Association between cardiometabolic index and albuminuria: Evidence from NHANES 2017-2020

被引:0
|
作者
Xu, Qiming [1 ,2 ]
Lin, Junyan [2 ]
Liao, Lin [1 ,2 ]
Hu, Jing [1 ,2 ]
Lu, Jianrao [1 ,2 ]
机构
[1] Shanghai Univ Tradit Chinese Med, Inst Kidney Dis, Shanghai, Peoples R China
[2] Shanghai Univ Tradit Chinese Med, Dept Nephrol, Peoples Hosp 7, Shanghai, Peoples R China
来源
PLOS ONE | 2025年 / 20卷 / 02期
关键词
BODY-MASS INDEX; CHRONIC KIDNEY-DISEASE; GENERAL-POPULATION; FAT DISTRIBUTION; RISK; PROTEINURIA; ADIPOSITY; OBESITY; HEALTH; HYPERTENSION;
D O I
10.1371/journal.pone.0318736
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Introduction Albuminuria is a crucial marker of kidney damage and serves as an early indicator of the risk for chronic kidney disease (CKD). Recent studies have suggested that the cardiometabolic index (CMI), could be valuable for screening renal insufficiency. However, the relationship between CMI and albuminuria remains underexplored. Therefore, the aim of this study was to investigate the association between CMI and albuminuria, with the goal of providing new insights for the clinical diagnosis, assessment, and early intervention of kidney disease. Methods The National Health and Nutrition Examination Survey (NHANES) for the period between 2017-2020 provided the data for this cross-sectional investigation. Triglyceride (TG) (mmol/L)/High density lipid-cholesterol (HDL-C) (mmol/L) x Waist height ratio (WHtR) was the formula used for calculating CMI. Using multifactorial logistic regression, the independent connection between albuminuria and CMI was investigated. The threshold effect was determined by means of a two-stage linear regression model. Additionally, subgroup analysis and interaction tests were carried out. Results A total of 3,339 participants were included, and 12.38% of them had albuminuria. As the CMI quartiles grew (quartile 1: 7.78%, quartile 2: 13.43%, quartile 3: 12.93%, quartile 4: 17.01%), so did the probability of albuminuria. The results of adjusted model 3 showed that a greater probability of albuminuria prevalence was strongly correlated with CMI (OR = 2.26, 95% CI: 1.58-3.23). This association held true for all subgroups (all P for trend > 0.05). Furthermore, with a two-stage linear regression model with an inflection point of 0.92, we discovered a nonlinear relationship between CMI and albuminuria. Conclusions Our findings indicate that CMI levels are significantly associated with the risk of albuminuria prevalence, suggesting that CMI could serve as a valuable biomarker for assessing the risk of albuminuria.
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页数:14
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