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Background: Remote unsupervised cognitive assessments have the potential to complement and facilitate cognitive assessment in clinical and research settings. Method: Here we evaluate the usability, validity and reliability of unsupervised remote memory assessments via mobile devices (see Figure 1) in individuals without dementia from the Swedish BioFINDER‐2 study and explore their prognostic utility regarding future cognitive decline in combination with a plasma marker for p‐tau217. Result: Usability was rated positively. The app was easy to use with clear instructions, a majority of participants enjoyed completing the tests and preferred digital assessments over pen‐and‐paper tests, and more than 80% of participants found the frequency and length of remote assessments were appropriate. Remote memory assessments showed good construct validity with traditional neuropsychological assessments (see Figure 2) and were significantly associated with medial temporal lobe subregional tau‐PET and downstream MRI measures (see Figure 3). Memory performance at baseline was associated with future cognitive decline and prediction of future cognitive decline was further improved by combining remote digital memory assessments with plasma p‐tau217. Finally, retest reliability was moderate for a single assessment and good for an aggregate of two sessions. Conclusion: Our results demonstrate that unsupervised digital memory assessments might be used for diagnosis and prognosis in Alzheimer's disease, potentially in combination with plasma biomarkers. [ABSTRACT FROM AUTHOR]

INTRODUCTION: We examined the relations of misfolded alpha synuclein (α‐synuclein) with Alzheimer's disease (AD) biomarkers in two large independent cohorts. METHODS: We included Biomarkers for Identifying Neurodegenerative Disorders Early and Reliably Two (BioFINDER‐2) and Alzheimer's Disease Neuroimaging Initiative (ADNI) participants (n = 2315, cognitively unimpaired, mild cognitive impairment, AD dementia) who had cross‐sectional cerebrospinal fluid (CSF) α‐synuclein measurement from seed‐amplification assay as well as cross‐sectional and longitudinal amyloid beta (Aβ) and tau levels (measured in CSF and/or by positron emission tomography). All analyses were adjusted for age, sex, and cognitive status. RESULTS: Across cohorts, the main biomarker associated with α‐synuclein positivity at baseline was higher levels of Aβ pathology (all p values ≤ 0.02), but not tau. Looking at longitudinal measures of AD biomarkers, α‐synuclein –positive participants had a statistically significant faster increase of Aβ load, although of modest magnitude (1.11 Centiloid/year, p = 0.02), compared to α‐synuclein –negative participants in BioFINDER‐2 but not in ADNI. DISCUSSION: We showed associations between concurrent misfolded α‐synuclein and Aβ levels, providing in vivo evidence of links between these two molecular disease pathways in humans. Highlights: Amyloid beta (Aβ), but not tau, was associated with alpha‐synuclein (α‐synuclein) positivity.Such association was consistent across two cohorts, beyond the effect of age, sex, and cognitive status.α‐synuclein–positive participants had a small, statistically significant faster increase in Aβ positron emission tomography levels in one of the two cohorts. [ABSTRACT FROM AUTHOR]

Background: Recently, we demonstrated the sensitivity of a novel CSF mass spectrometer‐based assay, which enabled the assessment of multiple phospho‐tau (pTau) forms (pT181, pS199, pS202, pT205, pT217, pT231, and pS396) and two non‐phosphorylated peptides. Initial results suggested that there may be disease associated differences across pTau species. Here, we aimed to investigate the associations between these CSF pTau forms and Alzheimer's disease (AD) core pathologies as measured by PET. Methods: We included 352 participants from the BioFINDER‐2 cohort with available CSF mass‐spectrometry quantification and who underwent [18F]flutemetamol and [18F]RO948 PET to assess amyloid‐β (Aβ) and tau burden, respectively (Table1). Aβ burden was determined globally and in an early accumulating region (precuneus). Tau burden was determined for a temporal meta‐ROI and Braak stages (I‐II, III‐IV, V‐VI). The ratio of tau phosphorylated to non‐phosphorylated peptides was additionally determined for pS199, pS202, pT205, pT217. CSF pTau‐forms (ratio‐)levels were log‐transformed. To assess the contribution of Aβ and tau burden on pTau‐forms (ratio‐)levels, we ran multivariable linear regression models including both Aβ and tau PET measures as independent variables, covarying for age and sex. Next, we determined the parsimonious model (i.e. minimal number of predictors within 2 Akaike points of optimal model) of pTau‐forms (ratio‐)levels using R package MuMIn to predict Aβ and tau PET burden in the different ROIs. Results: In the whole cohort, all CSF pTau‐forms peptide concentrations, except for pS396, were associated with both Aβ and tau PET burden, though to different extents (Figure1). pT217 and pT231 were more strongly associated with Aβ, while pS199 and pT205 were more strongly associated with tau. In Aβ‐positive subjects (Figure1C/D), the association with tau increased across pTau‐forms. For the multivariable parsimonious models, pTau217 consistently showed the strongest contribution to predicting amyloid and tau burden across ROIs (Table2&3). pT231 was also selected when early amyloid burden was assessed. pT205 was only selected to predict tau burden in early ROIs when corrected for the non‐phosphorylated peptide (Table3). In addition, sex significantly contributed to predicting tau‐PET burden across ROIs. Conclusion: We provide further evidence that measurement of multiple phosphorylated‐tau species could be used to track AD disease progression. [ABSTRACT FROM AUTHOR]

Background: The introduction of the A/T/(N) (β‐amyloid/tau/neurodegeneration) framework has revealed an A‐T+ (β‐amyloid‐negative/tau‐positive) group which has been named suspected non‐Alzheimer's pathophysiology. Cerebrospinal fluid (CSF) markers are often utilized to divide individuals in the A and T categories, but it is unclear what elevated CSF p‐tau levels signify in the absence of β‐amyloid. We aim to better understand A‐T+ as defined by CSF markers in two independent samples of cognitively unimpaired and impaired individuals by assessing baseline and longitudinal change in CSF, magnetic resonance (MRI), positron emission tomography (PET) and cognition measures. Method: Cognitively normal (CN) and Mild Cognitive Impairment subjects from BioFINDER‐1 and 2 were included. A status was based on two CSF measures of β‐amyloid 42/40 (Aβ42/40) where A‐ individuals had to be negative on both measures using previously established cut offs. T status was determined by the 75th, 80th and 85th percentile of A‐ CN subjects for Roche Elecsys p‐tau181 (Table 1). Analyses of covariance and linear mixed models were performed to assess baseline and change differences in CSF (p‐tau181, Aβ42/40 ratio, neurofilament light (NFL)), MRI (hippocampal volume, entorhinal cortex and temporal meta‐region of interest thickness), cognition (modified Preclinical Alzheimer's Cognitive Composite and delayed recall), and PET measures (entorhinal cortex Tau‐PET and early and global Alzheimer's Disease composite β‐amyloid‐PET uptake) over 8 years (10 for cognition) for BioFINDER‐1 and 4 years for BioFINDER‐2. Result: In A‐T+ versus A‐T‐, higher baseline NFL levels were found. No other baseline differences were found except for p‐tau181 and Aβ42/40 ratio by design. A‐T+ did not show significant change compared to A‐T‐ on any measure. Using different cut offs for p‐tau181 did not notably change the results. Comparisons with the A+ groups are shown in Figures 1‐2. Conclusion: Our results indicate that A‐T+ as determined by p‐tau181 does not seem to be clinically meaningful as this group shows no decline on any of the biomarkers or cognitive measures over several years. The high NFL levels in the A‐T+ group suggest the presence of a latent factor that affects both NFL and p‐tau levels. Analyses will be repeated with other p‐tau isotopes and assays. [ABSTRACT FROM AUTHOR]

Background: Although cerebrovascular pathology often co‐exists with Alzheimer's disease pathology, it remains incompletely understood to what extent it can contribute to the progression of Alzheimer's disease pathology. We investigated whether cerebrovascular pathology (white matter lesions [WML] and microbleeds) modified the association between amyloid‐β (Aβ) and both cross‐sectional and longitudinal tau in cognitively unimpaired (CU) and cognitively impaired (CI) individuals. Method: We included 1240 participants from the Swedish BioFINDER‐2 cohort including 402 Aβ‐negative CU, 147 Aβ‐positive CU, 257 Aβ‐negative CI (MCI and non‐AD dementia) and 434 Aβ‐positive CI participants (MCI and AD dementia) (Table 1). All underwent baseline [18F]RO948 (tau)‐PET, and a subset (n = 678) underwent longitudinal tau‐PET (2.6±1.0‐years). We extracted tau‐PET SUVr in a temporal meta‐region‐of‐interest. For Aβ, we used global [18F]flutemetamol‐PET SUVr for CU and Aβ‐status (+/‐) for CI participants. Global WML volume was calculated using the SPM lesion segmentation tool, corrected for intra‐cranial volume and log‐transformed. Presence of microbleeds was dichotomized into absent or present (≥1) by visual read. Primary analyses included age‐, sex‐ and APOE ε4‐corrected linear mixed models between tau‐PET (outcome) and interactions between time, Aβ and WML volume/microbleeds (predictors). Result: Upon correcting for age, sex and ε4‐carriership, Aβ‐positive CU and CI participants showed significantly more microbleeds compared to Aβ‐negative participants of the same cognitive stage (both p<0.05), but no differences in WML volume was observed (both p>0.05). In CU, we observed a significant interaction effect between ≥1 microbleed and higher Aβ‐load with both greater cross‐sectional tau (β = 0.67 [CI: 0.45‐0.89], p<0.001) and longitudinal tau (β = 0.11 [CI: 0.07‐0.16], p<0.001) (Fig‐1, Table‐2). In CU, we also observed a significant interaction effect between larger WML volumes and higher Aβ‐load with greater cross‐sectional tau (β = 0.14 [CI: 0.06‐0.22], p = 0.001). When including both microbleeds and WML volume in the same model, only effects of microbleeds remained significant (Table‐2). No significant effects were observed in CI participants. Conclusion: In CU (but not CI) individuals, cerebrovascular pathology may modify the association between Aβ and tau by accelerating tau accumulation in the presence of Aβ. These results might suggest an early effect of cerebrovascular pathology on the interplay between Aβ and tau. [ABSTRACT FROM AUTHOR]

Background: Staging Alzheimer's disease (AD) allows for the identification of key milestones and inflection points in the disease course. However, most current in vivo disease staging methods rely on costly and low‐accessible measures such as PET. Our objective was to generate and evaluate a staging model based on multiple cerebrospinal fluid (CSF) biomarkers. Method: We created a staging model using the Subtype and Stage Inference (SuStaIn) algorithm in 426 participants covering the whole AD spectrum (Table 1) by evaluating CSF Ab42/40 and 5 CSF tau markers: the proportion of tau phosphorylated at three threonine residues (pT205/T205, pT217/T217, and pT231/T231), and the concentrations of microtubule‐binding region of tau at residue 243 [MTBR‐tau243] and total‐tau [mid‐domain containing T212‐221]). We investigated associations between CSF stages and cross‐sectional and longitudinal changes in amyloid and tau pathologies, neurodegeneration and cognition using linear regression models. ROC curves were used to identify the optimal stage for predicting positivity in imaging biomarkers and to separate AD vs non‐AD cognitive symptoms. We also tested the relative risk of progression from cognitively unimpaired or MCI to AD‐type dementia using Kaplan‐Meier curves and Cox‐proportional Hazard models. Creation of the model and cross‐sectional analyses were replicated in an independent cohort (n = 222, Table 1). Result: SuStaIn revealed that only one subtype was present and that five CSF biomarkers (ordered: Ab42/40, pT217/T217, pT205/T205, MTBR‐tau243, T212‐221) were sufficient to create an accurate disease staging model (Fig.1A‐B). Increasing stages (0‐5) were associated with increased abnormality in other AD‐related biomarkers (Fig.1C). In both cohorts, stage ≥2 accurately predicted amyloid‐PET positivity (AUC≥0.89), stage ≥4 predicted tau‐PET (AUC≥0.94) and neurodegeneration positivity (AUC≥0.81). In the main cohort, stage≥2 separated non‐AD from AD‐related cognitive impairment (AUC = 0.96). Longitudinal changes for several AD‐biomarkers by stages are presented in (Fig.2A). Higher stages (4‐5) at baseline were associated with higher hazard ratios (HR) of progressing to AD‐type dementia (CU&MCI: HR = 4.6[2.0,10.9], MCI: HR = 3.5[1.5,8.1], Fig.2B‐C). Conclusion: Our results suggest that the AD continuum can be accurately staged with a single CSF sample, which may be useful in the clinical setting for patient management but also as a tool in clinical trials for selection and monitoring of study participants. [ABSTRACT FROM AUTHOR]

