In 2023, the American Heart Association (AHA) introduced the Cardiovascular-Kidney-Metabolic (CKM) syndrome concept to address the substantial burden of interrelated cardiovascular, kidney, and metabolic disorders. The framework highlights that chronic kidney disease (CKD) significantly accelerates CKM syndrome progression and increases cardiovascular risk, an effect that may be closely paralleled by aldosterone excess. Excess aldosterone can arise from renin-dependent aldosteronism (RDA), a primarily physiological state (not discussed in this review), or from renin-independent aldosteronism (RIA). RIA is a pathophysiologically relevant condition characterized by persistent autonomous activation, bypassing normal renin-angiotensin-aldosterone system (RAAS) regulation. Its most recognized form is PA, a prevalent, multidimensional disorder spanning a continuum from subclinical to overt autonomous aldosterone production. This leads to inappropriately elevated aldosterone relative to suppressed renin and sodium levels. PA is a leading cause of secondary hypertension and elevates the risk of metabolic and cardiorenal disorders, showing substantial overlap with CKM syndrome. Despite its clinical significance, the specific relationship between PA and CKM syndrome remains insufficiently investigated. This review synthesizes evidence from three key perspectives: (1) Epidemiology and clinical data show that PA spans a spectrum from subclinical to overt stages and is strongly associated with driving and accelerating the progression of CKM syndrome; (2) Therapeutically, targeted treatment of PA mitigates the adverse effects of aldosterone on CKM syndrome progression; and (3) Pathophysiologically, inappropriately elevated aldosterone primarily interacts with widely distributed mineralocorticoid receptors in tissues relevant to CKM syndrome, exacerbating key pathogenic pathways akin to adding fuel to the fire. Building on this synthesis, we emphasize that inappropriately elevated aldosterone is not merely a simple biomarker but an active driver and accelerator of CKM syndrome progression. This review also proposes future directions for integrated PA-CKM screening and management. Incorporating PA into the CKM syndrome framework could not only refine CKM syndrome care but also address the critical underdiagnosis of PA, whose screening rate regrettably remains below 2% in high-risk populations.

Objective: Our study aims to develop a personalized nomogram model for predicting the risk of nonalcoholic fatty liver disease (NAFLD) in hypertension (HTN) patients and further validate its effectiveness. Methods: A total of 1250 hypertensive (HTN) patients from Guangxi, China, were divided into a training group (875 patients, 70%) and a validation set (375 patients, 30%). LASSO regression, in combination with univariate and multivariate logistic regression analyses, was used to identify predictive factors associated with nonalcoholic fatty liver disease (NAFLD) in HTN patients within the training set. Subsequently, the performance of an NAFLD nomogram prediction model was evaluated in the separate validation group, including assessments of differentiation ability, calibration performance, and clinical applicability. This was carried out using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Results: The risk-prediction model for the HTN patients concomitant with NAFLD included oral antidiabetic drugs (OADs) (OR = 2.553, 95% CI: 1.368–4.763), antihypertensives (AHs) (OR = 7.303, 95% CI: 4.168–12.794), body mass index (BMI) (OR = 1.145, 95% CI: 1.084–1.209), blood urea nitrogen (BUN) (OR = 0.924, 95% CI: 0.860–0.992), triglycerides (TGs) (OR = 1.474, 95% CI: 1.201–1.809), aspartate aminotransferase (AST) (OR = 1.061, 95% CI: 1.018–1.105), and AST/ALT ratio (AAR) (OR = 0.249, 95% CI: 0.121–0.514) as significant predictors. The AUC of the NAFLD risk-prediction model in the training set and the validation set were 0.816 (95% CI: 0.785–0.847) and 0.794 (95% CI: 0.746–0.842), respectively. The Hosmer–Lemeshow test showed that the model has a good goodness-of-fit (p-values were 0.612 and 0.221). DCA suggested the net benefit of using a nomogram to predict the risk of HTN patients concomitant with NAFLD is higher. These results suggested that the model showed moderate predictive ability and good calibration. Conclusions: BMI, OADs, AHs, BUN, TGs, AST, and AAR were independent influencing factors of HTN combined with NAFLD, and the risk prediction model constructed based on this could help to identify the high-risk group of HTN combined with NAFLD at an early stage and guide the development of interventions. Larger cohorts with multiethnic populations are essential to verify our findings.

Yixuan Liu, M.M., Tingwei Jin, M.M., Yushuang Wei, M.M., Xuelian Qin, M.B.B.S., Siyu Deng, M.M., Jie Zheng, M.M., Boteng Yan, M.D., Ph.D., Yuanyuan Nong, M.M., Yu Ye, M.D., Ph.D., Shengzhu Huang, M.D., Ph.D., Yu Long, M.D., Ph.D., Jianmin Li, M.M., Ganqin Wang, M.B.B.S., Pei Huang, M.M., Jinghang Jiang, M.D., Ph.D., Fan Wu, M.M., Zengnan Mo, M.D., Ph.D., and Yonghua Jiang, M.D., Ph.D.. Ann Lab Med -0001;0:. https://doi.org/10.3343/alm.2025.0090