The Differential Effect of Excess Aldosterone on Skeletal Muscle Mass by Sex

The effects of excess aldosterone on skeletal muscle in individuals with primary aldosteronism (PA) are unknown. To examine the effects of aldosterone on skeletal muscle mass in patients with PA, by sex, 309 consecutive patients were enrolled. Skeletal muscle and fat mass of 62 patients with PA were compared with those of 247 controls with non-functioning adrenal incidentaloma (NFAI). Body composition parameters were measured using bioelectrical impedance analysis, and plasma aldosterone concentration (PAC) was measured using radioimmunoassay. The PAC in all women, but not in men, showed an inverse association with both appendicular skeletal muscle mass (ASM) (β = −0.197, P = 0.016) and height-adjusted ASM (HA-ASM) (β = −0.207, P = 0.009). HA-ASM in women (but not in men) with PA was 5.0% lower than that in women with NFAI (P = 0.036). Furthermore, women with PA had a lower HA-ASM than 1:1 age- and sex-matched controls with NFAI by 5.7% (P = 0.049) and tended to have a lower HA-ASM than 1:3 age-, sex-, and menopausal status-matched controls without adrenal incidentaloma (AI) by 7.3% (P = 0.053). The odds ratio (OR), per quartile increase in PAC, of low HA-ASM in women was 1.18 [95% confidence interval (CI), 1.01–1.39; P = 0.035]. The odds of HA-ASM in subjects with PA were 10.63-fold (95% CI: 0.83–135.50) higher, with marginal significance (P = 0.069) than in those with NFAI. Skeletal muscle mass in women with PA was lower than that in women with NFAI; suggesting that excess aldosterone has adverse effects on skeletal muscle metabolism

Korean Type 2 Diabetes Patients have Multiple Adenomatous Polyps Compared to Non-diabetic Controls

We tested the correlation between diabetes and aggressiveness of colorectal polyps in diabetic patients and matched non-diabetic controls. We retrospectively studied 3,505 type 2 diabetes (T2DM) patients without gastrointestinal symptoms who underwent colonoscopy for colorectal cancer at Samsung Medical Center, Seoul, Korea from August 1995 to August 2009. We matched 495 non-diabetic subjects with colon polyps to the diabetic patients in whom polyps were detected by year of colonoscopy, age, sex and body mass index (BMI). Among the 3,505 T2DM patients screened, 509 were found to have 1,136 colon polyps. Those with diabetes had a greater proportion of adenomatous polyps (62.8% vs 53.6%) compared to the control. Multivariate logistic regression analysis identified DM, male gender, age and BMI as independent risk factors for multiple polyps (more than three polyps). Polyp multiplicity in diabetic patients was significantly associated with male gender (OR 2.360, P = 0.005), age (OR 1.033, P = 0.005) and BMI (OR 1.077, P = 0.028). Neither aspirin nor metformin use affected either size or number of polyps in diabetic patients. Male patients older than 65 yr with T2DM and BMI greater than 25 have increased risk for multiple adenomatous polyps and should be screened with colonoscopy to prevent colorectal cancer

The Ability of β-Cells to Compensate for Insulin Resistance is Restored with a Reduction in Excess Growth Hormone in Korean Acromegalic Patients

The aim of this study was to assess the prevalence of diabetes and to study the effects of excess growth hormone (GH) on insulin sensitivity and β-cell function in Korean acromegalic patients. One hundred and eighty-four acromegalic patients were analyzed to assess the prevalence of diabetes, and 52 naïve acromegalic patients were enrolled in order to analyze insulin sensitivity and insulin secretion. Patients underwent a 75 g oral glucose tolerance test with measurements of GH, glucose, insulin, and C-peptide levels. The insulin sensitivity index and β-cell function index were calculated and compared according to glucose status. Changes in the insulin sensitivity index and β-cell function index were evaluated one to two months after surgery. Of the 184 patients, 17.4% were in the normal glucose tolerance (NGT) group, 45.1% were in the pre-diabetic group and 37.5% were in the diabetic group. The insulin sensitivity index (ISI0,120) was significantly higher and the HOMA-IR was lower in the NGT compared to the diabetic group (P = 0.001 and P = 0.037, respectively). The ISI0,120 and disposition index were significantly improved after tumor resection. Our findings suggest that both insulin sensitivity and β-cell function are improved by tumor resection in acromegalic patients

