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IntroductionDespite major advances in the field of neuroscience over the last three decades, the quality of assessments available to patients with memory problems in later life has barely changed. At the same time, a large proportion of dementia biomarker research is conducted in selected research samples that often poorly reflect the demographics of the population of patients who present to memory clinics. The Oxford Brain Health Clinic (BHC) is a newly developed clinical assessment service with embedded research in which all patients are offered high-quality clinical and research assessments, including MRI, as standard.Methods and analysisHere we describe the BHC protocol, including aligning our MRI scans with those collected in the UK Biobank. We evaluate rates of research consent for the first 108 patients (data collection ongoing) and the ability of typical psychiatry-led NHS memory-clinic patients to tolerate both clinical and research assessments.Ethics and disseminationOur ethics and consenting process enables patients to choose the level of research participation that suits them. This generates high rates of consent, enabling us to populate a research database with high-quality data that will be disseminated through a national platform (the Dementias Platform UK data portal).

BackgroundEquitable access to research studies needs to be increased for all patients. There is debate about which is the best approach to use to discuss participation in research in real-world clinical settings.ObjectiveWe aimed to determine the feasibility of asking all clinical staff within one hospital Trust (an organisation that provides secondary health services within the English and Welsh National Health Service) to use a newly created form on the Trust’s electronic patient records system, as a means of asking patients to consent to discuss participation in research (the opt-in approach). We also aimed to collect feedback from patients and clinicians about their views of the opt-in approach.MethodsFour pilot sites were selected in the Trust: two memory clinics, an adult mental health team and an acute adult ward. Data were collected in three phases: (1) for 6 months, pilot site staff were asked to complete a consent to discuss participation in research form with patients; (2) staff feedback on the form was collected through an online survey; and (3) patient feedback was collected through focus groups.FindingsOf 1779 patients attending services during the pilot period, 197 (11%) had a form completed by staff and 143 (8%) opted-in to finding out about research. Staff cited limited time, low priority and poor user experience of the electronic patient records system as reasons for low uptake of the form. Patients generally approved of the approach but offered suggestions for improvement.ConclusionsThere were mixed results for adopting an opt-in approach; uptake was very low, limiting its value as an effective strategy for improving access to research.Clinical implicationsAlternative strategies to the opt-in approach, such as transparent opt out approaches, warrant consideration to maximise access to research within routine clinical care.

Multimorbidity across the lifespan, especially during critical age windows, is associated with increased dementia risk. In this study, we sought to characterise the accumulation of long-term conditions (LTCs) in a real-world memory clinic population at the Oxford Brain Health Clinic (OBHC). We contextualise these prevalences with comparison to UK Biobank (UKB).BACKGROUNDMultimorbidity across the lifespan, especially during critical age windows, is associated with increased dementia risk. In this study, we sought to characterise the accumulation of long-term conditions (LTCs) in a real-world memory clinic population at the Oxford Brain Health Clinic (OBHC). We contextualise these prevalences with comparison to UK Biobank (UKB).By 2025, medical histories extracted from primary care records were available on the OBHC Research Database for 190 NHS memory clinic patients. 50 LTCs or categories of LTCs were prioritised during extraction and grouped according to body system (Figure 1), recording the first time each diagnosis was made. To align with previous comorbidity lists (Patel et al., medRxiv, 2024), some conditions were merged (e.g., cancers, types of arthritis), and others were extracted from free-text (e.g., anaemia, macular degeneration, osteoporosis, prostate, sleep disorders, hyperlipidaemia), resulting in a list of 31 LTCs for comparison. In line with the OBHC cohort, UKB participants younger than or deceased before age 65 were excluded. A supplementary comparison was performed including only those with a dementia diagnosis, excluding those from UKB who were diagnosed before age 65; only pre-dementia LTCs were considered.METHODBy 2025, medical histories extracted from primary care records were available on the OBHC Research Database for 190 NHS memory clinic patients. 50 LTCs or categories of LTCs were prioritised during extraction and grouped according to body system (Figure 1), recording the first time each diagnosis was made. To align with previous comorbidity lists (Patel et al., medRxiv, 2024), some conditions were merged (e.g., cancers, types of arthritis), and others were extracted from free-text (e.g., anaemia, macular degeneration, osteoporosis, prostate, sleep disorders, hyperlipidaemia), resulting in a list of 31 LTCs for comparison. In line with the OBHC cohort, UKB participants younger than or deceased before age 65 were excluded. A supplementary comparison was performed including only those with a dementia diagnosis, excluding those from UKB who were diagnosed before age 65; only pre-dementia LTCs were considered.On average, OBHC patients had 4 comorbid diagnoses before attending their memory clinic appointment; osteoarthritis and hypertension were most common, with a relative prevalence of 46.8% and 43.7%, respectively (Figure 1). In early adulthood, psychiatric conditions were most prevalent in OBHC patients, but cardiovascular conditions accumulated most rapidly across midlife to become the most prevalent (Figure 2). Of the 10 most prevalent LTCs in the OBHC, arthritis, depression, and IBS were more prevalent than in UKB (Figure 3A). OBHC dementia patients had significantly lower prevalences of hypertension, cardiovascular disease, and anaemia than UKB dementia patients (Figure 3B).RESULTOn average, OBHC patients had 4 comorbid diagnoses before attending their memory clinic appointment; osteoarthritis and hypertension were most common, with a relative prevalence of 46.8% and 43.7%, respectively (Figure 1). In early adulthood, psychiatric conditions were most prevalent in OBHC patients, but cardiovascular conditions accumulated most rapidly across midlife to become the most prevalent (Figure 2). Of the 10 most prevalent LTCs in the OBHC, arthritis, depression, and IBS were more prevalent than in UKB (Figure 3A). OBHC dementia patients had significantly lower prevalences of hypertension, cardiovascular disease, and anaemia than UKB dementia patients (Figure 3B).Although the rankings of conditions between cohorts were largely similar, we found potentially important differences in the prevalence of LTCs between a large population cohort and a real-world patient sample. Ascertaining comparable data across research and clinical cohorts is a harmonisation challenge that needs to be met so the impact of multimorbidity on brain health can inform the development of personalised interventions.CONCLUSIONAlthough the rankings of conditions between cohorts were largely similar, we found potentially important differences in the prevalence of LTCs between a large population cohort and a real-world patient sample. Ascertaining comparable data across research and clinical cohorts is a harmonisation challenge that needs to be met so the impact of multimorbidity on brain health can inform the development of personalised interventions.

