Objective: To explore the associations of glycated hemoglobin (HbA1c) with FPG and oral glucose tolerance test 2-hour (OGTT-2 h) in areas at different altitude in China. Methods: Subjects who participated in 2018-2019 China Chronic Disease and Risk Factor Surveillance and had no prior type 2 diabetes diagnosis were included. Subsequently, they were categorized into three groups based on altitude of living area (<2 000, 2 000- and ≥3 000 m). With adjustment for intracluster correlation, multivariable linear regression analysis was performed to evaluate the associations of HbA1c with FPG and OGTT-2 h in the context of HbA1c was normal (<5.7%) or abnormal (≥5.7%). Furthermore, the shape of relationships between HbA1c and glucose indicators was examined using restricted cubic spline. Finally, receiver operating characteristic curve was used to evaluate the diagnostic performance of HbA1c for diabetes. Results: A total of 157 277 subjects were included in the analysis. While FPG and OGTT-2 h levels gradually decreased with increase of altitude, HbA1c level was similar among the three groups. When HbA1c was <5.7%, its association with FPG and OGTT-2 h was weak and no obvious difference was observed among the three groups. When HbA1c was ≥5.7%, the FPG and OGTT-2 h increased by 15.45% (95%CI:14.71%- 16.18%) and 24.54% (95%CI:23.18%-25.91%) respectively per one standard deviation increase in HbA1c in group in area at altitude <2 000 m. However, the FPG and OGTT-2 h increased by 13.08% (95%CI:10.46%-15.76%) and 21.72% (95%CI:16.39%-27.31%), respectively, in group in area at altitude 2 000- m, and increased by 11.41% (95%CI:9.32%-13.53%) and 20.03% (95%CI:15.38%- 24.86%), respectively, in group of altitude ≥3 000 m. The restricted cubic spline indicated that the curve showing the association of HbA1c with FPG and OGTT-2 h was flat when HbA1c was <5.7%, but showed a positive linear relationship when HbA1c was ≥5.7%. The area under curve for detecting diabetes was 0.808 (95%CI:0.803-0.812) in group of altitude <2 000 m and 0.728 (95%CI:0.660-0.796, P=0.022) in group of altitude ≥3 000 m. The relevant optimal cutoff value of HbA1c was 5.7%, with a sensitivity of 65.4% and a specificity of 83.0%, and 6.0%, with a sensitivity of 48.3% and a specificity of 93.7%, respectively. Conclusions: When HbA1c was ≥5.7%, the association between HbA1c and glucose indicators became weaker as the increase of altitude. In the area at altitude ≥3 000 m, it may not be appropriate to use HbA1c in the diagnosis of diabetes.
Adult ; Humans ; Glycated Hemoglobin ; Diabetes Mellitus, Type 2/diagnosis* ; Blood Glucose/analysis* ; Glucose ; Altitude ; Fasting ; China/epidemiology* ; Diabetes Mellitus/epidemiology*

Most information used to evaluate diabetic statuses is collected at a special time-point, such as taking fasting plasma glucose test and providing a limited view of individual's health and disease risk. As a new parameter for continuously evaluating personal clinical statuses, the newly developed technique "continuous glucose monitoring" (CGM) can characterize glucose dynamics. By calculating the complexity of glucose time series index (CGI) with refined composite multi-scale entropy analysis of the CGM data, the study showed for the first time that the complexity of glucose time series in subjects decreased gradually from normal glucose tolerance to impaired glucose regulation and then to type 2 diabetes (P for trend < 0.01). Furthermore, CGI was significantly associated with various parameters such as insulin sensitivity/secretion (all P < 0.01), and multiple linear stepwise regression showed that the disposition index, which reflects β-cell function after adjusting for insulin sensitivity, was the only independent factor correlated with CGI (P < 0.01). Our findings indicate that the CGI derived from the CGM data may serve as a novel marker to evaluate glucose homeostasis.
Humans ; Glucose ; Blood Glucose ; Insulin Resistance/physiology* ; Diabetes Mellitus, Type 2/diagnosis* ; Blood Glucose Self-Monitoring ; Time Factors ; Insulin

