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- Author: Ruijie Xie x
- Metabolic Syndrome and Diabetes x
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Background
Depression has become a multifaceted global health issue, with complex connections to obesity. Weight-adjusted-waist index (WWI) can effectively evaluate central obesity, but the relationship between WWI and depression has not been well studied. The study aims to investigate the potential correlation between these two health parameters.
Methods
According to the data from National Health and Nutrition Examination Survey, this cross-sectional study used multiple regression analysis, subgroup analysis, and smooth curve fitting to explore the relationship between WWI and depression. The assessment ability of WWI was evaluated and compared to other obesity indicators using the receiver operating characteristic (ROC) curve.
Results
This study analyzed 38,154 participants. Higher WWI is associated with higher depression scores (β = 0.41; 95% CI, 0.36–0.47). After adjusting for various confounding factors, the positive correlation between WWI and depression remained significant (P for trend < 0.0001). Nonlinear positive correlation was detected with a breakpoint of 11.14. ROC analysis shows that compared to other obesity indicators (ROCWWI = 0.593; ROCBMI = 0.584; and ROCWC = 0.581), the correlation between WWI and depression has better discrimination and accuracy. DII mediated 4.93%, SII mediated 5.08%, and sedentary mediated 0.35% of the total association between WWI and depression.
Conclusion
WWI levels were related to an increased likelihood of depression and showed a stronger relationship than BMI and waist circumference. Our findings indicated that WWI may serve as a simple anthropometric index to evaluate depression.
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Objective
The aim of this study was to investigate the relationship between weight-adjusted-waist index (WWI) and diabetic kidney disease in individuals afflicted with type 2 diabetes.
Methods
Comprehensive data were ascertained from the National Health and Nutrition Examination Survey in 2013–March 2020. Weighted univariate, multivariate logistic regression models, subgroup analyses and tests for interaction were performed. Additionally, we employed smooth curve fitting to assess linear correlations and the threshold effects were calculated by applying a binary linear regression model. Breakpoints are identified by a model with maximum likelihood ratio and a two-step recursive approach. Receiver operating characteristic curve (ROC) along with the area under the curve (AUC) value predict the capability of WWI and body mass index for diabetic kidney disease.
Results
A total of 10,661 individuals diagnosed with type 2 diabetes were included, and the overall prevalence of diabetic kidney disease was 20.74%. WWI exhibited a positive correlation with the likelihood of diabetic kidney disease in type 2 diabetes patients (OR: 1.17, 95% CI: 1.03–1.33). The results of subgroup analysis showed significant interaction for gender (P < 0.05). Among female patients, U-shaped correlations were observed with a breakpoint at 11.48. Additionally, weight-adjusted waist index (AUC = 0.664) proved to be a more effective predictor of diabetic kidney disease compared to body mass index (AUC = 0.555).
Conclusion
In patients with type 2 diabetes, increased weight-adjusted-waist index is implicated with an increased risk of diabetic kidney disease. WWI can be used as a new anthropometric index to predict diabetic kidney disease, and its predictive ability is stronger than body mass index.
