Endocrinology and Metabolism最新文献

Backgruound: Current research has not investigated the effect of thyroid-stimulating hormone suppression therapy with levothyroxine on the risk for developing subsequent primary cancers (SPCs). This study aimed to investigate the association between levothyroxine dosage and the risk for SPCs in thyroid cancer patients.

Methods: We conducted a nationwide population-based retrospective cohort study form Korean National Health Insurance database. This cohort included 342,920 thyroid cancer patients between 2004 and 2018. Patients were divided into the non-levothyroxine and the levothyroxine groups, the latter consisting of four dosage subgroups according to quartiles. Cox proportional hazard models were performed to evaluate the risk for SPCs by adjusting for variables including cumulative doses of radioactive iodine (RAI) therapy.

Results: A total of 17,410 SPC cases were observed over a median 7.3 years of follow-up. The high-dose levothyroxine subgroups (Q3 and Q4) had a higher risk for SPC (adjusted hazard ratio [HR], 1.14 and 1.27; 95% confidence interval [CI], 1.05-1.24 and 1.17- 1.37; respectively) compared to the non-levothyroxine group. In particular, the adjusted HR of stomach (1.31), colorectal (1.60), liver and biliary tract (1.95), and pancreatic (2.48) cancers were increased in the Q4 subgroup. We consistently observed a positive association between high levothyroxine dosage per body weight and risk of SPCs, even after adjusting for various confounding variables. Moreover, similar results were identified in the stratified analyses according to thyroidectomy type and RAI therapy, as well as in a subgroup analysis of patients with good adherence.

Conclusion: High-dose levothyroxine use was associated with increased risk of SPCs among thyroid cancer patients regardless of RAI therapy.

Backgruound: This study investigated the effectiveness of a social networking site (SNS)-based automatic mobile message providing system on glycemic control in patients with type 2 diabetes mellitus (T2DM).

Methods: A 3-month, randomized, open-label, controlled, parallel-group trial was conducted. One hundred and ten participants with T2DM were randomized to a mobile message system (MMS) (n=55) or control group (n=55). The MMS group received protocolbased automated messages two times per day for 10 weeks regarding diabetes self-management through KakaoTalk SNS messenger. The primary outcome was the difference in the change in glycated hemoglobin (HbA1c) levels (%) from baseline to week 12.

Results: HbA1c levels were more markedly decreased in the MMS group (8.4%±0.7% to 8.0%±1.1%) than in the control group (8.5%±0.8% to 8.4%±0.8%), resulting in a significant between-group difference (P=0.027). No differences were observed in changes in fasting glucose levels, lipid profiles, and the number of participants who experienced hypoglycemia, or in changes in lifestyle behavior between groups. However, the self-monitoring of blood glucose frequency was significantly increased in the MMS group compared to the control group (P=0.003). In addition, sleep duration was increased in the MMS group, but was not changed in the control group.

Conclusion: An SNS-based automatic mobile message providing system was effective in improving glycemic control in patients in T2DM. Studies which based on a more individualized protocol, and investigate longer beneficial effect and sustainability will be required in the future.

Backgruound: This study evaluated the effects of a mobile diabetes management program called "iCareD" (College of Medicine, The Catholic University of Korea) which was integrated into the hospital's electronic medical records system to minimize the workload of the healthcare team in the real clinical practice setting.

Methods: In this retrospective observational study, we recruited 308 patients. We categorized these patients based on their compliance regarding their use of the iCareD program at home; compliance was determined through self-monitored blood glucose inputs and message subscription rates. We analyzed changes in the ABC (hemoglobin A1c, blood pressure, and low-density lipoprotein cholesterol) levels from the baseline to 12 months thereafter, based on the patients' iCareD usage patterns.

Results: The patients comprised 92 (30%) non-users, 170 (55%) poor-compliance users, and 46 (15%) good-compliance users; the ABC target achievement rate showed prominent changes in good-compliance groups from baseline to 12 months (10.9% vs. 23.9%, P<0.05), whereas no significant changes were observed for poor-compliance users and non-users (13.5% vs. 18.8%, P=0.106; 20.7% vs. 14.1%, P=0.201; respectively).

Conclusion: Implementing the iCareD can improve the ABC levels of patients with diabetes with minimal efforts of the healthcare team in real clinical settings. However, the improvement of patients' compliance concerning the use of the system without the vigorous intervention of the healthcare team needs to be solved in the future.

Backgruound: Delayed postoperative hyponatremia (DPH) is the most common cause of readmission after pituitary surgery. In this study, we aimed to evaluate the cutoff values of serum copeptin and determine the optimal timing for copeptin measurement for the prediction of the occurrence of DPH in patients who undergo endoscopic transsphenoidal approach (eTSA) surgery and tumor resection.

Methods: This was a prospective observational study of 73 patients who underwent eTSA surgery for pituitary or stalk lesions. Copeptin levels were measured before surgery, 1 hour after extubation, and on postoperative days 1, 2, 7, and 90.

