Publications by year
Bucholc M, James C, Al Khleifat A, Badhwar A, Tantiangco HM, Lourida I, Llewellyn DJ, Ranson JM (In Press). Artificial Intelligence for Dementia Research Methods Optimization.
Frisoni GB, Altomare D, Ribaldi F, Villain N, Brayne C, Mukadam N, Abramowicz M, Abramowicz F, Berthier M, Bieler-Aeschlimann M, et al (In Press). Dementia prevention in memory clinics: recommendations from the European task force for brain health services. The Lancet Regional Health - Europe
Nihat A, Ranson J, Harris D, McNiven K, Rudge P, Collinge J, Llewellyn D, Mead SH (In Press). Development of Prognostic Models for Survival and Care Status in Sporadic Creutzfeldt-Jakob disease. med Rxiv
Ranson J, Llewellyn D, Lourida I
(In Press). Harnessing the potential of machine learning and artificial intelligence for dementia research. Brain Informatics
Harnessing the potential of machine learning and artificial intelligence for dementia research
Progress in dementia research has been limited, with substantial gaps in our knowledge of targets for prevention, mechanisms for disease progression, and disease-modifying treatments. The growing availability of multimodal datasets opens possibilities for the application of machine learning and artificial intelligence (AI) to help answer key questions in the field. We provide an overview of the state of the science, highlighting current challenges and opportunities for utilisation of AI approaches to move the field forward in the areas of genetics, experimental medicine, drug discovery and trials optimisation, imaging, and prevention. Machine learning methods can enhance results of genetic studies, help determine biological effects and facilitate the identification of drug targets based on genetic and transcriptomic information. The use of unsupervised learning for understanding disease mechanisms for drug discovery is promising, while analysis of multimodal datasets to characterise and quantify disease severity and subtype are also beginning to contribute to optimisation of clinical trial recruitment. Data-driven experimental medicine is needed to analyse data across modalities and develop novel algorithms to translate insights from animal models to human disease biology. AI methods in neuroimaging outperform traditional approaches for diagnostic classification and although challenges around validation and translation remain, there is optimism for their meaningful integration to clinical practice in the near future. AI-based models can also clarify our understanding of the causality and commonality of dementia risk factors, informing and improving risk prediction models along with the development of preventative interventions. The complexity and heterogeneity of dementia requires an alternative approach beyond traditional design and analytical approaches. Although not yet widely used in dementia research, machine learning and AI have the potential to unlock current challenges and advance precision dementia medicine. Abstract
Bucholc M, James C, Khleifat AA, Badhwar A, Clarke N, Dehsarvi A, Madan CR, Marzi SJ, Shand C, Schilder BM, et al
(2023). Artificial Intelligence for Dementia Research Methods Optimization. ArXiv
Artificial Intelligence for Dementia Research Methods Optimization.
INTRODUCTION: Machine learning (ML) has been extremely successful in identifying key features from high-dimensional datasets and executing complicated tasks with human expert levels of accuracy or greater. METHODS: We summarize and critically evaluate current applications of ML in dementia research and highlight directions for future research. RESULTS: We present an overview of ML algorithms most frequently used in dementia research and highlight future opportunities for the use of ML in clinical practice, experimental medicine, and clinical trials. We discuss issues of reproducibility, replicability and interpretability and how these impact the clinical applicability of dementia research. Finally, we give examples of how state-of-the-art methods, such as transfer learning, multi-task learning, and reinforcement learning, may be applied to overcome these issues and aid the translation of research to clinical practice in the future. DISCUSSION: ML-based models hold great promise to advance our understanding of the underlying causes and pathological mechanisms of dementia. Abstract
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Du R, Zhou Y, you C, Liu K, King DA, Liang Z-S, Ranson JM, Llewellyn DJ, Huang J, Zhang Z, et al
(2023). Attention-deficit/hyperactivity disorder and ischemic stroke: a Mendelian randomization study. Int J Stroke
Attention-deficit/hyperactivity disorder and ischemic stroke: a Mendelian randomization study.
BACKGROUND: Observational studies have found an association between attention-deficit/hyperactivity disorder (ADHD) and ischemic stroke. AIMS: the purpose of this study was to investigate whether genetic liability to ADHD has a causal effect on ischemic stroke and its subtypes. METHODS: in this two-sample Mendelian randomization (MR) study, genetic variants (nine single-nucleotide polymorphisms; P Abstract
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Pain O, Jones A, Khleifat AA, Agarwal D, Hramyka D, Karoui H, Kubica J, Llewellyn DJ, Ranson JM, Yao Z, et al
(2023). Harnessing Transcriptomic Signals for Amyotrophic Lateral Sclerosis to Identify Novel Drugs and Enhance Risk Prediction. medRxiv
Harnessing Transcriptomic Signals for Amyotrophic Lateral Sclerosis to Identify Novel Drugs and Enhance Risk Prediction.
