Personalized breast cancer onset prediction from lifestyle and health history information
This article proposes a method for predicting when a woman will develop breast cancer (Bca) based on health and lifestyle history using data from 18,288 women in Alberta’s Tomorrow Project. Their approach produced seven actionable lifestyle features that a woman can modify to show how the model can predict the effects of such changes. This method can be used to identify interventions for those with a greater likelihood of developing BCa.
Dietary Intake and the Neighbourhood Environment in the BC Generations Project
This study examined how neighbourhood factors like access to amenities and social relationships, as well as greenness and walkability, can influence fruit and vegetable intake. ~28,000 participants from the BC Generations Project were involved. Those living in neighbourhoods with greater material and social deprivation were less likely to meet recommendations for fruit and vegetable consumption, while those living in neighbourhoods with higher greenness were more likely to meet recommendations. These findings highlight how multiple neighbourhood characteristics can impact dietary intake.
Patterns and predictors of adherence to breast cancer screening recommendations in Alberta’s Tomorrow Project
This study examined screening patterns in almost 5,000 women in Alberta’s Tomorrow Project. Most participants were up-to-date with screening at enrollment and follow-up, but 21.6% were not up-to-date at follow-up, and 3.2% had never participated. Having a family doctor was the strongest predictor of regular screening, while current smokers were less likely to be regular screeners. The study highlights the importance of promoting awareness of screening recommendations and the role of family doctors in encouraging screening.
The role of ultra-processed food consumption and depression on type 2 diabetes incidence: a prospective community study in Quebec, Canada
Researchers analyzed the association between depression and ultra-processed food (UPF) consumption as risk factors for developing type 2 diabetes (T2D) using baseline data (2009-2010) from 3,880 CARTaGENE participants. Participants with high depressive symptoms and high UPF consumption were at the highest risk for T2D. The study suggests that early management and monitoring of both risk factors could be essential for diabetes prevention.
Global Biobank Meta-analysis Initiative: Powering genetic discovery across human disease
The Global Biobank Meta-analysis Initiative is a collaborative network of 23 biobanks, representing more than 2.2M consented participants with genetic data linked to electronic health records. This collaborative effort will improve genome-wide association studies’ power for diseases, benefit understudied diseases, and improve risk prediction.
Association of dairy consumption patterns with the incidence of type 2 diabetes: Findings from Alberta’s Tomorrow Project
Researchers investigated the relationship between dairy consumption and the risk of developing type 2 diabetes (T2D) with data from Alberta’s Tomorrow Project (ATP). 15,016 women and 8,615 men completed a food-frequency questionnaire and were followed up over time to determine T2D incidence. They found that higher consumption of whole milk, regular cheese, and non-fat milk was associated with decreased risk of incident T2D only in men. The study suggests that combining different dairy products might be good for men’s health.
Population-Based Recalibration of the Framingham Risk Score and Pooled Cohort Equations
The Framingham Risk Score (FRS) and Pooled Cohort Equations (PCEs) overestimate risk in many contemporary cohorts. This study sought to determine if the recalibration of these scores using contemporary population-level data improves risk stratification for statin therapy.
Prediction of Cardiovascular Events by Pulse Waveform Parameters: Analysis of CARTaGENE
Researchers conducted the largest study to date evaluating non-invasive pulse waveform parameters’ association with cardiovascular events. By adding two waveform parameters to the existing atherosclerotic cardiovascular disease score, they improved cardiovascular prediction and reclassified up to 5.7% of patients in another risk category.
Harnessing the power of data linkage to enrich the cancer research ecosystem in Canada
This abstract discusses a project aimed at linking cancer registry and administrative health data to Canada’s largest population health study, the Canadian Partnership for Tomorrow’s Health (CanPath). The project seeks to enrich the cancer research ecosystem in Canada by providing researchers with a comprehensive dataset that includes genetics, environment, lifestyle, and behaviour data. The linked data will be made available through a cloud-based solution called the CanPath Data Safe Haven, which is accessible to researchers through secure access. The project will address concerns related to the accessibility of cancer data in Canada, bring more value to existing data, and support an enhanced understanding of the impacts of cancer on marginalized populations.
Cohort Profile: The Ontario Health Study (OHS)
OHS’s cohort profile outlines its research platform’s history and value for the broader scientific community. OHS follows 225,000 over their lifetime, actively and passively, making de-identified genomic, environmental, lifestyle, and electronic health data available to cancer and chronic disease researchers.