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Post-COVID condition (PCC) and multiple sclerosis (MS) share some clinical and demographic features, including cognitive symptoms and fatigue. Some pathophysiological mechanisms well-known in MS, such as autoimmunity, neuroinflammation and myelin damage, have also been implicated in PCC. In this study, we aimed to compare the cognitive phenotypes of two large cohorts of patients with PCC and MS, and to evaluate the relationship between fatigue and cognitive performance. Cross-sectional study including 218 patients with PCC and 218 with MS matched by age, sex, and years of education. Patients were evaluated with a comprehensive neuropsychological protocol and were categorized according to the International Classification of Cognitive Disorders system. Fatigue and depression were also assessed. Cognitive profiles of PCC and MS largely overlapped, with a greater impairment in episodic memory in MS, but with small effect sizes. The most salient deficits in both disorders were in attention and processing speed. The severity of fatigue was greater in patients with PCC. Still, the correlations between fatigue severity and neuropsychological tests were more prominent in the case of MS. There were no differences in the severity of depression among groups. Our study found similar cognitive profiles in PCC and MS. Fatigue was more severe in PCC, but was more associated with cognitive performance in MS. Further comparative studies addressing the mechanisms related to cognitive dysfunction and fatigue may be of interest to advance the knowledge of these disorders and develop new therapies.

Objectives: We aimed to evaluate and compare the diagnostic capacity of five cognitive screening tests for the diagnosis of mild cognitive impairment (MCI) in patients consulting by memory loss. Methods: A cross-sectional study involving 140 participants with a mean age of 74.42 ± 7.60 years, 87 (62.14%) women. Patients were classified as MCI or cognitively unimpaired according to a comprehensive neuropsychological battery. The diagnostic properties of the following screening tests were compared: Mini-Mental State Examination (MMSE), Addenbrooke’s Cognitive Examination III (ACE-III) and Mini-Addenbrooke (M-ACE), Memory Impairment Screen (MIS), Montreal Cognitive Assessment (MoCA), and Rowland Universal Dementia Assessment Scale (RUDAS). Results: The area under the curve (AUC) was 0.861 for the ACE-III, 0.867 for M-ACE, 0.791 for MoCA, 0.795 for MMSE, 0.731 for RUDAS, and 0.672 for MIS. For the memory components, the AUC was 0.869 for ACE-III, 0.717 for MMSE, 0.755 for MoCA, and 0.720 for RUDAS. Cronbach’s alpha was 0.827 for ACE-III, 0.505 for MMSE, 0.896 for MoCA, and 0.721 for RUDAS. Correlations with Free and Cued Selective Reminding Test were moderate with M-ACE, ACE-III, and MoCA, and moderate for the other tests. The M-ACE showed the best balance between diagnostic capacity and time of administration. Conclusions: ACE-III and its brief version M-ACE showed better diagnostic properties for the diagnosis of MCI than the other screening tests. MoCA and MMSE showed adequate properties, while the diagnostic capacity of MIS and RUDAS was limited.

Background/Objectives: Fatigue is a prevalent and debilitating symptom in Post-COVID Condition (PCC), fibromyalgia, and multiple sclerosis (MS). Although these conditions share clinical similarities, the underlying mechanisms of fatigue across these conditions may differ and remain poorly understood. This study aimed to compare the intensity and characteristics of fatigue in these three conditions to identify shared and distinct features. Methods: We conducted a cross-sectional study involving 429 participants: 219 with PCC, 112 with fibromyalgia, and 98 with MS. Participants completed a questionnaire specifically developed for the study via the Google Forms platform. This questionnaire was developed by a group of professionals in the hospital specializing in fatigue related to these three conditions, in collaboration with expert patients. The questionnaire was reported following the Checklist for Reporting Results of Internet E-Surveys (CHERRIES) recommendations. Results: Fatigue intensity was significantly higher in PCC and fibromyalgia compared to MS. Some differences in fatigue characteristics were observed: MS patients reported more fatigue in response to heat and a greater impact of mood on fatigue. Furthermore, delayed fatigue and reduced benefits from rest were more pronounced in both PCC and fibromyalgia. No significant differences were found regarding cognitive fatigue or difficulties in predicting the ability to perform activities. Conclusions: These results underscore some clinical characteristics in the intensity and quality of fatigue across PCC, fibromyalgia, and MS. These findings could suggest different mechanisms in the pathophysiology of the fatigue. Our study underscores the need for tailored diagnostic tools and interventions in managing fatigue in these three conditions.

