{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T12:48:34Z","timestamp":1775566114451,"version":"3.50.1"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T00:00:00Z","timestamp":1767571200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T00:00:00Z","timestamp":1767571200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Obes"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>People exist at a combination of different individual and neighbourhood deprivations. Each of these combinations may have a unique impact on health. However, little is known about the intersectional inequality of these combinations on general and central obesity, including when considering their demographics. This study aims to answer these questions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The sample comprised 452,339 participants from the UK Biobank study. Individuals were grouped into 320 intersectional strata according to their household income, neighbourhood deprivation, sex, ethnicity and age. Linear and logistic multilevel analysis of individual heterogeneity and discriminatory accuracy was used to establish the total, additive and interactive inequality of body mass index (BMI), fat mass index (FMI), and waist to height ratio (WHtR), as well as the associated obesity classifications.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>6.5%, 25.2% and 9.1% of the total variation in BMI, FMI and WHtR, respectively, was due to inequality between the strata. Of this, 26.5%, 3.5% and 22.0% is interactive. 79, 58 and 93 strata for BMI, FMI and WHtR demonstrate a significant interactive effect. We found some patterns; for example, affluent white women have an advantaged interactive effect, whilst deprived black women have a disadvantaged effect. Meanwhile men experience the inverse relationship. The relationship between individual and neighbourhood deprivation is not universally experienced by all strata. For example, black men living in areas of high deprivation have higher BMIs as their household income increases.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>A large proportion of variation in general and central obesity is due to intersectional inequality, with up to 26.5% being interactive. It is important that these intersectional effects are considered when designing policy interventions to avoid policy failure, such as by focussing on groups with high total and interactive risk.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1038\/s41366-025-01984-2","type":"journal-article","created":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T18:43:13Z","timestamp":1767638593000},"page":"731-740","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Intersectional inequality in general and central obesity: cross-sectional UK Biobank study"],"prefix":"10.1038","volume":"50","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8964-0984","authenticated-orcid":false,"given":"Joseph","family":"Hutchinson","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Natalie","family":"Darko","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rebecca","family":"Hardy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David","family":"Webb","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Francesco","family":"Zaccardi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0347-4354","authenticated-orcid":false,"given":"William","family":"Johnson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,5]]},"reference":[{"key":"1984_CR1","doi-asserted-by":"publisher","unstructured":"Ng M, et al. Global, regional, and national prevalence of adult overweight and obesity, 1990\u20132021, with forecasts to 2050: a forecasting study for the Global Burden of Disease Study 2021. Lancet. 