CSF sTREM2: Marking the tipping point between preclinical AD and dementia?

Biomarkers for Alzheimer's disease (AD) have improved our understanding of the temporal sequence of biological events that lead to AD dementia (Jack et al, 2013). AD is characterized neuropathologically by amyloid plaques comprised of the amyloid‐β peptide and neurofibrillary tangles comprised of tau. Brain amyloid deposition, as evidenced by a decline in amyloid‐β peptide 42 (Aβ42) in the cerebrospinal fluid (CSF) or by binding of amyloid PET ligands, is thought to be a key initiating event in AD and begins many years prior to the onset of dementia. A rise in CSF tau and phosphorylated tau in the setting of Aβ deposition appears to reflect neurodegeneration and also begins years prior to the onset of dementia but after Aβ deposition has begun to accumulate. Individuals with “preclinical AD,” that is, normal cognition but abnormal AD biomarkers, have a much higher risk for developing AD dementia but may remain cognitively normal for years (Vos et al, 2013). While deposition of amyloid and formation of tau tangles are necessary for AD to occur, it is likely that additional events involving inflammation or other processes contribute to crossing the tipping point from preclinical AD to AD dementia. Current efforts are aimed at defining the biomarker(s) that best predict the transition from cognitive normality to abnormality. A biomarker that is closely associated with the onset of cognitive decline could help us to understand the biological events that connect amyloid deposition and tangle formation to cognitive decline and could have significant practical value in AD diagnosis and clinical trial design

Association of acquired and heritable factors with intergenerational differences in age at symptomatic onset of Alzheimer disease between offspring and parents with dementia

Importance: Acquired and heritable traits are associated with dementia risk; however, how these traits are associated with age at symptomatic onset (AAO) of Alzheimer disease (AD) is unknown. Identifying the associations of acquired and heritable factors with variability in intergenerational AAO of AD could facilitate diagnosis, assessment, and counseling of the offspring of parents with AD. Objective: To quantify the associations of acquired and heritable factors with intergenerational differences in AAO of AD. Design, Setting, and Participants: This nested cohort study used data from the Knight Alzheimer Disease Research Center that included community-dwelling participants with symptomatic AD, parental history of dementia, and available DNA data who were enrolled in prospective studies of memory and aging from September 1, 2005, to August 31, 2016. Clinical, biomarker, and genetic data were extracted on January 17, 2017, and data analyses were conducted from July 1, 2017, to August 20, 2019. Main Outcomes and Measures: The associations of acquired (ie, years of education; body mass index; history of cardiovascular disease, hypertension, hypercholesterolemia, diabetes, active depression within 2 years, traumatic brain injury, tobacco use, and unhealthy alcohol use; and retrospective determination of AAO) and heritable factors (ie, ethnicity/race, paternal or maternal inheritance, parental history of early-onset dementia, APOE ε4 allele status, and AD polygenic risk scores) to intergenerational difference in AAO of AD were quantified using stepwise forward multivariable regression. Missense or frameshift variants within genes associated with AD pathogenesis were screened using whole-exome sequencing. Results: There were 164 participants with symptomatic AD, known parental history of dementia, and available DNA data (mean [SD] age, 70.9 [8.3] years; 90 [54.9%] women) included in this study. Offspring were diagnosed with symptomatic AD a mean (SD) 6.1 (10.7) years earlier than their parents (P \u3c .001). The adjusted R2 for measured acquired and heritable factors for intergenerational difference in AAO of AD was 0.29 (F8,155 = 9.13; P \u3c .001). Paternal (β = -9.52 [95% CI, -13.79 to -5.25]) and maternal (β = -6.68 [95% CI, -11.61 to -1.75]) history of dementia, more years of education (β = -0.58 [95% CI -1.08 to -0.09]), and retrospective determination of AAO (β = -3.46 [95% CI, -6.40 to -0.52]) were associated with earlier-than-expected intergenerational difference in AAO of AD. Parental history of early-onset dementia (β = 21.30 [95% CI, 15.01 to 27.59]), presence of 1 APOE ε4 allele (β = 5.00 [95% CI, 2.11 to 7.88]), and history of hypertension (β = 3.81 [95% CI, 0.88 to 6.74]) were associated with later-than-expected intergenerational difference in AAO of AD. Missense or frameshift variants within genes associated with AD pathogenesis were more common in participants with the greatest unexplained variability in intergenerational AAO of AD (19 of 48 participants [39.6%] vs 26 of 116 participants [22.4%]; P = .03). Conclusions and Relevance: Acquired and heritable factors were associated with a substantial proportion of variability in intergenerational AAO of AD. Variants in genes associated with AD pathogenesis may contribute to unexplained variability, justifying further study

Extraction of clinical phenotypes for Alzheimer\u27s disease dementia from clinical notes using natural language processing

