Marco Antônio De Bastiani

Emerging evidence suggests altered lactate metabolism in Alzheimer’s disease (AD), although findings remain inconsistent. We conducted a systematic review and meta-analysis to assess alterations in lactate metabolism in AD. PubMed and Web of Science were searched up to January 2026 for studies reporting lactate, pyruvate, monocarboxylate transporter, pyruvate kinase, mitochondrial pyruvate carrier, lactate dehydrogenase (LDH), pyruvate dehydrogenase (PDH), and pyruvate dehydrogenase kinase measured in the cerebrospinal fluid (CSF), blood and brain of AD and cognitively unimpaired (CU) individuals (PROSPERO CRD42023451816). Effect sizes were determined via the standardized mean difference (SMD; Hedge’s g with random effects) and heterogeneity was assessed by I² statistic. Of 4,451 articles, 27 met the final inclusion criteria (n = 540 CU/774 AD). AD individuals showed reduced PDH activity across the whole brain (SMD: -1.39 [95% CI: -1.76, -1.01]; p<0.001; I²: 55.27), cortical regions (SMD: -1.49 [95% CI: -1.95, -1.03]; p<0.001; I²: 52.04), and frontal cortex (SMD: -1.5 [95% CI: -2.09, -0.91]; p<0.001; I²: 23.61). In contrast, LDH activity was increased in the whole brain (SMD:0.38 [95% CI: 0.26, 0.49]; p<0.001; I²: 0) and cortical regions (SMD:0.34 [95% CI: 0.17, 0.51]; p<0.001; I²: 27) of AD individuals. Additionally, CSF pyruvate levels were higher in AD individuals (SMD: 2.09 [95% CI:0.36, 3.82]; p = 0.018; I²: 93.5). Overall, evidence supports altered lactate metabolism in AD, although heterogeneity across outcomes suggests variation by biological compartment, disease stage, and methodology. The consistent reduction in PDH and increase in LDH activity support impaired oxidative metabolism and altered pyruvate–lactate homeostasis in AD.

Purpose: Quantitative interpretation of brain [¹⁸F]FDG-PET increasingly relies on comparisons with normative datasets. However, normative values may be influenced by technical and biological factors, limiting their generalizability. We investigated the effects of scanner manufacturer, reference region, age, and sex on regional [¹⁸F]FDG uptake in cognitively normal (CN) adults and generated covariate-adjusted normative reference data. Methods: A total of 449 CN participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) were included. Regional SUVr were calculated using three reference regions (whole cerebellum, pons, cortical gray matter) and converted to Z-scores. Linear regression models were used to estimate standardized regression coefficients (β), and 10-fold cross-validation was performed to quantify the out- of-sample predictive contribution of each covariate using incremental explained variance (ΔR²). Results: Scanner manufacturer introduced large, spatially structured biases. Compared with Siemens systems, GE and Philips scanners yielded lower Z-scores in frontal and medial temporal regions, with effect sizes approaching one standard deviation in selected regions (β up to −0.85). Age showed region- specific associations with subcortical nuclei, medial temporal structures, and the posterior cingulate cortex, and was the strongest biological predictor in cross-validation (ΔR²≈0.11). Sex effects were negligible (ΔR²<0.001). Cortical gray matter normalization minimized biological and technical confounding, and the AD meta-ROI demonstrated high robustness across manufacturers and normalization strategies. Conclusion: Scanner manufacturer and age are the major sources of variance in brain [¹⁸F]FDG-PET quantification in CN subjects. Cortical gray matter provides the most stable reference region and supports harmonized, covariate-adjusted normative datasets for clinical and research applications.

