Subjective Cognitive Decline Patients Research Articles

The taxonomy of subjective cognitive decline: proposal and first clinical evidence from the Geneva memory clinic cohort.

Subjective Cognitive Decline (SCD) is characterized by subjective cognitive concerns without objective cognitive impairment and is considered a risk factor for cognitive decline and dementia. However, most SCD patients will not develop neurodegenerative disorders, yet they may suffer from minor psychiatric, neurological, or somatic comorbidities. The aim of the present study is to provide a taxonomy of the heterogeneous SCD entity by isolating homogenous SCD subgroups with specific clinical features and cognitive trajectoriesand to conduct a preliminary validation using data from a memory clinic sample. Participants were fifty-five SCD individuals consecutively recruited at the Geneva Memory Center. Based on clinical reports, they were classified into three clinically pre-defined subgroups: (i) those with psychological or psychiatric comorbidities (Psy), (ii) those with somatic comorbidities (SomCom), (iii) and those with no apparent cause (NAC). Baseline demographics, clinical, cognitive, and biomarker differences among the SCD subgroups were assessed. Longitudinal cognitive changes (average 3 years follow-up) were modeled using a linear mixed model. Out of the 55 SCD cases, 16 were SomCom, 18 Psy, and 21 NAC. 47% were female, mean age was 71 years. We observed higher frequency of APOE ε4 carriers in NAC (53%) compared to SomCom (14%) and Psy (0%, P=0.023) and lower level of plasma Aβ42 in NAC (6.8±1.0) compared to SomCom (8.4±1.1; P=0.031). SomCom subjects were older (74 years) than Psy (67 years, P=0.011), and had greater medial temporal lobe atrophy (1.0±1.0) than Psy (0.2±0.6) and NAC (0.4±0.5, P=0.005). SomCom have worse episodic memory performances (14.5±3.5) than Psy (15.8±0.4) and NAC (15.8±0.7, P=0.032). We observed a slightly steeper, yet not statistically significant, cognitive decline in NAC (β=-0.48) compared to Psy (β=-0.28) and SomCom (β=-0.24). NAC feature higher proportion of APOE ε4 carriers, lower plasma Aβ42 and a trend towards steeper cognitive decline than SomCom and Psy. Taken together, these findings suggest that NAC are at higher risk of cognitive decline due to AD. The proposed clinical taxonomy might be implemented in clinical practice to identify SCD at higher risk. However, such taxonomy should be tested on an independent cohort with larger sample size.

Altered dynamic amplitude of low-frequency fluctuation in individuals at high risk for Alzheimer's disease.

The brain's dynamic spontaneous neural activity is significant in supporting cognition; however, how brain dynamics go awry in subjective cognitive decline (SCD) and mild cognitive impairment (MCI) remains unclear. Thus, the current study aimed to investigate the dynamic amplitude of low-frequency fluctuation (dALFF) alterations in patients at high risk for Alzheimer's disease and to explore its correlation with clinical cognitive assessment scales, to identify an early imaging sign for these special populations. A total of 152 participants, including 72 SCD patients, 44 MCI patients and 36 healthy controls (HCs), underwent a resting-state functional magnetic resonance imaging and were assessed with various neuropsychological tests. The dALFF was measured using sliding-window analysis. We employed canonical correlation analysis (CCA) to examine the bi-multivariate correlations between neuropsychological scales and altered dALFF among multiple regions in SCD and MCI patients. Compared to those in the HC group, both the MCI and SCD groups showed higher dALFF values in the right opercular inferior frontal gyrus (voxel P < .001, cluster P < .05, correction). Moreover, the CCA models revealed that behavioural tests relevant to inattention correlated with the dALFF of the right middle frontal gyrus and right opercular inferior frontal gyrus, which are involved in frontoparietal networks (R = .43, P = .024). In conclusion, the brain dynamics of neural activity in frontal areas provide insights into the shared neural basis underlying SCD and MCI.

Disrupted morphological brain network organization in subjective cognitive decline and mild cognitive impairment.

We aim to investigate the alterations in gray matter for subjective cognitive decline (SCD) and mild cognitive impairment (MCI) from the perspective of the human connectome. High-resolution T1-weighted images were acquired from 54 patients with SCD, 95 patients with MCI, and 65 healthy controls (HC). Morphological brain networks (MBN) were constructed using similarities in the distribution of gray matter volumes between regions. The strength of morphological connections and topographic metrics derived from the graph-theoretical analysis were compared. Furthermore, we assessed the relationship between the observed morphological abnormalities and disease severity. According to the results, we found a significantly decreased morphological connection between the somatomotor network and ventral attention network in SCD compared to HC and MCI compared to SCD. The graph-theoretic analysis illustrated disruptions in the whole network organization, where the normalized shortest path increased and the global efficiency (Eg) decreased in MCI compared to SCD. In addition, Montreal Cognitive Assessment scores of SCD patients had a significantly negative correlation with Eg. The primary limitations of the present study include the cross-sectional design, no enrolled AD patients, no assessment of amyloidosis, and the need for more comprehensive neuropsychological tests. Our findings indicate the abnormalities of morphological networks at early stages in the AD continuum, which could be interpreted as compensatory changes to retain a normal level of cognitive function. The present study could provide new insight into the mechanism of AD.

