Professor Ramin Nilforooshan

Plasma biomarkers of dementia, including phosphorylated tau (p-tau217), offer promise as tools for diagnosis, stratification for clinical trials, monitoring disease progression, and assessing the success of interventions in those living with Alzheimer's disease. However, currently, it is unknown whether these dementia biomarker levels vary with the time of day, which could have implications for their clinical value. In two protocols, we studied 38 participants (70.8 ± 7.6 years; mean ± SD) in a 27-h laboratory protocol with either two samples taken 12 h apart or 3-hourly blood sampling for 24 h in the presence of a sleep-wake cycle. The study population comprised people living with mild Alzheimer's disease (PLWA, n = 8), partners/caregivers of PLWA (n = 6) and cognitively intact older adults (n = 24). Single-molecule array technology was used to measure phosphorylated tau (p-tau217) (ALZpath), amyloid-beta 40 (Aβ40), amyloid-beta 42 (Aβ42), glial fibrillary acidic protein, and neurofilament light (NfL) (Neuro 4-Plex E). Analysis with a linear mixed model (SAS, PROC MIXED) revealed a significant effect of time of day for p-tau217, Aβ40, Aβ42, and NfL, and a significant effect of participant group for p-tau217. For p-tau217, the lowest levels were observed in the morning upon waking and the highest values in the afternoon/early evening. The magnitude of the diurnal variation for p-tau217 was similar to the reported increase in p-tau217 over one year in amyloid-β-positive mild cognitively impaired people. Currently, the factors driving this diurnal variation are unknown and could be related to sleep, circadian mechanisms, activity, posture, or meals. Overall, this work implies that the time of day of sample collection may be relevant in the implementation and interpretation of plasma biomarkers in dementia research and care.

Schizophrenia is a severe psychiatric disorder with periods of remission and relapse. As discontinuation of antipsychotic medication is the most important reason for relapse, long-term maintenance treatment is key. Whether intramuscular long-acting (depot) antipsychotics are more efficacious than oral medication in preventing medication discontinuation is still unresolved. We aimed to compare time to all-cause discontinuation in patients randomly allocated to long-acting injectable (LAI) versus oral medication. EULAST was a pragmatic, randomised, open-label trial conducted at 50 general hospitals and psychiatric specialty clinics in 15 European countries and Israel. Patients aged 18 years and older, with DSM-IV schizophrenia (as confirmed by the Mini International Neuropsychiatric Interview 5 plus) and having experienced their first psychotic episode from 6 months to 7 years before screening, were randomly allocated (1:1:1:1) using block randomisation to LAI paliperidone, LAI aripiprazole, or the respective oral formulations of these antipsychotics. Randomisation was stratified by country and duration of illness (6 months up to 3 years vs 4 to 7 years). Patients were followed up for up to 19 months. The primary endpoint was discontinuation, regardless of the reason, during 19 months of treatment. We used survival analysis to assess the time until all-cause discontinuation in the intention-to-treat (ITT) group, and per protocol analyses were also done. This trial is registered with ClinicalTrials.gov, NCT02146547, and is complete. Between Feb 24, 2015, and Dec 15, 2018, 533 individuals were recruited and assessed for eligibility. The ITT population included 511 participants, with 171 (33%) women and 340 (67%) men, and a mean age of 30·5 (SD 9·6) years. 410 (80%) of 511 participants were White, 35 (7%) were Black, 20 (4%) were Asian, and 46 (9%) were other ethnicity. In the combined oral antipsychotics treatment group of 247 patients, 72 (29%) patients completed the study and 175 (71%) met all-cause discontinuation criteria. In the combined LAI treatment arm of 264 patients, 95 (36%) completed the study and 169 (64%) met the all-cause discontinuation criteria. Cox regression analyses showed that treatment discontinuation for any cause did not differ between the two combined treatment groups (hazard ration [HR] 1·16, 95% CI 0·94-1·43, p=0·18). No significant difference was found in the time to all-cause discontinuation between the combined oral and combined LAI treatment groups (log rank test χ =1·87 [df 1]; p=0·17). During the study, 121 psychiatric hospitalisations occurred in 103 patients, and one patient from each of the LAI groups died; the death of the patient assigned to paliperidone was assessed to be unrelated to the medication, but the cause of other patient's death was not shared with the study team. 86 (25%) of 350 participants with available data met akathisia criteria and 70 (20%) met parkinsonism criteria at some point during the study. We found no substantial advantage for LAI antipsychotic treatment over oral treatment regarding time to discontinuation in patients with early-phase schizophrenia, indicating that there is no reason to prescribe LAIs instead of oral antipsychotics if the goal is to prevent discontinuation of antipsychotic medication in daily clinical practice. Lundbeck and Otsuka.

