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Brain and cognitive correlates of subjective cognitive decline-plus features in a population-based cohort.

ABSTRACT: BACKGROUND:Subjective cognitive decline (SCD) consists of self-perceived decline in cognition over time. The occurrence of specific additional features in SCD (so-called SCDplus) confers a higher risk of future cognitive decline. However, it is not known whether SCDplus patients have a distinct cognitive and neuroimaging profile. Therefore, we aimed to study the associations between SCDplus features and cognitive and neuroimaging profiles in a population-based cohort. METHODS:A total of 2670 individuals from the ALFA cohort underwent clinical, cognitive, and MRI (n = 532) explorations. Subjects were classified as self-reporting cognitive decline (SCD) or not self-reporting cognitive decline (non-SCD). Within the SCD group, participants were also classified according to the number of SCDplus features they met (SCD+, > 3; SCD-, ≤ 3). RESULTS:The prevalence of SCD in the cohort was 21.4% (55.8% SCD-, 44.2% SCD+). SCD+ subjects performed worse than non-SCD and SCD- subjects in memory and executive function. Among the SCDplus features, confirmation of decline by an informant was the best predictor of worse cognitive performance and lower gray matter volumes. CONCLUSIONS:Our findings show that individuals with SCDplus features have a distinct cognitive and brain volumetric profile similar to that found in Alzheimer's disease and therefore support the use of the SCDplus concept as an enrichment criterion in population-based cohorts.

SUBMITTER: Sanchez-Benavides G

PROVIDER: S-EPMC6302483 | biostudies-other | 2018 Dec

REPOSITORIES: biostudies-other

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Brain and cognitive correlates of subjective cognitive decline-plus features in a population-based cohort.

Sánchez-Benavides Gonzalo G Grau-Rivera Oriol O Suárez-Calvet Marc M Minguillon Carolina C Cacciaglia Raffaele R Gramunt Nina N Falcon Carles C Gispert Juan Domingo JD Molinuevo José Luis JL

Alzheimer's research & therapy 20181220 1

<h4>Background</h4>Subjective cognitive decline (SCD) consists of self-perceived decline in cognition over time. The occurrence of specific additional features in SCD (so-called SCDplus) confers a higher risk of future cognitive decline. However, it is not known whether SCDplus patients have a distinct cognitive and neuroimaging profile. Therefore, we aimed to study the associations between SCDplus features and cognitive and neuroimaging profiles in a population-based cohort.<h4>Methods</h4>A to ...[more]

PMID: 30572953

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Project description:BackgroundSince SCD (plus) was standardized, little is known about its demographic characteristics and its outcomes of neuropsychological assessments, including the SCD questionnaire 9 (SCD-Q9).ObjectiveTo characterize SCD (plus) by comparing the neuropsychological features among its subgroups and with normal controls (NC). Also, to explore its demographics and to understand the relation of the chief complaints and the scores of SCD-Q9.MethodsMultistage stratified cluster random sampling was conducted to select participants. As a result, 84 NC and 517 SCD (plus) were included. SCD (plus) was further classified into several subgroups (SCD-C: concerned cognitive decline; SCD-F: complaints about SCD within the past five years; SCD-P: feeling performance being not as good as their peers; SCD+: presented> 3 of SCD (plus) features; SCD-: presented ? 3 of SCD (plus) features (see the diagnostic criteria for the details)) and between-group comparisons of neuropsychological scores were conducted. Point-biserial correlation and binary logistic regression analyses were performed to investigate the demographic characteristics of its subgroups. Finally, Spearman correlation was used to better understand the relation of SCD (plus) to SCD-Q9.Results(1) Scores of AVLT-LR (AVLT-LR: Auditory Verbal Learning Test-Long Delayed Recall) and MoCA-B (MoCA: Montreal Cognitive Assessment-Basic) were lower in the SCD-P group than those in the NC group, and the SCD+ group scored lower in the MoCA-B and CDT(CDT: Clock Drawing Test) than the SCD- group. (2) Females were more concerned than male participants. Individuals with lower education level felt that their cognitive performance were worse than their peers. Also, younger people might express concerns more than the more elderly. People who had complaints of SCD-P might be more likely to report SCD-C, but less likely to report SCD-F. (3) Positive correlations were found between the chief complaints of SCD (plus) and some items of SCD-Q9.ConclusionsSCD (plus) may be related to demographic factors. Individuals with SCD (plus) already exhibited cognitive impairment, which can be detected by SCD-Q9.

