The influence of economic policies on social environments and mental health

Despite increased advocacy and investments in mental health systems globally, there has been limited progress in reducing mental disorder prevalence. In this paper, we argue that meaningful advancements in population mental health necessitate addressing the fundamental sources of shared distress. Using a systems perspective, economic structures and policies are identified as the potential cause of causes of mental ill-health. Neoliberal ideologies, prioritizing economic optimization and continuous growth, contribute to the promotion of individualism, job insecurity, increasing demands on workers, parental stress, social disconnection and a broad range of manifestations well-recognized to erode mental health. We emphasize the need for mental health researchers and advocates to increasingly engage with the economic policy discourse to draw attention to mental health and well-being implications. We call for a shift towards a well-being economy to better align commercial interests with collective well-being and social prosperity. The involvement of individuals with lived mental ill-health experiences, practitioners and researchers is needed to mobilize communities for change and influence economic policies to safeguard well-being. Additionally, we call for the establishment of national mental wealth observatories to inform coordinated health, social and economic policies and realize the transition to a more sustainable well-being economy that offers promise for progress on population mental health outcomes.

ElliQ, an AI-Driven Social Robot to Alleviate Loneliness: Progress and Lessons Learned

Background

Loneliness is a significant issue in older adults and can increase the risk of morbidity and mortality.

Objective

To present the development of ElliQ, a proactive, AI-driven social robot with multiple social and health coaching functions specifically designed to address loneliness and support older people.

Development/Implementation

ElliQ, a consumer robot with a friendly appearance, uses voice, sounds, light, and buttons through a touch screen to facilitate conversation, music, video calls, well-being assessments, stress reduction, cognitive games, and health reminders. The robot was deployed by 15 government agencies in the USA. Initial experience suggests it is not only highly engaging for older people but may be able to improve their quality of life and reduce loneliness. In addition, the development of a weekly report that patients can share with their clinicians to allow better integration into routine care is described.

Conclusion

This paper describes the development and real-world implementation of this product innovation and discusses challenges encountered and future directions.

Identifying a stable and generalizable factor structure of major depressive disorder across three large longitudinal cohorts

The Patient Health Questionnaire 9 (PHQ-9) is the current standard outpatient screening tool for measuring and tracking the nine symptoms of major depressive disorder (MDD). While the PHQ-9 was originally conceptualized as a unidimensional measure, it has become clear that MDD is not a monolithic construct, as evidenced by high comorbidities with other theoretically distinct diagnoses and common symptom overlap between depression and other diagnoses. Therefore, identifying reliable and temporally stable subfactors of depressive symptoms could allow research and care to be tailored to different depression phenotypes. This study improved on previous factor analysis studies of the PHQ-9 by leveraging samples that were clinical (participants with depression only), large (N = 1483 depressed individuals in total), longitudinal (up to 5 years), and from three diverse (matching racial distribution of the United States) datasets. By refraining from assuming the number of factors or item loadings a priori, and thus utilizing a solely data-driven approach, we identified a ranked list of best-fitting models, with the parsimonious one achieving good model fit across studies at most timepoints (average TLI >= 0.90). This model categorizes the PHQ-9 items into four factors: (1) Affective (Anhedonia + Depressed Mood), (2) Somatic (Sleep + Fatigue + Appetite), (3) Internalizing (Worth/Guilt + Suicidality), (4) Sensorimotor (Concentration + Psychomotor), which may be used to further precision psychiatry by testing factor-specific interventions in research and clinical settings.

Deep learning-aided decision support for diagnosis of skin disease across skin tones

Although advances in deep learning systems for image-based medical diagnosis demonstrate their potential to augment clinical decision-making, the effectiveness of physician-machine partnerships remains an open question, in part because physicians and algorithms are both susceptible to systematic errors, especially for diagnosis of underrepresented populations. Here we present results from a large-scale digital experiment involving board-certified dermatologists (n = 389) and primary-care physicians (n = 459) from 39 countries to evaluate the accuracy of diagnoses submitted by physicians in a store-and-forward teledermatology simulation. In this experiment, physicians were presented with 364 images spanning 46 skin diseases and asked to submit up to four differential diagnoses. Specialists and generalists achieved diagnostic accuracies of 38% and 19%, respectively, but both specialists and generalists were four percentage points less accurate for the diagnosis of images of dark skin as compared to light skin. Fair deep learning system decision support improved the diagnostic accuracy of both specialists and generalists by more than 33%, but exacerbated the gap in the diagnostic accuracy of generalists across skin tones. These results demonstrate that well-designed physician-machine partnerships can enhance the diagnostic accuracy of physicians, illustrating that success in improving overall diagnostic accuracy does not necessarily address bias.

Individual bioenergetic capacity as a potential source of resilience to Alzheimer's disease

Impaired glucose uptake in the brain is one of the earliest presymptomatic manifestations of Alzheimer's disease (AD). The absence of symptoms for extended periods of time suggests that compensatory metabolic mechanisms can provide resilience. Here, we introduce the concept of a systemic 'bioenergetic capacity' as the innate ability to maintain energy homeostasis under pathological conditions, potentially serving as such a compensatory mechanism. We argue that fasting blood acylcarnitine profiles provide an approximate peripheral measure for this capacity that mirrors bioenergetic dysregulation in the brain. Using unsupervised subgroup identification, we show that fasting serum acylcarnitine profiles of participants from the AD Neuroimaging Initiative yields bioenergetically distinct subgroups with significant differences in AD biomarker profiles and cognitive function. To assess the potential clinical relevance of this finding, we examined factors that may offer diagnostic and therapeutic opportunities. First, we identified a genotype affecting the bioenergetic capacity which was linked to succinylcarnitine metabolism and significantly modulated the rate of future cognitive decline. Second, a potentially modifiable influence of beta-oxidation efficiency seemed to decelerate bioenergetic aging and disease progression. Our findings, which are supported by data from more than 9,000 individuals, suggest that interventions tailored to enhance energetic health and to slow bioenergetic aging could mitigate the risk of symptomatic AD, especially in individuals with specific mitochondrial genotypes.

Cerebral venous biomarkers and veno-arterial gradients: untapped resources in Alzheimer's disease

Blood based biomarkers (BBB) derived from forearm veins for estimating brain changes is becoming ubiquitous in Alzheimer's Disease (AD) research and could soon become standard in routine clinical diagnosis. However, there are many peripheral sources of contamination through which concentrations of these metabolites can be raised or lowered after leaving the brain and entering the central venous pool. This raises the issue of potential false conclusions that could lead to erroneous diagnosis or research findings. We propose the use of simultaneous sampling of internal jugular venous and arterial blood to calculate veno-arterial gradient, which can reveal either a surplus or a deficit of metabolites exiting the brain. Methods for sampling internal jugular venous and arterial blood are described along with examples of the use of the veno-arterial gradient in non-AD brain research. Such methods in turn could help better establish the accuracy of forearm venous biomarkers.

Computerized Cognitive Training in Mild Cognitive Impairment: Findings in African Americans and Caucasians

BACKGROUND

African Americans with MCI may be at increased risk for dementia compared to Caucasians. The effect of race on the efficacy of cognitive training in MCI is unclear.

METHODS

We used data from a two-site, 78-week randomized trial of MCI comparing intensive, home-based, computerized training with Web-based cognitive games or Web-based crossword puzzles to examine the effect of race on outcomes. The study outcomes were changes from baseline in cognitive and functional scales as well as MRI-measured changes in hippocampal volume and cortical thickness. Analyses used linear models adjusted for baseline scores. This was an exploratory study.