Malnutrition and dehydration are strongly associated with increased cognitive and functional decline in people living with dementia (PLWD), as well as an increased rate of hospitalisations in comparison to their healthy counterparts. Extreme changes in eating and drinking behaviours can often lead to malnutrition and dehydration, accelerating the progression of cognitive and functional decline and resulting in a marked reduction in quality of life. Unfortunately, there are currently no established methods by which to objectively detect such changes. Here, we present the findings of an extensive quantitative analysis conducted on in-home monitoring data collected from 73 households of PLWD using Internet of Things technologies. The Coronavirus 2019 (COVID-19) pandemic has previously been shown to have dramatically altered the behavioural habits, particularly the eating and drinking habits, of PLWD. Using the COVID-19 pandemic as a natural experiment, we conducted linear mixed-effects modelling to examine changes in mean kitchen activity within a subset of 21 households of PLWD that were continuously monitored for 499 days. We report an observable increase in day-time kitchen activity and a significant decrease in night-time kitchen activity (t(147) = -2.90, p < 0.001). We further propose a novel analytical approach to detecting changes in behaviours of PLWD using Markov modelling applied to remote monitoring data as a proxy for behaviours that cannot be directly measured. Together, these results pave the way to introduce improvements into the monitoring of PLWD in naturalistic settings and for shifting from reactive to proactive care.

Abstract Background People living with dementia (PLWD) have an increased susceptibility to developing adverse physical and psychological events. Internet of Things (IoT) technologies provides new ways to remotely monitor patients within the comfort of their homes, particularly important for the timely delivery of appropriate healthcare. Presented here is data collated as part of the on‐going UK Dementia Research Institute’s Care Research and Technology Centre cohort and Technology Integrated Health Management (TIHM) study. There are two main aims to this work: first, to investigate the effect of the COVID‐19 quarantine on the performance of daily living activities of PLWD, on which there is currently little research; and second, to create a simple classification model capable of effectively predicting agitation risk in PLWD, allowing for the generation of alerts with actionable information by which to prevent such outcomes. Method A within‐subject, date‐matched study was conducted on daily living activity data using the first COVID‐19 quarantine as a natural experiment. Supervised machine learning approaches were then applied to combined physiological and environmental data to create two simple classification models: a single marker model trained using ambient temperature as a feature, and a multi‐marker model using ambient temperature, body temperature, movement, and entropy as features. Result There are 102 PLWD total included in the dataset, with all patients having an established diagnosis of dementia, but with ranging types and severity. The COVID‐19 study was carried out on a sub‐group of 21 patient households. In 2020, PLWD had a significant increase in daily household activity (p = 1.40e‐08), one‐way repeated measures ANOVA). Moreover, there was a significant interaction between the pandemic quarantine and patient gender on night‐time bed‐occupancy duration (p = 3.00e‐02, two‐way mixed‐effect ANOVA). On evaluating the models using 10‐fold cross validation, both the single and multi‐marker model were shown to balance precision and recall well, having F1‐scores of 0.80 and 0.66, respectively. Conclusion Remote monitoring technologies provide a continuous and reliable way of monitoring patient day‐to‐day wellbeing. The application of statistical analyses and machine learning algorithms to combined physiological and environmental data has huge potential to positively impact the delivery of healthcare for PLWD.

Sleep and circadian rhythm disturbance are predictors of poor physical and mental health, including dementia. Long-term digital technology-enabled monitoring of sleep and circadian rhythms in the community has great potential for early diagnosis, monitoring of disease progression, and assessing the effectiveness of interventions. Before novel digital technology-based monitoring can be implemented at scale, its performance and acceptability need to be evaluated and compared to gold-standard methodology in relevant populations. Here, we describe our protocol for the evaluation of novel sleep and circadian technology which we have applied in cognitively intact older adults and are currently using in people living with dementia (PLWD). In this protocol, we test a range of technologies simultaneously at home (7-14 days) and subsequently in a clinical research facility in which gold standard methodology for assessing sleep and circadian physiology is implemented. We emphasize the importance of assessing both nocturnal and diurnal sleep (naps), valid markers of circadian physiology, and that evaluation of technology is best achieved in protocols in which sleep is mildly disturbed and in populations that are relevant to the intended use-case. We provide details on the design, implementation, challenges, and advantages of this protocol, along with examples of datasets.

