Cognitive complaints are common in elderly subjects and are a frequent reason for referral to memory clinics. If the complaints are not associated with objective cognitive impairment, the condition is labelled subjective cognitive decline (SCD). SCD is often considered as a stage antedating objective impairment, and an at‐risk condition for subsequent dementia. Recent large‐scale studies indicate that a significantly increased risk of clinical progression in subjects with SCD is associated with positivity for Alzheimer's disease (AD) biomarkers, a finding supporting the notion that SCD can be due to different mechanisms not associated with neurodegeneration, including functional cognitive disorders. In this paper we present a selective review of research on the relations among SCD, cognitive awareness, and metacognitive abilities. We propose that longitudinal studies of metacognitive efficiency in SCD may provide useful cues about the risk of progression to dementia and the possible presence of a functional cognitive disorder, with different implications for the management of this prevalent aging‐related condition.Highlights: Subjective cognitive decline (SCD), a common cause of referral to memory clinics, can be due to multiple conditions. The predictive value of SCD for progression to Alzheimer's disease (AD) dementia is high in association with AD biomarker positivity. The awareness of cognitive decline is the mechanism responsible for the emergence of SCD and metacognition is the underlying neuropsychological function. The awareness of cognitive decline in clinical patients is usually assessed comparing an informant rating to the patient self‐assessment, a method that can be affected by informant bias. While there is strong evidence that awareness starts to decline with the onset of objective cognitive impairment, progressively leading to the anosognosia of AD, the status of metacognitive efficiency in SCD needs to be further investigated. Quantitative, performance‐based indexes of metacognitive efficiency may contribute both to the assessment of progression risk and to the management of subjects with functional cognitive disorders.

Increasing evidence shows that neuroinflammation is a possible modulator of tau spread effects on cognitive impairment in Alzheimer’s disease. In this context, plasma levels of the glial fibrillary acidic protein (GFAP) have been suggested to have a robust association with Alzheimer’s disease pathophysiology. This study aims to assess the correlation between plasma GFAP and Alzheimer’s disease pathology, and their synergistic effect on cognitive performance and decline. A cohort of 122 memory clinic subjects with amyloid and tau positron emission tomography, MRI scans, plasma GFAP, and Mini-Mental State Examination (MMSE) was included in the study. A subsample of 94 subjects had a follow-up MMSE score at least one year after baseline. Regional and voxel-based correlations between Alzheimer’s disease biomarkers and plasma GFAP were assessed. Mediation analyses were performed to evaluate the effects of plasma GFAP on the association between amyloid and tau PET, and tau PET and cognitive impairment and decline. GFAP was associated with increased tau PET ligand uptake in the lateral temporal and inferior temporal lobes in a strong left-sided pattern independently of age, gender, education, amyloid, and APOE status (β=0.001, p < 0.01). The annual rate of MMSE change was significantly and independently correlated with both GFAP (β=0.006, p < 0.01) and global tau SUVR (β=4.33, p < 0.01), but not with amyloid burden. Partial mediation effects of GFAP were found on the association between amyloid and tau pathology (13.7%), and between tau pathology and cognitive decline (17.4%), but not on global cognition at baseline. Neuroinflammation measured by circulating GFAP is independently associated with tau Alzheimer’s disease pathology and with cognitive decline, suggesting neuroinflammation as a potential target for future disease-modifying trials targeting tau pathology. Peretti et al. show that a circulatory marker of neuroinflammation—glial fibrillary acidic protein—is associated with tau pathology in lateral temporal and frontal regions in patients with Alzheimer's disease, independent of amyloid load. Neuroinflammation appears to modulate the association between amyloid and tau biomarkers.

Background: Subjective cognitive decline (SCD) is the subjective perception of a decline in memory and/or other cognitive functions in the absence of objective evidence. Some SCD individuals however may suffer from very early stages of neurodegenerative diseases (such as Alzheimer’s disease, AD), minor psychiatric conditions, neurological, and/or somatic comorbidities. Even if a theoretical framework has been established, the etiology of SCD remains far from elucidated. Clinical observations recently lead to the hypothesis that individuals with incipient AD may have overestimated metacognitive judgements of their own cognitive performance, while those with psychiatric disorders typically present underestimated metacognitive judgements. Moreover, brain connectivity changes are known correlates of AD and psychiatric conditions and might be used as biomarkers to discriminate SCD individuals of different etiologies. The aim of the COSCODE study is to identify metacognition, connectivity, behavioral, and biomarker profiles associated with different etiologies of SCD. Here we present its rationale and study design. Methods: COSCODE is an observational, longitudinal (4 years), prospective clinical cohort study involving 120 SCD, and 80 control study participants (40 individuals with no cognitive impairment, and 40 living with mild cognitive impairment - MCI, or dementia due to AD), all of which will undergo diffusion magnetic resonance imaging (MRI) and functional magnetic resonance imaging (fMRI) as well as behavioral and biomarker assessments at baseline and after 1 and 2 years. Both hypothesis-driven and data-driven cluster analysis approaches will be used to identify SCD sub-types based on metacognition, connectivity, behavioral, and biomarker features. Conclusion: COSCODE will allow defining and interpreting the constellation of signs and symptoms associated with different etiologies of SCD, paving the way to the development of cost-effective risk assessment and prevention protocols.

