Ana Frank-García

Background: Discriminating mild cognitive impairment (MCI) from subjective cognitive decline (SCD), and healthy controls (HC) remains challenging. Digital tools may overcome paper-and-pencil screening’s limitations. Objective: This study evaluated the diagnostic accuracy and usability of CogniPaz (Daniel Blanco), an ecologically oriented, mobile health (mHealth), gamified cognitive assessment tool exploring memory and executive functions, in discriminating MCI (particularly amnestic phenotypes [aMCI]) from SCD, and HC. Methods: This prospective observational study recruited a convenience sample of consecutive patients with cognitive complaints and HC at La Paz University Hospital Cognitive Disorders Unit from April 2025 to January 2026. Demographic and clinical characteristics, and Montreal Cognitive Assessment (MoCA) scores were collected. An evaluator blinded to the MoCA scores oversaw the automated, self-administered CogniPaz assessment. Participants were classified into HC (no cognitive complaints, MoCA score≥26), SCD (cognitive complaints, MoCA score≥26), and MCI (cognitive complaints without functional impairment, MoCA scoreP =.003). CogniPaz scores correlated with MoCA scores (ρ = 0.81, 95% CI 0.75‐0.89; P< .001). CogniPaz showed good concurrent accuracy, independently of age: MCI vs HC (area under the curve [AUC]=0.880, 95% CI 0.798‐0.961), MCI vs SCD (AUC=0.915, 95% CI 0.841‐0.990), aMCI vs HC (AUC=0.910, 95% CI 0.830‐0.990), and aMCI vs SCD (AUC=0.943, 95% CI 0.878‐1.000). At the provisional P =.02). Median administration time was 9.39 (IQR 7.53‐11.38) minutes with 100% (84/84) completion. User satisfaction was high (median score 8.4/9, IQR 7.9‐8.6; Cronbach α=0.84). Conclusions: CogniPaz is an innovative, user-friendly mHealth tool that addresses classical cognitive screening tools’ limitations through a brief, multidomain, minimally supervised gamified format, with high patient acceptance. It correlates with MoCA scores, shows good concurrent capacity to differentiate MCI, particularly aMCI, from SCD and HC, and appears promising for identifying bND. Pending external longitudinal validation, these findings support CogniPaz as a candidate for early cognitive screening in primary care.

BACKGROUND Discriminating mild cognitive impairment (MCI) from subjective cognitive decline (SCD) and healthy controls (HC) remains challenging. Digital tools can overcome classic paper-and-pencil test limitations, offering sensitive and engaging screening for early cognitive impairment. OBJECTIVE To evaluate the diagnostic accuracy of CogniPaz, an ecologically oriented, tablet-based, gamified cognitive assessment tool composed of nine tasks designed to evaluate memory and executive functions, in discriminating MCI from SCD and HC. METHODS This prospective observational study recruited participants with cognitive complaints and HC at La Paz University Hospital Cognitive Impairment Unit (April 2025–January 2026). Following clinical and Montreal Cognitive Assessment (MoCA) evaluations, participants were classified into HC (no cognitive complaints, MoCA≥26), SCD (cognitive complaints, MoCA≥26), and MCI (cognitive complaints without functional impairment, MoCA<26; subdivided into amnestic [aMCI] and non-amnestic phenotypes). An evaluator blinded to MoCA results oversaw the automated, self-administered CogniPaz assessment (including automatic timing). Receiver operating characteristic (ROC) analyses (with age-adjusted models compared via DeLong's test) evaluated diagnostic accuracy, Spearman assessed CogniPaz-MoCA correlation, and a 0-9 scored questionnaire measured CogniPaz usability. A subset of participants with available biomarker data underwent an exploratory analysis to assess the tool's capacity to distinguish biologically confirmed neurodegeneration suggestive of Alzheimer Disease (AD). RESULTS Participants (N=84; median age: 72.0 years) included HC (n=27; 74.0), SCD (n=17; 59.0), and MCI (n=40; 74.0) groups, the latter subclassified into aMCI (n=22; 76.5) and non-amnestic MCI (n=18; 71.5). The aMCI group was significantly older than the SCD group (P=.003). The median administration time for CogniPaz was 9.39 minutes (IQR 7.53–11.38). CogniPaz scores correlated strongly with MoCA (r=.81, 95% CI .75-.89, P<.001). CogniPaz achieved high diagnostic accuracy, independently of age: MCI vs HC (AUC=0.880, 95% CI 0.798-0.961), MCI vs SCD (AUC=0.915, 95% CI 0.841-0.990), aMCI vs HC (AUC=0.910, 95% CI 0.830-0.990), and aMCI vs SCD (AUC=0.943, 95% CI 0.878-1.000). It showed capacity for detecting biologically confirmed neurodegeneration (n=39, AUC=0.810, 95% CI 0.661-0.958). At the<28.5 points cutoff, sensitivity ranged from 85.0% to 90.9% and specificity from 74.1% to 82.4% across comparisons. Particularly, it was accurate in differentiating aMCI vs SCD (90.9% sensitivity, 82.4% specificity). User satisfaction was high (median 8.4/9). CONCLUSIONS CogniPaz showed good capacity to differentiate MCI/aMCI from SCD and HC, and appears promising for identifying AD-related neurodegeneration, with a strong correlation with MoCA score and good patient acceptance. These results support its use as a digital screening tool for early-stage cognitive impairment. External, multi-centre longitudinal validation is essential before considering its implementation in primary care settings.

