Cerebrospinal fluid biomarker signature in Alzheimer's disease neuroimaging initiative subjects

Plasma phosphorylated tau181 predicts cognitive and functional decline

OBJECTIVE

To determine if plasma tau phosphorylated at threonine 181 (p-tau181) distinguishes pathology-confirmed Alzheimer's disease (AD) from normal cognition (NC) adults, to test if p-tau181 predicts cognitive and functional decline, and to validate findings in an external cohort.

METHODS

Thirty-one neuropathology-confirmed AD cases, participants with clinical diagnoses of mild cognitive impairment (MCI, N = 91) or AD dementia (N = 64), and NC (N = 241) had plasma collected at study entry. The clinical diagnosis groups had annual cognitive (Mini-Mental State Examination, MMSE) and functional (Clinical Dementia Rating Scale, CDR) measures. NC (N = 70), MCI (N = 75), and AD dementia (N = 50) cases from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were used as a validation cohort. Plasma p-tau181 was measured using the Quanterix SiMoA HD-X platform.

RESULTS

Plasma p-tau181 differentiated pathology-confirmed AD from NC with negative amyloid PET scans with an AUC of 0.93. A cut point of 3.44 pg/mL (maximum Youden Index) had a sensitivity of 0.77, specificity of 0.96. p-Tau181 values above the cut point were associated with the faster rate of decline in MMSE in AD dementia and MCI and a shorter time to a clinically significant functional decline in all groups. In a subset of MCI cases from ADNI, p-tau181 values above the cut point associated with faster rate of decline in MMSE, and a shorter time to a clinically significant functional decline and conversion to dementia.

INTERPRETATION

Plasma p-tau181 differentiates AD pathology cases from NC with high accuracy. Higher levels of plasma p-tau181 are associated with faster cognitive and functional decline.

Language patterns in Japanese patients with Alzheimer disease: A machine learning approach

AIM

The authors applied natural language processing and machine learning to explore the disease-related language patterns that warrant objective measures for assessing language ability in Japanese patients with Alzheimer disease (AD), while most previous studies have used large publicly available data sets in Euro-American languages.

METHODS

The authors obtained 276 speech samples from 42 patients with AD and 52 healthy controls, aged 50 years or older. A natural language processing library for Python was used, spaCy, with an add-on library, GiNZA, which is a Japanese parser based on Universal Dependencies designed to facilitate multilingual parser development. The authors used eXtreme Gradient Boosting for our classification algorithm. Each unit of part-of-speech and dependency was tagged and counted to create features such as tag-frequency and tag-to-tag transition-frequency. Each feature's importance was computed during the 100-fold repeated random subsampling validation and averaged.

RESULTS

The model resulted in an accuracy of 0.84 (SD = 0.06), and an area under the curve of 0.90 (SD = 0.03). Among the features that were important for such predictions, seven of the top 10 features were related to part-of-speech, while the remaining three were related to dependency. A box plot analysis demonstrated that the appearance rates of content words-related features were lower among the patients, whereas those with stagnation-related features were higher.

CONCLUSION

The current study demonstrated a promising level of accuracy for predicting AD and found the language patterns corresponding to the type of lexical-semantic decline known as 'empty speech', which is regarded as a characteristic of AD.

The impact of increasing levels of blood C-reactive protein on the inflammatory loci SPI1 and CD33 in Alzheimer's disease

Apolipoprotein ε4 (APOE ε4) is the most significant genetic risk factor for late-onset Alzheimer's disease (AD). Elevated blood C-reactive protein (CRP) further increases the risk of AD for people carrying the APOE ε4 allele. We hypothesized that CRP, as a key inflammatory element, could modulate the impact of other genetic variants on AD risk. We selected ten single nucleotide polymorphisms (SNPs) in reported AD risk loci encoding proteins related to inflammation. We then tested the interaction effects between these SNPs and blood CRP levels on AD incidence using the Cox proportional hazards model in UK Biobank (n = 279,176 white participants with 803 incident AD cases). The five top SNPs were tested for their interaction with different CRP cutoffs for AD incidence in the Framingham Heart Study (FHS) Generation 2 cohort (n = 3009, incident AD = 156). We found that for higher concentrations of serum CRP, the AD risk increased for SNP genotypes in 3 AD-associated genes (SPI1, CD33, and CLU). Using the Cox model in stratified genotype analysis, the hazard ratios (HRs) for the association between a higher CRP level (≥10 vs. <10 mg/L) and the risk of incident AD were 1.94 (95% CI: 1.33-2.84, p < 0.001) for the SPI1 rs1057233-AA genotype, 1.75 (95% CI: 1.20-2.55, p = 0.004) for the CD33 rs3865444-CC genotype, and 1.76 (95% CI: 1.25-2.48, p = 0.001) for the CLU rs9331896-C genotype. In contrast, these associations were not observed in the other genotypes of these genes. Finally, two SNPs were validated in 321 Alzheimer's Disease Neuroimaging (ADNI) Mild Cognitive Impairment (MCI) patients. We observed that the SPI1 and CD33 genotype effects were enhanced by elevated CRP levels for the risk of MCI to AD conversion. Furthermore, the SPI1 genotype was associated with CSF AD biomarkers, including t-Tau and p-Tau, in the ADNI cohort when the blood CRP level was increased (p < 0.01). Our findings suggest that elevated blood CRP, as a peripheral inflammatory biomarker, is an important moderator of the genetic effects of SPI1 and CD33 in addition to APOE ε4 on AD risk. Monitoring peripheral CRP levels may be helpful for precise intervention and prevention of AD for these genotype carriers.

Association of CSF GAP-43 and APOE ε4 with Cognition in Mild Cognitive Impairment and Alzheimer's Disease

The growth-associated protein 43 (GAP-43) is a presynaptic phosphoprotein in cerebrospinal fluid (CSF). The ε4 allele of apolipoprotein E (APOE) is an important genetic risk factor for Alzheimer's disease (AD). We aimed to evaluate the association of CSF GAP-43 with cognition and whether this correlation was related to the APOE ε4 status. We recruited participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, and they were divided into cognitively normal (CN) ε4 negative (CN ε4-), CN ε4 positive (CN ε4+), mild cognitive impairment (MCI) ε4 negative (MCI ε4-), MCI ε4 positive (MCI ε4+), AD ε4 negative (AD ε4-), and AD ε4 positive (AD ε4+) groups. Spearman's correlation was utilized to evaluate the relationship between CSF GAP-43 and core AD biomarkers at the baseline. We performed receiver-operating characteristic (ROC) curve analyses to investigate the diagnostic accuracy of CSF GAP-43. The correlations between CSF GAP-43 and the Mini-Mental State Examination (MMSE) scores and brain atrophy at baseline were assessed by using multiple linear regression, while the association between CSF GAP-43 and MMSE scores at the follow-up was tested by performing the generalized estimating equation (GEE). The role of CSF GAP-43 in the conversion from MCI to AD was evaluated using the Cox proportional hazard model. We found that the CSF GAP-43 level was significantly increased in MCI ε4+, AD ε4- and AD ε4+ groups compared with CN ε4- or MCI ε4- group. The negative associations between the CSF GAP-43 and MMSE scores at the baseline and follow-up were found in MCI ε4- and MCI ε4+ groups. In addition, baseline CSF GAP-43 was able to predict the clinical progression from MCI to AD. CSF GAP-43 may be a promising biomarker to screen cognition for AD. The effects of CSF GAP-43 on cognition were suspected to be relevant to APOE ε4 status.

Association of neurofilament light chain with renal function: mechanisms and clinical implications

BACKGROUND

Blood-based neurofilament light chain (NfL) is a promising biomarker of neurodegeneration across multiple neurodegenerative diseases. However, blood-based NfL is highly associated with renal function in older adults, which leads to the concern that blood-based NfL levels may be influenced by renal function, rather than neurodegeneration alone. Despite growing interest in using blood-based NfL as a biomarker of neurodegeneration in research and clinical practices, whether renal function should always be accounted for in these settings remains unclear. Moreover, the mechanisms underlying this association between blood-based measures of NfL and renal function remain elusive. In this study, we first evaluated the effect of renal function on the associations of plasma NfL with other measures of neurodegeneration. We then examined the extent of genetic and environmental contributions to the association between plasma NfL and renal function.

METHODS

In a sample of 393 adults (mean age=75.22 years, range=54-90), we examined the associations of plasma NfL with cerebrospinal fluid (CSF) NfL and brain volumetric measures before and after adjusting for levels of serum creatinine (an index of renal function). In an independent sample of 969 men (mean age=67.57 years, range=61-73) that include monozygotic and dizygotic twin pairs, we replicated the same analyses and leveraged biometrical twin modeling to examine the genetic and environmental influences on the plasma NfL and creatinine association.

RESULTS

Plasma NfL's associations with cerebrospinal fluid NfL and brain volumetric measures did not meaningfully change after adjusting for creatinine levels. Both plasma NfL and creatinine were significantly heritable (h2=0.54 and 0.60, respectively). Their phenotypic correlation (r=0.38) was moderately explained by shared genetic influences (genetic correlation=0.46) and unique environmental influences (unique environmental correlation=0.27).

CONCLUSIONS

Adjusting for renal function is unnecessary when assessing associations between plasma NfL and other measures of neurodegeneration but is necessary if plasma NfL is compared to a cutoff for classifying neurodegeneration-positive versus neurodegeneration-negative individuals. Blood-based measures of NfL and renal function are heritable and share common genetic influences.

