Alzheimer's Disease Neuroimaging Initiative Participants Research Articles

Contributions of the ADNI Biostatistics Core.

The goal of the Biostatistics Core of the Alzheimer's Disease Neuroimaging Initiative (ADNI) has been to ensure that sound study designs and statistical methods are used to meet the overall goals of ADNI. We have supported the creation of a well-validated and well-curated longitudinal database of clinical and biomarker information on ADNI participants and helped to make this accessible and usable for researchers. We have developed a statistical methodology for characterizing the trajectories of clinical and biomarker change for ADNI participants across the spectrum from cognitively normal to dementia, including multivariate patterns and evidence for heterogeneity in cognitive aging. We have applied these methods and adapted them to improve clinical trial design. ADNI-4 will offer us a chance to help extend these efforts to a more diverse cohort with an even richer panel of biomarker data to support better knowledge of and treatment for Alzheimer's disease and related dementias. HIGHLIGHTS: The Alzheimer's Disease Neuroimaging Initiative (ADNI) Biostatistics Core provides study design and analytic support to ADNI investigators. Core members develop and apply novel statistical methodology to work with ADNI data and support clinical trial design. The Core contributes to the standardization, validation, and harmonization of biomarker data. The Core serves as a resource to the wider research community to address questions related to the data and study as a whole.

Beyond Hypertension: Examining Variable Blood Pressure's Role in Cognition and Brain Structure.

Hypertension or high blood pressure (BP) is one of the 12 modifiable risk factors that contribute to 40% of dementia cases that could be delayed or prevented. Although hypertension is associated with cognitive decline and structural brain changes, less is known about the long-term association between variable BP and cognitive/brain changes. This study examined the relationship between variable BP and longitudinal cognitive, white matter hyperintensity (WMH), gray matter (GM), and white matter (WM) volume change over time and postmortem neuropathology. A total of 4,606 participants (32,776 follow-ups) from RADC Research Resource Sharing Hub (RUSH) and 2,114 participants (9,827 follow-ups) fromtheAlzheimer's Disease Neuroimaging Initiative (ADNI) were included. Participants were divided into 1 of 3 groups: normal, high, or variable BP. Linear-mixed models investigated the relationship between BP and cognition, brain structure, and neuropathology. Older adults with variable BP exhibited the highest rate of cognitive decline followed by high and then normal BP. Increased GM volume loss and WMH burden were also observed in variable compared to high and normal BP. In postmortem neuropathology, both variable and high BP had increased rates compared to normal BP. Results were consistent across the RUSH and ADNI participants, supporting the generalizability of the findings. Damages potentially associated with variable BP may reduce resilience to future dementia-related pathology and increased the risk of dementia more than that caused by high BP. Improved treatment and management of variable BP may help reduce cognitive decline in the older adult population.

The neutrophil to lymphocyte ratio associates with markers of Alzheimer's disease pathology in cognitively unimpaired elderly people.

An elevated neutrophil-lymphocyte ratio (NLR) in blood has been associated with Alzheimer's disease (AD). However, an elevated NLR has also been implicated in many other conditions that are risk factors for AD, prompting investigation into whether the NLR is directly linked with AD pathology or a result of underlying comorbidities. Herein, we explored the relationship between the NLR and AD biomarkers in the cerebrospinal fluid (CSF) of cognitively unimpaired (CU) subjects. Adjusting for sociodemographics, APOE4, and common comorbidities, we investigated these associations in two cohorts: the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the M.J. de Leon CSF repository at NYU. Specifically, we examined associations between the NLR and cross-sectional measures of amyloid-β42 (Aβ42), total tau (t-tau), and phosphorylated tau181 (p-tau), as well as the trajectories of these CSF measures obtained longitudinally. A total of 111 ADNI and 190 NYU participants classified as CU with available NLR, CSF, and covariate data were included. Compared to NYU, ADNI participants were older (73.79 vs. 61.53, p < 0.001), had a higher proportion of males (49.5% vs. 36.8%, p = 0.042), higher BMIs (27.94 vs. 25.79, p < 0.001), higher prevalence of hypertensive history (47.7% vs. 16.3%, p < 0.001), and a greater percentage of Aβ-positivity (34.2% vs. 20.0%, p = 0.009). In the ADNI cohort, we found cross-sectional associations between the NLR and CSF Aβ42 (β = -12.193, p = 0.021), but not t-tau or p-tau. In the NYU cohort, we found cross-sectional associations between the NLR and CSF t-tau (β = 26.812, p = 0.019) and p-tau (β = 3.441, p = 0.015), but not Aβ42. In the NYU cohort alone, subjects classified as Aβ + (n = 38) displayed a stronger association between the NLR and t-tau (β = 100.476, p = 0.037) compared to Aβ- subjects or the non-stratified cohort. In both cohorts, the same associations observed in the cross-sectional analyses were observed after incorporating longitudinal CSF data. We report associations between the NLR and Aβ42 in the older ADNI cohort, and between the NLR and t-tau and p-tau in the younger NYU cohort. Associations persisted after adjusting for comorbidities, suggesting a direct link between the NLR and AD. However, changes in associations between the NLR and specific AD biomarkers may occur as part of immunosenescence.