INTRODUCTION: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease. This study integrates the latest ALS genome-wide association study (GWAS) summary statistics with functional genomic annotations with the aim of providing mechanistic insights into ALS risk loci, inferring drug repurposing opportunities, and enhancing prediction of ALS risk and clinical characteristics. METHODS: Genes associated with ALS were identified using GWAS summary statistic methodology including SuSiE SNP-based fine-mapping, and transcriptome- and proteome-wide association study (TWAS/PWAS) analyses. Using several approaches, gene associations were integrated with the DrugTargetor drug-gene interaction database to identify drugs that could be repurposed for the treatment of ALS. Furthermore, ALS gene associations from TWAS were combined with observed blood expression in two external ALS case-control datasets to calculate polytranscriptomic scores and evaluate their utility for prediction of ALS risk and clinical characteristics, including site of onset, age at onset, and survival. RESULTS: SNP-based fine-mapping, TWAS and PWAS identified 117 genes associated with ALS, with TWAS and PWAS providing novel mechanistic insights. Drug repurposing analyses identified five drugs significantly enriched for interactions with ALS associated genes, with directional analyses highlighting α-glucosidase inhibitors may exacerbate ALS pathology. Additionally, drug class enrichment analysis showed calcium channel blockers may reduce ALS risk. Across the two observed expression target samples, ALS polytranscriptomic scores significantly predicted ALS risk ( R 2 = 4%; p -value = 2.1×10 -21 ). CONCLUSIONS: Functionally-informed analyses of ALS GWAS summary statistics identified novel mechanistic insights into ALS aetiology, highlighted several therapeutic research avenues, and enabled statistically significant prediction of ALS risk. Abstract
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Shannon OM, Ranson JM, Gregory S, Macpherson H, Milte C, Lentjes M, Mulligan A, McEvoy C, Griffiths A, Matu J, et al
(2023). Mediterranean diet adherence is associated with lower dementia risk, independent of genetic predisposition: findings from the UK Biobank prospective cohort study. BMC Med
Mediterranean diet adherence is associated with lower dementia risk, independent of genetic predisposition: findings from the UK Biobank prospective cohort study.
BACKGROUND: the identification of effective dementia prevention strategies is a major public health priority, due to the enormous and growing societal cost of this condition. Consumption of a Mediterranean diet (MedDiet) has been proposed to reduce dementia risk. However, current evidence is inconclusive and is typically derived from small cohorts with limited dementia cases. Additionally, few studies have explored the interaction between diet and genetic risk of dementia. METHODS: We used Cox proportional hazard regression models to explore the associations between MedDiet adherence, defined using two different scores (Mediterranean Diet Adherence Screener [MEDAS] continuous and Mediterranean diet Pyramid [PYRAMID] scores), and incident all-cause dementia risk in 60,298 participants from UK Biobank, followed for an average 9.1 years. The interaction between diet and polygenic risk for dementia was also tested. RESULTS: Higher MedDiet adherence was associated with lower dementia risk (MEDAS continuous: HR = 0.77, 95% CI = 0.65-0.91; PYRAMID: HR = 0.86, 95% CI = 0.73-1.02 for highest versus lowest tertiles). There was no significant interaction between MedDiet adherence defined by the MEDAS continuous and PYRAMID scores and polygenic risk for dementia. CONCLUSIONS: Higher adherence to a MedDiet was associated with lower dementia risk, independent of genetic risk, underlining the importance of diet in dementia prevention interventions. Abstract
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Klee M, Leist AK, Veldsman M, Ranson JM, Llewellyn DJ (2023). Socioeconomic Deprivation, Genetic Risk, and Incident Dementia. American Journal of Preventive Medicine
Tai XY, Veldsman M, Lyall DM, Littlejohns TJ, Langa KM, Husain M, Ranson JM, Llewellyn DJ (2022). Cardiometabolic multimorbidity, genetic risk and dementia. Alzheimer's & Dementia, 18(S11).
Tai XY, Veldsman M, Lyall DM, Littlejohns TJ, Langa KM, Husain M, Ranson J, Llewellyn DJ
(2022). Cardiometabolic multimorbidity, genetic risk, and dementia: a prospective cohort study. Lancet Healthy Longev
Cardiometabolic multimorbidity, genetic risk, and dementia: a prospective cohort study.
BACKGROUND: Individual cardiometabolic disorders and genetic factors are associated with an increased dementia risk; however, the relationship between dementia and cardiometabolic multimorbidity is unclear. We investigated whether cardiometabolic multimorbidity increases the risk of dementia, regardless of genetic risk, and examined for associated brain structural changes. METHODS: We examined health and genetic data from 203 038 UK Biobank participants of European ancestry, aged 60 years or older without dementia at baseline assessment (2006-10) and followed up until March 31, 2021, in England and Scotland and Feb 28, 2018, in Wales, as well as brain structural data in a nested imaging subsample of 12 236 participants. A cardiometabolic multimorbidity index comprising stroke, diabetes, and myocardial infarction (one point for each), and a polygenic risk score for dementia (with low, intermediate, and high risk groups) were calculated for each participant. The main outcome measures were incident all-cause dementia and brain structural metrics. FINDINGS: the dementia risk associated with high cardiometabolic multimorbidity was three times greater than that associated with high genetic risk (hazard ratio [HR] 5·55, 95% CI 3·39-9·08, p Abstract
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Nihat A, Ranson JM, Harris D, McNiven K, Mok T, Rudge P, Collinge J, Llewellyn DJ, Mead S
(2022). Development of prognostic models for survival and care status in sporadic Creutzfeldt-Jakob disease. Brain Commun
Development of prognostic models for survival and care status in sporadic Creutzfeldt-Jakob disease.