2025;405:813\u201338. https:\/\/doi.org\/10.1016\/S0140-6736(25)00355-1.","DOI":"10.1016\/S0140-6736(25)00355-1"},{"key":"1984_CR2","unstructured":"Baker C. Obesity statistics, House of Commons Library, (03336). 2023. https:\/\/researchbriefings.files.parliament.uk\/documents\/SN03336\/SN03336.pdf."},{"key":"1984_CR3","doi-asserted-by":"publisher","unstructured":"Bl\u00fcher M. Obesity: global epidemiology and pathogenesis. Nat Rev Endocrinol. 2019:288\u201398. https:\/\/doi.org\/10.1038\/s41574-019-0176-8.","DOI":"10.1038\/s41574-019-0176-8"},{"key":"1984_CR4","doi-asserted-by":"publisher","unstructured":"Bridger Staatz C, Kelly Y, Lacey R, Blodgett J, George A, Arnot M, et al. Life course socioeconomic position and body composition in adulthood: a systematic review and narrative synthesis. Int J Obes. 2021:2300\u201315. https:\/\/doi.org\/10.1038\/s41366-021-00898-z.","DOI":"10.1038\/s41366-021-00898-z"},{"key":"1984_CR5","doi-asserted-by":"publisher","unstructured":"Candio P, Mujica FP, Frew E. Socio-economic accounting of inequalities in excess weight: a population-based analysis. BMC Public Health. 2023;23(1). https:\/\/doi.org\/10.1186\/s12889-023-15592-0.","DOI":"10.1186\/s12889-023-15592-0"},{"key":"1984_CR6","doi-asserted-by":"publisher","unstructured":"Zhang Q, Youfa W. Socioeconomic inequality of obesity in the United States: do gender, age and ethnicity matter? Soc Sci Med. 2004;58(6):1171\u201380. https:\/\/doi.org\/10.1016\/S0277-9536(03)00288-0.","DOI":"10.1016\/S0277-9536(03)00288-0"},{"key":"1984_CR7","unstructured":"Crenshaw K. Demarginalizing the intersection of race and sex: a Black feminist critique of antidiscrimination doctrine, feminist theory and antiracist politics. U Chi Legal F. 1989;1989:8."},{"key":"1984_CR8","doi-asserted-by":"crossref","unstructured":"Crenshaw K. Mapping the margins: intersectionality, identity politics, and violence against women of color. Stanf Law Rev. 1991;43(6):1241\u201399.","DOI":"10.2307\/1229039"},{"key":"1984_CR9","doi-asserted-by":"publisher","unstructured":"Hankivsky O, Grace D, Hunting G, Giesbrecht M, Fridkin A, Rudrum S, et al. An intersectionality-based policy analysis framework: critical reflections on a methodology for advancing equity. Int J Equity Health. 2014;13(1). https:\/\/doi.org\/10.1186\/s12939-014-0119-x.","DOI":"10.1186\/s12939-014-0119-x"},{"key":"1984_CR10","doi-asserted-by":"publisher","unstructured":"Schulz A, Mullings L. Gender, race, class & health: intersectional approaches, gender & society. San Francisco: Jossey-Bass; 2006. https:\/\/doi.org\/10.1177\/0891243206297797.","DOI":"10.1177\/0891243206297797"},{"key":"1984_CR11","doi-asserted-by":"publisher","unstructured":"Farmer M. Are racial disparities in health conditional on socioeconomic status? Soc Sci Med. 2005;60. https:\/\/doi.org\/10.1016\/j.socscimed.2004.04.026.","DOI":"10.1016\/j.socscimed.2004.04.026"},{"key":"1984_CR12","doi-asserted-by":"crossref","unstructured":"Bowleg L. The problem with the phrase women and minorities: intersectionality\u2014an important theoretical framework for public health. Public Health. 2012;102(7):1267\u201373.","DOI":"10.2105\/AJPH.2012.300750"},{"key":"1984_CR13","doi-asserted-by":"publisher","unstructured":"Bentley C, Muyoya C, Vannini S, Oman S, Jimenez A. Intersectional approaches to data: the importance of an articulation mindset for intersectional data science. Big Data Soc. 2023;10(2). https:\/\/doi.org\/10.1177\/20539517231203667.","DOI":"10.1177\/20539517231203667"},{"key":"1984_CR14","doi-asserted-by":"publisher","unstructured":"Evans C, Leckie G, Subramanian S, Bell A, Merlo J. A tutorial for conducting intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA). SSM Popul Health. 