Longitudinal associations of apathy and regional tau in mild cognitive impairment and dementia: Findings from the Alzheimer's Disease Neuroimaging Initiative.

It is important to study apathy in Alzheimer's disease (AD) to better understand its underlying neurobiology and develop effective interventions. In the current study, we sought to examine the relationships between longitudinal apathy and regional tau burden in cognitively impaired older adults from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Three hundred and nineteen ADNI participants with mild cognitive impairment (MCI) or AD dementia underwent flortaucipir (FTP) tau positron emission tomography (PET) imaging and clinical assessment with the Neuropsychiatric Inventory (NPI) annually. Longitudinal NPI Apathy (NPI-A) scores were examined in relation to baseline tau PET signal in three a priori selected regions implicated in AD and AD-related apathy (supramarginal gyrus, entorhinal cortex [EC] and rostral anterior cingulate cortex [rACC]). Secondary models were adjusted for global cognition (Mini-Mental State Examination score) and cortical amyloid (florbetapir PET). Higher baseline supramarginal gyrus and EC tau burden were each significantly associated with greater NPI-A over time, while rACC tau was associated with higher NPI-A but did not predict its trajectory over time. These results were retained for supramarginal and EC tau after adjusting models for global cognition and cortical amyloid. Our findings suggest that baseline in vivo tau burden in parietal and temporal brain regions affected in AD, and less so in a medial frontal region involved in motivational control, is associated with increasing apathy over time in older adults with MCI and AD dementia. Future work studying emergent apathy in relation to not only core AD pathology but also circuit level dysfunction may provide additional insight into the neurobiology of apathy in AD and opportunities for intervention. Tau (Flortaucipir PET) in regions implicated in AD was associated with increasing apathy over timeCortical amyloid was also found to be a robust predictor of the trajectory of apathyEvidence of synergy between regional tau and amyloid in overall higher levels of apathy.

Association of APOE-ε4, Osteoarthritis, β-Amyloid, and Tau Accumulation in Primary Motor and Somatosensory Regions in Alzheimer Disease.

One of the most prevalent chronic diseases, osteoarthritis (OA), may work in conjunction with APOE-ε4 to accelerate Alzheimer disease (AD) alterations, particularly in the primary motor (precentral) and somatosensory (postcentral) cortices. To understand the reasoning behind this, we investigated how OA and APOE-ε4 influence the accumulation of β-amyloid (Aβ) and tau accumulation in primary motor and somatosensory regions in Aβ-positive (Aβ+) older individuals. We selected Aβ+ Alzheimer Disease Neuroimaging Initiative participants, defined by baseline 18F-florbetapir (FBP) Aβ PET standardized uptake value ratio (SUVR) of AD summary cortical regions, who had longitudinal Aβ PET, the records of OA medical history, and APOE-ε4 genotyping. We examined how OA and APOE-ε4 relate to baseline and longitudinal Aβ accumulation and tau deposition measured at follow-up in precentral and postcentral cortical areas and how they modulate Aβ-associated future higher tau levels, adjusting for age, sex, and diagnosis and using multiple comparison corrections. A total of 374 individuals (mean age 75 years, 49.2% female, 62.8% APOE-ε4 carriers) who underwent longitudinal FBP PET with a median follow-up of 3.3 years (interquartile range [IQR] 3.4, range 1.6-9.4) were analyzed, and 96 people had 18F-flortaucipir (FTP) tau PET measured at a median of 5.4 (IQR 1.9, range 4.0-9.3) years postbaseline FBP PET. Neither OA nor APOE-ε4 was related to baseline FBP SUVR in precentral and postcentral regions. At follow-up, OA rather than APOE-ε4 was associated with faster Aβ accumulation in postcentral region (β = 0.005, 95% CI 0.001-0.008) over time. In addition, OA but not the APOE-ε4 allele was strongly linked to higher follow-up FTP tau levels in precentral (β = 0.098, 95% CI 0.034-0.162) and postcentral (β = 0.105, 95% CI 0.040-0.169) cortices. OA and APOE-ε4 were also interactively associated with higher follow-up FTP tau deposition in precentral (β = 0.128, 95% CI 0.030-0.226) and postcentral (β = 0.124, 95% CI 0.027-0.223) regions. This study suggests that OA was associated with faster Aβ accumulation and higher Aβ-dependent future tau deposition in primary motor and somatosensory regions, providing novel insights into how OA increases the risk of AD.

Association of Data-Driven White Matter Hyperintensity Spatial Signatures With Distinct Cerebral Small Vessel Disease Etiologies.

