Disease staging of Alzheimer\u27s disease using a CSF-based biomarker model

Biological staging of individuals with Alzheimer\u27s 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: Aβ42/40, 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 Aβ-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

Prediction of Longitudinal Cognitive Decline in Preclinical Alzheimer Disease Using Plasma Biomarkers

IMPORTANCE: Alzheimer disease (AD) pathology starts with a prolonged phase of β-amyloid (Aβ) accumulation without symptoms. The duration of this phase differs greatly among individuals. While this disease phase has high relevance for clinical trial designs, it is currently unclear how to best predict the onset of clinical progression. OBJECTIVE: To evaluate combinations of different plasma biomarkers for predicting cognitive decline in Aβ-positive cognitively unimpaired (CU) individuals. DESIGN, SETTING, AND PARTICIPANTS: This prospective population-based prognostic study evaluated data from 2 prospective longitudinal cohort studies (the Swedish BioFINDER-1 and the Wisconsin Registry for Alzheimer Prevention [WRAP]), with data collected from February 8, 2010, to October 21, 2020, for the BioFINDER-1 cohort and from August 11, 2011, to June 27, 2021, for the WRAP cohort. Participants were CU individuals recruited from memory clinics who had brain Aβ pathology defined by cerebrospinal fluid (CSF) Aβ42/40 in the BioFINDER-1 study and by Pittsburgh Compound B (PiB) positron emission tomography (PET) in the WRAP study. A total of 564 eligible Aβ-positive and Aβ-negative CU participants with available relevant data from the BioFINDER-1 and WRAP cohorts were included in the study; of those, 171 Aβ-positive participants were included in the main analyses. EXPOSURES: Baseline P-tau181, P-tau217, P-tau231, glial fibrillary filament protein, and neurofilament light measured in plasma; CSF biomarkers in the BioFINDER-1 cohort, and PiB PET uptake in the WRAP cohort. MAIN OUTCOMES AND MEASURES: The primary outcome was longitudinal measures of cognition (using the Mini-Mental State Examination [MMSE] and the modified Preclinical Alzheimer Cognitive Composite [mPACC]) over a median of 6 years (range, 2-10 years). The secondary outcome was conversion to AD dementia. Baseline biomarkers were used in linear regression models to predict rates of longitudinal cognitive change (calculated separately). Models were adjusted for age, sex, years of education, apolipoprotein E ε4 allele status, and baseline cognition. Multivariable models were compared based on model R2 coefficients and corrected Akaike information criterion. RESULTS: Among 171 Aβ-positive CU participants included in the main analyses, 119 (mean [SD] age, 73.0 [5.4] years; 60.5% female) were from the BioFINDER-1 study, and 52 (mean [SD] age, 64.4 [4.6] years; 65.4% female) were from the WRAP study. In the BioFINDER-1 cohort, plasma P-tau217 was the best marker to predict cognitive decline in the mPACC (model R2 = 0.41) and the MMSE (model R2 = 0.34) and was superior to the covariates-only models (mPACC: R2 = 0.23; MMSE: R2 = 0.04; P < .001 for both comparisons). Results were validated in the WRAP cohort; for example, plasma P-tau217 was associated with mPACC slopes (R2 = 0.13 vs 0.01 in the covariates-only model; P = .01) and MMSE slopes (R2 = 0.29 vs 0.24 in the covariates-only model; P = .046). Sparse models were identified with plasma P-tau217 as a predictor of cognitive decline. Power calculations for enrichment in hypothetical clinical trials revealed large relative reductions in sample sizes when using plasma P-tau217 to enrich for CU individuals likely to experience cognitive decline over time. CONCLUSIONS AND RELEVANCE: In this study, plasma P-tau217 predicted cognitive decline in patients with preclinical AD. These findings suggest that plasma P-tau217 may be used as a complement to CSF or PET for participant selection in clinical trials of novel disease-modifying treatments

The Effects of the Mediterranean Diet on Biomarkers of Vascular Wall Inflammation and Plaque Vulnerability in Subjects with High Risk for Cardiovascular Disease. A Randomized Trial