Observational studies in Alzheimer disease: bridging preclinical studies and clinical trials

Recent high-profile failures of Alzheimer disease treatments at the clinical trial stage have led to renewed efforts to identify and test novel interventions for Alzheimer disease and related dementias (ADRD). In this Perspective, we highlight the importance of including well-designed observational studies as part of these efforts. Observational research is an important cornerstone for gathering evidence on risk factors and causes of ADRD; this evidence can then be combined with data from preclinical studies and randomized controlled trials to inform the development of effective interventions. Observational study designs can be particularly beneficial for hypothesis generation, posing questions that are unethical or impractical for a trial setting, studying life-course associations, research in populations typically not included in trials, and public health surveillance. Here, we discuss each of these situations in the specific context of ADRD research. We also highlight novel approaches to enhance causal inference and provide a timely discussion on how observational epidemiological studies help provide a bridge between preclinical studies and successful interventions for ADRD.

Altered functional connectivity pattern of hippocampal subfields in individuals with objectively-defined subtle cognitive decline and its association with cognition and cerebrospinal fluid biomarkers

Recent studies have shown that in the preclinical phase of Alzheimer's disease (AD), subtle cognitive changes can be detected using sensitive neuropsychological measures, and have proposed the concept of objectively-defined subtle cognitive decline (Obj-SCD). We aimed to assess the functional alteration of hippocampal subfields in individuals with Obj-SCD and its association with cognition and pathological biomarkers. Forty-two participants with cognitively normal (CN), 29 with Obj-SCD, and 55 with mild cognitive impairment (MCI) were retrospectively collected from the ADNI database. Neuropsychological performance, functional MRI, and cerebrospinal fluid (CSF) data were obtained. We calculated the seed-based functional connectivity (FC) of hippocampal subfields (cornu ammonis1 [CA1], CA2/3/dentate gyrus [DG], and subiculum) with whole-brain voxels. Additionally, we analyzed the correlation between FC values of significantly altered regions and neuropsychological performance and CSF biomarkers. The Obj-SCD group showed lower FC between left CA1-CA2/3/DG and right thalamus and higher FC between right subiculum and right superior parietal gyrus (SPG) compared with the CN and MCI groups. In the Obj-SCD group, FC values between left CA2/3/DG and right thalamus were positively associated with Auditory Verbal Learning Test (AVLT) recognition (r = 0.395, p = 0.046) and CSF Aβ1-42 levels (r = 0.466, p = 0.019), and FC values between left CA1 and right thalamus were positively correlated with CSF Aβ1-42 levels (r = 0.530, p = 0.006). Taken together, dysfunction in CA1-CA2/3/DG subregions suggests subtle cognitive impairment and AD-specific pathological changes in individuals with Obj-SCD. Additionally, increased subiculum connectivity may indicate early functional compensation for subtle cognitive changes.

Arterial Spin Labelling-Based Blood-Brain Barrier Assessment and Its Applications

The brain relies on the blood-brain barrier (BBB) for the selective absorption of nutrients and the exclusion of other big molecules from the circulating blood. Therefore, the integrity of BBB is critical to brain health, and assessing BBB condition is of great clinical importance. BBB is often examined using exogenous tracers that can travel across the BBB, but the tracers might cause severe side effects. To avoid the use of external tracers, researchers have used magnetically labeled arterial blood as the endogenous tracer to assess the water permeability of BBB as a surrogate index of BBB. This paper reviews the three major types of Arterial Spin Labelling (ASL) based BBB water permeability assessment techniques and their applications in brain diseases such as Alzheimer's Disease.

Multi-platform proteomic analysis of Alzheimer's disease cerebrospinal fluid and plasma reveals network biomarkers associated with proteostasis and the matrisome

Robust and accessible biomarkers that can capture the heterogeneity of Alzheimer's disease and its diverse pathological processes are urgently needed. Here, we undertook an investigation of Alzheimer's disease cerebrospinal fluid (CSF) and plasma from the same subjects (n=18 control, n=18 AD) using three different proteomic platforms-SomaLogic SomaScan, Olink proximity extension assay, and tandem mass tag-based mass spectrometry-to assess which protein markers in these two biofluids may serve as reliable biomarkers of AD pathophysiology observed from unbiased brain proteomics studies. Median correlation of overlapping protein measurements across platforms in CSF (r~0.7) and plasma (r~0.6) was good, with more variability in plasma. The SomaScan technology provided the most measurements in plasma. Surprisingly, many proteins altered in AD CSF were found to be altered in the opposite direction in plasma, including important members of AD brain co-expression modules. An exception was SMOC1, a key member of the brain matrisome module associated with amyloid-β deposition in AD, which was found to be elevated in both CSF and plasma. Protein co-expression analysis on greater than 7000 protein measurements in CSF and 9500 protein measurements in plasma across all proteomic platforms revealed strong changes in modules related to autophagy, ubiquitination, and sugar metabolism in CSF, and endocytosis and the matrisome in plasma. Cross-platform and cross-biofluid proteomics represents a promising approach for AD biomarker development.

CSF Biomarkers of Alzheimer Disease in Patients With Concomitant α-Synuclein Pathology

BACKGROUND AND OBJECTIVES

CSF biomarkers β-amyloid 1-42 (Aβ42), phosphorylated tau 181 (p-tau181), total tau (t-tau), and neurogranin (Ng) can diagnose Alzheimer disease (AD) in life. However, it is unknown whether CSF concentrations, and thus their accuracies, are affected by concomitant pathologies common in AD, such as α-synuclein (αSyn). Our primary goal was to test whether biomarkers in patients with AD are altered by concomitant αSyn. We compared CSF Aβ42, p-tau181, t-tau, and Ng levels across autopsy-confirmed AD and concomitant AD and αSyn (AD + αSyn). Antemortem CSF levels were related to postmortem accumulations of αSyn. Finally, we tested how concommitant AD + αSyn affected the diagnostic accuracy of 2 CSF-based strategies: the amyloid/tau/neurodegeneration (ATN) framework and the t-tau/Aβ42 ratio.

METHODS

Inclusion criteria were neuropathologic diagnoses of AD, mixed AD + αSyn, and αSyn. A convenience sample of nonimpaired controls was selected with available CSF and a Mini-Mental State Examination (MMSE) ≥ 27. αSyn without AD and controls were included as reference groups. Analyses of covariance (ANCOVAs) tested planned comparisons were CSF Aβ42, p-tau181, t-tau, and Ng differences across AD and AD + αSyn. Linear models tested how biomarkers were altered by αSyn accumulation in AD, accounting for pathologic β-amyloid and tau. Receiver operating characteristic and area under the curve (AUC), including 95% CI, evaluated diagnostic accuracy.

RESULTS

Participants were 61 patients with AD, 39 patients with mixed AD + αSyn, 20 patients with αSyn, and 61 controls. AD had similar median age (73 [interquartile range {IQR} = 12] years), MMSE (23 [IQR = 9]), and sex distribution (male = 49%) compared with AD + αSyn age (70 [IQR = 13] years; p = 0.3), MMSE (25 [IQR = 9.5]; p = 0.19), and sex distribution (male = 69%; p = 0.077). ANCOVAs showed that AD + αSyn had lower p-tau181 (F(1,94) = 17, p < 2.6e-16), t-tau (F(1,93) = 11, p = 0.0004), and Ng levels (F(1,50) = 12, p = 0.0004) than AD; there was no difference in Aβ42 (p = 0.44). Models showed increasing αSyn related to lower p-tau181 (β = -0.26, SE = 0.092, p = 0.0065), t-tau (β = -0.19, SE = 0.092, p = 0.041), and Ng levels (β = -0.2, SE = 0.066, p = 0.0046); αSyn was not a significant factor for Aβ42 (p = 1). T-tau/Aβ42 had the highest accuracy when detecting AD, including mixed AD + αSyn cases (AUC = 0.95; CI 0.92-0.98).

DISCUSSION

Findings demonstrate that concomitant αSyn pathology in AD is associated with lower CSF p-tau181, t-tau, and Ng levels and can affect diagnostic accuracy in patients with AD.

Quantitative systems pharmacology model of the amyloid pathway in Alzheimer's disease: Insights into the therapeutic mechanisms of clinical candidates

Despite considerable investment into potential therapeutic approaches for Alzheimer's disease (AD), currently approved treatment options are limited. Predictive modeling using quantitative systems pharmacology (QSP) can be used to guide the design of clinical trials in AD. This study developed a QSP model representing amyloid beta (Aβ) pathophysiology in AD. The model included mechanisms of Aβ monomer production and aggregation to form insoluble fibrils and plaques; the transport of soluble species between the compartments of brain, cerebrospinal fluid (CSF), and plasma; and the pharmacokinetics, transport, and binding of monoclonal antibodies to targets in the three compartments. Ordinary differential equations were used to describe these processes quantitatively. The model components were calibrated to data from the literature and internal studies, including quantitative data supporting the underlying AD biology and clinical data from clinical trials for anti-Aβ monoclonal antibodies (mAbs) aducanumab, crenezumab, gantenerumab, and solanezumab. The model was developed for an apolipoprotein E (APOE) ɛ4 allele carrier and tested for an APOE ɛ4 noncarrier. Results indicate that the model is consistent with data on clinical Aβ accumulation in untreated individuals and those treated with monoclonal antibodies, capturing increases in Aβ load accurately. This model may be used to investigate additional AD mechanisms and their impact on biomarkers, as well as predict Aβ load at different dose levels for mAbs with known targets and binding affinities. This model may facilitate the design of scientifically enriched and efficient clinical trials by enabling a priori prediction of biomarker dynamics in the brain and CSF.

SPARE-Tau: A flortaucipir machine-learning derived early predictor of cognitive decline

BACKGROUND

Recently, tau PET tracers have shown strong associations with clinical outcomes in individuals with cognitive impairment and cognitively unremarkable elderly individuals. flortaucipir PET scans to measure tau deposition in multiple brain areas as the disease progresses. This information needs to be summarized to evaluate disease severity and predict disease progression. We, therefore, sought to develop a machine learning-derived index, SPARE-Tau, which successfully detects pathology in the earliest disease stages and accurately predicts progression compared to a priori-based region of interest approaches (ROI).