RESULTS

A total of 105 subjects were included comprising 81 whites (77.1%) and 24 African Americans (22.8%). The effect of race on the change from baseline in ADAS-Cog-11 was not significant. The effect of race on change from baseline to week 78 in the Functional Activities Questionnaire (FAQ) was significant with African American participants' FAQ scores showing greater improvements at weeks 52 and 78 (P = 0.009, P = 0.0002, respectively) than white subjects. Within the CCT cohort, FAQ scores for African American participants showed greater improvement between baseline and week 78, compared to white participants randomized to CCT (P = 0.006). There was no effect of race on the UPSA. There was no effect of race on hippocampal or cortical thickness outcomes.

CONCLUSIONS

Our preliminary findings suggest that web-based cognitive training programs may benefit African Americans with MCI at least as much as Caucasians, and highlight the need to further study underrepresented minorities in AD prevention trials. (Supported by the National Institutes of Health, National Institute on Aging; ClinicalTrials.gov number, NCT03205709.).

Expectancy Does Not Predict 18-month Treatment Outcomes with Cognitive Training in Mild Cognitive Impairment

BACKGROUND

Computerized cognitive training (CCT) has emerged as a potential treatment option for mild cognitive impairment (MCI). It remains unclear whether CCT's effect is driven in part by expectancy of improvement.

OBJECTIVES

This study aimed to determine factors associated with therapeutic expectancy and the influence of therapeutic expectancy on treatment effects in a randomized clinical trial of CCT versus crossword puzzle training (CPT) for older adults with MCI.

DESIGN

Randomized clinical trial of CCT vs CPT with 78-week follow-up.

SETTING

Two-site study - New York State Psychiatric Institute and Duke University Medical Center.

PARTICIPANTS

107 patients with MCI.

INTERVENTION

12 weeks of intensive training with CCT or CPT with follow-up booster training over 78 weeks.

MEASUREMENTS

Patients rated their expectancies for CCT and CPT prior to randomization.

RESULTS

Patients reported greater expectancy for CCT than CPT. Lower patient expectancy was associated with lower global cognition at baseline and older age. Expectancy did not differ by sex or race. There was no association between expectancy and measures of everyday functioning, hippocampus volume, or apolipoprotein E genotype. Expectancy was not associated with change in measures of global cognition, everyday functioning, and hippocampus volume from baseline to week 78, nor did expectancy interact with treatment condition.

CONCLUSIONS

While greater cognitive impairment and increased age was associated with low expectancy of improvement, expectancy was not associated with the likelihood of response to treatment with CPT or CCT.

Personalized Computational Causal Modeling of the Alzheimer Disease Biomarker Cascade

BACKGROUND

Mathematical models of complex diseases, such as Alzheimer's disease, have the potential to play a significant role in personalized medicine. Specifically, models can be personalized by fitting parameters with individual data for the purpose of discovering primary underlying disease drivers, predicting natural history, and assessing the effects of theoretical interventions. Previous work in causal/mechanistic modeling of Alzheimer's Disease progression has modeled the disease at the cellular level and on a short time scale, such as minutes to hours. No previous studies have addressed mechanistic modeling on a personalized level using clinically validated biomarkers in individual subjects.

OBJECTIVES

This study aimed to investigate the feasibility of personalizing a causal model of Alzheimer's Disease progression using longitudinal biomarker data.

DESIGN/SETTING/PARTICIPANTS/MEASUREMENTS

We chose the Alzheimer Disease Biomarker Cascade model, a widely-referenced hypothetical model of Alzheimer's Disease based on the amyloid cascade hypothesis, which we had previously implemented mathematically as a mechanistic model. We used available longitudinal demographic and serial biomarker data in over 800 subjects across the cognitive spectrum from the Alzheimer's Disease Neuroimaging Initiative. The data included participants that were cognitively normal, had mild cognitive impairment, or were diagnosed with dementia (probable Alzheimer's Disease). The model consisted of a sparse system of differential equations involving four measurable biomarkers based on cerebrospinal fluid proteins, imaging, and cognitive testing data.

RESULTS

Personalization of the Alzheimer Disease Biomarker Cascade model with individual serial biomarker data yielded fourteen personalized parameters in each subject reflecting physiologically meaningful characteristics. These included growth rates, latency values, and carrying capacities of the various biomarkers, most of which demonstrated significant differences across clinical diagnostic groups. The model fits to training data across the entire cohort had a root mean squared error (RMSE) of 0.09 (SD 0.081) on a variable scale between zero and one, and were robust, with over 90% of subjects showing an RMSE of < 0.2. Similarly, in a subset of subjects with data on all four biomarkers in at least one test set, performance was high on the test sets, with a mean RMSE of 0.15 (SD 0.117), with 80% of subjects demonstrating an RMSE < 0.2 in the estimation of future biomarker points. Cluster analysis of parameters revealed two distinct endophenotypic groups, with distinct biomarker profiles and disease trajectories.

CONCLUSION

Results support the feasibility of personalizing mechanistic models based on individual biomarker trajectories and suggest that this approach may be useful for reclassifying subjects on the Alzheimer's clinical spectrum. This computational modeling approach is not limited to the Alzheimer Disease Biomarker Cascade hypothesis, and can be applied to any mechanistic hypothesis of disease progression in the Alzheimer's field that can be monitored with biomarkers. Thus, it offers a computational platform to compare and validate various disease hypotheses, personalize individual biomarker trajectories and predict individual response to theoretical prevention and therapeutic intervention strategies.

MRI-based Deep Learning Assessment of Amyloid, Tau, and Neurodegeneration Biomarker Status across the Alzheimer Disease Spectrum

Background PET can be used for amyloid-tau-neurodegeneration (ATN) classification in Alzheimer disease, but incurs considerable cost and exposure to ionizing radiation. MRI currently has limited use in characterizing ATN status. Deep learning techniques can detect complex patterns in MRI data and have potential for noninvasive characterization of ATN status. Purpose To use deep learning to predict PET-determined ATN biomarker status using MRI and readily available diagnostic data. Materials and Methods MRI and PET data were retrospectively collected from the Alzheimer's Disease Imaging Initiative. PET scans were paired with MRI scans acquired within 30 days, from August 2005 to September 2020. Pairs were randomly split into subsets as follows: 70% for training, 10% for validation, and 20% for final testing. A bimodal Gaussian mixture model was used to threshold PET scans into positive and negative labels. MRI data were fed into a convolutional neural network to generate imaging features. These features were combined in a logistic regression model with patient demographics, APOE gene status, cognitive scores, hippocampal volumes, and clinical diagnoses to classify each ATN biomarker component as positive or negative. Area under the receiver operating characteristic curve (AUC) analysis was used for model evaluation. Feature importance was derived from model coefficients and gradients. Results There were 2099 amyloid (mean patient age, 75 years ± 10 [SD]; 1110 male), 557 tau (mean patient age, 75 years ± 7; 280 male), and 2768 FDG PET (mean patient age, 75 years ± 7; 1645 male) and MRI pairs. Model AUCs for the test set were as follows: amyloid, 0.79 (95% CI: 0.74, 0.83); tau, 0.73 (95% CI: 0.58, 0.86); and neurodegeneration, 0.86 (95% CI: 0.83, 0.89). Within the networks, high gradients were present in key temporal, parietal, frontal, and occipital cortical regions. Model coefficients for cognitive scores, hippocampal volumes, and APOE status were highest. Conclusion A deep learning algorithm predicted each component of PET-determined ATN status with acceptable to excellent efficacy using MRI and other available diagnostic data. © RSNA, 2023 Supplemental material is available for this article.

