Medial temporal lobe atrophy patterns in early-versus late-onset amnestic Alzheimer's disease.

BACKGROUND: The medial temporal lobe (MTL) is hypothesized to be relatively spared in early-onset Alzheimer's disease (EOAD). Yet, detailed examination of MTL subfields and drivers of atrophy in amnestic EOAD is lacking. METHODS: BioFINDER-2 participants with memory impairment, abnormal amyloid-ß and tau-PET were included. Forty-one amnestic EOAD individuals ≤65 years and, as comparison, late-onset AD (aLOAD, ≥70 years, n = 154) and amyloid-ß-negative cognitively unimpaired controls were included. MTL subregions and biomarkers of (co-)pathologies were measured. RESULTS: AD groups showed smaller MTL subregions compared to controls. Atrophy patterns were similar across AD groups: aLOAD showed thinner entorhinal cortices than aEOAD; aEOAD showed thinner parietal regions than aLOAD. aEOAD showed lower white matter hyperintensities than aLOAD. No differences in MTL tau-PET or transactive response DNA binding protein 43-proxy positivity were found. CONCLUSIONS: We found evidence for MTL atrophy in amnestic EOAD and overall similar levels to aLOAD of MTL tau pathology and co-pathologies.

Lewy body pathology exacerbates brain hypometabolism and cognitive decline in Alzheimer's disease.

Identifying concomitant Lewy body (LB) pathology through seed amplification assays (SAA) might enhance the diagnostic and prognostic work-up of Alzheimer's disease (AD) in clinical practice and trials. This study examined whether LB pathology exacerbates AD-related disease progression in 795 cognitively impaired individuals (Mild Cognitive Impairment and dementia) from the longitudinal multi-center observational ADNI cohort. Participants were on average 75 years of age (SD = 7.89), 40.8% were female, 184 (23.1%) had no biomarker evidence of AD/LB pathology, 39 (4.9%) had isolated LB pathology (AD-LB+), 395 (49.7%) had only AD pathology (AD+LB-), and 177 (22.3%) had both pathologies (AD+LB+). The AD+LB+ group showed worst baseline performance for most cognitive outcomes and compared to the AD+LB- group faster global cognitive decline and more cortical hypometabolism, particularly in posterior brain regions. Neuropathological examination (n = 61) showed high sensitivity (26/27, 96.3%) and specificity (27/28, 96.4%) of the SAA-test. We showed that co-existing LB-positivity exacerbates cognitive decline and cortical brain hypometabolism in AD. In vivo LB pathology detection could enhance prognostic evaluations in clinical practice and could have implications for clinical AD trial design.

Associations between misfolded alpha-synuclein aggregates and Alzheimer's disease pathology in vivo.

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 (Aß) 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 Aß 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 Aß 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 Aß levels, providing in vivo evidence of links between these two molecular disease pathways in humans. HIGHLIGHTS: Amyloid beta (Aß), 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 Aß positron emission tomography levels in one of the two cohorts.

Proteomic changes in Alzheimer's disease associated with progressive Aß plaque and tau tangle pathologies.

Proteomics can shed light on the dynamic and multifaceted alterations in neurodegenerative disorders like Alzheimer's disease (AD). Combining radioligands measuring ß-amyloid (Aß) plaques and tau tangles with cerebrospinal fluid proteomics, we uncover molecular events mirroring different stages of AD pathology in living humans. We found 127 differentially abundant proteins (DAPs) across the AD spectrum. The strongest Aß-related proteins were mainly expressed in glial cells and included SMOC1 and ITGAM. A dozen proteins linked to ATP metabolism and preferentially expressed in neurons were independently associated with tau tangle load and tau accumulation. Only 20% of the DAPs were also altered in other neurodegenerative diseases, underscoring AD's distinct proteome. Two co-expression modules related, respectively, to protein metabolism and microglial immune response encompassed most DAPs, with opposing, staggered trajectories along the AD continuum. We unveil protein signatures associated with Aß and tau proteinopathy in vivo, offering insights into complex neural responses and potential biomarkers and therapeutics targeting different disease stages.

Prediction of future cognitive decline among cognitively unimpaired individuals using measures of soluble phosphorylated tau or tau tangle pathology.