Multidimensional assessment of time perception along the continuum of Alzheimer’s Disease and evidence of alterations in subjective cognitive decline

Timing alterations occur in Alzheimer’s disease (AD), even in early stages (mild cognitive impairment, MCI). Moreover, a stage named subjective cognitive decline (SCD), in which individuals perceive a change in cognitive performance not revealed by neuropsychological tests, has been identified as a preclinical phase of AD. However, no study to date has investigated different dimensions of time processing along the continuum from physiological to pathological aging, and whether timing alterations occur in SCD. Here a sample of participants with SCD, MCI, AD and healthy controls (HC) performed tasks assessing prospective duration estimation, production, reproduction, implicit temporal learning in conditions dependent from external cues (externally-cued learning, ECL) or independent from external cues (internally-based learning, IBL), retrospective duration estimation, the subjective experience of time and the temporal collocation of events. AD patients performed worse than HC and SCD in prospective timing, and in collocating events in time. The subjective experience of time did not differ between groups. Concerning temporal learning, AD performed worse in ECL than in IBL, whereas SCD performed worse in IBL than in ECL. SCD, MCI and AD patients all showed errors greater than HC in retrospective duration estimation. Results point to implicit temporal learning in externally-cued conditions and retrospective time estimation as possible early markers of cognitive decline.

Brain Volumetry‐based Amyloid PET Positivity Prediction in Subjective Cognitive Decline Patients

AbstractBackgroundAmyloid beta PET screening for subjective cognitive decline (SCD) patients would identify a risk factor of Alzheimer’s disease (AD) at an early stage but is generally infeasible due to the high cost and radiation exposure. Brain volumetry using 3D T1 MRI integrated with genetic information can alternatively suggest amyloid positivity in SCD subjects.MethodIndividuals of age between 60 and 85 feeling cognitive decline and scoring 7% to 50% on the memory test while exceeding 7% of the other tests comprises the SCD dataset via Seoul Neuropsychological Battery (SNSB) scheme (n = 119; 26 Aß+; 24 APOE4+). We measured the volume, volume to intracranial volume ratio, cortical thickness and their normative percentiles of 104 brain subregions, respectively, using VUNO‐Med DeepBrain, an automated brain volumetry and neurodegenerative disease diagnosis‐supporting software. The significance level is set to a = 0.005 to find differentiating regions between amyloid positive and negative groups. Furthermore, four machine learning models (Logistic Regression, Random Forest, XGBoost, TabNet) were trained using the combination of APOE4 information, age, and each of the six different volumetry results to predict amyloid positivity in SCD dataset. The training dataset (n = 102; 36 Aß+; 31 APOE4+) consists of subjects from significant memory concern (SMC) cohort of ADNI2 and ADNIGO studies.ResultSwell of the choroid plexus and shrink of the lingual cortex (both p &lt; .005) are statistically significant in Aß+ SCD patients compared to the Ab‐ patients. The random forest model trained by the volume data plus APOE4 and age information showed the highest performance (accuracy = 0.79, AUROC = 0.75, precision = 0.81, recall = 0.79, F1 = 0.80) in the amyloid positivity prediction task among the 24 models, higher than the model without APOE4 information (accuracy = 0.70, AUROC = 0.71, precision = 0.77, recall = 0.70, F1 = 0.72).ConclusionThe result indicates a statistically significant difference in choroid plexus volume and lingual cortex thickness between Aß+ and Aß‐ SCD patients. In addition, using both APOE e4 information and brain volumetry is crucial for amyloid positivity prediction. This study was supported by a grant from the Ministry of Health and Welfare, HI18C0530.

