Regional associations of white matter hyperintensities and early cortical amyloid pathology.

White matter hyperintensities (WMHs) have a heterogeneous aetiology, associated with both vascular risk factors and amyloidosis due to Alzheimer's disease. While spatial distribution of both amyloid and WM lesions carry important information for the underlying pathogenic mechanisms, the regional relationship between these two pathologies and their joint contribution to early cognitive deterioration remains largely unexplored. We included 662 non-demented participants from three Amyloid Imaging to Prevent Alzheimer's disease (AMYPAD)-affiliated cohorts: EPAD-LCS (N = 176), ALFA+ (N = 310), and EMIF-AD PreclinAD Twin60++ (N = 176). Using PET imaging, cortical amyloid burden was assessed regionally within early accumulating regions (medial orbitofrontal, precuneus, and cuneus) and globally, using the Centiloid method. Regional WMH volume was computed using Bayesian Model Selection. Global associations between WMH, amyloid, and cardiovascular risk scores (Framingham and CAIDE) were assessed using linear models. Partial least square (PLS) regression was used to identify regional associations. Models were adjusted for age, sex, and APOE-e4 status. Individual PLS scores were then related to cognitive performance in 4 domains (attention, memory, executive functioning, and language). While no significant global association was found, the PLS model yielded two components of interest. In the first PLS component, a fronto-parietal WMH pattern was associated with medial orbitofrontal-precuneal amyloid, vascular risk, and age. Component 2 showed a posterior WMH pattern associated with precuneus-cuneus amyloid, less related to age or vascular risk. Component 1 was associated with lower performance in all cognitive domains, while component 2 only with worse memory. In a large pre-dementia population, we observed two distinct patterns of regional associations between WMH and amyloid burden, and demonstrated their joint influence on cognitive processes. These two components could reflect the existence of vascular-dependent and -independent manifestations of WMH-amyloid regional association that might be related to distinct primary pathophysiology.

Increased CSF-decorin predicts brain pathological changes driven by Alzheimer's Aß amyloidosis.

Cerebrospinal fluid (CSF) biomarkers play an important role in diagnosing Alzheimer's disease (AD) which is characterized by amyloid-ß (Aß) amyloidosis. Here, we used two App knock-in mouse models, AppNL-F/NL-F and AppNL-G-F/NL-G-F, exhibiting AD-like Aß pathology to analyze how the brain pathologies translate to CSF proteomes by label-free mass spectrometry (MS). This identified several extracellular matrix (ECM) proteins as significantly altered in App knock-in mice. Next, we compared mouse CSF proteomes with previously reported human CSF MS results acquired from patients across the AD spectrum. Intriguingly, the ECM protein decorin was similarly and significantly increased in both AppNL-F/NL-F and AppNL-G-F/NL-G-F mice, strikingly already at three months of age in the AppNL-F/NL-F mice and preclinical AD subjects having abnormal CSF-Aß42 but normal cognition. Notably, in this group of subjects, CSF-decorin levels positively correlated with CSF-Aß42 levels indicating that the change in CSF-decorin is associated with early Aß amyloidosis. Importantly, receiver operating characteristic analysis revealed that CSF-decorin can predict a specific AD subtype having innate immune activation and potential choroid plexus dysfunction in the brain. Consistently, in AppNL-F/NL-F mice, increased CSF-decorin correlated with both Aß plaque load and with decorin levels in choroid plexus. In addition, a low concentration of human Aß42 induces decorin secretion from mouse primary neurons. Interestingly, we finally identify decorin to activate neuronal autophagy through enhancing lysosomal function. Altogether, the increased CSF-decorin levels occurring at an early stage of Aß amyloidosis in the brain may reflect pathological changes in choroid plexus, present in a subtype of AD subjects.

P-tau subgroups in AD relate to distinct amyloid production and synaptic integrity profiles.

