Modifiable Risk Factors Explain Socioeconomic Inequalities in Dementia Risk: Evidence from a Population-Based Prospective Cohort Study

BACKGROUND: Differences in dementia risk across the gradient of socioeconomic status (SES) exist, but their determinants are not well understood. OBJECTIVE: This study investigates whether health conditions and lifestyle-related risk factors explain the SES inequalities in dementia risk. METHODS: 6,346 participants from the English Longitudinal Study of Ageing were followed up from 2008/2009 until 2014/2015. We used Cox regression adjusted for age, gender, wealth/education, and clustering at the household level to examine the association between SES markers (wealth, education) and time to dementia in a structural equation model including potential mediation or effect modification by a weighted compound score of twelve modifiable risk and protective factors for dementia (‘LIfestyle for BRAin health’ (LIBRA) score). RESULTS: During a median follow-up of 6 years, 192 individuals (3.0%) developed dementia. LIBRA scores decreased with increasing wealth and higher educational level. A one-point increase in the LIBRA score was associated with a 13% increase in dementia risk (hazard ratio (HR) = 1.13, 95% confidence interval 1.07–1.19). Higher wealth was associated with a decreased dementia risk (HR = 0.58, 0.39–0.85). Mediation analysis showed that 52% of the risk difference between the highest and lowest wealth tertile was mediated by differences in LIBRA (indirect effect: HR = 0.75, 0.66–0.85). Education was not directly associated with dementia (HR = 1.05, 0.69–1.59), but was a distal risk factor for dementia by explaining differences in wealth and LIBRA scores (indirect effect high education: HR = 0.92, 0.88–0.95). CONCLUSION: Socioeconomic differences in dementia risk can be partly explained by differences in modifiable health conditions and lifestyle factors

Research protocol of the NeedYD-study (Needs in Young onset Dementia): a prospective cohort study on the needs and course of early onset dementia

Contains fulltext : 89407.pdf (publisher's version ) (Open Access)BACKGROUND: Early onset dementia has serious consequences for patients and their family members. Although there has been growing attention for this patient group, health care services are still mainly targeted at the elderly. Specific knowledge of the needs of early onset dementia patients and their families is limited but necessary for the development of adequate health care services and specific guidelines. This research project is mainly targeted at delineating the course of early onset dementia, the functional characteristics and needs of early onset dementia patients and their caregivers, the risk factors for institutionalization and the interaction with the caring environment. METHODS/DESIGN: The NeedYD-study (Needs in Young Onset Dementia) is a longitudinal observational study investigating early onset dementia patients and their caregivers (n = 217). Assessments are performed every six months over two years and consist of interviews and questionnaires with patients and caregivers. The main outcomes are (1) the needs of patients and caregivers, as measured by the Camberwell Assessment of Needs for the Elderly (CANE) and (2) neuropsychiatric symptoms, as measured by the NeuroPsychiatric Inventory (NPI). Qualitative analyses will be performed in order to obtain more in-depth information on the experiences of EOD patients and their family members. The results of this study will be compared with comparable data on late onset dementia from a historical cohort. DISCUSSION: The study protocol of the NeedYD-study is presented here. To our knowledge, this study is the first prospective cohort study in this research area. Although some limitations exist, these do not outweigh the strong points of this study design