Brain Volumetry‐based Amyloid PET Positivity Prediction in Subjective Cognitive Decline Patients

AbstractBackgroundAmyloid beta PET screening for subjective cognitive decline (SCD) patients would identify a risk factor of Alzheimer’s disease (AD) at an early stage but is generally infeasible due to the high cost and radiation exposure. Brain volumetry using 3D T1 MRI integrated with genetic information can alternatively suggest amyloid positivity in SCD subjects.MethodIndividuals of age between 60 and 85 feeling cognitive decline and scoring 7% to 50% on the memory test while exceeding 7% of the other tests comprises the SCD dataset via Seoul Neuropsychological Battery (SNSB) scheme (n = 119; 26 Aβ+; 24 APOE4+). We measured the volume, volume to intracranial volume ratio, cortical thickness and their normative percentiles of 104 brain subregions, respectively, using VUNO‐Med DeepBrain, an automated brain volumetry and neurodegenerative disease diagnosis‐supporting software. The significance level is set to a = 0.005 to find differentiating regions between amyloid positive and negative groups. Furthermore, four machine learning models (Logistic Regression, Random Forest, XGBoost, TabNet) were trained using the combination of APOE4 information, age, and each of the six different volumetry results to predict amyloid positivity in SCD dataset. The training dataset (n = 102; 36 Aβ+; 31 APOE4+) consists of subjects from significant memory concern (SMC) cohort of ADNI2 and ADNIGO studies.ResultSwell of the choroid plexus and shrink of the lingual cortex (both p &lt; .005) are statistically significant in Aβ+ SCD patients compared to the Ab‐ patients. The random forest model trained by the volume data plus APOE4 and age information showed the highest performance (accuracy = 0.79, AUROC = 0.75, precision = 0.81, recall = 0.79, F1 = 0.80) in the amyloid positivity prediction task among the 24 models, higher than the model without APOE4 information (accuracy = 0.70, AUROC = 0.71, precision = 0.77, recall = 0.70, F1 = 0.72).ConclusionThe result indicates a statistically significant difference in choroid plexus volume and lingual cortex thickness between Aβ+ and Aβ‐ SCD patients. In addition, using both APOE e4 information and brain volumetry is crucial for amyloid positivity prediction. This study was supported by a grant from the Ministry of Health and Welfare, HI18C0530.

Minor neuropsychological deficits in SCD: Association with AD biomarkers and cognitive decline

AbstractBackgroundThe clinical relevance and longitudinal impact of minor neuropsychological deficits (MND) in cognitively normal memory clinic patients with subjective cognitive decline (SCD) is still unclear. To address this, we set out to investigate whether MND in SCD patients are related to Alzheimer´s disease (AD) pathology and future cognitive decline. In addition, we wanted to assess whether SCD patients have an increased risk of neuropathology and cognitive decline even in the absence of MND.MethodWe included 233 cognitively normal, healthy control participants (HC) without subjective cognitive concerns and 439 memory clinic SCD patients with available baseline CSF and/or longitudinal cognitive data from the observational DZNE Longitudinal Cognitive Impairment and Dementia (DELCODE) study. Participants, who scored at least 0.5SD below the mean on a composite measure calculated from the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) neuropsychological assessment battery, were classified as having MND. We compared the resulting participant groups regarding their baseline CSF Aβ42/40, p‐tau181, t‐tau and Aβ42/p‐tau181 levels, longitudinal preclinical Alzheimer´s cognitive composite (PACC5) trajectories and risk of progression to incident mild cognitive impairment (MCI).ResultCompared to SCD patients without MND (n = 384), SCD patients with MND (n = 55, 12.5% of SCD sample) showed significantly elevated CSF AD biomarker levels, accelerated PACC5 decline and a higher risk of progression to incident MCI. Baseline MND had a positive predictive value of 57.0% (95%CI: 38.5‐75.4) and a negative predictive value of 86.0% (95%CI: 81.9‐90.1) for the progression to incident MCI within 3 years. SCD patients without MND showed increased PACC5 decline and a higher risk of incident MCI compared to HC participants without MND (n = 215), but did not differ from HC participants in their CSF biomarker levels.ConclusionMND are a risk factor for AD pathology and cognitive decline in memory clinic patients with SCD. The assessment of MND could thus provide useful information for AD risk stratification and enrichment in SCD samples. Importantly, SCD patients still have an increased risk for cognitive decline in the absence of MND.

Health‐related quality of life in full spectrum of Alzheimer’s disease: a longitudinal analysis

