Journal of trace elements in medicine and biology : organ of the Society for Minerals and Trace Elements (GMS) · 2026
Associations of individual and combined alterations in essential trace metal levels with breast cancer risk: A case-control study in Tanzania.
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Njale Emmanuel, Mahugija John A M, Shilla Dativa J, Mbare Nyimvua S
Аннотация
BACKGROUND: Both deficiency and excess of essential trace metals may be linked to an increased risk of breast cancer. Alterations in trace metal concentrations have been noted in breast cancer patients, but their relationships with breast cancer remain unclear. The aim of this study was to explore the associations between alterations in trace metals levels and breast cancer.
METHODS: Urinary trace metals were determined in 100 breast cancer patients and 80 controls using an inductively coupled plasma atomic emission spectrophotometer (ICP-AES). An unconditional binary logistic regression model was used to assess the impact of individual metals on breast cancer, while Bayesian kernel machine regression (BKMR) was applied to uncover possible non-linear, interactive, and combined metal effects on breast cancer.
RESULTS: In multivariable models, the second quartile of Cu (OR = 10.83, 95 % CI: 2.54-46.23) and Se in third (OR = 8.54, 95 % CI: 1.25-58.20) and highest (OR = 11.87, 95 % CI: (1.65-85.20) quartiles were significantly associated with increased breast cancer risk, while the second quartile of Co (OR = 0.15, 95 % CI: 0.02-0.96) was significantly connected to a reduced risk. The analysis of combined effects revealed that higher concentrations of trace metal mixtures were associated with breast cancer, with Cu contributing the most to the overall relationship within the mixture.
CONCLUSION: Alteration in urinary Co levels was linked to a lower risk of breast cancer, while Cu and Se were associated with increased risks. The combined effect of the metal mixture was also linked to breast cancer. It is recommended that further large-scale and population longitudinal studies can be conducted in order to clarify or confirm these findings.
METHODS: Urinary trace metals were determined in 100 breast cancer patients and 80 controls using an inductively coupled plasma atomic emission spectrophotometer (ICP-AES). An unconditional binary logistic regression model was used to assess the impact of individual metals on breast cancer, while Bayesian kernel machine regression (BKMR) was applied to uncover possible non-linear, interactive, and combined metal effects on breast cancer.
RESULTS: In multivariable models, the second quartile of Cu (OR = 10.83, 95 % CI: 2.54-46.23) and Se in third (OR = 8.54, 95 % CI: 1.25-58.20) and highest (OR = 11.87, 95 % CI: (1.65-85.20) quartiles were significantly associated with increased breast cancer risk, while the second quartile of Co (OR = 0.15, 95 % CI: 0.02-0.96) was significantly connected to a reduced risk. The analysis of combined effects revealed that higher concentrations of trace metal mixtures were associated with breast cancer, with Cu contributing the most to the overall relationship within the mixture.
CONCLUSION: Alteration in urinary Co levels was linked to a lower risk of breast cancer, while Cu and Se were associated with increased risks. The combined effect of the metal mixture was also linked to breast cancer. It is recommended that further large-scale and population longitudinal studies can be conducted in order to clarify or confirm these findings.