There are no disease-modifying treatments for Alzheimer disease (AD), the most common cause of dementia. Minocycline is anti-inflammatory, protects against the toxic effects of β-amyloid in vitro and in animal models of AD, and is a credible repurposed treatment candidate. To determine whether 24 months of minocycline treatment can modify cognitive and functional decline in patients with mild AD. Participants were recruited into a double-blind randomized clinical trial from May 23, 2014, to April 14, 2016, with 24 months of treatment and follow-up. This multicenter study in England and Scotland involved 32 National Health Service memory clinics within secondary specialist services for people with dementia. From 886 screened patients, 554 patients with a diagnosis of mild AD (Standardised Mini-Mental State Examination [sMMSE] score ≥24) were randomized. Participants were randomly allocated 1:1:1 in a semifactorial design to receive minocycline (400 mg/d or 200 mg/d) or placebo for 24 months. Primary outcome measures were decrease in sMMSE score and Bristol Activities of Daily Living Scale (BADLS), analyzed by intention-to-treat repeated-measures regression. Of 544 eligible participants (241 women and 303 men), the mean (SD) age was 74.3 (8.2) years, and the mean (SD) sMMSE score was 26.4 (1.9). Fewer participants completed 400-mg minocycline hydrochloride treatment (28.8% [53 of 184]) than 200-mg minocycline treatment (61.9% [112 of 181]) or placebo (63.7% [114 of 179]; P 

BackgroundType 2 diabetes is a risk factor for Alzheimer's disease (AD), and AD brain shows impaired insulin signalling. The role of peripheral insulin resistance on AD aetiopathogenesis in non-diabetic patients is still debated. Here we evaluated the influence of insulin resistance on brain glucose metabolism, grey matter volume and white matter lesions (WMLs) in non-diabetic AD subjects.MethodsIn total, 130 non-diabetic AD subjects underwent MRI and [18F]FDG PET scans with arterial cannula insertion for radioactivity measurement. T1 Volumetric and FLAIR sequences were acquired on a 3-T MRI scanner. These subjects also had measurement of glucose and insulin levels after a 4-h fast on the same day of the scan. Insulin resistance was calculated by the updated homeostatic model assessment (HOMA2). For [18F]FDG analysis, cerebral glucose metabolic rate (rCMRGlc) parametric images were generated using spectral analysis with arterial plasma input function.ResultsIn this non-diabetic AD population, HOMA2 was negatively associated with hippocampal rCMRGlc, along with total grey matter volumes. No significant correlation was observed between HOMA2, hippocampal volume and WMLs.ConclusionsIn non-diabetic AD, peripheral insulin resistance is independently associated with reduced hippocampal glucose metabolism and with lower grey matter volume, suggesting that peripheral insulin resistance might influence AD pathology by its action on cerebral glucose metabolism and on neurodegeneration.

Agitation is common in people with dementia and negatively affects the quality of life of both people with dementia and carers. Non-drug patient-centred care is the first-line treatment, but there is a need for other treatment when this care is not effective. Current evidence is sparse on safer and effective alternatives to antipsychotics. We assessed the efficacy and safety of mirtazapine, an antidepressant prescribed for agitation in dementia. This parallel-group, double-blind, placebo-controlled trial-the Study of Mirtazapine for Agitated Behaviours in Dementia trial (SYMBAD)-was done in 26 UK centres. Participants had probable or possible Alzheimer's disease, agitation unresponsive to non-drug treatment, and a Cohen-Mansfield Agitation Inventory (CMAI) score of 45 or more. They were randomly assigned (1:1) to receive either mirtazapine (titrated to 45 mg) or placebo. The primary outcome was reduction in CMAI score at 12 weeks. This trial is registered with ClinicalTrials.gov, NCT03031184, and ISRCTN17411897. Between Jan 26, 2017, and March 6, 2020, 204 participants were recruited and randomised. Mean CMAI scores at 12 weeks were not significantly different between participants receiving mirtazapine and participants receiving placebo (adjusted mean difference -1·74, 95% CI -7·17 to 3·69; p=0·53). The number of controls with adverse events (65 [64%] of 102 controls) was similar to that in the mirtazapine group (67 [66%] of 102 participants receiving mirtazapine). However, there were more deaths in the mirtazapine group (n=7) by week 16 than in the control group (n=1), with post-hoc analysis suggesting this difference was of marginal statistical significance (p=0·065). This trial found no benefit of mirtazapine compared with placebo, and we observed a potentially higher mortality with use of mirtazapine. The data from this study do not support using mirtazapine as a treatment for agitation in dementia. UK National Institute for Health Research Health Technology Assessment Programme.