Erratum to: Blueberry Supplementation Effects on Neuronal and Pathological Biomarkers in Subjects at Risk for Alzheimer's Disease: A Pilot Study

[This corrects the article DOI: 10.14283/jarlife.2023.13.].

Blueberry Supplementation Effects on Neuronal and Pathological Biomarkers in Subjects at Risk for Alzheimer's Disease: A Pilot Study

Background

There is a need to develop non-invasive practical lifestyle interventions for preventing Alzheimer's disease (AD) in people at risk, such as those with mild cognitive impairment (MCI). Blueberry consumption has been associated with reduced risk of dementia in some epidemiologic studies and with improvements in cognition in healthy aging adults. Blood-based biomarkers have emerged at the forefront of AD therapeutics research spurred by the development of reliable ultra-sensitive "single-molecule array" assays with 100-1000-fold greater sensitivity over traditional platforms.

Objective

The purpose of this study was to examine the effect of blueberry supplementation in MCI on six blood biomarkers: amyloid-beta 40 (Aβ40), amyloid-beta 42 (Aβ42), phosphorylated Tau181 (ptau181), neurofilament light (NfL), Glial Fibrillary acidic protein (GFAP), and Brain-Derived Neurotrophic Factor (BDNF).

Methods

This was a 12-week, open-label, pilot trial of 10 participants with MCI (mean age 80.2 years + 5.16). Subjects consumed 36 grams per day of lyophilized blueberry powder in a split dose consumed with breakfast and dinner. Baseline and endpoint venous blood was analyzed using an ultrasensitive SIMOA assay. Our aim was to test if blueberry supplementation would particularly impact p-tau181, NfL, and GFAP elevations associated with the neurodegenerative process.

Results

There were no statistically significant (p < 0.05) changes from baseline to endpoint for any of the biomarker values or in the ratios of Aβ42 / Aβ40 and ptau181/ Aβ42. Adverse effects were mild and transient; supplementation was relatively well tolerated with all subjects completing the study.

Conclusion

To our knowledge, this is the first study to prospectively examine the effects of blueberry supplementation on a panel of blood biomarkers reflecting the neurodegenerative process. Our findings raise two possibilities - a potential stabilization of the neurodegenerative process or a lack of a direct and acute effect on beta-amyloid/tau/glial markers. A larger controlled study is warranted.

Enhancing social connectedness with companion robots using AI

Companion robots with AI may usher a new science of social connectedness that requires the development of ethical frameworks.

Expanding the scope of health disparities research in Alzheimer's disease and related dementias: Recommendations from the "Leveraging Existing Data and Analytic Methods for Health Disparities Research Related to Aging and Alzheimer's Disease and Related Dementias" Workshop Series

Topics discussed at the "Leveraging Existing Data and Analytic Methods for Health Disparities Research Related to Aging and Alzheimer's Disease and Related Dementias" workshop, held by Duke University and the Alzheimer's Association with support from the National Institute on Aging, are summarized.  Ways in which existing data resources paired with innovative applications of both novel and well-known methodologies can be used to identify the effects of multi-level societal, community, and individual determinants of race/ethnicity, sex, and geography-related health disparities in Alzheimer's disease and related dementia are proposed.  Current literature on the population analyses of these health disparities is summarized with a focus on identifying existing gaps in knowledge, and ways to mitigate these gaps using data/method combinations are discussed at the workshop.  Substantive and methodological directions of future research capable of advancing health disparities research related to aging are formulated.

Impact of Computerized Cognitive Training on Default Mode Network Connectivity in Subjects at Risk for Alzheimer's Disease: A 78-week Randomized Controlled Trial

BACKGROUND

Mild cognitive impairment (MCI) represents a high risk group for Alzheimer's disease (AD). Computerized Cognitive Games Training (CCT) is an investigational strategy to improve targeted functions in MCI through the modulation of cognitive networks.

OBJECTIVE

The goal of this study was to examine the effect of CCT versus a non-targeted active brain exercise on functional cognitive networks.

METHODS

107 patients with MCI were randomized to CCT or web-based crossword puzzles. Resting-state functional MRI (fMRI) was obtained at baseline and 18 months to evaluate differences in fMRI measured within- and between-network functional connectivity (FC) of the default mode network (DMN) and other large-scale brain networks: the executive control, salience, and sensorimotor networks.

RESULTS

There were no differences between crosswords and games in the primary outcome, within-network DMN FC across all subjects. However, secondary analyses suggest differential effects on between-network connectivity involving the DMN and SLN, and within-network connectivity of the DMN in subjects with late MCI. Paradoxically, in both cases, there was a decrease in FC for games and an increase for the crosswords control (p < 0.05), accompanied by lesser cognitive decline in the crosswords group.

CONCLUSION

Results do not support a differential impact on within-network DMN FC between games and crossword puzzle interventions. However, crossword puzzles might result in cognitively beneficial remodeling between the DMN and other networks in more severely impaired MCI subjects, parallel to the observed clinical benefits.

IMPACT OF CALORIE RESTRICTION ON PLASMA ALZHEIMER’S DISEASE BIOMARKERS IN HEALTHY YOUNG AND MIDDLE-AGED ADULTS

Midlife cardiometabolic risk factors are associated with an increased risk of Alzheimer’s dementia (AD). Moderate calorie restriction (CR) in healthy, non-obese young and middle-aged adults improves cardiometabolic risk factors. Plasma concentrations of amyloid β oligomers (Aβ-42 and Aβ-40) and total tau are emerging biomarkers of AD pathology. Our objective was to determine the impact of two years of CR in healthy young and middle-aged adults on Aβ-42, Aβ-40, and total tau in the Comprehensive Assessment of Long term Effects of Reducing Intake of Energy (CALERIE) Study. Participants were randomized 2:1 to 24 months of CR (prescribed as 25% reduction in baseline calorie requirements) versus an ad libitum (AL) diet. We quantified plasma Aβ-42, Aβ-40, and total tau using the ultrasensitive single molecule array (SIMOA) technology at baseline and two years in a subset of CALERIE (N=133). We used linear regression to evaluate the impact of CR, including age, sex, and presence/absence of the APOE-ε4 risk allele as covariates. We hypothesized that there would be differential CR effects based on APOE-ε4 carrier status; to test this, we included an interaction term. As compared to AL, there was a trend towards decreased Aβ-40, controlling for age, baseline Aβ-40 concentrations, and APOE-ε4 carrier status (β=-12.59, 95% CI[-27.14, 1.96], p=0.093) with 12% (average achieved) CR. The CR*APOE-ε4 carrier status interaction term was significant at a pre-defined threshold of p<0.10 (p=0.062). Stratified by APOE-ε4 carrier status, CR was associated with a decrease in plasma Aβ-40 (β=-33.72, 95% CI[-65.16,-2.09], p=0.041). In conclusion, moderate CR in healthy, non-obese young and middle-aged adults impacts plasma biomarkers of AD risk, primarily in APOE-ε4 carriers.

Computerized Games versus Crosswords Training in Mild Cognitive Impairment

BACKGROUND

Mild cognitive impairment (MCI) increases the risk of dementia. The efficacy of cognitive training in patients with MCI is unclear.

