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Background: Dementia and urinary incontinence (UI) are etiologically complex clinical syndromes. Dementia and UI often occur in the same individuals, but underlying factors connecting them are incompletely understood. Objective: Query data from a community-based autopsy series to assess pathologies that underlie UI. Methods: Included research subjects came to autopsy from the University of Kentucky Alzheimer’s Disease Research Center longitudinal cohort. A total of 368 research volunteers met inclusion criteria for this cross-sectional study. The average age at death was 85.3 years and the average number of annual clinic visits was 5.2 visits. Statistical models were run to evaluate which pathologies were associated with UI. Data included pathologies scored according to conventional stage-based systems, and these studies were complemented by quantitative digital neuropathology. Results: Dementia was diagnosed at the final clinical visit in 208 (56.7% of the sample) and UI was documented in 156 (42.7%). UI was associated with depression and dementia (both p < 0.001). More women than men had a history of UI (p < 0.04), and women with UI had had more biological children than those without UI (p < 0.005). Participants with limbic predominant age-related TDP-43 encephalopathy neuropathologic changes (LATE-NC) were more likely to have UI than those without LATE-NC (p < 0.001). The presence of LATE-NC (Stage > 1) was associated with UI with or without severe Alzheimer’s disease neuropathologic changes and/or Lewy body pathology. Conclusion: In this community-based autopsy cohort, multiple factors were associated with UI, but the neuropathologic change most robustly associated with UI was LATE-NC.

This study aimed to examine gabapentin utilization trends among older adults with different cognitive statuses and investigate concurrent medication use of potentially inappropriate medications. Data were extracted from the National Alzheimer's Coordinating Center Uniform Data Set (2006-2019). We estimated the yearly prevalence of gabapentin use, both overall and within subgroups defined by cognitive status (normal, mild cognitive impairment and dementia) and demographics (age and sex) for participants aged 65+. Additionally, we assessed the prevalence of concurrent use of gabapentin with opioids, combined opioids and benzodiazepine, antidepressant and antipsychotic. From 35 205 eligible older adults (mean age [SD]: 75.7 [7.0]; male: 43.1%), gabapentin use increased from 2006 to 2019 in both overall and every participant subgroup. About 10%-30% of gabapentin users reported to concurrently use opioids, and the concurrent use of gabapentin, opioid and benzodiazepine was up to 7.5% throughout the study period. The frequency of concurrent use with antipsychotics or antidepressants was higher in participants with dementia than those with normal cognition or those who were mildly cognitively impaired. Given increasing use among older adults, rigorous studies are needed to examine the safety of gabapentin in this population.

Visinin-like protein 1 (VILIP-1) is a neuron-specific calcium sensor protein rapidly released into blood after mild traumatic brain injury (mTBI) and may be a suitable biomarker for identification of sports-related concussion (SRC). The objective of the study is to test if quantification of a specific post-translationally modified (ubiquitinated) form of VILIP-1 (ubVILIP-1) from a fingerstick blood sample using a point of care (POC) lateral flow device (LFD) can be used to rapidly identify athletes with SRC.Prospective cohort study.Side-line blood collection at football, soccer, and volleyball games/practices.Division I athletes with/without SRC.Blood ubVILIP-1 concentrations.Data collected over 2 athletic seasons from non-SRC athletes (controls) show a small but statistically significant elevation of ubVILIP-1 over an individual season for male athletes (P = 0.02) dependent on sport (P = 0.014) and no significant changes in ubVILIP-1 levels between seasons. For SRC athletes, the data show ubVILIP-1 levels substantially increase above baseline as soon as 30 minutes postdiagnosis with peak concentrations and times postinjury that vary based on injury severity.Results of the study suggest quantification of blood ubVILIP-1 levels measured using an LFD may provide an objective identification of athletes with SRC, setting the stage for further study with a larger number of SRC patients.

