Trajectories Of Cognitive Decline Research Articles

Sex differences in the trajectories of cognitive decline and affected cognitive domains among older adults with controlled and uncontrolled glycemia.

We aimed to analyze the trajectories of cognitive decline as a function of the presence of type 2 diabetes and glycemic control in analyzes stratified by sex in an eight-year follow-up period. A total of 1,752 men and 2,232 women aged ≥50 years who participated in the English Longitudinal Study of Ageing (ELSA), conducted from 2004 to 2012, were analyzed. The outcomes of interest were performance on the cognitive domains of memory, executive function, and temporal orientation as well as the global cognition score. Cognitive performance was standardized in z-scores in strata based on schooling and age. The participants were classified as without diabetes, with controlled glycemia and with uncontrolled glycemia, according to medical diagnosis, glucose-lowering medications use and HbA1c levels. Generalized linear mixed models controlled by sociodemographic, behavioral, and health-related characteristics were used for the trajectory analyses. No differences in z-scores were found for global cognition or cognitive domains based on diabetes classification in men and women at baseline. Over eight years of follow-up, women with uncontrolled glycemia had a greater decline in z-scores for global cognition (-0.037 SD/year [95%CI: -0.073; -0.001]) and executive function (-0.049 SD/year [95%CI: -0.092; -0.007]) compared to those without diabetes. No significant difference in trajectories of global cognition or any cognitive domain was found in men as a function of diabetes classification. Women with uncontrolled glycemia are at greater risk of a decline in global cognition and executive function than those without diabetes.

Disentangling sex-dependent effects of APOE on diverse trajectories of cognitive decline in Alzheimer's disease

Current diagnostic systems for Alzheimer's disease (AD) rely upon clinical signs and symptoms, despite the fact that the multiplicity of clinical symptoms renders various neuropsychological assessments inadequate to reflect the underlying pathophysiological mechanisms. Since putative neuroimaging biomarkers play a crucial role in understanding the etiology of AD, we sought to stratify the diverse relationships between AD biomarkers and cognitive decline in the aging population and uncover risk factors contributing to the diversities in AD. To do so, we capitalized on a large amount of neuroimaging data from the ADNI study to examine the inflection points along the dynamic relationship between cognitive decline trajectories and whole-brain neuroimaging biomarkers, using a state-of-the-art statistical model of change point detection. Our findings indicated that the temporal relationship between AD biomarkers and cognitive decline may differ depending on the synergistic effect of genetic risk and biological sex. Specifically, tauopathy-PET biomarkers exhibit a more dynamic and age-dependent association with Mini-Mental State Examination scores (p<0.05), with inflection points at 72, 78, and 83 years old, compared with amyloid-PET and neurodegeneration (cortical thickness from MRI) biomarkers. In the landscape of health disparities in AD, our analysis indicated that biological sex moderates the rate of cognitive decline associated with APOE4 genotype. Meanwhile, we found that higher education levels may moderate the effect of APOE4, acting as a marker of cognitive reserve.

The SNP rs6859 in NECTIN2 gene is associated with underlying heterogeneous trajectories of cognitive changes in older adults

BackgroundFunctional decline associated with dementia, including in Alzheimer’s disease (AD), is not uniform across individuals, and respective heterogeneity is not yet fully explained. Such heterogeneity may in part be related to genetic variability among individuals. In this study, we investigated whether the SNP rs6859 in nectin cell adhesion molecule 2 (NECTIN2) gene (a major risk factor for AD) influences trajectories of cognitive decline in older participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI).MethodsWe retrospectively analyzed records on 1310 participants from the ADNI database for the multivariate analysis. We used longitudinal measures of Mini-Mental State Examination (MMSE) scores in participants, who were cognitively normal, or having AD, or other cognitive deficits to investigate the trajectories of cognitive changes. Multiple linear regression, linear mixed models and latent class analyses were conducted to investigate the association of the SNP rs6859 with MMSE.ResultsThe regression coefficient per one allele dose of the SNP rs6859 was independently associated with MMSE in both cross-sectional (-2.23, p < 0.01) and linear mixed models (-2.26, p < 0.01) analyses. The latent class model with three distinct subgroups (class 1: stable and gradual decline, class 2: intermediate and late decline, and class 3: lowest and irregular) performed best in the posterior classification, 42.67% (n = 559), 21.45% (n = 281), 35.88% (n = 470) were classified as class 1, class 2, and class 3. In the heterogeneous linear mixed model, the regression coefficient per one allele dose of rs6859 – A risk allele was significantly associated with MMSE class 1 and class 2 memberships and related decline; Class 1 (-2.28, 95% CI: -4.05, -0.50, p < 0.05), Class 2 (-5.56, 95% CI: -9.61, -1.51, p < 0.01) and Class 3 (-0.37, 95% CI: -1.62, 0.87, p = 0.55).ConclusionsThis study found statistical evidence supporting the classification of three latent subclass groups representing complex MMSE trajectories in the ADNI cohort. The SNP rs6859 can be suggested as a candidate genetic predictor of variation in modeling MMSE trajectory, as well as for identifying latent classes with higher baseline MMSE. Functional studies may help further elucidate this relationship.