BACKGROUND: We previously identified four Alzheimer's disease (AD) subgroups with increasingly higher cerebrospinal fluid (CSF) levels of tau phosphorylated at threonine 181 (p-tau). These subgroups included individuals across the cognitive spectrum, suggesting p-tau subgroups could reflect distinct biological changes in AD, rather than disease severity. Therefore, in the current study, we further investigated which potential processes may be related with p-tau subgroups, by comparing individuals on CSF markers for presynaptic structure [vesicle-associated membrane protein 2 (VAMP2)], postsynaptic structure [neurogranin (NRGN)], axonal damage [neurofilament light (NfL)], and amyloid production [beta-secretase 1 (BACE1) and amyloid-beta 1-40 (Aß40)]. METHODS: We selected 348 amyloid-positive (A+) individuals (53 preclinical, 102 prodromal, 193 AD dementia) and 112 amyloid-negative (A-) cognitively normal (CN) individuals from the Amsterdam Dementia Cohort (ADC). Individuals were labeled according to their p-tau subgroup (subgroup 1: p-tau ≤ 56 pg/ml; subgroup 2: 57-96 pg/ml; subgroup 3: 97-159 pg/ml; subgroup 4: > 159 pg/ml). CSF protein levels were measured with ELISA (NRGN, BACE1, Aß40, NfL) or single-molecule array (Simoa) (VAMP2). We tested whether protein levels differed between the p-tau subgroups within A+ individuals with linear models corrected for age and sex and whether disease stage influenced these relationships. RESULTS: Among A+ individuals, higher p-tau subgroups showed a higher percentage of AD dementia [subgroup 1: n = 41/94 (44%); subgroup 2: n = 81/147 (55%); subgroup 3: n = 59/89 (66%); subgroup 4: n = 7/11 (64%)]. Relative to controls, subgroup 1 showed reduced CSF levels of BACE1, Aß40, and VAMP2 and higher levels of NfL. Subgroups 2 to 4 showed gradually increased CSF levels of all measured proteins, either across the first three (NfL and Aß40) or across all subgroups (VAMP2, NRGN, BACE1). The associations did not depend on the clinical stage (interaction p-values ranging between 0.19 and 0.87). CONCLUSIONS: The results suggest that biological heterogeneity in p-tau levels in AD is related to amyloid metabolism and synaptic integrity independent of clinical stage. Biomarkers reflecting amyloid metabolism and synaptic integrity may be useful outcome measures in clinical trials targeting tau pathology.

Cerebrospinal fluid proteomic profiling of individuals with mild cognitive impairment and suspected non-Alzheimer's disease pathophysiology.

BACKGROUND: Suspected non-Alzheimer's disease pathophysiology (SNAP) is a biomarker concept that encompasses individuals with neuronal injury but without amyloidosis. We aim to investigate the pathophysiology of SNAP, defined as abnormal tau without amyloidosis, in individuals with mild cognitive impairment (MCI) by cerebrospinal fluid (CSF) proteomics. METHODS: Individuals were classified based on CSF amyloid beta (Aß)1-42 (A) and phosphorylated tau (T), as cognitively normal A-T- (CN), MCI A-T+ (MCI-SNAP), and MCI A+T+ (MCI-AD). Proteomics analyses, Gene Ontology (GO), brain cell expression, and gene expression analyses in brain regions of interest were performed. RESULTS: A total of 96 proteins were decreased in MCI-SNAP compared to CN and MCI-AD. These proteins were enriched for extracellular matrix (ECM), hemostasis, immune system, protein processing/degradation, lipids, and synapse. Fifty-one percent were enriched for expression in the choroid plexus. CONCLUSION: The pathophysiology of MCI-SNAP (A-T+) is distinct from that of MCI-AD. Our findings highlight the need for a different treatment in MCI-SNAP compared to MCI-AD.

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

BACKGROUND AND OBJECTIVES: ß-amyloid (Aß) staging models assume a single spatial-temporal progression of amyloid accumulation. We assessed evidence for Aß accumulation subtypes by applying the data-driven Subtype and Stage Inference (SuStaIn) model to amyloid-PET data. METHODS: Amyloid-PET data of 3,010 participants were pooled from 6 cohorts (ALFA+, EMIF-AD, ABIDE, OASIS, and ADNI). Standardized uptake value ratios 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 and the most probable subtype/stage classification per scan. The effects of demographics and risk factors on subtype assignment were 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) years, 42.1% were APOE ε4 carriers, and 51.8% were female. A 1-subtype model recovered the traditional amyloid accumulation trajectory, but SuStaIn identified 3 optimal 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%), participants with dementia (frontal 19.7%, parietal 19.1%, occipital 31.0%), and lower age for the parietal subtype (frontal/occipital 72.1 years, parietal 69.3 years). Higher amyloid (Centiloid) and CSF p-tau burden was observed for the frontal subtype; parietal and occipital subtypes did not differ. At follow-up, most participants (81.1%) maintained baseline subtype assignment and 25.6% progressed to a later stage. DISCUSSION: Whereas a 1-trajectory model recovers the established pattern of amyloid accumulation, SuStaIn determined that 3 subtypes were optimal, showing distinct associations with Alzheimer disease risk factors. Further analyses to determine clinical utility are warranted.