Transactive response (TAR) DNA-binding protein 43 kDa (TDP-43) pathology is a hallmark of limbic-predominant age-related TDP-43 encephalopathy (LATE). The amygdala is affected early in the evolution of LATE neuropathologic change (LATE-NC), and heterogeneity of LATE-NC in amygdala has previously been observed. However, much remains to be learned about how LATE-NC originates and progresses in the brain. To address this, we assessed TDP-43 and other pathologies in the amygdala region of 184 autopsied subjects (median age = 85 years), blinded to clinical diagnoses, other neuropathologic diagnoses, and risk genotype information. As previously described, LATE-NC was associated with older age at death, cognitive impairment, and the TMEM106B risk allele. Pathologically, LATE-NC was associated with comorbid hippocampal sclerosis (HS), myelin loss, and vascular disease in white matter (WM). Unbiased hierarchical clustering of TDP-43 inclusion morphologies revealed discernable subtypes of LATE-NC with distinct clinical, genetic, and pathologic associations. The most common patterns were: Pattern 1, with lamina II TDP-43 + processes and preinclusion pathology in cortices of the amygdala region, and frequent LATE-NC Stage 3 with HS; Pattern 2, previously described as type-β, with neurofibrillary tangle-like TDP-43 neuronal cytoplasmic inclusions (NCIs), high Alzheimer’s disease neuropathologic change (ADNC), frequent APOE ε4, and usually LATE-NC Stage 2; Pattern 3, with round NCIs and thick neurites in amygdala, younger age at death, and often comorbid Lewy body disease; and Pattern 4 (the most common pattern), with tortuous TDP-43 processes in subpial and WM regions, low ADNC, rare HS, and lower dementia probability. TDP-43 pathology with features of patterns 1 and 2 were often comorbid in the same brains. Early and mild TDP-43 pathology was often best described to be localized in the “amygdala region” rather than the amygdala proper. There were also important shared attributes across patterns. For example, all four patterns were associated with the TMEM106B risk allele. Each pattern also demonstrated the potential to progress to higher LATE-NC stages with confluent anatomical and pathological patterns, and to contribute to dementia. Although LATE-NC showed distinct patterns of initiation in amygdala region, there was also apparent shared genetic risk and convergent pathways of clinico-pathological evolution.

Clinically, detection of disease-causing pathology associated with Alzheimer's disease (AD) and vascular contributions to cognitive impairment and dementia (VCID) is limited to magnetic resonance imaging and positron emission tomography scans, which are expensive and not widely accessible. Here, we assess angiogenic, inflammatory, and AD-related plasma biomarkers to determine their relationships with human post mortem neuropathology.Plasma samples were analyzed using a digital immunoassay and pathological evaluation was performed by University of Kentucky Alzheimer's Disease Research Center neuropathologists. The association of plasma markers with neuropathology was estimated via proportional odds and logistic regressions adjusted for age.Included cases (N = 90) showed increased tau/amyloid beta (Aβ)42 ratio, glial fibrillary acidic protein (GFAP), vascular endothelial growth factor A (VEGF-A), and placental growth factor (PlGF) were positively associated with higher level of AD neuropathological change, while higher Aβ42/Aβ40 ratio was inversely associated. Higher PlGF, VEGF-A, and interleukin 6 were inversely associated with chronic cerebrovascular disease, while Aβ42/Aβ40 ratio was positively associated.Our results provide support for the continued study of plasma biomarkers as a clinical screening tool for AD and VCID pathology.

Cancer and Alzheimer’s disease are common diseases in ageing populations. Previous research has reported a lower incidence of Alzheimer’s disease-type (amnestic) dementia among individuals with a diagnosis of cancer. Both cancer and amnestic dementia are prevalent and potentially lethal clinical syndromes. The current study was conducted to investigate the association of cancer diagnosis with neuropathological and cognitive features of dementia. Data were analysed from longitudinally evaluated participants in a community-based cohort study of brain ageing who came to autopsy at the University of Kentucky Alzheimer’s Disease Research Center. These data were linked to the Kentucky Cancer Registry, a population-based state cancer surveillance system, to obtain cancer-related data. We examined the relationship between cancer diagnosis, clinical dementia diagnosis, Mini-Mental State Examination scores and neuropathological features using inverse probability weighting to address bias due to confounding and missing data. To address bias due to inclusion of participants with dementia at cohort baseline, we repeated all analyses restricted to the participants who were cognitively normal at baseline. Included participants (n = 785) had a mean ± standard deviation age of death of 83.8 ± 8.6 years; 60.1% were female. Cancer diagnosis was determined in 190 (24.2%) participants, and a diagnosis of mild cognitive impairment or dementia was determined in 539 (68.7%). APOE ɛ4 allele dosage was lower among participants with cancer diagnosis compared to cancer-free participants overall (P = 0.0072); however, this association was not observed among those who were cognitively normal at baseline. Participants with cancer diagnosis had lower odds of mild cognitive impairment or dementia, and higher cognitive test scores (e.g. Mini-Mental State Examination scores evaluated 6 and ≤2 years ante-mortem, P Cancer diagnosis was associated with a lower burden of Alzheimer’s disease pathology and less cognitive impairment. These findings from a community-based cohort with neuropathological confirmation of substrates support the hypothesis that there is an inverse relationship between cancer and Alzheimer’s disease.