Topographical distribution of white matter hyperintensities (WMH) are hypothesized to vary by cerebrovascular risk factors. We used an unbiased pattern discovery approach to identify distinct WMH spatial patterns and investigate their association with different WMH etiologies. We performed a cross-sectional study on participants of the Alzheimer's Disease Neuroimaging Initiative (ADNI) to identify spatially distinct WMH distribution patterns using voxel-based spectral clustering analysis of aligned WMH probability maps. We included all participants from the ADNI Grand Opportunity/ADNI 2 study with available baseline 2D-FLAIR MRI scans, without history of stroke or presence of infarction on imaging. We evaluated the associations of these WMH spatial patterns with vascular risk factors, amyloid-β PET, and imaging biomarkers of cerebral amyloid angiopathy (CAA), characterizing different forms of cerebral small vessel disease (CSVD) using multivariable regression. We also used linear regression models to investigate whether WMH spatial distribution influenced cognitive impairment. We analyzed MRI scans of 1,046 ADNI participants with mixed vascular and amyloid-related risk factors (mean age 72.9, 47.7% female, 31.4% hypertensive, 48.3% with abnormal amyloid PET). We observed unbiased partitioning of WMH into 5 unique spatial patterns: deep frontal, periventricular, juxtacortical, parietal, and posterior. Juxtacortical WMH were independently associated with probable CAA, deep frontal WMH were associated with risk factors for arteriolosclerosis (hypertension and diabetes), and parietal WMH were associated with brain amyloid accumulation, consistent with an Alzheimer disease (AD) phenotype. Juxtacortical, deep frontal, and parietal WMH spatial patterns were associated with cognitive impairment. Periventricular and posterior WMH spatial patterns were unrelated to any disease phenotype or cognitive decline. Data-driven WMH spatial patterns reflect discrete underlying etiologies including arteriolosclerosis, CAA, AD, and normal aging. Global measures of WMH volume may miss important spatial distinctions. WMH spatial signatures may serve as etiology-specific imaging markers, helping to resolve WMH heterogeneity, identify the dominant underlying pathologic process, and improve prediction of clinical-relevant trajectories that influence cognitive decline.

Multi-method investigation of factors influencing amyloid onset and impairment in three cohorts.

Alzheimer's disease biomarkers are becoming increasingly important for characterizing the longitudinal course of disease, predicting the timing of clinical and cognitive symptoms, and for recruitment and treatment monitoring in clinical trials. In this work, we develop and evaluate three methods for modelling the longitudinal course of amyloid accumulation in three cohorts using amyloid PET imaging. We then use these novel approaches to investigate factors that influence the timing of amyloid onset and the timing from amyloid onset to impairment onset in the Alzheimer's disease continuum. Data were acquired from the Alzheimer's Disease Neuroimaging Initiative (ADNI), the Baltimore Longitudinal Study of Aging (BLSA) and the Wisconsin Registry for Alzheimer's Prevention (WRAP). Amyloid PET was used to assess global amyloid burden. Three methods were evaluated for modelling amyloid accumulation using 10-fold cross-validation and holdout validation where applicable. Estimated amyloid onset age was compared across all three modelling methods and cohorts. Cox regression and accelerated failure time models were used to investigate whether sex, apolipoprotein E genotype and e4 carriage were associated with amyloid onset age in all cohorts. Cox regression was used to investigate whether apolipoprotein E (e4 carriage and e3e3, e3e4, e4e4 genotypes), sex or age of amyloid onset were associated with the time from amyloid onset to impairment onset (global clinical dementia rating ≥1) in a subset of 595 ADNI participants that were not impaired before amyloid onset. Model prediction and estimated amyloid onset age were similar across all three amyloid modelling methods. Sex and apolipoprotein E e4 carriage were not associated with PET-measured amyloid accumulation rates. Apolipoprotein E genotype and e4 carriage, but not sex, were associated with amyloid onset age such that e4 carriers became amyloid positive at an earlier age compared to non-carriers, and greater e4 dosage was associated with an earlier amyloid onset age. In the ADNI, e4 carriage, being female and a later amyloid onset age were all associated with a shorter time from amyloid onset to impairment onset. The risk of impairment onset due to age of amyloid onset was non-linear and accelerated for amyloid onset age >65. These findings demonstrate the feasibility of modelling longitudinal amyloid accumulation to enable individualized estimates of amyloid onset age from amyloid PET imaging. These estimates provide a more direct way to investigate the role of amyloid and other factors that influence the timing of clinical impairment in Alzheimer's disease.

Novel polygenic risk score approach with transcriptome-based weighting for genetic risk prediction of late-onset Alzheimer's disease.