Plasma p-tau217 and Tau-PET are strong prognostic biomarkers in Alzheimer's disease (AD), but their relative performance in predicting future cognitive decline among cognitively unimpaired (CU) individuals is unclear. In this head-to-head comparison study including 9 cohorts and 1534 individuals, we found that plasma p-tau217 and medial temporal lobe Tau-PET signal showed similar associations with cognitive decline on a global cognitive composite test (R2 PET=0.32 vs R2 PLASMA=0.32, pdifference=0.812) and with progression to mild cognitive impairment (Hazard ratio[HR]PET=1.56[1.43-1.70] vs HRPLASMA=1.63[1.50-1.77], pdifference=0.627). Combined plasma and PET models were superior to the single biomarker models (R2=0.36, p<0.01). Furthermore, sequential selection using plasma p-tau217 and then Tau-PET reduced the number of participants required for a clinical trial by 94%, compared to a 75% reduction when using plasma p-tau217 alone. We conclude that plasma p-tau217 and Tau-PET showed similar performance for predicting future cognitive decline in CU individuals, and their sequential use (i.e., plasma p-tau217 followed by Tau-PET in a subset with high plasma p-tau217) is useful for screening in clinical trials in preclinical AD.

Predicting progression from subjective cognitive decline to mild cognitive impairment or dementia based on brain atrophy patterns.

BACKGROUND: Alzheimer's disease (AD) is a progressive neurodegenerative disorder where pathophysiological changes begin decades before the onset of clinical symptoms. Analysis of brain atrophy patterns using structural MRI and multivariate data analysis are an effective tool in identifying patients with subjective cognitive decline (SCD) at higher risk of progression to AD dementia. Atrophy patterns obtained from models trained to classify advanced AD versus normal subjects, may not be optimal for subjects at an early stage, like SCD. In this study, we compared the accuracy of the SCD progression prediction using the 'severity index' generated using a standard classification model trained on patients with AD dementia versus a new model trained on ß-amyloid (Aß) positive patients with amnestic mild cognitive impairment (aMCI). METHODS: We used structural MRI data of 504 patients from the Swedish BioFINDER-1 study cohort (cognitively normal (CN), Aß-negative = 220; SCD, Aß positive and negative = 139; aMCI, Aß-positive = 106; AD dementia = 39). We applied multivariate data analysis to create two predictive models trained to discriminate CN individuals from either individuals with Aß positive aMCI or AD dementia. Models were applied to individuals with SCD to classify their atrophy patterns as either high-risk "disease-like" or low-risk "CN-like". Clinical trajectory and model accuracy were evaluated using 8 years of longitudinal data. RESULTS: In predicting progression from SCD to MCI or dementia, the standard, dementia-based model, reached 100% specificity but only 10.6% sensitivity, while the new, aMCI-based model, reached 72.3% sensitivity and 60.9% specificity. The aMCI-based model was superior in predicting progression from SCD to MCI or dementia, reaching a higher receiver operating characteristic area under curve (AUC = 0.72; P = 0.037) in comparison with the dementia-based model (AUC = 0.57). CONCLUSION: When predicting conversion from SCD to MCI or dementia using structural MRI data, prediction models based on individuals with milder levels of atrophy (i.e. aMCI) may offer superior clinical value compared to standard dementia-based models.

Identification of distinct and shared biomarker panels in different manifestations of cerebral small vessel disease through proteomic profiling.

The pathophysiology underlying various manifestations of cerebral small vessel disease (cSVD) remains obscure. Using cerebrospinal fluid proximity extension assays and co-expression network analysis of 2,943 proteins, we found common and distinct proteomic signatures between white matter lesions (WML), microbleeds and infarcts measured in 856 living patients, and validated WML-associated proteins in three additional datasets. Proteins indicative of extracellular matrix dysregulation and vascular remodeling, including ELN, POSTN, CCN2 and MMP12 were elevated across all cSVD manifestations, with MMP12 emerging as an early cSVD indicator. cSVD-associated proteins formed a co-abundance network linked to metabolism and enriched in endothelial and arterial smooth muscle cells, showing elevated levels at early disease manifestations. Later disease stages involved changes in microglial proteins, associated with longitudinal WML progression, and changes in neuronal proteins mediating WML-associated cognitive decline. These findings provide an atlas of novel cSVD biomarkers and a promising roadmap for the next generation of cSVD therapeutics.

Blood Biomarkers to Detect Alzheimer Disease in Primary Care and Secondary Care.

