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The recent commercialisation of the first disease-modifying drugs for Alzheimer's disease emphasises the need for consensus recommendations on the rational use of biomarkers to diagnose people with suspected neurocognitive disorders in memory clinics. Most available recommendations and guidelines are either disease-centred or biomarker-centred. A European multidisciplinary taskforce consisting of 22 experts from 11 European scientific societies set out to define the first patient-centred diagnostic workflow that aims to prioritise testing for available biomarkers in individuals attending memory clinics. After an extensive literature review, we used a Delphi consensus procedure to identify 11 clinical syndromes, based on clinical history and examination, neuropsychology, blood tests, structural imaging, and, in some cases, EEG. We recommend first-line and, if needed, second-line testing for biomarkers according to the patient's clinical profile and the results of previous biomarker findings. This diagnostic workflow will promote consistency in the diagnosis of neurocognitive disorders across European countries.

Both late-onset Alzheimer's disease (AD) and ageing have a strong genetic component. In each case, many associated variants have been discovered, but how much missing heritability remains to be discovered is debated. Variability in the estimation of SNP-based heritability could explain the differences in reported heritability. We compute heritability in five large independent cohorts (N = 7,396, 1,566, 803, 12,528 and 3,963) to determine whether a consensus for the AD heritability estimate can be reached. These cohorts vary by sample size, age of cases and controls and phenotype definition. We compute heritability a) for all SNPs, b) excluding APOE region, c) excluding both APOE and genome-wide association study hit regions, and d) SNPs overlapping a microglia gene-set. SNP-based heritability of late onset Alzheimer's disease is between 38 and 66% when age and genetic disease architecture are correctly accounted for. The heritability estimates decrease by 12% [SD = 8%] on average when the APOE region is excluded and an additional 1% [SD = 3%] when genome-wide significant regions were removed. A microglia gene-set explains 69-84% of our estimates of SNP-based heritability using only 3% of total SNPs in all cohorts. The heritability of neurodegenerative disorders cannot be represented as a single number, because it is dependent on the ages of cases and controls. Genome-wide association studies pick up a large proportion of total AD heritability when age and genetic architecture are correctly accounted for. Around 13% of SNP-based heritability can be explained by known genetic loci and the remaining heritability likely resides around microglial related genes.

Background: It has been suggested that circulating Paraoxonase-1 (PON1) and apolipoprotein A1 (APOA1), which closely interacts with the antioxidant enzyme, could be implicated in Alzheimer’s disease (AD) and vascular dementia (VaD) development. This study aimed to evaluate PON1 changes in serum and cerebrospinal fluid (CSF) as evidence for its association with AD or VaD. Methods: Serum PON-arylesterase activity was measured in patients with AD, VaD, and CONTROLS distributed in two cohorts: Ferrara cohort (FC: n = 503, age = 74 years) and Amsterdam Dementia cohort (ADC: n = 71, age = 65 years). In the last cohort, CSF PON-arylesterase, CSF β-amyloid1-42, p-tau and t-tau, and imaging biomarkers were also measured. Results: AD and VaD patients of FC showed significantly lower levels of serum PON-arylesterase compared to CONTROLS, but this outcome was driven by older subjects (>71 years, p < 0.0001). In the younger ADC, a similar decreasing (but not significant) trend was observed in serum and CSF. Intriguingly, PON-arylesterase per APOA1 correlated with t-tau in AD group (r = −0.485, p = 0.002). Conclusion: These results suggest that decreased peripheral PON-arylesterase might be a specific feature of older AD/VaD patients. Moreover, we showed that PON-arylesterase/APOA1 is inversely related to neurodegeneration in AD patients, suggesting a prognostic usefulness of this composite parameter.

