Highlights • Our sample includes veterans with and without posttraumatic stress disorder (PTSD). • We examine if inflammatory markers are associated with hippocampal volume. • Higher sTNF-RII, but ...not IL-6, was associated with reduced hippocampal volume. • Neither current nor past PTSD diagnoses were associated with sTNF-RII or IL-6. • More severe PTSD symptoms were associated with elevated sTNF-RII and lower IL-6.
Background
Accurate identification of AD risk using non‐invasive biomarkers is a critical need. Recently plasma biomarkers of amyloid, phosphorylated tau, and neurodegeneration have been shown to ...accurately identify mild cognitive impairment (MCI), and brain amyloid levels. However, little is known about whether baseline plasma biomarker predict future progression from cognitively unimpaired (CN) to MCI. The overall goal of this study was to examine the predictive utility of markers that can easily be collected remotely.
Method
We identified a set of non‐invasive markers in ADNI: plasma p‐tau181 and neurofilament light (NfL), Neuropsychiatric Inventory Questionnaire (NPIQ), self‐ and study partner versions of a subjective cognitive decline instrument (Everyday Cognition Scale; Ecog), and APOE. In 300 ADNI participants who were CN at baseline, we determined the associations between these markers and progression to MCI using logistic regression. Models covaried for age, gender, and education level. Area under the receiver operator curve (AUC) evaluated discrimination accuracy. Likelihood ratio tests (LRT) determined the best fitting model.
Result
42 (14%) of participants progressed to MCI (Table 1). In the full model including all predictors (AUC = .879; Table 2 and Figure 1), study partner Ecog score was the only significant predictor associated with progression to MCI. Excluding study partner Ecog decreased model accuracy (AUC = .779; Figure 1). The LRT between this model and the full model was significant (c2(1) = 5.04, p < .05).
Conclusion
Even when accounting for the contributions of ptau181 and Nfl, study partner‐reported subjective cognitive decline independently predicts future progression to MCI. This approach can be used in the future to efficiently predict MCI risk without the need for in‐clinic assessment.
Background
The presence of co‐pathologies such as TDP‐43, Lewy bodies (LB), and cerebral amyloid angiopathy (CAA) is common in older individuals with AD, even those pathologically confirmed to have ...amyloid‐β and tau pathology, and contributes to cognitive and functional decline. Treatments targeting just one pathology will thus have variable and reduced overall measurable efficacy compared with an assumed population with no co‐pathologies. Tools to identify individuals with non‐AD co‐pathologies would be enabling technologies for a precision medicine approach to clinical trials in sporadic AD.
In the absence of biomarkers to measure these co‐pathologies directly, our aim was to develop AI models to identify autopsy‐confirmed structural MRI‐based signatures of non‐AD degenerative brain pathologies, to facilitate participant selection for AD trials. This builds on the recent development of models to impute amyloid and tau positivity from structural MRI scans and associated clinical data.
Method
ADNI and NACC participants with ante‐mortem MRI and histopathologic assessments for scoring of neuritic plaques, staging of neurofibrillary tangles, LB, CAA, and TDP inclusions were included (Table 1). A multilabel classifier was trained on demographics, AD pathology, and MRI to jointly model positivity for TDP‐43, LB, and CAA. The leave‐one‐out validated model was applied to a separate ADNI cohort without neuropathologic assessments to assess the imputed prevalence of co‐pathologies at baseline and variance in cognitive decline and atrophy explained by baseline AD and non‐AD pathologies.
Result
When optimized for 90% overall NPV (Fig. 1), the multi‐label classifier identified 16.0% of ADNI Dementia participants as TDP‐43+, 54.8% LB+, and 17.8% CAA+ (Fig. 2). While co‐pathology positivity was rare in cognitively unimpaired, LB and CAA positivity were common in MCI. Although dependent upon clinical disease stage, imputed probability of being TDP‐43+ explained a significant percentage of variance in clinical outcomes measures in cognitively impaired participants (Fig. 3).
Conclusion
These initial results provide promising evidence that imputed co‐pathology burden via widely available imaging and clinical data can be used to impute the presence of non‐AD co‐pathologies and their contribution to cognitive decline in vivo. The primary limitation of this approach is the limited sample size in the autopsy cohorts for model development.
Background
Clinical assessment of cognition and functional abilities plays an important role in identifying individuals at risk for cognitive decline, mild cognitive impairment (MCI), and dementia ...due to Alzheimer’s (AD). In‐person clinical assessments can be logistically challenging and deter broad participation in clinical studies, reducing diversity in AD research. Online assessments are an appealing alternative, but the validity of online compared to clinical in‐person assessments has not been established. Our goal was to determine whether the Everyday Cognition (ECog) Scale, which assesses decline in instrumental activities of daily living, collected online corresponds well with ECog collected in‐clinic. The correspondence of in‐clinic and online measures could not logically exceed the test‐retest reliability of the in‐clinic measure, so we used this as a benchmark for validity of the online measure.