2024;26. https:\/\/doi.org\/10.1016\/j.ssmph.2024.101664.","DOI":"10.1016\/j.ssmph.2024.101664"},{"key":"1984_CR15","doi-asserted-by":"publisher","unstructured":"Humphreys S. The unethical use of BMI in contemporary general practice. Br J Gen Pr. 2010;60(578):696\u20137. https:\/\/doi.org\/10.3399\/bjgp10X515548.","DOI":"10.3399\/bjgp10X515548"},{"key":"1984_CR16","doi-asserted-by":"publisher","unstructured":"Kelly TL, Wilson KE, Heymsfield SB. Dual energy X-ray absorptiometry body composition reference values from NHANES. PLoS ONE. 2009;4(9). https:\/\/doi.org\/10.1371\/journal.pone.0007038.","DOI":"10.1371\/journal.pone.0007038"},{"key":"1984_CR17","doi-asserted-by":"publisher","first-page":"953","DOI":"10.1038\/sj.ijo.0802037","volume":"26","author":"Y Schutz","year":"2002","unstructured":"Schutz Y, Kyle U, Pichard C. Fat-free mass index and fat mass index percentiles in Caucasians aged 18-98. Int J Obes. 2002;26:953\u201360. https:\/\/doi.org\/10.1038\/sj.ijo.0802037.","journal-title":"Int J Obes"},{"key":"1984_CR18","doi-asserted-by":"publisher","unstructured":"Jin X, Liu J, Cao Q, Lin J, Wu G, Liu L, et al. Normal-weight central obesity: implications for diabetes mellitus. Front Nutr. 2023;10. https:\/\/doi.org\/10.3389\/fnut.2023.1239493.","DOI":"10.3389\/fnut.2023.1239493"},{"key":"1984_CR19","doi-asserted-by":"publisher","unstructured":"Sahakyan KR, Somers VK, Rodriguez-Escudero JP, Hodge DO, Carter RE, Sochor O, et al. Normal-weight central obesity: implications for total and cardiovascular mortality. Ann Intern Med. 2015;163(11):827\u201335. https:\/\/doi.org\/10.7326\/M14-2525.","DOI":"10.7326\/M14-2525"},{"key":"1984_CR20","unstructured":"UK Biobank. UK Biobank. 2025. www.ukbiobank.ac.uk."},{"key":"1984_CR21","unstructured":"Office for National Statistics. Ethnic group variable: Census 2021. 2021. https:\/\/www.ons.gov.uk\/census\/census2021dictionary\/variablesbytopic\/ethnicgroupnationalidentitylanguageandreligionvariablescensus2021\/ethnicgroup?utm_source=chatgpt.com."},{"key":"1984_CR22","unstructured":"UK Biobank. Data-Field 2100. 2025. https:\/\/biobank.ndph.ox.ac.uk\/ukb\/field.cgi?id=21000."},{"key":"1984_CR23","unstructured":"UK Biobank. Data-Field 738. 2025. https:\/\/biobank.ndph.ox.ac.uk\/ukb\/field.cgi?id=738."},{"key":"1984_CR24","unstructured":"UK Biobank. Data-Field 26410. 2025. https:\/\/biobank.ndph.ox.ac.uk\/ukb\/field.cgi?id=26410."},{"key":"1984_CR25","unstructured":"UK Biobank. Data-Field 26427. 2025. https:\/\/biobank.ndph.ox.ac.uk\/ukb\/field.cgi?id=26427."},{"key":"1984_CR26","doi-asserted-by":"publisher","unstructured":"Abel G, Barclay M, Payne R. Adjusted indices of multiple deprivation to enable comparisons within and between constituent countries of the UK including an illustration using mortality rates. BMJ Open. 2016;6(11). https:\/\/doi.org\/10.1136\/bmjopen-2016-012750.","DOI":"10.1136\/bmjopen-2016-012750"},{"key":"1984_CR27","unstructured":"Payne R, Abel G. UK indices of multiple deprivation\u2014a way to make comparisons across constituent countries easier. Health Stat Q. 2021;53:22\u201337. https:\/\/www.researchgate.net\/publication\/221696538."},{"key":"1984_CR28","unstructured":"Gov.UK. Official Statistics English Indices of Deprivation 2010. 2011. https:\/\/www.gov.uk\/government\/statistics\/english-indices-of-deprivation-2010."},{"key":"1984_CR29","unstructured":"Scottish Government. Scottish Index of Multiple Deprivation. 2009. https:\/\/www.gov.scot\/publications\/scottish-index-multiple-deprivation-2009-general-report\/."},{"key":"1984_CR30","unstructured":"UK Biobank. Data-Field 21022. 2025. https:\/\/biobank.ndph.ox.ac.uk\/ukb\/field.cgi?id=21022."},{"key":"1984_CR31","unstructured":"UK Biobank. Data-Field 31. 2025. https:\/\/biobank.ndph.ox.ac.uk\/ukb\/field.cgi?id=31."},{"key":"1984_CR32","unstructured":"UK Biobank. Data-Field 48. 2025. https:\/\/biobank.ndph.ox.ac.uk\/ukb\/field.cgi?id=48."},{"key":"1984_CR33","unstructured":"UK Biobank. Data-Field 21001. 