Disentangling the Relationship Between Urinary Metal Exposure and Osteoporosis Risk Across a Broad Population: A Comprehensive Supervised and Unsupervised Analysis
<b>Background</b>: Limited evidence links urinary metal exposure to osteoporosis in broad populations, prompting this study to cover this knowledge gap using supervised and unsupervised approaches. <b>Methods</b>: This study included 15,923 participants from the National Heal...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Series: | Toxics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2305-6304/12/12/866 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850085463401955328 |
|---|---|
| author | Jianing Liu Kai Wang |
| author_facet | Jianing Liu Kai Wang |
| author_sort | Jianing Liu |
| collection | DOAJ |
| description | <b>Background</b>: Limited evidence links urinary metal exposure to osteoporosis in broad populations, prompting this study to cover this knowledge gap using supervised and unsupervised approaches. <b>Methods</b>: This study included 15,923 participants from the National Health and Nutrition Examination Survey (NHANES) spanning from 1999 to 2020. Urinary concentrations of nine metals—barium (Ba), cadmium (Cd), cobalt (Co), cesium (Cs), molybdenum (Mo), lead (Pb), antimony (Sb), thallium (Tl), and tungsten (Tu)—were measured using inductively coupled plasma mass spectrometry (ICP-MS). Osteoporosis was assessed via dual-energy X-ray absorptiometry. A weighted quantile sum (WQS) regression analysis evaluated each metal’s contribution to osteoporosis risk. Partitioning around medoids (PAM) clustering identified the high- and low-exposure groups, and their association with the risk and prognosis of osteoporosis was evaluated. <b>Results</b>: WQS regression identified Cd as a significant osteoporosis risk factor in the general population (odds ratio (OR) = 1.19, 95% confidence interval (CI): 1.08, 1.31, weight = 0.66). Pb notably affected those individuals aged 30–49 years and classified as Mexican American, while Sb impacted Black individuals. PAM clustering showed that the high-exposure group had a significantly higher risk of osteoporosis (OR = 1.74, 95% CI: 1.43, 2.12) and cumulative mortality risk. <b>Conclusions</b>: Urinary metals are associated with the risk and prognosis of osteoporosis. |
| format | Article |
| id | doaj-art-01b209bc84fe40bd891f8521a769a3d7 |
| institution | DOAJ |
| issn | 2305-6304 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Toxics |
| spelling | doaj-art-01b209bc84fe40bd891f8521a769a3d72025-08-20T02:43:43ZengMDPI AGToxics2305-63042024-11-01121286610.3390/toxics12120866Disentangling the Relationship Between Urinary Metal Exposure and Osteoporosis Risk Across a Broad Population: A Comprehensive Supervised and Unsupervised AnalysisJianing Liu0Kai Wang1Medical Faculty, Ulm University, 89075 Ulm, GermanyMedical School, Southeast University, Nanjing 210096, China<b>Background</b>: Limited evidence links urinary metal exposure to osteoporosis in broad populations, prompting this study to cover this knowledge gap using supervised and unsupervised approaches. <b>Methods</b>: This study included 15,923 participants from the National Health and Nutrition Examination Survey (NHANES) spanning from 1999 to 2020. Urinary concentrations of nine metals—barium (Ba), cadmium (Cd), cobalt (Co), cesium (Cs), molybdenum (Mo), lead (Pb), antimony (Sb), thallium (Tl), and tungsten (Tu)—were measured using inductively coupled plasma mass spectrometry (ICP-MS). Osteoporosis was assessed via dual-energy X-ray absorptiometry. A weighted quantile sum (WQS) regression analysis evaluated each metal’s contribution to osteoporosis risk. Partitioning around medoids (PAM) clustering identified the high- and low-exposure groups, and their association with the risk and prognosis of osteoporosis was evaluated. <b>Results</b>: WQS regression identified Cd as a significant osteoporosis risk factor in the general population (odds ratio (OR) = 1.19, 95% confidence interval (CI): 1.08, 1.31, weight = 0.66). Pb notably affected those individuals aged 30–49 years and classified as Mexican American, while Sb impacted Black individuals. PAM clustering showed that the high-exposure group had a significantly higher risk of osteoporosis (OR = 1.74, 95% CI: 1.43, 2.12) and cumulative mortality risk. <b>Conclusions</b>: Urinary metals are associated with the risk and prognosis of osteoporosis.https://www.mdpi.com/2305-6304/12/12/866environmental pollutantsbone healthmachine learningrisk factormortality |
| spellingShingle | Jianing Liu Kai Wang Disentangling the Relationship Between Urinary Metal Exposure and Osteoporosis Risk Across a Broad Population: A Comprehensive Supervised and Unsupervised Analysis Toxics environmental pollutants bone health machine learning risk factor mortality |
| title | Disentangling the Relationship Between Urinary Metal Exposure and Osteoporosis Risk Across a Broad Population: A Comprehensive Supervised and Unsupervised Analysis |
| title_full | Disentangling the Relationship Between Urinary Metal Exposure and Osteoporosis Risk Across a Broad Population: A Comprehensive Supervised and Unsupervised Analysis |
| title_fullStr | Disentangling the Relationship Between Urinary Metal Exposure and Osteoporosis Risk Across a Broad Population: A Comprehensive Supervised and Unsupervised Analysis |
| title_full_unstemmed | Disentangling the Relationship Between Urinary Metal Exposure and Osteoporosis Risk Across a Broad Population: A Comprehensive Supervised and Unsupervised Analysis |
| title_short | Disentangling the Relationship Between Urinary Metal Exposure and Osteoporosis Risk Across a Broad Population: A Comprehensive Supervised and Unsupervised Analysis |
| title_sort | disentangling the relationship between urinary metal exposure and osteoporosis risk across a broad population a comprehensive supervised and unsupervised analysis |
| topic | environmental pollutants bone health machine learning risk factor mortality |
| url | https://www.mdpi.com/2305-6304/12/12/866 |
| work_keys_str_mv | AT jianingliu disentanglingtherelationshipbetweenurinarymetalexposureandosteoporosisriskacrossabroadpopulationacomprehensivesupervisedandunsupervisedanalysis AT kaiwang disentanglingtherelationshipbetweenurinarymetalexposureandosteoporosisriskacrossabroadpopulationacomprehensivesupervisedandunsupervisedanalysis |