Early Decline Research Articles

SENSOR-ENHANCED CARE COORDINATION TO ADDRESS OLDER ADULT HEALTH: LESSONS FROM AN INTERDISCIPLINARY RESEARCH TEAM

Abstract Researchers at the University of Missouri have a history of developing and testing aging in place (AIP) care coordination models for clinical translation. Vital to this work are in-home, residentially installed sensors to detect early illness and functional decline over time. While much smart home research is focused on older adults living in continuing care retirement communities, our team developed a novel sensor-enhanced care coordination approach for community-dwelling and disabled older adults as part of two ongoing, federally funded projects. For this symposium, we will highlight the Age-friendly Sustainable Smart and Equitable Technologies for Aging in Place (ASSETs for AIP) demonstration program, funded by the Department of Health and Human Services. Our first presentation will overview our interdisciplinary approach to creating this novel care coordination model, with specifics about research team composition, population focus, and methods. Our second presentation will highlight recruitment strategies and share lessons learned from reaching and enrolling this population that often experience residential transitions. Our third presentation will overview the open-source smart home and consumer-grade technologies deployed in this work, and practical considerations for technology installation, support, and sustainability and clinical team training. Our symposium will close with a presentation of selected client case studies that illustrate how clinical care coordinators use sensor data to remotely monitor and empower older adults for behavior change via telehealth consultations. This symposium will help inform other care coordination research, particularly that which uses sensor technologies and telehealth to facilitate aging in place.

Spatiotemporal Correlation between Amyloid and Tau Accumulations Underlies Cognitive Changes in Aging.

It is poorly known how Aβ and tau accumulations associate at the spatiotemporal level in the in vivo human brain to impact cognitive changes in older adults prior to AD symptoms onset. In this study, we used a graph theory-based spatiotemporal analysis to characterize the cortical patterns of Aβ and tau deposits and their relationship with cognitive changes in the Harvard Aging Brain Study (HABS) cohort. We found that the temporal accumulations of interlinked Aβ and tau pathology display distinctive spatiotemporal correlations associated with early cognitive decline. Notably, we observed that baseline Aβ deposits-Thal amyloid phase Ⅱ-related to future increase of tau deposits, Braak stages Ⅰ-Ⅳ, both displaying linkage to the decline in multi-domain cognitive scores. We also found unimodal tau-to-tau and cognitive impairment associations in broad areas of Braak stages Ⅰ-Ⅳ. The unimodal Aβ-to-Aβ progressions were not associated with cognitive changes. Our results revealed a multifaceted correlation of the spatiotemporal Aβ and tau associations with cognitive decline over time, in which tau-to-tau and tau-Aβ interactions, and not Aβ independently, might be critical contributors to clinical trajectories toward AD in older adults.

Cerebrovascular CO2 reactivity and dynamic cerebral autoregulation through the eighth decade of life and their implications for cognitive decline

Aging is accompanied by a decrease in cerebral blood flow (CBF), especially in the presence of preclinical cognitive decline. The role of cerebrovascular physiology including regulatory mechanisms of CBF in processes underlying aging and subclinical cognitive decline is, however, not fully understood. We explored changes in cerebrovascular CO2 reactivity and dynamic cerebral autoregulation (dCA) through the eighth decade of life, and their relation with early cognitive decline. After 10.9 years, twenty-eight (age, 80.0 ± 3.5 years; 46% female) out of forty-eight healthy older adults who had participated in a previous study (age at baseline, 70 ± 4 years; 42% female), underwent repeated transcranial Doppler assessments. Linear mixed-model analyses revealed small reductions in cerebrovascular CO2 reactivity with aging (−0.37%/mmHg, P = 0.041), whereas dCA was modestly enhanced (gain: −0.009 cm/s/mmHg, P = 0.038; phase: +8.9 degrees, P = 0.004). These changes were more pronounced in participants who had developed subjective memory complaints at follow-up. Our observations confirm that dCA is not impaired in aging, despite lower cerebral perfusion and cerebrovascular reactivity. Altogether, this unique longitudinal study highlights the involvement of cerebrovascular health in preclinical cognitive decline, which is of clinical relevance in the development of dementia management strategies.

Organ aging signatures in the plasma proteome track health and disease

Animal studies show aging varies between individuals as well as between organs within an individual1–4, but whether this is true in humans and its effect on age-related diseases is unknown. We utilized levels of human blood plasma proteins originating from specific organs to measure organ-specific aging differences in living individuals. Using machine learning models, we analysed aging in 11 major organs and estimated organ age reproducibly in five independent cohorts encompassing 5,676 adults across the human lifespan. We discovered nearly 20% of the population show strongly accelerated age in one organ and 1.7% are multi-organ agers. Accelerated organ aging confers 20–50% higher mortality risk, and organ-specific diseases relate to faster aging of those organs. We find individuals with accelerated heart aging have a 250% increased heart failure risk and accelerated brain and vascular aging predict Alzheimer’s disease (AD) progression independently from and as strongly as plasma pTau-181 (ref. 5), the current best blood-based biomarker for AD. Our models link vascular calcification, extracellular matrix alterations and synaptic protein shedding to early cognitive decline. We introduce a simple and interpretable method to study organ aging using plasma proteomics data, predicting diseases and aging effects.

