Background Obesity is a chronic disease associated with metabolic diseases such as diabetes and cardiovascular disease. Since the U.S. Food and Drug Administration approved liraglutide as an anti-obesity drug for nondiabetic patients in 2014, it has been widely used for weight control in overweight and obese people. This study aimed to systematically analyze the effects of liraglutide on body weight and other cardiometabolic parameters. Methods We investigated articles from PubMed, EMBASE, and the Cochrane Library to search randomized clinical trials that examined body weight changes with liraglutide treatment. Results We included 31 studies with 8,060 participants for this meta-analysis. The mean difference (MD) between the liraglutide group and the placebo group was −4.19 kg (95% confidence interval [CI], −4.84 to −3.55), with a −4.16% change from the baseline (95% CI, −4.90 to −3.43). Liraglutide treatment correlated with a significantly reduced body mass index (MD: −1.55; 95% CI, −1.76 to −1.34) and waist circumference (MD: −3.11 cm; 95% CI, −3.59 to −2.62) and significantly decreased blood pressure (systolic blood pressure, MD: −2.85 mm Hg; 95% CI, −3.36 to −2.35; diastolic blood pressure, MD: −0.66 mm Hg; 95% CI, −1.02 to −0.30), glycated hemoglobin (MD: −0.40%; 95% CI, −0.49 to −0.31), and low-density lipoprotein cholesterol (MD: −2.91 mg/dL; 95% CI, −5.28 to −0.53; MD: −0.87% change from baseline; 95% CI, −1.17 to −0.56). Conclusion Liraglutide is effective for weight control and can be a promising drug for cardiovascular protection in overweight and obese people. Keywords: Liraglutide; Glucagon-like peptide 1; Obesity; Metabolic syndrome; Meta-analysis
Abstract Background Advanced glycation end products (AGEs) are accumulated with aging in various tissues of humans. The soluble receptor for AGEs (sRAGE) exerts a protective role against the development of aging‐related chronic disorders by neutralizing the action of AGEs. We investigated the implication of sRAGE on low muscle mass in Asian men and women. Methods This cross‐sectional study included a 390‐participant, nondiabetic subcohort recruited within the framework of the Korean Sarcopenic Obesity Study, an ongoing prospective cohort study. Low muscle mass was defined based on the distribution of appendicular skeletal muscle mass divided by body mass index, as proposed by the Foundation for the National Institutes Sarcopenia Project. Results Serum sRAGE levels were significantly lower in participants with low muscle mass than in participants without low muscle mass (0.76 [0.60‐1.00] ng/mL vs 0.87 [0.67‐1.15] ng/mL, P = .005). In age‐ and sex‐adjusted correlation analyses, appendicular skeletal muscle mass divided by body mass index was associated with sRAGE ( r = 0.109, P = .037). Furthermore, decreased circulating levels of sRAGE are independently associated with low muscle mass (odds ratio = 0.254, P = .002) after adjusting for confounding factors, including insulin resistance and inflammatory markers. Conclusions The present study shows that a low circulating level of sRAGE may be an independent risk factor for the presence of low muscle mass.
Appraisal of muscle mass is important when considering the serious consequences of sarcopenia in an aging society. However, the associations between sarcopenia and its clinical outcomes might vary according to the method applied in its diagnosis. We compared the relationships between cardiometabolic risk parameters and sarcopenia defined according to three different diagnostic methods using dual-energy X-ray absorptiometry (DXA) and computed tomography (CT). Appendicular skeletal muscle mass (ASM) adjusted by height squared and BMI (ASM/height2 and ASM/BMI) measured using DXA and thigh muscle cross-sectional area (tmCSA) adjusted by weight (tmCSA/weight) measured using CT were used as indices of muscle mass. Sarcopenia was defined as two standard deviations below either the mean ASM/height2, ASM/BMI, or tmCSA/weight of a young reference group. ASM/BMI and tmCSA/weight showed a negative relationship with several components of metabolic syndrome and HOMA-IR, whereas ASM/height2 was positively associated with theses cardiometabolic risk factors. Logistic regression analyses demonstrated that ASM/BMI-defined sarcopenia was significantly associated with increased HOMA-IR (P = 0.01) and prevalence of visceral obesity (P = 0.03) and metabolic syndrome (P = 0.025), while ASM/height2- and tmCSA/weight-defined sarcopenia were not. ASM/BMI-defined sarcopenia exhibits a closer relationship with cardiometabolic risk factors than does ASM/height2- or tmCSA/weight-defined sarcopenia.
