Specific Cognitive/Behavioral Domains Predict Neuropsychiatric Symptoms in Severe Dementia

Background: Neuropsychiatric symptoms (NPS) have high prevalence in Alzheimer’s disease and related disorders (ADRD), with nearly 100% of individuals experiencing some type of symptom over the course of dementia (Tschanz et al, 2011). The occurrence of NPS is highly variable and fluctuates in severity (Tschanz et al., 2016). Their occurrence differs by type of dementia and increases over time (Kazui et al., 2016). Although risk factors for NPS in ADRD have been studied (e.g., Steinberg et al., 2014; Treiber et al, 2008), greater understanding of the nature of NPS and their triggers is needed to inform care management strategies (Gauthier et al., 2010). While much research has examined NPS in mild-to-moderate dementia, fewer studies have examined NPS in severe dementia. We investigated the cognitive correlates of NPS in patients with severe dementia in a community-based sample, examining whether impairments in specific cognitive or behavioral domains were more predictive of specific NPS. We hypothesized that poorer cognitive abilities would be associated with more severe NPS (e.g., agitation) and higher cognitive scores with affective symptoms in severe dementia. Methods: Eighty-nine (27%) out of 328 total participants of a longitudinal study of dementia progression (the Cache County Dementia Progression Study) met criteria for severe dementia: Mini-Mental State Exam (MMSE) score of ≤10 or Clinical Dementia Rating of 3 (severe). Forty-eight (54%) of these individuals completed the Severe Cognitive Impairment Profile (SCIP), which assessed the following domains: Comportment, Attention, Language, Memory, Motor, Conceptualization, Arithmetic, and Visuospatial abilities. NPS were assessed by caregiver report using the Neuropsychiatric Inventory (NPI). The NPI assesses delusions, hallucinations, depression, anxiety, irritability, apathy, agitation/aggression, judgement, aberrant motor behaviors, euphoria, sleep and appetite. Demographic information, overall health, place of residence (private home, assisted living facility and nursing home), and dementia duration were also assessed. NPI severity scores (intensity x frequency) were summed across domains to yield a total NPI score (Total NPI-12) and domain clusters of psychotic symptoms (hallucinations and delusions), affective symptoms (depression, anxiety, and irritability), apathy, and agitation/aggression were examined. Bivariate correlations between SCIP domain scores and Total NPI-12 and the domain clusters were examined. SCIP domain scores that were significantly correlated with NPI scores in bivariate analyses were entered into multiple regression models. Covariates tested included the age at which severe dementia criteria was met, the duration of dementia from age of onset, gender, place of residence, overall health and years of education. Results: Mean (SD) age and education were 86.23 (6.12) and 13.13 (3.13), respectively. Total NPI-12 scores showed significant correlations with the SCIP sub scores of comportment ( r = -0.36, p = 0.017) and memory (r = - 0.31, p = 0.047). Apathy significantly correlated with comportment (r = -0.38, p = 0.010) while agitation/aggression correlated with conceptualization (r = -0.41, p = 0.007), language (r = -0.36, p = 0.017), memory (r = -0.48, p = 0.001), and visuospatial ability (r = -0.31, p = 0.045). In multiple regression models (with inclusion of significant covariates), total NPI-12 scores were significantly associated with comportment (β = -1.32, SE = 0.56, p = 0.02); apathy was significantly associated with comportment (β = -0.01, SE = 0.02, p = 0.003); and agitation/aggression was significantly associated with memory (β = -0.43, SE = 0.12, p = 0.001). NPI affective and psychotic scores were not associated with any SCIP domains. Conclusion: In this sample of individuals with severe dementia, we found several cognitive or behavioral domains were associated with NPS. Poorer abilities in Comportment, which consisted of responses to social questions (e.g., greetings) were associated with more severe apathy, and poorer abilities in conceptualization, language, memory and visuospatial skills were associated with more severe agitation/aggression. With the latter, multiple regression models found only memory scores to independently predict agitation/aggression, reflecting moderate correlation between cognitive domains. Our results suggest that poor cognitive abilities may increase vulnerability to NPS, possibly as a result of impaired comprehension of activities and events in the environment. Cognitive testing may be useful to identify those at greatest risk for NPS. Furthermore, environmental manipulations that aim to decrease the complexity and therefore degree of stimulation for persons with dementia to a level more appropriate to their level of cognitive function may help reduce the occurrence of NPS in severe dementia

The Empowering Role of Mobile Apps in Behavior Change Interventions: The Gray Matters Randomized Controlled Trial