2025. https:\/\/biobank.ndph.ox.ac.uk\/ukb\/field.cgi?id=21001."},{"key":"1984_CR34","doi-asserted-by":"publisher","unstructured":"Cuccu Z, Abi-Aad G, Duggal A. Characteristics of patients with body mass index recorded within the Kent Integrated Dataset (KID). BMJ Health Care Inform. 2019;26(1). https:\/\/doi.org\/10.1136\/bmjhci-2019-000026.","DOI":"10.1136\/bmjhci-2019-000026"},{"key":"1984_CR35","doi-asserted-by":"publisher","unstructured":"Jacobs EJ, Newton CC, Wang Y, Patel AV, McCullough ML, Campbell PT, et al. Waist circumference and all-cause mortality in a large US cohort. Arch Intern Med. 2010;15(170):1293\u2013301. https:\/\/doi.org\/10.1001\/archinternmed.2010.201.","DOI":"10.1001\/archinternmed.2010.201"},{"key":"1984_CR36","doi-asserted-by":"publisher","unstructured":"Cole TJ. Sympercents: symmetric percentage differences on the 100 log(e) scale simplify the presentation of log transformed data. Stat Med. 2000;19(22):3109\u201325. https:\/\/doi.org\/10.1002\/1097-0258(20001130)19:22<3109::AID-SIM558>3.0.CO;2-Fpp.","DOI":"10.1002\/1097-0258(20001130)19:22<3109::AID-SIM558>3.0.CO;2-Fpp"},{"key":"1984_CR37","unstructured":"National Institute for Health and Care Excellence. Obesity: identification, assessment and management. 2014. https:\/\/www.transformationpartners.nhs.uk\/wp-content\/uploads\/2018\/04\/Obesity-identifification-assessment-and-management-NICE-Guidlines.pdf."},{"key":"1984_CR38","doi-asserted-by":"publisher","unstructured":"Bauer G, Churchill S, Mahendran M, Walwyn C, Lizotte D, Villa-Rueda A, et al. Intersectionality in quantitative research: a systematic review of its emergence and applications of theory and methods. SSM Popul Health. 2021;14. https:\/\/doi.org\/10.1016\/j.ssmph.2021.100798.","DOI":"10.1016\/j.ssmph.2021.100798"},{"key":"1984_CR39","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.socscimed.2017.11.011","volume":"203","author":"C Evans","year":"2018","unstructured":"Evans C, Williams D, Onnela J, Subramanian S. A multilevel approach to modeling health inequalities at the intersection of multiple social identities. Soc Sci Med. 2018;203:64\u201373. https:\/\/doi.org\/10.1016\/j.socscimed.2017.11.011.","journal-title":"Soc Sci Med"},{"key":"1984_CR40","doi-asserted-by":"publisher","unstructured":"Merlo J. Invited commentary: multilevel analysis of individual heterogeneity\u2014a fundamental critique of the current probabilistic risk factor epidemiology. Am J Epidemiol. 2014;180(2):208\u201312. https:\/\/doi.org\/10.1093\/aje\/kwu108.","DOI":"10.1093\/aje\/kwu108"},{"key":"1984_CR41","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.socscimed.2017.12.026","volume":"203","author":"J Merlo","year":"2018","unstructured":"Merlo J. Multilevel analysis of individual heterogeneity and discriminatory accuracy (I-MAIHDA) within an intersectional framework. Soc Sci Med. 2018;203:74\u201380. https:\/\/doi.org\/10.1016\/j.socscimed.2017.12.026.","journal-title":"Soc Sci Med"},{"key":"1984_CR42","doi-asserted-by":"publisher","unstructured":"Bell A, Holman D, Jones K. Using shrinkage in multilevel models to understand Intersectionality: a simulation study and a guide for best practice. Methodology. 2019;15(2):88\u201396. https:\/\/doi.org\/10.1027\/1614-2241\/a000167.","DOI":"10.1027\/1614-2241\/a000167"},{"key":"1984_CR43","doi-asserted-by":"publisher","unstructured":"Mahendran M, Lizotte D, Bauer GR. Quantitative methods for descriptive intersectional analysis with binary health outcomes. SSM Popul Health. 2022;17. https:\/\/doi.org\/10.1016\/j.ssmph.2022.101032.","DOI":"10.1016\/j.ssmph.2022.101032"},{"key":"1984_CR44","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1097\/EDE.0000000000001466","volume":"33","author":"M Mahendran","year":"2022","unstructured":"Mahendran M, Lizotte D, Bauer GR. Describing intersectional health outcomes: an evaluation of data analysis methods. Epidemiology. 