Association of composite dietary antioxidant index and endometriosis risk in reproductive—age women: a cross-sectional study using big data-machine learning approach
BackgroundEndometriosis (EM) is a chronic gynecological disorder characterized by the growth of endometrial-like tissue outside the uterus, leading to pain and infertility. Recent studies suggest that antioxidants may play a protective role in the development of EM. However, the precise connection b...
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Frontiers Media S.A.
2025-03-01
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| Series: | Frontiers in Nutrition |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnut.2025.1572336/full |
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| author | Wenxin Chen Kui Xiao Chenyu Zhou Jiajia Cheng Zixuan Zeng Fang Zhang |
| author_facet | Wenxin Chen Kui Xiao Chenyu Zhou Jiajia Cheng Zixuan Zeng Fang Zhang |
| author_sort | Wenxin Chen |
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| description | BackgroundEndometriosis (EM) is a chronic gynecological disorder characterized by the growth of endometrial-like tissue outside the uterus, leading to pain and infertility. Recent studies suggest that antioxidants may play a protective role in the development of EM. However, the precise connection between the composite dietary antioxidant index (CDAI)—a key measure of dietary antioxidants—and EM risk remains unclear. This study aims to explore the relationship between CDAI and EM risk using data from the National Health and Nutrition Examination Survey (NHANES), potentially guiding dietary interventions for EM prevention.MethodsThis study analyzed data from the NHANES spanning 1999 to 2006. To investigate the relationship between the CDAI and the EM, a variety of statistical techniques were employed, including a weighted multiple logistic regression model, smooth curve fitting, machine learning analysis, and subgroup analyses.ResultsAfter controlling for potential confounding variables, the results indicated an inverse relationship between CDAI and EM (OR = 0.92, 95% CI 0.86–0.98, p = 0.011). Compared to participants in the lowest quartile (Q1), the odds ratios (OR) for higher CDAI in the other quartiles were as follows: Q2 (OR = 0.84, 95% CI 0.45–1.57, p = 0.576), Q3 (OR = 0.64, 95% CI 0.33–1.24, p = 0.172), and Q4 (OR = 0.47, 95% CI 0.26–0.87, p = 0.019). Among the various components of the CDAI, changes in vitamin A, vitamin E, and carotene were independently associated with the occurrence of EM, while both LASSO and RF machine learning algorithms consistently identified selenium and carotene as significant factors. Furthermore, subgroup analyses did not reveal significant interactions by age, body mass index, smoking, drinking, diabetes, or hypertension.ConclusionThe findings of this extensive cross-sectional study indicate a clear negative linear correlation between the CDAI and EM in American adult women. It is therefore recommended that women incorporate a greater proportion of antioxidant-rich foods into their diet to assist in the prevention of EM. |
| format | Article |
| id | doaj-art-469fc03bdf7543e1ad04256619e01d3e |
| institution | OA Journals |
| issn | 2296-861X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Nutrition |
| spelling | doaj-art-469fc03bdf7543e1ad04256619e01d3e2025-08-20T02:10:42ZengFrontiers Media S.A.Frontiers in Nutrition2296-861X2025-03-011210.3389/fnut.2025.15723361572336Association of composite dietary antioxidant index and endometriosis risk in reproductive—age women: a cross-sectional study using big data-machine learning approachWenxin Chen0Kui Xiao1Chenyu Zhou2Jiajia Cheng3Zixuan Zeng4Fang Zhang5Department of Gynaecology and Obstetrics, Affiliated Hengyang Hospital of Hunan Normal University & Hengyang Central Hospital, Hengyang, ChinaDepartment of Plastic Surgery, Guangzhou Red Cross Hospital, Jinan University, Guangzhou, ChinaReproductive Medicine Center, Hunan Provincial Maternal and Child Health Care Hospital, Changsha, ChinaDepartment of Plastic Surgery, Guangzhou Red Cross Hospital, Jinan University, Guangzhou, ChinaDepartment of Plastic Surgery, Guangzhou Red Cross Hospital, Jinan University, Guangzhou, ChinaDepartment of