Validation of Different Dementia Code-based Definitions in a Population-based Study of Rheumatoid Arthritis.

OBJECTIVE: Rheumatoid arthritis (RA) has been associated with an elevated dementia risk. The study aimed to examine how different diagnostic dementia definitions perform in those with RA compared to individuals without RA. METHODS: This study population included 2050 individuals (1025 with RA) from a retrospective population-based cohort in southern Minnesota and compared the performance of three code-based dementia diagnostic algorithms with medical record review diagnosis of dementia. For the overall comparison, no time frames were used, and each patient's complete medical history was used. Sensitivity analyses were performed using 1, 2, and 5-year windows around the date that dementia was identified in the medical record (reference standard). RESULTS: Algorithms performed very similarly in persons with and without RA. The algorithms generally had high specificity, negative predictive values, and accuracy, regardless of the time window studied (>88%). Sensitivity and positive predictive values varied depending on the algorithm and the time window studied. Sensitivity values ranged from 56.5% to 95.9%, and positive predictive values ranged from 55.2% to 83.1%. Performance measures declined with more restrictive time windows. CONCLUSION: Routinely collected electronic health record (EHR) data was used to define code-based dementia diagnosis algorithms with good performance (vs. diagnosis by medical record review). These results can inform future studies that use retrospective databases (especially in the same or similar EHR infrastructure) to identify dementia in individuals with RA.

Association of Anxiety and Unspecified Emotional Distress Obtained from a Medical Records Linkage System with Incident Cognitive Outcomes in a Population-Based Setting.

Background: Studies that assess cognition prospectively and study in detail anxiety history in the participants' medical records within the context of brain aging and Alzheimer's disease are limited. Objective: To examine the associations of anxiety and unspecified emotional distress (UED) acquired throughout a person's life with prospectively collected cognitive outcomes. Methods: Mayo Clinic Study of Aging participants who were cognitively unimpaired at baseline were included. Anxiety and UED data were abstracted from the medical record using the Rochester Epidemiology Project (REP) resources and were run separately as predictors in our models. The data were analyzed using Cox proportional hazards models for the outcomes of incident mild cognitive impairment (MCI) and dementia and using linear mixed effects models for the outcomes of global and domain specific cognitive z-scores and included key covariates. Results: The study sample (n = 1,808) had a mean (standard deviation) age of 74.5 (7.3) years and 51.4% were male. Anxiety was associated with increased risk of MCI and dementia and was associated with lower baseline cognitive z-scores and accelerated decline over time in the global, memory, and attention domains. UED was associated with faster decline in all domains except visuospatial but did not show evidence of association with incident cognitive outcomes. These results varied by medication use and timing of anxiety. Conclusions: Anxiety and UED both showed inverse associations with cognition. Utilization of anxiety and UED data from across the life course, as available, from the REP system adds robustness to our results.

Matching Patients to Clinical Trials using LLaMA 2 Embeddings and Siamese Neural Network.

Patient recruitment is a key desideratum for the success of a clinical trial that entails identifying eligible patients that match the selection criteria for the trial. However, the complexity of criteria information and heterogeneity of patient data render manual analysis a burdensome and time-consuming task. In an attempt to automate patient recruitment, this work proposes a Siamese Neural Network-based model, namely Siamese-PTM. Siamese-PTM employs the pretrained LLaMA 2 model to derive contextual representations of the EHR and criteria inputs and jointly encodes them using two weight-sharing identical subnetworks. We evaluate Siamese-PTM on structured and unstructured EHR to analyze their predictive informativeness as standalone and collective feature sets. We explore a variety of deep models for Siamese-PTM's encoders and compare their performance against the Single-encoder counterparts. We develop a baseline rule-based classifier, compared to which Siamese-PTM improved performance by 40%. Furthermore, visualization of Siamese-PTM's learned embedding space reinforces its predictive robustness.

Associations of mid-to-late-life inflammation with late-life mobility and the influences of chronic comorbidities, race, and social determinants of health: The Atherosclerosis Risk in Communities Study.