Background: CSF biomarkers are key AD descriptors, but the CSF volume as well as the production and clearance rates of proteins varies in individuals. Adjusting for reference proteins, which reflect these factors, might improve the accuracy of CSF biomarkers, similar to when normalizing CSF Aβ42 levels to Aβ40 levels. For other biomarkers, individual CSF dynamics are seldom adjusted for, which may result in disagreement with other techniques (e.g., PET). Additionally, this may contribute to inaccurate conclusions about CSF protein correlations that mainly exist due to high or low CSF dilution levels. The aim of this work is therefore to establish the use of CSF reference proteins to improve CSF AD biomarker accuracy. Method: We used measurements of 2944 CSF proteins in the Swedish BioFINDER‐2 cohort (n = 982, 80% train and 20% test). Reference protein candidates (reflecting CSF dilution levels without being related to any disease) were searched for in a data‐driven manner, using t‐distributed stochastic neighbor embedding (t‐SNE) and a semi‐supervised K‐means clustering algorithm. The final candidate proteins were evaluated in logistic regression models, using CSF P‐tau181 and CSF Aβ42 as main predictors of tau‐PET and amyloid‐PET, respectively. Result: Most proteins were associated with an individual dilution level, although at different strengths (Fig 1 and Fig 2e). From the different colorings of the t‐SNE space, proteins of similar expressions were placed nearby, creating areas of extra interest (yellow areas in subfigures 2c‐2h). The semi‐supervised K‐means algorithm highlighted an area (cluster 11) containing proteins with superior reference protein characteristics, from which single reference protein candidates were identified. All examined references (e.g., Aβ40, NTRK3, NTRK2, BLMH,CBLN4, PTPRN2 or PTPRS) outperformed using no reference for both the P‐tau181 and Aβ42 model (Figure 3). For P‐tau181, highest performance was achieved using CBLN4 (AUC = 0.944). For Aβ42, Aβ40 performed best as reference (AUC = 0.992). Conclusion: The individual CSF dilution level affects many CSF proteins. We describe several novel and previously suggested CSF reference proteins, which can significantly improve the accuracy of CSF biomarkers as key AD descriptors. [ABSTRACT FROM AUTHOR]

INTRODUCTION: β‐Synuclein is an emerging synaptic blood biomarker for Alzheimer's disease (AD) but differences in β‐synuclein levels in preclinical AD and its association with amyloid and tau pathology have not yet been studied. METHODS: We measured plasma β‐synuclein levels in cognitively unimpaired individuals with positive Aβ‐PET (i.e., preclinical AD, N = 48) or negative Aβ‐PET (N = 61), Aβ‐positive patients with mild cognitive impairment (MCI, N = 36), and Aβ‐positive AD dementia (N = 85). Amyloid (A) and tau (T) pathology were assessed by [18F]flutemetamol and [18F]RO948 PET. RESULTS: Plasma β‐synuclein levels were higher in preclinical AD and even higher in MCI and AD dementia. Stratification according to amyloid/tau pathology revealed higher β‐synuclein in A+T− and A+T+ subjects compared with A−T−. Plasma β‐synuclein levels were related to tau and Aβ pathology and associated with temporal cortical thinning and cognitive impairment. DISCUSSION: Our data indicate that plasma β‐synuclein might track synaptic dysfunction, even during the preclinical stages of AD. HIGHLIGHTS: Plasma β‐synuclein is already higher in preclinical AD.Plasma β‐synuclein is higher in MCI and AD dementia than in preclinical AD.Aβ‐ and tau‐PET SUVRs are associated with plasma β‐synuclein levels.Plasma β‐synuclein is already higher in tau‐PET negative subjects.Plasma β‐synuclein is related to temporal cortical atrophy and cognitive impairment. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Our objective was determining the optimal combinations of cerebrospinal fluid (CSF) biomarkers for predicting disease progression in Alzheimer's disease (AD) and other neurodegenerative diseases. METHODS: We included 1,983 participants from three different cohorts with longitudinal cognitive and clinical data, and baseline CSF levels of Aβ42, Aβ40, phosphorylated tau at threonine‐181 (p‐tau), neurofilament light (NfL), neurogranin, α‐synuclein, soluble triggering receptor expressed on myeloid cells 2 (sTREM2), glial fibrillary acidic protein (GFAP), YKL‐40, S100b, and interleukin 6 (IL‐6) (Elecsys NeuroToolKit). RESULTS: Change of modified Preclinical Alzheimer's Cognitive Composite (mPACC) in cognitively unimpaired (CU) was best predicted by p‐tau/Aβ42 alone (R2 ≥ 0.31) or together with NfL (R2 = 0.25), while p‐tau/Aβ42 (R2 ≥ 0.19) was sufficient to accurately predict change of the Mini‐Mental State Examination (MMSE) in mild cognitive impairment (MCI) patients. P‐tau/Aβ42 (AUC ≥ 0.87) and p‐tau/Aβ42 together with NfL (AUC ≥ 0.75) were the best predictors of conversion to AD and all‐cause dementia, respectively. DISCUSSION: P‐tau/Aβ42 is sufficient for predicting progression in AD, with very high accuracy. Adding NfL improves the prediction of all‐cause dementia conversion and cognitive decline. [ABSTRACT FROM AUTHOR]

Introduction: This study investigated the comparability of cerebrospinal fluid (CSF) cutoffs for Elecsys immunoassays for amyloid beta (Aβ)42/Aβ40 or Aβ42/phosphorylated tau (p‐tau)181 and the effects of measurement variability when predicting Alzheimer's disease (AD)‐related outcomes (i.e., Aβ‐positron emission tomography [PET] visual read and AD neuropathology). Methods: We studied 750 participants (BioFINDER study, Alzheimer's Disease Neuroimaging Initiative [ADNI], and University of California San Francisco [UCSF]). Youden's index was used to identify cutoffs and to calculate accuracy (Aβ‐PET visual read as outcome). Using longitudinal variability in Aβ‐negative controls, we identified a gray zone around cut‐points where the risk of an inconsistent predicted outcome was >5%. Results: For Aβ42/Aβ40, cutoffs across cohorts were <0.059 (BioFINDER), <0.057 (ADNI), and <0.058 (UCSF). For Aβ42/p‐tau181, cutoffs were <41.90 (BioFINDER), <39.20 (ADNI), and <46.02 (UCSF). Accuracy was ≈90% for both Aβ42/Aβ40 and Aβ42/p‐tau181 using these cutoffs. Using Aβ‐PET as an outcome, 8.7% of participants fell within a gray zone interval for Aβ42/Aβ40, compared to 4.5% for Aβ42/p‐tau181. Similar findings were observed using a measure of overall AD neuropathologic change (7.7% vs. 3.3%). In a subset with CSF and plasma Aβ42/40, the number of individuals within the gray zone was ≈1.5 to 3 times greater when using plasma Aβ42/40. Discussion: CSF Aβ42/p‐tau181 was more robust to the effects of measurement variability, suggesting that it may be the preferred Elecsys‐based measure in clinical practice and trials. [ABSTRACT FROM AUTHOR]

Background: The Amsterdam Instrumental ADL Questionnaire (A‐IADL‐Q) has shown promising performance in detecting subtle impairment in IADL independency. Importantly, it has not been validated in older people in a primary care setting, i.e., where the initial work‐up and triaging of cognitive impairment occurs. The main aim was to examine the accuracy of the A‐IADL‐Q for estimating the cognitive stage in a primary care sample, both in the whole population and within the Alzheimer's disease (AD) continuum. Secondary aims were to examine associations with the clinical dementia rating (CDR) scale and global cognition (MMSE), including the effects of non‐cognitive co‐morbidities and medications. Method: This cross‐sectional study includes informants of 362 participants from the BioFINDER‐Primary care study. They completed the A‐IADL‐Q (higher scores = "better") either on an iPad or at home on a computer. Cognitive stage was assessed by a dementia expert (blinded to A‐IADL‐Q scores). Amyloid status was determined using FDA‐approved CSF or PET measures. The main outcome was cognitive stage according to the NIA‐AA scale. Secondary outcomes were CDR‐sum of the boxes (CDR‐SOB) and MMSE. Receiver Operating Curves (ROC) analyses were used to calculate the area under the curves (AUCs). Result: The mean age of participants (44.9% female) was 76.1 (SD 7.2) years. Out of which, 83 (23%) had subjective cognitive decline (SCD), 169 (47%) mild cognitive impairment (MCI), and 110 (30%) dementia. The AUC (95% CI) for differentiating between SCD versus MCI/dementia was 0.89 (0.85‐0.92), and it was 0.89 (0.85‐0.93) for SCD/MCI versus dementia. In amyloid positive participants (n = 303), AUCs were similar. Better A‐IADL‐Q scores were associated with worse results on CDR‐SOB (r = ‐0.73, p<0.001) and better global cognition (MMSE, r = 0.361, p<0.001). The effects of different co‐morbidities on the A‐IADL score, adjusted for cognitive stage, will be presented at AAIC. Conclusion: Our findings suggest that the A‐IADL‐Q accurately estimates the cognitive stage among cognitively impaired older people (with high prevalence of comorbidities) in a primary care setting. This can aid primary care physicians in the diagnosis of cognitive impairment, clinical management/support, and as part of the work‐up for identifying eligible participants for anti‐amyloid treatment. [ABSTRACT FROM AUTHOR]

Introduction: Biomarkers for the prediction of cognitive decline in patients with amnestic mild cognitive impairment (MCI) and amnestic mild dementia are needed for both clinical practice and clinical trials. Methods: We evaluated the ability of tau‐PET (positron emission tomography), cortical atrophy on magnetic resonance imaging (MRI), baseline cognition, apolipoprotein E gene (APOE) status, plasma and cerebrospinal fluid (CSF) levels of phosphorylated tau‐217, neurofilament light (NfL), and amyloid beta (Aβ)42/40 ratio (individually and in combination) to predict cognitive decline over 2 years in BioFINDER‐2 and Alzheimer's Disease Neuroimaging Initiative (ADNI). Results: Baseline tau‐PET and a composite baseline cognitive score were the strongest independent predictors of cognitive decline. Cortical thickness and NfL provided some additional information. Using a predictive algorithm to enrich patient selection in a theoretical clinical trial led to a significantly lower required sample size. Discussion: Models including baseline tau‐PET and cognition consistently provided the best prediction of change in cognitive function over 2 years in patients with amnestic MCI or mild dementia. [ABSTRACT FROM AUTHOR]

Introduction: There is a great need for fully automated plasma assays that can measure amyloid beta (Aβ) pathology and predict future Alzheimer's disease (AD) dementia. Methods: Two cohorts (n = 920) were examined: Panel A+ (n = 32 cognitively unimpaired [CU], n = 106 mild cognitive impairment [MCI], and n = 89 AD) and BioFINDER‐1 (n = 461 CU, n = 232 MCI). Plasma Aβ42/Aβ40, phosphorylated tau (p‐tau)181, two p‐tau217 variants, ApoE4 protein, neurofilament light, and GFAP were measured using Elecsys prototype immunoassays. Results: The best biomarker for discriminating Aβ‐positive versus Aβ‐negative participants was Aβ42/Aβ40 (are under the curve [AUC] 0.83–0.87). Combining Aβ42/Aβ40, p‐tau181, and ApoE4 improved the AUCs significantly (0.90 to 0.93; P< 0.01). Adding additional biomarkers had marginal effects (ΔAUC ≤0.01). In BioFINDER, p‐tau181, p‐tau217, and ApoE4 predicted AD dementia within 6 years in CU (AUC 0.88) and p‐tau181, p‐tau217, and Aβ42/Aβ40 in MCI (AUC 0.87). Discussion: The high accuracies for Aβ pathology and future AD dementia using fully automated instruments are promising for implementing plasma biomarkers in clinical trials and clinical routine. [ABSTRACT FROM AUTHOR]

Background: In contrast to amyloid‐PET and MRI, tau‐PET imaging has not been integrated into clinical practice even though it shows a close relationship with clinical symptoms. With the present study, we aim to assess the performance of tau‐PET to predict conversion to dementia (all‐cause and AD) in individuals with mild cognitive impairment (MCI), and compare it with amyloid‐PET and MRI. Method: We selected participants from an ongoing multicenter study (n = 408, Table 1). The following ROIs were used to define quantified thresholds for positivity using an independent cognitively unimpaired reference group: a temporal meta‐region for tau‐PET and an AD‐susceptible temporoparietal region for MRI‐based cortical thickness. For amyloid‐PET, we used the standard Centiloid mask and a published threshold of 27 Centiloids to determine positivity. We additionally performed visual reads for all imaging modalities. We used Cox proportional hazard, logistic regression, and receiver operating curve (ROC) analyses to assess the performance of amyloid‐PET (A), tau‐PET (T), and MRI (N) to predict progression from MCI to dementia over a mean follow‐up period of 2 years. Result: Hazard ratios indicated that tau‐PET and amyloid‐PET positivity (both when assessed quantitatively and using visual reads) conveyed a greater risk of progression to dementia than MRI, and that a combination of A+ and T+ (or N+) conveyed a greater risk than A‐T‐N‐ or isolated A+ or T+ (Figure 1). ROC analyses revealed that only tau‐PET performed better than demographics alone when predicting all‐cause dementia while both amyloid‐ and tau‐PET performed better than demographics and MRI when predicting AD dementia. LASSO logistic regression revealed that the optimal model to predict conversion from MCI to all‐cause dementia included a quantitative MRI marker and tau‐PET visual read. For optimal prediction of AD dementia specifically, the quantitative measure of amyloid‐PET was added in addition to the quantitative MRI marker and the tau‐PET visual read (Table 2). Conclusion: Among individuals with MCI, tau‐PET performs best when predicting dementia, and can be optimized when combined with MRI. For AD dementia, tau‐PET and amyloid‐PET are the best‐performing modalities, but an optimal prediction is again achieved when combined with MRI. [ABSTRACT FROM AUTHOR]