Refereed/Peer-reviewed Limbic-predominant age-related TDP-43 encephalopathy neuropathologic change (LATE-NC) and Alzheimer’s disease neuropathologic change (ADNC) are each associated with substantial cognitive impairment in aging populations. However, the prevalence of LATE-NC across the full range of ADNC remains uncertain. To address this knowledge gap, neuropathologic, genetic, and clinical data were compiled from 13 high-quality community- and population-based longitudinal studies. Participants were recruited from United States (8 cohorts, including one focusing on Japanese–American men), United Kingdom (2 cohorts), Brazil, Austria, and Finland. The total number of participants included was 6196, and the average age of death was 88.1 years. Not all data were available on each individual and there were differences between the cohorts in study designs and the amount of missing data. Among those with known cognitive status before death (n = 5665), 43.0% were cognitively normal, 14.9% had MCI, and 42.4% had dementia—broadly consistent with epidemiologic data in this age group. Approximately 99% of participants (n = 6125) had available CERAD neuritic amyloid plaque score data. In this subsample, 39.4% had autopsy-confirmed LATE-NC of any stage. Among brains with “frequent” neuritic amyloid plaques, 54.9% had comorbid LATE-NC, whereas in brains with no detected neuritic amyloid plaques, 27.0% had LATE-NC. Data on LATE-NC stages were available for 3803 participants, of which 25% had LATE-NC stage > 1 (associated with cognitive impairment). In the subset of individuals with Thal Aβ phase = 0 (lacking detectable Aβ plaques), the brains with LATE-NC had relatively more severe primary age-related tauopathy (PART). A total of 3267 participants had available clinical data relevant to frontotemporal dementia (FTD), and none were given the clinical diagnosis of definite FTD nor the pathological diagnosis of frontotemporal lobar degeneration with TDP-43 inclusions (FTLD-TDP). In the 10 cohorts with detailed neurocognitive assessments proximal to death, cognition tended to be worse with LATE-NC across the full spectrum of ADNC severity. This study provided a credible estimate of the current prevalence of LATE-NC in advanced age. LATE-NC was seen in almost 40% of participants and often, but not always, coexisted with Alzheimer’s disease neuropathology.

Finite Markov chains are useful tools for studying transitions among health states; these chains can be complex consisting of a mix of transient and absorbing states. The transition probabilities, which are often affected by covariates, can be difficult to estimate due to the presence of many covariates and/or a subset of transitions that are rarely observed. The purpose of this article is to show how to estimate the effect of a subset of covariates of interest after adjusting for the presence of multiple other covariates by applying multidimensional dimension reduction to the latter. The case in which transitions within each row of the one-step transition probability matrix are estimated by multinomial logistic regression is discussed in detail. Dimension reduction for the adjustment covariates involves estimating the effect of the covariates by a product of matrices iteratively; at each iteration one matrix in the product is fixed while the second is estimated using either standard software or nonlinear estimation, depending on which of the matrices in the product is fixed. The algorithm is illustrated by an application where the effect of at least one Apolipoprotein-E (APOE) gene ϵ 4 allele on transition probability is estimated in a Markov Chain that includes adjustment for eight covariates and focuses on transitions from normal cognition to several forms of mild cognitive impairment, with possible absorption into dementia. Data were drawn from annual cognitive assessments of 649 participants enrolled in the BRAiNS cohort at the University of Kentucky's Alzheimer's Disease Research Center.

