Assessment centre ⏵ Imaging ⏵ Abdominal MRI ⏵ Abdominal composition
Description
This category contains derived data from the abdominal MRI, which were supplied by AMRA® Medical AB (Linköping, Sweden).Image-derived phenotypes relating to liver iron and fat, and abdominal composition have been generated by multiple institutes. The data from these other institutes are held in Category 126 and Category 158. Meaningful differences may exist between data across these categories and therefore, data should not be merged without very careful consideration.
Two highly comparable, but distinct pipelines have been used to generate the derived data in this category. Data for participants with only instance 2 have been generated using one pipeline (pipeline version 1). For those with data at both instance 2 and 3, data from instance 2 have been reanalysed in connection with analysis of the corresponding data from instance 3 to enable the highest possible precision for longitudinal assessment (pipeline version 2). A list of the participants with data at both instance 2 and 3, analysed with pipeline version 2, can currently be found in Field 41000.
The following OpenAccess publications provide details of the methods used to derive data in this category under pipeline version 1:
- Karlsson A, Rosander J, Romu T, et al. Automatic and quantitative assessment of regional muscle volume by multi-atlas segmentation using whole-body water-fat MRI. Journal of magnetic resonance imaging: JMRI. 2014. doi: 10.1002/jmri.24726 PMID: 25111561.
- Borga M, Thomas LE, Romu T, Rosander J, et al. Validation of a fast method for quantification of intra-abdominal and subcutaneous adipose tissue for large scale human studies. NMR in biomedicine. 2015; 28(12):1747-53. doi: 10.1002/nbm.3432 PMID: 26768490
- West J, Dahlqvist Leinhard O, Romu T, et al. (2016) Feasibility of MR-Based Body Composition Analysis in Large Scale Population Studies. PLoS ONE 11(9): e0163332. doi:10.1371/journal.pone.0163332
- Linge J, Borga M, West J et al. (2018) Body Composition Profiling in the UK Biobank Imaging Study. Obesity 26(11). doi: 10.1002/oby.22210
- Borga M, Ahlgren A, Romu T, et al. Reproducibility and repeatability of MRI-based body composition analysis. Magn Reson Med. 2020;84(6):3146-3156. doi: 10.1002/mrm.28360
When using the data for research purposes, the following publications may also be of interest:
- Borga M, West J, Bell JD, Harvey NC, Romu T, Heymsfield SB, Dahlqvist Leinhard O. Advanced body composition assessment: from body mass index to body composition profiling. J Investig Med. 2018 Jun;66(5):1-9. doi: 10.1136/jim-2018-000722. Epub 2018 Mar 25. PMID: 29581385; PMCID: PMC5992366.
- Linge J, Whitcher B, Borga M, Dahlqvist Leinhard O. Sub-phenotyping Metabolic Disorders Using Body Composition: An Individualized, Nonparametric Approach Utilizing Large Data Sets. Obesity (Silver Spring). 2019 Jul;27(7):1190-1199. doi: 10.1002/oby.22510. Epub 2019 May 16. PMID: 31094076; PMCID: PMC6617760.
- Linge J, Heymsfield SB, Dahlqvist Leinhard O. On the Definition of Sarcopenia in the Presence of Aging and Obesity-Initial Results from UK Biobank. J Gerontol A Biol Sci Med Sci. 2020 Jun 18;75(7):1309-1316. doi: 10.1093/gerona/glz229. PMID: 31642894; PMCID: PMC7302181.
- Linge J, Ekstedt M, Dahlqvist Leinhard O. Adverse muscle composition is linked to poor functional performance and metabolic comorbidities in NAFLD. JHEP Rep. 2020 Oct 28;3(1):100197. doi: 10.1016/j.jhepr.2020.100197. PMID: 33598647; PMCID: PMC7868647.
- Linge J, Petersson M, Forsgren MF, Sanyal AJ, Dahlqvist Leinhard O. Adverse muscle composition predicts all-cause mortality in the UK Biobank imaging study. J Cachexia Sarcopenia Muscle. 2021 Dec;12(6):1513-1526. doi: 10.1002/jcsm.12834. Epub 2021 Oct 29. PMID: 34713982; PMCID: PMC8718078.
- Tejani S, McCoy C, Ayers CR, Powell-Wiley TM, Després JP, Linge J, Leinhard OD, Petersson M, Borga M, Neeland IJ. Cardiometabolic Health Outcomes Associated With Discordant Visceral and Liver Fat Phenotypes: Insights From the Dallas Heart Study and UK Biobank. Mayo Clin Proc. 2022 Feb;97(2):225-237. doi: 10.1016/j.mayocp.2021.08.021. Epub 2021 Sep 28. PMID: 34598789; PMCID: PMC8818017.