METHODS

In a two-site, single-blinded, 78-week trial, participants with MCI - stratified by age, severity (early/late MCI), and site - were randomly assigned to 12 weeks of intensive, home-based, computerized training with Web-based cognitive games or Web-based crossword puzzles, followed by six booster sessions. In mixed-model analyses, the primary outcome was change from baseline in the 11-item Alzheimer's Disease Assessment Scale-Cognitive (ADAS-Cog) score, a 70 point scale in which higher scores indicate greater cognitive impairment at 78 weeks, adjusted for baseline. Secondary outcomes included change from baseline in neuropsychological composite score, University of California San Diego Performance-Based Skills Assessment (functional outcome) score, and Functional Activities Questionnaire (functional outcome) score at 78 weeks, adjusted for baseline. Changes in hippocampal volume and cortical thickness on magnetic resonance imaging were assessed.

RESULTS

Among 107 participants (n=51 [games]; n=56 [crosswords]), ADAS-Cog score worsened slightly for games and improved for crosswords at week 78 (least squares [LS] means difference, -1.44; 95% confidence interval [CI], -2.83 to -0.06; P=0.04). From baseline to week 78, mean ADAS-Cog score worsened for games (9.53 to 9.93) and improved for crosswords (9.59 to 8.61). The late MCI subgroup showed similar results (LS means difference, -2.45; SE, 0.89; 95% CI, -4.21 to -0.70). Among secondary outcomes, the Functional Activities Questionnaire score worsened more with games than with crosswords at week 78 (LS means difference, -1.08; 95% CI, -1.97 to -0.18). Other secondary outcomes showed no differences. Decreases in hippocampal volume and cortical thickness were greater for games than for crosswords (LS means difference, 34.07; SE, 17.12; 95% CI, 0.51 to 67.63 [hippocampal volume]; LS means difference, 0.02; SE, 0.01; 95% CI, 0.00 to 0.04 [cortical thickness]).

CONCLUSIONS

Home-based computerized training with crosswords demonstrated superior efficacy to games for the primary outcome of baseline-adjusted change in ADAS-Cog score over 78 weeks. (Supported by the National Institutes of Health, National Institute on Aging; ClinicalTrials.gov number, NCT03205709.).

General Anxiety Disorder-7 Questionnaire as a marker of low socioeconomic status and inequity

BACKGROUND

The General Anxiety Disorder-7 (GAD-7) questionnaire is a standard tool used for screening and follow-up of patients with Generalized Anxiety Disorder (GAD). Although it is generally accepted that anxiety correlates with clinical and psychosocial stressors, precise quantitative data is limited on the relations among GAD-7, traditional biomarkers, and other measures of health. Further research is needed about how GAD-7 relates to race, ethnicity, and socioeconomic status (SES) as an assembly. We determined how multiple demographic and socioeconomic data correlate with the participants' GAD-7 results when compared with laboratory, physical function, clinical, and other biological markers.

METHODS

The Project Baseline Health Study (BHS) is a prospective cohort of adults representing several populations in the USA. We analyzed a deeply phenotyped group of 2502 participants from that study. Measures of interest included: clinical markers or history of medical diagnoses; physical function markers including gait, grip strength, balance time, daily steps, and echocardiographic parameters; psychometric measurements; activities of daily living; socioeconomic characteristics; and laboratory results.

RESULTS

Higher GAD-7 scores were associated with female sex, younger age, and Hispanic ethnicity. Measures of low SES were also associated with higher scores, including unemployment, income ≤$25,000, and ≤12 years of education. After adjustment for 158 demographic, clinical, laboratory, and symptom characteristics, unemployment and overall higher SES risk scores were highly correlated with anxiety scores. Protective factors included Black race and older age.

LIMITATIONS

Correlations identified in this cross-sectional study cannot be used to infer causal relationships; further, we were not able to account for possible use of anxiety treatments by study participants.

CONCLUSIONS

These findings highlight the importance of understanding anxiety as a biopsychosocial entity. Clinicians and provider organizations need to consider both the physical manifestations of the disorder and their patients' social determinants of health when considering treatment pathways and designing interventions.

Data-driven causal model discovery and personalized prediction in Alzheimer's disease

With the explosive growth of biomarker data in Alzheimer’s disease (AD) clinical trials, numerous mathematical models have been developed to characterize disease-relevant biomarker trajectories over time. While some of these models are purely empiric, others are causal, built upon various hypotheses of AD pathophysiology, a complex and incompletely understood area of research. One of the most challenging problems in computational causal modeling is using a purely data-driven approach to derive the model’s parameters and the mathematical model itself, without any prior hypothesis bias. In this paper, we develop an innovative data-driven modeling approach to build and parameterize a causal model to characterize the trajectories of AD biomarkers. This approach integrates causal model learning, population parameterization, parameter sensitivity analysis, and personalized prediction. By applying this integrated approach to a large multicenter database of AD biomarkers, the Alzheimer’s Disease Neuroimaging Initiative, several causal models for different AD stages are revealed. In addition, personalized models for each subject are calibrated and provide accurate predictions of future cognitive status.

Importance of Social Determinants in Screening for Depression

IMPORTANCE

The most common screening tool for depression is the Patient Health Questionnaire-9 (PHQ-9). Despite extensive research on the clinical and behavioral implications of the PHQ-9, data are limited on the relationship between PHQ-9 scores and social determinants of health and disease.

OBJECTIVE

To assess the relationship between the PHQ-9 at intake and other measurements intended to assess social determinants of health.

DESIGN, SETTING, AND PARTICIPANTS

Cross-sectional analyses of 2502 participants from the Baseline Health Study (BHS), a prospective cohort of adults selected to represent major demographic groups in the US; participants underwent deep phenotyping on demographic, socioeconomic, clinical, laboratory, functional, and imaging findings.

INTERVENTIONS

None.

MAIN OUTCOMES AND MEASURES

Cross-sectional measures of clinical and socioeconomic status (SES).

RESULTS

In addition to a host of clinical and biological factors, higher PHQ-9 scores were associated with female sex, younger participants, people of color, and Hispanic ethnicity. Multiple measures of low SES, including less education, being unmarried, not currently working, and lack of insurance, were also associated with higher PHQ-9 scores across the entire spectrum of PHQ-9 scores. A summative score of SES, which was the 6th most predictive factor, was associated with higher PHQ-9 score after adjusting for 150 clinical, lab testing, and symptomatic characteristics.

CONCLUSIONS AND RELEVANCE

Our findings underscore that depression should be considered a comorbidity when social determinants of health are addressed, and both elements should be considered when designing appropriate interventions.

Measuring, Modeling, and Forecasting the Mental Wealth of Nations

The COVID-19 pandemic has exposed the deep links and fragility of economic, health and social systems. Discussions of reconstruction include renewed interest in moving beyond GDP and recognizing "human capital", "brain capital", "mental capital", and "wellbeing" as assets fundamental to economic reimagining, productivity, and prosperity. This paper describes how the conceptualization of Mental Wealth provides an important framing for measuring and shaping social and economic renewal to underpin healthy, productive, resilient, and thriving communities. We propose a transdisciplinary application of systems modeling to forecast a nation's Mental Wealth and understand the extent to which policy-mediated changes in economic, social, and health sectors could enhance collective mental health and wellbeing, social cohesion, and national prosperity. Specifically, simulation will allow comparison of the projected impacts of a range of cross-sector strategies (education sector, mental health system, labor market, and macroeconomic reforms) on GDP and national Mental Wealth, and provide decision support capability for future investments and actions to foster Mental Wealth. Finally, this paper introduces the Mental Wealth Initiative that is harnessing complex systems science to examine the interrelationships between social, commercial, and structural determinants of mental health and wellbeing, and working to empirically challenge the notion that fostering universal social prosperity is at odds with economic and commercial interests.