BACKGROUND: Relationships of midlife inflammation with late-life mobility and influences of chronic health conditions, race, and social determinants of health (SDoH) on these relationships are poorly understood. METHODS: Among 4758 community-dwelling participants (41% men, 20% Black), high-sensitivity C-reactive protein (hsCRP) was measured over 20+ years: in midlife at study visit 2 (V2: 1990-1992, 47-68 years); at V4 (1996-1998, 53-74 years); and with concurrent late-life 4-m gait speed at V5 (2011-2013, 67-88 years, mean 75 years). SDoH measures included race, the national-rank area deprivation index, education, and income. We examined associations of late-life gait speed with midlife hsCRP (V2 continuous and clinically high ≥3 mg/L), with 20-year hsCRP history from midlife (V2-V5 average continuous hsCRP and clinically high ≥3 mg/L) and with inflammation accumulation (visits and years with high hsCRP). Regression models adjusted for demographic, cardiovascular, and SDoH measures; effect modification by the presence of other common chronic conditions (obesity, diabetes, hypertension) and race were examined, with and without accounting for SDoH. RESULTS: High midlife hsCRP was associated with slower late-life gait speed, even among those without chronic conditions in midlife: -4.6 cm/s (95% CI: -6.4, -2.8). Importantly, sustained high hsCRP was associated with a 20-year slowing of -10.0 cm/s (-14.9, -5.1) among those who never experienced obesity, diabetes, or hypertension over the 20-year period. Associations were similar between Black participants, -3.8 cm/s (-6.9, -0.7) and White participants -3.3 (-4.5, -2.2) per interquartile range of midlife hsCRP; effect modifications by chronic conditions and race were unsupported throughout. Results were robust to accounting for SDoH or otherwise; however, worse SDoH was associated with higher inflammation and slower gait speed in both Black and White participants. CONCLUSIONS: Inflammation in midlife may contribute to clinically meaningful late-life slowing of gait speed, even among otherwise healthy-appearing adults and regardless of race and socioeconomic disadvantage. Regular monitoring and interventions for inflammation may be warranted from midlife.

Complex relationships of socioeconomic status with vascular and Alzheimer's pathways on cognition.

INTRODUCTION: AD and CVD, which frequently co-occur, are leading causes of age-related cognitive decline. We assessed how demographic factors, socioeconomic status (SES) as indicated by education and occupation, vascular risk factors, and a range of biomarkers associated with both CVD (including white matter hyperintensities [WMH], diffusion MRI abnormalities, infarctions, and microbleeds) and AD (comprising amyloid-PET and tau-PET) collectively influence cognitive function. METHODS: In this cross-sectional population study, structural equation models were utilized to understand these associations in 449 participants (mean age (SD) = 74.5 (8.4) years; 56% male; 7.5% cognitively impaired). RESULTS: (1) Higher SES had a protective effect on cognition with mediation through the vascular pathway. (2) The effect of amyloid directly on cognition and through tau was 11-fold larger than the indirect effect of amyloid on cognition through WMH. (3) There is a significant effect of vascular risk on tau deposition. DISCUSSION: The utilized biomarkers captured the impact of CVD and AD on cognition. The overall effect of vascular risk and SES on these biomarkers are complex and need further investigation.

Assessing the Impact of Overactive Bladder Medications on Cognition.

IMPORTANCE: Emerging literature has associated the use of anticholinergic medications to cognitive decline. OBJECTIVE: The aim of this study was to evaluate the association of overactive bladder medications on cognitive function with prospective longitudinal cognitive assessments. STUDY DESIGN: A population-based cohort of individuals 50 years and older who had serial validated cognitive assessment, in accordance with the Mayo Clinic Study of Aging, was evaluated from October 2004 through December 2021. Anticholinergic overactive bladder medications were grouped by traditional anticholinergic medications and central nervous system (CNS)- sparing anticholinergic medications and compared to no medication exposure. A linear mixed effects model with time-dependent exposures evaluated the association between overactive bladder anticholinergic medication exposure and subsequent trajectories of cognitive z-scores. RESULTS: We included 5,872 participants with a median follow-up of 6.4 years. Four hundred forty-three were exposed to traditional anticholinergic medications, 60 to CNS-sparing medications, and 5,369 had no exposure. On multivariable analyses, exposure to any anticholinergic overactive bladder medication was significantly associated with deterioration in longitudinal cognitive scores in the language and attention assessments compared to the control cohort. Traditional anticholinergic medication exposure was associated with worse attention scores than nonexposed participants. Exposure to CNS-sparing anticholinergic medications was associated with a deterioration in the language domain compared to those unexposed. Among women, traditional anticholinergic medication exposure was associated with worse global and visuospatial scores than nonexposed participants, but this association was not identified in the CNS-sparing group. CONCLUSION: Exposure to anticholinergic overactive bladder medications was associated with small but significantly worse decline in cognitive scoring in the language and attention domains when compared to nonexposed individuals.

