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Background: Motoric cognitive risk (MCR) is a syndrome characterised by measured slow gait speed and self‐reported cognitive complaints. MCR is a high‐risk state for adverse health outcomes in older adults, particularly cognitive impairment and dementia. Previous studies have identified risk factors for MCR, but the effect of socioeconomic status has, to date, been insufficiently examined. This study explored the association between MCR and socioeconomic status, as determined by occupational social class and years of education. Methods: Some 692 community‐based adults of the Lothian Birth Cohort 1936 (LBC1936), aged 70 years at baseline, were followed up after 6 years and classified into non‐MCR and MCR groups. We applied logistic regression analyses adjusting for demographic, lifestyle, and health covariates to investigate the association between MCR and years of education and occupational social class, categorised into manual versus non‐manual occupations. Results: MCR prevalence at age 76 years was 5.6% (95% CI 4.0–7.6). After multivariate adjustment, participants of lower socioeconomic status (manual occupation) had a greater than three‐fold increased likelihood of MCR (adjusted odds ratio 3.55, 95% CI 1.46–8.74; p = 0.005) compared with those of higher socioeconomic status (non‐manual occupation). Conclusions: Working in a manual job earlier in life triples the risk of MCR later in life, regardless of education. Unravelling this association will likely reveal important pathophysiological mechanisms underlying MCR and may unearth modifiable risk factors which could be targeted to reduce the incidence of MCR and, ultimately, dementia. Policy and healthcare practice addressing dementia risks such as MCR in their social context and early in the lifecourse could be effective strategies for reducing health inequalities in older age. [ABSTRACT FROM AUTHOR]

Background: The Lothian Birth Cohort 1936 (LBC1936) is a longitudinal study of ageing with well-characterised assessments, but until now, it has relied on self-report or proxies for dementia such as cognitive tests. Our aims were twofold: a) to describe a framework for identifying dementia in a cohort study. b) to report the age-specific incidence and prevalence of all-cause dementia and dementia subtypes in 865 individuals in the LBC1936. Methods: Electronic Health Records (EHR) of all participants were reviewed, and relevant information was extracted to form case vignettes for everyone with any record of cognitive dysfunction. The EHR data sources include hospital and clinic letters, general practitioner and hospital referrals, prescribed medications, imaging and laboratory results. Death certificate data were obtained separately. Clinician assessments were performed when there was concern about a participant's cognition. A diagnosis of probable dementia, possible dementia, or no dementia was agreed upon by a consensus diagnostic review board, comprised of a multidisciplinary team of clinical dementia experts who reviewed case vignettes and clinician assessment letters. For those with probable dementia, a subtype was also determined, where possible. We compared the agreement between our newly ascertained dementia diagnoses with the existing self-reported dementia diagnoses. Results: Self-reported dementia diagnoses were positive in only 17.8% of ascertained dementia diagnoses. The EHR review identified 163/865 (18.8%) individuals as having cognitive dysfunction. At the consensus diagnostic review board, 118/163 were diagnosed with probable all-cause dementia, a prevalence of 13.6%. Age-specific dementia prevalence increased with age from 0.8% (65–74.9 years) to 9.93% (85–89.9 years). Prevalence rates for women were higher in nearly all age groups. The most common subtype was dementia due to Alzheimer disease (49.2%), followed by mixed Alzheimer and cerebrovascular disease (17.0%), dementia of unknown or unspecified cause (16.1%), and dementia due to vascular disease (8.5%). Conclusions: We present a robust systematic framework and guide for other cohort teams wanting to ascertain dementia diagnoses. The newly ascertained dementia diagnosis provides vital data for further analyses of LBC1936 to allow exploration of lifecourse predictors of dementia. [ABSTRACT FROM AUTHOR]