BACKGROUND: Health education and behavior change programs targeting specific risk factors have demonstrated their effectiveness in reducing the development of future diseases. Alzheimer disease (AD) shares many of the same risk factors, most of which can be addressed via behavior change. It is therefore theorized that a behavior change intervention targeting these risk factors would likely result in favorable rates of AD prevention. OBJECTIVE: The objective of this study was to reduce the future risk of developing AD, while in the short term promoting vascular health, through behavior change. METHODS: The study was an interventional randomized controlled trial consisting of subjects who were randomly assigned into either treatment (n=102) or control group (n=42). Outcome measures included various blood-based biomarkers, anthropometric measures, and behaviors related to AD risk. The treatment group was provided with a bespoke “Gray Matters” mobile phone app designed to encourage and facilitate behavior change. The app presented evidence-based educational material relating to AD risk and prevention strategies, facilitated self-reporting of behaviors across 6 behavioral domains, and presented feedback on the user’s performance, calculated from reported behaviors against recommended guidelines. RESULTS: This paper explores the rationale for a mobile phone–led intervention and details the app’s effect on behavior change and subsequent clinical outcomes. Via the app, the average participant submitted 7.3 (SD 3.2) behavioral logs/day (n=122,719). Analysis of these logs against primary outcome measures revealed that participants who improved their high-density lipoprotein cholesterol levels during the study duration answered a statistically significant higher number of questions per day (mean 8.30, SD 2.29) than those with no improvement (mean 6.52, SD 3.612), t(97.74)=−3.051, P=.003. Participants who decreased their body mass index (BMI) performed significantly better in attaining their recommended daily goals (mean 56.21 SD 30.4%) than those who increased their BMI (mean 40.12 SD 29.1%), t(80) = −2.449, P=.017. In total, 69.2% (n=18) of those who achieved a mean performance percentage of 60% or higher, across all domains, reduced their BMI during the study, whereas 60.7% (n=34) who did not, increased their BMI. One-way analysis of variance of systolic blood pressure category changes showed a significant correlation between reported efforts to reduce stress and category change as a whole, P=.035. An exit survey highlighted that respondents (n=83) reported that the app motivated them to perform physical activity (85.4%) and make healthier food choices (87.5%). CONCLUSIONS: In this study, the ubiquitous nature of the mobile phone excelled as a delivery platform for the intervention, enabling the dissemination of educational intervention material while simultaneously monitoring and encouraging positive behavior change, resulting in desirable clinical effects. Sustained effort to maintain the achieved behaviors is expected to mitigate future AD risk. TRIAL REGISTRATION: ClinicalTrails.gov NCT02290912; https://clinicaltrials.gov/ct2/show/NCT02290912 (Archived by WebCite at http://www.webcitation.org/6ictUEwnm

Common DNA Variants Accurately Rank an Individual of Extreme Height

Polygenic scores (or genetic risk scores) quantify the aggregate of small effects from many common genetic loci that have been associated with a trait through genome-wide association. Polygenic scores were first used successfully in schizophrenia and have since been applied to multiple phenotypes including multiple sclerosis, rheumatoid arthritis, and height. Because human height is an easily-measured and complex polygenic trait, polygenic height scores provide exciting insights into the predictability of aggregate common variant effect on the phenotype. Shawn Bradley is an extremely tall former professional basketball player from Brigham Young University and the National Basketball Association (NBA), measuring 2.29 meters (7′6″, 99.99999th percentile for height) tall, with no known medical conditions. Here, we present a case where a rare combination of common SNPs in one individual results in an extremely high polygenic height score that is correlated with an extreme phenotype. While polygenic scores are not clinically significant in the average case, our findings suggest that for extreme phenotypes, polygenic scores may be more successful for the prediction of individuals

Rare coding variants in PLCG2, ABI3, and TREM2 implicate microglial-mediated innate immunity in Alzheimer's disease

We identified rare coding variants associated with Alzheimer’s disease (AD) in a 3-stage case-control study of 85,133 subjects. In stage 1, 34,174 samples were genotyped using a whole-exome microarray. In stage 2, we tested associated variants (P<1×10-4) in 35,962 independent samples using de novo genotyping and imputed genotypes. In stage 3, an additional 14,997 samples were used to test the most significant stage 2 associations (P<5×10-8) using imputed genotypes. We observed 3 novel genome-wide significant (GWS) AD associated non-synonymous variants; a protective variant in PLCG2 (rs72824905/p.P522R, P=5.38×10-10, OR=0.68, MAFcases=0.0059, MAFcontrols=0.0093), a risk variant in ABI3 (rs616338/p.S209F, P=4.56×10-10, OR=1.43, MAFcases=0.011, MAFcontrols=0.008), and a novel GWS variant in TREM2 (rs143332484/p.R62H, P=1.55×10-14, OR=1.67, MAFcases=0.0143, MAFcontrols=0.0089), a known AD susceptibility gene. These protein-coding changes are in genes highly expressed in microglia and highlight an immune-related protein-protein interaction network enriched for previously identified AD risk genes. These genetic findings provide additional evidence that the microglia-mediated innate immune response contributes directly to AD development

Atypical Antipsychotic Drugs Block Selective Components of Amphetamine-Induced Stereotypy

Individual items of behavior produced by 1.0 or 5.0 mg/kg d-amphetamine were monitored in rats pretreated 15 minutes earlier with vehicle or with behaviorally relevant doses of haloperidol (0.1 or 0.25 mg/kg), clozapine (1.0 or 5.0 mg/kg), or thioridazine (1.0 or 5.0 mg/kg). Unlike haloperidol, the atypical antipsychotics failed to block all components of either the low- or high-dose response to amphetamine. These drugs, however, did block selective items of amphetamine-induced stereotyped behavior. Clozapine significantly attenuated the sniffing produced by 1.0 mg/kg d-amphetamine as well as the oral behavior (licking and/or biting) produced by 5.0 mg/kg d-amphetamine. Thioridazine, at a dose 5.0 mg/kg, also reduced oral behavior and selectively blocked repetitive head bobbing. Taken together, these results suggest that although atypical antipsychotic drugs exert some common effects on the amphetamine behavioral response, these drugs do not influence all amphetamine-induced behaviors equally