Sporadic Creutzfeldt-Jakob disease, the most common human prion disease, typically presents as a rapidly progressive dementia and has a highly variable prognosis. Despite this heterogeneity, clinicians need to give timely advice on likely prognosis and care needs. No prognostic models have been developed that predict survival or time to increased care status from the point of diagnosis. We aimed to develop clinically useful prognostic models with data from a large prospective observational cohort study. Five hundred and thirty-seven patients were visited by mobile teams of doctors and nurses from the National Health Service National Prion Clinic within 5 days of notification of a suspected diagnosis of sporadic Creutzfeldt-Jakob disease, enrolled to the study between October 2008 and March 2020, and followed up until November 2020. Prediction of survival over 10-, 30- and 100-day periods was the main outcome. Escalation of care status over the same time periods was a secondary outcome for a subsample of 113 patients with low care status at initial assessment. Two hundred and eighty (52.1%) patients were female and the median age was 67.2 (interquartile range 10.5) years. Median survival from initial assessment was 24 days (range 0-1633); 414 patients died within 100 days (77%). Ten variables were included in the final prediction models: sex; days since symptom onset; baseline care status; PRNP codon 129 genotype; Medical Research Council Prion Disease Rating Scale, Motor and Cognitive Examination Scales; count of MRI abnormalities; Mini-Mental State Examination score and categorical disease phenotype. The strongest predictor was PRNP codon 129 genotype (odds ratio 6.65 for methionine homozygous compared with methionine-valine heterozygous; 95% confidence interval 3.02-14.68 for 30-day mortality). of 113 patients with lower care status at initial assessment, 88 (78%) had escalated care status within 100 days, with a median of 35 days. Area under the curve for models predicting outcomes within 10, 30 and 100 days was 0.94, 0.92 and 0.91 for survival, and 0.87, 0.87 and 0.95 for care status escalation, respectively. Models without PRNP codon 129 genotype, which is not immediately available at initial assessment, were also highly accurate. We have developed a model that can accurately predict survival and care status escalation in sporadic Creutzfeldt-Jakob disease patients using clinical, imaging and genetic data routinely available in a specialist national referral service. The utility and generalizability of these models to other settings could be prospectively evaluated when recruiting to clinical trials and providing clinical care. Abstract
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Ward DD, Ranson JM, Wallace LMK, Llewellyn DJ, Rockwood K
(2022). Frailty, lifestyle, genetics and dementia risk. J Neurol Neurosurg Psychiatry
Frailty, lifestyle, genetics and dementia risk.
OBJECTIVE: to optimise dementia prevention strategies, we must understand the complex relationships between lifestyle behaviours, frailty and genetics. METHODS: We explored relationships between frailty index, healthy lifestyle and polygenic risk scores (all assessed at study entry) and incident all-cause dementia as recorded on hospital admission records and death register data. RESULTS: the analytical sample had a mean age of 64.1 years at baseline (SD=2.9) and 53% were women. Incident dementia was detected in 1762 participants (median follow-up time=8.0 years). High frailty was associated with increased dementia risk independently of genetic risk (HR 3.68, 95% CI 3.11 to 4.35). Frailty mediated 44% of the relationship between healthy lifestyle behaviours and dementia risk (indirect effect HR 0.95, 95% CI 0.95 to 0.96). Participants at high genetic risk and with high frailty had 5.8 times greater risk of incident dementia compared with those at low genetic risk and with low frailty (HR 5.81, 95% CI 4.01 to 8.42). Higher genetic risk was most influential in those with low frailty (HR 1.31, 95% CI 1.22 to 1.40) but not influential in those with high frailty (HR 1.09, 95% CI 0.92 to 1.28). CONCLUSION: Frailty is strongly associated with dementia risk and affects the risk attributable to genetic factors. Frailty should be considered an important modifiable risk factor for dementia and a target for dementia prevention strategies, even among people at high genetic risk. Abstract
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Huang J, Liang Z-S, Pallotti S, Ranson JM, Llewellyn DJ, Zheng Z-J, King DA, Zhou Q, Zheng H, Napolioni V, et al
(2022). PAGEANT: personal access to genome and analysis of natural traits. Nucleic Acids Res
PAGEANT: personal access to genome and analysis of natural traits.
GWASs have identified numerous genetic variants associated with a wide variety of diseases, yet despite the wide availability of genetic testing the insights that would enhance the interpretability of these results are not widely available to members of the public. As a proof of concept and demonstration of technological feasibility, we developed PAGEANT (Personal Access to Genome & Analysis of Natural Traits), usable through Graphical User Interface or command line-based version, aiming to serve as a protocol and prototype that guides the overarching design of genetic reporting tools. PAGEANT is structured across five core modules, summarized by five Qs: (i) quality assurance of the genetic data; (ii) qualitative assessment of genetic characteristics; (iii) quantitative assessment of health risk susceptibility based on polygenic risk scores and population reference; (iv) query of third-party variant databases (e.g. ClinVAR and PharmGKB) and (v) quick Response code of genetic variants of interest. Literature review was conducted to compare PAGEANT with academic and industry tools. For 2504 genomes made publicly available through the 1000 Genomes Project, we derived their genomic characteristics for a suite of qualitative and quantitative traits. One exemplary trait is susceptibility to COVID-19, based on the most up-to-date scientific findings reported. Abstract
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Klee M, Leist AK, Veldsman M, Ranson JM, Llewellyn DJ (2022). Socioeconomic Deprivation, Genetics and Risk of Dementia. Alzheimer's & Dementia, 18(S11).
