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3. Reporting guidelines for precision medicine research of clinical relevance: the BePRECISE checklist

Author

Lim, Siew S., Semnani-Azad, Zhila, Morieri, Mario L., Ng, Ashley H., Ahmad, Abrar, Fitipaldi, Hugo, Boyle, Jacqueline, Collin, Christian, Dennis, John M., Langenberg, Claudia, Loos, Ruth J. F., Morrison, Melinda, Ramsay, Michele, Sanyal, Arun J., Sattar, Naveed, Hivert, Marie-France, Gomez, Maria F., Merino, Jordi, Tobias, Deirdre K., Trenell, Michael I., Rich, Stephen S., Sargent, Jennifer L., and Franks, Paul W.

Abstract

Precision medicine should aspire to reduce error and improve accuracy in medical and health recommendations by comparison with contemporary practice, while maintaining safety and cost-effectiveness. The etiology, clinical manifestation and prognosis of diseases such as obesity, diabetes, cardiovascular disease, kidney disease and fatty liver disease are heterogeneous. Without standardized reporting, this heterogeneity, combined with the diversity of research tools used in precision medicine studies, makes comparisons across studies and implementation of the findings challenging. Specific recommendations for reporting precision medicine research do not currently exist. The BePRECISE (Better Precision-data Reporting of Evidence from Clinical Intervention Studies & Epidemiology) consortium, comprising 23 experts in precision medicine, cardiometabolic diseases, statistics, editorial and lived experience, conducted a scoping review and participated in a modified Delphi and nominal group technique process to develop guidelines for reporting precision medicine research. The BePRECISE checklist comprises 23 items organized into 5 sections that align with typical sections of a scientific publication. A specific section about health equity serves to encourage precision medicine research to be inclusive of individuals and communities that are traditionally under-represented in clinical research and/or underserved by health systems. Adoption of BePRECISE by investigators, reviewers and editors will facilitate and accelerate equitable clinical implementation of precision medicine.

Published
2024
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22. Defining cardiac adaptations and safety of endurance training in patients with m.3243A>G-related mitochondrial disease

Author

Bates, Matthew G.D., Newman, Jane H., Jakovljevic, Djordje G., Hollingsworth, Kieren G., Alston, Charlotte L., Zalewski, Pawel, Klawe, Jacek J., Blamire, Andrew M., MacGowan, Guy A., Keavney, Bernard D., Bourke, John P., Schaefer, Andrew, McFarland, Robert, Newton, Julia L., Turnbull, Douglass M., Taylor, Robert W., Trenell, Michael I., and Gorman, Gráinne S.

Published
2013
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26. Unsupervised high-intensity interval training improves glycaemic control but not cardiovascular autonomic function in type 2 diabetes patients: A randomised controlled trial

Author

Cassidy, Sophie, Vaidya, Vivek, Houghton, David, Zalewski, Pawel, Seferovic, Jelena P, Hallsworth, Kate, MacGowan, Guy A, Trenell, Michael I, and Jakovljevic, Djordje G

Subjects
Blood Glucose, Glycated Hemoglobin, Male, Time Factors, type 2 diabetes mellitus, Blood Pressure, Original Articles, Baroreflex, High-Intensity Interval Training, Middle Aged, Autonomic Nervous System, Cardiovascular System, cardiovascular diseases, Treatment Outcome, Diabetes Mellitus, Type 2, Diabetic Neuropathies, England, Heart Rate, Humans, Female, Exercise, Biomarkers
Abstract

Background: This is the first randomised controlled trial to assess the impact of unsupervised high-intensity interval training on cardiovascular autonomic function in adults with type 2 diabetes. Methods: A total of 22 individuals with type 2 diabetes (age 60 ± 2 years, 17 males) lay in a supine position for 20 min for evaluation of cardiovascular autonomic function, which included (1) time domain measures of heart rate variability, (2) frequency domain measures of heart rate variability and blood pressure variability and (3) baroreflex receptor sensitivity. Participants were randomised into 12 weeks of high-intensity interval training (3 sessions/week) or standard care control group. Results: After 12 weeks, the between-group change in HbA1c (%) was significant (high-intensity interval training: 7.13 ± 0.31 to 6.87 ± 0.29 vs Control: 7.18 ± 0.17 to 7.36 ± 0.21, p = 0.03). There were no significant changes in measures of heart rate variability; R-R interval (ms) (high-intensity interval training: 954 ± 49 to 973 ± 53 vs Control: 920 ± 6 to 930 ± 32, p = 0.672), low frequency/high frequency (high-intensity interval training: 0.90 ± 0.21 to 0.73 ± 0.07 vs Control: 1.20 ± 0.29 to 1.00 ± 0.17, p = 0.203), or blood pressure variability; systolic blood pressure low frequency/high frequency (high-intensity interval training: 0.86 ± 0.21 to 0.73 ± 0.10 vs Control: 1.06 ± 0.26 to 0.91 ± 0.14, p = 0.169). At baseline, HbA1c was negatively correlated with baroreflex receptor sensitivity (r = –0.592, p