Dementia underdiagnosis persists because lengthy staging tools hinder routine clinical use. The Quick Dementia Rating System (QDRS) offers a brief, informant-based alternative. Objective: This study validates the Brazilian Portuguese QDRS and establishes optimal cutoff values for diagnostic accuracy. Methods: Individuals from primary care and a secondary memory clinic in Southern Brazil were included. Informants completed the QDRS prior to clinical evaluation. All participants underwent comprehensive neurological and neuropsychological assessment, including the Clinical Dementia Rating (CDR), Mini-Mental State Examination (MMSE), and Montreal Cognitive Assessment (MoCa). Psychometric properties (internal consistency and concurrent validity), diagnostic accuracy, and staging agreement between different diagnostic groups and CDR categories were investigated. Results: A total of 180 participants were included in this study (59 cognitively unimpaired, 63 with mild cognitive impairment [MCI], and 58 with dementia). The QDRS demonstrated excellent internal consistency (Cronbach’s α=0.94) and strong concurrent validity with the CDR-Sum of Boxes (p=0.82, p<0.001), MMSE and MoCa scores. Agreement between corresponding QDRS and CDR domains ranged from moderate to good (Intraclass Correlation Coefficient — ICC=0.73–0.89). Diagnostic accuracy for dementia was excellent relative to both clinical diagnosis (cutoff 3.25; Area Under the Curve — AUC=0.98) and CDR staging (cutoff 4.25; AUC=0.97), with sensitivities and specificities exceeding 92%. Staging agreement with the CDR was substantial (weighted Kappa=0.64). Conclusion: The Brazilian Portuguese QDRS is a reliable and efficient instrument for dementia screening and staging. Large-scale testing in clinical practice and population-based research are warranted.

Background: Alzheimer's disease (AD) is a progressive neurodegenerative disorder shaped by genetic factors. Expression quantitative trait loci (eQTL) mapping identifies how genetic variants regulate gene expression, offering a powerful tool for uncovering the mechanisms underlying the disease. In this study, we adopted a data‐driven approach to explore the relationships between genetic variants, blood gene expression, and FDG‐PET imaging using the ADNI dataset. We hypothesized that regulatory effects revealed through eQTL mapping are associated with changes in brain FDG‐PET imaging. Methods: We analyzed genomic and blood transcriptomic data from 746 individuals across the AD continuum in the ADNI dataset. eQTL mapping of gene–single nucleotide polymorphism (SNP) pairs was performed using the MatrixEQTL package in the R statistical environment, accounting for age and sex covariates. Significance was defined as an FDR‐adjusted p ‐value < 0.01 and an absolute beta value > 0.5. Subsequently, voxel‐wise regression analyses on FDG‐PET imaging data were conducted with the RMINC package to evaluate associations between gene–SNP interactions and brain glucose metabolism. Results: Out of 10,558 genes and 477,532 SNPs analyzed, 5,278 SNPs were significantly associated with changes in the expression of 217 genes. We analyzed the 11 SNP pairs with the strongest effects on expression patterns for each gene, including both upregulation and downregulation. Voxel‐wise regression analysis assessed the interaction between SNP carriership and gene expression on FDG‐PET. This revealed that carriers of the PLEK SNP rs8761, associated with PLEK gene downregulation (Figure 1A), exhibited FDG‐PET hypermetabolism in the frontal and temporal cortices. In contrast, the PLEK SNP rs1063479, linked to PLEK gene upregulation, did not show significant changes in the FDG‐PET signal (Figure 1B). Conclusion: The PLEK gene encodes pleckstrin, a plasma protein involved in the innate immune response and expressed in glial cells. Variants of PLEK associated with FDG‐PET imaging suggest a potential link between a plasma protein related to immune cells and brain glucose metabolism. To our knowledge, the role of PLEK in brain glucose metabolism has not been previously reported. Therefore, future studies should explore these associations to better understand the underlying mechanisms and their implications for AD pathology.

Background/Objectives: Hepatic encephalopathy (HE) is a severe neurological complication of acute liver failure (ALF) characterized by the accumulation of neurotoxic metabolites and impaired cerebral function. We aimed to examine the correlation between HE severity and cerebrospinal fluid (CSF) biomarker levels in a rat model of ALF induced by subtotal hepatectomy. Methods: Male Wistar rats underwent 92% hepatectomy and were monitored for neurological impairment via a standardized HE score. At twenty-four hours post surgery, CSF and blood were collected for biochemical analysis. Results: We found a significant positive correlation between neurological severity and CSF levels of glutamine (r = 0.929, p < 0.001) and albumin (r = 0.869, p < 0.001), both with HE grade I scores, highlighting their prominent role as HE biomarkers. Other amino acids, including aspartate (r = 0.790, p < 0.001), glutamate (r = 0.853, p < 0.001), isoleucine (r = 0.834, p < 0.001), leucine (r = 0.813, p < 0.001), lysine (r = 0.861, p < 0.001), methionine (r = 0.889, p < 0.001), phenylalanine (r = 0.916, p < 0.001), ornithine (r = 0.775, p < 0.001), tryptophan (r = 0.814, p < 0.001), and valine (r = 0.721, p < 0.001), also showed significant correlations with HE severity but not with HE grade I scores. Conclusions: These findings underscore the potential of glutamine and albumin in CSF as key biomarkers for assessing neurological severity in ALF patients.