Trajectories of cognitive decline in different domains prior to AD onset in persons with mild cognitive impairment

ObjectivesTo explore the trajectories and the change-points of global and five domain-specific cognitive functions before the onset of Alzheimer's disease (AD). MethodsData was retrieved from the Alzheimer's Disease Neuroimaging Initiative with follow-up from 2005 to 2022. Participants with mild cognitive impairment (MCI) at baseline and those who progressed to AD during follow-up were included. The time of AD onset was defined as the visit time when participant was first diagnosed as AD during follow-up. Global and five domain-specific cognitive functions (immediate memory, visuospatial ability, language, processing speed and executive function) were assessed by Mini-Mental State Examination, Immediate recalling trials of Rey Auditory Verbal Learning Test, Clock Drawing Test, Animal Fluency Test, Part A and B of Trail Making Test, respectively. Their trajectories and change-points before AD onset were explored by generalized additive mixed models and piecewise linear regression models, respectively. Results349 participants were diagnosed as MCI at baseline and converted to AD during follow-up, who were included in this study. They had been visited on an average of 4.6 times (SD = 2.1, range = 2.0–13.0), with a total of 1593 visits. Their mean baseline age and AD onset age were 74.4 (SD = 6.4, range = 60.0–88.4) and 77.0 (SD = 6.8, range = 60.5–94.7) years, respectively. Baseline age and educational year were significantly associated with global cognitive, immediate memory, language and executive function. Men presented better global cognitive function (β = 0.54, p < 0.05) but poorer immediate memory (β = −1.72, p < 0.05) than women. Immediate memory and visuospatial ability showed the earliest change-points at 4 years before the onset of AD (Note as T-4years), followed by language (T-3.5years), executive function (T-2.5 years), processing speed (T-2.0 years), and finally the global cognitive function (T-1.5years). ConclusionsThe trajectories of the six neuropsychological scores were non-linear and showed deterioration in functions over time. Immediate memory and visuospatial ability showed the earliest change-points prior to AD onset.

Genetic and Clinical Correlates of AI-Based Brain Aging Patterns in Cognitively Unimpaired Individuals

Brain aging elicits complex neuroanatomical changes influenced by multiple age-related pathologies. Understanding the heterogeneity of structural brain changes in aging may provide insights into preclinical stages of neurodegenerative diseases. To derive subgroups with common patterns of variation in participants without diagnosed cognitive impairment (WODCI) in a data-driven manner and relate them to genetics, biomedical measures, and cognitive decline trajectories. Data acquisition for this cohort study was performed from 1999 to 2020. Data consolidation and harmonization were conducted from July 2017 to July 2021. Age-specific subgroups of structural brain measures were modeled in 4 decade-long intervals spanning ages 45 to 85 years using a deep learning, semisupervised clustering method leveraging generative adversarial networks. Data were analyzed from July 2021 to February 2023 and were drawn from the Imaging-Based Coordinate System for Aging and Neurodegenerative Diseases (iSTAGING) international consortium. Individuals WODCI at baseline spanning ages 45 to 85 years were included, with greater than 50 000 data time points. Individuals WODCI at baseline scan. Three subgroups, consistent across decades, were identified within the WODCI population. Associations with genetics, cardiovascular risk factors (CVRFs), amyloid β (Aβ), and future cognitive decline were assessed. In a sample of 27 402 individuals (mean [SD] age, 63.0 [8.3] years; 15 146 female [55%]) WODCI, 3 subgroups were identified in contrast with the reference group: a typical aging subgroup, A1, with a specific pattern of modest atrophy and white matter hyperintensity (WMH) load, and 2 accelerated aging subgroups, A2 and A3, with characteristics that were more distinct at age 65 years and older. A2 was associated with hypertension, WMH, and vascular disease-related genetic variants and was enriched for Aβ positivity (ages ≥65 years) and apolipoprotein E (APOE) ε4 carriers. A3 showed severe, widespread atrophy, moderate presence of CVRFs, and greater cognitive decline. Genetic variants associated with A1 were protective for WMH (rs7209235: mean [SD] B = -0.07 [0.01]; P value = 2.31 × 10-9) and Alzheimer disease (rs72932727: mean [SD] B = 0.1 [0.02]; P value = 6.49 × 10-9), whereas the converse was observed for A2 (rs7209235: mean [SD] B = 0.1 [0.01]; P value = 1.73 × 10-15 and rs72932727: mean [SD] B = -0.09 [0.02]; P value = 4.05 × 10-7, respectively); variants in A3 were associated with regional atrophy (rs167684: mean [SD] B = 0.08 [0.01]; P value = 7.22 × 10-12) and white matter integrity measures (rs1636250: mean [SD] B = 0.06 [0.01]; P value = 4.90 × 10-7). The 3 subgroups showed distinct associations with CVRFs, genetics, and subsequent cognitive decline. These subgroups likely reflect multiple underlying neuropathologic processes and affect susceptibility to Alzheimer disease, paving pathways toward patient stratification at early asymptomatic stages and promoting precision medicine in clinical trials and health care.