Introduction: The effect of random error on the performance of blood‐based biomarkers for Alzheimer's disease (AD) must be determined before clinical implementation. Methods: We measured test‐retest variability of plasma amyloid beta (Aβ)42/Aβ40, neurofilament light (NfL), glial fibrillary acidic protein (GFAP), and phosphorylated tau (p‐tau)217 and simulated effects of this variability on biomarker performance when predicting either cerebrospinal fluid (CSF) Aβ status or conversion to AD dementia in 399 non‐demented participants with cognitive symptoms. Results: Clinical performance was highest when combining all biomarkers. Among single‐biomarkers, p‐tau217 performed best. Test‐retest variability ranged from 4.1% (Aβ42/Aβ40) to 25% (GFAP). This variability reduced the performance of the biomarkers (≈ΔAUC [area under the curve] −1% to −4%) with the least effects on models with p‐tau217. The percent of individuals with unstable predicted outcomes was lowest for the multi‐biomarker combination (14%). Discussion: Clinical prediction models combining plasma biomarkers—particularly p‐tau217—exhibit high performance and are less effected by random error. Individuals with unstable predicted outcomes ("gray zone") should be recommended for further tests. [ABSTRACT FROM AUTHOR]

Background: Amyloid‐beta (Aβ) is hypothesized to facilitate the spread of tau pathology beyond the medial temporal lobe (MTL). However, there is evidence that age‐related Aβ‐independent tau pathology is present outside the MTL (Kaufman et al., Acta Neuropathol, 2018). We examine tau deposition determined by positron emission tomography (PET) in regions typically involved earlier/later in AD and downstream effects on neurodegeneration and cognition in cognitively unimpaired older adults and a low‐Aβ subgroup. Methods: We included 488 adults (40‐91 years; low‐Aβ: n=355, 65.2±11.5 years) from the BioFINDER‐2 study. MTL volumes (dentate gyrus, subiculum (SUB), cornu ammonis 1) and thickness (entorhinal cortex, Brodmann areas (BA)35/36, and parahippocampal cortex) were obtained, using Automated Segmentation for Hippocampal Subfields packages for T1‐ and T2‐weighted magnetic resonance images. Thickness of early/late neocortical AD‐regions (anterior cingulate, precuneus/posterior cingulate (PPC), orbitofrontal, inferior parietal cortex; and middle frontal, lateral occipital, and precentral/postcentral gyrus) was determined using FreeSurfer. [18F]RO948‐ and [18F]flutemetamol‐PET standardized uptake value ratios (SUVRs) were calculated for local tau and global/local Aβ. Aβ status was determined using Aβ‐PET or cerebrospinal fluid Aβ‐42/40 ratio. Global cognition was measured using delayed word‐list recall, trail making test B, and animal fluency. Results: Increasing age was associated with higher tau‐PET SUVRs primarily in MTL/frontal/parietal regions. A significant association between age and local tau‐PET remained even when including Aβ‐PET as a mediator (Fig. 1). Age and local tau‐PET, but not Aβ‐PET, where negatively associated with structure in most examined regions (Figs. 2‐3). Age‐structure associations were serially mediated via tau‐PET in regions with early AD pathology (SUB/BA35/PPC). Also, in the low‐Aβ subgroup, tau‐PET mediated the age‐structure (SUB/BA35/PPC) associations (Fig. 3D). Finally, the age‐global cognition relationship was serially mediated via MTL tau‐PET and subiculum volume, even when including global Aβ‐PET as additional mediator (Fig. 4). Conclusion: We observe partially Aβ‐independent associations between age and tau‐PET signal across the neocortex. Interestingly, partially Aβ‐independent tau‐PET signal appears to mediate the age‐structure associations in and outside the MTL (PPC), also in the low‐Aβ group, and the age‐MTL structure‐cognition associations. This potentially provides in vivo support for Primary Age‐related Tauopathy downstream effects on structure, beyond the MTL, and cognition. [ABSTRACT FROM AUTHOR]

Background: The medial temporal lobe (MTL) is hypothesized to be relatively spared in early‐onset compared to late‐onset Alzheimer's disease (AD). However, detailed examination of MTL subfields comparing amnestic early‐ and late‐onset AD is lacking. We investigate MTL subfield atrophy patterns in individuals with amnestic early‐ and late‐onset cognitive impairment (EOCI and LOCI). Methods: Cognitively impaired (mild cognitive impairment and dementia) adults from the BioFINDER‐2 study with memory impairment (>1.5SD age‐ and education‐specific norms), amyloid‐beta (Aβ+ based on CSF Aβ42/40), and tau positivity were included (Table 1). [18F]RO948‐PET standardized uptake value ratios (SUVRs) were calculated for the MTL, and tau‐PET positivity was determined from a temporal meta‐ROI. EOCI individuals were <65 years, LOCI >70 years of age. Self‐reported age‐of‐onset matched with EO/LO status. Two reference groups of Aβ‐ cognitively unimpaired (CU) which were age‐matched to EOCI/LOCI were included. Hippocampal subfield volumes and thickness of entorhinal cortex, Brodmann areas (BA)35/36, and parahippocampal cortex were obtained using Automated Segmentation for Hippocampal Subfields packages for T1‐ and T2‐weighted MRI. We focused particularly on BA35, region with earliest tau accumulation. Episodic memory was determined (errors on delayed word‐list recall). Results: Compared to CU, LOCI showed widespread atrophy across all MTL subfields, while the atrophy pattern in EOCI was slightly more restrictive (Fig. 1). Compared to EOCI, LOCI showed more severe atrophy in hippocampal subfields and entorhinal cortex. There was no difference between EOCI and LOCI for MTL tau‐PET SUVR (Fig. 2). Zooming in on BA35, there was a trend for an association between MTL tau‐PET SUVR and BA35 atrophy in LOCI. This association was stronger for EOCI but non‐significant, likely due to limited power (Fig. 2C). In both groups, smaller BA35 was associated with worse episodic memory (Fig. 3A). In LOCI, higher MTL tau‐PET SUVR was associated with worse episodic memory (Fig. 3B). Conclusion: The findings suggest that MTL tau‐PET uptake and atrophy is present in both amnestic LOCI/EOCI and is related to the observed memory impairment in these groups. The MTL atrophy pattern is more extensive in LOCI, which may be due to longer presence of tau pathology and/or the involvement of comorbid pathologies. [ABSTRACT FROM AUTHOR]

Background: Amyloid‐β (Aβ) and tau markers have very good prognostic value for Alzheimer's disease (AD); however, novel neurodegeneration and glial‐related cerebrospinal fluid (CSF) biomarkers may help further improve prognosis. Using multiple CSF biomarkers from a single multi‐panel, we explored the combination of biomarkers that best predicts cognitive decline and clinical progression in three separate cohorts with relevance for clinical practice and trials. Method: In this study, we included 1,991 participants (cognitively unimpaired [CU], n=1,462; mild cognitive impairment [MCI], n=529) from three separate cohorts (BioFINDER‐1, BioFINDER‐2 and Wisconsin's: Wisconsin Registry for Alzheimer's Prevention [WRAP] & Wisconsin Alzheimer's Disease Research Center [WADRC]; Table 1) with longitudinal cognitive and clinical measurements. CSF was measured in all participants using the NeuroToolKit panel (a panel of automated Elecsys® and robust prototype immunoassays, Roche Diagnostics International Ltd) for biomarkers of AD pathology (p‐tau/Aβ42 ratio), neurodegeneration (neurofilament light chain [NfL], neurogranin, and α‐synuclein), and glial function (sTREM2, GFAP, YKL‐40, IL‐6, and S100b). Linear mixed models and generalized linear mixed models were used in each cohort to identify biomarker combinations most predictive of clinical progression (to Alzheimer's dementia or all‐cause dementia) or cognitive decline (Preclinical Alzheimer's cognitive composite [PACC] for CU and Mini‐Mental State Examination [MMSE] for MCI). Best performing models were compared to single‐predictor (p‐tau/Aβ42) models using Akaike information criterion (AIC) and areas under the curve (AUC), respectively. Result: Only p‐tau/Aβ42 ratio was selected as predictor in the parsimonious models for predicting conversion to Alzheimer's dementia in all cohorts and conversion to all‐cause dementia in the Wisconsin's cohort (Table 2, Figure 1). In the other two cohorts, model performance was significantly improved by the addition of NfL and neurogranin (BioFINDER‐1) and NfL alone (BioFINDER‐2). For predicting cognitive decline, parsimonious models included p‐tau/Aβ42 ratio alone (MMSE in BioFINDER‐1 and PACC in the other cohorts) or together with NfL (rest) as predictors. Conclusion: The CSF p‐tau/Aβ42 ratio is sufficient for prediction of AD progression, showing very high accuracy in three separate cohorts. The addition of NfL to p‐tau/Aβ42 can be applied to a clinical setting to further improve the prediction of all‐cause dementia and cognitive decline. [ABSTRACT FROM AUTHOR]

Background: Different tau biomarkers become abnormal at different stages of Alzheimer's disease (AD), with CSF p‐tau typically being elevated at subthreshold levels of tau‐PET binding. To capitalize on the temporal order of tau biomarker‐abnormality and capture the earliest changes of tau accumulation, we selected a group of amyloid‐β‐positive (A+) individuals with elevated CSF p‐tau levels but negative tau‐PET scans and assessed longitudinal changes in tau‐PET, cortical thickness and cognitive decline. Method: Individuals without dementia (i.e., cognitively unimpaired (CU) or mild cognitive impairment, n=231) were selected from the BioFINDER‐2 study. These subjects were categorized into biomarker groups based on Gaussian mixture modelling to determine cut‐offs for abnormal CSF Aβ42/40 (A; <0.078), CSF p‐tau217 (P; >110 pg/ml) and [18F]RO948 tau‐PET SUVR within a temporal meta‐ROI (T; SUVR >1.40). Resulting groups were: A+P‐T‐ (concordant, n=30), A+P+T‐ (discordant, n=48) and A+P+T+ (concordant, n=18). We additionally used 135 A‐ CU individuals (A‐ CU) as a reference group (Tables 1 and 2). Differences in annual change in regional tau‐PET SUVR, cortical thickness and cognition between the A+P+T‐ group and the other groups were assessed using general linear models, adjusted for age, sex, clinical diagnosis and (for cognitive measures) education. Result: Longitudinal change in tau‐PET was faster in the A+P+T‐ group than in the A‐ CU and A+P‐T‐ groups across medial temporal and neocortical regions, with the medial temporal increases being more pronounced. The A+P+T‐ group showed slower rate of increases in tau‐PET compared to the A+P+T+ group, primarily in neocortical regions (Figures 1 and 2). We did not detect differences in yearly change in cortical thickness (Figure 3) or in cognitive decline (Figure 3) between the A+P+T‐ and A+P‐T‐ groups. The A+P+T+ group, however, showed faster cognitive decline compared to all other groups. Conclusion: These findings suggest that the A+P+T‐ biomarker profile is associated with early tau accumulation, and with relative sparing of cortical thinning and cognitive decline compared to A+P+T+ individuals. Therefore, the A+P+T‐ group represents an interesting target‐group for early anti‐tau interventions and for examining the emergence of tau aggregates in early AD. [ABSTRACT FROM AUTHOR]

Background: While the temporal dynamics of biomarkers in Alzheimer's disease (AD) have been studied in much detail, the lack of established methods for accurately staging pre‐dementia individuals in a time‐consistent manner has resulted in uncertain trajectories and lack of accurate estimates on the temporal relations between biomarkers and clinical milestones. Methods: A novel latent‐time disease progression model was developed to estimate a multivariate time‐consistent trajectory describing the typical timeline of clinical scales (CDR‐SB, ADAS‐cog, MMSE) and amyloid PET along the AD continuum based on data from amyloid‐negative (A‐) cognitively unimpaired (CU) and the full cognitive spectrum of A+. For each subject, the model predicted amyloid‐cognition (AC) scores that represented the progression along the estimated timeline. Correlation to unseen variables was used to validate the AC score construct. AC scores were then used as a natural timescale for comparing a range of biomarker trajectories. Results: Using 1424 individuals (6236 visits, 4581 follow‐up years) from the Alzheimer's Disease Neuroimaging Initiative, we estimated a timeline of Alzheimer's disease that spanned almost 20 years from the earliest stages of CU amyloid PET positivity (AC score 0) to severe dementia (Figure 1). The predicted subject‐level AC scores had the best cross‐domain correlation among all compared staging methods (Table 1). CSF biomarkers (Aβ1‐42/p‐tau181) showed the earliest changes, followed by amyloid PET, plasma p‐tau181 and finally tau PET (Figure 2). In terms of abnormality relative to A‐ CU, CSF Aβ1‐42 reached a population‐level mean value above the A‐ CU 95%‐percentile 1.5 years before amyloid PET. Tau PET reached this level 8 years after amyloid PET (Figure 3). In early pre‐clinical stages, CSF Aβ1‐42 was the biomarker that was most sensitive to change. In the later pre‐clinical and early symptomatic stages amyloid PET was most sensitive followed by tau PET in prodromal stage, and measures of cognition and function in the dementia stage (Figure 4). Conclusion: This analysis supports the AC score as a data‐driven approach for studying biomarker trajectories across the Alzheimer's continuum. The analysis highlighted the value in using different biomarkers to detect subject‐level changes across disease stages. Replication analyses in the BioFINDER study are ongoing. [ABSTRACT FROM AUTHOR]