Importance: An accurate blood test for Alzheimer disease (AD) could streamline the diagnostic workup and treatment of AD. Objective: To prospectively evaluate a clinically available AD blood test in primary care and secondary care using predefined biomarker cutoff values. Design, Setting, and Participants: There were 1213 patients undergoing clinical evaluation due to cognitive symptoms who were examined between February 2020 and January 2024 in Sweden. The biomarker cutoff values had been established in an independent cohort and were applied to a primary care cohort (n = 307) and a secondary care cohort (n = 300); 1 plasma sample per patient was analyzed as part of a single batch for each cohort. The blood test was then evaluated prospectively in the primary care cohort (n = 208) and in the secondary care cohort (n = 398); 1 plasma sample per patient was sent for analysis within 2 weeks of collection. Exposure: Blood tests based on plasma analyses by mass spectrometry to determine the ratio of plasma phosphorylated tau 217 (p-tau217) to non-p-tau217 (expressed as percentage of p-tau217) alone and when combined with the amyloid-ß 42 and amyloid-ß 40 (Aß42:Aß40) plasma ratio (the amyloid probability score 2 [APS2]). Main Outcomes and Measures: The primary outcome was AD pathology (determined by abnormal cerebrospinal fluid Aß42:Aß40 ratio and p-tau217). The secondary outcome was clinical AD. The positive predictive value (PPV), negative predictive value (NPV), diagnostic accuracy, and area under the curve (AUC) values were calculated. Results: The mean age was 74.2 years (SD, 8.3 years), 48% were women, 23% had subjective cognitive decline, 44% had mild cognitive impairment, and 33% had dementia. In both the primary care and secondary care assessments, 50% of patients had AD pathology. When the plasma samples were analyzed in a single batch in the primary care cohort, the AUC was 0.97 (95% CI, 0.95-0.99) when the APS2 was used, the PPV was 91% (95% CI, 87%-96%), and the NPV was 92% (95% CI, 87%-96%); in the secondary care cohort, the AUC was 0.96 (95% CI, 0.94-0.98) when the APS2 was used, the PPV was 88% (95% CI, 83%-93%), and the NPV was 87% (95% CI, 82%-93%). When the plasma samples were analyzed prospectively (biweekly) in the primary care cohort, the AUC was 0.96 (95% CI, 0.94-0.98) when the APS2 was used, the PPV was 88% (95% CI, 81%-94%), and the NPV was 90% (95% CI, 84%-96%); in the secondary care cohort, the AUC was 0.97 (95% CI, 0.95-0.98) when the APS2 was used, the PPV was 91% (95% CI, 87%-95%), and the NPV was 91% (95% CI, 87%-95%). The diagnostic accuracy was high in the 4 cohorts (range, 88%-92%). Primary care physicians had a diagnostic accuracy of 61% (95% CI, 53%-69%) for identifying clinical AD after clinical examination, cognitive testing, and a computed tomographic scan vs 91% (95% CI, 86%-96%) using the APS2. Dementia specialists had a diagnostic accuracy of 73% (95% CI, 68%-79%) vs 91% (95% CI, 88%-95%) using the APS2. In the overall population, the diagnostic accuracy using the APS2 (90% [95% CI, 88%-92%]) was not different from the diagnostic accuracy using the percentage of p-tau217 alone (90% [95% CI, 88%-91%]). Conclusions and Relevance: The APS2 and percentage of p-tau217 alone had high diagnostic accuracy for identifying AD among individuals with cognitive symptoms in primary and secondary care using predefined cutoff values. Future studies should evaluate how the use of blood tests for these biomarkers influences clinical care.

MRI Signature of α-Synuclein Pathology in Asymptomatic Stages and a Memory Clinic Population.

