No abstract available.
Diabetes Mellitus, Type 2* ; Gastroesophageal Reflux* ; Humans ; Prevalence* ; Risk Factors*

No abstract available.
Diabetes Mellitus, Type 2* ; Gastroesophageal Reflux* ; Humans ; Prevalence* ; Risk Factors*

The aim of this study was to compare the fasting serum glucose level with the fasting plasma glucose level for diagnosing hyperglycemic states in real-life clinical situations. Additionally, we investigated a usual delay in sample processing and how such delays can impact the diagnosis of hyperglycemic states. Among 1,254 participants who had normoglycemia or impaired fasting glucose (IFG) assessed by the fasting serum glucose level, 20.9% were newly diagnosed with diabetes based on the plasma fasting glucose level. Of the participants with normoglycemia, 62.1% and 14.2% were newly diagnosed with IFG and diabetes, respectively, according to the plasma fasting glucose level. In our clinical laboratory for performing health examinations, the time delay from blood sampling to glycemic testing averaged 78±52 minutes. These findings show that the ordinary time delay for sample processing of the serum glucose for screening hyperglycemic states may be an important reason for these diagnoses to be underestimated in Korea.
Blood Glucose* ; Diagnosis* ; Fasting ; Glucose ; Korea ; Mass Screening* ; Plasma ; Prediabetic State*

One of the authors' names was misprinted.

The published Fig. 1 is to explain the expression and role of NPC1L1 in the intestine and liver. The arrows of NPC1L1 and ABCG5/ABCG8 in the liver were reversed by an inadvertent mistake.

No abstract available.
Blood Glucose* ; Critical Illness* ; Humans ; Mortality*

BACKGROUND: We aimed to quantify stress-induced hyperglycemia and differentiate the glucose response between normal animals and those with diabetes. We also examined the pattern in glucose fluctuation induced by stress according to type of diabetes. METHODS: To load psychological stress on animal models, we used a predator stress model by exposing rats to a cat for 60 minutes and measured glucose level from the beginning to the end of the test to monitor glucose fluctuation. We induced type 1 diabetes model (T1D) for ten Sprague-Dawley rats using streptozotocin and used five Otsuka Long-Evans Tokushima Fatty rats as obese type 2 diabetes model (OT2D) and 10 Goto-Kakizaki rats as nonobese type 2 diabetes model (NOT2D). We performed the stress loading test in both the normal and diabetic states and compared patterns of glucose fluctuation among the three models. We classified the pattern of glucose fluctuation into A, B, and C types according to speed of change in glucose level. RESULTS: Increase in glucose, total amount of hyperglycemic exposure, time of stress-induced hyperglycemia, and speed of glucose increase were significantly increased in all models compared to the normal state. While the early increase in glucose after exposure to stress was higher in T1D and NOT2D, it was slower in OT2D. The rate of speed of the decrease in glucose level was highest in NOT2D and lowest in OT2D. CONCLUSION: The diabetic state was more vulnerable to stress compared to the normal state in all models, and the pattern of glucose fluctuation differed among the three types of diabetes. The study provides basic evidence for stress-induced hyperglycemia patterns and characteristics used for the management of diabetes patients.
Animals ; Cats ; Glucose ; Humans ; Hyperglycemia* ; Models, Animal ; Rats ; Rats, Sprague-Dawley ; Streptozocin ; Stress, Psychological

BACKGROUND: This study compared the glycemic effectiveness of three metformin-based dual therapies according to baseline hemoglobin A1c (HbA1c) to evaluate the appropriateness of the guideline enforced by the National Health Insurance Corporation of Korea for initial medication of type 2 diabetes (T2D). METHODS: This prospective observational study was conducted across 24 weeks for drug-naive Korean T2D patients with HbA1c greater than 7.5%. Subjects were first divided into three groups based on the agent combined with metformin (group 1, gliclazide-modified release or glimepiride; group 2, pioglitazone; group 3, sitagliptin). Subjects were also classified into three categories according to baseline HbA1c (category I, 7.5%< or =HbA1c<9.0%; category II, 9.0%< or =HbA1c<11.0%; category III, 11.0%< or =HbA1c). RESULTS: Among 116 subjects, 99 subjects completed the study, with 88 subjects maintaining the initial medication. While each of the metformin-based dual therapies showed a significant decrease in HbA1c (group 1, 8.9% to 6.4%; group 2, 9.0% to 6.6%; group 3, 9.3% to 6.3%; P<0.001 for each), there was no significant difference in the magnitude of HbA1c change among the groups. While the three HbA1c categories showed significantly different baseline HbA1c levels (8.2% vs. 9.9% vs. 11.9%; P<0.001), endpoint HbA1c was not different (6.4% vs. 6.6% vs. 6.0%; P=0.051). CONCLUSION: The three dual therapies using a combination of metformin and either sulfonylurea, pioglitazone, or sitagliptin showed similar glycemic effectiveness among drug-naive Korean T2D patients. In addition, these regimens were similarly effective across a wide range of baseline HbA1c levels.
Diabetes Mellitus, Type 2 ; Humans ; Korea ; Metformin ; National Health Programs ; Prospective Studies ; Sitagliptin Phosphate

