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This study investigated the concentrations of essential and potentially toxic metals in the meat and liver of eighty small ruminants in Lorestan, Iran and assessed the risks associated with their consumption. Essential (Co, Se, Cu, Fe, Mg, Mn, and Zn) and potentially toxic (Ni, Cd, and Pb) metal concentrations were measured using ICP-OES. The sources and distribution patterns of essential and toxic trace metals were examined using multivariate statistical analyses including the Pearson correlation coefficient (PCC), principal component analysis (PCA) and hierarchical cluster analysis (HCA). Human health risk was evaluated using the lifetime cancer risk, the hazard ratio and the hazard index, and uncertainty in these parameters was quantified through Monte Carlo simulation. Most metals were present at concentrations consistent with international standards, whereas Zn with values from 75.33 to 132.5\u00a0mg\/kg, Ni with values from 0.76 to 1.2\u00a0mg \/kg and Pb with values from 0.12 to 0.65\u00a0mg\/kg exceeded the limits recommended by FAO\/WHO. The hazard index was less than one, indicating a low probability of non-carcinogenic effects. However, the lifetime cancer risk suggested a moderate carcinogenic risk associated with nickel in meat. Multivariate analysis showed that manganese, iron, copper and cadmium were primarily associated with natural terrestrial sources, while Ni, Pb and Zn were linked to anthropogenic inputs. These findings highlight the need to assess grazing environments and water sources, improve hygienic practices during slaughter and establish maximum permissible levels for trace elements to reinforce food safety monitoring.<\/jats:p>","DOI":"10.1038\/s41598-025-33838-2","type":"journal-article","created":{"date-parts":[[2025,12,27]],"date-time":"2025-12-27T14:54:20Z","timestamp":1766847260000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Potentially toxic metals in small ruminant tissues: multivariate analysis and health risk assessment via Monte Carlo simulation"],"prefix":"10.1038","volume":"16","author":[{"given":"Alireza","family":"Fathi-Beyranvand","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8935-8321","authenticated-orcid":false,"given":"Azadeh","family":"Rashidimehr","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4735-4095","authenticated-orcid":false,"given":"Elaheh","family":"Askari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fatemeh","family":"Esfarjani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fatemeh","family":"Mohammadi-Nasrabadi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,12,27]]},"reference":[{"key":"33838_CR1","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1016\/j.foodchem.2016.11.082","volume":"238","author":"DB Haytowitz","year":"2018","unstructured":"Haytowitz, D. 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