ArticlePloS one2025
Association between cardiometabolic index and albuminuria: Evidence from NHANES 2017-2020.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Article
- Podocytopathies.Nature reviews. Disease primers · 2025Review
- The association between the cardiac metabolic index and rapid kidney function decline and CKD in individuals with different glucose metabolism statuses: results from the China health and retirement longitudinal study.Lipids in health and disease · 2025Article
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Authors and funding
5 authors.
Funding
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Abstract
introductionAlbuminuria 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.
methodsThe 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) × 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.
resultsA 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.
conclusionsOur 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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