Explore the vast range of titles available.

Dementia is a global health priority. Early identification in asymptomatic or mildly symptomatic individuals (ie, dementia risk detection) is proposed as a clinical solution for early intervention and could support researchers to identify novel neuropathological targets and recruit to clinical trials. Digital biomarkers of behavioral or physiological markers, including sleep, are cited as a potential low-cost, noninvasive, and objective method for dementia risk detection. Understanding perspectives on digital biomarkers, particularly acceptability, from potential end users and clinical staff is required when considering implementation within any clinical service. With emerging evidence of sleep as a risk marker for dementia, the efficacy of the Dementia Research Institute Sleep Index (DRI-SI), based on continuous remote monitoring of sleep patterns detected by a digital sleep mat, for dementia risk detection, is currently being explored by the InSleep46 study. This qualitative substudy aimed to explore perspectives of potential end users and primary care physicians regarding the use of a digital sleep mat to measure the DRI-SI and its application towards dementia risk detection. Thirty-one potential end users (age: 31-82 years, 11 female and 20 male) from Newcastle and London, United Kingdom, with personal or caregiving experience related to dementia, participated in qualitative focus group workshops. They shared opinions on integrating the sleep mat into their homes, the DRI-SI's potential for identifying dementia risk, and the necessary information for engagement with related clinical services. Seven primary care physicians from across England participated in semistructured interviews regarding the potential application of the DRI-SI in dementia risk detection and its integration into current clinical practice. Inductive thematic analysis was conducted to identify key themes. Four key themes emerged from end user focus groups: (1) practical use, (2) prospective acceptability, (3) clinical management, and (4) data concerns. Three main themes came from the semistructured interviews with physicians: (1) prospective acceptability, (2) health care provision, and (3) practical considerations. Common themes were identified in both groups but held differing perspectives. End users were focused on practical aspects of integrating the digital sleep mat within their daily life, the effect of the DRI-SI on clinical care, and privacy concerns regarding data use. Primary care physicians were concerned more broadly with how the DRI-SI and dementia risk detection service would integrate into current clinical practice, the impact on clinical resources and patient well-being, and the need for clinical actionability and guidance on discussing results with patients. End users would find the DRI-SI acceptable as part of their clinical care, but primary care physicians require a more robust evidence base. Future research should explore the integration of the DRI-SI into clinical care/research pathways to enhance clinical acceptability. Five key recommendations have been made for further development of digital biomarkers for dementia risk populations.

Background Although age is the biggest known risk factor for dementia, there remains uncertainty about other factors over the life course that contribute to a person’s risk for cognitive decline later in life. Furthermore, the pathological processes leading to dementia are not fully understood. The main goals of Insight 46—a multi-phase longitudinal observational study—are to collect detailed cognitive, neurological, physical, cardiovascular, and sensory data; to combine those data with genetic and life-course information collected from the MRC National Survey of Health and Development (NSHD; 1946 British birth cohort); and thereby contribute to a better understanding of healthy ageing and dementia. Methods/Design Phase 1 of Insight 46 (2015–2018) involved the recruitment of 502 members of the NSHD (median age = 70.7 years; 49% female) and has been described in detail by Lane and Parker et al . 2017. The present paper describes phase 2 (2018–2021) and phase 3 (2021–ongoing). Of the 502 phase 1 study members who were invited to a phase 2 research visit, 413 were willing to return for a clinic visit in London and 29 participated in a remote research assessment due to COVID-19 restrictions. Phase 3 aims to recruit 250 study members who previously participated in both phases 1 and 2 of Insight 46 (providing a third data time point) and 500 additional members of the NSHD who have not previously participated in Insight 46. Discussion The NSHD is the oldest and longest continuously running British birth cohort. Members of the NSHD are now at a critical point in their lives for us to investigate successful ageing and key age-related brain morbidities. Data collected from Insight 46 have the potential to greatly contribute to and impact the field of healthy ageing and dementia by combining unique life course data with longitudinal multiparametric clinical, imaging, and biomarker measurements. Further protocol enhancements are planned, including in-home sleep measurements and the engagement of participants through remote online cognitive testing. Data collected are and will continue to be made available to the scientific community.

