{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T02:08:45Z","timestamp":1792030125788,"version":"4.4.0"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"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":["BMC Cardiovasc Disord"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Background<\/jats:title>\n            <jats:p>Cardiometabolic multimorbidity (CMM), the co-occurrence of two or more cardiometabolic conditions, poses a growing health concern as populations age. Linked to adverse outcomes like cognitive decline, poor COVID-19 prognosis, and higher mortality, its prevalence is rising due to shared risk factors among conditions such as diabetes, heart disease, and hypertension. While CMM is well-studied in high-income countries, data from low- and middle-income countries, particularly from India, is limited. This study investigates the prevalence and correlates of CMM among older Indian adults.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Methods<\/jats:title>\n            <jats:p>We analyzed data from 59,764 participants aged 45 years and older from the Longitudinal Ageing Study in India (LASI), wave 1, conducted from 2017 to 2019. CMM was defined as the co-occurrence of two or more of the following conditions: hypertension, diabetes, coronary heart disease, stroke, obesity, and hypercholesterolemia. Descriptive statistics were employed to calculate the prevalence of CMM with 95% confidence intervals to predict uncertainty. Multivariable logistic regression was used to examine the associations between CMM and various socio-demographic correlates, with results reported as adjusted odds ratios (AOR).<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>We observed higher CMM prevalence among females 27.3% (95% CI: 26.8%-27.8%) compared to males 23.04% (95% CI: 22.5%-23.5%), particularly in the 60\u201374 age group. CMM was more prevalent in urban areas, wealthier socioeconomic groups, and those with higher education. Increased odds of CMM were found among males from the OBC and other castes and females from SC, OBC, and \u201cOthers\u201d castes, while ST males had lower odds. Significant correlates for both genders included not working, urban residence, and higher wealth. Regionally, CMM rates were highest in the Southern and Western regions, and lowest in the North-East and Central regions of India.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>This study reveals a rising burden of CMM among middle-aged and older adults in India, with higher prevalence in females, urban residents, and wealthier groups. Regional and caste disparities highlight the need for targeted interventions. Effective management of CMM requires early screening and comprehensive primary care, especially as India ages and develops.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/s12872-025-05190-w","type":"journal-article","created":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T10:49:50Z","timestamp":1761043790000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Cardiometabolic multimorbidity (CMM) among older adults in India"],"prefix":"10.1186","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7582-1047","authenticated-orcid":false,"given":"Manish","family":"Barik","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0085-8878","authenticated-orcid":false,"given":"Sushree Nibedita","family":"Panda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,10,21]]},"reference":[{"issue":"7","key":"5190_CR1","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1093\/eurheartj\/ehac744","volume":"44","author":"A Dove","year":"2023","unstructured":"Dove A, Guo J, Marseglia A, Fastbom J, Vetrano DL, Fratiglioni L, et al. 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