Publications

These publications are examples of research made possible with data from CanPath and its regional cohorts.

2023

Non-fasting lipids and cardiovascular disease in those with and without diabetes in Alberta’s Tomorrow Project: A prospective cohort study

Authors: Olivia R. Weaver, Ming Ye, Jennifer E. Vena, Dean T. Eurich, Spencer D. Proctor

This study’s objective was to assess the relationship of non-fasting remnant cholesterol (RC) with cardiovascular disease (CVD) in those with and without diabetes using data from 13,631 Alberta’s Tomorrow Project participants. Researchers found that elevated non-fasting RC was associated with increased CVD risk in middle and older-aged adults without diabetes.

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2023

Mental health service use and associated predisposing, enabling and need factors in community living adults and older adults across Canada

Authors: Helen-Maria Vasiliadis, Jessica Spagnolo, Marie-Josée Fleury, Jean-Philippe Gouin, Pasquale Roberge, Mary Bartram, Sébastien Grenier, Grace Shen-Tu, Jennifer E. Vena, JianLi Wang

The authors utilized data from the CanPath COVID-19 health survey (May to December 2020) to conduct multivariate logistic regression analysis to determine the association between mental health service use (MHSU) and predisposing, enabling, and need factors — derived from Andersen’s model of healthcare-seeking behaviour — among five regional cohorts. Among the 45,542 adults in the study population, 6.3% of respondents reported MHSU and need factors were consistently associated with MHSU.

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2023

Physical activity-induced alterations of the gut microbiota are BMI dependent

Authors: Shrushti Shah, Chunlong Mu, Shirin Moossavi, Grace Shen-Tu, Kristina Schlicht, Nathalie Rohmann, Corinna Geisler, Matthias Laudes, Andre Franke, Thomas Züllig, Harald Köfeler, Jane Shearer

Researchers assessed physical activity and hand-grip strength’s role in gut microbiome composition in middle-aged adults with normal and overweight body mass index. Data from 443 participants from Alberta’s Tomorrow Project suggest that BMI plays a significant role in modelling PA-induced changes in gut microbiota.

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2023

Impact of Comorbidity on Hospitalization and Emergency Room Visits in Adults With Diabetes: A Longitudinal Study of Alberta’s Tomorrow Project

Authors: Ming Ye, Jennifer E Vena, Jeffrey A Johnson, Grace Shen-Tu, Dean T Eurich

Using data from Alberta’s Tomorrow Project, Ye et al. investigated the impact of comorbidities on hospitalization and emergency room visits in people with diabetes. Over the 5-year study period, the authors observed a significant association between the number of comorbidities and increased healthcare utilization among the 2,110 cases in the study population.

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2023

Associations between neighborhood walkability and walking following residential relocation: Findings from Alberta’s Tomorrow Project

Authors: Gavin R McCormack, Mohammad Javad Koohsari, Jennifer E Vena, Koichiro Oka, Tomoki Nakaya, Jonathan Chapman, Ryan Martinson, Graham Matsalla

This study aimed to estimate whether changes in neighbourhood walkability resulting from residential relocation were associated with leisure, transportation, and total walking levels. Using data from 5,977 urban adults (non-movers, movers to less walkability, and movers to more walkability), researchers found that time spent walking at follow-up was lower among those who moved to less walkable neighbourhoods, suggesting that relocating to less walkable neighbourhoods could negatively affect health.

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2023

A comparison of machine learning algorithms and traditional regression-based statistical modeling for predicting hypertension incidence in a Canadian population

Authors: Mohammad Ziaul Islam Chowdhury, Alexander A Leung, Robin L Walker, Khokan C Sikdar, Maeve O'Beirne, Hude Quan, Tanvir C Turin

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.

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2022

Personalized breast cancer onset prediction from lifestyle and health history information

Authors: Shi-Ang Qi, Neeraj Kumar, Jian-Yi Xu, Jaykumar Patel, Sambasivarao Damaraju, Grace Shen-Tu, Russell Greiner

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.

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2022

Patterns and predictors of adherence to breast cancer screening recommendations in Alberta’s Tomorrow Project

Authors: Olivia K Loewen, Navjot Sandila, Grace Shen-Tu, Jennifer E Vena, Huiming Yang, Kara Patterson, Jian-Yi Xu

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.

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2022

Association of dairy consumption patterns with the incidence of type 2 diabetes: Findings from Alberta’s Tomorrow Project

Authors: Emad Yuzbashian, Mohammadreza Pakseresht, Jennifer Vena, Catherine B Chan

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.

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2022

Development and validation of a hypertension risk prediction model and construction of a risk score in a Canadian population

Authors: Mohammad Ziaul Islam Chowdhury, Alexander A Leung, Khokan C Sikdar, Maeve O'Beirne, Hude Quan, Tanvir C Turin

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.

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