Physician perceptions of surveillance: Wearables, Apps, and Chatbots for COVID-19

Background and Purpose

To characterize the global physician community's opinions on the use of digital tools for COVID-19 public health surveillance and self-surveillance.

Materials and Methods

Cross-sectional, random, stratified survey done on Sermo, a physician networking platform, between September 9 and 15, 2020. We aimed to sample 1000 physicians divided among the USA, EU, and rest of the world. The survey questioned physicians on the risk-benefit ratio of digital tools, as well as matters of data privacy and trust.

Statistical Analysis Used

Descriptive statistics examined physicians' characteristics and opinions by age group, gender, frontline status, and geographic region. ANOVA, t-test, and Chi-square tests with P < 0.05 were viewed as qualitatively different. As this was an exploratory study, we did not adjust for small cell sizes or multiplicity. We used JMP Pro 15 (SAS), as well as Protobi.

Results

The survey was completed by 1004 physicians with a mean (standard deviation) age of 49.14 (12) years. Enthusiasm was highest for self-monitoring smartwatches (66%) and contact tracing apps (66%) and slightly lower (48-56%) for other tools. Trust was highest for health providers (68%) and lowest for technology companies (30%). Most respondents (69.8%) felt that loosening privacy standards to fight the pandemic would lead to misuse of privacy in the future.

Conclusion

The survey provides foundational insights into how physicians think of surveillance.

Research to Clinical Practice-Youth seeking mental health information online and its impact on the first steps in the patient journey

BACKGROUND

Online searches about anxiety and depression are recorded every 3-5 s. As such, information and communication technologies (ICT) have enormous potential to enable or impair help-seeking and patient-professional interactions. Youth studies indicate that ICT searches are undertaken before initial mental health consultations, but no publications have considered how this online activity affects the first steps of the patient journey in youth mental health settings.

METHODS

State-of-the-art review using an iterative, evidence mapping approach to identify key literature and expert consensus to synthesize and prioritise clinical and research issues.

RESULTS

Adolescents and young adults are more likely to seek health advice via online search engines or social media platforms than from a health professional. Young people not only search user-generated content and social media to obtain advice and support from online communities but increasingly contribute personal information online.

CONCLUSIONS

A major clinical challenge is to raise professional awareness of the likely impact of this activity on mental health consultations. Potential strategies range from modifying the structure of clinical consultations to ensure young people are able to disclose ICT activities related to mental health, through to the development and implementation of 'internet prescriptions' and a youth-focused 'toolkit'.

Validity of the Web-Based, Self-Directed, NeuroCognitive Performance Test in Mild Cognitive Impairment

BACKGROUND

Digital cognitive tests offer several potential advantages over established paper-pencil tests but have not yet been fully evaluated for the clinical evaluation of mild cognitive impairment.

OBJECTIVE

The NeuroCognitive Performance Test (NCPT) is a web-based, self-directed, modular battery intended for repeated assessments of multiple cognitive domains. Our objective was to examine its relationship with the Alzheimer's Disease Assessment Scale-Cognition Subscale (ADAS-Cog) and Mini-Mental State Examination (MMSE) as well as with established paper-pencil tests of cognition and daily functioning in mild cognitive impairment (MCI).

METHODS

We used Spearman correlations, regressions and principal components analysis followed by a factor analysis (varimax rotated) to examine our objectives.

RESULTS

In MCI subjects, the NCPT composite is significantly correlated with both a composite measure of established tests (r = 0.77, p <  0.0001) as well as with the ADAS-Cog (r = 0.55, p <  0.0001). Both NCPT and paper-pencil test batteries had a similar factor structure that included a large "g" component with a high eigenvalue. The correlation for the analogous tests (e.g., Trails A and B, learning memory tests) were significant (p <  0.0001). Further, both the NCPT and established tests significantly (p <  0.01) predicted the University of California San Diego Performance-Based Skills Assessment and Functional Activities Questionnaire, measures of daily functioning.

CONCLUSION

The NCPT, a web-based, self-directed, computerized test, shows high concurrent validity with established tests and hence offers promise for use as a research or clinical tool in MCI. Despite limitations such as a relatively small sample, absence of control group and cross-sectional nature, these findings are consistent with the growing literature on the promise of self-directed, web-based cognitive assessments for MCI.

Probing gut-brain links in Alzheimer's disease with rifaximin

Gut-microbiome-inflammation interactions have been linked to neurodegeneration in Alzheimer's disease (AD) and other disorders. We hypothesized that treatment with rifaximin, a minimally absorbed gut-specific antibiotic, may modify the neurodegenerative process by changing gut flora and reducing neurotoxic microbial drivers of inflammation. In a pilot, open-label trial, we treated 10 subjects with mild to moderate probable AD dementia (Mini-Mental Status Examination (MMSE) = 17 ± 3) with rifaximin for 3 months. Treatment was associated with a significant reduction in serum neurofilament-light levels (P < .004) and a significant increase in fecal phylum Firmicutes microbiota. Serum phosphorylated tau (pTau)181 and glial fibrillary acidic protein (GFAP) levels were reduced (effect sizes of -0.41 and -0.48, respectively) but did not reach statistical significance. In addition, there was a nonsignificant downward trend in serum cytokine interleukin (IL)-6 and IL-13 levels. Cognition was unchanged. Increases in stool Erysipelatoclostridium were correlated significantly with reductions in serum pTau181 and serum GFAP. Insights from this pilot trial are being used to design a larger placebo-controlled clinical trial to determine if specific microbial flora/products underlie neurodegeneration, and whether rifaximin is clinically efficacious as a therapeutic.

Biological and clinical correlates of the patient health questionnaire-9: exploratory cross-sectional analyses of the baseline health study

OBJECTIVES

We assessed the relationship between the Patient Health Questionnaire-9 (PHQ-9) at intake and other measurements intended to assess biological factors, markers of disease and health status.

DESIGN, SETTING AND PARTICIPANTS

We performed a cross-sectional analysis of 2365 participants from the Baseline Health Study, a prospective cohort of adults selected to represent major demographic groups in the USA. Participants underwent deep phenotyping on demographic, clinical, laboratory, functional and imaging findings.

IMPORTANCE

Despite extensive research on the clinical implications of the PHQ-9, data are limited on the relationship between PHQ-9 scores and other measures of health and disease; we sought to better understand this relationship.

INTERVENTIONS

None.

MAIN OUTCOMES AND MEASURES

Cross-sectional measures of medical illnesses, gait, balance strength, activities of daily living, imaging and laboratory tests.

RESULTS

Compared with lower PHQ-9 scores, higher scores were associated with female sex (46.9%-66.7%), younger participants (53.6-42.4 years) and compromised physical status (higher resting heart rates (65 vs 75 bpm), larger body mass index (26.5-30 kg/m²), greater waist circumference (91-96.5 cm)) and chronic conditions, including gastro-oesophageal reflux disease (13.2%-24.7%) and asthma (9.5%-20.4%) (p<0.0001). Increasing PHQ-9 score was associated with a higher frequency of comorbidities (migraines (6%-20.4%)) and active symptoms (leg cramps (6.4%-24.7%), mood change (1.2%-47.3%), lack of energy (1.2%-57%)) (p<0.0001). After adjustment for relevant demographic, socioeconomic, behavioural and medical characteristics, we found that memory change, tension, shortness of breath and indicators of musculoskeletal symptoms (backache and neck pain) are related to higher PHQ-9 scores (p<0.0001).

CONCLUSIONS

Our study highlights how: (1) even subthreshold depressive symptoms (measured by PHQ-9) may be indicative of several individual- and population-level concerns that demand more attention; and (2) depression should be considered a comorbidity in common disease.

