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Biological markers for therapeutic trials in Alzheimer’s disease: Proceedings of the biological markers working group; NIA initiative on neuroimaging in Alzheimer’s disease

Timely detection of the pathophysiological changes and cognitive impairment caused by Alzheimer’s disease (AD) is increasingly pressing because of the advent of biomarker-guided targeted therapies that may be most effective when provided early in the disease. Currently, diagnosis and management of early AD are largely guided by clinical symptoms. FDA-approved neuroimaging and cerebrospinal fluid biomarkers can aid detection and diagnosis, but the clinical implementation of these testing modalities is limited because of availability, cost, and perceived invasiveness. Blood-based biomarkers (BBBMs) may enable earlier and faster diagnoses as well as aid in risk assessment, early detection, prognosis, and management. Herein, we review data on BBBMs that are closest to clinical implementation, particularly those based on measures of amyloid-β peptides and phosphorylated tau species. We discuss key parameters and considerations for the development and potential deployment of these BBBMs under different contexts of use and highlight challenges at the methodological, clinical, and regulatory levels.

Neuropathological hallmarks of Alzheimer's disease (AD) include pathogenic accumulation of amyloid-β (Aβ) peptides and age-dependent formation of amyloid plaques in the brain. AD-associated Aβ neuropathology begins decades before onset of cognitive symptoms and slowly progresses over the course of the disease. We previously reported discovery of Aβ deposition, β-amyloidopathy, and co-localizing supranuclear cataracts (SNC) in lenses from people with AD, but not other neurodegenerative disorders or normal aging. We confirmed AD-associated Aβ molecular pathology in the lens by immunohistopathology, amyloid histochemistry, immunoblot analysis, epitope mapping, immunogold electron microscopy, quantitative immunoassays, and tryptic digest mass spectrometry peptide sequencing. Ultrastructural analysis revealed that AD-associated Aβ deposits in AD lenses localize as electron-dense microaggregates in the cytoplasm of supranuclear (deep cortex) fiber cells. These Aβ microaggregates also contain αB-crystallin and scatter light, thus linking Aβ pathology and SNC phenotype expression in the lenses of people with AD. Subsequent research identified Aβ lens pathology as the molecular origin of the distinctive cataracts associated with Down syndrome (DS, trisomy 21), a chromosomal disorder invariantly associated with early-onset Aβ accumulation and Aβ amyloidopathy in the brain. Investigation of 1249 participants in the Framingham Eye Study found that AD-associated quantitative traits in brain and lens are co-heritable. Moreover, AD-associated lens traits preceded MRI brain traits and cognitive deficits by a decade or more and predicted future AD. A genome-wide association study of bivariate outcomes in the same subjects identified a new AD risk factor locus in the CTNND2 gene encoding δ-catenin, a protein that modulates Aβ production in brain and lens. Here we report identification of AD-related human Aβ (hAβ) lens pathology and age-dependent SNC phenotype expression in the Tg2576 transgenic mouse model of AD. Tg2576 mice express Swedish mutant human amyloid precursor protein (APP-Swe), accumulate hAβ peptides and amyloid pathology in the brain, and exhibit cognitive deficits that slowly progress with increasing age. We found that Tg2576 trangenic (Tg⁺) mice, but not non-transgenic (Tg^–) control mice, also express human APP, accumulate hAβ peptides, and develop hAβ molecular and ultrastructural pathologies in the lens. Tg2576 Tg⁺ mice exhibit age-dependent Aβ supranuclear lens opacification that recapitulates lens pathology and SNC phenotype expression in human AD. In addition, we detected hAβ in conditioned medium from lens explant cultures prepared from Tg⁺ mice, but not Tg^– control mice, a finding consistent with constitutive hAβ generation in the lens. In vitro studies showed that hAβ promoted mouse lens protein aggregation detected by quasi-elastic light scattering (QLS) spectroscopy. These results support mechanistic (genotype-phenotype) linkage between Aβ pathology and AD-related phenotypes in lens and brain. Collectively, our findings identify Aβ pathology as the shared molecular etiology of two age-dependent AD-related cataracts associated with two human diseases (AD, DS) and homologous murine cataracts in the Tg2576 transgenic mouse model of AD. These results represent the first evidence of AD-related Aβ pathology outside the brain and point to lens Aβ as an optically-accessible AD biomarker for early detection and longitudinal monitoring of this devastating neurodegenerative disease.