COVID-19 Pandemic Impact on the Trajectories of Cognitive Decline and Depression Symptoms in Long-Term Care Facility Residents

ObjectivesTo compare the longitudinal rates of change in cognition and depressive symptoms between 2019 (pre–COVID-19 pandemic) and 2020 (COVID-19 pandemic) among long-term care facility (LTCF) residents in Iowa, which ranked among the top 10 US states that suffered from extreme nursing staff shortages during this crisis. DesignA longitudinal cohort study analyzing the Long-Term Care Minimum Data Set (MDS) version 3.0 between January 1, 2019, and December 31, 2020. Setting and ParticipantsLTCF residents from the state of Iowa, with a first assessment before March 10 for each year (2019 and 2020), LTCF stay period >60 days, and at least 2 documented assessments with a minimum of 45 days in between. LTCF residents with a Brief Interview for Mental Status score (BIMS) <3 were excluded. MethodsWe computed doubly robust estimators by combining regression and propensity score models for BIMS (cognitive decline) and Nine-item Patient Health Questionnaire (PHQ-9; depression symptoms) monthly scores for 2020 vs 2019. ResultsA total of 24,025 residents from 436 LTCFs were included. Our research revealed a marginally accelerated monthly decline in BIMS scores during 2020 as opposed to 2019, with a rate of −0.012 per month (95% CI −0.022, −0.002; P = .016). Simultaneously, we observed a monthly increment of 0.016 in the PHQ-9 scores among LTCF residents in 2020 (95% CI 0.006, 0.028; P = .003). Conclusions and ImplicationsOur study identified a notable yet modest increase in cognitive decline and depressive symptoms among NH residents in Iowa during the COVID-19 pandemic. Although statistically significant, the changes were small and may not have clinical relevance over 1 year. Further research is needed to examine risk factors for cognitive decline during pandemic scenarios such as social isolation, loneliness, inappropriate patient-centered care, and the relationship with staff shortage and facility resources.

Longitudinal trajectories of cognitive decline and cerebral blood flow abnormalities in octogenarian men with normal global cognition

AimsCognitive and perfusion changes have been previously observed in older men with Mini Mental State Examination scores>24 points. We aimed to investigate time change in cognitive domains in a cohort of non-demented men between age 68 and 82, and how they are expressed in regional defects estimated by Cerebral Blood Flow (rCBF). Methods103 men at age 81 with MMSE scores >24 (mean 28.4 ± 1.7), no dementia or stroke, were examined with the same cognitive test battery at age 68 and age 81: Synonyms (SRB-1), Block design (SRB-3), Paired Associates, Digit Symbol Substitution and Benton Visual Retention test. rCBF was estimated using 99mTc-HMPAO SPECT at age 82. ResultsBetween ages 68 and 82 we observed a relative decline (Δ%) of cognitive test scores: SRB-3 and Benton tests, -33.7 % (SD 16,8) and -25.8 % (SD 23.9) respectively, followed by Digit Symbol test: -22,6 % (SD 15,6). The cluster of men (46 %) could be detected, grouped on the largest test score decline and highest overall test predictors’ importance in decreasing order: Δ% SRB-3, Δ% Paired Associates, Δ% Digit Symbol, Δ% Benton VR and Δ% SRB-1. Compared to the cluster with stable cognitive functions, it expressed lower rCBF in frontal and parietal lobes, and in subcortical areas. ConclusionNearly half of the studied, community-dwelling cohort of non-demented, octogenarian men with MMSE > 24, had a combination of decreasing visuospatial ability and episodic memory during preceding years, expressed by widespread rCBF changes in fronto-subcortical areas.

Toward the right treatment at the right time: Modeling the trajectory of cognitive decline to identify the earliest age of change in people with Alzheimer's disease.

Age is the greatest risk factor for Alzheimer's disease (AD). A limitation of randomized control trials in AD is a lack of specificity in the age ranges of participants who are enrolled in studies of disease-modifying therapies. We aimed to apply Emax (i.e., maximum effect) modeling as a novel approach to identity ideal treatment windows. Emax curves were fitted to longitudinal cognitive data of 101 participants with AD and 1392 healthy controls. We included the Mini-Mental State Examination (MMSE) and tests of verbal fluency and executive functioning. In people with AD, the earliest decline in the MMSE could be detected in the 67-71 age band while verbal fluency declined from the 41-45 age band. In healthy controls, changes in cognition showed a later trajectory of decline. Emax modeling could be used to design more efficient trials which has implications for randomized control trials targeting the earlier stages of AD.

