Endocrinology and Metabolism

Original Article

Clinical Study Big Data Articles (National Health Insurance Service Database)
Variabilities in Weight and Waist Circumference and Risk of Myocardial Infarction, Stroke, and Mortality: A Nationwide Cohort Study: Da Hye Kim, Ga Eun Nam, Kyungdo Han, Yang-Hyun Kim, Kye-Yeung Park, Hwan-Sik Hwang, Byoungduck Han, Sung Jung Cho, Seung Jin Jung, Yeo-Joon Yoon, Yong Kyun Roh, Kyung Hwan Cho, Yong Gyu Park; Endocrinol Metab. 2020;35(4):933-942. Published online December 23, 2020; DOI: https://doi.org/10.3803/EnM.2020.871

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Background
Evidence regarding the association between variabilities in obesity measures and health outcomes is limited. We aimed to examine the association between variabilities in obesity measures and cardiovascular outcomes and all-cause mortality.
Methods
We identified 4,244,460 individuals who underwent health examination conducted by the Korean National Health Insurance Service during 2012, with ≥3 anthropometric measurements between 2009 and 2012. Variabilities in body weight (BW) and waist circumference (WC) were assessed using four indices including variability independent of the mean (VIM). We performed multivariable Cox proportional hazards regression analyses.
Results
During follow-up of 4.4 years, 16,095, 18,957, and 30,200 cases of myocardial infarction (MI), stroke, and all-cause mortality were recorded. Compared to individuals with the lowest quartiles, incrementally higher risks of study outcomes and those of stroke and all-cause mortality were observed among individuals in higher quartiles of VIM for BW and VIM for WC, respectively. The multivariable adjusted hazard ratios and 95% confidence intervals comparing the highest versus lowest quartile groups of VIM for BW were 1.17 (1.12 to 1.22) for MI, 1.20 (1.16 to 1.25) for stroke, and 1.66 (1.60 to 1.71) for all-cause mortality; 1.07 (1.03 to 1.12) for stroke and 1.29 (1.25 to 1.33) for all-cause mortality regarding VIM for WC. These associations were similar with respect to the other indices for variability.
Conclusion
This study revealed positive associations between variabilities in BW and WC and cardiovascular outcomes and allcause mortality. Our findings suggest that variabilities in obesity measures are associated with adverse health outcomes in the general population.

Citations

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Gender differences in midlife to later-life cumulative burden and variability of obesity measures and risk of all-cause and cause-specific mortality
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Review Article

Obesity and Metabolism
Effects of Cardiovascular Risk Factor Variability on Health Outcomes: Seung-Hwan Lee, Mee Kyoung Kim, Eun-Jung Rhee; Endocrinol Metab. 2020;35(2):217-226. Published online June 24, 2020; DOI: https://doi.org/10.3803/EnM.2020.35.2.217

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Innumerable studies have suggested “the lower, the better” for cardiovascular risk factors, such as body weight, lipid profile, blood pressure, and blood glucose, in terms of health outcomes. However, excessively low levels of these parameters cause health problems, as seen in cachexia, hypoglycemia, and hypotension. Body weight fluctuation is related to mortality, diabetes, obesity, cardiovascular disease, and cancer, although contradictory findings have been reported. High lipid variability is associated with increased mortality and elevated risks of cardiovascular disease, diabetes, end-stage renal disease, and dementia. High blood pressure variability is associated with increased mortality, myocardial infarction, hospitalization, and dementia, which may be caused by hypotension. Furthermore, high glucose variability, which can be measured by continuous glucose monitoring systems or self-monitoring of blood glucose levels, is associated with increased mortality, microvascular and macrovascular complications of diabetes, and hypoglycemic events, leading to hospitalization. Variability in metabolic parameters could be affected by medications, such as statins, antihypertensives, and hypoglycemic agents, and changes in lifestyle patterns. However, other mechanisms modify the relationships between biological variability and various health outcomes. In this study, we review recent evidence regarding the role of variability in metabolic parameters and discuss the clinical implications of these findings.

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Original Articles

Effects of Vildagliptin or Pioglitazone on Glycemic Variability and Oxidative Stress in Patients with Type 2 Diabetes Inadequately Controlled with Metformin Monotherapy: A 16-Week, Randomised, Open Label, Pilot Study: Nam Hoon Kim, Dong-Lim Kim, Kyeong Jin Kim, Nan Hee Kim, Kyung Mook Choi, Sei Hyun Baik, Sin Gon Kim; Endocrinol Metab. 2017;32(2):241-247. Published online June 23, 2017; DOI: https://doi.org/10.3803/EnM.2017.32.2.241

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Background

Glycemic variability is associated with the development of diabetic complications through the activation of oxidative stress. This study aimed to evaluate the effects of a dipeptidyl peptidase 4 inhibitor, vildagliptin, or a thiazolidinedione, pioglitazone, on glycemic variability and oxidative stress in patients with type 2 diabetes.