Background: Amyloid-β imaging through positron emission tomography (PET) has significantly transformed Alzheimer’s disease (AD) research. [¹¹C]PiB has been widely used for imaging β-amyloid plaques due to its high affinity and selectivity for amyloid deposits. [¹⁸F]AZD4694 is a more recently developed amyloid-PET imaging agent, which structurally resembles PiB and has less non-specific binding in the white matter than other ¹⁸F-labeled compounds. The purpose of this study is to compare the in vitro binding properties of the amyloid-PET radiotracers [¹¹C]PiB and [¹⁸F]AZD4694 in post-mortem human brain tissue. Total binding was assessed by autoradiography in prefrontal, inferior parietal, posterior cingulate cortices and hippocampal sections of healthy control (HC) and AD autopsy-confirmed brain tissues. Furthermore, the displacement of [¹⁸F]AZD4694 by unlabeled PiB was evaluated in the above-mentioned sections of AD brain tissues. Results: For both radiotracers, we found significant differences (p < 0.0001) between HC and AD tissues binding in the prefrontal cortex ([¹¹C]PiB Cohen’s d = 3.424, [¹⁸F]AZD4694 Cohen’s d = 5.070), inferior parietal cortex ([¹¹C]PiB Cohen’s d = 3.156, [¹⁸F]AZD4694 Cohen’s d = 3.959), posterior cingulate cortex ([¹¹C]PiB Cohen’s d = 1.781, [¹⁸F]AZD4694 Cohen’s d = 3.434), and hippocampus ([¹¹C]PiB Cohen’s d = 1.320, [¹⁸F]AZD4694 Cohen’s d = 3.696). Higher binding was detected for [¹⁸F]AZD4694 compared to [¹¹C]PiB in AD prefrontal, inferior parietal and posterior cingulate cortices, while binding in the hippocampus was comparable for both radioligands. Strong correlations between [¹⁸]AZD4694 and [¹¹C]PiB were found in the prefrontal (R = 0.959, p < 0.0001), inferior parietal (R = 0.893, p < 0.0001), posterior cingulate (R = 0.838, p = 0.0006) cortices and hippocampus (R = 0.750, p < 0.0001). Bland–Altman analyses revealed strong agreement between [¹¹C]PiB and [¹⁸F]AZD4694 in the prefrontal, inferior parietal, and posterior cingulate cortices, but lower agreement in the hippocampus. Displacement studies confirmed high binding affinity of PiB in all tissues, indicating that both amyloid-PET agents compete for the same binding sites. Conclusions: This head-to-head study provides evidence that while [¹⁸F]AZD4694 and [¹¹C]PiB bindings are highly correlated with both tracers competing for the same binding sites, [¹⁸F]AZD4694 has a slightly higher effect size when comparing between neuropathologically-confirmed AD and HC brain tissues.