ARTIFICIAL INTELLIGENCE IN MENTAL HEALTH FOR THE AGING POPULATION: A SCOPING REVIEW

Abstract Artificial intelligence (AI) has significant potential in mental health research and service delivery for the aging population. This poster provides an overview of its evolving utilization, including research methods, data sources, and predictors of mental health. The review of 26 articles from 2015 to 2023 found that surveys/questionnaires, clinical data, non-clinical data, and electronic health records are the most commonly used data sources, with clinical data being the most common, accounting for more than 50% of the articles. The frequently utilized AI techniques in mental health research for older adults were supervised learning (65.4%), deep learning (42.3%), unsupervised learning (7.7%), NLP (7.7%), and semi-supervised learning (3.8%). Mental health factors that received the most attention in the research were anxiety/depression (65.4%), dementia (Alzheimer’s) (38.5%), mild cognitive impairment (23.1%), suicidal ideation (15.4%), and trajectory of cognitive decline (7.7%). AI is currently employed in various aspects of mental health research for older adults, including identifying and diagnosing mental health conditions at an early stage, monitoring mental well-being, assessing the severity of mental health conditions, predicting cognitive trajectories, and providing assistance in treatment. The recent application of generative language models, such as ChatGPT, has further advanced the field of AI in mental health research. However, ethical concerns such as bias, privacy, and transparency must be addressed before widespread implementation. In conclusion, AI has great potential as well as risks and ethical concerns for mental health research and service delivery to aging populations, but further research is needed.

Inter‐ and intra‐individual variation in brain structural‐cognition relationships in aging

AbstractBackgroundThe sources of inter‐ and intra‐individual variability in age‐related cognitive decline remain poorly understood. We examined the association between 20‐year trajectories of cognitive decline and multimodal brain structure and morphology in older age.MethodWe used the Whitehall II Study, an extensively characterised cohort with 3T brain magnetic resonance images acquired at older age (mean age = 69.52 ± 4.9) and 5 repeated cognitive performance assessments between mid‐life (mean age = 53.2 ±4.9 years) and late‐life (mean age = 67.7 ± 4.9). Using non‐negative matrix factorisation, we identified 10 brain components integrating cortical thickness, surface area, fractional anisotropy, and mean and radial diffusivities (Figure 1).ResultWe observed two latent variables describing distinct brain‐cognition associations. The first describes variations in 5 structural components associated with low mid‐life performance across multiple cognitive domains, decline in reasoning, but maintenance of fluency abilities. The second describes variations in 6 structural components associated with low mid‐life performance in fluency and memory, but retention of multiple abilities (Figure 2). Expression of latent variables predicts future cognition 3.2 years later (mean age = 70.87 ± 4.9) (Figure 3).ConclusionLongitudinal cognitive decline and maintenance across diverse cognitive functions are both positively and negatively associated with distinct markers of cortical structure. Latent brain‐behaviour relationships predict future cognitive performance.

Sex differences in marmoset neurocognitive aging, a nonhuman primate model for brain aging and age‐related neurodegenerative diseases

AbstractBackgroundNonhuman primate models of human aging are necessary to understand functional decline and identify vulnerabilities to aging pathologies. Longitudinal studies are critical and common marmosets (Callithrix jacchus) are ideal as they have a short lifespan (∼12 yr) and naturally develop neuropathology relevant to Alzheimer’s disease (AD).MethodWe studied 28 marmosets (14 females) from midlife (∼5 yr old) to old age across four years. Simple discrimination and reversal learning was used to evaluate cognitive performance. Additional yearly tests included motor, stress reactivity, and social behavior assessments. A subset of animals (n = 17, 9 females) were studied for AD‐relevant neuropathology in post‐mortem tissue of the dorsal lateral prefrontal cortex (dlPFC) and CA1 of the hippocampus.ResultMultilevel models revealed age‐related changes for cognitive performance with improved reversal learning during the first three years due to practice followed by decline in the final year with the onset of old age. Cognitive decline was steeper and began earlier for females compared to males. Based on cognitive trajectories which were highly variable across individuals, we categorized marmosets as cognitively impaired or non‐impaired. Mild amyloid β (Aβ) deposition was observed in the dlPFC and CA1 but did not differ between males and females. Aβ burden was higher in cognitively impaired compared to non‐impaired marmosets, but this difference did not reach statistical significance. However, cognitively impaired marmosets showed signs of accelerated brain aging including higher astrogliosis associated with Aβ deposits and dendritic spine loss, both in the dlPFC and hippocampus. In addition, increased activation and changes in microglial phenotypes were observed in the dlPFC.ConclusionLike humans, aging marmosets displayed intra‐individual variability in cognitive decline, neurodegeneration and neuronal aging. Female marmosets showed greater vulnerability to cognitive impairment, which is translationally relevant to the higher prevalence of AD in women. While trajectories of cognitive decline did not strongly map onto Aβ burden for females or males, evidence of accelerated brain aging was found in the impaired marmosets. This study highlights the value of marmosets as a nonhuman primate model of AD to identify vulnerabilities to aging pathologies, such as sex differences.