Results: Among 73 patients, 23 patients (31.5%) developed DPH. The baseline ratio of copeptin to serum sodium level showed the highest predictive performance (area under the curve [AUROC], 0.699), and its optimal cutoff to maximize Youden's index was 2.5×10-11, with a sensitivity of 91.3% and negative predictive value of 92.0%. No significant predictors were identified for patients with transient arginine vasopressin (AVP) deficiency. However, for patients without transient AVP deficiency, the copeptin-to-urine osmolarity ratio at baseline demonstrated the highest predictive performance (AUROC, 0.725). An optimal cutoff of 6.5×10-12 maximized Youden's index, with a sensitivity of 92.9% and a negative predictive value of 94.1%.

Conclusion: The occurrence of DPH can be predicted using baseline copeptin and its ratio with serum sodium or urine osmolarity only in patients without transient AVP deficiency after pituitary surgery.

Obesity is the fifth leading risk factor for global deaths with numbers continuing to increase worldwide. In the last 20 years, the emergence of pharmacological treatments for obesity based on gastrointestinal hormones has transformed the therapeutic landscape. The successful development of glucagon-like peptide-1 (GLP-1) receptor agonists, followed by the synergistic combined effect of glucose-dependent insulinotropic polypeptide (GIP)/GLP-1 receptor agonists achieved remarkable weight loss and glycemic control in those with the diseases of obesity and type 2 diabetes. The multiple cardiometabolic benefits include improving glycemic control, lipid profiles, blood pressure, inflammation, and hepatic steatosis. The 2023 phase 2 double-blind, randomized controlled trial evaluating a GLP-1/GIP/glucagon receptor triagonist (retatrutide) in patients with the disease of obesity reported 24.2% weight loss at 48 weeks with 12 mg retatrutide. This review evaluates the current available evidence for GLP-1 receptor agonists, dual GLP-1/GIP receptor co-agonists with a focus on GLP-1/GIP/glucagon receptor triagonists and discusses the potential future benefits and research directions.

Backgruound: Hepatic steatosis, which involves the excessive accumulation of lipid droplets in hepatocytes, presents a significant global health concern due to its association with obesity and metabolic disorders. Inflammation plays a crucial role in the progression of hepatic steatosis; however, the precise molecular mechanisms responsible for this process remain unknown.

Methods: This study investigated the involvement of the nucleotide-binding oligomerization domain-like receptor pyrin domain-containing-3 (NLRP3) inflammasome and the forkhead box O6 (FoxO6) transcription factor in the pathogenesis of hepatic steatosis. We monitored the NLRP3 inflammasome and lipogenesis in mice overexpressing the constitutively active (CA)-FoxO6 allele and FoxO6-null mice. In an in vitro study, we administered palmitate to liver cells overexpressing CA-FoxO6 and measured changes in lipid metabolism.

Results: We administered palmitate treatment to clarify the mechanisms through which FoxO6 activates cytokine interleukin (IL)-1β through the NLRP3 inflammasome. The initial experiments revealed that dephosphorylation led to palmitate-induced FoxO6 transcriptional activity. Further palmitate experiments showed increased expression of IL-1β and the hepatic NLRP3 inflammasome complex, including adaptor protein apoptotic speck protein containing a caspase recruitment domain (ASC) and pro-caspase-1. Furthermore, thioredoxin-interacting protein (TXNIP), a key regulator of cellular redox conditions upstream of the NLRP3 inflammasome, was induced by FoxO6 in the liver and HepG2 cells.

Conclusion: The findings of this study shed light on the molecular mechanisms underpinning the FoxO6-NLRP3 inflammasome axis in promoting inflammation and lipid accumulation in the liver.

Backgruound: Cardiovascular disease is life-threatening yet preventable for patients with type 2 diabetes mellitus (T2DM). Because each patient with T2DM has a different risk of developing cardiovascular complications, the accurate stratification of cardiovascular risk is critical. In this study, we proposed cardiovascular risk engines based on machine-learning algorithms for newly diagnosed T2DM patients in Korea.

Methods: To develop the machine-learning-based cardiovascular disease engines, we retrospectively analyzed 26,166 newly diagnosed T2DM patients who visited Seoul St. Mary's Hospital between July 2009 and April 2019. To accurately measure diabetes-related cardiovascular events, we designed a buffer (1 year), an observation (1 year), and an outcome period (5 years). The entire dataset was split into training and testing sets in an 8:2 ratio, and this procedure was repeated 100 times. The area under the receiver operating characteristic curve (AUROC) was calculated by 10-fold cross-validation on the training dataset.

Results: The machine-learning-based risk engines (AUROC XGBoost=0.781±0.014 and AUROC gated recurrent unit [GRU]-ordinary differential equation [ODE]-Bayes=0.812±0.016) outperformed the conventional regression-based model (AUROC=0.723± 0.036).

Conclusion: GRU-ODE-Bayes-based cardiovascular risk engine is highly accurate, easily applicable, and can provide valuable information for the individualized treatment of Korean patients with newly diagnosed T2DM.