Background: For optimal design of anti‐amyloid‐β (Aβ) and anti‐tau clinical trials, it is important to understand how Aβ and soluble phosphorylated tau (p‐tau) relate to the accumulation of tau aggregates assessed with positron emission tomography (PET) and subsequent cognitive decline across the Alzheimer's disease (AD) continuum. Method: We included 327 participants from the Swedish BioFINDER‐2 cohort with cerebrospinal fluid (CSF) p‐tau217, Aβ‐PET, longitudinal tau‐PET, and longitudinal cognition. The main groups of interest were Aβ‐positive non‐demented participants and AD dementia patients (Table 1 and Figure 1), and analyses were conducted separately in each group. First, we investigated how soluble p‐tau217 and regional Aβ‐PET were associated with tau‐PET rate of change across the 200 brain parcels from the Schaefer atlas. We also tested the mediating effect of p‐tau217 between Aβ‐PET and tau‐PET change. Second, we investigated how soluble p‐tau217 and tau‐PET change related to change in cognition, and mediation between these variables. Result: In early AD stages (non‐demented participants), increased concentration of soluble p‐tau217 was the main driver of accumulation of insoluble tau aggregates across the brain (measured as tau‐PET rate of change), beyond the effect of regional Aβ‐PET and baseline tau‐PET (Figure 2A‐C). Further, averaged across all regions, soluble p‐tau217 mediated 54% of the association between Aβ and tau aggregation (Figure 2D). Higher soluble p‐tau217 concentrations were also associated with cognitive decline, which was mediated by faster increase of tau aggregates (Figure 3). Repeating the same analyses in the AD dementia group, results were different. In late stage of AD, when Aβ fibrils and soluble p‐tau levels have plateaued, soluble p‐tau217 was not associated with accumulation of tau aggregates beyond baseline tau‐PET (Figure 4A), and cognitive decline was driven by the accumulation rate of insoluble tau aggregates and not soluble p‐tau217 (Figure 4B‐C). Conclusion: Soluble p‐tau is a main driver of tau aggregation and future cognitive decline in earlier stages of AD, whereas tau aggregation accumulation is more likely an important driver of disease in later stages. Overall, our data suggest that therapeutic approaches reducing soluble p‐tau levels might be most favorable in early AD. [ABSTRACT FROM AUTHOR]

Introduction: We studied usefulness of combining blood amyloid beta (Aβ)42/Aβ40, phosphorylated tau (p‐tau)217, and neurofilament light (NfL) to detect abnormal brain Aβ deposition in different stages of early Alzheimer's disease (AD). Methods: Plasma biomarkers were measured using mass spectrometry (Aβ42/Aβ40) and immunoassays (p‐tau217 and NfL) in cognitively unimpaired individuals (CU, N = 591) and patients with mild cognitive impairment (MCI, N = 304) from two independent cohorts (BioFINDER‐1, BioFINDER‐2). Results: In CU, a combination of plasma Aβ42/Aβ40 and p‐tau217 detected abnormal brain Aβ status with area under the curve (AUC) of 0.83 to 0.86. In MCI, the models including p‐tau217 alone or Aβ42/Aβ40 and p‐tau217 had similar AUCs (0.86–0.88); however, the latter showed improved model fit. The models were implemented in an online application providing individualized risk assessments (https://brainapps.shinyapps.io/PredictABplasma/). Discussion: A combination of plasma Aβ42/Aβ40 and p‐tau217 discriminated Aβ status with relatively high accuracy, whereas p‐tau217 showed strongest associations with Aβ pathology in MCI but not in CU. [ABSTRACT FROM AUTHOR]

Background: Tau‐PET has great potential in Alzheimer's disease (AD) clinical trials as pre‐trial inclusion biomarker and as trial endpoint. However, it is unclear which Tau‐PET ROI is optimal for tracking treatment response in relevant trial populations, and how best to enrich trials if using Tau‐PET as an endpoint. We aimed to determine the sample required to achieve 80% power to observe a reduction in Tau‐PET increase when using different combinations of A/T/N biomarkers as trial inclusion measures. Tau‐PET outcome variables were selected based on ROIs that showed greatest increases using longitudinal data. Methods: We included 179 cognitively unimpaired (CU) individuals (39% Aβ‐positive) and 103 MCI (62% Aβ‐positive) with longitudinal Tau‐PET ([18F]RO948;baseline and after ∼18‐months [mean 18.81±4.02]) from BioFINDER‐2. Annual change in Tau‐PET SUVR was calculated across event‐based modelling (EBM) (Figure 1) ROIs. For the power analysis, linear models were fit with the best ROI identified in the first step as outcome and different combinations of A (Amyloid‐PET), T (CSF/plasma P‐tau217, baseline Tau‐PET in outcome ROI), and N (CSF/plasma NfL, hippocampal volume) biomarkers as predictors. Model fit was assessed using change in AIC relative to no enrichment (i.e. all subjects). Results: In CU, annual Tau‐PET increase was highest in EBM stage‐I (1.26%,95%CI [0.82‐1.70%]) (Figure 2); in MCI, the greatest change was seen in EBM stage‐II (1.89%, 95%CI[1.22‐2.56%]). Using these ROIs as outcome variables (assuming selection of top 50% of individuals), regression models showed that plasma P‐tau217 provided the best model fit in CU (44% fewer participants,∆AIC=‐5,P<0.01), followed by CSF P‐tau217 (‐32%, ∆AIC=‐3,P<0.05). In MCI, Amyloid‐PET provided the best model fit (‐71%,∆AIC=‐8, P<0.01), followed by plasma P‐tau217 (‐69%,∆AIC=‐6,P<0.01) and CSF P‐tau217 (‐71%,∆AIC=‐6,P<0.01). When combining biomarkers (Figure 3), plasma P‐tau217, CSF P‐tau217 and Amyloid‐PET proved the best model in CU (‐74%,∆AIC=‐3,P<0.0001). In MCI, Amyloid‐PET with EBM‐II baseline Tau‐PET and plasma P‐tau217 proved the best model (‐92%,∆AIC=‐3,P<0.0001). Combining plasma P‐tau217 with Amyloid‐PET (CU) or baseline Tau‐PET (MCI) provided similar results, however (69%,∆AIC=‐4,P<0.0001; 87%,∆AIC=‐6,P<0.0001, respectively). Conclusion: Plasma P‐tau217 combined with either Amyloid‐PET (preclinical AD) and baseline Tau‐PET (prodromal AD) may prove optimal and feasible enrichment strategies for trials with Tau‐PET as endpoint. [ABSTRACT FROM AUTHOR]

Background: A better understanding of drivers behind neuropsychiatric symptoms (NPS) in neurocognitive diseases, such as Alzheimer's disease, can potentially guide drug development. Cross‐sectional studies have related apathy, depression and anxiety to beta‐amyloid‐ (Aβ) and tau‐pathology, neurodegeneration as well as cognitive deficits already at preclinical stages. However, longitudinal studies are scarce and often small. We investigated effects of neuropathology and cognition on the longitudinal trajectories of NPS in a large group of cognitively unimpaired subjects (CU). Method: The sample originated from the Swedish BioFINDER study. CU subjects with at least one NPS assessment during the biennial follow‐up of maximum 8 years were included (n=356). The Apathy Evaluation Scale (AES) and the Hospital Depression and Anxiety Scale (HADS) assessed apathy as well as anxious and depressive symptoms, respectively. The Mini Mental State Examination (MMSE) and a modified Preclinical Alzheimer Cognitive Composite (mPACC) measured cognition longitudinally. Plasma P‐tau217, CSF Aβ42/Aβ40 ratio and CSF neurofilament light (NfL) were quantified at baseline using immunoassays. Magnetic Resonance Imaging quantified white matter lesions (WML). Subject‐specific cognitive change per year (slope) was obtained by linear regression models. Linear mixed effect models were used to test associations between longitudinal NPS and other measures (including longitudinal cognition). Results: Subjects with lower levels of CSF Aβ42/40 (β=‐0.060, p=0.01) or higher plasma P‐tau217 (β=0.253, p=0.015) at study start demonstrated accelerated levels of apathy over time. On a trend level, a high load of WML predicted over time high levels of apathy (β=0.183, p=0.053). Increased levels of longitudinal anxiety were predicted by the interaction between time and lower CSF Aβ42/40 (β=‐0.283, p=0.005) or higher CSF NfL (β=0.054, p=0.024). Furthermore, more rapid decline in mPACC was associated with more rapid progression of apathy (β=‐0.478, p=<0.001) and anxiety (β=‐0.066, p=<0.023). Similar results were obtained for longitudinal associations between MMSE and apathy (β=‐0.673, p=<0.001). Conclusion: These results suggests that the longitudinal trajectories of apathy and anxiety already in CU subjects are related to key hallmarks of Alzheimer´s disease, including Aβ‐ or tau‐pathology at baseline, and further are parallel with cognitive decline over time. [ABSTRACT FROM AUTHOR]

Background: Several studies indicate that increases in cerebrospinal fluid (CSF) phosphorylated tau (P‐tau) in Alzheimer's disease (AD) occur in response to very early amyloid‐β (Aβ) pathology and precede widespread tau aggregation. Thus far, however, there are no studies comparing different P‐tau isoforms—i.e., tau phosphorylated at threonine‐181 (P‐tau181), 217 (P‐tau217) and 213 (P‐tau231) levels—in relation to Aβ and Tau PET across the symptomatic stages of AD, nor data directly comparing their diagnostic performance for separating AD dementia from non‐AD neurodegenerative disorders and for identifying abnormal Aβ and Tau PET status. We herein aimed to address these questions using cross‐sectional data from the Swedish BioFINDER‐2 study. Method: We included cross‐sectional data for 629 participants (cognitively unimpaired, amyloid‐β (Aβ)‐positive mild cognitive impairment, Alzheimer's disease (AD) dementia and non‐AD disorders). In addition to comparing P‐tau181 and P‐tau217 measured using assays with the same capture antibodies from Eli Lilly (P‐tau181Lilly and P‐tau217Lilly; Meso Scale Discovery platform) with P‐tau231 from ADx NeuroSciences (P‐tau231ADx; sandwich ELISA), we also compared P‐tau181Lilly with P‐tau181 measurements from two commonly used assays (Innotest [P‐tau181Innotest; sandwich ELISA] and Elecsys [P‐tau181Elecsys; fully automated electrochemiluminescence immunoassay]). Aβ and Tau PET were performed using [18F]flutemetamol and [18F]RO948.The discriminative performance of CSF P‐tau measures was assessed using the area under the receiver operating characteristic curve (AUC). Result: Though all P‐tau variants increased across the AD continuum, P‐tau217Lilly showed the greatest dynamic range (13‐fold‐increase vs 1.9‐5.4‐fold‐increase for other P‐tau variants for AD dementia vs non‐AD) (Figure 1). P‐tau217Lillyshowed stronger correlations with Aβ‐ and tau‐PET (P<0.0001) (Figure 2). P‐tau217Lilly exhibited higher accuracy than other P‐tau variants for separating AD dementia from non‐AD (AUC, 0.991vs 0.906‐0.982, P<0.0001) and for identifying Aβ‐PET positivity (AUC, 0.951 vs 0.816‐0.924, P<0.0001) and tau‐PET positivity (AUC, 0.957 vs 0.836‐0.938, P<0.0001) (Figure 3). Finally, P‐tau181Lilly generally performed better than the other P‐tau181 assays, (e.g., AD dementia vs non‐AD, AUC, 0.976 vs 0.923, P<0.0001). Conclusion: CSF P‐tau217Lilly seem to be more useful than other included P‐tau assays in the diagnostic work‐up of AD and for tracking disease progression. Varied results across P‐tau181 assays also highlights the importance of anti‐tau antibodies for biomarker performance. [ABSTRACT FROM AUTHOR]