There are very few predictive indexes for long-term mortality among community-dwelling elderly Asian individuals, despite its importance, given the rapid and continuous increase in this population. We aimed to develop 10-year predictive mortality indexes for community-dwelling elderly Korean men and women based on routinely collected clinical data. We used data from 2244 elderly individuals (older than 60 years of age) from the southwest Seoul Study, a prospective cohort study, for the development of a prognostic index. An independent longitudinal cohort of 679 elderly participants was selected from the Korean Genome Epidemiology Study in Ansan City for validation. During a 10-year follow-up, 393 participants (17.5%) from the development cohort died. Nine risk factors were identified and weighed in the Cox proportional regression model to create a point scoring system: age, male sex, smoking, diabetes, systolic blood pressure, triglyceride, total cholesterol, white blood cell count, and hemoglobin. In the development cohort, the 10-year mortality risk was 6.6%, 14.8%, 18.2%, and 38.4% among subjects with 1 to 4, 5 to 7, 8 to 9, and ≥10 points, respectively. In the validation cohort, the 10-year mortality risk was 5.2%, 12.0%, 16.0%, and 16.0% according to these categories. The C-statistic for the point system was 0.73 and 0.67 in the development and validation cohorts, respectively. The present study provides valuable information for prognosis among elderly Koreans and may guide individualized approaches for appropriate care in a rapidly aging society.
Abstract Background — Athough an association exists between type 2 diabetes and Parkinson’s disease (PD), the implications of glycemic variability on PD are unknown. We assessed the future risk of incident PD according to visit-to-visit fasting plasma glucose (FPG) variability; this was calculated using standard deviation (FPG-SD), coefficient variance (FPG-CV), and variability independent of the mean (FPG-VIM). Methods — Using the Korean National Health Insurance Service–Health Screening Cohort, we followed 131,625 Korean adults without diabetes. This study population was divided into a midlife group (<65 years) and an elderly group (≥65 years), during a median follow-up of 8.4 years. Results — The adjusted hazard ratios (HRs) were calculated using a multivariable Cox proportional hazard analysis. In the midlife group, the HRs for incident PD in the highest quartile of FPG variability, as measured using SD, CV, and VIM, were 1.35 (95% confidence interval (CI), 1.07–1.70), 1.31 (95% CI, 1.04–1.65), and 1.33 (95% CI, 1.06–1.67), respectively, when compared to the lowest quartile group. However, the incident PD was not different depending on FPG variability in the elderly group. Kaplan–Meier curves of PD probability showed a progressively increasing risk of PD according to the higher FPG variability in the midlife group. According to a multivariable adjusted model, a 1-SD unit increment in glycemic variability was associated with a 9% higher risk for incident PD in the midlife group. Conclusions — Increased long-term glycemic variability is a preceding risk factor for developing PD in the midlife population without diabetes.
Abstract In health emergencies, such as in the COVID-19 pandemic, the need to expand or introduce the Paid sick leave(PSL) and Sickness benefits(SB) increases. They are key components of the universal health coverage(UHC) and active labor market policies(ALMPs) that enable workers to take care of their health and guarantee return-to-work after recovery. This study examines effects those policies in achieving economic stability and job security of covered workers through a scoping review. Studies were selected using the search terms ‘paid sick leave', ‘sickness benefits', ‘paid sick day', and ‘earned sick leave’ in PubMed and Web of Science. Our search conducted on 6th April 2021 yielded 1,030 articles, of which 22 articles were included in the review. All articles were analyzed by the 4 sub-groups(employees, families, employers, and government) and we investigated indicators of socio-economic impacts on their lives. Articles are largely PSL(90.9%)-focused. PSL guarantees not only workers’ job security by securing employment agreement, but also their income security by promising part of wages enough to afford healthcare and living expenses during the medical treatment and recovery. Additionally, PSL attenuates employers’ financial risk, as it reduces presenteeism while increasing the return-to-work rate. Moreover, PSL and SB reduce the total healthcare and social security expenditures of the government. To sum up, PSL and SB guarantee health and labor rights by ensuring income and job security to employees while assuring financial stability to both employers, and the government. However, as the previous studies paid less attention on the equity of these impacts at the system levels, future research should more focus on the dimension. Key messages • PSL and SB guarantee health and labour rights by ensuring income and job security for employees, while assuring financial stability for both employers and the government. • The previous studies that examined the effects of PSL and SB paid less attention on the equity of ensuring income and employment security, therefore future studies should focus more on this dimension.