Background Multimorbidity across the lifespan, especially during critical age windows, is associated with increased dementia risk. In this study, we sought to characterise the accumulation of long‐term conditions (LTCs) in a real‐world memory clinic population at the Oxford Brain Health Clinic (OBHC). We contextualise these prevalences with comparison to UK Biobank (UKB). Method By 2025, medical histories extracted from primary care records were available on the OBHC Research Database for 190 NHS memory clinic patients. 50 LTCs or categories of LTCs were prioritised during extraction and grouped according to body system (Figure 1), recording the first time each diagnosis was made. To align with previous comorbidity lists (Patel et al., medRxiv, 2024), some conditions were merged (e.g., cancers, types of arthritis), and others were extracted from free‐text (e.g., anaemia, macular degeneration, osteoporosis, prostate, sleep disorders, hyperlipidaemia), resulting in a list of 31 LTCs for comparison. In line with the OBHC cohort, UKB participants younger than or deceased before age 65 were excluded. A supplementary comparison was performed including only those with a dementia diagnosis, excluding those from UKB who were diagnosed before age 65; only pre‐dementia LTCs were considered. Result On average, OBHC patients had 4 comorbid diagnoses before attending their memory clinic appointment; osteoarthritis and hypertension were most common, with a relative prevalence of 46.8% and 43.7%, respectively (Figure 1). In early adulthood, psychiatric conditions were most prevalent in OBHC patients, but cardiovascular conditions accumulated most rapidly across midlife to become the most prevalent (Figure 2). Of the 10 most prevalent LTCs in the OBHC, arthritis, depression, and IBS were more prevalent than in UKB (Figure 3A). OBHC dementia patients had significantly lower prevalences of hypertension, cardiovascular disease, and anaemia than UKB dementia patients (Figure 3B). Conclusion Although the rankings of conditions between cohorts were largely similar, we found potentially important differences in the prevalence of LTCs between a large population cohort and a real‐world patient sample. Ascertaining comparable data across research and clinical cohorts is a harmonisation challenge that needs to be met so the impact of multimorbidity on brain health can inform the development of personalised interventions.