Prevalence Estimates of Amyloid Abnormality Across the Alzheimer Disease Clinical Spectrum.

IMPORTANCE: One characteristic histopathological event in Alzheimer disease (AD) is cerebral amyloid aggregation, which can be detected by biomarkers in cerebrospinal fluid (CSF) and on positron emission tomography (PET) scans. Prevalence estimates of amyloid pathology are important for health care planning and clinical trial design. OBJECTIVE: To estimate the prevalence of amyloid abnormality in persons with normal cognition, subjective cognitive decline, mild cognitive impairment, or clinical AD dementia and to examine the potential implications of cutoff methods, biomarker modality (CSF or PET), age, sex, APOE genotype, educational level, geographical region, and dementia severity for these estimates. DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional, individual-participant pooled study included participants from 85 Amyloid Biomarker Study cohorts. Data collection was performed from January 1, 2013, to December 31, 2020. Participants had normal cognition, subjective cognitive decline, mild cognitive impairment, or clinical AD dementia. Normal cognition and subjective cognitive decline were defined by normal scores on cognitive tests, with the presence of cognitive complaints defining subjective cognitive decline. Mild cognitive impairment and clinical AD dementia were diagnosed according to published criteria. EXPOSURES: Alzheimer disease biomarkers detected on PET or in CSF. MAIN OUTCOMES AND MEASURES: Amyloid measurements were dichotomized as normal or abnormal using cohort-provided cutoffs for CSF or PET or by visual reading for PET. Adjusted data-driven cutoffs for abnormal amyloid were calculated using gaussian mixture modeling. Prevalence of amyloid abnormality was estimated according to age, sex, cognitive status, biomarker modality, APOE carrier status, educational level, geographical location, and dementia severity using generalized estimating equations. RESULTS: Among the 19 097 participants (mean [SD] age, 69.1 [9.8] years; 10 148 women [53.1%]) included, 10 139 (53.1%) underwent an amyloid PET scan and 8958 (46.9%) had an amyloid CSF measurement. Using cohort-provided cutoffs, amyloid abnormality prevalences were similar to 2015 estimates for individuals without dementia and were similar across PET- and CSF-based estimates (24%; 95% CI, 21%-28%) in participants with normal cognition, 27% (95% CI, 21%-33%) in participants with subjective cognitive decline, and 51% (95% CI, 46%-56%) in participants with mild cognitive impairment, whereas for clinical AD dementia the estimates were higher for PET than CSF (87% vs 79%; mean difference, 8%; 95% CI, 0%-16%; P = .04). Gaussian mixture modeling-based cutoffs for amyloid measures on PET scans were similar to cohort-provided cutoffs and were not adjusted. Adjusted CSF cutoffs resulted in a 10% higher amyloid abnormality prevalence than PET-based estimates in persons with normal cognition (mean difference, 9%; 95% CI, 3%-15%; P = .004), subjective cognitive decline (9%; 95% CI, 3%-15%; P = .005), and mild cognitive impairment (10%; 95% CI, 3%-17%; P = .004), whereas the estimates were comparable in persons with clinical AD dementia (mean difference, 4%; 95% CI, -2% to 9%; P = .18). CONCLUSIONS AND RELEVANCE: This study found that CSF-based estimates using adjusted data-driven cutoffs were up to 10% higher than PET-based estimates in people without dementia, whereas the results were similar among people with dementia. This finding suggests that preclinical and prodromal AD may be more prevalent than previously estimated, which has important implications for clinical trial recruitment strategies and health care planning policies.

Complementary pre-screening strategies to uncover hidden prodromal and mild Alzheimer's disease: Results from the MOPEAD project.