2022;33:395\u2013405. https:\/\/doi.org\/10.1097\/EDE.0000000000001466.","journal-title":"Epidemiology"},{"key":"1984_CR45","doi-asserted-by":"publisher","unstructured":"Bann D, Johnson W, Li L, Kuh D, Hardy R. Socioeconomic inequalities in body mass index across adulthood: coordinated analyses of individual participant data from three British birth cohort studies initiated in 1946, 1958 and 1970. PLoS Med. 2017;14(1). https:\/\/doi.org\/10.1371\/journal.pmed.1002214.","DOI":"10.1371\/journal.pmed.1002214"},{"key":"1984_CR46","doi-asserted-by":"publisher","unstructured":"Autret K, Bekelman TA. Socioeconomic status and obesity. J Endocr Soc. 2024;8(11). https:\/\/doi.org\/10.1210\/jendso\/bvae176.","DOI":"10.1210\/jendso\/bvae176"},{"key":"1984_CR47","doi-asserted-by":"publisher","unstructured":"Hern\u00e1ndez-Yumar A, Wemrell M, Alesson I, Lopez-Valcarcel B, Leckie G, Merlo J. Socioeconomic differences in body mass index in Spain: an intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy. PLoS ONE. 2018;13(12). https:\/\/doi.org\/10.1371\/journal.pone.0208624.","DOI":"10.1371\/journal.pone.0208624"},{"key":"1984_CR48","doi-asserted-by":"publisher","unstructured":"Holman D, Bell A, Green M, Salway S. Neighbourhood deprivation and intersectional inequalities in biomarkers of healthy ageing in England. Health Place. 2022;77. https:\/\/doi.org\/10.1016\/j.healthplace.2022.102871.","DOI":"10.1016\/j.healthplace.2022.102871"},{"key":"1984_CR49","doi-asserted-by":"publisher","unstructured":"Fry A, Littlejohns T, Sudlow C, Doherty N, Adamska L, Sprosen T. Comparison of sociodemographic and health-related characteristics of UK Biobank participants with those of the general population. Am J Epidemiol. 2017;186(9):1026\u201334. https:\/\/doi.org\/10.1093\/aje\/kwx246.","DOI":"10.1093\/aje\/kwx246"},{"key":"1984_CR50","unstructured":"Garlick S. Ethnic group, England and Wales: census 2021. 2022. https:\/\/www.ons.gov.uk\/peoplepopulationandcommunity\/culturalidentity\/ethnicity\/bulletins\/ethnicgroupenglandandwales\/census2021."},{"key":"1984_CR51","doi-asserted-by":"publisher","unstructured":"Wilkes R, Karimi A. From intersectional invisibility to visibility: Black women in health disparity data and quantitative intersectional models. Curr Res Behav Sci. 2024;6. https:\/\/doi.org\/10.1016\/j.crbeha.2024.100149.","DOI":"10.1016\/j.crbeha.2024.100149"},{"key":"1984_CR52","doi-asserted-by":"publisher","unstructured":"Galobardes B, Shaw M, Lawlor D, Lynch J, Smith G. Indicators of socioeconomic position (part 1). J Epidemiol Community Health. 2006:7\u201312. https:\/\/doi.org\/10.1136\/jech.2004.023531.","DOI":"10.1136\/jech.2004.023531"}],"container-title":["International Journal of Obesity"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41366-025-01984-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41366-025-01984-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41366-025-01984-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T11:56:35Z","timestamp":1775562995000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41366-025-01984-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,5]]},"references-count":52,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["1984"],"URL":"https:\/\/doi.org\/10.1038\/s41366-025-01984-2","relation":{},"ISSN":["0307-0565","1476-5497"],"issn-type":[{"value":"0307-0565","type":"print"},{"value":"1476-5497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,5]]},"assertion":[{"value":"5 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 October 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 November 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 January 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}