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Cited by 38Body Composition Measurement TechniquesObesity, Physical Activity, DietDiabetes, Cardiovascular Risks, and Lipoproteins

Assessment of correlation between conventional anthropometric and imaging-derived measures of body fat composition: a systematic literature review and meta-analysis of observational studies

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Mouchti, Sofia, Orliacq, Josefina, Reeves, Gillian, Chen, Zhengming·2023·BMC Medical Imaging
DOI 10.1186/s12880-023-01063-wPMC PMC10503139
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Abstract

Background: In studies of the association of adiposity with disease risk, widely used anthropometric measures of adiposity (e.g. body-mass-index [BMI], waist circumference [WC], waist-hip ratio [WHR]) are simple and inexpensive to implement at scale. In contrast, imaging-based techniques (e.g. magnetic resonance imaging [MRI] and dual x-ray absorptiometry [DXA]) are expensive and labour intensive, but can provide more accurate quantification of body fat composition. There is, however, limited evidence about the relationship between conventional and imaging-derived measures of adiposity. Methods: We searched Scopus and Web of Science for published reports in English of conventional versus imaging-derived measurements of adiposity. We identified 42 articles (MRI = 22; DXA = 20) that met selection criteria, involving 42,556 (MRI = 15,130; DXA = 27,426) individuals recruited from community or hospital settings. Study-specific correlation coefficients (r) were transformed using Fisher’s Z transformation, and meta-analysed to yield weighted average correlations, both overall and by ancestry, sex and age, where feasible. Publication bias was investigated using funnel plots and Egger’s test. Results: Overall, 98% of participants were 18 + years old, 85% male and 95% White. BMI and WC were most strongly correlated with imaging-derived total abdominal (MRI-derived: r = 0.88-; DXA-derived: 0.50–0.86) and subcutaneous abdominal fat (MRI-derived: 0.83–0.85), but were less strongly correlated with visceral abdominal fat (MRI-derived: 0.76-0.79; DXA-derived: 0.80) and with DXA-derived %body fat (0.76). WHR was, at best, strongly correlated with imaging-derived total abdominal (MRI-derived: 0.60; DXA-derived: 0.13), and visceral abdominal fat (MRI-derived: 0.67; DXA-derived: 0.65), and moderately with subcutaneous abdominal (MRI-derived: 0.54), and with DXA-derived %body fat (0.58). All conventional adiposity measures were at best moderately correlated with hepatic fat (MRI-derived: 0.36–0.43). In general, correlations were stronger in women than in men, in Whites than in non-Whites, and in those aged 18 + years. Conclusions: In this meta-analysis, BMI and WC, but not WHR, were very strongly correlated with imaging-derived total and subcutaneous abdominal fat. By comparison, all three measures were moderately or strongly correlated with imaging-based visceral abdominal fat, with WC showing the greatest correlation. No anthropometric measure was substantially correlated with hepatic fat. Further larger studies are needed to compare these measures within the same study population, and to assess their relevance for disease risks in diverse populations.

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  1. [1]Collaboration NRF Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19°2 million participants The Lancet 2016 387 10026 1377 96 PMC7615134 27115820
  2. [3]Neeland IJ Hughes C Ayers CR Malloy CR Jin ES Effects of Visceral Adiposity on Glycerol Pathways in Gluconeogenesis Metabolism 2016 67 80 9 28081781 PMC5244471
  3. [4]Speliotes EK Massaro JM Hoffmann U Vasan RS Meigs JB Sahani DV Fatty liver is associated with dyslipidemia and dysglycemia independent of visceral fat: The Framingham heart study Hepatology 2010 51 6 1979 87 20336705 PMC3023160