AbstractBackgroundUnderstanding the health‐related quality of life (HR‐QoL) in subjective cognitive decline (SCD), mild cognitive impairment (MCI) and demented patients is essential from the health economic perspective for evaluating new disease‐modifying treatments and preventive interventions. This study aims to assess HR‐QoL and the factors associated with longitudinal change in HR‐QoL in the full spectrum of Alzheimer’s disease.MethodLongitudinal data from 918 SCD and 1,346 MCI patients from the MEMENTO cohort were included in the study. MCI was defined as performing one standard deviation (SD) worse than the age, sex and education‐level thresholds in one or more cognitive domains identified within six months of the study enrollment. SCD was defined as having isolated subjective cognitive complaints but without any objective cognitive deficit and aged 60 years or older. HR‐QoL was measured by EuroQol EQ‐5D‐3L patient‐reported version. Linear mixed‐effect models with disease stage as a time‐varying covariate were used to (1) assess the utility changes over time across the dementia spectrum and (2) compare the utility changes between amyloid positive and negative SCD and MCI patients.ResultAt baseline, mean utilities were 0.84 ± 0.16 and 0.81 ± 0.18 in SCD and MCI patients. Linear mixed effect models estimated that the utilities were ‐0.016 (SE 0.003), ‐0.07 (SE 0.009), ‐0.18 (SE 0.014) and ‐0.36 (SE 0.034) lower than SCD in patients with MCI, mild, moderate and severe dementia over the study period. Time is associated with a significant decrease in utility with ‐0.008 (SE 0.001) per year. Baseline age, BMI, female, diabetes, cardiovascular history, neuropsychiatric symptoms and inability to perform Instrumental activity of daily living (IADL) were negatively associated with utilities. Amyloid‐positive SCD patients showed a faster decline in utility than amyloid‐negative SCD patients (p &lt; 0.05). The decrease was not statistically significant in amyloid‐positive MCI patients compared to amyloid‐negative MCI.ConclusionHealth utilities decrease over time in all stages of dementia, and the decline is steeper in moderate and severe stages. Positive amyloid accelerates health utility decline in SCD but not MCI individuals. The ability to perform IADL is the major factor affecting HR‐QoL in all phases of dementia.

Diagnostic performance and clinical applicability of plasma pTau181 to predict dementia risk in a large real‐world cohort of a memory clinic

AbstractBackgroundThe Alzheimer Association has recently remarked the diagnostic and prognostic revolution that plasma biomarkers will mean for AD diagnosis, and highlighting the need of further research to translate them as a realistic option in the clinical practice. However, many studies to date have assessed them in research cohorts. Here we evaluated the diagnostic performance and clinical applicability of plasma pTau181 as a predictive marker of dementia risk in a real‐world memory clinic cohort.MethodThe ACE cohort encompass 1649 patients with subjective cognitive decline (SCD), mild cognitive impairment (MCI), AD dementia and other dementias, with a complete battery of neurological tests, biochemistry data, and paired CSF and plasma samples of the Ace Alzheimer Center Barcelona memory clinic. CSF AB42, AB40, pTau181 and tTau, and plasma pTau181 were measured using the Lumipulse G1200 automatic platform (Fujirebio Inc.). Complete cohort were divided in the Testing cohort (n = 1000) and Validation cohort (n = 649).ResultPlasma pTau181 significantly correlated (P&lt;0.0001) with CSF pTau181 in MCI, AD dementia and other dementias, but not in SCD. Sex appeared not significant in plasma pTau181 levels, whereas age significantly influenced in the biomarker’s performance. ROC curve of A+T+ patients vs rest showed an AUC = 0.8250 and P&lt;0.0001. Specifically for MCI patients, ROC curve of MCI A+T+ vs MCI A‐T‐ showed and AUC = 0.8874 and P&lt;0.0001. A cut off value of 1.300 pg/ml of plasma pTau181 exhibited a 93.6% sensibility, 72.4% specificity, 77.8% precision and only 8.3% of false negatives in MCI patients. Hazard ratio of survival of conversion analysis showed that MCI patients above this cut off value exhibited a 4.570 higher risk to convert to dementia than MCI under the cut off value. Validation cohort confirmed these findings.ConclusionPlasma pTau181 could be useful as a pre‐screening biomarker of MCI patients with high risk to progress to dementia. However, positive patients will also require a CSF/PET confirmatory test. Plasma pTau181 by itself does not present a suitable performance in SCD patients. Another biomarker or a combination of biomarkers would be needed to this population. Further studies are need to obtain more clinical data and improve the biomarker performance to finally substitute CSF/PET biomarkers.

Disruptions of Gut Microbiota are Associated with Cognitive Deficit of Preclinical Alzheimer's Disease: A Cross-Sectional Study.

