Diabetes mellitus, systemic inflammation and overactive bladder

被引:8
作者
He, Qingliu [2 ,3 ]
Wu, Lizhen [4 ]
Deng, Changqi [3 ]
He, Jiatai [3 ]
Wen, Jun [5 ]
Wei, Chengcheng [3 ,6 ]
You, Zhijiao [1 ]
机构
[1] JinJiang Municipal Hosp, Dept Urol, Quanzhou, Fujian, Peoples R China
[2] Fujian Med Univ, Affiliated Hosp 2, Dept Urol, Quanzhou, Peoples R China
[3] Huazhong Univ Sci & Technol, Union Hosp, Tongji Med Coll, Dept Urol, Wuhan, Hubei, Peoples R China
[4] Fujian Med Univ, Affiliated Hosp 2, Dept Endocrinol & Metab, Quanzhou, Fujian, Peoples R China
[5] Chongqing Med Univ, Dept Resp & Crit Care Med, Affiliated Hosp 1, Chongqing, Peoples R China
[6] Chongqing Med Univ, Affiliated Hosp 1, Dept Urol, Chongqing, Peoples R China
来源
FRONTIERS IN ENDOCRINOLOGY | 2024年 / 15卷
关键词
diabetes; inflammation; overactive bladder; NHANES; epidemiology; METABOLIC SYNDROME; CYSTOPATHY; RISK; DYSFUNCTION; PREVALENCE; MECHANISMS; MEDIATION; DIAGNOSIS; WOMEN;
D O I
10.3389/fendo.2024.1386639
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Background: Increasing evidence emphasizes the potential relationship between diabetes and OAB (overactive bladder). However, large population epidemiology is still lacking. Methods: This cross-sectional study included six cycle NHANES surveys, with a total of 23863 participants. Logistic regression models were constructed to analyze the association between diabetes mellitus, diabetes-related markers, and inflammatory biomarkers with OAB. Restricted cubic splines were used to analyze the non-linear associations. Mediating analysis was performed to test the effect of inflammatory biomarkers on the relationship between diabetes-related markers and OAB. Finally, machine learning models were applied to predict the relative importance and construct the best-fit model. Results: Diabetes mellitus participants' OAB prevalence increased by 77% compared with non-diabetes. As the quartiles of diabetes-related markers increased, the odds of OAB monotonically increased in three models (all p for trend < 0.001). Glycohemoglobin exhibited a linear association with OAB (p for nonlinearity > 0.05). White blood cells significantly mediated the associations between diabetes-related markers (glycohemoglobin, fasting glucose, and insulin) with OAB, and the proportions were 7.23%, 8.08%, and 17.74%, respectively (all p < 0.0001). Neutrophils partly mediated the correlation between (glycohemoglobin, fasting glucose, and insulin) and OAB at 6.58%, 9.64%, and 17.93%, respectively (all p < 0.0001). Machine learning of the XGBoost model constructs the best fit model, and XGBoost predicts glycohemoglobin is the most important indicator on OAB. Conclusion: Our research revealed diabetes mellitus and diabetes-related markers were remarkably associated with OAB, and systemic inflammation was an important mediator of this association.
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页数:11
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