Ranson JM, Khleifat AA, Lyall DM, Newby D, Winchester LM, Proitsi P, Veldsman M, Rittman T, Marzi S, Yao Z, et al (2022). The Deep Dementia Phenotyping (DEMON) Network: a global platform for innovation using data science and artificial intelligence. Alzheimer's & Dementia, 18(S11).
Ranson JM, Khleifat AA, Lyall DM, Newby D, Winchester LM, Proitsi P, Veldsman M, Rittman T, Marzi S, Yao Z, et al
(2022). The Deep Dementia Phenotyping (DEMON) Network: a global platform for innovation using data science and artificial intelligence. Alzheimers Dement
, 18 Suppl 2
The Deep Dementia Phenotyping (DEMON) Network: a global platform for innovation using data science and artificial intelligence.
BACKGROUND: the increasing availability of large high-dimensional data from experimental medicine, population-based and clinical cohorts, clinical trials, and electronic health records has the potential to transform dementia research. Our ability to make best use of this rich data will depend on utilisation of advanced machine learning and artificial intelligence (AI) techniques and collaboration across disciplinary and geographic boundaries. METHOD: the Deep Dementia Phenotyping (DEMON) Network launched in 20191 to support the growing interest in machine learning and AI. Led by Director Prof David Llewellyn and Deputy Director Dr Janice Ranson, the leadership team additionally includes 5 Theme Leads and 14 Working Group Leads, supported by an international Steering Committee of world-leading academics. Core funding is provided by Alzheimer's Research UK, the Alan Turing Institute and the University of Exeter, with additional support from strategic partners including the UK Dementia Research Institute and the Alzheimer's Society. Grand Challenges were established at a National Strategy Workshop in June 2020. Multidisciplinary Working Groups were formed to coordinate practical activities in seven key areas: Genetics and omics, experimental medicine, drug discovery and trials optimisation, biomarkers, imaging, dementia prevention, and applied models and digital health. Additional Special Interest Groups coordinate topic specific collaborations. RESULT: Membership on 4th February 2022 comprised 1,321 individuals from 61 countries across 6 continents (see Figure). Areas of expertise include dementia research (904; 68%), data science (692; 52%), clinical practice (244; 18%), industry (162; 12%), and regulation (26; 2%). Individual membership is free, and regular knowledge transfer events are provided including a monthly seminar series, talks and workshops, training, networking, and early career development. Each Working Group meets monthly, with multiple grants, reviews, and original research articles in progress. Eight state of the science position papers are in preparation, resulting from a Symposium held in April 2021. In January 2022, 110 early career researchers participated in the Network's flagship event 'NEUROHACK', a 4-day competitive global hackathon, with pilot grants awarded to those generating the most innovative solutions. CONCLUSION: the DEMON Network is a rapidly growing global platform for innovation that is supporting the global dementia research community to collaborate. Find out more at demondementia.com. Abstract
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Ranson JM, Bucholc M, Lyall D, Newby D, Winchester L, Oxtoby N, Veldsman M, Rittman T, Marzi S, Skene N, et al (2022). The Emerging Role of AI in Dementia Research and Healthcare. In (Ed) Artificial Intelligence in Healthcare, 95-106.
Jenkins ND, Hoogendijk EO, Armstrong JJ, Lewis NA, Ranson JM, Rijnhart JJM, Ahmed T, Ghachem A, Mullin DS, Ntanasi E, et al (2022). Trajectories of Frailty with Aging: Coordinated Analysis of Five Longitudinal Studies. Innovation in Aging, 6(2).
Borchert R, Azevedo T, Badhwar A, Bernal J, Betts M, Bruffaerts R, Burkhart M, Dewachter I, Gellersen H, Low A, et al (2021). Artificial intelligence for diagnosis and prognosis in neuroimaging for dementia; a systematic review.
