.042, R2=0.272. Only Aβ42/p-Tau181 contributed significantly to the model (B=8.09, p=0.022). At the threshold of 0.368, the model achieved a sensitivity of 75.8% and specificity of 54.5%, with an AUC of 66.6%, 95% CI: 0.52-0.79), p = 0.033 Conclusion: The plasmatic Aβ42/p-Tau181 ratio could predict CSF amyloid alteration with good sensitivity and low specificity in subjects at high dementia risk. As far as we know, this is the first study in a Brazilian sample to evaluate plasmatic Alzheimer’s biomarkers in potential pre-dementia subjects using gold-standard diagnostic methods like Elecsys and SIMOA.

Little is known about changes in the brain associated with frailty, in particular, which brain areas could be related to frailty in older people without cognitive impairment. This scoping review mapped evidence on functional and/or structural brain changes in frail older adults without cognitive impairment. The methodology proposed by the JBI® was used in this study. The search in PubMed, PubMed PMC, BVS/BIREME, EBSCOHOST, Scopus, Web of Science, Embase, and PROQUEST was conducted up to January 2023. Studies included following the population, concepts, context and the screening and data extraction were performed by two independent reviewers. A total of 9,912 records were identified, 5,676 were duplicates and were excluded. The remaining articles were screened; 31 were read in full and 17 articles were included. The results showed that lesions in white matter hyperintensities, reduced volume of the hippocampus, cerebellum, middle frontal gyrus, low gray matter volume, cortical atrophy, decreased connectivity of the supplementary motor area, presence of amyloid-beta peptide (aβ) in the anterior and posterior putamen and precuneus regions were more frequently observed in frail older adults, compared with non-frail individuals. Studies have suggested that such findings may be of neurodegenerative or cerebrovascular origin. The identification of these brain alterations in frail older adults through neuroimaging studies contributes to our understanding of the underlying mechanisms of frailty. Such findings may have implications for the early detection of frailty and implementation of intervention strategies.

Autoantibodies and auto-reactive B cells participate in the pathogenesis of systemic lupus erythematosus (SLE), affecting various organs and tissues, including the nervous system, referred to as neuropsychiatric SLE (NPSLE).The cytokine B-lymphocyte stimulator (BLyS), which induces B cell proliferation and survival, may play an important role in neuropsychiatric manifestations (NPM).Here, we examine BLyS levels in SLE patients with well-defined NPSLE symptoms as compared to SLE patients without NPSLE and individuals with depression and cognitive impairment and healthy controls. CONCLUSIONSLE patients with depression and cognitive impairment had higher BLyS levels when compared to SLE patients without NPM and control with NP.BLyS may play a role in NPSLE pathogenesis.

Abstract The causes of the neurodegenerative processes in Alzheimer's disease (AD) are not completely known. Recent studies have shown that white matter (WM) damage could be more severe and widespread than whole‐brain cortical atrophy and that such damage may appear even before the damage to the gray matter (GM). In AD, Amyloid‐beta (Aβ42) and tau proteins could directly affect WM, spreading across brain networks. Since hippocampal atrophy is common in the early phase of disease, it is reasonable to expect that hippocampal volume (HV) might be also related to WM integrity. Our study aimed to evaluate the integrity of the whole‐brain WM, through diffusion tensor imaging (DTI) parameters, in mild AD and amnestic mild cognitive impairment (aMCI) due to AD (with Aβ42 alteration in cerebrospinal fluid [CSF]) in relation to controls; and possible correlations between those measures and the CSF levels of Aβ42, phosphorylated tau protein (p‐Tau) and total tau (t‐Tau). We found a widespread WM alteration in the groups, and we also observed correlations between p‐Tau and t‐Tau with tracts directly linked to mesial temporal lobe (MTL) structures (fornix and hippocampal cingulum). However, linear regressions showed that the HV better explained the variation found in the DTI measures (with weak to moderate effect sizes, explaining from 9% to 31%) than did CSF proteins. In conclusion, we found widespread alterations in WM integrity, particularly in regions commonly affected by the disease in our group of early‐stage disease and patients with Alzheimer's disease. Nonetheless, in the statistical models, the HV better predicted the integrity of the MTL tracts than the biomarkers in CSF.