Global Prevalence of Young-Onset Dementia: A Systematic Review and Meta-analysis

Importance: Reliable prevalence estimates are lacking for young-onset dementia (YOD), in which symptoms of dementia start before the age of 65 years. Such estimates are needed for policy makers to organize appropriate health care. Objective: To determine the global prevalence of YOD. Data sources: The PubMed, Embase, CINAHL, and PsycInfo databases were systematically searched for population-based studies on the prevalence of YOD published between January 1, 1990, and March 31, 2020. Study selection: Studies containing data on the prevalence of dementia in individuals younger than 65 years were screened by 2 researchers for inclusion in a systematic review and meta-analysis. Data extraction and synthesis: Prevalence estimates on 5-year age bands, from 30 to 34 years to 60 to 64 years, were extracted. Random-effects meta-analyses were conducted to pool prevalence estimates. Results were age standardized for the World Standard Population. Heterogeneity was assessed by subgroup analyses for sex, dementia subtype, study design, and economic status based on the World Bank classification and by meta-regression. Main outcomes and measures: Prevalence estimates of YOD for 5-year age bands. Results: A total of 95 unique studies were included in this systematic review, of which 74 with 2 760 379 unique patients were also included in 5-year age band meta-analyses. Studies were mostly conducted in Europe and in older groups in Asia, North America, and Oceania. Age-standardized prevalence estimates increased from 1.1 per 100 000 population in the group aged 30 to 34 years to 77.4 per 100 000 population in the group aged 60 to 64 years. This gives an overall global age-standardized prevalence of 119.0 per 100 000 population in the age range of 30 to 64 years, corresponding to 3.9 million people aged 30 to 64 years living with YOD in the world. Subgroup analyses showed prevalence between men and women to be similar (crude estimates for men, 216.5 per 100 000 population; for women, 293.1 per 100 000 population), whereas prevalence was lower in high-income countries (crude estimate, 663.9 per 100 000 population) compared with upper-middle-income (crude estimate, 1873.6 per 100 000 population) and lower-middle-income (crude estimate, 764.2 per 100 000 population) countries. Meta-regression showed that age range (P < .001), sample size (P < .001), and study methodology (P = .02) significantly influenced heterogeneity between studies. Conclusions and relevance: This systematic review and meta-analysis found an age-standardized prevalence of YOD of 119.0 per 100 000 population, although estimates of the prevalence in low-income countries and younger age ranges remain scarce. These results should help policy makers organize sufficient health care for this subgroup of individuals with dementia. Study registration: PROSPERO CRD42019119288This study was supported by the Gieskes-Strijbis Foundation, Alzheimer Netherlands, and the Dutch Young-Onset Dementia Knowledge Centre

MRI predictors of amyloid pathology: results from the EMIF-AD Multimodal Biomarker Discovery study

BACKGROUND: With the shift of research focus towards the pre-dementia stage of Alzheimer's disease (AD), there is an urgent need for reliable, non-invasive biomarkers to predict amyloid pathology. The aim of this study was to assess whether easily obtainable measures from structural MRI, combined with demographic data, cognitive data and apolipoprotein E (APOE) ε4 genotype, can be used to predict amyloid pathology using machine-learning classification. METHODS: We examined 810 subjects with structural MRI data and amyloid markers from the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery study, including subjects with normal cognition (CN, n = 337, age 66.5 ± 7.2, 50% female, 27% amyloid positive), mild cognitive impairment (MCI, n = 375, age 69.1 ± 7.5, 53% female, 63% amyloid positive) and AD dementia (n = 98, age 67.0 ± 7.7, 48% female, 97% amyloid positive). Structural MRI scans were visually assessed and Freesurfer was used to obtain subcortical volumes, cortical thickness and surface area measures. We first assessed univariate associations between MRI measures and amyloid pathology using mixed models. Next, we developed and tested an automated classifier using demographic, cognitive, MRI and APOE ε4 information to predict amyloid pathology. A support vector machine (SVM) with nested 10-fold cross-validation was applied to identify a set of markers best discriminating between amyloid positive and amyloid negative subjects. RESULTS: In univariate associations, amyloid pathology was associated with lower subcortical volumes and thinner cortex in AD-signature regions in CN and MCI. The multi-variable SVM classifier provided an area under the curve (AUC) of 0.81 ± 0.07 in MCI and an AUC of 0.74 ± 0.08 in CN. In CN, selected features for the classifier included APOE ε4, age, memory scores and several MRI measures such as hippocampus, amygdala and accumbens volumes and cortical thickness in temporal and parahippocampal regions. In MCI, the classifier including demographic and APOE ε4 information did not improve after additionally adding imaging measures. CONCLUSIONS: Amyloid pathology is associated with changes in structural MRI measures in CN and MCI. An automated classifier based on clinical, imaging and APOE ε4 data can identify the presence of amyloid pathology with a moderate level of accuracy. These results could be used in clinical trials to pre-screen subjects for anti-amyloid therapies