Altomare D, Molinuevo JL, Ritchie C, Ribaldi F, Carrera E, Dubois B, Jessen F, McWhirter L, Scheltens P, van der Flier WM, et al
(2021). Brain Health Services: organization, structure, and challenges for implementation. A user manual for Brain Health Services—part 1 of 6. Alzheimer's Research and Therapy
Brain Health Services: organization, structure, and challenges for implementation. A user manual for Brain Health Services—part 1 of 6
Dementia has a devastating impact on the quality of life of patients and families and comes with a huge cost to society. Dementia prevention is considered a public health priority by the World Health Organization. Delaying the onset of dementia by treating associated risk factors will bring huge individual and societal benefit. Empirical evidence suggests that, in higher-income countries, dementia incidence is decreasing as a result of healthier lifestyles. This observation supports the notion that preventing dementia is possible and that a certain degree of prevention is already in action. Further reduction of dementia incidence through deliberate prevention plans is needed to counteract its growing prevalence due to increasing life expectancy. An increasing number of individuals with normal cognitive performance seek help in the current memory clinics asking an evaluation of their dementia risk, preventive interventions, or interventions to ameliorate their cognitive performance. Consistent evidence suggests that some of these individuals are indeed at increased risk of dementia. This new health demand asks for a shift of target population, from patients with cognitive impairment to worried but cognitively unimpaired individuals. However, current memory clinics do not have the programs and protocols in place to deal with this new population. We envision the development of new services, henceforth called Brain Health Services, devoted to respond to demands from cognitively unimpaired individuals concerned about their risk of dementia. The missions of Brain Health Services will be (i) dementia risk profiling, (ii) dementia risk communication, (iii) dementia risk reduction, and (iv) cognitive enhancement. In this paper, we present the organizational and structural challenges associated with the set-up of Brain Health Services. Abstract
Zhang N, Ranson J, Zheng Z-J, Hannon E, Zhou Z, Kong X, Llewellyn D, King D, Huang J (2021). Interaction Between Genetic Predisposition, Smoking, and Dementia Risk: a Population-based Cohort Study.
Zhang N, Ranson JM, Zheng Z-J, Hannon E, Zhou Z, Kong X, Llewellyn DJ, King DA, Huang J
(2021). Interaction between genetic predisposition, smoking, and dementia risk: a population-based cohort study. Sci Rep
Interaction between genetic predisposition, smoking, and dementia risk: a population-based cohort study.
We evaluated whether the association between cigarette smoking and dementia risk is modified by genetic predisposition including apolipoprotein E (APOE) genotype and polygenic risk (excluding the APOE region). We included 193,198 UK Biobank participants aged 60-73 years without dementia at baseline. of non-APOE-ε4 carriers, 0.89% (95% CI 0.73-1.08%) current smokers developed dementia compared with 0.49% (95% CI 0.44-0.55%) of never smokers (adjusted HR 1.78; 95% CI 1.39-2.29). In contrast, of one APOE-ε4 allele carriers, 1.69% (95% CI 1.31-2.12%) current smokers developed dementia compared with 1.40% (95% CI 1.25-1.55%) of never smokers (adjusted HR 1.06; 95% CI 0.77-1.45); of two APOE-ε4 alleles carriers, 4.90% (95% CI 2.92-7.61%) current smokers developed dementia compared with 3.87% (95% CI 3.11-4.74%) of never smokers (adjusted HR 0.94; 95% CI 0.49-1.79). of participants with high polygenic risk, 1.77% (95% CI 1.35-2.27%) current smokers developed dementia compared with 1.05% (95% CI 0.91-1.21%) of never smokers (adjusted HR 1.63; 95% CI 1.16-2.28). A significant interaction was found between APOE genotype and smoking status (P = 0.002) while no significant interaction was identified between polygenic risk and smoking status (P = 0.25). APOE genotype but not polygenic risk modified the effect of smoking on dementia risk. Abstract
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Ranson JM, Rittman T, Hayat S, Brayne C, Jessen F, Blennow K, van Duijn C, Barkhof F, Tang E, Mummery CJ, et al
(2021). Modifiable risk factors for dementia and dementia risk profiling. A user manual for Brain Health Services-part 2 of 6. Alzheimers Res Ther
Modifiable risk factors for dementia and dementia risk profiling. A user manual for Brain Health Services-part 2 of 6.
We envisage the development of new Brain Health Services to achieve primary and secondary dementia prevention. These services will complement existing memory clinics by targeting cognitively unimpaired individuals, where the focus is on risk profiling and personalized risk reduction interventions rather than diagnosing and treating late-stage disease. In this article, we review key potentially modifiable risk factors and genetic risk factors and discuss assessment of risk factors as well as additional fluid and imaging biomarkers that may enhance risk profiling. We then outline multidomain measures and risk profiling and provide practical guidelines for Brain Health Services, with consideration of outstanding uncertainties and challenges. Users of Brain Health Services should undergo risk profiling tailored to their age, level of risk, and availability of local resources. Initial risk assessment should incorporate a multidomain risk profiling measure. For users aged 39-64, we recommend the Cardiovascular Risk Factors, Aging, and Incidence of Dementia (CAIDE) Dementia Risk Score, whereas for users aged 65 and older, we recommend the Brief Dementia Screening Indicator (BDSI) and the Australian National University Alzheimer's Disease Risk Index (ANU-ADRI). The initial assessment should also include potentially modifiable risk factors including sociodemographic, lifestyle, and health factors. If resources allow, apolipoprotein E ɛ4 status testing and structural magnetic resonance imaging should be conducted. If this initial assessment indicates a low dementia risk, then low intensity interventions can be implemented. If the user has a high dementia risk, additional investigations should be considered if local resources allow. Common variant polygenic risk of late-onset AD can be tested in middle-aged or older adults. Rare variants should only be investigated in users with a family history of early-onset dementia in a first degree relative. Advanced imaging with 18-fluorodeoxyglucose positron emission tomography (FDG-PET) or amyloid PET may be informative in high risk users to clarify the nature and burden of their underlying pathologies. Cerebrospinal fluid biomarkers are not recommended for this setting, and blood-based biomarkers need further validation before clinical use. As new technologies become available, advances in artificial intelligence are likely to improve our ability to combine diverse data to further enhance risk profiling. Ultimately, Brain Health Services have the potential to reduce the future burden of dementia through risk profiling, risk communication, personalized risk reduction, and cognitive enhancement interventions. Abstract