Recent genetic studies showed that polygenic risk score (PRS) could be used to identify individuals at high risk of Alzheimer's disease (AD). Despite this success, early detection and intervention of AD still remain challenging. In this study, we suggest a novel PRS approach that is weighted by predicted tissue-specific gene expression levels. We used whole-genome sequencing and phenotypic data of 446 European participants (207 late-onset AD cases and 239 cognitively normal controls) from the Alzheimer's disease Neuroimaging Initiative (ADNI) (Table 1). We performed transcriptome-wide association studies (TWAS) in 13 brain regions by using MetaXcan (weights from GTEx V8) and GWAS summary statistics (IGAP stage 1 excluding ADNI participants) and integrated these 13 TWAS results using MultiXcan. To generate transcriptome-based weighting (TW)-PRS, expression weights in each gene were mapped to SNPs in the gene, and these were applied as additional weights to the SNPs' beta coefficients in the GWAS summary statistics. Then, PRS was derived based on the weighted GWAS summary statistics using PRSice-2. Finally, we evaluated prediction performance of a set of different models that incorporated demographic information and TW-PRS. A total of 17,588 gene expression weights were obtained from 13 brain tissues. These gene expression weights covered about 220,000 SNPs. Among them, expression weights were applied to about 140,000 SNPs overlapped with GWAS summary statistics. An AD prediction using conventional PRS yielded a pseudo-R2 of 0.0462 (P=1.30E-04). Compared with the conventional PRS, the TW-PRS improved the performance and statistical power with a pseudo-R2 of 0.0574 (P=1.74E-05). As shown in Table 2, a fully adjusted model achieved an AUC of 0.794 in which TW-PRS were significant even when APOE ε4 status and demographic information were adjusted (P=2.33E-05). This study proposes a novel TW-PRS approach that combines predicted tissue-specific transcriptomic weights and PRS. Expression weights of brain regions critical to AD progression enhanced the performance of conventional PRS. Our finding suggests that tissue-specific transcriptomic factors may be independent and complementary to conventional PRS and provide additional information for tissue-specific regulatory effects in AD.

Rates of longitudinal change in 18 F-flortaucipir PET vary by brain region, cognitive impairment, and age in atypical Alzheimer's disease.

IntroductionLongitudinal positron emission tomography (PET) studies of tau accumulation in Alzheimer's disease (AD) have noted reduced increases or frank decreases in tau signal. We investigated how such reductions related to analytical confounds and disease progression markers in atypical AD.MethodsWe assessed regional and interindividual variation in longitudinal change on 18F‐flortaucipir PET imaging in 24 amyloid beta (Aβ)+ patients with atypical, early‐onset amnestic or non‐amnestic AD plus 62 Aβ– and 132 Aβ+ Alzheimer's Disease Neuroimaging Initiative (ADNI) participants.ResultsIn atypical AD, 18F‐flortaucipir uptake slowed or declined over time in areas with high baseline signal and older, more impaired individuals. ADNI participants had reduced longitudinal change in early Braak stage regions relative to late‐stage areas.DiscussionResults suggested radioligand uptake plateaus or declines in advanced neurodegeneration. Further research should investigate whether results generalize to other radioligands and whether they relate to changes of the radioligand binding site structure or accessibility.

Impact of C-Reactive Protein on Cognition and Alzheimer Disease Biomarkers in Homozygous APOE ɛ4 Carriers.

Previous research has shown that elevated blood C-reactive protein (CRP) is associated with increased Alzheimer disease (AD) risk only in APOE ε4 allele carriers; the objective of this study was to examine the interactive effects of plasma CRP and APOE genotype on cognition and AD biomarkers. Data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study were analyzed, including APOE genotype; plasma CRP concentrations; diagnostic status (i.e., mild cognitive impairment and dementia due to AD); Mini-Mental State Examination (MMSE) and Clinical Dementia Rating Dementia Staging Instrument scores; CSF concentrations of β-amyloid peptide (Aβ42), total tau (t-Tau) and phosphorylated tau (p-Tau); and amyloid (AV45) PET imaging. Multivariable regression analyses tested the associations between plasma CRP and APOE on cognitive and biomarker outcomes. Among 566 ADNI participants, 274 (48.4%) had no, 222 (39.2%) had 1, and 70 (12.4%) had 2 APOE ε4 alleles. Among only participants who had 2 APOE ε4 alleles, elevated CRP was associated with lower MMSE score at baseline (β [95% confidence interval] -0.52 [-1.01, -0.12]) and 12-month follow-up (β -1.09 [-1.88, -0.17]) after adjustment for sex, age, and education. The interaction of 2 APOE ε4 alleles and elevated plasma CRP was associated with increased CSF levels of t-Tau (β = 11.21, SE 3.37, p < 0.001) and p-Tau (β = +2.74, SE 1.14, p < 0.01). Among those who had no APOE ε4 alleles, elevated CRP was associated with decreased CSF t-Tau and p-Tau. These effects were stronger at the 12-month follow-up. CRP released during peripheral inflammation could be a mediator in APOE ε4-related AD neurodegeneration and serve as a drug target for AD.