Alzheimer's Disease (AD) is the most prevalent type of dementia. The early change of gut microbiota is a potential biomarker for preclinical AD patients. The study aimed to explore changes in gut microbiota characteristics in preclinical AD patients, including those with Subjective Cognitive Decline (SCD) and Mild Cognitive Impairment (MCI), and detect the correlation between gut microbiota characteristics and cognitive performances. This study included 117 participants [33 MCI, 54 SCD, and 30 Healthy Controls (HC)]. We collected fresh fecal samples and blood samples from all participants and evaluated their cognitive performance. We analyzed the diversity and structure of gut microbiota in all participants through qPCR, screened characteristic microbial species through machine learning models, and explored the correlations between these species and cognitive performances and serum indicators. Compared to the healthy controls, the structure of gut microbiota in MCI and SCD patients was significantly different. The three characteristic microorganisms, including Bacteroides ovatus, Bifidobacterium adolescentis, and Roseburia inulinivorans, were screened based on the best classification model (HC and MCI) having intergroup differences. Bifidobacterium adolescentis is associated with better performance in multiple cognitive scores and several serum indicators. Roseburia inulinivorans showed negative correlations with the scores of the Functional Activities Questionnaire (FAQ). The gut microbiota in patients with preclinical AD has significantly changed in terms of composition and richness. Correlations have been discovered between changes in characteristic species and cognitive performances. Gut microbiota alterations have shown promise in affecting AD pathology and cognitive deficit.

Altered cortical and subcortical morphometric features and asymmetries in the subjective cognitive decline and mild cognitive impairment.

This study aimed to evaluate morphological changes in cortical and subcortical regions and their asymmetrical differences in individuals with subjective cognitive decline (SCD) and mild cognitive impairment (MCI). These morphological changes may provide valuable insights into the early diagnosis and treatment of Alzheimer's disease (AD). We conducted structural MRI scans on a cohort comprising 62 SCD patients, 97 MCI patients, and 70 age-, sex-, and years of education-matched healthy controls (HC). Using Freesurfer, we quantified surface area, thickness, the local gyrification index (LGI) of cortical regions, and the volume of subcortical nuclei. Asymmetry measures were also calculated. Additionally, we explored the correlation between morphological changes and clinical variables related to cognitive decline. Compared to HC, patients with MCI exhibited predominantly left-sided surface morphological changes in various brain regions, including the transverse temporal gyrus, superior temporal gyrus, insula, and pars opercularis. SCD patients showed relatively minor surface morphological changes, primarily in the insula and pars triangularis. Furthermore, MCI patients demonstrated reduced volumes in the anterior-superior region of the right hypothalamus, the fimbria of the bilateral hippocampus, and the anterior region of the left thalamus. These observed morphological changes were significantly associated with clinical ratings of cognitive decline. The findings of this study suggest that cortical and subcortical morphometric changes may contribute to cognitive impairment in MCI, while compensatory mechanisms may be at play in SCD to preserve cognitive function. These insights have the potential to aid in the early diagnosis and treatment of AD.

Changes in Multiparametric Magnetic Resonance Imaging and Plasma Amyloid-Beta Protein in Subjective Cognitive Decline.

The association between plasma amyloid-beta protein (Aβ) and subjective cognitive decline (SCD) remains controversial. We aimed to explore the correlation between neuroimaging findings, plasma Aβ, and neuropsychological scales using data from 53 SCD patients and 46 age- and sex-matched healthy controls (HCs). Magnetic resonance imaging (MRI) was used to obtain neuroimaging data for a whole-brain voxel-based morphometry analysis and cortical functional network topological features. The SCD group had slightly lower Montreal Cognitive Assessment (MoCA) scores than the HC group. The Aβ42 levels were significantly higher in the SCD group than in the HC group (p < 0.05). The SCD patients demonstrated reduced volumes in the left hippocampus, right rectal gyrus (REC.R), and right precentral gyrus (PreCG.R); an increased percentage fluctuation in the left thalamus (PerAF); and lower average small-world coefficient (aSigma) and average global efficiency (aEg) values. Correlation analyses with Aβ and neuropsychological scales revealed significant positive correlations between the volumes of the HIP.L, REC.R, PreCG.R, and MoCA scores. The HIP.L volume and Aβ42 were negatively correlated, as were the REC.R volume and Aβ42/40. PerAF and aSigma were negatively and positively correlated with the MoCA scores, respectively. The aEg was positively correlated with Aβ42/40. SCD patients may exhibit alterations in plasma biomarkers and multi-parameter MRI that resemble those observed in Alzheimer's disease, offering a theoretical foundation for early clinical intervention in SCD.

Plasma neurofilament light chain predicts Alzheimer's disease in patients with subjective cognitive decline and mild cognitive impairment: A cross-sectional and longitudinal study.