CSF biomarkers of immune activation and Alzheimer's disease for predicting cognitive impairment risk in the elderly.

The immune system substantially influences age-related cognitive decline and Alzheimer's disease (AD) progression, affected by genetic and environmental factors. In a Mayo Clinic Study of Aging cohort, we examined how risk factors like APOE genotype, age, and sex affect inflammatory molecules and AD biomarkers in cerebrospinal fluid (CSF). Among cognitively unimpaired individuals over 65 (N = 298), we measured 365 CSF inflammatory molecules, finding age, sex, and diabetes status predominantly influencing their levels. We observed age-related correlations with AD biomarkers such as total tau, phosphorylated tau-181, neurofilament light chain (NfL), and YKL40. APOE4 was associated with lower Aß42 and higher SNAP25 in CSF. We explored baseline variables predicting cognitive decline risk, finding age, CSF Aß42, NfL, and REG4 to be independently correlated. Subjects with older age, lower Aß42, higher NfL, and higher REG4 at baseline had increased cognitive impairment risk during follow-up. This suggests that assessing CSF inflammatory molecules and AD biomarkers could predict cognitive impairment risk in the elderly.

FedFSA: Hybrid and federated framework for functional status ascertainment across institutions.

INTRODUCTION: Patients' functional status assesses their independence in performing activities of daily living, including basic ADLs (bADL), and more complex instrumental activities (iADL). Existing studies have discovered that patients' functional status is a strong predictor of health outcomes, particularly in older adults. Depite their usefulness, much of the functional status information is stored in electronic health records (EHRs) in either semi-structured or free text formats. This indicates the pressing need to leverage computational approaches such as natural language processing (NLP) to accelerate the curation of functional status information. In this study, we introduced FedFSA, a hybrid and federated NLP framework designed to extract functional status information from EHRs across multiple healthcare institutions. METHODS: FedFSA consists of four major components: 1) individual sites (clients) with their private local data, 2) a rule-based information extraction (IE) framework for ADL extraction, 3) a BERT model for functional status impairment classification, and 4) a concept normalizer. The framework was implemented using the OHNLP Backbone for rule-based IE and open-source Flower and PyTorch library for federated BERT components. For gold standard data generation, we carried out corpus annotation to identify functional status-related expressions based on ICF definitions. Four healthcare institutions were included in the study. To assess FedFSA, we evaluated the performance of category- and institution-specific ADL extraction across different experimental designs. RESULTS: ADL extraction performance ranges from an F1-score of 0.907 to 0.986 for bADL and 0.825 to 0.951 for iADL across the four healthcare sites. The performance for ADL extraction with impairment ranges from an F1-score of 0.722 to 0.954 for bADL and 0.674 to 0.813 for iADL across four healthcare sites. For category-specific ADL extraction, laundry and transferring yielded relatively high performance, while dressing, medication, bathing, and continence achieved moderate-high performance. Conversely, food preparation and toileting showed low performance. CONCLUSION: NLP performance varied across ADL categories and healthcare sites. Federated learning using a FedFSA framework performed higher than non-federated learning for impaired ADL extraction at all healthcare sites. Our study demonstrated the potential of the federated learning framework in functional status extraction and impairment classification in EHRs, exemplifying the importance of a large-scale, multi-institutional collaborative development effort.

Dementia Prediction in Older Adults Using Sex-Specific Health Trajectory Clustering.

