Plasma Aldosterone Elevation in Hypertensive Patients and Association with Urinary Stone Formation: A Large-Scale Population Study from Northwest China
Bibliographic record
Abstract
Shuaiwei Song,1– 5,* Nanfang Li,1– 5,* Di Shen,1– 5 Junli Hu,1– 5 Xintian Cai,1– 5 Qing Zhu,1– 5 Yingying Zhang,1– 5 Rui Ma,1– 5 Pan Zhou,1– 5 Zhiqiang Zhang,1– 5 Wen Jiang,1– 5 Jing Hong1– 5 1Hypertension Center of People’s Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, 830001, People’s Republic of China; 2Xinjiang Hypertension Institute, Urumqi, Xinjiang, 830001, People’s Republic of China; 3NHC Key Laboratory of Hypertension Clinical Research, Urumqi, Xinjiang, 830001, People’s Republic of China; 4Key Laboratory of Xinjiang Uygur Autonomous Region “Hypertension Research Laboratory”, Urumqi, Xinjiang, 830001, People’s Republic of China; 5Xinjiang Clinical Medical Research Center for Hypertension (Cardio-Cerebrovascular) Diseases, Urumqi, Xinjiang, 830001, People’s Republic of China*These authors contributed equally to this workCorrespondence: Nanfang Li, Hypertension Center of People’s Hospital of Xinjiang Uygur Autonomous Region, No. 91 Tianchi Road, Urumuqi, Urumqi, Xinjiang, People’s Republic of China, 830001, Tel +86 8564818, Email lnanfang2016@sina.comBackground: Previous studies have suggested a potential association between plasma aldosterone concentration (PAC) and calcium regulation. However, it remains unclear whether elevated PAC levels increase the risk of urinary stones. Therefore, this study aimed to investigate the relationship between PAC levels and urinary stones, including their subtypes, in patients with hypertension.Methods: This large-scale study included a total of 35161 hypertensive patients. Multivariable logistic regression was used to analyze the association between PAC levels and urinary stones, as well as their subtypes. Additionally, a dose-response relationship was explored using restricted cubic spline (RCS) analysis, and a two-stage comparative analysis was conducted based on the RCS turning point. The importance of PAC was further confirmed through variable importance analysis. Finally, extensive subgroup analyses and sensitivity analyses were performed to assess the robustness of the findings.Results: Multivariable logistic regression revealed a significant association between elevated PAC levels and the occurrence of urinary stones and their subtypes. Specifically, for every 5 ng/dL increase in PAC, the risk of urinary stones increased by 26% (odds ratios [OR] 1.26, 95% confidence interval [CI], 1.22– 1.30, P< 0.001). Furthermore, RCS threshold analysis demonstrated a marked increase in urinary stone risk when PAC levels exceeded 14.2 ng/dL (OR 1.50, 95% CI, 1.38– 1.63, P< 0.001). These findings were consistent across subtypes, including kidney stones and ureteral stones. Subgroup analyses showed that the results were unaffected by stratification factors, and sensitivity analyses further confirmed the stability of the findings.Conclusion: This study demonstrated that elevated PAC levels are significantly associated with the occurrence of urinary stones and their subtypes in hypertensive patients. These findings suggest that controlling PAC levels in hypertensive patients may help reduce the risk of urinary stone formation. Keywords: Plasma aldosterone concentration, primary aldosteronism, Hypertension, Parathyroid hormone
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".