Identifying the optimal age to intervene in Alzheimer’s disease prevention trials: Validation of Emax modelling in sporadic Alzheimer’s disease

AbstractBackgroundMany clinical trials of disease‐modifying therapies which seek to delay or prevent Alzheimer’s disease (AD) currently exist. However, a limitation of these trials is the absence of considering the earliest age groups at which decline in cognition can be detected to guide optimal participant recruitment.Previous work has shown that in people with Down syndrome, Emax modelling can be used to identify the trajectory of cognitive decline in the preclinical and prodromal stages of AD. We aimed to validate Emaxmodelling to identify the earliest age of change in cognition in people who developed sporadic AD and compare these individuals to healthy controls without AD.MethodOur sample consisted of participants aged 65 years old from the Enquête Sante ́ Psychologique – Risques, Incidence et Traitement (ESPRIT) study. Participants were categorised into two groups based on whether they were ever diagnosed with possible and probable AD while enrolled in the study (n = 101) or did not receive a diagnosis of any subtype of dementia (n = 1392, our healthy control group). We included measures of general cognitive functioning (Mini Mental State Examination; MMSE), verbal fluency, visual memory (Benton Visual Retention Test) and executive functioning (Trail Making Test).ResultIn people with AD, the earliest decline in general cognitive functioning could be detected in the 67‐71 age band while verbal fluency appears to decline from the 41‐45 age band and visual memory in the 82‐86 age band according to our model. In healthy controls, general cognitive functioning shows early change in the 87‐91 age band while verbal fluency starts to decline in the 43‐47 age band and executive functioning in the 85‐89 age band. Models for Trail Making Models for people with AD and visual memory for controls were not well fitted to the data.ConclusionWe have shown that sensitivity to change depend on the primary outcome so authors designing trials need to ensure the research question, population and primary outcome are consistent. This has implications particularly for trials targeting the stages of disease before dementia diagnosis.

Trajectories of cognition and functional ability in people with dementia: Two‐year longitudinal results from the IDEAL programme

AbstractBackgroundImpairment in both cognition and instrumental activities of daily living (iADL) are key diagnostic features of dementia. Few studies have compared trajectories of cognition and iADL. By comparing trajectories of cognitive decline with both self‐rated and informant rated iADL, the study determines whether these aspects are related or decline in parallel and where there is a difference between perceptions of change.MethodData from the Improving the experience of Dementia and Enhancing Active Life (IDEAL) study were used. The study comprises 1537, 1183, and 851 people with dementia, and 1277, 977, and 749 carers at baseline (T1), 12 months later (Time 2, T2), and 24 months later (Time 3, T3), respectively. Cognition was assessed with the Addenbrooke’s Cognitive Examination‐III (ACE‐III), higher scores indicate fewer cognitive difficulties. iADL was assessed using the Functional Activities Questionnaire (FAQ), higher scores indicate more iADL difficulties. Both self‐ratings (FAQ‐S) and informant ratings (FAQ‐I) were collected. Because of the differences in the scale and distribution of FAQ and ACE‐III, scores were converted to ranks to enable comparison of trajectories between the two measures; deciles were calculated at T1 and imposed on T2 and T3 scores.ResultAs ACE‐III decreased both FAQ‐S and FAQ‐I increased suggesting that people with dementia with more cognitive difficulties were rated as having greater iADL difficulties by both people with dementia themselves and their carers. FAQ‐S declined on average by ‐0.10 (‐0.26, 0.06) decile points per timepoint more than ACE‐III. As confidence intervals overlapped zero this suggests that FAQ‐S and ACE‐III ratings declined at a similar rate. FAQ‐I declined on average by ‐0.31 (‐0.43, ‐0.19) decile points per timepoint more than ACE‐III. The trajectory over the two years suggests that FAQ‐I ratings declined at a significantly greater rate than ACE‐III scores.ConclusionBoth cognition and iADL, whether self‐ or informant‐rated declined over two years. Trajectory analysis suggests that self‐rated iADL was generally concordant with cognitive decline, whereas informant‐rated iADL reflected more perceived decline than cognition. Therefore, either cognition and perceived iADL decline at different rates or informants overestimate increasing iADL difficulties compared to both cognition and self‐ratings.

Mapping cognitive trajectories in older adults with essential tremor

AbstractBackgroundOlder adults with essential tremor (ET) are at increased risk for Alzheimer’s disease (AD) and related disorders. Examining cognitive trajectories within this population will enable better identification of individuals who evidence accelerated rates of change. Here we examine how variability in baseline cognitive performance affects trajectories of cognitive change within three subgroups of cognitively normal older adults: 1) those whose baseline neuropsychological scores were entirely within normal limits (WNL); 2) those with at least one low score considered to have unlikely clinical significance (UCS); and 3) those with at least one low score considered to have possible clinical significance (PCS).MethodOlder adults with ET completed comprehensive neuropsychological testing and clinical interviews, and 159 received a consensus diagnosis of cognitively normal. Participants were then subdivided into one of three categories defined above: WNL (N = 66, 75.38 ± 8.96 years of age), UCS (N = 54, 76.35 ± 10.25 years), and PCS (N = 37, 80.22 ± 9.00 years). Generalized estimating equations (GEE) were used to compare rates of change in global cognition, memory, executive function, attention, visuospatial, and language z‐scores over time across these groups. Participants were seen for two to four visits (24.98 ± 20.29 months). Models were adjusted for age, gender, and years of education.ResultAll three groups, WNL (β = ‐.005, p = .028), UCS (β = ‐.006, p = .011), and PCS (β = ‐.012, p = .002), evidenced decline in their memory performance over time, with the latter groups declining faster than the WNL group. Relative to the WNL and UCS groups, the PCS group also declined faster over time in overall cognition (β = ‐.010, p = .029) and in language (β = ‐.024, p = .035).ConclusionHeterogeneity in cognitive performance among cognitively normal older adults with ET predicts trajectories of cognitive decline longitudinally. Specifically, rates of decline were slowest in those with entirely normal test scores, and greatest in those whose scores were considered to have possible clinical significance. These preliminary results provide a starting point for further inquiry into the role of nuanced cognitive profiles in predicting rates of decline, impairment, and dementia in ET cases who may initially be labeled as “cognitively normal.”