METHODS

587 participants of the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort had flortaucipir scans, structural MRI scans, and an Aβ biomarker test (CSF or florbetapir PET) performed on the same visit. We derived the SPARE-Tau index in a subset of 367 participants. We evaluated associations with clinical measures for CSF p-tau, SPARE-MRI, and flortaucipir PET indices (SPARE-Tau, meta-temporal, and average Braak ROIs). Bootstrapped multivariate adaptive regression splines linear regression analyzed the association between the biomarkers and baseline ADAS-Cog13 scores. Bootstrapped multivariate linear regression models evaluated associations with clinical diagnosis. Cox-hazards and mixed-effects models investigated clinical progression and longitudinal ADAS-Cog13 changes. The Aβ positive cognitively unremarkable participants, not included in the SPARE-Tau training, served as an independent validation group.

RESULTS

Compared to CSF p-tau, meta-temporal, and averaged Braak tau PET ROIs, SPARE-Tau showed the strongest association with baseline ADAS-cog13 scores and diagnosis. SPARE-Tau also presented the strongest association with clinical progression in cognitively unremarkable participants and longitudinal ADAS-Cog13 changes. Results were confirmed in the Aβ+ cognitively unremarkable hold-out sample participants. CSF p-tau showed the weakest cross-sectional associations and longitudinal prediction.

DISCUSSION

Flortaucipir indices showed the strongest clinical association among the studied biomarkers (flortaucipir, florbetapir, structural MRI, and CSF p-tau) and were predictive in the preclinical disease stages. Among the flortaucipir indices, the machine-learning derived SPARE-Tau index was the most sensitive clinical progression biomarker. The combination of different biomarker modalities better predicted cognitive performance.

Sex differences in Alzheimer's disease: plasma MMP-9 and markers of disease severity

BACKGROUND

Studies have reported higher plasma matrix metalloproteinase-9 (MMP-9) levels in mild cognitive impairment (MCI) and Alzheimer's disease (AD). Despite evidence that MMP-9 activity and its influence on AD pathophysiology may be modulated by sex hormones, sex differences in the association between MMP-9 and AD biomarkers and cognition have not been explored.

METHODS

Our sample included 238 amyloid-β (Aβ)-positive participants with MCI or AD dementia from the Alzheimer's Disease Neuroimaging Initiative (37.4% women, 74.6 ± 7.3 years). We used linear regression models to examine whether sex modified free and total plasma MMP-9 associations with CSF t-tau, p-tau181, and Aβ₄₂. We used linear mixed effects models to examine whether sex modified total and free plasma MMP-9 associations with cognition, using longitudinal Mini-Mental Status Examination (MMSE) and Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-cog) data.

RESULTS

Total and free MMP-9 levels did not differ by sex, but AD dementia patients had higher total MMP-9 levels than participants with MCI (β = 0.06 [-0.11 to -0.01], p = 0.031). Sex modified the association of CSF t-tau with total (β = 128.68 [55.37 to 201.99], p < 0.001) and free MMP-9 (β = 98.61 [33.61 to 163.62], p = 0.003), whereby higher total and free MMP-9 correlated with higher CSF t-tau in women and lower CSF t-tau in men. Higher free MMP-9 correlated with lower CSF p-tau181 among men (β = -14.98 [-27.37 to -2.58], p = 0.018), but not women. In participants with MCI, higher free MMP-9 levels were associated with higher CSF Aβ₄₂ among men (β = 26.88 [4.03 to 49.73], p = 0.022) but not women. In the overall sample, higher free and total MMP-9 at baseline predicted worsening MMSE scores in women (β = -2.10 [-3.97 to -0.27], p = 0.027 and β = -2.24 [-4.32 to -0.18], p = 0.035) but not men. Higher free MMP-9 correlated with worse ADAS-cog scores (β = 12.34 [3.02 to 21.65], p = 0.011) in women (β = 12.34 [3.02 to 21.65], p = 0.011) but not men with AD dementia cross-sectionally but correlated with worsening ADAS-cog scores longitudinally only in men (β = 8.98 [0.27 to 17.68], p = 0.042).

CONCLUSIONS

MMP-9 may have more detrimental effects on AD-related pathological and cognitive changes in women. If replicated, our findings could help uncover potential mechanisms contributing to women's elevated susceptibility to AD.

Exploration of salient risk factors involved in mild cognitive impairment

Mild cognitive impairment (MCI) is a prevalent and complex condition among older adults that often progresses into Alzheimer's disease (AD). Although MCI affects individuals differently, there are specific indicators of risk commonly associated with the development of MCI. The present study explored the prevalence of seven established MCI risk categories within a large sample of older adults with and without MCI. We explored trends across the different diagnostic groups and extracted the most salient risk factors related to MCI using partial least squares. Neuropsychological risk categories showed the largest differences across groups, with the cognitively unimpaired groups outperforming the MCI groups on all measures. Apolipoprotein E4 (ApoE4) carriers were significantly more common among the more severe MCI group, whereas ApoE4 non-carriers were more common in the healthy controls. Participants with subjective and objective cognitive impairment were trending towards AD-like cerebral spinal fluid (CSF) biomarker levels. Increased age, being male and having fewer years of education were identified as important risk factors of MCI. Higher CSF tau levels were correlated with ApoE4 carrier status, age and a decrease in the ability to carry out daily activities across all diagnostic groups. Amyloid beta_1-42 CSF concentration was positively correlated with cognitive and memory performance and non-ApoE4 carrier status regardless of diagnostic status. Unlike previous research, poor cardiovascular health or being female had no relation to MCI. Altogether, the results highlighted risk factors that were specific to persons with MCI, findings that will inform future research in healthy aging, MCI and AD.

Elevated Plasma Phosphorylated Tau 181 in Amyotrophic Lateral Sclerosis

OBJECTIVE

Plasma phosphorylated tau (p-tau181 ) is reliably elevated in Alzheimer's disease (AD), but less explored is its specificity relative to other neurodegenerative conditions. Here, we find novel evidence that plasma p-tau181 is elevated in amyotrophic lateral sclerosis (ALS), a neurodegenerative condition typically lacking tau pathology. We performed a detailed evaluation to identify the clinical correlates of elevated p-tau181 in ALS.

METHODS

Patients were clinically or pathologically diagnosed with ALS (n = 130) or AD (n = 79), or were healthy non-impaired controls (n = 26). Receiver operating characteristic (ROC) curves were analyzed and area under the curve (AUC) was used to discriminate AD from ALS. Within ALS, Mann-Whitney-Wilcoxon tests compared analytes by presence/absence of upper motor neuron and lower motor neuron (LMN) signs. Spearman correlations tested associations between plasma p-tau181 and postmortem neuron loss.

RESULTS

A Wilcoxon test showed plasma p-tau181 was higher in ALS than controls (W = 2,600, p = 0.000015), and ROC analyses showed plasma p-tau181 poorly discriminated AD and ALS (AUC = 0.60). In ALS, elevated plasma p-tau181 was associated with LMN signs in cervical (W = 827, p = 0.0072), thoracic (W = 469, p = 0.00025), and lumbosacral regions (W = 851, p = 0.0000029). In support of LMN findings, plasma p-tau181 was associated with neuron loss in the spinal cord (rho = 0.46, p = 0.017), but not in the motor cortex (p = 0.41). Cerebrospinal spinal fluid p-tau181 and plasma neurofilament light chain were included as reference analytes, and demonstrate specificity of findings.

INTERPRETATION

We found strong evidence that plasma p-tau181 is elevated in ALS and may be a novel marker specific to LMN dysfunction. ANN NEUROL 2022;92:807-818.

Multi-task longitudinal forecasting with missing values on Alzheimer's disease

BACKGROUND AND OBJECTIVE

Machine learning techniques typically used in dementia assessment are not able to learn multiple tasks jointly and deal with time-dependent heterogeneous data containing missing values. In this paper, we reformulate SSHIBA, a recently introduced Bayesian multi-view latent variable model, for jointly learning diagnosis, ventricle volume, and ADAS score in dementia on longitudinal data with missing values.

METHODS

We propose a novel Bayesian Variational inference framework capable of simultaneously imputing missing values and combining information from several views. This way, we can combine different data views from different time-points in a common latent space and learn the relationships between each time-point, using the semi-supervised formulation to fully exploit the temporal structure of the data and handle missing values. In turn, the model can combine all the available information to simultaneously model and predict multiple output variables.

RESULTS

We applied the proposed model to jointly predict diagnosis, ventricle volume, and ADAS score in dementia. The comparison of imputation strategies demonstrated the superior performance of the semi-supervised formulation of the model, improving the best baseline methods. Moreover, the performance in simultaneous prediction of diagnosis, ventricle volume, and ADAS score led to an improved prediction performance over the best baseline method.

CONCLUSIONS

The results demonstrate that the proposed SSHIBA framework can learn an excellent imputation of the missing values and outperforming the baselines while simultaneously predicting three different tasks.

CSF proteome profiling across the Alzheimer's disease spectrum reflects the multifactorial nature of the disease and identifies specific biomarker panels

Development of disease-modifying therapies against Alzheimer's disease (AD) requires biomarkers reflecting the diverse pathological pathways specific for AD. We measured 665 proteins in 797 cerebrospinal fluid (CSF) samples from patients with mild cognitive impairment with abnormal amyloid (MCI(Aβ+): n = 50), AD-dementia (n = 230), non-AD dementias (n = 322) and cognitively unimpaired controls (n = 195) using proximity ligation-based immunoassays. Here we identified >100 CSF proteins dysregulated in MCI(Aβ+) or AD compared to controls or non-AD dementias. Proteins dysregulated in MCI(Aβ+) were primarily related to protein catabolism, energy metabolism and oxidative stress, whereas those specifically dysregulated in AD dementia were related to cell remodeling, vascular function and immune system. Classification modeling unveiled biomarker panels discriminating clinical groups with high accuracies (area under the curve (AUC): 0.85-0.99), which were translated into custom multiplex assays and validated in external and independent cohorts (AUC: 0.8-0.99). Overall, this study provides novel pathophysiological leads delineating the multifactorial nature of AD and potential biomarker tools for diagnostic settings or clinical trials.

