Daily Endocrinology Research Analysis

AIM: To estimate the efficacy and safety of the basal-bolus and premixed insulin as intensification regimens in patients with type 2 diabetes mellitus (T2DM). METHODS: A comprehensive search of online databases was performed until December 2022 to identify randomized controlled trials (RCTs) comparing premixed insulin versus basal-bolus regimen with treat-to-target intention. The Cochrane ROB-2 tool and GRADE approach were used for quality assessment and certainty of the evidence, respectively. Pooled weighted mean difference (WMD) and odds ratio (OR) were calculated using random-effects meta-analysis models. RESULTS: Eighteen RCTs were included in the meta-analysis, and 66% had a low risk of bias. We found no significant difference between the two regimens regarding HbA1c reduction (WMD: 0.03% [-0.05%, 0.10%]). The basal-bolus regimen improved fasting plasma glucose (FPG) more than the premixed regimen (WMD: 6.35 mg/dL [0.31, 12.39]). Both had similar effects on weight gain. The odds of developing overall, nocturnal, and severe hypoglycemia were comparable (pooled OR: 0.9, 1.02, and 1.00, respectively) with no heterogeneity. Findings of the model were robust. The certainty of the evidence was moderate to high for all outcomes except FPG. CONCLUSIONS: Two regimens are clinically comparable. Patient preference should be considered when adopting an individualized approach in a real-world setting.

BACKGROUND: Health care is experiencing new opportunities in the emerging digital landscape. The metaverse, a shared virtual space, integrates technologies such as augmented reality, virtual reality, blockchain, and artificial intelligence. It allows users to interact with immersive digital worlds, connect with others, and explore unknowns. While the metaverse is gaining traction across various medical disciplines, its application in thyroid diseases remains unexplored. Subclinical hypothyroidism (SCH) is the most common thyroid disorder during pregnancy and is frequently associated with adverse pregnancy outcomes. OBJECTIVE: This study aims to evaluate the safety and effectiveness of a metaverse platform in managing SCH during pregnancy. METHODS: A randomized controlled trial was conducted at Fujian Provincial Hospital, China, from July 2022 to December 2023. A total of 60 pregnant women diagnosed with SCH were randomly assigned into two groups: the standard group (n=30) and the metaverse group (n=30). Both groups received levothyroxine sodium tablets. Additionally, participants in the metaverse group had access to the metaverse virtual medical consultations and metaverse-based medical games. The primary outcomes were adverse maternal and offspring outcomes, and the secondary outcomes included the neurobehavioral development of offspring and maternal psychological assessments. RESULTS: Of the 30 participants in each group, adverse maternal outcomes were observed in 43% (n=13) of the standard group and 37% (n=11) of the metaverse group (P=.60). The incidence of adverse offspring outcomes was 33% (n=10) in the standard group, compared to 7% (n=2) in the metaverse group (P=.01). The Gesell Development Scale did not show significant differences between the two groups. Notably, the metaverse group demonstrated significantly improved scores on the Self-Rating Depression Scale and the Self-Rating Anxiety Scale scores compared to the standard group (P<.001 and P=.001, respectively). CONCLUSIONS: The use of metaverse technology significantly reduced the incidence of adverse offspring outcomes and positively impacted maternal mental health. Maternal adverse outcomes and offspring neurobehavioral development were comparable between the two groups. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR2300076803; https://www.chictr.org.cn/showproj.html?proj=205905.

INTRODUCTION: Elevated glycosylated hemoglobin (HbA1c) in individuals with type 2 diabetes is associated with increased risk of hospitalization and death after acute COVID-19, however the effect of HbA1c on Long COVID is unclear. OBJECTIVE: Evaluate the association of glycemic control with the development of Long COVID in patients with type 2 diabetes (T2D). RESEARCH DESIGN AND METHODS: We conducted a retrospective cohort study using electronic health record data from the National COVID Cohort Collaborative. Our cohort included individuals with T2D from eight sites with longitudinal natural language processing (NLP) data. The primary outcome was death or new-onset recurrent Long COVID symptoms within 30-180 days after COVID-19. Symptoms were identified as keywords from clinical notes using NLP in respiratory, brain fog, fatigue, loss of smell/taste, cough, cardiovascular and musculoskeletal symptom categories. Logistic regression was used to evaluate the risk of Long COVID by HbA1c range, adjusting for demographics, body mass index, comorbidities, and diabetes medication. A COVID-negative group was used as a control. RESULTS: Among 7430 COVID-positive patients, 1491 (20.1%) developed symptomatic Long COVID, and 380 (5.1%) died. The primary outcome of death or Long COVID was increased in patients with HbA1c 8% to <10% (OR 1.20, 95% CI 1.02 to 1.41) and ≥10% (OR 1.40, 95% CI 1.14 to 1.72) compared with those with HbA1c 6.5% to <8%. This association was not seen in the COVID-negative group. Higher HbA1c levels were associated with increased risk of Long COVID symptoms, especially respiratory and brain fog. There was no association between HbA1c levels and risk of death within 30-180 days following COVID-19. NLP identified more patients with Long COVID symptoms compared with diagnosis codes. CONCLUSION: Poor glycemic control (HbA1c≥8%) in people with T2D was associated with higher risk of Long COVID symptoms 30-180 days following COVID-19. Notably, this risk increased as HbA1c levels rose. However, this association was not observed in patients with T2D without a history of COVID-19. An NLP-based definition of Long COVID identified more patients than diagnosis codes and should be considered in future studies.