Ante‐mortem cognitive trajectories of older adults with cerebrovascular disease across Aβ and tau biomarker profiles determined at autopsy in the National Alzheimer’s Coordinating Center database

AbstractBackgroundAlzheimer’s disease (AD) is characterised biologically by beta‐amyloid (Aβ) plaques and tau tangles, but autopsy studies reveal that most older adults also present with cerebrovascular disease (CVD) markers. Effects of biological AD on cognition in the context of concurrent CVD remain unclear. In older adults with CVD pathology, this study sought to determine ante‐mortem cognitive trajectories associated with Aβ/tau positivity or negativity (+/−) at autopsy.MethodParticipants aged 65‐95 classified as cognitively unimpaired at baseline from the National Alzheimer’s Coordinating Center database, with ≥1 follow‐up between 2005‐2015, and available autopsy/APOE data were included (N = 924). Autopsy indicated that all participants had at least one of six CVD pathology markers. Participants were classified into four groups (A−T−, A+T−, A−T+, A+T+) based on semiquantitative Consortium to Establish a Registry for Alzheimer’s Disease neuritic plaque staging and Braak staging. Linear mixed models including group × time interactions assessed rate of change in Preclinical Alzheimer’s Cognitive Composite scores, episodic memory, and executive function. Interactions between age, sex, APOE ε4 × time and the interval between death/the final study visit were included as covariates.ResultA+T+ adults demonstrated significantly faster cognitive decline on all outcomes in the ∼10 years preceding death compared to A−T−, A+T−, and A−T+ adults (Table 1, Figure 1, d = 0.15 – 0.39). At final visit prior to death, a greater proportion of A+T+ adults (36%) received a dementia diagnosis compared to A−T+ (14%) or A+T− (15%) (p &lt;.001). When analyses were restricted to exclude dementia diagnoses, significantly faster decline on all outcomes (p’s &lt;.001, d = 0.06 – 0.36) was similarly observed in A+T+ adults compared to A−T−, A+T− and A−T+ adults (Figure 2). Cognitive trajectories were equivalent between A−T− and A+T− for all outcomes (p’s &gt;.55).Conclusionln older adults with CVD pathology, A+T+ at autopsy was associated with greater cognitive decline over 10 years preceding death compared to A+T−, A−T+, and A−T− older adults. Faster cognitive decline in this group in the context of low final visit dementia diagnoses may suggest that post‐mortem A+T+ is associated with a steep trajectory of cognitive decline ante‐mortem, but that dementia progression is not inevitable.

Cluster‐Derived Psychosocial Phenotypes, Cognitive Outcomes, and AD Plasma Biomarkers of Black and Hispanic/Latino Older Adults Enrolled in HABS‐HD

AbstractBackgroundAlthough Black and Hispanic/Latino older adults are at increased risk for Alzheimer’s disease (AD), there is considerable within‐group heterogeneity in psychosocial factors of risk and resiliency amongst these community members. The present study (1) used cluster analysis on measures of economic/social resources and psychiatric distress to identify distinct subgroups and (2) compared cognitive and AD biomarker outcomes of these groups in an effort to understand pathologic aging among racially/ethnically diverse older adults.MethodBaseline data from 816 Black and Hispanic/Latino participants without dementia enrolled in HABS‐HD were included. Variables in the cluster analysis included: annual income, state area deprivation index, social support, occupational complexity, chronic stress, and symptoms of depression and anxiety. A discriminate function analysis (DFA) tested the extent to which the individual variables predicted cluster‐group membership. ANOVAs and chi‐squared tests compared the groups on demographics. ANCOVAs controlling for age, education, sex, and race/ethnicity examined group differences in subjective cognitive concerns, objective memory and executive functioning composite scores, and plasma AD biomarkers (AB42/40, t‐tau, NfL).ResultThe cluster analysis identified 3 distinct groups: Group 1 was low resource/high distress (n = 215); Group 2 was high resource/low distress (n = 248); and Group 3 was low resource/low distress (n = 353; Figure 1). The DFA correctly classified 95.3% of participants. Results revealed significant group differences in education and the proportion of women and Black adults across the groups (see Table 1). ANCOVAs revealed significant group differences in subjective memory concerns, executive functioning composite, and plasma AD biomarkers; Group 1 consistently displayed poorer cognitive outcomes and more severe AD biomarker patterns relative to Groups 2 and 3, but there no differences in outcomes between Groups 2 and 3. No group differences on the memory composite were observed.ConclusionRisk for AD among racially/ethnically diverse older adults may differ as a function of current levels of resources and psychiatric distress. Group 1 represents individuals with low resources and high levels of distress that may be at increased risk for poor outcomes in late life. Future work should further clarify precise mechanisms of resiliency, such as social support, in Group 3 and explore longitudinal trajectories of cognitive decline across the groups.

