Harmonizing the preclinical Alzheimer cognitive composite for multicohort studies

Sex and gender differences in cognitive resilience to aging and Alzheimer's disease

Sex and gender-biological and social constructs-significantly impact the prevalence of protective and risk factors, influencing the burden of Alzheimer's disease (AD; amyloid beta and tau) and other pathologies (e.g., cerebrovascular disease) which ultimately shape cognitive trajectories. Understanding the interplay of these factors is central to understanding resilience and resistance mechanisms explaining maintained cognitive function and reduced pathology accumulation in aging and AD. In this narrative review, the ADDRESS! Special Interest Group (Alzheimer's Association) adopted a multidisciplinary approach to provide the foundations and recommendations for future research into sex- and gender-specific drivers of resilience, including a sex/gender-oriented review of risk factors, genetics, AD and non-AD pathologies, brain structure and function, and animal research. We urge the field to adopt a sex/gender-aware approach to resilience to advance our understanding of the intricate interplay of biological and social determinants and consider sex/gender-specific resilience throughout disease stages. HIGHLIGHTS: Sex differences in resilience to cognitive decline vary by age and cognitive status. Initial evidence supports sex-specific distinctions in brain pathology. Findings suggest sex differences in the impact of pathology on cognition. There is a sex-specific change in resilience in the transition to clinical stages. Gender and sex factors warrant study: modifiable, immune, inflammatory, and vascular.

Examining a Preclinical Alzheimer's Cognitive Composite for Telehealth Administration for Reliability Between In-Person and Remote Cognitive Testing with Neuroimaging Biomarkers

Background

The preclinical Alzheimer's cognitive composite (PACC) was developed for in-person administration to capture subtle cognitive decline. At the outset of the COVID-19 pandemic, cognitive testing was increasingly performed remotely by telephone or video administration. It is desirable to have a harmonized composite measurement derived from both in-person and remote assessments for identifying cognitive changes and to examine its relationship with common neuroimaging biomarkers.

Objective

We defined a telehealth compatible PACC (tPACC) and examined its relationship with neuroimaging biomarkers related to neurodegeneration, brain function and perfusion, white matter integrity, and amyloid-β.

Methods

We examined 648 participants' neuroimaging and in-person and remote cognitive testing data from the Wake Forest Alzheimer's Disease Research Center's Clinical Core cohort (observational study) to calculate a modified PACC (PACC5-RAVLT) score and tPACC scores (in-person and remote). We performed Spearman/intraclass correlation coefficient (ICC) analyses for reliability of tPACC scores and linear regression models to evaluate associations between tPACC and neuroimaging. Bland-Altman plots for agreement were constructed across cognitively normal and impaired (mild cognitive impairment and dementia) participants.

Results

There was a significant positive relationship between tPACCin - person and PACC5-RAVLT (Overall group: r2 = 0.94, N = 648), and tPACCin - person and tPACCremote (validation subgroup: ICC = 0.82, n = 53). Overall, tPACC showed significant associations with brain thickness/volume, gray matter perfusion, white matter free water, and amyloid-β deposition.

Conclusions

There is a good agreement between tPACCand PACC5-RAVLTfor cognitively normal and impaired individuals. The tPACC is associated with common neuroimaging markers of Alzheimer's disease.

The Alzheimer's Disease Neuroimaging Initiative in the era of Alzheimer's disease treatment: A review of ADNI studies from 2021 to 2022

The Alzheimer's Disease Neuroimaging Initiative (ADNI) aims to improve Alzheimer's disease (AD) clinical trials. Since 2006, ADNI has shared clinical, neuroimaging, and cognitive data, and biofluid samples. We used conventional search methods to identify 1459 publications from 2021 to 2022 using ADNI data/samples and reviewed 291 impactful studies. This review details how ADNI studies improved disease progression understanding and clinical trial efficiency. Advances in subject selection, detection of treatment effects, harmonization, and modeling improved clinical trials and plasma biomarkers like phosphorylated tau showed promise for clinical use. Biomarkers of amyloid beta, tau, neurodegeneration, inflammation, and others were prognostic with individualized prediction algorithms available online. Studies supported the amyloid cascade, emphasized the importance of neuroinflammation, and detailed widespread heterogeneity in disease, linked to genetic and vascular risk, co-pathologies, sex, and resilience. Biological subtypes were consistently observed. Generalizability of ADNI results is limited by lack of cohort diversity, an issue ADNI-4 aims to address by enrolling a diverse cohort.

A guide for researchers seeking training in retrospective data harmonization for population neuroscience studies of Alzheimer's disease and related dementias

Due to needs surrounding rigor and reproducibility, subgroup specific disease knowledge, and questions of external validity, data harmonization is an essential tool in population neuroscience of Alzheimer's disease and related dementias (ADRD). Systematic harmonization of data elements is necessary to pool information from heterogeneous samples, and such pooling allows more expansive evaluations of health disparities, more precise effect estimates, and more opportunities to discover effective prevention or treatment strategies. The key goal of this Tutorial in Population Neuroimaging Curriculum, Instruction, and Pedagogy article is to guide researchers in creating a customized population neuroscience of ADRD harmonization training plan to fit their needs or those of their mentees. We provide brief guidance for retrospective data harmonization of multiple data types in this area, including: (1) clinical and demographic, (2) neuropsychological, and (3) neuroimaging data. Core competencies and skills are reviewed, and resources are provided to fill gaps in training as well as data needs. We close with an example study in which harmonization is a critical tool. While several aspects of this tutorial focus specifically on ADRD, the concepts and resources are likely to benefit population neuroscientists working in a range of research areas.