Examining the influence of built environment on sleep disruption
Researchers sought to understand if modifying aspects of the built environment improved sleep. Using data from 28,385 BC Generations Project participants, they found that increased light-at-night, air pollution (SO2), and living <100 m from a main roadway were associated with insufficient sleep. Greenness had a positive effect on sleep.
A comparison of machine learning algorithms and traditional regression-based statistical modeling for predicting hypertension incidence in a Canadian population
This study evaluates different machine learning algorithms and compares their predictive performance with conventional models to predict hypertension incidence using data from 18,322 Alberta’s Tomorrow Project participants. The study found little difference in predictive performance between the machine learning algorithms and the conventional Cox PH model. The results suggest that conventional regression-based models can perform similarly to machine learning algorithms with good predictive accuracy in a moderate dataset with a reasonable number of features.
Agreement between self-report and administrative health data on occurrence of non-cancer chronic disease among participants of the BC generations project
Linked self-reported chronic disease history data to a Chronic Disease Registry (CDR) that applied algorithms to administrative health data to ascertain diagnoses of multiple chronic diseases in the Province of British Columbia.
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.