Safety and biomarker effects of candesartan in non-hypertensive adults with prodromal Alzheimer's disease

Observational studies suggest that angiotensin receptor blockers in hypertensive adults are associated with lower post-mortem indicators of Alzheimer's disease pathology. Candesartan, an angiotensin receptor blocker, has a positive cognitive effect in mild cognitive impairment with hypertension. However, its safety and effects in non-hypertensive individuals with Alzheimer's disease are unclear. This is the first double-blind randomized placebo-controlled trial aimed to assess safety and effects of 1-year therapy of candesartan on biomarkers and clinical indicators of Alzheimer's disease in non-hypertensive individuals with biomarker-confirmed prodromal Alzheimer's disease. Seventy-seven non-hypertensive participants 50 years or older (mean age: 68.1 years; 62% women; 20% African American) with mild cognitive impairment and biomarker confirmed Alzheimer's disease were randomized to escalating doses of once daily oral candesartan (up to 32 mg) or matched placebo. Main outcomes included safety and tolerability of candesartan, cerebrospinal fluid biomarkers (amyloid-β42, amyloid-β40, total tau and phospho-tau). Additional exploratory outcomes included PET imaging (Pittsburgh Compound-B (11C-PiB) and 18F-flortaucipir), brain MRI (structural and connectivity measures) and cognitive functioning. Analyses used intention-to-treat approach with group comparisons of safety measures using Chi-square test, and repeated measures mixed effects models were used to assess candesartan effects on main and exploratory outcomes (ClinicalTrials.gov, NCT02646982). Candesartan was found to be safe with no significant difference in safety measures: symptoms of hypotension, renal failure or hyperkalemia. Candesartan was also found to be associated with increases in cerebrospinal fluid Aβ40 (between-group mean difference: 1211.95 pg/ml, 95% confidence interval: 313.27, 2110.63) and Aβ42 (49.51 pg/ml, 95% confidence interval: -98.05, -0.98) reflecting lower brain amyloid accumulation. Candesartan was associated with decreased 11C-PiB in the parahippocampal region (-0.1104, 95% confidence interval: -0.19, -0.029) which remained significant after false discovery rate correction, and with an increase in functional network connectivity in the subcortical networks. Candesartan was further associated with improved executive function (Trail Making Test Part B) performance (-11.41 s, 95% confidence interval: -11.94, -10.89) and trended for an improved global cognitive functioning reflected by a composite cognitive score (0.002, 95% confidence interval: -0.0002, 0.005). We did not observe significant effects on tau levels, hippocampal volume or other cognitive measures (memory or clinical dementia rating scale-sum of boxes). In conclusion, among non-hypertensive prodromal Alzheimer's disease, candesartan is safe and likely decreases brain amyloid biomarkers, enhances subcortical brain connectivity and has favourable cognitive effects. These findings suggest that candesartan may have an important therapeutic role in Alzheimer's disease, and warrant further investigation given the lack of clear treatment options for this devastating illness.

Heart fatty acid-binding protein is associated with phosphorylated tau and longitudinal cognitive changes

Background

Perturbation of lipid metabolism is associated with Alzheimer’s disease (AD). Heart fatty acid-binding protein (HFABP) is an adipokine playing an important role in lipid metabolism regulation.

Materials and methods

Two datasets separately enrolled 303 and 197 participants. First, we examine the associations of cerebrospinal fluid (CSF) HFABP levels with cognitive measures [including Mini-Mental State Examination (MMSE), Clinical Dementia Rating sum of boxes (CDRSB), and the cognitive section of Alzheimer’s Disease Assessment Scale] and AD biomarkers (CSF amyloid beta and tau levels). Second, we examine the longitudinal associations of baseline CSF HFABP levels and the variability of HFABP with cognitive measures and AD biomarkers. Structural equation models explored the mediation effects of AD pathologies on cognition.

Results

We found a significant relationship between CSF HFABP level and P-tau (dataset 1: β = 2.04, p &lt; 0.001; dataset 2: β = 1.51, p &lt; 0.001). We found significant associations of CSF HFABP with longitudinal cognitive measures (dataset 1: ADAS13, β = 0.09, p = 0.008; CDRSB, β = 0.10, p = 0.003; MMSE, β = −0.15, p &lt; 0.001; dataset 2: ADAS13, β = 0.07, p = 0.004; CDRSB, β = 0.07, p = 0.005; MMSE, β = −0.09, p &lt; 0.001) in longitudinal analysis. The variability of HFABP was associated with CSF P-tau (dataset 2: β = 3.62, p = 0.003). Structural equation modeling indicated that tau pathology mediated the relationship between HFABP and cognition.

Conclusion

Our findings demonstrated that HFABP was significantly associated with longitudinal cognitive changes, which might be partially mediated by tau pathology.

Biological correlates of elevated soluble TREM2 in cerebrospinal fluid

Cerebrospinal fluid (CSF) soluble triggering receptor expressed on myeloid cells-2 (sTREM2) is an emerging biomarker of neuroinflammation in Alzheimer's disease (AD). Yet, sTREM2 expression has not been systematically evaluated in relation to concomitant drivers of neuroinflammation. While associations between sTREM2 and tau in CSF are established, we sought to determine additional biological correlates of CSF sTREM2 during the prodromal stages of AD by evaluating CSF Aβ species (Aβ_x-40), a fluid biomarker of blood-brain barrier integrity (CSF/plasma albumin ratio), and CSF biomarkers of neurodegeneration measured in 155 participants from the Vanderbilt Memory and Aging Project. A novel association between high CSF levels of both sTREM2 and Aβ_x-40 was observed and replicated in an independent dataset. Aβ_x-40 levels, as well as the CSF/plasma albumin ratio, explained additional and unique variance in sTREM2 levels above and beyond that of CSF biomarkers of neurodegeneration. The component of sTREM2 levels correlated with Aβ_x-40 levels best predicted future cognitive performance. We highlight potential contributions of Aβ homeostasis and blood-brain barrier integrity to elevated CSF sTREM2, underscoring novel biomarker associations relevant to disease progression and clinical outcome measures.

Higher baseline levels of CSF inflammation increase risk of incident mild cognitive impairment and Alzheimer's disease dementia

Introduction

Few studies have investigated how neuroinflammation early in the disease course may affect Alzheimer's disease (AD) progression over time despite evidence that neuroinflammation is associated with AD.

Methods

Research participants with cerebrospinal fluid (CSF) biomarkers from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were included in this study. Cox models were used to investigate whether baseline CSF neuroinflammation was associated with incident mild cognitive impairment (MCI) or AD. Moderating effects of sex and apolipoprotein E (APOE) ε4 were also examined.

Results

Elevated levels of tumor necrosis factor α (TNF-α), interleukin (IL)-9, and IL-12p40 at baseline were associated with higher rates of conversion to MCI/AD. Interactions with sex and APOE ε4 were observed, such that women with elevated TNF-α and all APOE ε4 carriers with elevated IL-9 levels had shorter times to conversion. In addition, TNF-α mediated the relationship between elevated IL-12p40 and IL-9.

Discussion

Elevated neuroinflammation markers are associated with incident MCI/AD, and the factors of sex and APOE ε4 status modify the time to conversion.

Osteoporosis is associated with elevated baseline cerebrospinal fluid biomarkers and accelerated brain structural atrophy among older people

Objective

The aim of this study was to examine whether osteoporosis (OP) is associated with Alzheimer’s disease-related cerebrospinal fluid (CSF) biomarkers and brain structures among older people.

Methods

From the Alzheimer’s disease Neuroimaging Initiative database, we grouped participants according to the OP status (OP+/OP−) and compared the Alzheimer’s disease (AD)-related CSF biomarker levels and the regional brain structural volumes between the two groups using multivariable models. These models were adjusted for covariates including age, education, gender, diagnosis of Alzheimer’s disease, and apolipoprotein E4 carrier status.

Results

In the cross-sectional analyses at baseline, OP was related to higher CSF t-tau (total tau) and p-tau₁₈₁ (tau phosphorylated at threonine-181) but not to CSF amyloid-beta (1–42) or the volumes of entorhinal cortex and hippocampus. In the longitudinal analyses, OP was not associated with the change in the three CSF biomarkers over time but was linked to a faster decline in the size of the entorhinal cortex and hippocampus.

Conclusion

OP was associated with elevated levels of CSF t-tau and p-tau₁₈₁ at baseline, and accelerated entorhinal cortex and hippocampal atrophies over time among older people.

The Feasibility of Early Alzheimer’s Disease Diagnosis Using a Neural Network Hybrid Platform

Early diagnosis of Alzheimer’s Disease (AD) is critical for disease prevention and cure. However, currently, techniques with the required high sensitivity and specificity are lacking. Recently, with the advances and increased accessibility of data analysis tools, such as machine learning, research efforts have increasingly focused on using these computational methods to solve this challenge. Here, we demonstrate a convolutional neural network (CNN)-based AD diagnosis approach using the surface-enhanced Raman spectroscopy (SERS) fingerprints of human cerebrospinal fluid (CSF). SERS and CNN were combined for biomarker detection to analyze disease-associated biochemical changes in the CSF. We achieved very high reproducibility in double-blind experiments for testing the feasibility of our system on human samples. We achieved an overall accuracy of 92% (100% for normal individuals and 88.9% for AD individuals) based on the clinical diagnosis. Further, we observed an excellent correlation coefficient between our test score and the Clinical Dementia Rating (CDR) score. Our findings offer a substantial indication of the feasibility of detecting AD biomarkers using the innovative combination of SERS and machine learning. We are hoping that this will serve as an incentive for future research in the field.