Development of Clinical Trial Simulation Tools for Alzheimer’s Disease through the Critical Path for Alzheimer’s Disease (CPAD) Consortium

AbstractBackgroundOptimized clinical trial design remains a major challenge in drug development for Alzheimer’s Disease AD). To advance our understanding of the underlying pathophysiology of disease and help optimize clinical trial design, CPAD developed a clinical trial simulation (CTS) tool for the pre‐dementia stages of Alzheimer’s disease, using CDR‐SB as the primary endpoint. The tool is based on a disease progression model integrating baseline hippocampal volume, the effect of patient drop‐out and relevant sources of variability. The CTS was enhanced using data from contemporary clinical trials, adding fluid and imaging biomarkers of amyloid (Aβ) and tau pathology. Also included are dropout models, a placebo effect model, as well as drug‐effect models. Positron Emission Tomography (PET) measures of tau deposition, leveraging ongoing collaborative efforts to develop a standardized quantification scale across different tau tracers, will also be incorporated, to develop the first disease progression models utilizing a harmonized tau‐PET scale.MethodData were harmonized and both linear and non‐linear mixed effects models were developed for multiple endpoints, including demographics, time from and to diagnosis, genetic status (APOE4). Monte Carlo simulations were performed to compare the statistical power by sample size in trials with and without enrichment using relevant covariates. A longitudinal mixed‐effects model was fit with longitudinal tau and neurodegeneration outcomes. Cognition and biomarkers were explored in a multivariate model.ResultAs of October 2022, CPAD’s clinical trial repository contains over 7,000 unique participants that have baseline CDR‐SB measurement, 4,000+ participants with baseline Aβ measures (fluid or PET), and 2,000+ participants with baseline measures of amyloid, tau (e.g., cerebrospinal fluid p‐tau and/or tau‐PET), and neurodegeneration (e.g., MRI or neurofilament light). With these data, we modeled longitudinal trajectories of cognitive decline using baseline levels of amyloid, tau, and neurodegeneration. Future work will add models of longitudinal biomarker trajectories.ConclusionThe disease progression models will serve as the basis for a clinical trial simulation tool to facilitate design of contemporary clinical trials based on a quantitative understanding of the biological progression of key pathological features, amyloid and tau deposition, of AD.

Longitudinal Trajectories of Normal versus Abnormal Cognitive Aging in the SIU Longitudinal Cognitive Aging Study as measured by the original Alzheimer’s disease Assessment Scale and a novel extended version

AbstractBackgroundThe original Alzheimer’s disease Assessment Scale‐cognitive subscale (oADAS) is a common measure of cognition used in Alzheimer’s disease (AD) clinical trials. The oADAS may not be sensitive to pre‐dementia syndromes such as mild cognitive impairment (MCI), possibly due to ceiling effects for many of the subtests. However, little is known about the trajectories of cognitive aging on the oADAS in cognitively‐normal individuals who decline over time. We used Linear Mixed Effects Modeling to examine trajectories of cognitive aging on the oADAS, a novel extended version (eADAS), and the MMSE. We hypothesized that, compared to non‐decliners, individuals who declined from normal to MCI and/or AD (decliners) would demonstrate increased error scores on the ADAS and lower scores on the MMSE over time.MethodsWe included data from 608 older adults (72% female, mean age = 69.0 years) who were enrolled in SIU Longitudinal Cognitive Aging Study (LCAS). Total years of educational attainment ranged from 8‐26 years. The vast majority of the participants were White/Not‐Hispanic. There were 50 decliners and 558 non‐decliners included in the present analysis. The eADAS included short‐delay recall/recognition trials for the first word list, a short‐delay true‐false word recognition trial for the second word list, a letter‐digit d psychomotor test, supermarket fluency, spontaneous clock drawing, three additional items to the object naming subtest, three additional figure copies, and a similarities test. All analyses were completed after controlling for gender, education, age at first visit, time between visits, and number of visits.ResultsThere was no significant difference between the groups for age at first visit. Greater age at first visit and lower education were associated with increased error scores for both ADAS versions and MMSE in the decliner group (p’s &lt;0.001). There was no significant effect of the number of days between visits. The mean coefficients for the change in error score over time between visits for the oADAS, eADAS, and MMSE was 2.84, 10.30, and ‐1.09, respectively.ConclusionOur results indicate that the oADAS and eADAS are useful measures for examining trajectories of cognitive decline in cognitively‐normal individuals at increased risk for AD.

