Changes in self- and study partner?perceived cognitive functioning in relation to amyloid status and future clinical progression: Findings from the SCIENCe project

Introduction: We investigated changes in self- and study partner?reported self-perceived cognitive decline in relation to amyloid pathology and clinical progression, in a sample of cognitively normal individuals. Methods: A total of 404 participants (63???9 years, 44% female) and their study partners completed the Cognitive Change Index (CCI) yearly (0.7?6.8 follow-up years; n visits?=?1436). Baseline and longitudinal associations between (change in) CCI scores, amyloid, and clinical progression were modeled in linear mixed models and Cox regressions. Results: CCI?study partner scores of amyloid-positive individuals increased over time (B?=?1.79, 95% confidence interval [CI]?=?[0.51, 3.06]), while CCI?self scores remained stable (B?=??0.45, 95% CI?=?[?1.77, 0.87]). Ten-point higher baseline CCI?study partner (hazard ratio [HR]?=?1.75, 95% CI?=?[1.30, 2.36]) and CCI?self scores (HR?=?1.90, 95% CI?=?[1.40, 2.58]) were associated with an approximately 2-fold increased risk of progression to mild cognitive impairment or dementia. Discussion: Study partner?reported but not self-perceived complaints increase over time in amyloid-positive individuals, supporting the value of longitudinal study partner report, even in initially cognitively normal individuals

Longitudinal change in ATN biomarkers in cognitively normal individuals

BACKGROUND: Biomarkers for amyloid, tau, and neurodegeneration (ATN) have predictive value for clinical progression, but it is not clear how individuals move through these stages. We examined changes in ATN profiles over time, and investigated determinants of change in A status, in a sample of cognitively normal individuals presenting with subjective cognitive decline (SCD). METHODS: We included 92 individuals with SCD from the SCIENCe project with [18F]florbetapir PET (A) available at two time points (65 ± 8y, 42% female, MMSE 29 ± 1, follow-up 2.5 ± 0.7y). We additionally used [18F]flortaucipir PET for T and medial temporal atrophy score on MRI for N. Thirty-nine individuals had complete biomarker data at baseline and follow-up, enabling the construction of ATN profiles at two time points. All underwent extensive neuropsychological assessments (follow-up time 4.9 ± 2.8y, median number of visits n = 4). We investigated changes in biomarker status and ATN profiles over time. We assessed which factors predisposed for a change from A- to A+ using logistic regression. We additionally used linear mixed models to assess change from A- to A+, compared to the group that remained A- at follow-up, as predictor for cognitive decline. RESULTS: At baseline, 62% had normal AD biomarkers (A-T-N- n = 24), 5% had non-AD pathologic change (A-T-N+ n = 2,) and 33% fell within the Alzheimer's continuum (A+T-N- n = 9, A+T+N- n = 3, A+T+N+ n = 1). Seventeen subjects (44%) changed to another ATN profile over time. Only 6/17 followed the Alzheimer's disease sequence of A → T → N, while 11/17 followed a different order (e.g., reverted back to negative biomarker status). APOE ε4 carriership inferred an increased risk of changing from A- to A+ (OR 5.2 (95% CI 1.2-22.8)). Individuals who changed from A- to A+, showed subtly steeper decline on Stroop I (β - 0.03 (SE 0.01)) and Stroop III (- 0.03 (0.01)), compared to individuals who remained A-. CONCLUSION: We observed considerable variability in the order of ATN biomarkers becoming abnormal. Individuals who became A+ at follow-up showed subtle decline on tests for attention and executive functioning, confirming clinical relevance of amyloid positivity

Psychosocial Effects of Corona Measures on Patients With Dementia, Mild Cognitive Impairment and Subjective Cognitive Decline