Method
Self‐reported ECog was collected both in‐clinic and online from 94 older adults (characteristics described in Table 1) enrolled in both the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Brain Health Registry (BHR). Associations between in‐clinic and online ECog scores were estimated using linear regression. Prediction error was corrected for optimism with 10‐fold cross‐validation. We estimated test‐retest reliability of in‐clinic ECog in 259 cognitively normal ADNI2 participants (characteristics described in Table 2) with two in‐clinic ECog measures, completed six months apart. Reliability was estimated using the intraclass correlation coefficient (ICC) and linear regression. Age, gender, education, and time between assessments were included as covariates in all regression models.
Result
(1) Validity of online ECog. Mean online ECog scores were significantly associated with mean in‐clinic ECog scores (β=0.81, 95% CI 0.64‐0.98, p=0.00). The optimism‐corrected R2 was 0.61 (95% CI 0.40‐0.82). (2) Reliability of In‐clinic ECog: The ICC was estimated to be 0.67 (95% CI 0.54‐0.79). Mean in‐clinic ECog scores collected at 6 months were significantly associated with in‐clinic ECog score collected at baseline (β=0.71, 95% CI 0.63‐0.78, p=0.00). The optimism‐corrected R2 was 0.61 (95% CI 0.51‐0.72).
Conclusion
The online ECog corresponded well with in‐clinic ECog and provided as much information as repeating the in‐clinic ECog. This supports the validity of online ECog, a measure of functional and cognitive decline.
A recent genome-wide association meta-analysis showed a suggestive association between alcohol intake in humans and a common single nucleotide polymorphism in the ras-specific guanine nucleotide ...releasing factor 2 gene. Here, we tested whether this variant - associated with lower alcohol consumption - showed associations with brain structure and longitudinal ventricular expansion over time, across two independent elderly cohorts, totaling 1,032 subjects. We first examined a large sample of 738 elderly participants with neuroimaging and genetic data from the Alzheimer's Disease Neuroimaging Initiative (ADNI1). Then, we assessed the generalizability of the findings by testing this polymorphism in a replication sample of 294 elderly subjects from a continuation of the first ADNI project (ADNI2) to minimize the risk of reporting false positive results. The minor allele - previously linked with lower alcohol intake - was associated with larger volumes in various cortical regions, notably the medial prefrontal cortex and cingulate gyrus in both cohorts. Intriguingly, the same allele also predicted faster ventricular expansion rates in the ADNI1 cohort at 1- and 2-year follow up. Despite a lack of alcohol consumption data in this study cohort, these findings, combined with earlier functional imaging investigations of the same gene, suggest the existence of reciprocal interactions between genes, brain, and drinking behavior.
Background
Traditional strategies have been largely unsuccessful in recruiting Latinos and other underrepresented groups for Alzheimer’s disease and related dementias (ADRD) clinical research. Online ...registries are a promising approach to recruit hard to reach populations. This project, funded by the California Department of Public Health Alzheimer’s Disease Program, aimed to use internet advertising and culturally‐tailored messaging to enroll participants in the Brain Healthy Registry (BHR), an online registry for ADRD research.
Methods
Researchers collaborated with marketing professionals experienced in marketing to Latinos. A community advisory board, comprised of professionals from different industries who identify as Latino and/or have knowledge and experience within local Latino communities, was formed to guide recruitment strategies tailored to Latinos’ values and beliefs. Custom audience targeting displayed Facebook and Google ads to Latinos living in California zip codes with large Latino populations. Recruitment strategies, including “look‐alike” audience targeting, ad retargeting, simplification of the BHR enrollment process, and revamped email campaigns to individuals that did not complete enrollment, were employed.
Results
Internet advertising from September 2020 and mid‐January 2021 resulted in email addresses from 5,095 individuals, of which 2,560 (50%) joined BHR. Seventy four percent of new participants self‐reported Latino ethnicity. Prior to this, there were only 3,761 Latino participants (out of total sample size of > 70,0000) since BHR’s inception in 2014. A larger percentage of new participants also self‐reported being non‐White/Caucasian compared to previously enrolled participants (Table 1). The cost to enroll a participant recruited from these efforts ranged from approximately $35 – $80.
Conclusion
Internet advertising with culturally tailored recruitment messaging substantially increased representation of Latino participants in BHR during a short timeframe. Future efforts include a Spanish‐language BHR website, Spanish digital ads, and messaging to improve engagement and retention of Latino participants once they are enrolled.