INTRODUCTION: The Models of Patient Engagement for Alzheimer's Disease (MOPEAD) project was conceived to explore innovative complementary strategies to uncover hidden prodromal and mild Alzheimer's disease (AD) dementia cases and to raise awareness both in the general public and among health professionals about the importance of early diagnosis. METHODS: Four different strategies or RUNs were used: (a) a web-based (WB) prescreening tool, (2) an open house initiative (OHI), (3) a primary care-based protocol for early detection of cognitive decline (PC), and (4) a tertiary care-based pre-screening at diabetologist clinics (DC). RESULTS: A total of 1129 patients at high risk of having prodromal AD or dementia were identified of 2847 pre-screened individuals (39.7%). The corresponding proportion for the different initiatives were 36.8% (WB), 35.6% (OHI), 44.4% (PC), and 58.3% (DC). CONCLUSION: These four complementary pre-screening strategies were useful for identifying individuals at high risk of having prodromal or mild AD.

Plasma amyloid-ß oligomerization assay as a pre-screening test for amyloid status.

OBJECTIVE: We assessed the performance of plasma amyloid oligomerization tendency (OAß) as a marker for abnormal amyloid status. Additionally, we examined long-term storage effects on plasma OAß. METHODS: We included 399 subjects regardless of clinical diagnosis from the Amsterdam Dementia Cohort and European Medical Information Framework for AD project (age, 63.8 ± 6.6; 44% female). Amyloid status was determined by visual read on positron emission tomography (PET; nabnormal = 206). Plasma OAß was measured using the multimer detection system (MDS). Long-term storage effects on MDS-OAß were assessed using general linear models. Associations between plasma MDS-OAß and Aß-PET status were assessed using logistic regression and receiver operating characteristics analyses. Correlations between plasma MDS-OAß and CSF biomarker levels were evaluated using Pearson correlation analyses. RESULTS: MDS-OAß was higher in individuals with abnormal amyloid, and it identified abnormal Aß-PET with an area under the curve (AUC) of 0.74 (95% CI, 0.67-0.81), especially in samples with a storage duration < 4 years. Combining APOEe4 and age with plasma MDS-OAß revealed an AUC of 81% for abnormal amyloid PET status (95% CI, 74-87%). Plasma MDS-OAß correlated negatively with MMSE (r = - 0.29, p < .01) and CSF Aß42 (r = - 0.20, p < 0.05) and positively with CSF Tau (r = 0.20, p = 0.01). CONCLUSIONS: Plasma MDS-OAß combined with APOEe4 and age accurately identifies brain amyloidosis in a large Aß-confirmed population. Using plasma MDS-OAß as a screener reduced the costs and number of PET scans needed to screen for amyloidosis, which is relevant for clinical trials. Additionally, plasma MDS-OAß levels appeared affected by long-term storage duration, which could be of interest for others measuring plasma Aß biomarkers.

Identification of undiagnosed dementia cases using a web-based pre-screening tool: The MOPEAD project.

INTRODUCTION: Innovative patient engagement models are required to identify people with prodromal and mild Alzheimer's disease who are "hidden" in their communities and not normally found in a memory clinic setting. METHODS: A marketing campaign and a web-based pre-screening tool were used to identify individuals at risk of dementia in five European countries. Harmonized clinical evaluation of these patients was performed in participating memory clinics within the MOPEAD project. RESULTS: A total of 1487 individuals completed the pre-screening, with 547 of them found to be at risk of dementia (36.8%). Among the subset of 91 patients with a positive pre-screening result that underwent full clinical evaluation, 49 (53.8%) were diagnosed with either mild cognitive impairment or Alzheimer's disease. CONCLUSION: This novel web-based pre-screening tool showed to be a valid strategy to identify undiagnosed people with cognitive impairment.

Multitracer model for staging cortical amyloid deposition using PET imaging.

