Daily Archives: 16 April 2024

Sex-specific blood biomarkers associated with memory alterations in middle-aged adults: The Framingham Heart Study

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Dementia is a collection of symptoms characterized by a deterioration in cognitive functions such as memory, language, reasoning, and the ability to perform daily activities. This condition can be caused by various factors, including Alzheimer’s disease, vascular complications, injuries to the brain due to trauma, and other health-related issues. Although advancing age is considered the primary risk factor, evidence suggests that women are at a higher risk of developing dementia. While cognitive changes with ageing are observed in both sexes, significant differences in the biological processes contributing to cognitive decline are seen between men and women.

Recent research conducted by the Boston University Chobanian & Avedisian School of Medicine and School of Public Health has uncovered specific blood biomarkers – notably lower levels of adiponectin (a hormone secreted by adipose tissue) and elevated fasting blood glucose levels – that are associated with more rapid memory decline in middle-aged individuals. Two biomarkers, elevated HbA1c and plasma insulin levels, have been found to correlate with quicker memory deterioration over time in women, but this association was not observed in men.

The disparity in dementia risk between genders, besides being attributed to differences in life expectancy, may also be linked to a variety of factors, including socio-economic risk elements, inflammation, reproductive indicators, and levels of the amyloid β protein42 (Aβ42). Hence, identifying blood biomarkers with varying connections to cognitive changes among men and women is essential for a deeper understanding of dementia’s root causes and for devising effective preventive measures, as pointed out by Huitong Ding, PhD, a Postdoctoral Associate at the Boston University Chobanian & Avedisian School of Medicine.

Leveraging data from the Framingham Heart Study (FHS), the team examined numerous blood markers and their relation to memory and cognitive shifts in 793 middle-aged participants, employing various statistical techniques for their analysis. In a forward-looking cohort study, this study marks the inaugural sex-specific examination of blood biomarkers and their association with memory alterations during midlife.

The research findings underscore a pressing need in the field of dementia prevention. It is crucial to shift our focus to cardio-metabolic risks and to authenticate biomarkers specific to midlife. These biomarkers could expedite the creation of primary prevention strategies, a point emphasized by Chunyu Liu, PhD, a professor of biostatistics at the Boston University School of Public Health and co-corresponding author. The study aims to pioneer the development of precise preventive strategies targeting cardio-metabolic risks associated with memory changes in middle age. By doing so, they aim to avert cognitive decline, thereby enhancing individuals’ health and quality of life throughout their lifespan.

More information: Huitong Ding et al, Sex-specific blood biomarkers linked to memory changes in middle-aged adults: The Framingham Heart Study, Alzheimer s & Dementia. DOI: 10.1002/dad2.12569

Journal information: Alzheimer s & Dementia Provided by Boston University School of Medicine

Do scientists react more swiftly than Google Trends in addressing COVID-19 topics? An innovative method for analysing textual big data

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In a groundbreaking development, a recent publication in Health Data Science, a partner journal of Science, introduces a sophisticated analytical framework. This framework, designed to navigate the extensive textual realm associated with COVID-19, incorporates keywords derived from Google Trends and abstracts from the WHO COVID-19 database. It offers a unique and detailed comprehension of the dynamic discourse surrounding the pandemic.

Throughout the crisis, research has been pivotal in shaping effective policy. However, tools like Google Trends frequently need to pay more attention to the intricate details that scholarly research captures. By comparing Google Trends data with academic articles, the study sheds light on the scope and intensity of scientific discourse on COVID-19 topics about public interest.

Benson Shu Yan Lam, an Associate Professor at The Hang Seng University of Hong Kong, emphasizes the study’s aim to examine the promptness and interconnectedness of these information sources, determining whether Google Trends can effectively indicate emerging public concerns or if scholarly discussions offer more immediate and thorough insights.

Amanda Man Ying Chu, an Assistant Professor at The Education University of Hong Kong, made a notable discovery: the academic community has precedence in tackling COVID-19 issues. Academic abstracts initiated discussions on these topics before they became prominent in Google Trends searches, providing deeper insights invaluable for policy development.

The research introduces the Coherent Topic Clustering (CTC) technique, a novel text-mining method that efficiently categorizes significant phrases from extensive research abstracts. This technique surpasses BERTopic, a modern deep-learning model, in identifying relevant themes.

Looking ahead, Mike Ka Pui So, a Professor at The Hong Kong University of Science and Technology, envisions a promising future for this analytical framework. He anticipates extending its application beyond health science to include financial news analysis. This potential expansion underscores the framework’s ability to integrate qualitative and quantitative insights, marking a significant advancement in the field of financial analysis.

Reflecting on the study’s broader implications, Professor So discusses the potential for combining various data types and integrating textual analysis with traditional numerical data sets to enhance financial analytics. This approach illustrates the research’s interdisciplinary possibilities, offering new pathways for comprehensive analysis across different fields.

The study’s findings reveal that research abstracts addressed the majority of COVID-19 topics before Google Trends highlighted them and offered a more comprehensive examination of these issues. That could significantly aid policymakers in recognizing the central issues related to COVID-19 and enable them to respond more promptly. Furthermore, the clustering technique more accurately captures the principal themes of the abstracts compared to a recent sophisticated deep learning-based approach to topic modelling. The academic community engages with COVID-19 topics more rapidly than Google Trends.

More information: Benson Shu Yan Lam et al, Do Scholars Respond Faster Than Google Trends in Discussing COVID-19 Issues? An Approach to Textual Big Data, Health Data Science. DOI: 10.34133/hds.0116

Journal information: Health Data Science