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Peer-reviewedCC BY 4.0Frontiers in Endocrinology

Dose–response association and predictive value of plasma aldosterone concentration for cardiometabolic multimorbidity in patients with hypertension: a retrospective cohort study

  • Zhiqiang Zhang1,2,3,4,5
  • Nanfang Li1,2,3,4,5
  • Qing Zhu1,2,3,4,5
  • Junli Hu1,2,3,4,5
  • Jing Hong1,2,3,4,5
  • Wenbo Yang1,2,3,4,5
  • Pan Zhou1,2,3,4,5
  • Huimin Ma1,2,3,4,5
  • Rui Ma1,2,3,4,5
  • Ting Wu1,2,3,4,5
5 affiliations
  1. 1Hypertension Center of People’s Hospital of Xinjiang Uygur Autonomous Region
  2. 2Xinjiang Hypertension Institute
  3. 3National Health Commission (NHC) Key Laboratory of Hypertension Clinical Research
  4. 4Key Laboratory of Xinjiang Uygur Autonomous Region “Hypertension Research Laboratory”
  5. 5Xinjiang Clinical Medical Research Center for Hypertension (Cardio-Cerebrovascular) Diseases
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Abstract

Background: The purpose of this study was to investigate the relationship between cardiometabolic multimorbidity (CMM) and plasma aldosterone concentration (PAC) among patients with hypertension, to characterize the potential dose–response pattern, and to assess whether the inclusion of PAC into a conventional risk model improves risk reclassification and clinical utility.

Methods: Between January 2010 and January 2026, 7,677 hospitalized hypertensive patients without CMM at baseline were included in this retrospective cohort analysis. The follow-up period was 3.52 years on average. Multivariable Cox proportional hazards regression and restricted cubic spline (RCS) analyses were performed to examine the association between PAC and the risk of incident CMM. The incremental predictive value of PAC was evaluated for 3-year risk prediction using time-dependent ROC analysis with inverse probability of censoring weighting (IPCW), survival-based net reclassification improvement (NRI), integrated discrimination improvement (IDI), calibration assessment, and decision curve analysis (DCA). Based on the potential interaction observed in the subgroup analysis, RCS analyses stratified by serum potassium level and a bivariate penalized spline model were used to characterize the joint nonlinear associations of serum potassium and PAC with the risk of CMM. The Boruta algorithm was additionally applied for feature selection and assessment of variable importance.

Results: During follow-up, 1,949 incident cases of CMM were documented. Patients in the highest quartile of baseline PAC (Q4) had a considerably greater cumulative likelihood of CMM than those in the other quartiles, according to Kaplan–Meier analysis (log-rank P < 0.05). In the multivariable Cox regression analysis, each 1-ng/dL increment in PAC was associated with a 2% higher hazard of incident CMM (hazard ratio [HR], 1.02; 95% confidence interval [CI], 1.02–1.03). Compared with the lowest quartile (Q1), the adjusted HRs were 1.19 (95% CI, 1.04–1.36) for Q2, 1.32 (95% CI, 1.16–1.52) for Q3, and 1.65 (95% CI, 1.45–1.87) for Q4, with a significant trend across quartiles ( P for trend<0.001). RCS analysis demonstrated a significant nonlinear association between PAC and the risk of CMM ( P for nonlinearity < 0.001), identifying an estimated inflection point at approximately 15.34 ng/dL, above which the risk curve showed an upward trend. Subgroup analyses indicated that the PAC–CMM association may differ by serum potassium status ( P for interaction < 0.05). After PAC was added to the baseline prediction model, the time-dependent AUC increased from 0.678 to 0.735 ( P < 0.001), with a net reclassification improvement of 0.372 and an integrated discrimination improvement of 0.038 ( P < 0.05). DCA suggested that incorporating PAC may provide additional estimated net benefit across clinically relevant threshold probabilities. The Boruta feature-selection algorithm identified PAC as an important predictor, and the overall findings remained consistent across subgroup and sensitivity analyses.

Conclusion: The possibility of a CMM incident was positively correlated with PAC in a nonlinear fashion, and this correlation differed depending on serum potassium levels. Incorporating PAC into a conventional risk model was associated with improved predictive performance and greater net clinical benefit, suggesting that PAC may provide additional prognostic information for cardiometabolic risk stratification among patients with hypertension.