Background Primary progressive aphasia (PPA) is a neurodegenerative syndrome characterized by the progressive impairment of language abilities. However, recent findings suggest that other cognitive domains, beyond language, may also be affected in the early stages of the disease. Exploring cognitive domains could facilitate the diagnosis of PPA and its classification into the three main variants (non‐fluent (nfvPPA), semantic (svPPA) and logopenic (lvPPA)), which remains challenging. This study aimed to identify non‐linguistic cognitive alterations in PPA and its variants. Method A cross‐sectional study including 157 patients with PPA and 74 cognitively unimpaired controls. Patients were classified into nfvPPA (N = 58), svPPA (N = 19), and lvPPA (N = 80) using a language battery, FDG‐PET, and CSF biomarkers. The mean age was 69,97 ± 8.12 years, and the 61.04% were females. Four cognitive domains (attention and working memory, executive functions, episodic memory and visuospatial abilities) were assessed using the ACE‐III and the following cognitive tests: Digit Span, TMT‐A and B, Rey‐Osterrieth Complex Figure (copy and 3‐minute recall), VOSP and Tower of London. Result All PPA variants scored significantly worse than the control group across most explored domains. The lvPPA and svPPA groups scored lower on episodic memory compared to the nfvPPA group. The lvPPA group performed worse on visuospatial abilities and executive function than the svPPA group. The nfvPPA group scored lower on attention and working memory tasks compared to the svPPA group. Conclusion This study highlights the impairment of non‐linguistic cognitive domains in PPA and its variants, identifying distinct cognitive profiles across variants that may aid in the patient diagnosis, classification, and monitoring of disease progression.

The detection of early biological and clinical changes in Alzheimer's disease (AD) is mandatory for therapeutic interventions. Blood p-tau markers, such as p-tau181 and p-tau217, have shown promising performance in identifying patients with early amyloid pathology, positioning themselves as potential first-line diagnostic tests. The development of plasma biomarkers for AD requires understanding their clinical implications and redefining the role of cognitive assessment in screening and diagnosing patients with early symptoms. Our study aimed to determine the optimal combination of biomarkers and cognitive tests to detect AD in its early stages. We included 124 patients consulting for memory loss with no functional impairment. The mean age was 69.50±6.54 years old, and 73 (58.9%) were women. The mean MMSE was 27.16±3.07. All patients were evaluated with neuropsychological assessment and CSF biomarkers. According to the results of the CSF biomarkers, patients were categorized as AD or non-AD. Patients with suspicion of other neurodegenerative disorders were not included. Serum p-tau181 and p-tau217 were measured afterwards with Lumipulse G600II. The neuropsychological assessment comprised the following cognitive tests: Mini-Mental State Examination (MMSE), Addenbrooke's Cognitive Examination III (ACE-III), digit span, Corsi test, Trail Making Test, Symbol Digit Modalities test, Boston Naming Test, Free and Cued Selective Reminding Test (FCSRT), Rey-Osterrieth Complex Figure (ROCF), verbal fluency, Visual Object and Space Perception Battery, Judgement Line Orientation test, Stroop Color Word and Interference Test, and Tower of London. The AUC for p-tau181 and p-tau217 was 0.935 and 0.886, respectively. The best AUC for neuropsychological tests was 0.865 when age, FCSRT, and ROCF-memory were combined. A logistic regression model including ptau181 and ACE-III (memory) showed an AUC of 0.949, and the AUC was 0.964 when combining ptau181 and ROCF (memory). Our study suggests that the combination of neuropsychological tests and blood biomarkers could improve diagnostic performance in the early stages of AD. However, the fact that the biomarker's importance in the statistical models was greater partially challenges the utility of cognitive assessments, particularly for screening purposes and in situations where clinical time may be limited.

