Background Acromegaly leads to various skeletal complications, and fragility fractures are emerging as a new concern in patients with acromegaly. Therefore, this study investigated the risk of fractures in Korean patients with acromegaly.
Methods We used the Korean nationwide claims database from 2009 to 2019. A total of 931 patients with acromegaly who had never used an osteoporosis drug before and were treated with surgery alone were selected as study participants, and a 1:29 ratio of 26,999 age- and sex-matched osteoporosis drug-naïve controls without acromegaly were randomly selected from the database.
Results The mean age was 46.2 years, and 50.0% were male. During a median follow-up of 54.1 months, there was no difference in the risks of all, vertebral, and non-vertebral fractures between the acromegaly and control groups. However, hip fracture risk was significantly higher (hazard ratio [HR], 2.73; 95% confidence interval [CI], 1.32 to 5.65), and non-hip and non-vertebral fractures risk was significantly lower (HR, 0.40; 95% CI, 0.17 to 0.98) in patients with acromegaly than in controls; these results remained robust even after adjustment for socioeconomic status and baseline comorbidities. Age, type 2 diabetes mellitus, cardio-cerebrovascular disease, fracture history, recent use of acid-suppressant medication, psychotropic medication, and opioids were risk factors for all fractures in patients with acromegaly (all P<0.05).
Conclusion Compared with controls, patients surgically treated for acromegaly had a higher risk of hip fractures. The risk factors for fracture in patients with acromegaly were consistent with widely accepted risk factors in the general population.
Lifestyle is a critical aspect of diabetes management. We aimed to define a healthy lifestyle using objectively measured parameters obtained from a wearable activity tracker (Fitbit) in patients with type 2 diabetes. This prospective observational study included 24 patients (mean age, 46.8 years) with type 2 diabetes. Expectation–maximization clustering analysis produced two groups: A (n=9) and B (n=15). Group A had a higher daily step count, lower resting heart rate, longer sleep duration, and lower mean time differences in going to sleep and waking up than group B. A Shapley additive explanation summary analysis indicated that sleep-related factors were key elements for clustering. The mean hemoglobin A1c level was 0.3 percentage points lower at the end of follow-up in group A than in group B. Factors related to regular sleep patterns could be possible determinants of lifestyle clustering in patients with type 2 diabetes.
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Original Articles
Diabetes, Obesity and Metabolism Big Data Articles (National Health Insurance Service Database)
Background There are differences in risk and risk factor findings of postpartum type 2 diabetes mellitus (T2DM) after gestational diabetes depending on study design and subjects of previous studies. This study aimed to assess these risk and risk factors more accurately through a population-based study to provide basic data for prevention strategies.
Methods This open retrospective cohort included data of 419,101 women with gestational diabetes and matched 1,228,802 control women who delivered between 2004 and 2016 from the South Korea National Health Information Database of the National Health Insurance Service. Following 14 (median 5.9) years of follow-up, the incidence and hazard ratio (HR) of postpartum T2DM were evaluated using Kaplan-Meier curves and Cox proportional regression models.
Results The incidence and HR of postpartum T2DM in women with gestational diabetes (compared to women without gestational diabetes) after the 14-year follow-up was 21.3% and 2.78 (95% confidence interval [CI], 2.74 to 2.82), respectively. Comorbid obesity (body mass index [BMI] ≥25 kg/m2) increased postpartum T2DM risk 7.59 times (95% CI, 7.33 to 7.86). Significant risk factors for postpartum T2DM were fasting glucose level, BMI, age, family history of diabetes, hypertension, and insulin use during pregnancy.
Conclusion This population-based study showed higher postpartum T2DM risk in women with gestational diabetes than in those without, which was further increased by comorbid obesity. BMI and fasting glucose level were important postpartum risk factors. The management of obesity and glycemic control may be important strategies to prevent the incidence of diabetes after delivery.
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Antenatal factors and risk of postpartum hyperglycemia in women with gestational diabetes mellitus: A central India prospective cohort study Nilajkumar Bagde, Madhuri Bagde, Vijayalakshmi Shanbhag, Pragati Trigunait, Nagma Sheikh, Sarita Agrawal Journal of Family Medicine and Primary Care.2024; 13(1): 59. CrossRef
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Background We investigated how 100-g oral glucose tolerance test (OGTT) results can be used to predict adverse pregnancy outcomes in gestational diabetes mellitus (GDM) patients.
Methods We analyzed 1,059 pregnant women who completed the 100-g OGTT between 24 and 28 weeks of gestation. We compared the risk of adverse pregnancy outcomes according to OGTT patterns by latent profile analysis (LPA), numbers to meet the OGTT criteria, and area under the curve (AUC) of the OGTT graph. Adverse pregnancy outcomes were defined as a composite of preterm birth, macrosomia, large for gestational age, low APGAR score at 1 minute, and pregnancy-induced hypertension.
Results Overall, 257 participants were diagnosed with GDM, with a median age of 34 years. An LPA led to three different clusters of OGTT patterns; however, there were no significant associations between the clusters and adverse pregnancy outcomes after adjusting for confounders. Notwithstanding, the risk of adverse pregnancy outcome increased with an increase in number to meet the OGTT criteria (P for trend=0.011); odds ratios in a full adjustment model were 1.27 (95% confidence interval [CI], 0.72 to 2.23), 2.16 (95% CI, 1.21 to 3.85), and 2.32 (95% CI, 0.66 to 8.15) in those meeting the 2, 3, and 4 criteria, respectively. The AUCs of the OGTT curves also distinguished the patients at risk of adverse pregnancy outcomes; the larger the AUC, the higher the risk (P for trend=0.007).
Conclusion The total number of abnormal values and calculated AUCs for the 100-g OGTT may facilitate tailored management of patients with GDM by predicting adverse pregnancy outcomes.
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