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Solomon A, Stephen R, Altomare D, Carrera E, Frisoni GB, Kulmala J, Molinuevo JL, Nilsson P, Ngandu T, Ribaldi F, et al
(2021). Multidomain interventions: state-of-the-art and future directions for protocols to implement precision dementia risk reduction. A user manual for Brain Health Services—part 4 of 6. Alzheimer's Research and Therapy
Multidomain interventions: state-of-the-art and future directions for protocols to implement precision dementia risk reduction. A user manual for Brain Health Services—part 4 of 6
Although prevention of dementia and late-life cognitive decline is a major public health priority, there are currently no generally established prevention strategies or operational models for implementing such strategies into practice. This article is a narrative review of available evidence from multidomain dementia prevention trials targeting several risk factors and disease mechanisms simultaneously, in individuals without dementia at baseline. Based on the findings, we formulate recommendations for implementing precision risk reduction strategies into new services called Brain Health Services. A literature search was conducted using medical databases (MEDLINE via PubMed and SCOPUS) to select relevant studies: non-pharmacological multidomain interventions (i.e. combining two or more intervention domains), target population including individuals without dementia, and primary outcomes including cognitive/functional performance changes and/or incident cognitive impairment or dementia. Further literature searches covered the following topics: sub-group analyses assessing potential modifiers for the intervention effect on cognition in the multidomain prevention trials, dementia risk scores used as surrogate outcomes in multidomain prevention trials, dementia risk scores in relation to brain pathology markers, and cardiovascular risk scores in relation to dementia. Multidomain intervention studies conducted so far appear to have mixed results and substantial variability in target populations, format and intensity of interventions, choice of control conditions, and outcome measures. Most trials were conducted in high-income countries. The differences in design between the larger, longer-term trials that met vs. did not meet their primary outcomes suggest that multidomain intervention effectiveness may be dependent on a precision prevention approach, i.e. successfully identifying the at-risk groups who are most likely to benefit. One such successful trial has already developed an operational model for implementing the intervention into practice. Evidence on the efficacy of risk reduction interventions is promising, but not yet conclusive. More long-term multidomain randomized controlled trials are needed to fill the current evidence gaps, especially concerning low- and middle-income countries and integration of dementia prevention with existing cerebrovascular prevention programs. A precision risk reduction approach may be most effective for dementia prevention. Such an approach could be implemented in Brain Health Services. Abstract
Huang J, Liang Z-S, Pallotti S, Ranson JM, Llewellyn DJ, Zheng Z-J, King DA, Zhou Q, Zheng H, Napolioni V, et al (2021). PAGEANT: Personal Access to Genome and Analysis of Natural Traits.
James C, Ranson JM, Everson R, Llewellyn DJ
(2021). Performance of Machine Learning Algorithms for Predicting Progression to Dementia in Memory Clinic Patients. JAMA Netw Open
Performance of Machine Learning Algorithms for Predicting Progression to Dementia in Memory Clinic Patients.
IMPORTANCE: Machine learning algorithms could be used as the basis for clinical decision-making aids to enhance clinical practice. OBJECTIVE: to assess the ability of machine learning algorithms to predict dementia incidence within 2 years compared with existing models and determine the optimal analytic approach and number of variables required. DESIGN, SETTING, AND PARTICIPANTS: This prognostic study used data from a prospective cohort of 15 307 participants without dementia at baseline to perform a secondary analysis of factors that could be used to predict dementia incidence. Participants attended National Alzheimer Coordinating Center memory clinics across the United States between 2005 and 2015. Analyses were conducted from March to May 2021. EXPOSURES: 258 variables spanning domains of dementia-related clinical measures and risk factors. MAIN OUTCOMES AND MEASURES: the main outcome was incident all-cause dementia diagnosed within 2 years of baseline assessment. RESULTS: in a sample of 15 307 participants (mean [SD] age, 72.3 [9.8] years; 9129 [60%] women and 6178 [40%] men) without dementia at baseline, 1568 (10%) received a diagnosis of dementia within 2 years of their initial assessment. Compared with 2 existing models for dementia risk prediction (ie, Cardiovascular Risk Factors, Aging, and Incidence of Dementia Risk Score, and the Brief Dementia Screening Indicator), machine learning algorithms were superior in predicting incident all-cause dementia within 2 years. The gradient-boosted trees algorithm had a mean (SD) overall accuracy of 92% (1%), sensitivity of 0.45 (0.05), specificity of 0.97 (0.01), and area under the curve of 0.92 (0.01) using all 258 variables. Analysis of variable importance showed that only 6 variables were required for machine learning algorithms to achieve an accuracy of 91% and area under the curve of at least 0.89. Machine learning algorithms also identified up to 84% of participants who received an initial dementia diagnosis that was subsequently reversed to mild cognitive impairment or cognitively unimpaired, suggesting possible misdiagnosis. CONCLUSIONS AND RELEVANCE: These findings suggest that machine learning algorithms could accurately predict incident dementia within 2 years in patients receiving care at memory clinics using only 6 variables. These findings could be used to inform the development and validation of decision-making aids in memory clinics. Abstract
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(2020). Development and validation of clinical prediction models for dementia identification in non-specialist settings.