Objective To investigate the cardiac structural and functional characteristics in the patients with heart failure with preserved ejection fraction (HFpEF) and type 2 diabetes mellitus (T2DM),and predict the factors influencing the characteristics. Methods A total of 783 HFpEF patients diagnosed in the Department of Geriatric Cardiology,the First Hospital of Lanzhou University from April 2009 to December 2020 were enrolled in this study.Echocardiography and tissue Doppler technique were employed to evaluate cardiac structure and function.According to the occurrence of T2DM,the patients were assigned into a HFpEF+T2DM group (n=332) and a HFpEF group (n=451).Propensity score matching (PSM)(in a 1∶1 ratio) was adopted to minimize confounding effect.According to urinary albumin excretion rate (UAER),the HFpEF+T2DM group was further divided into three subgroups with UAER<20 μg/min,of 20-200 μg/min,and>200 μg/min,respectively.The comorbidities,symptoms and signs,and cardiac structure and function were compared among the groups to clarify the features of diabetes related HFpEF.Multivariate linear regression was conducted to probe the relationship of systolic blood pressure,blood glucose,glycosylated hemoglobin,and UARE with cardiac structural and functional impairment. Results The HFpEF+T2DM group had higher prevalence of hypertension (P=0.001) and coronary heart disease (P=0.036),younger age (P=0.020),and larger body mass index (P=0.005) than the HFpEF group,with the median diabetic course of 10 (3,17) years.After PSM,the prevalence of hypertension and coronary heart disease,body mass index,and age had no significant differences between the two groups(all P>0.05).In addition,the HFpEF+T2DM group had higher interventricular septal thickness (P=0.015),left ventricular posterior wall thickness (P=0.040),and left ventricular mass (P=0.012) and lower early diastole velocity of mitral annular septum (P=0.030) and lateral wall (P=0.011) than the HFpEF group.Compared with the HFpEF group,the HFpEF+T2DM group showed increased ratio of early diastolic mitral filling velocity to early diastolic mitral annular velocity (E/e') (P=0.036).Glycosylated hemoglobin was correlated with left ventricular mass (P=0.011),and the natural logarithm of UAER with interventricular septal thickness (P=0.004),left ventricular posterior wall thickness (P=0.006),left ventricular mass (P<0.001),and E/e' ratio (P=0.049). Conclusion The patients with both T2DM and HFpEF have thicker left ventricular wall,larger left ventricular mass,more advanced left ventricular remodeling,severer impaired left ventricular diastolic function,and higher left ventricular filling pressure than the HFpEF patients without T2DM.Elevated blood glucose and diabetic microvascular diseases might play a role in the development of the detrimental structural and functional changes of the heart.
Humans ; Aged ; Heart Failure/diagnosis* ; Diabetes Mellitus, Type 2 ; Stroke Volume ; Glycated Hemoglobin ; Blood Glucose ; Propensity Score ; Ventricular Function, Left ; Hypertension

The present article demonstrates an unusual case of bilateral lower extremity edema caused by neurogenic areflexic bladder as the first physical symptom of diabetes. A 52-year-old man presented to the emergency department because of massive edema of his lower limbs. The edema had been present for 2 weeks, was symmetrical, and was progressively covering the lower limbs up to the inguinal area, scrotal bag, and penis and was accompanied by dysuria and an interrupted urine stream. Laboratory findings revealed a serum glucose level of 657 mg/dL and glycated hemoglobin (HbA1c) level of 15.6%. Computed tomography (CT) of the abdomen and pelvis revealed marked enlargement of the bladder with bilateral hydronephrosis and hydroureter. In addition, CT demonstrated bilateral compression of the iliac veins caused by the enlarged bladder. This case highlights the importance of a broad differential diagnosis for patients with diabetes and extensive peripheral edema. Neurogenic bladder should be considered in the differential diagnosis, even in newly diagnosed diabetic patients.
Abdomen ; Blood Glucose ; Diabetes Complications ; Diabetes Mellitus, Type 2 ; Diagnosis, Differential ; Dysuria ; Edema ; Emergency Service, Hospital ; Hemoglobin A, Glycosylated ; Humans ; Hydronephrosis ; Iliac Vein ; Lower Extremity ; Male ; Middle Aged ; Pelvis ; Penis ; Rivers ; Urinary Bladder ; Urinary Bladder, Neurogenic

The Committee of Clinical Practice Guidelines of the Korean Diabetes Association revised and updated the 6th Clinical Practice Guidelines in 2019. Targets of glycemic, blood pressure, and lipid control in type 2 diabetes mellitus (T2DM) were updated. The obese and overweight population is increasing steadily in Korea, and half of the Koreans with diabetes are obese. Evidence-based recommendations for weight-loss therapy for obesity management as treatment for hyperglycemia in T2DM were provided. In addition, evidence from large clinical studies assessing cardiovascular outcomes following the use of sodium-glucose cotransporter-2 inhibitors and glucagon-like peptide 1 receptor agonists in patients with T2DM were incorporated into the recommendations.
Blood Pressure ; Diabetes Mellitus, Type 2 ; Diagnosis ; Glucagon-Like Peptide 1 ; Humans ; Hyperglycemia ; Korea ; Obesity ; Overweight

