Environmental exposure to metal(loid)s: an emerging risk factor for osteoporosis in postmenopausal women?

Background: Osteoporosis is a major public health concern, particularly in the aging population, due to its association with increased fracture risk and reduced quality of life [1]. Although well-established risk factors contribute to bone mineral density (BMD) loss [2], environmental exposure to metals and metalloids is an emerging but underexplored determinant. Objective: To evaluate the association between exposure to metals and metalloids and osteoporosis in postmenopausal women aged 50 and older. Methods: This cross-sectional study included 380 postmenopausal women, aged 50–70 years, residing in Cascavel, Paraná, Brazil. BMD was measured using dual-energy X-ray absorptiometry. Urinary concentrations of 20 metals and metalloids ─ aluminium (Al), barium (Ba), cadmium (Cd), cobalt (Co), cesium (Cs), copper (Cu), mercury (Hg), lithium (Li), manganese (Mn), molybdenum (Mo), nickel (Ni), lead (Pb), rubidium (Rb), antimony (Sb), selenium (Se), tin (Sn), strontium (Sr), thallium (Tl), uranium (U), and zinc (Zn) ─ were quantified using inductively coupled plasma mass spectrometry, and corrected for creatinine levels. Univariate and multiple regression analyses were used to investigate the association between metal(loid) exposure and BMD or osteoporosis risk. Results: A total of 73 women (19%) were diagnosed with osteoporosis. Univariate analysis revealed that urinary levels of Cd, Mn, Pb, Sb, Sn, and Zn were significantly increased in the osteoporosis group after adjustment for confounders (p < 0.05). Additionally, significant negative correlations were found between BMD and urinary levels of Al, Cd, Hg, Mn, Sb, and U after adjustment for confounders (p < 0.05). Higher urinary levels of Cd (OR = 1.495, 95% CI: 1.048; 2.131, p = 0.026), Mn (OR = 1.014, 95% CI: 1.001; 1.028, p = 0.040), Pb (OR = 1.016, 95% CI: 1.000; 1.033, p = 0.048) and Sb (OR = 2.059, 95% CI: 1.073; 3.950, p = 0.030) were independently and significantly associated with increased odds of osteoporosis. Conclusions: Our findings highlight a significant and clinically relevant association between environmental exposure to Cd or Sb and an increased risk of osteoporosis in postmenopausal women. Further longitudinal studies are essential to better understand the long-term effects of metal(loid) exposure on bone health, which could guide the development of targeted prevention strategies for osteoporosis.

Publication Details

Journal
Open Repository of the University of Porto (University of Porto)
Published
2025-05-06
DOI
https://doi.org/10.48797/sl.2025.364
Primary Topic
Heavy Metal Exposure and Toxicity
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Environmental exposure to metal(loid)s: an emerging risk factor for osteoporosis in postmenopausal women?

Open Repository of the University of Porto (University of Porto)
Heavy Metal Exposure and Toxicity
article

Environmental exposure to metal(loid)s: an emerging risk factor for osteoporosis in postmenopausal women?

article en

Abstract

Background: Osteoporosis is a major public health concern, particularly in the aging population, due to its association with increased fracture risk and reduced quality of life [1]. Although well-established risk factors contribute to bone mineral density (BMD) loss [2], environmental exposure to metals and metalloids is an emerging but underexplored determinant. Objective: To evaluate the association between exposure to metals and metalloids and osteoporosis in postmenopausal women aged 50 and older. Methods: This cross-sectional study included 380 postmenopausal women, aged 50–70 years, residing in Cascavel, Paraná, Brazil. BMD was measured using dual-energy X-ray absorptiometry. Urinary concentrations of 20 metals and metalloids ─ aluminium (Al), barium (Ba), cadmium (Cd), cobalt (Co), cesium (Cs), copper (Cu), mercury (Hg), lithium (Li), manganese (Mn), molybdenum (Mo), nickel (Ni), lead (Pb), rubidium (Rb), antimony (Sb), selenium (Se), tin (Sn), strontium (Sr), thallium (Tl), uranium (U), and zinc (Zn) ─ were quantified using inductively coupled plasma mass spectrometry, and corrected for creatinine levels. Univariate and multiple regression analyses were used to investigate the association between metal(loid) exposure and BMD or osteoporosis risk. Results: A total of 73 women (19%) were diagnosed with osteoporosis. Univariate analysis revealed that urinary levels of Cd, Mn, Pb, Sb, Sn, and Zn were significantly increased in the osteoporosis group after adjustment for confounders (p < 0.05). Additionally, significant negative correlations were found between BMD and urinary levels of Al, Cd, Hg, Mn, Sb, and U after adjustment for confounders (p < 0.05). Higher urinary levels of Cd (OR = 1.495, 95% CI: 1.048; 2.131, p = 0.026), Mn (OR = 1.014, 95% CI: 1.001; 1.028, p = 0.040), Pb (OR = 1.016, 95% CI: 1.000; 1.033, p = 0.048) and Sb (OR = 2.059, 95% CI: 1.073; 3.950, p = 0.030) were independently and significantly associated with increased odds of osteoporosis. Conclusions: Our findings highlight a significant and clinically relevant association between environmental exposure to Cd or Sb and an increased risk of osteoporosis in postmenopausal women. Further longitudinal studies are essential to better understand the long-term effects of metal(loid) exposure on bone health, which could guide the development of targeted prevention strategies for osteoporosis.

Open Repository of the University of Porto (University of Porto)
Openalex Percentile: Top 48%
Heavy Metal Exposure and Toxicity
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.