Importance: The lack of an in vivo measure for α-synuclein (α-syn) pathology until recently has limited thorough characterization of its brain atrophy pattern, especially during early disease stages. Objective: To assess the association of state-of-the-art cerebrospinal fluid (CSF) seed amplification assays (SAA) α-syn positivity (SAA α-syn+) with magnetic resonance imaging (MRI) structural measures, across the continuum from clinically unimpaired (CU) to cognitively impaired (CI) individuals, in 3 independent cohorts, and separately in CU and CI individuals, the latter reflecting a memory clinic population. Design, Setting, and Participants: Cross-sectional data were used from the Swedish BioFINDER-2 study (inclusion, 2017-2023) as the discovery cohort and the Swedish BioFINDER-1 study (inclusion, 2007-2015) and Alzheimer's Disease Neuroimaging Initiative (ADNI; inclusion 2005-2022) as replication cohorts. All cohorts are from multicenter studies, but the BioFINDER cohorts used 1 MRI scanner. CU and CI individuals fulfilling inclusion criteria and without missing data points in relevant metrics were included in the study. All analyses were performed from 2023 to 2024. Exposures: Presence of α-syn pathology, estimated by baseline CSF SAA α-syn. Main Outcomes and Measures: The primary outcomes were cross-sectional structural MRI measures either through voxel-based morphometry (VBM) or regions of interest (ROI) including an automated pipeline for cholinergic basal forebrain nuclei CH4/4p (nucleus basalis of Meynert [NBM]) and CH1/2/3. Secondary outcomes were domain-specific cross-sectional cognitive measures. Analyses were adjusted for CSF biomarkers of Alzheimer pathology. Results: A total of 2961 participants were included in this study: 1388 (mean [SD] age, 71 [10] years; 702 female [51%]) from the BioFINDER-2 study, 752 (mean [SD] age, 72 [6] years; 406 female [54%]) from the BioFINDER-1 study, and 821 (mean [SD] age, 75 [8] years; 449 male [55%]) from ADNI. In the BioFINDER-2 study, VBM analyses in the whole cohort revealed a specific association between SAA α-syn+ and the cholinergic NBM, even when adjusting for Alzheimer copathology. ROI-based analyses in the BioFINDER-2 study focused on regions involved in the cholinergic system and confirmed that SAA α-syn+ was indeed independently associated with smaller NBM (ß = -0.271; 95% CI, -0.399 to -0.142; P <.001) and CH1/2/3 volumes (ß = -0.227; 95% CI, -0.377 to -0.076; P =.02). SAA α-syn+ was also independently associated with smaller NBM volumes in the separate CU (ß = -0.360; 95% CI, -0.603 to -0.117; P =.03) and CI (ß = -0.251; 95% CI, -0.408 to -0.095; P =.02) groups. Overall, the association between SAA α-syn+ and NBM volume was replicated in the BioFINDER-1 study and ADNI cohort. In CI individuals, NBM volumes partially mediated the association of SAA α-syn+ with attention/executive impairments in all cohorts (BioFINDER-2, ß = -0.017; proportion-mediated effect, 7%; P =.04; BioFINDER-1, ß = -0.096; proportion-mediated effect, 19%; P =.04; ADNI, ß = -0.061; proportion-mediated effect, 20%; P =.007). Conclusions and Relevance: In this cohort study, SAA α-syn+ was consistently associated with NBM atrophy already during asymptomatic stages. Further, in memory clinic CI populations, SAA α-syn+ was associated with NBM atrophy, which partially mediated α-syn-induced attention/executive impairment.

Plasma Phosphorylated Tau 217 and Aß42/40 to Predict Early Brain Aß Accumulation in People Without Cognitive Impairment.

Importance: Phase 3 trials of successful antiamyloid therapies in Alzheimer disease (AD) have demonstrated improved clinical efficacy in people with less severe disease. Plasma biomarkers will be essential for efficient screening of participants in future primary prevention clinical trials testing antiamyloid therapies in cognitively unimpaired (CU) individuals with initially low brain ß-amyloid (Aß) levels who are at high risk of accumulating Aß. Objective: To investigate if combining plasma biomarkers could be useful in predicting subsequent development of Aß pathology in CU individuals with subthreshold brain Aß levels (defined as Aß levels <40 Centiloids) at baseline. Design, Setting, and Participants: This was a longitudinal study including Swedish BioFINDER-2 (enrollment 2017-2022) and replication in 2 independent cohorts, the Knight Alzheimer Disease Research Center (Knight ADRC; enrollment 1988 and 2019) and Swedish BioFINDER-1 (enrollment 2009-2015). Included for analysis was a convenience sample of CU individuals with baseline plasma phosphorylated tau 217 (p-tau217) and Aß42/40 assessments and Aß assessments with positron emission tomography (Aß-PET) or cerebrospinal fluid (CSF) Aß42/40. Data were analyzed between April 2023 and May 2024. Exposures: Baseline plasma levels of Aß42/40, p-tau217, the ratio of p-tau217 to nonphosphorylated tau (%p-tau217), p-tau231, and glial fibrillary acidic protein (GFAP). Main Outcomes and Measures: Cross-sectional and longitudinal PET and CSF measures of brain Aß pathology. Results: This study included 495 (BioFINDER-2), 283 (Knight ADRC), and 205 (BioFINDER-1) CU participants. In BioFINDER-2, the mean (SD) age was 65.7 (14.4) with 261 females (52.7%). When detecting abnormal CSF Aß-status, a combination of plasma %p-tau217 and Aß42/40 showed better performance (area under the curve = 0.949; 95% CI, 0.929-0.970; P <.02) than individual biomarkers. In CU participants with subthreshold baseline Aß-PET, baseline plasma %p-tau217 and Aß42/40 levels were significantly associated with baseline Aß-PET (n = 384) and increases in Aß-PET over time (n = 224). Associations of plasma %p-tau217 and Aß42/40 and their interaction with baseline Aß-PET (%p-tau217: ß = 2.77; 95% CI, 1.84-3.70; Aß42/40: ß = -1.64; 95% CI, -2.53 to -0.75; %p-tau217 × Aß42/40: ß = -2.14; 95% CI, -2.79 to -1.49; P < .001) and longitudinal Aß-PET (%p-tau217: ß = 0.67; 95% CI, 0.48-0.87; Aß42/40: ß = -0.33; 95% CI, -0.51 to -0.15; %p-tau217 × Aß42/40: ß = -0.31; 95% CI, -0.44 to -0.18; P < .001) were also significant in the models combining the 2 baseline biomarkers as predictors. Similarly, baseline plasma p-tau217 and Aß42/40 were independently associated with longitudinal Aß-PET in Knight ADRC (%p-tau217: ß = 0.71; 95% CI, 0.26-1.16; P = .002; Aß42/40: ß = -0.74; 95% CI, -1.26 to -0.22; P = .006) and longitudinal CSF Aß42/40 in BioFINDER-1 (p-tau217: ß = -0.0003; 95% CI, -0.0004 to -0.0001; P = .01; Aß42/40: ß = 0.0004; 95% CI, 0.0002-0.0006; P < .001) in CU participants with subthreshold Aß levels at baseline. Plasma p-tau231 and GFAP did not provide any clear independent value. Conclusions and Relevance: Results of this cohort study suggest that combining plasma p-tau217and Aß42/40 levels could be useful for predicting development of Aß pathology in people with early stages of subthreshold Aß accumulation. These biomarkers might thus facilitate screening of participants for future primary prevention trials.