We aimed to analyze whether EVs carry antibodies against EBV antigens and the possibility that they could serve as diagnostic and disease activity blood biomarkers in RRMS. This was a prospective and observational study including patients with RRMS with active and inactive disease and healthy controls. Blood EVs were isolated by precipitation. Titers of antibodies against nuclear (anti-EBNA1) and capsid (anti-VCA) EBV antigens in EVs and in plasma, as well as content of myelin antibodies in EVs were determined by ELISA. An exploratory analysis of correlations with clinical and radiological data was performed. Patients with RRMS had higher titers of anti-VCA inside EVs and free in plasma than healthy controls. Patients with active disease showed higher levels of anti-EBNA1 in EVs, but not in plasma, than patients with inactive disease. EV anti-VCA levels correlated with disease duration and with decreased brain volume structures—total brain, white matter, gray matter, cerebellum, hippocampus, —but not with T2/FLAIR lesion volume or EDSS, SDMT, or 9HPT. In addition, EV anti-VCA correlated with EV anti-MBP. The anti-VCA and anti-EBNA1 content in EVs could represent diagnostic and disease activity blood biomarkers, respectively, in RRMS.

Therapeutic plasma exchange (TPE) was first used in neurology in the 1980s for myasthenia gravis (MG) and Guillain-Barré syndrome (GBS). Indications have since grown. Fear of complications with this treatment modality limit its use. A study of patients undergoing TPE for neurological diseases (1981-2020) in a university hospital in Madrid, Spain. Clinical indications, complications, procedure number, apheresis technique and replacement fluids were prospectively recorded and retrospectively analysed. Historical trends were studied. Over 40 years, 159 patients (48.69±18.15 years, 54.3% females) underwent TPE using central-venous catheter and replacement fluid albumin. We performed 1207 procedures over 189 cycles (6.4±3.8 procedures/cycle). Most patients underwent TPE for category I-II indications, mainly GBS and MG (77.7%). Complication rate was low (3.9% procedures), mostly hypotensive/vasovagal reactions (55.3%) and vascular access-related complications (38.3%). Most were mild-moderate (92.9%), permitting TPE completion, and somewhat more frequent during the first procedure (38.3%) and after periods of little TPE use. GBS patients were more prone to complications than MG patients (6.5% vs. 1.2%,p<0.001) mainly hypotensive/vasovagal reactions (3.7% vs. 1.0%,p=0.008). TPE is well-tolerated with low complication rate (<4% procedures), mainly hypotensive/vasovagal reactions. Patients with GBS seem more prone to them than MG patients. Acquaintance with this technique seems necessary.

Emerging studies have suggested several chromosomal regions as potential host genetic factors involved in the susceptibility to SARS-CoV-2 infection and disease outcome. We nested a COVID-19 genome-wide association study using the GR@ACE/DEGESCO study, searching for susceptibility factors associated with COVID-19 disease. To this end, we compared 221 COVID-19 confirmed cases with 17,035 individuals in whom the COVID-19 disease status was unknown. Then, we performed a meta-analysis with the publicly available data from the COVID-19 Host Genetics Initiative. Because the APOE locus has been suggested as a potential modifier of COVID-19 disease, we added sensitivity analyses stratifying by dementia status or by disease severity. We confirmed the existence of the 3p21.31 region (LZTFL1, SLC6A20) implicated in the susceptibility to SARS-CoV-2 infection and TYK2 gene might be involved in COVID-19 severity. Nevertheless, no statistically significant association was observed in the COVID-19 fatal outcome or in the stratified analyses (dementia-only and non-dementia strata) for the APOE locus not supporting its involvement in SARS-CoV-2 pathobiology or COVID-19 prognosis.

