Journal Article Research Support, Non-U.S. Gov't Epublish 1 citation
PubMed: 38057740 DOI: 10.1186/s12882-023-03407-8
Kidney shape statistical analysis: associations with disease and anthropometric factors.
BMC Nephrol 24(1):362
Authors: Marjola Thanaj, Nicolas Basty, Madeleine Cule, Elena P Sorokin, Brandon Whitcher, Ramprakash Srinivasan, Rachel Lennon, Jimmy D Bell, E Louise Thomas
Affiliations
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK.
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK.
- Calico Life Sciences LLC, South San Francisco, CA, USA.
- Wellcome Centre for Cell-Matrix Research, Division of Cell-Matrix Biology and Regenerative Medicine, School of Biological Sciences, Faculty of Biology Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK.
- Department of Paediatric Nephrology, Royal Manchester Children's Hospital, Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK.
Abstract
BACKGROUND
Organ measurements derived from magnetic resonance imaging (MRI) have the potential to enhance our understanding of the precise phenotypic variations underlying many clinical conditions.
METHODS
We applied morphometric methods to study the kidneys by constructing surface meshes from kidney segmentations from abdominal MRI data in 38,868 participants in the UK Biobank. Using mesh-based analysis techniques based on statistical parametric maps (SPMs), we were able to detect variations in specific regions of the kidney and associate those with anthropometric traits as well as disease states including chronic kidney disease (CKD), type-2 diabetes (T2D), and hypertension. Statistical shape analysis (SSA) based on principal component analysis was also used within the disease population and the principal component scores were used to assess the risk of disease events.
RESULTS
We show that CKD, T2D and hypertension were associated with kidney shape. Age was associated with kidney shape consistently across disease groups. Body mass index (BMI) and waist-to-hip ratio (WHR) were also associated with kidney shape for the participants with T2D. Using SSA, we were able to capture kidney shape variations, relative to size, angle, straightness, width, length, and thickness of the kidneys, within disease populations. We identified significant associations between both left and right kidney length and width and incidence of CKD (hazard ratio (HR): 0.74, 95% CI: 0.61-0.90, p < 0.05, in the left kidney; HR: 0.76, 95% CI: 0.63-0.92, p < 0.05, in the right kidney) and hypertension (HR: 1.16, 95% CI: 1.03-1.29, p < 0.05, in the left kidney; HR: 0.87, 95% CI: 0.79-0.96, p < 0.05, in the right kidney).
CONCLUSIONS
The results suggest that shape-based analysis of the kidneys can augment studies aiming at the better categorisation of pathologies associated with chronic kidney conditions.
MeSH
Hypertension, Kidney Diseases, Waist-Hip Ratio