Background: Relationship between genetic factors and pathological features of Alzheimer's disease (AD) may be studied with biomarker, using both polygenic risk scores (PRSs), as well as individual genetic variants in genome‐wide association studies (GWAS). Method: In the Swedish BioFINDER study, we used a priori PRS for AD based on findings in recent GWAS, and AD related biomarkers in cerebrospinal fluid (CSF) in cognitively unimpaired individuals (n = 751), Mild Cognitive Impairment (n = 212), and AD (n = 150) patients. AD related biomarkers were rank‐based inverse normal transformed to be used as dependent variables in linear regression models adjusted for age, gender, education, APOE ε4, ε2 count and significant principal components. We also tested individual genetic variants in GWAS for each biomarker. Analyses were performed in the total sample, and after stratification on MMSE results. Result: The PRS was associated with higher CSF P‐tau 181 (p ≤1.2e‐05) and T‐tau (p ≤ 8.14e‐05) and lower CSF Aβ42/40 (p ≤ 0.006) and Aβ42 (p ≤ 0.04) (Figure 1, 2). Gene Enrichment of PRS 5 genes [containing 1850 genetic variants mapped to 1607 genes] for Tau biomarker showed 13 Gene Ontology (GO) Biological Process (BP) terms at p‐value < e‐03 ("Dendrite Morphogenesis": top hit; p‐value ≤ 9.20e‐06) and 16 KEGG pathway term enriched for genes of PRS 5 ("Phosphatidylinositol signaling system": top hit; p‐value ≤ 5.5e‐06) (Figure 3, 4). Gene enrichment of PRS 7 [containing 62 genetic variants mapped to 58 genes] for Aβ biomarker returned 12 GO terms ("Integrin‐Mediated Signaling Pathway": top hit; p‐value ≤1.20e‐03) and 1 term enriched for KEGG pathway (Hematopoietic cell lineage). In our predefined list of genes interacting with MAPT (22 genes) and APP (69 genes) we found 3 genes from MAPT and APP set that were involved in PRS 5 and PRS 7 respectively. We also found 9 genes from APP set that was involved in PRS 5. Conclusion: Elevated levels of AD related biomarkers are associated with polygenic risk scores in AD. These findings further strengthens the link between genetic and biomarker disease predictors and indicate a potential role for these markers in disease prediction and patient stratification in AD. [ABSTRACT FROM AUTHOR]

Background: The rate of cognitive decline in the early stages of Alzheimer's disease (AD) is variable, which may be partly due to genetic factors. We therefore investigated genetic predictors of longitudinal cognitive decline in AD. Method: In the Swedish BioFINDER study, we used polygenic scores (PGS) (of AD, intelligence and educational attainment), and genetic variants (in a genome‐wide association study [GWAS]) to predict longitudinal change in cognition (measured by MMSE) over a mean of 4.2 years. We included 555 b‐amyloid (Aβ) negative cognitively unimpaired (CU) individuals, 206 Aβ‐positive CU (preclinical AD), 110 Aβ‐negative mild cognitive impairment (MCI) patients, and 146 Aβ‐positive MCI patients (prodromal AD). Mixed‐effect models were fitted with longitudinal MMSE data as dependent variable. Random slopes and intercepts were extracted and were rank‐based inverse normal transformed (INT) to be used as dependent variables in linear regression models. Result: AD polygenic risk score (PRS) and intelligence PGS (but not education PGS) were associated with rate of cognitive decline (Figure 1). The AD PRS was only associated with decline in Ab‐positive individuals, but the intelligence PGS was protective irrespective of Ab‐status (Figure 2). The model containing only the APOE burden (ε4 and ε2 counts) was associated with cognitive decline with a nominal level of significance, whereas this was not found for the early‐stage AD cohort (Figure 1 and 2). Our GWAS identified 8 genes (out of which 3 genes independent of Aβ‐status) associated with rate of cognitive decline at a p‐value ≤ 5e‐05 (Table 1). Conclusion: An a priori defined genetic risk score for AD was only associated with rate of cognitive decline in early stage AD (Aβ+ CU and Aβ+ MCI) and not in an unselected population, while a polygenic score for intelligence was protective irrespective of Aβ status. Together with novel genetic associations for rate of cognitive decline in AD, this may provide new insights into the pathophysiology of AD and new therapeutic development targets. [ABSTRACT FROM AUTHOR]

Background: The medial temporal lobe (MTL) is affected in aging and dementia. Aging, small vessel disease, β‐amyloid, tau pathology, and inflammation are hypothesized to play a role in neurodegeneration, but their interplay is still unclear. We investigate potential MTL atrophy mediators of (1) Aβ‐related effects in cognitively normal older adults and (2) aging effects in a β‐amyloid negative (A‐) subgroup. Method: 401 adults (41‐91 years) from the Swedish BioFINDER‐2 study were included ([mean±SD] 65.5±11.7 years; A‐ n=274, 62.7±11.8 years). Using the Automated Segmentation for Hippocampal Subfields for T1‐ and T2‐weighted MRI, hippocampal subfield volumes (dentate gyrus (DG), subiculum (Sub), cornu ammonis (CA) 1) and thickness of entorhinal cortex (ERC), Brodmann areas (BA) 35/36, and parahippocampal cortex (PHC) were obtained. [18F]RO948‐ and [18F]Flutemetamol‐PET standard uptake value ratios were calculated for mean MTL tau (excluding hippocampus; partial volume corrected (PVC)) and global Aβ (with and without PVC). White matter lesions (WML; log‐transformed) to estimate small vessel disease and cerebrospinal fluid (CSF) Glial fibrillary acidic protein (GFAP) to estimate neuroinflammation were analyzed. Both global Aβ‐PET and CSF Aβ‐42/40 ratio determined amyloid‐β status. Result: All predictors were negatively associated with Sub volume and BA35/PHC thickness in the whole sample, with roughly similar results for the A‐ subgroup (Table 1, Fig. 1). PVC global Aβ‐PET showed similar results. Mediation analyses therefore focus on these three regions. In the whole sample, MTL tau‐PET and GFAP fully mediated the associations between global Aβ‐PET and Sub/BA35/PHC (Fig. 2). When including age in the model only MTL tau‐PET, not GFAP, remained a significant mediator for Sub. In the A‐ group, MTL tau‐PET partially mediated the age‐Sub/BA35 associations. These mediations survived adjusting for Aβ, except for BA35 (p=.054). Global WML partially mediated the age‐PHC association (Fig. 3). No other predictors were significant mediators. Conclusion: As expected, global Aβ‐PET‐associated neurodegeneration is mediated by MTL tau pathology, but, interestingly, also potentially by inflammation. MTL tau pathology and small vessel disease seemingly drive age effects on the MTL, but in different MTL regions. The mediating effect of MTL tau is supportive of a role for Primary Age‐Related Tauopathy in age‐related neurodegeneration. [ABSTRACT FROM AUTHOR]

Quality of life and health utility are important outcomes for patients with Alzheimer's disease (AD) and central for demonstrating the value of new treatments. Estimates in biomarker‐confirmed AD populations are missing, potentially delaying payer approval of treatment. We examined whether health utility, assessed with the EuroQoL‐5 3‐level version (EQ‐5D‐3L), differed between individuals with a positive or negative amyloid beta (Aβ) biomarker in patients with mild cognitive impairment (MCI) and cognitively unimpaired (CU) participants from the Swedish BioFINDER study (n = 578). Participants with prodromal AD (Aβ‐positive MCI) reported better health utility (n = 79, mean = 0.81, 95% confidence interval [CI] 0.77–0.85) than Aβ‐negative MCI (mean = 0.71, 95% CI 0.64–0.78), but worse than controls (Aβ‐negative CU, mean = 0.87, 95% CI 0.86–0.89). Health utility in preclinical AD (Aβ‐positive CU; mean = 0.86, 95% CI 0.83–0.89) was similar to controls. This relatively good health utility in prodromal AD suggests a larger value of delaying progression to dementia than previously anticipated and a great value of delaying clinical progression in preclinical AD. [ABSTRACT FROM AUTHOR]

Background: Longitudinal tau‐PET is increasingly used as an outcome in clinical trials evaluating disease‐modifying therapies in Alzheimer's disease (AD). In order to quantify change in tau‐PET signal over time, regions of interest (ROIs) are used; to date, these have been based on neuropathological studies or data‐driven approaches where the same ROI is used for each subject (group‐level ROI). However, given the inter‐individual heterogeneity in spatial patterns of tau‐PET, a key question is whether the use of subject‐specific (individualized) ROIs might offer any advantages over group‐level ROIs in AD clinical trials. We aimed to address this question by comparing group‐level and individualized ROIs across several metrics using longitudinal tau‐PET in preclinical and prodromal AD. Methods: We included 174 participants with [18F]RO948 (97 preclinical AD [Aβ‐positive cognitively unimpaired], 77 prodromal AD [Aβ‐positive MCI] from BioFINDER‐2; average follow‐up 1.90±0.50 years). Group‐level ROIs included i) Braak and ii) event‐based modelling (EBM) stages and iii) temporal and iv) whole‐brain meta‐ROIs. Individualized ROIs included i) epicenter (regions with earliest tau), ii) Q1 (quartile closest to subject‐specific epicenter based on functional connectivity) and iii) probability‐based approach (regions selected based on their probability of being tau‐positivity at baseline using Gaussian mixture modelling). Group‐level and individualized ROIs were compared using yearly percent‐change in SUVR (ΔSUVR) and required sample size in a simulated intervention that reduces tau accumulation by 20, 30 or 40%. Results: In preclinical AD (Figure 1A), EBM stage‐I had the greatest ΔSUVR among group‐level ROIs (4.27%). Based on 95% confidence intervals, ΔSUVR was significantly higher using the probability‐based approach (5.51%). A similar pattern was seen in prodromal AD (Figure 1B), where ΔSUVR for the best individualized ROI (probability‐based, 7.86%) was significantly greater than that for the best group‐level ROI (EBM stage‐II, 5.45%). Across interventions (20/30/40%), sample size differences were on average ∼6% lower in preclinical AD and ∼20% lower in prodromal AD using individualized ROIs, relative to group‐level ROIs (Table 1). Further analysis are ongoing using a validation cohort (n = 278) scanned with [18F]flortaucipir. Conclusion: Preliminary findings using [18F]RO948 suggest that individualized ROIs carry an advantage over group‐level ROIs in AD clinical trials using longitudinal tau‐PET as outcome. [ABSTRACT FROM AUTHOR]

Background: Cerebral small vessel disease (SVD) is a cerebrovascular condition associated with age that contributes to dementia, stroke, and other neurological disorders. White matter lesions (WML), microbleeds, and infarcts are hallmarks of SVD, but their molecular mechanisms are poorly understood. The aim of this study was to investigate the CSF proteomic changes underlying the pathogenesis of SVD subtypes. Method: We analysed CSF samples of controls and SVD patients (295 with WML, 149 with microbleeds and 64 with infarcts, total 894 subjects) from the Swedish BioFINDER‐2 study using the Olink proteomics platform, measuring 1331 proteins. To evaluate differentially expressed proteins, we used generalized linear models, including age, sex and CSF dynamics as covariates. For cell type enrichment analysis, we used scRNA‐seq data (Allen Brain Map) obtained from the middle temporal gyrus. Result: We identified >400 CSF proteins dysregulated (pFDR<0.05) in subjects with WML and >20 proteins dysregulated in subjects with microbleeds and infarcts compared to controls. Proteins involved in the regulation of the extracellular matrix, including MMP12 (β = 0.2, p<0.0001 for WML; β = 0.07, p = 0.012 for microbleeds; β = 0.078, p = 0.004 for infarcts) and SMOC2 (β = 0.08, p<0.0001 for WML; β = 0.08, p<0.0001 for microbleeds; β = 0.04, p = 0.03 for infarcts), were significantly upregulated in all SVDs. Others were unique to a specific manifestation of SVD, including CHIT1 for microbleeds (β = 0.15, p<0.0001), and S100A4 for WML (β = 0.16, p<0.0001). Upregulated proteins associated with microbleeds were over‐represented in microglial cells (p = 0.037), but proteins associated with WML and infarcts were enriched in vascular leptomeningeal cells (VLMC) (p<0.0001). In addition, the downregulated proteins in WML were significantly enriched in glutamatergic and GABAergic neurons (p<0.0001). Conclusion: In this study, we revealed shared and distinct proteomic signatures of WML, microbleeds, and infarcts. We uncovered distinct cell type signatures among SVDs, which indicated that microglia might be important for the occurrence of microbleeds but vascular leptomeningeal cells for WML and infarcts. In summary, these findings offer new insights into the molecular mechanisms underlying SVDs. [ABSTRACT FROM AUTHOR]

Background: Cognitive screening tests are often used in primary care to aid in the decision whether further investigation is needed. The most widely used tests, MMSE and the Clock Drawing Test (CDT), were developed more than 40 years ago and there is a need to update the primary care neuropsychological toolbox. The objective for this pilot study was to examine the diagnostic accuracy of AD for different cognitive tests administered at primary care centers. Method: The BioFINDER Primary Care Study consecutively recruits patients with cognitive complaints (SCD, MCI, or mild dementia) at different primary care centers in Sweden. Cognitive tests are examined as well as plasma AD‐biomarkers. A test battery including MMSE, MoCA, CDT, Verbal fluency tests, TMT A and B, Symbol Digit Test, ADAS 10‐wordlist and Recall of Picture Test (RPT) are administered to all patients at their respective primary care center. Patients are then referred to a memory clinic where they undergo a full clinical work‐up (including neuropsychological assessment, lumbar puncture or Aβ‐PET, MRI and an examination by a physician specialized in dementia diseases) after which a diagnosis is made (blinded to the tests done in primary care). A clinical diagnosis of AD is supported by CSF or PET findings. The diagnosis of AD, either at the MCI or mild dementia stage, was here used as the dependent variable in logistic regression models with the different cognitive tests as predictors (adjusted for age, sex, education). Comparisons of AUC were performed using DeLong statistics. Result: In this pilot study the first 276 recruited patients were analyzed (Table 1). Four tests, MMSE (AUC = 0.73, P <0.01), MoCA (AUC = 0.71, P <0.05), ADAS 10‐wordlist (AUC = 0.80, P <0.001) and RPT (AUC = 0.79, P <0.001), performed significantly better than the basic model (age, sex, education) in predicting a diagnosis of AD. Both the ADAS 10‐wordlist and RPT performed significantly better than MMSE (P <0.05). Adding the CDT to MMSE did not improve the AUC (0.73). Conclusion: In a diverse population, as seen at primary care centers, brief tests of memory may provide better accuracy in detecting AD than standard of care tests as MMSE and MoCA. [ABSTRACT FROM AUTHOR]