The reconceptualization of Alzheimer’s disease (AD) as a clinical and biological construct has facilitated the development of biomarker-guided, pathway-based targeted therapies, many of which have reached late-stage development with the near-term potential to enter global clinical practice. These medical advances mark an unprecedented paradigm shift and requires an optimized global framework for clinical care pathways for AD. In this Perspective, we describe the blueprint for transitioning from the current, clinical symptom-focused and inherently late-stage diagnosis and management of AD to the next-generation pathway that incorporates biomarker-guided and digitally facilitated decision-making algorithms for risk stratification, early detection, timely diagnosis, and preventative or therapeutic interventions. We address critical and high-priority challenges, propose evidence-based strategic solutions, and emphasize that the perspectives of affected individuals and care partners need to be considered and integrated. This Perspective describes the blueprint, challenges and potential solutions for the transformation of Alzheimer’s disease clinical care pathway with biomarker-guided and digitally facilitated detection and intervention at early disease stages.

For many years, blood-based biomarkers for Alzheimer's disease seemed unattainable, but recent results have shown that they could become a reality. Convincing data generated with new high-sensitivity assays have emerged with remarkable consistency across different cohorts, but also independent of the precise analytical method used. Concentrations in blood of amyloid and phosphorylated tau proteins associate with the corresponding concentrations in CSF and with amyloid-PET or tau-PET scans. Moreover, other blood-based biomarkers of neurodegeneration, such as neurofilament light chain and glial fibrillary acidic protein, appear to provide information on disease progression and potential for monitoring treatment effects. Now the question emerges of when and how we can bring these biomarkers to clinical practice. This step would pave the way for blood-based biomarkers to support the diagnosis of, and development of treatments for, Alzheimer's disease and other dementias.

The beta-site APP cleaving enzyme 1 (BACE1) is known primarily for its initial cleavage of the amyloid precursor protein (APP), which ultimately leads to the generation of Aβ peptides. Here, we provide evidence that altered BACE1 levels and activity impact the degradation of Aβ40 and Aβ42 into a common Aβ34 intermediate. Using human cerebrospinal fluid (CSF) samples from the Amsterdam Dementia Cohort, we show that Aβ34 is elevated in individuals with mild cognitive impairment who later progressed to dementia. Furthermore, Aβ34 levels correlate with the overall Aβ clearance rates in amyloid positive individuals. Using CSF samples from the PREVENT-AD cohort (cognitively normal individuals at risk for Alzheimer’s disease), we further demonstrate that the Aβ34/Aβ42 ratio, representing Aβ degradation and cortical deposition, associates with pre-clinical markers of neurodegeneration. We propose that Aβ34 represents a marker of amyloid clearance and may be helpful for the characterization of Aβ turnover in clinical samples. Aβ34 is generated from degradation of Aβ40 and Aβ42 by β-secretase. Here, the authors show that Aβ34 is a marker for amyloid clearance and is elevated in the CSF of patients that go on to convert from mild cognitive impairment to Alzheimer’s disease, suggesting it may be a useful biomarker.

A growing awareness about brain health and Alzheimer's disease in the general population is leading to an increasing number of cognitively unimpaired individuals, who are concerned that they have reduced cognitive function, to approach the medical system for help. The term subjective cognitive decline (SCD) was conceived in 2014 to describe this condition. Epidemiological data provide evidence that the risk for mild cognitive impairment and dementia is increased in individuals with SCD. However, the majority of individuals with SCD will not show progressive cognitive decline. An individually tailored diagnostic process might be reasonable to identify or exclude underlying medical conditions in an individual with SCD who actively seeks medical help. An increasing number of studies are investigating the link between SCD and the very early stages of Alzheimer's disease and other neurodegenerative diseases.

[This corrects the article DOI: 10.1371/journal.pone.0281440.].