IntroductionHuman T-cell lymphotropic virus type-1 (HTLV-1) infection remains asymptomatic in >90% of the 20-million people infected worldwide. However in 3%, chronic inflammation within the thoracic spinal cord leads to progressive spastic paraparesis; HTLV-1 associated myelopathy (HAM). This pathological process is not limited to the thoracic cord. We present a case of HTLV-1 encephalomyelitis.CaseA 53-year-old woman with HAM of 9 years duration presented with subacute cerebellar dysfunc- tion, being unable to feed herself, 3-months after cessation of methotrexate. Investigations demonstrated extensive T2-hyperintensity within the brainstem, cortical and subcortical white matter with punctate contrast enhancement; lymphocytic CSF; and high blood (36%) and CSF (72%) HTLV-1 proviral load (DNA copies per 100 lymphocytes). We diagnosed HTLV-1 encephalomyelitis and commenced high dose methylprednisolone with slow steroid taper following which functional independence in the upper limbs was regained.DiscussionRisk of HAM rises exponentially once HTLV-1 proviral load exceeds 1%, while CSF:blood proviral load ratio ≥2:1 indicates CNS infiltration of HTLV-1 infected lymphocytes. This patient’s high HTLV-1 proviral load and widespread MRI changes indicated HTLV-1 associated inflammation of the brain and spine. Prompt immunosuppression resulted in significant recovery and highlights the importance of early rec- ognition and management of extraspinal manifestations of HTLV-1 infection.j.king-robson@nhs.net

Background Sleep and circadian disruption are associated with increased dementia risk. Digital sleep biomarkers may provide an ecologically valid and low‐burden means of remote population‐level screening for incipient dementia. We explored the feasibility and predictive value of a digital sleep biomarker, developed from data collected using the Withings Sleep Analyzer (WSA), a ballistocardiographic under‐mattress pressure sensor which collects sleep and physiological data unobtrusively, to detect Alzheimer‐related biomarkers in a presymptomatic cohort. Method Participants from the Insight 46 study (all born in March 1946) underwent serial assessment, including plasma phosphorylated tau (pTau)217 ALZpath and 18F‐Florbetapir β‐amyloid PET at age ∼73 and 18F‐MK‐6240 Tau PET at age ∼77. Amyloid status (‐/+) and Tau Braak staging (‐/Braak1+/Braak3+) were derived using automated pipelines. The WSA was deployed at age ∼78, installed under participants’ mattresses by the study participant/family. Continuous sleep, circadian, and physiological parameters were collected. A leave‐one‐out cross validation approach was employed to develop models predicting PET status after feature selection (Figure 1). Results were compared to plasma pTau217. Result n = 161 had both WSA and Tau PET data (12.4% Braak1+, 6.2% Braak3+); n = 153 participants also had β‐amyloid PET (25% β‐amyloid+ at Centiloid>=12). In total we collected 63,720 nights (174 years) of sleep data, corresponding to a mean±SD of 239.8±108.7 nights/participant (age at collection 78.3±0.2 yrs; 49% female). n = 404 had plasma pTau217. A final trained model identified asymptomatic individuals with Braak3+ tau pathology with area under the receiver operating characteristic curve (AUROC)=0.75; comparable to plasma pTau217 (Figure 2) after iterative feature selection (Figure 3). Trained models were less effective at identifying earlier pathological stages (Tau Braak1+, β‐amyloid+). Conclusion Deploying a remote sleep and circadian monitoring device in a countrywide population‐based cohort in their late 70s is feasible. A model based on iterative feature selection was able to identify individuals with significant Tau (Braak3+) pathology with AUROC similar to plasma pTau217. This provides proof‐of‐concept that digital sleep biomarkers may be useful in identifying individuals at high risk of developing clinical AD. Work is underway to refine the model further, replicate these results in other cohorts, and identify the shortest duration of recording required for robust prediction.

