Related amyloid burden and cortical atrophy in individuals with subtle cognitive decline

BACKGROUND AND PURPOSE

Subtle cognitive decline represents a stage of cognitive deterioration in which pathological biomarkers may be present, including early cortical atrophy and amyloid deposition. Using individual items from the Montreal Cognitive Assessment and k-modes cluster analysis, we previously identified three clusters of individuals without overt cognitive impairment: (1) High Performing (no deficits in performance), (2) Memory Deficits (lower memory performance), and (3) Compound Deficits (lower memory and executive function performance). In this study, we sought to understand the relationships found in our clusters between cortical atrophy on MR and amyloid burden on PET.

METHODS

Data were derived from the Alzheimer's Disease Neuroimaging Initiative and comprised individuals from our previous analyses with available MR and amyloid PET scans (n = 272). Using multiple-group structural equation modeling, we regressed amyloid standardized uptake value ratio on volumetric regions to simultaneously evaluate unique associations within each cluster.

RESULTS

In our Compound Deficits cluster, greater whole cerebral amyloid burden was significantly related to right entorhinal cortical and left hippocampal atrophy, rs  = -.412 (p = .005) and -.304 (p = .049), respectively. Within this cluster, right entorhinal cortical atrophy was significantly related to greater amyloid burden within multiple frontal regions.

CONCLUSIONS

The Compound Deficits cluster, which represents a group potentially at higher risk for decline, was observed to have significantly more cortical atrophy, particularly within the entorhinal cortex and hippocampus, associated with whole brain and frontal lobe amyloid burden. These findings point to a pattern of early pathological deterioration that may place these individuals at risk for future decline.

Using the Montreal cognitive assessment to identify individuals with subtle cognitive decline

OBJECTIVE

Dementia is a devastating neurological disease that may be better managed if diagnosed earlier when subclinical neurodegenerative changes are already present, including subtle cognitive decline and mild cognitive impairment. In this study, we used item-level performance on the Montreal Cognitive Assessment (MoCA) to identify individuals with subtle cognitive decline.

METHOD

Individual MoCA item data from the Alzheimer's Disease Neuroimaging Initiative was grouped using k-modes cluster analysis. These clusters were validated and examined for association with convergent neuropsychological tests. The clusters were then compared and characterized using multinomial logistic regression.

RESULTS

A three-cluster solution had 77.3% precision, with Cluster 1 (high performing) displaying no deficits in performance, Cluster 2 (memory deficits) displaying lower memory performance, and Cluster 3 (compound deficits) displaying lower performance on memory and executive function. Age at MoCA (older in compound deficits), gender (more females in memory deficits), and marital status (fewer married in compound deficits) were significantly different among clusters. Age was not associated with increased odds of membership in the high-performing cluster compared to the others.

CONCLUSIONS

We identified three clusters of individuals classified as cognitively unimpaired using cluster analysis. Individuals in the compound deficits cluster performed lower on the MoCA and were older and less often married than individuals in other clusters. Demographic analyses suggest that cluster identity was due to a combination of both cognitive and clinical factors. Identifying individuals at risk for future cognitive decline using the MoCA could help them receive earlier evidence-based interventions to slow further cognitive decline. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

A Tribute to the Mind, Methodology and Mentoring of Wayne Velicer

Wayne Velicer is remembered for a mind where mathematical concepts and calculations intrigued him, behavioral science beckoned him, and people fascinated him. Born in Green Bay, Wisconsin on March 4, 1944, he was raised on a farm, although early influences extended far beyond that beginning. His Mathematics BS and Psychology minor at Wisconsin State University in Oshkosh, and his PhD in Quantitative Psychology from Purdue led him to a fruitful and far-reaching career. He was honored several times as a high-impact author, was a renowned scholar in quantitative and health psychology, and had more than 300 scholarly publications and 54,000+ citations of his work, advancing the arenas of quantitative methodology and behavioral health. In his methodological work, Velicer sought out ways to measure, synthesize, categorize, and assess people and constructs across behaviors and time, largely through principal components analysis, time series, and cluster analysis. Further, he and several colleagues developed a method called Testing Theory-based Quantitative Predictions, successfully applied to predicting outcomes and effect sizes in smoking cessation, diet behavior, and sun protection, with the potential for wider applications. With $60,000,000 in external funding, Velicer also helped engage a large cadre of students and other colleagues to study methodological models for a myriad of health behaviors in a widely applied Transtheoretical Model of Change. Unwittingly, he has engendered indelible memories and gratitude to all who crossed his path. Although Wayne Velicer left this world on October 15, 2017 after battling an aggressive cancer, he is still very present among us.

Instructor and peer bullying in college students: Distinct typologies based on Latent Class Analysis

OBJECTIVE

Although bullying is traditionally considered within the context of primary and secondary school, recent evidence suggests that bullying continues into college and workplace settings. Participants/Method: Latent class analysis (LCA) was employed to classify college bullying involvement typologies among 325 college students attending a northeastern university.

RESULTS

Four classes concerning bullying involvement were revealed: Non-involved (36%); Instructor victim (30%); Peer bully-victim (22%); and Peer bully-victim/ Instructor victim (12%).

CONCLUSIONS

Findings from this study, which classified college bullying experiences by incorporating both peer and instructor (teacher and professor) bullying, add substantially to the literature by providing insight into patterns of relatively unexplored bullying behaviors.