With increasing number of people living with dementia, the problem of late diagnosis significantly impacts a person's quality of life while early signs of dementia may provide useful insights to facilitate better treatment plans. With time, this progressive neurodegenerative syndrome could progress from mild cognitive impairment to dementia. A pattern of health conditions can be characterized in unsupervised manner to help predict this progress. As a significant extension to our previous work with streaming clustering model, we consider additional information for predicting dementia onset. With empirical observations, we discover the importance of examining sex and age to predict dementia onset. To this end, we propose a sex-specific model with age-constraint for predicting dementia onset and validate the effectiveness of our models using data from Mayo Clinic Study of Aging (MCSA). The proposed sex-specific models for older adult populations (>=65 years of age) outperformed the previous models with F-score of 77% and 78% for male-specific and female-specific models, respectively. Our experiments of sex-specific temporal clustering of features in older adults demonstrate the potential of more personalized models for early alerts of dementia.

Neuroimaging Characteristics of Hearing Loss in the Mayo Clinic Study of Aging.

OBJECTIVE: To investigate the association between standard pure tone and speech audiometry with neuroimaging characteristics reflective of aging and dementia in older adults. STUDY DESIGN: Prospective population-based study. SETTING: Single tertiary care referral center. METHODS: Participants from the Mayo Clinic Study of aging 60 years old or older with normal cognition or mild cognitive impairment, baseline neuroimaging, and a behavioral audiogram associated with neuroimaging were eligible for study. Imaging modalities included structural MRI (sMRI) and fluid-attenuated inversion recovery MRI (FLAIR-MRI; N = 605), diffusion tensor imaging MRI (DTI-MRI; N = 444), and fluorodeoxyglucose-positron emission tomography (FDG-PET; N = 413). Multivariable logistic and linear regression models were used to evaluate associations with neuroimaging outcomes. RESULTS: Mean (SD) pure tone average (PTA) was 33 (15) dB HL and mean (SD) word recognition score (WRS) was 91% (14). There were no significant associations between audiometric performance and cortical thinning assessed by sMRI. Each 10-dB increase in PTA was associated with increased likelihood of abnormal white-matter hyperintensity (WMH) from FLAIR-MRI (odds ratio 1.26, P = .02). From DTI-MRI, participants with <100% WRSs had significantly lower fractional anisotropy in the genu of the corpus callosum (parameter estimate [PE] -0.012, P = .008) compared to those with perfect WRSs. From FDG-PET, each 10% decrease in WRSs was associated with decreased uptake in the anterior cingulate cortex (PE -0.013, P = .001). CONCLUSION: Poorer audiometric performance was not significantly associated with cortical thinning but was associated with white matter damage relevant to cerebrovascular disease (increased abnormal WMH, decreased corpus callosum diffusion). These neuroimaging results suggest a pathophysiologic link between hearing loss and cerebrovascular disease.

Vascular risk, gait, behavioral, and plasma indicators of VCID.

INTRODUCTION: Cost-effective screening tools for vascular contributions to cognitive impairment and dementia (VCID) has significant implications. We evaluated non-imaging indicators of VCID using magnetic resonance imaging (MRI)-measured white matter (WM) damage and hypothesized that these indicators differ based on age. METHODS: In 745 participants from the Mayo Clinic Study of Aging (≥50 years of age) with serial WM assessments from diffusion MRI and fluid-attenuated inversion recovery (FLAIR)-MRI, we examined associations between baseline non-imaging indicators (demographics, vascular risk factors [VRFs], gait, behavioral, plasma glial fibrillary acidic protein [GFAP], and plasma neurofilament light chain [NfL]) and WM damage across three age tertiles. RESULTS: VRFs and gait were associated with diffusion changes even in low age strata. All measures (VRFs, gait, behavioral, plasma GFAP, plasma NfL) were associated with white matter hyperintensities (WMHs) but mainly in intermediate and high age strata. DISCUSSION: Non-imaging indicators of VCID were related to WM damage and may aid in screening participants and assessing outcomes for VCID. HIGHLIGHTS: Non-imaging indicators of VCID can aid in prediction of MRI-measured WM damage but their importance differed by age. Vascular risk and gait measures were associated with early VCID changes measured using diffusion MRI. Plasma markers explained variability in WMH across age strata. Most non-imaging measures explained variability in WMH and vascular WM scores in intermediate and older age groups. The framework developed here can be used to evaluate new non-imaging VCID indicators proposed in the future.