“I didn’t personally think it would change my life, but it has”: The experience following emergency laparotomy for older people living with frailty

Abstract Introduction Older people living with frailty are at high risk of adverse clinical outcomes following emergency laparotomy, including early death, hospital readmission and functional decline. Despite this, there is a paucity of literature exploring patient experience of surgery in this group, particularly following hospital discharge. There is therefore limited information to guide the development of service delivery models that support optimal post-operative recovery and improve overall experience. Methods Twenty older people, aged ≥65 years, with a Clinical Frailty Scale score of ≥ 4 and who had undergone emergency laparotomy were recruited from eight hospital sites. Participants were interviewed 3 weeks following their surgery, or the earliest convenient date. Interviews were undertaken either face to face or via telephone and explored the peri-operative and early recovery experience. Data were analysed using reflexive thematic analysis. Results Participants described physical, psychological, and social implications following emergency laparotomy which extended further than hospital discharge. Recovery was perceived to be an ongoing and slow process of returning to ‘normal self’. Although generally satisfied with in-hospital care, a lack of access to discharge advice and community follow up left some participants feeling ‘abandoned’ and uncertain once they returned home, hindering recovery. Conclusions Older people living with frailty experience multifaceted consequences of emergency laparotomy that result in a prolonged recovery phase and care needs. Multi-disciplinary post-operative pathways are imperative in addressing these holistic care needs. Better access to information and enhanced community support could improve patient experience and facilitate ongoing recovery.

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 in Depressive Symptoms and Midlife Brain Health

AbstractBackgroundEvidence in older adults suggests that depressive symptoms may be part of prodromal mood changes in dementia, rather than a risk factor. However, it is unclear whether variations in depressive symptoms in adulthood exhibit distinct characteristics in brain structure and cognitive function in midlife.MethodsFrom the Coronary Artery Risk Development in Young Adults study, we identified 662 Black and White participants (age 23‐36 at baseline) who completed the Center for Epidemiological Studies Depression scale (CES‐D) at six time points over 20 years. 20 years after baseline, the participants underwent magnetic resonance imaging to characterize gray matter (GM) structures (total GM, temporal cortex, hippocampus, and entorhinal cortex) and white matter hyperintensities (WMHs). Participants also completed neuropsychological tests including the Digit Symbol Substitution Test (DSST), Rey‐Auditory Verbal Learning Test (RAVLT), Stroop Test, Montreal Cognitive Assessment (MoCA), and category and letter fluency tests, analyzed as z‐scores.ResultsUsing growth mixture modeling, we identified four trajectories of depressive symptoms (Figure 1): consistently low scorers (“steady low”; n = 509, 76.9%), a class with an early peak and decline in symptoms (“declining”; n = 63, 9.5%), a class with late increases in symptoms (“increasing”; n = 49, 7.4%), and consistently high scorers (“steady high”; n = 41, 6.2%). Compared to the steady low class, the steady high class had lower entorhinal cortex volume (β: ‐180.80, 95% CI: ‐336.69 to ‐24.91). The increasing class, had more WMHs (β: 0.55, 95% CI: 0.22 to 0.89) and less total brain volume (β: ‐9269.25, 95% CI: ‐17104.67 to ‐1444.82). Both the steady high and the increasing classes had poorer performance on the Stroop task (β: ‐0.40, 95% CI: ‐0.70 to ‐0.10; β: ‐0.39, 95% CI: ‐0.64 to ‐0.14, respectively), however the steady high also had poorer DSST performance (β: ‐0.40, 95% CI: ‐0.67 to ‐0.13), while the increasing class had poorer performance on the MoCA (β: ‐1.20, 95% CI: ‐2.04 to ‐0.37). The declining group was not significantly different from the steady low group on any brain or cognitive measures.ConclusionsOur findings suggest that trajectories in depressive symptoms in young to mid‐adulthood show different cognitive and brain phenotypes in midlife.