Association of rs2072446 in the NGFR gene with the risk of Alzheimer's disease and amyloid-β deposition in the brain

INTRODUCTION AND AIMS

Alzheimer's disease (AD) is the most common form of dementia with a complex genetic background. The cause of sporadic AD (sAD) remains largely unknown. Increasing evidence shows that genetic variations play a crucial role in sAD. P75 neurotrophin receptor (p75NTR, encoded by NGFR) plays a critical role in the pathogenesis of AD. Yet, the relationship between NGFR gene polymorphisms and AD was less studied. This study aims to analyze the relationship of NGFR gene polymorphism with the risk of AD in the Chinese Han population and amyloid-β deposition in the ADNI cohort.

METHODS

This case-control association study was conducted in a Chinese Han cohort consisting of 366 sporadic AD (sAD) patients and 390 age- and sex-matched controls. Twelve tag-SNPs were selected and genotyped with a multiplex polymerase chain reaction-ligase detection reaction (PCR-LDR) method. The associations between tag-SNPs and the risk of AD were analyzed by logistic regression. Moreover, another cohort from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database was included to examine the association of one tag-SNP (rs2072446) with indicators of amyloid deposition. Kaplan-Meier survival analysis and Cox proportional hazards models were used to test the predictive abilities of rs2072446 genotypes for AD progression. The mediation effects of Aβ deposition on this association were subsequently tested by mediation analyses.

RESULTS

After multiple testing corrections, one tag-SNP, rs2072446, was associated with an increased risk of sAD (additive model, OR = 1.79, P_adjustment  = 0.0144). Analyses of the ADNI cohort showed that the minor allele (T) of rs2072446 was significantly associated with the heavier Aβ burden, which further contributed to an increased risk of AD progression in APOE ε4 non-carrier.

CONCLUSION

Our study found that rs2072446 in NGFR is associated with both the risk of sAD in the Chinese Han population and the amyloid burden in the ADNI cohort, which reveals the role of p75NTR in AD from a genetic perspective.

Stage-dependent differential influence of metabolic and structural networks on memory across Alzheimer's disease continuum

Background

Large-scale neuronal network breakdown underlies memory impairment in Alzheimer's disease (AD). However, the differential trajectories of the relationships between network organisation and memory across pathology and cognitive stages in AD remain elusive. We determined whether and how the influences of individual-level structural and metabolic covariance network integrity on memory varied with amyloid pathology across clinical stages without assuming a constant relationship.

Methods

Seven hundred and eight participants from the Alzheimer's Disease Neuroimaging Initiative were studied. Individual-level structural and metabolic covariance scores in higher-level cognitive and hippocampal networks were derived from magnetic resonance imaging and [18F] fluorodeoxyglucose positron emission tomography using seed-based partial least square analyses. The non-linear associations between network scores and memory across cognitive stages in each pathology group were examined using sparse varying coefficient modelling.

Results

We showed that the associations of memory with structural and metabolic networks in the hippocampal and default mode regions exhibited pathology-dependent differential trajectories across cognitive stages using sparse varying coefficient modelling. In amyloid pathology group, there was an early influence of hippocampal structural network deterioration on memory impairment in the preclinical stage, and a biphasic influence of the angular gyrus-seeded default mode metabolic network on memory in both preclinical and dementia stages. In non-amyloid pathology groups, in contrast, the trajectory of the hippocampus-memory association was opposite and weaker overall, while no metabolism covariance networks were related to memory. Key findings were replicated in a larger cohort of 1280 participants.

Conclusions

Our findings highlight potential windows of early intervention targeting network breakdown at the preclinical AD stage.

Funding

Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). We also acknowledge the funding support from the Duke NUS/Khoo Bridge Funding Award (KBrFA/2019-0020) and NMRC Open Fund Large Collaborative Grant (OFLCG09May0035).

Assessing mild cognitive impairment using object-location memory in immersive virtual environments

Pathological changes in the medial temporal lobe (MTL) are found in the early stages of Alzheimer's disease (AD) and aging. The earliest pathological accumulation of tau colocalizes with the areas of the MTL involved in object processing as part of a wider anterolateral network. Here, we sought to assess the diagnostic potential of memory for object locations in iVR environments in individuals at high risk of AD dementia (amnestic mild cognitive impairment [aMCI] n = 23) as compared to age-related cognitive decline. Consistent with our primary hypothesis that early AD would be associated with impaired object location, aMCI patients exhibited impaired spatial feature binding. Compared to both older (n = 24) and younger (n = 53) controls, aMCI patients, recalled object locations with significantly less accuracy (p < .001), with a trend toward an impaired identification of the object's correct context (p = .05). Importantly, these findings were not explained by deficits in object recognition (p = .6). These deficits differentiated aMCI from controls with greater accuracy (AUC = 0.89) than the standard neuropsychological tests. Within the aMCI group, 16 had CSF biomarkers indicative of their likely AD status (MCI+ n = 9 vs. MCI- n = 7). MCI+ showed lower accuracy in the object-context association than MCI- (p = .03) suggesting a selective deficit in object-context binding postulated to be associated with anterior-temporal areas. MRI volumetric analysis across healthy older participants and aMCI revealed that test performance positively correlates with lateral entorhinal cortex volumes (p < .05) and hippocampus volumes (p < .01), consistent with their hypothesized role in binding contextual and spatial information with object identity. Our results indicate that tests relying on the anterolateral object processing stream, and in particular requiring successful binding of an object with spatial information, may aid detection of pre-dementia AD due to the underlying early spread of tau pathology.

Predicting conversion from mild cognitive impairment to Alzheimer's disease with multimodal latent factors

INTRODUCTION

We studied the ability of latent factor scores to predict conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) and investigated whether multimodal factor scores improve predictive power, relative to single-modal factor scores.

METHOD

We conducted exploratory factor analyses (EFAs) and confirmatory factor analyses (CFAs) of the baseline data of MCI subjects in the Alzheimer's Disease Neuroimaging Initiative (ADNI) to generate factor scores for three data modalities: neuropsychological (NP), magnetic resonance imaging (MRI), and cerebrospinal fluid (CSF). Factor scores from single or multiple modalities were entered in logistic regression models to predict MCI to AD conversion for 160 ADNI subjects over a 2-year interval.

RESULTS

NP factors attained an area under the curve (AUC) of .80, with a sensitivity of .66 and a specificity of .77. MRI factors reached a comparable level of performance (AUC = .80, sensitivity = .66, specificity = .78), whereas CSF factors produced weaker prediction (AUC = .70, sensitivity = .56, specificity = .79). Combining NP factors with MRI or CSF factors produced better prediction than either MRI or CSF factors alone. Similarly, adding MRI factors to NP or CSF factors produced improvements in prediction relative to NP or CSF factors alone. However, adding CSF factors to either NP or MRI factors produced no improvement in prediction.

CONCLUSIONS

Latent factor scores provided good accuracy for predicting MCI to AD conversion. Adding NP or MRI factors to factors from other modalities enhanced predictive power but adding CSF factors did not.

Sex differences in risk factors that predict progression from mild cognitive impairment to Alzheimer's dementia

OBJECTIVES

To evaluate whether cerebrospinal fluid biomarkers, apolipoprotein e4, neuroimaging abnormalities, and neuropsychological data differentially predict progression from mild cognitive impairment (MCI) to dementia for men and women.

METHODS

Participants who were diagnosed with MCI at baseline (n = 449) were classified as either progressing to Alzheimer's dementia at follow-up or as not progressing. Men and women were first compared using bivariate analyses. Sex-stratified Cox proportional hazard regressions were performed examining the relationship between baseline data and the likelihood of progressing to dementia. Sex interactions were subsequently examined.

RESULTS

Cox proportional hazard regression controlling for age and education indicated that all variables significantly predicted subsequent progression to dementia for men and women. Sex interactions indicated that only Rey Auditory Verbal Learning Test (RAVLT) delayed recall and Functional Activities Questionnaire (FAQ) were significantly stronger risk factors for women. When all variables were entered into a fully adjusted model, significant risk factors for women were Aβ42, hippocampal volume, RAVLT delayed recall, Boston Naming Test, and FAQ. In contrast, for men, Aβ42, p-tau181, p-tau181/Aβ42, hippocampal volume, category fluency and FAQ were significant risk factors. Interactions with sex were only significant for p-tau181/Aβ42 and RAVLT delayed recall for the fully adjusted model.

CONCLUSIONS

Men and women with MCI may to differ for which factors predict subsequent dementia although future analyses with greater power are needed to evaluate sex differences. We hypothesize that brain and cognitive reserve theories may partially explain these findings.

Proteomic Discovery and Validation of Novel Fluid Biomarkers for Improved Patient Selection and Prediction of Clinical Outcomes in Alzheimer’s Disease Patient Cohorts

Alzheimer’s disease (AD) is an irreversible neurodegenerative disease characterized by progressive cognitive decline. The two cardinal neuropathological hallmarks of AD include the buildup of cerebral β amyloid (Aβ) plaques and neurofibrillary tangles of hyperphosphorylated tau. The current disease-modifying treatments are still not effective enough to lower the rate of cognitive decline. There is an urgent need to identify early detection and disease progression biomarkers that can facilitate AD drug development. The current established readouts based on the expression levels of amyloid beta, tau, and phospho-tau have shown many discrepancies in patient samples when linked to disease progression. There is an urgent need to identify diagnostic and disease progression biomarkers from blood, cerebrospinal fluid (CSF), or other biofluids that can facilitate the early detection of the disease and provide pharmacodynamic readouts for new drugs being tested in clinical trials. Advances in proteomic approaches using state-of-the-art mass spectrometry are now being increasingly applied to study AD disease mechanisms and identify drug targets and novel disease biomarkers. In this report, we describe the application of quantitative proteomic approaches for understanding AD pathophysiology, summarize the current knowledge gained from proteomic investigations of AD, and discuss the development and validation of new predictive and diagnostic disease biomarkers.