Background: An important end‐point for disease‐modifying treatments is to target the accumulation of tau. This highlights the need to determine which individuals are most likely to accumulate tau in the future. Different biomarkers of tau become abnormal at different stages of Alzheimer's disease and capturing the evolution of tau pathology in its entirety will rely on multi‐modal assessments. Here, we examine longitudinal changes in tau accumulation associated with elevated CSF p‐tau and tau‐PET biomarkers. Method: A total of 231 amyloid‐positive (A+) individuals from the BioFINDER‐2 study were categorised into groups based on cut‐offs for abnormal CSF p‐tau217 (P) and [18F]RO948 tau‐PET (T), yielding groups of tau‐concordant‐negative (A+P‐T‐;n = 30), tau‐discordant (A+P+T‐;n = 48) and tau‐concordant‐positive (A+P+T+;n = 18) individuals. In addition, 135 amyloid‐β‐negative, unimpaired individuals served as controls. Differences in regional tau‐PET between groups were assessed using general‐linear models, adjusted for age, sex, clinical‐diagnosis and (for cognitive measures) education. We additionally assessed the effects on cortical thickness and cognition. Mean follow‐up time was ∼2 years. Result: In the A+P+T‐ group, even though below the threshold for positivity, tau‐PET was higher at baseline than in the control and A+P‐T‐ groups, while cortical thickness and cognition were not different between these groups. For the A+P+T+ group, tau‐PET was higher, and cortical thickness, and cognition were lower at baseline than for controls, A+P‐T‐ and A+P+T‐. Longitudinal increase in tau‐PET was faster in the A+P+T‐ group than in controls and A+P‐T‐, primarily in the medial‐temporal lobe. The A+P+T+ group showed a faster rate of tau‐PET compared to controls and A+P‐T‐ (across the entire brain), and compared to A+P+T‐ (primarily neocortex). There were no differences in cortical thinning or cognitive decline between controls, A+P+T‐ and A+P‐T‐. The A+P+T+ group showed faster cortical thinning and cognitive decline compared to controls, A+P‐T‐ and A+P+T‐. Conclusion: The A+P+T‐ group only shows tau accumulation and no cortical thinning or cognitive decline, and therefore represents an interesting population for early intervention in the AD pathological process, preventing downstream effects. The A+P+T+ group shows fast and widespread tau accumulation and will, therefore, show the greatest beneficial effect of tau‐targeted treatment but already shows cortical thinning and cognitive decline at baseline. [ABSTRACT FROM AUTHOR]

Background: CSF biomarkers are well characterised as surrogate markers for amyloid deposition and neurofibrillary tau tangles reflecting pathological features of Alzheimer's disease. Amyloid PET images provide a visual and quantitative assessment of brain amyloid burden. In diagnostic workup these biomarkers are considered interchangeable, however their relationship is not fully understood. Methods: Centiloid (CL) values for 387 [18F]flutemetamol images from the Swedish BioFINDER I study were derived following the methods of Klunk et.al. (Alzheimer's & Dementia, 2015). Subjects, comprising Controls, SCD and MCI (Table 1), were ranked by Centiloid values (lowest to highest) and the average for each consecutive group of 30 subjects calculated. Corresponding CSF biomarker values (Aβ42/40 and pTau181) were plotted for each group. Results: Subjects in groups averaging ‐7.7 CL to 29.0 CL had a mean(±SD) Aβ 42/40 ratio of 0.144 (±0.033, range 0.051 to 0.236, n = 240) with no statistically significant differences between groups (Fig. 1A). A statistically significant fall in CSF Aβ42/40 ratio between the 29.0 CL and 47.3 CL groups (from 0.134 (±0.041, 0.066 to 0.236) to 0.088(±0.031, 0.045 to 0.183), p<0.0001) was observed. The average CSF Aβ42/40 ratio value for all subjects ranging from 66.3 Cl to129.8 CL was 0.072 (±0.017, 0.030 to 0.127, n = 117) with no statistically significant difference in CSF levels across these CL bands. Subjects in the groups averaging ‐7.7 CL to 29.0 CL had a mean(±SD) pTau181 value of 48.9 pg/mL (±15.9, 22 to 138) with no statistically significant difference between groups (Fig. 1B). A statistically significant (p<0.05) increase between 29.0 CL (47.3±19.3, 24 to 116 pg/mL) and 47.3 CL groups (67.8±29.4, 24 to 175 pg/mL) was observed. For all Subjects from 66.3 CL to129.8 CL mean pTau181 levels were 79.6 (±30.3, 32 to 209 pg/mL) with no statistically significant difference across these CL bands. Conclusions: Comparison of [18F]flutemetamol image quantitation and CSF biomarkers indicates CSF Aβ42/40 and pTau181 starts to show amyloid positivity at approximately 30‐40 CL units. Recent comparisons between visual rating and Centiloids from PET imaging indicate visual positivity from approximately 20 CL units indicating sensitivity of amyloid PET imaging for the detection of early amyloid deposition. [ABSTRACT FROM AUTHOR]

Background: In the ATN (Amyloid b, Tau, Neurodegeneration) biomarker research framework proposed for Alzheimer's Disease (AD), a critical need is the identification of robust markers of neurodegeneration ("N") for more accurate disease state diagnosis and prognosis. Targeted proteomic profiling can be highly valuable for identifying such markers. Method: We reanalyzed Olink™ data from 270 CSF and plasma samples from the Swedish BioFINDER study (Whelan et al. 2019) using additional normalization and adjustments to reduce technical variation and inter‐subject heterogeneity. These include 1) plate‐level quantile normalization, 2) adjustment for age, gender, BMI, and anti‐inflammatory drugs, and 3) first principal component as a covariate to account for heterogeneity in control subjects from different cohorts. Our analysis cohort included 140 Aβ+ cognitively normal (ApCN), 65 Aβ+ mild cognitive impairment (ApMCI), and 138 Aβ + AD (ApAD) subjects. Proteins with false discovery rate < 5% and Cohen's D ≥ 0.5 were considered statistically significant. Multiscale Embedded Gene Co‐expression Network Analysis (MEGENA) was used to identify co‐expressing network modules and hub proteins. Result: Our analysis identified significant alterations of 65 CSF and 94 plasma proteins comparing different pairs of ApAD, ApMCI and ApCN groups. AUC ranged from 61% to 86% for differentiating ApAD vs ApCN. MMP‐10 was found to be significantly altered in all CSF comparisons, but not in plasma. We identified many significantly altered proteins not reported by Whelan et al (2019), including TFF3, EFNA4, and CTSC that are known to be associated with neurodegeneration or neuroinflammation. MEGENA analysis identified 10 network modules for CSF and 17 network modules for plasma. The most significant CSF modules were represented by IGFB‐2 and PLC hub proteins and enriched for positive regulation of cellular function. The most significant plasma modules were represented by SIRT2 and LAT and enriched for cellular response to stimulus. Conclusion: Targeted proteomic profiling of matching CSF and plasma samples along with co‐expression network analysis can yield additional markers in the ATN framework for early detection and screening for AD. [ABSTRACT FROM AUTHOR]

Background: Recent studies suggest that microtubule‐binding region (MTBR) tau fragments may be markers of tau tangles in Alzheimer's disease (AD) when used in ratio with total tau (MTBR‐tau/T‐tau) to normalize for T‐tau secretion. One such candidate marker is MTBR‐tau containing residue 368 (MTBR‐tau368). In this study, it was cross‐sectionally evaluated in participants from a memory clinic cohort and an autopsy series. Methods: We included Aβ‐negative cognitively unimpaired (CU) individuals, individuals in the AD continuum (Aβ‐positive CU, mild cognitive impairment (MCI) and AD dementia), and with non‐AD dementia from the BioFINDER‐1 study, who all underwent [18F]‐flortaucipir‐PET. Further, we included CU and CI participants from UCSD Alzheimer's disease research center (UCSD‐ADRC) who underwent lumbar puncture (LP) antemortem and then donated their brain for autopsy, examining AD neuropathologic change (ADNC) and non‐AD pathologies. All participants had immunoassay‐based CSF measures of T‐tau and MTBR‐tau368 by commercial immunoassays (Euroimmun [BioFINDER‐1] and Lumipulse [UCSD‐ADRC] T‐tau) and an in‐house Single molecule array (Simoa) assay, respectively. Statistical analyses were performed using linear models (covarying for age and sex) and Pearson correlations. Results: Sixty‐seven individuals from UCSD‐ADRC (mean [SD] age at LP 74.3 [8.4] years, 4.3 [2.1] years before death) and 181 from BioFINDER‐1 (72.5 [7.4] years) were included for this study. There were no group‐level differences in MTBR‐tau368 levels alone in any of the cohorts. In the UCSD‐ADRC cohort, MTBR‐tau368/T‐tau was decreased in participants with high (estimated marginal mean [EM = 0.06) vs. low (EM = 0.09, P = 0.01) or no (EM = 0.1, P = 0.001) ADNC and with higher Braak stage (V‐VI; EM = 0.06 vs. III‐IV; EM = 0.08, P<0.05, and I‐II; EM = 0.09, P<0.001). In BioFINDER‐1, Aβ‐positive MCI and AD dementia had lower MTBR‐tau368/T‐tau (EM∼0.04) compared with all other groups (EM = 0.06‐0.07, P<0.05‐<0.001). Further, MTBR‐tau368/T‐tau inversely correlated with tau‐PET in Braak‐like regions III‐IV and V‐VI (r = ‐0.35 and ‐0.42, P<0.001). MTBR‐tau368/T‐tau was unchanged in individuals in the frontotemporal dementia (FTD) spectrum (behavioral variant FTD/semantic dementia, and corticobasal syndrome/progressive supranuclear palsy). Conclusion: Concordant with recent data, MTBR‐tau fragments alone do not change in AD or other neurodegenerative disorders. However, when normalizing MTBR fragments to T‐tau secretion, their capability of reflecting the extent of tau pathology provides opportunities of staging AD using CSF biomarkers. [ABSTRACT FROM AUTHOR]

Background: The first and final level of care for most individuals with cognitive impairment is primary care. The aim was to examine the diagnostic accuracy of plasma phospho‐tau217/non‐phospho‐Tau217 (p‐tau217 Ratio) and Aβ42/40 Ratio for Alzheimer's disease (AD) as compared to usual care by primary care physicians (PCPs). Method: In the BioFINDER‐Primary Care study, we recruited patients examined due to cognitive complaints at 25 primary care centers in Sweden (n = 307). After care‐as‐usual (CT/MRI, cognitive testing, and clinical assessment), the PCPs documented the most likely underlying etiology and proposed a treatment plan. Plasma biomarkers included p‐tau217 Ratio, Aβ42/Aβ40 Ratio, and a predefined algorithm combining both (C2N mass‐spectrometry). The outcome was AD pathology defined using FDA‐approved cerebrospinal fluid Aβ42/p‐tau181 (Lumipulse®) or visual read of 18F‐Flutemetamol Aβ‐PET. Result: The mean age was 76±6.3 years, 48% were women, and 49% had AD pathology. Twenty‐five percent had subjective cognitive decline, 47% mild cognitive impairment, and 28% dementia. Using AD pathology as the outcome, the AUCs were 0.80 (95%CI 0.75‐0.86) for Aβ42/Aβ40 Ratio, 0.91 (0.88‐0.94) for pTau217 Ratio, and 0.94 (0.92‐0.97) for the algorithm. In those with available PCP questionnaires (n = 265), the PCPs correctly classified AD in 54% (50‐59%) of the cases, compared with 77% (73‐81%) for Aβ42/40 Ratio, 85% (82‐88%) for p‐tau217 Ratio, and 87% (84‐90%) for the algorithm. The mean certainty of the PCPs' diagnosis (1‐10, not at all to completely certain) was 4.7 (5.1 in those with AD pathology who were correctly diagnosed as AD and 4.6 in the missed AD cases). The PCPs' proposed treatment, which in 87% followed the treatment guidelines, led to 50% of true AD cases not receiving symptomatic treatment and 30% of non‐AD cases incorrectly receiving symptomatic AD treatment (DLB patients excluded). Conclusion: We found that it is difficult for PCPs to identify AD among patients with cognitive impairment in primary care, which leads to diagnostic uncertainty and incorrect treatment. The use of a blood test for AD pathology has great potential for improving the diagnostic accuracy and treatment of AD in primary care. At AAIC, a larger dataset will be presented including the effects of co‐morbidities and medications. [ABSTRACT FROM AUTHOR]