Background Sleep and circadian disruption are associated with increased dementia risk. Digital sleep biomarkers may provide an ecologically valid and low‐burden means of remote population‐level screening for incipient dementia. We explored the feasibility and predictive value of a digital sleep biomarker, developed from data collected using the Withings Sleep Analyzer (WSA), a ballistocardiographic under‐mattress pressure sensor which collects sleep and physiological data unobtrusively, to detect Alzheimer‐related biomarkers in a presymptomatic cohort. Method Participants from the Insight 46 study (all born in March 1946) underwent serial assessment, including plasma phosphorylated tau (pTau)217 ALZpath and 18F‐Florbetapir β‐amyloid PET at age ∼73 and 18F‐MK‐6240 Tau PET at age ∼77. Amyloid status (‐/+) and Tau Braak staging (‐/Braak1+/Braak3+) were derived using automated pipelines. The WSA was deployed at age ∼78, installed under participants’ mattresses by the study participant/family. Continuous sleep, circadian, and physiological parameters were collected. A leave‐one‐out cross validation approach was employed to develop models predicting PET status after feature selection (Figure 1). Results were compared to plasma pTau217. Result n = 161 had both WSA and Tau PET data (12.4% Braak1+, 6.2% Braak3+); n = 153 participants also had β‐amyloid PET (25% β‐amyloid+ at Centiloid>=12). In total we collected 63,720 nights (174 years) of sleep data, corresponding to a mean±SD of 239.8±108.7 nights/participant (age at collection 78.3±0.2 yrs; 49% female). n = 404 had plasma pTau217. A final trained model identified asymptomatic individuals with Braak3+ tau pathology with area under the receiver operating characteristic curve (AUROC)=0.75; comparable to plasma pTau217 (Figure 2) after iterative feature selection (Figure 3). Trained models were less effective at identifying earlier pathological stages (Tau Braak1+, β‐amyloid+). Conclusion Deploying a remote sleep and circadian monitoring device in a countrywide population‐based cohort in their late 70s is feasible. A model based on iterative feature selection was able to identify individuals with significant Tau (Braak3+) pathology with AUROC similar to plasma pTau217. This provides proof‐of‐concept that digital sleep biomarkers may be useful in identifying individuals at high risk of developing clinical AD. Work is underway to refine the model further, replicate these results in other cohorts, and identify the shortest duration of recording required for robust prediction.

A 53-year-old woman developed subacute onset of upper limb weakness, sensory loss and cerebellar dysfunction. She was known to have human T-lymphotropic virus type 1 (HTLV-1)-associated myelopathy. MR scan of the brain showed extensive T2 hyperintensity within the deep and subcortical white matter, with punctate contrast enhancement. Cerebrospinal fluid (CSF) was lymphocytic with very high levels of HTLV-1 provirus in both CSF and peripheral blood lymphocytes. We diagnosed HTLV-1 encephalomyelitis and started high-dose methylprednisolone followed by a slow corticosteroid taper. She recovered well and regained functional independence in the upper limbs. Neurological manifestations of HTLV-1 infection extend beyond classical ‘tropical spastic paraparesis’ and are under-recognised. We review the literature on HTLV-1 encephalitis and discuss its diagnosis and management.