Gynaecology and Obstetrics, Affiliated Hengyang Hospital of Hunan Normal University & Hengyang Central Hospital, Hengyang, ChinaBackgroundEndometriosis (EM) is a chronic gynecological disorder characterized by the growth of endometrial-like tissue outside the uterus, leading to pain and infertility. Recent studies suggest that antioxidants may play a protective role in the development of EM. However, the precise connection between the composite dietary antioxidant index (CDAI)—a key measure of dietary antioxidants—and EM risk remains unclear. This study aims to explore the relationship between CDAI and EM risk using data from the National Health and Nutrition Examination Survey (NHANES), potentially guiding dietary interventions for EM prevention.MethodsThis study analyzed data from the NHANES spanning 1999 to 2006. To investigate the relationship between the CDAI and the EM, a variety of statistical techniques were employed, including a weighted multiple logistic regression model, smooth curve fitting, machine learning analysis, and subgroup analyses.ResultsAfter controlling for potential confounding variables, the results indicated an inverse relationship between CDAI and EM (OR = 0.92, 95% CI 0.86–0.98, p = 0.011). Compared to participants in the lowest quartile (Q1), the odds ratios (OR) for higher CDAI in the other quartiles were as follows: Q2 (OR = 0.84, 95% CI 0.45–1.57, p = 0.576), Q3 (OR = 0.64, 95% CI 0.33–1.24, p = 0.172), and Q4 (OR = 0.47, 95% CI 0.26–0.87, p = 0.019). Among the various components of the CDAI, changes in vitamin A, vitamin E, and carotene were independently associated with the occurrence of EM, while both LASSO and RF machine learning algorithms consistently identified selenium and carotene as significant factors. Furthermore, subgroup analyses did not reveal significant interactions by age, body mass index, smoking, drinking, diabetes, or hypertension.ConclusionThe findings of this extensive cross-sectional study indicate a clear negative linear correlation between the CDAI and EM in American adult women. It is therefore recommended that women incorporate a greater proportion of antioxidant-rich foods into their diet to assist in the prevention of EM.https://www.frontiersin.org/articles/10.3389/fnut.2025.1572336/fullcomposite dietary antioxidant index (CDAI)endometriosis (EM)national health and nutrition examination survey (NHANES)cross-sectional researchmachine learning |
| spellingShingle | Wenxin Chen Kui Xiao Chenyu Zhou Jiajia Cheng Zixuan Zeng Fang Zhang Association of composite dietary antioxidant index and endometriosis risk in reproductive—age women: a cross-sectional study using big data-machine learning approach Frontiers in Nutrition composite dietary antioxidant index (CDAI) endometriosis (EM) national health and nutrition examination survey (NHANES) cross-sectional research machine learning |
| title | Association of composite dietary antioxidant index and endometriosis risk in reproductive—age women: a cross-sectional study using big data-machine learning approach |
| title_full | Association of composite dietary antioxidant index and endometriosis risk in reproductive—age women: a cross-sectional study using big data-machine learning approach |
| title_fullStr | Association of composite dietary antioxidant index and endometriosis risk in reproductive—age women: a cross-sectional study using big data-machine learning approach |
| title_full_unstemmed | Association of composite dietary antioxidant index and endometriosis risk in reproductive—age women: a cross-sectional study using big data-machine learning approach |
| title_short | Association of composite dietary antioxidant index and endometriosis risk in reproductive—age women: a cross-sectional study using big data-machine learning approach |
| title_sort | association of composite dietary antioxidant index and endometriosis risk in reproductive age women a cross sectional study using big data machine learning approach |
| topic | composite dietary antioxidant index (CDAI) endometriosis (EM) national health and nutrition examination survey (NHANES) cross-sectional research machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fnut.2025.1572336/full |
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