Currently, outpatient care in the UK is expensive and needs improvement, with traditional systems having been identified as no longer fit for purpose. Making sustainable changes to outpatient appointment systems is vital in order to meet increasing demands and cost. Shifting to data and technology-driven outpatient care may be one way to tackle these demands. As technology becomes more diverse and accessible, its implementation into healthcare systems can make services more efficient and help with transitioning from outdated practices to more effective protocols. Patient Recorded Outcome Measures (PROMs) and home-monitoring devices could be the key step in identifying which patients require input and help shift to more data-driven appointment scheduling based on clinical need, rather than at regular intervals of time. Virtual care and technology-driven service provision could also revolutionise outpatient systems, maintaining high quality care while improving accessibility to patients. Patient involvement and empowerment while making these changes will assist shared decision making surrounding their care and allow them to be champions of their own health, helping clinicians to provide a patient-centred service. Understanding how these may be implemented will help clinicians take an active role in the development of these practices.

Background: As the number of people with dementia increases, more families will be affected by the daily challenges of providing effective support, given its current incurable status. Once individuals are diagnosed with dementia, the earlier they access support, the more effective the outcome. However, once people receive a diagnosis, how they make sense of their dementia can impact on their help-seeking intentions. Exploring the illness beliefs of people with dementia and their caregivers and this relationship to help seeking may identify how best to facilitate early support. Aims: To systematically obtain and critically review relevant studies on the relationship between illness perceptions and help seeking of people with dementia and their caregivers. Method: A systematic search was conducted and included both quantitative and qualitative studies. The initial search was conducted in October 2018, with an adjacent search conducted in April 2020. Findings: A total of 14 articles met the inclusion criteria. Conceptually, the studies examined the association of illness perceptions and help-seeking post-diagnosis and revealed that people living with dementia and their caregivers sought help when symptoms became severe. Components of illness perceptions revealed that lack of knowledge, cultural beliefs, complexity of the healthcare system, threat to independence and acceptance were identified as major factors for delaying help seeking. Conclusion: Although research interest in the area of illness perceptions and their impact on help seeking for dementia is increasing, further work is needed to understand this area, particularly regarding the influence of the relationship between the person with dementia and their caregiver.

Objectives: To examine the costs and cost-effectiveness of mirtazapine compared to placebo over 12-week follow-up. Design: Economic evaluation in a double-blind randomized controlled trial of mirtazapine vs. placebo. Setting: Community settings and care homes in 26 UK centers. Participants: People with probable or possible Alzheimer's disease and agitation. Measurements: Primary outcome included incremental cost of participants' health and social care per 6-point difference in CMAI score at 12 weeks. Secondary cost-utility analyses examined participants' and unpaid carers' gain in quality-adjusted life years (derived from EQ-5D-5L, DEMQOL-Proxy-U, and DEMQOL-U) from the health and social care and societal perspectives. Results: One hundred and two participants were allocated to each group; 81 mirtazapine and 90 placebo participants completed a 12-week assessment (87 and 95, respectively, completed a 6-week assessment). Mirtazapine and placebo groups did not differ on mean CMAI scores or health and social care costs over the study period, before or after adjustment for center and living arrangement (independent living/care home). On the primary outcome, neither mirtazapine nor placebo could be considered a cost-effective strategy with a high level of confidence. Groups did not differ in terms of participant self- or proxy-rated or carer self-rated quality of life scores, health and social care or societal costs, before or after adjustment. Conclusions: On cost-effectiveness grounds, the use of mirtazapine cannot be recommended for agitated behaviors in people living with dementia. Effective and cost-effective medications for agitation in dementia remain to be identified in cases where non-pharmacological strategies for managing agitation have been unsuccessful.