Accurate blood-based biomarkers of Alzheimer's disease (AD) could constitute simple, inexpensive, and non-invasive tools for the early diagnosis and treatment of this devastating neurodegenerative disease. We sought to develop a robust AD biomarker panel by identifying alterations in plasma metabolites that persist throughout the continuum of AD pathophysiology. Using a multicenter, cross-sectional study design, we based our analysis on metabolites whose levels were altered both in AD patients and in patients with amnestic mild cognitive impairment (aMCI), the earliest identifiable stage of AD. UPLC coupled to mass spectrometry was used to independently compare the levels of 495 plasma metabolites in aMCI (n = 58) and AD (n = 100) patients with those of normal cognition controls (NC, n = 93). Metabolite alterations common to both aMCI and AD patients were used to generate a logistic regression model that accurately distinguished AD from NC patients. The final panel consisted of seven metabolites: three amino acids (glutamic acid, alanine, and aspartic acid), one non-esterified fatty acid (22:6n-3, DHA), one bile acid (deoxycholic acid), one phosphatidylethanolamine [PE(36:4)], and one sphingomyelin [SM(39:1)]. Detailed analysis ruled out the influence of potential confounding variables, including comorbidities and treatments, on each of the seven biomarkers. The final model accurately distinguished AD from NC patients (AUC, 0.918). Importantly, the model also distinguished aMCI from NC patients (AUC, 0.826), indicating its potential diagnostic utility in early disease stages. These findings describe a sensitive biomarker panel that may facilitate the specific detection of early-stage AD through the analysis of plasma samples.

1H-MRS variability increases due to normal aging and also as a result of atrophy in grey and white matter caused by neurodegeneration. In this work, an automatic process was developed to integrate data from spectra and high-resolution anatomical images to quantify metabolites, taking into account tissue partial volumes within the voxel of interest avoiding additional spectra acquisitions required for partial volume correction. To evaluate this method, we use a cohort of 135 subjects (47 male and 88 female, aged between 57 and 99 years) classified into 4 groups: 38 healthy participants, 20 amnesic mild cognitive impairment patients, 22 multi-domain mild cognitive impairment patients, and 55 Alzheimer's disease patients. Our findings suggest that knowing the voxel composition of white and grey matter and cerebrospinal fluid is necessary to avoid partial volume variations in a single-voxel study and to decrease part of the variability found in metabolites quantification, particularly in those studies involving elder patients and neurodegenerative diseases. The proposed method facilitates the use of 1H-MRS techniques in statistical studies in Alzheimer's disease, because it provides more accurate quantitative measurements, reduces the inter-subject variability, and improves statistical results when performing group comparisons.

The aim of this study was to characterize the neuropsychological and neuroimaging profiles of mild cognitive impairment (MCI) and Alzheimer's disease (AD) patients, and to study the magnitude of the differences by comparing both outcomes with healthy subjects in a cross-sectional manner. Five hundred and thirty-five subjects (356 cognitively normal adults (CONT), 79 MCI, and 100 AD) were assessed with the NEURONORMA neuropsychological battery. Thirty CONT, 23 MCI, and 23 AD subjects from this sample were included in the neuroimaging substudy. Patients' raw cognitive scores were converted to age and education-adjusted scaled ones (range 2-18) using co-normed reference values. Medians were plotted to examine the cognitive profile. MRIs were processed by means of FreeSurfer. Effect size indices (Cohen's d) were calculated in order to compare the standardized differences between patients and healthy subjects. Graphically, the observed cognitive profiles for MCI and AD groups produced near to parallel lines. Verbal and visual memories were the most impaired domains in both groups, followed by executive functions and linguistic/semantic ones. The largest effect size between AD and cognitively normal subjects was found for the FCSRT (d = 4.05, AD versus CONT), which doubled the value obtained by the best MRI measure, the right hippocampus (d = 1.65, AD versus CONT). Our results support the notion of a continuum in cognitive profile between MCI and AD. Neuropsychological outcomes, in particular the FCSRT, are better than neuroimaging ones at detecting differences among subjects.

Alzheimer's disease is a common debilitating dementia with known heritability, for which 20 late onset susceptibility loci have been identified, but more remain to be discovered. This study sought to identify new susceptibility genes, using an alternative gene-wide analytical approach which tests for patterns of association within genes, in the powerful genome-wide association dataset of the International Genomics of Alzheimer's Project Consortium, comprising over 7 m genotypes from 25,580 Alzheimer's cases and 48,466 controls. In addition to earlier reported genes, we detected genome-wide significant loci on chromosomes 8 (TP53INP1, p = 1.4×10⁻⁶) and 14 (IGHV1-67 p = 7.9×10⁻⁸) which indexed novel susceptibility loci. The additional genes identified in this study, have an array of functions previously implicated in Alzheimer's disease, including aspects of energy metabolism, protein degradation and the immune system and add further weight to these pathways as potential therapeutic targets in Alzheimer's disease.