Remote and unsupervised digital memory assessments can reliably detect cognitive impairment in Alzheimer's disease.

INTRODUCTION: Remote unsupervised cognitive assessments have the potential to complement and facilitate cognitive assessment in clinical and research settings. METHODS: Here, we evaluate the usability, validity, and reliability of unsupervised remote memory assessments via mobile devices in individuals without dementia from the Swedish BioFINDER-2 study and explore their prognostic utility regarding future cognitive decline. RESULTS: Usability was rated positively; remote memory assessments showed good construct validity with traditional neuropsychological assessments and were significantly associated with tau-positron emission tomography and downstream magnetic resonance imaging measures. 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. DISCUSSION: 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. HIGHLIGHTS: Remote and unsupervised digital memory assessments are feasible in older adults and individuals in early stages of Alzheimer's disease. Digital memory assessments are associated with neuropsychological in-clinic assessments, tau-positron emission tomography and magnetic resonance imaging measures. Combination of digital memory assessments with plasma p-tau217 holds promise for prognosis of future cognitive decline. Future validation in further independent, larger, and more diverse cohorts is needed to inform clinical implementation.

Associations of modifiable and non-modifiable risk factors with cognitive functions - a prospective, population-based, 17 years follow-up study of 3,229 individuals.

BACKGROUND: Although several cardiovascular, demographic, genetic and lifestyle factors have been associated with cognitive function, little is known about what type of cognitive impairment they are associated with. The aim was to examine the associations between different risk factors and future memory and attention/executive functions, and their interaction with APOE genotype. METHODS: Participants from a large, prospective, population-based, Swedish study were included (n = 3,229). Linear regression models were used to examine baseline hypertension, body mass index (BMI), long-term glucose levels (HbA1c), different lipid levels, physical activity, alcohol consumption, smoking, education, APOE genotype, age and sex. All models were adjusted for follow-up time and basic demographics, and, in a second step, all significant predictors were included to examine independent effects. Follow-up outcomes were memory and attention/executive functions. RESULTS: The mean age at baseline was 56.1 (SD 5.7) years and 59.7% were women. The mean follow-up time was 17.4 (range 14.3-20.8) years. When examining independent effects, APOE ε4 genotype(p < 0.01), and higher HbA1c(p < 0.001), were associated with future low memory function. Higher BMI (p < 0.05), and HbA1c(p < 0.05), lower high-density lipoprotein cholesterol (HDL-C)(p < 0.05)and stroke(p < 0.001) were associated with future low attention/executive function. The strongest factors associated with both better memory and attention/executive functions were higher education and alcohol consumption. Further, significant interaction effects between predictors and APOE genotype were found. For memory function, the protective effects of education were greater among ɛ4-carriers(p < 0.05). For attention/executive function, the protective effects of alcohol were greater among ɛ2 or ɛ4-carriers(p < 0.05). Also, attention/executive function was lower among ɛ4-carriers with higher BMI(p < 0.05) and ɛ2-carriers with higher HbA1c-levels(p < 0.05). CONCLUSIONS: Targeting cardiovascular risk factors in mid-life could have greater effect on future attention/executive functions rather than memory, whereas targeting diabetes could be beneficial for multiple cognitive domains. In addition, effects of different risk factors may vary depending on the APOE genotype. The varied cognitive profiles suggest that different mechanisms and brain regions are affected by the individual risk factors. Having detailed knowledge about the specific cognitive effects of different risk factors might be beneficial in preventive health counseling.

Medial temporal lobe atrophy patterns in early- versus late-onset amnestic Alzheimer's disease.

