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2 "Kyoung-Ah Kim"
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Cardiovascular Risk/Epidemiology
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Two-Year Changes in Diabetic Kidney Disease Phenotype and the Risk of Heart Failure: A Nationwide Population-Based Study in Korea
Seung Eun Lee, Juhwan Yoo, Han Seok Choi, Kyungdo Han, Kyoung-Ah Kim
Diabetes Metab J. 2023;47(4):523-534.   Published online April 25, 2023
DOI: https://doi.org/10.4093/dmj.2022.0096
  • 2,425 View
  • 112 Download
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
Diabetic kidney disease (DKD) is a risk factor for hospitalization for heart failure (HHF). DKD could be classified into four phenotypes by estimated glomerular filtration rate (eGFR, normal vs. low) and proteinuria (PU, negative vs. positive). Also, the phenotype often changes dynamically. This study examined HHF risk according to the DKD phenotype changes across 2-year assessments.
Methods
The study included 1,343,116 patients with type 2 diabetes mellitus (T2DM) from the Korean National Health Insurance Service database after excluding a very high-risk phenotype (eGFR <30 mL/min/1.73 m2) at baseline, who underwent two cycles of medical checkups between 2009 and 2014. From the baseline and 2-year eGFR and PU results, participants were divided into 10 DKD phenotypic change categories.
Results
During an average of 6.5 years of follow-up, 7,874 subjects developed HHF. The cumulative incidence of HHF from index date was highest in the eGFRlowPU– phenotype, followed by eGFRnorPU+ and eGFRnorPU. Changes in DKD phenotype differently affect HHF risk. When the persistent eGFRnorPU category was the reference, hazard ratios for HHF were 3.10 (95% confidence interval [CI], 2.73 to 3.52) in persistent eGFRnorPU+ and 1.86 (95% CI, 1.73 to 1.99) in persistent eGFRlowPU. Among altered phenotypes, the category converted to eGFRlowPU+ showed the highest risk. In the normal eGFR category at the second examination, those who converted from PU to PU+ showed a higher risk of HHF than those who converted from PU+ to PU.
Conclusion
Changes in DKD phenotype, particularly with the presence of PU, are more likely to reflect the risk of HHF, compared with DKD phenotype based on a single time point in patients with T2DM.
Cardiovascular Risk/Epidemiology
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Comparative Effects of Sodium-Glucose Cotransporter 2 Inhibitor and Thiazolidinedione Treatment on Risk of Stroke among Patients with Type 2 Diabetes Mellitus
Seung Eun Lee, Hyewon Nam, Han Seok Choi, Hoseob Kim, Dae-Sung Kyoung, Kyoung-Ah Kim
Diabetes Metab J. 2022;46(4):567-577.   Published online February 8, 2022
DOI: https://doi.org/10.4093/dmj.2021.0160
  • 6,537 View
  • 385 Download
  • 6 Web of Science
  • 6 Crossref
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
Although cardiovascular outcome trials using sodium-glucose cotransporter-2 inhibitors (SGLT-2i) showed a reduction in risk of 3-point major adverse cardiovascular events (MACE), they did not demonstrate beneficial effects on stroke risk. Additionally, meta-analysis showed SGLT-2i potentially had an adverse effect on stroke risk. Contrarily, pioglitazone, a type of thiazolidinedione (TZD), has been shown to reduce recurrent stroke risk. Thus, we aimed to compare the effect of SGLT-2i and TZD on the risk of stroke in type 2 diabetes mellitus (T2DM) patients.
Methods
Using the Korean National Health Insurance Service data, we compared a 1:1 propensity score-matched cohort of patients who used SGLT-2i or TZD from January 2014 to December 2018. The primary outcome was stroke. The secondary outcomes were myocardial infarction (MI), cardiovascular death, 3-point MACE, and heart failure (HF).
Results
After propensity-matching, each group included 56,794 patients. Baseline characteristics were well balanced. During the follow-up, 862 patients were newly hospitalized for stroke. The incidence rate of stroke was 4.11 and 4.22 per 1,000 person-years for the TZD and SGLT-2i groups respectively. The hazard ratio (HR) of stroke was 1.054 (95% confidence interval [CI], 0.904 to 1.229) in the SGLT-2i group compared to the TZD group. There was no difference in the risk of MI, cardiovascular death, 3-point MACE between groups. Hospitalization for HF was significantly decreased in SGLT-2i-treated patients (HR, 0.645; 95% CI, 0.466 to 0.893). Results were consistent regardless of prior cardiovascular disease.
Conclusion
In this real-world data, the risk of stroke was comparable in T2DM patients treated with SGLT-2i or TZD.

Citations

Citations to this article as recorded by  
  • Similar incidence of stroke with SGLT2 inhibitors and GLP-1 receptor agonists in real-world cohort studies among patients with type 2 diabetes
    André J. Scheen
    Diabetes Epidemiology and Management.2024; 13: 100179.     CrossRef
  • Diabetes and Stroke: Impact of Novel Therapies for the Treatment of Type 2 Diabetes Mellitus
    Inês Henriques Vieira, Tânia Santos Carvalho, Joana Saraiva, Leonor Gomes, Isabel Paiva
    Biomedicines.2024; 12(5): 1102.     CrossRef
  • Key results from observational studies and real‐world evidence of sodium‐glucose cotransporter‐2 inhibitor effectiveness and safety in reducing cardio‐renal risk
    Thomas Nyström
    Diabetes, Obesity and Metabolism.2024; 26(S5): 35.     CrossRef
  • Comparison of Statin With Ezetimibe Combination Therapy Versus Statin Monotherapy for Primary Prevention in Middle-Aged Adults
    Jung-Joon Cha, Soon Jun Hong, Subin Lim, Ju Hyeon Kim, Hyung Joon Joo, Jae Hyoung Park, Cheol Woong Yu, Do-Sun Lim, Jang Young Kim, Jin-Ok Jeong, Jeong-Hun Shin, Chi Young Shim, Jong-Young Lee, Young-Hyo Lim, Sung Ha Park, Eun Joo Cho, Hasung Kim, Jungkuk
    Korean Circulation Journal.2024; 54(9): 534.     CrossRef
  • Lobeglitazone, a novel thiazolidinedione, for secondary prevention in patients with ischemic stroke: a nationwide nested case-control study
    Joonsang Yoo, Jimin Jeon, Minyoul Baik, Jinkwon Kim
    Cardiovascular Diabetology.2023;[Epub]     CrossRef
  • Do SGLT2 inhibitors and GLP-1 receptor agonists modulate differently the risk of stroke ? Discordance between randomised controlled trials and observational studies
    André J. Scheen
    Diabetes & Metabolism.2023; 49(5): 101474.     CrossRef

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