OBJECTIVE: To develop and evaluate a model for staging cortical amyloid deposition using PET with high generalizability. METHODS: Three thousand twenty-seven individuals (1,763 cognitively unimpaired [CU], 658 impaired, 467 with Alzheimer disease [AD] dementia, 111 with non-AD dementia, and 28 with missing diagnosis) from 6 cohorts (European Medical Information Framework for AD, Alzheimer's and Family, Alzheimer's Biomarkers in Daily Practice, Amsterdam Dementia Cohort, Open Access Series of Imaging Studies [OASIS]-3, Alzheimer's Disease Neuroimaging Initiative [ADNI]) who underwent amyloid PET were retrospectively included; 1,049 individuals had follow-up scans. With application of dataset-specific cutoffs to global standard uptake value ratio (SUVr) values from 27 regions, single-tracer and pooled multitracer regional rankings were constructed from the frequency of abnormality across 400 CU individuals (100 per tracer). The pooled multitracer ranking was used to create a staging model consisting of 4 clusters of regions because it displayed a high and consistent correlation with each single-tracer ranking. Relationships between amyloid stage, clinical variables, and longitudinal cognitive decline were investigated. RESULTS: SUVr abnormality was most frequently observed in cingulate, followed by orbitofrontal, precuneal, and insular cortices and then the associative, temporal, and occipital regions. Abnormal amyloid levels based on binary global SUVr classification were observed in 1.0%, 5.5%, 17.9%, 90.0%, and 100.0% of individuals in stage 0 to 4, respectively. Baseline stage predicted decline in Mini-Mental State Examination (MMSE) score (ADNI: n = 867, F = 67.37, p < 0.001; OASIS: n = 475, F = 9.12, p < 0.001) and faster progression toward an MMSE score ≤25 (ADNI: n = 787, hazard ratio [HR]stage1 2.00, HRstage2 3.53, HRstage3 4.55, HRstage4 9.91, p < 0.001; OASIS: n = 469, HRstage4 4.80, p < 0.001). CONCLUSION: The pooled multitracer staging model successfully classified the level of amyloid burden in >3,000 individuals across cohorts and radiotracers and detects preglobal amyloid burden and distinct risk profiles of cognitive decline within globally amyloid-positive individuals.

Duration of Care Trajectories in Persons With Dementia Differs According to Demographic and Clinical Characteristics.

OBJECTIVES: To estimate (1) the duration of no formal care, home care, and institutional care after dementia diagnosis, and (2) the effect of age, sex, living situation, dementia medication, migration background, and income on this dementia care duration. DESIGN: Longitudinal retrospective study using routinely recorded general practice electronic health records linked with population-based healthcare and mortality data. SETTING AND PARTICIPANTS: In total, 11,012 community-dwelling persons who received an incident dementia diagnosis and were listed in a Dutch general practitioner database from 448 general practices in the Netherlands. METHODS: Using multistate modeling analyses, we estimated the mean duration of care types (no/home/institutional care) for different ages based on simulations of transition rates and examined the influence of demographic and clinical factors on these durations. RESULTS: From dementia diagnosis onward in 85-year-old men, the mean duration without formal care was 0.7 years, of home care 1.7, and institutional care 1.1 years. In 85-year-old women, the duration without formal care was 0.8 years, of home care 2.3, and institutional care 2.3 years. Total care duration was 3.5 years in 85-year-old men and 5.4 years in 85-year-old women. In men, the duration of home care was longer compared with no formal care and institutional care. The duration of no formal care was longer in persons not living alone, without prescribed dementia medication, with a non‒Western migration background, or with a higher income. The duration of home or institutional care was longer in women, persons without polypharmacy, in those living alone, or those with a Western background. CONCLUSIONS AND IMPLICATIONS: Our findings help to increase understanding of long-term dementia care trajectories and show that demographic and clinical factors determine the duration of care types. Our results can contribute to the organization of healthcare resource planning and monitoring of the effects of healthcare policy and interventions.

Time from diagnosis to institutionalization and death in people with dementia.

INTRODUCTION: Reliable estimates of time from diagnosis until institutionalization and death in people with dementia from routine nationally representative databases are lacking. METHODS: We selected 9230 people with dementia and 24,624 matched controls from family physicians' electronic records linked with national administrative databases to analyze time until institutionalization and death and associated factors. RESULTS: Median time from recorded diagnosis until institutionalization and until death for people with dementia was 3.9 and 5.0 years, respectively, which was considerably shorter than for controls. Once institutionalized, median time to death was longer for persons with dementia (2.5 years) than for controls (1.2 years). Older age and receiving home care were the strongest predictors of shorter time until institutionalization and death in people with dementia. Gender, cohabitation, migration status, frailty, polypharmacy, and dementia medication were other significant factors. DISCUSSION: The estimates could help to inform patients, their families, and policymakers about probable trajectories.

The MOPEAD project: Advancing patient engagement for the detection of "hidden" undiagnosed cases of Alzheimer's disease in the community.