The Oxford Brain Health Clinic (OBHC) has assessed over 300 NHS memory clinic patients with a magnetic resonance imaging (MRI) protocol aligned with the UK Biobank. We also acquired the same data from over 100 healthy volunteers (HV) of a similar age range. This work explores multimodal patterns of imaging-derived phenotypes (IDPs) across diagnostic groups in a real-world memory clinic setting.BACKGROUNDThe Oxford Brain Health Clinic (OBHC) has assessed over 300 NHS memory clinic patients with a magnetic resonance imaging (MRI) protocol aligned with the UK Biobank. We also acquired the same data from over 100 healthy volunteers (HV) of a similar age range. This work explores multimodal patterns of imaging-derived phenotypes (IDPs) across diagnostic groups in a real-world memory clinic setting.Scans from 342 OBHC patients and 107 HV (demographics in Table 1) were processed with an integrated quality control-analysis pipeline optimised for memory clinic use (Gillis et al., medRxiv, 2024). Subsequent diagnoses were extracted from electronic healthcare records and categorised as follows: dementia (ICD10 codes F00, F01, F02, F03), mild cognitive impairment (MCI - F06.7), and no dementia-related diagnoses (NDRD, including F10, F31, F32, F41). We performed ordinal regression analyses to test associations of IDPs with diagnoses, controlling for age, sex, head size, and applying hierarchical FDR correction.METHODScans from 342 OBHC patients and 107 HV (demographics in Table 1) were processed with an integrated quality control-analysis pipeline optimised for memory clinic use (Gillis et al., medRxiv, 2024). Subsequent diagnoses were extracted from electronic healthcare records and categorised as follows: dementia (ICD10 codes F00, F01, F02, F03), mild cognitive impairment (MCI - F06.7), and no dementia-related diagnoses (NDRD, including F10, F31, F32, F41). We performed ordinal regression analyses to test associations of IDPs with diagnoses, controlling for age, sex, head size, and applying hierarchical FDR correction.IDPs from all 6 MRI modalities significantly differed across groups (Figure 1). Pairwise post-hoc analyses revealed that healthy volunteers and dementia patients also significantly differed across all modalities (Figure 2A). In addition to structural changes, MCI patients had significantly higher cortical mean diffusivity, lower white matter integrity, and lower cerebral blood flow compared to HV (Figure 2B). Dementia patients had smaller volumes, localised increases in mean diffusivity, and more white matter hyperintensities (WMHs) than MCI patients (Figure 2C). Memory clinic patients who received no formal dementia-related diagnosis did not have significantly different brain volumes compared to HV, but the left hippocampal mean diffusivity was significantly higher (Figure 2D).RESULTIDPs from all 6 MRI modalities significantly differed across groups (Figure 1). Pairwise post-hoc analyses revealed that healthy volunteers and dementia patients also significantly differed across all modalities (Figure 2A). In addition to structural changes, MCI patients had significantly higher cortical mean diffusivity, lower white matter integrity, and lower cerebral blood flow compared to HV (Figure 2B). Dementia patients had smaller volumes, localised increases in mean diffusivity, and more white matter hyperintensities (WMHs) than MCI patients (Figure 2C). Memory clinic patients who received no formal dementia-related diagnosis did not have significantly different brain volumes compared to HV, but the left hippocampal mean diffusivity was significantly higher (Figure 2D).Thanks to the comprehensive multimodal MRI assessment offered in the OBHC, we observed distinct patterns of changes across the dementia spectrum. While structural IDPs may still provide best sensitivity, non-conventional MRI may give further insights on mechanisms of neurodegeneration. Microstructural and perfusion changes may precede the formation of overt WMH lesions, supporting the possibility of diffusion MRI and perfusion imaging as early signatures alongside structural imaging. Increased mean diffusivity in the left hippocampus in NDRD might explain memory problems that led to the referral to memory clinic.CONCLUSIONThanks to the comprehensive multimodal MRI assessment offered in the OBHC, we observed distinct patterns of changes across the dementia spectrum. While structural IDPs may still provide best sensitivity, non-conventional MRI may give further insights on mechanisms of neurodegeneration. Microstructural and perfusion changes may precede the formation of overt WMH lesions, supporting the possibility of diffusion MRI and perfusion imaging as early signatures alongside structural imaging. Increased mean diffusivity in the left hippocampus in NDRD might explain memory problems that led to the referral to memory clinic.

Background The Oxford Brain Health Clinic (OBHC) has assessed over 300 NHS memory clinic patients with a magnetic resonance imaging (MRI) protocol aligned with the UK Biobank. We also acquired the same data from over 100 healthy volunteers (HV) of a similar age range. This work explores multimodal patterns of imaging‐derived phenotypes (IDPs) across diagnostic groups in a real‐world memory clinic setting. Method Scans from 342 OBHC patients and 107 HV (demographics in Table 1) were processed with an integrated quality control‐analysis pipeline optimised for memory clinic use (Gillis et al., medRxiv, 2024). Subsequent diagnoses were extracted from electronic healthcare records and categorised as follows: dementia (ICD10 codes F00, F01, F02, F03), mild cognitive impairment (MCI ‐ F06.7), and no dementia‐related diagnoses (NDRD, including F10, F31, F32, F41). We performed ordinal regression analyses to test associations of IDPs with diagnoses, controlling for age, sex, head size, and applying hierarchical FDR correction. Result IDPs from all 6 MRI modalities significantly differed across groups (Figure 1). Pairwise post‐hoc analyses revealed that healthy volunteers and dementia patients also significantly differed across all modalities (Figure 2A). In addition to structural changes, MCI patients had significantly higher cortical mean diffusivity, lower white matter integrity, and lower cerebral blood flow compared to HV (Figure 2B). Dementia patients had smaller volumes, localised increases in mean diffusivity, and more white matter hyperintensities (WMHs) than MCI patients (Figure 2C). Memory clinic patients who received no formal dementia‐related diagnosis did not have significantly different brain volumes compared to HV, but the left hippocampal mean diffusivity was significantly higher (Figure 2D). Conclusion Thanks to the comprehensive multimodal MRI assessment offered in the OBHC, we observed distinct patterns of changes across the dementia spectrum. While structural IDPs may still provide best sensitivity, non‐conventional MRI may give further insights on mechanisms of neurodegeneration. Microstructural and perfusion changes may precede the formation of overt WMH lesions, supporting the possibility of diffusion MRI and perfusion imaging as early signatures alongside structural imaging. Increased mean diffusivity in the left hippocampus in NDRD might explain memory problems that led to the referral to memory clinic.