Background: The recent COVID-19 pandemic is not only a major healthcare problem in itself, but also poses enormous social challenges. Though nursing homes increasingly receive attention, the majority of people with cognitive decline and dementia live at home. We aimed to explore the psychosocial effects of corona measures in memory clinic (pre-)dementia patients and their caregivers. Methods: Between April 28th and July 13th 2020, n = 389 patients of Alzheimer center Amsterdam [n = 121 symptomatic (age = 69 ± 6, 33%F, MMSE = 23 ± 5), n = 268 cognitively normal (age = 66 ± 8, 40% F, MMSE = 29 ± 1)] completed a survey on psychosocial effects of the corona measures. Questions related to social isolation, worries for faster cognitive decline, behavioral problems and discontinuation of care. In addition, n = 147 caregivers of symptomatic patients completed a similar survey with additional questions on caregiver burden. Results: Social isolation was experienced by n = 42 (35%) symptomatic and n = 67 (25%) cognitively normal patients and two third of patients [n = 129 (66%); n = 58 (75%) symptomatic, n = 71 (61%) cognitively normal] reported that care was discontinued. Worries for faster cognitive decline were existed in symptomatic patients [n = 44 (44%)] and caregivers [n = 73 (53%)], but were also reported by a subgroup of cognitively normal patients [n = 27 (14%)]. Both patients [n = 56 (46%) symptomatic, n = 102 (38%) cognitively normal] and caregivers [n = 72 (48%)] reported an increase in psychological symptoms. More than three quarter of caregivers [n = 111(76%)] reported an increase in patients' behavioral problems. A higher caregiver burden was experienced by n = 69 (56%) of caregivers and n = 43 (29%) of them reported that a need for more support. Discontinuation of care (OR = 3.3 [1.3–7.9]), psychological (OR = 4.0 [1.6–9.9]) and behavioral problems (OR = 3.0 [1.0–9.0]) strongly related to experiencing a higher caregiver burden. Lastly, social isolation (OR = 3.2 [1.2–8.1]) and psychological symptoms (OR = 8.1 [2.8–23.7]) were red flags for worries for faster cognitive decline. Conclusion: Not only symptomatic patients, but also cognitively normal patients express worries for faster cognitive decline and psychological symptoms. Moreover, we identified patients who are at risk of adverse outcomes of the corona measures, i.e., discontinued care, social isolation, psychological and behavioral problems. This underlines the need for health care professionals to provide ways to warrant the continuation of care and support (informal) networks surrounding patients and caregivers to mitigate the higher risk of negative psychosocial effects

Gut Microbiota Composition Is Related to AD Pathology

Introduction: Several studies have reported alterations in gut microbiota composition of Alzheimer’s disease (AD) patients. However, the observed differences are not consistent across studies. We aimed to investigate associations between gut microbiota composition and AD biomarkers using machine learning models in patients with AD dementia, mild cognitive impairment (MCI) and subjective cognitive decline (SCD). Materials and Methods: We included 170 patients from the Amsterdam Dementia Cohort, comprising 33 with AD dementia (66 ± 8 years, 46%F, mini-mental state examination (MMSE) 21[19-24]), 21 with MCI (64 ± 8 years, 43%F, MMSE 27[25-29]) and 116 with SCD (62 ± 8 years, 44%F, MMSE 29[28-30]). Fecal samples were collected and gut microbiome composition was determined using 16S rRNA sequencing. Biomarkers of AD included cerebrospinal fluid (CSF) amyloid-beta 1-42 (amyloid) and phosphorylated tau (p-tau), and MRI visual scores (medial temporal atrophy, global cortical atrophy, white matter hyperintensities). Associations between gut microbiota composition and dichotomized AD biomarkers were assessed with machine learning classification models. The two models with the highest area under the curve (AUC) were selected for logistic regression, to assess associations between the 20 best predicting microbes and the outcome measures from these machine learning models while adjusting for age, sex, BMI, diabetes, medication use, and MMSE. Results: The machine learning prediction for amyloid and p-tau from microbiota composition performed best with AUCs of 0.64 and 0.63. Highest ranked microbes included several short chain fatty acid (SCFA)-producing species. Higher abundance of [Clostridium] leptum and lower abundance of [Eubacterium] ventriosum group spp., Lachnospiraceae spp., Marvinbryantia spp., Monoglobus spp., [Ruminococcus] torques group spp., Roseburia hominis, and Christensenellaceae R-7 spp., was associated with higher odds of amyloid positivity. We found associations between lower abundance of Lachnospiraceae spp., Lachnoclostridium spp., Roseburia hominis and Bilophila wadsworthia and higher odds of positive p-tau status. Conclusions: Gut microbiota composition was associated with amyloid and p-tau status. We extend on recent studies that observed associations between SCFA levels and AD CSF biomarkers by showing that lower abundances of SCFA-producing microbes were associated with higher odds of positive amyloid and p-tau status

Psychosocial Effects of COVID-19 Measures on (Pre-)Dementia Patients During Second Lockdown