Predicting cognitive decline in amyloid‐positive patients with mild cognitive impairment or mild dementia

AbstractBackgroundCognitive decline rates in Alzheimer’s disease (AD) vary greatly. Disease‐modifying treatments (DMT) may alter cognitive decline trajectories, rendering their prediction increasingly relevant. We aimed to construct clinically applicable prediction models of cognitive decline in amyloid‐positive patients with mild cognitive impairment (MCI) or mild dementia.MethodsFrom the Amsterdam Dementia Cohort, we selected amyloid‐positive participants with MCI (n = 317, 66±7yrs, 45%Female, baseline MMSE 26±2) or mild dementia (n = 666, 65±7yrs, 49%Female, MMSE 22±4) and ≥2 annual MMSEs. Follow‐up was 3±2yrs with 4±2 MMSEs. Amyloid positivity was based on cerebrospinal fluid (CSF) AD biomarker concentrations or amyloid PET. We used linear mixed modelling to predict MMSE over time. First, decline trajectories were described with cubic time curves (S‐shaped). Then, we backwards selected predictors from baseline MMSE, age, sex, APOE ε4 dose, magnetic resonance imaging (MRI; visual scores: medial temporal lobe [MTA], global atrophy), CSF concentrations (phosphorylated tau [pTau], Aβ1‐42), and their interactions with linear time. MCI and mild dementia were modelled separately as stratification increased log‐likelihood.ResultsFor MCI and mild dementia, rates of cognitive decline increased over time. In MCI, average MMSE declined from 26.4(95%CI:26.2‐26.7) to 25.8(25.5‐26.1) after 18 months, to 24.2(23.7‐24.6) after three years, and to 21.0(20.1‐21.7) after five years. In mild dementia, MMSE declined from 22.4(95%CI:22.0‐22.6) to 19.8(19.4‐20.2), 15.3(14.6‐15.9), and 7.8(6.7‐8.9). For MCI, selected predictors of future MMSE were baseline MMSE, and interactions of time with age, MTA and pTau (table 1). For mild dementia, additional selected interactions with time were baseline MMSE, Aβ1‐42, and APOE ε4. Figure 1 shows the predicted decline distribution. As an example; predicted MMSE after three years for a 65yrs female MCI patient with baseline MMSE 27, would be 26.3 with MTA 0 and pTau 20pg/ml, compared to 23.8 with MTA 1 and pTau 40pg/ml (figure 2a;2b). In the latter case, 27% slower decline would lengthen follow‐up to a predicted MMSE of twenty by fourteen months.ConclusionWe constructed models for MCI and mild dementia that predict MMSE over time. These models could inform patients about their potential cognitive trajectory and aid in conversations about DMT initiation and the evaluation of their potential effect.

Cross‐omics integration analysis revels synaptic dysregulation across brain regions at later stages of Alzheimer disease

AbstractBackgroundThe clinical and molecular heterogeneity of Alzheimer disease (AD) is increasingly recognized as a major factor impacting diagnosis accuracy, the design of therapeutic interventions and clinical trials, and the development of effective treatments for AD. Recently, research efforts have leveraged molecular data to characterize AD patients into subgroups. However, the influence that such AD molecular profiles may have on cognitive decline trajectories remains poorly understood. Comprehensive studies that aggregate multiple modalities of omics have proven to be a powerful way to identify molecular profiles for human diseases including cancer. Here, we sought to determine whether cross‐omics integration can reveal molecular profiles of AD that have clinical and biological relevance.MethodWe leveraged machine learning approaches, digital deconvolution, and traditional statistical approaches to integrate and analyze multiple high‐throughput omics data from different cortical regions and cohorts, including the parietal cortex (N = 278) from the Knight Alzheimer Disease Research Center (Knight ADRC), the dorsolateral prefrontal cortex (N = 237) from the Religious Orders Study and Memory and Aging Project (ROSMAP) cohort, and the parahippocampal gyrus (BM36; N = 116) from The Mount Sinai Brain Bank (MSBB) study.ResultWe identified four distinct molecular profiles, one of which was associated with significantly worse Clinical Dementia Rating (CDR) at death, shorter survival after symptom onset, more severe neurodegeneration and astrogliosis, and unique metabolomic profiles. This profile, present in multiple cortical regions affected at distinct stages of AD progression, showed significant dysregulation of synaptic genes and pathways, indicating neuron/synapse losses and dysfunction at later stages of AD and associated with worse cognitive function. Among other molecules, we found that the expression of alpha‐synuclein (SNCA) and SNAP25 were downregulated in this profile. The synaptic dysregulation associated with this profile was further validated in a mouse model of pathological aSyn aggregation.ConclusionTogether, our results demonstrate that cross‐omics approaches can reveal multiple molecular pathways associated with AD progression, and reinforce that, in AD, prominent synaptic loss and dysfunction are closely associated with worse cognition. This may open the possibility for new biomarkers for the molecular staging of AD and cognition, as well as potential therapeutic targets to ameliorate cognitive decline and AD progression.