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Jimi Choi 4 Articles
Calcium & bone metabolism
Increased Risk of Hip Fracture in Patients with Acromegaly: A Nationwide Cohort Study in Korea
Jiwon Kim, Namki Hong, Jimi Choi, Ju Hyung Moon, Eui Hyun Kim, Eun Jig Lee, Sin Gon Kim, Cheol Ryong Ku
Endocrinol Metab. 2023;38(6):690-700.   Published online October 30, 2023
DOI: https://doi.org/10.3803/EnM.2023.1782
  • 1,024 View
  • 80 Download
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
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.
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Diabetes, Obesity and Metabolism
Identification of Healthy and Unhealthy Lifestyles by a Wearable Activity Tracker in Type 2 Diabetes: A Machine Learning-Based Analysis
Kyoung Jin Kim, Jung-Been Lee, Jimi Choi, Ju Yeon Seo, Ji Won Yeom, Chul-Hyun Cho, Jae Hyun Bae, Sin Gon Kim, Heon-Jeong Lee, Nam Hoon Kim
Endocrinol Metab. 2022;37(3):547-551.   Published online June 29, 2022
DOI: https://doi.org/10.3803/EnM.2022.1479
  • 2,695 View
  • 119 Download
  • 2 Web of Science
  • 2 Crossref
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
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.

Citations

Citations to this article as recorded by  
  • Rethink nutritional management in chronic kidney disease care
    Fangyue Chen, Krit Pongpirul
    Frontiers in Nephrology.2023;[Epub]     CrossRef
  • Effect of a Wearable Device–Based Physical Activity Intervention in North Korean Refugees: Pilot Randomized Controlled Trial
    Ji Yoon Kim, Kyoung Jin Kim, Kyeong Jin Kim, Jimi Choi, Jinhee Seo, Jung-Been Lee, Jae Hyun Bae, Nam Hoon Kim, Hee Young Kim, Soo-Kyung Lee, Sin Gon Kim
    Journal of Medical Internet Research.2023; 25: e45975.     CrossRef
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Diabetes, Obesity and Metabolism
Big Data Articles (National Health Insurance Service Database)
Risk and Risk Factors for Postpartum Type 2 Diabetes Mellitus in Women with Gestational Diabetes: A Korean Nationwide Cohort Study
Mi Jin Choi, Jimi Choi, Chae Weon Chung
Endocrinol Metab. 2022;37(1):112-123.   Published online February 28, 2022
DOI: https://doi.org/10.3803/EnM.2021.1276
  • 3,621 View
  • 158 Download
  • 2 Web of Science
  • 2 Crossref
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
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.

Citations

Citations to this article as recorded by  
  • Integration of nutrigenomics, melatonin, serotonin and inflammatory cytokines in the pathophysiology of pregnancy-specific urinary incontinence in women with gestational diabetes mellitus
    Danielle Cristina Honorio França, Eduardo Luzía França, Luis Sobrevia, Angélica Mércia Pascon Barbosa, Adenilda Cristina Honorio-França, Marilza Vieira Cunha Rudge
    Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease.2023; 1869(6): 166737.     CrossRef
  • Risk factors associated with early postpartum glucose intolerance in women with a history of gestational diabetes mellitus: a systematic review and meta-analysis
    Zhe Liu, Qianghuizi Zhang, Leyang Liu, Weiwei Liu
    Endocrine.2023; 82(3): 498.     CrossRef
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Diabetes, Obesity and Metabolism
How Can We Adopt the Glucose Tolerance Test to Facilitate Predicting Pregnancy Outcome in Gestational Diabetes Mellitus?
Kyeong Jin Kim, Nam Hoon Kim, Jimi Choi, Sin Gon Kim, Kyung Ju Lee
Endocrinol Metab. 2021;36(5):988-996.   Published online October 15, 2021
DOI: https://doi.org/10.3803/EnM.2021.1107
  • 3,969 View
  • 112 Download
  • 2 Web of Science
  • 2 Crossref
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
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.

Citations

Citations to this article as recorded by  
  • Risk factors combine in a complex manner in assessment for macrosomia
    Yi-Wen Wang, Yan Chen, Yong-Jun Zhang
    BMC Public Health.2023;[Epub]     CrossRef
  • Association of the Severity of Hypertensive Disorders in Pregnancy with Birthweight, Childhood Obesity, and Blood Pressure at Age 7
    Yan Chen, Yiwen Wang, Yanjun Li, Guodong Ding, Yongjun Zhang
    Nutrients.2023; 15(14): 3104.     CrossRef
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