Background: The medial temporal lobe (MTL) is hypothesized to be relatively spared in early-onset Alzheimer's disease (EOAD). Yet, detailed examination of MTL subfield volumes and drivers of atrophy in amnestic EOAD is lacking. Methods: BioFINDER-2 participants with memory impairment, abnormal amyloid-ß status and tau-PET were included. Forty-one EOAD individuals aged ≤65 years and, as comparison, late-onset AD (LOAD, ≥70 years, n=154) and Aß-negative cognitively unimpaired controls were included. MTL subregions and biomarkers of (co-)pathologies were measured. Results: AD groups showed smaller MTL subregions compared to controls. Atrophy patterns were similar across AD groups, although LOAD showed thinner entorhinal cortices compared to EOAD. EOAD showed lower WMH compared to LOAD. No differences in MTL tau-PET or transactive response DNA binding protein 43-proxy positivity was found. Conclusions: We found in vivo evidence for MTL atrophy in amnestic EOAD and overall similar levels to LOAD of MTL tau pathology and co-pathologies.

A machine learning-based prediction of tau load and distribution in Alzheimer's disease using plasma, MRI and clinical variables.

Tau positron emission tomography (PET) is a reliable neuroimaging technique for assessing regional load of tau pathology in the brain, commonly used in Alzheimer's disease (AD) research and clinical trials. However, its routine clinical use is limited by cost and accessibility barriers. Here we explore using machine learning (ML) models to predict clinically useful tau-PET composites from low-cost and non-invasive features, e.g., basic clinical variables, plasma biomarkers, and structural magnetic resonance imaging (MRI). Results demonstrated that models including plasma biomarkers yielded the most accurate predictions of tau-PET burden (best model: R-squared=0.66-0.68), with especially high contribution from plasma P-tau217. In contrast, MRI variables stood out as best predictors (best model: R-squared=0.28-0.42) of asymmetric tau load between the two hemispheres (an example of clinically relevant spatial information). The models showed high generalizability to external test cohorts with data collected at multiple sites. Based on these results, we also propose a proof-of-concept two-step classification workflow, demonstrating how the ML models can be translated to a clinical setting. This study uncovers current potential in predicting tau-PET information from scalable cost-effective variables, which could improve diagnosis and prognosis of AD.

Tau Positron Emission Tomography for Predicting Dementia in Individuals With Mild Cognitive Impairment.

Importance: An accurate prognosis is especially pertinent in mild cognitive impairment (MCI), when individuals experience considerable uncertainty about future progression. Objective: To evaluate the prognostic value of tau positron emission tomography (PET) to predict clinical progression from MCI to dementia. Design, Setting, and Participants: This was a multicenter cohort study with external validation and a mean (SD) follow-up of 2.0 (1.1) years. Data were collected from centers in South Korea, Sweden, the US, and Switzerland from June 2014 to January 2024. Participant data were retrospectively collected and inclusion criteria were a baseline clinical diagnosis of MCI; longitudinal clinical follow-up; a Mini-Mental State Examination (MMSE) score greater than 22; and available tau PET, amyloid-ß (Aß) PET, and magnetic resonance imaging (MRI) scan less than 1 year from diagnosis. A total of 448 eligible individuals with MCI were included (331 in the discovery cohort and 117 in the validation cohort). None of these participants were excluded over the course of the study. Exposures: Tau PET, Aß PET, and MRI. Main Outcomes and Measures: Positive results on tau PET (temporal meta-region of interest), Aß PET (global; expressed in the standardized metric Centiloids), and MRI (Alzheimer disease [AD] signature region) was assessed using quantitative thresholds and visual reads. Clinical progression from MCI to all-cause dementia (regardless of suspected etiology) or to AD dementia (AD as suspected etiology) served as the primary outcomes. The primary analyses were receiver operating characteristics. Results: In the discovery cohort, the mean (SD) age was 70.9 (8.5) years, 191 (58%) were male, the mean (SD) MMSE score was 27.1 (1.9), and 110 individuals with MCI (33%) converted to dementia (71 to AD dementia). Only the model with tau PET predicted all-cause dementia (area under the receiver operating characteristic curve [AUC], 0.75; 95% CI, 0.70-0.80) better than a base model including age, sex, education, and MMSE score (AUC, 0.71; 95% CI, 0.65-0.77; P = .02), while the models assessing the other neuroimaging markers did not improve prediction. In the validation cohort, tau PET replicated in predicting all-cause dementia. Compared to the base model (AUC, 0.75; 95% CI, 0.69-0.82), prediction of AD dementia in the discovery cohort was significantly improved by including tau PET (AUC, 0.84; 95% CI, 0.79-0.89; P < .001), tau PET visual read (AUC, 0.83; 95% CI, 0.78-0.88; P = .001), and Aß PET Centiloids (AUC, 0.83; 95% CI, 0.78-0.88; P = .03). In the validation cohort, only the tau PET and the tau PET visual reads replicated in predicting AD dementia. Conclusions and Relevance: In this study, tau-PET showed the best performance as a stand-alone marker to predict progression to dementia among individuals with MCI. This suggests that, for prognostic purposes in MCI, a tau PET scan may be the best currently available neuroimaging marker.

