European journal of obstetrics, gynecology, and reproductive biology · 2025
Elevated cardiometabolic index as a potential risk factor for female Urinary incontinence.
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Wang Jiameng, Wang Boyu, Li Chengjia, Meng Tianwei, Zhou Yue, Chen Jia, Cong Huifang
Аннотация
BACKGROUND: Urinary incontinence (UI) is closely related to metabolic conditions. The cardiometabolic index (CMI) is a novel and easily obtainable indicator used to assess metabolic status. This study aims to explore the potential relationship between CMI and female UI.
METHODS: Data from the NHANES spanning seven consecutive survey cycles from 2005 to 2018 were used, including adult women who self-reported UI and had complete CMI calculation information. The CMI calculation formula is triglycerides(TG)/high-density lipoprotein cholesterol (HDL-C) × waist-to-height ratio (WHtR, WHtR = waist circumference/height). A multivariate logistic regression model was used to examine the linear relationship between CMI and UUI, SUI, and MUI, and subgroup analyses were conducted to explore potential influencing factors. Additionally, RCS curves were used to plot and analyze nonlinear relationships and threshold effects.
RESULTS: A total of 6,628 adult women were enrolled in this study. Multivariate logistic regression showed that in the fully adjusted model, compared to subjects with the lowest CMI, those with the highest CMI had an increased association with UUI, SUI, and MUI by 1.14 times (OR = 2.14; 95 % CI, 1.83-2.50, P < 0.0001), 0.38 times (OR = 1.38; 95 % CI, 1.17-1.63, P = 0.0001), and 0.83 times (OR = 1.83; 95 % CI, 1.49-2.23, P < 0.0001), respectively. Subgroup analysis revealed significant interaction between multiple subgroups and UUI, SUI, and MUI. Additionally, the association between CMI and UUI, SUI, and MUI was nonlinear, with inflection points at 2.98, 3.21, and 2.98, respectively.
CONCLUSIONS: Elevated CMI levels are closely associated with UI and suggest that CMI may serve as a useful reference indicator for assessing UI risk; however, larger prospective cohort studies are needed to validate these findings and clarify the causal relationship between CMI and UI to advance its potential clinical application.
METHODS: Data from the NHANES spanning seven consecutive survey cycles from 2005 to 2018 were used, including adult women who self-reported UI and had complete CMI calculation information. The CMI calculation formula is triglycerides(TG)/high-density lipoprotein cholesterol (HDL-C) × waist-to-height ratio (WHtR, WHtR = waist circumference/height). A multivariate logistic regression model was used to examine the linear relationship between CMI and UUI, SUI, and MUI, and subgroup analyses were conducted to explore potential influencing factors. Additionally, RCS curves were used to plot and analyze nonlinear relationships and threshold effects.
RESULTS: A total of 6,628 adult women were enrolled in this study. Multivariate logistic regression showed that in the fully adjusted model, compared to subjects with the lowest CMI, those with the highest CMI had an increased association with UUI, SUI, and MUI by 1.14 times (OR = 2.14; 95 % CI, 1.83-2.50, P < 0.0001), 0.38 times (OR = 1.38; 95 % CI, 1.17-1.63, P = 0.0001), and 0.83 times (OR = 1.83; 95 % CI, 1.49-2.23, P < 0.0001), respectively. Subgroup analysis revealed significant interaction between multiple subgroups and UUI, SUI, and MUI. Additionally, the association between CMI and UUI, SUI, and MUI was nonlinear, with inflection points at 2.98, 3.21, and 2.98, respectively.
CONCLUSIONS: Elevated CMI levels are closely associated with UI and suggest that CMI may serve as a useful reference indicator for assessing UI risk; however, larger prospective cohort studies are needed to validate these findings and clarify the causal relationship between CMI and UI to advance its potential clinical application.