Background: Aggregates of misfolded α‐synuclein are the underlying pathology of Lewy body (LB) disease. Little is known about the early effects of LB pathology in preclinical (i.e., pre‐symptomatic) individuals. Further, there is poor knowledge about the independent clinical effects of Lewy body (LB) pathology in patients with cognitive decline, especially when coexisting with Alzheimer's disease (AD) pathology. Method: Here, we examined the effects of LB pathology using a cerebrospinal fluid α‐synuclein seeding aggregation assay in 1,182 cognitively and neurologically unimpaired participants as well as 882 participants with mild cognitive impairment or dementia from the BioFINDER‐1 and BioFINDER‐2 studies. Result: In clinically unimpaired individuals, we found that 8% were LB‐positive, 26% Aβ‐positive (12% of those were also LB‐positive), and 16% tau‐positive. LB pathology had independent and significant negative effects on cross‐sectional and longitudinal global cognition and memory. These were similar in magnitude to the effects of tau pathology and more pronounced than those of Aβ pathology. Participants with both LB and AD (Aβ and tau) pathology exhibited faster cognitive decline than those with just LB or AD pathology. LB, but not AD, pathology was associated with reduced sense of smell. In cognitively impaired individuals, we found that 23% of participants had LB pathology, of whom only 32% fulfilled clinical criteria of Parkinson's disease or dementia with Lewy bodies, and 54% exhibited AD pathology (amyloid‐β+ and tau+). Within the LB‐positive group, 48% also had AD‐pathology. In the AD‐positive group, 17% of patients with mild cognitive impairment and 24% of those with dementia were also LB‐positive. LB pathology was associated with worse attention/executive, visuospatial, and motor function independently of AD pathology. Moreover, LB pathology was also independently associated with a faster longitudinal decline in all examined cognitive functions. Conclusion: This study highlights the cognitive effects of LB pathology, independent from AD pathology, in clinically unimpaired individuals, which will be important to consider for prognosis and design of both preclinical AD and LB disease trials. Further, analyzing LB status, in addition to amyloid‐β and tau, in cognitively impaired individuals provides a better precision medicine approach identifying patient subgroups with different clinical trajectories. [ABSTRACT FROM AUTHOR]

Background: CSF biomarkers are well characterised as surrogate markers for amyloid deposition and neurofibrillary tau tangles reflecting pathological features of Alzheimer's disease. Amyloid PET images provide a visual and quantitative assessment of brain amyloid burden. In diagnostic workup these biomarkers are considered interchangeable, however their relationship is not fully understood. Method: Centiloid (CL) values for 387 [18F]flutemetamol images from the Swedish BioFINDER I study were derived following the methods of Klunk et.al. (Alzheimer's & Dementia, 2015). Subjects, comprising Controls, SCD and MCI (Table 1), were ranked by Centiloid values (lowest to highest) and the average for each consecutive group of 30 subjects calculated. Corresponding CSF biomarker values (Aß42/40 and pTau181) were plotted for each group. Result: Subjects in groups averaging ‐7.7 CL to 29.0 CL had a mean(±SD) Aß 42/40 ratio of 0.144 (±0.033, range 0.051 to 0.236, n = 240) with no statistically significant differences between groups (Fig. 1A). A statistically significant fall in CSF Aß42/40 ratio between the 29.0 CL and 47.3 CL groups (from 0.134 (+0.041, 0.066 to 0.236) to 0.088(+0.031, 0.045 to 0.183), p<0.0001) was observed. The average CSF Aß42/40 ratio value for all subjects ranging from 66.3 Cl to129.8 CL was 0.072 (±0.017, 0.030 to 0.127, n = 117) with no statistically significant difference in CSF levels across these CL bands. Subjects in the groups averaging ‐7.7 CL to 29.0 CL had a mean(±SD) pTau181 value of 48.9 pg/mL (±15.9, 22 to 138) with no statistically significant difference between groups (Fig. 1B). A statistically significant (p<0.05) increase between 29.0 CL (47.3±19.3, 24 to 116 pg/mL) and 47.3 CL groups (67.8±29.4, 24 to 175 pg/mL) was observed. For all Subjects from 66.3 CL to129.8 CL mean pTau181 levels were 79.6 (±30.3, 32 to 209 pg/mL) with no statistically significant difference across these CL bands. Conclusion: Comparison of [18F]flutemetamol image quantitation and CSF biomarkers indicates CSF Aß42/40 and pTau181 starts to show amyloid positivity at approximately 30‐40 CL units. Recent comparisons between visual rating and Centiloids from PET imaging indicate visual positivity from approximately 20 CL units indicating sensitivity of amyloid PET imaging for the detection of early amyloid deposition. [ABSTRACT FROM AUTHOR]

Background: Cognitive resilience refers to processes involved in coping with effects of brain pathology on cognition. Factors contributing to cognitive resilience against brain atrophy are unclear, as there have been few longitudinal studies investigating this. Therefore we aimed to identify factors explaining the discrepancy between brain atrophy and longitudinal cognitive decline. One proposed method of investigating cognitive resilience is a residual approach, which suggests using the variance in cognition not explained by a specified predictor as a measure of resilience. Method: Cognitively unimpaired subjects (n=406) from The Swedish BioFINDER Study with magnetic resonance imaging (MRI) and cognitive test results from at least two time points were included. Using FreeSurfer, global cortical thickness was calculated. A cognitive composite score was calculated from Mini Mental State Examination, Alzheimer´s Disease Assessment Scale – Cognitive Subscale delayed word recall, Trail Making Test B, and Animal fluency. For each subject, the beta coefficients for change in cortical thickness and change in cognition over time were calculated. Linear regression was performed between the two beta coefficients, and the residuals were used as a measure of cognitive resilience. Associations were calculated between this resilience measure and baseline demographics, biomarkers of amyloid (cerebrospinal fluid (CSF) β‐amyloid (Aβ) 42/40 ratio), tau (CSF phosphorylated tau (p‐tau)), and vascular (MRI white matter lesion (WML) volume) pathology as well as neurodegeneration (CSF neurofilament light (NfL)). For dichotomous variables (sex and pathologic Aβ42/40 ratio) independent samples t‐tests, and for continuous variables (all other variables) Pearson correlations, were performed. Result: In univariate models, lower age at baseline, female sex, higher cortical thickness and better cognitive results at baseline were associated with higher cognitive resilience against cortical atrophy, while education was not. Further, pathological Aβ42/40 ratio and higher levels of p‐tau, NfL, and WMLs at baseline were negatively associated with cognitive resilience in univariate analyses. In a multivariable linear regression model, lower baseline cortical thickness, a pathological Aβ42/40 ratio, and higher NfL were significantly associated with worse cognitive resilience (Figure 1, Table 1). Conclusion: Markers of Alzheimer´s disease pathology and neurodegeneration are associated with worse than expected cognitive decline at a given rate of brain atrophy. [ABSTRACT FROM AUTHOR]

Background: Biomarkers for the prediction of cognitive decline in patients with mild cognitive impairment (MCI) due to Alzheimer’s disease (AD) and mild AD dementia are needed for inclusion of suitable patients in clinical trials. We therefore assessed the ability of tau‐PET, blood and cerebrospinal fluid (CSF) phospho‐Tau217 (pTau217), CSF neurofilament light (NfL) and cortical thickness to predict cognitive decline early symptomatic AD. Method: 73 amyloid‐β positive (CSF Aβ42/Aβ40<0.752) participants with MCI and mild AD dementia (MMSE ≥ 22) were assessed at baseline with cognitive testing, blood and cerebrospinal fluid (CSF)‐sampling, MRI and [18F]RO948 PET and were followed longitudinally with annual cognitive testing over 24 months. Slopes of change in MMSE and preclinical Alzheimer cognitive composite (PACC) were calculated over two years. We used linear regression models to assess the ability of biomarkers (plasma pTau217, CSF‐pTau217, temporal meta‐ROI [18F]RO948 PET standardized uptake value ratios (SUVR), CSF‐NfL and MRI “AD‐signature” cortical thickness) to predict cognitive decline both individually and in combination. The linear regression models were adjusted for age, sex, education, and baseline MMSE. Result: All tested biomarkers individually showed significant associations with cognitive decline using both MMSE and PACC as outcome variables. For predicting longitudinal change in MMSE using individual biomarkers, [18F]RO948 SUVR showed the highest R2‐value (0.39, 95% CI [0.28‐0.67]) and best model fit (lowest Akaike Information Criterion score)(Table 1). Adding other biomarkers yielded a slightly higher R2‐value (0.44 [0.33‐0.63]), but did not improve the model fit. In a sensitivity analysis we performed a similar analysis in a subset of participants having undergone longitudinal testing with a modified PACC test battery (n=53). Results again showed the best performance for [18F]RO948 SUVRs, when tested alone, but with improved model fit and R2‐values with the addition of cortical thickness and CSF‐NfL data (Table 2). Conclusion: Tau‐PET imaging outperformed CSF, plasma and MRI measures when predicting cognitive decline in prodromal AD and mild AD dementia. Specifically, tau‐PET provided the best prediction alone for change in MMSE and in combination with NfL and cortical thickness for change in PACC, indicating that it would be a suitable biomarker for inclusion in clinical trials. [ABSTRACT FROM AUTHOR]

Background: The exploratory NeuroToolKit (Roche Diagnostics International Ltd.) is a panel of automated robust prototype assays measuring cerebrospinal fluid (CSF) Alzheimer's disease (AD) biomarkers. NeuroToolKit includes core AD biomarkers Aβ42, Aβ40, p‐tau181 and t‐tau, markers of synaptic dysfunction (α‐synuclein and neurogranin), astrocytosis (S100b, YKL‐40, glial fibrillary acidic protein [GFAP]), microglial activation and inflammation (sTREM2, IL‐6) and axonal injury (neurofilament light [NfL]). In this study, we investigated additional effects of APOE e4 genotype, age and sex on the change of the biomarkers over time in CSF samples from the longitudinal Swedish BioFINDER cohort. Method: CSF samples were collected at baseline and then at 2‐, 4‐, 6‐, 8‐ and 10‐years' follow‐up from 1324 patients who were cognitively unimpaired (CU, n=801) or had mild cognitive impairment (n=265) or non‐AD dementias (n=258). Groups were stratified as Aβ positive (Aβ+) or negative (Aβ‐) according to CSF Aβ42/40 ratio (cut‐off<0.07). Linear mixed‐effect models were used to assess interaction between time and APOE e4 genotype (no e4 allele, heterozygous e4, homozygous e4), sex and age stratified by cognitive status (CU<65 years, CU≥65 years; cognitively impaired [CI] <65 years, CI≥65 years). Result: APOE e4 homozygous genotype was associated with a significant increase in Aβ42 and YKL‐40 levels in the Aβ+ group over time (versus subjects with no e4 allele) (Table 1A, Figure 1A‐B). In the Aβ‐ group, male sex was associated with a significant increase in IL‐6 and sTREM2 levels over time (Table 1B, Figure 1C‐D). Regardless of Aβ status, increased age was associated with increase in neurogranin, p‐tau181 and T‐tau over time and decrease in sTREM2 in the cohort as a whole (Table 2A). However, when stratified for age and clinical diagnosis, there was an increase in sTREM2 levels in the CI group (versus CU<65 years) over time (Table 2B, Figure 1E). No significant effects of age, sex and APOE genotype were observed in the other biomarkers. Conclusion: The longitudinal change in most biomarkers in the NeuroToolKit is not affected by APOE e4 genotype, age and sex; however, significant effects of these factors on the measurement of some biomarkers warrant adjustment in such studies. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease is characterized by β‐amyloid plaques and tau tangles. Plasma levels of phospho‐tau217 (P‐tau217) accurately differentiate Alzheimer's disease dementia from other dementias, but it is unclear to what degree this reflects β‐amyloid plaque accumulation, tau tangle accumulation, or both. Method: We studied plasma P‐tau217 in a cohort with post‐mortem neuropathological data (N=88), for correlations with plaque and tangle density, and in a cohort with β‐amyloid and tau PET imaging ("BioFINDER‐2", N=426). Result: In the cohort with post‐mortem neuropathological data, both plaque and tangle density contributed independently to higher P‐tau217 (Fig 1). Several findings were replicated in the cohort with PET imaging, where β‐amyloid and tau PET were independently associated to P‐tau217. P‐tau217 correlated with β‐amyloid PET (but not tau PET) in early disease stages, and with both β‐amyloid and (more strongly) tau PET in late disease stages (Fig 2). Finally, P‐tau217 mediated the association between β‐amyloid and tau in both cohorts, especially for tau outside of the medial temporal lobe (Fig 3). Conclusion: These findings support the hypothesis that plasma P‐tau217 is associated with both β‐amyloid plaques and tau tangles and is congruent with the hypothesis that P‐tau is involved in β‐amyloid‐dependent formation of neocortical tau tangles (Fig 4). [ABSTRACT FROM AUTHOR]

