Subjective memory complaint as a useful tool for the early detection of Alzheimer&amp;rsquo;s disease

Objectives Depressive symptoms and cognitive impairment often concur in older persons. Differentiating the cause of cognitive impairment in older persons with Depressive Disorder (DD) from other diseases such as Alzheimer's Disease (AD) is challenging. The goal of this study was to characterize cognitive impairment in older persons with DD. Design Cross-sectional retrospective observational clinical cohort study using patient records from 2014 to 2018. Setting Gerontopsychiatric services of Ulm University at Bezirkskrankenhaus Gü nzburg serving as primary psychiatric care institution and tertiary referral center for psychiatric care for older persons. Partcipants DD was diagnosed according to ICD-10 criteria. When indicated by the medical history or neuropsychological assessment further diagnostic procedures were initiated. Cerebrospinal fluid (CSF) tap was routinely the first additional procedure. If patients did not consent to CSF tap or contraindications were present, 18 F-fluordesoxyglucose-PET (FDG-PET) or Amyloid-PET (Am-PET) were performed. Materials and methods Extensive neuropsychological test battery to assess cognitive profile. Results 457 subjects were diagnosed with DD (DD-all; age 50-94; 159 males, 298 females). Biomarkers were assessed in 176 persons; in 90 of these subjects AD-biomarkers were negative (DD-BM-; age 54-89; 40 males, 50 females), and in 86 subjects at least one biomarker

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“…Subjective memory complaints have been found useful to detect early AD [33]. Likewise objective screening tests have been found useful [34].…”

Section: Discussionmentioning

confidence: 99%

On the conundrum of cognitive impairment due to depressive disorder in older patients

et al. 2020

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“…The lack of a uniform set of cognitive measures across studies makes it challenging to compare findings. In addition to the objective measures of cognition, subjective memory complaints, which can occur in the absence of objective cognitive decline, may predict future cognitive impairment and dementia (Choe et al, 2018;Jessen, 2010). Only a few studies, however, have examined the link between sleep quality and subjective memory complaints (Gamaldo et al, 2019;Kang et al, 2017), and these have indicated a possible connection between poor sleep and memory dysfunction.…”

Section: Introductionmentioning

confidence: 99%

Sleep quality, neurocognitive performance, and memory self-appraisal in middle-aged and older adults with memory complaints

Siddarth

Thana-udom

Ojha

et al. 2020

Int. Psychogeriatr.

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Objective: Because of inconsistent findings regarding the relationship between sleep quality and cognitive function in people with age-related memory complaints, we examined how self-reports of sleep quality were related to multiple domains of both objective and subjective cognitive function in middle-aged and older adults. Design: A cross-sectional study involving analysis of baseline data, collected as part of a clinical trial. Measurements: Two hundred and three participants (mean age = 60.4 [6.5] years, 69.0% female) with mild memory complaints were asked to rate their sleep quality using the Pittsburgh Sleep Quality Index (PSQI) and their memory performance using the Memory Functioning Questionnaire (MFQ), which measures self-awareness of memory ability. Neurocognitive performance was evaluated using the Continuous Performance Test (CPT), Trail Making Test, Buschke Selective Reminding Test, and the Brief Visuospatial Test – Revised (BVMT-R). Results: Total PSQI scores were significantly associated with objective measures of sustained attention (CPT hit reaction time by block and standard error by block) and subjective memory loss (MFQ frequency and seriousness of forgetting). The PSQI components of (poorer) sleep quality and (greater) sleep disturbance were related to (worse) sustained attention scores while increased sleep latency and daytime sleepiness were associated with greater frequency and seriousness of forgetting. Conclusions: Sleep quality is related to both objective measures of sustained attention and self-awareness of memory decline. These findings suggest that interventions for improving sleep quality may contribute not only to improving the ability to focus on a particular task but also in reducing memory complaints in middle-aged and older adults.

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“…Alternatively, while subjective memory complaint can be predictive (in early stages) for future onset and development of dementia, those individuals suffering from or exhibiting cognitive dementia who are diagnosed with AD (beyond mild cognitive impairment) usually deny or are unaware of their memory problems. And, complicating the specificity further, most people recognize and readily admit that their memories are not perfect [17][18][19].…”

Section: Discussionmentioning

confidence: 99%

Episodic-Memory Performance in Machine Learning Modeling for Predicting Cognitive Health Status Classification

Bergeron

Landset

Tarpin-Bernard³

et al. 2019

JAD

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Background: Memory dysfunction is characteristic of aging and often attributed to Alzheimer's disease (AD). An easily administered tool for preliminary assessment of memory function and early AD detection would be integral in improving patient management. Objective: Our primary aim was to utilize machine learning in determining initial viable models to serve as complementary instruments in demonstrating efficacy of the MemTrax online Continuous Recognition Tasks (M-CRT) test for episodicmemory screening and assessing cognitive impairment. Methods: We used an existing dataset subset (n = 18,395) of demographic information, general health screening questions (addressing memory, sleep quality, medications, and medical conditions affecting thinking), and test results from a convenience sample of adults who took the M-CRT test. M-CRT performance and participant features were used as independent attributes: true positive/negative, percent responses/correct, response time, age, sex, and recent alcohol consumption. For predictive modeling, we used demographic information and test scores to predict binary classification of the health-related questions (yes/no) and general health status (healthy/unhealthy), based on the screening questions. Results: ANOVA revealed significant differences among HealthQScore groups for response time true positive (p = 0.000) and true positive (p = 0.020), but none for true negative (p = 0.0551). Both %responses and %correct had significant differences (p = 0.026 and p = 0.037, respectively). Logistic regression was generally the top-performing learner with moderately robust prediction performance (AUC) for HealthQScore (0.648-0.680) and selected general health questions (0.713-0.769). Conclusion: Our novel application of supervised machine learning and predictive modeling helps to demonstrate and validate cross-sectional utility of MemTrax in assessing early-stage cognitive impairment and general screening for AD.

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Subjective memory complaint as a useful tool for the early detection of Alzheimer’s disease

Cited by 37 publications

References 52 publications

On the conundrum of cognitive impairment due to depressive disorder in older patients

On the conundrum of cognitive impairment due to depressive disorder in older patients

Sleep quality, neurocognitive performance, and memory self-appraisal in middle-aged and older adults with memory complaints

Episodic-Memory Performance in Machine Learning Modeling for Predicting Cognitive Health Status Classification

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