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Cite

BibTeXRIS
BibTeX
@article{zhang2026doseresponse,
  title = {{Dose–response association and predictive value of plasma aldosterone concentration for cardiometabolic multimorbidity in patients with hypertension: a retrospective cohort study}},
  author = {Zhiqiang Zhang and Nanfang Li and Qing Zhu and Junli Hu and Jing Hong and Wenbo Yang and Pan Zhou and Huimin Ma and Rui Ma and Ting Wu},
  journal = {Frontiers in Endocrinology},
  year = {2026},
  month = oct,
  doi = {10.3389/fendo.2026.1965927},
  url = {https://www.frontiersin.org/articles/10.3389/fendo.2026.1965927/full},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Dose–response association and predictive value of plasma aldosterone concentration for cardiometabolic multimorbidity in patients with hypertension: a retrospective cohort study
AU  - Zhiqiang Zhang
AU  - Nanfang Li
AU  - Qing Zhu
AU  - Junli Hu
AU  - Jing Hong
AU  - Wenbo Yang
AU  - Pan Zhou
AU  - Huimin Ma
AU  - Rui Ma
AU  - Ting Wu
PY  - 2026
DA  - 2026/10/16
JO  - Frontiers in Endocrinology
DO  - 10.3389/fendo.2026.1965927
UR  - https://www.frontiersin.org/articles/10.3389/fendo.2026.1965927/full
AB  - Background: The purpose of this study was to investigate the relationship between cardiometabolic multimorbidity (CMM) and plasma aldosterone concentration (PAC) among patients with hypertension, to characterize the potential dose–response pattern, and to assess whether the inclusion of PAC into a conventional risk model improves risk reclassification and clinical utility.  Methods: Between January 2010 and January 2026, 7,677 hospitalized hypertensive patients without CMM at baseline were included in this retrospective cohort analysis. The follow-up period was 3.52 years on average. Multivariable Cox proportional hazards regression and restricted cubic spline (RCS) analyses were performed to examine the association between PAC and the risk of incident CMM. The incremental predictive value of PAC was evaluated for 3-year risk prediction using time-dependent ROC analysis with inverse probability of censoring weighting (IPCW), survival-based net reclassification improvement (NRI), integrated discrimination improvement (IDI), calibration assessment, and decision curve analysis (DCA). Based on the potential interaction observed in the subgroup analysis, RCS analyses stratified by serum potassium level and a bivariate penalized spline model were used to characterize the joint nonlinear associations of serum potassium and PAC with the risk of CMM. The Boruta algorithm was additionally applied for feature selection and assessment of variable importance.  Results: During follow-up, 1,949 incident cases of CMM were documented. Patients in the highest quartile of baseline PAC (Q4) had a considerably greater cumulative likelihood of CMM than those in the other quartiles, according to Kaplan–Meier analysis (log-rank P < 0.05). In the multivariable Cox regression analysis, each 1-ng/dL increment in PAC was associated with a 2% higher hazard of incident CMM (hazard ratio [HR], 1.02; 95% confidence interval [CI], 1.02–1.03). Compared with the lowest quartile (Q1), the adjusted HRs were 1.19 (95% CI, 1.04–1.36) for Q2, 1.32 (95% CI, 1.16–1.52) for Q3, and 1.65 (95% CI, 1.45–1.87) for Q4, with a significant trend across quartiles ( P for trend<0.001). RCS analysis demonstrated a significant nonlinear association between PAC and the risk of CMM ( P for nonlinearity < 0.001), identifying an estimated inflection point at approximately 15.34 ng/dL, above which the risk curve showed an upward trend. Subgroup analyses indicated that the PAC–CMM association may differ by serum potassium status ( P for interaction < 0.05). After PAC was added to the baseline prediction model, the time-dependent AUC increased from 0.678 to 0.735 ( P < 0.001), with a net reclassification improvement of 0.372 and an integrated discrimination improvement of 0.038 ( P < 0.05). DCA suggested that incorporating PAC may provide additional estimated net benefit across clinically relevant threshold probabilities. The Boruta feature-selection algorithm identified PAC as an important predictor, and the overall findings remained consistent across subgroup and sensitivity analyses.  Conclusion: The possibility of a CMM incident was positively correlated with PAC in a nonlinear fashion, and this correlation differed depending on serum potassium levels. Incorporating PAC into a conventional risk model was associated with improved predictive performance and greater net clinical benefit, suggesting that PAC may provide additional prognostic information for cardiometabolic risk stratification among patients with hypertension.
SN  - 1664-2392
ER  -