Concomitant use of complementary, multimodal imaging measures and neurocognitive measures is reported to have higher accuracy as a biomarker in Alzheimer’s dementia. However, such an approach has not been examined to differentiate healthy individuals from Bipolar disorder. In this study, we examined the utility of support vector machine (SVM) technique to differentiate bipolar disorder patients and healthy using structural, functional and diffusion tensor images of brain and neurocognitive measures.

30 patients with Bipolar disorder-I and 30 age, sex matched individuals participated in the study. Structural MRI, resting state functional MRI and diffusion tensor images were obtained using a 1.5 T scanner. All participants were administered neuropsychological tests to measure executive functions. SVM, a supervised machine learning technique was applied to differentiate patients and healthy individuals with k-fold cross validation over 10 trials.

The composite marker consisting of both neuroimaging and neuropsychological measures, had an accuracy of 87.60 %, sensitivity of 82.3 % and specificity of 92.7 %. The performance of composite marker was better compared to that of individual markers on classificatory.

We were able to achieve a high accuracy for machine learning technique in distinguishing BD from HV using a combination of multimodal neuroimaging and neurocognitive measures. Findings of this proof of concept study, if replicated in larger samples, could have potential clinical applications.

Cognitive reserve has been described as offering protection against Alzheimer's disease (AD) and other neurodegenerative conditions, but also against age-associated brain changes. Using data from the Alzheimer's Disease Neuroimaging Initiative, we defined cognitive reserve using the residual reserve index: episodic memory performance residualized for 3T MRI-derived brain volumes and demographics. We examined whether cognitive reserve predicted executive function (EF) decline equally across 2 groups of older adults—AD biomarker–positive (n = 468) and –negative (n = 402)—defined by the tau-to-amyloid ratio in cerebrospinal fluid. A significant interaction between the residual reserve index and biomarker group revealed that the effect of cognitive reserve on EF decline was dependent on pathology status. In the biomarker-positive group, higher cognitive reserve predicted EF decline over five years. However, cognitive reserve did not predict EF decline in the biomarker-negative group. These results suggest a certain level of AD pathology may be needed before cognitive reserve exerts its protective effects on future cognition; however, further research that tracks cognitive reserve longitudinally is needed.

Alzheimer's disease (AD) neuropathology is extremely heterogeneous, and the evolution from preclinical to mild cognitive impairment until dementia is driven by interacting genetic/biological mechanisms not fully captured by current clinical/research criteria. We characterized the heterogeneous “construct” of AD through a cerebrospinal fluid biomarker-guided stratification approach. We analyzed 5 validated pathophysiological cerebrospinal fluid biomarkers (Aβ_1-42, t-tau, p-tau₁₈₁, NFL, YKL-40) in 113 participants (healthy controls [N = 20], subjective memory complainers [N = 36], mild cognitive impairment [N = 20], and AD dementia [N = 37], age: 66.7 ± 10.4, 70.4 ± 7.7, 71.7 ± 8.4, 76.2 ± 3.5 years [mean ± SD], respectively) using Density-Based Spatial Clustering of Applications with Noise, which does not require a priori determination of the number of clusters. We found 5 distinct clusters (sizes: N = 38, 16, 24, 14, and 21) whose composition was independent of phenotypical groups. Two clusters showed biomarker profiles linked to neurodegenerative processes not associated with classical AD-related pathophysiology. One cluster was characterized by the neuroinflammation biomarker YKL-40. Combining nonlinear data aggregation with informative biomarkers can generate novel patient strata which are representative of cellular/molecular pathophysiology and may aid in predicting disease evolution and mechanistic drug response.

Both high or low plasma amyloid levels have been associated with risk of dementia in nondemented subjects.

We examined baseline plasma β-amyloid (Aβ) levels in relationship to incident dementia during a period of 8.5 years in 2840 subjects age >75 years; 2381 were cognitively normal (CN) and 450 mild cognitive impairment.

Increased plasma Aβ1-40 and Aβ1-42 levels were associated with gender (women), age, low education, creatinine levels, history of stroke, and hypertension. CN participants who developed dementia had lower levels of Aβ1-42 and Aβ1-42/Aβ1-40 ratio compared with those who did not. Aβ levels did not predict dementia in mild cognitive impairment participants.

There was an inverse association between Aβ1-42 and Aβ1-42/Aβ1-40 ratio to risk of dementia in CN participants. Cerebral and cardiovascular disease and renal function are important determinants of increased Aβ levels and must be considered in evaluations of relationship of plasma Aβ and subsequent risk of dementia.