Microstructural integrity and structural connectivity of medial temporal lobe subregions is related to Alzheimer’s pathology

AbstractBackgroundThe earliest effects of Alzheimer’s disease pathology on medial temporal lobe (MTL) gray matter microstructure and white matter connectivity are still unknown. Here, we applied ultra‐high‐resolution diffusion MRI to characterize microstructural integrity and structural connectivity of the MTL, and tested associations with Alzheimer’s pathology and memory performance in cognitively normal older adults.Method83 participants (M = 71.7yrs, 66% female) received structural and ultra‐high‐resolution diffusion MRI (0.67×0.67×3mm). Subsamples received [18F]‐florbetapir (FBP) to measure Aß (n = 77; global summary SUVR), [18F]‐MK6240 to measure tau (n = 23; entorhinal SUVR), and performed an object mnemonic discrimination task (n = 74). Structural MRI was processed with ASHS to obtain MTL ROIs (Fig1). Diffusion MRI was processed with FSL DTIfit to calculate mean diffusivity (MD), an index of cellular integrity. Tractography was performed with DSI Studio. ROI‐to‐ROI streamlines were calculated (Fig3A) and used for structural connectivity graph analyses of the MTL network. Analyses controlled for age and sex, with scan interval and education in relevant models.ResultEntorhinal MK6240 SUVR was associated with increased MD in transentorhinal cortex (BA35; r = 0.72, p<0.001), entorhinal cortex (EC; r = 0.61, p = 0.004), and dentate gyrus (DG; r = 0.46, p = 0.04; Fig2A), while global FBP SUVR was associated with higher MD in EC (r = 0.25, p = 0.03). Worse mnemonic discrimination performance was associated with increased MD in DG (r = ‐0.29, p = 0.01), with trends in EC (r = ‐0.23, p = 0.06) and CA3 (r = ‐0.20, p = 0.09; Fig2B). The number of streamlines between EC and DG was not significantly associated with entorhinal MK6240 SUVR (r = ‐0.32, p = 0.17), FBP SUVR, or mnemonic discrimination (ps>0.37). Lower global efficiency of the MTL network was associated with increased entorhinal MK6240 SUVR (r = ‐0.50, p = 0.025) yet better mnemonic discrimination performance (r = ‐0.28, p = 0.02; Fig3B).ConclusionEntorhinal tau pathology and memory performance are closely related to microstructural integrity and structural connectivity of the MTL, suggesting tau‐related MTL neurodegeneration may drive early memory decline.

Primary reverse total shoulder arthroplasty for fractures requires more revisions than for degenerative conditions 1 year after surgery: an analysis from the Dutch Arthroplasty Register.

Although reverse total shoulder arthroplasty (RTSA) is considered a viable treatment strategy for proximal humeral fractures, there is an ongoing discussion of how its revision rate compares with indications performed in the elective setting. First, this study evaluated whether RTSA for fractures conveyed a higher revision rate than RTSA for degenerative conditions (osteoarthritis, rotator cuff arthropathy, rotator cuff tear, or rheumatoid arthritis). Second, this study assessed whether there was a difference in patient-reported outcomes between these 2 groups following primary replacement. Finally, the results of conventional stem designs were compared with those of fracture-specific designs within the fracture group. This was a retrospective comparative cohort study with registry data from the Netherlands, generated prospectively between 2014 and 2020. Patients (aged ≥ 18 years) were included if they underwent primary RTSA for a fracture (<4 weeks after trauma), osteoarthritis, rotator cuff arthropathy, rotator cuff tear, or rheumatoid arthritis, with follow-up until first revision, death, or the end of the study period. The primary outcome was the revision rate. The secondary outcomes were the Oxford Shoulder Score, EuroQol 5 Dimensions (EQ-5D) score, numerical rating scale score (pain at rest and during activity), recommendation score, and scores assessing change in daily functioning and change in pain. This study included 8753 patients in the degenerative condition group (mean age, 74.3±7.2 years) and 2104 patients in the fracture group (mean age, 74.3±7.8 years). RTSA performed for fractures showed an early steep decline in survivorship: Adjusted for time, age, sex, and arthroplasty brand, the revision risk after 1 year was significantly higher in these patients than in those with degenerative conditions (hazard ratio [HR], 2.50; 95% confidence interval, 1.66-3.77). Over time, the HR steadily decreased, with an HR of 0.98 at year 6. Apart from the recommendation score (which was slightly better within the fracture group), there were no clinically relevant differences in the patient-reported outcome measures after 12 months. Patients who received conventional stems (n=1137) did not have a higher likelihood of undergoing a revision procedure than those who received fracture-specific stems (n=675) (HR, 1.70; 95% confidence interval, 0.91-3.17). Patients undergoing primary RTSA for fractures have a substantially higher likelihood of undergoing revision within the first year following the procedure than patients with degenerative conditions preoperatively. Although RTSA is regarded as a reliable and safe treatment option for fractures, surgeons should inform patients accordingly and incorporate this information in decision making when opting for head replacement surgery. There were no differences in patient-reported outcomes between the 2 groups and no differences in revision rates between conventional and fracture-specific stem designs.