Cerebrospinal fluid reference proteins increase accuracy and interpretability of biomarkers for brain diseases.

Cerebrospinal fluid (CSF) biomarkers reflect brain pathophysiology and are used extensively in translational research as well as in clinical practice for diagnosis of neurological diseases, e.g., Alzheimer's disease (AD). However, CSF biomarker concentrations may be influenced by non-disease related inter-individual variability. Here we use a data-driven approach to demonstrate the existence of inter-individual variability in mean standardized CSF protein levels. We show that these non-disease related differences cause many commonly reported CSF biomarkers to be highly correlated, thereby producing misleading results if not accounted for. To adjust for this inter-individual variability, we identified and evaluated high-performing reference proteins which improved the diagnostic accuracy of key CSF AD biomarkers. Our reference protein method attenuates the risk for false positive findings, and improves the sensitivity and specificity of CSF biomarkers, with broad implications for both research and clinical practice.

Plasma p-tau217 in Alzheimer's disease: Lumipulse and ALZpath SIMOA head-to-head comparison.

Background: Plasma phosphorylated-tau217 (p-tau217) has been shown to be one of the most accurate diagnostic markers for Alzheimer's disease (AD). No studies have compared the clinical performance of p-tau217 as assessed by the fully automated Lumipulse and SIMOA ALZpath p-tau217. Aim: To evaluate the diagnostic accuracy of Lumipulse and SIMOA plasma p-tau217 assays for AD. Methods: The study included 392 participants, 162 with AD, 70 with other neurodegenerative diseases (NDD) with CSF biomarkers and 160 healthy controls. Plasma p-tau217 levels were measured using the Lumipulse and ALZpath SIMOA assays. The ability of p-tau217 assessed by both techniques to discriminate AD from NDD and controls was investigated using ROC analyses. Results: Both techniques showed high internal consistency of p-tau217 with similar correlation with CSF p-tau181 levels. In head-to-head comparison, Lumipulse and SIMOA showed similar diagnostic accuracy for differentiating AD from NDD (area under the curve [AUC] 0.952, 95%CI 0.927-0.978 vs 0.955, 95%CI 0.928-0.982, respectively) and HC (AUC 0.938, 95%CI 0.910-0.966 and 0.937, 95% CI0.907-0.967 for both assays). Conclusions: This study demonstrated the high precision and diagnostic accuracy of p-tau217 for the clinical diagnosis of Alzheimer's disease using either fully automated or semi-automated techniques.

A generalizable data-driven model of atrophy heterogeneity and progression in memory clinic settings.

Memory clinic patients are a heterogeneous population representing various aetiologies of pathological ageing. It is not known whether divergent spatiotemporal progression patterns of brain atrophy, as previously described in Alzheimer's disease patients, are prevalent and clinically meaningful in this group of older adults. To uncover distinct atrophy subtypes, we applied the Subtype and Stage Inference (SuStaIn) algorithm to baseline structural MRI data from 813 participants enrolled in the DELCODE cohort (mean ± standard deviation, age = 70.67 ± 6.07 years, 52% females). Participants were cognitively unimpaired (n = 285) or fulfilled diagnostic criteria for subjective cognitive decline (n = 342), mild cognitive impairment (n = 118) or dementia of the Alzheimer's type (n = 68). Atrophy subtypes were compared in baseline demographics, fluid Alzheimer's disease biomarker levels, the Preclinical Alzheimer Cognitive Composite (PACC-5) as well as episodic memory and executive functioning. PACC-5 trajectories over up to 240 weeks were examined. To test whether baseline atrophy subtype and stage predicted clinical trajectories before manifest cognitive impairment, we analysed PACC-5 trajectories and mild cognitive impairment conversion rates of cognitively unimpaired participants and those with subjective cognitive decline. Limbic-predominant and hippocampal-sparing atrophy subtypes were identified. Limbic-predominant atrophy initially affected the medial temporal lobes, followed by further temporal regions and, finally, the remaining cortical regions. At baseline, this subtype was related to older age, more pathological Alzheimer's disease biomarker levels, APOE ε4 carriership and an amnestic cognitive impairment. Hippocampal-sparing atrophy initially occurred outside the temporal lobe, with the medial temporal lobe spared up to advanced atrophy stages. This atrophy pattern also affected individuals with positive Alzheimer's disease biomarkers and was associated with more generalized cognitive impairment. Limbic-predominant atrophy, in all participants and in only unimpaired participants, was linked to more negative longitudinal PACC-5 slopes than observed in participants without or with hippocampal-sparing atrophy and increased the risk of mild cognitive impairment conversion. SuStaIn modelling was repeated in a sample from the Swedish BioFINDER-2 cohort. Highly similar atrophy progression patterns and associated cognitive profiles were identified. Cross-cohort model generalizability, at both the subject and the group level, was excellent, indicating reliable performance in previously unseen data. The proposed model is a promising tool for capturing heterogeneity among older adults at early at-risk states for Alzheimer's disease in applied settings. The implementation of atrophy subtype- and stage-specific end points might increase the statistical power of pharmacological trials targeting early Alzheimer's disease.

