Angelina Kirilova Kancheva

White matter hyperintensities (WMHs) relate to cognitive and physical impairment. Although WMHs typically progress over time, regression has also been observed. Our aim was to explore baseline clinical phenotypes associated with subsequent WMH volume change in the UK Biobank (UKB). We included participants with total volume of WMHs at first and follow-up brain MRI. We assessed 107 preselected clinical phenotypes measured at baseline and preprocessed using the Phenome Scan Analysis Tool (PHESANT). We derived a measure of WMH change as the residual from a linear regression of follow-up WMH volumes on baseline WMH volumes to account for baseline WMH burden. We ran (1) linear regressions for continuous WMH change as the outcome and (2) multinomial logistic regressions comparing progression, regression, and stable groups after categorizing WMH volume change using an established percentile-based approach. Models were unadjusted, partially adjusted (age, sex, total brain tissue volume, follow-up time), or fully adjusted (further adding blood pressure; BP). We corrected p values using false discovery rate (FDR) correction. We included 4,329 participants (median age 52; interquartile range 12), 54.6% female, with a median follow-up of 2.3 years (range 1-7 years). Across follow-up, total brain volume decreased (mean decrease 16,058 mm³) and WMH volume increased (median increase 293 mm³; both p < 0.001). WMHs progressed in 53.9% of participants, regressed in 26.01%, and remained stable in 20%. In fully adjusted linear models, higher diastolic BP was the only baseline phenotype associated with greater WMH progression (0.10 SD higher WMH change per 1 SD higher inverse-rank normal transformed diastolic BP; 95% CI 0.05-0.15, FDR p < 0.05). In multinomial models, hip pain lasting more than 3 months (odds ratio [OR] 0.25, 95% CI 0.11-0.58) and faster walking pace (OR 0.79, 95% CI 0.66-0.95) were inversely associated with WMH progression, whereas hip pain was also inversely associated with WMH regression vs stability (OR 0.31, 95% CI 0.12-0.79; all FDR p < 0.05). We established vascular risk factors associated with subsequent WMH volume change in the studied cohort. Distinct clinical and demographic profiles characterized WMH progression, regression, and stable groups. Results suggest that vascular factors relate to WMH change but are sensitive to covariate control. Further studies should establish factors differentially and causally related to WMH progression, regression, and stability.

Background: Plasma biomarkers may aid Alzheimer disease (AD) diagnosis and prognosis. Cardiovascular risk contributes to cognitive decline in AD, but whether it modifies the relationship between plasma biomarkers and cognitive status has not been assessed in a large multisite cohort. We aimed to explore if cardiovascular risk moderates plasma AD biomarkers’ relationship with cognitive status. Methods: We included cognitively normal (n=301) participants and participants with mild cognitive impairment or probable AD (n=444) from the Bio‐Hermes‐001 study. Cardiovascular risk was quantified using the Atherosclerotic Cardiovascular Disease risk calculator. Logistic regression analyzed associations of cardiovascular risk and plasma biomarkers (amyloid beta 42/amyloid beta 40, phosphorylated tau [p‐tau]181, p‐tau217, apoE4 [apolipoprotein E]) with cognitive status. Moderation by cardiovascular risk was tested in each model. Results: We included 745 participants (mean age=72.3 years; 423 [56.8%] female). Plasma biomarkers and cardiovascular risk were independently associated with cognitive status across models; the strongest association was with p‐tau217 (odds ratio [OR], 2.33 [95% CI, 1.89–2.9]; P P <0.05). Conclusions: Plasma AD biomarkers and cardiovascular risk were independently associated with cognitive status, with cardiovascular risk moderating the p‐tau181 and p‐tau217 cognitive status relationships. If certain plasma biomarkers and cardiovascular risk independently contribute to dementia risk, cardiovascular risk assessment should complement other biomarker evaluations in cognitive screening. Results should be interpreted with caution as associations might be primarily driven by age and sex. Future research including education and genetic risk is needed to clarify the studied relationships.