OBJECTIVES: The incidence of type 2 diabetes mellitus has increased significantly in recent years. With the development of artificial intelligence applications in healthcare, they are used for diagnosis, therapeutic decision making, and outcome prediction, especially in type 2 diabetes mellitus. This study aimed to identify the artificial intelligence (AI) applications for type 2 diabetes mellitus care. METHODS: This is a review conducted in 2018. We searched the PubMed, Web of Science, and Embase scientific databases, based on a combination of related mesh terms. The article selection process was based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Finally, 31 articles were selected after inclusion and exclusion criteria were applied. Data gathering was done by using a data extraction form. Data were summarized and reported based on the study objectives. RESULTS: The main applications of AI for type 2 diabetes mellitus care were screening and diagnosis in different stages. Among all of the reviewed AI methods, machine learning methods with 71% (n = 22) were the most commonly applied techniques. Many applications were in multi method forms (23%). Among the machine learning algorithms applications, support vector machine (21%) and naive Bayesian (19%) were the most commonly used methods. The most important variables that were used in the selected studies were body mass index, fasting blood sugar, blood pressure, HbA1c, triglycerides, low-density lipoprotein, high-density lipoprotein, and demographic variables. CONCLUSIONS: It is recommended to select optimal algorithms by testing various techniques. Support vector machine and naive Bayesian might achieve better performance than other applications due to the type of variables and targets in diabetes-related outcomes classification.
Artificial Intelligence ; Blood Glucose ; Blood Pressure ; Body Mass Index ; Classification ; Decision Making ; Delivery of Health Care ; Diabetes Mellitus ; Diabetes Mellitus, Type 2 ; Diagnosis ; Fasting ; Incidence ; Lipoproteins ; Machine Learning ; Mass Screening ; Methods ; Support Vector Machine ; Triglycerides

Type 2 diabetes mellitus (DM) is commonly linked to muscle weakness and metabolic abnormalities which increase healthcare costs. The study was undertaken to investigate if low handgrip strength, as a marker of muscle weakness, is associated with hyperglycemia and/or DM in Brazilian subjects. In a cross-sectional design, 415 individuals of both sexes (46.7% male) were interviewed by a questionnaire and the DM diagnostic was self-reported. Anthropometric measurements, such as weight, height, body mass index (BMI), arm circumference, mid-arm and calf circumference and handgrip strength, were obtained by trained nutritionists. Blood glucose concentrations were determined by portable monitor analysis. Student's t-test was applied to compare DM cases with non-diabetic individuals, and logistic regression analysis was performed to verify the odds for becoming diabetic or having altered glycemia and p < 0.05 was considered as significant. From 415 subjects, 9.2% (n = 35) were classified as DM. DM patients had significantly higher age, BMI, casual glycemia and lower handgrip strength and normalized (to body weight) handgrip strength (NHS) when compared with non-diabetic patients. Individuals with low NHS have 2.7 odds ratio to DM without adjustment for covariate (crude model, p = 0.006) and have 2.7 times higher the likelihood of DM than individuals with high NHS after adjusting for age (model 1, p = 0.006); however, this association disappeared after further adjusting for sex. In conclusion, low handgrip strength normalized or not to body weight, was not associated with hyperglycemia and DM diagnosis.
Arm ; Blood Glucose ; Body Height ; Body Weight ; Diabetes Mellitus ; Diabetes Mellitus, Type 2* ; Diagnosis ; Hand Strength ; Health Care Costs ; Humans ; Hyperglycemia* ; Logistic Models ; Muscle Weakness ; Nutritionists ; Odds Ratio