OBJECTIVES: There is much interest in utilizing clinical data for developing prediction models for Alzheimer\u27s disease (AD) risk, progression, and outcomes. Existing studies have mostly utilized curated research registries, image analysis, and structured electronic health record (EHR) data. However, much critical information resides in relatively inaccessible unstructured clinical notes within the EHR. MATERIALS AND METHODS: We developed a natural language processing (NLP)-based pipeline to extract AD-related clinical phenotypes, documenting strategies for success and assessing the utility of mining unstructured clinical notes. We evaluated the pipeline against gold-standard manual annotations performed by 2 clinical dementia experts for AD-related clinical phenotypes including medical comorbidities, biomarkers, neurobehavioral test scores, behavioral indicators of cognitive decline, family history, and neuroimaging findings. RESULTS: Documentation rates for each phenotype varied in the structured versus unstructured EHR. Interannotator agreement was high (Cohen\u27s kappa = 0.72-1) and positively correlated with the NLP-based phenotype extraction pipeline\u27s performance (average F1-score = 0.65-0.99) for each phenotype. DISCUSSION: We developed an automated NLP-based pipeline to extract informative phenotypes that may improve the performance of eventual machine learning predictive models for AD. In the process, we examined documentation practices for each phenotype relevant to the care of AD patients and identified factors for success. CONCLUSION: Success of our NLP-based phenotype extraction pipeline depended on domain-specific knowledge and focus on a specific clinical domain instead of maximizing generalizability

GPS driving: A digital biomarker for preclinical Alzheimer disease

BACKGROUND: Alzheimer disease (AD) is the most common cause of dementia. Preclinical AD is the period during which early AD brain changes are present but cognitive symptoms have not yet manifest. The presence of AD brain changes can be ascertained by molecular biomarkers obtained via imaging and lumbar puncture. However, the use of these methods is limited by cost, acceptability, and availability. The preclinical stage of AD may have a subtle functional signature, which can impact complex behaviours such as driving. The objective of the present study was to evaluate the ability of in-vehicle GPS data loggers to distinguish cognitively normal older drivers with preclinical AD from those without preclinical AD using machine learning methods. METHODS: We followed naturalistic driving in cognitively normal older drivers for 1 year with a commercial in-vehicle GPS data logger. The cohort included n = 64 individuals with and n = 75 without preclinical AD, as determined by cerebrospinal fluid biomarkers. Four Random Forest (RF) models were trained to detect preclinical AD. RF Gini index was used to identify the strongest predictors of preclinical AD. RESULTS: The F1 score of the RF models for identifying preclinical AD was 0.85 using APOE ε4 status and age only, 0.82 using GPS-based driving indicators only, 0.88 using age and driving indicators, and 0.91 using age, APOE ε4 status, and driving. The area under the receiver operating curve for the final model was 0.96. CONCLUSION: The findings suggest that GPS driving may serve as an effective and accurate digital biomarker for identifying preclinical AD among older adults

Photo-activatable Cre recombinase regulates gene expression in vivo

Techniques allowing precise spatial and temporal control of gene expression in the brain are needed. Herein we describe optogenetic approaches using a photo-activatable Cre recombinase (PA-Cre) to stably modify gene expression in the mouse brain. Blue light illumination for 12 hours via optical fibers activated PA-Cre in the hippocampus, a deep brain structure. Two-photon illumination through a thinned skull window for 100 minutes activated PA-Cre within a sub-millimeter region of cortex. Light activation of PA-Cre may allow permanent gene modification with improved spatiotemporal precision compared to standard methods

Apolipoprotein E O-glycosylation is associated with amyloid plaques and APOE genotype

Although the APOE ε4 allele is the strongest genetic risk factor for sporadic Alzheimer\u27s disease (AD), the relationship between apolipoprotein (apoE) and AD pathophysiology is not yet fully understood. Relatively little is known about the apoE protein species, including post-translational modifications, that exist in the human periphery and CNS. To better understand these apoE species, we developed a LC-MS/MS assay that simultaneously quantifies both unmodified and O-glycosylated apoE peptides. The study cohort included 47 older individuals (age 75.6 ± 5.7 years [mean ± standard deviation]), including 23 individuals (49%) with cognitive impairment. Paired plasma and cerebrospinal fluid samples underwent analysis. We quantified O-glycosylation of two apoE protein residues - one in the hinge region and one in the C-terminal region - and found that glycosylation occupancy of the hinge region in the plasma was significantly correlated with plasma total apoE levels, APOE genotype and amyloid status as determined by CSF Aβ42/Aβ40. A model with plasma glycosylation occupancy, plasma total apoE concentration, and APOE genotype distinguished amyloid status with an AUROC of 0.89. These results suggest that plasma apoE glycosylation levels could be a marker of brain amyloidosis, and that apoE glycosylation may play a role in the pathophysiology of AD

Cerebrospinal fluid neurofilament light chain is a marker of aging and white matter damage