Background: Blood‐based biomarkers show great promise to revolutionize diagnosis and prognosis of Alzheimer's disease (AD) and other dementias. Cardiovascular disease (CVD) risk factors, such as hypertension and diabetes, contribute to risk of cognitive impairment (CI), vascular and Alzheimer's disease dementia. The relationship between CVD risk and blood‐based biomarkers for AD remains unclear. We sought to explore whether CVD risk significantly moderates the relationship between blood‐based AD biomarkers and CI. Method: We included participants with mild cognitive impairment or probable AD (CI group), versus cognitively normal comparators (CN) from the Bio‐Hermes study. Clinical presentation of CI was determined based on clinical screening procedures. CVD risk was calculated using the Atherosclerotic CVD (ASCVD) risk score calculator. We conducted a series of logistic regression analyses to evaluate the association of each of several AD biomarkers (plasma amyloid beta (Aβ) 42/Aβ40, phosphorylated tau ( p ‐tau)181, p ‐tau217, apolipoprotein E ε4 allele (Apoε4) status) and CVD risk, with CI. We then tested moderation by CVD risk in each model. Result: We included 661 participants ( n = 266 CN, n = 395 CI; mean age 72.4 years; 55% male). In each of the models, blood‐based biomarkers and CVD risk were significantly associated with CI. Strongest association was for p ‐tau217 (OR = 2.34 [95% CI:1.89‐2.9]). CVD risk moderated the relationship between p ‐tau181 and CI (OR = 0.78 [95% CI: 0.64‐0.95]) but no moderation effect was observed for any of the other biomarkers. Conclusion: In our study, blood‐based dementia biomarkers and CVD risk significantly associated with CI, but CVD risk did not moderate the relationship between blood‐based biomarkers and CI. Blood‐based biomarkers and CVD risk might confer independent risk of CI. Given the multiple risk factors for dementia and large implications for social and healthcare systems globally, CVD risk assessment should complement other dementia biomarker assessments for accurate and expeditious prognosis.

Background and Objectives: White matter hyperintensities (WMHs) relate to cognitive and physical impairment. Although WMHs typically increase/progress with time, regression/decreasing has also been observed. We tested which baseline clinical phenotypes predict subsequent WMH volume change in UK Biobank (UKB). Methods: We included participants with total volume of WMHs at first brain magnetic resonance imaging (MRI) and follow-up brain MRI. We assessed whether 107 pre-selected clinical phenotypes at baseline related to follow-up WMH volume change. We derived a measure of WMH change as the residual from a linear regression of follow-up WMH volume on baseline WMH volume. We pre-processed clinical phenotypes using PHESANT. We ran (i) linear regressions for continuous WMH change as the outcome and (ii) multinomial logistic regressions comparing progression, regression, and stable groups after categorising WMH volume change using a quintile-based approach. Models were unadjusted, partially adjusted (age, sex, total brain tissue volume, follow-up time), or fully adjusted (further adding blood pressure; BP). We corrected all P -values for multiple testing using false discovery rate (FDR) correction. Results: We included 4329 participants (median age=52; IQR=12), 54.6% female. Median follow-up was 2.3 years (range=1-7 years). On average, total brain volume decreased across follow-up (mean decrease=16,058 mm ³ ) and WMH volume increased (median increase=293 mm ³ ; both p p <0.05). In group comparisons, progressors were older than regressors and stable individuals, on average. Regressors and progressors had higher BMI than the stable group. Regressors had the highest baseline WMH burden. In unadjusted models, progressors versus stable were more likely to have cataract and higher BP, while regressors had higher weight and systolic BP. These associations did not survive covariate adjustment. Discussion: Well-established vascular risk factors related to subsequent WMH volume change. Distinct clinical and demographic profiles characterized WMH progression, regression, and stable groups. Results suggest that vascular factors relate to WMH change but are sensitive to covariate control. Further studies should establish factors differentially related to WMH progression, regression, and stability.

Objectives: Plasma biomarkers may assist with diagnosis and prognosis of Alzheimer’s disease and other dementias. Cardiovascular risk is associated with impaired cognitive health, although mechanisms are not completely understood. We sought to explore whether cardiovascular risk moderates the relationship between plasma Alzheimer’s disease biomarkers and cognitive status. Methods: We included groups of cognitively normal (n=301) and combined mild cognitive impairment or probable Alzheimer’s disease (n=444), based on clinical assessment, from the Bio-Hermes-001 study. Cardiovascular risk was quantified using the Atherosclerotic CVD (ASCVD) risk calculator. We conducted a series of logistic regression analyses to evaluate the association of cardiovascular risk, each of several Alzheimer’s disease biomarkers (i.e., plasma amyloid beta (Aβ)42/Aβ40, phosphorylated tau (p-tau)181, p-tau217, and circulating levels of apolipoprotein E (ApoE4)), with cognitive status. We tested moderation by cardiovascular risk in each model. Results: We included 745 participants (mean age=72.3 years; 423 (56.8%) female) in the analysis. In each model, plasma biomarkers and cardiovascular risk were independently associated with cognitive status; the strongest association was found with p-tau217 (odds ratio (OR)=2.33; 95% confidence intervals (95%CI) [1.89-2.9]; p p <0.05). Discussion: Plasma Alzheimer’s disease biomarkers and cardiovascular risk were independently associated with cognitive status, but cardiovascular risk only marginally moderated the p-tau181- and p-tau217-cognitive status relationships. If plasma biomarkers and CVD risk potentially confer an independent risk of dementia, cardiovascular risk assessment should complement other dementia biomarker assessments in clinical and research cognitive screening.

