NIHR Queen Square Dementia Biomedical Research Unit

Abstract Importance Dementia is common in Parkinson’s disease (PD), causing greater disability than other symptoms, but varies in timing. Although visual deficits are linked with PD dementia, how these interact with genetic factors to predict PD dementia has not been characterised. Objective To investigate whether visual deficits and genetic factors predict PD dementia. Design Large prospective longitudinal case-control study, mean follow-up 32.7 (SD=12.3) months. Setting Cases were recruited between 2017-2020 at 35 UK PD clinics. Participants People with PD without dementia at baseline were included. Main outcomes and measures Visual function was measured using a web-based platform. The main outcome measure was global cognition, measured as the Montreal Cognitive Assessment (MoCA). Blood samples were collected for genetics. Results 450 patients with PD were included. Mean age of PD patients was 71.7 (SD=7.8), 68% male. Mean baseline MoCA was 27.7 (SD=1.7). 263 patients with PD were classed as poor-vision based on baseline visual tests: mean age 74.4 (SD=6.8) compared to 69.7 (SD=7.5) with good-vision. Poor-vision PD patients had higher rates of progression to mild cognitive impairment (PD-MCI) (HR=2.34, CI=1.58-3.48, p FDR=0.00062, age- and sex-corrected). The combination of genetic factors together with vision influenced outcomes. In good-vision PD patients, high-risk GBA1 gene variants were linked with greater progression to PD-MCI (HR=4.61, CI=1.73-12.28, p FDR=0.0068). Polygenic Risk Score (PRS) for both PD and Alzheimer’s disease (AD) also modified cognitive survival when combined with vision status. High PD-PRS was associated with greater progression to PD-MCI in good-vision patients (HR=2.66, CI=1.21-5.81, p FDR=0.0381); and high AD-PRS with greater progression to PD-MCI in poor-vision PD patients (HR=1.91, CI=1.10-3.32, p FDR=0.04999). Combining high PD- and AD-PRS, compared to low PD- and AD-PRS in good-vision PD showed even higher progression to PD-MCI (HR=6.14, CI=1.36-27.83, p FDR=0.046). Simulations showed that adding visual and genetic stratification reduced sample size from n=705 to n=160 for clinical trials. Conclusions and relevance Poor vision in PD predicts progression to PD-MCI and dementia. This combines with the effects of genetic factors including GBA risk variants and PD- and AD-PRS. These findings can enable enrichment of clinical trials for patients at higher risk of PD dementia, for more efficient trial design for interventions to slow progression. Key points Question Do clinical factors, measured by performance on visual tests, and genetic factors help predict which patients are more likely to develop cognitive involvement in Parkinson’s disease? Findings This prospective longitudinal study of 450 Parkinson’s patients, based in Parkinson’s clinics, with mean follow-up 32.7 months, found that Parkinson’s patients with poor vision are more likely to develop cognitive impairment; and that genetic factors in combination with poor vision further predict poor prognostic groups for Parkinson’s dementia. Meaning These data could enable selection of Parkinson’s patients at highest risk of dementia for clinical trials aimed at slowing Parkinson’s dementia.

BackgroundThere is substantial heterogeneity in clinical presentation of genetic Frontotemporal Dementia (FTD), even within the same family. This suggests that additional heritability may exist and contribute to this variable presentation. We examined whether gene‐based aggregate burden of genome‐wide rare variants (minor allele frequency [MAF]: ≤1%) contribute to variation in regional cortical and subcortical grey matter volumes, after controlling for effects of causative mutations in GRN, MAPT, and C9orf72.MethodThis study was embedded within the GENetic Frontotemporal dementia Initiative (GENFI), which recruits genetic FTD cases and their asymptomatic at‐risk family members, both carriers and non‐carriers of FTD mutations. We included 518 participants with genotype (Neurochip; imputed against TOPMed), and T1w‐MRI brain volumetric data. Gene‐based burden tests that aggregate the number of rare variants by gene were used to examine the association of rare variants (MAF: ≤1%) with regional cortical and subcortical grey matter volumes (70 regions of interest [ROIs]), controlling for age, sex, total intracranial volume, mutation status, scanner site, population stratification, and family membership (kinship matrix) using RVTests. Annotations for loss of function mutations (LOF): start gain, stop loss, start loss, essential splice site, stop gain, normal splice site, and non‐synonymous. Multiple testing correction accounted for the number of genes and number of independent grey matter volumes as calculated by matSpD (p‐value threshold: 0.05/(17,053x42) = 6.98 x10‐8).ResultAggregate burden of LOF mutations (DNAJB8‐AS1, WDR26, RDM1P5, BSND, CNOT2, DDA1, ASAH2B, PPM1A, HOXD13, ALDH1A1, CENATAC, ANKRD45) was associated with significantly lower volumes within the left temporal lobe (ROIs: left temporal and lateral temporal left), and greater volume in the putamen bilaterally (TSACC). All genes are protein coding, except the DNAJB8‐AS1 (antisense RNA) and RDM1P5 (pseudogene), and are variably expressed in the brain. Molecular functions of significant genes involve regulation of gene expression, transcription, and cell cycle, ion channel function, and chromosomal segregation. WDR26 and CNOT2 genes have been implicated in neurodevelopment and neurological disorders respectively; BSND gene is involved in neurotransmission.ConclusionIdentification of deleterious or protective rare variants contributing to FTD imaging phenotypes may help identify genetic modifiers of familial FTD. Replication in larger cohorts is needed.