Serum biomarkers for prognosis and monitoring in asymptomatic Alzheimer’s Disease

AbstractBackgroundPrognostic and monitoring capabilities of blood‐based biomarkers for early cognitive decline, amyloid‐β (Aβ) accumulation and grey matter (GM) loss in the asymptomatic phase of Alzheimer disease require further investigation over extended time periods. This longitudinal study investigated serum glial fibrillary acidic protein (GFAP), neurofilament light (NfL) and Aβ1‐42/Aβ1‐40 as potential prognostic and monitoring biomarkers in cognitively unimpaired (CU) elderly.MethodWe included 184 CU older adults (Table 1) who underwent baseline serum sampling and 2‐yearly neuropsychological assessment for up to 10 years (median: 6 years, Figure 1). A subset (N = 109) underwent serial blood sampling, Aβ‐PET and structural MRI at 2 (N = 73), 3 (N = 34), or 4 (N = 2) time points (time interval: 2‐10 years, median: 6 years). Cognition was evaluated in memory, language and executive functioning domains through conversion of neuropsychological tests scores to standardised z‐scores and subsequent averaging per domain. Aβ‐accumulation and GM loss were analysed in a voxelwise manner using Voxelstats with cluster‐level p RFT&lt;.05 and voxel‐level puncorrected&lt;.001 significance thresholds.[1] Aβ1‐42/Aβ1‐40 was inverted to facilitate biomarker comparison. Linear‐mixed effects models were used to (i) identify the predictive value of serum biomarkers for longitudinal cognitive decline, Aβ‐accumulation and GM loss, (ii) calculate annual serum biomarker rates of change and (iii) investigate associations between longitudinal biomarker changes and longitudinal changes in cognition, Aβ‐PET and GM volume.ResultHigh baseline GFAP and NfL predicted a longitudinal decline in memory (βGFAP*time = ‐0.020, pFDR = 0.03; βNfL*time = ‐0.031, pFDR = 0.002) and language (βGFAP*time = ‐0.025, pFDR&lt;0.001, βNfL*time = ‐0.017, pFDR = 0.04), and longitudinal GM loss within the hippocampus. Low Aβ1‐42/Aβ1‐40 predicted memory decline (βAβ1‐42/Aβ1‐40*time = ‐0.022, pFDR = 0.03) as well as Aβ accumulation within Aβ‐vulnerable regions (Figure 1). Serum GFAP (βtime = 0.022, pFDR&lt;.001) and NfL (βtime = 0.040, pFDR&lt;.001) increased over time. Longitudinal serum NfL increases were associated with a concomitant decline in memory (β∆NfL*time = ‐0.031, pFDR= .004), language (β∆NfL*time = ‐0.022, pFDR= .01) and GM volume (Figure 2). Serum GFAP increases were associated with precuneal Aβ accumulation. Aβ1‐42/Aβ1‐40 decreases were stronger in Aβ‐ subjects (pFDR&lt;.001) and did not associate with Aβ accumulation, nor any other longitudinal marker.ConclusionSerum GFAP, NfL and Aβ1‐42/Aβ1‐40 are prognostic and/or monitoring biomarkers providing complementary information by reflecting different Aβ‐dependent or ‐independent pathophysiological processes.REFERENCE[1] Mathotaarachchi et al. Front Neuroinform 2016.

Microstructural integrity and structural connectivity of medial temporal lobe subregions is related to Alzheimer’s pathology

AbstractBackgroundThe earliest effects of Alzheimer’s disease pathology on medial temporal lobe (MTL) gray matter microstructure and white matter connectivity are still unknown. Here, we applied ultra‐high‐resolution diffusion MRI to characterize microstructural integrity and structural connectivity of the MTL, and tested associations with Alzheimer’s pathology and memory performance in cognitively normal older adults.Method83 participants (M = 71.7yrs, 66% female) received structural and ultra‐high‐resolution diffusion MRI (0.67×0.67×3mm). Subsamples received [18F]‐florbetapir (FBP) to measure Aβ (n = 77; global summary SUVR), [18F]‐MK6240 to measure tau (n = 23; entorhinal SUVR), and performed an object mnemonic discrimination task (n = 74). Structural MRI was processed with ASHS to obtain MTL ROIs (Fig1). Diffusion MRI was processed with FSL DTIfit to calculate mean diffusivity (MD), an index of cellular integrity. Tractography was performed with DSI Studio. ROI‐to‐ROI streamlines were calculated (Fig3A) and used for structural connectivity graph analyses of the MTL network. Analyses controlled for age and sex, with scan interval and education in relevant models.ResultEntorhinal MK6240 SUVR was associated with increased MD in transentorhinal cortex (BA35; r = 0.72, p&lt;0.001), entorhinal cortex (EC; r = 0.61, p = 0.004), and dentate gyrus (DG; r = 0.46, p = 0.04; Fig2A), while global FBP SUVR was associated with higher MD in EC (r = 0.25, p = 0.03). Worse mnemonic discrimination performance was associated with increased MD in DG (r = ‐0.29, p = 0.01), with trends in EC (r = ‐0.23, p = 0.06) and CA3 (r = ‐0.20, p = 0.09; Fig2B). The number of streamlines between EC and DG was not significantly associated with entorhinal MK6240 SUVR (r = ‐0.32, p = 0.17), FBP SUVR, or mnemonic discrimination (ps&gt;0.37). Lower global efficiency of the MTL network was associated with increased entorhinal MK6240 SUVR (r = ‐0.50, p = 0.025) yet better mnemonic discrimination performance (r = ‐0.28, p = 0.02; Fig3B).ConclusionEntorhinal tau pathology and memory performance are closely related to microstructural integrity and structural connectivity of the MTL, suggesting tau‐related MTL neurodegeneration may drive early memory decline.