Development and validation of clinical prediction models for dementia identification in non-specialist settings
A timely dementia diagnosis is a public health priority. However, there is no single accurate test suitable for the identification of dementia in non-specialist settings, such as primary care. The aim of this thesis was to develop and validate clinical prediction models for a computerised clinical decision support system being developed (DECODE). The DECODE system estimates the probability of dementia for a given patient, and provides clinical recommendations such as whether a full dementia evaluation is appropriate. The thesis includes a narrative review, an investigation of dementia misclassification, and a series of model development and validation analyses. Abstract
The narrative review examined case-finding policy and practice in the UK and US. An evidence-based pathway for dementia case-finding was proposed, informing the rationale for and design of DECODE.
In the investigation of dementia misclassification three brief cognitive assessments were analysed in a population-based cohort of older adults. Misclassification ranged from 14-21% across the assessments, each associated with different patient characteristics. Only 3% of participants were misclassified by all three assessments, suggesting misclassification may occur due to test-specific biases.
Prediction model development began with identification of candidate predictors of dementia based on dementia-related systematic reviews, diagnostic criteria and expert judgement. A total of 40 candidate predictors included socio-demographics, subjective and objective cognition, functional impairment, family history and health factors available in non-specialist settings.
A series of bootstrapped fractional polynomial logistic regression analyses predicted current dementia status using two population-based cohorts from the US (Aging, Demographics, and Memory Study, N = 856) and Australia (Sydney Memory and Ageing Study, N = 707). Models were developed and internally evaluated for use with four different brief cognitive assessments; the Mini-Mental State Examination (MMSE), Memory Impairment Screen, General Practitioner Assessment of Cognition and 10-point Cognitive Screener.
Final models were externally validated using the US National Alzheimer’s Coordinating Center (NACC) memory clinic dataset (N = 27,235). There were no differences in area under the curve (AUC) between internal and external validation. All models were consistently more accurate than brief cognitive assessments alone (difference in AUC all p
Ranson JM, Lourida I, Hannon E, Littlejohns TJ, Ballard C, Langa KM, Hyppönen E, Kuzma E, Llewellyn DJ (2020). Genetic risk, education and incidence of dementia.
Ranson J, Lourida I, Hannon E, littlejohns T, Ballard C, langa K, hypponen E, Kuzma E, Llewellyn D (2020). Genetic risk, education and incidence of dementia. Alzheimer’s Association International Conference. 27th - 31st Jul 2020.
Ranson J, Lourida ILIANNA, Hannon E, littlejohns T, Ballard C, langa K, hypponen E, Kuzma E, Llewellyn D (2020). Stroke, genetic risk and incidence of dementia. Alzheimer's Association International Conference. 27th - 31st Jul 2020.
Ranson JM, James C, Routledge C, Everson R, Bauermeister SD, Llewellyn DJ (2020). The DEMON Network: the first UK network for deep dementia phenotyping. Alzheimer's & Dementia, 16(S10).
Ranson JM, Kuzma E, Hamilton W, Llewellyn DJ (2019). P2‐307: DECODE DEMENTIA: INITIAL DEVELOPMENT AND EXTERNAL VALIDATION OF CLINICAL PREDICTION MODELS FOR DEMENTIA IDENTIFICATION. Alzheimer's & Dementia, 15, p704-p705.
Ranson JM, Kuźma E, Hamilton W, Muniz-Terrera G, Langa KM, Llewellyn DJ
(2019). Predictors of dementia misclassification when using brief cognitive assessments. Neurol Clin Pract
Predictors of dementia misclassification when using brief cognitive assessments.
BACKGROUND: Brief cognitive assessments can result in false-positive and false-negative dementia misclassification. We aimed to identify predictors of misclassification by 3 brief cognitive assessments; the Mini-Mental State Examination (MMSE), Memory Impairment Screen (MIS) and animal naming (AN). METHODS: Participants were 824 older adults in the population-based US Aging, Demographics and Memory Study with adjudicated dementia diagnosis (DSM-III-R and DSM-IV criteria) as the reference standard. Predictors of false-negative, false-positive and overall misclassification by the MMSE (cut-point Abstract
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Ranson JM, Kuźma E, Hamilton W, Lang I, Llewellyn DJ
(2018). Case-finding in clinical practice: an appropriate strategy for dementia identification?. Alzheimer's and Dementia: Translational Research and Clinical Interventions
Case-finding in clinical practice: an appropriate strategy for dementia identification?