Recent advances in pre-clinical diagnosis of Alzheimer's disease

Alzheimer's disease (AD) is the most common dementia with currently no known cures or disease modifying treatments (DMTs), despite much time and effort from the field. Diagnosis and intervention of AD during the early pre-symptomatic phase of the disease is thought to be a more effective strategy. Therefore, the detection of biomarkers has emerged as a critical tool for monitoring the effect of new AD therapies, as well as identifying patients most likely to respond to treatment. The establishment of the amyloid/tau/neurodegeneration (A/T/N) framework in 2018 has codified the contexts of use of AD biomarkers in neuroimaging and bodily fluids for research and diagnostic purposes. Furthermore, a renewed drive for novel AD biomarkers and innovative methods of detection has emerged with the goals of adding additional insight to disease progression and discovery of new therapeutic targets. The use of biomarkers has accelerated the development of AD drugs and will bring new therapies to patients in need. This review highlights recent methods utilized to diagnose antemortem AD.

Prognosis of Alzheimer's Disease Using Quantitative Mass Spectrometry of Human Blood Plasma Proteins and Machine Learning

Early recognition of the risk of Alzheimer's disease (AD) onset is a global challenge that requires the development of reliable and affordable screening methods for wide-scale application. Proteomic studies of blood plasma are of particular relevance; however, the currently proposed differentiating markers are poorly consistent. The targeted quantitative multiple reaction monitoring (MRM) assay of the reported candidate biomarkers (CBs) can contribute to the creation of a consistent marker panel. An MRM-MS analysis of 149 nondepleted EDTA-plasma samples (MHRC, Russia) of patients with AD (n = 47), mild cognitive impairment (MCI, n = 36), vascular dementia (n = 8), frontotemporal dementia (n = 15), and an elderly control group (n = 43) was performed using the BAK 125 kit (MRM Proteomics Inc., Canada). Statistical analysis revealed a significant decrease in the levels of afamin, apolipoprotein E, biotinidase, and serum paraoxonase/arylesterase 1 associated with AD. Different training algorithms for machine learning were performed to identify the protein panels and build corresponding classifiers for the AD prognosis. Machine learning revealed 31 proteins that are important for AD differentiation and mostly include reported earlier CBs. The best-performing classifiers reached 80% accuracy, 79.4% sensitivity and 83.6% specificity and were able to assess the risk of developing AD over the next 3 years for patients with MCI. Overall, this study demonstrates the high potential of the MRM approach combined with machine learning to confirm the significance of previously identified CBs and to propose consistent protein marker panels.

A Systematic Review and Meta-Analysis of Cerebrospinal Fluid Amyloid and Tau Levels Identifies Mild Cognitive Impairment Patients Progressing to Alzheimer's Disease

Reported levels of amyloid-beta and tau in human cerebrospinal fluid (CSF) were evaluated to discover if these biochemical markers can predict the transition from Mild Cognitive Impairment (MCI) to Alzheimer’s disease (AD). A systematic review of the literature in PubMed and Web of Science (April 2021) was performed by a single researcher to identify studies reporting immunologically-based (xMAP or ELISA) measures of CSF analytes Aβ(1-42) and/or P-tau and/or T-tau in clinical studies with at least two timepoints and a statement of diagnostic criteria. Of 1137 screened publications, 22 met the inclusion criteria for CSF Aβ(1-42) measures, 20 studies included T-tau, and 17 included P-tau. Six meta-analyses were conducted to compare the analytes for healthy controls (HC) versus progressive MCI (MCI_AD) and for non-progressive MCI (Stable_MCI) versus MCI_AD; effect sizes were determined using random effects models. The heterogeneity of effect sizes across studies was confirmed with very high significance (p < 0.0001) for all meta-analyses except HC versus MCI_AD T-tau (p < 0.05) and P-tau (non-significant). Standard mean difference (SMD) was highly significant (p < 0.0001) for all comparisons (Stable_MCI versus MCI_AD: SMD [95%-CI] Aβ(1-42) = 1.19 [0.96,1.42]; T-tau = −1.03 [−1.24,−0.82]; P-tau = −1.03 [−1.47,−0.59]; HC versus MCI_AD: SMD Aβ(1-42) = 1.73 [1.39,2.07]; T-tau = −1.13 [−1.33,−0.93]; P-tau = −1.10 [−1.23,−0.96]). The follow-up interval in longitudinal evaluations was a critical factor in clinical study design, and the Aβ(1−42)/P-tau ratio most robustly differentiated progressive from non-progressive MCI. The value of amyloid-beta and tau as markers of patient outcome are supported by these findings.

Measurement of neurodegeneration using a multivariate early frame amyloid PET classifier

Introduction

Amyloid measurement provides important confirmation of pathology for Alzheimer's disease (AD) clinical trials. However, many amyloid positive (Am+) early-stage subjects do not worsen clinically during a clinical trial, and a neurodegenerative measure predictive of decline could provide critical information. Studies have shown correspondence between perfusion measured by early amyloid frames post-tracer injection and fluorodeoxyglucose (FDG) positron emission tomography (PET), but with limitations in sensitivity. Multivariate machine learning approaches may offer a more sensitive means for detection of disease related changes as we have demonstrated with FDG.

Methods

Using summed dynamic florbetapir image frames acquired during the first 6 minutes post-injection for 107 Alzheimer's Disease Neuroimaging Initiative subjects, we applied optimized machine learning to develop and test image classifiers aimed at measuring AD progression. Early frame amyloid (EFA) classification was compared to that of an independently developed FDG PET AD progression classifier by scoring the FDG scans of the same subjects at the same time point. Score distributions and correlation with clinical endpoints were compared to those obtained from FDG. Region of interest measures were compared between EFA and FDG to further understand discrimination performance.

Results

The EFA classifier produced a primary pattern similar to that of the FDG classifier whose expression correlated highly with the FDG pattern (R-squared 0.71), discriminated cognitively normal (NL) amyloid negative (Am-) subjects from all Am+ groups, and that correlated in Am+ subjects with Mini-Mental State Examination, Clinical Dementia Rating Sum of Boxes, and Alzheimer's Disease Assessment Scale-13-item Cognitive subscale (R = 0.59, 0.63, 0.73) and with subsequent 24-month changes in these measures (R = 0.67, 0.73, 0.50).

Discussion

Our results support the ability to use EFA with a multivariate machine learning-derived classifier to obtain a sensitive measure of AD-related loss in neuronal function that correlates with FDG PET in preclinical and early prodromal stages as well as in late mild cognitive impairment and dementia.

Highlights

The summed initial post-injection minutes of florbetapir positron emission tomography  correlate with fluorodeoxyglucose.A machine learning classifier enabled sensitive detection of early prodromal Alzheimer's disease.Early frame amyloid (EFA) classifier scores correlate with subsequent change in Mini-Mental State Examination, Clinical Dementia Rating Sum of Boxes, and Alzheimer's Disease Assessment Scale-13-item Cognitive subscale.EFA classifier effect sizes and clinical prediction outperformed region of interest standardized uptake value ratio.EFA classification may aid in stratifying patients to assess treatment effect.

Upregulated Blood miR-150-5p in Alzheimer’s Disease Dementia Is Associated with Cognition, Cerebrospinal Fluid Amyloid-β, and Cerebral Atrophy

Background: There is an urgent need for noninvasive, cost-effective biomarkers for Alzheimer’s disease (AD), such as blood-based biomarkers. They will not only support the clinical diagnosis of dementia but also allow for timely pharmacological and nonpharmacological interventions and evaluations. Objective: To identify and validate a novel blood-based microRNA biomarker for dementia of the Alzheimer’s type (DAT). Methods: We conducted microRNA sequencing using peripheral blood mononuclear cells isolated from a discovery cohort and validated the identified miRNAs in an independent cohort and AD postmortem tissues. miRNA correlations with AD pathology and AD clinical-radiological imaging were conducted. We also performed bioinformatics and cell-based assay to identify miRNA target genes. Results: We found that miR-150-5p expression was significantly upregulated in DAT compared to mild cognitive impairment and healthy subjects. Upregulation of miR-150-5p was observed in AD hippocampus. We further found that higher miR-150-5p levels were correlated with the clinical measures of DAT, including lower global cognitive scores, lower CSF Aβ 42, and higher CSF total tau. Interestingly, we observed that higher miR-150-5p levels were associated with MRI brain volumes within the default mode and executive control networks, two key networks implicated in AD. Furthermore, pathway analysis identified the targets of miR-150-5p to be enriched in the Wnt signaling pathway, including programmed cell death 4 (PDCD4). We found that PDCD4 was downregulated in DAT blood and was downregulated by miR-150-5p at both the transcriptional and protein levels Conclusion: Our findings demonstrated that miR-150-5p is a promising clinical blood-based biomarker for DAT

Non-tremor motor dysfunction in Lewy body dementias is associated with AD biomarkers

Motor features of Parkinson's disease (PD) are heterogeneous and well-studied; non-tremor features of postural instability and gait dysfunction (PIGD) have been linked to worse outcomes and Alzheimer's disease (AD) co-pathology. However, these features are understudied in Lewy body dementias (LBD). Here we perform retrospective analysis of a unique cohort of LBD (n = 30) with Unified Parkinson's Disease Rating Scale (UPDRS) data collected at baseline in proximity to cerebrospinal fluid collection to test the hypothesis that LBD patients with a positive AD biomarker profile (LBD + AD = 13) would have higher PIGD burden compared with LBD patients without AD biomarker positivity (LBD-AD = 17). We find novel evidence for selective impairment of PIGD burden in LBD + AD vs LBD-AD (OR = 1.95, 95%CI = 1.02-3.70, p = 0.04) and a direct association of increasing CSF tau/Aβ1-42 ratio with increasing PIGD disability in the total cohort (β = 0.23, SE = 0.08, p = 0.01). This unique biomarker stratification approach suggests AD co-pathology may contribute to PIGD motor signs in LBD.