BACKGROUND: Cerebrospinal fluid (CSF) neurofilament light chain (NfL) reflects neuro-axonal damage and is increasingly used to evaluate disease progression across neurological conditions including Alzheimer disease (AD). However, it is unknown how NfL relates to specific types of brain tissue. We sought to determine whether CSF NfL is more strongly associated with total gray matter, white matter, or white matter hyperintensity (WMH) volume, and to quantify the relative importance of brain tissue volume, age, and AD marker status (i.e., APOE genotype, brain amyloidosis, tauopathy, and cognitive status) in predicting CSF NfL. METHODS: 419 participants (Clinical Dementia Rating [CDR] Scale \u3e 0, N = 71) had CSF, magnetic resonance imaging (MRI), and neuropsychological data. A subset had amyloid positron emission tomography (PET) and tau PET. Pearson correlation analysis was used to determine the association between CSF NfL and age. Multiple regression was used to determine which brain volume (i.e., gray, white, or WMH volume) most strongly associated with CSF NfL. Stepwise regression and dominance analyses were used to determine the individual contributions and relative importance of brain volume, age, and AD marker status in predicting CSF NfL. RESULTS: CSF NfL increased with age (r = 0.59, p \u3c 0.001). Elevated CSF NfL was associated with greater total WMH volume (p \u3c 0.001), but not gray or white matter volume (p\u27s \u3e 0.05) when considered simultaneously. Age and WMH volume were consistently more important (i.e., have greater R CONCLUSIONS: CSF NfL is a non-specific marker of aging and white matter integrity with limited sensitivity to specific markers of AD. CSF NfL likely reflects processes associated with cerebrovascular disease

A map of neurofilament light chain species in brain and cerebrospinal fluid and alterations in Alzheimer\u27s disease

Neurofilament light is a well-established marker of both acute and chronic neuronal damage and is increased in multiple neurodegenerative diseases. However, the protein is not well characterized in brain tissue or body fluids, and it is unknown what neurofilament light species are detected by commercial assays and whether additional species exist. We developed an immunoprecipitation-mass spectrometry assay using custom antibodies targeting various neurofilament light domains, including antibodies targeting Coil 1A/1B of the rod domain (HJ30.13), Coil 2B of the rod domain (HJ30.4) and the tail region (HJ30.11). We utilized our assay to characterize neurofilament light in brain tissue and CSF of individuals with Alzheimer\u27s disease dementia and healthy controls. We then validated a quantitative version of our assay and measured neurofilament light concentrations using both our quantitative immunoprecipitation-mass spectrometry assay and the commercially available immunoassay from Uman diagnostics in individuals with and without Alzheimer\u27s disease dementia. Our validation cohort included CSF samples from 30 symptomatic amyloid-positive participants, 16 asymptomatic amyloid-positive participants, 10 symptomatic amyloid-negative participants and 25 amyloid-negative controls. We identified at least three major neurofilament light species in CSF, including N-terminal and C-terminal truncations, and a C-terminal fragment containing the tail domain. No full-length neurofilament light was identified in CSF. This contrasts with brain tissue, which contained mostly full-length neurofilament and a C-terminal tail domain fragment. We observed an increase in neurofilament light concentrations in individuals with Alzheimer\u27s disease compared with healthy controls, with larger differences for some neurofilament light species than for others. The largest differences were observed for neurofilament light fragments including NfL165 (in Coil 1B), NfL324 (in Coil 2B) and NfL530 (in the C-terminal tail domain). The Uman immunoassay correlated most with NfL324. This study provides a comprehensive evaluation of neurofilament light in brain and CSF and enables future investigations of neurofilament light biology and utility as a biomarker

Naturalistic assessment of reaction time variability in older adults at risk for Alzheimer\u27s disease

OBJECTIVE: Maintaining attention underlies many aspects of cognition and becomes compromised early in neurodegenerative diseases like Alzheimer\u27s disease (AD). The consistency of maintaining attention can be measured with reaction time (RT) variability. Previous work has focused on measuring such fluctuations during in-clinic testing, but recent developments in remote, smartphone-based cognitive assessments can allow one to test if these fluctuations in attention are evident in naturalistic settings and if they are sensitive to traditional clinical and cognitive markers of AD. METHOD: Three hundred and seventy older adults (aged 75.8 +/- 5.8 years) completed a week of remote daily testing on the Ambulatory Research in Cognition (ARC) smartphone platform and also completed clinical, genetic, and conventional in-clinic cognitive assessments. RT variability was assessed in a brief (20-40 seconds) processing speed task using two different measures of variability, the Coefficient of Variation (CoV) and the Root Mean Squared Successive Difference (RMSSD) of RTs on correct trials. RESULTS: Symptomatic participants showed greater variability compared to cognitively normal participants. When restricted to cognitively normal participants, APOE ε4 carriers exhibited greater variability than noncarriers. Both CoV and RMSSD showed significant, and similar, correlations with several in-clinic cognitive composites. Finally, both RT variability measures significantly mediated the relationship between APOE ε4 status and several in-clinic cognition composites. CONCLUSIONS: Attentional fluctuations over 20-40 seconds assessed in daily life, are sensitive to clinical status and genetic risk for AD. RT variability appears to be an important predictor of cognitive deficits during the preclinical disease stage