Background: Early prognosis of high-risk older adults for amnestic mild cognitive impairment (aMCI), using noninvasive and sensitive neuromarkers, is key for early prevention of Alzheimer’s disease. We have developed individualized measures in electrophysiological brain signals during working memory that distinguish patients with aMCI from age-matched cognitively intact older individuals. Objective: Here we test longitudinally the prognosis of the baseline neuromarkers for aMCI risk. We hypothesized that the older individuals diagnosed with incident aMCI already have aMCI-like brain signatures years before diagnosis. Methods: Electroencephalogram (EEG) and memory performance were recorded during a working memory task at baseline. The individualized baseline neuromarkers, annual cognitive status, and longitudinal changes in memory recall scores up to 10 years were analyzed. Results: Seven of the 19 cognitively normal older adults were diagnosed with incident aMCI for a median 5.2 years later. The seven converters’ frontal brainwaves were statistically identical to those patients with diagnosed aMCI (n = 14) at baseline. Importantly, the converters’ baseline memory-related brainwaves (reduced mean frontal responses to memory targets) were significantly different from those who remained normal. Furthermore, differentiation pattern of left frontal memory-related responses (targets versus nontargets) was associated with an increased risk hazard of aMCI (HR = 1.47, 95% CI 1.03, 2.08). Conclusion: The memory-related neuromarkers detect MCI-like brain signatures about five years before diagnosis. The individualized frontal neuromarkers index increased MCI risk at baseline. These noninvasive neuromarkers during our Bluegrass memory task have great potential to be used repeatedly for individualized prognosis of MCI risk and progression before clinical diagnosis.

As opioid prescriptions have risen, there has also been an increase in opioid use disorder (OUD) and its adverse outcomes. Accurate and complete epidemiologic surveillance of OUD, to inform prevention strategies, presents challenges. The objective of this study was to ascertain prevalence of OUD using two methods to identify OUD in electronic health records (EHR): applying natural language processing (NLP) for text mining of unstructured clinical notes and using ICD-10-CM diagnostic codes.Data were drawn from EHR records for hospital and emergency department patient visits to a large regional academic medical center from 2017 to 2019. International Classification of Disease, 10th Edition, Clinic Modification (ICD-10-CM) discharge codes were extracted for each visit. To develop the rule-based NLP algorithm, a stepwise process was used. First, a small sample of visits from 2017 was used to develop initial dictionaries. Next, EHR corresponding to 30,124 visits from 2018 were used to develop and evaluate the rule-based algorithm. A random sample of the results were manually reviewed to identify and address shortcomings in the algorithm, and to estimate sensitivity and specificity of the two methods of ascertainment. Last, the final algorithm was then applied to 29,212 visits from 2019 to estimate OUD prevalence.While there was substantial overlap in the identified records (n = 1,381 [59.2 %]), overall n = 2,332 unique visits were identified. Of the total unique visits, 430 (18.4 %) were identified only by ICD-10-CM codes, and 521 (22.3 %) were identified only by NLP. The prevalence of visits with evidence of an OUD diagnosis in this sample, ascertained using only ICD-10-CM codes, was 1,811/29,212 (6.1 %). Including the additional 521 visits identified only by NLP, the estimated prevalence of OUD is 2,332/29,212 (7.9 %), an increase of 29.5 % compared to the use of ICD-10-CM codes alone. The estimated sensitivity and specificity of the NLP-based OUD classification were 81.8 % and 97.5 %, respectively, relative to gold-standard manual review by an expert addiction medicine physician.NLP-based algorithms can automate data extraction and identify evidence of opioid use disorder from unstructured electronic healthcare records. The most complete ascertainment of OUD in EHR was combined NLP with ICD-10-CM codes. NLP should be considered for epidemiological studies involving EHR data.