We aimed to evaluate the accuracy of plasma neurofilament light chain (NfL) in predicting Alzheimer's disease (AD) and the progression of cognitive decline in patients with subjective cognitive decline (SCD) and mild cognitive impairment (MCI). This longitudinal cohort study involved 140 patients (45 with SCD, 73 with MCI, and 22 with AD dementia [AD-D]) who underwent plasma NfL and AD biomarker assessments (cerebrospinal fluid, amyloid positron emission tomography [PET], and 18 F-fluorodeoxyglucose-PET) at baseline. The patients were rated according to the amyloid/tau/neurodegeneration (A/T/N) system and followed up for a mean time of 2.72 ± 0.95 years to detect progression from SCD to MCI and from MCI to AD. Forty-eight patients (19 SCD, 29 MCI) also underwent plasma NfL measurements 2 years after baseline. At baseline, plasma NfL detected patients with biomarker profiles consistent with AD (A+/T+/N+ or A+/T+/N-) with high accuracy (area under the curve [AUC] 0.82). We identified cut-off values of 19.45 pg/mL for SCD and 20.45 pg/mL for MCI. During follow-up, nine SCD patients progressed to MCI (progressive SCD [p-SCD]), and 14 MCI patients developed AD dementia (progressive MCI [p-MCI]). The previously identified cut-off values provided good accuracy in identifying p-SCD (80% [95% confidence interval 65.69: 94.31]). The rate of NfL change was higher in p-MCI (3.52 ± 4.06 pg/mL) compared to non-progressive SCD (0.81 ± 1.25 pg/mL) and non-progressive MCI (-0.13 ± 3.24 pg/mL) patients. A rate of change lower than 1.64 pg/mL per year accurately excluded progression from MCI to AD (AUC 0.954). Plasma NfL concentration and change over time may be a reliable, non-invasive tool to detect AD and the progression of cognitive decline at the earliest stages of the disease.

PRedicting the EVolution of SubjectIvE Cognitive Decline to Alzheimer’s Disease With machine learning: the PREVIEW study protocol

BackgroundAs 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.MethodsWe 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 Aβ42, t-tau, and p-tau concentration and Aβ42/Aβ40 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.DiscussionThis 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.

Topological properties of individual gray matter morphological networks in identifying the preclinical stages of Alzheimer's disease: a preliminary study.

Subjective cognitive decline (SCD) and mild cognitive impairment (MCI) are preclinical stages of Alzheimer's disease (AD). Individual biomarkers are essential for evaluating altered neurological outcomes at both SCD and MCI stages for early diagnosis and intervention of AD. In this study, we aimed to investigate the relationships between topological properties of the individual brain morphological network and clinical cognitive performances among healthy controls (HCs) and patients with SCD or MCI. The topological measurements of individual morphological networks were analyzed using graph theory, and inter-group differences of standard graph topology were correlated and regressed to scores of clinical cognitive functions. Compared with HCs, the topology of the individual morphological networks in SCD and MCI patients was significantly altered. At the global level, altered topology was characterized by lower global efficiency, shorter characteristics path length, and normalized characteristics path length [all P<0.05, false discovery rate (FDR) corrected]. In addition, at the regional level, SCD and MCI patients exhibited abnormal degree centrality in the caudate nucleus and nodal efficiency in the caudate nucleus, right insula, lenticular nucleus, and putamen (all P<0.05, FDR corrected). The topological features of individual gray matter morphological networks may serve as biomarkers to improve disease prognosis and intervention in the early stages of AD, namely SCD and MCI. Moreover, these findings may further elucidate the relationships between brain morphological alterations and cognitive dysfunctions in SCD and MCI.

Homocysteine altered gyrification related to self‐awareness of everyday cognition

AbstractBackgroundPrevious studies on homocysteine have verified higher homocysteine may adversely influence cognition performance in individuals with subjective cognitive decline (SCD), whereas the potential mechanisms for this relationship are still unclear.MethodWe analyzed data from 23 individuals with SCD enrolled in the 500 nm light therapy cohort who filled the 12 items Everyday Cognition scale (ECog‐12) and had plasma homocysteine levels assessed at the time of their baseline 3T MRI scans. Gyrification and cortical thickness were obtained by using whole brain region of interest (ROI)‐based approach with the CAT12 toolbox (in SPM12). Statistical analyses used one‐sample t‐test including age and sex as covariates. Regions of significant group effects were used as regions of interest for cognitive performance analyses.ResultAt corrected thresholds (p &lt; 0.05, Holm‐Bonferroni‐corrected), there were significant negative correlations between homocysteine and gyrification in the left temporal pole (p = 0.012000) and right inferior temporal sulcus (p = 0.012268). For cortical thickness, we found a significant positive correlation of homocysteine and cortical thickness in the right inferior temporal sulcus (p = 0.032744). In addition, a significant positive correlation was found between the self‐report ECog‐12 scores and gyrification of the left temporal pole.ConclusionHomocysteine alters local surface shape in temporal lobule in SCD patients and localized low gyrification may imply impaired self‐awareness of everyday cognition.