Testing the Feasibility of Tracking Behavior and Physiology Patterns using Wearable Technology to Detect Potential Cognitive Risk among Rural, Multicultural Older Adults

AbstractBackgroundRural residents often experience multiple factors that increase their risk of cognitive impairment, e.g. less access to formal education and specialized healthcare (Livingston et al., 2020). Early, accurate diagnosis in the mild cognitive impairment stage could save up to 7.9 trillion dollars in health and long‐term care costs by 2050 (Alzheimer’s Association, 2021). First year results from this federally‐funded new investigator mentoring program between computer science researchers and a nurse investigator are reported. The purpose was to test the feasibility of a novel approach to detecting early cognitive decline using smartwatch technology among a 92% racially and ethnically diverse rural community challenged by low digital access (51%) and digital literacy (48%).MethodWe tested smartwatches programmed with the novel “n‐back” shape test for cognitive assessment, ecological momentary assessment, and movement tracking. Paper‐and pencil measures to assess cognitive risk were the PROMIS ® Applied Cognition, Mini‐Cog (Borson, 2003), and Mini‐MoCA (Nasreddine, 2019). Another novel aspect of the study was hiring the local high school students to mentor older adults in use of smartwatches during home visits. Student engagement with their mentees was monitored by their high school advisor, with oversight by well‐known faith‐based research assistants. Six additional pre‐post intervention surveys included “Technology Activities of Daily Living (T‐ADL) (Muñoz‐Neira, 2012) and “Relating to Older People” (Cherry and Palmore, 2008).ResultMean years lived rural of the n = 28 enrolled was 60.3 (SD = 18.2), with an average age of 71.6 (SD = 8.1). Results regarding cognitive risk using the n‐back Shape test included correlation with Ecological Momentary Assessment Question 7 (“Right now, my environment is distracting”; r = ‐.35, p = .046) and approached significance with Question 1 (“In the past 2 hours, do you think your mind has been as sharp as usual?”; r = .33, p = .058). Pre‐post correlations in T‐ADL (r = .87) and Applied Cognition (r = .38) were significant (p &lt; .001, p &lt; .04).ConclusionTesting this novel two‐pronged approach of detecting cognitive risk through smartwatch technology, and hiring high school students to mentor older adults in the home setting yielded implications for ADRD‐related research among larger samples of underrepresented groups.

Clinical Manifestations.

Subjective cognitive complaints (SCC) in the elderly may represent an early cognitive marker of Alzheimer's disease (AD). Currently, there is no recommended gold standard for evaluating SCC. In addition, available questionnaires were originally developed to identify individuals at later stages of AD. Based on previous research, we constructed the Subjective Cognitive Changes Questionnaire (SCC-Q), which includes items potentially sensitive to early cognitive decline. The study aimed to analyse reliability of the individual items and construct and convergent validity of the SCC-Q. A total of 211 non-demented participants over the age of 55 from the Czech Brain Aging Study (CBAS) underwent a comprehensive neuropsychological examination, based on which they were classified into two groups: patients with amnestic mild cognitive impairment (n = 88) and those with subjective cognitive decline (SCD) (n = 88). Further, we included cognitively healthy volunteers without SCC (n = 35). All participants completed the novel 26-item SCC-Q in which they evaluated change in specific cognitive abilities during the last 5 years on a 5-point Likert scale. The structure of the SCC-Q was explored using the principal component analysis (PCA) with varimax rotation. To analyse associations between the components and cognitive performance (convergent validity), non-parametric partial rank correlations controlled for sex, age and education were performed. Cognitive performance was expressed as composite z-scores representing memory, attention and working memory, language, executive, and visuospatial function. Item analysis showed that all included items were highly reliable (McDonald's Ω > 0.9, Cronbach's α > 0.9). Using the PCA, four components explaining 57.6% of the variance were identified. Two of them were significantly associated with memory, attention and working memory, and executive or visuospatial function, respectively (all ps ≤ 0.048). The component more closely associated with cognitive domains includes items related to spatial orientation changes. The SCC-Q has the potential to become a valuable method for clinical practice in identifying individuals at risk of developing cognitive deficits. Further analyses are needed to confirm its validity.