White matter lesions may be an early marker for age-related cognitive decline

BACKGROUND

Research suggests that cerebral small vessel disease (CSVD), amyloid, and pTau contribute to age-related cognitive decline. It remains unknown how these factors relate to one another and how they jointly contribute to cognitive decline in normal aging. This project examines the association between these factors and their relationship to cognitive decline in cognitively unimpaired older adults without subjective cognitive decline.

METHODS

A total of 230 subjects with cerebrospinal fluid (CSF) Aß42, CSF pTau181, white matter lesions (WMLs) used as a proxy of CSVD, and cognitive scores from the Alzheimer's Disease Neuroimaging Initiative were included. Associations between each factor and cognitive score were investigated using regression models. Furthermore, relationships between the three pathologies were also examined using regression models.

RESULTS

At baseline, there was an inverse association between WML load and Aß42 (t = -4.20, p <.001). There was no association between WML load and pTau (t = 0.32, p = 0.75), nor with Aß42 and pTau (t = 0.51, p =.61). Correcting for age, sex and education, baseline WML load was associated with baseline ADAS-13 scores (t = 2.59, p =.01) and lower follow-up executive functioning (t = -2.84, p =.005). Baseline Aß42 was associated with executive function at baseline (t = 3.58, p<.004) but not at follow-up (t = 1.05, p = 0.30), nor with ADAS-13 at baseline (t = -0.24, p = 0.81) or follow-up (t = 0.09, p = 0.93). Finally, baseline pTau was not associated with any cognitive measure at baseline or follow-up.

CONCLUSION

Both baseline Aß42 and WML load are associated with some baseline cognition scores, but only baseline WML load is associated with follow-up executive functioning. This finding suggests that WMLs may be one of the earliest clinical manifestations that contributes to future cognitive decline in cognitively healthy older adults. Given that healthy older adults with WMLs exhibit declines in cognitive functioning, they may be less resilient to future pathology increasing their risk for cognitive impairment due to dementia than those without WMLs.

A predictive model using the mesoscopic architecture of the living brain to detect Alzheimer's disease

Background

Alzheimer's disease, the most common cause of dementia, causes a progressive and irreversible deterioration of cognition that can sometimes be difficult to diagnose, leading to suboptimal patient care.

Methods

We developed a predictive model that computes multi-regional statistical morpho-functional mesoscopic traits from T1-weighted MRI scans, with or without cognitive scores. For each patient, a biomarker called "Alzheimer's Predictive Vector" (ApV) was derived using a two-stage least absolute shrinkage and selection operator (LASSO).

Results

The ApV reliably discriminates between people with (ADrp) and without (nADrp) Alzheimer's related pathologies (98% and 81% accuracy between ADrp - including the early form, mild cognitive impairment - and nADrp in internal and external hold-out test sets, respectively), without any a priori assumptions or need for neuroradiology reads. The new test is superior to standard hippocampal atrophy (26% accuracy) and cerebrospinal fluid beta amyloid measure (62% accuracy). A multiparametric analysis compared DTI-MRI derived fractional anisotropy, whose readout of neuronal loss agrees with ADrp phenotype, and SNPrs2075650 is significantly altered in patients with ADrp-like phenotype.

Conclusions

This new data analytic method demonstrates potential for increasing accuracy of Alzheimer diagnosis.

Establishment of combined diagnostic models of Alzheimer's disease in a Chinese cohort: the Chongqing Ageing & Dementia Study (CADS)

Cerebrospinal fluid (CSF) biomarkers are essential for the accurate diagnosis of Alzheimer's disease (AD), yet their measurement levels vary widely across centers and regions, leaving no uniform cutoff values to date. Diagnostic cutoff values of CSF biomarkers for AD are lacking for the Chinese population. As a member of the Alzheimer's Association Quality Control program for CSF biomarkers, we aimed to establish diagnostic models based on CSF biomarkers and risk factors for AD in a Chinese cohort. A total of 64 AD dementia patients and 105 age- and sex-matched cognitively normal (CN) controls from the Chongqing Ageing & Dementia Study cohort were included. CSF Aβ42, P-tau181, and T-tau levels were measured by ELISA. Combined biomarker models and integrative models with demographic characteristics were established by logistic regression. The cutoff values to distinguish AD from CN were 933 pg/mL for Aβ42, 48.7 pg/mL for P-tau181 and 313 pg/mL for T-tau. The AN model, including Aβ42 and T-tau, had a higher diagnostic accuracy of 89.9%. Integrating age and APOE ε4 status to AN model (the ANA'E model) increased the diagnostic accuracy to 90.5% and improved the model performance. This study established cutoff values of CSF biomarkers and optimal combined models for AD diagnosis in a Chinese cohort.

Cross-Sectional and Longitudinal Hippocampal Atrophy, Not Cortical Thinning, Occurs in Amyloid-Negative, p-Tau-Positive, Older Adults With Non-Amyloid Pathology and Mild Cognitive Impairment

Introduction

Alzheimer's disease (AD) is a degenerative disease characterized by pathological accumulation of amyloid and phosphorylated tau. Typically, the early stage of AD, also called mild cognitive impairment (MCI), shows amyloid pathology. A small but significant number of individuals with MCI do not exhibit amyloid pathology but have elevated phosphorylated tau levels (A-T+ MCI). We used CSF amyloid and phosphorylated tau to identify the individuals with A+T+ and A-T+ MCI as well as cognitively normal (A-T-) controls. To increase the sample size, we leveraged the Global Alzheimer's Association Interactive Network and identified 137 MCI+ and 61 A-T+ MCI participants. We compared baseline and longitudinal, hippocampal, and cortical atrophy between groups.

Methods

We applied ComBat harmonization to minimize site-related variability and used FreeSurfer for all measurements.

Results

Harmonization reduced unwanted variability in cortical thickness by 3.4% and in hippocampal volume measurement by 10.3%. Cross-sectionally, widespread cortical thinning with age was seen in the A+T+ and A-T+ MCI groups (p < 0.0005). A decrease in the hippocampal volume with age was faster in both groups (p < 0.05) than in the controls. Longitudinally also, hippocampal atrophy rates were significant (p < 0.05) when compared with the controls. No longitudinal cortical thinning was observed in A-T+ MCI group.

Discussion

A-T+ MCI participants showed similar baseline cortical thickness patterns with aging and longitudinal hippocampal atrophy rates as participants with A+T+ MCI, but did not show longitudinal cortical atrophy signature.

Disentangling tau and brain atrophy cluster heterogeneity across the Alzheimer's disease continuum

Introduction

Neuroimaging heterogeneity in dementia has been examined using single modalities. We evaluated the associations of magnetic resonance imaging (MRI) atrophy and flortaucipir positron emission tomography (PET) clusters across the Alzheimer's disease (AD) spectrum.

Methods

We included 496 Alzheimer's Disease Neuroimaging Initiative participants with brain MRI, flortaucipir PET scan, and amyloid beta biomarker measures obtained. We applied a novel robust collaborative clustering (RCC) approach on the MRI and flortaucipir PET scans. We derived indices for AD-like (SPARE-AD index) and brain age (SPARE-BA) atrophy.

Results

We identified four tau (I-IV) and three atrophy clusters. Tau clusters were associated with the apolipoprotein E genotype. Atrophy clusters were associated with white matter hyperintensity volumes. Only the hippocampal sparing atrophy cluster showed a specific association with brain aging imaging index. Tau clusters presented stronger clinical associations than atrophy clusters. Tau and atrophy clusters were partially associated.

Conclusions

Each neuroimaging modality captured different aspects of brain aging, genetics, vascular changes, and neurodegeneration leading to individual multimodal phenotyping.

Disentangling Alzheimer's disease neurodegeneration from typical brain ageing using machine learning

Neuroimaging biomarkers that distinguish between changes due to typical brain ageing and Alzheimer's disease are valuable for determining how much each contributes to cognitive decline. Supervised machine learning models can derive multivariate patterns of brain change related to the two processes, including the Spatial Patterns of Atrophy for Recognition of Alzheimer's Disease (SPARE-AD) and of Brain Aging (SPARE-BA) scores investigated herein. However, the substantial overlap between brain regions affected in the two processes confounds measuring them independently. We present a methodology, and associated results, towards disentangling the two. T1-weighted MRI scans of 4054 participants (48-95 years) with Alzheimer's disease, mild cognitive impairment (MCI), or cognitively normal (CN) diagnoses from the Imaging-based coordinate SysTem for AGIng and NeurodeGenerative diseases (iSTAGING) consortium were analysed. Multiple sets of SPARE scores were investigated, in order to probe imaging signatures of certain clinically or molecularly defined sub-cohorts. First, a subset of clinical Alzheimer's disease patients (n = 718) and age- and sex-matched CN adults (n = 718) were selected based purely on clinical diagnoses to train SPARE-BA1 (regression of age using CN individuals) and SPARE-AD1 (classification of CN versus Alzheimer's disease) models. Second, analogous groups were selected based on clinical and molecular markers to train SPARE-BA2 and SPARE-AD2 models: amyloid-positive Alzheimer's disease continuum group (n = 718; consisting of amyloid-positive Alzheimer's disease, amyloid-positive MCI, amyloid- and tau-positive CN individuals) and amyloid-negative CN group (n = 718). Finally, the combined group of the Alzheimer's disease continuum and amyloid-negative CN individuals was used to train SPARE-BA3 model, with the intention to estimate brain age regardless of Alzheimer's disease-related brain changes. The disentangled SPARE models, SPARE-AD2 and SPARE-BA3, derived brain patterns that were more specific to the two types of brain changes. The correlation between the SPARE-BA Gap (SPARE-BA minus chronological age) and SPARE-AD was significantly reduced after the decoupling (r = 0.56-0.06). The correlation of disentangled SPARE-AD was non-inferior to amyloid- and tau-related measurements and to the number of APOE ε4 alleles but was lower to Alzheimer's disease-related psychometric test scores, suggesting the contribution of advanced brain ageing to the latter. The disentangled SPARE-BA was consistently less correlated with Alzheimer's disease-related clinical, molecular and genetic variables. By employing conservative molecular diagnoses and introducing Alzheimer's disease continuum cases to the SPARE-BA model training, we achieved more dissociable neuroanatomical biomarkers of typical brain ageing and Alzheimer's disease.

