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BackgroundSubjective Cognitive Decline (SCD) is a self-experienced decline in cognitive capacity with normal performance on standardized cognitive tests, showing to increase risk of Alzheimer’s Disease (AD). Cognitive reserve seems to influence the progression from SCD to Mild Cognitive Impairment (MCI) and to AD. The aim of our study was to investigate gender differences in cognitive reserve evaluating how sex might modulate the role of cognitive reserve on SCD.MethodsWe included 381 SCD patients who underwent clinical evaluation, neuropsychological assessment, evaluation of premorbid intelligence by the Test di Intelligenza Breve (TIB), cognitive complaints by the Memory Assessment Clinics Questionnaire (MAC-Q), and apolipoprotein E (APOE) genotyping.ResultsThe proportion between women and men was significantly different (68.7% [95% CI 63.9–73.4 vs 31.4%, 95% CI 26.6–36.0]). Women were younger than men at onset of SCD and at the baseline visit (p = 0.021), had lower years of education (p = 0.007), lower TIB scores (p < 0.001), and higher MAC-Q scores (p = 0.012). TIB was directly associated with age at onset of SCD in both women and men, while years of education was inversely associated with age at onset only in women. Multivariate analysis showed that sex influences TIB independently from years of education. TIB was directly associated with MAC-Q in men.ConclusionsSex interacts with premorbid intelligence and education level in influencing the age at onset and the severity of SCD. As the effect of education was different between men and women, we speculated that education might act as a minor contributor of cognitive reserve in women.

Brain-derived natriuretic factor (BDNF) Val66Met polymorphism has been frequently reported to be associated with Alzheimer’s disease (AD) with contrasting results. Numerous studies showed that Met allele increased the risk of AD only in women, while other studies have found worse cognitive performance in Val/Val carriers. We aimed to inquire the effects of Val66Met polymorphism on the progression from subjective cognitive decline (SCD) to mild cognitive impairment (MCI) and from MCI to AD and to ascertain if this effect is modulated by demographic and cognitive variables. For this purpose, we followed up 74 subjects (48 SCD, 26 MCI) for a mean time of 9 years. All participants underwent extensive neuropsychological assessment, cognitive reserve estimation, BDNF and apolipoprotein E (ApoE) genotype analysis at baseline. Personality traits and leisure activities were assessed in a subgroup. Each patient underwent clinical–neuropsychological follow-up, during which 18 out of 48 SCD subjects progressed to MCI and 14 out of 26 MCI subjects progressed to AD. We found that Val66Met increased the risk of progression from SCD to MCI and from MCI to AD only in women. Nevertheless, Val/Val carriers who progressed from SCD to MCI had a shorter conversion time compared to Met carriers. We concluded that Val66Met polymorphism might play different roles depending on sex and stage of the disease.

We aimed to assess diagnostic accuracy of plasma p-tau181 and NfL separately and in combination in discriminating Subjective Cognitive Decline (SCD) and Mild Cognitive Impairment (MCI) patients carrying Alzheimer’s Disease (AD) pathology from non-carriers; to propose a flowchart for the interpretation of the results of plasma p-tau181 and NfL. We included 43 SCD, 41 MCI and 21 AD-demented (AD-d) patients, who underwent plasma p-tau181 and NfL analysis. Twenty-eight SCD, 41 MCI and 21 AD-d patients underwent CSF biomarkers analysis (Aβ1-42, Aβ1-42/1–40, p-tau, t-tau) and were classified as carriers of AD pathology (AP+) it they were A+/T+ , or non-carriers (AP−) when they were A−, A+/T−/N−, or A+/T−/N+ according to the A/T(N) system. Plasma p-tau181 and NfL separately showed a good accuracy (AUC = 0.88), while the combined model (NfL + p-tau181) showed an excellent accuracy (AUC = 0.92) in discriminating AP+ from AP− patients. Plasma p-tau181 and NfL results were moderately concordant (Coehn’s k = 0.50, p  < 0.001). Based on a logistic regression model, we estimated the risk of AD pathology considering the two biomarkers: 10.91% if both p-tau181 and NfL were negative; 41.10 and 76.49% if only one biomarker was positive (respectively p-tau18 and NfL); 94.88% if both p-tau181 and NfL were positive. Considering the moderate concordance and the risk of presenting an underlying AD pathology according to the positivity of plasma p-tau181 and NfL, we proposed a flow chart to guide the combined use of plasma p-tau181 and NfL and the interpretation of biomarker results to detect AD pathology.