We describe a digital platform developed in collaboration with clinicians and user groups to provide remote healthcare monitoring and support in a dementia care application. The platform uses data from sensory devices that are deployed in participants' homes and utilises a set machine learning and analytical algorithms to identify risks of adverse health conditions such as Urinary Tract Infections (UTIs) and hypertension in people with dementia. The platform includes a clinical interface that is used by a monitoring team to view alerts and notifications that are generated by the algorithms and to also browse the in-home activity and physiological data in a secure and privacy-aware system. The platform complies to the information governance requirements of the UK National Healthcare Service (NHS) and is registered as a class 1 medical device. The platform has been deployed and tested in over 150 homes.

Background: Internet of Things (IoT) technology enables physiological measurements to be recorded at home from people living with dementia and monitored remotely. However, measurements from people with dementia in this context have not been previously studied. We report on the distribution of physiological measurements from 82 people with dementia over approximately 2 years. Objective: Our objective was to characterize the physiology of people with dementia when measured in the context of their own homes. We also wanted to explore the possible use of an alerts-based system for detecting health deterioration and discuss the potential applications and limitations of this kind of system. Methods: We performed a longitudinal community-based cohort study of people with dementia using "Minder," our IoT remote monitoring platform. All people with dementia received a blood pressure machine for systolic and diastolic blood pressure, a pulse oximeter measuring oxygen saturation and heart rate, body weight scales, and a thermometer, and were asked to use each device once a day at any time. Timings, distributions, and abnormalities in measurements were examined, including the rate of significant abnormalities ("alerts") defined by various standardized criteria. We used our own study criteria for alerts and compared them with the National Early Warning Score 2 criteria. Results: A total of 82 people with dementia, with a mean age of 80.4 (SD 7.8) years, recorded 147,203 measurements over 958,000 participant-hours. The median percentage of days when any participant took any measurements (ie, any device) was 56.2% (IQR 33.2%-83.7%, range 2.3%-100%). Reassuringly, engagement of people with dementia with the system did not wane with time, reflected in there being no change in the weekly number of measurements with respect to time (1-sample t-test on slopes of linear fit, P=.45). A total of 45% of people with dementia met criteria for hypertension. People with dementia with alpha-synuclein-related dementia had lower systolic blood pressure; 30% had clinically significant weight loss. Depending on the criteria used, 3.03%-9.46% of measurements generated alerts, at 0.066-0.233 per day per person with dementia. We also report 4 case studies, highlighting the potential benefits and challenges of remote physiological monitoring in people with dementia. These include case studies of people with dementia developing acute infections and one of a person with dementia developing symptomatic bradycardia while taking donepezil. Conclusions: We present findings from a study of the physiology of people with dementia recorded remotely on a large scale. People with dementia and their carers showed acceptable compliance throughout, supporting the feasibility of the system. Our findings inform the development of technologies, care pathways, and policies for IoT-based remote monitoring. We show how IoT-based monitoring could improve the management of acute and chronic comorbidities in this clinically vulnerable group. Future randomized trials are required to establish if a system like this has measurable long-term benefits on health and quality of life outcomes.

Purpose: Treatment-resistant depression (TRD) is associated with profound morbidity for patients, placing a significant burden on those affected, the health service and wider society. Despite this, TRD remains chronically underserved in terms of viable treatment options. To address this gap, an advisory panel of psychiatrists and clinical researchers with experience in managing TRD convened to develop best practice statements on the use of esketamine nasal spray, one of the first TRD treatments to be licensed in 30 years. Methods: During a virtual meeting held on 12th November 2020, the advisory panel shared their experiences of using esketamine nasal spray in their clinical practice. The meeting focused on developing and refining recommendations for setting up and running an efficient esketamine nasal spray clinic for patients living with TRD. At the conclusion of the meeting, agreement was reached on all recommendation statements.Results: In setting up an esketamine nasal spray clinic, it is important to consider the logistical requirements involved and put measures in place to ensure it runs as efficiently as possible. Educating patients about the treatment and maintaining their well-being is paramount for preventing discontinuation. Putting in place checklists can be a useful strategy for ensuring treatment appointments run smoothly and safely.Conclusion: Providing additional treatment options for the management of TRD, such as esketamine nasal spray, is likely to be key to improving the long-term outcomes of this underserved patient population.