BackgroundDementia 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.ObjectiveThis 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.MethodsThirty-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.ResultsFour 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.ConclusionsEnd 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.

BackgroundFrontotemporal dementia (FTD) with right anterior temporal lobe (RATL) predominant atrophy is an emerging area of interest. Recent findings by the International Working Group (IWG) have identified this subtype as having a distinct clinical profile within the FTD spectrum (Ulugut et al., 2024, A&D). However, its genetic and pathological underpinnings remain unexplored in large, multicultural cohorts.MethodsRetrospective analyses encompassing clinical, genetic, pathological, and neuroimaging data from 23 IWG sites across 13 countries in Asia, Middle East, Europe, North and South America were conducted. The study included 444 patients with FTD exhibiting predominant RATL atrophy.ResultsGenetic screening was performed on 51% (n = 225) of the cohort for at least the major frontotemporal lobar degeneration (FTLD) mutations including microtubule‐associated protein tau (MAPT), progranulin (GRN), and chromosome 9 open reading frame 72 (C9orf72), or dementia panels encompassing extended sets of genes. Of these, 81% were sporadic, showing negative results for the screened genes and a modified Goldman Score of ≥ 3, indicating a negative family history for dementia. The MAPT mutation was the most common genetic variant, identified in 7% of the screened cases (Figure 1). Pathological confirmation was available for 63 patients. Among the sporadic cases, transactive response DNA‐binding protein 43 type C (TDP‐C) pathology was most prevalent (60%, n = 32), while tau‐MAPT pathology was most common in the genetic cases (38%, n = 16). Fifteen cases did not fit neatly into genetic or sporadic categories, displaying heterogeneous pathologies (Figure 2). At the initial visit, compared to genetic cases, patients with TDP‐C pathology were older, and more frequently exhibited semantic deficits, with less frequent attention difficulties and executive dysfunction. No differences were observed in sex distribution, symptom duration or disease severity between genetic and sporadic TDP‐C cases. However, left handedness was more common in TDP‐C cases (14%) compared to genetic cases (5%).ConclusionWhile FTD with RATL atrophy primarily appears sporadic, a significant proportion of cases exhibit genetic variants. These sporadic and genetic subtypes display distinct neuropathological features and clinical manifestations. Given the implications for therapeutic strategies, precise clinical and molecular subtyping is critical for enhancing patient management and ensuring appropriate enrollment in clinical trials.

ABSTRACTBackgroundApproximately one‐third of people with epilepsy (PWE) remain drug‐resistant. In these cases, surgical resection of the epileptogenic zone may significantly reduce or eliminate seizures. Surgery necessitates precise delineation of the epileptogenic zone (EZ) which proves especially challenging in the 20% of PWE that remain MRI‐negative. The purpose of this study was to analyze the feasibility and robustness of ultra‐high‐field MRSI in identifying and characterizing pathologies in focal epilepsy. In addition, the relationship of glutamate and glutamine was evaluated in the EZ.MethodsFifty‐six people with focal epilepsy were prospectively measured using 7 T concentric ring trajectory direct acquisition of free‐induction‐decay MRSI, which generated whole‐brain metabolic maps with an isotropic resolution of 3.4mm3. After exclusion criteria were applied, we assessed metabolite ratios in 15 lesional and 14 MRI‐negative PWE.ResultsIn the lesional group, metabolic alterations in the suspected EZ were present in 86.7% of maps normalized to N‐acetyl‐aspartate, whereas this was reduced to 80% in creatine ratios. Metabolites with the highest consistency in the lesional group included myo‐inositol and choline, showing increases in 92.3% of PWE. In MRI‐negative patients, changes were heterogeneous, with a detection rate of 57.1%. We also observed a tendency toward an inverse relationship of glutamate to glutamine in the EZ, with increases of glutamine in PWE with lower seizure frequencies, contrasting glutamate increases in higher seizure frequencies.ConclusionOur preliminary analysis suggests that 7 T CRT‐FID MRSI shows promise not only in identifying metabolic alterations in focal epilepsy but may also provide insights into disease pathomechanisms.

