Age Cohort Patterns of Socioeconomic Inequalities in High Risk Body Mass Index (BMI) and Waist to Hip Ratio (WHR) Composite: Findings from Nationally Representative Survey

Age Cohort Patterns of Socioeconomic Inequalities in High Risk Body Mass Index (BMI) and Waist to Hip Ratio (WHR) Composite: Findings from Nationally Representative Survey | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Age Cohort Patterns of Socioeconomic Inequalities in High Risk Body Mass Index (BMI) and Waist to Hip Ratio (WHR) Composite: Findings from Nationally Representative Survey Anil Pardeshi, Rayhan Rahman, Ankita Mathur, Vini Mehta This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7542656/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Obesity is a growing global health challenge, with central adiposity posing particularly high cardiometabolic risk. In low- and middle-income countries (LMICs) like India, the coexistence of undernutrition and obesity reflects a double burden of malnutrition. However, limited research has examined how socioeconomic inequalities in high-risk adiposity vary across age cohorts using combined anthropometric measures. Aim To assess age-cohort patterns of socioeconomic inequalities in high-risk adiposity defined using a composite of body mass index (BMI) and waist-to-hip ratio (WHR) in a nationally representative Indian population. Methods We analysed data from Wave-1 (2017–2018) of the Longitudinal Ageing Study in India (LASI), including 65,150 adults with complete anthropometric and socioeconomic data. High-risk body composition (BWC) was defined as overweight BMI with high-risk WHR, or obese BMI with any WHR. Descriptive, regression, and inequality measures (CI and Erreygers CI with decomposition) were used to examine disparities by socioeconomic status across age cohorts. Results The 18–44 cohort had the highest prevalence of overweight (28.4%) and obesity (10.9%), while WHR increased sharply with age (p < 0.001). Nearly all overweight (93%) and obese (91.6%) individuals were WHR high-risk. High-risk BWC was more common among women, urban residents, and individuals with higher education and wealth (p < 0.001). Inequality analysis showed a pro-rich concentration in all cohorts, peaking at ages 45–59 (ECI: 0.214), with education as the largest contributor (24–30%). Conclusion High-risk adiposity in India is patterned by age and SES. WHR captures substantial central obesity risk, highlighting the need for equity-focused, life-course obesity prevention strategies. Socioeconomic inequality LASI Age cohorts Concentration index Central Adiposity Waist-to-hip ratio Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7542656","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":533285823,"identity":"4396b008-5ec0-4431-aae5-ef661796fcaf","order_by":0,"name":"Anil Pardeshi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+0lEQVRIiWNgGAWjYDCCA0CcUMBgAObwMNgAScbGA4S1GMC1pIG0NBDWwoDQchghiAvw3T787MEDAwZjfrEzhh/etp23W9t+GGhLjU00Li2S59LMDYAOM5OcnWMsObftdvK2M4lALcfSchtwaDE4w2AmAdRiY3A7LUGaF6jF7ABQC2PDYTxa2L+BtdjfTkv+zdt2Ltns/ENCWnjAtpgZSCcfA9pywM7sBgFbJM/wlAG1SBhL3E4+ZjnnXHKC2Q2gLQl4/MJ3hn2b5I8KG8P+2YnNN96U2dmbnU9/+OBDjQ1OLVAgAWclglUm4FeOCuxJUTwKRsEoGAUjAwAAHr5f/u+PB3AAAAAASUVORK5CYII=","orcid":"","institution":"Dr. D. 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Survey","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Socioeconomic inequality, LASI, Age cohorts, Concentration index, Central Adiposity, Waist-to-hip ratio","lastPublishedDoi":"10.21203/rs.3.rs-7542656/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7542656/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eObesity is a growing global health challenge, with central adiposity posing particularly high cardiometabolic risk. In low- and middle-income countries (LMICs) like India, the coexistence of undernutrition and obesity reflects a double burden of malnutrition. However, limited research has examined how socioeconomic inequalities in high-risk adiposity vary across age cohorts using combined anthropometric measures.\u003c/p\u003e\u003ch2\u003eAim\u003c/h2\u003e\u003cp\u003eTo assess age-cohort patterns of socioeconomic inequalities in high-risk adiposity defined using a composite of body mass index (BMI) and waist-to-hip ratio (WHR) in a nationally representative Indian population.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe analysed data from Wave-1 (2017\u0026ndash;2018) of the Longitudinal Ageing Study in India (LASI), including 65,150 adults with complete anthropometric and socioeconomic data. High-risk body composition (BWC) was defined as overweight BMI with high-risk WHR, or obese BMI with any WHR. Descriptive, regression, and inequality measures (CI and Erreygers CI with decomposition) were used to examine disparities by socioeconomic status across age cohorts.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe 18\u0026ndash;44 cohort had the highest prevalence of overweight (28.4%) and obesity (10.9%), while WHR increased sharply with age (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Nearly all overweight (93%) and obese (91.6%) individuals were WHR high-risk. High-risk BWC was more common among women, urban residents, and individuals with higher education and wealth (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Inequality analysis showed a pro-rich concentration in all cohorts, peaking at ages 45\u0026ndash;59 (ECI: 0.214), with education as the largest contributor (24\u0026ndash;30%).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eHigh-risk adiposity in India is patterned by age and SES. WHR captures substantial central obesity risk, highlighting the need for equity-focused, life-course obesity prevention strategies.\u003c/p\u003e","manuscriptTitle":"Age Cohort Patterns of Socioeconomic Inequalities in High Risk Body Mass Index (BMI) and Waist to Hip Ratio (WHR) Composite: Findings from Nationally Representative Survey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-26 07:57:18","doi":"10.21203/rs.3.rs-7542656/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3d2ea26b-082e-4720-88d4-5049548e67c0","owner":[],"postedDate":"October 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-17T07:24:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-26 07:57:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7542656","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7542656","identity":"rs-7542656","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}