Mild-moderate CKD is not associated with cognitive impairment in older adults in the Alzheimer's Disease Neuroimaging Initiative cohort.

Chronic kidney disease (CKD) is associated with cognitive impairment and dementia. We examined whether this relationship hold true in older adults, who have a higher prevalence of both CKD and dementia. We conducted a cross-sectional secondary analysis of an established observational cohort. We analyzed data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), an NIH funded, multicenter longitudinal observational study, which includes participants with normal and impaired cognition and assesses cognition with a comprehensive battery of neuropsychological tests. We included a non-probability sample of all ADNI participants with serum creatinine measurements at baseline (N = 1181). Using multivariable linear regression analysis, we related the CKD Epidemiology Collaboration equation eGFR with validated composite scores for memory (ADNI-mem) and executive function (ADNI-EF). For the 1181 ADNI participants, the mean age was 73.7 ± 7.1 years. Mean estimated glomerular filtration rate (eGFR) was 76.4 ± 19.7; 6% had eGFR<45, 22% had eGFR of 45 to <60, 51% had eGFR of 60-90 and 21% had eGFR>90 ml/min/1.73 m2. The mean ADNI-Mem score was 0.241 ± 0.874 and mean ADNI-EF score was 0.160 ± 1.026. In separate multivariable linear regression models, adjusted for age, sex, race education and BMI, there was no association between each 10 ml/ min/1.73 m2 higher eGFR and ADNI-Mem (β -0.02, 95% CI -0.04, 0.02, p = 0.56) or ADNI-EF (β 0.01, 95% CI -0.03, 0.05, p = 0.69) scores. We did not observe an association between eGFR and cognition in the older ADNI participants.

Machine Learning Predictive Models Can Improve Efficacy of Clinical Trials for Alzheimer's Disease.

The ideal participants for Alzheimer's disease (AD) clinical trials would show cognitive decline in the absence of treatment (i.e., placebo arm) and also would be responsive to the therapeutic intervention being studied (i.e., drug arm). One strategy to boost the power of trials is to enroll individuals who are more likely to progress targeted using data-driven predictive models. To investigate if machine learning (ML) models can effectively predict clinical disease progression (cognitive decline) in mild-to-moderate AD patients during the timeframe of a phase III clinical trial. Data from 202 participants with a diagnosis of AD at baseline from the Alzheimer's Disease Neuroimaging Initiative (ADNI) was used to train ML classifiers that can differentiate between individuals who had declining cognitive function (DC) and individuals with stable cognitive function (SC). DC was defined as any downward change in the Alzheimer's Disease Assessment Scale cognitive subscale (ADAS-cog) score over 12 months of follow-up. SC was defined by the absence of decline in ADAS-cog. Trained models were applied to data from 77 participants from the placebo arm of the phase III trial of Semagacestat (LFAN study) to identify subgroups of SC versus DC. Only 74.8% of ADNI participants and 63.6% of LFAN participants had cognitive decline after one year of follow up. K-nearest neighbors (kNN) classifier had an accuracy of 68.3%, sensitivity of 80.1%, and specificity of 33.3% for identifying decliners in ADNI (training sample). In LFAN (validation sample), the model showed an overall accuracy of 61.3%, sensitivity of 65.5%, and specificity of 47.0% in identifying decliners at the 12 months of follow-up. The model had a positive predictive value of 80.8%, which was 17.2% more than the base prevalence of decliners. Machine learning predictive models can be effectively used to boost the power of clinical trials by reducing the sample size.

Characterization of Alzheimer Disease Biomarker Discrepancies Using Cerebrospinal Fluid Phosphorylated Tau and AV1451 Positron Emission Tomography