Published
2018

45. Low physical activity, high television viewing and poor sleep duration cluster in overweight and obese adults; a cross-sectional study of 398,984 participants from the UK Biobank

Author

Cassidy, Sophie, Chau, Josephine Y., Catt, Michael, Bauman, Adrian, and Trenell, Michael I.

Subjects
Adult, Male, Body Mass Index, Thinness, Surveys and Questionnaires, Odds Ratio, Humans, Obesity, Exercise, Life Style, lcsh:RC620-627, Aged, Biological Specimen Banks, Sedentary, Physical activity, Research, lcsh:Public aspects of medicine, lcsh:RA1-1270, Middle Aged, Overweight, Lifestyle, United Kingdom, lcsh:Nutritional diseases. Deficiency diseases, Cross-Sectional Studies, Logistic Models, Recreation, Female, Television, Sleep
Abstract

Background An unhealthy lifestyle is one of the greatest contributors to obesity. A number of behaviours are linked with obesity, but are often measured separately. The UK Biobank cohort of >500,000 participants allows us to explore these behaviours simultaneously. We therefore aimed to compare physical activity, television (TV) viewing and sleep duration across body mass index (BMI) categories in a large sample of UK adults. Methods UK Biobank participants were recruited and baseline measures were taken between 2007 and 2010 and data analysis was performed in 2015. BMI was measured objectively using trained staff. Self-report questionnaires were used to measure lifestyle behaviours including the international physical activity questionnaire (IPAQ-short form) for physical activity. During data analysis, six groups were defined based on BMI; ‘Underweight’ (n = 2026), ‘Normal weight’ (n = 132,372), ‘Overweight (n = 171,030), ‘Obese I’ (n = 67,903), ‘Obese II’ (n = 18,653) and ‘Obese III’ (n = 7000). The odds of reporting unhealthy lifestyle behaviours (low physical activity, high TV viewing or poor sleep duration) were compared across BMI groups using logistic regression analysis. Results Overweight and obese adults were more likely to report low levels of physical activity (≤967.5 MET.mins/wk) (‘Overweight’-OR [95% CI]: 1.23 [1.20 to 1.26], ‘Obese I’ 1.66 [1.61–1.71], ‘Obese II’ 2.21 [2.12–2.30], and ‘Obese III’ 3.13 [2.95 to 3.23]) compared to ‘Normal weight’ adults. The odds of reporting high TV viewing (3 h/day) was greater in ‘Overweight’ (1.52 [1.48 to 1.55]) and obese adults (‘Obese I’ 2.06 [2.00–2.12], ‘Obese II’ 2.69 [2.58–2.80], ‘Obese III’ 3.26 [3.07 to 3.47]), and poor sleep duration (8 h/night) was higher in ‘Overweight’ (1.09 [1.07 to 1.12]) and obese adults (‘Obese I’ 1.31 [1.27–1.34], ‘Obese II’ 1.50 [1.44–1.56], ‘Obese III’ (1.78 [1.68 to 1.89]) compared to the ‘Normal weight’ group. These lifestyle behaviours were clustered, the odds of reporting simultaneous low physical activity, high TV viewing and poor sleep (unhealthy behavioural phenotype) was higher than reporting these behaviours independently, in overweight and obese groups. ‘Obese III’ adults were almost six times more likely (5.47 [4.96 to 6.05]) to report an unhealthy behavioural phenotype compared to the ‘Normal weight’ group. Conclusions Overweight and obese adults report low levels of physical activity, high TV viewing and poor sleep duration. These behaviours seem to cluster and collectively expose individuals to greater risk of obesity. Multiple lifestyle behaviours should be targeted in future interventions. Electronic supplementary material The online version of this article (doi:10.1186/s12966-017-0514-y) contains supplementary material, which is available to authorized users.

Published
2017

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