Primary aldosteronism (PA) is associated with an increased risk of cardiometabolic diseases, especially in unilateral subtype. Despite its high prevalence, the case detection rate of PA is limited, partly because of no clinical models available in general practice to identify patients highly suspicious of unilateral subtype of PA, who should be referred to specialized centers. The aim of this retrospective cross-sectional study was to develop a predictive model for subtype diagnosis of PA based on machine learning methods using clinical data available in general practice. Overall, 91 patients with unilateral and 138 patients with bilateral PA were randomly assigned to the training and test cohorts. Four supervised machine learning classifiers; logistic regression, support vector machines, random forests (RF), and gradient boosting decision trees, were used to develop predictive models from 21 clinical variables. The accuracy and the area under the receiver operating characteristic curve (AUC) for predicting of subtype diagnosis of PA in the test cohort were compared among the optimized classifiers. Of the four classifiers, the accuracy and AUC were highest in RF, with 95.7% and 0.990, respectively. Serum potassium, plasma aldosterone, and serum sodium levels were highlighted as important variables in this model. For feature-selected RF with the three variables, the accuracy and AUC were 89.1% and 0.950, respectively. With an independent external PA cohort, we confirmed a similar accuracy for feature-selected RF (accuracy: 85.1%). Machine learning models developed using blood test can help predict subtype diagnosis of PA in general practice.
Unilateral subtype of primary aldosteronism (PA) is a common surgically curable form of endocrine hypertension. However, more than half of the patients with PA who undergo unilateral adrenalectomy suffer from persistent hypertension, which may discourage those with PA from undergoing adrenalectomy even when appropriate. The aim of this retrospective cross-sectional study was to develop machine learning-based models for predicting postoperative hypertensive remission using preoperative predictors that are readily available in routine clinical practice. A total of 107 patients with PA who achieved complete biochemical success after adrenalectomy were included and randomly assigned to the training and test datasets. Predictive models of complete clinical success were developed using supervised machine learning algorithms. Of 107 patients, 40 achieved complete clinical success after adrenalectomy in both datasets. Six clinical features associated with complete clinical success (duration of hypertension, defined daily dose (DDD) of antihypertensive medication, plasma aldosterone concentration (PAC), sex, body mass index (BMI), and age) were selected based on predictive performance in the machine learning-based model. The predictive accuracy and area under the curve (AUC) for the developed model in the test dataset were 77.3% and 0.884 (95% confidence interval: 0.737–1.000), respectively. In an independent external cohort, the performance of the predictive model was found to be comparable with an accuracy of 80.4% and AUC of 0.867 (95% confidence interval: 0.763–0.971). The duration of hypertension, DDD of antihypertensive medication, PAC, and BMI were non-linearly related to the prediction of complete clinical success. The developed predictive model may be useful in assessing the benefit of unilateral adrenalectomy and in selecting surgical treatment and antihypertensive medication for patients with PA in clinical practice.
Purpose Prolonged exposure to pathological cortisol, as in Cushing’s syndrome causes various age-related disorders including sarcopenia. However, it is unclear whether mild cortisol excess, for example, accelerates sarcopenia due to aging or chronic stress. We performed a Mendelian randomization (MR) analysis to assess whether cortisol was causally associated with muscle strength and mass. Methods Three single nucleotide polymorphisms associated with plasma cortisol concentrations in the CORtisol NETwork consortium (n = 12,597) were used as instrumental variables. Summary statistics with traits of interest were obtained from relevant genome-wide association studies. For the primary analysis, we used the fixed-effects inverse-variance weighted analysis accounting for genetic correlations between variants. Results One standard deviation (SD) increase in cortisol was associated with SD reduction in grip strength (estimate, -0.032; 95% confidence interval [CI] -0.044 ~ -0.020; P = 3e-04), whole-body lean mass (estimate, -0.032; 95%CI, -0.046 ~ -0.017; P = 0.004), and appendicular lean mass (estimate, -0.031; 95%CI, -0.049 ~ -0.012; P = 0.001). The results were supported by the weighted-median analysis, with no evidence of pleiotropy in the MR-Egger analysis. The association of cortisol with grip strength and lean mass was observed in women but not in men. The association was attenuated after adjusting for fasting glucose in the multivariable MR analysis, which was the top mediator for the association in the MR-Bayesian model averaging analysis. Conclusion This MR study provides evidence for the association of cortisol with reduced muscle strength and mass, suggesting the impact of cortisol on the development of sarcopenia.
Context Current clinical guidelines recommend confirmation of positive result in at least one confirmatory test in the diagnosis of primary aldosteronism (PA). Clinical implication of multiple confirmatory tests has not been established, especially when patients show discordant results. Objective The aim of the present study was to explore the role of two confirmatory tests in subtype diagnosis of PA. Design Retrospective cross-sectional study. Setting The study was conducted at two referral centers. Participants and Method We identified 360 hypertensive patients who underwent both captopril challenge test (CCT) and saline infusion test (SIT) and exhibited at least one positive result. Among them, we studied 193 patients with PA whose data were available for subtype diagnosis based on adrenal vein sampling (AVS). Main Outcome Measure The prevalence of bilateral subtype on AVS according to the results of the confirmatory tests. Results Of patients studied, 127 were positive for both CCT and SIT (double-positive), while 66 were positive for either CCT or SIT (single-positive) (n = 34 and n = 32, respectively). Altogether, 135 were diagnosed with bilateral subtype on AVS. The single-positive patients had milder clinical features of PA than the double-positive patients. The prevalence of bilateral subtype on AVS was significantly higher in the single-positive patients than in the double-positive patients. (63/66 [95.5%] vs. 72/127 [56.7%], P < 0.01). Several clinical parameters were different between CCT single-positive and SIT single-positive patients. Conclusion Patients with discordant results between CCT and SIT have a high probability of bilateral subtype of PA on AVS.
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