Glucocorticoid-Induced Diabetes Mellitus: An Important but Overlooked Problem

Glucocorticoids are widely used as potent anti-inflammatory and immunosuppressive drugs to treat a wide range of diseases. However, they are also associated with a number of side effects, including new-onset hyperglycemia in patients without a history of diabetes mellitus (DM) or severely uncontrolled hyperglycemia in patients with known DM. Glucocorticoid-induced diabetes mellitus (GIDM) is a common and potentially harmful problem in clinical practice, affecting almost all medical specialties, but is often difficult to detect in clinical settings. However, scientific evidence is lacking regarding the effects of GIDM, as well as strategies for prevention and treatment. Similarly to nonsteroid-related DM, the principles of early detection and risk factor modification apply. Screening for GIDM should be considered in all patients treated with medium to high doses of glucocorticoids. Challenges in the management of GIDM stem from wide fluctuations in postprandial hyperglycemia and the lack of clearly defined treatment protocols. Together with lifestyle measures, hypoglycemic drugs with insulin-sensitizing effects are indicated. However, insulin therapy is often unavoidable, to the point that insulin can be considered the drug of choice. The treatment of GIDM should take into account the degree and pattern of hyperglycemia, as well as the type, dose, and schedule of glucocorticoid used. Moreover, it is essential to instruct the patient and/or the patient's family about how to perform the necessary adjustments. Prospective studies are needed to answer the remaining questions regarding GIDM

Glycemic Variability: How Do We Measure It and Why Is It Important?

Chronic hyperglycemia is the primary risk factor for the development of complications in diabetes mellitus (DM); however, it is believed that frequent or large glucose fluctuations may independently contribute to diabetes-related complications. Postprandial spikes in blood glucose, as well as hypoglycemic events, are blamed for increased cardiovascular events in DM. Glycemic variability (GV) includes both of these events; hence, minimizing GV can prevent future cardiovascular events. Correcting GV emerges as a target to be pursued in clinical practice to safely reduce the mean blood glucose and to determine its direct effects on vascular complications in diabetes. Modern diabetes management modalities, including glucagon-related peptide-1-based therapy, newer insulins, modern insulin pumps and bariatric surgery, significantly reduce GV. However, defining GV remains a challenge primarily due to the difficulty of measuring it and the lack of consensus regarding the optimal approach for its management. The purpose of this manuscript was not only to review the most recent evidence on GV but also to help readers better understand the available measurement options and how the various definitions relate differently to the development of diabetic complications

Subclinical Hypothyroidism and Cardiovascular Disease

Subclinical hypothyroidism (SCH) is a common disorder that is characterized by elevated thyroid-stimulating hormone levels in conjunction with free thyroxine concentrations within the normal reference range. Thyroid hormones are known to affect the heart and vasculature and, as a result, the impact of SCH on the cardiovascular (CV) system has recently become an important topic of research. Strong evidence points to a link between SCH and CV risk factors such as alterations in blood pressure, lipid levels, and atherosclerosis. Additionally, accumulating evidence indicates that SCH is associated with metabolic syndrome and heart failure. The present review proposes that SCH may be a potentially modifiable risk factor of CV disease and mortality. However, large-scale clinical trials with appropriate power investigating the risks and benefits of SCH treatment are required to determine whether these benefits can be achieved with levothyroxine therapy

FedPSO: Federated Learning Using Particle Swarm Optimization to Reduce Communication Costs

Federated learning is a learning method that collects only learned models on a server to ensure data privacy. This method does not collect data on the server but instead proceeds with data directly from distributed clients. Because federated learning clients often have limited communication bandwidth, communication between servers and clients should be optimized to improve performance. Federated learning clients often use Wi-Fi and have to communicate in unstable network environments. However, as existing federated learning aggregation algorithms transmit and receive a large amount of weights, accuracy is significantly reduced in unstable network environments. In this study, we propose the algorithm using particle swarm optimization algorithm instead of FedAvg, which updates the global model by collecting weights of learned models that were mainly used in federated learning. The algorithm is named as federated particle swarm optimization (FedPSO), and we increase its robustness in unstable network environments by transmitting score values rather than large weights. Thus, we propose a FedPSO, a global model update algorithm with improved network communication performance, by changing the form of the data that clients transmit to servers. This study showed that applying FedPSO significantly reduced the amount of data used in network communication and improved the accuracy of the global model by an average of 9.47%. Moreover, it showed an improvement in loss of accuracy by approximately 4% in experiments on an unstable network

FedPSO: Federated Learning Using Particle Swarm Optimization to Reduce Communication Costs

Federated learning is a learning method that collects only learned models on a server to ensure data privacy. This method does not collect data on the server but instead proceeds with data directly from distributed clients. Because federated learning clients often have limited communication bandwidth, communication between servers and clients should be optimized to improve performance. Federated learning clients often use Wi-Fi and have to communicate in unstable network environments. However, as existing federated learning aggregation algorithms transmit and receive a large amount of weights, accuracy is significantly reduced in unstable network environments. In this study, we propose the algorithm using particle swarm optimization algorithm instead of FedAvg, which updates the global model by collecting weights of learned models that were mainly used in federated learning. The algorithm is named as federated particle swarm optimization (FedPSO), and we increase its robustness in unstable network environments by transmitting score values rather than large weights. Thus, we propose a FedPSO, a global model update algorithm with improved network communication performance, by changing the form of the data that clients transmit to servers. This study showed that applying FedPSO significantly reduced the amount of data used in network communication and improved the accuracy of the global model by an average of 9.47%. Moreover, it showed an improvement in loss of accuracy by approximately 4% in experiments on an unstable network