Methods

In this open label, randomised, active-controlled, pilot trial, individuals who were inadequately controlled with metformin monotherapy were assigned to either vildagliptin (50 mg twice daily, n=17) or pioglitazone (15 mg once daily, n=14) treatment groups for 16 weeks. Glycemic variability was assessed by calculating the mean amplitude of glycemic excursions (MAGE), which was obtained from continuous glucose monitoring. Urinary 8-iso prostaglandin F₂α, serum oxidised low density lipoprotein, and high-sensitivity C-reactive protein were used as markers of oxidative stress or inflammation.

Results

Both vildagliptin and pioglitazone significantly reduced glycated hemoglobin and mean plasma glucose levels during the 16-week treatment. Vildagliptin also significantly reduced the MAGE (from 93.8±38.0 to 70.8±19.2 mg/dL, P=0.046), and mean standard deviation of 24 hours glucose (from 38±17.3 to 27.7±6.9, P=0.026); however, pioglitazone did not, although the magnitude of decline was similar in both groups. Markers of oxidative stress or inflammation including urinary 8-iso prostaglandin F₂α did not change after treatment in both groups.

Conclusion

In this 16-week treatment trial, vildagliptin, but not pioglitazone, reduced glycemic variability in individuals with type 2 diabetes who was inadequately controlled with metformin monotherapy, although a reduction of oxidative stress markers was not observed.

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What is Glycaemic Variability and which Pharmacological Treatment Options are Effective? A Narrative Review
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Frontiers in Endocrinology.2022;[Epub] CrossRef
Comparison of Efficacy of Glimepiride, Alogliptin, and Alogliptin-Pioglitazone as the Initial Periods of Therapy in Patients with Poorly Controlled Type 2 Diabetes Mellitus: An Open-Label, Multicenter, Randomized, Controlled Study
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Clinical Study
1,5-Anhydro-D-Glucitol Could Reflect Hypoglycemia Risk in Patients with Type 2 Diabetes Receiving Insulin Therapy: Min Kyeong Kim, Hye Seung Jung, Soo Heon Kwak, Young Min Cho, Kyong Soo Park, Seong Yeon Kim; Endocrinol Metab. 2016;31(2):284-291. Published online May 27, 2016; DOI: https://doi.org/10.3803/EnM.2016.31.2.284

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Background

The identification of a marker for hypoglycemia could help patients achieve strict glucose control with a lower risk of hypoglycemia. 1,5-Anhydro-D-glucitol (1,5-AG) reflects postprandial hyperglycemia in patients with well-controlled diabetes, which contributes to glycemic variability. Because glycemic variability is related to hypoglycemia, we aimed to evaluate the value of 1,5-AG as a marker of hypoglycemia.

Methods

We enrolled 18 adults with type 2 diabetes mellitus (T2DM) receiving insulin therapy and assessed the occurrence of hypoglycemia within a 3-month period. We measured 1,5-AG level, performed a survey to score the severity of hypoglycemia, and applied a continuous glucose monitoring system (CGMS).

Results

1,5-AG was significantly lower in the high hypoglycemia-score group compared to the low-score group. Additionally, the duration of insulin treatment was significantly longer in the high-score group. Subsequent analyses were adjusted by the duration of insulin treatment and mean blood glucose, which was closely associated with both 1,5-AG level and hypoglycemia risk. In adjusted correlation analyses, 1,5-AG was negatively correlated with hypoglycemia score, area under the curve at 80 mg/dL, and low blood glucose index during CGMS (P=0.068, P=0.033, and P=0.060, respectively).

Conclusion

1,5-AG level was negatively associated with hypoglycemia score determined by recall and with documented hypoglycemia after adjusting for mean glucose and duration of insulin treatment. As a result, this level could be a marker of the risk of hypoglycemia in patients with well-controlled T2DM receiving insulin therapy.

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Obesity and Metabolism
Factors Associated with Glycemic Variability in Patients with Type 2 Diabetes: Focus on Oral Hypoglycemic Agents and Cardiovascular Risk Factors: Soyeon Yoo, Sang-Ouk Chin, Sang-Ah Lee, Gwanpyo Koh; Endocrinol Metab. 2015;30(3):352-360. Published online August 4, 2015; DOI: https://doi.org/10.3803/EnM.2015.30.3.352

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Background

The role of glycemic variability (GV) in development of cardiovascular diseases remains controversial, and factors that determine glucose fluctuation in patients with diabetes are unknown. We investigated relationships between GV indices, kinds of oral hypoglycemic agents (OHAs), and cardiovascular risk factors in patients with type 2 diabetes mellitus (T2DM).