TRIAL REGISTRATION NUMBER

NCT03154346.

Diffusion Tensor MRI Structural Connectivity and PET Amyloid Burden in Preclinical Autosomal Dominant Alzheimer Disease: The DIAN Cohort

Background Pathologic evidence of Alzheimer disease (AD) is detectable years before onset of clinical symptoms. Imaging-based identification of structural changes of the brain in people at genetic risk for early-onset AD may provide insights into how genes influence the pathologic cascade that leads to dementia. Purpose To assess structural connectivity differences in cortical networks between cognitively normal autosomal dominant Alzheimer disease (ADAD) mutation carriers versus noncarriers and to determine the cross-sectional relationship of structural connectivity and cortical amyloid burden with estimated years to symptom onset (EYO) of dementia in carriers. Materials and Methods In this exploratory analysis of a prospective trial, all participants enrolled in the Dominantly Inherited Alzheimer Network between January 2009 and July 2014 who had normal cognition at baseline, T1-weighted MRI scans, and diffusion tensor imaging (DTI) were analyzed. Amyloid PET imaging using Pittsburgh compound B was also analyzed for mutation carriers. Areas of the cerebral cortex were parcellated into three cortical networks: the default mode network, frontoparietal control network, and ventral attention network. The structural connectivity of the three networks was calculated from DTI. General linear models were used to examine differences in structural connectivity between mutation carriers and noncarriers and the relationship between structural connectivity, amyloid burden, and EYO in mutation carriers. Correlation network analysis was performed to identify clusters of related clinical and imaging markers. Results There were 30 mutation carriers (mean age ± standard deviation, 34 years ± 10; 17 women) and 38 noncarriers (mean age, 37 years ± 10; 20 women). There was lower structural connectivity in the frontoparietal control network in mutation carriers compared with noncarriers (estimated effect of mutation-positive status, -0.0266; P = .04). Among mutation carriers, there was a correlation between EYO and white matter structural connectivity in the frontoparietal control network (estimated effect of EYO, -0.0015, P = .01). There was no significant relationship between cortical global amyloid burden and EYO among mutation carriers (P > .05). Conclusion White matter structural connectivity was lower in autosomal dominant Alzheimer disease mutation carriers compared with noncarriers and correlated with estimated years to symptom onset. Clinical trial registration no. NCT00869817 © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by McEvoy in this issue.

Cortical thickness predicts remission of depression with antidepressants in patients with late-life depression and cognitive impairment

BACKGROUND

Depression (DEP) and cognitive impairment (CI) share etiological risk factors, anatomical underpinnings, and interact to produce deleterious treatment outcomes. Both DEP and CI exhibit altered patterns of cortical thickness which may impact the course of antidepressant treatment, though inconsistencies in directionality and affected brain regions have been reported. In this study, we examined the relationship between cortical thickness and treatment outcome in older adults with comorbid DEP-CI.

METHODS

55 patients with DEP-CI received baseline MRI scans as part of a larger clinical trial at NYSPI/Columbia University Medical Center and Duke University Medical Center. Mood was assessed using the Hamilton Depression Rating Scale. Patients received open antidepressant treatment for 8 weeks followed by another 8 weeks of the same medication or switch to another antidepressant for a total of 16 weeks. Cortical thickness was extracted using an automated brain segmentation program (FreeSurfer). Vertex-wise analyses evaluated the relationship between cortical thickness and treatment outcome.

RESULTS

Remitters exhibited diffuse clusters of greater cortical thickness and reduced cortical thickness compared to non-remitters. Thicker baseline middle frontal gyrus most consistently predicted greater likelihood and faster rate of remission. White matter hyperintensities and hippocampal volume were not associated with antidepressant treatment outcome.

LIMITATIONS

MRI was conducted at baseline only and sample size was small.

DISCUSSION

Cortical thickness predicts treatment remission and magnitude of early improvement. Results indicate that individuals with DEP-CI exhibit unique patterns of structural abnormalities compared to their depressed peers without CI that have consequences for their recovery with antidepressant treatment.

Gut-microbiota-microglia-brain interactions in Alzheimer's disease: knowledge-based, multi-dimensional characterization

BACKGROUND

Interactions between the gut microbiota, microglia, and aging may modulate Alzheimer's disease (AD) pathogenesis but the precise nature of such interactions is not known.

METHODS

We developed an integrated multi-dimensional, knowledge-driven, systems approach to identify interactions among microbial metabolites, microglia, and AD. Publicly available datasets were repurposed to create a multi-dimensional knowledge-driven pipeline consisting of an integrated network of microbial metabolite-gene-pathway-phenotype (MGPPN) consisting of 34,509 nodes (216 microbial metabolites, 22,982 genes, 1329 pathways, 9982 mouse phenotypes) and 1,032,942 edges.

RESULTS

We evaluated the network-based ranking algorithm by showing that abnormal microglia function and physiology are significantly associated with AD pathology at both genetic and phenotypic levels: AD risk genes were ranked at the top 6.4% among 22,982 genes, P < 0.001. AD phenotypes were ranked at the top 11.5% among 9982 phenotypes, P < 0.001. A total of 8094 microglia-microbial metabolite-gene-pathway-phenotype-AD interactions were identified for top-ranked AD-associated microbial metabolites. Short-chain fatty acids (SCFAs) were ranked at the top among prioritized AD-associated microbial metabolites. Through data-driven analyses, we provided evidence that SCFAs are involved in microglia-mediated gut-microbiota-brain interactions in AD at both genetic, functional, and phenotypic levels.

CONCLUSION

Our analysis produces a novel framework to offer insights into the mechanistic links between gut microbial metabolites, microglia, and AD, with the overall goal to facilitate disease mechanism understanding, therapeutic target identification, and designing confirmatory experimental studies.

Assessment of the Feasibility of Using Noninvasive Wearable Biometric Monitoring Sensors to Detect Influenza and the Common Cold Before Symptom Onset

IMPORTANCE

Currently, there are no presymptomatic screening methods to identify individuals infected with a respiratory virus to prevent disease spread and to predict their trajectory for resource allocation.

OBJECTIVE

To evaluate the feasibility of using noninvasive, wrist-worn wearable biometric monitoring sensors to detect presymptomatic viral infection after exposure and predict infection severity in patients exposed to H1N1 influenza or human rhinovirus.

DESIGN, SETTING, AND PARTICIPANTS

The cohort H1N1 viral challenge study was conducted during 2018; data were collected from September 11, 2017, to May 4, 2018. The cohort rhinovirus challenge study was conducted during 2015; data were collected from September 14 to 21, 2015. A total of 39 adult participants were recruited for the H1N1 challenge study, and 24 adult participants were recruited for the rhinovirus challenge study. Exclusion criteria for both challenges included chronic respiratory illness and high levels of serum antibodies. Participants in the H1N1 challenge study were isolated in a clinic for a minimum of 8 days after inoculation. The rhinovirus challenge took place on a college campus, and participants were not isolated.

EXPOSURES

Participants in the H1N1 challenge study were inoculated via intranasal drops of diluted influenza A/California/03/09 (H1N1) virus with a mean count of 106 using the median tissue culture infectious dose (TCID50) assay. Participants in the rhinovirus challenge study were inoculated via intranasal drops of diluted human rhinovirus strain type 16 with a count of 100 using the TCID50 assay.

MAIN OUTCOMES AND MEASURES

The primary outcome measures included cross-validated performance metrics of random forest models to screen for presymptomatic infection and predict infection severity, including accuracy, precision, sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve (AUC).