Background
Remote, internet‐based methods for recruitment, screening, and longitudinally assessing older adults have the potential to greatly facilitate Alzheimer’s disease and related research, ...including clinical trials and observational studies.
Methods
The Brain Health Registry (BHR) is an online website and registry that includes a comprehensive battery of self‐ and study partner‐report questionnaires and online neuropsychological tests. Participants are asked to return at 6‐month intervals for longitudinal follow‐up. Recently, new online infrastructure for managing remote biomarker (saliva, blood) collection and for linking in‐clinic and online data were added. Multiple current initiatives aim to increase recruitment and engagement of underrepresented populations using digital, community engaged research strategies to improve generalizability of results. These include the recent launch of a Spanish‐language website, and projects focused on increasing enrollment and task completion of Black/African American and Hispanic/Latinx individuals.
Results
BHR includes >95,000 participants, >9000 of whom have enrolled study partners, 40% return for longitudinal follow up, 64% are age 55+, 80% are female, 80% identify as Non‐Latinx White, and 10% identify as Hispanic/Latinx. Participants have an average of 16.2 years of education. BHR has made >86,000 referrals to other studies, resulting in >5000 BHR participants enrolled in 25 different aging and AD observational studies and treatment trials. Over 2400 participants are co‐enrolled in BHR and collaborator studies, with online data linked to in‐clinic data. 573 participants have undergone APOE genotyping using remote saliva collection, and 629 have had blood collected using local phlebotomy for AD plasma biomarker analysis. Accumulating evidence supports the feasibility and validity of the approach, including associations with in‐clinic assessments, the ability to accurately detect MCI and enrichment for amyloid positivity.
Conclusions
Major advantages of the BHR approach are scalability and accessibility. Challenges include compliance, retention, and cohort diversity. Lessons learned from BHR, and components of the existing infrastructure, can be used to inform future remote clinical trial design. One such future effort is ADNI4. To facilitate enrollment of new participants, ADNI4 will establish an online recruitment and screening portal, with a remote phlebotomy component, to efficiently identify those from underrepresented populations, and those likely to have preclinical and prodromal AD.
Background
An analysis of the ethnoracial and educational composition of Alzheimer's Disease Neuroimaging Initiative (ADNI) participants, and the relationship between ethnoracial/education and ...screening, enrollment, dropout, and biomarkers is needed to assess the generalizability of ADNI data to diverse populations, and to inform efforts to increase diversity.
Method
Data from all 4 ADNI phases were used to determine ethnoracial and educational breakdown and differences of the following ADNI participation metrics: screening, screen fails, enrollment, as well as drop‐out within and between ADNI phases. Multivariable logistic regression was used to analyze the association between ethnoracial and educational group and either amyloid positivity or ApoE e4 status, adjusting for age, gender, and diagnostic group.
Result
Across the 4 ADNI phases (screened: n=3,739; enrolled: n=2,286), 11% of screened and enrolled participants identified as Latinx, non‐Latinx Black, or non‐Latinx‐Asian and 16% of the screened and 15% of the enrolled participants reported having =<12 years of education. In contrast, the 2019 US Census American Community Survey for adults 60+ reports 25% non‐White, and 44 % =< 12 years of education. There were no observed differences between participants from historically underrepresented ethnoracial groups and NLW participants in screen fail and dropout rates. We found that individuals with =<12 years of education failed screening at a significantly higher rate (p=.01) and rolled‐over significantly less to subsequent ADNI phases (p<.001) compared to individuals with an education >12 years. Identifying as non‐Latinx Asian was associated with lower odds of being amyloid positive (OR=0.36; p=.014) and an ApoE4 carrier (OR=0.36; p=.008) compared to NLW. Identifying as Latinx was associated with being less likely to be amyloid positive than NLW participants (OR=0.54; p=.031).
Conclusion
Compared to the US Census, ADNI underrepresents enrollment of Latinx, non‐Latinx Black, and non‐Latinx Asian participants and those with low educational attainment. Hence, ADNI reflects the on‐going challenges with recruiting and enrolling underrepresented populations into most AD research. Our findings highlight the need for ADNI to increase enrollment and engagement of underrepresented ethnoracial and educational populations. ADNI is actively undertaking steps to implement improved recruitment approaches aimed at increasing ethnoracial and socioeconomical representation in multi‐center cohort studies.
Background
Differences in CSF and plasma Alzheimer’s disease biomarkers between African Americans (AA), Latinos (LA), and non‐Hispanic Whites (NHW) have been reported. The Alzheimer’s Disease ...Neuroimaging Initiative (ADNI) may offer insights into ethnoracial biomarker differences important for understanding disease trajectory variations among diverse populations.