  4. [5]Chen Z Smith M Du H Guo Y Clarke R Bian Z Blood pressure in relation to general and central adiposity among 500 000 adult Chinese men and women International Journal of Epidemiology 2015 44 4 1305 19 25747585 PMC4588860
  5. [6]Cutler JA Randomized clinical trials of weight reduction in nonhypertensive persons Ann Epidemiol 1991 1 4 363 70 1669517
  6. [8]Timpson NJ Harbord R Smith GD Zacho J Tybjærg-Hansen A Nordestgaard BG Does Greater Adiposity Increase Blood Pressure and Hypertension Risk?: Mendelian Randomization Using the FTO/MC4R Genotype Hypertension 2009 54 1 84 90 19470880
  7. [10]Klop B Elte JWF Cabezas MC Dyslipidemia in Obesity: Mechanisms and Potential Targets Nutrients 2013 5 4 1218 40 23584084 PMC3705344
  8. [11]Chen Z Iona A Parish S Chen Y Guo Y Bragg F Adiposity and risk of ischaemic and haemorrhagic stroke in 0°5 million Chinese men and women: a prospective cohort study The Lancet Global Health 2018 6 6 e630 e40 29773119 PMC5960068
  9. [12]Hubert HB Feinleib M McNamara PM Castelli WP Obesity as an independent risk factor for cardiovascular disease: A 26-year follow-up of participants in the Framingham Heart Study Circulation 1983 67 5 968 77 6219830
  10. [13]Mathieu P Pibarot P Larose E Poirier P Marette A Despres JP Visceral obesity and the heart Int J Biochem Cell Biol 2008 40 5 821 36 18201922
  11. [14]Lamarche B Lemieux S Dagenais GR Després JP Visceral obesity and the risk of ischaemic heart disease: insights from the Québec Cardiovascular Study Growth Horm IGF Res 1998 8 Suppl B 1 8 10990129
  12. [15]Im JP Kim D Chung SJ Jin EH Han YM Park MJ Visceral obesity as a risk factor for colorectal adenoma occurrence in surveillance colonoscopy Gastrointest Endosc 2018 88 1 119 27 e4 29510147
  13. [16]Schapira DV Clark RA Wolff PA Jarrett AR Kumar NB Aziz NM Visceral obesity and breast-cancer risk CANCER 1994 74 2 632 9 8033042
  14. [17]Hafe P Pina F Pérez A Tavares M Barros H Visceral Fat Accumulation as a Risk Factor for Prostate Cancer Obes Res 2004 12 12 1930 5 15687393
  15. [18]Kivimäki M Kuosma E Ferrie JE Luukkonen R Nyberg ST Alfredsson L Overweight, obesity, and risk of cardiometabolic multimorbidity: pooled analysis of individual-level data for 120 813 adults from 16 cohort studies from the USA and Europe The Lancet Public Health 2017 2 6 e277 e85 28626830 PMC5463032
  16. [20]Tchernof A Després J-P Pathophysiology of human visceral obesity: An update Physiol Rev 2013 93 1 359 404 23303913
  17. [21]Etchison WC Bloodgood EA Minton CP Thompson NJ Collins MA Hunter SC Body mass index and percentage of body fat as indicators for obesity in an adolescent athletic population Sports Health 2011 3 3 249 52 23016014 PMC3445161
  18. [22]Ode JJ Pivarnik JM Reeves MJ Knous JL Body mass index as a predictor of percent fat in college athletes and nonathletes Med Sci Sports Exerc 2007 39 3 403 9 17473765
  19. [23]Cerhan JR Moore SC Jacobs EJ Kitahara CM Rosenberg PS Adami HO A pooled analysis of waist circumference and mortality in 650,000 adults Mayo Clin Proc 2014 89 3 335 45 24582192 PMC4104704
  20. [24]InterAct C Langenberg C Sharp SJ Schulze MB Rolandsson O Overvad K Long-term risk of incident type 2 diabetes and measures of overall and regional obesity: the EPIC-InterAct case-cohort study PLoS Med 2012 9 6 e1001230 22679397 PMC3367997
  21. [25]Bragg F Tang K Guo Y Iona A Du H Holmes MV Associations of General and Central Adiposity With Incident Diabetes in Chinese Men and Women Diabetes Care 2018 41 3 494 502 29298802 PMC6548563
  22. [26]Cuthbertson DJ Steele T Wilding JP Halford JC Harrold JA Hamer M What have human experimental overfeeding studies taught us about adipose tissue expansion and susceptibility to obesity and metabolic complications? International Journal of Obesity 2017 41 6 853 65 28077863
