Glomerular Hyperfiltration Interacts With Abnormal Metabolism to Enhance Arterial Stiffness in Middle-Aged and Elderly People
Bibliographic record
Abstract
Introduction: Glomerular hyperfiltration (GHF) is an early kidney injury. We investigated whether GHF is associated with arterial stiffness expressed by increase of brachial–ankle pulse wave velocity (baPWV) and pulse pressure (PP), and whether the coexistence of GHF and abnormal metabolism increases the risk of arterial stiffness. Methods: In this prospective cohort study, 2,133 non-chronic kidney disease (CKD) participants aged ≥40 years were followed for a mean period of 3.3 years. The extent of arterial stiffness was expressed by measures of baPWV and PP. GHF was defined as eGFR exceeding the age- and sex-specific 90th percentile. Multivariate logistic regression models were used to assess the association between GHF/abnormal metabolism and increased baPWV/PP. The interaction indexes of GHF and abnormal metabolism on arterial stiffness were calculated based on the OR in a multivariate logistic regression model. Results: GHF alone was not associated with increased baPWV or PP in all participants in this study. However, when GHF coexisted with abnormal metabolism, the risk of increased PP increased 3.23-fold [OR = 3.23(1.47–7.13)] compared with participants with normal filtration and normal metabolism, in which the interaction accounted for 55.1% of the total effect and 79.8% of the effect from GHF and abnormal metabolism. After subtracting the independent effects of GHF and abnormal metabolism, their combined effect still resulted in a 1.78-fold increase in PP. Conclusion: GHF could interact with abnormal metabolism to significantly enhance arterial stiffness. Since abnormal metabolism commonly exists in the general population, even slight changes in renal function should be distinguished to prevent arterial stiffness risk.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".