Investigating the role of the metabolic score for visceral fat in assessing the prevalence of chronic kidney disease from the NHANES 1999-2018
This study investigates the association between the Metabolic Score for Visceral Fat (METS-VF) and chronic kidney disease (CKD), assessing METS-VF as a potential predictor of CKD risk. Utilizing data from the 1999-2018 National Health and Nutrition Examination Survey (NHANES), this cross-sectional s...
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| Main Authors: | , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
18 January 2025
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| In: |
Scientific reports
Year: 2025, Volume: 15, Pages: 1-13 |
| ISSN: | 2045-2322 |
| DOI: | 10.1038/s41598-025-86723-3 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1038/s41598-025-86723-3 Verlag, lizenzpflichtig, Volltext: https://www.nature.com/articles/s41598-025-86723-3 
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| Author Notes: | Xingcheng Zhou, Jiayi Xiang, Shuxian Zhang, Jun Yang, Yi Tang & Yalan Wang |
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| 520 | |a This study investigates the association between the Metabolic Score for Visceral Fat (METS-VF) and chronic kidney disease (CKD), assessing METS-VF as a potential predictor of CKD risk. Utilizing data from the 1999-2018 National Health and Nutrition Examination Survey (NHANES), this cross-sectional study included 24,387 adult participants. Multivariable logistic regression, restricted cubic spline models, and threshold effect analyses were employed to explore the relationship. The results revealed a significant positive association, with multivariable logistic regression showing that each unit increase in METS-VF was associated with an 86% higher risk of CKD (OR: 1.86, 95% CI: 1.48-2.34). Critical METS-VF thresholds (6.10 and 7.55) were identified, at which CKD risk increased substantially. Subgroup analyses indicated that the association was particularly pronounced among older adults and males. These findings suggest that METS-VF is a reliable predictor for assessing CKD risk and that lifestyle interventions, including dietary modifications and exercise programs, may mitigate this risk. | ||
| 650 | 4 | |a Computational biology and bioinformatics | |
| 650 | 4 | |a Endocrinology | |
| 650 | 4 | |a Kidney diseases | |
| 650 | 4 | |a Nephrology | |
| 650 | 4 | |a Public health | |
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| 700 | 1 | |a Yang, Jun |e VerfasserIn |4 aut | |
| 700 | 1 | |a Tang, Yi |e VerfasserIn |4 aut | |
| 700 | 1 | |a Wang, Yalan |e VerfasserIn |4 aut | |
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