Background: Positron emission tomography (PET) imaging greatly impacted Alzheimer’s disease (AD) research and diagnosis. which makes predicting PET brain imaging alterations using blood data is of high interest. Additionally, integrating PET and omics data can provide new insights into AD pathophysiology. Here, we implemented a module‐based framework combining blood transcriptomics with PET to search transcription factors (TFs) activities associated with brain metabolic changes in AD. We hypothesized that integrating omics and PET data will help advance our understanding of AD neurobiology and may reveal relevant new peripheral biomarkers.Methods: [¹⁸F]Fluorodeoxyglucose ([¹⁸F]FDG)‐PET imaging and transcriptomics data were acquired from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Blood microarray gene expression from ADNI, GSE63063 and GSE97760 (https://www.ncbi.nlm.nih.gov/geo/) datasets were submitted to differential expression (DE) analysis. Regulatory units (regulons) of TFs and their predicted target genes were reconstructed using the ARACNe method. Altered regulons in AD submitted gene set variation analysis to infer their TF activity prior to neuroimaging integration with [¹⁸F]FDG‐PET images using voxel‐wise eneralized linear regression (GLR) models adjusted for age, gender, years of education, and APOEε4 (RMINC package).Results: Sixty‐one regulatory units were significantly enriched with altered genes in at least ⅔ of the datasets explored, and 12 were altered in all three (Figure A‐B). The voxel‐wise correlation between [¹⁸F]FDG‐PET and regulons resulted in t‐statistical maps, where uncorrected t‐value > 2.0 was used as the threshol. We observed that ZNF653 has a positive correlation with [¹⁸F]FDG‐PET in the precuneus (24.24% left, 39.51% right), medial frontal gyrus (17.26% left), medial frontal‐orbital gyrus (12.50% left) and precentral gyrus (9.08% left, 8.28% right). Interestingly, the ZNF653 regulatory unit is composed majoritarily by genes related to energetic metabolism and protein kinase activity (Figure 1C‐D).Conclusion: We identified the activity of the ZNF653 regulatory unit associated with [18 F]FDG‐PET metabolism in the brain of AD individuals. Furthermore, ZNF653 activity could be modulating metabolic and protein kinase activity‐related genes, highlighting a potential role of this TF in AD.

Updates in Alzheimer disease (AD) diagnostic guidelines by the National Institute on Aging-Alzheimer's Association (NIA-AA) and the International Working Group (IWG) over the past 11 years may affect clinical diagnoses. We assessed how these guidelines affect clinical AD diagnosis in a cohort of cognitively unimpaired (CU) and cognitively impaired (CI) individuals. We applied clinical and biomarker data in algorithms to classify individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort according to the following diagnostic guidelines for AD: 2011 NIA-AA, 2016 IWG-2, 2018 NIA-AA, and 2021 IWG-3, assigning the following generic diagnostic labels: (1) not AD (nAD), (2) increased risk of developing AD (irAD), and (3) AD. Diagnostic labels were compared according to their frequency, convergence across guidelines, biomarker profiles, and prognostic value. We also evaluated the diagnostic discordance among the criteria. A total of 1,195 individuals (mean age 73.2 ± 7.2 years, mean education 16.1 ± 2.7, 44.0% female) presented different repartitions of diagnostic labels according to the 2011 NIA-AA (nAD = 37.8%, irAD = 23.0%, AD = 39.2%), 2016 IWG-2 (nAD = 37.7%, irAD = 28.7%, AD = 33.6%), 2018 NIA-AA (nAD = 40.7%, irAD = 9.3%, AD = 50.0%), and 2021 IWG-3 (nAD = 51.2%, irAD = 8.4%, AD = 48.3%) frameworks. Discordant diagnoses across all guidelines were found in 512 participants (42.8%) (138 [91.4%] occurring in only β-amyloid [CU 65.4%, CI 34.6%] and 191 [78.6%] in only tau-positive [CU 71.7%, CI 28.3%] individuals). Differences in predicting cognitive impairment between nAD and irAD groups were observed with the 2011 NIA-AA (hazard ratio [HR] 2.21, 95% CI 1.34-3.65, p = 0.002), 2016 IWG-2 (HR 2.81, 95% CI 1.59-4.96, p < 0.000), and 2021 IWG-3 (HR 3.61, 95% CI 2.09-6.23, p < 0.000), but not with 2018 NIA-AA (HR 1.69, 95% CI 0.87-3.28, p = 0.115). Over 42% of the studied population presented discordant diagnoses when using the different examined AD criteria, mostly in individuals with a single positive biomarker. Except for 2018 NIA-AA, all guidelines identified asymptomatic individuals at risk of cognitive impairment. Our findings highlight important differences between the guidelines, emphasizing the necessity for updated criteria with enhanced staging metrics, considering clinical, research, therapeutic, and trial design aspects.