Fluid and imaging biomarkers of Alzheimer disease (AD) are often used interchangeably, but some biomarkers may reveal earlier stages of disease. To characterize individuals with tau abnormality indicated by cerebrospinal fluid (CSF) assay or positron emission tomography (PET). Between 2010 and 2019, 322 participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) underwent CSF and PET assessments of tau pathology. Data-driven, clinically relevant thresholds for CSF phosphorylated tau (P-tau) (≥26.64 pg/mL) and flortaucipir-PET meta-regions of interest (ROI) (standard uptake value ratio ≥1.37) indicated participants' tau status as CSF-/PET-, CSF+/PET-, CSF-/PET+, and CSF+/PET+. Of 1659 ADNI participants with a CSF or flortaucipir assessment, 588 had both measures (1071 were excluded). Among these, 266 were further excluded because they did not have flortaucipir and CSF testing within less than 25 months, leaving 322 for analysis. Of these, 213 were cognitively unimpaired (CU); 98 had mild cognitive impairment (MCI); and 11 had AD dementia. We compared tau-positive vs tau-negative groups as indicated by either modality or demographic and clinical variables, amyloid β-PET burden, and flortaucipir-PET binding across Braak stage-related ROIs. We also compared 5-year rates of CSF P-tau accumulation and cognitive decline prior to flortaucipir-PET scanning. Among the 322 study participants, 180 were women (56%), and the mean (SD) age was 73.08 (7.37) years. Two hundred ten participants were CSF-/PET- (65%); 63 were CSF+/PET- (19.5%); 15 were CSF-/PET+ (4.6%); and 34 were CSF+/PET+ (10.5%). Most CSF-/PET+ participants had measures near CSF or PET tau thresholds. The CSF+/PET- participants showed faster 5-year accrual of P-tau and increased flortaucipir-PET binding in early Braak ROIs but similar memory decline compared with CSF-/PET- participants. Tau-positive individuals by either measure showed increased amyloid β-PET burden. All CSF+/PET+ individuals were amyloid-positive, and 26 had MCI or AD dementia (76%). Compared with the CSF-/PET- group, CSF+/PET+ individuals had experienced faster 5-year accrual of CSF P-tau and decline in memory and executive function, resulting in reduced cognitive abilities at the time of flortaucipir-PET assessment. Suprathreshold CSF P-tau without flortaucipir-PET abnormality may indicate a stage of AD development characterized by early tau abnormality without measurable loss in cognitive performance. Persons with both tau CSF and PET abnormality appear to have reduced cognitive capacities resulting from faster antecedent cognitive decline. Elevation of CSF P-tau appears to precede flortaucipir-PET positivity in the progression of AD pathogenesis and related cognitive decline.

Editors' note: Type 2 diabetes mellitus, brain atrophy, and cognitive decline

In the article “Type 2 diabetes mellitus, brain atrophy, and cognitive decline,” Moran et al. examined longitudinal relationships between type 2 diabetes mellitus (T2DM), cortical thickness, and cognitive function in older people with normal cognition, mild cognitive impairment (MCI), and Alzheimer disease (AD) in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. They concluded that T2DM contributed to cognitive decline via neurodegeneration (lower baseline cortical thickness) and that previous brain and cognitive reserve (reflected by greater education) may protect against this effect. In response, Ba et al. argue that it is unclear whether the authors properly controlled for other risk factors for cognitive decline such as hypertension, hyperlipidemia, white matter lesion burden, and depression and note that incorporating additional T2DM-related factors such as fasting blood glucose or disease duration would be relevant. They also critique the study's reliance on clinical diagnoses for determination of MCI and AD, which can often result in misattribution of symptoms to AD when supportive biomarkers are not included. Replying to these comments, Dr. Moran acknowledges the absence of detailed T2DM-specific information as a limitation of repurposing the ADNI dataset for their work but notes that the study reported data on fasting glucose, diabetes medication use, blood pressure, and measures of obesity. Although ADNI participants were selected to have a low burden of cerebrovascular disease, Dr. Moran acknowledges that there are additional potential confounders of the relationship between T2DM and brain health, such as other vascular risk factors, some of which were explored in other work using samples with a greater burden of cerebrovascular disease. He also agrees on the importance of incorporating biomarkers of underlying pathology into future work in this area. In the article “Type 2 diabetes mellitus, brain atrophy, and cognitive decline,” Moran et al. examined longitudinal relationships between type 2 diabetes mellitus (T2DM), cortical thickness, and cognitive function in older people with normal cognition, mild cognitive impairment (MCI), and Alzheimer disease (AD) in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. They concluded that T2DM contributed to cognitive decline via neurodegeneration (lower baseline cortical thickness) and that previous brain and cognitive reserve (reflected by greater education) may protect against this effect. In response, Ba et al. argue that it is unclear whether the authors properly controlled for other risk factors for cognitive decline such as hypertension, hyperlipidemia, white matter lesion burden, and depression and note that incorporating additional T2DM-related factors such as fasting blood glucose or disease duration would be relevant. They also critique the study's reliance on clinical diagnoses for determination of MCI and AD, which can often result in misattribution of symptoms to AD when supportive biomarkers are not included. Replying to these comments, Dr. Moran acknowledges the absence of detailed T2DM-specific information as a limitation of repurposing the ADNI dataset for their work but notes that the study reported data on fasting glucose, diabetes medication use, blood pressure, and measures of obesity. Although ADNI participants were selected to have a low burden of cerebrovascular disease, Dr. Moran acknowledges that there are additional potential confounders of the relationship between T2DM and brain health, such as other vascular risk factors, some of which were explored in other work using samples with a greater burden of cerebrovascular disease. He also agrees on the importance of incorporating biomarkers of underlying pathology into future work in this area.

Discrepancy-Based Evidence for Loss of Thinking Abilities (DELTA): Development and Validation of a Novel Approach to Identifying Cognitive Changes.