Effects of age, amyloid, sex, and APOE ε4 on the CSF proteome in normal cognition

Introduction

It is important to understand which biological processes change with aging, and how such changes are associated with increased Alzheimer's disease (AD) risk. We studied how cerebrospinal fluid (CSF) proteomics changed with age and tested if associations depended on amyloid status, sex, and apolipoprotein E Ɛ4 genotype.

Methods

We included 277 cognitively intact individuals aged 46 to 89 years from Alzheimer's Disease Neuroimaging Initiative, European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery, and Metabolic Syndrome in Men. In total, 1149 proteins were measured with liquid chromatography mass spectrometry with multiple reaction monitoring/Rules-Based Medicine, tandem mass tag mass spectrometry, and SOMAscan. We tested associations between age and protein levels in linear models and tested enrichment for Reactome pathways.

Results

Levels of 252 proteins increased with age independently of amyloid status. These proteins were associated with immune and signaling processes. Levels of 21 proteins decreased with older age exclusively in amyloid abnormal participants and these were enriched for extracellular matrix organization.

Discussion

We found amyloid-independent and -dependent CSF proteome changes with older age, perhaps representing physiological aging and early AD pathology.

Delusional Severity Is Associated with Abnormal Texture in FLAIR MRI

Background: This study examines the relationship between delusional severity in cognitively impaired adults with automatically computed volume and texture biomarkers from the Normal Appearing Brain Matter (NABM) in FLAIR MRI. Methods: Patients with mild cognitive impairment (MCI, n = 24) and Alzheimer’s Disease (AD, n = 18) with delusions of varying severities based on Neuropsychiatric Inventory-Questionnaire (NPI-Q) (1—mild, 2—moderate, 3—severe) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) were analyzed for this task. The NABM region, which is gray matter (GM) and white matter (WM) combined, was automatically segmented in FLAIR MRI volumes with intensity standardization and thresholding. Three imaging biomarkers were computed from this region, including NABM volume and two texture markers called “Integrity” and “Damage”. Together, these imaging biomarkers quantify structural changes in brain volume, microstructural integrity and tissue damage. Multivariable regression was used to investigate relationships between imaging biomarkers and delusional severities (1, 2 and 3). Sex, age, education, APOE4 and baseline cerebrospinal fluid (CSF) tau were included as co-variates. Results: Biomarkers were extracted from a total of 42 participants with longitudinal time points representing 164 imaging volumes. Significant associations were found for all three NABM biomarkers between delusion level 3 and level 1. Integrity was also sensitive enough to show differences between delusion level 1 and delusion level 2. A significant specified interaction was noted with severe delusions (level 3) and CSF tau for all imaging biomarkers (p < 0.01). APOE4 homozygotes were also significantly related to the biomarkers. Conclusion: Cognitively impaired older adults with more severe delusions have greater global brain disease burden in the WM and GM combined (NABM) as measured using FLAIR MRI. Relative to patients with mild delusions, tissue degeneration in the NABM was more pronounced in subjects with higher delusional symptoms, with a significant association with CSF tau. Future studies are required to establish potential tau-associated mechanisms of increased delusional severity.

Biomarker A+T-: is this Alzheimer's disease or not? A combined CSF and pathology study

The biological definition of Alzheimer's disease using CSF biomarkers requires abnormal levels of both amyloid (A) and tau (T). However, biomarkers and corresponding cutoffs may not always reflect the presence or absence of pathology. Previous studies suggest that up to 32% of individuals with autopsy-confirmed Alzheimer's disease show normal CSF p-tau levels in vivo, but these studies are sparse and had small sample sizes. Therefore, in three independent autopsy cohorts, we studied whether or not CSF A+T- excluded Alzheimer's disease based on autopsy. We included 215 individuals, for whom ante-mortem CSF collection and autopsy had been performed, from three cohorts: (i) the Amsterdam Dementia Cohort (ADC) [n = 80, 37 (46%) Alzheimer's disease at autopsy, time between CSF collection and death 4.5 ± 2.9 years]; (ii) the Antwerp Dementia Cohort (DEM) [n = 92, 84 (91%) Alzheimer's disease at autopsy, time CSF collection to death 1.7 ± 2.3 years]; and (iii) the Alzheimer's Disease Neuroimaging Initiative (ADNI) [n = 43, 31 (72%) Alzheimer's disease at autopsy, time CSF collection to death 5.1 ± 2.5 years]. Biomarker profiles were based on dichotomized CSF Aβ1-42 and p-tau levels. The accuracy of CSF AT profiles to detect autopsy-confirmed Alzheimer's disease was assessed. Lastly, we investigated whether the concordance of AT profiles with autopsy diagnosis improved when CSF was collected closer to death in 9 (10%) DEM and 30 (70%) ADNI individuals with repeated CSF measurements available. In total, 50-73% of A+T- individuals and 100% of A+T+ individuals had Alzheimer's disease at autopsy. Amyloid status showed the highest accuracy to detect autopsy-confirmed Alzheimer's disease (accuracy, sensitivity and specificity in the ADC: 88%, 92% and 84%; in the DEM: 87%, 94% and 12%; and in the ADNI cohort: 86%, 90% and 75%, respectively). The addition of CSF p-tau did not further improve these estimates. We observed no differences in demographics or degree of Alzheimer's disease neuropathology between A+T- and A+T+ individuals with autopsy-confirmed Alzheimer's disease. All individuals with repeated CSF measurements remained stable in Aβ1-42 status during follow-up. None of the Alzheimer's disease individuals with a normal p-tau status changed to abnormal; however, four (44%) DEM individuals and two (7%) ADNI individuals changed from abnormal to normal p-tau status over time, and all had Alzheimer's disease at autopsy. In summary, we found that up to 73% of A+T- individuals had Alzheimer's disease at autopsy. This should be taken into account in both research and clinical settings.

Practice Effects in Mild Cognitive Impairment Increase Reversion Rates and Delay Detection of New Impairments

Objective

Cognitive practice effects (PEs) can delay detection of progression from cognitively unimpaired to mild cognitive impairment (MCI). They also reduce diagnostic accuracy as suggested by biomarker positivity data. Even among those who decline, PEs can mask steeper declines by inflating cognitive scores. Within MCI samples, PEs may increase reversion rates and thus impede detection of further impairment. Within an MCI sample at baseline, we evaluated how PEs impact prevalence, reversion rates, and dementia progression after 1 year.

Methods

We examined 329 baseline Alzheimer's Disease Neuroimaging Initiative MCI participants (mean age = 73.1; SD = 7.4). We identified test-naïve participants who were demographically matched to returnees at their 1-year follow-up. Since the only major difference between groups was that one completed testing once and the other twice, comparison of scores in each group yielded PEs. PEs were subtracted from each test to yield PE-adjusted scores. Biomarkers included cerebrospinal fluid phosphorylated tau and amyloid beta. Cox proportional models predicted time until first dementia diagnosis using PE-unadjusted and PE-adjusted diagnoses.

Results

Accounting for PEs increased MCI prevalence at follow-up by 9.2% (272 vs. 249 MCI), and reduced reversion to normal by 28.8% (57 vs. 80 reverters). PEs also increased stability of single-domain MCI by 12.0% (164 vs. 147). Compared to PE-unadjusted diagnoses, use of PE-adjusted follow-up diagnoses led to a twofold increase in hazard ratios for incident dementia. We classified individuals as false reverters if they reverted to cognitively unimpaired status based on PE-unadjusted scores, but remained classified as MCI cases after accounting for PEs. When amyloid and tau positivity were examined together, 72.2% of these false reverters were positive for at least one biomarker.

Interpretation

Even when PEs are small, they can meaningfully change whether some individuals with MCI retain the diagnosis at a 1-year follow-up. Accounting for PEs resulted in increased MCI prevalence and altered stability/reversion rates. This improved diagnostic accuracy also increased the dementia-predicting ability of MCI diagnoses.

Cerebrospinal fluid catecholamines in Alzheimer's disease patients with and without biological disease

Noradrenergic and dopaminergic neurons are involved in cognitive functions, relate to behavioral and psychological symptoms in dementia and are affected in Alzheimer's disease (AD). Amyloid plaques (A), neurofibrillary tangles (T) and neurodegeneration (N) hallmarks the AD neuropathology. Today, the AT(N) pathophysiology can be assessed through biomarkers. Previous studies report cerebrospinal fluid (CSF) catecholamine concentrations in AD patients without biomarker refinement. We explored if CSF catecholamines relate to AD clinical presentation or neuropathology as reflected by CSF biomarkers. CSF catecholamines were analyzed in AD patients at the mild cognitive impairment (MCI; n = 54) or dementia stage (n = 240) and in cognitively unimpaired (n = 113). CSF biomarkers determined AT status and indicated synaptic damage (neurogranin). The AD patients (n = 294) had higher CSF noradrenaline and adrenaline concentrations, but lower dopamine concentrations compared to the cognitively unimpaired (n = 113). AD patients in the MCI and dementia stage of the disease had similar CSF catecholamine concentrations. In the CSF neurogranin positively associated with noradrenaline and adrenaline but not with dopamine. Adjusted regression analyses including AT status, CSF neurogranin, age, gender, and APOEε4 status verified the findings. In restricted analyses comparing A+T+ patients to A-T- cognitively unimpaired, the findings for CSF adrenaline remained significant (p < 0.001) but not for CSF noradrenaline (p = 0.07) and CSF dopamine (p = 0.33). There were no differences between A+T+ and A-T- cognitively unimpaired. Thus, we find alterations in CSF catecholamines in symptomatic AD and the CSF adrenergic transmitters to increase simultaneously with synaptic damage as indexed by CSF neurogranin.