Primary progressive aphasia (PPA) is a neurodegenerative syndrome characterized by the progressive impairment of language abilities. However, recent findings suggest that other cognitive domains, beyond language, may also be affected in the early stages of the disease. Exploring cognitive domains could facilitate the diagnosis of PPA and its classification into the three main variants (non-fluent (nfvPPA), semantic (svPPA) and logopenic (lvPPA)), which remains challenging. This study aimed to identify non-linguistic cognitive alterations in PPA and its variants. A cross-sectional study including 157 patients with PPA and 74 cognitively unimpaired controls. Patients were classified into nfvPPA (N = 58), svPPA (N = 19), and lvPPA (N = 80) using a language battery, FDG-PET, and CSF biomarkers. The mean age was 69,97 ± 8.12 years, and the 61.04% were females. Four cognitive domains (attention and working memory, executive functions, episodic memory and visuospatial abilities) were assessed using the ACE-III and the following cognitive tests: Digit Span, TMT-A and B, Rey-Osterrieth Complex Figure (copy and 3-minute recall), VOSP and Tower of London. All PPA variants scored significantly worse than the control group across most explored domains. The lvPPA and svPPA groups scored lower on episodic memory compared to the nfvPPA group. The lvPPA group performed worse on visuospatial abilities and executive function than the svPPA group. The nfvPPA group scored lower on attention and working memory tasks compared to the svPPA group. This study highlights the impairment of non-linguistic cognitive domains in PPA and its variants, identifying distinct cognitive profiles across variants that may aid in the patient diagnosis, classification, and monitoring of disease progression.

Background The detection of early biological and clinical changes in Alzheimer's disease (AD) is mandatory for therapeutic interventions. Blood p‐tau markers, such as p‐tau181 and p‐tau217, have shown promising performance in identifying patients with early amyloid pathology, positioning themselves as potential first‐line diagnostic tests. The development of plasma biomarkers for AD requires understanding their clinical implications and redefining the role of cognitive assessment in screening and diagnosing patients with early symptoms. Our study aimed to determine the optimal combination of biomarkers and cognitive tests to detect AD in its early stages. Method We included 124 patients consulting for memory loss with no functional impairment. The mean age was 69.50±6.54 years old, and 73 (58.9%) were women. The mean MMSE was 27.16±3.07. All patients were evaluated with neuropsychological assessment and CSF biomarkers. According to the results of the CSF biomarkers, patients were categorized as AD or non‐AD. Patients with suspicion of other neurodegenerative disorders were not included. Serum p‐tau181 and p‐tau217 were measured afterwards with Lumipulse G600II. The neuropsychological assessment comprised the following cognitive tests: Mini‐Mental State Examination (MMSE), Addenbrooke's Cognitive Examination III (ACE‐III), digit span, Corsi test, Trail Making Test, Symbol Digit Modalities test, Boston Naming Test, Free and Cued Selective Reminding Test (FCSRT), Rey‐Osterrieth Complex Figure (ROCF), verbal fluency, Visual Object and Space Perception Battery, Judgement Line Orientation test, Stroop Color Word and Interference Test, and Tower of London. Result The AUC for p‐tau181 and p‐tau217 was 0.935 and 0.886, respectively. The best AUC for neuropsychological tests was 0.865 when age, FCSRT, and ROCF‐memory were combined. A logistic regression model including ptau181 and ACE‐III (memory) showed an AUC of 0.949, and the AUC was 0.964 when combining ptau181 and ROCF (memory). Conclusion Our study suggests that the combination of neuropsychological tests and blood biomarkers could improve diagnostic performance in the early stages of AD. However, the fact that the biomarker's importance in the statistical models was greater partially challenges the utility of cognitive assessments, particularly for screening purposes and in situations where clinical time may be limited.