To develop and validate the Discrepancy-based Evidence for Loss of Thinking Abilities (DELTA) score. The DELTA score characterizes the strength of evidence for cognitive decline on a continuous spectrum using well-established psychometric principles for improving detection of cognitive changes. DELTA score development used neuropsychological test scores from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort (two tests each from Memory, Executive Function, and Language domains). We derived regression-based normative reference scores using age, gender, years of education, and word-reading ability from robust cognitively normal ADNI participants. Discrepancies between predicted and observed scores were used for calculating the DELTA score (range 0-15). We validated DELTA scores primarily against longitudinal Clinical Dementia Rating-Sum of Boxes (CDR-SOB) and Functional Activities Questionnaire (FAQ) scores (baseline assessment through Year 3) using linear mixed models and secondarily against cross-sectional Alzheimer's biomarkers. There were 1359 ADNI participants with calculable baseline DELTA scores (age 73.7 ± 7.1 years, 55.4% female, 100% white/Caucasian). Higher baseline DELTA scores (stronger evidence of cognitive decline) predicted higher baseline CDR-SOB (ΔR2 = .318) and faster rates of CDR-SOB increase over time (ΔR2 = .209). Longitudinal changes in DELTA scores tracked closely and in the same direction as CDR-SOB scores (fixed and random effects of mean + mean-centered DELTA, ΔR2 > .7). Results were similar for FAQ scores. High DELTA scores predicted higher PET-Aβ SUVr (ρ = 324), higher CSF-pTau/CSF-Aβ ratio (ρ = .460), and demonstrated PPV > .9 for positive Alzheimer's disease biomarker classification. Data support initial development and validation of the DELTA score through its associations with longitudinal functional changes and Alzheimer's biomarkers. We provide several considerations for future research and include an automated scoring program for clinical use.

Bridging Representation Gaps in Big Data: An Alzheimer's Disease Neuroimaging Initiative (ADNI) Investigation

Abstract Objective Large-scale research consortium studies are increasingly used to help aid in early identification of age-related brain changes and neurodegenerative disease risk. Racial/ethnic minorities (REM) are severely underrepresented in publicly available datasets related to neurodegenerative disease. This is problematic given that the US older adult population will be comprised of 40% REM by 2050. As this population grows more diverse, it is crucial that Alzheimer’s disease (AD) risk factors are appropriately characterized and REM are represented in longitudinal research. The purpose of this study was to examine memory, Apolipoprotein E (APOE) status, and hippocampal volume (HV) data among REM participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. Participants and Method We sampled 91 Latinx and Non-Hispanic Black (NHB) ADNI participants, which included 41 controls (CN; 39% Latinx) and 50 individuals with mild cognitive impairment (MCI; 52% Latinx). Measures included Logical Memory (LM) Delay, Rey Auditory Verbal Learning Test (RAVLT), Alzheimer’s Disease Assessment Cognitive 13-item Scale (ADAS13), APOE e4 and APOE e2 status, and HV. Groups did not differ by age, education, or memory performance. None of the Latinx sample were e2 carriers, whereas 32% of NHB were e2 carriers. Results For NHB and Latinx MCI groups, LM Delay scores positively predicted greater HV (NHB: R2 = .17, p = .04; Latinx: R2 = .16, p = .04). Better RAVLT performance predicted greater HV for the Latinx MCI group but not for the NHB MCI group (Latinx: R2 = .13, p = .06). For NHB CN, e4 status and ADAS13 were positively correlated (r = .59, p = .002) but negatively correlated for Latinx CN (r = -.58, p = .002). APOE e2 and RAVLT percent forgetting were negatively correlated for NHB MCI (r = -.49, p = .01). Conclusions There appears to be differential risk associated with heterozygous e4 status in Latinx and NHB. APOE e2 may have been protective in the context of e4 in NHB. The degree to which e2 is protective in Latinx is unknown. Results highlight the need for improved REM representation in large-scale studies to understand genetic risk and protective factors in REM.

Independent value added by diffusion MRI for prediction of cognitive function in older adults.

The purpose of this study was to determine whether white matter microstructure measured by diffusion magnetic resonance imaging (dMRI) provides independent information about baseline level or change in executive function (EF) or memory (MEM) in older adults with and without cognitive impairment. Longitudinal data was acquired from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study from phases GO and 2 (2009–2015). ADNI participants included were diagnosed as cognitively normal (n = 46), early mild cognitive impairment (MCI) (n = 48), late MCI (n = 29), and dementia (n = 39) at baseline. We modeled the association between dMRI-based global white matter mean diffusivity (MD) and baseline level and change in EF and MEM composite scores, in models controlling for baseline bilateral hippocampal volume, regional cerebral FDG PET metabolism and global cerebral AV45 PET uptake. EF and MEM composite scores were measured at baseline, 6, 12, 24 and 36 months. In the baseline late MCI and dementia groups, greater global MD was associated with lesser baseline EF, but not EF change nor MEM baseline or change. As expected, lesser hippocampal volume and lesser FDG PET metabolism was associated with greater rates of EF and MEM decline. In ADNI-GO/2 participants, white matter integrity provided independent information about current executive function, but was not sensitive to future cognitive change. Since individuals experiencing executive function declines progress to dementia more rapidly than those with only memory impairment, better biomarkers of future executive function decline are needed.