Introduction: Chronic pain is associated with single cardiometabolic diseases (CMDs). Less is known about the association of chronic pain with the co-occurrence of multiple CMDs, known as cardiometabolic multimorbidity (CMM). Objectives: This study aims to examine the association between chronic pain and incidence of CMM and if it existed, to what extent chronic pain relates to the progression of specific CMD-related multimorbidity (MM). Methods: We pooled individual-level data of 59,134 participants from 4 cohort studies across 18 countries between 2010 and 2020. Participants aged 45 years or older, free of CMDs (diabetes, heart diseases, and stroke), and with self-reported chronic pain status at baseline were included. Multinomial logistic regression was performed on the association of chronic pain with incident CMM and the progression of specific CMD-related MM. Results: One-third (21,204) of participants reported chronic pain at baseline. After 8 to 9 years, 1344 (2.3%) developed CMM. Chronic pain was associated with the onset of each CMD (odds ratio [OR] range 1.12–1.37) and CMM combinations (OR range 1.57–2.09). It is also linked with the increased odds of more CMDs (1, 2, and 3) during the follow-up. For example, OR increased from 1.31 for individuals with one CMD, to 1.57 for those with 2 CMDs, to 2.09 for those with 3 CMDs. Chronic pain was also associated with developing all CMD-related MM (OR range 1.26–1.88). Compared with those with diabetes only, participants with chronic pain were more likely to progress to diabetes and heart diseases, as well as diabetes, heart diseases, and stroke. Conclusion: Chronic pain is associated with incidence and progression of CMM, whose management should be considered in primary and secondary prevention of CMM among middle-aged and older populations.

Cerebral small vessel disease (cSVD) is the most common pathology underlying vascular cognitive impairment. Although other clinical features of cSVD are increasingly recognized, it is likely that certain symptoms are being overlooked. A comprehensive description of cSVD associations with clinical phenotypes at scale is lacking. The objective of this study was to conduct a large-scale, hypothesis-free study of associations between cSVD and clinical phenotypes in UK Biobank (UKB). We included participants from the UKB imaging study who had available information on total volume of white matter hyperintensities (WMHs), the most common cSVD neuroimaging feature. We included various UKB variables describing clinical phenotypes, defined as observable signs and symptoms of individuals with concurrent neuroimaging evidence of cSVD. We conducted a phenome scan using the open-source PHESANT software package. Total volume of WMHs was introduced as the independent variable and clinical phenotypes as the dependent variables in the regression model. The association of each phenotype with total volume of WMHs was tested using one of several regression analyses (all age at recruitment and sex-adjusted). All associations were corrected for multiple comparisons using the false discovery rate (FDR) correction method. We included 45,013 participants in the analysis (mean age = 54.97 years, SD = 7.55). We confirm previously reported associations with depression (odds ratio [OR] = 1.07 [95% CI 1.05-1.10]), apathy (OR = 1.11 [95% CI 1.08-1.14]), falls (OR = 1.11 [95% CI 1.09-1.13]), respiratory problems (OR = 1.14 [95% CI 1.04-1.25]), and sleep disturbance (OR = 1.07 [95% CI 1.04-1.09], all FDR-adjusted p < 0.001). We further identified associations with all-cause dental issues (OR = 0.94 [95% CI 0.96-0.92]), hearing problems (OR = 1.06 [95% CI 1.03-1.08]), and eye problems (OR = 0.93 [95% CI 0.91-0.95], all FDR-adjusted p < 0.001). Our findings suggest that presence of cSVD associates with concurrent clinical phenotypes across several body systems. We have corroborated established associations of cSVD and present novel ones. While our results do not provide causality or direction of association because of the cross-sectional nature of our study, they support the need for a more holistic view of cSVD in research, practice, and policy.