The analysis tools and statistical methods used in large neuroimaging research studies differ from those applied in clinical contexts, making it unclear whether these techniques can be translated to a memory clinic setting. The Oxford Brain Health Clinic (OBHC) was established in 2020 to bridge this gap between research studies and memory clinics. We optimised the UK Biobank imaging framework for the memory clinic setting by integrating enhanced quality control (QC) processes (MRIQC, QUAD, and DSE decomposition) and supplementary dementia‐informed analyses (lobar volumes, NBM volumes, WMH classification, PSMD, cortical diffusion MRI metrics, and tract volumes) into the analysis pipeline. We explored associations between resultant imaging‐derived phenotypes (IDPs) and clinical phenotypes in the OBHC patient population (N = 213), applying hierarchical FDR correction to account for multiple testing. 14%–24% of scans were flagged by automated QC tools, but upon visual inspection, only 0%–2.4% of outputs were excluded. The pipeline successfully generated 5683 IDPs aligned with UK Biobank and 110 IDPs targeted towards dementia‐related changes. We replicated established associations and found novel associations between brain metrics and age, cognition, and dementia‐related diagnoses. The imaging protocol is feasible, acceptable, and yields high‐quality data that is usable for both clinical and research purposes. We validated the use of this methodology in a real‐world memory clinic population, which demonstrates the potential of this enhanced pipeline to bridge the gap between big data studies and clinical settings. We optimised the UK Biobank imaging framework for memory clinic use by integrating enhanced quality control (QC) and supplementary analyses targeted towards dementia‐related changes. These analyses capture established and novel associations between brain metrics and dementia‐related phenotypes, creating a translational bridge between big data analytics and real‐world clinical populations.

Evidence has shown that utilization of antiosteoporotic medications does not correspond with risk, and studies on other therapies have shown that adequacy of pharmaceutical prescribing might vary between regions. Nevertheless, very few studies have addressed the variability in osteoporotic drug consumption. We aimed to describe variations in pharmaceutical utilization and spending on osteoporotic drugs between Health Areas (HA) in Spain. Population-based cross-sectional ecological study of expenditure and utilization of the five therapeutic groups marketed for osteoporosis treatment in Spain in 2009. Small area variation analysis (SAVA) methods were used. The units of analysis were the 168 HA of 13 Spanish regions, including 7.2 million women aged 50 years and older. The main outcomes were the defined daily dose (DDD) per 1000 inhabitants and day (DDD/1000/Day) dispensed according to the pharmaceutical claims reimbursed, and the expenditure on antiosteoporotics at retail price per woman ≥50 years old and per year. The average osteoporosis drug consumption was 116.8 DDD/1000W/Day, ranging from 78.5 to 158.7 DDD/1000W/Day between the HAs in the 5th and 95th percentiles. Seventy-five percent of the antiosteoporotics consumed was bisphosphonates, followed by raloxifene, strontium ranelate, calcitonins, and parathyroid hormones including teriparatide. Regarding variability by therapeutic groups, biphosphonates showed the lowest variation, while calcitonins and parathyroid hormones showed the highest variation. The annual expenditure on antiosteoporotics was €426.5 million, translating into an expenditure of €59.2 for each woman ≥50 years old and varying between €38.1 and €83.3 between HAs in the 5th and 95th percentiles. Biphosphonates, despite accounting for 79% of utilization, only represented 63% of total expenditure, while parathyroid hormones with only 1.6% of utilization accounted for 15% of the pharmaceutical spending. This study highlights a marked geographical variation in the prescription of antiosteoporotics, being more pronounced in the case of costly drugs such as parathyroid hormones. The differences in rates of prescribing explained almost all of the variance in drug spending, suggesting that the difference in prescription volume between territories, and not the price of the drugs, is the main source of variation in this setting. Data on geographical variation of prescription can help guide policy proposals for targeting areas with inadequate antiosteoporotic drug use.