Adherence to the Mediterranean diet (MD) is associated with reduced morbidity and mortality due to cardiovascular disease. However, how the MD exerts its effects is not fully known. Aim: To assess the 12-month effects of two enhanced MDs compared to a low-fat diet on inflammatory biomarkers related to atherosclerosis and plaque vulnerability in a subcohort of the PREDIMED (Prevencion con Dieta Mediterranea) study. Methods: A total of 164 participants at high risk for cardiovascular disease were randomized into three diet groups: MD supplemented with 50 mL/d of extra virgin olive oil (MD+EVOO) or 30 g/d of nuts (MD+Nuts) and a low-fat diet. Changes in classical cardiovascular risk factors, inflammatory biomarkers of atherosclerosis and plaque vulnerability were measured after 12 months of intervention. Results: Compared to participants in the low-fat diet group, those receiving MD+EVOO and MD+Nuts showed a higher decrease in systolic (6 mmHg)and diastolic (3 mmHg) blood pressure (P = 0.02; both), as well as a reduction of 10% and 8% in LDL-cholesterol (P = 0.04), respectively. Patients in the MD+Nuts group showed a significant reduction of 34% in CD40 expression on monocyte surface compared to low-fat diet patients (P = 0.03). In addition, inflammatory biomarkers related to plaque instability such as C-reactive protein and interleukin-6 were reduced by 45% and 35% and 95% and 90% in the MD+EVOO and MD+Nuts groups, respectively (P<0.05; all) compared to the low-fat diet group. Likewise, sICAM and Pselectin were also reduced by 50% and 27%, respectively in the MD+ EVOO group (P = 0.04) and P-selectin by 19% in MD+Nuts group (P = 0.04) compared to the low-fat diet group. Conclusions: Adherence to the MD is associated with an increase in serum markers of atheroma plaque stability which may explain, at least in part, the protective role of MD against ischemic heart disease

Quantification of amyloid PET for future clinical use: a state-of-the-art review

Amyloid-β (Aβ) pathology is one of the earliest detectable brain changes in Alzheimer's disease (AD) pathogenesis. The overall load and spatial distribution of brain Aβ can be determined in vivo using positron emission tomography (PET), for which three fluorine-18 labelled radiotracers have been approved for clinical use. In clinical practice, trained readers will categorise scans as either Aβ positive or negative, based on visual inspection. Diagnostic decisions are often based on these reads and patient selection for clinical trials is increasingly guided by amyloid status. However, tracer deposition in the grey matter as a function of amyloid load is an inherently continuous process, which is not sufficiently appreciated through binary cut-offs alone. State-of-the-art methods for amyloid PET quantification can generate tracer-independent measures of Aβ burden. Recent research has shown the ability of these quantitative measures to highlight pathological changes at the earliest stages of the AD continuum and generate more sensitive thresholds, as well as improving diagnostic confidence around established binary cut-offs. With the recent FDA approval of aducanumab and more candidate drugs on the horizon, early identification of amyloid burden using quantitative measures is critical for enrolling appropriate subjects to help establish the optimal window for therapeutic intervention and secondary prevention. In addition, quantitative amyloid measurements are used for treatment response monitoring in clinical trials. In clinical settings, large multi-centre studies have shown that amyloid PET results change both diagnosis and patient management and that quantification can accurately predict rates of cognitive decline. Whether these changes in management reflect an improvement in clinical outcomes is yet to be determined and further validation work is required to establish the utility of quantification for supporting treatment endpoint decisions. In this state-of-the-art review, several tools and measures available for amyloid PET quantification are summarised and discussed. Use of these methods is growing both clinically and in the research domain. Concurrently, there is a duty of care to the wider dementia community to increase visibility and understanding of these methods

Quantitative informant- and self-reports of subjective cognitive decline predict amyloid beta PET outcomes in cognitively unimpaired individuals independently of age and APOE ε4

Introduction: Amyloid beta (Aβ) pathology is an Alzheimer's disease early hallmark. Here we assess the value of longitudinal self- and informant reports of cognitive decline to predict Aβ positron emission tomography (PET) outcome in cognitively unimpaired middle-aged individuals. Methods: A total of 261 participants from the ALFA+ study underwent [18F]flutemetamol PET and Subjective Cognitive Decline Questionnaire (SCD-Q) concurrently, and 3 years before scan. We used logistic regressions to evaluate the ability of SCD-Q scores (self and informant) to predict Aβ PET visual read, and repeated analysis of variance to assess whether changes in SCD-Q scores relate to Aβ status. Results: Self-perception of decline in memory (odds ratio [OR] = 1.2), and informant perception of executive decline (OR = 1.6), increased the probability of a positive scan. Informant reports 3 years before scanning predicted Aβ PET outcome. Longitudinal increase of self-reported executive decline was predictive of Aβ in women (P = .003). Discussion: Subjective reports of cognitive decline are useful to predict Aβ and may improve recruitment strategies

