{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T10:02:27Z","timestamp":1791453747782,"version":"4.3.3"},"reference-count":30,"publisher":"American Medical Association (AMA)","issue":"16","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAMA"],"published-print":{"date-parts":[[2026,4,28]]},"abstract":"\n Importance<\/jats:title>\n Despite declines, lead exposure remains a major risk factor for cardiovascular disease (CVD) mortality. Quantifying the direct effects of cumulative bone lead exposure on CVD is essential to guide prevention and policy.<\/jats:p>\n <\/jats:sec>\n \n Objectives<\/jats:title>\n To estimate the association between bone lead levels and CVD, derive an exposure-response curve, and estimate global, regional, and national lead-attributable disease burden for 1990 to 2023.<\/jats:p>\n <\/jats:sec>\n \n Design, Setting, and Participants<\/jats:title>\n Analysis of 42\u202f028 adults from 9 cycles of the US National Health and Nutrition Examination Survey (NHANES) spanning 1988 through 2013, with follow-up through December 2015 capturing 1748 CVD deaths, was combined with a systematic review and meta-regression. Bone lead was estimated from blood lead, age, and cohort-specific exposure histories. Cox proportional hazards models adjusted for blood pressure and confounders estimated hazard ratios for CVD mortality. A bayesian meta-regression using the Burden of Proof framework pooled NHANES results with published data to estimate the risk curve, which was then applied to lead exposure and CVD estimates to estimate population-attributable fractions and lead-attributable CVD burden.<\/jats:p>\n <\/jats:sec>\n \n Exposure<\/jats:title>\n Estimated bone lead levels.<\/jats:p>\n <\/jats:sec>\n \n Main Outcomes and Measures<\/jats:title>\n Lead-attributable CVD mortality, years lived with disability, years of life lost, and disability-adjusted life-years (DALYs) by age, sex, year, and location.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n NHANES participants were aged 18 to 90 years (median, 46 [IQR, 32-63] years) and 51.5% of participants were female, with estimated bone lead levels ranging from 0.17 \u03bcg\/g to 301 \u03bcg\/g (median, 13.3 \u03bcg\/g). Higher bone lead levels were associated with higher CVD risk: relative to estimated preindustrial levels of 0.027 \u03bcg\/g, bone lead levels of 5 \u03bcg\/g, 10 \u03bcg\/g, 25 \u03bcg\/g, 50 \u03bcg\/g, and 100 \u03bcg\/g corresponded to 7.5% (95% uncertainty interval [UI], 1.7%-14.1%), 15.8% (95% UI, 12.7%-19.1%), 41.3% (95% UI, 34.2%-49.2%), 71.3% (95% UI, 55.4%-90.0%), and 87.9% (95% UI, 61.0%-121.2%) greater CVD mortality risk, respectively. Globally in 2023, 3.5 million (95% UI, 2.6 million to 4.4 million) deaths and 71.6 million (95% UI, 52.4 million to 90.3 million) DALYs were attributable to lead exposure, accounting for 5.8% (95% UI, 4.3%-7.2%) of all deaths and 2.6% (95% UI, 1.8%-3.2%) of all DALYs. Lead was the eighth leading risk for global mortality and second leading environmental risk.<\/jats:p>\n <\/jats:sec>\n \n Conclusions and Relevance<\/jats:title>\n Cumulative lead exposure remains a major, preventable contributor to global CVD mortality. Strengthened surveillance, regulation, and remediation are urgently required to reduce the lead-attributable burden.<\/jats:p>\n <\/jats:sec>","DOI":"10.1001\/jama.2026.2197","type":"journal-article","created":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T15:30:29Z","timestamp":1774884629000},"page":"1418","source":"Crossref","is-referenced-by-count":8,"title":["Lead-Attributable Cardiovascular Disease Burden"],"prefix":"10.1001","volume":"335","author":[{"name":"GBD 2023 Lead Collaborators","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeffrey D.","family":"Stanaway","sequence":"additional","affiliation":[{"name":"Department of Health Metrics Sciences, Institute for Health Metrics and Evaluation, Seattle, Washington"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sandra","family":"Spearman","sequence":"additional","affiliation":[{"name":"Department of Health Metrics Sciences, Institute for Health Metrics and Evaluation, Seattle, Washington"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicole K.","family":"DeCleene","sequence":"additional","affiliation":[{"name":"Department of Health Metrics Sciences, Institute for Health Metrics and Evaluation, Seattle, Washington"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vanessa","family":"Garcia","sequence":"additional","affiliation":[{"name":"Department of Health Metrics Sciences, Institute for Health Metrics and Evaluation, Seattle, Washington"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laura","family":"Lara-Castor","sequence":"additional","affiliation":[{"name":"Department of Health Metrics Sciences, Institute for Health Metrics and Evaluation, Seattle, Washington"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephen S.","family":"Lim","sequence":"additional","affiliation":[{"name":"Department of Health Metrics Sciences, Institute for Health Metrics and Evaluation, Seattle, Washington"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christopher J. 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