Introduction: Patients with mild cognitive impairment (MCI) have an unnatural cognitive loss of aging and have an increased chance of developing Alzheimer’s Disease (AD). Another factor that also increases this risk is the chronic inflammation caused by obesity, described by a body fat percentage (BF%) above healthy values. Objectives: This study aimed to investigate whether BF% in older adults with MCI correlates with AD biomarkers, such as total TAU protein and beta-amyloid. Methods: Twenty-one older adults with MCI were evaluated, 11 men and 10 women with a mean age of 66.3 (standard deviation, SD: ±5.88) years. The participants were submitted to the collection of 10 ml of cerebrospinal fluid (CSF) via lumbar puncture and evaluated regarding the dosages of CSF total TAU protein and beta-amyloid protein using immunoenzymatic ELISA kits: INNOTEST® ß-AMYLOID (1-42), INNOTESTR h TAU Ag (Innogenetics, Gent, Belgium). They were also evaluated for BF% on a bioimpedance scale (Tanita® BC-108). Kendall’s tau correlation coefficient was applied for statistical analysis using the IBM SPSS Statistics 22 Software. Results: Both men and women had an average BF% that classified them with higher BF (men: 27% (SD: ±4.23) and women: 36.7% (SD: ±4.84). We found a weak positive correlation (τ = 0.360*) between BF% and Total TAU protein concentration and between BF% and beta-amyloid protein concentration (τ = 0.341*). Conclusion: Our results provide an insight into the possible influence of BF% on concentrations of biomarkers related to neurodegeneration in elderly people at increased risk of developing AD. The higher the BF%, the higher the protein concentration that reflects neuroinflammation. Studies with more individuals with MCI and with different degrees of obesity are suggested to contribute to the investigation of the relationship between AD biomarkers and BF%.

The major current challenge in Alzheimer’s disease (AD) is the identification of individuals likely to develop dementia. Non-invasive imaging techniques that reflect the integrity of the brain’s white matter are potential parameters to study the pathogenesis of AD. Diffusion tensor imaging (DTI) provides tools to analyze AD-related brain changes despite the large amount of data generated. To process large amounts of data, computational methods capable of finding relationships among large and complex data sets are needed. In this context, machine learning (ML) techniques have been extended to address this issue. This study aimed to evaluate whether DTI features could predict future AD in non-AD patients using ML techniques. Sixty-two subjects, with mild cognitive impairment and healthy controls, were enrolled and followed for approximately eleven months. At the first assessment, MRI, neuropsychological tests, and cerebrospinal fluid analysis were performed. At the second assessment, ten had progressed to Alzheimer’s dementia. The clinical diagnosis was used to train the ML classification models. Five data sets were generated with different missing data strategies. Feature selection strategies measured by information gain were implemented and compared. Twenty ML algorithms distributed over several mathematical approaches were used in model training. A deep learning model was also implemented for comparison with ML results. The best model for predicting AD by DTI was Random Forest with kNN missing data imputation and features selected by information gain. This model achieved a test accuracy of 88.72%. The most relevant DTI tract that contributed to the prediction of the model was the fractional anisotropy (FA) of the cingulum - hippocampus . When other categories of data, such as neuropsychological tests, were included in the model, the accuracy hardly changed (90.26%), suggesting that DTI alone is a good predictor of Alzheimer’s disease. When the deep learning model was run, the constructed neural network achieved 92.31% accuracy. Our results suggest that DTI measures analyzed by ML models, such as Random Forest, may predict AD in non-AD patients. Furthermore, DTI measure of cingulum - hippocampus tract may be a sensitive marker of early AD pathology.