Subjective cognitive decline (SCD) refers to the subjective experience of cognitive decline in the absence of detectable cognitive impairment. SCD has been largely studied as a risk condition for cognitive decline. Empirical observations suggest that persons with SCD are heterogeneous, including individuals with early Alzheimer’s disease and others with psychological vulnerabilities and/or physical comorbidity. The semiology of SCD is still in its infancy, and the features predicting cognitive decline are poorly defined. The present study aims to identify subgroups of SCD using a data-driven approach and study their clinical evolution across 8 years. the study population is the InveCe.Ab population-based cohort, including cognitively unimpaired people aged 70–74 years and followed for 8 years. Hierarchical cluster analysis (HCA) was carried out to identify distinct SCD subgroups based on nine clinical and cognitive features. Longitudinal changes by baseline SCD status were estimated using linear mixed models for cognitive decline and Cox proportional-hazard model for all-cause dementia risk. out of 956 individuals, 513 were female (54%); and the mean age was 72.1 (SD = 1.3), education was 7.2 (3.3), and 370 (39%) reported cognitive complaints (SCD). The HCA resulted in two clusters (SCD1 and SCD2). SCD2 were less educated and had more comorbidities, cardiovascular risk and depressive symptoms than SCD1 and controls. SCD2 presented steeper cognitive decline (Mini-Mental State Examination; β = −0.31) and increased all-cause dementia risk (hazard-ratio = 3.4). at the population level, basic clinical information can differentiate individuals with SCD at higher risk of developing dementia, underlining the heterogeneous nature of this population even in a sample selected for a narrow age range, in a specific geographic area.

Background: Plasma biomarkers of Alzheimer’s disease (AD) pathology, neurodegeneration, and neuroinflammation are ideally suited for secondary prevention programs in self-sufficient persons at-risk of dementia. Plasma biomarkers have been shown to be highly correlated with traditional imaging biomarkers. However, their comparative predictive value versus traditional AD biomarkers is still unclear in cognitively unimpaired (CU) subjects and with mild cognitive impairment (MCI). Methods: Plasma (Aβ42/40, p-tau181, p-tau231, NfL, and GFAP) and neuroimaging (hippocampal volume, centiloid of amyloid-PET, and tau-SUVR of tau-PET) biomarkers were assessed at baseline in 218 non-demented subjects (CU = 140; MCI = 78) from the Geneva Memory Center. Global cognition (MMSE) was evaluated at baseline and at follow-ups up to 5.7 years. We used linear mixed-effects models and Cox proportional-hazards regression to assess the association between biomarkers and cognitive decline. Lastly, sample size calculations using the linear mixed-effects models were performed on subjects positive for amyloid-PET combined with tau-PET and plasma biomarker positivity. Results: Cognitive decline was significantly predicted in MCI by baseline plasma NfL (β=-0.55), GFAP (β=-0.36), hippocampal volume (β = 0.44), centiloid (β=-0.38), and tau-SUVR (β=-0.66) (all p < 0.05). Subgroup analysis with amyloid-positive MCI participants also showed that only NfL and GFAP were the only significant predictors of cognitive decline among plasma biomarkers. Overall, NfL and tau-SUVR showed the highest prognostic values (hazard ratios of 7.3 and 5.9). Lastly, we demonstrated that adding NfL to the inclusion criteria could reduce the sample sizes of future AD clinical trials by up to one-fourth in subjects with amyloid-PET positivity or by half in subjects with amyloid-PET and tau-PET positivity. Conclusions: Plasma NfL and GFAP predict cognitive decline in a similar manner to traditional imaging techniques in amyloid-positive MCI patients. Hence, even though they are non-specific biomarkers of AD, both can be implemented in memory clinic workups as important prognostic biomarkers. Likewise, future clinical trials might employ plasma biomarkers as additional inclusion criteria to stratify patients at higher risk of cognitive decline to reduce sample sizes and enhance effectiveness.