Lung cancer screening (LCS) combined with smoking cessation programs is a critical strategy for reducing lung cancer mortality. Understanding the perspectives of cigarette users and former ones on these interventions is essential for enhancing their acceptability and effectiveness. This study aimed to explore, in Italy, the perceptions and experiences of individuals eligible for LCS within the context of a smoking cessation program. This multicenter qualitative study was conducted in two Italian regions as part of a larger project the Italian League against Cancer promoted. Using purposive sampling, we included (a) cigarette users and former ones who participated in an Italian trial, ITALUNG study, and (b) cigarette users who had been offered individual or group smoking cessation interventions and were theoretically eligible for screening in the following years (aged 50-70, ≥15 pack-years). Data were collected through open-ended semi-structured interviews and focus group meetings and analyzed using reflexive thematic analysis. The data analysis yielded six themes covering participants' views on the interactions between the two types of interventions (screening and smoking cessation program). Across their data, we generated the following themes: (i) depreciation and fatalism toward the risk of smoking, (ii) self-blaming and ethicality, (iii) ambivalent impact of the screening on smoking, (iv) LCS-related information and concerns, (v) teachable and motivating moments, and (vi) non-stigmatizing communication and testimony by professionals. Our study underscores the importance of avoiding stigma and respecting the dignity of cigarette users in implementing LCS and smoking cessation programs. Clear communication and supportive interactions with healthcare providers are crucial for enhancing the acceptability and effectiveness of these interventions. Future research should focus on quantifying these findings and exploring additional factors influencing the acceptability and effectiveness of combined LCS and smoking cessation programs.

INTRODUCTION This study investigated gender differences in cognitive reserve (CR) in subjective cognitive decline (SCD) and examined the impact of gender‐CR interaction on the risk of progression to mild cognitive impairment (MCI). METHODS We enrolled 440 SCD patients and estimated CR using premorbid intelligence (Test di Intelligenza Breve [TIB]). To account for socio‐cultural differences, patients were stratified by birth cohort (pre‐/post‐1950). A Markov random‐field (MRF) model explored relationships between gender, CR, education, and age. Logistic regression assessed MCI progression risk. RESULTS Women showed lower TIB scores than men (p < 0.001). The MRF model revealed an inverse connection between TIB and female gender, while no link was observed between TIB and generation. Progression to MCI was predicted by age at onset (p < 0.001), apolipoprotein E (APOE) status (p = 0.002), and TIB (p = 0.018), but not gender. DISCUSSION Gender has an impact on CR, but not through socio‐economic variables. In turn, CR influenced the risk of MCI progression, whereas gender did not. Highlights Subjective cognitive decline (SCD) women presented lower cognitive reserve (CR) levels than men, despite similar education levels. Social‐cultural factors did not explain these gender differences in CR in SCD. The gender–CR interaction was not mediated by social–cultural factors. The risk of progression to mild cognitive impairment (MCI) was influenced by CR but not by gender.