Interaction Effects of Neighborhood Disadvantage and Individual Social Support on Frequency of Alcohol Use in Youth Living with HIV

Youth living with HIV (YLH) experience multiple disease-related stresses along with the same structural and developmental challenges faced by their uninfected peers; alcohol use among YLH represents a risk behavior by virtue of potential effects on youth health and increased likelihood of engaging in unprotected sex while drinking alcohol. Research aimed at better understanding the interplay of individual- and neighborhood-level influences on alcohol use for YLH is needed to inform interventions. This study examined whether socioeconomic disadvantage (SED) and social support influence, independently and through interaction, alcohol use in YLH. Data from the Adolescent Medicine Trials Network for HIV/AIDS Interventions (ATN) consisted of YLH across 538 neighborhoods in the United States who acquired HIV behaviorally. Neighborhood-specific data were compiled from the 2010 U.S. Census Bureau and matched with individual-level data from the ATN (N = 1,357) to examine effects that contribute to variation in frequency of alcohol use. Other drug use, being male, being non-Black, and older age were associated with greater alcohol use. Higher social support was negatively associated with alcohol use frequency. A cross-level interaction indicated that the association found between decreasing social support and increasing alcohol use frequency was weakened in areas with lower SED. Implications are discussed.

Longitudinal analysis of intervention effects on temptations and stages of change for dietary fat using parallel process latent growth modeling

The Dietary Guidelines for Americans recommend a 20-35 percent daily intake of fat. Resisting the temptation to eat high-fat foods, in conjunction with stage of readiness to avoid these foods, has been shown to influence healthy behavior change. Data ( N = 6516) from three randomized controlled trials were pooled to examine the relationships among direct intervention effects on temptations and stage of change for limiting high-fat foods. Findings demonstrate separate simultaneous growth processes in which baseline level of temptations, but not the rate of change in temptations, was significantly related to the change in readiness to avoid high-fat foods.

Extending Theory-Based Quantitative Predictions to New Health Behaviors

BACKGROUND

Traditional null hypothesis significance testing suffers many limitations and is poorly adapted to theory testing.

PURPOSE

A proposed alternative approach, called Testing Theory-based Quantitative Predictions, uses effect size estimates and confidence intervals to directly test predictions based on theory.

METHOD

This paper replicates findings from previous smoking studies and extends the approach to diet and sun protection behaviors using baseline data from a Transtheoretical Model behavioral intervention (N = 5407). Effect size predictions were developed using two methods: (1) applying refined effect size estimates from previous smoking research or (2) using predictions developed by an expert panel.

RESULTS

Thirteen of 15 predictions were confirmed for smoking. For diet, 7 of 14 predictions were confirmed using smoking predictions and 6 of 16 using expert panel predictions. For sun protection, 3 of 11 predictions were confirmed using smoking predictions and 5 of 19 using expert panel predictions.

CONCLUSION

Expert panel predictions and smoking-based predictions poorly predicted effect sizes for diet and sun protection constructs. Future studies should aim to use previous empirical data to generate predictions whenever possible. The best results occur when there have been several iterations of predictions for a behavior, such as with smoking, demonstrating that expected values begin to converge on the population effect size. Overall, the study supports necessity in strengthening and revising theory with empirical data.

Replicating cluster subtypes for the prevention of adolescent smoking and alcohol use

INTRODUCTION

Substance abuse interventions tailored to the individual level have produced effective outcomes for a wide variety of behaviors. One approach to enhancing tailoring involves using cluster analysis to identify prevention subtypes that represent different attitudes about substance use. This study applied this approach to better understand tailored interventions for smoking and alcohol prevention.

METHODS

Analyses were performed on a sample of sixth graders from 20 New England middle schools involved in a 36-month tailored intervention study. Most adolescents reported being in the Acquisition Precontemplation (aPC) stage at baseline: not smoking or not drinking and not planning to start in the next six months. For smoking (N=4059) and alcohol (N=3973), each sample was randomly split into five subsamples. Cluster analysis was performed within each subsample based on three variables: Pros and Cons (from Decisional Balance Scales), and Situational Temptations.

RESULTS

Across all subsamples for both smoking and alcohol, the following four clusters were identified: (1) Most Protected (MP; low Pros, high Cons, low Temptations); (2) Ambivalent (AM; high Pros, average Cons and Temptations); (3) Risk Denial (RD; average Pros, low Cons, average Temptations); and (4) High Risk (HR; high Pros, low Cons, and very high Temptations).

CONCLUSIONS

Finding the same four clusters within aPC for both smoking and alcohol, replicating the results across the five subsamples, and demonstrating hypothesized relations among the clusters with additional external validity analyses provide strong evidence of the robustness of these results. These clusters demonstrate evidence of validity and can provide a basis for tailoring interventions.

Testing 40 Predictions From the Transtheoretical Model Again, With Confidence

Testing Theory-based Quantitative Predictions (TTQP) represents an alternative to traditional Null Hypothesis Significance Testing (NHST) procedures and is more appropriate for theory testing. The theory generates explicit effect size predictions and these effect size estimates, with related confidence intervals, are used to test the predictions. The focus of a study is shifted to a quantitative approach in contrast to the NHST dyadic decision centered on testing a prediction not based on the theory. This article describes the TTQP as an alternative approach by replicating and extending a test of 40 a priori predictions based on the Transtheoretical Model (TTM). Specific quantitative predictions were made about the magnitude of the effect size (ω(2)). The predictions involved movement from 1 of 3 initial stages (Precontemplation, Contemplation, and Preparation) to stage membership 12 months later. In the initial study, 36 of the 40 predictions were confirmed. The same 40 predictions are evaluated on a sample (N = 3,923) of smokers recruited from a large New England HMO for a smoking cessation study. The predictions were recalibrated based on the first study and 99% confidence intervals were employed to test the predictions. Thirty-two of the 40 predictions were confirmed. Of the 8 failures, 4 were judged to reflect a need for further recalibration, 1 was attributed to sampling fluctuation, and 3 suggested revisions of the theory are needed. The results provide overall support for the TTM. The study also illustrates some of the challenges of testing quantitative predictions.