Methods

We analyzed 209 patients with T2DM. The GV index (standard deviation [SD] and mean absolute glucose change [MAG]) were calculated from 7-point self-monitoring of blood glucose profiles. The patients were classified into four groups according to whether they take OHAs known as GV-lowering (A) and GV-increasing (B): 1 (A only), 2 (neither), 3 (both A and B), and 4 (B only). The 10-year risk for atherosclerotic cardiovascular disease (ASCVD) was calculated using the Pooled Cohort Equations.

Results

GV indices were significantly higher in patients taking sulfonylureas (SUs), but lower in those taking dipeptidyl peptidase-4 inhibitors. In hierarchical regression analysis, the use of SUs remained independent correlates of the SD (β=0.209, P=0.009) and MAG (β=0.214, P=0.011). In four OHA groups, GV indices increased progressively from group 1 to group 4. However, these did not differ according to quartiles of 10-year ASCVD risk.

Conclusion

GV indices correlated significantly with the use of OHAs, particularly SU, and differed significantly according to combination of OHAs. However, cardiovascular risk factors and 10-year ASCVD risk were not related to GV indices. These findings suggest that GV is largely determined by properties of OHAs and not to cardiovascular complications in patients with T2DM.

Citations

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Review Article

Obesity and Metabolism
Clinical Implications of Glucose Variability: Chronic Complications of Diabetes: Hye Seung Jung; Endocrinol Metab. 2015;30(2):167-174. Published online June 30, 2015; DOI: https://doi.org/10.3803/EnM.2015.30.2.167

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Abstract PDF PubReader

Glucose variability has been identified as a potential risk factor for diabetic complications; oxidative stress is widely regarded as the mechanism by which glycemic variability induces diabetic complications. However, there remains no generally accepted gold standard for assessing glucose variability. Representative indices for measuring intraday variability include calculation of the standard deviation along with the mean amplitude of glycemic excursions (MAGE). MAGE is used to measure major intraday excursions and is easily measured using continuous glucose monitoring systems. Despite a lack of randomized controlled trials, recent clinical data suggest that long-term glycemic variability, as determined by variability in hemoglobin A1c, may contribute to the development of microvascular complications. Intraday glycemic variability is also suggested to accelerate coronary artery disease in high-risk patients.

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Original Article

Obesity and Metabolism
A1c Variability Can Predict Coronary Artery Disease in Patients with Type 2 Diabetes with Mean A1c Levels Greater than 7: Eun Ju Lee, You Jeong Kim, Tae Nyun Kim, Tae Ik Kim, Won Kee Lee, Mi-Kyung Kim, Jeong Hyun Park, Byoung Doo Rhee; Endocrinol Metab. 2013;28(2):125-132. Published online June 18, 2013; DOI: https://doi.org/10.3803/EnM.2013.28.2.125

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28 Download
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Abstract PDF PubReader

Background

Recent studies suggested that the association of acute glucose variability and diabetic complications was not consistent, and that A1c variability representing long term glucose fluctuation may be related to coronary atherosclerosis in patients with type 1 diabetes. In this study, we attempt to determine whether or not A1c variability can predict coronary artery disease (CAD) in patients with type 2 diabetes.

Methods

We reviewed data of patients with type 2 diabetes who had undergone coronary angiography (CAG) and had been followed up with for 5 years. The intrapersonal standard deviation (SD) of serially-measured A1c levels adjusted by the different number of assessments among patients (adj-A1c-SD) was considered to be a measure of the variability of A1c.

Results

Among the 269 patients, 121 of them had type 2 diabetes with CAD. In patients with A1c ≥7%, the mean A1c levels and A1c levels at the time of CAG among the three groups were significantly different. The ratio of patients with CAD was the highest in the high adj-A1c-SD group and the lowest in the low adj-A1c-SD group (P=0.017). In multiple regression analysis, adj-A1c-SD was an independent predictor for CAD in subjects with A1c ≥7% (odds ratio, 2.140; P=0.036).

Conclusion

Patients with higher A1c variability for several years showed higher mean A1c levels. A1c variability can be an independent predictor for CAD as seen in angiographs of patients with type 2 diabetes with mean A1c levels over 7%.

Citations

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Cardiovascular Diabetology.2018;[Epub] CrossRef
Cardiovascular Autonomic Neuropathy Predicts Higher HbA1c Variability in Subjects with Type 2 Diabetes Mellitus
Yeoree Yang, Eun-Young Lee, Jae-Hyoung Cho, Yong-Moon Park, Seung-Hyun Ko, Kun-Ho Yoon, Moo-Il Kang, Bong-Yun Cha, Seung-Hwan Lee
Diabetes & Metabolism Journal.2018; 42(6): 496. CrossRef
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