Background Sleep and circadian disruption are associated with increased dementia risk, yet the mechanism remains poorly understood. We examined the relationship between night/shift working in the fourth decade and late‐life brain health. We explored whether significant relationships were mediated by life course factors including cardiovascular risk. Methods Night/shift working (yes/no) was recorded prospectively at age 31. Smoking, alcohol intake, body mass index, exercise, blood pressure, and Framingham risk scores (FRS) were determined at 3‐6 timepoints across the life course (age 20, 36, 43, 53, 60‐64, 68‐70). Whole‐brain and hippocampal volumes, white matter hyperintensity volume (WMHV), and β‐amyloid PET SUVr were derived from T1, fluid‐attenuated inversion recovery, and 18F‐Florbetapir PET, respectively, at age ∼73. Associations between night/shift working, life course cardiovascular risk factors, and imaging metrics were examined with linear regression. Causal mediation analysis (gformula approach in R), examined whether significant relationships between night/shift working and imaging metrics were mediated by life course cardiovascular risk factors. Analyses were adjusted for gender, adult socioeconomic status, educational attainment, age at imaging, and intracranial volume for volumetric measures. Results 432 participants had available data, of whom 74 (17.1%) were night/shift workers. Night/shift workers had lower whole brain volume (‐26.2 ml, 95% CI ‐39.3, ‐13.0, P < 0.001, Figure 1), without evidence of a significant difference in hippocampal volume, WMHV, or amyloid‐β SUVr. Night/shift workers had 0.6% higher FRS (95% CI; 0.23%, 0.91%; P = 0.001) and higher alcohol consumption age 36 (5.7 g/day, 95% CI, 0.3, 11.2, P = 0.04); higher alcohol consumption age 60‐64 (10.7 g/day, 95% CI, 4.5, 16.9; P < 0.001); and smoked an additional 5.7 pack‐years by age 60‐64 (95% CI 1.9, 9.4; P = 0.003, Figure 2). 35% of the brain volume reduction in shift workers was mediated by cardiovascular risk factors (Figure 3). Conclusion Shift working in the early 30s is associated with lower brain volume in late‐life independent of amyloidogenic pathways. While partially mediated by increased cardiovascular risk factors in night/shift workers, the majority of the effect is unexplained and may be a direct effect of night/shift working on brain health.

Sleep and circadian disruption are associated with increased dementia risk. Digital sleep biomarkers may provide an ecologically valid and low-burden means of remote population-level screening for incipient dementia. We explored the feasibility and predictive value of a digital sleep biomarker, developed from data collected using the Withings Sleep Analyzer (WSA), a ballistocardiographic under-mattress pressure sensor which collects sleep and physiological data unobtrusively, to detect Alzheimer-related biomarkers in a presymptomatic cohort. Participants from the Insight 46 study (all born in March 1946) underwent serial assessment, including plasma phosphorylated tau (pTau)217 ALZpath and 18F-Florbetapir β-amyloid PET at age ∼73 and 18F-MK-6240 Tau PET at age ∼77. Amyloid status (-/+) and Tau Braak staging (-/Braak1+/Braak3+) were derived using automated pipelines. The WSA was deployed at age ∼78, installed under participants' mattresses by the study participant/family. Continuous sleep, circadian, and physiological parameters were collected. A leave-one-out cross validation approach was employed to develop models predicting PET status after feature selection (Figure 1). Results were compared to plasma pTau217. n = 161 had both WSA and Tau PET data (12.4% Braak1+, 6.2% Braak3+); n = 153 participants also had β-amyloid PET (25% β-amyloid+ at Centiloid>=12). In total we collected 63,720 nights (174 years) of sleep data, corresponding to a mean±SD of 239.8±108.7 nights/participant (age at collection 78.3±0.2 yrs; 49% female). n = 404 had plasma pTau217. A final trained model identified asymptomatic individuals with Braak3+ tau pathology with area under the receiver operating characteristic curve (AUROC)=0.75; comparable to plasma pTau217 (Figure 2) after iterative feature selection (Figure 3). Trained models were less effective at identifying earlier pathological stages (Tau Braak1+, β-amyloid+). Deploying a remote sleep and circadian monitoring device in a countrywide population-based cohort in their late 70s is feasible. A model based on iterative feature selection was able to identify individuals with significant Tau (Braak3+) pathology with AUROC similar to plasma pTau217. This provides proof-of-concept that digital sleep biomarkers may be useful in identifying individuals at high risk of developing clinical AD. Work is underway to refine the model further, replicate these results in other cohorts, and identify the shortest duration of recording required for robust prediction.

