The identification of reliable biomarkers in biological fluids is paramount to optimizing the diagnosis of Alzheimer’s disease (AD). Measurement of Aβ42, t-tau, and p-tau in cerebrospinal fluid (CSF) is the most accepted method to support the diagnosis of AD. However, lumbar puncture represents an invasive investigation, whereas saliva is one of the most accessible body fluids. The aim of our study was to investigate salivary concentrations in AD and evaluate the correlation between salivary and CSF Aβ42 concentrations in AD patients, patients with non-AD dementias, and controls. We recruited 100 subjects: 18 AD patients, 64 patients with non-AD dementias, and 18 controls. The mean saliva Aβ42 concentrations in AD patients were higher than in controls (p < 0.001), and to patients with non-AD dementias (p = 0.001). A significant negative correlation between salivary and CSF Aβ42 concentrations was found in the overall group (r = −0.562, p < 0.001) and in non-AD patients (r = −0.443, p < 0.001). Salivary Aβ42 concentrations positively correlated with CSF t-tau (r = 0.321, p = 0.001) and p-tau (r = 0.297, p = 0.001). Our study showed that in AD patients’ saliva, Aβ42 concentrations are specifically increased, and we found an interesting negative correlation between CSF and salivary Aβ42 concentrations that warrants further investigation.

Objective: In this systematic review and meta-analysis, we critically evaluate available evidence regarding the association between primary headaches and subsequent decline of cognitive function and dementia. Background: Recent studies suggested that headache disorders may increase the risk for dementia. However, available studies are conflicting. Methods: To identify qualifying studies, we searched scientific databases, including Pubmed, Scopus, Web of Science, Science Direct and BMC, screening for relevant papers. In order to reduce the heterogeneity between different studies, the analyses were further subdivided according to the clinical diagnoses and the study methodologies. Results: We identified 23 studies investigating the association between primary headaches and the risk of dementia. Of these, 18 met our inclusion criteria for meta-analysis (covering 924.140 individuals). Overall effect-size shows that primary headaches were associated with a small increase in dementia risk (OR = 1,15; CI 95%: 1,03–1,28; p = 0,02). Analyzing subgroups, we found that migraine was associated with both a moderate increased risk of all-cause dementia (OR = 1,26; p = 0,00; 95% CI: 1,13–1,40) as well as a moderate increased risk of Alzheimer’s disease (OR = 2,00; p = 0,00; 95% CI: 1,46–2,75). This association was significant in both case–control and retrospective cohort studies but not in prospective studies. Conclusions: Our study supports the presence of a link between primary headaches and dementia. However, in the subgroup analysis, only patients with migraine showed a moderate increase risk for all-cause dementia and for Alzheimer’s disease. Additional rigorous studies are needed to elucidate the possible role of primary headaches on the risk of developing cognitive impairment and dementia.

Several studies have revealed defects in autophagy in neurodegenerative disorders including Alzheimer’s disease (AD) and frontotemporal dementia (FTD). SQSTM1/p62 plays a key role in the autophagic machinery and may serve as a marker for autophagic flux in vivo. We investigated the role of p62 in neurodegeneration, analyzing its concentrations in the CSF of AD and FTD patients. We recruited 76 participants: 22 patients with AD, 28 patients with FTD, and 26 controls. CSF p62 concentrations were significantly increased in AD and FTD patients when compared to controls, which persisted after adjusting for age (p = 0.01 and p = 0.008, respectively). In female FTD patients, p62 positively correlated with the neurodegenerative biomarkers t-Tau and p-Tau. A significant correlation between CSF p62 concentrations and several clinical features of AD was found. Our data show that p62 is increased in CSF of AD and FTD patients, suggesting a key role of autophagy in these two disorders. The levels of p62 in CSF may reflect an altered autophagic flux, and p62 could represent a potential biomarker of neurodegeneration.

Migraine is a common neurovascular disorder characterized by recurrent episodes of headache and associated neurological symptoms. At present, a significant portion of patients do not obtain a satisfactory response to acute pain-relieving therapies, including NSAIDs and triptans. In this context, pharmacogenetics plays a key role in the understanding of such a diverse response. In order to investigate whether functional polymorphisms in proinflammatory cytokine genes (IL-1α, IL-1β, IL-1RN; IL-6 and TNF-α) may influence the response to acute treatment, 313 consecutive patients with episodic migraine without aura were enrolled. Pain relief by administration of NSAIDs or triptans for three consecutive migraine attacks was evaluated. We found a significant association between A allele of the TNF-α promoter (−308 A/G) and a lack of efficacy after NSAID administration (p < 0.01, OR 2.51, 95% CI: 1.33 < OR < 4.75 compared to the G allele). Remaining polymorphisms had no significant effect on pain relief. Our study showed that a functional polymorphism in the TNF-α gene significantly modulates the clinical response to NSAID administration in acute attacks. Patients with higher production of the active cytokine during stress showed a significantly lower anti-migraine effect. Our results further support a role for TNF-α in the pathophysiological mechanisms of migraine attack.

Early and differential diagnosis of dementia is essential for timely and targeted care. This study investigated the feasibility of using an artificial intelligence (AI)-based system to discriminate between different stages and etiologies of dementia by analyzing facial emotions. We collected video recordings of 64 participants exposed to standardized audio-visual stimuli. Facial emotion features in terms of valence and arousal were extracted and used to train machine learning models on multiple classification tasks, including distinguishing individuals with mild cognitive impairment (MCI) and overt dementia from healthy controls (HCs) and differentiating Alzheimer’s disease (AD) from other types of cognitive impairment. Nested cross-validation was adopted to evaluate the performance of different tested models (K-Nearest Neighbors, Logistic Regression, and Support Vector Machine models) and optimize their hyperparameters. The system achieved a cross-validation accuracy of 76.0% for MCI vs. HCs, 73.6% for dementia vs. HCs, and 64.1% in the three-class classification (MCI vs. dementia vs. HCs). Among cognitively impaired individuals, a 75.4% accuracy was reached in distinguishing AD from other etiologies. These results demonstrated the potential of AI-driven facial emotion analysis as a non-invasive tool for early detection of cognitive impairment and for supporting differential diagnosis of AD in clinical settings.

Altered facial expressivity is frequently recognized in cognitively impaired individuals. This makes facial emotion identification a promising tool with which to support the diagnostic process. We propose a novel, non-invasive approach for detecting cognitive impairment based on facial emotion analysis. We design a protocol for emotion elicitation using visual and auditory standardized stimuli. We collect facial emotion video recordings from 32 cognitively impaired and 28 healthy control subjects. To track the evolution of emotions during the experiment, we train a deep convolutional neural network on the AffectNet dataset for emotion recognition from facial images. Emotions are described using a dimensional affect model, namely the continuous dimensions of valence and arousal, rather than discrete categories, enabling a more nuanced analysis. The collected facial emotion data are used to train a classifier to distinguish cognitively impaired and healthy subjects. Our k-nearest neighbors model achieves a cross-validation accuracy of 76.7%, demonstrating the feasibility of automatic cognitive impairment detection from facial expressions. These results highlight the potential of facial expressions as early markers of cognitive impairment, which could enhance non-invasive screening methods for early diagnosis.