Association Between Chinese Visceral Adipose Index and Albuminuria in Chinese Adults: A Cross-Sectional Study
Bibliographic record
Abstract
Purpose: To explore the correlation between Chinese visceral adipose index (CVAI) and urinary microalbumin/creatinine ratio (UACR) and urinary albumin, and whether there is any difference in correlation between Han and Tujia ethnicity. Methods: This cross-sectional study was conducted in Changde, Hunan, China from May 2021 to December 2021. Biochemical indicators including anthropometric parameters, blood pressure, blood glucose, blood lipids, and UACR of the participants were measured. Univariate analysis, multivariate analyses and multinomial logistic regression analysis were carried out to assess the association between CVAI and albuminuria. In addition, curve fitting and threshold effect analysis were used to explore the nonlinear association between CVAI and albuminuria, and to observe whether there were ethnic differences in this association. Results: A total of 2026 adult residents were enrolled in this study, 500 of whom had albuminuria. Population-standardized prevalence of albuminuria is 19.06%. In the multivariable model adjusted for confounding factors, the odds ratio (OR) of albuminuria for pre-unit increase of CVAI and pre-SD increase of CVAI were 1.007 (1.003-1.010) and 1.298 (1.127-1.496), respectively. Multinomial logistic regression analysis confirmed the robustness and consistency of the results.The generalized additive model showed that CVAI and albuminuria had a nonlinear relationship with inflection point at 97.201 using the threshold effect. Compared with Han ethnic groups, the threshold between CVAI and albuminuria in Tujia people moved backward. The thresholds were 159.785 and 98.527, respectively. Conclusion: There was a positive nonlinear dose-response relationship between increased CVAI and higher levels of albuminuria. Maintaining appropriate CVAI levels may be important for the prevention of albuminuria.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".