Background As disease-modifying therapies (DMTs) for Alzheimer's disease (AD) are becoming a reality, there is an urgent need to select cost-effective tools that can accurately identify patients in the earliest stages of the disease. Subjective Cognitive Decline (SCD) is a condition in which individuals complain of cognitive decline with normal performances on neuropsychological evaluation. Many studies demonstrated a higher prevalence of Alzheimer’s pathology in patients diagnosed with SCD as compared to the general population. Consequently, SCD was suggested as an early symptomatic phase of AD. We will describe the study protocol of a prospective cohort study (PREVIEW) that aim to identify features derived from easily accessible, cost-effective and non-invasive assessment to accurately detect SCD patients who will progress to AD dementia. Methods We will include patients who self-referred to our memory clinic and are diagnosed with SCD. Participants will undergo: clinical, neurologic and neuropsychological examination, estimation of cognitive reserve and depression, evaluation of personality traits, APOE and BDNF genotyping, electroencephalography and event-related potential recording, lumbar puncture for measurement of Aβ 42 , t-tau, and p-tau concentration and Aβ 42 /Aβ 40 ratio. Recruited patients will have follow-up neuropsychological examinations every two years. Collected data will be used to train a machine learning algorithm to define the risk of being carriers of AD and progress to dementia in patients with SCD. Discussion This is the first study to investigate the application of machine learning to predict AD in patients with SCD. Since all the features we will consider can be derived from non-invasive and easily accessible assessments, our expected results may provide evidence for defining cost-effective and globally scalable tools to estimate the risk of AD and address the needs of patients with memory complaints. In the era of DMTs, this will have crucial implications for the early identification of patients suitable for treatment in the initial stages of AD. Trial registration number (TRN) NCT05569083.

Background Primary progressive aphasia (PPA) diagnostic criteria underestimate the complex presentation of semantic (sv) and logopenic (lv) variants, in which symptoms partially overlap, and mixed clinical presentation (mixed-PPA) and heterogenous profile (lvPPA +) are frequent. Conceptualization of similarities and differences of these clinical conditions is still scarce. Methods Lexical, semantic, phonological, and working memory errors from nine language tasks of sixty-seven PPA were analyzed using Profile Analysis based on Multidimensional Scaling, which allowed us to create a distributed representation of patients’ linguistic performance in a shared space. Patients had been studied with [ 18 F] FDG-PET. Correlations were performed between metabolic and behavioral data. Results Patients’ profiles were distributed across a continuum. All PPA, but two, presented a lexical retrieval impairment, in terms of reduced production of verbs and nouns. svPPA patients occupied a fairly clumped space along the continuum, showing a preponderant semantic deficit, which correlated to fusiform gyrus hypometabolism, while only few presented working memory deficits. Adjacently, lvPPA + presented a semantic impairment combined with phonological deficits, which correlated with metabolism in the anterior fusiform gyrus and posterior middle temporal gyrus. Starting from the shared phonological deficit side, a large portion of the space was occupied by all lvPPA, showing a combination of phonological, lexical, and working memory deficits, with the latter correlating with posterior temporo-parietal hypometabolism. Mixed PPA did not show unique profile, distributing across the space. Discussion Different clinical PPA entities exist but overlaps are frequent. Identifying shared and unique clinical markers is critical for research and clinical practice. Further research is needed to identify the role of genetic and pathological factors in such distribution, including also higher sample size of less represented groups.

BACKGROUND The study aimed to explore the applicability of plasma phosphorylated tau (p‐tau)217 in identifying patients with subjective cognitive decline (SCD) and mild cognitive impairment (MCI) carrying Alzheimer's disease (AD) pathology in real‐world settings. METHODS Fifty SCD, 87 MCI, and 50 AD‐demented patients underwent blood collection to dose plasma p‐tau217 with a fully automated Lumipulse G600II assay. Patients were classified according to the Revised Criteria of the Alzheimer's Association Workgroup as Core1+ or Core1– (based on amyloid positron emission tomography, cerebrospinal fluid [CSF] amyloid beta [Aβ]42/Aβ40, CSF p‐tau181/Aβ42). RESULTS Plasma p‐tau217 was accurate for discriminating between Core1+ and Core1– patients (area under the curve = 0.92) with an optimal cut‐off value of 0.274 pg/mL, revealing good accuracy (86.29%), positive predictive value (PPV; 88.18%), and negative predictive value (NPV; 83.09%). The two cut‐offs approach (0.229–0.516 pg/mL) showed higher accuracy (91.11%), a PPV of 96.25% and a NPV of 83.63%. CONCLUSION The two cut‐offs approach provides for stronger accuracy, PPV, and NPV than a single cut‐off, making reliable the clinical application of plasma p‐tau217 for early detection of AD in real‐world settings. Highlights Plasma phosphorylated tau (p‐tau)217 was highly accurate in detecting Alzheimer's disease (AD) pathology. The two cut‐offs approach increased plasma p‐tau217 accuracy for AD diagnosis. Even when measured with immunoassay, p‐tau217 is a good biomarker for AD diagnosis. Transition of p‐tau217 from research setting to clinical practice seems feasible.