Disease staging of Alzheimer's disease using a CSF-based biomarker model.

Biological staging of individuals with Alzheimer's disease (AD) may improve diagnostic and prognostic workup of dementia in clinical practice and the design of clinical trials. In this study, we used the Subtype and Stage Inference (SuStaIn) algorithm to establish a robust biological staging model for AD using cerebrospinal fluid (CSF) biomarkers. Our analysis involved 426 participants from BioFINDER-2 and was validated in 222 participants from the Knight Alzheimer Disease Research Center cohort. SuStaIn identified a singular biomarker sequence and revealed that five CSF biomarkers effectively constituted a reliable staging model (ordered: Aß42/40, pT217/T217, pT205/T205, MTBR-tau243 and non-phosphorylated mid-region tau). The CSF stages (0-5) demonstrated a correlation with increased abnormalities in other AD-related biomarkers, such as Aß-PET and tau-PET, and aligned with longitudinal biomarker changes reflective of AD progression. Higher CSF stages at baseline were associated with an elevated hazard ratio of clinical decline. This study highlights a common molecular pathway underlying AD pathophysiology across all patients, suggesting that a single CSF collection can accurately indicate the presence of AD pathologies and characterize the stage of disease progression. The proposed staging model has implications for enhancing diagnostic and prognostic assessments in both clinical practice and the design of clinical trials.

Comparing a pre-defined versus deep learning approach for extracting brain atrophy patterns to predict cognitive decline due to Alzheimer's disease in patients with mild cognitive symptoms.

BACKGROUND: Predicting future Alzheimer's disease (AD)-related cognitive decline among individuals with subjective cognitive decline (SCD) or mild cognitive impairment (MCI) is an important task for healthcare. Structural brain imaging as measured by magnetic resonance imaging (MRI) could potentially contribute when making such predictions. It is unclear if the predictive performance of MRI can be improved using entire brain images in deep learning (DL) models compared to using pre-defined brain regions. METHODS: A cohort of 332 individuals with SCD/MCI were included from the Swedish BioFINDER-1 study. The goal was to predict longitudinal SCD/MCI-to-AD dementia progression and change in Mini-Mental State Examination (MMSE) over four years. Four models were evaluated using different predictors: (1) clinical data only, including demographics, cognitive tests and APOE ε4 status, (2) clinical data plus hippocampal volume, (3) clinical data plus all regional MRI gray matter volumes (N = 68) extracted using FreeSurfer software, (4) a DL model trained using multi-task learning with MRI images, Jacobian determinant images and baseline cognition as input. A double cross-validation scheme, with five test folds and for each of those ten validation folds, was used. External evaluation was performed on part of the ADNI dataset, including 108 patients. Mann-Whitney U-test was used to determine statistically significant differences in performance, with p-values less than 0.05 considered significant. RESULTS: In the BioFINDER cohort, 109 patients (33%) progressed to AD dementia. The performance of the clinical data model for prediction of progression to AD dementia was area under the curve (AUC) = 0.85 and four-year cognitive decline was R2 = 0.14. The performance was improved for both outcomes when adding hippocampal volume (AUC = 0.86, R2 = 0.16). Adding FreeSurfer brain regions improved prediction of four-year cognitive decline but not progression to AD (AUC = 0.83, R2 = 0.17), while the DL model worsened the performance for both outcomes (AUC = 0.84, R2 = 0.08). A sensitivity analysis showed that the Jacobian determinant image was more informative than the MRI image, but that performance was maximized when both were included. In the external evaluation cohort from ADNI, 23 patients (21%) progressed to AD dementia. The results for predicted progression to AD dementia were similar to the results for the BioFINDER test data, while the performance for the cognitive decline was deteriorated. CONCLUSIONS: The DL model did not significantly improve the prediction of clinical disease progression in AD, compared to regression models with a single pre-defined brain region.