TAR-DNA binding protein-43 (TDP-43) proteinopathy is seen in multiple brain diseases. A standardized terminology was recommended recently for common age-related TDP-43 proteinopathy: limbic-predominant, age-related TDP-43 encephalopathy (LATE) and the underlying neuropathological changes, LATE-NC. LATE-NC may be co-morbid with Alzheimer’s disease neuropathological changes (ADNC). However, there currently are ill-defined diagnostic classification issues among LATE-NC, ADNC, and frontotemporal lobar degeneration with TDP-43 (FTLD-TDP). A practical challenge is that different autopsy cohorts are composed of disparate groups of research volunteers: hospital- and clinic-based cohorts are enriched for FTLD-TDP cases, whereas community-based cohorts have more LATE-NC cases. Neuropathological methods also differ across laboratories. Here, we combined both cases and neuropathologists’ diagnoses from two research centres—University of Pennsylvania and University of Kentucky. The study was designed to compare neuropathological findings between FTLD-TDP and pathologically severe LATE-NC. First, cases were selected from the University of Pennsylvania with pathological diagnoses of either FTLD-TDP (n = 33) or severe LATE-NC (mostly stage 3) with co-morbid ADNC (n = 30). Sections from these University of Pennsylvania cases were cut from amygdala, anterior cingulate, superior/mid-temporal, and middle frontal gyrus. These sections were stained for phospho-TDP-43 immunohistochemically and evaluated independently by two University of Kentucky neuropathologists blinded to case data. A simple set of criteria hypothesized to differentiate FTLD-TDP from LATE-NC was generated based on density of TDP-43 immunoreactive neuronal cytoplasmic inclusions in the neocortical regions. Criteria-based sensitivity and specificity of differentiating severe LATE-NC from FTLD-TDP cases with blind evaluation was ∼90%. Another proposed neuropathological feature related to TDP-43 proteinopathy in aged individuals is ‘Alpha’ versus ‘Beta’ in amygdala. Alpha and Beta status was diagnosed by neuropathologists from both universities (n = 5 raters). There was poor inter-rater reliability of Alpha/Beta classification (mean κ = 0.31). We next tested a separate cohort of cases from University of Kentucky with either FTLD-TDP (n = 8) or with relatively ‘pure’ severe LATE-NC (lacking intermediate or severe ADNC; n = 14). The simple criteria were applied by neuropathologists blinded to the prior diagnoses at University of Pennsylvania. Again, the criteria for differentiating LATE-NC from FTLD-TDP was effective, with sensitivity and specificity ∼90%. If more representative cases from each cohort (including less severe TDP-43 proteinopathy) had been included, the overall accuracy for identifying LATE-NC was estimated at >98% for both cohorts. Also across both cohorts, cases with FTLD-TDP died younger than those with LATE-NC (P

To better understand clinical and neuropathological features of TDP-43 proteinopathies, data were analyzed from autopsied research volunteers who were followed in the National Alzheimer's Coordinating Center (NACC) data set. All subjects (n = 495) had autopsy-proven TDP-43 proteinopathy as an inclusion criterion. Subjects underwent comprehensive longitudinal clinical evaluations yearly for 6.9 years before death on average. We tested whether an unsupervised clustering algorithm could detect coherent groups of TDP-43 immunopositive cases based on age at death and extensive neuropathologic data. Although many of the brains had mixed pathologies, four discernible clusters were identified. Key differentiating features were age at death and the severity of comorbid Alzheimer's disease neuropathologic changes (ADNC), particularly neuritic amyloid plaque densities. Cluster 1 contained mostly cases with a pathologic diagnosis of frontotemporal lobar degeneration (FTLD-TDP), consistent with enrichment of frontotemporal dementia clinical phenotypes including appetite/eating problems, disinhibition and primary progressive aphasia (PPA). Cluster 2 consisted of elderly limbic-predominant age-related TDP-43 encephalopathy (LATE-NC) subjects without severe neuritic amyloid plaques. Subjects in Cluster 2 had a relatively slow cognitive decline. Subjects in both Clusters 3 and 4 had severe ADNC + LATE-NC; however, Cluster 4 was distinguished by earlier disease onset, swifter disease course, more Lewy body pathology, less neocortical TDP-43 proteinopathy, and a suggestive trend in a subgroup analysis (n = 114) for increased C9orf72 risk SNP rs3849942 T allele (Fisher's exact test p value = 0.095). Overall, clusters enriched with neocortical TDP-43 proteinopathy (Clusters 1 and 2) tended to have lower levels of neuritic amyloid plaques, and those dying older (Clusters 2 and 3) had far less PPA or disinhibition, but more apathy. Indeed, 98% of subjects dying past age 85 years lacked clinical features of the frontotemporal dementia syndrome. Our study revealed discernible subtypes of LATE-NC and underscored the importance of age of death for differentiating FTLD-TDP and LATE-NC.