Objectives: Motoric Cognitive Risk (MCR) is a gait-based predementia syndrome that is easy to measure and prognostic of dementia and falls. We aimed to examine the prevalence and risk factors for MCR, and assess its overlap with Mild Cognitive Impairment, Prefrailty, and Frailty, in a cohort of older Scottish adults without dementia.Methods: In this longitudinal prospective study, we classified 690 participants (mean [SD] age 76.3 [0.8] years; wave 3) of the Lothian Birth Cohort 1936 (LBC1936) into non-MCR or MCR groups. We examined their baseline (age 69.5 [0.8] years; wave 1) risk factors for MCR at waves 3, 4, and 5 (6, 9, and 12 years later respectively).Results: MCR prevalence rate ranged from 5.3% to 5.7% across the three waves. The presence of MCR was associated with older baseline age (6 and 9 years later), lower occupational socioeconomic status (6 years later), and worse scores in a range of tests of executive function (6, 9 and 12 years later). Approximately 46% of the MCR group also had Mild Cognitive Impairment, and almost everyone in the MCR group had either Prefrailty or Frailty.Conclusions: The prevalence of MCR in this Scottish cohort is lower than the pooled global average, possibly reflecting the general good health of the LBC cohort. However, it is higher than the prevalence in two neighbouring countries' cohorts, which may reflect the younger average ages of those cohorts. Future LBC1936 research should assess the risk factors associated with MCR to validate previous findings and analyse novel predictive factors, particularly socioeconomic status. [ABSTRACT FROM AUTHOR]

Background: Multimorbidity (the co-occurrence of multiple chronic conditions) is increasingly common, especially among people with dementia. Few neuroimaging studies have explored amyloid biomarkers in people with multimorbidity.Objective: We aimed to conduct the first study of the association between multimorbidity and cerebrospinal fluid amyloid-β42 (CSF Aβ).Method: The European Prevention of Alzheimer's Dementia (EPAD) Longitudinal Cohort Study V500.0 dataset includes volunteers aged ≥50 years from 12 sites. Participants undergo detailed phenotyping, including CSF measures and a self-reported medical history. Using logistic and linear regression analyses, we explored the association between multimorbidity and continuous chronic condition count with CSF Aβ positivity (Aβ42 <1000pg/ml) and continuous CSF Aβ concentration. All models were adjusted for age, sex, APOE status, education, and family history of dementia.Results: Among 447 eligible participants without dementia, the mean (SD) age was 66.6 (6.6) years, 234 (52.3%) were women, and 157 (35.1%) were amyloid positive. With chronic conditions regarded as pseudo-continuous, each additional condition carried a decreased likelihood of amyloid positivity (OR = 0.82, 95% CI: 0.68-0.97; p = 0.026). With CSF Aβ as a continuous variable, each additional condition was associated with an increase of 54.2 pg/ml (95% CI: 9.9-98.5, p = 0.017). Having ≥2 conditions was inversely associated with amyloid positivity (OR 0.59, 95% CI: 0.37-0.95, p = 0.030) compared to one or none.Conclusion: Our findings suggest that the established association between multimorbidity and dementia may be due to a pathway other than amyloid. However, this cross-sectional study does not allow us to make causal inferences. Longitudinal work is required to confirm the inverse association found. [ABSTRACT FROM AUTHOR]

To quantify inconsistent self-reporting of chronic conditions between the baseline (2011–2015) and first follow-up surveys (2015–2018) in the Canadian Longitudinal Study on Aging (CLSA), and to explore methods to resolve inconsistent responses and impact on multimorbidity.Community-dwelling adults aged 45–85 years in the baseline and first follow-up surveys were included (n = 45,184). At each survey, participants self-reported whether they ever had a physician diagnosis of 35 chronic conditions. Identifiable inconsistent responses were enumerated.32–40% of participants had at least one inconsistent response across all conditions. Illness-related information (e.g., taking medication) resolved most inconsistent responses (>93%) while computer-assisted software asking participants to confirm their inconsistent disease status resolved ≤53%. Using these adjudication methods, multimorbidity prevalence at follow-up increased by ≤1.6% compared to the prevalence without resolving inconsistent responses.Inconsistent self-reporting of chronic conditions is common but may not substantially affect multimorbidity prevalence. Future research should validate methods to resolve inconsistencies. [ABSTRACT FROM AUTHOR]