BACKGROUND: The aim of this study was to explore the differences in the clinical characteristics and diagnostic rates of diabetes mellitus (DM) according to various criteria in different age groups and to evaluate the efficacy of each criterion for screening older patients. METHODS: We studied 515 patients and measured the fasting plasma glucose level (FPG), 2-hour plasma glucose level after the 75 g oral glucose tolerance test (2-hour postload glucose [2-h PG]), and glycosylated hemoglobin (HbA1c) for re-evaluation of hyperglycemia without a history of diabetes. Patients with newly diagnosed DM were grouped by age as younger ( < 65 years) or older (≥65 years). RESULTS: Older patients had significantly lower HbA1c, FPG, and 2-h PG levels and a higher homeostatic level of pancreatic β-cell function compared with younger patients (P < 0.001). The older group had the lowest diagnostic rate when using the FPG level (45.5%) and the highest diagnostic rate when using the 2-h PG level (84.6%). These results were mostly due to the higher frequency of isolated post-challenge hyperglycemia in the older patients than in the younger group (28.8% vs. 9.2%). The use of both the FPG and HbA1c levels significantly enhanced the low diagnostic power when employing only the FPG levels in the older group (71.2% vs. 45.5%). CONCLUSION: In the older patients, the 2-h PG level was the most accurate diagnostic criterion. When we consider the costs and convenience, a combination of the FPG and HbA1c criteria may be recommended as a screening test for DM in older people.
Blood Glucose* ; Diabetes Mellitus ; Diabetes Mellitus, Type 2* ; Early Diagnosis* ; Fasting ; Glucose ; Glucose Tolerance Test ; Hemoglobin A, Glycosylated ; Humans ; Hyperglycemia ; Mass Screening ; Plasma*

BACKGROUND: Little is known about the natural course of normal fasting glucose (NFG) in Asians and the risk factors for future diabetes. METHODS: A total of 370 Japanese Americans (163 men, 207 women) with NFG levels and no history of diabetes, aged 34 to 75 years, were enrolled. Oral glucose tolerance tests were performed at baseline, 2.5, 5, and 10 years after enrollment. RESULTS: During 10 years of follow-up, 16.1% of participants met criteria for diabetes diagnosis, and 39.6% of subjects still had NFG levels at the time of diabetes diagnosis. During 5 years of follow-up, age (odds ratio [OR], 1.05; 95% confidence interval [CI], 1.01 to 1.10; P=0.026) and family history of diabetes (OR, 3.24; 95% CI, 1.42 to 7.40; P=0.005) were independently associated with future diabetes diagnosis; however, fasting glucose level was not an independent predictor. During 10 years of follow-up, family history of diabetes (OR, 2.76; 95% CI, 1.37 to 5.54; P=0.004), fasting insulin level (OR, 1.01; 95% CI, 1.00 to 1.02; P=0.037), and fasting glucose level (OR, 3.69; 95% CI, 1.13 to 12.01; P=0.030) were associated with diabetes diagnosis independent of conventional risk factors for diabetes. CONCLUSION: A substantial number of subjects with NFG at baseline still remained in the NFG range at the time of diabetes diagnosis. A family history of diabetes and fasting insulin and glucose levels were associated with diabetes diagnosis during 10 years of follow-up; however, fasting glucose level was not associated with diabetes risk within the relatively short-term follow-up period of 5 years in subjects with NFG.
Asian Americans* ; Asian Continental Ancestry Group* ; Blood Glucose ; Diabetes Mellitus, Type 2* ; Diagnosis ; Epidemiology ; Fasting* ; Follow-Up Studies ; Glucose Tolerance Test ; Glucose* ; Humans ; Insulin ; Male ; Risk Factors

The prevalence of type 2 diabetes mellitus (T2DM) in children and adolescents has increased globally over the past 2 decades. Metabolic syndrome, including obesity and overweight at a young age, increases the occurrence of T2DM. Studies in Indonesia have found that obese children and adolescents are more likely to have insulin resistance, a risk factor for T2DM. There are no data on the current incidence of T2DM in youth in Indonesia, but there has been a significant rise in the prevalence of overweight in adolescents. The diagnosis of T2DM in youth is similar to that in adults, with special consideration of when to test asymptomatic children. Management of T2DM in Indonesia follows the recommendations of the Indonesian Pediatric Society, which include lifestyle modifications, such as improving dietary habits and exercise, as well as appropriate medications. Metformin is the drug of choice for young T2DM patients; if marked hyperglycemia is present, basal insulin is given with metformin. Monitoring of T2DM is generally done through selfmonitoring of blood glucose and glycosylated hemoglobin.
Adolescent* ; Adult ; Blood Glucose ; Child* ; Diabetes Mellitus, Type 2* ; Diagnosis ; Food Habits ; Hemoglobin A, Glycosylated ; Humans ; Hyperglycemia ; Incidence ; Indonesia ; Insulin ; Insulin Resistance ; Life Style ; Metformin ; Obesity ; Overweight ; Prevalence ; Risk Factors