Background The diagnosis of Alzheimer’s disease (AD) in its preclinical stages, such as subjective cognitive decline (SCD), is crucial for a timely management of the condition. However, current early diagnostic methods are unsuitable for preclinical screenings due to limited availability and diagnostic reliability. Additionally, reliance on invasive and scarcely available methods exacerbates the underdiagnosis of AD in its preclinical forms. Methods We introduce an early diagnostic pipeline based on the Digital Alzheimer’s Disease Diagnosis (DADD) digital twin model, which derives personalized AD biomarkers from non-invasive electroencephalographic (EEG) recordings. These biomarkers reconstruct patient-specific neurodegeneration, capturing synaptic and connectivity degeneration mechanisms. Digital biomarkers were used to predict cerebrospinal fluid (CSF) biomarker positivity for AD and clinical conversions at follow-up in 124 participants with varying degrees of cognitive decline, including a control group of 19 healthy subjects. Results Digital biomarkers derived from the DADD model: i) Robustly distinguished SCD from healthy participants, improving classification accuracy by 7% compared to standard EEG biomarkers; ii) Identified patients positive for CSF biomarkers of AD with 88% accuracy (significantly outperforming standard EEG biomarkers, which achieved 58% accuracy); iii) Predicted follow-up conversions to clinical cognitive decline with 87% accuracy (compared to 54% accuracy for standard EEG biomarkers). Conclusions The DADD model provided robust digital AD biomarkers with strong diagnostic and prognostic value for preclinical AD, enabling the prediction of CSF biomarkers and clinical conversions using only non-invasive EEG recordings. This is particularly important as preclinical patients, such as those with SCD, are often excluded from diagnostic procedures like lumbar puncture. Predicting CSF biomarkers by combining digital twins with non-invasive recordings could revolutionize AD diagnosis in its early stages, paving the way for the clinical application of digital twins in AD diagnostics. Trial registration Clinical Trial identifier: NCT05569083 (submitted 2022–08-24).

Background Picture naming performance is influenced by the properties of the stimuli and of the words to be retrieved, such as word length and lexical frequency. Significant inconsistencies, however, remain regarding the brain regions mediating these effects in neurodegenerative patients. In the present study, we addressed this issue by correlating regional cerebral metabolism with several naming-related variables in a large cohort of neurodegenerative patients, who are likely to exhibit naming impairments due to different mechanisms of cognitive dysfunction. Methods A total of 178 patients classified within the Frontotemporal (FTD) and Alzheimer’s Disease (AD) spectra were administered a picture naming test validated for the Italian language (CaGi) and underwent a FDG-PET scan. Principal Component Analysis on 10 psycholinguistic variables resulted in the extraction of four components, labelled as word-form, visual, lexical, and semantic, according to the variables populating each of them. Using an item-level approach, the influence of each component on patients' performance was assessed and correlated with brain metabolism data from 11 left hemispheric Regions of Interest. Results A simple word form and lexical structure were associated with better naming performance. The imaging findings reveal a distributed neural network, with fusiform gyrus supporting both visual and semantic features. Inferior frontal and posterior temporal/parietal gyri represented an interface between lexico-semantic and phonological properties. The anterior temporal lobe contributed to all the stages of picture naming. The two dementia spectra activated different areas in response to the same variables, in particular for the visual and semantic components, suggesting the presence of disease-specific compensatory mechanisms. Conclusions Our results suggest a distributed neural network showing both commonalities and specificities in how picture and word properties influence naming performance. The network also seems capable of compensatory changes in the face of the extension of neurodegenerative processes.