Earlier diagnosis of dementia is increasingly being recognized as a public health priority. As screening is not generally recommended, case-finding in clinical practice is encouraged as an alternative dementia identification strategy. The approaches of screening and case-finding are often confused, with uncertainty about what case-finding should involve and under what circumstances it is appropriate. We propose a formal definition of dementia case-finding with a clear distinction from screening. We critically examine case-finding policy and practice and propose evidence requirements for implementation in clinical practice. Finally, we present a case-finding pathway and discuss the available evidence for best practice at each stage, with recommendations for research and practice. In conclusion, dementia case-finding is a promising strategy but currently not appropriate due to the substantial gaps in the evidence base for several components of this approach. Abstract
Ranson JM, Kuzma E, Hamilton W, Llewellyn DJ (2017). [P1–498]: MISCLASSIFICATION OF DEMENTIA BY BRIEF COGNITIVE ASSESSMENTS IN THE AGING, DEMOGRAPHICS AND MEMORY STUDY. Alzheimer's & Dementia, 13(7S_Part_9), p481-p482.
Lourida I, Kuzma E, Ranson JM, Hunt H, Talens‐Bou J, Rogers M, Thompson‐Coon J, Llewellyn DJ (2017). [P2–094]: DEVELOPMENT OF a DEMENTIA META‐EVIDENCE DATABASE (EMANATE). Alzheimer's & Dementia, 13(7S_Part_13), p642-p643.
Kuźma E, Soni M, Littlejohns TJ, Ranson JM, van Schoor NM, Deeg DJH, Comijs H, Chaves PHM, Kestenbaum BR, Kuller LH, et al
(2016). Vitamin D and Memory Decline: Two Population-Based Prospective Studies. J Alzheimers Dis
Vitamin D and Memory Decline: Two Population-Based Prospective Studies.
BACKGROUND: Vitamin D deficiency has been linked with dementia risk, cognitive decline, and executive dysfunction. However, the association with memory remains largely unknown. OBJECTIVE: to investigate whether low serum 25-hydroxyvitamin D (25(OH)D) concentrations are associated with memory decline. METHODS: We used data on 1,291 participants from the US Cardiovascular Health Study (CHS) and 915 participants from the Dutch Longitudinal Aging Study Amsterdam (LASA) who were dementia-free at baseline, had valid vitamin D measurements, and follow-up memory assessments. The Benton Visual Retention Test (in the CHS) and Rey's Auditory Verbal Learning Test (in the LASA) were used to assess visual and verbal memory, respectively. RESULTS: in the CHS, those moderately and severely deficient in serum 25(OH)D changed -0.03 SD (95% CI: -0.06 to 0.01) and -0.10 SD (95% CI: -0.19 to -0.02) per year respectively in visual memory compared to those sufficient (p = 0.02). In the LASA, moderate and severe deficiency in serum 25(OH)D was associated with a mean change of 0.01 SD (95% CI: -0.01 to 0.02) and -0.01 SD (95% CI: -0.04 to 0.02) per year respectively in verbal memory compared to sufficiency (p = 0.34). CONCLUSIONS: Our findings suggest an association between severe vitamin D deficiency and visual memory decline but no association with verbal memory decline. They warrant further investigation in prospective studies assessing different memory subtypes. Abstract
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Ranson JM, Kuzma E, Langa KM, Llewellyn DJ (2015). P3‐192: Primary care‐relevant predictors of dementia status in the aging, demographics and memory study. Alzheimer's & Dementia, 11(7S_Part_15), p705-p706.
(2014). Development and validation of a dementia screening tool for use in primary care.
Development and validation of a dementia screening tool for use in primary care
Background: Brief cognitive assessments are biased by patient characteristics and offer inadequate diagnostic accuracy for primary care dementia screening. Integrating clinically available information with a brief cognitive assessment and optimizing the assessment scoring method may enhance accuracy and reduce bias. Abstract
Objective: Development of a novel dementia-screening tool for use in primary care.
Method: a representative sample of the U.S population aged 70 and over (N=856) from the Aging, Demographics and Memory Study (ADAMS) was used, during which a dementia reference diagnosis was reached by consensus expert panel review of neuropsychological assessments, informant reports and medical records. In a series of logistic regression models, we predicted current dementia status from a wide range of sociodemographic, health, lifestyle and cognition variables, including a brief cognitive assessment (Memory Impairment Screen; MIS). We also investigated the optimal MIS scoring method and importance of informant-reported information. The final set of predictors selected by stepwise regression was weighted to form a point-system dementia-screening tool. For validation, the tool was calibrated and discrimination accuracy was compared against the original MIS using receiver operating curve analysis.
Results: Dementia prevalence at baseline was 36% (n = 308). The final screening tool included stroke history (2 points), difficulties with bathing (points), using the phone (1 point), managing money (2 points), absence of heart medications (1 point), informant-reported memory decline (3 points), previous memory complaints (1 point) and categorical MIS score (0-9 points), AUC= 0.96, CI95% [0.95 – 0.98]. A threshold of 10 points yielded a sensitivity of 89% (MIS 69%,) and specificity of 93% (MIS 97%).
Conclusions: This composite screening tool incorporating a small set of key clinically relevant variables with an optimally scored brief cognitive assessment, provides an accurate and reliable measure suitable for use in primary care which compares favourably with the MIS.