  23. [27]Choe SS Huh JY Hwang IJ Kim JI Kim JB Adipose Tissue Remodeling: Its Role in Energy Metabolism and Metabolic Disorders Front Endocrinol (Lausanne) 2016 7 30 27148161 PMC4829583
  24. [28]Hemke R Buckless C Torriani M Quantitative Imaging of Body Composition Semin Musculoskelet Radiol 2020 24 4 375 85 32992366
  25. [30]Michale Borenstein LVH Julian PT Higgins Hannah R Rothestein Introduction to Meta-Analysis Edition S Wiley 2021
  26. [31]Hedges LV Olkin I Statistical methods for meta-analysis Academic Press Boston London 1985
  27. [32]Perez-Cornago A Dunneram Y Watts EL Key TJ Travis RC Adiposity and risk of prostate cancer death: a prospective analysis in UK Biobank and meta-analysis of published studies BMC Med 2022 20 1 143 35509091 PMC9069769
  28. [33]Matthias Egger GDS Minder Christoph Bias in meta-analysis detected by a simple, graphical test BMJ 315 629 34 PMC2127453 9310563
  29. [34]Yang JZ Dokpuang D Nemati R He KH Zheng AB Petrov MS Evaluation of Ethnic Variations in Visceral, Subcutaneous, Intra-Pancreatic, and Intra-Hepatic Fat Depositions by Magnetic Resonance Imaging among New Zealanders Biomedicines 2020 8 6 PMC7344761 32630574
  30. [35]Ulbrich EJ Nanz D Leinhard OD Marcon M Fischer MA Whole-body adipose tissue and lean muscle volumes and their distribution across gender and age: MR-derived normative values in a normal-weight Swiss population Magnetic Resonance in Medicine 2018 79 1 449 58 28432747
  31. [36]Lee V Blew R Hetherington-Rauth M Blew D Galons JP Hagio T Estimation of visceral fat in 9- to 13-year-old girls using dual-energy X-ray absorptiometry (DXA) and anthropometry Obesity Science & Practice 2018 4 5 437 47 30338114 PMC6180717
  32. [37]Eloi JC Epifanio M de Goncalves MM Pellicioli A Vieira PFG Dias HB Quantification of Abdominal Fat in Obese and Healthy Adolescents Using 3 Tesla Magnetic Resonance Imaging and Free Software for Image Analysis Plos One 2017 12 1 PMC5271344 28129354
  33. [38]Setiawan VW Lim U Lipworth L Lu SC Shepherd J Ernst T Sex and Ethnic Differences in the Association of Obesity With Risk of Hepatocellular Carcinoma Clin Gastroenterol Hepatol 2016 14 2 309 16 26404865 PMC4718778
  34. [39]Neeland IJ McGuire DK Eliasson B Ridderstråle M Zeller C Woerle HJ Correction to: Comparison of Adipose Distribution Indices with Gold Standard Body Composition Assessments in the EMPA-REG H2H SU Trial: A Body Composition Sub-Study Diabetes Ther 2015 9 4 1721 PMC6064599 29931507
  35. [40]Lange T Buechert M Baumstark MW Deibert P Gerner S Ryden H Value of MRI and MRS fat measurements to complement conventional screening methods for childhood obesity Journal of Magnetic Resonance Imaging 2015 42 5 1214 22 25881207
  36. [41]Zhi Dong YL Zhang Zhongwei Cai Huasong Li Yanbing Chan Tao Wu Ling Li Zi-Ping Feng Shi-Ting MR quantification of total liver fat in patients with impaired glucose tolerance and healthy subjects 2014 9 10 PMC4208854 25343445
  37. [42]Neamat-Allah J Wald D Husing A Teucher B Wendt A Delorme S Validation of Anthropometric Indices of Adiposity against Whole-Body Magnetic Resonance Imaging - A Study within the German European Prospective Investigation into Cancer and Nutrition (EPIC) Cohorts Plos One 2014 9 3 PMC3953447 24626110
  38. [43]Mantatzis M Milousis T Katergari S Delistamatis A Papachristou DN Prassopoulos P Abdominal Adipose Tissue Distribution on MRI and Diabetes Academic Radiology 2014 21 5 667 74 24703481
  39. [44]Koren D Marcus CL Kim C Gallagher PR Schwab R Bradford RM Anthropometric predictors of visceral adiposity in normal-weight and obese adolescents Pediatric Diabetes 2013 14 8 575 84 23710887 PMC4565510
  40. [45]Maislin G Ahmed MM Gooneratne N Thorne-Fitzgerald M Kim C Teff K Single Slice vs. Volumetric MR Assessment of Visceral Adipose Tissue: Reliability and Validity Among the Overweight and Obese Obesity 2012 20 10 2124 32 22395811 PMC3743719