Glutamatergic neurotransmission system dysregulation may play an important role in the pathophysiology of Alzheimer’s disease (AD). However, reported results on glutamatergic components across brain regions are contradictory. Here, we conducted a systematic review with meta-analysis to examine whether there are consistent glutamatergic abnormalities in the human AD brain. We searched PubMed and Web of Science (database origin-October 2023) reports evaluating glutamate, glutamine, glutaminase, glutamine synthetase, glutamate reuptake, aspartate, excitatory amino acid transporters, vesicular glutamate transporters, glycine, D-serine, metabotropic and ionotropic glutamate receptors in the AD human brain (PROSPERO #CDRD42022299518). The studies were synthesized by outcome and brain region. We included cortical regions, the whole brain (cortical and subcortical regions combined), the entorhinal cortex and the hippocampus. Pooled effect sizes were determined with standardized mean differences (SMD), random effects adjusted by false discovery rate, and heterogeneity was examined by I² statistics. The search retrieved 6 936 articles, 63 meeting the inclusion criteria (N = 709CN/786AD; mean age 75/79). We showed that the brain of AD individuals presents decreased glutamate (SMD = −0.82; I² = 74.54%; P < 0.001) and aspartate levels (SMD = −0.64; I² = 89.71%; P = 0.006), and reuptake (SMD = −0.75; I² = 83.04%; P < 0.001. We also found reduced α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPAR)-GluA2/3 levels (SMD = −0.63; I² = 95.55%; P = 0.046), hypofunctional N-methyl-D-aspartate receptor (NMDAR) (SMD = −0.60; I² = 91.47%; P < 0.001) and selective reduction of NMDAR-GluN2B subunit levels (SMD = −1.07; I² = 41.81%; P < 0.001). Regional differences include lower glutamate levels in cortical areas and aspartate levels in cortical areas and in the hippocampus, reduced glutamate reuptake, reduced AMPAR-GluA2/3 in the entorhinal cortex, hypofunction of NMDAR in cortical areas, and a decrease in NMDAR-GluN2B subunit levels in the entorhinal cortex and hippocampus. Other parameters studied were not altered. Our findings show depletion of the glutamatergic system and emphasize the importance of understanding glutamate-mediated neurotoxicity in AD. This study has implications for the development of therapies and biomarkers in AD.

Background: The relationship between high cortisol levels and brain atrophy in patients across the Alzheimer’s disease (AD) continuum has been relatively unexplored. Thus, this study sought to investigate the association between plasma cortisol levels and brain cortical thickness in patients across the biological and clinical continuum of AD.Method: Participants with baseline plasma cortisol concentrations (n = 544), structural MRI, and CSF Aβ_1‐42 and CSF p‐tau₁₈₁ were selected from the ADNI database. Three general linear models (GLMs) were constructed to assess the association between cortical thickness and cortisol levels (Freesurfer’s v7.1.1): (1) cortisol as an independent variable; (2) cortisol, cognitive status, and interaction; (3) cortisol, neuropathology (measured by Aβ_1‐42 and p‐tau₁₈₁ positivity), and interaction. These analyses were corrected for multiple comparisons through cluster formation (pTable 1. The cortical thickness showed a negative correlation with plasma cortisol concentrations in the following clusters (first GLM; Figure 1 and 2): left hemisphere – superior parietal (pcc = ‐0.13; p = 0.0002); superior frontal (pcc = ‐0.13; p = 0.0004); superior parietal (pcc = ‐0.13; p = 0.013); precentral (pcc = ‐0.12; p = 0.02); isthmus cingulate (pcc = ‐0.13; p = 0.02); caudal middle frontal (pcc = ‐0.11; p = 0.031); fusiform (pcc = ‐0.12; p = 0.046); right hemisphere – inferior parietal (pcc = ‐0.13; p = 0.0004); precuneus (pcc = ‐0.12; p = 0.0014); superior frontal (pcc = ‐0.12; p = 0.003); parahippocampal (pcc = ‐0.12; p = 0.003); superior parietal (pcc = ‐0.12; p = 0.041). Interestingly, no interactions between cortisol and AD cognitive status and neuropathology were identified (second and third GLMs – data not shown).Conclusion: This cross‐sectional study identified a negative correlation between plasma cortisol concentrations and cortical thickness in brain regions typically affected by AD, which was found to be independent of AD diagnosis and neuropathology. This finding suggests that high peripheral cortisol levels may create a vulnerability that is independent of AD neuropathology in regions commonly affected by the disease, which may accelerate disease progression.