In most, if not all health systems, dementia is underdiagnosed, and when diagnosis occurs, it is typically at a relatively late stage in the disease process despite mounting evidence showing that a timely diagnosis would result in numerous benefits for patients, families, and society. Moving toward earlier diagnoses in Alzheimer's disease (AD) requires a conscientious and collective effort to implement a global strategy addressing the multiple causes hindering patient engagement at different levels of society. This article describes the design of the Models of Patient Engagement for Alzheimer's Disease project, an ongoing EU-funded public-private multinational initiative that will compare four innovative patient engagement strategies across five European countries regarding their ability to identify individuals with prodromal AD and mild AD dementia, which are "hidden" in their communities and traditionally not found in the typical memory clinic setting. The strategies include an online AD citizen science platform, an open house initiative at the memory clinics, and patient engagement at primary care and diabetologist clinics.

Association Between Later Life Lifestyle Factors and Alzheimer's Disease Biomarkers in Non-Demented Individuals: A Longitudinal Descriptive Cohort Study.

BACKGROUND: Lifestyle factors have been associated with the risk of dementia, but the association with Alzheimer's disease (AD) remains unclear. OBJECTIVE: To examine the association between later life lifestyle factors and AD biomarkers (i.e., amyloid-ß 1-42 (Aß42) and tau in cerebrospinal fluid (CSF), and hippocampal volume) in individuals with subjective cognitive decline (SCD) and mild cognitive impairment (MCI). In addition, to examine the effect of later life lifestyle factors on developing AD-type dementia in individuals with MCI. METHODS: We selected individuals with SCD (n = 111) and MCI (n = 353) from the DESCRIPA and Kuopio Longitudinal MCI studies. CSF Aß42 and tau concentrations were assessed with ELISA assay and hippocampal volume with multi-atlas segmentation. Lifestyle was assessed by clinical interview at baseline for: social activity, physical activity, cognitive activity, smoking, alcohol consumption, and sleep. We performed logistic and Cox regression analyses adjusted for study site, age, gender, education, and diagnosis. Prediction for AD-type dementia was performed in individuals with MCI only. RESULTS: Later life lifestyle factors were not associated with AD biomarkers or with conversion to AD-type dementia. AD biomarkers were strongly associated with conversion to AD-type dementia, but these relations were not modulated by lifestyle factors. Apolipoprotein E (APOE) genotype did not influence the results. CONCLUSIONS: Later life lifestyle factors had no impact on key AD biomarkers in individuals with SCD and MCI or on conversion to AD-type dementia in MCI.

Strategic roadmap for an early diagnosis of Alzheimer's disease based on biomarkers.

The diagnosis of Alzheimer's disease can be improved by the use of biological measures. Biomarkers of functional impairment, neuronal loss, and protein deposition that can be assessed by neuroimaging (ie, MRI and PET) or CSF analysis are increasingly being used to diagnose Alzheimer's disease in research studies and specialist clinical settings. However, the validation of the clinical usefulness of these biomarkers is incomplete, and that is hampering reimbursement for these tests by health insurance providers, their widespread clinical implementation, and improvements in quality of health care. We have developed a strategic five-phase roadmap to foster the clinical validation of biomarkers in Alzheimer's disease, adapted from the approach for cancer biomarkers. Sufficient evidence of analytical validity (phase 1 of a structured framework adapted from oncology) is available for all biomarkers, but their clinical validity (phases 2 and 3) and clinical utility (phases 4 and 5) are incomplete. To complete these phases, research priorities include the standardisation of the readout of these assays and thresholds for normality, the evaluation of their performance in detecting early disease, the development of diagnostic algorithms comprising combinations of biomarkers, and the development of clinical guidelines for the use of biomarkers in qualified memory clinics.

Modifiable Risk Factors for Prevention of Dementia in Midlife, Late Life and the Oldest-Old: Validation of the LIBRA Index.