  41. [46]Zhang H Peng Y Liu ZX Li SL Lv ZL Tian LF Effects of Acupuncture Therapy on Abdominal Fat and Hepatic Fat Content in Obese Children: A Magnetic Resonance Imaging and Proton Magnetic Resonance Spectroscopy Study Journal of Alternative and Complementary Medicine 2011 17 5 413 20 21595560
  42. [47]Browning LM Mugridge O Dixon AK Aitken SW Prentice AM Jebb SA Measuring Abdominal Adipose Tissue: Comparison of Simpler Methods with MRI Obesity Facts 2011 4 1 9 15 21372606 PMC6450044
  43. [48]Ducluzeau PH Manchec-Poilblanc P Roullier V Cesbron E Lebigot J Bertrais S Distribution of abdominal adipose tissue as a predictor of hepatic steatosis assessed by MRI Clinical Radiology 2010 65 9 695 700 20696296
  44. [49]Ludescher B Machann J Eschweiler GW Vanhöfen S Maenz C Thamer C Correlation of fat distribution in whole body MRI with generally used anthropometric data Investigative Radiology 2009 44 11 712 9 19809346
  45. [50]Illouz F Roulier V Rod A Gallois Y Pelle CP Aube C Distribution of adipose tissue: Quantification and relationship with hepatic steatosis and vascular profiles of type 2 diabetic patients with metabolic syndrome Diabetes & Metabolism 2008 34 1 68 74 18243026
  46. [51]Kullberg J von Below C Lonn L Lind L Ahlstrom H Johansson L Practical approach for estimation of subcutaneous and visceral adipose tissue Clinical Physiology and Functional Imaging 2007 27 3 148 53 17445065
  47. [52]Ball GDC Huang TTK Cruz ML Shaibi GQ Weigensberg MJ Goran MI Predicting abdominal adipose tissue in overweight Latino youth International Journal of Pediatric Obesity 2006 1 4 210 6 17907327
  48. [53]Poll LW Wittsack HJ Willers R Mödder U Heinemann L Kapitza C Correlation between anthropometric parameters and abdominal fat volumes assessed by a magnetic resonance imaging method in patients with diabetes Diabetes Technology and Therapeutics 2004 6 6 844 9 15684638
  49. [54]Kamel EG McNeill G Wijk MCW Usefulness of Anthropometry and DXA in Predicting Intra-abdominal Fat in Obese Men and Women Obes Res 2000 8 1 36 42 10678257
  50. [55]Corrêa CR Formolo NPS Dezanetti T Speretta GFF Nunes EA Relative fat mass is a better tool to diagnose high adiposity when compared to body mass index in young male adults: A cross-section study Clin Nutr ESPEN 2021 41 225 33 33487268
  51. [56]Staynor JMD Smith MK Donnelly CJ Sallam AE Ackland TR DXA reference values and anthropometric screening for visceral obesity in Western Australian adults Sci Rep 2020 10 1 18731 33127940 PMC7599223
  52. [57]Redondo MJ Siller AF Gu XJ Tosur M Bondy M Devaraj S Sex differences in circulating leptin as a marker of adiposity in obese or overweight adolescents with type 1 diabetes BMJ Open Diabetes Research & Care 2020 8 1 PMC7580039 33087341
  53. [58]Martin CJ Muller E Labadarios D Veldman FJ Kassier SM Body composition of HIV-positive candidates for and recipients of a kidney transplant: comparative analysis between DEXA and anthropometric indices South African Journal of Clinical Nutrition 2020
  54. [59]Guzmán-León AE Velarde AG Vidal-Salas M Urquijo-Ruiz LG Caraveo-Gutiérrez LA Valencia ME External validation of the relative fat mass (RFM) index in adults from north-west Mexico using different reference methods PloS one 2019 14 12 e0226767 31891616 PMC6938316
  55. [60]Grzegorczyk J Woloszyn N Perenc L Comparison of selected body composition parameters in women using DXA and anthropometric method Journal of research in medical sciences 2019 24 1 70 31523256 PMC6734665
  56. [61]Vasan SK Osmond C Canoy D Christodoulides C Neville MJ Di Gravio C Comparison of regional fat measurements by dual-energy X-ray absorptiometry and conventional anthropometry and their association with markers of diabetes and cardiovascular disease risk Int J Obes (Lond) 2018 42 4 850 7 29151596 PMC5965665