Late‐life hypertension acts together with amyloid‐β pathology to promote early cognitive decline

AbstractBackgroundMidlife hypertension (HTN) is a known risk factor for Alzheimer’s Disease (AD) development. However, whether the same effect is observed in older individuals, at risk for AD, remains to be elucidated. Here, we aimed to assess whether late‐life HTN and the presence of amyloid‐β pathology (Aβ) interact to promote longitudinal cognitive decline in cognitively unimpaired (CU) individuals, and if systolic blood pressure (SBP) levels moderate this interaction. Interactive effect of disease pathophysiology is crucial for dementia prevention strategies.MethodWe used two independent cohorts. We evaluated 475 CU individuals over 65 years of age from the ADNI cohort, with available baseline medical data and CSF Elecsys biomarkers (Aβ1‐42 and p‐tau181), as well as longitudinal clinical assessments with neuropsychological testing (up to 6 years); the individuals were classified as (A)+ or (A)‐ based on a previously proposed cut‐off of CSF p‐tau181/Aβ1‐42 lower than 0.025. We also evaluated 162 CU individuals over 65 years of age from the TRIAD cohort with baseline clinical data and Aβ‐PET, as well as longitudinal clinical assessments with neuropsychological testing (up to 3 years). The individuals were classified as (A)+ or (A)‐ based on Aβ‐PET positivity. All individuals were classified as positive or negative for hypertension based on medical history. For the ADNI cohort, we could also evaluate the SBP levels as a continuous variable.ResultLinear mixed‐effects (LME) models showed that HTN and Aβ acted together to promote longitudinal cognitive decline (ADNI: HTN X Aβ X Time, β = ‐0.44, p = 0.001, figure 1, TRIAD: HTN X Aβ X Time, β = ‐0.64, p &lt; 0.001, figure 2). Also, we could see that higher SBP levels acted together with Aβ to promote further cognitive decline (SBP values X Aβ X Time, β = ‐0.011, p = 0.03, figure 3).ConclusionOur results support a framework in which late‐life HTN is a modifiable risk factor for cognitive decline in CU individuals at increased risk for AD. This supports further investigation in determining the best target of SBP levels for individuals at risk for AD, in the context of offering precision medicine for this population.

Modeling and Simulation to Inform Potential New Strategies for BACEi in Treatment of Alzheimers Disease

AbstractBackgroundThe β‐secretase‐1 inhibitors (BACEi), including verubecestat, were extensively studied in prodromal to moderate AD and demonstrated early cognitive decline at doses achieving &gt;50% inhibition of amyloid production. Questions remain as to whether BACEi may still have utility, if used earlier in disease, in combination with other therapies, and/or at lower doses. Modeling and simulation approaches were used to maximize learnings from the prior trials and to make predictions around potential alternative therapeutic approaches.MethodTwo models were developed: 1) A joint plaque turnover model, linking drug exposure with amyloid plaque load (PET data in centiloids), to describe natural progression and treatment response to BACEi and amyloid mAb therapies. All plaque data from natural progression (control arms), BACEi and amyloid mAb treatment arms were well described by a joint model, after accounting for differences in baseline plaque load, dosing and titration regimens, and mechanism of action. 2) Disease progression models for the clinical efficacy outcomes (ADAS‐cog11, CDR‐SB) were developed using a methodology similar to Ref. Evidence for BACEi drug effects (on progression rate and/or early detrimental offset) was tested and dependencies on dose, drug exposure and patient characteristics were explored.ResultThe joint plaque turnover model well described natural progression and treatment response to BACEi and amyloid mAb and allows for prediction of alternative regimens and therapeutic approaches. Simulations indicate that 20‐30% inhibition of BACE would be sufficient to stabilize plaque loads in prodromal patients. Disease progression results for ADAS‐cog11 and CDR‐SB identified a verubecestat drug effect of early detrimental offset with no effect on rate of disease progression supported. The magnitude of the early detrimental offset was small relative to the overall progression seen over 18‐24 months and results suggested that this effect may be reduced in advanced AD patients. A simulation engine utilizing the progression model allows for estimation of sample size needed to check for the detrimental effect in future trials.ConclusionModel‐based simulations can help inform future BACEi therapeutic strategies and trial designs. Reference: Clin Pharmacol Ther. 2015 Mar;97(3):210‐4