The long-term effects of consecutive COVID-19 waves on mental health.

BACKGROUND: Although several studies have documented the impact of the COVID-19 pandemic on mental health, the long-term effects remain unclear. AIMS: To examine longitudinal changes in mental health before and during the consecutive COVID-19 waves in a well-established probability sample. METHOD: An online survey was completed by the participants of the COVID-19 add-on study at four time points: pre-COVID-19 period (2014-2015, n = 1823), first COVID-19 wave (April to May 2020, n = 788), second COVID-19 wave (August to October 2020, n = 532) and third COVID-19 wave (March to April 2021, n = 383). Data were collected via a set of validated instruments, and analysed with latent growth models. RESULTS: During the pandemic, we observed a significant increase in stress levels (standardised ß = 0.473, P < 0.001) and depressive symptoms (standardised ß = 1.284, P < 0.001). The rate of increase in depressive symptoms (std. covariance = 0.784, P = 0.014), but not in stress levels (std. covariance = 0.057, P = 0.743), was associated with the pre-pandemic mental health status of the participants. Further analysis showed that secondary stressors played a predominant role in the increase in mental health difficulties. The main secondary stressors were loneliness, negative emotionality associated with the perception of COVID-19 disease, lack of resilience, female gender and younger age. CONCLUSIONS: The surge in stress levels and depressive symptoms persisted across all three consecutive COVID-19 waves. This persistence is attributable to the effects of secondary stressors, and particularly to the status of mental health before the COVID-19 pandemic. Our findings reveal mechanisms underlying the surge in mental health difficulties during the COVID-19 waves, with direct implications for strategies promoting mental health during pandemics.

Association of Cerebrovascular Imaging Biomarkers, Depression, and Anxiety, with Mild Cognitive Impairment.

The study included 1,738 Mayo Clinic Study of Aging participants (≥50 years old; 1,460 cognitively unimpaired and 278 with mild cognitive impairment (MCI)) and examined the cross-sectional association between cerebrovascular (CVD) imaging biomarkers (e.g., white matter hyperintensities (WMH), infarctions) and Beck Depression Inventory-II (BDI-II) and Beck Anxiety Inventory (BAI) scores, as well as their association with MCI. High (abnormal) WMH burden was significantly associated with having BDI-II>13 and BAI > 7 scores, and both (CVD imaging biomarkers and depression/anxiety) were significantly associated with MCI when included simultaneously in the model, suggesting that both were independently associated with the odds of MCI.

Characterizing Performance Gaps of a Code-Based Dementia Algorithm in a Population-Based Cohort of Cognitive Aging.

BACKGROUND: Multiple algorithms with variable performance have been developed to identify dementia using combinations of billing codes and medication data that are widely available from electronic health records (EHR). If the characteristics of misclassified patients are clearly identified, modifying existing algorithms to improve performance may be possible. OBJECTIVE: To examine the performance of a code-based algorithm to identify dementia cases in the population-based Mayo Clinic Study of Aging (MCSA) where dementia diagnosis (i.e., reference standard) is actively assessed through routine follow-up and describe the characteristics of persons incorrectly categorized. METHODS: There were 5,316 participants (age at baseline (mean (SD)): 73.3 (9.68) years; 50.7% male) without dementia at baseline and available EHR data. ICD-9/10 codes and prescription medications for dementia were extracted between baseline and one year after an MCSA dementia diagnosis or last follow-up. Fisher's exact or Kruskal-Wallis tests were used to compare characteristics between groups. RESULTS: Algorithm sensitivity and specificity were 0.70 (95% CI: 0.67, 0.74) and 0.95 (95% CI: 0.95, 0.96). False positives (i.e., participants falsely diagnosed with dementia by the algorithm) were older, with higher Charlson comorbidity index, more likely to have mild cognitive impairment (MCI), and longer follow-up (versus true negatives). False negatives (versus true positives) were older, more likely to have MCI, or have more functional limitations. CONCLUSIONS: We observed a moderate-high performance of the code-based diagnosis method against the population-based MCSA reference standard dementia diagnosis. Older participants and those with MCI at baseline were more likely to be misclassified.