Introduction: Differential patterns of brain atrophy on structural magnetic resonance imaging (MRI) revealed four reproducible subtypes of Alzheimer's disease (AD): (1) "typical", (2) "limbic‐predominant", (3) "hippocampal‐sparing", and (4) "mild atrophy". We examined the neurobiological characteristics and clinical progression of these atrophy‐defined subtypes. Methods: The four subtypes were replicated using a clustering method on MRI data in 260 amyloid‐β‐positive patients with mild cognitive impairment or AD dementia, and we subsequently tested whether the subtypes differed on [18F]flortaucipir (tau) positron emission tomography, white matter hyperintensity burden, and rate of global cognitive decline. Results: Voxel‐wise and region‐of‐interest analyses revealed the greatest neocortical tau load in hippocampal‐sparing (frontoparietal‐predominant) and typical (temporal‐predominant) patients, while limbic‐predominant patients showed particularly high entorhinal tau. Typical patients with AD had the most pronounced white matter hyperintensity load, and hippocampal‐sparing patients showed the most rapid global cognitive decline. Discussion: Our data suggest that structural MRI can be used to identify biologically and clinically meaningful subtypes of AD. [ABSTRACT FROM AUTHOR]

Background: Placental growth factor (PlGF) is a pro‐inflammatory marker of angiogenesis, which may play a role in cardiovascular disease and is upregulated by hyperglycemia. In non‐demented elderly, cerebrospinal fluid (CSF) PlGF is strongly associated with white matter lesions (WML), a commonly used measure of cerebrovascular disease. However, it is unclear to what degree CSF PlGF and WML provide overlapping or complementary information about pathological processes in the brain. To investigate this, we tested associations for different vascular risk factors with CSF PlGF and WML. Methods: 495 cognitively unimpaired (CU) elderly and 247 patients with mild cognitive impairment (MCI) from the Swedish BIOFINDER study were included. CSF samples were analyzed for PlGF, Aβ40 and Aβ42. Information about the vascular risk factors stroke (present in 8%), hypertension (35%), diabetes (9%), and ischemic heart disease (IHD) (10%) was collected. Associations between WML and PlGF as well as their associations with these risk factors were tested with linear regression models. Analyses were adjusted for age, gender, Aβ status, and intracranial volume. Results: PlGF was associated with WML in the whole group (β = 0.18; CI 0.14‐0.22), and in diagnostic subgroups CU (β = 0.16; CI 0.10‐0.21) and MCI (β = 0.17; CI 0.11‐0.24). Stroke (β = 0.44; CI 0.29‐0.59) and hypertension (β = 0.12; CI 0.03‐0.20) were associated with WML, whereas diabetes (β = 0.37; CI 0.15‐0.60) was associated with PlGF. There was also a nominally significant association between WML and IHD (β = 0.15; CI 0.017‐0.29). In diagnostic subgroups, associations between stroke and WML were significant in CU (β = 0.35; CI 0.12‐0.59), and MCI (β = 0.38; CI 0.20‐0.57) after correction for multiple comparison. Conclusion: We confirmed that PlGF is strongly associated with WML. However, PlGF and WML were differentially associated with vascular risk factors. PlGF was strongly associated with diabetes, which could indicate alterations in endothelial function, frequently seen in diabetes complications. In contrast, WML was associated with stroke and hypertension, both associated with cerebrovascular disease. This suggests that WML and PlGF contribute complementary information about vascular disease processes in the brain. [ABSTRACT FROM AUTHOR]

Background: Modifiable risk factors account for 40% of worldwide dementias. However, to what extent different risk factors are associated with different types of cognitive decline remains unclear. The aim of this study was to investigate how different risk factors contribute to future decline in memory and attention/executive function, respectively. Method: Baseline examination of individuals without dementia in the prospective Swedish population‐based Malmö Diet and Cancer Study (MDCS) took place in 1991‐1996. In 2007‐2012, a randomly selected subsample was invited to a re‐examination using cognitive assessments (the Mini‐Mental State Examination [MMSE], A Quick Test of cognitive speed (AQT) and, for a sub‐population, The Montreal Cognitive Assessment (MoCA). Participants with complete data on cognitive assessments, body measurements, education and APOE genotype were included in the study (n = 2,881; n = 720 with MoCA). Linear regression models were used to examine the modifiable risk factors (hypertension, body mass index [BMI], long‐term glucose levels [HbA1c], lipid levels, physical activity and alcohol consumption) and non‐modifiable risk factor (APOE‐genotype). Follow‐up outcomes were 1) AQT‐color/form (processing speed and executive function, higher scores indicating worse function), 2) delayed recall and orientation scores from MMSE (memory function), and 3) subscores from MoCA. All models were adjusted for age, sex, education and time between baseline and cognitive assessments. Result: Median follow‐up time was 17.3 (inter‐quartile range [IQR] 2.2) years. APOE‐e4 and higher HbA1c levels were independently associated with worse future memory function in a multivariable model (Table 1). Mean arterial blood pressure, obesity, lower HDL‐C, and higher HbA1c levels were independently associated with worse future processing speed/executive function (Table 2). Higher alcohol consumption was associated better performance in executive function (Table 2). Similar results were found using corresponding MoCA subscores (delayed recall and orientation subscores; visuospatial/executive and attention subscores). Conclusion: In this prospective, population‐based, 17‐years follow‐up study, cardiovascular risk factors in mid‐life were associated with future decline in processing speed and executive functions, while an APOE‐e4 genotype was associated with worse memory function. Alcohol consumption in mid‐life was associated with better performance in both cognitive domains. These results suggest that targeting cardiovascular risk factors in interventions may have a greater effect on future executive function than memory. [ABSTRACT FROM AUTHOR]

Background: Tau pathology is a key hallmark of Alzheimer's disease (AD), but factors modifying the rate of tau accumulation are still largely unknown. Tau‐PET allows tau pathology to be visualized in vivo in AD. Our aim was to study factors associated with the accumulation rate of tau pathology, using [18F]flortaucipir PET. Method: 419 participants from four longitudinal cohorts in the United States (AVID05, n=157; Expedition 3, n=82; and ADNI, n=123) and Sweden (n=57) were scanned between June 2014 and May 2019. Study participants were cognitively unimpaired (CU, n=153), or patients with mild cognitive impairment (MCI, n=139) or AD with dementia (AD, n=127).Participants underwent two to four [18F]‐flortaucipir PET scans with a mean of 538 (±163[SD]) days between the first and last scan. Amyloid‐β(Aβ) status was determined using PET. [18F]‐flortaucipir was quantified in a temporal meta‐region of interest (ROI) and a neocortical ROI. Subject‐specific slopes (reflecting annual change) of [18F]‐flortaucipir SUVR were derived from linear regression models for each ROI. Generalized linear models were used to predict the subject‐specific slopes using the following variables: diagnostic group, age, sex, baseline tau‐PET SUVR, baseline Aβ‐status, APOE‐ε4 status and study cohort. Result: Accelerated increase in [18F]‐flortaucipir retention was observed in Aβ+ MCI and AD dementia, respectively, when compared to either Aβ‐ CU and Aβ‐ MCI (Figure 1). We found associations between change in [18F]‐flortaucipir over time and female sex (temporal meta‐ROI: t=2.93, p=0.004; neocortical ROI: t=2.96, p=0.003), younger age (temporal‐meta ROI: t=‐2.42, p=0.02), and baseline [18F]‐flortaucipir retention (temporal‐meta ROI: t=3.60, p<0.001; neocortical ROI: t=4.88, p<0.001) (Figure 2). There was a significant interaction between age and Aβ‐status on change in [18F]‐flortaucipir SUVR (temporal‐meta ROI: t=‐2.50, p=0.013). There was no effect of study cohort or APOE‐ε4 genotype. In a similar analysis on longitudinal Aβ‐PET (in ADNI subjects only, n=639) we found significant associations between the rate of Aβ‐accumulation and APOE‐ε4 positivity, older age and baseline Aβ‐positivity, but no effect of sex. Conclusion: These findings indicate that the tau accumulation rate is increased in females and Aβ‐positive younger individuals, in contrast to Aβ‐accumulation which is increased in APOE‐ε4 carriers and older individuals. [ABSTRACT FROM AUTHOR]

Background: It is unlikely that biomarker testing contributes to a more accurate prognosis in every patient with Mild Cognitive Impairment (MCI). We therefore aimed to derive a stepwise approach to select MCI patients for cerebrospinal fluid (CSF) testing and determine the optimal proportion of patients to receive CSF testing. Methods: We selected n=402 MCI patients from the Amsterdam Dementia Cohort (ADC) and n=221 from the BioFinder study, with available demographic information, Magnetic Resonance Imaging (MRI) and CSF data. Three‐year progression probabilities to dementia were predicted for all patients, using previously published model (van Maurik et al. 2019, The Lancet Neurology) based on 1) patient characteristics (age, sex, MMSE; demographic model), or 2) additional MRI (hippocampal volume; MRI model). We incrementally augmented the proportion of patients to receive CSF testing, first the most uncertain 10% of patients (percentile 45‐55 of demographic‐ or MRI model derived probabilities), then 20% (percentile 40‐60), and so on. Prognostic performance of this stepwise model was measured with Harrell's C. The optimal proportion was selected as the point where the stepwise model performed similar to testing all patients. This proportion provides the range of probabilities where additional CSF testing would be indicated (Table 1). To study generalizability, we replicated this approach in BioFINDER. Results: On top of demographic characteristics, the stepwise approach showed that 60% of the patients required CSF testing in both cohorts in order to reach optimal prognostic performance (i.e. similar to CSF model; Figure 1A). This proportion corresponded with progression probability between 32%‐56%. This range was similar in BioFINDER (34%‐56%; Table 1). If we took the MRI model as a starting point, the optimal proportion was 60% in the ADC and 70% in BioFINDER (i.e. similar to ATN model; Figure 1B). This corresponded with a probability range of 19%‐40% in the ADC and 19%‐48% in BioFINDER (Table 1). Conclusion: We showed that CSF biomarker testing adds prognostic value on clinical and/or imaging information in 60‐70% of the MCI patients that had less certain probability of progression between 32%‐56% or 19%‐48%. As such, we achieve a generalizable CSF saving recommendation with optimal prognostic accuracy. [ABSTRACT FROM AUTHOR]

Background: Neuroinflammation may accelerate Alzheimer's disease (AD) progression. We profiled acute phase response (APR) proteins (stimulated by IL‐6 released from pro‐inflammatory glia) in CSF of people along the clinical and pathology (CSF t‐tau/Aβ42) spectrum of AD to investigate the association between disease outcomes and inflammatory changes. Methods: APR proteins in CSF from 920 subjects in the Swedish BioFINDER study were quantitively analyzed using multiple reaction modelling mass spectrometry: α1‐antitrypsin, α1‐antichymotrypsin, ceruloplasmin, complement C3, α‐fibrinogen, β‐fibrinogen, γ‐fibrinogen, haptoglobin, and hemopexin. Ferritin was measured using multiplex ELISA. CSF t‐tau and Aβ42 was measured using Euroimmun ELISA and the cohort was dichotomized for AD pathology using the t‐tau/Aβ42 ratio. Subjects were also stratified by the following clinical diagnoses: cognitively normal (CN); subjective cognitive decline (SCD); mild cognitive impairment (MCI); and AD dementia. N per group was: CN t‐tau/Aβ42‐ve=335; CN t‐tau/Aβ42+ve=104; SCD t‐tau/Aβ42‐ve=105; CN t‐tau/Aβ42+ve=44; MCI t‐tau/Aβ42‐ve=78; MCI t‐tau/Aβ42+ve=133; AD t‐tau/Aβ42+ve=121. Results: In separate multiple regressions (adjusting for diagnosis, age, sex, APOE‐ε4), CSF t‐tau/Aβ42 status did not affect APR protein levels in CN and SCD subjects (Figure 1). Rather, the APR elevated with symptomatic progression: MCI t‐tau/Aβ42+ve subjects had 4/10 proteins elevated compared to CN tau/Aβ42‐ve subjects (α1 antichymotrypsin, ferritin, α‐fibrinogen, β‐fibrinogen; 0.001

Background: Innate lymphoid cells (ILCs) are the most recently identified leukocytes of the immune system and these cells are increasingly acknowledged to play important roles in host defence and tissue repair. ILCs are also contributors of inflammatory diseases such as asthma and colitis. We analyzed the presence and relative proportions of the different ILC subsets (ILC1, ILC2 and ILC3) in gingivitis and periodontitis. Further, we investigated if ILCs express receptor activator of nuclear factor kappa-B ligand (RANKL), a cytokine crucial for osteoclast differentiation and bone resorption.Methods: We collected gingivitis and periodontitis soft tissue and characterized ILC subsets including RANKL expression in single-cell suspensions using flow cytometry.Results: ILCs were detected both in gingivitis and periodontitis. The majority of ILCs, in both conditions, were ILC1s. Furthermore, RANKL expression was detected on a fraction of the ILC1s.Conclusions: Our discovery of the presence of ILCs both in gingivitis and periodontitis and concomitant expression of RANKL on a fraction of the ILC1 population suggest that these cells may be of importance in periodontal disease. In addition, our findings provide a new insight into the field of oral immunology. [ABSTRACT FROM AUTHOR]