Predicting Conversion from Subjective Cognitive Decline to Mild Cognitive Impairment and Alzheimer's Disease Dementia Using Ensemble Machine Learning.

Subjective cognitive decline (SCD) may represent a preclinical stage of Alzheimer's disease (AD). Predicting progression of SCD patients is of great importance in AD-related research but remains a challenge. To develop and implement an ensemble machine learning (ML) algorithm to identify SCD subjects at risk of conversion to mild cognitive impairment (MCI) or AD. Ninety-nine SCD patients were included. Thirty-two progressed to MCI/AD, while 67 remained stable. To minimize the effect of class imbalance, both classes were balanced, and sensitivity was taken as evaluation metric. Bagging and boosting ML models were developed by using socio-demographic and clinical information, Mini-Mental State Examination and Geriatric Depression Scale (GDS) scores (feature-set 1a); socio-demographic characteristics and neuropsychological tests scores (feature-set 1b) and regional magnetic resonance imaging grey matter volumes (feature-set 2). The most relevant variables were combined to find the best model. Good prediction performances were obtained with feature-sets 1a and 2. The most relevant variables (variable importance exceeding 20%) were: Age, GDS, and grey matter volumes measured in four cortical regions of interests. Their combination provided the optimal classification performance (highest sensitivity and specificity) ensemble ML model, Extreme Gradient Boosting with over-sampling of the minority class, with performance metrics: sensitivity = 1.00, specificity = 0.92 and area-under-the-curve = 0.96. The median values based on fifty random train/test splits were sensitivity = 0.83 (interquartile range (IQR) = 0.17), specificity = 0.77 (IQR = 0.23) and area-under-the-curve = 0.75 (IQR = 0.11). A high-performance algorithm that could be translatable into practice was able to predict SCD conversion to MCI/AD by using only six predictive variables.

Plasma p-tau181 as a promising non-invasive biomarker of Alzheimer’s Disease pathology in Subjective Cognitive Decline. (P7-6.001)

<h3>Objective:</h3> To explore plasma phosphorylated tau (p-tau) 181 levels in Subjective Cognitive Decline and to investigate its role as a potential biomarker for Alzheimer’s Disease (AD) in Subjective Cognitive Decline (SCD). <h3>Background:</h3> One of the greatest challenges in AD is the discovery of new non-invasive, sensitive and specific biomarkers, which might be useful in the early stages such as SCD. Plasma p-tau181 is becoming increasingly notable in prodromal stages of AD, as a valuable marker for tauopathy, one of the core features of AD. <h3>Design/Methods:</h3> We included 27 SCD patients, who underwent clinical evaluation, neuropsychological assessment, Apolipoprotein E (APOE) genotyping, plasma p-tau181 analysis with SiMoA assay and cerebrospinal fluid (CSF) biomarkers analysis (Aβ1–42, Aβ1–42/1–40, p-tau, t-tau).Patients were rated according to A/T(N) system and classified as carrier of AD pathology (AP+) when A+ was associated with either T+ or N+, or non-carriers (AP−) when they were classified as A− (regardless of T and N classification), or A+/T−/N−. <h3>Results:</h3> Plasma p-tau181 levels were correlated with CSF p-tau (ϱ=0.819, <i>p</i>&lt;0.001) and also with age (ϱ=0.605, <i>p</i>=0.001), CSF Aβ1–42 (ϱ= −0.461, <i>p</i>=0.012), CSF Aβ1–42/1–40 (ϱ= −0.770, <i>p</i>&lt;0.001) and CSF t-tau (ϱ=0.535, <i>p</i>=0.009). A multivariate linear regression analysis showed that plasma p-tau181 were significantly associated only with CSF p-tau levels (<i>p</i>=0.027). When comparing AP+ and AP− subgroups, we found that plasma p-tau181 levels were significantly higher in the former group (2.85±0.53 vs 1.75±.66, <i>p</i>&lt;0.001). A ROC curve analysis demonstrated that plasma p-tau181 was highly accurate for discriminating between AP+ and AP− SCD patients (AUC = 0.908). <h3>Conclusions:</h3> Plasma p-tau181 levels strongly correlated with CSF p-tau. Plasma p-tau showed a high accuracy in differentiating SCD patients who were carriers from SCD patients who were non-carriers of AD pathology. Our preliminary results suggest that plasma p-tau181 might be a promising noninvasive biomarker of AD pathology which might be useful in preclinical stage of AD. <b>Disclosure:</b> Ms. Giacomucci has nothing to disclose. Mr. Mazzeo has nothing to disclose. Dr. Bagnoli has nothing to disclose. Dr. Ingannato has nothing to disclose. Sonia Padiglioni has nothing to disclose. Sandro Sorbi has nothing to disclose. Valentina Bessi has nothing to disclose. Benedetta Nacmias has received personal compensation in the range of $500-$4,999 for serving as an Editor, Associate Editor, or Editorial Advisory Board Member for Elsevier.