Cerebral small vessel disease (cSVD) causes lacunar and hemorrhagic stroke and is an important contributor to vascular cognitive impairment. Other potential physical and psychological consequences of cSVD have been described across various body systems. Descriptions of cSVD are available in journals specific to those individual body systems, but a comprehensive assessment of clinical manifestations across this disparate literature is lacking. We conducted an overview of systematic reviews describing clinical cSVD phenotypes. We searched multidisciplinary databases from inception to December 2023. We included reviews describing concurrent clinical phenotypes in individuals with neuroimaging evidence of cSVD, defined using the STandards for ReportIng Vascular changes on nEuroimaging criteria. We broadly classified phenotypes into cognitive, mood and neuropsychiatric, respiratory, cardiovascular, renal-urinary, peripheral nervous system, locomotor, and gastrointestinal. We included both studies assessing multiple cSVD features and studies examining individual cSVD markers. We extracted risk factor-adjusted effect estimates, where possible, and assessed methodologic quality using the Assessment of Multiple Systematic Reviews-2 tool. After screening 6,156 publications, we included 24 systematic reviews reporting on 685 original studies and 1,135,943 participants. Cognitive and neuropsychiatric phenotypes were examined most often, particularly in relation to white matter hyperintensities (range of risk ratios [RRs] for cognitive phenotypes 1.21-1.49, range of 95% CI 1.01-1.84; for neuropsychiatric, RR 1.02-5.71, 95% CI 0.96-19.69). Two reviews focused solely on perivascular spaces. No reviews assessed lacunes or small subcortical infarcts separately from other cSVD features. Reviews on peripheral nervous system, urinary, or gastrointestinal phenotypes were lacking. Fourteen reviews had high methodologic quality, 5 had moderate quality, and 5 had low quality. Heterogeneity in cSVD definitions and phenotypic assessments was substantial. Neuroimaging markers of cSVD are associated with various clinical manifestations, suggesting a multisystem phenotype. However, features classically associated with cSVD, for example, gait, had limited supporting evidence, and for many body systems, there were no available reviews. Similarly, while white matter hyperintensities were relatively well studied, there were limited data on phenotypes associated with other cSVD features. Future studies should characterize the full clinical spectrum of cSVD and explore clinical associations beyond neurocognitive and neuropsychiatric presentations.

Vascular cognitive impairment is common after stroke, in memory clinics, medicine for the elderly services, and undiagnosed in the community. Vascular disease is said to be the second most common cause of dementia after Alzheimer disease, yet vascular dysfunction is now known to predate cognitive decline in Alzheimer disease, and most dementias at older ages are mixed. Neuroimaging has a major role in identifying the proportion of vascular versus other likely pathologies in patients with cognitive impairment. Here, we aim to provide a pragmatic but evidence-based summary of the current state of potential imaging biomarkers, focusing on magnetic resonance imaging and computed tomography, which are relevant to diagnosing, estimating prognosis, monitoring vascular cognitive impairment, and incorporating our own experiences. We focus on markers that are well-established, with a known profile of association with cognitive measures, but also consider more recently described, including quantitative tissue markers of vascular injury. We highlight the gaps in accessibility and translation to more routine clinical practice.

Self-management interventions (SMIs) may enhance heart failure (HF) outcomes and address challenges associated with disease management. This study aims to review randomized evidence and identify knowledge gaps in SMIs for adult HF patients. Within the COMPAR-EU project, from 2010 to 2018, we conducted searches in the databases MEDLINE, CINAHL, Embase, Cochrane, and PsycINFO. We performed a descriptive analysis using predefined categories and developed an evidence map of randomized controlled trials (RCTs). We found 282 RCTs examining SMIs for HF patients, comparing two to four interventions, primarily targeting individual patients (97%) globally (34 countries, only 31% from an European country). These interventions involved support techniques such as information sharing (95%) and self-monitoring (62%), often through a mix of in-person and remote sessions (43%). Commonly assessed outcomes included quality of life, hospital admissions, mortality, exercise capacity, and self-efficacy. Few studies have focused on lower socio-economic or minority groups. Nurses (68%) and physicians (30%) were the primary providers, and most studies were at low risk of bias in generating a random sequence for participant allocation; however, the reporting was noticeably unclear of methods used to conceal the allocation process. Our analysis has revealed prevalent support techniques and delivery methods while highlighting methodological challenges. These findings provide valuable insights for researchers, clinicians, and policymakers striving to optimize SMIs for individuals living with HF.