Data‐driven neuroanatomical subtype trajectories of primary progressive aphasia

AbstractBackgroundPrimary progressive aphasia (PPA) is an atypical neurodegenerative dementia with three main clinical variants: semantic (svPPA), non‐fluent/agrammatic (nfvPPA) and logopenic (lvPPA). While svPPA is typically associated with left anterior temporal lobe atrophy, neuroimaging findings in nfvPPA, lvPPA and PPA not otherwise specified (PPA‐nos) are more variable. We therefore applied unsupervised machine learning to one of the largest databases of PPA magnetic resonance imaging (MRI) scans ever acquired.MethodBaseline MRI scans were collected from 273 people with PPA participating in the longitudinal research programme in the Queen Square Dementia Research Centre (svPPA n = 97; nfvPPA n = 109; lvPPA n = 51; PPA‐nos n = 16). Longitudinal scans were collected from 138 participants. We used Subtype and Stage Inference (SuStaIn), a model that combines disease progression modelling with unsupervised machine learning to identify subgroups of patients following a similar trajectory. We standardised the data with respect to cognitively normal controls (n = 360). Based on clinicians’ input we chose 19 brain regions of interest to model from those generated by GIF (Cardoso et al. 2012). The model was fit using baseline data, selecting the appropriate number of subtypes using cross‐validation (Young et al. 2018).ResultCross‐validation supported four subtypes of spatiotemporal atrophy (Figure 1). Clinical variant representation per subtype assignment (1/2/3/4) was: svPPA (n = 74/15/4/4), nfvPPA (n = 3/35/21/44), lvPPA (n = 1/18/30/2) and PPA‐nos (n = 7/4/3/2) (Figure 2). Subtype 1 showed early decline in the left anterior temporal lobe and sparing of the left frontal lobe. Subtype 2 showed early decline of the left insula and sparing of the temporal pole. Subtype 3 showed initial atrophy of the left temporoparietal junction; Subtype 4 showed early atrophy in the left precentral gyrus. Statistical analysis of posterior distributions found low similarity between subtypes (Hellinger distance &gt; 0.66) (Figure 3), validating the presence of four distinct subtypes.ConclusionWhile the data‐driven atrophy subtypes did not align perfectly with clinical variants, we found a correspondence between Subtype 1 and svPPA, the most coherent PPA variant. Subtypes 2,3 and 4 were associated with nfvPPA and lvPPA, corroborating work suggesting these diagnostic terms encompass multiple clinical phenotypes. Future work will investigate the association between clinical diagnosis, longitudinal subtype, and clinical measures.

Indexing the Predictive Utility of Real‐World Sleep to Discriminate Early Alzheimer’s Disease

AbstractBackgroundSleep dysfunction is one of earliest symptoms of Alzheimer’s disease (AD) and can accelerate cognitive decline, increasing risk of AD. Emerging technologies, like actigraphy, allow for continuous monitoring of real‐world sleep. Our goal is to determine the predictive utility of real‐world sleep dysfunction features for indexing warning signs of incipient dementia like mild cognitive impairment (MCI).MethodSubjects were classed as cognitively neurotypical (N = 31) or impaired: MCI (n = 51) or AD (N = 7) based on NIA‐AA diagnostic criteria, as evaluated by a neurologist from clinical exam, neuropsychological, and demographic assessments. (Table 1) Each subject was tracked for 3 months with wrist‐worn actigraphy. Self‐reported sleep diaries validated real‐world sleep data. Sleep duration and quality were estimated across means and standard deviations (SD) of: Total Sleep Time (TST), Sleep Latency (SL), Sleep Efficiency (SE), Wakefulness After Sleep Onset (WASO), and Awakening Count (AC). An optimal logistic regression model, adjusting for demographics (age, gender, education), was quantified and used to rank the explanatory value of sleep covariates to discriminate impaired vs. typical aging using stepwise selection.ResultAcross 6,530 validated, nightly sleep periods (mean = 73.4 ± 17.0 sleep periods per subject), we found best discrimination of impaired vs. typical aging, ranked from best to least discrimination (Table 2), for average sleep efficiency [SE] (b = 3.58, p = 0.03), nightly wakefulness [WASO] (b = 3.42, p = 0.04), variability of daily sleep quality [SE] (b = 1.65, p = 0.001), and awakening frequency [AC] (b = ‐0.79, p = .03). TST did not discriminate impaired from typical aging.ConclusionResults align with prior literature, demonstrating that sleep quality is an optimal feature to track early warning signs of dementia from real‐world sleep. Critically, SE shows the most promise for discriminating early decline while TST did not show predictive utility in this study. Digital fingerprints of sleep behavior in the real world offer unique added value to neurobiological biomarker evidence of preclinical disease, improving early detection of AD risk to bolster interventions aimed at treating AD—‐even its earliest stages.