Subtle Cognitive Decline and Biomarker Staging in Preclinical Alzheimer's Disease.

The NIA-AA criteria for "preclinical" Alzheimer's disease (AD) propose a staging method in which AD biomarkers follow an invariable temporal sequence in accordance with the amyloid cascade hypothesis. However, recent findings do not align with the proposed temporal sequence and "subtle cognitive decline," which has not been definitively operationalized, may occur earlier than suggested in preclinical AD. We aimed to define "subtle cognitive decline" using sensitive and reliable neuropsychological tests, and to examine the number and sequence of biomarker abnormalities in the Alzheimer's Disease Neuroimaging Initiative (ADNI). 570 cognitively normal ADNI participants were classified based on NIA-AA criteria and separately based on the number of abnormal biomarkers/cognitive markers associated with preclinical AD that each individual possessed. Results revealed that neurodegeneration alone was 2.5 times more common than amyloidosis alone at baseline. For those who demonstrated only one abnormal biomarker at baseline and later progressed to mild cognitive impairment/AD, neurodegeneration alone was most common, followed by amyloidosis alone or subtle cognitive decline alone, which were equally common. Findings suggest that most individuals do not follow the temporal order proposed by NIA-AA criteria. We provide an operational definition of subtle cognitive decline that captures both cognitive and functional decline. Additionally, we offer a new approach for staging preclinical AD based on number of abnormal biomarkers, without regard to their temporal order of occurrence. This method of characterizing preclinical AD is more parsimonious than the NIA-AA staging system and does not presume that all patients follow a singular invariant expression of the disease.

Memory, executive, and multidomain subtle cognitive impairment: clinical and biomarker findings.

We studied the biomarker signatures and prognoses of 3 different subtle cognitive impairment (SCI) groups (executive, memory, and multidomain) as well as the subjective memory complaints (SMC) group. We studied 522 healthy controls in the Alzheimer's Disease Neuroimaging Initiative (ADNI). Cutoffs for executive, memory, and multidomain SCI were defined using participants who remained cognitively normal (CN) for 7 years. CSF Alzheimer disease (AD) biomarkers, composite and region-of-interest (ROI) MRI, and fluorodeoxyglucose-PET measures were compared in these participants. Using a stringent cutoff (fifth percentile), 27.6% of the ADNI participants were classified as SCI. Most single ROI or global-based measures were not sensitive to detect differences between groups. Only MRI-SPARE-AD (Spatial Pattern of Abnormalities for Recognition of Early AD), a quantitative MRI pattern-based global index, showed differences between all groups, excluding the executive SCI group. Atrophy patterns differed in memory SCI and SMC. The CN and the SMC groups presented a similar distribution of preclinical dementia stages. Fifty percent of the participants with executive, memory, and multidomain SCI progressed to mild cognitive impairment or dementia at 7, 5, and 2 years, respectively. Our results indicate that (1) the different SCI categories have different clinical prognoses and biomarker signatures, (2) longitudinally followed CN subjects are needed to establish clinical cutoffs, (3) subjects with SMC show a frontal pattern of brain atrophy, and (4) pattern-based analyses outperform commonly used single ROI-based neuroimaging biomarkers and are needed to detect initial stages of cognitive impairment.

Susceptibility of the conventional criteria for mild cognitive impairment to false-positive diagnostic errors

BackgroundWe assessed whether mild cognitive impairment (MCI) subtypes could be empirically derived within the Alzheimer's Disease Neuroimaging Initiative (ADNI) MCI cohort and examined associated biomarkers and clinical outcomes. MethodsCluster analysis was performed on neuropsychological data from 825 MCI ADNI participants. ResultsFour subtypes emerged: (1) dysnomic (n = 153), (2) dysexecutive (n = 102), (3) amnestic (n = 288), and (4) cluster-derived normal (n = 282) who performed within normal limits on cognitive testing. The cluster-derived normal group had significantly fewer APOE ε4 carriers and fewer who progressed to dementia compared with the other subtypes; they also evidenced cerebrospinal fluid Alzheimer's disease biomarker profiles that did not differ from the normative reference group. ConclusionsIdentification of empirically derived MCI subtypes demonstrates heterogeneity in MCI cognitive profiles that is not captured by conventional criteria. The large cluster-derived normal group suggests that conventional diagnostic criteria are susceptible to false-positive errors, with the result that prior MCI studies may be diluting important biomarker relationships.