Method
We queried the ADNI database for all participants with available plasma pTau181 and NfL (Simoa) and linked CSF biomarkers within 1 year of blood draw (Aβ42, pTau181, T‐tau, Elecsys). Baseline data for 47 AA and 43 LA with plasma biomarkers were matched to separate groups of NHW (1:3 nearest neighbor matching on propensity score), considering age, sex, education, CDR, APOE ε4 alleles and family history of dementia (Table 1). Cross‐sectional analyses compared biomarker levels between matched cohorts without (non‐parametric Mann‐Whitney U‐test) and with adjustment for demographics, clinical, biomarker, and medical characteristics (multi‐linear regression). Statistical threshold was set at two‐tailed p<0.05.
Result
Unadjusted comparisons between AA and NHW revealed no significant differences in plasma NfL 29.3 (IQR: 21.6‐42.5) vs 35.5 (27.0‐49.6), p=.12, plasma pTau181 14.5 (9.4‐22.9) vs 15.5 (10.1‐22.8), p=.83, CSF Aβ42 933.1 (649.5‐1590.8) vs 926.7 (677.7‐1697.8), p=.83, CSF T‐tau 214.6 (152.4‐290.7) vs 251.9 (194.5‐351.4), p=.17 or CSF pTau181 19.6 (14.4‐27.3) vs 22.7 (17.0‐33.3), p=.32 (Fig. 1a‐b). No significant differences were identified in any of the biomarkers after adjustment for relevant covariates. Unadjusted comparisons between LA and NHW revealed no significant differences in plasma NfL 36.7 (24.4‐50.4) vs 35.6 (25.6‐47.1), p=1, plasma pTau181 18.0 (11.3‐25.0) vs 15.7 (10.9‐23.4), p=.81, or CSF Aβ42 852.7 (787.1‐1213.0) vs 951.6 (651.1‐1530.5), p=1. A trend towards lower CSF T‐Tau 216.5 (147.1‐270.5) vs 257.2 (197.2‐360.9), p=.076 and CSF pTau181 19.4 (13.6‐27.9) vs 24.7 (17.5‐33.4), p=.076 in LA was observed (Fig. 2a‐b). After adjustment for covariates, LA were found to have significantly lower levels of CSF T‐tau (β=0.154, p<.05) and CSF pTau181 (β=0.164, p<.05), while differences in plasma markers were not significant.
Conclusion
No significant biomarker differences between AA and NHW were observed. Lower CSF pTau181 and CSF T‐Tau in LA compared to NHW after covariate adjustment may represent subtle differences driven by biological factors or unmeasured sociocultural determinants (income, occupation, neighborhood environment); larger studies are needed.
Background
The Centiloid (CL) was proposed to harmonise the quantification of Amyloid PET images across tracers, scanners, and processing pipelines. However, several groups have reported differences ...across tracers and scanners. In this study, we aim to evaluate the impact of different pre/post‐processing harmonisation steps on the consistency of longitudinal data across multiple large studies.
Method
All Amyloid PET data in AIBL (N=3830), ADNI (N=3453) and OASIS3 (N=1398) were quantified using the Centiloid SPM pipeline. SUVR were converted into Centiloids using each tracer’s respective transform. All images were smoothed to a uniform 8mm FWHM PSF. Both Raw and 8mm FWHM smoothed images were quantified. For Florbetapir, we evaluated using both the standard Whole Cerebellum (WC) and a Composite WM+WC reference region, which has been previously shown to improve longitudinal consistency. Additionally, our recently proposed Non‐negative Matrix Factorisation quantification (NMF: Neuroimage 2021) was applied to all spatially and SUVR normalised images. Longitudinal consistency was evaluated using: fitting error when fitting a regression line to each subject; percentage of longitudinal changes exceeding 95th percentile of changes seen with PiB when using a single scanner (6.56CL/y in the negative and 17.06CL/y in the positive); Spearman rank correlation ρ between the baseline Centiloid and the rate of change.
Result
Figure 1 illustrates the longitudinal consistency, while the fitting error and percentage of outliers are presented in Table 1. These results indicate that the 8mm FWHM smoothing reduced some of the variability, but its impact was limited. Using the Composite reference region on Florbetapir had a bigger impact in reducing the variability. The best results were however obtained when using the NMF method, compared to SPM. Figure 2 shows the baseline vs rate of CL changes. These plots also show a moderate influence of the FWHM smoothing, and a stronger influence of the Composite reference region. They also show that the NMF method produced the highest Spearman and peak CL/y regardless of the pre‐processing or normalisation method used.
Conclusion
FWHM smoothing has moderate impact on longitudinal consistency or outliers. A Composite reference region including subcortical WM should be used for Florbetapir longitudinal Centiloids. NMF improves consistency over SPM.
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