Subjective cognitive decline: 15 years of follow-up experience from a memory clinic (P7-6.005)

<h3>Objective:</h3> To propose a protocol for management of subjective cognitive decline (SCD) based on our experience and on literature data. <h3>Background:</h3> Clinical research is focusing on SCD to identify patients at higher risk of dementia. Nevertheless, a consensus is lacking about how to manage patients with SCD. <h3>Design/Methods:</h3> From a sample of 445 SCD patients self-referred to our center, we considered: 119 patients followed-up for at least 10 years; 78 patients who underwent Alzheimer’s disease biomarker assessment (CSF or amyloid-PET), rated according to the AT(N) system. <h3>Results:</h3> During the follow-up, 55 patients (47.0%[38.0–56.1]) were diagnosed with MCI and 22 (18.8%[11.7–25.9]) with dementia. A patient developed Parkinson’s disease and a patient had a brain tumor. Mean progression time was 7.9(5.2) years to MCI and 10.39(4.7) to dementia. Thirty-eight (32.5%[24.0–41.0]) patients still were SCD at the end of the follow-up (15.2[5.4] years). In this group nine patients were diagnosed with depression, six had vascular leukoencephalopathy and two were diagnosed with obstructive sleep apnea syndrome (OSAS). Among 78 patients who underwent AD biomarker assessment 10 (12.8%[5.4–20.2]) were A+ (seven A+/T+/N− and five A+/T+/N+). <h3>Conclusions:</h3> SCD should not be confused with mood disorder nor with normal cognition. We elaborated a management protocol to be applied both in clinical and in research settings: at the baseline evaluation, all the conditions possibly associated with SCD should be considered. If none of the baseline exams shows a possible explanation for the SCD, we classify patients as “SCD of unknown cause”. We suggest stratifying patients according to age at onset and APOE genotype. Regarding patients who undergo AD biomarker analysis, we suggest applying the ATN classification to classify patients as affected by “SCD not due to AD”, “Alzheimer’s pathologic changes with SCD” or “AD with SCD”. Patients in the two last groups should be followed-up yearly and receive psychological support. <b>Disclosure:</b> Mr. Mazzeo has nothing to disclose. Sonia Padiglioni has nothing to disclose. Dr. Bagnoli has nothing to disclose. Ms. Giacomucci has nothing to disclose. Dr. Balestrini has nothing to disclose. Dr. Ingannato has nothing to disclose. Miss Moschini has nothing to disclose. Miss Morinelli has nothing to disclose. Dr. Emiliani has nothing to disclose. Dr. Galdo has nothing to disclose. Benedetta Nacmias has received personal compensation in the range of $500-$4,999 for serving as an Editor, Associate Editor, or Editorial Advisory Board Member for Elsevier. Sandro Sorbi has nothing to disclose. Valentina Bessi has nothing to disclose.

Associations Between Levels of Peripheral NCAPH2 Promoter Methylation and Different Stages of Alzheimer's Disease: A Cross-Sectional Study.

Several studies have examined NCAPH2 methylation in amnestic mild cognitive impairment (aMCI) and Alzheimer's disease (AD), but little is known of NCAPH2 methylation in subjective cognitive decline (SCD). To examine whether methylation of peripheral NCAPH2 are differentially changed at various phases of AD, and whether it could serve as a diagnostic biomarker for SCD. A total of 40 AD patients, 52 aMCI patients, 148 SCD patients, and 193 cognitively normal controls (NCs) were recruited in the current case-control study. Besides, 54 cognitively normal individuals have received amyloid positron emission tomography (amyloid PET) scans. Using bisulfite pyrosequencing method, we measured blood DNA methylation in the NCAPH2 gene promoter. The main outcomes were: 1) For SCD, there was no significant difference between SCD and NC regarding NCAPH2 methylation; 2) For aMCI, NCAPH2 methylation at CpG2 were significantly lower in aMCI compared with NC and SCD in the entire population and male subgroup; 3) For AD, NCAPH2 methylation at CpG1 were significantly lower in AD compared with NC among females; 4) A relationship with apolipoprotein E (APOE) ɛ4 status was shown. Receiver operating characteristic (ROC) analysis by combining NCAPH2 methylation, age, education, and APOEɛ4 status could distinguish between patients with aMCI (area under the curve (AUC): 0.742) and AD (AUC: 0.873) from NCs. NCAPH2 methylation levels were altered at the aMCI and AD stage and may be convenient and cost-effective biomarkers of AD and aMCI.