Background: Data on the effects of excess aldosterone on glucose metabolism are inconsistent. This study compared the changes in glucose metabolism in patients with primary aldosteronism (PA) after adrenalectomy or treatment with a mineralocorticoid receptor antagonist (MRA). Methods: Overall, 241 patients were enrolled; 153 underwent adrenalectomy and 88 received an MRA. Fasting glucose, homeostatic model assessment of insulin resistance (HOMA-IR), and homeostatic model assessment of β-cell function (HOMA-β) were compared between the treatment groups after 1 year. Plasma aldosterone concentration (PAC) and factors determining HOMA-IR and PAC were evaluated. Results: No baseline differences were observed between the groups. Fasting insulin, HOMA-IR, and HOMA-β increased in both groups and there were no significant differences in fasting glucose following treatment. Multiple regression analysis showed associations between PAC and HOMA-IR (β=0.172, P=0.017) after treatment. Treatment with spironolactone was the only risk factor associated with PAC >30 ng/dL (odds ratio, 5.2; 95% confidence interval [CI], 2.7 to 10; P<0.001) and conferred a 2.48-fold risk of insulin resistance after 1 year compared with surgery (95% CI, 1.3 to 4.8; P=0.007). Conclusion Spironolactone treatment might increase insulin resistance in patients with PA. This strengthened the current recommendation that adrenalectomy is the preferred strategy for patient with positive lateralization test. Achieving a post-treatment PAC of <30 ng/dL for improved insulin sensitivity may be appropriate.

This review article aims to explore the major challenges that the healthcare system is currently facing and propose a new paradigm shift that harnesses the potential of wearable devices and novel theoretical frameworks on health and disease. Lifestyle-induced diseases currently account for a significant portion of all healthcare spending, with this proportion projected to increase with population aging. Wearable devices have emerged as a key technology for implementing large-scale healthcare systems focused on disease prevention and management. Advancements in miniaturized sensors, system integration, the Internet of Things, artificial intelligence, 5G, and other technologies have enabled wearable devices to perform high-quality measurements comparable to medical devices. Through various physical, chemical, and biological sensors, wearable devices can continuously monitor physiological status information in a non-invasive or minimally invasive way, including electrocardiography, electroencephalography, respiration, blood oxygen, blood pressure, blood glucose, activity, and more. Furthermore, by combining concepts and methods from complex systems and nonlinear dynamics, we developed a novel theory of continuous dynamic physiological signal analysis-dynamical complexity. The results of dynamic signal analyses can provide crucial information for disease prevention, diagnosis, treatment, and management. Wearable devices can also serve as an important bridge connecting doctors and patients by tracking, storing, and sharing patient data with medical institutions, enabling remote or real-time health assessments of patients, and providing a basis for precision medicine and personalized treatment. Wearable devices have a promising future in the healthcare field and will be an important driving force for the transformation of the healthcare system, while also improving the health experience for individuals.
Humans ; Artificial Intelligence ; Wearable Electronic Devices ; Monitoring, Physiologic/methods*