Data‐driven neuroanatomical subtype trajectories of primary progressive aphasia

AbstractBackgroundPrimary progressive aphasia (PPA) is an atypical neurodegenerative dementia with three main clinical variants: semantic (svPPA), non‐fluent/agrammatic (nfvPPA) and logopenic (lvPPA). While svPPA is typically associated with left anterior temporal lobe atrophy, neuroimaging findings in nfvPPA, lvPPA and PPA not otherwise specified (PPA‐nos) are more variable. We therefore applied unsupervised machine learning to one of the largest databases of PPA magnetic resonance imaging (MRI) scans ever acquired.MethodBaseline MRI scans were collected from 273 people with PPA participating in the longitudinal research programme in the Queen Square Dementia Research Centre (svPPA n = 97; nfvPPA n = 109; lvPPA n = 51; PPA‐nos n = 16). Longitudinal scans were collected from 138 participants. We used Subtype and Stage Inference (SuStaIn), a model that combines disease progression modelling with unsupervised machine learning to identify subgroups of patients following a similar trajectory. We standardised the data with respect to cognitively normal controls (n = 360). Based on clinicians’ input we chose 19 brain regions of interest to model from those generated by GIF (Cardoso et al. 2012). The model was fit using baseline data, selecting the appropriate number of subtypes using cross‐validation (Young et al. 2018).ResultCross‐validation supported four subtypes of spatiotemporal atrophy (Figure 1). Clinical variant representation per subtype assignment (1/2/3/4) was: svPPA (n = 74/15/4/4), nfvPPA (n = 3/35/21/44), lvPPA (n = 1/18/30/2) and PPA‐nos (n = 7/4/3/2) (Figure 2). Subtype 1 showed early decline in the left anterior temporal lobe and sparing of the left frontal lobe. Subtype 2 showed early decline of the left insula and sparing of the temporal pole. Subtype 3 showed initial atrophy of the left temporoparietal junction; Subtype 4 showed early atrophy in the left precentral gyrus. Statistical analysis of posterior distributions found low similarity between subtypes (Hellinger distance &gt; 0.66) (Figure 3), validating the presence of four distinct subtypes.ConclusionWhile the data‐driven atrophy subtypes did not align perfectly with clinical variants, we found a correspondence between Subtype 1 and svPPA, the most coherent PPA variant. Subtypes 2,3 and 4 were associated with nfvPPA and lvPPA, corroborating work suggesting these diagnostic terms encompass multiple clinical phenotypes. Future work will investigate the association between clinical diagnosis, longitudinal subtype, and clinical measures.

Amygdala tau deposition best discriminates between stages of memory impairment

AbstractBackgroundThe recent development of positron emission tomography (PET) radiotracers for tau aggregates allows quantifying the regional tau burden in‐vivo. We aimed to investigate the associations between regional tauopathy and episodic memory outcomes in the AD spectrum from clinically normal to the dementia stage.MethodWe recruited 111 participants between 2019 and 2023 at the Memory Clinic of Saint‐Luc University Hospital. Each participant underwent [F18]‐MK6240 tau‐PET, 3DT1 MRI, cognitive assessment, and either lumbar puncture or [F18]‐Flutemetamol PET for amyloid status. Tau burden was quantified using Freesurfer by referring to the Desikan‐Kiliany lobe cortical parcellation, as well as in several medio‐temporal regions (MTL; i.e., entorhinal cortex, amygdala, hippocampus). Memory was assessed using three Z‐scores from the FCSRT: Sum of Free Recall (SFR), Sum of Total Recall (STR), and Sum of Delayed Free Recall (DFR). The average of these scores forms the Memory Composite Score (MCS). Participants were classified in three groups (Table 1): amyloid‐negative clinically normal participants (n = 44); non‐demented participants with AD pathology (early AD, n = 45); and AD dementia participants (n = 22). Based on their SFR and STR scores, subjects were also classified as having retrieval and/or encoding memory deficits according to a Stage of Objective Memory Impairment (SOMI) classification, inspired from Grober and colleagues (2018). Analyses were corrected for multiple comparisons.ResultIn early AD, SFR and DFR correlated with tauopathy in the bilateral entorhinal cortex (EC) and amygdala (Fig.1). MCS correlated with tauopathy in the bilateral amygdala, and the right frontal lobe. Tau levels did not correlate with STR in any regions. Moreover, MTL tauopathy differed between SOMI stages: Specifically, amygdala tauopathy gradually increased with retrieval deficits (SOMI1‐2) and encoding deficits (SOMI3‐4). In contrast, tauopathy in the EC, the temporal lobe, and the parietal lobe was elevated in all participants with memory deficits, but did not distinguish between retrieval and encoding deficits (Table 2).ConclusionTauopathy in the MTL ROIs is predictive of retrieval deficits in early stage of AD. SFR is a more sensitive measure of this early decline compared to STR. Amygdala tauopathy differs between patients with retrieval and encoding deficits.

Exploring mechanisms of improvement in the Active Brains intervention for older adults with chronic pain and early cognitive decline

ObjectivesWe iteratively developed, optimized, and established the feasibility of a virtual, group-based, mind-body activity program (Active Brains, AB), supported by Fitbit for older adults with chronic pain (CP) and early cognitive decline (ECD). Guided by the principles of the NIH stage model we 1) explore signals of improvement in AB outcomes and hypothesized mechanisms of action and 2) explore relationships between changes in outcomes with changes in mechanisms. MethodsParticipants were N = 15 older adults (age ≥ 60) with CP–ECD from two NIH stage 1 pilot studies of AB. We conducted paired t-tests to explore pre–post improvements, and correlations to investigate associations between changes in outcomes and mechanisms. ResultsWe observed small to large improvements across co-primary and secondary outcomes (d = 0.24–1.09). We observed small to medium improvements in 4 out of 5 hypothesized mechanisms (d = 0.23–0.47). Overall, improvements in outcomes had moderate to large associations with improvements in hypothesized mechanisms. ConclusionAB was associated with improvements across several co-primary and secondary outcomes, and hypothesized mechanisms. Pain-specific coping and general coping skills are promising treatment targets to address the CP–ECD comorbidity among older adults.