Background: The COVID-19 pandemic poses enormous social challenges, especially during lockdown. People with cognitive decline and their caregivers are particularly at risk of lockdown consequences. Objective: To investigate psychosocial effects in (pre-)dementia patients and caregivers during second lockdown and compare effects between first and second lockdown. Methods: We included n = 511 (pre-)dementia patients and n = 826 caregivers from the Amsterdam Dementia Cohort and via Alzheimer Nederland. All respondents completed a self-designed survey on psychosocial effects of COVID-19. We examined relations between experienced support and psychosocial and behavioral symptoms using logistic regression. In a subset of patients and caregivers we compared responses between first and second lockdown using generalized estimating equation (GEE). Results: The majority of patients (≥58%) and caregivers (≥60%) reported that family and friends, hobbies, and music helped them cope. Support from family and friends was strongly related to less negative feelings in patients (loneliness: OR = 0.3[0.1-0.6]) and caregivers (loneliness: OR = 0.2[0.1-0.3]; depression: OR = 0.4[0.2-0.5]; anxiety: OR = 0.4[0.3-0.6]; uncertainty: OR = 0.3[0.2-0.5]; fatigue: OR = 0.3[0.2-0.4]; stress: OR = 0.3[0.2-0.5]). In second lockdown, less psychosocial and behavioral symptoms were reported compared to first lockdown (patients; e.g., anxiety: 22% versus 13%, p = 0.007; apathy: 27% versus 8%, p < 0.001, caregivers; e.g., anxiety: 23% versus 16%, p = 0.033; patient's behavioral problems: 50% versus 35%, p < 0.001). Patients experienced more support (e.g., family and friends: 52% versus 93%, p < 0.001; neighbors: 28% versus 66%, p < 0.001). Conclusion: During second lockdown, patients and caregivers adapted to challenges posed by lockdown, as psychosocial and behavioral effects decreased, while patients experienced more social support compared to first lockdown. Support from family and friends is a major protective factor for negative outcomes in patients and caregivers

Dietary Patterns Are Related to Clinical Characteristics in Memory Clinic Patients with Subjective Cognitive Decline: The SCIENCe Project

As nutrition is one of the modifiable risk factors for cognitive decline, we studied the relationship between dietary quality and clinical characteristics in cognitively normal individuals with subjective cognitive decline (SCD). We included 165 SCD subjects (age: 64 &#177; 8 years; 45% female) from the SCIENCe project, a prospective memory clinic based cohort study on SCD. The Dutch Healthy Diet Food Frequency Questionnaire (DHD-FFQ) was used to assess adherence to Dutch guidelines on vegetable, fruit, fibers, fish, saturated fat, trans fatty acids, salt and alcohol intake (item score 0&#8211;10, higher score indicating better adherence). We measured global cognition (Mini Mental State Examination), cognitive complaints (Cognitive Change Index self-report; CCI) and depressive symptoms (Center for Epidemiologic Studies Depression Scale; CES-D). Using principal component analysis, we identified dietary components and investigated their relation to clinical characteristics using linear regression models adjusted for age, sex and education. We identified three dietary patterns: (i) &#8220;low-Fat-low-Salt&#8222;, (ii) &#8220;high-Veggy&#8222;, and (iii) &#8220;low-Alcohol-low-Fish&#8222;. Individuals with lower adherence on &#8220;low-Fat-low-Salt&#8222; had more depressive symptoms (&#946; &#8722;0.18 (&#8722;2.27&#8211;&#8722;0.16)). Higher adherence to &#8220;high-Veggy&#8222; was associated with higher MMSE scores (&#946; 0.30 (0.21&#8211;0.64)). No associations were found with the low-Alcohol-low-Fish component. We showed that in SCD subjects, dietary quality was related to clinically relevant outcomes. These findings could be useful to identify individuals that might benefit most from nutritional prevention strategies to optimize brain health

Dietary Patterns Are Related to Clinical Characteristics in Memory Clinic Patients with Subjective Cognitive Decline: The SCIENCe Project

As nutrition is one of the modifiable risk factors for cognitive decline, we studied the relationship between dietary quality and clinical characteristics in cognitively normal individuals with subjective cognitive decline (SCD). We included 165 SCD subjects (age: 64 ± 8 years; 45% female) from the SCIENCe project, a prospective memory clinic based cohort study on SCD. The Dutch Healthy Diet Food Frequency Questionnaire (DHD-FFQ) was used to assess adherence to Dutch guidelines on vegetable, fruit, fibers, fish, saturated fat, trans fatty acids, salt and alcohol intake (item score 0-10, higher score indicating better adherence). We measured global cognition (Mini Mental State Examination), cognitive complaints (Cognitive Change Index self-report; CCI) and depressive symptoms (Center for Epidemiologic Studies Depression Scale; CES-D). Using principal component analysis, we identified dietary components and investigated their relation to clinical characteristics using linear regression models adjusted for age, sex and education. We identified three dietary patterns: (i) "low-Fat-low-Salt", (ii) "high-Veggy", and (iii) "low-Alcohol-low-Fish". Individuals with lower adherence on "low-Fat-low-Salt" had more depressive symptoms (β -0.18 (-2.27--0.16)). Higher adherence to "high-Veggy" was associated with higher MMSE scores (β 0.30 (0.21-0.64)). No associations were found with the low-Alcohol-low-Fish component. We showed that in SCD subjects, dietary quality was related to clinically relevant outcomes. These findings could be useful to identify individuals that might benefit most from nutritional prevention strategies to optimize brain health.</p