Depressive symptoms and cognitive decline in community-dwelling older adults

OBJECTIVES: To examine the temporal association between depressive symptoms and cognitive functioning and estimate the effect measure modification of the apolipoprotein E (APOE) ε4 allele on this relationship. DESIGN: Prospective cohort study. SETTING: General community. PARTICIPANTS: Population-based sample of 598 cognitively intact older adults aged 60 and older, with re-assessments after 3 (N=479) and 6 years (N=412). MEASUREMENTS: Depressive symptoms (Symptom Checklist) and neurocognitive functioning (memory, Visual Verbal Learning Test; attention, Stroop Color-Word Test; processing speed, Letter Digit Substitution Test; general cognition, Mini-Mental State Examination). Longitudinal associations were assessed using linear mixed models. The risk for cognitive impairment, no dementia (CIND) was examined using logistic regression. RESULTS: Adjusting for age, sex, education, and baseline cognition, the rate of change in memory z-scores was 0.00, -0.11, -0.20, and -0.37 for those in the lowest (reference group), second, third, and highest depressive symptom quartiles at baseline, respectively (P<.001 for highest vs lowest quartile). The odds ratios for developing CIND with amnestic features were 1.00, 0.87, 0.69, and 2.98 for the four severity groups (P=.05 for highest vs lowest quartile). Associations were strongest for those with persistent depressive symptoms, defined as high depressive symptoms at baseline and at least one follow-up visit. Results were similar for processing speed and global cognitive function but were not as strong for attention. No APOE interaction was observed. CONCLUSION: Depression and APOE act independently to increase the risk for cognitive decline and may provide targets for prevention and early treatment. © 2010, Copyright the Authors. Journal compilation © 2010, The American Geriatrics Society

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical science. © The Author(s) 2019. Published by Oxford University Press