Structural brain changes in aging are known to occur even in the absence of dementia, but the magnitudes and regions involved vary between studies. To further characterize these changes, we analyzed paired MRI images acquired with identical protocols and scanner over a median 5.8-year interval. The normal study group comprised 78 elders (25M 53F, baseline age range 70–78 years) who underwent an annual standardized expert assessment of cognition and health and who maintained normal cognition for the duration of the study. We found a longitudinal grey matter (GM) loss rate of 2.56 ± 0.07 ml/year (0.20 ± 0.04%/year) and a cerebrospinal fluid (CSF) expansion rate of 2.97 ± 0.07 ml/year (0.22 ± 0.04%/year). Hippocampal volume loss rate was higher than the GM and CSF global rates, 0.0114 ± 0.0004 ml/year (0.49 ± 0.04%/year). Regions of greatest GM loss were posterior inferior frontal lobe, medial parietal lobe and dorsal cerebellum. Rates of GM loss and CSF expansion were on the low end of the range of other published values, perhaps due to the relatively good health of the elder volunteers in this study. An additional smaller group of 6 subjects diagnosed with MCI at baseline were followed as well, and comparisons were made with the normal group in terms of both global and regional GM loss and CSF expansion rates. An increased rate of GM loss was found in the hippocampus bilaterally for the MCI group.

PURPOSE Although polypharmacy in younger populations is a growing public health concern, most studies addressing polypharmacy focus on elderly populations. Thus, polypharmacy is not yet well understood in younger populations. METHODS Baseline data from the Maule Cohort (MAUCO) (adults aged 38-74 years) were used to study the prevalence of polypharmacy and associated participant characteristics using logistic and zero-inflated negative binomial regressions. Factors studied include age, sex, self-rated health, education, smoking, obesity, diabetes, hypertension, and other chronic conditions. RESULTS Polypharmacy was reported by 10% of participants overall, with higher prevalence among older (≥60 years) vs middle aged (

There is an urgent need for interventions that can prevent or delay cognitive decline and dementia. Decades of epidemiological research have identified potential pharmacological strategies for risk factor modification to prevent these serious conditions, but clinical trials have failed to confirm the potential efficacy for such interventions. Our multidisciplinary international group reviewed seven high-potential intervention strategies in an attempt to identify potential reasons for the mismatch between the observational and trial results. In considering our findings, we offer constructive recommendations for the next steps. Overall, we observed some differences in the observational evidence base for the seven strategies, but several common methodological themes that emerged. These themes included the appropriateness of trial populations and intervention strategies, including the timing of interventions and other aspects of trials methodology. To inform the design of future clinical trials, we provide recommendations for the next steps in finding strategies for effective dementia risk reduction.

The pathologic substrates or neuroanatomic regions responsible for similarities in behavioral features seen in autism spectrum disorder and late-life dementia remain unknown. The present study examined the neuropathologic features of late-life dementia in research volunteers with and without antemortem behaviors characteristic of autism spectrum disorders.Antemortem cross-sectional assessment of autistic spectrum behaviors proximal to death in persons with diagnosis of mild cognitive impairment or dementia was completed using the Gilliam Autism Rating Scale, 2nd edition (GARS-2), followed by postmortem quantitative and semiquantitative neuropathologic assessment. All individuals who completed the GARS-2 prior to autopsy were included (n = 56) and we note that no participants had known diagnosis of autism spectrum disorder. The GARS-2 was used as an antemortem screening tool to stratify participants into two groups: "Autism Possible/Very Likely" or "Autism Unlikely." Data were analyzed using nonparametric statistics comparing location and scale to evaluate between-group differences in pathologic features.Neurofibrillary tangles (NFT; p = 0.028) density and tau burden (p = 0.032) in the frontal region, the NFT density (p = 0.048) and neuritic plaque burden (p = 0.042), and the tau burden (p = 0.032) of the temporal region, were significantly different in scale between groups. For measures with significant group differences, the medians of the Autism Possible/Very Likely group were roughly equal to the 75th percentile of the Autism Unlikely group (i.e., the distributions were shifted to the right).This study links behaviors characteristic of autism to increased pathologic tau burden in the frontal and temporal lobes in persons with late-life dementia. Additional studies are needed to determine causal factors and treatment options for behaviors characteristic of autism behaviors in late-life dementias.