Cognitive Performance, Sociodemographic Factors, Pure-Tone Audiometry, and their Association with Speech Discrimination: A Prospective Population-Based Study of 1,061 Older Adults.

OBJECTIVE: Hearing loss is increasingly recognized as a chronic disease state with important health sequelae. Although considered a central component of routine audiometric testing, the degree to which various patient factors influence speech discrimination is poorly characterized to date. The primary objective of the current work was to describe associations of cognitive performance, sociodemographic factors, and pure-tone audiometry with speech discrimination in older adults. STUDY DESIGN: Prospective study. SETTING: Olmsted County, Minnesota. PATIENTS: There were 1,061 study participants 50 years or older at enrollment in the population-based Mayo Clinic Study of Aging between November 2004 and December 2019 who underwent formal audiometric and cognitive testing included in the current investigation. MAIN OUTCOME MEASURES: The primary outcome measure was word recognition scores (WRSs; measured as <100% vs 100% as well as continuous), with pure-tone averages (PTAs; 0.5, 1, 2, and 3 kHz), age, sex, years of education, state area deprivation index (ADI) quintiles, and global cognition z scores as explanatory features. RESULTS: The mean (SD) age among the 1,061 participants was 76 (9) years with 528 (50%) males. Participant age [OR (95% CI) for a 10-year increase of 1.8 (1.4-2.3), p < 0.001], male sex [OR = 2.6 (1.9-3.7), p < 0.001], and PTA [OR for a 10-dB hearing loss increase of 2.4 (2.1-2.8), p < 0.001] were all significantly associated with <100% WRSs, with the greatest explanatory ability attributable to the PTA. Years of education ( p = 0.9), state ADI quintile ( p = 0.6), and global cognitive performance ( p = 0.2) were not associated with WRS. The multivariable model demonstrated strong predictive ability for less than perfect WRSs, with a c index of 0.854. Similar results were seen for WRSs analyzed as continuous, with the multivariable model resulting in an R2 value of 0.433. CONCLUSIONS: Although PTA exhibited the greatest influence on speech discrimination, advancing age and male sex both independently increased the likelihood of having worse speech discrimination among older adults, even after accounting for years of education, neighborhood-level socioeconomic disadvantage, and cognitive function. These findings help identify patient factors that can be instrumental when designing screening programs for adult-onset hearing loss.

Eligibility for Anti-Amyloid Treatment in a Population-Based Study of Cognitive Aging.

BACKGROUND AND OBJECTIVES: Treatment options for Alzheimer disease (AD) are limited and have focused mainly on symptomatic therapy and improving quality of life. Recently, lecanemab, an anti-ß-amyloid monoclonal antibody (mAb), received accelerated approval by the US Food and Drug Administration for treatment in the early stages of biomarker-confirmed symptomatic AD. An additional anti-ß-amyloid mAb, aducanumab, was approved in 2021, and more will potentially become available in the near future. Research on the applicability and generalizability of the anti-ß-amyloid mAb eligibility criteria on adults with biomarkers available in the general population has been lacking. The study's primary aim was to apply the clinical trial eligibility criteria for lecanemab treatment to participants with early AD of the population-based Mayo Clinic Study of Aging (MCSA) and assess the generalizability of anti-amyloid treatment. The secondary aim of this study was to apply the clinical trial eligibility criteria for aducanumab treatment in MCSA participants. METHODS: This cross-sectional study aimed to apply the clinical trial eligibility criteria for lecanemab and aducanumab treatment to participants with early AD of the population-based MCSA and assess the generalizability of anti-amyloid treatment. RESULTS: Two hundred thirty-seven MCSA participants (mean age [SD] 80.9 [6.3] years, 54.9% male, and 97.5% White) with mild cognitive impairment (MCI) or mild dementia and increased brain amyloid burden by PiB PET comprised the study sample. Lecanemab trial's inclusion criteria reduced the study sample to 112 (47.3% of 237) participants. The trial's exclusion criteria further narrowed the number of potentially eligible participants to 19 (overall 8% of 237). Modifying the eligibility criteria to include all participants with MCI (instead of applying additional cognitive criteria) resulted in 17.4% of participants with MCI being eligible for lecanemab treatment. One hundred four participants (43.9% of 237) fulfilled the aducanumab clinical trial's inclusion criteria. The aducanumab trial's exclusion criteria further reduced the number of available participants, narrowing those eligible to 12 (5.1% of 237). Common exclusions were related to other chronic conditions and neuroimaging findings. DISCUSSION: Findings estimate the limited eligibility in typical older adults with cognitive impairment for anti-ß-amyloid mAbs.

