Stroke and dementia risk: A systematic review and meta-analysis
Kuźma E, Lourida I, Moore SF, Levine DA, Ukoumunne OC, Llewellyn DJ. Stroke and dementia risk: A systematic review and meta-analysis. Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association. . https://doi.org/10.1016/j.jalz.2018.06.3061.
Stroke is an established risk factor for all-cause dementia, though meta-analyses are needed to quantify this risk.
We searched Medline, PsycINFO, and Embase for studies assessing prevalent or incident stroke versus a no-stroke comparison group and the risk of all-cause dementia. Random effects meta-analysis was used to pool adjusted estimates across studies, and meta-regression was used to investigate potential effect modifiers.
We identified 36 studies of prevalent stroke (1.9 million participants) and 12 studies of incident stroke (1.3 million participants). For prevalent stroke, the pooled hazard ratio for all-cause dementia was 1.69 (95% confidence interval: 1.49–1.92; P < .00001; I2 = 87%). For incident stroke, the pooled risk ratio was 2.18 (95% confidence interval: 1.90–2.50; P < .00001; I2 = 88%). Study characteristics did not modify these associations, with the exception of sex which explained 50.2% of between-study heterogeneity for prevalent stroke.
Stroke is a strong, independent, and potentially modifiable risk factor for all-cause dementia.
Stroke and dementia risk: A systematic review and meta-analysis
Health management and pattern analysis of daily living activities of people with dementia using in-home sensors and machine learning techniques
Enshaeifar S, Zoha A, Markides A, et al. Health management and pattern analysis of daily living activities of people with dementia using in-home sensors and machine learning techniques. PLOS ONE. 2018;13(5):e0195605. https://doi.org/10.1371/journal.pone.0195605.
The number of people diagnosed with dementia is expected to rise in the coming years. Given that there is currently no definite cure for dementia and the cost of care for this condition soars dramatically, slowing the decline and maintaining independent living are important goals for supporting people with dementia. This paper discusses a study that is called Technology Integrated Health Management (TIHM). TIHM is a technology assisted monitoring system that uses Internet of Things (IoT) enabled solutions for continuous monitoring of people with dementia in their own homes. We have developed machine learning algorithms to analyse the correlation between environmental data collected by IoT technologies in TIHM in order to monitor and facilitate the physical well-being of people with dementia. The algorithms are developed with different temporal granularity to process the data for long-term and short-term analysis. We extract higher-level activity patterns which are then used to detect any change in patients’ routines. We have also developed a hierarchical information fusion approach for detecting agitation, irritability and aggression. We have conducted evaluations using sensory data collected from homes of people with dementia. The proposed techniques are able to recognise agitation and unusual patterns with an accuracy of up to 80%.
A Comparison of the Prevalence of Dementia in the United States in 2000 and 2012
Importance The aging of the US population is expected to lead to a large increase in the number of adults with dementia, but some recent studies in the United States and other high-income countries suggest that the age-specific risk of dementia may have declined over the past 25 years. Clarifying current and future population trends in dementia prevalence and risk has important implications for patients, families, and government programs.
Objective To compare the prevalence of dementia in the United States in 2000 and 2012.
Design, Setting, and Participants We used data from the Health and Retirement Study (HRS), a nationally representative, population-based longitudinal survey of individuals in the United States 65 years or older from the 2000 (n = 10 546) and 2012 (n = 10 511) waves of the HRS.
Main Outcomes and Measures Dementia was identified in each year using HRS cognitive measures and validated methods for classifying self-respondents, as well as those represented by a proxy. Logistic regression was used to identify socioeconomic and health variables associated with change in dementia prevalence between 2000 and 2012.
Results The study cohorts had an average age of 75.0 years (95% CI, 74.8-75.2 years) in 2000 and 74.8 years (95% CI, 74.5-75.1 years) in 2012 (P = .24); 58.4% (95% CI, 57.3%-59.4%) of the 2000 cohort was female compared with 56.3% (95% CI, 55.5%-57.0%) of the 2012 cohort (P < .001). Dementia prevalence among those 65 years or older decreased from 11.6% (95% CI, 10.7%-12.7%) in 2000 to 8.8% (95% CI, 8.2%-9.4%) (8.6% with age- and sex-standardization) in 2012 (P < .001). More years of education was associated with a lower risk for dementia, and average years of education increased significantly (from 11.8 years [95% CI, 11.6-11.9 years] to 12.7 years [95% CI, 12.6-12.9 years]; P < .001) between 2000 and 2012. The decline in dementia prevalence occurred even though there was a significant age- and sex-adjusted increase between years in the cardiovascular risk profile (eg, prevalence of hypertension, diabetes, and obesity) among older US adults.
Conclusions and Relevance The prevalence of dementia in the United States declined significantly between 2000 and 2012. An increase in educational attainment was associated with some of the decline in dementia prevalence, but the full set of social, behavioral, and medical factors contributing to the decline is still uncertain. Continued monitoring of trends in dementia incidence and prevalence will be important for better gauging the full future societal impact of dementia as the number of older adults increases in the decades ahead.
Schizophrenia risk from complex variation of complement component 4
Schizophrenia is a heritable brain illness with unknown pathogenic mechanisms. Schizophrenia’s strongest genetic association at a population level involves variation in the major histocompatibility complex (MHC) locus, but the genes and molecular mechanisms accounting for this have been challenging to identify. Here we show that this association arises in part from many structurally diverse alleles of the complement component 4 (C4) genes. We found that these alleles generated widely varying levels of C4A and C4B expression in the brain, with each common C4 allele associating with schizophrenia in proportion to its tendency to generate greater expression of C4A. Human C4 protein localized to neuronal synapses, dendrites, axons, and cell bodies. In mice, C4 mediated synapse elimination during postnatal development. These results implicate excessive complement activity in the development of schizophrenia and may help explain the reduced numbers of synapses in the brains of individuals with schizophrenia.
Nature (2016), doi:10.1038/nature16549, Published online 27 January 2016
Effects of aging on circadian patterns of gene expression in the human prefrontal cortex
With aging, significant changes in circadian rhythms occur, including a shift in phase toward a “morning” chronotype and a loss of rhythmicity in circulating hormones. However, the effects of aging on molecular rhythms in the human brain have remained elusive. Here, we used a previously described time-of-death analysis to identify transcripts throughout the genome that have a significant circadian rhythm in expression in the human prefrontal cortex [Brodmann’s area 11 (BA11) and BA47]. Expression levels were determined by microarray analysis in 146 individuals. Rhythmicity in expression was found in ∼10% of detected transcripts (P < 0.05). Using a metaanalysis across the two brain areas, we identified a core set of 235 genes (q < 0.05) with significant circadian rhythms of expression. These 235 genes showed 92% concordance in the phase of expression between the two areas. In addition to the canonical core circadian genes, a number of other genes were found to exhibit rhythmic expression in the brain. Notably, we identified more than 1,000 genes (1,186 in BA11; 1,591 in BA47) that exhibited age-dependent rhythmicity or alterations in rhythmicity patterns with aging. Interestingly, a set of transcripts gained rhythmicity in older individuals, which may represent a compensatory mechanism due to a loss of canonical clock function. Thus, we confirm that rhythmic gene expression can be reliably measured in human brain and identified for the first time (to our knowledge) significant changes in molecular rhythms with aging that may contribute to altered cognition, sleep, and mood in later life.
Cho-Yi Chen, Proceedings of the National Academy of Sciences 2015 of the USA ; published ahead of print December 22, 2015, doi:10.1073/pnas.1508249112.