  57. [62]Pasha EP Birdsill A Parker P Elmenshawy A Tanaka H Haley AP Visceral adiposity predicts subclinical white matter hyperintensities in middle-aged adults Obesity Research & Clinical Practice 2017 11 2 177 87 27133528
  58. [63]Verduin WM Van Den Helder R Doodeman HJ Struijf E Houdijk APJ Dexa Body Composition Assessment in 10-11 Year Healthy Children Plos One 2016 11 10 PMC5082851 27788168
  59. [64]Saki F Omrani GR Dabbaghmanesh MH Comparison between BMI and Inverted BMI in Evaluating Metabolic Risk and Body Composition in Iranian Children International Journal of Pediatrics-Mashhad 2016 4 4 1625 36
  60. [65]Grier T Canham-Chervak M Sharp M Jones BH Does body mass index misclassify physically active young men 2015 2 487 PMC4985185 27547717
  61. [66]Bhatia R Lesser DJ Oliveira F Tran WH Keens TG Khoo MCK Body Fat Composition: A Predictive Factor for Sleep Related Breathing Disorder in Obese Children Journal of Clinical Sleep Medicine 2015 11 9 1039 45 26094935 PMC4543248
  62. [67]Smith E Bailey PE Crawford J Samaras K Baune BT Campbell L Adiposity Estimated Using Dual Energy X-Ray Absorptiometry and Body Mass Index and Its Association with Cognition in Elderly Adults Journal of the American Geriatrics Society 2014 62 12 2311 8 25516027
  63. [68]Lam BCC Lim SC Wong MTK Shum E Ho CY Bosco JIE A method comparison study to validate a novel parameter of obesity, the body adiposity index, in chinese subjects Obesity 2013 21 12 E634 E9 23630126
  64. [69]Direk K Cecelja M Astle W Chowienczyk P Spector TD Falchi M The relationship between DXA-based and anthropometric measures of visceral fat and morbidity in women BMC Cardiovasc Disord 2013 13 1 25 23552273 PMC3769144
  65. [70]Segatto AFM Junior IFF Santos VRD Alves KCDLRP Barbosa DA Filho AMP Indices of body fat distribution for assessment of lipodysthrophy in people living with HIV/AIDS BMC Research Notes 2012 5 PMC3502469 23031203
  66. [71]Kaul S Rothney MP Peters DM Wacker WK Davis CE Shapiro MD Dual-Energy X-Ray Absorptiometry for Quantification of Visceral Fat Obesity 2012 20 6 1313 8 22282048 PMC3361068
  67. [72]Oreopoulos A Ezekowitz JA McAlister FA Kalantar-Zadeh K Fonarow GC Norris CM Association Between Direct Measures of Body Composition and Prognostic Factors in Chronic Heart Failure Mayo Clinic Proceedings 2010 85 7 609 17 20592169 PMC2894716
  68. [73]Liu J Fox CS Hickson D Bidulescu A Carr JJ Taylor HA Fatty liver, abdominal visceral fat, and cardiometabolic risk factors: the Jackson Heart Study Arterioscler Thromb Vasc Biol 2011 31 11 2715 22 21885852 PMC3228266
  69. [74]Liu J Fox CS Hickson DA May WD Hairston KG Carr JJ Impact of abdominal visceral and subcutaneous adipose tissue on cardiometabolic risk factors: the Jackson Heart Study J Clin Endocrinol Metab 2010 95 12 5419 26 20843952 PMC2999970
  70. [75]Preis SR Massaro JM Robins SJ Hoffmann U Vasan RS Irlbeck T Abdominal subcutaneous and visceral adipose tissue and insulin resistance in the Framingham heart study Obesity (Silver Spring) 2010 18 11 2191 8 20339361 PMC3033570
  71. [76]Liu J Fox CS Hickson D Sarpong D Ekunwe L May WD Pericardial adipose tissue, atherosclerosis, and cardiovascular disease risk factors: the Jackson heart study Diabetes Care 2010 33 7 1635 9 20413524 PMC2890373
  72. [77]Britton KAMDMPH Massaro JMP Murabito JMMDS Kreger BEMD Hoffmann UMDMPH Fox CSMDMPH Body Fat Distribution, Incident Cardiovascular Disease, Cancer, and All-Cause Mortality J Am Coll Cardiol 2013 62 10 921 5 23850922 PMC4142485
  73. [78]Verweij LM Terwee CB Proper KI Hulshof CTJ van Mechelen W Measurement error of waist circumference: gaps in knowledge Public Health Nutr 2013 16 2 281 8 22626254 PMC10271771
  74. [80]Sebo P Haller DM Pechère-Bertschi A Bovier P Herrmann FR Accuracy of doctors’ anthropometric measurements in general practice Swiss Med Wkly 2015 145 w14115 25701670
