Machine learning derived abdominal aortic calcification is associated with physical frailty in community-dwelling adults: the UK Biobank Imaging Study - Abadi K. Gebre; Marion Mundt; Elsa Dent; James Webster; Afsah Saleem; Syed Zulqarnain Gilani; Yuandan Zhang; Cassandra Smith; Parminder Raina; John P. Kemp; William D. Leslie; John T. Schousboe; et al. (GeroScience, 2026)

Automated abdominal aortic calcification scoring from vertebral fracture assessment images and fall-associated hospitalisations: the Manitoba Bone Mineral Density Registry - Marc Sim; Abadi K. Gebre; Jack Dalla Via; Siobhan Reid; Mohammad Jafari Jozani; Douglas Kimelman; Barret A. Monchka; Syed Zulqarnain Gilani; Zaid Ilyas; Cassandra Smith; David Suter; John T. Schousboe; et al. (GeroScience, 2025)
Abdominal aortic calcification (AAC), a subclinical measure of cardiovascular disease (CVD) that can be assessed on vertebral fracture assessment (VFA) images during osteoporosis screening, is reported to be a falls risk factor. A limitation to incorporating AAC clinically is that its scoring requires trained experts and is time-consuming. We examined if our machine learning (ML) algorithm for AAC (ML-AAC24) is associated with a higher fall-associated hospitalisation risk in the Manitoba Bone Mineral Density (BMD) Registry. A total of 8565 individuals (94.0% female, age 75.7 ± 6.8 years) who had a BMD and VFA image from DXA between February 2010 and December 2017 were included. ML-AAC24 was categorised based on established categories (ML-AAC24 = low < 2; moderate 2 to < 6; high ≥ 6). Cox proportional hazards models assessed the relationship between ML-AAC24 categories and incident fall-associated hospitalisations obtained from linked health records (mean ± SD follow-up, 3.9 ± 2.2 years). Individuals with moderate (9.6%) and high ML-AAC24 (11.7%) had a greater proportion of fall-associated hospitalisations, compared to those with low ML-AAC24 (6.0%). In age and sex-adjusted models, compared to low ML-AAC24, moderate (HR 1.49, 95% CI 1.24–1.79) and high ML-AAC24 (HR 1.89, 95% CI 1.56–2.28) were associated with greater hazards for a fall-associated hospitalisation. Results were comparable (HR 1.37, 95% CI 1.13–1.65 and HR 1.60, 95% CI 1.31–1.95, respectively) after multivariable adjustment, including prior falls and CVD, as well as medication use. Integrating ML-AAC24 into bone density machine software to identify high risk individuals would opportunistically provide important information on fall and cardiovascular disease risk to clinicians for evaluation and intervention.

Nitrate: The Dr. Jekyll and Mr. Hyde of human health? - Catherine P. Bondonno; Liezhou Zhong; Nicola P. Bondonno; Marc Sim; Lauren C. Blekkenhorst; Alex Liu; Anjana Rajendra; Pratik Pokharel; Dorit W. Erichsen; Oliver Neubauer; Kevin D. Croft; Jonathan M. Hodgson (Trends in Food Science & Technology, 2023)

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity - Chiara Fallerini; Nicola Picchiotti; Margherita Baldassarri; Kristina Zguro; Sergio Daga; Francesca Fava; Elisa Benetti; Sara Amitrano; Mirella Bruttini; Maria Palmieri; Susanna Croci; Mirjam Lista; et al. (Human Genetics, 2022)
The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management.

Gender differences in respiratory health outcomes among farming cohorts around the globe: findings from the AGRICOH consortium - Jonathan Fix; Isabella Annesi-Maesano; Isabelle Baldi; Mathilde Boulanger; Soo Cheng; Sandra Cortes; Jean- Charles Dalphin; Mohamed Aqiel Dalvie; Bruno Degano; Jeroen Douwes; Wijnand Eduard; Grethe Elholm; et al. (Journal of Agromedicine, 2021)

Iron considerations for the athlete: a narrative review - Marc Sim; Laura A. Garvican-Lewis; Gregory R. Cox; Andrew Govus; Alannah K. A. McKay; Trent Stellingwerff; Peter Peeling (European Journal of Applied Physiology, 2019)

Seven days of high carbohydrate ingestion does not attenuate post-exercise IL-6 and hepcidin levels - Claire E. Badenhorst; Brian Dawson; Gregory R. Cox; Marc Sim; Coby M. Laarakkers; Dorine W. Swinkels; Peter Peeling (European Journal of Applied Physiology, 2016)

Influence of post-exercise hypoxic exposure on hepcidin response in athletes - Claire E. Badenhorst; Brian Dawson; Carmel Goodman; Marc Sim; Gregory R. Cox; Christopher J. Gore; Harold Tjalsma; Dorine W. Swinkels; Peter Peeling (European Journal of Applied Physiology, 2014)

The effects of carbohydrate ingestion during endurance running on post-exercise inflammation and hepcidin levels - Marc Sim; Brian Dawson; Grant Landers; Erwin T. Wiegerinck; Dorine W. Swinkels; Mary-Anne Townsend; Debbie Trinder; Peter Peeling (European Journal of Applied Physiology, 2012)