Background There is a need for novel fluid biomarkers tracking neuroinflammatory responses in Alzheimer’s disease (AD). Our recent cerebrospinal fluid (CSF) proteomics study revealed that migration inhibitory factor (MIF) and soluble triggering receptor expressed on myeloid cells 1 (sTREM1) increased along the AD continuum. We aimed to assess the potential use of these proteins, in addition to sTREM2, as CSF biomarkers to monitor inflammatory processes in AD. Methods We included cognitively unimpaired controls ( n  = 67, 63 ± 9 years, 24% females, all amyloid negative), patients with mild cognitive impairment (MCI; n  = 92, 65 ± 7 years, 47% females, 65% amyloid positive), AD ( n  = 38, 67 ± 6 years, 8% females, all amyloid positive), and DLB ( n  = 50, 67 ± 6 years, 5% females, 54% amyloid positive). MIF, sTREM1, and sTREM2 levels were measured by validated immunoassays. Differences in protein levels between groups were tested with analysis of covariance (corrected for age and sex). Spearman correlation analysis was performed to evaluate the association between these neuroinflammatory markers with AD-CSF biomarkers (Aβ42, tTau, pTau) and mini-mental state examination (MMSE) scores. Results MIF levels were increased in MCI ( p  < 0.01), AD ( p  < 0.05), and DLB ( p  > 0.05) compared to controls. Levels of sTREM1 were specifically increased in AD compared to controls ( p  < 0.01), MCI ( p  < 0.05), and DLB patients ( p  > 0.05), while sTREM2 levels were increased specifically in MCI compared to all other groups (all p  < 0.001). Neuroinflammatory proteins were highly correlated with CSF pTau levels (MIF: all groups; sTREM1: MCI, AD and DLB; sTREM2: controls, MCI and DLB). Correlations with MMSE scores were observed in specific clinical groups (MIF in controls, sTREM1 in AD, and sTREM2 in DLB). Conclusion Inflammatory-related proteins show diverse expression profiles along different AD stages, with increased protein levels in the MCI stage (MIF and sTREM2) and AD stage (MIF and sTREM1). The associations of these inflammatory markers primarily with CSF pTau levels indicate an intertwined relationship between tau pathology and inflammation. These neuroinflammatory markers might be useful in clinical trials to capture dynamics in inflammatory responses or monitor drug–target engagement of inflammatory modulators.

The use of amyloid-PET in dementia workup is upcoming. At the same time, amyloid-PET is costly and limitedly available. While the appropriate use criteria (AUC) aim for optimal use of amyloid-PET, their limited sensitivity hinders the translation to clinical practice. Therefore, there is a need for tools that guide selection of patients for whom amyloid-PET has the most clinical utility. We aimed to develop a computerized decision support approach to select patients for amyloid-PET. We included 286 subjects (135 controls, 108 Alzheimer's disease dementia, 33 frontotemporal lobe dementia, and 10 vascular dementia) from the Amsterdam Dementia Cohort, with available neuropsychology, APOE, MRI and [18F]florbetaben amyloid-PET. In our computerized decision support approach, using supervised machine learning based on the DSI classifier, we first classified the subjects using only neuropsychology, APOE, and quantified MRI. Then, for subjects with uncertain classification (probability of correct class (PCC) < 0.75) we enriched classification by adding (hypothetical) amyloid positive (AD-like) and negative (normal) PET visual read results and assessed whether the diagnosis became more certain in at least one scenario (PPC≥0.75). If this was the case, the actual visual read result was used in the final classification. We compared the proportion of PET scans and patients diagnosed with sufficient certainty in the computerized approach with three scenarios: 1) without amyloid-PET, 2) amyloid-PET according to the AUC, and 3) amyloid-PET for all patients. The computerized approach advised PET in n = 60(21%) patients, leading to a diagnosis with sufficient certainty in n = 188(66%) patients. This approach was more efficient than the other three scenarios: 1) without amyloid-PET, diagnostic classification was obtained in n = 155(54%), 2) applying the AUC resulted in amyloid-PET in n = 113(40%) and diagnostic classification in n = 156(55%), and 3) performing amyloid-PET in all resulted in diagnostic classification in n = 154(54%). Our computerized data-driven approach selected 21% of memory clinic patients for amyloid-PET, without compromising diagnostic performance. Our work contributes to a cost-effective implementation and could support clinicians in making a balanced decision in ordering additional amyloid PET during the dementia workup.