Aging of the brain is characterized by deleterious processes at various levels including cellular/molecular and structural/functional changes. Many of these processes can be assessed invivo by means of modern neuroimaging procedures, allowing the quantification of brain age in different modalities. Brain age can be measured by suitable machine learning strategies. The deviation (in both directions) between a person's measured brain age and chronologic age is referred to as the brain age gap (BAG). Although brain age, as defined by these methods, generally is related to the chronologic age of a person, this relationship is not always parallel and can also vary significantly between individuals. Importantly, whereas neurodegenerative disorders are not equivalent to accelerated brain aging, they may induce brain changes that resemble those of older adults, which can be captured by brain age models. Inversely, healthy brain aging may involve a resistance or delay of the onset of neurodegenerative pathologies in the brain. This continuing education article elaborates how the BAG can be computed and explores how BAGs, derived from diverse neuroimaging modalities, offer unique insights into the phenotypes of age-related neurodegenerative diseases. Structural BAGs from T1-weighted MRI have shown promise as phenotypic biomarkers for monitoring neurodegenerative disease progression especially in Alzheimer disease. Additionally, metabolic and molecular BAGs from molecular imaging, functional BAGs from functional MRI, and microstructural BAGs from diffusion MRI, although researched considerably less, each may provide distinct perspectives on particular brain aging processes and their deviations from healthy aging. We suggest that BAG estimation, when based on the appropriate modality, could potentially be useful for disease monitoring and offer interesting insights concerning the impact of therapeutic interventions.

Alzheimer disease (AD)-modifying therapies are approved for treatment of early-symptomatic AD. Autosomal dominant AD (ADAD) provides a unique opportunity to test therapies in presymptomatic individuals. Using data from the Dominantly Inherited Alzheimer Network (DIAN), sample sizes for clinical trials were estimated for various cognitive, imaging, and CSF outcomes. Sample sizes were computed for detecting a reduction of either absolute levels of AD-related pathology (amyloid, tau) or change over time in neurodegeneration (atrophy, hypometabolism, cognitive change). Biomarkers measuring amyloid and tau pathology had required sample sizes below 200 participants per arm (examples CSF Aβ42/40: 47[95 %CI 25,104], cortical PIB 49[28,99], CSF p-tau181 74[48,125]) for a four-year trial in presymptomatic individuals (CDR=0) to have 80 % power (5 % statistical significance) to detect a 25 % reduction in absolute levels of pathology, allowing 40 % dropout. For cognitive, MRI, and FDG, it was more appropriate to detect a 50 % reduction in rate of change. Sample sizes ranged from 250 to 900 (examples hippocampal volume: 338[131,2096], cognitive composite: 326[157,1074]). MRI, FDG and cognitive outcomes had lower sample sizes when including indivduals with mild impairment (CDR=0.5 and 1) as well as presymptomatic individuals (CDR=0). Despite the rarity of ADAD, presymptomatic clinical trials with feasible sample sizes given the number of cases appear possible.

ABSTRACTAimsTo discuss the role of screening and treatment of affective symptoms, like anxiety and depression in patients with LUTD. A review of the literature regarding the bidirectional association and multidisciplinary approaches integrating psychometric assessments with personalized treatment plans to improve diagnostic accuracy and therapeutic outcomes of LUTD.MethodsThis review summarizes discussions and a narrative review of (recent) literature during an International Consultation on Incontinence‐Research Society 2024 research proposal with respect to the role of screening for anxiety and depression, effect of mental health symptoms on treatment outcomes and future implications.ResultsConsensus recognized the importance to incorporate attention to anxiety and depression in relation to LUTD. The awareness of this association can lead to better outcomes. Future research projects are proposed to evaluate the bidirectional relationship.ConclusionThe relationship between affective symptoms and LUTD underscores the need for integrated treatment approaches that address both psychological and urological dimensions. Further research is required to identify specific patient subgroups that would benefit most from these interventions, to develop standardized screening tools, and to refine treatment protocols. Multidisciplinary care, incorporating psychological assessment and personalized treatment strategies, could enhance outcomes for LUTD patients.

Stroke is one of the most common acute neurological disorders and a leading cause of disability worldwide. Evidence-based treatments over the last two decades have driven a revolution in the clinical management and design of stroke services. We need a highly skilled, multidisciplinary workforce that includes neurologists as core members to deliver modern stroke care. In the UK, the dedicated subspecialty training programme for stroke medicine has recently been integrated into the neurology curriculum. All neurologists will be trained to contribute to each aspect of the stroke care pathway. We discuss how training in stroke medicine is evolving for neurologists and the opportunities and challenges around practising stroke medicine in the UK and beyond.