  75. [81]Borga M West J Bell JD Harvey NC Romu T Heymsfield SB Advanced body composition assessment: from body mass index to body composition profiling Journal of Investigative Medicine 2018 66 5 887 95 PMC5992366 29581385
  76. [82]Lee V Blew R Hetherington-Rauth M Blew D Galons JP Hagio T Estimation of visceral fat in 9- to 13-year-old girls using dual-energy X-ray absorptiometry (DXA) and anthropometry Obes Sci Pract 2018 4 5 437 47 30338114 PMC6180717
  77. [83]Taylor AE Kuper H Varma RD Wells JC Bell JD Radhakrishna KV Validation of Dual Energy X-Ray Absorptiometry Measures of Abdominal Fat by Comparison with Magnetic Resonance Imaging in an Indian Population Plos One 2012 7 12 PMC3522679 23272086
  78. [84]Reinhardt M Piaggi P DeMers B Trinidad C Krakoff J Cross Calibration of Two Dual-Energy X-Ray Densitometers and Comparison of Visceral Adipose Tissue Measurements by iDXA and MRI Obesity 2017 25 2 332 7 28000375 PMC5269467
  79. [85]Kamel EG McNeill G Van Wijk MC Usefulness of anthropometry and DXA in predicting intra-abdominal fat in obese men and women Obes Res 2000 8 1 36 42 10678257
  80. [87]Enzi G Gasparo M Biondetti PR Fiore D Semisa M Zurlo F Subcutaneous and visceral fat distribution according to sex, age, and overweight, evaluated by computed tomography Am J Clin Nutr 1986 44 6 739 46 3788827
  81. [88]Tchernof A Despres JP Pathophysiology of human visceral obesity: an update Physiol Rev 2013 93 1 359 404 23303913
  82. [90]Kuk JL Saunders TJ Davidson LE Ross R Age-related changes in total and regional fat distribution Ageing Res Rev 2009 8 4 339 48 19576300
  83. [91]Dunne RF Loh KP Williams GR Jatoi A Mustian KM Mohile SG Cachexia and Sarcopenia in Older Adults with Cancer: A Comprehensive Review Cancers (Basel) 2019 11 12 PMC6966439 31769421
  84. [92]VA H R R W M WR F WJ E MA FS Anthropometric assessment of 10-y changes in body composition in the elderly Am J Clin Nutr 2004 Aug 80 2 475 82 10.1093/ajcn/802475.2004(0002-9165 (Print));475-82 15277173 · DOI
  85. [93]Frayn K Samra JS Summers LK Visceral fat in relation to health: is it a major culprit or simply an innocent bystander? International journal of obesity and related metabolic disorders: journal of the International Association for the Study of Obesity 1997 21 15481760
  86. [94]Hexsel D Dal’Forno Dini T Belem L Tanure Correa I Brandao A Gluteal subcutaneous adipose tissue in women of different ages and body mass index observed by magnetic resonance imaging J Cosmet Dermatol 2022 21 5 2169 75 35181990

Cited by

Sorted by citations

Authors
Mouchti, S., Orliacq, J., Reeves, G., & Chen, Z.
Affiliations
University of Oxford (GB)
Corresponding author
  • Reeves, Gillian
  • Chen, Zhengming
ORCID
Venue
BMC Medical Imaging
Publisher
Springer Science and Business Media LLC
Published
2023-09-14
Volume
23
Issue
1
Pages
127-127
ISSN
1471-2342 (ISSN-L)
DOI
10.1186/s12880-023-01063-w
PMC
PMC10503139
OpenAlex
W4386741493
Cited by
38
References
7
Citations by year
2026: 13 · 2025: 17 · 2024: 8
FWCI
1.36
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Medical Imaging (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Cancer Research UK
  • NDPH funded
Topics
  • Health Sciences › Medicine › Physiology › Body Composition Measurement Techniques
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Obesity, Physical Activity, Diet
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
Keywords
adiposity, body fat content, anthropometric measures, body mass index, waist circumference, waist-to-hip ratio, magnetic resonance imaging, dual-energy X-ray absorptiometry, visceral fat, subcutaneous abdominal fat, liver fat, sex differences, ancestry differences, age differences
MeSH terms
Adipose Tissue, Adolescent, Anthropometry, Body Composition, Diagnostic Imaging, Female, Humans, Male, Obesity, Body Mass Index

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