Quantification of amyloid PET for future clinical use: a state-of-the-art review

Amyloid-β (Aβ) pathology is one of the earliest detectable brain changes in Alzheimer's disease (AD) pathogenesis. The overall load and spatial distribution of brain Aβ can be determined in vivo using positron emission tomography (PET), for which three fluorine-18 labelled radiotracers have been approved for clinical use. In clinical practice, trained readers will categorise scans as either Aβ positive or negative, based on visual inspection. Diagnostic decisions are often based on these reads and patient selection for clinical trials is increasingly guided by amyloid status. However, tracer deposition in the grey matter as a function of amyloid load is an inherently continuous process, which is not sufficiently appreciated through binary cut-offs alone. State-of-the-art methods for amyloid PET quantification can generate tracer-independent measures of Aβ burden. Recent research has shown the ability of these quantitative measures to highlight pathological changes at the earliest stages of the AD continuum and generate more sensitive thresholds, as well as improving diagnostic confidence around established binary cut-offs. With the recent FDA approval of aducanumab and more candidate drugs on the horizon, early identification of amyloid burden using quantitative measures is critical for enrolling appropriate subjects to help establish the optimal window for therapeutic intervention and secondary prevention. In addition, quantitative amyloid measurements are used for treatment response monitoring in clinical trials. In clinical settings, large multi-centre studies have shown that amyloid PET results change both diagnosis and patient management and that quantification can accurately predict rates of cognitive decline. Whether these changes in management reflect an improvement in clinical outcomes is yet to be determined and further validation work is required to establish the utility of quantification for supporting treatment endpoint decisions. In this state-of-the-art review, several tools and measures available for amyloid PET quantification are summarised and discussed. Use of these methods is growing both clinically and in the research domain. Concurrently, there is a duty of care to the wider dementia community to increase visibility and understanding of these methods

Brain alterations in the early Alzheimer's continuum with amyloid-β, tau, glial and neurodegeneration CSF markers

Higher grey matter volumes/cortical thickness and fluorodeoxyglucose uptake have been consistently found in cognitively unimpaired individuals with abnormal Alzheimer's disease biomarkers compared with those with normal biomarkers. It has been hypothesized that such transient increases may be associated with neuroinflammatory mechanisms triggered in response to early Alzheimer's pathology. Here, we evaluated, in the earliest stages of the Alzheimer's continuum, associations between grey matter volume and fluorodeoxyglucose uptake with CSF biomarkers of several pathophysiological mechanisms known to be altered in preclinical Alzheimer's disease stages. We included 319 cognitively unimpaired participants from the ALFA+ cohort with available structural MRI, fluorodeoxyglucose PET and CSF biomarkers of amyloid-β and tau pathology (phosphorylated tau and total tau), synaptic dysfunction (neurogranin), neuronal and axonal injury (neurofilament light), glial activation (soluble triggering receptor on myeloid cells 2, YKL40, GFAP, interleukin-6 and S100b) and α-synuclein using the Roche NeuroToolKit. We first used the amyloid-β/tau framework to investigate differences in the neuroimaging biomarkers between preclinical Alzheimer's disease stages. Then, we looked for associations between the neuroimaging markers and all the CSF markers. Given the non-negative nature of the concentrations of CSF biomarkers and their high collinearity, we clustered them using non-negative matrix factorization approach (components) and sought associations with the imaging markers. By groups, higher grey matter volumes were found in the amyloid-β-positive tau-negative participants with respect to the reference amyloid-β-negative tau-negative group. Both amyloid-β and tau-positive participants showed higher fluorodeoxyglucose uptake than tau-negative individuals. Using the obtained components, we observed that tau pathology accompanied by YKL-40 (astrocytic marker) was associated with higher grey matter volumes and fluorodeoxyglucose uptake in extensive brain areas. Higher grey matter volumes in key Alzheimer-related regions were also found in association with two other components characterized by a higher expression of amyloid-β in combination with different glial markers: one with higher GFAP and S100b levels (astrocytic markers) and the other one with interleukin-6 (pro-inflammatory). Notably, these components' expression had different behaviours across amyloid-β/tau stages. Taken together, our results show that CSF amyloid-β and phosphorylated tau, in combination with different aspects of glial response, have distinctive associations with higher grey matter volumes and increased glucose metabolism in key Alzheimer-related regions. These mechanisms combine to produce transient higher grey matter volumes and fluorodeoxyglucose uptake at the earliest stages of the Alzheimer's continuum, which may revert later on the course of the disease when neurodegeneration drives structural and metabolic cerebral changes

Reactive astrogliosis is associated with higher cerebral glucose consumption in the early Alzheimer's continuum