RESULTS

A total of 31 participants with H1N1 (24 men [77.4%]; mean [SD] age, 34.7 [12.3] years) and 18 participants with rhinovirus (11 men [61.1%]; mean [SD] age, 21.7 [3.1] years) were included in the analysis after data preprocessing. Separate H1N1 and rhinovirus detection models, using only data on wearble devices as input, were able to distinguish between infection and noninfection with accuracies of up to 92% for H1N1 (90% precision, 90% sensitivity, 93% specificity, and 90% F1 score, 0.85 [95% CI, 0.70-1.00] AUC) and 88% for rhinovirus (100% precision, 78% sensitivity, 100% specificity, 88% F1 score, and 0.96 [95% CI, 0.85-1.00] AUC). The infection severity prediction model was able to distinguish between mild and moderate infection 24 hours prior to symptom onset with an accuracy of 90% for H1N1 (88% precision, 88% sensitivity, 92% specificity, 88% F1 score, and 0.88 [95% CI, 0.72-1.00] AUC) and 89% for rhinovirus (100% precision, 75% sensitivity, 100% specificity, 86% F1 score, and 0.95 [95% CI, 0.79-1.00] AUC).

CONCLUSIONS AND RELEVANCE

This cohort study suggests that the use of a noninvasive, wrist-worn wearable device to predict an individual's response to viral exposure prior to symptoms is feasible. Harnessing this technology would support early interventions to limit presymptomatic spread of viral respiratory infections, which is timely in the era of COVID-19.

Serum metabolites associated with brain amyloid beta deposition, cognition and dementia progression

Metabolomics in the Alzheimer’s Disease Neuroimaging Initiative cohort provides a powerful tool for mapping biochemical changes in Alzheimer’s disease, and a unique opportunity to learn about the association between circulating blood metabolites and brain amyloid-β deposition in Alzheimer’s disease. We examined 140 serum metabolites and their associations with brain amyloid-β deposition, cognition and conversion from mild cognitive impairment to Alzheimer’s disease in the Alzheimer’s Disease Neuroimaging Initiative. Processed [¹⁸F] Florbetapir PET images were used to perform a voxel-wise statistical analysis of the effect of metabolite levels on amyloid-β accumulation across the whole brain. We performed a multivariable regression analysis using age, sex, body mass index, apolipoprotein E ε4 status and study phase as covariates. We identified nine metabolites as significantly associated with amyloid-β deposition after multiple comparison correction. Higher levels of one acylcarnitine (C3; propionylcarnitine) and one biogenic amine (kynurenine) were associated with decreased amyloid-β accumulation and higher memory scores. However, higher levels of seven phosphatidylcholines (lysoPC a C18:2, PC aa C42:0, PC ae C42:3, PC ae C44:3, PC ae C44:4, PC ae C44:5 and PC ae C44:6) were associated with increased brain amyloid-β deposition. In addition, higher levels of PC ae C44:4 were significantly associated with lower memory and executive function scores and conversion from mild cognitive impairment to Alzheimer’s disease dementia. Our findings suggest that dysregulation of peripheral phosphatidylcholine metabolism is associated with earlier pathological changes noted in Alzheimer’s disease as measured by brain amyloid-β deposition as well as later clinical features including changes in memory and executive functioning. Perturbations in phosphatidylcholine metabolism may point to issues with membrane restructuring leading to the accumulation of amyloid-β in the brain. Additional studies are needed to explore whether these metabolites play a causal role in the pathogenesis of Alzheimer’s disease or if they are biomarkers for systemic changes during preclinical phases of the disease.

Evaluating Alzheimer Disease With Flortaucipir and Florbetapir PET: A Clinical Case Series

ABSTRACT

Early, accurate diagnosis of Alzheimer disease (AD) is essential but remains challenging. Neuropathological hallmarks of AD are β-amyloid neuritic plaques and tau protein neurofibrillary tangles. 18F-Florbetapir is one of several available PET tracers for imaging cortical fibrillary β-amyloid plaques. 18F-Flortaucipir PET was recently approved for evaluating the distribution and density of aggregated neurofibrillary tangles. We present cases of mild cognitive impairment or suspected AD to depict the nuances of flortaucipir distribution and scan interpretation as well as how combined information from amyloid and tau PET may help with differential diagnosis and prognosis.

Potential Neuroregenerative and Neuroprotective Effects of Uridine/Choline-Enriched Multinutrient Dietary Intervention for Mild Cognitive Impairment: A Narrative Review

In mild cognitive impairment (MCI) due to Alzheimer disease (AD), also known as prodromal AD, there is evidence for a pathologic shortage of uridine, choline, and docosahexaenoic acid [DHA]), which are key nutrients needed by the brain. Preclinical and clinical evidence shows the importance of nutrient bioavailability to support the development and maintenance of brain structure and function in MCI and AD. Availability of key nutrients is limited in MCI, creating a distinct nutritional need for uridine, choline, and DHA. Evidence suggests that metabolic derangements associated with ageing and disease-related pathology can affect the body's ability to generate and utilize nutrients. This is reflected in lower levels of nutrients measured in the plasma and brains of individuals with MCI and AD dementia, and progressive loss of cognitive performance. The uridine shortage cannot be corrected by normal diet, making uridine a conditionally essential nutrient in affected individuals. It is also challenging to correct the choline shortfall through diet alone, because brain uptake from the plasma significantly decreases with ageing. There is no strong evidence to support the use of single-agent supplements in the management of MCI due to AD. As uridine and choline work synergistically with DHA to increase phosphatidylcholine formation, there is a compelling rationale to combine these nutrients. A multinutrient enriched with uridine, choline, and DHA developed to support brain function has been evaluated in randomized controlled trials covering a spectrum of dementia from MCI to moderate AD. A randomized controlled trial in subjects with prodromal AD showed that multinutrient intervention slowed brain atrophy and improved some measures of cognition. Based on the available clinical evidence, nutritional intervention should be considered as a part of the approach to the management of individuals with MCI due to AD, including adherence to a healthy, balanced diet, and consideration of evidence-based multinutrient supplements.

Aggregated Tau Measured by Visual Interpretation of Flortaucipir Positron Emission Tomography and the Associated Risk of Clinical Progression of Mild Cognitive Impairment and Alzheimer Disease: Results From 2 Phase III Clinical Trials

Questions

What is the association between flortaucipir positron emission tomography (PET) imaging visual classification using a clinically applicable and US Food and Drug Administration–approved method and 18-month cognitive and functional decline in patients with clinically diagnosed mild cognitive impairment and dementia owing to Alzheimer disease (AD)?

Findings

In this analysis of 2 open-label clinical trials, visual read of an advanced flortaucipir PET AD pattern was associated with an increased risk of 18-month cognitive and functional decline compared with other scan patterns.

Meaning

Clinically applicable visual reads of flortaucipir PET scans may provide valuable information regarding the risk of near-term clinical deterioration among patients with clinically diagnosed mild cognitive impairment or dementia owing to AD.

Importance

Flortaucipir positron emission tomography (PET) scans, rated with a novel, US Food and Drug Administration–approved, clinically applicable visual interpretation method, provide valuable information regarding near-term clinical progression of patients with Alzheimer disease (AD) or mild cognitive impairment (MCI).

Objective

To evaluate the association between flortaucipir PET visual interpretation and patients’ near-term clinical progression.

