NDLI: Kidney shape statistical analysis: associations with disease and anthropometric factors

Content Provider	Springer Nature : BioMed Central
Author	Thanaj, Marjola Basty, Nicolas Cule, Madeleine Sorokin, Elena P. Whitcher, Brandon Srinivasan, Ramprakash Lennon, Rachel Bell, Jimmy D. Thomas, E. Louise
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.
Related Links	https://bmcnephrol.biomedcentral.com/counter/pdf/10.1186/s12882-023-03407-8.pdf
Ending Page	16
Page Count	16
Starting Page	1
File Format	HTM / HTML
ISSN	14712369
DOI	10.1186/s12882-023-03407-8
Journal	BMC Nephrology
Issue Number	1
Volume Number	24
Language	English
Publisher	BioMed Central
Publisher Date	2023-12-06
Access Restriction	Open
Subject Keyword	Nephrology Internal Medicine Magnetic resonance imaging Kidney volume 3D mesh-derived phenotype Statistical parametric maps Statistical shape analysis Chronic kidney disease Type-2 diabetes Hypertension
Content Type	Text
Resource Type	Article
Subject	Nephrology
Journal Impact Factor	2.2/2023
5-Year Journal Impact Factor	2.6/2023

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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