PURPOSE: Glial activation is one of the earliest mechanisms to be altered in Alzheimer's disease (AD). Glial fibrillary acidic protein (GFAP) relates to reactive astrogliosis and can be measured in both cerebrospinal fluid (CSF) and blood. Plasma GFAP has been suggested to become altered earlier in AD than its CSF counterpart. Although astrocytes consume approximately half of the glucose-derived energy in the brain, the relationship between reactive astrogliosis and cerebral glucose metabolism is poorly understood. Here, we aimed to investigate the association between fluorodeoxyglucose ([18F]FDG) uptake and reactive astrogliosis, by means of GFAP quantified in both plasma and CSF for the same participants. METHODS: We included 314 cognitively unimpaired participants from the ALFA + cohort, 112 of whom were amyloid-β (Aβ) positive. Associations between GFAP markers and [18F]FDG uptake were studied. We also investigated whether these associations were modified by Aβ and tau status (AT stages). RESULTS: Plasma GFAP was positively associated with glucose consumption in the whole brain, while CSF GFAP associations with [18F]FDG uptake were only observed in specific smaller areas like temporal pole and superior temporal lobe. These associations persisted when accounting for biomarkers of Aβ pathology but became negative in Aβ-positive and tau-positive participants (A + T +) in similar areas of AD-related hypometabolism. CONCLUSIONS: Higher astrocytic reactivity, probably in response to early AD pathological changes, is related to higher glucose consumption. With the onset of tau pathology, the observed uncoupling between astrocytic biomarkers and glucose consumption might be indicative of a failure to sustain the higher energetic demands required by reactive astrocytes

Spatial-Temporal Patterns of Amyloid-β Accumulation: A Subtype and Stage Inference Model Analysis

BACKGROUND AND OBJECTIVES: Currently, amyloid-β (Aβ) staging models assume a single spatial-temporal progression of amyloid accumulation. We assessed evidence for Aβ accumulation subtypes by applying the data-driven Subtype and Stage Inference (SuStaIn) model to amyloid-PET data. METHODS: Amyloid-PET data of 3010 subjects were pooled from 6 cohorts (ALFA+, EMIF-AD, ABIDE, OASIS, and ADNI). Standardized uptake value ratios (SUVr) were calculated for 17 regions. We applied the SuStaIn algorithm to identify consistent subtypes in the pooled dataset based on the cross-validation information criterion (CVIC) and the most probable subtype/stage classification per scan. The effect of demographics and risk factors on subtype assignment was assessed using multinomial logistic regression. RESULTS: Participants were mostly cognitively unimpaired (N=1890, 62.8%), had a mean age of 68.72 (SD=9.1), 42.1% was APOE-ε4 carrier, and 51.8% was female. While a one-subtype model recovered the traditional amyloid accumulation trajectory, SuStaIn identified an optimal of three subtypes, referred to as Frontal, Parietal, and Occipital based on the first regions to show abnormality. Of the 788 (26.2%) with strong subtype assignment (>50% probability), the majority was assigned to Frontal (N=415, 52.5%), followed by Parietal (N=199, 25.3%), and Occipital subtypes (N=175, 22.2%). Significant differences across subtypes included distinct proportions of APOE-ε4 carriers (Frontal:61.8%, Parietal:57.1%, Occipital:49.4%), subjects with dementia (Frontal:19.7%, Parietal:19.1%, Occipital:31.0%) and lower age for the Parietal subtype (Frontal/Occipital:72.1y, Parietal:69.3y). Higher amyloid (Centiloid) and CSF p-tau burden was observed for the Frontal subtype, while Parietal and Occipital did not differ. At follow-up, most subjects (81.1%) maintained baseline subtype assignment and 25.6% progressed to a later stage. DISCUSSION: While a one-trajectory model recovers the established pattern of amyloid accumulation, SuStaIn determined that three subtypes were optimal, showing distinct associations to AD risk factors. Nonetheless, further analyses to determine clinical utility is warranted

Oxidative stress is associated with an increased antioxidant defense in elderly subjects: a multilevel approach.

BACKGROUND: Studies of associations between plasma GSH-Px activity and cardiovascular risk factors have been done in humans, and contradictory results have been reported. The aim of our study was to assess the association between the scavenger antioxidant enzyme glutathione peroxidase (GSH-Px) activity in plasma and the presence of novel and classical cardiovascular risk factors in elderly patients. METHODS: We performed a cross-sectional study with baseline data from a subsample of the PREDIMED (PREvención con DIeta MEDiterránea) study in Spain. Participants were 1,060 asymptomatic subjects at high risk for cardiovascular disease (CVD), aged 55 to 80, selected from 8 primary health care centers (PHCCs). We assessed classical CVD risk factors, plasma oxidized low-density lipoproteins (ox-LDL), and glutathione peroxidase (GSH-Px) using multilevel statistical procedures. RESULTS: Mean GSH-Px value was 612 U/L (SE: 12 U/L), with variation between PHCCs ranging from 549 to 674 U/L (Variance =  013.5; P<0.001). Between-participants variability within a PHCC accounted for 89% of the total variation. Both glucose and oxidized LDL were positively associated with GSH-Px activity after adjustment for possible confounder variables (P = 0.03 and P = 0.01, respectively). CONCLUSION: In a population at high cardiovascular risk, a positive linear association was observed between plasma GSH-Px activity and both glucose and ox-LDL levels. The high GSH-Px activity observed when an oxidative stress situation occurred, such as hyperglycemia and lipid oxidative damage, could be interpreted as a healthy defensive response against oxidative injury in our cardiovascular risk population