Project description:BackgroundTraumatic brain injury (TBI) is extremely common across the lifespan and is an established risk factor for dementia. The cognitive profile of the large and growing population of older adults with prior TBI who do not have a diagnosis of dementia, however, has not been well described. Our aim was to describe the cognitive profile associated with prior TBI exposure among community-dwelling older adults without dementia-an understudied but potentially vulnerable population.Methods and findingsIn this population-based cohort study, we studied 984 community-dwelling older adults (age 51 y and older and their spouses) without dementia who had been randomly selected from respondents to the 2014 wave of the Health and Retirement Study to participate in a comprehensive TBI survey and who either reported no prior TBI (n = 737) or prior symptomatic TBI resulting in treatment in a hospital (n = 247). Mean time since first TBI was 38 ± 19 y. Outcomes assessed included measures of global cognitive function, verbal episodic memory, semantic fluency, and calculation as well as a measure of subjective memory ("How would you rate your memory at the present time?"). We compared outcomes between the two TBI groups using regression models adjusting for demographics, medical comorbidities, and depression. Sensitivity analyses were performed stratified by TBI severity (no TBI, TBI without loss of consciousness [LOC], and TBI with LOC). Respondents with TBI were younger (mean age 64 ± 10 y versus 68 ± 11 y), were less likely to be female, and had higher prevalence of medical comorbidities and depression than respondents without TBI. Respondents with TBI did not perform significantly differently from respondents without TBI on any measure of objective cognitive function in either raw or adjusted models (fully adjusted: global cognitive function score 15.4 versus 15.2, p = 0.68; verbal episodic memory score 4.4 versus 4.3, p = 0.79; semantic fluency score 15.7 versus 14.0, p = 0.21; calculation impairment 22% versus 26%, risk ratio [RR] [95% CI] = 0.86 [0.67-1.11], p = 0.24). Sensitivity analyses stratified by TBI severity produced similar results. TBI was associated with significantly increased risk for subjective memory impairment in models adjusted for demographics and medical comorbidities (29% versus 24%; RR [95% CI]: 1.26 [1.02-1.57], p = 0.036). After further adjustment for active depression, however, risk for subjective memory impairment was no longer significant (RR [95% CI]: 1.18 [0.95-1.47], p = 0.13). Sensitivity analyses revealed that risk of subjective memory impairment was increased only among respondents with TBI with LOC and not among those with TBI without LOC. Furthermore, the risk of subjective memory impairment was significantly greater among those with TBI with LOC versus those without TBI even after adjustment for depression (RR [95% CI]: partially adjusted, 1.38 [1.09-1.74], p = 0.008; fully adjusted, 1.28 [1.01-1.61], p = 0.039).ConclusionsIn this population-based study of community-dwelling older adults without dementia, those with prior TBI with LOC were more likely to report subjective memory impairment compared to those without TBI even after adjustment for demographics, medical comorbidities, and active depression. Lack of greater objective cognitive impairment among those with versus without TBI may be due to poor sensitivity of the cognitive battery or survival bias, or may suggest that post-TBI cognitive impairment primarily affects executive function and processing speed, which were not rigorously assessed in this study. Our findings show that among community-dwelling non-demented older adults, history of TBI is common but may not preferentially impact cognitive domains of episodic memory, attention, working memory, verbal semantic fluency, or calculation.

Project description:ObjectiveIndividuals with subjective cognitive decline (SCD) or amnestic mild cognitive impairment (aMCI) represent important targets for the early detection and intervention of Alzheimer's disease (AD). In this study, we employed a multi-kernel support vector machine (SVM) to examine whether white matter (WM) structural networks can be used for screening SCD and aMCI.MethodsA total of 138 right-handed participants [51 normal controls (NC), 36 SCD, 51 aMCI] underwent MRI brain scans. For each participant, three types of WM networks with different edge weights were constructed with diffusion MRI data: fiber number-weighted networks, mean fractional anisotropy-weighted networks, and mean diffusivity (MD)-weighted networks. By employing a multiple-kernel SVM, we seek to integrate information from three weighted networks to improve classification performance. The accuracy of classification between each pair of groups was evaluated via leave-one-out cross-validation.ResultsFor the discrimination between SCD and NC, an area under the curve (AUC) value of 0.89 was obtained, with an accuracy of 83.9%. Further analysis revealed that the methods using three types of WM networks outperformed other methods using single WM network. Moreover, we found that most of discriminative features were from MD-weighted networks, which distributed among frontal lobes. Similar classification performance was also reported in the differentiation between subjects with aMCI and NCs (accuracy = 83.3%). Between SCD and aMCI, an AUC value of 0.72 was obtained, with an accuracy of 72.4%, sensitivity of 74.5% and specificity of 69.4%. The highest accuracy was achieved with features only selected from MD-weighted networks.ConclusionWhite matter structural network features help machine learning algorithms accurately identify individuals with SCD and aMCI from NCs. Our findings have significant implications for the development of potential brain imaging markers for the early detection of AD.

Project description:BackgroundSubjective cognitive decline (SCD) has been proposed as a pre-MCI at-risk condition of Alzheimer's disease (AD). Current research is focusing on a refined assessment of specific SCD features associated with increased risk for AD, as proposed in the SCD-plus criteria. We developed a structured interview (SCD-I) for the assessment of these features and tested their relationship with AD biomarkers.MethodsWe analyzed data of 205 cognitively normal participants of the DELCODE study (mean age = 68.9 years; 52% female) with available CSF AD biomarkers (Aß-42, p-Tau181, Aß-42/Tau ratio, total Tau). For each of five cognitive domains (including memory, language, attention, planning, others), a study physician asked participants about the following SCD-plus features: the presence of subjective decline, associated worries, onset of SCD, feeling of worse performance than others of the same age group, and informant confirmation. We compared AD biomarkers of subjects endorsing each of these questions with those who did not, controlling for age. SCD was also quantified by two summary scores: the number of fulfilled SCD-plus features, and the number of domains with experienced decline. Covariate-adjusted linear regression analyses were used to test whether these SCD scores predicted abnormality in AD biomarkers.ResultsLower Aß-42 levels were associated with a reported decline in memory and language abilities, and with the following SCD-plus features: onset of subjective decline within 5 years, confirmation of cognitive decline by an informant, and decline-related worries. Furthermore, both quantitative SCD scores were associated with lower Aß42 and lower Aß42/Tau ratio, but not with total Tau or p-Tau181.ConclusionsFindings support the usefulness of a criterion-based interview approach to assess and quantify SCD in the context of AD and validate the current SCD-plus features as predictors of AD pathology. While some features seem to be more closely associated with AD biomarkers than others, aggregated scores over several SCD-plus features or SCD domains may be the best predictors of AD pathology.

Dataset Information

Brain and cognitive correlates of subjective cognitive decline-plus features in a population-based cohort.

Publications

Brain and cognitive correlates of subjective cognitive decline-plus features in a population-based cohort.

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