BACKGROUND: Recently, the LIfestyle for BRAin health (LIBRA) index was developed to assess an individual's prevention potential for dementia. OBJECTIVE: We investigated the predictive validity of the LIBRA index for incident dementia in midlife, late life, and the oldest-old. METHODS: 9,387 non-demented individuals were recruited from the European population-based DESCRIPA study. An individual's LIBRA index was calculated solely based on modifiable risk factors: depression, diabetes, physical activity, hypertension, obesity, smoking, hypercholesterolemia, coronary heart disease, and mild/moderate alcohol use. Cox regression was used to test the predictive validity of LIBRA for dementia at follow-up (mean 7.2 y, range 1-16). RESULTS: In midlife (55-69 y, n = 3,256) and late life (70-79 y, n = 4,320), the risk for dementia increased with higher LIBRA scores. Individuals in the intermediate- and high-risk groups had a higher risk of dementia than those in the low-risk group. In the oldest-old (80-97 y, n = 1,811), higher LIBRA scores did not increase the risk for dementia. CONCLUSION: LIBRA might be a useful tool to identify individuals for primary prevention interventions of dementia in midlife, and maybe in late life, but not in the oldest-old.

Clinical validity of medial temporal atrophy as a biomarker for Alzheimer's disease in the context of a structured 5-phase development framework.

Research criteria for Alzheimer's disease recommend the use of biomarkers for diagnosis, but whether biomarkers improve the diagnosis in clinical routine has not been systematically assessed. The aim is to evaluate the evidence for use of medial temporal lobe atrophy (MTA) as a biomarker for Alzheimer's disease at the mild cognitive impairment stage in routine clinical practice, with an adapted version of the 5-phase oncology framework for biomarker development. A literature review on visual assessment of MTA and hippocampal volumetry was conducted with other biomarkers addressed in parallel reviews. Ample evidence is available for phase 1 (rationale for use) and phase 2 (discriminative ability between diseased and control subjects). Phase 3 (early detection ability) is partly achieved: most evidence is derived from research cohorts or clinical populations with short follow-up, but validation in clinical mild cognitive impairment cohorts is required. In phase 4, only the practical feasibility has been addressed for visual rating of MTA. The rest of phase 4 and phase 5 have not yet been addressed.

NIA-AA staging of preclinical Alzheimer disease: discordance and concordance of CSF and imaging biomarkers.

The National Institute of Aging and Alzheimer's Association (NIA-AA) criteria for Alzheimer disease (AD) treat neuroimaging and cerebrospinal fluid (CSF) markers of AD pathology as if they would be interchangeable. We tested this assumption in 212 cognitively normal participants who have both neuroimaging and CSF measures of ß-amyloid (CSF Aß1-42 and positron emission tomography imaging with Pittsburgh Compound B) and neuronal injury (CSF t-tau and p-tau and structural magnetic resonance imaging) with longitudinal clinical follow-up. Participants were classified in preclinical AD stage 1 (ß-amyloidosis) or preclinical AD stage 2+ (ß-amyloidosis and neuronal injury) using the NIA-AA criteria, or in the normal or suspected non-Alzheimer disease pathophysiology group (neuronal injury without ß-amyloidosis). At baseline, 21% of participants had preclinical AD based on CSF and 28% based on neuroimaging. Between modalities, staging was concordant in only 47% of participants. Disagreement resulted from low concordance between biomarkers of neuronal injury. Still, individuals in stage 2+ using either criterion had an increased risk for clinical decline. This highlights the heterogeneity of the definition of neuronal injury and has important implications for clinical trials using biomarkers for enrollment or as surrogate end point measures.

The use of biomarkers for the etiologic diagnosis of MCI in Europe: an EADC survey.

We investigated the use of Alzheimer's disease (AD) biomarkers in European Alzheimer's Disease Consortium centers and assessed their perceived usefulness for the etiologic diagnosis of mild cognitive impairment (MCI). We surveyed availability, frequency of use, and confidence in diagnostic usefulness of markers of brain amyloidosis (amyloid positron emission tomography [PET], cerebrospinal fluid [CSF] Aß42) and neurodegeneration (medial temporal atrophy [MTA] on MR, fluorodeoxyglucose positron emission tomography [FDG-PET], CSF tau). The most frequently used biomarker is visually rated MTA (75% of the 37 responders reported using it "always/frequently") followed by CSF markers (22%), FDG-PET (16%), and amyloid-PET (3%). Only 45% of responders perceive MTA as contributing to diagnostic confidence, where the contribution was rated as "moderate". Seventy-nine percent of responders felt "very/extremely" comfortable delivering a diagnosis of MCI due to AD when both amyloid and neuronal injury biomarkers were abnormal (P < .02 versus any individual biomarker). Responders largely agreed that a combination of amyloidosis and neuronal injury biomarkers was a strongly indicative AD signature.