Background : The present study was conducted to examine the effects of a long-acting formulation of lanreotide (Somatulin-Autogel®) in Korean acromegalic patients who had undergone surgery. Methods : The subjects in the study were 11 acromegalic patients (5 men and 6 women) who had undergone transsphenoidal tumor resection at Korea University Guro Hospital. The anthropometric parameters, blood pressure, fasting blood glucose (FBG), IGF-1, HbA1C, mass size and GH level following a 75 gm oral glucose tolerance test (OGTT) were measured in each subject before and after treatment with a long-acting formulation of lanreotide. Results : The median age of the subjects was 41 yrs (range: 28-52 yrs) (Table 1). The mean pre-operative levels of serum IGF-1 in the 11 patients was 1185±323.58 ng/mL, and post-operatively it was 862±314.06 ng/mL. The mean serum IGF-1 concentration decreased from 862±314.06 ng/mL to 549±371.62 ng/mL after 6 months treatment with the long-acting formulation of lanreotide (p=0.003, vs baseline, n=11), and it decreased further to 439±342.53 ng/mL after 12 months treatment (p=0.005 vs baseline, n=10) (Table 3). Two patients achieved the target level of IGF-1. The HbA1C measured before and after lanreotide treatment was 5.8±0.5% and 5.9±0.3%, respectively. Conclusions : This study showed that a long-acting formulation of lanreotide decreased the IGF-1 and GH levels without significant side effects. In spite of the small number of subjects in this study, these findings suggest that this formulation of lanreotide is effective for the post-operative management of acromegaly.
To evaluate the clinical efficacy of sitagliptin for reducing plasma glucose levels in Korean subjects with type 2 diabetes mellitus during a 14-week treatment period.Our study design involved the addition of 100 mg sitagliptin once-daily to three ongoing combination therapy regimens and changing from glimepiride and metformin to sitagliptin and metformin.The addition of sitagliptin 100 mg/day produced a statistically significant reduction in mean HbA1c level (mean HbA1c reduction of 0.99±0.85%, P<0.01). In the group taking a combination of sitagliptin and metformin (n=143, initial mean HbA1c level=7.48%), the reductions in HbA1c, 2-hour postprandial glucose, and fasting glucose levels were 0.72±0.76% (P<0.01), 47±65 mg/dL (P<0.01), and 15±44 mg/dL (P<0.01), respectively. In the group taking a combination of sitagliptin, glimepiride, and metformin (n=125, initial mean HbA1c level=8.42%), the reductions in HbA1c, 2-hour postprandial glucose, and fasting glucose levels were 1.09±0.86% (P<0.01), 62±64 mg/dL (P<0.01), and 31±45 mg/dL (P<0.01), respectively. In the group taking a combination of sitagliptin, glimepiride, metformin, and α-glucosidase inhibitor (n=63, initial mean HbA1c level=9.19%), the reductions in HbA1c, 2-hour postprandial glucose, and fasting glucose levels were 1.27±0.70% (P<0.01), 72±65 mg/dL (P<0.01), and 35±51 mg/dL (P<0.01), respectively. In the group that had previous hypoglycemic events and that changed from glimepiride to sitagliptin, HbA1c level did not change but fasting glucose increased significantly (14±29 mg/dL, P<0.01).Sitagliptin combination therapy for 14 weeks significantly improved glycemic control and was well-tolerated in Korean subjects with type 2 diabetes mellitus.
This study was to determine whether glycemic variability is related to hypoglycemic events in type 1 diabetic patients, and whether the hypoglycemic events during a short-term continuous glucose monitoring system (CGMS) period parallel those measured during a 4-week self-monitoring of blood glucose (SMBG) period. We also evaluated whether glycemic variability indexes from a short-term CGMS correlate with those from a 4-week SMBG. A total of 49 type 1 diabetic patients wore CGMS devices for 3 days. These patients also performed SMBG for 4 weeks. Several indexes from the CGMS data were compared with indexes from the SMBG data. Hypoglycemic events (glucose levels <70 mg/dL) that occurred during the 3-day CGMS and 4-week SMBG periods were evaluated and compared. Hypoglycemic events were detected in 33 patients (67%) during the 3-day CGMS period. The patients with hypoglycemic events had a significantly higher glycemic variability index divided by mean glucose of CGMS, and a higher number of hypoglycemic events during the 4-week SMBG, compared to those with non-hypoglycemic events during the 3-day CGMS period. The percentage of hypoglycemic events using the 3-day CGMS was correlated with that from the 4-week SMBG (r=0.49, P<0.05) and low blood glucose index (r=0.51, P<0.05). The glycemic variability indexes from the 4-week SMBG correlated with the glycemic variability indexes from the 3-day CGMS. The short-term CGMS appears to be clinically useful for rapidly assessing the risk of hypoglycemic events and glycemic variability.
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