Design/Setting/Participants

Two prospective, open-label, longitudinal studies were conducted from December 2014 to September 2019. Study 1 screened 298 patients and enrolled 160 participants who had a flortaucipir scan at baseline visit. Study 2 selected 205 participants from the AMARANTH trial, which was terminated after futility analysis. Out of the 2218 AMARANTH participants, 424 had a flortaucipir scan around randomization, but 219 did not complete 18-month clinical dementia rating (CDR) assessments and thus were excluded. In both studies, all participants were diagnosed as clinically impaired, and they were longitudinally followed up for approximately 18 months after baseline.

Main Outcomes and Measures

Flortaucipir scans were rated as either advanced or nonadvanced AD pattern using a predetermined visual interpretation method. The CDR sum of box (CDR-SB) score was used as primary clinical end point measurement in both studies.

Results

Of the 364 study participants who had readable scans, 48% were female (n = 174 of 364), and the mean (SD) age was 71.8 (8.7) years. Two hundred forty participants were rated as having an advanced AD pattern. At 18 months follow-up, 70% of those with an advanced AD pattern (n = 147 of 210) had 1 point or more increase in CDR-SB, an event predefined as clinically meaningful deterioration. In contrast, only 46% of those with a nonadvanced AD pattern scan (n = 48 of 105) experienced the same event (risk ratio [RR], 1.40; 95% CI, 1.11-1.76; P = .005). The adjusted mean CDR-SB changes were 2.28 and 0.98 for advanced and nonadvanced AD pattern groups, respectively (P < .001). Analyses with other clinical end point assessments, as well as analyses with each individual study’s data, consistently indicated a higher risk of clinical deterioration associated with an advanced AD scan pattern.

Conclusions and Relevance

These results suggest that flortaucipir PET scans, when interpreted with an US Food and Drug Administration–approved, clinically applicable visual interpretation method, may provide valuable information regarding the risk of clinical deterioration over 18 months among patients with AD and MCI.

Trial Registration

ClinicalTrials.gov Identifier: NCT02016560 and NCT03901105

Computerized Cognitive Training by Healthy Older and Younger Adults: Age Comparisons of Overall Efficacy and Selective Effects on Cognition

Among the non-pharmacological methods under development for maintaining cognitive function across the lifespan is computerized cognitive training (CCT). There has been considerable interest in using CCT to slow or remediate age-related cognitive decline, both normal and pathological. Toward these ends, it would be useful to know how the effects of CCT on cognitive function vary over the course of normal cognitive aging. Are there changes in either 1) the overall efficacy of CCT or 2) which cognitive faculties are affected? To address these two questions, we reanalyzed results from a large online study by Hardy et al. (1) of 4,715 adults between 18 and 80 that examined effects of CCT on both a neuropsychological test battery and self-reported ratings of cognition and affect in daily living. Combined across all participants, Hardy et al. found greater improvement on both types of assessment following 10 weeks of CCT with the commercial program Lumosity, as compared to practice with a control activity involving computerized crossword puzzles. The present study compared the size of these effects on the older (50-80) and younger (18-49) participants. To address the question of overall efficacy, we examined CCT effects (treatment minus control) on overall performance of the test battery and mean rating. No significant difference on either measure was found between the two age cohorts. To address the question of whether the same magnitude of overall effects on both age cohorts was due to equivalent effects on the same set of underlying cognitive functions, we examined the patterns of CCT effects across individual subtests and rated items. These patterns did not differ significantly between the two age cohorts. Our findings suggest that benefits from CCT can occur to a similar degree and in a similar way across an extended part of the adult lifespan. Moreover, the overall effects of CCT delivered over the internet were of the same small to medium size as those typically found in the lab or clinic. Besides improving access and reducing the cost of CCT for older adults, delivery over the internet makes long-term training more practicable, which could potentially yield larger benefits.

Serum triglycerides in Alzheimer disease: Relation to neuroimaging and CSF biomarkers

OBJECTIVE

To investigate the association of triglyceride (TG) principal component scores with Alzheimer disease (AD) and the amyloid, tau, neurodegeneration, and cerebrovascular disease (A/T/N/V) biomarkers for AD.

METHODS

Serum levels of 84 TG species were measured with untargeted lipid profiling of 689 participants from the Alzheimer's Disease Neuroimaging Initiative cohort, including 190 cognitively normal older adults (CN), 339 with mild cognitive impairment (MCI), and 160 with AD. Principal component analysis with factor rotation was used for dimension reduction of TG species. Differences in principal components between diagnostic groups and associations between principal components and AD biomarkers (including CSF, MRI and [¹⁸F]fluorodeoxyglucose-PET) were assessed with a generalized linear model approach. In both cases, the Bonferroni method of adjustment was used to correct for multiple comparisons.

RESULTS

The 84 TGs yielded 9 principal components, 2 of which, consisting of long-chain, polyunsaturated fatty acid-containing TGs (PUTGs), were significantly associated with MCI and AD. Lower levels of PUTGs were observed in MCI and AD compared to CN. PUTG principal component scores were also significantly associated with hippocampal volume and entorhinal cortical thickness. In participants carrying the APOE ε4 allele, these principal components were significantly associated with CSF β-amyloid_1-42 values and entorhinal cortical thickness.

CONCLUSION

This study shows that PUTG component scores were significantly associated with diagnostic group and AD biomarkers, a finding that was more pronounced in APOE ε4 carriers. Replication in independent larger studies and longitudinal follow-up are warranted.

A multicentre longitudinal study of flortaucipir (18F) in normal ageing, mild cognitive impairment and Alzheimer's disease dementia

The advent of tau-targeted PET tracers such as flortaucipir (18F) (flortaucipir, also known as 18F-AV-1451 or 18F-T807) have made it possible to investigate the sequence of development of tau in relationship to age, amyloid-β, and to the development of cognitive impairment due to Alzheimer's disease. Here we report a multicentre longitudinal evaluation of the relationships between baseline tau, tau change and cognitive change, using flortaucipir PET imaging. A total of 202 participants 50 years old or older, including 57 cognitively normal subjects, 97 clinically defined mild cognitive impairment and 48 possible or probable Alzheimer's disease dementia patients, received flortaucipir PET scans of 20 min in duration beginning 80 min after intravenous administration of 370 MBq flortaucipir (18F). On separate days, subjects also received florbetapir amyloid PET imaging, and underwent a neuropsychological test battery. Follow-up flortaucipir scans and neuropsychological battery assessments were also performed at 9 and 18 months. Fifty-five amyloid-β+ and 90 amyloid-β- subjects completed the baseline and 18-month study visits and had valid quantifiable flortaucipir scans at both time points. There was a statistically significant increase in the global estimate of cortical tau burden as measured by standardized uptake value ratio (SUVr) from baseline to 18 months in amyloid-β+ but not amyloid-β- subjects (least squared mean change in flortaucipir SUVr : 0.0524 ± 0.0085, P < 0.0001 and 0.0007 ± 0.0024 P = 0.7850, respectively), and a significant association between magnitude of SUVr increase and baseline tau burden. Voxel-wise evaluations further suggested that the regional pattern of change in flortaucipir PET SUVr over the 18-month study period (i.e. which regions exhibited the greatest change) also varied as a function of baseline global estimate of tau burden. In subjects with lower global SUVr, temporal lobe regions showed the greatest flortaucipir retention, whereas in subjects with higher baseline SUVr, parietal and frontal regions were increasingly affected. Finally, baseline flortaucipir and change in flortaucipir SUVr were both significantly (P < 0.0001) associated with changes in cognitive performance. Taken together, these results provide a preliminary characterization of the longitudinal spread of tau in Alzheimer's disease and suggest that the amount and location of tau may have implications both for the spread of tau and the cognitive deterioration that may occur over an 18-month period.