Disease severity and mortality in Alzheimer's disease: an analysis using the U.S. National Alzheimer's Coordinating Center Uniform Data Set.

BACKGROUND: Evidence on the relative risk of death across all stages of Alzheimer's disease (AD) is lacking but greatly needed for the evaluation of new interventions. We used data from the Uniform Data Set (UDS) of the National Alzheimer's Coordinating Center (NACC) to assess the expected survival of a person progressing to a particular stage of AD and the relative risk of death for a person in a particular stage of AD compared with cognitively normal (CN) people. METHODS: This was a retrospective observational cohort study of mortality and its determinants in participants with incident mild cognitive impairment (MCI) due to AD or AD dementia compared with CN participants. Overall survival and hazard ratios of all-cause mortality in participants ≥ 50 years of age with clinically assessed or diagnosed MCI due to AD, or mild, moderate, or severe AD dementia, confirmed by Clinical Dementia Rating scores, versus CN participants were estimated, using NACC UDS data. Participants were followed until death, censoring, or until information to determine disease stage was missing. RESULTS: Aged between 50 and 104 years, 12,414 participants met the eligibility criteria for the study. Participants progressing to MCI due to AD or AD dementia survived a median of 3-12 years, with higher mortality observed in more severe stages. Risk of death increased with the severity of AD dementia, with the increase significantly higher at younger ages. Participants with MCI due to AD and CN participants had a similar risk of death after controlling for confounding factors. CONCLUSIONS: Relative all-cause mortality risk increases with AD severity, more so at younger ages. Mortality does not seem to be higher for those remaining in MCI due to AD. Findings might imply potential benefit of lower mortality if preventing or delaying the progression of AD is successful, and importantly, this potential benefit might be greater in relatively younger people. Future research should replicate our study in other samples more representative of the general US population as well as other populations around the world.

Plasma-derived biomarkers of Alzheimer's disease and neuropsychiatric symptoms: A community-based study.

INTRODUCTION: We examined associations between plasma-derived biomarkers of Alzheimer's disease (AD) and neuropsychiatric symptoms (NPS) in community-dwelling older adults. METHODS: Cross-sectional study involving 1005 persons ≥50 years of age (mean 74 years, 564 male, 118 cognitively impaired), who completed plasma-derived biomarker (amyloid beta 42 [Aß42]/Aß40, phosphorylated tau 181 [p-tau181], p-tau217, total tau [t-tau], neurofilament light [NfL]), and NPS assessment. RESULTS: P-tau181 (odds ratio [OR] 2.06, 95% confidence interval [CI] 1.41-3.00, p < 0.001), p-tau217 (OR 1.70, 95% CI 1.10-2.61, p = 0.016), and t-tau (OR 1.44, 95% CI 1.08-1.92, p = 0.012) were associated with appetite change. We also found that p-tau181 and p-tau217 were associated with increased symptoms of agitation (OR 1.93, 95% CI 1.20-3.11, p = 0.007 and OR 2.04, 95% CI 1.21-3.42, p = 0.007, respectively), and disinhibition (OR 2.39, 95% CI 1.45-3.93, p = 0.001 and OR 2.30, 95% CI 1.33-3.98, p = 0.003, respectively). Aß42/Aß40 and NfL were not associated with NPS. CONCLUSION: Higher plasma-derived p-tau181 and p-tau217 levels are associated with increased symptoms of appetite change, agitation, and disinhibition. These findings may support the validity of plasma tau biomarkers for predicting behavioral symptoms that often accompany cognitive impairment. HIGHLIGHTS: We studied 1005 community-dwelling persons aged ≥ 50 yearsHigher plasma tau levels are associated with increased neuropsychiatric symptomsAß42/Aß40 and NfL are not associated with neuropsychiatric symptomsClinicians should treat neuropsychiatric symptoms in persons with high plasma-derived tau.