BACKGROUND: Visceral adiposity is related to insulin resistance. Skeletal muscle plays a central role in insulin-mediated glucose disposal; however, little is known about the association between muscle mass and metabolic syndrome (MS). This study is to clarify the clinical role of skeletal muscle mass in development of MS. METHODS: A total of 1,042 subjects were enrolled. Subjects with prior MS and chronic diseases were excluded. After 24 months, development of MS was assessed using NCEP-ATP III criteria. Skeletal muscle mass (SMM; kg), body fat mass (BFM; kg), and visceral fat area (VFA; cm2) were obtained from bioelectrical analysis. Then, the following values were calculated as follows: percent of SMM (SMM%; %): SMM (kg)/weight (kg), skeletal muscle index (SMI; kg/m2): SMM (kg)/height (m2), skeletal muscle to body fat ratio (MFR): SMM (kg)/BFM (kg), and skeletal muscle to visceral fat ratio (SVR; kg/cm2): SMM (kg)/VFA (cm2). RESULTS: Among 838 subjects, 88 (10.5%) were newly diagnosed with MS. Development of MS increased according to increasing quintiles of BMI, SMM, VFA, and SMI, but was negatively associated with SMM%, MFR, and SVR. VFA was positively associated with high waist circumference (WC), high blood pressure (BP), dysglycemia, and high triglyceride (TG). In contrast, MFR was negatively associated with high WC, high BP, dysglycemia, and high TG. SVR was negatively associated with all components of MS. CONCLUSION: Relative SMM ratio to body composition, rather than absolute mass, may play a critical role in development of MS and could be used as a strong predictor.
Adipose Tissue ; Adiposity ; Body Composition ; Chronic Disease ; Glucose ; Hypertension ; Insulin Resistance ; Intra-Abdominal Fat ; Muscle, Skeletal* ; Muscles ; Triglycerides ; Waist Circumference

BACKGROUND: Recent evidence has suggested an association between subclinical hypothyroidism (SCH) and microalbuminuria in patients with type 2 diabetes. However, whether SCH is related to microalbuminuria among subjects with prediabetes has not been studied. Thus, we evaluated the association between SCH and microalbuminuria in a cohort of prediabetic Egyptian adults. METHODS: A total of 147 prediabetic subjects and 150 healthy controls matched for age and sex were enrolled in this study. Anthropometric measurements, plasma glucose, lipid profile, homeostasis model assessment of insulin resistance (HOMA-IR), thyroid stimulating hormone (TSH), free thyroxine, triiodothyronine levels, and urinary albumin-creatinine ratio (UACR) were assessed. RESULTS: The prevalence of SCH and microalbuminuria in the prediabetic subjects was higher than that in the healthy controls (16.3% vs. 4%, P<0.001; and 12.9% vs. 5.3%, P=0.02, respectively). Prediabetic subjects with SCH were characterized by significantly higher HOMA-IR, TSH levels, UACR, and prevalence of microalbuminuria than those with euthyroidism. TSH level was associated with total cholesterol (P=0.05), fasting insulin (P=0.01), HOMA-IR (P=0.01), and UACR (P=0.005). UACR was associated with waist circumference (P=0.01), fasting insulin (P=0.05), and HOMA-IR (P=0.02). With multiple logistic regression analysis, SCH was associated with microalbuminuria independent of confounding variables (beta=2.59; P=0.01). CONCLUSION: Our findings suggest that prediabetic subjects with SCH demonstrate higher prevalence of microalbuminuria than their non-SCH counterparts. SCH is also independently associated with microalbuminuria in prediabetic subjects. Screening and treatment for SCH may be warranted in those patients.
Adult* ; Blood Glucose ; Cholesterol ; Cohort Studies* ; Confounding Factors (Epidemiology) ; Fasting ; Homeostasis ; Humans ; Hypothyroidism* ; Insulin ; Insulin Resistance ; Logistic Models ; Mass Screening ; Prediabetic State ; Prevalence ; Thyrotropin ; Thyroxine ; Triiodothyronine ; Waist Circumference