Sleep and circadian disruption are associated with increased dementia risk. Digital sleep biomarkers may provide an ecologically valid and low-burden means of remote population-level screening for incipient dementia. We explored the feasibility and predictive value of a digital sleep biomarker, developed from data collected using the Withings Sleep Analyzer (WSA), a ballistocardiographic under-mattress pressure sensor which collects sleep and physiological data unobtrusively, to detect Alzheimer-related biomarkers in a presymptomatic cohort. Participants from the Insight 46 study (all born in March 1946) underwent serial assessment, including plasma phosphorylated tau (pTau)217 ALZpath and 18F-Florbetapir β-amyloid PET at age ∼73 and 18F-MK-6240 Tau PET at age ∼77. Amyloid status (-/+) and Tau Braak staging (-/Braak1+/Braak3+) were derived using automated pipelines. The WSA was deployed at age ∼78, installed under participants' mattresses by the study participant/family. Continuous sleep, circadian, and physiological parameters were collected. A leave-one-out cross validation approach was employed to develop models predicting PET status after feature selection (Figure 1). Results were compared to plasma pTau217. n = 161 had both WSA and Tau PET data (12.4% Braak1+, 6.2% Braak3+); n = 153 participants also had β-amyloid PET (25% β-amyloid+ at Centiloid>=12). In total we collected 63,720 nights (174 years) of sleep data, corresponding to a mean±SD of 239.8±108.7 nights/participant (age at collection 78.3±0.2 yrs; 49% female). n = 404 had plasma pTau217. A final trained model identified asymptomatic individuals with Braak3+ tau pathology with area under the receiver operating characteristic curve (AUROC)=0.75; comparable to plasma pTau217 (Figure 2) after iterative feature selection (Figure 3). Trained models were less effective at identifying earlier pathological stages (Tau Braak1+, β-amyloid+). Deploying a remote sleep and circadian monitoring device in a countrywide population-based cohort in their late 70s is feasible. A model based on iterative feature selection was able to identify individuals with significant Tau (Braak3+) pathology with AUROC similar to plasma pTau217. This provides proof-of-concept that digital sleep biomarkers may be useful in identifying individuals at high risk of developing clinical AD. Work is underway to refine the model further, replicate these results in other cohorts, and identify the shortest duration of recording required for robust prediction.

Pro-inflammatory cytokine-induced activation of nuclear factor, NF-κB has an important role in leukocyte adhesion to, and subsequent migration across, brain endothelial cells (BECs), which is crucial for the development of neuroinflammatory disorders such as multiple sclerosis (MS). In contrast, microRNA-146a (miR-146a) has emerged as an anti-inflammatory molecule by inhibiting NF-κB activity in various cell types, but its effect in BECs during neuroinflammation remains to be evaluated. Here, we show that miR-146a was upregulated in microvessels of MS-active lesions and the spinal cord of mice with experimental autoimmune encephalomyelitis. In vitro, TNFα and IFNγ treatment of human cerebral microvascular endothelial cells (hCMEC/D3) led to upregulation of miR-146a. Brain endothelial overexpression of miR-146a diminished, whereas knockdown of miR-146a augmented cytokine-stimulated adhesion of T cells to hCMEC/D3 cells, nuclear translocation of NF-κB, and expression of adhesion molecules in hCMEC/D3 cells. Furthermore, brain endothelial miR-146a modulates NF-κB activity upon cytokine activation through targeting two novel signaling transducers, RhoA and nuclear factor of activated T cells 5, as well as molecules previously identified, IL-1 receptor-associated kinase 1, and TNF receptor-associated factor 6. We propose brain endothelial miR-146a as an endogenous NF-κB inhibitor in BECs associated with decreased leukocyte adhesion during neuroinflammation.